toolset. Based on feature-driven borders, we divide a behavioral model of a product line into a set of separate components with interfaces and a driver process to coordinate them. Abstracting from irrelevant components, we verify properties over a smaller behavioral model, which not only simplifies the model checking task but also makes the result amenable for reuse. This is a fundamental step forward for the approach to scale up to industrial-size product lines.","Title: Communication and Compatibility in Systems of Systems: Correctness-by-Construction.\nYear: 2015\n\nAbstract: Society is still trying to catch up with technology in the wake of the digital revolution of the last twenty years. Current systems need to be both heterogeneous and able to deal with enormous volumes of data coming from uncertain environments; consequently it is essential to be able to automatically assess the correctness of interactions. To guarantee that a system of systems, comprising a conglomerate of cooperating reactive components, can be trusted, and that the system as a whole behaves as intended, requires a thorough understanding of its communication behaviour. Once local interactions are identified, abstractions can support the identification of incompatibility of systems that should cooperate within a larger system.","Title: Design and validation of variability in product lines\nYear: 2011\n\nAbstract: We propose an emerging solution technique, pushing the application of model-checking techniques to the design and validation of variability in a product line (PL), mainly aimed at those industrial domains where model-based development is adopted for the development of safety-critical systems.","Title: Using FMC for family-based analysis of software product lines\nYear: 2015\n\nAbstract: We show how the FMC model checker can successfully be used to model and analyze behavioural variability in Software Product Lines. FMC accepts parameterized specifications in a process-algebraic input language and allows the verification of properties of such models by means of efficient on-the-fly model checking. The properties can be expressed in a logic that allows to correlate the parameters of different actions within the same formula. We show how this feature can be used to tailor formulas to the verification of only a specific subset of products of a Software Product Line, thus allowing for scalable family-based analyses with FMC. We present a proof-of-concept that shows the application of FMC to an illustrative Featured Transition System from the literature.","Title: Applying the product lines paradigm to the quantitative analysis of collective adaptive systems\nYear: 2015\n\nAbstract: Engineering a Collective Adaptive System (CAS) requires the support of a framework for quantitative modeling and analysis of the system. In order to jointly address variability and quantitative analysis, we apply the Product Lines paradigm, considered at the level of system engineering, to a case study of the European project QUANTICOL, by first defining a reference feature model and then adding feature attributes and global quantitative constraints, in the form of a Clafer attributed feature model. ClaferMOOVisualizer is subsequently used for quantitative analyses and multi-objective optimization of the resulting attributed feature model.","Title: Statistical analysis of probabilistic models of software product lines with quantitative constraints\nYear: 2015\n\nAbstract: We investigate the suitability of statistical model checking for the analysis of probabilistic models of software product lines with complex quantitative constraints and advanced feature installation options. Such models are specified in the feature-oriented language QFLan, a rich process algebra whose operational behaviour interacts with a store of constraints, neatly separating product configuration from product behaviour. The resulting probabilistic configurations and behaviour converge seamlessly in a semantics based on DTMCs, thus enabling quantitative analyses ranging from the likelihood of certain behaviour to the expected average cost of products. This is supported by a Maude implementation of QFLan, integrated with the SMT solver Z3 and the distributed statistical model checker MultiVeStA. Our approach is illustrated with a bikes product line case study.","Title: Towards Automatic Decision Support for Bike-Sharing System Design\nYear: 2015\n\nAbstract: Public bike-sharing systems are a popular means of sustainable urban mobility, but their successful introduction in a city stands or falls with their specific designs. What kind of bikes and docking stations are needed, how many and where to install them? How to avoid as much as possible that stations are completely empty or full for some period? Hence, a bike-sharing system can be seen both as a highly reconfigurable system and as a collective adaptive system. In this paper, we present two complementary strategies for the evaluation of bike-sharing system designs by means of automated tool support. We use the Clafer toolset to perform multi-objective optimisation of attributed feature models known from software product line engineering and the recently developed mean field model checker FlyFast to assess performance and user satisfaction aspects of variants of large-scale bike-sharing systems. The combined use of these analysis approaches is a preliminary step in the direction of automatic decision support for the initial design of a bike-sharing system as well as its successive adaptations and reconfigurations that considers both qualitative and performance aspects.","Title: Towards A Feature Mu-Calculus Targeting Spl Verification\nYear: 2016\n\nAbstract: The modal mu-calculus mu L is a well-known fixpoint logic to express and model check properties interpreted over labeled transition systems. In this paper, we propose two variants of the mu-calculus, mu L-f and mu L-f', for feature transition systems. For this, we explicitly incorporate feature expressions into the logics, allowing operators to select transitions and behavior restricted to specific products and subfamilies. We provide semantics for mu L-f and mu L-f' and relate the two new mu-calculi and mu L to each other. Next, we focus on the analysis of SPL behavior and show how our formalism can be applied for product-based verification with mu L-f as well as family-based verification with mu L-f'. We illustrate by means of a toy example how properties can be model checked, exploiting an embedding of mu L-f' into the mu-calculus with data.","Title: Proceedings of the Workshop on FORmal methods for the quantitative Evaluation of Collective Adaptive SysTems.\nYear: 2016\n\nAbstract: Collective Adaptive Systems (CAS) consist of a large number of spatially distributed heterogeneous entities with decentralised control and varying degrees of complex autonomous behaviour that may be competing for shared resources even when collaborating to reach common goals. It is important to carry out thorough quantitative modelling and analysis and verification of their design to investigate all aspects of their behaviour before they are put into operation. This requires combinations of formal methods and applied mathematics which moreover scale to large-scale CAS. The primary goal of FORECAST is to raise awareness in the software engineering and formal methods communities of the particularities of CAS and the design and control problems which they bring.","Title: Supervisory Controller Synthesis for Product Lines Using CIF 3.\nYear: 2016\n\nAbstract: Using the CIF 3 toolset, we illustrate the general idea of controller synthesis for product line engineering for a prototypical example of a family of coffee machines. The challenge is to integrate a number of given components into a family of products such that the resulting behaviour is guaranteed to respect an attributed feature model as well as additional behavioural requirements. The proposed correctness-by-construction approach incrementally restricts the composed behaviour by subsequently incorporating feature constraints, attribute constraints and temporal constraints. The procedure as presented focusses on synthesis, but leaves ample opportunity to handle e.g.uncontrollable behaviour, dynamic reconfiguration, and product-and family-based analysis.","Title: Statistical Model Checking for Product Lines.\nYear: 2016\n\nAbstract: We report on the suitability of statistical model checking for the analysis of quantitative properties of product line models by an extended treatment of earlier work by the authors. The type of analysis that can be performed includes the likelihood of specific product behaviour, the expected average cost of products (in terms of the attributes of the products' features) and the probability of features to be (un) installed at runtime. The product lines must be modelled in QFLan, which extends the probabilistic feature-oriented language PFLan with novel quantitative constraints among features and on behaviour and with advanced feature installation options. QFLan is a rich process-algebraic specification language whose operational behaviour interacts with a store of constraints, neatly separating product configuration from product behaviour. The resulting probabilistic configurations and probabilistic behaviour converge in a discrete-time Markov chain semantics, enabling the analysis of quantitative properties. Technically, a Maude implementation of QFLan, integrated with Microsoft's SMT constraint solver Z3, is combined with the distributed statistical model checker MultiVeStA, developed by one of the authors. We illustrate the feasibility of our framework by applying it to a case study of a product line of bikes.","Title: Correctness-by-Construction and Post-hoc Verification: Friends or Foes?\nYear: 2016\n\nAbstract: While correctness-by-construction and post-hoc verification are traditionally considered to provide two opposing views on proving software systems to be free from errors, nowadays numerous techniques and application fields witness initiatives that try to integrate elements of both ends of the spectrum. The ultimate aim is not merely to improve the correctness of software systems but also to improve their time-to-market, and to do so at a reasonable cost. This track brings together researchers and practitioners interested in the inherent 'tension' that is usually felt when trying to balance the pros and cons of correctness-by-construction versus post-hoc verification.","Title: Variability-Based Design of Services for Smart Transportation Systems.\nYear: 2016\n\nAbstract: A smart transportation system can be seen as an aggregate of transportation opportunities and services, accompanied by advanced management services that make the access to the system easier for the user. In this paper, we exploit the product line paradigm to address the variability of an exemplary smart transportation system: a bike-sharing system. Improving the satisfaction of a user of a bike-sharing system includes providing information at runtime on the filling degree of the docking stations in the near future. To fulfill this expectation, a prediction service is needed to infer the probability that at a certain time of the day a user will return a bike to or take one from a station. In earlier studies, several possible advanced smart predictive services were identified. The choice of which services to offer to users by the managers of a bike-sharing system is influenced by minimizing the costs while maximizing customer satisfaction. To aid the managers, we modeled a family of smart bike-sharing services, after which an attributed feature model was used to augment the model with quantitative attributes related to cost and customer satisfaction, allowing for a multi-objective optimization by dedicated tools. We observe that the performance of the smart prediction services, and therefore of the related customer satisfaction, is highly dependent on the amount of collected historical data on which the predictive analysis is based. Therefore the result of the optimization also depends on this factor, which evolves over time.","Title: Family-Based Model Checking with mCRL2.\nYear: 2017\n\nAbstract: Family-based model checking targets the simultaneous verification of multiple system variants, a technique to handle feature-based variability that is intrinsic to software product lines SPLs. We present an approach for family-based verification based on the feature $$\\\\mu $$-calculus $$\\\\mu L {}_f$$, which combines modalities with feature expressions. This logic is interpreted over featured transition systems, a well-accepted model of SPLs, which allows one to reason over the collective behavior of a number of variants a family of products. Via an embedding into the modal $$\\\\mu $$-calculus with data, underpinned by the general-purpose mCRL2 toolset, off-the-shelf tool support for﾿$$\\\\mu L {}_f$$ becomes readily available. We illustrate the feasibility of our approach on an SPL benchmark model and show the runtime improvement that family-based model checking with mCRL2 offers with respect to model checking the benchmark product-by-product.","Title: A framework for quantitative modeling and analysis of highly (re)configurable systems.\nYear: 2017\n\nAbstract: This paper presents our approach to the quantitative modeling and analysis of highly (re)configurable systems, such as software product lines. Different combinations of the optional features of such a system give rise to combinatorially many individual system variants. We use a formal modeling language that allows us to model systems with probabilistic behavior, possibly subject to quantitative feature constraints, and able to dynamically install, remove or replace features. More precisely, our models are defined in the probabilistic feature-oriented language QFLAN, a rich domain specific language (DSL) for systems with variability defined in terms of features. QFLAN specifications are automatically encoded in terms of a process algebra whose operational behavior interacts with a store of constraints, and hence allows to separate system configuration from system behavior. The resulting probabilistic configurations and behavior converge seamlessly in a semantics based on discrete-time Markov chains, thus enabling quantitative analysis. Our analysis is based on statistical model checking techniques, which allow us to scale to larger models with respect to precise probabilistic analysis techniques. The analyses we can conduct range from the likelihood of specific behavior to the expected average cost, in terms of feature attributes, of specific system variants. Our approach is supported by a novel Eclipse-based tool which includes state-of-the-art DSL utilities for QFLAN based on the Xtext framework as well as analysis plug-ins to seamlessly run statistical model checking analyses. We provide a number of case studies that have driven and validated the development of our framework.","Title: Orchestration Of Dynamic Service Product Lines With Featured Modal Contract Automata\nYear: 2017\n\nAbstract: In Service-Oriented Computing, contracts provide a way to characterise the behavioural conformance of a composition of services, and to guarantee that the results do not lead to spurious compositions. Adding variability leads to a product line of services capable of adapting to customer requirements and to changes in the context in which they operate. To this aim, we extended a previously introduced formal model of service contracts. In particular, we included: (i) feature-based constraints and (ii) four classes of service requests to characterise different variants of service agreement. We then exploited Supervisory Control Theory to define an algorithm to synthesise an orchestration of services that satisfies: (i) all feature constraints of the service product line, and (ii) the maximal number of service requests for which an agreement can be reached. Moreover, such an orchestration of a service product line, whose number of products is potentially exponential in the number of features, can be synthesised from only a subset of its products. A prototypical tool supports the developed theory. In this short paper, we provide the intuition for our approach and illustrate it by means of a Hotel reservation service product line.","Title: Formal Methods for the Railway Sector.\nYear: 2018\n\nAbstract: Researchers from the Formal Methods and Tools group of ISTI-CNR are working on a review and assessment of the main formal modelling and verification languages and tools used in the railway domain, with the aim of evaluating the actual applicability of the most promising ones to a moving block signalling system model provided by an industrial partner. The research is being conducted in the context of the H2020 Shift2Rail project ASTRail.","Title: Replicated Computations Results (RCR) Report for \"Design and Verification of Trusted Collective Adaptive Systems\".\nYear: 2018\n\nAbstract: The article “Design and Verification of Trusted Collective Adaptive Systems” by Aldini proposes a process-algebraic framework for modeling and verifying trusted collective adaptive systems. To favor reuse, the system and trust models can be specified separately, only to be integrated at the semantic level. Through a combination of behavioral equivalence checking and model checking against branching-time temporal logic with trust predicates, the framework allows comparative analyses of different trust models as well as analyses of the effects of attacks to the trust models. The applicability of the formal framework is illustrated by means of two representative use cases: the security analysis of a trust-incentive service management system and a comparison of two different reputation systems.\n\nThis replicated computations results report focuses on the reproducibility of the experiments performed in the aforementioned article, i.e. on the automatic verification of properties against models of these use cases encoded in the well-known NuSMV model checker. It was straightforward to reproduce all results from the article in reasonable time using a standard laptop machine.","Title: QFLan: A Tool for the Quantitative Analysis of Highly Reconfigurable Systems.\nYear: 2018\n\nAbstract: QFLan offers modeling and analysis of highly reconfigurable systems, like product lines, which are characterized by combinatorially many system variants (or products) that can be obtained via different combinations of installed features. The tool offers a modern integrated development environment for the homonym probabilistic feature-oriented language. QFLan allows the specification of a family of products in terms of a feature model with quantitative attributes, which defines the valid feature combinations, and probabilistic behavior subject to quantitative constraints. The language's behavioral part enables dynamic installation, removal and replacement of features. QFLan has a discrete-time Markov chain semantics, permitting quantitative analyses. Thanks to a seamless integration with the statistical model checker MultiVeStA, it allows for analyses like the likelihood of specific behavior or the expected average value of non-functional aspects related to feature attributes.","Title: Orchestration Synthesis For Real-Time Service Contracts\nYear: 2018\n\nAbstract: Service contracts offer a way to define the desired behavioural compliance of a composition of services, characterised by the fulfilment of all requirements (e. g. service requests) by obligations (e. g. service offers). Depending on their granularity, requirements may vary according to their criticality and contain real-time aspects (e. g. service expiration time). Synthesis of safe orchestrations, the standard method to refine spurious compositions into compliant ones, is of paramount importance. Ideally, safe orchestrations solve competition among matching requests/ offers, respecting criticalities and time constraints, in the best possible way. The contribution of this paper is (i) the introduction of timed service contract automata, a novel formalisation of service contracts with (ii) real-time constraints and (iii) service requests with varying levels of criticality, and a means to compute their (iv) composition and (v) safe orchestration. Orchestration is based on the synthesis of the most permissive controller from supervisory control theory, computed using the concept of zones from timed games. An intuitive example illustrates the contribution.","Title: Product line models of large cyber-physical systems: the case of ERTMS/ETCS.\nYear: 2018\n\nAbstract: A product line perspective may help to understand the possible variants in interactions between the subsystems of a large, cyber-physical system. This observation is exemplified in this paper by proposing a feature model of the family of ERTMS/ETCS train control systems and their foreseen extensions. This model not only shows the different components that have to be installed when deploying the system at the different levels established by the ERTMS/ETCS standards, but it also helps to identify and discuss specific issues, such as the borders between onboard and wayside equipment, different manufacturers of the subsystems, interoperability among systems developed at different levels, backward compatibility of trains equipped with higher level equipment running on lines equipped with lower level equipment, and evolution towards future trends of railway signalling. The feature model forms the basis for formal modelling of the behaviour of the critical components of the system and for evaluating the overall cost, effectiveness and sustainability, for example by adding cost and performance attributes to the feature model.","Title: Modelling and analysis with featured modal contract automata.\nYear: 2018\n\nAbstract: Featured modal contract automata (FMCA) have been proposed as a suitable formalism for modelling contract-based dynamic service product lines. A contract is a behavioural description consisting of offers and necessary and permitted service requests with different levels of criticality, to be matched with corresponding offers of other FMCA. Each contract is equipped with a feature constraint, whose features are offers or requests, and characterises a valid product orchestration. A safe orchestration of a product fulfils all necessary and the maximum number of permitted requests, such that all enabled features are available and none of its disabled features is. The entire product line orchestration can be computed from a subset of valid product orchestrations, by exploiting their (partial) ordering. The open-source prototypical toolkit FMCAT supports the specification and orchestration of FMCA, and it interfaces with FeatureIDE for importing feature models and their valid products. In this experience report, we show how to model a Hotel service product line with FMCA and how to analyse it with FMCAT.","Title: On the expressiveness of modal transition systems with variability constraints.\nYear: 2019\n\nAbstract: We demonstrate that modal transition systems with variability constraints are equally expressive as featured transition systems, by defining a transformation of the latter into the former, a transformation of the former into the latter, and proving the soundness and completeness of both transformations. Modal transition systems and featured transition systems are widely recognised as fundamental behavioural models for software product lines and our results thus contribute to the expressiveness hierarchy of such basic models studied in other papers published in this journal.","Title: Quantitative Variability Modeling and Analysis\nYear: 2019\n\nAbstract: The explicit management of variability in the development cycle of software-intensive systems has led to a plethora of modeling and analysis techniques tailored to deal with behavioral validation of such configurable systems. Most of the work, however, focuses on qualitative (i.e. functional) requirements. Recently, there is growing interest in variability modeling and analysis techniques that do explicitly consider quantitative (i.e. non-functional) requirements, such as dependability, energy consumption, security, and cost.\n\nToday's software is embedded in a variety of smart and critical systems that run in environments where events occur randomly and affect the system, and to which it needs to adapt. Therefore, quantitative modeling and analysis is currently a hot topic. The panel on Quantitative Variability Modeling and Analysis (QSPL) discusses the latest quantitative techniques and how to apply them to variability modeling and analysis of software-intensive systems.","Title: Guest Editorial for the Special Issue on FORmal methods for the quantitative Evaluation of Collective Adaptive SysTems (FORECAST).\nYear: 2018\n\nAbstract: An abstract is not available.","Title: Formal methods and automated verification of critical systems.\nYear: 2018\n\nAbstract: Critical (software) systems are all around us. These systems are typically characterised by stringent dependability requirements and demand elevated levels of robustness and fault tolerance. To assure that they function as intended and provide a number of quality guarantees, formal methods and automated verification techniques and tools have been in use in the engineering of such critical systems for many years now. In this introduction to the special issue FMICS–AVoCS on “Formal Methods and Automated Verification of Critical Systems”, we outline a number of recent achievements concerning the use of formal methods and automated verification techniques and tools for the specification and analysis of critical systems from a variety of application domains. These achievements are represented by six selected papers: five were selected from the joint 21st International Workshop on Formal Methods for Industrial Critical Systems and 16th International Workshop on Automated Verification of Critical Systems (FMICS–AVoCS 2016), while one of them was selected after an open call for papers.","Title: Formal methods for transport systems.\nYear: 2018\n\nAbstract: Formal methods and verification tools have been in use in the engineering of safety-critical transport systems for well over 30 years. In both the railway and the avionics domain, for instance, formal methods are specifically recommended in current international certification standards for ultra-dependable systems and for products at the highest integrity level. In fact, traditionally, the applications of formal methods and tools to such transport systems concern demonstrating, with the highest levels of assurance, the correct functioning of the software systems involved, such as train signalling systems to avoid collisions. More recently, however, formal methods and verification tools have started to be applied also to the scheduling and management of transport systems or networks, for instance to optimise the exploitation of a railway line or to improve the operational efficiency of a bus network. In this introduction to the special issue on “Formal Methods for Transport Systems”, we outline some recent achievements for each of the above-mentioned types of application of formal methods and tools. These achievements are represented by three selected papers: one was selected from the “Formal Methods and Safety Certification: Challenges in the Railways Domain” track at the seventh International Symposium On Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2016); another one was selected from the 21st International Workshop on Formal Methods for Industrial Critical Systems and the 16th International Workshop on Automated Verification of Critical Systems (FMICS-AVoCS 2016); a final one was selected after an open call for contributions.","Title: Survey On Formal Methods And Tools In Railways: The Astrail Approach\nYear: 2019\n\nAbstract: Formal methods and tools have been widely applied to the development of railway systems during the last decades. However, no universally accepted formal framework has emerged, and railway companies wishing to introduce formal methods have little guidance for the selection of the most appropriate methods and tools to adopt. A work package (WP) of the European project ASTRail, funded under the Shift2Rail initiative, addresses this problem, by performing a survey that considers scientific literature, international projects, and practitioners' perspectives to identify a collection of formal methods and tools to be applied in railways. This paper summarises the current results of this WP. We surveyed 114 scientific publications, 44 practitioners, and 8 projects to come to a shortlist of 14 methods considered suitable for system modelling and verification in railways. The methods and tools were reviewed according to a set of functional, language-related, and quality features. The current paper extends the body of knowledge with a set of publicly available documents that can be leveraged by companies for guidance on formal methods selection in railway system development.","Title: Featured Team Automata\nYear: 2021\n\nAbstract: We propose featured team automata to support variability in the development and analysis of teams, which are systems of reactive components that communicate according to specified synchronisation types. A featured team automaton concisely describes a family of concrete product models for specific configurations determined by feature selection. We focus on the analysis of communication-safety properties, but doing so product-wise quickly becomes impractical. Therefore, we investigate how to lift notions of receptiveness (no message loss) to the level of family models. We show that featured (weak) receptiveness of featured team automata characterises (weak) receptiveness for all product instantiations. A prototypical tool supports the developed theory.","Title: Static Analysis and Family-based Model Checking of Featured Transition Systems with VMC\nYear: 2021\n\nAbstract: A Featured Transition System (FTS) is a formalism for modeling variability in configurable system behavior. The behavior of all variants (products) is modeled in a single compact FTS by associating the possibility to perform an action and transition from one state to another with feature expressions that condition the execution of an action in specific variants. We present a front-end for the research tool VMC. The resulting toolchain allows a modeler to analyze an FTS for ambiguities (dead or false optional transitions and hidden deadlock states), transform an ambiguous FTS into an unambiguous one, and perform an efficient kind of family-based verification of an FTS without hidden deadlock states. We use benchmarks from the literature to demonstrate the novelties offered by the toolchain.","Title: Efficient static analysis and verification of featured transition systems\nYear: 2022\n\nAbstract: A Featured Transition System (FTS) models the behaviour of all products of a Software Product Line (SPL) in a single compact structure, by associating action-labelled transitions with features that condition their presence in product behaviour. It may however be the case that the resulting featured transitions of an FTS cannot be executed in any product (so called dead transitions) or, on the contrary, can be executed in all products (so called false optional transitions). Moreover, an FTS may contain states from which a transition can be executed only in some products (so called hidden deadlock states). It is useful to detect such ambiguities and signal them to the modeller, because dead transitions indicate an anomaly in the FTS that must be corrected, false optional transitions indicate a redundancy that may be removed, and hidden deadlocks should be made explicit in the FTS to improve the understanding of the model and to enable efficient verification-if the deadlocks in the products should not be remedied in the first place. We provide an algorithm to analyse an FTS for ambiguities and a means to transform an ambiguous FTS into an unambiguous one. The scope is twofold: an ambiguous model is typically undesired as it gives an unclear idea of the SPL and, moreover, an unambiguous FTS can efficiently be model checked. We empirically show the suitability of the algorithm by applying it to a number of benchmark SPL examples from the literature, and we show how this facilitates a kind of family-based model checking of a wide range of properties on FTSs.","Title: Exploring the ERTMS/ETCS full moving block specification: an experience with formal methods\nYear: 2022\n\nAbstract: Shift2Rail is a joint undertaking funded by the EU via its Horizon 2020 program and by main railway stakeholders. Several Shift2Rail projects aim to investigate the application of formal methods to new ERTMS/ETCS railway signalling systems that promise to move European railway forward by guaranteeing high capacity, low cost and improved reliability. We explore the ERTMS/ETCS level 3 full moving block specifications stemming from different Shift2Rail projects using Uppaal and statistical model checking. The results range from novel rigorously formalised requirements to an operational model formally verified against scenarios with multiple trains on a single railway line. From the gained experience, we have distilled future research goals to improve the formal specification and verification of real-time systems, and we discuss some barriers concerning a possible uptake of formal methods and tools in the railway industry.","Title: Formal methods and tools for industrial critical systems\nYear: 2022\n\nAbstract: Formal methods and tools have become well established and widely applied to ensure the correctness of fundamental components of industrial critical systems in domains like railways, avionics and automotive. In this Introduction to the special issue, we outline a number of recent achievements concerning the use of formal methods and tools for the specification and verification of critical systems from a variety of industrial domains. These achievements are represented by eight properly revised and extended versions of papers that were selected from the 24th and 25th International Conference on Formal Methods for Industrial Critical Systems (FMICS 2019 and FMICS 2020)."],"string":"[\n \"Title: Vector team automata\\nYear: 2012\\n\\nAbstract: Vector team automata are team automata with an explicit representation of synchronizations. This makes a translation possible of a subclass of vector team automata into individual token net controllers, a model of labeled Petri nets developed within the framework of vector controlled concurrent systems.\",\n \"Title: Formal methods and analysis in software product line engineering: 3rd edition of FMSPLE workshop series\\nYear: 2012\\n\\nAbstract: FMSPLE 2012 is the third edition of the FMSPLE workshop series, traditionally affiliated with SPLC, which aims to connect researchers and practitioners interested in raising the efficiency and the effectiveness of SPLE through the application of innovative analysis approaches and formal methods.\",\n \"Title: Vmc: A Tool For The Analysis Of Variability In Software Product Lines\\nYear: 2013\\n\\nAbstract: Researchers from the Formal Methods and Tools group of ISTI-CNR have developed a tool for the computeraided verification of behavioural variability in product families.\",\n \"Title: Petri Net Control for Grammar Systems\\nYear: 2002\\n\\nAbstract: It is demonstrated how Petri nets may be used to control the derivations in systems of cooperating grammars. This allows to define grammar systems with a concurrent rewriting protocol. Some basic properties are established.\",\n \"Title: Formal verification of an automotive scenario in service-oriented computing\\nYear: 2008\\n\\nAbstract: We report on the successful application of academic experience with formal modelling and verification techniques to an automotive scenario from the service-oriented computing domain. The aim of this industrial case study is to verify a priori, thus before implementation, certain design issues. The specific scenario is a simplified version of one of possible new services for car drivers to be provided by the in-vehicle computers.\",\n \"Title: Demonstration of a model checker for the analysis of product variability\\nYear: 2012\\n\\nAbstract: We demonstrate an experimental tool for the modeling and analysis of behavioral variability in product families.\",\n \"Title: Detecting policy conflicts by model checking UML state machines\\nYear: 2009\\n\\nAbstract: Policies are convenient means to modify system behaviour at run-time. Nowadays, policies are created in great numbers by different actors, ranging from system administrators to lay-users. However, this situation may lead naturally to inconsistencies, a problem that has been recognized and termed policy conflict. The adoption of a widely-used notation, with good tool support, to express the policies, can not only support the detection, but also help all the involved actors in understanding and resolving the conflicts. In this respect, a natural candidate is UML due to its current wide use in the industrial practice. In this paper we show how to model check policies expressed in UML to verify whether they are free of conflicts: we define a correspondence between APPEL policies and UML state machines and use UMC as a model checker. We validate the approach with examples taken from the literature.\",\n \"Title: A formal approach supporting the comparative predictive assessment of the interruption-tolerance of interactive systems\\nYear: 2009\\n\\nAbstract: This paper presents an approach for investigating in a predictive way potential disruptive effects of interruptions on task performance in a multitasking environment. The approach combines previous work in the field of interruption analysis, formal description techniques for interactive systems and stochastic processes to support performance analysis of user activities constrained by the occurrence of interruptions. The approach uses formal description techniques to provide a precise description of user tasks, and both system and interruptions behavior. The detailed mechanism by which systems and interruptions behave is first described using a Petri nets-based formal description technique called Interactive Cooperative Objects (ICO). The use of a formal modeling technique for the description of these three components makes it possible to compare and analyze different interaction techniques. In particular, it allows us to determine which of the system states are most affected by the occurrence of interruptions. Once composed together, models describing the system, user tasks and interruptions behavior are transformed into PEPA models (i.e. Performance Evaluation Process Algebra) that are amenable to performance analysis using the PRISM model checker. The approach is exemplified by a simple example that models two interaction techniques for manipulating icons in a desktop environment.\",\n \"Title: Using mCRL2 for the analysis of software product lines\\nYear: 2014\\n\\nAbstract: We show how the formal specification language mCRL2 and its state-of-the-art toolset can be used successfully to model and analyze variability in software product lines. The mCRL2 toolset supports parametrized modeling, model reduction and quality assurance techniques like model checking. We present a proof-of-concept, which moreover illustrates the use of data in mCRL2 and also how to exploit its data language to manage feature attributes of software product lines and quantitative constraints between attributes and features.\",\n \"Title: Web Service Composition Approaches: From Industrial Standards to Formal Methods\\nYear: 2007\\n\\nAbstract: Ensuring dependability and security of Peer-to- Peer (P2P) systems is an intricate task due to the autonomous and volatile nature of peers and the decentralization that characterizes such systems. Dynamic verification provides the means of monitoring ...\",\n \"Title: Formal Modelling and Verification of an Asynchronous Extension of SOAP\\nYear: 2006\\n\\nAbstract: We present a goal-driven approach to model a choreographer for realizing composite Web services. In this framework, the users start with an abstract, and possibly incomplete functional specification of a desired goal service. This specification is used ...\",\n \"Title: Modularity for teams of I/O automata\\nYear: 2005\\n\\nAbstract: It is shown how Input/Output automata flt in the framework of team automata, thus making it possible to view certain notions and results regarding their modular structure as special instances of more general observations.\",\n \"Title: Formal Description of Variability in Product Families\\nYear: 2011\\n\\nAbstract: We illustrate how to manage variability in a single logical framework consisting of a Modal Transition System (MTS) and an associated set of formulae expressed in the branching-time temporal logic MHML interpreted in a deontic way over such MTSs. We discuss the commonalities and differences with the framework of Classen et al. based on Featured Transition Systems and Linear-time Temporal Logic.\",\n \"Title: Formal Modelling And Verification In Service-Oriented Computing\\nYear: 2007\\n\\nAbstract: Formal methods and tools are a popular means of analysing the correctness properties of computer network protocols, such as safety, liveness and security. First the protocol under scrutiny is described in a formal language, which often results in a more precise definition of its function. Subsequently, the properties to be analysed are specified in a suitable logic. Finally, to decide whether or not the protocol fulfils certain properties, automatic tools are used to analyse it. The outcome either proves the protocol to be correct with respect to the relevant properties or shows there to be a problem.\",\n \"Title: Resilience of Interaction Techniques to Interrupts: A Formal Model-Based Approach\\nYear: 2009\\n\\nAbstract: In many modern working environments interruptions are commonplace as users must temporarily suspend a task to complete an unexpected intervening activity. As users are faced with more and more sources of information competing for their attention, it is becoming increasingly important to understand how interruptions affect their abilities to complete tasks. This paper introduces a new perspective for research in this field by employing analytical, model-based techniques that are informed by well-established cognitive theories and empirical data available in the literature. We propose stochastic modelling and model checking to predict measures of the disruptive effects of interruptions to two well-known interaction techniques: Drag 'n Drop and Speak 'n Drop. The approach also provides a way to compare the resilience of different interaction techniques to the presence of external interruptions that users need to handle. The obtained results are in a form that allows validation with results obtained by empirical studies involving real users.\",\n \"Title: Associativity of Infinite Synchronized Shuffles and Team Automata\\nYear: 2009\\n\\nAbstract: Motivated by different ways to obtain team automata from synchronizing component automata, we consider various definitions of synchronized shuffles of words. A shuffle of two words is an interleaving of their symbol occurrenceswhich preserves the original order of these occurrences within each of the two words. In a synchronized shuffle, however, also two occurrences of one symbol, each from a different word, may be identified as a single occurrence. In case at least one of the words involved is infinite, a (synchronized) shuffle can also be unfair in the sense that an infinite word may prevail fromsome point onwards even when the other word still has occurrences to contribute to the shuffle. We prove that for the synchronized shuffle operations under consideration, every (fair or unfair) synchronized shuffle can be obtained as a limit of synchronized shuffles of the finite prefixes of the words involved. In addition, it is shown that with the exception of one, all synchronized shuffle operations that we consider satisfy a natural notion of associativity, also in case of unfairness. Finally, using these results, some compositionality results for team automata are established.\",\n \"Title: A Deontic Logical Framework for Modelling Product Families\\nYear: 2010\\n\\nAbstract: We discuss the application of deontic logics to the modelling of variabilities in product family descriptions. Deontic logics make it possible to express concepts like permission and obligation, and hence promise a direct mod- elling of constraints over the products of a family. Indeed, we first show how feature models can be straightforwardly characterised by means of a deontic logic. We then study the deontic modelling of the behavioural variability in product families by defining a deontic extension of a behavioural logic. This allows both constraints over the products of a family and constraints over their behaviour to be expressed in a single framework: a novelty in the field. We discuss how model-checking tools could support formal verification in this framework, and we indicate some future research into that direction.\",\n \"Title: Teams in Grammar Systems: Sub-Context-Free Cases\\nYear: 1997\\n\\nAbstract: The study of teams in grammar systems so far has evolved around teams being formed from a finite number of sets of context-free productions. Here, the generative power of teams in grammar systems consisting of regular, linear and metalinear sets of productions is investigated. For these sub-context-free cases the forming of teams strictly increases the generative power of the underlying grammar systems in many cases. 1 Introduction When an agent is unable to tackle a complex problem, due to...\",\n \"Title: Cooperating distributed grammar systems: components with nonincreasing competence\\nYear: 2011\\n\\nAbstract: We study the generative power of CD grammar systems (CDGSs) that use a cooperation protocol based on the level of competence on a sentential form -- and the competence of their components does not increase during rewriting. A component of a CDGS is k-competent on a sentential form if it can rewrite exactly k different nonterminals appearing in that string. A CDGS with components that are nonincreasing in competence works in = k-competence mode of derivation if no k-competent component can ever become l-competent, for some l k. We introduce a static and a dynamic definition to impose this restriction, and we relate the generative power of such CDGSs working either in a sequential or in a parallel manner and according to the aforementioned cooperation protocol, for various k, with that of context-free forbidding random context grammars, (random context) ET0L systems, and context-free programmed grammars with appearance checking.\",\n \"Title: Infinite unfair shuffles and associativity\\nYear: 2007\\n\\nAbstract: We consider a general shuffling operation for finite and infinite words which is not necessarily fair. This means that it may be the case that in a shuffle of two words, from some point onwards, one of these words prevails ad infinitum even though the other word still has letters to contribute. Prefixes and limits of shuffles are investigated, leading to a characterization of general shuffles in terms of shuffles of finite words, a result which does not hold for fair shuffles. Associativity of shuffling is an immediate corollary.\",\n \"Title: Guaranteeing Correct Evolution Of Software Product Lines\\nYear: 2012\\n\\nAbstract: Researchers from the Software Engineering and Architecture group, University of L'Aquila, together with the Formal Methods and Tools group of ISTI-CNR are developing a novel approach that extends and adapts assume-guarantee reasoning to evolving SPLs in order to guarantee resilience against changes in the product environment. The proposal is to selectively verify and test assume-guarantee properties over only the components affected by the changes.\",\n \"Title: Formal methods and analysis in software product line engineering: 4th edition of FMSPLE workshop series\\nYear: 2013\\n\\nAbstract: FMSPLE 2013 is the fourth edition of the FMSPLE workshop series aimed at connecting researchers and practitioners interested in raising the efficiency and the effectiveness of software product line engineering through the application of innovative analysis approaches and formal methods.\",\n \"Title: A Calculus for Team Automata\\nYear: 2008\\n\\nAbstract: Team automata are a formalism for the component-based specification of reactive, distributed systems. Their main feature is a flexible technique for specifying coordination patterns among systems, thus extending I/O automata. Furthermore, for some patterns the language recognized by a team automaton can be specified via those languages recognized by its components. We introduce a process calculus tailored over team automata. Each automaton is described by a process, such that its associated (fragment of a) labeled transition system is bisimilar to the original automaton. The mapping is moreover denotational, since the operators defined on processes are in a bijective correspondence with a chosen family of coordination patterns and that correspondence is preserved by the mapping. We thus extend to team automata a few classical results on I/O automata and their representation by process calculi. Moreover, besides providing a language for expressing team automata, we widen the family of coordination patterns for which an equational characterization of the language associated to a composite automaton can be provided. The latter result is obtained by providing a set of axioms, in ACP-style, for capturing bisimilarity in our calculus.\",\n \"Title: Teams in grammar systems: hybridity and weak rewriting\\nYear: 1996\\n\\nAbstract: Some new ideas in the theory of teams in grammar systems are introduced and studied. Traditionally, a team is formed from a finite number of sets of productions and in every derivation step, one production from each component is used to rewrite a symbol of the sentential form. Hence rewriting is done in parallel. Several derivation modes are considered, varying from using a team exactly one time to using it a maximal amount of times. Here, the possibility of different teams having different...\",\n \"Title: CMC-UMC: a framework for the verification of abstract service-oriented properties\\nYear: 2009\\n\\nAbstract: CMC and UMC are two prototypical instantiations of a common logical verification framework for the analysis of functional properties of service-oriented systems. The service-oriented SocL logic is used to describe the required system properties. Computational models of the system can be built either using the COWS specification language or designing the system as a collection of interacting UML state machines, and an on-the-fly model checker can be used to verify the satisfaction of the requirements and possibly to generate counterexamples or witnesses for them. An automotive case study is used to illustrate the overall framework.\",\n \"Title: A state/event-based model-checking approach for the analysis of abstract system properties\\nYear: 2011\\n\\nAbstract: We present the UMC framework for the formal analysis of concurrent systems specified by collections of UML state machines. The formal model of a system is given by a doubly labelled transition system, and the logic used to specify its properties is the state-based and event-based logic UCTL. UMC is an on-the-fly analysis framework which allows the user to interactively explore a UML model, to visualize abstract behavioural slices of it and to perform local model checking of UCTL formulae. An automotive scenario from the service-oriented computing (SOC) domain is used as case study to illustrate our approach.\",\n \"Title: Assisting the design of a groupware system\\nYear: 2009\\n\\nAbstract: Product Data Management (PDM) systems support the product/document management of design processes such as those typically used in the manufacturing industry. They allow enterprises to capture, organise, automate and share engineering information in an efficient way. The efficient handling of queries on product information and the uploading and downloading of families of related files for modification by designers are essential aspects of such systems. The efficiency of the system as perceived by its clients depends on its correct functioning, but also for a significant part on its performance aspects. In this article, we apply both qualitative and stochastic model-checking techniques to evaluate various usability and performance aspects of the thinkteam PDM system, and of several proposed extensions, thereby assisting the design phase of an industrial groupware system.\",\n \"Title: Team Automata for Security: - A Survey -.\\nYear: 2005\\n\\nAbstract: In [Kleijn, J., Team Automata for CSCW – A Survey –, Petri Net Technology for Communication-Based Systems—Advances in Petri Nets, LNCS 2472, Springer, 2003, 295–320], Kleijn presented a survey of the use of team automata for the specification and analysis of phenomena from the field of computer supported cooperative work, in particular notions related to groupware systems. In this paper we present a survey of the use of team automata for the specification and analysis of some issues from the field of security. In particular, we show how team automata can adequately be used to model and verify various access control policies, multicast/broadcast communication protocols, and general (cryptographic) communication protocols.\",\n \"Title: Team automata for spatial access control\\nYear: 2001\\n\\nAbstract: Team automata provide a framework for capturing notions like coordination, collaboration, and cooperation in distributed systems. They consist of an abstract specification of components of a system and allow one to describe different Interconnection mechanisms based upon the concept of \\\"shared actions\\\" This document considers access control mechanisms in the context of the team automata model. It demonstrates the model usage and utility for capturing information security and protection structures, and critical coordinations between these structures. On the basis of a spatial access metaphor, various known access control strategies are given a rigorous formal description in terms of synchronizations in team automata.\",\n \"Title: Model Checking Publish/Subscribe Notification for thinkteam®\\nYear: 2005\\n\\nAbstract: This paper reports on the fruitful combination of academic experience with formal modelling techniques and industrial experience with requirements exploration. We study the addition of a publish/subscribe notification service to thinkteamaathinkteam is a registered trademark of think3, Inc. For details: http://www.think3.com., a ready-to-use Product Data Management application developed by think3. thinkteam allows enterprises to capture, organise, automate, and share engineering product information and it is an example of an asynchronous and dispersed group-ware system. We define an abstract specification (model) of the groupware protocol underlying thinkteam and augment it with a publish/subscribe notification service. Consequently, we show a number of important correctness properties of the thinkteam model, some of which are also relevant to groupware protocols in general. In particular, we show that by adding a publish/subscribe notification service to thinkteam, the user's awareness of the status of the development of the engineering product and the activities of the design team increases.\",\n \"Title: Combining declarative and procedural views in the specification and analysis of product families\\nYear: 2013\\n\\nAbstract: We introduce the feature-oriented language FLan as a proof of concept for specifying both declarative aspects of product families, namely constraints on their features, and procedural aspects, namely feature configuration and run-time behaviour. FLan is inspired by the concurrent constraint programming paradigm. A store of constraints allows one to specify in a declarative way all common constraints on features, including inter-feature constraints. A standard yet rich set of process-algebraic operators allows one to specify in a procedural way the configuration and behaviour of products. There is a close interaction between both views: (i) the execution of a process is constrained by its store to forbid undesired configurations; (ii) a process can query a store to resolve design and behavioural choices; (iii) a process can update the store by adding new features. An implementation in the Maude framework allows for a variety of formal automated analyses of product families specified in FLan, ranging from consistency checking to model checking.\",\n \"Title: A compositional framework to derive product line behavioural descriptions\\nYear: 2012\\n\\nAbstract: Modelling variability in product families has been the subject of extensive study in the literature on Software Product Lines, especially that concerning Feature Modelling. In recent years, we have laid the basis for the study of the application of temporal logics to the formal modelling of behavioural variability in product family definitions. A critical point in this formalization is to give an adequate representation of the elements of the feature model and their relation with the behaviour of the many products that are to be derived from the family. To this aim, we propose a methodology to systematize this step as much as possible, in order to allow the derivation of behavioural models that are general enough to capture the behaviour of all consistent products belonging to the family.\",\n \"Title: On competence in CD grammar systems\\nYear: 2004\\n\\nAbstract: We investigate the generative power of cooperating distributed grammar systems (CDGSs), if the cooperation protocol is based on the level of competence on the underlying sentential form. A component is said to be = k-competent (≤ k-, ≥ k-competent, resp.) on a sentential form if it is able to rewrite exactly k (at most k, at least k, resp.) different nonterminals appearing in that string. In most cases CDGSs working according to the above described cooperation strategy turn out to give new characterizations of the language families based on random context conditions, namely random context (context-free) languages and the biologically motivated family of languages generated by ET0L systems with random context. Thus, the results presented in this paper can shed new light on some longstanding open problems in the theory of regulated rewriting.\",\n \"Title: Product Lines For Service Oriented Applications - Pl For Soa\\nYear: 2011\\n\\nAbstract: PL for SOA proposes, formally, a software engineering methodology, development techniques and support tools for the provision of service product lines. We propose rigorous modeling techniques for the specification and verification of formal notations and languages for service computing with inclinations of variability. Through these cutting-edge technologies, increased levels of flexibility and adaptivity can be achieved. This will involve developing semantics of variability over behavioural models of services. Such tools will assist organizations to plan, optimize and control the quality of software service provision, both at design and at run time by making it possible to develop flexible and cost-effective software systems that support high levels of reuse. We tackle this challenge from two levels. We use feature modeling from product line engineering and, from a services point of view, the orchestration language Orc. We introduce the Smart Grid as the service product line to apply the techniques to.\",\n \"Title: Variability and Rigour in Service Computing Engineering\\nYear: 2011\\n\\nAbstract: We present a research agenda on an emerging topic in software engineering, namely the synergy between Software Product Line Engineering (SPLE) and Service-Oriented Computing (SOC). Our proposal is to develop rigorous modelling techniques as well as analysis and verification support tools for assisting organisations to plan, optimise, and control the quality of 'software service' provision, both at design time and at run time. We foresee a flexible engineering methodology according to which 'software service line organisations' can develop novel classes of service-oriented applications that can easily be adapted to customer requirements as well as to changes in the context in which, and while, they execute. By superposing variability mechanisms on current languages for service engineering, based on policies and strategies defined by service providers, we envision the possibility of identifying variability points that can be triggered at run time to increase adaptability and optimise the (re)use of resources.\",\n \"Title: Assume-guarantee testing of evolving software product line architectures\\nYear: 2012\\n\\nAbstract: Despite some work on testing software product lines, maintaining the quality of products when a software product line evolves is still an open problem. In this paper, we propose a novel assume-guarantee testing approach as a solution to the following research question: how can we verify the correct functioning of products of an software product line when core components evolve? The underlying idea is to retest only some of the products that conform to the software product line architecture and to infer, using assume-guarantee reasoning, the correctness of the other products. Assume-guarantee reasoning moreover permits the retesting of only those components that are affected by the changes.\",\n \"Title: Validating reconfigurations of reo circuits in an e-Banking scenario\\nYear: 2013\\n\\nAbstract: We formalize dynamic reconfiguration of Reo circuits (which can be thought of as multi-party communication infrastructures built from primitive channels) through graph transformation, and apply it to a scenario from the Finance domain: a critical infrastructure controlling the business process of an e-banking system. In this scenario, reconfiguration is triggered as soon as the communication buffers reach specific predefined thresholds of congestion. These constraints are implemented inside the Reo model by associating suitable predicates to channels, thus extending previous results on the use of graph transformation for the reconfiguration of Reo's graphical structures.\",\n \"Title: Towards model checking stochastic aspects of the thinkteam user interface\\nYear: 2005\\n\\nAbstract: Stochastic model checking is a recent extension of traditional model-checking techniques for the integrated analysis of both qualitative and quantitative system properties. In this paper we show how stochastic model checking can be conveniently used to address a number of usability concerns that involve quantitative aspects of a user interface for the industrial groupware system thinkteam. thinkteam is a ready-to-use Product Data Management application developed by think3. It allows enterprises to capture, organise, automate, and share engineering product information and it is an example of an asynchronous and dispersed groupware system. Several aspects of the functional correctness, such as concurrency aspects and awareness aspects, of the groupware protocol underlying thinkteam and of its planned publish/subscribe notification service have been addressed in previous work by means of a traditional model-checking approach. In this paper we investigate the trade-off between two different design options for granting users access to files in the database: a retrial approach and a waiting-list approach and show how stochastic model checking can be used for such analyses.\",\n \"Title: A case study on the automated verification of groupware protocols\\nYear: 2005\\n\\nAbstract: We report on a fruitful combination of applying academic experience with formal modelling and verification techniques to an industrial case study. The goal of the case study was to investigate a priori, i.e. before implementation, the effects of adding a lightweight and easy-to-use publish/subscribe (event) notification service to thinkteam--an asynchronous and dispersed groupware system which was developed by think3. Researchers from the Formal Methods and Tools (FM&T) group of ISTI-CNR--with a longstanding experience in research on the development and application of formal methods, notations, and software tools for the specification, design, and verification of complex computer systems--therefore teamed up with think3--a global provider of integrated product development solutions that provides mechanical design and Product Data Management (PDM) software catering the product management needs of design processes in the manufacturing industry. The technical details of this joint research effort have been documented elsewhere, here we report on the lessons learned from this experience.\",\n \"Title: Towards Security Analyses of an Identity Federation Protocol for Web Services in Convergent Networks\\nYear: 2007\\n\\nAbstract: We describe a formal approach to the analysis of security aspects of an identity federation protocol for web services in convergent networks. This network protocol was proposed by Telecom Italia as a solution to allow end users to access services on the web through different access networks without explicitly providing any credentials, while the service providers can trust the user's identity information provided by the access networks and access some user data. As a first step towards a full-blown formal security analysis of the protocol, we specify three user scenarios in the process algebra Crypto-CCS and verify the vulnerability of one of these specifications w.r.t. a man-in-the-middle attack with the model checker PaMoChSA.\",\n \"Title: Coherent Branching Feature Bisimulation\\nYear: 2015\\n\\nAbstract: Progress in the behavioral analysis of software product lines at the family level benefits from further development of the underlying semantical theory. Here, we propose a behavioral equivalence for feature transition systems (FTS) generalizing branching bisimulation for labeled transition systems (LTS). We prove that branching feature bisimulation for an FTS of a family of products coincides with branching bisimulation for the LTS projection of each the individual products. For a restricted notion of coherent branching feature bisimulation we furthermore present a minimization algorithm and show its correctness. Although the minimization problem for coherent branching feature bisimulation is shown to be intractable, application of the algorithm in the setting of a small case study results in a significant speed-up of model checking of behavioral properties.\",\n \"Title: Quantitative Analysis Of Probabilistic Models Of Software Product Lines With Statistical Model Checking\\nYear: 2015\\n\\nAbstract: We investigate the suitability of statistical model checking techniques for analysing quantitative properties of software product line models with probabilistic aspects. For this purpose, we enrich the feature-oriented language FLAN with action rates, which specify the likelihood of exhibiting particular behaviour or of installing features at a specific moment or in a specific order. The enriched language (called PFLAN) allows us to specify models of software product lines with probabilistic configurations and behaviour, e.g. by considering a PFLAN semantics based on discrete-time Markov chains. The Maude implementation of PFLAN is combined with the distributed statistical model checker MultiVeStA to perform quantitative analyses of a simple product line case study. The presented analyses include the likelihood of certain behaviour of interest (e.g. product malfunctioning) and the expected average cost of products.\",\n \"Title: A Quantitative Approach To The Design And Analysis Of Collective Adaptive Systems For Smart Cities\\nYear: 2014\\n\\nAbstract: It's smart to be fair. Researchers from the Formal Methods and Tools group of ISTICNR are working on scalable analysis techniques to support smart applications for the efficient and equitable sharing of resources in the cities of our future. The research is being carried out under the European FET-Proactive project, QUANTICOL.\",\n \"Title: Kandisti: A Family Of Model Checkers For The Analysis Of Software Designs\\nYear: 2014\\n\\nAbstract: Driven by a series of European projects, researchers from the Formal Methods and Tools lab of ISTICNR have developed a family of model-checking tools for the computer-aided verification of the correctness of software designs. To date, these tools have been applied to a range of case studies in the railway, automotive and telecommunication fields.\",\n \"Title: On Distributed Cooperation and Synchronised Collaboration.\\nYear: 2014\\n\\nAbstract: In CD grammar systems, the rewriting process is distributed over component grammars that take turns in the derivation of new symbols. Team automata however collaborate by synchronising their action...\",\n \"Title: Model Checking Value-Passing Modal Specifications\\nYear: 2014\\n\\nAbstract: Formal modelling and verification of variability concepts in product families has been the subject of extensive study in the literature on Software Product Lines. In recent years, we have laid the basis for the use of modal specifications and branching-time temporal logics for the specification and analysis of behavioural variability in product family definitions. A critical point in this formalization is the lack of a possibility to model an adequate representation of the data that may need to be described when considering real systems. To this aim, we now extend the modelling and verification environment that we have developed for specifications interpreted over Modal Transition Systems, by adding the possibility to include data in the specifications. In concert with this, we also extend the variability-specific modal logic and the associated special-purpose model checker VMC. As a result, it offers the possibility to efficiently verify formulas over possibly infinite-state systems by using the on-the-fly bounded model-checking algorithms implemented in the model checker. We illustrate our approach by means of a simple yet intuitive example: a bike-sharing system.\",\n \"Title: VMC: recent advances and challenges ahead\\nYear: 2014\\n\\nAbstract: The variability model checker VMC accepts a product family specified as a Modal Transition System (MTS) with additional variability constraints. Consequently, it offers behavioral variability analyses over both the family and its valid product behavior. This ranges from product derivation and simulation to efficient on-the-fly model checking of logical properties expressed in a variability-aware version of action-based CTL. In this paper, we first explain the reasons and assumptions underlying the choice for a modeling and analysis framework based on MTSs. Subsequently, we present recent advances on proving inheritance of behavioral analysis properties from a product family to its valid products. Finally, we illustrate challenges remaining for the future.\",\n \"Title: Software product line analysis with mCRL2\\nYear: 2014\\n\\nAbstract: The mCRL2 language and supporting software provide a state-of-the-art tool suite for the verification of distributed systems. In this paper, we present the general principles, extrapolated from [7,8], which make us believe that mCRL2 can also be used for behavioral variability analysis of product families. The mCRL2 data language allows to smoothly deal with feature sets and attributes, its process language is sufficiently rich to model feature selection, as well as product behavior based on an FTS-like semantics. Because of the feature-orientation, our modeling strategy allows a natural refactoring of the semantic model of a product family into a parallel composition of components that reflects coherent sets of features. This opens the way for dedicated abstraction and reduction techniques that strengthen the prospect of a scalable verification approach to software product lines. In this paper, we sketch how to model product families in mCRL2 and how to apply a modular verification method, preparing the ground to further assess the scalability of our approach, in particular regarding model checking.\",\n \"Title: Challenges in Modelling and Analyzing Quantitative Aspects of Bike-Sharing Systems\\nYear: 2014\\n\\nAbstract: Bike-sharing systems are becoming popular not only as a sustainable means of transportation in the urban environment, but also as a challenging case study that presents interesting run-time optimization problems. As a side-study within a research project aimed at quantitative analysis that used such a case study, we have observed how the deployed systems enjoy a wide variety of different features. We have therefore applied variability analysis to define a family of bike-sharing systems, and we have sought support in available tools. We have so established a tool chain that includes academic tools that provide different functionalities regarding the analysis of software product lines, from feature modelling to product derivation and from quantitative evaluation of the attributes of products to model checking value-passing modal specifications. The tool chain is currently experimented inside the mentioned project as a complement to more sophisticated product-based analysis techniques.\",\n \"Title: Towards Modular Verification of Software Product Lines with mCRL2\\nYear: 2014\\n\\nAbstract: We introduce by means of an example a modular verification technique for analyzing the behavior of software product lines using the <Emphasis FontCategory=\\\\\\\"NonProportional\\\\\\\"mCRL2</Emphasis> toolset. Based on feature-driven borders, we divide a behavioral model of a product line into a set of separate components with interfaces and a driver process to coordinate them. Abstracting from irrelevant components, we verify properties over a smaller behavioral model, which not only simplifies the model checking task but also makes the result amenable for reuse. This is a fundamental step forward for the approach to scale up to industrial-size product lines.\",\n \"Title: Communication and Compatibility in Systems of Systems: Correctness-by-Construction.\\nYear: 2015\\n\\nAbstract: Society is still trying to catch up with technology in the wake of the digital revolution of the last twenty years. Current systems need to be both heterogeneous and able to deal with enormous volumes of data coming from uncertain environments; consequently it is essential to be able to automatically assess the correctness of interactions. To guarantee that a system of systems, comprising a conglomerate of cooperating reactive components, can be trusted, and that the system as a whole behaves as intended, requires a thorough understanding of its communication behaviour. Once local interactions are identified, abstractions can support the identification of incompatibility of systems that should cooperate within a larger system.\",\n \"Title: Design and validation of variability in product lines\\nYear: 2011\\n\\nAbstract: We propose an emerging solution technique, pushing the application of model-checking techniques to the design and validation of variability in a product line (PL), mainly aimed at those industrial domains where model-based development is adopted for the development of safety-critical systems.\",\n \"Title: Using FMC for family-based analysis of software product lines\\nYear: 2015\\n\\nAbstract: We show how the FMC model checker can successfully be used to model and analyze behavioural variability in Software Product Lines. FMC accepts parameterized specifications in a process-algebraic input language and allows the verification of properties of such models by means of efficient on-the-fly model checking. The properties can be expressed in a logic that allows to correlate the parameters of different actions within the same formula. We show how this feature can be used to tailor formulas to the verification of only a specific subset of products of a Software Product Line, thus allowing for scalable family-based analyses with FMC. We present a proof-of-concept that shows the application of FMC to an illustrative Featured Transition System from the literature.\",\n \"Title: Applying the product lines paradigm to the quantitative analysis of collective adaptive systems\\nYear: 2015\\n\\nAbstract: Engineering a Collective Adaptive System (CAS) requires the support of a framework for quantitative modeling and analysis of the system. In order to jointly address variability and quantitative analysis, we apply the Product Lines paradigm, considered at the level of system engineering, to a case study of the European project QUANTICOL, by first defining a reference feature model and then adding feature attributes and global quantitative constraints, in the form of a Clafer attributed feature model. ClaferMOOVisualizer is subsequently used for quantitative analyses and multi-objective optimization of the resulting attributed feature model.\",\n \"Title: Statistical analysis of probabilistic models of software product lines with quantitative constraints\\nYear: 2015\\n\\nAbstract: We investigate the suitability of statistical model checking for the analysis of probabilistic models of software product lines with complex quantitative constraints and advanced feature installation options. Such models are specified in the feature-oriented language QFLan, a rich process algebra whose operational behaviour interacts with a store of constraints, neatly separating product configuration from product behaviour. The resulting probabilistic configurations and behaviour converge seamlessly in a semantics based on DTMCs, thus enabling quantitative analyses ranging from the likelihood of certain behaviour to the expected average cost of products. This is supported by a Maude implementation of QFLan, integrated with the SMT solver Z3 and the distributed statistical model checker MultiVeStA. Our approach is illustrated with a bikes product line case study.\",\n \"Title: Towards Automatic Decision Support for Bike-Sharing System Design\\nYear: 2015\\n\\nAbstract: Public bike-sharing systems are a popular means of sustainable urban mobility, but their successful introduction in a city stands or falls with their specific designs. What kind of bikes and docking stations are needed, how many and where to install them? How to avoid as much as possible that stations are completely empty or full for some period? Hence, a bike-sharing system can be seen both as a highly reconfigurable system and as a collective adaptive system. In this paper, we present two complementary strategies for the evaluation of bike-sharing system designs by means of automated tool support. We use the Clafer toolset to perform multi-objective optimisation of attributed feature models known from software product line engineering and the recently developed mean field model checker FlyFast to assess performance and user satisfaction aspects of variants of large-scale bike-sharing systems. The combined use of these analysis approaches is a preliminary step in the direction of automatic decision support for the initial design of a bike-sharing system as well as its successive adaptations and reconfigurations that considers both qualitative and performance aspects.\",\n \"Title: Towards A Feature Mu-Calculus Targeting Spl Verification\\nYear: 2016\\n\\nAbstract: The modal mu-calculus mu L is a well-known fixpoint logic to express and model check properties interpreted over labeled transition systems. In this paper, we propose two variants of the mu-calculus, mu L-f and mu L-f', for feature transition systems. For this, we explicitly incorporate feature expressions into the logics, allowing operators to select transitions and behavior restricted to specific products and subfamilies. We provide semantics for mu L-f and mu L-f' and relate the two new mu-calculi and mu L to each other. Next, we focus on the analysis of SPL behavior and show how our formalism can be applied for product-based verification with mu L-f as well as family-based verification with mu L-f'. We illustrate by means of a toy example how properties can be model checked, exploiting an embedding of mu L-f' into the mu-calculus with data.\",\n \"Title: Proceedings of the Workshop on FORmal methods for the quantitative Evaluation of Collective Adaptive SysTems.\\nYear: 2016\\n\\nAbstract: Collective Adaptive Systems (CAS) consist of a large number of spatially distributed heterogeneous entities with decentralised control and varying degrees of complex autonomous behaviour that may be competing for shared resources even when collaborating to reach common goals. It is important to carry out thorough quantitative modelling and analysis and verification of their design to investigate all aspects of their behaviour before they are put into operation. This requires combinations of formal methods and applied mathematics which moreover scale to large-scale CAS. The primary goal of FORECAST is to raise awareness in the software engineering and formal methods communities of the particularities of CAS and the design and control problems which they bring.\",\n \"Title: Supervisory Controller Synthesis for Product Lines Using CIF 3.\\nYear: 2016\\n\\nAbstract: Using the CIF 3 toolset, we illustrate the general idea of controller synthesis for product line engineering for a prototypical example of a family of coffee machines. The challenge is to integrate a number of given components into a family of products such that the resulting behaviour is guaranteed to respect an attributed feature model as well as additional behavioural requirements. The proposed correctness-by-construction approach incrementally restricts the composed behaviour by subsequently incorporating feature constraints, attribute constraints and temporal constraints. The procedure as presented focusses on synthesis, but leaves ample opportunity to handle e.g.uncontrollable behaviour, dynamic reconfiguration, and product-and family-based analysis.\",\n \"Title: Statistical Model Checking for Product Lines.\\nYear: 2016\\n\\nAbstract: We report on the suitability of statistical model checking for the analysis of quantitative properties of product line models by an extended treatment of earlier work by the authors. The type of analysis that can be performed includes the likelihood of specific product behaviour, the expected average cost of products (in terms of the attributes of the products' features) and the probability of features to be (un) installed at runtime. The product lines must be modelled in QFLan, which extends the probabilistic feature-oriented language PFLan with novel quantitative constraints among features and on behaviour and with advanced feature installation options. QFLan is a rich process-algebraic specification language whose operational behaviour interacts with a store of constraints, neatly separating product configuration from product behaviour. The resulting probabilistic configurations and probabilistic behaviour converge in a discrete-time Markov chain semantics, enabling the analysis of quantitative properties. Technically, a Maude implementation of QFLan, integrated with Microsoft's SMT constraint solver Z3, is combined with the distributed statistical model checker MultiVeStA, developed by one of the authors. We illustrate the feasibility of our framework by applying it to a case study of a product line of bikes.\",\n \"Title: Correctness-by-Construction and Post-hoc Verification: Friends or Foes?\\nYear: 2016\\n\\nAbstract: While correctness-by-construction and post-hoc verification are traditionally considered to provide two opposing views on proving software systems to be free from errors, nowadays numerous techniques and application fields witness initiatives that try to integrate elements of both ends of the spectrum. The ultimate aim is not merely to improve the correctness of software systems but also to improve their time-to-market, and to do so at a reasonable cost. This track brings together researchers and practitioners interested in the inherent 'tension' that is usually felt when trying to balance the pros and cons of correctness-by-construction versus post-hoc verification.\",\n \"Title: Variability-Based Design of Services for Smart Transportation Systems.\\nYear: 2016\\n\\nAbstract: A smart transportation system can be seen as an aggregate of transportation opportunities and services, accompanied by advanced management services that make the access to the system easier for the user. In this paper, we exploit the product line paradigm to address the variability of an exemplary smart transportation system: a bike-sharing system. Improving the satisfaction of a user of a bike-sharing system includes providing information at runtime on the filling degree of the docking stations in the near future. To fulfill this expectation, a prediction service is needed to infer the probability that at a certain time of the day a user will return a bike to or take one from a station. In earlier studies, several possible advanced smart predictive services were identified. The choice of which services to offer to users by the managers of a bike-sharing system is influenced by minimizing the costs while maximizing customer satisfaction. To aid the managers, we modeled a family of smart bike-sharing services, after which an attributed feature model was used to augment the model with quantitative attributes related to cost and customer satisfaction, allowing for a multi-objective optimization by dedicated tools. We observe that the performance of the smart prediction services, and therefore of the related customer satisfaction, is highly dependent on the amount of collected historical data on which the predictive analysis is based. Therefore the result of the optimization also depends on this factor, which evolves over time.\",\n \"Title: Family-Based Model Checking with mCRL2.\\nYear: 2017\\n\\nAbstract: Family-based model checking targets the simultaneous verification of multiple system variants, a technique to handle feature-based variability that is intrinsic to software product lines SPLs. We present an approach for family-based verification based on the feature $$\\\\\\\\mu $$-calculus $$\\\\\\\\mu L {}_f$$, which combines modalities with feature expressions. This logic is interpreted over featured transition systems, a well-accepted model of SPLs, which allows one to reason over the collective behavior of a number of variants a family of products. Via an embedding into the modal $$\\\\\\\\mu $$-calculus with data, underpinned by the general-purpose mCRL2 toolset, off-the-shelf tool support for﾿$$\\\\\\\\mu L {}_f$$ becomes readily available. We illustrate the feasibility of our approach on an SPL benchmark model and show the runtime improvement that family-based model checking with mCRL2 offers with respect to model checking the benchmark product-by-product.\",\n \"Title: A framework for quantitative modeling and analysis of highly (re)configurable systems.\\nYear: 2017\\n\\nAbstract: This paper presents our approach to the quantitative modeling and analysis of highly (re)configurable systems, such as software product lines. Different combinations of the optional features of such a system give rise to combinatorially many individual system variants. We use a formal modeling language that allows us to model systems with probabilistic behavior, possibly subject to quantitative feature constraints, and able to dynamically install, remove or replace features. More precisely, our models are defined in the probabilistic feature-oriented language QFLAN, a rich domain specific language (DSL) for systems with variability defined in terms of features. QFLAN specifications are automatically encoded in terms of a process algebra whose operational behavior interacts with a store of constraints, and hence allows to separate system configuration from system behavior. The resulting probabilistic configurations and behavior converge seamlessly in a semantics based on discrete-time Markov chains, thus enabling quantitative analysis. Our analysis is based on statistical model checking techniques, which allow us to scale to larger models with respect to precise probabilistic analysis techniques. The analyses we can conduct range from the likelihood of specific behavior to the expected average cost, in terms of feature attributes, of specific system variants. Our approach is supported by a novel Eclipse-based tool which includes state-of-the-art DSL utilities for QFLAN based on the Xtext framework as well as analysis plug-ins to seamlessly run statistical model checking analyses. We provide a number of case studies that have driven and validated the development of our framework.\",\n \"Title: Orchestration Of Dynamic Service Product Lines With Featured Modal Contract Automata\\nYear: 2017\\n\\nAbstract: In Service-Oriented Computing, contracts provide a way to characterise the behavioural conformance of a composition of services, and to guarantee that the results do not lead to spurious compositions. Adding variability leads to a product line of services capable of adapting to customer requirements and to changes in the context in which they operate. To this aim, we extended a previously introduced formal model of service contracts. In particular, we included: (i) feature-based constraints and (ii) four classes of service requests to characterise different variants of service agreement. We then exploited Supervisory Control Theory to define an algorithm to synthesise an orchestration of services that satisfies: (i) all feature constraints of the service product line, and (ii) the maximal number of service requests for which an agreement can be reached. Moreover, such an orchestration of a service product line, whose number of products is potentially exponential in the number of features, can be synthesised from only a subset of its products. A prototypical tool supports the developed theory. In this short paper, we provide the intuition for our approach and illustrate it by means of a Hotel reservation service product line.\",\n \"Title: Formal Methods for the Railway Sector.\\nYear: 2018\\n\\nAbstract: Researchers from the Formal Methods and Tools group of ISTI-CNR are working on a review and assessment of the main formal modelling and verification languages and tools used in the railway domain, with the aim of evaluating the actual applicability of the most promising ones to a moving block signalling system model provided by an industrial partner. The research is being conducted in the context of the H2020 Shift2Rail project ASTRail.\",\n \"Title: Replicated Computations Results (RCR) Report for \\\"Design and Verification of Trusted Collective Adaptive Systems\\\".\\nYear: 2018\\n\\nAbstract: The article “Design and Verification of Trusted Collective Adaptive Systems” by Aldini proposes a process-algebraic framework for modeling and verifying trusted collective adaptive systems. To favor reuse, the system and trust models can be specified separately, only to be integrated at the semantic level. Through a combination of behavioral equivalence checking and model checking against branching-time temporal logic with trust predicates, the framework allows comparative analyses of different trust models as well as analyses of the effects of attacks to the trust models. The applicability of the formal framework is illustrated by means of two representative use cases: the security analysis of a trust-incentive service management system and a comparison of two different reputation systems.\\n\\nThis replicated computations results report focuses on the reproducibility of the experiments performed in the aforementioned article, i.e. on the automatic verification of properties against models of these use cases encoded in the well-known NuSMV model checker. It was straightforward to reproduce all results from the article in reasonable time using a standard laptop machine.\",\n \"Title: QFLan: A Tool for the Quantitative Analysis of Highly Reconfigurable Systems.\\nYear: 2018\\n\\nAbstract: QFLan offers modeling and analysis of highly reconfigurable systems, like product lines, which are characterized by combinatorially many system variants (or products) that can be obtained via different combinations of installed features. The tool offers a modern integrated development environment for the homonym probabilistic feature-oriented language. QFLan allows the specification of a family of products in terms of a feature model with quantitative attributes, which defines the valid feature combinations, and probabilistic behavior subject to quantitative constraints. The language's behavioral part enables dynamic installation, removal and replacement of features. QFLan has a discrete-time Markov chain semantics, permitting quantitative analyses. Thanks to a seamless integration with the statistical model checker MultiVeStA, it allows for analyses like the likelihood of specific behavior or the expected average value of non-functional aspects related to feature attributes.\",\n \"Title: Orchestration Synthesis For Real-Time Service Contracts\\nYear: 2018\\n\\nAbstract: Service contracts offer a way to define the desired behavioural compliance of a composition of services, characterised by the fulfilment of all requirements (e. g. service requests) by obligations (e. g. service offers). Depending on their granularity, requirements may vary according to their criticality and contain real-time aspects (e. g. service expiration time). Synthesis of safe orchestrations, the standard method to refine spurious compositions into compliant ones, is of paramount importance. Ideally, safe orchestrations solve competition among matching requests/ offers, respecting criticalities and time constraints, in the best possible way. The contribution of this paper is (i) the introduction of timed service contract automata, a novel formalisation of service contracts with (ii) real-time constraints and (iii) service requests with varying levels of criticality, and a means to compute their (iv) composition and (v) safe orchestration. Orchestration is based on the synthesis of the most permissive controller from supervisory control theory, computed using the concept of zones from timed games. An intuitive example illustrates the contribution.\",\n \"Title: Product line models of large cyber-physical systems: the case of ERTMS/ETCS.\\nYear: 2018\\n\\nAbstract: A product line perspective may help to understand the possible variants in interactions between the subsystems of a large, cyber-physical system. This observation is exemplified in this paper by proposing a feature model of the family of ERTMS/ETCS train control systems and their foreseen extensions. This model not only shows the different components that have to be installed when deploying the system at the different levels established by the ERTMS/ETCS standards, but it also helps to identify and discuss specific issues, such as the borders between onboard and wayside equipment, different manufacturers of the subsystems, interoperability among systems developed at different levels, backward compatibility of trains equipped with higher level equipment running on lines equipped with lower level equipment, and evolution towards future trends of railway signalling. The feature model forms the basis for formal modelling of the behaviour of the critical components of the system and for evaluating the overall cost, effectiveness and sustainability, for example by adding cost and performance attributes to the feature model.\",\n \"Title: Modelling and analysis with featured modal contract automata.\\nYear: 2018\\n\\nAbstract: Featured modal contract automata (FMCA) have been proposed as a suitable formalism for modelling contract-based dynamic service product lines. A contract is a behavioural description consisting of offers and necessary and permitted service requests with different levels of criticality, to be matched with corresponding offers of other FMCA. Each contract is equipped with a feature constraint, whose features are offers or requests, and characterises a valid product orchestration. A safe orchestration of a product fulfils all necessary and the maximum number of permitted requests, such that all enabled features are available and none of its disabled features is. The entire product line orchestration can be computed from a subset of valid product orchestrations, by exploiting their (partial) ordering. The open-source prototypical toolkit FMCAT supports the specification and orchestration of FMCA, and it interfaces with FeatureIDE for importing feature models and their valid products. In this experience report, we show how to model a Hotel service product line with FMCA and how to analyse it with FMCAT.\",\n \"Title: On the expressiveness of modal transition systems with variability constraints.\\nYear: 2019\\n\\nAbstract: We demonstrate that modal transition systems with variability constraints are equally expressive as featured transition systems, by defining a transformation of the latter into the former, a transformation of the former into the latter, and proving the soundness and completeness of both transformations. Modal transition systems and featured transition systems are widely recognised as fundamental behavioural models for software product lines and our results thus contribute to the expressiveness hierarchy of such basic models studied in other papers published in this journal.\",\n \"Title: Quantitative Variability Modeling and Analysis\\nYear: 2019\\n\\nAbstract: The explicit management of variability in the development cycle of software-intensive systems has led to a plethora of modeling and analysis techniques tailored to deal with behavioral validation of such configurable systems. Most of the work, however, focuses on qualitative (i.e. functional) requirements. Recently, there is growing interest in variability modeling and analysis techniques that do explicitly consider quantitative (i.e. non-functional) requirements, such as dependability, energy consumption, security, and cost.\\n\\nToday's software is embedded in a variety of smart and critical systems that run in environments where events occur randomly and affect the system, and to which it needs to adapt. Therefore, quantitative modeling and analysis is currently a hot topic. The panel on Quantitative Variability Modeling and Analysis (QSPL) discusses the latest quantitative techniques and how to apply them to variability modeling and analysis of software-intensive systems.\",\n \"Title: Guest Editorial for the Special Issue on FORmal methods for the quantitative Evaluation of Collective Adaptive SysTems (FORECAST).\\nYear: 2018\\n\\nAbstract: An abstract is not available.\",\n \"Title: Formal methods and automated verification of critical systems.\\nYear: 2018\\n\\nAbstract: Critical (software) systems are all around us. These systems are typically characterised by stringent dependability requirements and demand elevated levels of robustness and fault tolerance. To assure that they function as intended and provide a number of quality guarantees, formal methods and automated verification techniques and tools have been in use in the engineering of such critical systems for many years now. In this introduction to the special issue FMICS–AVoCS on “Formal Methods and Automated Verification of Critical Systems”, we outline a number of recent achievements concerning the use of formal methods and automated verification techniques and tools for the specification and analysis of critical systems from a variety of application domains. These achievements are represented by six selected papers: five were selected from the joint 21st International Workshop on Formal Methods for Industrial Critical Systems and 16th International Workshop on Automated Verification of Critical Systems (FMICS–AVoCS 2016), while one of them was selected after an open call for papers.\",\n \"Title: Formal methods for transport systems.\\nYear: 2018\\n\\nAbstract: Formal methods and verification tools have been in use in the engineering of safety-critical transport systems for well over 30 years. In both the railway and the avionics domain, for instance, formal methods are specifically recommended in current international certification standards for ultra-dependable systems and for products at the highest integrity level. In fact, traditionally, the applications of formal methods and tools to such transport systems concern demonstrating, with the highest levels of assurance, the correct functioning of the software systems involved, such as train signalling systems to avoid collisions. More recently, however, formal methods and verification tools have started to be applied also to the scheduling and management of transport systems or networks, for instance to optimise the exploitation of a railway line or to improve the operational efficiency of a bus network. In this introduction to the special issue on “Formal Methods for Transport Systems”, we outline some recent achievements for each of the above-mentioned types of application of formal methods and tools. These achievements are represented by three selected papers: one was selected from the “Formal Methods and Safety Certification: Challenges in the Railways Domain” track at the seventh International Symposium On Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2016); another one was selected from the 21st International Workshop on Formal Methods for Industrial Critical Systems and the 16th International Workshop on Automated Verification of Critical Systems (FMICS-AVoCS 2016); a final one was selected after an open call for contributions.\",\n \"Title: Survey On Formal Methods And Tools In Railways: The Astrail Approach\\nYear: 2019\\n\\nAbstract: Formal methods and tools have been widely applied to the development of railway systems during the last decades. However, no universally accepted formal framework has emerged, and railway companies wishing to introduce formal methods have little guidance for the selection of the most appropriate methods and tools to adopt. A work package (WP) of the European project ASTRail, funded under the Shift2Rail initiative, addresses this problem, by performing a survey that considers scientific literature, international projects, and practitioners' perspectives to identify a collection of formal methods and tools to be applied in railways. This paper summarises the current results of this WP. We surveyed 114 scientific publications, 44 practitioners, and 8 projects to come to a shortlist of 14 methods considered suitable for system modelling and verification in railways. The methods and tools were reviewed according to a set of functional, language-related, and quality features. The current paper extends the body of knowledge with a set of publicly available documents that can be leveraged by companies for guidance on formal methods selection in railway system development.\",\n \"Title: Featured Team Automata\\nYear: 2021\\n\\nAbstract: We propose featured team automata to support variability in the development and analysis of teams, which are systems of reactive components that communicate according to specified synchronisation types. A featured team automaton concisely describes a family of concrete product models for specific configurations determined by feature selection. We focus on the analysis of communication-safety properties, but doing so product-wise quickly becomes impractical. Therefore, we investigate how to lift notions of receptiveness (no message loss) to the level of family models. We show that featured (weak) receptiveness of featured team automata characterises (weak) receptiveness for all product instantiations. A prototypical tool supports the developed theory.\",\n \"Title: Static Analysis and Family-based Model Checking of Featured Transition Systems with VMC\\nYear: 2021\\n\\nAbstract: A Featured Transition System (FTS) is a formalism for modeling variability in configurable system behavior. The behavior of all variants (products) is modeled in a single compact FTS by associating the possibility to perform an action and transition from one state to another with feature expressions that condition the execution of an action in specific variants. We present a front-end for the research tool VMC. The resulting toolchain allows a modeler to analyze an FTS for ambiguities (dead or false optional transitions and hidden deadlock states), transform an ambiguous FTS into an unambiguous one, and perform an efficient kind of family-based verification of an FTS without hidden deadlock states. We use benchmarks from the literature to demonstrate the novelties offered by the toolchain.\",\n \"Title: Efficient static analysis and verification of featured transition systems\\nYear: 2022\\n\\nAbstract: A Featured Transition System (FTS) models the behaviour of all products of a Software Product Line (SPL) in a single compact structure, by associating action-labelled transitions with features that condition their presence in product behaviour. It may however be the case that the resulting featured transitions of an FTS cannot be executed in any product (so called dead transitions) or, on the contrary, can be executed in all products (so called false optional transitions). Moreover, an FTS may contain states from which a transition can be executed only in some products (so called hidden deadlock states). It is useful to detect such ambiguities and signal them to the modeller, because dead transitions indicate an anomaly in the FTS that must be corrected, false optional transitions indicate a redundancy that may be removed, and hidden deadlocks should be made explicit in the FTS to improve the understanding of the model and to enable efficient verification-if the deadlocks in the products should not be remedied in the first place. We provide an algorithm to analyse an FTS for ambiguities and a means to transform an ambiguous FTS into an unambiguous one. The scope is twofold: an ambiguous model is typically undesired as it gives an unclear idea of the SPL and, moreover, an unambiguous FTS can efficiently be model checked. We empirically show the suitability of the algorithm by applying it to a number of benchmark SPL examples from the literature, and we show how this facilitates a kind of family-based model checking of a wide range of properties on FTSs.\",\n \"Title: Exploring the ERTMS/ETCS full moving block specification: an experience with formal methods\\nYear: 2022\\n\\nAbstract: Shift2Rail is a joint undertaking funded by the EU via its Horizon 2020 program and by main railway stakeholders. Several Shift2Rail projects aim to investigate the application of formal methods to new ERTMS/ETCS railway signalling systems that promise to move European railway forward by guaranteeing high capacity, low cost and improved reliability. We explore the ERTMS/ETCS level 3 full moving block specifications stemming from different Shift2Rail projects using Uppaal and statistical model checking. The results range from novel rigorously formalised requirements to an operational model formally verified against scenarios with multiple trains on a single railway line. From the gained experience, we have distilled future research goals to improve the formal specification and verification of real-time systems, and we discuss some barriers concerning a possible uptake of formal methods and tools in the railway industry.\",\n \"Title: Formal methods and tools for industrial critical systems\\nYear: 2022\\n\\nAbstract: Formal methods and tools have become well established and widely applied to ensure the correctness of fundamental components of industrial critical systems in domains like railways, avionics and automotive. In this Introduction to the special issue, we outline a number of recent achievements concerning the use of formal methods and tools for the specification and verification of critical systems from a variety of industrial domains. These achievements are represented by eight properly revised and extended versions of papers that were selected from the 24th and 25th International Conference on Formal Methods for Industrial Critical Systems (FMICS 2019 and FMICS 2020).\"\n]","isConversational":false},"profile_size":{"kind":"number","value":82,"string":"82"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13241,"string":"13,241"}}},{"rowIdx":13242,"cells":{"question_id":{"kind":"string","value":"Shehzad Khalid"},"question":{"kind":"string","value":"Generate an abstract for the title \"Multiagent Semantical Annotation Enhancement Model for IoT-Based Energy-Aware Data\" using the following items: Internet of Things, Sensor-based data, Semantically enabled, Heterogeneous information sources, Semantic adjustment"},"answer":{"kind":"string","value":"AbstractThe Internet of Things (IoT) is involved in dealing with physical items, gadgets, vehicles, structures, and different things that are inserted into hardware, programming, sensors, and system availability, which empowers these items to gather and trade information. Improving extraction of sensor-based data for energy awareness and then annotating it and converting it into semantically enabled form for analyzing results with the use of improved tools and applications are the focus of this research. However, as the amount of real time data gets huge, it becomes difficult to track results when needed at once. Reconciliation of heterogeneous information sources into an interlinked data is a standout among the most pertinent difficulties for some learning based systems these days. This paper forms suitable elements by a methodology for adjustment of heterogeneous sensor-based Web assets, where different tools and applications like weather detection for self-observing and self-diagnostics use dispersed human specialists and learning. The proposed general model uses a capability of the Semantic Web innovation and concentrates on the part of a semantic adjustment of existing broadly utilized models of information representation to Resource Description Framework (RDF) based semantically rich arrangement. This work is valuable for sorting out and inquiry of the detecting information in the Internet of Things."},"correct_answer":{"kind":"string","value":"AbstractThe Internet of Things (IoT) is involved in dealing with physical items, gadgets, vehicles, structures, and different things that are inserted into hardware, programming, sensors, and system availability, which empowers these items to gather and trade information. Improving extraction of sensor-based data for energy awareness and then annotating it and converting it into semantically enabled form for analyzing results with the use of improved tools and applications are the focus of this research. However, as the amount of real time data gets huge, it becomes difficult to track results when needed at once. Reconciliation of heterogeneous information sources into an interlinked data is a standout among the most pertinent difficulties for some learning based systems these days. This paper forms suitable elements by a methodology for adjustment of heterogeneous sensor-based Web assets, where different tools and applications like weather detection for self-observing and self-diagnostics use dispersed human specialists and learning. The proposed general model uses a capability of the Semantic Web innovation and concentrates on the part of a semantic adjustment of existing broadly utilized models of information representation to Resource Description Framework (RDF) based semantically rich arrangement. This work is valuable for sorting out and inquiry of the detecting information in the Internet of Things."},"documents":{"kind":"list like","value":["Title: Activity classification and anomaly detection using m-mediods based modelling of motion patterns\nYear: 2010\n\nAbstract: Techniques for video object motion analysis, behaviour recognition and event detection are becoming increasingly important with the rapid increase in demand for and deployment of video surveillance systems. Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents a novel technique for classification of motion activity and anomaly detection using object motion trajectory. In the proposed motion learning system, trajectories are treated as time series and modelled using modified DFT-based coefficient feature space representation. A modelling technique, referred to as m-mediods, is proposed that models the class containing n members with m mediods. Once the m-mediods based model for all the classes have been learnt, the classification of new trajectories and anomaly detection can be performed by checking the closeness of said trajectory to the models of known classes. A mechanism based on agglomerative approach is proposed for anomaly detection. Four anomaly detection algorithms using m-mediods based representation of classes are proposed. These includes: (i)global merged anomaly detection (GMAD), (ii) localized merged anomaly detection (LMAD), (iii) global un-merged anomaly detection (GUAD), and (iv) localized un-merged anomaly detection (LUAD). Our proposed techniques are validated using variety of simulated and complex real life trajectory datasets.","Title: Evaluation of Matching Metrics for Trajectory-Based Indexing and Retrieval of Video Clips\nYear: 2005\n\nAbstract: This paper describes a comparative evaluation of three different similarity metrics for trajectory-based indexing and retrieval of video motion clips. The motion paths are generated using a low-level tracking algorithm incorporating first-order Kalman filter and colour appearance models. For simple motion paths, a RANSAC approach can be used to generate smooth trajectories for each tracked object described by low-order polynomials. This allows us to obtain a representative trajectory model even in the case of high numbers of outlier points caused by target mis-detection and multiple occlusions. We show that more complex trajectories including stop-start motions, can be modelled as time series using high order Chebyshev polynomials. Similarity metrics based on coefficient descriptors are shown to have comparable performance to a Hausdorff distance measure when retrieving trajectory-based motion clips but at substantially reduced computational cost. Experimental results are presented to illustrate the comparative performance of different matching metrics on real-world trajectory data collected by a retail store CCTV installation.","Title: Detection and classification of retinal lesions for grading of diabetic retinopathy\nYear: 2014\n\nAbstract: Diabetic Retinopathy (DR) is an eye abnormality in which the human retina is affected due to an increasing amount of insulin in blood. The early detection and diagnosis of DR is vital to save the vision of diabetes patients. The early signs of DR which appear on the surface of the retina are microaneurysms, haemorrhages, and exudates. In this paper, we propose a system consisting of a novel hybrid classifier for the detection of retinal lesions. The proposed system consists of preprocessing, extraction of candidate lesions, feature set formulation, and classification. In preprocessing, the system eliminates background pixels and extracts the blood vessels and optic disc from the digital retinal image. The candidate lesion detection phase extracts, using filter banks, all regions which may possibly have any type of lesion. A feature set based on different descriptors, such as shape, intensity, and statistics, is formulated for each possible candidate region: this further helps in classifying that region. This paper presents an extension of the m-Mediods based modeling approach, and combines it with a Gaussian Mixture Model in an ensemble to form a hybrid classifier to improve the accuracy of the classification. The proposed system is assessed using standard fundus image databases with the help of performance parameters, such as, sensitivity, specificity, accuracy, and the Receiver Operating Characteristics curves for statistical analysis.","Title: Intelligent Optimization Of Wireless Sensor Networks Through Bio-Inspired Computing: Survey And Future Directions\nYear: 2013\n\nAbstract: This survey article is a comprehensive discussion on Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing. The marvelous perfection of biological systems and its different aspects for optimized solutions for non-biological problems is presented here in detail. In the current research inclination, hiring of biological solutions to solve and optimize different aspects of artificial systems' problems has been shaped into an important field with the name of bio-inspired computing. We have tabulated the exploitation of key constituents of biological system for developing bio-inspired systems to represent its importance and emergence in problem solving trends. We have presented how the metaphoric relationship is developed between the two biological and non-biological systems by quoting an example of relationship between prevailing wireless system and the natural system. Interdisciplinary research is playing a splendid contribution for various problems' solving. The process of combining the individuals' output to form a single problem solving solution is depicted in three-stage ensemble design. Also the hybrid solutions from computational intelligence-based optimization are elongated to demonstrate the emergent involvement of these inspired systems with rich references for the interested readers. It is concluded that these perfect creations have remedies for most of the problems in non-biological system.","Title: Motion trajectory clustering for video retrieval using spatio-temporal approximations\nYear: 2005\n\nAbstract: A new technique is proposed for clustering and similarity retrieval of video motion clips based on spatio-temporal object trajectories. The trajectories are treated as motion time series and modelled using orthogonal basis polynomial approximations. Trajectory clustering is then carried out to discover patterns of similar object motion behaviour. The coefficients of the basis functions are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Clustering in the basis coefficient space leads to efficiency gains over existing approaches that encode trajectories as point-based flow vectors. Experiments on pedestrian motion data gathered from video surveillance demonstrate the effectiveness of our approach. Applications to motion data mining in video surveillance databases are envisaged.","Title: Incremental indexing and retrieval mechanism for scalable and robust shape matching.\nYear: 2012\n\nAbstract: Techniques for efficient and effective content-based image matching are becoming increasingly important with the widespread increase in digital image capturing systems. Shape of an object, represented by its contour, is one of the most important visual feature that is thought to be used by humans to determine the similarity of objects. The selected feature and its distance measure must be robust to different distortions such as noise, articulation, scale and rotation. Existing approaches provides invariance to these distortions at the cost of either the accuracy due to poor discrimination ability or the efficiency. In this paper, we present an effective representation of shape, using its boundary information, that is robust to arbitrary distortions and affine transformation. The contour representation of shape is converted into time series and is modeled using orthogonal basis function representations. Shape matching is then carried out in the chosen coefficient feature space resulting in efficient matching. The efficiency of shape matching is further improved by indexing the shape descriptors using hierarchical indexing structure. A novel distributed beam search based technique is proposed that operates on the indexing structure and ensures no false dismissal for a given k-NN query. Experimental evaluation demonstrates that the proposed shape representation and matching mechanism is robust, efficient and scalable to very large shape datasets.","Title: Motion Trajectory Learning in the DFT-Coefficient Feature Space\nYear: 2006\n\nAbstract: Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. In this paper we propose a novel vision system for clustering and classification of object-based video motion clips using spatiotemporal models. Object trajectories are modeled as motion time series using the lowest order Fourier coefficients obtained by Discrete Fourier Transform. Trajectory clustering is then carried out in the DFT-coefficient feature space to discover patterns of similar object motion activity. The DFT coefficients are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Assuming the clusters of trajectory points are distributed normally in the coefficient feature space, we propose a simple Mahalanobis classifier for the detection of anomalous trajectories. Our proposed techniques are validated on three different datasets - Australian sign language, handlabelled object trajectories from video surveillance footage and real-time tracking data obtained in the laboratory. Applications to event detection and motion data mining for visual surveillance systems are envisaged.","Title: Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space\nYear: 2006\n\nAbstract: This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal function approximations. Assuming the clusters of trajectory points are distributed normally in the coefficient feature space, we propose a Mahalanobis classifier for the detection of anomalous trajectories. Motion trajectories are considered as time series and modelled using orthogonal basis function representations. We have compared three different function approximations --- least squares polynomials, Chebyshev polynomials and Fourier series obtained by Discrete Fourier Transform (DFT). Trajectory clustering is then carried out in the chosen coefficient feature space to discover patterns of similar object motions. The coefficients of the basis functions are used as input feature vectors to a Self- Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Our proposed techniques are validated on three different datasets --- Australian sign language, hand-labelled object trajectories from video surveillance footage and real-time tracking data obtained in the laboratory. Applications to event detection and motion data mining for multimedia video surveillance systems are envisaged.","Title: Frameworks for multivariate m-mediods based modeling and classification in Euclidean and general feature spaces\nYear: 2012\n\nAbstract: This paper presents an extension of m-mediods based modeling technique to cater for multimodal distributions of sample within a pattern. The classification of new samples and anomaly detection is performed using a novel classification algorithm which can handle patterns with underlying multivariate probability distributions. We have proposed two frameworks, namely MMC-ES and MMC-GFS, to enable our proposed multivarite m-mediods based modeling and classification approach workable for any feature space with a computable distance metric. MMC-ES framework is specialized for finite dimensional features in Euclidean space whereas MMC-GFS works on any feature space with a computable distance metric. Experimental results using simulated and complex real life dataset show that multivariate m-mediods based frameworks are effective and give superior performance than competitive modeling and classification techniques especially when the patterns exhibit multivariate probability density functions.","Title: Motion Clustering Using Spatiotemporal Approximations\nYear: 2005\n\nAbstract: In this paper a new technique is proposed for the clustering and classification of spatio-temporal object trajectories extracted from video motion clips. The trajectories are represented as motion time series and modelled using Chebyshev polynomial approximations. Trajectory clustering is then performed to discover patterns of similar object motion. The coefficients of the basis ftinctions are used as an input feature vector to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. It is shown that applying machine learning techniques in the Chebyshev parameter subspace leads to significant performance gains over previous approaches that encode trajectories as point-based flow (PBF) vectors. Experiments using the PETS'04 tracking dataset demonstrate the effectiveness of clustering in the parameter subspace and improvements in overall classification accuracy in comparison with PBF vector encoding. We also show how this technique can be further extended to the detection of anomalous motion paths. Applications to motion data mining and event detection in video surveillance systems are envisaged.","Title: Motion-based behaviour learning, profiling and classification in the presence of anomalies\nYear: 2010\n\nAbstract: Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents a novel technique for clustering and classification of motion. In the proposed motion learning system, trajectories are treated as time series and modelled using modified DFT (discrete fourier transform)-based coefficient feature space representation. A framework (iterative HSACT-LVQ (hierarchical semi-agglomerative clustering-learning vector quantization)) is proposed for learning of patterns in the presence of significant number of anomalies in training data. A novel modelling technique, referred to as m-Mediods, is also proposed that models the class containing n members with m Mediods. Once the m-Mediods-based model for all the classes have been learnt, the classification of new trajectories and anomaly detection can be performed by checking the closeness of said trajectory to the models of known classes. A mechanism based on agglomerative approach is proposed for anomaly detection. Our proposed techniques are validated using variety of simulated and complex real life trajectory data sets.","Title: Detection of neovascularization in retinal images using multivariate m-Mediods based classifier.\nYear: 2013\n\nAbstract: Diabetic retinopathy is a progressive eye disease and one of the leading causes of blindness all over the world. New blood vessels (neovascularization) start growing at advance stage of diabetic retinopathy known as proliferative diabetic retinopathy. Early and accurate detection of proliferative diabetic retinopathy is very important and crucial for protection of patient's vision. Automated systems for detection of proliferative diabetic retinopathy should identify between normal and abnormal vessels present in digital retinal image. In this paper, we proposed a new method for detection of abnormal blood vessels and grading of proliferative diabetic retinopathy using multivariate m-Mediods based classifier. The system extracts the vascular pattern and optic disc using a multilayered thresholding technique and Hough transform respectively. It grades the fundus image in different categories of proliferative diabetic retinopathy using classification and optic disc coordinates. The proposed method is evaluated using publicly available retinal image databases and results show that the proposed system detects and grades proliferative diabetic retinopathy with high accuracy.","Title: Identification and classification of microaneurysms for early detection of diabetic retinopathy\nYear: 2013\n\nAbstract: Diabetic retinopathy is a progressive eye disease which may cause blindness if not detected and treated in time. The early detection and diagnosis of diabetic retinopathy is important to protect the patient's vision. The accurate detection of microaneurysms (MAs) is a critical step for early detection of diabetic retinopathy because they appear as the first sign of disease. In this paper, we propose a three-stage system for early detection of MAs using filter banks. In the first stage, the system extracts all possible candidate regions for MAs present in retinal image. In order to classify a candidate region as MA or non-MA, the system formulates a feature vector for each region depending upon certain properties, i.e. shape, color, intensity and statistics. We present a hybrid classifier which combines the Gaussian mixture model (GMM), support vector machine (SVM) and an extension of multimodel mediod based modeling approach in an ensemble to improve the accuracy of classification. The proposed system is evaluated using publicly available retinal image databases and achieved higher accuracy which is better than previously published methods.","Title: Classifying spatiotemporal object trajectories using unsupervised learning of basis function coefficients\nYear: 2005\n\nAbstract: This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal functional approximations. A Mahalanobis classifier is then used for the detection of anomalous trajectories. Motion trajectories are considered as time series and modeled using the leading Fourier coefficients obtained by a Discrete Fourier Transform. Trajectory clustering is then carried out in the Fourier coefficient feature space to discover patterns of similar object motions. The coefficients of the basis functions are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Experiments are performed on two different datasets -- synthetic and pedestrian object tracking - to demonstrate the effectiveness of our approach. Applications to motion data mining in video surveillance databases are envisaged.","Title: Preprocessing Approach Identifying and Removing Noise which Affects Shape\nYear: 2014\n\nAbstract: In this paper, we propose a noise removal approach that significantly improves the performance of sophisticated shape matching techniques. Existing shape matching techniques focus on complex algorithms without giving much consideration to the noise removing preprocessing techniques. These preprocessing techniques can reasonably improve the accuracy where the shapes are affected by cracks. We present a preprocessing approach to identify and remove small cracks and slits in the shape contour. It has been observed that these cracks or slits change the shape altogether in a manner which makes classification of shapes difficult. This technique is simple and effective and may be used to preprocess the images before applying sophisticated shape representation techniques to improve their accuracies. The effectiveness of proposed preprocessing approach is verified using publicly available shape datasets.","Title: Robust shape matching using global feature space representation of contours\nYear: 2012\n\nAbstract: The fundamental ingredient of content-based image retrieval is the selection of appropriate features to describe the content of the image. Shape of an object, represented by its contour, is the most important visual feature that is thought to be used by humans to determine the similarity of objects. In this paper, we present an effective representation of shape, using its boundary information, that is robust to arbitrary distortions and affine transformation. The contour representation of shape is converted into time series and is modeled using orthogonal basis function representations. Encoding contour representation of shapes in this manner leads to efficiency gains over existing approaches such as structural shape representation and techniques that use discrete point-based flow vectors to represent the contour. Experimental evaluation demonstrates that the proposed shape representation and matching mechanism is effective, efficient and robust to different arbitrary and affine distortions.","Title: Energy efficient strategy for throughput improvement in wireless sensor networks.\nYear: 2015\n\nAbstract: Network lifetime and throughput are one of the prime concerns while designing routing protocols for wireless sensor networks (WSNs). However, most of the existing schemes are either geared towards prolonging network lifetime or improving throughput. This paper presents an energy efficient routing scheme for throughput improvement in WSN. The proposed scheme exploits multilayer cluster design for energy efficient forwarding node selection, cluster heads rotation and both inter- and intra-cluster routing. To improve throughput, we rotate the role of cluster head among various nodes based on two threshold levels which reduces the number of dropped packets. We conducted simulations in the NS2 simulator to validate the performance of the proposed scheme. Simulation results demonstrate the performance efficiency of the proposed scheme in terms of various metrics compared to similar approaches published in the literature.","Title: TOBAE: A Density-based Agglomerative Clustering Algorithm\nYear: 2015\n\nAbstract: This paper presents a novel density based agglomerative clustering algorithm named TOBAE which is a parameter-less algorithm and automatically filters noise. It finds the appropriate number of clusters while giving a competitive running time. TOBAE works by tracking the cumulative density distribution of the data points on a grid and only requires the original data set as input. The clustering problem is solved by automatically finding the optimal density threshold for the clusters. It is applicable to any N-dimensional data set which makes it highly relevant for real world scenarios. The algorithm outperforms state of the art clustering algorithms by the additional feature of automatic noise filtration around clusters. The concept behind the algorithm is explained using the analogy of puddles ('tobae'), which the algorithm is inspired from. This paper provides a detailed algorithm for TOBAE along with the complexity analysis for both time and space. We show experimental results against known data sets and show how TOBAE competes with the best algorithms in the field while providing its own set of advantages.","Title: Behaviour recognition using multivariate m-mediod based modelling of motion trajectories.\nYear: 2015\n\nAbstract: The importance of behaviour analysis and activity recognition systems continue to increase with the increasing demand and deployment of video surveillance systems. Motion trajectories provide rich spatio-temporal information about an object’s activity. In this article, we present a supervised feature extraction and multivariate modelling approach for motion-based behaviour recognition and anomaly detection. In the proposed motion learning system, trajectories are treated as time series and modelled using modified DFT-based coefficient feature space representation. We employ supervised dimensionality reduction using Local Fisher Discriminant Analysis to enhance the feature space representation of trajectories. A modelling approach, referred to as multivariate m-mediods, is proposed that can cater for the presence of multivariate distribution of samples within a given motion pattern. A hierarchical indexing of mediods and retrieval approach is presented to improve the efficiency of proposed classifier. Our proposed techniques are validated using variety of simulated and complex real-life trajectory datasets.","Title: Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels\nYear: 2015\n\nAbstract: Retinal blood vessels are the source to provide oxygen and nutrition to retina and any change in the normal structure may lead to different retinal abnormalities. Automated detection of vascular structure is very important while designing a computer aided diagnostic system for retinal diseases. Most popular methods for vessel segmentation are based on matched filters and Gabor wavelets which give good response against blood vessels. One major drawback in these techniques is that they also give strong response for lesion (exudates, hemorrhages) boundaries which give rise to false vessels. These false vessels may lead to incorrect detection of vascular changes. In this paper, we propose a new hybrid feature set along with new classification technique for accurate detection of blood vessels. The main motivation is to lower the false positives especially from retinal images with severe disease level. A novel region based hybrid feature set is presented for proper discrimination between true and false vessels. A new modified m-mediods based classification is also presented which uses most discriminating features to categorize vessel regions into true and false vessels. The evaluation of proposed system is done thoroughly on publicly available databases along with a locally gathered database with images of advanced level of retinal diseases. The results demonstrate the validity of the proposed system as compared to existing state of the art techniques.","Title: Recognition of Urdu ligatures - a holistic approach\nYear: 2015\n\nAbstract: This paper presents an effective segmentation-free and scale-invariant technique for recognition of Urdu ligatures in Nastaliq font. The proposed technique relies on separating the main body of ligatures from the secondary components and training a separate hidden Markov model for each. Features capturing projection, concavity and curvature information of ligatures are extracted using right-to-left sliding windows and are fed to the models for training. The system trained and evaluated on a total of more than 2,000 frequently occurring Urdu ligatures from a standard database realized a recognition rate of 97.93%.","Title: Online Knowledge-Based Model for Big Data Topic Extraction\nYear: 2016\n\nAbstract: AbstractLifelong machine learning LML models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model OAMC to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half.","Title: Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3D retinal surfaces.\nYear: 2016\n\nAbstract: •An automated method is proposed here to diagnose two common macular syndromes by reconstructing 3D retinal surfaces.•To the best of our knowledge, this paper proposes a first ever novel self-diagnosis system to incorporate 3D OCT scans.•The self-diagnosis in our proposed system is based on multilevel support vector machines (SVM) classifier.•90 OCT volumes (30 healthy, 30 CSR and 30 ME) of 73 patients were considered in this research to test the proposed system.","Title: A three-way approach for learning rules in automatic knowledge-based topic models.\nYear: 2017\n\nAbstract: A three-way approach is proposed for selecting rules in automatic knowledge-based topic models.The selection of rules in the three-way approach is controlled based on a pair of thresholds.Effective thresholds are determined based on a tradeoff between the quantity of rules and the quality of the rules.The game-theoretic rough set (GTRS) model is used to implement the tradeoff.","Title: Digital Image Preprocessing And Hair Artifact Removal By Using Gabor Wavelet\nYear: 2016\n\nAbstract: Diagnosis of skin cancer needs specialized equipment, doctors and continuous monitoring to treat it well. Patients living in remote areas normally cannot access such facilities. To overcome these barriers of access, Computer Aided Diagnostics, an emerging field in computer science, often called telemedicine, is being considered a promising approach. In this research, preprocessing and hair artifact removal experiment was performed on dermatoscope images by using Morphological and Gabor wavelet-based techniques. It has been found that, in some cases, wavelet transformations provide better results as compared to other techniques like gel, water bubbles and dark hair around the surface affected by cancer, i.e. these artifacts are removed with less effort.","Title: IoT-based students interaction framework using attention-scoring assessment in eLearning.\nYear: 2018\n\nAbstract: Students’ interaction and collaboration using Internet of Things (IoT) based interoperable infrastructure is a convenient way. Measuring student attention is an essential part of educational assessment. As new learning styles develop, new tools and assessment methods are also needed. The focus of this paper is to develop IoT-based interaction framework and analysis of the student experience of electronic learning (eLearning). The learning behaviors of students attending remote video lectures are assessed by logging their behavior and analyzing the resulting multimedia data using machine learning algorithms. An attention-scoring algorithm, its workflow, and the mathematical formulation for the smart assessment of the student learning experience are established. This setup has a data collection module, which can be reproduced by implementing the algorithm in any modern programming language. Some faces, eyes, and status of eyes are extracted from video stream taken from a webcam using this module. The extracted information is saved in a dataset for further analysis. The analysis of the dataset produces interesting results for student learning assessments. Modern learning management systems can integrate the developed tool to take student learning behaviors into account when assessing electronic learning strategies.","Title: Precise shape matching of large shape datasets using hybrid approach.\nYear: 2017\n\nAbstract: Precise and fast shape matching and retrieval from very large datasets is a challenging task because of the existence of many distortions such as noise, occlusion and affine distortions. In this paper, we aim to propose a time-saving and effective shape matching and retrieval framework, that employs pruning which will enable online shape retrieval from extremely large datasets. First, using a hierarchical tree-based structure supporting parallel processing and efficient feature descriptors, irrelevant shapes are pruned and a subset of shapes relevant to the query is selected, then using more sophisticated feature descriptors, accurate retrieval is performed. Contour representation of an object is considered as most significant visual shape similarity measure by the humans. Using boundary information, we generate two simplified and efficient feature descriptors for fast pruning, and a sophisticated feature descriptor for effective and accurate retrieval. Tests performed on standard datasets unveil that the proposed technique is computationally more efficient than the state-of-the-art techniques while maintaining comparable matching and retrieval performance. Its performance is scalable for huge datasets and is robust against affine transformations, articulations and occlusion.","Title: Segmentation-free optical character recognition for printed Urdu text.\nYear: 2017\n\nAbstract: This paper presents a segmentation-free optical character recognition system for printed Urdu Nastaliq font using ligatures as units of recognition. The proposed technique relies on statistical features and employs Hidden Markov Models for classification. A total of 1525 unique high-frequency Urdu ligatures from the standard Urdu Printed Text Images (UPTI) database are considered in our study. Ligatures extracted from text lines are first split into primary (main body) and secondary (dots and diacritics) ligatures and multiple instances of the same ligature are grouped into clusters using a sequential clustering algorithm. Hidden Markov Models are trained separately for each ligature using the examples in the respective cluster by sliding right-to-left the overlapped windows and extracting a set of statistical features. Given the query text, the primary and secondary ligatures are separately recognized and later associated together using a set of heuristics to recognize the complete ligature. The system evaluated on the standard UPTI Urdu database reported a ligature recognition rate of 92% on more than 6000 query ligatures.","Title: Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning.\nYear: 2017\n\nAbstract: Multimedia is an essential and integral part of electronic learning (e-learning). In this study, teaching performance and student learning experience are measured using real-time multimedia processing tools and techniques for the e-learning paradigm. Visual attention and visual engagement analysis are performed using two developed algorithms. Video lectures are recorded and delivered to students in e-learning pedagogical setup, which are examined for the visual attention and visual engagement of the student and teacher, respectively. Proposed methodology integrates the assessment on both student and teacher ends. Multimedia processing of video lectures for teaching performance produces scoring dataset. The same methodology on student end for visual attention is used to investigate student experience. These types of datasets then reduced to time-based datasets from the image-based dataset. Correlation and association of both datasets provide the opportunity to relate both student experience and teaching performance as well as to move forward to create content that is more useful. Computational performance of the developed algorithms is compared using different video lectures with their processed frames per second, which is analyzed as per their corresponding bins. Mean, max, and median of the processed frames of all the processed videos are also compared.","Title: Fuzzy based multi-criteria vertical handover decision modeling in heterogeneous wireless networks.\nYear: 2017\n\nAbstract: Vertical handover gain significant importance due to the enhancements in mobility models by the Fourth Generation (4G) technologies. However, these enhancements are limited to specific scenarios and hence do not provide support for generic mobility. Similarly, various schemes are proposed based on these mobility models but most of them are suffered from the high packet loss, frequent handovers, too early and late handovers, inappropriate network selection, etc. To address these challenges, a generic vertical handover management scheme for heterogeneous wireless networks is proposed in this article. The proposed scheme works in three phases. In the first phase, a handover triggering approach is designed to identify the appropriate place for initiating handover based on the estimated coverage area of a WLAN access point or cellular base station. In the second phase, fuzzy rule based system is designed to eliminate the inappropriate networks before deciding an optimal network for handover. In the third phase, a network selection scheme is developed based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision mechanism. Various parameters such as delay, jitter, Bit Error Rate (BER), packet loss, communication cost, response time, and network load are considered for selecting an optimal network. The proposed scheme is tested in a mobility scenario with different speeds of a mobile node ranging from very low to very high. The simulation results are compared with the existing decision models used for network selection and handover triggering approaches. The proposed scheme outperforms these schemes in terms of energy consumption, handover delay and time, packet loss, good put, etc.","Title: Multimedia based qualitative assessment methodology in eLearning: student teacher engagement analysis.\nYear: 2018\n\nAbstract: Multimedia-based Electronic learning (eLearning) is an effective method of knowledge transfer. Multimedia-based eLearning provides the opportunity to the students that they can use once delivered and recorded video lecture any time. Multimedia tools and its applications if used in eLearning enable the students and teachers to take such kind of advantages. Multimedia applications provide the advantages to students and teacher in eLearning but challenges are also striking features. One of the challenges is to measure and analyze visual engagement and visual attention for teachers and students respectively. Visual engagement of the video lecture is measured and analyzed to verify the effectiveness of the teachers’ lectures. In the same way, the visual attention for the students is also be measured and analyzed. We have presented a qualitative assessment methodology and an algorithm for both of the measures and their correlation. Covariance and relationship between the two measures are the key technique of this paper. Multimedia-based software tools have been developed for this purpose. Results of both the tools are very interesting and promising. They are interpreted using descriptive and graphical statistics.","Title: Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.\nYear: 2018\n\nAbstract: Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.","Title: Pattern Based Comprehensive Urdu Stemmer and Short Text Classification.\nYear: 2018\n\nAbstract: Urdu language is used by approximately 200 million people for spoken and written communications. The bulk of unstructured Urdu textual data is available in the world. We can employ data mining techniques to extract useful information from such a large, potentially informative base data. There are many text processing systems available to process unstructured textual data. However, these systems are mostly language specific with the large proportion of systems applicable to English text. This is primarily due to language-dependent preprocessing systems, mainly the stemming requirement. Stemming is a vital preprocessing step in the text mining process and its primary aim is to reduce grammatical words form, e.g., parts of speech, gender, tense, and so on, to their root form. In the proposed work, we have developed a rule-based comprehensive stemming method for Urdu text. This proposed Urdu stemmer has the ability to generate the stem of Urdu words as well as loan words that belong to borrowed languages, such as Arabic, Persian, and Turkish, by removing prefix, infix, and suffix from the words. In the proposed stemming technique, we introduced six novel Urdu infix words classes and a minimum word length rule to generate the stem of Urdu text. In order to cope with the challenge of Urdu infix stemming, we have developed infix stripping rules for introduced infix words classes and generic stemming rules for prefix and suffix stemming. We also present a probabilistic classification approach to classify Urdu short text. Different experiments are performed to demonstrate the effectiveness and efficacy of the proposed approach. Comparison with existing state-of-the art approaches is also made. Stemming accuracy results demonstrate the adoptability of the proposed stemming approach for a variety text processing applications.","Title: Sentiment analysis and the complex natural language.\nYear: 2016\n\nAbstract: There is huge amount of content produced online by amateur authors, covering a large variety of topics. Sentiment analysis (SA) extracts and aggregates users’ sentiments towards a target entity. Machine learning (ML) techniques are frequently used as the natural language data is in abundance and has definite patterns. ML techniques adapt to domain specific solution at high accuracy depending upon the feature set used. The lexicon-based techniques, using external dictionary, are independent of data to prevent overfitting but they miss context too in specialized domains. Corpus-based statistical techniques require large data to stabilize. Complex network based techniques are highly resourceful, preserving order, proximity, context and relationships. Recent applications developed incorporate the platform specific structural information i.e. meta-data. New sub-domains are introduced as influence analysis, bias analysis, and data leakage analysis. The nature of data is also evolving where transcribed customer-agent phone conversation are also used for sentiment analysis. This paper reviews sentiment analysis techniques and highlight the need to address natural language processing (NLP) specific open challenges. Without resolving the complex NLP challenges, ML techniques cannot make considerable advancements. The open issues and challenges in the area are discussed, stressing on the need of standard datasets and evaluation methodology. It also emphasized on the need of better language models that could capture context and proximity.","Title: Lifelong aspect extraction from big data: knowledge engineering.\nYear: 2016\n\nAbstract: Traditional machine learning techniques follow a single shot learning approach. It includes all supervised, semi-supervised, transfer learning, hybrid and unsupervised techniques having a single target domain known prior to analysis. Learning from one task is not carried to the next task, therefore, they cannot scale up to big data having many unknown domains. Lifelong learning models are tailored for big data having a knowledge module that is maintained automatically. The knowledge-base grows with experience where knowledge from previous tasks helps in current task. This paper surveys topic models leading the discussion to knowledge-based topic models and lifelong learning models. The issues and challenges in learning knowledge, its abstraction, retention and transfer are elaborated. The state-of-the art models store word pairs as knowledge having positive or negative co-relations called must-links and cannot-links. The need for innovative ideas from other research fields is stressed to learn more varieties of knowledge to improve accuracy and reveal more semantic structures from within the data.","Title: Melanocytic and nevus lesion detection from diseased dermoscopic images using fuzzy and wavelet techniques.\nYear: 2018\n\nAbstract: The use of computer-assisted decision system (CAD) for the diagnosis of skin cancer dermoscopy is aggravated by the potential gains of its excellent performance. It automates the skin lesion analysis and reduces the amount of repetitive and tedious tasks to be done by physicians. This research is mainly focused on the computer vision perspective to design a CAD system which will facilitate the physicians. An automated PR system includes four inter-related processes to analyze skin lesions by the clinicians: image preprocessing, segmentation, feature extraction, feature selection and classification. The dataset contains images and annotations provided by physicians. Segmentation is an imperative preprocessing step for CAD system of skin lesions. Feature extraction of segmented skin lesions is a pivotal step for implementing accurate decision support systems. Physicians are interested in examining a specific clinically significant region in a lesion. Such a region is expected to have more information in the form of texture that can be relevant for detection. In case of detection of melanoma, various local features, for example, pigment network and streaks, usually occur in peripheral region of the lesion. This led to the extraction of peripheral part for feature extraction instead of whole lesion processing. We propose novel techniques for lesion detection and classification on peripheral part of the lesion using m-mediod classifier along with the contrast of patterns. Classification results obtained from the proposed feature matrix were compared with some other texture descriptors, showing the superiority of our proposed descriptor.","Title: Counter Measuring Conceivable Security Threats on Smart Healthcare Devices.\nYear: 2018\n\nAbstract: Smart devices, the carriers of a huge amount of private, sensitive and confidential data are pervasive in today's world with innovative and enhanced functionalities. Smartphones have brought tremendous change in people's lives with the launch of a new platform of communication and an ease of access to a wide range of applications. Due to the swift increase in the users of Android smartphones and the increasing demands based on advanced ease and features, developers are working hard to achieve the needful. Easy access to certain features and applications gave rise to the powerfulness and an efficacy of various threats, risks and vulnerabilities that can victimize users' private data residing in smartphone paradigm. With the developments and enhancements in malware, for Android-based smartphones, attacks continue to occur. In this paper, we investigate one of the possibly most destructive attacks for Android, that is, screenshot attack. We have developed \"ScreenStealer\" application and explored the vulnerabilities which make Android more inclined to risks and threats. Furthermore, we evaluated capture ratio of screenshots, resources consumption and execution time to determine effectiveness, efficiency and stealthiness of such a malicious application.","Title: Semantic Interoperability in Heterogeneous IoT Infrastructure for Healthcare.\nYear: 2017\n\nAbstract: Interoperability remains a significant burden to the developers of Internet of Things' Systems. This is due to the fact that the IoT devices are highly heterogeneous in terms of underlying communication protocols, data formats, and technologies. Secondly due to lack of worldwide acceptable standards, interoperability tools remain limited. In this paper, we proposed an IoT based Semantic Interoperability Model (IoT-SIM) to provide Semantic Interoperability among heterogeneous IoT devices in healthcare domain. Physicians communicate their patients with heterogeneous IoT devices to monitor their current health status. Information between physician and patient is semantically annotated and communicated in a meaningful way. A lightweight model for semantic annotation of data using heterogeneous devices in IoT is proposed to provide annotations for data. Resource Description Framework (RDF) is a semantic web framework that is used to relate things using triples to make it semantically meaningful. RDF annotated patients' data has made it semantically interoperable. SPARQL query is used to extract records from RDF graph. For simulation of system, we used Tableau, Gruff-6.2.0, and Mysql tools.","Title: Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network.\nYear: 2018\n\nAbstract: Among constituents of communication architecture, routing is the most energy squeezing process. In this survey article, we are targeting an innovative aspect of analysis on routing in wireless sensor network (WSN) that has never been seen in the available literature before. This article can be a guiding light for new researchers to comprehend the WSN technology, energy aware routing, and the factors that affect the energy aware routing in WSN. This insight comprehension then makes the ways easy for them in designing such types of algorithms as well as evaluating the authenticity and extending the existing algorithms of this category, since algebraic and graphical modelling of these factors is also demonstrated. Various available techniques used by existing routing algorithms to handle these factors in making themselves energy aware are also given. Further, they are analyzed along with the suggested improvements for the researchers. At the end, we presented our previously published research work as an example and case study of discussed factors. A rich list of references is also cited for interested readers to explore the related given points.","Title: Designing an Energy-Aware Mechanism for Lifetime Improvement of Wireless Sensor Networks: a Comprehensive Study.\nYear: 2018\n\nAbstract: In this paper, we have presented a comprehensive study on designing an aware energy architecture of Wireless Sensor Networks. A summary and modelling of various major techniques in designing the constituents of clustered wireless sensor network architecture are given in detail. In continuation of it, we have also analysed our proposed scheme, Extended-Multilayer Cluster Designing Algorithm (E-MCDA) in a large network. Among its components, algorithms for time slot allocation, minimising the CH competition candidates, and cluster member selection to CH play underpinning roles to achieve the target. These incorporations in MCDA result in minimising transmissions, suppressing the unneeded response of transmissions and near equal size and equal load clusters. We have done extensive simulations in NS2 and evaluate the performance of E-MCDA in energy consumption at various aspects of energy, packets transmission, the number of designed clusters, the number of nodes per cluster and un-clustered nodes. It is found that the proposed mechanism optimistically outperforms the competition with MCDA and EADUC concerning parameters above.","Title: An enhanced framework for multimedia data: Green transmission and portrayal for smart traffic system.\nYear: 2018\n\nAbstract: The object tracking in video surveillance for intelligent traffic handling in smart cities requires an enormous amount of data called big data to be transmitted over the network using the Internet of Things. Manual monitoring and surveillance are impossible because traditional computer vision technologies are no more useful for massive processing and intelligent decision making. In this paper, a framework is proposed which enables both on spot data processing and intelligent decision making by using cloud computing. The developed application is a trained on Artificial Neural Network, which can handle different traffic techniques with congested traffic scenario and priorities traffic such as ambulance handling. The Message Queue Telemetry Transport protocol is used for green transmission with mobile access to traffic data. The results analyzed with thirty videos processed data which handle real-time data prioritization for the people for smart surveillance to fastest route and enhance the intelligent data transmission.","Title: BotDet: A System for Real Time Botnet Command and Control Traffic Detection.\nYear: 2018\n\nAbstract: Over the past decade, the digitization of services transformed the healthcare sector leading to a sharp rise in cybersecurity threats. Poor cybersecurity in the healthcare sector, coupled with high value of patient records attracted the attention of hackers. Sophisticated advanced persistent threats and malware have significantly contributed to increasing risks to the health sector. Many recent attacks are attributed to the spread of malicious software, e.g., ransomware or bot malware. Machines infected with bot malware can be used as tools for remote attack or even cryptomining. This paper presents a novel approach, called BotDet, for botnet Command and Control (C&C) traffic detection to defend against malware attacks in critical ultrastructure systems. There are two stages in the development of the proposed system: 1) we have developed four detection modules to detect different possible techniques used in botnet C&C communications and 2) we have designed a correlation framework to reduce the rate of false alarms raised by individual detection modules. Evaluation results show that BotDet balances the true positive rate and the false positive rate with 82.3% and 13.6%, respectively. Furthermore, it proves BotDet capability of real time detection.","Title: A generic methodology for geo-related data semantic annotation.\nYear: 2018\n\nAbstract: Geo-related data, also known as spatial data, is represented using a vector used for representing longitude, elevation, and latitude. Specially built systems, well known as Geographical Information Systems (GIS), made use of such data for querying, manipulating, navigation, and analyzing. In the current era of data, science needs to involve smart interactive investigation involving Internet of Data (IoD) on predicting upcoming changes and spatial updates on the map is growing rapidly. To resolve issues concerning real-time spatial data, transformation using semantic annotation can provide a better way to translate spatial relationships. These spatial relationships will support spatial analysis by linking different cause and effect with the help of reasoning mechanism. This research's major focus is on a data transformation methodology for geo-related semantic annotation. Spatial dataset gets stored in a database and then transformed into Extensible Markup Language (XML) and Resource Description Framework (RDF). Even for bi-directional transformation to work properly, we need to map different schema level transformations. A deep research is conducted to consider available mappings, implementations, and updates to further improving data fusion for having better compatibility. Then, transformed data as results get analyzed and discussed based on the data mapping rules formulated. It is aimed to show the importance of reducing the response time of investigation and offer compatibility between the web and semantically enriched spatial data.","Title: Enhanced Network Anomaly Detection Based on Deep Neural Networks.\nYear: 2018\n\nAbstract: Due to the monumental growth of Internet applications in the last decade, the need for security of information network has increased manifolds. As a primary defense of network infrastructure, an intrusion detection system is expected to adapt to dynamically changing threat landscape. Many supervised and unsupervised techniques have been devised by researchers from the discipline of machine learning and data mining to achieve reliable detection of anomalies. Deep learning is an area of machine learning which applies neuron-like structure for learning tasks. Deep learning has profoundly changed the way we approach learning tasks by delivering monumental progress in different disciplines like speech processing, computer vision, and natural language processing to name a few. It is only relevant that this new technology must be investigated for information security applications. The aim of this paper is to investigate the suitability of deep learning approaches for anomaly-based intrusion detection system. For this research, we developed anomaly detection models based on different deep neural network structures, including convolutional neural networks, autoencoders, and recurrent neural networks. These deep models were trained on NSLKDD training data set and evaluated on both test data sets provided by NSLKDD, namely NSLKDDTest+ and NSLKDDTest21. All experiments in this paper are performed by authors on a GPU-based test bed. Conventional machine learning-based intrusion detection models were implemented using well-known classification techniques, including extreme learning machine, nearest neighbor, decision-tree, random-forest, support vector machine, naive-bays, and quadratic discriminant analysis. Both deep and conventional machine learning models were evaluated using well-known classification metrics, including receiver operating characteristics, area under curve, precision-recall curve, mean average precision and accuracy of classification. Experimental results of deep IDS models showed promising results for real-world application in anomaly detection systems.","Title: Fundus Images-Based Detection and Grading of Macular Edema Using Robust Macula Localization.\nYear: 2018\n\nAbstract: The macula is an oval-shaped area near the center of the human retina, and at its center, there is a small pit known as the fovea. The fovea contains large concentrations of cone cells and is responsible for sharp, colored vision. Macular disorders are the group of diseases that damage the macula, resulting in blurred vision or even blindness. Macular edema (ME), one of the most common types of macular disorder, is caused by fluid accumulation beneath the macula. In this paper, we present an automated system for the detection of ME from fundus images. We introduce a new automated system for the detailed grading of the severity of disease using knowledge of exudates and maculae. A new set of features is used along with a minimum distance classifier for accurate localization of the fovea, which is important for the grading of ME. The proposed system uses different hybrid features and support vector machines for segmentation of exudates. The detailed grading of ME-as both clinically significant ME and non-clinically significant ME-is done using localized foveae and segmented exudates. The proposed algorithm is validated using public and local data sets. We have achieved an average accuracy of 96.1% in the detection and grading of ME with our proposed method.","Title: A Parallel Architecture for the Partitioning Around Medoids (PAM) Algorithm for Scalable Multi-Core Processor Implementation with Applications in Healthcare.\nYear: 2018\n\nAbstract: Clustering is the most common method for organizing unlabeled data into its natural groups (called clusters), based on similarity (in some sense or another) among data objects. The Partitioning Around Medoids (PAM) algorithm belongs to the partitioning-based methods of clustering widely used for objects categorization, image analysis, bioinformatics and data compression, but due to its high time complexity, the PAM algorithm cannot be used with large datasets or in any embedded or real-time application. In this work, we propose a simple and scalable parallel architecture for the PAM algorithm to reduce its running time. This architecture can easily be implemented either on a multi-core processor system to deal with big data or on a reconfigurable hardware platform, such as FPGA and MPSoCs, which makes it suitable for real-time clustering applications. Our proposed model partitions data equally among multiple processing cores. Each core executes the same sequence of tasks simultaneously on its respective data subset and shares intermediate results with other cores to produce results. Experiments show that the computational complexity of the PAM algorithm is reduced exponentially as we increase the number of cores working in parallel. It is also observed that the speedup graph of our proposed model becomes more linear with the increase in number of data points and as the clusters become more uniform. The results also demonstrate that the proposed architecture produces the same results as the actual PAM algorithm, but with reduced computational complexity.","Title: Socio-cyber network: The potential of cyber-physical system to define human behaviors using big data analytics.\nYear: 2019\n\nAbstract: The growing gap between users and the big data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze such massive volume of data. Moreover, advancements in the field of big data application and data science leads toward a new paradigm of human behavior, where various smart devices integrate with each other and establish a relationship. However, majority of the systems are either memoryless or computational inefficient, which are unable to define or predict human behavior. Therefore, keeping in view the aforementioned needs, there is a requirement for a system that can efficiently analyze a stream of big data within their requirements. Hence, this paper presents a system architecture that integrates social network with the technical network. We derive a novel notion of ‘Socio-Cyber Network’, where a friendship is made based on the geo-location information of the user, where trust index is used based on graphs theory. The proposed graph theory provides a better understanding of extraction knowledge from the data and finding relationship between different users. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce. MapReduce for cyber-physical system (CPS) is supported by a parallel algorithm that efficiently process a huge volume of data sets. The system is implemented using Spark GraphX tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.","Title: Comprehensive Stemmer For Morphologically Rich Urdu Language\nYear: 2019\n\nAbstract: Urdu language is used by approximately 200 million people for spoken and written communication. Bulk of unstructured Urdu textual data is available in the world. We can employ data mining techniques to extract useful information from such a large potential information base. There are many text processing systems that are available. However, these systems are mostly language specific with the large proportion of systems are applicable to English text. This is primarily due to the language dependant pre-processing systems mainly the stemming requirement. Stemming is a vital pre-processing step in the text mining process and its core aim is to reduce many grammatical words form e.g., parts of speech, gender, tense etc. to their root form. In this proposed work, we have developed a rule based comprehensive stemming method for Urdu text. This proposed Urdu stemmer has the ability to generate the stem of Urdu words as well as loan words (words belonging to borrowed language i.e. Arabic, Persian, Turkish, etc) by removing prefix infix, and suffix. This proposed stemming technique introduced six novel Urdu infix words classes and minimum word length rule. In order to cope with the challenge of Urdu infix stemming, we have developed infix stripping rules for introduced infix words classes and generic rules for prefix and suffix stemming. The experimental results show the superiority of our proposed stemming approach as compared to existing technique.","Title: Deepgender: real-time gender classification using deep learning for smartphones\nYear: 2019\n\nAbstract: Face recognition, expression identification, age determination, racial binding and gender classification are common examples of image processing computerization. Gender classification is very straightforward for us like we can tell by the person’s hair, nose, eyes, mouth and skin whether that person is a male or female with a relatively high degree of confidence and accuracy; however, can we program a computer to perform just as well at gender classification? The very problem is the main focus of this research. The conventional sequence for recent real-time facial image processing consists of five steps: face detection, noise removal, face alignment, feature representation and classification. With the aim of human gender classification, face alignment and feature vector extraction stages have been re-examined keeping in view the application of the system on smartphones. Face alignment has been made by spotting out 83 facial landmarks and 3-D facial model with the purpose of applying affine transformation. Furthermore, ‘feature representation’ is prepared through proposed modification in multilayer deep neural network, and hence we name it Deepgender. This convolutional deep neural network consists of some locally connected hidden layers without common weights of kernels as previously followed in legacy layered architecture. This specific case study involves deep learning as four convolutional layers, three max-pool layers (for downsizing of unrelated data), two fully connected layers (connection of outcome to all inputs) and a single layer of ‘multinomial logistic regression.’ Training has been made using CAS-PEAL and FEI which contain 99,594 face images of 1040 people and 2800 face images of 200 individuals, respectively. These images are either in different poses or taken under uncontrolled conditions which are close to real-time input facial image for gender classification application. The proposed system ‘Deepgender’ has registered 98% accuracy by combined use of both databases with the specific preprocess procedure, i.e., exhibiting alignment before resizing. Experiments suggest that accuracy is nearly 100% with frontal and nonblurred facial images. State-of-the-art steps have been taken to overcome memory and battery constraints in mobiles.","Title: Using casual reasoning for anomaly detection among ECG live data streams in ubiquitous healthcare monitoring systems\nYear: 2019\n\nAbstract: Anomalies in cardiac functionality can be fatal. Early detection of these anomalies, and in many cases their precursors, can save lives. The probability of the occurrence of these anomalies is extremely among people with a pre-diagnosed heart condition. In this research, we discovered that much remote Electrocardiography (ECG) monitoring systems do not convey “enough” information to the diagnosing doctor or the nominated caregiver. A few examples of this information can be the type of cardiac abnormality, the exact waveform of the ECG signal, time and frequency of the occurrence of the anomaly, machine-understandable part so that medical SCADA be alerted about the case, and immediate preventative urgent steps correlated to that emergency. It is also important to delivery surrounding context to the Health Information System so that the medical expert make his/her diagnosis with ample support data. The most important component in this communication is the security of contents from cybercrimes. We propose a cost-efficient and non-invasive health monitoring system that is secure and quickly deployable. The presented system embeds an intelligent wearable data acquisition system with unique identification algorithms requiring very little computational time and simple threshold-based classification.","Title: Multimedia based IoT-centric smart framework for eLearning paradigm.\nYear: 2019\n\nAbstract: Multimedia content boosts the learning trends. This paper is aimed to presents an electronic learning system based on Internet of Things (IoT) for the synchronous and asynchronous communications. The infrastructure of IoT provides the adaptable, scalable and open access for the eLearning paradigm. The multimedia-based IoT-centric environment is suitable to enhance the effectiveness of the delivery of learning contents. Students can take full advantage of 7As of IoT, which provides the opportunity to the students that they can access everything on the internet at any time and place. It creates a flexible eLearning paradigm for the teachers and students. The proposed eLearning modeluses sensors to detect the student location, temperature, and mobile camera to identify the student activeness in thelearning environment. Virtual campuses are controlled from a centralized location that may be called the head office. The MAQAS framework provides the solutions to the problems and analyzes the results for the efficient and connected eLearning paradigm. The MAQAS system is used to answer student’s queries, which are responded to automatically by agent-based question answering system. The results show that the students’ participation towards learning and teacher’s pedagogy are more efficient in synchronous and asynchronous modes. Performance evaluated by comparison to the existing question answering Live QA Trak, Quora Yoda QA Live and AskMSR-QA with MAQAS.","Title: A Holistic Approach for Recognition of Complete Urdu Ligatures Using Hidden Markov Models\nYear: 2017\n\nAbstract: Optical Character Recognition (OCR) is one of the continuously explored problems. Presently, commercial character recognizers are available reporting near to 100% recognition rates on text in a number of scripts. Despite these advancements, OCR systems however, have yet to mature for cursive scripts like Urdu. This study presents a holistic technique for recognition of Urdu text in Nastaliq font using \"complete\" ligatures as recognition units. The term \"complete\" refers to a partial word including its main body and secondary components (dots and diacritic marks). Discrete Wavelet Transform (DWT) is employed as feature extractor while a separate Hidden Markov Model (HMM) is trained for each ligature considered in our study. More than 2000 frequently used unique Urdu ligatures from the standard CLE (Center of Language Engineering) dataset are considered in our evaluations. The system reads a promising accuracy of 88.87% on more than 10,000 partial words.","Title: A Methodology of Real-Time Data Fusion for Localized Big Data Analytics.\nYear: 2018\n\nAbstract: The traditional big-data analytical approaches use data clustering as small buckets while providing distributed computation among different child nodes. These approaches bring the issues especially concerning network capacity, specialized tools and applications not capable of being trained in a short period. Furthermore, raw data generated through IoT forming big data comes with the capability of producing highly unstructured and heterogeneous form of data. Such form of data grows into challenging task for the real-time analytics. It is highly valuable to have computational values available locally instead of through distributed resources to reduce real-time analytical challenges. This paper proposes a fusion of three different data models like relational, semantical, and big data based data and metadata involving their issues and enhanced capabilities. A case study is used to represent data fusion in action from RDB to Resource Description Framework. Whereas, issues and their feasible solutions are also being discussed in this paper.","Title: Loss Based Congestion Control Module for Health Centers Deployed by Using Advanced IoT Based SDN Communication Networks\nYear: 2020\n\nAbstract: Many healthcare centers are deploying advanced Internet of Things (IoT) based on Software-Defined Networks (SDNs). Transmission Control Protocol (TCP) was developed to control the data transmission in wide range of networks and provides reliable communication by using many caching and congestion control schemes. TCP is predestined to always increase and decrease its congestion window size to make changes in traffic. Nowadays, about 50% IoT based SDN traffic is controlled by TCP CUBIC, which is the default congestion control scheme in Linux operating system. The aim of this research is to develop a new content-caching based congestion control scheme for advanced IoT enabled SDN networks to achieve better performance in healthcare infrastructure network environments. In this research, Congestion Control Module for Loss Event (CCM-LE) is proposed to enhance the performance of TCP CUBIC in advanced IoT based on SDN. Network Simulator 2 (NS-2) is used to simulate the experiments of CCM-LE and state-of-the-art schemes. Results show that the performance of CCM-LE outperforms by 19% as compared to state-of-the-art schemes.","Title: Software Plagiarism Detection In Multiprogramming Languages Using Machine Learning Approach\nYear: 2021\n\nAbstract: The Software plagiarism, which arises the problem of software piracy is a growing major concern nowadays. It is a serious risk to the software industry that gives huge economic damages every year. The customers may develop a modified version of the original software in other types of programming languages. Furthermore, the plagiarism detection in different types of source codes is a challenging task because each source code may have specific syntax rules. In this paper, we proposed a methodology for software plagiarism detection in multiprogramming languages based on machine learning approaches. The Principal Component Analysis (PCA) is applied for features extraction from source codes without losing the actual information. It extracts features by factor analysis and converts the dataset into normalized linear principal components which are further useful for predictions analysis. Then, the multinomial logistic regression model (MLR) is applied to these components to classify the source codes documents based on predictions. It gives the generalization of logistic regression to handle multiclass problems. Further, the predictors' performance in MLR is evaluated by 2 tailed z test. To apply the experiment, the dataset is collected in five different and popular languages, ie, C, C++, Java, C#, and Python. Each programming language taken in two different case studies, ie, binary search and Stack.","Title: Graph Clustering Based Size Varying Rules for Lifelong Topic Modeling\nYear: 2018\n\nAbstract: Lifelong learning topic models identify the hidden concepts discussed in the collection of documents. The concepts are represented as topics having groups of ordered words based on their relevance to the topic. Lifelong learning models have an automatic learning mechanism which allows continuous learning without external support. In the process, the model gets more knowledgeable with experience as it learns from the past in the form of rules. It is carries rules to the future and utilize them when a similar scenario arises. The existing lifelong learning topic models heavily rely on statistical measures to learn rules that leads to two limitations. The rules are evaluated for fixed number of words while ignoring the natural arrangement of words within the documents. Moreover, the rules have arbitrary orientation that causes repeated patterns of transferring the impact of a rule into a topic during the early iterations of the inference technique. In this research work, we introduce complex networks analysis for learning rules which addresses both of the limitations discussed. The rules are obtained through hierarchical clustering of the complex network that have different number of words within a rule and have directed orientation. The proposed approach improves the utilization of rules for improved quality of topics at higher performance with unidirectional rules on the standard lifelong learning dataset.","Title: VerSig: a new approach for online signature verification\nYear: 2019\n\nAbstract: This paper introduces, VerSig, a new proposed scheme for online signature verification. The proposed scheme is based on creation of a signature envelope by employing dynamic time warping method. This envelope provides the basis for decision of forged and authentic signatures. The scheme only uses basic features such as X, Y coordinates of the signature. A well known and standardized Japanese handwritten dataset (provided for ICDAR 2013 signature verification competition) is used to evaluate the performance of proposed method. Proposed method is compared with state of art methods and observed to offer significant improvements in terms of overall accuracy of prediction.","Title: Plagiarism detection in students’ programming assignments based on semantics: multimedia e-learning based smart assessment methodology\nYear: 2020\n\nAbstract: The multimedia-based e-Learning methodology provides virtual classrooms to students. The teacher uploads learning materials, programming assignments and quizzes on university’ Learning Management System (LMS). The students learn lessons from uploaded videos and then solve the given programming tasks and quizzes. The source code plagiarism is a serious threat to academia. However, identifying similar source code fragments between different programming languages is a challenging task. To solve the problem, this paper proposed a new plagiarism detection technique between C++ and Java source codes based on semantics in multimedia-based e-Learning and smart assessment methodology. First, it transforms source codes into tokens to calculate semantic similarity in token by token comparison. After that, it finds semantic similarity in scalar value for the complete source codes written in C++ and Java. To analyse the experiment, we have taken the dataset consists of four (4) case studies of Factorial, Bubble Sort, Binary Search and Stack data structure in both C++ and Java. The entire experiment is done in R Studio with R version 3.4.2. The experimental results show better semantic similarity results for plagiarism detection based on comparison.","Title: Energy efficient edge-of-things.\nYear: 2019\n\nAbstract: Edge-of-Things (EoT) emerged as a novel computing and storage paradigm to overcome the limitations of IoT-cloud environment by providing cloud-like services at edge of the network. EoT offers a vast area for research and development as the invention has laid out great opportunities to experiment the possibilities for handling large data sets produced by the growing Internet-of-Things (IoT). The EoT offers a framework that lies between the cloud-to-end to perform the processing and cater the storage demands of the IoT applications. However, the exponential increase in EoT infrastructure resulted into extreme energy consumption. This paper finds the opportunity to address the issue of energy consumption in IoT-EoT environment by introducing dynamic speed scaling mechanism in EoT devices. The proposed approach is rigorously evaluated, and the verification is acquired through the simulations carried out on the simulator, iFogSim. The results show significant improvement in energy conservation by dynamically scaling the processor frequency of EoT devices according to the load variations in IoT traffic.","Title: Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features\nYear: 2019\n\nAbstract: The cancer of lung has been one of the major threats to human life for decades in developed and developing countries. The Computer Aided Detection CAD could be a powerful tool for initial lung nodule detection and preventing the deaths caused by the lung tumor. In this paper, an advanced technique for lung-nodule detection by using a hybrid feature set and artificial neural network is proposed. Initially, the lung volume is segmented from the input Computed Tomography image using optimal thresholding which is followed by image enhancement using with multi scale dot augmentation filtering. Next, lung nodule candidates have been detected from enhanced image and certain features are extracted. The set feature consists of the texture features, shape 2D and 3D and intensity. Finally, lung nodule’s classification is attained using two-layer feed forward neural network. The Lung Image Database Consortium dataset has been used to evaluate the novel system which achieved a sensitivity of 95.5% with only 5.72 FP per scan.","Title: Security and privacy based access control model for internet of connected vehicles\nYear: 2019\n\nAbstract: Vehicular networks are continually gaining popularity, owing to the evolution of the Internet of things, by means of rapid communication in IP-based networks. Vehicles are being fitted with sensory devices to gain inputs from the external environment for information processing. Numerous applications of vehicular networks depend on the use of communication networks. The security and privacy of data communication are significant challenges, particularly in wireless applications, for example, the Internet of connected vehicles. When an official fleet of vehicles (government or military) begins its journey, the vehicles need to communicate with one another to share sensitive information. The security of such a fleet is maintained in various security layers, each of which coordinates and communicates with the others, but avoids disclosing sensitive information of higher security layers to lower security layers. No such access control model exists that can enforce the authority of security officials in layers. We propose a novel security and privacy based access control (SPBAC) model for the Internet of connected vehicles, which allows security officials to access information in combination with permissions and roles, instead of roles only, for officials traveling in vehicles belonging to the same fleet. Vehicle-to-vehicle communication is performed with the aid of onboard unit sensory devices. The vehicles can also communicate through wireless technology, namely WiFi and cellular communications (4G/5G). The SPBAC model provides communication among security layers in a secure, private, and efficient manner compared to other state-of-the-art algorithms. The SPBAC model is demonstrated here using mathematical modeling along with implementation examples.","Title: Utilization and load balancing in fog servers for health applications\nYear: 2019\n\nAbstract: With the evolution of fog computing, processing takes place locally in a virtual platform rather than in a centralized cloud server. Fog computing combined with cloud computing is more efficient as fog computing alone does not serve the purpose. Inefficient resource management and load balancing leads to degradation in quality of service as well as energy losses. Traffic overhead is increased because all the requests are sent to the main server causing delays which cannot be tolerated in healthcare scenarios. To overcome this problem, the authors are consolidating fog computing resources so that requests are handled by foglets and only critical requests are sent to the cloud for processing. Servers are placed locally in each city to handle the nearby requests in order to utilize the resources efficiently along with load balancing among all the servers, which leads to reduced latency and traffic overhead with the improved quality of service.","Title: Digital Watermarking Technique for Text Document Protection Using Data Mining Analysis.\nYear: 2019\n\nAbstract: In the current era, information security is on its top priority for all organizations. The individuals, government officials, and military with the rapid development of Internet technologies like the Internet of Things (IoT), big data, and cloud computing facing data security problems. As the massive rate of data growth, it is a challenging task for the researchers, that how to manage the vast amount of data safely and effectively while designing smart cities. It has been quite easy to produce an illegal copy of digital contents. The verification of digital content is one of the major issues because digital contents are generated daily and shared via the internet. The limited techniques are available for document copyright protection. However, most of the existing techniques produce distortion during watermark insertion or lack of capacity. In the said perspective, a digital watermarking technique is proposed for document copyright protection and ownership verification with the help of data mining. The techniques of data mining are applied to find suitable properties from the document for embedding watermark. The proposed model provides copyright protection to text documents on local and cloud computing paradigm. For the evaluation of the proposed technique, 20 different text documents are used to perform many attacks such as formatting, insertion, and deletion attacks. The proposed technique attained a high-level of imperceptibility where peak signal to noise ratio (PSNR) values are between 64.67% and 71.03%, and similarity (SIM) percentage is between 99.92% and 99.99%. The proposed technique is robust and resists from formatting attacks and capacity of the proposed technique is also improved as compared to the previous techniques.","Title: Recognition of printed Urdu ligatures using convolutional neural networks\nYear: 2019\n\nAbstract: We present a holistic technique for recognition of text in cursive scripts using printed Urdu ligatures as a case study. Convolutional neural networks (CNNs) are trained on high-frequency ligature clusters for feature extraction and classification. A query ligature presented to the system is first divided into primary and secondary ligatures that are separately recognized and later associated in a postprocessing step to recognize the complete ligature. Experiments are carried out using transfer learning on pretrained networks as well as by training a network from scratch. The technique is evaluated on ligatures extracted from two standard databases of printed Urdu text, Urdu printed text image (UPTI) and Center of Language Engineering (CLE), as well as by combining the ligatures of the two datasets. The system realizes high recognition rates of 97.81% and 89.20% on the UPTI and CLE databases, respectively. (C) 2019 SPIE and IS&T","Title: A Deep Learning Based Ensemble Learning Method For Epileptic Seizure Prediction\nYear: 2021\n\nAbstract: In epilepsy, patients suffer from seizures which cannot be controlled with medicines or surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is extremely important so that they can be controlled with medication before they actually occur. Researchers have proposed multiple machine/deep learning based methods to predict epileptic seizures; however, accurate prediction of epileptic seizures with low false positive rate is still a challenge. In this research, we propose a deep learning based ensemble learning method to predict epileptic seizures. In the proposed method, EEG signals are preprocessed using empirical mode decomposition followed by bandpass filtering for noise removal. The class imbalance problem has been mitigated with synthetic preictal segments generated using generative adversarial networks. A three-layer customized convolutional neural network has been proposed to extract automated features from preprocessed EEG signals and combined them with handcrafted features to get a comprehensive feature set. The feature set is then used to train an ensemble classifier that combines the output of SVM, CNN and LSTM using Model agnostic meta learning. An average sensitivity of 96.28% and specificity of 95.65% with an average anticipation time of 33 min on all subjects of CHBMIT has been achieved by the proposed method, whereas, on American epilepsy society-Kaggle seizure prediction dataset, an average sensitivity of 94.2% and specificity of 95.8% has been achieved on all subjects."],"string":"[\n \"Title: Activity classification and anomaly detection using m-mediods based modelling of motion patterns\\nYear: 2010\\n\\nAbstract: Techniques for video object motion analysis, behaviour recognition and event detection are becoming increasingly important with the rapid increase in demand for and deployment of video surveillance systems. Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents a novel technique for classification of motion activity and anomaly detection using object motion trajectory. In the proposed motion learning system, trajectories are treated as time series and modelled using modified DFT-based coefficient feature space representation. A modelling technique, referred to as m-mediods, is proposed that models the class containing n members with m mediods. Once the m-mediods based model for all the classes have been learnt, the classification of new trajectories and anomaly detection can be performed by checking the closeness of said trajectory to the models of known classes. A mechanism based on agglomerative approach is proposed for anomaly detection. Four anomaly detection algorithms using m-mediods based representation of classes are proposed. These includes: (i)global merged anomaly detection (GMAD), (ii) localized merged anomaly detection (LMAD), (iii) global un-merged anomaly detection (GUAD), and (iv) localized un-merged anomaly detection (LUAD). Our proposed techniques are validated using variety of simulated and complex real life trajectory datasets.\",\n \"Title: Evaluation of Matching Metrics for Trajectory-Based Indexing and Retrieval of Video Clips\\nYear: 2005\\n\\nAbstract: This paper describes a comparative evaluation of three different similarity metrics for trajectory-based indexing and retrieval of video motion clips. The motion paths are generated using a low-level tracking algorithm incorporating first-order Kalman filter and colour appearance models. For simple motion paths, a RANSAC approach can be used to generate smooth trajectories for each tracked object described by low-order polynomials. This allows us to obtain a representative trajectory model even in the case of high numbers of outlier points caused by target mis-detection and multiple occlusions. We show that more complex trajectories including stop-start motions, can be modelled as time series using high order Chebyshev polynomials. Similarity metrics based on coefficient descriptors are shown to have comparable performance to a Hausdorff distance measure when retrieving trajectory-based motion clips but at substantially reduced computational cost. Experimental results are presented to illustrate the comparative performance of different matching metrics on real-world trajectory data collected by a retail store CCTV installation.\",\n \"Title: Detection and classification of retinal lesions for grading of diabetic retinopathy\\nYear: 2014\\n\\nAbstract: Diabetic Retinopathy (DR) is an eye abnormality in which the human retina is affected due to an increasing amount of insulin in blood. The early detection and diagnosis of DR is vital to save the vision of diabetes patients. The early signs of DR which appear on the surface of the retina are microaneurysms, haemorrhages, and exudates. In this paper, we propose a system consisting of a novel hybrid classifier for the detection of retinal lesions. The proposed system consists of preprocessing, extraction of candidate lesions, feature set formulation, and classification. In preprocessing, the system eliminates background pixels and extracts the blood vessels and optic disc from the digital retinal image. The candidate lesion detection phase extracts, using filter banks, all regions which may possibly have any type of lesion. A feature set based on different descriptors, such as shape, intensity, and statistics, is formulated for each possible candidate region: this further helps in classifying that region. This paper presents an extension of the m-Mediods based modeling approach, and combines it with a Gaussian Mixture Model in an ensemble to form a hybrid classifier to improve the accuracy of the classification. The proposed system is assessed using standard fundus image databases with the help of performance parameters, such as, sensitivity, specificity, accuracy, and the Receiver Operating Characteristics curves for statistical analysis.\",\n \"Title: Intelligent Optimization Of Wireless Sensor Networks Through Bio-Inspired Computing: Survey And Future Directions\\nYear: 2013\\n\\nAbstract: This survey article is a comprehensive discussion on Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing. The marvelous perfection of biological systems and its different aspects for optimized solutions for non-biological problems is presented here in detail. In the current research inclination, hiring of biological solutions to solve and optimize different aspects of artificial systems' problems has been shaped into an important field with the name of bio-inspired computing. We have tabulated the exploitation of key constituents of biological system for developing bio-inspired systems to represent its importance and emergence in problem solving trends. We have presented how the metaphoric relationship is developed between the two biological and non-biological systems by quoting an example of relationship between prevailing wireless system and the natural system. Interdisciplinary research is playing a splendid contribution for various problems' solving. The process of combining the individuals' output to form a single problem solving solution is depicted in three-stage ensemble design. Also the hybrid solutions from computational intelligence-based optimization are elongated to demonstrate the emergent involvement of these inspired systems with rich references for the interested readers. It is concluded that these perfect creations have remedies for most of the problems in non-biological system.\",\n \"Title: Motion trajectory clustering for video retrieval using spatio-temporal approximations\\nYear: 2005\\n\\nAbstract: A new technique is proposed for clustering and similarity retrieval of video motion clips based on spatio-temporal object trajectories. The trajectories are treated as motion time series and modelled using orthogonal basis polynomial approximations. Trajectory clustering is then carried out to discover patterns of similar object motion behaviour. The coefficients of the basis functions are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Clustering in the basis coefficient space leads to efficiency gains over existing approaches that encode trajectories as point-based flow vectors. Experiments on pedestrian motion data gathered from video surveillance demonstrate the effectiveness of our approach. Applications to motion data mining in video surveillance databases are envisaged.\",\n \"Title: Incremental indexing and retrieval mechanism for scalable and robust shape matching.\\nYear: 2012\\n\\nAbstract: Techniques for efficient and effective content-based image matching are becoming increasingly important with the widespread increase in digital image capturing systems. Shape of an object, represented by its contour, is one of the most important visual feature that is thought to be used by humans to determine the similarity of objects. The selected feature and its distance measure must be robust to different distortions such as noise, articulation, scale and rotation. Existing approaches provides invariance to these distortions at the cost of either the accuracy due to poor discrimination ability or the efficiency. In this paper, we present an effective representation of shape, using its boundary information, that is robust to arbitrary distortions and affine transformation. The contour representation of shape is converted into time series and is modeled using orthogonal basis function representations. Shape matching is then carried out in the chosen coefficient feature space resulting in efficient matching. The efficiency of shape matching is further improved by indexing the shape descriptors using hierarchical indexing structure. A novel distributed beam search based technique is proposed that operates on the indexing structure and ensures no false dismissal for a given k-NN query. Experimental evaluation demonstrates that the proposed shape representation and matching mechanism is robust, efficient and scalable to very large shape datasets.\",\n \"Title: Motion Trajectory Learning in the DFT-Coefficient Feature Space\\nYear: 2006\\n\\nAbstract: Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. In this paper we propose a novel vision system for clustering and classification of object-based video motion clips using spatiotemporal models. Object trajectories are modeled as motion time series using the lowest order Fourier coefficients obtained by Discrete Fourier Transform. Trajectory clustering is then carried out in the DFT-coefficient feature space to discover patterns of similar object motion activity. The DFT coefficients are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Assuming the clusters of trajectory points are distributed normally in the coefficient feature space, we propose a simple Mahalanobis classifier for the detection of anomalous trajectories. Our proposed techniques are validated on three different datasets - Australian sign language, handlabelled object trajectories from video surveillance footage and real-time tracking data obtained in the laboratory. Applications to event detection and motion data mining for visual surveillance systems are envisaged.\",\n \"Title: Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space\\nYear: 2006\\n\\nAbstract: This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal function approximations. Assuming the clusters of trajectory points are distributed normally in the coefficient feature space, we propose a Mahalanobis classifier for the detection of anomalous trajectories. Motion trajectories are considered as time series and modelled using orthogonal basis function representations. We have compared three different function approximations --- least squares polynomials, Chebyshev polynomials and Fourier series obtained by Discrete Fourier Transform (DFT). Trajectory clustering is then carried out in the chosen coefficient feature space to discover patterns of similar object motions. The coefficients of the basis functions are used as input feature vectors to a Self- Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Our proposed techniques are validated on three different datasets --- Australian sign language, hand-labelled object trajectories from video surveillance footage and real-time tracking data obtained in the laboratory. Applications to event detection and motion data mining for multimedia video surveillance systems are envisaged.\",\n \"Title: Frameworks for multivariate m-mediods based modeling and classification in Euclidean and general feature spaces\\nYear: 2012\\n\\nAbstract: This paper presents an extension of m-mediods based modeling technique to cater for multimodal distributions of sample within a pattern. The classification of new samples and anomaly detection is performed using a novel classification algorithm which can handle patterns with underlying multivariate probability distributions. We have proposed two frameworks, namely MMC-ES and MMC-GFS, to enable our proposed multivarite m-mediods based modeling and classification approach workable for any feature space with a computable distance metric. MMC-ES framework is specialized for finite dimensional features in Euclidean space whereas MMC-GFS works on any feature space with a computable distance metric. Experimental results using simulated and complex real life dataset show that multivariate m-mediods based frameworks are effective and give superior performance than competitive modeling and classification techniques especially when the patterns exhibit multivariate probability density functions.\",\n \"Title: Motion Clustering Using Spatiotemporal Approximations\\nYear: 2005\\n\\nAbstract: In this paper a new technique is proposed for the clustering and classification of spatio-temporal object trajectories extracted from video motion clips. The trajectories are represented as motion time series and modelled using Chebyshev polynomial approximations. Trajectory clustering is then performed to discover patterns of similar object motion. The coefficients of the basis ftinctions are used as an input feature vector to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. It is shown that applying machine learning techniques in the Chebyshev parameter subspace leads to significant performance gains over previous approaches that encode trajectories as point-based flow (PBF) vectors. Experiments using the PETS'04 tracking dataset demonstrate the effectiveness of clustering in the parameter subspace and improvements in overall classification accuracy in comparison with PBF vector encoding. We also show how this technique can be further extended to the detection of anomalous motion paths. Applications to motion data mining and event detection in video surveillance systems are envisaged.\",\n \"Title: Motion-based behaviour learning, profiling and classification in the presence of anomalies\\nYear: 2010\\n\\nAbstract: Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents a novel technique for clustering and classification of motion. In the proposed motion learning system, trajectories are treated as time series and modelled using modified DFT (discrete fourier transform)-based coefficient feature space representation. A framework (iterative HSACT-LVQ (hierarchical semi-agglomerative clustering-learning vector quantization)) is proposed for learning of patterns in the presence of significant number of anomalies in training data. A novel modelling technique, referred to as m-Mediods, is also proposed that models the class containing n members with m Mediods. Once the m-Mediods-based model for all the classes have been learnt, the classification of new trajectories and anomaly detection can be performed by checking the closeness of said trajectory to the models of known classes. A mechanism based on agglomerative approach is proposed for anomaly detection. Our proposed techniques are validated using variety of simulated and complex real life trajectory data sets.\",\n \"Title: Detection of neovascularization in retinal images using multivariate m-Mediods based classifier.\\nYear: 2013\\n\\nAbstract: Diabetic retinopathy is a progressive eye disease and one of the leading causes of blindness all over the world. New blood vessels (neovascularization) start growing at advance stage of diabetic retinopathy known as proliferative diabetic retinopathy. Early and accurate detection of proliferative diabetic retinopathy is very important and crucial for protection of patient's vision. Automated systems for detection of proliferative diabetic retinopathy should identify between normal and abnormal vessels present in digital retinal image. In this paper, we proposed a new method for detection of abnormal blood vessels and grading of proliferative diabetic retinopathy using multivariate m-Mediods based classifier. The system extracts the vascular pattern and optic disc using a multilayered thresholding technique and Hough transform respectively. It grades the fundus image in different categories of proliferative diabetic retinopathy using classification and optic disc coordinates. The proposed method is evaluated using publicly available retinal image databases and results show that the proposed system detects and grades proliferative diabetic retinopathy with high accuracy.\",\n \"Title: Identification and classification of microaneurysms for early detection of diabetic retinopathy\\nYear: 2013\\n\\nAbstract: Diabetic retinopathy is a progressive eye disease which may cause blindness if not detected and treated in time. The early detection and diagnosis of diabetic retinopathy is important to protect the patient's vision. The accurate detection of microaneurysms (MAs) is a critical step for early detection of diabetic retinopathy because they appear as the first sign of disease. In this paper, we propose a three-stage system for early detection of MAs using filter banks. In the first stage, the system extracts all possible candidate regions for MAs present in retinal image. In order to classify a candidate region as MA or non-MA, the system formulates a feature vector for each region depending upon certain properties, i.e. shape, color, intensity and statistics. We present a hybrid classifier which combines the Gaussian mixture model (GMM), support vector machine (SVM) and an extension of multimodel mediod based modeling approach in an ensemble to improve the accuracy of classification. The proposed system is evaluated using publicly available retinal image databases and achieved higher accuracy which is better than previously published methods.\",\n \"Title: Classifying spatiotemporal object trajectories using unsupervised learning of basis function coefficients\\nYear: 2005\\n\\nAbstract: This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal functional approximations. A Mahalanobis classifier is then used for the detection of anomalous trajectories. Motion trajectories are considered as time series and modeled using the leading Fourier coefficients obtained by a Discrete Fourier Transform. Trajectory clustering is then carried out in the Fourier coefficient feature space to discover patterns of similar object motions. The coefficients of the basis functions are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Experiments are performed on two different datasets -- synthetic and pedestrian object tracking - to demonstrate the effectiveness of our approach. Applications to motion data mining in video surveillance databases are envisaged.\",\n \"Title: Preprocessing Approach Identifying and Removing Noise which Affects Shape\\nYear: 2014\\n\\nAbstract: In this paper, we propose a noise removal approach that significantly improves the performance of sophisticated shape matching techniques. Existing shape matching techniques focus on complex algorithms without giving much consideration to the noise removing preprocessing techniques. These preprocessing techniques can reasonably improve the accuracy where the shapes are affected by cracks. We present a preprocessing approach to identify and remove small cracks and slits in the shape contour. It has been observed that these cracks or slits change the shape altogether in a manner which makes classification of shapes difficult. This technique is simple and effective and may be used to preprocess the images before applying sophisticated shape representation techniques to improve their accuracies. The effectiveness of proposed preprocessing approach is verified using publicly available shape datasets.\",\n \"Title: Robust shape matching using global feature space representation of contours\\nYear: 2012\\n\\nAbstract: The fundamental ingredient of content-based image retrieval is the selection of appropriate features to describe the content of the image. Shape of an object, represented by its contour, is the most important visual feature that is thought to be used by humans to determine the similarity of objects. In this paper, we present an effective representation of shape, using its boundary information, that is robust to arbitrary distortions and affine transformation. The contour representation of shape is converted into time series and is modeled using orthogonal basis function representations. Encoding contour representation of shapes in this manner leads to efficiency gains over existing approaches such as structural shape representation and techniques that use discrete point-based flow vectors to represent the contour. Experimental evaluation demonstrates that the proposed shape representation and matching mechanism is effective, efficient and robust to different arbitrary and affine distortions.\",\n \"Title: Energy efficient strategy for throughput improvement in wireless sensor networks.\\nYear: 2015\\n\\nAbstract: Network lifetime and throughput are one of the prime concerns while designing routing protocols for wireless sensor networks (WSNs). However, most of the existing schemes are either geared towards prolonging network lifetime or improving throughput. This paper presents an energy efficient routing scheme for throughput improvement in WSN. The proposed scheme exploits multilayer cluster design for energy efficient forwarding node selection, cluster heads rotation and both inter- and intra-cluster routing. To improve throughput, we rotate the role of cluster head among various nodes based on two threshold levels which reduces the number of dropped packets. We conducted simulations in the NS2 simulator to validate the performance of the proposed scheme. Simulation results demonstrate the performance efficiency of the proposed scheme in terms of various metrics compared to similar approaches published in the literature.\",\n \"Title: TOBAE: A Density-based Agglomerative Clustering Algorithm\\nYear: 2015\\n\\nAbstract: This paper presents a novel density based agglomerative clustering algorithm named TOBAE which is a parameter-less algorithm and automatically filters noise. It finds the appropriate number of clusters while giving a competitive running time. TOBAE works by tracking the cumulative density distribution of the data points on a grid and only requires the original data set as input. The clustering problem is solved by automatically finding the optimal density threshold for the clusters. It is applicable to any N-dimensional data set which makes it highly relevant for real world scenarios. The algorithm outperforms state of the art clustering algorithms by the additional feature of automatic noise filtration around clusters. The concept behind the algorithm is explained using the analogy of puddles ('tobae'), which the algorithm is inspired from. This paper provides a detailed algorithm for TOBAE along with the complexity analysis for both time and space. We show experimental results against known data sets and show how TOBAE competes with the best algorithms in the field while providing its own set of advantages.\",\n \"Title: Behaviour recognition using multivariate m-mediod based modelling of motion trajectories.\\nYear: 2015\\n\\nAbstract: The importance of behaviour analysis and activity recognition systems continue to increase with the increasing demand and deployment of video surveillance systems. Motion trajectories provide rich spatio-temporal information about an object’s activity. In this article, we present a supervised feature extraction and multivariate modelling approach for motion-based behaviour recognition and anomaly detection. In the proposed motion learning system, trajectories are treated as time series and modelled using modified DFT-based coefficient feature space representation. We employ supervised dimensionality reduction using Local Fisher Discriminant Analysis to enhance the feature space representation of trajectories. A modelling approach, referred to as multivariate m-mediods, is proposed that can cater for the presence of multivariate distribution of samples within a given motion pattern. A hierarchical indexing of mediods and retrieval approach is presented to improve the efficiency of proposed classifier. Our proposed techniques are validated using variety of simulated and complex real-life trajectory datasets.\",\n \"Title: Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels\\nYear: 2015\\n\\nAbstract: Retinal blood vessels are the source to provide oxygen and nutrition to retina and any change in the normal structure may lead to different retinal abnormalities. Automated detection of vascular structure is very important while designing a computer aided diagnostic system for retinal diseases. Most popular methods for vessel segmentation are based on matched filters and Gabor wavelets which give good response against blood vessels. One major drawback in these techniques is that they also give strong response for lesion (exudates, hemorrhages) boundaries which give rise to false vessels. These false vessels may lead to incorrect detection of vascular changes. In this paper, we propose a new hybrid feature set along with new classification technique for accurate detection of blood vessels. The main motivation is to lower the false positives especially from retinal images with severe disease level. A novel region based hybrid feature set is presented for proper discrimination between true and false vessels. A new modified m-mediods based classification is also presented which uses most discriminating features to categorize vessel regions into true and false vessels. The evaluation of proposed system is done thoroughly on publicly available databases along with a locally gathered database with images of advanced level of retinal diseases. The results demonstrate the validity of the proposed system as compared to existing state of the art techniques.\",\n \"Title: Recognition of Urdu ligatures - a holistic approach\\nYear: 2015\\n\\nAbstract: This paper presents an effective segmentation-free and scale-invariant technique for recognition of Urdu ligatures in Nastaliq font. The proposed technique relies on separating the main body of ligatures from the secondary components and training a separate hidden Markov model for each. Features capturing projection, concavity and curvature information of ligatures are extracted using right-to-left sliding windows and are fed to the models for training. The system trained and evaluated on a total of more than 2,000 frequently occurring Urdu ligatures from a standard database realized a recognition rate of 97.93%.\",\n \"Title: Online Knowledge-Based Model for Big Data Topic Extraction\\nYear: 2016\\n\\nAbstract: AbstractLifelong machine learning LML models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model OAMC to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half.\",\n \"Title: Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3D retinal surfaces.\\nYear: 2016\\n\\nAbstract: •An automated method is proposed here to diagnose two common macular syndromes by reconstructing 3D retinal surfaces.•To the best of our knowledge, this paper proposes a first ever novel self-diagnosis system to incorporate 3D OCT scans.•The self-diagnosis in our proposed system is based on multilevel support vector machines (SVM) classifier.•90 OCT volumes (30 healthy, 30 CSR and 30 ME) of 73 patients were considered in this research to test the proposed system.\",\n \"Title: A three-way approach for learning rules in automatic knowledge-based topic models.\\nYear: 2017\\n\\nAbstract: A three-way approach is proposed for selecting rules in automatic knowledge-based topic models.The selection of rules in the three-way approach is controlled based on a pair of thresholds.Effective thresholds are determined based on a tradeoff between the quantity of rules and the quality of the rules.The game-theoretic rough set (GTRS) model is used to implement the tradeoff.\",\n \"Title: Digital Image Preprocessing And Hair Artifact Removal By Using Gabor Wavelet\\nYear: 2016\\n\\nAbstract: Diagnosis of skin cancer needs specialized equipment, doctors and continuous monitoring to treat it well. Patients living in remote areas normally cannot access such facilities. To overcome these barriers of access, Computer Aided Diagnostics, an emerging field in computer science, often called telemedicine, is being considered a promising approach. In this research, preprocessing and hair artifact removal experiment was performed on dermatoscope images by using Morphological and Gabor wavelet-based techniques. It has been found that, in some cases, wavelet transformations provide better results as compared to other techniques like gel, water bubbles and dark hair around the surface affected by cancer, i.e. these artifacts are removed with less effort.\",\n \"Title: IoT-based students interaction framework using attention-scoring assessment in eLearning.\\nYear: 2018\\n\\nAbstract: Students’ interaction and collaboration using Internet of Things (IoT) based interoperable infrastructure is a convenient way. Measuring student attention is an essential part of educational assessment. As new learning styles develop, new tools and assessment methods are also needed. The focus of this paper is to develop IoT-based interaction framework and analysis of the student experience of electronic learning (eLearning). The learning behaviors of students attending remote video lectures are assessed by logging their behavior and analyzing the resulting multimedia data using machine learning algorithms. An attention-scoring algorithm, its workflow, and the mathematical formulation for the smart assessment of the student learning experience are established. This setup has a data collection module, which can be reproduced by implementing the algorithm in any modern programming language. Some faces, eyes, and status of eyes are extracted from video stream taken from a webcam using this module. The extracted information is saved in a dataset for further analysis. The analysis of the dataset produces interesting results for student learning assessments. Modern learning management systems can integrate the developed tool to take student learning behaviors into account when assessing electronic learning strategies.\",\n \"Title: Precise shape matching of large shape datasets using hybrid approach.\\nYear: 2017\\n\\nAbstract: Precise and fast shape matching and retrieval from very large datasets is a challenging task because of the existence of many distortions such as noise, occlusion and affine distortions. In this paper, we aim to propose a time-saving and effective shape matching and retrieval framework, that employs pruning which will enable online shape retrieval from extremely large datasets. First, using a hierarchical tree-based structure supporting parallel processing and efficient feature descriptors, irrelevant shapes are pruned and a subset of shapes relevant to the query is selected, then using more sophisticated feature descriptors, accurate retrieval is performed. Contour representation of an object is considered as most significant visual shape similarity measure by the humans. Using boundary information, we generate two simplified and efficient feature descriptors for fast pruning, and a sophisticated feature descriptor for effective and accurate retrieval. Tests performed on standard datasets unveil that the proposed technique is computationally more efficient than the state-of-the-art techniques while maintaining comparable matching and retrieval performance. Its performance is scalable for huge datasets and is robust against affine transformations, articulations and occlusion.\",\n \"Title: Segmentation-free optical character recognition for printed Urdu text.\\nYear: 2017\\n\\nAbstract: This paper presents a segmentation-free optical character recognition system for printed Urdu Nastaliq font using ligatures as units of recognition. The proposed technique relies on statistical features and employs Hidden Markov Models for classification. A total of 1525 unique high-frequency Urdu ligatures from the standard Urdu Printed Text Images (UPTI) database are considered in our study. Ligatures extracted from text lines are first split into primary (main body) and secondary (dots and diacritics) ligatures and multiple instances of the same ligature are grouped into clusters using a sequential clustering algorithm. Hidden Markov Models are trained separately for each ligature using the examples in the respective cluster by sliding right-to-left the overlapped windows and extracting a set of statistical features. Given the query text, the primary and secondary ligatures are separately recognized and later associated together using a set of heuristics to recognize the complete ligature. The system evaluated on the standard UPTI Urdu database reported a ligature recognition rate of 92% on more than 6000 query ligatures.\",\n \"Title: Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning.\\nYear: 2017\\n\\nAbstract: Multimedia is an essential and integral part of electronic learning (e-learning). In this study, teaching performance and student learning experience are measured using real-time multimedia processing tools and techniques for the e-learning paradigm. Visual attention and visual engagement analysis are performed using two developed algorithms. Video lectures are recorded and delivered to students in e-learning pedagogical setup, which are examined for the visual attention and visual engagement of the student and teacher, respectively. Proposed methodology integrates the assessment on both student and teacher ends. Multimedia processing of video lectures for teaching performance produces scoring dataset. The same methodology on student end for visual attention is used to investigate student experience. These types of datasets then reduced to time-based datasets from the image-based dataset. Correlation and association of both datasets provide the opportunity to relate both student experience and teaching performance as well as to move forward to create content that is more useful. Computational performance of the developed algorithms is compared using different video lectures with their processed frames per second, which is analyzed as per their corresponding bins. Mean, max, and median of the processed frames of all the processed videos are also compared.\",\n \"Title: Fuzzy based multi-criteria vertical handover decision modeling in heterogeneous wireless networks.\\nYear: 2017\\n\\nAbstract: Vertical handover gain significant importance due to the enhancements in mobility models by the Fourth Generation (4G) technologies. However, these enhancements are limited to specific scenarios and hence do not provide support for generic mobility. Similarly, various schemes are proposed based on these mobility models but most of them are suffered from the high packet loss, frequent handovers, too early and late handovers, inappropriate network selection, etc. To address these challenges, a generic vertical handover management scheme for heterogeneous wireless networks is proposed in this article. The proposed scheme works in three phases. In the first phase, a handover triggering approach is designed to identify the appropriate place for initiating handover based on the estimated coverage area of a WLAN access point or cellular base station. In the second phase, fuzzy rule based system is designed to eliminate the inappropriate networks before deciding an optimal network for handover. In the third phase, a network selection scheme is developed based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision mechanism. Various parameters such as delay, jitter, Bit Error Rate (BER), packet loss, communication cost, response time, and network load are considered for selecting an optimal network. The proposed scheme is tested in a mobility scenario with different speeds of a mobile node ranging from very low to very high. The simulation results are compared with the existing decision models used for network selection and handover triggering approaches. The proposed scheme outperforms these schemes in terms of energy consumption, handover delay and time, packet loss, good put, etc.\",\n \"Title: Multimedia based qualitative assessment methodology in eLearning: student teacher engagement analysis.\\nYear: 2018\\n\\nAbstract: Multimedia-based Electronic learning (eLearning) is an effective method of knowledge transfer. Multimedia-based eLearning provides the opportunity to the students that they can use once delivered and recorded video lecture any time. Multimedia tools and its applications if used in eLearning enable the students and teachers to take such kind of advantages. Multimedia applications provide the advantages to students and teacher in eLearning but challenges are also striking features. One of the challenges is to measure and analyze visual engagement and visual attention for teachers and students respectively. Visual engagement of the video lecture is measured and analyzed to verify the effectiveness of the teachers’ lectures. In the same way, the visual attention for the students is also be measured and analyzed. We have presented a qualitative assessment methodology and an algorithm for both of the measures and their correlation. Covariance and relationship between the two measures are the key technique of this paper. Multimedia-based software tools have been developed for this purpose. Results of both the tools are very interesting and promising. They are interpreted using descriptive and graphical statistics.\",\n \"Title: Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.\\nYear: 2018\\n\\nAbstract: Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.\",\n \"Title: Pattern Based Comprehensive Urdu Stemmer and Short Text Classification.\\nYear: 2018\\n\\nAbstract: Urdu language is used by approximately 200 million people for spoken and written communications. The bulk of unstructured Urdu textual data is available in the world. We can employ data mining techniques to extract useful information from such a large, potentially informative base data. There are many text processing systems available to process unstructured textual data. However, these systems are mostly language specific with the large proportion of systems applicable to English text. This is primarily due to language-dependent preprocessing systems, mainly the stemming requirement. Stemming is a vital preprocessing step in the text mining process and its primary aim is to reduce grammatical words form, e.g., parts of speech, gender, tense, and so on, to their root form. In the proposed work, we have developed a rule-based comprehensive stemming method for Urdu text. This proposed Urdu stemmer has the ability to generate the stem of Urdu words as well as loan words that belong to borrowed languages, such as Arabic, Persian, and Turkish, by removing prefix, infix, and suffix from the words. In the proposed stemming technique, we introduced six novel Urdu infix words classes and a minimum word length rule to generate the stem of Urdu text. In order to cope with the challenge of Urdu infix stemming, we have developed infix stripping rules for introduced infix words classes and generic stemming rules for prefix and suffix stemming. We also present a probabilistic classification approach to classify Urdu short text. Different experiments are performed to demonstrate the effectiveness and efficacy of the proposed approach. Comparison with existing state-of-the art approaches is also made. Stemming accuracy results demonstrate the adoptability of the proposed stemming approach for a variety text processing applications.\",\n \"Title: Sentiment analysis and the complex natural language.\\nYear: 2016\\n\\nAbstract: There is huge amount of content produced online by amateur authors, covering a large variety of topics. Sentiment analysis (SA) extracts and aggregates users’ sentiments towards a target entity. Machine learning (ML) techniques are frequently used as the natural language data is in abundance and has definite patterns. ML techniques adapt to domain specific solution at high accuracy depending upon the feature set used. The lexicon-based techniques, using external dictionary, are independent of data to prevent overfitting but they miss context too in specialized domains. Corpus-based statistical techniques require large data to stabilize. Complex network based techniques are highly resourceful, preserving order, proximity, context and relationships. Recent applications developed incorporate the platform specific structural information i.e. meta-data. New sub-domains are introduced as influence analysis, bias analysis, and data leakage analysis. The nature of data is also evolving where transcribed customer-agent phone conversation are also used for sentiment analysis. This paper reviews sentiment analysis techniques and highlight the need to address natural language processing (NLP) specific open challenges. Without resolving the complex NLP challenges, ML techniques cannot make considerable advancements. The open issues and challenges in the area are discussed, stressing on the need of standard datasets and evaluation methodology. It also emphasized on the need of better language models that could capture context and proximity.\",\n \"Title: Lifelong aspect extraction from big data: knowledge engineering.\\nYear: 2016\\n\\nAbstract: Traditional machine learning techniques follow a single shot learning approach. It includes all supervised, semi-supervised, transfer learning, hybrid and unsupervised techniques having a single target domain known prior to analysis. Learning from one task is not carried to the next task, therefore, they cannot scale up to big data having many unknown domains. Lifelong learning models are tailored for big data having a knowledge module that is maintained automatically. The knowledge-base grows with experience where knowledge from previous tasks helps in current task. This paper surveys topic models leading the discussion to knowledge-based topic models and lifelong learning models. The issues and challenges in learning knowledge, its abstraction, retention and transfer are elaborated. The state-of-the art models store word pairs as knowledge having positive or negative co-relations called must-links and cannot-links. The need for innovative ideas from other research fields is stressed to learn more varieties of knowledge to improve accuracy and reveal more semantic structures from within the data.\",\n \"Title: Melanocytic and nevus lesion detection from diseased dermoscopic images using fuzzy and wavelet techniques.\\nYear: 2018\\n\\nAbstract: The use of computer-assisted decision system (CAD) for the diagnosis of skin cancer dermoscopy is aggravated by the potential gains of its excellent performance. It automates the skin lesion analysis and reduces the amount of repetitive and tedious tasks to be done by physicians. This research is mainly focused on the computer vision perspective to design a CAD system which will facilitate the physicians. An automated PR system includes four inter-related processes to analyze skin lesions by the clinicians: image preprocessing, segmentation, feature extraction, feature selection and classification. The dataset contains images and annotations provided by physicians. Segmentation is an imperative preprocessing step for CAD system of skin lesions. Feature extraction of segmented skin lesions is a pivotal step for implementing accurate decision support systems. Physicians are interested in examining a specific clinically significant region in a lesion. Such a region is expected to have more information in the form of texture that can be relevant for detection. In case of detection of melanoma, various local features, for example, pigment network and streaks, usually occur in peripheral region of the lesion. This led to the extraction of peripheral part for feature extraction instead of whole lesion processing. We propose novel techniques for lesion detection and classification on peripheral part of the lesion using m-mediod classifier along with the contrast of patterns. Classification results obtained from the proposed feature matrix were compared with some other texture descriptors, showing the superiority of our proposed descriptor.\",\n \"Title: Counter Measuring Conceivable Security Threats on Smart Healthcare Devices.\\nYear: 2018\\n\\nAbstract: Smart devices, the carriers of a huge amount of private, sensitive and confidential data are pervasive in today's world with innovative and enhanced functionalities. Smartphones have brought tremendous change in people's lives with the launch of a new platform of communication and an ease of access to a wide range of applications. Due to the swift increase in the users of Android smartphones and the increasing demands based on advanced ease and features, developers are working hard to achieve the needful. Easy access to certain features and applications gave rise to the powerfulness and an efficacy of various threats, risks and vulnerabilities that can victimize users' private data residing in smartphone paradigm. With the developments and enhancements in malware, for Android-based smartphones, attacks continue to occur. In this paper, we investigate one of the possibly most destructive attacks for Android, that is, screenshot attack. We have developed \\\"ScreenStealer\\\" application and explored the vulnerabilities which make Android more inclined to risks and threats. Furthermore, we evaluated capture ratio of screenshots, resources consumption and execution time to determine effectiveness, efficiency and stealthiness of such a malicious application.\",\n \"Title: Semantic Interoperability in Heterogeneous IoT Infrastructure for Healthcare.\\nYear: 2017\\n\\nAbstract: Interoperability remains a significant burden to the developers of Internet of Things' Systems. This is due to the fact that the IoT devices are highly heterogeneous in terms of underlying communication protocols, data formats, and technologies. Secondly due to lack of worldwide acceptable standards, interoperability tools remain limited. In this paper, we proposed an IoT based Semantic Interoperability Model (IoT-SIM) to provide Semantic Interoperability among heterogeneous IoT devices in healthcare domain. Physicians communicate their patients with heterogeneous IoT devices to monitor their current health status. Information between physician and patient is semantically annotated and communicated in a meaningful way. A lightweight model for semantic annotation of data using heterogeneous devices in IoT is proposed to provide annotations for data. Resource Description Framework (RDF) is a semantic web framework that is used to relate things using triples to make it semantically meaningful. RDF annotated patients' data has made it semantically interoperable. SPARQL query is used to extract records from RDF graph. For simulation of system, we used Tableau, Gruff-6.2.0, and Mysql tools.\",\n \"Title: Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network.\\nYear: 2018\\n\\nAbstract: Among constituents of communication architecture, routing is the most energy squeezing process. In this survey article, we are targeting an innovative aspect of analysis on routing in wireless sensor network (WSN) that has never been seen in the available literature before. This article can be a guiding light for new researchers to comprehend the WSN technology, energy aware routing, and the factors that affect the energy aware routing in WSN. This insight comprehension then makes the ways easy for them in designing such types of algorithms as well as evaluating the authenticity and extending the existing algorithms of this category, since algebraic and graphical modelling of these factors is also demonstrated. Various available techniques used by existing routing algorithms to handle these factors in making themselves energy aware are also given. Further, they are analyzed along with the suggested improvements for the researchers. At the end, we presented our previously published research work as an example and case study of discussed factors. A rich list of references is also cited for interested readers to explore the related given points.\",\n \"Title: Designing an Energy-Aware Mechanism for Lifetime Improvement of Wireless Sensor Networks: a Comprehensive Study.\\nYear: 2018\\n\\nAbstract: In this paper, we have presented a comprehensive study on designing an aware energy architecture of Wireless Sensor Networks. A summary and modelling of various major techniques in designing the constituents of clustered wireless sensor network architecture are given in detail. In continuation of it, we have also analysed our proposed scheme, Extended-Multilayer Cluster Designing Algorithm (E-MCDA) in a large network. Among its components, algorithms for time slot allocation, minimising the CH competition candidates, and cluster member selection to CH play underpinning roles to achieve the target. These incorporations in MCDA result in minimising transmissions, suppressing the unneeded response of transmissions and near equal size and equal load clusters. We have done extensive simulations in NS2 and evaluate the performance of E-MCDA in energy consumption at various aspects of energy, packets transmission, the number of designed clusters, the number of nodes per cluster and un-clustered nodes. It is found that the proposed mechanism optimistically outperforms the competition with MCDA and EADUC concerning parameters above.\",\n \"Title: An enhanced framework for multimedia data: Green transmission and portrayal for smart traffic system.\\nYear: 2018\\n\\nAbstract: The object tracking in video surveillance for intelligent traffic handling in smart cities requires an enormous amount of data called big data to be transmitted over the network using the Internet of Things. Manual monitoring and surveillance are impossible because traditional computer vision technologies are no more useful for massive processing and intelligent decision making. In this paper, a framework is proposed which enables both on spot data processing and intelligent decision making by using cloud computing. The developed application is a trained on Artificial Neural Network, which can handle different traffic techniques with congested traffic scenario and priorities traffic such as ambulance handling. The Message Queue Telemetry Transport protocol is used for green transmission with mobile access to traffic data. The results analyzed with thirty videos processed data which handle real-time data prioritization for the people for smart surveillance to fastest route and enhance the intelligent data transmission.\",\n \"Title: BotDet: A System for Real Time Botnet Command and Control Traffic Detection.\\nYear: 2018\\n\\nAbstract: Over the past decade, the digitization of services transformed the healthcare sector leading to a sharp rise in cybersecurity threats. Poor cybersecurity in the healthcare sector, coupled with high value of patient records attracted the attention of hackers. Sophisticated advanced persistent threats and malware have significantly contributed to increasing risks to the health sector. Many recent attacks are attributed to the spread of malicious software, e.g., ransomware or bot malware. Machines infected with bot malware can be used as tools for remote attack or even cryptomining. This paper presents a novel approach, called BotDet, for botnet Command and Control (C&C) traffic detection to defend against malware attacks in critical ultrastructure systems. There are two stages in the development of the proposed system: 1) we have developed four detection modules to detect different possible techniques used in botnet C&C communications and 2) we have designed a correlation framework to reduce the rate of false alarms raised by individual detection modules. Evaluation results show that BotDet balances the true positive rate and the false positive rate with 82.3% and 13.6%, respectively. Furthermore, it proves BotDet capability of real time detection.\",\n \"Title: A generic methodology for geo-related data semantic annotation.\\nYear: 2018\\n\\nAbstract: Geo-related data, also known as spatial data, is represented using a vector used for representing longitude, elevation, and latitude. Specially built systems, well known as Geographical Information Systems (GIS), made use of such data for querying, manipulating, navigation, and analyzing. In the current era of data, science needs to involve smart interactive investigation involving Internet of Data (IoD) on predicting upcoming changes and spatial updates on the map is growing rapidly. To resolve issues concerning real-time spatial data, transformation using semantic annotation can provide a better way to translate spatial relationships. These spatial relationships will support spatial analysis by linking different cause and effect with the help of reasoning mechanism. This research's major focus is on a data transformation methodology for geo-related semantic annotation. Spatial dataset gets stored in a database and then transformed into Extensible Markup Language (XML) and Resource Description Framework (RDF). Even for bi-directional transformation to work properly, we need to map different schema level transformations. A deep research is conducted to consider available mappings, implementations, and updates to further improving data fusion for having better compatibility. Then, transformed data as results get analyzed and discussed based on the data mapping rules formulated. It is aimed to show the importance of reducing the response time of investigation and offer compatibility between the web and semantically enriched spatial data.\",\n \"Title: Enhanced Network Anomaly Detection Based on Deep Neural Networks.\\nYear: 2018\\n\\nAbstract: Due to the monumental growth of Internet applications in the last decade, the need for security of information network has increased manifolds. As a primary defense of network infrastructure, an intrusion detection system is expected to adapt to dynamically changing threat landscape. Many supervised and unsupervised techniques have been devised by researchers from the discipline of machine learning and data mining to achieve reliable detection of anomalies. Deep learning is an area of machine learning which applies neuron-like structure for learning tasks. Deep learning has profoundly changed the way we approach learning tasks by delivering monumental progress in different disciplines like speech processing, computer vision, and natural language processing to name a few. It is only relevant that this new technology must be investigated for information security applications. The aim of this paper is to investigate the suitability of deep learning approaches for anomaly-based intrusion detection system. For this research, we developed anomaly detection models based on different deep neural network structures, including convolutional neural networks, autoencoders, and recurrent neural networks. These deep models were trained on NSLKDD training data set and evaluated on both test data sets provided by NSLKDD, namely NSLKDDTest+ and NSLKDDTest21. All experiments in this paper are performed by authors on a GPU-based test bed. Conventional machine learning-based intrusion detection models were implemented using well-known classification techniques, including extreme learning machine, nearest neighbor, decision-tree, random-forest, support vector machine, naive-bays, and quadratic discriminant analysis. Both deep and conventional machine learning models were evaluated using well-known classification metrics, including receiver operating characteristics, area under curve, precision-recall curve, mean average precision and accuracy of classification. Experimental results of deep IDS models showed promising results for real-world application in anomaly detection systems.\",\n \"Title: Fundus Images-Based Detection and Grading of Macular Edema Using Robust Macula Localization.\\nYear: 2018\\n\\nAbstract: The macula is an oval-shaped area near the center of the human retina, and at its center, there is a small pit known as the fovea. The fovea contains large concentrations of cone cells and is responsible for sharp, colored vision. Macular disorders are the group of diseases that damage the macula, resulting in blurred vision or even blindness. Macular edema (ME), one of the most common types of macular disorder, is caused by fluid accumulation beneath the macula. In this paper, we present an automated system for the detection of ME from fundus images. We introduce a new automated system for the detailed grading of the severity of disease using knowledge of exudates and maculae. A new set of features is used along with a minimum distance classifier for accurate localization of the fovea, which is important for the grading of ME. The proposed system uses different hybrid features and support vector machines for segmentation of exudates. The detailed grading of ME-as both clinically significant ME and non-clinically significant ME-is done using localized foveae and segmented exudates. The proposed algorithm is validated using public and local data sets. We have achieved an average accuracy of 96.1% in the detection and grading of ME with our proposed method.\",\n \"Title: A Parallel Architecture for the Partitioning Around Medoids (PAM) Algorithm for Scalable Multi-Core Processor Implementation with Applications in Healthcare.\\nYear: 2018\\n\\nAbstract: Clustering is the most common method for organizing unlabeled data into its natural groups (called clusters), based on similarity (in some sense or another) among data objects. The Partitioning Around Medoids (PAM) algorithm belongs to the partitioning-based methods of clustering widely used for objects categorization, image analysis, bioinformatics and data compression, but due to its high time complexity, the PAM algorithm cannot be used with large datasets or in any embedded or real-time application. In this work, we propose a simple and scalable parallel architecture for the PAM algorithm to reduce its running time. This architecture can easily be implemented either on a multi-core processor system to deal with big data or on a reconfigurable hardware platform, such as FPGA and MPSoCs, which makes it suitable for real-time clustering applications. Our proposed model partitions data equally among multiple processing cores. Each core executes the same sequence of tasks simultaneously on its respective data subset and shares intermediate results with other cores to produce results. Experiments show that the computational complexity of the PAM algorithm is reduced exponentially as we increase the number of cores working in parallel. It is also observed that the speedup graph of our proposed model becomes more linear with the increase in number of data points and as the clusters become more uniform. The results also demonstrate that the proposed architecture produces the same results as the actual PAM algorithm, but with reduced computational complexity.\",\n \"Title: Socio-cyber network: The potential of cyber-physical system to define human behaviors using big data analytics.\\nYear: 2019\\n\\nAbstract: The growing gap between users and the big data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze such massive volume of data. Moreover, advancements in the field of big data application and data science leads toward a new paradigm of human behavior, where various smart devices integrate with each other and establish a relationship. However, majority of the systems are either memoryless or computational inefficient, which are unable to define or predict human behavior. Therefore, keeping in view the aforementioned needs, there is a requirement for a system that can efficiently analyze a stream of big data within their requirements. Hence, this paper presents a system architecture that integrates social network with the technical network. We derive a novel notion of ‘Socio-Cyber Network’, where a friendship is made based on the geo-location information of the user, where trust index is used based on graphs theory. The proposed graph theory provides a better understanding of extraction knowledge from the data and finding relationship between different users. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce. MapReduce for cyber-physical system (CPS) is supported by a parallel algorithm that efficiently process a huge volume of data sets. The system is implemented using Spark GraphX tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.\",\n \"Title: Comprehensive Stemmer For Morphologically Rich Urdu Language\\nYear: 2019\\n\\nAbstract: Urdu language is used by approximately 200 million people for spoken and written communication. Bulk of unstructured Urdu textual data is available in the world. We can employ data mining techniques to extract useful information from such a large potential information base. There are many text processing systems that are available. However, these systems are mostly language specific with the large proportion of systems are applicable to English text. This is primarily due to the language dependant pre-processing systems mainly the stemming requirement. Stemming is a vital pre-processing step in the text mining process and its core aim is to reduce many grammatical words form e.g., parts of speech, gender, tense etc. to their root form. In this proposed work, we have developed a rule based comprehensive stemming method for Urdu text. This proposed Urdu stemmer has the ability to generate the stem of Urdu words as well as loan words (words belonging to borrowed language i.e. Arabic, Persian, Turkish, etc) by removing prefix infix, and suffix. This proposed stemming technique introduced six novel Urdu infix words classes and minimum word length rule. In order to cope with the challenge of Urdu infix stemming, we have developed infix stripping rules for introduced infix words classes and generic rules for prefix and suffix stemming. The experimental results show the superiority of our proposed stemming approach as compared to existing technique.\",\n \"Title: Deepgender: real-time gender classification using deep learning for smartphones\\nYear: 2019\\n\\nAbstract: Face recognition, expression identification, age determination, racial binding and gender classification are common examples of image processing computerization. Gender classification is very straightforward for us like we can tell by the person’s hair, nose, eyes, mouth and skin whether that person is a male or female with a relatively high degree of confidence and accuracy; however, can we program a computer to perform just as well at gender classification? The very problem is the main focus of this research. The conventional sequence for recent real-time facial image processing consists of five steps: face detection, noise removal, face alignment, feature representation and classification. With the aim of human gender classification, face alignment and feature vector extraction stages have been re-examined keeping in view the application of the system on smartphones. Face alignment has been made by spotting out 83 facial landmarks and 3-D facial model with the purpose of applying affine transformation. Furthermore, ‘feature representation’ is prepared through proposed modification in multilayer deep neural network, and hence we name it Deepgender. This convolutional deep neural network consists of some locally connected hidden layers without common weights of kernels as previously followed in legacy layered architecture. This specific case study involves deep learning as four convolutional layers, three max-pool layers (for downsizing of unrelated data), two fully connected layers (connection of outcome to all inputs) and a single layer of ‘multinomial logistic regression.’ Training has been made using CAS-PEAL and FEI which contain 99,594 face images of 1040 people and 2800 face images of 200 individuals, respectively. These images are either in different poses or taken under uncontrolled conditions which are close to real-time input facial image for gender classification application. The proposed system ‘Deepgender’ has registered 98% accuracy by combined use of both databases with the specific preprocess procedure, i.e., exhibiting alignment before resizing. Experiments suggest that accuracy is nearly 100% with frontal and nonblurred facial images. State-of-the-art steps have been taken to overcome memory and battery constraints in mobiles.\",\n \"Title: Using casual reasoning for anomaly detection among ECG live data streams in ubiquitous healthcare monitoring systems\\nYear: 2019\\n\\nAbstract: Anomalies in cardiac functionality can be fatal. Early detection of these anomalies, and in many cases their precursors, can save lives. The probability of the occurrence of these anomalies is extremely among people with a pre-diagnosed heart condition. In this research, we discovered that much remote Electrocardiography (ECG) monitoring systems do not convey “enough” information to the diagnosing doctor or the nominated caregiver. A few examples of this information can be the type of cardiac abnormality, the exact waveform of the ECG signal, time and frequency of the occurrence of the anomaly, machine-understandable part so that medical SCADA be alerted about the case, and immediate preventative urgent steps correlated to that emergency. It is also important to delivery surrounding context to the Health Information System so that the medical expert make his/her diagnosis with ample support data. The most important component in this communication is the security of contents from cybercrimes. We propose a cost-efficient and non-invasive health monitoring system that is secure and quickly deployable. The presented system embeds an intelligent wearable data acquisition system with unique identification algorithms requiring very little computational time and simple threshold-based classification.\",\n \"Title: Multimedia based IoT-centric smart framework for eLearning paradigm.\\nYear: 2019\\n\\nAbstract: Multimedia content boosts the learning trends. This paper is aimed to presents an electronic learning system based on Internet of Things (IoT) for the synchronous and asynchronous communications. The infrastructure of IoT provides the adaptable, scalable and open access for the eLearning paradigm. The multimedia-based IoT-centric environment is suitable to enhance the effectiveness of the delivery of learning contents. Students can take full advantage of 7As of IoT, which provides the opportunity to the students that they can access everything on the internet at any time and place. It creates a flexible eLearning paradigm for the teachers and students. The proposed eLearning modeluses sensors to detect the student location, temperature, and mobile camera to identify the student activeness in thelearning environment. Virtual campuses are controlled from a centralized location that may be called the head office. The MAQAS framework provides the solutions to the problems and analyzes the results for the efficient and connected eLearning paradigm. The MAQAS system is used to answer student’s queries, which are responded to automatically by agent-based question answering system. The results show that the students’ participation towards learning and teacher’s pedagogy are more efficient in synchronous and asynchronous modes. Performance evaluated by comparison to the existing question answering Live QA Trak, Quora Yoda QA Live and AskMSR-QA with MAQAS.\",\n \"Title: A Holistic Approach for Recognition of Complete Urdu Ligatures Using Hidden Markov Models\\nYear: 2017\\n\\nAbstract: Optical Character Recognition (OCR) is one of the continuously explored problems. Presently, commercial character recognizers are available reporting near to 100% recognition rates on text in a number of scripts. Despite these advancements, OCR systems however, have yet to mature for cursive scripts like Urdu. This study presents a holistic technique for recognition of Urdu text in Nastaliq font using \\\"complete\\\" ligatures as recognition units. The term \\\"complete\\\" refers to a partial word including its main body and secondary components (dots and diacritic marks). Discrete Wavelet Transform (DWT) is employed as feature extractor while a separate Hidden Markov Model (HMM) is trained for each ligature considered in our study. More than 2000 frequently used unique Urdu ligatures from the standard CLE (Center of Language Engineering) dataset are considered in our evaluations. The system reads a promising accuracy of 88.87% on more than 10,000 partial words.\",\n \"Title: A Methodology of Real-Time Data Fusion for Localized Big Data Analytics.\\nYear: 2018\\n\\nAbstract: The traditional big-data analytical approaches use data clustering as small buckets while providing distributed computation among different child nodes. These approaches bring the issues especially concerning network capacity, specialized tools and applications not capable of being trained in a short period. Furthermore, raw data generated through IoT forming big data comes with the capability of producing highly unstructured and heterogeneous form of data. Such form of data grows into challenging task for the real-time analytics. It is highly valuable to have computational values available locally instead of through distributed resources to reduce real-time analytical challenges. This paper proposes a fusion of three different data models like relational, semantical, and big data based data and metadata involving their issues and enhanced capabilities. A case study is used to represent data fusion in action from RDB to Resource Description Framework. Whereas, issues and their feasible solutions are also being discussed in this paper.\",\n \"Title: Loss Based Congestion Control Module for Health Centers Deployed by Using Advanced IoT Based SDN Communication Networks\\nYear: 2020\\n\\nAbstract: Many healthcare centers are deploying advanced Internet of Things (IoT) based on Software-Defined Networks (SDNs). Transmission Control Protocol (TCP) was developed to control the data transmission in wide range of networks and provides reliable communication by using many caching and congestion control schemes. TCP is predestined to always increase and decrease its congestion window size to make changes in traffic. Nowadays, about 50% IoT based SDN traffic is controlled by TCP CUBIC, which is the default congestion control scheme in Linux operating system. The aim of this research is to develop a new content-caching based congestion control scheme for advanced IoT enabled SDN networks to achieve better performance in healthcare infrastructure network environments. In this research, Congestion Control Module for Loss Event (CCM-LE) is proposed to enhance the performance of TCP CUBIC in advanced IoT based on SDN. Network Simulator 2 (NS-2) is used to simulate the experiments of CCM-LE and state-of-the-art schemes. Results show that the performance of CCM-LE outperforms by 19% as compared to state-of-the-art schemes.\",\n \"Title: Software Plagiarism Detection In Multiprogramming Languages Using Machine Learning Approach\\nYear: 2021\\n\\nAbstract: The Software plagiarism, which arises the problem of software piracy is a growing major concern nowadays. It is a serious risk to the software industry that gives huge economic damages every year. The customers may develop a modified version of the original software in other types of programming languages. Furthermore, the plagiarism detection in different types of source codes is a challenging task because each source code may have specific syntax rules. In this paper, we proposed a methodology for software plagiarism detection in multiprogramming languages based on machine learning approaches. The Principal Component Analysis (PCA) is applied for features extraction from source codes without losing the actual information. It extracts features by factor analysis and converts the dataset into normalized linear principal components which are further useful for predictions analysis. Then, the multinomial logistic regression model (MLR) is applied to these components to classify the source codes documents based on predictions. It gives the generalization of logistic regression to handle multiclass problems. Further, the predictors' performance in MLR is evaluated by 2 tailed z test. To apply the experiment, the dataset is collected in five different and popular languages, ie, C, C++, Java, C#, and Python. Each programming language taken in two different case studies, ie, binary search and Stack.\",\n \"Title: Graph Clustering Based Size Varying Rules for Lifelong Topic Modeling\\nYear: 2018\\n\\nAbstract: Lifelong learning topic models identify the hidden concepts discussed in the collection of documents. The concepts are represented as topics having groups of ordered words based on their relevance to the topic. Lifelong learning models have an automatic learning mechanism which allows continuous learning without external support. In the process, the model gets more knowledgeable with experience as it learns from the past in the form of rules. It is carries rules to the future and utilize them when a similar scenario arises. The existing lifelong learning topic models heavily rely on statistical measures to learn rules that leads to two limitations. The rules are evaluated for fixed number of words while ignoring the natural arrangement of words within the documents. Moreover, the rules have arbitrary orientation that causes repeated patterns of transferring the impact of a rule into a topic during the early iterations of the inference technique. In this research work, we introduce complex networks analysis for learning rules which addresses both of the limitations discussed. The rules are obtained through hierarchical clustering of the complex network that have different number of words within a rule and have directed orientation. The proposed approach improves the utilization of rules for improved quality of topics at higher performance with unidirectional rules on the standard lifelong learning dataset.\",\n \"Title: VerSig: a new approach for online signature verification\\nYear: 2019\\n\\nAbstract: This paper introduces, VerSig, a new proposed scheme for online signature verification. The proposed scheme is based on creation of a signature envelope by employing dynamic time warping method. This envelope provides the basis for decision of forged and authentic signatures. The scheme only uses basic features such as X, Y coordinates of the signature. A well known and standardized Japanese handwritten dataset (provided for ICDAR 2013 signature verification competition) is used to evaluate the performance of proposed method. Proposed method is compared with state of art methods and observed to offer significant improvements in terms of overall accuracy of prediction.\",\n \"Title: Plagiarism detection in students’ programming assignments based on semantics: multimedia e-learning based smart assessment methodology\\nYear: 2020\\n\\nAbstract: The multimedia-based e-Learning methodology provides virtual classrooms to students. The teacher uploads learning materials, programming assignments and quizzes on university’ Learning Management System (LMS). The students learn lessons from uploaded videos and then solve the given programming tasks and quizzes. The source code plagiarism is a serious threat to academia. However, identifying similar source code fragments between different programming languages is a challenging task. To solve the problem, this paper proposed a new plagiarism detection technique between C++ and Java source codes based on semantics in multimedia-based e-Learning and smart assessment methodology. First, it transforms source codes into tokens to calculate semantic similarity in token by token comparison. After that, it finds semantic similarity in scalar value for the complete source codes written in C++ and Java. To analyse the experiment, we have taken the dataset consists of four (4) case studies of Factorial, Bubble Sort, Binary Search and Stack data structure in both C++ and Java. The entire experiment is done in R Studio with R version 3.4.2. The experimental results show better semantic similarity results for plagiarism detection based on comparison.\",\n \"Title: Energy efficient edge-of-things.\\nYear: 2019\\n\\nAbstract: Edge-of-Things (EoT) emerged as a novel computing and storage paradigm to overcome the limitations of IoT-cloud environment by providing cloud-like services at edge of the network. EoT offers a vast area for research and development as the invention has laid out great opportunities to experiment the possibilities for handling large data sets produced by the growing Internet-of-Things (IoT). The EoT offers a framework that lies between the cloud-to-end to perform the processing and cater the storage demands of the IoT applications. However, the exponential increase in EoT infrastructure resulted into extreme energy consumption. This paper finds the opportunity to address the issue of energy consumption in IoT-EoT environment by introducing dynamic speed scaling mechanism in EoT devices. The proposed approach is rigorously evaluated, and the verification is acquired through the simulations carried out on the simulator, iFogSim. The results show significant improvement in energy conservation by dynamically scaling the processor frequency of EoT devices according to the load variations in IoT traffic.\",\n \"Title: Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features\\nYear: 2019\\n\\nAbstract: The cancer of lung has been one of the major threats to human life for decades in developed and developing countries. The Computer Aided Detection CAD could be a powerful tool for initial lung nodule detection and preventing the deaths caused by the lung tumor. In this paper, an advanced technique for lung-nodule detection by using a hybrid feature set and artificial neural network is proposed. Initially, the lung volume is segmented from the input Computed Tomography image using optimal thresholding which is followed by image enhancement using with multi scale dot augmentation filtering. Next, lung nodule candidates have been detected from enhanced image and certain features are extracted. The set feature consists of the texture features, shape 2D and 3D and intensity. Finally, lung nodule’s classification is attained using two-layer feed forward neural network. The Lung Image Database Consortium dataset has been used to evaluate the novel system which achieved a sensitivity of 95.5% with only 5.72 FP per scan.\",\n \"Title: Security and privacy based access control model for internet of connected vehicles\\nYear: 2019\\n\\nAbstract: Vehicular networks are continually gaining popularity, owing to the evolution of the Internet of things, by means of rapid communication in IP-based networks. Vehicles are being fitted with sensory devices to gain inputs from the external environment for information processing. Numerous applications of vehicular networks depend on the use of communication networks. The security and privacy of data communication are significant challenges, particularly in wireless applications, for example, the Internet of connected vehicles. When an official fleet of vehicles (government or military) begins its journey, the vehicles need to communicate with one another to share sensitive information. The security of such a fleet is maintained in various security layers, each of which coordinates and communicates with the others, but avoids disclosing sensitive information of higher security layers to lower security layers. No such access control model exists that can enforce the authority of security officials in layers. We propose a novel security and privacy based access control (SPBAC) model for the Internet of connected vehicles, which allows security officials to access information in combination with permissions and roles, instead of roles only, for officials traveling in vehicles belonging to the same fleet. Vehicle-to-vehicle communication is performed with the aid of onboard unit sensory devices. The vehicles can also communicate through wireless technology, namely WiFi and cellular communications (4G/5G). The SPBAC model provides communication among security layers in a secure, private, and efficient manner compared to other state-of-the-art algorithms. The SPBAC model is demonstrated here using mathematical modeling along with implementation examples.\",\n \"Title: Utilization and load balancing in fog servers for health applications\\nYear: 2019\\n\\nAbstract: With the evolution of fog computing, processing takes place locally in a virtual platform rather than in a centralized cloud server. Fog computing combined with cloud computing is more efficient as fog computing alone does not serve the purpose. Inefficient resource management and load balancing leads to degradation in quality of service as well as energy losses. Traffic overhead is increased because all the requests are sent to the main server causing delays which cannot be tolerated in healthcare scenarios. To overcome this problem, the authors are consolidating fog computing resources so that requests are handled by foglets and only critical requests are sent to the cloud for processing. Servers are placed locally in each city to handle the nearby requests in order to utilize the resources efficiently along with load balancing among all the servers, which leads to reduced latency and traffic overhead with the improved quality of service.\",\n \"Title: Digital Watermarking Technique for Text Document Protection Using Data Mining Analysis.\\nYear: 2019\\n\\nAbstract: In the current era, information security is on its top priority for all organizations. The individuals, government officials, and military with the rapid development of Internet technologies like the Internet of Things (IoT), big data, and cloud computing facing data security problems. As the massive rate of data growth, it is a challenging task for the researchers, that how to manage the vast amount of data safely and effectively while designing smart cities. It has been quite easy to produce an illegal copy of digital contents. The verification of digital content is one of the major issues because digital contents are generated daily and shared via the internet. The limited techniques are available for document copyright protection. However, most of the existing techniques produce distortion during watermark insertion or lack of capacity. In the said perspective, a digital watermarking technique is proposed for document copyright protection and ownership verification with the help of data mining. The techniques of data mining are applied to find suitable properties from the document for embedding watermark. The proposed model provides copyright protection to text documents on local and cloud computing paradigm. For the evaluation of the proposed technique, 20 different text documents are used to perform many attacks such as formatting, insertion, and deletion attacks. The proposed technique attained a high-level of imperceptibility where peak signal to noise ratio (PSNR) values are between 64.67% and 71.03%, and similarity (SIM) percentage is between 99.92% and 99.99%. The proposed technique is robust and resists from formatting attacks and capacity of the proposed technique is also improved as compared to the previous techniques.\",\n \"Title: Recognition of printed Urdu ligatures using convolutional neural networks\\nYear: 2019\\n\\nAbstract: We present a holistic technique for recognition of text in cursive scripts using printed Urdu ligatures as a case study. Convolutional neural networks (CNNs) are trained on high-frequency ligature clusters for feature extraction and classification. A query ligature presented to the system is first divided into primary and secondary ligatures that are separately recognized and later associated in a postprocessing step to recognize the complete ligature. Experiments are carried out using transfer learning on pretrained networks as well as by training a network from scratch. The technique is evaluated on ligatures extracted from two standard databases of printed Urdu text, Urdu printed text image (UPTI) and Center of Language Engineering (CLE), as well as by combining the ligatures of the two datasets. The system realizes high recognition rates of 97.81% and 89.20% on the UPTI and CLE databases, respectively. (C) 2019 SPIE and IS&T\",\n \"Title: A Deep Learning Based Ensemble Learning Method For Epileptic Seizure Prediction\\nYear: 2021\\n\\nAbstract: In epilepsy, patients suffer from seizures which cannot be controlled with medicines or surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is extremely important so that they can be controlled with medication before they actually occur. Researchers have proposed multiple machine/deep learning based methods to predict epileptic seizures; however, accurate prediction of epileptic seizures with low false positive rate is still a challenge. In this research, we propose a deep learning based ensemble learning method to predict epileptic seizures. In the proposed method, EEG signals are preprocessed using empirical mode decomposition followed by bandpass filtering for noise removal. The class imbalance problem has been mitigated with synthetic preictal segments generated using generative adversarial networks. A three-layer customized convolutional neural network has been proposed to extract automated features from preprocessed EEG signals and combined them with handcrafted features to get a comprehensive feature set. The feature set is then used to train an ensemble classifier that combines the output of SVM, CNN and LSTM using Model agnostic meta learning. An average sensitivity of 96.28% and specificity of 95.65% with an average anticipation time of 33 min on all subjects of CHBMIT has been achieved by the proposed method, whereas, on American epilepsy society-Kaggle seizure prediction dataset, an average sensitivity of 94.2% and specificity of 95.8% has been achieved on all subjects.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":65,"string":"65"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13242,"string":"13,242"}}},{"rowIdx":13243,"cells":{"question_id":{"kind":"string","value":"Barbara Vantaggi"},"question":{"kind":"string","value":"Generate an abstract for the title \"Rationality principles for preferences on belief functions.\" using the following items: Generalized lottery, belief function, preference relation, representability, utility."},"answer":{"kind":"string","value":"A generalized notion of lottery is considered, where the uncertainty is expressed by a belief function. Given a partial preference relation on an arbitrary set of generalized lotteries all on the same finite totally ordered set of prizes, conditions for the representability, either by a linear utility or a Choquet expected utility are provided. Both the cases of a finite and an infinite set of generalized lotteries are investigated."},"correct_answer":{"kind":"string","value":"A generalized notion of lottery is considered, where the uncertainty is expressed by a belief function. Given a partial preference relation on an arbitrary set of generalized lotteries all on the same finite totally ordered set of prizes, conditions for the representability, either by a linear utility or a Choquet expected utility are provided. Both the cases of a finite and an infinite set of generalized lotteries are investigated."},"documents":{"kind":"list like","value":["Title: Comparative Conditional Possibilities\nYear: 2005\n\nAbstract: Any dynamic decision model or procedure for acquisition of knowledge must deal with conditional events and should refer to\n (not necessarily structured) domains containing only the elements and the information of interest. We consider conditional\n possibility theory as numerical reference model to handle uncertainty and to study binary relations, defined on an arbitrary\n set of conditional events expressing the idea of “no more possible than”. We give the necessary conditions for the representability\n of a relation by a T-conditional possibility, for any triangular norm T, and we provide a complete characterization in terms of necessary and sufficient conditions for the representability by a\n conditional possibility (i.e. when T is the minimum).","Title: The role of coherence for handling probabilistic evaluations and independence\nYear: 2005\n\nAbstract: This paper deals with coherent conditional probability able to manage uncertainty, partial knowledge and conditional independence, overcoming the critical situations presented by the classic independence definition. When a probability is not complete (i.e. it is defined on an arbitrary set of conditional events) the conditional independence statements are not necessarily automatically induced by the values of the assessment, so given a set of independence statements its compatibility with the numerical values (conditional probability) need to be checked. This problem related to the compatibility of independence statements and conditional probability assessment is studied and a procedure for checking the compatibility is proposed.","Title: 2D Probability-Possibility Transformations.\nYear: 2012\n\nAbstract: Probability-possibility transformations are useful whenever probabilistic information must be dealt with in the possibility theory. In this paper, two-dimensional probability-possibility transformations of joint probability densities are considered, to build joint possibilities such that the marginals preserve the same information content as the marginals of the joint probability densities.","Title: Inferential processes leading to possibility and necessity\nYear: 2013\n\nAbstract: This paper deals with the upper and lower bounds of a class of uncertainty measures endowed with particular characteristics (decomposability, monotonicity, partial additivity and so on). We consider an initial partial assessment consistent with either probability or possibility or necessity, then we study the upper and lower envelopes of all possible extensions. By resorting to a notion of weak logical independence we get as lower or upper envelope a possibility or a necessity, respectively, starting either from a probability or from a possibility or from a necessity.","Title: The L-separation criterion for description of cs-independence models\nYear: 2002\n\nAbstract: Different conditional independence models have been proposed in literature; in this paper we consider models induced by conditional probabilities based on the definition of conditional cs-independence. These models need not comply with the symmetry property, so that they have not the graphoid structure. Hence, the well-known d-separation criterion for directed acyclic graphs may not be able to represent such independence models. Therefore, we introduce a new separation criterion called L-separation. We study its main properties and show how it allows to represent the above-mentioned independence models through directed acyclic graphs.","Title: Possibility theory: Conditional independence\nYear: 2006\n\nAbstract: The subtle notion of conditioning is controversial in several contexts, for example in possibility theory where, in fact, different definitions have been introduced. We refer to a general axiomatic definition of conditional possibility and then we deal with ''partial assessments'' on (not necessarily structured) domains containing only elements of interest. We study a notion of coherence, which assures the extendability of an assessment as a conditional possibility and we introduce a procedure for checking coherence. Moreover, we propose a definition of independence for conditional possibility, which avoids some counterintuitive situations, and we study its main properties in order to compare it with other definitions introduced in literature. Then, we check which properties among the graphoid ones are satisfied: this allows to compare our definition with other independence notions given in the context of other uncertainty formalisms. This analysis is relevant for graphical models in order to single out and visualize dependence relations among random variables.","Title: Graphical Representation of Asymmetric Graphoid Structures\nYear: 2003\n\nAbstract: Independence models induced by some uncertainty measures (e.g. condi- tional probability, possibility) do not obey the usual graphoid properties, since they do not satisfy the symmetry property. They are efciently rep- resentable through directed acyclic l-graphs by using L-separation criterion. In this paper, we show that in general there is not a l-graph which de- scribes completely all the independence statements of a given model; hence we introduce in this context the notion of minimal I-map and we show how to build it, given an ordering on the variables. In addition, we prove that, for any ordering, there exists an I-map for any asymmetric graphoid structure.","Title: Locally Strong Coherence in Inference Processes\nYear: 2002\n\nAbstract: In this paper we deal with probabilistic inference in the most general form of coherent conditional probability assessments. In particular, our aim is to reduce computational difficulties that could arise with a direct application of the main characterization results. We reach our goal by introducing the notion of locally strong coherence and characterizing it by logical conditions. Hence, some of the numerical constraints are replaced by Boolean satisfiability conditions. An automatic procedure is proposed and its efficiency is proved. Some examples are reported to make easier the understanding of the machinery and to show its effectiveness.","Title: Conditional Independence in A Coherent Finite Setting\nYear: 2001\n\nAbstract: A definition of stochastic independence which avoids the inconsistencies (related to events of probability 0 or 1) of the classic one has been proposed by Coletti and Scozzafava for two events. We extend it to iconditional independence among finite sets of events. In particular, the case of (finite) discrete random variables is studied. We check which of the relevant properties connected with graphical structures hold. Hence, an axiomatic characterization of these independence models is given and it is compared to the classic ones.","Title: Conditional independence structure and its closure: Inferential rules and algorithms\nYear: 2009\n\nAbstract: In this paper, we deal with conditional independence models closed with respect to graphoid properties. Such models come from different uncertainty measures, in particular in a probabilistic setting. We study some inferential rules and describe methods and algorithms to compute efficiently the closure of a set of conditional independence statements.","Title: Statistical matching of multiple sources: A look through coherence\nYear: 2008\n\nAbstract: In several applications there is the need to consider different data sources and to integrate information: a specific case is the so-called statistical matching, where data sources have just a set of common variables and inference is required on the other variables. The traditional way to cope with such situations is to combine the available data with assumptions strong enough to identify pointwise the joint probability. Such assumptions cannot always be justified and inference should take into account all the set of compatible probabilities. In this paper, we show how statistical matching problems can be managed by means of coherent conditional probability: coherence allows us to combine the knowledge coming from different multiple sources, included those given from field experts, without necessarily assuming further hypothesis. Moreover, inferences and decisions can be dealt with by taking in consideration also logical constraints among the variables, which arise naturally in the applications. An example showing advantages and drawbacks of the proposed method is given.","Title: Axiomatic characterization of partial ordinal relations\nYear: 2000\n\nAbstract: In this paper we focus on the theoretical properties of non-numerical representation of the uncertainty. As usual, this representation is realized by an “ordinal relation” (or, equivalently, by a “comparative scale”) among the “entities” (events, alternatives or acts) of a specific problem. After giving an overview of different known axioms characterizing some classes of ordinal relations (and their duals), we introduce some axioms to enclose the necessary and sufficient conditions for the representability of ordinal relations (defined on arbitrary finite sets of events) by the most-known uncertainty measures.","Title: Acyclic directed graphs representing independence models\nYear: 2011\n\nAbstract: In this paper we study the problem of representing probabilistic independence models, in particular those closed under graphoid properties. We focus on acyclic directed graph (DAG): a new algorithm to build a DAG, given an ordering among random variables, is described and peculiarities and advantages of this approach are discussed. Moreover, we provide a necessary and sufficient condition for the existence of a perfect map representing an independence model and we describe an algorithm based on this characterization.","Title: Independence And Conditional Possibility For Strictly Monotone Triangular Norms\nYear: 2006\n\nAbstract: In the literature there are different definitions of conditional possibility. Starting from a general axiomatic definition, we propose a definition of independence for circle dot-conditional possibility, in the case that circle dot is a strictly monotone triangular norm. We study its main properties to compare it to other definitions introduced in possibility theory. Then, we show that the controversial aspects related to logical dependencies (structural zeros) can be circumvented. Moreover, a set of properties (the well-known graphoid properties) has been considered to be tested, allowing us to compare the proposed definition to the independence notions given in the context of other uncertainty formalisms. (c) 2006 Wiley Periodicals, Inc.","Title: Probabilistic Fuzzy Reasoning in a Coherent Setting.\nYear: 2013\n\nAbstract: We use the interpretation of fuzzy sets in terms of coherent conditional probabilities for handling probabilistic fuzzy IF-THEN rules. We show by some examples how this interpretation can help when fuzzy and statistical information need to be combined and the available probabilistic information on the fuzzy events is possibly imprecise or incomplete.","Title: An Axiomatization of Conditional Possibilistic Preference Functionals\nYear: 2007\n\nAbstract: The aim of the paper is to extend the Savage like axiomatization of possibilistic preference functionals in qualitative decision theory to conditional acts, so as to make a step towards the dynamic decision setting. To this end, the de Finetti style approach to conditional possibility recently advocated by Coletti and Vantaggi is exploited, extending to conditional acts the basic axioms pertaining to conditional events.","Title: A view on conditional measures through local representability of binary relations\nYear: 2008\n\nAbstract: We consider binary relations @? admitting a conditional measure of uncertainty @f (precisely plausibility, possibility and their dual functions), which locally represents @?: for every pair A@?B, the measure @f(.|C) almost represents for any hypothesis C@?A@?B, and represents @? under the most specific hypothesis C=A@?B.","Title: Conditional independence structures and graphical models\nYear: 2003\n\nAbstract: In this paper we study conditional independence structures arising from conditional probabilities and lower conditional probabilities. Such models are based on notions of stochastic independence apt to manage also those situations where zero evaluations on possible events are present: this is particularly crucial for lower probability.The \"graphoid\" properties of such models are investigated, and the representation problem of conditional independence structures is dealt with by generalizing the well-known classic separation criteria for undirected and directed acyclic graphs. Our graphical models describe the independence statements and the possible logical dependencies among the random variables.","Title: A bridge between probability and possibility in a comparative framework\nYear: 2011\n\nAbstract: The paper studies the connection between comparative probability and comparative plausibility, with a particular emphasis on comparative possibility. We consider a comparative probability on an algebra and extend it to a different algebra. We prove that, in general, the upper extension of the given comparative probability is a comparative plausibility. By considering a suitable condition of weak logical independence between the two partitions related to the atoms of the two algebras, we prove that the upper ordinal relation is a comparative possibility. These results hold for comparative probability not necessarily representable by a numerical probability.","Title: Incomplete preferences on conditional random quantities: Representability by conditional previsions\nYear: 2010\n\nAbstract: We study “partial” preference relations, defined on an arbitrary set of conditional bounded random quantities: we provide a condition of rationality (interpretable in terms of betting scheme) characterizing preference relations representable by a conditional expectation. Moreover, we study the problem of extending a rational preference relation.","Title: Hybrid Models: Probabilistic and Fuzzy Information.\nYear: 2012\n\nAbstract: Under the interpretation of fuzzy set as coherent conditional probability, we study inferential processes starting from a probability distribution (on a random variable) and a coherent conditional probability on \"fuzzy conditional events\". We characterize the coherent extensions and we analyze an example proposed by Zadeh.","Title: Finding P-maps and I-maps to represent conditional independencies\nYear: 2011\n\nAbstract: The representation problem of independence models is studied by focusing on acyclic directed graph (DAG). We present the algorithm PC* in order to look for a perfect map. However, when a perfect map does not exist, so that PC* fails, it is interesting to find a minimal I--map, which represents as many triples as possible in J*. Therefore we describe an algorithm which finds such a map by means of a backtracking procedure.","Title: Qualitative combination of independence models\nYear: 2013\n\nAbstract: We deal with the problem of combining sets of independence statements coming from different experts. It is known that the independence model induced by a strictly positive probability distribution has a graphoid structure, but the explicit computation and storage of the closure (w.r.t. graphoid properties) of a set of independence statements is a computational hard problem. For this, we rely on a compact symbolic representation of the closure called fast closure and study three different combination strategies of two sets of independence statements, working on fast closures. We investigate when the complete DAG representability of the given models is preserved in the combined one.","Title: Generalized Bayesian inference in a fuzzy context: From theory to a virtual reality application\nYear: 2012\n\nAbstract: A generalized Bayesian inference framework in order to embed fuzzy sets and partial probabilistic information is provided. The general framework of reference is that of coherent conditional probabilities, which allows giving a rigorous interpretation of membership function as a conditional probability, regarded as a function of the conditioning event. The inferential problem needs to be studied in situations where the prior can be partial; moreover, membership and prior can be given on different classes of events. This inferential model is applied for the virtual representation of a female avatar.","Title: Incoherence correction strategies in statistical matching\nYear: 2012\n\nAbstract: Several economic applications require to consider different data sources and to integrate the information coming from them. This paper focuses on statistical matching, in particular we deal with incoherences. In fact, when logical constraints among the variables are present incoherencies on the probability evaluations can arise. The aim of this paper is to remove such incoherences by using different methods based on distances minimization or least commitment imprecise probabilities extensions. An illustrative example shows peculiarities of the different correction methods. Finally, limited to pseudo distance minimization, we performed a systematic comparison through a simulation study.","Title: Acyclic Directed Graphs to Represent Conditional Independence Models\nYear: 2009\n\nAbstract: In this paper we consider conditional independence models closed under graphoid properties. We investigate their representation by means of acyclic directed graphs (DAG). A new algorithm to build a DAG, given an ordering among random variables, is described and peculiarities and advantages of this approach are discussed. Finally, some properties ensuring the existence of perfect maps are provided. These conditions can be used to define a procedure able to find a perfect map for some classes of independence models.","Title: Possibility Measures in Probabilistic Inference\nYear: 2008\n\nAbstract: By means of a logical condition between two partitions L and L' (\"weak logical independence\"), we find connections between probabilities and possibilities. We show that the upper envelope of the extensions of a probability on L is a possibility on the algebra generated by L'. Moreover we characterize the set of possibilities obtained as extensions of a coherent probability on an arbitrary set: in particular, we find the two \"extreme\" (i.e., dominated and dominating) possibilities.","Title: Independence And Conditional Decomposable Information Measures\nYear: 2010\n\nAbstract: We deal with conditional decomposable information measures, directly defined as functions on a suitable set of conditional events satisfying a class of axioms. For these general measures we introduce a notion of independence and study its main properties in order to compare it with classical definitions present in the literature. The particular case of Wiener-Shannon information measure is taken in consideration and the links between the provided independence for information measures and the independence for the underlying probability are analyzed.","Title: Locally strong coherence and inference with lower-upper probabilities\nYear: 2003\n\nAbstract: We introduce an operational way to reduce the spatial complexity in inference processes based on conditional lower–upper\n probabilities assessments. To reach such goal we must suitably exploit zero probabilities taking account of logical conditions\n characterizing locally strong coherence. We actually re-formulate for conditional lower–upper probabilities the notion of\n locally strong coherence already introduced for conditional precise probabilities. Thanks to the characterization, we avoid\n to build all atoms, so that several real problems become feasible. In fact, the real complexity problem is connected to the\n number of atoms. Since for an inferential process with lower–upper probabilities several sequences of constraints must be\n fulfilled, our simplification can have either a “global” or a “partial” effect, being applicable to all or just to some sequences.\n The whole procedure has been implemented by XLisp-Stat language. A comparison with other approaches will be done by an example.","Title: Simplification Rules for the Coherent Probability Assessment Problem\nYear: 2002\n\nAbstract: In this paper we develop a procedure for checking the consistency (coherence) of a partial probability assessment. The general problem (called CPA) is NP-complete, hence, to have a reasonable application some heuristic is needed. Our proposal differs from others because it is based on a skilful use of the logical relations present among the events. In other approaches the consistency problem is reduced directly to the satisfiability of a system of linear constraints. Here, thanks to the characterization of particular configurations and to the elimination of variables, an instance of the problem is reduced to smaller instances. To obtain such results, we introduce a procedure based on rules resembling those given by Davis–Putnam for the satisfiability of Boolean formulas. At the end a particularized description of an actual implementation is given.","Title: Likelihood in a Possibilistic and Probabilistic Context: A Comparison\nYear: 2010\n\nAbstract: We provide a comparison between a probabilistic and a possibilistic likelihood both as point and set functions.","Title: An Algorithm To Find A Perfect Map For Graphoid Structures\nYear: 2010\n\nAbstract: We provide a necessary and sufficient condition for the existence of a perfect; map representing an independence model and we give an algorithm for checking this condition and drawing a perfect map, when it exists.","Title: Exploiting independencies to compute semigraphoid and graphoid structures\nYear: 2011\n\nAbstract: We deal with conditional independencies, which have a fundamental role in probability and multivariate statistics. The structure of probabilistic independencies is described by semigraphoids or, for strictly positive probabilities, by graphoids. In this paper, given a set of independencies compatible with a probability, the attention is focused toward the problem of computing efficiently the closure with respect to the semigraphoid and graphoid structures. We introduce a suitable notion of projection in order to provide a new method which properly uses conditional independence statements.","Title: Elimination of Boolean variables for probabilistic coherence\nYear: 2000\n\nAbstract: In this paper we deal with the computational complexity problem of checking the coherence of a partial probability assessment (called CPA). The CPA problem, like its analogous PSAT, is NP-complete so we look for an heuristic procedure to make tractable reasonable instances of the problem. Starting from the characteristic feature of de Finetti's approach (i.e. the explicit distinction between the probabilistic assessment and the logical relations among the sentences) we introduce several rules...","Title: Independence in possibility theory under different triangular norms\nYear: 2013\n\nAbstract: In this paper we consider coherent T-conditional possibility assessments, with T a continuous t-norm, and introduce for them a concept of independence already studied for the minimum and strict t-norms. As a significant particular case of T-conditional possibility we explicitly consider TDP-conditional possibility (obtained through the minimum specificity principle) introduced by Dubois and Prade.","Title: T-conditional possibilities: Coherence and inference\nYear: 2009\n\nAbstract: In this paper we refer to an axiomatic definition of T-conditional possibility, where T is any t-norm. We characterize a full T-conditional possibility in terms of a suitable set of unconditional possibilities. Starting from this characterization we are able to manage coherent conditional possibility assessments and their enlargements. To compare T-conditional possibility related to different t-norm T, we study binary relations locally representable by a T-conditional possibility.","Title: A general interpretation of conditioning and its implication on coherence\nYear: 1999\n\nAbstract: In this paper we introduce a relation between Boolean events that can be interpreted as a conditioning relation. The goal\n is to find “elementary” events (atoms) derived from a finite family of conditional events and for this purpose we adopt MV-algebra\n operations. Two different decompositions (whose elements are called dicotomic and tricotomic conditional atoms, respectively)\n are proposed and their interpretations are given. Finally, we examine them as a tool for checking the coherence of a partial\n conditional probability assessment.","Title: Inference with fuzzy and probabilistic information\nYear: 2010\n\nAbstract: In the paper we deal with fuzzy sets under the interpretation given in a coherent probabilistic setting. We provide a general Bayesian inference process involving fuzzy and partial probabilistic information by showing its peculiarities.","Title: The Role Of Coherence For The Integration Of Different Sources\nYear: 2005\n\nAbstract: In several economic applications (e.g. marketing research, microsimulation models) there is the need to consider different data sources and to integrate the information coming from them. In this paper we show how integration problems can be managed by means of coherence for partial conditional probabilistic assessments. Coherence allows us to combine the knowledge coming from the different sources, included those (possibly) given from field experts, without necessarily assuming further hypothesis (as conditional independence). Moreover, inferences and decisions can be drawn taking in consideration also logical constraints among the variables. An example showing advantages and drawbacks of the proposed method is given.","Title: Integrated Likelihood in a Finitely Additive Setting\nYear: 2009\n\nAbstract: Without a clear, precise and rigorous mathematical frame, is the likelihood \"per se\" a proper tool to deal with statistical inference and to manage partial and vague information? Since (as Basu puts it) \"the likelihood function is after all a bunch of conditional probabilities\", a proper discussion of the various extensions of a likelihood from a point function to a set function is carried out by looking at a conditional probability as a general non-additive \"uncertainty\" measure P (E | · ) on the set of conditioning events.","Title: Comparative models ruled by possibility and necessity: A conditional world\nYear: 2007\n\nAbstract: Any dynamic decision model needs to deal with conditional events and conditional uncertainty measures. Moreover, to avoid the introduction of not available information in decision processes, we should refer to domains containing only the elements of interest. According to the aforementioned guidelines we study the comparative uncertainty framework for decision models referring to possibility and necessity measures: we consider binary relations, defined on an arbitrary set of conditional events and we provide a complete characterization in terms of necessary and sufficient conditions for their representability by a conditional possibility and by a conditional necessity.","Title: Measuring the Quality of Health-Care Services: A Likelihood-Based Fuzzy Modeling Approach\nYear: 2007\n\nAbstract: We face the problem of constructing a model which is suited for an effective evaluation of the quality of a health-care provider: to this purpose, we focus on some relevant indicators characterizing the various services run by the provider. We rely on a fuzzy modeling approach by using the interpretation (in terms of coherent conditional probability) of a membership function of a fuzzy set as a suitable likelihood.","Title: Fuzzy inclusion and similarity through coherent conditional probability\nYear: 2009\n\nAbstract: Our aim is to study some specific fuzzy relations (inclusion and similarity) in the framework of the interpretation of fuzzy theory in terms of coherent conditional probability.","Title: Probabilistic Reasoning as a General Unifying Tool\nYear: 2001\n\nAbstract: Our starting point is the approach to probabilistic logic through coherence; but we give up de Finetti's idea of a conditional event E\\H being a 3-valued entity, with the third value being just an undetermined common value for all ordered pairs (E,H). We let instead the \"third\" value of E\\H suitably depend on the given pair. In this way we get, through a direct assignment of conditional probability, a general theory of probabilistic reasoning able to encompass other approaches to uncertain reasoning, such as fuzziness and default reasoning. We are also able to put forward a meaningful concept of conditional independence, which avoids many of the usual inconsistencies related to logical dependence. We give an example in which we put together different kinds of information and show how coherent conditional probability can act as a unifying tool.","Title: Weak Implication in Terms of Conditional Uncertainty Measures\nYear: 2007\n\nAbstract: We define weak implication $H \\longmapsto_{\\varphi} E$ (\"H weakly implies E under $\\mathit{\\varphi}$\") through the relation $\\mathit{\\varphi}(E|H)$ = 1, where $\\mathit{\\varphi}$ is a (coherent) conditional uncertainty measure. By considering various such measures with different levels of generality, we get different sets of \"inferential rules\", that correspond to those of default logic when $\\mathit{\\varphi}$ reduces to a conditional probability.","Title: Possibilistic and probabilistic likelihood functions and their extensions: Common features and specific characteristics\nYear: 2014\n\nAbstract: We deal with conditional probability in the sense of de Finetti and with T-conditional possibility (with T a triangular norm). We prove that Dubois and Prade conditional possibility is a particular min-conditional possibility and then we compare the two notions of conditioning by an inferential point of view. Moreover, we study T-conditional possibilities as functions of the conditioning event, putting in evidence analogies and differences with conditional probabilities. This allows to characterize likelihood functions (and their aggregations) consistent either with a T-conditional possibility or a conditional probability. This analysis highlights many syntactical coincidences. Nevertheless the main difference is a weak form of monotonicity, which arises only in the possibilistic case.","Title: Bayesian inference: the role of coherence to deal with a prior belief function.\nYear: 2014\n\nAbstract: Starting from a likelihood function and a prior information represented by a belief function, a closed form expression is provided for the lower envelope of the set of all the possible “posterior probabilities” in finite spaces. The same problem, removing the hypothesis of finiteness for the domain of the prior, is then studied in the finitely additive probability framework by considering either the whole set of coherent extensions or the subset of disintegrable extensions.","Title: Choquet Expected Utility Representation of Preferences on Generalized Lotteries.\nYear: 2014\n\nAbstract: The classical von Neumann-Morgenstern's notion of lottery is generalized by replacing a probability distribution on a finite support with a belief function on the power set of the support. Given a partial preference relation on a finite set of generalized lotteries, a necessary and sufficient condition (weak rationality) is provided for its representation as a Choquet expected utility of a strictly increasing utility function.","Title: Coherent T-conditional Possibility Envelopes and Nonmonotonic Reasoning.\nYear: 2014\n\nAbstract: Envelopes of coherent T-conditional possibilities and coherent T-conditional necessities are studied and an analysis of some inference rules which play an important role in nonmonotonic reasoning is carried out.","Title: A 2-D Metrology-Sound Probability–Possibility Transformation\nYear: 2013\n\nAbstract: In recent years, the possibility theory has been investigated by many authors in the field of mathematics and engineering. A possibility distribution (PD) is, from the mathematical point of view, a generalization of a probability distribution, since it can represent the upper envelope of a family of probability distributions. Given a probability distribution, different probability-possibility transformations can be applied to transform the probability distribution into different PDs. Probability-possibility transformations are useful in metrology whenever statistical data must be dealt with inside the possibility theory, for instance, when they have to be associated with other kinds of uncertain and imprecise data to evaluate measurement uncertainty. This paper shows how uncorrelated and correlated random contributions to uncertainty can be effectively represented, processed, and combined in terms of PDs, by means of an original probability-possibility transformation.","Title: Standard and nonstandard representability of positive uncertainty orderings.\nYear: 2014\n\nAbstract: Axioms are given for positive comparative probabilities and plausibilities defined either on Boolean algebras or on arbitrary sets of events. These axioms allow to characterize binary relations representable by either standard or nonstandard measures (i. e. taking values either on the real field or on a hyperreal field). We also study relations between conditional events induced by preferences on conditional acts.","Title: Non additive ordinal relations representable by lower or upper probabilities.\nYear: 1998\n\nAbstract: We characterize (in terms of necessary and sufficient conditions) binary relations representable by a lower probability. Such relations can be non-additive (as the relations representable by a probability) and also not \"partially monotone\" (as the relations representable by a belief function). Moreover we characterize relations representable by upper probabilities and those representable by plausibility. In fact the conditions characterizing these relations are not immediately deducible by means of \"dual\" conditions given on the contrary events, like in the numerical case.","Title: Preferences on Gambles Representable by a Choquet Expected Value with Respect to Conditional Belief and Plausibility Functions.\nYear: 2016\n\nAbstract: We deal with preference relations on finite sets of (conditional) gambles, providing necessary and sufficient conditions for their representability by a (conditional) Choquet expected value with respect to a (conditional) belief function or a (conditional) plausibility function.","Title: Partial identification in statistical matching with misclassification.\nYear: 2017\n\nAbstract: The main target of statistical matching is to make inference on variables observed in different sources by using information on common variables. The partial information generally implies that the model is not identifiable. The aim of this paper is to study the case when common variables are affected by a misclassification. The partially identifiable region, that is the class of probabilities extending the conditional probabilities obtained by the information in different sources, is determined. These regions are determined in the general case and under some specific restrictions on the misclassification mechanism. An application to real data is used to show in practice the results achieved in the paper.","Title: Envelopes of conditional probabilities extending a strategy and a prior probability.\nYear: 2017\n\nAbstract: Any assessment formed by a strategy and a prior probability is a coherent conditional probability and can be extended, generally not in a unique way, to a full conditional probability. The corresponding class of all extensions is studied and a closed form expression for its envelopes is provided. Subclasses of extensions meeting further analytical properties are considered by imposing conglomerability and a conditional version of conglomerability, respectively. Then, the envelopes of extensions satisfying these conditions are characterized. Envelopes of the class of all full conditional probabilities extending a strategy and a prior probability.Conglomerability and conditional conglomerability for full conditional probability extensions.Envelopes of the subclass of conglomerable full conditional probability extensions.Envelopes of the subclass of conditionally conglomerable full conditional probability extensions.","Title: Fuzzy Weighted Attribute Combinations Based Similarity Measures.\nYear: 2017\n\nAbstract: Some similarity measures for fuzzy subsets are introduced: they are based on fuzzy set-theoretic operations and on a weight capacity expressing the degree of contribution of each group of attributes. For such measures, the properties of dominance and T-transitivity are investigated.","Title: Interval-Based Possibilistic Logic in a Coherent Setting.\nYear: 2017\n\nAbstract: In probability theory the notion of coherence has been introduced by de Finetti in terms of bets and it reveals to be equivalent to the notion of consistence of a partial assessment with a finitely additive probability. An important feature of coherent assessments is their coherent extendibility: in general we obtain a class of coherent extensions, determining a lower and an upper envelope. A similar notion of coherence has been recently introduced for (T-conditional) possibility measures, where T is a t-norm. The extendibility of coherent possibility assessments reveals to be particularly suitable for studying interval-based possibilistic logic. Our aim is to compare the results implied by the coherent setting with those obtained in different approaches, in particular, that relying on classical T-based conditioning.","Title: Fuzzy memberships as likelihood functions in a possibilistic framework.\nYear: 2017\n\nAbstract: Likelihood functions are studied in a probabilistic and possibilistic setting: inferential conclusions are drawn from a set of likelihood functions and prior information relying on the notion of disintegrability. The present study allows for a new interpretation of fuzzy membership functions as coherent conditional possibilities. The concept of possibility of a fuzzy event is then introduced and a comparison with the probability of a fuzzy event is provided.","Title: Knowledge processing in decisions under fuzziness and uncertainty.\nYear: 2017\n\nAbstract: Some decision making applications require to encode statistics and fuzziness. In this work two frameworks are considered: coherent conditional probabilities and possibilities, which allow to give a rigorous interpretation of membership function. A comparison of the two interpretations is given to employ a general Bayesian inferential approach able to embed fuzzy information.","Title: Priorconditioned CGLS-Based Quasi-MAP Estimate, Statistical Stopping Rule, and Ranking of Priors.\nYear: 2017\n\nAbstract: We consider linear discrete ill-posed problems within the Bayesian framework, assuming a Gaussian additive noise model and a Gaussian prior whose covariance matrices may be known modulo multiplicative scaling factors. In that context, we propose a new pointwise estimator for the posterior density, the prtorcondtttoned CGLS-based quasi-MAP (qMAP) as a computationally attractive approximation of the classical maximum a posteriori (MAP) estimate, in particular when the effective rank of the matrix A is much smaller than the dimension of the unknown. Exploiting the Bayesian paradigm and the connection between standard normal random variables and the chi(2) distribution of their squared Euclidean norms, we propose a new stopping rule, the Max chi(2) rule, to terminate the CGLS iterations in the computation of the qMAP estimate. Moreover, we show that the proposed stopping rule can be used to estimate the possibly unknown scaling factor of the noise covariance, which is tantamount to estimating the noise level. It is shown that the Max chi(2) stopping rule and the proposed noise level estimation are affected by the correlation structure of priorconditioner, i.e., prior-related right preconditioner, but not by its scaling, which in the classical regularization framework would correspond to the Tikhonov regularization parameter. We will also show how the relation between priorconditioners and whitening transformations of the unknown can be used to rank different priorconditioners according to their agreement with the data.","Title: Bayes Meets Krylov: Statistically Inspired Preconditioners for CGLS.\nYear: 2018\n\nAbstract: The solution of linear inverse problems when the unknown parameters outnumber data requires addressing the problem of a nontrivial null space. After restating the problem within the Bayesian framework, a priori information about the unknown can be utilized for determining the null space contribution to the solution. More specifically, if the solution of the associated linear system is computed by the conjugate gradient for least squares (CGLS) method, the additional information can be encoded in the form of a right preconditioner. In this paper we study how the right preconditioner changes the Krylov subspaces where the CGLS iterates live, and we draw a tighter connection between Bayesian inference and Krylov subspace methods. The advantages of a Bayes-meets-Krylov approach to the solution of underdetermined linear inverse problems is illustrated with computed examples.","Title: Conditional Submodular Coherent Risk Measures.\nYear: 2018\n\nAbstract: A family of conditional risk measures is introduced by considering a single period financial market, relying on a notion of conditioning for submodular capacities, which generalizes that introduced by Dempster. The resulting measures are expressed as discounted conditional Choquet expected values, take into account ambiguity towards uncertainty and allow for conditioning to \"null\" events. We also provide a characterisation of consistence of a partial assessment with a conditional submodular coherent risk measure. The latter amounts to test the solvability of a suitable sequence of linear systems.","Title: Envelopes of Joint Probabilities with Given Marginals Under Absolute Continuity or Equivalence Constraints.\nYear: 2016\n\nAbstract: The aim is to determine the envelopes of the class of joint probabilities (provided it is not empty) with assigned marginals, under the constraint of absolute continuity or equivalence with respect to a given reference measure.","Title: A Savage-Like Representation Theorem for Preferences on Multi-acts.\nYear: 2016\n\nAbstract: We deal with a Savage-like decision problem under uncertainty where, for every state of the world, the consequence of each decision (multi-act) is generally uncertain: the decision maker only knows the set of possible alternatives where it can range (multi-consequence). A Choquet expected utility representation theorem for a preference relation on multi-acts is provided, relying on a state-independent cardinal utility function defined on the (finite) set of all alternatives.","Title: Characterization of Conditional Submodular Capacities: Coherence and Extension.\nYear: 2018\n\nAbstract: We provide a representation in terms of a linearly ordered class of (unconditional) submodular capacities of an axiomatically defined conditional submodular capacity. This allows to provide a notion of coherence for a partial assessment and a related notion of coherent extension.","Title: Models for pessimistic or optimistic decisions under different uncertain scenarios.\nYear: 2019\n\nAbstract: In many decision problems under uncertainty, agents are only able to provide a possibly incomplete preference relation on gambles, that can be “irrational” according to the classical expected utility paradigm. Furthermore, agents can find it easier to express their preference relation taking one or more specific scenarios as hypothesis. In order to handle these situations two betting scheme rationality conditions are introduced, which characterize those preference relations representable by a conditional Choquet expected value with respect to a conditional belief or plausibility function. Such conditions encode a pessimistic or an optimistic attitude towards uncertainty, respectively, and allow to take into account (possible) “null” scenarios by means of a weak ordering of “unexpectation”. Moreover, they are the basis of a completion technique for enlarging the set of preferences by preserving rationality.","Title: Inferential Rules for Weak Graphoid\nYear: 2010\n\nAbstract: We deal with the problem of computing efficiently the closure of a set of independencies, compatible with a coherent conditional probability, under cs-independence. For this aim we provide two inferential rules, winch allow to build a basis for the closure.","Title: Qualitative Bayesian Networks with Logical Constraints\nYear: 2003\n\nAbstract: An important feature of Qualitative Bayesian Networks is that they describe conditional independence models. However, they are not able to handle models involving logical constraints among the given variables. The aim of this paper is to show how this theory can be extended in such a way to represent also the logical constraints in the graph through an enhanced version of Qualitative Bayesian Networks. The relative algorithm for building these graphs (which is a generalization of the well-known algorithm based on D-separation criterion) is given. This theory is particularly fit for conditional probabilistic independence models based on the notion of cs-independence. This notion avoids the usual critical situations shown by the classic definition when logical constraints are present.","Title: Betting Schemes for Assessing Coherent Numerical and Comparative Conditional Possibilities\nYear: 2021\n\nAbstract: We introduce coherence conditions having a betting scheme interpretation both for a numerical and a comparative conditional possibility assessment. The conditional bets are considered under partially resolving uncertainty and assuming consonance. This means that we allow situations in which the agent may only acquire the information that a non-impossible event occurs, without knowing which is the true state of the world. Further, he/she can only consider families of nested non-impossible events in computing the gain and has a systematically optimistic behavior. Both conditions are proved to be equivalent to the existence of a conditional possibility agreeing with an axiomatic definition based on the algebraic product t-norm, that extends, either numerically or comparatively (through the induced comparative conditional possibility relation), the given assessment.","Title: Dempster-Shafer Approximations and Probabilistic Bounds in Statistical Matching\nYear: 2021\n\nAbstract: Many economic applications require to integrate information coming from different data sources. In this work we consider a specific integration problem called statistical matching, referring to probabilistic distributions of Y vertical bar X, Z vertical bar X and X, where X, Y, Z are categorical (possibly multi-dimensional) random variables. Here, we restrict to the case of no logical relations among random variables X, Y, Z. The non-uniqueness of the conditional distribution of (Y, Z)vertical bar X suggests to deal with sets of probabilities. For that we consider different strategies to get a conditional belief function for (Y, Z)vertical bar X that approximates the initial assessment in a reasonable way. In turn, such conditional belief function, together with the marginal probability distribution of X, gives rise to a joint belief function for the distribution of V = (X, Y, Z).","Title: Conditional decisions under objective and subjective ambiguity in Dempster-Shafer theory\nYear: 2022\n\nAbstract: This paper deals with conditional decisions on generalized Anscombe-Aumann acts mapping states of the world to finitely additive probabilities on the set of menus of consequences, the latter conveying a form of “objective” ambiguity. If the decision maker has a systematic pessimistic/optimistic attitude towards “objective” ambiguity, acts reduce to functions mapping states of the world to belief/plausibility functions on consequences. We provide a system of axioms assuring the representability of a family of conditional preference relations on such acts by a conditional functional in which “subjective” uncertainty is modeled through a conditional belief/plausibility function on the states of the world, obeying to a suitable axiomatic definition.","Title: Probability envelopes and their Dempster-Shafer approximations in statistical matching\nYear: 2022\n\nAbstract: Many economic applications require to integrate information coming from different data sources. In this work we consider a specific integration problem, called statistical matching, referring to integration of data sets where some variables are separately observed and some others are observed in all the data sets. This problem leads to the issue of non-uniqueness for the compatible (conditional) distributions and so it suggests to deal with sets of probabilities. For that we consider different strategies to get a (conditional) belief function that approximates the lower envelope of the class of compatible (conditional) probabilities. We first analyze the case without logical constraints among the variables and then generalize the obtained results by allowing for logical constraints. We finally show an application to real data."],"string":"[\n \"Title: Comparative Conditional Possibilities\\nYear: 2005\\n\\nAbstract: Any dynamic decision model or procedure for acquisition of knowledge must deal with conditional events and should refer to\\n (not necessarily structured) domains containing only the elements and the information of interest. We consider conditional\\n possibility theory as numerical reference model to handle uncertainty and to study binary relations, defined on an arbitrary\\n set of conditional events expressing the idea of “no more possible than”. We give the necessary conditions for the representability\\n of a relation by a T-conditional possibility, for any triangular norm T, and we provide a complete characterization in terms of necessary and sufficient conditions for the representability by a\\n conditional possibility (i.e. when T is the minimum).\",\n \"Title: The role of coherence for handling probabilistic evaluations and independence\\nYear: 2005\\n\\nAbstract: This paper deals with coherent conditional probability able to manage uncertainty, partial knowledge and conditional independence, overcoming the critical situations presented by the classic independence definition. When a probability is not complete (i.e. it is defined on an arbitrary set of conditional events) the conditional independence statements are not necessarily automatically induced by the values of the assessment, so given a set of independence statements its compatibility with the numerical values (conditional probability) need to be checked. This problem related to the compatibility of independence statements and conditional probability assessment is studied and a procedure for checking the compatibility is proposed.\",\n \"Title: 2D Probability-Possibility Transformations.\\nYear: 2012\\n\\nAbstract: Probability-possibility transformations are useful whenever probabilistic information must be dealt with in the possibility theory. In this paper, two-dimensional probability-possibility transformations of joint probability densities are considered, to build joint possibilities such that the marginals preserve the same information content as the marginals of the joint probability densities.\",\n \"Title: Inferential processes leading to possibility and necessity\\nYear: 2013\\n\\nAbstract: This paper deals with the upper and lower bounds of a class of uncertainty measures endowed with particular characteristics (decomposability, monotonicity, partial additivity and so on). We consider an initial partial assessment consistent with either probability or possibility or necessity, then we study the upper and lower envelopes of all possible extensions. By resorting to a notion of weak logical independence we get as lower or upper envelope a possibility or a necessity, respectively, starting either from a probability or from a possibility or from a necessity.\",\n \"Title: The L-separation criterion for description of cs-independence models\\nYear: 2002\\n\\nAbstract: Different conditional independence models have been proposed in literature; in this paper we consider models induced by conditional probabilities based on the definition of conditional cs-independence. These models need not comply with the symmetry property, so that they have not the graphoid structure. Hence, the well-known d-separation criterion for directed acyclic graphs may not be able to represent such independence models. Therefore, we introduce a new separation criterion called L-separation. We study its main properties and show how it allows to represent the above-mentioned independence models through directed acyclic graphs.\",\n \"Title: Possibility theory: Conditional independence\\nYear: 2006\\n\\nAbstract: The subtle notion of conditioning is controversial in several contexts, for example in possibility theory where, in fact, different definitions have been introduced. We refer to a general axiomatic definition of conditional possibility and then we deal with ''partial assessments'' on (not necessarily structured) domains containing only elements of interest. We study a notion of coherence, which assures the extendability of an assessment as a conditional possibility and we introduce a procedure for checking coherence. Moreover, we propose a definition of independence for conditional possibility, which avoids some counterintuitive situations, and we study its main properties in order to compare it with other definitions introduced in literature. Then, we check which properties among the graphoid ones are satisfied: this allows to compare our definition with other independence notions given in the context of other uncertainty formalisms. This analysis is relevant for graphical models in order to single out and visualize dependence relations among random variables.\",\n \"Title: Graphical Representation of Asymmetric Graphoid Structures\\nYear: 2003\\n\\nAbstract: Independence models induced by some uncertainty measures (e.g. condi- tional probability, possibility) do not obey the usual graphoid properties, since they do not satisfy the symmetry property. They are efciently rep- resentable through directed acyclic l-graphs by using L-separation criterion. In this paper, we show that in general there is not a l-graph which de- scribes completely all the independence statements of a given model; hence we introduce in this context the notion of minimal I-map and we show how to build it, given an ordering on the variables. In addition, we prove that, for any ordering, there exists an I-map for any asymmetric graphoid structure.\",\n \"Title: Locally Strong Coherence in Inference Processes\\nYear: 2002\\n\\nAbstract: In this paper we deal with probabilistic inference in the most general form of coherent conditional probability assessments. In particular, our aim is to reduce computational difficulties that could arise with a direct application of the main characterization results. We reach our goal by introducing the notion of locally strong coherence and characterizing it by logical conditions. Hence, some of the numerical constraints are replaced by Boolean satisfiability conditions. An automatic procedure is proposed and its efficiency is proved. Some examples are reported to make easier the understanding of the machinery and to show its effectiveness.\",\n \"Title: Conditional Independence in A Coherent Finite Setting\\nYear: 2001\\n\\nAbstract: A definition of stochastic independence which avoids the inconsistencies (related to events of probability 0 or 1) of the classic one has been proposed by Coletti and Scozzafava for two events. We extend it to iconditional independence among finite sets of events. In particular, the case of (finite) discrete random variables is studied. We check which of the relevant properties connected with graphical structures hold. Hence, an axiomatic characterization of these independence models is given and it is compared to the classic ones.\",\n \"Title: Conditional independence structure and its closure: Inferential rules and algorithms\\nYear: 2009\\n\\nAbstract: In this paper, we deal with conditional independence models closed with respect to graphoid properties. Such models come from different uncertainty measures, in particular in a probabilistic setting. We study some inferential rules and describe methods and algorithms to compute efficiently the closure of a set of conditional independence statements.\",\n \"Title: Statistical matching of multiple sources: A look through coherence\\nYear: 2008\\n\\nAbstract: In several applications there is the need to consider different data sources and to integrate information: a specific case is the so-called statistical matching, where data sources have just a set of common variables and inference is required on the other variables. The traditional way to cope with such situations is to combine the available data with assumptions strong enough to identify pointwise the joint probability. Such assumptions cannot always be justified and inference should take into account all the set of compatible probabilities. In this paper, we show how statistical matching problems can be managed by means of coherent conditional probability: coherence allows us to combine the knowledge coming from different multiple sources, included those given from field experts, without necessarily assuming further hypothesis. Moreover, inferences and decisions can be dealt with by taking in consideration also logical constraints among the variables, which arise naturally in the applications. An example showing advantages and drawbacks of the proposed method is given.\",\n \"Title: Axiomatic characterization of partial ordinal relations\\nYear: 2000\\n\\nAbstract: In this paper we focus on the theoretical properties of non-numerical representation of the uncertainty. As usual, this representation is realized by an “ordinal relation” (or, equivalently, by a “comparative scale”) among the “entities” (events, alternatives or acts) of a specific problem. After giving an overview of different known axioms characterizing some classes of ordinal relations (and their duals), we introduce some axioms to enclose the necessary and sufficient conditions for the representability of ordinal relations (defined on arbitrary finite sets of events) by the most-known uncertainty measures.\",\n \"Title: Acyclic directed graphs representing independence models\\nYear: 2011\\n\\nAbstract: In this paper we study the problem of representing probabilistic independence models, in particular those closed under graphoid properties. We focus on acyclic directed graph (DAG): a new algorithm to build a DAG, given an ordering among random variables, is described and peculiarities and advantages of this approach are discussed. Moreover, we provide a necessary and sufficient condition for the existence of a perfect map representing an independence model and we describe an algorithm based on this characterization.\",\n \"Title: Independence And Conditional Possibility For Strictly Monotone Triangular Norms\\nYear: 2006\\n\\nAbstract: In the literature there are different definitions of conditional possibility. Starting from a general axiomatic definition, we propose a definition of independence for circle dot-conditional possibility, in the case that circle dot is a strictly monotone triangular norm. We study its main properties to compare it to other definitions introduced in possibility theory. Then, we show that the controversial aspects related to logical dependencies (structural zeros) can be circumvented. Moreover, a set of properties (the well-known graphoid properties) has been considered to be tested, allowing us to compare the proposed definition to the independence notions given in the context of other uncertainty formalisms. (c) 2006 Wiley Periodicals, Inc.\",\n \"Title: Probabilistic Fuzzy Reasoning in a Coherent Setting.\\nYear: 2013\\n\\nAbstract: We use the interpretation of fuzzy sets in terms of coherent conditional probabilities for handling probabilistic fuzzy IF-THEN rules. We show by some examples how this interpretation can help when fuzzy and statistical information need to be combined and the available probabilistic information on the fuzzy events is possibly imprecise or incomplete.\",\n \"Title: An Axiomatization of Conditional Possibilistic Preference Functionals\\nYear: 2007\\n\\nAbstract: The aim of the paper is to extend the Savage like axiomatization of possibilistic preference functionals in qualitative decision theory to conditional acts, so as to make a step towards the dynamic decision setting. To this end, the de Finetti style approach to conditional possibility recently advocated by Coletti and Vantaggi is exploited, extending to conditional acts the basic axioms pertaining to conditional events.\",\n \"Title: A view on conditional measures through local representability of binary relations\\nYear: 2008\\n\\nAbstract: We consider binary relations @? admitting a conditional measure of uncertainty @f (precisely plausibility, possibility and their dual functions), which locally represents @?: for every pair A@?B, the measure @f(.|C) almost represents for any hypothesis C@?A@?B, and represents @? under the most specific hypothesis C=A@?B.\",\n \"Title: Conditional independence structures and graphical models\\nYear: 2003\\n\\nAbstract: In this paper we study conditional independence structures arising from conditional probabilities and lower conditional probabilities. Such models are based on notions of stochastic independence apt to manage also those situations where zero evaluations on possible events are present: this is particularly crucial for lower probability.The \\\"graphoid\\\" properties of such models are investigated, and the representation problem of conditional independence structures is dealt with by generalizing the well-known classic separation criteria for undirected and directed acyclic graphs. Our graphical models describe the independence statements and the possible logical dependencies among the random variables.\",\n \"Title: A bridge between probability and possibility in a comparative framework\\nYear: 2011\\n\\nAbstract: The paper studies the connection between comparative probability and comparative plausibility, with a particular emphasis on comparative possibility. We consider a comparative probability on an algebra and extend it to a different algebra. We prove that, in general, the upper extension of the given comparative probability is a comparative plausibility. By considering a suitable condition of weak logical independence between the two partitions related to the atoms of the two algebras, we prove that the upper ordinal relation is a comparative possibility. These results hold for comparative probability not necessarily representable by a numerical probability.\",\n \"Title: Incomplete preferences on conditional random quantities: Representability by conditional previsions\\nYear: 2010\\n\\nAbstract: We study “partial” preference relations, defined on an arbitrary set of conditional bounded random quantities: we provide a condition of rationality (interpretable in terms of betting scheme) characterizing preference relations representable by a conditional expectation. Moreover, we study the problem of extending a rational preference relation.\",\n \"Title: Hybrid Models: Probabilistic and Fuzzy Information.\\nYear: 2012\\n\\nAbstract: Under the interpretation of fuzzy set as coherent conditional probability, we study inferential processes starting from a probability distribution (on a random variable) and a coherent conditional probability on \\\"fuzzy conditional events\\\". We characterize the coherent extensions and we analyze an example proposed by Zadeh.\",\n \"Title: Finding P-maps and I-maps to represent conditional independencies\\nYear: 2011\\n\\nAbstract: The representation problem of independence models is studied by focusing on acyclic directed graph (DAG). We present the algorithm PC* in order to look for a perfect map. However, when a perfect map does not exist, so that PC* fails, it is interesting to find a minimal I--map, which represents as many triples as possible in J*. Therefore we describe an algorithm which finds such a map by means of a backtracking procedure.\",\n \"Title: Qualitative combination of independence models\\nYear: 2013\\n\\nAbstract: We deal with the problem of combining sets of independence statements coming from different experts. It is known that the independence model induced by a strictly positive probability distribution has a graphoid structure, but the explicit computation and storage of the closure (w.r.t. graphoid properties) of a set of independence statements is a computational hard problem. For this, we rely on a compact symbolic representation of the closure called fast closure and study three different combination strategies of two sets of independence statements, working on fast closures. We investigate when the complete DAG representability of the given models is preserved in the combined one.\",\n \"Title: Generalized Bayesian inference in a fuzzy context: From theory to a virtual reality application\\nYear: 2012\\n\\nAbstract: A generalized Bayesian inference framework in order to embed fuzzy sets and partial probabilistic information is provided. The general framework of reference is that of coherent conditional probabilities, which allows giving a rigorous interpretation of membership function as a conditional probability, regarded as a function of the conditioning event. The inferential problem needs to be studied in situations where the prior can be partial; moreover, membership and prior can be given on different classes of events. This inferential model is applied for the virtual representation of a female avatar.\",\n \"Title: Incoherence correction strategies in statistical matching\\nYear: 2012\\n\\nAbstract: Several economic applications require to consider different data sources and to integrate the information coming from them. This paper focuses on statistical matching, in particular we deal with incoherences. In fact, when logical constraints among the variables are present incoherencies on the probability evaluations can arise. The aim of this paper is to remove such incoherences by using different methods based on distances minimization or least commitment imprecise probabilities extensions. An illustrative example shows peculiarities of the different correction methods. Finally, limited to pseudo distance minimization, we performed a systematic comparison through a simulation study.\",\n \"Title: Acyclic Directed Graphs to Represent Conditional Independence Models\\nYear: 2009\\n\\nAbstract: In this paper we consider conditional independence models closed under graphoid properties. We investigate their representation by means of acyclic directed graphs (DAG). A new algorithm to build a DAG, given an ordering among random variables, is described and peculiarities and advantages of this approach are discussed. Finally, some properties ensuring the existence of perfect maps are provided. These conditions can be used to define a procedure able to find a perfect map for some classes of independence models.\",\n \"Title: Possibility Measures in Probabilistic Inference\\nYear: 2008\\n\\nAbstract: By means of a logical condition between two partitions L and L' (\\\"weak logical independence\\\"), we find connections between probabilities and possibilities. We show that the upper envelope of the extensions of a probability on L is a possibility on the algebra generated by L'. Moreover we characterize the set of possibilities obtained as extensions of a coherent probability on an arbitrary set: in particular, we find the two \\\"extreme\\\" (i.e., dominated and dominating) possibilities.\",\n \"Title: Independence And Conditional Decomposable Information Measures\\nYear: 2010\\n\\nAbstract: We deal with conditional decomposable information measures, directly defined as functions on a suitable set of conditional events satisfying a class of axioms. For these general measures we introduce a notion of independence and study its main properties in order to compare it with classical definitions present in the literature. The particular case of Wiener-Shannon information measure is taken in consideration and the links between the provided independence for information measures and the independence for the underlying probability are analyzed.\",\n \"Title: Locally strong coherence and inference with lower-upper probabilities\\nYear: 2003\\n\\nAbstract: We introduce an operational way to reduce the spatial complexity in inference processes based on conditional lower–upper\\n probabilities assessments. To reach such goal we must suitably exploit zero probabilities taking account of logical conditions\\n characterizing locally strong coherence. We actually re-formulate for conditional lower–upper probabilities the notion of\\n locally strong coherence already introduced for conditional precise probabilities. Thanks to the characterization, we avoid\\n to build all atoms, so that several real problems become feasible. In fact, the real complexity problem is connected to the\\n number of atoms. Since for an inferential process with lower–upper probabilities several sequences of constraints must be\\n fulfilled, our simplification can have either a “global” or a “partial” effect, being applicable to all or just to some sequences.\\n The whole procedure has been implemented by XLisp-Stat language. A comparison with other approaches will be done by an example.\",\n \"Title: Simplification Rules for the Coherent Probability Assessment Problem\\nYear: 2002\\n\\nAbstract: In this paper we develop a procedure for checking the consistency (coherence) of a partial probability assessment. The general problem (called CPA) is NP-complete, hence, to have a reasonable application some heuristic is needed. Our proposal differs from others because it is based on a skilful use of the logical relations present among the events. In other approaches the consistency problem is reduced directly to the satisfiability of a system of linear constraints. Here, thanks to the characterization of particular configurations and to the elimination of variables, an instance of the problem is reduced to smaller instances. To obtain such results, we introduce a procedure based on rules resembling those given by Davis–Putnam for the satisfiability of Boolean formulas. At the end a particularized description of an actual implementation is given.\",\n \"Title: Likelihood in a Possibilistic and Probabilistic Context: A Comparison\\nYear: 2010\\n\\nAbstract: We provide a comparison between a probabilistic and a possibilistic likelihood both as point and set functions.\",\n \"Title: An Algorithm To Find A Perfect Map For Graphoid Structures\\nYear: 2010\\n\\nAbstract: We provide a necessary and sufficient condition for the existence of a perfect; map representing an independence model and we give an algorithm for checking this condition and drawing a perfect map, when it exists.\",\n \"Title: Exploiting independencies to compute semigraphoid and graphoid structures\\nYear: 2011\\n\\nAbstract: We deal with conditional independencies, which have a fundamental role in probability and multivariate statistics. The structure of probabilistic independencies is described by semigraphoids or, for strictly positive probabilities, by graphoids. In this paper, given a set of independencies compatible with a probability, the attention is focused toward the problem of computing efficiently the closure with respect to the semigraphoid and graphoid structures. We introduce a suitable notion of projection in order to provide a new method which properly uses conditional independence statements.\",\n \"Title: Elimination of Boolean variables for probabilistic coherence\\nYear: 2000\\n\\nAbstract: In this paper we deal with the computational complexity problem of checking the coherence of a partial probability assessment (called CPA). The CPA problem, like its analogous PSAT, is NP-complete so we look for an heuristic procedure to make tractable reasonable instances of the problem. Starting from the characteristic feature of de Finetti's approach (i.e. the explicit distinction between the probabilistic assessment and the logical relations among the sentences) we introduce several rules...\",\n \"Title: Independence in possibility theory under different triangular norms\\nYear: 2013\\n\\nAbstract: In this paper we consider coherent T-conditional possibility assessments, with T a continuous t-norm, and introduce for them a concept of independence already studied for the minimum and strict t-norms. As a significant particular case of T-conditional possibility we explicitly consider TDP-conditional possibility (obtained through the minimum specificity principle) introduced by Dubois and Prade.\",\n \"Title: T-conditional possibilities: Coherence and inference\\nYear: 2009\\n\\nAbstract: In this paper we refer to an axiomatic definition of T-conditional possibility, where T is any t-norm. We characterize a full T-conditional possibility in terms of a suitable set of unconditional possibilities. Starting from this characterization we are able to manage coherent conditional possibility assessments and their enlargements. To compare T-conditional possibility related to different t-norm T, we study binary relations locally representable by a T-conditional possibility.\",\n \"Title: A general interpretation of conditioning and its implication on coherence\\nYear: 1999\\n\\nAbstract: In this paper we introduce a relation between Boolean events that can be interpreted as a conditioning relation. The goal\\n is to find “elementary” events (atoms) derived from a finite family of conditional events and for this purpose we adopt MV-algebra\\n operations. Two different decompositions (whose elements are called dicotomic and tricotomic conditional atoms, respectively)\\n are proposed and their interpretations are given. Finally, we examine them as a tool for checking the coherence of a partial\\n conditional probability assessment.\",\n \"Title: Inference with fuzzy and probabilistic information\\nYear: 2010\\n\\nAbstract: In the paper we deal with fuzzy sets under the interpretation given in a coherent probabilistic setting. We provide a general Bayesian inference process involving fuzzy and partial probabilistic information by showing its peculiarities.\",\n \"Title: The Role Of Coherence For The Integration Of Different Sources\\nYear: 2005\\n\\nAbstract: In several economic applications (e.g. marketing research, microsimulation models) there is the need to consider different data sources and to integrate the information coming from them. In this paper we show how integration problems can be managed by means of coherence for partial conditional probabilistic assessments. Coherence allows us to combine the knowledge coming from the different sources, included those (possibly) given from field experts, without necessarily assuming further hypothesis (as conditional independence). Moreover, inferences and decisions can be drawn taking in consideration also logical constraints among the variables. An example showing advantages and drawbacks of the proposed method is given.\",\n \"Title: Integrated Likelihood in a Finitely Additive Setting\\nYear: 2009\\n\\nAbstract: Without a clear, precise and rigorous mathematical frame, is the likelihood \\\"per se\\\" a proper tool to deal with statistical inference and to manage partial and vague information? Since (as Basu puts it) \\\"the likelihood function is after all a bunch of conditional probabilities\\\", a proper discussion of the various extensions of a likelihood from a point function to a set function is carried out by looking at a conditional probability as a general non-additive \\\"uncertainty\\\" measure P (E | · ) on the set of conditioning events.\",\n \"Title: Comparative models ruled by possibility and necessity: A conditional world\\nYear: 2007\\n\\nAbstract: Any dynamic decision model needs to deal with conditional events and conditional uncertainty measures. Moreover, to avoid the introduction of not available information in decision processes, we should refer to domains containing only the elements of interest. According to the aforementioned guidelines we study the comparative uncertainty framework for decision models referring to possibility and necessity measures: we consider binary relations, defined on an arbitrary set of conditional events and we provide a complete characterization in terms of necessary and sufficient conditions for their representability by a conditional possibility and by a conditional necessity.\",\n \"Title: Measuring the Quality of Health-Care Services: A Likelihood-Based Fuzzy Modeling Approach\\nYear: 2007\\n\\nAbstract: We face the problem of constructing a model which is suited for an effective evaluation of the quality of a health-care provider: to this purpose, we focus on some relevant indicators characterizing the various services run by the provider. We rely on a fuzzy modeling approach by using the interpretation (in terms of coherent conditional probability) of a membership function of a fuzzy set as a suitable likelihood.\",\n \"Title: Fuzzy inclusion and similarity through coherent conditional probability\\nYear: 2009\\n\\nAbstract: Our aim is to study some specific fuzzy relations (inclusion and similarity) in the framework of the interpretation of fuzzy theory in terms of coherent conditional probability.\",\n \"Title: Probabilistic Reasoning as a General Unifying Tool\\nYear: 2001\\n\\nAbstract: Our starting point is the approach to probabilistic logic through coherence; but we give up de Finetti's idea of a conditional event E\\\\H being a 3-valued entity, with the third value being just an undetermined common value for all ordered pairs (E,H). We let instead the \\\"third\\\" value of E\\\\H suitably depend on the given pair. In this way we get, through a direct assignment of conditional probability, a general theory of probabilistic reasoning able to encompass other approaches to uncertain reasoning, such as fuzziness and default reasoning. We are also able to put forward a meaningful concept of conditional independence, which avoids many of the usual inconsistencies related to logical dependence. We give an example in which we put together different kinds of information and show how coherent conditional probability can act as a unifying tool.\",\n \"Title: Weak Implication in Terms of Conditional Uncertainty Measures\\nYear: 2007\\n\\nAbstract: We define weak implication $H \\\\longmapsto_{\\\\varphi} E$ (\\\"H weakly implies E under $\\\\mathit{\\\\varphi}$\\\") through the relation $\\\\mathit{\\\\varphi}(E|H)$ = 1, where $\\\\mathit{\\\\varphi}$ is a (coherent) conditional uncertainty measure. By considering various such measures with different levels of generality, we get different sets of \\\"inferential rules\\\", that correspond to those of default logic when $\\\\mathit{\\\\varphi}$ reduces to a conditional probability.\",\n \"Title: Possibilistic and probabilistic likelihood functions and their extensions: Common features and specific characteristics\\nYear: 2014\\n\\nAbstract: We deal with conditional probability in the sense of de Finetti and with T-conditional possibility (with T a triangular norm). We prove that Dubois and Prade conditional possibility is a particular min-conditional possibility and then we compare the two notions of conditioning by an inferential point of view. Moreover, we study T-conditional possibilities as functions of the conditioning event, putting in evidence analogies and differences with conditional probabilities. This allows to characterize likelihood functions (and their aggregations) consistent either with a T-conditional possibility or a conditional probability. This analysis highlights many syntactical coincidences. Nevertheless the main difference is a weak form of monotonicity, which arises only in the possibilistic case.\",\n \"Title: Bayesian inference: the role of coherence to deal with a prior belief function.\\nYear: 2014\\n\\nAbstract: Starting from a likelihood function and a prior information represented by a belief function, a closed form expression is provided for the lower envelope of the set of all the possible “posterior probabilities” in finite spaces. The same problem, removing the hypothesis of finiteness for the domain of the prior, is then studied in the finitely additive probability framework by considering either the whole set of coherent extensions or the subset of disintegrable extensions.\",\n \"Title: Choquet Expected Utility Representation of Preferences on Generalized Lotteries.\\nYear: 2014\\n\\nAbstract: The classical von Neumann-Morgenstern's notion of lottery is generalized by replacing a probability distribution on a finite support with a belief function on the power set of the support. Given a partial preference relation on a finite set of generalized lotteries, a necessary and sufficient condition (weak rationality) is provided for its representation as a Choquet expected utility of a strictly increasing utility function.\",\n \"Title: Coherent T-conditional Possibility Envelopes and Nonmonotonic Reasoning.\\nYear: 2014\\n\\nAbstract: Envelopes of coherent T-conditional possibilities and coherent T-conditional necessities are studied and an analysis of some inference rules which play an important role in nonmonotonic reasoning is carried out.\",\n \"Title: A 2-D Metrology-Sound Probability–Possibility Transformation\\nYear: 2013\\n\\nAbstract: In recent years, the possibility theory has been investigated by many authors in the field of mathematics and engineering. A possibility distribution (PD) is, from the mathematical point of view, a generalization of a probability distribution, since it can represent the upper envelope of a family of probability distributions. Given a probability distribution, different probability-possibility transformations can be applied to transform the probability distribution into different PDs. Probability-possibility transformations are useful in metrology whenever statistical data must be dealt with inside the possibility theory, for instance, when they have to be associated with other kinds of uncertain and imprecise data to evaluate measurement uncertainty. This paper shows how uncorrelated and correlated random contributions to uncertainty can be effectively represented, processed, and combined in terms of PDs, by means of an original probability-possibility transformation.\",\n \"Title: Standard and nonstandard representability of positive uncertainty orderings.\\nYear: 2014\\n\\nAbstract: Axioms are given for positive comparative probabilities and plausibilities defined either on Boolean algebras or on arbitrary sets of events. These axioms allow to characterize binary relations representable by either standard or nonstandard measures (i. e. taking values either on the real field or on a hyperreal field). We also study relations between conditional events induced by preferences on conditional acts.\",\n \"Title: Non additive ordinal relations representable by lower or upper probabilities.\\nYear: 1998\\n\\nAbstract: We characterize (in terms of necessary and sufficient conditions) binary relations representable by a lower probability. Such relations can be non-additive (as the relations representable by a probability) and also not \\\"partially monotone\\\" (as the relations representable by a belief function). Moreover we characterize relations representable by upper probabilities and those representable by plausibility. In fact the conditions characterizing these relations are not immediately deducible by means of \\\"dual\\\" conditions given on the contrary events, like in the numerical case.\",\n \"Title: Preferences on Gambles Representable by a Choquet Expected Value with Respect to Conditional Belief and Plausibility Functions.\\nYear: 2016\\n\\nAbstract: We deal with preference relations on finite sets of (conditional) gambles, providing necessary and sufficient conditions for their representability by a (conditional) Choquet expected value with respect to a (conditional) belief function or a (conditional) plausibility function.\",\n \"Title: Partial identification in statistical matching with misclassification.\\nYear: 2017\\n\\nAbstract: The main target of statistical matching is to make inference on variables observed in different sources by using information on common variables. The partial information generally implies that the model is not identifiable. The aim of this paper is to study the case when common variables are affected by a misclassification. The partially identifiable region, that is the class of probabilities extending the conditional probabilities obtained by the information in different sources, is determined. These regions are determined in the general case and under some specific restrictions on the misclassification mechanism. An application to real data is used to show in practice the results achieved in the paper.\",\n \"Title: Envelopes of conditional probabilities extending a strategy and a prior probability.\\nYear: 2017\\n\\nAbstract: Any assessment formed by a strategy and a prior probability is a coherent conditional probability and can be extended, generally not in a unique way, to a full conditional probability. The corresponding class of all extensions is studied and a closed form expression for its envelopes is provided. Subclasses of extensions meeting further analytical properties are considered by imposing conglomerability and a conditional version of conglomerability, respectively. Then, the envelopes of extensions satisfying these conditions are characterized. Envelopes of the class of all full conditional probabilities extending a strategy and a prior probability.Conglomerability and conditional conglomerability for full conditional probability extensions.Envelopes of the subclass of conglomerable full conditional probability extensions.Envelopes of the subclass of conditionally conglomerable full conditional probability extensions.\",\n \"Title: Fuzzy Weighted Attribute Combinations Based Similarity Measures.\\nYear: 2017\\n\\nAbstract: Some similarity measures for fuzzy subsets are introduced: they are based on fuzzy set-theoretic operations and on a weight capacity expressing the degree of contribution of each group of attributes. For such measures, the properties of dominance and T-transitivity are investigated.\",\n \"Title: Interval-Based Possibilistic Logic in a Coherent Setting.\\nYear: 2017\\n\\nAbstract: In probability theory the notion of coherence has been introduced by de Finetti in terms of bets and it reveals to be equivalent to the notion of consistence of a partial assessment with a finitely additive probability. An important feature of coherent assessments is their coherent extendibility: in general we obtain a class of coherent extensions, determining a lower and an upper envelope. A similar notion of coherence has been recently introduced for (T-conditional) possibility measures, where T is a t-norm. The extendibility of coherent possibility assessments reveals to be particularly suitable for studying interval-based possibilistic logic. Our aim is to compare the results implied by the coherent setting with those obtained in different approaches, in particular, that relying on classical T-based conditioning.\",\n \"Title: Fuzzy memberships as likelihood functions in a possibilistic framework.\\nYear: 2017\\n\\nAbstract: Likelihood functions are studied in a probabilistic and possibilistic setting: inferential conclusions are drawn from a set of likelihood functions and prior information relying on the notion of disintegrability. The present study allows for a new interpretation of fuzzy membership functions as coherent conditional possibilities. The concept of possibility of a fuzzy event is then introduced and a comparison with the probability of a fuzzy event is provided.\",\n \"Title: Knowledge processing in decisions under fuzziness and uncertainty.\\nYear: 2017\\n\\nAbstract: Some decision making applications require to encode statistics and fuzziness. In this work two frameworks are considered: coherent conditional probabilities and possibilities, which allow to give a rigorous interpretation of membership function. A comparison of the two interpretations is given to employ a general Bayesian inferential approach able to embed fuzzy information.\",\n \"Title: Priorconditioned CGLS-Based Quasi-MAP Estimate, Statistical Stopping Rule, and Ranking of Priors.\\nYear: 2017\\n\\nAbstract: We consider linear discrete ill-posed problems within the Bayesian framework, assuming a Gaussian additive noise model and a Gaussian prior whose covariance matrices may be known modulo multiplicative scaling factors. In that context, we propose a new pointwise estimator for the posterior density, the prtorcondtttoned CGLS-based quasi-MAP (qMAP) as a computationally attractive approximation of the classical maximum a posteriori (MAP) estimate, in particular when the effective rank of the matrix A is much smaller than the dimension of the unknown. Exploiting the Bayesian paradigm and the connection between standard normal random variables and the chi(2) distribution of their squared Euclidean norms, we propose a new stopping rule, the Max chi(2) rule, to terminate the CGLS iterations in the computation of the qMAP estimate. Moreover, we show that the proposed stopping rule can be used to estimate the possibly unknown scaling factor of the noise covariance, which is tantamount to estimating the noise level. It is shown that the Max chi(2) stopping rule and the proposed noise level estimation are affected by the correlation structure of priorconditioner, i.e., prior-related right preconditioner, but not by its scaling, which in the classical regularization framework would correspond to the Tikhonov regularization parameter. We will also show how the relation between priorconditioners and whitening transformations of the unknown can be used to rank different priorconditioners according to their agreement with the data.\",\n \"Title: Bayes Meets Krylov: Statistically Inspired Preconditioners for CGLS.\\nYear: 2018\\n\\nAbstract: The solution of linear inverse problems when the unknown parameters outnumber data requires addressing the problem of a nontrivial null space. After restating the problem within the Bayesian framework, a priori information about the unknown can be utilized for determining the null space contribution to the solution. More specifically, if the solution of the associated linear system is computed by the conjugate gradient for least squares (CGLS) method, the additional information can be encoded in the form of a right preconditioner. In this paper we study how the right preconditioner changes the Krylov subspaces where the CGLS iterates live, and we draw a tighter connection between Bayesian inference and Krylov subspace methods. The advantages of a Bayes-meets-Krylov approach to the solution of underdetermined linear inverse problems is illustrated with computed examples.\",\n \"Title: Conditional Submodular Coherent Risk Measures.\\nYear: 2018\\n\\nAbstract: A family of conditional risk measures is introduced by considering a single period financial market, relying on a notion of conditioning for submodular capacities, which generalizes that introduced by Dempster. The resulting measures are expressed as discounted conditional Choquet expected values, take into account ambiguity towards uncertainty and allow for conditioning to \\\"null\\\" events. We also provide a characterisation of consistence of a partial assessment with a conditional submodular coherent risk measure. The latter amounts to test the solvability of a suitable sequence of linear systems.\",\n \"Title: Envelopes of Joint Probabilities with Given Marginals Under Absolute Continuity or Equivalence Constraints.\\nYear: 2016\\n\\nAbstract: The aim is to determine the envelopes of the class of joint probabilities (provided it is not empty) with assigned marginals, under the constraint of absolute continuity or equivalence with respect to a given reference measure.\",\n \"Title: A Savage-Like Representation Theorem for Preferences on Multi-acts.\\nYear: 2016\\n\\nAbstract: We deal with a Savage-like decision problem under uncertainty where, for every state of the world, the consequence of each decision (multi-act) is generally uncertain: the decision maker only knows the set of possible alternatives where it can range (multi-consequence). A Choquet expected utility representation theorem for a preference relation on multi-acts is provided, relying on a state-independent cardinal utility function defined on the (finite) set of all alternatives.\",\n \"Title: Characterization of Conditional Submodular Capacities: Coherence and Extension.\\nYear: 2018\\n\\nAbstract: We provide a representation in terms of a linearly ordered class of (unconditional) submodular capacities of an axiomatically defined conditional submodular capacity. This allows to provide a notion of coherence for a partial assessment and a related notion of coherent extension.\",\n \"Title: Models for pessimistic or optimistic decisions under different uncertain scenarios.\\nYear: 2019\\n\\nAbstract: In many decision problems under uncertainty, agents are only able to provide a possibly incomplete preference relation on gambles, that can be “irrational” according to the classical expected utility paradigm. Furthermore, agents can find it easier to express their preference relation taking one or more specific scenarios as hypothesis. In order to handle these situations two betting scheme rationality conditions are introduced, which characterize those preference relations representable by a conditional Choquet expected value with respect to a conditional belief or plausibility function. Such conditions encode a pessimistic or an optimistic attitude towards uncertainty, respectively, and allow to take into account (possible) “null” scenarios by means of a weak ordering of “unexpectation”. Moreover, they are the basis of a completion technique for enlarging the set of preferences by preserving rationality.\",\n \"Title: Inferential Rules for Weak Graphoid\\nYear: 2010\\n\\nAbstract: We deal with the problem of computing efficiently the closure of a set of independencies, compatible with a coherent conditional probability, under cs-independence. For this aim we provide two inferential rules, winch allow to build a basis for the closure.\",\n \"Title: Qualitative Bayesian Networks with Logical Constraints\\nYear: 2003\\n\\nAbstract: An important feature of Qualitative Bayesian Networks is that they describe conditional independence models. However, they are not able to handle models involving logical constraints among the given variables. The aim of this paper is to show how this theory can be extended in such a way to represent also the logical constraints in the graph through an enhanced version of Qualitative Bayesian Networks. The relative algorithm for building these graphs (which is a generalization of the well-known algorithm based on D-separation criterion) is given. This theory is particularly fit for conditional probabilistic independence models based on the notion of cs-independence. This notion avoids the usual critical situations shown by the classic definition when logical constraints are present.\",\n \"Title: Betting Schemes for Assessing Coherent Numerical and Comparative Conditional Possibilities\\nYear: 2021\\n\\nAbstract: We introduce coherence conditions having a betting scheme interpretation both for a numerical and a comparative conditional possibility assessment. The conditional bets are considered under partially resolving uncertainty and assuming consonance. This means that we allow situations in which the agent may only acquire the information that a non-impossible event occurs, without knowing which is the true state of the world. Further, he/she can only consider families of nested non-impossible events in computing the gain and has a systematically optimistic behavior. Both conditions are proved to be equivalent to the existence of a conditional possibility agreeing with an axiomatic definition based on the algebraic product t-norm, that extends, either numerically or comparatively (through the induced comparative conditional possibility relation), the given assessment.\",\n \"Title: Dempster-Shafer Approximations and Probabilistic Bounds in Statistical Matching\\nYear: 2021\\n\\nAbstract: Many economic applications require to integrate information coming from different data sources. In this work we consider a specific integration problem called statistical matching, referring to probabilistic distributions of Y vertical bar X, Z vertical bar X and X, where X, Y, Z are categorical (possibly multi-dimensional) random variables. Here, we restrict to the case of no logical relations among random variables X, Y, Z. The non-uniqueness of the conditional distribution of (Y, Z)vertical bar X suggests to deal with sets of probabilities. For that we consider different strategies to get a conditional belief function for (Y, Z)vertical bar X that approximates the initial assessment in a reasonable way. In turn, such conditional belief function, together with the marginal probability distribution of X, gives rise to a joint belief function for the distribution of V = (X, Y, Z).\",\n \"Title: Conditional decisions under objective and subjective ambiguity in Dempster-Shafer theory\\nYear: 2022\\n\\nAbstract: This paper deals with conditional decisions on generalized Anscombe-Aumann acts mapping states of the world to finitely additive probabilities on the set of menus of consequences, the latter conveying a form of “objective” ambiguity. If the decision maker has a systematic pessimistic/optimistic attitude towards “objective” ambiguity, acts reduce to functions mapping states of the world to belief/plausibility functions on consequences. We provide a system of axioms assuring the representability of a family of conditional preference relations on such acts by a conditional functional in which “subjective” uncertainty is modeled through a conditional belief/plausibility function on the states of the world, obeying to a suitable axiomatic definition.\",\n \"Title: Probability envelopes and their Dempster-Shafer approximations in statistical matching\\nYear: 2022\\n\\nAbstract: Many economic applications require to integrate information coming from different data sources. In this work we consider a specific integration problem, called statistical matching, referring to integration of data sets where some variables are separately observed and some others are observed in all the data sets. This problem leads to the issue of non-uniqueness for the compatible (conditional) distributions and so it suggests to deal with sets of probabilities. For that we consider different strategies to get a (conditional) belief function that approximates the lower envelope of the class of compatible (conditional) probabilities. We first analyze the case without logical constraints among the variables and then generalize the obtained results by allowing for logical constraints. We finally show an application to real data.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":72,"string":"72"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13243,"string":"13,243"}}},{"rowIdx":13244,"cells":{"question_id":{"kind":"string","value":"Antonio Plaza"},"question":{"kind":"string","value":"Generate an abstract for the title \"Distributed Computing for Efficient Hyperspectral Imaging Using Fully Heterogeneous Networks of Workstations\" using the following items: Java messaging, JMS, distributed applications, publish/subscribe, message routing."},"answer":{"kind":"string","value":"The Java messaging service (JMS) is a means to organize communication among distributed applications according to the publish/subscribe principle. If the subscribers install filter rules on the JMS server, JMS can be used as a message routing platform, ..."},"correct_answer":{"kind":"string","value":"The Java messaging service (JMS) is a means to organize communication among distributed applications according to the publish/subscribe principle. If the subscribers install filter rules on the JMS server, JMS can be used as a message routing platform, ..."},"documents":{"kind":"list like","value":["Title: Hyperspectral band selection using a collaborative sparse model\nYear: 2012\n\nAbstract: In our previous research, we have proposed band-similarity-based unsupervised band selection approaches, which are proven to be very efficient. In this paper, we propose to use a collaborative sparse model for further improvement. Specifically, the pre-selected bands using the fast method, called NFINDR+LP, are further refined using a collaborative sparse model. It not only requires that the linear regression coefficients are sparse, but also requires that the same set of active bands is shared by all the bands to be removed. With the collaborative sparseness constraint being relaxed, the final selected bands can be further improved, that is, the band subset with the same number of bands can provide better classification accuracy. Based on the preliminary result, the proposed sparse model is also capable of finding the minimum number of bands to be selected.","Title: New Postprocessing Methods for Remote Sensing Image Classification: A Systematic Study\nYear: 2014\n\nAbstract: This paper develops several new strategies for remote sensing image classification postprocessing (CPP) and conducts a systematic study in this area. CPP is defined as a refinement of the labeling in a classified image in order to enhance its original classification accuracy. The current mainstream classification methods (preprocessing) extract additional spatial features in order to complement spectral information and enhance classification using spectral responses alone. On the other hand, however, the CPP methods, providing a new solution to improve classification accuracy by refining the initial result, have not received sufficient attention. They have potential for achieving comparable accuracy to the preprocessing methods but in a more direct and succinct way. In this paper, we consider four groups of CPP strategies: filtering; random field; object-based voting; and relearning. In addition to the state-of-the-art CPP algorithms, we also propose a series of new ones, e.g., anisotropic probability diffusion and primitive cooccurrence matrix. In experiments, a number of multisource remote sensing data sets are used for evaluation of the considered CPP algorithms. It is shown that all the CPP strategies are capable of providing more accurate results than the raw classification. Among them, the relearning approaches achieve the best results. In addition, our relearning algorithms are compared with the state-of-the-art spectral-spatial classification. The results obtained further verify the effectiveness of CPP in different remote sensing applications.","Title: Parallel hyperspectral image compression using iterative error analysis on graphics processing units\nYear: 2012\n\nAbstract: In this paper, we develop a new parallel implementation of the iterative error analysis (IEA) algorithm for lossy hyperspectral image compression on graphics processing units (GPUs), an inexpensive parallel computing platform that has recently become very popular in hyperspectral imaging applications. The proposed GPU implementation is tested on several different architectures from NVidia, the main GPU vendor worldwide, and is shown to exhibit real-time performance in the analysis of AVIRIS data sets. The GPU implementation of the IEA represents a step forward towards real-time onboard (lossy) compression of hyperspectral data where the quality of the compression can be also adjusted in real-time.","Title: Impact of Vector Ordering Strategies on Morphological Unmixing of Remotely Sensed Hyperspectral Images\nYear: 2010\n\nAbstract: Hyper spectral imaging is a new technique in remote sensing that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have explored the application of morphological operations to integrate both spatial and spectral responses in hyper spectral data analysis. These operations rely on ordering pixel vectors in spectral space, but there is no unambiguous means of defining the minimum and maximum values between two vectors of more than one dimension. Our original contribution in this paper is to examine the impact of different vector ordering strategies on the definition of multi-channel morphological operations. Our focus is on morphological unmixing, which decomposes each pixel vector in the hyper spectral scene into a combination of pure spectral signatures (called end members) and their associated abundance fractions, allowing sub-pixel characterization. Experiments are conducted using real hyper spectral data sets collected by NASA/JPL's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system.","Title: FPGA implementation of the pixel purity index algorithm for remotely sensed hyperspectral image analysis\nYear: 2010\n\nAbstract: Hyperspectral imaging is a new emerging technology in remote sensing which generates hundreds of images, at different wavelength channels, for the same area on the surface of the Earth. Over the last years, many algorithms have been developed with the purpose of finding endmembers, assumed to be pure spectral signatures in remotely sensed hyperspectral data sets. One of the most popular techniques has been the pixel purity index (PPI). This algorithm is very time-consuming. The reconfigurability, compact size, and high computational power of Field programmable gate arrays (FPGAs) make them particularly attractive for exploitation in remote sensing applications with (near) real-time requirements. In this paper, we present an FPGA design for implementation of the PPI algorithm. Our systolic array design includes a DMA and implements a prefetching technique to reduce the penalties due to the I/O communications. We have also included a hardware module for random number generation. The proposed method has been tested using real hyperspectral data collected by NASA's Airborne Visible Infrared Imaging Spectrometer over the Cuprite mining district in Nevada. Experimental results reveal that the proposed hardware system is easily scalable and able to provide accurate results with compact size in (near) real-time, whichmake our reconfigurable system appealing for on-board hyperspectral data processing.","Title: Morphological Feature Extraction For Automatic Registration Of Multispectral Images\nYear: 2007\n\nAbstract: The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.","Title: Parallel unmixing of remotely sensed hyperspectral images on commodity graphics processing units\nYear: 2011\n\nAbstract: Hyperspectral imaging instruments are capable of collecting hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. One of the main problems in the analysis of hyperspectral data cubes is the presence of mixed pixels, which arise when the spatial resolution of the sensor is not enough to separate spectrally distinct materials. Hyperspectral unmixing is one of the most popular techniques to analyze hyperspectral data. It comprises two stages: (i) automatic identification of pure spectral signatures (endmembers) and (ii) estimation of the fractional abundance of each endmember in each pixel. The spectral unmixing process is quite expensive in computational terms, mainly due to the extremely high dimensionality of hyperspectral data cubes. Although this process maps nicely to high performance systems such as clusters of computers, these systems are generally expensive and difficult to adapt to real-time data processing requirements introduced by several applications, such as wildland fire tracking, biological threat detection, monitoring of oil spills, and other types of chemical contamination. In this paper, we develop an implementation of the full hyperspectral unmixing chain on commodity graphics processing units (GPUs). The proposed methodology has been implemented, using the CUDA (compute device unified architecture), and tested on three different GPU architectures: NVidia Tesla C1060, NVidia GeForce GTX 275, and NVidia GeForce 9800 GX2, achieving near real-time unmixing performance in some configurations tested when analyzing two different hyperspectral images, collected over the World Trade Center complex in New York City and the Cuprite mining district in Nevada. Copyright © 2011 John Wiley & Sons, Ltd.","Title: Spectral Characterisation Of Land Surface Composition To Determine Soil Erosion Within Semiarid Rainfed Cultivated Areas\nYear: 2012\n\nAbstract: In a Mediterranean semiarid area in Central Spain, with dominant rainfed agriculture, hyperspectral airborne data, supported by field spectroscopy have been obtained, allowing a spectral identification of bare soil with the corresponding erosion stages. The definition of soil erosion stages is based on a spectral characterization supported by morphological, physical and chemical features of contrasted soil surfaces as a result of soil loss. Different soil erosion stages were defined within two bare-soil sites representing the soil variability of the area, and where such stages are spatially represented. The validation of selected image derived endmebers was a key step to carry out a partial unmixing for determining soil erosion stages. A preliminary spatial distribution of advanced and intermediate erosion stages was obtained for the most representative soil types.","Title: Towards real-time compression of hyperspectral images using virtex-iI FPGAs\nYear: 2007\n\nAbstract: Hyperspectral imagery is a new type of high-dimensional image data which is now used in many Earth-based and planetary exploration applications. Many efforts have been devoted to designing and developing compression algorithms for hyperspectral imagery. Unfortunately, most available approaches have largely overlooked the impact of mixed pixels and subpixel targets, which can be accurately modeled and uncovered by resorting to the wealth of spectral information provided by hyperspectral image data. In this paper, we develop an FPGA-based data compression technique which relies on the concept of spectral unmixing, one of the most popular approaches to deal with mixed pixels and subpixel targets in hyperspectral analysis. The proposed method uses a two-stage approach in which the purest pixels in the image (endmembers) are first extracted and then used to express mixed pixels as linear combinations of end-members. The result is an intelligent, applicationbased compression technique which has been implemented and tested on a Xilinx Virtex-II FPGA.","Title: Parallel Hyperspectral Unmixing on GPUs.\nYear: 2014\n\nAbstract: This letter presents a new parallel method for hyperspectral unmixing composed by the efficient combination of two popular methods: vertex component analysis (VCA) and sparse unmixing by variable splitting and augmented Lagrangian (SUNSAL). First, VCA extracts the endmember signatures, and then, SUNSAL is used to estimate the abundance fractions. Both techniques are highly parallelizable, which si...","Title: Parallel implementation of vertex component analysis for hyperspectral endmember extraction\nYear: 2012\n\nAbstract: Vertex component analysis (VCA) has become a very popular and useful tool to linear unmix large hyperspectral datasets without the use of any a priori knowledge of the constituent spectra. Although VCA is fast method, many hyperspectral imagery applications require a response in real time or near-real time. This paper proposes two different optimizations for accelerating the computational performance of VCA: the first one focus a parallel implementation based on graphics computing units (GPUs) to alleviate the VCA computational burden; The second one is focused on the development of a strategy to remove a large proportion of mixed pixels that play no effect on the VCA functioning. Experiments are conducted using simulated and real hyperspectral datasets. These results reveal considerable acceleration factors, which satisfies the real-time constraints given by the data acquisition rate.","Title: Parallel Morphological/Neural Classification of Remote Sensing Images Using Fully Heterogeneous and Homogeneous Commodity Clusters\nYear: 2006\n\nAbstract: The wealth spatial and spectral information available from last-generation Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel techniques treat remotely sensed data not as images, but as unordered listings of spectral measurements with no spatial arrangement. In thematic classification applications, however, the integration of spatial and spectral information can be greatly beneficial. Although such integrated approaches can be efficiently mapped in homogeneous commodity clusters, low-cost heterogeneous networks of computers (HNOCs) have soon become a standard tool of choice in Earth and planetary missions. In this paper, we develop a new morphological/neural parallel algorithm for commodity cluster-based analysis of high-dimensional remotely sensed image data sets. The algorithms accuracy and parallel performance are tested (in the context of a real precision agriculture application) using two parallel platforms: a fully heterogeneous cluster made up of 16 workstations at University of Maryland, and a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center","Title: Real-time spectral unmixing using iterative error analysis on commodity graphics processing units\nYear: 2011\n\nAbstract: Spectral unmixing is an important task for hyperspectral data exploitation. It generally consists of two steps: identification of pure spectral signatures (endmembers) and estimation of the fractional abundance of each endmember in each pixel of the scene. A successful algorithm to perform both tasks in simultaneous fashion is the iterative error analysis (IEA) algorithm, which applies an iterative process in which the next endmember to be detected depends on the set of previously extracted ones, which can be computationally expensive for hyperspectral images with a large number of endmembers. In this paper, we propose a new parallel implementation of the IEA algorithm for graphics processing units (GPUs). The proposed implementation is tested on three different GPUs fromNVidia™, and is shown to exhibit real-time performance in the analysis of an Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) data set collected over the Cuprite mining district in Nevada.","Title: Parallel Implementation Of Hyperspectral Image Processing Algorithms\nYear: 2006\n\nAbstract: High computing performance of algorithm analysis is essential in many hyperspectral imaging applications, including automatic target recognition for homeland defense and security, risk/hazard prevention and monitoring, wild-land fire tracking and biological threat detection. Despite the growing interest in hyperspectral imaging research, only a few efforts devoted to designing and implementing well-conformed parallel processing solutions currently exist in the open literature. With the recent explosion in the amount and dimensionality of hyperspectral imagery, parallel processing is expected to become a requirement in most remote sensing missions. In this paper, we take a necessary first step towards the quantitative comparison of parallel techniques and strategies for analyzing hyperspectral data sets. Our focus is on three types of algorithms: automatic target recognition, spectral mixture analysis and data compression. Three types of high performance computing platforms are used for demonstration purposes, including commodity cluster-based systems, heterogeneous networks of distributed workstations and hardware-based computer architectures. Combined, these parts deliver a snapshot of the state of the art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of incorporating parallel computing models into hyperspectral remote sensing problems.","Title: Parallel Processing of High-Dimensional Remote Sensing Images Using Cluster Computer Architectures\nYear: 2007\n\nAbstract: Hyperspectral sensors represent the most advanced instruments currently available for remote sensing of the Earth. The high spatial and spectral resolution of the images supplied by systems like the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS), developed by NASA Jet Propulsion Laboratory, allows their exploitation in diverse applications, such as detection and control of wildland fires and hazardous agents in water and atmosphere, detection of military targets and management of natural resources. Even though the above applications generally require a response in near real time, few solutions are currently available to provide fast and efficient processing of such high-dimensional image data sets. This is mainly due to the extremely high volume of data collected by hyperspectral sensors, which often limits their exploitation in analysis scenarios where the spatial and temporal requirements are very high. In this paper, we describe new parallel processing methodologies for hyperspectral image processing, based on neural architectures and morphological concepts. The computational performance of the proposed methods is demonstrated using real analysis scenarios based on the exploitation of AVIRIS data using two parallel computer systems, an SGI Origin 2000 multicomputer located at the","Title: Spatial/spectral endmember extraction by multidimensional morphological operations\nYear: 2002\n\nAbstract: Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data.","Title: Parallel morphological/neural processing of hyperspectral images using heterogeneous and homogeneous platforms\nYear: 2008\n\nAbstract: The wealth spatial and spectral information available from last-generation Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel techniques treat remotely sensed data not as images, but as unordered listings of spectral measurements with no spatial arrangement. In thematic classification applications, however, the integration of spatial and spectral information can be greatly beneficial. Although such integrated approaches can be efficiently mapped in homogeneous commodity clusters, low-cost heterogeneous networks of computers (HNOCs) have soon become a standard tool of choice for dealing with the massive amount of image data produced by Earth observation missions. In this paper, we develop a new morphological/neural algorithm for parallel classification of high-dimensional (hyperspectral) remotely sensed image data sets. The algorithm's accuracy and parallel performance is tested in a variety of homogeneous and heterogeneous computing platforms, using two networks of workstations distributed among different locations, and also a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center in Maryland.","Title: Efficient Collective Communication Paradigms for Hyperspectral Imaging Algorithms Using HeteroMPI\nYear: 2008\n\nAbstract: Most of the parallel strategies used for information extraction in remotely sensed hyperspectral imaging applications have been implemented in the form of parallel algorithms on both homogeneous and heterogeneous networks of computers. In this paper, we develop a study on efficient collective communications based on the usage of HeteroMPI for a parallel heterogeneous hyperspectral imaging algorithm which uses concepts of mathematical morphology.","Title: Improving the Performance of Hyperspectral Image and Signal Processing Algorithms Using Parallel, Distributed and Specialized Hardware-Based Systems\nYear: 2010\n\nAbstract: Advances in sensor technology are revolutionizing the way remotely sensed data is collected, managed and analyzed. The incorporation of latest-generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral signal processing is a new technique in remote sensing that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. Many current and future applications of remote sensing in Earth science, space science, and soon in exploration science will require (near) real-time processing capabilities. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) systems and architectures in remote sensing missions. With the aim of providing an overview of current and new trends in parallel and distributed systems for remote sensing applications, this paper explores three HPC-based paradigms for efficient implementation of the Pixel Purity Index (PPI) algorithm, available from the popular Kodak's Research Systems ENVI software package, as a representative case study for demonstration purposes. Several different parallel programming techniques are used to improve the performance of the PPI on a variety of parallel platforms, including a set of message passing interface (MPI)-based implementations on a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center in Maryland and on a variety of heterogeneous networks of workstations at University of Maryland; a Handel-C implementation of the algorithm on a Virtex-II field programmable gate array (FPGA); and a compute unified device architecture (CUDA)-based implementation on graphical processing units (GPUs) of NVidia. Combined, these parts deliver an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of adapting HPC systems to remote sensing problems.","Title: The Promise of Reconfigurable Computing for Hyperspectral Imaging Onboard Systems: A Review and Trends\nYear: 2013\n\nAbstract: Hyperspectral imaging is an important technique in remote sensing which is characterized by high spectral resolutions. With the advent of new hyperspectral remote sensing missions and their increased temporal resolutions, the availability and dimensionality of hyperspectral data is continuously increasing. This demands fast processing solutions that can be used to compress and/or interpret hyperspectral data onboard spacecraft imaging platforms in order to reduce downlink connection requirements and perform a more efficient exploitation of hyperspectral data sets in various applications. Over the last few years, reconfigurable hardware solutions such as field-programmable gate arrays (FPGAs) have been consolidated as the standard choice for onboard remote sensing processing due to their smaller size, weight, and power consumption when compared with other high-performance computing systems, as well as to the availability of more FPGAs with increased tolerance to ionizing radiation in space. Although there have been many literature sources on the use of FPGAs in remote sensing in general and in hyperspectral remote sensing in particular, there is no specific reference discussing the state-of-the-art and future trends of applying this flexible and dynamic technology to such missions. In this work, a necessary first step in this direction is taken by providing an extensive review and discussion of the (current and future) capabilities of reconfigurable hardware and FPGAs in the context of hyperspectral remote sensing missions. The review covers both technological aspects of FPGA hardware and implementation issues, providing two specific case studies in which FPGAs are successfully used to improve the compression and interpretation (through spectral unmixing concepts) of remotely sensed hyperspectral data. Based on the two considered case studies, we also highlight the major challenges to be addressed in the near future in this emerging and fast growing research area.","Title: On the Use of Cluster Computing Architectures for Implementation of Hyperspectral Image Analysis Algorithms\nYear: 2005\n\nAbstract: Hyperspectral sensors represent the most advanced instruments currently available for remote sensing of the Earth. The high spatial and spectral resolution of the images supplied by systems like the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS), developed by NASA Jet Propulsion Laboratory, allows their exploitation in diverse applications, such as detection and control of wild fires and hazardous agents in water and atmosphere, detection of military targets and management of natural resources. Even though the above applications require a response in real time, few solutions are available to provide fast and efficient analysis of these types of data. This is mainly caused by the dimensionality of hyperspectral images, which limits their exploitation in analysis scenarios where the spatial and temporal requirements are very high. In the present work, we describe a new parallel methodology which deals with most of the previously addressed problems. The computational performance of the proposed analysis methodology is evaluated using two parallel computer systems, an SGI Origin 2000 shared memory system located at the European Center of Parallelism of Barcelona, and the Thunderhead Beowulf cluster at NASAýs Goddard Space Flight Center.","Title: A New Minimum-Volume Enclosing Algorithm for Endmember Identification and Abundance Estimation in Hyperspectral Data\nYear: 2012\n\nAbstract: Spectral unmixing is an important technique for hyperspectral data exploitation, in which a mixed spectral signature is decomposed into a collection of spectrally pure constituent spectra, called endmembers, and a set of correspondent fractions, or abundances, that indicate the proportion of each endmember present in the mixture. Over the last years, several algorithms have been developed for automatic or semiautomatic endmember extraction. Some available approaches assume that the input data set contains at least one pure spectral signature for each distinct material and further conduct a search for the most spectrally pure signatures in the high-dimensional space spanned by the hyperspectral data. Among these approaches, those aimed at maximizing the volume of the simplex that can be formed using available spectral signatures have found wide acceptance. However, the presence of spectrally pure constituents is unlikely in remotely sensed hyperspectral scenes due to spatial resolution, mixing phenomena, and other considerations. In order to address this issue, other available algorithms have been developed to generate virtual endmembers (not necessarily present among the input data samples) by finding the simplex with minimum volume that encloses all available observations. In this paper, we discuss maximum-volume versus minimum-volume enclosing solutions and further develop a novel algorithm in the latter category which incorporates the fractional abundance estimation as an internal step of the endmember searching process (i.e., it does not require an external method to produce endmember fractional abundances). The method is based on iteratively enclosing the observations in a lower dimensional space and removing observations that are most likely not to be enclosed by the simplex of the endmembers to be estimated. The performance of the algorithm is investigated and compared to that of other algorithms (with and without the pure pixel assumption) using synthetic a- d real hyperspectral data sets collected by a variety of hyperspectral imaging instruments.","Title: A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral Data\nYear: 2004\n\nAbstract: Linear spectral unmixing is a commonly accepted approach to mixed-pixel classification in hyperspectral imagery. This approach involves two steps. First, to find spectrally unique signatures of pure ground components, usually known as endmembers, and, second, to express mixed pixels as linear combinations of endmember materials. Over the past years, several algorithms have been developed for auton...","Title: Parallel CBIR System for Efficient Hyperspectral Image Retrieval from Heterogeneous Networks of Workstations\nYear: 2007\n\nAbstract: The purpose of content-based image retrieval (CBIR) is to retrieve, from real data stored in a database, information that is relevant to a query. In remote sensing applications, the wealth of spectral information provided by last-generation (hyperspectral) instruments has quickly introduced the need for parallel CBIR systems able to effectively retrieve features of interest from ever-growing data archives. To address this need, this paper develops a new parallel CBIR system which has been specifically designed to be run on heterogeneous networks of computers (HNOCs). These platforms have soon become a standard computing architecture in remote sensing missions due to the distributed nature of data repositories. The proposed heterogeneous system first extracts an image feature vector able to characterize image content with sub-pixel precision, and then uses the obtained feature as a search reference. The system is validated using a complex hyperspectral image database, and implemented on several networks of workstations at University of Maryland.","Title: Commodity cluster-based parallel processing of hyperspectral imagery\nYear: 2006\n\nAbstract: The rapid development of space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral Earth observing sensors such as the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of fast techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. This paper describes commodity cluster-based parallel data analysis strategies for hyperspectral imagery, a new class of image data that comprises hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. An unsupervised technique that integrates the spatial and spectral information in the image data using multi-channel morphological transformations is parallelized and compared to other available parallel algorithms. The code's portability, reusability and scalability are illustrated by using two high-performance parallel computing architectures: a distributed memory, multiple instruction multiple data (MIMD)-style multicomputer at European Center for Parallelism of Barcelona, and a Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results suggest that Beowulf clusters are a source of computational power that is both accessible and applicable to obtaining results in valid response times in information extraction applications from hyperspectral imagery.","Title: Automated generation of semi-labeled training samples for nonlinear neural network-based abundance estimation in hyperspectral data\nYear: 2005\n\nAbstract: As the initial stage of a supervised classification, the quality or training has a significant effect oil the entire classification process and its accuracy. In hyperspectral data analysis, a judicious selection of training samples call be tremendously difficult due to the presence of subpixel targets and mixed pixels, in particular, when no prior knowledge about the data is available. For instance, the Multi-Layer Perceptron (MLP) neural network call provide very accurate nonlinear estimations of fractional abundances, provided that the training set contains all possible mixture conditions. However, the requirement of large volumes of training data is a serious limitation in remote sensing because, even if classes concurring to a per-pixel cover class mixture are known, proportions of these classes are very difficult to be estimated a priori. This paper investigates, explores and further proposes solutions to resolve the issues above. Specifically, we develop a nonlinear neural net-work-based mixture model, coupled with unsupervised algorithms for automated generation of semi-labeled samples that can be effectively used for mixed pixel classification. These unsupervised algorithms, intended for situations where ancilliary-information is difficult to be collected prior to data analysis, rely on the principle that patterns that lie close to the location of decision boundaries in feature space are more informative than patterns drawn from the class cores. Computer simulations and real experiments are conducted for performance analysis of nonlinear unmixing techniques based on training samples.","Title: Improving the scalability of hyperspectral imaging applications on heterogeneous platforms using adaptive run-time data compression\nYear: 2010\n\nAbstract: Latest generation remote sensing instruments (called hyperspectral imagers) are now able to generate hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have reported that the scalability of parallel processing algorithms dealing with these high-dimensional data volumes is affected by the amount of data to be exchanged through the communication network of the system. However, large messages are common in hyperspectral imaging applications since processing algorithms are pixel-based, and each pixel vector to be exchanged through the communication network is made up of hundreds of spectral values. Thus, decreasing the amount of data to be exchanged could improve the scalability and parallel performance. In this paper, we propose a new framework based on intelligent utilization of wavelet-based data compression techniques for improving the scalability of a standard hyperspectral image processing chain on heterogeneous networks of workstations. This type of parallel platform is quickly becoming a standard in hyperspectral image processing due to the distributed nature of collected hyperspectral data as well as its flexibility and low cost. Our experimental results indicate that adaptive lossy compression can lead to improvements in the scalability of the hyperspectral processing chain without sacrificing analysis accuracy, even at sub-pixel precision levels.","Title: Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields.\nYear: 2012\n\nAbstract: This paper introduces a new supervised segmentation algorithm for remotely sensed hyperspectral image data which integrates the spectral and spatial information in a Bayesian framework. A multinomial logistic regression (MLR) algorithm is first used to learn the posterior probability distributions from the spectral information, using a subspace projection method to better characterize noise and hi...","Title: Generalized Composite Kernel Framework for Hyperspectral Image Classification\nYear: 2013\n\nAbstract: This paper presents a new framework for the development of generalized composite kernel machines for hyperspectral image classification. We construct a new family of generalized composite kernels which exhibit great flexibility when combining the spectral and the spatial information contained in the hyperspectral data, without any weight parameters. The classifier adopted in this work is the multinomial logistic regression, and the spatial information is modeled from extended multiattribute profiles. In order to illustrate the good performance of the proposed framework, support vector machines are also used for evaluation purposes. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the proposed framework leads to state-of-the-art classification performance in complex analysis scenarios.","Title: Real-Time Endmember Extraction on Multicore Processors.\nYear: 2011\n\nAbstract: In this letter, we discuss the use of multicore processors in the acceleration of endmember extraction algorithms for hyperspectral image unmixing. Specifically, we develop computationally efficient versions of two popular fully automatic endmember extraction algorithms: orthogonal subspace projection and N-FINDR. Our experimental results, based on the analysis of hyperspectral data collected by t...","Title: Performance versus energy consumption of hyperspectral unmixing algorithms on multi-core platforms.\nYear: 2013\n\nAbstract: Hyperspectral imaging is a growing area in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the Earth. Hyperspectral images are extremely high-dimensional, and require on-board processing algorithms able to satisfy near real-time constraints in applications such as wildland fire monitoring, mapping of oil spills and chemical contamination, etc. One of the most widely used techniques for analyzing hyperspectral images is spectral unmixing, which allows for sub-pixel data characterization. This is particularly important since the available spatial resolution in hyperspectral images is typically of several meters, and therefore it is reasonable to assume that several spectrally pure substances (called endmembers in hyperspectral imaging terminology) can be found within each imaged pixel. There have been several efforts towards the efficient implementation of hyperspectral unmixing algorithms on architectures susceptible of being mounted onboard imaging instruments, including field programmable gate arrays (FPGAs) and graphics processing units (GPUs). While FPGAs are generally difficult to program, GPUs are difficult to adapt to onboard processing requirements in spaceborne missions due to its extremely high power consumption. In turn, with the increase in the number of cores, multi-core platforms have recently emerged as an easier to program platform compared to FPGAs, and also more tolerable radiation and power consumption requirements. However, a detailed assessment of the performance versus energy consumption of these architectures has not been conducted as of yet in the field of hyperspectral imaging, in which it is particularly important to achieve processing results in real-time. In this article, we provide a thoughtful perspective on this relevant issue and further analyze the performance versus energy consumption ratio of different processing chains for spectral unmixing when implemented on multi-core platforms.","Title: Impact of platform heterogeneity on the design of parallel algorithms for morphological processing of high-dimensional image data\nYear: 2007\n\nAbstract: The main objective of this paper is to describe a realistic framework to understand parallel performance of high-dimensional image processing algorithms in the context of heterogeneous networks of workstations (NOWs). As a case study, this paper explores techniques for mapping hyperspectral image analysis techniques onto fully heterogeneous NOWs. Hyperspectral imaging is a new technique in remote sensing that has gained tremendous popularity in many research areas, including satellite imaging and aerial reconnaissance. The automation of techniques able to transform massive amounts of hyperspectral data into scientific understanding in valid response times is critical for space-based Earth science and planetary exploration. Using an evaluation strategy which is based on comparing the efficiency achieved by an heterogeneous algorithm on a fully heterogeneous NOW with that evidenced by its homogeneous version on a homogeneous NOW with the same aggregate performance as the heterogeneous one, we develop a detailed analysis of parallel algorithms that integrate the spatial and spectral information in the image data through mathematical morphology concepts. For comparative purposes, performance data for the tested algorithms on Thunderhead (a large-scale Beowulf cluster at NASA's Goddard Space Flight Center) are also provided. Our detailed investigation of the parallel properties of the proposed morphological algorithms provides several intriguing findings that may help image analysts in selection of parallel techniques and strategies for specific applications.","Title: Cluster versus GPU implementation of an Orthogonal Target Detection Algorithm for Remotely Sensed Hyperspectral Images\nYear: 2010\n\nAbstract: Remotely sensed hyperspectral imaging instruments provide high-dimensional data containing rich information in both the spatial and the spectral domain. In many surveillance applications, detecting objects (targets) is a very important task. In particular, algorithms for detecting (moving or static) targets, or targets that could expand their size (such as propagating fires) often require timely responses for swift decisions that depend upon high computing performance of algorithm analysis. In this paper, we develop parallel versions of a target detection algorithm based on orthogonal subspace projections. The parallel implementations are tested in two types of parallel computing architectures: a massively parallel cluster of computers called Thunderhead and available at NASA’s Goddard Space Flight Center in Maryland, and a commodity graphics processing unit (GPU) of NVidia GeForce GTX 275 type. While the cluster-based implementation reveals itself as appealing for information extraction from remote sensing data already transmitted to Earth, the GPU implementation allows us to perform near real-time anomaly detection in hyperspectral scenes, with speedups over 50x with regards to a highly optimized serial version. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system over the World Trade Center (WTC) in New York, five days after the attacks that collapsed the two main towers in the WTC complex.","Title: High-performance computing in remotely sensed hyperspectral imaging: the pixel purity index algorithm as a case study\nYear: 2006\n\nAbstract: The incorporation of last-generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral imaging is a new technique in remote sensing that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. The price paid for such a wealth of spectral information available from latest-generation sensors is the enormous amounts of data that they generate. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) models in remote sensing missions. This paper explores three HPC-based paradigms for efficient information extraction from remote sensing data using the Pixel Purity Index (PPI) algorithm (available from the popular Kodak's Research Systems ENVI software) as a case study for algorithm optimization. The three considered approaches are: 1) Commodity cluster-based parallel computing; 2) Distributed computing using heterogeneous networks of workstations; and 3) FPGAbased hardware implementations. Combined, these parts deliver an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of adapting HPC models to remote sensing problems.","Title: Semisupervised Hyperspectral Image Classification Using Soft Sparse Multinomial Logistic Regression\nYear: 2013\n\nAbstract: In this letter, we propose a new semisupervised learning (SSL) algorithm for remotely sensed hyperspectral image classification. Our main contribution is the development of a new soft sparse multinomial logistic regression model which exploits both hard and soft labels. In our terminology, these labels respectively correspond to labeled and unlabeled training samples. The proposed algorithm represents an innovative contribution with regard to conventional SSL algorithms that only assign hard labels to unlabeled samples. The effectiveness of our proposed method is evaluated via experiments with real hyperspectral images, in which comparisons with conventional semisupervised self-learning algorithms with hard labels are carried out. In such comparisons, our method exhibits state-of-the-art performance.","Title: Design and Implementation of a Parallel Heterogeneous Algorithm for Hyperspectral Image Analysis Using HeteroMPI\nYear: 2006\n\nAbstract: The development of efficient techniques for transforming the massive volume of remotely sensed hyperspectral data collected on a daily basis into scientific understanding is critical for space-based Earth science and planetary exploration. Although most available parallel processing strategies for hyperspectral image analysis assume homogeneity in the computing platform, heterogeneous networks of computers represent a promising cost-effective solution expected to play a major role in many on-going and planned remote sensing missions. To address the need for cost-effective parallel hyperspectral imaging algorithms, this paper develops an innovative heterogeneous parallel algorithm for spatial/spectral morphological analysis of hyperspectral image data. The algorithm has been developed using Heterogeneous MPI (HeteroMPI), an extension of MPI for programming high-performance computations on heterogeneous networks of computers. Experimental results are presented and discussed in the context of a realistic application, based on hyperspectral data collected by NASA's Jet Propulsion Laboratory.","Title: Spectral-Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization.\nYear: 2014\n\nAbstract: Remotely sensed hyperspectral image classification is a very challenging task. This is due to many different aspects, such as the presence of mixed pixels in the data or the limited information available a priori. This has fostered the need to develop techniques able to exploit the rich spatial and spectral information present in the scenes while, at the same time, dealing with mixed pixels and li...","Title: Use of FPGA or GPU-based architectures for remotely sensed hyperspectral image processing\nYear: 2013\n\nAbstract: Hyperspectral imaging is a growing area in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the Earth. Hyperspectral images are extremely high-dimensional, and require advanced on-board processing algorithms able to satisfy near real-time constraints in applications such as wildland fire monitoring, mapping of oil spills and chemical contamination, etc. One of the most widely used techniques for analyzing hyperspectral images is spectral unmixing, which allows for sub-pixel data characterization. This is particularly important since the available spatial resolution in hyperspectral images is typically of several meters, and therefore it is reasonable to assume that several spectrally pure substances (called endmembers in hyperspectral imaging terminology) can be found within each imaged pixel. In this paper we explore the role of hardware accelerators in hyperspectral remote sensing missions and further inter-compare two types of solutions: field programmable gate arrays (FPGAs) and graphics processing units (GPUs). A full spectral unmixing chain is implemented and tested in this work, using both types of accelerators, in the context of a real hyperspectral mapping application using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). The paper provides a thoughtful perspective on the potential and emerging challenges of applying these types of accelerators in hyperspectral remote sensing missions, indicating that the reconfigurability of FPGA systems (on the one hand) and the low cost of GPU systems (on the other) open many innovative perspectives toward fast on-board and on-the-ground processing of remotely sensed hyperspectral images.","Title: On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images\nYear: 2009\n\nAbstract: In this work, neural network-based models involved in hyperspectral image spectra separation are considered. Focus is on how to select the most highly informative samples for effectively training the neural architecture. This issue is addressed here by several new algorithms for intelligent selection of training samples: (1) a border-training algorithm (BTA) which selects training samples located in the vicinity of the hyperplanes that can optimally separate the classes; (2) a mixed-signature algorithm (MSA) which selects the most spectrally mixed pixels in the hyperspectral data as training samples; and (3) a morphological-erosion algorithm (MEA) which incorporates spatial information (via mathematical morphology concepts) to select spectrally mixed training samples located in spatially homogeneous regions. These algorithms, along with other standard techniques based on orthogonal projections and a simple Maximin-distance algorithm, are used to train a multi-layer perceptron (MLP), selected in this work as a representative neural architecture for spectral mixture analysis. Experimental results are provided using both a database of nonlinear mixed spectra with absolute ground truth and a set of real hyperspectral images, collected at different altitudes by the digital airborne imaging spectrometer (DAIS 7915) and reflective optics system imaging spectrometer (ROSIS) operating simultaneously at multiple spatial resolutions.","Title: AMEEPAR: parallel morphological algorithm for hyperspectral image classification on heterogeneous networks of workstations\nYear: 2006\n\nAbstract: Hyperspectral imaging is a new technique in remote sensing that generates hundreds of images corresponding to different wavelength channels for the same area on the surface of the Earth. Most available techniques for hyperspectral image classification focus on analyzing the data without incorporating the spatial information; i.e. the data is treated not as an image but as an unordered listing of spectral measurements where the spatial coordinates can be shuffled arbitrarily without affecting the final analysis. Despite the growing interest in the development of techniques for interpretation and classification of such high-dimensional imagery, only a few efforts devoted to the design of parallel implementations exist in the open literature. In this paper, we describe AMEEPAR, a parallel morphological algorithm that integrates the spatial and spectral information. The algorithm has been specifically optimized in this work for execution on heterogeneous networks of workstations. The parallel properties and classification accuracy of the proposed approach are evaluated using four networks of workstations distributed among different locations, and a massively parallel Beowulf cluster at NASA’s Goddard Space Flight Center.","Title: Network Performance Testing System Integrating Models for Automatic QoE Evaluation of Popular Services: YouTube and Facebook\nYear: 2015\n\nAbstract: Smartphones have significantly changed the traffic pattern usage of mobile subscribers. In addition to the classical file sharing and web browsing usage, social networking applications and access to video content are now a significant percentage of the typical user data traffic consumption. As the change in usage pattern affects the performance of mobile networks, operators need methods to estimate how well the network behaves. The metrics to use for the evaluation of the quality of experience (QoE) are only well documented for traditional applications. In this paper a novel QoE system that automatically captures and processes the metrics needed to evaluate two commonly used applications, the social networking application Facebook and the video download application YouTube, is presented. The system uses a test application developed for Android phones, which report the results to a centralized platform capable of processing and presenting the results in different formats. The system has been used to test those two services over 3G/LTE network technologies. In the case of the YouTube services, performance results show a subjective score very similar for the 3G and LTE because of the automatic adaptation of the video coding rate to the network conditions. Testing of the Facebook application has led to the proposal of a new MOS expression that takes into account the content type being posted, which provides more reasonable MOS estimations than the generic web browsing models available in the literature and commonly used to estimate Facebook QoE.","Title: Informative Change Detection by Unmixing for Hyperspectral Images\nYear: 2015\n\nAbstract: Applying spectral unmixing on a series of multitemporal hyperspectral images for change detection has the potential to reveal important subpixel-level information, such as the abundance variation of each underlying material in a given location or the change in the distribution of materials throughout the scene, with time or resulting from significant events such as a natural disaster. However, change detection by spectral unmixing for hyperspectral images has not been extensively studied up to now, and most studies have been limited to specific cases and data sets. This is caused by the scarcity of real multitemporal hyperspectral data and the inherent difficulties in applying unmixing to multitemporal hyperspectral data in a coherent way. In this letter, we investigate change detection for hyperspectral images by spectral unmixing and systematically present the advantages that can be gained by using such an approach, supported by experimental studies conducted on carefully prepared synthetic data sets and also with real data sets.","Title: Learning Discriminative Sparse Representations for Hyperspectral Image Classification\nYear: 2015\n\nAbstract: In sparse representation (SR) driven hyperspectral image classification, signal-to-reconstruction rule based classification may lack generalization performance. In order to overcome this limitation, we presents a new method for discriminative sparse representation of hyperspectral data by learning a reconstructive dictionary and a discriminative classifier in a SR model regularized with total variation (TV). The proposed method features the following components. First, we adopt a spectral unmixing by variable splitting augmented Lagrangian and TV method to guarantee the spatial homogeneity of sparse representations. Second, we embed dictionary learning in the method to enhance the representative power of sparse representations via gradient descent in a class-wise manner. Finally, we adopt a sparse multinomial logistic regression (SMLR) model and design a class-oriented optimization strategy to obtain a powerful classifier, which improves the performance of the learnt model for specific classes. The first two components are beneficial to produce discriminative sparse representations. Whereas, adopting SMLR allows for effectively modeling the discriminative information. Experimental results with both simulated and real hyperspectral data sets in a number of experimental comparisons with other related approaches demonstrate the superiority of the proposed method.","Title: Fast and Reliable Noise Estimation for Hyperspectral Subspace Identification\nYear: 2015\n\nAbstract: In this letter, we introduce an efficient algorithm to estimate the noise correlation matrix in the initial stage of the hyperspectral signal identification by minimum error (HySime) method, commonly used for signal subspace identification in remotely sensed hyperspectral images. Compared with the current implementations of this stage, the new algorithm for noise estimation relies on the reliable QR factorization, producing correct results even when operating with single-precision arithmetic. Additionally, our algorithm exhibits a lower computational cost, and it is highly parallel. The experiments on a multicore server, using two real hyperspectral scenes, expose that these theoretical advantages carry over to the practical results.","Title: Fast determination of the number of endmembers for real-time hyperspectral unmixing on GPUs\nYear: 2014\n\nAbstract: Spectral unmixing is a very important task for remotely sensed hyperspectral data exploitation. It amounts at identifying a set of spectrally pure components (called endmembers) and their associated per-pixel coverage fractions (called abundances). A challenging problem in spectral unmixing is how to determine the number of endmembers in a given scene. Several automatic techniques exist for this purpose, including the virtual dimensionality (VD) concept or the hyperspectral signal identification by minimum error (HySime). Due to the complexity and high dimensionality of hyperspectral scenes, these techniques are computationally expensive. In this paper, we develop new fast implementations of VD and HySime using commodity graphics processing units. The proposed parallel implementations are validated in terms of accuracy and computational performance, showing significant speedups with regards to optimized serial implementations. The newly developed implementations are integrated in a fully operational unmixing chain which exhibits real-time performance with regards to the time that the hyperspectral instrument takes to collect the image data.","Title: MUSIC-CSR: Hyperspectral Unmixing via Multiple Signal Classification and Collaborative Sparse Regression\nYear: 2014\n\nAbstract: Spectral unmixing aims at finding the spectrally pure constituent materials (also called endmembers) and their respective fractional abundances in each pixel of a hyperspectral image scene. In recent years, sparse unmixing has been widely used as a reliable spectral unmixing methodology. In this approach, the observed spectral vectors are expressed as linear combinations of spectral signatures assumed to be known a priori and presented in a large collection, termed spectral library or dictionary, usually acquired in laboratory. Sparse unmixing has attracted much attention as it sidesteps two common limitations of classic spectral unmixing approaches, namely, the lack of pure pixels in hyperspectral scenes and the need to estimate the number of endmembers in a given scene, which are very difficult tasks. However, the high mutual coherence of spectral libraries, jointly with their ever-growing dimensionality, strongly limits the operational applicability of sparse unmixing. In this paper, we introduce a two-step algorithm aimed at mitigating the aforementioned limitations. The algorithm exploits the usual low dimensionality of the hyperspectral data sets. The first step, which is similar to the multiple signal classification array signal processing algorithm, identifies a subset of the library elements, which contains the endmember signatures. Because this subset has cardinality much smaller than the initial number of library elements, the sparse regression we are led to is much more well conditioned than the initial one using the complete library. The second step applies collaborative sparse regression, which is a form of structured sparse regression, exploiting the fact that only a few spectral signatures in the library are active. The effectiveness of the proposed approach, termed MUSIC-CSR, is extensively validated using both simulated and real hyperspectral data sets.","Title: A Subspace-Based Multinomial Logistic Regression for Hyperspectral Image Classification\nYear: 2014\n\nAbstract: In this letter, we propose a multinomial-logistic-regression method for pixelwise hyperspectral classification. The feature vectors are formed by the energy of the spectral vectors projected on class-indexed subspaces. In this way, we model not only the linear mixing process that is often present in the hyperspectral measurement process but also the nonlinearities that are separable in the feature space defined by the aforementioned feature vectors. Our experimental results have been conducted using both simulated and real hyperspectral data sets, which are collected using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the Reflective Optics System Imaging Spectrographic (ROSIS) system. These results indicate that the proposed method provides competitive results in comparison with other state-of-the-art approaches.","Title: Collaborative Sparse Regression for Hyperspectral Unmixing\nYear: 2014\n\nAbstract: Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In this paper, we present a refinement of the sparse unmixing methodology recently introduced which exploits the usual very low number of endmembers present in real images, out of a very large library. Specifically, we adopt the collaborative (also called “multitask” or “simultaneous”) sparse regression framework that improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. Our experimental results with both synthetic and real hyperspectral data sets show clearly the advantages obtained using the new joint sparse regression strategy, compared with the pixelwise independent approach.","Title: Semi-supervised active learning for urban hyperspectral image classification\nYear: 2012\n\nAbstract: In this paper, we develop a new framework for semi-supervised learning which exploits active learning for unlabeled sample selection in hyperspectral data classification. Specifically, we use active learning to select the most informative unlabeled training samples with the ultimate goal of systematically achieving noticeable improvements in classification results with regard to those found by randomly selected training sets of the same size. Our experimental results, conducted with an urban hyperspectral scene collected by the Reflective Optics Spectrographic Imaging Instrument (ROSIS) of the Deutschen Zentrum for Luftund Raumfahrt (DLR, the German Aerospace Agency) over the city of Pavia, Italy, indicate that using active learning for unlabeled sample selection represents an effective and promising strategy in the context of urban hyperspectral data classification.","Title: Parallel implementation of a hyperspectral unmixing chain: Graphic processing units versus multi-core processors\nYear: 2012\n\nAbstract: Spectral unmixing is an important tast for remotely sensed hyperspectral imaging. Spectral unmixing algorithms can be computationally expensive. In this paper, we develop two efficient implementations of a full hyperspectral unmixing chain on two different kinds of high performance computing architectures: graphics processing units (GPUs) and multi-core processors. These platforms are inter-compared in the context of hyperspectral unmixing. Our experimental results reveal that both GPUs and multi-core processors can provide real-time unmixing performance when properly implemented in this kind of parallel platforms.","Title: HyperMix: A new tool for quantitative evaluation of end member identification and spectral unmixing techniques\nYear: 2012\n\nAbstract: In this paper, we present a new open source system for evaluating and inter-comparing new spectral unmixing applications. The proposed tool, called HyperMix, comprises several open source implementations of algorithms for end member identification and spectral unmixing. The tool also includes a database of synthetic hyperspectral images (generated using fractals to simulate natural patterns) which can be used to evaluate the precision of the algorithms for end member identification and abundance estimation which are already incorporated in the tool. The paper also presents an exhaustive inter-comparison of algorithms for end member extraction and abundance estimation using the proposed tool.","Title: Hyperspectral coded aperture (HYCA): A new technique for hyperspectral compressive sensing\nYear: 2012\n\nAbstract: Hyperspectral imaging is an active research area in remote sensing. Due to the high volume of hyperspectral image data, the exploration of compression strategies has received a lot of attention in recent years. In this paper, we introduce a new compressed sensing methodology, termed Hyperspectral coded aperture (HYCA), which exploits the high correlation existing among the components of remotely sensed hyperspectral data sets to reduce the number of measurements necessary to correctly reconstruct the original data. HYCA relies on two central properties of most hyperspectral images: the spectral vectors live on a low dimensional subspace and the spectral bands are piecewise smooth. The former property allows to represent the data vectors using a small number of coordinates, and the latter implies that each coordinate is piecewise smooth and thus compressible on local differences. The reconstruction of the data cube is obtained by minimizing a convex objective function containing a data term associated to the compressed measurements and a total variation spatial regularizer. A series of experiments with simulated and real data show the effectiveness of the newly developed HYCA, indicating that the proposed scheme has a high potential in real-world applications.","Title: Collaborative sparse unmixing of hyperspectral data\nYear: 2012\n\nAbstract: Sparse unmixing aims at estimating the constituent materials (endmembers) and their respective fractional abundances in each pixel of a hyperspectral image by assuming that the endmembers are present in a large collection of pure spectral signatures (spectral library), known a priori. In this paper, we propose a refinement of the sparse unmixing approach by taking into account the fact that all the pixels of the image share the same set of endmembers, thus lying in a lower dimensional subspace. Our idea is based on the collaborative lasso, which enforces sparsity across the pixels. The goal of this line of attack is to obtain higher accuracy of the estimated fractional abundances, at the same time with a decrease in the number of endmembers used to explain the observed data. The experimental results, obtained with both simulated and real data, confirm the potential of the proposed approach in the unmixing problem.","Title: Efficient information extraction from hyperspectral imagery using networks of workstations\nYear: 2005\n\nAbstract: The rapid development in space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral sensors such as the Airborne VisibleInfrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of efficient techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. Heterogeneous networks of workstations are a very promising cost-effective parallel computing architecture. Unlike traditional homogeneous parallel platforms, heterogeneous architectures are composed of processors running at different speeds. This heterogeneity results in distributed-memory parallel computing systems created from commodity components that can satisfy, specific computational requirements for the Earth and space sciences community. This paper explores techniques for mapping hyperspectral image analysis algorithms onto heterogeneous networks of workstations. Important aspects in algorithm design such as portability, reusability and scalability are illustrated by using homogeneous and heterogeneous parallel computing facilities at NASA's Goddard Space Flight Center and, European Center for Parallelism of Barcelona, and University of Extremadura in Spain. Hyperspectral image data from the AVIRIS data repository is used in experiments, which reveal that heterogeneous networks of workstations are a source of computational power that is both accessible and applicable to obtaining results quickly enough for practical use in information extraction applications from hyperspectral imagery.","Title: Parallel Detection of Targets in Hyperspectral Images Using Heterogeneous Networks of Workstations\nYear: 2007\n\nAbstract: Heterogeneous networks of workstations have rapidly become a cost-effective computing solution in many application areas. This paper develops several highly innovative parallel algorithms for target detection in hyperspectral imagery, considered to be a crucial goal in remote sensing-based homeland security and defense applications. In order to illustrate parallel performance, we consider four (partially and fully) heterogeneous networks of workstations distributed among different locations at University of Maryland, and also a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results indicate that heterogeneous networks can be used as a viable low-cost alternative to homogeneous parallel systems in many on-going and planned remote sensing missions","Title: Spatial Preprocessing for Endmember Extraction\nYear: 2009\n\nAbstract: Endmember extraction is the process of selecting a collection of pure signature spectra of the materials present in a remotely sensed hyperspectral scene. These pure signatures are then used to decompose the scene into abundance fractions by means of a spectral unmixing algorithm. Most techniques avail- able in the endmember extraction literature rely on exploiting the spectral properties of the data alone. As a result, the search for endmembers in a scene is conducted by treating the data as a collection of spectral measurements with no spatial arrangement. In this paper, we propose a novel strategy to incorporate spatial information into the traditional spectral-based endmember search process. Specifically, we propose to estimate, for each pixel vector, a scalar spatially derived factor that relates to the spectral simi- larity of pixels lying within a certain spatial neighborhood. This scalar value is then used to weigh the importance of the spectral information associated to each pixel in terms of its spatial context. Two key aspects of the proposed methodology are given as follows: 1) No modification of existing image spectral-based endmember extraction methods is necessary in order to apply the proposed approach. 2) The proposed preprocessing method enhances the search for image spectral endmembers in spatially homogeneous areas. Our experimental results, which were obtained using both synthetic and real hyperspectral data sets, indicate that the spec- tral endmembers obtained after spatial preprocessing can be used to accurately model the original hyperspectral scene using a linear mixture model. The proposed approach is suitable for jointly combining spectral and spatial information when searching for image-derived endmembers in highly representative hyperspectral image data sets.","Title: An overview on hyperspectral unmixing: Geometrical, statistical, and sparse regression based approaches\nYear: 2011\n\nAbstract: Hyperspectral instruments acquire electromagnetic energy scattered within their ground instantaneous field view in hundreds of spectral channels with high spectral resolution. Very often, however, owing to low spatial resolution of the scanner or to the presence of intimate mixtures (mixing of the materials at a very small scale) in the scene, the spectral vectors (collection of signals acquired at different spectral bands from a given pixel) acquired by the hyperspectral scanners are actually mixtures of the spectral signatures of the materials present in the scene. Given a set of mixed spectral vectors, spectral mixture analysis (or spectral unmixing) aims at estimating the number of reference materials, also called endmembers, their spectral signatures, and their fractional abundances. Spectral unmixing is, thus, a source separation problem. This paper presents an overview of the principal research directions in hyperspectral unmixing. The paper is organized into six main topics: i) mixing models, ii) signal subspace identification, iii) geometrical-based spectral unmixing, iv) statistical-based spectral unmixing, v) sparse regression based unmixing, and vi) spatial-contextual information. For each topic, we summarize what is the mathematical problem involved and give relevant pointers to state-of-the-art algorithms to address these problems.","Title: Noise-robust spatial preprocessing prior to endmember extraction from hyperspectral data\nYear: 2011\n\nAbstract: This paper develops a noise-robust spatial preprocessing module which can be used prior to spectral unmixing of remotely sensed hyperspectral images. The method first derives a spatial homogeneity index which is relatively insensitive to the noise present in the original hyperspectral data. Then, it fuses this index with a spectral-based classification, obtaining a set of pure regions which are used to guide the unmixing process. An experimental comparison of the proposed method with other spatial-spectral unmixing approaches is conducted using both synthetic and real hyperspectral data collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Our experiments indicate that spectral unmixing can benefit from the proposed pre-processing approach, in particular, when the noise level present in the original hypespectral scene is relatively high.","Title: Hyperspectral unmixingwith sparse group lasso\nYear: 2011\n\nAbstract: Sparse unmixing has been recently introduced as a mechanism to characterize mixed pixels in remotely sensed hyper-spectral images. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In available spectral libraries, it is observed that the spectral signatures appear organized in groups (e.g. different alterations of a single mineral in the U.S. Geological Survey spectral library). In this paper, we explore the potential of the sparse group lasso technique in solving hyperspectral unmixing problems. Our introspection in this work is that, when the spectral signatures appear in groups, this technique has the potential to yield better results than the standard sparse regression approach. Experimental results with both synthetic and real hyperspectral data are given to investigate this issue.","Title: FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis\nYear: 2012\n\nAbstract: Hyperspectral remote sensing attempts to identify features in the surface of the Earth using sensors that generally provide large amounts of data. The data are usually collected by a satellite or an airborne instrument and sent to a ground station that processes it. The main bottleneck of this approach is the (often reduced) bandwidth connection between the satellite and the station, which drastically limits the information that can be sent and processed in real time. A possible way to overcome this problem is to include onboard computing resources able to preprocess the data, reducing its size by orders of magnitude. Reconfigurable field-programmable gate arrays (FPGAs) are a promising platform that allows hardware/software codesign and the potential to provide powerful onboard computing capability and flexibility at the same time. Since FPGAs can implement custom hardware solutions, they can reach very high performance levels. Moreover, using run-time reconfiguration, the functionality of the FPGA can be updated at run time as many times as needed to perform different computations. Hence, the FPGA can be reused for several applications reducing the number of computing resources needed. One of the most popular and widely used techniques for analyzing hyperspectral data is linear spectral unmixing, which relies on the identification of pure spectral signatures via a so-called endmember extraction algorithm. In this paper, we present the first FPGA design for N-FINDR, a widely used endmember extraction algorithm in the literature. Our system includes a direct memory access module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-4 XC4VFX60 FPGA (a model that is similar to radiation-hardened FPGAs certified for space operation) and tested using real hyperspectral data collected by NASA's Earth Observing-1 Hyperion (a satellite instrument) and the Airborne Visible Infra-- ed Imaging Spectrometer over the Cuprite mining district in Nevada and the Jasper Ridge Biological Preserve in California. Experimental results demonstrate that our hardware version of the N-FINDR algorithm can significantly outperform an equivalent software version and is able to provide accurate results in near real time, which makes our reconfigurable system appealing for onboard hyperspectral data processing.","Title: Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing\nYear: 2012\n\nAbstract: Spectral unmixing aims at estimating the fractional abundances of pure spectral signatures (also called endmembers) in each mixed pixel collected by a remote sensing hyperspectral imaging instrument. In recent work, the linear spectral unmixing problem has been approached in semisupervised fashion as a sparse regression one, under the assumption that the observed image signatures can be expressed as linear combinations of pure spectra, known a priori and available in a library. It happens, however, that sparse unmixing focuses on analyzing the hyperspectral data without incorporating spatial information. In this paper, we include the total variation (TV) regularization to the classical sparse regression formulation, thus exploiting the spatial-contextual information present in the hyperspectral images and developing a new algorithm called sparse unmixing via variable splitting augmented Lagrangian and TV. Our experimental results, conducted with both simulated and real hyperspectral data sets, indicate the potential of including spatial information (through the TV term) on sparse unmixing formulations for improved characterization of mixed pixels in hyperspectral imagery.","Title: Unmixing sparse hyperspectral mixtures\nYear: 2009\n\nAbstract: Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or atoms ), is a hard combinatorial problem which, as in other areas, has been increasingly addressed. This paper studies the efficiency of the sparse regression techniques in the spectral unmixing problem by conducting a comparison between four different approaches: Moore-Penrose Pseudoinverse, Orthogonal Matching Pursuit (OMP ) (1), Iterative Spectral Mixture Analysis ( ISMA ) (2) and 1 2 l l - sparse regression techniques, which are of widespread use in compressed sensing. We conclude that the 1 2 l l - sparse regression techniques, implemented here by Iterative Shrinkage/Thresholding ( TwIST ) algorithm (3), yield the state-of- the-art in the hyperspectral unmixing area. 2 l l - norm minimization, convex optimization","Title: Analysis of different strategies for incorporating spatial information in the design of endmember extraction algorithms from hyperspectral data\nYear: 2009\n\nAbstract: Over the last decade, several algorithms have been developed for automatic or semi-automatic extraction of spectral endmembers from hyperspectral image data. In this paper, we present a thorough analytical comparison of endmember extraction methods which include spatial information in the search of spectral endmembers versus a few algorithms which are exclusively based on spectral information. Our quantitative and comparative assessment of algorithm accuracy and computational performance, conducted using both synthetic and real hyperspectral data, provides interesting findings about the potential benefits that can be obtained after incorporating spatial information into the design of endmember extraction algorithms.","Title: Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning\nYear: 2010\n\nAbstract: This paper presents a new semisupervised segmentation algorithm, suited to high-dimensional data, of which remotely sensed hyperspectral image data sets are an example. The algorithm implements two main steps: 1) semisupervised learning of the posterior class distributions followed by 2) segmentation, which infers an image of class labels from a posterior distribution built on the learned class distributions and on a Markov random field. The posterior class distributions are modeled using multinomial logistic regression, where the regressors are learned using both labeled and, through a graph-based technique, unlabeled samples. Such unlabeled samples are actively selected based on the entropy of the corresponding class label. The prior on the image of labels is a multilevel logistic model, which enforces segmentation results in which neighboring labels belong to the same class. The maximum a posteriori segmentation is computed by the α-expansion min-cut-based integer optimization algorithm. Our experimental results, conducted using synthetic and real hyperspectral image data sets collected by the Airborne Visible/Infrared Imaging Spectrometer system of the National Aeronautics and Space Administration Jet Propulsion Laboratory over the regions of Indian Pines, IN, and Salinas Valley, CA, reveal that the proposed approach can provide classification accuracies that are similar or higher than those achieved by other supervised methods for the considered scenes. Our results also indicate that the use of a spatial prior can greatly improve the final results with respect to a case in which only the learned class densities are considered, confirming the importance of jointly considering spatial and spectral information in hyperspectral image segmentation.","Title: Sparse Unmixing of Hyperspectral Data\nYear: 2011\n\nAbstract: Linear spectral unmixing is a popular tool in remotely sensed hyperspectral data interpretation. It aims at estimating the fractional abundances of pure spectral signatures (also called as endmembers) in each mixed pixel collected by an imaging spectrometer. In many situations, the identification of the end-member signatures in the original data set may be challenging due to insufficient spatial resolution, mixtures happening at different scales, and unavailability of completely pure spectral signatures in the scene. However, the unmixing problem can also be approached in semisupervised fashion, i.e., by assuming that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In practice, this is a combinatorial problem which calls for efficient linear sparse regression (SR) techniques based on sparsity-inducing regularizers, since the number of endmembers participating in a mixed pixel is usually very small compared with the (ever-growing) dimensionality (and availability) of spectral libraries. Linear SR is an area of very active research, with strong links to compressed sensing, basis pursuit (BP), BP denoising, and matching pursuit. In this paper, we study the linear spectral unmixing problem under the light of recent theoretical results published in those referred to areas. Furthermore, we provide a comparison of several available and new linear SR algorithms, with the ultimate goal of analyzing their potential in solving the spectral unmixing problem by resorting to available spectral libraries. Our experimental results, conducted using both simulated and real hyperspectral data sets collected by the NASA Jet Propulsion Laboratory's Airborne Visible I- - nfrared Imaging Spectrometer and spectral libraries publicly available from the U.S. Geological Survey, indicate the potential of SR techniques in the task of accurately characterizing the mixed pixels using the library spectra. This opens new perspectives for spectral unmixing, since the abundance estimation process no longer depends on the availability of pure spectral signatures in the input data nor on the capacity of a certain endmember extraction algorithm to identify such pure signatures.","Title: Improved signal unmixing of vegetation using sparse group selection\nYear: 2013\n\nAbstract: Recently, signal unmixing was proposed in remote sensing with the goal to infer physical parameters of materials of interest, such as vegetation, on the ground. The typical approach uses large collections of pure spectra, called spectral libraries, in which many possible states of the vegetation are modeled by simulated or on-site acquired spectra. Spectra randomly selected from these libraries are used as input to dedicated unmixing methods, such as Multiple Endmember Spectral Mixture Analysis (MESMA). The spectra leading to the lowest reconstruction error are considered to be representative for the materials present in the pixel, such that the physical parameters of the ground vegetation can be inferred. However, the large number of spectra in the library imposes limits to the performance of this combinatorial approach, mainly related to running time constraints. In this paper, we propose the inclusion of a pre-processing step in the processing chain, based on the group lasso, which has the goal of selecting groups of signatures likely to be present in the mixtures. In this sense, the Group Sparse Unmixing via variable Splitting and Augmented Lagrangian (GSUnSAL) algorithm is used. The signatures contained in the groups selected by GSUnSAL are then used as input for MESMA. Our experiments using a real dataset acquired by an ASD spectrometer in a South-African orchard show that the proposed approach introduces important improvements in the signal unmixing solutions.","Title: Parallel Hyperspectral Image and Signal Processing [Applications Corner]\nYear: 2011\n\nAbstract: Remotely sensed hyperspectral imaging instruments are capable of collecting hundreds of images corresponding to different wave length channels for the same area on the surface of the Earth. For instance, NASA is continuously gathering high dimensional image data with instruments such as the Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS). This advanced sensor for Earth observation records the visible and near-infrared spectrum of the reflected light using more than 200 spectral bands, thus producing a stack of images in which each pixel (vector) is represented by a spectral signal that uniquely characterizes the underlying objects. The resulting data volume typically comprises several gigabytes per flight. In this article, we describe the state of the art in the devel opment and application of image and signal processing techniques for advanced information extraction from hyperspectral data. The article also describes new trends for efficient pro cessing of such data using parallel and distributed processing techniques in the context of time-critical applications.","Title: Spatial-spectral endmember extraction from hyperspectral imagery using multi-band morphology and volume optimization\nYear: 2009\n\nAbstract: We develop a new approach for characterization of mixed pixels in remotely sensed hyperspectral images. The proposed method first performs joint spatial-spectral pixel characterization via extended morphological transformations, and then automatically extracts pure spectral signatures (called end-members) using volume optimization and convex geometry concepts. The proposed method outperforms other widely used approaches in the analysis of a real hyperspectral scene collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite mining district in Nevada. Ground-truth information available from U.S. Geological Survey is used to substantiate our findings.","Title: Semi-supervised hyperspectral image classification based on a Markov random field and sparse multinomial logistic regression\nYear: 2009\n\nAbstract: This paper introduces a new semi-supervised classification and segmentation approach tailored to hyperspectral images. The poste- rior distributions of the classes are modeled by the multinomial lo- gistic regression. The contextual information inherent to the spatial configuration of the image pixels is modeled by a Multi-Level Lo- gistic (MLL) Markov-Gibbs random field. The multinomial logistic regressors, assumed to be random vectors with independent Lapla- cian components, are learned using the recently introduced LOR- SAL algorithm. The maximum a posteriori (MAP) segmentation is computed via the -Expansion algorithm, a powerful graph cut based approach to integer optimization. The effectiveness of the pro- posed methodology is illustrated by classifying simulated and real data sets. Comparisons with state-of-art methods are also included.","Title: High performance computing for hyperspectral image analysis: Perspective and state-of-the-art\nYear: 2009\n\nAbstract: The main purpose of this paper is to describe available (HPC)-based implementations of remotely sensed hyperspectral image processing algorithms on multi-computer clusters, heterogeneous networks of computers, and specialized hardware architectures such as field programmable gate arrays (FPGAs) and graphic processing units (GPUs). Combined, the revision of existing techniques conducted in this paper, along with the description of performance results for a parallel hyperspectral processing chain on different architectures, delivers an excellent snapshot of the state-of-the-art in the area of HPC-based hyperspectral image processing and a thoughtful perspective of the potential and emerging challenges of applying HPC paradigms to hyperspectral imaging problems.","Title: Improving the scalability of parallel algorithms for hyperspectral image analysis using adaptive message compression\nYear: 2009\n\nAbstract: In previous work, we have reported that the scalability of parallel processing algorithms for hyperspectral image analysis is affected by the amount of data to exchanged through the communication network of the parallel system. However, large messages are common in hyperspectral imaging applications since processing algorithms are often pixel-based, and each pixel vector to be exchanged through the communication network is made up of hundreds of spectral values. Thus, decreasing the amount of data to be exchanged could improve the scalability and parallel performance. In this paper, we propose a new framework based on intelligent utilization of data compression techniques for improving the scalability of a standard spectral unmixin-based parallel hyperspectral processing chain on heterogeneous networks of workstations. Our experimental results indicate that adaptive, wavelet-based lossy compression can lead to improvements in the scalability of the parallel algorithms without significantly sacrificing algorithm analysis accuracy.","Title: Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over an Agricultural Area.\nYear: 2009\n\nAbstract: In this paper we compare two different methodologies for Fractional Vegetation Cover (FVC) retrieval from Compact High Resolution Imaging Spectrometer (CHRIS) data onboard the European Space Agency (ESA) Project for On-Board Autonomy (PROBA) platform. The first methodology is based on empirical approaches using Vegetation Indices (VIs), in particular the Normalized Difference Vegetation Index (NDVI) and the Variable Atmospherically Resistant Index (VARI). The second methodology is based on the Spectral Mixture Analysis (SMA) technique, in which a Linear Spectral Unmixing model has been considered in order to retrieve the abundance of the different constituent materials within pixel elements, called Endmembers (EMs). These EMs were extracted from the image using three different methods: i) manual extraction using a land cover map, ii) Pixel Purity Index (PPI) and iii) Automated Morphological Endmember Extraction (AMEE). The different methodologies for FVC retrieval were applied to one PROBA/CHRIS image acquired over an agricultural area in Spain, and they were calibrated and tested against in situ measurements of FVC estimated with hemispherical photographs. The results obtained from VIs show that VARI correlates better with FVC than NDVI does, with standard errors of estimation of less than 8% in the case of VARI and less than 13% in the case of NDVI when calibrated using the in situ measurements. The results obtained from the SMA-LSU technique show Root Mean Square Errors (RMSE) below 12% when EMs are extracted from the AMEE method and around 9% when extracted from the PPI method. A RMSE value below 9% was obtained for manual extraction of EMs using a land cover use map.","Title: Sparse Unmixing-Based Content Retrieval of Hyperspectral Images on Graphics Processing Units.\nYear: 2015\n\nAbstract: Content-based image retrieval (CBIR) systems have gained significant importance in the remotely sensed hyperspectral imaging community due to the increasing availability of hyperspectral data collected from different instruments. Spectral unmixing has been a popular technique for not only interpreting hyperspectral images but also retrieving them precisely from databases based on information conte...","Title: Real-Time Implementation of the Sparse Multinomial Logistic Regression for Hyperspectral Image Classification on GPUs\nYear: 2015\n\nAbstract: In this letter, a real-time implementation of the logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm for sparse multinomial logistic regression is presented on commodity graphics processing units (GPUs) using Nvidia's compute unified device architecture. The proposed parallel method properly exploits the GPU architecture at the low level, including its shared memory, and takes full advantage of the computational power of GPUs to achieve real-time classification performance of hyperspectral images for the first time in the hyperspectral imaging literature. Our experimental results reveal remarkable acceleration factors and real-time performance, while retaining exactly the same classification accuracy with regard to the serial and multicore versions of the classifier.","Title: Multiple Morphological Component Analysis Based Decomposition for Remote Sensing Image Classification.\nYear: 2016\n\nAbstract: Remote sensing images exhibit significant contrast and intensity regions and edges, which makes them highly suitable for using different texture features to properly represent and classify the objects that they contain. In this paper, we present a new technique based on multiple morphological component analysis (MMCA) that exploits multiple textural features for decomposition of remote sensing ima...","Title: Real-time implementation of remotely sensed hyperspectral image unmixing on GPUs.\nYear: 2015\n\nAbstract: Spectral unmixing is one of the most popular techniques to analyze remotely sensed hyperspectral images. It generally comprises three stages: (1) reduction of the dimensionality of the original image to a proper subspace; (2) automatic identification of pure spectral signatures (called endmembers); and (3) estimation of the fractional abundance of each endmember in each pixel of the scene. The spectral unmixing process allows sub-pixel analysis of hyperspectral images, but can be computationally expensive due to the high dimensionality of the data. In this paper, we develop the first real-time implementation of a full spectral unmixing chain in commodity graphics processing units (GPUs). These hardware accelerators offer a source of computational power that is very appealing in hyperspectral remote sensing applications, mainly due to their low cost and adaptivity to on-board processing scenarios. The implementation has been developed using the compute device unified architecture (CUDA) and tested on an NVidia™ GTX 580 GPU, achieving real-time unmixing performance in two different case studies: (1) characterization of thermal hot spots in hyperspectral images collected by NASA’s Airborne Visible Infra-red Imaging Spectrometer (AVIRIS) during the terrorist attack to the World Trade Center complex in New York City, and (2) sub-pixel mapping of minerals in AVIRIS hyperspectral data collected over the Cuprite mining district in Nevada.","Title: Fusion of hyperspectral and lidar data using generalized composite kernels: A case study in Extremadura, Spain\nYear: 2015\n\nAbstract: The light detection and ranging (LiDAR) data provides very valuable information about the height of the surveyed area which can be used as a source of complementary information for the classification of hyperspectral data, in particular when it is difficult to separate complex classes. In this work, we suggest to exploit the generalized composite kernel strategy for fusion and classification of hyperspectral and LiDAR data. Our experimental results, conducted using a hyperspectral image and a LiDAR derived intensity image collected over a rural area in Extremadura, Spain, indicate that the proposed framework for the fusion of hyperspactral and LiDAR data provides significant classification results.","Title: Fast Principal Component Analysis For Hyperspectral Imaging Based On Cloud Computing\nYear: 2015\n\nAbstract: Principal component analysis (PCA) is an important method for feature extraction of hyperspectral remote sensing image. With the development of hyperspectral sensors, the magnitude of hyperspectral data grows quickly, and it is a challenging task to efficiently reduce the data dimension and compress massive data volumes in hyperspectral imaging. In this paper, a distributed parallel optimization of PCA algorithm (PCA_DP) is presented on cloud computing architecture. The realization of the proposed method using Apache Hadoop and MapReduce model is described and evaluated. The experiments conducted on real hyperspectral images of different sizes, demonstrate significant acceleration factor of PCA_DP. It is efficient for massive hyperspectral data processing.","Title: Class-Oriented Spectral Partitioning For Hyperspectral Image Classification\nYear: 2015\n\nAbstract: This paper presents a new approach for class-oriented spectral partitioning for hyperspectral image classification. First, without empirical information, we automatically search the spectral bands that correspond to a specific class by using different band selection approaches. Then, the obtained class oriented spectral partitions are used respectively as the input of a group of classifiers, the results of which are combined together to generate a final one by a multiple classifier system. Our experimental results, conducted with the well-known Indians Pines test site hyperspectral image collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) in NW Indiana, suggest that our presented spectral partitioning method leads to competitive results when compared with other state-of-the-art approaches.","Title: GPU Implementation of Spatial-Spectral Preprocessing for Hyperspectral Unmixing.\nYear: 2016\n\nAbstract: Spectral unmixing pursues the identification of spectrally pure constituents, called endmembers, and their corresponding abundances in each pixel of a hyperspectral image. Most unmixing techniques have focused on the exploitation of spectral information alone. Recently, some techniques have been developed to take advantage of the complementary information provided by the spatial correlation of the...","Title: Big Data for Remote Sensing: Challenges and Opportunities.\nYear: 2016\n\nAbstract: Every day a large number of Earth observation (EO) spaceborne and airborne sensors from many different countries provide a massive amount of remotely sensed data. Those data are used for different applications, such as natural hazard monitoring, global climate change, urban planning, etc. The applications are data driven and mostly interdisciplinary. Based on this it can truly be stated that we ar...","Title: Active Learning Based Autoencoder For Hyperspectral Imagery Classification\nYear: 2016\n\nAbstract: In this paper, we joint autoencoder with active learning for hyperspectral imagery classification. Specifically, we learn the classifier via autoencoder, where the most informative samples are acitvely selected through the interaction between the autoencoder and active learning. Experimental results, conducted using both the Kennedy Space Center and the Indian Pines hyperspectral images, show that driven by active learning, the performance of autoencoder can be greatly improved.","Title: On The Optimization Of Memory Access To Increase The Performance Of Spatial Preprocessing Techniques On Graphics Processing Units\nYear: 2016\n\nAbstract: The use of spatial information prior to spectral unmixing of hyperspectral data is a very active research line in recent years. There are many approximations that consider spatial characteristics of the data in order to guide the endmember identification/extraction procedure. In particular, the spatial preprocessing (SPP) algorithm can be used prior to most existing spectral-based endmember identification techniques, thus promoting the selection of endmembers in spatially representative parts of the scene. The main concern regarding SPP and this kind of preprocessing techniques is that they are computational expensive, adding a significant burden to the spectral unmixing process which should be alleviated. In this paper we revisit and enhance a previously developed implementation of SPP for graphical processing units (GPUs) in order to increase its performance by exhaustively using the level one (L1)-cache level of the GPU. The performance of the proposed implementation is evaluated using an NVidiaT-MGeForce GTX 580. Our experimental validation reveals that real-time processing performance can be obtained for real hyperspectral data sets collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).","Title: Fast Spatial-Spectral Preprocessing For Endmember Extraction And Spectral Unmixing Using Graphic Processing Units\nYear: 2016\n\nAbstract: Linear spectral unmixing consists on the identification of spectrally pure constituents, called endmembers and their corresponding proportions or abundances using a linear model. Traditionally, most of the attention has been focussed on the exploitation of spectral information when identifying a set of endmembers and, only recently, some techniques try to take advantage of complementary information such as the one provided by the spatial correlation of the pixels in the image. Computational complexity represents a major problem in most of these spatial-spectral based techniques, as hyperspectral images provide very rich information in both the spatial and the spectral domain. In this paper we provide a computationally efficient implementation of a spatial-spectral processing (SSPP) algorithm which can be used prior to endmember identification and spectral unmixing. Specifically we present an implementation optimized for commodity graphics processing units (GPUs), which is evaluated using two different GPU architectures from NVidia: GeForce GTX580 and GeForce GT740. Our experimental validation reveals that significant speedups can be achieved when processing hyperspectral images of different sizes.","Title: Oil Spill Detection via Multitemporal Optical Remote Sensing Images: A Change Detection Perspective\nYear: 2017\n\nAbstract: Oil spill monitoring in optical remote sensing (RS) images is a challenging task due to the complexity of target discrimination in an oil spill scenario. Differently from traditional oil spill detection methods that are mainly carried out in a monotemporal image, in this letter, a novel solution is given in a multitemporal domain by investigating potential capability of change detection (CD) techn...","Title: Using Linear Spectral Unmixing for Subpixel Mapping of Hyperspectral Imagery: A Quantitative Assessment\nYear: 2017\n\nAbstract: Subpixel mapping techniques have been widely utilized to determine the spatial distribution of the different land-cover classes in mixed pixels at a subpixel scale by converting low-resolution fractional abundance maps (estimated by a linear mixture model) into a finer classification map. Over the past decades, many subpixel mapping algorithms have been proposed to tackle this problem. It has been...","Title: A New Sparse Subspace Clustering Algorithm for Hyperspectral Remote Sensing Imagery.\nYear: 2017\n\nAbstract: Robust techniques such as sparse subspace clustering (SSC) have been recently developed for hyperspectral images (HSIs) based on the assumption that pixels belonging to the same land-cover class approximately lie in the same subspace. In order to account for the spatial information contained in HSIs, SSC models incorporating spatial information have become very popular. However, such models are of...","Title: Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification.\nYear: 2016\n\nAbstract: Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the classification of remotely sensed images, discarding the other convolutional layer features which may also be helpful for classification purposes. In this paper, we propose a new adaptive deep pyramid matching (ADPM) model that takes advantage of the features from all of the convolutional layers for remote sensing image classification. To this end, the optimal fusing weights for different convolutional layers are learned from the data itself. In remotely sensed scenes, the objects of interest exhibit different scales in distinct scenes, and even a single scene may contain objects with different sizes. To address this issue, we select the CNN with spatial pyramid pooling (SPP-net) as the basic deep network, and further construct a multi-scale ADPM model to learn complementary information from multi-scale images. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.","Title: Spatial Technology and Social Media in Remote Sensing: A Survey\nYear: 2017\n\nAbstract: The rapid development of social media data and the associated growth in volume, velocity, and variety has fostered the idea of using these data to guide traditional remote sensing image retrieval and information extraction tasks. Although important progress has been made in recent years in harvesting spatial and temporal data from social media, the exploitation of these data for decision making still needs further investigation, particularly in the context of its integration with remote sensing and geographic information systems. In this paper, we first discuss the relation between localization techniques and spatial technologies, pointing out their similarities and differences. Then, we provide a discussion on location analysis of social media data, and the fusion of multiple data sources, with specific attention to the integration of social media content (including localization) with remote sensing-based spatial technologies. Next, we provide specific examples addressing the use of social media data to perform information extraction from large remote sensing data repositories. Although significant possibilities for the integration of localization and spatial technologies can be seen in the examples provided, our survey suggests that the convergence of remote sensing and social media data will continue to deeply transform these technologies.","Title: Performance-Power Evaluation of an OpenCL Implementation of the Simplex Growing Algorithm for Hyperspectral Unmixing.\nYear: 2017\n\nAbstract: Over the last few years, several new strategies for spectral unmixing of remotely sensed hyperspectral data have been proposed. Many of them have been developed to solve the most time-consuming and relevant step: endmember extraction. However, unmixing algorithms can be computationally very expensive in terms of processing time and energy consumption, a fact that compromises their use in applicati...","Title: Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation.\nYear: 2017\n\nAbstract: Spectral unmixing is an important technique in hyperspectral image applications. Recently, sparse regression has been widely used in hyperspectral unmixing, but its performance is limited by the high mutual coherence of spectral libraries. To address this issue, a new sparse unmixing algorithm, called double reweighted sparse unmixing and total variation (TV), is proposed in this letter. Specifica...","Title: Discriminative Low-Rank Gabor Filtering for Spectral-Spatial Hyperspectral Image Classification.\nYear: 2017\n\nAbstract: Spectral-spatial classification of remotely sensed hyperspectral images has attracted a lot of attention in recent years. Although Gabor filtering has been used for feature extraction from hyperspectral images, its capacity to extract relevant information from both the spectral and the spatial domains of the image has not been fully explored yet. In this paper, we present a new discriminative low-...","Title: A Novel Preunmixing Framework for Efficient Detection of Linear Mixtures in Hyperspectral Images.\nYear: 2017\n\nAbstract: In order to provide reliable information about the instantaneous field of view considered in hyperspectral images through spectral unmixing, understanding the kind of mixture that occurs over each pixel plays a crucial role. In this paper, in order to detect nonlinear mixtures, a method for fast identification of linear mixtures is introduced. The proposed method does not need statistical informat...","Title: Multisensor Coupled Spectral Unmixing for Time-Series Analysis.\nYear: 2017\n\nAbstract: We present a new framework, called multisensor coupled spectral unmixing (MuCSUn), that solves unmixing problems involving a set of multisensor time-series spectral images in order to understand dynamic changes of the surface at a subpixel scale. The proposed methodology couples multiple unmixing problems based on regularization on graphs between the time-series data to obtain robust and stable un...","Title: Spatial technology and social media in remote sensing: challenges and opportunities [point of view].\nYear: 2017\n\nAbstract: The convergence of remote sensing technologies with social media, coupled with advances in other location-aware technologies such as WiFi and smartphones, is moving us on a fast track to a situation in which we can readily know, at any time, where everybody and everything are located on the surface of the Earth, and to exploit the power of social media in different contexts. Remote sensing technol...","Title: Integration of LiDAR and Hyperspectral Data for Land-cover Classification: A Case Study.\nYear: 2017\n\nAbstract: In this paper, an approach is proposed to fuse LiDAR and hyperspectral data, which considers both spectral and spatial information in a single framework. Here, an extended self-dual attribute profile (ESDAP) is investigated to extract spatial information from a hyperspectral data set. To extract spectral information, a few well-known classifiers have been used such as support vector machines (SVMs), random forests (RFs), and artificial neural networks (ANNs). The proposed method accurately classify the relatively volumetric data set in a few CPU processing time in a real ill-posed situation where there is no balance between the number of training samples and the number of features. The classification part of the proposed approach is fully-automatic.","Title: Decision fusion based on extended multi-attribute profiles for hyperspectral image classification\nYear: 2013\n\nAbstract: In this paper, a new classification strategy for remotely sensed hyperspectral image data is presented and discussed. The proposed approach adopts a decision fusion strategy which combines a well-established classifier, such as the support vector machine (SVM), with the information provided by extended multi-attribute morphological profiles (EMAPs). EMAPs provide a multilevel characterization of an image created by using a sequence of morphological attribute filters to model different kinds of the structural information contained in such image. In our proposed decision fusion strategy, the SVM classifier is first applied to each of the components (which are associated to different attributes) provided by the EMAP. Then, we apply a decision fusion rule to obtain the final classification result. In our experimental results, conducted with a reference urban hyperspectral data sets widely used in the literature, we show that the proposed strategy provides state of the art results (especially when limited training sets are used.)","Title: A New Extended Linear Mixing Model To Address Spectral Variability\nYear: 2014\n\nAbstract: Spectral variability is a phenomenon due, to a grand extend, to variations in the illumination and atmospheric conditions within a hyperspectral image, causing the spectral signature of a material to vary within a image. Data spectral fluctuation due to spectral variability compromises the linear mixing model (LMM) sum-to-one constraint, and is an important source of error in hyperspectral image analysis. Recently, spectral variability has raised more attention and some techniques have been proposed to address this issue, i.e. spectral bundles. Here, we propose the definition of an extended LMM (ELMM) to model spectral variability and we show that the use of spectral bundles models the ELMM implicitly. We also show that the constrained least squares (CLS) is an explicit modelling of the ELMM when the spectral variability is due to scaling effects. We give experimental validation that spectral bundles (and sparsity) and CLS are complementary techniques addressing spectral variability. We finally discuss on future research avenues to fully exploit the proposed ELMM.","Title: Integrating Multiple Nonlinear Estimators Into Hyperspectral Unmixing\nYear: 2014\n\nAbstract: Linear spectral unmixing has been widely used for hyperspectral data interpretation. However, there is a need for nonlinear unmixing methods that can model more complex geometries without the need to resort to prior knowledge about the objects in the scene. In this paper, we present a novel strategy for nonlinear spectral unmixing which combines polytope decomposition (POD) with artificial neural network (ANN)-based learning. Even if no ground-truth information is available, the ANN can still efficiently estimate the order of the nonlinearity involved in the problem and enhance the capacity of the POD method to deliver unmixing performance for a wider range of nonlinearities. The proposed method has been evaluated using both simulated and real scenes, providing promising results.","Title: On the use of collaborative sparse regression in hyperspectral unmixing chains\nYear: 2014\n\nAbstract: Hyperspectral unmixing is a complex process in which several steps are consecutively executed to derive the desired results: the image endmembers and their corresponding fractional abundance maps. Each of these unmixing stages benefits nowadays from a plethora of algorithms, continuously developed and improved. In this paper, we analyze three of the general unmixing steps: band selection (data dimensionality reduction), endmember extraction and fractional abundance inference (inversion) from a multi-measurement vector problem point of view. We show that these particular steps can be expressed as a convex optimization problem in which the concept of data collaborativity is exploited and one single algorithm can be efficiently used to solve them. Our experimental results obtained in an urban dataset acquired over Berlin, Germany, show the potential of this approach in remote sensing applications.","Title: Validation Of Spectral Unmixing Methods Using Photometry And Topography Information\nYear: 2013\n\nAbstract: In this work the performance of spectral unmixing procedures applied on hyperspectral images of granular mixtures are compared. For that purpose we consider a laboratory image and synthetic images, the latter being created using a original algorithmic process while borrowing some aspects of the real data. The nonlinear effects of light multiple scattering within the mixture can be partially compensated by transfounation of the image using the Hapke model under different levels of injected infounation regarding topography and photometry of the scene. The validation of the different methods and deconvolution processes is established by comparing the results with a \"ground truth\" through the mean Spectral Angle for the extracted endmembers and the mean Abundances Root Mean Square Error for the estimated abundances. Interpretation of the results indicates that is safer to extract the endmembers from the original version of image unless topography and most importantly photometry are precisely known. On the other hand better distribution maps are obtained in general from the transfouned version of the image.","Title: GPU implementation of a constrained hyperspectral coded aperture algorithm for compressive sensing\nYear: 2015\n\nAbstract: In this paper, a parallel implementation of a previously constrained hyperspectral coded aperture (CHYCA) algorithm for compressive sensing on graphics processing units (GPUs) is proposed. CHYCA method combines the ideas of spectral unmixing and compressive sensing exploiting the high spatial correlation that can be observed in the data and the generally low number of endmembers needed in order to explain the data. The performance of CHYCA relies which does not depend on the tuning of a regularization parameter, which is a time consuming task offering good performance compared with a previously hyperspectral coded aperture (HYCA) method. The proposed implementation exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs using shared memory and coalesced accesses to memory. Experimental results using simulated data reveals speedups up to 56 times, with regards to serial implementation.","Title: FPGA implementation of a maximum volume algorithm for endmember extraction from hyperspectral imagery\nYear: 2015\n\nAbstract: Endmember extraction is a very important technique in hyperspectral unmixing. Due to the fact that more applications require real or near real-time processing capabilities, high performance computing based on field-programmable gate array (FPGA) for endmember extraction has received considerable interest in recent years. In this paper, we propose a real-time implementation of maximum volume algorithms (RT-MaxV) using a Kintex-7 FPGA. The proposed RT-MaxV does not need dimensionality reduction like other real-time fast simplex growing algorithms (RT-FSGA). Our experimental results, obtained using the AVIRIS Cuprite data set, indicate that the proposed algorithm has better accuracy and performance than RT-FSGA, and its FPGA implementation achieves real-time processing capability in the considered problem.","Title: Binary partition tree-based local spectral unmixing\nYear: 2014\n\nAbstract: The linear mixing model (LMM) is a widely used methodology for the spectral unmixing (SU) of hyperspectral data. In this model, hyperspectral data is formed as a linear combination of spectral signatures corresponding to macroscopically pure materials (endmembers), weighted by their fractional abundances. Some of the drawbacks of the LMM are the presence of multiple mixtures and the spectral variability of the endmembers due to illumination and atmospheric effects. These issues appear as variations of the spectral conditions of the image along its spatial domain. However, these effects are not so severe locally and could be at least mitigated by working in smaller regions of the image. The proposed local SU works over a partition of the image, performing the spectral unmixing locally in each region of the partition. In this work, we first introduce the general local SU methodology, then we propose an implementation of the local SU based on a binary partition tree representation of the hyperspectral image and finally we give an experimental validation of the approach using real data.","Title: Hyperspectral change detection by sparse unmixing with dictionary pruning\nYear: 2015\n\nAbstract: Hyperspectral change detection is used in many applications ranging from environmental monitoring to city planning and military surveillance. Change detection by unmixing has the potential of not only providing the locations of the changes, but also the nature of the change, and sub-pixel level information. Change detection by sparse unmixing using spectral libraries, with respect to regular spectral unmixing, has the added benefits of circumventing the process of endmember extraction and providing specific mission-based information. However, sparse unmixing is generally a severely ill-conditioned and time-consuming problem. Recently, an approach that aims to identify a subset of signatures of the spectral library that contribute most to the observed data has been proposed in order to improve the conditioning of the problem, hence decreasing computation time and enhancing performance. In this work, sparse unmixing with dictionary pruning is explored for change detection in multi-temporal hyperspectral images. Experimental results validate the performance of the proposed approach.","Title: Spectral unmixing-based post-processing for hyperspectral image classification\nYear: 2013\n\nAbstract: Spectral unmixing and classification have been widely used in the recent literature to analyze remotely sensed hyperspectral data. However, possible connections between classification and spectral unmixing concepts have been rarely investigated. In this work, we propose a simple spectral unmixing-based post-processing method to improve the classification accuracies provided by supervised and semi-supervised techniques for hyperspectral image classification. The proposed approach exploits the information retrieved with spectral unmixing in order to complement the results obtained after the classification stage (which can be either supervised or semi-supervised), thus bridging the gap between unmixing and classification and exploiting both techniques in synergistic fashion for hyperspectral data interpretation. The proposed method is experimentally validated using a real hyperspectral data set collected by the Reflective Optics Spectrographic Imaging System (ROSIS). Our experimental results indicate that the proposed unmixing-based preprocessing can improve the classification results for some of the classes, particularly the most highly mixed ones, in supervised and semi-supervised scenarios using limited training samples.","Title: Plant production system monitoring via multiple signal classification and sparse regression\nYear: 2013\n\nAbstract: In this paper, we propose a dictionary pruning technique for signal unmixing, which is a recent unmixing concept aimed at extracting the signal contribution of an individual class of endmembers to each observed pixel in a hyperspectral scene. We focus on a specific application in the area of plant production system monitoring. The pruning technique allows to infer the physical state of vegetation in a region of interest on the ground, by selecting appropriate spectra from a very large spectral library designed to model a wide variety of such possible states. On the one hand, Multiple Endmember Spectral Mixture Analysis (MESMA), a combinatorial technique, was intensively used to solve the signal unmixing problem. Sparse unmixing, a fast approach dealing with spectral libraries, was recently proposed as a reliable technique for spectral unmixing. The performances of both techniques are influenced by the large number of spectra collected in the libraries. The aim of this paper is two-fold: 1) to present a subspace-based method for library pruning, such that only spectra likely to be present in the image are used in unmixing, which is expected to improve MESMA results, and 2) to introduce sparse unmixing as a reliable signal unmixing technique, given the natural ability of these methods to deal with applications in which a low number of atoms is used to accurately describe every observed vector. The results obtained in a simulated environment show the potential of the proposed scheme to boost the signal unmixing performance both in terms of accuracy and running time.","Title: Potential And Limitations Of Band Selection And Library Pruning In Sparse Hyperspectral Unmixing\nYear: 2015\n\nAbstract: Sparse regression using spectral libraries is nowadays a widely used technique for hyperspectral data unmixing. Assuming that the potential endmembers are collected in a large database of spectra, sparse unmixing finds the fractional abundances of a reduced set of constituent materials by solving convex optimization problems which target, at the same time, low data reconstruction errors. The large amount of data, jointly with other limitations related to the internal characteristics of the large spectral libraries (such as spectra similarity), affect the performance of sparse unmixing algorithms in terms of accuracy and running time. Recently, an efficient method based on a multi-measurement vector (MMV) approach was proposed to select, from a large library, suitable spectra for unmixing. Many research efforts have also been devoted to data dimensionality reduction techniques, of which band selection is very popular. In this work, we investigate the effects on the unmixing performance when the two techniques are applied simultaneously. Our experiments show that important improvements can be achieved, depending on several factors, such as: data noise, number of endmembers present in the dataset, number of spectra retained from the library, the order of operations, accuracy of data subspace estimation.","Title: Spatial Technology and Social Media [Scanningthe Issue].\nYear: 2017\n\nAbstract: The significant development of social media over the past decade has been complemented by the rise of spatial technologies to provide new mapping mechanisms that allow users engage with online information services and also with each other in an unprecedented way. Users of these technologies now provide a comprehensive geosocial overlay of the physical environment of the planet.","Title: Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis.\nYear: 2017\n\nAbstract: The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. ...","Title: Structured Sparse Coding-Based Hyperspectral Imagery Denoising With Intracluster Filtering.\nYear: 2017\n\nAbstract: Sparse coding can exploit the intrinsic sparsity of hyperspectral images (HSIs) by representing it as a group of sparse codes. This strategy has been shown to be effective for HSI denoising. However, how to effectively exploit the structural information within the sparse codes (structured sparsity) has not been widely studied. In this paper, we propose a new method for HSI denoising, which uses st...","Title: Multicore Real-Time Implementation of a Full Hyperspectral Unmixing Chain.\nYear: 2018\n\nAbstract: Solving the mixture problem in remotely sensed hyperspectral images remains a challenging task. In particular, solutions are needed in order to obtain a response for applications with real-time constraints. In the last decade, several efforts have been developed, many of them using graphics processing units (GPUs) and focused on the exploitation of spectral information alone. However, a few spectr...","Title: Convex Formulation for Multiband Image Classification With Superpixel-Based Spatial Regularization.\nYear: 2018\n\nAbstract: Superpixels are a powerful device to characterize the spatial-contextual information in remotely sensed hyperspectral image (HSI) interpretation. However, the exploitation of superpixels in classification problems is not straightforward, often leading to unbearable NP-hard discrete integer optimization problems. In this paper, we attack this hurdle by leveraging on a convex relaxation of the origi...","Title: Parallel real-time virtual dimensionality estimation for hyperspectral images.\nYear: 2018\n\nAbstract: One of the most important tasks in hyperspectral imaging is the estimation of the number of endmembers in a scene, where the endmembers are the most spectrally pure components. The high dimensionality of hyperspectral data makes this calculation computationally expensive. In this paper, we present several new real-time implementations of the well-known Harsanyi–Farrand–Chang method for virtual dimensionality estimation. The proposed solutions exploit multi-core processors and graphic processing units for achieving real-time performance of this algorithm, together with better performance than other works in the literature. Our experimental results are obtained using both synthetic and real images. The obtained processing times show that the proposed implementations outperform other hardware-based solutions.","Title: Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion.\nYear: 2018\n\nAbstract: Probabilistic topic models have recently shown a great potential in the remote sensing image fusion field, which is particularly helpful in land-cover categorization tasks. This letter first studies the application of probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation to remote sensing synthetic aperture radar (SAR) and multispectral imaging (MSI) unsupervised land-cover...","Title: Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification.\nYear: 2018\n\nAbstract: Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification.","Title: A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution.\nYear: 2018\n\nAbstract: Super-resolution (SR) brings an excellent opportunity to improve a wide range of different remote sensing applications. SR techniques are concerned about increasing the image resolution while providing finer spatial details than those captured by the original acquisition instrument. Therefore, SR techniques are particularly useful to cope with the increasing demand remote sensing imaging applicati...","Title: Hyperspectral Unmixing Based on Dual-Depth Sparse Probabilistic Latent Semantic Analysis.\nYear: 2018\n\nAbstract: This paper presents a novel approach for spectral unmixing of remotely sensed hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the semantics pervading the latent topic space when identifying spectral signatures and estimating fractional abundances from hyperspectral images. Despite the contrasted potential of topic models to uncover image semantics, they ha...","Title: Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach.\nYear: 2018\n\nAbstract: Hyperspectral imaging is a widely used technique in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the earth. In the last two decades, several methods (unsupervised, supervised, and semisupervised) have been proposed to deal with the hyperspectral image classification problem. Supervised techniques ...","Title: Spatial Discontinuity-Weighted Sparse Unmixing of Hyperspectral Images.\nYear: 2018\n\nAbstract: Spectral unmixing is an important technique for remotely sensed hyperspectral image interpretation, of which the goal is to decompose the image into a set of pure spectral components (endmembers) and their abundance fractions in each pixel of the scene. Sparse-representation-based approaches have been widely studied for remotely sensed hyperspectral unmixing. A recent trend is to incorporate the s...","Title: A New Spectral-Spatial Sub-Pixel Mapping Model for Remotely Sensed Hyperspectral Imagery.\nYear: 2018\n\nAbstract: In this paper, a new joint spectral-spatial subpixel mapping model is proposed for hyperspectral remotely sensed imagery. Conventional approaches generally use an intermediate step based on the derivation of fractional abundance maps obtained after a spectral unmixing process, and thus the rich spectral information contained in the original hyperspectral data set may not be utilized fully. In this...","Title: Hyperspectral Unmixing Using Sparsity-Constrained Deep Nonnegative Matrix Factorization With Total Variation.\nYear: 2018\n\nAbstract: Hyperspectral unmixing is an important processing step for many hyperspectral applications, mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation of the abundance of each endmember in each pixel of the image. In recent years, nonnegative matrix factorization (NMF) has been highly attractive for this purpose due to the nonnegativity constraint that is often impo...","Title: Estimating Nonlinearities in p-Linear Hyperspectral Mixtures.\nYear: 2018\n\nAbstract: Accurately estimating the elements in Earth observations is crucial when assessing specific features such as air quality index, water pollution, or urbanization process behavior. Moreover, physical-chemical composition can be retrieved from hyperspectral images when proper spectral unmixing architectures are employed. Specifically, when linear and nonlinear combinations of endmembers (pure spectra...","Title: Fast dimensionality reduction and classification of hyperspectral images with extreme learning machines.\nYear: 2018\n\nAbstract: Recent advances in remote sensing techniques allow for the collection of hyperspectral images with enhanced spatial and spectral resolution. In many applications, these images need to be processed and interpreted in real-time, since analysis results need to be obtained almost instantaneously. However, the large amount of data that these images comprise introduces significant processing challenges. This also complicates the analysis performed by traditional machine learning algorithms. To address this issue, dimensionality reduction techniques aim at reducing the complexity of data while retaining the relevant information for the analysis, removing noise and redundant information. In this paper, we present a new real-time method for dimensionality reduction and classification of hyperspectral images. The newly proposed method exploits artificial neural networks, which are used to develop a fast compressor based on the extreme learning machine. The obtained experimental results indicate that the proposed method has the ability to compress and classify high-dimensional images fast enough for practical use in real-time applications.","Title: A suite of parallel algorithms for efficient band selection from hyperspectral images.\nYear: 2018\n\nAbstract: The analysis of hyperspectral images is usually very heavy from the computational point-of-view, due to their high dimensionality. In order to avoid this problem, band selection (BS)\n has been widely used to reduce the dimensionality before the analysis. The aim is to extract a subset of the original bands of the hyperspectral image, preserving most of the information contained in the original data. The BS technique can be performed by prioritizing the bands on the basis of a score, assigned by specific criteria; in this case, BS turns out in the so-called band prioritization (BP). This paper focuses on BP algorithms based on the following parameters: signal-to-noise ratio, kurtosis, entropy, information divergence, variance and linearly constrained minimum variance. In particular, an optimized C serial version has been developed for each algorithm from which two parallel versions have been derived using OpenMP and NVIDIA’s compute unified device architecture. The former is designed for a multi-core CPU, while the latter is designed for a many-core graphics processing unit. For each version of these algorithms, several tests have been performed on a large database containing both synthetic and real hyperspectral images. In this way, scientists can integrate the proposed suite of efficient BP algorithms into existing frameworks, choosing the most suitable technique for their specific applications.","Title: A real-time unsupervised background extraction-based target detection method for hyperspectral imagery.\nYear: 2018\n\nAbstract: Target detection is an important technique in hyperspectral image analysis. The high dimensionality of hyperspectral data provides the possibility of deeply mining the information hiding in spectra, and many targets that cannot be visualized by inspection can be detected. But this also brings some problems such as unknown background interferences at the same time. In this way, extracting and taking advantage of the background information in the region of interest becomes a task of great significance. In this paper, we present an unsupervised background extraction-based target detection method, which is called UBETD for short. The proposed UBETD takes advantage of the method of endmember extraction in hyperspectral unmixing, another important technique that can extract representative material signatures from the images. These endmembers represent most of the image information, so they can be reasonably seen as the combination of targets and background signatures. Since the background information is known, algorithm like target-constrained interference-minimized filter could then be introduced to detect the targets while inhibiting the interferences. To meet the rapidly rising demand of real-time processing capabilities, the proposed algorithm is further simplified in computation and implemented on a FPGA board. Experiments with synthetic and real hyperspectral images have been conducted comparing with constrained energy minimization, adaptive coherence/cosine estimator and adaptive matched filter to evaluate the detection and computational performance of our proposed method. The results indicate that UBETD and its hardware implementation RT-UBETD can achieve better performance and are particularly prominent in inhibiting interferences in the background. On the other hand, the hardware implementation of RT-UBETD can complete the target detection processing in far less time than the data acquisition time of hyperspectral sensor like HyMap, which confirms strict real-time processing capability of the proposed system.","Title: A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images.\nYear: 2018\n\nAbstract: Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA's efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data.","Title: Remote Sensing Scene Classification Using Multilayer Stacked Covariance Pooling.\nYear: 2018\n\nAbstract: This paper proposes a new method, called multilayer stacked covariance pooling (MSCP), for remote sensing scene classification. The innovative contribution of the proposed method is that it is able to naturally combine multilayer feature maps, obtained by pretrained convolutional neural network (CNN) models. Specifically, the proposed MSCP-based classification framework consists of the following t...","Title: Sparse Unmixing With Dictionary Pruning for Hyperspectral Change Detection.\nYear: 2017\n\nAbstract: The localization of changes that occur between the images in a multitemporal series is crucial for many applications, ranging from environmental monitoring to military surveillance. In contrast to traditional change detection methods, unmixing-based change detection has been shown to have the important added benefit of providing subpixel-level information on the nature of the changes, instead of o...","Title: Superpixel-Based Active Learning and Online Feature Importance Learning for Hyperspectral Image Analysis.\nYear: 2017\n\nAbstract: The rapid development of multichannel optical imaging sensors has led to increased utilization of hyperspectral data for remote sensing. For classification of hyperspectral data, an informative training set is necessary for ensuring robust performance. However, in remote sensing and other image analysis applications, labeled samples are often difficult, expensive, and time-consuming to obtain. Thi...","Title: A New Cloud Computing Architecture for the Classification of Remote Sensing Data.\nYear: 2017\n\nAbstract: This paper proposes a new distributed architecture for supervised classification of large volumes of earth observation data on a cloud computing environment. The architecture supports distributed execution, network communication, and fault tolerance in a transparent way to the user. The architecture is composed of three abstraction layers, which support the definition and implementation of applica...","Title: Class-Oriented Spectral Partitioning for Remotely Sensed Hyperspectral Image Classification.\nYear: 2017\n\nAbstract: Remotely sensed hyperspectral images exhibit very high dimensionality in the spectral domain. As opposed to band selection techniques, which extract a subset of the original spectral bands in the image, spectral partitioning (SP) techniques reassign the original bands into subgroups that are then processed separately. From a classification perspective, this strategy has the advantage that all the ...","Title: Parallel Implementation of a Full Hyperspectral Unmixing Chain Using OpenCL.\nYear: 2017\n\nAbstract: Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. Due to the fact that the spatial resolution of the sensor may not be able to separate different spectrally pure components (endmembers), spectral unmixing faces important challenges in order to characterize mixed pixels. As a result, several hyperspectral unmixing chains have been proposed to find the spect...","Title: Fast Spatial Preprocessing for Spectral Unmixing of Hyperspectral Data on Graphics Processing Units.\nYear: 2016\n\nAbstract: Spectral unmixing is an important technique for hyperspectral data exploitation. It amounts at finding a set of pure spectral signatures (endmembers) of the most representative materials in the scene, and estimating their abundance fractions. The integration of spatial information prior to spectral unmixing of hyperspectral data has attracted much attention in recent years. Several approaches have...","Title: A Discontinuity Preserving Relaxation Scheme for Spectral-Spatial Hyperspectral Image Classification.\nYear: 2016\n\nAbstract: In remote sensing image processing, relaxation is defined as a method that uses the local relationship among neighboring pixels to correct spectral or spatial distortions. In recent years, relaxation methods have shown great success in classification of remotely sensed data. Relaxation, as a preprocessing step, can reduce noise and improve the class separability in the spectral domain. On the othe...","Title: One-Class Classification of Remote Sensing Images Using Kernel Sparse Representation.\nYear: 2016\n\nAbstract: Sparse representations have been widely studied in remote sensing image analysis in recent years. In this paper, we develop a novel method for one-class classification (OCC) using a kernel sparse representation model for remotely sensed imagery. Training samples taken from the target class alone are used to build a learning dictionary for the sparse representation model, which is then optimized to...","Title: Automatic Change Detection in High-Resolution Remote Sensing Images by Using a Multiple Classifier System and Spectral-Spatial Features.\nYear: 2016\n\nAbstract: Change detection (CD) is an active research topic in remote sensing applications including urban studies, disaster assessment, and deforestation monitoring. In this paper, we propose an automatic method for CD in high-resolution remote sensing images that uses a novel strategy for the selection of training samples and an ensemble of multiple classifiers. As for the selection of training samples, o...","Title: A Technique for Subpixel Analysis of Dynamic Mangrove Ecosystems With Time-Series Hyperspectral Image Data.\nYear: 2018\n\nAbstract: Changes in mangrove forests are mostly caused by natural disasters, anthropogenic forces, and uncontrolled population growth. These phenomena often lead to a strong competition between existing mangrove species for their survival. Due to its enhanced spectral resolution, remotely sensed hyperspectral image data can play an important role in the task of mapping and monitoring changes in mangrove fo...","Title: Impervious Surface Extraction From Multispectral Images via Morphological Attribute Profiles Based on Spectral Analysis.\nYear: 2018\n\nAbstract: Impervious surfaces exhibit unique spatial characteristics in urban environments. The estimation of impervious surfaces is critical for the analysis of these environments. In this paper, we propose a new technique based on morphological attribute profiles for mapping impervious surfaces under a spectral mixture analysis model. A main feature of our newly developed method is that it can model diffe...","Title: Remote Sensing Image Fusion Using Hierarchical Multimodal Probabilistic Latent Semantic Analysis.\nYear: 2018\n\nAbstract: The generative semantic nature of probabilistic topic models has recently shown encouraging results within the remote sensing image fusion field when conducting land cover categorization. However, standard topic models have not yet been adapted to the inherent complexity of remotely sensed data, which eventually may limit their resulting performance. In this scenario, this paper presents a new top...","Title: Feature Extraction With Multiscale Covariance Maps for Hyperspectral Image Classification.\nYear: 2019\n\nAbstract: The classification of hyperspectral images (HSIs) using convolutional neural networks (CNNs) has recently drawn significant attention. However, it is important to address the potential overfitting problems that CNN-based methods suffer when dealing with HSIs. Unlike common natural images, HSIs are essentially three-order tensors which contain two spatial dimensions and one spectral dimension. As a...","Title: Spatial-Spectral Hyperspectral Image Classification Using Random Multiscale Representation.\nYear: 2016\n\nAbstract: This paper presents a novel spatial-spectral classification method for remotely sensed hyperspectral images. First of all, a multiscale representation technique based on random projection, referred as random multiscale representation (RMSR), is proposed to extract the spatial features from the given scene. The idea behind RMSR is to properly model the spatial characteristics comprised by each pixe...","Title: FPGA Implementation of an Algorithm for Automatically Detecting Targets in Remotely Sensed Hyperspectral Images.\nYear: 2016\n\nAbstract: Timely detection of targets continues to be a relevant challenge for hyperspectral remote sensing capability. The automatic target-generation process using an orthogonal projection operator (ATGP-OSP) has been widely used for this purpose. Hyperspectral target-detection applications require timely responses for swift decisions, which depend upon (near) real-time performance of algorithm analysis. ...","Title: Harmonic Mixture Modeling for Efficient Nonlinear Hyperspectral Unmixing.\nYear: 2016\n\nAbstract: Higher order nonlinear material mixtures provide a good model to explain the effects of physical-chemical phenomena on hyperspectral remote sensing measurements. Therefore, inverting nonlinear effects starting from the measured spectral values is a very challenging yet fundamental task to provide a thorough and reliable characterization of the materials in a scene. In this paper, this task is achi...","Title: Approximate Computing of Remotely Sensed Data: SVM Hyperspectral Image Classification as a Case Study.\nYear: 2016\n\nAbstract: Onboard processing systems are becoming very important in remote sensing data processing. However, a main problem with specialized hardware architectures used for onboard processing is their high power consumption, which limits their exploitation in earth observation missions. In this paper, a novel strategy for approximate computing is proposed for reducing energy consumption in remotely sensed o...","Title: A New Algorithm for Bilinear Spectral Unmixing of Hyperspectral Images Using Particle Swarm Optimization.\nYear: 2016\n\nAbstract: Spectral unmixing is an important technique for exploiting hyperspectral data. The presence of nonlinear mixing effects poses an important problem when attempting to provide accurate estimates of the abundance fractions of pure spectral components (endmembers) in a scene. This problem complicates the development of algorithms that can address all types of nonlinear mixtures in the scene. In this p...","Title: A New Genetic Method for Subpixel Mapping Using Hyperspectral Images.\nYear: 2016\n\nAbstract: Subpixel mapping techniques aim to obtain the spatial location and distribution of subpixels by transforming the information coming from a set of input abundance maps into a classification result with higher spatial resolution. However, traditional subpixel mapping algorithms generally ignore the possible errors that are due to abundance estimation inaccuracies by spectral unmixing techniques. In ...","Title: Parallel and Distributed Dimensionality Reduction of Hyperspectral Data on Cloud Computing Architectures.\nYear: 2016\n\nAbstract: Cloud computing offers the possibility to store and process massive amounts of remotely sensed hyperspectral data in a distributed way. Dimensionality reduction is an important task in hyperspectral imaging, as hyperspectral data often contains redundancy that can be removed prior to analysis of the data in repositories. In this regard, the development of dimensionality reduction techniques in clo...","Title: Cloud implementation of the K-means algorithm for hyperspectral image analysis.\nYear: 2017\n\nAbstract: Remotely sensed hyperspectral imaging offers the possibility to collect hundreds of images, at different wavelength channels, for the same area on the surface of the Earth. Hyperspectral images are characterized by their large volume and dimensionality, which makes their processing and storage difficult. As a result, several techniques have been developed in previous years to perform hyperspectral image analysis on high-performance computing architectures. However, the application of cloud computing techniques has not been as widespread. There are many potential advantages in exploiting cloud computing architectures for distributed hyperspectral image analysis. In this paper, we present a cloud implementation (developed using Apache Spark) of the popular K-means algorithm for unsupervised hyperspectral image clustering. The experimental results suggest that cloud architectures allow for the efficient distributed processing of large hyperspectral image data sets.","Title: Robust Matrix Discriminative Analysis for Feature Extraction From Hyperspectral Images\nYear: 2017\n\nAbstract: Linear discriminative analysis (LDA) is an effective feature extraction method for hyperspectral image (HSI) classification. Most of the existing LDA-related methods are based on spectral features, ignoring spatial information. Recently, a matrix discriminative analysis (MDA) model has been proposed to incorporate the spatial information into the LDA. However, due to sensor interferers, calibratio...","Title: FPGA implementation of a maximum simplex volume algorithm for endmember extraction from remotely sensed hyperspectral images\nYear: 2019\n\nAbstract: Spectral unmixing is a very important technique for remotely sensed hyperspectral unmixing. Since more hyperspectral applications now require real or near real-time processing capabilities, fast spectral unmixing using field-programmable gate arrays (FPGAs) has received considerable interest in recent years. FPGAs can provide onboard, high computing performance at low power consumption. Another important characteristic of FPGA-based systems is reconfigurability, which makes them more flexible to process different kind of scenes. Pure signature (endmember) extraction is a fundamental step in spectral unmixing, which has been tackled using the maximum volume principle by several algorithms, most notably N-FINDR and simplex growing algorithm (SGA). These algorithms find out the simplex with maximum volume as a mechanism to extract endmembers. However, a previous dimensionality reduction step is generally required, which introduces information loss and additional computational burden. To address these issues, in this work we introduce a new volume calculation formula and further develop a new real-time implementation of a maximum simplex volume algorithm (called RT-MSVA). The proposed RT-MSVA does not need dimensionality reduction, so all spectral bands can be used without losing any information to ensure robust endmember extraction accuracy. Experiments with synthetic and real hyperspectral images have been conducted to evaluate the accuracy and computational performance of our proposed method. Our experimental results indicate that proposed FPGA-based implementation significantly outperforms the corresponding software version and achieves real-time processing performance in the considered problem. It also exhibits better endmember extraction accuracy and comparable performance to other available techniques, such as a real-time implementation of a simplex growing algorithm (RT-FSGA).","Title: GPU Parallel Implementation of Spatially Adaptive Hyperspectral Image Classification.\nYear: 2018\n\nAbstract: Image classification is a very important tool for remotely sensed hyperspectral image processing. Techniques able to exploit the rich spectral information contained in the data, as well as its spatial-contextual information, have shown success in recent years. Due to the high dimensionality of hyperspectral data, spectral-spatial classification techniques are quite demanding from a computational v...","Title: Capsule Networks for Hyperspectral Image Classification\nYear: 2019\n\nAbstract: Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral–spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral–spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton’s capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral–spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral–spatial capsules, which are able to learn HSI spectral–spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted using five well-known HSI data sets and several state-of-the-art classification methods, reveal that our HSI classification approach based on spectral–spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time.","Title: Pansharpening via Detail Injection Based Convolutional Neural Networks.\nYear: 2019\n\nAbstract: Pansharpening aims to fuse a multispectral (MS) image with an associated panchromatic (PAN) image, producing a composite image with the spectral resolution of the former and the spatial resolution of the latter. Traditional pansharpening methods can be ascribed to a unified detail injection context, which views the injected MS details as the integration of PAN details and bandwise injection gains....","Title: Low–High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification\nYear: 2019\n\nAbstract: Convolutional neural networks have emerged as an excellent tool for remotely sensed hyperspectral image (HSI) classification. Nonetheless, the high computational complexity and energy requirements of these models typically limit their application in on-board remote sensing scenarios. In this context, low-power consumption architectures are promising platforms that may provide acceptable on-board computing capabilities to achieve satisfactory classification results with reduced energy demand. For instance, the new NVIDIA Jetson Tegra TX2 device is an efficient solution for on-board processing applications using deep-learning (DL) approaches. So far, very few efforts have been devoted to exploiting this or other similar computing platforms in on-board remote sensing procedures. This letter explores the use of low-power consumption architectures and DL algorithms for HSI classification. The conducted experimental study reveals that the NVIDIA Jetson Tegra TX2 device offers a good choice in terms of performance, cost, and energy consumption for on-board HSI classification tasks.","Title: DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing\nYear: 2019\n\nAbstract: Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep autoencoder network (DAEN). Our newly developed DAEN consists of two parts. The first part of the n...","Title: An Efficient and Scalable Framework for Processing Remotely Sensed Big Data in Cloud Computing Environments\nYear: 2019\n\nAbstract: The large amount of data produced by satellites and airborne remote sensing instruments has posed important challenges to efficient and scalable processing of remotely sensed data in the context of various applications. In this paper, we propose a new big data framework for processing massive amounts of remote sensing images on cloud computing platforms. In addition to taking advantage of the para...","Title: Sentinel-2 and Sentinel-3 Intersensor Vegetation Estimation via Constrained Topic Modeling\nYear: 2019\n\nAbstract: This letter presents a novel intersensor vegetation estimation framework, which aims at combining Sentinel-2 (S2) spatial resolution with Sentinel-3 (S3) spectral characteristics in order to generate fused vegetation maps. On the one hand, the multispectral instrument (MSI), carried by S2, provides high spatial resolution images. On the other hand, the Ocean and Land Color Instrument (OLCI), one o...","Title: Intersensor Remote Sensing Image Registration Using Multispectral Semantic Embeddings\nYear: 2019\n\nAbstract: This letter presents a novel intersensor registration framework specially designed to register Sentinel-3 (S3) operational data using the Sentinel-2 (S2) instrument as a reference. The substantially higher resolution of the Multispectral Instrument (MSI), on-board S2, with respect to the Ocean and Land Color Instrument (OLCI), carried by S3, makes the former sensor a suitable spatial reference to ...","Title: Subpixel Component Analysis for Hyperspectral Image Classification\nYear: 2019\n\nAbstract: Land-cover classification with hyperspectral imagery has been an active topic in the remote sensing community. It aims at relating a unique class label to each pixel in the scene, so that it can be well defined by a given land cover type. In this paper, we explore the intrinsic characteristics of hyperspectral imagery from a subpixel-level perspective and propose a new subpixel component analysis (SCA) approach for feature extraction and land-cover classification. The core idea of SCA is that we extract a subpixel attribute component feature from the abundance maps. Compared with the abundance maps, the extracted subpixel feature image shows higher signal-to-noise level and clearer spatial distribution details. In order to deal with spectral variability, as well as obtain representative image endmember signatures and their corresponding abundance maps, we adopt a regional clustering-based spatial preprocessing (RCSPP) strategy for endmember identification, and a partial unmixing model based on mixture tuned matched filtering (MTMF) for abundance estimation. Furthermore, to highlight the spatial distribution details as well as eliminate the noise disturbance in the derived abundance maps, we perform sparse image decomposition on the obtained abundance maps, thus achieving a new subpixel feature representation for classification. Our experimental results reveal that the proposed SCA approach can obtain feature representation with explicit physical meaning, clear spatial distribution details, and better noise robustness, leading to state-of-the-art classification results.","Title: Scale-Free Convolutional Neural Network for Remote Sensing Scene Classification\nYear: 2019\n\nAbstract: Fine-tuning of pretrained convolutional neural networks (CNNs) has been proven to be an effective strategy for remote sensing image scene classification, particularly when a limited number of labeled data sets are available for training purposes. However, such a fine-tuning process often needs that the input images are resized into a fixed size to generate input vectors of the size required by ful...","Title: Hyperspectral Image Classification Using Random Occlusion Data Augmentation\nYear: 2019\n\nAbstract: Convolutional neural networks (CNNs) have become a powerful tool for remotely sensed hyperspectral image (HSI) classification due to their great generalization ability and high accuracy. However, owing to the huge amount of parameters that need to be learned and to the complex nature of HSI data itself, these approaches must deal with the important problem of overfitting, which can lead to inadequate generalization and loss of accuracy. In order to mitigate this problem, in this letter, we adopt random occlusion, a recently developed data augmentation (DA) method for training CNNs, in which the pixels of different rectangular spatial regions in the HSI are randomly occluded, generating training images with various levels of occlusion and reducing the risk of overfitting. Our results with two well-known HSIs reveal that the proposed method helps to achieve better classification accuracy with low computational cost.","Title: Skip-Connected Covariance Network for Remote Sensing Scene Classification.\nYear: 2020\n\nAbstract: This paper proposes a novel end-to-end learning model, called skip-connected covariance (SCCov) network, for remote sensing scene classification (RSSC). The innovative contribution of this paper is to embed two novel modules into the traditional convolutional neural network (CNN) model, i.e., skip connections and covariance pooling. The advantages of newly developed SCCov are twofold. First, by means of the skip connections, the multi-resolution feature maps produced by the CNN are combined together, which provides important benefits to address the presence of large-scale variance in RSSC data sets. Second, by using covariance pooling, we can fully exploit the second-order information contained in such multi-resolution feature maps. This allows the CNN to achieve more representative feature learning when dealing with RSSC problems. Experimental results, conducted using three large-scale benchmark data sets, demonstrate that our newly proposed SCCov network exhibits very competitive or superior classification performance when compared with the current state-of-the-art RSSC techniques, using a much lower amount of parameters. Specifically, our SCCov only needs 10% of the parameters used by its counterparts.","Title: SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers\nYear: 2022\n\nAbstract: Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Due to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image classification. However, CNNs fail to mine and represent the sequence attributes of spectral signatures well due to the limitations of their inherent network backbone. To solve this issue, we rethink HS image classification from a sequential perspective with transformers and propose a novel backbone network called SpectralFormer. Beyond bandwise representations in classic transformers, SpectralFormer is capable of learning spectrally local sequence information from neighboring bands of HS images, yielding groupwise spectral embeddings. More significantly, to reduce the possibility of losing valuable information in the layerwise propagation process, we devise a cross-layer skip connection to convey memory-like components from shallow to deep layers by adaptively learning to fuse \"soft\" residuals across layers. It is worth noting that the proposed SpectralFormer is a highly flexible backbone network, which can be applicable to both pixelwise and patchwise inputs. We evaluate the classification performance of the proposed SpectralFormer on three HS datasets by conducting extensive experiments, showing the superiority over classic transformers and achieving a significant improvement in comparison with state-of-the-art backbone networks. The codes of this work will be available at https://github.com/danfenghong/IEEE_TGRS_SpectralFormer for the sake of reproducibility.","Title: Distributed Deep Learning for Remote Sensing Data Interpretation\nYear: 2021\n\nAbstract: As a newly emerging technology, deep learning (DL) is a very promising field in big data applications. Remote sensing often involves huge data volumes obtained daily by numerous in-orbit satellites. This makes it a perfect target area for data-driven applications. Nowadays, technological advances in terms of software and hardware have a noticeable impact on Earth observation applications, more spe...","Title: An Overview of the Contributions of Jose Manuel Bioucas-Dias to Remote Sensing Image Processing\nYear: 2021\n\nAbstract: José Manuel Bioucas-Dias was an outstanding expert in many different IEEE-related areas, including inverse problems in imaging, signal and image processing, pattern recognition, optimization, and remote sensing. He authored or coauthored more than 250 publications, including more than 100 journal papers (66 of which published in IEEE journals) and over 200 peer-reviewed international conference pa...","Title: Hyperspectral Classification via Global-Local Hierarchical Weighting Fusion Network\nYear: 2022\n\nAbstract: The fusion of spectral–spatial features based on deep learning has become the focus of research in hyperspectral image (HSI) classification. However, previous deep frameworks based on spectral–spatial fusion usually performed feature aggregation only at the branch ends. Furthermore, only first-order statistical features are considered in the fusion process, which is not conducive to improving the discrimination of spectral–spatial features. This article proposes a global–local hierarchical weighted fusion end-to-end classification architecture. The architecture includes two subnetworks for spectral classification and spatial classification. For the spectral subnetwork, two band-grouping strategies are designed, and bidirectional long short-term memory is used to capture spectral context information from global to local perspectives. For the spatial subnetwork, a pooling strategy based on local attention is combined to construct a global–local pooling fusion module to enhance the discriminability of spatial features learned by a convolutional neural network. For the fusion stage, a hierarchical weighting fusion mechanism is developed to obtain the nonlinear relationship between both spectral and spatial features. The experimental results on four real HSI datasets and a GF-5 satellite dataset demonstrate that the method proposed is more competitive in terms of accuracy and generalization.","Title: Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review\nYear: 2022\n\nAbstract: Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions, including constrained NMF, structured NMF, and generalized NMF. Furthermore, several experiments are conducted to illustrate the effectiveness of associated algorithms. Finally, we conclude this article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing.","Title: Hyperspectral Unmixing Based on Spectral and Sparse Deep Convolutional Neural Networks\nYear: 2021\n\nAbstract: Hyperspectral unmixing refers to the process of obtaining endmembers and abundance vectors through linear or nonlinear models. The traditional linear unmixing model assumes that each mixed pixel can be represented by a linear combination of endmembers. Considering real-world situations, a sparse constraint is normally added to the linear unmixing model. However, the linear model does not take into account that the spectrum of mixed pixels is not simply linearlymixed. To fully study themixing characteristics of ground object spectra before being imaged by the sensor, we propose a supervised unmixing architecture based on a one-dimensional convolutional neural network (CNN) by considering the spectral information and the sparse characteristics in the mixed pixel. Since 1-D CNN only considers feature learning, we combine the traditional root-mean-square error (RMSE) and l(1) regularization in its loss function to minimize training error. The performance of our proposed unmixing model is assessed by comparing the unmixing resultswith three traditional linear sparse unmixing algorithms and the fuzzy ARTMAP neural network in a simulated dataset and three real datasets. The RMSE was used to verify the unmixing accuracy of the different methods. The results showed that the RMSE obtained by our proposed CNN-based method was the lowest among themethods on all three real datasets, proving the effectiveness and stability of the CNN in unmixing tasks.","Title: Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China\nYear: 2021\n\nAbstract: Landslide detection mapping (LDM) is the basis of the field of landslide disaster prevention; however, it has faced certain difficulties. The Three Gorges Reservoir area of the Yangtze River has been one of the most intensively evaluated areas for landslide prevention in the world, due to the high frequency of landslide disasters here. In this article, we constructed an accurate LDM model based on convolutional neural networks, residual neural networks, and dense convolutional neural networks (DenseNets) that considers \"ZY-3\" high spatial resolution (HSR) data and conditioning factors (CFs). In this article, 19 factors based on remote sensing (RS) images, topographical and geological data associated with historical landslide locations were randomly divided into training (70% of total) and testing (30%) datasets. The experimental results show that the accuracy (ACC) of these three LDM models is above 0.95, indicating that the deep neural networks aimed at landslide detection performed well. Furthermore, DenseNet with RS images and CFs can accurately detect landslides. Specifically, DenseNet with RS images and CFs outperforms the other five models by considering the evaluation metrics, which exhibited Kappa coefficient improvements of 0.01-0.04 and ACC improvements of 0.02-0.3%. Among all the factors, elevation factor has a high importance of 0.727, which is the most important factors found in this landslide model construction experiment.","Title: Ensemble Entropy Metric for Hyperspectral Anomaly Detection\nYear: 2022\n\nAbstract: In hyperspectral anomaly detection, anomalies are rare targets that exhibit distinct spectral signatures from the background. Thus, anomalies are with low probabilities of occurrence in hyperspectral images. In this article, we develop a new technique for hyperspectral anomaly detection that adopts a new information theory perspective, to fully utilize the aforementioned concepts. Our goal is to transform system entropy into quantitative metrics of anomaly conspicuousness of pixels. To do so, two tasks are first completed: first, the construction of occurrence probability of pixels based on the density peak clustering algorithm, and second, the valid system definitions for pixels in specific anomaly detection problems with multiviews. Specifically, three types of systems are separately established by pixel pairs to conform to the definitions of three entropy definitions in information theory, i.e., Shannon entropy, joint entropy, and relative entropy. Then, three individual entropy-based metrics that assess the anomaly conspicuousness are defined. In addition, we design a standard deviation-based ensemble strategy for the integrated representation of the three individual metrics, which considers both logic ``or'' and ``and'' operations to simultaneously improve the detection rate and reduce the false alarm rate. Our experimental results obtained on two publicly available datasets with anomalies of different sizes and shapes demonstrate the superiority of our newly proposed anomaly detection method.","Title: Hashing for Localization (HfL): A Baseline for Fast Localizing Objects in a Large-Scale Scene\nYear: 2022\n\nAbstract: Advanced remote-sensing instruments produce massively large scenes from the surface of the earth, with very high spatial resolution and dimensionality. Developing methods for efficiently localizing specific objects in a large-scale scene presents a significant challenge, mainly because of the high computational requirements involved. To tackle this issue, we propose a new hashing for localization (HfL) framework that efficiently searches for specific objects in the large-scale scene. It begins by dividing the scene into a large number of overlapping local patches. A lightweight deep hash model, referred to as a tiny hashing network (THNet), encodes the local patches into hash codes. The Hamming distances between the hash code of an object image, i.e., an image containing the specific class of objects to be localized in the scene, and those of all local patches are computed. Small values of the Hamming distance indicate local patches that are similar to the object image. The positions of these local patches in the large-scale scene reflect the regional locations of the specific objects. The hash codes are binary and do not take up much space, and the Hamming distance carries very low-computational overheads. Further, we exploit a class center loss as the THNet training objective, which can comprehensively manage multiple object classes. These features mean that the HfL framework can localize specific objects very quickly, regardless of the size of the scene. Extensive experiments validate the effectiveness and efficiency of the framework. For instance, HfL can find objects in a remote-sensing image of 19,584 x 19,584 pixels in only 4.388 s (on a single RTX2080ti), with remarkable localization results. The source codes and datasets are available at https://github.com/lrhan/HfL, together providing a baseline for fast localizing objects in a large-scale scene.","Title: Self-Supervised Robust Deep Matrix Factorization for Hyperspectral Unmixing\nYear: 2022\n\nAbstract: Hyperspectral unmixing is a critical step to process hyperspectral images (HSIs). Nonnegative matrix factorization (NMF) has drawn extensive attention in remotely sensed hyperspectral unmixing since it does not require prior knowledge about the pure spectral constituents (endmembers) in the scene. However, this approach is normally implemented as a single-layer procedure, which does not allow for a refinement of the obtained endmember abundances. In addition, HSIs suffer from the interference of sparse noise (besides Gaussian noise), which brings challenges when pursuing efficient hyperspectral unmixing. To address these issues, we propose a new self-supervised robust deep matrix factorization (SSRDMF) model for hyperspectral unmixing, which consists of two parts: encoder and decoder. In the encoder, a multilayer nonlinear structure is designed to directly map the observed HSI data to the corresponding abundances. The abundances are then decoded by the decoder, in which the connected weights are treated as the extracted endmembers. By modeling the sparse noise explicitly, the proposed method can reduce the effect caused by both Gaussian and sparse noise. Furthermore, a self-supervised constraint is included for exploring the spectral information, which is beneficial to further improve unmixing performance. To validate our method, we have conducted extensive experiments on both synthetic and real datasets. Our experiments reveal that our newly developed SSRDMF achieves superior unmixing performance compared to other state-of-the-art methods.","Title: Endmember Estimation From Hyperspectral Images Using Geometric Distances\nYear: 2022\n\nAbstract: Endmember estimation consists of two tasks, that is, determining the number of pure spectral constituents (endmembers) and extracting their spectral signatures. We present a new geometric distance-based method for endmember estimation from hyperspectral images (HSIs), which does not need to know the number of endmembers in advance. Our strategy optimizes the widely used maximum distance analysis (MDA) method from two viewpoints. First, the traditional MDA method performs endmember estimation by computing the maximum distances between any pixel and one specific pixel, line, plane, or affine hull (AH) composed by the endmembers that have been formerly extracted. Instead, our new strategy only requires computing the maximum distance between any pixel and one specific AH. This operation provides a simpler way than MDA to estimate endmembers. Second, our strategy exploits a new distance computation between any pixel and an AH and just needs the normal vector (compared to the traditional MDA method, which uses the normal vector and offset). The new distance computation in our method is much more efficient than that in the traditional MDA method.","Title: CNN-Based Hyperspectral Pansharpening With Arbitrary Resolution\nYear: 2022\n\nAbstract: Traditional hyperspectral (HS) pansharpening aims at fusing a HS image with its panchromatic (PAN) counterpart, to bring the spatial resolution of the HS image to that of the PAN image. However, in many practical applications, arbitrary resolution HS (ARHS) pansharpening is required, where the HS and PAN images need to be integrated to generate a pansharpened HS image with arbitrary resolution (usually higher than that of the PAN image). Such an innovative task brings forth new challenges for the pansharpening technique, mainly including how to reconstruct HS images beyond the training scale and how to guarantee spectral fidelity at any spatial resolutions. To tackle the challenges, we present a novel convolutional neural network (CNN)-based method for ARHS pansharpening called ARHS-CNN. It is based on a two-step relay optimization process, which is associated with a multilevel enhancement subnetwork and a rescaling subnetwork. With a careful design following the thread, our ARHS-CNN is able to pansharpen HS images to any spatial resolutions using just a single CNN model trained on a limited number of scales while meantime to keep spectral fidelity at those resolutions, which wins an obvious advantage over traditional pansharpening methods. Experimental results obtained on several datasets verify the excellent performance of our ARHS-CNN method.","Title: Efficient Semantic Segmentation of Hyperspectral Images Using Adaptable Rectangular Convolution\nYear: 2022\n\nAbstract: Convolutional neural networks (CNNs) are relevant tools for remote sensing data processing in the last few years. Kernels process and integrate the spatial information of remotely sensed hyperspectral images (HSIs) accurately enough, so as to reduce the noise and spectral variations present in the data. Despite the great efficiency of the CNNs with HSI data, kernels need to be composed of a large number of layers and parameters to be able to obtain good performance, forcing the model to process a large amount of information from the datasets and making the model prone to overfitting, due to the limited number of labels in some cases. On the other hand, with that huge amount of data to consume, a large number of kernels are needed, making the model less efficient due to computational complexity. To overcome these challenges, this letter presents a new technique to reduce the computational cost and increase the model accuracy [based on adaptable rectangular convolutions (ARCs)], leading to a substantial reduction of the number of parameters and improving the model, in order to achieve better results in the context of HSI semantic segmentation. Thus, the model learns through the convolution the dimensions and offsets of the kernel of this Adaptable layer, performing the average operation based on integral image works to achieve better results with fewer parameters, reducing the risk of overfitting and computational cost. The source code can be found in the repository available at the link. [https://github.com/jlgs96/segHSI]","Title: Optimized Spatial Gradient Transfer for Hyperspectral-LiDAR Data Classification\nYear: 2022\n\nAbstract: The classification accuracy of ground objects is improved due to the combined use of the same scene data collected by different sensors. We propose to fuse the spatial planar distribution and spectral information of the hyperspectral images (HSIs) with the spatial 3D information of the objects captured by light detection and ranging (LiDAR). In this paper, we use the optimized spatial gradient transfer method for data fusion, which can effectively solve the strong heterogeneity of heterogeneous data fusion. The entropy rate superpixel segmentation algorithm over-segments HSI and LiDAR to extract local spatial and elevation information, and a Gaussian density-based regularization strategy normalizes the local spatial and elevation information. Then, the spatial gradient transfer model and l(1)-total variation minimization are introduced to realize the fusion of local multi-attribute features of different sources, and fully exploit the complementary information of different features for the description of ground objects. Finally, the fused local spatial features are reconstructed into a guided image, and the guided filtering acts on each dimension of the original HSI, so that the output maintains the complete spectral information and detailed changes of the spatial fusion features. It is worth mentioning that we have carried out two versions of expansion on the basis of the proposed method to improve the joint utilization of multi-source data. Experimental results on two real datasets indicated that the fused features of the proposed method have a better effect on ground object classification than the mainstream stacking or cascade fusion methods.","Title: Enhanced Spatiotemporal Fusion via MODIS-Like Images\nYear: 2022\n\nAbstract: Spatiotemporal fusion (STF) aims at generating remote-sensing data with both high spatial and temporal resolution. In the literature, one of the most widely used strategies to accomplish this goal is to fuse high temporal resolution images collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) with images with finer spatial resolution than those provided by MODIS (e.g., those collected by other satellite instruments such as Landsat or Sentinel-2). Current STF methods generally fuse an upsampled MODIS image with finer spatial resolution images. This leads to two main problems. First of all, the model uncertainty errors (resulting from the ill-posed upsampling problem) will be propagated into the fusion results, leading to spatial and spectral distortion. Furthermore, the spatial details of the upsampled MODIS image may be significantly different from those of the finer spatial resolution images, making the STF problem even more challenging. In order to tackle these issues, in this work, we develop a new linear regression-based STF strategy (LiSTF), which performs the reconstruction from a MODIS-like image (instead of from an upsampled MODIS image), thus reducing the model uncertainty errors and preserving better the spatial information. The MODIS-like images are built from the finer spatial resolution images via downsampling. Our experimental results, conducted using two publicly available datasets of Landsat-MODIS image pairs and one publicly available dataset of Sentinel-MODIS image pairs, reveal that our newly proposed LiSTF approach can significantly enhance the quantitative and qualitative performance of STF, particularly in terms of preserving the spatial information.","Title: (DSL)-L-4: Deep Semisupervised Shared Subspace Learning for Hyperspectral Image Classification\nYear: 2022\n\nAbstract: Hyperspectral image (HSI) classification is essential in remote sensing image analysis. The classification methods based on deep learning have attracted more and more attention. However, classification accuracy is seriously affected by the quantity of labeled data and redundant information. Therefore, a deep semisupervised shared subspace learning ((DSL)-L-4) model is developed to overcome these problems in this article. (DSL)-L-4 is composed of two parts. First, the basic feature extraction (BFE) network is constructed to preliminary extract high-dimensional-space features of multiscale data and fusion them to one shared subspace. Then, a deep shared subspace learning (DSSL) network is proposed to obtain a deeper and more representative low-dimensional subspace. Moreover, to obtain a more representative subspace and alleviate dependence on labeled samples, the regular, irregular constraint, and cross-entropy (CE) loss are integrated into the model. The regular constraint is adopted to reconstruct the multiscale patches to ensure the quality of the subspace in an unsupervised manner. The irregular constraint can well embed labeled and unlabeled samples into the procedure of subspace learning (SL). Then, the CE loss is used to extract more discriminative subspace using the limited labeled samples. Finally, we perform experiments on three widely used HSI datasets. Compared with the basic SL model, the (DSL)-L-4's classification accuracy on the popular Salinas, Indian Pines, and PaviaU datasets are increased by 4.68%, 4.25%, and 1.57%, respectively.","Title: DFLLR: Deep Feature Learning With Latent Relationship Embedding for Remote Sensing Image Retrieval\nYear: 2022\n\nAbstract: For deep networks, accurate image similarities cannot be well characterized with limited iterations, so the latent relationships between images can be embedded to enhance image retrieval performance. In this article, we propose a method named DFLLR to learn deep image features and accurate image similarities for remote sensing image retrieval (RSIR) simultaneously. First, the AlexNet is employed to extract high-level semantic features. Second, to obtain accurate image similarities, latent relationships between images are constructed with manifold learning and embedded in the AlexNet model with fully connected layers; in this way, the latent relationships and image features can be jointly learned. Third, to boost the RSIR performance further, the constraints of central and margin for jointly learning latent relationships and image features are integrated into our DFLLR. The central constraint is used to reduce the discrepancy of the latent relationships at the intraclass level and enhance the accuracies of image features. Moreover, the margin constraint is designed to enhance the accuracies of the latent relationships by maximizing the manifold margin between the latent relationships at the intraclass and interclass levels. To validate our method, we perform comprehensive experiments on three publicly available remote sensing image datasets, and the results demonstrate that it significantly outperforms other state-of-the-art methods.","Title: Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution\nYear: 2022\n\nAbstract: The hyperspectral images are composed of a variety of textures across the different bands which increase the spectral similarity and make it difficult to predict the pixel-wise labels without inducing additional complexity at the feature level. To extract robust and discriminative features from the different regions of land cover, the hyperspectral research community is still seeking such type of convolutions which can efficiently deal with fine-grained texture information during the feature extraction phase, which often overlook this aspect by vanilla convolution. To overcome the above shortcoming, this article proposes a generalized gradient centralized 3D convolution (G2C-Conv3D) operation, which is a weighted combination between the vanilla and gradient centralized 3D convolutions (GC-Conv3D) to extract both the intensity-level semantic information and gradient-level information. This can be easily plugged into the existing HSI feature extraction networks to boost the performance of accurate prediction for land-cover types. To validate the feasibility of the proposed G2C-Conv3D, we have considered the existing CNN3D, MS3DNet, ContextNet, and SSRN feature extraction models and as well as CAE3D, VAE3D, and SAE3D autoencoder (AE) networks, respectively. All these networks are embedded with G2C-Conv3D convolution to implement both generalized gradient centralized feature extraction networks (G2C-FE) and generalized gradient centralized AE networks (G2C-AE) for fine-grained spectral-spatial feature learning. In addition, G2C-Conv2D is also considered with few networks. The extensive experiments are conducted on four most widely used hyperspectral datasets i.e., IP, KSC, UH, and UP, respectively, and compared with the nine methods. The results demonstrate that the proposed G2C-Conv3D can effectively enhance the feature learning ability of the existing networks and both the qualitative and quantitative results show the superiority and effectiveness of the proposed G2C-Conv3D. The source codes will he publicly available at https://github.com/danfenghong/G2C-Conv3D-HSI.","Title: A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping\nYear: 2022\n\nAbstract: •Fully used the deep learning model as the base model for heterogeneous ensemble.•Stacking ensemble method improved the performance of the base model effectively.•Ensemble learning methods reduced the uncertainty of the single models’ results.•The importance and correlation of all LCFs were analyzed before training.","Title: Rotation-Invariant Deep Embedding for Remote Sensing Images\nYear: 2022\n\nAbstract: Endowing convolutional neural networks (CNNs) with the rotation-invariant capability is important for characterizing the semantic contents of remote sensing (RS) images since they do not have typical orientations. Most of the existing deep methods for learning rotation-invariant CNN models are based on the design of proper convolutional or pooling layers, which aims at predicting the correct category labels of the rotated RS images equivalently. However, a few works have focused on learning rotation-invariant embeddings in the framework of deep metric learning for modeling the fine-grained semantic relationships among RS images in the embedding space. To fill this gap, we first propose a rule that the deep embeddings of rotated images should be closer to each other than those of any other images (including the images belonging to the same class). Then, we propose to maximize the joint probability of the leave-one-out image classification and rotational image identification. With the assumption of independence, such optimization leads to the minimization of a novel loss function composed of two terms: 1) a class-discrimination term and 2) a rotation-invariant term. Furthermore, we introduce a penalty parameter that balances these two terms and further propose a final loss to Rotation-invariant Deep embedding for RS images, termed RiDe. Extensive experiments conducted on two benchmark RS datasets validate the effectiveness of the proposed approach and demonstrate its superior performance when compared to other state-of-the-art methods. The codes of this article will be publicly available at https://github.com/jiankang1991/TGRS_RiDe.","Title: DisOptNet: Distilling Semantic Knowledge From Optical Images for Weather-Independent Building Segmentation\nYear: 2022\n\nAbstract: Synthetic aperture radar (SAR) images provide all-weather and all-time capabilities for Earth observation, which becomes highly beneficial in the field of intelligent remote sensing (RS) image interpretation. Due to these advantages, SAR images have been widely exploited in automatic building segmentation tasks under poor weather conditions, especially when disasters happen. However, compared to optical images, the semantics inherent to SAR images are less rich and interpretable due to factors such as speckle noise and imaging geometry. In this scenario, most state-of-the-art methods are focused on designing advanced network architectures or loss functions for building footprint extraction. However, few works have been oriented toward improving segmentation performance through knowledge transfer from optical images. In this article, we propose a novel method based on the DisOptNet network, which can distill the useful semantic knowledge from optical images into a network only trained with SAR data. Specifically, we first analyze the multilevel feature discrepancies between multiple stages of the networks pretrained on the two image modalities. We observe that feature discrepancies start to increase as the encoding stage gradually changes from low level to high level. Based on such observation, we reuse the early stage features and construct parallel convolutional neural network (CNN) branches that are responsible for capturing high-level domain-specific knowledge for each image modality. The optical branch is aimed at mimicking feature generation at the optical pretrained network given the input SAR images. Then, an aggregation module is introduced to calibrate and fuse the features from different modalities while generating the building segments. Extensive experiments were conducted on a large-scale multisensor all-weather building segmentation dataset with state-of-the-art methods used for comparison. Our experimental results validate the effectiveness of DisOptNet, which demonstrates great potential in the task of weather-independent building footprint generation under real scenarios. The codes of this article will be made publicly available at https://github.com/jiankang1991/TGRS_DisOptNet.","Title: Semisupervised Discriminative Random Field for Hyperspectral Image Classification\nYear: 2021\n\nAbstract: The integration of spectral and spatial information is crucial in remotely sensed hyperspectral image classification. Some available approaches extract spatial features before classification, while other techniques include spatial information as a spatial regularizer. Due to the model complexity, these methods generally assume that a given pixel is only dependent on itself and its neighboring pixels. However, it is known that there are strong interactions among the pixels in different areas of the image, and as a result, this general assumption can limit the classifier performance. In this work, we develop a new semisupervised discriminative random field (DRF) technique for spatial-spectral classification of hyperspectral images, which constructs the complete model using the whole image. Furthermore, to cope with the unlabeled samples in the model, a new expectation maximization-based algorithm is introduced to optimize the learning of DRF parameters, where the missing labels of these samples serve as the latent variable. As compared to other conventional learning approaches such as maximum a posteriori marginal labeling, the proposed approach can obtain better optimization that leads to improved classification performance. Our experimental results, conducted using real hyperspectral datasets, indicate that the newly developed DRF algorithm exhibits very good performance.","Title: A Siamese Network Based U-Net for Change Detection in High Resolution Remote Sensing Images\nYear: 2022\n\nAbstract: Remote sensing image change detection (RSICD) is a technique that explores the change of surface coverage in a certain time series by studying the difference between multiple remote sensing images (RSIs) collected over the same area. Traditional RSICD algorithms exhibit poor performance on complex change detection (CD) tasks. In recent years, deep learning (DL) techniques have achieved outstanding results in the fields of RSI segmentation and target recognition. In CD research, most of the methods treat multitemporal remote sensing data as one input and directly apply DL-based image segmentation theory on it while ignoring the spatio-temporal information in these images. In this article, a new siamese neural network is designed by combing an attention mechanism (Siamese_AUNet) with UNet to solve the problems of RSICD algorithms. SiameseNet encodes the feature extraction of RSIs by two branches in the siamese network, respectively. The weights are shared between these two branches in siamese networks. Subsequently, an attention mechanism is added to the model in order to improve its detection ability for changed objects. The models are then compared with conventional neural networks using three benchmark datasets. The results show that the Siamese_AUNet newly proposed in this article exhibits better performance than other standard methods when solving problems related to weak CD and noise suppression.","Title: Toward Tightness of Scalable Neighborhood Component Analysis for Remote-Sensing Image Characterization\nYear: 2022\n\nAbstract: Deep metric learning methods have recently drawn significant attention in the field of remote sensing (RS), owing to their prominent capabilities for modeling relations among RS images based on their semantic contents. In the context of scene classification and large-scale image retrieval, one of the most prominent deep metric learning methods is the scalable neighborhood component analysis (SNCA), which has demonstrated excellent performance on the locality neighborhood structure in the metric space. However, the standard SNCA has important constraints on separating the hard positive and other negative images in the metric space, and this may become a major limitation when dealing with the large-scale variance problem inherent to RS data. To address this issue, we propose a novel deep metric learning formulation that introduces a new margin parameter to enforce the compactness of the within-class feature embeddings. Based on this innovative scheme, we propose two novel loss functions: 1) T-SNCA-c, where the parameter is based on the cosine similarity, and 2) T-SNCA-a, where the parameter is based on the angular distance. Besides, we exploit memory bank optimization to further enhance the semantic diversity during training. Our experimental results, conducted using three downstream applications (K-NN classification, clustering, and image retrieval) and two large-scale RS benchmark datasets, demonstrate that the proposed approach can achieve superior performance when compared to current state-of-the-art deep metric learning methods. The codes of this work will be made available online (https://github.com/jiankang1991/GRSL_TSNCA).","Title: Hyperspectral and LiDAR Data Classification Using Joint CNNs and Morphological Feature Learning\nYear: 2022\n\nAbstract: Convolutional neural networks (CNNs) have been extensively utilized for hyperspectral image (HSI) and light detection and ranging (LiDAR) data classification. However, CNNs have not been much explored for joint HSI and LiDAR image classification. Therefore, this article proposes a joint feature learning (HSI and LiDAR) and fusion mechanism using CNN and spatial morphological blocks, which generates highly accurate land-cover maps. The CNN model comprises three Conv3D layers and is directly applied to the HSIs for extracting discriminative spectral-spatial feature representation. On the contrary, the spatial morphological block is able to capture the information relevant to the height or shape of the different land-cover regions from LiDAR data. The LiDAR features are extracted using morphological dilation and erosion layers that increase the robustness of the proposed model by considering elevation information as an additional feature. Finally, both the obtained features from CNNs and spatial morphological blocks are combined using an additive operation prior to the classification. Extensive experiments are shown with widely used HSIs and LiDAR datasets, i.e., University of Houston (UH), Trento, and MUUFL Gulfport scene. The reported results show that the proposed model significantly outperforms traditional methods and other state-of-the-art deep learning models. The source code for the proposed model will be made available publicly at https://github.com/AnkurDeria/HSI+LiDAR.","Title: Open-Pit Mine Area Mapping With Gaofen-2 Satellite Images Using U-Net\nYear: 2022\n\nAbstract: Obtaining information on the surface coverage of open-pit mining areas (OPMAs) is of great significance to ecological governance and restoration. The current methods to map the OPMAs face problems such as low mapping accuracy due to complex landscapes. In this article, we propose a hybrid open-pit mining mapping (OPMM) framework with Gaofen-2 (GF-2) high-spatial resolution satellite images (HSRSIs), using an improved U-Net neural network (U-Net+). By concatenating the previous layers with each subsequent layer to ensure that there is a maximum of feature maps of each layer in the network, the U-Net+ can reduce the loss of feature information and make the extraction capability of the network more powerful. Two independent OPMAs were selected as the study area for the OPMM. By taking advantage of GF-2 HSRSIs, a total of 111 open-pit mine sites (OPMSs) were mapped and each OPMS boundary was validated by field surveys. Then, these OPMSs were used as input to assess the accuracy of the OPMM results obtained by the U-Net+. By comparing our results with those provided by five state-of-the-art deep learning algorithms: Fully Convolutional Network (FCN), SegNet, U-Net, Residual U-Net (ResU-Net), and U-Net++, we conclude that the proposed framework outperformed these methods by more than 0.02% in Overall Accuracy, 0.06% in Kappa Coefficient, 0.03% in Mean Intersection over union, 8.36% in producer accuracy and 4.44% in user accuracy. Therefore, the proposed framework thus exhibits very promising applicability in the ecological restoration and governance of OPMAs.","Title: Multiattribute Sample Learning for Hyperspectral Image Classification Using Hierarchical Peak Attribute Propagation\nYear: 2022\n\nAbstract: Although hyperspectral image (HSI) classification methods have gained popularity in the remote sensing community, their performance tends to be limited by the quantity and quality of training samples. Actually, the production (i.e., capture and annotation) of training samples requires abundant labor costs and time consumption. Multiattribute sample learning can effectively solve the cost problem for the acquisition of training samples; for this, this article proposes a novel hierarchical peak attribute propagation (HPAP) method for the annotation of multiattribute samples, which is composed of the following key technologies. First, superpixel-oriented guided filtering (SOGF) is designed to improve the distinguishability of spectral features between pixels. Then, peak attribute-driven manual labeling is performed on each multiattribute region to provide prior knowledge for label propagation. Next, peak attribute-based label propagation is applied to each multiattribute region to achieve the single-label information of pixels. Finally, multiattribute samples with a single class label are augmented to the training process of HSI supervised model to enhance their classification performance. Experimental results on several public HSI datasets indicated that the proposed HPAP method can obtain higher annotation accuracy for multiattribute samples and promote the classification accuracy of HSI classification supervised models.","Title: Hyperspectral Anomaly Detection With Relaxed Collaborative Representation\nYear: 2022\n\nAbstract: Anomaly detection has become an important remote sensing application due to the abundant spectral and spatial information contained in hyperspectral images. Recently, hyperspectral anomaly detection methods based on the collaborative representation (CR) model have attracted significant attention. Nevertheless, these methods have to face two main challenges: 1) all features (spectral signatures) are constrained to share the same representation coefficient, which ignores the differences among features and 2) existing dictionaries for pixel-by-pixel detection models are usually not reliable. To address these issues, this article proposes a new relaxed CR detector for hyperspectral anomaly detection by using a novel nonglobal dictionary. The proposed detector conducts CR on each feature dimension of the pixel under test and simultaneously constrains the coding vectors of different features to be similar. To the best of our knowledge, this is the first time that a detection model is built from each feature dimension. To adjust the contributions of each feature, an adaptive feature weight constrained version of the method is also proposed. The nonglobal dictionary is constructed by combining the k-nearest neighbor method and an existing global dictionary, which is more reliable and practical than the widely used dual-window dictionary. In addition, this article also designs a band selection strategy for the proposed method. Experiments on five real datasets indicate that the proposed method suppresses background well and outperforms other classical and state-of-the-art methods.","Title: Pseudo Complex-Valued Deformable ConvLSTM Neural Network With Mutual Attention Learning for Hyperspectral Image Classification\nYear: 2022\n\nAbstract: Convolutional long short-term memory (ConvLSTM) has received much attention for hyperspectral image (HSI) classification due to its ability of modeling long-range correlations, which, however, is vulnerable to too many parameters and insufficient training, limiting its classification accuracy, especially for small samples. Different from it, traditional hand-crafted methods extract the features with basic attributes of HSIs, which can provide the lack of details and interpretability of deep semantic features. However, existing methods fail to incorporate their complementarity for HSI classification. As such, a Pseudo complex-valued (CV) Deformable ConvLSTM Neural Network with mutual Attention learning (APDCLNN) is proposed, providing a new way to realize the collaborative learning of hand-crafted and deep features for HSI classification. First, a 2-D pseudo CV deformable ConvLSTM (PDConvLSTM2D) cell is designed using deformable convolution and complex operations, with which a spatial-spectral PDConvLSTM2D neural network (SSPDCL2DNN) is built to extract scale- and spectral-enhanced deep spatial-spectral features. Then, 3-D Gabor filter is used to extract hand-crafted features, and a mutual attention-based multimodality feature learning and fusion (MAMLF) module is designed to integrate them into deep features for training and optimization of SSPDCL2DNN. Finally, an attention loss subnetwork is designed to refine the classification results. As we know, this is the first attempt to apply the idea of mutual attention learning to fuse hand-crafted and deep features for HSI classification. Extensive experiments on three widely used HSI datasets show the advantages of our model over other deep methods in terms of both quantitative and visual quality.","Title: DRFL-VAT: Deep Representative Feature Learning With Virtual Adversarial Training for Semisupervised Classification of Hyperspectral Image\nYear: 2022\n\nAbstract: While deep learning algorithms have achieved good results in hyperspectral image (HSI) classification, several supervised classification algorithms rely on a large number of labeled samples to get adequate performance. Collecting a large number of labeled samples is expensive in many real applications. To address this issue, a novel semisupervised HSI classification framework called deep representative feature learning (DRFL) with virtual adversarial training (DRFL-VAT) is developed in this article. By embedding the local manifold learning (LML) into the fully connected layers of a convolutional neural network (CNN), our newly developed DRFL can learn representative features. The VAT regularization is adopted to exploit the prediction label distribution of training samples and addresses the overfitting problem. Finally, the objective function of DRFL-VAT is solved by a customized algorithm. We test our method on three widely public HSI datasets and our results show that our method is competitive when compared to other state-of-the-art approaches.","Title: Variable Subpixel Convolution Based Arbitrary-Resolution Hyperspectral Pansharpening\nYear: 2022\n\nAbstract: Standard hyperspectral (HS) pansharpening relies on fusion to enhance low-resolution HS (LRHS) images to the resolution of their matching panchromatic (PAN) images, whose practical implementation is normally under a stipulation of scale invariance of the model across the training phase and the pansharpening phase. By contrast, arbitrary resolution HS (ARHS) pansharpening seeks to pansharpen LRHS images to any user-customized resolutions. For such a new HS pansharpening task, it is not feasible to train and store convolution neural network (CNN) models for all possible candidate scales, which implies that the single model acquired from the training phase should be capable of being generalized to yield HS images with any resolutions in the pansharpening phase. To address the challenge, a novel variable subpixel convolution (VSPC)-based CNN (VSPC-CNN) method following our arbitrary upsampling CNN (AU-CNN) framework is developed for ARHS pansharpening. The VSPC-CNN method comprises a two-stage elevating thread. The first stage is to improve the spatial resolution of the input HS image to that of the PAN image through a prepansharpening module, and then, a VSPC-encapsulated arbitrary scale attention upsampling (ASAU) module is cascaded for arbitrary resolution adjustment. After training with given scales, it can be generalized to pansharpen HS image to arbitrary scales under the spatial patterns invariance across the training and pansharpening phases. Experimental results from several specific VSPC-CNNs on both simulated and real HS datasets show the superiority of the proposed method.","Title: Multi-resolution Pyramid Enhanced Non-local Feature Extraction for Hyperspectral Classification\nYear: 2022\n\nAbstract: Hyperspectral image (HSI) is applied to accurately distinguish ground objects in many fields, owing to its abundant structural and spectral information. To fully mine the spectral-spatial information, feature extraction methods are employed to further improve the classification performance in numerous research works. Among them, integrating contextual information into pixels is an active topic and achieved significant performance. However, it is inevitable to come with unwanted and superfluous distracting contents in the process of introducing contextual information. This article proposes a multi-resolution pyramid enhanced non-local feature extraction (MRPEN) method by exploiting non-local information from different resolutions to alleviate the effects of noise contents. The feature extraction task is implemented through by first constructing a multi-resolution feature pyramid via performing spectral-spatial reduction and resize operation on the HSI. With the feature pyramid, three kinds of feature extraction manners (i.e., short-range, long-range, and non-local) are utilized to extract specific features for each layers. In particular, the non-local feature is extracted by introducing adjacent superpixels from the upper layer into our feature extraction operation. After this, the extracted features of each layer are concatenated together and fed on a sparse autoencoders. Finally, a decision rule is introduced to blend the prediction results. Experiments on three public HSI datasets and a Gaofen-5 satellite dataset demonstrate that our method outperforms several state-of-the-art classification approaches.","Title: Fusing Sentinel-2 and Landsat-8 Surface Reflectance Data via Pixel-Wise Local Normalization\nYear: 2022\n\nAbstract: Medium spatial resolution surface reflectance image series from the combination of Landsat-8 Operational Land Imager and Sentinel-2 Multispectral Imager observations have great importance to the land surface monitoring tasks, for which great efforts have been paid for blending the two data. However, most of the efforts focus on the image series with spatial resolution of 30 m, which cannot meet the data demand of some applications. Therefore, it is necessary to fuse Landsat-8 and Sentinel-2 images to provide 10-m image series. Currently, there are three means to achieve that, including the area-to-point regression kriging fusion approach (ATPRK), spatiotemporal fusion methods, and deep-learning-based fusion models. However, the ATPRK and spatiotemporal fusion methods suffer from the limited fusion performance, while the deep-learning-based fusion models are hardware dependent, i.e., requiring the graphics processing units, which may not be satisfied sometimes. To address these issues, in this article, we develop a new pixel-wise local normalization-based fusion method (LN-FM) for fusing Sentinel-2 and Landsat-8 images. The newly proposed LN-FM is compared to the ATPRK and three representative spatiotemporal fusion methods in experiments, which use imagery collected from both a rural area and an urban area. The experimental results demonstrate that the newly developed LN-FM exhibits excellent qualitative and quantitative performance, as well as remarkable spatial, spectral, and pixel distribution fidelity. Furthermore, this approach is fast, which may improve its applicability","Title: Local Semantic Feature Aggregation-Based Transformer for Hyperspectral Image Classification\nYear: 2022\n\nAbstract: Hyperspectral images (HSIs) contain abundant information in the spatial and spectral domains, allowing for a precise characterization of categories of materials. Convolutional neural networks (CNNs) have achieved great success in HSI classification, owing to their excellent ability in local contextual modeling. However, CNNs suffer from fixed filter weights and deep convolutional layers, which lead to a limited receptive field and high computational burden. The recent vision transformer (ViT) models long-range dependencies with a self-attention mechanism and has been an alternative backbone to CNNs traditionally used in HSI classification. However, such transformer-based architectures designate all the input pixels of the receptive field as feature tokens in terms of feature embedding and self-attention, which inevitably limits the ability for learning multiscale features and increases the computational cost. To overcome this issue, we propose a local semantic feature aggregation-based transformer (LSFAT) architecture which allows transformers to represent long-range dependencies of multiscale features more efficiently. We introduce the concept of the homogeneous region into the transformer by considering a pixel aggregation strategy and further propose neighborhood-aggregation-based embedding (NAE) and attention (NAA) modules, which are able to adaptively form multiscale features and capture locally spatial semantics among them in a hierarchical transformer architecture. A reusable classification token is included together with the feature tokens in the attention calculation. In the last stage, a fully connected layer is used to perform classification on the reusable token after transformer encoding. We verify the effectiveness of the NAE and NAA modules compared with the traditional ViT through extensive experiments. Our results demonstrate the excellent classification performance of the proposed method in comparison to other state-of-the-art approaches on several public HSIs.","Title: A New Deep Convolutional Network for Effective Hyperspectral Unmixing\nYear: 2022\n\nAbstract: Hyperspectral unmixing extracts pure spectral constituents (endmembers) and their corresponding abundance fractions from remotely sensed scenes. Most traditional hyperspectral unmixing methods require the results of other endmember extraction algorithms to complete the abundance estimation step. Due to the impressive learning and data fitting capabilities of convolutional neural networks (CNNs), deep learning (DL)-based hyperspectral unmixing technologies have rapidly developed in the literature. According to the procedure used to combine different layers (i.e., fully connected layers, convolution layers, and activation layers), these techniques are mainly divided into three main categories, i.e., those based on autoencoder networks, convolutional neural networks, and convolutional autoencoder networks. They usually extract the weight and output of a specific activation layer as endmember signatures and abundance maps, respectively. Moreover, most existing DL-based unmixing approaches usually use 2-D CNNs to learn the features contained in hyperspectral images, and very few approaches employ 3-D CNNs to extract spectral and spatial information. However, 2-D CNN-based techniques cannot capture good discriminative feature maps from the spectral viewpoint, and 3-D CNN-based techniques usually have high computational overload. In this work, to further exploit the feature extraction capability of CNNs, we combine 3- and 2-D convolutions to propose a cross-convolution unmixing network (CrossCUN) for hyperspectral unmixing. Simultaneously, to better illustrate the improvements of our proposed CrossCUN, we also build the corresponding 2-D convolution unmixing network (2-DCUN) and 3-D convolution unmixing network (3-DCUN). We evaluate the performance of our newly developed networks on two types of synthetic datasets and three real hyperspectral images. Experimental results show that the proposed networks not only obtain better results than other DL-based unmixing methods but also do not require any prior knowledge (e.g., the results of other endmember extraction algorithms) to estimate the abundance maps.","Title: Learning Orientation Information From Frequency-Domain for Oriented Object Detection in Remote Sensing Images\nYear: 2022\n\nAbstract: Object detection in remote sensing images (RSIs) poses great difficulties due to arbitrary orientations, various scales, and dense location of the targets over the ground. Recent evidence suggests that encoding the orientation information is of great use for training an accurate object detector for oriented object detection (OOD). In this article, we propose a new frequency-domain orientation learning (FDOL) module with two main components: the frequency-domain feature extraction (FFE) network and an orientation enhanced self-attention layer (OES-Layer). The FFE network models the interactions among spatial locations in the frequency domain to determine the frequency of spatial features. Then, these features are fed into our OES-Layer to learn the orientation information. Moreover, the orientation weights are adopted to guide the feature selection in a self-attention (SA) architecture, using them as a control gate to emphasize the spatial responses of target instances. Considering that the original similarity weights (calculated by the SA algorithm) do not distinctly model the orientation variation, the considered orientation weights provide an efficient asset to emphasize the orientation of objects. Extensive experiments on the DOTA and HRSC2016 datasets demonstrate that our method achieves state-of-the-art performance among single-scale methods while achieving competitive performance over multiscale methods.","Title: Automatic Detection of Aquatic Weeds: A Case Study in the Guadiana River, Spain\nYear: 2022\n\nAbstract: The spread of aquatic invasive plants is a major concern in several zones of the world's geography. These plants, which are not part of the natural ecosystem, cause a negative impact to the environment, as well as to economy and society. In Spain, large areas of Guadiana (the second-longest river in Spain) have been invaded by such plants. Among the strategies to address this problem, monitoring and detection play an important role to control the spatiotemporal distribution of the invasive plants. The main objective of this work is to develop a methodology able to automatically detect the geolocation of aquatic invasive plants using remote sensing and machine learning techniques. To this end, several classification algorithms have been applied to freely available multispectral satellite imagery, collected by ESA's Sentinel-2 satellite. A quantitative and comparative assessment is conducted using different machine and deep learning algorithms from classical methods, such as unsupervised K-means to supervised random forests and convolutional neural networks. This study also proposes a methodology for validating the obtained classification results, generating synthetic ground truth images based on available high spatial resolution imagery. The obtained results demonstrate the suitability of some of the considered algorithms for automatic detection of aquatic weeds in satellite images with medium spatial resolution."],"string":"[\n \"Title: Hyperspectral band selection using a collaborative sparse model\\nYear: 2012\\n\\nAbstract: In our previous research, we have proposed band-similarity-based unsupervised band selection approaches, which are proven to be very efficient. In this paper, we propose to use a collaborative sparse model for further improvement. Specifically, the pre-selected bands using the fast method, called NFINDR+LP, are further refined using a collaborative sparse model. It not only requires that the linear regression coefficients are sparse, but also requires that the same set of active bands is shared by all the bands to be removed. With the collaborative sparseness constraint being relaxed, the final selected bands can be further improved, that is, the band subset with the same number of bands can provide better classification accuracy. Based on the preliminary result, the proposed sparse model is also capable of finding the minimum number of bands to be selected.\",\n \"Title: New Postprocessing Methods for Remote Sensing Image Classification: A Systematic Study\\nYear: 2014\\n\\nAbstract: This paper develops several new strategies for remote sensing image classification postprocessing (CPP) and conducts a systematic study in this area. CPP is defined as a refinement of the labeling in a classified image in order to enhance its original classification accuracy. The current mainstream classification methods (preprocessing) extract additional spatial features in order to complement spectral information and enhance classification using spectral responses alone. On the other hand, however, the CPP methods, providing a new solution to improve classification accuracy by refining the initial result, have not received sufficient attention. They have potential for achieving comparable accuracy to the preprocessing methods but in a more direct and succinct way. In this paper, we consider four groups of CPP strategies: filtering; random field; object-based voting; and relearning. In addition to the state-of-the-art CPP algorithms, we also propose a series of new ones, e.g., anisotropic probability diffusion and primitive cooccurrence matrix. In experiments, a number of multisource remote sensing data sets are used for evaluation of the considered CPP algorithms. It is shown that all the CPP strategies are capable of providing more accurate results than the raw classification. Among them, the relearning approaches achieve the best results. In addition, our relearning algorithms are compared with the state-of-the-art spectral-spatial classification. The results obtained further verify the effectiveness of CPP in different remote sensing applications.\",\n \"Title: Parallel hyperspectral image compression using iterative error analysis on graphics processing units\\nYear: 2012\\n\\nAbstract: In this paper, we develop a new parallel implementation of the iterative error analysis (IEA) algorithm for lossy hyperspectral image compression on graphics processing units (GPUs), an inexpensive parallel computing platform that has recently become very popular in hyperspectral imaging applications. The proposed GPU implementation is tested on several different architectures from NVidia, the main GPU vendor worldwide, and is shown to exhibit real-time performance in the analysis of AVIRIS data sets. The GPU implementation of the IEA represents a step forward towards real-time onboard (lossy) compression of hyperspectral data where the quality of the compression can be also adjusted in real-time.\",\n \"Title: Impact of Vector Ordering Strategies on Morphological Unmixing of Remotely Sensed Hyperspectral Images\\nYear: 2010\\n\\nAbstract: Hyper spectral imaging is a new technique in remote sensing that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have explored the application of morphological operations to integrate both spatial and spectral responses in hyper spectral data analysis. These operations rely on ordering pixel vectors in spectral space, but there is no unambiguous means of defining the minimum and maximum values between two vectors of more than one dimension. Our original contribution in this paper is to examine the impact of different vector ordering strategies on the definition of multi-channel morphological operations. Our focus is on morphological unmixing, which decomposes each pixel vector in the hyper spectral scene into a combination of pure spectral signatures (called end members) and their associated abundance fractions, allowing sub-pixel characterization. Experiments are conducted using real hyper spectral data sets collected by NASA/JPL's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system.\",\n \"Title: FPGA implementation of the pixel purity index algorithm for remotely sensed hyperspectral image analysis\\nYear: 2010\\n\\nAbstract: Hyperspectral imaging is a new emerging technology in remote sensing which generates hundreds of images, at different wavelength channels, for the same area on the surface of the Earth. Over the last years, many algorithms have been developed with the purpose of finding endmembers, assumed to be pure spectral signatures in remotely sensed hyperspectral data sets. One of the most popular techniques has been the pixel purity index (PPI). This algorithm is very time-consuming. The reconfigurability, compact size, and high computational power of Field programmable gate arrays (FPGAs) make them particularly attractive for exploitation in remote sensing applications with (near) real-time requirements. In this paper, we present an FPGA design for implementation of the PPI algorithm. Our systolic array design includes a DMA and implements a prefetching technique to reduce the penalties due to the I/O communications. We have also included a hardware module for random number generation. The proposed method has been tested using real hyperspectral data collected by NASA's Airborne Visible Infrared Imaging Spectrometer over the Cuprite mining district in Nevada. Experimental results reveal that the proposed hardware system is easily scalable and able to provide accurate results with compact size in (near) real-time, whichmake our reconfigurable system appealing for on-board hyperspectral data processing.\",\n \"Title: Morphological Feature Extraction For Automatic Registration Of Multispectral Images\\nYear: 2007\\n\\nAbstract: The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.\",\n \"Title: Parallel unmixing of remotely sensed hyperspectral images on commodity graphics processing units\\nYear: 2011\\n\\nAbstract: Hyperspectral imaging instruments are capable of collecting hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. One of the main problems in the analysis of hyperspectral data cubes is the presence of mixed pixels, which arise when the spatial resolution of the sensor is not enough to separate spectrally distinct materials. Hyperspectral unmixing is one of the most popular techniques to analyze hyperspectral data. It comprises two stages: (i) automatic identification of pure spectral signatures (endmembers) and (ii) estimation of the fractional abundance of each endmember in each pixel. The spectral unmixing process is quite expensive in computational terms, mainly due to the extremely high dimensionality of hyperspectral data cubes. Although this process maps nicely to high performance systems such as clusters of computers, these systems are generally expensive and difficult to adapt to real-time data processing requirements introduced by several applications, such as wildland fire tracking, biological threat detection, monitoring of oil spills, and other types of chemical contamination. In this paper, we develop an implementation of the full hyperspectral unmixing chain on commodity graphics processing units (GPUs). The proposed methodology has been implemented, using the CUDA (compute device unified architecture), and tested on three different GPU architectures: NVidia Tesla C1060, NVidia GeForce GTX 275, and NVidia GeForce 9800 GX2, achieving near real-time unmixing performance in some configurations tested when analyzing two different hyperspectral images, collected over the World Trade Center complex in New York City and the Cuprite mining district in Nevada. Copyright © 2011 John Wiley & Sons, Ltd.\",\n \"Title: Spectral Characterisation Of Land Surface Composition To Determine Soil Erosion Within Semiarid Rainfed Cultivated Areas\\nYear: 2012\\n\\nAbstract: In a Mediterranean semiarid area in Central Spain, with dominant rainfed agriculture, hyperspectral airborne data, supported by field spectroscopy have been obtained, allowing a spectral identification of bare soil with the corresponding erosion stages. The definition of soil erosion stages is based on a spectral characterization supported by morphological, physical and chemical features of contrasted soil surfaces as a result of soil loss. Different soil erosion stages were defined within two bare-soil sites representing the soil variability of the area, and where such stages are spatially represented. The validation of selected image derived endmebers was a key step to carry out a partial unmixing for determining soil erosion stages. A preliminary spatial distribution of advanced and intermediate erosion stages was obtained for the most representative soil types.\",\n \"Title: Towards real-time compression of hyperspectral images using virtex-iI FPGAs\\nYear: 2007\\n\\nAbstract: Hyperspectral imagery is a new type of high-dimensional image data which is now used in many Earth-based and planetary exploration applications. Many efforts have been devoted to designing and developing compression algorithms for hyperspectral imagery. Unfortunately, most available approaches have largely overlooked the impact of mixed pixels and subpixel targets, which can be accurately modeled and uncovered by resorting to the wealth of spectral information provided by hyperspectral image data. In this paper, we develop an FPGA-based data compression technique which relies on the concept of spectral unmixing, one of the most popular approaches to deal with mixed pixels and subpixel targets in hyperspectral analysis. The proposed method uses a two-stage approach in which the purest pixels in the image (endmembers) are first extracted and then used to express mixed pixels as linear combinations of end-members. The result is an intelligent, applicationbased compression technique which has been implemented and tested on a Xilinx Virtex-II FPGA.\",\n \"Title: Parallel Hyperspectral Unmixing on GPUs.\\nYear: 2014\\n\\nAbstract: This letter presents a new parallel method for hyperspectral unmixing composed by the efficient combination of two popular methods: vertex component analysis (VCA) and sparse unmixing by variable splitting and augmented Lagrangian (SUNSAL). First, VCA extracts the endmember signatures, and then, SUNSAL is used to estimate the abundance fractions. Both techniques are highly parallelizable, which si...\",\n \"Title: Parallel implementation of vertex component analysis for hyperspectral endmember extraction\\nYear: 2012\\n\\nAbstract: Vertex component analysis (VCA) has become a very popular and useful tool to linear unmix large hyperspectral datasets without the use of any a priori knowledge of the constituent spectra. Although VCA is fast method, many hyperspectral imagery applications require a response in real time or near-real time. This paper proposes two different optimizations for accelerating the computational performance of VCA: the first one focus a parallel implementation based on graphics computing units (GPUs) to alleviate the VCA computational burden; The second one is focused on the development of a strategy to remove a large proportion of mixed pixels that play no effect on the VCA functioning. Experiments are conducted using simulated and real hyperspectral datasets. These results reveal considerable acceleration factors, which satisfies the real-time constraints given by the data acquisition rate.\",\n \"Title: Parallel Morphological/Neural Classification of Remote Sensing Images Using Fully Heterogeneous and Homogeneous Commodity Clusters\\nYear: 2006\\n\\nAbstract: The wealth spatial and spectral information available from last-generation Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel techniques treat remotely sensed data not as images, but as unordered listings of spectral measurements with no spatial arrangement. In thematic classification applications, however, the integration of spatial and spectral information can be greatly beneficial. Although such integrated approaches can be efficiently mapped in homogeneous commodity clusters, low-cost heterogeneous networks of computers (HNOCs) have soon become a standard tool of choice in Earth and planetary missions. In this paper, we develop a new morphological/neural parallel algorithm for commodity cluster-based analysis of high-dimensional remotely sensed image data sets. The algorithms accuracy and parallel performance are tested (in the context of a real precision agriculture application) using two parallel platforms: a fully heterogeneous cluster made up of 16 workstations at University of Maryland, and a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center\",\n \"Title: Real-time spectral unmixing using iterative error analysis on commodity graphics processing units\\nYear: 2011\\n\\nAbstract: Spectral unmixing is an important task for hyperspectral data exploitation. It generally consists of two steps: identification of pure spectral signatures (endmembers) and estimation of the fractional abundance of each endmember in each pixel of the scene. A successful algorithm to perform both tasks in simultaneous fashion is the iterative error analysis (IEA) algorithm, which applies an iterative process in which the next endmember to be detected depends on the set of previously extracted ones, which can be computationally expensive for hyperspectral images with a large number of endmembers. In this paper, we propose a new parallel implementation of the IEA algorithm for graphics processing units (GPUs). The proposed implementation is tested on three different GPUs fromNVidia™, and is shown to exhibit real-time performance in the analysis of an Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) data set collected over the Cuprite mining district in Nevada.\",\n \"Title: Parallel Implementation Of Hyperspectral Image Processing Algorithms\\nYear: 2006\\n\\nAbstract: High computing performance of algorithm analysis is essential in many hyperspectral imaging applications, including automatic target recognition for homeland defense and security, risk/hazard prevention and monitoring, wild-land fire tracking and biological threat detection. Despite the growing interest in hyperspectral imaging research, only a few efforts devoted to designing and implementing well-conformed parallel processing solutions currently exist in the open literature. With the recent explosion in the amount and dimensionality of hyperspectral imagery, parallel processing is expected to become a requirement in most remote sensing missions. In this paper, we take a necessary first step towards the quantitative comparison of parallel techniques and strategies for analyzing hyperspectral data sets. Our focus is on three types of algorithms: automatic target recognition, spectral mixture analysis and data compression. Three types of high performance computing platforms are used for demonstration purposes, including commodity cluster-based systems, heterogeneous networks of distributed workstations and hardware-based computer architectures. Combined, these parts deliver a snapshot of the state of the art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of incorporating parallel computing models into hyperspectral remote sensing problems.\",\n \"Title: Parallel Processing of High-Dimensional Remote Sensing Images Using Cluster Computer Architectures\\nYear: 2007\\n\\nAbstract: Hyperspectral sensors represent the most advanced instruments currently available for remote sensing of the Earth. The high spatial and spectral resolution of the images supplied by systems like the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS), developed by NASA Jet Propulsion Laboratory, allows their exploitation in diverse applications, such as detection and control of wildland fires and hazardous agents in water and atmosphere, detection of military targets and management of natural resources. Even though the above applications generally require a response in near real time, few solutions are currently available to provide fast and efficient processing of such high-dimensional image data sets. This is mainly due to the extremely high volume of data collected by hyperspectral sensors, which often limits their exploitation in analysis scenarios where the spatial and temporal requirements are very high. In this paper, we describe new parallel processing methodologies for hyperspectral image processing, based on neural architectures and morphological concepts. The computational performance of the proposed methods is demonstrated using real analysis scenarios based on the exploitation of AVIRIS data using two parallel computer systems, an SGI Origin 2000 multicomputer located at the\",\n \"Title: Spatial/spectral endmember extraction by multidimensional morphological operations\\nYear: 2002\\n\\nAbstract: Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data.\",\n \"Title: Parallel morphological/neural processing of hyperspectral images using heterogeneous and homogeneous platforms\\nYear: 2008\\n\\nAbstract: The wealth spatial and spectral information available from last-generation Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel techniques treat remotely sensed data not as images, but as unordered listings of spectral measurements with no spatial arrangement. In thematic classification applications, however, the integration of spatial and spectral information can be greatly beneficial. Although such integrated approaches can be efficiently mapped in homogeneous commodity clusters, low-cost heterogeneous networks of computers (HNOCs) have soon become a standard tool of choice for dealing with the massive amount of image data produced by Earth observation missions. In this paper, we develop a new morphological/neural algorithm for parallel classification of high-dimensional (hyperspectral) remotely sensed image data sets. The algorithm's accuracy and parallel performance is tested in a variety of homogeneous and heterogeneous computing platforms, using two networks of workstations distributed among different locations, and also a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center in Maryland.\",\n \"Title: Efficient Collective Communication Paradigms for Hyperspectral Imaging Algorithms Using HeteroMPI\\nYear: 2008\\n\\nAbstract: Most of the parallel strategies used for information extraction in remotely sensed hyperspectral imaging applications have been implemented in the form of parallel algorithms on both homogeneous and heterogeneous networks of computers. In this paper, we develop a study on efficient collective communications based on the usage of HeteroMPI for a parallel heterogeneous hyperspectral imaging algorithm which uses concepts of mathematical morphology.\",\n \"Title: Improving the Performance of Hyperspectral Image and Signal Processing Algorithms Using Parallel, Distributed and Specialized Hardware-Based Systems\\nYear: 2010\\n\\nAbstract: Advances in sensor technology are revolutionizing the way remotely sensed data is collected, managed and analyzed. The incorporation of latest-generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral signal processing is a new technique in remote sensing that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. Many current and future applications of remote sensing in Earth science, space science, and soon in exploration science will require (near) real-time processing capabilities. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) systems and architectures in remote sensing missions. With the aim of providing an overview of current and new trends in parallel and distributed systems for remote sensing applications, this paper explores three HPC-based paradigms for efficient implementation of the Pixel Purity Index (PPI) algorithm, available from the popular Kodak's Research Systems ENVI software package, as a representative case study for demonstration purposes. Several different parallel programming techniques are used to improve the performance of the PPI on a variety of parallel platforms, including a set of message passing interface (MPI)-based implementations on a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center in Maryland and on a variety of heterogeneous networks of workstations at University of Maryland; a Handel-C implementation of the algorithm on a Virtex-II field programmable gate array (FPGA); and a compute unified device architecture (CUDA)-based implementation on graphical processing units (GPUs) of NVidia. Combined, these parts deliver an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of adapting HPC systems to remote sensing problems.\",\n \"Title: The Promise of Reconfigurable Computing for Hyperspectral Imaging Onboard Systems: A Review and Trends\\nYear: 2013\\n\\nAbstract: Hyperspectral imaging is an important technique in remote sensing which is characterized by high spectral resolutions. With the advent of new hyperspectral remote sensing missions and their increased temporal resolutions, the availability and dimensionality of hyperspectral data is continuously increasing. This demands fast processing solutions that can be used to compress and/or interpret hyperspectral data onboard spacecraft imaging platforms in order to reduce downlink connection requirements and perform a more efficient exploitation of hyperspectral data sets in various applications. Over the last few years, reconfigurable hardware solutions such as field-programmable gate arrays (FPGAs) have been consolidated as the standard choice for onboard remote sensing processing due to their smaller size, weight, and power consumption when compared with other high-performance computing systems, as well as to the availability of more FPGAs with increased tolerance to ionizing radiation in space. Although there have been many literature sources on the use of FPGAs in remote sensing in general and in hyperspectral remote sensing in particular, there is no specific reference discussing the state-of-the-art and future trends of applying this flexible and dynamic technology to such missions. In this work, a necessary first step in this direction is taken by providing an extensive review and discussion of the (current and future) capabilities of reconfigurable hardware and FPGAs in the context of hyperspectral remote sensing missions. The review covers both technological aspects of FPGA hardware and implementation issues, providing two specific case studies in which FPGAs are successfully used to improve the compression and interpretation (through spectral unmixing concepts) of remotely sensed hyperspectral data. Based on the two considered case studies, we also highlight the major challenges to be addressed in the near future in this emerging and fast growing research area.\",\n \"Title: On the Use of Cluster Computing Architectures for Implementation of Hyperspectral Image Analysis Algorithms\\nYear: 2005\\n\\nAbstract: Hyperspectral sensors represent the most advanced instruments currently available for remote sensing of the Earth. The high spatial and spectral resolution of the images supplied by systems like the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS), developed by NASA Jet Propulsion Laboratory, allows their exploitation in diverse applications, such as detection and control of wild fires and hazardous agents in water and atmosphere, detection of military targets and management of natural resources. Even though the above applications require a response in real time, few solutions are available to provide fast and efficient analysis of these types of data. This is mainly caused by the dimensionality of hyperspectral images, which limits their exploitation in analysis scenarios where the spatial and temporal requirements are very high. In the present work, we describe a new parallel methodology which deals with most of the previously addressed problems. The computational performance of the proposed analysis methodology is evaluated using two parallel computer systems, an SGI Origin 2000 shared memory system located at the European Center of Parallelism of Barcelona, and the Thunderhead Beowulf cluster at NASAýs Goddard Space Flight Center.\",\n \"Title: A New Minimum-Volume Enclosing Algorithm for Endmember Identification and Abundance Estimation in Hyperspectral Data\\nYear: 2012\\n\\nAbstract: Spectral unmixing is an important technique for hyperspectral data exploitation, in which a mixed spectral signature is decomposed into a collection of spectrally pure constituent spectra, called endmembers, and a set of correspondent fractions, or abundances, that indicate the proportion of each endmember present in the mixture. Over the last years, several algorithms have been developed for automatic or semiautomatic endmember extraction. Some available approaches assume that the input data set contains at least one pure spectral signature for each distinct material and further conduct a search for the most spectrally pure signatures in the high-dimensional space spanned by the hyperspectral data. Among these approaches, those aimed at maximizing the volume of the simplex that can be formed using available spectral signatures have found wide acceptance. However, the presence of spectrally pure constituents is unlikely in remotely sensed hyperspectral scenes due to spatial resolution, mixing phenomena, and other considerations. In order to address this issue, other available algorithms have been developed to generate virtual endmembers (not necessarily present among the input data samples) by finding the simplex with minimum volume that encloses all available observations. In this paper, we discuss maximum-volume versus minimum-volume enclosing solutions and further develop a novel algorithm in the latter category which incorporates the fractional abundance estimation as an internal step of the endmember searching process (i.e., it does not require an external method to produce endmember fractional abundances). The method is based on iteratively enclosing the observations in a lower dimensional space and removing observations that are most likely not to be enclosed by the simplex of the endmembers to be estimated. The performance of the algorithm is investigated and compared to that of other algorithms (with and without the pure pixel assumption) using synthetic a- d real hyperspectral data sets collected by a variety of hyperspectral imaging instruments.\",\n \"Title: A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral Data\\nYear: 2004\\n\\nAbstract: Linear spectral unmixing is a commonly accepted approach to mixed-pixel classification in hyperspectral imagery. This approach involves two steps. First, to find spectrally unique signatures of pure ground components, usually known as endmembers, and, second, to express mixed pixels as linear combinations of endmember materials. Over the past years, several algorithms have been developed for auton...\",\n \"Title: Parallel CBIR System for Efficient Hyperspectral Image Retrieval from Heterogeneous Networks of Workstations\\nYear: 2007\\n\\nAbstract: The purpose of content-based image retrieval (CBIR) is to retrieve, from real data stored in a database, information that is relevant to a query. In remote sensing applications, the wealth of spectral information provided by last-generation (hyperspectral) instruments has quickly introduced the need for parallel CBIR systems able to effectively retrieve features of interest from ever-growing data archives. To address this need, this paper develops a new parallel CBIR system which has been specifically designed to be run on heterogeneous networks of computers (HNOCs). These platforms have soon become a standard computing architecture in remote sensing missions due to the distributed nature of data repositories. The proposed heterogeneous system first extracts an image feature vector able to characterize image content with sub-pixel precision, and then uses the obtained feature as a search reference. The system is validated using a complex hyperspectral image database, and implemented on several networks of workstations at University of Maryland.\",\n \"Title: Commodity cluster-based parallel processing of hyperspectral imagery\\nYear: 2006\\n\\nAbstract: The rapid development of space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral Earth observing sensors such as the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of fast techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. This paper describes commodity cluster-based parallel data analysis strategies for hyperspectral imagery, a new class of image data that comprises hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. An unsupervised technique that integrates the spatial and spectral information in the image data using multi-channel morphological transformations is parallelized and compared to other available parallel algorithms. The code's portability, reusability and scalability are illustrated by using two high-performance parallel computing architectures: a distributed memory, multiple instruction multiple data (MIMD)-style multicomputer at European Center for Parallelism of Barcelona, and a Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results suggest that Beowulf clusters are a source of computational power that is both accessible and applicable to obtaining results in valid response times in information extraction applications from hyperspectral imagery.\",\n \"Title: Automated generation of semi-labeled training samples for nonlinear neural network-based abundance estimation in hyperspectral data\\nYear: 2005\\n\\nAbstract: As the initial stage of a supervised classification, the quality or training has a significant effect oil the entire classification process and its accuracy. In hyperspectral data analysis, a judicious selection of training samples call be tremendously difficult due to the presence of subpixel targets and mixed pixels, in particular, when no prior knowledge about the data is available. For instance, the Multi-Layer Perceptron (MLP) neural network call provide very accurate nonlinear estimations of fractional abundances, provided that the training set contains all possible mixture conditions. However, the requirement of large volumes of training data is a serious limitation in remote sensing because, even if classes concurring to a per-pixel cover class mixture are known, proportions of these classes are very difficult to be estimated a priori. This paper investigates, explores and further proposes solutions to resolve the issues above. Specifically, we develop a nonlinear neural net-work-based mixture model, coupled with unsupervised algorithms for automated generation of semi-labeled samples that can be effectively used for mixed pixel classification. These unsupervised algorithms, intended for situations where ancilliary-information is difficult to be collected prior to data analysis, rely on the principle that patterns that lie close to the location of decision boundaries in feature space are more informative than patterns drawn from the class cores. Computer simulations and real experiments are conducted for performance analysis of nonlinear unmixing techniques based on training samples.\",\n \"Title: Improving the scalability of hyperspectral imaging applications on heterogeneous platforms using adaptive run-time data compression\\nYear: 2010\\n\\nAbstract: Latest generation remote sensing instruments (called hyperspectral imagers) are now able to generate hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have reported that the scalability of parallel processing algorithms dealing with these high-dimensional data volumes is affected by the amount of data to be exchanged through the communication network of the system. However, large messages are common in hyperspectral imaging applications since processing algorithms are pixel-based, and each pixel vector to be exchanged through the communication network is made up of hundreds of spectral values. Thus, decreasing the amount of data to be exchanged could improve the scalability and parallel performance. In this paper, we propose a new framework based on intelligent utilization of wavelet-based data compression techniques for improving the scalability of a standard hyperspectral image processing chain on heterogeneous networks of workstations. This type of parallel platform is quickly becoming a standard in hyperspectral image processing due to the distributed nature of collected hyperspectral data as well as its flexibility and low cost. Our experimental results indicate that adaptive lossy compression can lead to improvements in the scalability of the hyperspectral processing chain without sacrificing analysis accuracy, even at sub-pixel precision levels.\",\n \"Title: Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields.\\nYear: 2012\\n\\nAbstract: This paper introduces a new supervised segmentation algorithm for remotely sensed hyperspectral image data which integrates the spectral and spatial information in a Bayesian framework. A multinomial logistic regression (MLR) algorithm is first used to learn the posterior probability distributions from the spectral information, using a subspace projection method to better characterize noise and hi...\",\n \"Title: Generalized Composite Kernel Framework for Hyperspectral Image Classification\\nYear: 2013\\n\\nAbstract: This paper presents a new framework for the development of generalized composite kernel machines for hyperspectral image classification. We construct a new family of generalized composite kernels which exhibit great flexibility when combining the spectral and the spatial information contained in the hyperspectral data, without any weight parameters. The classifier adopted in this work is the multinomial logistic regression, and the spatial information is modeled from extended multiattribute profiles. In order to illustrate the good performance of the proposed framework, support vector machines are also used for evaluation purposes. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the proposed framework leads to state-of-the-art classification performance in complex analysis scenarios.\",\n \"Title: Real-Time Endmember Extraction on Multicore Processors.\\nYear: 2011\\n\\nAbstract: In this letter, we discuss the use of multicore processors in the acceleration of endmember extraction algorithms for hyperspectral image unmixing. Specifically, we develop computationally efficient versions of two popular fully automatic endmember extraction algorithms: orthogonal subspace projection and N-FINDR. Our experimental results, based on the analysis of hyperspectral data collected by t...\",\n \"Title: Performance versus energy consumption of hyperspectral unmixing algorithms on multi-core platforms.\\nYear: 2013\\n\\nAbstract: Hyperspectral imaging is a growing area in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the Earth. Hyperspectral images are extremely high-dimensional, and require on-board processing algorithms able to satisfy near real-time constraints in applications such as wildland fire monitoring, mapping of oil spills and chemical contamination, etc. One of the most widely used techniques for analyzing hyperspectral images is spectral unmixing, which allows for sub-pixel data characterization. This is particularly important since the available spatial resolution in hyperspectral images is typically of several meters, and therefore it is reasonable to assume that several spectrally pure substances (called endmembers in hyperspectral imaging terminology) can be found within each imaged pixel. There have been several efforts towards the efficient implementation of hyperspectral unmixing algorithms on architectures susceptible of being mounted onboard imaging instruments, including field programmable gate arrays (FPGAs) and graphics processing units (GPUs). While FPGAs are generally difficult to program, GPUs are difficult to adapt to onboard processing requirements in spaceborne missions due to its extremely high power consumption. In turn, with the increase in the number of cores, multi-core platforms have recently emerged as an easier to program platform compared to FPGAs, and also more tolerable radiation and power consumption requirements. However, a detailed assessment of the performance versus energy consumption of these architectures has not been conducted as of yet in the field of hyperspectral imaging, in which it is particularly important to achieve processing results in real-time. In this article, we provide a thoughtful perspective on this relevant issue and further analyze the performance versus energy consumption ratio of different processing chains for spectral unmixing when implemented on multi-core platforms.\",\n \"Title: Impact of platform heterogeneity on the design of parallel algorithms for morphological processing of high-dimensional image data\\nYear: 2007\\n\\nAbstract: The main objective of this paper is to describe a realistic framework to understand parallel performance of high-dimensional image processing algorithms in the context of heterogeneous networks of workstations (NOWs). As a case study, this paper explores techniques for mapping hyperspectral image analysis techniques onto fully heterogeneous NOWs. Hyperspectral imaging is a new technique in remote sensing that has gained tremendous popularity in many research areas, including satellite imaging and aerial reconnaissance. The automation of techniques able to transform massive amounts of hyperspectral data into scientific understanding in valid response times is critical for space-based Earth science and planetary exploration. Using an evaluation strategy which is based on comparing the efficiency achieved by an heterogeneous algorithm on a fully heterogeneous NOW with that evidenced by its homogeneous version on a homogeneous NOW with the same aggregate performance as the heterogeneous one, we develop a detailed analysis of parallel algorithms that integrate the spatial and spectral information in the image data through mathematical morphology concepts. For comparative purposes, performance data for the tested algorithms on Thunderhead (a large-scale Beowulf cluster at NASA's Goddard Space Flight Center) are also provided. Our detailed investigation of the parallel properties of the proposed morphological algorithms provides several intriguing findings that may help image analysts in selection of parallel techniques and strategies for specific applications.\",\n \"Title: Cluster versus GPU implementation of an Orthogonal Target Detection Algorithm for Remotely Sensed Hyperspectral Images\\nYear: 2010\\n\\nAbstract: Remotely sensed hyperspectral imaging instruments provide high-dimensional data containing rich information in both the spatial and the spectral domain. In many surveillance applications, detecting objects (targets) is a very important task. In particular, algorithms for detecting (moving or static) targets, or targets that could expand their size (such as propagating fires) often require timely responses for swift decisions that depend upon high computing performance of algorithm analysis. In this paper, we develop parallel versions of a target detection algorithm based on orthogonal subspace projections. The parallel implementations are tested in two types of parallel computing architectures: a massively parallel cluster of computers called Thunderhead and available at NASA’s Goddard Space Flight Center in Maryland, and a commodity graphics processing unit (GPU) of NVidia GeForce GTX 275 type. While the cluster-based implementation reveals itself as appealing for information extraction from remote sensing data already transmitted to Earth, the GPU implementation allows us to perform near real-time anomaly detection in hyperspectral scenes, with speedups over 50x with regards to a highly optimized serial version. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system over the World Trade Center (WTC) in New York, five days after the attacks that collapsed the two main towers in the WTC complex.\",\n \"Title: High-performance computing in remotely sensed hyperspectral imaging: the pixel purity index algorithm as a case study\\nYear: 2006\\n\\nAbstract: The incorporation of last-generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral imaging is a new technique in remote sensing that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. The price paid for such a wealth of spectral information available from latest-generation sensors is the enormous amounts of data that they generate. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) models in remote sensing missions. This paper explores three HPC-based paradigms for efficient information extraction from remote sensing data using the Pixel Purity Index (PPI) algorithm (available from the popular Kodak's Research Systems ENVI software) as a case study for algorithm optimization. The three considered approaches are: 1) Commodity cluster-based parallel computing; 2) Distributed computing using heterogeneous networks of workstations; and 3) FPGAbased hardware implementations. Combined, these parts deliver an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of adapting HPC models to remote sensing problems.\",\n \"Title: Semisupervised Hyperspectral Image Classification Using Soft Sparse Multinomial Logistic Regression\\nYear: 2013\\n\\nAbstract: In this letter, we propose a new semisupervised learning (SSL) algorithm for remotely sensed hyperspectral image classification. Our main contribution is the development of a new soft sparse multinomial logistic regression model which exploits both hard and soft labels. In our terminology, these labels respectively correspond to labeled and unlabeled training samples. The proposed algorithm represents an innovative contribution with regard to conventional SSL algorithms that only assign hard labels to unlabeled samples. The effectiveness of our proposed method is evaluated via experiments with real hyperspectral images, in which comparisons with conventional semisupervised self-learning algorithms with hard labels are carried out. In such comparisons, our method exhibits state-of-the-art performance.\",\n \"Title: Design and Implementation of a Parallel Heterogeneous Algorithm for Hyperspectral Image Analysis Using HeteroMPI\\nYear: 2006\\n\\nAbstract: The development of efficient techniques for transforming the massive volume of remotely sensed hyperspectral data collected on a daily basis into scientific understanding is critical for space-based Earth science and planetary exploration. Although most available parallel processing strategies for hyperspectral image analysis assume homogeneity in the computing platform, heterogeneous networks of computers represent a promising cost-effective solution expected to play a major role in many on-going and planned remote sensing missions. To address the need for cost-effective parallel hyperspectral imaging algorithms, this paper develops an innovative heterogeneous parallel algorithm for spatial/spectral morphological analysis of hyperspectral image data. The algorithm has been developed using Heterogeneous MPI (HeteroMPI), an extension of MPI for programming high-performance computations on heterogeneous networks of computers. Experimental results are presented and discussed in the context of a realistic application, based on hyperspectral data collected by NASA's Jet Propulsion Laboratory.\",\n \"Title: Spectral-Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization.\\nYear: 2014\\n\\nAbstract: Remotely sensed hyperspectral image classification is a very challenging task. This is due to many different aspects, such as the presence of mixed pixels in the data or the limited information available a priori. This has fostered the need to develop techniques able to exploit the rich spatial and spectral information present in the scenes while, at the same time, dealing with mixed pixels and li...\",\n \"Title: Use of FPGA or GPU-based architectures for remotely sensed hyperspectral image processing\\nYear: 2013\\n\\nAbstract: Hyperspectral imaging is a growing area in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the Earth. Hyperspectral images are extremely high-dimensional, and require advanced on-board processing algorithms able to satisfy near real-time constraints in applications such as wildland fire monitoring, mapping of oil spills and chemical contamination, etc. One of the most widely used techniques for analyzing hyperspectral images is spectral unmixing, which allows for sub-pixel data characterization. This is particularly important since the available spatial resolution in hyperspectral images is typically of several meters, and therefore it is reasonable to assume that several spectrally pure substances (called endmembers in hyperspectral imaging terminology) can be found within each imaged pixel. In this paper we explore the role of hardware accelerators in hyperspectral remote sensing missions and further inter-compare two types of solutions: field programmable gate arrays (FPGAs) and graphics processing units (GPUs). A full spectral unmixing chain is implemented and tested in this work, using both types of accelerators, in the context of a real hyperspectral mapping application using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). The paper provides a thoughtful perspective on the potential and emerging challenges of applying these types of accelerators in hyperspectral remote sensing missions, indicating that the reconfigurability of FPGA systems (on the one hand) and the low cost of GPU systems (on the other) open many innovative perspectives toward fast on-board and on-the-ground processing of remotely sensed hyperspectral images.\",\n \"Title: On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images\\nYear: 2009\\n\\nAbstract: In this work, neural network-based models involved in hyperspectral image spectra separation are considered. Focus is on how to select the most highly informative samples for effectively training the neural architecture. This issue is addressed here by several new algorithms for intelligent selection of training samples: (1) a border-training algorithm (BTA) which selects training samples located in the vicinity of the hyperplanes that can optimally separate the classes; (2) a mixed-signature algorithm (MSA) which selects the most spectrally mixed pixels in the hyperspectral data as training samples; and (3) a morphological-erosion algorithm (MEA) which incorporates spatial information (via mathematical morphology concepts) to select spectrally mixed training samples located in spatially homogeneous regions. These algorithms, along with other standard techniques based on orthogonal projections and a simple Maximin-distance algorithm, are used to train a multi-layer perceptron (MLP), selected in this work as a representative neural architecture for spectral mixture analysis. Experimental results are provided using both a database of nonlinear mixed spectra with absolute ground truth and a set of real hyperspectral images, collected at different altitudes by the digital airborne imaging spectrometer (DAIS 7915) and reflective optics system imaging spectrometer (ROSIS) operating simultaneously at multiple spatial resolutions.\",\n \"Title: AMEEPAR: parallel morphological algorithm for hyperspectral image classification on heterogeneous networks of workstations\\nYear: 2006\\n\\nAbstract: Hyperspectral imaging is a new technique in remote sensing that generates hundreds of images corresponding to different wavelength channels for the same area on the surface of the Earth. Most available techniques for hyperspectral image classification focus on analyzing the data without incorporating the spatial information; i.e. the data is treated not as an image but as an unordered listing of spectral measurements where the spatial coordinates can be shuffled arbitrarily without affecting the final analysis. Despite the growing interest in the development of techniques for interpretation and classification of such high-dimensional imagery, only a few efforts devoted to the design of parallel implementations exist in the open literature. In this paper, we describe AMEEPAR, a parallel morphological algorithm that integrates the spatial and spectral information. The algorithm has been specifically optimized in this work for execution on heterogeneous networks of workstations. The parallel properties and classification accuracy of the proposed approach are evaluated using four networks of workstations distributed among different locations, and a massively parallel Beowulf cluster at NASA’s Goddard Space Flight Center.\",\n \"Title: Network Performance Testing System Integrating Models for Automatic QoE Evaluation of Popular Services: YouTube and Facebook\\nYear: 2015\\n\\nAbstract: Smartphones have significantly changed the traffic pattern usage of mobile subscribers. In addition to the classical file sharing and web browsing usage, social networking applications and access to video content are now a significant percentage of the typical user data traffic consumption. As the change in usage pattern affects the performance of mobile networks, operators need methods to estimate how well the network behaves. The metrics to use for the evaluation of the quality of experience (QoE) are only well documented for traditional applications. In this paper a novel QoE system that automatically captures and processes the metrics needed to evaluate two commonly used applications, the social networking application Facebook and the video download application YouTube, is presented. The system uses a test application developed for Android phones, which report the results to a centralized platform capable of processing and presenting the results in different formats. The system has been used to test those two services over 3G/LTE network technologies. In the case of the YouTube services, performance results show a subjective score very similar for the 3G and LTE because of the automatic adaptation of the video coding rate to the network conditions. Testing of the Facebook application has led to the proposal of a new MOS expression that takes into account the content type being posted, which provides more reasonable MOS estimations than the generic web browsing models available in the literature and commonly used to estimate Facebook QoE.\",\n \"Title: Informative Change Detection by Unmixing for Hyperspectral Images\\nYear: 2015\\n\\nAbstract: Applying spectral unmixing on a series of multitemporal hyperspectral images for change detection has the potential to reveal important subpixel-level information, such as the abundance variation of each underlying material in a given location or the change in the distribution of materials throughout the scene, with time or resulting from significant events such as a natural disaster. However, change detection by spectral unmixing for hyperspectral images has not been extensively studied up to now, and most studies have been limited to specific cases and data sets. This is caused by the scarcity of real multitemporal hyperspectral data and the inherent difficulties in applying unmixing to multitemporal hyperspectral data in a coherent way. In this letter, we investigate change detection for hyperspectral images by spectral unmixing and systematically present the advantages that can be gained by using such an approach, supported by experimental studies conducted on carefully prepared synthetic data sets and also with real data sets.\",\n \"Title: Learning Discriminative Sparse Representations for Hyperspectral Image Classification\\nYear: 2015\\n\\nAbstract: In sparse representation (SR) driven hyperspectral image classification, signal-to-reconstruction rule based classification may lack generalization performance. In order to overcome this limitation, we presents a new method for discriminative sparse representation of hyperspectral data by learning a reconstructive dictionary and a discriminative classifier in a SR model regularized with total variation (TV). The proposed method features the following components. First, we adopt a spectral unmixing by variable splitting augmented Lagrangian and TV method to guarantee the spatial homogeneity of sparse representations. Second, we embed dictionary learning in the method to enhance the representative power of sparse representations via gradient descent in a class-wise manner. Finally, we adopt a sparse multinomial logistic regression (SMLR) model and design a class-oriented optimization strategy to obtain a powerful classifier, which improves the performance of the learnt model for specific classes. The first two components are beneficial to produce discriminative sparse representations. Whereas, adopting SMLR allows for effectively modeling the discriminative information. Experimental results with both simulated and real hyperspectral data sets in a number of experimental comparisons with other related approaches demonstrate the superiority of the proposed method.\",\n \"Title: Fast and Reliable Noise Estimation for Hyperspectral Subspace Identification\\nYear: 2015\\n\\nAbstract: In this letter, we introduce an efficient algorithm to estimate the noise correlation matrix in the initial stage of the hyperspectral signal identification by minimum error (HySime) method, commonly used for signal subspace identification in remotely sensed hyperspectral images. Compared with the current implementations of this stage, the new algorithm for noise estimation relies on the reliable QR factorization, producing correct results even when operating with single-precision arithmetic. Additionally, our algorithm exhibits a lower computational cost, and it is highly parallel. The experiments on a multicore server, using two real hyperspectral scenes, expose that these theoretical advantages carry over to the practical results.\",\n \"Title: Fast determination of the number of endmembers for real-time hyperspectral unmixing on GPUs\\nYear: 2014\\n\\nAbstract: Spectral unmixing is a very important task for remotely sensed hyperspectral data exploitation. It amounts at identifying a set of spectrally pure components (called endmembers) and their associated per-pixel coverage fractions (called abundances). A challenging problem in spectral unmixing is how to determine the number of endmembers in a given scene. Several automatic techniques exist for this purpose, including the virtual dimensionality (VD) concept or the hyperspectral signal identification by minimum error (HySime). Due to the complexity and high dimensionality of hyperspectral scenes, these techniques are computationally expensive. In this paper, we develop new fast implementations of VD and HySime using commodity graphics processing units. The proposed parallel implementations are validated in terms of accuracy and computational performance, showing significant speedups with regards to optimized serial implementations. The newly developed implementations are integrated in a fully operational unmixing chain which exhibits real-time performance with regards to the time that the hyperspectral instrument takes to collect the image data.\",\n \"Title: MUSIC-CSR: Hyperspectral Unmixing via Multiple Signal Classification and Collaborative Sparse Regression\\nYear: 2014\\n\\nAbstract: Spectral unmixing aims at finding the spectrally pure constituent materials (also called endmembers) and their respective fractional abundances in each pixel of a hyperspectral image scene. In recent years, sparse unmixing has been widely used as a reliable spectral unmixing methodology. In this approach, the observed spectral vectors are expressed as linear combinations of spectral signatures assumed to be known a priori and presented in a large collection, termed spectral library or dictionary, usually acquired in laboratory. Sparse unmixing has attracted much attention as it sidesteps two common limitations of classic spectral unmixing approaches, namely, the lack of pure pixels in hyperspectral scenes and the need to estimate the number of endmembers in a given scene, which are very difficult tasks. However, the high mutual coherence of spectral libraries, jointly with their ever-growing dimensionality, strongly limits the operational applicability of sparse unmixing. In this paper, we introduce a two-step algorithm aimed at mitigating the aforementioned limitations. The algorithm exploits the usual low dimensionality of the hyperspectral data sets. The first step, which is similar to the multiple signal classification array signal processing algorithm, identifies a subset of the library elements, which contains the endmember signatures. Because this subset has cardinality much smaller than the initial number of library elements, the sparse regression we are led to is much more well conditioned than the initial one using the complete library. The second step applies collaborative sparse regression, which is a form of structured sparse regression, exploiting the fact that only a few spectral signatures in the library are active. The effectiveness of the proposed approach, termed MUSIC-CSR, is extensively validated using both simulated and real hyperspectral data sets.\",\n \"Title: A Subspace-Based Multinomial Logistic Regression for Hyperspectral Image Classification\\nYear: 2014\\n\\nAbstract: In this letter, we propose a multinomial-logistic-regression method for pixelwise hyperspectral classification. The feature vectors are formed by the energy of the spectral vectors projected on class-indexed subspaces. In this way, we model not only the linear mixing process that is often present in the hyperspectral measurement process but also the nonlinearities that are separable in the feature space defined by the aforementioned feature vectors. Our experimental results have been conducted using both simulated and real hyperspectral data sets, which are collected using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the Reflective Optics System Imaging Spectrographic (ROSIS) system. These results indicate that the proposed method provides competitive results in comparison with other state-of-the-art approaches.\",\n \"Title: Collaborative Sparse Regression for Hyperspectral Unmixing\\nYear: 2014\\n\\nAbstract: Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In this paper, we present a refinement of the sparse unmixing methodology recently introduced which exploits the usual very low number of endmembers present in real images, out of a very large library. Specifically, we adopt the collaborative (also called “multitask” or “simultaneous”) sparse regression framework that improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. Our experimental results with both synthetic and real hyperspectral data sets show clearly the advantages obtained using the new joint sparse regression strategy, compared with the pixelwise independent approach.\",\n \"Title: Semi-supervised active learning for urban hyperspectral image classification\\nYear: 2012\\n\\nAbstract: In this paper, we develop a new framework for semi-supervised learning which exploits active learning for unlabeled sample selection in hyperspectral data classification. Specifically, we use active learning to select the most informative unlabeled training samples with the ultimate goal of systematically achieving noticeable improvements in classification results with regard to those found by randomly selected training sets of the same size. Our experimental results, conducted with an urban hyperspectral scene collected by the Reflective Optics Spectrographic Imaging Instrument (ROSIS) of the Deutschen Zentrum for Luftund Raumfahrt (DLR, the German Aerospace Agency) over the city of Pavia, Italy, indicate that using active learning for unlabeled sample selection represents an effective and promising strategy in the context of urban hyperspectral data classification.\",\n \"Title: Parallel implementation of a hyperspectral unmixing chain: Graphic processing units versus multi-core processors\\nYear: 2012\\n\\nAbstract: Spectral unmixing is an important tast for remotely sensed hyperspectral imaging. Spectral unmixing algorithms can be computationally expensive. In this paper, we develop two efficient implementations of a full hyperspectral unmixing chain on two different kinds of high performance computing architectures: graphics processing units (GPUs) and multi-core processors. These platforms are inter-compared in the context of hyperspectral unmixing. Our experimental results reveal that both GPUs and multi-core processors can provide real-time unmixing performance when properly implemented in this kind of parallel platforms.\",\n \"Title: HyperMix: A new tool for quantitative evaluation of end member identification and spectral unmixing techniques\\nYear: 2012\\n\\nAbstract: In this paper, we present a new open source system for evaluating and inter-comparing new spectral unmixing applications. The proposed tool, called HyperMix, comprises several open source implementations of algorithms for end member identification and spectral unmixing. The tool also includes a database of synthetic hyperspectral images (generated using fractals to simulate natural patterns) which can be used to evaluate the precision of the algorithms for end member identification and abundance estimation which are already incorporated in the tool. The paper also presents an exhaustive inter-comparison of algorithms for end member extraction and abundance estimation using the proposed tool.\",\n \"Title: Hyperspectral coded aperture (HYCA): A new technique for hyperspectral compressive sensing\\nYear: 2012\\n\\nAbstract: Hyperspectral imaging is an active research area in remote sensing. Due to the high volume of hyperspectral image data, the exploration of compression strategies has received a lot of attention in recent years. In this paper, we introduce a new compressed sensing methodology, termed Hyperspectral coded aperture (HYCA), which exploits the high correlation existing among the components of remotely sensed hyperspectral data sets to reduce the number of measurements necessary to correctly reconstruct the original data. HYCA relies on two central properties of most hyperspectral images: the spectral vectors live on a low dimensional subspace and the spectral bands are piecewise smooth. The former property allows to represent the data vectors using a small number of coordinates, and the latter implies that each coordinate is piecewise smooth and thus compressible on local differences. The reconstruction of the data cube is obtained by minimizing a convex objective function containing a data term associated to the compressed measurements and a total variation spatial regularizer. A series of experiments with simulated and real data show the effectiveness of the newly developed HYCA, indicating that the proposed scheme has a high potential in real-world applications.\",\n \"Title: Collaborative sparse unmixing of hyperspectral data\\nYear: 2012\\n\\nAbstract: Sparse unmixing aims at estimating the constituent materials (endmembers) and their respective fractional abundances in each pixel of a hyperspectral image by assuming that the endmembers are present in a large collection of pure spectral signatures (spectral library), known a priori. In this paper, we propose a refinement of the sparse unmixing approach by taking into account the fact that all the pixels of the image share the same set of endmembers, thus lying in a lower dimensional subspace. Our idea is based on the collaborative lasso, which enforces sparsity across the pixels. The goal of this line of attack is to obtain higher accuracy of the estimated fractional abundances, at the same time with a decrease in the number of endmembers used to explain the observed data. The experimental results, obtained with both simulated and real data, confirm the potential of the proposed approach in the unmixing problem.\",\n \"Title: Efficient information extraction from hyperspectral imagery using networks of workstations\\nYear: 2005\\n\\nAbstract: The rapid development in space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral sensors such as the Airborne VisibleInfrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of efficient techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. Heterogeneous networks of workstations are a very promising cost-effective parallel computing architecture. Unlike traditional homogeneous parallel platforms, heterogeneous architectures are composed of processors running at different speeds. This heterogeneity results in distributed-memory parallel computing systems created from commodity components that can satisfy, specific computational requirements for the Earth and space sciences community. This paper explores techniques for mapping hyperspectral image analysis algorithms onto heterogeneous networks of workstations. Important aspects in algorithm design such as portability, reusability and scalability are illustrated by using homogeneous and heterogeneous parallel computing facilities at NASA's Goddard Space Flight Center and, European Center for Parallelism of Barcelona, and University of Extremadura in Spain. Hyperspectral image data from the AVIRIS data repository is used in experiments, which reveal that heterogeneous networks of workstations are a source of computational power that is both accessible and applicable to obtaining results quickly enough for practical use in information extraction applications from hyperspectral imagery.\",\n \"Title: Parallel Detection of Targets in Hyperspectral Images Using Heterogeneous Networks of Workstations\\nYear: 2007\\n\\nAbstract: Heterogeneous networks of workstations have rapidly become a cost-effective computing solution in many application areas. This paper develops several highly innovative parallel algorithms for target detection in hyperspectral imagery, considered to be a crucial goal in remote sensing-based homeland security and defense applications. In order to illustrate parallel performance, we consider four (partially and fully) heterogeneous networks of workstations distributed among different locations at University of Maryland, and also a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results indicate that heterogeneous networks can be used as a viable low-cost alternative to homogeneous parallel systems in many on-going and planned remote sensing missions\",\n \"Title: Spatial Preprocessing for Endmember Extraction\\nYear: 2009\\n\\nAbstract: Endmember extraction is the process of selecting a collection of pure signature spectra of the materials present in a remotely sensed hyperspectral scene. These pure signatures are then used to decompose the scene into abundance fractions by means of a spectral unmixing algorithm. Most techniques avail- able in the endmember extraction literature rely on exploiting the spectral properties of the data alone. As a result, the search for endmembers in a scene is conducted by treating the data as a collection of spectral measurements with no spatial arrangement. In this paper, we propose a novel strategy to incorporate spatial information into the traditional spectral-based endmember search process. Specifically, we propose to estimate, for each pixel vector, a scalar spatially derived factor that relates to the spectral simi- larity of pixels lying within a certain spatial neighborhood. This scalar value is then used to weigh the importance of the spectral information associated to each pixel in terms of its spatial context. Two key aspects of the proposed methodology are given as follows: 1) No modification of existing image spectral-based endmember extraction methods is necessary in order to apply the proposed approach. 2) The proposed preprocessing method enhances the search for image spectral endmembers in spatially homogeneous areas. Our experimental results, which were obtained using both synthetic and real hyperspectral data sets, indicate that the spec- tral endmembers obtained after spatial preprocessing can be used to accurately model the original hyperspectral scene using a linear mixture model. The proposed approach is suitable for jointly combining spectral and spatial information when searching for image-derived endmembers in highly representative hyperspectral image data sets.\",\n \"Title: An overview on hyperspectral unmixing: Geometrical, statistical, and sparse regression based approaches\\nYear: 2011\\n\\nAbstract: Hyperspectral instruments acquire electromagnetic energy scattered within their ground instantaneous field view in hundreds of spectral channels with high spectral resolution. Very often, however, owing to low spatial resolution of the scanner or to the presence of intimate mixtures (mixing of the materials at a very small scale) in the scene, the spectral vectors (collection of signals acquired at different spectral bands from a given pixel) acquired by the hyperspectral scanners are actually mixtures of the spectral signatures of the materials present in the scene. Given a set of mixed spectral vectors, spectral mixture analysis (or spectral unmixing) aims at estimating the number of reference materials, also called endmembers, their spectral signatures, and their fractional abundances. Spectral unmixing is, thus, a source separation problem. This paper presents an overview of the principal research directions in hyperspectral unmixing. The paper is organized into six main topics: i) mixing models, ii) signal subspace identification, iii) geometrical-based spectral unmixing, iv) statistical-based spectral unmixing, v) sparse regression based unmixing, and vi) spatial-contextual information. For each topic, we summarize what is the mathematical problem involved and give relevant pointers to state-of-the-art algorithms to address these problems.\",\n \"Title: Noise-robust spatial preprocessing prior to endmember extraction from hyperspectral data\\nYear: 2011\\n\\nAbstract: This paper develops a noise-robust spatial preprocessing module which can be used prior to spectral unmixing of remotely sensed hyperspectral images. The method first derives a spatial homogeneity index which is relatively insensitive to the noise present in the original hyperspectral data. Then, it fuses this index with a spectral-based classification, obtaining a set of pure regions which are used to guide the unmixing process. An experimental comparison of the proposed method with other spatial-spectral unmixing approaches is conducted using both synthetic and real hyperspectral data collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Our experiments indicate that spectral unmixing can benefit from the proposed pre-processing approach, in particular, when the noise level present in the original hypespectral scene is relatively high.\",\n \"Title: Hyperspectral unmixingwith sparse group lasso\\nYear: 2011\\n\\nAbstract: Sparse unmixing has been recently introduced as a mechanism to characterize mixed pixels in remotely sensed hyper-spectral images. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In available spectral libraries, it is observed that the spectral signatures appear organized in groups (e.g. different alterations of a single mineral in the U.S. Geological Survey spectral library). In this paper, we explore the potential of the sparse group lasso technique in solving hyperspectral unmixing problems. Our introspection in this work is that, when the spectral signatures appear in groups, this technique has the potential to yield better results than the standard sparse regression approach. Experimental results with both synthetic and real hyperspectral data are given to investigate this issue.\",\n \"Title: FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis\\nYear: 2012\\n\\nAbstract: Hyperspectral remote sensing attempts to identify features in the surface of the Earth using sensors that generally provide large amounts of data. The data are usually collected by a satellite or an airborne instrument and sent to a ground station that processes it. The main bottleneck of this approach is the (often reduced) bandwidth connection between the satellite and the station, which drastically limits the information that can be sent and processed in real time. A possible way to overcome this problem is to include onboard computing resources able to preprocess the data, reducing its size by orders of magnitude. Reconfigurable field-programmable gate arrays (FPGAs) are a promising platform that allows hardware/software codesign and the potential to provide powerful onboard computing capability and flexibility at the same time. Since FPGAs can implement custom hardware solutions, they can reach very high performance levels. Moreover, using run-time reconfiguration, the functionality of the FPGA can be updated at run time as many times as needed to perform different computations. Hence, the FPGA can be reused for several applications reducing the number of computing resources needed. One of the most popular and widely used techniques for analyzing hyperspectral data is linear spectral unmixing, which relies on the identification of pure spectral signatures via a so-called endmember extraction algorithm. In this paper, we present the first FPGA design for N-FINDR, a widely used endmember extraction algorithm in the literature. Our system includes a direct memory access module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-4 XC4VFX60 FPGA (a model that is similar to radiation-hardened FPGAs certified for space operation) and tested using real hyperspectral data collected by NASA's Earth Observing-1 Hyperion (a satellite instrument) and the Airborne Visible Infra-- ed Imaging Spectrometer over the Cuprite mining district in Nevada and the Jasper Ridge Biological Preserve in California. Experimental results demonstrate that our hardware version of the N-FINDR algorithm can significantly outperform an equivalent software version and is able to provide accurate results in near real time, which makes our reconfigurable system appealing for onboard hyperspectral data processing.\",\n \"Title: Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing\\nYear: 2012\\n\\nAbstract: Spectral unmixing aims at estimating the fractional abundances of pure spectral signatures (also called endmembers) in each mixed pixel collected by a remote sensing hyperspectral imaging instrument. In recent work, the linear spectral unmixing problem has been approached in semisupervised fashion as a sparse regression one, under the assumption that the observed image signatures can be expressed as linear combinations of pure spectra, known a priori and available in a library. It happens, however, that sparse unmixing focuses on analyzing the hyperspectral data without incorporating spatial information. In this paper, we include the total variation (TV) regularization to the classical sparse regression formulation, thus exploiting the spatial-contextual information present in the hyperspectral images and developing a new algorithm called sparse unmixing via variable splitting augmented Lagrangian and TV. Our experimental results, conducted with both simulated and real hyperspectral data sets, indicate the potential of including spatial information (through the TV term) on sparse unmixing formulations for improved characterization of mixed pixels in hyperspectral imagery.\",\n \"Title: Unmixing sparse hyperspectral mixtures\\nYear: 2009\\n\\nAbstract: Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or atoms ), is a hard combinatorial problem which, as in other areas, has been increasingly addressed. This paper studies the efficiency of the sparse regression techniques in the spectral unmixing problem by conducting a comparison between four different approaches: Moore-Penrose Pseudoinverse, Orthogonal Matching Pursuit (OMP ) (1), Iterative Spectral Mixture Analysis ( ISMA ) (2) and 1 2 l l - sparse regression techniques, which are of widespread use in compressed sensing. We conclude that the 1 2 l l - sparse regression techniques, implemented here by Iterative Shrinkage/Thresholding ( TwIST ) algorithm (3), yield the state-of- the-art in the hyperspectral unmixing area. 2 l l - norm minimization, convex optimization\",\n \"Title: Analysis of different strategies for incorporating spatial information in the design of endmember extraction algorithms from hyperspectral data\\nYear: 2009\\n\\nAbstract: Over the last decade, several algorithms have been developed for automatic or semi-automatic extraction of spectral endmembers from hyperspectral image data. In this paper, we present a thorough analytical comparison of endmember extraction methods which include spatial information in the search of spectral endmembers versus a few algorithms which are exclusively based on spectral information. Our quantitative and comparative assessment of algorithm accuracy and computational performance, conducted using both synthetic and real hyperspectral data, provides interesting findings about the potential benefits that can be obtained after incorporating spatial information into the design of endmember extraction algorithms.\",\n \"Title: Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning\\nYear: 2010\\n\\nAbstract: This paper presents a new semisupervised segmentation algorithm, suited to high-dimensional data, of which remotely sensed hyperspectral image data sets are an example. The algorithm implements two main steps: 1) semisupervised learning of the posterior class distributions followed by 2) segmentation, which infers an image of class labels from a posterior distribution built on the learned class distributions and on a Markov random field. The posterior class distributions are modeled using multinomial logistic regression, where the regressors are learned using both labeled and, through a graph-based technique, unlabeled samples. Such unlabeled samples are actively selected based on the entropy of the corresponding class label. The prior on the image of labels is a multilevel logistic model, which enforces segmentation results in which neighboring labels belong to the same class. The maximum a posteriori segmentation is computed by the α-expansion min-cut-based integer optimization algorithm. Our experimental results, conducted using synthetic and real hyperspectral image data sets collected by the Airborne Visible/Infrared Imaging Spectrometer system of the National Aeronautics and Space Administration Jet Propulsion Laboratory over the regions of Indian Pines, IN, and Salinas Valley, CA, reveal that the proposed approach can provide classification accuracies that are similar or higher than those achieved by other supervised methods for the considered scenes. Our results also indicate that the use of a spatial prior can greatly improve the final results with respect to a case in which only the learned class densities are considered, confirming the importance of jointly considering spatial and spectral information in hyperspectral image segmentation.\",\n \"Title: Sparse Unmixing of Hyperspectral Data\\nYear: 2011\\n\\nAbstract: Linear spectral unmixing is a popular tool in remotely sensed hyperspectral data interpretation. It aims at estimating the fractional abundances of pure spectral signatures (also called as endmembers) in each mixed pixel collected by an imaging spectrometer. In many situations, the identification of the end-member signatures in the original data set may be challenging due to insufficient spatial resolution, mixtures happening at different scales, and unavailability of completely pure spectral signatures in the scene. However, the unmixing problem can also be approached in semisupervised fashion, i.e., by assuming that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In practice, this is a combinatorial problem which calls for efficient linear sparse regression (SR) techniques based on sparsity-inducing regularizers, since the number of endmembers participating in a mixed pixel is usually very small compared with the (ever-growing) dimensionality (and availability) of spectral libraries. Linear SR is an area of very active research, with strong links to compressed sensing, basis pursuit (BP), BP denoising, and matching pursuit. In this paper, we study the linear spectral unmixing problem under the light of recent theoretical results published in those referred to areas. Furthermore, we provide a comparison of several available and new linear SR algorithms, with the ultimate goal of analyzing their potential in solving the spectral unmixing problem by resorting to available spectral libraries. Our experimental results, conducted using both simulated and real hyperspectral data sets collected by the NASA Jet Propulsion Laboratory's Airborne Visible I- - nfrared Imaging Spectrometer and spectral libraries publicly available from the U.S. Geological Survey, indicate the potential of SR techniques in the task of accurately characterizing the mixed pixels using the library spectra. This opens new perspectives for spectral unmixing, since the abundance estimation process no longer depends on the availability of pure spectral signatures in the input data nor on the capacity of a certain endmember extraction algorithm to identify such pure signatures.\",\n \"Title: Improved signal unmixing of vegetation using sparse group selection\\nYear: 2013\\n\\nAbstract: Recently, signal unmixing was proposed in remote sensing with the goal to infer physical parameters of materials of interest, such as vegetation, on the ground. The typical approach uses large collections of pure spectra, called spectral libraries, in which many possible states of the vegetation are modeled by simulated or on-site acquired spectra. Spectra randomly selected from these libraries are used as input to dedicated unmixing methods, such as Multiple Endmember Spectral Mixture Analysis (MESMA). The spectra leading to the lowest reconstruction error are considered to be representative for the materials present in the pixel, such that the physical parameters of the ground vegetation can be inferred. However, the large number of spectra in the library imposes limits to the performance of this combinatorial approach, mainly related to running time constraints. In this paper, we propose the inclusion of a pre-processing step in the processing chain, based on the group lasso, which has the goal of selecting groups of signatures likely to be present in the mixtures. In this sense, the Group Sparse Unmixing via variable Splitting and Augmented Lagrangian (GSUnSAL) algorithm is used. The signatures contained in the groups selected by GSUnSAL are then used as input for MESMA. Our experiments using a real dataset acquired by an ASD spectrometer in a South-African orchard show that the proposed approach introduces important improvements in the signal unmixing solutions.\",\n \"Title: Parallel Hyperspectral Image and Signal Processing [Applications Corner]\\nYear: 2011\\n\\nAbstract: Remotely sensed hyperspectral imaging instruments are capable of collecting hundreds of images corresponding to different wave length channels for the same area on the surface of the Earth. For instance, NASA is continuously gathering high dimensional image data with instruments such as the Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS). This advanced sensor for Earth observation records the visible and near-infrared spectrum of the reflected light using more than 200 spectral bands, thus producing a stack of images in which each pixel (vector) is represented by a spectral signal that uniquely characterizes the underlying objects. The resulting data volume typically comprises several gigabytes per flight. In this article, we describe the state of the art in the devel opment and application of image and signal processing techniques for advanced information extraction from hyperspectral data. The article also describes new trends for efficient pro cessing of such data using parallel and distributed processing techniques in the context of time-critical applications.\",\n \"Title: Spatial-spectral endmember extraction from hyperspectral imagery using multi-band morphology and volume optimization\\nYear: 2009\\n\\nAbstract: We develop a new approach for characterization of mixed pixels in remotely sensed hyperspectral images. The proposed method first performs joint spatial-spectral pixel characterization via extended morphological transformations, and then automatically extracts pure spectral signatures (called end-members) using volume optimization and convex geometry concepts. The proposed method outperforms other widely used approaches in the analysis of a real hyperspectral scene collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite mining district in Nevada. Ground-truth information available from U.S. Geological Survey is used to substantiate our findings.\",\n \"Title: Semi-supervised hyperspectral image classification based on a Markov random field and sparse multinomial logistic regression\\nYear: 2009\\n\\nAbstract: This paper introduces a new semi-supervised classification and segmentation approach tailored to hyperspectral images. The poste- rior distributions of the classes are modeled by the multinomial lo- gistic regression. The contextual information inherent to the spatial configuration of the image pixels is modeled by a Multi-Level Lo- gistic (MLL) Markov-Gibbs random field. The multinomial logistic regressors, assumed to be random vectors with independent Lapla- cian components, are learned using the recently introduced LOR- SAL algorithm. The maximum a posteriori (MAP) segmentation is computed via the -Expansion algorithm, a powerful graph cut based approach to integer optimization. The effectiveness of the pro- posed methodology is illustrated by classifying simulated and real data sets. Comparisons with state-of-art methods are also included.\",\n \"Title: High performance computing for hyperspectral image analysis: Perspective and state-of-the-art\\nYear: 2009\\n\\nAbstract: The main purpose of this paper is to describe available (HPC)-based implementations of remotely sensed hyperspectral image processing algorithms on multi-computer clusters, heterogeneous networks of computers, and specialized hardware architectures such as field programmable gate arrays (FPGAs) and graphic processing units (GPUs). Combined, the revision of existing techniques conducted in this paper, along with the description of performance results for a parallel hyperspectral processing chain on different architectures, delivers an excellent snapshot of the state-of-the-art in the area of HPC-based hyperspectral image processing and a thoughtful perspective of the potential and emerging challenges of applying HPC paradigms to hyperspectral imaging problems.\",\n \"Title: Improving the scalability of parallel algorithms for hyperspectral image analysis using adaptive message compression\\nYear: 2009\\n\\nAbstract: In previous work, we have reported that the scalability of parallel processing algorithms for hyperspectral image analysis is affected by the amount of data to exchanged through the communication network of the parallel system. However, large messages are common in hyperspectral imaging applications since processing algorithms are often pixel-based, and each pixel vector to be exchanged through the communication network is made up of hundreds of spectral values. Thus, decreasing the amount of data to be exchanged could improve the scalability and parallel performance. In this paper, we propose a new framework based on intelligent utilization of data compression techniques for improving the scalability of a standard spectral unmixin-based parallel hyperspectral processing chain on heterogeneous networks of workstations. Our experimental results indicate that adaptive, wavelet-based lossy compression can lead to improvements in the scalability of the parallel algorithms without significantly sacrificing algorithm analysis accuracy.\",\n \"Title: Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over an Agricultural Area.\\nYear: 2009\\n\\nAbstract: In this paper we compare two different methodologies for Fractional Vegetation Cover (FVC) retrieval from Compact High Resolution Imaging Spectrometer (CHRIS) data onboard the European Space Agency (ESA) Project for On-Board Autonomy (PROBA) platform. The first methodology is based on empirical approaches using Vegetation Indices (VIs), in particular the Normalized Difference Vegetation Index (NDVI) and the Variable Atmospherically Resistant Index (VARI). The second methodology is based on the Spectral Mixture Analysis (SMA) technique, in which a Linear Spectral Unmixing model has been considered in order to retrieve the abundance of the different constituent materials within pixel elements, called Endmembers (EMs). These EMs were extracted from the image using three different methods: i) manual extraction using a land cover map, ii) Pixel Purity Index (PPI) and iii) Automated Morphological Endmember Extraction (AMEE). The different methodologies for FVC retrieval were applied to one PROBA/CHRIS image acquired over an agricultural area in Spain, and they were calibrated and tested against in situ measurements of FVC estimated with hemispherical photographs. The results obtained from VIs show that VARI correlates better with FVC than NDVI does, with standard errors of estimation of less than 8% in the case of VARI and less than 13% in the case of NDVI when calibrated using the in situ measurements. The results obtained from the SMA-LSU technique show Root Mean Square Errors (RMSE) below 12% when EMs are extracted from the AMEE method and around 9% when extracted from the PPI method. A RMSE value below 9% was obtained for manual extraction of EMs using a land cover use map.\",\n \"Title: Sparse Unmixing-Based Content Retrieval of Hyperspectral Images on Graphics Processing Units.\\nYear: 2015\\n\\nAbstract: Content-based image retrieval (CBIR) systems have gained significant importance in the remotely sensed hyperspectral imaging community due to the increasing availability of hyperspectral data collected from different instruments. Spectral unmixing has been a popular technique for not only interpreting hyperspectral images but also retrieving them precisely from databases based on information conte...\",\n \"Title: Real-Time Implementation of the Sparse Multinomial Logistic Regression for Hyperspectral Image Classification on GPUs\\nYear: 2015\\n\\nAbstract: In this letter, a real-time implementation of the logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm for sparse multinomial logistic regression is presented on commodity graphics processing units (GPUs) using Nvidia's compute unified device architecture. The proposed parallel method properly exploits the GPU architecture at the low level, including its shared memory, and takes full advantage of the computational power of GPUs to achieve real-time classification performance of hyperspectral images for the first time in the hyperspectral imaging literature. Our experimental results reveal remarkable acceleration factors and real-time performance, while retaining exactly the same classification accuracy with regard to the serial and multicore versions of the classifier.\",\n \"Title: Multiple Morphological Component Analysis Based Decomposition for Remote Sensing Image Classification.\\nYear: 2016\\n\\nAbstract: Remote sensing images exhibit significant contrast and intensity regions and edges, which makes them highly suitable for using different texture features to properly represent and classify the objects that they contain. In this paper, we present a new technique based on multiple morphological component analysis (MMCA) that exploits multiple textural features for decomposition of remote sensing ima...\",\n \"Title: Real-time implementation of remotely sensed hyperspectral image unmixing on GPUs.\\nYear: 2015\\n\\nAbstract: Spectral unmixing is one of the most popular techniques to analyze remotely sensed hyperspectral images. It generally comprises three stages: (1) reduction of the dimensionality of the original image to a proper subspace; (2) automatic identification of pure spectral signatures (called endmembers); and (3) estimation of the fractional abundance of each endmember in each pixel of the scene. The spectral unmixing process allows sub-pixel analysis of hyperspectral images, but can be computationally expensive due to the high dimensionality of the data. In this paper, we develop the first real-time implementation of a full spectral unmixing chain in commodity graphics processing units (GPUs). These hardware accelerators offer a source of computational power that is very appealing in hyperspectral remote sensing applications, mainly due to their low cost and adaptivity to on-board processing scenarios. The implementation has been developed using the compute device unified architecture (CUDA) and tested on an NVidia™ GTX 580 GPU, achieving real-time unmixing performance in two different case studies: (1) characterization of thermal hot spots in hyperspectral images collected by NASA’s Airborne Visible Infra-red Imaging Spectrometer (AVIRIS) during the terrorist attack to the World Trade Center complex in New York City, and (2) sub-pixel mapping of minerals in AVIRIS hyperspectral data collected over the Cuprite mining district in Nevada.\",\n \"Title: Fusion of hyperspectral and lidar data using generalized composite kernels: A case study in Extremadura, Spain\\nYear: 2015\\n\\nAbstract: The light detection and ranging (LiDAR) data provides very valuable information about the height of the surveyed area which can be used as a source of complementary information for the classification of hyperspectral data, in particular when it is difficult to separate complex classes. In this work, we suggest to exploit the generalized composite kernel strategy for fusion and classification of hyperspectral and LiDAR data. Our experimental results, conducted using a hyperspectral image and a LiDAR derived intensity image collected over a rural area in Extremadura, Spain, indicate that the proposed framework for the fusion of hyperspactral and LiDAR data provides significant classification results.\",\n \"Title: Fast Principal Component Analysis For Hyperspectral Imaging Based On Cloud Computing\\nYear: 2015\\n\\nAbstract: Principal component analysis (PCA) is an important method for feature extraction of hyperspectral remote sensing image. With the development of hyperspectral sensors, the magnitude of hyperspectral data grows quickly, and it is a challenging task to efficiently reduce the data dimension and compress massive data volumes in hyperspectral imaging. In this paper, a distributed parallel optimization of PCA algorithm (PCA_DP) is presented on cloud computing architecture. The realization of the proposed method using Apache Hadoop and MapReduce model is described and evaluated. The experiments conducted on real hyperspectral images of different sizes, demonstrate significant acceleration factor of PCA_DP. It is efficient for massive hyperspectral data processing.\",\n \"Title: Class-Oriented Spectral Partitioning For Hyperspectral Image Classification\\nYear: 2015\\n\\nAbstract: This paper presents a new approach for class-oriented spectral partitioning for hyperspectral image classification. First, without empirical information, we automatically search the spectral bands that correspond to a specific class by using different band selection approaches. Then, the obtained class oriented spectral partitions are used respectively as the input of a group of classifiers, the results of which are combined together to generate a final one by a multiple classifier system. Our experimental results, conducted with the well-known Indians Pines test site hyperspectral image collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) in NW Indiana, suggest that our presented spectral partitioning method leads to competitive results when compared with other state-of-the-art approaches.\",\n \"Title: GPU Implementation of Spatial-Spectral Preprocessing for Hyperspectral Unmixing.\\nYear: 2016\\n\\nAbstract: Spectral unmixing pursues the identification of spectrally pure constituents, called endmembers, and their corresponding abundances in each pixel of a hyperspectral image. Most unmixing techniques have focused on the exploitation of spectral information alone. Recently, some techniques have been developed to take advantage of the complementary information provided by the spatial correlation of the...\",\n \"Title: Big Data for Remote Sensing: Challenges and Opportunities.\\nYear: 2016\\n\\nAbstract: Every day a large number of Earth observation (EO) spaceborne and airborne sensors from many different countries provide a massive amount of remotely sensed data. Those data are used for different applications, such as natural hazard monitoring, global climate change, urban planning, etc. The applications are data driven and mostly interdisciplinary. Based on this it can truly be stated that we ar...\",\n \"Title: Active Learning Based Autoencoder For Hyperspectral Imagery Classification\\nYear: 2016\\n\\nAbstract: In this paper, we joint autoencoder with active learning for hyperspectral imagery classification. Specifically, we learn the classifier via autoencoder, where the most informative samples are acitvely selected through the interaction between the autoencoder and active learning. Experimental results, conducted using both the Kennedy Space Center and the Indian Pines hyperspectral images, show that driven by active learning, the performance of autoencoder can be greatly improved.\",\n \"Title: On The Optimization Of Memory Access To Increase The Performance Of Spatial Preprocessing Techniques On Graphics Processing Units\\nYear: 2016\\n\\nAbstract: The use of spatial information prior to spectral unmixing of hyperspectral data is a very active research line in recent years. There are many approximations that consider spatial characteristics of the data in order to guide the endmember identification/extraction procedure. In particular, the spatial preprocessing (SPP) algorithm can be used prior to most existing spectral-based endmember identification techniques, thus promoting the selection of endmembers in spatially representative parts of the scene. The main concern regarding SPP and this kind of preprocessing techniques is that they are computational expensive, adding a significant burden to the spectral unmixing process which should be alleviated. In this paper we revisit and enhance a previously developed implementation of SPP for graphical processing units (GPUs) in order to increase its performance by exhaustively using the level one (L1)-cache level of the GPU. The performance of the proposed implementation is evaluated using an NVidiaT-MGeForce GTX 580. Our experimental validation reveals that real-time processing performance can be obtained for real hyperspectral data sets collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).\",\n \"Title: Fast Spatial-Spectral Preprocessing For Endmember Extraction And Spectral Unmixing Using Graphic Processing Units\\nYear: 2016\\n\\nAbstract: Linear spectral unmixing consists on the identification of spectrally pure constituents, called endmembers and their corresponding proportions or abundances using a linear model. Traditionally, most of the attention has been focussed on the exploitation of spectral information when identifying a set of endmembers and, only recently, some techniques try to take advantage of complementary information such as the one provided by the spatial correlation of the pixels in the image. Computational complexity represents a major problem in most of these spatial-spectral based techniques, as hyperspectral images provide very rich information in both the spatial and the spectral domain. In this paper we provide a computationally efficient implementation of a spatial-spectral processing (SSPP) algorithm which can be used prior to endmember identification and spectral unmixing. Specifically we present an implementation optimized for commodity graphics processing units (GPUs), which is evaluated using two different GPU architectures from NVidia: GeForce GTX580 and GeForce GT740. Our experimental validation reveals that significant speedups can be achieved when processing hyperspectral images of different sizes.\",\n \"Title: Oil Spill Detection via Multitemporal Optical Remote Sensing Images: A Change Detection Perspective\\nYear: 2017\\n\\nAbstract: Oil spill monitoring in optical remote sensing (RS) images is a challenging task due to the complexity of target discrimination in an oil spill scenario. Differently from traditional oil spill detection methods that are mainly carried out in a monotemporal image, in this letter, a novel solution is given in a multitemporal domain by investigating potential capability of change detection (CD) techn...\",\n \"Title: Using Linear Spectral Unmixing for Subpixel Mapping of Hyperspectral Imagery: A Quantitative Assessment\\nYear: 2017\\n\\nAbstract: Subpixel mapping techniques have been widely utilized to determine the spatial distribution of the different land-cover classes in mixed pixels at a subpixel scale by converting low-resolution fractional abundance maps (estimated by a linear mixture model) into a finer classification map. Over the past decades, many subpixel mapping algorithms have been proposed to tackle this problem. It has been...\",\n \"Title: A New Sparse Subspace Clustering Algorithm for Hyperspectral Remote Sensing Imagery.\\nYear: 2017\\n\\nAbstract: Robust techniques such as sparse subspace clustering (SSC) have been recently developed for hyperspectral images (HSIs) based on the assumption that pixels belonging to the same land-cover class approximately lie in the same subspace. In order to account for the spatial information contained in HSIs, SSC models incorporating spatial information have become very popular. However, such models are of...\",\n \"Title: Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification.\\nYear: 2016\\n\\nAbstract: Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the classification of remotely sensed images, discarding the other convolutional layer features which may also be helpful for classification purposes. In this paper, we propose a new adaptive deep pyramid matching (ADPM) model that takes advantage of the features from all of the convolutional layers for remote sensing image classification. To this end, the optimal fusing weights for different convolutional layers are learned from the data itself. In remotely sensed scenes, the objects of interest exhibit different scales in distinct scenes, and even a single scene may contain objects with different sizes. To address this issue, we select the CNN with spatial pyramid pooling (SPP-net) as the basic deep network, and further construct a multi-scale ADPM model to learn complementary information from multi-scale images. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.\",\n \"Title: Spatial Technology and Social Media in Remote Sensing: A Survey\\nYear: 2017\\n\\nAbstract: The rapid development of social media data and the associated growth in volume, velocity, and variety has fostered the idea of using these data to guide traditional remote sensing image retrieval and information extraction tasks. Although important progress has been made in recent years in harvesting spatial and temporal data from social media, the exploitation of these data for decision making still needs further investigation, particularly in the context of its integration with remote sensing and geographic information systems. In this paper, we first discuss the relation between localization techniques and spatial technologies, pointing out their similarities and differences. Then, we provide a discussion on location analysis of social media data, and the fusion of multiple data sources, with specific attention to the integration of social media content (including localization) with remote sensing-based spatial technologies. Next, we provide specific examples addressing the use of social media data to perform information extraction from large remote sensing data repositories. Although significant possibilities for the integration of localization and spatial technologies can be seen in the examples provided, our survey suggests that the convergence of remote sensing and social media data will continue to deeply transform these technologies.\",\n \"Title: Performance-Power Evaluation of an OpenCL Implementation of the Simplex Growing Algorithm for Hyperspectral Unmixing.\\nYear: 2017\\n\\nAbstract: Over the last few years, several new strategies for spectral unmixing of remotely sensed hyperspectral data have been proposed. Many of them have been developed to solve the most time-consuming and relevant step: endmember extraction. However, unmixing algorithms can be computationally very expensive in terms of processing time and energy consumption, a fact that compromises their use in applicati...\",\n \"Title: Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation.\\nYear: 2017\\n\\nAbstract: Spectral unmixing is an important technique in hyperspectral image applications. Recently, sparse regression has been widely used in hyperspectral unmixing, but its performance is limited by the high mutual coherence of spectral libraries. To address this issue, a new sparse unmixing algorithm, called double reweighted sparse unmixing and total variation (TV), is proposed in this letter. Specifica...\",\n \"Title: Discriminative Low-Rank Gabor Filtering for Spectral-Spatial Hyperspectral Image Classification.\\nYear: 2017\\n\\nAbstract: Spectral-spatial classification of remotely sensed hyperspectral images has attracted a lot of attention in recent years. Although Gabor filtering has been used for feature extraction from hyperspectral images, its capacity to extract relevant information from both the spectral and the spatial domains of the image has not been fully explored yet. In this paper, we present a new discriminative low-...\",\n \"Title: A Novel Preunmixing Framework for Efficient Detection of Linear Mixtures in Hyperspectral Images.\\nYear: 2017\\n\\nAbstract: In order to provide reliable information about the instantaneous field of view considered in hyperspectral images through spectral unmixing, understanding the kind of mixture that occurs over each pixel plays a crucial role. In this paper, in order to detect nonlinear mixtures, a method for fast identification of linear mixtures is introduced. The proposed method does not need statistical informat...\",\n \"Title: Multisensor Coupled Spectral Unmixing for Time-Series Analysis.\\nYear: 2017\\n\\nAbstract: We present a new framework, called multisensor coupled spectral unmixing (MuCSUn), that solves unmixing problems involving a set of multisensor time-series spectral images in order to understand dynamic changes of the surface at a subpixel scale. The proposed methodology couples multiple unmixing problems based on regularization on graphs between the time-series data to obtain robust and stable un...\",\n \"Title: Spatial technology and social media in remote sensing: challenges and opportunities [point of view].\\nYear: 2017\\n\\nAbstract: The convergence of remote sensing technologies with social media, coupled with advances in other location-aware technologies such as WiFi and smartphones, is moving us on a fast track to a situation in which we can readily know, at any time, where everybody and everything are located on the surface of the Earth, and to exploit the power of social media in different contexts. Remote sensing technol...\",\n \"Title: Integration of LiDAR and Hyperspectral Data for Land-cover Classification: A Case Study.\\nYear: 2017\\n\\nAbstract: In this paper, an approach is proposed to fuse LiDAR and hyperspectral data, which considers both spectral and spatial information in a single framework. Here, an extended self-dual attribute profile (ESDAP) is investigated to extract spatial information from a hyperspectral data set. To extract spectral information, a few well-known classifiers have been used such as support vector machines (SVMs), random forests (RFs), and artificial neural networks (ANNs). The proposed method accurately classify the relatively volumetric data set in a few CPU processing time in a real ill-posed situation where there is no balance between the number of training samples and the number of features. The classification part of the proposed approach is fully-automatic.\",\n \"Title: Decision fusion based on extended multi-attribute profiles for hyperspectral image classification\\nYear: 2013\\n\\nAbstract: In this paper, a new classification strategy for remotely sensed hyperspectral image data is presented and discussed. The proposed approach adopts a decision fusion strategy which combines a well-established classifier, such as the support vector machine (SVM), with the information provided by extended multi-attribute morphological profiles (EMAPs). EMAPs provide a multilevel characterization of an image created by using a sequence of morphological attribute filters to model different kinds of the structural information contained in such image. In our proposed decision fusion strategy, the SVM classifier is first applied to each of the components (which are associated to different attributes) provided by the EMAP. Then, we apply a decision fusion rule to obtain the final classification result. In our experimental results, conducted with a reference urban hyperspectral data sets widely used in the literature, we show that the proposed strategy provides state of the art results (especially when limited training sets are used.)\",\n \"Title: A New Extended Linear Mixing Model To Address Spectral Variability\\nYear: 2014\\n\\nAbstract: Spectral variability is a phenomenon due, to a grand extend, to variations in the illumination and atmospheric conditions within a hyperspectral image, causing the spectral signature of a material to vary within a image. Data spectral fluctuation due to spectral variability compromises the linear mixing model (LMM) sum-to-one constraint, and is an important source of error in hyperspectral image analysis. Recently, spectral variability has raised more attention and some techniques have been proposed to address this issue, i.e. spectral bundles. Here, we propose the definition of an extended LMM (ELMM) to model spectral variability and we show that the use of spectral bundles models the ELMM implicitly. We also show that the constrained least squares (CLS) is an explicit modelling of the ELMM when the spectral variability is due to scaling effects. We give experimental validation that spectral bundles (and sparsity) and CLS are complementary techniques addressing spectral variability. We finally discuss on future research avenues to fully exploit the proposed ELMM.\",\n \"Title: Integrating Multiple Nonlinear Estimators Into Hyperspectral Unmixing\\nYear: 2014\\n\\nAbstract: Linear spectral unmixing has been widely used for hyperspectral data interpretation. However, there is a need for nonlinear unmixing methods that can model more complex geometries without the need to resort to prior knowledge about the objects in the scene. In this paper, we present a novel strategy for nonlinear spectral unmixing which combines polytope decomposition (POD) with artificial neural network (ANN)-based learning. Even if no ground-truth information is available, the ANN can still efficiently estimate the order of the nonlinearity involved in the problem and enhance the capacity of the POD method to deliver unmixing performance for a wider range of nonlinearities. The proposed method has been evaluated using both simulated and real scenes, providing promising results.\",\n \"Title: On the use of collaborative sparse regression in hyperspectral unmixing chains\\nYear: 2014\\n\\nAbstract: Hyperspectral unmixing is a complex process in which several steps are consecutively executed to derive the desired results: the image endmembers and their corresponding fractional abundance maps. Each of these unmixing stages benefits nowadays from a plethora of algorithms, continuously developed and improved. In this paper, we analyze three of the general unmixing steps: band selection (data dimensionality reduction), endmember extraction and fractional abundance inference (inversion) from a multi-measurement vector problem point of view. We show that these particular steps can be expressed as a convex optimization problem in which the concept of data collaborativity is exploited and one single algorithm can be efficiently used to solve them. Our experimental results obtained in an urban dataset acquired over Berlin, Germany, show the potential of this approach in remote sensing applications.\",\n \"Title: Validation Of Spectral Unmixing Methods Using Photometry And Topography Information\\nYear: 2013\\n\\nAbstract: In this work the performance of spectral unmixing procedures applied on hyperspectral images of granular mixtures are compared. For that purpose we consider a laboratory image and synthetic images, the latter being created using a original algorithmic process while borrowing some aspects of the real data. The nonlinear effects of light multiple scattering within the mixture can be partially compensated by transfounation of the image using the Hapke model under different levels of injected infounation regarding topography and photometry of the scene. The validation of the different methods and deconvolution processes is established by comparing the results with a \\\"ground truth\\\" through the mean Spectral Angle for the extracted endmembers and the mean Abundances Root Mean Square Error for the estimated abundances. Interpretation of the results indicates that is safer to extract the endmembers from the original version of image unless topography and most importantly photometry are precisely known. On the other hand better distribution maps are obtained in general from the transfouned version of the image.\",\n \"Title: GPU implementation of a constrained hyperspectral coded aperture algorithm for compressive sensing\\nYear: 2015\\n\\nAbstract: In this paper, a parallel implementation of a previously constrained hyperspectral coded aperture (CHYCA) algorithm for compressive sensing on graphics processing units (GPUs) is proposed. CHYCA method combines the ideas of spectral unmixing and compressive sensing exploiting the high spatial correlation that can be observed in the data and the generally low number of endmembers needed in order to explain the data. The performance of CHYCA relies which does not depend on the tuning of a regularization parameter, which is a time consuming task offering good performance compared with a previously hyperspectral coded aperture (HYCA) method. The proposed implementation exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs using shared memory and coalesced accesses to memory. Experimental results using simulated data reveals speedups up to 56 times, with regards to serial implementation.\",\n \"Title: FPGA implementation of a maximum volume algorithm for endmember extraction from hyperspectral imagery\\nYear: 2015\\n\\nAbstract: Endmember extraction is a very important technique in hyperspectral unmixing. Due to the fact that more applications require real or near real-time processing capabilities, high performance computing based on field-programmable gate array (FPGA) for endmember extraction has received considerable interest in recent years. In this paper, we propose a real-time implementation of maximum volume algorithms (RT-MaxV) using a Kintex-7 FPGA. The proposed RT-MaxV does not need dimensionality reduction like other real-time fast simplex growing algorithms (RT-FSGA). Our experimental results, obtained using the AVIRIS Cuprite data set, indicate that the proposed algorithm has better accuracy and performance than RT-FSGA, and its FPGA implementation achieves real-time processing capability in the considered problem.\",\n \"Title: Binary partition tree-based local spectral unmixing\\nYear: 2014\\n\\nAbstract: The linear mixing model (LMM) is a widely used methodology for the spectral unmixing (SU) of hyperspectral data. In this model, hyperspectral data is formed as a linear combination of spectral signatures corresponding to macroscopically pure materials (endmembers), weighted by their fractional abundances. Some of the drawbacks of the LMM are the presence of multiple mixtures and the spectral variability of the endmembers due to illumination and atmospheric effects. These issues appear as variations of the spectral conditions of the image along its spatial domain. However, these effects are not so severe locally and could be at least mitigated by working in smaller regions of the image. The proposed local SU works over a partition of the image, performing the spectral unmixing locally in each region of the partition. In this work, we first introduce the general local SU methodology, then we propose an implementation of the local SU based on a binary partition tree representation of the hyperspectral image and finally we give an experimental validation of the approach using real data.\",\n \"Title: Hyperspectral change detection by sparse unmixing with dictionary pruning\\nYear: 2015\\n\\nAbstract: Hyperspectral change detection is used in many applications ranging from environmental monitoring to city planning and military surveillance. Change detection by unmixing has the potential of not only providing the locations of the changes, but also the nature of the change, and sub-pixel level information. Change detection by sparse unmixing using spectral libraries, with respect to regular spectral unmixing, has the added benefits of circumventing the process of endmember extraction and providing specific mission-based information. However, sparse unmixing is generally a severely ill-conditioned and time-consuming problem. Recently, an approach that aims to identify a subset of signatures of the spectral library that contribute most to the observed data has been proposed in order to improve the conditioning of the problem, hence decreasing computation time and enhancing performance. In this work, sparse unmixing with dictionary pruning is explored for change detection in multi-temporal hyperspectral images. Experimental results validate the performance of the proposed approach.\",\n \"Title: Spectral unmixing-based post-processing for hyperspectral image classification\\nYear: 2013\\n\\nAbstract: Spectral unmixing and classification have been widely used in the recent literature to analyze remotely sensed hyperspectral data. However, possible connections between classification and spectral unmixing concepts have been rarely investigated. In this work, we propose a simple spectral unmixing-based post-processing method to improve the classification accuracies provided by supervised and semi-supervised techniques for hyperspectral image classification. The proposed approach exploits the information retrieved with spectral unmixing in order to complement the results obtained after the classification stage (which can be either supervised or semi-supervised), thus bridging the gap between unmixing and classification and exploiting both techniques in synergistic fashion for hyperspectral data interpretation. The proposed method is experimentally validated using a real hyperspectral data set collected by the Reflective Optics Spectrographic Imaging System (ROSIS). Our experimental results indicate that the proposed unmixing-based preprocessing can improve the classification results for some of the classes, particularly the most highly mixed ones, in supervised and semi-supervised scenarios using limited training samples.\",\n \"Title: Plant production system monitoring via multiple signal classification and sparse regression\\nYear: 2013\\n\\nAbstract: In this paper, we propose a dictionary pruning technique for signal unmixing, which is a recent unmixing concept aimed at extracting the signal contribution of an individual class of endmembers to each observed pixel in a hyperspectral scene. We focus on a specific application in the area of plant production system monitoring. The pruning technique allows to infer the physical state of vegetation in a region of interest on the ground, by selecting appropriate spectra from a very large spectral library designed to model a wide variety of such possible states. On the one hand, Multiple Endmember Spectral Mixture Analysis (MESMA), a combinatorial technique, was intensively used to solve the signal unmixing problem. Sparse unmixing, a fast approach dealing with spectral libraries, was recently proposed as a reliable technique for spectral unmixing. The performances of both techniques are influenced by the large number of spectra collected in the libraries. The aim of this paper is two-fold: 1) to present a subspace-based method for library pruning, such that only spectra likely to be present in the image are used in unmixing, which is expected to improve MESMA results, and 2) to introduce sparse unmixing as a reliable signal unmixing technique, given the natural ability of these methods to deal with applications in which a low number of atoms is used to accurately describe every observed vector. The results obtained in a simulated environment show the potential of the proposed scheme to boost the signal unmixing performance both in terms of accuracy and running time.\",\n \"Title: Potential And Limitations Of Band Selection And Library Pruning In Sparse Hyperspectral Unmixing\\nYear: 2015\\n\\nAbstract: Sparse regression using spectral libraries is nowadays a widely used technique for hyperspectral data unmixing. Assuming that the potential endmembers are collected in a large database of spectra, sparse unmixing finds the fractional abundances of a reduced set of constituent materials by solving convex optimization problems which target, at the same time, low data reconstruction errors. The large amount of data, jointly with other limitations related to the internal characteristics of the large spectral libraries (such as spectra similarity), affect the performance of sparse unmixing algorithms in terms of accuracy and running time. Recently, an efficient method based on a multi-measurement vector (MMV) approach was proposed to select, from a large library, suitable spectra for unmixing. Many research efforts have also been devoted to data dimensionality reduction techniques, of which band selection is very popular. In this work, we investigate the effects on the unmixing performance when the two techniques are applied simultaneously. Our experiments show that important improvements can be achieved, depending on several factors, such as: data noise, number of endmembers present in the dataset, number of spectra retained from the library, the order of operations, accuracy of data subspace estimation.\",\n \"Title: Spatial Technology and Social Media [Scanningthe Issue].\\nYear: 2017\\n\\nAbstract: The significant development of social media over the past decade has been complemented by the rise of spatial technologies to provide new mapping mechanisms that allow users engage with online information services and also with each other in an unprecedented way. Users of these technologies now provide a comprehensive geosocial overlay of the physical environment of the planet.\",\n \"Title: Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis.\\nYear: 2017\\n\\nAbstract: The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. ...\",\n \"Title: Structured Sparse Coding-Based Hyperspectral Imagery Denoising With Intracluster Filtering.\\nYear: 2017\\n\\nAbstract: Sparse coding can exploit the intrinsic sparsity of hyperspectral images (HSIs) by representing it as a group of sparse codes. This strategy has been shown to be effective for HSI denoising. However, how to effectively exploit the structural information within the sparse codes (structured sparsity) has not been widely studied. In this paper, we propose a new method for HSI denoising, which uses st...\",\n \"Title: Multicore Real-Time Implementation of a Full Hyperspectral Unmixing Chain.\\nYear: 2018\\n\\nAbstract: Solving the mixture problem in remotely sensed hyperspectral images remains a challenging task. In particular, solutions are needed in order to obtain a response for applications with real-time constraints. In the last decade, several efforts have been developed, many of them using graphics processing units (GPUs) and focused on the exploitation of spectral information alone. However, a few spectr...\",\n \"Title: Convex Formulation for Multiband Image Classification With Superpixel-Based Spatial Regularization.\\nYear: 2018\\n\\nAbstract: Superpixels are a powerful device to characterize the spatial-contextual information in remotely sensed hyperspectral image (HSI) interpretation. However, the exploitation of superpixels in classification problems is not straightforward, often leading to unbearable NP-hard discrete integer optimization problems. In this paper, we attack this hurdle by leveraging on a convex relaxation of the origi...\",\n \"Title: Parallel real-time virtual dimensionality estimation for hyperspectral images.\\nYear: 2018\\n\\nAbstract: One of the most important tasks in hyperspectral imaging is the estimation of the number of endmembers in a scene, where the endmembers are the most spectrally pure components. The high dimensionality of hyperspectral data makes this calculation computationally expensive. In this paper, we present several new real-time implementations of the well-known Harsanyi–Farrand–Chang method for virtual dimensionality estimation. The proposed solutions exploit multi-core processors and graphic processing units for achieving real-time performance of this algorithm, together with better performance than other works in the literature. Our experimental results are obtained using both synthetic and real images. The obtained processing times show that the proposed implementations outperform other hardware-based solutions.\",\n \"Title: Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion.\\nYear: 2018\\n\\nAbstract: Probabilistic topic models have recently shown a great potential in the remote sensing image fusion field, which is particularly helpful in land-cover categorization tasks. This letter first studies the application of probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation to remote sensing synthetic aperture radar (SAR) and multispectral imaging (MSI) unsupervised land-cover...\",\n \"Title: Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification.\\nYear: 2018\\n\\nAbstract: Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification.\",\n \"Title: A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution.\\nYear: 2018\\n\\nAbstract: Super-resolution (SR) brings an excellent opportunity to improve a wide range of different remote sensing applications. SR techniques are concerned about increasing the image resolution while providing finer spatial details than those captured by the original acquisition instrument. Therefore, SR techniques are particularly useful to cope with the increasing demand remote sensing imaging applicati...\",\n \"Title: Hyperspectral Unmixing Based on Dual-Depth Sparse Probabilistic Latent Semantic Analysis.\\nYear: 2018\\n\\nAbstract: This paper presents a novel approach for spectral unmixing of remotely sensed hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the semantics pervading the latent topic space when identifying spectral signatures and estimating fractional abundances from hyperspectral images. Despite the contrasted potential of topic models to uncover image semantics, they ha...\",\n \"Title: Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach.\\nYear: 2018\\n\\nAbstract: Hyperspectral imaging is a widely used technique in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the earth. In the last two decades, several methods (unsupervised, supervised, and semisupervised) have been proposed to deal with the hyperspectral image classification problem. Supervised techniques ...\",\n \"Title: Spatial Discontinuity-Weighted Sparse Unmixing of Hyperspectral Images.\\nYear: 2018\\n\\nAbstract: Spectral unmixing is an important technique for remotely sensed hyperspectral image interpretation, of which the goal is to decompose the image into a set of pure spectral components (endmembers) and their abundance fractions in each pixel of the scene. Sparse-representation-based approaches have been widely studied for remotely sensed hyperspectral unmixing. A recent trend is to incorporate the s...\",\n \"Title: A New Spectral-Spatial Sub-Pixel Mapping Model for Remotely Sensed Hyperspectral Imagery.\\nYear: 2018\\n\\nAbstract: In this paper, a new joint spectral-spatial subpixel mapping model is proposed for hyperspectral remotely sensed imagery. Conventional approaches generally use an intermediate step based on the derivation of fractional abundance maps obtained after a spectral unmixing process, and thus the rich spectral information contained in the original hyperspectral data set may not be utilized fully. In this...\",\n \"Title: Hyperspectral Unmixing Using Sparsity-Constrained Deep Nonnegative Matrix Factorization With Total Variation.\\nYear: 2018\\n\\nAbstract: Hyperspectral unmixing is an important processing step for many hyperspectral applications, mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation of the abundance of each endmember in each pixel of the image. In recent years, nonnegative matrix factorization (NMF) has been highly attractive for this purpose due to the nonnegativity constraint that is often impo...\",\n \"Title: Estimating Nonlinearities in p-Linear Hyperspectral Mixtures.\\nYear: 2018\\n\\nAbstract: Accurately estimating the elements in Earth observations is crucial when assessing specific features such as air quality index, water pollution, or urbanization process behavior. Moreover, physical-chemical composition can be retrieved from hyperspectral images when proper spectral unmixing architectures are employed. Specifically, when linear and nonlinear combinations of endmembers (pure spectra...\",\n \"Title: Fast dimensionality reduction and classification of hyperspectral images with extreme learning machines.\\nYear: 2018\\n\\nAbstract: Recent advances in remote sensing techniques allow for the collection of hyperspectral images with enhanced spatial and spectral resolution. In many applications, these images need to be processed and interpreted in real-time, since analysis results need to be obtained almost instantaneously. However, the large amount of data that these images comprise introduces significant processing challenges. This also complicates the analysis performed by traditional machine learning algorithms. To address this issue, dimensionality reduction techniques aim at reducing the complexity of data while retaining the relevant information for the analysis, removing noise and redundant information. In this paper, we present a new real-time method for dimensionality reduction and classification of hyperspectral images. The newly proposed method exploits artificial neural networks, which are used to develop a fast compressor based on the extreme learning machine. The obtained experimental results indicate that the proposed method has the ability to compress and classify high-dimensional images fast enough for practical use in real-time applications.\",\n \"Title: A suite of parallel algorithms for efficient band selection from hyperspectral images.\\nYear: 2018\\n\\nAbstract: The analysis of hyperspectral images is usually very heavy from the computational point-of-view, due to their high dimensionality. In order to avoid this problem, band selection (BS)\\n has been widely used to reduce the dimensionality before the analysis. The aim is to extract a subset of the original bands of the hyperspectral image, preserving most of the information contained in the original data. The BS technique can be performed by prioritizing the bands on the basis of a score, assigned by specific criteria; in this case, BS turns out in the so-called band prioritization (BP). This paper focuses on BP algorithms based on the following parameters: signal-to-noise ratio, kurtosis, entropy, information divergence, variance and linearly constrained minimum variance. In particular, an optimized C serial version has been developed for each algorithm from which two parallel versions have been derived using OpenMP and NVIDIA’s compute unified device architecture. The former is designed for a multi-core CPU, while the latter is designed for a many-core graphics processing unit. For each version of these algorithms, several tests have been performed on a large database containing both synthetic and real hyperspectral images. In this way, scientists can integrate the proposed suite of efficient BP algorithms into existing frameworks, choosing the most suitable technique for their specific applications.\",\n \"Title: A real-time unsupervised background extraction-based target detection method for hyperspectral imagery.\\nYear: 2018\\n\\nAbstract: Target detection is an important technique in hyperspectral image analysis. The high dimensionality of hyperspectral data provides the possibility of deeply mining the information hiding in spectra, and many targets that cannot be visualized by inspection can be detected. But this also brings some problems such as unknown background interferences at the same time. In this way, extracting and taking advantage of the background information in the region of interest becomes a task of great significance. In this paper, we present an unsupervised background extraction-based target detection method, which is called UBETD for short. The proposed UBETD takes advantage of the method of endmember extraction in hyperspectral unmixing, another important technique that can extract representative material signatures from the images. These endmembers represent most of the image information, so they can be reasonably seen as the combination of targets and background signatures. Since the background information is known, algorithm like target-constrained interference-minimized filter could then be introduced to detect the targets while inhibiting the interferences. To meet the rapidly rising demand of real-time processing capabilities, the proposed algorithm is further simplified in computation and implemented on a FPGA board. Experiments with synthetic and real hyperspectral images have been conducted comparing with constrained energy minimization, adaptive coherence/cosine estimator and adaptive matched filter to evaluate the detection and computational performance of our proposed method. The results indicate that UBETD and its hardware implementation RT-UBETD can achieve better performance and are particularly prominent in inhibiting interferences in the background. On the other hand, the hardware implementation of RT-UBETD can complete the target detection processing in far less time than the data acquisition time of hyperspectral sensor like HyMap, which confirms strict real-time processing capability of the proposed system.\",\n \"Title: A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images.\\nYear: 2018\\n\\nAbstract: Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA's efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data.\",\n \"Title: Remote Sensing Scene Classification Using Multilayer Stacked Covariance Pooling.\\nYear: 2018\\n\\nAbstract: This paper proposes a new method, called multilayer stacked covariance pooling (MSCP), for remote sensing scene classification. The innovative contribution of the proposed method is that it is able to naturally combine multilayer feature maps, obtained by pretrained convolutional neural network (CNN) models. Specifically, the proposed MSCP-based classification framework consists of the following t...\",\n \"Title: Sparse Unmixing With Dictionary Pruning for Hyperspectral Change Detection.\\nYear: 2017\\n\\nAbstract: The localization of changes that occur between the images in a multitemporal series is crucial for many applications, ranging from environmental monitoring to military surveillance. In contrast to traditional change detection methods, unmixing-based change detection has been shown to have the important added benefit of providing subpixel-level information on the nature of the changes, instead of o...\",\n \"Title: Superpixel-Based Active Learning and Online Feature Importance Learning for Hyperspectral Image Analysis.\\nYear: 2017\\n\\nAbstract: The rapid development of multichannel optical imaging sensors has led to increased utilization of hyperspectral data for remote sensing. For classification of hyperspectral data, an informative training set is necessary for ensuring robust performance. However, in remote sensing and other image analysis applications, labeled samples are often difficult, expensive, and time-consuming to obtain. Thi...\",\n \"Title: A New Cloud Computing Architecture for the Classification of Remote Sensing Data.\\nYear: 2017\\n\\nAbstract: This paper proposes a new distributed architecture for supervised classification of large volumes of earth observation data on a cloud computing environment. The architecture supports distributed execution, network communication, and fault tolerance in a transparent way to the user. The architecture is composed of three abstraction layers, which support the definition and implementation of applica...\",\n \"Title: Class-Oriented Spectral Partitioning for Remotely Sensed Hyperspectral Image Classification.\\nYear: 2017\\n\\nAbstract: Remotely sensed hyperspectral images exhibit very high dimensionality in the spectral domain. As opposed to band selection techniques, which extract a subset of the original spectral bands in the image, spectral partitioning (SP) techniques reassign the original bands into subgroups that are then processed separately. From a classification perspective, this strategy has the advantage that all the ...\",\n \"Title: Parallel Implementation of a Full Hyperspectral Unmixing Chain Using OpenCL.\\nYear: 2017\\n\\nAbstract: Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. Due to the fact that the spatial resolution of the sensor may not be able to separate different spectrally pure components (endmembers), spectral unmixing faces important challenges in order to characterize mixed pixels. As a result, several hyperspectral unmixing chains have been proposed to find the spect...\",\n \"Title: Fast Spatial Preprocessing for Spectral Unmixing of Hyperspectral Data on Graphics Processing Units.\\nYear: 2016\\n\\nAbstract: Spectral unmixing is an important technique for hyperspectral data exploitation. It amounts at finding a set of pure spectral signatures (endmembers) of the most representative materials in the scene, and estimating their abundance fractions. The integration of spatial information prior to spectral unmixing of hyperspectral data has attracted much attention in recent years. Several approaches have...\",\n \"Title: A Discontinuity Preserving Relaxation Scheme for Spectral-Spatial Hyperspectral Image Classification.\\nYear: 2016\\n\\nAbstract: In remote sensing image processing, relaxation is defined as a method that uses the local relationship among neighboring pixels to correct spectral or spatial distortions. In recent years, relaxation methods have shown great success in classification of remotely sensed data. Relaxation, as a preprocessing step, can reduce noise and improve the class separability in the spectral domain. On the othe...\",\n \"Title: One-Class Classification of Remote Sensing Images Using Kernel Sparse Representation.\\nYear: 2016\\n\\nAbstract: Sparse representations have been widely studied in remote sensing image analysis in recent years. In this paper, we develop a novel method for one-class classification (OCC) using a kernel sparse representation model for remotely sensed imagery. Training samples taken from the target class alone are used to build a learning dictionary for the sparse representation model, which is then optimized to...\",\n \"Title: Automatic Change Detection in High-Resolution Remote Sensing Images by Using a Multiple Classifier System and Spectral-Spatial Features.\\nYear: 2016\\n\\nAbstract: Change detection (CD) is an active research topic in remote sensing applications including urban studies, disaster assessment, and deforestation monitoring. In this paper, we propose an automatic method for CD in high-resolution remote sensing images that uses a novel strategy for the selection of training samples and an ensemble of multiple classifiers. As for the selection of training samples, o...\",\n \"Title: A Technique for Subpixel Analysis of Dynamic Mangrove Ecosystems With Time-Series Hyperspectral Image Data.\\nYear: 2018\\n\\nAbstract: Changes in mangrove forests are mostly caused by natural disasters, anthropogenic forces, and uncontrolled population growth. These phenomena often lead to a strong competition between existing mangrove species for their survival. Due to its enhanced spectral resolution, remotely sensed hyperspectral image data can play an important role in the task of mapping and monitoring changes in mangrove fo...\",\n \"Title: Impervious Surface Extraction From Multispectral Images via Morphological Attribute Profiles Based on Spectral Analysis.\\nYear: 2018\\n\\nAbstract: Impervious surfaces exhibit unique spatial characteristics in urban environments. The estimation of impervious surfaces is critical for the analysis of these environments. In this paper, we propose a new technique based on morphological attribute profiles for mapping impervious surfaces under a spectral mixture analysis model. A main feature of our newly developed method is that it can model diffe...\",\n \"Title: Remote Sensing Image Fusion Using Hierarchical Multimodal Probabilistic Latent Semantic Analysis.\\nYear: 2018\\n\\nAbstract: The generative semantic nature of probabilistic topic models has recently shown encouraging results within the remote sensing image fusion field when conducting land cover categorization. However, standard topic models have not yet been adapted to the inherent complexity of remotely sensed data, which eventually may limit their resulting performance. In this scenario, this paper presents a new top...\",\n \"Title: Feature Extraction With Multiscale Covariance Maps for Hyperspectral Image Classification.\\nYear: 2019\\n\\nAbstract: The classification of hyperspectral images (HSIs) using convolutional neural networks (CNNs) has recently drawn significant attention. However, it is important to address the potential overfitting problems that CNN-based methods suffer when dealing with HSIs. Unlike common natural images, HSIs are essentially three-order tensors which contain two spatial dimensions and one spectral dimension. As a...\",\n \"Title: Spatial-Spectral Hyperspectral Image Classification Using Random Multiscale Representation.\\nYear: 2016\\n\\nAbstract: This paper presents a novel spatial-spectral classification method for remotely sensed hyperspectral images. First of all, a multiscale representation technique based on random projection, referred as random multiscale representation (RMSR), is proposed to extract the spatial features from the given scene. The idea behind RMSR is to properly model the spatial characteristics comprised by each pixe...\",\n \"Title: FPGA Implementation of an Algorithm for Automatically Detecting Targets in Remotely Sensed Hyperspectral Images.\\nYear: 2016\\n\\nAbstract: Timely detection of targets continues to be a relevant challenge for hyperspectral remote sensing capability. The automatic target-generation process using an orthogonal projection operator (ATGP-OSP) has been widely used for this purpose. Hyperspectral target-detection applications require timely responses for swift decisions, which depend upon (near) real-time performance of algorithm analysis. ...\",\n \"Title: Harmonic Mixture Modeling for Efficient Nonlinear Hyperspectral Unmixing.\\nYear: 2016\\n\\nAbstract: Higher order nonlinear material mixtures provide a good model to explain the effects of physical-chemical phenomena on hyperspectral remote sensing measurements. Therefore, inverting nonlinear effects starting from the measured spectral values is a very challenging yet fundamental task to provide a thorough and reliable characterization of the materials in a scene. In this paper, this task is achi...\",\n \"Title: Approximate Computing of Remotely Sensed Data: SVM Hyperspectral Image Classification as a Case Study.\\nYear: 2016\\n\\nAbstract: Onboard processing systems are becoming very important in remote sensing data processing. However, a main problem with specialized hardware architectures used for onboard processing is their high power consumption, which limits their exploitation in earth observation missions. In this paper, a novel strategy for approximate computing is proposed for reducing energy consumption in remotely sensed o...\",\n \"Title: A New Algorithm for Bilinear Spectral Unmixing of Hyperspectral Images Using Particle Swarm Optimization.\\nYear: 2016\\n\\nAbstract: Spectral unmixing is an important technique for exploiting hyperspectral data. The presence of nonlinear mixing effects poses an important problem when attempting to provide accurate estimates of the abundance fractions of pure spectral components (endmembers) in a scene. This problem complicates the development of algorithms that can address all types of nonlinear mixtures in the scene. In this p...\",\n \"Title: A New Genetic Method for Subpixel Mapping Using Hyperspectral Images.\\nYear: 2016\\n\\nAbstract: Subpixel mapping techniques aim to obtain the spatial location and distribution of subpixels by transforming the information coming from a set of input abundance maps into a classification result with higher spatial resolution. However, traditional subpixel mapping algorithms generally ignore the possible errors that are due to abundance estimation inaccuracies by spectral unmixing techniques. In ...\",\n \"Title: Parallel and Distributed Dimensionality Reduction of Hyperspectral Data on Cloud Computing Architectures.\\nYear: 2016\\n\\nAbstract: Cloud computing offers the possibility to store and process massive amounts of remotely sensed hyperspectral data in a distributed way. Dimensionality reduction is an important task in hyperspectral imaging, as hyperspectral data often contains redundancy that can be removed prior to analysis of the data in repositories. In this regard, the development of dimensionality reduction techniques in clo...\",\n \"Title: Cloud implementation of the K-means algorithm for hyperspectral image analysis.\\nYear: 2017\\n\\nAbstract: Remotely sensed hyperspectral imaging offers the possibility to collect hundreds of images, at different wavelength channels, for the same area on the surface of the Earth. Hyperspectral images are characterized by their large volume and dimensionality, which makes their processing and storage difficult. As a result, several techniques have been developed in previous years to perform hyperspectral image analysis on high-performance computing architectures. However, the application of cloud computing techniques has not been as widespread. There are many potential advantages in exploiting cloud computing architectures for distributed hyperspectral image analysis. In this paper, we present a cloud implementation (developed using Apache Spark) of the popular K-means algorithm for unsupervised hyperspectral image clustering. The experimental results suggest that cloud architectures allow for the efficient distributed processing of large hyperspectral image data sets.\",\n \"Title: Robust Matrix Discriminative Analysis for Feature Extraction From Hyperspectral Images\\nYear: 2017\\n\\nAbstract: Linear discriminative analysis (LDA) is an effective feature extraction method for hyperspectral image (HSI) classification. Most of the existing LDA-related methods are based on spectral features, ignoring spatial information. Recently, a matrix discriminative analysis (MDA) model has been proposed to incorporate the spatial information into the LDA. However, due to sensor interferers, calibratio...\",\n \"Title: FPGA implementation of a maximum simplex volume algorithm for endmember extraction from remotely sensed hyperspectral images\\nYear: 2019\\n\\nAbstract: Spectral unmixing is a very important technique for remotely sensed hyperspectral unmixing. Since more hyperspectral applications now require real or near real-time processing capabilities, fast spectral unmixing using field-programmable gate arrays (FPGAs) has received considerable interest in recent years. FPGAs can provide onboard, high computing performance at low power consumption. Another important characteristic of FPGA-based systems is reconfigurability, which makes them more flexible to process different kind of scenes. Pure signature (endmember) extraction is a fundamental step in spectral unmixing, which has been tackled using the maximum volume principle by several algorithms, most notably N-FINDR and simplex growing algorithm (SGA). These algorithms find out the simplex with maximum volume as a mechanism to extract endmembers. However, a previous dimensionality reduction step is generally required, which introduces information loss and additional computational burden. To address these issues, in this work we introduce a new volume calculation formula and further develop a new real-time implementation of a maximum simplex volume algorithm (called RT-MSVA). The proposed RT-MSVA does not need dimensionality reduction, so all spectral bands can be used without losing any information to ensure robust endmember extraction accuracy. Experiments with synthetic and real hyperspectral images have been conducted to evaluate the accuracy and computational performance of our proposed method. Our experimental results indicate that proposed FPGA-based implementation significantly outperforms the corresponding software version and achieves real-time processing performance in the considered problem. It also exhibits better endmember extraction accuracy and comparable performance to other available techniques, such as a real-time implementation of a simplex growing algorithm (RT-FSGA).\",\n \"Title: GPU Parallel Implementation of Spatially Adaptive Hyperspectral Image Classification.\\nYear: 2018\\n\\nAbstract: Image classification is a very important tool for remotely sensed hyperspectral image processing. Techniques able to exploit the rich spectral information contained in the data, as well as its spatial-contextual information, have shown success in recent years. Due to the high dimensionality of hyperspectral data, spectral-spatial classification techniques are quite demanding from a computational v...\",\n \"Title: Capsule Networks for Hyperspectral Image Classification\\nYear: 2019\\n\\nAbstract: Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral–spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral–spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton’s capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral–spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral–spatial capsules, which are able to learn HSI spectral–spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted using five well-known HSI data sets and several state-of-the-art classification methods, reveal that our HSI classification approach based on spectral–spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time.\",\n \"Title: Pansharpening via Detail Injection Based Convolutional Neural Networks.\\nYear: 2019\\n\\nAbstract: Pansharpening aims to fuse a multispectral (MS) image with an associated panchromatic (PAN) image, producing a composite image with the spectral resolution of the former and the spatial resolution of the latter. Traditional pansharpening methods can be ascribed to a unified detail injection context, which views the injected MS details as the integration of PAN details and bandwise injection gains....\",\n \"Title: Low–High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification\\nYear: 2019\\n\\nAbstract: Convolutional neural networks have emerged as an excellent tool for remotely sensed hyperspectral image (HSI) classification. Nonetheless, the high computational complexity and energy requirements of these models typically limit their application in on-board remote sensing scenarios. In this context, low-power consumption architectures are promising platforms that may provide acceptable on-board computing capabilities to achieve satisfactory classification results with reduced energy demand. For instance, the new NVIDIA Jetson Tegra TX2 device is an efficient solution for on-board processing applications using deep-learning (DL) approaches. So far, very few efforts have been devoted to exploiting this or other similar computing platforms in on-board remote sensing procedures. This letter explores the use of low-power consumption architectures and DL algorithms for HSI classification. The conducted experimental study reveals that the NVIDIA Jetson Tegra TX2 device offers a good choice in terms of performance, cost, and energy consumption for on-board HSI classification tasks.\",\n \"Title: DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing\\nYear: 2019\\n\\nAbstract: Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep autoencoder network (DAEN). Our newly developed DAEN consists of two parts. The first part of the n...\",\n \"Title: An Efficient and Scalable Framework for Processing Remotely Sensed Big Data in Cloud Computing Environments\\nYear: 2019\\n\\nAbstract: The large amount of data produced by satellites and airborne remote sensing instruments has posed important challenges to efficient and scalable processing of remotely sensed data in the context of various applications. In this paper, we propose a new big data framework for processing massive amounts of remote sensing images on cloud computing platforms. In addition to taking advantage of the para...\",\n \"Title: Sentinel-2 and Sentinel-3 Intersensor Vegetation Estimation via Constrained Topic Modeling\\nYear: 2019\\n\\nAbstract: This letter presents a novel intersensor vegetation estimation framework, which aims at combining Sentinel-2 (S2) spatial resolution with Sentinel-3 (S3) spectral characteristics in order to generate fused vegetation maps. On the one hand, the multispectral instrument (MSI), carried by S2, provides high spatial resolution images. On the other hand, the Ocean and Land Color Instrument (OLCI), one o...\",\n \"Title: Intersensor Remote Sensing Image Registration Using Multispectral Semantic Embeddings\\nYear: 2019\\n\\nAbstract: This letter presents a novel intersensor registration framework specially designed to register Sentinel-3 (S3) operational data using the Sentinel-2 (S2) instrument as a reference. The substantially higher resolution of the Multispectral Instrument (MSI), on-board S2, with respect to the Ocean and Land Color Instrument (OLCI), carried by S3, makes the former sensor a suitable spatial reference to ...\",\n \"Title: Subpixel Component Analysis for Hyperspectral Image Classification\\nYear: 2019\\n\\nAbstract: Land-cover classification with hyperspectral imagery has been an active topic in the remote sensing community. It aims at relating a unique class label to each pixel in the scene, so that it can be well defined by a given land cover type. In this paper, we explore the intrinsic characteristics of hyperspectral imagery from a subpixel-level perspective and propose a new subpixel component analysis (SCA) approach for feature extraction and land-cover classification. The core idea of SCA is that we extract a subpixel attribute component feature from the abundance maps. Compared with the abundance maps, the extracted subpixel feature image shows higher signal-to-noise level and clearer spatial distribution details. In order to deal with spectral variability, as well as obtain representative image endmember signatures and their corresponding abundance maps, we adopt a regional clustering-based spatial preprocessing (RCSPP) strategy for endmember identification, and a partial unmixing model based on mixture tuned matched filtering (MTMF) for abundance estimation. Furthermore, to highlight the spatial distribution details as well as eliminate the noise disturbance in the derived abundance maps, we perform sparse image decomposition on the obtained abundance maps, thus achieving a new subpixel feature representation for classification. Our experimental results reveal that the proposed SCA approach can obtain feature representation with explicit physical meaning, clear spatial distribution details, and better noise robustness, leading to state-of-the-art classification results.\",\n \"Title: Scale-Free Convolutional Neural Network for Remote Sensing Scene Classification\\nYear: 2019\\n\\nAbstract: Fine-tuning of pretrained convolutional neural networks (CNNs) has been proven to be an effective strategy for remote sensing image scene classification, particularly when a limited number of labeled data sets are available for training purposes. However, such a fine-tuning process often needs that the input images are resized into a fixed size to generate input vectors of the size required by ful...\",\n \"Title: Hyperspectral Image Classification Using Random Occlusion Data Augmentation\\nYear: 2019\\n\\nAbstract: Convolutional neural networks (CNNs) have become a powerful tool for remotely sensed hyperspectral image (HSI) classification due to their great generalization ability and high accuracy. However, owing to the huge amount of parameters that need to be learned and to the complex nature of HSI data itself, these approaches must deal with the important problem of overfitting, which can lead to inadequate generalization and loss of accuracy. In order to mitigate this problem, in this letter, we adopt random occlusion, a recently developed data augmentation (DA) method for training CNNs, in which the pixels of different rectangular spatial regions in the HSI are randomly occluded, generating training images with various levels of occlusion and reducing the risk of overfitting. Our results with two well-known HSIs reveal that the proposed method helps to achieve better classification accuracy with low computational cost.\",\n \"Title: Skip-Connected Covariance Network for Remote Sensing Scene Classification.\\nYear: 2020\\n\\nAbstract: This paper proposes a novel end-to-end learning model, called skip-connected covariance (SCCov) network, for remote sensing scene classification (RSSC). The innovative contribution of this paper is to embed two novel modules into the traditional convolutional neural network (CNN) model, i.e., skip connections and covariance pooling. The advantages of newly developed SCCov are twofold. First, by means of the skip connections, the multi-resolution feature maps produced by the CNN are combined together, which provides important benefits to address the presence of large-scale variance in RSSC data sets. Second, by using covariance pooling, we can fully exploit the second-order information contained in such multi-resolution feature maps. This allows the CNN to achieve more representative feature learning when dealing with RSSC problems. Experimental results, conducted using three large-scale benchmark data sets, demonstrate that our newly proposed SCCov network exhibits very competitive or superior classification performance when compared with the current state-of-the-art RSSC techniques, using a much lower amount of parameters. Specifically, our SCCov only needs 10% of the parameters used by its counterparts.\",\n \"Title: SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers\\nYear: 2022\\n\\nAbstract: Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Due to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image classification. However, CNNs fail to mine and represent the sequence attributes of spectral signatures well due to the limitations of their inherent network backbone. To solve this issue, we rethink HS image classification from a sequential perspective with transformers and propose a novel backbone network called SpectralFormer. Beyond bandwise representations in classic transformers, SpectralFormer is capable of learning spectrally local sequence information from neighboring bands of HS images, yielding groupwise spectral embeddings. More significantly, to reduce the possibility of losing valuable information in the layerwise propagation process, we devise a cross-layer skip connection to convey memory-like components from shallow to deep layers by adaptively learning to fuse \\\"soft\\\" residuals across layers. It is worth noting that the proposed SpectralFormer is a highly flexible backbone network, which can be applicable to both pixelwise and patchwise inputs. We evaluate the classification performance of the proposed SpectralFormer on three HS datasets by conducting extensive experiments, showing the superiority over classic transformers and achieving a significant improvement in comparison with state-of-the-art backbone networks. The codes of this work will be available at https://github.com/danfenghong/IEEE_TGRS_SpectralFormer for the sake of reproducibility.\",\n \"Title: Distributed Deep Learning for Remote Sensing Data Interpretation\\nYear: 2021\\n\\nAbstract: As a newly emerging technology, deep learning (DL) is a very promising field in big data applications. Remote sensing often involves huge data volumes obtained daily by numerous in-orbit satellites. This makes it a perfect target area for data-driven applications. Nowadays, technological advances in terms of software and hardware have a noticeable impact on Earth observation applications, more spe...\",\n \"Title: An Overview of the Contributions of Jose Manuel Bioucas-Dias to Remote Sensing Image Processing\\nYear: 2021\\n\\nAbstract: José Manuel Bioucas-Dias was an outstanding expert in many different IEEE-related areas, including inverse problems in imaging, signal and image processing, pattern recognition, optimization, and remote sensing. He authored or coauthored more than 250 publications, including more than 100 journal papers (66 of which published in IEEE journals) and over 200 peer-reviewed international conference pa...\",\n \"Title: Hyperspectral Classification via Global-Local Hierarchical Weighting Fusion Network\\nYear: 2022\\n\\nAbstract: The fusion of spectral–spatial features based on deep learning has become the focus of research in hyperspectral image (HSI) classification. However, previous deep frameworks based on spectral–spatial fusion usually performed feature aggregation only at the branch ends. Furthermore, only first-order statistical features are considered in the fusion process, which is not conducive to improving the discrimination of spectral–spatial features. This article proposes a global–local hierarchical weighted fusion end-to-end classification architecture. The architecture includes two subnetworks for spectral classification and spatial classification. For the spectral subnetwork, two band-grouping strategies are designed, and bidirectional long short-term memory is used to capture spectral context information from global to local perspectives. For the spatial subnetwork, a pooling strategy based on local attention is combined to construct a global–local pooling fusion module to enhance the discriminability of spatial features learned by a convolutional neural network. For the fusion stage, a hierarchical weighting fusion mechanism is developed to obtain the nonlinear relationship between both spectral and spatial features. The experimental results on four real HSI datasets and a GF-5 satellite dataset demonstrate that the method proposed is more competitive in terms of accuracy and generalization.\",\n \"Title: Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review\\nYear: 2022\\n\\nAbstract: Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions, including constrained NMF, structured NMF, and generalized NMF. Furthermore, several experiments are conducted to illustrate the effectiveness of associated algorithms. Finally, we conclude this article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing.\",\n \"Title: Hyperspectral Unmixing Based on Spectral and Sparse Deep Convolutional Neural Networks\\nYear: 2021\\n\\nAbstract: Hyperspectral unmixing refers to the process of obtaining endmembers and abundance vectors through linear or nonlinear models. The traditional linear unmixing model assumes that each mixed pixel can be represented by a linear combination of endmembers. Considering real-world situations, a sparse constraint is normally added to the linear unmixing model. However, the linear model does not take into account that the spectrum of mixed pixels is not simply linearlymixed. To fully study themixing characteristics of ground object spectra before being imaged by the sensor, we propose a supervised unmixing architecture based on a one-dimensional convolutional neural network (CNN) by considering the spectral information and the sparse characteristics in the mixed pixel. Since 1-D CNN only considers feature learning, we combine the traditional root-mean-square error (RMSE) and l(1) regularization in its loss function to minimize training error. The performance of our proposed unmixing model is assessed by comparing the unmixing resultswith three traditional linear sparse unmixing algorithms and the fuzzy ARTMAP neural network in a simulated dataset and three real datasets. The RMSE was used to verify the unmixing accuracy of the different methods. The results showed that the RMSE obtained by our proposed CNN-based method was the lowest among themethods on all three real datasets, proving the effectiveness and stability of the CNN in unmixing tasks.\",\n \"Title: Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China\\nYear: 2021\\n\\nAbstract: Landslide detection mapping (LDM) is the basis of the field of landslide disaster prevention; however, it has faced certain difficulties. The Three Gorges Reservoir area of the Yangtze River has been one of the most intensively evaluated areas for landslide prevention in the world, due to the high frequency of landslide disasters here. In this article, we constructed an accurate LDM model based on convolutional neural networks, residual neural networks, and dense convolutional neural networks (DenseNets) that considers \\\"ZY-3\\\" high spatial resolution (HSR) data and conditioning factors (CFs). In this article, 19 factors based on remote sensing (RS) images, topographical and geological data associated with historical landslide locations were randomly divided into training (70% of total) and testing (30%) datasets. The experimental results show that the accuracy (ACC) of these three LDM models is above 0.95, indicating that the deep neural networks aimed at landslide detection performed well. Furthermore, DenseNet with RS images and CFs can accurately detect landslides. Specifically, DenseNet with RS images and CFs outperforms the other five models by considering the evaluation metrics, which exhibited Kappa coefficient improvements of 0.01-0.04 and ACC improvements of 0.02-0.3%. Among all the factors, elevation factor has a high importance of 0.727, which is the most important factors found in this landslide model construction experiment.\",\n \"Title: Ensemble Entropy Metric for Hyperspectral Anomaly Detection\\nYear: 2022\\n\\nAbstract: In hyperspectral anomaly detection, anomalies are rare targets that exhibit distinct spectral signatures from the background. Thus, anomalies are with low probabilities of occurrence in hyperspectral images. In this article, we develop a new technique for hyperspectral anomaly detection that adopts a new information theory perspective, to fully utilize the aforementioned concepts. Our goal is to transform system entropy into quantitative metrics of anomaly conspicuousness of pixels. To do so, two tasks are first completed: first, the construction of occurrence probability of pixels based on the density peak clustering algorithm, and second, the valid system definitions for pixels in specific anomaly detection problems with multiviews. Specifically, three types of systems are separately established by pixel pairs to conform to the definitions of three entropy definitions in information theory, i.e., Shannon entropy, joint entropy, and relative entropy. Then, three individual entropy-based metrics that assess the anomaly conspicuousness are defined. In addition, we design a standard deviation-based ensemble strategy for the integrated representation of the three individual metrics, which considers both logic ``or'' and ``and'' operations to simultaneously improve the detection rate and reduce the false alarm rate. Our experimental results obtained on two publicly available datasets with anomalies of different sizes and shapes demonstrate the superiority of our newly proposed anomaly detection method.\",\n \"Title: Hashing for Localization (HfL): A Baseline for Fast Localizing Objects in a Large-Scale Scene\\nYear: 2022\\n\\nAbstract: Advanced remote-sensing instruments produce massively large scenes from the surface of the earth, with very high spatial resolution and dimensionality. Developing methods for efficiently localizing specific objects in a large-scale scene presents a significant challenge, mainly because of the high computational requirements involved. To tackle this issue, we propose a new hashing for localization (HfL) framework that efficiently searches for specific objects in the large-scale scene. It begins by dividing the scene into a large number of overlapping local patches. A lightweight deep hash model, referred to as a tiny hashing network (THNet), encodes the local patches into hash codes. The Hamming distances between the hash code of an object image, i.e., an image containing the specific class of objects to be localized in the scene, and those of all local patches are computed. Small values of the Hamming distance indicate local patches that are similar to the object image. The positions of these local patches in the large-scale scene reflect the regional locations of the specific objects. The hash codes are binary and do not take up much space, and the Hamming distance carries very low-computational overheads. Further, we exploit a class center loss as the THNet training objective, which can comprehensively manage multiple object classes. These features mean that the HfL framework can localize specific objects very quickly, regardless of the size of the scene. Extensive experiments validate the effectiveness and efficiency of the framework. For instance, HfL can find objects in a remote-sensing image of 19,584 x 19,584 pixels in only 4.388 s (on a single RTX2080ti), with remarkable localization results. The source codes and datasets are available at https://github.com/lrhan/HfL, together providing a baseline for fast localizing objects in a large-scale scene.\",\n \"Title: Self-Supervised Robust Deep Matrix Factorization for Hyperspectral Unmixing\\nYear: 2022\\n\\nAbstract: Hyperspectral unmixing is a critical step to process hyperspectral images (HSIs). Nonnegative matrix factorization (NMF) has drawn extensive attention in remotely sensed hyperspectral unmixing since it does not require prior knowledge about the pure spectral constituents (endmembers) in the scene. However, this approach is normally implemented as a single-layer procedure, which does not allow for a refinement of the obtained endmember abundances. In addition, HSIs suffer from the interference of sparse noise (besides Gaussian noise), which brings challenges when pursuing efficient hyperspectral unmixing. To address these issues, we propose a new self-supervised robust deep matrix factorization (SSRDMF) model for hyperspectral unmixing, which consists of two parts: encoder and decoder. In the encoder, a multilayer nonlinear structure is designed to directly map the observed HSI data to the corresponding abundances. The abundances are then decoded by the decoder, in which the connected weights are treated as the extracted endmembers. By modeling the sparse noise explicitly, the proposed method can reduce the effect caused by both Gaussian and sparse noise. Furthermore, a self-supervised constraint is included for exploring the spectral information, which is beneficial to further improve unmixing performance. To validate our method, we have conducted extensive experiments on both synthetic and real datasets. Our experiments reveal that our newly developed SSRDMF achieves superior unmixing performance compared to other state-of-the-art methods.\",\n \"Title: Endmember Estimation From Hyperspectral Images Using Geometric Distances\\nYear: 2022\\n\\nAbstract: Endmember estimation consists of two tasks, that is, determining the number of pure spectral constituents (endmembers) and extracting their spectral signatures. We present a new geometric distance-based method for endmember estimation from hyperspectral images (HSIs), which does not need to know the number of endmembers in advance. Our strategy optimizes the widely used maximum distance analysis (MDA) method from two viewpoints. First, the traditional MDA method performs endmember estimation by computing the maximum distances between any pixel and one specific pixel, line, plane, or affine hull (AH) composed by the endmembers that have been formerly extracted. Instead, our new strategy only requires computing the maximum distance between any pixel and one specific AH. This operation provides a simpler way than MDA to estimate endmembers. Second, our strategy exploits a new distance computation between any pixel and an AH and just needs the normal vector (compared to the traditional MDA method, which uses the normal vector and offset). The new distance computation in our method is much more efficient than that in the traditional MDA method.\",\n \"Title: CNN-Based Hyperspectral Pansharpening With Arbitrary Resolution\\nYear: 2022\\n\\nAbstract: Traditional hyperspectral (HS) pansharpening aims at fusing a HS image with its panchromatic (PAN) counterpart, to bring the spatial resolution of the HS image to that of the PAN image. However, in many practical applications, arbitrary resolution HS (ARHS) pansharpening is required, where the HS and PAN images need to be integrated to generate a pansharpened HS image with arbitrary resolution (usually higher than that of the PAN image). Such an innovative task brings forth new challenges for the pansharpening technique, mainly including how to reconstruct HS images beyond the training scale and how to guarantee spectral fidelity at any spatial resolutions. To tackle the challenges, we present a novel convolutional neural network (CNN)-based method for ARHS pansharpening called ARHS-CNN. It is based on a two-step relay optimization process, which is associated with a multilevel enhancement subnetwork and a rescaling subnetwork. With a careful design following the thread, our ARHS-CNN is able to pansharpen HS images to any spatial resolutions using just a single CNN model trained on a limited number of scales while meantime to keep spectral fidelity at those resolutions, which wins an obvious advantage over traditional pansharpening methods. Experimental results obtained on several datasets verify the excellent performance of our ARHS-CNN method.\",\n \"Title: Efficient Semantic Segmentation of Hyperspectral Images Using Adaptable Rectangular Convolution\\nYear: 2022\\n\\nAbstract: Convolutional neural networks (CNNs) are relevant tools for remote sensing data processing in the last few years. Kernels process and integrate the spatial information of remotely sensed hyperspectral images (HSIs) accurately enough, so as to reduce the noise and spectral variations present in the data. Despite the great efficiency of the CNNs with HSI data, kernels need to be composed of a large number of layers and parameters to be able to obtain good performance, forcing the model to process a large amount of information from the datasets and making the model prone to overfitting, due to the limited number of labels in some cases. On the other hand, with that huge amount of data to consume, a large number of kernels are needed, making the model less efficient due to computational complexity. To overcome these challenges, this letter presents a new technique to reduce the computational cost and increase the model accuracy [based on adaptable rectangular convolutions (ARCs)], leading to a substantial reduction of the number of parameters and improving the model, in order to achieve better results in the context of HSI semantic segmentation. Thus, the model learns through the convolution the dimensions and offsets of the kernel of this Adaptable layer, performing the average operation based on integral image works to achieve better results with fewer parameters, reducing the risk of overfitting and computational cost. The source code can be found in the repository available at the link. [https://github.com/jlgs96/segHSI]\",\n \"Title: Optimized Spatial Gradient Transfer for Hyperspectral-LiDAR Data Classification\\nYear: 2022\\n\\nAbstract: The classification accuracy of ground objects is improved due to the combined use of the same scene data collected by different sensors. We propose to fuse the spatial planar distribution and spectral information of the hyperspectral images (HSIs) with the spatial 3D information of the objects captured by light detection and ranging (LiDAR). In this paper, we use the optimized spatial gradient transfer method for data fusion, which can effectively solve the strong heterogeneity of heterogeneous data fusion. The entropy rate superpixel segmentation algorithm over-segments HSI and LiDAR to extract local spatial and elevation information, and a Gaussian density-based regularization strategy normalizes the local spatial and elevation information. Then, the spatial gradient transfer model and l(1)-total variation minimization are introduced to realize the fusion of local multi-attribute features of different sources, and fully exploit the complementary information of different features for the description of ground objects. Finally, the fused local spatial features are reconstructed into a guided image, and the guided filtering acts on each dimension of the original HSI, so that the output maintains the complete spectral information and detailed changes of the spatial fusion features. It is worth mentioning that we have carried out two versions of expansion on the basis of the proposed method to improve the joint utilization of multi-source data. Experimental results on two real datasets indicated that the fused features of the proposed method have a better effect on ground object classification than the mainstream stacking or cascade fusion methods.\",\n \"Title: Enhanced Spatiotemporal Fusion via MODIS-Like Images\\nYear: 2022\\n\\nAbstract: Spatiotemporal fusion (STF) aims at generating remote-sensing data with both high spatial and temporal resolution. In the literature, one of the most widely used strategies to accomplish this goal is to fuse high temporal resolution images collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) with images with finer spatial resolution than those provided by MODIS (e.g., those collected by other satellite instruments such as Landsat or Sentinel-2). Current STF methods generally fuse an upsampled MODIS image with finer spatial resolution images. This leads to two main problems. First of all, the model uncertainty errors (resulting from the ill-posed upsampling problem) will be propagated into the fusion results, leading to spatial and spectral distortion. Furthermore, the spatial details of the upsampled MODIS image may be significantly different from those of the finer spatial resolution images, making the STF problem even more challenging. In order to tackle these issues, in this work, we develop a new linear regression-based STF strategy (LiSTF), which performs the reconstruction from a MODIS-like image (instead of from an upsampled MODIS image), thus reducing the model uncertainty errors and preserving better the spatial information. The MODIS-like images are built from the finer spatial resolution images via downsampling. Our experimental results, conducted using two publicly available datasets of Landsat-MODIS image pairs and one publicly available dataset of Sentinel-MODIS image pairs, reveal that our newly proposed LiSTF approach can significantly enhance the quantitative and qualitative performance of STF, particularly in terms of preserving the spatial information.\",\n \"Title: (DSL)-L-4: Deep Semisupervised Shared Subspace Learning for Hyperspectral Image Classification\\nYear: 2022\\n\\nAbstract: Hyperspectral image (HSI) classification is essential in remote sensing image analysis. The classification methods based on deep learning have attracted more and more attention. However, classification accuracy is seriously affected by the quantity of labeled data and redundant information. Therefore, a deep semisupervised shared subspace learning ((DSL)-L-4) model is developed to overcome these problems in this article. (DSL)-L-4 is composed of two parts. First, the basic feature extraction (BFE) network is constructed to preliminary extract high-dimensional-space features of multiscale data and fusion them to one shared subspace. Then, a deep shared subspace learning (DSSL) network is proposed to obtain a deeper and more representative low-dimensional subspace. Moreover, to obtain a more representative subspace and alleviate dependence on labeled samples, the regular, irregular constraint, and cross-entropy (CE) loss are integrated into the model. The regular constraint is adopted to reconstruct the multiscale patches to ensure the quality of the subspace in an unsupervised manner. The irregular constraint can well embed labeled and unlabeled samples into the procedure of subspace learning (SL). Then, the CE loss is used to extract more discriminative subspace using the limited labeled samples. Finally, we perform experiments on three widely used HSI datasets. Compared with the basic SL model, the (DSL)-L-4's classification accuracy on the popular Salinas, Indian Pines, and PaviaU datasets are increased by 4.68%, 4.25%, and 1.57%, respectively.\",\n \"Title: DFLLR: Deep Feature Learning With Latent Relationship Embedding for Remote Sensing Image Retrieval\\nYear: 2022\\n\\nAbstract: For deep networks, accurate image similarities cannot be well characterized with limited iterations, so the latent relationships between images can be embedded to enhance image retrieval performance. In this article, we propose a method named DFLLR to learn deep image features and accurate image similarities for remote sensing image retrieval (RSIR) simultaneously. First, the AlexNet is employed to extract high-level semantic features. Second, to obtain accurate image similarities, latent relationships between images are constructed with manifold learning and embedded in the AlexNet model with fully connected layers; in this way, the latent relationships and image features can be jointly learned. Third, to boost the RSIR performance further, the constraints of central and margin for jointly learning latent relationships and image features are integrated into our DFLLR. The central constraint is used to reduce the discrepancy of the latent relationships at the intraclass level and enhance the accuracies of image features. Moreover, the margin constraint is designed to enhance the accuracies of the latent relationships by maximizing the manifold margin between the latent relationships at the intraclass and interclass levels. To validate our method, we perform comprehensive experiments on three publicly available remote sensing image datasets, and the results demonstrate that it significantly outperforms other state-of-the-art methods.\",\n \"Title: Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution\\nYear: 2022\\n\\nAbstract: The hyperspectral images are composed of a variety of textures across the different bands which increase the spectral similarity and make it difficult to predict the pixel-wise labels without inducing additional complexity at the feature level. To extract robust and discriminative features from the different regions of land cover, the hyperspectral research community is still seeking such type of convolutions which can efficiently deal with fine-grained texture information during the feature extraction phase, which often overlook this aspect by vanilla convolution. To overcome the above shortcoming, this article proposes a generalized gradient centralized 3D convolution (G2C-Conv3D) operation, which is a weighted combination between the vanilla and gradient centralized 3D convolutions (GC-Conv3D) to extract both the intensity-level semantic information and gradient-level information. This can be easily plugged into the existing HSI feature extraction networks to boost the performance of accurate prediction for land-cover types. To validate the feasibility of the proposed G2C-Conv3D, we have considered the existing CNN3D, MS3DNet, ContextNet, and SSRN feature extraction models and as well as CAE3D, VAE3D, and SAE3D autoencoder (AE) networks, respectively. All these networks are embedded with G2C-Conv3D convolution to implement both generalized gradient centralized feature extraction networks (G2C-FE) and generalized gradient centralized AE networks (G2C-AE) for fine-grained spectral-spatial feature learning. In addition, G2C-Conv2D is also considered with few networks. The extensive experiments are conducted on four most widely used hyperspectral datasets i.e., IP, KSC, UH, and UP, respectively, and compared with the nine methods. The results demonstrate that the proposed G2C-Conv3D can effectively enhance the feature learning ability of the existing networks and both the qualitative and quantitative results show the superiority and effectiveness of the proposed G2C-Conv3D. The source codes will he publicly available at https://github.com/danfenghong/G2C-Conv3D-HSI.\",\n \"Title: A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping\\nYear: 2022\\n\\nAbstract: •Fully used the deep learning model as the base model for heterogeneous ensemble.•Stacking ensemble method improved the performance of the base model effectively.•Ensemble learning methods reduced the uncertainty of the single models’ results.•The importance and correlation of all LCFs were analyzed before training.\",\n \"Title: Rotation-Invariant Deep Embedding for Remote Sensing Images\\nYear: 2022\\n\\nAbstract: Endowing convolutional neural networks (CNNs) with the rotation-invariant capability is important for characterizing the semantic contents of remote sensing (RS) images since they do not have typical orientations. Most of the existing deep methods for learning rotation-invariant CNN models are based on the design of proper convolutional or pooling layers, which aims at predicting the correct category labels of the rotated RS images equivalently. However, a few works have focused on learning rotation-invariant embeddings in the framework of deep metric learning for modeling the fine-grained semantic relationships among RS images in the embedding space. To fill this gap, we first propose a rule that the deep embeddings of rotated images should be closer to each other than those of any other images (including the images belonging to the same class). Then, we propose to maximize the joint probability of the leave-one-out image classification and rotational image identification. With the assumption of independence, such optimization leads to the minimization of a novel loss function composed of two terms: 1) a class-discrimination term and 2) a rotation-invariant term. Furthermore, we introduce a penalty parameter that balances these two terms and further propose a final loss to Rotation-invariant Deep embedding for RS images, termed RiDe. Extensive experiments conducted on two benchmark RS datasets validate the effectiveness of the proposed approach and demonstrate its superior performance when compared to other state-of-the-art methods. The codes of this article will be publicly available at https://github.com/jiankang1991/TGRS_RiDe.\",\n \"Title: DisOptNet: Distilling Semantic Knowledge From Optical Images for Weather-Independent Building Segmentation\\nYear: 2022\\n\\nAbstract: Synthetic aperture radar (SAR) images provide all-weather and all-time capabilities for Earth observation, which becomes highly beneficial in the field of intelligent remote sensing (RS) image interpretation. Due to these advantages, SAR images have been widely exploited in automatic building segmentation tasks under poor weather conditions, especially when disasters happen. However, compared to optical images, the semantics inherent to SAR images are less rich and interpretable due to factors such as speckle noise and imaging geometry. In this scenario, most state-of-the-art methods are focused on designing advanced network architectures or loss functions for building footprint extraction. However, few works have been oriented toward improving segmentation performance through knowledge transfer from optical images. In this article, we propose a novel method based on the DisOptNet network, which can distill the useful semantic knowledge from optical images into a network only trained with SAR data. Specifically, we first analyze the multilevel feature discrepancies between multiple stages of the networks pretrained on the two image modalities. We observe that feature discrepancies start to increase as the encoding stage gradually changes from low level to high level. Based on such observation, we reuse the early stage features and construct parallel convolutional neural network (CNN) branches that are responsible for capturing high-level domain-specific knowledge for each image modality. The optical branch is aimed at mimicking feature generation at the optical pretrained network given the input SAR images. Then, an aggregation module is introduced to calibrate and fuse the features from different modalities while generating the building segments. Extensive experiments were conducted on a large-scale multisensor all-weather building segmentation dataset with state-of-the-art methods used for comparison. Our experimental results validate the effectiveness of DisOptNet, which demonstrates great potential in the task of weather-independent building footprint generation under real scenarios. The codes of this article will be made publicly available at https://github.com/jiankang1991/TGRS_DisOptNet.\",\n \"Title: Semisupervised Discriminative Random Field for Hyperspectral Image Classification\\nYear: 2021\\n\\nAbstract: The integration of spectral and spatial information is crucial in remotely sensed hyperspectral image classification. Some available approaches extract spatial features before classification, while other techniques include spatial information as a spatial regularizer. Due to the model complexity, these methods generally assume that a given pixel is only dependent on itself and its neighboring pixels. However, it is known that there are strong interactions among the pixels in different areas of the image, and as a result, this general assumption can limit the classifier performance. In this work, we develop a new semisupervised discriminative random field (DRF) technique for spatial-spectral classification of hyperspectral images, which constructs the complete model using the whole image. Furthermore, to cope with the unlabeled samples in the model, a new expectation maximization-based algorithm is introduced to optimize the learning of DRF parameters, where the missing labels of these samples serve as the latent variable. As compared to other conventional learning approaches such as maximum a posteriori marginal labeling, the proposed approach can obtain better optimization that leads to improved classification performance. Our experimental results, conducted using real hyperspectral datasets, indicate that the newly developed DRF algorithm exhibits very good performance.\",\n \"Title: A Siamese Network Based U-Net for Change Detection in High Resolution Remote Sensing Images\\nYear: 2022\\n\\nAbstract: Remote sensing image change detection (RSICD) is a technique that explores the change of surface coverage in a certain time series by studying the difference between multiple remote sensing images (RSIs) collected over the same area. Traditional RSICD algorithms exhibit poor performance on complex change detection (CD) tasks. In recent years, deep learning (DL) techniques have achieved outstanding results in the fields of RSI segmentation and target recognition. In CD research, most of the methods treat multitemporal remote sensing data as one input and directly apply DL-based image segmentation theory on it while ignoring the spatio-temporal information in these images. In this article, a new siamese neural network is designed by combing an attention mechanism (Siamese_AUNet) with UNet to solve the problems of RSICD algorithms. SiameseNet encodes the feature extraction of RSIs by two branches in the siamese network, respectively. The weights are shared between these two branches in siamese networks. Subsequently, an attention mechanism is added to the model in order to improve its detection ability for changed objects. The models are then compared with conventional neural networks using three benchmark datasets. The results show that the Siamese_AUNet newly proposed in this article exhibits better performance than other standard methods when solving problems related to weak CD and noise suppression.\",\n \"Title: Toward Tightness of Scalable Neighborhood Component Analysis for Remote-Sensing Image Characterization\\nYear: 2022\\n\\nAbstract: Deep metric learning methods have recently drawn significant attention in the field of remote sensing (RS), owing to their prominent capabilities for modeling relations among RS images based on their semantic contents. In the context of scene classification and large-scale image retrieval, one of the most prominent deep metric learning methods is the scalable neighborhood component analysis (SNCA), which has demonstrated excellent performance on the locality neighborhood structure in the metric space. However, the standard SNCA has important constraints on separating the hard positive and other negative images in the metric space, and this may become a major limitation when dealing with the large-scale variance problem inherent to RS data. To address this issue, we propose a novel deep metric learning formulation that introduces a new margin parameter to enforce the compactness of the within-class feature embeddings. Based on this innovative scheme, we propose two novel loss functions: 1) T-SNCA-c, where the parameter is based on the cosine similarity, and 2) T-SNCA-a, where the parameter is based on the angular distance. Besides, we exploit memory bank optimization to further enhance the semantic diversity during training. Our experimental results, conducted using three downstream applications (K-NN classification, clustering, and image retrieval) and two large-scale RS benchmark datasets, demonstrate that the proposed approach can achieve superior performance when compared to current state-of-the-art deep metric learning methods. The codes of this work will be made available online (https://github.com/jiankang1991/GRSL_TSNCA).\",\n \"Title: Hyperspectral and LiDAR Data Classification Using Joint CNNs and Morphological Feature Learning\\nYear: 2022\\n\\nAbstract: Convolutional neural networks (CNNs) have been extensively utilized for hyperspectral image (HSI) and light detection and ranging (LiDAR) data classification. However, CNNs have not been much explored for joint HSI and LiDAR image classification. Therefore, this article proposes a joint feature learning (HSI and LiDAR) and fusion mechanism using CNN and spatial morphological blocks, which generates highly accurate land-cover maps. The CNN model comprises three Conv3D layers and is directly applied to the HSIs for extracting discriminative spectral-spatial feature representation. On the contrary, the spatial morphological block is able to capture the information relevant to the height or shape of the different land-cover regions from LiDAR data. The LiDAR features are extracted using morphological dilation and erosion layers that increase the robustness of the proposed model by considering elevation information as an additional feature. Finally, both the obtained features from CNNs and spatial morphological blocks are combined using an additive operation prior to the classification. Extensive experiments are shown with widely used HSIs and LiDAR datasets, i.e., University of Houston (UH), Trento, and MUUFL Gulfport scene. The reported results show that the proposed model significantly outperforms traditional methods and other state-of-the-art deep learning models. The source code for the proposed model will be made available publicly at https://github.com/AnkurDeria/HSI+LiDAR.\",\n \"Title: Open-Pit Mine Area Mapping With Gaofen-2 Satellite Images Using U-Net\\nYear: 2022\\n\\nAbstract: Obtaining information on the surface coverage of open-pit mining areas (OPMAs) is of great significance to ecological governance and restoration. The current methods to map the OPMAs face problems such as low mapping accuracy due to complex landscapes. In this article, we propose a hybrid open-pit mining mapping (OPMM) framework with Gaofen-2 (GF-2) high-spatial resolution satellite images (HSRSIs), using an improved U-Net neural network (U-Net+). By concatenating the previous layers with each subsequent layer to ensure that there is a maximum of feature maps of each layer in the network, the U-Net+ can reduce the loss of feature information and make the extraction capability of the network more powerful. Two independent OPMAs were selected as the study area for the OPMM. By taking advantage of GF-2 HSRSIs, a total of 111 open-pit mine sites (OPMSs) were mapped and each OPMS boundary was validated by field surveys. Then, these OPMSs were used as input to assess the accuracy of the OPMM results obtained by the U-Net+. By comparing our results with those provided by five state-of-the-art deep learning algorithms: Fully Convolutional Network (FCN), SegNet, U-Net, Residual U-Net (ResU-Net), and U-Net++, we conclude that the proposed framework outperformed these methods by more than 0.02% in Overall Accuracy, 0.06% in Kappa Coefficient, 0.03% in Mean Intersection over union, 8.36% in producer accuracy and 4.44% in user accuracy. Therefore, the proposed framework thus exhibits very promising applicability in the ecological restoration and governance of OPMAs.\",\n \"Title: Multiattribute Sample Learning for Hyperspectral Image Classification Using Hierarchical Peak Attribute Propagation\\nYear: 2022\\n\\nAbstract: Although hyperspectral image (HSI) classification methods have gained popularity in the remote sensing community, their performance tends to be limited by the quantity and quality of training samples. Actually, the production (i.e., capture and annotation) of training samples requires abundant labor costs and time consumption. Multiattribute sample learning can effectively solve the cost problem for the acquisition of training samples; for this, this article proposes a novel hierarchical peak attribute propagation (HPAP) method for the annotation of multiattribute samples, which is composed of the following key technologies. First, superpixel-oriented guided filtering (SOGF) is designed to improve the distinguishability of spectral features between pixels. Then, peak attribute-driven manual labeling is performed on each multiattribute region to provide prior knowledge for label propagation. Next, peak attribute-based label propagation is applied to each multiattribute region to achieve the single-label information of pixels. Finally, multiattribute samples with a single class label are augmented to the training process of HSI supervised model to enhance their classification performance. Experimental results on several public HSI datasets indicated that the proposed HPAP method can obtain higher annotation accuracy for multiattribute samples and promote the classification accuracy of HSI classification supervised models.\",\n \"Title: Hyperspectral Anomaly Detection With Relaxed Collaborative Representation\\nYear: 2022\\n\\nAbstract: Anomaly detection has become an important remote sensing application due to the abundant spectral and spatial information contained in hyperspectral images. Recently, hyperspectral anomaly detection methods based on the collaborative representation (CR) model have attracted significant attention. Nevertheless, these methods have to face two main challenges: 1) all features (spectral signatures) are constrained to share the same representation coefficient, which ignores the differences among features and 2) existing dictionaries for pixel-by-pixel detection models are usually not reliable. To address these issues, this article proposes a new relaxed CR detector for hyperspectral anomaly detection by using a novel nonglobal dictionary. The proposed detector conducts CR on each feature dimension of the pixel under test and simultaneously constrains the coding vectors of different features to be similar. To the best of our knowledge, this is the first time that a detection model is built from each feature dimension. To adjust the contributions of each feature, an adaptive feature weight constrained version of the method is also proposed. The nonglobal dictionary is constructed by combining the k-nearest neighbor method and an existing global dictionary, which is more reliable and practical than the widely used dual-window dictionary. In addition, this article also designs a band selection strategy for the proposed method. Experiments on five real datasets indicate that the proposed method suppresses background well and outperforms other classical and state-of-the-art methods.\",\n \"Title: Pseudo Complex-Valued Deformable ConvLSTM Neural Network With Mutual Attention Learning for Hyperspectral Image Classification\\nYear: 2022\\n\\nAbstract: Convolutional long short-term memory (ConvLSTM) has received much attention for hyperspectral image (HSI) classification due to its ability of modeling long-range correlations, which, however, is vulnerable to too many parameters and insufficient training, limiting its classification accuracy, especially for small samples. Different from it, traditional hand-crafted methods extract the features with basic attributes of HSIs, which can provide the lack of details and interpretability of deep semantic features. However, existing methods fail to incorporate their complementarity for HSI classification. As such, a Pseudo complex-valued (CV) Deformable ConvLSTM Neural Network with mutual Attention learning (APDCLNN) is proposed, providing a new way to realize the collaborative learning of hand-crafted and deep features for HSI classification. First, a 2-D pseudo CV deformable ConvLSTM (PDConvLSTM2D) cell is designed using deformable convolution and complex operations, with which a spatial-spectral PDConvLSTM2D neural network (SSPDCL2DNN) is built to extract scale- and spectral-enhanced deep spatial-spectral features. Then, 3-D Gabor filter is used to extract hand-crafted features, and a mutual attention-based multimodality feature learning and fusion (MAMLF) module is designed to integrate them into deep features for training and optimization of SSPDCL2DNN. Finally, an attention loss subnetwork is designed to refine the classification results. As we know, this is the first attempt to apply the idea of mutual attention learning to fuse hand-crafted and deep features for HSI classification. Extensive experiments on three widely used HSI datasets show the advantages of our model over other deep methods in terms of both quantitative and visual quality.\",\n \"Title: DRFL-VAT: Deep Representative Feature Learning With Virtual Adversarial Training for Semisupervised Classification of Hyperspectral Image\\nYear: 2022\\n\\nAbstract: While deep learning algorithms have achieved good results in hyperspectral image (HSI) classification, several supervised classification algorithms rely on a large number of labeled samples to get adequate performance. Collecting a large number of labeled samples is expensive in many real applications. To address this issue, a novel semisupervised HSI classification framework called deep representative feature learning (DRFL) with virtual adversarial training (DRFL-VAT) is developed in this article. By embedding the local manifold learning (LML) into the fully connected layers of a convolutional neural network (CNN), our newly developed DRFL can learn representative features. The VAT regularization is adopted to exploit the prediction label distribution of training samples and addresses the overfitting problem. Finally, the objective function of DRFL-VAT is solved by a customized algorithm. We test our method on three widely public HSI datasets and our results show that our method is competitive when compared to other state-of-the-art approaches.\",\n \"Title: Variable Subpixel Convolution Based Arbitrary-Resolution Hyperspectral Pansharpening\\nYear: 2022\\n\\nAbstract: Standard hyperspectral (HS) pansharpening relies on fusion to enhance low-resolution HS (LRHS) images to the resolution of their matching panchromatic (PAN) images, whose practical implementation is normally under a stipulation of scale invariance of the model across the training phase and the pansharpening phase. By contrast, arbitrary resolution HS (ARHS) pansharpening seeks to pansharpen LRHS images to any user-customized resolutions. For such a new HS pansharpening task, it is not feasible to train and store convolution neural network (CNN) models for all possible candidate scales, which implies that the single model acquired from the training phase should be capable of being generalized to yield HS images with any resolutions in the pansharpening phase. To address the challenge, a novel variable subpixel convolution (VSPC)-based CNN (VSPC-CNN) method following our arbitrary upsampling CNN (AU-CNN) framework is developed for ARHS pansharpening. The VSPC-CNN method comprises a two-stage elevating thread. The first stage is to improve the spatial resolution of the input HS image to that of the PAN image through a prepansharpening module, and then, a VSPC-encapsulated arbitrary scale attention upsampling (ASAU) module is cascaded for arbitrary resolution adjustment. After training with given scales, it can be generalized to pansharpen HS image to arbitrary scales under the spatial patterns invariance across the training and pansharpening phases. Experimental results from several specific VSPC-CNNs on both simulated and real HS datasets show the superiority of the proposed method.\",\n \"Title: Multi-resolution Pyramid Enhanced Non-local Feature Extraction for Hyperspectral Classification\\nYear: 2022\\n\\nAbstract: Hyperspectral image (HSI) is applied to accurately distinguish ground objects in many fields, owing to its abundant structural and spectral information. To fully mine the spectral-spatial information, feature extraction methods are employed to further improve the classification performance in numerous research works. Among them, integrating contextual information into pixels is an active topic and achieved significant performance. However, it is inevitable to come with unwanted and superfluous distracting contents in the process of introducing contextual information. This article proposes a multi-resolution pyramid enhanced non-local feature extraction (MRPEN) method by exploiting non-local information from different resolutions to alleviate the effects of noise contents. The feature extraction task is implemented through by first constructing a multi-resolution feature pyramid via performing spectral-spatial reduction and resize operation on the HSI. With the feature pyramid, three kinds of feature extraction manners (i.e., short-range, long-range, and non-local) are utilized to extract specific features for each layers. In particular, the non-local feature is extracted by introducing adjacent superpixels from the upper layer into our feature extraction operation. After this, the extracted features of each layer are concatenated together and fed on a sparse autoencoders. Finally, a decision rule is introduced to blend the prediction results. Experiments on three public HSI datasets and a Gaofen-5 satellite dataset demonstrate that our method outperforms several state-of-the-art classification approaches.\",\n \"Title: Fusing Sentinel-2 and Landsat-8 Surface Reflectance Data via Pixel-Wise Local Normalization\\nYear: 2022\\n\\nAbstract: Medium spatial resolution surface reflectance image series from the combination of Landsat-8 Operational Land Imager and Sentinel-2 Multispectral Imager observations have great importance to the land surface monitoring tasks, for which great efforts have been paid for blending the two data. However, most of the efforts focus on the image series with spatial resolution of 30 m, which cannot meet the data demand of some applications. Therefore, it is necessary to fuse Landsat-8 and Sentinel-2 images to provide 10-m image series. Currently, there are three means to achieve that, including the area-to-point regression kriging fusion approach (ATPRK), spatiotemporal fusion methods, and deep-learning-based fusion models. However, the ATPRK and spatiotemporal fusion methods suffer from the limited fusion performance, while the deep-learning-based fusion models are hardware dependent, i.e., requiring the graphics processing units, which may not be satisfied sometimes. To address these issues, in this article, we develop a new pixel-wise local normalization-based fusion method (LN-FM) for fusing Sentinel-2 and Landsat-8 images. The newly proposed LN-FM is compared to the ATPRK and three representative spatiotemporal fusion methods in experiments, which use imagery collected from both a rural area and an urban area. The experimental results demonstrate that the newly developed LN-FM exhibits excellent qualitative and quantitative performance, as well as remarkable spatial, spectral, and pixel distribution fidelity. Furthermore, this approach is fast, which may improve its applicability\",\n \"Title: Local Semantic Feature Aggregation-Based Transformer for Hyperspectral Image Classification\\nYear: 2022\\n\\nAbstract: Hyperspectral images (HSIs) contain abundant information in the spatial and spectral domains, allowing for a precise characterization of categories of materials. Convolutional neural networks (CNNs) have achieved great success in HSI classification, owing to their excellent ability in local contextual modeling. However, CNNs suffer from fixed filter weights and deep convolutional layers, which lead to a limited receptive field and high computational burden. The recent vision transformer (ViT) models long-range dependencies with a self-attention mechanism and has been an alternative backbone to CNNs traditionally used in HSI classification. However, such transformer-based architectures designate all the input pixels of the receptive field as feature tokens in terms of feature embedding and self-attention, which inevitably limits the ability for learning multiscale features and increases the computational cost. To overcome this issue, we propose a local semantic feature aggregation-based transformer (LSFAT) architecture which allows transformers to represent long-range dependencies of multiscale features more efficiently. We introduce the concept of the homogeneous region into the transformer by considering a pixel aggregation strategy and further propose neighborhood-aggregation-based embedding (NAE) and attention (NAA) modules, which are able to adaptively form multiscale features and capture locally spatial semantics among them in a hierarchical transformer architecture. A reusable classification token is included together with the feature tokens in the attention calculation. In the last stage, a fully connected layer is used to perform classification on the reusable token after transformer encoding. We verify the effectiveness of the NAE and NAA modules compared with the traditional ViT through extensive experiments. Our results demonstrate the excellent classification performance of the proposed method in comparison to other state-of-the-art approaches on several public HSIs.\",\n \"Title: A New Deep Convolutional Network for Effective Hyperspectral Unmixing\\nYear: 2022\\n\\nAbstract: Hyperspectral unmixing extracts pure spectral constituents (endmembers) and their corresponding abundance fractions from remotely sensed scenes. Most traditional hyperspectral unmixing methods require the results of other endmember extraction algorithms to complete the abundance estimation step. Due to the impressive learning and data fitting capabilities of convolutional neural networks (CNNs), deep learning (DL)-based hyperspectral unmixing technologies have rapidly developed in the literature. According to the procedure used to combine different layers (i.e., fully connected layers, convolution layers, and activation layers), these techniques are mainly divided into three main categories, i.e., those based on autoencoder networks, convolutional neural networks, and convolutional autoencoder networks. They usually extract the weight and output of a specific activation layer as endmember signatures and abundance maps, respectively. Moreover, most existing DL-based unmixing approaches usually use 2-D CNNs to learn the features contained in hyperspectral images, and very few approaches employ 3-D CNNs to extract spectral and spatial information. However, 2-D CNN-based techniques cannot capture good discriminative feature maps from the spectral viewpoint, and 3-D CNN-based techniques usually have high computational overload. In this work, to further exploit the feature extraction capability of CNNs, we combine 3- and 2-D convolutions to propose a cross-convolution unmixing network (CrossCUN) for hyperspectral unmixing. Simultaneously, to better illustrate the improvements of our proposed CrossCUN, we also build the corresponding 2-D convolution unmixing network (2-DCUN) and 3-D convolution unmixing network (3-DCUN). We evaluate the performance of our newly developed networks on two types of synthetic datasets and three real hyperspectral images. Experimental results show that the proposed networks not only obtain better results than other DL-based unmixing methods but also do not require any prior knowledge (e.g., the results of other endmember extraction algorithms) to estimate the abundance maps.\",\n \"Title: Learning Orientation Information From Frequency-Domain for Oriented Object Detection in Remote Sensing Images\\nYear: 2022\\n\\nAbstract: Object detection in remote sensing images (RSIs) poses great difficulties due to arbitrary orientations, various scales, and dense location of the targets over the ground. Recent evidence suggests that encoding the orientation information is of great use for training an accurate object detector for oriented object detection (OOD). In this article, we propose a new frequency-domain orientation learning (FDOL) module with two main components: the frequency-domain feature extraction (FFE) network and an orientation enhanced self-attention layer (OES-Layer). The FFE network models the interactions among spatial locations in the frequency domain to determine the frequency of spatial features. Then, these features are fed into our OES-Layer to learn the orientation information. Moreover, the orientation weights are adopted to guide the feature selection in a self-attention (SA) architecture, using them as a control gate to emphasize the spatial responses of target instances. Considering that the original similarity weights (calculated by the SA algorithm) do not distinctly model the orientation variation, the considered orientation weights provide an efficient asset to emphasize the orientation of objects. Extensive experiments on the DOTA and HRSC2016 datasets demonstrate that our method achieves state-of-the-art performance among single-scale methods while achieving competitive performance over multiscale methods.\",\n \"Title: Automatic Detection of Aquatic Weeds: A Case Study in the Guadiana River, Spain\\nYear: 2022\\n\\nAbstract: The spread of aquatic invasive plants is a major concern in several zones of the world's geography. These plants, which are not part of the natural ecosystem, cause a negative impact to the environment, as well as to economy and society. In Spain, large areas of Guadiana (the second-longest river in Spain) have been invaded by such plants. Among the strategies to address this problem, monitoring and detection play an important role to control the spatiotemporal distribution of the invasive plants. The main objective of this work is to develop a methodology able to automatically detect the geolocation of aquatic invasive plants using remote sensing and machine learning techniques. To this end, several classification algorithms have been applied to freely available multispectral satellite imagery, collected by ESA's Sentinel-2 satellite. A quantitative and comparative assessment is conducted using different machine and deep learning algorithms from classical methods, such as unsupervised K-means to supervised random forests and convolutional neural networks. This study also proposes a methodology for validating the obtained classification results, generating synthetic ground truth images based on available high spatial resolution imagery. The obtained results demonstrate the suitability of some of the considered algorithms for automatic detection of aquatic weeds in satellite images with medium spatial resolution.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":200,"string":"200"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13244,"string":"13,244"}}},{"rowIdx":13245,"cells":{"question_id":{"kind":"string","value":"Hugues Talbot"},"question":{"kind":"string","value":"Generate an abstract for the title \"Image quantization under spatial smoothness constraints\" using the following items: 1. Quantization\n2. Regularity constraints\n3. Optimization approach\n4. SNR improvement\n5. Lower entropy"},"answer":{"kind":"string","value":"Quantization, defined as the act of attributing a finite number of grey-levels to an image, is an essential task in image acquisition and coding. It is also intricately linked to various image analysis tasks, such as denoising and segmentation. In this paper, we investigate quantization combined with regularity constraints, a little-studied area which is of interest, in particular, when quantizing in the presence of noise or other acquisition artifacts. We present an optimization approach to the problem involving a novel two-step, iterative, flexible, joint quantizing-regularization method featuring both convex and combinatorial optimization techniques. We show that when using a small number of grey-levels, our approach can yield better quality images in terms of SNR, with lower entropy, than conventional optimal quantization methods."},"correct_answer":{"kind":"string","value":"Quantization, defined as the act of attributing a finite number of grey-levels to an image, is an essential task in image acquisition and coding. It is also intricately linked to various image analysis tasks, such as denoising and segmentation. In this paper, we investigate quantization combined with regularity constraints, a little-studied area which is of interest, in particular, when quantizing in the presence of noise or other acquisition artifacts. We present an optimization approach to the problem involving a novel two-step, iterative, flexible, joint quantizing-regularization method featuring both convex and combinatorial optimization techniques. We show that when using a small number of grey-levels, our approach can yield better quality images in terms of SNR, with lower entropy, than conventional optimal quantization methods."},"documents":{"kind":"list like","value":["Title: Directional Morphological Filtering\nYear: 2001\n\nAbstract: We show that a translation invariant implementation of min/max filters along a line segment of slope in the form of an irreducible fraction $dy/dx$ can be achieved at the cost of $2+k$ min/max comparisons per image pixel, where $k=\\max(|dx|,|dy|)$. Therefore, for a given slope, the computation time is constant and independent of the length of the line segment. We then present the notion of periodic moving histogram algorithm. This allows for a similar performance to be achieved in the more general case of rank filters and rank-based morphological filters. Applications to the filtering of thin nets and computation of both granulometries and orientation fields are detailed. Finally, two extensions are developed. The first deals with the decomposition of discrete disks and arbitrarily oriented discrete rectangles, while the second concerns min/max filters along gray tone periodic line segments.","Title: Well-Composed Images And Rigid Transformations\nYear: 2013\n\nAbstract: We study the conditions under which the topological properties of a 2D well-composed binary image are preserved under arbitrary rigid transformations. This work initiates a more global study of digital image topological properties under such transformations, which is a crucial but under-considered problem in the context of image processing, e.g., for image registration and warping.","Title: Path-Based Morphological Openings\nYear: 2004\n\nAbstract: A well-known problem in image analysis is the extraction of thin and elongated features such as edges, fractures, fibres, vessels. etc. In many cases, Such features are not linear but Curved rather than straight. A well-known morphological tool for the extraction of linear features is the opening by straight line segments of a certain length. In this paper we will extend this class of morphological filters with openings with narrow structuring elements which are not necessarily straight line segments, but which can be connected paths defined by an adjacency relation.","Title: Path Openings and Closings\nYear: 2005\n\nAbstract: This paper lays the theoretical foundations to path openings and closings.The traditional morphological filter used for the analysis of linear structures in images is the union of openings (or the intersection of closings) by linear segments. However structures in images are rarely strictly straight, and as a result a more flexible approach is needed.An extension to the idea of using straight line segments as structuring elements is to use constrained paths, i.e. discrete, one-pixel thick successions of pixels oriented in a particular direction, but in general forming curved lines rather than perfectly straight lines. However the number of such paths is prohibitive and the resulting algorithm by simple composition is inefficient.In this paper we propose a way to compute openings and closings over large numbers of constrained, oriented paths in an efficient manner, suitable for building filters with applications to the analysis of oriented features, such as for example texture.","Title: The Phase Only Transform For Unsupervised Surface Defect Detection\nYear: 2010\n\nAbstract: We present a simple, fast, and effective method to detect defects on textured surfaces. Our method is unsupervised and contains no learning stage or information on the texture being inspected. The new method is based on the Phase Only Transform (PHOT) which correspond to the Discrete Fourier Transform (DFT), normalized by the magnitude. The PHOT removes any regularities, at arbitrary scales, from the image while preserving only irregular patterns considered to represent defects. The localization is obtained by the inverse transform followed by adaptive thresholding using a simple standard statistical method. The main computational requirement is thus to apply the DFT on the input image. The new method is also easy to implement in a few lines of code. Despite its simplicity, the methods is shown to be effective and generic as tested on various inputs, requiring only one parameter for sensitivity. We provide theoretical justification based on a simple model and show results on various kinds of patterns. We also discuss some limitations.","Title: Dual Constrained TV-based Regularization on Graphs.\nYear: 2013\n\nAbstract: Algorithms based on total variation (TV) minimization are prevalent in image processing. They play a key role in a variety of applications such as image denoising, compressive sensing, and inverse problems in general. In this work, we extend the TV dual framework that includes Chambolle's and Gilboa and Osher's projection algorithms for TV minimization. We use a flexible graph data representation that allows us to generalize the constraint on the projection variable. We show how this new formulation of the TV problem may be solved by means of fast parallel proximal algorithms. In denoising and deblurring examples, the proposed approach is shown not only to perform better than recent TV-based approaches, but also to perform well on arbitrary graphs instead of regular grids. The proposed method consequently applies to a variety of other inverse problems including image fusion and mesh filtering.","Title: Outer surface reconstruction for 3D fractured objects\nYear: 2010\n\nAbstract: We study surface reconstruction using a combination of anisotropic Gaussian filter and image segmentation technique -the minimum surface method. Anisotropic Gaussian filtering allows to manage a contrast between intensities of the discontinuity and the object in a desired direction. The minimum surface method detects properly outer boundaries even affected by boundary leakage in the vicinity of blurred edges. The algorithm is tested on a set of real 3D images of large corrosion cracks in stainless steel that initiated at the surface of the tested samples. Results are presented and discussed.","Title: A Memory Gradient algorithm for ℓ2 - ℓ0 regularization with applications to image restoration.\nYear: 2011\n\nAbstract: In this paper, we consider a class of differentiable criteria for sparse image recovery problems. The regularization is applied to a linear transform of the target image. As special cases, it includes edge preserving measures or frame analysis potentials. As shown by our asymptotic results, the considered ℓ2 - ℓ0 penalties may be employed to approximate solutions to ℓ0 penalized optimization problems. One of the advantages of the approach is that it allows us to derive an efficient Majorize-Minimize Memory Gradient algorithm. The fast convergence properties of the proposed optimization algorithm are illustrated through image restoration examples.","Title: Globally Optimal Surfaces by Continuous Maximal Flows\nYear: 2003\n\nAbstract: In this paper we solve the problem of computing exact con- tinuous optimal curves and surfaces for image segmentation and 3D reconstruction, using a maximal flow approach expressed by means of a PDE model. Previously existing techniques yield either grid-biased (graph-based approaches) or sub-optimal answers (active contours and surfaces). The proposed algorithm simulates the flow of an ideal fluid with spatially varying velocity constraint. A proof is given that the algorithm gives the globally maximal flow at convergence, along with an implementation method. The globally minimal surface may be obtained trivially from its output. The new algorithm is applied to segmentation in 2D and 3D medical im- ages and to 3D reconstruction from a stereo image pair. The results in 2D agree remarkably well with an existing planar minimal surface algorithm and the results in 3D segmentation and reconstruction demonstrate that the new algorithm does not exhibit grid bias.","Title: Image tampering detection using Bayer interpolation and JPEG compression.\nYear: 2008\n\nAbstract: In this paper, we describe a technique to detect image tampering using two different methods. The first is based on the Bayer interpolation process and its consequences in the Fourier domain. The second uses artifacts of the JPEG compression and more particularly in the JPEG frame observable in the Fourier domain.","Title: Efficient Robust d-Dimensional Path Operators.\nYear: 2012\n\nAbstract: Path openings and closings are efficient morphological operators that use flexible oriented paths as structuring elements. They are employed in a similar way to operators with rotated line segments as structuring elements, but are more effective at detecting linear structures that are not necessarily locally perfectly straight. While their theory has always allowed paths in arbitrary dimensions, d...","Title: A hybrid algorithm for automatic heart segmentation in ct angiography.\nYear: 2012\n\nAbstract: In this work, we present a hybrid algorithm to automatically delineate the heart volume in 3D cardiac computed tomography (CT) datasets for the visualization of coronary arteries. Our work eliminates the tedious and time consuming step of manually removing obscuring structures around the heart (ribs, sternum, liver...). It quickly provides a clear and well defined view of the coronaries. So far, works related to heart segmentation have mainly focused on heart cavities delineation, which is not suited for coronaries visualization. In contrast, our algorithm extracts the heart cavities, the myocardium and coronaries as a single object. The proposed approach is based on the fitting of a geometric model of the heart to a set of automatically extracted 3D points lying on the heart shell. A novel two-stage fitting scheme is used to improve the robustness to the outliers. The fitting result is further refined using a Random Walker (RW) segmentation approach. Qualitative analysis of results obtained on a 70 exam database shows the efficiency and the accuracy of our approach.","Title: A fast solver for truncated-convex priors: quantized-convex split moves\nYear: 2011\n\nAbstract: This paper addresses the problem of minimizing multilabel energies with truncated convex priors. Such priors are known to be useful but difficult and slow to optimize because they are not convex. We propose two novel classes of binary Graph-Cuts (GC) moves, namely the convex move and the quantized move. The moves are complementary. To significantly improve efficiency, the label range is divided into even intervals. The quantized move tends to efficiently put pixel labels into the correct intervals for the energy with truncated convex prior. Then the convex move assigns the labels more precisely within these intervals for the same energy. The quantized move is a modified a-expansion move, adapted to handle a generalized Potts prior, which assigns a constant penalty to arguments above some threshold. Our convex move is a GC representation of the efficient Murota's algorithm. We assume that the data terms are convex, since this is a requirement for Murota's algorithm. We introduce Quantized-Convex Split Moves algorithm which minimizes energies with truncated priors by alternating both moves. This algorithm is a fast solver for labeling problems with a high number of labels and convex data terms. We illustrate its performance on image restoration.","Title: Fast computation of morphological operations with arbitrary structuring elements\nYear: 1996\n\nAbstract: This paper presents a general algorithm that performs basic mathematical morphology operations, like erosions and openings, with any arbitrary shaped structuring element in an efficient way. It is shown that our algorithm has a lower or equal complexity but better computing time than all comparable known methods. Key words: Mathematical morphology, algorithm, arbitrary, shape.","Title: Efficiently Computing Optimal Consensus of Digital Line Fitting\nYear: 2010\n\nAbstract: Given a set of discrete points in a 2D digital image containing noise, we formulate our problem as robust digital line fitting. More precisely, we seek the maximum subset whose points are included in a digital line, called the optimal consensus. The paper presents an efficient method for exactly computing the optimal consensus by using the topological sweep, which provides us with the quadratic time complexity and the linear space complexity with respect to the number of input points.","Title: Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest\nYear: 2009\n\nAbstract: In this work, we extend a common framework for seeded image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function with differing choices of a parameter q acting as an exponent on the differences between neighboring nodes. Introducing a new parameter p that fixes a power for the edge weights allows us to also include the optimal spanning forest algorithm for watersheds in this same framework. We then propose a new family of segmentation algorithms that fixes p to produce an optimal spanning forest but varies the power q beyond the usual watershed algorithm, which we term power watersheds. Placing the watershed algorithm in this energy minimization framework also opens new possibilities for using unary terms in traditional watershed segmentation and using watersheds to optimize more general models of use in application beyond image segmentation.","Title: Curvilinear structure enhancement with the polygonal path image--application to guide-wire segmentation in X-ray fluoroscopy.\nYear: 2012\n\nAbstract: Curvilinear structures are common in medical imaging, which typically require dedicated processing techniques. We present a new structure to process these, that we call the polygonal path image, denoted (see text for symbol). We derive from (see text for symbol) some curvilinear structure enhancement and analysis algorithms. We show that (see text for symbol) has some interesting properties: it generalizes several concepts found in other methods; it makes it possible to control the smoothness and length of the structures under study; and it can be computed efficiently. We estimate quantitatively its performance in the context of interventional cardiology for the detection of guide-wires in Xray images. We show that (see text for symbol) is particularly well suited for this task where it appears to outperform previous state of the art techniques.","Title: Topology-Preserving Conditions for 2D Digital Images Under Rigid Transformations\nYear: 2014\n\nAbstract: In the continuous domain $\\mathbb{R}^{n}$, rigid transformations are topology-preserving operations. Due to digitization, this is not the case when considering digital images, i.e., images defined on $\\mathbb{Z}^{n}$. In this article, we begin to investigate this problem by studying conditions for digital images to preserve their topological properties under all rigid transformations on $\\mathbb{Z}^{2}$. Based on (i) the recently introduced notion of DRT graph, and (ii) the notion of simple point, we propose an algorithm for evaluating digital images topological invariance.","Title: Motion Segmentation Using Seeded Region Growing.\nYear: 2000\n\nAbstract: Motion based segmentation techniques usually begin with an estimate of the velocity Row field and utilise clustering techniques to segment the flow field. This paper investigates the practicality of performing segmentation using the information typically applied to the velocity estimation stage. A region growing process is described that can reliably segment rigid objects observed by moving cameras without requiring shape or motion models.","Title: A Spatial Regularization Approach for Vector Quantization\nYear: 2011\n\nAbstract: Quantization, defined as the act of attributing a finite number of levels to an image, is an essential task in image acquisition and coding. It is also intricately linked to image analysis tasks, such as denoising and segmentation. In this paper, we investigate vector quantization combined with regularity constraints, a little-studied area which is of interest, in particular, when quantizing in the presence of noise or other acquisition artifacts. We present an optimization approach to the problem involving a novel two-step, iterative, flexible, joint quantizing-regularization method featuring both convex and combinatorial optimization techniques. We show that when using a small number of levels, our approach can yield better quality images in terms of SNR, with lower entropy, than conventional optimal quantization methods.","Title: Efficient robust digital hyperplane fitting with bounded error\nYear: 2011\n\nAbstract: We consider the following fitting problem: given an arbitrary set of N points in a bounded grid in dimension d, find a digital hyperplane that contains the largest possible number of points. We first observe that the problem is 3SUM-hard in the plane, so that it probably cannot be solved exactly with computational complexity better than O(N2), and it is conjectured that optimal computational complexity in dimension d is in fact O(Nd). We therefore propose two approximation methods featuring linear time complexity. As the latter one is easily implemented, we present experimental results that show the runtime in practice.","Title: Efficient robust digital annulus fitting with bounded error\nYear: 2013\n\nAbstract: A digital annulus is defined as a set of grid points lying between two circles sharing an identical center and separated by a given width. This paper deals with the problem of fitting a digital annulus to a given set of points in a 2D bounded grid. More precisely, we tackle the problem of finding a digital annulus that contains the largest number of inliers. As the current best algorithm for exact optimal fitting has a computational complexity in O(N3 logN) where N is the number of grid points, we present an approximation method featuring linear time complexity and bounded error in annulus width, by extending the approximation method previously proposed for digital hyperplane fitting. Experiments show some results and runtime in practice.","Title: Flexible Linear Openings and Closings.\nYear: 2000\n\nAbstract: We present here a new solution to a problem which is commonly encountered in applied image analysis: the problem of extracting linens features from an image. Examples of linear features are fibres, fractures and object boundaries. Such features, though linear, are usually not straight - in general they are curved. A standard morphological tool for extraction of dark linear features is the minimum of straight-line closings. If the linear Features: are light in colour, the dual, a maximum of straight-line openings may be used. This approach, however, is not robust to curvature in the linear features: especially if they are narrow. In this paper we present an efficient algorithm inspired by local shortest-paths which is robust to curvature. This approach fields filters which are morphological closings or openings. Examples show the effect of the new method.","Title: Globally Optimal Geodesic Active Contours\nYear: 2005\n\nAbstract: An approach to optimal object segmentation in the geodesic active contour framework is presented with application to automated image segmentation. The new segmentation scheme seeks the geodesic active contour of globally minimal energy under the sole restriction that it contains a specified internal point pint. This internal point selects the object of interest and may be used as the only input parameter to yield a highly automated segmentation scheme. The image to be segmented is represented as a Riemannian space S with an associated metric induced by the image. The metric is an isotropic and decreasing function of the local image gradient at each point in the image, encoding the local homogeneity of image features. Optimal segmentations are then the closed geodesics which partition the object from the background with minimal similarity across the partitioning. An efficient algorithm is presented for the computation of globally optimal segmentations and applied to cell microscopy, x-ray, magnetic resonance and cDNA microarray images.","Title: Efficient Path Openings and Closings.\nYear: 2005\n\nAbstract: Path openings and closings are algebraic morphological operators using families of thin and oriented structuring elements that are not necessarily perfectly straight. These operators are naturally translation invariant and can be used in filtering applications instead of operators based on the more standard families of straight line structuring elements. They give similar results to area or attribute-based operators but with more flexibility in the constraints. Trivial implementations of this idea using actual suprema or infima of morphological operators with paths as structuring elements would imply exponential complexity. Fortunately a linear complexity algorithm exists in the literature, which has similar running times as an efficient implementation of algebraic operators using straight lines as structuring elements. However even this implementation is sometimes not fast enough, leading practitioners to favour some attribute-based operators instead, which in some applications is not optimal. In this paper we propose an implementation of path-based morphological operators which is shown experimentally to exhibit logarithmic complexity and comparable computing times with those of attribute-based operators.","Title: Efficient complete and incomplete path openings and closings\nYear: 2007\n\nAbstract: Path openings and closings are algebraic morphological operators using families of thin and oriented structuring elements that are not necessarily perfectly straight. These operators can typically be used in filtering applications in lieu of operators based on the more standard families of straight line structuring elements. They yield results which are less constrained than filters based on straight line segments, yet more constrained than connected area or other attribute-based operators. Furthermore, path operators can be parametrised to behave more like either extreme. Natural implementations of this idea using actual suprema or infima of morphological operators with paths as structuring elements would imply exponential complexity. Fortunately, a linear complexity algorithm exists in the literature. This algorithm has similar running times as the best known implementation of morphological operators using straight lines as structuring elements. However, even this implementation is sometimes not fast enough, leading practitioners to favour some attribute-based operators instead, which in some applications is not the best solution. In this paper, we propose an implementation of path-based morphological operators that is shown experimentally to exhibit a logarithmic complexity and comparable computing times with those of attribute-based operators. This implementation has the added benefit of allowing the computation of the related opening transform at no extra computational cost. In order to give additional flexibility and noise-robustness to these operators, we also investigate the case when some pixels are left ignored from the path (i.e. ''jumps'' are allowed) and form incomplete paths.","Title: Globally minimal surfaces by continuous maximal flows.\nYear: 2006\n\nAbstract: In this paper, we address the computation of globally minimal curves and surfaces for image segmentation and stereo reconstruction. We present a solution, simulating a continuous maximal flow by a novel system of partial differential equations. Existing methods are either grid-biased (graph-based methods) or suboptimal (active contours and surfaces). The solution simulates the flow of an ideal fluid with isotropic velocity constraints. Velocity constraints are defined by a metric derived from image data. An auxiliary potential function is introduced to create a system of partial differential equations. It is proven that the algorithm produces a globally maximal continuous flow at convergence, and that the globally minimal surface may be obtained trivially from the auxiliary potential. The bias of minimal surface methods toward small objects is also addressed. An efficient implementation is given for the flow simulation. The globally minimal surface algorithm is applied to segmentation in 2D and 3D as well as to stereo matching. Results in 2D agree with an existing minimal contour algorithm for planar images. Results in 3D segmentation and stereo matching demonstrate that the new algorithm is robust and free from grid bias.","Title: Anisotropic diffusion using power watersheds\nYear: 2010\n\nAbstract: Many computer vision applications such as image filtering, segmentation and stereo-vision can be formulated as optimization problems. Whereas in previous decades continuous-domain, iterative procedures were common, recently discrete, convex, globally optimal methods have received a lot of attention. However not all problems in computer vision are convex, for instance L0 norm optimization such as seen in compressive sensing. Recently, a novel discrete framework encompassing many known segmentation methods was proposed: power watershed. We are interested to explore the possibilities of this minimizer to solve other problems than segmentation, in particular with respect to unusual norms optimization. In this article we reformulate the problem of anisotropic diffusion as an L0 optimization problem, and we show that power watersheds are able to optimize this energy quickly and effectively. This study paves the way for using the power watershed as a useful general-purpose minimizer in many different computer vision contexts.","Title: Sufficient conditions for topological invariance of 2d images under rigid transformations\nYear: 2013\n\nAbstract: In ℝ2, rigid transformations are topology-preserving operations. However, this property is generally no longer true when considering digital images instead of continuous ones, due to digitization effects. In this article, we investigate this issue by studying discrete rigid transformations (DRTs) on ℤ2. More precisely, we define conditions under which digital images preserve their topological properties under any arbitrary DRTs. Based on the recently introduced notion of DRT graph and the classical notion of simple point, we first identify a family of local patterns that authorize topological invariance under DRTs. These patterns are then involved in a local analysis process that guarantees topological invariance of whole digital images in linear time.","Title: Topology-Preserving Rigid Transformation of 2D Digital Images\nYear: 2014\n\nAbstract: We provide conditions under which 2D digital images preserve their topological properties under rigid transformations. We consider the two most common digital topology models, namely dual adjacency and well-composedness. This paper leads to the proposal of optimal preprocessing strategies that ensure the topological invariance of images under arbitrary rigid transformations. These results and methods are proved to be valid for various kinds of images (binary, gray-level, label), thus providing generic and efficient tools, which can be used in particular in the context of image registration and warping.","Title: Elliptical distance transforms and applications\nYear: 2006\n\nAbstract: Discrete Euclidean distance transforms, both exact and approximate, have been studied for some time, in particular by the Discrete Geometry community. In this paper we extend the notion of Euclidean distance transform (EDT) to elliptical distance transform (LDT) The LDT takes an additional two fixed parameters (eccentricity and orientation) in 2-D and an additional four in 3-D (two ratios and two angles) in 3-D, instead of 1 for the EDT in all cases We study first how the LDT can be computed efficiently with good approximation in the case where all parameters are constant. We provide an application to binary object segmentation as motivation for this work.","Title: Dual Constrained Tv-Based Regularization\nYear: 2011\n\nAbstract: Algorithms based on the minimization of the Total Variation are prevalent in computer vision. They are used in a variety of applications such as image denoising, compressive sensing and inverse problems in general. In this work, we extend the TV dual framework that includes Chambolle's and Gilboa-Osher's projection algorithms for TV minimization in a flexible graph data representation by generalizing the constraint on the projection variable. We show how this new formulation of the TV problem may be solved by means of a fast parallel proximal algorithm, which performs better than the classical TV approach for denoising, and is also applicable to inverse problems such as image deblurring.","Title: Thin Structure Filtering Framework With Non-Local Means, Gaussian Derivatives And Spatially-Variant Mathematical Morphology\nYear: 2013\n\nAbstract: Thin structure filtering is an important preprocessing task for the analysis of 2D and 3D bio-medical images in various contexts. We propose a filtering framework that relies on three approaches that are distinct and infrequently used together: linear, non-linear and non-local. This strategy, based on recent progress both in algorithmic/computational and methodological points of view, provides results that benefit from the advantages of each approach, while reducing their respective weaknesses. Its relevance is demonstrated by validations on 2D and 3D images.","Title: Recursive filtering of images with symmetric extension\nYear: 2005\n\nAbstract: Recursive filters are widely used in image analysis due to their efficiency and simple implementation. However these filters have an initialisation problem which either produces unusable results near the image boundaries or requires costly approximate solutions such as extending the boundary manually.In this paper, we describe a method for the recursive filtering of symmetrically extended images for filters with symmetric denominator. We begin with an analysis of symmetric extensions and their effect on non-recursive filtering operators. Based on the non-recursive case, we derive a formulation of recursive filtering on symmetric domains as a linear but spatially varying implicit operator. We then give an efficient method for decomposing and solving the linear implicit system, along with a proof that this decomposition always exists.This decomposition needs to be performed only once for each dimension of the image. This yields a filtering which is both stable and consistent with the ideal infinite extension. The filter is efficient, requiring less computation than the standard recursive filtering. We give experimental evidence to verify these claims.","Title: Surface reconstruction using power watershed\nYear: 2011\n\nAbstract: Surface reconstruction from a set of noisy point measurements has been a well studied problem for several decades. Recently, variational and discrete optimization approaches have been applied to solve it, demonstrating good robustness to outliers thanks to a global energy minimization scheme. In this work, we use a recent approach embedding several optimization algorithms into a common framework named power watershed. We derive a specific watershed algorithm for surface reconstruction which is fast, robust to markers placement, and produces smooth surfaces. Experiments also show that our proposed algorithm compares favorably in terms of speed, memory requirement and accuracy with existing algorithms.","Title: A Majorize-Minimize Memory Gradient Algorithm Applied To X-Ray Tomography\nYear: 2013\n\nAbstract: Tomography is an image reconstruction task that may be viewed as a linear inverse problem akin to deconvolution. Recent progresses in optimization methods have made it possible to formulate this task so that fewer projections and higher amounts of noise can be dealt with, making use of a-priori information and domain constraints. In this article, we investigate 3MG, a new optimization method that is highly flexible and effective. In particular, we propose and compare convex and non-convex regularization potentials on both synthetic and real images. We further investigate the possibility to deal with continuous angular integration, i.e. where projections rays are no longer straight lines, but cones. This is encountered in a variety of real-life situations, but is difficult or impossible to deal with exactly using traditional reconstruction algorithms. We show that in this situation it may be beneficial to acquire fewer projections than would be required using classical methods.","Title: Unseeded region growing for 3D image segmentation\nYear: 2000\n\nAbstract: Unseeded region growing is a versatile and fully automatic segmentation technique suitable for multispectral and 3D images. This approach integrates region-based segmentation with image processing techniques based on adaptive anisotropic diffusion filters.The segmentation method is fast, reliable and free of tuning parameters. It is indeed a general purpose segmentation method and has been successfully applied in a range of image analysis tasks. This paper describes the algorithm, and briefly discusses its properties and applications. Segmentation results will also be shown at the end of the paper","Title: Spatially-Variant Morpho-Hessian Filter: Efficient Implementation and Application\nYear: 2009\n\nAbstract: Elongated objects are more difficult to filter than more isotropic ones because they locally comprise fewer pixels. For thin linear objects, this problem is compounded because there is only a restricted set of directions that can be used for filtering, and finding this local direction is not a simple problem. In addition, disconnections can easily appear due to noise. In this paper we tackle both issues by combining a linear filter for direction finding and a morphological one for filtering. More specifically, we use the eigen-analysis of the Hessian for detecting thin, linear objects, and a spatially-variant opening or closing for their enhancement and reconnection. We discuss the theory of spatially-variant morphological filters and present an efficient algorithm. The resulting spatially-variant morphological filter is shown to successfully enhance directions in 2D and 3D examples illustrated with a brain blood vessel segmentation problem.","Title: Robust skeletonization using the discrete λ-medial axis\nYear: 2011\n\nAbstract: Medial axes and skeletons are notoriously sensitive to contour irregularities. This lack of stability is a serious problem for applications in e.g. shape analysis and recognition. In 2005, Chazal and Lieutier introduced the @l-medial axis as a new concept for computing the medial axis of a shape subject to single parameter filtering. The @l-medial axis is stable under small shape perturbations, as proved by these authors. In this article, a discrete @l-medial axis (DLMA) is introduced and compared with the recently introduced integer medial axis (GIMA). We show that DLMA provides measurably better results than GIMA, with regard to stability and sensibility to rotations. We give efficient algorithms to compute the DLMA, and we also introduce a variant of the DLMA which may be computed in linear-time.","Title: Combinatorial structure of rigid transformations in 2D digital images\nYear: 2013\n\nAbstract: Rigid transformations are involved in a wide range of digital image processing applications. When applied on discrete images, rigid transformations are usually performed in their associated continuous space, requiring a subsequent digitization of the result. In this article, we propose to study rigid transformations of digital images as fully discrete processes. In particular, we investigate a combinatorial structure modelling the whole space of digital rigid transformations on arbitrary subset of Z^2 of size NxN. We describe this combinatorial structure, which presents a space complexity O(N^9) and we propose an algorithm enabling to construct it in linear time with respect to its space complexity. This algorithm, which handles real (i.e., non-rational) values related to the continuous transformations associated to the discrete ones, is however defined in a fully discrete form, leading to exact computation.","Title: Power Watersheds: A Unifying Graph-Based Optimization Framework.\nYear: 2011\n\nAbstract: In this work, we extend a common framework for graph-based image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function with differing choices of a parameter $q$ acting as an exponent on the differences between neighboring nodes. Introducing a new parameter $p$ that fixes a power for the edge weights allows us to also include the optimal spanning forest algorithm for watershed in this same framework. We then propose a new family of segmentation algorithms that fixes $p$ to produce an optimal spanning forest but varies the power $q$ beyond the usual watershed algorithm, which we term power watershed. In particular when $q=2$, the power watershed leads to a multilabel, scale and contrast invariant, unique global optimum obtained in practice in quasi-linear time. Placing the watershed algorithm in this energy minimization framework also opens new possibilities for using unary terms in traditional watershed segmentation and using watershed to optimize more general models of use in applications beyond image segmentation.","Title: Segmentation by Adaptive Prediction and Region Merging\nYear: 2003\n\nAbstract: This paper presents a segmentation technique based on pre- diction and adaptive region merging. While many techniques for segmentation exist, few of them are suited for the segmentation of natural images containing regular textures defined on non-rectangular segments. In this paper, we propose a description of regions based on a deconvolution algorithm whose purpose is to remove the influence of the shape on region contents. The decoupling of shape and texture information is achieved either by adapting waveforms to the segment shape, which is a time-consuming task that needs to be repeated for each segment shape, or by the extrapolation of a signal to fit a rectangular window, which is the chosen path. The deconvolution algorithm is the key of a new segmentation technique that uses extrapolation as a prediction of neighbouring regions. When the prediction of a region fits the actual content of a connected region rea- sonably well, both regions are merged. The segmentation process starts with an over-segmented image. It progressively merges neighbouring re- gions whose extrapolations fit according to an energy criterion. After each merge, the algorithm updates the values of the merging criterion for regions connected to the merged region pair. It stops when no fur- ther gain is achieved in merging regions or when mean values of adjacent regions are too different. Simulation results indicate that, although our technique is tailored for natural images containing periodic signals and flat regions, it is in fact usable for a large set of natural images.","Title: Optimal Consensus Set For Digital Line And Plane Fitting\nYear: 2011\n\nAbstract: This article presents a new method for fitting a digital line or plane to a given set of points in a 2D or 3D image in the presence of noise by maximizing the number of inliers, namely the consensus set. By using a digital model instead of a continuous one, we show that we can generate all possible consensus sets for model fitting. We present a deterministic algorithm that efficiently searches the optimal solution with time complexity O(N-d log N) for dimension d, where d = 2,3, together with space complexity O(N) where N is the number of points. (c) 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 45-57, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ima.20269","Title: Efficient and consistent recursive filtering of images with reflective extension\nYear: 2003\n\nAbstract: Recursive filters are commonly used in scale space construction for their efficiency and simple implementation. However these filters have an initialisation problem which either produces unusable results near the image boundaries or requires costly approximate solutions such as extending the boundary manually. In this paper, we describe a method for the recursive filtering of reflectively extended images for filters with symmetric denominator. We begin with an analysis of reflective extensions and their effect on nonrecursive filtering operators. Based on the non-recursive case, we derive a formulation of recursive filtering on reflective domains as a linear but time-varying implicit operator. We then give an efficient method for decomposing and solving the linear implicit system. This decomposition needs to be performed only once for each dimension of the image. This yields a filtering which is both stable and consistent with the ideal infinite extension. The filter is efficient, requiring the same order of computation as the standard recursive filtering. We give experimental evidence to verify these claims.","Title: Mathematical morphology: A useful set of tools for image analysis\nYear: 2000\n\nAbstract: In this paper we give an overview of both classical and more modern morphological techniques. We will demonstrate their utility through a range of practical examples. After discussing the fundamental morphological ideas, we show how the classic morphological opening and closing filters lead to measures of size via granulometries, and we will discuss briefly their implementation. We also present an overview of morphological segmentation techniques, and the use of connected openings and thinnings will be demonstrated. This then leads us into the more recent set-theoretic notions of graph based approaches to image analysis.","Title: Automatic retinal vessel extraction based on directional mathematical morphology and fuzzy classification\nYear: 2014\n\nAbstract: The problem of detecting blood vessels in retinal color fundus images is addressed. An unsupervised method based on the extraction of two vessel features vectors in order to detect the pixels belonging to the vessel tree is presented. The proposed vessel features rely on the contrast of vessels and their linear connectivity. The extraction of these features is performed by using advanced morphological directional filter called path openings. The resulting features are used to carry out a data fusion task based on fuzzy set theory. As a result, pixel classification can easily be performed to construct a vessel map. Experimental results using real data have demonstrated the ability of the proposed method to successfully extract a good quality vessel tree. The obtained results are compared with results obtained by classical vessel extraction techniques.","Title: 3D CFD in Complex Vascular Systems: A Case Study.\nYear: 2014\n\nAbstract: Modeling the flowing blood in vascular structures is crucial to perform in silico simulations in various clinical contexts. This remains however an emerging and challenging research field, that raises several open issues. In particular, a compromise is generally made between the completeness of the simulation and the complicated architecture of the vasculature: reduced order simulations (lumped parameter models) represent vascular networks, whereas detailed models are devoted to small regions of interest. However, technical improvements enable targeting of compartments of the blood circulation rather than focusing on vascular branched segments. This article aims at investigating the cerebral flow in the entire venous drainage that can be reconstructed from medical imaging.","Title: Combining interior tomography reconstruction and spatial regularization.\nYear: 2014\n\nAbstract: Interior tomography, also called local or region-of-interest tomography is a special case of computed tomography, in which the object under study is larger than the detector. In this modality, reconstructing the tomography image is an even more ill-posed problem than standard tomography and characteristic artefacts are typically observed, even when a large number of measurements are taken. In this work we propose a reconstruction algorithm designed for interior tomography. We also investigate the case of under-sampled measurements in the case of gradient-sparse images. Our algorithm optimizes the sum of a spatial regularization terms for the image inside the region of interest, and a sinogram regularization term for the projection of the non-reconstructed part of the sample, using convex optimization techniques. We present results on simulated and real data.","Title: Tubular Structure Filtering By Ranking Orientation Responses Of Path Operators\nYear: 2014\n\nAbstract: Thin objects in 3D volumes, for instance vascular networks in medical imaging or various kinds of fibres in materials science, have been of interest for some time to computer vision. Particularly, tubular objects are everywhere elongated in one principal direction-which varies spatially-and are thin in the other two perpendicular directions. Filters for detecting such structures use for instance an analysis of the three principal directions of the Hessian, which is a local feature. In this article, we present a low-level tubular structure detection filter. This filter relies on paths, which are semi-global features that avoid any blurring effect induced by scale-space convolution. More precisely, our filter is based on recently developed morphological path operators. These require sampling only in a few principal directions, are robust to noise and do not assume feature regularity. We show that by ranking the directional response of this operator, we are further able to efficiently distinguish between blob, thin planar and tubular structures. We validate this approach on several applications, both from a qualitative and a quantitative point of view, demonstrating noise robustness and an efficient response on tubular structures.","Title: Efficient Neighbourhood Computing For Discrete Rigid Transformation Graph Search\nYear: 2014\n\nAbstract: Rigid transformations are involved in a wide variety of image processing applications, including image registration. In this context, we recently proposed to deal with the associated optimization problem from a purely discrete point of view, using the notion of discrete rigid transformation (DRT) graph. In particular, a local search scheme within the DRT graph to compute a locally optimal solution without any numerical approximation was formerly proposed. In this article, we extend this study, with the purpose to reduce the algorithmic complexity of the proposed optimization scheme. To this end, we propose a novel algorithmic framework for just-in-time computation of sub-graphs of interest within the DRT graph. Experimental results illustrate the potential usefulness of our approach for image registration.","Title: Iterative poisson-Gaussian noise parametric estimation for blind image denoising\nYear: 2014\n\nAbstract: This paper deals with noise parameter estimation from a single image under Poisson-Gaussian noise statistics. The problem is formulated within a mixed discrete-continuous optimization framework. The proposed approach jointly estimates the signal of interest and the noise parameters. This is achieved by introducing an adjustable regularization term inside an optimized criterion, together with a data fidelity error measure. The optimal solution is sought iteratively by alternating the minimization of a label field and of a noise parameter vector. Noise parameters are updated at each iteration using an Expectation-Maximization approach. The proposed algorithm is inspired from a spatial regularization approach for vector quantization. We illustrate the usefulness of our approach on macroconfocal images. The identified noise parameters are applied to a denoising algorithm, so yielding a complete denoising scheme.","Title: Curvilinear morpho-Hessian filter\nYear: 2008\n\nAbstract: The motivation of this paper is the filtering of thin elongated objects, such as veins, fibres etc. In particular, we focus our attention on detecting thin segments and linking them when disconnection is due to noise. A hybrid Hessian-based and morphological linear filtering method is proposed within the framework of scale space theory. For each pixel the second- derivative response kernel is collected and formed into a Hessian matrix which undergoes eigen analysis. From there, the best eigenvalue response along with the corresponding eigenvector is chosen on different scales. Using the resulting principal directions of linear segment pixels, morphological filters are used to track and connect the linears.","Title: A primal-dual proximal splitting approach for restoring data corrupted with poisson-gaussian noise\nYear: 2012\n\nAbstract: A Poisson-Gaussian model accurately describes the noise present in many imaging systems such as CCD cameras or fluorescence microscopy. However most existing restoration strategies rely on approximations of the Poisson-Gaussian noise statistics. We propose a convex optimization algorithm for the reconstruction of signals degraded by a linear operator and corrupted with mixed Poisson-Gaussian noise. The originality of our approach consists of considering the exact continuous-discrete model corresponding to the data statistics. After establishing the Lipschitz differentiability of the Poisson-Gaussian log-likelihood, we derive a primal-dual iterative scheme for minimizing the associated penalized criterion. The proposed method is applicable to a large choice of penalty terms. The robustness of our scheme allows us to handle computational difficulties due to infinite sums arising from the computation of the gradient of the criterion. The proposed approach is validated on image restoration examples.","Title: Direction-adaptive grey-level morphology. application to 3D vascular brain imaging\nYear: 2009\n\nAbstract: Segmentation and analysis of blood vessels is an important issue in medical imaging. In 3D cerebral angiographic data, the vascular signal is however hard to accurately detect and can, in particular, be disconnected. In this article, we present a procedure utilising both linear, Hessian-based and morphological methods for blood vessel edge enhancement and reconnection. More specifically, multi-scale second-order derivative analysis is performed to detect candidate vessels as well as their orientation. This information is then fed to a spatially-variant morphological filter for reconnection and reconstruction. The result is a fast and effective vessel-reconnecting method.","Title: Selective and robust d-dimensional path operators\nYear: 2014\n\nAbstract: Path operators are powerful tools for the enhancement of thin and elongated objects in an image. In order to cope with noisy acquisition a variant of the path operators was recently proposed. However, both approaches cannot properly handle thin objects with tortuous shapes since strong variations of an object curvature produce disconnections in the paths. In order to address this issue, we propose a novel operator able to properly handle paths in tortuous shapes. It relies on the coupling of attribute filters based on the geodesic tortuosity and conventional path operators. Analogously to the complete version of the path operators, by allowing disconnections within paths it is possible also to define a path operator that is both robust and selective. The effectiveness of the proposed operators in filtering thin and tortuous image objects is proved on a 2D and 3D biomedical image.","Title: An EM approach for Poisson-Gaussian noise modeling\nYear: 2011\n\nAbstract: This paper deals with noise parameter estimation. We assume observations corrupted by noise modelled as a sum of two random processes: one Poisson and the other a (nonzero mean) Gaussian. Such problems arise in various applications, e.g. in astronomy and confocal microscopy imaging. To estimate noise parameters, we propose an iterative algorithm based on an Expectation-Maximization approach. This allows us to jointly estimate the scale parameter of the Poisson component and the mean and variance of the Gaussian one. Moreover, an adequate initialization based on cumulants is provided. Numerical difficulties arising from the procedure are also addressed. To validate the proposed method in terms of accuracy and robustness, tests are performed on synthetic data. The good performance of the method is also demonstrated in a denoising experiment on real data.","Title: Global image registration and video color correction in presence of illumination variations\nYear: 2015\n\nAbstract: In high-speed video imaging acquisition, artificial lighting can cause non-uniform luminosity and chroma variation between frames, which is linked to the interaction between the frequency of the lighting power source and of the video acquisition. This problem is commonly dubbed “flicker”. Deflickering techniques are also useful for resolving similar problems encountered in other applications for instance in archived film sequences, image-video compression and time-lapse videos. In this context, this paper introduces a rigid registration method using point descriptors that is robust to illumination variations, and followed by a method for stabilizing brightness variations in image sequences. This method is based on a Least Square adjustment approach. Our results demonstrate the efficacy of our method at correcting global brightness fluctuations in image sequences while maintaining image quality and without introducing artifacts.","Title: A Convex Approach for Image Restoration with Exact Poisson--Gaussian Likelihood\nYear: 2015\n\nAbstract: The Poisson-Gaussian model can accurately describe the noise present in a number of imaging systems. However most existing restoration methods rely on approximations of the Poisson-Gaussian noise statistics. We propose a convex optimization strategy for the reconstruction of images degraded by a linear operator and corrupted with a mixed Poisson-Gaussian noise. The originality of our approach consists of considering the exact, mixed continuous-discrete model corresponding to the data statistics. After establishing the Lipschitz differentiability and convexity of the Poisson-Gaussian neg-log-likelihood, we derive a primal-dual iterative scheme for minimizing the associated penalized criterion. The proposed method is applicable to a large choice of convex penalty terms. The robustness of our scheme allows us to handle computational difficulties due to infinite sums arising from the computation of the gradient of the criterion. We propose finite bounds for these sums, that are dependent on the current image estimate, and thus adapted to each iteration of our algorithm. The proposed approach is validated on image restoration examples. Then, the exact data fidelity term is used as a reference for studying some of its various approximations. We show that in a variational framework the shifted Poisson and exponential approximations lead to very good restoration results.","Title: Directed Connected Operators: Asymmetric Hierarchies for Image Filtering and Segmentation\nYear: 2015\n\nAbstract: Connected operators provide well-established solutions for digital image processing, typically in conjunction with hierarchical schemes. In graph-based frameworks, such operators basically rely on symmetric adjacency relations between pixels. In this article, we introduce a notion of directed connected operators for hierarchical image processing, by also considering non-symmetric adjacency relatio...","Title: Multicriteria 3D PET image segmentation\nYear: 2015\n\nAbstract: The analysis of images acquired with Positron Emission Tomography (PET) is challenging. In particular, there is no consensus on the best criterion to quantify the metabolic activity for lesion detection and segmentation purposes. Based on this consideration, we propose a versatile knowledge-based segmentation methodology for 3D PET imaging. In contrast to previous methods, an arbitrary number of quantitative criteria can be involved and the experts behaviour learned and reproduced in order to guide the segmentation process. The classification part of the scheme relies on example-based learning strategies, allowing interactive example definition and more generally incremental refinement. The image processing part relies on hierarchical segmentation, allowing vectorial attribute handling. Preliminary results on synthetic and real images confirm the relevance of this methodology, both as a segmentation approach and as an experimental framework for criteria evaluation.","Title: Automated heart rate estimation in fish embryo\nYear: 2015\n\nAbstract: Transparent organisms such as fish embryos are being increasingly used for environmental toxicology studies. These studies require estimating a number of physiological parameters. These estimations may be diverse in nature and can be a challenge to automate. Among these, an example is the development of reliable and repeatable automated assays for the determination of heart rates. To achieve this, most existing method rely on cyclical luminance variations, since as the heart fills and empties, it become respectively brighter and darker. However, sometimes direct measurement of the heart rate may be difficult, depending on the age of the embryo, its actual transparency, and its aspect under the microscope. It may be easier to seek an indirect measurement. In this article, we estimate the heart function parameters, such as heart frequency, either from measuring the heart motion or from blood flow in arteries. This measurement is more complex from the image analysis point of view, but it is more precise, more physically meaningful and easier to use in practice and to automate than measuring illumination changes. It may also be more informative. We illustrate on medaka embryos.","Title: Image Noise and Digital Image Forensics.\nYear: 2015\n\nAbstract: Noise is an intrinsic specificity of all forms of imaging, and can be found in various forms in all domains of digital imagery. This paper offers an overall review of digital image noise, from its causes and models to the degradations it suffers along the image acquisition pipeline. We show that by the end of the pipeline, the noise may have widely different characteristics compared to the raw image, and consider the consequences in forensic and counter-forensic imagery.","Title: Ranking Orientation Responses of Path Operators: Motivations, Choices and Algorithmics.\nYear: 2015\n\nAbstract: A new morphological operator, namely RORPO (Ranking Orientation Responses of Path Operators), was recently introduced as a semi-global, morphological alternative to the local, Hessian-based operators for thin structure filtering in 3D images. In this context, a previous study has already provided experimental proof of its relevance by comparison to such differential operators. In this article, we present a methodological study of RORPO, which completes the presentation of this new morphological filter. In particular, we expose the motivations of RORPO with respect to previous morphological strategies; we present algorithmic developments of this filter and the underlying robust path operator; and we discuss computational issues related to parametricity and time efficiency. We conclude this study by a discussion on the methodological and applicative potentiality of RORPO in various fields of image processing and analysis.","Title: Shape-Based Analysis on Component-Graphs for Multivalued Image Processing\nYear: 2015\n\nAbstract: The extension of mathematical morphology to multivalued images is an important issue. This is particularly true in the context of connected operators based on morphological hierarchies, which aim to provide efficient image filtering and segmentation tools in various application fields, e.g. (bio) medical imaging, remote sensing, or astronomy. In this article, we propose a preliminary study that describes how two notions recently introduced for connected filtering, namely component-graphs (that extend component-trees from a spectral point of view) and shaping (that extend component-trees from a conceptual point of view) can be associated for the effective processing of multivalued images. Structural, algorithmic and experimental developments are proposed. This study opens the way to new paradigms for connected filtering based on hierarchies.","Title: When Convex Analysis Meets Mathematical Morphology on Graphs.\nYear: 2015\n\nAbstract: In recent years, variational methods, i.e., the formulation of problems under optimization forms, have had a great deal of success in image processing. This may be accounted for by their good performance and versatility. Conversely, mathematical morphology (MM) is a widely recognized methodology for solving a wide array of image processing-related tasks. It thus appears useful and timely to build bridges between these two fields. In this article, we propose a variational approach to implement the four basic, structuring element-based operators of MM: dilation, erosion, opening, and closing. We rely on discrete calculus and convex analysis for our formulation. We show that we are able to propose a variety of continuously varying operators in between the dual extremes, i.e., between erosions and dilation; and perhaps more interestingly between openings and closings. This paves the way to the use of morphological operators in a number of new applications.","Title: An Automated Assay for the Evaluation of Mortality in Fish Embryo\nYear: 2015\n\nAbstract: Fish embryo models are used increasingly for human disease modeling, chemical toxicology screening, drug discovery and environmental toxicology studies. These studies are devoted to the analysis of a wide spectrum of physiological parameters, such as mortality ratio. In this article, we develop an assay to determine Medaka (Oryzias latipes) embryo mortality. Based on video sequences, our purpose is to obtain reliable, repeatable results in a fully automated fashion. To reach that challenging goal, we develop an efficient morphological pipeline that analyses image sequences in a multiscale paradigm, from the global scene to the embryo, and then to its heart, finally analysing its putative motion, characterized by intensity variations. Our pipeline, based on robust morphological operators, has a low computational cost, and was experimentally assessed on a dataset consisting of 660 images, providing a success ratio higher than 99%.","Title: Hierarchies And Shape-Space For Pet Image Segmentation\nYear: 2015\n\nAbstract: Positron Emission Tomography ( PET) image segmentation is essential for detecting lesions and quantifying their metabolic activity. Due to the spatial and spectral properties of PET images, most methods rely on intensity-based strategies. Recent methods also propose to integrate anatomical priors to improve the segmentation process. In this article, we show how the hierarchical approaches proposed in mathematical morphology can efficiently handle these different strategies. Our contribution is twofold. First, we present the component-tree as a relevant data-structure for developing interactive, real-time, intensity-based segmentation of PET images. Second, we prove that thanks to the recent concept of shaping, we can efficiently involve a priori knowledge for lesion segmentation, while preserving the good properties of component-tree segmentation. Preliminary experiments on synthetic and real PET images of lymphoma demonstrate the relevance of our approach.","Title: Discrete Calculus, Optimisation and Inverse Problems in Imaging.\nYear: 2016\n\nAbstract: Inverse problems in science are the process of estimating the causal factors that produced a set of observations. Many image processing tasks can be cast as inverse problems: image restoration: noise reduction, deconvolution; segmentation, tomography, demosaicing, inpaiting, and many others, are examples of such tasks. Typically, inverse problems are ill-posed, and solving these problems efficiently and effectively is a major, ongoing topic of research. While imaging is often thought of as occurring on regular grids, it is also useful to be able to solve these problems on arbitrary graphs. The combined frameworks of discrete calculus and modern optimisation allow us to formulate and provide solutions to many of these problems in an elegant way. This tutorial article summarizes and illustrates some of the research results of the last decade from this point of view. We provide illustrations and major references.","Title: Automated Quantification Of The Epidermal Aging Process Using In-Vivo Confocal Microscopy\nYear: 2016\n\nAbstract: Reflectance confocal microscopy (RCM) is a powerful tool to visualize the skin layers at cellular resolution. The epidermal layer appears as a honeycomb pattern, whose regularity decreases with age. Our aim is to provide a method to automatically quantify the regularity of the honeycomb pattern. The proposed strategy relies on a cell-level supervised classification as regular or irregular using spatial information given by a prior segmentation. The aggregated scores defined by the classification results show significant correlation with chronological aging and photo-aging. Thus, our method enables practitioners to more objectively assess the quality of the epidermal layers on large cohort of subjects.","Title: Countering Noise-based Splicing Detection Using Noise Density Transfer.\nYear: 2016\n\nAbstract: Image splicing is a common and widespread type of manipulation, which is defined as pasting a portion of an image onto a second image. Several forensic methods have been developed to detect splicing, using various image properties. Some of these methods exploit the noise statistics of the image to try and find discrepancies. In this paper, we propose a new counter-forensic approach to eliminate the noise differences that can appear in a spliced image. This approach can also be used when creating computer graphics images, in order to endow them with a realistic noise. This is performed by changing the noise statistics of the spliced elements so that they are closer to those of the original image. The proposed method makes use of a novel way to transfer density functions. We apply this to image noise in order to impose identical noise density functions from a source to a destination image. This method can be used with arbitrary noise distributions. The method is tested against several noise-based splicing detection methods, in order to prove its efficacy.","Title: Automatic Image Splicing Detection Based on Noise Density Analysis in Raw Images.\nYear: 2016\n\nAbstract: Image splicing is a common manipulation which consists in copying part of an image in a second image. In this paper, we exploit the variation in noise characteristics in spliced images, caused by the difference in camera and lighting conditions during the image acquisition. The proposed method automatically gives a probability of alteration for any area of the image, using a local analysis of noise density. We consider both Gaussian and Poisson noise components to modelize the noise in the image. The efficiency and robustness of our method is demonstrated on a large set of images generated with an automated splicing.","Title: From Real MRA to Virtual MRA: Towards an Open-Source Framework.\nYear: 2016\n\nAbstract: Angiographic imaging is a crucial domain of medical imaging. In particular , Magnetic Resonance Angiography (MRA) is used for both clinical and research purposes. This article presents the first framework geared toward the design of virtual MRA images from real MRA images. It relies on a pipeline that involves image processing, vascular modeling, computational fluid dynamics and MR image simulation, with several purposes. It aims to provide to the whole scientific community (1) software tools for MRA analysis and blood flow simulation ; and (2) data (computational meshes, virtual MRAs with associated ground truth), in an open-source / open-data paradigm. Beyond these purposes, it constitutes a versatile tool for progressing in the understanding of vascular networks, especially in the brain, and the associated imaging technologies.","Title: Discrete rigid registration: A local graph-search approach.\nYear: 2017\n\nAbstract: Image registration has become a crucial step in a wide range of imaging domains, from computer vision to computer graphics. The core of image registration consists of determining the transformation that induces the best mapping between two images. This problem is ill-posed; it is also difficult to handle, due to the high size of the images and the high dimension of the transformation parameter spaces. Computing an actually optimal solution is practically impossible when transformations are assumed continuous (i.e., defined on Rn). In this article, we initiate the exploration of a new way of considering image registration. Since digital images are basically defined in a discrete framework (i.e., in Zn), the transformation spaces–despite a potentially high complexity–actually remain finite, allowing for the development of explicit exploration of the parameter space via discrete optimization schemes. We propose an analysis of the very basis of registration, by considering rigid registration between 2D images. We show, in particular, how this problem can be handled in a fully discrete fashion, by computing locally the combinatorial structure of the parameter space of discrete rigid transformations, and by navigating on-the-flight within this space via gradient descent paradigms. This registration framework is applied in real imaging cases, emphasizing the relevance of our approach, and the potential usefulness of its further extension to higher dimension images and richer transformations.","Title: Numerical Implementation Of The Ambrosio-Tortorelli Functional Using Discrete Calculus And Application To Image Restoration And Inpainting\nYear: 2016\n\nAbstract: The Mumford-Shah (MS) functional is one of the most influential variational model in image segmentation, restoration, and cartooning. Difficult to solve, the Ambrosio-Tortorelli (AT) functional is of particular interest, because minimizers of AT can be shown to converge to a minimizer of MS. This paper takes an interest in a new method for numerically solving the AT model [11]. This method formulates the AT functional in a discrete calculus setting, and by this way is able to capture the set of discontinuities as a one-dimensional set. It is also shown that this model is competitive with total variation restoration methods. We present here the discrete AT models in details, and compare its merit with recent convex relaxations of AT and MS functionals. We also examine the potential of this model for inpainting, and describe its implementation in the DGtal library, an open-source project.","Title: Morphological PDEs on graphs for analyzing unorganized data in 3D and higher\nYear: 2016\n\nAbstract: Mathematical morphology operators can be defined in terms of algebraic (discrete) sets or as partial differential equations (PDEs). In our previous works [1, 2], we have proposed a simple method to solve PDEs (Partial Differential Equations) on dataset using the framework PdEs (Partial difference Equations) on graphs. In this paper, we propose to apply morphological-based operators on unorganized dataset.","Title: Morphological Analysis Of Brownian Motion For Physical Measurements\nYear: 2017\n\nAbstract: Brownian motion is a well-known, apparently chaotic motion affecting microscopic objects in fluid media. The mathematical and physical basis of Brownian motion have been well studied but not often exploited. In this article we propose a particle tracking methodology based on mathematical morphology, suitable for Brownian motion analysis, which can provide difficult physical measurements such as the local temperature and viscosity. We illustrate our methodology on simulation and real data, showing that interesting phenomena and good precision can be achieved.","Title: Evaluation Of Combinations Of Watershed Hierarchies\nYear: 2017\n\nAbstract: The main goal of this paper is to evaluate the potential of some combinations of watershed hierarchies. We also propose a new combination based on merging level sets of hierarchies. Experiments were performed on natural image datasets and were based on evaluating the segmentations extracted from level sets of each hierarchy against the image ground truths. Our experiments show that most of combinations studied here are superior to their individual counterparts, which opens a path for a deeper investigation on combination of hierarchies.","Title: Classification of the dermal-epidermal junction using in-vivo confocal microscopy\nYear: 2017\n\nAbstract: Reflectance confocal microscopy (RCM) is a powerful tool to visualize the skin layers at cellular resolution. The dermal-epidermal junction (DEJ) is a thin complex 3D structure. It appears as a low-contrasted structure in confocal en-face sections, which is difficult to recognize visually, leading to uncertainty in the classification. In this article, we propose an automated method for segmenting the DEJ with reduced uncertainty. The proposed approach relies on a 3D Conditional Random Field to model the skin biological properties and impose regularization constraints. We improve the restitution of the epidermal and dermal labels while reducing the thickness of the uncertainty area in a coherent biological way from 16.9 μm (ground-truth) to 10.3 μm.","Title: Automated 3D lymphoma lesion segmentation from PET/CT characteristics\nYear: 2017\n\nAbstract: Positron Emission Tomography (PET) using \n<sup xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">18</sup>\nF-FDG is recognized as the modality of choice for lymphoma, due to its high sensitivity and specificity. Its wider use for the detection of lesions, quantification of their metabolic activity and evaluation of response to treatment demands the development of accurate and reproducible quantitative image interpretation tools. An accurate tumour delineation remains a challenge in PET, due to the limitations the modality suffers from, despite being essential for quantifying reliable changes in tumour tissues. Due to the spatial and spectral properties of PET images, most methods rely on intensity-based strategies. Recent methods also propose to integrate anatomical priors to improve the segmentation process. However, the current routinely-used approach remains a local relative thresholding and requires important user interaction, leading to a process that is not only user-dependent but very laborious in the case of lymphomas. In this paper, we propose to rely on hierarchical image models embedding multimodality PET/CT descriptors for a fully automated PET lesion detection / segmentation, performed via a machine learning process. More precisely, we propose to perform random forest classification within the mixed spatial-spectral space of component-trees modeling PET/CT mages. This new approach, combining the strengths of machine learning and morphological hierarchy models leads to intelligent thresholding based on high-level PET/CT knowledge. We evaluate our approach on a database of multi-centric PET/CT images of patients treated for lymphoma, delineated by an expert. Our method provides good efficiency, with the detection of 92% of all lesions, and accurate segmentation results with mean sensitivity and specificity of 0.73 and 0.99 respectively, without any user interaction.","Title: Periodic area-of-motion characterization for bio-medical applications\nYear: 2017\n\nAbstract: Many bio-medical applications involve the analysis of sequences for motion characterization. In this article, we consider 2D+t sequences where a particular motion (e.g. a blood flow) is associated with a specific area of the 2D image (e.g. an artery) but multiple motions may exist simultaneously in the same sequences (e.g. there may be several blood vessels present, each with their specific flow). The characterization of this type of motion typically involves first finding the areas where motion is present, followed by an analysis of these motions: speed, regularity, frequency, etc. In this article, we propose a methodology called “area-of-motion characterization” suitable for simultaneously detecting and characterizing areas where motion is present in a sequence. We can then classify this motion into consistent areas using unsupervised learning and produce directly usable metrics for various applications. We illustrate this methodology for the analysis of cilia motion on ex-vivo human samples, and we apply and validate the same methodology for blood flow analysis in fish embryo.","Title: Spatially Variant Psf Modeling In Confocal Macroscopy\nYear: 2018\n\nAbstract: The point spread function (PSF) of imaging systems plays an essential role in image reconstruction. In the context of confocal microscopy, optical performance degrades towards the edge of the field of view. In confocal macroscopy, the related artifacts are even stronger, as the field of view is much larger. Because the related PSFs are strongly spatially variant, it is essential to be able to model them with few parameters. The field aberrations in fluorescence macroscopy were observed to be symmetrical and to increase with the distance from the center of the field of view. In this paper we propose an experiment and an optimization method for assessing the center of the field of view. The obtained results constitute an important step towards reducing the number of parameters in macroscopy PSF models.","Title: Curvilinear Structure Analysis by Ranking the Orientation Responses of Path Operators.\nYear: 2018\n\nAbstract: The analysis of thin curvilinear objects in 3D images is a complex and challenging task. In this article, we introduce a new, non-linear operator, called RORPO (Ranking the Orientation Responses of Path Operators). Inspired by the multidirectional paradigm currently used in linear filtering for thin structure analysis, RORPO is built upon the notion of path operator from mathematical morphology. T...","Title: Flicker removal and superpixel-based motion tracking for high speed videos\nYear: 2017\n\nAbstract: Flicker removal consists of filtering out rapid, artefactual changes of luminosity and colorimetry from image sequences in order to improve colorimetry consistency between video frames. It is a necessary and fundamental task in multiple applications, for instance in archived film sequences, image/video compression and time-lapse videos. In recent years, the wider availability of fast video acquisition technology has renewed the interest in the flicker removal problem, in particular periodic flicker. In this context, rapid, undesirable intensity and chroma variations are due to acquisition hardware performing faster than the frequency of alternating current powering artificial light sources. This paper proposes both theoretical and efficient experimental solutions for flicker removal from image sequences by performing simultaneous motion tracking and color correction using a superpixel image representation.","Title: A Multicore Convex Optimization Algorithm With Applications To Video Restoration\nYear: 2018\n\nAbstract: In this paper, we present a new distributed algorithm for minimizing a sum of non-necessarily differentiable convex functions composed with arbitrary linear operators. The overall cost function is assumed strongly convex. Each involved function is associated with a node of a hypergraph having the ability to communicate with neighboring nodes sharing the same hyperedge. Our algorithm relies on a primal-dual splitting strategy with established convergence guarantees. We show how it can be efficiently implemented to take full advantage of a multicore architecture. The good numerical performance of the proposed approach is illustrated in a problem of video sequence denoising, where a significant speedup is achieved.","Title: Generation of patient-specific cardiac vascular networks: a hybrid image-based and synthetic geometric model.\nYear: 2019\n\nAbstract: Image-based models derived from CT angiography are being used clinically to simulate blood flow in the coronary arteries of individual patients to aid in the diagnosis of disease and planning treatments. However, image resolution limits vessel segmentation to larger epicardial arteries. The generated model can be used to simulate blood flow and derived quantities from the aorta into the myocardium. This is an important step for diagnosis an treatment planning of coronary artery disease.","Title: High throughput automated detection of axial malformations in Medaka embryo.\nYear: 2019\n\nAbstract: Fish embryo models are widely used as screening tools to assess the efficacy and/or toxicity of chemicals. This assessment involves the analysis of embryo morphological abnormalities. In this article, we propose a multi-scale pipeline to allow automated classification of fish embryos (Medaka: Oryzias latipes) based on the presence or absence of spine malformations. The proposed pipeline relies on the acquisition of fish embryo 2D images, on feature extraction based on mathematical morphology operators and on machine learning classification. After image acquisition, segmentation tools are used to detect the embryo before analysing several morphological features. An approach based on machine learning is then applied to these features to automatically classify embryos according to the presence of axial malformations. We built and validated our learning model on 1459 images with a 10-fold cross-validation by comparison with the gold standard of 3D observations performed under a microscope by a trained operator. Our pipeline results in correct classification in 85% of the cases included in the database. This percentage is similar to the percentage of success of a trained human operator working on 2D images. The key benefit of our approach is the low computational cost of our image analysis pipeline, which guarantees optimal throughput analysis.","Title: Combinatorial Continuous Maximum Flow\nYear: 2011\n\nAbstract: Maximum flow (and minimum cut) algorithms have had a strong impact on computer vision. In particular, graph cut algorithms provide a mechanism for the discrete optimization of an energy functional which has been used in a variety of applications such as image segmentation, stereo, image stitching, and texture synthesis. Algorithms based on the classical formulation of max-flow defined on a graph are known to exhibit metrication artifacts in the solution. Therefore, a recent trend has been to instead employ a spatially continuous maximum flow (or the dual min-cut problem) in these same applications to produce solutions with no metrication errors. However, known fast continuous max-flow algorithms have no stopping criteria or have not been proved to converge. In this work, we revisit the continuous max-flow problem and show that the analogous discrete formulation is different from the classical max-flow problem. We then apply an appropriate combinatorial optimization technique to this combinatorial continuous max-flow (CCMF) problem to find a null-divergence solution that exhibits no metrication artifacts and may be solved exactly by a fast, efficient algorithm with provable convergence. Finally, by exhibiting the dual problem of our CCMF formulation, we clarify the fact, already proved by Nozawa in the continuous setting, that the max-flow and the total variation problems are not always equivalent.","Title: nD variational restoration of curvilinear structures with prior-based directional regularization.\nYear: 2019\n\nAbstract: Curvilinear structure restoration in image processing procedures is a difficult task, which can be compounded when these structures are thin, i.e., when their smallest dimension is close to the resolution of the sensor. Many recent restoration methods involve considering a local gradient-based regularization term as prior, assuming gradient sparsity. An isotropic gradient operator is typically not...","Title: Morphological PDEs on Graphs for Image Processing on Surfaces and Point Clouds.\nYear: 2016\n\nAbstract: Partial Differential Equations (PDEs)-based morphology offers a wide range of continuous operators to address various image processing problems. Most of these operators are formulated as Hamilton-Jacobi equations or curve evolution level set and morphological flows. In our previous works, we have proposed a simple method to solve PDEs on point clouds using the framework of PdEs (Partial difference Equations) on graphs. In this paper, we propose to apply a large class of morphological-based operators on graphs for processing raw 3D point clouds and extend their applications for the processing of colored point clouds of geo-informatics 3D data. Through illustrations, we show that this simple framework can be used in the resolution of many applications for geo-informatics purposes.","Title: An automated assay for the assessment of cardiac arrest in fish embryo.\nYear: 2017\n\nAbstract: Studies on fish embryo models are widely developed in research. They are used in several research fields including drug discovery or environmental toxicology. In this article, we propose an entirely automated assay to detect cardiac arrest in Medaka (Oryzias latipes) based on image analysis. We propose a multi-scale pipeline based on mathematical morphology. Starting from video sequences of entire wells in 24-well plates, we focus on the embryo, detect its heart, and ascertain whether or not the heart is beating based on intensity variation analysis. Our image analysis pipeline only uses commonly available operators. It has a low computational cost, allowing analysis at the same rate as acquisition. From an initial dataset of 3192 videos, 660 were discarded as unusable (20.7%), 655 of them correctly so (99.25%) and only 5 incorrectly so (0.75%). The 2532 remaining videos were used for our test. On these, 45 errors were made, leading to a success rate of 98.23%. Graphical abstractDisplay Omitted HighlightsAn automated method to detect the presence of a heartbeat in fish embryos, using video sequence analysis is proposed.Heartbeat detection is based on measuring cyclic intensity variations in the thorax region of the embryo.Image stabilization and spurious motion detection is performed to eliminate motion artifacts.The procedure works on both eggs and hatched embryo.","Title: 2d Filtering Of Curvilinear Structures By Ranking The Orientation Responses Of Path Operators (Rorpo)\nYear: 2017\n\nAbstract: We present a filtering method for 2D curvilinear structures, called RORPO (Ranking the Orientation Responses of Path Operators). RORPO is based on path operators, a recently developed family of mathematical morphology filters. Compared with state of the art methods, RORPO is non-local and well adapted to the intrinsic anisotropy of curvilinear structures. Since RORPO does not depend on a linear scale-space framework, it tends to preserve object contours without a blurring effect. Due to these properties, RORPO is a useful low-level filter and can also serve as a curvilinear prior in segmentation frameworks. In this article, after introducing RORPO, we develop the 2D version of the algorithm and present a few applications.Source CodeThe C++ implementation of RORPO 2D along with an online demo are available on the IPOL webpage of this article(1)","Title: Atlas-based automated detection of swim bladder in Medaka embryo.\nYear: 2019\n\nAbstract: Fish embryo models are increasingly being used both for the assessment of chemicals efficacy and potential toxicity. This article proposes a methodology to automatically detect the swim bladder on 2D images of medaka fish embryos seen either in dorsal view or in lateral view. After embryo segmentation and for each studied orientation, the method builds an atlas of a healthy embryo. This atlas is then used to define the region of interest and to guide the swim bladder segmentation with a discrete globally optimal active contour. Descriptors are subsequently designed from this segmentation. An automated random forest classifier is built from these descriptors in order to classify embryos with and without a swim bladder. The proposed method is assessed on a dataset of 261 images, containing 202 embryos with a swim bladder (where 196 are in dorsal view and 6 are in lateral view) and 59 without (where 43 are in dorsal view and 16 are in lateral view). We obtain an average precision rate of 95% in the total dataset following 5-fold cross-validation.","Title: Spherical Fluorescent Particle Segmentation and Tracking in 3D Confocal Microscopy.\nYear: 2019\n\nAbstract: Spherical fluorescent particle are micrometer-scale spherical beads used in various areas of physics, chemistry or biology as markers associated with local physical media. They are useful for example in fluid dynamics to characterize flows, diffusion coefficients, viscosity or temperature; they are used in cells dynamics to estimate mechanical strain and stress at the micrometer scale. In order to estimate these physical measurements, tracking these particles is necessary. Numerous approaches and existing packages, both open-source and proprietary are available to achieve tracking with a high degree of precision in 2D. However, little such software is available to achieve tracking in 3D. One major difficulty is that 3D confocal microscopy acquisition is not typically fast enough to assume that the beads are stationary during the whole 3D scan. As a result, beads may move between planar scans. Classical approaches to 3D segmentation may yield objects are not spherical. In this article, we propose a 3D bead segmentation that deals with this situation.","Title: Self-dual Pattern Spectra for Characterising the Dermal-Epidermal Junction in 3D Reflectance Confocal Microscopy Imaging.\nYear: 2019\n\nAbstract: The Dermal-Epidermal Junction (DEJ) is a 2D surface separating the epidermis from the dermis which undergoes multiple changes under pathological or ageing conditions. Recent advances in reflectance confocal microscopy now enables the extraction of the DEJ from in-vivo imaging. This articles proposes a method to automatically analyse DEJ surfaces using self-dual morphological filters. We use self-dual pattern spectra with non-increasing attributes and we propose a novel measure in order to characterize the evolution of the surface under the filtering process. The proposed method is assessed on a specifically constituted dataset and we show that the proposed surface feature significantly correlates with both chronological ageing and photo-ageing.","Title: Machine-Learning-Assisted Intelligent Imaging Flow Cytometry: A Review\nYear: 2021\n\nAbstract: Imaging flow cytometry has been widely adopted in numerous applications such as optical sensing, environmental monitoring, clinical diagnostics, and precision agriculture. The system, with the assistance of machine learning, shows unprecedented advantages in automated image analysis, thus enabling high-throughput measurement, identification, and sorting of biological entities. Recently, with the burgeoning developments of machine learning algorithms, deep learning has taken over most of data analysis and promised tremendous performance in intelligent imaging flow cytometry. Herein, an overview of the basic knowledge of intelligent imaging flow cytometry, the evolution of machine learning and the typical applications, and how machine learning can be applied to assist intelligent imaging flow cytometry is provided. Perspectives of emerging machine learning algorithms in implementing future intelligent imaging flow cytometry are also discussed.","Title: Object Detection With Component-Graphs in Multi-Band Images: Application to Source Detection in Astronomical Images\nYear: 2021\n\nAbstract: In the context of mathematical morphology, component-graphs are complex but powerful structures for multi-band image modeling, processing, and analysis. In this work, we propose a novel multi-band object detection method relying on the component-graphs and statistical hypothesis tests. Our analysis shows that component-graphs are better at capturing image structures compared to the classical component-trees, with significantly higher detection capacity. Besides, we introduce two filtering algorithms to identify duplicated and partial nodes in the component-graphs. The proposed method, applied to the detection of sources on astronomical images, demonstrates a significant improvement in detecting faint objects on both multi-band simulated and real astronomical images compared to the state of the art.","Title: Transformer Graph Network for Coronary Plaque Localization in CCTA\nYear: 2022\n\nAbstract: Coronary CT angiography (CCTA) is the only non-invasive imaging technique that reliably depicts the anatomic extent of Coronary Artery Disease (CAD). While occlusion remains a highly predictive indicator of major cardiovascular events (MACE), there is growing evidence that the presence and characteristics of coronary atherosclerosis provide additional prognostic information. In CCTA calcified plaq...","Title: Distributed algorithms for scalable proximity operator computation and application to video denoising\nYear: 2022\n\nAbstract: Optimization problems arising in signal and image processing involve an increasingly large number of variables. In addition to the curse of dimensionality, another difficulty to overcome is that the cost function usually reads as the sum of several loss or regularization terms, which are non-necessarily smooth and possibly composed with large-size linear operators. Proximal splitting approaches are fundamental tools to address such problems, with demonstrated efficiency in many applicative fields. In this paper, we present a new distributed algorithm for computing the proximity operator of a sum of non-necessarily smooth convex functions composed with arbitrary linear operators. Our algorithm relies on a primal-dual splitting strategy, and benefits from established convergence guaranties. Each involved function is associated with a node of a hypergraph, with the ability to communicate with neighboring nodes sharing the same hyperedge. Thanks to this structure, our method can be efficiently implemented on modern parallel computing architectures, distributing the computations on multiple nodes or machines, with controlled requirements for synchronization steps. Good numerical performance and scalability properties are demonstrated on a problem of video sequence denoising. Our code implemented in Julia is made available at https://github.com/MarinENSTA/distributed_julia_denoising.","Title: Unsupervised Nuclei Segmentation Using Spatial Organization Priors\nYear: 2022\n\nAbstract: In digital pathology, various biomarkers (e.g., KI67, HER2, CD3/CD8) are routinely analyzed by pathologists through immuno-histochemistry-stained slides. Identifying these biomarkers on patient biopsies allows for a more informed design of their treatment regimen. The diversity and specificity of these types of images make the availability of annotated databases sparse. Consequently, robust and efficient learning-based diagnostic systems are difficult to develop and apply in a clinical setting. Our study builds on the observation that the overall organization and structure of the observed tissues are similar across different staining protocols. In this paper, we propose to leverage both the wide availability of haematoxylineosin stained databases and the invariance of tissue organization and structure in order to perform unsupervised nuclei segmentation on immunohistochemistry images. We implement and evaluate a generative adversarial method that relies on high-level nuclei distribution priors through comparison with largely available haematoxylin-eosin stained cell nuclei masks. Our approach shows promising results compared to classic unsupervised and supervised methods, as we quantitatively demonstrate on two publicly available datasets. Our code is publicly available to encourage further contributions (https://github.com/loic-lb/Unsupervised-Nuclei-Segmentation-using-Spatial-Organization-Priors)."],"string":"[\n \"Title: Directional Morphological Filtering\\nYear: 2001\\n\\nAbstract: We show that a translation invariant implementation of min/max filters along a line segment of slope in the form of an irreducible fraction $dy/dx$ can be achieved at the cost of $2+k$ min/max comparisons per image pixel, where $k=\\\\max(|dx|,|dy|)$. Therefore, for a given slope, the computation time is constant and independent of the length of the line segment. We then present the notion of periodic moving histogram algorithm. This allows for a similar performance to be achieved in the more general case of rank filters and rank-based morphological filters. Applications to the filtering of thin nets and computation of both granulometries and orientation fields are detailed. Finally, two extensions are developed. The first deals with the decomposition of discrete disks and arbitrarily oriented discrete rectangles, while the second concerns min/max filters along gray tone periodic line segments.\",\n \"Title: Well-Composed Images And Rigid Transformations\\nYear: 2013\\n\\nAbstract: We study the conditions under which the topological properties of a 2D well-composed binary image are preserved under arbitrary rigid transformations. This work initiates a more global study of digital image topological properties under such transformations, which is a crucial but under-considered problem in the context of image processing, e.g., for image registration and warping.\",\n \"Title: Path-Based Morphological Openings\\nYear: 2004\\n\\nAbstract: A well-known problem in image analysis is the extraction of thin and elongated features such as edges, fractures, fibres, vessels. etc. In many cases, Such features are not linear but Curved rather than straight. A well-known morphological tool for the extraction of linear features is the opening by straight line segments of a certain length. In this paper we will extend this class of morphological filters with openings with narrow structuring elements which are not necessarily straight line segments, but which can be connected paths defined by an adjacency relation.\",\n \"Title: Path Openings and Closings\\nYear: 2005\\n\\nAbstract: This paper lays the theoretical foundations to path openings and closings.The traditional morphological filter used for the analysis of linear structures in images is the union of openings (or the intersection of closings) by linear segments. However structures in images are rarely strictly straight, and as a result a more flexible approach is needed.An extension to the idea of using straight line segments as structuring elements is to use constrained paths, i.e. discrete, one-pixel thick successions of pixels oriented in a particular direction, but in general forming curved lines rather than perfectly straight lines. However the number of such paths is prohibitive and the resulting algorithm by simple composition is inefficient.In this paper we propose a way to compute openings and closings over large numbers of constrained, oriented paths in an efficient manner, suitable for building filters with applications to the analysis of oriented features, such as for example texture.\",\n \"Title: The Phase Only Transform For Unsupervised Surface Defect Detection\\nYear: 2010\\n\\nAbstract: We present a simple, fast, and effective method to detect defects on textured surfaces. Our method is unsupervised and contains no learning stage or information on the texture being inspected. The new method is based on the Phase Only Transform (PHOT) which correspond to the Discrete Fourier Transform (DFT), normalized by the magnitude. The PHOT removes any regularities, at arbitrary scales, from the image while preserving only irregular patterns considered to represent defects. The localization is obtained by the inverse transform followed by adaptive thresholding using a simple standard statistical method. The main computational requirement is thus to apply the DFT on the input image. The new method is also easy to implement in a few lines of code. Despite its simplicity, the methods is shown to be effective and generic as tested on various inputs, requiring only one parameter for sensitivity. We provide theoretical justification based on a simple model and show results on various kinds of patterns. We also discuss some limitations.\",\n \"Title: Dual Constrained TV-based Regularization on Graphs.\\nYear: 2013\\n\\nAbstract: Algorithms based on total variation (TV) minimization are prevalent in image processing. They play a key role in a variety of applications such as image denoising, compressive sensing, and inverse problems in general. In this work, we extend the TV dual framework that includes Chambolle's and Gilboa and Osher's projection algorithms for TV minimization. We use a flexible graph data representation that allows us to generalize the constraint on the projection variable. We show how this new formulation of the TV problem may be solved by means of fast parallel proximal algorithms. In denoising and deblurring examples, the proposed approach is shown not only to perform better than recent TV-based approaches, but also to perform well on arbitrary graphs instead of regular grids. The proposed method consequently applies to a variety of other inverse problems including image fusion and mesh filtering.\",\n \"Title: Outer surface reconstruction for 3D fractured objects\\nYear: 2010\\n\\nAbstract: We study surface reconstruction using a combination of anisotropic Gaussian filter and image segmentation technique -the minimum surface method. Anisotropic Gaussian filtering allows to manage a contrast between intensities of the discontinuity and the object in a desired direction. The minimum surface method detects properly outer boundaries even affected by boundary leakage in the vicinity of blurred edges. The algorithm is tested on a set of real 3D images of large corrosion cracks in stainless steel that initiated at the surface of the tested samples. Results are presented and discussed.\",\n \"Title: A Memory Gradient algorithm for ℓ2 - ℓ0 regularization with applications to image restoration.\\nYear: 2011\\n\\nAbstract: In this paper, we consider a class of differentiable criteria for sparse image recovery problems. The regularization is applied to a linear transform of the target image. As special cases, it includes edge preserving measures or frame analysis potentials. As shown by our asymptotic results, the considered ℓ2 - ℓ0 penalties may be employed to approximate solutions to ℓ0 penalized optimization problems. One of the advantages of the approach is that it allows us to derive an efficient Majorize-Minimize Memory Gradient algorithm. The fast convergence properties of the proposed optimization algorithm are illustrated through image restoration examples.\",\n \"Title: Globally Optimal Surfaces by Continuous Maximal Flows\\nYear: 2003\\n\\nAbstract: In this paper we solve the problem of computing exact con- tinuous optimal curves and surfaces for image segmentation and 3D reconstruction, using a maximal flow approach expressed by means of a PDE model. Previously existing techniques yield either grid-biased (graph-based approaches) or sub-optimal answers (active contours and surfaces). The proposed algorithm simulates the flow of an ideal fluid with spatially varying velocity constraint. A proof is given that the algorithm gives the globally maximal flow at convergence, along with an implementation method. The globally minimal surface may be obtained trivially from its output. The new algorithm is applied to segmentation in 2D and 3D medical im- ages and to 3D reconstruction from a stereo image pair. The results in 2D agree remarkably well with an existing planar minimal surface algorithm and the results in 3D segmentation and reconstruction demonstrate that the new algorithm does not exhibit grid bias.\",\n \"Title: Image tampering detection using Bayer interpolation and JPEG compression.\\nYear: 2008\\n\\nAbstract: In this paper, we describe a technique to detect image tampering using two different methods. The first is based on the Bayer interpolation process and its consequences in the Fourier domain. The second uses artifacts of the JPEG compression and more particularly in the JPEG frame observable in the Fourier domain.\",\n \"Title: Efficient Robust d-Dimensional Path Operators.\\nYear: 2012\\n\\nAbstract: Path openings and closings are efficient morphological operators that use flexible oriented paths as structuring elements. They are employed in a similar way to operators with rotated line segments as structuring elements, but are more effective at detecting linear structures that are not necessarily locally perfectly straight. While their theory has always allowed paths in arbitrary dimensions, d...\",\n \"Title: A hybrid algorithm for automatic heart segmentation in ct angiography.\\nYear: 2012\\n\\nAbstract: In this work, we present a hybrid algorithm to automatically delineate the heart volume in 3D cardiac computed tomography (CT) datasets for the visualization of coronary arteries. Our work eliminates the tedious and time consuming step of manually removing obscuring structures around the heart (ribs, sternum, liver...). It quickly provides a clear and well defined view of the coronaries. So far, works related to heart segmentation have mainly focused on heart cavities delineation, which is not suited for coronaries visualization. In contrast, our algorithm extracts the heart cavities, the myocardium and coronaries as a single object. The proposed approach is based on the fitting of a geometric model of the heart to a set of automatically extracted 3D points lying on the heart shell. A novel two-stage fitting scheme is used to improve the robustness to the outliers. The fitting result is further refined using a Random Walker (RW) segmentation approach. Qualitative analysis of results obtained on a 70 exam database shows the efficiency and the accuracy of our approach.\",\n \"Title: A fast solver for truncated-convex priors: quantized-convex split moves\\nYear: 2011\\n\\nAbstract: This paper addresses the problem of minimizing multilabel energies with truncated convex priors. Such priors are known to be useful but difficult and slow to optimize because they are not convex. We propose two novel classes of binary Graph-Cuts (GC) moves, namely the convex move and the quantized move. The moves are complementary. To significantly improve efficiency, the label range is divided into even intervals. The quantized move tends to efficiently put pixel labels into the correct intervals for the energy with truncated convex prior. Then the convex move assigns the labels more precisely within these intervals for the same energy. The quantized move is a modified a-expansion move, adapted to handle a generalized Potts prior, which assigns a constant penalty to arguments above some threshold. Our convex move is a GC representation of the efficient Murota's algorithm. We assume that the data terms are convex, since this is a requirement for Murota's algorithm. We introduce Quantized-Convex Split Moves algorithm which minimizes energies with truncated priors by alternating both moves. This algorithm is a fast solver for labeling problems with a high number of labels and convex data terms. We illustrate its performance on image restoration.\",\n \"Title: Fast computation of morphological operations with arbitrary structuring elements\\nYear: 1996\\n\\nAbstract: This paper presents a general algorithm that performs basic mathematical morphology operations, like erosions and openings, with any arbitrary shaped structuring element in an efficient way. It is shown that our algorithm has a lower or equal complexity but better computing time than all comparable known methods. Key words: Mathematical morphology, algorithm, arbitrary, shape.\",\n \"Title: Efficiently Computing Optimal Consensus of Digital Line Fitting\\nYear: 2010\\n\\nAbstract: Given a set of discrete points in a 2D digital image containing noise, we formulate our problem as robust digital line fitting. More precisely, we seek the maximum subset whose points are included in a digital line, called the optimal consensus. The paper presents an efficient method for exactly computing the optimal consensus by using the topological sweep, which provides us with the quadratic time complexity and the linear space complexity with respect to the number of input points.\",\n \"Title: Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest\\nYear: 2009\\n\\nAbstract: In this work, we extend a common framework for seeded image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function with differing choices of a parameter q acting as an exponent on the differences between neighboring nodes. Introducing a new parameter p that fixes a power for the edge weights allows us to also include the optimal spanning forest algorithm for watersheds in this same framework. We then propose a new family of segmentation algorithms that fixes p to produce an optimal spanning forest but varies the power q beyond the usual watershed algorithm, which we term power watersheds. Placing the watershed algorithm in this energy minimization framework also opens new possibilities for using unary terms in traditional watershed segmentation and using watersheds to optimize more general models of use in application beyond image segmentation.\",\n \"Title: Curvilinear structure enhancement with the polygonal path image--application to guide-wire segmentation in X-ray fluoroscopy.\\nYear: 2012\\n\\nAbstract: Curvilinear structures are common in medical imaging, which typically require dedicated processing techniques. We present a new structure to process these, that we call the polygonal path image, denoted (see text for symbol). We derive from (see text for symbol) some curvilinear structure enhancement and analysis algorithms. We show that (see text for symbol) has some interesting properties: it generalizes several concepts found in other methods; it makes it possible to control the smoothness and length of the structures under study; and it can be computed efficiently. We estimate quantitatively its performance in the context of interventional cardiology for the detection of guide-wires in Xray images. We show that (see text for symbol) is particularly well suited for this task where it appears to outperform previous state of the art techniques.\",\n \"Title: Topology-Preserving Conditions for 2D Digital Images Under Rigid Transformations\\nYear: 2014\\n\\nAbstract: In the continuous domain $\\\\mathbb{R}^{n}$, rigid transformations are topology-preserving operations. Due to digitization, this is not the case when considering digital images, i.e., images defined on $\\\\mathbb{Z}^{n}$. In this article, we begin to investigate this problem by studying conditions for digital images to preserve their topological properties under all rigid transformations on $\\\\mathbb{Z}^{2}$. Based on (i) the recently introduced notion of DRT graph, and (ii) the notion of simple point, we propose an algorithm for evaluating digital images topological invariance.\",\n \"Title: Motion Segmentation Using Seeded Region Growing.\\nYear: 2000\\n\\nAbstract: Motion based segmentation techniques usually begin with an estimate of the velocity Row field and utilise clustering techniques to segment the flow field. This paper investigates the practicality of performing segmentation using the information typically applied to the velocity estimation stage. A region growing process is described that can reliably segment rigid objects observed by moving cameras without requiring shape or motion models.\",\n \"Title: A Spatial Regularization Approach for Vector Quantization\\nYear: 2011\\n\\nAbstract: Quantization, defined as the act of attributing a finite number of levels to an image, is an essential task in image acquisition and coding. It is also intricately linked to image analysis tasks, such as denoising and segmentation. In this paper, we investigate vector quantization combined with regularity constraints, a little-studied area which is of interest, in particular, when quantizing in the presence of noise or other acquisition artifacts. We present an optimization approach to the problem involving a novel two-step, iterative, flexible, joint quantizing-regularization method featuring both convex and combinatorial optimization techniques. We show that when using a small number of levels, our approach can yield better quality images in terms of SNR, with lower entropy, than conventional optimal quantization methods.\",\n \"Title: Efficient robust digital hyperplane fitting with bounded error\\nYear: 2011\\n\\nAbstract: We consider the following fitting problem: given an arbitrary set of N points in a bounded grid in dimension d, find a digital hyperplane that contains the largest possible number of points. We first observe that the problem is 3SUM-hard in the plane, so that it probably cannot be solved exactly with computational complexity better than O(N2), and it is conjectured that optimal computational complexity in dimension d is in fact O(Nd). We therefore propose two approximation methods featuring linear time complexity. As the latter one is easily implemented, we present experimental results that show the runtime in practice.\",\n \"Title: Efficient robust digital annulus fitting with bounded error\\nYear: 2013\\n\\nAbstract: A digital annulus is defined as a set of grid points lying between two circles sharing an identical center and separated by a given width. This paper deals with the problem of fitting a digital annulus to a given set of points in a 2D bounded grid. More precisely, we tackle the problem of finding a digital annulus that contains the largest number of inliers. As the current best algorithm for exact optimal fitting has a computational complexity in O(N3 logN) where N is the number of grid points, we present an approximation method featuring linear time complexity and bounded error in annulus width, by extending the approximation method previously proposed for digital hyperplane fitting. Experiments show some results and runtime in practice.\",\n \"Title: Flexible Linear Openings and Closings.\\nYear: 2000\\n\\nAbstract: We present here a new solution to a problem which is commonly encountered in applied image analysis: the problem of extracting linens features from an image. Examples of linear features are fibres, fractures and object boundaries. Such features, though linear, are usually not straight - in general they are curved. A standard morphological tool for extraction of dark linear features is the minimum of straight-line closings. If the linear Features: are light in colour, the dual, a maximum of straight-line openings may be used. This approach, however, is not robust to curvature in the linear features: especially if they are narrow. In this paper we present an efficient algorithm inspired by local shortest-paths which is robust to curvature. This approach fields filters which are morphological closings or openings. Examples show the effect of the new method.\",\n \"Title: Globally Optimal Geodesic Active Contours\\nYear: 2005\\n\\nAbstract: An approach to optimal object segmentation in the geodesic active contour framework is presented with application to automated image segmentation. The new segmentation scheme seeks the geodesic active contour of globally minimal energy under the sole restriction that it contains a specified internal point pint. This internal point selects the object of interest and may be used as the only input parameter to yield a highly automated segmentation scheme. The image to be segmented is represented as a Riemannian space S with an associated metric induced by the image. The metric is an isotropic and decreasing function of the local image gradient at each point in the image, encoding the local homogeneity of image features. Optimal segmentations are then the closed geodesics which partition the object from the background with minimal similarity across the partitioning. An efficient algorithm is presented for the computation of globally optimal segmentations and applied to cell microscopy, x-ray, magnetic resonance and cDNA microarray images.\",\n \"Title: Efficient Path Openings and Closings.\\nYear: 2005\\n\\nAbstract: Path openings and closings are algebraic morphological operators using families of thin and oriented structuring elements that are not necessarily perfectly straight. These operators are naturally translation invariant and can be used in filtering applications instead of operators based on the more standard families of straight line structuring elements. They give similar results to area or attribute-based operators but with more flexibility in the constraints. Trivial implementations of this idea using actual suprema or infima of morphological operators with paths as structuring elements would imply exponential complexity. Fortunately a linear complexity algorithm exists in the literature, which has similar running times as an efficient implementation of algebraic operators using straight lines as structuring elements. However even this implementation is sometimes not fast enough, leading practitioners to favour some attribute-based operators instead, which in some applications is not optimal. In this paper we propose an implementation of path-based morphological operators which is shown experimentally to exhibit logarithmic complexity and comparable computing times with those of attribute-based operators.\",\n \"Title: Efficient complete and incomplete path openings and closings\\nYear: 2007\\n\\nAbstract: Path openings and closings are algebraic morphological operators using families of thin and oriented structuring elements that are not necessarily perfectly straight. These operators can typically be used in filtering applications in lieu of operators based on the more standard families of straight line structuring elements. They yield results which are less constrained than filters based on straight line segments, yet more constrained than connected area or other attribute-based operators. Furthermore, path operators can be parametrised to behave more like either extreme. Natural implementations of this idea using actual suprema or infima of morphological operators with paths as structuring elements would imply exponential complexity. Fortunately, a linear complexity algorithm exists in the literature. This algorithm has similar running times as the best known implementation of morphological operators using straight lines as structuring elements. However, even this implementation is sometimes not fast enough, leading practitioners to favour some attribute-based operators instead, which in some applications is not the best solution. In this paper, we propose an implementation of path-based morphological operators that is shown experimentally to exhibit a logarithmic complexity and comparable computing times with those of attribute-based operators. This implementation has the added benefit of allowing the computation of the related opening transform at no extra computational cost. In order to give additional flexibility and noise-robustness to these operators, we also investigate the case when some pixels are left ignored from the path (i.e. ''jumps'' are allowed) and form incomplete paths.\",\n \"Title: Globally minimal surfaces by continuous maximal flows.\\nYear: 2006\\n\\nAbstract: In this paper, we address the computation of globally minimal curves and surfaces for image segmentation and stereo reconstruction. We present a solution, simulating a continuous maximal flow by a novel system of partial differential equations. Existing methods are either grid-biased (graph-based methods) or suboptimal (active contours and surfaces). The solution simulates the flow of an ideal fluid with isotropic velocity constraints. Velocity constraints are defined by a metric derived from image data. An auxiliary potential function is introduced to create a system of partial differential equations. It is proven that the algorithm produces a globally maximal continuous flow at convergence, and that the globally minimal surface may be obtained trivially from the auxiliary potential. The bias of minimal surface methods toward small objects is also addressed. An efficient implementation is given for the flow simulation. The globally minimal surface algorithm is applied to segmentation in 2D and 3D as well as to stereo matching. Results in 2D agree with an existing minimal contour algorithm for planar images. Results in 3D segmentation and stereo matching demonstrate that the new algorithm is robust and free from grid bias.\",\n \"Title: Anisotropic diffusion using power watersheds\\nYear: 2010\\n\\nAbstract: Many computer vision applications such as image filtering, segmentation and stereo-vision can be formulated as optimization problems. Whereas in previous decades continuous-domain, iterative procedures were common, recently discrete, convex, globally optimal methods have received a lot of attention. However not all problems in computer vision are convex, for instance L0 norm optimization such as seen in compressive sensing. Recently, a novel discrete framework encompassing many known segmentation methods was proposed: power watershed. We are interested to explore the possibilities of this minimizer to solve other problems than segmentation, in particular with respect to unusual norms optimization. In this article we reformulate the problem of anisotropic diffusion as an L0 optimization problem, and we show that power watersheds are able to optimize this energy quickly and effectively. This study paves the way for using the power watershed as a useful general-purpose minimizer in many different computer vision contexts.\",\n \"Title: Sufficient conditions for topological invariance of 2d images under rigid transformations\\nYear: 2013\\n\\nAbstract: In ℝ2, rigid transformations are topology-preserving operations. However, this property is generally no longer true when considering digital images instead of continuous ones, due to digitization effects. In this article, we investigate this issue by studying discrete rigid transformations (DRTs) on ℤ2. More precisely, we define conditions under which digital images preserve their topological properties under any arbitrary DRTs. Based on the recently introduced notion of DRT graph and the classical notion of simple point, we first identify a family of local patterns that authorize topological invariance under DRTs. These patterns are then involved in a local analysis process that guarantees topological invariance of whole digital images in linear time.\",\n \"Title: Topology-Preserving Rigid Transformation of 2D Digital Images\\nYear: 2014\\n\\nAbstract: We provide conditions under which 2D digital images preserve their topological properties under rigid transformations. We consider the two most common digital topology models, namely dual adjacency and well-composedness. This paper leads to the proposal of optimal preprocessing strategies that ensure the topological invariance of images under arbitrary rigid transformations. These results and methods are proved to be valid for various kinds of images (binary, gray-level, label), thus providing generic and efficient tools, which can be used in particular in the context of image registration and warping.\",\n \"Title: Elliptical distance transforms and applications\\nYear: 2006\\n\\nAbstract: Discrete Euclidean distance transforms, both exact and approximate, have been studied for some time, in particular by the Discrete Geometry community. In this paper we extend the notion of Euclidean distance transform (EDT) to elliptical distance transform (LDT) The LDT takes an additional two fixed parameters (eccentricity and orientation) in 2-D and an additional four in 3-D (two ratios and two angles) in 3-D, instead of 1 for the EDT in all cases We study first how the LDT can be computed efficiently with good approximation in the case where all parameters are constant. We provide an application to binary object segmentation as motivation for this work.\",\n \"Title: Dual Constrained Tv-Based Regularization\\nYear: 2011\\n\\nAbstract: Algorithms based on the minimization of the Total Variation are prevalent in computer vision. They are used in a variety of applications such as image denoising, compressive sensing and inverse problems in general. In this work, we extend the TV dual framework that includes Chambolle's and Gilboa-Osher's projection algorithms for TV minimization in a flexible graph data representation by generalizing the constraint on the projection variable. We show how this new formulation of the TV problem may be solved by means of a fast parallel proximal algorithm, which performs better than the classical TV approach for denoising, and is also applicable to inverse problems such as image deblurring.\",\n \"Title: Thin Structure Filtering Framework With Non-Local Means, Gaussian Derivatives And Spatially-Variant Mathematical Morphology\\nYear: 2013\\n\\nAbstract: Thin structure filtering is an important preprocessing task for the analysis of 2D and 3D bio-medical images in various contexts. We propose a filtering framework that relies on three approaches that are distinct and infrequently used together: linear, non-linear and non-local. This strategy, based on recent progress both in algorithmic/computational and methodological points of view, provides results that benefit from the advantages of each approach, while reducing their respective weaknesses. Its relevance is demonstrated by validations on 2D and 3D images.\",\n \"Title: Recursive filtering of images with symmetric extension\\nYear: 2005\\n\\nAbstract: Recursive filters are widely used in image analysis due to their efficiency and simple implementation. However these filters have an initialisation problem which either produces unusable results near the image boundaries or requires costly approximate solutions such as extending the boundary manually.In this paper, we describe a method for the recursive filtering of symmetrically extended images for filters with symmetric denominator. We begin with an analysis of symmetric extensions and their effect on non-recursive filtering operators. Based on the non-recursive case, we derive a formulation of recursive filtering on symmetric domains as a linear but spatially varying implicit operator. We then give an efficient method for decomposing and solving the linear implicit system, along with a proof that this decomposition always exists.This decomposition needs to be performed only once for each dimension of the image. This yields a filtering which is both stable and consistent with the ideal infinite extension. The filter is efficient, requiring less computation than the standard recursive filtering. We give experimental evidence to verify these claims.\",\n \"Title: Surface reconstruction using power watershed\\nYear: 2011\\n\\nAbstract: Surface reconstruction from a set of noisy point measurements has been a well studied problem for several decades. Recently, variational and discrete optimization approaches have been applied to solve it, demonstrating good robustness to outliers thanks to a global energy minimization scheme. In this work, we use a recent approach embedding several optimization algorithms into a common framework named power watershed. We derive a specific watershed algorithm for surface reconstruction which is fast, robust to markers placement, and produces smooth surfaces. Experiments also show that our proposed algorithm compares favorably in terms of speed, memory requirement and accuracy with existing algorithms.\",\n \"Title: A Majorize-Minimize Memory Gradient Algorithm Applied To X-Ray Tomography\\nYear: 2013\\n\\nAbstract: Tomography is an image reconstruction task that may be viewed as a linear inverse problem akin to deconvolution. Recent progresses in optimization methods have made it possible to formulate this task so that fewer projections and higher amounts of noise can be dealt with, making use of a-priori information and domain constraints. In this article, we investigate 3MG, a new optimization method that is highly flexible and effective. In particular, we propose and compare convex and non-convex regularization potentials on both synthetic and real images. We further investigate the possibility to deal with continuous angular integration, i.e. where projections rays are no longer straight lines, but cones. This is encountered in a variety of real-life situations, but is difficult or impossible to deal with exactly using traditional reconstruction algorithms. We show that in this situation it may be beneficial to acquire fewer projections than would be required using classical methods.\",\n \"Title: Unseeded region growing for 3D image segmentation\\nYear: 2000\\n\\nAbstract: Unseeded region growing is a versatile and fully automatic segmentation technique suitable for multispectral and 3D images. This approach integrates region-based segmentation with image processing techniques based on adaptive anisotropic diffusion filters.The segmentation method is fast, reliable and free of tuning parameters. It is indeed a general purpose segmentation method and has been successfully applied in a range of image analysis tasks. This paper describes the algorithm, and briefly discusses its properties and applications. Segmentation results will also be shown at the end of the paper\",\n \"Title: Spatially-Variant Morpho-Hessian Filter: Efficient Implementation and Application\\nYear: 2009\\n\\nAbstract: Elongated objects are more difficult to filter than more isotropic ones because they locally comprise fewer pixels. For thin linear objects, this problem is compounded because there is only a restricted set of directions that can be used for filtering, and finding this local direction is not a simple problem. In addition, disconnections can easily appear due to noise. In this paper we tackle both issues by combining a linear filter for direction finding and a morphological one for filtering. More specifically, we use the eigen-analysis of the Hessian for detecting thin, linear objects, and a spatially-variant opening or closing for their enhancement and reconnection. We discuss the theory of spatially-variant morphological filters and present an efficient algorithm. The resulting spatially-variant morphological filter is shown to successfully enhance directions in 2D and 3D examples illustrated with a brain blood vessel segmentation problem.\",\n \"Title: Robust skeletonization using the discrete λ-medial axis\\nYear: 2011\\n\\nAbstract: Medial axes and skeletons are notoriously sensitive to contour irregularities. This lack of stability is a serious problem for applications in e.g. shape analysis and recognition. In 2005, Chazal and Lieutier introduced the @l-medial axis as a new concept for computing the medial axis of a shape subject to single parameter filtering. The @l-medial axis is stable under small shape perturbations, as proved by these authors. In this article, a discrete @l-medial axis (DLMA) is introduced and compared with the recently introduced integer medial axis (GIMA). We show that DLMA provides measurably better results than GIMA, with regard to stability and sensibility to rotations. We give efficient algorithms to compute the DLMA, and we also introduce a variant of the DLMA which may be computed in linear-time.\",\n \"Title: Combinatorial structure of rigid transformations in 2D digital images\\nYear: 2013\\n\\nAbstract: Rigid transformations are involved in a wide range of digital image processing applications. When applied on discrete images, rigid transformations are usually performed in their associated continuous space, requiring a subsequent digitization of the result. In this article, we propose to study rigid transformations of digital images as fully discrete processes. In particular, we investigate a combinatorial structure modelling the whole space of digital rigid transformations on arbitrary subset of Z^2 of size NxN. We describe this combinatorial structure, which presents a space complexity O(N^9) and we propose an algorithm enabling to construct it in linear time with respect to its space complexity. This algorithm, which handles real (i.e., non-rational) values related to the continuous transformations associated to the discrete ones, is however defined in a fully discrete form, leading to exact computation.\",\n \"Title: Power Watersheds: A Unifying Graph-Based Optimization Framework.\\nYear: 2011\\n\\nAbstract: In this work, we extend a common framework for graph-based image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function with differing choices of a parameter $q$ acting as an exponent on the differences between neighboring nodes. Introducing a new parameter $p$ that fixes a power for the edge weights allows us to also include the optimal spanning forest algorithm for watershed in this same framework. We then propose a new family of segmentation algorithms that fixes $p$ to produce an optimal spanning forest but varies the power $q$ beyond the usual watershed algorithm, which we term power watershed. In particular when $q=2$, the power watershed leads to a multilabel, scale and contrast invariant, unique global optimum obtained in practice in quasi-linear time. Placing the watershed algorithm in this energy minimization framework also opens new possibilities for using unary terms in traditional watershed segmentation and using watershed to optimize more general models of use in applications beyond image segmentation.\",\n \"Title: Segmentation by Adaptive Prediction and Region Merging\\nYear: 2003\\n\\nAbstract: This paper presents a segmentation technique based on pre- diction and adaptive region merging. While many techniques for segmentation exist, few of them are suited for the segmentation of natural images containing regular textures defined on non-rectangular segments. In this paper, we propose a description of regions based on a deconvolution algorithm whose purpose is to remove the influence of the shape on region contents. The decoupling of shape and texture information is achieved either by adapting waveforms to the segment shape, which is a time-consuming task that needs to be repeated for each segment shape, or by the extrapolation of a signal to fit a rectangular window, which is the chosen path. The deconvolution algorithm is the key of a new segmentation technique that uses extrapolation as a prediction of neighbouring regions. When the prediction of a region fits the actual content of a connected region rea- sonably well, both regions are merged. The segmentation process starts with an over-segmented image. It progressively merges neighbouring re- gions whose extrapolations fit according to an energy criterion. After each merge, the algorithm updates the values of the merging criterion for regions connected to the merged region pair. It stops when no fur- ther gain is achieved in merging regions or when mean values of adjacent regions are too different. Simulation results indicate that, although our technique is tailored for natural images containing periodic signals and flat regions, it is in fact usable for a large set of natural images.\",\n \"Title: Optimal Consensus Set For Digital Line And Plane Fitting\\nYear: 2011\\n\\nAbstract: This article presents a new method for fitting a digital line or plane to a given set of points in a 2D or 3D image in the presence of noise by maximizing the number of inliers, namely the consensus set. By using a digital model instead of a continuous one, we show that we can generate all possible consensus sets for model fitting. We present a deterministic algorithm that efficiently searches the optimal solution with time complexity O(N-d log N) for dimension d, where d = 2,3, together with space complexity O(N) where N is the number of points. (c) 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 45-57, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ima.20269\",\n \"Title: Efficient and consistent recursive filtering of images with reflective extension\\nYear: 2003\\n\\nAbstract: Recursive filters are commonly used in scale space construction for their efficiency and simple implementation. However these filters have an initialisation problem which either produces unusable results near the image boundaries or requires costly approximate solutions such as extending the boundary manually. In this paper, we describe a method for the recursive filtering of reflectively extended images for filters with symmetric denominator. We begin with an analysis of reflective extensions and their effect on nonrecursive filtering operators. Based on the non-recursive case, we derive a formulation of recursive filtering on reflective domains as a linear but time-varying implicit operator. We then give an efficient method for decomposing and solving the linear implicit system. This decomposition needs to be performed only once for each dimension of the image. This yields a filtering which is both stable and consistent with the ideal infinite extension. The filter is efficient, requiring the same order of computation as the standard recursive filtering. We give experimental evidence to verify these claims.\",\n \"Title: Mathematical morphology: A useful set of tools for image analysis\\nYear: 2000\\n\\nAbstract: In this paper we give an overview of both classical and more modern morphological techniques. We will demonstrate their utility through a range of practical examples. After discussing the fundamental morphological ideas, we show how the classic morphological opening and closing filters lead to measures of size via granulometries, and we will discuss briefly their implementation. We also present an overview of morphological segmentation techniques, and the use of connected openings and thinnings will be demonstrated. This then leads us into the more recent set-theoretic notions of graph based approaches to image analysis.\",\n \"Title: Automatic retinal vessel extraction based on directional mathematical morphology and fuzzy classification\\nYear: 2014\\n\\nAbstract: The problem of detecting blood vessels in retinal color fundus images is addressed. An unsupervised method based on the extraction of two vessel features vectors in order to detect the pixels belonging to the vessel tree is presented. The proposed vessel features rely on the contrast of vessels and their linear connectivity. The extraction of these features is performed by using advanced morphological directional filter called path openings. The resulting features are used to carry out a data fusion task based on fuzzy set theory. As a result, pixel classification can easily be performed to construct a vessel map. Experimental results using real data have demonstrated the ability of the proposed method to successfully extract a good quality vessel tree. The obtained results are compared with results obtained by classical vessel extraction techniques.\",\n \"Title: 3D CFD in Complex Vascular Systems: A Case Study.\\nYear: 2014\\n\\nAbstract: Modeling the flowing blood in vascular structures is crucial to perform in silico simulations in various clinical contexts. This remains however an emerging and challenging research field, that raises several open issues. In particular, a compromise is generally made between the completeness of the simulation and the complicated architecture of the vasculature: reduced order simulations (lumped parameter models) represent vascular networks, whereas detailed models are devoted to small regions of interest. However, technical improvements enable targeting of compartments of the blood circulation rather than focusing on vascular branched segments. This article aims at investigating the cerebral flow in the entire venous drainage that can be reconstructed from medical imaging.\",\n \"Title: Combining interior tomography reconstruction and spatial regularization.\\nYear: 2014\\n\\nAbstract: Interior tomography, also called local or region-of-interest tomography is a special case of computed tomography, in which the object under study is larger than the detector. In this modality, reconstructing the tomography image is an even more ill-posed problem than standard tomography and characteristic artefacts are typically observed, even when a large number of measurements are taken. In this work we propose a reconstruction algorithm designed for interior tomography. We also investigate the case of under-sampled measurements in the case of gradient-sparse images. Our algorithm optimizes the sum of a spatial regularization terms for the image inside the region of interest, and a sinogram regularization term for the projection of the non-reconstructed part of the sample, using convex optimization techniques. We present results on simulated and real data.\",\n \"Title: Tubular Structure Filtering By Ranking Orientation Responses Of Path Operators\\nYear: 2014\\n\\nAbstract: Thin objects in 3D volumes, for instance vascular networks in medical imaging or various kinds of fibres in materials science, have been of interest for some time to computer vision. Particularly, tubular objects are everywhere elongated in one principal direction-which varies spatially-and are thin in the other two perpendicular directions. Filters for detecting such structures use for instance an analysis of the three principal directions of the Hessian, which is a local feature. In this article, we present a low-level tubular structure detection filter. This filter relies on paths, which are semi-global features that avoid any blurring effect induced by scale-space convolution. More precisely, our filter is based on recently developed morphological path operators. These require sampling only in a few principal directions, are robust to noise and do not assume feature regularity. We show that by ranking the directional response of this operator, we are further able to efficiently distinguish between blob, thin planar and tubular structures. We validate this approach on several applications, both from a qualitative and a quantitative point of view, demonstrating noise robustness and an efficient response on tubular structures.\",\n \"Title: Efficient Neighbourhood Computing For Discrete Rigid Transformation Graph Search\\nYear: 2014\\n\\nAbstract: Rigid transformations are involved in a wide variety of image processing applications, including image registration. In this context, we recently proposed to deal with the associated optimization problem from a purely discrete point of view, using the notion of discrete rigid transformation (DRT) graph. In particular, a local search scheme within the DRT graph to compute a locally optimal solution without any numerical approximation was formerly proposed. In this article, we extend this study, with the purpose to reduce the algorithmic complexity of the proposed optimization scheme. To this end, we propose a novel algorithmic framework for just-in-time computation of sub-graphs of interest within the DRT graph. Experimental results illustrate the potential usefulness of our approach for image registration.\",\n \"Title: Iterative poisson-Gaussian noise parametric estimation for blind image denoising\\nYear: 2014\\n\\nAbstract: This paper deals with noise parameter estimation from a single image under Poisson-Gaussian noise statistics. The problem is formulated within a mixed discrete-continuous optimization framework. The proposed approach jointly estimates the signal of interest and the noise parameters. This is achieved by introducing an adjustable regularization term inside an optimized criterion, together with a data fidelity error measure. The optimal solution is sought iteratively by alternating the minimization of a label field and of a noise parameter vector. Noise parameters are updated at each iteration using an Expectation-Maximization approach. The proposed algorithm is inspired from a spatial regularization approach for vector quantization. We illustrate the usefulness of our approach on macroconfocal images. The identified noise parameters are applied to a denoising algorithm, so yielding a complete denoising scheme.\",\n \"Title: Curvilinear morpho-Hessian filter\\nYear: 2008\\n\\nAbstract: The motivation of this paper is the filtering of thin elongated objects, such as veins, fibres etc. In particular, we focus our attention on detecting thin segments and linking them when disconnection is due to noise. A hybrid Hessian-based and morphological linear filtering method is proposed within the framework of scale space theory. For each pixel the second- derivative response kernel is collected and formed into a Hessian matrix which undergoes eigen analysis. From there, the best eigenvalue response along with the corresponding eigenvector is chosen on different scales. Using the resulting principal directions of linear segment pixels, morphological filters are used to track and connect the linears.\",\n \"Title: A primal-dual proximal splitting approach for restoring data corrupted with poisson-gaussian noise\\nYear: 2012\\n\\nAbstract: A Poisson-Gaussian model accurately describes the noise present in many imaging systems such as CCD cameras or fluorescence microscopy. However most existing restoration strategies rely on approximations of the Poisson-Gaussian noise statistics. We propose a convex optimization algorithm for the reconstruction of signals degraded by a linear operator and corrupted with mixed Poisson-Gaussian noise. The originality of our approach consists of considering the exact continuous-discrete model corresponding to the data statistics. After establishing the Lipschitz differentiability of the Poisson-Gaussian log-likelihood, we derive a primal-dual iterative scheme for minimizing the associated penalized criterion. The proposed method is applicable to a large choice of penalty terms. The robustness of our scheme allows us to handle computational difficulties due to infinite sums arising from the computation of the gradient of the criterion. The proposed approach is validated on image restoration examples.\",\n \"Title: Direction-adaptive grey-level morphology. application to 3D vascular brain imaging\\nYear: 2009\\n\\nAbstract: Segmentation and analysis of blood vessels is an important issue in medical imaging. In 3D cerebral angiographic data, the vascular signal is however hard to accurately detect and can, in particular, be disconnected. In this article, we present a procedure utilising both linear, Hessian-based and morphological methods for blood vessel edge enhancement and reconnection. More specifically, multi-scale second-order derivative analysis is performed to detect candidate vessels as well as their orientation. This information is then fed to a spatially-variant morphological filter for reconnection and reconstruction. The result is a fast and effective vessel-reconnecting method.\",\n \"Title: Selective and robust d-dimensional path operators\\nYear: 2014\\n\\nAbstract: Path operators are powerful tools for the enhancement of thin and elongated objects in an image. In order to cope with noisy acquisition a variant of the path operators was recently proposed. However, both approaches cannot properly handle thin objects with tortuous shapes since strong variations of an object curvature produce disconnections in the paths. In order to address this issue, we propose a novel operator able to properly handle paths in tortuous shapes. It relies on the coupling of attribute filters based on the geodesic tortuosity and conventional path operators. Analogously to the complete version of the path operators, by allowing disconnections within paths it is possible also to define a path operator that is both robust and selective. The effectiveness of the proposed operators in filtering thin and tortuous image objects is proved on a 2D and 3D biomedical image.\",\n \"Title: An EM approach for Poisson-Gaussian noise modeling\\nYear: 2011\\n\\nAbstract: This paper deals with noise parameter estimation. We assume observations corrupted by noise modelled as a sum of two random processes: one Poisson and the other a (nonzero mean) Gaussian. Such problems arise in various applications, e.g. in astronomy and confocal microscopy imaging. To estimate noise parameters, we propose an iterative algorithm based on an Expectation-Maximization approach. This allows us to jointly estimate the scale parameter of the Poisson component and the mean and variance of the Gaussian one. Moreover, an adequate initialization based on cumulants is provided. Numerical difficulties arising from the procedure are also addressed. To validate the proposed method in terms of accuracy and robustness, tests are performed on synthetic data. The good performance of the method is also demonstrated in a denoising experiment on real data.\",\n \"Title: Global image registration and video color correction in presence of illumination variations\\nYear: 2015\\n\\nAbstract: In high-speed video imaging acquisition, artificial lighting can cause non-uniform luminosity and chroma variation between frames, which is linked to the interaction between the frequency of the lighting power source and of the video acquisition. This problem is commonly dubbed “flicker”. Deflickering techniques are also useful for resolving similar problems encountered in other applications for instance in archived film sequences, image-video compression and time-lapse videos. In this context, this paper introduces a rigid registration method using point descriptors that is robust to illumination variations, and followed by a method for stabilizing brightness variations in image sequences. This method is based on a Least Square adjustment approach. Our results demonstrate the efficacy of our method at correcting global brightness fluctuations in image sequences while maintaining image quality and without introducing artifacts.\",\n \"Title: A Convex Approach for Image Restoration with Exact Poisson--Gaussian Likelihood\\nYear: 2015\\n\\nAbstract: The Poisson-Gaussian model can accurately describe the noise present in a number of imaging systems. However most existing restoration methods rely on approximations of the Poisson-Gaussian noise statistics. We propose a convex optimization strategy for the reconstruction of images degraded by a linear operator and corrupted with a mixed Poisson-Gaussian noise. The originality of our approach consists of considering the exact, mixed continuous-discrete model corresponding to the data statistics. After establishing the Lipschitz differentiability and convexity of the Poisson-Gaussian neg-log-likelihood, we derive a primal-dual iterative scheme for minimizing the associated penalized criterion. The proposed method is applicable to a large choice of convex penalty terms. The robustness of our scheme allows us to handle computational difficulties due to infinite sums arising from the computation of the gradient of the criterion. We propose finite bounds for these sums, that are dependent on the current image estimate, and thus adapted to each iteration of our algorithm. The proposed approach is validated on image restoration examples. Then, the exact data fidelity term is used as a reference for studying some of its various approximations. We show that in a variational framework the shifted Poisson and exponential approximations lead to very good restoration results.\",\n \"Title: Directed Connected Operators: Asymmetric Hierarchies for Image Filtering and Segmentation\\nYear: 2015\\n\\nAbstract: Connected operators provide well-established solutions for digital image processing, typically in conjunction with hierarchical schemes. In graph-based frameworks, such operators basically rely on symmetric adjacency relations between pixels. In this article, we introduce a notion of directed connected operators for hierarchical image processing, by also considering non-symmetric adjacency relatio...\",\n \"Title: Multicriteria 3D PET image segmentation\\nYear: 2015\\n\\nAbstract: The analysis of images acquired with Positron Emission Tomography (PET) is challenging. In particular, there is no consensus on the best criterion to quantify the metabolic activity for lesion detection and segmentation purposes. Based on this consideration, we propose a versatile knowledge-based segmentation methodology for 3D PET imaging. In contrast to previous methods, an arbitrary number of quantitative criteria can be involved and the experts behaviour learned and reproduced in order to guide the segmentation process. The classification part of the scheme relies on example-based learning strategies, allowing interactive example definition and more generally incremental refinement. The image processing part relies on hierarchical segmentation, allowing vectorial attribute handling. Preliminary results on synthetic and real images confirm the relevance of this methodology, both as a segmentation approach and as an experimental framework for criteria evaluation.\",\n \"Title: Automated heart rate estimation in fish embryo\\nYear: 2015\\n\\nAbstract: Transparent organisms such as fish embryos are being increasingly used for environmental toxicology studies. These studies require estimating a number of physiological parameters. These estimations may be diverse in nature and can be a challenge to automate. Among these, an example is the development of reliable and repeatable automated assays for the determination of heart rates. To achieve this, most existing method rely on cyclical luminance variations, since as the heart fills and empties, it become respectively brighter and darker. However, sometimes direct measurement of the heart rate may be difficult, depending on the age of the embryo, its actual transparency, and its aspect under the microscope. It may be easier to seek an indirect measurement. In this article, we estimate the heart function parameters, such as heart frequency, either from measuring the heart motion or from blood flow in arteries. This measurement is more complex from the image analysis point of view, but it is more precise, more physically meaningful and easier to use in practice and to automate than measuring illumination changes. It may also be more informative. We illustrate on medaka embryos.\",\n \"Title: Image Noise and Digital Image Forensics.\\nYear: 2015\\n\\nAbstract: Noise is an intrinsic specificity of all forms of imaging, and can be found in various forms in all domains of digital imagery. This paper offers an overall review of digital image noise, from its causes and models to the degradations it suffers along the image acquisition pipeline. We show that by the end of the pipeline, the noise may have widely different characteristics compared to the raw image, and consider the consequences in forensic and counter-forensic imagery.\",\n \"Title: Ranking Orientation Responses of Path Operators: Motivations, Choices and Algorithmics.\\nYear: 2015\\n\\nAbstract: A new morphological operator, namely RORPO (Ranking Orientation Responses of Path Operators), was recently introduced as a semi-global, morphological alternative to the local, Hessian-based operators for thin structure filtering in 3D images. In this context, a previous study has already provided experimental proof of its relevance by comparison to such differential operators. In this article, we present a methodological study of RORPO, which completes the presentation of this new morphological filter. In particular, we expose the motivations of RORPO with respect to previous morphological strategies; we present algorithmic developments of this filter and the underlying robust path operator; and we discuss computational issues related to parametricity and time efficiency. We conclude this study by a discussion on the methodological and applicative potentiality of RORPO in various fields of image processing and analysis.\",\n \"Title: Shape-Based Analysis on Component-Graphs for Multivalued Image Processing\\nYear: 2015\\n\\nAbstract: The extension of mathematical morphology to multivalued images is an important issue. This is particularly true in the context of connected operators based on morphological hierarchies, which aim to provide efficient image filtering and segmentation tools in various application fields, e.g. (bio) medical imaging, remote sensing, or astronomy. In this article, we propose a preliminary study that describes how two notions recently introduced for connected filtering, namely component-graphs (that extend component-trees from a spectral point of view) and shaping (that extend component-trees from a conceptual point of view) can be associated for the effective processing of multivalued images. Structural, algorithmic and experimental developments are proposed. This study opens the way to new paradigms for connected filtering based on hierarchies.\",\n \"Title: When Convex Analysis Meets Mathematical Morphology on Graphs.\\nYear: 2015\\n\\nAbstract: In recent years, variational methods, i.e., the formulation of problems under optimization forms, have had a great deal of success in image processing. This may be accounted for by their good performance and versatility. Conversely, mathematical morphology (MM) is a widely recognized methodology for solving a wide array of image processing-related tasks. It thus appears useful and timely to build bridges between these two fields. In this article, we propose a variational approach to implement the four basic, structuring element-based operators of MM: dilation, erosion, opening, and closing. We rely on discrete calculus and convex analysis for our formulation. We show that we are able to propose a variety of continuously varying operators in between the dual extremes, i.e., between erosions and dilation; and perhaps more interestingly between openings and closings. This paves the way to the use of morphological operators in a number of new applications.\",\n \"Title: An Automated Assay for the Evaluation of Mortality in Fish Embryo\\nYear: 2015\\n\\nAbstract: Fish embryo models are used increasingly for human disease modeling, chemical toxicology screening, drug discovery and environmental toxicology studies. These studies are devoted to the analysis of a wide spectrum of physiological parameters, such as mortality ratio. In this article, we develop an assay to determine Medaka (Oryzias latipes) embryo mortality. Based on video sequences, our purpose is to obtain reliable, repeatable results in a fully automated fashion. To reach that challenging goal, we develop an efficient morphological pipeline that analyses image sequences in a multiscale paradigm, from the global scene to the embryo, and then to its heart, finally analysing its putative motion, characterized by intensity variations. Our pipeline, based on robust morphological operators, has a low computational cost, and was experimentally assessed on a dataset consisting of 660 images, providing a success ratio higher than 99%.\",\n \"Title: Hierarchies And Shape-Space For Pet Image Segmentation\\nYear: 2015\\n\\nAbstract: Positron Emission Tomography ( PET) image segmentation is essential for detecting lesions and quantifying their metabolic activity. Due to the spatial and spectral properties of PET images, most methods rely on intensity-based strategies. Recent methods also propose to integrate anatomical priors to improve the segmentation process. In this article, we show how the hierarchical approaches proposed in mathematical morphology can efficiently handle these different strategies. Our contribution is twofold. First, we present the component-tree as a relevant data-structure for developing interactive, real-time, intensity-based segmentation of PET images. Second, we prove that thanks to the recent concept of shaping, we can efficiently involve a priori knowledge for lesion segmentation, while preserving the good properties of component-tree segmentation. Preliminary experiments on synthetic and real PET images of lymphoma demonstrate the relevance of our approach.\",\n \"Title: Discrete Calculus, Optimisation and Inverse Problems in Imaging.\\nYear: 2016\\n\\nAbstract: Inverse problems in science are the process of estimating the causal factors that produced a set of observations. Many image processing tasks can be cast as inverse problems: image restoration: noise reduction, deconvolution; segmentation, tomography, demosaicing, inpaiting, and many others, are examples of such tasks. Typically, inverse problems are ill-posed, and solving these problems efficiently and effectively is a major, ongoing topic of research. While imaging is often thought of as occurring on regular grids, it is also useful to be able to solve these problems on arbitrary graphs. The combined frameworks of discrete calculus and modern optimisation allow us to formulate and provide solutions to many of these problems in an elegant way. This tutorial article summarizes and illustrates some of the research results of the last decade from this point of view. We provide illustrations and major references.\",\n \"Title: Automated Quantification Of The Epidermal Aging Process Using In-Vivo Confocal Microscopy\\nYear: 2016\\n\\nAbstract: Reflectance confocal microscopy (RCM) is a powerful tool to visualize the skin layers at cellular resolution. The epidermal layer appears as a honeycomb pattern, whose regularity decreases with age. Our aim is to provide a method to automatically quantify the regularity of the honeycomb pattern. The proposed strategy relies on a cell-level supervised classification as regular or irregular using spatial information given by a prior segmentation. The aggregated scores defined by the classification results show significant correlation with chronological aging and photo-aging. Thus, our method enables practitioners to more objectively assess the quality of the epidermal layers on large cohort of subjects.\",\n \"Title: Countering Noise-based Splicing Detection Using Noise Density Transfer.\\nYear: 2016\\n\\nAbstract: Image splicing is a common and widespread type of manipulation, which is defined as pasting a portion of an image onto a second image. Several forensic methods have been developed to detect splicing, using various image properties. Some of these methods exploit the noise statistics of the image to try and find discrepancies. In this paper, we propose a new counter-forensic approach to eliminate the noise differences that can appear in a spliced image. This approach can also be used when creating computer graphics images, in order to endow them with a realistic noise. This is performed by changing the noise statistics of the spliced elements so that they are closer to those of the original image. The proposed method makes use of a novel way to transfer density functions. We apply this to image noise in order to impose identical noise density functions from a source to a destination image. This method can be used with arbitrary noise distributions. The method is tested against several noise-based splicing detection methods, in order to prove its efficacy.\",\n \"Title: Automatic Image Splicing Detection Based on Noise Density Analysis in Raw Images.\\nYear: 2016\\n\\nAbstract: Image splicing is a common manipulation which consists in copying part of an image in a second image. In this paper, we exploit the variation in noise characteristics in spliced images, caused by the difference in camera and lighting conditions during the image acquisition. The proposed method automatically gives a probability of alteration for any area of the image, using a local analysis of noise density. We consider both Gaussian and Poisson noise components to modelize the noise in the image. The efficiency and robustness of our method is demonstrated on a large set of images generated with an automated splicing.\",\n \"Title: From Real MRA to Virtual MRA: Towards an Open-Source Framework.\\nYear: 2016\\n\\nAbstract: Angiographic imaging is a crucial domain of medical imaging. In particular , Magnetic Resonance Angiography (MRA) is used for both clinical and research purposes. This article presents the first framework geared toward the design of virtual MRA images from real MRA images. It relies on a pipeline that involves image processing, vascular modeling, computational fluid dynamics and MR image simulation, with several purposes. It aims to provide to the whole scientific community (1) software tools for MRA analysis and blood flow simulation ; and (2) data (computational meshes, virtual MRAs with associated ground truth), in an open-source / open-data paradigm. Beyond these purposes, it constitutes a versatile tool for progressing in the understanding of vascular networks, especially in the brain, and the associated imaging technologies.\",\n \"Title: Discrete rigid registration: A local graph-search approach.\\nYear: 2017\\n\\nAbstract: Image registration has become a crucial step in a wide range of imaging domains, from computer vision to computer graphics. The core of image registration consists of determining the transformation that induces the best mapping between two images. This problem is ill-posed; it is also difficult to handle, due to the high size of the images and the high dimension of the transformation parameter spaces. Computing an actually optimal solution is practically impossible when transformations are assumed continuous (i.e., defined on Rn). In this article, we initiate the exploration of a new way of considering image registration. Since digital images are basically defined in a discrete framework (i.e., in Zn), the transformation spaces–despite a potentially high complexity–actually remain finite, allowing for the development of explicit exploration of the parameter space via discrete optimization schemes. We propose an analysis of the very basis of registration, by considering rigid registration between 2D images. We show, in particular, how this problem can be handled in a fully discrete fashion, by computing locally the combinatorial structure of the parameter space of discrete rigid transformations, and by navigating on-the-flight within this space via gradient descent paradigms. This registration framework is applied in real imaging cases, emphasizing the relevance of our approach, and the potential usefulness of its further extension to higher dimension images and richer transformations.\",\n \"Title: Numerical Implementation Of The Ambrosio-Tortorelli Functional Using Discrete Calculus And Application To Image Restoration And Inpainting\\nYear: 2016\\n\\nAbstract: The Mumford-Shah (MS) functional is one of the most influential variational model in image segmentation, restoration, and cartooning. Difficult to solve, the Ambrosio-Tortorelli (AT) functional is of particular interest, because minimizers of AT can be shown to converge to a minimizer of MS. This paper takes an interest in a new method for numerically solving the AT model [11]. This method formulates the AT functional in a discrete calculus setting, and by this way is able to capture the set of discontinuities as a one-dimensional set. It is also shown that this model is competitive with total variation restoration methods. We present here the discrete AT models in details, and compare its merit with recent convex relaxations of AT and MS functionals. We also examine the potential of this model for inpainting, and describe its implementation in the DGtal library, an open-source project.\",\n \"Title: Morphological PDEs on graphs for analyzing unorganized data in 3D and higher\\nYear: 2016\\n\\nAbstract: Mathematical morphology operators can be defined in terms of algebraic (discrete) sets or as partial differential equations (PDEs). In our previous works [1, 2], we have proposed a simple method to solve PDEs (Partial Differential Equations) on dataset using the framework PdEs (Partial difference Equations) on graphs. In this paper, we propose to apply morphological-based operators on unorganized dataset.\",\n \"Title: Morphological Analysis Of Brownian Motion For Physical Measurements\\nYear: 2017\\n\\nAbstract: Brownian motion is a well-known, apparently chaotic motion affecting microscopic objects in fluid media. The mathematical and physical basis of Brownian motion have been well studied but not often exploited. In this article we propose a particle tracking methodology based on mathematical morphology, suitable for Brownian motion analysis, which can provide difficult physical measurements such as the local temperature and viscosity. We illustrate our methodology on simulation and real data, showing that interesting phenomena and good precision can be achieved.\",\n \"Title: Evaluation Of Combinations Of Watershed Hierarchies\\nYear: 2017\\n\\nAbstract: The main goal of this paper is to evaluate the potential of some combinations of watershed hierarchies. We also propose a new combination based on merging level sets of hierarchies. Experiments were performed on natural image datasets and were based on evaluating the segmentations extracted from level sets of each hierarchy against the image ground truths. Our experiments show that most of combinations studied here are superior to their individual counterparts, which opens a path for a deeper investigation on combination of hierarchies.\",\n \"Title: Classification of the dermal-epidermal junction using in-vivo confocal microscopy\\nYear: 2017\\n\\nAbstract: Reflectance confocal microscopy (RCM) is a powerful tool to visualize the skin layers at cellular resolution. The dermal-epidermal junction (DEJ) is a thin complex 3D structure. It appears as a low-contrasted structure in confocal en-face sections, which is difficult to recognize visually, leading to uncertainty in the classification. In this article, we propose an automated method for segmenting the DEJ with reduced uncertainty. The proposed approach relies on a 3D Conditional Random Field to model the skin biological properties and impose regularization constraints. We improve the restitution of the epidermal and dermal labels while reducing the thickness of the uncertainty area in a coherent biological way from 16.9 μm (ground-truth) to 10.3 μm.\",\n \"Title: Automated 3D lymphoma lesion segmentation from PET/CT characteristics\\nYear: 2017\\n\\nAbstract: Positron Emission Tomography (PET) using \\n<sup xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">18</sup>\\nF-FDG is recognized as the modality of choice for lymphoma, due to its high sensitivity and specificity. Its wider use for the detection of lesions, quantification of their metabolic activity and evaluation of response to treatment demands the development of accurate and reproducible quantitative image interpretation tools. An accurate tumour delineation remains a challenge in PET, due to the limitations the modality suffers from, despite being essential for quantifying reliable changes in tumour tissues. Due to the spatial and spectral properties of PET images, most methods rely on intensity-based strategies. Recent methods also propose to integrate anatomical priors to improve the segmentation process. However, the current routinely-used approach remains a local relative thresholding and requires important user interaction, leading to a process that is not only user-dependent but very laborious in the case of lymphomas. In this paper, we propose to rely on hierarchical image models embedding multimodality PET/CT descriptors for a fully automated PET lesion detection / segmentation, performed via a machine learning process. More precisely, we propose to perform random forest classification within the mixed spatial-spectral space of component-trees modeling PET/CT mages. This new approach, combining the strengths of machine learning and morphological hierarchy models leads to intelligent thresholding based on high-level PET/CT knowledge. We evaluate our approach on a database of multi-centric PET/CT images of patients treated for lymphoma, delineated by an expert. Our method provides good efficiency, with the detection of 92% of all lesions, and accurate segmentation results with mean sensitivity and specificity of 0.73 and 0.99 respectively, without any user interaction.\",\n \"Title: Periodic area-of-motion characterization for bio-medical applications\\nYear: 2017\\n\\nAbstract: Many bio-medical applications involve the analysis of sequences for motion characterization. In this article, we consider 2D+t sequences where a particular motion (e.g. a blood flow) is associated with a specific area of the 2D image (e.g. an artery) but multiple motions may exist simultaneously in the same sequences (e.g. there may be several blood vessels present, each with their specific flow). The characterization of this type of motion typically involves first finding the areas where motion is present, followed by an analysis of these motions: speed, regularity, frequency, etc. In this article, we propose a methodology called “area-of-motion characterization” suitable for simultaneously detecting and characterizing areas where motion is present in a sequence. We can then classify this motion into consistent areas using unsupervised learning and produce directly usable metrics for various applications. We illustrate this methodology for the analysis of cilia motion on ex-vivo human samples, and we apply and validate the same methodology for blood flow analysis in fish embryo.\",\n \"Title: Spatially Variant Psf Modeling In Confocal Macroscopy\\nYear: 2018\\n\\nAbstract: The point spread function (PSF) of imaging systems plays an essential role in image reconstruction. In the context of confocal microscopy, optical performance degrades towards the edge of the field of view. In confocal macroscopy, the related artifacts are even stronger, as the field of view is much larger. Because the related PSFs are strongly spatially variant, it is essential to be able to model them with few parameters. The field aberrations in fluorescence macroscopy were observed to be symmetrical and to increase with the distance from the center of the field of view. In this paper we propose an experiment and an optimization method for assessing the center of the field of view. The obtained results constitute an important step towards reducing the number of parameters in macroscopy PSF models.\",\n \"Title: Curvilinear Structure Analysis by Ranking the Orientation Responses of Path Operators.\\nYear: 2018\\n\\nAbstract: The analysis of thin curvilinear objects in 3D images is a complex and challenging task. In this article, we introduce a new, non-linear operator, called RORPO (Ranking the Orientation Responses of Path Operators). Inspired by the multidirectional paradigm currently used in linear filtering for thin structure analysis, RORPO is built upon the notion of path operator from mathematical morphology. T...\",\n \"Title: Flicker removal and superpixel-based motion tracking for high speed videos\\nYear: 2017\\n\\nAbstract: Flicker removal consists of filtering out rapid, artefactual changes of luminosity and colorimetry from image sequences in order to improve colorimetry consistency between video frames. It is a necessary and fundamental task in multiple applications, for instance in archived film sequences, image/video compression and time-lapse videos. In recent years, the wider availability of fast video acquisition technology has renewed the interest in the flicker removal problem, in particular periodic flicker. In this context, rapid, undesirable intensity and chroma variations are due to acquisition hardware performing faster than the frequency of alternating current powering artificial light sources. This paper proposes both theoretical and efficient experimental solutions for flicker removal from image sequences by performing simultaneous motion tracking and color correction using a superpixel image representation.\",\n \"Title: A Multicore Convex Optimization Algorithm With Applications To Video Restoration\\nYear: 2018\\n\\nAbstract: In this paper, we present a new distributed algorithm for minimizing a sum of non-necessarily differentiable convex functions composed with arbitrary linear operators. The overall cost function is assumed strongly convex. Each involved function is associated with a node of a hypergraph having the ability to communicate with neighboring nodes sharing the same hyperedge. Our algorithm relies on a primal-dual splitting strategy with established convergence guarantees. We show how it can be efficiently implemented to take full advantage of a multicore architecture. The good numerical performance of the proposed approach is illustrated in a problem of video sequence denoising, where a significant speedup is achieved.\",\n \"Title: Generation of patient-specific cardiac vascular networks: a hybrid image-based and synthetic geometric model.\\nYear: 2019\\n\\nAbstract: Image-based models derived from CT angiography are being used clinically to simulate blood flow in the coronary arteries of individual patients to aid in the diagnosis of disease and planning treatments. However, image resolution limits vessel segmentation to larger epicardial arteries. The generated model can be used to simulate blood flow and derived quantities from the aorta into the myocardium. This is an important step for diagnosis an treatment planning of coronary artery disease.\",\n \"Title: High throughput automated detection of axial malformations in Medaka embryo.\\nYear: 2019\\n\\nAbstract: Fish embryo models are widely used as screening tools to assess the efficacy and/or toxicity of chemicals. This assessment involves the analysis of embryo morphological abnormalities. In this article, we propose a multi-scale pipeline to allow automated classification of fish embryos (Medaka: Oryzias latipes) based on the presence or absence of spine malformations. The proposed pipeline relies on the acquisition of fish embryo 2D images, on feature extraction based on mathematical morphology operators and on machine learning classification. After image acquisition, segmentation tools are used to detect the embryo before analysing several morphological features. An approach based on machine learning is then applied to these features to automatically classify embryos according to the presence of axial malformations. We built and validated our learning model on 1459 images with a 10-fold cross-validation by comparison with the gold standard of 3D observations performed under a microscope by a trained operator. Our pipeline results in correct classification in 85% of the cases included in the database. This percentage is similar to the percentage of success of a trained human operator working on 2D images. The key benefit of our approach is the low computational cost of our image analysis pipeline, which guarantees optimal throughput analysis.\",\n \"Title: Combinatorial Continuous Maximum Flow\\nYear: 2011\\n\\nAbstract: Maximum flow (and minimum cut) algorithms have had a strong impact on computer vision. In particular, graph cut algorithms provide a mechanism for the discrete optimization of an energy functional which has been used in a variety of applications such as image segmentation, stereo, image stitching, and texture synthesis. Algorithms based on the classical formulation of max-flow defined on a graph are known to exhibit metrication artifacts in the solution. Therefore, a recent trend has been to instead employ a spatially continuous maximum flow (or the dual min-cut problem) in these same applications to produce solutions with no metrication errors. However, known fast continuous max-flow algorithms have no stopping criteria or have not been proved to converge. In this work, we revisit the continuous max-flow problem and show that the analogous discrete formulation is different from the classical max-flow problem. We then apply an appropriate combinatorial optimization technique to this combinatorial continuous max-flow (CCMF) problem to find a null-divergence solution that exhibits no metrication artifacts and may be solved exactly by a fast, efficient algorithm with provable convergence. Finally, by exhibiting the dual problem of our CCMF formulation, we clarify the fact, already proved by Nozawa in the continuous setting, that the max-flow and the total variation problems are not always equivalent.\",\n \"Title: nD variational restoration of curvilinear structures with prior-based directional regularization.\\nYear: 2019\\n\\nAbstract: Curvilinear structure restoration in image processing procedures is a difficult task, which can be compounded when these structures are thin, i.e., when their smallest dimension is close to the resolution of the sensor. Many recent restoration methods involve considering a local gradient-based regularization term as prior, assuming gradient sparsity. An isotropic gradient operator is typically not...\",\n \"Title: Morphological PDEs on Graphs for Image Processing on Surfaces and Point Clouds.\\nYear: 2016\\n\\nAbstract: Partial Differential Equations (PDEs)-based morphology offers a wide range of continuous operators to address various image processing problems. Most of these operators are formulated as Hamilton-Jacobi equations or curve evolution level set and morphological flows. In our previous works, we have proposed a simple method to solve PDEs on point clouds using the framework of PdEs (Partial difference Equations) on graphs. In this paper, we propose to apply a large class of morphological-based operators on graphs for processing raw 3D point clouds and extend their applications for the processing of colored point clouds of geo-informatics 3D data. Through illustrations, we show that this simple framework can be used in the resolution of many applications for geo-informatics purposes.\",\n \"Title: An automated assay for the assessment of cardiac arrest in fish embryo.\\nYear: 2017\\n\\nAbstract: Studies on fish embryo models are widely developed in research. They are used in several research fields including drug discovery or environmental toxicology. In this article, we propose an entirely automated assay to detect cardiac arrest in Medaka (Oryzias latipes) based on image analysis. We propose a multi-scale pipeline based on mathematical morphology. Starting from video sequences of entire wells in 24-well plates, we focus on the embryo, detect its heart, and ascertain whether or not the heart is beating based on intensity variation analysis. Our image analysis pipeline only uses commonly available operators. It has a low computational cost, allowing analysis at the same rate as acquisition. From an initial dataset of 3192 videos, 660 were discarded as unusable (20.7%), 655 of them correctly so (99.25%) and only 5 incorrectly so (0.75%). The 2532 remaining videos were used for our test. On these, 45 errors were made, leading to a success rate of 98.23%. Graphical abstractDisplay Omitted HighlightsAn automated method to detect the presence of a heartbeat in fish embryos, using video sequence analysis is proposed.Heartbeat detection is based on measuring cyclic intensity variations in the thorax region of the embryo.Image stabilization and spurious motion detection is performed to eliminate motion artifacts.The procedure works on both eggs and hatched embryo.\",\n \"Title: 2d Filtering Of Curvilinear Structures By Ranking The Orientation Responses Of Path Operators (Rorpo)\\nYear: 2017\\n\\nAbstract: We present a filtering method for 2D curvilinear structures, called RORPO (Ranking the Orientation Responses of Path Operators). RORPO is based on path operators, a recently developed family of mathematical morphology filters. Compared with state of the art methods, RORPO is non-local and well adapted to the intrinsic anisotropy of curvilinear structures. Since RORPO does not depend on a linear scale-space framework, it tends to preserve object contours without a blurring effect. Due to these properties, RORPO is a useful low-level filter and can also serve as a curvilinear prior in segmentation frameworks. In this article, after introducing RORPO, we develop the 2D version of the algorithm and present a few applications.Source CodeThe C++ implementation of RORPO 2D along with an online demo are available on the IPOL webpage of this article(1)\",\n \"Title: Atlas-based automated detection of swim bladder in Medaka embryo.\\nYear: 2019\\n\\nAbstract: Fish embryo models are increasingly being used both for the assessment of chemicals efficacy and potential toxicity. This article proposes a methodology to automatically detect the swim bladder on 2D images of medaka fish embryos seen either in dorsal view or in lateral view. After embryo segmentation and for each studied orientation, the method builds an atlas of a healthy embryo. This atlas is then used to define the region of interest and to guide the swim bladder segmentation with a discrete globally optimal active contour. Descriptors are subsequently designed from this segmentation. An automated random forest classifier is built from these descriptors in order to classify embryos with and without a swim bladder. The proposed method is assessed on a dataset of 261 images, containing 202 embryos with a swim bladder (where 196 are in dorsal view and 6 are in lateral view) and 59 without (where 43 are in dorsal view and 16 are in lateral view). We obtain an average precision rate of 95% in the total dataset following 5-fold cross-validation.\",\n \"Title: Spherical Fluorescent Particle Segmentation and Tracking in 3D Confocal Microscopy.\\nYear: 2019\\n\\nAbstract: Spherical fluorescent particle are micrometer-scale spherical beads used in various areas of physics, chemistry or biology as markers associated with local physical media. They are useful for example in fluid dynamics to characterize flows, diffusion coefficients, viscosity or temperature; they are used in cells dynamics to estimate mechanical strain and stress at the micrometer scale. In order to estimate these physical measurements, tracking these particles is necessary. Numerous approaches and existing packages, both open-source and proprietary are available to achieve tracking with a high degree of precision in 2D. However, little such software is available to achieve tracking in 3D. One major difficulty is that 3D confocal microscopy acquisition is not typically fast enough to assume that the beads are stationary during the whole 3D scan. As a result, beads may move between planar scans. Classical approaches to 3D segmentation may yield objects are not spherical. In this article, we propose a 3D bead segmentation that deals with this situation.\",\n \"Title: Self-dual Pattern Spectra for Characterising the Dermal-Epidermal Junction in 3D Reflectance Confocal Microscopy Imaging.\\nYear: 2019\\n\\nAbstract: The Dermal-Epidermal Junction (DEJ) is a 2D surface separating the epidermis from the dermis which undergoes multiple changes under pathological or ageing conditions. Recent advances in reflectance confocal microscopy now enables the extraction of the DEJ from in-vivo imaging. This articles proposes a method to automatically analyse DEJ surfaces using self-dual morphological filters. We use self-dual pattern spectra with non-increasing attributes and we propose a novel measure in order to characterize the evolution of the surface under the filtering process. The proposed method is assessed on a specifically constituted dataset and we show that the proposed surface feature significantly correlates with both chronological ageing and photo-ageing.\",\n \"Title: Machine-Learning-Assisted Intelligent Imaging Flow Cytometry: A Review\\nYear: 2021\\n\\nAbstract: Imaging flow cytometry has been widely adopted in numerous applications such as optical sensing, environmental monitoring, clinical diagnostics, and precision agriculture. The system, with the assistance of machine learning, shows unprecedented advantages in automated image analysis, thus enabling high-throughput measurement, identification, and sorting of biological entities. Recently, with the burgeoning developments of machine learning algorithms, deep learning has taken over most of data analysis and promised tremendous performance in intelligent imaging flow cytometry. Herein, an overview of the basic knowledge of intelligent imaging flow cytometry, the evolution of machine learning and the typical applications, and how machine learning can be applied to assist intelligent imaging flow cytometry is provided. Perspectives of emerging machine learning algorithms in implementing future intelligent imaging flow cytometry are also discussed.\",\n \"Title: Object Detection With Component-Graphs in Multi-Band Images: Application to Source Detection in Astronomical Images\\nYear: 2021\\n\\nAbstract: In the context of mathematical morphology, component-graphs are complex but powerful structures for multi-band image modeling, processing, and analysis. In this work, we propose a novel multi-band object detection method relying on the component-graphs and statistical hypothesis tests. Our analysis shows that component-graphs are better at capturing image structures compared to the classical component-trees, with significantly higher detection capacity. Besides, we introduce two filtering algorithms to identify duplicated and partial nodes in the component-graphs. The proposed method, applied to the detection of sources on astronomical images, demonstrates a significant improvement in detecting faint objects on both multi-band simulated and real astronomical images compared to the state of the art.\",\n \"Title: Transformer Graph Network for Coronary Plaque Localization in CCTA\\nYear: 2022\\n\\nAbstract: Coronary CT angiography (CCTA) is the only non-invasive imaging technique that reliably depicts the anatomic extent of Coronary Artery Disease (CAD). While occlusion remains a highly predictive indicator of major cardiovascular events (MACE), there is growing evidence that the presence and characteristics of coronary atherosclerosis provide additional prognostic information. In CCTA calcified plaq...\",\n \"Title: Distributed algorithms for scalable proximity operator computation and application to video denoising\\nYear: 2022\\n\\nAbstract: Optimization problems arising in signal and image processing involve an increasingly large number of variables. In addition to the curse of dimensionality, another difficulty to overcome is that the cost function usually reads as the sum of several loss or regularization terms, which are non-necessarily smooth and possibly composed with large-size linear operators. Proximal splitting approaches are fundamental tools to address such problems, with demonstrated efficiency in many applicative fields. In this paper, we present a new distributed algorithm for computing the proximity operator of a sum of non-necessarily smooth convex functions composed with arbitrary linear operators. Our algorithm relies on a primal-dual splitting strategy, and benefits from established convergence guaranties. Each involved function is associated with a node of a hypergraph, with the ability to communicate with neighboring nodes sharing the same hyperedge. Thanks to this structure, our method can be efficiently implemented on modern parallel computing architectures, distributing the computations on multiple nodes or machines, with controlled requirements for synchronization steps. Good numerical performance and scalability properties are demonstrated on a problem of video sequence denoising. Our code implemented in Julia is made available at https://github.com/MarinENSTA/distributed_julia_denoising.\",\n \"Title: Unsupervised Nuclei Segmentation Using Spatial Organization Priors\\nYear: 2022\\n\\nAbstract: In digital pathology, various biomarkers (e.g., KI67, HER2, CD3/CD8) are routinely analyzed by pathologists through immuno-histochemistry-stained slides. Identifying these biomarkers on patient biopsies allows for a more informed design of their treatment regimen. The diversity and specificity of these types of images make the availability of annotated databases sparse. Consequently, robust and efficient learning-based diagnostic systems are difficult to develop and apply in a clinical setting. Our study builds on the observation that the overall organization and structure of the observed tissues are similar across different staining protocols. In this paper, we propose to leverage both the wide availability of haematoxylineosin stained databases and the invariance of tissue organization and structure in order to perform unsupervised nuclei segmentation on immunohistochemistry images. We implement and evaluate a generative adversarial method that relies on high-level nuclei distribution priors through comparison with largely available haematoxylin-eosin stained cell nuclei masks. Our approach shows promising results compared to classic unsupervised and supervised methods, as we quantitatively demonstrate on two publicly available datasets. Our code is publicly available to encourage further contributions (https://github.com/loic-lb/Unsupervised-Nuclei-Segmentation-using-Spatial-Organization-Priors).\"\n]","isConversational":false},"profile_size":{"kind":"number","value":99,"string":"99"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13245,"string":"13,245"}}},{"rowIdx":13246,"cells":{"question_id":{"kind":"string","value":"Tzong Chen Wu"},"question":{"kind":"string","value":"Generate an abstract for the title \"Comments on Gateway-Oriented Password-Based Authenticated Key Exchange Protocol\" using the following items: 1. GPAKE \n2. Gateway-oriented \n3. Authenticated Key Exchange \n4. Security requirements \n5. Improved Scheme"},"answer":{"kind":"string","value":"A Gateway-oriented Password-based Authenticated Key Exchange (GPAKE) scheme allows a client to establish an authenticated session key with a gateway via the help of an authentication server, where the client has pre-shared a password with the server. The desirable security properties of a GPAKE include session key semantic security, key privacy against the server, and password guessing attacks resistance. This letter shows that both Byun et al.’s schemes [2] failed to commit the security requirements. The improved scheme would be proposed in the extended version."},"correct_answer":{"kind":"string","value":"A Gateway-oriented Password-based Authenticated Key Exchange (GPAKE) scheme allows a client to establish an authenticated session key with a gateway via the help of an authentication server, where the client has pre-shared a password with the server. The desirable security properties of a GPAKE include session key semantic security, key privacy against the server, and password guessing attacks resistance. This letter shows that both Byun et al.’s schemes [2] failed to commit the security requirements. The improved scheme would be proposed in the extended version."},"documents":{"kind":"list like","value":["Title: Highly Efficient Password-Based Three-Party Key Exchange in Random Oracle Model\nYear: 2008\n\nAbstract: A password-based three-party encrypted key exchange (3PEKE) is a protocol enables any pair of two registered clients to establish session keys via the help of a trusted server such that each client shares only one password with the server. This approach greatly improves the scalability of key agreement protocol in distributed environments, and provides great user convenience. This paper proposes a new password-based 3PEKE scheme with only four message steps, which is the minimum among the published works. The proposed scheme is secure in the random oracle model.","Title: Efficient Convertible Multi-authenticated Encryption Scheme for Group Communications\nYear: 2012\n\nAbstract: In 2008, a convertible multi-authenticated encryption (CMAE) scheme for group communications is proposed by Ting-Yi Chang. Chang's mechanism enables multiple signers to generate a multi-authenticated cipher text associated with the targeted message or document. After receiving this multi-authenticated cipher text, multiple verifiers can recover and verify the message from it. If those signers deny the message was sent by them afterward, the verifiers can convert the corresponding multi-authenticated signature into a traditional one and then submit it to the trusted third party to resolve the dispute. In this study, we propose a new CMAE scheme for group communications based on one-way hash function and discrete logarithm problem (DLP). The proposed scheme is more efficient than Chang's scheme in terms of computation cost. In addition, security robustness of the proposed CMAE scheme is evaluated under four general attack patterns.","Title: Two RFID-based solutions for secure inpatient medication administration.\nYear: 2012\n\nAbstract: Medication error can easily cause serious health damage to inpatients in hospital. Consequently, the whole society has to spend huge amount of extra resources for additional therapies and medication on those affected inpatients. In order to prevent medication errors, secure inpatient medication administration system is required in a hospital. Using RFID technology, such administration system provides automated medication verification for inpatient's medicine doses and generates corresponding medication evidence, which may be audited later for medical dispute. Recently, Peris-Lopez et al. (Int. J. Med. Inform., 2011) proposed an IS-RFID system to enhance inpatient medication safety. Nevertheless, IS-RFID system does not detect the denial of proof attack efficiently and the generated medication evidence cannot defend against counterfeit evidence generated from the hospital. That is, the hospital possesses enough privilege from the design of IS-RFID system to modify generated medication evidence whenever it is necessary. Hence, we design two lightweight RFID-based solutions for secure inpatient medication administration, one for online verification environment and the other for offline validation situation, to achieve system security on evidence generation and provide early detection on denial of proof attack.","Title: ID-Based Authenticated Group Key Agreement Protocol from Bilinear Pairings for Wireless Mobile Devices.\nYear: 2013\n\nAbstract: A group key agreement protocol is a group communication mechanism that allows a set of parties to determine a group key used for encrypting/decrypting subsequent communication messages through an insecure channel. Based on the rapid development of wireless and mobile technologies, researchers have proposed several group key protocols for mobile environments. In these protocols, the computational cost of each mobile device is fixed, but each mobile user must hold the public keys of all other users. This study proposes an identity-based authenticated group key agreement protocol for mobile environments based on bilinear pairings. In the proposed scheme, the public key of the user can be easily derived from the user's identity. Therefore, each user is not required to hold the public keys of the other entities. In this approach, the computational cost of each mobile user is independent of the number of mobile users; that is, the computational cost of each mobile user is fixed. The proposed protocol meets the usual security requirements and resists well-known attacks.","Title: ID-Based Aggregate Proxy Signature Scheme Realizing Warrant-Based Delegation.\nYear: 2013\n\nAbstract: This paper proposes a novel ID-based aggregate proxy signature scheme that realizes a warrant-based delegation for an original signer to transfer his/her signing power to a given set of proxy signers. Our proposed scheme allows n distinct proxy signers to sign n distinct messages in such a way that these n individual signatures can be aggregated into a single one without expansion. In the practical applications, such specific kind of aggregate signatures is significantly applausive for enforcing the delegation of authority with both bandwidth and computation savings. Our proposed scheme requires constant bilinear pairing operations for signature verification. Besides, the size of the aggregate proxy signature is the same as that of each of the individual proxy signatures, regardless of the number of participant proxy signers has involved. We also formally sketch the security model of our proposed scheme and show that it is secure against the chosen message attacks under the computational Diffie-Hellman (CDH) assumption.","Title: Two Id-Based Multisignature Protocols For Sequential And Broadcasting Architectures\nYear: 1996\n\nAbstract: Based on the difficulty of factorization problem, two ID-based digital multisignature protocols are proposed, one suitable for the sequential architecture and the other for the broadcasting architecture. Both protocols have common characteristics: (I) the size of multisignature is fixed, despite the number of participants increasing; (2) it is easy to implement the multisignature generation and verification procedures; (3) the key management among participants is simplified. As for the sequential multisignature protocol, it additionally has the following characteristics: (4) the signing order (if necessary) can be flexibly defined by the system or the document issuer in advance, without affecting the predefined setup parameters; (5) each signer can easily verify the partial multisignature produced by all preceding signers; also, each signer can check whether all preceding signers have indeed signed the document.","Title: Novel Anonymous Authentication Scheme Using Smart Cards\nYear: 2013\n\nAbstract: Smart card based authentication scheme has been widely utilized for various transaction-oriented services such as electronic currency exchange, social insurance payment and e-commerce payment charge in modern society. How to develop a smart card based authentication scheme to support initiator untraceability and defend against major security threats for a transaction service user has become a crucial topic for researchers. Recent efforts for developing anonymous authentication scheme with smart card have failed to provide initiator untraceability for user or been vulnerable to some security attacks. This paper first presents a security model for anonymous authentication and then proposes a new anonymous authentication scheme using smart card. Security robustness of the proposed scheme is constructed by one-way hash function and elliptic curve cryptosystem. Our security analysis shows that the proposed scheme achieves general security requirement and offers initiator untraceability for user without requiring database support. Performance analysis on communication overhead and computation cost shows that the proposed scheme has better or similar efficiency in comparison with other existing smart card based authentication schemes.","Title: An efficient method for computing the feasible region with translational containment between two convex polygons\nYear: 2001\n\nAbstract: A convex polygon containment problem is studied: whether a given convex polygon P can be translated to fit inside another fixed convex polygon Q. An O(pq log q) time algorithm is presented for solving such a problem, where p and q are the numbers of vertices of P and Q. In addition, by utilizing the existence algorithm, it takes O(pq log q) time to find the set of all placements of P that fit inside Q","Title: Cryptanalysis of digital multisignature schemes for authenticating delegates in mobile code systems\nYear: 2003\n\nAbstract: Shieh et al. proposed two multisignature schemes for message recovery -the parallel multisignature scheme and the serial multisignature scheme - for authenticating delegates in mobile code systems. In this paper, we will show that Shieh et al.'s multisignature schemes are vulnerable to insider forgery attacks hence cannot achieve the security requirements they claimed.","Title: Retrieving similar pictures from iconic databases using G-tree\nYear: 1997\n\nAbstract: G-tree, combining the features of both grids and B-trees, is a disk-based spatial data structure for organizing multidimen- sional data. This paper presents an efficient access method for retrieving similar pictures from iconic databases using G-tree. The proposed method preserves the merits of G-tree, and hence indexing the pictures in the database, processing range queries for similarity retrieval, inserting/deleting a picture into/from the database, and effective memory utilization can be achieved in practical applications. In order to improve the acceptability of similarity retrieval as compared to previous works, we define a similarity function by the usage of query ranges, and each range corresponds to one object in the query. This similarity function reflects both the direction and the distance knowledge among objects in the pictures. Besides, the result of similarity retrieval can be further refined by simply adjusting the query ranges or increasing the threshold value of the predefined similarity function. (~) 1997 Elsevier Science B.V.","Title: Group Commitment Protocol Based On Zero-Knowledge Proofs\nYear: 1995\n\nAbstract: Based on the concept of zero knowledge proofs, this research note presents a group commitment protocol which can verify the members of a certain group without revealing the prover's or verifier's secret information. The proposed protocol give the commitment answer quickly. The commitment protocol is achieved successfully only if both the prover and verifier are in the same group. We show that the proposed protocol can withstand the attack on replaying the previously intercepted commitment challenges (or dialogues). Further, the problems of dynamically adding and deleting group members in the existing system are discussed.","Title: Provably Secure Password-Based Three-Party Key Exchange With Optimal Message Steps\nYear: 2009\n\nAbstract: A password-based three-party encrypted key exchange (3PEKE) is a protocol that enables any pair of two registered clients to establish session keys with the help of a trusted server such that each client shares only one password with the server. This approach greatly improves the scalability of key agreement protocol in distributed environments and provides great user convenience. This article proposes a new password-based 3PEKE scheme that completes its entire job in only a record low four steps, which we prove that it is optimal for password-based 3PEKE protocols. The security of the proposed scheme, as we will detail later, has been proved in the random oracle model.","Title: Threshold Convertible Authenticated Encryption Scheme For Hierarchical Organizations\nYear: 2010\n\nAbstract: A convertible authenticated encryption (CAE) scheme is a technique which allows the signer to generate a valid authenticated ciphertext on the chosen message so that only the designated recipient can recover and verify the message. In case of a dispute, the recipient has the ability to convert the authenticated ciphertext into ordinary signature that can be verified by anyone. In this paper, we elaborate on the merits of CAE and secret sharing schemes to propose two new (t, n) threshold authenticated encryption schemes for hierarchical structures. Our proposed schemes not only allow i-th level participants to support the (i+1)-th level participants to generate the authenticated encryption messages, but also allow the participants of i-th or higher levels to cooperatively generate the valid ones. Any authenticated ciphertext for a message is addressed to a specified group of signers in such a way that the ability to generate the ciphertext is regulated by the adopted (t, n) threshold schemes. Furthermore, the proposed schemes achieve the security requirements of an authenticated encryption scheme.","Title: A Structured Multisignature Scheme from the Gap Diffie-Hellman Group\nYear: 2003\n\nAbstract: In this paper, the authors propose a new structured multisignature scheme that considers the signing order among co-signers. The proposed scheme can resolve signing structures of serial, parallel, and the mix of them. Moreover, the size and the verification of a structured multisignature is the same as those of an individual signature generated by any co-signer. Arithmetically, the proposed scheme makes use of the Gap Diffie-Hellman (GDH) signature scheme recently presented by Boneh, Shacham, and Lynn. Due to the underlying GDH group, our scheme has the merits of simplicity in construction and efficiency in performance.","Title: Exploring Design-Fits For The Strategic Alignment Of Information Systems With Business Objectives\nYear: 2013\n\nAbstract: Introduction. This paper proposes the development of the concept of design-5 its and theoretical profiles for aligning information systems design and business strategies in terms of March and Smith's four main information technology artefacts: constructs, models, methods and instantiations.Method. The main research strategy adopted is an interpretative, in-depth case study. We selected SaveCom, a Taiwanese telecommunications company, to explore issues related to design-5 its. This research is primarily based on twenty-two interviews with middle and top managers at SaveCom, supported by secondary information.Analysis. The analysis incorporated three major phases of description (relying heavily on verbatim quotes from interviewees), analysis (identifying important factors, themes, and relationships), and interpretation (making sense of meanings in context).Results. The research proposes three mechanisms of design fit: content fit, which offers an alignment between the nature of business strategic requirements and the spirit of information technology design; structural fit, which involves the alignment of the organizational structural conditions and information technology procedures; and social fit, which considers the alignment of human factors and information technology usage design.Conclusions. Design fit is a complex process and effort must be made to understand dynamic and adaptive alignment processes that can aid the execution of information technology strategy.","Title: Remote Login Authentication Scheme Based On A Geometric Approach\nYear: 1995\n\nAbstract: A smart card-oriented remote login authentication scheme is presented. The proposed scheme can be divided into three phases: registration, login and authentication. In the registration phase, the registering user chooses a password only known to himself. The central authority (CA) assigns an identity for the user, and delivers a smart card to the registered user. The smart card contains some necessary public parameters used in the login and authentication phases. Based on some simple properties of Euclidean geometry, the login and authentication phases can be achieved easily. Impersonation and replay attacks on the proposed scheme are discussed.","Title: New dynamic ID authentication scheme using smart cards\nYear: 2010\n\nAbstract: Recently, Yoon et al. and Wang et al. independently proposed a dynamic ID authentication scheme and claimed that their schemes can withstand the risk of ID-theft, and several attacks. In this paper, we first demonstrate that both of the proposed schemes cannot achieve the security requirement for user anonymity and resist an impersonation attack yet. Then, we shall propose a new dynamic ID authentication scheme to overcome the above security weaknesses. We formally analyzed our proposed scheme with the BAN-logic and shown that it can withstand the several possible attacks. Copyright © 2010 John Wiley & Sons, Ltd.","Title: Information Security: 11th International Conference, ISC 2008, Taipei, Taiwan, September 15-18, 2008, Proceedings\nYear: 2008\n\nAbstract: This book constitutes the refereed proceedings of the 11th International Conference on Information Security Conference, ISC 2008, held in Taipei, Taiwan, September 15-18, 2008. The 33 revised full papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in topical sections on trusted computing, database and system security, intrusion detection, network security, cryptanalysis, digital signatures, AES, symmetric cryptography and hash functions, authentication as well as security protocols.","Title: Authenticated Group Key Agreement Protocol for Unbalanced Wireless Mobile Networks\nYear: 2010\n\nAbstract: In 2004, Bresson et al. proposed an authentication and group key agreement protocol for unbalanced wireless networks. Tseng recently proposed a novel secure protocol to improve Bresson et al.'s protocol. However, both protocols are insecure against the so-called impersonation attacks. This paper proposes a mutual authenticated group key agreement protocol based on RSA, discrete logarithms (DL) and one-way hash function (OHF) assumptions. The proposed protocol is more secure and efficient than previously proposed protocols for unbalanced wireless mobile networks.","Title: Enhancing convenience and situation-aware intelligence of smart home services using passive RFID and intelligent controls\nYear: 2010\n\nAbstract: Smart home services have been quite popular for some time and many kinds of technologies can be applied to enhance smart home services. Conventionally, active radio frequency identification (RFID) has been widely used in smart home services to identify users, and there was less consideration for intelligence in these services. However, it is inconvenient and costly for users, as carrying active tags requires battery recharging, is costly and can be bulky. Distinct user preferences and complex situations call for more system intelligence. A new approach is to utilise passive RFID like (EPC Gen 2 and MIFARE) as these have several advantages. This includes no battery requirement, low cost, light-weight sizes, moderate storage, and a moderate reading range. In this paper, we design an intelligent smart home system that integrates passive RFID and a smart engine along with other technologies to enhance the cost-effectiveness, convenience, safety, comfort and intelligence of smart home services. Prototyping and field-testing demonstrate that the proper application of passive RFIDs (like Gen 2 and MIFARE) and intelligent controls into existing smart home services improves the services in several aspects.","Title: An exact match retrieval scheme based upon principal component analysis\nYear: 1995\n\nAbstract: How to perceive spatial relationships among objects in a picture is an important criterion to retrieve the pictures in a symbolic picture database. In 1991, Chang proposed an indexing structure, called the 9DLT matrix, to logically represent symbolic pictures by pairwise spatial relationships. The corresponding 9DLT matrix preserves the spatial relationships among objects in a symbolic picture. In this paper, a match retrieval of pictures in a symbolic picture database by the use of 9DLT matrix is proposed. The proposed scheme is based upon a famous multivariant statistic technique — principal component analysis. By the proposed scheme, it requires O(log n ) search time in the worst case for the exact match retrieval of symbolic pictures, where n is the total number of pictures in the data base.","Title: New Secure Broadcasting Scheme Realizing Information Granularity\nYear: 2010\n\nAbstract: This paper proposes a new secure broadcasting scheme to realize the property of \"information granularity\", wherein a receiver with a higher security clearance level has the natural capability to recover a larger amount of information from the broadcasted message block. Based on the intractability of the product of the e-weak Bilinear Diffie-Hellman Inversion problem and the n-modified Bilinear Diffie-Hellman problem, the proposed scheme achieves the following features: (i) the length of the enabling block is independent of the number of receivers and the number of security clearance levels; (ii) each receiver holds only one small fixed-size decryption key corresponding to his/her security clearance level; (iii) it is computationally feasible for any receiver to derive a session key of a lower but never a higher security clearance level, even taking into account collusion with other receivers; (iv) any receiver can dynamically join or leave the system without resolving the re-keying problem for the existing receivers.","Title: Digital product transaction mechanism for electronic auction environment\nYear: 2010\n\nAbstract: The rapid development in electronic commerce and information technology drives the traditional physical product trading evolved to digital product trading. With the effect of the multi-agents system in the Internet environment and the promotions of Government, digital product industry grows fast. The authors proposed a digital product transaction mechanism for electronic auction in the multi-agents system environment. The research introduced a convenient platform to protect the privacies of both buyers and sellers, and track digital product further in an electronic auction environment. In addition, by using simple cryptography techniques supplemented with encryption, the authors ensure the security of information transactions, thereby providing a mechanism of safe and fair digital product electronic auction.","Title: ID-Based Structured Mulitsignature Schemes\nYear: 2001\n\nAbstract: The signing structure of a multisignature scheme specifies the signing order for all signers when signing messages, and any\n multisignature not obeying the specified signing order will be verified as invalid. In accordance with the different responsibilities\n of the participant signers, the signing structure of a multisignature scheme could be further classified as the following\n three types: serial, parallel and mixed, where the mixed structure is regarded as the mix of the serial and the parallel.\n Based on the well-known ID-based public key system, we will propose three ID-based structured multisignature schemes and each\n scheme respectively realizes the serial, parallel and mixed signing structures. In the proposed schemes, the length of a multisignature\n is fixed and the verification of a multisignature is efficient, regardless of the number of signers involved. Besides, any\n invalid partial multisignature can be effectively identified during the generation of the multisignature.","Title: Cryptanalysis of Chang---Wu's group-oriented authentication and key exchange protocols\nYear: 2001\n\nAbstract: In 1998, Chang and Wu proposed a group-oriented authentication mechanism with key exchange [Computer Communications 21 (1998) 485–497]. The authors show that the Chang–Wu protocols are vulnerable to the impersonation attack. They also give improvements that can not only eliminate the security flaw, but also reduce the redundant messages inherent in the original protocols.","Title: Standard Operating Procedure and Privilege Management in Taiwan Digital Forensics\nYear: 2007\n\nAbstract: Recently, a new class of crime scenes has become more prevalent. In a variety of criminal activities technologies are used to maintain records, communication, and commit crimes. For this reason, the challenge to investigative professionals is how to retrieve the digital evidence from storage devices and how to take the suspect to court. Digital evidence is different from physical. When the investigative professionals face the cases of computer crime, they must handle digital evidences properly to guarantee integrity and non-repudiation. This paper discusses a standard operating procedure to conduct inquires by the Ministry of Justice, Investigation Bureau (MJIB) in Taiwan, to achieve above goals. To enhance the quality and security of managing digital evidence, we also consider access control and fingerprint mechanisms to protect digital evidence from being misused, disclosed, or modified by malicious users. The results of this paper can be used to improve digital forensics in Taiwan.","Title: Analysis of an eHealth Care System with Smart Card Based Authentication\nYear: 2012\n\nAbstract: In recent years, smart card based authentication has been massively adopted in the electronic health care and medical treatment services in which the user and administrator can agree a robust session key based on human-memorable passwords with the hospital server. In this paper, we investigate the robustness of an eHealth care system with smart card based authentication mechanisms. This research is from International Journal of Innovative Computing, Information and Control in 2011, and its out-standing contribution and rigorous analysis make itself one of the most promising techniques in the research area of secure eHealth care services. However, it still exist spaces for security enhancement, therefore, we present the analysis of this research and de-liver contributions in the design of the secure eHealth care application development.","Title: Application of geometric hashing to iconic database retrieval\nYear: 1994\n\nAbstract: This paper presents one application of geometric hashing to retrieve the most similar picture from an iconic image database. Here, iconic pictures or objects are some kind of gross panorama of simple symbolic objects and any one iconic object is not distinguished from any other object of the same kind. In our proposed scheme, we transform each iconic picture or query into a set of ordered triples ( O i , O j , R ij )'s, where O i and O j are two iconic objects and R ij is the spatial relationship code between O i and O j . Then, applying the geooetric hashing scheme proposed by Comer and O'Donnell, we can construct a set of hashing functions corresponding to predefined relationship codes. Associated with these hashing functions, the most similar iconic pictures satisfying a specified query can be easily and fast determined.","Title: Access control with binary keys\nYear: 1994\n\nAbstract: A new access control scheme for implementing the access control matrix is proposed. The proposed scheme is different from the methods which are based on the concept of key- lock pairs. In the proposed scheme, each user is assigned a binary key, which is derived from the access rights with respect to the files. The binary key is possessed by the user, and can be used to derive the access right to the files. The characteristics of the proposed scheme are that the system can protect the files without explicitly maintaining an access control matrix, the derivation of the access rights is simple, and the dynamic access control can be easily achieved.","Title: A New Password-Based Multi-server Authentication Scheme Robust to Password Guessing Attacks\nYear: 2013\n\nAbstract: A multi-server authentication scheme is a useful authentication mechanism in which a remote user can access the services of multiple servers after registering with the registration center (RC). This study shows that the password-based multi-server authentication scheme proposed by Yeh and Lo is vulnerable to undetectable password-guessing attack and offline password-guessing attack. This study proposes a new password-based multi-server authentication scheme to overcome these vulnerabilities. The proposed protocol introduces a new mechanism for protecting user password. The RC sends an alternative key to help the server verify the legitimacy of user instead of the user's password. The values of these keys are changed with a random large nonce in each session. Therefore, the password-guessing attack cannot work successfully on the proposed scheme.","Title: The Design of a Conference Key Distribution System\nYear: 1992\n\nAbstract: In this paper, we propose a conference key distribution system for generating a common secret key for two or more users. In our system, each user possesses a secret key and a public key. Initially, the chairperson constructs a conference key associated with his secret key and the conference members' public keys. Then each member can obtain and authenticate the conference key by using his secret key. Further, we have shown that the security of our proposed system is based on the difficulty of breaking the Diffie-Hellman key distribution system.","Title: Convertible multi-authenticated encryption scheme\nYear: 2008\n\nAbstract: A convertible authenticated encryption (CAE) scheme allows the signer to generate a valid authenticated ciphertext on his chosen message such that only the designated recipient can retrieve the message. Further, the recipient has the ability to convert the authenticated ciphertext into ordinary signature in case of a dispute or repudiation. The previous proposed CAE schemes can only allow one signer to produce the authenticated ciphertext. It might be inadequate for multiparty environments. In this paper, we elaborate on the merits of CAE and multi-signature schemes to propose a convertible multi-authenticated encryption scheme which has the following advantages: (i) The size of the generated authenticated ciphertext is independent of the number of total participating signers. (ii) Except for the designated recipient, no one can obtain the signed message and verify its corresponding signature. (iii) The signature is cooperatively produced by a group of signers instead of a single signer. (iv) In case of a later dispute on repudiation, the recipient has the ability to convert the authenticated ciphertext into an ordinary one for convincing anyone of the signers' dishonesty.","Title: A letter-oriented perfect hashing scheme based upon sparse table compression\nYear: 1991\n\nAbstract: SUMMARY","Title: Comments on IMBAS: identity-based multi-user broadcast authentication in wireless sensor networks.\nYear: 2013\n\nAbstract: <title content-type=\"main\">ABSTRACT In 2008, Cao et al. proposed an identity-based multi-user broadcast authentication scheme (called IMBAS) for wireless sensor networks. The IMBAS consists of one elliptic curve-based signature scheme, called variant of Bellare-Namprempre-Neven' Identity-Based Signature (vBNN-IBS), for user broadcast authentication; a new Schnorr signature with partial message recovery for sink broadcast authentication; and a password-based private key protection to eliminate the threat caused by possible user device compromise. Compared with its counterparts, IMBAS owns better performance in terms of security, scalability, and efficiency. Unfortunately, this letter will show that (1) the password-based protection scheme and the vBNN-IBS scheme are not securea compromised user device will disclose the user private keyand (2) the new Schnorr signature with partial message recovery is vulnerable to existential forgery attack. Copyright (c) 2012 John Wiley & Sons, Ltd.","Title: A Fair-Exchange E-Payment Protocol For Digital Products With Customer Unlinkability.\nYear: 2012\n\nAbstract: Along with the development of Information Technology, online transactions through Internet have become more popular for the reasons of convenience and efficiency. In order to provide secure and reliable online transactions, an effective electronic payment protocol is crucial. In this paper, we propose a novel electronic payment protocol for digital product transactions with an offline arbiter to achieve fair exchange, automated dispute resolution, customer anonymity, and customer unlinkability. In our protocol a product token is adopted to eliminate the need of key management for digital product decryption in the offline arbiter. In addition, Elliptic Curve Cryptography (ECC)-based self-certified public key is utilized to further reduce computing overheads. According to our analysis, the efficiency of our protocol can be greatly increased in comparison with previous literatures.","Title: New identity-based society oriented signature schemes from pairings on elliptic curves\nYear: 2005\n\nAbstract: In this paper, we will propose two identity-based society oriented signature schemes that allow a group of co-signers to collaboratively generate a single signature for a message. The first proposed scheme is designated with known signers and the second scheme is with anonymous signers. Both schemes make use of pairings on elliptic curves in construction and thus have the merits of simplicity in design and efficiency in performance. In the proposed scheme with anonymous signers, a signer may participate in several different signing groups and may join or leave a signing group dynamically in a secure and efficient manner.","Title: A Refinement of a Compression-Oriented Addressing Scheme.\nYear: 1993\n\nAbstract: Chang and Wu have proposed a letter-oriented perfect hashing scheme based on sparse matrix compression. We present a method which is a refinement of the Chang-Wu scheme. By experimental evaluation, we show that the hashing of our refinement has more efficient storage utilization than Chang-Wu's method. Our refinement is valuable in practical implementations of hashing for large sets of keys.","Title: Recursive protocol for group-oriented authentication with key distribution\nYear: 2008\n\nAbstract: The authors propose a recursive protocol for group-oriented authentication with key exchange, in which a group of n entities can authenticate with each other and share a group session key. The proposed protocol has the following characteristics: First, it requires O(n) rounds of messages, O(logn) completion time, O(logn) waiting time, and O(nlogn) communication overhead in average for the completion of the recursion. Second, it not only meets the five principles suggested by Diffie et al. [Diffie, W., van Oorschot, P.C., Wiener, M.J., 1992. Authentication and authenticated key exchange. Designs, Codes, and Cryptography 2 (2), 107-125] on the design of a secure key exchange protocol, but also achieves the properties of nondisclosure, independency, and integrity addressed by Janson and Tsudik [Janson, P., Tsudik, G., 1995. Secure and minimal protocols for authenticated key distribution. Computer Communications 18 (9), 645-653] for the authentication of the group session key. Third, we describe the beliefs of trustworthy entities involved in our authentication protocol and the evolution of these beliefs as a consequence of communication by using BAN logic. Finally, it is practical and efficient, because only one-way hash function and exclusive-or (XOR) operations are used in implementation.","Title: Cryptanalysis Of A Password Authentication Scheme Using Quadratic Residues\nYear: 1995\n\nAbstract: In 1988, Laih, Harn and Huang proposed a password authentication scheme using quadratic residues. In their scheme, the user's password is effectively bound to the user's identification, regardless of the state of the password file. Furthermore, passwords are not stored in the system directory, and the password file is replaced by a public verification table. This research note shows that Laih et al.'s scheme is breakable. Also, a possible attack on Laih et al.'s scheme is discussed.","Title: Efficient Proxy Signature Scheme For Mobile Devices Using Bilinear Pairings\nYear: 2013\n\nAbstract: A proxy signature scheme is useful and convenient because it allows a proxy signer to sign a message on behalf of an entity. This study proposes a novel efficient proxy signature scheme for mobile devices using bilinear pairings. The computational cost of the proposed signature scheme is extremely low, and the length of the proposed signature is limited. In addition, our scheme does not require a special hash function, namely the Map-To-Point hash function. We also show that the proposed scheme is secure against adaptive chosen message attacks under a random oracle.","Title: Delegated multisignature scheme with document decomposition\nYear: 2001\n\nAbstract: Consider the case that a large document including different subjects can be decomposed into a disjoint set of subdocuments and is to be delegated to a group of signatories with different knowledge domains for signing. Based on the intractability of the discrete logarithm problem, the authors propose a novel multisignature scheme, namely the delegated multisignature scheme in which each qualified signatory only signs a subset of the set of subdocuments according to the justification that his/her knowledge domain completely covers these delegated subdocuments' subjects. Meanwhile, any verifier can verify the multisignature of the document with only knowing the group's public key, i.e., the multisignature verification key, not necessarily to know the results regarding document decomposition and subdocument delegation. The proposed scheme has the merit that the size of the subdocuments delegated to the qualified signatories can be further reduced and as “balanced” as possible. Besides, the proposed scheme is more realistic to practical aspects for multisignature construction and requires smaller communication bandwidth as compared to conventional multisignature schemes.","Title: A hashing-oriented nearest neighbor searching scheme\nYear: 1993\n\nAbstract: This paper presents a hashing-oriented nearest neighbor searching scheme. Given n points in the Euclidean two-dimensional plane, we first construct a Voronoi diagram and record the Voronoi vertices and the Voronoi edges. By passing each Voronoi vertex, we use two perpendicular lines, one is horizontal and the other is vertical, to partition the plane into some rectangular subdivisions. Here, each rectangular subdivision dominates at most two given points. Then we establish two hashing functions corresponding to horizontal slabs and vertical slabs, respectively. By applying the established hashing functions, we can quickly determine the proper rectangular subdivision containing the query point. After that, we compare the distance between the query point and the dominated points to determine the nearest neighbor. The searching time by our scheme is O(1). The preprocessing time and the amount of required storage are O( n 2 + t ), respectively, where n is the number of given points and t is the size of the hashing table needed by the established hashing functions.","Title: Establish a secure and trustworthy ICT environment for educational systems: a case study\nYear: 2012\n\nAbstract: Nowadays information and communication security has recently emerged as one of the most important tasks in the field of network management, operations and maintenance. The information security issue is of particular importance to the Taiwan Academic Network, a network which connects the networks of educational and research institutions in Taiwan. ICT environment in educational systems involves vast connected units and is featuring complexity, diversity and openness. In this paper, we investigate current situation of Taiwan Ministry of Education ICT security development and provide a case study. We also discussed challenges and solutions for improving ICT security environment in educational system.","Title: Two RFID-based solutions to enhance inpatient medication safety.\nYear: 2011\n\nAbstract: Owing to the low cost and convenience of identifying an object without physical contact, Radio Frequency Identification (RFID) systems provide innovative, promising and efficient applications in many domains. An RFID grouping protocol is a protocol that allows an off-line verifier to collect and verify the evidence of two or more tags simultaneously present. Recently, Huang and Ku (J. Med. Syst, 2009) proposed an efficient grouping protocol to enhance medication safety for inpatients based on low-cost tags. However, the Huang-Ku scheme is not secure; an attacker can easily make up fake grouping records to cheat the verifier. This weakness would seriously endanger the safety of inpatient medication safety. This paper will show the weaknesses, and then propose two RFID-based solutions to enhance medication safety for two different scenarios. The proposed schemes are practical, secure and efficient for medication applications.","Title: Retrieving the most similar symbolic pictures from pictorial databases\nYear: 1992\n\nAbstract: In this article, we suggest an iconic indexing mechanism for spatial similarity retrieval on iconic image databases based upon the spatial relationships among the objects in a picture. The iconic objects we deal with are some kinds of gross panorama of simple objects. We also assume that any one iconic object is not distinguished from any other object of the same kind. For our mechanism, we first transform each iconic picture into a set of ordered triples ( O i , O j , R ij ) where O i and O j are objects and R ij is the predefined spatial relationship codes between O i and O j . Then we construct a set of hashing functions for all spatial relationship codes R ij , separately, associated with all ordered pairs ( O i , O j ) extracted from the ordered triples ( O i , O j , R ij ). Thereafter, an iconic index table can be established according to the constructed hashing functions for all predefined spatial relationship codes. By applying the constructed hashing functions, the most similar pictures in the database satisfying a specified query can be fast determined. We can easily extend our mechanism for handling the case when some new spatial relationship codes are defined later for the considerations of refined spatial similarity retrieval under the maximum-likelihood measure criterion.","Title: Secure Handover Authentication Protocol Based on Bilinear Pairings\nYear: 2013\n\nAbstract: Handover authentication protocol enables a mobile node to switch from one base station to another without loss or interruption of service when the node exits the transmission area of his or her current base station. This paper proposes a secure prime-order handover authentication protocol based on bilinear pairings. The proposed protocol adapts the concept of pseudonyms to provide user anonymity and user unlinkability. It withstands well-known security threats and achieves mutual authentication, user unlinkability. A batch signature verification mechanism to verify a mass of signatures is presented in our scheme. We also prove that our scheme is secure under random oracle.","Title: Secure Delegation-Based Authentication Protocol for Wireless Roaming Service\nYear: 2012\n\nAbstract: Portable devices, with wireless communication capability, are used widely in everyday life. Preventing personal sensitive information from being revealed to an adversary through insecure wireless communication channels has therefore become a serious concern. This study proposes a novel ECC-based authentication protocol for portable communication systems. The proposed protocol resists DoS attacks and requires less computation cost when authenticating a communication session. In addition, the proposed protocol provides user unlinkability.","Title: Group-Oriented Authentication Mechanism With Key Exchange\nYear: 1998\n\nAbstract: Most of the previously proposed authentication mechanisms are based on the individual-oriented approach and hence may be inefficient and not suitable for group-oriented applications. In this paper, we propose a group-oriented authentication mechanism with key exchange that can effectively resolve the security requirements of group-oriented communications. The proposed mechanism consists of three basics: the entity-to-entity (EE), the entity-to-group (EG) and the group-to-group (GG) subprotocols. All these subprotocols meet the authentication and secure key exchange requirements separately addressed by other authors. By the proposed mechanism, a set of temporal authenticated subgroups can be recursively formed in a divide-and-conquer manner and finally all entities in the target group can authenticate each other and exchange a group session key. Experimental analyses show that the proposed mechanism requires O(n log n) rounds of messages (n is the number of entities in the target group) for achieving the authentication and key exchange among the entities in the target group, which outperforms the naive approach that is based on iteratively running either two-parties or three-parties authentication protocols for achieving the same purposes. Moreover, the proposed mechanism can be practically implemented in a large-scaled group-oriented application since only one-way hashing functions and exclusive-or operations are involved. (C) 1998 Elsevier Science B.V.","Title: Establishment of the Standard Operating Procedure (SOP) for Gathering Digital Evidence\nYear: 2005\n\nAbstract: Numerous high-profile computer intrusions and denial of service (DOS) incidents occurred during the latter months of 1999 and early 2000, including a significant intrusion of Speakeasy, an Internet service provider based in Seattle. The Federal Bureau ...","Title: A binary access control method using prime factorization\nYear: 1997\n\nAbstract: Based on binary coding and prime factorization, an improvement of the single-key-lock method for access control is presented. In our method, each accessor is associated with a key and each resource is associated with a lock vector. Through simple mathematical operations on the corresponding key and lock vector, the system can easily verify the access right for an accessor with respect to the referenced resource. Furthermore, by our improvement, the system can easily handle the dynamic access control problem, such as adding/deleting accessors or resources, without much effort. (C) Elsevier Science Inc. 1997.","Title: Group-oriented signature scheme with distinguished signing authorities\nYear: 2004\n\nAbstract: Elaborating on Shamir's (t, n) threshold scheme and ElGamal's digital signature scheme, we propose a group-oriented signature with distinguished signing authorities. The proposed scheme works under the following restrictions: (1) the signing document can be divided into any t smaller subdocuments in such a way that each subdocument is meaningful and will be signed as a unit by one discretionary signatory; (2) every signatory has the same knowledge domain that covers the subjects within the signing document; (3) any t out of n signatories within the group can be on behalf of the group for signing any document, and at most t - 1 participants can conspire to attack Shamir's scheme for revealing the other ones' private keys (or shadows). In the proposed scheme, every discretionary signatory signs only a smaller part of the document, instead of the whole one. Such an approach achieves the justification of labor-division and responsibility-sharing inherent in the group works. Moreover, the bandwidth of data transmission for group signature construction can be further reduced. The proposed scheme also has the property that the discretionary signatories are anonymous to the verifier outside the group, and the size of a group signature is equivalent to that of any individual signature.","Title: Mutual anonymity protocol with integrity protection for mobile peer-to-peer networks\nYear: 2010\n\nAbstract: A mobile peer-to-peer network (MOPNET) has drawn increasing attentions for its communication convenience and ease of resource sharing in an unfixed infrastructure. The essential requirements for applications in a MOPNET are to enforce trust of transmitted data and security of peers. However, most of mutual anonymity protocols for a MOPNET lack integrity protection, and hence, any peer might suffer from some potential security threats such as virus-infected or Trojan horse files. This paper proposes a mutual anonymity protocol with integrity protection for a MOPNET, in which the initiator and responder anonymously share data with each other, and ensure its integrity.","Title: New nonrepudiable threshold proxy signature scheme with known signers\nYear: 2001\n\nAbstract: Based on Kim et al.'s threshold proxy signature scheme, Sun proposed an nonrepudiable threshold proxy signature scheme with known signers. In Sun's scheme, actual proxy signers cannot deny the signatures they have signed. However, his scheme is vulnerable against the conspiracy attack. Any t malicious proxy signers can collusively derive the secret keys of other proxy signers and impersonate some other proxy signers to generate proxy signatures. In this paper, we proposed a new nonrepudiable threshold proxy signature scheme that overcomes the above weakness. Furthermore, the proposed scheme is more efficient than Sun's in terms of computational complexities and communication costs.","Title: Provably Secure Gateway-Oriented Password-Based Authenticated Key Exchange Protocol Resistant to Password Guessing Attacks.\nYear: 2013\n\nAbstract: A Gateway-oriented Password-based Authenticated Key Exchange (GPAKE) scheme allows a client to establish an authenticated session key with a gateway via the help of an authentication server, where the client has pre-shared a password with the server. The desirable security properties of a GPAKE include session key semantic security, key privacy against servers, and password guessing attacks resistance. Abdalla et al.'s scheme (Asiacrypt 2005) [1] proposed the first GPAKE scheme, and then Abdalla et al. [13] and Byun et al.'s [2] had respectively proposed their improvements to enhance the security. Unfortunately, we find that all the improved schemes fail to commit the security requirements. In this paper, we point out security weaknesses of the improved scheme. To enhance the security, we propose a new GPAKE scheme, and prove its security in an enhanced model.","Title: Id-Based Group-Oriented Cryptosystem And Its Digital Signature Scheme\nYear: 1997\n\nAbstract: Inspired from Tsujii and Itoh's system, we propose an ID-based group-oriented cryptosystem and its digital signature scheme. In the proposed cryptosystem, any entity for decrypting or signing a message is dedicated to \"group\", instead of \"individual\". A group, consisting of a set of individuals, could be specified as high-authorized, low-authorized or threshold-authorized according to the predefined authorization strategy. Any sender can encrypt a message addressed to a group with only knowing the group's identity and the corresponding public information, so that individuals of the group should cooperate to recover the message by following the authorization strategy. For signing a message, individuals of the group should cooperate to generate a group signature by following the authorization strategy, so that any verifier can validate the group signature with only requiring the group's identity and the corresponding public information. The sender of a secret message or the verifier of a group signature does not necessarily know the authorization strategy within that group in advance. (C) 1997 Elsevier Science B.V.","Title: Controlling the access requests in an information protection system\nYear: 1993\n\nAbstract: A new mechanism used for controlling the access requests in information systems is proposed. In our scheme, each access privilege associates to a recursively generated function based on Newton's interpolating polynomial. The insertion of a new user/file into the system and deletion of an old user/file from the system can be easily implemented. Besides, the updating of a privilege of a user to a file can also be successfully implemented without reconstructing all associated functions.","Title: GAnGS: gather, authenticate 'n group securely\nYear: 2008\n\nAbstract: Establishing secure communication among a group of physically collocated people is a challenge. This problem can be reduced to establishing authentic public keys among all the participants - these public keys then serve to establish a shared secret symmetric key for encryption and authentication of messages. Unfortunately, in most real-world settings, public key infrastructures (PKI) are uncommon and distributing a secret in a public space is difficult. Thus, it is a challenge to exchange authentic public keys in a scalable, secure, and easy to use fashion. In this paper, we propose GAnGS, a protocol for the secure exchange of authenticated information among a group of people. In contrast to prior work, GAnGS resists Group-in-the-Middle and Sybil attacks by malicious insiders, as well as infiltration attacks by malicious bystanders. GAnGS is designed to be robust to user errors, such as miscounting the number of participants or incorrectly comparing checksums. We have implemented and evaluated GAnGS on Nokia N70 phones. The GAnGS system is viable and achieves a good balance between scalability, security, and ease of use.","Title: New Identity-Based Sequential Aggregate Signature Scheme from RSA\nYear: 2013\n\nAbstract: An identity-based sequential aggregate signature (IBSAS) scheme provides a shorter aggregate signature for multiple signers in which each signer has signed his/her own message and all generated signatures are aggregated in sequence. During aggregate signature verification process, a verifier can identify the generated order and the validity of signatures. In 2012, Dou et al. first proposed two new IBSAS schemes based on RSA. One scheme is non-interactive and the other is interactive. This study discovers that the non-interactive IBSAS scheme of Dou et al. is vulnerable to a forgery attack. In our attack, a legal but malicious signer is capable to forge an IBSAS without knowing the other signers' private keys. In order to overcome the weakness, we propose a non-interactive IBSAS scheme based on the identity-based signature scheme of Qian and Cao. We then show that our non-interactive IBSAS scheme is secure against proposed forgery attack.","Title: ElGamal-like digital signature and multisignature schemes using self-certified public keys\nYear: 2000\n\nAbstract: In this paper, we first present a digital signature scheme using self-certified public key. Subsequently, we present a digital multisignature scheme extended from the proposed digital signature. Both the proposed schemes have the advantage that the authentication of the public key can be accomplished with the verification of the signature or multisignature. The security of both the proposed schemes is based on the one-way hash function, the factorization and the discrete logarithm assumptions. Moreover, the proposed multisignature scheme preserves the main merits inherent in most of the previously developed schemes. We also demonstrate that the proposed multisignature scheme can withstand the active attack that some malicious impostors try to universally forge an individual signature or a multisignature for a given message that is rejected to be signed by the other co-signers.","Title: Secure E-Health System On Passive Rfid: Outpatient Clinic And Emergency Care\nYear: 2013\n\nAbstract: In recent years, many researches have demonstrated several RFID-based solutions to enhance patient medication safety and avoid human errors. Although RFID-based procedure is more efficient than traditional process, patient's information may be attacked (or stolen) during the data transmission period. This will cause inappropriate medication use and medical errors. In this paper, we introduce a robust RFID-based e-Health system which strengthens the system security and protects the patient's privacy as well. In addition, our e-Health system can provide better efficiency of outpatient clinic procedure and emergency care procedure in hospital environment.","Title: Secure Anonymous Authentication Scheme Without Verification Table For Mobile Satellite Communication Systems\nYear: 2014\n\nAbstract: An authentication scheme is one of the most basic and important security mechanisms for satellite communication systems because it prevents illegal access by an adversary. Lee et al. recently proposed an efficient authentication scheme for mobile satellite communication systems. However, we observed that this authentication scheme is vulnerable to a denial of service (DoS) attack and does not offer perfect forward secrecy. Therefore, we propose a novel secure authentication scheme without verification table for mobile satellite communication systems. The proposed scheme can simultaneously withstand DoS attacks and support user anonymity and user unlinkability. In addition, the proposed scheme is based on the elliptic curve cryptosystem, has low client-side and server-side computation costs, and achieves perfect forward secrecy. Copyright (c) 2013 John Wiley & Sons, Ltd.","Title: Efficient English Auction Scheme Without A Secure Channel\nYear: 2015\n\nAbstract: English auctions become tremendously popular on the Internet today. This paper presents a new English auction scheme that can be realized in the public network environments without any additional secure channel. Our scheme not only satisfies security requirements of anonymity, traceability, no framing, fairness, public verifiability, unlinkability among different rounds of auction and linkability in an auction round, but also provides one-time registration and easy revocation. Furthermore, as compared with the pervious works, the proposed scheme has a better performance in terms of the computation and the size of bidding information.","Title: Weaknesses and improvements of an efficient certificateless signature scheme without using bilinear pairings\nYear: 2014\n\nAbstract: AbstractThe certificateless signature CLS scheme is a special signature scheme that solves the key escrow problem in identity-based signature schemes. In CLS schemes, the private key is generated cooperatively by the key generator center KGC and signer, such that a malicious KGC cannot masquerade as the signer and sign a message. He et al. in 2011 proposed an efficient CLS scheme without using bilinear pairings. However, we discovered that the CLS scheme by He et al. cannot resist a strong type 2 adversary if this adversary replaces the master public key of the KGC. This work proposes an improved scheme that overcomes this weakness. Copyright © 2012 John Wiley & Sons, Ltd.","Title: Provably Secure Anonymous Authentication Scheme for Roaming Service in Global Mobility Networks.\nYear: 2015\n\nAbstract: This paper presents an anonymous authentication scheme for roaming service in global mobility networks, in which the foreign agent cannot obtain the identity information of the mobile user who is sending the roaming request. In addition, the home agent does not have to maintain any verification table for authenticating the mobile user. We give formal analyses to show that our proposed scheme satisfies the security requirements of user anonymity, mutual authentication, session-key security, and perfect forward secrecy. Besides, some possible attacks on the proposed scheme are discussed, such as the replay attack, the man-in-middle attack, the impersonation attack, and the insider attack.","Title: Improvement of threshold proxy signature scheme\nYear: 2003\n\nAbstract: Recently, Sun et al. pointed out some weaknesses of Zhang's threshold proxy signature scheme and a disadvantage of Kim's threshold proxy signature scheme. They further proposed a new (t,n) threshold proxy signature scheme based on Zhang's scheme to eliminate these drawbacks. Unfortunately, their scheme suffered from a weakness which is shown in this paper. The authors also propose an improvement to counter it.","Title: Improvement of modified authenticated key agreement protocol\nYear: 2003\n\nAbstract: Recently, Ku and Wang showed that Tseng's modified authenticated key agreement protocol is vulnerable to two attacks and proposed an improvement to withstand these attacks. However, this letter will show that this improvement is still vulnerable to the modification attack, which is contrary to their claims. Additionally, we proposed an improvement to eliminate this security flaw.","Title: Rfid Authentication With Un-Traceability And Forward Secrecy In The Partial-Distributed-Server Model\nYear: 2015\n\nAbstract: Secure authentication of low cost Radio Frequency Identification (RFID) tag with limited resources is a big challenge, especially when we simultaneously consider anonymity, un-traceability, and forward secrecy. The popularity of Internet of Things (IoT) further amplifies this challenge, as we should authenticate these mobile tags in the partial-distributed-server environments. In this paper, we propose an RFID authentication scheme in the partial-distributed-server environments. The proposed scheme owns excellent performance in terms of computational complexity and scalability as well as security properties.","Title: An identity-based ring signature scheme from bilinear pairings\nYear: 2004\n\nAbstract: At the conference Asiacrypt 2001, Rivest, Shamir and Tauman firstly addressed the concept of ring signature. We propose an identity-based ring signature scheme from bilinear pairings. As compared with the Zhang-Kim scheme (presented at the conference Asiacrypt 2002), our scheme is more efficient in computation and requires fewer pairing operations."],"string":"[\n \"Title: Highly Efficient Password-Based Three-Party Key Exchange in Random Oracle Model\\nYear: 2008\\n\\nAbstract: A password-based three-party encrypted key exchange (3PEKE) is a protocol enables any pair of two registered clients to establish session keys via the help of a trusted server such that each client shares only one password with the server. This approach greatly improves the scalability of key agreement protocol in distributed environments, and provides great user convenience. This paper proposes a new password-based 3PEKE scheme with only four message steps, which is the minimum among the published works. The proposed scheme is secure in the random oracle model.\",\n \"Title: Efficient Convertible Multi-authenticated Encryption Scheme for Group Communications\\nYear: 2012\\n\\nAbstract: In 2008, a convertible multi-authenticated encryption (CMAE) scheme for group communications is proposed by Ting-Yi Chang. Chang's mechanism enables multiple signers to generate a multi-authenticated cipher text associated with the targeted message or document. After receiving this multi-authenticated cipher text, multiple verifiers can recover and verify the message from it. If those signers deny the message was sent by them afterward, the verifiers can convert the corresponding multi-authenticated signature into a traditional one and then submit it to the trusted third party to resolve the dispute. In this study, we propose a new CMAE scheme for group communications based on one-way hash function and discrete logarithm problem (DLP). The proposed scheme is more efficient than Chang's scheme in terms of computation cost. In addition, security robustness of the proposed CMAE scheme is evaluated under four general attack patterns.\",\n \"Title: Two RFID-based solutions for secure inpatient medication administration.\\nYear: 2012\\n\\nAbstract: Medication error can easily cause serious health damage to inpatients in hospital. Consequently, the whole society has to spend huge amount of extra resources for additional therapies and medication on those affected inpatients. In order to prevent medication errors, secure inpatient medication administration system is required in a hospital. Using RFID technology, such administration system provides automated medication verification for inpatient's medicine doses and generates corresponding medication evidence, which may be audited later for medical dispute. Recently, Peris-Lopez et al. (Int. J. Med. Inform., 2011) proposed an IS-RFID system to enhance inpatient medication safety. Nevertheless, IS-RFID system does not detect the denial of proof attack efficiently and the generated medication evidence cannot defend against counterfeit evidence generated from the hospital. That is, the hospital possesses enough privilege from the design of IS-RFID system to modify generated medication evidence whenever it is necessary. Hence, we design two lightweight RFID-based solutions for secure inpatient medication administration, one for online verification environment and the other for offline validation situation, to achieve system security on evidence generation and provide early detection on denial of proof attack.\",\n \"Title: ID-Based Authenticated Group Key Agreement Protocol from Bilinear Pairings for Wireless Mobile Devices.\\nYear: 2013\\n\\nAbstract: A group key agreement protocol is a group communication mechanism that allows a set of parties to determine a group key used for encrypting/decrypting subsequent communication messages through an insecure channel. Based on the rapid development of wireless and mobile technologies, researchers have proposed several group key protocols for mobile environments. In these protocols, the computational cost of each mobile device is fixed, but each mobile user must hold the public keys of all other users. This study proposes an identity-based authenticated group key agreement protocol for mobile environments based on bilinear pairings. In the proposed scheme, the public key of the user can be easily derived from the user's identity. Therefore, each user is not required to hold the public keys of the other entities. In this approach, the computational cost of each mobile user is independent of the number of mobile users; that is, the computational cost of each mobile user is fixed. The proposed protocol meets the usual security requirements and resists well-known attacks.\",\n \"Title: ID-Based Aggregate Proxy Signature Scheme Realizing Warrant-Based Delegation.\\nYear: 2013\\n\\nAbstract: This paper proposes a novel ID-based aggregate proxy signature scheme that realizes a warrant-based delegation for an original signer to transfer his/her signing power to a given set of proxy signers. Our proposed scheme allows n distinct proxy signers to sign n distinct messages in such a way that these n individual signatures can be aggregated into a single one without expansion. In the practical applications, such specific kind of aggregate signatures is significantly applausive for enforcing the delegation of authority with both bandwidth and computation savings. Our proposed scheme requires constant bilinear pairing operations for signature verification. Besides, the size of the aggregate proxy signature is the same as that of each of the individual proxy signatures, regardless of the number of participant proxy signers has involved. We also formally sketch the security model of our proposed scheme and show that it is secure against the chosen message attacks under the computational Diffie-Hellman (CDH) assumption.\",\n \"Title: Two Id-Based Multisignature Protocols For Sequential And Broadcasting Architectures\\nYear: 1996\\n\\nAbstract: Based on the difficulty of factorization problem, two ID-based digital multisignature protocols are proposed, one suitable for the sequential architecture and the other for the broadcasting architecture. Both protocols have common characteristics: (I) the size of multisignature is fixed, despite the number of participants increasing; (2) it is easy to implement the multisignature generation and verification procedures; (3) the key management among participants is simplified. As for the sequential multisignature protocol, it additionally has the following characteristics: (4) the signing order (if necessary) can be flexibly defined by the system or the document issuer in advance, without affecting the predefined setup parameters; (5) each signer can easily verify the partial multisignature produced by all preceding signers; also, each signer can check whether all preceding signers have indeed signed the document.\",\n \"Title: Novel Anonymous Authentication Scheme Using Smart Cards\\nYear: 2013\\n\\nAbstract: Smart card based authentication scheme has been widely utilized for various transaction-oriented services such as electronic currency exchange, social insurance payment and e-commerce payment charge in modern society. How to develop a smart card based authentication scheme to support initiator untraceability and defend against major security threats for a transaction service user has become a crucial topic for researchers. Recent efforts for developing anonymous authentication scheme with smart card have failed to provide initiator untraceability for user or been vulnerable to some security attacks. This paper first presents a security model for anonymous authentication and then proposes a new anonymous authentication scheme using smart card. Security robustness of the proposed scheme is constructed by one-way hash function and elliptic curve cryptosystem. Our security analysis shows that the proposed scheme achieves general security requirement and offers initiator untraceability for user without requiring database support. Performance analysis on communication overhead and computation cost shows that the proposed scheme has better or similar efficiency in comparison with other existing smart card based authentication schemes.\",\n \"Title: An efficient method for computing the feasible region with translational containment between two convex polygons\\nYear: 2001\\n\\nAbstract: A convex polygon containment problem is studied: whether a given convex polygon P can be translated to fit inside another fixed convex polygon Q. An O(pq log q) time algorithm is presented for solving such a problem, where p and q are the numbers of vertices of P and Q. In addition, by utilizing the existence algorithm, it takes O(pq log q) time to find the set of all placements of P that fit inside Q\",\n \"Title: Cryptanalysis of digital multisignature schemes for authenticating delegates in mobile code systems\\nYear: 2003\\n\\nAbstract: Shieh et al. proposed two multisignature schemes for message recovery -the parallel multisignature scheme and the serial multisignature scheme - for authenticating delegates in mobile code systems. In this paper, we will show that Shieh et al.'s multisignature schemes are vulnerable to insider forgery attacks hence cannot achieve the security requirements they claimed.\",\n \"Title: Retrieving similar pictures from iconic databases using G-tree\\nYear: 1997\\n\\nAbstract: G-tree, combining the features of both grids and B-trees, is a disk-based spatial data structure for organizing multidimen- sional data. This paper presents an efficient access method for retrieving similar pictures from iconic databases using G-tree. The proposed method preserves the merits of G-tree, and hence indexing the pictures in the database, processing range queries for similarity retrieval, inserting/deleting a picture into/from the database, and effective memory utilization can be achieved in practical applications. In order to improve the acceptability of similarity retrieval as compared to previous works, we define a similarity function by the usage of query ranges, and each range corresponds to one object in the query. This similarity function reflects both the direction and the distance knowledge among objects in the pictures. Besides, the result of similarity retrieval can be further refined by simply adjusting the query ranges or increasing the threshold value of the predefined similarity function. (~) 1997 Elsevier Science B.V.\",\n \"Title: Group Commitment Protocol Based On Zero-Knowledge Proofs\\nYear: 1995\\n\\nAbstract: Based on the concept of zero knowledge proofs, this research note presents a group commitment protocol which can verify the members of a certain group without revealing the prover's or verifier's secret information. The proposed protocol give the commitment answer quickly. The commitment protocol is achieved successfully only if both the prover and verifier are in the same group. We show that the proposed protocol can withstand the attack on replaying the previously intercepted commitment challenges (or dialogues). Further, the problems of dynamically adding and deleting group members in the existing system are discussed.\",\n \"Title: Provably Secure Password-Based Three-Party Key Exchange With Optimal Message Steps\\nYear: 2009\\n\\nAbstract: A password-based three-party encrypted key exchange (3PEKE) is a protocol that enables any pair of two registered clients to establish session keys with the help of a trusted server such that each client shares only one password with the server. This approach greatly improves the scalability of key agreement protocol in distributed environments and provides great user convenience. This article proposes a new password-based 3PEKE scheme that completes its entire job in only a record low four steps, which we prove that it is optimal for password-based 3PEKE protocols. The security of the proposed scheme, as we will detail later, has been proved in the random oracle model.\",\n \"Title: Threshold Convertible Authenticated Encryption Scheme For Hierarchical Organizations\\nYear: 2010\\n\\nAbstract: A convertible authenticated encryption (CAE) scheme is a technique which allows the signer to generate a valid authenticated ciphertext on the chosen message so that only the designated recipient can recover and verify the message. In case of a dispute, the recipient has the ability to convert the authenticated ciphertext into ordinary signature that can be verified by anyone. In this paper, we elaborate on the merits of CAE and secret sharing schemes to propose two new (t, n) threshold authenticated encryption schemes for hierarchical structures. Our proposed schemes not only allow i-th level participants to support the (i+1)-th level participants to generate the authenticated encryption messages, but also allow the participants of i-th or higher levels to cooperatively generate the valid ones. Any authenticated ciphertext for a message is addressed to a specified group of signers in such a way that the ability to generate the ciphertext is regulated by the adopted (t, n) threshold schemes. Furthermore, the proposed schemes achieve the security requirements of an authenticated encryption scheme.\",\n \"Title: A Structured Multisignature Scheme from the Gap Diffie-Hellman Group\\nYear: 2003\\n\\nAbstract: In this paper, the authors propose a new structured multisignature scheme that considers the signing order among co-signers. The proposed scheme can resolve signing structures of serial, parallel, and the mix of them. Moreover, the size and the verification of a structured multisignature is the same as those of an individual signature generated by any co-signer. Arithmetically, the proposed scheme makes use of the Gap Diffie-Hellman (GDH) signature scheme recently presented by Boneh, Shacham, and Lynn. Due to the underlying GDH group, our scheme has the merits of simplicity in construction and efficiency in performance.\",\n \"Title: Exploring Design-Fits For The Strategic Alignment Of Information Systems With Business Objectives\\nYear: 2013\\n\\nAbstract: Introduction. This paper proposes the development of the concept of design-5 its and theoretical profiles for aligning information systems design and business strategies in terms of March and Smith's four main information technology artefacts: constructs, models, methods and instantiations.Method. The main research strategy adopted is an interpretative, in-depth case study. We selected SaveCom, a Taiwanese telecommunications company, to explore issues related to design-5 its. This research is primarily based on twenty-two interviews with middle and top managers at SaveCom, supported by secondary information.Analysis. The analysis incorporated three major phases of description (relying heavily on verbatim quotes from interviewees), analysis (identifying important factors, themes, and relationships), and interpretation (making sense of meanings in context).Results. The research proposes three mechanisms of design fit: content fit, which offers an alignment between the nature of business strategic requirements and the spirit of information technology design; structural fit, which involves the alignment of the organizational structural conditions and information technology procedures; and social fit, which considers the alignment of human factors and information technology usage design.Conclusions. Design fit is a complex process and effort must be made to understand dynamic and adaptive alignment processes that can aid the execution of information technology strategy.\",\n \"Title: Remote Login Authentication Scheme Based On A Geometric Approach\\nYear: 1995\\n\\nAbstract: A smart card-oriented remote login authentication scheme is presented. The proposed scheme can be divided into three phases: registration, login and authentication. In the registration phase, the registering user chooses a password only known to himself. The central authority (CA) assigns an identity for the user, and delivers a smart card to the registered user. The smart card contains some necessary public parameters used in the login and authentication phases. Based on some simple properties of Euclidean geometry, the login and authentication phases can be achieved easily. Impersonation and replay attacks on the proposed scheme are discussed.\",\n \"Title: New dynamic ID authentication scheme using smart cards\\nYear: 2010\\n\\nAbstract: Recently, Yoon et al. and Wang et al. independently proposed a dynamic ID authentication scheme and claimed that their schemes can withstand the risk of ID-theft, and several attacks. In this paper, we first demonstrate that both of the proposed schemes cannot achieve the security requirement for user anonymity and resist an impersonation attack yet. Then, we shall propose a new dynamic ID authentication scheme to overcome the above security weaknesses. We formally analyzed our proposed scheme with the BAN-logic and shown that it can withstand the several possible attacks. Copyright © 2010 John Wiley & Sons, Ltd.\",\n \"Title: Information Security: 11th International Conference, ISC 2008, Taipei, Taiwan, September 15-18, 2008, Proceedings\\nYear: 2008\\n\\nAbstract: This book constitutes the refereed proceedings of the 11th International Conference on Information Security Conference, ISC 2008, held in Taipei, Taiwan, September 15-18, 2008. The 33 revised full papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in topical sections on trusted computing, database and system security, intrusion detection, network security, cryptanalysis, digital signatures, AES, symmetric cryptography and hash functions, authentication as well as security protocols.\",\n \"Title: Authenticated Group Key Agreement Protocol for Unbalanced Wireless Mobile Networks\\nYear: 2010\\n\\nAbstract: In 2004, Bresson et al. proposed an authentication and group key agreement protocol for unbalanced wireless networks. Tseng recently proposed a novel secure protocol to improve Bresson et al.'s protocol. However, both protocols are insecure against the so-called impersonation attacks. This paper proposes a mutual authenticated group key agreement protocol based on RSA, discrete logarithms (DL) and one-way hash function (OHF) assumptions. The proposed protocol is more secure and efficient than previously proposed protocols for unbalanced wireless mobile networks.\",\n \"Title: Enhancing convenience and situation-aware intelligence of smart home services using passive RFID and intelligent controls\\nYear: 2010\\n\\nAbstract: Smart home services have been quite popular for some time and many kinds of technologies can be applied to enhance smart home services. Conventionally, active radio frequency identification (RFID) has been widely used in smart home services to identify users, and there was less consideration for intelligence in these services. However, it is inconvenient and costly for users, as carrying active tags requires battery recharging, is costly and can be bulky. Distinct user preferences and complex situations call for more system intelligence. A new approach is to utilise passive RFID like (EPC Gen 2 and MIFARE) as these have several advantages. This includes no battery requirement, low cost, light-weight sizes, moderate storage, and a moderate reading range. In this paper, we design an intelligent smart home system that integrates passive RFID and a smart engine along with other technologies to enhance the cost-effectiveness, convenience, safety, comfort and intelligence of smart home services. Prototyping and field-testing demonstrate that the proper application of passive RFIDs (like Gen 2 and MIFARE) and intelligent controls into existing smart home services improves the services in several aspects.\",\n \"Title: An exact match retrieval scheme based upon principal component analysis\\nYear: 1995\\n\\nAbstract: How to perceive spatial relationships among objects in a picture is an important criterion to retrieve the pictures in a symbolic picture database. In 1991, Chang proposed an indexing structure, called the 9DLT matrix, to logically represent symbolic pictures by pairwise spatial relationships. The corresponding 9DLT matrix preserves the spatial relationships among objects in a symbolic picture. In this paper, a match retrieval of pictures in a symbolic picture database by the use of 9DLT matrix is proposed. The proposed scheme is based upon a famous multivariant statistic technique — principal component analysis. By the proposed scheme, it requires O(log n ) search time in the worst case for the exact match retrieval of symbolic pictures, where n is the total number of pictures in the data base.\",\n \"Title: New Secure Broadcasting Scheme Realizing Information Granularity\\nYear: 2010\\n\\nAbstract: This paper proposes a new secure broadcasting scheme to realize the property of \\\"information granularity\\\", wherein a receiver with a higher security clearance level has the natural capability to recover a larger amount of information from the broadcasted message block. Based on the intractability of the product of the e-weak Bilinear Diffie-Hellman Inversion problem and the n-modified Bilinear Diffie-Hellman problem, the proposed scheme achieves the following features: (i) the length of the enabling block is independent of the number of receivers and the number of security clearance levels; (ii) each receiver holds only one small fixed-size decryption key corresponding to his/her security clearance level; (iii) it is computationally feasible for any receiver to derive a session key of a lower but never a higher security clearance level, even taking into account collusion with other receivers; (iv) any receiver can dynamically join or leave the system without resolving the re-keying problem for the existing receivers.\",\n \"Title: Digital product transaction mechanism for electronic auction environment\\nYear: 2010\\n\\nAbstract: The rapid development in electronic commerce and information technology drives the traditional physical product trading evolved to digital product trading. With the effect of the multi-agents system in the Internet environment and the promotions of Government, digital product industry grows fast. The authors proposed a digital product transaction mechanism for electronic auction in the multi-agents system environment. The research introduced a convenient platform to protect the privacies of both buyers and sellers, and track digital product further in an electronic auction environment. In addition, by using simple cryptography techniques supplemented with encryption, the authors ensure the security of information transactions, thereby providing a mechanism of safe and fair digital product electronic auction.\",\n \"Title: ID-Based Structured Mulitsignature Schemes\\nYear: 2001\\n\\nAbstract: The signing structure of a multisignature scheme specifies the signing order for all signers when signing messages, and any\\n multisignature not obeying the specified signing order will be verified as invalid. In accordance with the different responsibilities\\n of the participant signers, the signing structure of a multisignature scheme could be further classified as the following\\n three types: serial, parallel and mixed, where the mixed structure is regarded as the mix of the serial and the parallel.\\n Based on the well-known ID-based public key system, we will propose three ID-based structured multisignature schemes and each\\n scheme respectively realizes the serial, parallel and mixed signing structures. In the proposed schemes, the length of a multisignature\\n is fixed and the verification of a multisignature is efficient, regardless of the number of signers involved. Besides, any\\n invalid partial multisignature can be effectively identified during the generation of the multisignature.\",\n \"Title: Cryptanalysis of Chang---Wu's group-oriented authentication and key exchange protocols\\nYear: 2001\\n\\nAbstract: In 1998, Chang and Wu proposed a group-oriented authentication mechanism with key exchange [Computer Communications 21 (1998) 485–497]. The authors show that the Chang–Wu protocols are vulnerable to the impersonation attack. They also give improvements that can not only eliminate the security flaw, but also reduce the redundant messages inherent in the original protocols.\",\n \"Title: Standard Operating Procedure and Privilege Management in Taiwan Digital Forensics\\nYear: 2007\\n\\nAbstract: Recently, a new class of crime scenes has become more prevalent. In a variety of criminal activities technologies are used to maintain records, communication, and commit crimes. For this reason, the challenge to investigative professionals is how to retrieve the digital evidence from storage devices and how to take the suspect to court. Digital evidence is different from physical. When the investigative professionals face the cases of computer crime, they must handle digital evidences properly to guarantee integrity and non-repudiation. This paper discusses a standard operating procedure to conduct inquires by the Ministry of Justice, Investigation Bureau (MJIB) in Taiwan, to achieve above goals. To enhance the quality and security of managing digital evidence, we also consider access control and fingerprint mechanisms to protect digital evidence from being misused, disclosed, or modified by malicious users. The results of this paper can be used to improve digital forensics in Taiwan.\",\n \"Title: Analysis of an eHealth Care System with Smart Card Based Authentication\\nYear: 2012\\n\\nAbstract: In recent years, smart card based authentication has been massively adopted in the electronic health care and medical treatment services in which the user and administrator can agree a robust session key based on human-memorable passwords with the hospital server. In this paper, we investigate the robustness of an eHealth care system with smart card based authentication mechanisms. This research is from International Journal of Innovative Computing, Information and Control in 2011, and its out-standing contribution and rigorous analysis make itself one of the most promising techniques in the research area of secure eHealth care services. However, it still exist spaces for security enhancement, therefore, we present the analysis of this research and de-liver contributions in the design of the secure eHealth care application development.\",\n \"Title: Application of geometric hashing to iconic database retrieval\\nYear: 1994\\n\\nAbstract: This paper presents one application of geometric hashing to retrieve the most similar picture from an iconic image database. Here, iconic pictures or objects are some kind of gross panorama of simple symbolic objects and any one iconic object is not distinguished from any other object of the same kind. In our proposed scheme, we transform each iconic picture or query into a set of ordered triples ( O i , O j , R ij )'s, where O i and O j are two iconic objects and R ij is the spatial relationship code between O i and O j . Then, applying the geooetric hashing scheme proposed by Comer and O'Donnell, we can construct a set of hashing functions corresponding to predefined relationship codes. Associated with these hashing functions, the most similar iconic pictures satisfying a specified query can be easily and fast determined.\",\n \"Title: Access control with binary keys\\nYear: 1994\\n\\nAbstract: A new access control scheme for implementing the access control matrix is proposed. The proposed scheme is different from the methods which are based on the concept of key- lock pairs. In the proposed scheme, each user is assigned a binary key, which is derived from the access rights with respect to the files. The binary key is possessed by the user, and can be used to derive the access right to the files. The characteristics of the proposed scheme are that the system can protect the files without explicitly maintaining an access control matrix, the derivation of the access rights is simple, and the dynamic access control can be easily achieved.\",\n \"Title: A New Password-Based Multi-server Authentication Scheme Robust to Password Guessing Attacks\\nYear: 2013\\n\\nAbstract: A multi-server authentication scheme is a useful authentication mechanism in which a remote user can access the services of multiple servers after registering with the registration center (RC). This study shows that the password-based multi-server authentication scheme proposed by Yeh and Lo is vulnerable to undetectable password-guessing attack and offline password-guessing attack. This study proposes a new password-based multi-server authentication scheme to overcome these vulnerabilities. The proposed protocol introduces a new mechanism for protecting user password. The RC sends an alternative key to help the server verify the legitimacy of user instead of the user's password. The values of these keys are changed with a random large nonce in each session. Therefore, the password-guessing attack cannot work successfully on the proposed scheme.\",\n \"Title: The Design of a Conference Key Distribution System\\nYear: 1992\\n\\nAbstract: In this paper, we propose a conference key distribution system for generating a common secret key for two or more users. In our system, each user possesses a secret key and a public key. Initially, the chairperson constructs a conference key associated with his secret key and the conference members' public keys. Then each member can obtain and authenticate the conference key by using his secret key. Further, we have shown that the security of our proposed system is based on the difficulty of breaking the Diffie-Hellman key distribution system.\",\n \"Title: Convertible multi-authenticated encryption scheme\\nYear: 2008\\n\\nAbstract: A convertible authenticated encryption (CAE) scheme allows the signer to generate a valid authenticated ciphertext on his chosen message such that only the designated recipient can retrieve the message. Further, the recipient has the ability to convert the authenticated ciphertext into ordinary signature in case of a dispute or repudiation. The previous proposed CAE schemes can only allow one signer to produce the authenticated ciphertext. It might be inadequate for multiparty environments. In this paper, we elaborate on the merits of CAE and multi-signature schemes to propose a convertible multi-authenticated encryption scheme which has the following advantages: (i) The size of the generated authenticated ciphertext is independent of the number of total participating signers. (ii) Except for the designated recipient, no one can obtain the signed message and verify its corresponding signature. (iii) The signature is cooperatively produced by a group of signers instead of a single signer. (iv) In case of a later dispute on repudiation, the recipient has the ability to convert the authenticated ciphertext into an ordinary one for convincing anyone of the signers' dishonesty.\",\n \"Title: A letter-oriented perfect hashing scheme based upon sparse table compression\\nYear: 1991\\n\\nAbstract: SUMMARY\",\n \"Title: Comments on IMBAS: identity-based multi-user broadcast authentication in wireless sensor networks.\\nYear: 2013\\n\\nAbstract: <title content-type=\\\"main\\\">ABSTRACT In 2008, Cao et al. proposed an identity-based multi-user broadcast authentication scheme (called IMBAS) for wireless sensor networks. The IMBAS consists of one elliptic curve-based signature scheme, called variant of Bellare-Namprempre-Neven' Identity-Based Signature (vBNN-IBS), for user broadcast authentication; a new Schnorr signature with partial message recovery for sink broadcast authentication; and a password-based private key protection to eliminate the threat caused by possible user device compromise. Compared with its counterparts, IMBAS owns better performance in terms of security, scalability, and efficiency. Unfortunately, this letter will show that (1) the password-based protection scheme and the vBNN-IBS scheme are not securea compromised user device will disclose the user private keyand (2) the new Schnorr signature with partial message recovery is vulnerable to existential forgery attack. Copyright (c) 2012 John Wiley & Sons, Ltd.\",\n \"Title: A Fair-Exchange E-Payment Protocol For Digital Products With Customer Unlinkability.\\nYear: 2012\\n\\nAbstract: Along with the development of Information Technology, online transactions through Internet have become more popular for the reasons of convenience and efficiency. In order to provide secure and reliable online transactions, an effective electronic payment protocol is crucial. In this paper, we propose a novel electronic payment protocol for digital product transactions with an offline arbiter to achieve fair exchange, automated dispute resolution, customer anonymity, and customer unlinkability. In our protocol a product token is adopted to eliminate the need of key management for digital product decryption in the offline arbiter. In addition, Elliptic Curve Cryptography (ECC)-based self-certified public key is utilized to further reduce computing overheads. According to our analysis, the efficiency of our protocol can be greatly increased in comparison with previous literatures.\",\n \"Title: New identity-based society oriented signature schemes from pairings on elliptic curves\\nYear: 2005\\n\\nAbstract: In this paper, we will propose two identity-based society oriented signature schemes that allow a group of co-signers to collaboratively generate a single signature for a message. The first proposed scheme is designated with known signers and the second scheme is with anonymous signers. Both schemes make use of pairings on elliptic curves in construction and thus have the merits of simplicity in design and efficiency in performance. In the proposed scheme with anonymous signers, a signer may participate in several different signing groups and may join or leave a signing group dynamically in a secure and efficient manner.\",\n \"Title: A Refinement of a Compression-Oriented Addressing Scheme.\\nYear: 1993\\n\\nAbstract: Chang and Wu have proposed a letter-oriented perfect hashing scheme based on sparse matrix compression. We present a method which is a refinement of the Chang-Wu scheme. By experimental evaluation, we show that the hashing of our refinement has more efficient storage utilization than Chang-Wu's method. Our refinement is valuable in practical implementations of hashing for large sets of keys.\",\n \"Title: Recursive protocol for group-oriented authentication with key distribution\\nYear: 2008\\n\\nAbstract: The authors propose a recursive protocol for group-oriented authentication with key exchange, in which a group of n entities can authenticate with each other and share a group session key. The proposed protocol has the following characteristics: First, it requires O(n) rounds of messages, O(logn) completion time, O(logn) waiting time, and O(nlogn) communication overhead in average for the completion of the recursion. Second, it not only meets the five principles suggested by Diffie et al. [Diffie, W., van Oorschot, P.C., Wiener, M.J., 1992. Authentication and authenticated key exchange. Designs, Codes, and Cryptography 2 (2), 107-125] on the design of a secure key exchange protocol, but also achieves the properties of nondisclosure, independency, and integrity addressed by Janson and Tsudik [Janson, P., Tsudik, G., 1995. Secure and minimal protocols for authenticated key distribution. Computer Communications 18 (9), 645-653] for the authentication of the group session key. Third, we describe the beliefs of trustworthy entities involved in our authentication protocol and the evolution of these beliefs as a consequence of communication by using BAN logic. Finally, it is practical and efficient, because only one-way hash function and exclusive-or (XOR) operations are used in implementation.\",\n \"Title: Cryptanalysis Of A Password Authentication Scheme Using Quadratic Residues\\nYear: 1995\\n\\nAbstract: In 1988, Laih, Harn and Huang proposed a password authentication scheme using quadratic residues. In their scheme, the user's password is effectively bound to the user's identification, regardless of the state of the password file. Furthermore, passwords are not stored in the system directory, and the password file is replaced by a public verification table. This research note shows that Laih et al.'s scheme is breakable. Also, a possible attack on Laih et al.'s scheme is discussed.\",\n \"Title: Efficient Proxy Signature Scheme For Mobile Devices Using Bilinear Pairings\\nYear: 2013\\n\\nAbstract: A proxy signature scheme is useful and convenient because it allows a proxy signer to sign a message on behalf of an entity. This study proposes a novel efficient proxy signature scheme for mobile devices using bilinear pairings. The computational cost of the proposed signature scheme is extremely low, and the length of the proposed signature is limited. In addition, our scheme does not require a special hash function, namely the Map-To-Point hash function. We also show that the proposed scheme is secure against adaptive chosen message attacks under a random oracle.\",\n \"Title: Delegated multisignature scheme with document decomposition\\nYear: 2001\\n\\nAbstract: Consider the case that a large document including different subjects can be decomposed into a disjoint set of subdocuments and is to be delegated to a group of signatories with different knowledge domains for signing. Based on the intractability of the discrete logarithm problem, the authors propose a novel multisignature scheme, namely the delegated multisignature scheme in which each qualified signatory only signs a subset of the set of subdocuments according to the justification that his/her knowledge domain completely covers these delegated subdocuments' subjects. Meanwhile, any verifier can verify the multisignature of the document with only knowing the group's public key, i.e., the multisignature verification key, not necessarily to know the results regarding document decomposition and subdocument delegation. The proposed scheme has the merit that the size of the subdocuments delegated to the qualified signatories can be further reduced and as “balanced” as possible. Besides, the proposed scheme is more realistic to practical aspects for multisignature construction and requires smaller communication bandwidth as compared to conventional multisignature schemes.\",\n \"Title: A hashing-oriented nearest neighbor searching scheme\\nYear: 1993\\n\\nAbstract: This paper presents a hashing-oriented nearest neighbor searching scheme. Given n points in the Euclidean two-dimensional plane, we first construct a Voronoi diagram and record the Voronoi vertices and the Voronoi edges. By passing each Voronoi vertex, we use two perpendicular lines, one is horizontal and the other is vertical, to partition the plane into some rectangular subdivisions. Here, each rectangular subdivision dominates at most two given points. Then we establish two hashing functions corresponding to horizontal slabs and vertical slabs, respectively. By applying the established hashing functions, we can quickly determine the proper rectangular subdivision containing the query point. After that, we compare the distance between the query point and the dominated points to determine the nearest neighbor. The searching time by our scheme is O(1). The preprocessing time and the amount of required storage are O( n 2 + t ), respectively, where n is the number of given points and t is the size of the hashing table needed by the established hashing functions.\",\n \"Title: Establish a secure and trustworthy ICT environment for educational systems: a case study\\nYear: 2012\\n\\nAbstract: Nowadays information and communication security has recently emerged as one of the most important tasks in the field of network management, operations and maintenance. The information security issue is of particular importance to the Taiwan Academic Network, a network which connects the networks of educational and research institutions in Taiwan. ICT environment in educational systems involves vast connected units and is featuring complexity, diversity and openness. In this paper, we investigate current situation of Taiwan Ministry of Education ICT security development and provide a case study. We also discussed challenges and solutions for improving ICT security environment in educational system.\",\n \"Title: Two RFID-based solutions to enhance inpatient medication safety.\\nYear: 2011\\n\\nAbstract: Owing to the low cost and convenience of identifying an object without physical contact, Radio Frequency Identification (RFID) systems provide innovative, promising and efficient applications in many domains. An RFID grouping protocol is a protocol that allows an off-line verifier to collect and verify the evidence of two or more tags simultaneously present. Recently, Huang and Ku (J. Med. Syst, 2009) proposed an efficient grouping protocol to enhance medication safety for inpatients based on low-cost tags. However, the Huang-Ku scheme is not secure; an attacker can easily make up fake grouping records to cheat the verifier. This weakness would seriously endanger the safety of inpatient medication safety. This paper will show the weaknesses, and then propose two RFID-based solutions to enhance medication safety for two different scenarios. The proposed schemes are practical, secure and efficient for medication applications.\",\n \"Title: Retrieving the most similar symbolic pictures from pictorial databases\\nYear: 1992\\n\\nAbstract: In this article, we suggest an iconic indexing mechanism for spatial similarity retrieval on iconic image databases based upon the spatial relationships among the objects in a picture. The iconic objects we deal with are some kinds of gross panorama of simple objects. We also assume that any one iconic object is not distinguished from any other object of the same kind. For our mechanism, we first transform each iconic picture into a set of ordered triples ( O i , O j , R ij ) where O i and O j are objects and R ij is the predefined spatial relationship codes between O i and O j . Then we construct a set of hashing functions for all spatial relationship codes R ij , separately, associated with all ordered pairs ( O i , O j ) extracted from the ordered triples ( O i , O j , R ij ). Thereafter, an iconic index table can be established according to the constructed hashing functions for all predefined spatial relationship codes. By applying the constructed hashing functions, the most similar pictures in the database satisfying a specified query can be fast determined. We can easily extend our mechanism for handling the case when some new spatial relationship codes are defined later for the considerations of refined spatial similarity retrieval under the maximum-likelihood measure criterion.\",\n \"Title: Secure Handover Authentication Protocol Based on Bilinear Pairings\\nYear: 2013\\n\\nAbstract: Handover authentication protocol enables a mobile node to switch from one base station to another without loss or interruption of service when the node exits the transmission area of his or her current base station. This paper proposes a secure prime-order handover authentication protocol based on bilinear pairings. The proposed protocol adapts the concept of pseudonyms to provide user anonymity and user unlinkability. It withstands well-known security threats and achieves mutual authentication, user unlinkability. A batch signature verification mechanism to verify a mass of signatures is presented in our scheme. We also prove that our scheme is secure under random oracle.\",\n \"Title: Secure Delegation-Based Authentication Protocol for Wireless Roaming Service\\nYear: 2012\\n\\nAbstract: Portable devices, with wireless communication capability, are used widely in everyday life. Preventing personal sensitive information from being revealed to an adversary through insecure wireless communication channels has therefore become a serious concern. This study proposes a novel ECC-based authentication protocol for portable communication systems. The proposed protocol resists DoS attacks and requires less computation cost when authenticating a communication session. In addition, the proposed protocol provides user unlinkability.\",\n \"Title: Group-Oriented Authentication Mechanism With Key Exchange\\nYear: 1998\\n\\nAbstract: Most of the previously proposed authentication mechanisms are based on the individual-oriented approach and hence may be inefficient and not suitable for group-oriented applications. In this paper, we propose a group-oriented authentication mechanism with key exchange that can effectively resolve the security requirements of group-oriented communications. The proposed mechanism consists of three basics: the entity-to-entity (EE), the entity-to-group (EG) and the group-to-group (GG) subprotocols. All these subprotocols meet the authentication and secure key exchange requirements separately addressed by other authors. By the proposed mechanism, a set of temporal authenticated subgroups can be recursively formed in a divide-and-conquer manner and finally all entities in the target group can authenticate each other and exchange a group session key. Experimental analyses show that the proposed mechanism requires O(n log n) rounds of messages (n is the number of entities in the target group) for achieving the authentication and key exchange among the entities in the target group, which outperforms the naive approach that is based on iteratively running either two-parties or three-parties authentication protocols for achieving the same purposes. Moreover, the proposed mechanism can be practically implemented in a large-scaled group-oriented application since only one-way hashing functions and exclusive-or operations are involved. (C) 1998 Elsevier Science B.V.\",\n \"Title: Establishment of the Standard Operating Procedure (SOP) for Gathering Digital Evidence\\nYear: 2005\\n\\nAbstract: Numerous high-profile computer intrusions and denial of service (DOS) incidents occurred during the latter months of 1999 and early 2000, including a significant intrusion of Speakeasy, an Internet service provider based in Seattle. The Federal Bureau ...\",\n \"Title: A binary access control method using prime factorization\\nYear: 1997\\n\\nAbstract: Based on binary coding and prime factorization, an improvement of the single-key-lock method for access control is presented. In our method, each accessor is associated with a key and each resource is associated with a lock vector. Through simple mathematical operations on the corresponding key and lock vector, the system can easily verify the access right for an accessor with respect to the referenced resource. Furthermore, by our improvement, the system can easily handle the dynamic access control problem, such as adding/deleting accessors or resources, without much effort. (C) Elsevier Science Inc. 1997.\",\n \"Title: Group-oriented signature scheme with distinguished signing authorities\\nYear: 2004\\n\\nAbstract: Elaborating on Shamir's (t, n) threshold scheme and ElGamal's digital signature scheme, we propose a group-oriented signature with distinguished signing authorities. The proposed scheme works under the following restrictions: (1) the signing document can be divided into any t smaller subdocuments in such a way that each subdocument is meaningful and will be signed as a unit by one discretionary signatory; (2) every signatory has the same knowledge domain that covers the subjects within the signing document; (3) any t out of n signatories within the group can be on behalf of the group for signing any document, and at most t - 1 participants can conspire to attack Shamir's scheme for revealing the other ones' private keys (or shadows). In the proposed scheme, every discretionary signatory signs only a smaller part of the document, instead of the whole one. Such an approach achieves the justification of labor-division and responsibility-sharing inherent in the group works. Moreover, the bandwidth of data transmission for group signature construction can be further reduced. The proposed scheme also has the property that the discretionary signatories are anonymous to the verifier outside the group, and the size of a group signature is equivalent to that of any individual signature.\",\n \"Title: Mutual anonymity protocol with integrity protection for mobile peer-to-peer networks\\nYear: 2010\\n\\nAbstract: A mobile peer-to-peer network (MOPNET) has drawn increasing attentions for its communication convenience and ease of resource sharing in an unfixed infrastructure. The essential requirements for applications in a MOPNET are to enforce trust of transmitted data and security of peers. However, most of mutual anonymity protocols for a MOPNET lack integrity protection, and hence, any peer might suffer from some potential security threats such as virus-infected or Trojan horse files. This paper proposes a mutual anonymity protocol with integrity protection for a MOPNET, in which the initiator and responder anonymously share data with each other, and ensure its integrity.\",\n \"Title: New nonrepudiable threshold proxy signature scheme with known signers\\nYear: 2001\\n\\nAbstract: Based on Kim et al.'s threshold proxy signature scheme, Sun proposed an nonrepudiable threshold proxy signature scheme with known signers. In Sun's scheme, actual proxy signers cannot deny the signatures they have signed. However, his scheme is vulnerable against the conspiracy attack. Any t malicious proxy signers can collusively derive the secret keys of other proxy signers and impersonate some other proxy signers to generate proxy signatures. In this paper, we proposed a new nonrepudiable threshold proxy signature scheme that overcomes the above weakness. Furthermore, the proposed scheme is more efficient than Sun's in terms of computational complexities and communication costs.\",\n \"Title: Provably Secure Gateway-Oriented Password-Based Authenticated Key Exchange Protocol Resistant to Password Guessing Attacks.\\nYear: 2013\\n\\nAbstract: A Gateway-oriented Password-based Authenticated Key Exchange (GPAKE) scheme allows a client to establish an authenticated session key with a gateway via the help of an authentication server, where the client has pre-shared a password with the server. The desirable security properties of a GPAKE include session key semantic security, key privacy against servers, and password guessing attacks resistance. Abdalla et al.'s scheme (Asiacrypt 2005) [1] proposed the first GPAKE scheme, and then Abdalla et al. [13] and Byun et al.'s [2] had respectively proposed their improvements to enhance the security. Unfortunately, we find that all the improved schemes fail to commit the security requirements. In this paper, we point out security weaknesses of the improved scheme. To enhance the security, we propose a new GPAKE scheme, and prove its security in an enhanced model.\",\n \"Title: Id-Based Group-Oriented Cryptosystem And Its Digital Signature Scheme\\nYear: 1997\\n\\nAbstract: Inspired from Tsujii and Itoh's system, we propose an ID-based group-oriented cryptosystem and its digital signature scheme. In the proposed cryptosystem, any entity for decrypting or signing a message is dedicated to \\\"group\\\", instead of \\\"individual\\\". A group, consisting of a set of individuals, could be specified as high-authorized, low-authorized or threshold-authorized according to the predefined authorization strategy. Any sender can encrypt a message addressed to a group with only knowing the group's identity and the corresponding public information, so that individuals of the group should cooperate to recover the message by following the authorization strategy. For signing a message, individuals of the group should cooperate to generate a group signature by following the authorization strategy, so that any verifier can validate the group signature with only requiring the group's identity and the corresponding public information. The sender of a secret message or the verifier of a group signature does not necessarily know the authorization strategy within that group in advance. (C) 1997 Elsevier Science B.V.\",\n \"Title: Controlling the access requests in an information protection system\\nYear: 1993\\n\\nAbstract: A new mechanism used for controlling the access requests in information systems is proposed. In our scheme, each access privilege associates to a recursively generated function based on Newton's interpolating polynomial. The insertion of a new user/file into the system and deletion of an old user/file from the system can be easily implemented. Besides, the updating of a privilege of a user to a file can also be successfully implemented without reconstructing all associated functions.\",\n \"Title: GAnGS: gather, authenticate 'n group securely\\nYear: 2008\\n\\nAbstract: Establishing secure communication among a group of physically collocated people is a challenge. This problem can be reduced to establishing authentic public keys among all the participants - these public keys then serve to establish a shared secret symmetric key for encryption and authentication of messages. Unfortunately, in most real-world settings, public key infrastructures (PKI) are uncommon and distributing a secret in a public space is difficult. Thus, it is a challenge to exchange authentic public keys in a scalable, secure, and easy to use fashion. In this paper, we propose GAnGS, a protocol for the secure exchange of authenticated information among a group of people. In contrast to prior work, GAnGS resists Group-in-the-Middle and Sybil attacks by malicious insiders, as well as infiltration attacks by malicious bystanders. GAnGS is designed to be robust to user errors, such as miscounting the number of participants or incorrectly comparing checksums. We have implemented and evaluated GAnGS on Nokia N70 phones. The GAnGS system is viable and achieves a good balance between scalability, security, and ease of use.\",\n \"Title: New Identity-Based Sequential Aggregate Signature Scheme from RSA\\nYear: 2013\\n\\nAbstract: An identity-based sequential aggregate signature (IBSAS) scheme provides a shorter aggregate signature for multiple signers in which each signer has signed his/her own message and all generated signatures are aggregated in sequence. During aggregate signature verification process, a verifier can identify the generated order and the validity of signatures. In 2012, Dou et al. first proposed two new IBSAS schemes based on RSA. One scheme is non-interactive and the other is interactive. This study discovers that the non-interactive IBSAS scheme of Dou et al. is vulnerable to a forgery attack. In our attack, a legal but malicious signer is capable to forge an IBSAS without knowing the other signers' private keys. In order to overcome the weakness, we propose a non-interactive IBSAS scheme based on the identity-based signature scheme of Qian and Cao. We then show that our non-interactive IBSAS scheme is secure against proposed forgery attack.\",\n \"Title: ElGamal-like digital signature and multisignature schemes using self-certified public keys\\nYear: 2000\\n\\nAbstract: In this paper, we first present a digital signature scheme using self-certified public key. Subsequently, we present a digital multisignature scheme extended from the proposed digital signature. Both the proposed schemes have the advantage that the authentication of the public key can be accomplished with the verification of the signature or multisignature. The security of both the proposed schemes is based on the one-way hash function, the factorization and the discrete logarithm assumptions. Moreover, the proposed multisignature scheme preserves the main merits inherent in most of the previously developed schemes. We also demonstrate that the proposed multisignature scheme can withstand the active attack that some malicious impostors try to universally forge an individual signature or a multisignature for a given message that is rejected to be signed by the other co-signers.\",\n \"Title: Secure E-Health System On Passive Rfid: Outpatient Clinic And Emergency Care\\nYear: 2013\\n\\nAbstract: In recent years, many researches have demonstrated several RFID-based solutions to enhance patient medication safety and avoid human errors. Although RFID-based procedure is more efficient than traditional process, patient's information may be attacked (or stolen) during the data transmission period. This will cause inappropriate medication use and medical errors. In this paper, we introduce a robust RFID-based e-Health system which strengthens the system security and protects the patient's privacy as well. In addition, our e-Health system can provide better efficiency of outpatient clinic procedure and emergency care procedure in hospital environment.\",\n \"Title: Secure Anonymous Authentication Scheme Without Verification Table For Mobile Satellite Communication Systems\\nYear: 2014\\n\\nAbstract: An authentication scheme is one of the most basic and important security mechanisms for satellite communication systems because it prevents illegal access by an adversary. Lee et al. recently proposed an efficient authentication scheme for mobile satellite communication systems. However, we observed that this authentication scheme is vulnerable to a denial of service (DoS) attack and does not offer perfect forward secrecy. Therefore, we propose a novel secure authentication scheme without verification table for mobile satellite communication systems. The proposed scheme can simultaneously withstand DoS attacks and support user anonymity and user unlinkability. In addition, the proposed scheme is based on the elliptic curve cryptosystem, has low client-side and server-side computation costs, and achieves perfect forward secrecy. Copyright (c) 2013 John Wiley & Sons, Ltd.\",\n \"Title: Efficient English Auction Scheme Without A Secure Channel\\nYear: 2015\\n\\nAbstract: English auctions become tremendously popular on the Internet today. This paper presents a new English auction scheme that can be realized in the public network environments without any additional secure channel. Our scheme not only satisfies security requirements of anonymity, traceability, no framing, fairness, public verifiability, unlinkability among different rounds of auction and linkability in an auction round, but also provides one-time registration and easy revocation. Furthermore, as compared with the pervious works, the proposed scheme has a better performance in terms of the computation and the size of bidding information.\",\n \"Title: Weaknesses and improvements of an efficient certificateless signature scheme without using bilinear pairings\\nYear: 2014\\n\\nAbstract: AbstractThe certificateless signature CLS scheme is a special signature scheme that solves the key escrow problem in identity-based signature schemes. In CLS schemes, the private key is generated cooperatively by the key generator center KGC and signer, such that a malicious KGC cannot masquerade as the signer and sign a message. He et al. in 2011 proposed an efficient CLS scheme without using bilinear pairings. However, we discovered that the CLS scheme by He et al. cannot resist a strong type 2 adversary if this adversary replaces the master public key of the KGC. This work proposes an improved scheme that overcomes this weakness. Copyright © 2012 John Wiley & Sons, Ltd.\",\n \"Title: Provably Secure Anonymous Authentication Scheme for Roaming Service in Global Mobility Networks.\\nYear: 2015\\n\\nAbstract: This paper presents an anonymous authentication scheme for roaming service in global mobility networks, in which the foreign agent cannot obtain the identity information of the mobile user who is sending the roaming request. In addition, the home agent does not have to maintain any verification table for authenticating the mobile user. We give formal analyses to show that our proposed scheme satisfies the security requirements of user anonymity, mutual authentication, session-key security, and perfect forward secrecy. Besides, some possible attacks on the proposed scheme are discussed, such as the replay attack, the man-in-middle attack, the impersonation attack, and the insider attack.\",\n \"Title: Improvement of threshold proxy signature scheme\\nYear: 2003\\n\\nAbstract: Recently, Sun et al. pointed out some weaknesses of Zhang's threshold proxy signature scheme and a disadvantage of Kim's threshold proxy signature scheme. They further proposed a new (t,n) threshold proxy signature scheme based on Zhang's scheme to eliminate these drawbacks. Unfortunately, their scheme suffered from a weakness which is shown in this paper. The authors also propose an improvement to counter it.\",\n \"Title: Improvement of modified authenticated key agreement protocol\\nYear: 2003\\n\\nAbstract: Recently, Ku and Wang showed that Tseng's modified authenticated key agreement protocol is vulnerable to two attacks and proposed an improvement to withstand these attacks. However, this letter will show that this improvement is still vulnerable to the modification attack, which is contrary to their claims. Additionally, we proposed an improvement to eliminate this security flaw.\",\n \"Title: Rfid Authentication With Un-Traceability And Forward Secrecy In The Partial-Distributed-Server Model\\nYear: 2015\\n\\nAbstract: Secure authentication of low cost Radio Frequency Identification (RFID) tag with limited resources is a big challenge, especially when we simultaneously consider anonymity, un-traceability, and forward secrecy. The popularity of Internet of Things (IoT) further amplifies this challenge, as we should authenticate these mobile tags in the partial-distributed-server environments. In this paper, we propose an RFID authentication scheme in the partial-distributed-server environments. The proposed scheme owns excellent performance in terms of computational complexity and scalability as well as security properties.\",\n \"Title: An identity-based ring signature scheme from bilinear pairings\\nYear: 2004\\n\\nAbstract: At the conference Asiacrypt 2001, Rivest, Shamir and Tauman firstly addressed the concept of ring signature. We propose an identity-based ring signature scheme from bilinear pairings. As compared with the Zhang-Kim scheme (presented at the conference Asiacrypt 2002), our scheme is more efficient in computation and requires fewer pairing operations.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":68,"string":"68"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13246,"string":"13,246"}}},{"rowIdx":13247,"cells":{"question_id":{"kind":"string","value":"Kurt Konolige"},"question":{"kind":"string","value":"Generate an abstract for the title \"Large-scale map-making\" using the following items: 1. Mapping algorithms\n2. Consistent Pose Estimation (CPE)\n3. Computation growth\n4. Abstraction method\n5. Connection graph."},"answer":{"kind":"string","value":"Current mapping algorithms using Consistent Pose Estimation (CPE) algorithms can successfully map areas of 104 square meters, using thousands of poses. However, the computation to construct the map grows as O(n log n), so larger maps get increasingly difficult to build. We present an abstraction method for postponing the growth in computation. This method solves a much smaller problem in the space of the connection graph of the map."},"correct_answer":{"kind":"string","value":"Current mapping algorithms using Consistent Pose Estimation (CPE) algorithms can successfully map areas of 104 square meters, using thousands of poses. However, the computation to construct the map grows as O(n log n), so larger maps get increasingly difficult to build. We present an abstraction method for postponing the growth in computation. This method solves a much smaller problem in the space of the connection graph of the map."},"documents":{"kind":"list like","value":["Title: Hierarchic autoepistemic theories for non-monotonic reasoning: preliminary report\nYear: 1988\n\nAbstract: Nonmonotonic logics are meant to be a formalization of nonmonotonic reasoning. However, for the most part they fail to embody two of the most important aspects of such reasoning: the explicit computational nature of nonmonotonic inference, and the assignment of preferences among competing inferences. We propose a method of nonmonotonic reasoning in which the notion of inference from specific bodies of evidence plays a fundamental role. The formalization is based on autoepistemic logic, but introduces additional structure, a hierarchy of evidential spaces. The method offers a natural formalization of many different applications of nonmonotonic reasoning, including reasoning about action, speech acts, belief revision, and various situations involving competing defaults.","Title: Ideal Introspective Belief\nYear: 1992\n\nAbstract: Autoepistemic (AE) logic is a formal system characterizing agents that have complete introspective access to their own beliefs. AE logic relies on a fixed point definition that has two significant parts. The first part is a set of assumptions or hypotheses about the contents of the fixed point. The second part is a set of reflection principles that link sentences with statements about their provability. We characterize a family of ideal AE reasoners in terms of the minimal hypotheses that they can make, and the weakest and strongest reflection principles that they can have, while still maintaining the interpretation of AE logic as self-belief. These results can help in analyzing metatheoretic systems in logic programming.","Title: Explanatory Belief Ascription\nYear: 1990\n\nAbstract: In this paper we discuss the problenl of ascribing beliefs to an agent, given partial knowledge of his beliefs. The particular kind of ascription we are interested in we call explanatory ascription, since it ascribes beliefs to an agent as a means of explaining the beliefs we already know he has. We explore two approaches to explanatory ascription. In the first, we develop a model of belief called the derivational model, in which the derivation of one belief fl'om another is made explicit. This model and its proof theory are formalized and used to solve a variation of the Wise Man Puzzle. For comparison, a second approach using a.n abductive framework and a standard modal logic of belief is developed. This approach leads to weaker conclusions than the derivational model, because closure conditions on derivations cannot be stated. On the other hand, the representational power-of the two approaches differs, the abductive system being more expressive with respect to disjunctive information about belief, and the derivationM model allowing nonmonotonic reasoning by the believer. The next section of the paper gives some background in the area of belief ascription. The third section describes the problem of explanatory ascription, and some general properties that a successful approach should have. The fourth and fifth sections develop the derivational model and its proof theory, while the sixth describes an abductive framework and compares it to the derivational approach. Finally, we discuss some extensions to the language of the derivational model.","Title: Projected texture stereo\nYear: 2010\n\nAbstract: Passive stereo vision is widely used as a range sensing technology in robots, but suffers from dropouts: areas of low texture where stereo matching fails. By supplementing a stereo system with a strong texture projector, dropouts can be eliminated or reduced. This paper develops a practical stereo projector system, first by finding good patterns to project in the ideal case, then by analyzing the effects of system blur and phase noise on these patterns, and finally by designing a compact projector that is capable of good performance out to 3m in indoor scenes. The system has been implemented and has excellent depth precision and resolution, especially in the range out to 1.5m.","Title: Compact signatures for high-speed interest point description and matching\nYear: 2009\n\nAbstract: Prominent feature point descriptors such as SIFT and SURF allow reliable real-time matching but at a compu- tational cost that limits the number of points that can be handled on PCs, and even more on less powerful mobile devices. A recently proposed technique that relies on statis- tical classification to compute signatures has the potential to be much faster but at the cost of using very large amounts of memory, which makes it impractical for implementation on low-memory devices. In this paper, we show that we can exploit the sparseness of these signatures to compact them, speed up the compu- tation, and drastically reduce memory usage. We base our approach on Compressive Sensing theory. We also high- light its effectiveness by incorporating it into two very dif- ferent SLAM packages and demonstrating substantial per- formance increases.","Title: Point feature extraction on 3D range scans taking into account object boundaries.\nYear: 2011\n\nAbstract: In this paper we address the topic of feature extraction in 3D point cloud data for object recognition and pose identification. We present a novel interest keypoint extraction method that operates on range images generated from arbitrary 3D point clouds, which explicitly considers the borders of the objects identified by transitions from foreground to background. We furthermore present a feature descriptor that takes the same information into account. We have implemented our approach and present rigorous experiments in which we analyze the individual components with respect to their repeatability and matching capabilities and evaluate the usefulness for point feature based object detection methods.","Title: Ascribing Plans to Agents\nYear: 1989\n\nAbstract: Intelligent agents who are situated in nuiltiagent domains must reason about one anothers' actions and plans. Following the tradition of earlier work in AI, we present a model of plan recognition as belief and intention ascription, an inherently defeasible reasoning process. However, we encode this process using a direct argumentation system. Within this system, we can make explicit statements about why one candidate ascription should be preferred over another. And we can avoid the overly strong assumption that the actor's plan is correct from the perspective of the observer--an assumption that was necessary in previous formalizations of plan recognition.","Title: Multiple-Agent Planning Systems\nYear: 1980\n\nAbstract: We analyze problems confronted by computer agents that synthesize plans that take into account (and employ) the plans of other, similar, cooperative agents. From the point of view of each of these agents, the others are dynamic entities that possess information about the world, have goals, make plans to achieve these goals, and execute these plans. Thus, each agent must represent not only the usual information about objects in the world and the preconditions and effects of its own actions, but it must also represent and reason about what other agents believe and what they may do. We describe a planning system that addresses these issues and show how it solves a sample problem.","Title: Markov Localization using Correlation\nYear: 1999\n\nAbstract: Localization is one of the most important capabilities for autonomous mobile agents. Markov Localization (ML), applied to dense range images, has proven to be an effective technique. But its computational and storage requirements put a large burden on robot systems, and make it difficult to update the map dynamically. In this paper we introduce a new technique, based on correlation of a sensor scan with the map, that is several orders of magnitude more efficient than ML. CBML (correlation-based ML) permits video-rate localization using dense range scans, dynamic map updates, and a more precise error model than ML. In this paper we present the basic method of CBML, and validate its efficiency and correctness in a series of experiments on an implemented mobile robot base.","Title: Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS\nYear: 2006\n\nAbstract: We describe a real-time, low-cost system to localize a mobile robot in outdoor environments. Our system relies on stereo vision to robustly estimate frame-to-frame motion in real time (also known as visual odometry). The motion estimation problem is formulated efficiently in the disparity space and results in accurate and robust estimates of the motion even for a small-baseline configuration. Our system uses inertial measurements to fill in motion estimates when visual odometry fails. This incremental motion is then fused with a low-cost GPS sensor using a Kalman Filter to prevent long-term drifts. Experimental results are presented for outdoor localization in moderately sized environments (\\geqslant 100 meters)","Title: Sparse Sparse Bundle Adjustment\nYear: 2010\n\nAbstract: Sparse Bundle Adjustment (SBA) is a method for simultaneously optimizing a set of camera poses and visible points. It exploits the sparse primary structure of the problem, where connections exist just between points and cameras. In this paper, we implement an efficient version of SBA for systems where the secondary structure (relations among cameras) is also sparse. The method, which we call Sparse SBA (sSBA), integrates an efficient method for setting up the linear subproblem with recent advances in direct sparse Cholesky solvers. sSBA outperforms the current SBA standard implementation on datasets with sparse secondary structure by at least an order of magnitude, while also being more efficient on dense datasets.","Title: Towards lifelong visual maps\nYear: 2009\n\nAbstract: The typical SLAM mapping system assumes a static environment and constructs a map that is then used without regard for ongoing changes. Most SLAM systems, such as FastSLAM, also require a single connected run to create a map. In this paper we present a system of visual mapping, using only input from a stereo camera, that continually updates an optimized metric map in large indoor spaces with movable objects: people, furniture, partitions, etc. The system can be stopped and restarted at arbitrary disconnected points, is robust to occlusion and localization failures, and efficiently maintains alternative views of a dynamic environment. It operates completely online at a 30 Hz frame rate. A mobile robot existing in a space over a period of time has to deal with a changing environment. Some of these changes are ephemeral and can be filtered, such as moving people. Others are more permanent: objects like furniture are moved, posters change, doors open and close, and more infrequently, walls are torn down or built. The typical SLAM mapping system assumes a static environment and constructs a map, usually in a single continuous run. This map is then enshrined as the ground truth, and used without regard for ongoing changes, with the hope that a robust localization filter will be sufficient for navigation and other tasks.","Title: Robots That Take Advice\nYear: 1995\n\nAbstract: Mobile robots, if they are to perform useful tasks and become accepted in open environments, must interact with humans. Robot response to humans at the most basic level means not injuring them directly or indirectly, e.g., treating humans as obstacles to be avoided in the performance of a task. Robots will be more useful if they can interact in more sophisticated ways. Here we identify a set of useful robot/human interaction modes: attending, taking advice, and tasking. All three involve complex sensing and planning operations on the part of the robot, including the use of visual tracking of humans, gesture recognition, and speech recognition and understanding. We show how these capabilities are integrated in the Saphira architecture on Flakey, a mobile robot testbed. We demonstrate a scenario in which an untrained supervisor is able to introduce Flakey to an office environment, and command it to perform delivery tasks.","Title: Reasoning with Analogical Representations\nYear: 1992\n\nAbstract: Analogical representations have long been of interest to the knowledge representation community. Such representations provide compact encodings of information that can be cumbersome to represent and inefficient to manipulate in sentential languages. In this document, we address the problem of using analogical representations effectively in automated deduction systems. The primary contribution is a formal framework for combining analogical and deductive reasoning. The framework consists of a set of generic operations on analogical structures and accompanying inference methods for integrating analogical and sentential information. The capabilities of the framework are demonstrated for the task of reasoning to extend incomplete maps. The examples presented here have all been solved automatically by an implementation of the integration framework.","Title: Navigation in hybrid metric-topological maps\nYear: 2011\n\nAbstract: We present an approach for navigation in hybrid maps consisting of a topological graph overlaid with local occupancy grids. The topological graph is built on top of a graph SLAM system, which can be efficiently optimized even for very large environments. The novel feature of our system is that it navigates locally using local metric maps, while the overall plan is formed on the topological graph. Unlike many current SLAM methods, we never reconstruct a full occupancy grid of the environment for localization or path planning. We show that our method generates near-optimal plans, and deals gracefully with changes to the map.","Title: An Autoepistemic Analysis of Metalevel Reasoning in Logic Programming\nYear: 1992\n\nAbstract: An introspective agent can represent the connection between her own beliefs and the state of the world, using introspection principles to reason about this connection. An ideal introspective agent is one who makes the fewest possible assumptions about her beliefs. There is a strong formal connection between ideal introspection and metalogical systems in Logic Programming. We trace this connection in two cases: Reflective Prolog and negation as failure. The main contribution of this paper is to show how principles of soundness, completeness, and groundedness can be carried over from introspective to metalogic systems.","Title: ERRATIC Competes with the Big Boys\nYear: 1995\n\nAbstract: This article discusses the development of the robot ERRATIC, the second-place winner of the 1994 AAAI Robot Competition and Exhibition. I detail the robot's history and describe the perceptual and control architecture. The success of the robot is highlighted in a description of the robot's performance during the competition.","Title: Frame-Frame Matching for Realtime Consistent Visual Mapping\nYear: 2007\n\nAbstract: Many successful indoor mapping techniques employ frame-to-frame matching of laser scans to produce detailed local maps, as well as closing large loops. In this paper, we propose a framework for applying the same techniques to visual imagery, matching visual frames with large numbers of point features. The relationship between frames is kept as a nonlinear measurement, and can be used to solve large loop closures quickly. Both monocular (bearing-only) and binocular vision can be used to generate matches. Other advantages of our system are that no special landmark initialization is required, and large loops can be solved very quickly.","Title: A fuzzy controller for flakey, the robot\nYear: 1993\n\nAbstract: SRI International has a long tradition in the field of qualitative analysis and control of complex systems, starting with the development of the early mobile robot Shakey. More recently, we have developed a fuzzy controller for our new platform, Flakey. Flakey's controller can pursue strategic goals while operating under conditions of uncertainty, incompleteness, and imprecision. This controller includes capabilities for: • Robust, uncertainty-tolerating goal-directed activity. • Real-time reactivity to unexpected contingencies (e.g., unknown obstacles). • Blending of multiple goals (e.g., reaching a position while avoiding static and moving obstacles).","Title: Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes\nYear: 2012\n\nAbstract: We propose a framework for automatic modeling, detection, and tracking of 3D objects with a Kinect. The detection part is mainly based on the recent template-based LINEMOD approach [1] for object detection. We show how to build the templates automatically from 3D models, and how to estimate the 6 degrees-of-freedom pose accurately and in real-time. The pose estimation and the color information allow us to check the detection hypotheses and improves the correct detection rate by 13% with respect to the original LINEMOD. These many improvements make our framework suitable for object manipulation in Robotics applications. Moreover we propose a new dataset made of 15 registered, 1100+ frame video sequences of 15 various objects for the evaluation of future competing methods.","Title: Autonomous door opening and plugging in with a personal robot\nYear: 2010\n\nAbstract: We describe an autonomous robotic system capable of navigating through an office environment, opening doors along the way, and plugging itself into electrical outlets to recharge as needed. We demonstrate through extensive experimentation that our robot executes these tasks reliably, without requiring any modification to the environment. We present robust detection algorithms for doors, door handles, and electrical plugs and sockets, combining vision and laser sensors. We show how to overcome the unavoidable shortcoming of perception by integrating compliant control into manipulation motions. We present a visual-differencing approach to high-precision plug-insertion that avoids the need for high-precision hand-eye calibration.","Title: Censure: Center Surround Extremas For Realtime Feature Detection And Matching\nYear: 2008\n\nAbstract: We explore the suitability of different feature detectors for the task of image registration, and in particular for visual odometry, using two criteria: stability (persistence across viewpoint change) and accuracy (consistent localization across viewpoint change). In addition to the now-standard SIFT, SURF, FAST, and Harris detectors, we introduce a suite of scale-invariant center-surround detectors (CenSurE) that outperform the other detectors, yet have better computational characteristics than other scale-space detectors, and are capable of real-time implementation.","Title: A multivalued logic approach to integrating planning and control\nYear: 1995\n\nAbstract: Intelligent agents embedded in a dynamic, uncertain environment should incorporatecapabilities for both planned and reactive behavior. Many current solutions tothis dual need focus on one aspect, and treat the other one as secondary. We proposean approach for integrating planning and control based on behavior schemas,which link physical movements to abstract action descriptions. Behavior schemasdescribe behaviors of an agent, expressed as trajectories of control actions in an environment,...","Title: A hierarchical Bayesian approach to the revisiting problem in mobile robot map building\nYear: 2003\n\nAbstract: We present an application of hierarchical Bayesian estimation to robot map building. The revisiting problem occurs when a robot has to decide whether it is seeing apreviously built portion of a map, or is exploring new territory. This is a difficult decision problem, requiring the probability of being outside of the current known map. To estimate this probability, we model the structure of a \"typical\" environment as a hidden Markov model that generates sequences of views observed by a robot navigating through the environment. A Dirichlet prior over structural models is learned from previously explored environments. Whenever a robot explores a new environment, the posterior over the model is estimated using Dirichlet hyperparameters. Our approach is implemented and tested in the context of multi-robot map merging, a particularly difficult instance of the revisiting problem. Experiments with robot data show that the technique yields strong improvements over alternative methods.","Title: Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes\nYear: 2011\n\nAbstract: We present a method for detecting 3D objects using multi-modalities. While it is generic, we demonstrate it on the combination of an image and a dense depth map which give complementary object information. It works in real-time, under heavy clutter, does not require a time consuming training stage, and can handle untextured objects. It is based on an efficient representation of templates that capture the different modalities, and we show in many experiments on commodity hardware that our approach significantly outperforms state-of-the-art methods on single modalities.","Title: A gradient method for realtime robot control\nYear: 2000\n\nAbstract: Despite many decades of research into mobile robot control, reliable, high-speed motion in complicated, uncertain environments remains an unachieved goal. In this paper we present a solution to realtime motion control that can competently maneuver a robot at optimal speed even as it explores a new region or encounters new obstacles. The method uses a navigation function to generate a gradient field that represents the optimal (lowest-cost) path to the goal at every point in the workspace. Additionally, we present an integrated sensor fusion system that allows incremental construction of an unknown or uncertain environment. Under modest assumptions, the robot is guaranteed to get to the goal in an arbitrary static unexplored environment, as long as such a path exists. We present preliminary experiments to show that the gradient method is better than expert human controllers in both known and unknown environments","Title: A computational theory of belief introspection\nYear: 1985\n\nAbstract: Introspection is a general term covering the ability of an agent to reflect upon the workings of his own cognitive functions. In this paper we will be concerned with developing an explanatory theory of a particular type of introspection: a robot agent's knowledge of his own beliefs. The development is both descriptive, in the sense of being able to capture introspective behavior as it exist; and prescriptive, in yielding an effective means of adding introspective reasoning abilities to robot agents.","Title: Technical demonstration on model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes\nYear: 2012\n\nAbstract: In this technical demonstration, we will show our framework of automatic modeling, detection, and tracking of arbitrary texture-less 3D objects with a Kinect. The detection is mainly based on the recent template-based LINEMOD approach [1] while the automatic template learning from reconstructed 3D models, the fast pose estimation and the quick and robust false positive removal is a novel addition. In this demonstration, we will show each step of our pipeline, starting with the fast reconstruction of arbitrary 3D objects, followed by the automatic learning and the robust detection and pose estimation of the reconstructed objects in real-time. As we will show, this makes our framework suitable for object manipulation e.g. in robotics applications.","Title: Real-Time Detection of Independent Motion using Stereo\nYear: 2005\n\nAbstract: We describe a system that detects independently moving objects from a mobile platform in real time using a calibrated stereo camera. Interest points are first detected and tracked through the images. These tracks are used to obtain the motion of the platform by using an efficient three-point algorithm in a RANSAC framework for outlier detection. We use a formulation based on disparity space for our inlier computation. In the disparity space, two disparity images of a rigid object are related by a homography that depends on the object's euclidean rigid motion. We use the homography obtained from the camera motion to detect the independently moving objects from the disparity maps obtained by an efficient stereo algorithm. Our system is able to reliably detect the independently moving objects at 16 Hz for a 320 x 240 stereo image sequence using a standard laptop computer.","Title: Learning kinematic models for articulated objects\nYear: 2009\n\nAbstract: Robots operating in home environments must be able to interact with articulated objects such as doors or drawers. Ideally, robots are able to autonomously infer articulation models by observation. In this paper, we present an approach to learn kinematic models by inferring the connectivity of rigid parts and the articulation models for the corresponding links. Our method uses a mixture of parameterized and parameter-free (Gaussian process) representations and finds low-dimensional manifolds that provide the best explanation of the given observations. Our approach has been implemented and evaluated using real data obtained in various realistic home environment settings.","Title: A Contour Method For Real-Time Range Image Parsing\nYear: 1998\n\nAbstract: Real-time or near real-time range images are available from a variety of sources. Many proposed uses for these devices require significant post-processing to extract relevant 3-D information. We propose a new method based on a generalization of contour maps that has several advantages over current methods in tasks such as terrain reconstruction and obstacle avoidance. The method is illustrated with examples from obstacle avoidance on a mobile robot.","Title: Outdoor mapping and navigation using stereo vision\nYear: 2006\n\nAbstract: We consider the problem of autonomous navigation in an unstructured outdoor envi-ronment. The goal is for a small outdoor robot to come into a new area, learn about and map its environment, and move to a given goal at modest speeds (1 m/s). This problem is especially difficult in outdoor, off-road environments, where tall grass, shadows, deadfall, and other obstacles predominate. Not surprisingly, the biggest challenge is acquiring and using a reliable map of the new area. Although work in outdoor navigation has preferentially used laser rangefinders [13, 2, 6], we use stereo vision as the main sensor. Vision sensors allow us to use more distant objects as landmarks for navigation, and to learn and use color and texture models of the environment, in looking further ahead than is possible with range sensors alone. In this paper we show how to build a consistent, globally correct map in real time, using a combination of the following vision-based techniques:","Title: Double window optimisation for constant time visual SLAM\nYear: 2011\n\nAbstract: We present a novel and general optimisation framework for visual SLAM, which scales for both local, highly accurate reconstruction and large-scale motion with long loop closures. We take a two-level approach that combines accurate pose-point constraints in the primary region of interest with a stabilising periphery of pose-pose soft constraints. Our algorithm automatically builds a suitable connected graph of keyposes and constraints, dynamically selects inner and outer window membership and optimises both simultaneously. We demonstrate in extensive simulation experiments that our method approaches the accuracy of offline bundle adjustment while maintaining constant-time operation, even in the hard case of very loopy monocular camera motion. Furthermore, we present a set of real experiments for various types of visual sensor and motion, including large scale SLAM with both monocular and stereo cameras, loopy local browsing with either monocular or RGB-D cameras, and dense RGB-D object model building.","Title: Using Default and Causal Reasoning in Diagnosis\nYear: 1992\n\nAbstract: We present a theory of default reasoning that is specifically targeted to causal domains. These domains encompass a wide variety of current applications of default reasoning, but here we concentrate on model-based diagnosis. The theory is unique in that it integrates a formal notion of causality with nonmonotonic reasoning techniques based on default logic and abduction. The main structure of the theory is a default causal net (DCN) representing the causal connections among propositions in the domain. The causal net provides a framework for the two nonmonotonic reasoning techniques of assuming defaults and generating explanations for observations, allowing them to be combined in a principled way.","Title: Tracking People from a Mobile Platform\nYear: 2002\n\nAbstract: Tracking people from a moving platform is a useful skill for the coming generation of service and human-interaction robots. It is also a challenging problem, involving sensing, interpretation, planning, and control, all in a real time and dynamic environment. Techniques used in existing people-trackers that operate from fixed locations, noting changes against a fixed background, are not applicable. Instead, we apply a vision-based approach using real-time stereo and 31) reconstruction, that explicitly models both foreground and background objects in an efficient manner. The main novelties of our approach include (1) remapping the stereo disparities to an orthographic \"occupancy map\", which simplifies person modeling, and (2) updating a background occupancy map based on robot motion. The current version of our system, running on a Pioneer II mobile robot, can follow people at up to 1.2 m/s in an indoor environment.","Title: A practical nonmonotonic theory for reasoning about speech acts\nYear: 1988\n\nAbstract: A prerequisite to a theory of the way agents understand speech acts is a theory of how their beliefs and intentions are revised as a consequence of events. This process of attitude revision is an interesting domain for the application of nonmonotonic reasoning because speech acts have a conventional aspect that is readily represented by defaults, but that interacts with an agent's beliefs and intentions in many complex ways that may override the defaults. Perrault has developed a theory of speech acts, based on Rieter's default logic, that captures the conventional aspect; it does not, however, adequately account for certain easily observed facts about attitude revision resulting from speech acts. A natural theory of attitude revision seems to require a method of stating preferences among competing defaults. We present here a speech act theory, formalized in hierarchic autoepistemic logic (a refinement of Moore's autoepistemic logic), in which revision of both the speaker's and hearer's attitudes can be adequately described. As a collateral benefit, efficient automatic reasoning methods for the formalism exist. The theory has been implemented and is now being employed by an utterance-planning system.","Title: Visually Realistic Mapping of a Planar Environment with Stereo\nYear: 2000\n\nAbstract: We present a hybrid technique for constructing geometrically a c- curate, visually realistic planar environments from stereo vision informa- tion. The technique is unique in estimating camera motion from two sources: range information from stereo, and visual alignment of images.","Title: Approximating a Single Viewpoint in Panoramic Imaging Devices\nYear: 2000\n\nAbstract: Recent panoramic cameras present a very wide field of view from a single viewpoint. A single viewpoint is useful in mobile robotics for a number of reasons, including perspective reprojection and stereo analysis. However, the requirement of single viewpoint for panoramic cameras restricts the optical and geometrical design of these devices. In this paper, we present a method for approximating a single viewpoint in panoramic devices that allows much greater freedom in design. We illustrate the method with a compact catadioptric device using a spherical mirror and standard optics, and apply it to perspective reprojection. The resultant panoramic camera has been integrated as a surveillance device on a small mobile robot.","Title: A Practical Stereo System Based On Regularization And Texture Projection\nYear: 2009\n\nAbstract: In this paper we investigate the suitability of stereo vision for robot manipulation tasks, which require high-fidelity real-time 3D information in the presence of motion. We compare spatial regularization methods for stereo and spacetime stereo, the latter relying on integration of information over time as well as space. In both cases we augment the scene with textured projection, to alleviate the well-known problem of noise in low-textured areas. We also propose a new spatial regularization method, local smoothing, that is more efficient than current methods, and produces almost equivalent results. We show that in scenes with moving objects spatial regularization methods are more accurate than spacetime stereo, while remaining computationally simpler. Finally, we propose an extension of regularization-based algorithms to the temporal domain, so to further improve the performance of regularization methods within dynamic scenes.","Title: Map Merging For Distributed Robot Navigation\nYear: 2003\n\nAbstract: A set of robots mapping an area can potentially combine their information to produce a distributed map more efficiently than a single robot alone. We describe a general framework for distributed map building in the presence of uncertain communication. Within this framework, we then present a technical solution to the key decision problem of determining relative location within partial maps.","Title: A Deduction Model of Belief\nYear: 1983\n\nAbstract: Representing and reasoning about the knowledge an agent (human or computer) must have to accomplish some task is becoming an increasingly important issue in artificial intelligence (AI) research. To reason about an agent's beliefs, an AI system must assume some formal model of those beliefs. An attractive candidate is the Deductive Belief model: an agent's beliefs are described as a set of sentences in some formal language (the base sentences), together with a deductive process for deriving consequences of those beliefs. In particular, a Deductive Belief model can account for the effect of resource limitations on deriving consequences of the base set: an agent need not believe all the logical consequences of his beliefs. In this paper we develop a belief model based on the notion of deduction, and contrast it with current AI formalisms for belief derived from Hintikka/Kripke possible-worlds semantics for knowledge.","Title: A Tutorial on Nonmonotonic Reasoning\nYear: 1991\n\nAbstract: Nonmonotonic reasoning, in its broadest sense, is reasoning to conclusions on the basis of incomplete information. Given more information, we are prepared to retract previously drawn inferences. To exhibit the classic example: if all we know about Tweety is that he is bird, then we plausibly conclude that he can fly; on learning that Tweety is a penguin, we withdraw that conclusion. We call this reasoning nonmonotonic because the set of plausible conclusions does not grow monotonically with increasing information.","Title: A Representationalist Theory of Intention\nYear: 1993\n\nAbstract: The problem of finding a set of assumptions which explain a given proposition is in general NP-hard, even when the background theory is an acyclic Horn theory. In this paper it is shown that when the background theory is acyclic Horn and its pseudo-completion ...","Title: Designing the 1993 robot competition\nYear: 1994\n\nAbstract: The Second Annual Robotics Competition and Exhibition was held in July 1993 in conjunction with the National Conference on Artificial Intelligence. This article reports some of my experiences in helping to design and run the contest and some reflections, drawn from post mortem abstracts written by the competitors, on the relation of the contest to current research efforts in mobile robotics.","Title: Large-Scale Visual Odometry for Rough Terrain\nYear: 2007\n\nAbstract: Motion estimation from stereo imagery, sometimes called visual odometry, is a well-known process. However, it is difficult to achieve good performance using standard techniques. We present the results of several years of work on an integrated system to localize a mobile robot in rough outdoor terrain using visual odometry, with an increasing degree of precision. We discuss issues that are important, for real-time, high-precision performance: choice of features, matching strategies; incremental bundle adjustment, and filtering with inertial measurement sensors. Using data with ground truth from an Risk GPS system, we show experimentally that our algorithms can track motion, in off-road terrain, over distances of 10 km, with an error of less than 10 m (0.1%).","Title: Mapping, navigation, and learning for off-road traversal\nYear: 2009\n\nAbstract: The challenge in the DARPA Learning Applied to Ground Robots (LAGR) project is to autonomously navigate a small robot using stereo vision as the main sensor. During this project, we demonstrated a complete autonomous system for off-road navigation in unstructured environments, using stereo vision as the main sensor. The system is very robust—we can typically give it a goal position several hundred meters away and expect it to get there. In this paper we describe the main components that comprise the system, including stereo processing, obstacle and free space interpretation, long-range perception, online terrain traversability learning, visual odometry, map registration, planning, and control. At the end of 3 years, the system we developed outperformed all nine other teams in final blind tests over previously unseen terrain. © 2008 Wiley Periodicals, Inc.","Title: Localization and Mapping for Autonomous Navigation in Outdoor Terrains: A Stereo Vision Approach\nYear: 2007\n\nAbstract: We consider the problem of autonomous navigation in unstructured outdoor terrains using vision sensors. The goal is for a robot to come into a new environment, map it and move to a given goal at modest speeds (1 m/sec). The biggest challenges are in building good maps and keeping the robot well localized as it advances towards the goal. In this paper, we concentrate on showing how it is possible to build a consistent, globally correct map in real time, using efficient precise stereo algorithms for map making and visual odometry for localization. While we have made advances in both localization and mapping using stereo vision, it is the integration of the techniques that is the biggest contribution of the research. The validity of our approach is tested in blind experiments, where we submit our code to an independent testing group that runs and validates it on an outdoor robot","Title: An inference net compiler for the prospector rule-based consultation system\nYear: 1979\n\nAbstract: A compiler for the production system used in a rule-based consultation system is described. This compiler produces machine code which performs antecedent or data-driven inference In an efficient manner. Timing results and some limitations of the compiler are discussed.","Title: Background Modeling for Segmentation of Video-Rate Stereo Sequences\nYear: 1998\n\nAbstract: Stereo sequences promise to be a powerful method for segmenting images for applications such as tracking human figures. We present a method of statistical background modeling for stereo sequences that improves the reliability and sensitivity of segmentation in the presence of object clutter. The dynamic version of the method, called gated background adaptation, can reliably learn background statistics in the presence of corrupting foreground motion. The method has been used with a simple head discriminator to detect and track people using a stereo head mounted on a pan/tilt platform. It runs at video rates using standard PC hardware.","Title: A Refined Method for Occupancy Grid Interpretation\nYear: 1995\n\nAbstract: Occupancy grids are a probabilistic method for fusing multiple sensor readings. Although the underlying theory has been understood for many years, the intricacies of applying it to realtime sensor interpretation have been somewhat neglected. In this paper I analyze how refined sensor models (including specularity models) and assumptions about independence are crucial issues for occupancy grid interpretation. Using this analysis, I develop the MURIEL method for occupancy grid calculations, and show how it can dramatically improve the fidelity of occupancy grid interpretations in specular and realtime environments.","Title: SLAM via Variable Reduction from Constraint Maps\nYear: 2005\n\nAbstract: The two dominant forms of SLAM are based on Extended Kalman Filtering and Consistent Pose Estimation. We show that these are particular subsets of a more general view of the SLAM problem, in which variables representing all robot poses and features are kept. The general technique of variable reduction is a unifying view of these methods that is mathematically sound, and which enables us to explore other interesting and computationally compelling forms for solving SLAM problems.","Title: On the relation between default theories and autoepistemic logic\nYear: 1987\n\nAbstract: Default theories are a formal means of reasoning about defaults: what normally is the case, in the absence of contradicting information. Autoepistemic theories, on the other hand, are meant to describe the consequences of reasoning about ignorance: what must be true if a certain fact is not known. Although the motivation and formal character of these systems are different, a closer analysis shows that they bear a common trait, which is the indexical nature of certain elements in the theory. In this paper we compare the expressive power of the two systems. First, we give an effective translation of default theories in to autoepistemic logic; default theories can thus be embedded into autoepistemic logic. A more suprising result is that the reverse translation is also possible: every set of sentences in autoepistemic logic can be effectively rewritten as a default theory. The formal equivalence of these two differing systems is thus established. Some benefits of this analysis are that it gives an interpretive semantics to default theories, and yields insight i n to the nature of defaults in autoepistemic reasoning.","Title: Improved Occupancy Grids for Map Building\nYear: 1997\n\nAbstract: Occupancy grids are a probabilistic method for fusing multiplesensor readings into surface maps of the environment. Although theunderlying theory has been understood for many years, the intricacies ofapplying it to realtime sensor interpretation have been neglected. Thispaper analyzes how refined sensor models (including specularity models) andassumptions about independence are crucial issues for occupancy gridinterpretation. Using this analysis, the MURIEL method for occupancy gridupdate is developed. Experiments show how it can dramatically improve thefidelity of occupancy grid map-making in specular and realtimeenvironments.","Title: Abduction versus closure in causal theories\nYear: 1992\n\nAbstract: There are two distinct formalizations for reasoning from observations to explanations. as in diagnostic tasks. The consistency based approach treats the task as a deductive one, in which the explanation is deduced from a background theory and a minimal set of abnormalities. The abductive method, on the other hand, treats explanations as sentences that, when added to the background theory, derive the observations. We show that there is a close connection between these two formalizations in the context of simple causal theories: domain theories in which a set of sentences are singled out as the explanatorily relevant causes of observations. There are two main results, which show that (with certain caveats) the consistency based approach can emulate abductive reasoning by adding closure axioms to a causal theory; and that abductive techniques can be used in place of the consistency based method in the domain of logic based diagnosis. It is especially interesting that in the latter case, the abductive techniques generate only relevant explanations, while diagnoses may have irrelevant elements.","Title: Computing the extensions of autoepistemic and default logics with a truth maintenance system\nYear: 1990\n\nAbstract: In this paper we develop a proof procedure for autoepistemic (AEL) and default logics (DL), based on translating them into a Truth Maintenance System (TMS). The translation is decidable if the theory consists of a finite number of defaults and premises and classical derivability for the base language is decidable. To determine all extensions of a network, we develop variants of Doyle's labelling algorithms.","Title: The Saphira architecture: a design for autonomy\nYear: 1997\n\nAbstract: Mobile robots, if they are to perform useful tasks and become accepted in open environments, must be fully autonomous. Autonomy has many different aspects; here the focus is on three central ones: the ability to attend to another agent, to take advice about the environment, and to carry out assigned tasks. All three involve complex sensing and planning operations on the part of the robot, including the use of visual tracking of humans, coordination of motor controls, and planning. It is shown how these capabilities are integrated in the Saphira architecture, using the concepts of coordination of behaviour, coherence of modelling, and communication with other agents.","Title: An abductive framework for general logic programs and other nonmonotonic systems\nYear: 1993\n\nAbstract: We present an abductive semantics for general propositional logic programs which defines the meaning of a logic program in terms of its extensions. This approach extends the stable model semantics for normal logic programs in a natural way. The new semantics is equivalent to stable semantics for a logic program P whenever P is normal and has a stable model. The abductive semantics can also be applied to generalize default logic and autoepistemic logic in a like manner. Our approach is based on an idea recently proposed by Konolige for causal reasoning. Instead of maximizing the set of hypotheses alone we maximize the union of the hypotheses, along with possible hypotheses that are excused or refuted by the theory.","Title: COLBERT: A Language for Reactive Control in Sapphira\nYear: 1997\n\nAbstract: this paper we are concerned with how a user can write sequencer programs toeffectively control the robot. Our emphasis is on issues of language and semantics: whatis a good language for robot programs, what kind of semantics is appropriate for thesequencer, and how does the language fit the semantics. The result of our inquiries is thesequencer language Colbert, a part of the Sapphira architecture.","Title: A non-monotonic logic for reasoning about speech acts and belief revision\nYear: 1988\n\nAbstract: A prerequisite to a theory of the way agents understand speech acts is a theory of how their beliefs and intentions are revised as a consequence of events. This process of attitude revision is an interesting domain for the application of nonmonotonic reasoning because speech acts have a conventional aspect that is readily represented by defaults, but that interacts with an agent's beliefs and intentions in many complex ways that may override the defaults. Perrault has developed a theory of speech acts, based on Rieter's default logic, that captures the conventional aspect; it does not, however, adequately account for certain easily observed facts about attitude revision resulting from speech acts. A natural theory of attitude revision seems to require a method of stating preferences among competing defaults. We present here a speech act theory, formalized in hierarchic autoepistemic logic (a refinement of Moore's autoepistemic logic), in which revision of both the speaker's and hearer's attitudes can be adequately described. As a collateral benefit, efficient automatic reasoning methods for the formalism exist. The theory has been implemented and is now being employed by an utterance-planning system.","Title: Distributed multirobot exploration, mapping, and task allocation\nYear: 2008\n\nAbstract: We present an integrated approach to multirobot exploration, mapping and searching suitable for large teams of robots operating in unknown areas lacking an existing supporting communications infrastructure. We present a set of algorithms that have been both implemented and experimentally verified on teams--of what we refer to as Centibots--consisting of as many as 100 robots. The results that we present involve search tasks that can be divided into a mapping stage in which robots must jointly explore a large unknown area with the goal of generating a consistent map from the fragment, a search stage in which robots are deployed within the environment in order to systematically search for an object of interest, and a protection phase in which robots are distributed to track any intruders in the search area. During the first stage, the robots actively seek to verify their relative locations in order to ensure consistency when combining data into shared maps; they must also coordinate their exploration strategies so as to maximize the efficiency of exploration. In the second and third stages, robots allocate search tasks among themselves; since tasks are not defined a priori, the robots first produce a topological graph of the area of interest and then generate a set of tasks that reflect spatial and communication constraints. Our system was evaluated under extremely realistic real-world conditions. An outside evaluation team found the system to be highly efficient and robust.","Title: Change Their Perception: RGB-D for 3-D Modeling and Recognition\nYear: 2013\n\nAbstract: RGB-D cameras, such as Microsoft Kinect, are active sensors that provide high-resolution dense color and depth information at real-time frame rates. The wide availability of affordable RGB-D cameras is causing a revolution in perception and changing the landscape of robotics and related fields. RGB-D perception has been the focus of a great deal of attention and many research efforts by various fields in the last three years. In this article, we summarize and discuss our ongoing research on the promising uses of RGB-D in three-dimensional (3-D) mapping and 3-D recognition. Combining the strengths of optical cameras and laser rangefinders, the joint use of color and depth in RGB-D sensing makes visual perception more robust and efficient, leading to practical systems that build detailed 3-D models of large indoor spaces, as well as systems that reliably recognize everyday objects in complex scenes. RGB-D perception is yet a burgeoning technology: a rapidly growing number of research projects are being conducted on or using RGB-D perception while RGB-D hardware quickly improves. We believe that RGB-D perception will be on the center stage of perception and, by making robots see much better than before, will enable a variety of perception-based research and applications.","Title: An experimental comparison of localization methods\nYear: 1998\n\nAbstract: Localization is the process of updating the pose of a robot in an environment, based on sensor readings. In this experimental study, we compare two recent methods for localization of indoor mobile robots: Markov localiza- tion, which uses a probability distribution across a grid of robot poses; and scan matching, which uses Kalman filter- ing techniques based on matching sensor scans. Both these techniques are dense matching methods, that is, they match dense sets of environment features to an a priori map. To arrive at results for a range of situations, we utilize sev- eral different types of environments, and add noise to both the dead-reckoning and the sensors. Analysis shows that, roughly, the scan-matching techniques are more efficient and accurate, but Markov localization is better able to cope with large amounts of noise. These results suggest hybrid methods that are efficient, accurate and robust to noise.","Title: Incremental mapping of large cyclic environments\nYear: 1999\n\nAbstract: Mobile robots can use geometric or topological maps of their environment to navigate reliably. Automatic cre- ation of such maps is still an unrealized goal, especially in environments that have large cyclical structures. Drawing on recent techniques of global registration and correlation, we present a method, called Local Registration and Global Correlation (LRGC), for reliable reconstruction of consis- tent global maps from dense range data. The method is attractive because it is incremental, producing an updated map with every new sensor input; and runs in constant time independent of the size of the map (except when closing large cycles). A real-time implementation and results are presented for several indoor environments.","Title: Fast color/texture segmentation for outdoor robots\nYear: 2008\n\nAbstract: We present a fast integrated approach for online segmentation of images for outdoor robots. A compact color and texture descriptor has been developed to describe local color and texture variations in an image. This descriptor is then used in a two-stage fast clustering framework using K-means to perform online segmentation of natural images. We present results of applying our descriptor for segmenting a synthetic image and compare it against other state-of-the-art descriptors. We also apply our segmentation algorithm to the task of detecting natural paths in outdoor images. The whole system has been demonstrated to work online alongside localization, 3D obstacle detection, and planning.","Title: ORB: An efficient alternative to SIFT or SURF\nYear: 2011\n\nAbstract: Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several real-world applications, including object detection and patch-tracking on a smart phone.","Title: Exploiting segmentation for robust 3D object matching\nYear: 2012\n\nAbstract: While Iterative Closest Point (ICP) algorithms have been successful at aligning 3D point clouds, they do not take into account constraints arising from sensor viewpoints. More recent beam-based models take into account sensor noise and viewpoint, but problems still remain. In particular, good optimization strategies are still lacking for the beam-based model. In situations of occlusion and clutter, both beam-based and ICP approaches can fail to find good solutions. In this paper, we present both an optimization method for beambased models and a novel framework for modeling observation dependencies in beam-based models using over-segmentations. This technique enables reasoning about object extents and works well in heavy clutter. We also make available a ground-truth 3D dataset for testing algorithms in this area.","Title: Vision-based detection for learning articulation models of cabinet doors and drawers in household environments\nYear: 2010\n\nAbstract: Service robots deployed in domestic environments generally need the capability to deal with articulated objects such as doors and drawers in order to fulfill certain mobile manipulation tasks. This however, requires, that the robots are able to perceive the articulation models of such objects. In this paper, we present an approach for detecting, tracking, and learning articulation models for cabinet doors and drawers without using artificial markers. Our approach uses a highly efficient and sampling-based approach to rectangle detection in depth images obtained from a self-developed active stereo system. The robot can use the generative models learned for the articulated objects to estimate their articulation type, their current configuration, and to make predictions about possible configurations not observed before. We present experiments carried out on real data obtained from our active stereo system. The results demonstrate that our technique is able to learn accurate articulation models. We furthermore provide a detailed error analysis based on ground truth data obtained in a motion capturing studio.","Title: Going Further With Point Pair Features\nYear: 2017\n\nAbstract: Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of clutter and sensor noise. Our experiments show that with our improvements, PPFs become competitive against state-of-the-art methods as it outperforms them on several objects from challenging benchmarks, at a low computational cost.","Title: Yale-CMU-Berkeley dataset for robotic manipulation research\nYear: 2017\n\nAbstract: AbstractIn this paper, we present an image and model dataset of the real-life objects from the Yale-CMU-Berkeley Object Set, which is specifically designed for benchmarking in manipulation research. For each object, the dataset presents 600 high-resolution RGB images, 600 RGB-D images and five sets of textured three-dimensional geometric models. Segmentation masks and calibration information for each image are also provided. These data are acquired using the BigBIRD Object Scanning Rig and Google Scanners. Together with the dataset, Python scripts and a Robot Operating System node are provided to download the data, generate point clouds and create Unified Robot Description Files. The dataset is also supported by our website, www.ycbbenchmarks.org, which serves as a portal for publishing and discussing test results along with proposing task protocols and benchmarks.","Title: Visual Odometry Using Sparse Bundle Adjustment on an Autonomous Outdoor Vehicle\nYear: 2005\n\nAbstract: Visual Odometry is the process of estimating the movement of a (stereo) camera through its environment by matching point features between pairs of consecutive image frames. No prior knowledge of the scene nor the motion is necessary. In this work, we present a visual odom- etry approach using a specialized method of Sparse Bundle Adjustment. We show experimental results that proof our approach to be a feasible method for estimating motion in unstructured outdoor environments.","Title: Between regions and objects: surfaces and volumes\nYear: 1977\n\nAbstract: Several modular processes are used to build relationships between a) two-dimensional regions and boundaries in a segmented image, and b) the hypothesized surfaces, volumes and objects of a three-dimensional world. A variety of bottom-up and top-down strategies coordinate the processes of SHAPE, PERSPECTIVE, SHADOW, and OCCLUSION to construct these relationships in the VISIONS system."],"string":"[\n \"Title: Hierarchic autoepistemic theories for non-monotonic reasoning: preliminary report\\nYear: 1988\\n\\nAbstract: Nonmonotonic logics are meant to be a formalization of nonmonotonic reasoning. However, for the most part they fail to embody two of the most important aspects of such reasoning: the explicit computational nature of nonmonotonic inference, and the assignment of preferences among competing inferences. We propose a method of nonmonotonic reasoning in which the notion of inference from specific bodies of evidence plays a fundamental role. The formalization is based on autoepistemic logic, but introduces additional structure, a hierarchy of evidential spaces. The method offers a natural formalization of many different applications of nonmonotonic reasoning, including reasoning about action, speech acts, belief revision, and various situations involving competing defaults.\",\n \"Title: Ideal Introspective Belief\\nYear: 1992\\n\\nAbstract: Autoepistemic (AE) logic is a formal system characterizing agents that have complete introspective access to their own beliefs. AE logic relies on a fixed point definition that has two significant parts. The first part is a set of assumptions or hypotheses about the contents of the fixed point. The second part is a set of reflection principles that link sentences with statements about their provability. We characterize a family of ideal AE reasoners in terms of the minimal hypotheses that they can make, and the weakest and strongest reflection principles that they can have, while still maintaining the interpretation of AE logic as self-belief. These results can help in analyzing metatheoretic systems in logic programming.\",\n \"Title: Explanatory Belief Ascription\\nYear: 1990\\n\\nAbstract: In this paper we discuss the problenl of ascribing beliefs to an agent, given partial knowledge of his beliefs. The particular kind of ascription we are interested in we call explanatory ascription, since it ascribes beliefs to an agent as a means of explaining the beliefs we already know he has. We explore two approaches to explanatory ascription. In the first, we develop a model of belief called the derivational model, in which the derivation of one belief fl'om another is made explicit. This model and its proof theory are formalized and used to solve a variation of the Wise Man Puzzle. For comparison, a second approach using a.n abductive framework and a standard modal logic of belief is developed. This approach leads to weaker conclusions than the derivational model, because closure conditions on derivations cannot be stated. On the other hand, the representational power-of the two approaches differs, the abductive system being more expressive with respect to disjunctive information about belief, and the derivationM model allowing nonmonotonic reasoning by the believer. The next section of the paper gives some background in the area of belief ascription. The third section describes the problem of explanatory ascription, and some general properties that a successful approach should have. The fourth and fifth sections develop the derivational model and its proof theory, while the sixth describes an abductive framework and compares it to the derivational approach. Finally, we discuss some extensions to the language of the derivational model.\",\n \"Title: Projected texture stereo\\nYear: 2010\\n\\nAbstract: Passive stereo vision is widely used as a range sensing technology in robots, but suffers from dropouts: areas of low texture where stereo matching fails. By supplementing a stereo system with a strong texture projector, dropouts can be eliminated or reduced. This paper develops a practical stereo projector system, first by finding good patterns to project in the ideal case, then by analyzing the effects of system blur and phase noise on these patterns, and finally by designing a compact projector that is capable of good performance out to 3m in indoor scenes. The system has been implemented and has excellent depth precision and resolution, especially in the range out to 1.5m.\",\n \"Title: Compact signatures for high-speed interest point description and matching\\nYear: 2009\\n\\nAbstract: Prominent feature point descriptors such as SIFT and SURF allow reliable real-time matching but at a compu- tational cost that limits the number of points that can be handled on PCs, and even more on less powerful mobile devices. A recently proposed technique that relies on statis- tical classification to compute signatures has the potential to be much faster but at the cost of using very large amounts of memory, which makes it impractical for implementation on low-memory devices. In this paper, we show that we can exploit the sparseness of these signatures to compact them, speed up the compu- tation, and drastically reduce memory usage. We base our approach on Compressive Sensing theory. We also high- light its effectiveness by incorporating it into two very dif- ferent SLAM packages and demonstrating substantial per- formance increases.\",\n \"Title: Point feature extraction on 3D range scans taking into account object boundaries.\\nYear: 2011\\n\\nAbstract: In this paper we address the topic of feature extraction in 3D point cloud data for object recognition and pose identification. We present a novel interest keypoint extraction method that operates on range images generated from arbitrary 3D point clouds, which explicitly considers the borders of the objects identified by transitions from foreground to background. We furthermore present a feature descriptor that takes the same information into account. We have implemented our approach and present rigorous experiments in which we analyze the individual components with respect to their repeatability and matching capabilities and evaluate the usefulness for point feature based object detection methods.\",\n \"Title: Ascribing Plans to Agents\\nYear: 1989\\n\\nAbstract: Intelligent agents who are situated in nuiltiagent domains must reason about one anothers' actions and plans. Following the tradition of earlier work in AI, we present a model of plan recognition as belief and intention ascription, an inherently defeasible reasoning process. However, we encode this process using a direct argumentation system. Within this system, we can make explicit statements about why one candidate ascription should be preferred over another. And we can avoid the overly strong assumption that the actor's plan is correct from the perspective of the observer--an assumption that was necessary in previous formalizations of plan recognition.\",\n \"Title: Multiple-Agent Planning Systems\\nYear: 1980\\n\\nAbstract: We analyze problems confronted by computer agents that synthesize plans that take into account (and employ) the plans of other, similar, cooperative agents. From the point of view of each of these agents, the others are dynamic entities that possess information about the world, have goals, make plans to achieve these goals, and execute these plans. Thus, each agent must represent not only the usual information about objects in the world and the preconditions and effects of its own actions, but it must also represent and reason about what other agents believe and what they may do. We describe a planning system that addresses these issues and show how it solves a sample problem.\",\n \"Title: Markov Localization using Correlation\\nYear: 1999\\n\\nAbstract: Localization is one of the most important capabilities for autonomous mobile agents. Markov Localization (ML), applied to dense range images, has proven to be an effective technique. But its computational and storage requirements put a large burden on robot systems, and make it difficult to update the map dynamically. In this paper we introduce a new technique, based on correlation of a sensor scan with the map, that is several orders of magnitude more efficient than ML. CBML (correlation-based ML) permits video-rate localization using dense range scans, dynamic map updates, and a more precise error model than ML. In this paper we present the basic method of CBML, and validate its efficiency and correctness in a series of experiments on an implemented mobile robot base.\",\n \"Title: Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS\\nYear: 2006\\n\\nAbstract: We describe a real-time, low-cost system to localize a mobile robot in outdoor environments. Our system relies on stereo vision to robustly estimate frame-to-frame motion in real time (also known as visual odometry). The motion estimation problem is formulated efficiently in the disparity space and results in accurate and robust estimates of the motion even for a small-baseline configuration. Our system uses inertial measurements to fill in motion estimates when visual odometry fails. This incremental motion is then fused with a low-cost GPS sensor using a Kalman Filter to prevent long-term drifts. Experimental results are presented for outdoor localization in moderately sized environments (\\\\geqslant 100 meters)\",\n \"Title: Sparse Sparse Bundle Adjustment\\nYear: 2010\\n\\nAbstract: Sparse Bundle Adjustment (SBA) is a method for simultaneously optimizing a set of camera poses and visible points. It exploits the sparse primary structure of the problem, where connections exist just between points and cameras. In this paper, we implement an efficient version of SBA for systems where the secondary structure (relations among cameras) is also sparse. The method, which we call Sparse SBA (sSBA), integrates an efficient method for setting up the linear subproblem with recent advances in direct sparse Cholesky solvers. sSBA outperforms the current SBA standard implementation on datasets with sparse secondary structure by at least an order of magnitude, while also being more efficient on dense datasets.\",\n \"Title: Towards lifelong visual maps\\nYear: 2009\\n\\nAbstract: The typical SLAM mapping system assumes a static environment and constructs a map that is then used without regard for ongoing changes. Most SLAM systems, such as FastSLAM, also require a single connected run to create a map. In this paper we present a system of visual mapping, using only input from a stereo camera, that continually updates an optimized metric map in large indoor spaces with movable objects: people, furniture, partitions, etc. The system can be stopped and restarted at arbitrary disconnected points, is robust to occlusion and localization failures, and efficiently maintains alternative views of a dynamic environment. It operates completely online at a 30 Hz frame rate. A mobile robot existing in a space over a period of time has to deal with a changing environment. Some of these changes are ephemeral and can be filtered, such as moving people. Others are more permanent: objects like furniture are moved, posters change, doors open and close, and more infrequently, walls are torn down or built. The typical SLAM mapping system assumes a static environment and constructs a map, usually in a single continuous run. This map is then enshrined as the ground truth, and used without regard for ongoing changes, with the hope that a robust localization filter will be sufficient for navigation and other tasks.\",\n \"Title: Robots That Take Advice\\nYear: 1995\\n\\nAbstract: Mobile robots, if they are to perform useful tasks and become accepted in open environments, must interact with humans. Robot response to humans at the most basic level means not injuring them directly or indirectly, e.g., treating humans as obstacles to be avoided in the performance of a task. Robots will be more useful if they can interact in more sophisticated ways. Here we identify a set of useful robot/human interaction modes: attending, taking advice, and tasking. All three involve complex sensing and planning operations on the part of the robot, including the use of visual tracking of humans, gesture recognition, and speech recognition and understanding. We show how these capabilities are integrated in the Saphira architecture on Flakey, a mobile robot testbed. We demonstrate a scenario in which an untrained supervisor is able to introduce Flakey to an office environment, and command it to perform delivery tasks.\",\n \"Title: Reasoning with Analogical Representations\\nYear: 1992\\n\\nAbstract: Analogical representations have long been of interest to the knowledge representation community. Such representations provide compact encodings of information that can be cumbersome to represent and inefficient to manipulate in sentential languages. In this document, we address the problem of using analogical representations effectively in automated deduction systems. The primary contribution is a formal framework for combining analogical and deductive reasoning. The framework consists of a set of generic operations on analogical structures and accompanying inference methods for integrating analogical and sentential information. The capabilities of the framework are demonstrated for the task of reasoning to extend incomplete maps. The examples presented here have all been solved automatically by an implementation of the integration framework.\",\n \"Title: Navigation in hybrid metric-topological maps\\nYear: 2011\\n\\nAbstract: We present an approach for navigation in hybrid maps consisting of a topological graph overlaid with local occupancy grids. The topological graph is built on top of a graph SLAM system, which can be efficiently optimized even for very large environments. The novel feature of our system is that it navigates locally using local metric maps, while the overall plan is formed on the topological graph. Unlike many current SLAM methods, we never reconstruct a full occupancy grid of the environment for localization or path planning. We show that our method generates near-optimal plans, and deals gracefully with changes to the map.\",\n \"Title: An Autoepistemic Analysis of Metalevel Reasoning in Logic Programming\\nYear: 1992\\n\\nAbstract: An introspective agent can represent the connection between her own beliefs and the state of the world, using introspection principles to reason about this connection. An ideal introspective agent is one who makes the fewest possible assumptions about her beliefs. There is a strong formal connection between ideal introspection and metalogical systems in Logic Programming. We trace this connection in two cases: Reflective Prolog and negation as failure. The main contribution of this paper is to show how principles of soundness, completeness, and groundedness can be carried over from introspective to metalogic systems.\",\n \"Title: ERRATIC Competes with the Big Boys\\nYear: 1995\\n\\nAbstract: This article discusses the development of the robot ERRATIC, the second-place winner of the 1994 AAAI Robot Competition and Exhibition. I detail the robot's history and describe the perceptual and control architecture. The success of the robot is highlighted in a description of the robot's performance during the competition.\",\n \"Title: Frame-Frame Matching for Realtime Consistent Visual Mapping\\nYear: 2007\\n\\nAbstract: Many successful indoor mapping techniques employ frame-to-frame matching of laser scans to produce detailed local maps, as well as closing large loops. In this paper, we propose a framework for applying the same techniques to visual imagery, matching visual frames with large numbers of point features. The relationship between frames is kept as a nonlinear measurement, and can be used to solve large loop closures quickly. Both monocular (bearing-only) and binocular vision can be used to generate matches. Other advantages of our system are that no special landmark initialization is required, and large loops can be solved very quickly.\",\n \"Title: A fuzzy controller for flakey, the robot\\nYear: 1993\\n\\nAbstract: SRI International has a long tradition in the field of qualitative analysis and control of complex systems, starting with the development of the early mobile robot Shakey. More recently, we have developed a fuzzy controller for our new platform, Flakey. Flakey's controller can pursue strategic goals while operating under conditions of uncertainty, incompleteness, and imprecision. This controller includes capabilities for: • Robust, uncertainty-tolerating goal-directed activity. • Real-time reactivity to unexpected contingencies (e.g., unknown obstacles). • Blending of multiple goals (e.g., reaching a position while avoiding static and moving obstacles).\",\n \"Title: Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes\\nYear: 2012\\n\\nAbstract: We propose a framework for automatic modeling, detection, and tracking of 3D objects with a Kinect. The detection part is mainly based on the recent template-based LINEMOD approach [1] for object detection. We show how to build the templates automatically from 3D models, and how to estimate the 6 degrees-of-freedom pose accurately and in real-time. The pose estimation and the color information allow us to check the detection hypotheses and improves the correct detection rate by 13% with respect to the original LINEMOD. These many improvements make our framework suitable for object manipulation in Robotics applications. Moreover we propose a new dataset made of 15 registered, 1100+ frame video sequences of 15 various objects for the evaluation of future competing methods.\",\n \"Title: Autonomous door opening and plugging in with a personal robot\\nYear: 2010\\n\\nAbstract: We describe an autonomous robotic system capable of navigating through an office environment, opening doors along the way, and plugging itself into electrical outlets to recharge as needed. We demonstrate through extensive experimentation that our robot executes these tasks reliably, without requiring any modification to the environment. We present robust detection algorithms for doors, door handles, and electrical plugs and sockets, combining vision and laser sensors. We show how to overcome the unavoidable shortcoming of perception by integrating compliant control into manipulation motions. We present a visual-differencing approach to high-precision plug-insertion that avoids the need for high-precision hand-eye calibration.\",\n \"Title: Censure: Center Surround Extremas For Realtime Feature Detection And Matching\\nYear: 2008\\n\\nAbstract: We explore the suitability of different feature detectors for the task of image registration, and in particular for visual odometry, using two criteria: stability (persistence across viewpoint change) and accuracy (consistent localization across viewpoint change). In addition to the now-standard SIFT, SURF, FAST, and Harris detectors, we introduce a suite of scale-invariant center-surround detectors (CenSurE) that outperform the other detectors, yet have better computational characteristics than other scale-space detectors, and are capable of real-time implementation.\",\n \"Title: A multivalued logic approach to integrating planning and control\\nYear: 1995\\n\\nAbstract: Intelligent agents embedded in a dynamic, uncertain environment should incorporatecapabilities for both planned and reactive behavior. Many current solutions tothis dual need focus on one aspect, and treat the other one as secondary. We proposean approach for integrating planning and control based on behavior schemas,which link physical movements to abstract action descriptions. Behavior schemasdescribe behaviors of an agent, expressed as trajectories of control actions in an environment,...\",\n \"Title: A hierarchical Bayesian approach to the revisiting problem in mobile robot map building\\nYear: 2003\\n\\nAbstract: We present an application of hierarchical Bayesian estimation to robot map building. The revisiting problem occurs when a robot has to decide whether it is seeing apreviously built portion of a map, or is exploring new territory. This is a difficult decision problem, requiring the probability of being outside of the current known map. To estimate this probability, we model the structure of a \\\"typical\\\" environment as a hidden Markov model that generates sequences of views observed by a robot navigating through the environment. A Dirichlet prior over structural models is learned from previously explored environments. Whenever a robot explores a new environment, the posterior over the model is estimated using Dirichlet hyperparameters. Our approach is implemented and tested in the context of multi-robot map merging, a particularly difficult instance of the revisiting problem. Experiments with robot data show that the technique yields strong improvements over alternative methods.\",\n \"Title: Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes\\nYear: 2011\\n\\nAbstract: We present a method for detecting 3D objects using multi-modalities. While it is generic, we demonstrate it on the combination of an image and a dense depth map which give complementary object information. It works in real-time, under heavy clutter, does not require a time consuming training stage, and can handle untextured objects. It is based on an efficient representation of templates that capture the different modalities, and we show in many experiments on commodity hardware that our approach significantly outperforms state-of-the-art methods on single modalities.\",\n \"Title: A gradient method for realtime robot control\\nYear: 2000\\n\\nAbstract: Despite many decades of research into mobile robot control, reliable, high-speed motion in complicated, uncertain environments remains an unachieved goal. In this paper we present a solution to realtime motion control that can competently maneuver a robot at optimal speed even as it explores a new region or encounters new obstacles. The method uses a navigation function to generate a gradient field that represents the optimal (lowest-cost) path to the goal at every point in the workspace. Additionally, we present an integrated sensor fusion system that allows incremental construction of an unknown or uncertain environment. Under modest assumptions, the robot is guaranteed to get to the goal in an arbitrary static unexplored environment, as long as such a path exists. We present preliminary experiments to show that the gradient method is better than expert human controllers in both known and unknown environments\",\n \"Title: A computational theory of belief introspection\\nYear: 1985\\n\\nAbstract: Introspection is a general term covering the ability of an agent to reflect upon the workings of his own cognitive functions. In this paper we will be concerned with developing an explanatory theory of a particular type of introspection: a robot agent's knowledge of his own beliefs. The development is both descriptive, in the sense of being able to capture introspective behavior as it exist; and prescriptive, in yielding an effective means of adding introspective reasoning abilities to robot agents.\",\n \"Title: Technical demonstration on model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes\\nYear: 2012\\n\\nAbstract: In this technical demonstration, we will show our framework of automatic modeling, detection, and tracking of arbitrary texture-less 3D objects with a Kinect. The detection is mainly based on the recent template-based LINEMOD approach [1] while the automatic template learning from reconstructed 3D models, the fast pose estimation and the quick and robust false positive removal is a novel addition. In this demonstration, we will show each step of our pipeline, starting with the fast reconstruction of arbitrary 3D objects, followed by the automatic learning and the robust detection and pose estimation of the reconstructed objects in real-time. As we will show, this makes our framework suitable for object manipulation e.g. in robotics applications.\",\n \"Title: Real-Time Detection of Independent Motion using Stereo\\nYear: 2005\\n\\nAbstract: We describe a system that detects independently moving objects from a mobile platform in real time using a calibrated stereo camera. Interest points are first detected and tracked through the images. These tracks are used to obtain the motion of the platform by using an efficient three-point algorithm in a RANSAC framework for outlier detection. We use a formulation based on disparity space for our inlier computation. In the disparity space, two disparity images of a rigid object are related by a homography that depends on the object's euclidean rigid motion. We use the homography obtained from the camera motion to detect the independently moving objects from the disparity maps obtained by an efficient stereo algorithm. Our system is able to reliably detect the independently moving objects at 16 Hz for a 320 x 240 stereo image sequence using a standard laptop computer.\",\n \"Title: Learning kinematic models for articulated objects\\nYear: 2009\\n\\nAbstract: Robots operating in home environments must be able to interact with articulated objects such as doors or drawers. Ideally, robots are able to autonomously infer articulation models by observation. In this paper, we present an approach to learn kinematic models by inferring the connectivity of rigid parts and the articulation models for the corresponding links. Our method uses a mixture of parameterized and parameter-free (Gaussian process) representations and finds low-dimensional manifolds that provide the best explanation of the given observations. Our approach has been implemented and evaluated using real data obtained in various realistic home environment settings.\",\n \"Title: A Contour Method For Real-Time Range Image Parsing\\nYear: 1998\\n\\nAbstract: Real-time or near real-time range images are available from a variety of sources. Many proposed uses for these devices require significant post-processing to extract relevant 3-D information. We propose a new method based on a generalization of contour maps that has several advantages over current methods in tasks such as terrain reconstruction and obstacle avoidance. The method is illustrated with examples from obstacle avoidance on a mobile robot.\",\n \"Title: Outdoor mapping and navigation using stereo vision\\nYear: 2006\\n\\nAbstract: We consider the problem of autonomous navigation in an unstructured outdoor envi-ronment. The goal is for a small outdoor robot to come into a new area, learn about and map its environment, and move to a given goal at modest speeds (1 m/s). This problem is especially difficult in outdoor, off-road environments, where tall grass, shadows, deadfall, and other obstacles predominate. Not surprisingly, the biggest challenge is acquiring and using a reliable map of the new area. Although work in outdoor navigation has preferentially used laser rangefinders [13, 2, 6], we use stereo vision as the main sensor. Vision sensors allow us to use more distant objects as landmarks for navigation, and to learn and use color and texture models of the environment, in looking further ahead than is possible with range sensors alone. In this paper we show how to build a consistent, globally correct map in real time, using a combination of the following vision-based techniques:\",\n \"Title: Double window optimisation for constant time visual SLAM\\nYear: 2011\\n\\nAbstract: We present a novel and general optimisation framework for visual SLAM, which scales for both local, highly accurate reconstruction and large-scale motion with long loop closures. We take a two-level approach that combines accurate pose-point constraints in the primary region of interest with a stabilising periphery of pose-pose soft constraints. Our algorithm automatically builds a suitable connected graph of keyposes and constraints, dynamically selects inner and outer window membership and optimises both simultaneously. We demonstrate in extensive simulation experiments that our method approaches the accuracy of offline bundle adjustment while maintaining constant-time operation, even in the hard case of very loopy monocular camera motion. Furthermore, we present a set of real experiments for various types of visual sensor and motion, including large scale SLAM with both monocular and stereo cameras, loopy local browsing with either monocular or RGB-D cameras, and dense RGB-D object model building.\",\n \"Title: Using Default and Causal Reasoning in Diagnosis\\nYear: 1992\\n\\nAbstract: We present a theory of default reasoning that is specifically targeted to causal domains. These domains encompass a wide variety of current applications of default reasoning, but here we concentrate on model-based diagnosis. The theory is unique in that it integrates a formal notion of causality with nonmonotonic reasoning techniques based on default logic and abduction. The main structure of the theory is a default causal net (DCN) representing the causal connections among propositions in the domain. The causal net provides a framework for the two nonmonotonic reasoning techniques of assuming defaults and generating explanations for observations, allowing them to be combined in a principled way.\",\n \"Title: Tracking People from a Mobile Platform\\nYear: 2002\\n\\nAbstract: Tracking people from a moving platform is a useful skill for the coming generation of service and human-interaction robots. It is also a challenging problem, involving sensing, interpretation, planning, and control, all in a real time and dynamic environment. Techniques used in existing people-trackers that operate from fixed locations, noting changes against a fixed background, are not applicable. Instead, we apply a vision-based approach using real-time stereo and 31) reconstruction, that explicitly models both foreground and background objects in an efficient manner. The main novelties of our approach include (1) remapping the stereo disparities to an orthographic \\\"occupancy map\\\", which simplifies person modeling, and (2) updating a background occupancy map based on robot motion. The current version of our system, running on a Pioneer II mobile robot, can follow people at up to 1.2 m/s in an indoor environment.\",\n \"Title: A practical nonmonotonic theory for reasoning about speech acts\\nYear: 1988\\n\\nAbstract: A prerequisite to a theory of the way agents understand speech acts is a theory of how their beliefs and intentions are revised as a consequence of events. This process of attitude revision is an interesting domain for the application of nonmonotonic reasoning because speech acts have a conventional aspect that is readily represented by defaults, but that interacts with an agent's beliefs and intentions in many complex ways that may override the defaults. Perrault has developed a theory of speech acts, based on Rieter's default logic, that captures the conventional aspect; it does not, however, adequately account for certain easily observed facts about attitude revision resulting from speech acts. A natural theory of attitude revision seems to require a method of stating preferences among competing defaults. We present here a speech act theory, formalized in hierarchic autoepistemic logic (a refinement of Moore's autoepistemic logic), in which revision of both the speaker's and hearer's attitudes can be adequately described. As a collateral benefit, efficient automatic reasoning methods for the formalism exist. The theory has been implemented and is now being employed by an utterance-planning system.\",\n \"Title: Visually Realistic Mapping of a Planar Environment with Stereo\\nYear: 2000\\n\\nAbstract: We present a hybrid technique for constructing geometrically a c- curate, visually realistic planar environments from stereo vision informa- tion. The technique is unique in estimating camera motion from two sources: range information from stereo, and visual alignment of images.\",\n \"Title: Approximating a Single Viewpoint in Panoramic Imaging Devices\\nYear: 2000\\n\\nAbstract: Recent panoramic cameras present a very wide field of view from a single viewpoint. A single viewpoint is useful in mobile robotics for a number of reasons, including perspective reprojection and stereo analysis. However, the requirement of single viewpoint for panoramic cameras restricts the optical and geometrical design of these devices. In this paper, we present a method for approximating a single viewpoint in panoramic devices that allows much greater freedom in design. We illustrate the method with a compact catadioptric device using a spherical mirror and standard optics, and apply it to perspective reprojection. The resultant panoramic camera has been integrated as a surveillance device on a small mobile robot.\",\n \"Title: A Practical Stereo System Based On Regularization And Texture Projection\\nYear: 2009\\n\\nAbstract: In this paper we investigate the suitability of stereo vision for robot manipulation tasks, which require high-fidelity real-time 3D information in the presence of motion. We compare spatial regularization methods for stereo and spacetime stereo, the latter relying on integration of information over time as well as space. In both cases we augment the scene with textured projection, to alleviate the well-known problem of noise in low-textured areas. We also propose a new spatial regularization method, local smoothing, that is more efficient than current methods, and produces almost equivalent results. We show that in scenes with moving objects spatial regularization methods are more accurate than spacetime stereo, while remaining computationally simpler. Finally, we propose an extension of regularization-based algorithms to the temporal domain, so to further improve the performance of regularization methods within dynamic scenes.\",\n \"Title: Map Merging For Distributed Robot Navigation\\nYear: 2003\\n\\nAbstract: A set of robots mapping an area can potentially combine their information to produce a distributed map more efficiently than a single robot alone. We describe a general framework for distributed map building in the presence of uncertain communication. Within this framework, we then present a technical solution to the key decision problem of determining relative location within partial maps.\",\n \"Title: A Deduction Model of Belief\\nYear: 1983\\n\\nAbstract: Representing and reasoning about the knowledge an agent (human or computer) must have to accomplish some task is becoming an increasingly important issue in artificial intelligence (AI) research. To reason about an agent's beliefs, an AI system must assume some formal model of those beliefs. An attractive candidate is the Deductive Belief model: an agent's beliefs are described as a set of sentences in some formal language (the base sentences), together with a deductive process for deriving consequences of those beliefs. In particular, a Deductive Belief model can account for the effect of resource limitations on deriving consequences of the base set: an agent need not believe all the logical consequences of his beliefs. In this paper we develop a belief model based on the notion of deduction, and contrast it with current AI formalisms for belief derived from Hintikka/Kripke possible-worlds semantics for knowledge.\",\n \"Title: A Tutorial on Nonmonotonic Reasoning\\nYear: 1991\\n\\nAbstract: Nonmonotonic reasoning, in its broadest sense, is reasoning to conclusions on the basis of incomplete information. Given more information, we are prepared to retract previously drawn inferences. To exhibit the classic example: if all we know about Tweety is that he is bird, then we plausibly conclude that he can fly; on learning that Tweety is a penguin, we withdraw that conclusion. We call this reasoning nonmonotonic because the set of plausible conclusions does not grow monotonically with increasing information.\",\n \"Title: A Representationalist Theory of Intention\\nYear: 1993\\n\\nAbstract: The problem of finding a set of assumptions which explain a given proposition is in general NP-hard, even when the background theory is an acyclic Horn theory. In this paper it is shown that when the background theory is acyclic Horn and its pseudo-completion ...\",\n \"Title: Designing the 1993 robot competition\\nYear: 1994\\n\\nAbstract: The Second Annual Robotics Competition and Exhibition was held in July 1993 in conjunction with the National Conference on Artificial Intelligence. This article reports some of my experiences in helping to design and run the contest and some reflections, drawn from post mortem abstracts written by the competitors, on the relation of the contest to current research efforts in mobile robotics.\",\n \"Title: Large-Scale Visual Odometry for Rough Terrain\\nYear: 2007\\n\\nAbstract: Motion estimation from stereo imagery, sometimes called visual odometry, is a well-known process. However, it is difficult to achieve good performance using standard techniques. We present the results of several years of work on an integrated system to localize a mobile robot in rough outdoor terrain using visual odometry, with an increasing degree of precision. We discuss issues that are important, for real-time, high-precision performance: choice of features, matching strategies; incremental bundle adjustment, and filtering with inertial measurement sensors. Using data with ground truth from an Risk GPS system, we show experimentally that our algorithms can track motion, in off-road terrain, over distances of 10 km, with an error of less than 10 m (0.1%).\",\n \"Title: Mapping, navigation, and learning for off-road traversal\\nYear: 2009\\n\\nAbstract: The challenge in the DARPA Learning Applied to Ground Robots (LAGR) project is to autonomously navigate a small robot using stereo vision as the main sensor. During this project, we demonstrated a complete autonomous system for off-road navigation in unstructured environments, using stereo vision as the main sensor. The system is very robust—we can typically give it a goal position several hundred meters away and expect it to get there. In this paper we describe the main components that comprise the system, including stereo processing, obstacle and free space interpretation, long-range perception, online terrain traversability learning, visual odometry, map registration, planning, and control. At the end of 3 years, the system we developed outperformed all nine other teams in final blind tests over previously unseen terrain. © 2008 Wiley Periodicals, Inc.\",\n \"Title: Localization and Mapping for Autonomous Navigation in Outdoor Terrains: A Stereo Vision Approach\\nYear: 2007\\n\\nAbstract: We consider the problem of autonomous navigation in unstructured outdoor terrains using vision sensors. The goal is for a robot to come into a new environment, map it and move to a given goal at modest speeds (1 m/sec). The biggest challenges are in building good maps and keeping the robot well localized as it advances towards the goal. In this paper, we concentrate on showing how it is possible to build a consistent, globally correct map in real time, using efficient precise stereo algorithms for map making and visual odometry for localization. While we have made advances in both localization and mapping using stereo vision, it is the integration of the techniques that is the biggest contribution of the research. The validity of our approach is tested in blind experiments, where we submit our code to an independent testing group that runs and validates it on an outdoor robot\",\n \"Title: An inference net compiler for the prospector rule-based consultation system\\nYear: 1979\\n\\nAbstract: A compiler for the production system used in a rule-based consultation system is described. This compiler produces machine code which performs antecedent or data-driven inference In an efficient manner. Timing results and some limitations of the compiler are discussed.\",\n \"Title: Background Modeling for Segmentation of Video-Rate Stereo Sequences\\nYear: 1998\\n\\nAbstract: Stereo sequences promise to be a powerful method for segmenting images for applications such as tracking human figures. We present a method of statistical background modeling for stereo sequences that improves the reliability and sensitivity of segmentation in the presence of object clutter. The dynamic version of the method, called gated background adaptation, can reliably learn background statistics in the presence of corrupting foreground motion. The method has been used with a simple head discriminator to detect and track people using a stereo head mounted on a pan/tilt platform. It runs at video rates using standard PC hardware.\",\n \"Title: A Refined Method for Occupancy Grid Interpretation\\nYear: 1995\\n\\nAbstract: Occupancy grids are a probabilistic method for fusing multiple sensor readings. Although the underlying theory has been understood for many years, the intricacies of applying it to realtime sensor interpretation have been somewhat neglected. In this paper I analyze how refined sensor models (including specularity models) and assumptions about independence are crucial issues for occupancy grid interpretation. Using this analysis, I develop the MURIEL method for occupancy grid calculations, and show how it can dramatically improve the fidelity of occupancy grid interpretations in specular and realtime environments.\",\n \"Title: SLAM via Variable Reduction from Constraint Maps\\nYear: 2005\\n\\nAbstract: The two dominant forms of SLAM are based on Extended Kalman Filtering and Consistent Pose Estimation. We show that these are particular subsets of a more general view of the SLAM problem, in which variables representing all robot poses and features are kept. The general technique of variable reduction is a unifying view of these methods that is mathematically sound, and which enables us to explore other interesting and computationally compelling forms for solving SLAM problems.\",\n \"Title: On the relation between default theories and autoepistemic logic\\nYear: 1987\\n\\nAbstract: Default theories are a formal means of reasoning about defaults: what normally is the case, in the absence of contradicting information. Autoepistemic theories, on the other hand, are meant to describe the consequences of reasoning about ignorance: what must be true if a certain fact is not known. Although the motivation and formal character of these systems are different, a closer analysis shows that they bear a common trait, which is the indexical nature of certain elements in the theory. In this paper we compare the expressive power of the two systems. First, we give an effective translation of default theories in to autoepistemic logic; default theories can thus be embedded into autoepistemic logic. A more suprising result is that the reverse translation is also possible: every set of sentences in autoepistemic logic can be effectively rewritten as a default theory. The formal equivalence of these two differing systems is thus established. Some benefits of this analysis are that it gives an interpretive semantics to default theories, and yields insight i n to the nature of defaults in autoepistemic reasoning.\",\n \"Title: Improved Occupancy Grids for Map Building\\nYear: 1997\\n\\nAbstract: Occupancy grids are a probabilistic method for fusing multiplesensor readings into surface maps of the environment. Although theunderlying theory has been understood for many years, the intricacies ofapplying it to realtime sensor interpretation have been neglected. Thispaper analyzes how refined sensor models (including specularity models) andassumptions about independence are crucial issues for occupancy gridinterpretation. Using this analysis, the MURIEL method for occupancy gridupdate is developed. Experiments show how it can dramatically improve thefidelity of occupancy grid map-making in specular and realtimeenvironments.\",\n \"Title: Abduction versus closure in causal theories\\nYear: 1992\\n\\nAbstract: There are two distinct formalizations for reasoning from observations to explanations. as in diagnostic tasks. The consistency based approach treats the task as a deductive one, in which the explanation is deduced from a background theory and a minimal set of abnormalities. The abductive method, on the other hand, treats explanations as sentences that, when added to the background theory, derive the observations. We show that there is a close connection between these two formalizations in the context of simple causal theories: domain theories in which a set of sentences are singled out as the explanatorily relevant causes of observations. There are two main results, which show that (with certain caveats) the consistency based approach can emulate abductive reasoning by adding closure axioms to a causal theory; and that abductive techniques can be used in place of the consistency based method in the domain of logic based diagnosis. It is especially interesting that in the latter case, the abductive techniques generate only relevant explanations, while diagnoses may have irrelevant elements.\",\n \"Title: Computing the extensions of autoepistemic and default logics with a truth maintenance system\\nYear: 1990\\n\\nAbstract: In this paper we develop a proof procedure for autoepistemic (AEL) and default logics (DL), based on translating them into a Truth Maintenance System (TMS). The translation is decidable if the theory consists of a finite number of defaults and premises and classical derivability for the base language is decidable. To determine all extensions of a network, we develop variants of Doyle's labelling algorithms.\",\n \"Title: The Saphira architecture: a design for autonomy\\nYear: 1997\\n\\nAbstract: Mobile robots, if they are to perform useful tasks and become accepted in open environments, must be fully autonomous. Autonomy has many different aspects; here the focus is on three central ones: the ability to attend to another agent, to take advice about the environment, and to carry out assigned tasks. All three involve complex sensing and planning operations on the part of the robot, including the use of visual tracking of humans, coordination of motor controls, and planning. It is shown how these capabilities are integrated in the Saphira architecture, using the concepts of coordination of behaviour, coherence of modelling, and communication with other agents.\",\n \"Title: An abductive framework for general logic programs and other nonmonotonic systems\\nYear: 1993\\n\\nAbstract: We present an abductive semantics for general propositional logic programs which defines the meaning of a logic program in terms of its extensions. This approach extends the stable model semantics for normal logic programs in a natural way. The new semantics is equivalent to stable semantics for a logic program P whenever P is normal and has a stable model. The abductive semantics can also be applied to generalize default logic and autoepistemic logic in a like manner. Our approach is based on an idea recently proposed by Konolige for causal reasoning. Instead of maximizing the set of hypotheses alone we maximize the union of the hypotheses, along with possible hypotheses that are excused or refuted by the theory.\",\n \"Title: COLBERT: A Language for Reactive Control in Sapphira\\nYear: 1997\\n\\nAbstract: this paper we are concerned with how a user can write sequencer programs toeffectively control the robot. Our emphasis is on issues of language and semantics: whatis a good language for robot programs, what kind of semantics is appropriate for thesequencer, and how does the language fit the semantics. The result of our inquiries is thesequencer language Colbert, a part of the Sapphira architecture.\",\n \"Title: A non-monotonic logic for reasoning about speech acts and belief revision\\nYear: 1988\\n\\nAbstract: A prerequisite to a theory of the way agents understand speech acts is a theory of how their beliefs and intentions are revised as a consequence of events. This process of attitude revision is an interesting domain for the application of nonmonotonic reasoning because speech acts have a conventional aspect that is readily represented by defaults, but that interacts with an agent's beliefs and intentions in many complex ways that may override the defaults. Perrault has developed a theory of speech acts, based on Rieter's default logic, that captures the conventional aspect; it does not, however, adequately account for certain easily observed facts about attitude revision resulting from speech acts. A natural theory of attitude revision seems to require a method of stating preferences among competing defaults. We present here a speech act theory, formalized in hierarchic autoepistemic logic (a refinement of Moore's autoepistemic logic), in which revision of both the speaker's and hearer's attitudes can be adequately described. As a collateral benefit, efficient automatic reasoning methods for the formalism exist. The theory has been implemented and is now being employed by an utterance-planning system.\",\n \"Title: Distributed multirobot exploration, mapping, and task allocation\\nYear: 2008\\n\\nAbstract: We present an integrated approach to multirobot exploration, mapping and searching suitable for large teams of robots operating in unknown areas lacking an existing supporting communications infrastructure. We present a set of algorithms that have been both implemented and experimentally verified on teams--of what we refer to as Centibots--consisting of as many as 100 robots. The results that we present involve search tasks that can be divided into a mapping stage in which robots must jointly explore a large unknown area with the goal of generating a consistent map from the fragment, a search stage in which robots are deployed within the environment in order to systematically search for an object of interest, and a protection phase in which robots are distributed to track any intruders in the search area. During the first stage, the robots actively seek to verify their relative locations in order to ensure consistency when combining data into shared maps; they must also coordinate their exploration strategies so as to maximize the efficiency of exploration. In the second and third stages, robots allocate search tasks among themselves; since tasks are not defined a priori, the robots first produce a topological graph of the area of interest and then generate a set of tasks that reflect spatial and communication constraints. Our system was evaluated under extremely realistic real-world conditions. An outside evaluation team found the system to be highly efficient and robust.\",\n \"Title: Change Their Perception: RGB-D for 3-D Modeling and Recognition\\nYear: 2013\\n\\nAbstract: RGB-D cameras, such as Microsoft Kinect, are active sensors that provide high-resolution dense color and depth information at real-time frame rates. The wide availability of affordable RGB-D cameras is causing a revolution in perception and changing the landscape of robotics and related fields. RGB-D perception has been the focus of a great deal of attention and many research efforts by various fields in the last three years. In this article, we summarize and discuss our ongoing research on the promising uses of RGB-D in three-dimensional (3-D) mapping and 3-D recognition. Combining the strengths of optical cameras and laser rangefinders, the joint use of color and depth in RGB-D sensing makes visual perception more robust and efficient, leading to practical systems that build detailed 3-D models of large indoor spaces, as well as systems that reliably recognize everyday objects in complex scenes. RGB-D perception is yet a burgeoning technology: a rapidly growing number of research projects are being conducted on or using RGB-D perception while RGB-D hardware quickly improves. We believe that RGB-D perception will be on the center stage of perception and, by making robots see much better than before, will enable a variety of perception-based research and applications.\",\n \"Title: An experimental comparison of localization methods\\nYear: 1998\\n\\nAbstract: Localization is the process of updating the pose of a robot in an environment, based on sensor readings. In this experimental study, we compare two recent methods for localization of indoor mobile robots: Markov localiza- tion, which uses a probability distribution across a grid of robot poses; and scan matching, which uses Kalman filter- ing techniques based on matching sensor scans. Both these techniques are dense matching methods, that is, they match dense sets of environment features to an a priori map. To arrive at results for a range of situations, we utilize sev- eral different types of environments, and add noise to both the dead-reckoning and the sensors. Analysis shows that, roughly, the scan-matching techniques are more efficient and accurate, but Markov localization is better able to cope with large amounts of noise. These results suggest hybrid methods that are efficient, accurate and robust to noise.\",\n \"Title: Incremental mapping of large cyclic environments\\nYear: 1999\\n\\nAbstract: Mobile robots can use geometric or topological maps of their environment to navigate reliably. Automatic cre- ation of such maps is still an unrealized goal, especially in environments that have large cyclical structures. Drawing on recent techniques of global registration and correlation, we present a method, called Local Registration and Global Correlation (LRGC), for reliable reconstruction of consis- tent global maps from dense range data. The method is attractive because it is incremental, producing an updated map with every new sensor input; and runs in constant time independent of the size of the map (except when closing large cycles). A real-time implementation and results are presented for several indoor environments.\",\n \"Title: Fast color/texture segmentation for outdoor robots\\nYear: 2008\\n\\nAbstract: We present a fast integrated approach for online segmentation of images for outdoor robots. A compact color and texture descriptor has been developed to describe local color and texture variations in an image. This descriptor is then used in a two-stage fast clustering framework using K-means to perform online segmentation of natural images. We present results of applying our descriptor for segmenting a synthetic image and compare it against other state-of-the-art descriptors. We also apply our segmentation algorithm to the task of detecting natural paths in outdoor images. The whole system has been demonstrated to work online alongside localization, 3D obstacle detection, and planning.\",\n \"Title: ORB: An efficient alternative to SIFT or SURF\\nYear: 2011\\n\\nAbstract: Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several real-world applications, including object detection and patch-tracking on a smart phone.\",\n \"Title: Exploiting segmentation for robust 3D object matching\\nYear: 2012\\n\\nAbstract: While Iterative Closest Point (ICP) algorithms have been successful at aligning 3D point clouds, they do not take into account constraints arising from sensor viewpoints. More recent beam-based models take into account sensor noise and viewpoint, but problems still remain. In particular, good optimization strategies are still lacking for the beam-based model. In situations of occlusion and clutter, both beam-based and ICP approaches can fail to find good solutions. In this paper, we present both an optimization method for beambased models and a novel framework for modeling observation dependencies in beam-based models using over-segmentations. This technique enables reasoning about object extents and works well in heavy clutter. We also make available a ground-truth 3D dataset for testing algorithms in this area.\",\n \"Title: Vision-based detection for learning articulation models of cabinet doors and drawers in household environments\\nYear: 2010\\n\\nAbstract: Service robots deployed in domestic environments generally need the capability to deal with articulated objects such as doors and drawers in order to fulfill certain mobile manipulation tasks. This however, requires, that the robots are able to perceive the articulation models of such objects. In this paper, we present an approach for detecting, tracking, and learning articulation models for cabinet doors and drawers without using artificial markers. Our approach uses a highly efficient and sampling-based approach to rectangle detection in depth images obtained from a self-developed active stereo system. The robot can use the generative models learned for the articulated objects to estimate their articulation type, their current configuration, and to make predictions about possible configurations not observed before. We present experiments carried out on real data obtained from our active stereo system. The results demonstrate that our technique is able to learn accurate articulation models. We furthermore provide a detailed error analysis based on ground truth data obtained in a motion capturing studio.\",\n \"Title: Going Further With Point Pair Features\\nYear: 2017\\n\\nAbstract: Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of clutter and sensor noise. Our experiments show that with our improvements, PPFs become competitive against state-of-the-art methods as it outperforms them on several objects from challenging benchmarks, at a low computational cost.\",\n \"Title: Yale-CMU-Berkeley dataset for robotic manipulation research\\nYear: 2017\\n\\nAbstract: AbstractIn this paper, we present an image and model dataset of the real-life objects from the Yale-CMU-Berkeley Object Set, which is specifically designed for benchmarking in manipulation research. For each object, the dataset presents 600 high-resolution RGB images, 600 RGB-D images and five sets of textured three-dimensional geometric models. Segmentation masks and calibration information for each image are also provided. These data are acquired using the BigBIRD Object Scanning Rig and Google Scanners. Together with the dataset, Python scripts and a Robot Operating System node are provided to download the data, generate point clouds and create Unified Robot Description Files. The dataset is also supported by our website, www.ycbbenchmarks.org, which serves as a portal for publishing and discussing test results along with proposing task protocols and benchmarks.\",\n \"Title: Visual Odometry Using Sparse Bundle Adjustment on an Autonomous Outdoor Vehicle\\nYear: 2005\\n\\nAbstract: Visual Odometry is the process of estimating the movement of a (stereo) camera through its environment by matching point features between pairs of consecutive image frames. No prior knowledge of the scene nor the motion is necessary. In this work, we present a visual odom- etry approach using a specialized method of Sparse Bundle Adjustment. We show experimental results that proof our approach to be a feasible method for estimating motion in unstructured outdoor environments.\",\n \"Title: Between regions and objects: surfaces and volumes\\nYear: 1977\\n\\nAbstract: Several modular processes are used to build relationships between a) two-dimensional regions and boundaries in a segmented image, and b) the hypothesized surfaces, volumes and objects of a three-dimensional world. A variety of bottom-up and top-down strategies coordinate the processes of SHAPE, PERSPECTIVE, SHADOW, and OCCLUSION to construct these relationships in the VISIONS system.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":71,"string":"71"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13247,"string":"13,247"}}},{"rowIdx":13248,"cells":{"question_id":{"kind":"string","value":"Ratul Mahajan"},"question":{"kind":"string","value":"Generate an abstract for the title \"Inferring link weights using end-to-end measurements\" using the following items: 1) Constraint-based approach\n2) ISP link weights\n3) Inferred weights \n4) Consistent routing model \n5) Accurate short path characterization"},"answer":{"kind":"string","value":"We describe a novel constraint-based approach to approximate ISP link weights using only end-to-end measurements. Common routing protocols such as OSPF and IS-IS choose least-cost paths using link weights, so inferred weights provide a simple, concise, and useful model of intradomain routing. Our approach extends router-level ISP maps, which include only connectivity, with link weights that are consistent with routing. Our inferred weights agree well with observed routing: while our inferred weights fully characterize the set of shortest paths between 84--99% of the router-pairs, alternative models based on hop count and latency do so for only 47--81% of the pairs."},"correct_answer":{"kind":"string","value":"We describe a novel constraint-based approach to approximate ISP link weights using only end-to-end measurements. Common routing protocols such as OSPF and IS-IS choose least-cost paths using link weights, so inferred weights provide a simple, concise, and useful model of intradomain routing. Our approach extends router-level ISP maps, which include only connectivity, with link weights that are consistent with routing. Our inferred weights agree well with observed routing: while our inferred weights fully characterize the set of shortest paths between 84--99% of the router-pairs, alternative models based on hop count and latency do so for only 47--81% of the pairs."},"documents":{"kind":"list like","value":["Title: Understanding BGP misconfiguration\nYear: 2002\n\nAbstract: It is well-known that simple, accidental BGP configuration errors can disrupt Internet connectivity. Yet little is known about the frequency of misconfiguration or its causes, except for the few spectacular incidents of widespread outages. In this paper, we present the first quantitative study of BGP misconfiguration. Over a three week period, we analyzed routing table advertisements from 23 vantage points across the Internet backbone to detect incidents of misconfiguration. For each incident we polled the ISP operators involved to verify whether it was a misconfiguration, and to learn the cause of the incident. We also actively probed the Internet to determine the impact of misconfiguration on connectivity.Surprisingly, we find that configuration errors are pervasive, with 200-1200 prefixes (0.2-1.0% of the BGP table size) suffering from misconfiguration each day. Close to 3 in 4 of all new prefix advertisements were results of misconfiguration. Fortunately, the connectivity seen by end users is surprisingly robust to misconfigurations. While misconfigurations can substantially increase the update load on routers, only one in twenty five affects connectivity. While the causes of misconfiguration are diverse, we argue that most could be prevented through better router design.","Title: Glasnost: enabling end users to detect traffic differentiation\nYear: 2010\n\nAbstract: Holding residential ISPs to their contractual or legal obligations of \"unlimited service\" or \"network neutrality\" is hard because their traffic management policies are opaque to end users and governmental regulatory agencies. We have built and deployed Glasnost, a system that improves network transparency by enabling ordinary Internet users to detect whether their ISPs are differentiating between flows of specific applications. We identify three key challenges in designing such a system: (a) to attract many users, the system must have low barrier of use and generate results in a timely manner, (b) the results must be robust to measurement noise and avoid false accusations of differentiation, which can adversely affect ISPs' reputation and business, (c) the system must include mechanisms to keep it up-to-date with the continuously changing differentiation policies of ISPs worldwide. We describe how Glasnost addresses each of these challenges. Glasnost has been operational for over a year. More than 350,000 users from over 5,800 ISPs worldwide have used Glasnost to detect differentiation, validating many of our design choices. We show how data from individual Glasnost users can be aggregated to provide regulators and monitors with useful information on ISP-wide deployment of various differentiation policies.","Title: Diversity in smartphone usage\nYear: 2010\n\nAbstract: Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.","Title: Finding protocol manipulation attacks\nYear: 2011\n\nAbstract: We develop a method to help discover manipulation attacks in protocol implementations. In these attacks, adversaries induce honest nodes to exhibit undesirable behaviors by misrepresenting their intent or network conditions. Our method is based on a novel combination of static analysis with symbolic execution and dynamic analysis with concrete execution. The former finds code paths that are likely vulnerable, and the latter emulates adversarial actions that lead to effective attacks. Our method is precise (i.e., no false positives) and we show that it scales to complex protocol implementations. We apply it to four diverse protocols, including TCP, the 802.11 MAC, ECN, and SCTP, and show that it is able to find all manipulation attacks that have been previously reported for these protocols. We also find a previously unreported attack for SCTP. This attack is a variant of a TCP attack but must be mounted differently in SCTP because of subtle semantic differences between the two protocols.","Title: An operating system for the home\nYear: 2012\n\nAbstract: Network devices for the home such as remotely controllable locks, lights, thermostats, cameras, and motion sensors are now readily available and inexpensive. In theory, this enables scenarios like remotely monitoring cameras from a smartphone or customizing climate control based on occupancy patterns. However, in practice today, such smarthome scenarios are limited to expert hobbyists and the rich because of the high overhead of managing and extending current technology. We present HomeOS, a platform that bridges this gap by presenting users and developers with a PC-like abstraction for technology in the home. It presents network devices as peripherals with abstract interfaces, enables cross-device tasks via applications written against these interfaces, and gives users a management interface designed for the home environment. HomeOS already has tens of applications and supports a wide range of devices. It has been running in 12 real homes for 4-8 months, and 42 students have built new applications and added support for additional devices independent of our efforts.","Title: HomeLab: shared infrastructure for home technology field studies\nYear: 2012\n\nAbstract: Researchers who develop new home technologies using connected devices (e.g. sensors) often want to conduct large-scale field studies in homes to evaluate their technology, but conducting such studies today is quite challenging, if not impossible. Considerable custom engineering is required to ensure hardware and software prototypes work robustly, and recruiting and managing more than a handful of households can be difficult and cost-prohibitive. To lower the barrier to developing and evaluating new technologies for the home environment, we call for the development of a shared infrastructure, called HomeLab. HomeLab consists of a large number of geographically distributed households, each running a common, flexible framework (e.g., HomeOS [4]) in which experiments are implemented. The use of a common framework enables engineering effort, along with experience and expertise, to be shared among many research groups. Recruitment of households to HomeLab can be organic: as a research group recruits (a few) households to participate in its field study, these households can be invited to join HomeLab and participate in future studies conducted by other groups. As the pool of households participating in HomeLab grows, we hope that researchers will find it easier to recruit a large number of households to participate in field studies.","Title: Augmenting homes with custom devices using .NET Gadgeteer and HomeOS.\nYear: 2012\n\nAbstract: We describe how two of our recent research technologies, .NET Gadgeteer and HomeOS, complement each other in enabling research using custom devices in home deployments. .NET Gadgeteer enables rapid prototyping of custom devices based on solderless assembly from a wide range of publicly available hardware modules, and managed C# software. HomeOS provides abstraction layers so that \"apps\" can be written which leverage devices in the home (e.g. wireless sensors, webcams) using standard APIs. In combination, HomeOS and .NET Gadgeteer make it easy to construct custom devices which interact with HomeOS apps. We showcase an example custom device and application a plumbing leak sensor and leak notification application.","Title: A general model of wireless interference\nYear: 2007\n\nAbstract: We develop a general model to estimate the throughput and goodput between arbitrary pairs of nodes in the presence of interference from other nodes in a wireless network. Our model is based on measurements from the underlying network itself and is thus more accurate than abstract models of RF propagation such as those based on distance. The seed measurements are easy to gather, requiring only O(N) measurements in an N-node networks. Compared to existing measurement-based models, our model advances the state of the art in three important ways. First, it goes beyond pairwise interference and models interference among an arbitrary number of senders. Second, it goes beyond broadcast transmissions and models the more common case of unicast transmissions. Third, it goes beyond homogeneous nodes and models the general case of heterogeneous nodes with different traffic demands and different radio characteristics. Using simulations and measurements from two different wireless testbeds, we show that the predictions of our model are accurate in a wide range of scenarios.","Title: Digital neighborhood watch: investigating the sharing of camera data amongst neighbors\nYear: 2013\n\nAbstract: In a neighborhood watch group, neighbors cooperate to prevent crime by sharing information and alerting police of suspicious activities. We propose a digital neighborhood watch (DNW) in which security cameras of individual homes work together to monitor the neighborhood. DNW could augment neighborhood watch by providing digital evidence of crime, increasing visibility of neighborhood activity, and automatically sending alerts when suspicious events occur. We investigate the appeal of sharing camera data with neighbors through semi-structured interviews with 11 households. Our participants validated the potential of sharing data with neighbors, particularly to provide evidence after an incident. But they also had security and privacy concerns about divulging their cameras' field of view and giving ongoing access to neighbors. For some participants, these concerns can be alleviated by enabling sharing of processed cameras views that include only the fore-ground activity or only public property (e.g., sidewalks).","Title: Predictable performance optimization for wireless networks\nYear: 2008\n\nAbstract: We present a novel approach to optimize the performance of IEEE 802.11-based multi-hop wireless networks. A unique feature of our approach is that it enables an accurate prediction of the resulting throughput of individual flows. At its heart lies a simple yet model of the network that captures interference, traffic, and MAC-induced dependencies. Unless properly accounted for, these dependencies lead to unpredictable behaviors. For instance, we show that even a simple network of two links with one flow is vulnerable to severe performance degradation. We design algorithms that build on this model to optimize the network for fairness and throughput. Given traffic demands as input, these algorithms compute rates at which individual flows must send to meet the objective. Evaluation using a multi-hop wireless testbed as well as simulations show that our approach is very effective. When optimizing for fairness, our methods result in close to perfect fairness. When optimizing for throughput, they lead to 100-200% improvement for UDP traffic and 10-50% for TCP traffic.","Title: Sampling biases in network path measurements and what to do about it\nYear: 2009\n\nAbstract: We show that currently prevalent practices for network path measurements can produce inaccurate inferences because of sampling biases. The inferred mean path latency can be more than a factor of two off the true mean. We present the Broom toolkit that has three methods to correct for this bias. Broom places no burden on the measurement process itself and can be applied post hoc to any measured data set. Our evaluation finds that two of the methods are particularly effective. One of them estimates missing path samples by embedding the nodes in a low-dimensional coordinate space. For realistic sampling rates, the quality of its estimates for path latency approximates ideal, unbiased sampling. The other method is based on a view of network paths as being composed of source-specific, destination-specific, and shared components. It reduces bias for a wide range of path properties, such as latency, hop count and capacity. Applying Broom to data from a real measurement study leads to substantial changes in the resulting inferences. For some networks, the post-correction estimate is 30% higher than the original.","Title: Measurement-based models of delivery and interference in static wireless networks\nYear: 2006\n\nAbstract: We present practical models for the physical layer behaviors of packet reception and carrier sense with interference in static wireless networks. These models use measurements of a real network rather than abstract RF propagation models as the basis for accuracy in complex environments. Seeding our models requires N trials in an N node network, in which each sender transmits in turn and receivers measure RSSI values and packet counts, both of which are easily obtainable. The models then predict packet delivery and throughput in the same network for different sets of transmitters with the same node placements. We evaluate our models for the base case of two senders that broadcast packets simultaneously. We find that they are effective at predicting when there will be significant interference effects. Across many predictions, we obtain an RMS error for 802.11a and 802.11b of a half and a third, respectively, of a measurement-based model that ignores interference.","Title: A provider-side view of web search response time\nYear: 2013\n\nAbstract: Using a large Web search service as a case study, we highlight the challenges that modern Web services face in understanding and diagnosing the response time experienced by users. We show that search response time (SRT) varies widely over time and also exhibits counter-intuitive behavior. It is actually higher during off-peak hours, when the query load is lower, than during peak hours. To resolve this paradox and explain SRT variations in general, we develop an analysis framework that separates systemic variations due to periodic changes in service usage and anomalous variations due to unanticipated events such as failures and denial-of-service attacks. We find that systemic SRT variations are primarily caused by systemic changes in aggregate network characteristics, nature of user queries, and browser types. For instance, one reason for higher SRTs during off-peak hours is that during those hours a greater fraction of queries come from slower, mainly-residential networks. We also develop a technique that, by factoring out the impact of such variations, robustly detects and diagnoses performance anomalies in SRT. Deployment experience shows that our technique detects three times more true (operator-verified) anomalies than existing techniques.","Title: Negotiation-based routing between neighboring ISPs\nYear: 2005\n\nAbstract: We explore negotiation as the basis for cooperation between competing entities, for the specific case of routing between two neighboring ISPs. Interdomain routing is often driven by self-interest and based on a limited view of the internetwork, which hurts the stability and efficiency of routing. We present a negotiation framework in which adjacent ISPs share information using coarse preferences and jointly decide the paths for the traffic flows they exchange. Our framework enables pairs of ISPs to agree on routing paths based on their specific relationship, even if they have different optimization criteria. We use simulation with over sixty measured ISP topologies to evaluate our framework. We find that the quality of negotiated routing is close to that of globally optimal routing that uses complete, detailed information about both ISPs. We also find that ISPs have incentive to negotiate because both of them benefit compared to routing independently based on local information.","Title: Human-guided machine learning for fast and accurate network alarm triage\nYear: 2011\n\nAbstract: Network alarm triage refers to grouping and prioritizing a stream of low-level device health information to help operators find and fix problems. Today, this process tends to be largely manual because existing rule-based tools cannot easily evolve with the network. We present CueT, a system that uses interactive machine learning to constantly learn from the triaging decisions of operators. It then uses that learning in novel visualizations to help them quickly and accurately triage alarms. Unlike prior interactive machine learning systems, CueT handles a highly dynamic environment where the groups of interest are not known a priori and evolve constantly. Our evaluations with real operators and data from a large network show that CueT significantly improves the speed and accuracy of alarm triage.","Title: High performance vehicular connectivity with opportunistic erasure coding\nYear: 2012\n\nAbstract: Motivated by poor network connectivity from moving vehicles, we develop a new loss recovery method called opportunistic erasure coding (OEC). Unlike existing erasure codingmethods, which are oblivious to the level of spare capacity along a path, OEC transmits coded packets only during instantaneous openings in a path's spare capacity. This transmission strategy ensures that coded packets provide as much protection as the level of spare capacity allows, without delaying or stealing capacity fromdata packets. OEC uses a novel encoding that greedily maximizes the amount of new data recovered by the receiver with each coded packet. We design and implement a system called PluriBus that uses OEC in the vehicular context. We deploy it on two buses for two months and show that PluriBus reduces the mean flow completion time by a factor of 4 for a realistic workload. We also show that OEC outperforms existing loss recovery methods in a range of lossy environments.","Title: How to build a research system in your spare time\nYear: 2010\n\nAbstract: This paper is based on a talk that I gave at CoNEXT 2009. Inspired by Hal Varian's paper on building economic models, it describes are search method for building computer systems. I find this method useful in my work and hope that some readers will find it helpful as well.","Title: Mutually controlled routing with independent ISPs\nYear: 2007\n\nAbstract: We present Wiser, an Internet routing protocol that enables ISPs to jointly control routing in a way that produces efficient end-to-end paths even when they act in their own interests. Wiser is a simple extension of BGP, uses only existing peering contracts for monetary exchange, and can be incrementally deployed. Each ISP selects paths in a way that presents a compromise between its own considerations and those of other ISPs. Done over many routes, this allows each ISP to improve its situation by its own optimization criteria compared to the use of BGP today. We evaluate Wiser using a routerlevel prototype and simulation on measured ISP topologies. We find that, unlike Internet routing today, Wiser consistently finds routes that are close in efficiency to that of global optimization for metrics such as path length. We further show that the overhead of Wiser is similar to that of BGP in terms of routing messages and computation.","Title: Analyzing the MAC-level behavior of wireless networks in the wild\nYear: 2006\n\nAbstract: We present Wit, a non-intrusive tool that builds on passive monitoring to analyze the detailed MAC-level behavior of operational wireless networks. Wit uses three processing steps to construct an enhanced trace of system activity. First, a robust merging procedure combines the necessarily incomplete views from multiple, independent monitors into a single, more complete trace of wireless activity. Next, a novel inference engine based on formal language methods reconstructs packets that were not captured by any monitor and determines whether each packet was received by its destination. Finally, Wit derives network performance measures from this enhanced trace; we show how to estimate the number of stations competing for the medium. We assess Wit with a mix of real traces and simulation tests. We find that merging and inference both significantly enhance the originally captured trace. We apply Wit to multi-monitor traces from a live network to show how it facilitates 802.11 MAC analyses that would otherwise be difficult or rely on less accurate heuristics.","Title: A case for adapting channel width in wireless networks\nYear: 2008\n\nAbstract: We study a fundamental yet under-explored facet in wireless communication -- the width of the spectrum over which transmitters spread their signals, or the channel width. Through detailed measurements in controlled and live environments, and using only commodity 802.11 hardware, we first quantify the impact of channel width on throughput, range, and power consumption. Taken together, our findings make a strong case for wireless systems that adapt channel width. Such adaptation brings unique benefits. For instance, when the throughput required is low, moving to a narrower channel increases range and reduces power consumption; in fixed-width systems, these two quantities are always in conflict. We then present a channel width adaptation algorithm, called SampleWidth, for the base case of two communicating nodes. This algorithm is based on a simple search process that builds on top of existing techniques for adapting modulation. Per specified policy, it can maximize throughput or minimize power consumption. Evaluation using a prototype implementation shows that SampleWidth correctly identities the optimal width under a range of scenarios. In our experiments with mobility, it increases throughput by more than 60% compared to the best fixed-width configuration.","Title: Sustaining cooperation in multi-hop wireless networks\nYear: 2005\n\nAbstract: Multi-hop wireless networks are vulnerable to free-riders because they require nodes to forward packets for each other. Deployed routing protocols ignore this issue while proposed solutions incorporate complicated mechanisms with the intent of making free-riding impossible. We present Catch, a protocol that falls between these extremes. It achieves nearly the low mechanism requirements of the former while imposing nearly as effective barriers to free-riding as the latter. Catch is made possible by novel techniques based on anonymous messages. These techniques enable cooperative nodes to detect nearby free-riders and disconnect them from the rest of the network. Catch has low overhead and is broadly applicable across routing protocols and traffic workloads. We evaluate it on an 802.11 wireless testbed as well as through simulation.","Title: Augmenting mobile 3G using WiFi\nYear: 2010\n\nAbstract: We investigate if WiFi access can be used to augment 3G capacity in mobile environments. We rst conduct a detailed study of 3G and WiFi access from moving vehicles, in three different cities. We find that the average 3G and WiFi availability across the cities is 87% and 11%, respectively. WiFi throughput is lower than 3G through-put, and WiFi loss rates are higher. We then design a system, called Wiffler, to augments mobile 3G capacity. It uses two key ideas leveraging delay tolerance and fast switching -- to overcome the poor availability and performance of WiFi. For delay tolerant applications, Wiffler uses a simple model of the environment to predict WiFi connectivity. It uses these predictions to delays transfers to offload more data on WiFi, but only if delaying reduces 3G usage and the transfers can be completed within the application's tolerance threshold. For applications that are extremely sensitive to delay or loss (e.g., VoIP), Wiffler quickly switches to 3G if WiFi is unable to successfully transmit the packet within a small time window. We implement and deploy Wiffler in our vehicular testbed. Our experiments show that Wiffler significantly reduces 3G usage. For a realistic workload, the reduction is 45% for a delay tolerance of 60 seconds.","Title: A study of the performance potential of DHT-based overlays\nYear: 2003\n\nAbstract: We use simulation to study whether overlays based on the recent distributed hash tables (DHTs) have the potential to deliver performance comparable to that of overlays based on measurements. Our work is motivated by the use of DHTs for services such as multicast, which is already targeted by measurement-based overlays; there is currently little understanding of how the two approaches compare at scales where both are viable. We compare three DHT-based overlays (CAN, Chord and Pastry) with two measurement-based overlays (Narada and NICE), as well as power-law random graphs (PLRGs) that represent Gnutella. To enable comparisons, we configure the overlays with the same average out-degree and focus on moderate scale. To gauge potential, we look at current and idealized DHT algorithms. We find that basic versions of DHTs have a latency stretch that is at least twice that of NICE and Narada, but similar performance in terms of bandwidth hotspots. However, DHT performance can be improved considerably with routing heuristics and topology-aware overlay construction, which have the potential to bring DHT performance at par with NICE. We also report on performance of overlays with power-law structure and the impact of hierarchy on performance.","Title: Detailed diagnosis in enterprise networks\nYear: 2009\n\nAbstract: By studying trouble tickets from small enterprise networks, we conclude that their operators need detailed fault diagnosis. That is, the diagnostic system should be able to diagnose not only generic faults (e.g., performance-related) but also application specific faults (e.g., error codes). It should also identify culprits at a fine granularity such as a process or firewall configuration. We build a system, called NetMedic, that enables detailed diagnosis by harnessing the rich information exposed by modern operating systems and applications. It formulates detailed diagnosis as an inference problem that more faithfully captures the behaviors and interactions of fine-grained network components such as processes. The primary challenge in solving this problem is inferring when a component might be impacting another. Our solution is based on an intuitive technique that uses the joint behavior of two components in the past to estimate the likelihood of them impacting one another in the present. We find that our deployed prototype is effective at diagnosing faults that we inject in a live environment. The faulty component is correctly identified as the most likely culprit in 80% of the cases and is almost always in the list of top five culprits.","Title: Differentially-private network trace analysis\nYear: 2010\n\nAbstract: We consider the potential for network trace analysis while providing the guarantees of \"differential privacy.\" While differential privacy provably obscures the presence or absence of individual records in a dataset, it has two major limitations: analyses must (presently) be expressed in a higher level declarative language; and the analysis results are randomized before returning to the analyst. We report on our experiences conducting a diverse set of analyses in a differentially private manner. We are able to express all of our target analyses, though for some of them an approximate expression is required to keep the error-level low. By running these analyses on real datasets, we find that the error introduced for the sake of privacy is often (but not always) low even at high levels of privacy. We factor our learning into a toolkit that will be likely useful for other analyses. Overall, we conclude that differential privacy shows promise for a broad class of network analyses.","Title: SystemSens: a tool for monitoring usage in smartphone research deployments\nYear: 2011\n\nAbstract: By deploying several research applications, we found that capturing usage context (e.g., CPU and memory) is highly valuable for debugging and interpreting results, even if that context information appears unrelated to the application. We have developed a general tool called SystemSens to help researchers capture usage context in their deployments in an extendible way. This paper describes and motivates the design choices underlying our tool and evaluates its overheads in terms of phone resources and data.","Title: A first look at traffic on smartphones\nYear: 2010\n\nAbstract: Using data from 43 users across two platforms, we present a detailed look at smartphone traffic. We find that browsing contributes over half of the traffic, while each of email, media, and maps contribute roughly 10%. We also find that the overhead of lower layer protocols is high because of small transfer sizes. For half of the transfers that use transport-level security, header bytes correspond to 40% of the total. We show that while packet loss is the main factor that limits the throughput of smartphone traffic, larger send buffers at Internet servers can improve the throughput of a quarter of the transfers. Finally, by studying the interaction between smartphone traffic and the radio power management policy, we find that the power consumption of the radio can be reduced by 35% with minimal impact on the performance of packet exchanges.","Title: Home automation in the wild: challenges and opportunities\nYear: 2011\n\nAbstract: Visions of smart homes have long caught the attention of researchers and considerable effort has been put toward enabling home automation. However, these technologies have not been widely adopted despite being available for over three decades. To gain insight into this state of affairs, we conducted semi-structured home visits to 14 households with home automation. The long term experience, both positive and negative, of the households we interviewed illustrates four barriers that need to be addressed before home automation becomes amenable to broader adoption. These barriers are high cost of ownership, inflexibility, poor manageability, and difficulty achieving security. Our findings also provide several directions for further research, which include eliminating the need for structural changes for installing home automation, providing users with simple security primitives that they can confidently configure, and enabling composition of home devices.","Title: Translating XSLT programs to Efficient SQL queries\nYear: 2002\n\nAbstract: We present an algorithm for translating XSLT programs into SQL. Our context is that of virtual XML publishing, in which a single XML view is defined from a relational database, and subsequently queried with XSLT programs. Each XSLT program is translated into a single SQL query and run entirely in the database engine. Our translation works for a large fragment of XSLT, which we define, that includes descendant/ancestor axis, recursive templates, modes, parameters, and aggregates. We put considerable effort in generating correct and efficient SQL queries and describe several optimization techniques to achieve this efficiency. We have tested our system on all 22 SQL queries of the TPC-H database benchmark which we represented in XSLT and then translated back to SQL using our translator.","Title: NetClinic: Interactive visualization to enhance automated fault diagnosis in enterprise networks\nYear: 2010\n\nAbstract: Diagnosing faults in an operational computer network is a frustrating, time-consuming exercise. Despite advances, automatic diagnostic tools are far from perfect: they occasionally miss the true culprit and are mostly only good at narrowing down the search to a few potential culprits. This uncertainty and the inability to extract useful sense from tool output renders most tools not usable to administrators. To bridge this gap, we present NetClinic, a visual analytics system that couples interactive visualization with an automated diagnostic tool for enterprise networks. It enables administrators to verify the output of the automatic analysis at different levels of detail and to move seamlessly across levels while retaining appropriate context. A qualitative user study shows that NetClinic users can accurately identify the culprit, even when it is not present in the suggestions made by the automated component. We also find that supporting a variety of sensemaking strategies is a key to the success of systems that enhance automated diagnosis.","Title: Controlling the cost of reliability in peer-to-peer overlays\nYear: 2003\n\nAbstract: Structured peer-to-peer overlay networks provide a useful substrate for building distributed applications but there are general concerns over the cost of maintaining these overlays. The current approach is to configure the overlays statically and conservatively to achieve the desired reliability even under uncommon adverse conditions. This results in high cost in the common case, or poor reliability in worse than expected conditions. We analyze the cost of overlay maintenance in realistic dynamic environments and design novel techniques to reduce this cost by adapting to the operating conditions. With our techniques, the concerns over the overlay maintenance cost are no longer warranted. Simulations using real traces show that they enable high reliability and performance even in very adverse conditions with low maintenance cost.","Title: Controlling high bandwidth aggregates in the network\nYear: 2002\n\nAbstract: The current Internet infrastructure has very few built-in protection mechanisms, and is therefore vulnerable to attacks and failures. In particular, recent events have illustrated the Internet's vulnerability to both denial of service (DoS) attacks and flash crowds in which one or more links in the network (or servers at the edge of the network) become severely congested. In both DoS attacks and flash crowds the congestion is due neither to a single flow, nor to a general increase in traffic, but to a well-defined subset of the traffic --- an aggregate. This paper proposes mechanisms for detecting and controlling such high bandwidth aggregates. Our design involves both a local mechanism for detecting and controlling an aggregate at a single router, and a cooperative pushback mechanism in which a router can ask upstream routers to control an aggregate. While certainly not a panacea, these mechanisms could provide some needed relief from flash crowds and flooding-style DoS attacks. The presentation in this paper is a first step towards a more rigorous evaluation of these mechanisms.","Title: Diagnosing mobile applications in the wild\nYear: 2010\n\nAbstract: There are a lot of applications that run on modern mobile operating systems. Inevitably, some of these applications fail in the hands of users. Diagnosing a failure to identify the culprit, or merely reproducing that failure in the lab is difficult. To get insight into this problem, we interviewed developers of five mobile applications and analyzed hundreds of trouble tickets. We find that support for diagnosing unexpected application behavior is lacking across major mobile platforms. Even when developers implement heavy-weight logging during controlled trials, they do not discover many dependencies that are then stressed in the wild. They are also not well-equipped to understand how to monitor the large number of dependencies without impacting the phone's limited resources such as CPU and battery. Based on these findings, we argue for three fundamental changes to failure reporting on mobile phones. The first is spatial spreading, which exploits the large number of phones in the field by spreading the monitoring work across them. The second is statistical inference, which builds a conditional distribution model between application behavior and its dependencies in the presence of partial information. The third is adaptive sampling, which dynamically varies what each phone monitors, to adapt to both the varying population of phones and what is being learned about each failure. We propose a system called MobiBug that combines these three techniques to simplify the task of diagnosing mobile applications.","Title: Uncovering performance differences among backbone ISPs with Netdiff\nYear: 2008\n\nAbstract: We design and implement Netdiff, a system that enables detailed performance comparisons among ISP networks. It helps customers and applications determine, for instance, which ISP offers the best performance for their specific workload. Netdiff is easy to deploy because it requires only a modest number of nodes and does not require active cooperation from ISPs. Realizing such a system, however, is challenging as we must aggressively reduce probing cost and ensure that the results are robust to measurement noise. We describe the techniques that Netdiff uses to address these challenges. Netdiff has been measuring eighteen backbone ISPs since February 2007. Its techniques allow it to capture an accurate view of an ISP's performance in terms of latency within fifteen minutes. Using Netdiff, we find that the relative performance of ISPs depends on many factors, including the geographic properties of traffic and the popularity of destinations. Thus, the detailed comparison that Netdiff provides is important for identifying ISPs that perform well for a given workload.","Title: User-level internet path diagnosis\nYear: 2003\n\nAbstract: Diagnosing faults in the Internet is arduous and time-consuming, in part because the network is composed of diverse components spread across many administrative domains. We consider an extreme form of this problem: can end users, with no special privileges, identify and pinpoint faults inside the network that degrade the performance of their applications? To answer this question, we present both an architecture for user-level Internet path diagnosis and a practical tool to diagnose paths in the current Internet. Our architecture requires only a small amount of network support, yet it is nearly as complete as analyzing a packet trace collected at all routers along the path. Our tool, tulip, diagnoses reordering, loss and significant queuing events by leveraging well deployed but little exploited router features that approximate our architecture. Tulip can locate points of reordering and loss to within three hops and queuing to within four hops on most paths that we measured. This granularity is comparable to that of a hypothetical network tomography tool that uses 65 diverse hosts to localize faults on a given path. We conclude by proposing several simple changes to the Internet to further improve its diagnostic capabilities.","Title: Timecard: controlling user-perceived delays in server-based mobile applications\nYear: 2013\n\nAbstract: Providing consistent response times to users of mobile applications is challenging because there are several variable delays between the start of a user's request and the completion of the response. These delays include location lookup, sensor data acquisition, radio wake-up, network transmissions, and processing on both the client and server. To allow applications to achieve consistent response times in the face of these variable delays, this paper presents the design, implementation, and evaluation of the Timecard system. Timecard provides two abstractions: the first returns the time elapsed since the user started the request, and the second returns an estimate of the time it would take to transmit the response from the server to the client and process the response at the client. With these abstractions, the server can adapt its processing time to control the end-to-end delay for the request. Implementing these abstractions requires Timecard to track delays across multiple asynchronous activities, handle time skew between client and server, and estimate network transfer times. Experiments with Timecard incorporated into two mobile applications show that the end-to-end delay is within 50 ms of the target delay of 1200 ms over 90% of the time.","Title: Achieving high utilization with software-driven WAN\nYear: 2013\n\nAbstract: We present SWAN, a system that boosts the utilization of inter-datacenter networks by centrally controlling when and how much traffic each service sends and frequently re-configuring the network's data plane to match current traffic demand. But done simplistically, these re-configurations can also cause severe, transient congestion because different switches may apply updates at different times. We develop a novel technique that leverages a small amount of scratch capacity on links to apply updates in a provably congestion-free manner, without making any assumptions about the order and timing of updates at individual switches. Further, to scale to large networks in the face of limited forwarding table capacity, SWAN greedily selects a small set of entries that can best satisfy current demand. It updates this set without disrupting traffic by leveraging a small amount of scratch capacity in forwarding tables. Experiments using a testbed prototype and data-driven simulations of two production networks show that SWAN carries 60% more traffic than the current practice.","Title: The causes of path inflation\nYear: 2003\n\nAbstract: Researchers have shown that the Internet exhibits path inflation -- end-to-end paths can be significantly longer than necessary. We present a trace-driven study of 65 ISPs that characterizes the root causes of path inflation, namely topology and routing policy choices within an ISP, between pairs of ISPs, and across the global Internet. To do so, we develop and validate novel techniques to infer intra-domain and peering policies from end-to-end measurements. We provide the first measured characterization of ISP peering policies. In addition to \"early-exit,\" we observe a significant degree of helpful non-early-exit, load-balancing, and other policies in use between peers. We find that traffic engineering (the explicit addition of policy constraints on top of topology constraints) is widespread in both intra- and inter-domain routing. However, intra-domain traffic engineering has minimal impact on path inflation, while peering policies and inter-domain routing lead to significant inflation. We argue that the underlying cause of inter-domain path inflation is the lack of BGP policy controls to provide convenient engineering of good paths across ISPs.","Title: The case for psychological computing\nYear: 2013\n\nAbstract: This paper envisions a new research direction that we call psychological computing. The key observation is that, even though computing systems are missioned to satisfy human needs, there has been little attempt to bring understandings of human need/psychology into core system design. This paper makes the case that percolating psychological insights deeper into the computing layers is valuable, even essential. Through examples from content caching, vehicular systems, and network scheduling, we argue that psychological awareness can not only offer performance gains to known technological problems, but also spawn new kinds of systems that are difficult to conceive otherwise.","Title: Optimizing cost and performance in online service provider networks\nYear: 2010\n\nAbstract: We present a method to jointly optimize the cost and the performance of delivering traffic from an online service provider (OSP) network to its users. Our method, called Entact, is based on two key techniques. First, it uses a novel route-injection mechanism to measure the performance of alternative paths that are not being currently used, without disturbing current traffic. Second, based on the cost, performance, traffic, and link capacity information, it computes the optimal cost vs. performance curve for the OSP. Each point on the curve represents a potential operating point for the OSP such that no other operating point offers a simultaneous improvement in cost and performance. The OSP can then pick the operating point that represents the desired trade-off (e.g., the \"sweet spot\"). We evaluate the benefit and overhead of Entact using trace-driven evaluation in a large OSP with 11 geographically distributed data centers. We find that by using Entact this OSP can reduce its traffic cost by 40% without any increase in path latency and with acceptably low overheads.","Title: Interactive wifi connectivity for moving vehicles\nYear: 2008\n\nAbstract: We ask if the ubiquity of WiFi can be leveraged to provide cheap connectivity from moving vehicles for common applications such as Web browsing and VoIP. Driven by this question, we conduct a study of connection quality available to vehicular WiFi clients based on measurements from testbeds in two different cities. We find that current WiFi handoff methods, in which clients communicate with one basestation at a time, lead to frequent disruptions in connectivity. We also find that clients can overcome many disruptions by communicating with multiple basestations simultaneously. These findings lead us to develop ViFi, a protocol that opportunistically exploits basestation diversity to minimize disruptions and support interactive applications for mobile clients. ViFi uses a decentralized and lightweight probabilistic algorithm for coordination between participating basestations. Our evaluation using a two-month long deployment and trace-driven simulations shows that its link-layer performance comes close to an ideal diversity-based protocol. Using two applications, VoIP and short TCP transfers, we show that the link layer performance improvement translates to better application performance. In our deployment, ViFi doubles the number of successful short TCP transfers and doubles the length of disruption-free VoIP sessions compared to an existing WiFi-style handoff protocol.","Title: CueT: human-guided fast and accurate network alarm triage\nYear: 2011\n\nAbstract: Network alarm triage refers to grouping and prioritizing a stream of low-level device health information to help operators find and fix problems. Today, this process tends to be largely manual because existing tools cannot easily evolve with the network. We present CueT, a system that uses interactive machine learning to learn from the triaging decisions of operators. It then uses that learning in novel visualizations to help them quickly and accurately triage alarms. Unlike prior interactive machine learning systems, CueT handles a highly dynamic environment where the groups of interest are not known a-priori and evolve constantly. A user study with real operators and data from a large network shows that CueT significantly improves the speed and accuracy of alarm triage compared to the network's current practice.","Title: Lab of things: a platform for conducting studies with connected devices in multiple homes\nYear: 2013\n\nAbstract: Researchers who develop new home technologies using connected devices often want to conduct large-scale field studies in homes to evaluate their technology, but conducting such studies today is extremely challenging. Inspired by the success of PlanetLab, which enabled development and evaluation of global network services, we are developing a shared infrastructure for home environments, called Lab of Things. Our goal is to substantially lower the barrier to developing and evaluating new technologies for the home environment.","Title: Measurement-based characterization of 802.11 in a hotspot setting\nYear: 2005\n\nAbstract: We analyze wireless measurements taken during the SIGCOMM 2004 conference to understand how well 802.11 operates in real deployments. We find that the overhead of 802.11 is high, with only 40% of the transmission time spent in sending original data. Most of the remaining time is consumed by retransmissions due to packet losses that are caused by both contention and transmission errors. Our analysis also shows that wireless nodes adapt their transmission rates with an extremely high frequency. We comment on the difficulties and opportunities of working with wireless traces, rather than the wired traces of wireless activity that are presently more common.","Title: Bolt: data management for connected homes\nYear: 2014\n\nAbstract: We present Bolt, a data management system for an emerging class of applications--those that manipulate data from connected devices in the home. It abstracts this data as a stream of time-tag-value records, with arbitrary, application-defined tags. For reliable sharing among applications, some of which may be running outside the home, Bolt uses untrusted cloud storage as seamless extension of local storage. It organizes data into chunks that contains multiple records and are individually compressed and encrypted. While chunking enables efficient transfer and storage, it also implies that data is retrieved at the granularity of chunks, instead of records. We show that the resulting overhead, however, is small because applications in this domain frequently query for multiple proximate records. We develop three diverse applications on top of Bolt and find that the performance needs of each are easily met. We also find that compared to OpenTSDB, a popular time-series database system, Bolt is up to 40 times faster than OpenTSDB while requiring 3-5 times less storage space.","Title: Advancing the state of home networking\nYear: 2011\n\nAbstract: Before building the network or its components, first understand the home and the behavior of its human inhabitants.","Title: On consistent updates in software defined networks\nYear: 2013\n\nAbstract: We argue for the development of efficient methods to update the data plane state of an SDN, while maintaining desired consistency properties (e.g., no packet should be dropped). We highlight the inherent trade-off between the strength of the consistency property and dependencies it imposes among rules at different switches; these dependencies fundamentally limit how quickly data plane can be updated. For one basic consistency property---no packet should loop---we develop an update algorithm that has provably minimal dependency structure. We also sketch a general architecture for consistent updates that separates the twin concerns of consistency and efficiency.","Title: The home needs an operating system (and an app store)\nYear: 2010\n\nAbstract: We argue that heterogeneity is hindering technological innovation in the home---homes differ in terms of their devices and how those devices are connected and used. To abstract these differences, we propose to develop a home-wide operating system. A HomeOS can simplify application development and let users easily add functionality by installing new devices or applications. The development of such an OS is an inherently inter-disciplinary exercise. Not only must the abstractions meet the usual goals of being efficient and easy to program, but the underlying primitives must also match how users want to manage and secure their home. We describe the preliminary design of HomeOS and our experience with developing applications for it.","Title: Latency inflation with MPLS-based traffic engineering\nYear: 2011\n\nAbstract: While MPLS has been extensively deployed in recent years, little is known about its behavior in practice. We examine the performance of MPLS in Microsoft's online service network (MSN), a well-provisioned multi-continent production network connecting tens of data centers. Using detailed traces collected over a 2-month period, we find that many paths experience significantly inflated latencies. We correlate occurrences of latency inflation with routers, links, and DC-pairs. This analysis sheds light on the causes of latency inflation and suggests several avenues for alleviating the problem.","Title: AppInsight: mobile app performance monitoring in the wild\nYear: 2012\n\nAbstract: The mobile-app marketplace is highly competitive. To maintain and improve the quality of their apps, developers need data about how their app is performing in the wild. The asynchronous, multithreaded nature of mobile apps makes tracing difficult. The difficulties are compounded by the resource limitations inherent in the mobile platform. To address this challenge, we develop AppInsight, a system that instruments mobile-app binaries to automatically identify the critical path in user transactions, across asynchronous-call boundaries. AppInsight is lightweight, it does not require any input from the developer, and it does not require any changes to the OS. We used AppInsight to instrument 30 marketplace apps, and carried out a field trial with 30 users for over 4 months. We report on the characteristics of the critical paths that AppInsight found in this data. We also give real-world examples of how AppInsight helped developers improve the quality of their app.","Title: Understanding wifi-based connectivity from moving vehicles\nYear: 2007\n\nAbstract: Using measurements from VanLAN, a modest-size testbed that we have deployed, we analyze the fundamental characteristics of WiFi-based connectivity between basestations and vehicles in urban settings. Our results uncover a more complex picture than previous work which was conducted in more benign settings. The interval between a vehicle coming into and going out of range of a basestation is often marred by intermittent periods of very poor connectivity. These \"gray periods\" are hard to reliably predict because their arrival is not signaled by metrics such as signal strength, loss rate, speed or distance from the basestation. At the same time, they also do not consistently occur at the same spot. Our analysis suggests that gray periods are not caused by the motion of the vehicle per se but by the variability in the urban radio environment combined with the vehicle traversing locations that are poorly covered by the basestation. We also find that knowledge of past connectivity can be used to identify regions where gray periods are more likely to occur as well as regions where the vehicle is likely to experience good connectivit.","Title: sTrack: Secure Tracking in Community Surveillance\nYear: 2014\n\nAbstract: We present sTrack, a system that can track objects across multiple cameras without sharing any visual information between two cameras except whether an object was seen by both. To achieve this challenging privacy goal, we leverage recent advances in secure two-party computation and multi-camera tracking. We derive a new distance metric learning technique that is more suited for secure computation. Compared to the existing methods, our technique has lower complexity in secure computation without sacrificing the tracking accuracy. We implement it using a new Boolean circuit for secure tracking. Experiments using real datasets show that the performance overhead of secure tracking is low, adding only a few seconds over non-private tracking.","Title: Gestalt: fast, unified fault localization for networked systems\nYear: 2014\n\nAbstract: We show that the performance of existing fault localization algorithms differs markedly for different networks; and no algorithm simultaneously provides high localization accuracy and low computational overhead. We develop a framework to explain these behaviors by anatomizing the algorithms with respect to six important characteristics of real networks, such as uncertain dependencies, noise, and covering relationships. We use this analysis to develop Gestalt, a new algorithm that combines the best elements of existing ones and includes a new technique to explore the space of fault hypotheses. We run experiments on three real, diverse networks. For each, Gestalt has either significantly higher localization accuracy or an order of magnitude lower running time. For example, when applied to the Lync messaging system that is used widely within corporations, Gestalt localizes faults with the same accuracy as Sherlock, while reducing fault localization time from days to 23 seconds.","Title: A Call to Arms for Management Plane Analytics\nYear: 2014\n\nAbstract: Over the last few decades, the networking community has developed numerous techniques for understanding how real networks behave through analyzing their data and control planes. In this paper, we call upon the community to similarly develop techniques to analyze the network management plane, that is, activities that underlie network design and operation. Such analytics can shed light on why a network behaves as observed and the relative merits of different management practices. While the management plane is often not directly observable, we argue that many relevant aspects can be inferred through data that most networks already gather (e.g., snapshots of configurations). Using preliminary analysis of such data from many large networks, we demonstrate the feasibility and the value of management plane analytics.","Title: Traffic engineering with forward fault correction\nYear: 2014\n\nAbstract: Faults such as link failures and high switch configuration delays can cause heavy congestion and packet loss. Because it takes time to detect and react to faults, these conditions can last long---even tens of seconds. We propose forward fault correction (FFC), a proactive approach to handling faults. FFC spreads network traffic such that freedom from congestion is guaranteed under arbitrary combinations of up to k faults. We show how FFC can be practically realized by compactly encoding the constraints that arise from this large number of possible faults and solving them efficiently using sorting networks. Experiments with data from real networks show that, with negligible loss in overall network throughput, FFC can reduce data loss by a factor of 7--130 in well-provisioned networks, and reduce the loss of high-priority traffic to almost zero in well-utilized networks.","Title: Dynamic scheduling of network updates\nYear: 2014\n\nAbstract: We present Dionysus, a system for fast, consistent network updates in software-defined networks. Dionysus encodes as a graph the consistency-related dependencies among updates at individual switches, and it then dynamically schedules these updates based on runtime differences in the update speeds of different switches. This dynamic scheduling is the key to its speed; prior update methods are slow because they pre-determine a schedule, which does not adapt to runtime conditions. Testbed experiments and data-driven simulations show that Dionysus improves the median update speed by 53--88% in both wide area and data center networks compared to prior methods.","Title: A network-state management service\nYear: 2014\n\nAbstract: We present Statesman, a network-state management service that allows multiple network management applications to operate independently, while maintaining network-wide safety and performance invariants. Network state captures various aspects of the network such as which links are alive and how switches are forwarding traffic. Statesman uses three views of the network state. In observed state, it maintains an up-to-date view of the actual network state. Applications read this state and propose state changes based on their individual goals. Using a model of dependencies among state variables, Statesman merges these proposed states into a target state that is guaranteed to maintain the safety and performance invariants. It then updates the network to the target state. Statesman has been deployed in ten Microsoft Azure datacenters for several months, and three distinct applications have been built on it. We use the experience from this deployment to demonstrate how Statesman enables each application to meet its goals, while maintaining network-wide invariants.","Title: Analyzing Protocol Implementations for Interoperability.\nYear: 2015\n\nAbstract: We propose PIC, a tool that helps developers search for non-interoperabilities in protocol implementations. We formulate this problem using intersection of the sets of messages that one protocol participant can send but another will reject as non-compliant. PIC leverages symbolic execution to characterize these sets and uses two novel techniques to scale to real-world implementations. First, it uses joint symbolic execution, in which receiver-side program analysis is constrained based on sender-side constraints, dramatically reducing the number of execution paths to consider. Second, it incorporates a search strategy that steers symbolic execution toward likely non-interoperabilities. We show that PIC is able to find multiple previously unknown noninteroperabilities in large and mature implementations of the SIP and SPDY (v2 through v3.1) protocols, some of which have since been fixed by the respective developers.","Title: A General Approach to Network Configuration Analysis.\nYear: 2015\n\nAbstract: We present an approach to detect network configuration errors, which combines the benefits of two prior approaches. Like prior techniques that analyze configuration files, our approach can find errors proactively, before the configuration is applied, and answer what if questions. Like prior techniques that analyze data-plane snapshots, our approach can check a broad range of forwarding properties and produce actual packets that violate checked properties. We accomplish this combination by faithfully deriving and then analyzing the data plane that would emerge from the configuration. Our derivation of the data plane is fully declarative, employing a set of logical relations that represent the control plane, the data plane, and their relationship. Operators can query these relations to understand identified errors and their provenance. We use our approach to analyze two large university networks with qualitatively different routing designs and find many misconfigurations in each. Operators have confirmed the majority of these as errors and have fixed their configurations accordingly.","Title: Measuring ISP topologies with Rocketfuel\nYear: 2002\n\nAbstract: To date, realistic ISP topologies have not been accessible to the research community, leaving work that depends on topology on an uncertain footing. In this paper, we present new Internet mapping techniques that have enabled us to directly measure router-level ISP topologies. Our techniques reduce the number of required traces compared to a brute-force, all-to-all approach by three orders of magnitude without a significant loss in accuracy. They include the use of BGP routing tables to focus the measurements, exploiting properties of IP routing to eliminate redundant measurements, better alias resolution, and the use of DNS to divide each map into POPs and backbone. We collect maps from ten diverse ISPs using our techniques, and find that our maps are substantially more complete than those of earlier Internet mapping efforts. We also report on properties of these maps, including the size of POPs, distribution of router outdegree, and the inter-domain peering structure. As part of this work, we release our maps to the community.","Title: Deployment Study Length: How Long Should a System Be Evaluated in the Wild?\nYear: 2015\n\nAbstract: There are many different ways to evaluate a novel interactive system. However, placing that system into the hands of real users and allowing them to use it as they would like in their natural environments may arguably be the best approach to understand if it \\\"really\\\" works. This is because findings learned from user studies conducted in the lab or a controlled setting are limited in external validity and therefore might not generalize beyond the studied usage scenario. Furthermore, the scenario used in a controlled study often lacks full authenticity, and thus it may not faithfully represent situations from the users' live.","Title: Lab of Things in Education\nYear: 2015\n\nAbstract: Lately, much has been said about the Internet of Things and how it is going to change how we will live, work and play. Strategy Analytics forecasts that by 2020, every person on the planet will carry four connected devices [1]. The majority of this growth will be driven by the interconnection of devices, sensors, smart objects and the like, and is expected to usher in changes to almost all aspects of our lives. This change will require, on the one hand, large-scale design, development and deployment of cloud and network systems; and on the other hand, design of hardware sensors, actuators, software middleware, and network protocols.","Title: Analyzing the Performance of an Anycast CDN\nYear: 2015\n\nAbstract: Content delivery networks must balance a number of trade-offs when deciding how to direct a client to a CDN server. Whereas DNS-based redirection requires a complex global traffic manager, anycast depends on BGP to direct a client to a CDN front-end. Anycast is simple to operate, scalable, and naturally resilient to DDoS attacks. This simplicity, however, comes at the cost of precise control of client redirection. We examine the performance implications of using anycast in a global, latency-sensitive, CDN. We analyze millions of client-side measurements from the Bing search service to capture anycast versus unicast performance to nearby front-ends. We find that anycast usually performs well despite the lack of precise control but that it directs roughly 20% of clients to a suboptimal front-end. We also show that the performance of these clients can be improved through a simple history-based prediction scheme.","Title: Management Plane Analytics\nYear: 2015\n\nAbstract: While it is generally held that network management is tedious and error-prone, it is not well understood which specific management practices increase the risk of failures. Indeed, our survey of 51 network operators reveals a significant diversity of opinions, and our characterization of the management practices in the 850+ networks of a large online service provider shows significant diversity in prevalent practices. Motivated by these observations, we develop a management plane analytics (MPA) framework that an organization can use to: (i) infer which management practices impact network health, and (ii) develop a predictive model of health, based on observed practices, to improve network management. We overcome the challenges of sparse and skewed data by aggregating data from many networks, reducing data dimensionality, and oversampling minority cases. Our learned models predict network health with an accuracy of 76-89%, and our causal analysis uncovers some high impact practices that operators thought had a low impact on network health. Our tool is publicly available, so organizations can analyze their own management practices.","Title: Systematically Exploring the Behavior of Control Programs\nYear: 2015\n\nAbstract: Many networked systems today, ranging from home automation networks to global wide-area networks, are operated using centralized control programs. Bugs in such programs pose serious risks to system security and stability. We develop a new technique to systematically explore the behavior of control programs. Because control programs depend intimately on absolute and relative timing of inputs, a key challenge that we face is to systematically handle time. We develop an approach that models programs as timed automata and incorporates novel mechanisms to enable scalable and comprehensive exploration. We implement our approach in a tool called DeLorean and apply it to real control programs for home automation and software-defined networks. DeLorean is able to finds bugs in these programs as well as provide significantly better code coverage--up to 94% compared to 76% for existing techniques.","Title: Packet-Level Telemetry in Large Datacenter Networks\nYear: 2015\n\nAbstract: Debugging faults in complex networks often requires capturing and analyzing traffic at the packet level. In this task, datacenter networks (DCNs) present unique challenges with their scale, traffic volume, and diversity of faults. To troubleshoot faults in a timely manner, DCN administrators must a) identify affected packets inside large volume of traffic; b) track them across multiple network components; c) analyze traffic traces for fault patterns; and d) test or confirm potential causes. To our knowledge, no tool today can achieve both the specificity and scale required for this task. We present Everflow, a packet-level network telemetry system for large DCNs. Everflow traces specific packets by implementing a powerful packet filter on top of \\\"match and mirror\\\" functionality of commodity switches. It shuffles captured packets to multiple analysis servers using load balancers built on switch ASICs, and it sends \\\"guided probes\\\" to test or confirm potential faults. We present experiments that demonstrate Everflow's scalability, and share experiences of troubleshooting network faults gathered from running it for over 6 months in Microsoft's DCNs.","Title: Consistent updates in software defined networks: On dependencies, loop freedom, and blackholes\nYear: 2016\n\nAbstract: We consider the problem of finding efficient methods to update forwarding rules in Software Defined Networks (SDNs). While the original and updated set of rules might both be consistent, disseminating the rule updates is an inherently asynchronous process, resulting in potentially inconsistent states. We highlight the inherent trade-off between the strength of the consistency property and the dependencies it imposes among rule updates at different switches; these dependencies fundamentally limit how quickly the SDN can be updated. Additionally, we consider the impact of resource constraints and show that fast blackhole free migration of rules with memory limits is NP-hard for the controller. For the basic consistency property of loop freedom, we prove that maximizing the number of loop free update rules is NP-hard for interval-based routing and longest-prefix matching. We also consider the basic case of just one destination in the network and show that the greedy approach can be nearly arbitrarily bad. However, minimizing the number of not updated rules can be approximated well for destination-based routing. For applying all updates, we develop an update algorithm that has a provably minimal dependency structure. We also sketch a general architecture for consistent updates that separates the twin concerns of consistency and efficiency, and lastly, evaluate our algorithm on ISP topologies.","Title: Beam: Ending Monolithic Applications for Connected Devices.\nYear: 2016\n\nAbstract: The proliferation of connected sensing devices (or Internet of Things) can in theory enable a range of applications that make rich inferences about users and their environment. But in practice developing such applications today is arduous because they must implement all data sensing and inference logic, even as devices move or are temporarily disconnected. We develop Beam, a framework that simplifies IoT applications by letting them specify \\\"what should be sensed or inferred,\\\" without worrying about \\\"how it is sensed or inferred.\\\" Beam introduces the key abstraction of an inference graph to decouple applications from the mechanics of sensing and drawing inferences. The inference graph allows Beam to address three important challenges: (1) device selection in heterogeneous environments, (2) efficient resource usage, and (3) handling device disconnections. Using Beam we develop two diverse applications that use several different types of devices and show that their implementations required up to 12× fewer source lines of code while resulting in up to 3× higher inference accuracy.","Title: Fast Control Plane Analysis Using an Abstract Representation.\nYear: 2016\n\nAbstract: Networks employ complex, and hence error-prone, routing control plane configurations. In many cases, the impact of errors manifests only under failures and leads to devastating effects. Thus, it is important to proactively verify control plane behavior under arbitrary link failures. State-of-the-art verifiers are either too slow or impractical to use for such verification tasks. In this paper we propose a new high level abstraction for control planes, ARC, that supports fast control plane analyses under arbitrary failures. ARC can check key invariants without generating the data plane--which is the main reason for current tools' ineffectiveness. This is possible because of the nature of verification tasks and the constrained nature of control plane designs in networks today. We develop algorithms to derive a network's ARC from its configuration files. Our evaluation over 314 networks shows that ARC computation is quick, and that ARC can verify key invariants in under 1s in most cases, which is orders-of-magnitude faster than the state-of-the-art.","Title: ProjecToR: Agile Reconfigurable Data Center Interconnect.\nYear: 2016\n\nAbstract: We explore a novel, free-space optics based approach for building data center interconnects. It uses a digital micromirror device (DMD) and mirror assembly combination as a transmitter and a photodetector on top of the rack as a receiver (Figure 1). Our approach enables all pairs of racks to establish direct links, and we can reconfigure such links (i.e., connect different rack pairs) within 12 us. To carry traffic from a source to a destination rack, transmitters and receivers in our interconnect can be dynamically linked in millions of ways. We develop topology construction and routing methods to exploit this flexibility, including a flow scheduling algorithm that is a constant factor approximation to the offline optimal solution. Experiments with a small prototype point to the feasibility of our approach. Simulations using realistic data center workloads show that, compared to the conventional folded-Clos interconnect, our approach can improve mean flow completion time by 30-95% and reduce cost by 25-40%.","Title: Don't Mind the Gap: Bridging Network-wide Objectives and Device-level Configurations.\nYear: 2016\n\nAbstract: We develop Propane, a language and compiler to help network operators with a challenging, error-prone task—bridging the gap between network-wide routing objectives and low-level configurations of devices that run complex, distributed protocols. The language allows operators to specify their objectives naturally, using high-level constraints on both the shape and relative preference of traffic paths. The compiler automatically translates these specifications to router-level BGP configurations, using an effective intermediate representation that compactly encodes the flow of routing information along policy-compliant paths. It guarantees that the compiled configurations correctly implement the specified policy under all possible combinations of failures. We show that Propane can effectively express the policies of datacenter and backbone networks of a large cloud provider; and despite its strong guarantees, our compiler scales to networks with hundreds or thousands of routers.","Title: Optical Layer Failures in a Large Backbone.\nYear: 2016\n\nAbstract: We analyze optical layer outages in a large backbone, using data for over a year from thousands of optical channels carrying live IP layer traffic. Our analysis uncovers several findings that can help improve network management and routing. For instance, we find that optical links have a wide range of availabilities, which questions the common assumption in fault-tolerant routing designs that all links have equal failure probabilities. We also find that by monitoring changes in optical signal quality (not visible at IP layer), we can better predict (probabilistically) future outages. Our results suggest that backbone traffic engineering strategies should consider current and past optical layer performance and route computation should be based on the outage-risk profile of the underlying optical links.","Title: Efficient Network Reachability Analysis Using a Succinct Control Plane Representation.\nYear: 2016\n\nAbstract: To guarantee network availability and security, operators must ensure that their reachability policies (e.g., A can or cannot talk to B) are correctly implemented. This is a difficult task due to the complexity of network configuration and the constant churn in a network's environment, e.g., new route announcements arrive and links fail. Current network reachability analysis techniques are limited as they can only reason about the current \\\"incarnation\\\" of the network, cannot analyze all configuration features, or are too slow to enable exploration of many environments. We build ERA, a tool for efficient reasoning about network reachability. Instead of reasoning about individual incarnations of the network, ERA directly reasons about the network \\\"control plane\\\" that generates these incarnations. We address key expressiveness and scalability challenges by building (i) a succinct model for the network control plane (i.e., various routing protocols and their interactions), and (ii) a repertoire of techniques for scalable (taking a few seconds for a network with > 1000 routers) exploration of this model. We have used ERA to successfully find both known and new violations of a range of common intended polices.","Title: Modeling Actuation Constraints for IoT Applications.\nYear: 2017\n\nAbstract: Internet of Things (IoT) promises to bring ease of monitoring, better efficiency and innovative services across many domains with connected devices around us. With information from critical parts of infrastructure and powerful cloud-based data analytics, many applications can be developed to gain insights about IoT systems as well as transform their capabilities. Actuation applications form an essential part of these IoT systems, as they enable automation as well as fast low-level decision making. However, modern IoT systems are designed for data acquisition, and actuation applications are implemented in an ad-hoc manner. We identify modeling constraints in a systematic manner as indispensable to support actuation applications because constraints encompass high-level policies dictated by laws of physics, legal policies, user preferences. We explore data models for constraints inIoT system with the example of a home heating system and illustrate the challenges in enforcing these constraints in theIoT system architecture.","Title: Network configuration synthesis with abstract topologies.\nYear: 2017\n\nAbstract: We develop Propane/AT, a system to synthesize provably-correct BGP (border gateway protocol) configurations for large, evolving networks from high-level specifications of topology, routing policy, and fault-tolerance requirements. Propane/AT is based on new abstractions for capturing parameterized network topologies and their evolution, and algorithms to analyze the impact of topology and routing policy on fault tolerance. Our algorithms operate entirely on abstract topologies. We prove that the properties established by our analyses hold for every concrete instantiation of the given abstract topology. Propane/AT also guarantees that only incremental changes to existing device configurations are required when the network evolves to add or remove devices and links. Our experiments with real-world topologies and policies show that our abstractions and algorithms are effective, and that, for large networks, Propane/AT synthesizes configurations two orders of magnitude faster than systems that operate on concrete topologies.","Title: Rail: A Case For Redundant Arrays Of Inexpensive Links In Data Center Networks\nYear: 2017\n\nAbstract: While there are many proposals to reduce the cost of data center networks (DCN), little attention has been paid to the role played by the physical links that carry packets. By studying over 300K optical links across many production DCNs, we show that these links are operating quite conservatively relative to the requirements in the IEEE standards. Motivated by this observation, to reduce DCN costs, we propose using transceivers-a key contributor to DCN cost-beyond their currently specified limit. Our experiments with multiple commodity transceivers show that their reach can be \"stretched\" 1.6 to 4 times their specification. However, with stretching, the performance of 1-5% of the DCN paths can fall below the IEEE standard. We develop RAIL, a system to ensure that in such a network, applications only use paths that meet their performance needs. Our proposal can reduce the network cost by up to 10% for 10Gbps networks and 44% for 40Gbps networks, without affecting the applications' performance.","Title: Efficiently Delivering Online Services Over Integrated Infrastructure\nYear: 2016\n\nAbstract: We present Footprint, a system for delivering online services in the \"integrated\" setting, where the same provider operates multiple elements of the infrastructure (e.g., proxies, data centers, and the wide area network). Such integration can boost system efficiency and performance by finely modulating how traffic enters and traverses the infrastructure. But fully realizing its benefits requires managing complex dynamics of service workloads. For instance, when a group of users are directed to a new proxy, their ongoing sessions continue to arrive at the old proxy, and this load at the old proxy declines gradually. Footprint harnesses such dynamics using a high-fidelity model that is also efficient to solve. Simulations based on a partial deployment of Footprint in Microsoft's infrastructure show that, compared to the current method, it can carry at least 50% more traffic and reduce user delays by at least 30%.","Title: Programming Distributed Control Planes: Invited Demo: Demo.\nYear: 2017\n\nAbstract: We describe Propane, a system that consists of a language for specifying the end-to-end routing policy for a network and a compiler for implementing the policy by generating a collection of device configurations for the BGP routing protocol that run on unmodified vendor hardware. Propane allows operators to describe their policy through high-level constraints on both the shape and preference of paths for different types of traffic. Constraints can describe paths both through the user's network, as well as through other networks that are not under the user's control. When Propane compiles a policy, the resulting BGP configurations are guaranteed to correctly implement the centralized policy in a distributed fashion -- without any centralized coordination and regardless of any number of network failures.","Title: Understanding and Mitigating Packet Corruption in Data Center Networks\nYear: 2017\n\nAbstract: We take a comprehensive look at packet corruption in data center networks, which leads to packet losses and application performance degradation. By studying 350K links across 15 production data centers, we find that the extent of corruption losses is significant and that its characteristics differ markedly from congestion losses. Corruption impacts fewer links than congestion, but imposes a heavier loss rate; and unlike congestion, corruption rate on a link is stable over time and is not correlated with its utilization. Based on these observations, we developed CorrOpt, a system to mitigate corruption. To minimize corruption losses, it intelligently selects which corrupting links can be safely disabled, while ensuring that each top-of-rack switch has a minimum number of paths to reach other switches. CorrOpt also recommends specific actions (e.g., replace cables, clean connectors) to repair disabled links, based on our analysis of common symptoms of different root causes of corruption. Our recommendation engine has been deployed in over seventy data centers of a large cloud provider. Our analysis shows that, compared to current state of the art, CorrOpt can reduce corruption losses by three to six orders of magnitude and improve repair accuracy by 60%.","Title: A General Approach to Network Configuration Verification\nYear: 2017\n\nAbstract: We present Minesweeper, a tool to verify that a network satisfies a wide range of intended properties such as reachability or isolation among nodes, waypointing, black holes, bounded path length, load-balancing, functional equivalence of two routers, and fault-tolerance. Minesweeper translates network configuration files into a logical formula that captures the stable states to which the network forwarding will converge as a result of interactions between routing protocols such as OSPF, BGP and static routes. It then combines the formula with constraints that describe the intended property. If the combined formula is satisfiable, there exists a stable state of the network in which the property does not hold. Otherwise, no stable state (if any) violates the property. We used Minesweeper to check four properties of 152 real networks from a large cloud provider. We found 120 violations, some of which are potentially serious security vulnerabilities. We also evaluated Minesweeper on synthetic benchmarks, and found that it can verify rich properties for networks with hundreds of routers in under five minutes. This performance is due to a suite of model-slicing and hoisting optimizations that we developed, which reduce runtime by over 460x for large networks.","Title: Automatically Repairing Network Control Planes Using an Abstract Representation.\nYear: 2017\n\nAbstract: The forwarding behavior of computer networks is governed by the configuration of distributed routing protocols and access filters---collectively known as the network control plane. Unfortunately, control plane configurations are often buggy, causing networks to violate important policies: e.g., specific traffic classes (defined in terms of source and destination endpoints) should always be able to reach their destination, or always traverse a waypoint. Manually repairing these configurations is daunting because of their inter-twined nature across routers, traffic classes, and policies.\n\nInspired by recent work in automatic program repair, we introduce CPR, a system that automatically computes correct, minimal repairs for network control planes. CPR casts configuration repair as a MaxSMT problem whose constraints are based on a digraph-based representation of a control plane's semantics. Crucially, this representation must capture the dependencies between traffic classes arising from the cross-traffic-class nature of control plane constructs. The MaxSMT formulation must account for these dependencies whilst also accounting for all policies and preferring repairs that minimize the size (e.g., number of lines) of the configuration changes. Using configurations from 96 data center networks, we show that CPR produces repairs in less than a minute for 98% of the networks, and these repairs requiring changing the same or fewer lines of configuration than hand-written repairs in 79% of cases.","Title: Control Plane Compression.\nYear: 2018\n\nAbstract: We develop an algorithm capable of compressing large networks into smaller ones with similar control plane behavior: For every stable routing solution in the large, original network, there exists a corresponding solution in the compressed network, and vice versa. Our compression algorithm preserves a wide variety of network properties including reachability, loop freedom, and path length. Consequently, operators may speed up network analysis, based on simulation, emulation, or verification, by analyzing only the compressed network. Our approach is based on a new theory of control plane equivalence. We implement these ideas in a tool called Bonsai and apply it to real and synthetic networks. Bonsai can shrink real networks by over a factor of 5 and speed up analysis by several orders of magnitude.","Title: Odin: Microsoft'S Scalable Fault-Tolerant Cdn Measurement System\nYear: 2018\n\nAbstract: Content delivery networks (CDNs) are critical for delivering high performance Internet services. Using worldwide deployments of front-ends, CDNs can direct users to the front-end that provides them with the best latency and availability. The key challenges arise from the heterogeneous connectivity of clients and the dynamic nature of the Internet that influences latency and availability. Without continuous insight on performance between users, front-ends, and external networks, CDNs will not be able to attain their full potential performance.We describe Odin, Microsoft's Internet measurement platform for its first-party and third-party customers. Odin is designed to handle Microsoft's large user base and need for large-scale measurements from users around the world. Odin integrates with Microsoft's varied suite of web-client and thick-client applications, all while being mindful of the regulatory and privacy concerns of enterprise customers. Odin has been operational for 2 years. We present the first detailed study of an Internet measurement platform of this scale and complexity.","Title: Efficient Verification Of Network Fault Tolerance Via Counterexample-Guided Refinement\nYear: 2019\n\nAbstract: We show how to verify that large data center networks satisfy key properties such as all-pairs reachability under a bounded number of faults. To scale the analysis, we develop algorithms that identify network symmetries and compute small abstract networks from large concrete ones. Using counter-example guided abstraction refinement, we successively refine the computed abstractions until the given property may be verified. The soundness of our approach relies on a novel notion of network approximation: routing paths in the concrete network are not precisely simulated by those in the abstract network but are guaranteed to be \"at least as good.\" We implement our algorithms in a tool called Origami and use them to verify reachability under faults for standard data center topologies. We find that Origami computes abstract networks with 1-3 orders of magnitude fewer edges, which makes it possible to verify large networks that are out of reach of existing techniques.","Title: Test coverage metrics for the network\nYear: 2021\n\nAbstract: ABSTRACTTesting and verification have emerged as key tools in the battle to improve the reliability of networks and the services they provide. However, the success of even the best technology of this sort is limited by how effectively it is applied, and in today's enormously complex industrial networks, it is surprisingly easy to overlook particular interfaces, routes, or flows when creating a test suite. Moreover, network engineers, unlike their software counterparts, have no help to battle this problem—there are no metrics or systems to compute the quality of their test suites or the extent to which their networks have been verified. To address this gap, we develop a general framework to define and compute network coverage for stateless network data planes. It computes coverage for a range of network components (\\EG, interfaces, devices, paths) and supports many types of tests (e.g., concrete versus symbolic; local versus end-to-end; tests that check network state versus those that analyze behavior). Our framework is based on the observation that any network dataplane component can be decomposed into forwarding rules and all types of tests ultimately exercise these rules using one or more packets. We build a system called Yardstick based on this framework and deploy it in Microsoft Azure. Within the first month of its deployment inside one of the production networks, it uncovered several testing gaps and helped improve testing by covering 89% more forwarding rules and 17% more network interfaces."],"string":"[\n \"Title: Understanding BGP misconfiguration\\nYear: 2002\\n\\nAbstract: It is well-known that simple, accidental BGP configuration errors can disrupt Internet connectivity. Yet little is known about the frequency of misconfiguration or its causes, except for the few spectacular incidents of widespread outages. In this paper, we present the first quantitative study of BGP misconfiguration. Over a three week period, we analyzed routing table advertisements from 23 vantage points across the Internet backbone to detect incidents of misconfiguration. For each incident we polled the ISP operators involved to verify whether it was a misconfiguration, and to learn the cause of the incident. We also actively probed the Internet to determine the impact of misconfiguration on connectivity.Surprisingly, we find that configuration errors are pervasive, with 200-1200 prefixes (0.2-1.0% of the BGP table size) suffering from misconfiguration each day. Close to 3 in 4 of all new prefix advertisements were results of misconfiguration. Fortunately, the connectivity seen by end users is surprisingly robust to misconfigurations. While misconfigurations can substantially increase the update load on routers, only one in twenty five affects connectivity. While the causes of misconfiguration are diverse, we argue that most could be prevented through better router design.\",\n \"Title: Glasnost: enabling end users to detect traffic differentiation\\nYear: 2010\\n\\nAbstract: Holding residential ISPs to their contractual or legal obligations of \\\"unlimited service\\\" or \\\"network neutrality\\\" is hard because their traffic management policies are opaque to end users and governmental regulatory agencies. We have built and deployed Glasnost, a system that improves network transparency by enabling ordinary Internet users to detect whether their ISPs are differentiating between flows of specific applications. We identify three key challenges in designing such a system: (a) to attract many users, the system must have low barrier of use and generate results in a timely manner, (b) the results must be robust to measurement noise and avoid false accusations of differentiation, which can adversely affect ISPs' reputation and business, (c) the system must include mechanisms to keep it up-to-date with the continuously changing differentiation policies of ISPs worldwide. We describe how Glasnost addresses each of these challenges. Glasnost has been operational for over a year. More than 350,000 users from over 5,800 ISPs worldwide have used Glasnost to detect differentiation, validating many of our design choices. We show how data from individual Glasnost users can be aggregated to provide regulators and monitors with useful information on ISP-wide deployment of various differentiation policies.\",\n \"Title: Diversity in smartphone usage\\nYear: 2010\\n\\nAbstract: Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.\",\n \"Title: Finding protocol manipulation attacks\\nYear: 2011\\n\\nAbstract: We develop a method to help discover manipulation attacks in protocol implementations. In these attacks, adversaries induce honest nodes to exhibit undesirable behaviors by misrepresenting their intent or network conditions. Our method is based on a novel combination of static analysis with symbolic execution and dynamic analysis with concrete execution. The former finds code paths that are likely vulnerable, and the latter emulates adversarial actions that lead to effective attacks. Our method is precise (i.e., no false positives) and we show that it scales to complex protocol implementations. We apply it to four diverse protocols, including TCP, the 802.11 MAC, ECN, and SCTP, and show that it is able to find all manipulation attacks that have been previously reported for these protocols. We also find a previously unreported attack for SCTP. This attack is a variant of a TCP attack but must be mounted differently in SCTP because of subtle semantic differences between the two protocols.\",\n \"Title: An operating system for the home\\nYear: 2012\\n\\nAbstract: Network devices for the home such as remotely controllable locks, lights, thermostats, cameras, and motion sensors are now readily available and inexpensive. In theory, this enables scenarios like remotely monitoring cameras from a smartphone or customizing climate control based on occupancy patterns. However, in practice today, such smarthome scenarios are limited to expert hobbyists and the rich because of the high overhead of managing and extending current technology. We present HomeOS, a platform that bridges this gap by presenting users and developers with a PC-like abstraction for technology in the home. It presents network devices as peripherals with abstract interfaces, enables cross-device tasks via applications written against these interfaces, and gives users a management interface designed for the home environment. HomeOS already has tens of applications and supports a wide range of devices. It has been running in 12 real homes for 4-8 months, and 42 students have built new applications and added support for additional devices independent of our efforts.\",\n \"Title: HomeLab: shared infrastructure for home technology field studies\\nYear: 2012\\n\\nAbstract: Researchers who develop new home technologies using connected devices (e.g. sensors) often want to conduct large-scale field studies in homes to evaluate their technology, but conducting such studies today is quite challenging, if not impossible. Considerable custom engineering is required to ensure hardware and software prototypes work robustly, and recruiting and managing more than a handful of households can be difficult and cost-prohibitive. To lower the barrier to developing and evaluating new technologies for the home environment, we call for the development of a shared infrastructure, called HomeLab. HomeLab consists of a large number of geographically distributed households, each running a common, flexible framework (e.g., HomeOS [4]) in which experiments are implemented. The use of a common framework enables engineering effort, along with experience and expertise, to be shared among many research groups. Recruitment of households to HomeLab can be organic: as a research group recruits (a few) households to participate in its field study, these households can be invited to join HomeLab and participate in future studies conducted by other groups. As the pool of households participating in HomeLab grows, we hope that researchers will find it easier to recruit a large number of households to participate in field studies.\",\n \"Title: Augmenting homes with custom devices using .NET Gadgeteer and HomeOS.\\nYear: 2012\\n\\nAbstract: We describe how two of our recent research technologies, .NET Gadgeteer and HomeOS, complement each other in enabling research using custom devices in home deployments. .NET Gadgeteer enables rapid prototyping of custom devices based on solderless assembly from a wide range of publicly available hardware modules, and managed C# software. HomeOS provides abstraction layers so that \\\"apps\\\" can be written which leverage devices in the home (e.g. wireless sensors, webcams) using standard APIs. In combination, HomeOS and .NET Gadgeteer make it easy to construct custom devices which interact with HomeOS apps. We showcase an example custom device and application a plumbing leak sensor and leak notification application.\",\n \"Title: A general model of wireless interference\\nYear: 2007\\n\\nAbstract: We develop a general model to estimate the throughput and goodput between arbitrary pairs of nodes in the presence of interference from other nodes in a wireless network. Our model is based on measurements from the underlying network itself and is thus more accurate than abstract models of RF propagation such as those based on distance. The seed measurements are easy to gather, requiring only O(N) measurements in an N-node networks. Compared to existing measurement-based models, our model advances the state of the art in three important ways. First, it goes beyond pairwise interference and models interference among an arbitrary number of senders. Second, it goes beyond broadcast transmissions and models the more common case of unicast transmissions. Third, it goes beyond homogeneous nodes and models the general case of heterogeneous nodes with different traffic demands and different radio characteristics. Using simulations and measurements from two different wireless testbeds, we show that the predictions of our model are accurate in a wide range of scenarios.\",\n \"Title: Digital neighborhood watch: investigating the sharing of camera data amongst neighbors\\nYear: 2013\\n\\nAbstract: In a neighborhood watch group, neighbors cooperate to prevent crime by sharing information and alerting police of suspicious activities. We propose a digital neighborhood watch (DNW) in which security cameras of individual homes work together to monitor the neighborhood. DNW could augment neighborhood watch by providing digital evidence of crime, increasing visibility of neighborhood activity, and automatically sending alerts when suspicious events occur. We investigate the appeal of sharing camera data with neighbors through semi-structured interviews with 11 households. Our participants validated the potential of sharing data with neighbors, particularly to provide evidence after an incident. But they also had security and privacy concerns about divulging their cameras' field of view and giving ongoing access to neighbors. For some participants, these concerns can be alleviated by enabling sharing of processed cameras views that include only the fore-ground activity or only public property (e.g., sidewalks).\",\n \"Title: Predictable performance optimization for wireless networks\\nYear: 2008\\n\\nAbstract: We present a novel approach to optimize the performance of IEEE 802.11-based multi-hop wireless networks. A unique feature of our approach is that it enables an accurate prediction of the resulting throughput of individual flows. At its heart lies a simple yet model of the network that captures interference, traffic, and MAC-induced dependencies. Unless properly accounted for, these dependencies lead to unpredictable behaviors. For instance, we show that even a simple network of two links with one flow is vulnerable to severe performance degradation. We design algorithms that build on this model to optimize the network for fairness and throughput. Given traffic demands as input, these algorithms compute rates at which individual flows must send to meet the objective. Evaluation using a multi-hop wireless testbed as well as simulations show that our approach is very effective. When optimizing for fairness, our methods result in close to perfect fairness. When optimizing for throughput, they lead to 100-200% improvement for UDP traffic and 10-50% for TCP traffic.\",\n \"Title: Sampling biases in network path measurements and what to do about it\\nYear: 2009\\n\\nAbstract: We show that currently prevalent practices for network path measurements can produce inaccurate inferences because of sampling biases. The inferred mean path latency can be more than a factor of two off the true mean. We present the Broom toolkit that has three methods to correct for this bias. Broom places no burden on the measurement process itself and can be applied post hoc to any measured data set. Our evaluation finds that two of the methods are particularly effective. One of them estimates missing path samples by embedding the nodes in a low-dimensional coordinate space. For realistic sampling rates, the quality of its estimates for path latency approximates ideal, unbiased sampling. The other method is based on a view of network paths as being composed of source-specific, destination-specific, and shared components. It reduces bias for a wide range of path properties, such as latency, hop count and capacity. Applying Broom to data from a real measurement study leads to substantial changes in the resulting inferences. For some networks, the post-correction estimate is 30% higher than the original.\",\n \"Title: Measurement-based models of delivery and interference in static wireless networks\\nYear: 2006\\n\\nAbstract: We present practical models for the physical layer behaviors of packet reception and carrier sense with interference in static wireless networks. These models use measurements of a real network rather than abstract RF propagation models as the basis for accuracy in complex environments. Seeding our models requires N trials in an N node network, in which each sender transmits in turn and receivers measure RSSI values and packet counts, both of which are easily obtainable. The models then predict packet delivery and throughput in the same network for different sets of transmitters with the same node placements. We evaluate our models for the base case of two senders that broadcast packets simultaneously. We find that they are effective at predicting when there will be significant interference effects. Across many predictions, we obtain an RMS error for 802.11a and 802.11b of a half and a third, respectively, of a measurement-based model that ignores interference.\",\n \"Title: A provider-side view of web search response time\\nYear: 2013\\n\\nAbstract: Using a large Web search service as a case study, we highlight the challenges that modern Web services face in understanding and diagnosing the response time experienced by users. We show that search response time (SRT) varies widely over time and also exhibits counter-intuitive behavior. It is actually higher during off-peak hours, when the query load is lower, than during peak hours. To resolve this paradox and explain SRT variations in general, we develop an analysis framework that separates systemic variations due to periodic changes in service usage and anomalous variations due to unanticipated events such as failures and denial-of-service attacks. We find that systemic SRT variations are primarily caused by systemic changes in aggregate network characteristics, nature of user queries, and browser types. For instance, one reason for higher SRTs during off-peak hours is that during those hours a greater fraction of queries come from slower, mainly-residential networks. We also develop a technique that, by factoring out the impact of such variations, robustly detects and diagnoses performance anomalies in SRT. Deployment experience shows that our technique detects three times more true (operator-verified) anomalies than existing techniques.\",\n \"Title: Negotiation-based routing between neighboring ISPs\\nYear: 2005\\n\\nAbstract: We explore negotiation as the basis for cooperation between competing entities, for the specific case of routing between two neighboring ISPs. Interdomain routing is often driven by self-interest and based on a limited view of the internetwork, which hurts the stability and efficiency of routing. We present a negotiation framework in which adjacent ISPs share information using coarse preferences and jointly decide the paths for the traffic flows they exchange. Our framework enables pairs of ISPs to agree on routing paths based on their specific relationship, even if they have different optimization criteria. We use simulation with over sixty measured ISP topologies to evaluate our framework. We find that the quality of negotiated routing is close to that of globally optimal routing that uses complete, detailed information about both ISPs. We also find that ISPs have incentive to negotiate because both of them benefit compared to routing independently based on local information.\",\n \"Title: Human-guided machine learning for fast and accurate network alarm triage\\nYear: 2011\\n\\nAbstract: Network alarm triage refers to grouping and prioritizing a stream of low-level device health information to help operators find and fix problems. Today, this process tends to be largely manual because existing rule-based tools cannot easily evolve with the network. We present CueT, a system that uses interactive machine learning to constantly learn from the triaging decisions of operators. It then uses that learning in novel visualizations to help them quickly and accurately triage alarms. Unlike prior interactive machine learning systems, CueT handles a highly dynamic environment where the groups of interest are not known a priori and evolve constantly. Our evaluations with real operators and data from a large network show that CueT significantly improves the speed and accuracy of alarm triage.\",\n \"Title: High performance vehicular connectivity with opportunistic erasure coding\\nYear: 2012\\n\\nAbstract: Motivated by poor network connectivity from moving vehicles, we develop a new loss recovery method called opportunistic erasure coding (OEC). Unlike existing erasure codingmethods, which are oblivious to the level of spare capacity along a path, OEC transmits coded packets only during instantaneous openings in a path's spare capacity. This transmission strategy ensures that coded packets provide as much protection as the level of spare capacity allows, without delaying or stealing capacity fromdata packets. OEC uses a novel encoding that greedily maximizes the amount of new data recovered by the receiver with each coded packet. We design and implement a system called PluriBus that uses OEC in the vehicular context. We deploy it on two buses for two months and show that PluriBus reduces the mean flow completion time by a factor of 4 for a realistic workload. We also show that OEC outperforms existing loss recovery methods in a range of lossy environments.\",\n \"Title: How to build a research system in your spare time\\nYear: 2010\\n\\nAbstract: This paper is based on a talk that I gave at CoNEXT 2009. Inspired by Hal Varian's paper on building economic models, it describes are search method for building computer systems. I find this method useful in my work and hope that some readers will find it helpful as well.\",\n \"Title: Mutually controlled routing with independent ISPs\\nYear: 2007\\n\\nAbstract: We present Wiser, an Internet routing protocol that enables ISPs to jointly control routing in a way that produces efficient end-to-end paths even when they act in their own interests. Wiser is a simple extension of BGP, uses only existing peering contracts for monetary exchange, and can be incrementally deployed. Each ISP selects paths in a way that presents a compromise between its own considerations and those of other ISPs. Done over many routes, this allows each ISP to improve its situation by its own optimization criteria compared to the use of BGP today. We evaluate Wiser using a routerlevel prototype and simulation on measured ISP topologies. We find that, unlike Internet routing today, Wiser consistently finds routes that are close in efficiency to that of global optimization for metrics such as path length. We further show that the overhead of Wiser is similar to that of BGP in terms of routing messages and computation.\",\n \"Title: Analyzing the MAC-level behavior of wireless networks in the wild\\nYear: 2006\\n\\nAbstract: We present Wit, a non-intrusive tool that builds on passive monitoring to analyze the detailed MAC-level behavior of operational wireless networks. Wit uses three processing steps to construct an enhanced trace of system activity. First, a robust merging procedure combines the necessarily incomplete views from multiple, independent monitors into a single, more complete trace of wireless activity. Next, a novel inference engine based on formal language methods reconstructs packets that were not captured by any monitor and determines whether each packet was received by its destination. Finally, Wit derives network performance measures from this enhanced trace; we show how to estimate the number of stations competing for the medium. We assess Wit with a mix of real traces and simulation tests. We find that merging and inference both significantly enhance the originally captured trace. We apply Wit to multi-monitor traces from a live network to show how it facilitates 802.11 MAC analyses that would otherwise be difficult or rely on less accurate heuristics.\",\n \"Title: A case for adapting channel width in wireless networks\\nYear: 2008\\n\\nAbstract: We study a fundamental yet under-explored facet in wireless communication -- the width of the spectrum over which transmitters spread their signals, or the channel width. Through detailed measurements in controlled and live environments, and using only commodity 802.11 hardware, we first quantify the impact of channel width on throughput, range, and power consumption. Taken together, our findings make a strong case for wireless systems that adapt channel width. Such adaptation brings unique benefits. For instance, when the throughput required is low, moving to a narrower channel increases range and reduces power consumption; in fixed-width systems, these two quantities are always in conflict. We then present a channel width adaptation algorithm, called SampleWidth, for the base case of two communicating nodes. This algorithm is based on a simple search process that builds on top of existing techniques for adapting modulation. Per specified policy, it can maximize throughput or minimize power consumption. Evaluation using a prototype implementation shows that SampleWidth correctly identities the optimal width under a range of scenarios. In our experiments with mobility, it increases throughput by more than 60% compared to the best fixed-width configuration.\",\n \"Title: Sustaining cooperation in multi-hop wireless networks\\nYear: 2005\\n\\nAbstract: Multi-hop wireless networks are vulnerable to free-riders because they require nodes to forward packets for each other. Deployed routing protocols ignore this issue while proposed solutions incorporate complicated mechanisms with the intent of making free-riding impossible. We present Catch, a protocol that falls between these extremes. It achieves nearly the low mechanism requirements of the former while imposing nearly as effective barriers to free-riding as the latter. Catch is made possible by novel techniques based on anonymous messages. These techniques enable cooperative nodes to detect nearby free-riders and disconnect them from the rest of the network. Catch has low overhead and is broadly applicable across routing protocols and traffic workloads. We evaluate it on an 802.11 wireless testbed as well as through simulation.\",\n \"Title: Augmenting mobile 3G using WiFi\\nYear: 2010\\n\\nAbstract: We investigate if WiFi access can be used to augment 3G capacity in mobile environments. We rst conduct a detailed study of 3G and WiFi access from moving vehicles, in three different cities. We find that the average 3G and WiFi availability across the cities is 87% and 11%, respectively. WiFi throughput is lower than 3G through-put, and WiFi loss rates are higher. We then design a system, called Wiffler, to augments mobile 3G capacity. It uses two key ideas leveraging delay tolerance and fast switching -- to overcome the poor availability and performance of WiFi. For delay tolerant applications, Wiffler uses a simple model of the environment to predict WiFi connectivity. It uses these predictions to delays transfers to offload more data on WiFi, but only if delaying reduces 3G usage and the transfers can be completed within the application's tolerance threshold. For applications that are extremely sensitive to delay or loss (e.g., VoIP), Wiffler quickly switches to 3G if WiFi is unable to successfully transmit the packet within a small time window. We implement and deploy Wiffler in our vehicular testbed. Our experiments show that Wiffler significantly reduces 3G usage. For a realistic workload, the reduction is 45% for a delay tolerance of 60 seconds.\",\n \"Title: A study of the performance potential of DHT-based overlays\\nYear: 2003\\n\\nAbstract: We use simulation to study whether overlays based on the recent distributed hash tables (DHTs) have the potential to deliver performance comparable to that of overlays based on measurements. Our work is motivated by the use of DHTs for services such as multicast, which is already targeted by measurement-based overlays; there is currently little understanding of how the two approaches compare at scales where both are viable. We compare three DHT-based overlays (CAN, Chord and Pastry) with two measurement-based overlays (Narada and NICE), as well as power-law random graphs (PLRGs) that represent Gnutella. To enable comparisons, we configure the overlays with the same average out-degree and focus on moderate scale. To gauge potential, we look at current and idealized DHT algorithms. We find that basic versions of DHTs have a latency stretch that is at least twice that of NICE and Narada, but similar performance in terms of bandwidth hotspots. However, DHT performance can be improved considerably with routing heuristics and topology-aware overlay construction, which have the potential to bring DHT performance at par with NICE. We also report on performance of overlays with power-law structure and the impact of hierarchy on performance.\",\n \"Title: Detailed diagnosis in enterprise networks\\nYear: 2009\\n\\nAbstract: By studying trouble tickets from small enterprise networks, we conclude that their operators need detailed fault diagnosis. That is, the diagnostic system should be able to diagnose not only generic faults (e.g., performance-related) but also application specific faults (e.g., error codes). It should also identify culprits at a fine granularity such as a process or firewall configuration. We build a system, called NetMedic, that enables detailed diagnosis by harnessing the rich information exposed by modern operating systems and applications. It formulates detailed diagnosis as an inference problem that more faithfully captures the behaviors and interactions of fine-grained network components such as processes. The primary challenge in solving this problem is inferring when a component might be impacting another. Our solution is based on an intuitive technique that uses the joint behavior of two components in the past to estimate the likelihood of them impacting one another in the present. We find that our deployed prototype is effective at diagnosing faults that we inject in a live environment. The faulty component is correctly identified as the most likely culprit in 80% of the cases and is almost always in the list of top five culprits.\",\n \"Title: Differentially-private network trace analysis\\nYear: 2010\\n\\nAbstract: We consider the potential for network trace analysis while providing the guarantees of \\\"differential privacy.\\\" While differential privacy provably obscures the presence or absence of individual records in a dataset, it has two major limitations: analyses must (presently) be expressed in a higher level declarative language; and the analysis results are randomized before returning to the analyst. We report on our experiences conducting a diverse set of analyses in a differentially private manner. We are able to express all of our target analyses, though for some of them an approximate expression is required to keep the error-level low. By running these analyses on real datasets, we find that the error introduced for the sake of privacy is often (but not always) low even at high levels of privacy. We factor our learning into a toolkit that will be likely useful for other analyses. Overall, we conclude that differential privacy shows promise for a broad class of network analyses.\",\n \"Title: SystemSens: a tool for monitoring usage in smartphone research deployments\\nYear: 2011\\n\\nAbstract: By deploying several research applications, we found that capturing usage context (e.g., CPU and memory) is highly valuable for debugging and interpreting results, even if that context information appears unrelated to the application. We have developed a general tool called SystemSens to help researchers capture usage context in their deployments in an extendible way. This paper describes and motivates the design choices underlying our tool and evaluates its overheads in terms of phone resources and data.\",\n \"Title: A first look at traffic on smartphones\\nYear: 2010\\n\\nAbstract: Using data from 43 users across two platforms, we present a detailed look at smartphone traffic. We find that browsing contributes over half of the traffic, while each of email, media, and maps contribute roughly 10%. We also find that the overhead of lower layer protocols is high because of small transfer sizes. For half of the transfers that use transport-level security, header bytes correspond to 40% of the total. We show that while packet loss is the main factor that limits the throughput of smartphone traffic, larger send buffers at Internet servers can improve the throughput of a quarter of the transfers. Finally, by studying the interaction between smartphone traffic and the radio power management policy, we find that the power consumption of the radio can be reduced by 35% with minimal impact on the performance of packet exchanges.\",\n \"Title: Home automation in the wild: challenges and opportunities\\nYear: 2011\\n\\nAbstract: Visions of smart homes have long caught the attention of researchers and considerable effort has been put toward enabling home automation. However, these technologies have not been widely adopted despite being available for over three decades. To gain insight into this state of affairs, we conducted semi-structured home visits to 14 households with home automation. The long term experience, both positive and negative, of the households we interviewed illustrates four barriers that need to be addressed before home automation becomes amenable to broader adoption. These barriers are high cost of ownership, inflexibility, poor manageability, and difficulty achieving security. Our findings also provide several directions for further research, which include eliminating the need for structural changes for installing home automation, providing users with simple security primitives that they can confidently configure, and enabling composition of home devices.\",\n \"Title: Translating XSLT programs to Efficient SQL queries\\nYear: 2002\\n\\nAbstract: We present an algorithm for translating XSLT programs into SQL. Our context is that of virtual XML publishing, in which a single XML view is defined from a relational database, and subsequently queried with XSLT programs. Each XSLT program is translated into a single SQL query and run entirely in the database engine. Our translation works for a large fragment of XSLT, which we define, that includes descendant/ancestor axis, recursive templates, modes, parameters, and aggregates. We put considerable effort in generating correct and efficient SQL queries and describe several optimization techniques to achieve this efficiency. We have tested our system on all 22 SQL queries of the TPC-H database benchmark which we represented in XSLT and then translated back to SQL using our translator.\",\n \"Title: NetClinic: Interactive visualization to enhance automated fault diagnosis in enterprise networks\\nYear: 2010\\n\\nAbstract: Diagnosing faults in an operational computer network is a frustrating, time-consuming exercise. Despite advances, automatic diagnostic tools are far from perfect: they occasionally miss the true culprit and are mostly only good at narrowing down the search to a few potential culprits. This uncertainty and the inability to extract useful sense from tool output renders most tools not usable to administrators. To bridge this gap, we present NetClinic, a visual analytics system that couples interactive visualization with an automated diagnostic tool for enterprise networks. It enables administrators to verify the output of the automatic analysis at different levels of detail and to move seamlessly across levels while retaining appropriate context. A qualitative user study shows that NetClinic users can accurately identify the culprit, even when it is not present in the suggestions made by the automated component. We also find that supporting a variety of sensemaking strategies is a key to the success of systems that enhance automated diagnosis.\",\n \"Title: Controlling the cost of reliability in peer-to-peer overlays\\nYear: 2003\\n\\nAbstract: Structured peer-to-peer overlay networks provide a useful substrate for building distributed applications but there are general concerns over the cost of maintaining these overlays. The current approach is to configure the overlays statically and conservatively to achieve the desired reliability even under uncommon adverse conditions. This results in high cost in the common case, or poor reliability in worse than expected conditions. We analyze the cost of overlay maintenance in realistic dynamic environments and design novel techniques to reduce this cost by adapting to the operating conditions. With our techniques, the concerns over the overlay maintenance cost are no longer warranted. Simulations using real traces show that they enable high reliability and performance even in very adverse conditions with low maintenance cost.\",\n \"Title: Controlling high bandwidth aggregates in the network\\nYear: 2002\\n\\nAbstract: The current Internet infrastructure has very few built-in protection mechanisms, and is therefore vulnerable to attacks and failures. In particular, recent events have illustrated the Internet's vulnerability to both denial of service (DoS) attacks and flash crowds in which one or more links in the network (or servers at the edge of the network) become severely congested. In both DoS attacks and flash crowds the congestion is due neither to a single flow, nor to a general increase in traffic, but to a well-defined subset of the traffic --- an aggregate. This paper proposes mechanisms for detecting and controlling such high bandwidth aggregates. Our design involves both a local mechanism for detecting and controlling an aggregate at a single router, and a cooperative pushback mechanism in which a router can ask upstream routers to control an aggregate. While certainly not a panacea, these mechanisms could provide some needed relief from flash crowds and flooding-style DoS attacks. The presentation in this paper is a first step towards a more rigorous evaluation of these mechanisms.\",\n \"Title: Diagnosing mobile applications in the wild\\nYear: 2010\\n\\nAbstract: There are a lot of applications that run on modern mobile operating systems. Inevitably, some of these applications fail in the hands of users. Diagnosing a failure to identify the culprit, or merely reproducing that failure in the lab is difficult. To get insight into this problem, we interviewed developers of five mobile applications and analyzed hundreds of trouble tickets. We find that support for diagnosing unexpected application behavior is lacking across major mobile platforms. Even when developers implement heavy-weight logging during controlled trials, they do not discover many dependencies that are then stressed in the wild. They are also not well-equipped to understand how to monitor the large number of dependencies without impacting the phone's limited resources such as CPU and battery. Based on these findings, we argue for three fundamental changes to failure reporting on mobile phones. The first is spatial spreading, which exploits the large number of phones in the field by spreading the monitoring work across them. The second is statistical inference, which builds a conditional distribution model between application behavior and its dependencies in the presence of partial information. The third is adaptive sampling, which dynamically varies what each phone monitors, to adapt to both the varying population of phones and what is being learned about each failure. We propose a system called MobiBug that combines these three techniques to simplify the task of diagnosing mobile applications.\",\n \"Title: Uncovering performance differences among backbone ISPs with Netdiff\\nYear: 2008\\n\\nAbstract: We design and implement Netdiff, a system that enables detailed performance comparisons among ISP networks. It helps customers and applications determine, for instance, which ISP offers the best performance for their specific workload. Netdiff is easy to deploy because it requires only a modest number of nodes and does not require active cooperation from ISPs. Realizing such a system, however, is challenging as we must aggressively reduce probing cost and ensure that the results are robust to measurement noise. We describe the techniques that Netdiff uses to address these challenges. Netdiff has been measuring eighteen backbone ISPs since February 2007. Its techniques allow it to capture an accurate view of an ISP's performance in terms of latency within fifteen minutes. Using Netdiff, we find that the relative performance of ISPs depends on many factors, including the geographic properties of traffic and the popularity of destinations. Thus, the detailed comparison that Netdiff provides is important for identifying ISPs that perform well for a given workload.\",\n \"Title: User-level internet path diagnosis\\nYear: 2003\\n\\nAbstract: Diagnosing faults in the Internet is arduous and time-consuming, in part because the network is composed of diverse components spread across many administrative domains. We consider an extreme form of this problem: can end users, with no special privileges, identify and pinpoint faults inside the network that degrade the performance of their applications? To answer this question, we present both an architecture for user-level Internet path diagnosis and a practical tool to diagnose paths in the current Internet. Our architecture requires only a small amount of network support, yet it is nearly as complete as analyzing a packet trace collected at all routers along the path. Our tool, tulip, diagnoses reordering, loss and significant queuing events by leveraging well deployed but little exploited router features that approximate our architecture. Tulip can locate points of reordering and loss to within three hops and queuing to within four hops on most paths that we measured. This granularity is comparable to that of a hypothetical network tomography tool that uses 65 diverse hosts to localize faults on a given path. We conclude by proposing several simple changes to the Internet to further improve its diagnostic capabilities.\",\n \"Title: Timecard: controlling user-perceived delays in server-based mobile applications\\nYear: 2013\\n\\nAbstract: Providing consistent response times to users of mobile applications is challenging because there are several variable delays between the start of a user's request and the completion of the response. These delays include location lookup, sensor data acquisition, radio wake-up, network transmissions, and processing on both the client and server. To allow applications to achieve consistent response times in the face of these variable delays, this paper presents the design, implementation, and evaluation of the Timecard system. Timecard provides two abstractions: the first returns the time elapsed since the user started the request, and the second returns an estimate of the time it would take to transmit the response from the server to the client and process the response at the client. With these abstractions, the server can adapt its processing time to control the end-to-end delay for the request. Implementing these abstractions requires Timecard to track delays across multiple asynchronous activities, handle time skew between client and server, and estimate network transfer times. Experiments with Timecard incorporated into two mobile applications show that the end-to-end delay is within 50 ms of the target delay of 1200 ms over 90% of the time.\",\n \"Title: Achieving high utilization with software-driven WAN\\nYear: 2013\\n\\nAbstract: We present SWAN, a system that boosts the utilization of inter-datacenter networks by centrally controlling when and how much traffic each service sends and frequently re-configuring the network's data plane to match current traffic demand. But done simplistically, these re-configurations can also cause severe, transient congestion because different switches may apply updates at different times. We develop a novel technique that leverages a small amount of scratch capacity on links to apply updates in a provably congestion-free manner, without making any assumptions about the order and timing of updates at individual switches. Further, to scale to large networks in the face of limited forwarding table capacity, SWAN greedily selects a small set of entries that can best satisfy current demand. It updates this set without disrupting traffic by leveraging a small amount of scratch capacity in forwarding tables. Experiments using a testbed prototype and data-driven simulations of two production networks show that SWAN carries 60% more traffic than the current practice.\",\n \"Title: The causes of path inflation\\nYear: 2003\\n\\nAbstract: Researchers have shown that the Internet exhibits path inflation -- end-to-end paths can be significantly longer than necessary. We present a trace-driven study of 65 ISPs that characterizes the root causes of path inflation, namely topology and routing policy choices within an ISP, between pairs of ISPs, and across the global Internet. To do so, we develop and validate novel techniques to infer intra-domain and peering policies from end-to-end measurements. We provide the first measured characterization of ISP peering policies. In addition to \\\"early-exit,\\\" we observe a significant degree of helpful non-early-exit, load-balancing, and other policies in use between peers. We find that traffic engineering (the explicit addition of policy constraints on top of topology constraints) is widespread in both intra- and inter-domain routing. However, intra-domain traffic engineering has minimal impact on path inflation, while peering policies and inter-domain routing lead to significant inflation. We argue that the underlying cause of inter-domain path inflation is the lack of BGP policy controls to provide convenient engineering of good paths across ISPs.\",\n \"Title: The case for psychological computing\\nYear: 2013\\n\\nAbstract: This paper envisions a new research direction that we call psychological computing. The key observation is that, even though computing systems are missioned to satisfy human needs, there has been little attempt to bring understandings of human need/psychology into core system design. This paper makes the case that percolating psychological insights deeper into the computing layers is valuable, even essential. Through examples from content caching, vehicular systems, and network scheduling, we argue that psychological awareness can not only offer performance gains to known technological problems, but also spawn new kinds of systems that are difficult to conceive otherwise.\",\n \"Title: Optimizing cost and performance in online service provider networks\\nYear: 2010\\n\\nAbstract: We present a method to jointly optimize the cost and the performance of delivering traffic from an online service provider (OSP) network to its users. Our method, called Entact, is based on two key techniques. First, it uses a novel route-injection mechanism to measure the performance of alternative paths that are not being currently used, without disturbing current traffic. Second, based on the cost, performance, traffic, and link capacity information, it computes the optimal cost vs. performance curve for the OSP. Each point on the curve represents a potential operating point for the OSP such that no other operating point offers a simultaneous improvement in cost and performance. The OSP can then pick the operating point that represents the desired trade-off (e.g., the \\\"sweet spot\\\"). We evaluate the benefit and overhead of Entact using trace-driven evaluation in a large OSP with 11 geographically distributed data centers. We find that by using Entact this OSP can reduce its traffic cost by 40% without any increase in path latency and with acceptably low overheads.\",\n \"Title: Interactive wifi connectivity for moving vehicles\\nYear: 2008\\n\\nAbstract: We ask if the ubiquity of WiFi can be leveraged to provide cheap connectivity from moving vehicles for common applications such as Web browsing and VoIP. Driven by this question, we conduct a study of connection quality available to vehicular WiFi clients based on measurements from testbeds in two different cities. We find that current WiFi handoff methods, in which clients communicate with one basestation at a time, lead to frequent disruptions in connectivity. We also find that clients can overcome many disruptions by communicating with multiple basestations simultaneously. These findings lead us to develop ViFi, a protocol that opportunistically exploits basestation diversity to minimize disruptions and support interactive applications for mobile clients. ViFi uses a decentralized and lightweight probabilistic algorithm for coordination between participating basestations. Our evaluation using a two-month long deployment and trace-driven simulations shows that its link-layer performance comes close to an ideal diversity-based protocol. Using two applications, VoIP and short TCP transfers, we show that the link layer performance improvement translates to better application performance. In our deployment, ViFi doubles the number of successful short TCP transfers and doubles the length of disruption-free VoIP sessions compared to an existing WiFi-style handoff protocol.\",\n \"Title: CueT: human-guided fast and accurate network alarm triage\\nYear: 2011\\n\\nAbstract: Network alarm triage refers to grouping and prioritizing a stream of low-level device health information to help operators find and fix problems. Today, this process tends to be largely manual because existing tools cannot easily evolve with the network. We present CueT, a system that uses interactive machine learning to learn from the triaging decisions of operators. It then uses that learning in novel visualizations to help them quickly and accurately triage alarms. Unlike prior interactive machine learning systems, CueT handles a highly dynamic environment where the groups of interest are not known a-priori and evolve constantly. A user study with real operators and data from a large network shows that CueT significantly improves the speed and accuracy of alarm triage compared to the network's current practice.\",\n \"Title: Lab of things: a platform for conducting studies with connected devices in multiple homes\\nYear: 2013\\n\\nAbstract: Researchers who develop new home technologies using connected devices often want to conduct large-scale field studies in homes to evaluate their technology, but conducting such studies today is extremely challenging. Inspired by the success of PlanetLab, which enabled development and evaluation of global network services, we are developing a shared infrastructure for home environments, called Lab of Things. Our goal is to substantially lower the barrier to developing and evaluating new technologies for the home environment.\",\n \"Title: Measurement-based characterization of 802.11 in a hotspot setting\\nYear: 2005\\n\\nAbstract: We analyze wireless measurements taken during the SIGCOMM 2004 conference to understand how well 802.11 operates in real deployments. We find that the overhead of 802.11 is high, with only 40% of the transmission time spent in sending original data. Most of the remaining time is consumed by retransmissions due to packet losses that are caused by both contention and transmission errors. Our analysis also shows that wireless nodes adapt their transmission rates with an extremely high frequency. We comment on the difficulties and opportunities of working with wireless traces, rather than the wired traces of wireless activity that are presently more common.\",\n \"Title: Bolt: data management for connected homes\\nYear: 2014\\n\\nAbstract: We present Bolt, a data management system for an emerging class of applications--those that manipulate data from connected devices in the home. It abstracts this data as a stream of time-tag-value records, with arbitrary, application-defined tags. For reliable sharing among applications, some of which may be running outside the home, Bolt uses untrusted cloud storage as seamless extension of local storage. It organizes data into chunks that contains multiple records and are individually compressed and encrypted. While chunking enables efficient transfer and storage, it also implies that data is retrieved at the granularity of chunks, instead of records. We show that the resulting overhead, however, is small because applications in this domain frequently query for multiple proximate records. We develop three diverse applications on top of Bolt and find that the performance needs of each are easily met. We also find that compared to OpenTSDB, a popular time-series database system, Bolt is up to 40 times faster than OpenTSDB while requiring 3-5 times less storage space.\",\n \"Title: Advancing the state of home networking\\nYear: 2011\\n\\nAbstract: Before building the network or its components, first understand the home and the behavior of its human inhabitants.\",\n \"Title: On consistent updates in software defined networks\\nYear: 2013\\n\\nAbstract: We argue for the development of efficient methods to update the data plane state of an SDN, while maintaining desired consistency properties (e.g., no packet should be dropped). We highlight the inherent trade-off between the strength of the consistency property and dependencies it imposes among rules at different switches; these dependencies fundamentally limit how quickly data plane can be updated. For one basic consistency property---no packet should loop---we develop an update algorithm that has provably minimal dependency structure. We also sketch a general architecture for consistent updates that separates the twin concerns of consistency and efficiency.\",\n \"Title: The home needs an operating system (and an app store)\\nYear: 2010\\n\\nAbstract: We argue that heterogeneity is hindering technological innovation in the home---homes differ in terms of their devices and how those devices are connected and used. To abstract these differences, we propose to develop a home-wide operating system. A HomeOS can simplify application development and let users easily add functionality by installing new devices or applications. The development of such an OS is an inherently inter-disciplinary exercise. Not only must the abstractions meet the usual goals of being efficient and easy to program, but the underlying primitives must also match how users want to manage and secure their home. We describe the preliminary design of HomeOS and our experience with developing applications for it.\",\n \"Title: Latency inflation with MPLS-based traffic engineering\\nYear: 2011\\n\\nAbstract: While MPLS has been extensively deployed in recent years, little is known about its behavior in practice. We examine the performance of MPLS in Microsoft's online service network (MSN), a well-provisioned multi-continent production network connecting tens of data centers. Using detailed traces collected over a 2-month period, we find that many paths experience significantly inflated latencies. We correlate occurrences of latency inflation with routers, links, and DC-pairs. This analysis sheds light on the causes of latency inflation and suggests several avenues for alleviating the problem.\",\n \"Title: AppInsight: mobile app performance monitoring in the wild\\nYear: 2012\\n\\nAbstract: The mobile-app marketplace is highly competitive. To maintain and improve the quality of their apps, developers need data about how their app is performing in the wild. The asynchronous, multithreaded nature of mobile apps makes tracing difficult. The difficulties are compounded by the resource limitations inherent in the mobile platform. To address this challenge, we develop AppInsight, a system that instruments mobile-app binaries to automatically identify the critical path in user transactions, across asynchronous-call boundaries. AppInsight is lightweight, it does not require any input from the developer, and it does not require any changes to the OS. We used AppInsight to instrument 30 marketplace apps, and carried out a field trial with 30 users for over 4 months. We report on the characteristics of the critical paths that AppInsight found in this data. We also give real-world examples of how AppInsight helped developers improve the quality of their app.\",\n \"Title: Understanding wifi-based connectivity from moving vehicles\\nYear: 2007\\n\\nAbstract: Using measurements from VanLAN, a modest-size testbed that we have deployed, we analyze the fundamental characteristics of WiFi-based connectivity between basestations and vehicles in urban settings. Our results uncover a more complex picture than previous work which was conducted in more benign settings. The interval between a vehicle coming into and going out of range of a basestation is often marred by intermittent periods of very poor connectivity. These \\\"gray periods\\\" are hard to reliably predict because their arrival is not signaled by metrics such as signal strength, loss rate, speed or distance from the basestation. At the same time, they also do not consistently occur at the same spot. Our analysis suggests that gray periods are not caused by the motion of the vehicle per se but by the variability in the urban radio environment combined with the vehicle traversing locations that are poorly covered by the basestation. We also find that knowledge of past connectivity can be used to identify regions where gray periods are more likely to occur as well as regions where the vehicle is likely to experience good connectivit.\",\n \"Title: sTrack: Secure Tracking in Community Surveillance\\nYear: 2014\\n\\nAbstract: We present sTrack, a system that can track objects across multiple cameras without sharing any visual information between two cameras except whether an object was seen by both. To achieve this challenging privacy goal, we leverage recent advances in secure two-party computation and multi-camera tracking. We derive a new distance metric learning technique that is more suited for secure computation. Compared to the existing methods, our technique has lower complexity in secure computation without sacrificing the tracking accuracy. We implement it using a new Boolean circuit for secure tracking. Experiments using real datasets show that the performance overhead of secure tracking is low, adding only a few seconds over non-private tracking.\",\n \"Title: Gestalt: fast, unified fault localization for networked systems\\nYear: 2014\\n\\nAbstract: We show that the performance of existing fault localization algorithms differs markedly for different networks; and no algorithm simultaneously provides high localization accuracy and low computational overhead. We develop a framework to explain these behaviors by anatomizing the algorithms with respect to six important characteristics of real networks, such as uncertain dependencies, noise, and covering relationships. We use this analysis to develop Gestalt, a new algorithm that combines the best elements of existing ones and includes a new technique to explore the space of fault hypotheses. We run experiments on three real, diverse networks. For each, Gestalt has either significantly higher localization accuracy or an order of magnitude lower running time. For example, when applied to the Lync messaging system that is used widely within corporations, Gestalt localizes faults with the same accuracy as Sherlock, while reducing fault localization time from days to 23 seconds.\",\n \"Title: A Call to Arms for Management Plane Analytics\\nYear: 2014\\n\\nAbstract: Over the last few decades, the networking community has developed numerous techniques for understanding how real networks behave through analyzing their data and control planes. In this paper, we call upon the community to similarly develop techniques to analyze the network management plane, that is, activities that underlie network design and operation. Such analytics can shed light on why a network behaves as observed and the relative merits of different management practices. While the management plane is often not directly observable, we argue that many relevant aspects can be inferred through data that most networks already gather (e.g., snapshots of configurations). Using preliminary analysis of such data from many large networks, we demonstrate the feasibility and the value of management plane analytics.\",\n \"Title: Traffic engineering with forward fault correction\\nYear: 2014\\n\\nAbstract: Faults such as link failures and high switch configuration delays can cause heavy congestion and packet loss. Because it takes time to detect and react to faults, these conditions can last long---even tens of seconds. We propose forward fault correction (FFC), a proactive approach to handling faults. FFC spreads network traffic such that freedom from congestion is guaranteed under arbitrary combinations of up to k faults. We show how FFC can be practically realized by compactly encoding the constraints that arise from this large number of possible faults and solving them efficiently using sorting networks. Experiments with data from real networks show that, with negligible loss in overall network throughput, FFC can reduce data loss by a factor of 7--130 in well-provisioned networks, and reduce the loss of high-priority traffic to almost zero in well-utilized networks.\",\n \"Title: Dynamic scheduling of network updates\\nYear: 2014\\n\\nAbstract: We present Dionysus, a system for fast, consistent network updates in software-defined networks. Dionysus encodes as a graph the consistency-related dependencies among updates at individual switches, and it then dynamically schedules these updates based on runtime differences in the update speeds of different switches. This dynamic scheduling is the key to its speed; prior update methods are slow because they pre-determine a schedule, which does not adapt to runtime conditions. Testbed experiments and data-driven simulations show that Dionysus improves the median update speed by 53--88% in both wide area and data center networks compared to prior methods.\",\n \"Title: A network-state management service\\nYear: 2014\\n\\nAbstract: We present Statesman, a network-state management service that allows multiple network management applications to operate independently, while maintaining network-wide safety and performance invariants. Network state captures various aspects of the network such as which links are alive and how switches are forwarding traffic. Statesman uses three views of the network state. In observed state, it maintains an up-to-date view of the actual network state. Applications read this state and propose state changes based on their individual goals. Using a model of dependencies among state variables, Statesman merges these proposed states into a target state that is guaranteed to maintain the safety and performance invariants. It then updates the network to the target state. Statesman has been deployed in ten Microsoft Azure datacenters for several months, and three distinct applications have been built on it. We use the experience from this deployment to demonstrate how Statesman enables each application to meet its goals, while maintaining network-wide invariants.\",\n \"Title: Analyzing Protocol Implementations for Interoperability.\\nYear: 2015\\n\\nAbstract: We propose PIC, a tool that helps developers search for non-interoperabilities in protocol implementations. We formulate this problem using intersection of the sets of messages that one protocol participant can send but another will reject as non-compliant. PIC leverages symbolic execution to characterize these sets and uses two novel techniques to scale to real-world implementations. First, it uses joint symbolic execution, in which receiver-side program analysis is constrained based on sender-side constraints, dramatically reducing the number of execution paths to consider. Second, it incorporates a search strategy that steers symbolic execution toward likely non-interoperabilities. We show that PIC is able to find multiple previously unknown noninteroperabilities in large and mature implementations of the SIP and SPDY (v2 through v3.1) protocols, some of which have since been fixed by the respective developers.\",\n \"Title: A General Approach to Network Configuration Analysis.\\nYear: 2015\\n\\nAbstract: We present an approach to detect network configuration errors, which combines the benefits of two prior approaches. Like prior techniques that analyze configuration files, our approach can find errors proactively, before the configuration is applied, and answer what if questions. Like prior techniques that analyze data-plane snapshots, our approach can check a broad range of forwarding properties and produce actual packets that violate checked properties. We accomplish this combination by faithfully deriving and then analyzing the data plane that would emerge from the configuration. Our derivation of the data plane is fully declarative, employing a set of logical relations that represent the control plane, the data plane, and their relationship. Operators can query these relations to understand identified errors and their provenance. We use our approach to analyze two large university networks with qualitatively different routing designs and find many misconfigurations in each. Operators have confirmed the majority of these as errors and have fixed their configurations accordingly.\",\n \"Title: Measuring ISP topologies with Rocketfuel\\nYear: 2002\\n\\nAbstract: To date, realistic ISP topologies have not been accessible to the research community, leaving work that depends on topology on an uncertain footing. In this paper, we present new Internet mapping techniques that have enabled us to directly measure router-level ISP topologies. Our techniques reduce the number of required traces compared to a brute-force, all-to-all approach by three orders of magnitude without a significant loss in accuracy. They include the use of BGP routing tables to focus the measurements, exploiting properties of IP routing to eliminate redundant measurements, better alias resolution, and the use of DNS to divide each map into POPs and backbone. We collect maps from ten diverse ISPs using our techniques, and find that our maps are substantially more complete than those of earlier Internet mapping efforts. We also report on properties of these maps, including the size of POPs, distribution of router outdegree, and the inter-domain peering structure. As part of this work, we release our maps to the community.\",\n \"Title: Deployment Study Length: How Long Should a System Be Evaluated in the Wild?\\nYear: 2015\\n\\nAbstract: There are many different ways to evaluate a novel interactive system. However, placing that system into the hands of real users and allowing them to use it as they would like in their natural environments may arguably be the best approach to understand if it \\\\\\\"really\\\\\\\" works. This is because findings learned from user studies conducted in the lab or a controlled setting are limited in external validity and therefore might not generalize beyond the studied usage scenario. Furthermore, the scenario used in a controlled study often lacks full authenticity, and thus it may not faithfully represent situations from the users' live.\",\n \"Title: Lab of Things in Education\\nYear: 2015\\n\\nAbstract: Lately, much has been said about the Internet of Things and how it is going to change how we will live, work and play. Strategy Analytics forecasts that by 2020, every person on the planet will carry four connected devices [1]. The majority of this growth will be driven by the interconnection of devices, sensors, smart objects and the like, and is expected to usher in changes to almost all aspects of our lives. This change will require, on the one hand, large-scale design, development and deployment of cloud and network systems; and on the other hand, design of hardware sensors, actuators, software middleware, and network protocols.\",\n \"Title: Analyzing the Performance of an Anycast CDN\\nYear: 2015\\n\\nAbstract: Content delivery networks must balance a number of trade-offs when deciding how to direct a client to a CDN server. Whereas DNS-based redirection requires a complex global traffic manager, anycast depends on BGP to direct a client to a CDN front-end. Anycast is simple to operate, scalable, and naturally resilient to DDoS attacks. This simplicity, however, comes at the cost of precise control of client redirection. We examine the performance implications of using anycast in a global, latency-sensitive, CDN. We analyze millions of client-side measurements from the Bing search service to capture anycast versus unicast performance to nearby front-ends. We find that anycast usually performs well despite the lack of precise control but that it directs roughly 20% of clients to a suboptimal front-end. We also show that the performance of these clients can be improved through a simple history-based prediction scheme.\",\n \"Title: Management Plane Analytics\\nYear: 2015\\n\\nAbstract: While it is generally held that network management is tedious and error-prone, it is not well understood which specific management practices increase the risk of failures. Indeed, our survey of 51 network operators reveals a significant diversity of opinions, and our characterization of the management practices in the 850+ networks of a large online service provider shows significant diversity in prevalent practices. Motivated by these observations, we develop a management plane analytics (MPA) framework that an organization can use to: (i) infer which management practices impact network health, and (ii) develop a predictive model of health, based on observed practices, to improve network management. We overcome the challenges of sparse and skewed data by aggregating data from many networks, reducing data dimensionality, and oversampling minority cases. Our learned models predict network health with an accuracy of 76-89%, and our causal analysis uncovers some high impact practices that operators thought had a low impact on network health. Our tool is publicly available, so organizations can analyze their own management practices.\",\n \"Title: Systematically Exploring the Behavior of Control Programs\\nYear: 2015\\n\\nAbstract: Many networked systems today, ranging from home automation networks to global wide-area networks, are operated using centralized control programs. Bugs in such programs pose serious risks to system security and stability. We develop a new technique to systematically explore the behavior of control programs. Because control programs depend intimately on absolute and relative timing of inputs, a key challenge that we face is to systematically handle time. We develop an approach that models programs as timed automata and incorporates novel mechanisms to enable scalable and comprehensive exploration. We implement our approach in a tool called DeLorean and apply it to real control programs for home automation and software-defined networks. DeLorean is able to finds bugs in these programs as well as provide significantly better code coverage--up to 94% compared to 76% for existing techniques.\",\n \"Title: Packet-Level Telemetry in Large Datacenter Networks\\nYear: 2015\\n\\nAbstract: Debugging faults in complex networks often requires capturing and analyzing traffic at the packet level. In this task, datacenter networks (DCNs) present unique challenges with their scale, traffic volume, and diversity of faults. To troubleshoot faults in a timely manner, DCN administrators must a) identify affected packets inside large volume of traffic; b) track them across multiple network components; c) analyze traffic traces for fault patterns; and d) test or confirm potential causes. To our knowledge, no tool today can achieve both the specificity and scale required for this task. We present Everflow, a packet-level network telemetry system for large DCNs. Everflow traces specific packets by implementing a powerful packet filter on top of \\\\\\\"match and mirror\\\\\\\" functionality of commodity switches. It shuffles captured packets to multiple analysis servers using load balancers built on switch ASICs, and it sends \\\\\\\"guided probes\\\\\\\" to test or confirm potential faults. We present experiments that demonstrate Everflow's scalability, and share experiences of troubleshooting network faults gathered from running it for over 6 months in Microsoft's DCNs.\",\n \"Title: Consistent updates in software defined networks: On dependencies, loop freedom, and blackholes\\nYear: 2016\\n\\nAbstract: We consider the problem of finding efficient methods to update forwarding rules in Software Defined Networks (SDNs). While the original and updated set of rules might both be consistent, disseminating the rule updates is an inherently asynchronous process, resulting in potentially inconsistent states. We highlight the inherent trade-off between the strength of the consistency property and the dependencies it imposes among rule updates at different switches; these dependencies fundamentally limit how quickly the SDN can be updated. Additionally, we consider the impact of resource constraints and show that fast blackhole free migration of rules with memory limits is NP-hard for the controller. For the basic consistency property of loop freedom, we prove that maximizing the number of loop free update rules is NP-hard for interval-based routing and longest-prefix matching. We also consider the basic case of just one destination in the network and show that the greedy approach can be nearly arbitrarily bad. However, minimizing the number of not updated rules can be approximated well for destination-based routing. For applying all updates, we develop an update algorithm that has a provably minimal dependency structure. We also sketch a general architecture for consistent updates that separates the twin concerns of consistency and efficiency, and lastly, evaluate our algorithm on ISP topologies.\",\n \"Title: Beam: Ending Monolithic Applications for Connected Devices.\\nYear: 2016\\n\\nAbstract: The proliferation of connected sensing devices (or Internet of Things) can in theory enable a range of applications that make rich inferences about users and their environment. But in practice developing such applications today is arduous because they must implement all data sensing and inference logic, even as devices move or are temporarily disconnected. We develop Beam, a framework that simplifies IoT applications by letting them specify \\\\\\\"what should be sensed or inferred,\\\\\\\" without worrying about \\\\\\\"how it is sensed or inferred.\\\\\\\" Beam introduces the key abstraction of an inference graph to decouple applications from the mechanics of sensing and drawing inferences. The inference graph allows Beam to address three important challenges: (1) device selection in heterogeneous environments, (2) efficient resource usage, and (3) handling device disconnections. Using Beam we develop two diverse applications that use several different types of devices and show that their implementations required up to 12× fewer source lines of code while resulting in up to 3× higher inference accuracy.\",\n \"Title: Fast Control Plane Analysis Using an Abstract Representation.\\nYear: 2016\\n\\nAbstract: Networks employ complex, and hence error-prone, routing control plane configurations. In many cases, the impact of errors manifests only under failures and leads to devastating effects. Thus, it is important to proactively verify control plane behavior under arbitrary link failures. State-of-the-art verifiers are either too slow or impractical to use for such verification tasks. In this paper we propose a new high level abstraction for control planes, ARC, that supports fast control plane analyses under arbitrary failures. ARC can check key invariants without generating the data plane--which is the main reason for current tools' ineffectiveness. This is possible because of the nature of verification tasks and the constrained nature of control plane designs in networks today. We develop algorithms to derive a network's ARC from its configuration files. Our evaluation over 314 networks shows that ARC computation is quick, and that ARC can verify key invariants in under 1s in most cases, which is orders-of-magnitude faster than the state-of-the-art.\",\n \"Title: ProjecToR: Agile Reconfigurable Data Center Interconnect.\\nYear: 2016\\n\\nAbstract: We explore a novel, free-space optics based approach for building data center interconnects. It uses a digital micromirror device (DMD) and mirror assembly combination as a transmitter and a photodetector on top of the rack as a receiver (Figure 1). Our approach enables all pairs of racks to establish direct links, and we can reconfigure such links (i.e., connect different rack pairs) within 12 us. To carry traffic from a source to a destination rack, transmitters and receivers in our interconnect can be dynamically linked in millions of ways. We develop topology construction and routing methods to exploit this flexibility, including a flow scheduling algorithm that is a constant factor approximation to the offline optimal solution. Experiments with a small prototype point to the feasibility of our approach. Simulations using realistic data center workloads show that, compared to the conventional folded-Clos interconnect, our approach can improve mean flow completion time by 30-95% and reduce cost by 25-40%.\",\n \"Title: Don't Mind the Gap: Bridging Network-wide Objectives and Device-level Configurations.\\nYear: 2016\\n\\nAbstract: We develop Propane, a language and compiler to help network operators with a challenging, error-prone task—bridging the gap between network-wide routing objectives and low-level configurations of devices that run complex, distributed protocols. The language allows operators to specify their objectives naturally, using high-level constraints on both the shape and relative preference of traffic paths. The compiler automatically translates these specifications to router-level BGP configurations, using an effective intermediate representation that compactly encodes the flow of routing information along policy-compliant paths. It guarantees that the compiled configurations correctly implement the specified policy under all possible combinations of failures. We show that Propane can effectively express the policies of datacenter and backbone networks of a large cloud provider; and despite its strong guarantees, our compiler scales to networks with hundreds or thousands of routers.\",\n \"Title: Optical Layer Failures in a Large Backbone.\\nYear: 2016\\n\\nAbstract: We analyze optical layer outages in a large backbone, using data for over a year from thousands of optical channels carrying live IP layer traffic. Our analysis uncovers several findings that can help improve network management and routing. For instance, we find that optical links have a wide range of availabilities, which questions the common assumption in fault-tolerant routing designs that all links have equal failure probabilities. We also find that by monitoring changes in optical signal quality (not visible at IP layer), we can better predict (probabilistically) future outages. Our results suggest that backbone traffic engineering strategies should consider current and past optical layer performance and route computation should be based on the outage-risk profile of the underlying optical links.\",\n \"Title: Efficient Network Reachability Analysis Using a Succinct Control Plane Representation.\\nYear: 2016\\n\\nAbstract: To guarantee network availability and security, operators must ensure that their reachability policies (e.g., A can or cannot talk to B) are correctly implemented. This is a difficult task due to the complexity of network configuration and the constant churn in a network's environment, e.g., new route announcements arrive and links fail. Current network reachability analysis techniques are limited as they can only reason about the current \\\\\\\"incarnation\\\\\\\" of the network, cannot analyze all configuration features, or are too slow to enable exploration of many environments. We build ERA, a tool for efficient reasoning about network reachability. Instead of reasoning about individual incarnations of the network, ERA directly reasons about the network \\\\\\\"control plane\\\\\\\" that generates these incarnations. We address key expressiveness and scalability challenges by building (i) a succinct model for the network control plane (i.e., various routing protocols and their interactions), and (ii) a repertoire of techniques for scalable (taking a few seconds for a network with > 1000 routers) exploration of this model. We have used ERA to successfully find both known and new violations of a range of common intended polices.\",\n \"Title: Modeling Actuation Constraints for IoT Applications.\\nYear: 2017\\n\\nAbstract: Internet of Things (IoT) promises to bring ease of monitoring, better efficiency and innovative services across many domains with connected devices around us. With information from critical parts of infrastructure and powerful cloud-based data analytics, many applications can be developed to gain insights about IoT systems as well as transform their capabilities. Actuation applications form an essential part of these IoT systems, as they enable automation as well as fast low-level decision making. However, modern IoT systems are designed for data acquisition, and actuation applications are implemented in an ad-hoc manner. We identify modeling constraints in a systematic manner as indispensable to support actuation applications because constraints encompass high-level policies dictated by laws of physics, legal policies, user preferences. We explore data models for constraints inIoT system with the example of a home heating system and illustrate the challenges in enforcing these constraints in theIoT system architecture.\",\n \"Title: Network configuration synthesis with abstract topologies.\\nYear: 2017\\n\\nAbstract: We develop Propane/AT, a system to synthesize provably-correct BGP (border gateway protocol) configurations for large, evolving networks from high-level specifications of topology, routing policy, and fault-tolerance requirements. Propane/AT is based on new abstractions for capturing parameterized network topologies and their evolution, and algorithms to analyze the impact of topology and routing policy on fault tolerance. Our algorithms operate entirely on abstract topologies. We prove that the properties established by our analyses hold for every concrete instantiation of the given abstract topology. Propane/AT also guarantees that only incremental changes to existing device configurations are required when the network evolves to add or remove devices and links. Our experiments with real-world topologies and policies show that our abstractions and algorithms are effective, and that, for large networks, Propane/AT synthesizes configurations two orders of magnitude faster than systems that operate on concrete topologies.\",\n \"Title: Rail: A Case For Redundant Arrays Of Inexpensive Links In Data Center Networks\\nYear: 2017\\n\\nAbstract: While there are many proposals to reduce the cost of data center networks (DCN), little attention has been paid to the role played by the physical links that carry packets. By studying over 300K optical links across many production DCNs, we show that these links are operating quite conservatively relative to the requirements in the IEEE standards. Motivated by this observation, to reduce DCN costs, we propose using transceivers-a key contributor to DCN cost-beyond their currently specified limit. Our experiments with multiple commodity transceivers show that their reach can be \\\"stretched\\\" 1.6 to 4 times their specification. However, with stretching, the performance of 1-5% of the DCN paths can fall below the IEEE standard. We develop RAIL, a system to ensure that in such a network, applications only use paths that meet their performance needs. Our proposal can reduce the network cost by up to 10% for 10Gbps networks and 44% for 40Gbps networks, without affecting the applications' performance.\",\n \"Title: Efficiently Delivering Online Services Over Integrated Infrastructure\\nYear: 2016\\n\\nAbstract: We present Footprint, a system for delivering online services in the \\\"integrated\\\" setting, where the same provider operates multiple elements of the infrastructure (e.g., proxies, data centers, and the wide area network). Such integration can boost system efficiency and performance by finely modulating how traffic enters and traverses the infrastructure. But fully realizing its benefits requires managing complex dynamics of service workloads. For instance, when a group of users are directed to a new proxy, their ongoing sessions continue to arrive at the old proxy, and this load at the old proxy declines gradually. Footprint harnesses such dynamics using a high-fidelity model that is also efficient to solve. Simulations based on a partial deployment of Footprint in Microsoft's infrastructure show that, compared to the current method, it can carry at least 50% more traffic and reduce user delays by at least 30%.\",\n \"Title: Programming Distributed Control Planes: Invited Demo: Demo.\\nYear: 2017\\n\\nAbstract: We describe Propane, a system that consists of a language for specifying the end-to-end routing policy for a network and a compiler for implementing the policy by generating a collection of device configurations for the BGP routing protocol that run on unmodified vendor hardware. Propane allows operators to describe their policy through high-level constraints on both the shape and preference of paths for different types of traffic. Constraints can describe paths both through the user's network, as well as through other networks that are not under the user's control. When Propane compiles a policy, the resulting BGP configurations are guaranteed to correctly implement the centralized policy in a distributed fashion -- without any centralized coordination and regardless of any number of network failures.\",\n \"Title: Understanding and Mitigating Packet Corruption in Data Center Networks\\nYear: 2017\\n\\nAbstract: We take a comprehensive look at packet corruption in data center networks, which leads to packet losses and application performance degradation. By studying 350K links across 15 production data centers, we find that the extent of corruption losses is significant and that its characteristics differ markedly from congestion losses. Corruption impacts fewer links than congestion, but imposes a heavier loss rate; and unlike congestion, corruption rate on a link is stable over time and is not correlated with its utilization. Based on these observations, we developed CorrOpt, a system to mitigate corruption. To minimize corruption losses, it intelligently selects which corrupting links can be safely disabled, while ensuring that each top-of-rack switch has a minimum number of paths to reach other switches. CorrOpt also recommends specific actions (e.g., replace cables, clean connectors) to repair disabled links, based on our analysis of common symptoms of different root causes of corruption. Our recommendation engine has been deployed in over seventy data centers of a large cloud provider. Our analysis shows that, compared to current state of the art, CorrOpt can reduce corruption losses by three to six orders of magnitude and improve repair accuracy by 60%.\",\n \"Title: A General Approach to Network Configuration Verification\\nYear: 2017\\n\\nAbstract: We present Minesweeper, a tool to verify that a network satisfies a wide range of intended properties such as reachability or isolation among nodes, waypointing, black holes, bounded path length, load-balancing, functional equivalence of two routers, and fault-tolerance. Minesweeper translates network configuration files into a logical formula that captures the stable states to which the network forwarding will converge as a result of interactions between routing protocols such as OSPF, BGP and static routes. It then combines the formula with constraints that describe the intended property. If the combined formula is satisfiable, there exists a stable state of the network in which the property does not hold. Otherwise, no stable state (if any) violates the property. We used Minesweeper to check four properties of 152 real networks from a large cloud provider. We found 120 violations, some of which are potentially serious security vulnerabilities. We also evaluated Minesweeper on synthetic benchmarks, and found that it can verify rich properties for networks with hundreds of routers in under five minutes. This performance is due to a suite of model-slicing and hoisting optimizations that we developed, which reduce runtime by over 460x for large networks.\",\n \"Title: Automatically Repairing Network Control Planes Using an Abstract Representation.\\nYear: 2017\\n\\nAbstract: The forwarding behavior of computer networks is governed by the configuration of distributed routing protocols and access filters---collectively known as the network control plane. Unfortunately, control plane configurations are often buggy, causing networks to violate important policies: e.g., specific traffic classes (defined in terms of source and destination endpoints) should always be able to reach their destination, or always traverse a waypoint. Manually repairing these configurations is daunting because of their inter-twined nature across routers, traffic classes, and policies.\\n\\nInspired by recent work in automatic program repair, we introduce CPR, a system that automatically computes correct, minimal repairs for network control planes. CPR casts configuration repair as a MaxSMT problem whose constraints are based on a digraph-based representation of a control plane's semantics. Crucially, this representation must capture the dependencies between traffic classes arising from the cross-traffic-class nature of control plane constructs. The MaxSMT formulation must account for these dependencies whilst also accounting for all policies and preferring repairs that minimize the size (e.g., number of lines) of the configuration changes. Using configurations from 96 data center networks, we show that CPR produces repairs in less than a minute for 98% of the networks, and these repairs requiring changing the same or fewer lines of configuration than hand-written repairs in 79% of cases.\",\n \"Title: Control Plane Compression.\\nYear: 2018\\n\\nAbstract: We develop an algorithm capable of compressing large networks into smaller ones with similar control plane behavior: For every stable routing solution in the large, original network, there exists a corresponding solution in the compressed network, and vice versa. Our compression algorithm preserves a wide variety of network properties including reachability, loop freedom, and path length. Consequently, operators may speed up network analysis, based on simulation, emulation, or verification, by analyzing only the compressed network. Our approach is based on a new theory of control plane equivalence. We implement these ideas in a tool called Bonsai and apply it to real and synthetic networks. Bonsai can shrink real networks by over a factor of 5 and speed up analysis by several orders of magnitude.\",\n \"Title: Odin: Microsoft'S Scalable Fault-Tolerant Cdn Measurement System\\nYear: 2018\\n\\nAbstract: Content delivery networks (CDNs) are critical for delivering high performance Internet services. Using worldwide deployments of front-ends, CDNs can direct users to the front-end that provides them with the best latency and availability. The key challenges arise from the heterogeneous connectivity of clients and the dynamic nature of the Internet that influences latency and availability. Without continuous insight on performance between users, front-ends, and external networks, CDNs will not be able to attain their full potential performance.We describe Odin, Microsoft's Internet measurement platform for its first-party and third-party customers. Odin is designed to handle Microsoft's large user base and need for large-scale measurements from users around the world. Odin integrates with Microsoft's varied suite of web-client and thick-client applications, all while being mindful of the regulatory and privacy concerns of enterprise customers. Odin has been operational for 2 years. We present the first detailed study of an Internet measurement platform of this scale and complexity.\",\n \"Title: Efficient Verification Of Network Fault Tolerance Via Counterexample-Guided Refinement\\nYear: 2019\\n\\nAbstract: We show how to verify that large data center networks satisfy key properties such as all-pairs reachability under a bounded number of faults. To scale the analysis, we develop algorithms that identify network symmetries and compute small abstract networks from large concrete ones. Using counter-example guided abstraction refinement, we successively refine the computed abstractions until the given property may be verified. The soundness of our approach relies on a novel notion of network approximation: routing paths in the concrete network are not precisely simulated by those in the abstract network but are guaranteed to be \\\"at least as good.\\\" We implement our algorithms in a tool called Origami and use them to verify reachability under faults for standard data center topologies. We find that Origami computes abstract networks with 1-3 orders of magnitude fewer edges, which makes it possible to verify large networks that are out of reach of existing techniques.\",\n \"Title: Test coverage metrics for the network\\nYear: 2021\\n\\nAbstract: ABSTRACTTesting and verification have emerged as key tools in the battle to improve the reliability of networks and the services they provide. However, the success of even the best technology of this sort is limited by how effectively it is applied, and in today's enormously complex industrial networks, it is surprisingly easy to overlook particular interfaces, routes, or flows when creating a test suite. Moreover, network engineers, unlike their software counterparts, have no help to battle this problem—there are no metrics or systems to compute the quality of their test suites or the extent to which their networks have been verified. To address this gap, we develop a general framework to define and compute network coverage for stateless network data planes. It computes coverage for a range of network components (\\\\EG, interfaces, devices, paths) and supports many types of tests (e.g., concrete versus symbolic; local versus end-to-end; tests that check network state versus those that analyze behavior). Our framework is based on the observation that any network dataplane component can be decomposed into forwarding rules and all types of tests ultimately exercise these rules using one or more packets. We build a system called Yardstick based on this framework and deploy it in Microsoft Azure. Within the first month of its deployment inside one of the production networks, it uncovered several testing gaps and helped improve testing by covering 89% more forwarding rules and 17% more network interfaces.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":85,"string":"85"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13248,"string":"13,248"}}},{"rowIdx":13249,"cells":{"question_id":{"kind":"string","value":"Daniel Kudenko"},"question":{"kind":"string","value":"Generate an abstract for the title \"Interactions Between Stereotypes\" using the following items: Stereotyping, recommender systems, user models, data fusion, performance."},"answer":{"kind":"string","value":"Despite the fact that stereotyping has been used many times in recommender systems, little is known about why stereotyping\n is successful for some users but unsuccessful for others. To begin to address this issue, we conducted experiments in which\n stereotype-based user models were automatically constructed and the performance of overall user models and individual stereotypes\n observed. We have shown how concepts from data fusion, a previously unconnected field, can be applied to illustrate why the\n performance of stereotyping varies between users. Our study illustrates clearly that the interactions between stereotypes,\n in terms of their ratings of items, is a major factor in overall user model performance and that poor performance on the part\n of an individual stereotype need not directly cause poor overall user model performance."},"correct_answer":{"kind":"string","value":"Despite the fact that stereotyping has been used many times in recommender systems, little is known about why stereotyping\n is successful for some users but unsuccessful for others. To begin to address this issue, we conducted experiments in which\n stereotype-based user models were automatically constructed and the performance of overall user models and individual stereotypes\n observed. We have shown how concepts from data fusion, a previously unconnected field, can be applied to illustrate why the\n performance of stereotyping varies between users. Our study illustrates clearly that the interactions between stereotypes,\n in terms of their ratings of items, is a major factor in overall user model performance and that poor performance on the part\n of an individual stereotype need not directly cause poor overall user model performance."},"documents":{"kind":"list like","value":["Title: Game intelligence.\nYear: 2014\n\nAbstract: Game Intelligence is knowledge gained by the player or by analysing the data players generate by playing digital games. Serious games for education, raising public awarenesss or changing the players' behaviour are well established and have provided Game Intelligence for decades [1]. However, more recently a trend has begun, inspired by the success of FoldIt [2], [3], of developing games for scientific discovery. These games lower the barrier of entry to complex scientific topics, allowing gamers to contribute to cutting edge research. We argue that this approach is currently underutilized and explore a vision where these games have wider impact. Furthermore, we will discuss the potential of extracting Game Intelligence from games designed originally for entertainment, potentially making all games into scientific discovery games.","Title: An Empirical Analysis of the Impact of Prioritised Sweeping on the DynaQ's Performance\nYear: 2008\n\nAbstract: Reinforcement learning tackles the problem of how to act optimally given observations of the current world state. Agents that learn from reinforcements execute actions in an environment and receive feedback (reward) that can be used to guide the learning process. The distinguishing feature of reinforcement learning is that the model of the environment (i.e., effects of actions or the reward function) are not known in advance. Model-based approaches represent a class of reinforcement learning algorithms which learn the model of dynamics. This model can be used by the learning agent to simulate interactions with the environment. DynaQ and its extended version with prioritised sweeping are the most popular examples of model-based approaches. This paper shows that, contrary to common belief, DynaQ with prioritised sweeping may perform worse than pure DynaQ in domains where the agent can be easily misled by a sub-optimal solution.","Title: Duality of Actor and Character Goals in Virtual Drama\nYear: 2009\n\nAbstract: Actor agents in a virtual reality and interactive drama must deliver a believable performance. This is challenging and many issues need to be resolved. One prominent problem is that the goals of a character in an emergent interactive drama may conflict with demands for coherency in the drama's progress. We propose an approach to resolve this that uses Object Oriented Bayesian networks and intelligent agents with dual roles: an actor role focused on the dramatic goals of the story, and a character role focused on believable actions in line with the character's personality and goals.","Title: Algorithms for Distributed Exploration\nYear: 2004\n\nAbstract: In this paper we propose algorithms for a set of problems where a distributed team of agents tries to compile a global map of the environment from local observations. We focus on two approaches: one based on behavioural agent technology where agents are pulled (or repelled) by various forces, and another where agents follow a approximate planning approach that is based on dynamic programming. We study these approaches under different conditions, such as different types of environments, varying sensor and communication ranges, and the availability of prior knowledge of the map. The results show that in most cases the simpler behavioural agent teams perform at least as well, if not better, than the teams based on approximate planning and dynamic programming. The research has not only practical implications for distributed exploration tasks, but also for analogous distributed search or optimisation problems.","Title: Reinforcement learning approaches to coordination in cooperative multi-agent systems\nYear: 2002\n\nAbstract: We report on an investigation of reinforcement learning techniques for the learning of coordination in cooperative multi-agent systems. Specifically, we focus on two novel approaches: one is based on a new action selection strategy for Q-learning [10], and the other is based on model estimation with a shared action-selection protocol. The new techniques are applicable to scenarios where mutual observation of actions is not possible. To date, reinforcement learning approaches for such independent agents did not guarantee convergence to the optimal joint action in scenarios with high miscoordination costs. We improve on previous results [2] by demonstrating empirically that our extension causes the agents to converge almost always to the optimal joint action even in these difficult cases.","Title: Multi-Agent Reinforcement Learning for Intrusion Detection: A Case Study and Evaluation\nYear: 2008\n\nAbstract: In this paper we propose a novel approach to train Multi-Agent Reinforcement Learning(MARL) agents to cooperate to detect intrusions in the form of normal and abnormal states in the network. We present an architecture of distributed sensor and decision agents that learn how to identify normal and abnormal states of the network using Reinforcement Learning(RL). Sensor agents extract network-state information using tile-coding as a function approximation technique and send communication signals in the form of actions to decision agents. By means of an on line process, sensor and decision agents learn the semantics of the communication actions. In this paper we detail the learning process and the operation of the agent architecture. We also present tests and results of our research work in an intrusion detection case study, using a realistic network simulation where sensor and decision agents learn to identify normal and abnormal states of the network.","Title: Innovative integrated architecture for educational games: challenges and merits\nYear: 2011\n\nAbstract: Interactive Narrative in game environments acts as the main catalyst to provide a motivating learning experience. In previous work, we have described how the use of a dual narrative generation technique could help to resolve the conflict between allowing high player student agency and also the track of the learning process. In this paper, we define a novel architecture that assists the dual narrative generation technique to be employed effectively in an adaptive educational game environment. The architecture composes components that individually have shown effectiveness in educational games environments. These components are graph structured narrative, dynamically generated narrative, evolving agents and a student model. An adaptive educational game, AEINS, has been developed to investigate the synergy of the architecture components. AEINS aims to foster character education at 8-12 year old children through the use of various interactive moral dilemmas that attempt the different student's cognitive levels. AEINS was evaluated through a study involved 20 participants who interacted with AEINS on an individual basis.","Title: A pattern restore method for restoring missing patterns in server side clickstream data\nYear: 2005\n\nAbstract: When analyzing patterns in server side data, it becomes quickly apparent that some of the data originating from the client is lost, mainly due to the caching of web pages. Missing data is a very important issue when using server side data to analyze a user's browsing behavior, since the quality of the browsing patterns that can be identified depends on the quality of the data. In this paper, we present a series of experiments to demonstrate the extent of the data loss in different browsing environments and illustrate the difference this makes in the resulting browsing patterns when visualized as footstep graphs. We propose an algorithm, called the Pattern Restore Method (PRM), for restoring some of the data that has been lost and evaluate the efficiency and accuracy of this algorithm.","Title: Automated Event Recognition for Football Commentary Generation\nYear: 2010\n\nAbstract: The enjoyment of many games can be enhanced by in-game commentaries. In this paper, the authors focus on the automatic generation of commentaries for football games, using Championship Manager as a case study. The basis of this approach is a real-time mapping of game states to commentary concepts, such as \"dangerous situation for team A\". While in some cases it is feasible to provide such a mapping by hand-coding, in some cases it is not straight-forward because the meaning of the concepts cannot be easily formalized. In these cases, the authors propose to use inductive learning techniques that learn such a mapping from annotated game traces.","Title: Combining reinforcement learning with symbolic planning\nYear: 2007\n\nAbstract: One of the major difficulties in applying Q-learning to realworld domains is the sharp increase in the number of learning steps required to converge towards an optimal policy as the size of the state space is increased. In this paper we propose a method, PLANQ-learning, that couples a Q-learner with a STRIPS planner. The planner shapes the reward function, and thus guides the Q-learner quickly to the optimal policy. We demonstrate empirically that this combination of highlevel reasoning and low-level learning displays significant improvements in scaling-up behaviour as the state-space grows larger, compared to both standard Q-learning and hierarchical Q-learning methods.","Title: UBB Mining: Finding Unexpected Browsing Behaviour in Clickstream Data to Improve a Web Site's Design\nYear: 2005\n\nAbstract: This paper describes a novel web usage mining approach to discover patterns in the navigation of websites known as Unexpected Browsing Behaviours (UBBs). By reviewing these UBBs, a website designer can choose to modify the design of their website or redesign the site completely. UBB mining is based on the Continuous Common Subsequence (CCS), a special instance of Common Subsequence (CS), which is used to define a set of expected routes. The predefined expected routes are then treated as rules and stored in a rule base. By using the predefined route and the UBB mining algorithm, interesting browsing behaviours can be discovered. This paper will introduce the format of the expected route and describe the UBB algorithms. The paper also describes a series of experiments designed to evaluate how well UBB mining algorithms work.","Title: Knowledge-Based Reinforcement Learning for Data Mining\nYear: 2009\n\nAbstract: Data Mining is the process of extracting patterns from data. Two general avenues of research in the intersecting areas of agents and data mining can be distinguished. The first approach is concerned with mining an agent's observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent's actions and goals are often independent of the data mining task. The data collection is mainly considered as a side effect of the agent's activities. Machine learning techniques applied in such situations fall into the class of supervised learning. In contrast, the second scenario occurs where an agent is actively performing the data mining, and is responsible for the data collection itself. For example, a mobile network agent is acquiring and processing data (where the acquisition may incur a certain cost), or a mobile sensor agent is moving in a (perhaps hostile) environment, collecting and processing sensor readings. In these settings, the tasks of the agent and the data mining are highly intertwined and interdependent (or even identical). Supervised learning is not a suitable technique for these cases. Reinforcement Learning (RL) enables an agent to learn from experience (in form of reward and punishment for explorative actions) and adapt to new situations, without a teacher. RL is an ideal learning technique for these data mining scenarios, because it fits the agent paradigm of continuous sensing and acting, and the RL agent is able to learn to make decisions on the sampling of the environment which provides the data. Nevertheless, RL still suffers from scalability problems, which have prevented its successful use in many complex real-world domains. The more complex the tasks, the longer it takes a reinforcement learning algorithm to converge to a good solution. For many real-world tasks, human expert knowledge is available. For example, human experts have developed heuristics that help them in planning and scheduling resources in their work place. However, this domain knowledge is often rough and incomplete. When the domain knowledge is used directly by an automated expert system, the solutions are often sub-optimal, due to the incompleteness of the knowledge, the uncertainty of environments, and the possibility to encounter unexpected situations. RL, on the other hand, can overcome the weaknesses of the heuristic domain knowledge and produce optimal solutions. In the talk we propose two techniques, which represent first steps in the area of knowledge-based RL (KBRL). The first technique [1] uses high-level STRIPS operator knowledge in reward shaping to focus the search for the optimal policy. Empirical results show that the plan-based reward shaping approach outperforms other RL techniques, including alternative manual and MDP-based reward shaping when it is used in its basic form. We showed that MDP-based reward shaping may fail and successful experiments with STRIPS-based shaping suggest modifications which can overcome encountered problems. The STRIPSbased method we propose allows expressing the same domain knowledge in a different way and the domain expert can choose whether to define an MDP or STRIPS planning task. We also evaluated the robustness of the proposed STRIPS-based technique to errors in the plan knowledge. In case that STRIPS knowledge is not available, we propose a second technique [2] that shapes the reward with hierarchical tile coding. Where the Q-function is represented with low-level tile coding, a V-function with coarser tile coding can be learned in parallel and used to approximate the potential for ground states. In the context of data mining, our KBRL approaches can also be used for any data collection task where the acquisition of data may incur considerable cost. In addition, observing the data collection agent in specific scenarios may lead to new insights into optimal data collection behaviour in the respective domains. In future work, we intend to demonstrate and evaluate our techniques on concrete real-world data mining applications.","Title: Machine learning and inductive logic programming for multi-agent systems\nYear: 2001\n\nAbstract: Learning is a crucial ability of intelligent agents. Rather than presenting a complete literature review, we focus in this paper on important issues surrounding the application of machine learning (ML) techniques to agents and multi-agent systems (MAS). In this discussion we move from disembodied ML over single-agent learning to full multi-agent learning. In the second part of the paper we focus on the application of Inductive Logic Programming, a knowledge-based ML technique, to MAS, and present an implemented framework in which multi-agent learning experiments can be carried out.","Title: A Study Of Concurrency In The Ant Colony System Algorithm\nYear: 2006\n\nAbstract: This paper reports the results of a study of a specific type of concurrency in the Ant Colony System (ACS) algorithm. Studies of Cellular Automata (CA) have shown that the update mechanism used can have a dramatic influence on the dynamics of the CA. ACS is usually implemented with a sequential update mechanism. A new method for controlling the concurrency in a nature-inspired algorithm is introduced. Comprehensive tests on a wide range of problem instances are reported. The study found that concurrency levels had no statistically significant effect on ACS performance. This result is interesting because it contradicts what has been observed in another form of nature-inspired algorithm, namely CAs.","Title: Reinforcement Learning with Reward Shaping and Mixed Resolution Function Approximation\nYear: 2009\n\nAbstract: A crucial trade-off is involved in the design process when function approximation is used in reinforcement learning. Ideally the chosen representation should allow representing as close as possible an approximation of the value function. However, the more expressive the representation the more training data is needed because the space of candidate hypotheses is bigger. A less expressive representation has a smaller hypotheses space and a good candidate can be found faster. The core idea of this paper is the use of a mixed resolution function approximation, that is, the use of a less expressive function approximation to provide useful guidance during learning, and the use of a more expressive function approximation to obtain a final result of high quality. A major question is how to combine the two representations. Two approaches are proposed and evaluated empirically.","Title: Generation of dilemma-based interactive narratives with a changeable story goal.\nYear: 2008\n\nAbstract: This paper describes the Generator of Adaptive Dilemma-based Interactive Narratives (GADIN) system. This system automatically generates interactive narratives which are focused on dilemmas in order to create dramatic tension. The user interacts with the system by making decisions on relevant dilemmas and by freely choosing their own actions. In this paper we introduce the version of GADIN which is able to create a finite story. The narrative finishes - in a manner which is satisfying to the user - when a dynamically determined story goal is achieved. Satisfaction of this goal may involve the user acting in a way which changes the dispositions of other characters. If the user actions cause the goal to become impossible or unlikely then they cause the story goal to be re-selected, thus meaning that the user is able to fundamentally change the overall narrative while still experiencing a coherent narrative and clear ending. This method has been applied within the children's story domain of a dinosaur adventure but is applicable in any domain which makes use of clichéd storylines. The story designer is required only to provide genre-specific storyworld knowledge and dilemmas.","Title: Potential-based difference rewards for multiagent reinforcement learning\nYear: 2014\n\nAbstract: Difference rewards and potential-based reward shaping can both significantly improve the joint policy learnt by multiple reinforcement learning agents acting simultaneously in the same environment. Difference rewards capture an agent's contribution to the system's performance. Potential-based reward shaping has been proven to not alter the Nash equilibria of the system but requires domain-specific knowledge. This paper introduces two novel reward functions that combine these methods to leverage the benefits of both. Using the difference reward's Counterfactual as Potential (CaP) allows the application of potential-based reward shaping to a wide range of multiagent systems without the need for domain specific knowledge whilst still maintaining the theoretical guarantee of consistent Nash equilibria. Alternatively, Difference Rewards incorporating Potential-Based Reward Shaping (DRiP) uses potential-based reward shaping to further shape difference rewards. By exploiting prior knowledge of a problem domain, this paper demonstrates agents using this approach can converge either up to 23.8 times faster than or to joint policies up to 196% better than agents using difference rewards alone.","Title: Multi-agent reinforcement learning for intrusion detection\nYear: 2007\n\nAbstract: Intrusion Detection Systems (IDS) have been investigated for many years and the field has matured. Nevertheless, there are still important challenges, e.g., how an IDS can detect new and complex distributed attacks. To tackle these problems, we propose a distributed Reinforcement Learning (RL) approach in a hierarchical architecture of network sensor agents. Each network sensor agent learns to interpret local state observations, and communicates them to a central agent higher up in the agent hierarchy. These central agents, in turn, learn to send signals up the hierarchy, based on the signals that they receive. Finally, the agent at the top of the hierarchy learns when to signal an intrusion alarm. We evaluate our approach in an abstract network domain.","Title: Reinforcement Learning in RoboCup KeepAway with Partial Observability\nYear: 2009\n\nAbstract: Partially observable environments pose a major challenge to the application of reinforcement learning algorithms. In such environments, due to the Markov property frequently being violated in the system state representation, situations can occur where an agent has insufficient information to decide on the optimal action. In such cases, it is necessary to determine when information gathering actions should be executed, that is, when the agent needs to reduce uncertainty about the current state before deciding on how to act. One possible solution that has been proposed in past research is to manually code rules for execution of information gathering actions in the policy using heuristic (and likely faulty) knowledge. However, such a solution requires explicit expert knowledge about actions which are information gathering. In this paper a flexible solution is proposed which automatically learns when to execute information gathering actions and furthermore to automatically discover which actions gather information. We present an evaluation in the Robo{C}up Keep{A}way domain that empirically shows the robustness of the proposed approach and its success in learning under varying degrees of partial observability. Hence, it eliminates the need for hand-coded rules, is flexible in different situations and does not require knowledge about information gathering actions.","Title: An analysis of problem difficulty for a class of optimisation heuristics\nYear: 2007\n\nAbstract: This paper investigates the effect of the cost matrix standard deviation of Travelling Salesman Problem (TSP) instances on the performance of a class of combinatorial optimisation heuristics. Ant Colony Optimisation (ACO) is the class of heuristic investigated. Results demonstrate that for a given instance size, an increase in the standard deviation of the cost matrix of instances results in an increase in the difficulty of the instances. This implies that for ACO, it is insufficient to report results on problems classified only by problem size, as has been commonly done in most ACO research to date. Some description of the cost matrix distribution is also required when attempting to explain and predict the performance of these algorithms on the TSP.","Title: Multi-agent, reward shaping for RoboCup KeepAway\nYear: 2011\n\nAbstract: This paper investigates the impact of reward shaping in multi-agent reinforcement learning as a way to incorporate domain knowledge about good strategies. In theory [2], potential-based reward shaping does not alter the Nash Equilibria of a stochastic game, only the exploration of the shaped agent. We demonstrate empirically the performance of state-based and state-action-based reward shaping in RoboCup KeepAway. The results illustrate that reward shaping can alter both the learning time required to reach a stable joint policy and the final group performance for better or worse.","Title: An Empirical Study of Potential-Based Reward Shaping and Advice in Complex, Multi-Agent Systems\nYear: 2011\n\nAbstract: This paper investigates the impact of reward shaping in multi-agent reinforcement learning as a way to incorporate domain knowledge about good strategies. In theory, potential-based reward shaping does not alter the Nash Equilibria of a stochastic game, only the exploration of the shaped agent. We demonstrate empirically the performance of reward shaping in two problem domains within the context of RoboCup KeepAway by designing three reward shaping schemes, encouraging specific behaviour such as keeping a minimum distance from other players on the same team and taking on specific roles. The results illustrate that reward shaping with multiple, simultaneous learning agents can reduce the time needed to learn a suitable policy and can alter the final group performance.","Title: Learning to Coordinate Using Commitment Sequences in Cooperative Multi-agent Systems\nYear: 2005\n\nAbstract: We report on an investigation of the learning of coordination in cooperative multi-agent systems. Specifically, we study solutions that are applicable to independent agents i.e. agents that do not observe one another's actions. In previous research [5] we have presented a reinforcement learning approach that converges to the optimal joint action even in scenarios with high miscoordination costs. However, this approach failed in fully stochastic environments. In this paper, we present a novel approach based on reward estimation with a shared action-selection protocol. The new technique is applicable in fully stochastic environments where mutual observation of actions is not possible. We demonstrate empirically that our approach causes the agents to converge almost always to the optimal joint action even in difficult stochastic scenarios with high miscoordination penalties.","Title: Moving Nature-Inspired Algorithms to Parallel, Asynchronous and Decentralised Environments\nYear: 2005\n\nAbstract: This paper motivates research into implementing nature-inspired algorithms in decentralised, asynchronous and parallel environments. These characteristics typify environments such as Peer-To-Peer systems, the Grid and autonomic computing which demand robustness, decentralisation, parallelism, asynchronicity and self-organisation. Nature-inspired systems promise these properties. However, current implementations of nature-inspired systems are only loosely based on their natural counterparts. They are generally implemented as synchronous, sequential, centralised algorithms that loop through passive data structures. For their successes to be relevant to the aforementioned new computing environments, variants of these algorithms must work in truely decentralised, parallel and asynchronous Multi-Agent System (MAS) environments. A general methodology is presented for engineering the transfer of nature-inspired algorithms to such a MAS framework. The concept of pheromone infrastructures is reviewed in light of emerging standards for agent platform architecture and interoperability. These ideas are illustrated using a particularly successful nature-inspired algorithm, Ant Colony System for the Travelling Salesman Problem.","Title: Adaptive Interactive Narrative Model to Teach Ethics\nYear: 2010\n\nAbstract: Promoting ethical, responsible, and caring young people is a perennial aim of education. Efforts have been made to find ways of teaching other than traditional ones like games and role play. Narrative-based computer games are engaging learning platforms that allow collaboration of humans and computers in the creation of innovative experiences. In this paper, the authors examine the design of an adaptive, interactive narrative model that uses a student model to provide an individualized story-path and an individualized learning process. In this regard, the authors comprise strong learning objectives underpinned by effective story telling. The adaptive narrative model has been deployed in the educational game environment, AEINS, along with the use of the Socratic Method and pedagogical agents to support teaching in the ethics domain. Evaluation results indicate the usefulness of the design and provide evidence on the development of moral reasoning and the transfer of moral virtues to its users.","Title: Baselines for Joint-Action Reinforcement Learning of Coordination in Cooperative Multi-agent Systems\nYear: 2005\n\nAbstract: A common assumption for the study of reinforcement learning of coordination is that agents can observe each other's actions (so-called joint-action learning). We present in this paper a number of simple joint-action learning algorithms and show that they perform very well when compared against more complex approaches such as OAL [1], while still maintaining convergence guarantees. Based on the empirical results, we argue that these simple algorithms should be used as baselines for any future research on joint-action learning of coordination.","Title: Tuning the performance of the MMAS heuristic\nYear: 2007\n\nAbstract: This paper presents an in-depth Design of Experiments (DOE) methodology for the performance analysis of a stochastic heuristic. The heuristic under investigation is Max-Min Ant System (MMAS). for the Travelling Salesperson Problem (TSP). Specifically, the Response Surface Methodology is used to model and tune MMAS performance with regard to 10 tuning parameters, 2 problem characteristics and 2 performance metrics--solution quality and solution time. The accuracy of these predictions is methodically verified in a separate series of confirmation experiments. The two conflicting responses are simultaneously optimised using desirability functions. Recommendations on optimal parameter settings are made. The optimal parameters are methodically verified. The large number of degrees-of-freedom in the MMAS design are overcome with a Minimum Run Resolution V design. Publicly available algorithm and problem generator implementations are used throughout. The paper should therefore serve as an illustrative case study of the principled engineering of a stochastic heuristic.","Title: Potential-based reward shaping for POMDPs\nYear: 2013\n\nAbstract: We address the problem of suboptimal behavior caused by short horizons during online POMDP planning. Our solution extends potential-based reward shaping from the related field of reinforcement learning to online POMDP planning in order to improve planning without increasing the planning horizon. In our extension, information about the quality of belief states is added to the function optimized by the agent during planning. This information provides hints of where the agent might find high future rewards, and thus achieve greater cumulative rewards.","Title: PAC-MDP learning with knowledge-based admissible models\nYear: 2010\n\nAbstract: PAC-MDP algorithms approach the exploration-exploitation problem of reinforcement learning agents in an effective way which guarantees that with high probability, the algorithm performs near optimally for all but a polynomial number of steps. The performance of these algorithms can be further improved by incorporating domain knowledge to guide their learning process. In this paper we propose a framework to use partial knowledge about effects of actions in a theoretically well-founded way. Empirical evaluation shows that our proposed method is more efficient than reward shaping which represents an alternative approach to incorporate background knowledge. Our solution is also very competitive when compared with the Bayesian Exploration Bonus (BEB) algorithm. BEB is not PAC-MDP, however it can exploit domain knowledge via informative priors. We show how to use the same kind of knowledge in the PAC-MDP framework in a way which preserves all theoretical guarantees of PAC-MDP learning.","Title: Feature generation for sequence categorization\nYear: 1998\n\nAbstract: The problem of sequence categorization is to generalize from a corpus of labeled sequences procedures for accurately labeling future unlabeled sequences. The choice of representation of sequences can have a major impact on this task, and in the absence of background knowledge a good representation is often not known and straightforward representations are often far from optimal. We propose a feature generation method (called FGEN) that creates Boolean features that check for the presence or absence of heuristically selected collections of subsequences. We show empirically that the representation computed by FGEN improves the accuracy of two commonly used learning systems (C4.5 and Ripper) when the new features are added to existing representations of sequence data. We show the superiority of FGEN across a range of tasks selected from three domains: DNA sequences, Unix command sequences, and English text.","Title: Knowledge Revision for Reinforcement Learning with Abstract MDPs\nYear: 2014\n\nAbstract: Reward shaping is a method often used in RL so as to provide domain knowledge to agents and thus improve learning. An unrealistic assumption however is that the provided knowledge is always correct. This assumption can lead to poor performance in terms of total reward and convergence speed in case it is not met. Previous research demonstrated the use of plan-based reward shaping with knowledge revision in a single agent scenario where agents showed that they can quickly identify and revise erroneous knowledge and thus benefit from more accurate plans. This method however has no mechanism to deal with non-deterministic scenarios and is thus limited to deterministic domains. In this paper we present a method to provide heuristic knowledge via abstract MDPs, coupled with a revision algorithm to manage the cases where the provided domain knowledge is wrong. We show empirically that our method can efficiently revise erroneous knowledge even in the cases where the environment is non-deterministic and also removes the need for some of the assumptions present in plan-based reward shaping with knowledge revision.","Title: Applying Web Usage Mining Techniques To Discover Potential Browsing Problems Of Users\nYear: 2007\n\nAbstract: In this paper, a web usage mining based approach is proposed to discover potential browsing problems. Two web usage mining techniques in the approach are introduced, including Automatic Pattern Discovery (APD) and Co-occurrence Pattern Mining with Distance Measurement (CPMDM). A combination method is also discussed to show how potential browsing problems can be identified","Title: Theoretical and Empirical Analysis of Reward Shaping in Reinforcement Learning\nYear: 2009\n\nAbstract: Reinforcement learning suffers scalability problems due to the state space explosion and the temporal credit assignment problem. Knowledge-based approaches have received a significant attention in the area. Reward shaping is a particular approach to incorporate domain knowledge into reinforcement learning. Theoretical and empirical analysis of this paper reveals important properties of this principle, especially the influence of the reward type, MDP discount factor, and the way of evaluating the potential function on the performance.","Title: Dynamic potential-based reward shaping\nYear: 2012\n\nAbstract: Potential-based reward shaping can significantly improve the time needed to learn an optimal policy and, in multi-agent systems, the performance of the final joint-policy. It has been proven to not alter the optimal policy of an agent learning alone or the Nash equilibria of multiple agents learning together. However, a limitation of existing proofs is the assumption that the potential of a state does not change dynamically during the learning. This assumption often is broken, especially if the reward-shaping function is generated automatically. In this paper we prove and demonstrate a method of extending potential-based reward shaping to allow dynamic shaping and maintain the guarantees of policy invariance in the single-agent case and consistent Nash equilibria in the multi-agent case.","Title: Multigrid Reinforcement Learning with Reward Shaping\nYear: 2008\n\nAbstract: Potential-based reward shaping has been shown to be a powerful method to improve the convergence rate of reinforcement learning agents. It is a flexible technique to incorporate background knowledge into temporal-difference learning in a principled way. However, the question remains how to compute the potential which is used to shape the reward that is given to the learning agent. In this paper we propose a way to solve this problem in reinforcement learning with state space discretisation. In particular, we show that the potential function can be learned online in parallel with the actual reinforcement learning process. If the Q-function is learned for states determined by a given grid, a V-function for states with lower resolution can be learned in parallel and used to approximate the potential for ground learning. The novel algorithm is presented and experimentally evaluated.","Title: Feature-Based Learners for Description Logics\nYear: 1999\n\nAbstract: Inductive learning algorithms that have beenapplied to learning in description logics (DL)have not been as well studied and optimized asthe more general class of feature-based learningalgorithms. This paper proposes a wayto apply feature-based learners to DL learningtasks by presenting a method to compute afeature-vector representation for DL instances.The representation is based on concepts computedby a DL learning algorithm and by afeature generation method that has previouslybeen...","Title: Educational Narrative and Student Modeling for Ill-Defined Domains\nYear: 2009\n\nAbstract: This paper introduces an adaptive narrative-based learning environment (AEINS) which supports teaching in the domain of ethics and citizenship [1]. AEINS is an inquiry-based teaching system that adopts educational theories and classroom strategies, such as the Socratic Method. AEINS targets students aged 8 to 11 years. The idea is centered around presenting and involving students in different moral dilemmas (teaching moments), where the Socratic Method is used to lead the student and allows him to examine the validity of an opinion or belief. AEINS monitors and analyzes students' actions in order to provide an individualized story-path and a personalized learning process. We present preliminary evaluation results.","Title: Representing sequences in description logics\nYear: 1997\n\nAbstract: Representing and manipulating sequences in description logics (DLs) has typically been achieved through the use of new sequence-specific operators or by relying on host-language functions. This paper shows that it is not necessary to add additional features to a DL to handle sequences, and instead describes an approach for dealing with sequences as first-class entities directly within a DL without the need for extensions or extra-linguistic constructs. The key idea is to represent sequences using suffix trees, then represent the resulting trees in a DL using traditional (tractable) concept and role operators. This approach supports the representation of a variety of information about a sequence, such as the locations and numbers of occurrences of all subsequences of the sequence. Moreover, subsequence testing and pattern matching reduce to subsumption checking in this representation, while computing the least common subsumer of two terms supports the application of inductive learning to sequences.","Title: Improving optimistic exploration in model-free reinforcement learning\nYear: 2009\n\nAbstract: The key problem in reinforcement learning is the explorationexploitation tradeoff. An optimistic initialisation of the value function is a popular RL strategy. The problem of this approach is that the algorithm may have relatively low performance after many episodes of learning. In this paper, two extensions to standard optimistic exploration are proposed. The first one is based on different initialisation of the value function of goal states. The second one which builds on the previous idea explicitly separates propagation of low and high values in the state space. Proposed extensions show improvement in empirical comparisons with basic optimistic initialisation. Additionally, they improve anytime performance and help on domains where learning takes place on the subspace of the large state space, that is, where the standard optimistic approach faces more difficulties.","Title: APD-A Tool for Identifying Behavioural Patterns Automatically from Clickstream Data\nYear: 2007\n\nAbstract: Clickstream can be a rich source of data for analysing user behaviour, but the volume of these logs makes it difficult to identify and categorise behavioural patterns. In this paper, we introduce the Automatic Pattern Discovery (APD) method, a technique for automated processing of Clickstream data to identify a user's browsing patterns. The paper also includes case study that is used to illustrate the use of the APD and to evaluate its performance.","Title: Learning in multi-agent systems\nYear: 2001\n\nAbstract: In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.","Title: Transferring and retraining learned information filters\nYear: 1997\n\nAbstract: Any system that learns how to filter documents will suffer poor performance during an initial training phase. One way of addressing this problem is to exploit filters learned by other users in a collaborative fashion. We investigate \"direct transfer\" of learned filters in this setting--a limiting case for any collaborative learning system. We evaluate the stability of several different learning methods under direct transfer, and conclude that symbolic learning methods that use negatively correlated features of the data perform poorly in transfer, even when they perform well in more conventional evaluation settings. This effect is robust: it holds for several learning methods, when a diverse set of users is used in training the classifier, and even when the learned classifiers can be adapted to the new user's distribution. Our experiments give rise to several concrete proposals for improving generalization performance in a collaborative setting, including a beneficial variation on a feature selection method that has been widely used in text categorization.","Title: Parallel Reinforcement Learning with Linear Function Approximation\nYear: 2007\n\nAbstract: In this paper, we investigate the use of parallelization in reinforcement learning (RL), with the goal of learning optimal\n policies for single-agent RL problems more quickly by using parallel hardware. Our approach is based on agents using the SARSA(λ) algorithm, with value functions represented using linear function approximators. In our proposed method, each agent learns\n independently in a separate simulation of the single-agent problem. The agents periodically exchange information extracted from the weights of their\n approximators, accelerating convergence towards the optimal policy. We develop three increasingly efficient versions of this\n approach to parallel RL, and present empirical results for an implementation of the methods on a Beowulf cluster.","Title: Online learning of shaping rewards in reinforcement learning.\nYear: 2010\n\nAbstract: Potential-based reward shaping has been shown to be a powerful method to improve the convergence rate of reinforcement learning agents. It is a flexible technique to incorporate background knowledge into temporal-difference learning in a principled way. However, the question remains of how to compute the potential function which is used to shape the reward that is given to the learning agent. In this paper, we show how, in the absence of knowledge to define the potential function manually, this function can be learned online in parallel with the actual reinforcement learning process. Two cases are considered. The first solution which is based on the multi-grid discretisation is designed for model-free reinforcement learning. In the second case, the approach for the prototypical model-based R-max algorithm is proposed. It learns the potential function using the free space assumption about the transitions in the environment. Two novel algorithms are presented and evaluated.","Title: Dynamic Generation of Dilemma-based Interactive Narratives\nYear: 2007\n\nAbstract: This paper presents a system which automatically gen- erates interactive stories. These are focused on dilem- mas in order to create dramatic tension. The system is provided with knowledge of generic story actions and dilemmas based on those cliches encountered in many of today's soap operas. The story designer is only re- quired to provide genre specific storyworld knowledge, such as information on characters and their relations, lo- cations and actions. These dilemmas and story actions are instantiated for the given storyworld and a story planner creates sequences of actions that each lead to a dilemma for a character (who can be the user). The user interacts with the story by making decisions on relevant dilemmas and by freely choosing their own actions. Us- ing this input the system chooses and adapts future story lines according to the user's past behaviour.","Title: Schemas in Directed Emergent Drama\nYear: 2008\n\nAbstract: A common problem in creating interactive drama is the authoring bottleneck. If pre-authored stories are directly incorporated into an interactive virtual environment then there is a need to consider all possible interactions and story twists, which for a sizeable drama is infeasible. One proposed solution to this problem is to use search and planning algorithms along with narrative structures. This leads to a huge state space and planning becomes intractable for real-time solutions. A way to address this problem is to distribute the story planning to autonomous characters so that the drama emerges from their interactions. However without predefined structure or directives the drama is unlikely to emerge into the intended story or even the intended genre. We propose to divide the drama into narrative episodes which we call schemas. Schemas are used by a director and a set of actors to structure the drama so that it emerges into a fully developed drama. The schemas are pre-authored in an abstract way such that they can be deployed multiple times in the same drama, which removes the authoring bottleneck. In this article, we define the structure of the schemas and how the director and actors use schemas in Directed Emergent Drama (DED).","Title: Group decision making through mediated discussions\nYear: 2003\n\nAbstract: To date, product recommendation systems have mainly been looked at from a single-agent perspective, where only the interests of a single user are taken into account. We extend this scenario and consider the case where multiple users are planning a joint purchase, and therefore many (potentially conflicting) interests have to be considered. In this paper we present an overview of a system that assists a group of users to reach a joint decision on an online catalogue purchase. This is done by acquiring individual user models and using these models to mediate a kind of group discussion with the goal to arrive at a compromise that is acceptable to all group members.","Title: Reinforcement learning of coordination in cooperative multi-agent systems\nYear: 2002\n\nAbstract: We report on an investigation of reinforcement learning techniques for the learning of coordination in cooperative multi-agent systems. Specifically, we focus on a novel action selection strategy for Q-learning (Watkins 1989). The new technique is applicable to scenarios where mutual observation of actions is not possible.To date, reinforcement learning approaches for such independent agents did not guarantee convergence to the optimal joint action in scenarios with high miscoordination costs. We improve on previous results (Claus & Boutilier 1998) by demonstrating empirically that our extension causes the agents to converge almost always to the optimal joint action even in these difficult cases.","Title: Theoretical considerations of potential-based reward shaping for multi-agent systems\nYear: 2011\n\nAbstract: Potential-based reward shaping has previously been proven to both be equivalent to Q-table initialisation and guarantee policy invariance in single-agent reinforcement learning. The method has since been used in multi-agent reinforcement learning without consideration of whether the theoretical equivalence and guarantees hold. This paper extends the existing proofs to similar results in multi-agent systems, providing the theoretical background to explain the success of previous empirical studies. Specifically, it is proven that the equivalence to Q-table initialisation remains and the Nash Equilibria of the underlying stochastic game are not modified. Furthermore, we demonstrate empirically that potential-based reward shaping affects exploration and, consequentially, can alter the joint policy converged upon.","Title: An empirical analysis of terminological representation systems\nYear: 1992\n\nAbstract: The family of terminological representation systems has its roots in therepresentation system kl-one. Since the development of kl-one morethan a dozen similar representation systems have been developed by variousresearch groups. These systems vary along a number of dimensions.In this paper, we present the results of an empirical analysis of six suchsystems. Surprisingly, the systems turned out to be quite diverse, leadingto problems when transporting knowledge bases from one system to...","Title: Overcoming erroneous domain knowledge in plan-based reward shaping\nYear: 2013\n\nAbstract: Reward shaping has been shown to significantly improve an agent's performance in reinforcement learning. Plan-based reward shaping is a successful approach in which a STRIPS plan is used in order to guide the agent to the optimal behaviour. However, if the provided domain knowledge is wrong, it has been shown the agent will take longer to learn the optimal policy. Previously, in some cases, it was better to ignore all prior knowledge despite it only being partially erroneous. This paper introduces a novel use of knowledge revision to overcome erroneous domain knowledge when provided to an agent receiving plan-based reward shaping. Empirical results show that an agent using this method can outperform the previous agent receiving plan-based reward shaping without knowledge revision.","Title: Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-agent Systems\nYear: 2004\n\nAbstract: Most approaches to the learning of coordination in multi-agent systems (MAS) to date require all agents to use the same learning\n algorithm with similar (or even the same) parameter settings. In today’s open networks and high inter-connectivity such an\n assumption becomes increasingly unrealistic. Developers are starting to have less control over the agents that join the system\n and the learning algorithms they employ. This makes effective coordination and good learning performance extremely difficult\n to achieve, especially in the absence of learning agent standards. In this paper we investigate the problem of learning to\n coordinate with heterogeneous agents. We show that an agent employing the FMQ algorithm, a recently developed multi-agent\n learning method, has the ability to converge towards the optimal joint action when teamed-up with one or more simple Q-learners.\n Specifically, we show such convergence in scenarios where simple Q-learners alone are unable to converge towards an optimum.\n Our results show that system designers may improve learning and coordination performance by adding a “smart” agent to the\n MAS.","Title: Finding unexpected navigation behaviour in clickstream data for website design improvement\nYear: 2009\n\nAbstract: This paper describes a novel web usage mining approach to discover patterns in the navigation of websites known as Unexpected Navigation Behaviours (UNBs). The approach provides a web designer with a means of identifying and classifying patterns of browsing and, by reviewing these patterns, the designer can then choose to modify the design of their site or redesign it completely. UNB mining is based on the Consecutive Common Subsequence (CCS), a special instance of Common Subsequence (CS), which is used to define a set of expected routes. The predefined expected routes are then treated as rules and stored in a rule base. By using the predefined route and the UNB mining algorithm, interesting navigation behaviours can be discovered. This paper will introduce the format of the expected route and describe the UNB algorithms. It will also describe a tool that a website designer can use to define the expected route more efficiently, which can help the website designer to make decision about where and how the design of website can be improved. The paper concludes with a series of experiments designed to evaluate how well the UNB mining algorithms work and demonstrate how UNB mining can be useful for improving website design.","Title: A comparison of plan-based and abstract MDP reward shaping\nYear: 2014\n\nAbstract: Reward shaping has been shown to significantly improve an agent's performance in reinforcement learning. As attention is shifting away from tabula-rasa approaches many different reward shaping methods have been developed. In this paper, we compare two different methods for reward shaping; plan-based, in which an agent is provided with a plan and extra rewards are given according to the steps of the plan the agent satisfies, and reward shaping via abstract Markov decision process (MDPs), in which an abstract high-level MDP of the environment is solved and the resulting value function is used to shape the agent. The comparison is conducted in terms of total reward, convergence speed and scaling up to more complex environments. Empirical results demonstrate the need to correctly select and set up reward shaping methods according to the needs of the environment the agents are acting in. This leads to the more interesting question, is there a reward shaping method which is universally better than all other approaches regardless of the environment dynamics?","Title: Analyzing heuristic performance with response surface models: prediction, optimization and robustness\nYear: 2007\n\nAbstract: This research uses a Design of Experiments (DOE) approach to build a predictive model of the performance of a combinatorial optimization heuristic over a range of heuristic tuning parameter settings and problem instance characteristics. The heuristic is Ant Colony System (ACS) for the Travelling Salesperson Problem. 10 heurstic tuning parameters and 2 problem characteristics are considered. Response Surface Models (RSM) of the solution quality and solution time predicted ACS performance on both new instances from a publicly available problem generator and new real-world instances from the TSPLIB benchmark library. A numerical optimisation of the RSMs is used to find the tuning parameter settings that yield optimal performance in terms of solution quality and solution time. This paper is the first use of desirability functions, a well-established technique in DOE, to simultaneously optimise these conflicting goals. Finally, overlay plots are used to examine the robustness of the performance of the optimised heuristic across a range of problem instance characteristics. These plots give predictions on the range of problem instances for which a given solutionquality can be expected within a given solution time.","Title: Screening the parameters affecting heuristic performance\nYear: 2007\n\nAbstract: This research screens the tuning parameters of a combinatorial optimization heuristic. Specifically, it presents a Design of Experiments (DOE) approach that uses a Fractional Factorial Design to screen the tuning parameters of Ant Colony System (ACS) for the Travelling Sales person problem. Screening is a preliminary step towards building a full Response Surface Model (RSM) [2]. It identifies parametersthat have little influence on performance and can be omittedfrom the RSM design. This reduces the complexity andexpense of the RSM design. 10 algorithm parameters and 2 problem characteristics are considered. Open questionson the effect of 3 parameters on performance are answered.A further parameter, sometimes assumed important, was shown to have no effect on performance. A new problem characteristic that effects performance was identified. A full version of this paper is available [3].","Title: Robustness Analysis of SARSA(lambda): Different Models of Reward and Initialisation\nYear: 2008\n\nAbstract: In the paper the robustness of SARSA(lambda), the reinforcement learning algorithm with eligibility traces, is confronted with different models of reward and initialisation of the Q-table. Most of the empirical analyses of eligibility traces in the literature have focused mainly on the step-penalty reward. We analyse two general types of rewards (final goal and step-penalty rewards) and show that learning with long traces, i.e., with high values of lambda, can lead to suboptimal solutions in some situations. Problems are identified and discussed. Specifically, obtained results show that SARSA(lambda) is sensitive to different models of reward and initialisation. In some cases the asymptotic performance can be significantly reduced.","Title: Generation of Dilemma-Based Narratives: Method and Turing Test Evaluation\nYear: 2008\n\nAbstract: This paper describes the analysis of the narrative generation aspect of the Generator of Adaptive Dilemma-based Interactive Narratives (GADIN). This system automatically generates narratives that are focused around dilemmas to create dramatic tension. The dilemmas are based on those clichés encountered in many of today's soap operas. A story planner creates sequences of actions that lead to dilemmas for storyworld characters. A narrative generated by GADIN is compared to that which took place in a television soap opera. The stories were subjected to a Turing-style test, with 127 participants, which showed that the readers could not determine which narrative had been written by a computer.","Title: Distributed response to network intrusions using multiagent reinforcement learning\nYear: 2015\n\nAbstract: Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to throttle or rate-limit traffic towards a victim server. It has been demonstrated to perform well against DDoS attacks in small-scale network topologies. The focus of this paper is to tackle the scalability challenge. Scalability is one of the most important aspects of a defence system since a non-scalable defence mechanism will never be considered, let alone adopted, for wide deployment by a company or organisation. In this paper we introduce Coordinated Team Learning (CTL) which is a novel design to the original Multiagent Router Throttling approach. One of the novel characteristics of our approach is that it provides a decentralised coordinated response to the DDoS problem. It incorporates several mechanisms, namely, hierarchical team-based communication, task decomposition and team rewards and its scalability is successfully demonstrated in experiments involving up to 100 reinforcement learning agents. We compare our proposed approach against a baseline and a popular state-of-the-art router throttling technique from the network security literature and we show that our approach significantly outperforms both of them in a series of scenarios with increasingly sophisticated attack dynamics. Furthermore, we show that our approach is more resilient and adaptable than the existing throttling approaches.","Title: Coordinated Team Learning and Difference Rewards for Distributed Intrusion Response.\nYear: 2014\n\nAbstract: Distributed denial of service attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to respond to such attacks. We demonstrate that our approach can significantly scale-up using hierarchical communication and coordinated team learning. Furthermore, we incorporate a form of reward shaping called difference rewards and show that the scalability of our system is significantly improved in experiments involving over 100 reinforcement learning agents. We also demonstrate that difference rewards constitute an ideal online learning mechanism for network intrusion response. We compare our proposed approach against a popular state-of-the-art router throttling technique from the network security literature, and we show that our proposed approach significantly outperforms it. We note that our approach can be useful in other related multiagent domains.","Title: Improving Robustness of Gaussian Process-Based Inferential Control System Using Kernel Principle Component Analysis\nYear: 2014\n\nAbstract: The plausibility and robustness of an inferential control system entirely depend on the prediction accuracy of the estimator used as the feedback element. This paper is based on a previously proposed Gaussian process inferential controller that employs Gaussian process soft sensor as an estimator. The paper enhances the robustness and the reliability of the control system, particularly, during sensor input failures. The contribution of the paper is i) alleviating the affect of the failure on the prediction accuracy of feedback element (soft sensor) and thus improving the robustness of the overall control system. ii) Hybridising Kernel Principal Component Analysis with Gaussian process Inferential Control System to achieve this robustness during all process operating conditions. The paper empirically shows the effectiveness and the plausibility of the processed hybrid system on a simulated chemical reactor process.","Title: Generation of Adaptive Dilemma-Based Interactive Narratives\nYear: 2009\n\nAbstract: The generator of adaptive dilemma-based interactive narratives (GADIN) presented in this paper dynamically generates interactive narratives which are focused on dilemmas to create dramatic tension. The system is provided with knowledge of generic story actions and dilemmas based on those clichE??s encountered in many storytelling domains. The domain designer is only required to provide domain-specific information, for example, regarding characters and their relationships, locations, and actions. A planner creates sequences of actions that all lead to a dilemma for a character (who can be the user). The user interacts with the storyworld by making decisions on relevant dilemmas and by freely choosing their own actions. Using this input, the system chooses and adapts future storylines according to the user's past behavior. Previous interactive narrative systems often have content creation and ordering requirements that restrict the possibility for sustaining the dramatic interest of the narrative over a long time period. In addition, many of these systems are not easily transferable between domains. In this paper, the GADIN system is demonstrated to both be able to maintain the dramatic interest of generated narratives over a long time period and to have a core architecture that is applicable to any domain.","Title: Bayesian networks: Real-time applicable decision mechanisms for intelligent agents in interactive drama\nYear: 2010\n\nAbstract: The growing use of intelligent agents in virtual realities calls for agents that can interact with users in a fluent and believable way. Intelligent agents are expected to react in an increasingly human like manner, showing distinct characteristics and to express emotion. Which calls for increasingly larger datasets for the agents to make informed and plausible decisions. Intelligent agents need to be able to process large knowledge bases and make decisions based on a wide range of relevant factors in real-time in order for the user to find them believable. This is very important for autonomous agents in emergent interactive drama because they need to respond fluently to user interactions in a manner that resembles that of a real actor. Bayesian networks (BNs) are particularly suitable to implement intelligent agents decision mechanism because they support transient emotions and decision making and accommodate for conflicts between the virtual agents goals. In their basic form, BN reasoning algorithms do not scale well, since the cost of updating values in a BN is NP-hard in the worst case. We propose efficient and scalable BN techniques using relevance reasoning, that are suitable for the needs of our autonomous agents in directed emergent drama and allow for real-time decision making.","Title: Using plan-based reward shaping to learn strategies in StarCraft: Broodwar\nYear: 2013\n\nAbstract: StarCraft: Broodwar (SC:BW) is a very popular commercial real strategy game (RTS) which has been extensively used in AI research. Despite being a popular test-bed reinforcement learning (RL) has not been evaluated extensively. A successful attempt was made to show the use of RL in a small-scale combat scenario involving an overpowered agent battling against multiple enemy units [1]. However, the chosen scenario was very small and not representative of the complexity of the game in its entirety. In order to build an RL agent that can manage the complexity of the full game, more efficient approaches must be used to tackle the state-space explosion. In this paper, we demonstrate how plan-based reward shaping can help an agent scale up to larger, more complex scenarios and significantly speed up the learning process as well as how high level planning can be combined with learning focusing on learning the Starcraft strategy, Battlecruiser Rush. We empirically show that the agent with plan-based reward shaping is significantly better both in terms of the learnt policy, as well as convergence speed when compared to baseline approaches which fail at reaching a good enough policy within a practical amount of time.","Title: Plan-based reward shaping for multi-agent reinforcement learning.\nYear: 2016\n\nAbstract: Recent theoretical results have justified the use of potential-based reward shaping as a way to improve the performance of multi-agent reinforcement learning (MARL). However, the question remains of how to generate a useful potential function. Previous research demonstrated the use of STRIPS operator knowledge to automatically generate a potential function for single-agent reinforcement learning. Following up on this work, we investigate the use of STRIPS planning knowledge in the context of MARL. Our results show that a potential function based on joint or individual plan knowledge can significantly improve MARL performance compared with no shaping. In addition, we investigate the limitations of individual plan knowledge as a source of reward shaping in cases where the combination of individual agent plans causes conflict.","Title: Context-sensitive reward shaping for sparse interaction multi-agent systems.\nYear: 2016\n\nAbstract: Potential-based reward shaping is a commonly used approach in reinforcement learning to direct exploration based on prior knowledge. Both in single and multi-agent settings this technique speeds up learning without losing any theoretical convergence guarantees. However, if speed ups through reward shaping are to be achieved in multi-agent environments, a different shaping signal should be used for each context in which agents have a different subgoal or when agents are involved in a different interaction situation. This paper describes the use of context-aware potential functions in a multi-agent system in which the interactions between agents are sparse. This means that, unknown to the agents a priori, the interactions between the agents only occur sporadically in certain regions of the state space. During these interactions, agents need to coordinate in order to reach the global optimal solution. We demonstrate how different reward shaping functions can be used on top of Future Coordinating Q-learning (FCQ-learning); an algorithm capable of automatically detecting when agents should take each other into consideration. Using FCQ-learning, coordination problems can even be anticipated before the actual problems occur, allowing the problems to be solved timely. We evaluate our approach on a range of gridworld problems, as well as a simulation of air traffic control.","Title: Overcoming incorrect knowledge in plan-based reward shaping.\nYear: 2016\n\nAbstract: Reward shaping has been shown to significantly improve an agent's performance in reinforcement learning. Plan-based reward shaping is a successful approach in which a STRIPS plan is used in order to guide the agent to the optimal behaviour. However, if the provided knowledge is wrong, it has been shown the agent will take longer to learn the optimal policy. Previously, in some cases, it was better to ignore all prior knowledge despite it only being partially incorrect. This paper introduces a novel use of knowledge revision to overcome incorrect domain knowledge when provided to an agent receiving plan-based reward shaping. Empirical results show that an agent using this method can outperform the previous agent receiving plan-based reward shaping without knowledge revision.","Title: Distributed reinforcement learning for adaptive and robust network intrusion response\nYear: 2015\n\nAbstract: Distributed denial of service DDoS attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. The focus of this paper is on online learning and scalability. We propose an approach that incorporates task decomposition, team rewards and a form of reward shaping called difference rewards. One of the novel characteristics of the proposed system is that it provides a decentralised coordinated response to the DDoS problem, thus being resilient to DDoS attacks themselves. The proposed system learns remarkably fast, thus being suitable for online learning. Furthermore, its scalability is successfully demonstrated in experiments involving 1000 learning agents. We compare our approach against a baseline and a popular state-of-the-art throttling technique from the network security literature and show that the proposed approach is more effective, adaptive to sophisticated attack rate dynamics and robust to agent failures.","Title: Predicting player disengagement and first purchase with event-frequency based data representation\nYear: 2015\n\nAbstract: In the game industry, especially for free to play games, player retention and purchases are important issues. There have been several approaches investigated towards predicting them by players' behaviours during game sessions. However, most current methods are only available for specific games because the data representations utilised are usually game specific. This work intends to use frequency of game events as data representations to predict both players' disengagement from game and the decisions of their first purchases. This method is able to provide better generality because events exist in every game and no knowledge of any event but their frequency is needed. In addition, this event frequency based method will also be compared with a recent work by Runge et al. [1] in terms of disengagement prediction.","Title: Knowledge Revision For Reinforcement Learning With Abstract Mdps\nYear: 2015\n\nAbstract: Reward shaping is a method often used in RL so as to provide domain knowledge to agents and thus improve learning. An unrealistic assumption however is that the provided knowledge is always correct. This assumption can lead to poor performance in terms of total reward and convergence speed in case it is not met. Previous research demonstrated the use of plan-based reward shaping with knowledge revision in a single agent scenario where agents showed that they can quickly identify and revise erroneous knowledge and thus benefit from more accurate plans. This method however has no mechanism to deal with non-deterministic scenarios and is thus limited to deterministic domains. In this paper we present a method to provide heuristic knowledge via abstract MDPs, coupled with a revision algorithm to manage the cases where the provided domain knowledge is wrong. We show empirically that our method can efficiently revise erroneous knowledge even in the cases where the environment is non-deterministic and also removes the need for some of the assumptions present in plan-based reward shaping with knowledge revision.","Title: Higher Graphical Fidelity Decreases Players' Access to Aggressive Concepts in Violent Video Games\nYear: 2015\n\nAbstract: Several features of violent video games (hence VVGs) may cause gamers to access aggressive concepts during play. Two of these features are graphical fidelity and use of narrative. In particular, the increased graphical fidelity of modern VVGs is widely theorised to cause increased access to aggressive concepts. However, despite this theoretical speculation, there is little empirical evidence of whether these formal features of VVGs actually do influence players to access aggressive concepts during play. Therefore, a 2x2 between-subjects factorial online experiment (N = 710) was employed. This examined the effects of graphical fidelity and narrative content on the extent to which players accessed aggression-related concepts when playing a VVG. Results indicated that the presence of realistic graphics in a video game representing aggression caused players to access aggressive concepts less (p = 0.014). The presence of narrative content was not found to have a statistically significant effect. Under an influential (though controversial) model of VVG effects known as the General Aggression Model, increased access to aggressive concepts causally contributes to anti-social behaviour by players of VVGs. These findings suggest that even if playing modern VVGs did lead to these negative effects, they would not be influenced by the increased graphical fidelity of modern VVGs.","Title: Gaussian Process-Based Inferential Control System\nYear: 2014\n\nAbstract: Gaussian process are emerging as a relatively new soft sensor building technique with promising results. This paper proposes a Gaussian Process Inferential Control System (GP-ICS) to control infrequently-measured variables in industrial processes. This is achieved by utilising an adaptive Gaussian process-based soft sensor to provide accurate reliable and continuous online predictions of difficult to measure variables and feeding them back to a PI controller. The contributions of the paper are i) the introduction of Gaussian process-based soft sensors in building inferential control systems, ii) we empirically show that the Gaussian process based inferential controller outperforms the ANN-based controller.","Title: Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems.\nYear: 2016\n\nAbstract: Real-world congestion problems (e.g. traffic congestion) are typically very complex and large-scale. Multiagent reinforcement learning (MARL) is a promising candidate for dealing with this emerging complexity by providing an autonomous and distributed solution to these problems. However, there are three limiting factors that affect the deployability of MARL approaches to congestion problems. These are learning time, scalability and decentralised coordination i.e. no communication between the learning agents. In this paper we introduce Resource Abstraction, an approach that addresses these challenges by allocating the available resources into abstract groups. This abstraction creates new reward functions that provide a more informative signal to the learning agents and aid the coordination amongst them. Experimental work is conducted on two benchmark domains from the literature, an abstract congestion problem and a realistic traffic congestion problem. The current state-of-the-art for solving multiagent congestion problems is a form of reward shaping called difference rewards. We show that the system using Resource Abstraction significantly improves the learning speed and scalability, and achieves the highest possible or near-highest joint performance/social welfare for both congestion problems in large-scale scenarios involving up to 1000 reinforcement learning agents.","Title: Distribution Data Across Multiple Cloud Storage using Reinforcement Learning Method.\nYear: 2017\n\nAbstract: Storing data on a single cloud storage service may cause several potential problems for the data owner such as service continuity, availability, performance, security, and the risk of vendor lock-in. A promising solution to tackle some of these issues is to distribute the data across multiple cloud storage services (MCSS). However, the distinguishing characteristics of different cloud providers, in terms of pricing schemes and service performance, make it difficult to optimise the cost and the performance concurrently on MCSS. This paper proposes a framework for automatically tuning the data distribution policies across MCSS from the client side based on file access patterns. The aim of this work is to optimise the average cost and the average service performance (mainly latency time) on MCSS. To achieve this goal, two different machine learning algorithms are used in this work: (1) supervised learning to predict file access patterns, and (2) reinforcement learning to control data distribution parameters based on the prediction of file access pattern. The framework was tested on a cloud storage emulator, where its was set to act like several common cloud storage services. The result of testing this framework shows a significant improvement in the cost and performance of storing data in multiple clouds, as compared to the commonly used uniform file distribution.","Title: Assured Reinforcement Learning with Formally Verified Abstract Policies.\nYear: 2017\n\nAbstract: We present a new reinforcement learning (RL) approach that enables an autonomous agent to solve decision making problems under constraints. Our assured reinforcement learning approach models the uncertain environment as a high-level, abstract Markov decision process (AMDP), and uses probabilistic model checking to establish AMDP policies that satisfy a set of constraints defined in probabilistic temporal logic. These formally verified abstract policies are then used to restrict the RL agent's exploration of the solution space so as to avoid constraint violations. We validate our RL approach by using it to develop autonomous agents for a flag-collection navigation task and an assisted-living planning problem.","Title: Behavioural realism and the activation of aggressive concepts in violent video games.\nYear: 2018\n\nAbstract: •Two large scale online game experiments (total n = 2778).•Behavioural realism does not increase the activation of aggressive concepts in VVGs.•Challenges theories which assert that more VVG realism leads to greater VVG effects.","Title: No priming in video games.\nYear: 2018\n\nAbstract: Video games depict a variety of different concepts. Models of learning in games like the GLM (General Learning Model) and GAM (General Aggression Model) predict that exposing players to these in-game concepts can lead to important changes in player behaviour.","Title: Deep Learning of Cell Classification Using Microscope Images of Intracellular Microtubule Networks\nYear: 2017\n\nAbstract: Microtubule networks (MTs) are a component of a cell that may indicate the presence of various chemical compounds and can be used to recognize properties such as treatment resistance. Therefore, the classification of MT images is of great relevance for cell diagnostics. Human experts find it particularly difficult to recognize the levels of chemical compound exposure of a cell. Improving the accuracy with automated techniques would have a significant impact on cell therapy. In this paper we present the application of Deep Learning to MT image classification and evaluate it on a large MT image dataset of animal cells with three degrees of exposure to a chemical agent. The results demonstrate that the learned deep network performs on par or better at the corresponding cell classification task than human experts. Specifically, we show that the task of recognizing different levels of chemical agent exposure can be handled significantly better by the neural network than by human experts.","Title: iSynchronizer: A Tool for Extracting, Integration and Analysis of MovieLens and IMDb Datasets.\nYear: 2018\n\nAbstract: The growing popularity of e-commerce has ignited the interest of the research community in e-commerce application research and development. For this purpose, variety of applications and resources such as MovieLens and IMDb datasets have been utilized, which present a rich database of items (movies), their attributes and ratings. It is a common practice to utilize both MovieLens and IMDb datasets to produce richer dataset about items and user-generated information such as tags and ratings for conducting research in recommender systems domain. Moreover, the combined dataset contains contextual feature represented by timestamps. However, dealing with a big chunk of the IMDb dataset and its integration with MovieLens dataset remains a challenge due to the various issues such as resource constraints and broken links. To address these issues, a more complex procedure has to be adopted for the integration of both datasets. Hence, this paper introduces iSynchronizer, which is an open-source tool for synchronizing MovieLens and IMDb datasets autonomously with minimum effort. Moreover, the tool includes code to generate a matrix of item features and user preferences over item features from the raw integrated dataset.","Title: Introspective Reinforcement Learning and Learning from Demonstration.\nYear: 2018\n\nAbstract: Reinforcement learning is a paradigm to model how an autonomous agent learns to maximise its cumulative reward by interacting with the environment. One challenge faced by reinforcement learning is that in many environments the reward signal is sparse, leading to slow improvement of the agentu0027s performance in early learning episodes. Potential-based reward shaping is a technique to resolve the aforementioned issue of sparse reward by incorporating an expertu0027s domain knowledge in the learning via a potential function. Past work on reinforcement learning from demonstration directly mapped (sub-optimal) human expert demonstration to a potential function, which can speed up reinforcement learning. In this paper we propose an introspective reinforcement learning agent that significantly speeds up the learning further. An introspective Reinforcement learning agent records its state-action decisions and experience during learning in a priority queue. Good quality decisions will be kept in the queue, while poorer decisions will be rejected. The queue is then used as demonstration to speed up reinforcement learning via reward shaping. A human expertu0027s demonstration can be used to initialise the priority queue before the learning process starts. Experimental validations in the 4-dimensional CartPole domain and the 27-dimensional Super Mario AI domain show that our approach significantly outperforms state-of-the-art approaches to reinforcement learning from demonstration in both domains.","Title: Deep Multi-Agent Reinforcement Learning with Relevance Graphs.\nYear: 2018\n\nAbstract: Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains. In this paper, we propose a novel approach, called MAGnet, to multi-agent reinforcement learning (MARL) that utilizes a relevance graph representation of the environment obtained by a self-attention mechanism, and a message-generation technique inspired by the NerveNet architecture. We applied our MAGnet approach to the Pommerman game and the results show that it significantly outperforms state-of-the-art MARL solutions, including DQN, MADDPG, and MCTS.","Title: Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data\nYear: 2018\n\nAbstract: Learning to produce efficient movement behaviour for humanoid robots from scratch is a hard problem, as has been illustrated by the “Learning to run” competition at NIPS 2017. The goal of this competition was to train a two-legged model of a humanoid body to run in a simulated race course with maximum speed. All submissions took a tabula rasa approach to reinforcement learning (RL) and were able to produce relatively fast, but not optimal running behaviour. In this paper, we demonstrate how data from videos of human running (e.g. taken from YouTube) can be used to shape the reward of the humanoid learning agent to speed up the learning and produce a better result. Specifically, we are using the positions of key body parts at regular time intervals to define a potential function for potential-based reward shaping (PBRS). Since PBRS does not change the optimal policy, this approach allows the RL agent to overcome sub-optimalities in the human movements that are shown in the videos. We present experiments in which we combine selected techniques from the top ten approaches from the NIPS competition with further optimizations to create an high-performing agent as a baseline. We then demonstrate how video-based reward shaping improves the performance further, resulting in an RL agent that runs twice as fast as the baseline in 12 hours of training. We furthermore show that our approach can overcome sub-optimal running behaviour in videos, with the learned policy significantly outperforming that of the running agent from the video.","Title: Continuous Gesture Recognition From Semg Sensor Data With Recurrent Neural Networks And Adversarial Domain Adaptation\nYear: 2018\n\nAbstract: Movement control of artificial limbs has made big advances in recent years. New sensor and control technology enhanced the functionality and usefulness of artificial limbs to the point that complex movements, such as grasping, can be performed to a limited extent. To date, the most successful results were achieved by applying recurrent neural networks (RNNs). However, in the domain of artificial hands, experiments so far were limited to non-mobile wrists, which significantly reduces the functionality of such prostheses. In this paper, for the first time, we present empirical results on gesture recognition with both mobile and non-mobile wrists. Furthermore, we demonstrate that recurrent neural networks with simple recurrent units (SRU) outperform regular RNNs in both cases in terms of gesture recognition accuracy, on data acquired by an arm band sensing electromagnetic signals from arm muscles (via surface electromyography or sEMG). Finally, we show that adding domain adaptation techniques to continuous gesture recognition with RNN improves the transfer ability between subjects, where a limb controller trained on data from one person is used for another person.","Title: A Reinforcement Learning Based Workflow Application Scheduling Approach in Dynamic Cloud Environment.\nYear: 2017\n\nAbstract: Workflow technology is an efficient means for constructing complex applications which involve multiple applications with different functions. In recent years, with the rapid development of cloud computing, deploying such workflow applications in cloud environment is becoming increasingly popular in many fields, such as scientific computing, big data analysis, collaborative design and manufacturing. In this context, how to schedule cloud-based workflow applications using heterogeneous and changing cloud resources is a formidable challenge. In this paper, we regard the service composition problem as a sequential decision making process and solve it by means of reinforcement learning. The experimental results demonstrate that our approach can find near-optimal solutions through continuous learning in the dynamic cloud market.","Title: A Comparative Evaluation of Machine Learning Methods for Robot Navigation Through Human Crowds\nYear: 2018\n\nAbstract: Robot navigation through crowds poses a difficult challenge to AI systems, since the methods should result in fast and efficient movement but at the same time are not allowed to compromise safety. Most approaches to date were focused on the combination of pathfinding algorithms with machine learning for pedestrian walking prediction. More recently, reinforcement learning techniques have been proposed in the research literature. In this paper, we perform a comparative evaluation of pathfinding/prediction and reinforcement learning approaches on a crowd movement dataset collected from surveillance videos taken at Grand Central Station in New York. The results demonstrate the strong superiority of state-of-the-art reinforcement learning approaches over pathfinding with state-of-the-art behavior prediction techniques.","Title: Artificial Intelligence for Prosthetics - challenge solutions.\nYear: 2019\n\nAbstract: In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector. Top participants were invited to describe their algorithms. In this work, we describe the challenge and present thirteen solutions that used deep reinforcement learning approaches. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each team implemented different modifications of the known algorithms by, for example, dividing the task into subtasks, learning low-level control, or by incorporating expert knowledge and using imitation learning.","Title: An Online Learning Algorithm for Non-stationary Imbalanced Data by Extra-Charging Minority Class\nYear: 2021\n\nAbstract: Online learning is one of the trending areas of machine learning in recent years. How to update the model based on new data is the core question in developing an online classifier. When new data arrives, the classifier should keep its model up-to-date by (1) learn new knowledge, (2) keep relevant learned knowledge, and (3) forget obsolete knowledge. This problem becomes more challenging in imbalanced non-stationary scenarios. Previous approaches save arriving instances, then utilize up/down sampling techniques to balance preserved samples and update their models. However, this strategy comes with two drawbacks: first, a delay in updating the models, and second, the up/down sampling causes information loss for the majority classes and introduces noise for the minority classes. To address these drawbacks, we propose the Hyper-Ellipses-Extra-Margin model (HEEM), which properly addresses the class imbalance challenge in online learning by reacting to every new instance as it arrives. HEEM keeps an ensemble of hyper-extended-ellipses for the minority class. Misclassified instances of the majority class are then used to shrink the ellipse, and correctly predicted instances of the minority class are used to enlarge the ellipse. Experimental results show that HEEM mitigates the class imbalance problem and outperforms the state-of-the-art methods.","Title: Utilising Assured Multi-Agent Reinforcement Learning within Safety-Critical Scenarios\nYear: 2021\n\nAbstract: Multi-agent reinforcement learning allows a team of agents to learn how to work together to solve complex decision-making problems in a shared environment. However, this learning process utilises stochastic mechanisms, meaning that its use in safety-critical domains can be problematic. To overcome this issue, we propose an Assured Multi-Agent Reinforcement Learning (AMARL) approach that uses a model checking technique called quantitative verification to provide formal guarantees of agent compliance with safety, performance, and other non-functional requirements during and after the reinforcement learning process. We demonstrate the applicability of our AMARL approach in three different patrolling navigation domains in which multi-agent systems must learn to visit key areas by using different types of reinforcement learning algorithms (temporal difference learning, game theory, and direct policy search). Furthermore, we compare the effectiveness of these algorithms when used in combination with and without our approach. Our extensive experiments with both homogeneous and heterogeneous multi-agent systems of different sizes show that the use of AMARL leads to safety requirements being consistently satisfied and to better overall results than standard reinforcement learning. (C) 2021 The Authors. Published by Elsevier B.V.","Title: Improving cold-start recommendations using item-based stereotypes\nYear: 2021\n\nAbstract: Recommender systems (RSs) have become key components driving the success of e-commerce and other platforms where revenue and customer satisfaction is dependent on the user's ability to discover desirable items in large catalogues. As the number of users and items on a platform grows, the computational complexity and the sparsity problem constitute important challenges for any recommendation algorithm. In addition, the most widely studied filtering-based RSs, while effective in providing suggestions for established users and items, are known for their poor performance for the new user and new item (cold-start) problems. Stereotypical modelling of users and items is a promising approach to solving these problems. A stereotype represents an aggregation of the characteristics of the items or users which can be used to create general user or item classes. We propose a set of methodologies for the automatic generation of stereotypes to address the cold-start problem. The novelty of the proposed approach rests on the findings that stereotypes built independently of the user-to-item ratings improve both recommendation metrics and computational performance during cold-start phases. The resulting RS can be used with any machine learning algorithm as a solver, and the improved performance gains due to rate-agnostic stereotypes are orthogonal to the gains obtained using more sophisticated solvers. The paper describes how such item-based stereotypes can be evaluated via a series of statistical tests prior to being used for recommendation. The proposed approach improves recommendation quality under a variety of metrics and significantly reduces the dimension of the recommendation model."],"string":"[\n \"Title: Game intelligence.\\nYear: 2014\\n\\nAbstract: Game Intelligence is knowledge gained by the player or by analysing the data players generate by playing digital games. Serious games for education, raising public awarenesss or changing the players' behaviour are well established and have provided Game Intelligence for decades [1]. However, more recently a trend has begun, inspired by the success of FoldIt [2], [3], of developing games for scientific discovery. These games lower the barrier of entry to complex scientific topics, allowing gamers to contribute to cutting edge research. We argue that this approach is currently underutilized and explore a vision where these games have wider impact. Furthermore, we will discuss the potential of extracting Game Intelligence from games designed originally for entertainment, potentially making all games into scientific discovery games.\",\n \"Title: An Empirical Analysis of the Impact of Prioritised Sweeping on the DynaQ's Performance\\nYear: 2008\\n\\nAbstract: Reinforcement learning tackles the problem of how to act optimally given observations of the current world state. Agents that learn from reinforcements execute actions in an environment and receive feedback (reward) that can be used to guide the learning process. The distinguishing feature of reinforcement learning is that the model of the environment (i.e., effects of actions or the reward function) are not known in advance. Model-based approaches represent a class of reinforcement learning algorithms which learn the model of dynamics. This model can be used by the learning agent to simulate interactions with the environment. DynaQ and its extended version with prioritised sweeping are the most popular examples of model-based approaches. This paper shows that, contrary to common belief, DynaQ with prioritised sweeping may perform worse than pure DynaQ in domains where the agent can be easily misled by a sub-optimal solution.\",\n \"Title: Duality of Actor and Character Goals in Virtual Drama\\nYear: 2009\\n\\nAbstract: Actor agents in a virtual reality and interactive drama must deliver a believable performance. This is challenging and many issues need to be resolved. One prominent problem is that the goals of a character in an emergent interactive drama may conflict with demands for coherency in the drama's progress. We propose an approach to resolve this that uses Object Oriented Bayesian networks and intelligent agents with dual roles: an actor role focused on the dramatic goals of the story, and a character role focused on believable actions in line with the character's personality and goals.\",\n \"Title: Algorithms for Distributed Exploration\\nYear: 2004\\n\\nAbstract: In this paper we propose algorithms for a set of problems where a distributed team of agents tries to compile a global map of the environment from local observations. We focus on two approaches: one based on behavioural agent technology where agents are pulled (or repelled) by various forces, and another where agents follow a approximate planning approach that is based on dynamic programming. We study these approaches under different conditions, such as different types of environments, varying sensor and communication ranges, and the availability of prior knowledge of the map. The results show that in most cases the simpler behavioural agent teams perform at least as well, if not better, than the teams based on approximate planning and dynamic programming. The research has not only practical implications for distributed exploration tasks, but also for analogous distributed search or optimisation problems.\",\n \"Title: Reinforcement learning approaches to coordination in cooperative multi-agent systems\\nYear: 2002\\n\\nAbstract: We report on an investigation of reinforcement learning techniques for the learning of coordination in cooperative multi-agent systems. Specifically, we focus on two novel approaches: one is based on a new action selection strategy for Q-learning [10], and the other is based on model estimation with a shared action-selection protocol. The new techniques are applicable to scenarios where mutual observation of actions is not possible. To date, reinforcement learning approaches for such independent agents did not guarantee convergence to the optimal joint action in scenarios with high miscoordination costs. We improve on previous results [2] by demonstrating empirically that our extension causes the agents to converge almost always to the optimal joint action even in these difficult cases.\",\n \"Title: Multi-Agent Reinforcement Learning for Intrusion Detection: A Case Study and Evaluation\\nYear: 2008\\n\\nAbstract: In this paper we propose a novel approach to train Multi-Agent Reinforcement Learning(MARL) agents to cooperate to detect intrusions in the form of normal and abnormal states in the network. We present an architecture of distributed sensor and decision agents that learn how to identify normal and abnormal states of the network using Reinforcement Learning(RL). Sensor agents extract network-state information using tile-coding as a function approximation technique and send communication signals in the form of actions to decision agents. By means of an on line process, sensor and decision agents learn the semantics of the communication actions. In this paper we detail the learning process and the operation of the agent architecture. We also present tests and results of our research work in an intrusion detection case study, using a realistic network simulation where sensor and decision agents learn to identify normal and abnormal states of the network.\",\n \"Title: Innovative integrated architecture for educational games: challenges and merits\\nYear: 2011\\n\\nAbstract: Interactive Narrative in game environments acts as the main catalyst to provide a motivating learning experience. In previous work, we have described how the use of a dual narrative generation technique could help to resolve the conflict between allowing high player student agency and also the track of the learning process. In this paper, we define a novel architecture that assists the dual narrative generation technique to be employed effectively in an adaptive educational game environment. The architecture composes components that individually have shown effectiveness in educational games environments. These components are graph structured narrative, dynamically generated narrative, evolving agents and a student model. An adaptive educational game, AEINS, has been developed to investigate the synergy of the architecture components. AEINS aims to foster character education at 8-12 year old children through the use of various interactive moral dilemmas that attempt the different student's cognitive levels. AEINS was evaluated through a study involved 20 participants who interacted with AEINS on an individual basis.\",\n \"Title: A pattern restore method for restoring missing patterns in server side clickstream data\\nYear: 2005\\n\\nAbstract: When analyzing patterns in server side data, it becomes quickly apparent that some of the data originating from the client is lost, mainly due to the caching of web pages. Missing data is a very important issue when using server side data to analyze a user's browsing behavior, since the quality of the browsing patterns that can be identified depends on the quality of the data. In this paper, we present a series of experiments to demonstrate the extent of the data loss in different browsing environments and illustrate the difference this makes in the resulting browsing patterns when visualized as footstep graphs. We propose an algorithm, called the Pattern Restore Method (PRM), for restoring some of the data that has been lost and evaluate the efficiency and accuracy of this algorithm.\",\n \"Title: Automated Event Recognition for Football Commentary Generation\\nYear: 2010\\n\\nAbstract: The enjoyment of many games can be enhanced by in-game commentaries. In this paper, the authors focus on the automatic generation of commentaries for football games, using Championship Manager as a case study. The basis of this approach is a real-time mapping of game states to commentary concepts, such as \\\"dangerous situation for team A\\\". While in some cases it is feasible to provide such a mapping by hand-coding, in some cases it is not straight-forward because the meaning of the concepts cannot be easily formalized. In these cases, the authors propose to use inductive learning techniques that learn such a mapping from annotated game traces.\",\n \"Title: Combining reinforcement learning with symbolic planning\\nYear: 2007\\n\\nAbstract: One of the major difficulties in applying Q-learning to realworld domains is the sharp increase in the number of learning steps required to converge towards an optimal policy as the size of the state space is increased. In this paper we propose a method, PLANQ-learning, that couples a Q-learner with a STRIPS planner. The planner shapes the reward function, and thus guides the Q-learner quickly to the optimal policy. We demonstrate empirically that this combination of highlevel reasoning and low-level learning displays significant improvements in scaling-up behaviour as the state-space grows larger, compared to both standard Q-learning and hierarchical Q-learning methods.\",\n \"Title: UBB Mining: Finding Unexpected Browsing Behaviour in Clickstream Data to Improve a Web Site's Design\\nYear: 2005\\n\\nAbstract: This paper describes a novel web usage mining approach to discover patterns in the navigation of websites known as Unexpected Browsing Behaviours (UBBs). By reviewing these UBBs, a website designer can choose to modify the design of their website or redesign the site completely. UBB mining is based on the Continuous Common Subsequence (CCS), a special instance of Common Subsequence (CS), which is used to define a set of expected routes. The predefined expected routes are then treated as rules and stored in a rule base. By using the predefined route and the UBB mining algorithm, interesting browsing behaviours can be discovered. This paper will introduce the format of the expected route and describe the UBB algorithms. The paper also describes a series of experiments designed to evaluate how well UBB mining algorithms work.\",\n \"Title: Knowledge-Based Reinforcement Learning for Data Mining\\nYear: 2009\\n\\nAbstract: Data Mining is the process of extracting patterns from data. Two general avenues of research in the intersecting areas of agents and data mining can be distinguished. The first approach is concerned with mining an agent's observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent's actions and goals are often independent of the data mining task. The data collection is mainly considered as a side effect of the agent's activities. Machine learning techniques applied in such situations fall into the class of supervised learning. In contrast, the second scenario occurs where an agent is actively performing the data mining, and is responsible for the data collection itself. For example, a mobile network agent is acquiring and processing data (where the acquisition may incur a certain cost), or a mobile sensor agent is moving in a (perhaps hostile) environment, collecting and processing sensor readings. In these settings, the tasks of the agent and the data mining are highly intertwined and interdependent (or even identical). Supervised learning is not a suitable technique for these cases. Reinforcement Learning (RL) enables an agent to learn from experience (in form of reward and punishment for explorative actions) and adapt to new situations, without a teacher. RL is an ideal learning technique for these data mining scenarios, because it fits the agent paradigm of continuous sensing and acting, and the RL agent is able to learn to make decisions on the sampling of the environment which provides the data. Nevertheless, RL still suffers from scalability problems, which have prevented its successful use in many complex real-world domains. The more complex the tasks, the longer it takes a reinforcement learning algorithm to converge to a good solution. For many real-world tasks, human expert knowledge is available. For example, human experts have developed heuristics that help them in planning and scheduling resources in their work place. However, this domain knowledge is often rough and incomplete. When the domain knowledge is used directly by an automated expert system, the solutions are often sub-optimal, due to the incompleteness of the knowledge, the uncertainty of environments, and the possibility to encounter unexpected situations. RL, on the other hand, can overcome the weaknesses of the heuristic domain knowledge and produce optimal solutions. In the talk we propose two techniques, which represent first steps in the area of knowledge-based RL (KBRL). The first technique [1] uses high-level STRIPS operator knowledge in reward shaping to focus the search for the optimal policy. Empirical results show that the plan-based reward shaping approach outperforms other RL techniques, including alternative manual and MDP-based reward shaping when it is used in its basic form. We showed that MDP-based reward shaping may fail and successful experiments with STRIPS-based shaping suggest modifications which can overcome encountered problems. The STRIPSbased method we propose allows expressing the same domain knowledge in a different way and the domain expert can choose whether to define an MDP or STRIPS planning task. We also evaluated the robustness of the proposed STRIPS-based technique to errors in the plan knowledge. In case that STRIPS knowledge is not available, we propose a second technique [2] that shapes the reward with hierarchical tile coding. Where the Q-function is represented with low-level tile coding, a V-function with coarser tile coding can be learned in parallel and used to approximate the potential for ground states. In the context of data mining, our KBRL approaches can also be used for any data collection task where the acquisition of data may incur considerable cost. In addition, observing the data collection agent in specific scenarios may lead to new insights into optimal data collection behaviour in the respective domains. In future work, we intend to demonstrate and evaluate our techniques on concrete real-world data mining applications.\",\n \"Title: Machine learning and inductive logic programming for multi-agent systems\\nYear: 2001\\n\\nAbstract: Learning is a crucial ability of intelligent agents. Rather than presenting a complete literature review, we focus in this paper on important issues surrounding the application of machine learning (ML) techniques to agents and multi-agent systems (MAS). In this discussion we move from disembodied ML over single-agent learning to full multi-agent learning. In the second part of the paper we focus on the application of Inductive Logic Programming, a knowledge-based ML technique, to MAS, and present an implemented framework in which multi-agent learning experiments can be carried out.\",\n \"Title: A Study Of Concurrency In The Ant Colony System Algorithm\\nYear: 2006\\n\\nAbstract: This paper reports the results of a study of a specific type of concurrency in the Ant Colony System (ACS) algorithm. Studies of Cellular Automata (CA) have shown that the update mechanism used can have a dramatic influence on the dynamics of the CA. ACS is usually implemented with a sequential update mechanism. A new method for controlling the concurrency in a nature-inspired algorithm is introduced. Comprehensive tests on a wide range of problem instances are reported. The study found that concurrency levels had no statistically significant effect on ACS performance. This result is interesting because it contradicts what has been observed in another form of nature-inspired algorithm, namely CAs.\",\n \"Title: Reinforcement Learning with Reward Shaping and Mixed Resolution Function Approximation\\nYear: 2009\\n\\nAbstract: A crucial trade-off is involved in the design process when function approximation is used in reinforcement learning. Ideally the chosen representation should allow representing as close as possible an approximation of the value function. However, the more expressive the representation the more training data is needed because the space of candidate hypotheses is bigger. A less expressive representation has a smaller hypotheses space and a good candidate can be found faster. The core idea of this paper is the use of a mixed resolution function approximation, that is, the use of a less expressive function approximation to provide useful guidance during learning, and the use of a more expressive function approximation to obtain a final result of high quality. A major question is how to combine the two representations. Two approaches are proposed and evaluated empirically.\",\n \"Title: Generation of dilemma-based interactive narratives with a changeable story goal.\\nYear: 2008\\n\\nAbstract: This paper describes the Generator of Adaptive Dilemma-based Interactive Narratives (GADIN) system. This system automatically generates interactive narratives which are focused on dilemmas in order to create dramatic tension. The user interacts with the system by making decisions on relevant dilemmas and by freely choosing their own actions. In this paper we introduce the version of GADIN which is able to create a finite story. The narrative finishes - in a manner which is satisfying to the user - when a dynamically determined story goal is achieved. Satisfaction of this goal may involve the user acting in a way which changes the dispositions of other characters. If the user actions cause the goal to become impossible or unlikely then they cause the story goal to be re-selected, thus meaning that the user is able to fundamentally change the overall narrative while still experiencing a coherent narrative and clear ending. This method has been applied within the children's story domain of a dinosaur adventure but is applicable in any domain which makes use of clichéd storylines. The story designer is required only to provide genre-specific storyworld knowledge and dilemmas.\",\n \"Title: Potential-based difference rewards for multiagent reinforcement learning\\nYear: 2014\\n\\nAbstract: Difference rewards and potential-based reward shaping can both significantly improve the joint policy learnt by multiple reinforcement learning agents acting simultaneously in the same environment. Difference rewards capture an agent's contribution to the system's performance. Potential-based reward shaping has been proven to not alter the Nash equilibria of the system but requires domain-specific knowledge. This paper introduces two novel reward functions that combine these methods to leverage the benefits of both. Using the difference reward's Counterfactual as Potential (CaP) allows the application of potential-based reward shaping to a wide range of multiagent systems without the need for domain specific knowledge whilst still maintaining the theoretical guarantee of consistent Nash equilibria. Alternatively, Difference Rewards incorporating Potential-Based Reward Shaping (DRiP) uses potential-based reward shaping to further shape difference rewards. By exploiting prior knowledge of a problem domain, this paper demonstrates agents using this approach can converge either up to 23.8 times faster than or to joint policies up to 196% better than agents using difference rewards alone.\",\n \"Title: Multi-agent reinforcement learning for intrusion detection\\nYear: 2007\\n\\nAbstract: Intrusion Detection Systems (IDS) have been investigated for many years and the field has matured. Nevertheless, there are still important challenges, e.g., how an IDS can detect new and complex distributed attacks. To tackle these problems, we propose a distributed Reinforcement Learning (RL) approach in a hierarchical architecture of network sensor agents. Each network sensor agent learns to interpret local state observations, and communicates them to a central agent higher up in the agent hierarchy. These central agents, in turn, learn to send signals up the hierarchy, based on the signals that they receive. Finally, the agent at the top of the hierarchy learns when to signal an intrusion alarm. We evaluate our approach in an abstract network domain.\",\n \"Title: Reinforcement Learning in RoboCup KeepAway with Partial Observability\\nYear: 2009\\n\\nAbstract: Partially observable environments pose a major challenge to the application of reinforcement learning algorithms. In such environments, due to the Markov property frequently being violated in the system state representation, situations can occur where an agent has insufficient information to decide on the optimal action. In such cases, it is necessary to determine when information gathering actions should be executed, that is, when the agent needs to reduce uncertainty about the current state before deciding on how to act. One possible solution that has been proposed in past research is to manually code rules for execution of information gathering actions in the policy using heuristic (and likely faulty) knowledge. However, such a solution requires explicit expert knowledge about actions which are information gathering. In this paper a flexible solution is proposed which automatically learns when to execute information gathering actions and furthermore to automatically discover which actions gather information. We present an evaluation in the Robo{C}up Keep{A}way domain that empirically shows the robustness of the proposed approach and its success in learning under varying degrees of partial observability. Hence, it eliminates the need for hand-coded rules, is flexible in different situations and does not require knowledge about information gathering actions.\",\n \"Title: An analysis of problem difficulty for a class of optimisation heuristics\\nYear: 2007\\n\\nAbstract: This paper investigates the effect of the cost matrix standard deviation of Travelling Salesman Problem (TSP) instances on the performance of a class of combinatorial optimisation heuristics. Ant Colony Optimisation (ACO) is the class of heuristic investigated. Results demonstrate that for a given instance size, an increase in the standard deviation of the cost matrix of instances results in an increase in the difficulty of the instances. This implies that for ACO, it is insufficient to report results on problems classified only by problem size, as has been commonly done in most ACO research to date. Some description of the cost matrix distribution is also required when attempting to explain and predict the performance of these algorithms on the TSP.\",\n \"Title: Multi-agent, reward shaping for RoboCup KeepAway\\nYear: 2011\\n\\nAbstract: This paper investigates the impact of reward shaping in multi-agent reinforcement learning as a way to incorporate domain knowledge about good strategies. In theory [2], potential-based reward shaping does not alter the Nash Equilibria of a stochastic game, only the exploration of the shaped agent. We demonstrate empirically the performance of state-based and state-action-based reward shaping in RoboCup KeepAway. The results illustrate that reward shaping can alter both the learning time required to reach a stable joint policy and the final group performance for better or worse.\",\n \"Title: An Empirical Study of Potential-Based Reward Shaping and Advice in Complex, Multi-Agent Systems\\nYear: 2011\\n\\nAbstract: This paper investigates the impact of reward shaping in multi-agent reinforcement learning as a way to incorporate domain knowledge about good strategies. In theory, potential-based reward shaping does not alter the Nash Equilibria of a stochastic game, only the exploration of the shaped agent. We demonstrate empirically the performance of reward shaping in two problem domains within the context of RoboCup KeepAway by designing three reward shaping schemes, encouraging specific behaviour such as keeping a minimum distance from other players on the same team and taking on specific roles. The results illustrate that reward shaping with multiple, simultaneous learning agents can reduce the time needed to learn a suitable policy and can alter the final group performance.\",\n \"Title: Learning to Coordinate Using Commitment Sequences in Cooperative Multi-agent Systems\\nYear: 2005\\n\\nAbstract: We report on an investigation of the learning of coordination in cooperative multi-agent systems. Specifically, we study solutions that are applicable to independent agents i.e. agents that do not observe one another's actions. In previous research [5] we have presented a reinforcement learning approach that converges to the optimal joint action even in scenarios with high miscoordination costs. However, this approach failed in fully stochastic environments. In this paper, we present a novel approach based on reward estimation with a shared action-selection protocol. The new technique is applicable in fully stochastic environments where mutual observation of actions is not possible. We demonstrate empirically that our approach causes the agents to converge almost always to the optimal joint action even in difficult stochastic scenarios with high miscoordination penalties.\",\n \"Title: Moving Nature-Inspired Algorithms to Parallel, Asynchronous and Decentralised Environments\\nYear: 2005\\n\\nAbstract: This paper motivates research into implementing nature-inspired algorithms in decentralised, asynchronous and parallel environments. These characteristics typify environments such as Peer-To-Peer systems, the Grid and autonomic computing which demand robustness, decentralisation, parallelism, asynchronicity and self-organisation. Nature-inspired systems promise these properties. However, current implementations of nature-inspired systems are only loosely based on their natural counterparts. They are generally implemented as synchronous, sequential, centralised algorithms that loop through passive data structures. For their successes to be relevant to the aforementioned new computing environments, variants of these algorithms must work in truely decentralised, parallel and asynchronous Multi-Agent System (MAS) environments. A general methodology is presented for engineering the transfer of nature-inspired algorithms to such a MAS framework. The concept of pheromone infrastructures is reviewed in light of emerging standards for agent platform architecture and interoperability. These ideas are illustrated using a particularly successful nature-inspired algorithm, Ant Colony System for the Travelling Salesman Problem.\",\n \"Title: Adaptive Interactive Narrative Model to Teach Ethics\\nYear: 2010\\n\\nAbstract: Promoting ethical, responsible, and caring young people is a perennial aim of education. Efforts have been made to find ways of teaching other than traditional ones like games and role play. Narrative-based computer games are engaging learning platforms that allow collaboration of humans and computers in the creation of innovative experiences. In this paper, the authors examine the design of an adaptive, interactive narrative model that uses a student model to provide an individualized story-path and an individualized learning process. In this regard, the authors comprise strong learning objectives underpinned by effective story telling. The adaptive narrative model has been deployed in the educational game environment, AEINS, along with the use of the Socratic Method and pedagogical agents to support teaching in the ethics domain. Evaluation results indicate the usefulness of the design and provide evidence on the development of moral reasoning and the transfer of moral virtues to its users.\",\n \"Title: Baselines for Joint-Action Reinforcement Learning of Coordination in Cooperative Multi-agent Systems\\nYear: 2005\\n\\nAbstract: A common assumption for the study of reinforcement learning of coordination is that agents can observe each other's actions (so-called joint-action learning). We present in this paper a number of simple joint-action learning algorithms and show that they perform very well when compared against more complex approaches such as OAL [1], while still maintaining convergence guarantees. Based on the empirical results, we argue that these simple algorithms should be used as baselines for any future research on joint-action learning of coordination.\",\n \"Title: Tuning the performance of the MMAS heuristic\\nYear: 2007\\n\\nAbstract: This paper presents an in-depth Design of Experiments (DOE) methodology for the performance analysis of a stochastic heuristic. The heuristic under investigation is Max-Min Ant System (MMAS). for the Travelling Salesperson Problem (TSP). Specifically, the Response Surface Methodology is used to model and tune MMAS performance with regard to 10 tuning parameters, 2 problem characteristics and 2 performance metrics--solution quality and solution time. The accuracy of these predictions is methodically verified in a separate series of confirmation experiments. The two conflicting responses are simultaneously optimised using desirability functions. Recommendations on optimal parameter settings are made. The optimal parameters are methodically verified. The large number of degrees-of-freedom in the MMAS design are overcome with a Minimum Run Resolution V design. Publicly available algorithm and problem generator implementations are used throughout. The paper should therefore serve as an illustrative case study of the principled engineering of a stochastic heuristic.\",\n \"Title: Potential-based reward shaping for POMDPs\\nYear: 2013\\n\\nAbstract: We address the problem of suboptimal behavior caused by short horizons during online POMDP planning. Our solution extends potential-based reward shaping from the related field of reinforcement learning to online POMDP planning in order to improve planning without increasing the planning horizon. In our extension, information about the quality of belief states is added to the function optimized by the agent during planning. This information provides hints of where the agent might find high future rewards, and thus achieve greater cumulative rewards.\",\n \"Title: PAC-MDP learning with knowledge-based admissible models\\nYear: 2010\\n\\nAbstract: PAC-MDP algorithms approach the exploration-exploitation problem of reinforcement learning agents in an effective way which guarantees that with high probability, the algorithm performs near optimally for all but a polynomial number of steps. The performance of these algorithms can be further improved by incorporating domain knowledge to guide their learning process. In this paper we propose a framework to use partial knowledge about effects of actions in a theoretically well-founded way. Empirical evaluation shows that our proposed method is more efficient than reward shaping which represents an alternative approach to incorporate background knowledge. Our solution is also very competitive when compared with the Bayesian Exploration Bonus (BEB) algorithm. BEB is not PAC-MDP, however it can exploit domain knowledge via informative priors. We show how to use the same kind of knowledge in the PAC-MDP framework in a way which preserves all theoretical guarantees of PAC-MDP learning.\",\n \"Title: Feature generation for sequence categorization\\nYear: 1998\\n\\nAbstract: The problem of sequence categorization is to generalize from a corpus of labeled sequences procedures for accurately labeling future unlabeled sequences. The choice of representation of sequences can have a major impact on this task, and in the absence of background knowledge a good representation is often not known and straightforward representations are often far from optimal. We propose a feature generation method (called FGEN) that creates Boolean features that check for the presence or absence of heuristically selected collections of subsequences. We show empirically that the representation computed by FGEN improves the accuracy of two commonly used learning systems (C4.5 and Ripper) when the new features are added to existing representations of sequence data. We show the superiority of FGEN across a range of tasks selected from three domains: DNA sequences, Unix command sequences, and English text.\",\n \"Title: Knowledge Revision for Reinforcement Learning with Abstract MDPs\\nYear: 2014\\n\\nAbstract: Reward shaping is a method often used in RL so as to provide domain knowledge to agents and thus improve learning. An unrealistic assumption however is that the provided knowledge is always correct. This assumption can lead to poor performance in terms of total reward and convergence speed in case it is not met. Previous research demonstrated the use of plan-based reward shaping with knowledge revision in a single agent scenario where agents showed that they can quickly identify and revise erroneous knowledge and thus benefit from more accurate plans. This method however has no mechanism to deal with non-deterministic scenarios and is thus limited to deterministic domains. In this paper we present a method to provide heuristic knowledge via abstract MDPs, coupled with a revision algorithm to manage the cases where the provided domain knowledge is wrong. We show empirically that our method can efficiently revise erroneous knowledge even in the cases where the environment is non-deterministic and also removes the need for some of the assumptions present in plan-based reward shaping with knowledge revision.\",\n \"Title: Applying Web Usage Mining Techniques To Discover Potential Browsing Problems Of Users\\nYear: 2007\\n\\nAbstract: In this paper, a web usage mining based approach is proposed to discover potential browsing problems. Two web usage mining techniques in the approach are introduced, including Automatic Pattern Discovery (APD) and Co-occurrence Pattern Mining with Distance Measurement (CPMDM). A combination method is also discussed to show how potential browsing problems can be identified\",\n \"Title: Theoretical and Empirical Analysis of Reward Shaping in Reinforcement Learning\\nYear: 2009\\n\\nAbstract: Reinforcement learning suffers scalability problems due to the state space explosion and the temporal credit assignment problem. Knowledge-based approaches have received a significant attention in the area. Reward shaping is a particular approach to incorporate domain knowledge into reinforcement learning. Theoretical and empirical analysis of this paper reveals important properties of this principle, especially the influence of the reward type, MDP discount factor, and the way of evaluating the potential function on the performance.\",\n \"Title: Dynamic potential-based reward shaping\\nYear: 2012\\n\\nAbstract: Potential-based reward shaping can significantly improve the time needed to learn an optimal policy and, in multi-agent systems, the performance of the final joint-policy. It has been proven to not alter the optimal policy of an agent learning alone or the Nash equilibria of multiple agents learning together. However, a limitation of existing proofs is the assumption that the potential of a state does not change dynamically during the learning. This assumption often is broken, especially if the reward-shaping function is generated automatically. In this paper we prove and demonstrate a method of extending potential-based reward shaping to allow dynamic shaping and maintain the guarantees of policy invariance in the single-agent case and consistent Nash equilibria in the multi-agent case.\",\n \"Title: Multigrid Reinforcement Learning with Reward Shaping\\nYear: 2008\\n\\nAbstract: Potential-based reward shaping has been shown to be a powerful method to improve the convergence rate of reinforcement learning agents. It is a flexible technique to incorporate background knowledge into temporal-difference learning in a principled way. However, the question remains how to compute the potential which is used to shape the reward that is given to the learning agent. In this paper we propose a way to solve this problem in reinforcement learning with state space discretisation. In particular, we show that the potential function can be learned online in parallel with the actual reinforcement learning process. If the Q-function is learned for states determined by a given grid, a V-function for states with lower resolution can be learned in parallel and used to approximate the potential for ground learning. The novel algorithm is presented and experimentally evaluated.\",\n \"Title: Feature-Based Learners for Description Logics\\nYear: 1999\\n\\nAbstract: Inductive learning algorithms that have beenapplied to learning in description logics (DL)have not been as well studied and optimized asthe more general class of feature-based learningalgorithms. This paper proposes a wayto apply feature-based learners to DL learningtasks by presenting a method to compute afeature-vector representation for DL instances.The representation is based on concepts computedby a DL learning algorithm and by afeature generation method that has previouslybeen...\",\n \"Title: Educational Narrative and Student Modeling for Ill-Defined Domains\\nYear: 2009\\n\\nAbstract: This paper introduces an adaptive narrative-based learning environment (AEINS) which supports teaching in the domain of ethics and citizenship [1]. AEINS is an inquiry-based teaching system that adopts educational theories and classroom strategies, such as the Socratic Method. AEINS targets students aged 8 to 11 years. The idea is centered around presenting and involving students in different moral dilemmas (teaching moments), where the Socratic Method is used to lead the student and allows him to examine the validity of an opinion or belief. AEINS monitors and analyzes students' actions in order to provide an individualized story-path and a personalized learning process. We present preliminary evaluation results.\",\n \"Title: Representing sequences in description logics\\nYear: 1997\\n\\nAbstract: Representing and manipulating sequences in description logics (DLs) has typically been achieved through the use of new sequence-specific operators or by relying on host-language functions. This paper shows that it is not necessary to add additional features to a DL to handle sequences, and instead describes an approach for dealing with sequences as first-class entities directly within a DL without the need for extensions or extra-linguistic constructs. The key idea is to represent sequences using suffix trees, then represent the resulting trees in a DL using traditional (tractable) concept and role operators. This approach supports the representation of a variety of information about a sequence, such as the locations and numbers of occurrences of all subsequences of the sequence. Moreover, subsequence testing and pattern matching reduce to subsumption checking in this representation, while computing the least common subsumer of two terms supports the application of inductive learning to sequences.\",\n \"Title: Improving optimistic exploration in model-free reinforcement learning\\nYear: 2009\\n\\nAbstract: The key problem in reinforcement learning is the explorationexploitation tradeoff. An optimistic initialisation of the value function is a popular RL strategy. The problem of this approach is that the algorithm may have relatively low performance after many episodes of learning. In this paper, two extensions to standard optimistic exploration are proposed. The first one is based on different initialisation of the value function of goal states. The second one which builds on the previous idea explicitly separates propagation of low and high values in the state space. Proposed extensions show improvement in empirical comparisons with basic optimistic initialisation. Additionally, they improve anytime performance and help on domains where learning takes place on the subspace of the large state space, that is, where the standard optimistic approach faces more difficulties.\",\n \"Title: APD-A Tool for Identifying Behavioural Patterns Automatically from Clickstream Data\\nYear: 2007\\n\\nAbstract: Clickstream can be a rich source of data for analysing user behaviour, but the volume of these logs makes it difficult to identify and categorise behavioural patterns. In this paper, we introduce the Automatic Pattern Discovery (APD) method, a technique for automated processing of Clickstream data to identify a user's browsing patterns. The paper also includes case study that is used to illustrate the use of the APD and to evaluate its performance.\",\n \"Title: Learning in multi-agent systems\\nYear: 2001\\n\\nAbstract: In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.\",\n \"Title: Transferring and retraining learned information filters\\nYear: 1997\\n\\nAbstract: Any system that learns how to filter documents will suffer poor performance during an initial training phase. One way of addressing this problem is to exploit filters learned by other users in a collaborative fashion. We investigate \\\"direct transfer\\\" of learned filters in this setting--a limiting case for any collaborative learning system. We evaluate the stability of several different learning methods under direct transfer, and conclude that symbolic learning methods that use negatively correlated features of the data perform poorly in transfer, even when they perform well in more conventional evaluation settings. This effect is robust: it holds for several learning methods, when a diverse set of users is used in training the classifier, and even when the learned classifiers can be adapted to the new user's distribution. Our experiments give rise to several concrete proposals for improving generalization performance in a collaborative setting, including a beneficial variation on a feature selection method that has been widely used in text categorization.\",\n \"Title: Parallel Reinforcement Learning with Linear Function Approximation\\nYear: 2007\\n\\nAbstract: In this paper, we investigate the use of parallelization in reinforcement learning (RL), with the goal of learning optimal\\n policies for single-agent RL problems more quickly by using parallel hardware. Our approach is based on agents using the SARSA(λ) algorithm, with value functions represented using linear function approximators. In our proposed method, each agent learns\\n independently in a separate simulation of the single-agent problem. The agents periodically exchange information extracted from the weights of their\\n approximators, accelerating convergence towards the optimal policy. We develop three increasingly efficient versions of this\\n approach to parallel RL, and present empirical results for an implementation of the methods on a Beowulf cluster.\",\n \"Title: Online learning of shaping rewards in reinforcement learning.\\nYear: 2010\\n\\nAbstract: Potential-based reward shaping has been shown to be a powerful method to improve the convergence rate of reinforcement learning agents. It is a flexible technique to incorporate background knowledge into temporal-difference learning in a principled way. However, the question remains of how to compute the potential function which is used to shape the reward that is given to the learning agent. In this paper, we show how, in the absence of knowledge to define the potential function manually, this function can be learned online in parallel with the actual reinforcement learning process. Two cases are considered. The first solution which is based on the multi-grid discretisation is designed for model-free reinforcement learning. In the second case, the approach for the prototypical model-based R-max algorithm is proposed. It learns the potential function using the free space assumption about the transitions in the environment. Two novel algorithms are presented and evaluated.\",\n \"Title: Dynamic Generation of Dilemma-based Interactive Narratives\\nYear: 2007\\n\\nAbstract: This paper presents a system which automatically gen- erates interactive stories. These are focused on dilem- mas in order to create dramatic tension. The system is provided with knowledge of generic story actions and dilemmas based on those cliches encountered in many of today's soap operas. The story designer is only re- quired to provide genre specific storyworld knowledge, such as information on characters and their relations, lo- cations and actions. These dilemmas and story actions are instantiated for the given storyworld and a story planner creates sequences of actions that each lead to a dilemma for a character (who can be the user). The user interacts with the story by making decisions on relevant dilemmas and by freely choosing their own actions. Us- ing this input the system chooses and adapts future story lines according to the user's past behaviour.\",\n \"Title: Schemas in Directed Emergent Drama\\nYear: 2008\\n\\nAbstract: A common problem in creating interactive drama is the authoring bottleneck. If pre-authored stories are directly incorporated into an interactive virtual environment then there is a need to consider all possible interactions and story twists, which for a sizeable drama is infeasible. One proposed solution to this problem is to use search and planning algorithms along with narrative structures. This leads to a huge state space and planning becomes intractable for real-time solutions. A way to address this problem is to distribute the story planning to autonomous characters so that the drama emerges from their interactions. However without predefined structure or directives the drama is unlikely to emerge into the intended story or even the intended genre. We propose to divide the drama into narrative episodes which we call schemas. Schemas are used by a director and a set of actors to structure the drama so that it emerges into a fully developed drama. The schemas are pre-authored in an abstract way such that they can be deployed multiple times in the same drama, which removes the authoring bottleneck. In this article, we define the structure of the schemas and how the director and actors use schemas in Directed Emergent Drama (DED).\",\n \"Title: Group decision making through mediated discussions\\nYear: 2003\\n\\nAbstract: To date, product recommendation systems have mainly been looked at from a single-agent perspective, where only the interests of a single user are taken into account. We extend this scenario and consider the case where multiple users are planning a joint purchase, and therefore many (potentially conflicting) interests have to be considered. In this paper we present an overview of a system that assists a group of users to reach a joint decision on an online catalogue purchase. This is done by acquiring individual user models and using these models to mediate a kind of group discussion with the goal to arrive at a compromise that is acceptable to all group members.\",\n \"Title: Reinforcement learning of coordination in cooperative multi-agent systems\\nYear: 2002\\n\\nAbstract: We report on an investigation of reinforcement learning techniques for the learning of coordination in cooperative multi-agent systems. Specifically, we focus on a novel action selection strategy for Q-learning (Watkins 1989). The new technique is applicable to scenarios where mutual observation of actions is not possible.To date, reinforcement learning approaches for such independent agents did not guarantee convergence to the optimal joint action in scenarios with high miscoordination costs. We improve on previous results (Claus & Boutilier 1998) by demonstrating empirically that our extension causes the agents to converge almost always to the optimal joint action even in these difficult cases.\",\n \"Title: Theoretical considerations of potential-based reward shaping for multi-agent systems\\nYear: 2011\\n\\nAbstract: Potential-based reward shaping has previously been proven to both be equivalent to Q-table initialisation and guarantee policy invariance in single-agent reinforcement learning. The method has since been used in multi-agent reinforcement learning without consideration of whether the theoretical equivalence and guarantees hold. This paper extends the existing proofs to similar results in multi-agent systems, providing the theoretical background to explain the success of previous empirical studies. Specifically, it is proven that the equivalence to Q-table initialisation remains and the Nash Equilibria of the underlying stochastic game are not modified. Furthermore, we demonstrate empirically that potential-based reward shaping affects exploration and, consequentially, can alter the joint policy converged upon.\",\n \"Title: An empirical analysis of terminological representation systems\\nYear: 1992\\n\\nAbstract: The family of terminological representation systems has its roots in therepresentation system kl-one. Since the development of kl-one morethan a dozen similar representation systems have been developed by variousresearch groups. These systems vary along a number of dimensions.In this paper, we present the results of an empirical analysis of six suchsystems. Surprisingly, the systems turned out to be quite diverse, leadingto problems when transporting knowledge bases from one system to...\",\n \"Title: Overcoming erroneous domain knowledge in plan-based reward shaping\\nYear: 2013\\n\\nAbstract: Reward shaping has been shown to significantly improve an agent's performance in reinforcement learning. Plan-based reward shaping is a successful approach in which a STRIPS plan is used in order to guide the agent to the optimal behaviour. However, if the provided domain knowledge is wrong, it has been shown the agent will take longer to learn the optimal policy. Previously, in some cases, it was better to ignore all prior knowledge despite it only being partially erroneous. This paper introduces a novel use of knowledge revision to overcome erroneous domain knowledge when provided to an agent receiving plan-based reward shaping. Empirical results show that an agent using this method can outperform the previous agent receiving plan-based reward shaping without knowledge revision.\",\n \"Title: Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-agent Systems\\nYear: 2004\\n\\nAbstract: Most approaches to the learning of coordination in multi-agent systems (MAS) to date require all agents to use the same learning\\n algorithm with similar (or even the same) parameter settings. In today’s open networks and high inter-connectivity such an\\n assumption becomes increasingly unrealistic. Developers are starting to have less control over the agents that join the system\\n and the learning algorithms they employ. This makes effective coordination and good learning performance extremely difficult\\n to achieve, especially in the absence of learning agent standards. In this paper we investigate the problem of learning to\\n coordinate with heterogeneous agents. We show that an agent employing the FMQ algorithm, a recently developed multi-agent\\n learning method, has the ability to converge towards the optimal joint action when teamed-up with one or more simple Q-learners.\\n Specifically, we show such convergence in scenarios where simple Q-learners alone are unable to converge towards an optimum.\\n Our results show that system designers may improve learning and coordination performance by adding a “smart” agent to the\\n MAS.\",\n \"Title: Finding unexpected navigation behaviour in clickstream data for website design improvement\\nYear: 2009\\n\\nAbstract: This paper describes a novel web usage mining approach to discover patterns in the navigation of websites known as Unexpected Navigation Behaviours (UNBs). The approach provides a web designer with a means of identifying and classifying patterns of browsing and, by reviewing these patterns, the designer can then choose to modify the design of their site or redesign it completely. UNB mining is based on the Consecutive Common Subsequence (CCS), a special instance of Common Subsequence (CS), which is used to define a set of expected routes. The predefined expected routes are then treated as rules and stored in a rule base. By using the predefined route and the UNB mining algorithm, interesting navigation behaviours can be discovered. This paper will introduce the format of the expected route and describe the UNB algorithms. It will also describe a tool that a website designer can use to define the expected route more efficiently, which can help the website designer to make decision about where and how the design of website can be improved. The paper concludes with a series of experiments designed to evaluate how well the UNB mining algorithms work and demonstrate how UNB mining can be useful for improving website design.\",\n \"Title: A comparison of plan-based and abstract MDP reward shaping\\nYear: 2014\\n\\nAbstract: Reward shaping has been shown to significantly improve an agent's performance in reinforcement learning. As attention is shifting away from tabula-rasa approaches many different reward shaping methods have been developed. In this paper, we compare two different methods for reward shaping; plan-based, in which an agent is provided with a plan and extra rewards are given according to the steps of the plan the agent satisfies, and reward shaping via abstract Markov decision process (MDPs), in which an abstract high-level MDP of the environment is solved and the resulting value function is used to shape the agent. The comparison is conducted in terms of total reward, convergence speed and scaling up to more complex environments. Empirical results demonstrate the need to correctly select and set up reward shaping methods according to the needs of the environment the agents are acting in. This leads to the more interesting question, is there a reward shaping method which is universally better than all other approaches regardless of the environment dynamics?\",\n \"Title: Analyzing heuristic performance with response surface models: prediction, optimization and robustness\\nYear: 2007\\n\\nAbstract: This research uses a Design of Experiments (DOE) approach to build a predictive model of the performance of a combinatorial optimization heuristic over a range of heuristic tuning parameter settings and problem instance characteristics. The heuristic is Ant Colony System (ACS) for the Travelling Salesperson Problem. 10 heurstic tuning parameters and 2 problem characteristics are considered. Response Surface Models (RSM) of the solution quality and solution time predicted ACS performance on both new instances from a publicly available problem generator and new real-world instances from the TSPLIB benchmark library. A numerical optimisation of the RSMs is used to find the tuning parameter settings that yield optimal performance in terms of solution quality and solution time. This paper is the first use of desirability functions, a well-established technique in DOE, to simultaneously optimise these conflicting goals. Finally, overlay plots are used to examine the robustness of the performance of the optimised heuristic across a range of problem instance characteristics. These plots give predictions on the range of problem instances for which a given solutionquality can be expected within a given solution time.\",\n \"Title: Screening the parameters affecting heuristic performance\\nYear: 2007\\n\\nAbstract: This research screens the tuning parameters of a combinatorial optimization heuristic. Specifically, it presents a Design of Experiments (DOE) approach that uses a Fractional Factorial Design to screen the tuning parameters of Ant Colony System (ACS) for the Travelling Sales person problem. Screening is a preliminary step towards building a full Response Surface Model (RSM) [2]. It identifies parametersthat have little influence on performance and can be omittedfrom the RSM design. This reduces the complexity andexpense of the RSM design. 10 algorithm parameters and 2 problem characteristics are considered. Open questionson the effect of 3 parameters on performance are answered.A further parameter, sometimes assumed important, was shown to have no effect on performance. A new problem characteristic that effects performance was identified. A full version of this paper is available [3].\",\n \"Title: Robustness Analysis of SARSA(lambda): Different Models of Reward and Initialisation\\nYear: 2008\\n\\nAbstract: In the paper the robustness of SARSA(lambda), the reinforcement learning algorithm with eligibility traces, is confronted with different models of reward and initialisation of the Q-table. Most of the empirical analyses of eligibility traces in the literature have focused mainly on the step-penalty reward. We analyse two general types of rewards (final goal and step-penalty rewards) and show that learning with long traces, i.e., with high values of lambda, can lead to suboptimal solutions in some situations. Problems are identified and discussed. Specifically, obtained results show that SARSA(lambda) is sensitive to different models of reward and initialisation. In some cases the asymptotic performance can be significantly reduced.\",\n \"Title: Generation of Dilemma-Based Narratives: Method and Turing Test Evaluation\\nYear: 2008\\n\\nAbstract: This paper describes the analysis of the narrative generation aspect of the Generator of Adaptive Dilemma-based Interactive Narratives (GADIN). This system automatically generates narratives that are focused around dilemmas to create dramatic tension. The dilemmas are based on those clichés encountered in many of today's soap operas. A story planner creates sequences of actions that lead to dilemmas for storyworld characters. A narrative generated by GADIN is compared to that which took place in a television soap opera. The stories were subjected to a Turing-style test, with 127 participants, which showed that the readers could not determine which narrative had been written by a computer.\",\n \"Title: Distributed response to network intrusions using multiagent reinforcement learning\\nYear: 2015\\n\\nAbstract: Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to throttle or rate-limit traffic towards a victim server. It has been demonstrated to perform well against DDoS attacks in small-scale network topologies. The focus of this paper is to tackle the scalability challenge. Scalability is one of the most important aspects of a defence system since a non-scalable defence mechanism will never be considered, let alone adopted, for wide deployment by a company or organisation. In this paper we introduce Coordinated Team Learning (CTL) which is a novel design to the original Multiagent Router Throttling approach. One of the novel characteristics of our approach is that it provides a decentralised coordinated response to the DDoS problem. It incorporates several mechanisms, namely, hierarchical team-based communication, task decomposition and team rewards and its scalability is successfully demonstrated in experiments involving up to 100 reinforcement learning agents. We compare our proposed approach against a baseline and a popular state-of-the-art router throttling technique from the network security literature and we show that our approach significantly outperforms both of them in a series of scenarios with increasingly sophisticated attack dynamics. Furthermore, we show that our approach is more resilient and adaptable than the existing throttling approaches.\",\n \"Title: Coordinated Team Learning and Difference Rewards for Distributed Intrusion Response.\\nYear: 2014\\n\\nAbstract: Distributed denial of service attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to respond to such attacks. We demonstrate that our approach can significantly scale-up using hierarchical communication and coordinated team learning. Furthermore, we incorporate a form of reward shaping called difference rewards and show that the scalability of our system is significantly improved in experiments involving over 100 reinforcement learning agents. We also demonstrate that difference rewards constitute an ideal online learning mechanism for network intrusion response. We compare our proposed approach against a popular state-of-the-art router throttling technique from the network security literature, and we show that our proposed approach significantly outperforms it. We note that our approach can be useful in other related multiagent domains.\",\n \"Title: Improving Robustness of Gaussian Process-Based Inferential Control System Using Kernel Principle Component Analysis\\nYear: 2014\\n\\nAbstract: The plausibility and robustness of an inferential control system entirely depend on the prediction accuracy of the estimator used as the feedback element. This paper is based on a previously proposed Gaussian process inferential controller that employs Gaussian process soft sensor as an estimator. The paper enhances the robustness and the reliability of the control system, particularly, during sensor input failures. The contribution of the paper is i) alleviating the affect of the failure on the prediction accuracy of feedback element (soft sensor) and thus improving the robustness of the overall control system. ii) Hybridising Kernel Principal Component Analysis with Gaussian process Inferential Control System to achieve this robustness during all process operating conditions. The paper empirically shows the effectiveness and the plausibility of the processed hybrid system on a simulated chemical reactor process.\",\n \"Title: Generation of Adaptive Dilemma-Based Interactive Narratives\\nYear: 2009\\n\\nAbstract: The generator of adaptive dilemma-based interactive narratives (GADIN) presented in this paper dynamically generates interactive narratives which are focused on dilemmas to create dramatic tension. The system is provided with knowledge of generic story actions and dilemmas based on those clichE??s encountered in many storytelling domains. The domain designer is only required to provide domain-specific information, for example, regarding characters and their relationships, locations, and actions. A planner creates sequences of actions that all lead to a dilemma for a character (who can be the user). The user interacts with the storyworld by making decisions on relevant dilemmas and by freely choosing their own actions. Using this input, the system chooses and adapts future storylines according to the user's past behavior. Previous interactive narrative systems often have content creation and ordering requirements that restrict the possibility for sustaining the dramatic interest of the narrative over a long time period. In addition, many of these systems are not easily transferable between domains. In this paper, the GADIN system is demonstrated to both be able to maintain the dramatic interest of generated narratives over a long time period and to have a core architecture that is applicable to any domain.\",\n \"Title: Bayesian networks: Real-time applicable decision mechanisms for intelligent agents in interactive drama\\nYear: 2010\\n\\nAbstract: The growing use of intelligent agents in virtual realities calls for agents that can interact with users in a fluent and believable way. Intelligent agents are expected to react in an increasingly human like manner, showing distinct characteristics and to express emotion. Which calls for increasingly larger datasets for the agents to make informed and plausible decisions. Intelligent agents need to be able to process large knowledge bases and make decisions based on a wide range of relevant factors in real-time in order for the user to find them believable. This is very important for autonomous agents in emergent interactive drama because they need to respond fluently to user interactions in a manner that resembles that of a real actor. Bayesian networks (BNs) are particularly suitable to implement intelligent agents decision mechanism because they support transient emotions and decision making and accommodate for conflicts between the virtual agents goals. In their basic form, BN reasoning algorithms do not scale well, since the cost of updating values in a BN is NP-hard in the worst case. We propose efficient and scalable BN techniques using relevance reasoning, that are suitable for the needs of our autonomous agents in directed emergent drama and allow for real-time decision making.\",\n \"Title: Using plan-based reward shaping to learn strategies in StarCraft: Broodwar\\nYear: 2013\\n\\nAbstract: StarCraft: Broodwar (SC:BW) is a very popular commercial real strategy game (RTS) which has been extensively used in AI research. Despite being a popular test-bed reinforcement learning (RL) has not been evaluated extensively. A successful attempt was made to show the use of RL in a small-scale combat scenario involving an overpowered agent battling against multiple enemy units [1]. However, the chosen scenario was very small and not representative of the complexity of the game in its entirety. In order to build an RL agent that can manage the complexity of the full game, more efficient approaches must be used to tackle the state-space explosion. In this paper, we demonstrate how plan-based reward shaping can help an agent scale up to larger, more complex scenarios and significantly speed up the learning process as well as how high level planning can be combined with learning focusing on learning the Starcraft strategy, Battlecruiser Rush. We empirically show that the agent with plan-based reward shaping is significantly better both in terms of the learnt policy, as well as convergence speed when compared to baseline approaches which fail at reaching a good enough policy within a practical amount of time.\",\n \"Title: Plan-based reward shaping for multi-agent reinforcement learning.\\nYear: 2016\\n\\nAbstract: Recent theoretical results have justified the use of potential-based reward shaping as a way to improve the performance of multi-agent reinforcement learning (MARL). However, the question remains of how to generate a useful potential function. Previous research demonstrated the use of STRIPS operator knowledge to automatically generate a potential function for single-agent reinforcement learning. Following up on this work, we investigate the use of STRIPS planning knowledge in the context of MARL. Our results show that a potential function based on joint or individual plan knowledge can significantly improve MARL performance compared with no shaping. In addition, we investigate the limitations of individual plan knowledge as a source of reward shaping in cases where the combination of individual agent plans causes conflict.\",\n \"Title: Context-sensitive reward shaping for sparse interaction multi-agent systems.\\nYear: 2016\\n\\nAbstract: Potential-based reward shaping is a commonly used approach in reinforcement learning to direct exploration based on prior knowledge. Both in single and multi-agent settings this technique speeds up learning without losing any theoretical convergence guarantees. However, if speed ups through reward shaping are to be achieved in multi-agent environments, a different shaping signal should be used for each context in which agents have a different subgoal or when agents are involved in a different interaction situation. This paper describes the use of context-aware potential functions in a multi-agent system in which the interactions between agents are sparse. This means that, unknown to the agents a priori, the interactions between the agents only occur sporadically in certain regions of the state space. During these interactions, agents need to coordinate in order to reach the global optimal solution. We demonstrate how different reward shaping functions can be used on top of Future Coordinating Q-learning (FCQ-learning); an algorithm capable of automatically detecting when agents should take each other into consideration. Using FCQ-learning, coordination problems can even be anticipated before the actual problems occur, allowing the problems to be solved timely. We evaluate our approach on a range of gridworld problems, as well as a simulation of air traffic control.\",\n \"Title: Overcoming incorrect knowledge in plan-based reward shaping.\\nYear: 2016\\n\\nAbstract: Reward shaping has been shown to significantly improve an agent's performance in reinforcement learning. Plan-based reward shaping is a successful approach in which a STRIPS plan is used in order to guide the agent to the optimal behaviour. However, if the provided knowledge is wrong, it has been shown the agent will take longer to learn the optimal policy. Previously, in some cases, it was better to ignore all prior knowledge despite it only being partially incorrect. This paper introduces a novel use of knowledge revision to overcome incorrect domain knowledge when provided to an agent receiving plan-based reward shaping. Empirical results show that an agent using this method can outperform the previous agent receiving plan-based reward shaping without knowledge revision.\",\n \"Title: Distributed reinforcement learning for adaptive and robust network intrusion response\\nYear: 2015\\n\\nAbstract: Distributed denial of service DDoS attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. The focus of this paper is on online learning and scalability. We propose an approach that incorporates task decomposition, team rewards and a form of reward shaping called difference rewards. One of the novel characteristics of the proposed system is that it provides a decentralised coordinated response to the DDoS problem, thus being resilient to DDoS attacks themselves. The proposed system learns remarkably fast, thus being suitable for online learning. Furthermore, its scalability is successfully demonstrated in experiments involving 1000 learning agents. We compare our approach against a baseline and a popular state-of-the-art throttling technique from the network security literature and show that the proposed approach is more effective, adaptive to sophisticated attack rate dynamics and robust to agent failures.\",\n \"Title: Predicting player disengagement and first purchase with event-frequency based data representation\\nYear: 2015\\n\\nAbstract: In the game industry, especially for free to play games, player retention and purchases are important issues. There have been several approaches investigated towards predicting them by players' behaviours during game sessions. However, most current methods are only available for specific games because the data representations utilised are usually game specific. This work intends to use frequency of game events as data representations to predict both players' disengagement from game and the decisions of their first purchases. This method is able to provide better generality because events exist in every game and no knowledge of any event but their frequency is needed. In addition, this event frequency based method will also be compared with a recent work by Runge et al. [1] in terms of disengagement prediction.\",\n \"Title: Knowledge Revision For Reinforcement Learning With Abstract Mdps\\nYear: 2015\\n\\nAbstract: Reward shaping is a method often used in RL so as to provide domain knowledge to agents and thus improve learning. An unrealistic assumption however is that the provided knowledge is always correct. This assumption can lead to poor performance in terms of total reward and convergence speed in case it is not met. Previous research demonstrated the use of plan-based reward shaping with knowledge revision in a single agent scenario where agents showed that they can quickly identify and revise erroneous knowledge and thus benefit from more accurate plans. This method however has no mechanism to deal with non-deterministic scenarios and is thus limited to deterministic domains. In this paper we present a method to provide heuristic knowledge via abstract MDPs, coupled with a revision algorithm to manage the cases where the provided domain knowledge is wrong. We show empirically that our method can efficiently revise erroneous knowledge even in the cases where the environment is non-deterministic and also removes the need for some of the assumptions present in plan-based reward shaping with knowledge revision.\",\n \"Title: Higher Graphical Fidelity Decreases Players' Access to Aggressive Concepts in Violent Video Games\\nYear: 2015\\n\\nAbstract: Several features of violent video games (hence VVGs) may cause gamers to access aggressive concepts during play. Two of these features are graphical fidelity and use of narrative. In particular, the increased graphical fidelity of modern VVGs is widely theorised to cause increased access to aggressive concepts. However, despite this theoretical speculation, there is little empirical evidence of whether these formal features of VVGs actually do influence players to access aggressive concepts during play. Therefore, a 2x2 between-subjects factorial online experiment (N = 710) was employed. This examined the effects of graphical fidelity and narrative content on the extent to which players accessed aggression-related concepts when playing a VVG. Results indicated that the presence of realistic graphics in a video game representing aggression caused players to access aggressive concepts less (p = 0.014). The presence of narrative content was not found to have a statistically significant effect. Under an influential (though controversial) model of VVG effects known as the General Aggression Model, increased access to aggressive concepts causally contributes to anti-social behaviour by players of VVGs. These findings suggest that even if playing modern VVGs did lead to these negative effects, they would not be influenced by the increased graphical fidelity of modern VVGs.\",\n \"Title: Gaussian Process-Based Inferential Control System\\nYear: 2014\\n\\nAbstract: Gaussian process are emerging as a relatively new soft sensor building technique with promising results. This paper proposes a Gaussian Process Inferential Control System (GP-ICS) to control infrequently-measured variables in industrial processes. This is achieved by utilising an adaptive Gaussian process-based soft sensor to provide accurate reliable and continuous online predictions of difficult to measure variables and feeding them back to a PI controller. The contributions of the paper are i) the introduction of Gaussian process-based soft sensors in building inferential control systems, ii) we empirically show that the Gaussian process based inferential controller outperforms the ANN-based controller.\",\n \"Title: Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems.\\nYear: 2016\\n\\nAbstract: Real-world congestion problems (e.g. traffic congestion) are typically very complex and large-scale. Multiagent reinforcement learning (MARL) is a promising candidate for dealing with this emerging complexity by providing an autonomous and distributed solution to these problems. However, there are three limiting factors that affect the deployability of MARL approaches to congestion problems. These are learning time, scalability and decentralised coordination i.e. no communication between the learning agents. In this paper we introduce Resource Abstraction, an approach that addresses these challenges by allocating the available resources into abstract groups. This abstraction creates new reward functions that provide a more informative signal to the learning agents and aid the coordination amongst them. Experimental work is conducted on two benchmark domains from the literature, an abstract congestion problem and a realistic traffic congestion problem. The current state-of-the-art for solving multiagent congestion problems is a form of reward shaping called difference rewards. We show that the system using Resource Abstraction significantly improves the learning speed and scalability, and achieves the highest possible or near-highest joint performance/social welfare for both congestion problems in large-scale scenarios involving up to 1000 reinforcement learning agents.\",\n \"Title: Distribution Data Across Multiple Cloud Storage using Reinforcement Learning Method.\\nYear: 2017\\n\\nAbstract: Storing data on a single cloud storage service may cause several potential problems for the data owner such as service continuity, availability, performance, security, and the risk of vendor lock-in. A promising solution to tackle some of these issues is to distribute the data across multiple cloud storage services (MCSS). However, the distinguishing characteristics of different cloud providers, in terms of pricing schemes and service performance, make it difficult to optimise the cost and the performance concurrently on MCSS. This paper proposes a framework for automatically tuning the data distribution policies across MCSS from the client side based on file access patterns. The aim of this work is to optimise the average cost and the average service performance (mainly latency time) on MCSS. To achieve this goal, two different machine learning algorithms are used in this work: (1) supervised learning to predict file access patterns, and (2) reinforcement learning to control data distribution parameters based on the prediction of file access pattern. The framework was tested on a cloud storage emulator, where its was set to act like several common cloud storage services. The result of testing this framework shows a significant improvement in the cost and performance of storing data in multiple clouds, as compared to the commonly used uniform file distribution.\",\n \"Title: Assured Reinforcement Learning with Formally Verified Abstract Policies.\\nYear: 2017\\n\\nAbstract: We present a new reinforcement learning (RL) approach that enables an autonomous agent to solve decision making problems under constraints. Our assured reinforcement learning approach models the uncertain environment as a high-level, abstract Markov decision process (AMDP), and uses probabilistic model checking to establish AMDP policies that satisfy a set of constraints defined in probabilistic temporal logic. These formally verified abstract policies are then used to restrict the RL agent's exploration of the solution space so as to avoid constraint violations. We validate our RL approach by using it to develop autonomous agents for a flag-collection navigation task and an assisted-living planning problem.\",\n \"Title: Behavioural realism and the activation of aggressive concepts in violent video games.\\nYear: 2018\\n\\nAbstract: •Two large scale online game experiments (total n = 2778).•Behavioural realism does not increase the activation of aggressive concepts in VVGs.•Challenges theories which assert that more VVG realism leads to greater VVG effects.\",\n \"Title: No priming in video games.\\nYear: 2018\\n\\nAbstract: Video games depict a variety of different concepts. Models of learning in games like the GLM (General Learning Model) and GAM (General Aggression Model) predict that exposing players to these in-game concepts can lead to important changes in player behaviour.\",\n \"Title: Deep Learning of Cell Classification Using Microscope Images of Intracellular Microtubule Networks\\nYear: 2017\\n\\nAbstract: Microtubule networks (MTs) are a component of a cell that may indicate the presence of various chemical compounds and can be used to recognize properties such as treatment resistance. Therefore, the classification of MT images is of great relevance for cell diagnostics. Human experts find it particularly difficult to recognize the levels of chemical compound exposure of a cell. Improving the accuracy with automated techniques would have a significant impact on cell therapy. In this paper we present the application of Deep Learning to MT image classification and evaluate it on a large MT image dataset of animal cells with three degrees of exposure to a chemical agent. The results demonstrate that the learned deep network performs on par or better at the corresponding cell classification task than human experts. Specifically, we show that the task of recognizing different levels of chemical agent exposure can be handled significantly better by the neural network than by human experts.\",\n \"Title: iSynchronizer: A Tool for Extracting, Integration and Analysis of MovieLens and IMDb Datasets.\\nYear: 2018\\n\\nAbstract: The growing popularity of e-commerce has ignited the interest of the research community in e-commerce application research and development. For this purpose, variety of applications and resources such as MovieLens and IMDb datasets have been utilized, which present a rich database of items (movies), their attributes and ratings. It is a common practice to utilize both MovieLens and IMDb datasets to produce richer dataset about items and user-generated information such as tags and ratings for conducting research in recommender systems domain. Moreover, the combined dataset contains contextual feature represented by timestamps. However, dealing with a big chunk of the IMDb dataset and its integration with MovieLens dataset remains a challenge due to the various issues such as resource constraints and broken links. To address these issues, a more complex procedure has to be adopted for the integration of both datasets. Hence, this paper introduces iSynchronizer, which is an open-source tool for synchronizing MovieLens and IMDb datasets autonomously with minimum effort. Moreover, the tool includes code to generate a matrix of item features and user preferences over item features from the raw integrated dataset.\",\n \"Title: Introspective Reinforcement Learning and Learning from Demonstration.\\nYear: 2018\\n\\nAbstract: Reinforcement learning is a paradigm to model how an autonomous agent learns to maximise its cumulative reward by interacting with the environment. One challenge faced by reinforcement learning is that in many environments the reward signal is sparse, leading to slow improvement of the agentu0027s performance in early learning episodes. Potential-based reward shaping is a technique to resolve the aforementioned issue of sparse reward by incorporating an expertu0027s domain knowledge in the learning via a potential function. Past work on reinforcement learning from demonstration directly mapped (sub-optimal) human expert demonstration to a potential function, which can speed up reinforcement learning. In this paper we propose an introspective reinforcement learning agent that significantly speeds up the learning further. An introspective Reinforcement learning agent records its state-action decisions and experience during learning in a priority queue. Good quality decisions will be kept in the queue, while poorer decisions will be rejected. The queue is then used as demonstration to speed up reinforcement learning via reward shaping. A human expertu0027s demonstration can be used to initialise the priority queue before the learning process starts. Experimental validations in the 4-dimensional CartPole domain and the 27-dimensional Super Mario AI domain show that our approach significantly outperforms state-of-the-art approaches to reinforcement learning from demonstration in both domains.\",\n \"Title: Deep Multi-Agent Reinforcement Learning with Relevance Graphs.\\nYear: 2018\\n\\nAbstract: Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains. In this paper, we propose a novel approach, called MAGnet, to multi-agent reinforcement learning (MARL) that utilizes a relevance graph representation of the environment obtained by a self-attention mechanism, and a message-generation technique inspired by the NerveNet architecture. We applied our MAGnet approach to the Pommerman game and the results show that it significantly outperforms state-of-the-art MARL solutions, including DQN, MADDPG, and MCTS.\",\n \"Title: Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data\\nYear: 2018\\n\\nAbstract: Learning to produce efficient movement behaviour for humanoid robots from scratch is a hard problem, as has been illustrated by the “Learning to run” competition at NIPS 2017. The goal of this competition was to train a two-legged model of a humanoid body to run in a simulated race course with maximum speed. All submissions took a tabula rasa approach to reinforcement learning (RL) and were able to produce relatively fast, but not optimal running behaviour. In this paper, we demonstrate how data from videos of human running (e.g. taken from YouTube) can be used to shape the reward of the humanoid learning agent to speed up the learning and produce a better result. Specifically, we are using the positions of key body parts at regular time intervals to define a potential function for potential-based reward shaping (PBRS). Since PBRS does not change the optimal policy, this approach allows the RL agent to overcome sub-optimalities in the human movements that are shown in the videos. We present experiments in which we combine selected techniques from the top ten approaches from the NIPS competition with further optimizations to create an high-performing agent as a baseline. We then demonstrate how video-based reward shaping improves the performance further, resulting in an RL agent that runs twice as fast as the baseline in 12 hours of training. We furthermore show that our approach can overcome sub-optimal running behaviour in videos, with the learned policy significantly outperforming that of the running agent from the video.\",\n \"Title: Continuous Gesture Recognition From Semg Sensor Data With Recurrent Neural Networks And Adversarial Domain Adaptation\\nYear: 2018\\n\\nAbstract: Movement control of artificial limbs has made big advances in recent years. New sensor and control technology enhanced the functionality and usefulness of artificial limbs to the point that complex movements, such as grasping, can be performed to a limited extent. To date, the most successful results were achieved by applying recurrent neural networks (RNNs). However, in the domain of artificial hands, experiments so far were limited to non-mobile wrists, which significantly reduces the functionality of such prostheses. In this paper, for the first time, we present empirical results on gesture recognition with both mobile and non-mobile wrists. Furthermore, we demonstrate that recurrent neural networks with simple recurrent units (SRU) outperform regular RNNs in both cases in terms of gesture recognition accuracy, on data acquired by an arm band sensing electromagnetic signals from arm muscles (via surface electromyography or sEMG). Finally, we show that adding domain adaptation techniques to continuous gesture recognition with RNN improves the transfer ability between subjects, where a limb controller trained on data from one person is used for another person.\",\n \"Title: A Reinforcement Learning Based Workflow Application Scheduling Approach in Dynamic Cloud Environment.\\nYear: 2017\\n\\nAbstract: Workflow technology is an efficient means for constructing complex applications which involve multiple applications with different functions. In recent years, with the rapid development of cloud computing, deploying such workflow applications in cloud environment is becoming increasingly popular in many fields, such as scientific computing, big data analysis, collaborative design and manufacturing. In this context, how to schedule cloud-based workflow applications using heterogeneous and changing cloud resources is a formidable challenge. In this paper, we regard the service composition problem as a sequential decision making process and solve it by means of reinforcement learning. The experimental results demonstrate that our approach can find near-optimal solutions through continuous learning in the dynamic cloud market.\",\n \"Title: A Comparative Evaluation of Machine Learning Methods for Robot Navigation Through Human Crowds\\nYear: 2018\\n\\nAbstract: Robot navigation through crowds poses a difficult challenge to AI systems, since the methods should result in fast and efficient movement but at the same time are not allowed to compromise safety. Most approaches to date were focused on the combination of pathfinding algorithms with machine learning for pedestrian walking prediction. More recently, reinforcement learning techniques have been proposed in the research literature. In this paper, we perform a comparative evaluation of pathfinding/prediction and reinforcement learning approaches on a crowd movement dataset collected from surveillance videos taken at Grand Central Station in New York. The results demonstrate the strong superiority of state-of-the-art reinforcement learning approaches over pathfinding with state-of-the-art behavior prediction techniques.\",\n \"Title: Artificial Intelligence for Prosthetics - challenge solutions.\\nYear: 2019\\n\\nAbstract: In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector. Top participants were invited to describe their algorithms. In this work, we describe the challenge and present thirteen solutions that used deep reinforcement learning approaches. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each team implemented different modifications of the known algorithms by, for example, dividing the task into subtasks, learning low-level control, or by incorporating expert knowledge and using imitation learning.\",\n \"Title: An Online Learning Algorithm for Non-stationary Imbalanced Data by Extra-Charging Minority Class\\nYear: 2021\\n\\nAbstract: Online learning is one of the trending areas of machine learning in recent years. How to update the model based on new data is the core question in developing an online classifier. When new data arrives, the classifier should keep its model up-to-date by (1) learn new knowledge, (2) keep relevant learned knowledge, and (3) forget obsolete knowledge. This problem becomes more challenging in imbalanced non-stationary scenarios. Previous approaches save arriving instances, then utilize up/down sampling techniques to balance preserved samples and update their models. However, this strategy comes with two drawbacks: first, a delay in updating the models, and second, the up/down sampling causes information loss for the majority classes and introduces noise for the minority classes. To address these drawbacks, we propose the Hyper-Ellipses-Extra-Margin model (HEEM), which properly addresses the class imbalance challenge in online learning by reacting to every new instance as it arrives. HEEM keeps an ensemble of hyper-extended-ellipses for the minority class. Misclassified instances of the majority class are then used to shrink the ellipse, and correctly predicted instances of the minority class are used to enlarge the ellipse. Experimental results show that HEEM mitigates the class imbalance problem and outperforms the state-of-the-art methods.\",\n \"Title: Utilising Assured Multi-Agent Reinforcement Learning within Safety-Critical Scenarios\\nYear: 2021\\n\\nAbstract: Multi-agent reinforcement learning allows a team of agents to learn how to work together to solve complex decision-making problems in a shared environment. However, this learning process utilises stochastic mechanisms, meaning that its use in safety-critical domains can be problematic. To overcome this issue, we propose an Assured Multi-Agent Reinforcement Learning (AMARL) approach that uses a model checking technique called quantitative verification to provide formal guarantees of agent compliance with safety, performance, and other non-functional requirements during and after the reinforcement learning process. We demonstrate the applicability of our AMARL approach in three different patrolling navigation domains in which multi-agent systems must learn to visit key areas by using different types of reinforcement learning algorithms (temporal difference learning, game theory, and direct policy search). Furthermore, we compare the effectiveness of these algorithms when used in combination with and without our approach. Our extensive experiments with both homogeneous and heterogeneous multi-agent systems of different sizes show that the use of AMARL leads to safety requirements being consistently satisfied and to better overall results than standard reinforcement learning. (C) 2021 The Authors. Published by Elsevier B.V.\",\n \"Title: Improving cold-start recommendations using item-based stereotypes\\nYear: 2021\\n\\nAbstract: Recommender systems (RSs) have become key components driving the success of e-commerce and other platforms where revenue and customer satisfaction is dependent on the user's ability to discover desirable items in large catalogues. As the number of users and items on a platform grows, the computational complexity and the sparsity problem constitute important challenges for any recommendation algorithm. In addition, the most widely studied filtering-based RSs, while effective in providing suggestions for established users and items, are known for their poor performance for the new user and new item (cold-start) problems. Stereotypical modelling of users and items is a promising approach to solving these problems. A stereotype represents an aggregation of the characteristics of the items or users which can be used to create general user or item classes. We propose a set of methodologies for the automatic generation of stereotypes to address the cold-start problem. The novelty of the proposed approach rests on the findings that stereotypes built independently of the user-to-item ratings improve both recommendation metrics and computational performance during cold-start phases. The resulting RS can be used with any machine learning algorithm as a solver, and the improved performance gains due to rate-agnostic stereotypes are orthogonal to the gains obtained using more sophisticated solvers. The paper describes how such item-based stereotypes can be evaluated via a series of statistical tests prior to being used for recommendation. The proposed approach improves recommendation quality under a variety of metrics and significantly reduces the dimension of the recommendation model.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":89,"string":"89"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13249,"string":"13,249"}}},{"rowIdx":13250,"cells":{"question_id":{"kind":"string","value":"Bo Lang"},"question":{"kind":"string","value":"Generate an abstract for the title \"An image representation method based on retina mechanism for the promotion of SIFT and segmentation\" using the following items: 1. Bio-inspired neural network\n2. Image understanding\n3. Retinal ganglion cells\n4. Non-classical receptive field\n5. Image segmentation."},"answer":{"kind":"string","value":"In this paper, a bio-inspired neural network was constructed. It could represent images effectively and provide a processing method for image understanding. Our model adopted the retinal ganglion cells (GCs) and their non-classical receptive field (nCRF) can dynamic self-adjusts according to the characteristics of the image. Extensive experimental evaluations to demonstrate that this kind of representation method was able to make for SIFT detector for focus on foreground object more correctly, and promote the result of image segmentation significantly."},"correct_answer":{"kind":"string","value":"In this paper, a bio-inspired neural network was constructed. It could represent images effectively and provide a processing method for image understanding. Our model adopted the retinal ganglion cells (GCs) and their non-classical receptive field (nCRF) can dynamic self-adjusts according to the characteristics of the image. Extensive experimental evaluations to demonstrate that this kind of representation method was able to make for SIFT detector for focus on foreground object more correctly, and promote the result of image segmentation significantly."},"documents":{"kind":"list like","value":["Title: A Hybrid Intelligent Algorithm for Grain Logistics Vehicle Routing Problem\nYear: 2009\n\nAbstract: Vehicle routing problems (VRP) arise in many real-life applications within transportation and logistics. This paper considers vehicle routing models in grain logistics (GLVRP) and its hybrid intelligent algorithm. The objective of GLVRP is to use a fleet of vehicles with specific capacity to serve a number of customers with fixed demand and time window constraints. A hybrid intelligent algorithm base on particle swarm and ant colony optimization (PSACO-GLVRP) is proposed to solve this problem. Both particle swarm optimization (PSO) and ant colony optimization (ACO) are co-operative, population-based global search swarm intelligence metaheuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACO imitates foraging behavior of real life ants. In the experiments, a number of numerical examples are carried out for testing and verification. The Computational results confirm the efficiency of the proposed methodology.","Title: Hash Bit Selection: A Unified Solution for Selection Problems in Hashing\nYear: 2013\n\nAbstract: Recent years have witnessed the active development of hashing techniques for nearest neighbor search over big datasets. However, to apply hashing techniques successfully, there are several important issues remaining open in selecting features, hashing algorithms, parameter settings, kernels, etc. In this work, we unify all these selection problems into a hash bit selection framework, i.e., selecting the most informative hash bits from a pool of candidate bits generated by different types of hashing methods using different feature spaces and/or parameter settings, etc. We represent the bit pool as a vertex- and edge-weighted graph with the candidate bits as vertices. The vertex weight represents the bit quality in terms of similarity preservation, and the edge weight reflects independence (non-redundancy) between bits. Then we formulate the bit selection problem as quadratic programming on the graph, and solve it efficiently by replicator dynamics. Moreover, a theoretical study is provided to reveal a very interesting insight: the selected bits actually are the normalized dominant set of the candidate bit graph. We conducted extensive large-scale experiments for three important application scenarios of hash techniques, i.e., hashing with multiple features, multiple hashing algorithms, and multiple bit hashing. We demonstrate that our bit selection approach can achieve superior performance over both naive selection methods and state-of-the-art hashing methods under each scenario, with significant accuracy gains ranging from 10% to 50% relatively.","Title: Compact kernel hashing with multiple features\nYear: 2012\n\nAbstract: Hashing methods, which generate binary codes to preserve certain similarity, recently have become attractive in many applications like large scale visual search. However, most of state-of-the-art hashing methods only utilize single feature type, while combining multiple features has been proved very helpful in image search. In this paper we propose a novel hashing approach that utilizes the information conveyed by different features. The multiple feature hashing can be formulated as a similarity preserving problem with optimal linearly-combined multiple kernels. Such formulation is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also helpful to achieve fast training and search. We present an efficient alternating optimization to learn the hashing functions and the optimal kernel combination. Experimental results on two well-known benchmarks CIFAR-10 and NUS-WIDE show that the proposed method can achieve 11% and 34% performance gains over state-of-the-art methods.","Title: A Combined Music Label Propagation Model\nYear: 2011\n\nAbstract: Music labels, especially those related to high level semantics are very useful in music retrieval and recommendation, but normally hard to acquire. In the submission to ISMIR'07, Mohamed Sordo proposed a novel model, i.e., propagation of labels, to annotate music with existing labels, by using the content-based music similarity distance. In that model, a partially annotated collection with a lot of non-labeled music was annotated at a high precision and recall. In this paper, we proposed a new model -- label probability prediction model --and introduce it into the Sordo's work, which makes a combined model, to improve the accuracy of propagation without exploiting any other information. In addition, we also made some modifications to the original Sordo's model that could make the algorithm works better. Then we compare the result of combined model to that yielded by the original on a publicly accessible ground truth data, and find that, the new approach can reach a higher recall. Furthermore, with the same recall, our method obtains a better precision.","Title: An image representation of infrastructure based on non-classical receptive field\nYear: 2014\n\nAbstract: Biological vision systems have become highly optimized over millions of years of evolution, developing complex neural structures to represent and process stimuli. Moreover, biological systems of vision are typically far more efficient than current human-made machine vision systems. The present report describes a non-task-dependent image representation schema that simulates the early phase of a biological neural vision mechanism. We designed a neural model involving multiple types of computational units to simulate ganglion cells and their non-classical receptive fields, local feedback control circuits and receptive field dynamic self-adjustment mechanisms in the retina. We found that, beyond the pixel level, our model was able to represent images self-adaptively and rapidly. A series of statistical analyses revealed that this model not only produces compact and abstract approximations of images, but also retains their primary visual features. In addition, the improved representation was found to substantially facilitate contour detection and image segmentation. We propose that this improvement arose because ganglion cells can resize their receptive fields, enabling multi-scale analysis functionality, a neighborhood referring function and a localized synthesis function. The ganglion cell layer is the starting point of subsequent diverse visual processing. The universality of this cell type and its functional mechanisms suggests that it will be useful for designing image processing algorithms in future.","Title: A tetrahedral data model for unstructured data management.\nYear: 2010\n\nAbstract: This paper proposes a tetrahedral data model for unstructured data management. The model defines the four components of unstructured\n data including: basic attributes, semantic characteristics, low-level features and raw data on its four facets, and the relations\n between these components. The internal implementation structure of the model and the data query language are designed and\n briefly introduced. This model provides a unified, integrated and associated description for different kinds of unstructured\n data, and supports intelligent data services such as associated retrieval and data mining. An example is given to demonstrate\n how to use the model for describing and manipulating data from a sample video base.","Title: Robust clustering for social annotated images\nYear: 2013\n\nAbstract: Clustering plays an important role in large-scale image data management. The supervised clustering methods can discover the meaningful data groups according to the image labels. However, social annotated labels usually contain lots of noises like semantic ambiguity and redundancy, and thus the accuracy of clustering cannot be guaranteed. This paper firstly analyzes the characteristics of social annotated labels: label co-occurrence, ambiguity and redundancy, and then proposes a new image clustering method using semantic labels and their co-occurrence statistics and designs the similarity metric for social annotated images. Moreover, this paper presents a dynamic and efficient thresholding scheme for adaptively terminate the spectral clustering process. Finally, a social annotated image data set is constructed for algorithm evaluation. In our experiments, we compared our method with classic ones, and the results show that our method has better robustness and efficiency on social annotated images in terms of both accuracy and balance.","Title: A Bio-inspired Model for Image Representation and Image Analysis\nYear: 2011\n\nAbstract: This paper proposes a model for image representation and image analysis using a multi-layer neural network, which is rooted in the human vision system. Having complex neural layers to represent and process information, the biological vision system is far more efficient than machine vision system. The neural model simulate non-classical receptive field of ganglion cell and its local feedback control circuit, and can represent images, beyond pixel level, self-adaptively and regularly. The results of experiments, rebuilding, distribution and contour detection, prove this method can represent image faithfully with low cost, and can produce a compact and abstract approximation to facilitate successive image segmentation and integration. This representation schema is good at extracting spatial relationships from different components of images and highlighting foreground objects from background, especially for nature images with complicated scenes. Further it can be applied to object recognition or image classification tasks in future.","Title: An XML Data Placement Strategy for Distributed XML Storage and Parallel Query\nYear: 2011\n\nAbstract: Since there has been significant amount of XML documents generated in various application domains, efficient XML management has become an important problem. Distributed XML storage and parallel query based on Map Reduce can be an effective solution to this problem. As XML data placement strategy is a key factor of parallel system performance, in this paper we present an XML placement strategy, which is Query Workload Estimation based XML Placement strategy (QWEXP) for efficient distributed XML storage and parallel query. To achieve query workload balance, it partitions XML based on query workload estimation which is calculated by XML structure without knowing of user queries, considering that in common application scenarios user queries are unknown in advance. The partitioned XML segments are around an XML storage unit W0, to support scalability of parallel XML database. Finally segments are distributed to each processing node evenly to ensure workload balance on parallel query execution. Experimental results have shown that QWEXP promotes the speedup and scale up properties of parallel XML system greatly.","Title: Image retrieval in the unstructured data management system AUDR\nYear: 2012\n\nAbstract: The explosive growth of image data leads to severe challenges to the traditional image retrieval methods. In order to manage massive images more accurate and efficient, this paper firstly proposes a scalable architecture for image retrieval based on a uniform data model and makes this function a sub-engine of AUDR, an advanced unstructured data management system, which can simultaneously manage several kinds of unstructured data including image, video, audio and text. The paper then proposes a new image retrieval algorithm, which incorporates rich visual features and two text models for multi-modal retrieval. Experiments on both ImageNet dataset and ImageCLEF medical dataset show that our proposed architecture and the new retrieval algorithm are appropriate for efficient management of massive image.","Title: Multiple feature kernel hashing for large-scale visual search\nYear: 2014\n\nAbstract: Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods.","Title: Contour detection model with multi-scale integration based on non-classical receptive field\nYear: 2013\n\nAbstract: The broad region outside the classical receptive field (CRF) of a vision neuron, known as the non-classical receptive field (nCRF), exerts a robust modulatory effect on the responses to visual stimuli presented within the CRF, and plays an important role in visual information processing. One possible role for the nCRF is the extract object contours from disorderly background textures. In this study, a multi-scale integration based contour extraction model, inspired by the inhibitory and disinhibitory interactions between the CRF and the nCRF is presented. Unlike previous models, our model not only includes both the simple and complex cell mechanisms but also introduces pre-processing of the external information by the retinal ganglion cells at an early stage. The multi-scale representation of a physical scene acquired through such pre-processing was filtered through Gabor filters, and then inhibited or disinhibited at different spatial locations on different scales until a final response was obtained. Our results show that by introducing this kind of mechanism into the model, numbers of non-meaningful texture elements can be removed significantly, while at the same time, the object contours can be detected effectively. In addition to the superior contour detection performance in comparison to other contour detection models, our model provides a better understanding of the role of the nCRF and a novel approach for computer vision and pattern recognition.","Title: Efficient geometric re-ranking for mobile visual search\nYear: 2012\n\nAbstract: The state-of-the-art mobile visual search approaches are based on the bag-of-visual-word (BoW). As BoW representation ignores geometric relationship among the local features, a full geometric constraint like RANSAC is usually used as a post-processing step to re-rank the matched images, which has been shown to greatly improve the precision but at high computational cost. In this paper we present a novel and efficient geometric re-ranking method. Our basic idea is that the true matching local features should be not only in a similar spatial context, but also have a consistent spatial relationship, thus we simultaneously introduce context similarity and spatial similarity to describe the geometric consistency. By incorporating these two geometric constraints, the co-occurring visual words in the same spatial context can be regarded as a \"visual phrase\"and significantly improve the discriminative power than single visual word. To evaluate our approach, we perform experiments on Star5k and ImageNet100k dataset. The comparison with the BoW method and Soft-assignment method highlights the effectiveness of our approach in both accuracy and speed.","Title: A Flexible Attribute Based Access Control Method for Grid Computing\nYear: 2009\n\nAbstract: Grid systems have huge and changeable user groups, and different autonomous domains always have different security policies.\n The attribute based access control (ABAC) model, which is flexible and scalable, is more suitable for Grid systems. This paper\n describes a method of building a flexible access control mechanism that is based on ABAC and supports multiple policies for\n Grid computing. Firstly an attribute based multipolicy access control model ABMAC is submitted. Compared with ABAC, ABMAC\n can describe multiple heterogeneous policies, and each policy is encapsulated without changing its descriptions. Then by extending\n the authorization architecture of XACML, the paper puts forward an authorization framework that supports ABMAC and is implemented\n in the Globus Toolkit release 4 (GT4) (Few parts of the authorization framework described in this paper can only be found\n in Globus Toolkit CVS repository. A more completed authorization framework will be appeared in the Globus Toolkit release\n 4.2). Basing on the concept of policy encapsulation, the framework provides a flexible and scalable authorization mechanism\n that can support multiple existing policies in a Grid system. The design and implementation details of GT4 authorization framework\n are also well discussed.","Title: Managing and Collaboratively Processing Medical Image via the Web.\nYear: 2012\n\nAbstract: This article presents a strategy for medical image management. Based on the database named AUDR (Advanced Unstructured Data Repository) developed by Beihang University, a web-based system for medical image management and collaborative processing is built. Different from other applications, the system is accessed via web browser, and allows both text-based and content-based retrieval of medical images in DICOM format. Besides, the system allows geographically separated radiologists to process and interpret the same medical image simultaneously. To import medical image into AUDR, a strategy for DICOM image import is proposed. To implement the system, a collaboration framework and a method for image transmission is proposed. System evaluation in terms of functionality and performance has been performed in order to prove the availability of the system. © 2012 Springer-Verlag.","Title: Compact hashing for mixed image-keyword query over multi-label images\nYear: 2012\n\nAbstract: Recently locality-sensitive hashing (LSH) algorithms have attracted much attention owing to its empirical success and theoretic guarantee in large-scale visual search. In this paper we address the new topic of hashing with multi-label data, in which images in the database are assumed to be associated with missing or noisy multiple labels and each query consists of a query image and several textual search terms, similar to the new \"Search with Image\" function introduced by the Google Image Search. The returned images are judged based on the combination of visual similarity and semantic information conveyed by search terms. In most of the state-of-the-art approaches, the learned hashing functions are universal for all labels. To further enhance the hashing efficiency for such multi-label data, we propose a novel scheme \"boosted shared hashing\". Our basic observation is that image labels typically form cliques in the feature space. Hashing efficacy can be greatly improved by making each hashing function more targeted at and only shared across such cliques instead of all labels in conventional hashing methods. In other words, each hashing function is deliberately designed such that it is especially effective for a subset of labels. The targeted, but sparse association between labels and hash bits reduces the computation and storage when indexing a new datum, since only a small number of relevant hashing functions become active given the labels. We develop a Boosting-style algorithm for simultaneously optimizing the label subset and hashing function in a unified framework. Experimental results on standard image benchmarks like CIFAR-10 and NUS-WIDE show that the proposed hashing scheme achieves substantially superior performances over conventional methods in terms of accuracy under the same hash bit budget.","Title: A General Image Representation Scheme and Its Improvement for Image Analysis\nYear: 2013\n\nAbstract: In this paper, a bio-inspired neural network is developed to represent images and analysis features of images effectively. This model adopts schemes of retinal ganglion cells (GC) working and GCs' non-classical receptive fields (nCRF) that can dynamically adjust their sizes/scales according to the visual information. Extensive experiments are provided to value the effect of image representing, and experimental results show that this neural network model can represent images at a low cost and with a favor in improving both segmentation and integration processing. Most importantly, the GC-array model provides a basic infrastructure for image semantic extraction.","Title: Hash Bit Selection Using Markov Process for Approximate Nearest Neighbor Search\nYear: 2013\n\nAbstract: Hashing for nearest neighbor search has attracted great attentions in the past years. Many hashing methods have been successfully applied in real-world applications like the mobile product search. The performance of these applications usually highly relies on the quality of hash bits. However, it still lacks of a general method that can provide good hash bits for different scenarios. In this paper, we propose a novel method that can select compact, independent and informative hash bits using the Markov Process. Our method can serve as a unified framework compatible with different hashing methods. We design two algorithms, BS-CMP and BS-DMP, and formulate the selection problem as the subgraph discovery on a graph. Experiments are conducted for two important selection scenarios when applying hash techniques, i.e., hashing using different hashing algorithms and hashing with multiple features. The result indicates that our proposed bit selection approaches outperform naive selection methods significantly under aforementioned two scenarios.","Title: Reciprocal Hash Tables for Nearest Neighbor Search.\nYear: 2013\n\nAbstract: Recent years have witnessed the success of hashing techniques in approximate nearest neighbor search. In practice, multiple hash tables are usually employed to retrieve more desired results from all hit buckets of each table. However, there are rare works studying the unified approach to constructing multiple informative hash tables except the widely used random way. In this paper, we regard the table construction as a selection problem over a set of candidate hash functions. With the graph representation of the function set, we propose an efficient solution that sequentially applies normalized dominant set to finding the most informative and independent hash functions for each table. To further reduce the redundancy between tables, we explore the reciprocal hash tables in a boosting manner, where the hash function graph is updated with high weights emphasized on the misclassified neighbor pairs of previous hash tables. The construction method is general and compatible with different types of hashing algorithms using different feature spaces and/or parameter settings. Extensive experiments on two large-scale benchmarks demonstrate that the proposed method outperforms both naive construction method and state-of-the-art hashing algorithms, with up to 65.93% accuracy gains. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.","Title: Trust Degree based Access Control for Social Networks\nYear: 2010\n\nAbstract: Trust brings a new method for building scalable and fine-grained access control mechanism of social networks, a new kind of P2P systems. In this paper, the semantics of trust in the context of access control is described, and a Trust Degree Based Access Control model named TDBAC is proposed. Trust degree computing on a trust network is a key part in TDBAC. A novel algorithm that transforms a trust network to a computable expression is given. The algorithm simplifies the computation process and is also flexible.","Title: MIAPS: a web-based system for remotely accessing and presenting medical images.\nYear: 2014\n\nAbstract: MIAPS (medical image access and presentation system) is a web-based system designed for remotely accessing and presenting DICOM image. MIAPS is accessed with web browser through the Internet. MIAPS provides four features: DICOM image retrieval, maintenance, presentation and output. MIAPS does not intent to replace sophisticated commercial and open source packages, but it provides a web-based solution for teleradiology and medical image sharing. The system has been evaluated by 39 hospitals in China for 10 months.","Title: Multi-scale Image Analysis Based on Non-Classical Receptive Field Mechanism.\nYear: 2011\n\nAbstract: In the real world, the biological visual system is more efficient than the machine visual system in analyzing visual information. Physiology theories show that this efficiency owes to the multi-layer neural network in human visual system, in which every layer accomplishes different tasks and is related with other layers. The low-level stages of the human visual system, especially the retina, can provide certain scale information for the high-level stages of visual system through using the non-classical receptive field (nCRF) mechanism. This mechanism that the nCRF size can be adjusted automatically by ganglion cell (GC) can achieve a multi-scale image analysis. The results, reflecting the distribution of the image information, can be shared by several algorithms or processes solving different visual tasks, such as contour detection and image segmentation. A model of multi-scale image analysis based on GC has been proposed in this paper, which retains the key information and reduces the redundancy information for the further stages of the visual system. Experimental results on N-cut and contour detection show that this multi-scale image analysis model provides distinctive improvement for these image processing tasks.","Title: Feature grouping and local soft match for mobile visual search\nYear: 2012\n\nAbstract: More powerful mobile devices stimulate mobile visual search to become a popular and unique image retrieval application. A number of challenges come up with such application, resulting from appearance variations in mobile images. Performance of state-of-the-art image retrieval systems is improved using bag-of-words approaches. However, for visual search by mobile images with large variations, there are at least two critical issues unsolved: (1) the loss of features discriminative power due to quantization; and (2) the underuse of spatial relationships among visual words. To address both issues, this paper presents a novel visual search method based on feature grouping and local soft match, which considers properties of mobile images and couples visual and spatial information consistently. First features of the query image are grouped using both matched visual features and their spatial relationships; and then grouped features are softly matched to alleviate quantization loss. An efficient score scheme is devised to utilize inverted file index and compared with vocabulary-guided pyramid kernels. Finally experiments on Stanford mobile visual search database and a collected database with more than one million images show that the proposed method achieves promising improvement over the approach with a vocabulary tree, especially when large variations exist in query images.","Title: Online ngram-enhanced topic model for academic retrieval\nYear: 2011\n\nAbstract: Applying topic model to text mining has achieved a great success. However, state-of-art topic modeling methods still have potential to improve in academic retrieval field. In this paper, we propose an online unified topic model, which is ngram-enhanced. Our model discovers topics with unigrams as well as topical bigrams and is updated by an online inference algorithm with the new incoming data streams. On this basis, we combine our model into the query likelihood model and develop an integrated academic searching system. Experiment results on ACM collection show that our proposed methods outperform the existing approaches on document modeling and searching accuracy. Especially, we prove the efficiency of our system on academic retrieval problem.","Title: A computational trust model for access control in P2P.\nYear: 2010\n\nAbstract: Trust brings a new method for building scalable and fine-grained access control mechanism in P2P systems. The quantificational\n expression of trust and the calculation of trust in a trust network are the basis of trust degree based access control. In\n this paper, the properties of trust is analyzed by referring to the fruits from social science; the semantics of trust in\n the context of access control is described, and a trust degree based access control model named TDBAC is introduced. Basing\n on the properties and semantics of trust, a computational trust model which includes a multilevel comprehensive evaluation\n method for expressing direct trust and calculators for computing recommended trust is put forward. To compute trust in a trust\n network, an algorithm that transforms a trust network to a computable expression is given. The algorithm simplifies the computation\n process and is also flexible.","Title: A retina-inspired neurocomputing circuit for image representation\nYear: 2013\n\nAbstract: Biological vision systems have become highly optimized over millions of years of evolution, developing complex neural structures to represent and process stimuli. Moreover, biological systems of vision are typically far more efficient than current human-made machine vision systems. The present report describes a non-task-dependent image representation schema that simulates the early phase of a biological neural vision mechanism. We designed a neural model involving multiple types of computational units to simulate ganglion cells and their non-classical receptive fields, local feedback control circuits and receptive field dynamic self-adjustment mechanisms in the retina. We found that, beyond the pixel level, our model was able to represent images self-adaptively and rapidly. In addition, the improved representation was found to substantially facilitate contour detection. We propose that this improvement arose because ganglion cells can resize their receptive fields, enabling multiscale analysis functionality, a neighborhood referring function and a localized synthesis function. The ganglion cell layer is the starting point of subsequent diverse visual processing. The universality of this cell type and its functional mechanisms suggests that it will be useful for designing image processing algorithms in future.","Title: Attribute-Based Access Control for Layered Grid Resources.\nYear: 2010\n\nAbstract: Attribute-Based Access Control (ABAC) is a fine-grained and flexible authorization method. In this paper, considering the layered structure of Grid resources, an ABAC model named Grid_ABAC is presented, and the implementation architecture of Grid_ABAC basing on XACML is proposed. The paper also describes the method for integrating Grid_ABAC seamlessly into the authorization framework of the Globus Tloolkit. The test result shows that Grid_ABAC is efficient and provides a more flexible and open access control method for grid computing.","Title: An intrusion detection method based on system call temporal serial analysis\nYear: 2007\n\nAbstract: System call sequences are useful criteria to judge the behaviors of processes. How to generate an efficient matching algorithm and how to build up an implementable system are two of the most difficult problems. In this paper, we explore the possibility of extending consecutive system call to incorporate temporal signature to the Host-based Intrusion Detection System. In this model, we use the real-time detected system call sequences and their consecutive time interval as the data source, and use temporal signature to filter the real model. During the monitoring procedure, we use data mining methods to analyze the source dynamically and implement incremental learning mechanism. Through studying small size samples and incremental learning, the detecting ability of the system can be still good when the sample's size is small. This paper also introduces the key technologies to build such a system, and verifies this intrusion detection method in real time environment. Finally, this paper gives the experiments results to verify the availability and efficiency of our system.","Title: Search by mobile image based on visual and spatial consistency\nYear: 2011\n\nAbstract: Performance of state-of-the-art image retrieval systems has been improved significantly using bag-of-words approaches. After represented by visual words quantized from local features, images can be indexed and retrieved using scalable textual retrieval approaches. However, there exist at least two issues unsolved, especially for search by mobile images with large variations: (1) the loss of features discriminative power due to quantization; and (2) the underuse of spatial relationships among visual words. To address both issues, considering properties of mobile images, this paper presents a novel method coupling visual and spatial information consistently: to improve discriminative power, features of the query image are first grouped using both matched visual features and their spatial relationships; Then grouped features are softly matched to alleviate quantization loss. Experiments on both UKBench database and a collected database with more than one million images show that the proposed method achieves 10% improvement over the approach with a vocabulary tree and bundled feature method.","Title: A Scale-Changeable Image Analysis Method.\nYear: 2011\n\nAbstract: The biological vision system is far more efficient than machine vision system. This is due to the former has rich neural layers for representation and process. In order to obtain a non-task-dependent image representation schema, the early phase of neural vision mechanism is worth simulating. We design a neural model to simulate non-classical receptive field of ganglion cell and its local feedback control circuit, and find it can represent image, beyond pixel level, self-adaptively and regularly. The experimental results prove this method can represent image faithfully with low cost, and can produce a compact and abstract approximation to facilitate successive image segmentation as well as integration operation. This representation schema is good at extracting spatial relationship from different components of image, thus it can be applied to formalize image semantics. Further it can be applied to object recognition or image classification tasks in future.","Title: Efficient semi-supervised annotation with Proxy-based Local Consistency Propagation\nYear: 2013\n\nAbstract: Semi-supervised learning methods can largely leverage the image annotation problem using both labeled and unlabeled data, especially when the labeled information is quite limited. However, most of them suffer the expensive computation stemming from the batch learning on large training dataset. In this paper we proposed a highly efficient semi-supervised annotation approach with the partial label propagation based on the graph representation. Specifically, the label information is first propagated from labeled samples to the unlabeled ones, and then spreads only among unlabeled ones like a spreading activation network. Our approach takes advantage of the decomposed formulation to achieve a fast incremental learning instead of the expensive batch one without accuracy loss. Extensive evaluations over two large datasets demonstrate the superior performance of the proposed method and its significant efficiency.","Title: Mixed image-keyword query adaptive hashing over multilabel images\nYear: 2014\n\nAbstract: This article defines a new hashing task motivated by real-world applications in content-based image retrieval, that is, effective data indexing and retrieval given mixed query (query image together with user-provided keywords). Our work is distinguished from state-of-the-art hashing research by two unique features: (1) Unlike conventional image retrieval systems, the input query is a combination of an exemplar image and several descriptive keywords, and (2) the input image data are often associated with multiple labels. It is an assumption that is more consistent with the realistic scenarios. The mixed image-keyword query significantly extends traditional image-based query and better explicates the user intention. Meanwhile it complicates semantics-based indexing on the multilabel data. Though several existing hashing methods can be adapted to solve the indexing task, unfortunately they all prove to suffer from low effectiveness. To enhance the hashing efficiency, we propose a novel scheme “boosted shared hashing”. Unlike prior works that learn the hashing functions on either all image labels or a single label, we observe that the hashing function can be more effective if it is designed to index over an optimal label subset. In other words, the association between labels and hash bits are moderately sparse. The sparsity of the bit-label association indicates greatly reduced computation and storage complexities for indexing a new sample, since only limited number of hashing functions will become active for the specific sample. We develop a Boosting style algorithm for simultaneously optimizing both the optimal label subsets and hashing functions in a unified formulation, and further propose a query-adaptive retrieval mechanism based on hash bit selection for mixed queries, no matter whether or not the query words exist in the training data. Moreover, we show that the proposed method can be easily extended to the case where the data similarity is gauged by nonlinear kernel functions. Extensive experiments are conducted on standard image benchmarks like CIFAR-10, NUS-WIDE and a-TRECVID. The results validate both the sparsity of the bit-label association and the convergence of the proposed algorithm, and demonstrate that the proposed hashing scheme achieves substantially superior performances over state-of-the-art methods under the same hash bit budget.","Title: A Multipolicy Authorization Framework for Grid Security\nYear: 2006\n\nAbstract: A Grid system is a Virtual Organization that is composed of several autonomous domains. Authorization in such a system needs to be flexible and scalable to support multiple security policies. Basing on the Web Services security specifications such as XACML, SAML, and the special security needs of the Grid computing, we have constructed an authorization framework in the Globus Toolkit 4 that can support multiple policies. This paper describes the concepts of our design and introduces the structure and the components of the authorization framework. To show the flexibility and scalability of the framework, we introduce a new blacklist/whitelistbased authorization mechanism that can be seamlessly integrated into the framework.","Title: Music identification via vocabulary tree with MFCC peaks\nYear: 2011\n\nAbstract: In this paper, a Vocabulary Tree based framework is proposed for music identification whose target is to recognize a fragment from a song database. The key to a high recognition precision within this framework is a novel feature, namely MFCC Peaks, which is a combination of MFCC and Spectral Peaks features. Our approach consists of three stages. We first build the Vocabulary Tree with 2 million MFCC Peaks features extracted from hundreds of music. Then each song in the database is quantified into some words by traveling from root down to a certain leaf. Given a query input, we apply the same quantization procedure to this fragment, score the archive according to the TF-IDF scheme and return the best matches. The experimental results demonstrate that our proposed feature has strong identifying and generalization ability. Other trials show that our approach scales well with the size of database. Further comparison also demonstrates that while our algorithm achieves approximately the same retrieval precision as other state-of-the-art methods, it cost less time and memory.","Title: Online semi-supervised annotation via proxy-based local consistency propagation.\nYear: 2015\n\nAbstract: In this paper, we propose a novel label propagation algorithm named Proxy-based Local Consistency Propagation (PLCP), in which the label information is first propagated from labeled examples to the unlabeled ones, and then spreads only among unlabeled ones mutually until a steady state is reached. To meet the requirements of efficiency in many real-world image annotation applications, we propose an online semi-supervised annotation framework where the new examples can be predicted and used to update the model. Specifically, we extend PLCP to work under an inductive setting and propose an incremental model updating method that can incorporate the new examples including labeled and unlabeled examples. The comprehensive experiments on MNIST, CIFAR-10 and PIE datasets show that our proposed PLCP achieves superior performance compared with the baselines, and our proposed incremental model updating method can achieve significant promotion in efficiency, with the nearly identical accuracy compared to re-training.","Title: Self-contained Data Protection Scheme Based on CP-ABE.\nYear: 2013\n\nAbstract: Self-protection capabilities of outsourced data become note-worthily important in cloud computing. Ciphertext-Policy Attribute Based Encryption (CP-ABE) can dynamically control the user group of the encrypted data by defining decryption attributes; hence has certain ability of access control. Although there are different schemes of CP-ABE, as far as we know, most of these schemes can only express simple policies with AND, OR and threshold attribute operations, which cannot support traditional access control policies. In order to effectively integrate access control with encryption to build a self-contained data protection mechanism, this paper proposed an Extended CP-ABE (ECP-ABE) scheme based on the existing CP-ABE scheme. The ECP-ABE scheme can express any AttributeBased Access Control (ABAC) policies represented by arithmetic comparison and logical expressions that involve NOT, <, <=, >, >=, [], (), (] and [) operators in addition to AND, OR and threshold operators. We prove the Chosen-plaintext Attack (CPA) security of our scheme under the Decisional Bilinear Diffie-Hellman (DBDH) assumption in the standard model, and also discuss the experimental results of the efficiency of ECP-ABE.","Title: Query-Adaptive Hash Code Ranking for Fast Nearest Neighbor Search\nYear: 2014\n\nAbstract: Recently hash-based nearest neighbor search has become attractive in many applications due to its compressed storage and fast query speed. However, the quantization in the hashing process usually degenerates its discriminative power when using Hamming distance ranking. To enable fine-grained ranking, hash bit weighting has been proved as a promising solution. Though achieving satisfying performance improvement, state-of-the-art weighting methods usually heavily rely on the projection's distribution assumption, and thus can hardly be directly applied to more general types of hashing algorithms. In this paper, we propose a new ranking method named QRank with query-adaptive bitwise weights by exploiting both the discriminative power of each hash function and their complement for nearest neighbor search. QRank is a general weighting method for all kinds of hashing algorithms without any strict assumptions. Experimental results on two well-known benchmarks MNIST and NUS-WIDE show that the proposed method can achieve up to 17.11\\\\% performance gains over state-of-the-art methods.","Title: Collaborative Hashing\nYear: 2014\n\nAbstract: Hashing technique has become a promising approach for fast similarity search. Most of existing hashing research pursue the binary codes for the same type of entities by preserving their similarities. In practice, there are many scenarios involving nearest neighbor search on the data given in matrix form, where two different types of, yet naturally associated entities respectively correspond to its two dimensions or views. To fully explore the duality between the two views, we propose a collaborative hashing scheme for the data in matrix form to enable fast search in various applications such as image search using bag of words and recommendation using user-item ratings. By simultaneously preserving both the entity similarities in each view and the interrelationship between views, our collaborative hashing effectively learns the compact binary codes and the explicit hash functions for out-of-sample extension in an alternating optimization way. Extensive evaluations are conducted on three well-known datasets for search inside a single view and search across different views, demonstrating that our proposed method outperforms state-of-the-art baselines, with significant accuracy gains ranging from 7.67% to 45.87% relatively.","Title: Ontology-based concept similarity integrating image semantic and visual information\nYear: 2014\n\nAbstract: In recent years, the concept similarity measure has received wide attention in many applications, such as ontology construction, text analysis, image retrieval, etc. Currently, the concept similarity measure depends on the information mining in various knowledge bases, like dictionaries, ontologies, image annotation labels, and search engines. However, these knowledge bases usually only contain semantic information. With the development of the Internet and the popularity of the digital imaging devices, a lot of images and related texts have appeared, which help us to further mine the concept similarity relationships. The concept similarity is the outcome of human subjective perception. In addition to analysis of semantic information, the content of image itself precisely provides the visual perception information, which also plays an important role in the access of concept similarity relationships. To integrate both image semantic and visual information, in this paper we propose an ontology concept similarity measure that simultaneously utilizes the image semantic annotations and visual features to optimize the ontology-based metrics. The experiment result on the Corel dataset demonstrates the effectiveness of our proposed method.","Title: Graph-based active semi-supervised learning: A new perspective for relieving multi-class annotation labor\nYear: 2014\n\nAbstract: Semi-supervised learning and active learning are important techniques to build more accurate model while labeled data are scarce. The objective of this paper is combining both to effectively relieve user labor for multi-class annotation. We propose a novel graph-based active semi-supervised learning framework which aim at efficiently learning a multi-class model with minimal human labor. In particular, we propose Minimize Expected Global Uncertainty algorithm to actively select examples (for labels), which naturally integrates with the probabilistic results of graph-based semi-supervised learning. Meanwhile, we update the model incrementally by decomposed formulation while the new example are incorporated for training, which only has the time complexity of O(n), compared to the original re-training of O(n3). Extensive evaluations over three real-world datasets demonstrate that our proposed method has the superior performance comparing with the baselines and the capability to efficiently build more accurate model with fractional human labor.","Title: A general image representation scheme and its improvement for image analysis\nYear: 2014\n\nAbstract: In this paper, a bio-vision neural network was presented could provide a novel approach for image representation and a processing method for image analysis. This model using ganglion cells (GC) in retina and its non-classical receptive field (nCRF) can dynamically self-adjusts based on different input images. We provide extensive experimental evaluation to demonstrate that, this GC array can represent image with a low cost and could substantially improve image processing efficiency. Most importantly, the image representation based on GC-array model provides new approach for image semantic extraction later.","Title: Large-Scale Unsupervised Hashing with Shared Structure Learning.\nYear: 2015\n\nAbstract: Hashing methods are effective in generating compact binary signatures for images and videos. This paper addresses an important open issue in the literature, i.e., how to learn compact hash codes by enhancing the complementarity among different hash functions. Most of prior studies solve this problem either by adopting time-consuming sequential learning algorithms or by generating the hash functions which are subject to some deliberately-designed constraints (e.g., enforcing hash functions orthogonal to one another). We analyze the drawbacks of past works and propose a new solution to this problem. Our idea is to decompose the feature space into a subspace shared by all hash functions and its complementary subspace. On one hand, the shared subspace, corresponding to the common structure across different hash functions, conveys most relevant information for the hashing task. Similar to data de-noising, irrelevant information is explicitly suppressed during hash function generation. On the other hand, in case that the complementary subspace also contains useful information for specific hash functions, the final form of our proposed hashing scheme is a compromise between these two kinds of subspaces. To make hash functions not only preserve the local neighborhood structure but also capture the global cluster distribution of the whole data, an objective function incorporating spectral embedding loss, binary quantization loss, and shared subspace contribution is introduced to guide the hash function learning. We propose an efficient alternating optimization method to simultaneously learn both the shared structure and the hash functions. Experimental results on three well-known benchmarks CIFAR-10, NUS-WIDE, and a-TRECVID demonstrate that our approach significantly outperforms state-of-the-art hashing methods.","Title: A CP-ABE scheme with hidden policy and its application in cloud computing.\nYear: 2015\n\nAbstract: With flexible and scalable features for fine-grained access control, ciphertext policy attribute-based encryption (CP-ABE) is widely used as a kind of data protection mechanism in cloud computing. However, the access policy of CP-ABE scheme may contain sensitive information which causes privacy revelation of the data provider or receiver. Some papers proposed hidden policy CP-ABE schemes, which were based on And-gate access structure whose expressive ability of access policy was limited. CP-ABE with the tree-based access structure has stronger expressive ability and more flexible access control capability. Therefore, it has broad application prospects compared to other mechanisms. This paper proposed a tree-based access structure CP-ABE scheme with hidden policy (CP-ABE-HP), and also proved that the scheme had chosen-plaintext attack (CPA) security. CP-ABE-HP could both protect the policy and had flexible access control capability. Then, considering the characteristics of cloud computing environment, the ...","Title: Query-Adaptive Reciprocal Hash Tables for Nearest Neighbor Search\nYear: 2016\n\nAbstract: Recent years have witnessed the success of binary hashing techniques in approximate nearest neighbor search. In practice, multiple hash tables are usually built using hashing to cover more desired results in the hit buckets of each table. However, rare work studies the unified approach to constructing multiple informative hash tables using any type of hashing algorithms. Meanwhile, for multiple ta...","Title: Multi-View Complementary Hash Tables for Nearest Neighbor Search\nYear: 2015\n\nAbstract: Recent years have witnessed the success of hashing techniques in fast nearest neighbor search. In practice many applications (eg., visual search, object detection, image matching, etc.) have enjoyed the benefits of complementary hash tables and information fusion over multiple views. However, most of prior research mainly focused on compact hash code cleaning, and rare work studies how to build multiple complementary hash tables, much less to adaptively integrate information stemming from multiple views. In this paper we first present a novel multi-view complementary hash table method that learns complementarity hash tables from the data with multiple views. For single multi-view table, using exemplar based feature fusion, we approximate the inherent data similarities with a low-rank matrix, and learn discriminative hash functions in an efficient way. To build complementary tables and meanwhile maintain scalable training and fast out-of-sample extension, an exemplar reweighting scheme is introduced to update the induced low-rank similarity in the sequential table construction framework, which indeed brings mutual benefits between tables by placing greater importance on exemplars shared by mis-separated neighbors. Extensive experiments on three large-scale image datasets demonstrate that the proposed method significantly outperforms various naive solutions and state-of-the-art multi-table methods.","Title: Outsourced KP-ABE with Enhanced Security.\nYear: 2014\n\nAbstract: Although Key-Policy Attribute-Based Encryption (KPABE) has been widely applied to protect data in cloud computing, it is always criticized for its inefficiency drawbacks, coming from both key-issuing and decryption. Recently, some papers proposed the outsourcing solutions. But adversaries in the attack model of these researches were divided into two categories, and it is assumed that the two cannot communicate with each other, which is obviously unrealistic. In this paper, we first proved that there are severe security vulnerabilities in these schemes for this assumption, and then proposed a security enhanced Chosen Plaintext Attack (SE-CPA) model, which eliminates the improper limitations. By utilizing Proxy Re-Encryption (PRE), we also constructed a concrete KP-ABE Outsourcing scheme (O-KP-ABE) and proved its security under SE-CPA model. Comparisons with existing schemes show that our construction has comprehensive advantages in security and efficiency.","Title: Compound Concept Semantic Similarity Calculation Based on Ontology and Concept Constitution Features\nYear: 2015\n\nAbstract: The computation of semantic similarity between words is important in information retrieval, knowledge acquisition and many other fields. The existing studies are mainly aiming at single concepts composed of single terms. For the compound concepts composed of multiple terms, they usually neglect the special constitution features of compounds and only process them as single concepts, which may affect the ultimate accuracy. In this paper, we propose a novel ontology-based Compound Concept Semantic Similarity calculation approach called CCSS which exploits concept constitution features. In CCSS, the compound is decomposed into Subject headings and Auxiliary words (SaA), and the relationships between these two sets are used to measure the similarity. Besides, the errors that may be caused by SaA recognition are corrected. Moreover, several information sources of ontology such as taxonomical features, local density and depth are considered. Extensive experimental evaluations demonstrate that our approach significantly outperforms existing approaches.","Title: Exploiting Human Mobility Patterns for Gas Station Site Selection.\nYear: 2016\n\nAbstract: Advances in sensor, wireless communication, and information infrastructure such as GPS have enabled us to collect massive amounts of human mobility data, which are fine-grained and have global road coverage. These human mobility data, if properly encoded with semantic information (i.e. combined with Point of Interests (POIs)), is appealing for changing the paradigm for gas station site selection. To this end, in this paper, we investigate how to exploit newly-generated human mobility data for enhancing gas station selection. Specifically, we develop a ranking system for evaluating the business performances of gas stations based on waiting time of refueling events by mining human mobility data. Along this line, we first design a method for detecting taxi refueling events by jointly tracking dwell times, GPS trace angles, location sequences, and refueling cycles of the vehicles. Also, we extract the fine-grained discriminative features strategically from POI data, human mobility data and road network data within the neighborhood of gas stations, and perform feature selection by simultaneously maximizing relevance and minimizing redundancy based on mutual information. In addition, we learn a ranking model for predicting gas station crowdedness by exploiting learning to rank techniques. The extensive experimental evaluation on real-world data also show the advantages of the proposed method over existing approaches for gas site selection.","Title: Fast Graph Similarity Search via Locality Sensitive Hashing.\nYear: 2015\n\nAbstract: Similarity search in graph databases has been widely studied in graph query processing in recent years. With the fast accumulation of graph databases, it is worthwhile to develop a fast algorithm to support similarity search in large-scale graph databases. In this paper, we study k-NN similarity search problem via locality sensitive hashing. We propose a fast graph search algorithm, which first transforms complex graphs into vectorial representations based on the prototypes in the database and then accelerates query efficiency in Euclidean space by employing locality sensitive hashing. Additionally, a general retrieval framework is established in our approach. Experiments on three real datasets show that our work achieves high performance both on the accuracy and the efficiency of the presented algorithm.","Title: A novel rotation adaptive object detection method based on pair Hough model.\nYear: 2016\n\nAbstract: This paper proposes a novel Hough-based object shape representation model called Pair Hough Model (PHM) and its corresponding object detection framework. PHM constructs the voting models implicitly with automatically detected interest points and their local descriptors for unseen object categories. In addition, by casting votes according to key point pairs instead of individual key points and taking the orientations of objects as well as their sizes into consideration, PHM can recognize and localize objects after their scaling and/or rotation, which makes it suitable for processing images with major rotations such as pictures taken by mobile devices. Evaluation experiments proved that PHM does not need to be trained on rotated images to recognize rotated objects, and PHM achieved comparable results to the state-of-the-art methods on several widely used public data sets.","Title: Query-Adaptive Hash Code Ranking for Large-Scale Multi-View Visual Search.\nYear: 2016\n\nAbstract: Hash-based nearest neighbor search has become attractive in many applications. However, the quantization in hashing usually degenerates the discriminative power when using Hamming distance ranking. Besides, for large-scale visual search, existing hashing methods cannot directly support the efficient search over the data with multiple sources, and while the literature has shown that adaptively inco...","Title: An Efficient Kp-Abe Scheme For Content Protection In Information-Centric Networking\nYear: 2016\n\nAbstract: Media streaming has largely dominated the Internet traffic and the trend will keep increasing in the next years. To efficiently distribute the media content, Information-Centric Networking (ICN) has attracted many researchers. Since end users usually obtain content from indeterminate caches in ICN, the publisher cannot reinforce data security and access control depending on the caches. Hence, the ability of self-contained protection is important for the cached contents. Attribute-based encryption (ABE) is considered the preferred solution to achieve this goal. However, the existing ABE schemes usually have problems regarding efficiency. The exponentiation in key generation and pairing operation in decryption respectively increases linearly with the number of attributes involved, which make it costly. In this paper, we propose an efficient key-policy ABE with fast key generation and decryption (FKP-ABE). In the key generation, we get rid of exponentiation and only require multiplications/divisions for each attribute in the access policy. And in the decryption, we reduce the pairing operations to a constant number, no matter how many attributes are used. The efficiency analysis indicates that our scheme has better performance than the existing KP-ABE schemes. Finally, we present an implementation framework that incorporates the proposed FKP-ABE with the ICN architecture.","Title: Structure Sensitive Hashing With Adaptive Product Quantization.\nYear: 2016\n\nAbstract: Hashing has been proved as an attractive solution to approximate nearest neighbor search, owing to its theoretical guarantee and computational efficiency. Though most of prior hashing algorithms can achieve low memory and computation consumption by pursuing compact hash codes, however, they are still far beyond the capability of learning discriminative hash functions from the data with complex inh...","Title: A bio-inspired image local feature representation based on vision network model.\nYear: 2016\n\nAbstract: In this paper, a hierarchical network model based on human vision physiological mechanism was put forward. Firstly, simple cell and complex cell in primary visual cortex is modeled, then studied the response pattern of V4 area and inferior temporal cortex on ventral side channel and representing the local features of input image utilized the computational model. The experiment results show that local image features extracted by computational model have sufficient discrimination; furthermore, the local image features extracted using biological visual model demonstrated much more excellent generalization ability in natural scene with complicated background.","Title: GKSH: Graph based Image Retrieval using Supervised Kernel Hashing.\nYear: 2016\n\nAbstract: The explosive growth of the massive image database brings great challenge to the fast and accurate image retrieval. To address this issue, we propose a kind of supervised hashing method based on the graph representation of the images, which translates images into representative attribute structural graphs. Compared with the traditional supervised methods, this kind of structural graphs can consider the spatial relations among regions in the image, and integrate unsupervised properties of images and supervised information of labels by adopting hashing with graph kernel based on random walks. The learnt hash codes can be a good tradeoff among retrieval speed, memory requirements and retrieval accuracy. The experiments on three image datasets PASCAL, MNIST and HOLIDAY demonstrate that, with the hash codes of the same length, the proposed supervised hashing method can achieve a higher precision and a better efficiency.","Title: Projection Based Weight Normalization for Deep Neural Networks.\nYear: 2017\n\nAbstract: Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling-based weight space symmetry property in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over Oblique manifold. A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem. PBWN executes standard gradient updates, followed by projecting the updated weight back to Oblique manifold. This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique. We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art convolutional neural networks, such as Inception, VGG and residual networks. The results show that our method is able to improve the performance of DNNs with different architectures consistently. We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method outperforms the state-of-the-art methods: we obtain test errors as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples, respectively.","Title: Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks.\nYear: 2018\n\nAbstract: Orthogonal matrix has shown advantages in training Recurrent Neural Networks (RNNs), but such matrix is limited to be square for the hidden-to-hidden transformation in RNNs. In this paper, we generalize such square orthogonal matrix to orthogonal rectangular matrix and formulating this problem in feed-forward Neural Networks (FNNs) as Optimization over Multiple Dependent Stiefel Manifolds (OMDSM). We show that the rectangular orthogonal matrix can stabilize the distribution of network activations and regularize FNNs. We also propose a novel orthogonal weight normalization method to solve OMDSM. Particularly, it constructs orthogonal transformation over proxy parameters to ensure the weight matrix is orthogonal and back-propagates gradient information through the transformation during training. To guarantee stability, we minimize the distortions between proxy parameters and canonical weights over all tractable orthogonal transformations. In addition, we design an orthogonal linear module (OLM) to learn orthogonal filter banks in practice, which can be used as an alternative to standard linear module. Extensive experiments demonstrate that by simply substituting OLM for standard linear module without revising any experimental protocols, our method largely improves the performance of the state-of-the-art networks, including Inception and residual networks on CIFAR and ImageNet datasets. In particular, we have reduced the test error of wide residual network on CIFAR-100 from 20.04% to 18.61% with such simple substitution. Our code is available online for result reproduction.","Title: Calculating Semantic Similarity between Academic Articles using Topic Event and Ontology.\nYear: 2017\n\nAbstract: Determining semantic similarity between academic documents is crucial to many tasks such as plagiarism detection, automatic technical survey and semantic search. Current studies mostly focus on semantic similarity between concepts, sentences and short text fragments. However, document-level semantic matching is still based on statistical information in surface level, neglecting article structures and global semantic meanings, which may cause the deviation in document understanding. In this paper, we focus on the document-level semantic similarity issue for academic literatures with a novel method. We represent academic articles with topic events that utilize multiple information profiles, such as research purposes, methodologies and domains to integrally describe the research work, and calculate the similarity between topic events based on the domain ontology to acquire the semantic similarity between articles. Experiments show that our approach achieves significant performance compared to state-of-the-art methods.","Title: DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model.\nYear: 2018\n\nAbstract: •We present a graph convolutional neural network (g-CNN) model that can perform feature learning on graph data directly.•We present the disordered graph convolutional layer that can change the discrete parameter learning into the sampling.•The experimental results show that the proposed method outperforms the existing g-CNN methods and other types of methods.","Title: An efficient adaptive algorithm for removal of impulse noises\nYear: 2017\n\nAbstract: An efficient algorithm Adaptive Min-Max Average Filters (AMMAF) for the removal of impulse noises is proposed in this paper. AMMAF divides all pixels of the corrupted image into three types and has two phases denoising with adaptive small filters, which preserve most original signals and restore more details. Experiments results show that compared with state-of-the-art salt & pepper Denoising algorithm, AMMAF performs better and is also less time-consuming, especially for the high-density noise images.","Title: Decorrelated Batch Normalization\nYear: 2018\n\nAbstract: Batch Normalization (BN) is capable of accelerating the training of deep models by centering and scaling activations within mini-batches. In this work, we propose Decorrelated Batch Normalization (DBN), which not just centers and scales activations but whitens them. We explore multiple whitening techniques, and find that PCA whitening causes a problem we call stochastic axis swapping, which is detrimental to learning. We show that ZCA whitening does not suffer from this problem, permitting successful learning. DBN retains the desirable qualities of BN and further improves BN's optimization efficiency and generalization ability. We design comprehensive experiments to show that DBN can improve the performance of BN on multilayer perceptrons and convolutional neural networks. Furthermore, we consistently improve the accuracy of residual networks on CIFAR-10, CIFAR-100, and ImageNet.","Title: Fast graph similarity search via hashing and its application on image retrieval\nYear: 2018\n\nAbstract: Similarity search in graph databases has been widely investigated. It is worthwhile to develop a fast algorithm to support similarity search in large-scale graph databases. In this paper, we investigate a -NN (-Nearest Neighbor) similarity search problem by locality sensitive hashing (LSH). We propose an innovative fast graph search algorithm named LSH-GSS, which first transforms complex graphs into vectorial representations based on prototypes in the database and later accelerates a query in Euclidean space by employing LSH. Because images can be represented as attributed graphs, we propose an approach to transform attributed graphs into n-dimensional vectors and apply LSH-GSS to execute further image retrieval. Experiments on three real graph datasets and two image datasets show that our methods are highly accurate and efficient.","Title: CNN and RNN based payload classification methods for attack detection.\nYear: 2019\n\nAbstract: In recent years, machine learning has been widely applied to problems in detecting network attacks, particularly novel attacks. However, traditional machine learning methods depend heavily on feature engineering, and extracting features is often time-consuming and complex. Thus, it is impractical to detect attacks with traditional machine learning methods in real-time applications. To discover network attacks efficiently, we propose an end-to-end detection approach. We implement deep learning models to analyze payloads and propose a convolutional neural network-based payload classification approach (PL-CNN) and a recurrent neural network-based payload classification approach (PL-RNN) for use in attack detection. Our two approaches learn feature representations from original payloads without feature engineering and support end-to-end detection. These approaches achieve accuracies of 99.36% and 99.98% when applied to the DARPA1998 dataset, respectively; these accuracies are comparable to or better than those of state-of-the-art methods. In addition, our methods are efficient and practical.","Title: An Efficient and Privacy Preserving CP-ABE Scheme for Internet-Based Collaboration.\nYear: 2017\n\nAbstract: The development of Internet applications facilitates enterprises and individuals to share information and work together across physical barriers. In such environment, flexible and efficient data-protection methods are required because data are out of the control domain of its owners. By encrypting with an access policy, ciphertext-policy attribute-based encryption (CP-ABE) can simultaneously achieve data encryption and access control, making it an ideal mechanism for data protection in Internet-based environments. However, the existing CP-ABE schemes usually have limitations regarding efficiency and privacy leakage from the access policy. In this paper, we propose a CP-ABE scheme with hidden access policy and fast decryption that improves the decryption efficiency and preserves the privacy of the access policy. In addition, by adopting dual-system encryption methodology, our scheme achieves full security, which is a higher security level in CP-ABE. The performance analysis revealed that the comprehensive capability of our scheme outperforms the existing CP-ABE schemes.","Title: Multidimensional data tight aggregation and fine-grained access control in smart grid.\nYear: 2018\n\nAbstract: •A multidimensional data secure aggregation scheme in smart grid is proposed.•The scheme achieves fine-grained access control at dimension-level.•Access permission can be updated flexibly and securely.•The proposed scheme is implemented efficiently.","Title: Topic Model Based Android Malware Detection.\nYear: 2019\n\nAbstract: Nowadays, the security risks brought by Android malwares are increasing. Machine learning is considered as a potential solution for promoting the performance of malware detection. For machine learning based Android malware detection, feature extraction plays a key role. Thinking the source codes of applications are comparable with text documents, we propose a new Android malware detection method based on the topic model which is an effective technique in text feature extraction. Our method regards the decompiled codes of an application as a text document, and the topic model is used to mine the potential topics in the codes which can reflect the semantic feature of the application. The experimental results demonstrate that, our approach performs better than the state-of-the-art methods. Also, our method mines the features in the application files automatically without manually design, and therefore overcomes the limitation in present methods which relies on experts’ prior knowledge.","Title: Semantic-based Compound Keyword Search over Encrypted Cloud Data\nYear: 2021\n\nAbstract: Keyword search over encrypted data is essential for accessing outsourced sensitive data in cloud computing. In some circumstances, the keywords that the user searches on are only semantically related to the data rather than via an exact or fuzzy match. Hence, semantic-based keyword search over encrypted cloud data becomes of paramount importance. However, existing schemes usually depend upon a glo...","Title: Network Traffic Classification Method Supporting Unknown Protocol Detection\nYear: 2021\n\nAbstract: At present, private protocols are widely used on the Internet. As a result, traditional traffic classification methods including port-based and DPI methods have become restricted. Existing machine learning-based methods depend on feature engineering, which makes feature design difficult. In addition, classification models can only classify data as predefined categories, which restricts the models ...","Title: Interpretable deep learning method for attack detection based on spatial domain attention\nYear: 2021\n\nAbstract: Deep learning methods can directly extract effective features from original data. However, this type of model is complex and considered to be a “black box”, which leads to low interpretability of the models. Since the results of attack detection are significant to cybersecurity, every decision should be supported with convincing reasons. Hence, the problem of interpretability has become a bottlene...","Title: Bridging Text Space and Knowledge Space via Transference Methods\nYear: 2021\n\nAbstract: Introducing the words of texts, entities, and relations of a knowledge graph (KG) into the same semantic space has great significance in KG complement and knowledge computing. Current methods mainly utilize the \"alignment constraint\" of words and entities to construct uniform objective functions. However, the \"alignment constraint\" limits the joint representation space to specific KGs and texts. Meanwhile, the representation effect still suffers from the scale of the \"alignment constraint\". This paper propose a novel transference framework, the method firstly learns the text representation space and KG representation space independently, and then transfers the word representation in the text space to the knowledge space with projection models, and finally constructs a joint representation space. Our approach can decrease the dependency on\"alignment constraint\", and allow two spaces to be optimized and extended independently. Hence, it has better flexibility, general applicability and helps improve the capability of the joint representation space. Further more, to enhance the word transference performance, we incorporate the relation constraint into the mapping models. To the best of our knowledge, this is the first study using transference method to construct the joint semantic space. The experimental results show that linear mapping models are more suitable than nonlinear models during the projection process. The results of word analogy and relation extraction tasks illustrate the effectiveness of our method compared with state-of-the-art methods.","Title: Exploring the Risky Travel Area and Behavior of Car-hailing Service\nYear: 2022\n\nAbstract: AbstractRecent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually emerged and attracted more and more attention. While car-hailing service providers have made considerable efforts on developing real-time trajectory tracking systems and alarm mechanisms, most of them only focus on providing rescue-supporting information rather than preventing potential crimes. Recently, the newly available large-scale car-hailing order data have provided an unparalleled chance for researchers to explore the risky travel area and behavior of car-hailing services, which can be used for building an intelligent crime early warning system. To this end, in this article, we propose a Risky Area and Risky Behavior Evaluation System (RARBEs) based on the real-world car-hailing order data. In RARBEs, we first mine massive multi-source urban data and train an effective area risk prediction model, which estimates area risk at the urban block level. Then, we propose a transverse and longitudinal double detection method, which estimates behavior risk based on two aspects, including fraud trajectory recognition and fraud patterns mining. In particular, we creatively propose a bipartite graph-based algorithm to model the implicit relationship between areas and behaviors, which collaboratively adjusts area risk and behavior risk estimation based on random walk regularization. Finally, extensive experiments on multi-source real-world urban data clearly validate the effectiveness and efficiency of our system.","Title: PT-LSTM: Extending LSTM for Efficient Processing Time Attributes in Time Series Prediction\nYear: 2021\n\nAbstract: Long Short-Term Memory (LSTM) has been widely applied in time series predictions. Time attributes are important factors in time series prediction. However, existing studies often ignore the influence of time attributes when splitting the time series data, and seldom utilize the time information in the LSTM models. In this paper, we propose a novel method named Position encoding and Time gate LSTM (PT-LSTM). We first propose a position-encoding based time attributes integration method, which obtains the vector representation of time attributes through position encoding, and integrate it with the observed value vectors of the data. Moreover, we propose a LSTM variant by adding a new time gate which is specially designed to process time attributes. Therefore, PT-LSTM can make good use of time attributes in the key phases of data prediction. Experimental results on three public datasets show that our PT-LSTM model outperforms the state-of-the-art methods in time series prediction.","Title: ARRPNGAN: Text-to-image GAN with attention regularization and region proposal networks\nYear: 2022\n\nAbstract: Although text-to-image synthesis has shown remarkable success in generating high-resolution photorealistic images and semantic consistency, it still faces challenges in generating images with complex backgrounds. In this paper, we address this problem by proposing a novel generative adversarial text-to-image synthesis framework based on attention regularization modules and region proposal networks (ARRPNGAN). ARRPNGAN can precisely locate the keywords in text by exploiting attention model advantages and improving the accuracy in locating the subimage of target objects with the help of an RPN. Leveraging both attention regularization and the RPN a generative adversarial network (GAN) can obtain the most text description semantics and reduce the interference of complex background information. The results of extensive experiments on the Caltech-UCSD Birds and MS COCO datasets demonstrate that the proposed ARRPNGAN significantly outperforms other state-of-the-art text-to-image methods, especially in generating photorealistic images with complex backgrounds. Codes are available at: https://github.com/quanFN/ARRPNGAN.","Title: An Efficient and Fine-Grained Access Control Scheme for Multidimensional Data Aggregation in Smart Grid\nYear: 2016\n\nAbstract: Smart grid has a large number of users and numerous data analysis organizations. The privacy-preserving and access control for multidimensional power data have become vital security concerns. For correct aggregation of multidimensional data, existing schemes require transmitting attribute sets of data together with ciphertexts to the gateway (GW), which leaks the relevant information of power data and increases the security risk. Moreover, to implement access control of the multidimensional data, control center (CC) must firstly decrypt the data of all dimensions. It means CC can access all of the data without authorization, which may result in the data leakage. For coping with these problems, we propose an efficient and Fine-grained Access control scheme for Multidimensional data Aggregation, named FAMA. It uses the extended Boneh-gohnissim homomorphic encryption (EBGN) to enforce multidimensional privacy-preserving data aggregation without transmitting attribute sets and attribute-based encryption (ABE) to implement fine-grained access control at dimension-level. By using FAMA, GW does not need to distinguish different ciphertexts. And every receiver can only extract the specific dimensions that it has been authorized, which not only restricts the privileges of CC, but also simplifies the operations of GW and CC. Moreover, after optimizing the scalar multiplication on elliptic curve, the encryption of FAMA is more efficient than the existing schemes."],"string":"[\n \"Title: A Hybrid Intelligent Algorithm for Grain Logistics Vehicle Routing Problem\\nYear: 2009\\n\\nAbstract: Vehicle routing problems (VRP) arise in many real-life applications within transportation and logistics. This paper considers vehicle routing models in grain logistics (GLVRP) and its hybrid intelligent algorithm. The objective of GLVRP is to use a fleet of vehicles with specific capacity to serve a number of customers with fixed demand and time window constraints. A hybrid intelligent algorithm base on particle swarm and ant colony optimization (PSACO-GLVRP) is proposed to solve this problem. Both particle swarm optimization (PSO) and ant colony optimization (ACO) are co-operative, population-based global search swarm intelligence metaheuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACO imitates foraging behavior of real life ants. In the experiments, a number of numerical examples are carried out for testing and verification. The Computational results confirm the efficiency of the proposed methodology.\",\n \"Title: Hash Bit Selection: A Unified Solution for Selection Problems in Hashing\\nYear: 2013\\n\\nAbstract: Recent years have witnessed the active development of hashing techniques for nearest neighbor search over big datasets. However, to apply hashing techniques successfully, there are several important issues remaining open in selecting features, hashing algorithms, parameter settings, kernels, etc. In this work, we unify all these selection problems into a hash bit selection framework, i.e., selecting the most informative hash bits from a pool of candidate bits generated by different types of hashing methods using different feature spaces and/or parameter settings, etc. We represent the bit pool as a vertex- and edge-weighted graph with the candidate bits as vertices. The vertex weight represents the bit quality in terms of similarity preservation, and the edge weight reflects independence (non-redundancy) between bits. Then we formulate the bit selection problem as quadratic programming on the graph, and solve it efficiently by replicator dynamics. Moreover, a theoretical study is provided to reveal a very interesting insight: the selected bits actually are the normalized dominant set of the candidate bit graph. We conducted extensive large-scale experiments for three important application scenarios of hash techniques, i.e., hashing with multiple features, multiple hashing algorithms, and multiple bit hashing. We demonstrate that our bit selection approach can achieve superior performance over both naive selection methods and state-of-the-art hashing methods under each scenario, with significant accuracy gains ranging from 10% to 50% relatively.\",\n \"Title: Compact kernel hashing with multiple features\\nYear: 2012\\n\\nAbstract: Hashing methods, which generate binary codes to preserve certain similarity, recently have become attractive in many applications like large scale visual search. However, most of state-of-the-art hashing methods only utilize single feature type, while combining multiple features has been proved very helpful in image search. In this paper we propose a novel hashing approach that utilizes the information conveyed by different features. The multiple feature hashing can be formulated as a similarity preserving problem with optimal linearly-combined multiple kernels. Such formulation is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also helpful to achieve fast training and search. We present an efficient alternating optimization to learn the hashing functions and the optimal kernel combination. Experimental results on two well-known benchmarks CIFAR-10 and NUS-WIDE show that the proposed method can achieve 11% and 34% performance gains over state-of-the-art methods.\",\n \"Title: A Combined Music Label Propagation Model\\nYear: 2011\\n\\nAbstract: Music labels, especially those related to high level semantics are very useful in music retrieval and recommendation, but normally hard to acquire. In the submission to ISMIR'07, Mohamed Sordo proposed a novel model, i.e., propagation of labels, to annotate music with existing labels, by using the content-based music similarity distance. In that model, a partially annotated collection with a lot of non-labeled music was annotated at a high precision and recall. In this paper, we proposed a new model -- label probability prediction model --and introduce it into the Sordo's work, which makes a combined model, to improve the accuracy of propagation without exploiting any other information. In addition, we also made some modifications to the original Sordo's model that could make the algorithm works better. Then we compare the result of combined model to that yielded by the original on a publicly accessible ground truth data, and find that, the new approach can reach a higher recall. Furthermore, with the same recall, our method obtains a better precision.\",\n \"Title: An image representation of infrastructure based on non-classical receptive field\\nYear: 2014\\n\\nAbstract: Biological vision systems have become highly optimized over millions of years of evolution, developing complex neural structures to represent and process stimuli. Moreover, biological systems of vision are typically far more efficient than current human-made machine vision systems. The present report describes a non-task-dependent image representation schema that simulates the early phase of a biological neural vision mechanism. We designed a neural model involving multiple types of computational units to simulate ganglion cells and their non-classical receptive fields, local feedback control circuits and receptive field dynamic self-adjustment mechanisms in the retina. We found that, beyond the pixel level, our model was able to represent images self-adaptively and rapidly. A series of statistical analyses revealed that this model not only produces compact and abstract approximations of images, but also retains their primary visual features. In addition, the improved representation was found to substantially facilitate contour detection and image segmentation. We propose that this improvement arose because ganglion cells can resize their receptive fields, enabling multi-scale analysis functionality, a neighborhood referring function and a localized synthesis function. The ganglion cell layer is the starting point of subsequent diverse visual processing. The universality of this cell type and its functional mechanisms suggests that it will be useful for designing image processing algorithms in future.\",\n \"Title: A tetrahedral data model for unstructured data management.\\nYear: 2010\\n\\nAbstract: This paper proposes a tetrahedral data model for unstructured data management. The model defines the four components of unstructured\\n data including: basic attributes, semantic characteristics, low-level features and raw data on its four facets, and the relations\\n between these components. The internal implementation structure of the model and the data query language are designed and\\n briefly introduced. This model provides a unified, integrated and associated description for different kinds of unstructured\\n data, and supports intelligent data services such as associated retrieval and data mining. An example is given to demonstrate\\n how to use the model for describing and manipulating data from a sample video base.\",\n \"Title: Robust clustering for social annotated images\\nYear: 2013\\n\\nAbstract: Clustering plays an important role in large-scale image data management. The supervised clustering methods can discover the meaningful data groups according to the image labels. However, social annotated labels usually contain lots of noises like semantic ambiguity and redundancy, and thus the accuracy of clustering cannot be guaranteed. This paper firstly analyzes the characteristics of social annotated labels: label co-occurrence, ambiguity and redundancy, and then proposes a new image clustering method using semantic labels and their co-occurrence statistics and designs the similarity metric for social annotated images. Moreover, this paper presents a dynamic and efficient thresholding scheme for adaptively terminate the spectral clustering process. Finally, a social annotated image data set is constructed for algorithm evaluation. In our experiments, we compared our method with classic ones, and the results show that our method has better robustness and efficiency on social annotated images in terms of both accuracy and balance.\",\n \"Title: A Bio-inspired Model for Image Representation and Image Analysis\\nYear: 2011\\n\\nAbstract: This paper proposes a model for image representation and image analysis using a multi-layer neural network, which is rooted in the human vision system. Having complex neural layers to represent and process information, the biological vision system is far more efficient than machine vision system. The neural model simulate non-classical receptive field of ganglion cell and its local feedback control circuit, and can represent images, beyond pixel level, self-adaptively and regularly. The results of experiments, rebuilding, distribution and contour detection, prove this method can represent image faithfully with low cost, and can produce a compact and abstract approximation to facilitate successive image segmentation and integration. This representation schema is good at extracting spatial relationships from different components of images and highlighting foreground objects from background, especially for nature images with complicated scenes. Further it can be applied to object recognition or image classification tasks in future.\",\n \"Title: An XML Data Placement Strategy for Distributed XML Storage and Parallel Query\\nYear: 2011\\n\\nAbstract: Since there has been significant amount of XML documents generated in various application domains, efficient XML management has become an important problem. Distributed XML storage and parallel query based on Map Reduce can be an effective solution to this problem. As XML data placement strategy is a key factor of parallel system performance, in this paper we present an XML placement strategy, which is Query Workload Estimation based XML Placement strategy (QWEXP) for efficient distributed XML storage and parallel query. To achieve query workload balance, it partitions XML based on query workload estimation which is calculated by XML structure without knowing of user queries, considering that in common application scenarios user queries are unknown in advance. The partitioned XML segments are around an XML storage unit W0, to support scalability of parallel XML database. Finally segments are distributed to each processing node evenly to ensure workload balance on parallel query execution. Experimental results have shown that QWEXP promotes the speedup and scale up properties of parallel XML system greatly.\",\n \"Title: Image retrieval in the unstructured data management system AUDR\\nYear: 2012\\n\\nAbstract: The explosive growth of image data leads to severe challenges to the traditional image retrieval methods. In order to manage massive images more accurate and efficient, this paper firstly proposes a scalable architecture for image retrieval based on a uniform data model and makes this function a sub-engine of AUDR, an advanced unstructured data management system, which can simultaneously manage several kinds of unstructured data including image, video, audio and text. The paper then proposes a new image retrieval algorithm, which incorporates rich visual features and two text models for multi-modal retrieval. Experiments on both ImageNet dataset and ImageCLEF medical dataset show that our proposed architecture and the new retrieval algorithm are appropriate for efficient management of massive image.\",\n \"Title: Multiple feature kernel hashing for large-scale visual search\\nYear: 2014\\n\\nAbstract: Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods.\",\n \"Title: Contour detection model with multi-scale integration based on non-classical receptive field\\nYear: 2013\\n\\nAbstract: The broad region outside the classical receptive field (CRF) of a vision neuron, known as the non-classical receptive field (nCRF), exerts a robust modulatory effect on the responses to visual stimuli presented within the CRF, and plays an important role in visual information processing. One possible role for the nCRF is the extract object contours from disorderly background textures. In this study, a multi-scale integration based contour extraction model, inspired by the inhibitory and disinhibitory interactions between the CRF and the nCRF is presented. Unlike previous models, our model not only includes both the simple and complex cell mechanisms but also introduces pre-processing of the external information by the retinal ganglion cells at an early stage. The multi-scale representation of a physical scene acquired through such pre-processing was filtered through Gabor filters, and then inhibited or disinhibited at different spatial locations on different scales until a final response was obtained. Our results show that by introducing this kind of mechanism into the model, numbers of non-meaningful texture elements can be removed significantly, while at the same time, the object contours can be detected effectively. In addition to the superior contour detection performance in comparison to other contour detection models, our model provides a better understanding of the role of the nCRF and a novel approach for computer vision and pattern recognition.\",\n \"Title: Efficient geometric re-ranking for mobile visual search\\nYear: 2012\\n\\nAbstract: The state-of-the-art mobile visual search approaches are based on the bag-of-visual-word (BoW). As BoW representation ignores geometric relationship among the local features, a full geometric constraint like RANSAC is usually used as a post-processing step to re-rank the matched images, which has been shown to greatly improve the precision but at high computational cost. In this paper we present a novel and efficient geometric re-ranking method. Our basic idea is that the true matching local features should be not only in a similar spatial context, but also have a consistent spatial relationship, thus we simultaneously introduce context similarity and spatial similarity to describe the geometric consistency. By incorporating these two geometric constraints, the co-occurring visual words in the same spatial context can be regarded as a \\\"visual phrase\\\"and significantly improve the discriminative power than single visual word. To evaluate our approach, we perform experiments on Star5k and ImageNet100k dataset. The comparison with the BoW method and Soft-assignment method highlights the effectiveness of our approach in both accuracy and speed.\",\n \"Title: A Flexible Attribute Based Access Control Method for Grid Computing\\nYear: 2009\\n\\nAbstract: Grid systems have huge and changeable user groups, and different autonomous domains always have different security policies.\\n The attribute based access control (ABAC) model, which is flexible and scalable, is more suitable for Grid systems. This paper\\n describes a method of building a flexible access control mechanism that is based on ABAC and supports multiple policies for\\n Grid computing. Firstly an attribute based multipolicy access control model ABMAC is submitted. Compared with ABAC, ABMAC\\n can describe multiple heterogeneous policies, and each policy is encapsulated without changing its descriptions. Then by extending\\n the authorization architecture of XACML, the paper puts forward an authorization framework that supports ABMAC and is implemented\\n in the Globus Toolkit release 4 (GT4) (Few parts of the authorization framework described in this paper can only be found\\n in Globus Toolkit CVS repository. A more completed authorization framework will be appeared in the Globus Toolkit release\\n 4.2). Basing on the concept of policy encapsulation, the framework provides a flexible and scalable authorization mechanism\\n that can support multiple existing policies in a Grid system. The design and implementation details of GT4 authorization framework\\n are also well discussed.\",\n \"Title: Managing and Collaboratively Processing Medical Image via the Web.\\nYear: 2012\\n\\nAbstract: This article presents a strategy for medical image management. Based on the database named AUDR (Advanced Unstructured Data Repository) developed by Beihang University, a web-based system for medical image management and collaborative processing is built. Different from other applications, the system is accessed via web browser, and allows both text-based and content-based retrieval of medical images in DICOM format. Besides, the system allows geographically separated radiologists to process and interpret the same medical image simultaneously. To import medical image into AUDR, a strategy for DICOM image import is proposed. To implement the system, a collaboration framework and a method for image transmission is proposed. System evaluation in terms of functionality and performance has been performed in order to prove the availability of the system. © 2012 Springer-Verlag.\",\n \"Title: Compact hashing for mixed image-keyword query over multi-label images\\nYear: 2012\\n\\nAbstract: Recently locality-sensitive hashing (LSH) algorithms have attracted much attention owing to its empirical success and theoretic guarantee in large-scale visual search. In this paper we address the new topic of hashing with multi-label data, in which images in the database are assumed to be associated with missing or noisy multiple labels and each query consists of a query image and several textual search terms, similar to the new \\\"Search with Image\\\" function introduced by the Google Image Search. The returned images are judged based on the combination of visual similarity and semantic information conveyed by search terms. In most of the state-of-the-art approaches, the learned hashing functions are universal for all labels. To further enhance the hashing efficiency for such multi-label data, we propose a novel scheme \\\"boosted shared hashing\\\". Our basic observation is that image labels typically form cliques in the feature space. Hashing efficacy can be greatly improved by making each hashing function more targeted at and only shared across such cliques instead of all labels in conventional hashing methods. In other words, each hashing function is deliberately designed such that it is especially effective for a subset of labels. The targeted, but sparse association between labels and hash bits reduces the computation and storage when indexing a new datum, since only a small number of relevant hashing functions become active given the labels. We develop a Boosting-style algorithm for simultaneously optimizing the label subset and hashing function in a unified framework. Experimental results on standard image benchmarks like CIFAR-10 and NUS-WIDE show that the proposed hashing scheme achieves substantially superior performances over conventional methods in terms of accuracy under the same hash bit budget.\",\n \"Title: A General Image Representation Scheme and Its Improvement for Image Analysis\\nYear: 2013\\n\\nAbstract: In this paper, a bio-inspired neural network is developed to represent images and analysis features of images effectively. This model adopts schemes of retinal ganglion cells (GC) working and GCs' non-classical receptive fields (nCRF) that can dynamically adjust their sizes/scales according to the visual information. Extensive experiments are provided to value the effect of image representing, and experimental results show that this neural network model can represent images at a low cost and with a favor in improving both segmentation and integration processing. Most importantly, the GC-array model provides a basic infrastructure for image semantic extraction.\",\n \"Title: Hash Bit Selection Using Markov Process for Approximate Nearest Neighbor Search\\nYear: 2013\\n\\nAbstract: Hashing for nearest neighbor search has attracted great attentions in the past years. Many hashing methods have been successfully applied in real-world applications like the mobile product search. The performance of these applications usually highly relies on the quality of hash bits. However, it still lacks of a general method that can provide good hash bits for different scenarios. In this paper, we propose a novel method that can select compact, independent and informative hash bits using the Markov Process. Our method can serve as a unified framework compatible with different hashing methods. We design two algorithms, BS-CMP and BS-DMP, and formulate the selection problem as the subgraph discovery on a graph. Experiments are conducted for two important selection scenarios when applying hash techniques, i.e., hashing using different hashing algorithms and hashing with multiple features. The result indicates that our proposed bit selection approaches outperform naive selection methods significantly under aforementioned two scenarios.\",\n \"Title: Reciprocal Hash Tables for Nearest Neighbor Search.\\nYear: 2013\\n\\nAbstract: Recent years have witnessed the success of hashing techniques in approximate nearest neighbor search. In practice, multiple hash tables are usually employed to retrieve more desired results from all hit buckets of each table. However, there are rare works studying the unified approach to constructing multiple informative hash tables except the widely used random way. In this paper, we regard the table construction as a selection problem over a set of candidate hash functions. With the graph representation of the function set, we propose an efficient solution that sequentially applies normalized dominant set to finding the most informative and independent hash functions for each table. To further reduce the redundancy between tables, we explore the reciprocal hash tables in a boosting manner, where the hash function graph is updated with high weights emphasized on the misclassified neighbor pairs of previous hash tables. The construction method is general and compatible with different types of hashing algorithms using different feature spaces and/or parameter settings. Extensive experiments on two large-scale benchmarks demonstrate that the proposed method outperforms both naive construction method and state-of-the-art hashing algorithms, with up to 65.93% accuracy gains. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.\",\n \"Title: Trust Degree based Access Control for Social Networks\\nYear: 2010\\n\\nAbstract: Trust brings a new method for building scalable and fine-grained access control mechanism of social networks, a new kind of P2P systems. In this paper, the semantics of trust in the context of access control is described, and a Trust Degree Based Access Control model named TDBAC is proposed. Trust degree computing on a trust network is a key part in TDBAC. A novel algorithm that transforms a trust network to a computable expression is given. The algorithm simplifies the computation process and is also flexible.\",\n \"Title: MIAPS: a web-based system for remotely accessing and presenting medical images.\\nYear: 2014\\n\\nAbstract: MIAPS (medical image access and presentation system) is a web-based system designed for remotely accessing and presenting DICOM image. MIAPS is accessed with web browser through the Internet. MIAPS provides four features: DICOM image retrieval, maintenance, presentation and output. MIAPS does not intent to replace sophisticated commercial and open source packages, but it provides a web-based solution for teleradiology and medical image sharing. The system has been evaluated by 39 hospitals in China for 10 months.\",\n \"Title: Multi-scale Image Analysis Based on Non-Classical Receptive Field Mechanism.\\nYear: 2011\\n\\nAbstract: In the real world, the biological visual system is more efficient than the machine visual system in analyzing visual information. Physiology theories show that this efficiency owes to the multi-layer neural network in human visual system, in which every layer accomplishes different tasks and is related with other layers. The low-level stages of the human visual system, especially the retina, can provide certain scale information for the high-level stages of visual system through using the non-classical receptive field (nCRF) mechanism. This mechanism that the nCRF size can be adjusted automatically by ganglion cell (GC) can achieve a multi-scale image analysis. The results, reflecting the distribution of the image information, can be shared by several algorithms or processes solving different visual tasks, such as contour detection and image segmentation. A model of multi-scale image analysis based on GC has been proposed in this paper, which retains the key information and reduces the redundancy information for the further stages of the visual system. Experimental results on N-cut and contour detection show that this multi-scale image analysis model provides distinctive improvement for these image processing tasks.\",\n \"Title: Feature grouping and local soft match for mobile visual search\\nYear: 2012\\n\\nAbstract: More powerful mobile devices stimulate mobile visual search to become a popular and unique image retrieval application. A number of challenges come up with such application, resulting from appearance variations in mobile images. Performance of state-of-the-art image retrieval systems is improved using bag-of-words approaches. However, for visual search by mobile images with large variations, there are at least two critical issues unsolved: (1) the loss of features discriminative power due to quantization; and (2) the underuse of spatial relationships among visual words. To address both issues, this paper presents a novel visual search method based on feature grouping and local soft match, which considers properties of mobile images and couples visual and spatial information consistently. First features of the query image are grouped using both matched visual features and their spatial relationships; and then grouped features are softly matched to alleviate quantization loss. An efficient score scheme is devised to utilize inverted file index and compared with vocabulary-guided pyramid kernels. Finally experiments on Stanford mobile visual search database and a collected database with more than one million images show that the proposed method achieves promising improvement over the approach with a vocabulary tree, especially when large variations exist in query images.\",\n \"Title: Online ngram-enhanced topic model for academic retrieval\\nYear: 2011\\n\\nAbstract: Applying topic model to text mining has achieved a great success. However, state-of-art topic modeling methods still have potential to improve in academic retrieval field. In this paper, we propose an online unified topic model, which is ngram-enhanced. Our model discovers topics with unigrams as well as topical bigrams and is updated by an online inference algorithm with the new incoming data streams. On this basis, we combine our model into the query likelihood model and develop an integrated academic searching system. Experiment results on ACM collection show that our proposed methods outperform the existing approaches on document modeling and searching accuracy. Especially, we prove the efficiency of our system on academic retrieval problem.\",\n \"Title: A computational trust model for access control in P2P.\\nYear: 2010\\n\\nAbstract: Trust brings a new method for building scalable and fine-grained access control mechanism in P2P systems. The quantificational\\n expression of trust and the calculation of trust in a trust network are the basis of trust degree based access control. In\\n this paper, the properties of trust is analyzed by referring to the fruits from social science; the semantics of trust in\\n the context of access control is described, and a trust degree based access control model named TDBAC is introduced. Basing\\n on the properties and semantics of trust, a computational trust model which includes a multilevel comprehensive evaluation\\n method for expressing direct trust and calculators for computing recommended trust is put forward. To compute trust in a trust\\n network, an algorithm that transforms a trust network to a computable expression is given. The algorithm simplifies the computation\\n process and is also flexible.\",\n \"Title: A retina-inspired neurocomputing circuit for image representation\\nYear: 2013\\n\\nAbstract: Biological vision systems have become highly optimized over millions of years of evolution, developing complex neural structures to represent and process stimuli. Moreover, biological systems of vision are typically far more efficient than current human-made machine vision systems. The present report describes a non-task-dependent image representation schema that simulates the early phase of a biological neural vision mechanism. We designed a neural model involving multiple types of computational units to simulate ganglion cells and their non-classical receptive fields, local feedback control circuits and receptive field dynamic self-adjustment mechanisms in the retina. We found that, beyond the pixel level, our model was able to represent images self-adaptively and rapidly. In addition, the improved representation was found to substantially facilitate contour detection. We propose that this improvement arose because ganglion cells can resize their receptive fields, enabling multiscale analysis functionality, a neighborhood referring function and a localized synthesis function. The ganglion cell layer is the starting point of subsequent diverse visual processing. The universality of this cell type and its functional mechanisms suggests that it will be useful for designing image processing algorithms in future.\",\n \"Title: Attribute-Based Access Control for Layered Grid Resources.\\nYear: 2010\\n\\nAbstract: Attribute-Based Access Control (ABAC) is a fine-grained and flexible authorization method. In this paper, considering the layered structure of Grid resources, an ABAC model named Grid_ABAC is presented, and the implementation architecture of Grid_ABAC basing on XACML is proposed. The paper also describes the method for integrating Grid_ABAC seamlessly into the authorization framework of the Globus Tloolkit. The test result shows that Grid_ABAC is efficient and provides a more flexible and open access control method for grid computing.\",\n \"Title: An intrusion detection method based on system call temporal serial analysis\\nYear: 2007\\n\\nAbstract: System call sequences are useful criteria to judge the behaviors of processes. How to generate an efficient matching algorithm and how to build up an implementable system are two of the most difficult problems. In this paper, we explore the possibility of extending consecutive system call to incorporate temporal signature to the Host-based Intrusion Detection System. In this model, we use the real-time detected system call sequences and their consecutive time interval as the data source, and use temporal signature to filter the real model. During the monitoring procedure, we use data mining methods to analyze the source dynamically and implement incremental learning mechanism. Through studying small size samples and incremental learning, the detecting ability of the system can be still good when the sample's size is small. This paper also introduces the key technologies to build such a system, and verifies this intrusion detection method in real time environment. Finally, this paper gives the experiments results to verify the availability and efficiency of our system.\",\n \"Title: Search by mobile image based on visual and spatial consistency\\nYear: 2011\\n\\nAbstract: Performance of state-of-the-art image retrieval systems has been improved significantly using bag-of-words approaches. After represented by visual words quantized from local features, images can be indexed and retrieved using scalable textual retrieval approaches. However, there exist at least two issues unsolved, especially for search by mobile images with large variations: (1) the loss of features discriminative power due to quantization; and (2) the underuse of spatial relationships among visual words. To address both issues, considering properties of mobile images, this paper presents a novel method coupling visual and spatial information consistently: to improve discriminative power, features of the query image are first grouped using both matched visual features and their spatial relationships; Then grouped features are softly matched to alleviate quantization loss. Experiments on both UKBench database and a collected database with more than one million images show that the proposed method achieves 10% improvement over the approach with a vocabulary tree and bundled feature method.\",\n \"Title: A Scale-Changeable Image Analysis Method.\\nYear: 2011\\n\\nAbstract: The biological vision system is far more efficient than machine vision system. This is due to the former has rich neural layers for representation and process. In order to obtain a non-task-dependent image representation schema, the early phase of neural vision mechanism is worth simulating. We design a neural model to simulate non-classical receptive field of ganglion cell and its local feedback control circuit, and find it can represent image, beyond pixel level, self-adaptively and regularly. The experimental results prove this method can represent image faithfully with low cost, and can produce a compact and abstract approximation to facilitate successive image segmentation as well as integration operation. This representation schema is good at extracting spatial relationship from different components of image, thus it can be applied to formalize image semantics. Further it can be applied to object recognition or image classification tasks in future.\",\n \"Title: Efficient semi-supervised annotation with Proxy-based Local Consistency Propagation\\nYear: 2013\\n\\nAbstract: Semi-supervised learning methods can largely leverage the image annotation problem using both labeled and unlabeled data, especially when the labeled information is quite limited. However, most of them suffer the expensive computation stemming from the batch learning on large training dataset. In this paper we proposed a highly efficient semi-supervised annotation approach with the partial label propagation based on the graph representation. Specifically, the label information is first propagated from labeled samples to the unlabeled ones, and then spreads only among unlabeled ones like a spreading activation network. Our approach takes advantage of the decomposed formulation to achieve a fast incremental learning instead of the expensive batch one without accuracy loss. Extensive evaluations over two large datasets demonstrate the superior performance of the proposed method and its significant efficiency.\",\n \"Title: Mixed image-keyword query adaptive hashing over multilabel images\\nYear: 2014\\n\\nAbstract: This article defines a new hashing task motivated by real-world applications in content-based image retrieval, that is, effective data indexing and retrieval given mixed query (query image together with user-provided keywords). Our work is distinguished from state-of-the-art hashing research by two unique features: (1) Unlike conventional image retrieval systems, the input query is a combination of an exemplar image and several descriptive keywords, and (2) the input image data are often associated with multiple labels. It is an assumption that is more consistent with the realistic scenarios. The mixed image-keyword query significantly extends traditional image-based query and better explicates the user intention. Meanwhile it complicates semantics-based indexing on the multilabel data. Though several existing hashing methods can be adapted to solve the indexing task, unfortunately they all prove to suffer from low effectiveness. To enhance the hashing efficiency, we propose a novel scheme “boosted shared hashing”. Unlike prior works that learn the hashing functions on either all image labels or a single label, we observe that the hashing function can be more effective if it is designed to index over an optimal label subset. In other words, the association between labels and hash bits are moderately sparse. The sparsity of the bit-label association indicates greatly reduced computation and storage complexities for indexing a new sample, since only limited number of hashing functions will become active for the specific sample. We develop a Boosting style algorithm for simultaneously optimizing both the optimal label subsets and hashing functions in a unified formulation, and further propose a query-adaptive retrieval mechanism based on hash bit selection for mixed queries, no matter whether or not the query words exist in the training data. Moreover, we show that the proposed method can be easily extended to the case where the data similarity is gauged by nonlinear kernel functions. Extensive experiments are conducted on standard image benchmarks like CIFAR-10, NUS-WIDE and a-TRECVID. The results validate both the sparsity of the bit-label association and the convergence of the proposed algorithm, and demonstrate that the proposed hashing scheme achieves substantially superior performances over state-of-the-art methods under the same hash bit budget.\",\n \"Title: A Multipolicy Authorization Framework for Grid Security\\nYear: 2006\\n\\nAbstract: A Grid system is a Virtual Organization that is composed of several autonomous domains. Authorization in such a system needs to be flexible and scalable to support multiple security policies. Basing on the Web Services security specifications such as XACML, SAML, and the special security needs of the Grid computing, we have constructed an authorization framework in the Globus Toolkit 4 that can support multiple policies. This paper describes the concepts of our design and introduces the structure and the components of the authorization framework. To show the flexibility and scalability of the framework, we introduce a new blacklist/whitelistbased authorization mechanism that can be seamlessly integrated into the framework.\",\n \"Title: Music identification via vocabulary tree with MFCC peaks\\nYear: 2011\\n\\nAbstract: In this paper, a Vocabulary Tree based framework is proposed for music identification whose target is to recognize a fragment from a song database. The key to a high recognition precision within this framework is a novel feature, namely MFCC Peaks, which is a combination of MFCC and Spectral Peaks features. Our approach consists of three stages. We first build the Vocabulary Tree with 2 million MFCC Peaks features extracted from hundreds of music. Then each song in the database is quantified into some words by traveling from root down to a certain leaf. Given a query input, we apply the same quantization procedure to this fragment, score the archive according to the TF-IDF scheme and return the best matches. The experimental results demonstrate that our proposed feature has strong identifying and generalization ability. Other trials show that our approach scales well with the size of database. Further comparison also demonstrates that while our algorithm achieves approximately the same retrieval precision as other state-of-the-art methods, it cost less time and memory.\",\n \"Title: Online semi-supervised annotation via proxy-based local consistency propagation.\\nYear: 2015\\n\\nAbstract: In this paper, we propose a novel label propagation algorithm named Proxy-based Local Consistency Propagation (PLCP), in which the label information is first propagated from labeled examples to the unlabeled ones, and then spreads only among unlabeled ones mutually until a steady state is reached. To meet the requirements of efficiency in many real-world image annotation applications, we propose an online semi-supervised annotation framework where the new examples can be predicted and used to update the model. Specifically, we extend PLCP to work under an inductive setting and propose an incremental model updating method that can incorporate the new examples including labeled and unlabeled examples. The comprehensive experiments on MNIST, CIFAR-10 and PIE datasets show that our proposed PLCP achieves superior performance compared with the baselines, and our proposed incremental model updating method can achieve significant promotion in efficiency, with the nearly identical accuracy compared to re-training.\",\n \"Title: Self-contained Data Protection Scheme Based on CP-ABE.\\nYear: 2013\\n\\nAbstract: Self-protection capabilities of outsourced data become note-worthily important in cloud computing. Ciphertext-Policy Attribute Based Encryption (CP-ABE) can dynamically control the user group of the encrypted data by defining decryption attributes; hence has certain ability of access control. Although there are different schemes of CP-ABE, as far as we know, most of these schemes can only express simple policies with AND, OR and threshold attribute operations, which cannot support traditional access control policies. In order to effectively integrate access control with encryption to build a self-contained data protection mechanism, this paper proposed an Extended CP-ABE (ECP-ABE) scheme based on the existing CP-ABE scheme. The ECP-ABE scheme can express any AttributeBased Access Control (ABAC) policies represented by arithmetic comparison and logical expressions that involve NOT, <, <=, >, >=, [], (), (] and [) operators in addition to AND, OR and threshold operators. We prove the Chosen-plaintext Attack (CPA) security of our scheme under the Decisional Bilinear Diffie-Hellman (DBDH) assumption in the standard model, and also discuss the experimental results of the efficiency of ECP-ABE.\",\n \"Title: Query-Adaptive Hash Code Ranking for Fast Nearest Neighbor Search\\nYear: 2014\\n\\nAbstract: Recently hash-based nearest neighbor search has become attractive in many applications due to its compressed storage and fast query speed. However, the quantization in the hashing process usually degenerates its discriminative power when using Hamming distance ranking. To enable fine-grained ranking, hash bit weighting has been proved as a promising solution. Though achieving satisfying performance improvement, state-of-the-art weighting methods usually heavily rely on the projection's distribution assumption, and thus can hardly be directly applied to more general types of hashing algorithms. In this paper, we propose a new ranking method named QRank with query-adaptive bitwise weights by exploiting both the discriminative power of each hash function and their complement for nearest neighbor search. QRank is a general weighting method for all kinds of hashing algorithms without any strict assumptions. Experimental results on two well-known benchmarks MNIST and NUS-WIDE show that the proposed method can achieve up to 17.11\\\\\\\\% performance gains over state-of-the-art methods.\",\n \"Title: Collaborative Hashing\\nYear: 2014\\n\\nAbstract: Hashing technique has become a promising approach for fast similarity search. Most of existing hashing research pursue the binary codes for the same type of entities by preserving their similarities. In practice, there are many scenarios involving nearest neighbor search on the data given in matrix form, where two different types of, yet naturally associated entities respectively correspond to its two dimensions or views. To fully explore the duality between the two views, we propose a collaborative hashing scheme for the data in matrix form to enable fast search in various applications such as image search using bag of words and recommendation using user-item ratings. By simultaneously preserving both the entity similarities in each view and the interrelationship between views, our collaborative hashing effectively learns the compact binary codes and the explicit hash functions for out-of-sample extension in an alternating optimization way. Extensive evaluations are conducted on three well-known datasets for search inside a single view and search across different views, demonstrating that our proposed method outperforms state-of-the-art baselines, with significant accuracy gains ranging from 7.67% to 45.87% relatively.\",\n \"Title: Ontology-based concept similarity integrating image semantic and visual information\\nYear: 2014\\n\\nAbstract: In recent years, the concept similarity measure has received wide attention in many applications, such as ontology construction, text analysis, image retrieval, etc. Currently, the concept similarity measure depends on the information mining in various knowledge bases, like dictionaries, ontologies, image annotation labels, and search engines. However, these knowledge bases usually only contain semantic information. With the development of the Internet and the popularity of the digital imaging devices, a lot of images and related texts have appeared, which help us to further mine the concept similarity relationships. The concept similarity is the outcome of human subjective perception. In addition to analysis of semantic information, the content of image itself precisely provides the visual perception information, which also plays an important role in the access of concept similarity relationships. To integrate both image semantic and visual information, in this paper we propose an ontology concept similarity measure that simultaneously utilizes the image semantic annotations and visual features to optimize the ontology-based metrics. The experiment result on the Corel dataset demonstrates the effectiveness of our proposed method.\",\n \"Title: Graph-based active semi-supervised learning: A new perspective for relieving multi-class annotation labor\\nYear: 2014\\n\\nAbstract: Semi-supervised learning and active learning are important techniques to build more accurate model while labeled data are scarce. The objective of this paper is combining both to effectively relieve user labor for multi-class annotation. We propose a novel graph-based active semi-supervised learning framework which aim at efficiently learning a multi-class model with minimal human labor. In particular, we propose Minimize Expected Global Uncertainty algorithm to actively select examples (for labels), which naturally integrates with the probabilistic results of graph-based semi-supervised learning. Meanwhile, we update the model incrementally by decomposed formulation while the new example are incorporated for training, which only has the time complexity of O(n), compared to the original re-training of O(n3). Extensive evaluations over three real-world datasets demonstrate that our proposed method has the superior performance comparing with the baselines and the capability to efficiently build more accurate model with fractional human labor.\",\n \"Title: A general image representation scheme and its improvement for image analysis\\nYear: 2014\\n\\nAbstract: In this paper, a bio-vision neural network was presented could provide a novel approach for image representation and a processing method for image analysis. This model using ganglion cells (GC) in retina and its non-classical receptive field (nCRF) can dynamically self-adjusts based on different input images. We provide extensive experimental evaluation to demonstrate that, this GC array can represent image with a low cost and could substantially improve image processing efficiency. Most importantly, the image representation based on GC-array model provides new approach for image semantic extraction later.\",\n \"Title: Large-Scale Unsupervised Hashing with Shared Structure Learning.\\nYear: 2015\\n\\nAbstract: Hashing methods are effective in generating compact binary signatures for images and videos. This paper addresses an important open issue in the literature, i.e., how to learn compact hash codes by enhancing the complementarity among different hash functions. Most of prior studies solve this problem either by adopting time-consuming sequential learning algorithms or by generating the hash functions which are subject to some deliberately-designed constraints (e.g., enforcing hash functions orthogonal to one another). We analyze the drawbacks of past works and propose a new solution to this problem. Our idea is to decompose the feature space into a subspace shared by all hash functions and its complementary subspace. On one hand, the shared subspace, corresponding to the common structure across different hash functions, conveys most relevant information for the hashing task. Similar to data de-noising, irrelevant information is explicitly suppressed during hash function generation. On the other hand, in case that the complementary subspace also contains useful information for specific hash functions, the final form of our proposed hashing scheme is a compromise between these two kinds of subspaces. To make hash functions not only preserve the local neighborhood structure but also capture the global cluster distribution of the whole data, an objective function incorporating spectral embedding loss, binary quantization loss, and shared subspace contribution is introduced to guide the hash function learning. We propose an efficient alternating optimization method to simultaneously learn both the shared structure and the hash functions. Experimental results on three well-known benchmarks CIFAR-10, NUS-WIDE, and a-TRECVID demonstrate that our approach significantly outperforms state-of-the-art hashing methods.\",\n \"Title: A CP-ABE scheme with hidden policy and its application in cloud computing.\\nYear: 2015\\n\\nAbstract: With flexible and scalable features for fine-grained access control, ciphertext policy attribute-based encryption (CP-ABE) is widely used as a kind of data protection mechanism in cloud computing. However, the access policy of CP-ABE scheme may contain sensitive information which causes privacy revelation of the data provider or receiver. Some papers proposed hidden policy CP-ABE schemes, which were based on And-gate access structure whose expressive ability of access policy was limited. CP-ABE with the tree-based access structure has stronger expressive ability and more flexible access control capability. Therefore, it has broad application prospects compared to other mechanisms. This paper proposed a tree-based access structure CP-ABE scheme with hidden policy (CP-ABE-HP), and also proved that the scheme had chosen-plaintext attack (CPA) security. CP-ABE-HP could both protect the policy and had flexible access control capability. Then, considering the characteristics of cloud computing environment, the ...\",\n \"Title: Query-Adaptive Reciprocal Hash Tables for Nearest Neighbor Search\\nYear: 2016\\n\\nAbstract: Recent years have witnessed the success of binary hashing techniques in approximate nearest neighbor search. In practice, multiple hash tables are usually built using hashing to cover more desired results in the hit buckets of each table. However, rare work studies the unified approach to constructing multiple informative hash tables using any type of hashing algorithms. Meanwhile, for multiple ta...\",\n \"Title: Multi-View Complementary Hash Tables for Nearest Neighbor Search\\nYear: 2015\\n\\nAbstract: Recent years have witnessed the success of hashing techniques in fast nearest neighbor search. In practice many applications (eg., visual search, object detection, image matching, etc.) have enjoyed the benefits of complementary hash tables and information fusion over multiple views. However, most of prior research mainly focused on compact hash code cleaning, and rare work studies how to build multiple complementary hash tables, much less to adaptively integrate information stemming from multiple views. In this paper we first present a novel multi-view complementary hash table method that learns complementarity hash tables from the data with multiple views. For single multi-view table, using exemplar based feature fusion, we approximate the inherent data similarities with a low-rank matrix, and learn discriminative hash functions in an efficient way. To build complementary tables and meanwhile maintain scalable training and fast out-of-sample extension, an exemplar reweighting scheme is introduced to update the induced low-rank similarity in the sequential table construction framework, which indeed brings mutual benefits between tables by placing greater importance on exemplars shared by mis-separated neighbors. Extensive experiments on three large-scale image datasets demonstrate that the proposed method significantly outperforms various naive solutions and state-of-the-art multi-table methods.\",\n \"Title: Outsourced KP-ABE with Enhanced Security.\\nYear: 2014\\n\\nAbstract: Although Key-Policy Attribute-Based Encryption (KPABE) has been widely applied to protect data in cloud computing, it is always criticized for its inefficiency drawbacks, coming from both key-issuing and decryption. Recently, some papers proposed the outsourcing solutions. But adversaries in the attack model of these researches were divided into two categories, and it is assumed that the two cannot communicate with each other, which is obviously unrealistic. In this paper, we first proved that there are severe security vulnerabilities in these schemes for this assumption, and then proposed a security enhanced Chosen Plaintext Attack (SE-CPA) model, which eliminates the improper limitations. By utilizing Proxy Re-Encryption (PRE), we also constructed a concrete KP-ABE Outsourcing scheme (O-KP-ABE) and proved its security under SE-CPA model. Comparisons with existing schemes show that our construction has comprehensive advantages in security and efficiency.\",\n \"Title: Compound Concept Semantic Similarity Calculation Based on Ontology and Concept Constitution Features\\nYear: 2015\\n\\nAbstract: The computation of semantic similarity between words is important in information retrieval, knowledge acquisition and many other fields. The existing studies are mainly aiming at single concepts composed of single terms. For the compound concepts composed of multiple terms, they usually neglect the special constitution features of compounds and only process them as single concepts, which may affect the ultimate accuracy. In this paper, we propose a novel ontology-based Compound Concept Semantic Similarity calculation approach called CCSS which exploits concept constitution features. In CCSS, the compound is decomposed into Subject headings and Auxiliary words (SaA), and the relationships between these two sets are used to measure the similarity. Besides, the errors that may be caused by SaA recognition are corrected. Moreover, several information sources of ontology such as taxonomical features, local density and depth are considered. Extensive experimental evaluations demonstrate that our approach significantly outperforms existing approaches.\",\n \"Title: Exploiting Human Mobility Patterns for Gas Station Site Selection.\\nYear: 2016\\n\\nAbstract: Advances in sensor, wireless communication, and information infrastructure such as GPS have enabled us to collect massive amounts of human mobility data, which are fine-grained and have global road coverage. These human mobility data, if properly encoded with semantic information (i.e. combined with Point of Interests (POIs)), is appealing for changing the paradigm for gas station site selection. To this end, in this paper, we investigate how to exploit newly-generated human mobility data for enhancing gas station selection. Specifically, we develop a ranking system for evaluating the business performances of gas stations based on waiting time of refueling events by mining human mobility data. Along this line, we first design a method for detecting taxi refueling events by jointly tracking dwell times, GPS trace angles, location sequences, and refueling cycles of the vehicles. Also, we extract the fine-grained discriminative features strategically from POI data, human mobility data and road network data within the neighborhood of gas stations, and perform feature selection by simultaneously maximizing relevance and minimizing redundancy based on mutual information. In addition, we learn a ranking model for predicting gas station crowdedness by exploiting learning to rank techniques. The extensive experimental evaluation on real-world data also show the advantages of the proposed method over existing approaches for gas site selection.\",\n \"Title: Fast Graph Similarity Search via Locality Sensitive Hashing.\\nYear: 2015\\n\\nAbstract: Similarity search in graph databases has been widely studied in graph query processing in recent years. With the fast accumulation of graph databases, it is worthwhile to develop a fast algorithm to support similarity search in large-scale graph databases. In this paper, we study k-NN similarity search problem via locality sensitive hashing. We propose a fast graph search algorithm, which first transforms complex graphs into vectorial representations based on the prototypes in the database and then accelerates query efficiency in Euclidean space by employing locality sensitive hashing. Additionally, a general retrieval framework is established in our approach. Experiments on three real datasets show that our work achieves high performance both on the accuracy and the efficiency of the presented algorithm.\",\n \"Title: A novel rotation adaptive object detection method based on pair Hough model.\\nYear: 2016\\n\\nAbstract: This paper proposes a novel Hough-based object shape representation model called Pair Hough Model (PHM) and its corresponding object detection framework. PHM constructs the voting models implicitly with automatically detected interest points and their local descriptors for unseen object categories. In addition, by casting votes according to key point pairs instead of individual key points and taking the orientations of objects as well as their sizes into consideration, PHM can recognize and localize objects after their scaling and/or rotation, which makes it suitable for processing images with major rotations such as pictures taken by mobile devices. Evaluation experiments proved that PHM does not need to be trained on rotated images to recognize rotated objects, and PHM achieved comparable results to the state-of-the-art methods on several widely used public data sets.\",\n \"Title: Query-Adaptive Hash Code Ranking for Large-Scale Multi-View Visual Search.\\nYear: 2016\\n\\nAbstract: Hash-based nearest neighbor search has become attractive in many applications. However, the quantization in hashing usually degenerates the discriminative power when using Hamming distance ranking. Besides, for large-scale visual search, existing hashing methods cannot directly support the efficient search over the data with multiple sources, and while the literature has shown that adaptively inco...\",\n \"Title: An Efficient Kp-Abe Scheme For Content Protection In Information-Centric Networking\\nYear: 2016\\n\\nAbstract: Media streaming has largely dominated the Internet traffic and the trend will keep increasing in the next years. To efficiently distribute the media content, Information-Centric Networking (ICN) has attracted many researchers. Since end users usually obtain content from indeterminate caches in ICN, the publisher cannot reinforce data security and access control depending on the caches. Hence, the ability of self-contained protection is important for the cached contents. Attribute-based encryption (ABE) is considered the preferred solution to achieve this goal. However, the existing ABE schemes usually have problems regarding efficiency. The exponentiation in key generation and pairing operation in decryption respectively increases linearly with the number of attributes involved, which make it costly. In this paper, we propose an efficient key-policy ABE with fast key generation and decryption (FKP-ABE). In the key generation, we get rid of exponentiation and only require multiplications/divisions for each attribute in the access policy. And in the decryption, we reduce the pairing operations to a constant number, no matter how many attributes are used. The efficiency analysis indicates that our scheme has better performance than the existing KP-ABE schemes. Finally, we present an implementation framework that incorporates the proposed FKP-ABE with the ICN architecture.\",\n \"Title: Structure Sensitive Hashing With Adaptive Product Quantization.\\nYear: 2016\\n\\nAbstract: Hashing has been proved as an attractive solution to approximate nearest neighbor search, owing to its theoretical guarantee and computational efficiency. Though most of prior hashing algorithms can achieve low memory and computation consumption by pursuing compact hash codes, however, they are still far beyond the capability of learning discriminative hash functions from the data with complex inh...\",\n \"Title: A bio-inspired image local feature representation based on vision network model.\\nYear: 2016\\n\\nAbstract: In this paper, a hierarchical network model based on human vision physiological mechanism was put forward. Firstly, simple cell and complex cell in primary visual cortex is modeled, then studied the response pattern of V4 area and inferior temporal cortex on ventral side channel and representing the local features of input image utilized the computational model. The experiment results show that local image features extracted by computational model have sufficient discrimination; furthermore, the local image features extracted using biological visual model demonstrated much more excellent generalization ability in natural scene with complicated background.\",\n \"Title: GKSH: Graph based Image Retrieval using Supervised Kernel Hashing.\\nYear: 2016\\n\\nAbstract: The explosive growth of the massive image database brings great challenge to the fast and accurate image retrieval. To address this issue, we propose a kind of supervised hashing method based on the graph representation of the images, which translates images into representative attribute structural graphs. Compared with the traditional supervised methods, this kind of structural graphs can consider the spatial relations among regions in the image, and integrate unsupervised properties of images and supervised information of labels by adopting hashing with graph kernel based on random walks. The learnt hash codes can be a good tradeoff among retrieval speed, memory requirements and retrieval accuracy. The experiments on three image datasets PASCAL, MNIST and HOLIDAY demonstrate that, with the hash codes of the same length, the proposed supervised hashing method can achieve a higher precision and a better efficiency.\",\n \"Title: Projection Based Weight Normalization for Deep Neural Networks.\\nYear: 2017\\n\\nAbstract: Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling-based weight space symmetry property in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over Oblique manifold. A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem. PBWN executes standard gradient updates, followed by projecting the updated weight back to Oblique manifold. This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique. We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art convolutional neural networks, such as Inception, VGG and residual networks. The results show that our method is able to improve the performance of DNNs with different architectures consistently. We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method outperforms the state-of-the-art methods: we obtain test errors as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples, respectively.\",\n \"Title: Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks.\\nYear: 2018\\n\\nAbstract: Orthogonal matrix has shown advantages in training Recurrent Neural Networks (RNNs), but such matrix is limited to be square for the hidden-to-hidden transformation in RNNs. In this paper, we generalize such square orthogonal matrix to orthogonal rectangular matrix and formulating this problem in feed-forward Neural Networks (FNNs) as Optimization over Multiple Dependent Stiefel Manifolds (OMDSM). We show that the rectangular orthogonal matrix can stabilize the distribution of network activations and regularize FNNs. We also propose a novel orthogonal weight normalization method to solve OMDSM. Particularly, it constructs orthogonal transformation over proxy parameters to ensure the weight matrix is orthogonal and back-propagates gradient information through the transformation during training. To guarantee stability, we minimize the distortions between proxy parameters and canonical weights over all tractable orthogonal transformations. In addition, we design an orthogonal linear module (OLM) to learn orthogonal filter banks in practice, which can be used as an alternative to standard linear module. Extensive experiments demonstrate that by simply substituting OLM for standard linear module without revising any experimental protocols, our method largely improves the performance of the state-of-the-art networks, including Inception and residual networks on CIFAR and ImageNet datasets. In particular, we have reduced the test error of wide residual network on CIFAR-100 from 20.04% to 18.61% with such simple substitution. Our code is available online for result reproduction.\",\n \"Title: Calculating Semantic Similarity between Academic Articles using Topic Event and Ontology.\\nYear: 2017\\n\\nAbstract: Determining semantic similarity between academic documents is crucial to many tasks such as plagiarism detection, automatic technical survey and semantic search. Current studies mostly focus on semantic similarity between concepts, sentences and short text fragments. However, document-level semantic matching is still based on statistical information in surface level, neglecting article structures and global semantic meanings, which may cause the deviation in document understanding. In this paper, we focus on the document-level semantic similarity issue for academic literatures with a novel method. We represent academic articles with topic events that utilize multiple information profiles, such as research purposes, methodologies and domains to integrally describe the research work, and calculate the similarity between topic events based on the domain ontology to acquire the semantic similarity between articles. Experiments show that our approach achieves significant performance compared to state-of-the-art methods.\",\n \"Title: DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model.\\nYear: 2018\\n\\nAbstract: •We present a graph convolutional neural network (g-CNN) model that can perform feature learning on graph data directly.•We present the disordered graph convolutional layer that can change the discrete parameter learning into the sampling.•The experimental results show that the proposed method outperforms the existing g-CNN methods and other types of methods.\",\n \"Title: An efficient adaptive algorithm for removal of impulse noises\\nYear: 2017\\n\\nAbstract: An efficient algorithm Adaptive Min-Max Average Filters (AMMAF) for the removal of impulse noises is proposed in this paper. AMMAF divides all pixels of the corrupted image into three types and has two phases denoising with adaptive small filters, which preserve most original signals and restore more details. Experiments results show that compared with state-of-the-art salt & pepper Denoising algorithm, AMMAF performs better and is also less time-consuming, especially for the high-density noise images.\",\n \"Title: Decorrelated Batch Normalization\\nYear: 2018\\n\\nAbstract: Batch Normalization (BN) is capable of accelerating the training of deep models by centering and scaling activations within mini-batches. In this work, we propose Decorrelated Batch Normalization (DBN), which not just centers and scales activations but whitens them. We explore multiple whitening techniques, and find that PCA whitening causes a problem we call stochastic axis swapping, which is detrimental to learning. We show that ZCA whitening does not suffer from this problem, permitting successful learning. DBN retains the desirable qualities of BN and further improves BN's optimization efficiency and generalization ability. We design comprehensive experiments to show that DBN can improve the performance of BN on multilayer perceptrons and convolutional neural networks. Furthermore, we consistently improve the accuracy of residual networks on CIFAR-10, CIFAR-100, and ImageNet.\",\n \"Title: Fast graph similarity search via hashing and its application on image retrieval\\nYear: 2018\\n\\nAbstract: Similarity search in graph databases has been widely investigated. It is worthwhile to develop a fast algorithm to support similarity search in large-scale graph databases. In this paper, we investigate a -NN (-Nearest Neighbor) similarity search problem by locality sensitive hashing (LSH). We propose an innovative fast graph search algorithm named LSH-GSS, which first transforms complex graphs into vectorial representations based on prototypes in the database and later accelerates a query in Euclidean space by employing LSH. Because images can be represented as attributed graphs, we propose an approach to transform attributed graphs into n-dimensional vectors and apply LSH-GSS to execute further image retrieval. Experiments on three real graph datasets and two image datasets show that our methods are highly accurate and efficient.\",\n \"Title: CNN and RNN based payload classification methods for attack detection.\\nYear: 2019\\n\\nAbstract: In recent years, machine learning has been widely applied to problems in detecting network attacks, particularly novel attacks. However, traditional machine learning methods depend heavily on feature engineering, and extracting features is often time-consuming and complex. Thus, it is impractical to detect attacks with traditional machine learning methods in real-time applications. To discover network attacks efficiently, we propose an end-to-end detection approach. We implement deep learning models to analyze payloads and propose a convolutional neural network-based payload classification approach (PL-CNN) and a recurrent neural network-based payload classification approach (PL-RNN) for use in attack detection. Our two approaches learn feature representations from original payloads without feature engineering and support end-to-end detection. These approaches achieve accuracies of 99.36% and 99.98% when applied to the DARPA1998 dataset, respectively; these accuracies are comparable to or better than those of state-of-the-art methods. In addition, our methods are efficient and practical.\",\n \"Title: An Efficient and Privacy Preserving CP-ABE Scheme for Internet-Based Collaboration.\\nYear: 2017\\n\\nAbstract: The development of Internet applications facilitates enterprises and individuals to share information and work together across physical barriers. In such environment, flexible and efficient data-protection methods are required because data are out of the control domain of its owners. By encrypting with an access policy, ciphertext-policy attribute-based encryption (CP-ABE) can simultaneously achieve data encryption and access control, making it an ideal mechanism for data protection in Internet-based environments. However, the existing CP-ABE schemes usually have limitations regarding efficiency and privacy leakage from the access policy. In this paper, we propose a CP-ABE scheme with hidden access policy and fast decryption that improves the decryption efficiency and preserves the privacy of the access policy. In addition, by adopting dual-system encryption methodology, our scheme achieves full security, which is a higher security level in CP-ABE. The performance analysis revealed that the comprehensive capability of our scheme outperforms the existing CP-ABE schemes.\",\n \"Title: Multidimensional data tight aggregation and fine-grained access control in smart grid.\\nYear: 2018\\n\\nAbstract: •A multidimensional data secure aggregation scheme in smart grid is proposed.•The scheme achieves fine-grained access control at dimension-level.•Access permission can be updated flexibly and securely.•The proposed scheme is implemented efficiently.\",\n \"Title: Topic Model Based Android Malware Detection.\\nYear: 2019\\n\\nAbstract: Nowadays, the security risks brought by Android malwares are increasing. Machine learning is considered as a potential solution for promoting the performance of malware detection. For machine learning based Android malware detection, feature extraction plays a key role. Thinking the source codes of applications are comparable with text documents, we propose a new Android malware detection method based on the topic model which is an effective technique in text feature extraction. Our method regards the decompiled codes of an application as a text document, and the topic model is used to mine the potential topics in the codes which can reflect the semantic feature of the application. The experimental results demonstrate that, our approach performs better than the state-of-the-art methods. Also, our method mines the features in the application files automatically without manually design, and therefore overcomes the limitation in present methods which relies on experts’ prior knowledge.\",\n \"Title: Semantic-based Compound Keyword Search over Encrypted Cloud Data\\nYear: 2021\\n\\nAbstract: Keyword search over encrypted data is essential for accessing outsourced sensitive data in cloud computing. In some circumstances, the keywords that the user searches on are only semantically related to the data rather than via an exact or fuzzy match. Hence, semantic-based keyword search over encrypted cloud data becomes of paramount importance. However, existing schemes usually depend upon a glo...\",\n \"Title: Network Traffic Classification Method Supporting Unknown Protocol Detection\\nYear: 2021\\n\\nAbstract: At present, private protocols are widely used on the Internet. As a result, traditional traffic classification methods including port-based and DPI methods have become restricted. Existing machine learning-based methods depend on feature engineering, which makes feature design difficult. In addition, classification models can only classify data as predefined categories, which restricts the models ...\",\n \"Title: Interpretable deep learning method for attack detection based on spatial domain attention\\nYear: 2021\\n\\nAbstract: Deep learning methods can directly extract effective features from original data. However, this type of model is complex and considered to be a “black box”, which leads to low interpretability of the models. Since the results of attack detection are significant to cybersecurity, every decision should be supported with convincing reasons. Hence, the problem of interpretability has become a bottlene...\",\n \"Title: Bridging Text Space and Knowledge Space via Transference Methods\\nYear: 2021\\n\\nAbstract: Introducing the words of texts, entities, and relations of a knowledge graph (KG) into the same semantic space has great significance in KG complement and knowledge computing. Current methods mainly utilize the \\\"alignment constraint\\\" of words and entities to construct uniform objective functions. However, the \\\"alignment constraint\\\" limits the joint representation space to specific KGs and texts. Meanwhile, the representation effect still suffers from the scale of the \\\"alignment constraint\\\". This paper propose a novel transference framework, the method firstly learns the text representation space and KG representation space independently, and then transfers the word representation in the text space to the knowledge space with projection models, and finally constructs a joint representation space. Our approach can decrease the dependency on\\\"alignment constraint\\\", and allow two spaces to be optimized and extended independently. Hence, it has better flexibility, general applicability and helps improve the capability of the joint representation space. Further more, to enhance the word transference performance, we incorporate the relation constraint into the mapping models. To the best of our knowledge, this is the first study using transference method to construct the joint semantic space. The experimental results show that linear mapping models are more suitable than nonlinear models during the projection process. The results of word analogy and relation extraction tasks illustrate the effectiveness of our method compared with state-of-the-art methods.\",\n \"Title: Exploring the Risky Travel Area and Behavior of Car-hailing Service\\nYear: 2022\\n\\nAbstract: AbstractRecent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually emerged and attracted more and more attention. While car-hailing service providers have made considerable efforts on developing real-time trajectory tracking systems and alarm mechanisms, most of them only focus on providing rescue-supporting information rather than preventing potential crimes. Recently, the newly available large-scale car-hailing order data have provided an unparalleled chance for researchers to explore the risky travel area and behavior of car-hailing services, which can be used for building an intelligent crime early warning system. To this end, in this article, we propose a Risky Area and Risky Behavior Evaluation System (RARBEs) based on the real-world car-hailing order data. In RARBEs, we first mine massive multi-source urban data and train an effective area risk prediction model, which estimates area risk at the urban block level. Then, we propose a transverse and longitudinal double detection method, which estimates behavior risk based on two aspects, including fraud trajectory recognition and fraud patterns mining. In particular, we creatively propose a bipartite graph-based algorithm to model the implicit relationship between areas and behaviors, which collaboratively adjusts area risk and behavior risk estimation based on random walk regularization. Finally, extensive experiments on multi-source real-world urban data clearly validate the effectiveness and efficiency of our system.\",\n \"Title: PT-LSTM: Extending LSTM for Efficient Processing Time Attributes in Time Series Prediction\\nYear: 2021\\n\\nAbstract: Long Short-Term Memory (LSTM) has been widely applied in time series predictions. Time attributes are important factors in time series prediction. However, existing studies often ignore the influence of time attributes when splitting the time series data, and seldom utilize the time information in the LSTM models. In this paper, we propose a novel method named Position encoding and Time gate LSTM (PT-LSTM). We first propose a position-encoding based time attributes integration method, which obtains the vector representation of time attributes through position encoding, and integrate it with the observed value vectors of the data. Moreover, we propose a LSTM variant by adding a new time gate which is specially designed to process time attributes. Therefore, PT-LSTM can make good use of time attributes in the key phases of data prediction. Experimental results on three public datasets show that our PT-LSTM model outperforms the state-of-the-art methods in time series prediction.\",\n \"Title: ARRPNGAN: Text-to-image GAN with attention regularization and region proposal networks\\nYear: 2022\\n\\nAbstract: Although text-to-image synthesis has shown remarkable success in generating high-resolution photorealistic images and semantic consistency, it still faces challenges in generating images with complex backgrounds. In this paper, we address this problem by proposing a novel generative adversarial text-to-image synthesis framework based on attention regularization modules and region proposal networks (ARRPNGAN). ARRPNGAN can precisely locate the keywords in text by exploiting attention model advantages and improving the accuracy in locating the subimage of target objects with the help of an RPN. Leveraging both attention regularization and the RPN a generative adversarial network (GAN) can obtain the most text description semantics and reduce the interference of complex background information. The results of extensive experiments on the Caltech-UCSD Birds and MS COCO datasets demonstrate that the proposed ARRPNGAN significantly outperforms other state-of-the-art text-to-image methods, especially in generating photorealistic images with complex backgrounds. Codes are available at: https://github.com/quanFN/ARRPNGAN.\",\n \"Title: An Efficient and Fine-Grained Access Control Scheme for Multidimensional Data Aggregation in Smart Grid\\nYear: 2016\\n\\nAbstract: Smart grid has a large number of users and numerous data analysis organizations. The privacy-preserving and access control for multidimensional power data have become vital security concerns. For correct aggregation of multidimensional data, existing schemes require transmitting attribute sets of data together with ciphertexts to the gateway (GW), which leaks the relevant information of power data and increases the security risk. Moreover, to implement access control of the multidimensional data, control center (CC) must firstly decrypt the data of all dimensions. It means CC can access all of the data without authorization, which may result in the data leakage. For coping with these problems, we propose an efficient and Fine-grained Access control scheme for Multidimensional data Aggregation, named FAMA. It uses the extended Boneh-gohnissim homomorphic encryption (EBGN) to enforce multidimensional privacy-preserving data aggregation without transmitting attribute sets and attribute-based encryption (ABE) to implement fine-grained access control at dimension-level. By using FAMA, GW does not need to distinguish different ciphertexts. And every receiver can only extract the specific dimensions that it has been authorized, which not only restricts the privileges of CC, but also simplifies the operations of GW and CC. Moreover, after optimizing the scalar multiplication on elliptic curve, the encryption of FAMA is more efficient than the existing schemes.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":74,"string":"74"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13250,"string":"13,250"}}},{"rowIdx":13251,"cells":{"question_id":{"kind":"string","value":"J zsef Balogh"},"question":{"kind":"string","value":"Generate an abstract for the title \"Hereditary Properties of Tournaments.\" using the following items: 1. Hereditary property\n2. Speed of P\n3. Jump in possible speeds\n4. Exponential speed\n5. Unique hereditary property"},"answer":{"kind":"string","value":"A collection of unlabelled tournaments P is called a hereditary property if it is closed under isomorphism and under taking induced sub-tournaments. The speed of P is the function n bar right arrow vertical bar P-n vertical bar, where P-n = {T epsilon P:vertical bar V(T)vertical bar=n}. In this paper, we prove that there is a jump in the possible speeds of a hereditary property of tournaments, from polynomial to exponential speed. Moreover, we determine the minimal exponential speed, vertical bar P-n vertical bar=c((1+o(1))n), where c similar or equal to 1.47 is the largest real root of the polynomial x(3)=x(2)+1, and the unique hereditary property with this speed."},"correct_answer":{"kind":"string","value":"A collection of unlabelled tournaments P is called a hereditary property if it is closed under isomorphism and under taking induced sub-tournaments. The speed of P is the function n bar right arrow vertical bar P-n vertical bar, where P-n = {T epsilon P:vertical bar V(T)vertical bar=n}. In this paper, we prove that there is a jump in the possible speeds of a hereditary property of tournaments, from polynomial to exponential speed. Moreover, we determine the minimal exponential speed, vertical bar P-n vertical bar=c((1+o(1))n), where c similar or equal to 1.47 is the largest real root of the polynomial x(3)=x(2)+1, and the unique hereditary property with this speed."},"documents":{"kind":"list like","value":["Title: Game domination number\nYear: 2002\n\nAbstract: The game domination number of a (simple, undirected) graph is defined by the following game. Two players, A and D, orient the edges of the graph alternately until all edges are oriented. Player D starts the game, and his goal is to decrease the domination number of the resulting digraph, while A is trying to increase it. The game domination number of the graph G, denoted by γg(G), is the domination number of the directed graph resulting from this game. This is well defined if we suppose that both players follow their optimal strategies. We determine the game domination number for several classes of graphs and provide general inequalities relating it to other graph parameters.","Title: Graph bootstrap percolation\nYear: 2012\n\nAbstract: Graph bootstrap percolation is a deterministic cellular automaton which was introduced by Bollobás in 1968, and is defined as follows. Given a graph H, and a set \\documentclass{article}\\usepackage{mathrsfs}\\usepackage{amsmath, amssymb}\\pagestyle{empty}\\begin{document}\\begin{align*}G \\subset E(K_n)\\end{align*} \\end{document} **image** of initially ‘infected’ edges, we infect, at each time step, a new edge e if there is a copy of H in \\documentclass{article}\\usepackage{mathrsfs}\\usepackage{amsmath, amssymb}\\pagestyle{empty}\\begin{document}\\begin{align*}K_n\\end{align*} \\end{document} **image** such that e is the only not-yet infected edge of H. We say that G percolates in the H-bootstrap process if eventually every edge of \\documentclass{article}\\usepackage{mathrsfs}\\usepackage{amsmath, amssymb}\\pagestyle{empty}\\begin{document}\\begin{align*}K_n\\end{align*} \\end{document} **image** is infected. The extremal questions for this model, when H is the complete graph \\documentclass{article}\\usepackage{mathrsfs}\\usepackage{amsmath, amssymb}\\pagestyle{empty}\\begin{document}\\begin{align*}K_r\\end{align*} \\end{document} **image** , were solved (independently) by Alon, Kalai and Frankl almost thirty years ago. In this paper we study the random questions, and determine the critical probability \\documentclass{article}\\usepackage{mathrsfs}\\usepackage{amsmath, amssymb}\\pagestyle{empty}\\begin{document}\\begin{align*}p_c(n,K_r)\\end{align*} \\end{document} **image** for the \\documentclass{article}\\usepackage{mathrsfs}\\usepackage{amsmath, amssymb}\\pagestyle{empty}\\begin{document}\\begin{align*}K_r\\end{align*} \\end{document} **image** -process up to a poly-logarithmic factor. In the case \\documentclass{article}\\usepackage{mathrsfs}\\usepackage{amsmath, amssymb}\\pagestyle{empty}\\begin{document}\\begin{align*}r = 4\\end{align*} \\end{document} **image** we prove a stronger result, and determine the threshold for \\documentclass{article}\\usepackage{mathrsfs}\\usepackage{amsmath, amssymb}\\pagestyle{empty}\\begin{document}\\begin{align*}p_c(n,K_4)\\end{align*} \\end{document} **image** . © 2012 Wiley Periodicals, Inc. Random Struct. Alg., 2012 (Supported by CNPq bolsa de Produtividade em Pesquisa.)","Title: The diameter game\nYear: 2009\n\nAbstract: A large class of Positional Games are defined on the complete graph on n vertices. The players, Maker and Breaker, take the edges of the graph in turns, and Maker wins iff his subgraph has a given — usually monotone — property. Here we introduce the d-diameter game, which means that Maker wins iff the diameter of his subgraph is at most d. We investigate the biased version of the game; i.e., when the players may take more than one, and not necessarily the same number of edges, in a turn. Our main result is that we proved that the 2-diameter game has the following surprising property: Breaker wins the game in which each player chooses one edge per turn, but Maker wins as long as he is permitted to choose 2 edges in each turn whereas Breaker can choose as many as (1-9)n1-8-(lnn)3-8. In addition, we investigate d-diameter games for d ≥ 3. The diameter games are strongly related to the degree games. Thus, we also provide a generalization of the fair degree game for the biased case. © 2009 Wiley Periodicals, Inc. Random Struct. Alg., 2009","Title: Almost all triangle-free triple systems are tripartite\nYear: 2012\n\nAbstract: A triangle in a triple system is a collection of three edges isomorphic to {123,124,345}. A triple system is triangle-free if it contains no three edges forming a triangle. It is tripartite if it has a vertex partition into three parts such that every edge has exactly one point in each part. It is easy to see that every tripartite triple system is triangle-free. We prove that almost all triangle-free triple systems with vertex set [n] are tripartite. Our proof uses the hypergraph regularity lemma of Frankl and Rödl [13], and a stability theorem for triangle-free triple systems due to Keevash and the second author [15].","Title: A jump to the bell number for hereditary graph properties\nYear: 2005\n\nAbstract: A hereditary graph property is a collection of labeled graphs, closed under isomorphism and also under the taking of induced subgraphs. Its speed is the number of graphs in the property as a function of the number of vertices in the graph. Earlier research has characterized the speeds for hereditary graph properties up to n(1+o(1))n, and described the properties that have those smaller speeds. The present work provides the minimal speed possible above that range, and gives a structural characterization for properties which exhibit such speeds.More precisely, this paper sheds light on the jump from below n(1+o(1))n to the range that includes n(1+o(1))n. A measure jumps when there are two functions with positive distance such that the measure can take no values between those functions. A clean jump occurs when the bounding functions are well-defined and occur as possible values of the measure. It has been known for some time that the density of a graph jumps; recent work on hereditary graph properties has shown that speeds jump for properties with \"large\" or \"small\" speeds.The current work shows that there is a clean jump for properties with speed in a middle range. In particular, we show that when the speed of a hereditary graph property has speed greater than ncn for all c Bn, the nth Bell number. Equality occurs only for the property containing all disjoint unions of cliques or its complement.","Title: On Avoider-Enforcer Games\nYear: 2009\n\nAbstract: In the Avoider-Enforcer game on the complete graph $K_n$, the players (Avoider and Enforcer) each take an edge in turn. Given a graph property $\\mathcal{P}$, Enforcer wins the game if Avoider's graph has the property $\\mathcal{P}$. An important parameter is $\\tau_E(\\mathcal{P})$, the smallest integer $t$ such that Enforcer can win the game against any opponent in $t$ rounds. In this paper, let $\\mathcal{F}$ be an arbitrary family of graphs and $\\mathcal{P}$ be the property that a member of $\\mathcal{F}$ is a subgraph or is an induced subgraph. We determine the asymptotic value of $\\tau_E(\\mathcal{P})$ when $\\mathcal{F}$ contains no bipartite graph and establish that $\\tau_E(\\mathcal{P})=o(n^2)$ if $\\mathcal{F}$ contains a bipartite graph. The proof uses the game of JumbleG and the Szemerédi regularity lemma.","Title: Hereditary properties of words\nYear: 2005\n\nAbstract: Let P be a hereditary property of words, i.e., an infinite class of finite words such that every subword (block) of a word belonging to P is also in P. Extending the classical Morse-Hedlund theorem, we show that either P contains at least n+1 words of length n for every n or, for some N, it contains at most N words of length n for every n. More importantly, we prove the following quantitative extension of this result: if P has m <= n words of length n then, for every k >= n + m, it contains at most [(m + 1)/2] [(m + 1)/2] words of length k.","Title: Erdős–ko–rado in random hypergraphs\nYear: 2009\n\nAbstract: Let 3 ≤ k n/2. We prove the analogue of the Erdős–Ko–Rado theorem for the random k-uniform hypergraph Gk(n, p) when k n/2)1/3; that is, we show that with probability tending to 1 as n → ∞, the maximum size of an intersecting subfamily of Gk(n, p) is the size of a maximum trivial family. The analogue of the Erdős–Ko–Rado theorem does not hold for all p when k ≫ n1/3. We give quite precise results for k n1/2−ϵ. For larger k we show that the random Erdős–Ko–Rado theorem holds as long as p is not too small, and fails to hold for a wide range of smaller values of p. Along the way, we prove that every non-trivial intersecting k-uniform hypergraph can be covered by k2 − k + 1 pairs, which is sharp as evidenced by projective planes. This improves upon a result of Sanders [7]. Several open questions remain.","Title: Large harmonic sets of noncrossing edges for n randomly labeled vertices in convex position\nYear: 2007\n\nAbstract: Consider a set of n points in the plane in convex position,where each point has an integer label from {0,1,...,n - 1}.This naturally induces a labeling of the edges: each edge(i,j) is assigned the label i + j,modulo n. We propose algorithms for finding largenoncrossing harmonic matchings or paths, i.e., the matchingsor paths in which no two edges have the same label. When the pointlabels are chosen uniformly at random, and independently of eachother, our matching algorithm with high probability delivers anearlyperfect matching, a matching of size n/2 -O(n1/3 lnn). We show that, in sharpcontrast, a near-perfect path is unlikely: with high probabilitythe length of the longest path is below 0.96n. In a seriesof computer experiments, one of our algorithms invariably found apath of length above 0.76n, and the likely path length forour empirically second best algorithm is proved to be above0.66n. © 2006 Wiley Periodicals, Inc. Random Struct.Alg., 2007Part of this research was done at the The Ohio StateUniversity, Columbus, Ohio","Title: The number of Ks;t-free graphs\nYear: 2011\n\nAbstract: Abstract Denote by fn(H) the number of (labeled) H-free graphs on a,xed vertex set of size n. Erd} os conjectured that whenever H contains a cycle, fn(H) = 2, which is asymptotically sharp for all those values of s and t for which the order of magnitude of the Tur an number ex( n;Ks;t) is known. Our methods allow us to prove, among other things, that there is a positive constant c, such that almost all K2;t-free graphs of order n have at least 1=12 ex(n;K2;t) and at most (1 c) ex(n;K2;t) edges. Moreover, our results have some interesting applications to the study of some Ramsey- and Tur,an-type problems.","Title: The number of Km, m-free graphs.\nYear: 2011\n\nAbstract: A graph is called H-free if it contains no copy of H. Denote by fn(H) the number of (labeled) H-free graphs on n vertices. Erd} os conjectured (see (7)) that fn(H) 2(1+o(1))ex(n;H). This was rst shown to be true for cliques (9) and then Erd} os, Frankl and Rodl (8) proved it for all graphs H with (H) 3. For most bipartite H, the question is still wide open, and even the correct order of magnitude of log2fn(H) is not known. We prove that for every m 2, fn(Km;m) 2O(n 2 1=m) , extending the result of Kleitman and Winston (15) and answering a question of Erd} os. This bound is asymptotically sharp for m2 f2; 3g, and possibly for other values of m, for which the order of ex(n;Km;m) is not known, but it is conjectured to be ( n2 1=m). Our method also yields a bound on the number of Km;m-free graphs with xed order and size, extending the result of Furedi (11). Using this bound, we prove a relaxed version of a conjecture due to Haxell, Kohayakawa and Luczak (13) and show that almost all K3;3-free graphs of order n have more than 1=20 ex(n;K3;3) edges.","Title: The number of graphs without forbidden subgraphs\nYear: 2004\n\nAbstract: Given a family L of graphs, set p = p(L) = minL ⊂ L χ(L) - 1 and, for n ≥ 1, denote by P(n, L) the set of graphs with vertex set [n] containing no member of L as a subgraph, and write ex(n, L) for the maximal size of a member of P(n, L). Extending a result of Erdös, Frankl and Rödl (Graphs Combin. 2 (1986) 113), we prove that |P(n, L)|≤ 2½(1-1/p)n2 . O(n2-σ) for some constant γ = γ(L) 0, and characterize γ in terms of some related extremal graph problems. In fact, if ex(n, L) = O(n2 δ), then any γ","Title: Note: A new short proof of a theorem of Ahlswede and Khachatrian\nYear: 2008\n\nAbstract: Ahlswede and Khachatrian [R. Ahlswede, L.H. Khachatrian, The complete nontrivial-intersection theorem for systems of finite sets, J. Combin. Theory Ser. A 76 (1996) 121-138] proved the following theorem, which answered a question of Frankl and Furedi [P. Frankl, Z. Furedi, Nontrivial intersecting families, J. Combin. Theory Ser. A 41 (1986) 150-153]. Let 2==(t+1)(k-t+1). Suppose that F is a family of k-subsets of an n-set, every two of which have at least t common elements. If |@?\"F\"@?\"FF|","Title: A sharp edge bound on the interval number of a graph\nYear: 1999\n\nAbstract: The interval number of a graph G, denoted by i(G), is the least natural number t such that G is the intersection graph of sets, each of which is the union of at most t intervals. Here we settle a conjecture of Griggs and West about bounding i(G) in terms of e, that is, the number of edges in G. Namely, it is shown that i(G) ≤ $\\lceil\\sqrt{e}/2\\rceil$ + 1. It is also observed that the edge bound induces i(G) ≤ $\\sqrt{3 \\gamma(G)/2}+O(1)$, where &ggr;(G) is the genus of G. © 1999 John Wiley & Sons, Inc. J Graph Theory 32: 153–159, 1999","Title: Towards a Weighted Version of the Hajnal-Szemerédi Theorem.\nYear: 2013\n\nAbstract: For a positive integer r >= 2, a K-r-factor of a graph is a collection vertex-disjoint copies of K-r which covers all the vertices of the given graph. The celebrated theorem of Hajnal and Szemeredi asserts that every graph on n vertices with minimum degree at least (1 - 1/r)n contains a K-r-factor. In this note, we propose investigating the relation between minimum degree and existence of perfect K-r-packing for edge-weighted graphs. The main question we study is the following. Suppose that a positive integer r >= 2 and a real t is an element of [0, 1] is given. What is the minimum weighted degree of K-n that guarantees the existence of a K-r-factor such that every factor has total edge weight at least t((r)(2))? We provide some lower and upper bounds and make a conjecture on the asymptotics of the threshold as n goes to infinity.","Title: On the First-Fit Chromatic Number of Graphs\nYear: 2008\n\nAbstract: The first-fit chromatic number of a graph is the number of colors needed in the worst case of a greedy coloring. It is also called the Grundy number, which is defined to be the maximum number of classes in an ordered partition of the vertex set of a graph $G$ into independent sets $V_1, V_2, \\dots, V_k$ so that for each $1\\le i","Title: Turán Densities of Some Hypergraphs Related to Kk+1k.\nYear: 2012\n\nAbstract: Let B-i((k)) be the k-uniform hypergraph whose vertex set is of the form S boolean OR T, where vertical bar S vertical bar = i, vertical bar T vertical bar = k - 1, and S boolean AND T = circle divide, and whose edges are the k-subsets of S boolean OR T that contain either S or T. We derive upper and lower bounds for the Turan density of B-i((k)) that are close to each other as k -> infinity. We also obtain asymptotically tight bounds for the Turan density of several other infinite families of hypergraphs. The constructions that imply the lower bounds are derived from elementary number theory by probabilistic arguments, and the upper bounds follow from some results of de Caen, Sidorenko, and Keevash.","Title: Long monotone paths in line arrangements\nYear: 2004\n\nAbstract: We show how to construct an arrangement of n lines having a monotone path of length Ω(n 2 – (d/\\sqrt{log n})), where d > 0 is some constant, and thus nearly settle the long standing question on monotone path length in line arrangements.","Title: The Positive Minimum Degree Game on Sparse Graphs.\nYear: 2012\n\nAbstract: In this note we investigate a special form of degree games defined by D. Hefetz, M. Krivelevich, M. Stojakovic and T. Szabo [4]. Usually the board of a graph game is the edge set of K-n, the complete graph on n vertices. Maker and Breaker alternately claim an edge, and Maker wins if his edges form a subgraph with prescribed properties; here a certain minimum degree. In the special form the board is no longer the whole edge set of K-n, Maker first selects as few edges of K-n as possible in order to win, and our goal is to compute the necessary size of that board. Solving a question of [4], we show, using the discharging method, that the sharp bound is around 10n/7 for the positive minimum degree game.","Title: Private computation using a PEZ dispenser\nYear: 2003\n\nAbstract: We show how a (big) PEZ dispenser can be used by two or more players to compute a function of their inputs while hiding the values of the inputs from each other. In contrast to traditional approaches for solving this problem, ours does not require any use of randomness.","Title: On the interval number of special graphs\nYear: 2004\n\nAbstract: The interval number of a graph G is the least natural number t such that G is the intersection graph of sets, each of which is the union of at most t intervals, denoted by i(G). Griggs and West showed that $i(G)\\le \\lceil {1\\over 2} (d+1)\\rceil $. We describe the extremal graphs for that inequality when d is even. For three special perfect graph classes we give bounds on the interval number in terms of the independence number. Finally, we show that a graph needs to contain large complete bipartite induced subgraphs in order to have interval number larger than the random graph on the same number of vertices. © 2004 Wiley Periodicals, Inc. J Graph Theory 46: 241–253, 2004","Title: k-Sets, Convex Quadrilaterals, and the Rectilinear Crossing Number of Kn\nYear: 2006\n\nAbstract: AbstractWe use circular sequences to give an improved lower bound on the minimum number of (≤ k)-sets in a set of points in general position. We then use this to show that if S is a set of n points in general position, then the number ź(S) of convex quadrilaterals determined by the points in S is at least 0.37553(n4) + O(n3). This in turn implies that the rectilinear crossing number cr(Kn) of the complete graph Knis at least 0.37553(n4) + O(n3), and that Sylvester's Four Point Problem Constant is at least 0.37553. These improved bounds refine results recently obtained by Abrego and Fernandez-Merchant and by Lovasz, Vesztergombi, Wagner, and Welzl.","Title: Disjoint representability of sets and their complements\nYear: 2005\n\nAbstract: For a hypergraph H and a set S, the trace of H on S is the set of all intersections of edges of H with S. We will consider forbidden trace problems, in which we want to find the largest hypergraph H that does not contain some list of forbidden configurations as traces, possibly with some restriction on the number of vertices or the size of the edges in H. In this paper we will focus on combinations of three forbidden configurations: the k-singleton [k](1), the k-co-singleton [k](k-1) and the k-chain Ck={φ, {1}, [1, 2], ..., [1, k-1]}, where we write [k](l) for the set of all l-subsets of [k]={1, ..., k}. Our main topic is hypergraphs with no k-singleton or k-co-singleton trace. We obtain an exact result in the case k = 3, both for uniform and non-uniform hypergraphs, and classify the extremal examples. In the general case, we show that the number of edges in the largest r-uniform hypergraph with no k-singleton or k-co-singleton trace is of order rk-2. By contrast, Frankl and Pach showed that the number of edges in the largest r-uniform hypergraph with no k-singleton trace is of order rk-1. We also give a very short proof of the recent result of Balogh and Bollobás that there is a finite bound on the number of sets in any hypergraph without a k-singleton, k-co-singleton or k-chain trace, independently of the number of vertices or the size of the edges.","Title: Majority bootstrap percolation on the hypercube\nYear: 2009\n\nAbstract: In majority bootstrap percolation on a graph G, an infection spreads according to the following deterministic rule: if at least half of the neighbours of a vertex v are already infected, then v is also infected, and infected vertices remain infected forever. We say that percolation occurs if eventually every vertex is infected. The elements of the set of initially infected vertices, A ⊂ V(G), are normally chosen independently at random, each with probability p, say. This process has been extensively studied on the sequence of torus graphs [n]d, for n = 1,2,. . ., where d = d(n) is either fixed or a very slowly growing function of n. For example, Cerf and Manzo [17] showed that the critical probability is o(1) if d(n) ≤ log*n, i.e., if p = p(n) is bounded away from zero then the probability of percolation on [n]d tends to one as n → ∞. In this paper we study the case when the growth of d to ∞ is not excessively slow; in particular, we show that the critical probability is 1/2 + o(1) if d ≥ (log log n)2 log log log n, and give much stronger bounds in the case that G is the hypercube, [2]d.","Title: Random disease on the square grid\nYear: 1998\n\nAbstract: We introduce some generalizations of a nice combinatorial problem, the central notion of which is the so-called Disease Process. Let us color independently each square of an nxn chessboard black with a probability p(n); this is a random initial configuration of our process. Then we have a deterministic painting or expansion rule, and the question is the behavior of the disease process determined by this rule of spreading. In particular, how large must p(n) be to paint the whole chessboard black? The main result of this paper is the almost exact determination of the threshold function in the fundamental case of this Random Disease Problem. We include further investigations into the general randomized and deterministic cases. (C) 1998 John Wiley & Sons, Inc.","Title: The Turán density of triple systems is not principal\nYear: 2002\n\nAbstract: The Erdös-Stone-Simonovits Theorem implies that the Turán density of a family of graphs is the minimum of the Turán densities of the individual graphs from the family. It was conjectured by Mubayi and Rödl (J. Combin. Theory Ser. A, submitted) that this is not necessarily true for hypergraphs, in particular for triple systems. We give an example, which shows that their conjecture is true.","Title: The unlabelled speed of a hereditary graph property\nYear: 2009\n\nAbstract: A property of graphs is a collection P of graphs closed under isomorphism; we call Phereditary if it is closed under taking induced subgraphs. Given a property P, we write P^n for the set of graphs in P with vertex set [n]={1,...,n}, and P\"n for the isomorphism classes of graphs of order n that are in P. The cardinality |P^n| is the labelled speed of P and |P\"n| is the unlabelled speed. In the last decade numerous results have been proved about the labelled speeds of hereditary properties, with emphasis on the striking phenomenon that only certain speeds are possible: there are various pairs of functions (f(n),F(n)), with F(n)much larger than f(n), such that if the labelled speed is infinitely often larger than f(n) then it is also larger than F(n) for all sufficiently large values of n. Putting it concisely: the speed jumps from f(n) to F(n). Recent work on hereditary graph properties has shown that ''large'' and ''small'' labelled speeds of hereditary graph properties do jump. The aim of this paper is to study the unlabelled speed of a hereditary property, with emphasis on jumps. Among other results, we shall show that the unlabelled speed of a hereditary graph property is either of polynomial order or at least S(n), the number of ways of partitioning a set with n indistinguishable elements.","Title: On the edge-bandwidth of graph products\nYear: 2006\n\nAbstract: The edge-bandwidth of a graph G is the bandwidth of the line graph of G. We show asymptotically tight bounds on the edge-bandwidth of two-dimensional grids and tori, the product of two cliques and the n-dimensional hypercube.","Title: Local resilience of almost spanning trees in random graphs\nYear: 2011\n\nAbstract: We prove that for fixed integer D and positive reals α and γ, there exists a constant C0 such that for all p satisfying p(n) ≥ C0/n, the random graph G(n,p) asymptotically almost surely contains a copy of every tree with maximum degree at most D and at most (1 - α)n vertices, even after we delete a (1/2 - γ)-fraction of the edges incident to each vertex. The proof uses Szemerédi's regularity lemma for sparse graphs and a bipartite variant of the theorem of Friedman and Pippenger on embedding bounded degree trees into expanding graphs. © 2010 Wiley Periodicals, Inc. Random Struct. Alg., 2011 © 2011 Wiley Periodicals, Inc.","Title: Corrádi and hajnal's theorem for sparse random graphs\nYear: 2012\n\nAbstract: In this paper we extend a classical theorem of Corrádi and Hajnal into the setting of sparse random graphs. We show that if p(n) â聣芦 (log n/n)1/2, then asymptotically almost surely every subgraph of G(n, p) with minimum degree at least (2/3 + o(1))np contains a triangle packing that covers all but at most O(p-2) vertices. Moreover, the assumption on p is optimal up to the (log n)1/2 factor and the presence of the set of O(p-2) uncovered vertices is indispensable. The main ingredient in the proof, which might be of independent interest, is an embedding theorem which says that if one imposes certain natural regularity conditions on all three pairs in a balanced 3-partite graph, then this graph contains a perfect triangle packing.","Title: On k-coverage in a mostly sleeping sensor network\nYear: 2008\n\nAbstract: Sensor networks are often desired to last many times longer than the active lifetime of individual sensors. This is usually achieved by putting sensors to sleep for most of their lifetime. On the other hand, surveillance kind of applications require guaranteed k-coverage of the protected region at all times. As a result, determining the appropriate number of sensors to deploy that achieves both goals simultaneously becomes a challenging problem. In this paper, we consider three kinds of deployments for a sensor network on a unit square - a √n x √n grid, random uniform (for all n points), and Poisson (with density n). In all three deployments, each sensor is active with probability p, independently from the others. Then, we claim that the critical value of the function npπr2/log(np) is 1 for the event of k-coverage of every point. We also provide an upper bound on the window of this phase transition. Although the conditions for the three deployments are similar, we obtain sharper bounds for the random deployments than the grid deployment, which occurs due to the boundary condition. In this paper, we also provide corrections to previously published results for the grid deployment model. Finally, we use simulation to show the usefulness of our analysis in real deployment scenarios.","Title: Hereditary properties of partitions, ordered graphs and ordered hypergraphs\nYear: 2006\n\nAbstract: In this paper we use the Klazar-Marcus-Tardos method (see [A. Marcus, G. Tardos, Excluded permutation matrices and the Stanley-Wilf conjecture. J. Combin. Theory Ser. A 107 (2004) 153-160]) to prove that, if a hereditary property of partitions P has super-exponential speed, then, for every k- permutation π, P contains the partition of |2k| with parts {{i, π(i) + k} : i ∈ ⌈ k ⌉}. We also prove a similar jump, from exponential to factorial, in the possible speeds of monotone properties of ordered graphs, and of hereditary properties of ordered graphs not containing large complete, or complete bipartite ordered graphs.Our results generalize the Stanley-Wilf conjecture on the number of n-permutations avoiding a fixed permutation, which was recently proved by the combined results of Klazar [M. Klazar, The Füredi-Hajnal conjecture implies the Stanley-Wilf conjecture, in: D. Krob, A.A. Mikhalev, A.V. Mikhalev (Eds.), Formal Power Series and Algebraic Combinatorics, Springer, Berlin, 2000, pp. 250-255] and Marcus and Tardos [A. Marcus, G. Tardos, Excluded permutation matrices and the Stanley-Wilf conjecture, J. Combin. Theory Ser. A 107 (2004) 153-160]. Our main results follow from a generalization to ordered hypergraphs of the theorem of Marcus and Tardos.","Title: The structure of almost all graphs in a hereditary property\nYear: 2011\n\nAbstract: A hereditary property of graphs is a collection of graphs which is closed under taking induced subgraphs. The speed of P is the function n@?|P\"n|, where P\"n denotes the graphs of order n in P. It was shown by Alekseev, and by Bollobas and Thomason, that if P is a hereditary property of graphs then|P\"n|=2^(^1^-^1^/^r^+^o^(^1^)^)^(^n^2^), where r=r(P)@?N is the so-called 'colouring number' of P. However, their results tell us very little about the structure of a typical graph G@?P. In this paper we describe the structure of almost every graph in a hereditary property of graphs, P. As a consequence, we derive essentially optimal bounds on the speed of P, improving the Alekseev-Bollobas-Thomason Theorem, and also generalising results of Balogh, Bollobas and Simonovits.","Title: An extended lower bound on the number of (k)-edges to generalized configurations of points and the pseudolinear crossing number of Kn.\nYear: 2008\n\nAbstract: Recently, Aichholzer, García, Orden, and Ramos derived a remarkably improved lower bound for the number of (⩽k)-edges in an n-point set, and as an immediate corollary, an improved lower bound on the rectilinear crossing number of Kn. We use simple allowable sequences to extend all their results to the more general setting of simple generalized configurations of points and slightly improve the lower bound on Sylvester's constant from 0.37963 to 0.379688. In other words, we prove that the pseudolinear (and consequently the rectilinear) crossing number of Kn is at least 0.379688(n4)+Θ(n3). We use this to determine the exact pseudolinear crossing numbers of Kn and the maximum number of halving pseudolines in an n-point set for n=10,11,12,13,15,17,19, and 21. All these values coincide with the corresponding rectilinear numbers obtained by Aichholzer et al.","Title: Excluding induced subgraphs: Critical graphs\nYear: 2011\n\nAbstract: Determining the cardinality and describing the structure of H-free graphs is well-investigated for many graphs H. In the nineties, Prömel and Steger proved that for a graph H with chromatic number k + 1 almost all graphs not containing H as a subgraph are k-colorable if and only if H contains a color-critical edge. We strengthen the concept of H-free to induced subgraph containment, proving that if H has coloring number k + 1 then almost all H-free graphs can be covered by k graphs that are cliques or independent sets if and only if H is in some well-defined sense critical. The family of critical graphs includes C4 and C2k+1 for all k ≥ 3. © 2010 Wiley Periodicals, Inc. Random Struct. Alg., 2011 © 2011 Wiley Periodicals, Inc.","Title: The penultimate rate of growth for graph properties\nYear: 2001\n\nAbstract: Given a property P ofgraphs, write P n for the set of graphs with vertex set (n) having property P. We call |P n| the speed of P. Recent research has shown that the speed ofa monotone or hereditary property P can be a constant, polynomial, or exponential function of n, and the structure ofthe graphs in P can then be well described. Similarly, |P n| can be ofthe f orm","Title: Bootstrap percolation on the random regular graph\nYear: 2007\n\nAbstract: The k-parameter bootstrap percolation on a graph is a model of an interacting particle system, which can also be viewed as a variant of a cellular automaton growth process with threshold k >= 2. At the start, each of the graph vertices is active with probability p and inactive with probability I - p, independently of other vertices. Presence of active vertices triggers a bootstrap percolation process controlled by a recursive rule: an active vertex remains active forever, and a currently inactive vertex becomes active when at least k of its neighbors are active. The basic problem is to identify, for a given graph, p(-) and p(+) such that for p < p(-) (p > p(+) resp.) the probability that all vertices are eventually active is very close to 0 (1 resp.). The bootstrap percolation process is a deterministic process on the space of subsets of the vertex set, which is easy to describe but hard to analyze rigorously in general. We study the percolation on the random d-regular graph, d >= 3, via analysis of the process on the multigraph counterpart of the graph. Here, thanks to a \"principle of deferred decisions,\" the percolation dynamics is described by a surprisingly simple Markov chain. Its generic state is formed by the counts of currently active and nonactive vertices having various degrees of activation capabilities. We replace the chain by a deterministic dynamical system, and use its integrals to show-via exponential supermartingales-that the percolation process undergoes relatively small fluctuations around the deterministic trajectory. This allows us to show existence of the phase transition within an interval [p(-)(n),p(+)(n)], such that (1)p(+/-)(n) -> p* = 1 - min(y epsilon(0,1)) y/P(Bin(d - 1, 1 -y) < k); (2)p(+)(n)-p(-)(n) is of order n(-1/2) for k < d(-1), and n(-epsilon n), (epsilon(n) down arrow 0, epsilon(n) log n -> infinity), for k = d - 1. Note that p* is the same as the critical probability of the process on the corresponding infinite regular tree. (c) 2006 Wiley Periodicals, Inc.","Title: Measures on monotone properties of graphs\nYear: 2002\n\nAbstract: Given a monotone property P of graphs, write P n for the set of graphs with vertex set [ n ] having property P . Building on recent results in the enumeration of graphical properties, we prove numerous results about the structure of graphs in P n and the functions | P n | . We also examine the measure e P (n) , the maximum number of edges in a graph of P n .","Title: Bootstrap percolation in high dimensions\nYear: 2010\n\nAbstract: In r-neighbour bootstrap percolation on a graph G, a set of initially infected vertices A ⊂ V(G) is chosen independently at random, with density p, and new vertices are subsequently infected if they have at least r infected neighbours. The set A is said to percolate if eventually all vertices are infected. Our aim is to understand this process on the grid, [n]d, for arbitrary functions n = n(t), d = d(t) and r = r(t), as t → ∞. The main question is to determine the critical probability pc([n]d, r) at which percolation becomes likely, and to give bounds on the size of the critical window. In this paper we study this problem when r = 2, for all functions n and d satisfying d ≫ log n. The bootstrap process has been extensively studied on [n]d when d is a fixed constant and 2 ⩽ r ⩽ d, and in these cases pc([n]d, r) has recently been determined up to a factor of 1 + o(1) as n → ∞. At the other end of the scale, Balogh and Bollobás determined pc([2]d, 2) up to a constant factor, and Balogh, Bollobás and Morris determined pc([n]d, d) asymptotically if d ≥ (log log n)2+ϵ, and gave much sharper bounds for the hypercube. Here we prove the following result. Let λ be the smallest positive root of the equation \\[\\sum_{k=0}^\\infty \\frac{(-1)^k \\lambda^k}{2^{k^2-k} k!} = 0,\\] so λ ≈ 1.166. Then \\[ \\frac{16\\lambda}{d^2} \\biggl(1 + \\frac{\\log d}{\\sqrt{d}} \\biggr)\\: 2^{-2\\sqrt{d}} \\leq p_c([2]^d,2) \\leq \\frac{16\\lambda}{d^2} \\biggl(1 + \\frac{5(\\log d)^2}{\\sqrt{d}} \\biggr) \\: 2^{-2\\sqrt{d}}\\] if d is sufficiently large, and moreover \\[p_c\\bigl([n]^d,2 \\bigr) = \\bigl(4\\lambda + o(1) \\bigr) \\biggl(\\frac{n}{n-1} \\biggr)^2 \\, \\frac{1}{d^2} \\, 2^{-2\\sqrt{d \\log_2 n}}\\] as d → ∞, for every function n = n(d) with d ≫ log n.","Title: Hereditary properties of combinatorial structures: Posets and oriented graphs\nYear: 2007\n\nAbstract: A hereditary property of combinatorial structures is acollection of structures (e.g., graphs, posets) which is closedunder isomorphism, closed under taking induced substructures (e.g.,induced subgraphs), and contains arbitrarily large structures.Given a property P, we writeP\\ for the collection of distinct (i.e.,non-isomorphic) structures in a property P withn vertices, and call the function n ➛|P\\| the speed (or unlabeled speed) ofP. Also, we write P\\ for thecollection of distinct labeled structures in Pwith vertices labeled 1,...,n, and call the functionn ➛ |P\\| the labeled speedof P. The possible labeled speeds of a hereditaryproperty of graphs have been extensively studied, and the aim ofthis article is to investigate the possible speeds of othercombinatorial structures, namely posets and oriented graphs. Moreprecisely, we show that (for sufficiently large n), thelabeled speed of a hereditary property of posets is either 1, orexactly a polynomial, or at least 2n-1. We alsoshow that there is an initial jump in the possible unlabeled speedsof hereditary properties of posets, tournaments, and directedgraphs, from bounded to linear speed, and give a sharp lower boundon the possible linear speeds in each case. © 2007 WileyPeriodicals, Inc. J Graph Theory 56: 311332, 2007The work was done while Robert Morris was at the University ofMemphis.","Title: Edit Distance and its Computation.\nYear: 2008\n\nAbstract: In this paper, we provide a method for determining the asymptotic value of the maximum edit distance from a given hereditary property. This method permits the edit distance to be computed without using Szemeredi's Regularity Lemma directly. Using this new method, we are able to compute the edit distance from hereditary properties for which it was previously unknown. For some graphs H, the edit distance from Forb(H) is computed, where Forb(H) is the class of graphs which contain no induced copy of graph H. Those graphs for which we determine the edit distance asymptotically are H = K-a + E-b, an a-clique with b isolated vertices, and H = K-3,K-3, a complete bipartite graph. We also provide a graph, the first such construction, for which the edit distance cannot be determined just by considering partitions of the vertex set into cliques and cocliques. In the process, we develop weighted generalizations of Turan's theorem, which may be of independent interest.","Title: Covering planar graphs with forests\nYear: 2005\n\nAbstract: We study the problem of covering graphs with trees and a graph of bounded maximum degree. By a classical theorem of Nash-Williams, every planar graph can be covered by three trees. We show that every planar graph can be covered by two trees and a forest, and the maximum degree of the forest is at most 8. Stronger results are obtained for some special classes of planar graphs.","Title: Bootstrap Percolation on Infinite Trees and Non-Amenable Groups\nYear: 2006\n\nAbstract: Bootstrap percolation on an arbitrary graph has a random initial configuration, where each vertex is occupied with probability $p$, independently of each other, and a deterministic spreading rule with a fixed parameter $k$: if a vacant site has at least $k$ occupied neighbours at a certain time step, then it becomes occupied in the next step. This process is well studied on ${\\mathbb Z}^d$; here we investigate it on regular and general infinite trees and on non-amenable Cayley graphs. The critical probability is the infimum of those values of $p$ for which the process achieves complete occupation with positive probability. On trees we find the following discontinuity: if the branching number of a tree is strictly smaller than $k$, then the critical probability is 1, while it is $1-1/k$ on the $k$-ary tree. A related result is that in any rooted tree $T$ there is a way of erasing $k$ children of the root, together with all their descendants, and repeating this for all remaining children, and so on, such that the remaining tree $T'$ has branching number $\\mbox{\\rm br}(T')\\leq \\max\\{\\mbox{\\rm br}(T)-k,\\,0\\}$. We also prove that on any $2k$-regular non-amenable graph, the critical probability for the $k$-rule is strictly positive.","Title: On the variance of Shannon products of graphs\nYear: 2008\n\nAbstract: We study the combinatorial problem of finding an arrangement of distinct integers into the d-dimensional N-cube so that the maximal variance of the numbers on each ℓ-dimensional section is minimized. Our main tool is an inequality on the Laplacian of a Shannon product of graphs, which might be a subject of independent interest. We describe applications of the inequality to multiple description scalar quantizers (MDSQ), to get bounds on the bandwidth of products of graphs, and to balance edge-colorings of regular, d-uniform, d-partite hypergraphs.","Title: The typical structure of graphs without given excluded subgraphs\nYear: 2009\n\nAbstract: Let $ {\\cal L}$ be a finite family of graphs. We describe the typical structure of $ {\\cal L}$-free graphs, improving our earlier results (Balogh et al., J Combinat Theory Ser B 91 (2004), 1–24) on the Erdős–Frankl–Rödl theorem (Erdős et al., Graphs Combinat 2 (1986), 113–121), by proving our earlier conjecture that, for $ p = p ({\\cal L}) = {\\rm min}_L \\in {\\cal L} \\chi (L) - 1 $, the structure of almost all $ {\\cal L}$-free graphs is very similar to that of a random subgraph of the Turán graph Tn,p. The “similarity” is measured in terms of graph theoretical parameters of $ {\\cal L}$.© 2008 Wiley Periodicals, Inc. Random Struct. Alg., 2009","Title: On the Tree Packing Conjecture.\nYear: 2013\n\nAbstract: The Gyarfas tree packing conjecture states that any set of n - 1 trees T-1, T-2, ... , Tn-1 such that T-i has n - i + 1 vertices packs into K-n (for n large enough). We show that t = 1/10n(1/4) trees T-1, T-2, ... , T-t such that T-i has n - i + 1 vertices packs into Kn+1 (for n large enough). We also prove that any set of t = 1/10n(1/4) trees T-1, T-2, ... , T-t such that no tree is a star and T-i has n - i + 1 vertices packs into K-n (for n large enough). Finally, we prove that t = 1/4 n(1/3) trees T-1, T-2, ... , T-t such that T-i has n - i + 1 vertices packs into K-n as long as each tree has maximum degree at least 2n(2/3) (for n large enough). One of the main tools used in the paper is the famous spanning tree embedding theorem of Komlos, Sarkozy, and Szemeredi [Combin. Probab. Comput., 10 (2001), pp. 397-416].","Title: Complete minors, independent sets, and chordal graphs.\nYear: 2011\n\nAbstract: The Hadwiger number h(G) of a graph G is the maximum size of a complete minor of G. Hadwiger's Conjecture states that h(G) >= \\chi(G). Since \\chi(G) \\alpha(G) >= |V(G)|, Hadwiger's Conjecture implies that \\alpha(G) h(G) >= |V(G)|. We show that (2 \\alpha(G) - \\lceil log_t(t \\alpha(G)/2) \\rceil) h(G) \\geq |V(G)| where t is approximately 6.83. For graphs with \\alpha(G) \\geq 14, this improves on a recent result of Kawarabayashi and Song who showed (2 \\alpha(G) - 2) h(G) >= |V(G)| when \\alpha(G) >= 3.","Title: Minimum Difference Representations of Graphs\nYear: 2009\n\nAbstract: Define a k-minimum-difference-representation (k-MDR) of a graph G to be a family of sets $${\\{S(v): v\\in V(G)\\}}$$such that u and v are adjacent in G if and only if min{|S(u)−S(v)|, |S(v)−S(u)|} ≥ k. Define ρ min(G) to be the smallest k for which G has a k-MDR. In this note, we show that {ρ min(G)} is unbounded. In particular, we prove that for every k there is an n 0 such that for n n 0 ‘almost all’ graphs of order n satisfy ρ min(G) k. As our main tool, we prove a Ramsey-type result on traces of hypergraphs.","Title: A remark on the number of edge colorings of graphs\nYear: 2006\n\nAbstract: Fix a 2-coloring Hk + 1 of the edges of a complete graph Kk + 1. Let C(n, Hk + 1) denote the maximum possible number of distinct edge-colorings of a simple graph on n vertices with two colors, which contain no copy of Kk + 1 colored exactly as Hk + 1. It is shown that for every fixed k and all n n0(k), if in the colored graph Hk + 1 both colors were used, then C(n, Hk + 1) = 2tk(n), where tk(n) is the maximum possible number of edges of a graph on n vertices containing no K k + 1. The proofs are based on Szemerédi's Regularity Lemma together with the Simonovits Stability Theorem, and provide one of the growing number of examples of a precise result proved by applying the Regularity Lemma.","Title: On the Chvátal-Erdös Triangle Game.\nYear: 2011\n\nAbstract: Given a graph G and positive integers n and q, let G(G; n, q) be the game played on the edges of the complete graph K-n in which the two players, Maker and Breaker, alternately claim 1 and q edges, respectively. Maker's goal is to occupy all edges in some copy of G; Breaker tries to prevent it. In their seminal paper on positional games,Chvatal and Erdos proved that in the game G (K-3;n,q), Maker has a winning strategy if q < root 2n+2 - 5/2, and if q >= 2 root n, then Breaker has a winning strategy. In this note, we improve the latter of these bounds by describing a randomized strategy that allows Breaker to win the game G(K-3; n, q) whenever q >= (2 - 1/24)root n. Moreover, we provide additional evidence supporting the belief that this bound can be further improved to (root 2 + o(1))root n.","Title: Linear algebra and bootstrap percolation\nYear: 2012\n\nAbstract: In H-bootstrap percolation, a set A@?V(H) of initially 'infected' vertices spreads by infecting vertices which are the only uninfected vertex in an edge of the hypergraph H. A particular case of this is the H-bootstrap process, in which H encodes copies of H in a graph G. We find the minimum size of a set A that leads to complete infection when G and H are powers of complete graphs and H encodes induced copies of H in G. The proof uses linear algebra, a technique that is new in bootstrap percolation, although standard in the study of weakly saturated graphs, which are equivalent to (edge) H-bootstrap percolation on a complete graph.","Title: Upper bounds on the size of 4- and 6-cycle-free subgraphs of the hypercube\nYear: 2014\n\nAbstract: In this paper we modify slightly Razborov's flag algebra machinery to be suitable for the hypercube. We use this modified method to show that the maximum number of edges of a 4-cycle-free subgraph of the n-dimensional hypercube is at most 0.6068 times the number of its edges. We also improve the upper bound on the number of edges for 6-cycle-free subgraphs of the n-dimensional hypercube from 2-1 to 0.3755 times the number of its edges. Additionally, we show that if the n-dimensional hypercube is considered as a poset then the maximum vertex density of three middle layers in an induced subgraph without 4-cycles is at most 2.15121(n@?n/2@?).","Title: On Perfect Packings in Dense Graphs.\nYear: 2013\n\nAbstract: We say that a graph G has a perfect H-packing if there exists a set of vertex-disjoint copies of H which cover all the vertices in G. We consider various problems concerning perfect H-packings: Given n, r, D is an element of N, we characterise the edge density threshold that ensures a perfect K-r-packing in any graph G on n vertices and with minimum degree delta(G) >= D. We also give two conjectures concerning degree sequence conditions which force a graph to contain a perfect H-packing. Other related embedding problems are also considered. Indeed, we give a structural result concerning K-r-free graphs that satisfy a certain degree sequence condition.","Title: Unavoidable Traces Of Set Systems\nYear: 2005\n\nAbstract: Sauer, Shelah, Vapnik and Chervonenkis proved that if a set system on n vertices contains many sets, then the set system has full trace on a large set. Although the restriction on the size of the groundset cannot be lifted, Frankl and Pach found a trace structure that is guaranteed to occur in uniform set systems even if we do not bound the size of the groundset. In this note we shall give three sequences of structures such that every set system consisting of sufficiently many sets contains at least one of these structures with many sets.","Title: The interval number of dense graphs\nYear: 2002\n\nAbstract: The interval number of a graph G, denoted by i(G), is the least natural number t such that G is the intersection graph of sets, each of which is the union of at most t closed intervals. Most known bounds on i(G) are grossly excessive when G has more than half of the possible edges. A plausible remedy is to develop bounds on i(G) that are monotone decreasing in G. Here we bound i(G) in terms of e(G), the number of edges in the complement of G. We prove that i(G) ≤ ⌈½√e(G)⌉ + O(n/logn).","Title: Almost all triple systems with independent neighborhoods are semi-bipartite\nYear: 2011\n\nAbstract: The neighborhood of a pair of vertices u, v in a triple system is the set of vertices w such that uvw is an edge. A triple system H is semi-bipartite if its vertex set contains a vertex subset X such that every edge of H intersects X in exactly two points. It is easy to see that if H is semi-bipartite, then the neighborhood of every pair of vertices in H is an independent set. We show a partial converse of this statement by proving that almost all triple systems with vertex sets [n] and independent neighborhoods are semi-bipartite. Our result can be viewed as an extension of the Erdos-Kleitman-Rothschild theorem to triple systems. The proof uses the Frankl-Rodl hypergraph regularity lemma, and stability theorems. Similar results have recently been proved for hypergraphs with various other local constraints.","Title: Online Ramsey games for triangles in random graphs\nYear: 2010\n\nAbstract: In the online F-avoidance edge-coloring game with r colors, a graph on n vertices is generated by randomly adding a new edge at each stage. The player must color each new edge as it appears; the goal is to avoid a monochromatic copy of F. Let N\"0(F,r,n) be the threshold function for the number of edges that the player is asymptotically almost surely able to paint before he/she loses. Even when F=K\"3, the order of magnitude of N\"0(F,r,n) is unknown for r>=3. In particular, the only known upper bound is the threshold function for the number of edges in the offline version of the problem, in which an entire random graph on n vertices with m edges is presented to the player to be r edge-colored. We improve the upper bound for the online triangle-avoidance game with r colors, providing the first result that separates the online threshold function from the offline bound for r>=3. This supports a conjecture of Marciniszyn, Spohel, and Steger that the known lower bound is tight for cliques and cycles for all r.","Title: On 2-Detour Subgraphs of the Hypercube\nYear: 2008\n\nAbstract: A spanning subgraph H of a graph G is a 2-detour subgraph of G if for each x, y ∈ V(G), d H (x, y) ≤ d G (x, y) + 2. We prove a conjecture of Erdős, Hamburger, Pippert, and Weakley by showing that for some positive constant c and every n, each 2-detour subgraph of the n-dimensional hypercube Q n has at least clog2 n · 2n edges.","Title: On the minimal degree implying equality of the largest triangle-free and bipartite subgraphs\nYear: 2006\n\nAbstract: Erdos posed the problem of finding conditions on a graph G that imply t(G)=b(G), where t(G) is the largest number of edges in a triangle-free subgraph and b(G) is the largest number of edges in a bipartite subgraph. Let @d\"c be the least number so that any graph G on n vertices with minimum degree @d\"cn has t(G)=b(G). Extending results of Bondy, Shen, Thomasse and Thomassen we show that 0.75=","Title: Large minors in graphs with given independence number\nYear: 2011\n\nAbstract: A weakening of Hadwiger's conjecture states that every n-vertex graph with independence number @a has a clique minor of size at least n@a. Extending ideas of Fox (2010) [6], we prove that such a graph has a clique minor with at least n(2-c)@a vertices where c1/19.2.","Title: The Klee–Minty random edge chain moves with linear speed\nYear: 2007\n\nAbstract: An infinite sequence of 0's and 1's evolves by flipping each 1 to a 0 exponentially at rate 1. When a 1 flips, all bits to its right also flip. Starting from any configuration with finitely many 1's to the left of the origin, we show that the leftmost 1 moves right with bounded speed. Upper and lower bounds are given on the speed. A consequence is that a lower bound for the run time of the random-edge simplex algorithm on a Klee–Minty cube is improved so as to be quadratic, in agreement with the upper bound. © 2006 Wiley Periodicals, Inc. Random Struct. Alg., 2007","Title: The fine structure of octahedron-free graphs\nYear: 2011\n\nAbstract: This paper is one of a series of papers in which, for a family L of graphs, we describe the typical structure of graphs not containing any L@?L. In this paper, we prove sharp results about the case L={O\"6}, where O\"6 is the graph with 6 vertices and 12 edges, given by the edges of an octahedron. Among others, we prove the following results. (a) The vertex set of almost every O\"6-free graph can be partitioned into two classes of almost equal sizes, U\"1 and U\"2, where the graph spanned by U\"1 is a C\"4-free and that by U\"2 is P\"3-free. (b) Similar assertions hold when L is the family of all graphs with 6 vertices and 12 edges. (c) If H is a graph with a color-critical edge and @g(H)=p+1, then almost every sH-free graph becomes p-chromatic after the deletion of some s-1 vertices, where sH is the graph formed by s vertex disjoint copies of H. These results are natural extensions of theorems of classical extremal graph theory. To show that results like those above do not hold in great generality, we provide examples for which the analogs of our results do not hold.","Title: The speed of hereditary properties of graphs\nYear: 2000\n\nAbstract: Given a property P of graphs, write P-n for the set of graphs with vertex set [n] having property P. The growth or speed of a property P can be discussed in terms of the values of \\P-n\\. For properties with \\P-n\\ < n(n) hereditary properties are surprisingly well determined by their speeds. Sharpening results of E. R. Scheinerman and J. Zito (1994, J. Combin. Theory Ser. B 61, 16-39), we prove numerous results about the possible functions \\P-n\\ and describe in derail the properties exhibiting each type of growth. We also list minimal properties exhibiting each type of growth. (C) 2000 Academic Press.","Title: The Erdős-Lovász Tihany conjecture for quasi-line graphs\nYear: 2009\n\nAbstract: Erdos and Lovasz conjectured in 1968 that for every graph G with @g(G)@w(G) and any two integers s,t=2 with s+t=@g(G)+1, there is a partition (S,T) of the vertex set V(G) such that @g(G[S])=s and @g(G[T])=t. Except for a few cases, this conjecture is still unsolved. In this note we prove the conjecture for quasi-line graphs and for graphs with independence number 2.","Title: Sizes of induced subgraphs of ramsey graphs\nYear: 2009\n\nAbstract: An n-vertex graph G is c-Ramsey if it contains neither a complete nor an empty induced subgraph of size greater than c log n. Erdős, Faudree and Sós conjectured that every c-Ramsey graph with n vertices contains Ω(n5/2) induced subgraphs, any two of which differ either in the number of vertices or in the number of edges, i.e., the number of distinct pairs (|V(H)|, |E(H)|), as H ranges over all induced subgraphs of G, is Ω(n5/2). We prove an Ω(n2.3693) lower bound.","Title: A new short proof of a theorem of Ahlswede and Khachatrian\nYear: 2008\n\nAbstract: Ahlswede and Khachatrian [R. Ahlswede, L.H. Khachatrian, The complete nontrivial-intersection theorem for systems of finite sets, J. Combin. Theory Ser. A 76 (1996) 121–138] proved the following theorem, which answered a question of Frankl and Füredi [P. Frankl, Z. Füredi, Nontrivial intersecting families, J. Combin. Theory Ser. A 41 (1986) 150–153]. Let 2⩽t+1⩽k⩽2t+1 and n⩾(t+1)(k−t+1). Suppose that F is a family of k-subsets of an n-set, every two of which have at least t common elements. If |⋂F∈FF|<t, then |F|⩽(t+2)(n−t−2k−t−1)+(n−t−2k−t−2), and this is best possible. We give a new, short proof of this result. The proof in [R. Ahlswede, L.H. Khachatrian, The complete nontrivial-intersection theorem for systems of finite sets, J. Combin. Theory Ser. A 76 (1996) 121–138] requires the entire machinery of the proof of the complete intersection theorem, while our proof uses only ordinary compression and an earlier result of Wilson [R.M. Wilson, The exact bound in the Erdős–Ko–Rado theorem, Combinatorica 4 (1984) 247–257].","Title: Intersecting families of discrete structures are typically trivial\nYear: 2015\n\nAbstract: The study of intersecting structures is central to extremal combinatorics. A family of permutations F S n is t-intersecting if any two permutations in F agree on some t indices, and is trivial if all permutations in F agree on the same t indices. A k-uniform hypergraph is t-intersecting if any two of its edges have t vertices in common, and trivial if all its edges share the same t vertices.The fundamental problem is to determine how large an intersecting family can be. Ellis, Friedgut and Pilpel proved that for n sufficiently large with respect to t, the largest t-intersecting families in S n are the trivial ones. The classic Erd s-Ko-Rado theorem shows that the largest t-intersecting k-uniform hypergraphs are also trivial when n is large. We determine the typical structure of t-intersecting families, extending these results to show that almost all intersecting families are trivial. We also obtain sparse analogues of these extremal results, showing that they hold in random settings.Our proofs use the Bollobás set-pairs inequality to bound the number of maximal intersecting families, which can then be combined with known stability theorems. We also obtain similar results for vector spaces.","Title: A random version of Sperner's theorem.\nYear: 2014\n\nAbstract: Let P(n) denote the power set of [n], ordered by inclusion, and let P(n,p) be obtained from P(n) by selecting elements from P(n) independently at random with probability p. A classical result of Sperner [12] asserts that every antichain in P(n) has size at most that of the middle layer, (n⌊n/2⌋). In this note we prove an analogous result for P(n,p): If pn→∞ then, with high probability, the size of the largest antichain in P(n,p) is at most (1+o(1))p(n⌊n/2⌋). This solves a conjecture of Osthus [9] who proved the result in the case when pn/logn→∞. Our condition on p is best-possible. In fact, we prove a more general result giving an upper bound on the size of the largest antichain for a wider range of values of p.","Title: On the number of K4-saturating edges.\nYear: 2014\n\nAbstract: Let G be a K4-free graph; an edge in its complement is a K4-saturating edge if the addition of this edge to G creates a copy of K4. Erdős and Tuza conjectured that for any n-vertex K4-free graph G with ⌊n2/4⌋+1 edges, one can find at least (1+o(1))n216 K4-saturating edges. We construct a graph with only 2n233 K4-saturating edges. Furthermore, we prove that it is best possible, i.e., one can always find at least (1+o(1))2n233 K4-saturating edges in an n-vertex K4-free graph with ⌊n2/4⌋+1 edges.","Title: Hypergraphs with Zero Chromatic Threshold\nYear: 2016\n\nAbstract: Abstract Let F be an r-uniform hypergraph. The chromatic threshold of the family of F-free, r-uniform hypergraphs is the infimum of all non-negative reals c such that the subfamily of F-free, r-uniform hypergraphs H with minimum degree at least \\(c \\left( {\\begin{array}{c}|V(H)|\\\\ r-1\\end{array}}\\right) \\) has bounded chromatic number. The study of chromatic thresholds of various graphs has a long history, beginning with the early work of Erdős and Simonovits. One interesting problem, first proposed by Łuczak and Thomassé and then solved by Allen, Böttcher, Griffiths, Kohayakawa and Morris, is the characterization of graphs having zero chromatic threshold, in particular the fact that there are graphs with non-zero Turán density that have zero chromatic threshold. Here, we make progress on this problem for r-uniform hypergraphs, showing that a large class of hypergraphs have zero chromatic threshold in addition to exhibiting a family of constructions showing another large class of hypergraphs have non-zero chromatic threshold. Our construction is based on a particular product of the Bollobás–Erdős graphs defined earlier by the authors.","Title: Rainbow triangles in three-colored graphs\nYear: 2017\n\nAbstract: Erdős and Sós proposed the problem of determining the maximum number F(n) of rainbow triangles in 3-edge-colored complete graphs on n vertices. They conjectured that F(n)=F(a)+F(b)+F(c)+F(d)+abc+abd+acd+bcd, where a+b+c+d=n and a,b,c,d are as equal as possible. We prove that the conjectured recurrence holds for sufficiently large n. We also prove the conjecture for n=4k for all k≥0. These results imply that limF(n)(n3)=0.4, and determine the unique limit object. In the proof we use flag algebras combined with stability arguments.","Title: A sharp bound on the number of maximal sum-free sets\nYear: 2015\n\nAbstract: Cameron and Erdős [P. Cameron and P. Erdős, Notes on sum-free and related sets, Combin. Probab.Comput., 8, (1999), 95–107] asked whether the number of maximal sum-free sets in {1,…,n} is much smaller than the number of sum-free sets. In the same paper they gave a lower bound of 2⌊n/4⌋ for the number of maximal sum-free sets. We prove the following: For each 1≤i≤4, there is a constant Ci such that, given any n≡i mod 4, {1,…,n} contains (Ci+o(1))2n/4 maximal sum-free sets. Our proof makes use of container and removal lemmas of Green [B. Green, The Cameron-Erdős conjecture, Bull. London Math. Soc., 36, (2004), 769–778; B. Green, A Szemerédi-type regularity lemma in abelian groups, with applications, Geom. Funct. Anal., 15, (2005), 340–376], a structural result of Deshouillers, Freiman, Sós and Temkin [J. Deshouillers, G. Freiman, V. Sós and M. Temkin, On the structure of sumfree sets II, Astérisque, 258, (1999), 149–161] and a recent bound on the number of subsets of integers with small sumset by Green and Morris [B. Green, R. Morris, Counting sets with small sumset and applications, Combinatorica, to appear].","Title: On the Chromatic Thresholds of Hypergraphs.\nYear: 2016\n\nAbstract: Let F be a family of r-uniform hypergraphs. The chromatic threshold of F is the infimum of all non-negative reals c such that the subfamily of F comprising hypergraphs H with minimum degree at least c((broken vertical bar V(H)broken vertical bar)(r-1)) has bounded chromatic number. This parameter has a long history for graphs (r = 2), and in this paper we begin its systematic study for hypergraphs. Luczak and Thomasse recently proved that the chromatic threshold of the so-called near bipartite graphs is zero, and our main contribution is to generalize this result to r-uniform hypergraphs. For this class of hypergraphs, we also show that the exact Turan number is achieved uniquely by the complete (r + 1)-partite hypergraph with nearly equal part sizes. This is one of very few infinite families of non-degenerate hypergraphs whose Turan number is determined exactly. In an attempt to generalize Thomassen's result that the chromatic threshold of triangle-free graphs is 1/3, we prove bounds for the chromatic threshold of the family of 3-uniform hypergraphs not containing {abc, abd, cde}, the so-called generalized triangle. In order to prove upper bounds we introduce the concept of fibre bundles, which can be thought of as a hypergraph analogue of directed graphs. This leads to the notion of fibre bundle dimension, a structural property of fibre bundles that is based on the idea of Vapnik-Chervonenkis dimension in hypergraphs. Our lower bounds follow from explicit constructions, many of which use a hypergraph analogue of the Kneser graph. Using methods from extremal set theory, we prove that these Kneser hypergraphs have unbounded chromatic number. This generalizes a result of Szemeredi for graphs and might be of independent interest. Many open problems remain.","Title: Minimum Number of Monotone Subsequences of Length 4 in Permutations.\nYear: 2015\n\nAbstract: We show that for every sufficiently large n, the number of monotone subsequences of length four in a permutation on n points is at least [GRAPHICS] . Furthermore, we characterize all permutations on [n] that attain this lower bound. The proof uses the flag algebra framework together with some additional stability arguments. This problem is equivalent to some specific type of edge colourings of complete graphs with two colours, where the number of monochromatic K-4 is minimized. We show that all the extremal colourings must contain monochromatic K-4 only in one of the two colours. This translates back to permutations, where all the monotone subsequences of length four are all either increasing, or decreasing only.","Title: On the Decay of Crossing Numbers of Sparse Graphs\nYear: 2015\n\nAbstract: AbstractRichter and Thomassen proved that every graph has an edge e such that the crossing number crG-e of G-e is at least 2/5crG-O1. Fox and Cs. Tóth proved that dense graphs have large sets of edges proportional in the total number of edges whose removal leaves a graph with crossing number proportional to the crossing number of the original graph; this result was later strenghthened by Černý, Kynčl, and G. Tóth. These results make our understanding of the decay of crossing numbers in dense graphs essentially complete. In this article we prove a similar result for large sparse graphs in which the number of edges is not artificially inflated by operations such as edge subdivisions. We also discuss the connection between the decay of crossing numbers and expected crossing numbers, a concept recently introduced by Mohar and Tamon.","Title: Maximum density of induced 5-cycle is achieved by an iterated blow-up of 5-cycle.\nYear: 2016\n\nAbstract: Let C(n) denote the maximum number of induced copies of 5-cycles in graphs on n vertices. For n large enough, we show that C(n)=a⋅b⋅c⋅d⋅e+C(a)+C(b)+C(c)+C(d)+C(e), where a+b+c+d+e=n and a,b,c,d,e are as equal as possible.","Title: Book Embeddings of Regular Graphs\nYear: 2015\n\nAbstract: In the influential paper in which he proved that every graph with m edges can be embedded in a book with O(m(1/2)) pages, Malitz proved the existence of d-regular n-vertex graphs that require Omega(root dn 1/2-1d) pages. In view of the O(m(1/2)) bound, this last bound is tight when d > log n, and Malitz asked if it is also tight when d < log n. We answer negatively to this question by showing that there exist d-regular graphs that require Omega(n(1/2-1/2(d-1))) pages. In addition, we show that the bound O(m(1/2)) is not tight either for most d-regular graphs by proving that for each fixed d, with high probability the random d-regular graph can be embedded in o(m(1/2)) pages. We also give a simpler proof of Malitz's O(m(1/2)) bound and improve the proportionality constant.","Title: Applications of graph containers in the Boolean lattice.\nYear: 2016\n\nAbstract: We apply the graph container method to prove a number of counting results for the Boolean lattice P(n). In particular, we: i. Give a partial answer to a question of Sapozhenko estimating the number of t error correcting codes in P(n), and we also give an upper bound on the number of transportation codes; ii. Provide an alternative proof of Kleitman's theorem on the number of antichains in P(n) and give a two-coloured analogue; iii. Give an asymptotic formula for the number of (p, q)-tilted Sperner families in P(n); iv. Prove a random version of Katona's t-intersection theorem. In each case, to apply the container method, we first prove corresponding supersaturation results. We also give a construction which disproves two conjectures of Ilinca and Kahn on maximal independent sets and antichains in the Boolean lattice. A number of open questions are also given. (C) 2016 Wiley Periodicals, Inc.","Title: Triangle factors of graphs without large independent sets and of weighted graphs.\nYear: 2016\n\nAbstract: The classical Corradi-Hajnal theorem claims that every n-vertex graph G with delta(G) >= 2n/3 contains a triangle factor, when 3|n. In this paper we present two related results that both use the absorbing technique of Rodl, Rucinski and Szemeredi. Our main result determines the minimum degree condition necessary to guarantee a triangle factor in graphs with sublinear independence number. In particular, we show that if G is an n-vertex graph with alpha(G) = o(n) and delta(G) >= (1/2 + o(1)) n, then G has a triangle factor and this is asymptotically best possible. Furthermore, it is shown for every r that if every linear size vertex set of a graph G spans quadratically many edges, and delta(G) >= (1/2 + o(1)) n, then G has a K-r -factor for n sufficiently large. We also propose many related open problems whose solutions could show a relationship with Ramsey-Turan theory. Additionally, we also consider a fractional variant of the Corradi-Hajnal Theorem, settling a conjecture of Balogh-Kemkes-Lee-Young. Let t is an element of(0, 1) and w : E(K-n) -> [0, 1]. We call a triangle t-heavy if the sum of the weights on its edges is more than 3t. We prove that if 3|n and w is such that for every vertex v the sum of w(e) over edges e incident to v is at least (1+2t/3 + o(1)) n, then there are n/3 vertex disjoint heavy triangles in G. (C) 2016Wiley Periodicals, Inc. Random Struct. Alg., 49, 669-693, 2016","Title: Stability for Vertex Cycle Covers.\nYear: 2017\n\nAbstract: In 1996 Kouider and Lonc proved the following natural generalization of Dirac's Theorem: for any integer k >= 2, if G is an n-vertex graph with minimum degree at least n/k, then there are k - 1 cycles in G that together cover all the vertices. This is tight in the sense that there are n-vertex graphs that have minimum degree n/k - 1 and that do not contain k - 1 cycles with this property. A concrete example is given by I-n,I-k = K-n \\ K(k-1)n/k +1 (an edge-maximal graph on n vertices with an independent set of size (k - 1)(n/k) + 1). This graph has minimum degree n = k 1 and cannot be covered with fewer than k cycles. More generally, given positive integers k(1) ,..., k(r) summing to k, the disjoint union I-k1n/k,I-k1 + ... + I-krn/k.kr is an n-vertex graph with the same properties. In this paper, we show that there are no extremal examples that differ substantially from the ones given by this construction. More precisely, we obtain the following stability result: if a graph G has n vertices and minimum degree n early n/k, then it either contains k - 1 cycles covering all vertices, or else it must be close (in 'edit distance') to a subgraph of I-k1n/(k,k1) +...+ I-krn/k,I-kr, for some sequence k(1),...,k(r) of positive integers that sum to k. Our proof uses Szemeredi's Regularity Lemma and the related machinery.","Title: On Two Problems in Ramsey-Turán Theory.\nYear: 2017\n\nAbstract: Alon, Balogh, Keevash, and Sudakov proved that the (k-1)-partite Turan graph maximizes the number of distinct r-edge-colorings with no monochromatic K-k for all fixed k and r = 2, 3, among all n-vertex graphs. In this paper, we determine this function asymptotically for r = 2 among n-vertex graphs with a sublinear independence number. Somewhat surprisingly, unlike Alon, Balog, Keevash, and Sudakov's result, the extremal construction from Ramsey-Turan theory, as a natural candidate, does not maximize the number of distinct edge-colorings with no monochromatic cliques among all graphs with a sublinear independence number, even in the 2-colored case. In the second problem, we determine the maximum number of triangles asymptotically in an n-vertex K-k free graph G with alpha(G) = o(n). The extremal graphs have a similar structure to the extremal graphs for the classical Ramsey-Turan problem, i.e., when the number of edges is maximized.","Title: Packing chromatic number of cubic graphs.\nYear: 2018\n\nAbstract: A packing\n k-coloring of a graph G is a partition of V(G) into sets V1,…,Vk such that for each 1≤i≤k the distance between any two distinct x,y∈Vi is at least i+1. The packing chromatic number, χp(G), of a graph G is the minimum k such that G has a packing k-coloring. Sloper showed that there are 4-regular graphs with arbitrarily large packing chromatic number. The question whether the packing chromatic number of subcubic graphs is bounded appears in several papers. We answer this question in the negative. Moreover, we show that for every fixed k and g≥2k+2, almost every n-vertex cubic graph of girth at least g has the packing chromatic number greater than k.","Title: Triangle-Tilings in Graphs Without Large Independent Sets.\nYear: 2018\n\nAbstract: We study the minimum degree necessary to guarantee the existence of perfect and almost-perfect triangle-tilings in an n-vertex graph G with sublinear independence number. In this setting, we show that if delta (G) >= n/3+ o(n), then G has a triangle-tiling covering all but at most four vertices. Also, for every r >= 5, we asymptotically determine the minimum degree threshold for a perfect triangle-tiling under the additional assumptions that G is K-r-free and n is divisible by 3.","Title: Two results about the hypercube.\nYear: 2018\n\nAbstract: First we consider families in the hypercube Qn with bounded VC dimension. Frankl raised the problem of estimating the number m(n,k) of maximum families of VC dimension k. Alon, Moran and Yehudayoff showed that n(1+o(1))1k+1nk≤m(n,k)≤n(1+o(1))nk.We close the gap by showing that logm(n,k)=(1+o(1))nklogn. We also show how a bound on the number of induced matchings between two adjacent small layers of Qn follows as a corollary.","Title: Rainbow spanning trees in properly coloured complete graphs.\nYear: 2018\n\nAbstract: In this short note, we study pairwise edge-disjoint rainbow spanning trees in properly edge-coloured complete graphs, where a graph is rainbow if its edges have distinct colours. Brualdi and Hollingsworth conjectured that every Kn properly edge-coloured by n−1 colours has n∕2 edge-disjoint rainbow spanning trees. Kaneko, Kano and Suzuki later suggested this should hold for every properly edge-coloured Kn. Improving the previous best known bound, we show that every properly edge-coloured Kn contains Ω(n) pairwise edge-disjoint rainbow spanning trees.","Title: The number of hypergraphs without linear cycles.\nYear: 2019\n\nAbstract: The r-uniform linear k-cycle Ckr is the r-uniform hypergraph on k(r−1) vertices whose edges are sets of r consecutive vertices in a cyclic ordering of the vertex set chosen in such a way that every pair of consecutive edges share exactly one vertex. Here, we prove a balanced supersaturation result for linear cycles which we then use in conjunction with the method of hypergraph containers to show that for any fixed pair of integers r,k≥3, the number of Ckr-free r-uniform hypergraphs on n vertices is 2Θ(nr−1), thereby settling a conjecture due to Mubayi and Wang from 2017.","Title: Packing Chromatic Number of Subdivisions of Cubic Graphs.\nYear: 2019\n\nAbstract: A packing k-coloring of a graph G is a partition of V(G) into sets \n \n \n \n $$V_1,\\ldots ,V_k$$\n \n \n \n such that for each \n \n \n \n $$1\\le i\\le k$$\n \n \n \n the distance between any two distinct \n \n \n \n $$x,y\\in V_i$$\n \n \n \n is at least \n \n \n \n $$i+1$$\n \n \n \n . The packing chromatic number, \n \n \n \n $$\\chi _p(G)$$\n \n \n \n , of a graph G is the minimum k such that G has a packing k-coloring. For a graph G, let D(G) denote the graph obtained from G by subdividing every edge. The questions on the value of the maximum of \n \n \n \n $$\\chi _p(G)$$\n \n \n \n and of \n \n \n \n $$\\chi _p(D(G))$$\n \n \n \n over the class of subcubic graphs G appear in several papers. Gastineau and Togni asked whether \n \n \n \n $$\\chi _p(D(G))\\le 5$$\n \n \n \n for any subcubic G, and later Brešar, Klavžar, Rall and Wash conjectured this, but no upper bound was proved. Recently the authors proved that \n \n \n \n $$\\chi _p(G)$$\n \n \n \n is not bounded in the class of subcubic graphs G. In contrast, in this paper we show that \n \n \n \n $$\\chi _p(D(G))$$\n \n \n \n is bounded in this class, and does not exceed 8.","Title: Transitive triangle tilings in oriented graphs\nYear: 2017\n\nAbstract: In this paper, we prove an analogue of Corrádi and Hajnal's classical theorem. There exists n0 such that for every n∈3Z when n≥n0 the following holds. If G is an oriented graph on n vertices and every vertex has both indegree and outdegree at least 7n/18, then G contains a perfect transitive triangle tiling, which is a collection of vertex-disjoint transitive triangles covering every vertex of G. This result is best possible, as, for every n∈3Z, there exists an oriented graph G on n vertices without a perfect transitive triangle tiling in which every vertex has both indegree and outdegree at least ⌈7n/18⌉−1.","Title: Tilings in Randomly Perturbed Dense Graphs\nYear: 2019\n\nAbstract: A perfect H-tiling in a graph G is a collection of vertex-disjoint copies of a graph H in G that together cover all the vertices in G. In this paper we investigate perfect H-tilings in a random graph model introduced by Bohman, Frieze and Martin [6] in which one starts with a dense graph and then adds m random edges to it. Specifically, for any fixed graph H, we determine the number of random edges required to add to an arbitrary graph of linear minimum degree in order to ensure the resulting graph contains a perfect H-tiling with high probability. Our proof utilizes Szemeredi's Regularity Lemma [29] as well as a special case of a result of Komlos [18] concerning almost perfect H-tilings in dense graphs.","Title: Partitioning 2-edge-colored graphs by monochromatic paths and cycles\nYear: 2014\n\nAbstract: We present results on partitioning the vertices of 2-edge-colored graphs into monochromatic paths and cycles. We prove asymptotically the two-color case of a conjecture of Sárközy: the vertex set of every 2-edge-colored graph can be partitioned into at most 2 ( G ) monochromatic cycles, where ( G ) denotes the independence number of G . Another direction, emerged recently from a conjecture of Schelp, is to consider colorings of graphs with given minimum degree. We prove that apart from o (| V ( G )|) vertices, the vertex set of any 2-edge-colored graph G with minimum degree at least $$\\tfrac{{(1 + \\varepsilon )3|V(G)|}} {4}$$ can be covered by the vertices of two vertex disjoint monochromatic cycles of distinct colors. Finally, under the assumption that $$\\bar G$$ does not contain a fixed bipartite graph H , we show that in every 2-edge-coloring of G , | V ( G )| c ( H ) vertices can be covered by two vertex disjoint paths of different colors, where c ( H ) is a constant depending only on H . In particular, we prove that c ( C 4)=1, which is best possible.","Title: The Ramsey number of the Fano plane versus the tight path\nYear: 2020\n\nAbstract: The hypergraph Ramsey number of two 3-uniform hypergraphs G and H, denoted by R(G, H), is the least integer N such that every red-blue edge-coloring of the complete 3-uniform hypergraph on N vertices contains a red copy of G or a blue copy of H. The Fano plane F is the unique 3-uniform hypergraph with seven edges on seven vertices in which every pair of vertices is contained in a unique edge. There is a simple construction showing that R(G, F) >= 2(v(G) -1) +1 for every connected G. Hypergraphs G for which the equality R(G, F) = 2(v(G) - 1) + 1 holds are called F-good. Conlon posed the problem to determine all G that are F-good. In this short paper we make progress on this problem by proving that the tight path of length n is F-good.","Title: On the sizes of large subgraphs of the binomial random graph\nYear: 2022\n\nAbstract: We consider the binomial random graph G(n, p), where p is a constant, and answer the following two questions. First, given e(k) = p((k)(2))+ O(k), what is the maximum k such that a.a.s. the binomial random 2 graph G(n, p) has an induced subgraph with k vertices and e(k) edges? We prove that this maximum is not concentrated in any finite set (in contrast to the case of a small e(k)). Moreover, for every constant C > 0, with probability bounded away from 0, the size of the concentration set is bigger than C root n/ lnn, and, for every omega(n) -> infinity, a.a.s. it is smaller than omega root nn/ lnn. Second, given k > epsilon n, what is the maximum mu such that a.a.s. the set of sizes of k vertex subgraphs of G(n, p) contains a full interval of length mu? The answer is mu = Theta(root(n - k)n ln ((n)(k)) (c) 2021 Published by Elsevier B.V.","Title: Monochromatic connected matchings in 2-edge-colored multipartite graphs\nYear: 2022\n\nAbstract: A matching M $M$ in a graph G $G$ is connected if all the edges of M $M$ are in the same component of G $G$ . Following Luczak, there have been many results using the existence of large connected matchings in cluster graphs with respect to regular partitions of large graphs to show the existence of long paths and other structures in these graphs. We prove exact Ramsey-type bounds on the sizes of monochromatic connected matchings in 2-edge-colored multipartite graphs. In addition, we prove a stability theorem for such matchings.","Title: Counting r-graphs without forbidden configurations\nYear: 2022\n\nAbstract: One of the major problems in combinatorics is to determine the number of r-uniform hypergraphs (r-graphs) on n vertices which are free of certain forbidden structures. This problem dates back to the work of Erdős, Kleitman and Rothschild, who showed that the number of Kr-free graphs on n vertices is 2ex(n,Kr)+o(n2). Their work was later extended to forbidding graphs as induced subgraphs by Prömel and Steger.","Title: A strengthening of the Erdos-Szekeres Theorem\nYear: 2022\n\nAbstract: The Erdos-Szekeres Theorem stated in terms of graphs says that any red-blue coloring of the edges of the ordered complete graph Krs+1 contains a red copy of the monotone increasing path with r edges or a blue copy of the monotone increasing path with s edges. Although rs + 1 is the minimum number of vertices needed for this result, not all edges of Krs+1 are necessary. We characterize the subgraphs of Krs+1 with this coloring property as follows: they are exactly the subgraphs that contain all the edges of a graph we call the circus tent graph CT(r, s). Additionally, we use similar proof techniques to improve upon the bounds on the online ordered size Ramsey number of a path given by Perez-Gimenez, Pralat, and West. (C) 2021 Elsevier Ltd. All rights reserved."],"string":"[\n \"Title: Game domination number\\nYear: 2002\\n\\nAbstract: The game domination number of a (simple, undirected) graph is defined by the following game. Two players, A and D, orient the edges of the graph alternately until all edges are oriented. Player D starts the game, and his goal is to decrease the domination number of the resulting digraph, while A is trying to increase it. The game domination number of the graph G, denoted by γg(G), is the domination number of the directed graph resulting from this game. This is well defined if we suppose that both players follow their optimal strategies. We determine the game domination number for several classes of graphs and provide general inequalities relating it to other graph parameters.\",\n \"Title: Graph bootstrap percolation\\nYear: 2012\\n\\nAbstract: Graph bootstrap percolation is a deterministic cellular automaton which was introduced by Bollobás in 1968, and is defined as follows. Given a graph H, and a set \\\\documentclass{article}\\\\usepackage{mathrsfs}\\\\usepackage{amsmath, amssymb}\\\\pagestyle{empty}\\\\begin{document}\\\\begin{align*}G \\\\subset E(K_n)\\\\end{align*} \\\\end{document} **image** of initially ‘infected’ edges, we infect, at each time step, a new edge e if there is a copy of H in \\\\documentclass{article}\\\\usepackage{mathrsfs}\\\\usepackage{amsmath, amssymb}\\\\pagestyle{empty}\\\\begin{document}\\\\begin{align*}K_n\\\\end{align*} \\\\end{document} **image** such that e is the only not-yet infected edge of H. We say that G percolates in the H-bootstrap process if eventually every edge of \\\\documentclass{article}\\\\usepackage{mathrsfs}\\\\usepackage{amsmath, amssymb}\\\\pagestyle{empty}\\\\begin{document}\\\\begin{align*}K_n\\\\end{align*} \\\\end{document} **image** is infected. The extremal questions for this model, when H is the complete graph \\\\documentclass{article}\\\\usepackage{mathrsfs}\\\\usepackage{amsmath, amssymb}\\\\pagestyle{empty}\\\\begin{document}\\\\begin{align*}K_r\\\\end{align*} \\\\end{document} **image** , were solved (independently) by Alon, Kalai and Frankl almost thirty years ago. In this paper we study the random questions, and determine the critical probability \\\\documentclass{article}\\\\usepackage{mathrsfs}\\\\usepackage{amsmath, amssymb}\\\\pagestyle{empty}\\\\begin{document}\\\\begin{align*}p_c(n,K_r)\\\\end{align*} \\\\end{document} **image** for the \\\\documentclass{article}\\\\usepackage{mathrsfs}\\\\usepackage{amsmath, amssymb}\\\\pagestyle{empty}\\\\begin{document}\\\\begin{align*}K_r\\\\end{align*} \\\\end{document} **image** -process up to a poly-logarithmic factor. In the case \\\\documentclass{article}\\\\usepackage{mathrsfs}\\\\usepackage{amsmath, amssymb}\\\\pagestyle{empty}\\\\begin{document}\\\\begin{align*}r = 4\\\\end{align*} \\\\end{document} **image** we prove a stronger result, and determine the threshold for \\\\documentclass{article}\\\\usepackage{mathrsfs}\\\\usepackage{amsmath, amssymb}\\\\pagestyle{empty}\\\\begin{document}\\\\begin{align*}p_c(n,K_4)\\\\end{align*} \\\\end{document} **image** . © 2012 Wiley Periodicals, Inc. Random Struct. Alg., 2012 (Supported by CNPq bolsa de Produtividade em Pesquisa.)\",\n \"Title: The diameter game\\nYear: 2009\\n\\nAbstract: A large class of Positional Games are defined on the complete graph on n vertices. The players, Maker and Breaker, take the edges of the graph in turns, and Maker wins iff his subgraph has a given — usually monotone — property. Here we introduce the d-diameter game, which means that Maker wins iff the diameter of his subgraph is at most d. We investigate the biased version of the game; i.e., when the players may take more than one, and not necessarily the same number of edges, in a turn. Our main result is that we proved that the 2-diameter game has the following surprising property: Breaker wins the game in which each player chooses one edge per turn, but Maker wins as long as he is permitted to choose 2 edges in each turn whereas Breaker can choose as many as (1-9)n1-8-(lnn)3-8. In addition, we investigate d-diameter games for d ≥ 3. The diameter games are strongly related to the degree games. Thus, we also provide a generalization of the fair degree game for the biased case. © 2009 Wiley Periodicals, Inc. Random Struct. Alg., 2009\",\n \"Title: Almost all triangle-free triple systems are tripartite\\nYear: 2012\\n\\nAbstract: A triangle in a triple system is a collection of three edges isomorphic to {123,124,345}. A triple system is triangle-free if it contains no three edges forming a triangle. It is tripartite if it has a vertex partition into three parts such that every edge has exactly one point in each part. It is easy to see that every tripartite triple system is triangle-free. We prove that almost all triangle-free triple systems with vertex set [n] are tripartite. Our proof uses the hypergraph regularity lemma of Frankl and Rödl [13], and a stability theorem for triangle-free triple systems due to Keevash and the second author [15].\",\n \"Title: A jump to the bell number for hereditary graph properties\\nYear: 2005\\n\\nAbstract: A hereditary graph property is a collection of labeled graphs, closed under isomorphism and also under the taking of induced subgraphs. Its speed is the number of graphs in the property as a function of the number of vertices in the graph. Earlier research has characterized the speeds for hereditary graph properties up to n(1+o(1))n, and described the properties that have those smaller speeds. The present work provides the minimal speed possible above that range, and gives a structural characterization for properties which exhibit such speeds.More precisely, this paper sheds light on the jump from below n(1+o(1))n to the range that includes n(1+o(1))n. A measure jumps when there are two functions with positive distance such that the measure can take no values between those functions. A clean jump occurs when the bounding functions are well-defined and occur as possible values of the measure. It has been known for some time that the density of a graph jumps; recent work on hereditary graph properties has shown that speeds jump for properties with \\\"large\\\" or \\\"small\\\" speeds.The current work shows that there is a clean jump for properties with speed in a middle range. In particular, we show that when the speed of a hereditary graph property has speed greater than ncn for all c Bn, the nth Bell number. Equality occurs only for the property containing all disjoint unions of cliques or its complement.\",\n \"Title: On Avoider-Enforcer Games\\nYear: 2009\\n\\nAbstract: In the Avoider-Enforcer game on the complete graph $K_n$, the players (Avoider and Enforcer) each take an edge in turn. Given a graph property $\\\\mathcal{P}$, Enforcer wins the game if Avoider's graph has the property $\\\\mathcal{P}$. An important parameter is $\\\\tau_E(\\\\mathcal{P})$, the smallest integer $t$ such that Enforcer can win the game against any opponent in $t$ rounds. In this paper, let $\\\\mathcal{F}$ be an arbitrary family of graphs and $\\\\mathcal{P}$ be the property that a member of $\\\\mathcal{F}$ is a subgraph or is an induced subgraph. We determine the asymptotic value of $\\\\tau_E(\\\\mathcal{P})$ when $\\\\mathcal{F}$ contains no bipartite graph and establish that $\\\\tau_E(\\\\mathcal{P})=o(n^2)$ if $\\\\mathcal{F}$ contains a bipartite graph. The proof uses the game of JumbleG and the Szemerédi regularity lemma.\",\n \"Title: Hereditary properties of words\\nYear: 2005\\n\\nAbstract: Let P be a hereditary property of words, i.e., an infinite class of finite words such that every subword (block) of a word belonging to P is also in P. Extending the classical Morse-Hedlund theorem, we show that either P contains at least n+1 words of length n for every n or, for some N, it contains at most N words of length n for every n. More importantly, we prove the following quantitative extension of this result: if P has m <= n words of length n then, for every k >= n + m, it contains at most [(m + 1)/2] [(m + 1)/2] words of length k.\",\n \"Title: Erdős–ko–rado in random hypergraphs\\nYear: 2009\\n\\nAbstract: Let 3 ≤ k n/2. We prove the analogue of the Erdős–Ko–Rado theorem for the random k-uniform hypergraph Gk(n, p) when k n/2)1/3; that is, we show that with probability tending to 1 as n → ∞, the maximum size of an intersecting subfamily of Gk(n, p) is the size of a maximum trivial family. The analogue of the Erdős–Ko–Rado theorem does not hold for all p when k ≫ n1/3. We give quite precise results for k n1/2−ϵ. For larger k we show that the random Erdős–Ko–Rado theorem holds as long as p is not too small, and fails to hold for a wide range of smaller values of p. Along the way, we prove that every non-trivial intersecting k-uniform hypergraph can be covered by k2 − k + 1 pairs, which is sharp as evidenced by projective planes. This improves upon a result of Sanders [7]. Several open questions remain.\",\n \"Title: Large harmonic sets of noncrossing edges for n randomly labeled vertices in convex position\\nYear: 2007\\n\\nAbstract: Consider a set of n points in the plane in convex position,where each point has an integer label from {0,1,...,n - 1}.This naturally induces a labeling of the edges: each edge(i,j) is assigned the label i + j,modulo n. We propose algorithms for finding largenoncrossing harmonic matchings or paths, i.e., the matchingsor paths in which no two edges have the same label. When the pointlabels are chosen uniformly at random, and independently of eachother, our matching algorithm with high probability delivers anearlyperfect matching, a matching of size n/2 -O(n1/3 lnn). We show that, in sharpcontrast, a near-perfect path is unlikely: with high probabilitythe length of the longest path is below 0.96n. In a seriesof computer experiments, one of our algorithms invariably found apath of length above 0.76n, and the likely path length forour empirically second best algorithm is proved to be above0.66n. © 2006 Wiley Periodicals, Inc. Random Struct.Alg., 2007Part of this research was done at the The Ohio StateUniversity, Columbus, Ohio\",\n \"Title: The number of Ks;t-free graphs\\nYear: 2011\\n\\nAbstract: Abstract Denote by fn(H) the number of (labeled) H-free graphs on a,xed vertex set of size n. Erd} os conjectured that whenever H contains a cycle, fn(H) = 2, which is asymptotically sharp for all those values of s and t for which the order of magnitude of the Tur an number ex( n;Ks;t) is known. Our methods allow us to prove, among other things, that there is a positive constant c, such that almost all K2;t-free graphs of order n have at least 1=12 ex(n;K2;t) and at most (1 c) ex(n;K2;t) edges. Moreover, our results have some interesting applications to the study of some Ramsey- and Tur,an-type problems.\",\n \"Title: The number of Km, m-free graphs.\\nYear: 2011\\n\\nAbstract: A graph is called H-free if it contains no copy of H. Denote by fn(H) the number of (labeled) H-free graphs on n vertices. Erd} os conjectured (see (7)) that fn(H) 2(1+o(1))ex(n;H). This was rst shown to be true for cliques (9) and then Erd} os, Frankl and Rodl (8) proved it for all graphs H with (H) 3. For most bipartite H, the question is still wide open, and even the correct order of magnitude of log2fn(H) is not known. We prove that for every m 2, fn(Km;m) 2O(n 2 1=m) , extending the result of Kleitman and Winston (15) and answering a question of Erd} os. This bound is asymptotically sharp for m2 f2; 3g, and possibly for other values of m, for which the order of ex(n;Km;m) is not known, but it is conjectured to be ( n2 1=m). Our method also yields a bound on the number of Km;m-free graphs with xed order and size, extending the result of Furedi (11). Using this bound, we prove a relaxed version of a conjecture due to Haxell, Kohayakawa and Luczak (13) and show that almost all K3;3-free graphs of order n have more than 1=20 ex(n;K3;3) edges.\",\n \"Title: The number of graphs without forbidden subgraphs\\nYear: 2004\\n\\nAbstract: Given a family L of graphs, set p = p(L) = minL ⊂ L χ(L) - 1 and, for n ≥ 1, denote by P(n, L) the set of graphs with vertex set [n] containing no member of L as a subgraph, and write ex(n, L) for the maximal size of a member of P(n, L). Extending a result of Erdös, Frankl and Rödl (Graphs Combin. 2 (1986) 113), we prove that |P(n, L)|≤ 2½(1-1/p)n2 . O(n2-σ) for some constant γ = γ(L) 0, and characterize γ in terms of some related extremal graph problems. In fact, if ex(n, L) = O(n2 δ), then any γ\",\n \"Title: Note: A new short proof of a theorem of Ahlswede and Khachatrian\\nYear: 2008\\n\\nAbstract: Ahlswede and Khachatrian [R. Ahlswede, L.H. Khachatrian, The complete nontrivial-intersection theorem for systems of finite sets, J. Combin. Theory Ser. A 76 (1996) 121-138] proved the following theorem, which answered a question of Frankl and Furedi [P. Frankl, Z. Furedi, Nontrivial intersecting families, J. Combin. Theory Ser. A 41 (1986) 150-153]. Let 2==(t+1)(k-t+1). Suppose that F is a family of k-subsets of an n-set, every two of which have at least t common elements. If |@?\\\"F\\\"@?\\\"FF|\",\n \"Title: A sharp edge bound on the interval number of a graph\\nYear: 1999\\n\\nAbstract: The interval number of a graph G, denoted by i(G), is the least natural number t such that G is the intersection graph of sets, each of which is the union of at most t intervals. Here we settle a conjecture of Griggs and West about bounding i(G) in terms of e, that is, the number of edges in G. Namely, it is shown that i(G) ≤ $\\\\lceil\\\\sqrt{e}/2\\\\rceil$ + 1. It is also observed that the edge bound induces i(G) ≤ $\\\\sqrt{3 \\\\gamma(G)/2}+O(1)$, where &ggr;(G) is the genus of G. © 1999 John Wiley & Sons, Inc. J Graph Theory 32: 153–159, 1999\",\n \"Title: Towards a Weighted Version of the Hajnal-Szemerédi Theorem.\\nYear: 2013\\n\\nAbstract: For a positive integer r >= 2, a K-r-factor of a graph is a collection vertex-disjoint copies of K-r which covers all the vertices of the given graph. The celebrated theorem of Hajnal and Szemeredi asserts that every graph on n vertices with minimum degree at least (1 - 1/r)n contains a K-r-factor. In this note, we propose investigating the relation between minimum degree and existence of perfect K-r-packing for edge-weighted graphs. The main question we study is the following. Suppose that a positive integer r >= 2 and a real t is an element of [0, 1] is given. What is the minimum weighted degree of K-n that guarantees the existence of a K-r-factor such that every factor has total edge weight at least t((r)(2))? We provide some lower and upper bounds and make a conjecture on the asymptotics of the threshold as n goes to infinity.\",\n \"Title: On the First-Fit Chromatic Number of Graphs\\nYear: 2008\\n\\nAbstract: The first-fit chromatic number of a graph is the number of colors needed in the worst case of a greedy coloring. It is also called the Grundy number, which is defined to be the maximum number of classes in an ordered partition of the vertex set of a graph $G$ into independent sets $V_1, V_2, \\\\dots, V_k$ so that for each $1\\\\le i\",\n \"Title: Turán Densities of Some Hypergraphs Related to Kk+1k.\\nYear: 2012\\n\\nAbstract: Let B-i((k)) be the k-uniform hypergraph whose vertex set is of the form S boolean OR T, where vertical bar S vertical bar = i, vertical bar T vertical bar = k - 1, and S boolean AND T = circle divide, and whose edges are the k-subsets of S boolean OR T that contain either S or T. We derive upper and lower bounds for the Turan density of B-i((k)) that are close to each other as k -> infinity. We also obtain asymptotically tight bounds for the Turan density of several other infinite families of hypergraphs. The constructions that imply the lower bounds are derived from elementary number theory by probabilistic arguments, and the upper bounds follow from some results of de Caen, Sidorenko, and Keevash.\",\n \"Title: Long monotone paths in line arrangements\\nYear: 2004\\n\\nAbstract: We show how to construct an arrangement of n lines having a monotone path of length Ω(n 2 – (d/\\\\sqrt{log n})), where d > 0 is some constant, and thus nearly settle the long standing question on monotone path length in line arrangements.\",\n \"Title: The Positive Minimum Degree Game on Sparse Graphs.\\nYear: 2012\\n\\nAbstract: In this note we investigate a special form of degree games defined by D. Hefetz, M. Krivelevich, M. Stojakovic and T. Szabo [4]. Usually the board of a graph game is the edge set of K-n, the complete graph on n vertices. Maker and Breaker alternately claim an edge, and Maker wins if his edges form a subgraph with prescribed properties; here a certain minimum degree. In the special form the board is no longer the whole edge set of K-n, Maker first selects as few edges of K-n as possible in order to win, and our goal is to compute the necessary size of that board. Solving a question of [4], we show, using the discharging method, that the sharp bound is around 10n/7 for the positive minimum degree game.\",\n \"Title: Private computation using a PEZ dispenser\\nYear: 2003\\n\\nAbstract: We show how a (big) PEZ dispenser can be used by two or more players to compute a function of their inputs while hiding the values of the inputs from each other. In contrast to traditional approaches for solving this problem, ours does not require any use of randomness.\",\n \"Title: On the interval number of special graphs\\nYear: 2004\\n\\nAbstract: The interval number of a graph G is the least natural number t such that G is the intersection graph of sets, each of which is the union of at most t intervals, denoted by i(G). Griggs and West showed that $i(G)\\\\le \\\\lceil {1\\\\over 2} (d+1)\\\\rceil $. We describe the extremal graphs for that inequality when d is even. For three special perfect graph classes we give bounds on the interval number in terms of the independence number. Finally, we show that a graph needs to contain large complete bipartite induced subgraphs in order to have interval number larger than the random graph on the same number of vertices. © 2004 Wiley Periodicals, Inc. J Graph Theory 46: 241–253, 2004\",\n \"Title: k-Sets, Convex Quadrilaterals, and the Rectilinear Crossing Number of Kn\\nYear: 2006\\n\\nAbstract: AbstractWe use circular sequences to give an improved lower bound on the minimum number of (≤ k)-sets in a set of points in general position. We then use this to show that if S is a set of n points in general position, then the number ź(S) of convex quadrilaterals determined by the points in S is at least 0.37553(n4) + O(n3). This in turn implies that the rectilinear crossing number cr(Kn) of the complete graph Knis at least 0.37553(n4) + O(n3), and that Sylvester's Four Point Problem Constant is at least 0.37553. These improved bounds refine results recently obtained by Abrego and Fernandez-Merchant and by Lovasz, Vesztergombi, Wagner, and Welzl.\",\n \"Title: Disjoint representability of sets and their complements\\nYear: 2005\\n\\nAbstract: For a hypergraph H and a set S, the trace of H on S is the set of all intersections of edges of H with S. We will consider forbidden trace problems, in which we want to find the largest hypergraph H that does not contain some list of forbidden configurations as traces, possibly with some restriction on the number of vertices or the size of the edges in H. In this paper we will focus on combinations of three forbidden configurations: the k-singleton [k](1), the k-co-singleton [k](k-1) and the k-chain Ck={φ, {1}, [1, 2], ..., [1, k-1]}, where we write [k](l) for the set of all l-subsets of [k]={1, ..., k}. Our main topic is hypergraphs with no k-singleton or k-co-singleton trace. We obtain an exact result in the case k = 3, both for uniform and non-uniform hypergraphs, and classify the extremal examples. In the general case, we show that the number of edges in the largest r-uniform hypergraph with no k-singleton or k-co-singleton trace is of order rk-2. By contrast, Frankl and Pach showed that the number of edges in the largest r-uniform hypergraph with no k-singleton trace is of order rk-1. We also give a very short proof of the recent result of Balogh and Bollobás that there is a finite bound on the number of sets in any hypergraph without a k-singleton, k-co-singleton or k-chain trace, independently of the number of vertices or the size of the edges.\",\n \"Title: Majority bootstrap percolation on the hypercube\\nYear: 2009\\n\\nAbstract: In majority bootstrap percolation on a graph G, an infection spreads according to the following deterministic rule: if at least half of the neighbours of a vertex v are already infected, then v is also infected, and infected vertices remain infected forever. We say that percolation occurs if eventually every vertex is infected. The elements of the set of initially infected vertices, A ⊂ V(G), are normally chosen independently at random, each with probability p, say. This process has been extensively studied on the sequence of torus graphs [n]d, for n = 1,2,. . ., where d = d(n) is either fixed or a very slowly growing function of n. For example, Cerf and Manzo [17] showed that the critical probability is o(1) if d(n) ≤ log*n, i.e., if p = p(n) is bounded away from zero then the probability of percolation on [n]d tends to one as n → ∞. In this paper we study the case when the growth of d to ∞ is not excessively slow; in particular, we show that the critical probability is 1/2 + o(1) if d ≥ (log log n)2 log log log n, and give much stronger bounds in the case that G is the hypercube, [2]d.\",\n \"Title: Random disease on the square grid\\nYear: 1998\\n\\nAbstract: We introduce some generalizations of a nice combinatorial problem, the central notion of which is the so-called Disease Process. Let us color independently each square of an nxn chessboard black with a probability p(n); this is a random initial configuration of our process. Then we have a deterministic painting or expansion rule, and the question is the behavior of the disease process determined by this rule of spreading. In particular, how large must p(n) be to paint the whole chessboard black? The main result of this paper is the almost exact determination of the threshold function in the fundamental case of this Random Disease Problem. We include further investigations into the general randomized and deterministic cases. (C) 1998 John Wiley & Sons, Inc.\",\n \"Title: The Turán density of triple systems is not principal\\nYear: 2002\\n\\nAbstract: The Erdös-Stone-Simonovits Theorem implies that the Turán density of a family of graphs is the minimum of the Turán densities of the individual graphs from the family. It was conjectured by Mubayi and Rödl (J. Combin. Theory Ser. A, submitted) that this is not necessarily true for hypergraphs, in particular for triple systems. We give an example, which shows that their conjecture is true.\",\n \"Title: The unlabelled speed of a hereditary graph property\\nYear: 2009\\n\\nAbstract: A property of graphs is a collection P of graphs closed under isomorphism; we call Phereditary if it is closed under taking induced subgraphs. Given a property P, we write P^n for the set of graphs in P with vertex set [n]={1,...,n}, and P\\\"n for the isomorphism classes of graphs of order n that are in P. The cardinality |P^n| is the labelled speed of P and |P\\\"n| is the unlabelled speed. In the last decade numerous results have been proved about the labelled speeds of hereditary properties, with emphasis on the striking phenomenon that only certain speeds are possible: there are various pairs of functions (f(n),F(n)), with F(n)much larger than f(n), such that if the labelled speed is infinitely often larger than f(n) then it is also larger than F(n) for all sufficiently large values of n. Putting it concisely: the speed jumps from f(n) to F(n). Recent work on hereditary graph properties has shown that ''large'' and ''small'' labelled speeds of hereditary graph properties do jump. The aim of this paper is to study the unlabelled speed of a hereditary property, with emphasis on jumps. Among other results, we shall show that the unlabelled speed of a hereditary graph property is either of polynomial order or at least S(n), the number of ways of partitioning a set with n indistinguishable elements.\",\n \"Title: On the edge-bandwidth of graph products\\nYear: 2006\\n\\nAbstract: The edge-bandwidth of a graph G is the bandwidth of the line graph of G. We show asymptotically tight bounds on the edge-bandwidth of two-dimensional grids and tori, the product of two cliques and the n-dimensional hypercube.\",\n \"Title: Local resilience of almost spanning trees in random graphs\\nYear: 2011\\n\\nAbstract: We prove that for fixed integer D and positive reals α and γ, there exists a constant C0 such that for all p satisfying p(n) ≥ C0/n, the random graph G(n,p) asymptotically almost surely contains a copy of every tree with maximum degree at most D and at most (1 - α)n vertices, even after we delete a (1/2 - γ)-fraction of the edges incident to each vertex. The proof uses Szemerédi's regularity lemma for sparse graphs and a bipartite variant of the theorem of Friedman and Pippenger on embedding bounded degree trees into expanding graphs. © 2010 Wiley Periodicals, Inc. Random Struct. Alg., 2011 © 2011 Wiley Periodicals, Inc.\",\n \"Title: Corrádi and hajnal's theorem for sparse random graphs\\nYear: 2012\\n\\nAbstract: In this paper we extend a classical theorem of Corrádi and Hajnal into the setting of sparse random graphs. We show that if p(n) â聣芦 (log n/n)1/2, then asymptotically almost surely every subgraph of G(n, p) with minimum degree at least (2/3 + o(1))np contains a triangle packing that covers all but at most O(p-2) vertices. Moreover, the assumption on p is optimal up to the (log n)1/2 factor and the presence of the set of O(p-2) uncovered vertices is indispensable. The main ingredient in the proof, which might be of independent interest, is an embedding theorem which says that if one imposes certain natural regularity conditions on all three pairs in a balanced 3-partite graph, then this graph contains a perfect triangle packing.\",\n \"Title: On k-coverage in a mostly sleeping sensor network\\nYear: 2008\\n\\nAbstract: Sensor networks are often desired to last many times longer than the active lifetime of individual sensors. This is usually achieved by putting sensors to sleep for most of their lifetime. On the other hand, surveillance kind of applications require guaranteed k-coverage of the protected region at all times. As a result, determining the appropriate number of sensors to deploy that achieves both goals simultaneously becomes a challenging problem. In this paper, we consider three kinds of deployments for a sensor network on a unit square - a √n x √n grid, random uniform (for all n points), and Poisson (with density n). In all three deployments, each sensor is active with probability p, independently from the others. Then, we claim that the critical value of the function npπr2/log(np) is 1 for the event of k-coverage of every point. We also provide an upper bound on the window of this phase transition. Although the conditions for the three deployments are similar, we obtain sharper bounds for the random deployments than the grid deployment, which occurs due to the boundary condition. In this paper, we also provide corrections to previously published results for the grid deployment model. Finally, we use simulation to show the usefulness of our analysis in real deployment scenarios.\",\n \"Title: Hereditary properties of partitions, ordered graphs and ordered hypergraphs\\nYear: 2006\\n\\nAbstract: In this paper we use the Klazar-Marcus-Tardos method (see [A. Marcus, G. Tardos, Excluded permutation matrices and the Stanley-Wilf conjecture. J. Combin. Theory Ser. A 107 (2004) 153-160]) to prove that, if a hereditary property of partitions P has super-exponential speed, then, for every k- permutation π, P contains the partition of |2k| with parts {{i, π(i) + k} : i ∈ ⌈ k ⌉}. We also prove a similar jump, from exponential to factorial, in the possible speeds of monotone properties of ordered graphs, and of hereditary properties of ordered graphs not containing large complete, or complete bipartite ordered graphs.Our results generalize the Stanley-Wilf conjecture on the number of n-permutations avoiding a fixed permutation, which was recently proved by the combined results of Klazar [M. Klazar, The Füredi-Hajnal conjecture implies the Stanley-Wilf conjecture, in: D. Krob, A.A. Mikhalev, A.V. Mikhalev (Eds.), Formal Power Series and Algebraic Combinatorics, Springer, Berlin, 2000, pp. 250-255] and Marcus and Tardos [A. Marcus, G. Tardos, Excluded permutation matrices and the Stanley-Wilf conjecture, J. Combin. Theory Ser. A 107 (2004) 153-160]. Our main results follow from a generalization to ordered hypergraphs of the theorem of Marcus and Tardos.\",\n \"Title: The structure of almost all graphs in a hereditary property\\nYear: 2011\\n\\nAbstract: A hereditary property of graphs is a collection of graphs which is closed under taking induced subgraphs. The speed of P is the function n@?|P\\\"n|, where P\\\"n denotes the graphs of order n in P. It was shown by Alekseev, and by Bollobas and Thomason, that if P is a hereditary property of graphs then|P\\\"n|=2^(^1^-^1^/^r^+^o^(^1^)^)^(^n^2^), where r=r(P)@?N is the so-called 'colouring number' of P. However, their results tell us very little about the structure of a typical graph G@?P. In this paper we describe the structure of almost every graph in a hereditary property of graphs, P. As a consequence, we derive essentially optimal bounds on the speed of P, improving the Alekseev-Bollobas-Thomason Theorem, and also generalising results of Balogh, Bollobas and Simonovits.\",\n \"Title: An extended lower bound on the number of (k)-edges to generalized configurations of points and the pseudolinear crossing number of Kn.\\nYear: 2008\\n\\nAbstract: Recently, Aichholzer, García, Orden, and Ramos derived a remarkably improved lower bound for the number of (⩽k)-edges in an n-point set, and as an immediate corollary, an improved lower bound on the rectilinear crossing number of Kn. We use simple allowable sequences to extend all their results to the more general setting of simple generalized configurations of points and slightly improve the lower bound on Sylvester's constant from 0.37963 to 0.379688. In other words, we prove that the pseudolinear (and consequently the rectilinear) crossing number of Kn is at least 0.379688(n4)+Θ(n3). We use this to determine the exact pseudolinear crossing numbers of Kn and the maximum number of halving pseudolines in an n-point set for n=10,11,12,13,15,17,19, and 21. All these values coincide with the corresponding rectilinear numbers obtained by Aichholzer et al.\",\n \"Title: Excluding induced subgraphs: Critical graphs\\nYear: 2011\\n\\nAbstract: Determining the cardinality and describing the structure of H-free graphs is well-investigated for many graphs H. In the nineties, Prömel and Steger proved that for a graph H with chromatic number k + 1 almost all graphs not containing H as a subgraph are k-colorable if and only if H contains a color-critical edge. We strengthen the concept of H-free to induced subgraph containment, proving that if H has coloring number k + 1 then almost all H-free graphs can be covered by k graphs that are cliques or independent sets if and only if H is in some well-defined sense critical. The family of critical graphs includes C4 and C2k+1 for all k ≥ 3. © 2010 Wiley Periodicals, Inc. Random Struct. Alg., 2011 © 2011 Wiley Periodicals, Inc.\",\n \"Title: The penultimate rate of growth for graph properties\\nYear: 2001\\n\\nAbstract: Given a property P ofgraphs, write P n for the set of graphs with vertex set (n) having property P. We call |P n| the speed of P. Recent research has shown that the speed ofa monotone or hereditary property P can be a constant, polynomial, or exponential function of n, and the structure ofthe graphs in P can then be well described. Similarly, |P n| can be ofthe f orm\",\n \"Title: Bootstrap percolation on the random regular graph\\nYear: 2007\\n\\nAbstract: The k-parameter bootstrap percolation on a graph is a model of an interacting particle system, which can also be viewed as a variant of a cellular automaton growth process with threshold k >= 2. At the start, each of the graph vertices is active with probability p and inactive with probability I - p, independently of other vertices. Presence of active vertices triggers a bootstrap percolation process controlled by a recursive rule: an active vertex remains active forever, and a currently inactive vertex becomes active when at least k of its neighbors are active. The basic problem is to identify, for a given graph, p(-) and p(+) such that for p < p(-) (p > p(+) resp.) the probability that all vertices are eventually active is very close to 0 (1 resp.). The bootstrap percolation process is a deterministic process on the space of subsets of the vertex set, which is easy to describe but hard to analyze rigorously in general. We study the percolation on the random d-regular graph, d >= 3, via analysis of the process on the multigraph counterpart of the graph. Here, thanks to a \\\"principle of deferred decisions,\\\" the percolation dynamics is described by a surprisingly simple Markov chain. Its generic state is formed by the counts of currently active and nonactive vertices having various degrees of activation capabilities. We replace the chain by a deterministic dynamical system, and use its integrals to show-via exponential supermartingales-that the percolation process undergoes relatively small fluctuations around the deterministic trajectory. This allows us to show existence of the phase transition within an interval [p(-)(n),p(+)(n)], such that (1)p(+/-)(n) -> p* = 1 - min(y epsilon(0,1)) y/P(Bin(d - 1, 1 -y) < k); (2)p(+)(n)-p(-)(n) is of order n(-1/2) for k < d(-1), and n(-epsilon n), (epsilon(n) down arrow 0, epsilon(n) log n -> infinity), for k = d - 1. Note that p* is the same as the critical probability of the process on the corresponding infinite regular tree. (c) 2006 Wiley Periodicals, Inc.\",\n \"Title: Measures on monotone properties of graphs\\nYear: 2002\\n\\nAbstract: Given a monotone property P of graphs, write P n for the set of graphs with vertex set [ n ] having property P . Building on recent results in the enumeration of graphical properties, we prove numerous results about the structure of graphs in P n and the functions | P n | . We also examine the measure e P (n) , the maximum number of edges in a graph of P n .\",\n \"Title: Bootstrap percolation in high dimensions\\nYear: 2010\\n\\nAbstract: In r-neighbour bootstrap percolation on a graph G, a set of initially infected vertices A ⊂ V(G) is chosen independently at random, with density p, and new vertices are subsequently infected if they have at least r infected neighbours. The set A is said to percolate if eventually all vertices are infected. Our aim is to understand this process on the grid, [n]d, for arbitrary functions n = n(t), d = d(t) and r = r(t), as t → ∞. The main question is to determine the critical probability pc([n]d, r) at which percolation becomes likely, and to give bounds on the size of the critical window. In this paper we study this problem when r = 2, for all functions n and d satisfying d ≫ log n. The bootstrap process has been extensively studied on [n]d when d is a fixed constant and 2 ⩽ r ⩽ d, and in these cases pc([n]d, r) has recently been determined up to a factor of 1 + o(1) as n → ∞. At the other end of the scale, Balogh and Bollobás determined pc([2]d, 2) up to a constant factor, and Balogh, Bollobás and Morris determined pc([n]d, d) asymptotically if d ≥ (log log n)2+ϵ, and gave much sharper bounds for the hypercube. Here we prove the following result. Let λ be the smallest positive root of the equation \\\\[\\\\sum_{k=0}^\\\\infty \\\\frac{(-1)^k \\\\lambda^k}{2^{k^2-k} k!} = 0,\\\\] so λ ≈ 1.166. Then \\\\[ \\\\frac{16\\\\lambda}{d^2} \\\\biggl(1 + \\\\frac{\\\\log d}{\\\\sqrt{d}} \\\\biggr)\\\\: 2^{-2\\\\sqrt{d}} \\\\leq p_c([2]^d,2) \\\\leq \\\\frac{16\\\\lambda}{d^2} \\\\biggl(1 + \\\\frac{5(\\\\log d)^2}{\\\\sqrt{d}} \\\\biggr) \\\\: 2^{-2\\\\sqrt{d}}\\\\] if d is sufficiently large, and moreover \\\\[p_c\\\\bigl([n]^d,2 \\\\bigr) = \\\\bigl(4\\\\lambda + o(1) \\\\bigr) \\\\biggl(\\\\frac{n}{n-1} \\\\biggr)^2 \\\\, \\\\frac{1}{d^2} \\\\, 2^{-2\\\\sqrt{d \\\\log_2 n}}\\\\] as d → ∞, for every function n = n(d) with d ≫ log n.\",\n \"Title: Hereditary properties of combinatorial structures: Posets and oriented graphs\\nYear: 2007\\n\\nAbstract: A hereditary property of combinatorial structures is acollection of structures (e.g., graphs, posets) which is closedunder isomorphism, closed under taking induced substructures (e.g.,induced subgraphs), and contains arbitrarily large structures.Given a property P, we writeP\\\\ for the collection of distinct (i.e.,non-isomorphic) structures in a property P withn vertices, and call the function n ➛|P\\\\| the speed (or unlabeled speed) ofP. Also, we write P\\\\ for thecollection of distinct labeled structures in Pwith vertices labeled 1,...,n, and call the functionn ➛ |P\\\\| the labeled speedof P. The possible labeled speeds of a hereditaryproperty of graphs have been extensively studied, and the aim ofthis article is to investigate the possible speeds of othercombinatorial structures, namely posets and oriented graphs. Moreprecisely, we show that (for sufficiently large n), thelabeled speed of a hereditary property of posets is either 1, orexactly a polynomial, or at least 2n-1. We alsoshow that there is an initial jump in the possible unlabeled speedsof hereditary properties of posets, tournaments, and directedgraphs, from bounded to linear speed, and give a sharp lower boundon the possible linear speeds in each case. © 2007 WileyPeriodicals, Inc. J Graph Theory 56: 311332, 2007The work was done while Robert Morris was at the University ofMemphis.\",\n \"Title: Edit Distance and its Computation.\\nYear: 2008\\n\\nAbstract: In this paper, we provide a method for determining the asymptotic value of the maximum edit distance from a given hereditary property. This method permits the edit distance to be computed without using Szemeredi's Regularity Lemma directly. Using this new method, we are able to compute the edit distance from hereditary properties for which it was previously unknown. For some graphs H, the edit distance from Forb(H) is computed, where Forb(H) is the class of graphs which contain no induced copy of graph H. Those graphs for which we determine the edit distance asymptotically are H = K-a + E-b, an a-clique with b isolated vertices, and H = K-3,K-3, a complete bipartite graph. We also provide a graph, the first such construction, for which the edit distance cannot be determined just by considering partitions of the vertex set into cliques and cocliques. In the process, we develop weighted generalizations of Turan's theorem, which may be of independent interest.\",\n \"Title: Covering planar graphs with forests\\nYear: 2005\\n\\nAbstract: We study the problem of covering graphs with trees and a graph of bounded maximum degree. By a classical theorem of Nash-Williams, every planar graph can be covered by three trees. We show that every planar graph can be covered by two trees and a forest, and the maximum degree of the forest is at most 8. Stronger results are obtained for some special classes of planar graphs.\",\n \"Title: Bootstrap Percolation on Infinite Trees and Non-Amenable Groups\\nYear: 2006\\n\\nAbstract: Bootstrap percolation on an arbitrary graph has a random initial configuration, where each vertex is occupied with probability $p$, independently of each other, and a deterministic spreading rule with a fixed parameter $k$: if a vacant site has at least $k$ occupied neighbours at a certain time step, then it becomes occupied in the next step. This process is well studied on ${\\\\mathbb Z}^d$; here we investigate it on regular and general infinite trees and on non-amenable Cayley graphs. The critical probability is the infimum of those values of $p$ for which the process achieves complete occupation with positive probability. On trees we find the following discontinuity: if the branching number of a tree is strictly smaller than $k$, then the critical probability is 1, while it is $1-1/k$ on the $k$-ary tree. A related result is that in any rooted tree $T$ there is a way of erasing $k$ children of the root, together with all their descendants, and repeating this for all remaining children, and so on, such that the remaining tree $T'$ has branching number $\\\\mbox{\\\\rm br}(T')\\\\leq \\\\max\\\\{\\\\mbox{\\\\rm br}(T)-k,\\\\,0\\\\}$. We also prove that on any $2k$-regular non-amenable graph, the critical probability for the $k$-rule is strictly positive.\",\n \"Title: On the variance of Shannon products of graphs\\nYear: 2008\\n\\nAbstract: We study the combinatorial problem of finding an arrangement of distinct integers into the d-dimensional N-cube so that the maximal variance of the numbers on each ℓ-dimensional section is minimized. Our main tool is an inequality on the Laplacian of a Shannon product of graphs, which might be a subject of independent interest. We describe applications of the inequality to multiple description scalar quantizers (MDSQ), to get bounds on the bandwidth of products of graphs, and to balance edge-colorings of regular, d-uniform, d-partite hypergraphs.\",\n \"Title: The typical structure of graphs without given excluded subgraphs\\nYear: 2009\\n\\nAbstract: Let $ {\\\\cal L}$ be a finite family of graphs. We describe the typical structure of $ {\\\\cal L}$-free graphs, improving our earlier results (Balogh et al., J Combinat Theory Ser B 91 (2004), 1–24) on the Erdős–Frankl–Rödl theorem (Erdős et al., Graphs Combinat 2 (1986), 113–121), by proving our earlier conjecture that, for $ p = p ({\\\\cal L}) = {\\\\rm min}_L \\\\in {\\\\cal L} \\\\chi (L) - 1 $, the structure of almost all $ {\\\\cal L}$-free graphs is very similar to that of a random subgraph of the Turán graph Tn,p. The “similarity” is measured in terms of graph theoretical parameters of $ {\\\\cal L}$.© 2008 Wiley Periodicals, Inc. Random Struct. Alg., 2009\",\n \"Title: On the Tree Packing Conjecture.\\nYear: 2013\\n\\nAbstract: The Gyarfas tree packing conjecture states that any set of n - 1 trees T-1, T-2, ... , Tn-1 such that T-i has n - i + 1 vertices packs into K-n (for n large enough). We show that t = 1/10n(1/4) trees T-1, T-2, ... , T-t such that T-i has n - i + 1 vertices packs into Kn+1 (for n large enough). We also prove that any set of t = 1/10n(1/4) trees T-1, T-2, ... , T-t such that no tree is a star and T-i has n - i + 1 vertices packs into K-n (for n large enough). Finally, we prove that t = 1/4 n(1/3) trees T-1, T-2, ... , T-t such that T-i has n - i + 1 vertices packs into K-n as long as each tree has maximum degree at least 2n(2/3) (for n large enough). One of the main tools used in the paper is the famous spanning tree embedding theorem of Komlos, Sarkozy, and Szemeredi [Combin. Probab. Comput., 10 (2001), pp. 397-416].\",\n \"Title: Complete minors, independent sets, and chordal graphs.\\nYear: 2011\\n\\nAbstract: The Hadwiger number h(G) of a graph G is the maximum size of a complete minor of G. Hadwiger's Conjecture states that h(G) >= \\\\chi(G). Since \\\\chi(G) \\\\alpha(G) >= |V(G)|, Hadwiger's Conjecture implies that \\\\alpha(G) h(G) >= |V(G)|. We show that (2 \\\\alpha(G) - \\\\lceil log_t(t \\\\alpha(G)/2) \\\\rceil) h(G) \\\\geq |V(G)| where t is approximately 6.83. For graphs with \\\\alpha(G) \\\\geq 14, this improves on a recent result of Kawarabayashi and Song who showed (2 \\\\alpha(G) - 2) h(G) >= |V(G)| when \\\\alpha(G) >= 3.\",\n \"Title: Minimum Difference Representations of Graphs\\nYear: 2009\\n\\nAbstract: Define a k-minimum-difference-representation (k-MDR) of a graph G to be a family of sets $${\\\\{S(v): v\\\\in V(G)\\\\}}$$such that u and v are adjacent in G if and only if min{|S(u)−S(v)|, |S(v)−S(u)|} ≥ k. Define ρ min(G) to be the smallest k for which G has a k-MDR. In this note, we show that {ρ min(G)} is unbounded. In particular, we prove that for every k there is an n 0 such that for n n 0 ‘almost all’ graphs of order n satisfy ρ min(G) k. As our main tool, we prove a Ramsey-type result on traces of hypergraphs.\",\n \"Title: A remark on the number of edge colorings of graphs\\nYear: 2006\\n\\nAbstract: Fix a 2-coloring Hk + 1 of the edges of a complete graph Kk + 1. Let C(n, Hk + 1) denote the maximum possible number of distinct edge-colorings of a simple graph on n vertices with two colors, which contain no copy of Kk + 1 colored exactly as Hk + 1. It is shown that for every fixed k and all n n0(k), if in the colored graph Hk + 1 both colors were used, then C(n, Hk + 1) = 2tk(n), where tk(n) is the maximum possible number of edges of a graph on n vertices containing no K k + 1. The proofs are based on Szemerédi's Regularity Lemma together with the Simonovits Stability Theorem, and provide one of the growing number of examples of a precise result proved by applying the Regularity Lemma.\",\n \"Title: On the Chvátal-Erdös Triangle Game.\\nYear: 2011\\n\\nAbstract: Given a graph G and positive integers n and q, let G(G; n, q) be the game played on the edges of the complete graph K-n in which the two players, Maker and Breaker, alternately claim 1 and q edges, respectively. Maker's goal is to occupy all edges in some copy of G; Breaker tries to prevent it. In their seminal paper on positional games,Chvatal and Erdos proved that in the game G (K-3;n,q), Maker has a winning strategy if q < root 2n+2 - 5/2, and if q >= 2 root n, then Breaker has a winning strategy. In this note, we improve the latter of these bounds by describing a randomized strategy that allows Breaker to win the game G(K-3; n, q) whenever q >= (2 - 1/24)root n. Moreover, we provide additional evidence supporting the belief that this bound can be further improved to (root 2 + o(1))root n.\",\n \"Title: Linear algebra and bootstrap percolation\\nYear: 2012\\n\\nAbstract: In H-bootstrap percolation, a set A@?V(H) of initially 'infected' vertices spreads by infecting vertices which are the only uninfected vertex in an edge of the hypergraph H. A particular case of this is the H-bootstrap process, in which H encodes copies of H in a graph G. We find the minimum size of a set A that leads to complete infection when G and H are powers of complete graphs and H encodes induced copies of H in G. The proof uses linear algebra, a technique that is new in bootstrap percolation, although standard in the study of weakly saturated graphs, which are equivalent to (edge) H-bootstrap percolation on a complete graph.\",\n \"Title: Upper bounds on the size of 4- and 6-cycle-free subgraphs of the hypercube\\nYear: 2014\\n\\nAbstract: In this paper we modify slightly Razborov's flag algebra machinery to be suitable for the hypercube. We use this modified method to show that the maximum number of edges of a 4-cycle-free subgraph of the n-dimensional hypercube is at most 0.6068 times the number of its edges. We also improve the upper bound on the number of edges for 6-cycle-free subgraphs of the n-dimensional hypercube from 2-1 to 0.3755 times the number of its edges. Additionally, we show that if the n-dimensional hypercube is considered as a poset then the maximum vertex density of three middle layers in an induced subgraph without 4-cycles is at most 2.15121(n@?n/2@?).\",\n \"Title: On Perfect Packings in Dense Graphs.\\nYear: 2013\\n\\nAbstract: We say that a graph G has a perfect H-packing if there exists a set of vertex-disjoint copies of H which cover all the vertices in G. We consider various problems concerning perfect H-packings: Given n, r, D is an element of N, we characterise the edge density threshold that ensures a perfect K-r-packing in any graph G on n vertices and with minimum degree delta(G) >= D. We also give two conjectures concerning degree sequence conditions which force a graph to contain a perfect H-packing. Other related embedding problems are also considered. Indeed, we give a structural result concerning K-r-free graphs that satisfy a certain degree sequence condition.\",\n \"Title: Unavoidable Traces Of Set Systems\\nYear: 2005\\n\\nAbstract: Sauer, Shelah, Vapnik and Chervonenkis proved that if a set system on n vertices contains many sets, then the set system has full trace on a large set. Although the restriction on the size of the groundset cannot be lifted, Frankl and Pach found a trace structure that is guaranteed to occur in uniform set systems even if we do not bound the size of the groundset. In this note we shall give three sequences of structures such that every set system consisting of sufficiently many sets contains at least one of these structures with many sets.\",\n \"Title: The interval number of dense graphs\\nYear: 2002\\n\\nAbstract: The interval number of a graph G, denoted by i(G), is the least natural number t such that G is the intersection graph of sets, each of which is the union of at most t closed intervals. Most known bounds on i(G) are grossly excessive when G has more than half of the possible edges. A plausible remedy is to develop bounds on i(G) that are monotone decreasing in G. Here we bound i(G) in terms of e(G), the number of edges in the complement of G. We prove that i(G) ≤ ⌈½√e(G)⌉ + O(n/logn).\",\n \"Title: Almost all triple systems with independent neighborhoods are semi-bipartite\\nYear: 2011\\n\\nAbstract: The neighborhood of a pair of vertices u, v in a triple system is the set of vertices w such that uvw is an edge. A triple system H is semi-bipartite if its vertex set contains a vertex subset X such that every edge of H intersects X in exactly two points. It is easy to see that if H is semi-bipartite, then the neighborhood of every pair of vertices in H is an independent set. We show a partial converse of this statement by proving that almost all triple systems with vertex sets [n] and independent neighborhoods are semi-bipartite. Our result can be viewed as an extension of the Erdos-Kleitman-Rothschild theorem to triple systems. The proof uses the Frankl-Rodl hypergraph regularity lemma, and stability theorems. Similar results have recently been proved for hypergraphs with various other local constraints.\",\n \"Title: Online Ramsey games for triangles in random graphs\\nYear: 2010\\n\\nAbstract: In the online F-avoidance edge-coloring game with r colors, a graph on n vertices is generated by randomly adding a new edge at each stage. The player must color each new edge as it appears; the goal is to avoid a monochromatic copy of F. Let N\\\"0(F,r,n) be the threshold function for the number of edges that the player is asymptotically almost surely able to paint before he/she loses. Even when F=K\\\"3, the order of magnitude of N\\\"0(F,r,n) is unknown for r>=3. In particular, the only known upper bound is the threshold function for the number of edges in the offline version of the problem, in which an entire random graph on n vertices with m edges is presented to the player to be r edge-colored. We improve the upper bound for the online triangle-avoidance game with r colors, providing the first result that separates the online threshold function from the offline bound for r>=3. This supports a conjecture of Marciniszyn, Spohel, and Steger that the known lower bound is tight for cliques and cycles for all r.\",\n \"Title: On 2-Detour Subgraphs of the Hypercube\\nYear: 2008\\n\\nAbstract: A spanning subgraph H of a graph G is a 2-detour subgraph of G if for each x, y ∈ V(G), d H (x, y) ≤ d G (x, y) + 2. We prove a conjecture of Erdős, Hamburger, Pippert, and Weakley by showing that for some positive constant c and every n, each 2-detour subgraph of the n-dimensional hypercube Q n has at least clog2 n · 2n edges.\",\n \"Title: On the minimal degree implying equality of the largest triangle-free and bipartite subgraphs\\nYear: 2006\\n\\nAbstract: Erdos posed the problem of finding conditions on a graph G that imply t(G)=b(G), where t(G) is the largest number of edges in a triangle-free subgraph and b(G) is the largest number of edges in a bipartite subgraph. Let @d\\\"c be the least number so that any graph G on n vertices with minimum degree @d\\\"cn has t(G)=b(G). Extending results of Bondy, Shen, Thomasse and Thomassen we show that 0.75=\",\n \"Title: Large minors in graphs with given independence number\\nYear: 2011\\n\\nAbstract: A weakening of Hadwiger's conjecture states that every n-vertex graph with independence number @a has a clique minor of size at least n@a. Extending ideas of Fox (2010) [6], we prove that such a graph has a clique minor with at least n(2-c)@a vertices where c1/19.2.\",\n \"Title: The Klee–Minty random edge chain moves with linear speed\\nYear: 2007\\n\\nAbstract: An infinite sequence of 0's and 1's evolves by flipping each 1 to a 0 exponentially at rate 1. When a 1 flips, all bits to its right also flip. Starting from any configuration with finitely many 1's to the left of the origin, we show that the leftmost 1 moves right with bounded speed. Upper and lower bounds are given on the speed. A consequence is that a lower bound for the run time of the random-edge simplex algorithm on a Klee–Minty cube is improved so as to be quadratic, in agreement with the upper bound. © 2006 Wiley Periodicals, Inc. Random Struct. Alg., 2007\",\n \"Title: The fine structure of octahedron-free graphs\\nYear: 2011\\n\\nAbstract: This paper is one of a series of papers in which, for a family L of graphs, we describe the typical structure of graphs not containing any L@?L. In this paper, we prove sharp results about the case L={O\\\"6}, where O\\\"6 is the graph with 6 vertices and 12 edges, given by the edges of an octahedron. Among others, we prove the following results. (a) The vertex set of almost every O\\\"6-free graph can be partitioned into two classes of almost equal sizes, U\\\"1 and U\\\"2, where the graph spanned by U\\\"1 is a C\\\"4-free and that by U\\\"2 is P\\\"3-free. (b) Similar assertions hold when L is the family of all graphs with 6 vertices and 12 edges. (c) If H is a graph with a color-critical edge and @g(H)=p+1, then almost every sH-free graph becomes p-chromatic after the deletion of some s-1 vertices, where sH is the graph formed by s vertex disjoint copies of H. These results are natural extensions of theorems of classical extremal graph theory. To show that results like those above do not hold in great generality, we provide examples for which the analogs of our results do not hold.\",\n \"Title: The speed of hereditary properties of graphs\\nYear: 2000\\n\\nAbstract: Given a property P of graphs, write P-n for the set of graphs with vertex set [n] having property P. The growth or speed of a property P can be discussed in terms of the values of \\\\P-n\\\\. For properties with \\\\P-n\\\\ < n(n) hereditary properties are surprisingly well determined by their speeds. Sharpening results of E. R. Scheinerman and J. Zito (1994, J. Combin. Theory Ser. B 61, 16-39), we prove numerous results about the possible functions \\\\P-n\\\\ and describe in derail the properties exhibiting each type of growth. We also list minimal properties exhibiting each type of growth. (C) 2000 Academic Press.\",\n \"Title: The Erdős-Lovász Tihany conjecture for quasi-line graphs\\nYear: 2009\\n\\nAbstract: Erdos and Lovasz conjectured in 1968 that for every graph G with @g(G)@w(G) and any two integers s,t=2 with s+t=@g(G)+1, there is a partition (S,T) of the vertex set V(G) such that @g(G[S])=s and @g(G[T])=t. Except for a few cases, this conjecture is still unsolved. In this note we prove the conjecture for quasi-line graphs and for graphs with independence number 2.\",\n \"Title: Sizes of induced subgraphs of ramsey graphs\\nYear: 2009\\n\\nAbstract: An n-vertex graph G is c-Ramsey if it contains neither a complete nor an empty induced subgraph of size greater than c log n. Erdős, Faudree and Sós conjectured that every c-Ramsey graph with n vertices contains Ω(n5/2) induced subgraphs, any two of which differ either in the number of vertices or in the number of edges, i.e., the number of distinct pairs (|V(H)|, |E(H)|), as H ranges over all induced subgraphs of G, is Ω(n5/2). We prove an Ω(n2.3693) lower bound.\",\n \"Title: A new short proof of a theorem of Ahlswede and Khachatrian\\nYear: 2008\\n\\nAbstract: Ahlswede and Khachatrian [R. Ahlswede, L.H. Khachatrian, The complete nontrivial-intersection theorem for systems of finite sets, J. Combin. Theory Ser. A 76 (1996) 121–138] proved the following theorem, which answered a question of Frankl and Füredi [P. Frankl, Z. Füredi, Nontrivial intersecting families, J. Combin. Theory Ser. A 41 (1986) 150–153]. Let 2⩽t+1⩽k⩽2t+1 and n⩾(t+1)(k−t+1). Suppose that F is a family of k-subsets of an n-set, every two of which have at least t common elements. If |⋂F∈FF|<t, then |F|⩽(t+2)(n−t−2k−t−1)+(n−t−2k−t−2), and this is best possible. We give a new, short proof of this result. The proof in [R. Ahlswede, L.H. Khachatrian, The complete nontrivial-intersection theorem for systems of finite sets, J. Combin. Theory Ser. A 76 (1996) 121–138] requires the entire machinery of the proof of the complete intersection theorem, while our proof uses only ordinary compression and an earlier result of Wilson [R.M. Wilson, The exact bound in the Erdős–Ko–Rado theorem, Combinatorica 4 (1984) 247–257].\",\n \"Title: Intersecting families of discrete structures are typically trivial\\nYear: 2015\\n\\nAbstract: The study of intersecting structures is central to extremal combinatorics. A family of permutations F S n is t-intersecting if any two permutations in F agree on some t indices, and is trivial if all permutations in F agree on the same t indices. A k-uniform hypergraph is t-intersecting if any two of its edges have t vertices in common, and trivial if all its edges share the same t vertices.The fundamental problem is to determine how large an intersecting family can be. Ellis, Friedgut and Pilpel proved that for n sufficiently large with respect to t, the largest t-intersecting families in S n are the trivial ones. The classic Erd s-Ko-Rado theorem shows that the largest t-intersecting k-uniform hypergraphs are also trivial when n is large. We determine the typical structure of t-intersecting families, extending these results to show that almost all intersecting families are trivial. We also obtain sparse analogues of these extremal results, showing that they hold in random settings.Our proofs use the Bollobás set-pairs inequality to bound the number of maximal intersecting families, which can then be combined with known stability theorems. We also obtain similar results for vector spaces.\",\n \"Title: A random version of Sperner's theorem.\\nYear: 2014\\n\\nAbstract: Let P(n) denote the power set of [n], ordered by inclusion, and let P(n,p) be obtained from P(n) by selecting elements from P(n) independently at random with probability p. A classical result of Sperner [12] asserts that every antichain in P(n) has size at most that of the middle layer, (n⌊n/2⌋). In this note we prove an analogous result for P(n,p): If pn→∞ then, with high probability, the size of the largest antichain in P(n,p) is at most (1+o(1))p(n⌊n/2⌋). This solves a conjecture of Osthus [9] who proved the result in the case when pn/logn→∞. Our condition on p is best-possible. In fact, we prove a more general result giving an upper bound on the size of the largest antichain for a wider range of values of p.\",\n \"Title: On the number of K4-saturating edges.\\nYear: 2014\\n\\nAbstract: Let G be a K4-free graph; an edge in its complement is a K4-saturating edge if the addition of this edge to G creates a copy of K4. Erdős and Tuza conjectured that for any n-vertex K4-free graph G with ⌊n2/4⌋+1 edges, one can find at least (1+o(1))n216 K4-saturating edges. We construct a graph with only 2n233 K4-saturating edges. Furthermore, we prove that it is best possible, i.e., one can always find at least (1+o(1))2n233 K4-saturating edges in an n-vertex K4-free graph with ⌊n2/4⌋+1 edges.\",\n \"Title: Hypergraphs with Zero Chromatic Threshold\\nYear: 2016\\n\\nAbstract: Abstract Let F be an r-uniform hypergraph. The chromatic threshold of the family of F-free, r-uniform hypergraphs is the infimum of all non-negative reals c such that the subfamily of F-free, r-uniform hypergraphs H with minimum degree at least \\\\(c \\\\left( {\\\\begin{array}{c}|V(H)|\\\\\\\\ r-1\\\\end{array}}\\\\right) \\\\) has bounded chromatic number. The study of chromatic thresholds of various graphs has a long history, beginning with the early work of Erdős and Simonovits. One interesting problem, first proposed by Łuczak and Thomassé and then solved by Allen, Böttcher, Griffiths, Kohayakawa and Morris, is the characterization of graphs having zero chromatic threshold, in particular the fact that there are graphs with non-zero Turán density that have zero chromatic threshold. Here, we make progress on this problem for r-uniform hypergraphs, showing that a large class of hypergraphs have zero chromatic threshold in addition to exhibiting a family of constructions showing another large class of hypergraphs have non-zero chromatic threshold. Our construction is based on a particular product of the Bollobás–Erdős graphs defined earlier by the authors.\",\n \"Title: Rainbow triangles in three-colored graphs\\nYear: 2017\\n\\nAbstract: Erdős and Sós proposed the problem of determining the maximum number F(n) of rainbow triangles in 3-edge-colored complete graphs on n vertices. They conjectured that F(n)=F(a)+F(b)+F(c)+F(d)+abc+abd+acd+bcd, where a+b+c+d=n and a,b,c,d are as equal as possible. We prove that the conjectured recurrence holds for sufficiently large n. We also prove the conjecture for n=4k for all k≥0. These results imply that limF(n)(n3)=0.4, and determine the unique limit object. In the proof we use flag algebras combined with stability arguments.\",\n \"Title: A sharp bound on the number of maximal sum-free sets\\nYear: 2015\\n\\nAbstract: Cameron and Erdős [P. Cameron and P. Erdős, Notes on sum-free and related sets, Combin. Probab.Comput., 8, (1999), 95–107] asked whether the number of maximal sum-free sets in {1,…,n} is much smaller than the number of sum-free sets. In the same paper they gave a lower bound of 2⌊n/4⌋ for the number of maximal sum-free sets. We prove the following: For each 1≤i≤4, there is a constant Ci such that, given any n≡i mod 4, {1,…,n} contains (Ci+o(1))2n/4 maximal sum-free sets. Our proof makes use of container and removal lemmas of Green [B. Green, The Cameron-Erdős conjecture, Bull. London Math. Soc., 36, (2004), 769–778; B. Green, A Szemerédi-type regularity lemma in abelian groups, with applications, Geom. Funct. Anal., 15, (2005), 340–376], a structural result of Deshouillers, Freiman, Sós and Temkin [J. Deshouillers, G. Freiman, V. Sós and M. Temkin, On the structure of sumfree sets II, Astérisque, 258, (1999), 149–161] and a recent bound on the number of subsets of integers with small sumset by Green and Morris [B. Green, R. Morris, Counting sets with small sumset and applications, Combinatorica, to appear].\",\n \"Title: On the Chromatic Thresholds of Hypergraphs.\\nYear: 2016\\n\\nAbstract: Let F be a family of r-uniform hypergraphs. The chromatic threshold of F is the infimum of all non-negative reals c such that the subfamily of F comprising hypergraphs H with minimum degree at least c((broken vertical bar V(H)broken vertical bar)(r-1)) has bounded chromatic number. This parameter has a long history for graphs (r = 2), and in this paper we begin its systematic study for hypergraphs. Luczak and Thomasse recently proved that the chromatic threshold of the so-called near bipartite graphs is zero, and our main contribution is to generalize this result to r-uniform hypergraphs. For this class of hypergraphs, we also show that the exact Turan number is achieved uniquely by the complete (r + 1)-partite hypergraph with nearly equal part sizes. This is one of very few infinite families of non-degenerate hypergraphs whose Turan number is determined exactly. In an attempt to generalize Thomassen's result that the chromatic threshold of triangle-free graphs is 1/3, we prove bounds for the chromatic threshold of the family of 3-uniform hypergraphs not containing {abc, abd, cde}, the so-called generalized triangle. In order to prove upper bounds we introduce the concept of fibre bundles, which can be thought of as a hypergraph analogue of directed graphs. This leads to the notion of fibre bundle dimension, a structural property of fibre bundles that is based on the idea of Vapnik-Chervonenkis dimension in hypergraphs. Our lower bounds follow from explicit constructions, many of which use a hypergraph analogue of the Kneser graph. Using methods from extremal set theory, we prove that these Kneser hypergraphs have unbounded chromatic number. This generalizes a result of Szemeredi for graphs and might be of independent interest. Many open problems remain.\",\n \"Title: Minimum Number of Monotone Subsequences of Length 4 in Permutations.\\nYear: 2015\\n\\nAbstract: We show that for every sufficiently large n, the number of monotone subsequences of length four in a permutation on n points is at least [GRAPHICS] . Furthermore, we characterize all permutations on [n] that attain this lower bound. The proof uses the flag algebra framework together with some additional stability arguments. This problem is equivalent to some specific type of edge colourings of complete graphs with two colours, where the number of monochromatic K-4 is minimized. We show that all the extremal colourings must contain monochromatic K-4 only in one of the two colours. This translates back to permutations, where all the monotone subsequences of length four are all either increasing, or decreasing only.\",\n \"Title: On the Decay of Crossing Numbers of Sparse Graphs\\nYear: 2015\\n\\nAbstract: AbstractRichter and Thomassen proved that every graph has an edge e such that the crossing number crG-e of G-e is at least 2/5crG-O1. Fox and Cs. Tóth proved that dense graphs have large sets of edges proportional in the total number of edges whose removal leaves a graph with crossing number proportional to the crossing number of the original graph; this result was later strenghthened by Černý, Kynčl, and G. Tóth. These results make our understanding of the decay of crossing numbers in dense graphs essentially complete. In this article we prove a similar result for large sparse graphs in which the number of edges is not artificially inflated by operations such as edge subdivisions. We also discuss the connection between the decay of crossing numbers and expected crossing numbers, a concept recently introduced by Mohar and Tamon.\",\n \"Title: Maximum density of induced 5-cycle is achieved by an iterated blow-up of 5-cycle.\\nYear: 2016\\n\\nAbstract: Let C(n) denote the maximum number of induced copies of 5-cycles in graphs on n vertices. For n large enough, we show that C(n)=a⋅b⋅c⋅d⋅e+C(a)+C(b)+C(c)+C(d)+C(e), where a+b+c+d+e=n and a,b,c,d,e are as equal as possible.\",\n \"Title: Book Embeddings of Regular Graphs\\nYear: 2015\\n\\nAbstract: In the influential paper in which he proved that every graph with m edges can be embedded in a book with O(m(1/2)) pages, Malitz proved the existence of d-regular n-vertex graphs that require Omega(root dn 1/2-1d) pages. In view of the O(m(1/2)) bound, this last bound is tight when d > log n, and Malitz asked if it is also tight when d < log n. We answer negatively to this question by showing that there exist d-regular graphs that require Omega(n(1/2-1/2(d-1))) pages. In addition, we show that the bound O(m(1/2)) is not tight either for most d-regular graphs by proving that for each fixed d, with high probability the random d-regular graph can be embedded in o(m(1/2)) pages. We also give a simpler proof of Malitz's O(m(1/2)) bound and improve the proportionality constant.\",\n \"Title: Applications of graph containers in the Boolean lattice.\\nYear: 2016\\n\\nAbstract: We apply the graph container method to prove a number of counting results for the Boolean lattice P(n). In particular, we: i. Give a partial answer to a question of Sapozhenko estimating the number of t error correcting codes in P(n), and we also give an upper bound on the number of transportation codes; ii. Provide an alternative proof of Kleitman's theorem on the number of antichains in P(n) and give a two-coloured analogue; iii. Give an asymptotic formula for the number of (p, q)-tilted Sperner families in P(n); iv. Prove a random version of Katona's t-intersection theorem. In each case, to apply the container method, we first prove corresponding supersaturation results. We also give a construction which disproves two conjectures of Ilinca and Kahn on maximal independent sets and antichains in the Boolean lattice. A number of open questions are also given. (C) 2016 Wiley Periodicals, Inc.\",\n \"Title: Triangle factors of graphs without large independent sets and of weighted graphs.\\nYear: 2016\\n\\nAbstract: The classical Corradi-Hajnal theorem claims that every n-vertex graph G with delta(G) >= 2n/3 contains a triangle factor, when 3|n. In this paper we present two related results that both use the absorbing technique of Rodl, Rucinski and Szemeredi. Our main result determines the minimum degree condition necessary to guarantee a triangle factor in graphs with sublinear independence number. In particular, we show that if G is an n-vertex graph with alpha(G) = o(n) and delta(G) >= (1/2 + o(1)) n, then G has a triangle factor and this is asymptotically best possible. Furthermore, it is shown for every r that if every linear size vertex set of a graph G spans quadratically many edges, and delta(G) >= (1/2 + o(1)) n, then G has a K-r -factor for n sufficiently large. We also propose many related open problems whose solutions could show a relationship with Ramsey-Turan theory. Additionally, we also consider a fractional variant of the Corradi-Hajnal Theorem, settling a conjecture of Balogh-Kemkes-Lee-Young. Let t is an element of(0, 1) and w : E(K-n) -> [0, 1]. We call a triangle t-heavy if the sum of the weights on its edges is more than 3t. We prove that if 3|n and w is such that for every vertex v the sum of w(e) over edges e incident to v is at least (1+2t/3 + o(1)) n, then there are n/3 vertex disjoint heavy triangles in G. (C) 2016Wiley Periodicals, Inc. Random Struct. Alg., 49, 669-693, 2016\",\n \"Title: Stability for Vertex Cycle Covers.\\nYear: 2017\\n\\nAbstract: In 1996 Kouider and Lonc proved the following natural generalization of Dirac's Theorem: for any integer k >= 2, if G is an n-vertex graph with minimum degree at least n/k, then there are k - 1 cycles in G that together cover all the vertices. This is tight in the sense that there are n-vertex graphs that have minimum degree n/k - 1 and that do not contain k - 1 cycles with this property. A concrete example is given by I-n,I-k = K-n \\\\ K(k-1)n/k +1 (an edge-maximal graph on n vertices with an independent set of size (k - 1)(n/k) + 1). This graph has minimum degree n = k 1 and cannot be covered with fewer than k cycles. More generally, given positive integers k(1) ,..., k(r) summing to k, the disjoint union I-k1n/k,I-k1 + ... + I-krn/k.kr is an n-vertex graph with the same properties. In this paper, we show that there are no extremal examples that differ substantially from the ones given by this construction. More precisely, we obtain the following stability result: if a graph G has n vertices and minimum degree n early n/k, then it either contains k - 1 cycles covering all vertices, or else it must be close (in 'edit distance') to a subgraph of I-k1n/(k,k1) +...+ I-krn/k,I-kr, for some sequence k(1),...,k(r) of positive integers that sum to k. Our proof uses Szemeredi's Regularity Lemma and the related machinery.\",\n \"Title: On Two Problems in Ramsey-Turán Theory.\\nYear: 2017\\n\\nAbstract: Alon, Balogh, Keevash, and Sudakov proved that the (k-1)-partite Turan graph maximizes the number of distinct r-edge-colorings with no monochromatic K-k for all fixed k and r = 2, 3, among all n-vertex graphs. In this paper, we determine this function asymptotically for r = 2 among n-vertex graphs with a sublinear independence number. Somewhat surprisingly, unlike Alon, Balog, Keevash, and Sudakov's result, the extremal construction from Ramsey-Turan theory, as a natural candidate, does not maximize the number of distinct edge-colorings with no monochromatic cliques among all graphs with a sublinear independence number, even in the 2-colored case. In the second problem, we determine the maximum number of triangles asymptotically in an n-vertex K-k free graph G with alpha(G) = o(n). The extremal graphs have a similar structure to the extremal graphs for the classical Ramsey-Turan problem, i.e., when the number of edges is maximized.\",\n \"Title: Packing chromatic number of cubic graphs.\\nYear: 2018\\n\\nAbstract: A packing\\n k-coloring of a graph G is a partition of V(G) into sets V1,…,Vk such that for each 1≤i≤k the distance between any two distinct x,y∈Vi is at least i+1. The packing chromatic number, χp(G), of a graph G is the minimum k such that G has a packing k-coloring. Sloper showed that there are 4-regular graphs with arbitrarily large packing chromatic number. The question whether the packing chromatic number of subcubic graphs is bounded appears in several papers. We answer this question in the negative. Moreover, we show that for every fixed k and g≥2k+2, almost every n-vertex cubic graph of girth at least g has the packing chromatic number greater than k.\",\n \"Title: Triangle-Tilings in Graphs Without Large Independent Sets.\\nYear: 2018\\n\\nAbstract: We study the minimum degree necessary to guarantee the existence of perfect and almost-perfect triangle-tilings in an n-vertex graph G with sublinear independence number. In this setting, we show that if delta (G) >= n/3+ o(n), then G has a triangle-tiling covering all but at most four vertices. Also, for every r >= 5, we asymptotically determine the minimum degree threshold for a perfect triangle-tiling under the additional assumptions that G is K-r-free and n is divisible by 3.\",\n \"Title: Two results about the hypercube.\\nYear: 2018\\n\\nAbstract: First we consider families in the hypercube Qn with bounded VC dimension. Frankl raised the problem of estimating the number m(n,k) of maximum families of VC dimension k. Alon, Moran and Yehudayoff showed that n(1+o(1))1k+1nk≤m(n,k)≤n(1+o(1))nk.We close the gap by showing that logm(n,k)=(1+o(1))nklogn. We also show how a bound on the number of induced matchings between two adjacent small layers of Qn follows as a corollary.\",\n \"Title: Rainbow spanning trees in properly coloured complete graphs.\\nYear: 2018\\n\\nAbstract: In this short note, we study pairwise edge-disjoint rainbow spanning trees in properly edge-coloured complete graphs, where a graph is rainbow if its edges have distinct colours. Brualdi and Hollingsworth conjectured that every Kn properly edge-coloured by n−1 colours has n∕2 edge-disjoint rainbow spanning trees. Kaneko, Kano and Suzuki later suggested this should hold for every properly edge-coloured Kn. Improving the previous best known bound, we show that every properly edge-coloured Kn contains Ω(n) pairwise edge-disjoint rainbow spanning trees.\",\n \"Title: The number of hypergraphs without linear cycles.\\nYear: 2019\\n\\nAbstract: The r-uniform linear k-cycle Ckr is the r-uniform hypergraph on k(r−1) vertices whose edges are sets of r consecutive vertices in a cyclic ordering of the vertex set chosen in such a way that every pair of consecutive edges share exactly one vertex. Here, we prove a balanced supersaturation result for linear cycles which we then use in conjunction with the method of hypergraph containers to show that for any fixed pair of integers r,k≥3, the number of Ckr-free r-uniform hypergraphs on n vertices is 2Θ(nr−1), thereby settling a conjecture due to Mubayi and Wang from 2017.\",\n \"Title: Packing Chromatic Number of Subdivisions of Cubic Graphs.\\nYear: 2019\\n\\nAbstract: A packing k-coloring of a graph G is a partition of V(G) into sets \\n \\n \\n \\n $$V_1,\\\\ldots ,V_k$$\\n \\n \\n \\n such that for each \\n \\n \\n \\n $$1\\\\le i\\\\le k$$\\n \\n \\n \\n the distance between any two distinct \\n \\n \\n \\n $$x,y\\\\in V_i$$\\n \\n \\n \\n is at least \\n \\n \\n \\n $$i+1$$\\n \\n \\n \\n . The packing chromatic number, \\n \\n \\n \\n $$\\\\chi _p(G)$$\\n \\n \\n \\n , of a graph G is the minimum k such that G has a packing k-coloring. For a graph G, let D(G) denote the graph obtained from G by subdividing every edge. The questions on the value of the maximum of \\n \\n \\n \\n $$\\\\chi _p(G)$$\\n \\n \\n \\n and of \\n \\n \\n \\n $$\\\\chi _p(D(G))$$\\n \\n \\n \\n over the class of subcubic graphs G appear in several papers. Gastineau and Togni asked whether \\n \\n \\n \\n $$\\\\chi _p(D(G))\\\\le 5$$\\n \\n \\n \\n for any subcubic G, and later Brešar, Klavžar, Rall and Wash conjectured this, but no upper bound was proved. Recently the authors proved that \\n \\n \\n \\n $$\\\\chi _p(G)$$\\n \\n \\n \\n is not bounded in the class of subcubic graphs G. In contrast, in this paper we show that \\n \\n \\n \\n $$\\\\chi _p(D(G))$$\\n \\n \\n \\n is bounded in this class, and does not exceed 8.\",\n \"Title: Transitive triangle tilings in oriented graphs\\nYear: 2017\\n\\nAbstract: In this paper, we prove an analogue of Corrádi and Hajnal's classical theorem. There exists n0 such that for every n∈3Z when n≥n0 the following holds. If G is an oriented graph on n vertices and every vertex has both indegree and outdegree at least 7n/18, then G contains a perfect transitive triangle tiling, which is a collection of vertex-disjoint transitive triangles covering every vertex of G. This result is best possible, as, for every n∈3Z, there exists an oriented graph G on n vertices without a perfect transitive triangle tiling in which every vertex has both indegree and outdegree at least ⌈7n/18⌉−1.\",\n \"Title: Tilings in Randomly Perturbed Dense Graphs\\nYear: 2019\\n\\nAbstract: A perfect H-tiling in a graph G is a collection of vertex-disjoint copies of a graph H in G that together cover all the vertices in G. In this paper we investigate perfect H-tilings in a random graph model introduced by Bohman, Frieze and Martin [6] in which one starts with a dense graph and then adds m random edges to it. Specifically, for any fixed graph H, we determine the number of random edges required to add to an arbitrary graph of linear minimum degree in order to ensure the resulting graph contains a perfect H-tiling with high probability. Our proof utilizes Szemeredi's Regularity Lemma [29] as well as a special case of a result of Komlos [18] concerning almost perfect H-tilings in dense graphs.\",\n \"Title: Partitioning 2-edge-colored graphs by monochromatic paths and cycles\\nYear: 2014\\n\\nAbstract: We present results on partitioning the vertices of 2-edge-colored graphs into monochromatic paths and cycles. We prove asymptotically the two-color case of a conjecture of Sárközy: the vertex set of every 2-edge-colored graph can be partitioned into at most 2 ( G ) monochromatic cycles, where ( G ) denotes the independence number of G . Another direction, emerged recently from a conjecture of Schelp, is to consider colorings of graphs with given minimum degree. We prove that apart from o (| V ( G )|) vertices, the vertex set of any 2-edge-colored graph G with minimum degree at least $$\\\\tfrac{{(1 + \\\\varepsilon )3|V(G)|}} {4}$$ can be covered by the vertices of two vertex disjoint monochromatic cycles of distinct colors. Finally, under the assumption that $$\\\\bar G$$ does not contain a fixed bipartite graph H , we show that in every 2-edge-coloring of G , | V ( G )| c ( H ) vertices can be covered by two vertex disjoint paths of different colors, where c ( H ) is a constant depending only on H . In particular, we prove that c ( C 4)=1, which is best possible.\",\n \"Title: The Ramsey number of the Fano plane versus the tight path\\nYear: 2020\\n\\nAbstract: The hypergraph Ramsey number of two 3-uniform hypergraphs G and H, denoted by R(G, H), is the least integer N such that every red-blue edge-coloring of the complete 3-uniform hypergraph on N vertices contains a red copy of G or a blue copy of H. The Fano plane F is the unique 3-uniform hypergraph with seven edges on seven vertices in which every pair of vertices is contained in a unique edge. There is a simple construction showing that R(G, F) >= 2(v(G) -1) +1 for every connected G. Hypergraphs G for which the equality R(G, F) = 2(v(G) - 1) + 1 holds are called F-good. Conlon posed the problem to determine all G that are F-good. In this short paper we make progress on this problem by proving that the tight path of length n is F-good.\",\n \"Title: On the sizes of large subgraphs of the binomial random graph\\nYear: 2022\\n\\nAbstract: We consider the binomial random graph G(n, p), where p is a constant, and answer the following two questions. First, given e(k) = p((k)(2))+ O(k), what is the maximum k such that a.a.s. the binomial random 2 graph G(n, p) has an induced subgraph with k vertices and e(k) edges? We prove that this maximum is not concentrated in any finite set (in contrast to the case of a small e(k)). Moreover, for every constant C > 0, with probability bounded away from 0, the size of the concentration set is bigger than C root n/ lnn, and, for every omega(n) -> infinity, a.a.s. it is smaller than omega root nn/ lnn. Second, given k > epsilon n, what is the maximum mu such that a.a.s. the set of sizes of k vertex subgraphs of G(n, p) contains a full interval of length mu? The answer is mu = Theta(root(n - k)n ln ((n)(k)) (c) 2021 Published by Elsevier B.V.\",\n \"Title: Monochromatic connected matchings in 2-edge-colored multipartite graphs\\nYear: 2022\\n\\nAbstract: A matching M $M$ in a graph G $G$ is connected if all the edges of M $M$ are in the same component of G $G$ . Following Luczak, there have been many results using the existence of large connected matchings in cluster graphs with respect to regular partitions of large graphs to show the existence of long paths and other structures in these graphs. We prove exact Ramsey-type bounds on the sizes of monochromatic connected matchings in 2-edge-colored multipartite graphs. In addition, we prove a stability theorem for such matchings.\",\n \"Title: Counting r-graphs without forbidden configurations\\nYear: 2022\\n\\nAbstract: One of the major problems in combinatorics is to determine the number of r-uniform hypergraphs (r-graphs) on n vertices which are free of certain forbidden structures. This problem dates back to the work of Erdős, Kleitman and Rothschild, who showed that the number of Kr-free graphs on n vertices is 2ex(n,Kr)+o(n2). Their work was later extended to forbidding graphs as induced subgraphs by Prömel and Steger.\",\n \"Title: A strengthening of the Erdos-Szekeres Theorem\\nYear: 2022\\n\\nAbstract: The Erdos-Szekeres Theorem stated in terms of graphs says that any red-blue coloring of the edges of the ordered complete graph Krs+1 contains a red copy of the monotone increasing path with r edges or a blue copy of the monotone increasing path with s edges. Although rs + 1 is the minimum number of vertices needed for this result, not all edges of Krs+1 are necessary. We characterize the subgraphs of Krs+1 with this coloring property as follows: they are exactly the subgraphs that contain all the edges of a graph we call the circus tent graph CT(r, s). Additionally, we use similar proof techniques to improve upon the bounds on the online ordered size Ramsey number of a path given by Perez-Gimenez, Pralat, and West. (C) 2021 Elsevier Ltd. All rights reserved.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":95,"string":"95"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13251,"string":"13,251"}}},{"rowIdx":13252,"cells":{"question_id":{"kind":"string","value":"Shengli Zhang"},"question":{"kind":"string","value":"Generate an abstract for the title \"Zero-forcing based MIMO two-way relay with relay antenna selection: Transmission scheme and diversity analysis\" using the following items: Physical-layer network coding, MIMO, two-way relay, zero-forcing, antenna selection."},"answer":{"kind":"string","value":"The combination of physical-layer network coding (PNC) and multiple-input multiple-output (MIMO) is expected to improve the throughput of two-way relay network. In this paper, we propose a zero-forcing based MIMO two-way relay scheme in conjunction with a simple Max-Min relay antenna selection. This scheme solves the unpractical constraint encountered by many existing MIMO two-way relay schemes for application, which requires the relay to equip fewer antennas than the end node. Our scheme, on the other hand, benefits from the dedicated relay that has more antennas than the end node. A notable diversity advantage is obtained from judicious relay antenna selection. The reliability of the simple ZF based MIMO two-way relay is therefore improved. Of particular note, this paper extends our previous study to 1) support the more general application with non-binary PNC and 2) give a complete analysis on the attained end-to-end diversity with explicit theoretical result under i.i.d. Rayleigh fading channel."},"correct_answer":{"kind":"string","value":"The combination of physical-layer network coding (PNC) and multiple-input multiple-output (MIMO) is expected to improve the throughput of two-way relay network. In this paper, we propose a zero-forcing based MIMO two-way relay scheme in conjunction with a simple Max-Min relay antenna selection. This scheme solves the unpractical constraint encountered by many existing MIMO two-way relay schemes for application, which requires the relay to equip fewer antennas than the end node. Our scheme, on the other hand, benefits from the dedicated relay that has more antennas than the end node. A notable diversity advantage is obtained from judicious relay antenna selection. The reliability of the simple ZF based MIMO two-way relay is therefore improved. Of particular note, this paper extends our previous study to 1) support the more general application with non-binary PNC and 2) give a complete analysis on the attained end-to-end diversity with explicit theoretical result under i.i.d. Rayleigh fading channel."},"documents":{"kind":"list like","value":["Title: Applying physical-layer network coding in wireless networks\nYear: 2010\n\nAbstract: A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes, simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11). This paper shows that the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity-boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC) scheme to coordinate transmissions among nodes. In contrast to \"straightforward\" network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM) waves for equivalent coding operation. And in doing so, PNC can potentially achieve 100% and 50% throughput increases compared with traditional transmission and straightforward network coding, respectively, in 1D regular linear networks with multiple random flows. The throughput improvements are even larger in 2D regular networks: 200% and 100%, respectively.","Title: Five Kinds of Contradictory Relations and Opposite Relations in Inconsistent Knowledge\nYear: 2007\n\nAbstract: Inconsistent knowledge processing is an important problem in the study of knowledge, especially in fuzzy knowledge. In this paper, we consider that research of inconsistent knowledge should differentiate between contradictory concept and opposite concept. Based on this cognition, we discovered a sort of character on opposite concepts, which a pair of opposite concepts is fuzzy concepts if and only if there is medium concept between them, and five kinds of contradictory relations and opposite relations in the distinct knowledge and fuzzy knowledge were defined. Furthermore, we studied these relations using the medium predicate logic MF and the infinite-valued model Phi of MF, and obtained the conditions of processing these relations.","Title: Blind Known Interference Cancellation\nYear: 2013\n\nAbstract: This paper investigates interference-cancellation schemes at the receiver, in which the interference data, which is valid data intended for another receiver, is known a priori. The interference channel, however, is unknown (the blind part). Such a priori knowledge is common in wireless relay networks. For example, a relay could be relaying data that was previously transmitted by a node A. If node A is now receiving a signal from another node B, the interference from the relay is actually self-information known to node A. Besides the case of self-information, the node could also have overheard or received the interference data in a prior transmission by another node. Directly removing the known interference requires accurate estimate of the interference channel, which may be difficult in many situations. In this paper, we propose a novel scheme, Blind Known-Interference Cancellation (BKIC), to cancel known interference without interference channel information. BKIC consists of two steps. The first step combines adjacent symbols to cancel the interference, exploiting the fact that the channel coefficients are almost the same between successive symbols. After such interference cancellation, however, the signal of interest is distorted. The second step recovers the signal of interest amidst the distortion. We propose two algorithms for the critical second steps. The first algorithm (BKIC-S) is based on the principle of smoothing. It is simple and has near optimal performance in the slow fading scenario. The second algorithm (BKIC-RBP) is based on the principle of real-valued belief propagation. Since there is no loop in the Tanner graph, BKIC-RBP can achieve MAP-optimal performance with fast convergence, and has near interference-free performance even in the fast fading scenario. Both BKIC schemes outperform the traditional self-interference cancellation schemes that have perfect initial channel information by a large margin, while having lower complexities.","Title: Interference with Symbol-misalignment\nYear: 2009\n\nAbstract: This paper studies the impact of interference asynchrony among different\nlinks in a wireless network. Without deliberate coordination and cooperation\namong the active links, there is a naturally occurring misalignment between the\nsymbols of the targeted signal of a receiver and the symbols of the interfering\nsignals. Interestingly, we show that the interference asynchrony can actually\nimprove the BER performance, compared with the situation in which symbols of al\nsignals ay aligned. In particular, we show that symbol misalignment can\ndecrease the \"effective interference power\" and change the distribution of the\ninterfering signals, in a way that results in lower BER. To ensure that symbol\nmisalignment can be consistently attained, we propose two simple schemes that\nintroduce time-varying symbol offsets to obtain an \"average\" performance of\nrandom symbol misalignment. Notably, our schemes do not change the simple\nreceiver design structure; only the transmitters are modified in a minor way.","Title: Physical Layer Network Coding\nYear: 2007\n\nAbstract: A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11). The goal of this paper is to show how the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity- boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC) scheme to coordinate transmissions among nodes. In contrast to \"straightforward\" network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM) waves for equivalent coding operation. PNC can yield higher capacity than straightforward network coding when applied to wireless networks. We believe this is a first paper that ventures into EM-wave-based network coding at the physical layer and demonstrates its potential for boosting network capacity. PNC opens up a whole new research area because of its implications and new design requirements for the physical, MAC, and network layers of ad hoc wireless stations. The resolution of the many outstanding but interesting issues in PNC may lead to a revolutionary new paradigm for wireless ad hoc networking.","Title: Physical-Layer Network Coding: Tutorial, Survey, and Beyond\nYear: 2013\n\nAbstract: The concept of physical-layer network coding (PNC) was proposed in 2006 for application in wireless networks. Since then it has developed into a subfield of network coding with wide implications. The basic idea of PNC is to exploit the mixing of signals that occurs naturally when electromagnetic (EM) waves are superimposed on one another. In particular, at a receiver, the simultaneous transmissions by several transmitters result in the reception of a weighted sum of the signals. This weighted sum is a form of network coding operation by itself. Alternatively, the received signal could be transformed and mapped to other forms of network coding. Exploiting these facts turns out to have profound and fundamental ramifications. Subsequent works by various researchers have led to many new results in the domains of (1) wireless communication, (2) information theory, and (3) wireless networking. The purpose of this paper is fourfold. First, we give a brief tutorial on the basic concept of PNC. Second, we survey and discuss recent key results in the three aforementioned areas. Third, we examine a critical issue in PNC: synchronization. It has been a common belief that PNC requires tight synchronization. Recent results suggest, however, that PNC may actually benefit from asynchrony. Fourth, we propose that PNC is not just for wireless networks; it can also be useful in optical networks. We provide an example showing that the throughput of a passive optical network (PON) could potentially be raised by 100% with PNC.","Title: Differentiation and Processing of Contradictory Knowledge and Opposite Knowledge\nYear: 2007\n\nAbstract: How to distinguish and deal with inconsistent knowledge in knowledge processing is an outstanding problem, especially in fuzzy knowledge processing. In this paper, we proposes that knowledge research should differentiate between contradictory concepts and opposite concepts in knowledge, and that discovered a character of opposite concepts, there exists medium concept between the two opposite concepts if and only if opposite concepts are fuzzy concepts. The contradictory relations and opposite relations in distinct concepts and fuzzy concepts were confirmed, some properties of them were analyzed. Furthermore, we study these concepts and relations using the medium predicate logic MF and the infinite-valued model Phi of MF, and get processing conditions on the five kinds of contradictory relations and opposite relations in knowledge.","Title: Optimal Decoding Algorithm for Asynchronous Physical-Layer Network Coding\nYear: 2011\n\nAbstract: A key issue in physical-layer network coding (PNC) is how to deal with the asynchrony between signals transmitted by multiple transmitters. That is, symbols transmitted by different transmitters could arrive at the receiver with symbol misalignment as well as relative carrier-phase offset. In this paper, 1) we propose and investigate a general framework based on belief propagation (BP) that can effectively deal with symbol and phase asynchronies; 2) we show that for BPSK and QPSK modulations, our BP method can significantly reduce the SNR penalty due to asynchrony compared with prior methods; 3) we find that symbol misalignment makes the system performance less sensitive and more robust against carrier-phase offset. Observation 3) has the following practical implication. It is relatively easier to control symbol timing than carrier-phase offset. Our results indicate that if we could control the symbol offset in PNC, it would actually be advantageous to deliberately introduce symbol misalignment to desensitize the system to phase offset.","Title: The Capacity Of Two Way Relay Channel\nYear: 2008\n\nAbstract: This paper investigates the capacity of a wireless two-way relay channel in which two end nodes exchange information via a relay node. The capacity is defined in the information-theoretic sense as the maximum information exchange rate between the two end nodes. We give an upper bound of the capacity by applying the cut-set theorem. We prove that this upper bound can be approached in low SNR region using \"separated\" multiple access for uplinks from the end nodes to the relay in which the data from the end nodes are individually decoded at the relay; and network-coding broadcast for downlinks from the relay to the end nodes in which the relay mixes the information from end nodes before forwarding. We further prove that the capacity is approachable in high SNR region using physical-layer network coding (PNC) multiple access for uplinks, and network-coding broadcast for downlinks. From our proof and observations, we conjecture that the upper bound may be achieved with PNC in all SNR regions.","Title: Channel quantization based physical-layer network coding.\nYear: 2013\n\nAbstract: This paper studies a MIMO Two-Way Relay Channel (TWRC) where two single-antenna nodes communicate with each other through a K-antenna relay. In the uplink phase, the two end nodes send their respective information simultaneously to the relay, and it is usually regarded as a virtual MIMO system. With traditional VBLAST MIMO detection, the maximum achievable diversity is K - 1. By noting that only the network coded packet, rather than the two individual packets, is needed at the relay node in TWRC, we propose a channel quantized physical-layer network coding (CQ-PNC) scheme based on VBLAST to achieve the full diversity K. Specifically, relay first uses QR decomposition to convert the K received signals into two valid signals, the first signal is a weighted summation of the two end nodes' symbols and the second signal is a scaled version of one end node's symbol. The relay first quantizes the first signal with the channel coefficient of one node and estimate the Gaussian integer summation of the two end nodes' information. At the same time, the quantization error is further canceled with the help of the second signal. After that, we adaptively map the Gaussian integer weighted summation to the network coding form. Moreover, we theoretically prove that our CQ-PNC can achieve the maximum diversity K. Finally, the numerical simulation shows that CQ-PNC performs within 2dB gap from the theoretical bound. © 2013 IEEE.","Title: Physical Layer Network Coding with Multiple Antennas\nYear: 2010\n\nAbstract: The two-phase MIMO NC (network coding) scheme can be used to boost the throughput in a two-way relay channel in which nodes are equipped with multiple antennas. The obvious strategy is for the relay node to extract the individual packets from the two end nodes and mix the two packets to form a network-coded packet. In this paper, we propose a new scheme called MIMO PNC (physical network coding), in which the relay extracts the summation and difference of the two end packets and then converts them to the network-coded form. MIMO PNC is a natural combination of the single-antenna PNC scheme and the linear MIMO detection scheme. The advantages of MIMO PNC are many. First, it removes the stringent carrier-phase requirement in single-antenna PNC. Second, it is linear in complexity with respect to the constellation size and the number of simultaneous data streams in MIMO. Simulation shows that MIMO PNC outperforms the straightforward MIMO NC significantly under random Rayleigh fading channel. Based on our analysis, we further conjecture that MIMO PNC outperforms MIMO NC under all possible realizations of the channel.","Title: Collision Resolution by Exploiting Symbol Misalignment\nYear: 2008\n\nAbstract: This paper presents CRESM, a novel collision resolution method for decoding\ncollided packets in random-access wireless networks. In a collision,\noverlapping signals from several sources are received simultaneously at a\nreceiver. CRESM exploits symbol misalignment among the overlapping signals to\nrecover the individual packets. CRESM can be adopted in 802.11 networks without\nmodification of the transmitter design; only a simple DSP technique is needed\nat the receiver to decode the overlapping signals. Our simulations indicate\nthat CRESM has better BER performance than the simplistic Successive\nInterference Cancellation (SIC) technique that treats interference as noise,\nfor almost all SNR regimes. The implication of CRESM for random-access\nnetworking is significant: in general, using CRESM to resolve collisions of up\nto n packets, network throughput can be boosted by more than n times if the\ntransmitters are allowed to transmit more aggressively in the MAC protocol.","Title: A novel type of fuzzy propositional logic\nYear: 2013\n\nAbstract: Negative knowledge is considered as important as positive knowledge in the theory of knowledge processing, which is investigated by many researchers. In this paper, a novel fuzzy propositional logic calculus system equipped with three kinds of negations, say contradictory, opposite and medium negation, is established, and the soundness theorem of the system is proved.","Title: Blind Known Interference Cancellation with parallel real valued belief propagation algorithm\nYear: 2012\n\nAbstract: This paper investigates interference-cancellation schemes at the receiver, in which the original data of the interference is known a priori. Such a priori knowledge is common in wireless relay networks. Directly removing the known interference requires accurate estimate of the interference channel, which may be difficult in many situations. In [1], we proposed a novel scheme, Blind Known Interference Cancellation (BKIC), for blind cancellation of known interference without interference channel information. BKIC consists of two steps. The first step combines adjacent symbols to cancel the interference, exploiting the fact that the channel coefficients are almost the same between successive symbols. After such interference cancellation, however, the signal of interest is also distorted. The second step recovers the signal of interest amidst the distortion. Two schemes for the second step, BKIC-S and successive BKIC-RBP, were proposed in [1]. BKIC-S removes distortion by smoothing while BKIC-RBP does so using a real-value belief propagation algorithm. Although successive BKIC-RBP performs well and is superior to BKIC-S, it requires a long processing time proportional to the packet length. To overcome this problem, this paper proposes a parallel BKIC-RBP algorithm. Parallel BKIC-RBP has similar performance as successive BKIC-RBP. It has the advantage of being amenable to parallel implementation with a much shorter processing time.","Title: Synchronization Analysis in Physical Layer Network Coding\nYear: 2010\n\nAbstract: Physical-layer Network Coding (PNC) makes use of the additive nature of the\nelectromagnetic (EM) waves to apply network coding arithmetic at the physical\nlayer. With PNC,the destructive effect of interference in wireless networks is\neliminated and the capacity of networks can be boosted significantly. This\npaper addresses a key outstanding issue in PNC: synchronization among\ntransmitting nodes. We first investigate the impact of imperfect\nsynchronization (i.e., finite synchronization errors) in a 3-node network. It\nis shown that with QPSK modulation, PNC still yields significantly higher\ncapacity than straightforward network coding when there are synchronization\nerrors. Significantly, this remains to be so even in the extreme case when\nsynchronization is not performed at all. Moving beyond a 3-node network, we\npropose and investigate a synchronization scheme for PNC in a general chain\nnetwork. At last, numerical simulation verifies that PNC is robust to\nsynchronization errors. In particular, for the mutual information performance,\nthere is about 0.5dB loss without time synchronization and there is at most 2dB\nloss without phase synchronization.","Title: Capacity of Two-Way Relay Channel\nYear: 2009\n\nAbstract: This paper investigates the capacity of a wireless two-way relay channel in which two end nodes exchange information via a\n relay node. The capacity is defined in the information-theoretic sense as the maximum information exchange rate between the\n two end nodes. We give an upper bound of the capacity by applying the cut-set theorem. We prove that this upper bound can\n be approached in low SNR region using “separated” multiple access for uplinks from the end nodes to the relay in which the\n data from the end nodes are individually decoded at the relay; and network-coding broadcast for downlinks from the relay to\n the end nodes in which the relay mixes the information from end nodes before forwarding. We further prove that the capacity\n is approachable in high SNR region using physical-layer network coding (PNC) multiple access for uplinks, and network-coding\n broadcast for downlinks. From our proof and observations, we conjecture that the upper bound may be achieved with PNC in all\n SNR regions.","Title: Channel coding and decoding in a MIMO TWRC with physical-layer network coding\nYear: 2012\n\nAbstract: In this paper, we propose a joint design of MIMO-PNC (multiple-input multiple-output physical layer network coding) and channel decoding in two way relay channels. The paper shows that if we adopt the same Repeat Accumulate (RA) channel code at the two end nodes, there would be a compatible decoder at the relay which can transform the received superimposed packet to the network coding form of the two packets efficiently. Specifically, we redesign the belief propagation decoding algorithm of the RA code for traditional point-to-point channel to suit the need of the MIMO PNC channel. The simulation results show that our new scheme outperforms the previously proposed schemes which combined MIMO NC with channel coding/decoding significantly in terms of BER with little added complexity.","Title: Joint Design of Physical-Layer Network Coding and Channel Coding\nYear: 2008\n\nAbstract: Physical-layer Network Coding (PNC) can significantly improve the throughput of wireless two way relay channel (TWRC) by allowing the two end nodes to transmit messages to the relay simultaneously. To achieve reliable communication, channel coding could be applied on top of PNC. This paper investigates link-by-link channel-coded PNC, in which a critical process at the relay is to transform the superimposed channel-coded packets received from the two end nodes plus noise, 3 1 2 3 Y X X W","Title: Multi-pair physical layer network coding with beamforming systems.\nYear: 2012\n\nAbstract: In this paper, we propose a spatial multi-user physical layer network coding (SM-PNC) scheme by jointly utilizing PNC and beamforming techniques in a multi-user one relay communication system. The relay is equipped with multiple antennas and each user node is only equipped with a single antenna. The two way relay transmission consists of two phases. In multiple access phase, the summation and difference of the user data are combined with log likelihood ratio combination, which has a superior robustness to the traditional separation detection under any channel condition; In broadcast phase, optimal adaptive select-group broadcasting scheme is proposed, which can efficiently overcome the problem of near interference in beamforming. Simulation results validate the ability of the proposed algorithms. © 2012 IEEE.","Title: A Refined Convergence Condition for Iterative Waterfilling Algorithm\nYear: 2014\n\nAbstract: In a multi-user spectrum optimization problem, iterative waterfilling is normally used as a distributed power allocation algorithm. In this letter, we derive a new sufficient condition for its convergence, which is more relaxed than previous conditions. Our result strengthens the fact that the convergence of iterative waterfilling is dependent on signal-to-noise ratio (SNR), and that convergence is much easier in the low SNR regime.","Title: One New Interpretation of Infinite Valued for Medium Proposition Logic\nYear: 2009\n\nAbstract: Since three-valued model of medium logic (ML) was established, ML has been affirmed one kind of three-valued logic by many scholars. However, for medium proposition logic(MP) being as kernel theory of ML, Pan gave out a model of infinite valued of MP, on which based, proved the theorems of completeness and reliability. Based on this, this paper sets up a new model of infinite valued of MP whose truth valued range is [0, λ)∪(λ,1](λ∈ (0. 5, 1)), some properties of the model are investigated, and the theorems of completeness and reliability are proved, also. This new model not only further shows that “medium proposition logic is one kind of three valued logic” is incorrect, but also rightly depicts the medium principle which is basic idea of MP, and offered foundation for MP used in more other fields.","Title: Secrecy rate study in two-hop relay channel with finite constellations\nYear: 2013\n\nAbstract: Two-hop security communication with an eavesdropper in wireless environment is a hot research direction. The basic idea is that the destination, simultaneously with the source, sends a jamming signal to interfere the eavesdropper near to or co-located with the relay. Similar as physical layer network coding, the friendly jamming signal will prevent the eavesdropper from detecting the useful information originated from the source and will not affect the destination on detecting the source information with the presence of the known jamming signal. However, existing investigations are confined to Gaussian distributed signals, which are seldom used in real systems. When finite constellation signals are applied, the behavior of the secrecy rate becomes very different. For example, the secrecy rate depends on phase difference between the input signals with finite constellations, which is not observed with Gaussian signals. In this paper, we investigate the secrecy capacity and derive its upper bound for the two-hop relay model, by assuming an eavesdropper near the relay and the widely used M-PSK modulation. With our upper bound, the best and worst phase differences in high SNR region are then given. Numerical studies verify our analysis and show that the derived upper bound is relatively tight.","Title: Joint Design Of Network Coding And Channel Decoding For Wireless Networks\nYear: 2007\n\nAbstract: Network coding has been receiving much attention recently for its ability to improve network throughput and enhance network robustness. In this paper, we investigate the design of network coding in wireless networks and propose a combined low complexity network coding and channel decoding scheme. We analyze the capacity of the proposed scheme for both the binary symmetric channel (BSC) and AWGN channel and show that it can achieve almost the same channel capacity as traditional network coding with a small degradation in the system bit error rate (BER) performance while achieving almost 50% complexity reduction. It is also shown that the proposed network coding design can be applied in wireless cooperative networks.","Title: Synchronization Analysis for Wireless TWRC Operated with Physical-layer Network Coding\nYear: 2013\n\nAbstract: Physical-layer network coding (PNC) makes use of the additive nature of the electromagnetic waves to apply network coding arithmetic at the physical layer. With PNC, the destructive effect of interference in wireless networks is eliminated and the capacity of networks can be boosted significantly. This paper addresses a key outstanding issue in PNC: synchronization among transmitting nodes. We first investigate the impact of imperfect synchronization in a 3-node network with a straightforward detection scheme. It is shown that with QPSK modulation, PNC on average still yields significantly higher capacity than straightforward network coding when there are synchronization errors. Significantly, this remains to be so even in the extreme case when synchronization is not performed at all. Moving beyond a 3-node network, we propose and investigate a synchronization scheme for PNC in a general chain network. And we argue that if the synchronization errors can be bounded in the 3-node case, they can also be bounded in the general N-node case. Lastly, we present simulation results showing that PNC is robust to synchronization errors. In particular, for the mutual information performance, there is about 2 dB loss without phase or symbol synchronization.","Title: Soft network coding in wireless two-way relay channels.\nYear: 2008\n\nAbstract: Application of network coding in wireless two-way relay channels (TWRC) has received much attention recently because its ability to improve throughput significantly. In traditional designs, network coding operates at upper layers above (including) the link layer and it requires the input packets to be correctly decoded. However, this requirement may limit the performance and application of network coding due to the unavoidable fading and noise in wireless networks. In this paper, we propose a new wireless network coding scheme for TWRC, which is referred to as soft network coding (SoftNC), where the relay nodes applies symbol-by-symbol soft decisions on the received signals from the two end nodes to come up with the network coded information to be forwarded. We do not assume further channel coding on top of SoftNC at the relay node (channel coding is assumed at the end nodes). According to measures of the soft information adopted, two kinds of SoftNC are proposed: amplify-and-forward SoftNC (AF-SoftNC) and soft-bit-forward SoftNC (SBF-SoftNC). We analyze the both the ergodic capacity and the outage capacity of the two SoftNC schemes. Specifically, analytical form approximations of the ergodic capacity and the outage capacity of the two schemes are given and validated. Numerical simulation shows that our SoftNC schemes can outperform the traditional network coding based two-way relay protocol, where channel decoding and re-encoding are used at the relay node. Notable is the fact that performance improvement is achieved using only simple symbol-level operations at the relay node. © 2008 KICS.","Title: Non-Memoryless Analog Network Coding in Two-Way Relay Channel\nYear: 2011\n\nAbstract: Physical-layer Network Coding (PNC) can significantly improve the throughput of two-way relay channels. An interesting variant of PNC is Analog Network Coding (ANC). Almost all ANC schemes proposed to date, however, operate in a symbol by symbol manner (memoryless) and cannot exploit the redundant information in channel-coded packets to enhance performance. This paper proposes a non-memoryless ANC scheme. In particular, we design a soft-input soft-output decoder for the relay node to process the superimposed packets from the two end nodes to yield an estimated MMSE packet for forwarding back to the end nodes. Our decoder takes into account the correlation among different symbols in the packets due to channel coding, and provides significantly improved MSE performance. Our analysis shows that the SNR improvement at the relay node is lower bounded by IIR (R is the code rate) with the simplest LDPC code (repeat code). The SNR improvement is also verified by numerical simulation with LDPC code. Our results indicate that LDPC codes of different degrees are preferred in different SNR regions. Generally speaking, smaller degrees are preferred for lower SNRs.","Title: Physical Layer Network Coding Over Finite And Infinite Fields\nYear: 2008\n\nAbstract: Direct application of network coding at the physical layer - physical layer\nnetwork coding (PNC) - is a promising technique for two-way relay wireless\nnetworks. In a two-way relay network, relay nodes are used to relay two-way\ninformation flows between pairs of end nodes. This paper proposes a precise\ndefinition for PNC. Specifically, in PNC, a relay node does not decode the\nsource information from the two ends separately, but rather directly maps the\ncombined signals received simultaneously to a signal to be relayed. Based on\nthis definition, PNC can be further sub-classed into two categories - PNCF (PNC\nover finite field) and PNCI (PNC over infinite field) - according to whether\nthe network-code field (or groups, rings) adopted is finite or infinite. For\neach of PNCF and PNCI, we consider two specific estimation techniques for\ndealing with noise in the mapping process. The performance of the four schemes\nis investigated by means of analysis and simulation, assuming symbol-level\nsynchronization only.","Title: Mapping and Coding Design for Channel Coded Physical-layer Network Coding.\nYear: 2013\n\nAbstract: Although BICM can significantly improves the BER performance by iteration processing between the demapping and the decoding in a traditional receiver, its design and performance in PNC system has fewer studied. This paper investigates a bit interleaved coded modulation (BICM) scheme in a Gaussian two-way relay channel operated with physical layer network coding (PNC). In particular, we first present an iterative demapping and decoding framework specially designed for PNC. After that, we compare different constellation mapping schemes in this framework, with the convergence analysis by using the EXIT chart. It is found that the anti-Gray mapping outperforms the Gray mapping, which is the best mapping in the traditional decoding schemes. Finally, the numerical simulation shows the better performance of our framework and verifies the mapping design. © 2013 IEEE.","Title: Real-time implementation of physical-layer network coding\nYear: 2013\n\nAbstract: This paper presents the first real-time physical-layer network coding (PNC) prototype for the two-way relay wireless channel (TWRC). Theoretically, PNC could boost the throughput of TWRC by a factor of 2 compared with traditional scheduling (TS) in the high signal-to-noise (SNR) regime. Although there have been many theoretical studies on PNC performance, there have been few experimental and implementation efforts. We built the first prototype of PNC about a year ago. It was, however, an offline system in which an offline PNC decoder was used at the relay. For a real-time PNC system, there are many additional challenges, including the needs for tighter coordination of the transmissions by the two end nodes, fast real-time PNC decoding at the relay, and a PNC-compatible retransmission scheme (i.e., an ARQ protocol) to ensure reliability of packet delivery. In this paper, we describe a real-time PNC prototype, referred to as RPNC, that provides practical solutions to these challenges. Indoor environment experimental results show that RPNC boosts the throughput of TWRC by a factor of 2 compared with TS, as predicted theoretically. RPNC prototype provides an interface to the application layer, with which we demonstrate the exchange of two image data files between the two end nodes.","Title: Hot topic: physical-layer network coding\nYear: 2006\n\nAbstract: A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11). The goal of this paper is to show how the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity-boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC) scheme to coordinate transmissions among nodes. In contrast to \"straightforward\" network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM) waves for equivalent coding operation. PNC can yield higher capacity than straight-forward network coding when applied to wireless networks. We believe this is a first paper that ventures into EM-wave-based network coding at the physical layer and demonstrates its potential for boosting network capacity. PNC opens up a whole new research area because of its implications and new design requirements for the physical, MAC, and network layers of ad hoc wireless stations. The resolution of the many outstanding but interesting issues in PNC may lead to a revolutionary new paradigm for wireless ad hoc networking.","Title: Physical Layer Network Coding Schemes over Finite and Infinite Fields\nYear: 2008\n\nAbstract: Direct application of network coding at the physical layer - physical layer network coding (PNC) - is a promising technique for two-way relay wireless networks. In a two-way relay network, relay nodes are used to relay two-way information flows between pairs of end nodes. This paper proposes a precise definition for PNC. Specifically, in PNC, a relay node does not decode the source information from the two ends separately, but rather directly maps the combined signals received simultaneously to a signal to be relayed. Based on this definition, PNC can be further sub-classed into two categories - PNCF (PNC over finite field) and PNCI (PNC over infinite field) - according to whether the network-code field (or groups, rings) adopted is finite or infinite. For each of PNCF and PNCI, we consider two specific estimation techniques for dealing with noise in the mapping process. The performance of the four schemes is investigated by means of analysis and simulation, assuming symbol-level time synchronization only.","Title: On Fuzzy Propositional Logic with Different Negations.\nYear: 2013\n\nAbstract: Pan pointed out that there are three kinds of different negations: contradictory negation, opposite negation and medium negation. He also proposed the novel fuzzy set FScom with the above three kinds of negations, followed by one improved fuzzy set IFScom that is established based on FScom. In this paper, a novel fuzzy propositional logic calculus system that equipped with three kinds of negations is set up to correspond to the FScom and IFScom. The soundness theorem and completeness theorem of the system is proved.","Title: Throughput analysis of CSMA wireless networks with finite offered-load\nYear: 2013\n\nAbstract: This paper proposes an approximate method, equivalent access intensity (EAI), for the throughput analysis of CSMA wireless networks in which links have finite offered-load and their MAC-layer transmit buffers may be empty from time to time. Different from prior works that mainly considered the saturated network, we take into account in our analysis the impacts of empty transmit buffers on the interactions and dependencies among links in the network that is more common in practice. It is known that the empty transmit buffer incurs extra waiting time for a link to compete for the channel airtime usage, since when it has no packet waiting for transmission, the link will not perform channel competition. The basic idea behind EAI is that this extra waiting time can be mapped to an equivalent “longer” backoff countdown time for the unsaturated link, yielding a lower link access intensity that is defined as the mean packet transmission time divided by the mean backoff countdown time. That is, we can compute the “equivalent access intensity” of an unsaturated link to incorporate the effects of the empty transmit buffer on its behavior of channel competition. Then, prior saturated ideal CSMA network (ICN) model can be adopted for link throughput computation. Specifically, we propose an iterative algorithm, “Compute-and-Compare”, to identify which links are unsaturated under current offered-load and protocol settings, compute their “equivalent access intensities” and calculate link throughputs. Simulation shows that our algorithm has high accuracy under various offered-load and protocol settings. We believe the ability to identify unsaturated links and compute links throughputs as established in this paper will serve an important first step toward the design and optimization of general CSMA wireless networks with offered-load control.","Title: Implementation of physical-layer network coding\nYear: 2013\n\nAbstract: This paper presents the first implementation of a two-way relay network based on the principle of physical-layer network coding. To date, only a simplified version of physical-layer network coding (PNC), called analog network coding (ANC), has been successfully implemented. The advantage of ANC is that it is simple to implement; the disadvantage, on the other hand, is that the relay amplifies the noise along with the signal before forwarding the signal. PNC systems in which the relay performs XOR or other denoising PNC mappings of the received signal have the potential for significantly better performance. However, their implementation also poses many challenges. For example, the relay must be able to deal with symbol and carrier-phase asynchronies of the simultaneous signals received from the two end nodes, and the relay must perform channel estimation before decoding. We investigate a PNC implementation in the frequency domain, referred to as FPNC, to tackle these challenges. FPNC is based on OFDM. In FPNC, XOR mapping is performed on the OFDM samples in each subcarrier rather than on the samples in the time domain. We implement FPNC on the universal soft radio peripheral (USRP) platform. Our implementation requires only moderate modifications of the packet preamble design of 802.11a/g OFDM PHY. With the help of the cyclic prefix (CP) in OFDM, symbol asynchrony and the multi-path fading effects can be dealt with simultaneously in a similar fashion. Our experimental results show that symbol-synchronous and symbol-asynchronous FPNC have essentially the same BER performance, for both channel-coded and unchannel-coded FPNC.","Title: Channel coding and decoding in a relay system operated with physical-layer network coding\nYear: 2009\n\nAbstract: This paper investigates link-by-link channel-coded PNC (Physical layer Network Coding), in which a critical process at the relay is to transform the superimposed channel-coded packets received from the two end nodes (plus noise), Y3 = X1+ X2+W3, to the network-coded combination of the source packets, S1 ⊕ S2. This is in contrast to the traditional multiple-access problem, in which the goal is to obtain both S1 and S2 explicitly at the relay node. Trying to obtain S1 and S2 explicitly is an overkill if we are only interested in S1 ⊕ S2. In this paper, we refer to the transformation Y3 → S1 ⊕ S2 as the Channel-decoding-Network-Coding process (CNC) in that it involves both channel decoding and network coding operations. This paper shows that if we adopt the Repeat Accumulate (RA) channel code at the two end nodes, then there is a compatible decoder at the relay that can perform the transformation Y3 → S1 ⊕ S2 efficiently. Specifically, we redesign the belief propagation decoding algorithm of the RA code for traditional point-to-point channel to suit the need of the PNC multiple-access channel. Simulation results show that our new scheme outperforms the previously proposed schemes significantly in terms of BER without added complexity.","Title: Pareto Optimal Power Control via Bisection Searching in Wireless Networks.\nYear: 2013\n\nAbstract: In this letter, we first prove that the upper boundary of the feasible signal to interference-plus-noise ratio (SINR) region achieved by power control is Pareto optimal under the constraints of minimum SINR requirements and limited power. Thanks to the Pareto optimal nature, we further propose a bisection searching based low-complexity algorithm that achieves arbitrary Pareto optimal power control over the entire feasible SINR region. More importantly, we can easily approach the upper boundary of the feasible SINR region through the proposed algorithm. © 1997-2012 IEEE.","Title: Blind interference alignment over homogeneous 3-user 2 × 1 broadcast channel\nYear: 2013\n\nAbstract: This paper studies the blind interference alignment (BIA) in a homogeneous 3-user 2 × 1 broadcast channel (BC). In a homogeneous BC, each link connecting the transmitter and a user undergoes independent fading with identical coherence time. BIA refers to the implementation of interference alignment with no need of CSIT. It is shown in our early work [1] that, BIA can be easily accomplished in a homogeneous 2-user 2 × 1 BC with no requirement of using time-frequency extension, or mounting an extra antenna to each single-antenna receiver. In this paper, we extend the idea to a homogeneous 3-user 2 × 1 BC. We show that, if the three homogeneous 2 × 1 links satisfy certain offset condition, a homogeneous 3-user 2 × 1 BC can be completely decomposed into finite-time channel blocks, and each of which is BIA-feasible. By using a new technique that is different with the one in our early work, we prove a sufficient condition for a homogeneous 3-user 2 × 1 BC to be completely decomposable, i.e., to be suitable for BIA to achieve the optimal 3over2 DoF. We further study a multi-user 2 × 1 broadcast network, in which there are K ≥ 3 homogeneous users and their coherence time offsets are independently and uniformly distributed. We show that, if K ≥ 15, a 2-antenna transmitter can find, with more than 99% certainty, three users to form a BIA-feasible 3-user BC and achieve the optimal 3over2 DoF with no need of CSIT. © 2013 IEEE.","Title: Distributed Two-Way Switch and Stay Combining with a Single Amplify-and-Forward Relay.\nYear: 2013\n\nAbstract: This letter considers a two-phase two-way relay network (TWRN) consisting of two sources and a single amplify-and-forward (AF) relay. In order to efficiently exploit the direct link between sources for achieving full diversity order, we propose to utilize a distributed switch and stay combining (DSSC) in the two-phase TWRN. In DSSC, one branch out of the direct and relay branches is activated, and the branch switching occurs when the minimum end-to-end signal-to-noise ratio (SNR) of bi-directional communication falls below a preset threshold. We analyze the system performance over Rayleigh fading channels, by deriving the lower bounds and asymptotic expressions for the outage probability and symbol error rate (SER). It is shown that the DSSC preserves the same spectral efficiency as pure analogy network coding (ANC) protocol, while can concurrently achieve full diversity order as the optimal selection (OS) with less channel estimation complexity and lower switching rate.} Numerical and simulation results demonstrate the proposed studies. © 2013 IEEE.","Title: The Capacity of Known Interference Channel\nYear: 2015\n\nAbstract: In this paper, we investigate the capacity of a known interference channel, where a transmitter sends information to a receiver in the presence of a block-fading interference link, and the receiver knows the interference data but not the channel gain of the interference link. An upper bound and a lower bound for the capacity of this known interference channel are derived. Specifically, the capacity lower bound is achieved by a blind known interference cancellation (BKIC) scheme, which can remove the interference without the knowledge of the interference channel gain. We further show that the achievable lower bound of BKIC can approach the upper bound in high SNR regime. Our results show that the lack of the knowledge of the channel gain of the interfering link causes only a small fractional loss of degrees of freedom (capacity prelog).","Title: Distance-based Location Management Utilizing Initial Position for Mobile Communication Networks\nYear: 2016\n\nAbstract: This paper aims at improving the distance-based location management scheme for mobile communication networks. In location management, a mobile terminal (MT) is tracked based on its location-update area (LA). The improvement is brought about by joint optimization of LA center and LA size. For LA center optimization (LCO), we determine the optimal center position of the LA given the initial position of the MT upon each location update. The investigation of optimal LA center has eluded research to date. Based on the popular continuous-time random walk (CTRW) mobility model, we propose an analytical framework that uses a diffusion equation to determine the optimal LA center that minimizes the total cost of location management, consisting of the location update cost and terminal paging cost. This framework allows us to easily model the non- Markovian movement of the MT and evaluate the impact of various measurable physical parameters (such as length of road section, angle between road sections, road section crossing time) and LA center. In particular, we show that proper LA center can significantly reduce the total cost. For example, for the circular LA and low Poisson call-arrival rate, optimizing the LA center alone has the potential of reducing the cost by up to 37%. Joint optimization of the LA center and terminal paging scheme can reduce the cost even further. Simulations results match the theoretical analysis to a gap within 3%, indicating that our theoretical model is very accurate.","Title: System Utility Maximization with Interference Processing for Cognitive Radio Networks\nYear: 2015\n\nAbstract: In spectrum underlay cognitive radio networks, secondary users (SUs) are allowed to reuse the spectrum allocated to a primary system. The interference between SUs actually carries information and can potentially be exploited to improve the network performance through information-theoretic interference processing. In this paper, we design an optimal joint power and rate control algorithm that maximizes the secondary system utility subject to the interference temperature constraints of primary users based on the capacity-approaching interference processing scheme called as the Han-Kobayashi scheme. The optimal solution is difficult to achieve, because the optimization problem is in general non-convex. To make the optimization problem tractable, this paper first transforms the problem into a monotonic optimization problem through exploiting its hidden monotonicity. We then devise an effective algorithm to obtain the global optimal solution to the joint power and rate control problem in the Han-Kobayashi scheme. The key idea behind the proposed algorithm is to construct a sequence of shrinking polyblocks that approximate the upper boundary of the feasible region with increasing precision. Numerical results further show that the achieved utility of our scheme significantly outperforms the utility of conventional schemes which treat the interference between SUs as the noise.","Title: Full Diversity Physical-Layer Network Coding in Two-Way Relay Channels With Multiple Antennas\nYear: 2014\n\nAbstract: This paper studies a two-way relay channel where two single-antenna users exchange messages via a relay with K antennas. A new physical-layer network coding (PNC) method is proposed, referred to as channel-quantized PNC (CQ-PNC), that can achieve full diversity gain of K. The proposed method converts K received signals at the relay into two signals by a QR decomposition. The first one is a weighted summation of the two users' messages and the second one is a scaled version of one user's message. Then, the first signal is quantized at the relay by using the channel coefficient and the quantization error is cancelled by using the side information of the second signal. Finally, a Gaussian-integer weighted summation of the two users' messages is obtained, mapped to network codeword, and then broadcast to the two users. It is proved that the proposed CQ-PNC can achieve the full diversity gain of K with receiver side channel information, low computational complexity and symbol level synchronization. Simulation results demonstrate that the proposed method obtains optimum performance results with negligible gap.","Title: Near-Optimal Modulo-and-Forward Scheme for the Untrusted Relay Channel.\nYear: 2015\n\nAbstract: This paper studies an untrusted relay channel, in which the destination sends artificial noise simultaneously with the source sending a message to the relay, in order to protect the source's confidential message. The traditional amplify-and-forward (AF) scheme shows poor performance in this situation because of the interference power dilemma. Providing better security by using stronger artificial ...","Title: Opportunistic relaying with analogue and digital network coding for two-way parallel relay network.\nYear: 2014\n\nAbstract: A pair of terminals exchanging information via a layer of parallel relay nodes under slow fading is considered. Two protocols are proposed based on the combination of opportunistic relaying (OR) with analogue network coding (ANC), named ORANC, or with digital network coding (DNC), named ORDNC, respectively. Two schemes/versions of ORDNC, including 2-phase ORDNC (2P-ORDNC) and 3-phase ORDNC (3P-ORD...","Title: Exploring Coding Benefits in CDN-Based VoD Systems.\nYear: 2014\n\nAbstract: Currently, video-on-demand (VoD) streaming over Internet is a popular application. Because of the rapidly growing video population and user population, how to maintain high user quality of experience (QoE) with low cost is a challenging problem for Internet video streaming providers. A promising technique to potentially benefit VoD streaming system is network coding. A number of recent works studi...","Title: Survey on cooperative strategies for wireless relay channels\nYear: 2014\n\nAbstract: AbstractCooperative communication has the potential of providing better throughput and reliability to wireless systems when compared with direct communication. To realise the potential gain, it is important to design cooperative strategies for some representative scenarios. This survey deals with three basic wireless relay channels, namely, the parallel relay channel, the multiple-access relay channel and the broadcast relay channel. For the first channel, which models a single unicast connection, various forwarding strategies are studied. For the second and third channels, which model, respectively, the uplink and downlink scenarios with multiple unicast connections; network codes that exploit the possibility of coding among the connections are studied. The common aim pertaining to the studies of all these three channels is to use the limited radio resource in the most efficient way. Beside the aforementioned conventional works, studies that have state-of-the-art assumptions for relay networks, including outdated channel state information at the transmitter, full duplex relay and practical rateless-coded cooperation, are also extensively reviewed. Copyright © 2014 John Wiley & Sons, Ltd.","Title: MIMO physical layer network coding based on VBLAST detection\nYear: 2012\n\nAbstract: For MIMO two-way relay channel, this paper proposes a novel scheme, VBLAST-PNC, to transform the two superimposed packets received by the relay to their network coding form. Different from traditional schemes, which tries to detect each packet before network coding them, VBLAST-PNC detects the summation of the two packets before network coding. In particular, after firstly detecting the second layer signal in 2-by-2 MIMO system with VBLAST, we only cancel part of the detected signal, rather than canceling all the components, from the first layer. Then we directly map the obtained signal, which is a weighted summation of the first layer and the second layer, to the network coding form. With such partial interference cancellation, the error propagation effect is significantly mitigated and the performance is thus improved as shown in simulations.","Title: Joint Cooperative MAC and Network Coding for Two-way Relaying Networks\nYear: 2014\n\nAbstract: This paper studies a three-hop two-way relaying network, in which two end nodes exchange their information data via two relays. In literature, it usually assumes that each link of the two-way relaying network is error-free. Under this assumption, if considering scheduling and network coding (NC), one only needs three time slots to exchange a pair of messages, obtaining throughput 2/3 messages/time slot. This paper shows that when each link is not zero, the throughput of NC decreases with the increasing of FER. To improve the throughput of the NC scheme, this paper integrates the technology of cooperative MAC to propose a new scheme, namely joint cooperative MAC and NC (JCNC) scheme. Simulation and analytical results demonstrate that the proposed JCNC scheme overperforms the NC scheme. An appealing observation is that each relay node only needs four cooperative nodes to fully gain the advantage of JCNC against NC.","Title: Capacity of known interference channel with Gaussian modulations\nYear: 2014\n\nAbstract: In this paper, we investigate the capacity of known interference channel, where the receiver knows the interference data but not the channel gain of the interference data, assuming the use of Gaussian modulations. We first present a tight upper bound for the capacity of this known-interference channel. After that, we obtain the achievable rate of the channel with a blind known interference cancellation (BKIC) scheme in closed form. We prove that this achievable rate can approach the aforementioned upper bound in the high SNR regime. Moreover, it is much larger than the achievable rate of the traditional interference cancellation scheme. In particular, the achievable rate of BKIC continues to increase with SNR in the high SNR regime, while that of the traditional scheme approaches a fixed bound that does not improve with SNR.","Title: Interference-aware system utility maximization for cognitive radio networks\nYear: 2014\n\nAbstract: In this paper, we aim to obtain the optimal joint power and rate control that maximizes the interference-aware secondary system utility subject to the interference temperature constraints of primary users (PUs) on the basis of the information-theoretic interference processing technique. In particular, we first draw the capacity-approaching rate region of secondary users (SUs) with given transmit power and channel information of SUs based on the near optimal interference cancellation and power splitting technique. Then, we formulate the power controlled secondary system utility maximization problem under the interference temperature constraints of PUs in the capacity-approaching rate region. To solve the optimization problem, we decompose it into one convex subproblem over the rate region with fixed power allocation and the other partitioning subproblem of the power allocations. Finally, we present an efficient algorithm, which can quickly achieve the utility-maximized power and rate control based on the idea of simulated annealing.","Title: Two-Way Decode-and-Forward for Low-Complexity Wireless Relaying: Selective Forwarding Versus One-Bit Soft Forwarding.\nYear: 2016\n\nAbstract: Motivated by applications such as battery-operated wireless sensor networks (WSN), we propose an easy-to-implement low-complexity two-way relaying scheme. In particular, we address the challenge of improving the standard two-way selective decode-and-forward protocol (TW-SDF) in terms of block-error-rate (BLER) with minor additional complexity and energy consumption. By following the principle of s...","Title: Secure Switch-and-Stay Combining (SSSC) for Cognitive Relay Networks.\nYear: 2016\n\nAbstract: In this paper, we study a two-phase underlay cognitive relay network, where there exists an eavesdropper who can overhear the message. The secure data transmission from the secondary source to secondary destination is assisted by two decode-and-forward (DF) relays. Although the traditional opportunistic relaying technique can choose one relay to provide the best secure performance, it needs to con...","Title: Overhearing Protocol Design Exploiting Intercell Interference in Cooperative Green Networks.\nYear: 2016\n\nAbstract: The conventional two-way relaying (TWR) protocol requires that the transmitter (receiver) in one direction must be the receiver (transmitter) in the other direction, a limitation precluding the application of the TWR for sophisticated real-world wireless networks. In this paper, we study a more general multicell system consisting of a downlink (DL) traffic in one cell and an uplink (UL) traffic in an adjacent cell, with a multiantenna relay located in the cell edge and shared by both cells. For the coexistence of DL and UL transmissions, we propose exploiting the overheard signals from the adjacent cell (commonly known as the intercell interference) to improve the quality of signal reception in both cells. To reduce the power consumption to suit for green networks, we design the optimum relay precoder to minimize the total power at the relay yet satisfying the rate constraints for both the DL and UL traffic flows. The original precoder design is a nonconvex problem that is difficult to solve. To make the problem tractable, we transform the nonconvex problem to an equivalent quadratically constrained quadratic program (QCQP), which is then solved by the semidefinite relaxation (SDR) technique. Finally, simulations validate the effectiveness of our proposed protocol together with the optimized relay precoder.","Title: Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM.\nYear: 2015\n\nAbstract: Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.","Title: Demo: Software-Defined Device to Device Communication in Multiple Cells\nYear: 2015\n\nAbstract: This work aims to design a novel multi-hop device-to-device (D2D) communication system across multiple cells, by applying the spirit of software-defined networking, where the controllers regulate the data flows between the device-level switches in a centralized way. We utilize a hierarchical control plane, where the global controller handles the cross-cell D2D transmission, and the local controller determines the intra-cell D2D routing and scheduling. Specifically, by collecting the network information periodically, the controllers can generate the topology of D2D network and provide it to the applications. Through the use of USRP hardware, we demonstrate the feasibility of our proposed system.","Title: Switch-and-Stay Combining Relaying for Security Enhancement in Cognitive Radio Networks\nYear: 2015\n\nAbstract: Opportunistic relaying scheme (ORS), where the best relay is selected for dual-hop communication, has been widely considered as the global optimum relaying technique. However, due to the requirement of acquiring the full channel state information (CSI) of all links, ORS has increased the system's complexity and might be harmful to the network stability, especially for the large-scale networks. In this paper, we therefore proposed an alternative scheme, namely, secure switch-and-stay combining (SSSC) protocol for providing the best secure performance. In particular, a two-phase underlay cognitive relay network, where one out of two decode-and-forward (DF) is activated to assist the secure data transmission. The secure relay switching occurs when the relay cannot support the secure communication any longer. We study the system secure performance of SSSC protocol by deriving an analytical secrecy outage probability as well as an asymptotic expression in the high main-to-eavesdropper ratio (MER) region. It is shown that SSSC can substantially reduce the switching rate with lower channel estimation complexity, and approach the full diversity meanwhile.","Title: Ergodic Rate Analysis for User Access in Downlink Heterogeneous Cloud Radio Access Networks.\nYear: 2015\n\nAbstract: Characterizing user access methods in heterogeneous cloud radio access networks (H-CRANs) is critical for performance optimization. Different from the user access in cloud radio access networks (C-RANs), the inter-tier interference from macro base station has a great impact on user access in HCRANs. In this paper, after considering the inter-tier interference, the ergodic rates of downlink H-CRANs for two proposed user access methods, namely distance based and cluster based, are analyzed. The corresponding mathematical expressions of ergodic rates have been derived. In particular, the closed-form expression for the upper bound of ergodic rate is proposed. Simulation results corroborate the accuracy of the derived expressions for these two methods. Furthermore, the cluster based user access method outperforms the distance based user access method when the intensity of remote radio heads is sufficiently high.","Title: Practicing a record-and-replay system on USRP\nYear: 2013\n\nAbstract: Signal recording and replaying is widely used in data analysis, device test, etc. Motivated by this fact, our paper focuses on developing a low-cost record-and-replay prototype based on USRP (Universal Software Radio Peripheral). In particular, we designed and developed a GPS (Global Positioning System) record-and-replay system, which can sample the real GPS signal and then output the analog GPS signal to other devices with cable. In this prototype, we first design the record flow graph and the replay flow graph for USRP, which is verified in the FM band with the FM (Frequency Modulation) broadcasting. After that, we replace the passive antenna with one active antenna suit to amplify the small power GPS signal, lower the quantization bits to reduce the data rate in order to support the wide bandwidth of the GPS signal. Finally, our GPS record-and-replay system is tested and verified by a real GPS receiver. The good performance of our GPS record-and-replay system showed that USRPs can not only used in academia as experiment devices but also used in industry as low-cost wireless auxiliary devices.","Title: Exploiting the UEP property of polar codes to reduce image distortions induced by transmission errors.\nYear: 2015\n\nAbstract: This paper considers the application of polar codes in image transmissions. When the polar codeword length is finite, the information bits in different positions experience different but predictable unequal post-decoding error probabilities. Exploiting this property, we propose to order the information bits in a polar codeword according to their erroneously transmission induced image distortions. Specifically, the most significant bits (MSB, left-most bits) of each pixel in an image are transmitted through the lower error probability bit-channels (logical channel post the polar code) and the Least Significant Bits (LSB, right-most bits) of each pixel are transmitted through higher error probability bit-channels. In this way, the Polar codes output bit streams with different importance will be given unequal error protection (UEP), resulting in smaller distortion to the image. Our analysis and simulation results reveal that the image transmission system coded by polar codes with our UEP scheme yields more than 7dB gain over the same polar codes image transmission system without UEP, when the delivered image Peak Signal to Noise Ratio (PSNR) is near 40dB.","Title: STAC: Simultaneous Transmitting and Air Computing in Wireless Data Center Networks\nYear: 2015\n\nAbstract: The data center network (DCN), wired or wireless, features large amounts of Many-to-One (M2O) sessions. Each M2O session is currently established using Point-to-Point (P2P) communications and Store-and-Forward (SAF) relays, and is generally followed by a certain computation at the destination, typically a weighted summation of the received information digits. Fundamentally different from this separate P2P/SAF-based-transmission and computation framework, this paper proposes STAC, a novel physical layer scheme that achieves Simultaneous Transmission and Air Computation in wireless DCNs. In particular, STAC builds on a number of distinguishing characteristics of DCs to take advantage of the superposition nature of electromagnetic signals. With STAC, multiple sources transmit in the same time slot with appropriately chosen parameters, such that the superimposed signal can be directly transformed to the desired summation at the receiver. To enable STAC, we propose an enhanced Software Defined Network (SDN) architecture, where a wired low-bandwidth backbone provides wireless transceivers with external reference signals. Theoretical analysis and simulation results show that STAC can significantly improve both bandwidth and energy efficiency in DCNs.","Title: Secure TWRC system based on channel quantized PNC\nYear: 2015\n\nAbstract: Because of the broadcasting nature of wireless communications, passive eavesdropping is a well-known security loophole. Generally there are two basic ideas to deal with the eavesdropper at physical layer: friendly jamming and channel state information dependent encoding. Based on the channel quantized physical-layer network coding (CQ-PNC), we propose a secured CQ-PNC scheme to combine both ideas aforementioned to prevent the eavesdropper from obtaining the messages. Specifically, the CQ-PNC consists of co-channel signal superposition in the air (friendly jamming) and channel state information dependent mapping (PNC mapping). As a result, the information is more difficult to be detected at the eavesdropper, meanwhile there is almost no rate loss at the legal receiver. To verify the effectiveness of the proposed secure CQ-PNC, we implemented the scheme on a software defined radio (USRP) experimental platform.","Title: Design of (4, 8) Binary Code with MDS and Zigzag-Decodable Property.\nYear: 2016\n\nAbstract: Dedicated to network code structured distributed storage system, a novel (4, 8) storage code is designed. The code possesses the following properties: operation within binary field , maximum distance separable property, zigzag decodable, requiring symmetric storage overhead among multiple storage nodes, and requiring little storage overhead . In the design of such a storage code, we first let original packets shift to the right by several bits and then add them together within binary field in bitwise manner . We propose a smart design on a cyclic matrix that represents the number of bits shifted by those packet. Existing works do not hold these properties simultaneously , which have the drawback of high encoding and decoding complexity, large storage overhead, and complicated storage room allocation procedure, etc.","Title: Design and implementation of device-to-device software-defined networks.\nYear: 2016\n\nAbstract: To support multi-hop device-to-device (D2D) transmission, this paper proposes a novel wireless architecture, device to -device software defined networks (D2D-SDN). In D2D-SDN, mobile devices are not only terminals but also can act as wireless switches to forward packets for others based on the routing and scheduling instructions from controllers. Under the proposed D2D-SDN architecture, we test the routing and adaptive resource allocation by jointly exploiting network information collection, and network abstraction. We also developed a testbed based on USRP to conduct experiments and demonstrate the feasibility and superiority of our proposed D2D-SDN.","Title: Expressive power of linear-temporal logic based on generalized possibility measures\nYear: 2016\n\nAbstract: Model checking of linear-time properties based on possibility measures was developed by Li and Li (2013). However, the linear-time properties considered in the previous work were classical and qualitative, possibility information of the systems was not considered thoroughly at all. Therefore, the quantitative model checking of fuzzy linear-time properties based on generalized possibility measures was studied by Li (2016). This paper is a continuation of the above work. In this paper, we study the expressive power of possibilistic linear-temporal logic (PoLTL). Unlike possibilistic computation tree logic (PoCTL) in which the expressiveness of PoCTL is more powerful than computation tree logic (CTL), it is surprising that PoLTL is as expressive as linear-temporal logic (LTL). Furthermore, the comparison between PoLTL and generalized PoLTL (GPoLTL) is given. It is shown that the expressiveness of GPoLTL with restrained conditions is the same as PoLTL when semantics of GPoLTL is interpreted in the frame of possibilistic Kripke structure.","Title: An Accelerometer-Assisted Transmission Power Control Solution for Energy-Efficient Communications in WBAN.\nYear: 2016\n\nAbstract: Energy efficiency is a key issue in wireless body area networks (WBANs). A number of transmission power control (TPC) schemes have been developed to improve the efficiency of transmission, which is one of the most energy consuming operations in WBAN. To save energy, these schemes only probe the link quality from the received data packets. However, due to large intervals between data packets and fast dynamic on-body link characteristics in WBAN, the obtained link information is usually outdated. In this case, the performance of the current TPC scheme is poor. This paper proposes an accelerometer-assisted TPC (AA-TPC) scheme, which exploits the periodic fluctuations of link qualities to improve the transmission energy efficiency. Consider the relationship between link quality and body movement, AA-TPC makes transmissions at ideal channel points that are identified by using the local accelerometer. We first conduct experiments to investigate the correlation between periodic movements and link quality. Then, we propose an algorithm to locate the time point in each period with the best link quality to transmit packets. The specific transmission power is then determined by the feedback information from the receiver. Finally, we evaluate the energy efficiency of AA-TPC based on a CC2420 platform in both a periodic scenario (without any aperiodic movement to break the periodicity) and a realistic scenario (which has aperiodic movements 20% of the time). The results show that about 26.4% and 18% of total energy consumption can be saved on average in the periodic and realistic scenarios, respectively.","Title: STAC: Simultaneous Transmitting and Air Computing in Wireless Data Center Networks.\nYear: 2016\n\nAbstract: The data center network (DCN), wired or wireless, features large amounts of many-to-one (M2O) sessions. Each M2O session is currently established using point-to-point (P2P) communications and store-and-forward (SAF) relays, and is generally followed by a certain computation at the destination, typically a weighted summation of the received information digits. Fundamentally different from this separate P2P/SAF-based-transmission and computation framework, this paper proposes simultaneous transmission and air computation (STAC), a novel physical layer scheme that achieves STAC in wireless DCNs. In particular, STAC builds on a number of distinguishing characteristics of DCs to take advantage of the superposition nature of electromagnetic signals. With STAC, multiple sources transmit in the same time slot with appropriately chosen parameters, such that the superimposed signal can be directly transformed to the desired summation at the receiver. To enable STAC, we propose an enhanced software-defined network architecture, where a wired low-bandwidth backbone provides wireless transceivers with external reference signals. We also discuss some new challenges that STAC brings to scheduling and routing. Theoretical analysis and simulation results show that STAC can significantly improve both bandwidth and energy efficiency in DCNs.","Title: Orbital Angular Momentum (OAM) Generation by Cylinder Dielectric Resonator Antenna for Future Wireless Communications.\nYear: 2016\n\nAbstract: In this paper, we demonstrate that the solid cylinder dielectric resonator antenna (CDRA) is a high radiation efficient device to generate orbital angular momentum (OAM) waves with different OAM modes at different frequencies. Theoretical formulations of its radiation fields are deduced in the cylindrical coordinate system, which shows that all components of the electric and magnetic fields have the azimuth dependence of e(jm phi). A full-wave simulation on a practical size CDRA is then implemented to validate the theoretical derivations. The simulation results show that different separated OAM modes can be obtained at different frequencies. Specifically, the higher order OAM modes are generated at higher electron magnetic modes, which makes CDRA a promising method to overcome the narrow band and multiband issues existing in the design of current OAM devices. Furthermore, a maximum radiation efficiency of 97 : 1% at 13.3 GHz, and a minimum radiation efficiency of 72 : 4% at 19.45 GHz are obtained in our simulation, which is a high radiation efficiency feature at the OAM resonant frequencies.","Title: On-off Analog Beamforming for Massive MIMO.\nYear: 2018\n\nAbstract: This paper investigates a new analog beamforming architecture for massive multiple-input multiple-output (MIMO) systems, where each of the multiple transmit antennas is switched to be on or off to form a beam according to the channel state information at transmitters. This on-off analog beamforming (OABF) scheme has the advantages in terms of both hardware complexities and algorithmic complexities...","Title: Experiment verified physical-layer collision separation of passive UHF tags.\nYear: 2016\n\nAbstract: In this paper, we propose a low complexity algorithm to separate collided RFID signals. By carefully analyzing the collided signals when two tags with the same bit durations respond to a reader simultaneously, we observed that the individual amplitudes of each tag appear in pairs and the two tags' information bits are different, and regular RFID signals with larger amplitudes appear if the two tags' information bits are the same. Exploiting this feature, our algorithm can separate the collided signals of two tags to enhance the identification efficiency. Moreover, we implement our algorithm based on a software-defined reader platform to verify the validity of our algorithm. Simulation and experiment results show that the overall system throughput with our method is significantly increased in comparison with the conventional Gen-2 MAC scheme.","Title: Asynchronous NOMA for Downlink Transmissions.\nYear: 2017\n\nAbstract: In this letter, we propose a novel symbol-asynchronous non-orthogonal multiple access (ANOMA) scheme for downlink transmissions. First, ANOMA introduces an artificial symbol-offset between packets destined for different user equipment (UE) in order to reduce their mutual interference. Second, ANOMA adopts a pre-coding at the base station and a whitening-and-decomposing (WD) detection scheme at the receiving UE to simplify signal detection. With these two novel techniques, ANOMA can significantly enlarge the achievable rate region of the traditional non-orthogonal multiple access (NOMA). Moreover, the impact of pulse shape and symbol offset on the performance of ANOMA scheme are investigated in both Gaussian and Rayleigh block-fading channels. The numerical results show that ANOMA with precoding and WD detection outperforms asynchronous NOMA with successive interference cancellation detection, both outperform synchronous NOMA.","Title: The Decision Delay in Finite-Length MMSE---DFE Systems\nYear: 2015\n\nAbstract: This paper investigates the performance of the minimum mean square error (MMSE) decision feedback equalization system and optimizes its key parameter, decision delay, to minimize the system MSE. For the first typical scenario where the feedback filter length is larger than or equal to the channel order , we present an analytical expression of the system MSE in terms of decision delay. A few results in previous literature can be regarded as special cases of this expression. For the second typical scenario where feedback filter length is less than the channel order , it is difficult to obtain the explicit solutions. We prove that at a given decision delay, the MSE for is always larger than or equal to that for . As a corollary of two derived theorems, the optimum feedback filter length should be set to the channel order for large . Furthermore, in this scenario, the optimal decision delay is conventionally found with an exhaustive search over all possible delays, whose computational complexity is linear with the searching space and unaffordable. To lower the complexity, we then propose an efficient algorithm to find the optimal decision delay. The complexity of our algorithm is only a log function of the searching space and its MSE and symbol error rate performance is almost the same as the traditional exhaustive search .","Title: Multi-Antenna Enabled Cluster-Based Cooperation in Wireless Powered Communication Networks.\nYear: 2017\n\nAbstract: In this paper, we consider a wireless powered communication network (WPCN) consisting of a multi-antenna hybrid access point (HAP) that transfers wireless energy to and receives sensing data from a cluster of low-power wireless devices (WDs). To enhance the throughput performance of some faraway WDs, we allow one of the WDs to act as the cluster head (CH) that helps forward the messages of the other cluster members. However, the performance of the proposed cluster-based cooperation is fundamentally limited by the high energy consumption of the CH, who needs to transmit all the WDs' messages including its own. To tackle this issue, we exploit the capability of multi-antenna energy beamforming (EB) at the HAP, which can focus more transferred power to the CH to balance its energy consumption in assisting the other WDs. Specifically, we first derive the throughput performance of each individual WD under the proposed scheme. Then, we jointly optimize the EB design, the transmit time allocation among the HAP and the WDs, and the transmit power allocation of the CH to maximize the minimum data rate achievable among all the WDs (the max-min throughput) for improved throughput fairness among the WDs. An efficient optimal algorithm is proposed to solve the joint optimization problem. Moreover, we simulate under practical network setups and show that the proposed multi-antenna enabled cluster-based cooperation can effectively improve the throughput fairness of WPCN.","Title: Novel Intersymbol Interference Cancellation Scheme to Enable Parallel Computational and High-Performance Faster-Than-Nyquist Signaling.\nYear: 2017\n\nAbstract: In this paper, we deal with the intersymbol interference (ISI) cancellation problem induced by the faster-than-Nyquist (FTN) signaling. In the traditional FTN signaling, the detection delay at the receiver depends on the number of states of the ISI trellis. In this case, the corresponding Viterbi algorithm or the BCJR algorithm would be far too complex and introduce a huge delay when the ISI tap set is large. In this paper, we propose a novel interference cancellation scheme to combat the ISI for the FTN communication system which enables the parallel computations. Our proposed scheme adopts a pre-coding at the transmitter and a decomposition detection at the receiver. Particularly, with the help of the parallel computations, the running time of our proposed scheme is independent of the ISI trellis, which allows the application of a more severe FTN system with a smaller time acceleration factor. Besides, based on the pre-coding scheme and the decomposition detection, an adaptive transmission strategy is developed, which can improve the performance of the proposed scheme dramatically. Finally, we compare our scheme with the offset BCJR algorithm and the offset Viterbi algorithm benchmarks. The simulation results verify that our scheme can outperform previous decoders with a better bit error rate and a much less delay.","Title: An Opportunistic Source Selection Spectrum Sharing Protocol for Overlay Cognitive Two-Way Relaying System.\nYear: 2017\n\nAbstract: In this paper, an overlay two-way relaying spectrum sharing protocol is proposed by employing an opportunistic source selection strategy. In this protocol, a secondary user acts as a relay and allocates a fraction of its transmit power to assist two-way communication between two primary users (PUs), thus, gaining spectrum sharing as long as without degrading the primary system performance. Moreover, to exploit the multiuser diversity inherent in the multiuser primary systems, the two PUs transmit their own messages in an opportunistic manner, depending on the instantaneous channel conditions. Specifically, at each transaction, only one of the PUs is selected for transmission such that the instantaneous mutual information on the whole primary network is maximized and as a result, the multiuser diversity gain is achieved by the primary system. Then, we derive the exact closed-form outage probabilities for both the primary and secondary systems. To gain more insights, closed-form asymptotic outage probability expressions are also derived. Based on the approximations, we further obtain analytical results on the diversity order of the primary system and the power-allocation threshold above which the spectrum sharing is allowed for secondary transmission. Numerical results are presented to verify our analysis and highlight the advantages of our proposed scheme compared to another two-phase spectrum sharing protocol based on analog network coding.","Title: A novel table look-up scheme based on GFScom and its application.\nYear: 2017\n\nAbstract: This work considers Mamdani fuzzy systems constructed from finite input–output data pairs using the generalized fuzzy sets with contradictory, opposite and medium negation (GFScom). Considering that the information available often consists of a set of finite numerical data pairs, a new table look-up scheme for constructing Mamdani fuzzy systems is presented. The designed fuzzy system is proved to be capable of approximating any real continuous function on a compact set to arbitrary degree of accuracy. We use this fuzzy modeling method for the truck back-upper control problem. The effectiveness of the proposed method is demonstrated by a comparison with the traditional table look-up scheme.","Title: Artificial-Noise-Aided Secure Channel with a Full-duplex Source.\nYear: 2017\n\nAbstract: This paper consider a new secure communication scene where a full-duplex transmitter (Alan) need to transmit confidential information to a half-duplex receiver (Bob), with a silent eavesdropper (Eve) that tries to eavesdrop the confidential information. For realizing secure communication between Alan and Bob, a novel two phases communication scheme is proposed: in Phase 1, Alan and Bob send artificial noises (AN) simultaneously, while in Phase 2, Alan superimposes the AN received in Phase 1 with its confidential signal and sends the mixed signalto Bob. Since the mixed AN could degrade the SINR (Signal to Interference and Noise Ratio) of Eve, but does not affect the SINR of Bob, a secrecy capacity can be achieved. We also derive the conditions that the secrecy capacity of the proposed scheme exists, and analyze the secrecy outage probability under Rayleigh fading channel. Numerical results show that the secrecy capacity is about two times higher than without AN, even though in the proposed scheme half of the time is used to transmit ANs, and the outage probability is about five times lower than that without AN.","Title: A wavelet frame coefficient total variational model for image restoration.\nYear: 2017\n\nAbstract: In this paper, we propose a vector total variation (VTV) of feature image model for image restoration. The VTV imposes different smoothing powers on different features (e.g. edges and cartoons) based on choosing various regularization parameters. Thus, the model can simultaneously preserve edges and remove noises. Next, the existence of solution for the model is proved and the split Bregman algorithm is used to solve the model. At last, we use the wavelet filter banks to explicitly define the feature operator and present some experimental results to show its advantage over the related methods in both quality and efficiency.","Title: Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning.\nYear: 2018\n\nAbstract: Early diagnosis of gear transmission has been a significant challenge, because gear faults occur primarily at microstructure or even material level but their effects can only be observed indirectly at a system level. The performance of a gear fault diagnosis system depends significantly on the features extracted and the classifier subsequently applied. Traditionally, fault-related features are extracted and identified based on domain expertise through data preprocessing which are system-specific and may not be easily generalized. On the other hand, although recently the deep neural networks based approaches featuring adaptive feature extractions and inherent classifications have attracted attention, they usually require a substantial set of training data. Aiming at tackling these issues, this paper presents a deep convolutional neural network-based transfer learning approach. The proposed transfer learning architecture consists of two parts; the first part is constructed with a pre-trained deep neural network that serves to extract the features automatically from the input, and the second part is a fully connected stage to classify the features that needs to be trained using gear fault experimental data. Case analyses using experimental data from a benchmark gear system indicate that the proposed approach not only entertains preprocessing free adaptive feature extractions, but also requires only a small set of training data.","Title: Multi-pair two-way relaying systems with physical layer network coding.\nYear: 2018\n\nAbstract: Physical layer network coding (PNC) significantly improves the throughput of the two-way relay channel (TWRC). However, most of current work on PNC just focuses on one pair users, and there are limit work on multi-pair PNC, in which the main bottleneck on system performance is the interference caused by the remaining communicating pairs. In this paper, we consider a scenario where multiple pairs communicate with each other through one relay node. For a purpose of comparison, we present the analytical capacities of the multi-pair version of the existing TWRC schemes in closed forms. Considering more practical issues such as the direction distribution of incident signals, we propose a spatial multi-pair physical layer network coding scheme. The novelty includes: in the multiple access phase, the log likelihood ratio combination scheme is proposed, which is robust in bad communication conditions; in the broadcast phase, two adaptive group broadcasting schemes are proposed, which can efficiently overcome the near interference problem. Simulation results validate the superiority of the proposed algorithms.","Title: DCAP: Improving the Capacity of WiFi Networks with Distributed Cooperative Access Points.\nYear: 2018\n\nAbstract: This paper presents the Distributed Cooperative Access Points (DCAP) system that can simultaneously serve multiple clients using cooperative beamforming to increase the capacity of WiFi-type wireless networks. The distributed APs are connected by Ethernet and driven by independent low-cost local oscillators. To facilitate cooperative beamforming, we address three major challenges: the phase synchronization, the channel state information (CSI) measurement, and the user selection. Specifically, we develop 1) a cooperative tracking scheme to track signal phase drifts at symbol level without adding extra hardware complexity; 2) an incremental CSI estimation mechanism that removes the per-frame CSI measurement overhead of previous approaches; and 3) a simple random user selection algorithm that scales the network capacity linearly and delivers over 70 percent performance compared to the optimal but complex greedy algorithm. We implement DCAP on the Sora software radio platform and evaluate it in a wireless network with nine nodes. Experimental results show that the cooperative beamforming is feasible in practice, and our cooperative phase tracking can ensure strict phase alignment ( 0.03 radian) among APs during the entire beamforming period (1.2 ms). Otherwise, without tracking, phases may drift by 0.3 radian over merely 600 s, causing that the symbol SNR decreases as large as 20 dB.","Title: LoS communications using large circular array for mmWave C-RAN network\nYear: 2017\n\nAbstract: This paper considers the application of large circular array to Line-of-Sight (LoS) multi-user Communications for C-RAN networks using the millimeter wave (mmWave) spectrum. Based on the symmetric geometry of the arrays, a spatial-harmonic-based interference suppressor (SHBIS) is proposed and evaluated in closed-form. For this receiver to work, a 2-D direction-of-arrival (DoA) information estimation method based on synthesizing spatial harmonics to form a scanning function is put forward.","Title: Receiver Originated Physical-Layer Robust Secure Transmissions in Wireless Systems.\nYear: 2018\n\nAbstract: This letter proposes a receiver-originated physical-layer secure transmissions scheme (ROST) which consists of two-phase communications. In phase 1, the receiver sends a pseudorandom signal (only known by itself) to the transmitter and in phase 2, the transmitter directly sends out the mixture of its confidential message and the received pseudorandom signal. The receiver could estimate the channel with the help of the pseudorandom signal before cancelling it from the received mixed signal, and finally detect the confidential information. By contrast, the eavesdropper can hardly obtain any confidential information because it cannot estimate the channel without any pilot signal in both phases transmissions and is interfered by the pseudorandom signal. Furthermore, we discuss the secrecy capacity of ROST and show that ROST can always keep the security of the confidential message, achieve much higher secrecy capacity than the traditional scheme, even without any channel information at the transmitter.","Title: D2D communications based scarcity-aware two-stage multicast for video streaming.\nYear: 2017\n\nAbstract: This paper proposes a device-to-device (D2D) communications based scarcity-aware two-stage multicast mechanism for delivering video streaming, in which the Base Station (BS) and D2D perform cooperative retransmissions to improve the mobile video quality and relieve the load of BS as well. In prior works, after BS's one-round multicast, the clients directly resort to D2D communications to obtain the missing frames by forming social groups. However, after the first-round multicast of BS, it is possible that some frames have not been correctly received by most of the clients, especially when the channel conditions are not good. That is, there are scarce frames in the network and the clients have very limited copies of them. In this case, it is inefficient for the clients to form social groups and directly exchange frames through D2D communications. Instead, this paper proposes an adaptive two-stage multicast mechanism: the BS first multicast video packets to the clients who request the same video services, after which the clients collect the information of missing frames and send it to the BS. Then the BS evaluates the scarcity of frames and re-multicast the scarce frames, if any, to the clients. After the BS's second-round multicast, the clients can then form groups to perform D2D communications so as to obtain the remaining missing packets from each other. Numerical studies show that the proposed simple scarcity-aware two-stage multicast mechanism can achieve about 5% video quality improvement than the existing one-stage multicast mechanism and have the potential to enhance the downlink capacity of the future 5G wireless network.","Title: Impact of Residual Hardware Impairments on Non-Orthogonal Multiple Access Based Amplify-and-Forward Relaying Networks.\nYear: 2018\n\nAbstract: We quantify the impact of residual hardware impairments (RHI) on a non-orthogonal multiple access (NOMA)-based relaying network, where a source communications simultaneously with multiple users via an amplify-and-forward relay. Specifically, exact and asymptotic expressions for the outage probability are first derived in closed-form over Nakagami-m fading channels, accounting for RHI at the source, relay, and all users. Our results show that the outage performance loss induced by RHI is small in the low signal-to-noise ratio (SNR) regime or/and at low target rates, however it is significant at high SNRs or/and target rates. Furthermore, we present new tight analytical approximated and asymptotic expressions for the system ergodic sum rate (ESR). For comparison, we also discuss the ESR of the conventional hardware-impaired relaying system employing orthogonal multiple access (OMA) transmission and obtain the asymptotic high-SNR expression of the ESR. The provided numerical results demonstrate that in the absence of RHI, the ESR in either the NOMA or the OMA system increases monotonically with the increase of SNR, whereas an unavoidable ESR ceiling is introduced in the hardware-impaired scenario for both systems. Notably, since the ESR ceiling value is only depended on the RHI levels, the NOMA-based and OMA-based systems achieve the same ESR performance in high SNRs.","Title: A Low Storage Room Requirement Framework for Distributed Ledger in Blockchain.\nYear: 2018\n\nAbstract: Traditional centralized commerce on the Internet relies on trusted third parties to process electronic payments. It suffers from the weakness of the trust-based model. A pure decentralized mechanism called blockchain tackles the above problem and has become a hot research area. However, since each node in a blockchain system needs to store all transactions of the other nodes, as time continues, the storage room required to store the entire blockchain will be huge. Therefore, the current storage mechanism needs to be revised to cater to the rapidly increasing need for storage. Network coded (NC) distributed storage (DS) can significantly reduce the required storage room. This paper proposes a NC-DS framework to store the blockchain and proposes corresponding solutions to apply the NC-DS to the blockchain systems. Analysis shows that the proposed scheme achieves significant improvement in saving storage room.","Title: Energy-Efficient Beamforming And Time Allocation In Wireless Powered Communication Networks\nYear: 2018\n\nAbstract: This paper investigates multi-antenna beamforming and time allocation to maximize the network energy-efficiency (EE) in a wireless powered communication network (WPCN). Since the EE optimization problem has an inherent fractional form, it is difficult to obtain the optimal value directly due to the lack of convexity in the objective function. To overcome this challenge, we first convert the original problem into a more tractable one by the fractional programming. Then, two schemes are proposed to find the optimal value. In the first scheme, the iterative value is updated according to the EE based on energy beamforming and time allocation derived in the current iteration. In the second one, the optimal value is obtained by consecutively shrinking the region in which it is located. Simulation results show that the proposed two schemes can improve the network EE significantly compared with the algorithm that only pursues high throughput. In addition, it is shown that the two schemes have simlilar performance in the network EE. However, the computation complexity of the first one is lower than that of the second one.","Title: Detecting and Localizing Adversarial Nodes Usig Neural Networks.\nYear: 2018\n\nAbstract: This work proposes a new method for securing the gossip algorithm for average consensus on communication networks. The gossip algorithm is appealing for its ability to harness distributed computational resources while adapting to arbitrarily connected networks without coordination overhead, however it is inherently vulnerable to the insider attack by adversarial node since each node locally updates its local states and passes information to its neighbors without supervision. In light of this, this work proposes new methods for detecting and localizing adversarial nodes using a neural network system. We show that our neural network-based method delivers a significantly improved detection and localization performance, compared to the state of the art.","Title: Blind Authentication at the Physical Layer Under Time-Varying Fading Channels.\nYear: 2018\n\nAbstract: Authentication is a key requirement for secure communications in modern wireless systems. Compared with the conventional authentication at the upper layer using a cryptographic tool, authentication at the physical layer has many advantages, including enhanced security through the introduction of uncertainty to adversaries and increased efficiency and compatibility through the avoidance of operatio...","Title: V2X-Enabled Energy-Efficient Transmission in Cellular Networks\nYear: 2018\n\nAbstract: In this paper, we propose an energy-efficient vehicle-to-everything (V2X)-enabled transmission scheme in uplink cellular networks. We aim to minimize the uplink energy consumption while considering both circuit power and transmit power. The optimal resource allocation algorithm for direct transmission and V2X-enabled transmission are derived, respectively. The simulation results show that the proposed V2X-enabled transmission scheme can achieve significant energy saving compared with the traditional direct transmission, the decode-and-forward (DF)-based transmission and the opportunistic store-carry and forward (SCF)-based transmission schemes, especially when the cell radius is large.","Title: Energy Efficient V2X-Enabled Communications in Cellular Networks.\nYear: 2019\n\nAbstract: Vehicle-to-everything (V2X) communications, which provide wireless connectivity among vehicles, roadside drivers, passengers, and pedestrians, are attracting great interest. In this paper, we propose an energy-efficient relay assisted transmission scheme based on V2X communications in the uplink cellular networks for delay insensitive applications. We aim to maximize energy efficiency of the uplin...","Title: Deep Learning for Joint Adaptations of Transmission Rate and Payload Length in Vehicular Networks.\nYear: 2019\n\nAbstract: Recently, vehicular networks have emerged to facilitate intelligent transportation systems (ITS). They enable vehicles to communicate with each other in order to provide various services such as traffic safety, autonomous driving, and entertainments. The vehicle-to-vehicle (V2V) communication channel is doubly selective, where the channel changes within the transmission bandwidth and the frame duration. This necessitates robust algorithms to provide reliable V2V communications. In this paper, we propose a scheme that provides joint adaptive modulation, coding and payload length selection (AMCPLS) for V2V communications. Our AMCPLS scheme selects both the modulation and coding scheme (MCS) and the payload length of transmission frames for V2V communication links, according to the V2V channel condition. Our aim is to achieve both reliability and spectrum efficiency. Our proposed AMCPLS scheme improves the V2V effective throughput performance while satisfying a predefined frame error rate (FER). Furthermore, we present a deep learning approach that exploits deep convolutional neural networks (DCNN) for implementing the proposed AMCPLS. Simulation results reveal that the proposed DCNN-based AMCPLS approach outperforms other competing machine learning algorithms such as k-nearest neighbors (k-NN) and support vector machines (SVM) in terms of FER, effective throughput, and prediction time.","Title: To Establish a Secure Channel from a Full-duplex Transmitter to a Half-duplex Receiver: An Artificial-Noise-Aided Scheme\nYear: 2019\n\nAbstract: This letter considers a new secure communication scenario where a full-duplex transmitter (Alan) needs to transmit confidential information to a half-duplex receiver (Bob), with an eavesdropper (Eve) that tries to overhear the confidential information. For realizing the secure communication, we design an effective artificial-noise-aided secure scheme (ANAS) which is composed of two phases' transmi...","Title: Parametric Modeling for Two-Dimensional Harmonic Signals With Missing Harmonics.\nYear: 2019\n\nAbstract: The problem of parametric modeling for the two-dimensional (2-D) harmonic signals with missing harmonics is addressed. The modeling process consists of two parts. First, we devise the joint spatial-temporal linearly constrained minimum variance beamformer and perform the joint estimation of the spatial and temporal fundamental frequencies for each pitch of the signal based on the maximum harmonic model. Second, we differentiate the competing signal models by the number of harmonic components and derive the maximum a posteriori criterion for the 2-D harmonic signals. As a result, the harmonic components of each pitch are detected according to their spectral powers. The simulation and experimental results are provided to show the superiority of the proposed signal modeling methodology over other existing schemes.","Title: Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems\nYear: 2019\n\nAbstract: This letter proposes a deep convolutional neural network (DCNN) approach for adaptive modulation and coding in practical multiple-input, multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Our target is to maximize the throughput and fulfill a packet error rate constraint. We consider practical impairments of MIMO-OFDM receiver, such as imperfect timing synchronization,...","Title: Computation Offloading for Mobile Edge Computing Enabled Vehicular Networks.\nYear: 2019\n\nAbstract: The emergence of computation-intensive and delay-sensitive vehicular applications poses a great challenge for individual vehicles with limited computation resources. Mobile edge computing (MEC) is a new paradigm shift that can enhance vehicular services through computation offloading. However, the high mobility of vehicles will affect offloading performance. In this paper, we investigate the vehicular user (VU) computation overhead minimization problem in MEC-enabled vehicular networks by jointly optimizing the computation and communication resources' allocation (transmit power and uploading time for communication, and the offloading ratio and local CPU frequency for computation). This optimization problem is nonconvex and difficult to solve directly. To deal with this issue, we first transform the original problem into an equivalent one. Then, we decompose the equivalent problem into a two-level problem. In addition, we develop a low-complexity algorithm to obtain the optimal solution. The numerical results demonstrate that the proposed algorithm can significantly outperform benchmark algorithms in terms of computation overhead.","Title: Molecular Insights Into The Recruiting Between Ucp2 And Ddx5/Ubap2l In The Metabolic Plasticity Of Non-Small-Cell Lung Cancer\nYear: 2021\n\nAbstract: Mitochondrial uncoupling protein 2 (UCP2) is distributed in tumor cells with a link to the support of systemic metabolic deregulation, and the downregulation of UCP2 has been unveiled as a biomarker of oncogenesis and chemoresistance in non-small-cell lung cancer (NSCLC) cells. However, the underlying mechanism of how UCP2 cooperates with other proteins in this metabolic reprogramming remains largely unsolved. We employed a combined computational and experimental strategy to explore into the recruiting of DDX5 with other proteins, and we unraveled the underlying structural mechanisms. We found that recruiting by ATP-dependent RNA helicase DDX5 (DDX5)/ubiquitin-associated protein 2-like (UBAP2L) might help UCP2 to play the pathological roles in NSCLC cells. According to the view of thermodynamics in physics, UCP2 tends to recruit DDX5 rather than UBAP2L, as shown by the ensemble-based docking, molecular dynamics simulations and molecular mechanics generalized Born surface area (MM/GBSA) approach. Cellular immunofluorescence assays further demonstrated that UCP2 associate with DDX5, and the recruiting of DDX5 with UCP2 at least partially contribute to the metabolic plasticity of NSCLCs via the AKT/mTOR pathway. Our study proposed an efficient way for detecting the protein- protein association via the experimentally validated molecular simulation. Our results shed light on the functional annotation of UCP and DDX family proteins in dysregulated metabolism, and the identification of candidate therapeutic targets for NSCLC.","Title: Ir5hmcsc: Identifying Rna 5-Hydroxymethylcytosine With Multiple Features Based On Stacking Learning\nYear: 2021\n\nAbstract: RNA 5-hydroxymethylcytosine (5hmC) modification is the basis of the translation of genetic information and the biological evolution. The study of its distribution in transcriptome is fundamentally crucial to reveal the biological significance of 5hmC. Biochemical experiments can use a variety of sequencing-based technologies to achieve high-throughput identification of 5hmC; however, they are labor-intensive, time-consuming, as well as expensive. Therefore, it is urgent to develop more effective and feasible computational methods. In this paper, a novel and powerful model called iR5hmcSC is designed for identifying 5hmC. Firstly, we extract the different features by K-mer, Pseudo Structure Status Composition and One-Hot encoding. Subsequently, the combination of chi-square test and logistic regression is utilized as the feature selection method to select the optimal feature sets. And then stacking learning, an ensemble learning method including random forest (RF), extra trees (EX), AdaBoost (Ada), gradient boosting decision tree (GBDT), and support vector machine (SVM), is used to recognize 5hmC and non-5hmC. Finally, 10-fold cross-validation test is performed to evaluate the model. The accuracy reaches 85.27% and 79.92% on benchmark dataset and independent dataset, respectively. The result is better than the state-of-the-art methods, which indicates that our model is a feasible tool to identify 5hmC. The datasets and source code are freely available at https://github.com/HongyanShi026/iR5hmcSC.","Title: Investigation on negative capacitance FinEFT beyond 7 nm node from device to circuit\nYear: 2021\n\nAbstract: In this study, a SPICE model for Negative Capacitance FinFET (NC-FinFET) based on the BSIM-CMG model and Landau-Khalatnikov (L-K) equation is developed. The areas of the ferroelectric layer (AFE) and the work functions (WFs) of n-NC-FinFET and p-NC-FinFET are adjusted to reduce the shift of the threshold voltage. As a result, the NC-FinFET-based seven-stage ring oscillator (RO) has a smaller delay, and energy can down to 72.2 % compared with the FinFET-based RO under the same delay. Furthermore, the NC-FinFET-based 6T SRAM has a smaller read and write time and lower static power than the FinFET-based 6T SRAM. Besides, under two key parameters (the length of the gate (Lg), the width of gate (Wfin), of NC-FinFET), the NC-FinFET-based 6T SRAM has great advantages in static power, read and write time. Therefore, the research on NC-FinFET from the device to the circuit can provide meaningful guidance for the further application of NC-FinFET.","Title: Secure Blockchain Platform for Industrial IoT with Trusted Computing Hardware\nYear: 2021\n\nAbstract: As a disruptive technology that originates from cryptocurrency, blockchain provides a trusted platform to facilitate Industrial IoT (IIoT) applications. However, implementing a blockchain platform in IIoT scenarios faces various security challenges due to the rigorous deployment conditions. To this end, we present a novel design of secure blockchain based on trusted computing hardware for IIoT applications. Specifically, we employ a trusted execution environment (TEE) module and a customized security chip to safeguard the blockchain against different attack vectors. Furthermore, we implement the proposed secure IIoT blockchain on an ARM-based embedded device and build a small-scale IIoT network to evaluate its performance. Our experimental results show that the secure blockchain platform achieves high throughput (150 TPS) with low transaction confirmation delay (below 66 ms), demonstrating its feasibility in practical IIoT scenarios. Finally, we outline the open challenges and future research directions.","Title: Deep learning based adaptive modulation and coding for uplink multi-user SIMO transmissions in IEEE 802.11ax WLANs\nYear: 2021\n\nAbstract: The IEEE 802.11ax standard defined a set of new specifications to improve spectrum efficiency, power efficiency, and reliability of future wireless local area networks (WLANs). Among these specifications, the uplink multi-user multiple-input, multiple-output (MU-MIMO) remedies the uplink shortcomings of existing WLANs and enables high-efficiency uplink transmissions. This paper considers the problem of adaptive modulation and coding (AMC) for uplink MU-single-input, multiple-output (MU-SIMO) in the upcoming WLANs. Our target is to select the appropriate modulation and coding scheme (MCS) for the existing users so as to maximize the throughput and at the same time guarantee that all users can satisfy a frame error constraint. We adopt a deep learning approach to tackle this problem. We propose to let the access point (AP) leverage the estimated channel state information (CSI) of all users along with the estimated noise standard deviation as the input features to a deep convolutional neural network (DCNN) that is trained to perform AMC. Simulation results reveal that the proposed DCNN for the uplink MU-SIMO AMC outperforms rival machine learning techniques such as fully-connected deep neural network (DNN), k-nearest neighbors (KNN), support vector machines (SVM), Gaussian Naive Bayes (GNB), and random forest (RF) in terms of the throughput and the frame error rate (FER).","Title: Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network\nYear: 2021\n\nAbstract: Signal features can be obscured in noisy environments, resulting in low accuracy of radar emitter signal recognition based on traditional methods. To improve the ability of learning features from noisy signals, a new radar emitter signal recognition method based on one-dimensional (1D) deep residual shrinkage network (DRSN) is proposed, which offers the following advantages: (i) Unimportant features are eliminated using the soft thresholding function, and the thresholds are automatically set based on the attention mechanism; (ii) without any professional knowledge of signal processing or dimension conversion of data, the 1D DRSN can automatically learn the features characterizing the signal directly from the 1D data and achieve a high recognition rate for noisy signals. The effectiveness of the 1D DRSN was experimentally verified under different types of noise. In addition, comparison with other deep learning methods revealed the superior performance of the DRSN. Last, the mechanism of eliminating redundant features using the soft thresholding function was analyzed.","Title: Computation Offloading and User Association for Blockchain-enabled Heterogeneous Cellular Networks\nYear: 2021\n\nAbstract: In this paper, we investigate a novel blockchain-enabled heterogeneous cellular network (HetNet). In this network, mobile users associate with the serving base stations (BSs) and offload their computation-intensive proof-of-work mining tasks to the mobile edge computing server of the macro-cell BS. We initialize the user association strategy by using the traditional nearest BS association algorithm in the blockchain-enabled HetNet. Then, we formulate the computation offloading problem and design an alternating iterative algorithm to attain the computing demand strategies for all users. Based on obtained computing demand strategies, we propose a modified user association algorithm in order to improve the data congestion of BSs. Analytical results show that the blockchain-enabled HetNet can serve more users, and the proposed algorithms can effectively obtain strategies of user association and computation offloading. Numerical results demonstrate that the proposed alternating iterative algorithm for computation offloading has fast convergence and good stability, and the proposed modified user association algorithm can avoid data congestion better than the traditional nearest BS association algorithm.","Title: A Comparative Study of Magnetic-induction based Cooperative Communication for Underwater Data Collection\nYear: 2021\n\nAbstract: Magnetic induction (MI) based communication is an important means for underwater data collection due to its less susceptibility to surrounding environments, predictable channel condition and high data rate. However, the communication rate is affected by the severe path loss in underwater channel and the orientation of the MI transceiver coils. In this case, allowing the coils to transmit collabora...","Title: iPro-GAN: A novel model based on generative adversarial learning for identifying promoters and their strength\nYear: 2022\n\nAbstract: Background and Objective: Promoter is a component of the gene, which can specifically bind with RNA polymerase and determine where transcription starts, and also determine the transcription efficiency of the gene. Promoters can be divided into strong promoters and weak promoters because their structures and the interaction time interval are quite different. The functional variation of the promoter can lead to a variety of diseases. Therefore, identifying promoters and their strength is necessary and has important biological significance. A novel and promising model based on deep learning is proposed to achieve it. Methods: In this work, we build a power model named iPro-GAN for identification of promoters and their strength. First, we collect benchmark datasets and independent datasets for training and testing. Then, Moran-based spatial auto-cross correlation method is used as feature extraction method. Finally, deep convolution generative adversarial network with 10-fold cross validation is applied for classifying. The first layer of the model is used to identify the promoter and the second layer is used to determine its type. Results: On the benchmark data set, the accuracy of the first layer predictor is 93.15%, and the accuracy of the second layer predictor is 92.30%. On the independent data set, the accuracy of the first layer predictor is 86.77%, and the accuracy of the second layer predictor is 91.66%. In particular, breakthrough progress has been made in the identification of promoters' strength. Conclusions: These results are far higher than the existing best predictor, which indicate that our model is serviceable and practicable to identify promoters and their strength. Furthermore, the datasets and source codes are available from this link: https://github.com/Bovbene/iPro-GAN . (c) 2022 Elsevier B.V. All rights reserved.","Title: Robust Spectral Analysis of Multi-Channel Sinusoidal Signals in Impulsive Noise Environments\nYear: 2022\n\nAbstract: Robust spectral analysis of the sinusoidal signals corrupted by impulsive noise poses a big challenge in the signal processing community. In this paper, we address the issue of robust spectral analysis for multi-channel sinusoidal signals, including order detection and parameter estimation. The successive robust low-rank decomposition is firstly designed to extract the common signal subspace from the multi-channel data matrix. Subsequently, the number of sinusoidal poles is determined with a model order selection criterion, based on the so-obtained subspace. With the signal order information, the sinusoidal parameters and outliers are jointly estimated according to the maximum \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">a posteriori</i>\n criterion. To find a robust initial guess of the sinusoidal parameters, an estimator based on robust weighted linear prediction is developed. Additionally, the performance analysis is provided, which includes computational complexity, convergence verification of the sinusoidal parameter estimation, and asymptotic consistency of the signal order detection. Simulation results demonstrate the advantages of the proposed robust spectral analysis framework compared to state-of-the-art schemes.","Title: Blockchain Storage, Computation Offloading, and User Association for Heterogeneous Cellular Networks\nYear: 2022\n\nAbstract: To support more Internet-of-Things devices, we present a novel blockchain-enabled heterogeneous cellular network (HetNet). In this network, devices store block data to the cloud service provider, offload the proof-of-work mining tasks to base stations (BSs), and associate with the macrocell BS or small-cell BSs. Then, we analyze the user association problem, computation offloading problem, and block storage problem in the blockchain-enabled HetNet. We also design corresponding algorithms to solve these problems. Furthermore, to tackle the challenge of data congestion of BSs, based on the obtained computation offloading and block storage strategies, we propose a modified user association algorithm. The analysis shows that the proposed blockchain-enabled HetNet can effectively attain computing offloading, block storage, and user association strategies, and more devices can access the blockchain network. Analytical results show that the proposed modified user association algorithm can greatly avoid data congestion of BSs. Numerical results demonstrate the effectiveness of our proposed algorithms for computation offloading and block storage, and the proposed modified user association algorithm has a significantly great advantage compared with the traditional nearest BS association algorithm in terms of avoiding data congestion.","Title: An MAP Method for Closed-Loop Channel Training in Massive MIMO Systems\nYear: 2022\n\nAbstract: Accurate channel state information is of great importance in high-speed wireless communication. In this work, the problem of channel training for massive multiple-input multiple-output (MIMO) systems is investigated and a closed-loop channel training scheme is devised upon the maximum \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">a posteriori</i>\n (MAP) criterion. More specifically, at each channel use, the channel estimation is performed at the receiver according to the MAP criterion, and the expectation-maximization (EM) algorithm is employed to cope with the MAP estimation problem. Based on the parameter estimates, the predicted mean square error (PMSE) of the channel estimate is minimized to determine a preferred sounding beam for the next channel use, of which the index is fed back to the transmitter via a limited-rate channel. The performance superiority of the proposed approach is evidenced by comparing with various channel training schemes.","Title: Downsampling and Transparent Coding for Blockchain\nYear: 2022\n\nAbstract: Blockchain is considered to be able to solve the problem of trust between distributed nodes. However, it's a challenge to store the large amount of data produced by blockchain. Therefore, it is necessary to reduce node storage overhead while ensuring node independence and data recoverability. This paper proposes to downsample these data to reduce the storage overhead of nodes. These nodes keep good independence, if downsampling follows the entropy of blockchain and the block bodies are continuously stored. Moreover, it demonstrates that the entire blockchain history can be efficiently recovered through the cooperative decoding of a group of nodes like fountain codes, if reserved data over these nodes obey the soliton distribution. However, these data on nodes are transparent (stored data are uncoded and can be used directly by the node without decoding). Thus, the proposed algorithm not only keeps decentralization and security, but also has good scalability in independence and recoverability.","Title: An improved residual network using deep fusion for identifying RNA 5-methylcytosine sites\nYear: 2022\n\nAbstract: Motivation: 5-Methylcytosine (m5C) is a crucial post-transcriptional modification. With the development of technology, it is widely found in various RNAs. Numerous studies have indicated that m5C plays an essential role in various activities of organisms, such as tRNA recognition, stabilization of RNA structure, RNA metabolism and so on. Traditional identification is costly and time-consuming by wet biological experiments. Therefore, computational models are commonly used to identify the m5C sites. Due to the vast computing advantages of deep learning, it is feasible to construct the predictive model through deep learning algorithms. Results: In this study, we construct a model to identify m5C based on a deep fusion approach with an improved residual network. First, sequence features are extracted from the RNA sequences using Kmer, K-tuple nucleotide frequency component (KNFC), Pseudo dinucleotide composition (PseDNC) and Physical and chemical property (PCP). Kmer and KNFC extract information from a statistical point of view. PseDNC and PCP extract information from the physicochemical properties of RNA sequences. Then, two parts of information are fused with new features using bidirectional long- and short-term memory and attention mechanisms, respectively. Immediately after, the fused features are fed into the improved residual network for classification. Finally, 10-fold cross-validation and independent set testing are used to verify the credibility of the model. The results show that the accuracy reaches 91.87%, 95.55%, 92.27% and 95.60% on the training sets and independent test sets of Arabidopsis thaliana and M.musculus, respectively. This is a considerable improvement compared to previous studies and demonstrates the robust performance of our model.","Title: R5hmCFDV: computational identification of RNA 5-hydroxymethylcytosine based on deep feature fusion and deep voting\nYear: 2022\n\nAbstract: RNA 5-hydroxymethylcytosine (5hmC) is a kind of RNA modification, which is related to the life activities of many organisms. Studying its distribution is very important to reveal its biological function. Previously, high-throughput sequencing was used to identify 5hmC, but it is expensive and inefficient. Therefore, machine learning is used to identify 5hmC sites. Here, we design a model called R5hmCFDV, which is mainly divided into feature representation, feature fusion and classification. (i) Pseudo dinucleotide composition, dinucleotide binary profile and frequency, natural vector and physicochemical property are used to extract features from four aspects: nucleotide composition, coding, natural language and physical and chemical properties. (ii) To strengthen the relevance of features, we construct a novel feature fusion method. Firstly, the attention mechanism is employed to process four single features, stitch them together and feed them to the convolution layer. After that, the output data are processed by BiGRU and BiLSTM, respectively. Finally, the features of these two parts are fused by the multiply function. (iii) We design the deep voting algorithm for classification by imitating the soft voting mechanism in the Python package. The base classifiers contain deep neural network (DNN), convolutional neural network (CNN) and improved gated recurrent unit (GRU). And then using the principle of soft voting, the corresponding weights are assigned to the predicted probabilities of the three classifiers. The predicted probability values are multiplied by the corresponding weights and then summed to obtain the final prediction results. We use 10-fold cross-validation to evaluate the model, and the evaluation indicators are significantly improved. The prediction accuracy of the two datasets is as high as 95.41% and 93.50%, respectively. It demonstrates the stronger competitiveness and generalization performance of our model. In addition, all datasets and source codes can be found at https://github.com/HongyanShi026/R5hmCFDV.","Title: A Deep Autoencoder Trust Model for Mitigating Jamming Attack in IoT Assisted by Cognitive Radio\nYear: 2022\n\nAbstract: This article proposes deep learning (DL) framework constructed using deep autoencoder (DAE) to detect the malicious nodes in an Internet of Things (IoT) network assisted by the cognitive radio (CR) technology. In the IoT era, a plethora of nodes are connected to the network for the purpose of collecting and exchanging data. CR technology finds its role in IoT applications because of its ability to efficiently exploit the available spectrum. In this article, we consider IoT nodes as secondary users that perform cooperative spectrum sensing (CSS). Specifically, these IoT nodes sense the spectrum and send their reports to the fusion center (FC) to determine whether the spectrum is occupied by the primary user or not. In such a scenario, the existence of malicious IoT nodes might mislead the FC. To determine which of these IoT nodes are benevolent and which are malicious is crucial. We adopt a DAE-based DL framework, called DAE-TRUST, to detect malicious nodes in CR-assisted IoT. The proposed DAE-TRUST is able to identify the malicious nodes, whose reports can be excluded from the spectrum detection process. Simulation results, in six different real-world environments, show that our DAE-TRUST enhances the performance of CSS in IoT applications.","Title: A Low-Complexity Codebook Design Scheme for SCMA Systems Over an AWGN Channel\nYear: 2022\n\nAbstract: As a promising wireless access technique, sparse code multiple access (SCMA) directly maps the input bits to the transmitted multidimensional codewords according to the SCMA codebook. In general, SCMA codebook optimization and detector simplification are independent of each other. In this article, we propose a simplified SCMA codebook with a separable structure for implementing a low-complexity detector. For codebook design, a separable codebook structure is proposed to simplify the maximization of the minimum Euclidean distance (MED). With this structure, multiple one-dimensional complex codebooks corresponding to one resource element (RE) are determined by maximizing the MED of the superimposed codewords. Then, the entire codebook set is obtained based on a structure indicator matrix and the separable codebook structure. The proposed codebook has a larger MED than obtained by existing codebooks. Benefiting from the separable codebook structure, the signals transmitted over each RE can be independently used to recover the corresponding input bit information. Therefore, we propose a parallel maximum a posteriori (P-MAP) detector consisting of several low-complexity MAP detectors to reduce the complexity of the conventional multiuser message passing (MP) detector in SCMA. At the receiver, each RE is assigned a low-complexity MAP detector. Simulation results show that the proposed codebook outperforms the known optimal codebook over an AWGN channel. Moreover, the run time of the P-MAP detection algorithm is reduced by 99.8% compared to the Log-MPA algorithm. With the proposed codebook design scheme and the P-MAP detector, an SCMA unit can be separated into two independent non-SCMA units, which significantly increases system flexibility.","Title: Edge Computing-Aided Coded Vertical Federated Linear Regression\nYear: 2022\n\nAbstract: For the training process of federated linear regression (FLR), which is the simplest form of federated learning, the integrated computation at each company is slowed down either by huge volume data or by time-consuming homomorphic encryption. Targetted at accelerating the training process of FLR, through the incorporation of edge computing aided coded distributed computing (CDC) into intensive computation (matrix multiplication), a novel coded FLR framework is proposed where several edge nodes aid the computing of one company. Two schemes, including linear combination (LC)-based vertical FLR and Matdot-based vertical FLR, are proposed and designed, which enjoy in-parallel computation and homomorphic encryption at the edge nodes. Since workload at each edge node is reduced significantly, the training runtime of these two schemes may be reduced significantly. Numerical studies show that our proposed coded schemes outperform traditional uncoded schemes significantly in terms of overall runtime (sum of encoding, computing, and decoding phases) of the training process. Besides, among the two proposed coded schemes, LC-based scheme and Matdot-based scheme each has its own advantage scenarios which conforms with the analysis.","Title: An Improved Thru-Line De-Embedding Technique up to 80 GHz\nYear: 2022\n\nAbstract: With the rapid increase of high-speed communications, the frequency range of integrated circuits enters the field of millimeter wave, and the size of devices has become smaller and smaller. In the measurement of microwave devices, the probe cannot contact the device directly. The actual device under test (DUT) is surrounded by metal traces called leads, which provide the interface between the probing pads on the wafer and the DUT itself. The probes are pressed on the pads at both ends of the DUT. The parasitic effect of pads and transmission lines is significant at high frequency. The established technique such as Short-Open-Load-Thru (SOLT) and Line-Reflect-Reflect-Match (LRRM) move the reference plane up to the tip of the probes by means of Cascade Microtech impedance standard substrate (ISS). To obtain accurate parameters of DUT, a deembedding method must be implemented to eliminate the influence of pads and interconnect transmission lines."],"string":"[\n \"Title: Applying physical-layer network coding in wireless networks\\nYear: 2010\\n\\nAbstract: A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes, simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11). This paper shows that the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity-boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC) scheme to coordinate transmissions among nodes. In contrast to \\\"straightforward\\\" network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM) waves for equivalent coding operation. And in doing so, PNC can potentially achieve 100% and 50% throughput increases compared with traditional transmission and straightforward network coding, respectively, in 1D regular linear networks with multiple random flows. The throughput improvements are even larger in 2D regular networks: 200% and 100%, respectively.\",\n \"Title: Five Kinds of Contradictory Relations and Opposite Relations in Inconsistent Knowledge\\nYear: 2007\\n\\nAbstract: Inconsistent knowledge processing is an important problem in the study of knowledge, especially in fuzzy knowledge. In this paper, we consider that research of inconsistent knowledge should differentiate between contradictory concept and opposite concept. Based on this cognition, we discovered a sort of character on opposite concepts, which a pair of opposite concepts is fuzzy concepts if and only if there is medium concept between them, and five kinds of contradictory relations and opposite relations in the distinct knowledge and fuzzy knowledge were defined. Furthermore, we studied these relations using the medium predicate logic MF and the infinite-valued model Phi of MF, and obtained the conditions of processing these relations.\",\n \"Title: Blind Known Interference Cancellation\\nYear: 2013\\n\\nAbstract: This paper investigates interference-cancellation schemes at the receiver, in which the interference data, which is valid data intended for another receiver, is known a priori. The interference channel, however, is unknown (the blind part). Such a priori knowledge is common in wireless relay networks. For example, a relay could be relaying data that was previously transmitted by a node A. If node A is now receiving a signal from another node B, the interference from the relay is actually self-information known to node A. Besides the case of self-information, the node could also have overheard or received the interference data in a prior transmission by another node. Directly removing the known interference requires accurate estimate of the interference channel, which may be difficult in many situations. In this paper, we propose a novel scheme, Blind Known-Interference Cancellation (BKIC), to cancel known interference without interference channel information. BKIC consists of two steps. The first step combines adjacent symbols to cancel the interference, exploiting the fact that the channel coefficients are almost the same between successive symbols. After such interference cancellation, however, the signal of interest is distorted. The second step recovers the signal of interest amidst the distortion. We propose two algorithms for the critical second steps. The first algorithm (BKIC-S) is based on the principle of smoothing. It is simple and has near optimal performance in the slow fading scenario. The second algorithm (BKIC-RBP) is based on the principle of real-valued belief propagation. Since there is no loop in the Tanner graph, BKIC-RBP can achieve MAP-optimal performance with fast convergence, and has near interference-free performance even in the fast fading scenario. Both BKIC schemes outperform the traditional self-interference cancellation schemes that have perfect initial channel information by a large margin, while having lower complexities.\",\n \"Title: Interference with Symbol-misalignment\\nYear: 2009\\n\\nAbstract: This paper studies the impact of interference asynchrony among different\\nlinks in a wireless network. Without deliberate coordination and cooperation\\namong the active links, there is a naturally occurring misalignment between the\\nsymbols of the targeted signal of a receiver and the symbols of the interfering\\nsignals. Interestingly, we show that the interference asynchrony can actually\\nimprove the BER performance, compared with the situation in which symbols of al\\nsignals ay aligned. In particular, we show that symbol misalignment can\\ndecrease the \\\"effective interference power\\\" and change the distribution of the\\ninterfering signals, in a way that results in lower BER. To ensure that symbol\\nmisalignment can be consistently attained, we propose two simple schemes that\\nintroduce time-varying symbol offsets to obtain an \\\"average\\\" performance of\\nrandom symbol misalignment. Notably, our schemes do not change the simple\\nreceiver design structure; only the transmitters are modified in a minor way.\",\n \"Title: Physical Layer Network Coding\\nYear: 2007\\n\\nAbstract: A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11). The goal of this paper is to show how the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity- boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC) scheme to coordinate transmissions among nodes. In contrast to \\\"straightforward\\\" network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM) waves for equivalent coding operation. PNC can yield higher capacity than straightforward network coding when applied to wireless networks. We believe this is a first paper that ventures into EM-wave-based network coding at the physical layer and demonstrates its potential for boosting network capacity. PNC opens up a whole new research area because of its implications and new design requirements for the physical, MAC, and network layers of ad hoc wireless stations. The resolution of the many outstanding but interesting issues in PNC may lead to a revolutionary new paradigm for wireless ad hoc networking.\",\n \"Title: Physical-Layer Network Coding: Tutorial, Survey, and Beyond\\nYear: 2013\\n\\nAbstract: The concept of physical-layer network coding (PNC) was proposed in 2006 for application in wireless networks. Since then it has developed into a subfield of network coding with wide implications. The basic idea of PNC is to exploit the mixing of signals that occurs naturally when electromagnetic (EM) waves are superimposed on one another. In particular, at a receiver, the simultaneous transmissions by several transmitters result in the reception of a weighted sum of the signals. This weighted sum is a form of network coding operation by itself. Alternatively, the received signal could be transformed and mapped to other forms of network coding. Exploiting these facts turns out to have profound and fundamental ramifications. Subsequent works by various researchers have led to many new results in the domains of (1) wireless communication, (2) information theory, and (3) wireless networking. The purpose of this paper is fourfold. First, we give a brief tutorial on the basic concept of PNC. Second, we survey and discuss recent key results in the three aforementioned areas. Third, we examine a critical issue in PNC: synchronization. It has been a common belief that PNC requires tight synchronization. Recent results suggest, however, that PNC may actually benefit from asynchrony. Fourth, we propose that PNC is not just for wireless networks; it can also be useful in optical networks. We provide an example showing that the throughput of a passive optical network (PON) could potentially be raised by 100% with PNC.\",\n \"Title: Differentiation and Processing of Contradictory Knowledge and Opposite Knowledge\\nYear: 2007\\n\\nAbstract: How to distinguish and deal with inconsistent knowledge in knowledge processing is an outstanding problem, especially in fuzzy knowledge processing. In this paper, we proposes that knowledge research should differentiate between contradictory concepts and opposite concepts in knowledge, and that discovered a character of opposite concepts, there exists medium concept between the two opposite concepts if and only if opposite concepts are fuzzy concepts. The contradictory relations and opposite relations in distinct concepts and fuzzy concepts were confirmed, some properties of them were analyzed. Furthermore, we study these concepts and relations using the medium predicate logic MF and the infinite-valued model Phi of MF, and get processing conditions on the five kinds of contradictory relations and opposite relations in knowledge.\",\n \"Title: Optimal Decoding Algorithm for Asynchronous Physical-Layer Network Coding\\nYear: 2011\\n\\nAbstract: A key issue in physical-layer network coding (PNC) is how to deal with the asynchrony between signals transmitted by multiple transmitters. That is, symbols transmitted by different transmitters could arrive at the receiver with symbol misalignment as well as relative carrier-phase offset. In this paper, 1) we propose and investigate a general framework based on belief propagation (BP) that can effectively deal with symbol and phase asynchronies; 2) we show that for BPSK and QPSK modulations, our BP method can significantly reduce the SNR penalty due to asynchrony compared with prior methods; 3) we find that symbol misalignment makes the system performance less sensitive and more robust against carrier-phase offset. Observation 3) has the following practical implication. It is relatively easier to control symbol timing than carrier-phase offset. Our results indicate that if we could control the symbol offset in PNC, it would actually be advantageous to deliberately introduce symbol misalignment to desensitize the system to phase offset.\",\n \"Title: The Capacity Of Two Way Relay Channel\\nYear: 2008\\n\\nAbstract: This paper investigates the capacity of a wireless two-way relay channel in which two end nodes exchange information via a relay node. The capacity is defined in the information-theoretic sense as the maximum information exchange rate between the two end nodes. We give an upper bound of the capacity by applying the cut-set theorem. We prove that this upper bound can be approached in low SNR region using \\\"separated\\\" multiple access for uplinks from the end nodes to the relay in which the data from the end nodes are individually decoded at the relay; and network-coding broadcast for downlinks from the relay to the end nodes in which the relay mixes the information from end nodes before forwarding. We further prove that the capacity is approachable in high SNR region using physical-layer network coding (PNC) multiple access for uplinks, and network-coding broadcast for downlinks. From our proof and observations, we conjecture that the upper bound may be achieved with PNC in all SNR regions.\",\n \"Title: Channel quantization based physical-layer network coding.\\nYear: 2013\\n\\nAbstract: This paper studies a MIMO Two-Way Relay Channel (TWRC) where two single-antenna nodes communicate with each other through a K-antenna relay. In the uplink phase, the two end nodes send their respective information simultaneously to the relay, and it is usually regarded as a virtual MIMO system. With traditional VBLAST MIMO detection, the maximum achievable diversity is K - 1. By noting that only the network coded packet, rather than the two individual packets, is needed at the relay node in TWRC, we propose a channel quantized physical-layer network coding (CQ-PNC) scheme based on VBLAST to achieve the full diversity K. Specifically, relay first uses QR decomposition to convert the K received signals into two valid signals, the first signal is a weighted summation of the two end nodes' symbols and the second signal is a scaled version of one end node's symbol. The relay first quantizes the first signal with the channel coefficient of one node and estimate the Gaussian integer summation of the two end nodes' information. At the same time, the quantization error is further canceled with the help of the second signal. After that, we adaptively map the Gaussian integer weighted summation to the network coding form. Moreover, we theoretically prove that our CQ-PNC can achieve the maximum diversity K. Finally, the numerical simulation shows that CQ-PNC performs within 2dB gap from the theoretical bound. © 2013 IEEE.\",\n \"Title: Physical Layer Network Coding with Multiple Antennas\\nYear: 2010\\n\\nAbstract: The two-phase MIMO NC (network coding) scheme can be used to boost the throughput in a two-way relay channel in which nodes are equipped with multiple antennas. The obvious strategy is for the relay node to extract the individual packets from the two end nodes and mix the two packets to form a network-coded packet. In this paper, we propose a new scheme called MIMO PNC (physical network coding), in which the relay extracts the summation and difference of the two end packets and then converts them to the network-coded form. MIMO PNC is a natural combination of the single-antenna PNC scheme and the linear MIMO detection scheme. The advantages of MIMO PNC are many. First, it removes the stringent carrier-phase requirement in single-antenna PNC. Second, it is linear in complexity with respect to the constellation size and the number of simultaneous data streams in MIMO. Simulation shows that MIMO PNC outperforms the straightforward MIMO NC significantly under random Rayleigh fading channel. Based on our analysis, we further conjecture that MIMO PNC outperforms MIMO NC under all possible realizations of the channel.\",\n \"Title: Collision Resolution by Exploiting Symbol Misalignment\\nYear: 2008\\n\\nAbstract: This paper presents CRESM, a novel collision resolution method for decoding\\ncollided packets in random-access wireless networks. In a collision,\\noverlapping signals from several sources are received simultaneously at a\\nreceiver. CRESM exploits symbol misalignment among the overlapping signals to\\nrecover the individual packets. CRESM can be adopted in 802.11 networks without\\nmodification of the transmitter design; only a simple DSP technique is needed\\nat the receiver to decode the overlapping signals. Our simulations indicate\\nthat CRESM has better BER performance than the simplistic Successive\\nInterference Cancellation (SIC) technique that treats interference as noise,\\nfor almost all SNR regimes. The implication of CRESM for random-access\\nnetworking is significant: in general, using CRESM to resolve collisions of up\\nto n packets, network throughput can be boosted by more than n times if the\\ntransmitters are allowed to transmit more aggressively in the MAC protocol.\",\n \"Title: A novel type of fuzzy propositional logic\\nYear: 2013\\n\\nAbstract: Negative knowledge is considered as important as positive knowledge in the theory of knowledge processing, which is investigated by many researchers. In this paper, a novel fuzzy propositional logic calculus system equipped with three kinds of negations, say contradictory, opposite and medium negation, is established, and the soundness theorem of the system is proved.\",\n \"Title: Blind Known Interference Cancellation with parallel real valued belief propagation algorithm\\nYear: 2012\\n\\nAbstract: This paper investigates interference-cancellation schemes at the receiver, in which the original data of the interference is known a priori. Such a priori knowledge is common in wireless relay networks. Directly removing the known interference requires accurate estimate of the interference channel, which may be difficult in many situations. In [1], we proposed a novel scheme, Blind Known Interference Cancellation (BKIC), for blind cancellation of known interference without interference channel information. BKIC consists of two steps. The first step combines adjacent symbols to cancel the interference, exploiting the fact that the channel coefficients are almost the same between successive symbols. After such interference cancellation, however, the signal of interest is also distorted. The second step recovers the signal of interest amidst the distortion. Two schemes for the second step, BKIC-S and successive BKIC-RBP, were proposed in [1]. BKIC-S removes distortion by smoothing while BKIC-RBP does so using a real-value belief propagation algorithm. Although successive BKIC-RBP performs well and is superior to BKIC-S, it requires a long processing time proportional to the packet length. To overcome this problem, this paper proposes a parallel BKIC-RBP algorithm. Parallel BKIC-RBP has similar performance as successive BKIC-RBP. It has the advantage of being amenable to parallel implementation with a much shorter processing time.\",\n \"Title: Synchronization Analysis in Physical Layer Network Coding\\nYear: 2010\\n\\nAbstract: Physical-layer Network Coding (PNC) makes use of the additive nature of the\\nelectromagnetic (EM) waves to apply network coding arithmetic at the physical\\nlayer. With PNC,the destructive effect of interference in wireless networks is\\neliminated and the capacity of networks can be boosted significantly. This\\npaper addresses a key outstanding issue in PNC: synchronization among\\ntransmitting nodes. We first investigate the impact of imperfect\\nsynchronization (i.e., finite synchronization errors) in a 3-node network. It\\nis shown that with QPSK modulation, PNC still yields significantly higher\\ncapacity than straightforward network coding when there are synchronization\\nerrors. Significantly, this remains to be so even in the extreme case when\\nsynchronization is not performed at all. Moving beyond a 3-node network, we\\npropose and investigate a synchronization scheme for PNC in a general chain\\nnetwork. At last, numerical simulation verifies that PNC is robust to\\nsynchronization errors. In particular, for the mutual information performance,\\nthere is about 0.5dB loss without time synchronization and there is at most 2dB\\nloss without phase synchronization.\",\n \"Title: Capacity of Two-Way Relay Channel\\nYear: 2009\\n\\nAbstract: This paper investigates the capacity of a wireless two-way relay channel in which two end nodes exchange information via a\\n relay node. The capacity is defined in the information-theoretic sense as the maximum information exchange rate between the\\n two end nodes. We give an upper bound of the capacity by applying the cut-set theorem. We prove that this upper bound can\\n be approached in low SNR region using “separated” multiple access for uplinks from the end nodes to the relay in which the\\n data from the end nodes are individually decoded at the relay; and network-coding broadcast for downlinks from the relay to\\n the end nodes in which the relay mixes the information from end nodes before forwarding. We further prove that the capacity\\n is approachable in high SNR region using physical-layer network coding (PNC) multiple access for uplinks, and network-coding\\n broadcast for downlinks. From our proof and observations, we conjecture that the upper bound may be achieved with PNC in all\\n SNR regions.\",\n \"Title: Channel coding and decoding in a MIMO TWRC with physical-layer network coding\\nYear: 2012\\n\\nAbstract: In this paper, we propose a joint design of MIMO-PNC (multiple-input multiple-output physical layer network coding) and channel decoding in two way relay channels. The paper shows that if we adopt the same Repeat Accumulate (RA) channel code at the two end nodes, there would be a compatible decoder at the relay which can transform the received superimposed packet to the network coding form of the two packets efficiently. Specifically, we redesign the belief propagation decoding algorithm of the RA code for traditional point-to-point channel to suit the need of the MIMO PNC channel. The simulation results show that our new scheme outperforms the previously proposed schemes which combined MIMO NC with channel coding/decoding significantly in terms of BER with little added complexity.\",\n \"Title: Joint Design of Physical-Layer Network Coding and Channel Coding\\nYear: 2008\\n\\nAbstract: Physical-layer Network Coding (PNC) can significantly improve the throughput of wireless two way relay channel (TWRC) by allowing the two end nodes to transmit messages to the relay simultaneously. To achieve reliable communication, channel coding could be applied on top of PNC. This paper investigates link-by-link channel-coded PNC, in which a critical process at the relay is to transform the superimposed channel-coded packets received from the two end nodes plus noise, 3 1 2 3 Y X X W\",\n \"Title: Multi-pair physical layer network coding with beamforming systems.\\nYear: 2012\\n\\nAbstract: In this paper, we propose a spatial multi-user physical layer network coding (SM-PNC) scheme by jointly utilizing PNC and beamforming techniques in a multi-user one relay communication system. The relay is equipped with multiple antennas and each user node is only equipped with a single antenna. The two way relay transmission consists of two phases. In multiple access phase, the summation and difference of the user data are combined with log likelihood ratio combination, which has a superior robustness to the traditional separation detection under any channel condition; In broadcast phase, optimal adaptive select-group broadcasting scheme is proposed, which can efficiently overcome the problem of near interference in beamforming. Simulation results validate the ability of the proposed algorithms. © 2012 IEEE.\",\n \"Title: A Refined Convergence Condition for Iterative Waterfilling Algorithm\\nYear: 2014\\n\\nAbstract: In a multi-user spectrum optimization problem, iterative waterfilling is normally used as a distributed power allocation algorithm. In this letter, we derive a new sufficient condition for its convergence, which is more relaxed than previous conditions. Our result strengthens the fact that the convergence of iterative waterfilling is dependent on signal-to-noise ratio (SNR), and that convergence is much easier in the low SNR regime.\",\n \"Title: One New Interpretation of Infinite Valued for Medium Proposition Logic\\nYear: 2009\\n\\nAbstract: Since three-valued model of medium logic (ML) was established, ML has been affirmed one kind of three-valued logic by many scholars. However, for medium proposition logic(MP) being as kernel theory of ML, Pan gave out a model of infinite valued of MP, on which based, proved the theorems of completeness and reliability. Based on this, this paper sets up a new model of infinite valued of MP whose truth valued range is [0, λ)∪(λ,1](λ∈ (0. 5, 1)), some properties of the model are investigated, and the theorems of completeness and reliability are proved, also. This new model not only further shows that “medium proposition logic is one kind of three valued logic” is incorrect, but also rightly depicts the medium principle which is basic idea of MP, and offered foundation for MP used in more other fields.\",\n \"Title: Secrecy rate study in two-hop relay channel with finite constellations\\nYear: 2013\\n\\nAbstract: Two-hop security communication with an eavesdropper in wireless environment is a hot research direction. The basic idea is that the destination, simultaneously with the source, sends a jamming signal to interfere the eavesdropper near to or co-located with the relay. Similar as physical layer network coding, the friendly jamming signal will prevent the eavesdropper from detecting the useful information originated from the source and will not affect the destination on detecting the source information with the presence of the known jamming signal. However, existing investigations are confined to Gaussian distributed signals, which are seldom used in real systems. When finite constellation signals are applied, the behavior of the secrecy rate becomes very different. For example, the secrecy rate depends on phase difference between the input signals with finite constellations, which is not observed with Gaussian signals. In this paper, we investigate the secrecy capacity and derive its upper bound for the two-hop relay model, by assuming an eavesdropper near the relay and the widely used M-PSK modulation. With our upper bound, the best and worst phase differences in high SNR region are then given. Numerical studies verify our analysis and show that the derived upper bound is relatively tight.\",\n \"Title: Joint Design Of Network Coding And Channel Decoding For Wireless Networks\\nYear: 2007\\n\\nAbstract: Network coding has been receiving much attention recently for its ability to improve network throughput and enhance network robustness. In this paper, we investigate the design of network coding in wireless networks and propose a combined low complexity network coding and channel decoding scheme. We analyze the capacity of the proposed scheme for both the binary symmetric channel (BSC) and AWGN channel and show that it can achieve almost the same channel capacity as traditional network coding with a small degradation in the system bit error rate (BER) performance while achieving almost 50% complexity reduction. It is also shown that the proposed network coding design can be applied in wireless cooperative networks.\",\n \"Title: Synchronization Analysis for Wireless TWRC Operated with Physical-layer Network Coding\\nYear: 2013\\n\\nAbstract: Physical-layer network coding (PNC) makes use of the additive nature of the electromagnetic waves to apply network coding arithmetic at the physical layer. With PNC, the destructive effect of interference in wireless networks is eliminated and the capacity of networks can be boosted significantly. This paper addresses a key outstanding issue in PNC: synchronization among transmitting nodes. We first investigate the impact of imperfect synchronization in a 3-node network with a straightforward detection scheme. It is shown that with QPSK modulation, PNC on average still yields significantly higher capacity than straightforward network coding when there are synchronization errors. Significantly, this remains to be so even in the extreme case when synchronization is not performed at all. Moving beyond a 3-node network, we propose and investigate a synchronization scheme for PNC in a general chain network. And we argue that if the synchronization errors can be bounded in the 3-node case, they can also be bounded in the general N-node case. Lastly, we present simulation results showing that PNC is robust to synchronization errors. In particular, for the mutual information performance, there is about 2 dB loss without phase or symbol synchronization.\",\n \"Title: Soft network coding in wireless two-way relay channels.\\nYear: 2008\\n\\nAbstract: Application of network coding in wireless two-way relay channels (TWRC) has received much attention recently because its ability to improve throughput significantly. In traditional designs, network coding operates at upper layers above (including) the link layer and it requires the input packets to be correctly decoded. However, this requirement may limit the performance and application of network coding due to the unavoidable fading and noise in wireless networks. In this paper, we propose a new wireless network coding scheme for TWRC, which is referred to as soft network coding (SoftNC), where the relay nodes applies symbol-by-symbol soft decisions on the received signals from the two end nodes to come up with the network coded information to be forwarded. We do not assume further channel coding on top of SoftNC at the relay node (channel coding is assumed at the end nodes). According to measures of the soft information adopted, two kinds of SoftNC are proposed: amplify-and-forward SoftNC (AF-SoftNC) and soft-bit-forward SoftNC (SBF-SoftNC). We analyze the both the ergodic capacity and the outage capacity of the two SoftNC schemes. Specifically, analytical form approximations of the ergodic capacity and the outage capacity of the two schemes are given and validated. Numerical simulation shows that our SoftNC schemes can outperform the traditional network coding based two-way relay protocol, where channel decoding and re-encoding are used at the relay node. Notable is the fact that performance improvement is achieved using only simple symbol-level operations at the relay node. © 2008 KICS.\",\n \"Title: Non-Memoryless Analog Network Coding in Two-Way Relay Channel\\nYear: 2011\\n\\nAbstract: Physical-layer Network Coding (PNC) can significantly improve the throughput of two-way relay channels. An interesting variant of PNC is Analog Network Coding (ANC). Almost all ANC schemes proposed to date, however, operate in a symbol by symbol manner (memoryless) and cannot exploit the redundant information in channel-coded packets to enhance performance. This paper proposes a non-memoryless ANC scheme. In particular, we design a soft-input soft-output decoder for the relay node to process the superimposed packets from the two end nodes to yield an estimated MMSE packet for forwarding back to the end nodes. Our decoder takes into account the correlation among different symbols in the packets due to channel coding, and provides significantly improved MSE performance. Our analysis shows that the SNR improvement at the relay node is lower bounded by IIR (R is the code rate) with the simplest LDPC code (repeat code). The SNR improvement is also verified by numerical simulation with LDPC code. Our results indicate that LDPC codes of different degrees are preferred in different SNR regions. Generally speaking, smaller degrees are preferred for lower SNRs.\",\n \"Title: Physical Layer Network Coding Over Finite And Infinite Fields\\nYear: 2008\\n\\nAbstract: Direct application of network coding at the physical layer - physical layer\\nnetwork coding (PNC) - is a promising technique for two-way relay wireless\\nnetworks. In a two-way relay network, relay nodes are used to relay two-way\\ninformation flows between pairs of end nodes. This paper proposes a precise\\ndefinition for PNC. Specifically, in PNC, a relay node does not decode the\\nsource information from the two ends separately, but rather directly maps the\\ncombined signals received simultaneously to a signal to be relayed. Based on\\nthis definition, PNC can be further sub-classed into two categories - PNCF (PNC\\nover finite field) and PNCI (PNC over infinite field) - according to whether\\nthe network-code field (or groups, rings) adopted is finite or infinite. For\\neach of PNCF and PNCI, we consider two specific estimation techniques for\\ndealing with noise in the mapping process. The performance of the four schemes\\nis investigated by means of analysis and simulation, assuming symbol-level\\nsynchronization only.\",\n \"Title: Mapping and Coding Design for Channel Coded Physical-layer Network Coding.\\nYear: 2013\\n\\nAbstract: Although BICM can significantly improves the BER performance by iteration processing between the demapping and the decoding in a traditional receiver, its design and performance in PNC system has fewer studied. This paper investigates a bit interleaved coded modulation (BICM) scheme in a Gaussian two-way relay channel operated with physical layer network coding (PNC). In particular, we first present an iterative demapping and decoding framework specially designed for PNC. After that, we compare different constellation mapping schemes in this framework, with the convergence analysis by using the EXIT chart. It is found that the anti-Gray mapping outperforms the Gray mapping, which is the best mapping in the traditional decoding schemes. Finally, the numerical simulation shows the better performance of our framework and verifies the mapping design. © 2013 IEEE.\",\n \"Title: Real-time implementation of physical-layer network coding\\nYear: 2013\\n\\nAbstract: This paper presents the first real-time physical-layer network coding (PNC) prototype for the two-way relay wireless channel (TWRC). Theoretically, PNC could boost the throughput of TWRC by a factor of 2 compared with traditional scheduling (TS) in the high signal-to-noise (SNR) regime. Although there have been many theoretical studies on PNC performance, there have been few experimental and implementation efforts. We built the first prototype of PNC about a year ago. It was, however, an offline system in which an offline PNC decoder was used at the relay. For a real-time PNC system, there are many additional challenges, including the needs for tighter coordination of the transmissions by the two end nodes, fast real-time PNC decoding at the relay, and a PNC-compatible retransmission scheme (i.e., an ARQ protocol) to ensure reliability of packet delivery. In this paper, we describe a real-time PNC prototype, referred to as RPNC, that provides practical solutions to these challenges. Indoor environment experimental results show that RPNC boosts the throughput of TWRC by a factor of 2 compared with TS, as predicted theoretically. RPNC prototype provides an interface to the application layer, with which we demonstrate the exchange of two image data files between the two end nodes.\",\n \"Title: Hot topic: physical-layer network coding\\nYear: 2006\\n\\nAbstract: A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11). The goal of this paper is to show how the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity-boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC) scheme to coordinate transmissions among nodes. In contrast to \\\"straightforward\\\" network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM) waves for equivalent coding operation. PNC can yield higher capacity than straight-forward network coding when applied to wireless networks. We believe this is a first paper that ventures into EM-wave-based network coding at the physical layer and demonstrates its potential for boosting network capacity. PNC opens up a whole new research area because of its implications and new design requirements for the physical, MAC, and network layers of ad hoc wireless stations. The resolution of the many outstanding but interesting issues in PNC may lead to a revolutionary new paradigm for wireless ad hoc networking.\",\n \"Title: Physical Layer Network Coding Schemes over Finite and Infinite Fields\\nYear: 2008\\n\\nAbstract: Direct application of network coding at the physical layer - physical layer network coding (PNC) - is a promising technique for two-way relay wireless networks. In a two-way relay network, relay nodes are used to relay two-way information flows between pairs of end nodes. This paper proposes a precise definition for PNC. Specifically, in PNC, a relay node does not decode the source information from the two ends separately, but rather directly maps the combined signals received simultaneously to a signal to be relayed. Based on this definition, PNC can be further sub-classed into two categories - PNCF (PNC over finite field) and PNCI (PNC over infinite field) - according to whether the network-code field (or groups, rings) adopted is finite or infinite. For each of PNCF and PNCI, we consider two specific estimation techniques for dealing with noise in the mapping process. The performance of the four schemes is investigated by means of analysis and simulation, assuming symbol-level time synchronization only.\",\n \"Title: On Fuzzy Propositional Logic with Different Negations.\\nYear: 2013\\n\\nAbstract: Pan pointed out that there are three kinds of different negations: contradictory negation, opposite negation and medium negation. He also proposed the novel fuzzy set FScom with the above three kinds of negations, followed by one improved fuzzy set IFScom that is established based on FScom. In this paper, a novel fuzzy propositional logic calculus system that equipped with three kinds of negations is set up to correspond to the FScom and IFScom. The soundness theorem and completeness theorem of the system is proved.\",\n \"Title: Throughput analysis of CSMA wireless networks with finite offered-load\\nYear: 2013\\n\\nAbstract: This paper proposes an approximate method, equivalent access intensity (EAI), for the throughput analysis of CSMA wireless networks in which links have finite offered-load and their MAC-layer transmit buffers may be empty from time to time. Different from prior works that mainly considered the saturated network, we take into account in our analysis the impacts of empty transmit buffers on the interactions and dependencies among links in the network that is more common in practice. It is known that the empty transmit buffer incurs extra waiting time for a link to compete for the channel airtime usage, since when it has no packet waiting for transmission, the link will not perform channel competition. The basic idea behind EAI is that this extra waiting time can be mapped to an equivalent “longer” backoff countdown time for the unsaturated link, yielding a lower link access intensity that is defined as the mean packet transmission time divided by the mean backoff countdown time. That is, we can compute the “equivalent access intensity” of an unsaturated link to incorporate the effects of the empty transmit buffer on its behavior of channel competition. Then, prior saturated ideal CSMA network (ICN) model can be adopted for link throughput computation. Specifically, we propose an iterative algorithm, “Compute-and-Compare”, to identify which links are unsaturated under current offered-load and protocol settings, compute their “equivalent access intensities” and calculate link throughputs. Simulation shows that our algorithm has high accuracy under various offered-load and protocol settings. We believe the ability to identify unsaturated links and compute links throughputs as established in this paper will serve an important first step toward the design and optimization of general CSMA wireless networks with offered-load control.\",\n \"Title: Implementation of physical-layer network coding\\nYear: 2013\\n\\nAbstract: This paper presents the first implementation of a two-way relay network based on the principle of physical-layer network coding. To date, only a simplified version of physical-layer network coding (PNC), called analog network coding (ANC), has been successfully implemented. The advantage of ANC is that it is simple to implement; the disadvantage, on the other hand, is that the relay amplifies the noise along with the signal before forwarding the signal. PNC systems in which the relay performs XOR or other denoising PNC mappings of the received signal have the potential for significantly better performance. However, their implementation also poses many challenges. For example, the relay must be able to deal with symbol and carrier-phase asynchronies of the simultaneous signals received from the two end nodes, and the relay must perform channel estimation before decoding. We investigate a PNC implementation in the frequency domain, referred to as FPNC, to tackle these challenges. FPNC is based on OFDM. In FPNC, XOR mapping is performed on the OFDM samples in each subcarrier rather than on the samples in the time domain. We implement FPNC on the universal soft radio peripheral (USRP) platform. Our implementation requires only moderate modifications of the packet preamble design of 802.11a/g OFDM PHY. With the help of the cyclic prefix (CP) in OFDM, symbol asynchrony and the multi-path fading effects can be dealt with simultaneously in a similar fashion. Our experimental results show that symbol-synchronous and symbol-asynchronous FPNC have essentially the same BER performance, for both channel-coded and unchannel-coded FPNC.\",\n \"Title: Channel coding and decoding in a relay system operated with physical-layer network coding\\nYear: 2009\\n\\nAbstract: This paper investigates link-by-link channel-coded PNC (Physical layer Network Coding), in which a critical process at the relay is to transform the superimposed channel-coded packets received from the two end nodes (plus noise), Y3 = X1+ X2+W3, to the network-coded combination of the source packets, S1 ⊕ S2. This is in contrast to the traditional multiple-access problem, in which the goal is to obtain both S1 and S2 explicitly at the relay node. Trying to obtain S1 and S2 explicitly is an overkill if we are only interested in S1 ⊕ S2. In this paper, we refer to the transformation Y3 → S1 ⊕ S2 as the Channel-decoding-Network-Coding process (CNC) in that it involves both channel decoding and network coding operations. This paper shows that if we adopt the Repeat Accumulate (RA) channel code at the two end nodes, then there is a compatible decoder at the relay that can perform the transformation Y3 → S1 ⊕ S2 efficiently. Specifically, we redesign the belief propagation decoding algorithm of the RA code for traditional point-to-point channel to suit the need of the PNC multiple-access channel. Simulation results show that our new scheme outperforms the previously proposed schemes significantly in terms of BER without added complexity.\",\n \"Title: Pareto Optimal Power Control via Bisection Searching in Wireless Networks.\\nYear: 2013\\n\\nAbstract: In this letter, we first prove that the upper boundary of the feasible signal to interference-plus-noise ratio (SINR) region achieved by power control is Pareto optimal under the constraints of minimum SINR requirements and limited power. Thanks to the Pareto optimal nature, we further propose a bisection searching based low-complexity algorithm that achieves arbitrary Pareto optimal power control over the entire feasible SINR region. More importantly, we can easily approach the upper boundary of the feasible SINR region through the proposed algorithm. © 1997-2012 IEEE.\",\n \"Title: Blind interference alignment over homogeneous 3-user 2 × 1 broadcast channel\\nYear: 2013\\n\\nAbstract: This paper studies the blind interference alignment (BIA) in a homogeneous 3-user 2 × 1 broadcast channel (BC). In a homogeneous BC, each link connecting the transmitter and a user undergoes independent fading with identical coherence time. BIA refers to the implementation of interference alignment with no need of CSIT. It is shown in our early work [1] that, BIA can be easily accomplished in a homogeneous 2-user 2 × 1 BC with no requirement of using time-frequency extension, or mounting an extra antenna to each single-antenna receiver. In this paper, we extend the idea to a homogeneous 3-user 2 × 1 BC. We show that, if the three homogeneous 2 × 1 links satisfy certain offset condition, a homogeneous 3-user 2 × 1 BC can be completely decomposed into finite-time channel blocks, and each of which is BIA-feasible. By using a new technique that is different with the one in our early work, we prove a sufficient condition for a homogeneous 3-user 2 × 1 BC to be completely decomposable, i.e., to be suitable for BIA to achieve the optimal 3over2 DoF. We further study a multi-user 2 × 1 broadcast network, in which there are K ≥ 3 homogeneous users and their coherence time offsets are independently and uniformly distributed. We show that, if K ≥ 15, a 2-antenna transmitter can find, with more than 99% certainty, three users to form a BIA-feasible 3-user BC and achieve the optimal 3over2 DoF with no need of CSIT. © 2013 IEEE.\",\n \"Title: Distributed Two-Way Switch and Stay Combining with a Single Amplify-and-Forward Relay.\\nYear: 2013\\n\\nAbstract: This letter considers a two-phase two-way relay network (TWRN) consisting of two sources and a single amplify-and-forward (AF) relay. In order to efficiently exploit the direct link between sources for achieving full diversity order, we propose to utilize a distributed switch and stay combining (DSSC) in the two-phase TWRN. In DSSC, one branch out of the direct and relay branches is activated, and the branch switching occurs when the minimum end-to-end signal-to-noise ratio (SNR) of bi-directional communication falls below a preset threshold. We analyze the system performance over Rayleigh fading channels, by deriving the lower bounds and asymptotic expressions for the outage probability and symbol error rate (SER). It is shown that the DSSC preserves the same spectral efficiency as pure analogy network coding (ANC) protocol, while can concurrently achieve full diversity order as the optimal selection (OS) with less channel estimation complexity and lower switching rate.} Numerical and simulation results demonstrate the proposed studies. © 2013 IEEE.\",\n \"Title: The Capacity of Known Interference Channel\\nYear: 2015\\n\\nAbstract: In this paper, we investigate the capacity of a known interference channel, where a transmitter sends information to a receiver in the presence of a block-fading interference link, and the receiver knows the interference data but not the channel gain of the interference link. An upper bound and a lower bound for the capacity of this known interference channel are derived. Specifically, the capacity lower bound is achieved by a blind known interference cancellation (BKIC) scheme, which can remove the interference without the knowledge of the interference channel gain. We further show that the achievable lower bound of BKIC can approach the upper bound in high SNR regime. Our results show that the lack of the knowledge of the channel gain of the interfering link causes only a small fractional loss of degrees of freedom (capacity prelog).\",\n \"Title: Distance-based Location Management Utilizing Initial Position for Mobile Communication Networks\\nYear: 2016\\n\\nAbstract: This paper aims at improving the distance-based location management scheme for mobile communication networks. In location management, a mobile terminal (MT) is tracked based on its location-update area (LA). The improvement is brought about by joint optimization of LA center and LA size. For LA center optimization (LCO), we determine the optimal center position of the LA given the initial position of the MT upon each location update. The investigation of optimal LA center has eluded research to date. Based on the popular continuous-time random walk (CTRW) mobility model, we propose an analytical framework that uses a diffusion equation to determine the optimal LA center that minimizes the total cost of location management, consisting of the location update cost and terminal paging cost. This framework allows us to easily model the non- Markovian movement of the MT and evaluate the impact of various measurable physical parameters (such as length of road section, angle between road sections, road section crossing time) and LA center. In particular, we show that proper LA center can significantly reduce the total cost. For example, for the circular LA and low Poisson call-arrival rate, optimizing the LA center alone has the potential of reducing the cost by up to 37%. Joint optimization of the LA center and terminal paging scheme can reduce the cost even further. Simulations results match the theoretical analysis to a gap within 3%, indicating that our theoretical model is very accurate.\",\n \"Title: System Utility Maximization with Interference Processing for Cognitive Radio Networks\\nYear: 2015\\n\\nAbstract: In spectrum underlay cognitive radio networks, secondary users (SUs) are allowed to reuse the spectrum allocated to a primary system. The interference between SUs actually carries information and can potentially be exploited to improve the network performance through information-theoretic interference processing. In this paper, we design an optimal joint power and rate control algorithm that maximizes the secondary system utility subject to the interference temperature constraints of primary users based on the capacity-approaching interference processing scheme called as the Han-Kobayashi scheme. The optimal solution is difficult to achieve, because the optimization problem is in general non-convex. To make the optimization problem tractable, this paper first transforms the problem into a monotonic optimization problem through exploiting its hidden monotonicity. We then devise an effective algorithm to obtain the global optimal solution to the joint power and rate control problem in the Han-Kobayashi scheme. The key idea behind the proposed algorithm is to construct a sequence of shrinking polyblocks that approximate the upper boundary of the feasible region with increasing precision. Numerical results further show that the achieved utility of our scheme significantly outperforms the utility of conventional schemes which treat the interference between SUs as the noise.\",\n \"Title: Full Diversity Physical-Layer Network Coding in Two-Way Relay Channels With Multiple Antennas\\nYear: 2014\\n\\nAbstract: This paper studies a two-way relay channel where two single-antenna users exchange messages via a relay with K antennas. A new physical-layer network coding (PNC) method is proposed, referred to as channel-quantized PNC (CQ-PNC), that can achieve full diversity gain of K. The proposed method converts K received signals at the relay into two signals by a QR decomposition. The first one is a weighted summation of the two users' messages and the second one is a scaled version of one user's message. Then, the first signal is quantized at the relay by using the channel coefficient and the quantization error is cancelled by using the side information of the second signal. Finally, a Gaussian-integer weighted summation of the two users' messages is obtained, mapped to network codeword, and then broadcast to the two users. It is proved that the proposed CQ-PNC can achieve the full diversity gain of K with receiver side channel information, low computational complexity and symbol level synchronization. Simulation results demonstrate that the proposed method obtains optimum performance results with negligible gap.\",\n \"Title: Near-Optimal Modulo-and-Forward Scheme for the Untrusted Relay Channel.\\nYear: 2015\\n\\nAbstract: This paper studies an untrusted relay channel, in which the destination sends artificial noise simultaneously with the source sending a message to the relay, in order to protect the source's confidential message. The traditional amplify-and-forward (AF) scheme shows poor performance in this situation because of the interference power dilemma. Providing better security by using stronger artificial ...\",\n \"Title: Opportunistic relaying with analogue and digital network coding for two-way parallel relay network.\\nYear: 2014\\n\\nAbstract: A pair of terminals exchanging information via a layer of parallel relay nodes under slow fading is considered. Two protocols are proposed based on the combination of opportunistic relaying (OR) with analogue network coding (ANC), named ORANC, or with digital network coding (DNC), named ORDNC, respectively. Two schemes/versions of ORDNC, including 2-phase ORDNC (2P-ORDNC) and 3-phase ORDNC (3P-ORD...\",\n \"Title: Exploring Coding Benefits in CDN-Based VoD Systems.\\nYear: 2014\\n\\nAbstract: Currently, video-on-demand (VoD) streaming over Internet is a popular application. Because of the rapidly growing video population and user population, how to maintain high user quality of experience (QoE) with low cost is a challenging problem for Internet video streaming providers. A promising technique to potentially benefit VoD streaming system is network coding. A number of recent works studi...\",\n \"Title: Survey on cooperative strategies for wireless relay channels\\nYear: 2014\\n\\nAbstract: AbstractCooperative communication has the potential of providing better throughput and reliability to wireless systems when compared with direct communication. To realise the potential gain, it is important to design cooperative strategies for some representative scenarios. This survey deals with three basic wireless relay channels, namely, the parallel relay channel, the multiple-access relay channel and the broadcast relay channel. For the first channel, which models a single unicast connection, various forwarding strategies are studied. For the second and third channels, which model, respectively, the uplink and downlink scenarios with multiple unicast connections; network codes that exploit the possibility of coding among the connections are studied. The common aim pertaining to the studies of all these three channels is to use the limited radio resource in the most efficient way. Beside the aforementioned conventional works, studies that have state-of-the-art assumptions for relay networks, including outdated channel state information at the transmitter, full duplex relay and practical rateless-coded cooperation, are also extensively reviewed. Copyright © 2014 John Wiley & Sons, Ltd.\",\n \"Title: MIMO physical layer network coding based on VBLAST detection\\nYear: 2012\\n\\nAbstract: For MIMO two-way relay channel, this paper proposes a novel scheme, VBLAST-PNC, to transform the two superimposed packets received by the relay to their network coding form. Different from traditional schemes, which tries to detect each packet before network coding them, VBLAST-PNC detects the summation of the two packets before network coding. In particular, after firstly detecting the second layer signal in 2-by-2 MIMO system with VBLAST, we only cancel part of the detected signal, rather than canceling all the components, from the first layer. Then we directly map the obtained signal, which is a weighted summation of the first layer and the second layer, to the network coding form. With such partial interference cancellation, the error propagation effect is significantly mitigated and the performance is thus improved as shown in simulations.\",\n \"Title: Joint Cooperative MAC and Network Coding for Two-way Relaying Networks\\nYear: 2014\\n\\nAbstract: This paper studies a three-hop two-way relaying network, in which two end nodes exchange their information data via two relays. In literature, it usually assumes that each link of the two-way relaying network is error-free. Under this assumption, if considering scheduling and network coding (NC), one only needs three time slots to exchange a pair of messages, obtaining throughput 2/3 messages/time slot. This paper shows that when each link is not zero, the throughput of NC decreases with the increasing of FER. To improve the throughput of the NC scheme, this paper integrates the technology of cooperative MAC to propose a new scheme, namely joint cooperative MAC and NC (JCNC) scheme. Simulation and analytical results demonstrate that the proposed JCNC scheme overperforms the NC scheme. An appealing observation is that each relay node only needs four cooperative nodes to fully gain the advantage of JCNC against NC.\",\n \"Title: Capacity of known interference channel with Gaussian modulations\\nYear: 2014\\n\\nAbstract: In this paper, we investigate the capacity of known interference channel, where the receiver knows the interference data but not the channel gain of the interference data, assuming the use of Gaussian modulations. We first present a tight upper bound for the capacity of this known-interference channel. After that, we obtain the achievable rate of the channel with a blind known interference cancellation (BKIC) scheme in closed form. We prove that this achievable rate can approach the aforementioned upper bound in the high SNR regime. Moreover, it is much larger than the achievable rate of the traditional interference cancellation scheme. In particular, the achievable rate of BKIC continues to increase with SNR in the high SNR regime, while that of the traditional scheme approaches a fixed bound that does not improve with SNR.\",\n \"Title: Interference-aware system utility maximization for cognitive radio networks\\nYear: 2014\\n\\nAbstract: In this paper, we aim to obtain the optimal joint power and rate control that maximizes the interference-aware secondary system utility subject to the interference temperature constraints of primary users (PUs) on the basis of the information-theoretic interference processing technique. In particular, we first draw the capacity-approaching rate region of secondary users (SUs) with given transmit power and channel information of SUs based on the near optimal interference cancellation and power splitting technique. Then, we formulate the power controlled secondary system utility maximization problem under the interference temperature constraints of PUs in the capacity-approaching rate region. To solve the optimization problem, we decompose it into one convex subproblem over the rate region with fixed power allocation and the other partitioning subproblem of the power allocations. Finally, we present an efficient algorithm, which can quickly achieve the utility-maximized power and rate control based on the idea of simulated annealing.\",\n \"Title: Two-Way Decode-and-Forward for Low-Complexity Wireless Relaying: Selective Forwarding Versus One-Bit Soft Forwarding.\\nYear: 2016\\n\\nAbstract: Motivated by applications such as battery-operated wireless sensor networks (WSN), we propose an easy-to-implement low-complexity two-way relaying scheme. In particular, we address the challenge of improving the standard two-way selective decode-and-forward protocol (TW-SDF) in terms of block-error-rate (BLER) with minor additional complexity and energy consumption. By following the principle of s...\",\n \"Title: Secure Switch-and-Stay Combining (SSSC) for Cognitive Relay Networks.\\nYear: 2016\\n\\nAbstract: In this paper, we study a two-phase underlay cognitive relay network, where there exists an eavesdropper who can overhear the message. The secure data transmission from the secondary source to secondary destination is assisted by two decode-and-forward (DF) relays. Although the traditional opportunistic relaying technique can choose one relay to provide the best secure performance, it needs to con...\",\n \"Title: Overhearing Protocol Design Exploiting Intercell Interference in Cooperative Green Networks.\\nYear: 2016\\n\\nAbstract: The conventional two-way relaying (TWR) protocol requires that the transmitter (receiver) in one direction must be the receiver (transmitter) in the other direction, a limitation precluding the application of the TWR for sophisticated real-world wireless networks. In this paper, we study a more general multicell system consisting of a downlink (DL) traffic in one cell and an uplink (UL) traffic in an adjacent cell, with a multiantenna relay located in the cell edge and shared by both cells. For the coexistence of DL and UL transmissions, we propose exploiting the overheard signals from the adjacent cell (commonly known as the intercell interference) to improve the quality of signal reception in both cells. To reduce the power consumption to suit for green networks, we design the optimum relay precoder to minimize the total power at the relay yet satisfying the rate constraints for both the DL and UL traffic flows. The original precoder design is a nonconvex problem that is difficult to solve. To make the problem tractable, we transform the nonconvex problem to an equivalent quadratically constrained quadratic program (QCQP), which is then solved by the semidefinite relaxation (SDR) technique. Finally, simulations validate the effectiveness of our proposed protocol together with the optimized relay precoder.\",\n \"Title: Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM.\\nYear: 2015\\n\\nAbstract: Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.\",\n \"Title: Demo: Software-Defined Device to Device Communication in Multiple Cells\\nYear: 2015\\n\\nAbstract: This work aims to design a novel multi-hop device-to-device (D2D) communication system across multiple cells, by applying the spirit of software-defined networking, where the controllers regulate the data flows between the device-level switches in a centralized way. We utilize a hierarchical control plane, where the global controller handles the cross-cell D2D transmission, and the local controller determines the intra-cell D2D routing and scheduling. Specifically, by collecting the network information periodically, the controllers can generate the topology of D2D network and provide it to the applications. Through the use of USRP hardware, we demonstrate the feasibility of our proposed system.\",\n \"Title: Switch-and-Stay Combining Relaying for Security Enhancement in Cognitive Radio Networks\\nYear: 2015\\n\\nAbstract: Opportunistic relaying scheme (ORS), where the best relay is selected for dual-hop communication, has been widely considered as the global optimum relaying technique. However, due to the requirement of acquiring the full channel state information (CSI) of all links, ORS has increased the system's complexity and might be harmful to the network stability, especially for the large-scale networks. In this paper, we therefore proposed an alternative scheme, namely, secure switch-and-stay combining (SSSC) protocol for providing the best secure performance. In particular, a two-phase underlay cognitive relay network, where one out of two decode-and-forward (DF) is activated to assist the secure data transmission. The secure relay switching occurs when the relay cannot support the secure communication any longer. We study the system secure performance of SSSC protocol by deriving an analytical secrecy outage probability as well as an asymptotic expression in the high main-to-eavesdropper ratio (MER) region. It is shown that SSSC can substantially reduce the switching rate with lower channel estimation complexity, and approach the full diversity meanwhile.\",\n \"Title: Ergodic Rate Analysis for User Access in Downlink Heterogeneous Cloud Radio Access Networks.\\nYear: 2015\\n\\nAbstract: Characterizing user access methods in heterogeneous cloud radio access networks (H-CRANs) is critical for performance optimization. Different from the user access in cloud radio access networks (C-RANs), the inter-tier interference from macro base station has a great impact on user access in HCRANs. In this paper, after considering the inter-tier interference, the ergodic rates of downlink H-CRANs for two proposed user access methods, namely distance based and cluster based, are analyzed. The corresponding mathematical expressions of ergodic rates have been derived. In particular, the closed-form expression for the upper bound of ergodic rate is proposed. Simulation results corroborate the accuracy of the derived expressions for these two methods. Furthermore, the cluster based user access method outperforms the distance based user access method when the intensity of remote radio heads is sufficiently high.\",\n \"Title: Practicing a record-and-replay system on USRP\\nYear: 2013\\n\\nAbstract: Signal recording and replaying is widely used in data analysis, device test, etc. Motivated by this fact, our paper focuses on developing a low-cost record-and-replay prototype based on USRP (Universal Software Radio Peripheral). In particular, we designed and developed a GPS (Global Positioning System) record-and-replay system, which can sample the real GPS signal and then output the analog GPS signal to other devices with cable. In this prototype, we first design the record flow graph and the replay flow graph for USRP, which is verified in the FM band with the FM (Frequency Modulation) broadcasting. After that, we replace the passive antenna with one active antenna suit to amplify the small power GPS signal, lower the quantization bits to reduce the data rate in order to support the wide bandwidth of the GPS signal. Finally, our GPS record-and-replay system is tested and verified by a real GPS receiver. The good performance of our GPS record-and-replay system showed that USRPs can not only used in academia as experiment devices but also used in industry as low-cost wireless auxiliary devices.\",\n \"Title: Exploiting the UEP property of polar codes to reduce image distortions induced by transmission errors.\\nYear: 2015\\n\\nAbstract: This paper considers the application of polar codes in image transmissions. When the polar codeword length is finite, the information bits in different positions experience different but predictable unequal post-decoding error probabilities. Exploiting this property, we propose to order the information bits in a polar codeword according to their erroneously transmission induced image distortions. Specifically, the most significant bits (MSB, left-most bits) of each pixel in an image are transmitted through the lower error probability bit-channels (logical channel post the polar code) and the Least Significant Bits (LSB, right-most bits) of each pixel are transmitted through higher error probability bit-channels. In this way, the Polar codes output bit streams with different importance will be given unequal error protection (UEP), resulting in smaller distortion to the image. Our analysis and simulation results reveal that the image transmission system coded by polar codes with our UEP scheme yields more than 7dB gain over the same polar codes image transmission system without UEP, when the delivered image Peak Signal to Noise Ratio (PSNR) is near 40dB.\",\n \"Title: STAC: Simultaneous Transmitting and Air Computing in Wireless Data Center Networks\\nYear: 2015\\n\\nAbstract: The data center network (DCN), wired or wireless, features large amounts of Many-to-One (M2O) sessions. Each M2O session is currently established using Point-to-Point (P2P) communications and Store-and-Forward (SAF) relays, and is generally followed by a certain computation at the destination, typically a weighted summation of the received information digits. Fundamentally different from this separate P2P/SAF-based-transmission and computation framework, this paper proposes STAC, a novel physical layer scheme that achieves Simultaneous Transmission and Air Computation in wireless DCNs. In particular, STAC builds on a number of distinguishing characteristics of DCs to take advantage of the superposition nature of electromagnetic signals. With STAC, multiple sources transmit in the same time slot with appropriately chosen parameters, such that the superimposed signal can be directly transformed to the desired summation at the receiver. To enable STAC, we propose an enhanced Software Defined Network (SDN) architecture, where a wired low-bandwidth backbone provides wireless transceivers with external reference signals. Theoretical analysis and simulation results show that STAC can significantly improve both bandwidth and energy efficiency in DCNs.\",\n \"Title: Secure TWRC system based on channel quantized PNC\\nYear: 2015\\n\\nAbstract: Because of the broadcasting nature of wireless communications, passive eavesdropping is a well-known security loophole. Generally there are two basic ideas to deal with the eavesdropper at physical layer: friendly jamming and channel state information dependent encoding. Based on the channel quantized physical-layer network coding (CQ-PNC), we propose a secured CQ-PNC scheme to combine both ideas aforementioned to prevent the eavesdropper from obtaining the messages. Specifically, the CQ-PNC consists of co-channel signal superposition in the air (friendly jamming) and channel state information dependent mapping (PNC mapping). As a result, the information is more difficult to be detected at the eavesdropper, meanwhile there is almost no rate loss at the legal receiver. To verify the effectiveness of the proposed secure CQ-PNC, we implemented the scheme on a software defined radio (USRP) experimental platform.\",\n \"Title: Design of (4, 8) Binary Code with MDS and Zigzag-Decodable Property.\\nYear: 2016\\n\\nAbstract: Dedicated to network code structured distributed storage system, a novel (4, 8) storage code is designed. The code possesses the following properties: operation within binary field , maximum distance separable property, zigzag decodable, requiring symmetric storage overhead among multiple storage nodes, and requiring little storage overhead . In the design of such a storage code, we first let original packets shift to the right by several bits and then add them together within binary field in bitwise manner . We propose a smart design on a cyclic matrix that represents the number of bits shifted by those packet. Existing works do not hold these properties simultaneously , which have the drawback of high encoding and decoding complexity, large storage overhead, and complicated storage room allocation procedure, etc.\",\n \"Title: Design and implementation of device-to-device software-defined networks.\\nYear: 2016\\n\\nAbstract: To support multi-hop device-to-device (D2D) transmission, this paper proposes a novel wireless architecture, device to -device software defined networks (D2D-SDN). In D2D-SDN, mobile devices are not only terminals but also can act as wireless switches to forward packets for others based on the routing and scheduling instructions from controllers. Under the proposed D2D-SDN architecture, we test the routing and adaptive resource allocation by jointly exploiting network information collection, and network abstraction. We also developed a testbed based on USRP to conduct experiments and demonstrate the feasibility and superiority of our proposed D2D-SDN.\",\n \"Title: Expressive power of linear-temporal logic based on generalized possibility measures\\nYear: 2016\\n\\nAbstract: Model checking of linear-time properties based on possibility measures was developed by Li and Li (2013). However, the linear-time properties considered in the previous work were classical and qualitative, possibility information of the systems was not considered thoroughly at all. Therefore, the quantitative model checking of fuzzy linear-time properties based on generalized possibility measures was studied by Li (2016). This paper is a continuation of the above work. In this paper, we study the expressive power of possibilistic linear-temporal logic (PoLTL). Unlike possibilistic computation tree logic (PoCTL) in which the expressiveness of PoCTL is more powerful than computation tree logic (CTL), it is surprising that PoLTL is as expressive as linear-temporal logic (LTL). Furthermore, the comparison between PoLTL and generalized PoLTL (GPoLTL) is given. It is shown that the expressiveness of GPoLTL with restrained conditions is the same as PoLTL when semantics of GPoLTL is interpreted in the frame of possibilistic Kripke structure.\",\n \"Title: An Accelerometer-Assisted Transmission Power Control Solution for Energy-Efficient Communications in WBAN.\\nYear: 2016\\n\\nAbstract: Energy efficiency is a key issue in wireless body area networks (WBANs). A number of transmission power control (TPC) schemes have been developed to improve the efficiency of transmission, which is one of the most energy consuming operations in WBAN. To save energy, these schemes only probe the link quality from the received data packets. However, due to large intervals between data packets and fast dynamic on-body link characteristics in WBAN, the obtained link information is usually outdated. In this case, the performance of the current TPC scheme is poor. This paper proposes an accelerometer-assisted TPC (AA-TPC) scheme, which exploits the periodic fluctuations of link qualities to improve the transmission energy efficiency. Consider the relationship between link quality and body movement, AA-TPC makes transmissions at ideal channel points that are identified by using the local accelerometer. We first conduct experiments to investigate the correlation between periodic movements and link quality. Then, we propose an algorithm to locate the time point in each period with the best link quality to transmit packets. The specific transmission power is then determined by the feedback information from the receiver. Finally, we evaluate the energy efficiency of AA-TPC based on a CC2420 platform in both a periodic scenario (without any aperiodic movement to break the periodicity) and a realistic scenario (which has aperiodic movements 20% of the time). The results show that about 26.4% and 18% of total energy consumption can be saved on average in the periodic and realistic scenarios, respectively.\",\n \"Title: STAC: Simultaneous Transmitting and Air Computing in Wireless Data Center Networks.\\nYear: 2016\\n\\nAbstract: The data center network (DCN), wired or wireless, features large amounts of many-to-one (M2O) sessions. Each M2O session is currently established using point-to-point (P2P) communications and store-and-forward (SAF) relays, and is generally followed by a certain computation at the destination, typically a weighted summation of the received information digits. Fundamentally different from this separate P2P/SAF-based-transmission and computation framework, this paper proposes simultaneous transmission and air computation (STAC), a novel physical layer scheme that achieves STAC in wireless DCNs. In particular, STAC builds on a number of distinguishing characteristics of DCs to take advantage of the superposition nature of electromagnetic signals. With STAC, multiple sources transmit in the same time slot with appropriately chosen parameters, such that the superimposed signal can be directly transformed to the desired summation at the receiver. To enable STAC, we propose an enhanced software-defined network architecture, where a wired low-bandwidth backbone provides wireless transceivers with external reference signals. We also discuss some new challenges that STAC brings to scheduling and routing. Theoretical analysis and simulation results show that STAC can significantly improve both bandwidth and energy efficiency in DCNs.\",\n \"Title: Orbital Angular Momentum (OAM) Generation by Cylinder Dielectric Resonator Antenna for Future Wireless Communications.\\nYear: 2016\\n\\nAbstract: In this paper, we demonstrate that the solid cylinder dielectric resonator antenna (CDRA) is a high radiation efficient device to generate orbital angular momentum (OAM) waves with different OAM modes at different frequencies. Theoretical formulations of its radiation fields are deduced in the cylindrical coordinate system, which shows that all components of the electric and magnetic fields have the azimuth dependence of e(jm phi). A full-wave simulation on a practical size CDRA is then implemented to validate the theoretical derivations. The simulation results show that different separated OAM modes can be obtained at different frequencies. Specifically, the higher order OAM modes are generated at higher electron magnetic modes, which makes CDRA a promising method to overcome the narrow band and multiband issues existing in the design of current OAM devices. Furthermore, a maximum radiation efficiency of 97 : 1% at 13.3 GHz, and a minimum radiation efficiency of 72 : 4% at 19.45 GHz are obtained in our simulation, which is a high radiation efficiency feature at the OAM resonant frequencies.\",\n \"Title: On-off Analog Beamforming for Massive MIMO.\\nYear: 2018\\n\\nAbstract: This paper investigates a new analog beamforming architecture for massive multiple-input multiple-output (MIMO) systems, where each of the multiple transmit antennas is switched to be on or off to form a beam according to the channel state information at transmitters. This on-off analog beamforming (OABF) scheme has the advantages in terms of both hardware complexities and algorithmic complexities...\",\n \"Title: Experiment verified physical-layer collision separation of passive UHF tags.\\nYear: 2016\\n\\nAbstract: In this paper, we propose a low complexity algorithm to separate collided RFID signals. By carefully analyzing the collided signals when two tags with the same bit durations respond to a reader simultaneously, we observed that the individual amplitudes of each tag appear in pairs and the two tags' information bits are different, and regular RFID signals with larger amplitudes appear if the two tags' information bits are the same. Exploiting this feature, our algorithm can separate the collided signals of two tags to enhance the identification efficiency. Moreover, we implement our algorithm based on a software-defined reader platform to verify the validity of our algorithm. Simulation and experiment results show that the overall system throughput with our method is significantly increased in comparison with the conventional Gen-2 MAC scheme.\",\n \"Title: Asynchronous NOMA for Downlink Transmissions.\\nYear: 2017\\n\\nAbstract: In this letter, we propose a novel symbol-asynchronous non-orthogonal multiple access (ANOMA) scheme for downlink transmissions. First, ANOMA introduces an artificial symbol-offset between packets destined for different user equipment (UE) in order to reduce their mutual interference. Second, ANOMA adopts a pre-coding at the base station and a whitening-and-decomposing (WD) detection scheme at the receiving UE to simplify signal detection. With these two novel techniques, ANOMA can significantly enlarge the achievable rate region of the traditional non-orthogonal multiple access (NOMA). Moreover, the impact of pulse shape and symbol offset on the performance of ANOMA scheme are investigated in both Gaussian and Rayleigh block-fading channels. The numerical results show that ANOMA with precoding and WD detection outperforms asynchronous NOMA with successive interference cancellation detection, both outperform synchronous NOMA.\",\n \"Title: The Decision Delay in Finite-Length MMSE---DFE Systems\\nYear: 2015\\n\\nAbstract: This paper investigates the performance of the minimum mean square error (MMSE) decision feedback equalization system and optimizes its key parameter, decision delay, to minimize the system MSE. For the first typical scenario where the feedback filter length is larger than or equal to the channel order , we present an analytical expression of the system MSE in terms of decision delay. A few results in previous literature can be regarded as special cases of this expression. For the second typical scenario where feedback filter length is less than the channel order , it is difficult to obtain the explicit solutions. We prove that at a given decision delay, the MSE for is always larger than or equal to that for . As a corollary of two derived theorems, the optimum feedback filter length should be set to the channel order for large . Furthermore, in this scenario, the optimal decision delay is conventionally found with an exhaustive search over all possible delays, whose computational complexity is linear with the searching space and unaffordable. To lower the complexity, we then propose an efficient algorithm to find the optimal decision delay. The complexity of our algorithm is only a log function of the searching space and its MSE and symbol error rate performance is almost the same as the traditional exhaustive search .\",\n \"Title: Multi-Antenna Enabled Cluster-Based Cooperation in Wireless Powered Communication Networks.\\nYear: 2017\\n\\nAbstract: In this paper, we consider a wireless powered communication network (WPCN) consisting of a multi-antenna hybrid access point (HAP) that transfers wireless energy to and receives sensing data from a cluster of low-power wireless devices (WDs). To enhance the throughput performance of some faraway WDs, we allow one of the WDs to act as the cluster head (CH) that helps forward the messages of the other cluster members. However, the performance of the proposed cluster-based cooperation is fundamentally limited by the high energy consumption of the CH, who needs to transmit all the WDs' messages including its own. To tackle this issue, we exploit the capability of multi-antenna energy beamforming (EB) at the HAP, which can focus more transferred power to the CH to balance its energy consumption in assisting the other WDs. Specifically, we first derive the throughput performance of each individual WD under the proposed scheme. Then, we jointly optimize the EB design, the transmit time allocation among the HAP and the WDs, and the transmit power allocation of the CH to maximize the minimum data rate achievable among all the WDs (the max-min throughput) for improved throughput fairness among the WDs. An efficient optimal algorithm is proposed to solve the joint optimization problem. Moreover, we simulate under practical network setups and show that the proposed multi-antenna enabled cluster-based cooperation can effectively improve the throughput fairness of WPCN.\",\n \"Title: Novel Intersymbol Interference Cancellation Scheme to Enable Parallel Computational and High-Performance Faster-Than-Nyquist Signaling.\\nYear: 2017\\n\\nAbstract: In this paper, we deal with the intersymbol interference (ISI) cancellation problem induced by the faster-than-Nyquist (FTN) signaling. In the traditional FTN signaling, the detection delay at the receiver depends on the number of states of the ISI trellis. In this case, the corresponding Viterbi algorithm or the BCJR algorithm would be far too complex and introduce a huge delay when the ISI tap set is large. In this paper, we propose a novel interference cancellation scheme to combat the ISI for the FTN communication system which enables the parallel computations. Our proposed scheme adopts a pre-coding at the transmitter and a decomposition detection at the receiver. Particularly, with the help of the parallel computations, the running time of our proposed scheme is independent of the ISI trellis, which allows the application of a more severe FTN system with a smaller time acceleration factor. Besides, based on the pre-coding scheme and the decomposition detection, an adaptive transmission strategy is developed, which can improve the performance of the proposed scheme dramatically. Finally, we compare our scheme with the offset BCJR algorithm and the offset Viterbi algorithm benchmarks. The simulation results verify that our scheme can outperform previous decoders with a better bit error rate and a much less delay.\",\n \"Title: An Opportunistic Source Selection Spectrum Sharing Protocol for Overlay Cognitive Two-Way Relaying System.\\nYear: 2017\\n\\nAbstract: In this paper, an overlay two-way relaying spectrum sharing protocol is proposed by employing an opportunistic source selection strategy. In this protocol, a secondary user acts as a relay and allocates a fraction of its transmit power to assist two-way communication between two primary users (PUs), thus, gaining spectrum sharing as long as without degrading the primary system performance. Moreover, to exploit the multiuser diversity inherent in the multiuser primary systems, the two PUs transmit their own messages in an opportunistic manner, depending on the instantaneous channel conditions. Specifically, at each transaction, only one of the PUs is selected for transmission such that the instantaneous mutual information on the whole primary network is maximized and as a result, the multiuser diversity gain is achieved by the primary system. Then, we derive the exact closed-form outage probabilities for both the primary and secondary systems. To gain more insights, closed-form asymptotic outage probability expressions are also derived. Based on the approximations, we further obtain analytical results on the diversity order of the primary system and the power-allocation threshold above which the spectrum sharing is allowed for secondary transmission. Numerical results are presented to verify our analysis and highlight the advantages of our proposed scheme compared to another two-phase spectrum sharing protocol based on analog network coding.\",\n \"Title: A novel table look-up scheme based on GFScom and its application.\\nYear: 2017\\n\\nAbstract: This work considers Mamdani fuzzy systems constructed from finite input–output data pairs using the generalized fuzzy sets with contradictory, opposite and medium negation (GFScom). Considering that the information available often consists of a set of finite numerical data pairs, a new table look-up scheme for constructing Mamdani fuzzy systems is presented. The designed fuzzy system is proved to be capable of approximating any real continuous function on a compact set to arbitrary degree of accuracy. We use this fuzzy modeling method for the truck back-upper control problem. The effectiveness of the proposed method is demonstrated by a comparison with the traditional table look-up scheme.\",\n \"Title: Artificial-Noise-Aided Secure Channel with a Full-duplex Source.\\nYear: 2017\\n\\nAbstract: This paper consider a new secure communication scene where a full-duplex transmitter (Alan) need to transmit confidential information to a half-duplex receiver (Bob), with a silent eavesdropper (Eve) that tries to eavesdrop the confidential information. For realizing secure communication between Alan and Bob, a novel two phases communication scheme is proposed: in Phase 1, Alan and Bob send artificial noises (AN) simultaneously, while in Phase 2, Alan superimposes the AN received in Phase 1 with its confidential signal and sends the mixed signalto Bob. Since the mixed AN could degrade the SINR (Signal to Interference and Noise Ratio) of Eve, but does not affect the SINR of Bob, a secrecy capacity can be achieved. We also derive the conditions that the secrecy capacity of the proposed scheme exists, and analyze the secrecy outage probability under Rayleigh fading channel. Numerical results show that the secrecy capacity is about two times higher than without AN, even though in the proposed scheme half of the time is used to transmit ANs, and the outage probability is about five times lower than that without AN.\",\n \"Title: A wavelet frame coefficient total variational model for image restoration.\\nYear: 2017\\n\\nAbstract: In this paper, we propose a vector total variation (VTV) of feature image model for image restoration. The VTV imposes different smoothing powers on different features (e.g. edges and cartoons) based on choosing various regularization parameters. Thus, the model can simultaneously preserve edges and remove noises. Next, the existence of solution for the model is proved and the split Bregman algorithm is used to solve the model. At last, we use the wavelet filter banks to explicitly define the feature operator and present some experimental results to show its advantage over the related methods in both quality and efficiency.\",\n \"Title: Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning.\\nYear: 2018\\n\\nAbstract: Early diagnosis of gear transmission has been a significant challenge, because gear faults occur primarily at microstructure or even material level but their effects can only be observed indirectly at a system level. The performance of a gear fault diagnosis system depends significantly on the features extracted and the classifier subsequently applied. Traditionally, fault-related features are extracted and identified based on domain expertise through data preprocessing which are system-specific and may not be easily generalized. On the other hand, although recently the deep neural networks based approaches featuring adaptive feature extractions and inherent classifications have attracted attention, they usually require a substantial set of training data. Aiming at tackling these issues, this paper presents a deep convolutional neural network-based transfer learning approach. The proposed transfer learning architecture consists of two parts; the first part is constructed with a pre-trained deep neural network that serves to extract the features automatically from the input, and the second part is a fully connected stage to classify the features that needs to be trained using gear fault experimental data. Case analyses using experimental data from a benchmark gear system indicate that the proposed approach not only entertains preprocessing free adaptive feature extractions, but also requires only a small set of training data.\",\n \"Title: Multi-pair two-way relaying systems with physical layer network coding.\\nYear: 2018\\n\\nAbstract: Physical layer network coding (PNC) significantly improves the throughput of the two-way relay channel (TWRC). However, most of current work on PNC just focuses on one pair users, and there are limit work on multi-pair PNC, in which the main bottleneck on system performance is the interference caused by the remaining communicating pairs. In this paper, we consider a scenario where multiple pairs communicate with each other through one relay node. For a purpose of comparison, we present the analytical capacities of the multi-pair version of the existing TWRC schemes in closed forms. Considering more practical issues such as the direction distribution of incident signals, we propose a spatial multi-pair physical layer network coding scheme. The novelty includes: in the multiple access phase, the log likelihood ratio combination scheme is proposed, which is robust in bad communication conditions; in the broadcast phase, two adaptive group broadcasting schemes are proposed, which can efficiently overcome the near interference problem. Simulation results validate the superiority of the proposed algorithms.\",\n \"Title: DCAP: Improving the Capacity of WiFi Networks with Distributed Cooperative Access Points.\\nYear: 2018\\n\\nAbstract: This paper presents the Distributed Cooperative Access Points (DCAP) system that can simultaneously serve multiple clients using cooperative beamforming to increase the capacity of WiFi-type wireless networks. The distributed APs are connected by Ethernet and driven by independent low-cost local oscillators. To facilitate cooperative beamforming, we address three major challenges: the phase synchronization, the channel state information (CSI) measurement, and the user selection. Specifically, we develop 1) a cooperative tracking scheme to track signal phase drifts at symbol level without adding extra hardware complexity; 2) an incremental CSI estimation mechanism that removes the per-frame CSI measurement overhead of previous approaches; and 3) a simple random user selection algorithm that scales the network capacity linearly and delivers over 70 percent performance compared to the optimal but complex greedy algorithm. We implement DCAP on the Sora software radio platform and evaluate it in a wireless network with nine nodes. Experimental results show that the cooperative beamforming is feasible in practice, and our cooperative phase tracking can ensure strict phase alignment ( 0.03 radian) among APs during the entire beamforming period (1.2 ms). Otherwise, without tracking, phases may drift by 0.3 radian over merely 600 s, causing that the symbol SNR decreases as large as 20 dB.\",\n \"Title: LoS communications using large circular array for mmWave C-RAN network\\nYear: 2017\\n\\nAbstract: This paper considers the application of large circular array to Line-of-Sight (LoS) multi-user Communications for C-RAN networks using the millimeter wave (mmWave) spectrum. Based on the symmetric geometry of the arrays, a spatial-harmonic-based interference suppressor (SHBIS) is proposed and evaluated in closed-form. For this receiver to work, a 2-D direction-of-arrival (DoA) information estimation method based on synthesizing spatial harmonics to form a scanning function is put forward.\",\n \"Title: Receiver Originated Physical-Layer Robust Secure Transmissions in Wireless Systems.\\nYear: 2018\\n\\nAbstract: This letter proposes a receiver-originated physical-layer secure transmissions scheme (ROST) which consists of two-phase communications. In phase 1, the receiver sends a pseudorandom signal (only known by itself) to the transmitter and in phase 2, the transmitter directly sends out the mixture of its confidential message and the received pseudorandom signal. The receiver could estimate the channel with the help of the pseudorandom signal before cancelling it from the received mixed signal, and finally detect the confidential information. By contrast, the eavesdropper can hardly obtain any confidential information because it cannot estimate the channel without any pilot signal in both phases transmissions and is interfered by the pseudorandom signal. Furthermore, we discuss the secrecy capacity of ROST and show that ROST can always keep the security of the confidential message, achieve much higher secrecy capacity than the traditional scheme, even without any channel information at the transmitter.\",\n \"Title: D2D communications based scarcity-aware two-stage multicast for video streaming.\\nYear: 2017\\n\\nAbstract: This paper proposes a device-to-device (D2D) communications based scarcity-aware two-stage multicast mechanism for delivering video streaming, in which the Base Station (BS) and D2D perform cooperative retransmissions to improve the mobile video quality and relieve the load of BS as well. In prior works, after BS's one-round multicast, the clients directly resort to D2D communications to obtain the missing frames by forming social groups. However, after the first-round multicast of BS, it is possible that some frames have not been correctly received by most of the clients, especially when the channel conditions are not good. That is, there are scarce frames in the network and the clients have very limited copies of them. In this case, it is inefficient for the clients to form social groups and directly exchange frames through D2D communications. Instead, this paper proposes an adaptive two-stage multicast mechanism: the BS first multicast video packets to the clients who request the same video services, after which the clients collect the information of missing frames and send it to the BS. Then the BS evaluates the scarcity of frames and re-multicast the scarce frames, if any, to the clients. After the BS's second-round multicast, the clients can then form groups to perform D2D communications so as to obtain the remaining missing packets from each other. Numerical studies show that the proposed simple scarcity-aware two-stage multicast mechanism can achieve about 5% video quality improvement than the existing one-stage multicast mechanism and have the potential to enhance the downlink capacity of the future 5G wireless network.\",\n \"Title: Impact of Residual Hardware Impairments on Non-Orthogonal Multiple Access Based Amplify-and-Forward Relaying Networks.\\nYear: 2018\\n\\nAbstract: We quantify the impact of residual hardware impairments (RHI) on a non-orthogonal multiple access (NOMA)-based relaying network, where a source communications simultaneously with multiple users via an amplify-and-forward relay. Specifically, exact and asymptotic expressions for the outage probability are first derived in closed-form over Nakagami-m fading channels, accounting for RHI at the source, relay, and all users. Our results show that the outage performance loss induced by RHI is small in the low signal-to-noise ratio (SNR) regime or/and at low target rates, however it is significant at high SNRs or/and target rates. Furthermore, we present new tight analytical approximated and asymptotic expressions for the system ergodic sum rate (ESR). For comparison, we also discuss the ESR of the conventional hardware-impaired relaying system employing orthogonal multiple access (OMA) transmission and obtain the asymptotic high-SNR expression of the ESR. The provided numerical results demonstrate that in the absence of RHI, the ESR in either the NOMA or the OMA system increases monotonically with the increase of SNR, whereas an unavoidable ESR ceiling is introduced in the hardware-impaired scenario for both systems. Notably, since the ESR ceiling value is only depended on the RHI levels, the NOMA-based and OMA-based systems achieve the same ESR performance in high SNRs.\",\n \"Title: A Low Storage Room Requirement Framework for Distributed Ledger in Blockchain.\\nYear: 2018\\n\\nAbstract: Traditional centralized commerce on the Internet relies on trusted third parties to process electronic payments. It suffers from the weakness of the trust-based model. A pure decentralized mechanism called blockchain tackles the above problem and has become a hot research area. However, since each node in a blockchain system needs to store all transactions of the other nodes, as time continues, the storage room required to store the entire blockchain will be huge. Therefore, the current storage mechanism needs to be revised to cater to the rapidly increasing need for storage. Network coded (NC) distributed storage (DS) can significantly reduce the required storage room. This paper proposes a NC-DS framework to store the blockchain and proposes corresponding solutions to apply the NC-DS to the blockchain systems. Analysis shows that the proposed scheme achieves significant improvement in saving storage room.\",\n \"Title: Energy-Efficient Beamforming And Time Allocation In Wireless Powered Communication Networks\\nYear: 2018\\n\\nAbstract: This paper investigates multi-antenna beamforming and time allocation to maximize the network energy-efficiency (EE) in a wireless powered communication network (WPCN). Since the EE optimization problem has an inherent fractional form, it is difficult to obtain the optimal value directly due to the lack of convexity in the objective function. To overcome this challenge, we first convert the original problem into a more tractable one by the fractional programming. Then, two schemes are proposed to find the optimal value. In the first scheme, the iterative value is updated according to the EE based on energy beamforming and time allocation derived in the current iteration. In the second one, the optimal value is obtained by consecutively shrinking the region in which it is located. Simulation results show that the proposed two schemes can improve the network EE significantly compared with the algorithm that only pursues high throughput. In addition, it is shown that the two schemes have simlilar performance in the network EE. However, the computation complexity of the first one is lower than that of the second one.\",\n \"Title: Detecting and Localizing Adversarial Nodes Usig Neural Networks.\\nYear: 2018\\n\\nAbstract: This work proposes a new method for securing the gossip algorithm for average consensus on communication networks. The gossip algorithm is appealing for its ability to harness distributed computational resources while adapting to arbitrarily connected networks without coordination overhead, however it is inherently vulnerable to the insider attack by adversarial node since each node locally updates its local states and passes information to its neighbors without supervision. In light of this, this work proposes new methods for detecting and localizing adversarial nodes using a neural network system. We show that our neural network-based method delivers a significantly improved detection and localization performance, compared to the state of the art.\",\n \"Title: Blind Authentication at the Physical Layer Under Time-Varying Fading Channels.\\nYear: 2018\\n\\nAbstract: Authentication is a key requirement for secure communications in modern wireless systems. Compared with the conventional authentication at the upper layer using a cryptographic tool, authentication at the physical layer has many advantages, including enhanced security through the introduction of uncertainty to adversaries and increased efficiency and compatibility through the avoidance of operatio...\",\n \"Title: V2X-Enabled Energy-Efficient Transmission in Cellular Networks\\nYear: 2018\\n\\nAbstract: In this paper, we propose an energy-efficient vehicle-to-everything (V2X)-enabled transmission scheme in uplink cellular networks. We aim to minimize the uplink energy consumption while considering both circuit power and transmit power. The optimal resource allocation algorithm for direct transmission and V2X-enabled transmission are derived, respectively. The simulation results show that the proposed V2X-enabled transmission scheme can achieve significant energy saving compared with the traditional direct transmission, the decode-and-forward (DF)-based transmission and the opportunistic store-carry and forward (SCF)-based transmission schemes, especially when the cell radius is large.\",\n \"Title: Energy Efficient V2X-Enabled Communications in Cellular Networks.\\nYear: 2019\\n\\nAbstract: Vehicle-to-everything (V2X) communications, which provide wireless connectivity among vehicles, roadside drivers, passengers, and pedestrians, are attracting great interest. In this paper, we propose an energy-efficient relay assisted transmission scheme based on V2X communications in the uplink cellular networks for delay insensitive applications. We aim to maximize energy efficiency of the uplin...\",\n \"Title: Deep Learning for Joint Adaptations of Transmission Rate and Payload Length in Vehicular Networks.\\nYear: 2019\\n\\nAbstract: Recently, vehicular networks have emerged to facilitate intelligent transportation systems (ITS). They enable vehicles to communicate with each other in order to provide various services such as traffic safety, autonomous driving, and entertainments. The vehicle-to-vehicle (V2V) communication channel is doubly selective, where the channel changes within the transmission bandwidth and the frame duration. This necessitates robust algorithms to provide reliable V2V communications. In this paper, we propose a scheme that provides joint adaptive modulation, coding and payload length selection (AMCPLS) for V2V communications. Our AMCPLS scheme selects both the modulation and coding scheme (MCS) and the payload length of transmission frames for V2V communication links, according to the V2V channel condition. Our aim is to achieve both reliability and spectrum efficiency. Our proposed AMCPLS scheme improves the V2V effective throughput performance while satisfying a predefined frame error rate (FER). Furthermore, we present a deep learning approach that exploits deep convolutional neural networks (DCNN) for implementing the proposed AMCPLS. Simulation results reveal that the proposed DCNN-based AMCPLS approach outperforms other competing machine learning algorithms such as k-nearest neighbors (k-NN) and support vector machines (SVM) in terms of FER, effective throughput, and prediction time.\",\n \"Title: To Establish a Secure Channel from a Full-duplex Transmitter to a Half-duplex Receiver: An Artificial-Noise-Aided Scheme\\nYear: 2019\\n\\nAbstract: This letter considers a new secure communication scenario where a full-duplex transmitter (Alan) needs to transmit confidential information to a half-duplex receiver (Bob), with an eavesdropper (Eve) that tries to overhear the confidential information. For realizing the secure communication, we design an effective artificial-noise-aided secure scheme (ANAS) which is composed of two phases' transmi...\",\n \"Title: Parametric Modeling for Two-Dimensional Harmonic Signals With Missing Harmonics.\\nYear: 2019\\n\\nAbstract: The problem of parametric modeling for the two-dimensional (2-D) harmonic signals with missing harmonics is addressed. The modeling process consists of two parts. First, we devise the joint spatial-temporal linearly constrained minimum variance beamformer and perform the joint estimation of the spatial and temporal fundamental frequencies for each pitch of the signal based on the maximum harmonic model. Second, we differentiate the competing signal models by the number of harmonic components and derive the maximum a posteriori criterion for the 2-D harmonic signals. As a result, the harmonic components of each pitch are detected according to their spectral powers. The simulation and experimental results are provided to show the superiority of the proposed signal modeling methodology over other existing schemes.\",\n \"Title: Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems\\nYear: 2019\\n\\nAbstract: This letter proposes a deep convolutional neural network (DCNN) approach for adaptive modulation and coding in practical multiple-input, multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Our target is to maximize the throughput and fulfill a packet error rate constraint. We consider practical impairments of MIMO-OFDM receiver, such as imperfect timing synchronization,...\",\n \"Title: Computation Offloading for Mobile Edge Computing Enabled Vehicular Networks.\\nYear: 2019\\n\\nAbstract: The emergence of computation-intensive and delay-sensitive vehicular applications poses a great challenge for individual vehicles with limited computation resources. Mobile edge computing (MEC) is a new paradigm shift that can enhance vehicular services through computation offloading. However, the high mobility of vehicles will affect offloading performance. In this paper, we investigate the vehicular user (VU) computation overhead minimization problem in MEC-enabled vehicular networks by jointly optimizing the computation and communication resources' allocation (transmit power and uploading time for communication, and the offloading ratio and local CPU frequency for computation). This optimization problem is nonconvex and difficult to solve directly. To deal with this issue, we first transform the original problem into an equivalent one. Then, we decompose the equivalent problem into a two-level problem. In addition, we develop a low-complexity algorithm to obtain the optimal solution. The numerical results demonstrate that the proposed algorithm can significantly outperform benchmark algorithms in terms of computation overhead.\",\n \"Title: Molecular Insights Into The Recruiting Between Ucp2 And Ddx5/Ubap2l In The Metabolic Plasticity Of Non-Small-Cell Lung Cancer\\nYear: 2021\\n\\nAbstract: Mitochondrial uncoupling protein 2 (UCP2) is distributed in tumor cells with a link to the support of systemic metabolic deregulation, and the downregulation of UCP2 has been unveiled as a biomarker of oncogenesis and chemoresistance in non-small-cell lung cancer (NSCLC) cells. However, the underlying mechanism of how UCP2 cooperates with other proteins in this metabolic reprogramming remains largely unsolved. We employed a combined computational and experimental strategy to explore into the recruiting of DDX5 with other proteins, and we unraveled the underlying structural mechanisms. We found that recruiting by ATP-dependent RNA helicase DDX5 (DDX5)/ubiquitin-associated protein 2-like (UBAP2L) might help UCP2 to play the pathological roles in NSCLC cells. According to the view of thermodynamics in physics, UCP2 tends to recruit DDX5 rather than UBAP2L, as shown by the ensemble-based docking, molecular dynamics simulations and molecular mechanics generalized Born surface area (MM/GBSA) approach. Cellular immunofluorescence assays further demonstrated that UCP2 associate with DDX5, and the recruiting of DDX5 with UCP2 at least partially contribute to the metabolic plasticity of NSCLCs via the AKT/mTOR pathway. Our study proposed an efficient way for detecting the protein- protein association via the experimentally validated molecular simulation. Our results shed light on the functional annotation of UCP and DDX family proteins in dysregulated metabolism, and the identification of candidate therapeutic targets for NSCLC.\",\n \"Title: Ir5hmcsc: Identifying Rna 5-Hydroxymethylcytosine With Multiple Features Based On Stacking Learning\\nYear: 2021\\n\\nAbstract: RNA 5-hydroxymethylcytosine (5hmC) modification is the basis of the translation of genetic information and the biological evolution. The study of its distribution in transcriptome is fundamentally crucial to reveal the biological significance of 5hmC. Biochemical experiments can use a variety of sequencing-based technologies to achieve high-throughput identification of 5hmC; however, they are labor-intensive, time-consuming, as well as expensive. Therefore, it is urgent to develop more effective and feasible computational methods. In this paper, a novel and powerful model called iR5hmcSC is designed for identifying 5hmC. Firstly, we extract the different features by K-mer, Pseudo Structure Status Composition and One-Hot encoding. Subsequently, the combination of chi-square test and logistic regression is utilized as the feature selection method to select the optimal feature sets. And then stacking learning, an ensemble learning method including random forest (RF), extra trees (EX), AdaBoost (Ada), gradient boosting decision tree (GBDT), and support vector machine (SVM), is used to recognize 5hmC and non-5hmC. Finally, 10-fold cross-validation test is performed to evaluate the model. The accuracy reaches 85.27% and 79.92% on benchmark dataset and independent dataset, respectively. The result is better than the state-of-the-art methods, which indicates that our model is a feasible tool to identify 5hmC. The datasets and source code are freely available at https://github.com/HongyanShi026/iR5hmcSC.\",\n \"Title: Investigation on negative capacitance FinEFT beyond 7 nm node from device to circuit\\nYear: 2021\\n\\nAbstract: In this study, a SPICE model for Negative Capacitance FinFET (NC-FinFET) based on the BSIM-CMG model and Landau-Khalatnikov (L-K) equation is developed. The areas of the ferroelectric layer (AFE) and the work functions (WFs) of n-NC-FinFET and p-NC-FinFET are adjusted to reduce the shift of the threshold voltage. As a result, the NC-FinFET-based seven-stage ring oscillator (RO) has a smaller delay, and energy can down to 72.2 % compared with the FinFET-based RO under the same delay. Furthermore, the NC-FinFET-based 6T SRAM has a smaller read and write time and lower static power than the FinFET-based 6T SRAM. Besides, under two key parameters (the length of the gate (Lg), the width of gate (Wfin), of NC-FinFET), the NC-FinFET-based 6T SRAM has great advantages in static power, read and write time. Therefore, the research on NC-FinFET from the device to the circuit can provide meaningful guidance for the further application of NC-FinFET.\",\n \"Title: Secure Blockchain Platform for Industrial IoT with Trusted Computing Hardware\\nYear: 2021\\n\\nAbstract: As a disruptive technology that originates from cryptocurrency, blockchain provides a trusted platform to facilitate Industrial IoT (IIoT) applications. However, implementing a blockchain platform in IIoT scenarios faces various security challenges due to the rigorous deployment conditions. To this end, we present a novel design of secure blockchain based on trusted computing hardware for IIoT applications. Specifically, we employ a trusted execution environment (TEE) module and a customized security chip to safeguard the blockchain against different attack vectors. Furthermore, we implement the proposed secure IIoT blockchain on an ARM-based embedded device and build a small-scale IIoT network to evaluate its performance. Our experimental results show that the secure blockchain platform achieves high throughput (150 TPS) with low transaction confirmation delay (below 66 ms), demonstrating its feasibility in practical IIoT scenarios. Finally, we outline the open challenges and future research directions.\",\n \"Title: Deep learning based adaptive modulation and coding for uplink multi-user SIMO transmissions in IEEE 802.11ax WLANs\\nYear: 2021\\n\\nAbstract: The IEEE 802.11ax standard defined a set of new specifications to improve spectrum efficiency, power efficiency, and reliability of future wireless local area networks (WLANs). Among these specifications, the uplink multi-user multiple-input, multiple-output (MU-MIMO) remedies the uplink shortcomings of existing WLANs and enables high-efficiency uplink transmissions. This paper considers the problem of adaptive modulation and coding (AMC) for uplink MU-single-input, multiple-output (MU-SIMO) in the upcoming WLANs. Our target is to select the appropriate modulation and coding scheme (MCS) for the existing users so as to maximize the throughput and at the same time guarantee that all users can satisfy a frame error constraint. We adopt a deep learning approach to tackle this problem. We propose to let the access point (AP) leverage the estimated channel state information (CSI) of all users along with the estimated noise standard deviation as the input features to a deep convolutional neural network (DCNN) that is trained to perform AMC. Simulation results reveal that the proposed DCNN for the uplink MU-SIMO AMC outperforms rival machine learning techniques such as fully-connected deep neural network (DNN), k-nearest neighbors (KNN), support vector machines (SVM), Gaussian Naive Bayes (GNB), and random forest (RF) in terms of the throughput and the frame error rate (FER).\",\n \"Title: Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network\\nYear: 2021\\n\\nAbstract: Signal features can be obscured in noisy environments, resulting in low accuracy of radar emitter signal recognition based on traditional methods. To improve the ability of learning features from noisy signals, a new radar emitter signal recognition method based on one-dimensional (1D) deep residual shrinkage network (DRSN) is proposed, which offers the following advantages: (i) Unimportant features are eliminated using the soft thresholding function, and the thresholds are automatically set based on the attention mechanism; (ii) without any professional knowledge of signal processing or dimension conversion of data, the 1D DRSN can automatically learn the features characterizing the signal directly from the 1D data and achieve a high recognition rate for noisy signals. The effectiveness of the 1D DRSN was experimentally verified under different types of noise. In addition, comparison with other deep learning methods revealed the superior performance of the DRSN. Last, the mechanism of eliminating redundant features using the soft thresholding function was analyzed.\",\n \"Title: Computation Offloading and User Association for Blockchain-enabled Heterogeneous Cellular Networks\\nYear: 2021\\n\\nAbstract: In this paper, we investigate a novel blockchain-enabled heterogeneous cellular network (HetNet). In this network, mobile users associate with the serving base stations (BSs) and offload their computation-intensive proof-of-work mining tasks to the mobile edge computing server of the macro-cell BS. We initialize the user association strategy by using the traditional nearest BS association algorithm in the blockchain-enabled HetNet. Then, we formulate the computation offloading problem and design an alternating iterative algorithm to attain the computing demand strategies for all users. Based on obtained computing demand strategies, we propose a modified user association algorithm in order to improve the data congestion of BSs. Analytical results show that the blockchain-enabled HetNet can serve more users, and the proposed algorithms can effectively obtain strategies of user association and computation offloading. Numerical results demonstrate that the proposed alternating iterative algorithm for computation offloading has fast convergence and good stability, and the proposed modified user association algorithm can avoid data congestion better than the traditional nearest BS association algorithm.\",\n \"Title: A Comparative Study of Magnetic-induction based Cooperative Communication for Underwater Data Collection\\nYear: 2021\\n\\nAbstract: Magnetic induction (MI) based communication is an important means for underwater data collection due to its less susceptibility to surrounding environments, predictable channel condition and high data rate. However, the communication rate is affected by the severe path loss in underwater channel and the orientation of the MI transceiver coils. In this case, allowing the coils to transmit collabora...\",\n \"Title: iPro-GAN: A novel model based on generative adversarial learning for identifying promoters and their strength\\nYear: 2022\\n\\nAbstract: Background and Objective: Promoter is a component of the gene, which can specifically bind with RNA polymerase and determine where transcription starts, and also determine the transcription efficiency of the gene. Promoters can be divided into strong promoters and weak promoters because their structures and the interaction time interval are quite different. The functional variation of the promoter can lead to a variety of diseases. Therefore, identifying promoters and their strength is necessary and has important biological significance. A novel and promising model based on deep learning is proposed to achieve it. Methods: In this work, we build a power model named iPro-GAN for identification of promoters and their strength. First, we collect benchmark datasets and independent datasets for training and testing. Then, Moran-based spatial auto-cross correlation method is used as feature extraction method. Finally, deep convolution generative adversarial network with 10-fold cross validation is applied for classifying. The first layer of the model is used to identify the promoter and the second layer is used to determine its type. Results: On the benchmark data set, the accuracy of the first layer predictor is 93.15%, and the accuracy of the second layer predictor is 92.30%. On the independent data set, the accuracy of the first layer predictor is 86.77%, and the accuracy of the second layer predictor is 91.66%. In particular, breakthrough progress has been made in the identification of promoters' strength. Conclusions: These results are far higher than the existing best predictor, which indicate that our model is serviceable and practicable to identify promoters and their strength. Furthermore, the datasets and source codes are available from this link: https://github.com/Bovbene/iPro-GAN . (c) 2022 Elsevier B.V. All rights reserved.\",\n \"Title: Robust Spectral Analysis of Multi-Channel Sinusoidal Signals in Impulsive Noise Environments\\nYear: 2022\\n\\nAbstract: Robust spectral analysis of the sinusoidal signals corrupted by impulsive noise poses a big challenge in the signal processing community. In this paper, we address the issue of robust spectral analysis for multi-channel sinusoidal signals, including order detection and parameter estimation. The successive robust low-rank decomposition is firstly designed to extract the common signal subspace from the multi-channel data matrix. Subsequently, the number of sinusoidal poles is determined with a model order selection criterion, based on the so-obtained subspace. With the signal order information, the sinusoidal parameters and outliers are jointly estimated according to the maximum \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">a posteriori</i>\\n criterion. To find a robust initial guess of the sinusoidal parameters, an estimator based on robust weighted linear prediction is developed. Additionally, the performance analysis is provided, which includes computational complexity, convergence verification of the sinusoidal parameter estimation, and asymptotic consistency of the signal order detection. Simulation results demonstrate the advantages of the proposed robust spectral analysis framework compared to state-of-the-art schemes.\",\n \"Title: Blockchain Storage, Computation Offloading, and User Association for Heterogeneous Cellular Networks\\nYear: 2022\\n\\nAbstract: To support more Internet-of-Things devices, we present a novel blockchain-enabled heterogeneous cellular network (HetNet). In this network, devices store block data to the cloud service provider, offload the proof-of-work mining tasks to base stations (BSs), and associate with the macrocell BS or small-cell BSs. Then, we analyze the user association problem, computation offloading problem, and block storage problem in the blockchain-enabled HetNet. We also design corresponding algorithms to solve these problems. Furthermore, to tackle the challenge of data congestion of BSs, based on the obtained computation offloading and block storage strategies, we propose a modified user association algorithm. The analysis shows that the proposed blockchain-enabled HetNet can effectively attain computing offloading, block storage, and user association strategies, and more devices can access the blockchain network. Analytical results show that the proposed modified user association algorithm can greatly avoid data congestion of BSs. Numerical results demonstrate the effectiveness of our proposed algorithms for computation offloading and block storage, and the proposed modified user association algorithm has a significantly great advantage compared with the traditional nearest BS association algorithm in terms of avoiding data congestion.\",\n \"Title: An MAP Method for Closed-Loop Channel Training in Massive MIMO Systems\\nYear: 2022\\n\\nAbstract: Accurate channel state information is of great importance in high-speed wireless communication. In this work, the problem of channel training for massive multiple-input multiple-output (MIMO) systems is investigated and a closed-loop channel training scheme is devised upon the maximum \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">a posteriori</i>\\n (MAP) criterion. More specifically, at each channel use, the channel estimation is performed at the receiver according to the MAP criterion, and the expectation-maximization (EM) algorithm is employed to cope with the MAP estimation problem. Based on the parameter estimates, the predicted mean square error (PMSE) of the channel estimate is minimized to determine a preferred sounding beam for the next channel use, of which the index is fed back to the transmitter via a limited-rate channel. The performance superiority of the proposed approach is evidenced by comparing with various channel training schemes.\",\n \"Title: Downsampling and Transparent Coding for Blockchain\\nYear: 2022\\n\\nAbstract: Blockchain is considered to be able to solve the problem of trust between distributed nodes. However, it's a challenge to store the large amount of data produced by blockchain. Therefore, it is necessary to reduce node storage overhead while ensuring node independence and data recoverability. This paper proposes to downsample these data to reduce the storage overhead of nodes. These nodes keep good independence, if downsampling follows the entropy of blockchain and the block bodies are continuously stored. Moreover, it demonstrates that the entire blockchain history can be efficiently recovered through the cooperative decoding of a group of nodes like fountain codes, if reserved data over these nodes obey the soliton distribution. However, these data on nodes are transparent (stored data are uncoded and can be used directly by the node without decoding). Thus, the proposed algorithm not only keeps decentralization and security, but also has good scalability in independence and recoverability.\",\n \"Title: An improved residual network using deep fusion for identifying RNA 5-methylcytosine sites\\nYear: 2022\\n\\nAbstract: Motivation: 5-Methylcytosine (m5C) is a crucial post-transcriptional modification. With the development of technology, it is widely found in various RNAs. Numerous studies have indicated that m5C plays an essential role in various activities of organisms, such as tRNA recognition, stabilization of RNA structure, RNA metabolism and so on. Traditional identification is costly and time-consuming by wet biological experiments. Therefore, computational models are commonly used to identify the m5C sites. Due to the vast computing advantages of deep learning, it is feasible to construct the predictive model through deep learning algorithms. Results: In this study, we construct a model to identify m5C based on a deep fusion approach with an improved residual network. First, sequence features are extracted from the RNA sequences using Kmer, K-tuple nucleotide frequency component (KNFC), Pseudo dinucleotide composition (PseDNC) and Physical and chemical property (PCP). Kmer and KNFC extract information from a statistical point of view. PseDNC and PCP extract information from the physicochemical properties of RNA sequences. Then, two parts of information are fused with new features using bidirectional long- and short-term memory and attention mechanisms, respectively. Immediately after, the fused features are fed into the improved residual network for classification. Finally, 10-fold cross-validation and independent set testing are used to verify the credibility of the model. The results show that the accuracy reaches 91.87%, 95.55%, 92.27% and 95.60% on the training sets and independent test sets of Arabidopsis thaliana and M.musculus, respectively. This is a considerable improvement compared to previous studies and demonstrates the robust performance of our model.\",\n \"Title: R5hmCFDV: computational identification of RNA 5-hydroxymethylcytosine based on deep feature fusion and deep voting\\nYear: 2022\\n\\nAbstract: RNA 5-hydroxymethylcytosine (5hmC) is a kind of RNA modification, which is related to the life activities of many organisms. Studying its distribution is very important to reveal its biological function. Previously, high-throughput sequencing was used to identify 5hmC, but it is expensive and inefficient. Therefore, machine learning is used to identify 5hmC sites. Here, we design a model called R5hmCFDV, which is mainly divided into feature representation, feature fusion and classification. (i) Pseudo dinucleotide composition, dinucleotide binary profile and frequency, natural vector and physicochemical property are used to extract features from four aspects: nucleotide composition, coding, natural language and physical and chemical properties. (ii) To strengthen the relevance of features, we construct a novel feature fusion method. Firstly, the attention mechanism is employed to process four single features, stitch them together and feed them to the convolution layer. After that, the output data are processed by BiGRU and BiLSTM, respectively. Finally, the features of these two parts are fused by the multiply function. (iii) We design the deep voting algorithm for classification by imitating the soft voting mechanism in the Python package. The base classifiers contain deep neural network (DNN), convolutional neural network (CNN) and improved gated recurrent unit (GRU). And then using the principle of soft voting, the corresponding weights are assigned to the predicted probabilities of the three classifiers. The predicted probability values are multiplied by the corresponding weights and then summed to obtain the final prediction results. We use 10-fold cross-validation to evaluate the model, and the evaluation indicators are significantly improved. The prediction accuracy of the two datasets is as high as 95.41% and 93.50%, respectively. It demonstrates the stronger competitiveness and generalization performance of our model. In addition, all datasets and source codes can be found at https://github.com/HongyanShi026/R5hmCFDV.\",\n \"Title: A Deep Autoencoder Trust Model for Mitigating Jamming Attack in IoT Assisted by Cognitive Radio\\nYear: 2022\\n\\nAbstract: This article proposes deep learning (DL) framework constructed using deep autoencoder (DAE) to detect the malicious nodes in an Internet of Things (IoT) network assisted by the cognitive radio (CR) technology. In the IoT era, a plethora of nodes are connected to the network for the purpose of collecting and exchanging data. CR technology finds its role in IoT applications because of its ability to efficiently exploit the available spectrum. In this article, we consider IoT nodes as secondary users that perform cooperative spectrum sensing (CSS). Specifically, these IoT nodes sense the spectrum and send their reports to the fusion center (FC) to determine whether the spectrum is occupied by the primary user or not. In such a scenario, the existence of malicious IoT nodes might mislead the FC. To determine which of these IoT nodes are benevolent and which are malicious is crucial. We adopt a DAE-based DL framework, called DAE-TRUST, to detect malicious nodes in CR-assisted IoT. The proposed DAE-TRUST is able to identify the malicious nodes, whose reports can be excluded from the spectrum detection process. Simulation results, in six different real-world environments, show that our DAE-TRUST enhances the performance of CSS in IoT applications.\",\n \"Title: A Low-Complexity Codebook Design Scheme for SCMA Systems Over an AWGN Channel\\nYear: 2022\\n\\nAbstract: As a promising wireless access technique, sparse code multiple access (SCMA) directly maps the input bits to the transmitted multidimensional codewords according to the SCMA codebook. In general, SCMA codebook optimization and detector simplification are independent of each other. In this article, we propose a simplified SCMA codebook with a separable structure for implementing a low-complexity detector. For codebook design, a separable codebook structure is proposed to simplify the maximization of the minimum Euclidean distance (MED). With this structure, multiple one-dimensional complex codebooks corresponding to one resource element (RE) are determined by maximizing the MED of the superimposed codewords. Then, the entire codebook set is obtained based on a structure indicator matrix and the separable codebook structure. The proposed codebook has a larger MED than obtained by existing codebooks. Benefiting from the separable codebook structure, the signals transmitted over each RE can be independently used to recover the corresponding input bit information. Therefore, we propose a parallel maximum a posteriori (P-MAP) detector consisting of several low-complexity MAP detectors to reduce the complexity of the conventional multiuser message passing (MP) detector in SCMA. At the receiver, each RE is assigned a low-complexity MAP detector. Simulation results show that the proposed codebook outperforms the known optimal codebook over an AWGN channel. Moreover, the run time of the P-MAP detection algorithm is reduced by 99.8% compared to the Log-MPA algorithm. With the proposed codebook design scheme and the P-MAP detector, an SCMA unit can be separated into two independent non-SCMA units, which significantly increases system flexibility.\",\n \"Title: Edge Computing-Aided Coded Vertical Federated Linear Regression\\nYear: 2022\\n\\nAbstract: For the training process of federated linear regression (FLR), which is the simplest form of federated learning, the integrated computation at each company is slowed down either by huge volume data or by time-consuming homomorphic encryption. Targetted at accelerating the training process of FLR, through the incorporation of edge computing aided coded distributed computing (CDC) into intensive computation (matrix multiplication), a novel coded FLR framework is proposed where several edge nodes aid the computing of one company. Two schemes, including linear combination (LC)-based vertical FLR and Matdot-based vertical FLR, are proposed and designed, which enjoy in-parallel computation and homomorphic encryption at the edge nodes. Since workload at each edge node is reduced significantly, the training runtime of these two schemes may be reduced significantly. Numerical studies show that our proposed coded schemes outperform traditional uncoded schemes significantly in terms of overall runtime (sum of encoding, computing, and decoding phases) of the training process. Besides, among the two proposed coded schemes, LC-based scheme and Matdot-based scheme each has its own advantage scenarios which conforms with the analysis.\",\n \"Title: An Improved Thru-Line De-Embedding Technique up to 80 GHz\\nYear: 2022\\n\\nAbstract: With the rapid increase of high-speed communications, the frequency range of integrated circuits enters the field of millimeter wave, and the size of devices has become smaller and smaller. In the measurement of microwave devices, the probe cannot contact the device directly. The actual device under test (DUT) is surrounded by metal traces called leads, which provide the interface between the probing pads on the wafer and the DUT itself. The probes are pressed on the pads at both ends of the DUT. The parasitic effect of pads and transmission lines is significant at high frequency. The established technique such as Short-Open-Load-Thru (SOLT) and Line-Reflect-Reflect-Match (LRRM) move the reference plane up to the tip of the probes by means of Cascade Microtech impedance standard substrate (ISS). To obtain accurate parameters of DUT, a deembedding method must be implemented to eliminate the influence of pads and interconnect transmission lines.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":114,"string":"114"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13252,"string":"13,252"}}},{"rowIdx":13253,"cells":{"question_id":{"kind":"string","value":"Yimin D Zhang"},"question":{"kind":"string","value":"Generate an abstract for the title \"Multi-Task Adaptive Matching Pursuit For Sparse Signal Recovery Exploiting Signal Structures\" using the following items: 1. Multi-task compressive sensing \n2. Hierarchical Bayesian model \n3. Sparse signal recovery \n4. Intra-group correlation \n5. Superiority over existing algorithms"},"answer":{"kind":"string","value":"Multi-task compressive sensing is a framework that, by leveraging the useful information contained in multiple tasks, significantly reduces the number of measurements required for sparse signal recovery and achieves improved sparse reconstruction performance of all tasks. In this paper, a novel multi-task adaptive matching pursuit (MT-AMP) algorithm based on a hierarchical Bayesian model is proposed with the exploitation of both the group structure across different tasks and the intra-group correlation, yielding an effective means to simultaneously perform sparse recovery as well as learn the statistical inter-task and intra-group relationships. Experimental results using both synthetic data and real data sets demonstrate the superiorities of the proposed method over existing state-of-the-art algorithms."},"correct_answer":{"kind":"string","value":"Multi-task compressive sensing is a framework that, by leveraging the useful information contained in multiple tasks, significantly reduces the number of measurements required for sparse signal recovery and achieves improved sparse reconstruction performance of all tasks. In this paper, a novel multi-task adaptive matching pursuit (MT-AMP) algorithm based on a hierarchical Bayesian model is proposed with the exploitation of both the group structure across different tasks and the intra-group correlation, yielding an effective means to simultaneously perform sparse recovery as well as learn the statistical inter-task and intra-group relationships. Experimental results using both synthetic data and real data sets demonstrate the superiorities of the proposed method over existing state-of-the-art algorithms."},"documents":{"kind":"list like","value":["Title: Instantaneous altitude estimation of maneuvering target in over-the-horizon radar exploiting multipath Doppler signatures.\nYear: 2013\n\nAbstract: Over-the-horizon radar systems are capable of localizing targets in range and azimuth but are unable to achieve reliable altitude estimation. Past work has shown that the time-varying Doppler signatures of micro-multipath signals provide rich information for reliable estimation of altitude changes. In this paper, we develop a new technique for the estimation of instantaneous altitude of maneuvering targets by exploiting the estimated multicomponent Doppler signatures. A key contribution of this paper is to apply effective non-stationary signal analysis techniques for estimating the time-varying Doppler signature of each individual multipath. This enables reliable target altitude estimation in an extended Kalman filter setup. The maximum a-posteriori criterion is used for the estimation of initial target states.","Title: Robust Motion Parameter Estimation In Multistatic Passive Radar\nYear: 2013\n\nAbstract: In this paper, we develop a new algorithm for motion parameter estimation of moving targets in a multistatic passive radar. Existing methods for motion parameter estimation rely on the estimated Doppler signatures of the observed signals corresponding to each transmitter. These techniques may fail for weak signals where the individual Doppler signature cannot be properly estimated. The focus of this paper is on motion parameter estimation from weak signals observed using multiple illuminators. Utilizing the sparsity of the motion parameters, the proposed technique obtains robust motion parameter estimates through the fusion of the data corresponding to all available illuminators, achieving signal enhancement and multistatic diversity. To reduce the computational cost, the acceleration and velocity parameters are decoupled and sequentially estimated.","Title: Reduced Interference Time-Frequency Representations And Sparse Reconstruction Of Undersampled Data\nYear: 2013\n\nAbstract: In this paper, we examine the time-frequency representation (TFR) and sparse reconstruction of non-stationary signals in the presence of missing data samples. These samples lend themselves to missing entries in the instantaneous auto-correlation function (IAF) which, in turn, induce artifacts in the time-frequency distribution and ambiguity function. The artifacts are additive noise-like and, as such, can be mitigated by using proper time-frequency kernels. We show that the sparse signal reconstruction methods applied to the time-lag domain improve the TFR over the direct application of Fourier transform to the IAF. Additionally, the paper demonstrates that the use of signal-adaptive kernels provides superior performance compared to data-independent kernels when missing data are present.","Title: Optimum Performance Boundaries of OSTBC Based AF-MIMO Relay System With Energy Harvesting Receiver\nYear: 2013\n\nAbstract: This paper studies the optimum performance boundaries of a two-hop multi-antenna amplify-and-forward (AF) relay system with a multi-antenna energy harvesting (EH) receiver. The source and relay nodes employ orthogonal space-time block codes for data transmission. When instantaneous channel state information (CSI) is available, we design joint optimal source and relay precoders to achieve different tradeoffs between the energy and information transfers, which are characterized by the boundary of the rate-energy (R-E) region. For this purpose, the optimization problem is formulated as a relaxed convex problem but its optimality is confirmed with a proof that rank-one optimal precoders can always be obtained. As a consequence, it is shown that the full-rate OSTBC, like the Alamouti code, can be employed for an arbitrary number of antennas at the transmit nodes (source and relay) and support up to seven simultaneously existing EH receivers. When only second order statistics of the CSI is available, the tradeoff between outage probability (OP) and energy is characterized by the boundary of the OP-energy (OP-E) region. In this case, the precoder design problem is formulated using a convex upper bound approximation to the OP, since the exact OP expression is difficult for tractable optimization. Numerical results show that the OP-E region obtained with the upper bound of the OP is better than that with the approach based on maximization of the long-term average signal-to-noise ratio (SNR). The role of the different parameters such as average SNR, numbers of antennas, and spatial correlation on the boundaries of the R-E and OP-E regions is demonstrated via simulations.","Title: Fall Detection Based on Sequential Modeling of Radar Signal Time-Frequency Features\nYear: 2013\n\nAbstract: Falls are one of the greatest threats to elderly health as they carry out their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be rendered. Radar is an effective non-intrusive sensing modality which is well suited for this purpose. It can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. In this paper, we use micro-Doppler features in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition for feature extraction and fall detection. The extracted features include the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, we use the extracted signal features for training and testing hidden Markov models and support vector machines indifferent falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections.","Title: Local CSI Based Selection Beamforming for Amplify-and-Forward MIMO Relay Networks\nYear: 2012\n\nAbstract: We investigate selection beamforming for a cooperative network that consists of a source, a destination, and two amplify-and-forward (AF) relays, which are all equipped with multiple antennas. The transmit and receive beamforming techniques are respectively applied at the source and destination, and the linear processing operator of the selected AF multiple-input multiple-output (MIMO) relay is optimized. We consider that the source has the instantaneous channel state information (CSI) of the channels from the source to relays but no information regarding the CSI of the channels from the relays to the destination. Partial relay selection (PRS) is employed, i.e., the source routes its information signal to the relay which offers better first-hop signal-to-noise ratio (SNR). Considering a Rayleigh fading environment, we derive an exact closed-form expression for the outage probability at the destination. The validity of the outage probability expression has been confirmed with numerical simulations. We also present the asymptotic analysis of the PRS scheme for the considered multiantenna system and derive the expressions of diversity gain. It is shown that, the overall diversity gain is $\\min(\\min(n_{{\\rm r},1}n_{\\rm d},n_{{\\rm r},2}n_{\\rm d}),n_{{\\rm r},1}n_{\\rm s}+n_{{\\rm r},2}n_{\\rm s})$ where $n_{\\rm s}$, $n_{\\rm d}$, and $n_{{\\rm r},q}$ are, respectively, the numbers of antennas at the source, destination and the $q$th relay. For the condition $C:\\min(n_{{\\rm r},1}n_{\\rm d},n_{{\\rm r},2}n_{\\rm d})\\geq n_{{\\rm r},1}n_{\\rm s}+n_{{\\rm r},2}n_{\\rm s}$ that can be easily met with practically realizable antenna configurations, it is shown that PRS provides the same diversity order as opportunistic relaying (OR).","Title: Local CSI Based Full Diversity Achieving Relay Selection for Amplify-and-Forward Cooperative Systems\nYear: 2013\n\nAbstract: This paper proposes a new relay selection method based on local channel state information (CSI) for a system consisting of a source, a destination and an arbitrary number of amplify-and-forward relay nodes. A set of candidate relays, whose source-relay (S-R) links are not in outage, is formed. An S-R link is in outage if its signal-to-noise ratio (SNR) is below the predefined threshold value of the destination SNR plus some adjustable margin. The candidate relay that yields the maximum second-hop SNR is then selected at the destination. Unlike the opportunistic relaying (OR) based on full CSI (F-CSI), the proposed method requires to equip the destination node with the instantaneous CSI of the S-R channel corresponding to only the selected relay. It is qualitatively shown that the training overhead of the proposed relay selection is less than or comparable to that of the partial relay selection (PRS) but can be much smaller than that of the OR with F-CSI. However, the proposed scheme requires to estimate a higher number of single-input single-output (SISO) channels than in the PRS scheme. The exact and asymptotic expressions of outage probability are derived and it is shown that the proposed method achieves full diversity. Simulation results verify theoretical analysis, and show that, for the optimized margin, the proposed scheme provides performance which is comparable to the method having F-CSI. The results also show that the proposed method significantly outperforms PRS, which may justify the linear order of the computational complexity associated with the estimation of the additional SISO channels and the low-cost one-dimensional line search used for optimizing the margin.","Title: High-resolution direction finding of non-stationary signals using matching pursuit.\nYear: 2013\n\nAbstract: One of the main goals of time–frequency (TF) signal representations in non-stationary array processing is to equip multi-antenna receivers with the ability to separate sources in the TF domain prior to direction finding. This permits high-resolution direction-of-arrival (DOA) estimation of individual sources and of more sources than sensors. In this paper, we use linear decomposition of sensor data based on matching pursuit (MP). The leading atoms of the MP, which capture most of the source TF signatures, can be different for different sources and, as such, provide the desired source discrimination. The MP coefficients with high signal-to-noise ratio (SNR) and corresponding to the leading decomposition atoms are used to develop the MP-MUSIC DOA estimation for non-stationary source signals. We demonstrate the source discriminatory capability of the proposed technique using linear FM, nonlinear FM, and other non-stationary signals. Further, we compare MP-MUSIC performance with conventional MUSIC and the time–frequency MUSIC, which incorporates bilinear transforms.","Title: Joint Optimization of Source Power Allocation and Relay Beamforming in Multiuser Cooperative Wireless Networks\nYear: 2011\n\nAbstract: Relay beamforming techniques have been shown to significantly enhance the sum capacity of a multiuser cooperative wireless network through the optimization of the relay weights, where concurrent communications of multiple source-destination pairs are achieved via spatial multiplexing. Further optimization of the transmit power allocation over the source nodes is expected to improve the network throughput as well. In this paper, we maximize the sum capacity of a multiuser cooperative wireless network through the joint optimization of power allocation among source nodes and relay beamforming weights across the relay nodes. We consider a two-hop cooperative wireless network, consisting of single-antenna nodes, in which multiple concurrent links are relayed by a number of cooperative nodes. When a large number of relay nodes are available, the channels of different source-destination pairs can be orthogonalized, yielding enhanced sum network capacity. Such cooperative advantage is particularly significant in high signal-to-noise ratio (SNR) regime, in which the capacity follows a logarithm law with the SNR, whereas exploiting spatial multiplexing of multiple links yields capacity increment linear to the number of users. However, the capacity performance is compromised when the input SNR is low and/or when the number of relay nodes is limited. Joint optimization of source power allocation and relay beamforming is important when the input SNR and/or the number of relay nodes are moderate or the wireless channels experience different channel variances. In these cases, joint optimization of source power and distributed beamforming weights achieves significant capacity increment over both source selection and equal source power spatial multiplexing schemes. With consideration of the needs to deliver data from each source node, we further examine the optimization of global sum capacity in the presence of individual capacity requirements by maximizing sum capacity of the network subject to a minimum capacity constraint over each individual user.","Title: Target localization in a multi-static passive radar system through convex optimization.\nYear: 2014\n\nAbstract: We propose efficient target localization methods for a passive radar system using time-of-arrival (TOA) information of the signals received from multiple illuminators, where the position of the receiver is subject to random errors. Since the maximum likelihood (ML) formulation of this target localization problem is a non-convex optimization problem, semi-definite relaxation (SDR)-based optimization methods in general do not provide satisfactory performance. As a result, approximated ML optimization problems are proposed and solved with SDR plus bisection methods. For the case without position error, it is shown that the relaxation guarantees a rank-one solution. The optimization problem for the case with position error involves only a relaxation of a scalar quadratic term. Simulation results show that the proposed algorithms outperform existing methods and provide root mean-square error performance very close to the Cramer–Rao lower bound.","Title: Source Separation and Localization Using Time-Frequency Distributions: An Overview.\nYear: 2013\n\nAbstract: In this article, we describe the role of time-frequency distributions (TFDs) in array processing. We particularly focus on quadratic TFDs (QTFDs). We demonstrate how these distributions can be properly integrated with the spatial dimension to enhance individual source signal recovery and angular estimation. The framework that enables such integration is referred to as spatial TFD (STFD). We presen...","Title: Azimuth Resampling Processing for Highly Squinted Synthetic Aperture Radar Imaging With Several Modes\nYear: 2014\n\nAbstract: The linear range walk yields a significant range-azimuth coupling effect in a highly squinted synthetic aperture radar (SAR). Although the linear range walk correction (LRWC) technique can effectively mitigate such coupling effect, it causes azimuth variation in the resulting signal and, as such, the so-called “azimuth-shift invariance” property becomes invalid. In order to eliminate the azimuth variation, a new spectrum processing approach based on azimuth resampling is proposed in this paper. After performing the LRWC, the azimuth resampling is carried out in the 2-D frequency domain and transforms the signal spectrum to be equivalent to that of a broadside SAR. For squinted beamsteering SAR (BS-SAR), e.g., spotlight SAR, sliding spotlight SAR, and Terrain Observation by Progressive Scans SAR, the azimuth resampling is combined with the azimuth signal reconstruction algorithm. As a result, both the azimuth variation, which is induced by the LRWC, and the aliasing, which is caused by antenna beam steering, can be avoided. Therefore, after the azimuth resampling, the squinted SAR data can be focused by exploiting a conventional broadside SAR imaging algorithm. An analysis of the motion error for airborne SAR data processing is also provided. Simulation and real data results show the effectiveness of the proposed algorithm.","Title: Generalized Coprime Array Configurations for Direction-of-Arrival Estimation\nYear: 2015\n\nAbstract: A coprime array uses two uniform linear subarrays to construct an effective difference coarray with certain desirable characteristics, such as a high number of degrees-of-freedom for direction-of-arrival (DOA) estimation. In this paper, we generalize the coprime array concept with two operations. The first operation is through the compression of the inter-element spacing of one subarray and the resulting structure treats the existing variations of coprime array configurations as well as the nested array structure as its special cases. The second operation exploits two displaced subarrays, and the resulting coprime array structure allows the minimum inter-element spacing to be much larger than the typical half-wavelength requirement, making them useful in applications where a small interelement spacing is infeasible. The performance of the generalized coarray structures is evaluated using their difference coarray equivalence. In particular, we derive the analytical expressions for the coarray aperture, the achievable number of unique lags, and the maximum number of consecutive lags for quantitative evaluation, comparison, and design of coprime arrays. The usefulness of these results is demonstrated using examples applied for DOA estimations utilizing both subspace-based and sparse signal reconstruction techniques.","Title: Multi-Task Bayesian Compressive Sensing Exploiting Intra-Task Dependency\nYear: 2015\n\nAbstract: In this letter, we propose a multi-task compressive sensing algorithm for the reconstruction of clustered sparse entries based on hierarchical Bayesian framework. By extending a paired spike-and-slab prior to a general multi-task model, the proposed algorithm has the capability of modeling both inter-task and intra-task dependencies of the observation data. The latter is achieved by imposing a clustered prior on non-zero entries and finds applications in radar where targets exhibit spatial extent. Simulation results verify that the proposed algorithm outperforms state-of-the-art group sparse Bayesian learning algorithms.","Title: Spectrum Compression Space–Time Adaptive Processing for TOPS SAR System\nYear: 2015\n\nAbstract: A multichannel terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR) system is capable of imaging a wider swath with a higher azimuth resolution for improved moving target detection. For TOPS SAR, due to antenna beam steering, the azimuth bandwidth of background clutter is much larger than the instantaneous signal bandwidth. To overcome this problem, a method referred to as spectrum compression space–time adaptive processing (SC-STAP) is proposed in this letter. Through the SC process, both the Doppler spectrum and the spatial spectrum of the background clutter are simultaneously compressed. This key step achieves fully overlapped clutter space–time spectrum lines and, as such, enables effective clutter suppression and target signal alias compensation by applying linearly constrained STAP. Furthermore, in order to avoid the target ambiguities arising from the spectral wrapping, an approach based on deramp processing is proposed to focus the moving targets for TOPS SAR mode. Simulation results validate the effectiveness of the proposed algorithm.","Title: Low-complexity sparse reconstruction for high-resolution multi-static passive SAR imaging.\nYear: 2014\n\nAbstract: Bi-static passive synthetic aperture radar (SAR) systems using ground broadcast and wireless network signals suffer from a low spatial resolution due to the narrow bandwidths and low carrier frequencies. By exploiting multiple distributed illuminators, multi-static passive radar has the possibility of producing high-resolution SAR images. In this paper, a two-stage image formation approach, which combines the Fourier transform and sparse reconstruction strategies, is proposed to process multi-static SAR data. This method exploits the group sparsity of the sparse scene, i.e., the observations associated with different bi-static pairs share the same support of the sparse scene but correspond to aspect-dependent scattering coefficients. Such observations are described as a number of generally disjoint sub-bands in the two-dimensional spatial frequency domain. In each sub-band, the sampling satisfies the Nyquist criterion, whereas different sub-bands are sparsely distributed. In the proposed approach, Fourier-based reconstruction is applied to the sub-band data to produce the coarse-resolution images, which are then combined to produce a high-resolution image through the exploitation of sparse reconstruction techniques. The proposed approach greatly improves the imaging quality as compared to Fourier-based reconstruction, whereas it exhibits significant reduction of the computational complexity when compared to direct application of sparse reconstruction techniques. The exploitation of block sparsity-based techniques also permits practical treatment of the angle-dependent target scattering characteristics in SAR image reconstruction. The advantages of the proposed approach are delineated using analysis and simulations.","Title: Motion parameter estimation of multiple ground moving targets in multi-static passive radar systems.\nYear: 2014\n\nAbstract: Multi-static passive radar (MPR) systems typically use narrowband signals and operate under weak signal conditions, making them difficult to reliably estimate motion parameters of ground moving targets. On the other hand, the availability of multiple spatially separated illuminators of opportunity provides a means to achieve multi-static diversity and overall signal enhancement. In this paper, we consider the problem of estimating motion parameters, including velocity and acceleration, of multiple closely located ground moving targets in a typical MPR platform with focus on weak signal conditions, where traditional time-frequency analysis-based methods become unreliable or infeasible. The underlying problem is reformulated as a sparse signal reconstruction problem in a discretized parameter search space. While the different bistatic links have distinct Doppler signatures, they share the same set of motion parameters of the ground moving targets. Therefore, such motion parameters act as a common sparse support to enable the exploitation of group sparsity-based methods for robust motion parameter estimation. This provides a means of combining signal energy from all available illuminators of opportunity and, thereby, obtaining a reliable estimation even when each individual signal is weak. Because the maximum likelihood (ML) estimation of motion parameters involves a multi-dimensional search and its performance is sensitive to target position errors, we also propose a technique that decouples the target motion parameters, yielding a two-step process that sequentially estimates the acceleration and velocity vectors with a reduced dimensionality of the parameter search space. We compare the performance of the sequential method against the ML estimation with the consideration of imperfect knowledge of the initial target positions. The Cramér-Rao bound (CRB) of the underlying parameter estimation problem is derived for a general multiple-target scenario in an MPR system. Simulation results are provided to compare the performance of the sparse signal reconstruction-based methods against the traditional time-frequency-based methods as well as the CRB.","Title: Segment-Sliding Reconstruction of Pulsed Radar Echoes with Sub-Nyquist Sampling.\nYear: 2015\n\nAbstract: It has been shown that analog-to-information conversion (AIC) is an efficient scheme to perform sub-Nyquist sampling of pulsed radar echoes. However, it is often impractical, if not infeasible, to reconstruct full-range Nyquist samples because of huge storage and computational load requirements. Based on the analyses of AIC measurement system, this paper develops a novel segment-sliding reconstruction (SegSR) scheme to effectively reconstruct the Nyquist samples. The SegSR performs segment-by-segment reconstruction in a sliding mode and can be implemented in real time. An important characteristic that distinguishes the proposed SegSR from existing methods is that the measurement matrix in each segment satisfies the restricted isometry property (RIP) condition. Partial support in the previous segment can be incorporated into the estimation of the Nyquist samples in the current segment. The effect of interference introduced from adjacent segments is theoretically analyzed, and it is revealed that the interference consists of two interference levels with different impacts to the signal reconstruction performance. With these observations, a two-step orthogonal matching pursuit (OMP) procedure is proposed for segment reconstruction, which takes into account different interference levels and partially known support of the previous segment. The proposed SegSR scheme achieves near-optimal reconstruction performance with a significant reduction of computational loads and storage requirements. Theoretical analyses and simulations verify its effectiveness.","Title: Gridless Quadrature Compressive Sampling with Interpolated Array Technique.\nYear: 2015\n\nAbstract: Quadrature compressive sampling (QuadCS) is a sub-Nyquist sampling scheme for acquiring in-phase and quadrature (I/Q) components in radar. In this scheme, the received intermediate frequency (IF) signals are expressed as a linear combination of time-delayed and scaled replicas of the transmitted waveforms. For sparse IF signals on discrete grids of time-delay space, the QuadCS can efficiently reconstruct the I/Q components from sub-Nyquist samples. In practice, the signals are characterized by a set of unknown time-delay parameters in a continuous space. Then conventional sparse signal reconstruction will deteriorate the QuadCS reconstruction performance. This paper focuses on the reconstruction of the I/Q components with continuous delay parameters. A parametric spectrum-matched dictionary is defined, which sparsely describes the IF signals in the frequency domain by delay parameters and gain coefficients, and the QuadCS system is reexamined under the new dictionary. With the inherent structure of the QuadCS system, it is found that the estimation of delay parameters can be decoupled from that of sparse gain coefficients, yielding a beamspace direction-of-arrival (DOA) estimation formulation with a time-varying beamforming matrix. Then an interpolated beamspace DOA method is developed to perform the DOA estimation. An optimal interpolated array is established and sufficient conditions to guarantee the successful estimation of the delay parameters are derived. With the estimated delays, the gain coefficients can be conveniently determined by solving a linear least-squares problem. Extensive simulation results evidently demonstrate the superiority of the proposed algorithms in achieving super-resolution time-delay estimation and high-accuracy sparse signal reconstruction. Quadrature compressive sampling system is described in the frequency domain.A beamspace DOA-based formulation is established for gridless sparse reconstruction.Time-delays are estimated with interpolated arrays.Sufficient condition to guarantee the estimation of the delay parameters is derived.","Title: Sparse Discrete Fractional Fourier Transform and Its Applications\nYear: 2014\n\nAbstract: The discrete fractional Fourier transform is a powerful signal processing tool with broad applications for nonstationary signals. In this paper, we propose a sparse discrete fractional Fourier transform (SDFrFT) algorithm to reduce the computational complexity when dealing with large data sets that are sparsely represented in the fractional Fourier domain. The proposed technique achieves multicomponent resolution in addition to its low computational complexity and robustness against noise. In addition, we apply the SDFrFT to the synchronization of high dynamic direct-sequence spread-spectrum signals. Furthermore, a sparse fractional cross ambiguity function (SFrCAF) is developed, and the application of SFrCAF to a passive coherent location system is presented. The experiment results confirm that the proposed approach can substantially reduce the computation complexity without degrading the precision.","Title: Complex multitask Bayesian compressive sensing\nYear: 2014\n\nAbstract: An effective complex multitask Bayesian compressive sensing (CMT-BCS) algorithm is proposed to recover sparse or group sparse complex signals. The existing multitask Bayesian compressive sensing (MT-CS) algorithm is powerful in recovering multiple real-valued sparse solutions. However, a large class of sensing problems deal with complex values. A simple approach, which decomposes a complex value into independent real and imaginary components, does not take into account the group sparsity of these two components and thus yields poor recovery performance. In this paper, we first introduce the CMT-BCS algorithm that jointly treats the real and imaginary components, and then derive a fast and accurate algorithm for the estimation of the prior parameters by solving a surrogate convex function. The proposed CMT-BCS algorithm achieves effective complex sparse signal recovery and outperforms MT-CS and complex group Lasso.","Title: Motion parameter estimation of multiple targets in multistatic passive radar through sparse signal recovery\nYear: 2014\n\nAbstract: The problem of estimating motion parameters of multiple closely located ground moving targets in a multistatic passive radar system is considered, with a focus on weak signal conditions. The proposed method provides a means of combining signal energy from all available, spatially separated, illuminators of opportunity to achieve multistatic diversity and overall signal enhancement. The proposed technique is based on sparse signal recovery and exploits a two-step process that sequentially estimates the acceleration and velocity vectors in order to reduce the dimensionality of parameter search space.","Title: Structural health monitoring exploiting MIMO ultrasonic sensing and group sparse Bayesian learning\nYear: 2014\n\nAbstract: In this paper, we propose the exploitation of sparse Bayesian learning in multiple-input multiple-output (MIMO) systems to account for the multi-dimensional group sparse nature of extended defects in guided ultrasonic wave based structural health monitoring. The multi-dimensional group sparsity in the underlying reconstruction problems arises due to the clustered spatial occupancy of extended defects and the multiple-aspect MIMO observations. Sparse Bayesian learning techniques have been shown to provide robustness for high-resolution signal reconstruction due to their insensitivity to dictionary coherence and have the flexibility of effective exploitation of the signal structure. The superiority of the proposed technique over the state-of-the-art sparse signal reconstruction techniques is demonstrated through simulations and preliminary experiments.","Title: Robust multipath exploitation radar imaging in urban sensing based on Bayesian compressive sensing\nYear: 2014\n\nAbstract: In through-the-wall radar imaging applications, exploitation of group sparsity of the targets under multipath propagation allows high-resolution ghost-free imaging. However, such multipath exploitation schemes may suffer from imperfect knowledge of the surrounding scatterers, such as interior walls. In this paper, a novel two-stage Bayesian compressive sensing approach is proposed for joint scene reconstruction and wall location estimation. The proposed method is capable of not only acquiring enhanced images by exploiting multipath propagation, but also estimating wall locations with high accuracy. In addition, compared to computationally demanding genetic approaches, the proposed method achieves robust imaging with a low complexity.","Title: Continuous structure based Bayesian compressive sensing for sparse reconstruction of time-frequency distributions\nYear: 2014\n\nAbstract: In this paper, we propose a Bayesian compressive sensing algorithm for effective reconstruction of sparse signals that demonstrate sparsity as continuous but irregular narrow strips in a multi-dimensional space. Among many applications of this class of representations are the two-dimensional time-frequency distributions (TFDs) of radar signals, which are often modeled as frequency modulated (FM) signals characterized by their sparse and continuous instantaneous frequencies. A spike-and-slab prior is introduced to statistically encourage sparsity of the time-frequency representations (TFRs) across each segmented time-frequency region, and a patterned prior is imposed to enforce the continuous structure of the TFR. Compared with the existing sparse signal reconstruction techniques, the proposed technique achieves improved interpretation of the TFD, particularly when the signals are noisy or with missing samples.","Title: Large-scale sparse reconstruction through partitioned compressive sensing\nYear: 2014\n\nAbstract: Compressive sensing (CS) finds broad applications in various sparse reconstruction problems. It has been clearly established that CS techniques achieve improved quality and resolution for many radar imaging problems where the scene is sparse or can be sparsely represented. One of the major issues that limits the applicability of CS techniques in radar systems is the prohibitive complexity in large-scale imaging problems encountered in, for example, synthetic aperture radar. However, as the actual scene and the back-projection images are associated with the point spreading function which has a finite support, it becomes possible to reconstruct the sparse scene based only on local observations. In this paper, we develop a novel segmented CS technique that achieves nearly optimal sparse reconstruction performance with significant reduction of computation complexity and memory requirements. The effect of interference from neighboring segments is examined, and the conditions of interference-free reconstruction of segmented compressive sensing are devised. The effectiveness of the proposed technique is verified by simulation results.","Title: Multistatic radar imaging via decentralized and collaborative subspace pursuit\nYear: 2014\n\nAbstract: The task of multistatic radar imaging can be converted to the problem of jointly sparse signal recovery. In this paper, the algorithm named decentralized and collaborative subspace pursuit (DCSP) is utilized in multistatic radar systems to obtain a high-resolution image. By embedding collaboration among radar nodes and fusion strategy into each iteration of the standard subspace pursuit (SP) algorithm, DCSP is capable of providing satisfactory image even if some radar nodes suffer from relatively low signal-to-noise ratios (SNRs). Compared to the existing algorithms based on linear programming, DCSP has much lower computational complexity at the cost of increased communication overhead in the radar network.","Title: Generalized coprime array configurations\nYear: 2014\n\nAbstract: A coprime array consists of two uniform linear subarrays that construct an effective difference co-array with certain desirable characteristics. In this paper, we propose a generalized coprime array concept through the compression of the interelement spacing of one constituting subarray. As such, the existing variations of coprime array and nested array structures are represented as special cases. The achievable unique lags as well as consecutive lags in the resulting virtual array are analytically expressed, and the direction-of-arrival estimation performance is examined using both the MUSIC algorithm and sparse signal reconstruction techniques.","Title: Time-frequency kernel design for sparse joint-variable signal representations\nYear: 2014\n\nAbstract: Highly localized quadratic time-frequency distributions cast nonstationary signals as sparse in the joint-variable representations. The linear model relating the ambiguity domain and time-frequency domain permits the application of sparse signal reconstruction techniques to yield high-resolution time-frequency representations. In this paper, we design signal-dependent kernels that enable the resulting time-frequency distribution to meet the two objectives of reduced cross-term interference and increased sparsity. It is shown that, for random undersampling schemes, the new adaptive kernel is superior to traditional reduced interference distribution kernels.","Title: Doa estimation exploiting coprime arrays with sparse sensor spacing\nYear: 2014\n\nAbstract: In this paper, we propose effective coprime array configurations in which the minimum interelement spacing is much larger than the typical half-wavelength requirement. Such configurations are important in many applications where the half-wavelength requirement cannot be met due to the physical sensors size or to avoid spatial oversampling in wideband operations. The application of such coprime arrays in direction-of-arrival estimations is examined using different algorithms.","Title: Time-frequency signature reconstruction from random observations using multiple measurement vectors\nYear: 2014\n\nAbstract: A new approach for sparse nonstationary signal reconstruction based on multiple windows is introduced. Signals which are localizable in the time-frequency (TF) domain give rise to sparsity in the same domain. When combined, sparse reconstructions, applied to randomly sampled data and corresponding to different selected windows, provide enhanced TF signature estimation. Among possible orthogonal windows, we consider those which characterize the eigen-decomposition of reduced-interference quadratic time-frequency distribution kernels. The highly overlapping TF support of the windows' full-data spectrograms inspires the use of the multiple measurement vectors, in lieu of individual windowed signal recovery. It is shown that the proposed approach outperforms other reconstruction methods when only a single window is applied and is superior to reduced interference time-frequency distributions of random observations.","Title: Simultaneous transfer of energy and information for MIMO-OFDM relay system\nYear: 2012\n\nAbstract: This paper investigates performance tradeoffs between energy and information transfer in a relay based two-hop wireless system involving an energy harvesting receiver. The source and relay nodes of the two-hop amplify-and-forward (AF) system employ orthogonal space-time block codes along with orthogonal frequency-division multiplexing (OFDM) scheme. The joint optimal source and relay precoders over the subchannels in frequencies are designed to achieve different tradeoffs between the overall energy transfer capability and the information rate, which are characterized by the boundary of the so-called rate-energy (R-E) region. The effect of various parameters on the boundary of the R-E region is demonstrated for different frequency selective channel models.","Title: Successive convex approximation for system performance optimization in a multiuser network with multiple mimo relays\nYear: 2011\n\nAbstract: We consider the problem of jointly optimizing amplify-and-forward (AF) multiple-input multiple-output (MIMO) relays and single-antenna receivers for multipoint-to-multipoint (MP2MP) communications using weighted summean-square-error (MSE) as a system performance measure. This optimization problem is nonconvex and the optimal solution cannot be obtained in polynomial time. By applying first-order approximation for the quadratic term, we show that this problem can be approximately reformulated as a semidefinite programming (SDP) problem, which is then solved successively in an iterative way. The performance of the proposed scheme is compared with the lower bound (LB) of the global optimal solution that is obtained with increased complexity using the MSE-profile approach, and the bisection and semidefinite relaxation (SDR) techniques. Numerical results show that the performance of the proposed successive convex approximation (SCA) approach is very close to the LB of the global optimal solution and is much better than the zero-forcing MIMO relays.","Title: Compressive sensing in nonstationary array processing using bilinear transforms\nYear: 2012\n\nAbstract: Compressive sensing (CS) has successfully been applied to reconstruct sparse signals and images from few observations. For multi-component nonstationary signals characterized by instantaneous frequency laws, the sparsity exhibits itself in the time-frequency domain as well as the ambiguity domain. In this paper, we examine CS in the context of nonstationary array processing. We show that the spatial averaging of the ambiguity function across the array improves the CS performance by reducing both noise and cross-terms. The corresponding time-frequency distribution which is reconstructed through L1 minimizations yields significant improvement in time-frequency signature localizations and characterizations.","Title: A novel partial relay selection method for amplify-and-forward relay systems\nYear: 2012\n\nAbstract: Although partial relay selection (PRS) for amplify-and-forward relay systems requires only the knowledge of source-relay (S-R) channels, in general, it incurs a significant performance loss. In cooperative systems with all single-antenna nodes, irrespective of the number of relays, the diversity order of PRS is limited to only one. This paper proposes a novel relay selection method for improving the performance of PRS scheme. In particular, as in the conventional PRS scheme, the relay that gives the best first-hop signal-to-noise ratio (SNR) is selected. However, this selection is made from only a subset of relays, for which the corresponding S-R and relay-destination (R-D) links are not in outage. An R-D link is considered to be in outage if its SNR is below the predefined threshold value of the end-to-end SNR plus some adjustable margin. The additional overhead required for implementing the proposed scheme is comparable to that of the conventional PRS method. For conciseness and better exposition of the proposed method, we limit our theoretical analysis to a system with two to three relays. The exact expressions of the end-to-end outage probability are derived and it is shown that full diversity order is achieved. Simulation results verify theoretical analysis and show that the proposed method significantly outperforms the conventional PRS method. Moreover, the results demonstrate that, for properly selected margin, the performance of the proposed method is very close or comparable to the method with full channel state information.","Title: Sparsity-based DOA estimation using co-prime arrays\nYear: 2013\n\nAbstract: In this paper, we propose co-prime arrays for effective direction-of-arrival (DOA) estimation. To fully utilize the virtual aperture achieved in the difference co-array constructed from a co-prime array structure, sparsity-based spatial spectrum estimation technique is exploited. Compared to existing techniques, the proposed technique achieves better utilization of the co-array aperture and thus results in increased degrees-of-freedom as well as improved DOA estimation performance.","Title: DOA estimation using a sparse uniform linear array with two CW signals of co-prime frequencies\nYear: 2013\n\nAbstract: In this paper, we propose the use of a sparse uniform linear array to estimate the direction-of-arrival (DOA) of more sources than the number of sensors by exploiting two continuous wave signals whose frequencies satisfy a certain co-prime relationship. This extends the co-prime array and filter concept, which was developed in either the spectral or spatial domain, to a joint spatio-spectral domain, thereby achieving high flexibility in array structure design to meet the degrees-of-freedom and system complexity constraints. The DOA estimation is implemented in the difference co-array context to avoid spatial undersampling, and group sparsity based compressive sensing techniques are used to determine the direction of signal arrivals.","Title: Pulse-doppler signal processing with quadrature compressive sampling\nYear: 2013\n\nAbstract: Quadrature compressive sampling (QuadCS) is a recently introduced sub-Nyquist sampling scheme for effective acquisition of inphase and quadrature (I/Q) components of sparse radio frequency signals. In applications to pulse-Doppler radars, the QuadCS outputs can be arranged into a two-dimensional data format, in terms of slow time and virtual fast time, similar to that by Nyquist sampling. This paper develops a compressive sampling pulse-Doppler (CoSaPD) processing scheme which performs Doppler estimation/detection and range estimation from the sub-Nyquist data without recovering the Nyquist samples. The Doppler estimation is realized through a spectrum analyzer as in classical processing, whereas the detection is performed using the Doppler bin data. The range estimation is performed using sparse recovery algorithms only for the detected targets to reduce the computational load. A low detection threshold is used to improve the detection probability and the introduced false targets are then removed in the range estimation stage by exploiting the inherent target detection capability of the recovery algorithms. Simulation results verify the effectiveness of the proposed CoSaPD scheme, which requires only one-eighth of the Nyquist rate to achieve similar performance to the classical processing with Nyquist samples, provided that the input signal-to-noise ratio (SNR) is above ???25 dB.","Title: Maneuvering target altitude tracking in over-the-horizon radars exploiting multipath Doppler signatures\nYear: 2013\n\nAbstract: Over-the-horizon radar (OTHR) systems provide wide-area surveillance capabilities to detect and track targets far beyond the range of conventional line-of-sight radars. Because of the narrowband waveforms, OTHR systems do not achieve reliable altitude estimation. In this paper, we develop a new technique to track the instantaneous altitude of maneuvering targets by exploiting the estimated multi-component Doppler signatures. The main contribution of this paper is to apply effective non-stationary signal analysis for estimating the time-varying Doppler signature of each individual multipath, which is then applied to an extended Kalman filter to reliably track the instantaneous target altitude.","Title: A Novel Two-Dimensional Sparse-Weight NLMS Filtering Scheme for Passive Bistatic Radar.\nYear: 2016\n\nAbstract: In passive bistatic radars, weak target echoes may often be masked by direct path interference, multipath components, and strong target echoes, making weak target detection a challenging problem. The conventional 1-D adaptive cancelation algorithms, such as the normalized least mean square (NLMS), cannot effectively suppress strong target echoes when their Doppler frequencies spread. In addition, ...","Title: Low-Complexity Direction-of-Arrival Estimation Based on Wideband Co-Prime Arrays\nYear: 2015\n\nAbstract: A class of low-complexity compressive sensing-based direction-of-arrival (DOA) estimation methods for wideband co-prime arrays is proposed. It is based on a recently proposed narrowband estimation method, where a virtual array model is generated by directly vectorizing the covariance matrix and then using a sparse signal recovery method to obtain the estimation result. As there are a large number of redundant entries in both the auto-correlation and cross-correlation matrices of the two sub-arrays, they can be combined together to form a model with a significantly reduced dimension, thereby leading to a solution with much lower computational complexity without sacrificing performance. A further reduction in complexity is achieved by removing noise power estimation from the formulation. Then, the two proposed low-complexity methods are extended to the wideband realm utilizing a group sparsity based signal reconstruction method. A particular advantage of group sparsity is that it allows a much larger unit inter-element spacing than the standard co-prime array and therefore leads to further improved performance.","Title: Space–Time Adaptive Processing and Motion Parameter Estimation in Multistatic Passive Radar Using Sparse Bayesian Learning\nYear: 2016\n\nAbstract: Conventional space-time adaptive processing suffers from the requirement of a large number of secondary samples. In this paper, a novel method is proposed to accurately estimate the clutter covariance matrix based on a small number of secondary samples, by exploiting the common clutter support across nearby range cells in the angle-Doppler domain. By taking advantage of the intrinsic sparsity of the clutter in the angle-Doppler domain, the recently developed sparse Bayesian learning technique is employed for high-resolution clutter profile estimation. The proposed method does not require the independent and identically distributed secondary sample assumption, and the required number of secondary data samples can be significantly reduced. In addition, we propose a sparse reconstruction-based approach to acquire the 2-D motion parameters of moving targets, by exploiting their group sparsity in the velocity domain in the multistatic passive radar systems. Simulation results verify the effectiveness of the proposed algorithm.","Title: Dual-Function Radar-Communications: Information Embedding Using Sidelobe Control and Waveform Diversity.\nYear: 2016\n\nAbstract: We develop a new technique for a dual-function system with joint radar and communication platforms. Sidelobe control of the transmit beamforming in tandem with waveform diversity enables communication links using the same pulse radar spectrum. Multiple simultaneously transmitted orthogonal waveforms are used for embedding a sequence of LB bits during each radar pulse. Two weight vectors are design...","Title: Special issue on Co-prime sampling and arrays\nYear: 2015\n\nAbstract: Co-prime sampling and co-prime arrays have recently been shown to improve active and passive sensing in radar and under-water acoustics using both narrowband and wideband signal plat-forms. Co-prime processing provides a systematical framework for sparse sampling and array configuration with increased aperture and improved spatial resolution. Co-prime based approaches to sampling and arrays can combat ambiguity and provide unique answers to target coordinates under forced coarse sampling in time, frequency, and space. Temporal and spatial co-prime samplings make advances in direction-of-arrival (DOA) estimation, spectrum sensing, and sparse covariance sketching. They find broad applications in synthetic aperture radar (SAR), radio frequency (RF) surveillance, target localization, wideband urban radar processing, space–time adaptive processing, and nonstationary array processing utilizing Doppler signatures and instantaneous frequency source characteristics.","Title: Radar Signal Processing for Elderly Fall Detection: The future for in-home monitoring.\nYear: 2016\n\nAbstract: Radar is considered an important technology for health monitoring and fall detection in elderly assisted living due to a number of attributes not shared by other sensing modalities. In this article, we describe the signal processing algorithms and techniques involved in elderly fall detection using radar. A human?s radar signal returns differ in their Doppler characteristics, depending on the natu...","Title: High-Resolution Passive SAR Imaging Exploiting Structured Bayesian Compressive Sensing\nYear: 2015\n\nAbstract: In this paper, we develop a novel structured Bayesian compressive sensing algorithm with location dependence for high-resolution imaging in ultra-narrowband passive synthetic aperture radar (SAR) systems. The proposed technique exploits wide-angle and/or multi-angle observations for image resolution enhancement. We first introduce a forward model based on sparse synthetic apertures. The problem of...","Title: Sparsity-based multi-target localization exploiting multi-frequency coprime array\nYear: 2015\n\nAbstract: In this paper, a novel sparsity-based multi-target localization approach is proposed by exploiting a coprime array operated with multiple narrowband signals of distinct but closely separated carrier frequencies. The cross-covariance matrix is formulated between baseband array data corresponding to different sensing frequencies to generate virtual difference coarrays, which enable direction-of-arrival (DOA) estimation of more targets than the number of physical sensors. In addition, the use of well designed multi-frequency signals unwraps the propagation phase information, thereby enabling unambiguous estimation of the target ranges. The DOA and range estimations are cast as a sparse reconstruction problem and are solved using the complex mulititask Bayesian compressive sensing (CMT-BCS) technique. The effectiveness of the proposed technique is verified through simulation results.","Title: DOA estimation of sparsely sampled nonstationary signals\nYear: 2015\n\nAbstract: The paper deals with sparsely sampled nonstationary signals in a multi-sensor array platform. We examine direction-of-arrival (DOA) estimation using sparsity-based time-frequency signal representation (TFSR). While conventional time-frequency analysis techniques suffer from noise-like artifacts due to missing data samples, high-fidelity time-frequency signatures can be obtained by applying kernelled processing and sparse reconstruction. Since the signals received at different sensors occupy the same time-frequency regions and share a common nonzero support, the recovery of TFSRs can be cast as a group sparse reconstruction problem. The reconstructed auto- and cross-sensor TFSRs enable the formation of the spatial time-frequency distribution (STFD) matrix, which is used, in turn, to propose the sparse time-frequency MUSIC (STF-MUSIC). The proposed STF-MUSIC method achieves effective source discrimination capability, leading to improved DOA estimation performance.","Title: Through-the-wall radar imaging based on modified Bayesian compressive sensing\nYear: 2014\n\nAbstract: In this paper, a novel modified complex multi-task Bayesian compressive sensing (MCMT-BCS) algorithm is proposed to acquire high-resolution images in stepped-frequency through-the-wall radar imaging (TWRI) exploiting multipath. Unlike traditional TWRI approaches that assume frequency-independent scattering model, we develop a practical subband scattering model to characterize real-world scattering mechanisms. The target imaging is reformulated as a multi-task sparse signal recovery problem across all frequency subbands as well as multipath modes, where the sparse entries of each task share the same support in the imaged scene. The proposed MCMT-BCS algorithm accounts for both types of coexisting group sparsity to achieve improved high-resolution imaging capability. Simulation results verify the effectiveness of the proposed algorithm.","Title: Group sparsity based wideband DOA estimation for co-prime arrays\nYear: 2014\n\nAbstract: A novel wideband direction-of-arrival (DOA) estimation method is proposed for co-prime arrays. After decomposing the wideband signals into different frequencies/subbands through a discrete Fourier transform or, more generally, a filter bank system, the increased degrees of freedom provided by co-prime arrays are fully exploited with a group sparsity based signal reconstruction method. Simulation results show that this novel method can distinguish much more sources than the number of physical sensors. Compared with the existing narrowband DOA estimation method for co-prime arrays, the proposed wideband method achieves a significant performance improvement.","Title: Consensus-Based Loud Detection For True And False Warhead Recognition\nYear: 2015\n\nAbstract: The micro-motion characteristics of warheads have been utilized to discriminate false warheads from true ones. To obtain accurate dimensional measurements, a multiple-input multiple-output (MIMO) radar is adopted to observe the kinetic information of the warhead. A distributed state space model (SSM) is built and the differences between the true and false warheads are characterized as different system parameters of the SSM. In this paper, we extend the locally optimal unknown direction (LOUD) detector, which has shown its effectiveness for hypothesis testing, to the underlying distributed detection problem, and a novel consensus-based LOUD detector is proposed. The superior detection performance of the proposed detection algorithm in identifying the true and false warheads is verified using simulation results.","Title: Radar-based fall detection exploiting time-frequency features\nYear: 2014\n\nAbstract: Falls of the elderly are a major public health concern. In this paper, we develop an effective fall detection algorithm for application in continuous-wave radar systems. The proposed algorithm exploits time-frequency characteristics of the radar Doppler signatures, and the motion events are classified using the joint statistics of three different features. The effectiveness of the proposed technique is verified through measurement data.","Title: Wideband DOA estimation for uniform linear arrays based on the co-array concept\nYear: 2015\n\nAbstract: A novel design for wideband uniform linear arrays (ULAs) with the associated group-sparsity based direction-of arrival (DOA) estimation method is proposed. This design allows the number of source signals to significantly exceed the number of sensors. Linear frequency modulated continuous wave (LFMCW) is used as the transmitted signal to ensure the required correlation property among different frequencies. The received echo signals from multiple targets are decomposed into different frequencies by discrete Fourier transform (DFT). Then these frequency bins are divided into several pairs to increase the degrees of freedom (DOFs) based on the co-array concept in the spatio-spectral domain. Group sparsity based signal reconstruction method is employed to jointly estimate the DOA results across multiple frequency pairs. Simulation results demonstrate a significantly improved performance achieved by the proposed method.","Title: Dual-function radar-communications using phase-rotational invariance\nYear: 2015\n\nAbstract: In this paper, we develop a new technique for dual-function radar-communications in a transmit: multi-sensor array where information embedding is achieved using phase-rotational invariance. A sequence of Q bits is first mapped into a dictionary of 2(Q) phase rotations. Then, one pair of transmit orthogonal waveforms is used in tandem with 2(Q) pairs of transmit beamforming weight vectors for embedding a certain entry of the phase-rotation dictionary during each radar pulse. The same pair of waveforms is used during all pulses while the pair of transmit beamforming weight vectors changes from pulse to pulse based on which entry of the phase rotation dictionary is embedded. During each pulse, the receiver detects the embedded phase rotation and employ it to decipher the transmitted bit sequence, The proposed information embedding technique is angle-dependant and, therefore, the communication process is inherently secure against interception from directions other than the desired communication direction. The performance of the proposed technique is investigated in terms of the bit error rate (BER).","Title: Doa Estimation Of Nonparametric Spreading Spatial Spectrum Based On Bayesian Compressive Sensing Exploiting Intra-Task Dependency\nYear: 2015\n\nAbstract: For spatially distributed targets encountered in radar and sonar applications, direct application of subspace-based methods usually do not lead to an accurate estimation of the direction and angular extent of the signal arrivals. If the spatial distribution of the targets can be parameterized with a known model a priori, the direction-of-arrival (DOA) estimation problems can be simplified as parameter estimation problems. However, these methods do not apply when the targets are not parameterizable. Motivated by this fact, we propose an effective approach for the DOA estimation of nonparametric spatially extended targets. In the proposed approach, the spatially extended targets are modeled as a continuous sparse structure, which are effectively estimated using the Bayesian compressive sensing techniques based on a paired spike-and-slab prior accounting for the angular target spread. In particular, the problem is examined under a collocated multiple-input multiple-output (MIMO) radar platform. Signal transmission at multiple coprime transmit frequencies are also considered to achieve increased degrees-of-freedom. The group sparsity of the targets across different frequencies is exploited to achieve improved DOA estimation performance.","Title: Structured Bayesian Compressive Sensing Exploiting Spatial Location Dependence\nYear: 2015\n\nAbstract: In this paper, we propose a novel structured compressive sensing algorithm based on non-parametric Bayesian framework for the reconstruction of sparse entries with a continuous structure. A paired spike-and-slab prior is first employed to impose signal sparsity. A logistic Gaussian kernel model, which involves the logistic model and location-dependent Gaussian kernel, is then proposed to encourage the underlying structure of a sparse signal. A closed-form and analytical posterior inference is carried out in a Gibbs sampling scheme. Simulation results demonstrate that the proposed algorithm outperforms existing state-of-the-art sparse Bayesian learning algorithms.","Title: Autofocus Bayesian compressive sensing for multipath exploitation in urban sensing\nYear: 2015\n\nAbstract: Exploitation of group sparsity under multipath propagation enables high-resolution ghost-free imaging in urban sensing and through-the-wall radar imaging applications. Multipath exploitation schemes typically require exact prior information of the indoor scene layout and transceiver locations to eliminate ghosts targets. Imperfections in the prior knowledge lead to performance degradation of such schemes. In this paper, a novel autofocus Bayesian compressive sensing approach is proposed for joint scene reconstruction and correction of phase errors resulted from transceiver position uncertainties. Supporting simulation results are provided to demonstrate the effectiveness of the proposed algorithm.","Title: Sparse reconstruction of multi-component Doppler signature exploiting target dynamics\nYear: 2015\n\nAbstract: In this paper, we propose a novel technique for a non-parametric sparse reconstruction of the Doppler signature of multiple ground moving targets exploiting prior knowledge about the target dynamics. The Doppler signature of multiple non-stationary targets is commonly modeled as a multi-component polynomial phase signal, whose order depends on the complexity of target dynamics and the coherent processing interval. In recent years, sparse signal reconstruction and compressive sensing algorithms have been extensively deployed in both parametric and non-parametric estimation of Doppler signatures. The performance of sparsity based Doppler signature reconstruction algorithms is significantly compromised in the presence of strong noise and a large number of missing samples. Herein, we present an effective technique for a non-parametric sparse reconstruction of a multi-component Doppler signature by exploiting the a priori information on target dynamics. Simulation results show that the proposed technique significantly outperforms the conventional sparse reconstruction based methods in such challenging conditions.","Title: Sparse Arrays and Sampling for Interference Mitigation and DOA Estimation in GNSS.\nYear: 2016\n\nAbstract: This paper establishes the role of sparse arrays and sparse sampling in antijam global navigation satellite systems (GNSS). We show that both jammer direction of arrival estimation methods and mitigation techniques benefit from the design flexibility of sparse arrays and their extended virtual apertures or coarrays. Taking advantage of information redundancy, significant reduction in hardware and ...","Title: Group Sparsity Based Multi-Target Tracking in Passive Multi-Static Radar Systems Using Doppler-Only Measurements.\nYear: 2016\n\nAbstract: In this paper, we consider the problem of tracking multiple targets in a passive multi-static radar system using Doppler-only measurements. The number of targets is assumed unknown and time-varying. The Doppler measurements are subject to additive noise, clutter, and missed detections. Doppler-only measurements from a single sensor provide incomplete information about the target state, commonly re...","Title: DOA Estimation From One-Bit Compressed Array Data via Joint Sparse Representation.\nYear: 2016\n\nAbstract: A one-bit joint sparse representation direction of arrival (OBJSR-DOA) estimation approach is proposed in this letter. By exploiting the joint spatial and spectral correlations inherent in acoustic sensor array data, the proposed OBJSR-DOA approach provides reliable DOA estimation from only the sign bit of randomly subsampled acoustic sensor data. The random subsampling and single-bit quantization...","Title: Underdetermined wideband DOA estimation of off-grid sources employing the difference co-array concept.\nYear: 2017\n\nAbstract: A wideband off-grid model is proposed to represent dictionary mismatch under the compressive sensing framework exploiting difference co-arrays. A group sparsity based off-grid method is proposed for underdetermined wideband direction of arrival (DOA) estimation which provides improved performance over the existing group sparsity based method with a same search grid. A two-step approach is then proposed which achieves an even better performance with significantly reduced computational complexity.","Title: DOA estimation exploiting a uniform linear array with multiple co-prime frequencies.\nYear: 2017\n\nAbstract: The co-prime array, which utilizes a co-prime pair of uniform linear sub-arrays, provides a systematical means for sparse array construction. By choosing two co-prime integers M and N, O ( MN ) co-array elements can be formed from only O ( M + N ) physical sensors. As such, a higher number of degrees-of-freedom (DOFs) is achieved, enabling direction-of-arrival (DOA) estimation of more targets than the number of physical sensors. In this paper, we propose an alternative structure to implement co-prime arrays. A single sparse uniform linear array is used to exploit two or more continuous-wave signals whose frequencies satisfy a co-prime relationship. This extends the co-prime array and filtering to a joint spatio-spectral domain, thereby achieving high flexibility in array structure design to meet system complexity constraints. The DOA estimation is obtained using group sparsity-based compressive sensing techniques. In particular, we use the recently developed complex multitask Bayesian compressive sensing for group sparse signal reconstruction. The achievable number of DOFs is derived for the two-frequency case, and an upper bound of the available DOFs is provided for multi-frequency scenarios. Simulation results demonstrate the effectiveness of the proposed technique and verify the analysis results. HighlightsWe have proposed a novel co-prime array structure that exploits a single uniform linear array and a co-prime set of frequencies.We have derived the analytical expressions of the array aperture and the number of DOFs with respect to two and multiple co-prime frequencies.DOA estimation is formulated as a group sparse compressive sensing problem, and is effectively solved by the complex multi-task Bayesian compressive sensing technique.","Title: Generalized Coprime Sampling of Toeplitz Matrices for Spectrum Estimation.\nYear: 2017\n\nAbstract: Increased demand on spectrum sensing over a broad frequency band requires a high sampling rate and thus leads to a prohibitive volume of data samples. In some applications, e.g., spectrum estimation, only the second-order statistics are required. In this case, we may use a reduced data-sampling rate by exploiting a low-dimensional representation of the original high-dimensional signals. In particular, the covariance matrix can be reconstructed from compressed data by utilizing its specific structure, e.g., the Toeplitz property. Among a number of techniques for compressive covariance sampler design, the coprime sampler is considered attractive because it enables a systematic design capability with a significantly reduced sampling rate. In this paper, we propose a general coprime sampling scheme that implements effective compression of Toeplitz covariance matrices. Given a fixed number of data samples, we examine different schemes on covariance matrix acquisition for performance evaluation, comparison, and optimal design, based on segmented data sequences.","Title: Two-dimensional DOA estimation using parallel coprime subarrays\nYear: 2016\n\nAbstract: A conventional coprime array is a linear array, which consists of two uniform linear subarrays to construct an effective difference coarray with certain desirable characteristics. In this paper, we propose a parallel coprime array structure and a novel algorithm for two-dimensional (2-D) direction-of-arrival (DOA) estimation. By vectorizing the cross-covariance matrix of subarray data, the resulting virtual difference coarray enables resolving more signals than the number of antennas. The 2-D DOA estimation problem is cast as two separate one-dimensional DOA estimation problems, where the estimated azimuth and elevation angles can be properly associated. Compared with other methods, such as, the propagator method (PM) and the rank-reduction (RARE) based algorithms, the proposed method resolves more signals and achieves improved estimation performance.","Title: Ultra-wideband radar and vision based human motion classification for assisted living.\nYear: 2016\n\nAbstract: Fall detection for elderly is one of the most important areas in elderly healthcare. Both video and radar based detections are being developed for this purpose. This paper presents a new approach to classify different human motions through machine learning. In particular, our objective is to achieve high-accuracy fall detection through the exploitation of both video and radar data. Motion history image is applied to extract temporal features from video clips, and hidden Markov models are trained with the features extracted from both video and radar data to discern the types of motion. Experiment results indicate that the proposed approach provides improved performance in distinguishing falls from other motions such as sitting.","Title: Robust group compressive sensing for DOA estimation with partially distorted observations.\nYear: 2016\n\nAbstract: In this paper, we propose a robust direction-of-arrival (DOA) estimation algorithm based on group sparse reconstruction algorithm utilizing signals observed at multiple frequencies. The group sparse reconstruction scheme for DOA estimation is solved through the complex multitask Bayesian compressive sensing algorithm by exploiting the group sparse property of the received multi-frequency signals. Then, we propose a robust reconstruction algorithm in the presence of distorted signals. In particular, we consider a problem where the observed data in some frequencies are distorted due to, e.g., interference contamination. In this case, the residual error will follow the impulsive Gaussian mixture distribution instead of the Gaussian distribution due to the fact that some of the estimation errors significantly depart from the mean value of the estimation error distribution. Thus, the minimum least square restriction used in the conventional sparse reconstruction algorithm may lead to a failed reconstruction result. By exploiting the maximum correntropy criterion which is inherently insensitive to the impulsive noise, a weighting vector is derived to automatically mitigate the effect of the distorted narrowband signals, and a robust group compressive sensing approach is developed to achieve reliable DOA estimation. The robustness and effectiveness of the proposed algorithm are verified using simulation results.","Title: Low-Complexity 2D Direction-of-Arrival Estimation for Acoustic Sensor Arrays.\nYear: 2016\n\nAbstract: We use the phase difference among sensors, by solving the phase wrapping problem based on the cross-spectrum between sensors, to obtain the direction of arrival without exhaustive search in the two-dimensional angle space. The proposed approach does not need the typical sensor separation or interfrequency separation requirements. Simulation results with narrowband, wideband signals (bird chirps), ...","Title: Focused Compressive Sensing for Underdetermined Wideband DOA Estimation Exploiting High-Order Difference Coarrays.\nYear: 2017\n\nAbstract: Group-sparsity-based method is applied to the 2qth-order difference coarray for underdetermined wideband direction of arrival (DOA) estimation. For complexity reduction, a focused compressive-sensing-based approach is proposed, without sacrificing its performance. Different from the conventional focusing approach, in the proposed one, focusing is applied to the virtual arrays and no preliminary DO...","Title: Frequency Diverse Coprime Arrays With Coprime Frequency Offsets for Multitarget Localization.\nYear: 2017\n\nAbstract: Different from conventional phased-array radars, the frequency diverse array (FDA) radar offers a range-dependent beampattern capability that is attractive in various applications. The spatial and range resolutions of an FDA radar are fundamentally limited by the array geometry and the frequency offset. In this paper, we overcome this limitation by introducing a novel sparsity-based multitarget lo...","Title: A dual-function MIMO radar-communications system using PSK modulation.\nYear: 2016\n\nAbstract: In this paper, we develop a new technique for information embedding into the emission of multiple-input multipleoutput (MIMO) radar using dual-functionality platforms. A set of orthogonal waveforms occupying the same band is used to implement the primary MIMO radar operation. The secondary communication function is implemented by embedding one phase-shift keying (PSK) communication symbol in each orthogonal waveform, i.e., the number of embedded communication symbols during each radar pulse equals the number of transmit antennas. We show that the communication operation is transparent to the MIMO radar operation. The communication receiver detects the embedded PSK symbols using standard ratio testing. The achievable data rate is proportional to the pulse repetition frequency, the number of transmit elements, and the size of the PSK constellation. The performance of the proposed technique is investigated in terms of the symbol error rate. Simulations examples demonstrate that data rates in the range of several Mbps can be embedded and reliably detected.","Title: Robust DOA Estimation in the Presence of Miscalibrated Sensors.\nYear: 2017\n\nAbstract: In this letter, we propose a robust direction-of-arrival (DOA) estimation algorithm in the context of sparse reconstruction, where some array sensors are miscalibrated. In this case, conventional DOA estimation algorithms suffer from degraded performance or even failed operations. In the proposed approach, the miscalibrated sensor observations are treated as outliers, and a weighting factor is ada...","Title: Wideband Radar Based Fall Motion Detection For A Generic Elderly\nYear: 2016\n\nAbstract: Radar-based automated fall detection systems are considered as an important and emerging technology for elderly assisted living. These radar systems provide non-intrusive sensing capabilities to detect fall events. Various studies have used micro-Doppler signatures to determine falls. However, Doppler radar fall detection systems suffer false alarms stemming from other sudden non-rhythmic motion articulations. In this work, we consider a textural-based feature extraction method which can determine the density variations between various motion articulations. For this purpose, textural features are extracted from the gray level co-occurrence matrix for each motion using time-integrated range-Doppler maps and micro-Doppler signatures. Textural features are then used to train the support vector machine classifier. The sequential forward selection method is implemented to identify essential features and minimize the feature space while maximizing the fall detection rate. The results show that well selected range-Doppler based textural features can provide improved classification results compared to textural features based only on micro-Doppler signatures.","Title: Nonstationary Jammer Suppression Based On Parametric Sparse Reconstruction\nYear: 2016\n\nAbstract: We propose a nonstationary jammer signal suppression technique based on parametric sparse reconstruction, where sparsely sampled data are considered. By assuming nonstationary jammers that are characterized by polynomial phase signatures, a data-dependent parametric dictionary matrix is designed. In order to obtain an accurate instantaneous frequency estimation at a low computational complexity, we exploit an iterative multi-round sparse reconstruction scheme in which the dictionary matrix is updated with a finer grid size, thus leading to dictionary entries that are closer to the true jammer signatures. Simulation results are provided to verify the effectiveness of the proposed technique.","Title: Detection of weak astronomical signals with frequency-hopping interference suppression.\nYear: 2018\n\nAbstract: This paper addresses the detection of weak astronomical signals that are contaminated by strong frequency-hopping (FH) interferers and suffer from missing samples. The problem is considered in the time–frequency domain and we successively suppress artifacts due to missing samples, estimate and remove FH interferers, and detect the weak astronomical signals. More specifically, we first suppress the artifacts due to missing samples by developing a waveform-adaptive time–frequency kernel. The instantaneous spectra of the FH interferers are then estimated using a sparsity-based approach that takes the inherent properties of FH signals into account. Finally, a sparse coherent integrated cubic phase function is applied to effectively detect weak astronomical chirp components over a long integration time. Simulation results are provided to demonstrate the effectiveness of the proposed approach.","Title: Cramer-Rao type bounds for sparsity-aware multi-sensor multi-target tracking.\nYear: 2018\n\nAbstract: •This paper develops the Cramer–Rao type of bounds for the sparsity-aware multi-sensor multi-target tracking techniques.•The results enable a clear understanding of the effect of the input signal-to-noise ratio and missing samples to the performance of sparsity-aware multi-sensor multi-target tracking techniques.•It provides theoretical support and insightful understanding to the offerings of the recursive feedback in the sparsity-aware multi-sensor multi-target tracking techniques.","Title: Structure-Aware Bayesian Compressive Sensing for Frequency-Hopping Spectrum Estimation With Missing Observations.\nYear: 2018\n\nAbstract: In this paper, we address the problem of spectrum estimation of multiple frequency-hopping (FH) signals in the presence of random missing observations. The signals are analyzed within the bilinear time-frequency (TF) representation framework, where a TF kernel is designed by exploiting the inherent FH signal structures. The designed kernel permits effective suppression of cross-terms and artifacts...","Title: Performance Analysis for Uniform Linear Arrays Exploiting Two Coprime Frequencies.\nYear: 2018\n\nAbstract: Sparse arrays can achieve a higher number of degrees of freedom (DOFs) compared with uniform linear array (ULA) counterparts. To further reduce the number of physical sensors while keeping a high number of DOFs, a direction of arrival (DOA) estimation algorithm by exploiting coprime frequencies base on a sparse ULA is recently proposed. However, the performance of such approach is not properly ana...","Title: Coprime array-based DOA estimation in unknown nonuniform noise environment.\nYear: 2018\n\nAbstract: In this paper, we propose direction-of-arrival (DOA) estimation techniques, respectively based on covariance matrix reconstruction and matrix completion, to achieve robust DOA estimation capability in nonuniform noise environments using coprime arrays. For the covariance matrix reconstruction-based approach, by exploring the diagonal structure of the covariance matrix of the noise, the covariance matrix of the received signal vector is reconstructed through averaging its diagonal elements. Moreover, in order to handle more sources than the number of sensors, the difference coarray of coprime arrays is utilized through the vectorization of the reconstructed covariance matrix. A compressive sensing (CS) based DOA estimator is then formulated to provide sparsity-based DOA estimation. For the matrix completion-based approach, we take the full advantage of the difference coarray lags and obtain the noise-free covariance matrix of the virtual uniform linear array by using the matrix completion technique to recover the removed diagonal elements and missing holes in the virtual array covariance matrix. Then CS-based and MUSIC-based DOA estimators are respectively designed to perform DOA estimation using the estimated noise-free covariance matrix. Simulation results verify the effectiveness of the proposed algorithms.","Title: DOA Estimation Using Compressed Sparse Array.\nYear: 2018\n\nAbstract: Sparse arrays, such as nested arrays and coprime arrays, can achieve a high number of degrees of freedom (DOFs) for direction of arrival (DOA) estimation with a reduced number of antennas. On the other hand, the compressive measurement method provides an effective way to reduce the number of frontend circuit chains. In this paper, we generalized current works on the two categories of methods to a ...","Title: Coarray Interpolation-Based Coprime Array Doa Estimation Via Covariance Matrix Reconstruction.\nYear: 2018\n\nAbstract: Coprime arrays are capable of achieving an increased number of degrees-of-freedom by operating the coarray signals. However, their non-uniform coarrays prevent the full utilization of the available signals. To address this problem, a novel coarray interpolation-based direction-of-arrival (DOA) estimation algorithm via covariance matrix reconstruction is proposed in this paper. In particular, we formulate a gridless optimization problem to reconstruct the covariance matrix of the interpolated coarray, such that all the coarray observations are fully utilized. We also investigate the rotational invariance in the coarray domain to retrieve the DOAs. Neither spatial sampling nor spectrum searching is required in the proposed algorithm, indicating the capability of resolving off-grid DOAs. Simulation results demonstrate the effectiveness of the proposed DOA estimation algorithm.","Title: Image Reconstruction in Electrical Impedance Tomography Based on Structure-Aware Sparse Bayesian Learning.\nYear: 2018\n\nAbstract: Electrical impedance tomography (EIT) is developed to investigate the internal conductivity changes of an object through a series of boundary electrodes, and has become increasingly attractive in a broad spectrum of applications. However, the design of optimal tomography image reconstruction algorithms has not achieved the adequate level of progress and matureness. In this paper, we propose an eff...","Title: Dual-function radar-communications using QAM-based sidelobe modulation.\nYear: 2018\n\nAbstract: Spectrum sharing using a joint platform for radar and communication systems has attracted significant attention in recent years. In this paper, we propose a novel dual-function radar-communications (DFRC) strategy to embed quadrature amplitude modulation (QAM) based communication information in the radar waveforms by exploiting sidelobe control and waveform diversity. The proposed information embedding technique can support multiple communication receivers located in the sidelobe region. In addition to the information broadcasting, the developed approach enables multi-user access by allowing simultaneous transmission of distinct information streams to the communication receivers located in different directions. We prove that the proposed technique ensures a significant data rate enhancement compared to the existing techniques. Moreover, the developed DFRC strategy generalizes the mathematical framework of the existing sidelobe control-based information embedding techniques.","Title: Direction-of-Arrival Estimation for Coprime Array via Virtual Array Interpolation.\nYear: 2018\n\nAbstract: Coprime arrays can achieve an increased number of degrees of freedom by deriving the equivalent signals of a virtual array. However, most existing methods fail to utilize all information received by the coprime array due to the non-uniformity of the derived virtual array, resulting in an inevitable estimation performance loss. To address this issue, we propose a novel virtual array interpolation-b...","Title: Off-Grid Direction-of-Arrival Estimation Using Coprime Array Interpolation.\nYear: 2018\n\nAbstract: In this letter, we propose a coprime array interpolation approach to provide an off-grid direction-of-arrival (DOA) estimation. Through array interpolation, a uniform linear array (ULA) with the same aperture is generated from the deterministic non-uniform coprime array. Taking the observed correlations calculated from the signals received at the coprime array, a gridless convex optimization probl...","Title: Coprime array-based robust beamforming using covariance matrix reconstruction technique.\nYear: 2018\n\nAbstract: A novel robust adaptive beamforming algorithm based on a coprime array is proposed in this study. First, the authors exploit the virtual array structure of the coprime coarray to construct two subspaces. The first subspace is obtained from the covariance matrix of the virtual uniform linear array, whereas the second one is obtained by integrating a spatial spectrum over each angular sector of the ...","Title: Sparsity-based time-frequency representation of FM signals with burst missing samples.\nYear: 2019\n\nAbstract: •The effects of burst missing samples are studied in different domains for FM signals.•A novel sparse reconstruction-based method is developed for time-frequency analysis.•Reliable time-frequency representations are achieved with low complexity.•Both cross-terms and artifacts due to missing samples are effectively suppressed.","Title: COMPRESSED SENSING BASED JOINT DOA AND POLARIZATION ANGLE ESTIMATION FOR SPARSE ARRAYS WITH DUAL-POLARIZED ANTENNAS\nYear: 2018\n\nAbstract: In this paper, a compressed sensing (CS)-based joint estimation of direction of arrival (DoA) and polarization parameters is proposed using a sparse array consisting of dual-polarized antenna elements. Since this joint estimation with the CS as well as conventional subspace based methods turn out to be computationally prohibitive, we propose a novel method so that DoAs can be accurately estimated using the proposed CS algorithm, whereas the polarization parameters can be estimated utilizing the estimated DoAs and the least-squares method. We verify that the proposed method can accurately resolve both the DoAs and polarization parameters of much more uncorrelated sources than the number of sparse array sensors.","Title: DOA Estimation Exploiting Moving Dilated Nested Arrays.\nYear: 2019\n\nAbstract: A novel dilated nested array is presented to obtain an enhanced fully filled difference coarray exploiting array motions. The proposed sparse array is suitable for the cases when the sensing environment can be assumed stationary over an array motion of half wavelength or shorter. Closed-form expressions of the number of degrees of freedom in the difference coarray of the combined array before and ...","Title: Design of wideband MIMO radar waveforms with low peak-to-average ratio\nYear: 2015\n\nAbstract: Multiple-input multiple-output (MIMO) radar systems allow array antennas to transmit different waveforms and, as such, enable flexible transmit beampattern synthesis. Most existing transmit beampattern synthesis methods focus on narrowband MIMO radar systems. In this paper, we propose a novel method to design transmit waveforms for wideband MIMO radar systems. The proposed technique is based on the optimization of the cross-spectral density matrix and achieves a low peak-to-average power ratio (PAR) as desired in practical radar operations. Simulations results are provided to verify the low PAR waveform design capability corresponding to arbitrary beampatterns.","Title: Human motion recognition exploiting radar with stacked recurrent neural network.\nYear: 2019\n\nAbstract: We develop a novel radar-based human motion recognition technique that exploits the temporal sequentiality of human motions. The stacked recurrent neural network (RNN) with long short-term memory (LSTM) units is employed to extract sequential features for automatic motion classification. The spectrogram of raw radar data is used as the network input to utilize the time-varying Doppler and micro-Doppler signatures for human motion characterization. Based on experimental data, we verified that a stacked RNN with two 36-cell LSTM layers successfully classifies six different types of human motions.","Title: DOA estimation of mixed coherent and uncorrelated targets exploiting coprime MIMO radar.\nYear: 2017\n\nAbstract: We propose a new scheme to estimate the directions-of-arrival (DOAs) of mixed coherent and uncorrelated targets exploiting a collocated multiple-input multiple-output (MIMO) radar with transmit/receive coprime arrays. In the proposed scheme, the DOAs of the uncorrelated targets are first estimated using subspace-based methods, whereas those of the coherent targets are resolved using Bayesian compressive sensing. Compared with the previous works, the proposed approach achieves improved DOA estimation accuracy with a flexible coprime array configuration and may resolve more targets than the number of coarray elements. Theoretical analysis and simulation results validate the effectiveness of the proposed technique.","Title: Time-Frequency Analysis for GNSSs: From interference mitigation to system monitoring.\nYear: 2017\n\nAbstract: In this article, we discuss the important role of time-frequency (TF) signal representations in enhancing global navigation satellite system (GNSS) receiver performance. Both linear transforms and quadratic TF distributions (QTFDs) are considered. We review recent advances of antijam techniques that exploit the distinction in the TF signatures between the desired and undesired signals, enabling ef...","Title: MISC Array: A New Sparse Array Design Achieving Increased Degrees of Freedom and Reduced Mutual Coupling Effect.\nYear: 2019\n\nAbstract: Recently, nested and coprime arrays have attracted considerable interest due to their capability of providing increased array aperture, enhanced degrees of freedom (DOFs), and reduced mutual coupling effect compared to uniform linear arrays (ULAs). These features are critical to improving the performance of direction-of-arrival estimation and adaptive beamforming. In this paper, a new sparse array...","Title: Information-Theoretic Pilot Design for Downlink Channel Estimation in FDD Massive MIMO Systems.\nYear: 2019\n\nAbstract: Massive multiple-input multiple-output (MIMO) is one of the most promising techniques for next generation wireless communications due to its superior capability to provide high spectrum and energy efficiency. Considering the very large number of antennas employed at the base station, however, the pilot overhead for downlink channel estimation becomes unaffordable in frequency division duplex (FDD)...","Title: DOA Estimation Exploiting Sparse Array Motions.\nYear: 2019\n\nAbstract: This paper utilizes sparse array motion to increase the numbers of achievable both degrees of freedom (DOFs) and consecutive lags in direction-of-arrival (DOA) estimation problems. We use commonly employed environment-independent sparse array configurations. The design of these arrays is not dependent on the sources in the field of view, but rather aims at achieving desirable difference co-arrays....","Title: Multi-Target Motion Parameter Estimation Exploiting Collaborative Uav Network\nYear: 2019\n\nAbstract: We propose a distributed unmanned aerial vehicle (UAV) network performing collaborative radar sensing for multi-target localization and motion parameter estimation. Two UAV network topologies are considered for data propagation and information fusion. In the former, we form a sequential UAV node chain, whereas in the latter, the UAV nodes are grouped into clusters and the information among different clusters is propagated through the cluster master nodes. Sparse reconstruction methods are used to fuse the target state information from previous nodes or clusters with the data measured in the underlying nodes or clusters, depending on the adopted topology, to achieve improved target state estimates. In order to minimize the communication traffic in the UAV network, each node transmits the estimated Doppler signatures or sparse target state estimates to the next UAV node in the network or the cluster master node, in lieu of the large volume of raw sampled data. Simulation results verify the effectiveness of the proposed approaches and compare the performance between the two UAV network topologies.","Title: Coprime Array Design With Minimum Lag Redundancy\nYear: 2019\n\nAbstract: In this paper, we propose a new sparse coprime array design that achieves a higher number of degrees-of-freedom for direction-of-arrival (DOA) estimation. The proposed array design completely avoids lag redundancies between the two constituting subarrays of the coprime array, thus achieving the maximum number of unique correlation lags under the coprime array framework. As a result, given the same number of physical sensors, the proposed design resolves more sources than other coprime array designs with enhanced DOA estimation performance. Simulation results demonstrate the superior performance of the proposed coprime array design.","Title: Analysis Of Coprime Arrays On Moving Platform\nYear: 2019\n\nAbstract: Moving platforms enable sparse arrays to assume higher degrees of freedom and lead to increased number of lags. In essence, array motion can fill the holes in the spatial auto correlation lags associated with a fixed platform and, therefore, increase the number of sources detectable by the same physical array. In this paper, we consider coprime arrays, and assume quasi-stationarity of the environment, where the source locations and waveforms are assumed invariant over array motion of half wavelength. Expressions of the synthetic array comprising the original coprime array and its shifted version are derived. Analysis of the difference co-array corresponding to the combined array positions before and after motion is provided. It is shown that majority, if not all, of the holes in the original array position can be filled by just a small translation shift along the coprime array axis.","Title: FOUR-DIMENSIONAL HIGH-RESOLUTION AUTOMOTIVE RADAR IMAGING EXPLOITING JOINT SPARSE-FREQUENCY AND SPARSE-ARRAY DESIGN\nYear: 2021\n\nAbstract: We propose a novel automotive radar imaging technique to provide high-resolution information in four dimensions, i.e., range, Doppler, azimuth, and elevation, by exploiting a joint sparsity design in frequency spectrum and array configurations. Random sparse step-frequency waveform is proposed to synthesize a large effective bandwidth and achieve high range resolution profiles. This concept is extended to multi-input multi-output (MIMO) radar by applying phase codes along the slow time to synthesize a two-dimensional (2D) sparse array with a high number of virtual array elements which enable high-resolution direction finding in both azimuth and elevation. The 2D sparse array acts as a sub-Nyquist sampler of the corresponding uniform rectangular array (URA), and the corresponding URA response is recovered by completing a low-rank block Hankel matrix. The proposed imaging radar provides point clouds with a resolution comparable to light detection and ranging (LiDAR) but with a much lower cost and is insensitive to weather conditions.","Title: Joint Radar-Communications Exploiting Optimized OFDM Waveforms\nYear: 2021\n\nAbstract: We propose novel Joint Radar-communication spectrum sharing strategies exploiting orthogonal frequency-division multiplexing (OFDM) waveforms that concurrently achieve the objectives of both radar and communication systems. An OFDM transmitter is considered that transmits dual-purpose OFDM subcarriers such that all the subcarriers are exploited for the primary radar function and further exclusively allocated to the secondary communication function serving multiple users. The waveform optimization is performed by employing mutual information (MI) as the optimization criterion for both radar and communication operations. For the purpose of radar performance optimization, we consider the MI between the frequency-dependent target response and the transmit OFDM waveforms. On the other hand, communication system performance is evaluated in terms of the MI between the frequency-dependent communication channels of communication users with the transmit OFDM subcarriers. These optimization objectives not only enable the transmit power allocation of the OFDM subcarriers, but also govern the subcarrier distribution among the communication users. Two resource optimization scenarios are considered, resulting in radar-centric and cooperative resource allocation strategies that exploit convex and mixed-integer linear programming optimization problems for power allocation and subcarrier distribution, respectively. We further present a chunk subcarrier allocation approach that applies to both optimization strategies to reduce the computational complexity with a trivial performance loss. Simulation results are presented to illustrate the effectiveness of the proposed strategies.","Title: Crossterm-free time-frequency representation exploiting deep convolutional neural network\nYear: 2022\n\nAbstract: •We developed a deep neural network-based approach to generate crossterm-free time-frequency representations.•We show that, provided that the neural networks are adequately trained, the proposed method works robustly and provides significant performance improvement compared to existing time-frequency representation reconstruction algorithms.•We evaluated the generalization capability of the proposed method, including the effects of noise levels, amplitude difference, variation speed of the IFs, fading, number of signal components, and frequency spreading.","Title: Joint DoA-Range Estimation Using Space-Frequency Virtual Difference Coarray\nYear: 2022\n\nAbstract: In this paper, we address the problem of joint direction-of-arrival (DoA) and range estimation using frequency diverse coprime array (FDCA). By incorporating the coprime array structure and coprime frequency offsets, a two-dimensional space-frequency virtual difference coarray corresponding to uniform array and uniform frequency offset is considered to increase the number of degrees-of-freedom (DoFs). However, the reconstruction of the doubly-Toeplitz covariance matrix is computationally prohibitive. To solve this problem, we propose an interpolation algorithm based on decoupled atomic norm minimization (DANM), which converts the coarray signal to a simple matrix form. On this basis, a relaxation-based optimization problem is formulated to achieve joint DoA-range estimation with enhanced DoFs. The reconstructed coarray signal enables application of existing subspace-based spectral estimation methods. The proposed DANM problem is further reformulated as an equivalent rank-minimization problem which is solved by cyclic rank minimization. This approach avoids the approximation errors introduced in nuclear norm-based approach, thereby achieving superior root-mean-square error which is closer to the Cramer-Rao bound. The effectiveness of the proposed method is confirmed by theoretical analyses and numerical simulations.","Title: Enhanced DOA Estimation Exploiting Multi-Frequency Sparse Array\nYear: 2021\n\nAbstract: In this paper, we develop a general framework of multi-frequency sparse array to estimate the direction-of-arrival (DOA) of a significantly higher number of targets than the number of physical sensors. The multi-frequency sparse arrays are designed to offer zero lag redundancy in the rendered difference coarray so that the available degrees of freedom are fully utilized to enable high-resolution DOA estimation. A modified sensor interpolation technique is developed to accurately estimate the signal correlation matrix so that the effect of holes in the difference coarray is mitigated. The proposed technique accounts for both self-lags between signals corresponding to the same frequencies and the cross-lags between signals corresponding to different frequencies. As such, it enhances the DOA estimation performance compared to existing methods that either perform array interpolation utilizing only the self-lags or carry out group sparse reconstruction without exploiting array interpolation. Simulation results verify the offerings of the multi-frequency sparse arrays.","Title: Target Altitude Estimation in Over-the-Horizon Radar\nYear: 2022\n\nAbstract: Doppler signatures of local multipath signals provide useful information for target altitude estimation in over-the-horizon radar surveillance. In this paper, we develop a method to improve the resolution of these multipath Doppler signatures and enable enhanced altitude estimation of aircraft target which maintains a constant altitude. Moreover, we consider the impact of ionospheric layer motion on target parameter estimation and show that target parameters can be estimated under both stationary as well as time-varying ionospheric layer conditions. In order to improve the resolution and estimation accuracy of the target parameters and ionosphere velocity with a significantly reduced complexity, we exploit a frequency focused transform to the de-chirped target signals for dimension reduction before applying a least absolute shrinkage and selection operator (LASSO)-based high-resolution spectrum estimation technique. The proposed strategy outperforms fractional Fourier transform and classical subspace-based frequency estimation methods with a much lower computational complexity. The effectiveness of the proposed approach is especially evident for challenging cases where the multipath signal components have spectrally close Doppler signatures. Simulation results confirm the effectiveness of the proposed method.","Title: Structured Bayesian compressive sensing exploiting dirichlet process priors\nYear: 2022\n\nAbstract: •A novel structured and clustered multi-task compressive sensing framework based on a hierarchical Bayesian model is proposed to exploit the inner sparse pattern and the statistical dependence between tasks.•The spike-and-slab priors encode sparse patterns of the signals and are further generalized by imposing the Gaussian process for modeling inner structures. The Dirichlet Process priors imposed on the support reveal the clustering mechanisms among tasks.•A new inference scheme based on expectation propagation is derived to approximate the posterior distribution for simplifying the computation and deriving analytical expression.","Title: Joint Target and Ionosphere Parameter Estimation in Over-the-Horizon Radar\nYear: 2022\n\nAbstract: Target localization, especially the estimation of target altitude, is a challenging task in over-the-horizon radar (OTHR) because of the narrow signal bandwidth as well as the complexity and uncertainty involved in the ionosphere conditions. This task becomes further complicated when the height of the ionosphere layer varies over time. Therefore, it is important to jointly estimate the instantaneous height of the ionosphere and the target altitude as well as other motion parameters. In this article, we achieve these objectives by analyzing the Doppler frequencies of the target local-multipath signals and the clutter. We reveal that the change of the ionosphere height can either enhance or deteriorate the performance of target parameter estimation depending on its direction of motion relative to the target’s motion profile. In addition, it is found that the received target and clutter Doppler signatures follow the chirp signal profile at the OTHR receiver. Based on these observations, we develop a general framework that achieves joint target and ionosphere parameter estimation and accounts for the velocity and accelerations of both target and ionosphere layer. Unlike existing time-frequency-based strategies for target localization and tracking in OTHR, where the Doppler signatures only directly determine target vertical velocity and the target altitude is estimated indirectly, the proposed model enables direct estimation of target altitude and ionosphere parameters. The parameter estimation problem in the proposed strategy is analytically derived and the effectiveness is verified using extensive simulation results.","Title: Neural Network Approach to Iterative Optimization of Compressive Measurement Matrix in Massive Mimo System\nYear: 2022\n\nAbstract: Massive multiple-input multiple-output (MIMO) systems equipped with a high number of antenna elements are becoming one of the most exciting emerging technologies in next-generation wireless communication systems. The use of a high number of anten-nas makes it challenging to implement separate radio frequency front-end circuits for each antenna due to hardware cost and power consumption restrictions. To make massive MIMO feasible, an ef-ficient solution is to perform hybrid beamforming that compresses the received signals at the MIMO receiver before the signals are dig-itized. We have proposed optimized compressive measurement by maximizing the mutual information between the compressed mea-surement and the signal directions-of-arrival (DOAs) by utilizing a coarse a priori probability distribution of the signal DOAs. Despite its superior performance to effectively reduce the number of required front-end circuits, the requirement of a coarse a priori distribution of signal DOAs makes it difficult to apply in some situations where such information is unavailable. In this paper, we propose a data-driven approach based on neural network to iteratively update the signal DOA distribution. The neural network estimate signal DOA spectrum, which is then fed back to refine the prior information, thereby making this method practical even in the situations where no prior information of the signal DOAs is available.","Title: Sparse Channel Estimation for IRS-Aided Systems Exploiting 2-D Sparse Arrays\nYear: 2022\n\nAbstract: Intelligent reflecting surface (IRS) is a promising next-generation technology for increasing channel capacity and reducing power consumption. In this paper, we present a novel IRS configuration consisting of a small number of active elements in an optimized L-shaped sparse array to separately estimate the channels between the base station and the IRS, and the channel between multiple user equipment and the IRS. Structured matrix completion techniques are used to attain superior direction-of-arrival estimation performance with an increased number of degrees of freedom. The training overhead is minimized in the proposed system and is not directly related to the number of IRS reflecting elements. The proposed sparse array strategy simultaneously resolves multiple sources with a high accuracy and outperforms the L-shaped uniform array counterpart using the same number of active elements. The effectiveness of the proposed strategy is confirmed using simulation results."],"string":"[\n \"Title: Instantaneous altitude estimation of maneuvering target in over-the-horizon radar exploiting multipath Doppler signatures.\\nYear: 2013\\n\\nAbstract: Over-the-horizon radar systems are capable of localizing targets in range and azimuth but are unable to achieve reliable altitude estimation. Past work has shown that the time-varying Doppler signatures of micro-multipath signals provide rich information for reliable estimation of altitude changes. In this paper, we develop a new technique for the estimation of instantaneous altitude of maneuvering targets by exploiting the estimated multicomponent Doppler signatures. A key contribution of this paper is to apply effective non-stationary signal analysis techniques for estimating the time-varying Doppler signature of each individual multipath. This enables reliable target altitude estimation in an extended Kalman filter setup. The maximum a-posteriori criterion is used for the estimation of initial target states.\",\n \"Title: Robust Motion Parameter Estimation In Multistatic Passive Radar\\nYear: 2013\\n\\nAbstract: In this paper, we develop a new algorithm for motion parameter estimation of moving targets in a multistatic passive radar. Existing methods for motion parameter estimation rely on the estimated Doppler signatures of the observed signals corresponding to each transmitter. These techniques may fail for weak signals where the individual Doppler signature cannot be properly estimated. The focus of this paper is on motion parameter estimation from weak signals observed using multiple illuminators. Utilizing the sparsity of the motion parameters, the proposed technique obtains robust motion parameter estimates through the fusion of the data corresponding to all available illuminators, achieving signal enhancement and multistatic diversity. To reduce the computational cost, the acceleration and velocity parameters are decoupled and sequentially estimated.\",\n \"Title: Reduced Interference Time-Frequency Representations And Sparse Reconstruction Of Undersampled Data\\nYear: 2013\\n\\nAbstract: In this paper, we examine the time-frequency representation (TFR) and sparse reconstruction of non-stationary signals in the presence of missing data samples. These samples lend themselves to missing entries in the instantaneous auto-correlation function (IAF) which, in turn, induce artifacts in the time-frequency distribution and ambiguity function. The artifacts are additive noise-like and, as such, can be mitigated by using proper time-frequency kernels. We show that the sparse signal reconstruction methods applied to the time-lag domain improve the TFR over the direct application of Fourier transform to the IAF. Additionally, the paper demonstrates that the use of signal-adaptive kernels provides superior performance compared to data-independent kernels when missing data are present.\",\n \"Title: Optimum Performance Boundaries of OSTBC Based AF-MIMO Relay System With Energy Harvesting Receiver\\nYear: 2013\\n\\nAbstract: This paper studies the optimum performance boundaries of a two-hop multi-antenna amplify-and-forward (AF) relay system with a multi-antenna energy harvesting (EH) receiver. The source and relay nodes employ orthogonal space-time block codes for data transmission. When instantaneous channel state information (CSI) is available, we design joint optimal source and relay precoders to achieve different tradeoffs between the energy and information transfers, which are characterized by the boundary of the rate-energy (R-E) region. For this purpose, the optimization problem is formulated as a relaxed convex problem but its optimality is confirmed with a proof that rank-one optimal precoders can always be obtained. As a consequence, it is shown that the full-rate OSTBC, like the Alamouti code, can be employed for an arbitrary number of antennas at the transmit nodes (source and relay) and support up to seven simultaneously existing EH receivers. When only second order statistics of the CSI is available, the tradeoff between outage probability (OP) and energy is characterized by the boundary of the OP-energy (OP-E) region. In this case, the precoder design problem is formulated using a convex upper bound approximation to the OP, since the exact OP expression is difficult for tractable optimization. Numerical results show that the OP-E region obtained with the upper bound of the OP is better than that with the approach based on maximization of the long-term average signal-to-noise ratio (SNR). The role of the different parameters such as average SNR, numbers of antennas, and spatial correlation on the boundaries of the R-E and OP-E regions is demonstrated via simulations.\",\n \"Title: Fall Detection Based on Sequential Modeling of Radar Signal Time-Frequency Features\\nYear: 2013\\n\\nAbstract: Falls are one of the greatest threats to elderly health as they carry out their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be rendered. Radar is an effective non-intrusive sensing modality which is well suited for this purpose. It can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. In this paper, we use micro-Doppler features in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition for feature extraction and fall detection. The extracted features include the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, we use the extracted signal features for training and testing hidden Markov models and support vector machines indifferent falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections.\",\n \"Title: Local CSI Based Selection Beamforming for Amplify-and-Forward MIMO Relay Networks\\nYear: 2012\\n\\nAbstract: We investigate selection beamforming for a cooperative network that consists of a source, a destination, and two amplify-and-forward (AF) relays, which are all equipped with multiple antennas. The transmit and receive beamforming techniques are respectively applied at the source and destination, and the linear processing operator of the selected AF multiple-input multiple-output (MIMO) relay is optimized. We consider that the source has the instantaneous channel state information (CSI) of the channels from the source to relays but no information regarding the CSI of the channels from the relays to the destination. Partial relay selection (PRS) is employed, i.e., the source routes its information signal to the relay which offers better first-hop signal-to-noise ratio (SNR). Considering a Rayleigh fading environment, we derive an exact closed-form expression for the outage probability at the destination. The validity of the outage probability expression has been confirmed with numerical simulations. We also present the asymptotic analysis of the PRS scheme for the considered multiantenna system and derive the expressions of diversity gain. It is shown that, the overall diversity gain is $\\\\min(\\\\min(n_{{\\\\rm r},1}n_{\\\\rm d},n_{{\\\\rm r},2}n_{\\\\rm d}),n_{{\\\\rm r},1}n_{\\\\rm s}+n_{{\\\\rm r},2}n_{\\\\rm s})$ where $n_{\\\\rm s}$, $n_{\\\\rm d}$, and $n_{{\\\\rm r},q}$ are, respectively, the numbers of antennas at the source, destination and the $q$th relay. For the condition $C:\\\\min(n_{{\\\\rm r},1}n_{\\\\rm d},n_{{\\\\rm r},2}n_{\\\\rm d})\\\\geq n_{{\\\\rm r},1}n_{\\\\rm s}+n_{{\\\\rm r},2}n_{\\\\rm s}$ that can be easily met with practically realizable antenna configurations, it is shown that PRS provides the same diversity order as opportunistic relaying (OR).\",\n \"Title: Local CSI Based Full Diversity Achieving Relay Selection for Amplify-and-Forward Cooperative Systems\\nYear: 2013\\n\\nAbstract: This paper proposes a new relay selection method based on local channel state information (CSI) for a system consisting of a source, a destination and an arbitrary number of amplify-and-forward relay nodes. A set of candidate relays, whose source-relay (S-R) links are not in outage, is formed. An S-R link is in outage if its signal-to-noise ratio (SNR) is below the predefined threshold value of the destination SNR plus some adjustable margin. The candidate relay that yields the maximum second-hop SNR is then selected at the destination. Unlike the opportunistic relaying (OR) based on full CSI (F-CSI), the proposed method requires to equip the destination node with the instantaneous CSI of the S-R channel corresponding to only the selected relay. It is qualitatively shown that the training overhead of the proposed relay selection is less than or comparable to that of the partial relay selection (PRS) but can be much smaller than that of the OR with F-CSI. However, the proposed scheme requires to estimate a higher number of single-input single-output (SISO) channels than in the PRS scheme. The exact and asymptotic expressions of outage probability are derived and it is shown that the proposed method achieves full diversity. Simulation results verify theoretical analysis, and show that, for the optimized margin, the proposed scheme provides performance which is comparable to the method having F-CSI. The results also show that the proposed method significantly outperforms PRS, which may justify the linear order of the computational complexity associated with the estimation of the additional SISO channels and the low-cost one-dimensional line search used for optimizing the margin.\",\n \"Title: High-resolution direction finding of non-stationary signals using matching pursuit.\\nYear: 2013\\n\\nAbstract: One of the main goals of time–frequency (TF) signal representations in non-stationary array processing is to equip multi-antenna receivers with the ability to separate sources in the TF domain prior to direction finding. This permits high-resolution direction-of-arrival (DOA) estimation of individual sources and of more sources than sensors. In this paper, we use linear decomposition of sensor data based on matching pursuit (MP). The leading atoms of the MP, which capture most of the source TF signatures, can be different for different sources and, as such, provide the desired source discrimination. The MP coefficients with high signal-to-noise ratio (SNR) and corresponding to the leading decomposition atoms are used to develop the MP-MUSIC DOA estimation for non-stationary source signals. We demonstrate the source discriminatory capability of the proposed technique using linear FM, nonlinear FM, and other non-stationary signals. Further, we compare MP-MUSIC performance with conventional MUSIC and the time–frequency MUSIC, which incorporates bilinear transforms.\",\n \"Title: Joint Optimization of Source Power Allocation and Relay Beamforming in Multiuser Cooperative Wireless Networks\\nYear: 2011\\n\\nAbstract: Relay beamforming techniques have been shown to significantly enhance the sum capacity of a multiuser cooperative wireless network through the optimization of the relay weights, where concurrent communications of multiple source-destination pairs are achieved via spatial multiplexing. Further optimization of the transmit power allocation over the source nodes is expected to improve the network throughput as well. In this paper, we maximize the sum capacity of a multiuser cooperative wireless network through the joint optimization of power allocation among source nodes and relay beamforming weights across the relay nodes. We consider a two-hop cooperative wireless network, consisting of single-antenna nodes, in which multiple concurrent links are relayed by a number of cooperative nodes. When a large number of relay nodes are available, the channels of different source-destination pairs can be orthogonalized, yielding enhanced sum network capacity. Such cooperative advantage is particularly significant in high signal-to-noise ratio (SNR) regime, in which the capacity follows a logarithm law with the SNR, whereas exploiting spatial multiplexing of multiple links yields capacity increment linear to the number of users. However, the capacity performance is compromised when the input SNR is low and/or when the number of relay nodes is limited. Joint optimization of source power allocation and relay beamforming is important when the input SNR and/or the number of relay nodes are moderate or the wireless channels experience different channel variances. In these cases, joint optimization of source power and distributed beamforming weights achieves significant capacity increment over both source selection and equal source power spatial multiplexing schemes. With consideration of the needs to deliver data from each source node, we further examine the optimization of global sum capacity in the presence of individual capacity requirements by maximizing sum capacity of the network subject to a minimum capacity constraint over each individual user.\",\n \"Title: Target localization in a multi-static passive radar system through convex optimization.\\nYear: 2014\\n\\nAbstract: We propose efficient target localization methods for a passive radar system using time-of-arrival (TOA) information of the signals received from multiple illuminators, where the position of the receiver is subject to random errors. Since the maximum likelihood (ML) formulation of this target localization problem is a non-convex optimization problem, semi-definite relaxation (SDR)-based optimization methods in general do not provide satisfactory performance. As a result, approximated ML optimization problems are proposed and solved with SDR plus bisection methods. For the case without position error, it is shown that the relaxation guarantees a rank-one solution. The optimization problem for the case with position error involves only a relaxation of a scalar quadratic term. Simulation results show that the proposed algorithms outperform existing methods and provide root mean-square error performance very close to the Cramer–Rao lower bound.\",\n \"Title: Source Separation and Localization Using Time-Frequency Distributions: An Overview.\\nYear: 2013\\n\\nAbstract: In this article, we describe the role of time-frequency distributions (TFDs) in array processing. We particularly focus on quadratic TFDs (QTFDs). We demonstrate how these distributions can be properly integrated with the spatial dimension to enhance individual source signal recovery and angular estimation. The framework that enables such integration is referred to as spatial TFD (STFD). We presen...\",\n \"Title: Azimuth Resampling Processing for Highly Squinted Synthetic Aperture Radar Imaging With Several Modes\\nYear: 2014\\n\\nAbstract: The linear range walk yields a significant range-azimuth coupling effect in a highly squinted synthetic aperture radar (SAR). Although the linear range walk correction (LRWC) technique can effectively mitigate such coupling effect, it causes azimuth variation in the resulting signal and, as such, the so-called “azimuth-shift invariance” property becomes invalid. In order to eliminate the azimuth variation, a new spectrum processing approach based on azimuth resampling is proposed in this paper. After performing the LRWC, the azimuth resampling is carried out in the 2-D frequency domain and transforms the signal spectrum to be equivalent to that of a broadside SAR. For squinted beamsteering SAR (BS-SAR), e.g., spotlight SAR, sliding spotlight SAR, and Terrain Observation by Progressive Scans SAR, the azimuth resampling is combined with the azimuth signal reconstruction algorithm. As a result, both the azimuth variation, which is induced by the LRWC, and the aliasing, which is caused by antenna beam steering, can be avoided. Therefore, after the azimuth resampling, the squinted SAR data can be focused by exploiting a conventional broadside SAR imaging algorithm. An analysis of the motion error for airborne SAR data processing is also provided. Simulation and real data results show the effectiveness of the proposed algorithm.\",\n \"Title: Generalized Coprime Array Configurations for Direction-of-Arrival Estimation\\nYear: 2015\\n\\nAbstract: A coprime array uses two uniform linear subarrays to construct an effective difference coarray with certain desirable characteristics, such as a high number of degrees-of-freedom for direction-of-arrival (DOA) estimation. In this paper, we generalize the coprime array concept with two operations. The first operation is through the compression of the inter-element spacing of one subarray and the resulting structure treats the existing variations of coprime array configurations as well as the nested array structure as its special cases. The second operation exploits two displaced subarrays, and the resulting coprime array structure allows the minimum inter-element spacing to be much larger than the typical half-wavelength requirement, making them useful in applications where a small interelement spacing is infeasible. The performance of the generalized coarray structures is evaluated using their difference coarray equivalence. In particular, we derive the analytical expressions for the coarray aperture, the achievable number of unique lags, and the maximum number of consecutive lags for quantitative evaluation, comparison, and design of coprime arrays. The usefulness of these results is demonstrated using examples applied for DOA estimations utilizing both subspace-based and sparse signal reconstruction techniques.\",\n \"Title: Multi-Task Bayesian Compressive Sensing Exploiting Intra-Task Dependency\\nYear: 2015\\n\\nAbstract: In this letter, we propose a multi-task compressive sensing algorithm for the reconstruction of clustered sparse entries based on hierarchical Bayesian framework. By extending a paired spike-and-slab prior to a general multi-task model, the proposed algorithm has the capability of modeling both inter-task and intra-task dependencies of the observation data. The latter is achieved by imposing a clustered prior on non-zero entries and finds applications in radar where targets exhibit spatial extent. Simulation results verify that the proposed algorithm outperforms state-of-the-art group sparse Bayesian learning algorithms.\",\n \"Title: Spectrum Compression Space–Time Adaptive Processing for TOPS SAR System\\nYear: 2015\\n\\nAbstract: A multichannel terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR) system is capable of imaging a wider swath with a higher azimuth resolution for improved moving target detection. For TOPS SAR, due to antenna beam steering, the azimuth bandwidth of background clutter is much larger than the instantaneous signal bandwidth. To overcome this problem, a method referred to as spectrum compression space–time adaptive processing (SC-STAP) is proposed in this letter. Through the SC process, both the Doppler spectrum and the spatial spectrum of the background clutter are simultaneously compressed. This key step achieves fully overlapped clutter space–time spectrum lines and, as such, enables effective clutter suppression and target signal alias compensation by applying linearly constrained STAP. Furthermore, in order to avoid the target ambiguities arising from the spectral wrapping, an approach based on deramp processing is proposed to focus the moving targets for TOPS SAR mode. Simulation results validate the effectiveness of the proposed algorithm.\",\n \"Title: Low-complexity sparse reconstruction for high-resolution multi-static passive SAR imaging.\\nYear: 2014\\n\\nAbstract: Bi-static passive synthetic aperture radar (SAR) systems using ground broadcast and wireless network signals suffer from a low spatial resolution due to the narrow bandwidths and low carrier frequencies. By exploiting multiple distributed illuminators, multi-static passive radar has the possibility of producing high-resolution SAR images. In this paper, a two-stage image formation approach, which combines the Fourier transform and sparse reconstruction strategies, is proposed to process multi-static SAR data. This method exploits the group sparsity of the sparse scene, i.e., the observations associated with different bi-static pairs share the same support of the sparse scene but correspond to aspect-dependent scattering coefficients. Such observations are described as a number of generally disjoint sub-bands in the two-dimensional spatial frequency domain. In each sub-band, the sampling satisfies the Nyquist criterion, whereas different sub-bands are sparsely distributed. In the proposed approach, Fourier-based reconstruction is applied to the sub-band data to produce the coarse-resolution images, which are then combined to produce a high-resolution image through the exploitation of sparse reconstruction techniques. The proposed approach greatly improves the imaging quality as compared to Fourier-based reconstruction, whereas it exhibits significant reduction of the computational complexity when compared to direct application of sparse reconstruction techniques. The exploitation of block sparsity-based techniques also permits practical treatment of the angle-dependent target scattering characteristics in SAR image reconstruction. The advantages of the proposed approach are delineated using analysis and simulations.\",\n \"Title: Motion parameter estimation of multiple ground moving targets in multi-static passive radar systems.\\nYear: 2014\\n\\nAbstract: Multi-static passive radar (MPR) systems typically use narrowband signals and operate under weak signal conditions, making them difficult to reliably estimate motion parameters of ground moving targets. On the other hand, the availability of multiple spatially separated illuminators of opportunity provides a means to achieve multi-static diversity and overall signal enhancement. In this paper, we consider the problem of estimating motion parameters, including velocity and acceleration, of multiple closely located ground moving targets in a typical MPR platform with focus on weak signal conditions, where traditional time-frequency analysis-based methods become unreliable or infeasible. The underlying problem is reformulated as a sparse signal reconstruction problem in a discretized parameter search space. While the different bistatic links have distinct Doppler signatures, they share the same set of motion parameters of the ground moving targets. Therefore, such motion parameters act as a common sparse support to enable the exploitation of group sparsity-based methods for robust motion parameter estimation. This provides a means of combining signal energy from all available illuminators of opportunity and, thereby, obtaining a reliable estimation even when each individual signal is weak. Because the maximum likelihood (ML) estimation of motion parameters involves a multi-dimensional search and its performance is sensitive to target position errors, we also propose a technique that decouples the target motion parameters, yielding a two-step process that sequentially estimates the acceleration and velocity vectors with a reduced dimensionality of the parameter search space. We compare the performance of the sequential method against the ML estimation with the consideration of imperfect knowledge of the initial target positions. The Cramér-Rao bound (CRB) of the underlying parameter estimation problem is derived for a general multiple-target scenario in an MPR system. Simulation results are provided to compare the performance of the sparse signal reconstruction-based methods against the traditional time-frequency-based methods as well as the CRB.\",\n \"Title: Segment-Sliding Reconstruction of Pulsed Radar Echoes with Sub-Nyquist Sampling.\\nYear: 2015\\n\\nAbstract: It has been shown that analog-to-information conversion (AIC) is an efficient scheme to perform sub-Nyquist sampling of pulsed radar echoes. However, it is often impractical, if not infeasible, to reconstruct full-range Nyquist samples because of huge storage and computational load requirements. Based on the analyses of AIC measurement system, this paper develops a novel segment-sliding reconstruction (SegSR) scheme to effectively reconstruct the Nyquist samples. The SegSR performs segment-by-segment reconstruction in a sliding mode and can be implemented in real time. An important characteristic that distinguishes the proposed SegSR from existing methods is that the measurement matrix in each segment satisfies the restricted isometry property (RIP) condition. Partial support in the previous segment can be incorporated into the estimation of the Nyquist samples in the current segment. The effect of interference introduced from adjacent segments is theoretically analyzed, and it is revealed that the interference consists of two interference levels with different impacts to the signal reconstruction performance. With these observations, a two-step orthogonal matching pursuit (OMP) procedure is proposed for segment reconstruction, which takes into account different interference levels and partially known support of the previous segment. The proposed SegSR scheme achieves near-optimal reconstruction performance with a significant reduction of computational loads and storage requirements. Theoretical analyses and simulations verify its effectiveness.\",\n \"Title: Gridless Quadrature Compressive Sampling with Interpolated Array Technique.\\nYear: 2015\\n\\nAbstract: Quadrature compressive sampling (QuadCS) is a sub-Nyquist sampling scheme for acquiring in-phase and quadrature (I/Q) components in radar. In this scheme, the received intermediate frequency (IF) signals are expressed as a linear combination of time-delayed and scaled replicas of the transmitted waveforms. For sparse IF signals on discrete grids of time-delay space, the QuadCS can efficiently reconstruct the I/Q components from sub-Nyquist samples. In practice, the signals are characterized by a set of unknown time-delay parameters in a continuous space. Then conventional sparse signal reconstruction will deteriorate the QuadCS reconstruction performance. This paper focuses on the reconstruction of the I/Q components with continuous delay parameters. A parametric spectrum-matched dictionary is defined, which sparsely describes the IF signals in the frequency domain by delay parameters and gain coefficients, and the QuadCS system is reexamined under the new dictionary. With the inherent structure of the QuadCS system, it is found that the estimation of delay parameters can be decoupled from that of sparse gain coefficients, yielding a beamspace direction-of-arrival (DOA) estimation formulation with a time-varying beamforming matrix. Then an interpolated beamspace DOA method is developed to perform the DOA estimation. An optimal interpolated array is established and sufficient conditions to guarantee the successful estimation of the delay parameters are derived. With the estimated delays, the gain coefficients can be conveniently determined by solving a linear least-squares problem. Extensive simulation results evidently demonstrate the superiority of the proposed algorithms in achieving super-resolution time-delay estimation and high-accuracy sparse signal reconstruction. Quadrature compressive sampling system is described in the frequency domain.A beamspace DOA-based formulation is established for gridless sparse reconstruction.Time-delays are estimated with interpolated arrays.Sufficient condition to guarantee the estimation of the delay parameters is derived.\",\n \"Title: Sparse Discrete Fractional Fourier Transform and Its Applications\\nYear: 2014\\n\\nAbstract: The discrete fractional Fourier transform is a powerful signal processing tool with broad applications for nonstationary signals. In this paper, we propose a sparse discrete fractional Fourier transform (SDFrFT) algorithm to reduce the computational complexity when dealing with large data sets that are sparsely represented in the fractional Fourier domain. The proposed technique achieves multicomponent resolution in addition to its low computational complexity and robustness against noise. In addition, we apply the SDFrFT to the synchronization of high dynamic direct-sequence spread-spectrum signals. Furthermore, a sparse fractional cross ambiguity function (SFrCAF) is developed, and the application of SFrCAF to a passive coherent location system is presented. The experiment results confirm that the proposed approach can substantially reduce the computation complexity without degrading the precision.\",\n \"Title: Complex multitask Bayesian compressive sensing\\nYear: 2014\\n\\nAbstract: An effective complex multitask Bayesian compressive sensing (CMT-BCS) algorithm is proposed to recover sparse or group sparse complex signals. The existing multitask Bayesian compressive sensing (MT-CS) algorithm is powerful in recovering multiple real-valued sparse solutions. However, a large class of sensing problems deal with complex values. A simple approach, which decomposes a complex value into independent real and imaginary components, does not take into account the group sparsity of these two components and thus yields poor recovery performance. In this paper, we first introduce the CMT-BCS algorithm that jointly treats the real and imaginary components, and then derive a fast and accurate algorithm for the estimation of the prior parameters by solving a surrogate convex function. The proposed CMT-BCS algorithm achieves effective complex sparse signal recovery and outperforms MT-CS and complex group Lasso.\",\n \"Title: Motion parameter estimation of multiple targets in multistatic passive radar through sparse signal recovery\\nYear: 2014\\n\\nAbstract: The problem of estimating motion parameters of multiple closely located ground moving targets in a multistatic passive radar system is considered, with a focus on weak signal conditions. The proposed method provides a means of combining signal energy from all available, spatially separated, illuminators of opportunity to achieve multistatic diversity and overall signal enhancement. The proposed technique is based on sparse signal recovery and exploits a two-step process that sequentially estimates the acceleration and velocity vectors in order to reduce the dimensionality of parameter search space.\",\n \"Title: Structural health monitoring exploiting MIMO ultrasonic sensing and group sparse Bayesian learning\\nYear: 2014\\n\\nAbstract: In this paper, we propose the exploitation of sparse Bayesian learning in multiple-input multiple-output (MIMO) systems to account for the multi-dimensional group sparse nature of extended defects in guided ultrasonic wave based structural health monitoring. The multi-dimensional group sparsity in the underlying reconstruction problems arises due to the clustered spatial occupancy of extended defects and the multiple-aspect MIMO observations. Sparse Bayesian learning techniques have been shown to provide robustness for high-resolution signal reconstruction due to their insensitivity to dictionary coherence and have the flexibility of effective exploitation of the signal structure. The superiority of the proposed technique over the state-of-the-art sparse signal reconstruction techniques is demonstrated through simulations and preliminary experiments.\",\n \"Title: Robust multipath exploitation radar imaging in urban sensing based on Bayesian compressive sensing\\nYear: 2014\\n\\nAbstract: In through-the-wall radar imaging applications, exploitation of group sparsity of the targets under multipath propagation allows high-resolution ghost-free imaging. However, such multipath exploitation schemes may suffer from imperfect knowledge of the surrounding scatterers, such as interior walls. In this paper, a novel two-stage Bayesian compressive sensing approach is proposed for joint scene reconstruction and wall location estimation. The proposed method is capable of not only acquiring enhanced images by exploiting multipath propagation, but also estimating wall locations with high accuracy. In addition, compared to computationally demanding genetic approaches, the proposed method achieves robust imaging with a low complexity.\",\n \"Title: Continuous structure based Bayesian compressive sensing for sparse reconstruction of time-frequency distributions\\nYear: 2014\\n\\nAbstract: In this paper, we propose a Bayesian compressive sensing algorithm for effective reconstruction of sparse signals that demonstrate sparsity as continuous but irregular narrow strips in a multi-dimensional space. Among many applications of this class of representations are the two-dimensional time-frequency distributions (TFDs) of radar signals, which are often modeled as frequency modulated (FM) signals characterized by their sparse and continuous instantaneous frequencies. A spike-and-slab prior is introduced to statistically encourage sparsity of the time-frequency representations (TFRs) across each segmented time-frequency region, and a patterned prior is imposed to enforce the continuous structure of the TFR. Compared with the existing sparse signal reconstruction techniques, the proposed technique achieves improved interpretation of the TFD, particularly when the signals are noisy or with missing samples.\",\n \"Title: Large-scale sparse reconstruction through partitioned compressive sensing\\nYear: 2014\\n\\nAbstract: Compressive sensing (CS) finds broad applications in various sparse reconstruction problems. It has been clearly established that CS techniques achieve improved quality and resolution for many radar imaging problems where the scene is sparse or can be sparsely represented. One of the major issues that limits the applicability of CS techniques in radar systems is the prohibitive complexity in large-scale imaging problems encountered in, for example, synthetic aperture radar. However, as the actual scene and the back-projection images are associated with the point spreading function which has a finite support, it becomes possible to reconstruct the sparse scene based only on local observations. In this paper, we develop a novel segmented CS technique that achieves nearly optimal sparse reconstruction performance with significant reduction of computation complexity and memory requirements. The effect of interference from neighboring segments is examined, and the conditions of interference-free reconstruction of segmented compressive sensing are devised. The effectiveness of the proposed technique is verified by simulation results.\",\n \"Title: Multistatic radar imaging via decentralized and collaborative subspace pursuit\\nYear: 2014\\n\\nAbstract: The task of multistatic radar imaging can be converted to the problem of jointly sparse signal recovery. In this paper, the algorithm named decentralized and collaborative subspace pursuit (DCSP) is utilized in multistatic radar systems to obtain a high-resolution image. By embedding collaboration among radar nodes and fusion strategy into each iteration of the standard subspace pursuit (SP) algorithm, DCSP is capable of providing satisfactory image even if some radar nodes suffer from relatively low signal-to-noise ratios (SNRs). Compared to the existing algorithms based on linear programming, DCSP has much lower computational complexity at the cost of increased communication overhead in the radar network.\",\n \"Title: Generalized coprime array configurations\\nYear: 2014\\n\\nAbstract: A coprime array consists of two uniform linear subarrays that construct an effective difference co-array with certain desirable characteristics. In this paper, we propose a generalized coprime array concept through the compression of the interelement spacing of one constituting subarray. As such, the existing variations of coprime array and nested array structures are represented as special cases. The achievable unique lags as well as consecutive lags in the resulting virtual array are analytically expressed, and the direction-of-arrival estimation performance is examined using both the MUSIC algorithm and sparse signal reconstruction techniques.\",\n \"Title: Time-frequency kernel design for sparse joint-variable signal representations\\nYear: 2014\\n\\nAbstract: Highly localized quadratic time-frequency distributions cast nonstationary signals as sparse in the joint-variable representations. The linear model relating the ambiguity domain and time-frequency domain permits the application of sparse signal reconstruction techniques to yield high-resolution time-frequency representations. In this paper, we design signal-dependent kernels that enable the resulting time-frequency distribution to meet the two objectives of reduced cross-term interference and increased sparsity. It is shown that, for random undersampling schemes, the new adaptive kernel is superior to traditional reduced interference distribution kernels.\",\n \"Title: Doa estimation exploiting coprime arrays with sparse sensor spacing\\nYear: 2014\\n\\nAbstract: In this paper, we propose effective coprime array configurations in which the minimum interelement spacing is much larger than the typical half-wavelength requirement. Such configurations are important in many applications where the half-wavelength requirement cannot be met due to the physical sensors size or to avoid spatial oversampling in wideband operations. The application of such coprime arrays in direction-of-arrival estimations is examined using different algorithms.\",\n \"Title: Time-frequency signature reconstruction from random observations using multiple measurement vectors\\nYear: 2014\\n\\nAbstract: A new approach for sparse nonstationary signal reconstruction based on multiple windows is introduced. Signals which are localizable in the time-frequency (TF) domain give rise to sparsity in the same domain. When combined, sparse reconstructions, applied to randomly sampled data and corresponding to different selected windows, provide enhanced TF signature estimation. Among possible orthogonal windows, we consider those which characterize the eigen-decomposition of reduced-interference quadratic time-frequency distribution kernels. The highly overlapping TF support of the windows' full-data spectrograms inspires the use of the multiple measurement vectors, in lieu of individual windowed signal recovery. It is shown that the proposed approach outperforms other reconstruction methods when only a single window is applied and is superior to reduced interference time-frequency distributions of random observations.\",\n \"Title: Simultaneous transfer of energy and information for MIMO-OFDM relay system\\nYear: 2012\\n\\nAbstract: This paper investigates performance tradeoffs between energy and information transfer in a relay based two-hop wireless system involving an energy harvesting receiver. The source and relay nodes of the two-hop amplify-and-forward (AF) system employ orthogonal space-time block codes along with orthogonal frequency-division multiplexing (OFDM) scheme. The joint optimal source and relay precoders over the subchannels in frequencies are designed to achieve different tradeoffs between the overall energy transfer capability and the information rate, which are characterized by the boundary of the so-called rate-energy (R-E) region. The effect of various parameters on the boundary of the R-E region is demonstrated for different frequency selective channel models.\",\n \"Title: Successive convex approximation for system performance optimization in a multiuser network with multiple mimo relays\\nYear: 2011\\n\\nAbstract: We consider the problem of jointly optimizing amplify-and-forward (AF) multiple-input multiple-output (MIMO) relays and single-antenna receivers for multipoint-to-multipoint (MP2MP) communications using weighted summean-square-error (MSE) as a system performance measure. This optimization problem is nonconvex and the optimal solution cannot be obtained in polynomial time. By applying first-order approximation for the quadratic term, we show that this problem can be approximately reformulated as a semidefinite programming (SDP) problem, which is then solved successively in an iterative way. The performance of the proposed scheme is compared with the lower bound (LB) of the global optimal solution that is obtained with increased complexity using the MSE-profile approach, and the bisection and semidefinite relaxation (SDR) techniques. Numerical results show that the performance of the proposed successive convex approximation (SCA) approach is very close to the LB of the global optimal solution and is much better than the zero-forcing MIMO relays.\",\n \"Title: Compressive sensing in nonstationary array processing using bilinear transforms\\nYear: 2012\\n\\nAbstract: Compressive sensing (CS) has successfully been applied to reconstruct sparse signals and images from few observations. For multi-component nonstationary signals characterized by instantaneous frequency laws, the sparsity exhibits itself in the time-frequency domain as well as the ambiguity domain. In this paper, we examine CS in the context of nonstationary array processing. We show that the spatial averaging of the ambiguity function across the array improves the CS performance by reducing both noise and cross-terms. The corresponding time-frequency distribution which is reconstructed through L1 minimizations yields significant improvement in time-frequency signature localizations and characterizations.\",\n \"Title: A novel partial relay selection method for amplify-and-forward relay systems\\nYear: 2012\\n\\nAbstract: Although partial relay selection (PRS) for amplify-and-forward relay systems requires only the knowledge of source-relay (S-R) channels, in general, it incurs a significant performance loss. In cooperative systems with all single-antenna nodes, irrespective of the number of relays, the diversity order of PRS is limited to only one. This paper proposes a novel relay selection method for improving the performance of PRS scheme. In particular, as in the conventional PRS scheme, the relay that gives the best first-hop signal-to-noise ratio (SNR) is selected. However, this selection is made from only a subset of relays, for which the corresponding S-R and relay-destination (R-D) links are not in outage. An R-D link is considered to be in outage if its SNR is below the predefined threshold value of the end-to-end SNR plus some adjustable margin. The additional overhead required for implementing the proposed scheme is comparable to that of the conventional PRS method. For conciseness and better exposition of the proposed method, we limit our theoretical analysis to a system with two to three relays. The exact expressions of the end-to-end outage probability are derived and it is shown that full diversity order is achieved. Simulation results verify theoretical analysis and show that the proposed method significantly outperforms the conventional PRS method. Moreover, the results demonstrate that, for properly selected margin, the performance of the proposed method is very close or comparable to the method with full channel state information.\",\n \"Title: Sparsity-based DOA estimation using co-prime arrays\\nYear: 2013\\n\\nAbstract: In this paper, we propose co-prime arrays for effective direction-of-arrival (DOA) estimation. To fully utilize the virtual aperture achieved in the difference co-array constructed from a co-prime array structure, sparsity-based spatial spectrum estimation technique is exploited. Compared to existing techniques, the proposed technique achieves better utilization of the co-array aperture and thus results in increased degrees-of-freedom as well as improved DOA estimation performance.\",\n \"Title: DOA estimation using a sparse uniform linear array with two CW signals of co-prime frequencies\\nYear: 2013\\n\\nAbstract: In this paper, we propose the use of a sparse uniform linear array to estimate the direction-of-arrival (DOA) of more sources than the number of sensors by exploiting two continuous wave signals whose frequencies satisfy a certain co-prime relationship. This extends the co-prime array and filter concept, which was developed in either the spectral or spatial domain, to a joint spatio-spectral domain, thereby achieving high flexibility in array structure design to meet the degrees-of-freedom and system complexity constraints. The DOA estimation is implemented in the difference co-array context to avoid spatial undersampling, and group sparsity based compressive sensing techniques are used to determine the direction of signal arrivals.\",\n \"Title: Pulse-doppler signal processing with quadrature compressive sampling\\nYear: 2013\\n\\nAbstract: Quadrature compressive sampling (QuadCS) is a recently introduced sub-Nyquist sampling scheme for effective acquisition of inphase and quadrature (I/Q) components of sparse radio frequency signals. In applications to pulse-Doppler radars, the QuadCS outputs can be arranged into a two-dimensional data format, in terms of slow time and virtual fast time, similar to that by Nyquist sampling. This paper develops a compressive sampling pulse-Doppler (CoSaPD) processing scheme which performs Doppler estimation/detection and range estimation from the sub-Nyquist data without recovering the Nyquist samples. The Doppler estimation is realized through a spectrum analyzer as in classical processing, whereas the detection is performed using the Doppler bin data. The range estimation is performed using sparse recovery algorithms only for the detected targets to reduce the computational load. A low detection threshold is used to improve the detection probability and the introduced false targets are then removed in the range estimation stage by exploiting the inherent target detection capability of the recovery algorithms. Simulation results verify the effectiveness of the proposed CoSaPD scheme, which requires only one-eighth of the Nyquist rate to achieve similar performance to the classical processing with Nyquist samples, provided that the input signal-to-noise ratio (SNR) is above ???25 dB.\",\n \"Title: Maneuvering target altitude tracking in over-the-horizon radars exploiting multipath Doppler signatures\\nYear: 2013\\n\\nAbstract: Over-the-horizon radar (OTHR) systems provide wide-area surveillance capabilities to detect and track targets far beyond the range of conventional line-of-sight radars. Because of the narrowband waveforms, OTHR systems do not achieve reliable altitude estimation. In this paper, we develop a new technique to track the instantaneous altitude of maneuvering targets by exploiting the estimated multi-component Doppler signatures. The main contribution of this paper is to apply effective non-stationary signal analysis for estimating the time-varying Doppler signature of each individual multipath, which is then applied to an extended Kalman filter to reliably track the instantaneous target altitude.\",\n \"Title: A Novel Two-Dimensional Sparse-Weight NLMS Filtering Scheme for Passive Bistatic Radar.\\nYear: 2016\\n\\nAbstract: In passive bistatic radars, weak target echoes may often be masked by direct path interference, multipath components, and strong target echoes, making weak target detection a challenging problem. The conventional 1-D adaptive cancelation algorithms, such as the normalized least mean square (NLMS), cannot effectively suppress strong target echoes when their Doppler frequencies spread. In addition, ...\",\n \"Title: Low-Complexity Direction-of-Arrival Estimation Based on Wideband Co-Prime Arrays\\nYear: 2015\\n\\nAbstract: A class of low-complexity compressive sensing-based direction-of-arrival (DOA) estimation methods for wideband co-prime arrays is proposed. It is based on a recently proposed narrowband estimation method, where a virtual array model is generated by directly vectorizing the covariance matrix and then using a sparse signal recovery method to obtain the estimation result. As there are a large number of redundant entries in both the auto-correlation and cross-correlation matrices of the two sub-arrays, they can be combined together to form a model with a significantly reduced dimension, thereby leading to a solution with much lower computational complexity without sacrificing performance. A further reduction in complexity is achieved by removing noise power estimation from the formulation. Then, the two proposed low-complexity methods are extended to the wideband realm utilizing a group sparsity based signal reconstruction method. A particular advantage of group sparsity is that it allows a much larger unit inter-element spacing than the standard co-prime array and therefore leads to further improved performance.\",\n \"Title: Space–Time Adaptive Processing and Motion Parameter Estimation in Multistatic Passive Radar Using Sparse Bayesian Learning\\nYear: 2016\\n\\nAbstract: Conventional space-time adaptive processing suffers from the requirement of a large number of secondary samples. In this paper, a novel method is proposed to accurately estimate the clutter covariance matrix based on a small number of secondary samples, by exploiting the common clutter support across nearby range cells in the angle-Doppler domain. By taking advantage of the intrinsic sparsity of the clutter in the angle-Doppler domain, the recently developed sparse Bayesian learning technique is employed for high-resolution clutter profile estimation. The proposed method does not require the independent and identically distributed secondary sample assumption, and the required number of secondary data samples can be significantly reduced. In addition, we propose a sparse reconstruction-based approach to acquire the 2-D motion parameters of moving targets, by exploiting their group sparsity in the velocity domain in the multistatic passive radar systems. Simulation results verify the effectiveness of the proposed algorithm.\",\n \"Title: Dual-Function Radar-Communications: Information Embedding Using Sidelobe Control and Waveform Diversity.\\nYear: 2016\\n\\nAbstract: We develop a new technique for a dual-function system with joint radar and communication platforms. Sidelobe control of the transmit beamforming in tandem with waveform diversity enables communication links using the same pulse radar spectrum. Multiple simultaneously transmitted orthogonal waveforms are used for embedding a sequence of LB bits during each radar pulse. Two weight vectors are design...\",\n \"Title: Special issue on Co-prime sampling and arrays\\nYear: 2015\\n\\nAbstract: Co-prime sampling and co-prime arrays have recently been shown to improve active and passive sensing in radar and under-water acoustics using both narrowband and wideband signal plat-forms. Co-prime processing provides a systematical framework for sparse sampling and array configuration with increased aperture and improved spatial resolution. Co-prime based approaches to sampling and arrays can combat ambiguity and provide unique answers to target coordinates under forced coarse sampling in time, frequency, and space. Temporal and spatial co-prime samplings make advances in direction-of-arrival (DOA) estimation, spectrum sensing, and sparse covariance sketching. They find broad applications in synthetic aperture radar (SAR), radio frequency (RF) surveillance, target localization, wideband urban radar processing, space–time adaptive processing, and nonstationary array processing utilizing Doppler signatures and instantaneous frequency source characteristics.\",\n \"Title: Radar Signal Processing for Elderly Fall Detection: The future for in-home monitoring.\\nYear: 2016\\n\\nAbstract: Radar is considered an important technology for health monitoring and fall detection in elderly assisted living due to a number of attributes not shared by other sensing modalities. In this article, we describe the signal processing algorithms and techniques involved in elderly fall detection using radar. A human?s radar signal returns differ in their Doppler characteristics, depending on the natu...\",\n \"Title: High-Resolution Passive SAR Imaging Exploiting Structured Bayesian Compressive Sensing\\nYear: 2015\\n\\nAbstract: In this paper, we develop a novel structured Bayesian compressive sensing algorithm with location dependence for high-resolution imaging in ultra-narrowband passive synthetic aperture radar (SAR) systems. The proposed technique exploits wide-angle and/or multi-angle observations for image resolution enhancement. We first introduce a forward model based on sparse synthetic apertures. The problem of...\",\n \"Title: Sparsity-based multi-target localization exploiting multi-frequency coprime array\\nYear: 2015\\n\\nAbstract: In this paper, a novel sparsity-based multi-target localization approach is proposed by exploiting a coprime array operated with multiple narrowband signals of distinct but closely separated carrier frequencies. The cross-covariance matrix is formulated between baseband array data corresponding to different sensing frequencies to generate virtual difference coarrays, which enable direction-of-arrival (DOA) estimation of more targets than the number of physical sensors. In addition, the use of well designed multi-frequency signals unwraps the propagation phase information, thereby enabling unambiguous estimation of the target ranges. The DOA and range estimations are cast as a sparse reconstruction problem and are solved using the complex mulititask Bayesian compressive sensing (CMT-BCS) technique. The effectiveness of the proposed technique is verified through simulation results.\",\n \"Title: DOA estimation of sparsely sampled nonstationary signals\\nYear: 2015\\n\\nAbstract: The paper deals with sparsely sampled nonstationary signals in a multi-sensor array platform. We examine direction-of-arrival (DOA) estimation using sparsity-based time-frequency signal representation (TFSR). While conventional time-frequency analysis techniques suffer from noise-like artifacts due to missing data samples, high-fidelity time-frequency signatures can be obtained by applying kernelled processing and sparse reconstruction. Since the signals received at different sensors occupy the same time-frequency regions and share a common nonzero support, the recovery of TFSRs can be cast as a group sparse reconstruction problem. The reconstructed auto- and cross-sensor TFSRs enable the formation of the spatial time-frequency distribution (STFD) matrix, which is used, in turn, to propose the sparse time-frequency MUSIC (STF-MUSIC). The proposed STF-MUSIC method achieves effective source discrimination capability, leading to improved DOA estimation performance.\",\n \"Title: Through-the-wall radar imaging based on modified Bayesian compressive sensing\\nYear: 2014\\n\\nAbstract: In this paper, a novel modified complex multi-task Bayesian compressive sensing (MCMT-BCS) algorithm is proposed to acquire high-resolution images in stepped-frequency through-the-wall radar imaging (TWRI) exploiting multipath. Unlike traditional TWRI approaches that assume frequency-independent scattering model, we develop a practical subband scattering model to characterize real-world scattering mechanisms. The target imaging is reformulated as a multi-task sparse signal recovery problem across all frequency subbands as well as multipath modes, where the sparse entries of each task share the same support in the imaged scene. The proposed MCMT-BCS algorithm accounts for both types of coexisting group sparsity to achieve improved high-resolution imaging capability. Simulation results verify the effectiveness of the proposed algorithm.\",\n \"Title: Group sparsity based wideband DOA estimation for co-prime arrays\\nYear: 2014\\n\\nAbstract: A novel wideband direction-of-arrival (DOA) estimation method is proposed for co-prime arrays. After decomposing the wideband signals into different frequencies/subbands through a discrete Fourier transform or, more generally, a filter bank system, the increased degrees of freedom provided by co-prime arrays are fully exploited with a group sparsity based signal reconstruction method. Simulation results show that this novel method can distinguish much more sources than the number of physical sensors. Compared with the existing narrowband DOA estimation method for co-prime arrays, the proposed wideband method achieves a significant performance improvement.\",\n \"Title: Consensus-Based Loud Detection For True And False Warhead Recognition\\nYear: 2015\\n\\nAbstract: The micro-motion characteristics of warheads have been utilized to discriminate false warheads from true ones. To obtain accurate dimensional measurements, a multiple-input multiple-output (MIMO) radar is adopted to observe the kinetic information of the warhead. A distributed state space model (SSM) is built and the differences between the true and false warheads are characterized as different system parameters of the SSM. In this paper, we extend the locally optimal unknown direction (LOUD) detector, which has shown its effectiveness for hypothesis testing, to the underlying distributed detection problem, and a novel consensus-based LOUD detector is proposed. The superior detection performance of the proposed detection algorithm in identifying the true and false warheads is verified using simulation results.\",\n \"Title: Radar-based fall detection exploiting time-frequency features\\nYear: 2014\\n\\nAbstract: Falls of the elderly are a major public health concern. In this paper, we develop an effective fall detection algorithm for application in continuous-wave radar systems. The proposed algorithm exploits time-frequency characteristics of the radar Doppler signatures, and the motion events are classified using the joint statistics of three different features. The effectiveness of the proposed technique is verified through measurement data.\",\n \"Title: Wideband DOA estimation for uniform linear arrays based on the co-array concept\\nYear: 2015\\n\\nAbstract: A novel design for wideband uniform linear arrays (ULAs) with the associated group-sparsity based direction-of arrival (DOA) estimation method is proposed. This design allows the number of source signals to significantly exceed the number of sensors. Linear frequency modulated continuous wave (LFMCW) is used as the transmitted signal to ensure the required correlation property among different frequencies. The received echo signals from multiple targets are decomposed into different frequencies by discrete Fourier transform (DFT). Then these frequency bins are divided into several pairs to increase the degrees of freedom (DOFs) based on the co-array concept in the spatio-spectral domain. Group sparsity based signal reconstruction method is employed to jointly estimate the DOA results across multiple frequency pairs. Simulation results demonstrate a significantly improved performance achieved by the proposed method.\",\n \"Title: Dual-function radar-communications using phase-rotational invariance\\nYear: 2015\\n\\nAbstract: In this paper, we develop a new technique for dual-function radar-communications in a transmit: multi-sensor array where information embedding is achieved using phase-rotational invariance. A sequence of Q bits is first mapped into a dictionary of 2(Q) phase rotations. Then, one pair of transmit orthogonal waveforms is used in tandem with 2(Q) pairs of transmit beamforming weight vectors for embedding a certain entry of the phase-rotation dictionary during each radar pulse. The same pair of waveforms is used during all pulses while the pair of transmit beamforming weight vectors changes from pulse to pulse based on which entry of the phase rotation dictionary is embedded. During each pulse, the receiver detects the embedded phase rotation and employ it to decipher the transmitted bit sequence, The proposed information embedding technique is angle-dependant and, therefore, the communication process is inherently secure against interception from directions other than the desired communication direction. The performance of the proposed technique is investigated in terms of the bit error rate (BER).\",\n \"Title: Doa Estimation Of Nonparametric Spreading Spatial Spectrum Based On Bayesian Compressive Sensing Exploiting Intra-Task Dependency\\nYear: 2015\\n\\nAbstract: For spatially distributed targets encountered in radar and sonar applications, direct application of subspace-based methods usually do not lead to an accurate estimation of the direction and angular extent of the signal arrivals. If the spatial distribution of the targets can be parameterized with a known model a priori, the direction-of-arrival (DOA) estimation problems can be simplified as parameter estimation problems. However, these methods do not apply when the targets are not parameterizable. Motivated by this fact, we propose an effective approach for the DOA estimation of nonparametric spatially extended targets. In the proposed approach, the spatially extended targets are modeled as a continuous sparse structure, which are effectively estimated using the Bayesian compressive sensing techniques based on a paired spike-and-slab prior accounting for the angular target spread. In particular, the problem is examined under a collocated multiple-input multiple-output (MIMO) radar platform. Signal transmission at multiple coprime transmit frequencies are also considered to achieve increased degrees-of-freedom. The group sparsity of the targets across different frequencies is exploited to achieve improved DOA estimation performance.\",\n \"Title: Structured Bayesian Compressive Sensing Exploiting Spatial Location Dependence\\nYear: 2015\\n\\nAbstract: In this paper, we propose a novel structured compressive sensing algorithm based on non-parametric Bayesian framework for the reconstruction of sparse entries with a continuous structure. A paired spike-and-slab prior is first employed to impose signal sparsity. A logistic Gaussian kernel model, which involves the logistic model and location-dependent Gaussian kernel, is then proposed to encourage the underlying structure of a sparse signal. A closed-form and analytical posterior inference is carried out in a Gibbs sampling scheme. Simulation results demonstrate that the proposed algorithm outperforms existing state-of-the-art sparse Bayesian learning algorithms.\",\n \"Title: Autofocus Bayesian compressive sensing for multipath exploitation in urban sensing\\nYear: 2015\\n\\nAbstract: Exploitation of group sparsity under multipath propagation enables high-resolution ghost-free imaging in urban sensing and through-the-wall radar imaging applications. Multipath exploitation schemes typically require exact prior information of the indoor scene layout and transceiver locations to eliminate ghosts targets. Imperfections in the prior knowledge lead to performance degradation of such schemes. In this paper, a novel autofocus Bayesian compressive sensing approach is proposed for joint scene reconstruction and correction of phase errors resulted from transceiver position uncertainties. Supporting simulation results are provided to demonstrate the effectiveness of the proposed algorithm.\",\n \"Title: Sparse reconstruction of multi-component Doppler signature exploiting target dynamics\\nYear: 2015\\n\\nAbstract: In this paper, we propose a novel technique for a non-parametric sparse reconstruction of the Doppler signature of multiple ground moving targets exploiting prior knowledge about the target dynamics. The Doppler signature of multiple non-stationary targets is commonly modeled as a multi-component polynomial phase signal, whose order depends on the complexity of target dynamics and the coherent processing interval. In recent years, sparse signal reconstruction and compressive sensing algorithms have been extensively deployed in both parametric and non-parametric estimation of Doppler signatures. The performance of sparsity based Doppler signature reconstruction algorithms is significantly compromised in the presence of strong noise and a large number of missing samples. Herein, we present an effective technique for a non-parametric sparse reconstruction of a multi-component Doppler signature by exploiting the a priori information on target dynamics. Simulation results show that the proposed technique significantly outperforms the conventional sparse reconstruction based methods in such challenging conditions.\",\n \"Title: Sparse Arrays and Sampling for Interference Mitigation and DOA Estimation in GNSS.\\nYear: 2016\\n\\nAbstract: This paper establishes the role of sparse arrays and sparse sampling in antijam global navigation satellite systems (GNSS). We show that both jammer direction of arrival estimation methods and mitigation techniques benefit from the design flexibility of sparse arrays and their extended virtual apertures or coarrays. Taking advantage of information redundancy, significant reduction in hardware and ...\",\n \"Title: Group Sparsity Based Multi-Target Tracking in Passive Multi-Static Radar Systems Using Doppler-Only Measurements.\\nYear: 2016\\n\\nAbstract: In this paper, we consider the problem of tracking multiple targets in a passive multi-static radar system using Doppler-only measurements. The number of targets is assumed unknown and time-varying. The Doppler measurements are subject to additive noise, clutter, and missed detections. Doppler-only measurements from a single sensor provide incomplete information about the target state, commonly re...\",\n \"Title: DOA Estimation From One-Bit Compressed Array Data via Joint Sparse Representation.\\nYear: 2016\\n\\nAbstract: A one-bit joint sparse representation direction of arrival (OBJSR-DOA) estimation approach is proposed in this letter. By exploiting the joint spatial and spectral correlations inherent in acoustic sensor array data, the proposed OBJSR-DOA approach provides reliable DOA estimation from only the sign bit of randomly subsampled acoustic sensor data. The random subsampling and single-bit quantization...\",\n \"Title: Underdetermined wideband DOA estimation of off-grid sources employing the difference co-array concept.\\nYear: 2017\\n\\nAbstract: A wideband off-grid model is proposed to represent dictionary mismatch under the compressive sensing framework exploiting difference co-arrays. A group sparsity based off-grid method is proposed for underdetermined wideband direction of arrival (DOA) estimation which provides improved performance over the existing group sparsity based method with a same search grid. A two-step approach is then proposed which achieves an even better performance with significantly reduced computational complexity.\",\n \"Title: DOA estimation exploiting a uniform linear array with multiple co-prime frequencies.\\nYear: 2017\\n\\nAbstract: The co-prime array, which utilizes a co-prime pair of uniform linear sub-arrays, provides a systematical means for sparse array construction. By choosing two co-prime integers M and N, O ( MN ) co-array elements can be formed from only O ( M + N ) physical sensors. As such, a higher number of degrees-of-freedom (DOFs) is achieved, enabling direction-of-arrival (DOA) estimation of more targets than the number of physical sensors. In this paper, we propose an alternative structure to implement co-prime arrays. A single sparse uniform linear array is used to exploit two or more continuous-wave signals whose frequencies satisfy a co-prime relationship. This extends the co-prime array and filtering to a joint spatio-spectral domain, thereby achieving high flexibility in array structure design to meet system complexity constraints. The DOA estimation is obtained using group sparsity-based compressive sensing techniques. In particular, we use the recently developed complex multitask Bayesian compressive sensing for group sparse signal reconstruction. The achievable number of DOFs is derived for the two-frequency case, and an upper bound of the available DOFs is provided for multi-frequency scenarios. Simulation results demonstrate the effectiveness of the proposed technique and verify the analysis results. HighlightsWe have proposed a novel co-prime array structure that exploits a single uniform linear array and a co-prime set of frequencies.We have derived the analytical expressions of the array aperture and the number of DOFs with respect to two and multiple co-prime frequencies.DOA estimation is formulated as a group sparse compressive sensing problem, and is effectively solved by the complex multi-task Bayesian compressive sensing technique.\",\n \"Title: Generalized Coprime Sampling of Toeplitz Matrices for Spectrum Estimation.\\nYear: 2017\\n\\nAbstract: Increased demand on spectrum sensing over a broad frequency band requires a high sampling rate and thus leads to a prohibitive volume of data samples. In some applications, e.g., spectrum estimation, only the second-order statistics are required. In this case, we may use a reduced data-sampling rate by exploiting a low-dimensional representation of the original high-dimensional signals. In particular, the covariance matrix can be reconstructed from compressed data by utilizing its specific structure, e.g., the Toeplitz property. Among a number of techniques for compressive covariance sampler design, the coprime sampler is considered attractive because it enables a systematic design capability with a significantly reduced sampling rate. In this paper, we propose a general coprime sampling scheme that implements effective compression of Toeplitz covariance matrices. Given a fixed number of data samples, we examine different schemes on covariance matrix acquisition for performance evaluation, comparison, and optimal design, based on segmented data sequences.\",\n \"Title: Two-dimensional DOA estimation using parallel coprime subarrays\\nYear: 2016\\n\\nAbstract: A conventional coprime array is a linear array, which consists of two uniform linear subarrays to construct an effective difference coarray with certain desirable characteristics. In this paper, we propose a parallel coprime array structure and a novel algorithm for two-dimensional (2-D) direction-of-arrival (DOA) estimation. By vectorizing the cross-covariance matrix of subarray data, the resulting virtual difference coarray enables resolving more signals than the number of antennas. The 2-D DOA estimation problem is cast as two separate one-dimensional DOA estimation problems, where the estimated azimuth and elevation angles can be properly associated. Compared with other methods, such as, the propagator method (PM) and the rank-reduction (RARE) based algorithms, the proposed method resolves more signals and achieves improved estimation performance.\",\n \"Title: Ultra-wideband radar and vision based human motion classification for assisted living.\\nYear: 2016\\n\\nAbstract: Fall detection for elderly is one of the most important areas in elderly healthcare. Both video and radar based detections are being developed for this purpose. This paper presents a new approach to classify different human motions through machine learning. In particular, our objective is to achieve high-accuracy fall detection through the exploitation of both video and radar data. Motion history image is applied to extract temporal features from video clips, and hidden Markov models are trained with the features extracted from both video and radar data to discern the types of motion. Experiment results indicate that the proposed approach provides improved performance in distinguishing falls from other motions such as sitting.\",\n \"Title: Robust group compressive sensing for DOA estimation with partially distorted observations.\\nYear: 2016\\n\\nAbstract: In this paper, we propose a robust direction-of-arrival (DOA) estimation algorithm based on group sparse reconstruction algorithm utilizing signals observed at multiple frequencies. The group sparse reconstruction scheme for DOA estimation is solved through the complex multitask Bayesian compressive sensing algorithm by exploiting the group sparse property of the received multi-frequency signals. Then, we propose a robust reconstruction algorithm in the presence of distorted signals. In particular, we consider a problem where the observed data in some frequencies are distorted due to, e.g., interference contamination. In this case, the residual error will follow the impulsive Gaussian mixture distribution instead of the Gaussian distribution due to the fact that some of the estimation errors significantly depart from the mean value of the estimation error distribution. Thus, the minimum least square restriction used in the conventional sparse reconstruction algorithm may lead to a failed reconstruction result. By exploiting the maximum correntropy criterion which is inherently insensitive to the impulsive noise, a weighting vector is derived to automatically mitigate the effect of the distorted narrowband signals, and a robust group compressive sensing approach is developed to achieve reliable DOA estimation. The robustness and effectiveness of the proposed algorithm are verified using simulation results.\",\n \"Title: Low-Complexity 2D Direction-of-Arrival Estimation for Acoustic Sensor Arrays.\\nYear: 2016\\n\\nAbstract: We use the phase difference among sensors, by solving the phase wrapping problem based on the cross-spectrum between sensors, to obtain the direction of arrival without exhaustive search in the two-dimensional angle space. The proposed approach does not need the typical sensor separation or interfrequency separation requirements. Simulation results with narrowband, wideband signals (bird chirps), ...\",\n \"Title: Focused Compressive Sensing for Underdetermined Wideband DOA Estimation Exploiting High-Order Difference Coarrays.\\nYear: 2017\\n\\nAbstract: Group-sparsity-based method is applied to the 2qth-order difference coarray for underdetermined wideband direction of arrival (DOA) estimation. For complexity reduction, a focused compressive-sensing-based approach is proposed, without sacrificing its performance. Different from the conventional focusing approach, in the proposed one, focusing is applied to the virtual arrays and no preliminary DO...\",\n \"Title: Frequency Diverse Coprime Arrays With Coprime Frequency Offsets for Multitarget Localization.\\nYear: 2017\\n\\nAbstract: Different from conventional phased-array radars, the frequency diverse array (FDA) radar offers a range-dependent beampattern capability that is attractive in various applications. The spatial and range resolutions of an FDA radar are fundamentally limited by the array geometry and the frequency offset. In this paper, we overcome this limitation by introducing a novel sparsity-based multitarget lo...\",\n \"Title: A dual-function MIMO radar-communications system using PSK modulation.\\nYear: 2016\\n\\nAbstract: In this paper, we develop a new technique for information embedding into the emission of multiple-input multipleoutput (MIMO) radar using dual-functionality platforms. A set of orthogonal waveforms occupying the same band is used to implement the primary MIMO radar operation. The secondary communication function is implemented by embedding one phase-shift keying (PSK) communication symbol in each orthogonal waveform, i.e., the number of embedded communication symbols during each radar pulse equals the number of transmit antennas. We show that the communication operation is transparent to the MIMO radar operation. The communication receiver detects the embedded PSK symbols using standard ratio testing. The achievable data rate is proportional to the pulse repetition frequency, the number of transmit elements, and the size of the PSK constellation. The performance of the proposed technique is investigated in terms of the symbol error rate. Simulations examples demonstrate that data rates in the range of several Mbps can be embedded and reliably detected.\",\n \"Title: Robust DOA Estimation in the Presence of Miscalibrated Sensors.\\nYear: 2017\\n\\nAbstract: In this letter, we propose a robust direction-of-arrival (DOA) estimation algorithm in the context of sparse reconstruction, where some array sensors are miscalibrated. In this case, conventional DOA estimation algorithms suffer from degraded performance or even failed operations. In the proposed approach, the miscalibrated sensor observations are treated as outliers, and a weighting factor is ada...\",\n \"Title: Wideband Radar Based Fall Motion Detection For A Generic Elderly\\nYear: 2016\\n\\nAbstract: Radar-based automated fall detection systems are considered as an important and emerging technology for elderly assisted living. These radar systems provide non-intrusive sensing capabilities to detect fall events. Various studies have used micro-Doppler signatures to determine falls. However, Doppler radar fall detection systems suffer false alarms stemming from other sudden non-rhythmic motion articulations. In this work, we consider a textural-based feature extraction method which can determine the density variations between various motion articulations. For this purpose, textural features are extracted from the gray level co-occurrence matrix for each motion using time-integrated range-Doppler maps and micro-Doppler signatures. Textural features are then used to train the support vector machine classifier. The sequential forward selection method is implemented to identify essential features and minimize the feature space while maximizing the fall detection rate. The results show that well selected range-Doppler based textural features can provide improved classification results compared to textural features based only on micro-Doppler signatures.\",\n \"Title: Nonstationary Jammer Suppression Based On Parametric Sparse Reconstruction\\nYear: 2016\\n\\nAbstract: We propose a nonstationary jammer signal suppression technique based on parametric sparse reconstruction, where sparsely sampled data are considered. By assuming nonstationary jammers that are characterized by polynomial phase signatures, a data-dependent parametric dictionary matrix is designed. In order to obtain an accurate instantaneous frequency estimation at a low computational complexity, we exploit an iterative multi-round sparse reconstruction scheme in which the dictionary matrix is updated with a finer grid size, thus leading to dictionary entries that are closer to the true jammer signatures. Simulation results are provided to verify the effectiveness of the proposed technique.\",\n \"Title: Detection of weak astronomical signals with frequency-hopping interference suppression.\\nYear: 2018\\n\\nAbstract: This paper addresses the detection of weak astronomical signals that are contaminated by strong frequency-hopping (FH) interferers and suffer from missing samples. The problem is considered in the time–frequency domain and we successively suppress artifacts due to missing samples, estimate and remove FH interferers, and detect the weak astronomical signals. More specifically, we first suppress the artifacts due to missing samples by developing a waveform-adaptive time–frequency kernel. The instantaneous spectra of the FH interferers are then estimated using a sparsity-based approach that takes the inherent properties of FH signals into account. Finally, a sparse coherent integrated cubic phase function is applied to effectively detect weak astronomical chirp components over a long integration time. Simulation results are provided to demonstrate the effectiveness of the proposed approach.\",\n \"Title: Cramer-Rao type bounds for sparsity-aware multi-sensor multi-target tracking.\\nYear: 2018\\n\\nAbstract: •This paper develops the Cramer–Rao type of bounds for the sparsity-aware multi-sensor multi-target tracking techniques.•The results enable a clear understanding of the effect of the input signal-to-noise ratio and missing samples to the performance of sparsity-aware multi-sensor multi-target tracking techniques.•It provides theoretical support and insightful understanding to the offerings of the recursive feedback in the sparsity-aware multi-sensor multi-target tracking techniques.\",\n \"Title: Structure-Aware Bayesian Compressive Sensing for Frequency-Hopping Spectrum Estimation With Missing Observations.\\nYear: 2018\\n\\nAbstract: In this paper, we address the problem of spectrum estimation of multiple frequency-hopping (FH) signals in the presence of random missing observations. The signals are analyzed within the bilinear time-frequency (TF) representation framework, where a TF kernel is designed by exploiting the inherent FH signal structures. The designed kernel permits effective suppression of cross-terms and artifacts...\",\n \"Title: Performance Analysis for Uniform Linear Arrays Exploiting Two Coprime Frequencies.\\nYear: 2018\\n\\nAbstract: Sparse arrays can achieve a higher number of degrees of freedom (DOFs) compared with uniform linear array (ULA) counterparts. To further reduce the number of physical sensors while keeping a high number of DOFs, a direction of arrival (DOA) estimation algorithm by exploiting coprime frequencies base on a sparse ULA is recently proposed. However, the performance of such approach is not properly ana...\",\n \"Title: Coprime array-based DOA estimation in unknown nonuniform noise environment.\\nYear: 2018\\n\\nAbstract: In this paper, we propose direction-of-arrival (DOA) estimation techniques, respectively based on covariance matrix reconstruction and matrix completion, to achieve robust DOA estimation capability in nonuniform noise environments using coprime arrays. For the covariance matrix reconstruction-based approach, by exploring the diagonal structure of the covariance matrix of the noise, the covariance matrix of the received signal vector is reconstructed through averaging its diagonal elements. Moreover, in order to handle more sources than the number of sensors, the difference coarray of coprime arrays is utilized through the vectorization of the reconstructed covariance matrix. A compressive sensing (CS) based DOA estimator is then formulated to provide sparsity-based DOA estimation. For the matrix completion-based approach, we take the full advantage of the difference coarray lags and obtain the noise-free covariance matrix of the virtual uniform linear array by using the matrix completion technique to recover the removed diagonal elements and missing holes in the virtual array covariance matrix. Then CS-based and MUSIC-based DOA estimators are respectively designed to perform DOA estimation using the estimated noise-free covariance matrix. Simulation results verify the effectiveness of the proposed algorithms.\",\n \"Title: DOA Estimation Using Compressed Sparse Array.\\nYear: 2018\\n\\nAbstract: Sparse arrays, such as nested arrays and coprime arrays, can achieve a high number of degrees of freedom (DOFs) for direction of arrival (DOA) estimation with a reduced number of antennas. On the other hand, the compressive measurement method provides an effective way to reduce the number of frontend circuit chains. In this paper, we generalized current works on the two categories of methods to a ...\",\n \"Title: Coarray Interpolation-Based Coprime Array Doa Estimation Via Covariance Matrix Reconstruction.\\nYear: 2018\\n\\nAbstract: Coprime arrays are capable of achieving an increased number of degrees-of-freedom by operating the coarray signals. However, their non-uniform coarrays prevent the full utilization of the available signals. To address this problem, a novel coarray interpolation-based direction-of-arrival (DOA) estimation algorithm via covariance matrix reconstruction is proposed in this paper. In particular, we formulate a gridless optimization problem to reconstruct the covariance matrix of the interpolated coarray, such that all the coarray observations are fully utilized. We also investigate the rotational invariance in the coarray domain to retrieve the DOAs. Neither spatial sampling nor spectrum searching is required in the proposed algorithm, indicating the capability of resolving off-grid DOAs. Simulation results demonstrate the effectiveness of the proposed DOA estimation algorithm.\",\n \"Title: Image Reconstruction in Electrical Impedance Tomography Based on Structure-Aware Sparse Bayesian Learning.\\nYear: 2018\\n\\nAbstract: Electrical impedance tomography (EIT) is developed to investigate the internal conductivity changes of an object through a series of boundary electrodes, and has become increasingly attractive in a broad spectrum of applications. However, the design of optimal tomography image reconstruction algorithms has not achieved the adequate level of progress and matureness. In this paper, we propose an eff...\",\n \"Title: Dual-function radar-communications using QAM-based sidelobe modulation.\\nYear: 2018\\n\\nAbstract: Spectrum sharing using a joint platform for radar and communication systems has attracted significant attention in recent years. In this paper, we propose a novel dual-function radar-communications (DFRC) strategy to embed quadrature amplitude modulation (QAM) based communication information in the radar waveforms by exploiting sidelobe control and waveform diversity. The proposed information embedding technique can support multiple communication receivers located in the sidelobe region. In addition to the information broadcasting, the developed approach enables multi-user access by allowing simultaneous transmission of distinct information streams to the communication receivers located in different directions. We prove that the proposed technique ensures a significant data rate enhancement compared to the existing techniques. Moreover, the developed DFRC strategy generalizes the mathematical framework of the existing sidelobe control-based information embedding techniques.\",\n \"Title: Direction-of-Arrival Estimation for Coprime Array via Virtual Array Interpolation.\\nYear: 2018\\n\\nAbstract: Coprime arrays can achieve an increased number of degrees of freedom by deriving the equivalent signals of a virtual array. However, most existing methods fail to utilize all information received by the coprime array due to the non-uniformity of the derived virtual array, resulting in an inevitable estimation performance loss. To address this issue, we propose a novel virtual array interpolation-b...\",\n \"Title: Off-Grid Direction-of-Arrival Estimation Using Coprime Array Interpolation.\\nYear: 2018\\n\\nAbstract: In this letter, we propose a coprime array interpolation approach to provide an off-grid direction-of-arrival (DOA) estimation. Through array interpolation, a uniform linear array (ULA) with the same aperture is generated from the deterministic non-uniform coprime array. Taking the observed correlations calculated from the signals received at the coprime array, a gridless convex optimization probl...\",\n \"Title: Coprime array-based robust beamforming using covariance matrix reconstruction technique.\\nYear: 2018\\n\\nAbstract: A novel robust adaptive beamforming algorithm based on a coprime array is proposed in this study. First, the authors exploit the virtual array structure of the coprime coarray to construct two subspaces. The first subspace is obtained from the covariance matrix of the virtual uniform linear array, whereas the second one is obtained by integrating a spatial spectrum over each angular sector of the ...\",\n \"Title: Sparsity-based time-frequency representation of FM signals with burst missing samples.\\nYear: 2019\\n\\nAbstract: •The effects of burst missing samples are studied in different domains for FM signals.•A novel sparse reconstruction-based method is developed for time-frequency analysis.•Reliable time-frequency representations are achieved with low complexity.•Both cross-terms and artifacts due to missing samples are effectively suppressed.\",\n \"Title: COMPRESSED SENSING BASED JOINT DOA AND POLARIZATION ANGLE ESTIMATION FOR SPARSE ARRAYS WITH DUAL-POLARIZED ANTENNAS\\nYear: 2018\\n\\nAbstract: In this paper, a compressed sensing (CS)-based joint estimation of direction of arrival (DoA) and polarization parameters is proposed using a sparse array consisting of dual-polarized antenna elements. Since this joint estimation with the CS as well as conventional subspace based methods turn out to be computationally prohibitive, we propose a novel method so that DoAs can be accurately estimated using the proposed CS algorithm, whereas the polarization parameters can be estimated utilizing the estimated DoAs and the least-squares method. We verify that the proposed method can accurately resolve both the DoAs and polarization parameters of much more uncorrelated sources than the number of sparse array sensors.\",\n \"Title: DOA Estimation Exploiting Moving Dilated Nested Arrays.\\nYear: 2019\\n\\nAbstract: A novel dilated nested array is presented to obtain an enhanced fully filled difference coarray exploiting array motions. The proposed sparse array is suitable for the cases when the sensing environment can be assumed stationary over an array motion of half wavelength or shorter. Closed-form expressions of the number of degrees of freedom in the difference coarray of the combined array before and ...\",\n \"Title: Design of wideband MIMO radar waveforms with low peak-to-average ratio\\nYear: 2015\\n\\nAbstract: Multiple-input multiple-output (MIMO) radar systems allow array antennas to transmit different waveforms and, as such, enable flexible transmit beampattern synthesis. Most existing transmit beampattern synthesis methods focus on narrowband MIMO radar systems. In this paper, we propose a novel method to design transmit waveforms for wideband MIMO radar systems. The proposed technique is based on the optimization of the cross-spectral density matrix and achieves a low peak-to-average power ratio (PAR) as desired in practical radar operations. Simulations results are provided to verify the low PAR waveform design capability corresponding to arbitrary beampatterns.\",\n \"Title: Human motion recognition exploiting radar with stacked recurrent neural network.\\nYear: 2019\\n\\nAbstract: We develop a novel radar-based human motion recognition technique that exploits the temporal sequentiality of human motions. The stacked recurrent neural network (RNN) with long short-term memory (LSTM) units is employed to extract sequential features for automatic motion classification. The spectrogram of raw radar data is used as the network input to utilize the time-varying Doppler and micro-Doppler signatures for human motion characterization. Based on experimental data, we verified that a stacked RNN with two 36-cell LSTM layers successfully classifies six different types of human motions.\",\n \"Title: DOA estimation of mixed coherent and uncorrelated targets exploiting coprime MIMO radar.\\nYear: 2017\\n\\nAbstract: We propose a new scheme to estimate the directions-of-arrival (DOAs) of mixed coherent and uncorrelated targets exploiting a collocated multiple-input multiple-output (MIMO) radar with transmit/receive coprime arrays. In the proposed scheme, the DOAs of the uncorrelated targets are first estimated using subspace-based methods, whereas those of the coherent targets are resolved using Bayesian compressive sensing. Compared with the previous works, the proposed approach achieves improved DOA estimation accuracy with a flexible coprime array configuration and may resolve more targets than the number of coarray elements. Theoretical analysis and simulation results validate the effectiveness of the proposed technique.\",\n \"Title: Time-Frequency Analysis for GNSSs: From interference mitigation to system monitoring.\\nYear: 2017\\n\\nAbstract: In this article, we discuss the important role of time-frequency (TF) signal representations in enhancing global navigation satellite system (GNSS) receiver performance. Both linear transforms and quadratic TF distributions (QTFDs) are considered. We review recent advances of antijam techniques that exploit the distinction in the TF signatures between the desired and undesired signals, enabling ef...\",\n \"Title: MISC Array: A New Sparse Array Design Achieving Increased Degrees of Freedom and Reduced Mutual Coupling Effect.\\nYear: 2019\\n\\nAbstract: Recently, nested and coprime arrays have attracted considerable interest due to their capability of providing increased array aperture, enhanced degrees of freedom (DOFs), and reduced mutual coupling effect compared to uniform linear arrays (ULAs). These features are critical to improving the performance of direction-of-arrival estimation and adaptive beamforming. In this paper, a new sparse array...\",\n \"Title: Information-Theoretic Pilot Design for Downlink Channel Estimation in FDD Massive MIMO Systems.\\nYear: 2019\\n\\nAbstract: Massive multiple-input multiple-output (MIMO) is one of the most promising techniques for next generation wireless communications due to its superior capability to provide high spectrum and energy efficiency. Considering the very large number of antennas employed at the base station, however, the pilot overhead for downlink channel estimation becomes unaffordable in frequency division duplex (FDD)...\",\n \"Title: DOA Estimation Exploiting Sparse Array Motions.\\nYear: 2019\\n\\nAbstract: This paper utilizes sparse array motion to increase the numbers of achievable both degrees of freedom (DOFs) and consecutive lags in direction-of-arrival (DOA) estimation problems. We use commonly employed environment-independent sparse array configurations. The design of these arrays is not dependent on the sources in the field of view, but rather aims at achieving desirable difference co-arrays....\",\n \"Title: Multi-Target Motion Parameter Estimation Exploiting Collaborative Uav Network\\nYear: 2019\\n\\nAbstract: We propose a distributed unmanned aerial vehicle (UAV) network performing collaborative radar sensing for multi-target localization and motion parameter estimation. Two UAV network topologies are considered for data propagation and information fusion. In the former, we form a sequential UAV node chain, whereas in the latter, the UAV nodes are grouped into clusters and the information among different clusters is propagated through the cluster master nodes. Sparse reconstruction methods are used to fuse the target state information from previous nodes or clusters with the data measured in the underlying nodes or clusters, depending on the adopted topology, to achieve improved target state estimates. In order to minimize the communication traffic in the UAV network, each node transmits the estimated Doppler signatures or sparse target state estimates to the next UAV node in the network or the cluster master node, in lieu of the large volume of raw sampled data. Simulation results verify the effectiveness of the proposed approaches and compare the performance between the two UAV network topologies.\",\n \"Title: Coprime Array Design With Minimum Lag Redundancy\\nYear: 2019\\n\\nAbstract: In this paper, we propose a new sparse coprime array design that achieves a higher number of degrees-of-freedom for direction-of-arrival (DOA) estimation. The proposed array design completely avoids lag redundancies between the two constituting subarrays of the coprime array, thus achieving the maximum number of unique correlation lags under the coprime array framework. As a result, given the same number of physical sensors, the proposed design resolves more sources than other coprime array designs with enhanced DOA estimation performance. Simulation results demonstrate the superior performance of the proposed coprime array design.\",\n \"Title: Analysis Of Coprime Arrays On Moving Platform\\nYear: 2019\\n\\nAbstract: Moving platforms enable sparse arrays to assume higher degrees of freedom and lead to increased number of lags. In essence, array motion can fill the holes in the spatial auto correlation lags associated with a fixed platform and, therefore, increase the number of sources detectable by the same physical array. In this paper, we consider coprime arrays, and assume quasi-stationarity of the environment, where the source locations and waveforms are assumed invariant over array motion of half wavelength. Expressions of the synthetic array comprising the original coprime array and its shifted version are derived. Analysis of the difference co-array corresponding to the combined array positions before and after motion is provided. It is shown that majority, if not all, of the holes in the original array position can be filled by just a small translation shift along the coprime array axis.\",\n \"Title: FOUR-DIMENSIONAL HIGH-RESOLUTION AUTOMOTIVE RADAR IMAGING EXPLOITING JOINT SPARSE-FREQUENCY AND SPARSE-ARRAY DESIGN\\nYear: 2021\\n\\nAbstract: We propose a novel automotive radar imaging technique to provide high-resolution information in four dimensions, i.e., range, Doppler, azimuth, and elevation, by exploiting a joint sparsity design in frequency spectrum and array configurations. Random sparse step-frequency waveform is proposed to synthesize a large effective bandwidth and achieve high range resolution profiles. This concept is extended to multi-input multi-output (MIMO) radar by applying phase codes along the slow time to synthesize a two-dimensional (2D) sparse array with a high number of virtual array elements which enable high-resolution direction finding in both azimuth and elevation. The 2D sparse array acts as a sub-Nyquist sampler of the corresponding uniform rectangular array (URA), and the corresponding URA response is recovered by completing a low-rank block Hankel matrix. The proposed imaging radar provides point clouds with a resolution comparable to light detection and ranging (LiDAR) but with a much lower cost and is insensitive to weather conditions.\",\n \"Title: Joint Radar-Communications Exploiting Optimized OFDM Waveforms\\nYear: 2021\\n\\nAbstract: We propose novel Joint Radar-communication spectrum sharing strategies exploiting orthogonal frequency-division multiplexing (OFDM) waveforms that concurrently achieve the objectives of both radar and communication systems. An OFDM transmitter is considered that transmits dual-purpose OFDM subcarriers such that all the subcarriers are exploited for the primary radar function and further exclusively allocated to the secondary communication function serving multiple users. The waveform optimization is performed by employing mutual information (MI) as the optimization criterion for both radar and communication operations. For the purpose of radar performance optimization, we consider the MI between the frequency-dependent target response and the transmit OFDM waveforms. On the other hand, communication system performance is evaluated in terms of the MI between the frequency-dependent communication channels of communication users with the transmit OFDM subcarriers. These optimization objectives not only enable the transmit power allocation of the OFDM subcarriers, but also govern the subcarrier distribution among the communication users. Two resource optimization scenarios are considered, resulting in radar-centric and cooperative resource allocation strategies that exploit convex and mixed-integer linear programming optimization problems for power allocation and subcarrier distribution, respectively. We further present a chunk subcarrier allocation approach that applies to both optimization strategies to reduce the computational complexity with a trivial performance loss. Simulation results are presented to illustrate the effectiveness of the proposed strategies.\",\n \"Title: Crossterm-free time-frequency representation exploiting deep convolutional neural network\\nYear: 2022\\n\\nAbstract: •We developed a deep neural network-based approach to generate crossterm-free time-frequency representations.•We show that, provided that the neural networks are adequately trained, the proposed method works robustly and provides significant performance improvement compared to existing time-frequency representation reconstruction algorithms.•We evaluated the generalization capability of the proposed method, including the effects of noise levels, amplitude difference, variation speed of the IFs, fading, number of signal components, and frequency spreading.\",\n \"Title: Joint DoA-Range Estimation Using Space-Frequency Virtual Difference Coarray\\nYear: 2022\\n\\nAbstract: In this paper, we address the problem of joint direction-of-arrival (DoA) and range estimation using frequency diverse coprime array (FDCA). By incorporating the coprime array structure and coprime frequency offsets, a two-dimensional space-frequency virtual difference coarray corresponding to uniform array and uniform frequency offset is considered to increase the number of degrees-of-freedom (DoFs). However, the reconstruction of the doubly-Toeplitz covariance matrix is computationally prohibitive. To solve this problem, we propose an interpolation algorithm based on decoupled atomic norm minimization (DANM), which converts the coarray signal to a simple matrix form. On this basis, a relaxation-based optimization problem is formulated to achieve joint DoA-range estimation with enhanced DoFs. The reconstructed coarray signal enables application of existing subspace-based spectral estimation methods. The proposed DANM problem is further reformulated as an equivalent rank-minimization problem which is solved by cyclic rank minimization. This approach avoids the approximation errors introduced in nuclear norm-based approach, thereby achieving superior root-mean-square error which is closer to the Cramer-Rao bound. The effectiveness of the proposed method is confirmed by theoretical analyses and numerical simulations.\",\n \"Title: Enhanced DOA Estimation Exploiting Multi-Frequency Sparse Array\\nYear: 2021\\n\\nAbstract: In this paper, we develop a general framework of multi-frequency sparse array to estimate the direction-of-arrival (DOA) of a significantly higher number of targets than the number of physical sensors. The multi-frequency sparse arrays are designed to offer zero lag redundancy in the rendered difference coarray so that the available degrees of freedom are fully utilized to enable high-resolution DOA estimation. A modified sensor interpolation technique is developed to accurately estimate the signal correlation matrix so that the effect of holes in the difference coarray is mitigated. The proposed technique accounts for both self-lags between signals corresponding to the same frequencies and the cross-lags between signals corresponding to different frequencies. As such, it enhances the DOA estimation performance compared to existing methods that either perform array interpolation utilizing only the self-lags or carry out group sparse reconstruction without exploiting array interpolation. Simulation results verify the offerings of the multi-frequency sparse arrays.\",\n \"Title: Target Altitude Estimation in Over-the-Horizon Radar\\nYear: 2022\\n\\nAbstract: Doppler signatures of local multipath signals provide useful information for target altitude estimation in over-the-horizon radar surveillance. In this paper, we develop a method to improve the resolution of these multipath Doppler signatures and enable enhanced altitude estimation of aircraft target which maintains a constant altitude. Moreover, we consider the impact of ionospheric layer motion on target parameter estimation and show that target parameters can be estimated under both stationary as well as time-varying ionospheric layer conditions. In order to improve the resolution and estimation accuracy of the target parameters and ionosphere velocity with a significantly reduced complexity, we exploit a frequency focused transform to the de-chirped target signals for dimension reduction before applying a least absolute shrinkage and selection operator (LASSO)-based high-resolution spectrum estimation technique. The proposed strategy outperforms fractional Fourier transform and classical subspace-based frequency estimation methods with a much lower computational complexity. The effectiveness of the proposed approach is especially evident for challenging cases where the multipath signal components have spectrally close Doppler signatures. Simulation results confirm the effectiveness of the proposed method.\",\n \"Title: Structured Bayesian compressive sensing exploiting dirichlet process priors\\nYear: 2022\\n\\nAbstract: •A novel structured and clustered multi-task compressive sensing framework based on a hierarchical Bayesian model is proposed to exploit the inner sparse pattern and the statistical dependence between tasks.•The spike-and-slab priors encode sparse patterns of the signals and are further generalized by imposing the Gaussian process for modeling inner structures. The Dirichlet Process priors imposed on the support reveal the clustering mechanisms among tasks.•A new inference scheme based on expectation propagation is derived to approximate the posterior distribution for simplifying the computation and deriving analytical expression.\",\n \"Title: Joint Target and Ionosphere Parameter Estimation in Over-the-Horizon Radar\\nYear: 2022\\n\\nAbstract: Target localization, especially the estimation of target altitude, is a challenging task in over-the-horizon radar (OTHR) because of the narrow signal bandwidth as well as the complexity and uncertainty involved in the ionosphere conditions. This task becomes further complicated when the height of the ionosphere layer varies over time. Therefore, it is important to jointly estimate the instantaneous height of the ionosphere and the target altitude as well as other motion parameters. In this article, we achieve these objectives by analyzing the Doppler frequencies of the target local-multipath signals and the clutter. We reveal that the change of the ionosphere height can either enhance or deteriorate the performance of target parameter estimation depending on its direction of motion relative to the target’s motion profile. In addition, it is found that the received target and clutter Doppler signatures follow the chirp signal profile at the OTHR receiver. Based on these observations, we develop a general framework that achieves joint target and ionosphere parameter estimation and accounts for the velocity and accelerations of both target and ionosphere layer. Unlike existing time-frequency-based strategies for target localization and tracking in OTHR, where the Doppler signatures only directly determine target vertical velocity and the target altitude is estimated indirectly, the proposed model enables direct estimation of target altitude and ionosphere parameters. The parameter estimation problem in the proposed strategy is analytically derived and the effectiveness is verified using extensive simulation results.\",\n \"Title: Neural Network Approach to Iterative Optimization of Compressive Measurement Matrix in Massive Mimo System\\nYear: 2022\\n\\nAbstract: Massive multiple-input multiple-output (MIMO) systems equipped with a high number of antenna elements are becoming one of the most exciting emerging technologies in next-generation wireless communication systems. The use of a high number of anten-nas makes it challenging to implement separate radio frequency front-end circuits for each antenna due to hardware cost and power consumption restrictions. To make massive MIMO feasible, an ef-ficient solution is to perform hybrid beamforming that compresses the received signals at the MIMO receiver before the signals are dig-itized. We have proposed optimized compressive measurement by maximizing the mutual information between the compressed mea-surement and the signal directions-of-arrival (DOAs) by utilizing a coarse a priori probability distribution of the signal DOAs. Despite its superior performance to effectively reduce the number of required front-end circuits, the requirement of a coarse a priori distribution of signal DOAs makes it difficult to apply in some situations where such information is unavailable. In this paper, we propose a data-driven approach based on neural network to iteratively update the signal DOA distribution. The neural network estimate signal DOA spectrum, which is then fed back to refine the prior information, thereby making this method practical even in the situations where no prior information of the signal DOAs is available.\",\n \"Title: Sparse Channel Estimation for IRS-Aided Systems Exploiting 2-D Sparse Arrays\\nYear: 2022\\n\\nAbstract: Intelligent reflecting surface (IRS) is a promising next-generation technology for increasing channel capacity and reducing power consumption. In this paper, we present a novel IRS configuration consisting of a small number of active elements in an optimized L-shaped sparse array to separately estimate the channels between the base station and the IRS, and the channel between multiple user equipment and the IRS. Structured matrix completion techniques are used to attain superior direction-of-arrival estimation performance with an increased number of degrees of freedom. The training overhead is minimized in the proposed system and is not directly related to the number of IRS reflecting elements. The proposed sparse array strategy simultaneously resolves multiple sources with a high accuracy and outperforms the L-shaped uniform array counterpart using the same number of active elements. The effectiveness of the proposed strategy is confirmed using simulation results.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":109,"string":"109"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13253,"string":"13,253"}}},{"rowIdx":13254,"cells":{"question_id":{"kind":"string","value":"Glenn Van Wallendael"},"question":{"kind":"string","value":"Generate an abstract for the title \"Subjective Quality Assessment of Longer Duration Video Sequences Delivered Over HTTP Adaptive Streaming to Tablet Devices.\" using the following items: HTTP adaptive streaming, mobile devices, video quality, subjective studies, longer duration video sequences"},"answer":{"kind":"string","value":"HTTP adaptive streaming facilitates video streaming to mobile devices connected through heterogeneous networks without the need for a dedicated streaming infrastructure. By splitting different encoded versions of the same video into small segments, clients can continuously decide which segments to download based on available network resources and device characteristics. These encoded versions can, for example, differ in terms of bitrate and spatial or temporal resolution. However, as a result of dynamically selecting video segments, perceived video quality can fluctuate during playback which will impact end-users' quality of experience. Subjective studies have already been conducted to assess the influence of video delivery using HTTP Adaptive Streaming to mobile devices. Nevertheless, existing studies are limited to the evaluation of short video sequences in controlled environments. Research has already shown that video duration and assessment environment influence quality perception. Therefore, in this article, we go beyond the traditional ways for subjective quality evaluation by conducting novel experiments on tablet devices in more ecologically valid testing environments using longer duration video sequences. As such, we want to mimic realistic viewing behavior as much as possible. Our results show that both video content and the range of quality switches significantly influence end-users' rating behavior. In general, quality level switches are only perceived in high motion sequences or in case switching occurs between high and low quality video segments. Moreover, we also found that video stallings should be avoided during playback at all times."},"correct_answer":{"kind":"string","value":"HTTP adaptive streaming facilitates video streaming to mobile devices connected through heterogeneous networks without the need for a dedicated streaming infrastructure. By splitting different encoded versions of the same video into small segments, clients can continuously decide which segments to download based on available network resources and device characteristics. These encoded versions can, for example, differ in terms of bitrate and spatial or temporal resolution. However, as a result of dynamically selecting video segments, perceived video quality can fluctuate during playback which will impact end-users' quality of experience. Subjective studies have already been conducted to assess the influence of video delivery using HTTP Adaptive Streaming to mobile devices. Nevertheless, existing studies are limited to the evaluation of short video sequences in controlled environments. Research has already shown that video duration and assessment environment influence quality perception. Therefore, in this article, we go beyond the traditional ways for subjective quality evaluation by conducting novel experiments on tablet devices in more ecologically valid testing environments using longer duration video sequences. As such, we want to mimic realistic viewing behavior as much as possible. Our results show that both video content and the range of quality switches significantly influence end-users' rating behavior. In general, quality level switches are only perceived in high motion sequences or in case switching occurs between high and low quality video segments. Moreover, we also found that video stallings should be avoided during playback at all times."},"documents":{"kind":"list like","value":["Title: 3D video compression based on high efficiency video coding.\nYear: 2012\n\nAbstract: With the advent of autostereoscopic displays, questions rise on how to efficiently compress the video information needed by such displays. Additionally, for gradual market acceptance of this new technology it is valuable to have a solution offering forward compatibility with stereo 3D video as it is used nowadays. In this paper, a multiview compression scheme making use of the efficient single-view coding tools used in High Efficiency Video Coding (HEVC) is provided. Although efficient single view compression can be obtained with HEVC, a multiview adaptation of this standard under development is proposed, offering additional coding gains. On average, for the texture information, the total bitrate can be reduced by 37.2% compared to simulcast HEVC. For depth map compression, gains largely depend on the quality of the captured content. Additionally, a forward compatible solution is proposed offering the possibility for a gradual upgrade from H.264/AVC based stereoscopic 3D systems to an HEVC-based autostereoscopic environment. With the proposed system, significant rate savings compared to Multiview Video Coding (MVC) are presented(1).","Title: Encryption for high efficiency video coding with video adaptation capabilities.\nYear: 2013\n\nAbstract: Video encryption techniques enable applications like digital rights management and video scrambling. Applying encryption on the entire video stream can be computationally costly and prevents advanced video modifications by an untrusted middlebox in the network, like splicing, quality monitoring, watermarking, and transcoding. Therefore, encryption techniques are proposed which influence a small amount of the video stream while keeping the video compliant with its compression standard, High Efficiency Video Coding. Encryption while guaranteeing standard compliance can cause degraded compression efficiency, so depending on their bitrate impact, a selection of encrypted syntax elements should be made. Each element also impacts the quality for untrusted decoders differently, so this aspect should also be considered. In this paper, multiple techniques for partial video encryption are investigated, most of them having a low impact on rate-distortion performance and having a broad range in scrambling performance.","Title: High definition H.264/AVC subjective video database for evaluating the influence of slice losses on quality perception\nYear: 2013\n\nAbstract: Prior to the construction or validation of objective video quality metrics, ground-truth data must be collected by means of a subjective video database. This database consists of (impaired) video sequences and corresponding subjective quality ratings. However, creating this subjective database is a time-consuming and expensive task. There is an ongoing effort towards publishing such subjective video databases into the public domain. This facilitates the development of new objective quality metrics. In this paper, we present a new subjective video database consisting of impaired High Definition H.264/AVC encoded video sequences and associated quality ratings gathered from a subjective experiment. This database can be used freely to determine impairment visibility or estimate overall quality of a video in the case of lost slices due to network impairments.","Title: Fast Channel Switching Based on SVC in an IPTV Environment\nYear: 2009\n\nAbstract: An IPTV network contains characteristics of both a unicast environment and a broadcast environment. For both environments, techniques to accelerate channel switching exist. In a unicast environment a bandwidth efficient implementation to accelerate channel switching can be obtained with non scalable video coding, while for a broadcast environment the same acceleration is more optimal using scalable video coding. For a combination of both environments, as is the case for an IPTV environment, none of the solutions is optimal. In this paper, we propose a compromise between scalable and non scalable video compression to adapt to the properties of an IPTV environment. One of the proposed configurations obtains bandwidth reductions on the access network of the IPTV network between 2.4% and 4.3% with a small increase between 0.1% and 1.6% on the core network of the IPTV network.","Title: Combining Open- And Closed-Loop Architectures For H.264/Avc-To-Svc Transcoding\nYear: 2011\n\nAbstract: Scalable video coding (SVC) allows encoded bitstreams to be adapted. However, most bitstreams do not incorporate this scalability so bitstreams have to be adapted multiple times to accommodate for varying network conditions or end-user devices. Each adaptation incorporates an additional loss of quality due to transcoding. To overcome this issue, we propose a single transcoding step from H.264/AVC to SVC. Doing so, the resulting bitstream can be freely adapted without any additional quality reduction. Open-loop transcoding architectures can be used for H.264/AVC-to-SVC transcoding with a low complexity, although these architectures suffer from drift artifacts. Closed-loop transcoding, on the other hand, requires a higher complexity. To overcome the drawbacks of both systems, we propose combining both techniques.","Title: Fast transcoding for video delivery by means of a control stream.\nYear: 2012\n\nAbstract: When a video delivery system provides different representations of the same sequence, solutions can be found in simulcast coding, scalable coding, and transcoding. The major downside of transcoding is the additional complexity needed to re-encode the video sequence in its new form. When one fidelity version of the video stream should be transmitted over the network, scalable coding is less efficient compared to single layer coding because of the layering overhead. Finally, simulcast encoding results in large storage requirements. In this paper, we propose an intermediate solution providing transcoding at a low complexity by the aid of control streams. We define a control stream as a regular video stream from which residual information is removed. With these control streams, the complexity of the encoding step in the transcoder can be reduced to decoder complexity. As a result of the removal of residual information, these control streams take up 62.8% less bitrate compared to simulcast coding for a test set based on High Efficiency Video Coding (HEVC). Additionally, when compared to scalable coding, an efficient single layer video stream can be provided without the 16.6% bitrate increase caused by transmitting a layered bitstream.","Title: An enhanced fast mode decision model for spatial enhancement layers in scalable video coding\nYear: 2012\n\nAbstract: Recently, the H.264/AVC standard has been extended to incorporate Scalable Video Coding (SVC). SVC offers the advantage of scalable (layered) coding, but has the disadvantage of a highly increased computational complexity at the encoder side when dealing with spatial scalability. To restrict the increase in required processing power, fast mode decision models for spatial enhancement layers have been proposed in literature. We propose a novel generic fast mode decision model for spatial enhancement layers for both P and B frames based on both the quantization of the enhancement layer and the correlation between the macroblock type in the enhancement layer and the co-located macroblock in the reference layer. In this paper, an evaluation of the proposed model and comparison with a state-of-the-art model is given. Results show that the proposed technique performs exceptionally well for spatial scalability. For both dyadic and non-dyadic spatial scalability, we achieve an average time saving of 75%, while only a slight bit rate increase of 2.23% and a minor PSNR decrease of 0.46 dB are measured. Compared with state of the art techniques, we further halve the complexity while having comparable rate-distortion results.","Title: Fast Channel Switching Based on SVC in IPTV Environments\nYear: 2012\n\nAbstract: In Internet Protocol TeleVision (IPTV) systems, the use of video compression results in a trade-off between random access and bandwidth consumption. Most of the time, priority is given to compression efficiency, resulting in low channel switching performance. In this paper, a Scalable Video Coding (SVC) configuration is proposed, wherein base and enhancement layer are coded in such a way that fast channel switching can be offered without impacting the access network bandwidth consumption. This is achieved by encoding a full quality slow channel switching version of the video stream in the base layer extended with a reduced quality fast switching enhancement layer. Additionally, this configuration offers backward compatibility with H.264/AVC, enabling a gradual upgrade of the IPTV system from H.264/AVC to SVC. When comparing this technique to single layer H.264/AVC, 25.0% bandwidth reduction on the access network is obtained without impacting channel switching speed, but only at the cost of a slightly reduced quality ( -1 dB PSNR) during a short transition period (maximum 48 frames). If a comparison is made with a regular SVC configuration, 39.4% bandwidth reduction on the access network during steady state is obtained. Compared to simulcast H.264/AVC, 32.0% bandwidth reduction on the core network is observed.","Title: Fast transrating for high efficiency video coding based on machine learning\nYear: 2013\n\nAbstract: To incorporate the newly developed High Efficiency Video Coding (HEVC) standard in real-life network applications, efficient transrating algorithms are required. We propose a fast transrating scheme, based on the early prediction of the partition split-flags in P pictures. Using machine learning techniques, the correlation between co-located partitions at different quantizations is investigated. This results in a model which predicts the split-flag and gives the associated prediction accuracy so that the splitting process in the transcoder is optimized. At each partition depth, the model indicates whether the full rate-distortion cost evaluations should be performed at the current depth, or if the partition can be split immediately. Experimental results show that the proposed transcoder reduces the complexity of the transrating process by 76.04%, while maintaining the coding efficiency of a cascaded decoder-encoder.","Title: Format-compliant encryption techniques for high efficiency video coding.\nYear: 2013\n\nAbstract: When middlebox devices should be able to adapt an encrypted video stream in the network without having the decryption key, format-compliant partial encryption schemes should be applied. In this paper, we propose such encryption schemes for the recently standardized High Efficiency Video Coding (HEVC) standard. By encrypting specific syntax elements like the sign of the residual information, the sign of the motion vector (MV) difference, the MV prediction index, and the MV reference index, format compliance and the possibility for adaptation are offered. Scrambling performance gradually increases when shifting from encrypting the motion information to encrypting the residual sign and finally to the combination thereof. Applying all these techniques has a negligible impact on the compression efficiency.","Title: Adaptive transform skipping for improved coding of motion compensated residuals\nYear: 2013\n\nAbstract: New generations of video compression algorithms, such as those included in the under development High Efficiency Video Coding (HEVC) standard, provide substantially higher compression compared to their ancestors. The gain is achieved by improved prediction of pixels, both within a frame and between frames. Novel coding tools that contribute to the gain provide highly uncorrelated prediction residuals for which classical frequency decomposition methods, such as the discrete cosine transform, may not be able to supply a compact representation with few significant coefficients. To further increase the compression gains, this paper proposes transform skip modes which allow skipping one or both 1-D constituent transforms (i.e., vertical and horizontal), which is more suitable for sparse residuals. The proposed transform skip mode is tested in the HEVC codec and is able to provide bitrate reductions of up to 10% at the same objective quality when compared with the application of 2-D block transforms only. Moreover, the proposed transform skip mode outperforms the full transform skip currently investigated for possible adoption in the HEVC standard.","Title: Assessing the importance of audio/video synchronization for simultaneous translation of video sequences.\nYear: 2012\n\nAbstract: Lip synchronization is considered a key parameter during interactive communication. In the case of video conferencing and television broadcasting, the differential delay between audio and video should remain below certain thresholds, as recommended by several standardization bodies. However, further research has also shown that these thresholds can be relaxed, depending on the targeted application and use case. In this article, we investigate the influence of lip sync on the ability to perform real-time language interpretation during video conferencing. Furthermore, we are also interested in determining proper lip sync visibility thresholds applicable to this use case. Therefore, we conducted a subjective experiment using expert interpreters, which were required to perform a simultaneous translation, and non-experts. Our results show that significant differences are obtained when conducting subjective experiments with expert interpreters. As interpreters are primarily focused on performing the simultaneous translation, lip sync detectability thresholds are higher compared with existing recommended thresholds. As such, primary focus and the targeted application and use case are important factors to be considered when selecting proper lip sync acceptability thresholds.","Title: Multi-Loop Quality Scalability Based On High Efficiency Video Coding\nYear: 2012\n\nAbstract: Scalable video coding performance largely depends on the underlying single layer coding efficiency. In this paper, the quality scalability capabilities are evaluated on a base of the new High Efficiency Video Coding (HEVC) standard under development. To enable the evaluation, a multi-loop codec has been designed using HEVC. Adaptive inter-layer prediction is realized by including the lower layer in the reference list of the enhancement layer. As a result, adaptive scalability on frame level and on prediction unit level is accomplished. Compared to single layer coding, 19.4% Bjontegaard Delta bitrate increase is measured over approximately a 30dB to 40dB PSNR range. When compared to simulcast, 20.6% bitrate reduction can be achieved. Under equivalent conditions, the presented technique achieves 43.8% bitrate reduction over Coarse Grain Scalability of the SVC - H.264/AVC-based standard.","Title: No-reference bitstream-based impairment detection for high efficiency video coding.\nYear: 2012\n\nAbstract: Video distribution over error-prone Internet Protocol (IP) networks results in visual impairments on the received video streams. Objective impairment detection algorithms are crucial for maintaining a high Quality of Experience (QoE) as provided with IPTV distribution. There is a lot of research invested in H.264/AVC impairment detection models and questions rise if these turn obsolete with a transition to the successor of H.264/AVC, called High Efficiency Video Coding (HEVC). In this paper, first we show that impairments on HEVC compressed sequences are more visible compaired to H.264/AVC encoded sequences. We also show that an impairment detection model designed for H.264/AVC could be reused on HEVC, but that caution is advised. A more accurate model taking into account content classification needed slight modification to remain applicable for HEVC compression video content.","Title: Improved intra mode signaling for HEVC\nYear: 2011\n\nAbstract: In the current development of HEVC, compression performance improved significantly compared to H.264/AVC for both inter pictures and intra pictures. With intra compression, the main reason for this improvement is the large in crease in intra prediction directions (up to 34). The downside of having a larger number of modes is that they increase the signaling overhead in the bitstream. In this paper, a low complexity intra mode prediction algorithm is proposed which improves the mode prediction accuracy. This is achieved by exploiting the correlation between the prediction directions of the neighboring prediction units and that of the encoded prediction unit. As a result, more efficient intra mode signaling can be achieved with minimal impact on encoder and decoder complexity. On average, 0.33% bitrate improvement is obtained by employing the proposed algorithm. For sequences that are encoded with a high number of directional intra modes, around 1% bitrate improvement is measured.","Title: A complexity-scalable hybrid H.264/AVC-to-SVC transcoder.\nYear: 2013\n\nAbstract: Transcoding of H.264/AVC bitstreams is frequently used for adapting video streams to varying network conditions or different end-user device characteristics. Transcoding requires a relatively high complexity (and thus high operational cost) for each adaptation operation, and possibly reduces the video quality. In comparison, transcoding the input bitstream to SVC requires only one adaptation step. Afterwards, the resulting SVC bitstream can be adapted instantly. To reduce the H.264/AVC-to-SVC transcoding complexity, a transcoding architecture which combines existing open-loop transcoding techniques with optimized closed-loop transcoding is proposed. Furthermore, this approach allows to scale the complexity of the system. Doing so, compared with a cascaded decoder-encoder only 8.48% of the complexity is required for the highest complexity, while a high rate-distortion efficiency is maintained and a high degree of scalability is guaranteed. When only limited resources are available, the transcoding complexity can be scaled such that over 99% of the complexity is reduced compared to a cascaded decoder-encoder.","Title: Fast channel switching for single-loop scalable HEVC.\nYear: 2014\n\nAbstract: In an IPTV environment, different techniques exist to provide faster random access or equivalently, a faster channel switching experience, but none of them provide backward compatibility and limited overhead at the same time. In this paper, a technique is proposed to increase the random access frequency for the High Efficiency Video Coding (HEVC) standard by using a single-loop scalable version of it. This is achieved by encoding an enhancement layer with a higher frequency of random access points compared to the base layer. With this scalable configuration, a backward compatible base stream remains present. The proposed technique requires 12.4% less bitrate compared to sending the fast and slow switching streams in a non-scalable way. Compared to the almost standardized multi-loop scalable extension of HEVC, 5.5% less bitrate is needed on the core of the IPTV network and 12.5% less bitrate on the access network during steady state conditions. Moreover, the multi-loop scalable extension does not provide backward compatibility with legacy HEVC decoders.","Title: Efficient transcoding for spatially misaligned compositions for HEVC\nYear: 2014\n\nAbstract: The visualization of (ultra) high-resolution compositions created from multiple input bitstreams requires several decoders at the receiving device. Therefore, not all devices can properly display such compositions. To address this problem, the input streams are decoded, merged into a single video, and re-encoded by a transcoder in the network. However, this approach requires a computationally complex re-encoding step. To reduce this complexity, information from the input bit-streams can be reused during transcoding. In HEVC, simply reusing the original encoding information is not compression efficient if the inserted content is not aligned with the grid of coded blocks. In this paper, we applied feature selection based on a decision tree, which was used in a fast HEVC transcoding model for misaligned content. The performance varies depending on the shift and average transform size in the original sequence, resulting in complexity reductions of up to 76%.","Title: Adaptive guided image filtering for screen content coding\nYear: 2014\n\nAbstract: The capabilities of off-the-shelf consumer electronics are continuously increasing, making them interesting candidates to integrate into industrial visualization systems for reducing the cost of these systems or speed up development. However, in the context of video compression, hardware acceleration on consumer electronics is typically only provided for more common, consumer oriented YUV 4:2:0 profiles. Meanwhile, industrial applications often require YUV 4:4:4 formats due to synthetic visual data. Upscaling YUV 4:2:0 to YUV 4:4:4 using conventional filters results in visual artifacts, specifically for synthetic content. To improve the quality of the chroma components, this paper proposes to extend the well-known guided image filter through content-adaptive selection of the filter radius, and analyze different strategies for limiting complexity. This results in a 3.5 dB improvement of the chroma PSNR compared to conventional filters.","Title: Fast motion estimation for closed-loop HEVC transrating\nYear: 2014\n\nAbstract: Transrating is a useful tool for adapting the bitrate of a video stream. Reducing the complexity of transrating is extremely important, especially for high efficiency video coding (HEVC) based transrating where the encoder is very time consuming. TZSearch is currently the default integer motion estimation (ME) algorithm in the HEVC reference encoder thanks to its excellent performance in reducing the complexity of ME. However, this algorithm is characterized by a fixed search area and search pattern, which can be considered sub optimal for transrating. In this paper, we improve the performance of HEVC transrating by optimizing this TZSearch algorithm. Utilizing the correlation between input and output motion vectors, we propose a fast search scheme including three steps. First, the initial search point is selected. Then, using the rate-distortion cost of this starting point, the search size is determined according to an online-trained Bayes decision rule. Finally, two proposed search algorithms are described for refining the starting point. Experimental results show that our proposed TZSearch scheme can reduce the complexity of transrating while improving the coding performance in terms of bit rate saving.","Title: Guided depth filtering to improve the quality of experience for autostereoscopic displays.\nYear: 2014\n\nAbstract: One of the drawbacks of current 3DTV systems are the glasses required to separate the 3D views. This problem can be solved by using autostereoscopic 3D displays, which generates a 3D scene from different viewpoints. These viewpoints are synthesized in the display based on depth information of the scene. However, currently no depth information is available for stereoscopic video. Therefore, depth estimation based on the stereoscopic input is required. This depth estimation should be low-complex and real-time for consumer electronic devices. Due to this low-complexity requirement, the quality of such depth maps will be limited.In this paper, we propose a guided depth filter, which filters the estimated depth map by using the downsampled chroma signal of the corresponding texture view as a guide. This allows to refine the borders of objects which reduces visible inpainting artifacts caused by the view synthesis. The proposed algorithm has been evaluated by expert viewing to show that it is independent of the depth map estimation and view synthesis of the autostereoscopic display.","Title: Performance analysis of machine learning for arbitrary downsizing of pre-encoded HEVC video.\nYear: 2015\n\nAbstract: Nowadays, broadcasters deliver ultra-high resolution video to their consumers. This live video is sent to a set-top box for display on a television. However, if one or more users in the home want to view the same video on their personal mobile devices with a lower display resolution and limited processing power, decoding the original ultra-high resolution video would result in stuttering and quick...","Title: Objective Video Quality Assessment - Towards Large Scale Video Database Enhanced Model Development\nYear: 2015\n\nAbstract: The current development of video quality assessment algorithms suffers from the lack of available video sequences for training, verification and validation to determine and enhance the algorithm's application scope. The Joint Effort Group of the Video Quality Experts Group (VQEG-JEG) is currently driving efforts towards the creation of large scale, reproducible, and easy to use databases. These databases will contain bitstreams of recent video encoders (H.264, H.265), packet loss impairment patterns and impaired bitstreams, pre-parsed bitstream information into files in XML syntax, and well-known objective video quality measurement outputs. The database is continuously updated and enlarged using reproducible processing chains. Currently, more than 70,000 sequences are available for statistical analysis of video quality measurement algorithms. New research questions are posed as the database is designed to verify and validate models on a very large scale, testing and validating various scopes of applications, while subjective assessment has to be limited to a comparably small subset of the database. Special focus is given on the principles guiding the database development, and some results are given to illustrate the practical usefulness of such a database with respect to the detailed new research questions.","Title: Real-Time Reservoir Computing Network-Based Systems for Detection Tasks on Visual Contents\nYear: 2015\n\nAbstract: Among the various types of artificial neural networks used for event detection in visual contents, those with the ability of processing temporal information, such as recurrent neural networks, have been proved to be more effective. However, training of such networks is often difficult and time consuming. In this work, we show how Reservoir Computing Networks (RCNs) can be used for detecting purposes on raw images. The applicability of RCNs is illustrated using two example challenges, namely isolated digit handwriting recognition on the MNIST dataset as well as detection of the status of a door using self-developed moving pictures from a surveillance camera. Achieving an error rate of 0.92 percent on MNIST, we show that RCN can be a serious competitor to the state-of-the-art. Moreover, we show how RCNs with their simple and yet robust training procedure can be practically used for real surveillance tasks using very low resolution camera sensors.","Title: A Motion Vector Re-Use Algorithm for H.264/AVC and HEVC Simultaneous Video Encoding.\nYear: 2015\n\nAbstract: The rapid emergence and rise of a wide range of electronic devices has led to the need for providing very different levels of video transmission services. The capabilities and performance of these devices determine the type of video streams that they are able to decode. As a way to fulfil their requirements, this paper presents a heterogeneous simultaneous encoding framework of H.264/Advanced Video Coding (AVC) and High Efficiency Video Coding (HEVC) that shares information between encoders in order to reduce the overall encoding time. In this regard, the proposed approach utilizes the H.264/AVC motion vectors of the 16x16 pixels macroblocks as predictors for the HEVC prediction units. As a consequence, the size of the motion estimation search area can be significantly reduced. Results show that an encoding time reduction of 8.95% can be achieved with negligible losses in terms of rate-distortion.","Title: Fast encoding for personalized views extracted from beyond high definition content\nYear: 2015\n\nAbstract: Broadcast providers are looking for new opportunities to increase user experience and user interaction on their content. Their main goal is to attract and preserve viewer attention to create a big and stable audience. This could be achieved with a second screen application that lets the users select their own viewpoint in an extremely high resolution video to direct their own first screen. By allowing the users to create their own personalized video stream, they become involved with the content creation itself. However, encoding a personalized view for each user is computationally complex. This paper describes a machine learning approach to speed up the encoding of each personal view. Simulation results of zoom, pan and tilt scenarios show bit rate increases between 2% and 9% for complexity reductions between 69% and 79% compared to full encoding.","Title: Predicting full-reference video quality measures using HEVC bitstream-based no-reference features\nYear: 2015\n\nAbstract: This paper presents bitstream-based features for perceptual quality estimation of HEVC coded videos. Various factors including the impact of different sizes of block-partitions, use of reference-frames, the relative amount of various prediction modes, statistics of motion vectors and quantization parameters are taken into consideration for producing 52 features relevant for perceptual quality prediction. The used test stimuli constitutes 560 bitstreams that have been carefully extracted for this analysis from the 59, 520 bistreams of the large-scale database generated by the Joint Effort Group (JEG) of the Video Quality Experts Group (VQEG). The obtained results show the significance of the considered features through reasonably accurate and monotonic prediction of a number of objective quality metrics.","Title: Fast simultaneous video encoder for adaptive streaming\nYear: 2015\n\nAbstract: Content providers create different versions of a video to accommodate different end-user devices and network conditions. However, each of these versions requires a resource intensive encoding process. To reduce the computational complexity of the encodings, this paper proposes a fast simultaneous encoder. This encoder takes a single video as input and creates a number of bit streams encoded with different parameters. Only one version of the video is created with a full encode, whereas encoding of the other versions is accelerated by exploiting the correlation with the fully encoded version using machine learning techniques. In a practical scenario, the fast simultaneous encoder achieves a complexity reduction of 67.3% with a bit rate increase of 5.2% compared to performing a full encode of each version.","Title: Image Quality of Experience: A Subjective Test Targeting the Consumer's Experience.\nYear: 2016\n\nAbstract: This paper presents a subjective image quality experiment design that specifically targets understanding consumer perception of visual quality. With this consumer-oriented goal in mind, images were captured from a large number of representative consumer devices. This design choice is fundamentally different from previous work in the field, in that it is not based on simulated software impairments, but rather on naturally occurring artifacts due to the device and conditions. New subjective questions were posed, which allowed exploration of differences in the quality of experience (QoE) provided by various devices. This paper presents the experimental design implemented to capture ratings and feedback on these consumer devices. The study is analyzed to highlight and demonstrate the unique capabilities provided by this method.","Title: Spatially misaligned HEVC transcoding with computational-complexity scalability.\nYear: 2016\n\nAbstract: Two methods to transcode a spatially misaligned video sequence are proposed.The machine learning method outperforms the trivial method for all shifts.Both methods perform best when the shifts align with a grid.Predicting both CU and PU information is more efficient than CU only. In control rooms, video walls display footage from multiple sources. Often, a composition of these sources is sent to other devices in a single video stream. To minimize the computational complexity of this composition process, information from the original bitstreams can be reused. However, in High Efficiency Video Coding (HEVC), simply copying the original encoding decisions is not compression efficient if the individual videos are spatially misaligned with the grid of coded blocks of the composition. Our proposed HEVC-based transcoder reduces the computational complexity by predicting encoding decisions of misaligned sequences by using a trivial method or a more adaptive, computational-complexity scalable machine learning method. Higher compression efficiency is observed when more alignment is preserved with the original block grid. Overall, the machine learning method achieves a higher compression efficiency than the trivial method. Both methods attain a complexity reduction of up to 82% compared to the reference software.","Title: Simultaneous encoder for high-dynamic-range and low-dynamic-range video.\nYear: 2016\n\nAbstract: High-dynamic-range (HDR) technology is an emerging video technology that allows displays to produce a higher range of luminance to better approximate the range of brightness perceived by the human eye. However, during the transition to this new technology, not all consumer devices will support the full range of luminance values offered by HDR. In order to also support these devices with lower dyna...","Title: CTU splitting algorithm for H.264/AVC and HEVC simultaneous encoding.\nYear: 2017\n\nAbstract: High-Efficiency Video Coding (HEVC) was conceived by the Joint Collaborative Team on Video Coding as the natural successor of the H.264/AVC standard, which has been the most extended digital video standard in all segments of the domestic and professional markets for over 10 years. HEVC roughly doubles the compression performance of H.264/AVC in Rate-Distortion terms at the expense of a high computational cost. Thus, most of the previous-generation devices can decode H.264/AVC streams but they cannot decode HEVC yet, while emerging devices are supposed to be able to decode both standards. Video providers should take advantage of bandwidth reduction using HEVC when possible, but they should also provide compatible streams to older devices, making it necessary to encode the same stream using both H.264/AVC and HEVC standards. This paper presents a coding tree unit splitting algorithm for a heterogeneous simultaneous encoding scenario which makes use of information from H.264/AVC encoder to make faster decisions in HEVC. Experimental results show that the proposed algorithm can achieve a good trade-off between coding efficiency and complexity.","Title: Efficient Encoding of Interactive Personalized Views Extracted from Immersive Video Content\nYear: 2016\n\nAbstract: Traditional television limits people to a single viewpoint. However, with new technologies such as virtual reality glasses, the way in which people experience video will change. Instead of being limited to a single viewpoint, people will demand a more immersive experience that gives them a sense of being present in a sports stadium, a concert hall, or at other events. To satisfy these users, video such as 360-degree or panoramic video needs to be transported to their homes. Since these videos have an extremely high resolution, sending the entire video requires a high bandwidth capacity and also results in a high decoding complexity at the viewer. The traditional approach to this problem is to split the original video into tiles and only send the required tiles to the viewer. However, this approach still has a large bit rate overhead compared to sending only the required view. Therefore, we propose to send only a personalized view to each user. Since this paper focuses on reducing the computational cost of such a system, we accelerate the encoding of each personalized view based on coding information obtained from a pre-analysis on the entire ultra-high-resolution video. By doing this using the High Efficiency Video Coding Test Model (HM), the complexity of each individual encode of a personalized view is reduced by more than 96.5% compared to a full encode of the view. This acceleration results in a bit rate overhead of at most 19.5%, which is smaller compared to the bit rate overhead of the tile-based method.","Title: AFRESh: an adaptive framework for compression of reads and assembled sequences with random access functionality.\nYear: 2017\n\nAbstract: Motivation: The past decade has seen the introduction of new technologies that lowered the cost of genomic sequencing increasingly. We can even observe that the cost of sequencing is dropping significantly faster than the cost of storage and transmission. The latter motivates a need for continuous improvements in the area of genomic data compression, not only at the level of effectiveness (compression rate), but also at the level of functionality (e.g. random access), configurability (effectiveness versus complexity, coding tool set...) and versatility (support for both sequenced reads and assembled sequences). In that regard, we can point out that current approaches mostly do not support random access, requiring full files to be transmitted, and that current approaches are restricted to either read or sequence compression. Results: We propose AFRESh, an adaptive framework for no-reference compression of genomic data with random access functionality, targeting the effective representation of the raw genomic symbol streams of both reads and assembled sequences. AFRESh makes use of a configurable set of prediction and encoding tools, extended by a Context-Adaptive Binary Arithmetic Coding scheme (CABAC), to compress raw genetic codes. To the best of our knowledge, our paper is the first to describe an effective implementation CABAC outside of its' original application. By applying CABAC, the compression effectiveness improves by up to 19% for assembled sequences and up to 62% for reads. By applying AFRESh to the genomic symbols of the MPEG genomic compression test set for reads, a compression gain is achieved of up to 51% compared to SCALCE, 42% compared to LFQC and 44% compared to ORCOM. When comparing to generic compression approaches, a compression gain is achieved of up to 41% compared to GNU Gzip and 22% compared to 7-Zip at the Ultra setting. Additionaly, when compressing assembled sequences of the Human Genome, a compression gain is achieved up to 34% compared to GNU Gzip and 16% compared to 7-Zip at the Ultra setting.","Title: Intra-Frame Sharing For Low-Complexity Decoding Of Shvc Video\nYear: 2016\n\nAbstract: Scalable video coding provides an efficient adaptation solution for delivering video to consumer devices with a great variety of characteristics. This technique encodes video using a base layer and several enhancement layers with different qualities. In order to reduce the total bit rate of the video stream, inter layer prediction (ILP) is enabled. However, the introduction of ILP results in a high decoding complexity since multiple layers need to be decoded at the same time. In this paper, we propose a method to reduce the decoding complexity of the scalable video stream by sharing the intra frame between layers and eliminating all other inter-layer dependencies. The experimental results show that the proposed method results in a video stream with a lower bit rate than a simulcast approach. Additionally, this approach reduces the decoder complexity compared to scalable video compression.","Title: The crowd as a cameraman: on-stage display of crowdsourced mobile video at large-scale events.\nYear: 2018\n\nAbstract: Recording videos with smartphones at large-scale events such as concerts and festivals is very common nowadays. These videos register the atmosphere of the event as it is experienced by the crowd and offer a perspective that is hard to capture by the professional cameras installed throughout the venue. In this article, we present a framework to collect videos from smartphones in the public and blend these into a mosaic that can be readily mixed with professional camera footage and shown on displays during the event. The video upload is prioritized by matching requests of the event director with video metadata, while taking into account the available wireless network capacity. The proposed framework’s main novelty is its scalability, supporting the real-time transmission, processing and display of videos recorded by hundreds of simultaneous users in ultra-dense Wi-Fi environments, as well as its proven integration in commercial production environments. The framework has been extensively validated in a controlled lab setting with up to 1 000 clients as well as in a field trial where 1 183 videos were collected from 135 participants recruited from an audience of 8 050 people. 90 % of those videos were uploaded within 6.8 minutes.","Title: Reducing the Complexity of a Multiview H.264/AVC and HEVC Hybrid Architecture.\nYear: 2018\n\nAbstract: With the advent of 3D displays, an efficient encoder is required to compress the video information needed by them. Moreover, for gradual market acceptance of this new technology, it is advisable to offer backward compatibility with existing devices. Thus, a multiview H.264/Advance Video Coding (AVC) and High Efficiency Video Coding (HEVC) hybrid architecture was proposed in the standardization process of HEVC. However, it requires long encoding times due to the use of HEVC. With the aim of tackling this problem, this paper presents an algorithm that reduces the complexity of this hybrid architecture by reducing the encoding complexity of the HEVC views. By using Naïve-Bayes classifiers, the proposed technique exploits the information gathered in the encoding of the H.264/AVC view to make decisions on the splitting of coding units in HEVC side views. Given the novelty of the proposal, the only similar work found in the literature is an unoptimized version of the algorithm presented here. Experimental results show that the proposed algorithm can achieve a good tradeoff between coding efficiency and complexity.","Title: A novel video watermarking approach based on implicit distortions.\nYear: 2018\n\nAbstract: Copyright-sensitive videos are commonly leaked or illegally distributed by so-called digital pirates. Video owners aim to prevent this by hiding a unique watermark in every video that contains information about the receiver. If the video is then illegally distributed, the copyright owner can extract the watermark and identify the malicious consumer. However, pirates may manipulate the video in the...","Title: Framework for reproducible objective video quality research with case study on PSNR implementations.\nYear: 2018\n\nAbstract: Reproducibility is an important and recurrent issue in objective video quality research because the presented algorithms are complex, depend on specific implementations in software packages or their parameters need to be trained on a particular, sometimes unpublished, dataset. Textual descriptions often lack the required detail and even for the simple Peak Signal to Noise Ratio (PSNR) several mutations exist for images and videos, in particular considering the choice of the peak value and the temporal pooling. This work presents results achieved through the analysis of objective video quality measures evaluated on a reproducible large scale database containing about 60,000 HEVC coded video sequences. We focus on PSNR, one of the most widespread measures, considering its two most common definitions. The sometimes largely different results achieved by applying the two definitions highlight the importance of the strict reproducibility of the research in video quality evaluation in particular. Reproducibility is also often a question of computational power and PSNR is a computationally inexpensive algorithm running faster than realtime. Complex algorithms cannot be reasonably developed and evaluated on the abovementioned 160 hours of video sequences. Therefore, techniques to select subsets of coding parameters are then introduced. Results show that an accurate selection can preserve the variety of the results seen on the large database but with much lower complexity. Finally, note that our SoftwareX accompanying paper presents the software framework which allows the full reproducibility of all the research results presented here, as well as how the same framework can be used to produce derived work for other measures or indexes proposed by other researchers which we strongly encourage for integration in our open framework.","Title: A Just Noticeable Difference Subjective Test for High Dynamic Range Images\nYear: 2018\n\nAbstract: High Dynamic Range (HDR) imaging captures a wide range of luminance existing in real-world scenes. Due to large luminance levels and higher brightness of HDR displays, artefacts can be more noticeable to the Human Visual System (HVS). In a first attempt to experimentally quantify those noticeable levels for HDR images, we pioneered in conducting an exhaustive and comprehensive Just Noticeable Difference (JND) subjective experiment of which the outcome is presented in this paper. Six distortions including JPEG, JPEG2000, noise, blur, contrast change, and quantization artefacts have been considered in the test. The distortions were applied to 10 HDR images in 100 distortion levels resulting a database of 6000 HDR test images. The subjects were asked to find the image JND location on each set of 100 images they had the freedom to explore. The effect of content features on the noticeable threshold selection is investigated per distortion type. Our results in some cases show a significant correlation between content features and JNDs. We are hoping that our results can contribute to further exploitation of a precise HVS model for HDR quality assessment and optimization of the coding and bit allocation in HDR compression.","Title: Steered Mixture-of-Experts Approximation of Spherical Image Data.\nYear: 2018\n\nAbstract: Steered Mixture-of-Experts (SMoE) is a novel framework for approximating multidimensional image modalities. Our goal is to provide full Six Degrees-of-Freedom capabilities for camera captured content. Previous research concerned only limited translational movement for which the 4D light field representation is sufficient. However, our goal is to arrive at a representation that allows for unlimited translational-rotational freedom, i.e. our goal is to approximate the full 5D plenoptic function. Until now, SMoE was only applied on Euclidean spaces. However, the plenoptic function contains two spherical coordinate dimensions. In this paper, we propose a methodology to extend the SMoE framework to spherical dimensions. Furthermore, we propose a method to reduce the parameter space to the same two dimensional Euclidean space as for planar 2D images by using a projection of the covariance matrices onto tangent spaces perpendicular to the unit sphere. Finally, we propose a novel training technique for spherical dimensions based on these observations. Experiments performed on omnidirectional 360 degrees images show that the introduction of the dimensionality-reduction projection step results in very low quality loss.","Title: Real-Time Low-Complexity Digital Video Stabilization in the Compressed Domain\nYear: 2018\n\nAbstract: Undesired vibration in videos is more and more common due to the rise of hand-held or vehicle mounted cameras. Non-indented motion of a video capturing device causes an unpleasant vibrating effect for the consumer. Therefore, video stabilization has been an extremely active field of research in the past years. Motion estimation is the most computationally expensive step of the video stabilization process. Our goal is to circumvent this expensive step in order to achieve real-time performance. We do so by using the precomputed motion vectors from the encoded video streams and thus operate in the compressed domain. These vectors already contain an approximation of the needed motion information. A low-level motion model is used for mitigating complexity, and a low-pass filter performs motion smoothing before the final motion compensation step which is used for correcting the video. In many real-time applications where the video vibration is moderate, the proposed framework can reach online video stabilization at 30 frames per second for high definition video and 60 frames per second for lower resolutions, while retaining satisfactory performance in video correction, comparable to pixel-based equivalent algorithms.","Title: Highly parallel steered mixture-of-experts rendering at pixel-level for image and light field data\nYear: 2020\n\nAbstract: A novel image approximation framework called steered mixture-of-experts (SMoE) was recently presented. SMoE has multiple applications in coding, scale-conversion, and general processing of image modalities. In particular, it has strong potential for coding and streaming higher dimensional image modalities that are necessary to leverage full translational and rotational freedom (6 degrees-of-freedom) in virtual reality for camera captured images. In this paper, we analyze the rendering performance of SMoE for 2D images and 4D light fields. Two different GPU implementations that parallelize the SMoE regression step at pixel-level are presented, including experimental evaluations based on rendering performance and quality. In this paper it is shown that on appropriate hardware, an OpenCL implementation can achieve 85 fps and 22 fps for, respectively, 1080p and 4K renderings of large models with more than 100,000 of Gaussian kernels.","Title: Traitor Tracing After Visible Watermark Removal\nYear: 2018\n\nAbstract: Watermarks are often used to protect copyright-protected videos from illegal re-distribution. More specifically, a unique watermark that represents the receiver's identifier is embedded into the video. In this way, malicious users can be identified when they leak their received version of the video. However, when the watermark is embedded as visible text in the video, it is easy for digital pirates to delete it such that they can no longer be identified. Therefore, copyright owners will benefit from a technique that allows the detection of a visible watermark when it is removed. This paper demonstrates how a visible watermark indirectly generates imperceptible variations over the entire video. As such, these variations in the non-watermarked area can be used as an alternative watermark representation, and thus enable watermark detection even after watermark removal. The experimental results prove that the watermark can be detected as long as the quality of the watermarked video is not significantly reduced, especially if the originally-distributed watermarked video has a high quality. Moreover, the watermark should be embedded into a video with sufficient motion. In conclusion, the proposed technique enables copyright-owners to identify pirates when they illegally distribute visibly-watermarked videos, even when the watermarked area is removed.","Title: A Scalable Architecture for Uncompressed-Domain Watermarked Videos\nYear: 2019\n\nAbstract: Video watermarking is a well-established technology to help identify digital pirates when they illegally re-distribute multimedia content. In order to provide every client with a unique, watermarked video, the traditional distribution architectures separately encode each watermarked video. However, since these encodings require a high amount of computational resources, such architectures do not scale well to a large number of users. Therefore, this paper proposes a novel architecture that uses fast encoders instead of traditional, full encoders. The fast encoders re-use the coding information from a single, previously-encoded, unwatermarked video in order to speed up the encodings of the watermarked videos. As a result, the complexity of a fast encoder is only a fraction of the complexity of a full encoder. Due to a high correlation of the re-used coding information with the optimal coding information, the compression efficiency and watermark robustness decrease only slightly. Most importantly, the proposed fast encoder speeds up the compression process with a factor of 115, resulting in a low complexity similar to that of a video decoder. Consequently, video distributors can use the proposed architecture to deliver high-quality watermarked videos on a large-scale without requiring an excessive amount of computational resources.","Title: Evaluation of full-reference objective video quality metrics on high efficiency video coding.\nYear: 2013\n\nAbstract: For the purpose of automatic video quality evaluation, Peak Signal-to-Noise Ratio (PSNR) has been the most well-known full reference quality metric since a long time. Improving on PSNR, several other pixel-based quality metrics have been developed, namely Structural SIMilarity (SSIM), Multi Scale-SSIM (MS-SSIM), and Video Quality Metric (VQM). The goal of these objective video quality metrics is to replace time-consuming and expensive subjective quality assessment experiments. These alternative objective metrics have already been evaluated on several video compression schemes, such as MPEG-2 and H. 264/AVC, transported over different kinds of network protocols and under a large variation of network characteristics. In January 2013, the successor of the Advanced Video Coding (H. 264/AVC) standard, named High Efficiency Video Coding (HEVC), has been finalized. Although HEVC is still a block based hybrid video compression standard, some radical changes are made to subjectively improve the compression efficiency compared to H. 264/AVC. Until now, the alternative quality metrics have never been evaluated on this new compression scheme. Therefore, in this paper, we analyze the difference in performance of these full reference metrics. Based on subjective evaluations, a performance analysis is presented which shows the validity of these models when applied to HEVC compressed video content. I. INTRODUCTION","Title: Efficient Bit Rate Transcoding for High Efficiency Video Coding.\nYear: 2016\n\nAbstract: High efficiency video coding (HEVC) shows a significant advance in compression efficiency and is considered to be the successor of H.264/AVC. To incorporate the HEVC standard into real-life network applications and a diversity of other applications, efficient bit rate adaptation (transrating) algorithms are required. A current problem of transrating for HEVC is the high computational complexity associated with the encoder part of such a cascaded pixel domain transcoder. This paper focuses on deriving an optimal strategy for reducing the transcoding complexity with a complexity-scalable scheme. We propose different transcoding techniques which are able to reduce the transcoding complexity in both CU and PU optimization levels. At the CU level, CUs can be evaluated in top-to-bottom or bottom-to-top flows, in which the coding information of the input video stream is utilized to reduce the number of evaluations or to early terminate certain evaluations. At the PU level, the PU candidates are adaptively selected based on the probability of PU sizes and the co-located input PU partitioning. Moreover, with the use of different proposed methods, a complexity-scalable transrating scheme can be achieved. Furthermore, the transcoding complexity can be effectively controlled by the machine learning based approach. Simulations show that the proposed techniques provide a superior transcoding performance compared to the state-of-the-art related works. Additionally, the proposed methods can achieve a range of trade-offs between transrating complexity and coding performance. From the proposed schemes, the fastest approach is able to reduce the complexity by 82% while keeping the bitrate loss below 3%.","Title: Low Delay Complexity Constrained Encoding\nYear: 2016\n\nAbstract: Complex software appliances typically consist of multiple software processes running concurrently to exploit the available computational resources in the hardware. However, the computational complexity of these software processes is often variable and the processes can interfere with each other. This can be an issue for real-time applications with a fixed deadline like low delay video encoding. In the context of High Efficiency Video Coding (HEVC), a limited number of publications have focused on controlling the complexity of an HEVC video encoder. In this paper, we propose a technique to control complexity by deciding between 2Nx2N merge mode and full encoding, at different Coding Unit (CU) depths. Our results demonstrate fast convergence to a given complexity threshold after a maximum of 10 frames, and a limited loss in rate-distortion performance (on average 2.84% Bjontegaard delta rate for 60% complexity reduction).","Title: Video Encoder Architecture for Low-Delay Live-Streaming Events.\nYear: 2017\n\nAbstract: Video-streaming events such as virtual classrooms and video conferences require a low delay between sender and receiver. In order to achieve this requirement, and to make full use of the bandwidth capacity of each receiver, each client can be provided with a personalized bitstream of which the bit rate is continuously adapted to his current network bandwidth capacity. However, such an approach req...","Title: AQUa: an adaptive framework for compression of sequencing quality scores with random access functionality.\nYear: 2018\n\nAbstract: Motivation: The past decade has seen the introduction of new technologies that significantly lowered the cost of genome sequencing. As a result, the amount of genomic data that must be stored and transmitted is increasing exponentially. To mitigate storage and transmission issues, we introduce a framework for lossless compression of quality scores. Results: This article proposes AQUa, an adaptive framework for lossless compression of quality scores. To compress these quality scores, AQUa makes use of a configurable set of coding tools, extended with a Context-Adaptive Binary Arithmetic Coding scheme. When benchmarking AQUa against generic single-pass compressors, file sizes are reduced by up to 38.49% when comparing with GNU Gzip and by up to 6.48% when comparing with 7-Zip at the Ultra Setting, while still providing support for random access. When comparing AQUa with the purpose-built, single-pass, and state-of-the-art compressor SCALCE, which does not support random access, file sizes are reduced by up to 21.14%. When comparing AQUa with the purpose-built, dual-pass, and state-of-the-art compressor QVZ, which does not support random access, file sizes are larger by 6.42-33.47%. However, for one test file, the file size is 0.38% smaller, illustrating the strength of our single-pass compression framework. This work has been spurred by the current activity on genomic information representation (MPEG-G) within the ISO/IEC SC29/WG11 technical committee.","Title: A highly scalable parallel encoder version of the emergent JEM video encoder.\nYear: 2019\n\nAbstract: In 2016, 73% of total Internet traffic came from video transmission and this percentage is expected to reach 82% by 2021. These figures show the importance of using video compression standards that maximize video quality while minimizing the necessary bandwidth. In 2013, the HEVC standard was released accounting for an approximate 50% bit rate saving compared to H.264/AVC while maintaining the same reconstruction quality. To address increases in video IP traffic, a new generation of video coding techniques is required that achieve higher compression rates. Compression improvements are being implemented in a software package known as the Joint Exploration Test Model. In this work, we present two parallel JEM model solutions specifically designed for distributed memory platforms for both All Intra and Random Access coding modes. The proposed parallel algorithms achieved high levels of efficiency, in particular for the All Intra mode. They also showed great scalability.","Title: Improving relevant subjective testing for validation: Comparing machine learning algorithms for finding similarities in VQA datasets using objective measures.\nYear: 2019\n\nAbstract: Subjective quality assessment is a necessary activity to validate objective measures or to assess the performance of innovative video processing technologies. However, designing and performing comprehensive tests requires expertise and a large effort especially for the execution part. In this work we propose a methodology that, given a set of processed video sequences prepared by video quality experts, attempts to reduce the number of subjective tests by selecting a subset with minimum size which is expected to yield the same conclusions of the larger set. To this aim, we combine information coming from different types of objective quality metrics with clustering and machine learning algorithms that perform the actual selection, therefore reducing the required subjective assessment effort while trying to preserve the variety of content and conditions needed to ensure the validity of the conclusions. Experiments are conducted on one of the largest publicly available subjectively annotated video sequence dataset. As performance criterion, we chose the validation criteria for video quality measurement algorithms established by the International Telecommunication Union.","Title: Improved Performance Measures for Video Quality Assessment Algorithms Using Training and Validation Sets\nYear: 2019\n\nAbstract: The training and performance analysis of objective video quality assessment algorithms is complex due to the huge variety of possible content classes and transmission distortions. Several secondary issues such as free parameters in machine learning algorithms and alignment of subjective datasets put an additional burden on the developer. In this paper, three subsequent steps are presented to address such issues. First, the content and coding parameter space of a large-scale database is used to select dedicated subsets for training objective algorithms. This aims at providing a method for selecting the most significant contents and coding parameters from all imaginable combinations. In the practical case where only a limited set is available, it also helps us to avoid redundancy in the training subset selection. The second step is a discussion on performance measures for algorithms that employ machine-learning methods. The particularity of the performance measures is that the quality of the training and verification datasets is taken into consideration. Common issues that often use existing measures are presented, and improved or complementary methods are proposed. The measures are applied to two examples of no-reference objective assessment algorithms using the aforementioned subsets of the large-scale database. While limited in terms of practical applications, this sandbox approach of objectively predicting an objectively evaluated video sequences allows for eliminating additional influence factors from subjective studies. In the third step, the proposed performance measures are applied to the practical case of training and analyzing assessment algorithms on readily available subjectively annotated image datasets. The presentation method in this part of the paper can also be used as an exemplified recommendation for reporting in-depth information on the performance. Using this presentation method, future publications presenting newly developed quality assessment algorithms may be significantly improved.","Title: Computing Quality-of-Experience Ranges for Video Quality Estimation\nYear: 2019\n\nAbstract: Typically, the measurement of the Quality of Experience for video sequences aims at a single value, in most cases the Mean Opinion Score (MOS). Predicting this value using various algorithms has been widely studied. However, deviation from the MOS is often handled as an unpredictable error. The approach in this contribution estimates intervals of video quality instead of the single valued MOS. Well-known video quality estimators are fused together to output a lower and upper border for the expected video quality, on the basis of a model derived from a well-known subjectively annotated dataset. Results on different datasets provide insight on the suitability of the well-known estimators for this particular approach.","Title: SILVR: a synthetic immersive large-volume plenoptic dataset.\nYear: 2022\n\nAbstract: In six-degrees-of-freedom light-field (LF) experiences, the viewer's freedom is limited by the extent to which the plenoptic function was sampled. Existing LF datasets represent only small portions of the plenoptic function, such that they either cover a small volume, or they have limited field of view. Therefore, we propose a new LF image dataset \"SILVR\" that allows for six-degrees-of-freedom navigation in much larger volumes while maintaining full panoramic field of view. We rendered three different virtual scenes in various configurations, where the number of views ranges from 642 to 2226. One of these scenes (called Zen Garden) is a novel scene, and is made publicly available. We chose to position the virtual cameras closely together in large cuboid and spherical organisations ($2.2m^3$ to $48m^3$), equipped with 180{\\deg} fish-eye lenses. Every view is rendered to a color image and depth map of 2048px $\\times$ 2048px. Additionally, we present the software used to automate the multi-view rendering process, as well as a lens-reprojection tool that converts between images with panoramic or fish-eye projection to a standard rectilinear (i.e., perspective) projection. Finally, we demonstrate how the proposed dataset and software can be used to evaluate LF coding/rendering techniques(in this case for training NeRFs with instant-ngp). As such, we provide the first publicly-available LF dataset for large volumes of light with full panoramic field of view","Title: On the Link Between Subjective Score Prediction and Disagreement of Video Quality Metrics\nYear: 2021\n\nAbstract: Several video quality metrics (VQMs) have been proposed in many publications to predict how humans perceive video quality. It is common to observe significant disagreements amongst the quality predictions of these VQMs for the same video sequence. Following an extensive literature search, we found no publicised work that has investigated if such disagreements convey useful information on the accuracy of VQMs. Herein, a measure for quantifying the disagreement between VQMs is proposed. A small-scale subjective study is carried out to assess the effectiveness of our proposal. In particular, the proposed disagreement measure is shown to be extremely effective in determining whether the quality of any given processed video sequence (PVS) can be accurately predicted by the VQMs. This type of information is particularly useful for identifying video sequences that are likely to degrade the end-user's quality of experience (QoE). Our proposal is also useful in selecting the most effective PVSs to be employed in a subjective test. We show that the proposed disagreement measure can be effectively predicted from bitstream features. This establishes a link between the capability to accurately assess the quality of a PVS and the way it is encoded. In addition, an analysis is conducted to compare the performances of some well-known and widely used open-source metrics and two proprietary metrics. The two proprietary metrics are used by a large media company for enhancing its delivery pipeline. The outcome of this comparison highlights the suitability of the open-source VQM, Video Multi-method Assessment Fusion (VMAF), as a good benchmark quality measure for both the industrial and academic environments.","Title: Subjective Evaluation of Visual Quality and Simulator Sickness of Short 360 degrees Videos: ITU-T Rec. P.919\nYear: 2022\n\nAbstract: Recently an impressive development in immersive technologies, such as Augmented Reality (AR), Virtual Reality (VR) and 360 degrees video, has been witnessed. However, methods for quality assessment have not been keeping up. This paper studies quality assessment of 360 degrees video from the cross-lab tests (involving ten laboratories and more than 300 participants) carried out by the Immersive Media Group (IMG) of the Video Quality Experts Group (VQEG). These tests were addressed to assess and validate subjective evaluation methodologies for 360 degrees video. Audiovisual quality, simulator sickness symptoms, and exploration behavior were evaluated with short (from 10 seconds to 30 seconds) 360 degrees sequences. The following factors' influences were also analyzed: assessment methodology, sequence duration, Head-Mounted Display (HMD) device, uniform and non-uniform coding degradations, and simulator sickness assessment methods. The obtained results have demonstrated the validity of Absolute Category Rating (ACR) and Degradation Category Rating (DCR) for subjective tests with 360 degrees videos, the possibility of using 10-second videos (with or without audio) when addressing quality evaluation of coding artifacts, as well as any commercial HMD (satisfying minimum requirements). Also, more efficient methods than the long Simulator Sickness Questionnaire (SSQ) have been proposed to evaluate related symptoms with 360 degrees videos. These results have been instrumental for the development of the ITU-T Recommendation P.919. Finally, the annotated dataset from the tests is made publicly available for the research community.","Title: Keyframe Insertion for Random Access and Packet-Loss Repair in H.264/AVC, H.265/HEVC, and H.266/VVC\nYear: 2022\n\nAbstract: Sending low-delay live video over error-prone channels comes with packet-loss-repair and random-access challenges. Existing solutions have a negative impact on end-users with reliable connections or users that do not switch channels. To minimize this impact, the keyframe-insertion technique extends a compression-efficient normal stream (NS) with a companion stream (CS) solely consisting of keyframes [1].","Title: Linking the cognitive load induced by route instruction types and building configuration during indoor route guidance, a usability study in VR\nYear: 2022\n\nAbstract: Every route instruction type (e.g. map, symbol, photo) induces a specific cognitive load. However, when these types are used at different decision points in a building, the building configuration of these points also influences the induced cognitive load. Therefore, the process of route guidance results in an interaction between the instruction type and the decision point, which determines the induced cognitive load. One way of reducing cognitive load during route guidance is by using adaptive systems that show specific route instruction types at specific decision points. Therefore, in this VR experiment, the usability of such an adaptive indoor route guidance system is tested by tracking the wayfinding and gaze behavior of the users. First, the difference in wayfinding and gaze behavior between all route instruction types is compared. Next, the building configuration at the decision points is quantified through the architectural theory of space syntax, and the correlation with the wayfinding and gaze behavior is determined. Our findings indicate that adapting the route instruction type does make a difference for the user.","Title: Watermarking for Large-Scale Video Distribution through Out-of-the-Loop Frame Replacement\nYear: 2022\n\nAbstract: Forensic watermarking is used in large-scale video-distribution applications to track digital pirates after they illegally leak videos. Existing methods either have a high embedding complexity or have troubles providing imperceptibility and robustness. Therefore, this paper proposes a novel watermarking method that has a low embedding complexity, as well as sufficient imperceptibility and robustness. The main novelty is that frames are replaced out of the loop, which has a very low complexity. This is inspired by A/B watermarking, yet applied on frame level rather than video-segment level. The resulting drift-error artifacts are used for robust watermark detection. Additionally, a faster watermark detection method is proposed at the cost of a decrease in embedding capacity. We show that the drift errors caused by frame replacement are imperceptible, and that our method has a negligible impact on the video bitrate. Moreover, we demonstrate a high level of robustness. These interesting properties come at the cost of a non-blind detection with a relatively high complexity, that is partially solved by the proposed fast detection method. Additionally, a high detection complexity is not necessarily a problem since detection occurs infrequently and recent speed-up solutions exist. In conclusion, the proposed method is a novel and scalable solution that enables secure large-scale video distribution."],"string":"[\n \"Title: 3D video compression based on high efficiency video coding.\\nYear: 2012\\n\\nAbstract: With the advent of autostereoscopic displays, questions rise on how to efficiently compress the video information needed by such displays. Additionally, for gradual market acceptance of this new technology it is valuable to have a solution offering forward compatibility with stereo 3D video as it is used nowadays. In this paper, a multiview compression scheme making use of the efficient single-view coding tools used in High Efficiency Video Coding (HEVC) is provided. Although efficient single view compression can be obtained with HEVC, a multiview adaptation of this standard under development is proposed, offering additional coding gains. On average, for the texture information, the total bitrate can be reduced by 37.2% compared to simulcast HEVC. For depth map compression, gains largely depend on the quality of the captured content. Additionally, a forward compatible solution is proposed offering the possibility for a gradual upgrade from H.264/AVC based stereoscopic 3D systems to an HEVC-based autostereoscopic environment. With the proposed system, significant rate savings compared to Multiview Video Coding (MVC) are presented(1).\",\n \"Title: Encryption for high efficiency video coding with video adaptation capabilities.\\nYear: 2013\\n\\nAbstract: Video encryption techniques enable applications like digital rights management and video scrambling. Applying encryption on the entire video stream can be computationally costly and prevents advanced video modifications by an untrusted middlebox in the network, like splicing, quality monitoring, watermarking, and transcoding. Therefore, encryption techniques are proposed which influence a small amount of the video stream while keeping the video compliant with its compression standard, High Efficiency Video Coding. Encryption while guaranteeing standard compliance can cause degraded compression efficiency, so depending on their bitrate impact, a selection of encrypted syntax elements should be made. Each element also impacts the quality for untrusted decoders differently, so this aspect should also be considered. In this paper, multiple techniques for partial video encryption are investigated, most of them having a low impact on rate-distortion performance and having a broad range in scrambling performance.\",\n \"Title: High definition H.264/AVC subjective video database for evaluating the influence of slice losses on quality perception\\nYear: 2013\\n\\nAbstract: Prior to the construction or validation of objective video quality metrics, ground-truth data must be collected by means of a subjective video database. This database consists of (impaired) video sequences and corresponding subjective quality ratings. However, creating this subjective database is a time-consuming and expensive task. There is an ongoing effort towards publishing such subjective video databases into the public domain. This facilitates the development of new objective quality metrics. In this paper, we present a new subjective video database consisting of impaired High Definition H.264/AVC encoded video sequences and associated quality ratings gathered from a subjective experiment. This database can be used freely to determine impairment visibility or estimate overall quality of a video in the case of lost slices due to network impairments.\",\n \"Title: Fast Channel Switching Based on SVC in an IPTV Environment\\nYear: 2009\\n\\nAbstract: An IPTV network contains characteristics of both a unicast environment and a broadcast environment. For both environments, techniques to accelerate channel switching exist. In a unicast environment a bandwidth efficient implementation to accelerate channel switching can be obtained with non scalable video coding, while for a broadcast environment the same acceleration is more optimal using scalable video coding. For a combination of both environments, as is the case for an IPTV environment, none of the solutions is optimal. In this paper, we propose a compromise between scalable and non scalable video compression to adapt to the properties of an IPTV environment. One of the proposed configurations obtains bandwidth reductions on the access network of the IPTV network between 2.4% and 4.3% with a small increase between 0.1% and 1.6% on the core network of the IPTV network.\",\n \"Title: Combining Open- And Closed-Loop Architectures For H.264/Avc-To-Svc Transcoding\\nYear: 2011\\n\\nAbstract: Scalable video coding (SVC) allows encoded bitstreams to be adapted. However, most bitstreams do not incorporate this scalability so bitstreams have to be adapted multiple times to accommodate for varying network conditions or end-user devices. Each adaptation incorporates an additional loss of quality due to transcoding. To overcome this issue, we propose a single transcoding step from H.264/AVC to SVC. Doing so, the resulting bitstream can be freely adapted without any additional quality reduction. Open-loop transcoding architectures can be used for H.264/AVC-to-SVC transcoding with a low complexity, although these architectures suffer from drift artifacts. Closed-loop transcoding, on the other hand, requires a higher complexity. To overcome the drawbacks of both systems, we propose combining both techniques.\",\n \"Title: Fast transcoding for video delivery by means of a control stream.\\nYear: 2012\\n\\nAbstract: When a video delivery system provides different representations of the same sequence, solutions can be found in simulcast coding, scalable coding, and transcoding. The major downside of transcoding is the additional complexity needed to re-encode the video sequence in its new form. When one fidelity version of the video stream should be transmitted over the network, scalable coding is less efficient compared to single layer coding because of the layering overhead. Finally, simulcast encoding results in large storage requirements. In this paper, we propose an intermediate solution providing transcoding at a low complexity by the aid of control streams. We define a control stream as a regular video stream from which residual information is removed. With these control streams, the complexity of the encoding step in the transcoder can be reduced to decoder complexity. As a result of the removal of residual information, these control streams take up 62.8% less bitrate compared to simulcast coding for a test set based on High Efficiency Video Coding (HEVC). Additionally, when compared to scalable coding, an efficient single layer video stream can be provided without the 16.6% bitrate increase caused by transmitting a layered bitstream.\",\n \"Title: An enhanced fast mode decision model for spatial enhancement layers in scalable video coding\\nYear: 2012\\n\\nAbstract: Recently, the H.264/AVC standard has been extended to incorporate Scalable Video Coding (SVC). SVC offers the advantage of scalable (layered) coding, but has the disadvantage of a highly increased computational complexity at the encoder side when dealing with spatial scalability. To restrict the increase in required processing power, fast mode decision models for spatial enhancement layers have been proposed in literature. We propose a novel generic fast mode decision model for spatial enhancement layers for both P and B frames based on both the quantization of the enhancement layer and the correlation between the macroblock type in the enhancement layer and the co-located macroblock in the reference layer. In this paper, an evaluation of the proposed model and comparison with a state-of-the-art model is given. Results show that the proposed technique performs exceptionally well for spatial scalability. For both dyadic and non-dyadic spatial scalability, we achieve an average time saving of 75%, while only a slight bit rate increase of 2.23% and a minor PSNR decrease of 0.46 dB are measured. Compared with state of the art techniques, we further halve the complexity while having comparable rate-distortion results.\",\n \"Title: Fast Channel Switching Based on SVC in IPTV Environments\\nYear: 2012\\n\\nAbstract: In Internet Protocol TeleVision (IPTV) systems, the use of video compression results in a trade-off between random access and bandwidth consumption. Most of the time, priority is given to compression efficiency, resulting in low channel switching performance. In this paper, a Scalable Video Coding (SVC) configuration is proposed, wherein base and enhancement layer are coded in such a way that fast channel switching can be offered without impacting the access network bandwidth consumption. This is achieved by encoding a full quality slow channel switching version of the video stream in the base layer extended with a reduced quality fast switching enhancement layer. Additionally, this configuration offers backward compatibility with H.264/AVC, enabling a gradual upgrade of the IPTV system from H.264/AVC to SVC. When comparing this technique to single layer H.264/AVC, 25.0% bandwidth reduction on the access network is obtained without impacting channel switching speed, but only at the cost of a slightly reduced quality ( -1 dB PSNR) during a short transition period (maximum 48 frames). If a comparison is made with a regular SVC configuration, 39.4% bandwidth reduction on the access network during steady state is obtained. Compared to simulcast H.264/AVC, 32.0% bandwidth reduction on the core network is observed.\",\n \"Title: Fast transrating for high efficiency video coding based on machine learning\\nYear: 2013\\n\\nAbstract: To incorporate the newly developed High Efficiency Video Coding (HEVC) standard in real-life network applications, efficient transrating algorithms are required. We propose a fast transrating scheme, based on the early prediction of the partition split-flags in P pictures. Using machine learning techniques, the correlation between co-located partitions at different quantizations is investigated. This results in a model which predicts the split-flag and gives the associated prediction accuracy so that the splitting process in the transcoder is optimized. At each partition depth, the model indicates whether the full rate-distortion cost evaluations should be performed at the current depth, or if the partition can be split immediately. Experimental results show that the proposed transcoder reduces the complexity of the transrating process by 76.04%, while maintaining the coding efficiency of a cascaded decoder-encoder.\",\n \"Title: Format-compliant encryption techniques for high efficiency video coding.\\nYear: 2013\\n\\nAbstract: When middlebox devices should be able to adapt an encrypted video stream in the network without having the decryption key, format-compliant partial encryption schemes should be applied. In this paper, we propose such encryption schemes for the recently standardized High Efficiency Video Coding (HEVC) standard. By encrypting specific syntax elements like the sign of the residual information, the sign of the motion vector (MV) difference, the MV prediction index, and the MV reference index, format compliance and the possibility for adaptation are offered. Scrambling performance gradually increases when shifting from encrypting the motion information to encrypting the residual sign and finally to the combination thereof. Applying all these techniques has a negligible impact on the compression efficiency.\",\n \"Title: Adaptive transform skipping for improved coding of motion compensated residuals\\nYear: 2013\\n\\nAbstract: New generations of video compression algorithms, such as those included in the under development High Efficiency Video Coding (HEVC) standard, provide substantially higher compression compared to their ancestors. The gain is achieved by improved prediction of pixels, both within a frame and between frames. Novel coding tools that contribute to the gain provide highly uncorrelated prediction residuals for which classical frequency decomposition methods, such as the discrete cosine transform, may not be able to supply a compact representation with few significant coefficients. To further increase the compression gains, this paper proposes transform skip modes which allow skipping one or both 1-D constituent transforms (i.e., vertical and horizontal), which is more suitable for sparse residuals. The proposed transform skip mode is tested in the HEVC codec and is able to provide bitrate reductions of up to 10% at the same objective quality when compared with the application of 2-D block transforms only. Moreover, the proposed transform skip mode outperforms the full transform skip currently investigated for possible adoption in the HEVC standard.\",\n \"Title: Assessing the importance of audio/video synchronization for simultaneous translation of video sequences.\\nYear: 2012\\n\\nAbstract: Lip synchronization is considered a key parameter during interactive communication. In the case of video conferencing and television broadcasting, the differential delay between audio and video should remain below certain thresholds, as recommended by several standardization bodies. However, further research has also shown that these thresholds can be relaxed, depending on the targeted application and use case. In this article, we investigate the influence of lip sync on the ability to perform real-time language interpretation during video conferencing. Furthermore, we are also interested in determining proper lip sync visibility thresholds applicable to this use case. Therefore, we conducted a subjective experiment using expert interpreters, which were required to perform a simultaneous translation, and non-experts. Our results show that significant differences are obtained when conducting subjective experiments with expert interpreters. As interpreters are primarily focused on performing the simultaneous translation, lip sync detectability thresholds are higher compared with existing recommended thresholds. As such, primary focus and the targeted application and use case are important factors to be considered when selecting proper lip sync acceptability thresholds.\",\n \"Title: Multi-Loop Quality Scalability Based On High Efficiency Video Coding\\nYear: 2012\\n\\nAbstract: Scalable video coding performance largely depends on the underlying single layer coding efficiency. In this paper, the quality scalability capabilities are evaluated on a base of the new High Efficiency Video Coding (HEVC) standard under development. To enable the evaluation, a multi-loop codec has been designed using HEVC. Adaptive inter-layer prediction is realized by including the lower layer in the reference list of the enhancement layer. As a result, adaptive scalability on frame level and on prediction unit level is accomplished. Compared to single layer coding, 19.4% Bjontegaard Delta bitrate increase is measured over approximately a 30dB to 40dB PSNR range. When compared to simulcast, 20.6% bitrate reduction can be achieved. Under equivalent conditions, the presented technique achieves 43.8% bitrate reduction over Coarse Grain Scalability of the SVC - H.264/AVC-based standard.\",\n \"Title: No-reference bitstream-based impairment detection for high efficiency video coding.\\nYear: 2012\\n\\nAbstract: Video distribution over error-prone Internet Protocol (IP) networks results in visual impairments on the received video streams. Objective impairment detection algorithms are crucial for maintaining a high Quality of Experience (QoE) as provided with IPTV distribution. There is a lot of research invested in H.264/AVC impairment detection models and questions rise if these turn obsolete with a transition to the successor of H.264/AVC, called High Efficiency Video Coding (HEVC). In this paper, first we show that impairments on HEVC compressed sequences are more visible compaired to H.264/AVC encoded sequences. We also show that an impairment detection model designed for H.264/AVC could be reused on HEVC, but that caution is advised. A more accurate model taking into account content classification needed slight modification to remain applicable for HEVC compression video content.\",\n \"Title: Improved intra mode signaling for HEVC\\nYear: 2011\\n\\nAbstract: In the current development of HEVC, compression performance improved significantly compared to H.264/AVC for both inter pictures and intra pictures. With intra compression, the main reason for this improvement is the large in crease in intra prediction directions (up to 34). The downside of having a larger number of modes is that they increase the signaling overhead in the bitstream. In this paper, a low complexity intra mode prediction algorithm is proposed which improves the mode prediction accuracy. This is achieved by exploiting the correlation between the prediction directions of the neighboring prediction units and that of the encoded prediction unit. As a result, more efficient intra mode signaling can be achieved with minimal impact on encoder and decoder complexity. On average, 0.33% bitrate improvement is obtained by employing the proposed algorithm. For sequences that are encoded with a high number of directional intra modes, around 1% bitrate improvement is measured.\",\n \"Title: A complexity-scalable hybrid H.264/AVC-to-SVC transcoder.\\nYear: 2013\\n\\nAbstract: Transcoding of H.264/AVC bitstreams is frequently used for adapting video streams to varying network conditions or different end-user device characteristics. Transcoding requires a relatively high complexity (and thus high operational cost) for each adaptation operation, and possibly reduces the video quality. In comparison, transcoding the input bitstream to SVC requires only one adaptation step. Afterwards, the resulting SVC bitstream can be adapted instantly. To reduce the H.264/AVC-to-SVC transcoding complexity, a transcoding architecture which combines existing open-loop transcoding techniques with optimized closed-loop transcoding is proposed. Furthermore, this approach allows to scale the complexity of the system. Doing so, compared with a cascaded decoder-encoder only 8.48% of the complexity is required for the highest complexity, while a high rate-distortion efficiency is maintained and a high degree of scalability is guaranteed. When only limited resources are available, the transcoding complexity can be scaled such that over 99% of the complexity is reduced compared to a cascaded decoder-encoder.\",\n \"Title: Fast channel switching for single-loop scalable HEVC.\\nYear: 2014\\n\\nAbstract: In an IPTV environment, different techniques exist to provide faster random access or equivalently, a faster channel switching experience, but none of them provide backward compatibility and limited overhead at the same time. In this paper, a technique is proposed to increase the random access frequency for the High Efficiency Video Coding (HEVC) standard by using a single-loop scalable version of it. This is achieved by encoding an enhancement layer with a higher frequency of random access points compared to the base layer. With this scalable configuration, a backward compatible base stream remains present. The proposed technique requires 12.4% less bitrate compared to sending the fast and slow switching streams in a non-scalable way. Compared to the almost standardized multi-loop scalable extension of HEVC, 5.5% less bitrate is needed on the core of the IPTV network and 12.5% less bitrate on the access network during steady state conditions. Moreover, the multi-loop scalable extension does not provide backward compatibility with legacy HEVC decoders.\",\n \"Title: Efficient transcoding for spatially misaligned compositions for HEVC\\nYear: 2014\\n\\nAbstract: The visualization of (ultra) high-resolution compositions created from multiple input bitstreams requires several decoders at the receiving device. Therefore, not all devices can properly display such compositions. To address this problem, the input streams are decoded, merged into a single video, and re-encoded by a transcoder in the network. However, this approach requires a computationally complex re-encoding step. To reduce this complexity, information from the input bit-streams can be reused during transcoding. In HEVC, simply reusing the original encoding information is not compression efficient if the inserted content is not aligned with the grid of coded blocks. In this paper, we applied feature selection based on a decision tree, which was used in a fast HEVC transcoding model for misaligned content. The performance varies depending on the shift and average transform size in the original sequence, resulting in complexity reductions of up to 76%.\",\n \"Title: Adaptive guided image filtering for screen content coding\\nYear: 2014\\n\\nAbstract: The capabilities of off-the-shelf consumer electronics are continuously increasing, making them interesting candidates to integrate into industrial visualization systems for reducing the cost of these systems or speed up development. However, in the context of video compression, hardware acceleration on consumer electronics is typically only provided for more common, consumer oriented YUV 4:2:0 profiles. Meanwhile, industrial applications often require YUV 4:4:4 formats due to synthetic visual data. Upscaling YUV 4:2:0 to YUV 4:4:4 using conventional filters results in visual artifacts, specifically for synthetic content. To improve the quality of the chroma components, this paper proposes to extend the well-known guided image filter through content-adaptive selection of the filter radius, and analyze different strategies for limiting complexity. This results in a 3.5 dB improvement of the chroma PSNR compared to conventional filters.\",\n \"Title: Fast motion estimation for closed-loop HEVC transrating\\nYear: 2014\\n\\nAbstract: Transrating is a useful tool for adapting the bitrate of a video stream. Reducing the complexity of transrating is extremely important, especially for high efficiency video coding (HEVC) based transrating where the encoder is very time consuming. TZSearch is currently the default integer motion estimation (ME) algorithm in the HEVC reference encoder thanks to its excellent performance in reducing the complexity of ME. However, this algorithm is characterized by a fixed search area and search pattern, which can be considered sub optimal for transrating. In this paper, we improve the performance of HEVC transrating by optimizing this TZSearch algorithm. Utilizing the correlation between input and output motion vectors, we propose a fast search scheme including three steps. First, the initial search point is selected. Then, using the rate-distortion cost of this starting point, the search size is determined according to an online-trained Bayes decision rule. Finally, two proposed search algorithms are described for refining the starting point. Experimental results show that our proposed TZSearch scheme can reduce the complexity of transrating while improving the coding performance in terms of bit rate saving.\",\n \"Title: Guided depth filtering to improve the quality of experience for autostereoscopic displays.\\nYear: 2014\\n\\nAbstract: One of the drawbacks of current 3DTV systems are the glasses required to separate the 3D views. This problem can be solved by using autostereoscopic 3D displays, which generates a 3D scene from different viewpoints. These viewpoints are synthesized in the display based on depth information of the scene. However, currently no depth information is available for stereoscopic video. Therefore, depth estimation based on the stereoscopic input is required. This depth estimation should be low-complex and real-time for consumer electronic devices. Due to this low-complexity requirement, the quality of such depth maps will be limited.In this paper, we propose a guided depth filter, which filters the estimated depth map by using the downsampled chroma signal of the corresponding texture view as a guide. This allows to refine the borders of objects which reduces visible inpainting artifacts caused by the view synthesis. The proposed algorithm has been evaluated by expert viewing to show that it is independent of the depth map estimation and view synthesis of the autostereoscopic display.\",\n \"Title: Performance analysis of machine learning for arbitrary downsizing of pre-encoded HEVC video.\\nYear: 2015\\n\\nAbstract: Nowadays, broadcasters deliver ultra-high resolution video to their consumers. This live video is sent to a set-top box for display on a television. However, if one or more users in the home want to view the same video on their personal mobile devices with a lower display resolution and limited processing power, decoding the original ultra-high resolution video would result in stuttering and quick...\",\n \"Title: Objective Video Quality Assessment - Towards Large Scale Video Database Enhanced Model Development\\nYear: 2015\\n\\nAbstract: The current development of video quality assessment algorithms suffers from the lack of available video sequences for training, verification and validation to determine and enhance the algorithm's application scope. The Joint Effort Group of the Video Quality Experts Group (VQEG-JEG) is currently driving efforts towards the creation of large scale, reproducible, and easy to use databases. These databases will contain bitstreams of recent video encoders (H.264, H.265), packet loss impairment patterns and impaired bitstreams, pre-parsed bitstream information into files in XML syntax, and well-known objective video quality measurement outputs. The database is continuously updated and enlarged using reproducible processing chains. Currently, more than 70,000 sequences are available for statistical analysis of video quality measurement algorithms. New research questions are posed as the database is designed to verify and validate models on a very large scale, testing and validating various scopes of applications, while subjective assessment has to be limited to a comparably small subset of the database. Special focus is given on the principles guiding the database development, and some results are given to illustrate the practical usefulness of such a database with respect to the detailed new research questions.\",\n \"Title: Real-Time Reservoir Computing Network-Based Systems for Detection Tasks on Visual Contents\\nYear: 2015\\n\\nAbstract: Among the various types of artificial neural networks used for event detection in visual contents, those with the ability of processing temporal information, such as recurrent neural networks, have been proved to be more effective. However, training of such networks is often difficult and time consuming. In this work, we show how Reservoir Computing Networks (RCNs) can be used for detecting purposes on raw images. The applicability of RCNs is illustrated using two example challenges, namely isolated digit handwriting recognition on the MNIST dataset as well as detection of the status of a door using self-developed moving pictures from a surveillance camera. Achieving an error rate of 0.92 percent on MNIST, we show that RCN can be a serious competitor to the state-of-the-art. Moreover, we show how RCNs with their simple and yet robust training procedure can be practically used for real surveillance tasks using very low resolution camera sensors.\",\n \"Title: A Motion Vector Re-Use Algorithm for H.264/AVC and HEVC Simultaneous Video Encoding.\\nYear: 2015\\n\\nAbstract: The rapid emergence and rise of a wide range of electronic devices has led to the need for providing very different levels of video transmission services. The capabilities and performance of these devices determine the type of video streams that they are able to decode. As a way to fulfil their requirements, this paper presents a heterogeneous simultaneous encoding framework of H.264/Advanced Video Coding (AVC) and High Efficiency Video Coding (HEVC) that shares information between encoders in order to reduce the overall encoding time. In this regard, the proposed approach utilizes the H.264/AVC motion vectors of the 16x16 pixels macroblocks as predictors for the HEVC prediction units. As a consequence, the size of the motion estimation search area can be significantly reduced. Results show that an encoding time reduction of 8.95% can be achieved with negligible losses in terms of rate-distortion.\",\n \"Title: Fast encoding for personalized views extracted from beyond high definition content\\nYear: 2015\\n\\nAbstract: Broadcast providers are looking for new opportunities to increase user experience and user interaction on their content. Their main goal is to attract and preserve viewer attention to create a big and stable audience. This could be achieved with a second screen application that lets the users select their own viewpoint in an extremely high resolution video to direct their own first screen. By allowing the users to create their own personalized video stream, they become involved with the content creation itself. However, encoding a personalized view for each user is computationally complex. This paper describes a machine learning approach to speed up the encoding of each personal view. Simulation results of zoom, pan and tilt scenarios show bit rate increases between 2% and 9% for complexity reductions between 69% and 79% compared to full encoding.\",\n \"Title: Predicting full-reference video quality measures using HEVC bitstream-based no-reference features\\nYear: 2015\\n\\nAbstract: This paper presents bitstream-based features for perceptual quality estimation of HEVC coded videos. Various factors including the impact of different sizes of block-partitions, use of reference-frames, the relative amount of various prediction modes, statistics of motion vectors and quantization parameters are taken into consideration for producing 52 features relevant for perceptual quality prediction. The used test stimuli constitutes 560 bitstreams that have been carefully extracted for this analysis from the 59, 520 bistreams of the large-scale database generated by the Joint Effort Group (JEG) of the Video Quality Experts Group (VQEG). The obtained results show the significance of the considered features through reasonably accurate and monotonic prediction of a number of objective quality metrics.\",\n \"Title: Fast simultaneous video encoder for adaptive streaming\\nYear: 2015\\n\\nAbstract: Content providers create different versions of a video to accommodate different end-user devices and network conditions. However, each of these versions requires a resource intensive encoding process. To reduce the computational complexity of the encodings, this paper proposes a fast simultaneous encoder. This encoder takes a single video as input and creates a number of bit streams encoded with different parameters. Only one version of the video is created with a full encode, whereas encoding of the other versions is accelerated by exploiting the correlation with the fully encoded version using machine learning techniques. In a practical scenario, the fast simultaneous encoder achieves a complexity reduction of 67.3% with a bit rate increase of 5.2% compared to performing a full encode of each version.\",\n \"Title: Image Quality of Experience: A Subjective Test Targeting the Consumer's Experience.\\nYear: 2016\\n\\nAbstract: This paper presents a subjective image quality experiment design that specifically targets understanding consumer perception of visual quality. With this consumer-oriented goal in mind, images were captured from a large number of representative consumer devices. This design choice is fundamentally different from previous work in the field, in that it is not based on simulated software impairments, but rather on naturally occurring artifacts due to the device and conditions. New subjective questions were posed, which allowed exploration of differences in the quality of experience (QoE) provided by various devices. This paper presents the experimental design implemented to capture ratings and feedback on these consumer devices. The study is analyzed to highlight and demonstrate the unique capabilities provided by this method.\",\n \"Title: Spatially misaligned HEVC transcoding with computational-complexity scalability.\\nYear: 2016\\n\\nAbstract: Two methods to transcode a spatially misaligned video sequence are proposed.The machine learning method outperforms the trivial method for all shifts.Both methods perform best when the shifts align with a grid.Predicting both CU and PU information is more efficient than CU only. In control rooms, video walls display footage from multiple sources. Often, a composition of these sources is sent to other devices in a single video stream. To minimize the computational complexity of this composition process, information from the original bitstreams can be reused. However, in High Efficiency Video Coding (HEVC), simply copying the original encoding decisions is not compression efficient if the individual videos are spatially misaligned with the grid of coded blocks of the composition. Our proposed HEVC-based transcoder reduces the computational complexity by predicting encoding decisions of misaligned sequences by using a trivial method or a more adaptive, computational-complexity scalable machine learning method. Higher compression efficiency is observed when more alignment is preserved with the original block grid. Overall, the machine learning method achieves a higher compression efficiency than the trivial method. Both methods attain a complexity reduction of up to 82% compared to the reference software.\",\n \"Title: Simultaneous encoder for high-dynamic-range and low-dynamic-range video.\\nYear: 2016\\n\\nAbstract: High-dynamic-range (HDR) technology is an emerging video technology that allows displays to produce a higher range of luminance to better approximate the range of brightness perceived by the human eye. However, during the transition to this new technology, not all consumer devices will support the full range of luminance values offered by HDR. In order to also support these devices with lower dyna...\",\n \"Title: CTU splitting algorithm for H.264/AVC and HEVC simultaneous encoding.\\nYear: 2017\\n\\nAbstract: High-Efficiency Video Coding (HEVC) was conceived by the Joint Collaborative Team on Video Coding as the natural successor of the H.264/AVC standard, which has been the most extended digital video standard in all segments of the domestic and professional markets for over 10 years. HEVC roughly doubles the compression performance of H.264/AVC in Rate-Distortion terms at the expense of a high computational cost. Thus, most of the previous-generation devices can decode H.264/AVC streams but they cannot decode HEVC yet, while emerging devices are supposed to be able to decode both standards. Video providers should take advantage of bandwidth reduction using HEVC when possible, but they should also provide compatible streams to older devices, making it necessary to encode the same stream using both H.264/AVC and HEVC standards. This paper presents a coding tree unit splitting algorithm for a heterogeneous simultaneous encoding scenario which makes use of information from H.264/AVC encoder to make faster decisions in HEVC. Experimental results show that the proposed algorithm can achieve a good trade-off between coding efficiency and complexity.\",\n \"Title: Efficient Encoding of Interactive Personalized Views Extracted from Immersive Video Content\\nYear: 2016\\n\\nAbstract: Traditional television limits people to a single viewpoint. However, with new technologies such as virtual reality glasses, the way in which people experience video will change. Instead of being limited to a single viewpoint, people will demand a more immersive experience that gives them a sense of being present in a sports stadium, a concert hall, or at other events. To satisfy these users, video such as 360-degree or panoramic video needs to be transported to their homes. Since these videos have an extremely high resolution, sending the entire video requires a high bandwidth capacity and also results in a high decoding complexity at the viewer. The traditional approach to this problem is to split the original video into tiles and only send the required tiles to the viewer. However, this approach still has a large bit rate overhead compared to sending only the required view. Therefore, we propose to send only a personalized view to each user. Since this paper focuses on reducing the computational cost of such a system, we accelerate the encoding of each personalized view based on coding information obtained from a pre-analysis on the entire ultra-high-resolution video. By doing this using the High Efficiency Video Coding Test Model (HM), the complexity of each individual encode of a personalized view is reduced by more than 96.5% compared to a full encode of the view. This acceleration results in a bit rate overhead of at most 19.5%, which is smaller compared to the bit rate overhead of the tile-based method.\",\n \"Title: AFRESh: an adaptive framework for compression of reads and assembled sequences with random access functionality.\\nYear: 2017\\n\\nAbstract: Motivation: The past decade has seen the introduction of new technologies that lowered the cost of genomic sequencing increasingly. We can even observe that the cost of sequencing is dropping significantly faster than the cost of storage and transmission. The latter motivates a need for continuous improvements in the area of genomic data compression, not only at the level of effectiveness (compression rate), but also at the level of functionality (e.g. random access), configurability (effectiveness versus complexity, coding tool set...) and versatility (support for both sequenced reads and assembled sequences). In that regard, we can point out that current approaches mostly do not support random access, requiring full files to be transmitted, and that current approaches are restricted to either read or sequence compression. Results: We propose AFRESh, an adaptive framework for no-reference compression of genomic data with random access functionality, targeting the effective representation of the raw genomic symbol streams of both reads and assembled sequences. AFRESh makes use of a configurable set of prediction and encoding tools, extended by a Context-Adaptive Binary Arithmetic Coding scheme (CABAC), to compress raw genetic codes. To the best of our knowledge, our paper is the first to describe an effective implementation CABAC outside of its' original application. By applying CABAC, the compression effectiveness improves by up to 19% for assembled sequences and up to 62% for reads. By applying AFRESh to the genomic symbols of the MPEG genomic compression test set for reads, a compression gain is achieved of up to 51% compared to SCALCE, 42% compared to LFQC and 44% compared to ORCOM. When comparing to generic compression approaches, a compression gain is achieved of up to 41% compared to GNU Gzip and 22% compared to 7-Zip at the Ultra setting. Additionaly, when compressing assembled sequences of the Human Genome, a compression gain is achieved up to 34% compared to GNU Gzip and 16% compared to 7-Zip at the Ultra setting.\",\n \"Title: Intra-Frame Sharing For Low-Complexity Decoding Of Shvc Video\\nYear: 2016\\n\\nAbstract: Scalable video coding provides an efficient adaptation solution for delivering video to consumer devices with a great variety of characteristics. This technique encodes video using a base layer and several enhancement layers with different qualities. In order to reduce the total bit rate of the video stream, inter layer prediction (ILP) is enabled. However, the introduction of ILP results in a high decoding complexity since multiple layers need to be decoded at the same time. In this paper, we propose a method to reduce the decoding complexity of the scalable video stream by sharing the intra frame between layers and eliminating all other inter-layer dependencies. The experimental results show that the proposed method results in a video stream with a lower bit rate than a simulcast approach. Additionally, this approach reduces the decoder complexity compared to scalable video compression.\",\n \"Title: The crowd as a cameraman: on-stage display of crowdsourced mobile video at large-scale events.\\nYear: 2018\\n\\nAbstract: Recording videos with smartphones at large-scale events such as concerts and festivals is very common nowadays. These videos register the atmosphere of the event as it is experienced by the crowd and offer a perspective that is hard to capture by the professional cameras installed throughout the venue. In this article, we present a framework to collect videos from smartphones in the public and blend these into a mosaic that can be readily mixed with professional camera footage and shown on displays during the event. The video upload is prioritized by matching requests of the event director with video metadata, while taking into account the available wireless network capacity. The proposed framework’s main novelty is its scalability, supporting the real-time transmission, processing and display of videos recorded by hundreds of simultaneous users in ultra-dense Wi-Fi environments, as well as its proven integration in commercial production environments. The framework has been extensively validated in a controlled lab setting with up to 1 000 clients as well as in a field trial where 1 183 videos were collected from 135 participants recruited from an audience of 8 050 people. 90 % of those videos were uploaded within 6.8 minutes.\",\n \"Title: Reducing the Complexity of a Multiview H.264/AVC and HEVC Hybrid Architecture.\\nYear: 2018\\n\\nAbstract: With the advent of 3D displays, an efficient encoder is required to compress the video information needed by them. Moreover, for gradual market acceptance of this new technology, it is advisable to offer backward compatibility with existing devices. Thus, a multiview H.264/Advance Video Coding (AVC) and High Efficiency Video Coding (HEVC) hybrid architecture was proposed in the standardization process of HEVC. However, it requires long encoding times due to the use of HEVC. With the aim of tackling this problem, this paper presents an algorithm that reduces the complexity of this hybrid architecture by reducing the encoding complexity of the HEVC views. By using Naïve-Bayes classifiers, the proposed technique exploits the information gathered in the encoding of the H.264/AVC view to make decisions on the splitting of coding units in HEVC side views. Given the novelty of the proposal, the only similar work found in the literature is an unoptimized version of the algorithm presented here. Experimental results show that the proposed algorithm can achieve a good tradeoff between coding efficiency and complexity.\",\n \"Title: A novel video watermarking approach based on implicit distortions.\\nYear: 2018\\n\\nAbstract: Copyright-sensitive videos are commonly leaked or illegally distributed by so-called digital pirates. Video owners aim to prevent this by hiding a unique watermark in every video that contains information about the receiver. If the video is then illegally distributed, the copyright owner can extract the watermark and identify the malicious consumer. However, pirates may manipulate the video in the...\",\n \"Title: Framework for reproducible objective video quality research with case study on PSNR implementations.\\nYear: 2018\\n\\nAbstract: Reproducibility is an important and recurrent issue in objective video quality research because the presented algorithms are complex, depend on specific implementations in software packages or their parameters need to be trained on a particular, sometimes unpublished, dataset. Textual descriptions often lack the required detail and even for the simple Peak Signal to Noise Ratio (PSNR) several mutations exist for images and videos, in particular considering the choice of the peak value and the temporal pooling. This work presents results achieved through the analysis of objective video quality measures evaluated on a reproducible large scale database containing about 60,000 HEVC coded video sequences. We focus on PSNR, one of the most widespread measures, considering its two most common definitions. The sometimes largely different results achieved by applying the two definitions highlight the importance of the strict reproducibility of the research in video quality evaluation in particular. Reproducibility is also often a question of computational power and PSNR is a computationally inexpensive algorithm running faster than realtime. Complex algorithms cannot be reasonably developed and evaluated on the abovementioned 160 hours of video sequences. Therefore, techniques to select subsets of coding parameters are then introduced. Results show that an accurate selection can preserve the variety of the results seen on the large database but with much lower complexity. Finally, note that our SoftwareX accompanying paper presents the software framework which allows the full reproducibility of all the research results presented here, as well as how the same framework can be used to produce derived work for other measures or indexes proposed by other researchers which we strongly encourage for integration in our open framework.\",\n \"Title: A Just Noticeable Difference Subjective Test for High Dynamic Range Images\\nYear: 2018\\n\\nAbstract: High Dynamic Range (HDR) imaging captures a wide range of luminance existing in real-world scenes. Due to large luminance levels and higher brightness of HDR displays, artefacts can be more noticeable to the Human Visual System (HVS). In a first attempt to experimentally quantify those noticeable levels for HDR images, we pioneered in conducting an exhaustive and comprehensive Just Noticeable Difference (JND) subjective experiment of which the outcome is presented in this paper. Six distortions including JPEG, JPEG2000, noise, blur, contrast change, and quantization artefacts have been considered in the test. The distortions were applied to 10 HDR images in 100 distortion levels resulting a database of 6000 HDR test images. The subjects were asked to find the image JND location on each set of 100 images they had the freedom to explore. The effect of content features on the noticeable threshold selection is investigated per distortion type. Our results in some cases show a significant correlation between content features and JNDs. We are hoping that our results can contribute to further exploitation of a precise HVS model for HDR quality assessment and optimization of the coding and bit allocation in HDR compression.\",\n \"Title: Steered Mixture-of-Experts Approximation of Spherical Image Data.\\nYear: 2018\\n\\nAbstract: Steered Mixture-of-Experts (SMoE) is a novel framework for approximating multidimensional image modalities. Our goal is to provide full Six Degrees-of-Freedom capabilities for camera captured content. Previous research concerned only limited translational movement for which the 4D light field representation is sufficient. However, our goal is to arrive at a representation that allows for unlimited translational-rotational freedom, i.e. our goal is to approximate the full 5D plenoptic function. Until now, SMoE was only applied on Euclidean spaces. However, the plenoptic function contains two spherical coordinate dimensions. In this paper, we propose a methodology to extend the SMoE framework to spherical dimensions. Furthermore, we propose a method to reduce the parameter space to the same two dimensional Euclidean space as for planar 2D images by using a projection of the covariance matrices onto tangent spaces perpendicular to the unit sphere. Finally, we propose a novel training technique for spherical dimensions based on these observations. Experiments performed on omnidirectional 360 degrees images show that the introduction of the dimensionality-reduction projection step results in very low quality loss.\",\n \"Title: Real-Time Low-Complexity Digital Video Stabilization in the Compressed Domain\\nYear: 2018\\n\\nAbstract: Undesired vibration in videos is more and more common due to the rise of hand-held or vehicle mounted cameras. Non-indented motion of a video capturing device causes an unpleasant vibrating effect for the consumer. Therefore, video stabilization has been an extremely active field of research in the past years. Motion estimation is the most computationally expensive step of the video stabilization process. Our goal is to circumvent this expensive step in order to achieve real-time performance. We do so by using the precomputed motion vectors from the encoded video streams and thus operate in the compressed domain. These vectors already contain an approximation of the needed motion information. A low-level motion model is used for mitigating complexity, and a low-pass filter performs motion smoothing before the final motion compensation step which is used for correcting the video. In many real-time applications where the video vibration is moderate, the proposed framework can reach online video stabilization at 30 frames per second for high definition video and 60 frames per second for lower resolutions, while retaining satisfactory performance in video correction, comparable to pixel-based equivalent algorithms.\",\n \"Title: Highly parallel steered mixture-of-experts rendering at pixel-level for image and light field data\\nYear: 2020\\n\\nAbstract: A novel image approximation framework called steered mixture-of-experts (SMoE) was recently presented. SMoE has multiple applications in coding, scale-conversion, and general processing of image modalities. In particular, it has strong potential for coding and streaming higher dimensional image modalities that are necessary to leverage full translational and rotational freedom (6 degrees-of-freedom) in virtual reality for camera captured images. In this paper, we analyze the rendering performance of SMoE for 2D images and 4D light fields. Two different GPU implementations that parallelize the SMoE regression step at pixel-level are presented, including experimental evaluations based on rendering performance and quality. In this paper it is shown that on appropriate hardware, an OpenCL implementation can achieve 85 fps and 22 fps for, respectively, 1080p and 4K renderings of large models with more than 100,000 of Gaussian kernels.\",\n \"Title: Traitor Tracing After Visible Watermark Removal\\nYear: 2018\\n\\nAbstract: Watermarks are often used to protect copyright-protected videos from illegal re-distribution. More specifically, a unique watermark that represents the receiver's identifier is embedded into the video. In this way, malicious users can be identified when they leak their received version of the video. However, when the watermark is embedded as visible text in the video, it is easy for digital pirates to delete it such that they can no longer be identified. Therefore, copyright owners will benefit from a technique that allows the detection of a visible watermark when it is removed. This paper demonstrates how a visible watermark indirectly generates imperceptible variations over the entire video. As such, these variations in the non-watermarked area can be used as an alternative watermark representation, and thus enable watermark detection even after watermark removal. The experimental results prove that the watermark can be detected as long as the quality of the watermarked video is not significantly reduced, especially if the originally-distributed watermarked video has a high quality. Moreover, the watermark should be embedded into a video with sufficient motion. In conclusion, the proposed technique enables copyright-owners to identify pirates when they illegally distribute visibly-watermarked videos, even when the watermarked area is removed.\",\n \"Title: A Scalable Architecture for Uncompressed-Domain Watermarked Videos\\nYear: 2019\\n\\nAbstract: Video watermarking is a well-established technology to help identify digital pirates when they illegally re-distribute multimedia content. In order to provide every client with a unique, watermarked video, the traditional distribution architectures separately encode each watermarked video. However, since these encodings require a high amount of computational resources, such architectures do not scale well to a large number of users. Therefore, this paper proposes a novel architecture that uses fast encoders instead of traditional, full encoders. The fast encoders re-use the coding information from a single, previously-encoded, unwatermarked video in order to speed up the encodings of the watermarked videos. As a result, the complexity of a fast encoder is only a fraction of the complexity of a full encoder. Due to a high correlation of the re-used coding information with the optimal coding information, the compression efficiency and watermark robustness decrease only slightly. Most importantly, the proposed fast encoder speeds up the compression process with a factor of 115, resulting in a low complexity similar to that of a video decoder. Consequently, video distributors can use the proposed architecture to deliver high-quality watermarked videos on a large-scale without requiring an excessive amount of computational resources.\",\n \"Title: Evaluation of full-reference objective video quality metrics on high efficiency video coding.\\nYear: 2013\\n\\nAbstract: For the purpose of automatic video quality evaluation, Peak Signal-to-Noise Ratio (PSNR) has been the most well-known full reference quality metric since a long time. Improving on PSNR, several other pixel-based quality metrics have been developed, namely Structural SIMilarity (SSIM), Multi Scale-SSIM (MS-SSIM), and Video Quality Metric (VQM). The goal of these objective video quality metrics is to replace time-consuming and expensive subjective quality assessment experiments. These alternative objective metrics have already been evaluated on several video compression schemes, such as MPEG-2 and H. 264/AVC, transported over different kinds of network protocols and under a large variation of network characteristics. In January 2013, the successor of the Advanced Video Coding (H. 264/AVC) standard, named High Efficiency Video Coding (HEVC), has been finalized. Although HEVC is still a block based hybrid video compression standard, some radical changes are made to subjectively improve the compression efficiency compared to H. 264/AVC. Until now, the alternative quality metrics have never been evaluated on this new compression scheme. Therefore, in this paper, we analyze the difference in performance of these full reference metrics. Based on subjective evaluations, a performance analysis is presented which shows the validity of these models when applied to HEVC compressed video content. I. INTRODUCTION\",\n \"Title: Efficient Bit Rate Transcoding for High Efficiency Video Coding.\\nYear: 2016\\n\\nAbstract: High efficiency video coding (HEVC) shows a significant advance in compression efficiency and is considered to be the successor of H.264/AVC. To incorporate the HEVC standard into real-life network applications and a diversity of other applications, efficient bit rate adaptation (transrating) algorithms are required. A current problem of transrating for HEVC is the high computational complexity associated with the encoder part of such a cascaded pixel domain transcoder. This paper focuses on deriving an optimal strategy for reducing the transcoding complexity with a complexity-scalable scheme. We propose different transcoding techniques which are able to reduce the transcoding complexity in both CU and PU optimization levels. At the CU level, CUs can be evaluated in top-to-bottom or bottom-to-top flows, in which the coding information of the input video stream is utilized to reduce the number of evaluations or to early terminate certain evaluations. At the PU level, the PU candidates are adaptively selected based on the probability of PU sizes and the co-located input PU partitioning. Moreover, with the use of different proposed methods, a complexity-scalable transrating scheme can be achieved. Furthermore, the transcoding complexity can be effectively controlled by the machine learning based approach. Simulations show that the proposed techniques provide a superior transcoding performance compared to the state-of-the-art related works. Additionally, the proposed methods can achieve a range of trade-offs between transrating complexity and coding performance. From the proposed schemes, the fastest approach is able to reduce the complexity by 82% while keeping the bitrate loss below 3%.\",\n \"Title: Low Delay Complexity Constrained Encoding\\nYear: 2016\\n\\nAbstract: Complex software appliances typically consist of multiple software processes running concurrently to exploit the available computational resources in the hardware. However, the computational complexity of these software processes is often variable and the processes can interfere with each other. This can be an issue for real-time applications with a fixed deadline like low delay video encoding. In the context of High Efficiency Video Coding (HEVC), a limited number of publications have focused on controlling the complexity of an HEVC video encoder. In this paper, we propose a technique to control complexity by deciding between 2Nx2N merge mode and full encoding, at different Coding Unit (CU) depths. Our results demonstrate fast convergence to a given complexity threshold after a maximum of 10 frames, and a limited loss in rate-distortion performance (on average 2.84% Bjontegaard delta rate for 60% complexity reduction).\",\n \"Title: Video Encoder Architecture for Low-Delay Live-Streaming Events.\\nYear: 2017\\n\\nAbstract: Video-streaming events such as virtual classrooms and video conferences require a low delay between sender and receiver. In order to achieve this requirement, and to make full use of the bandwidth capacity of each receiver, each client can be provided with a personalized bitstream of which the bit rate is continuously adapted to his current network bandwidth capacity. However, such an approach req...\",\n \"Title: AQUa: an adaptive framework for compression of sequencing quality scores with random access functionality.\\nYear: 2018\\n\\nAbstract: Motivation: The past decade has seen the introduction of new technologies that significantly lowered the cost of genome sequencing. As a result, the amount of genomic data that must be stored and transmitted is increasing exponentially. To mitigate storage and transmission issues, we introduce a framework for lossless compression of quality scores. Results: This article proposes AQUa, an adaptive framework for lossless compression of quality scores. To compress these quality scores, AQUa makes use of a configurable set of coding tools, extended with a Context-Adaptive Binary Arithmetic Coding scheme. When benchmarking AQUa against generic single-pass compressors, file sizes are reduced by up to 38.49% when comparing with GNU Gzip and by up to 6.48% when comparing with 7-Zip at the Ultra Setting, while still providing support for random access. When comparing AQUa with the purpose-built, single-pass, and state-of-the-art compressor SCALCE, which does not support random access, file sizes are reduced by up to 21.14%. When comparing AQUa with the purpose-built, dual-pass, and state-of-the-art compressor QVZ, which does not support random access, file sizes are larger by 6.42-33.47%. However, for one test file, the file size is 0.38% smaller, illustrating the strength of our single-pass compression framework. This work has been spurred by the current activity on genomic information representation (MPEG-G) within the ISO/IEC SC29/WG11 technical committee.\",\n \"Title: A highly scalable parallel encoder version of the emergent JEM video encoder.\\nYear: 2019\\n\\nAbstract: In 2016, 73% of total Internet traffic came from video transmission and this percentage is expected to reach 82% by 2021. These figures show the importance of using video compression standards that maximize video quality while minimizing the necessary bandwidth. In 2013, the HEVC standard was released accounting for an approximate 50% bit rate saving compared to H.264/AVC while maintaining the same reconstruction quality. To address increases in video IP traffic, a new generation of video coding techniques is required that achieve higher compression rates. Compression improvements are being implemented in a software package known as the Joint Exploration Test Model. In this work, we present two parallel JEM model solutions specifically designed for distributed memory platforms for both All Intra and Random Access coding modes. The proposed parallel algorithms achieved high levels of efficiency, in particular for the All Intra mode. They also showed great scalability.\",\n \"Title: Improving relevant subjective testing for validation: Comparing machine learning algorithms for finding similarities in VQA datasets using objective measures.\\nYear: 2019\\n\\nAbstract: Subjective quality assessment is a necessary activity to validate objective measures or to assess the performance of innovative video processing technologies. However, designing and performing comprehensive tests requires expertise and a large effort especially for the execution part. In this work we propose a methodology that, given a set of processed video sequences prepared by video quality experts, attempts to reduce the number of subjective tests by selecting a subset with minimum size which is expected to yield the same conclusions of the larger set. To this aim, we combine information coming from different types of objective quality metrics with clustering and machine learning algorithms that perform the actual selection, therefore reducing the required subjective assessment effort while trying to preserve the variety of content and conditions needed to ensure the validity of the conclusions. Experiments are conducted on one of the largest publicly available subjectively annotated video sequence dataset. As performance criterion, we chose the validation criteria for video quality measurement algorithms established by the International Telecommunication Union.\",\n \"Title: Improved Performance Measures for Video Quality Assessment Algorithms Using Training and Validation Sets\\nYear: 2019\\n\\nAbstract: The training and performance analysis of objective video quality assessment algorithms is complex due to the huge variety of possible content classes and transmission distortions. Several secondary issues such as free parameters in machine learning algorithms and alignment of subjective datasets put an additional burden on the developer. In this paper, three subsequent steps are presented to address such issues. First, the content and coding parameter space of a large-scale database is used to select dedicated subsets for training objective algorithms. This aims at providing a method for selecting the most significant contents and coding parameters from all imaginable combinations. In the practical case where only a limited set is available, it also helps us to avoid redundancy in the training subset selection. The second step is a discussion on performance measures for algorithms that employ machine-learning methods. The particularity of the performance measures is that the quality of the training and verification datasets is taken into consideration. Common issues that often use existing measures are presented, and improved or complementary methods are proposed. The measures are applied to two examples of no-reference objective assessment algorithms using the aforementioned subsets of the large-scale database. While limited in terms of practical applications, this sandbox approach of objectively predicting an objectively evaluated video sequences allows for eliminating additional influence factors from subjective studies. In the third step, the proposed performance measures are applied to the practical case of training and analyzing assessment algorithms on readily available subjectively annotated image datasets. The presentation method in this part of the paper can also be used as an exemplified recommendation for reporting in-depth information on the performance. Using this presentation method, future publications presenting newly developed quality assessment algorithms may be significantly improved.\",\n \"Title: Computing Quality-of-Experience Ranges for Video Quality Estimation\\nYear: 2019\\n\\nAbstract: Typically, the measurement of the Quality of Experience for video sequences aims at a single value, in most cases the Mean Opinion Score (MOS). Predicting this value using various algorithms has been widely studied. However, deviation from the MOS is often handled as an unpredictable error. The approach in this contribution estimates intervals of video quality instead of the single valued MOS. Well-known video quality estimators are fused together to output a lower and upper border for the expected video quality, on the basis of a model derived from a well-known subjectively annotated dataset. Results on different datasets provide insight on the suitability of the well-known estimators for this particular approach.\",\n \"Title: SILVR: a synthetic immersive large-volume plenoptic dataset.\\nYear: 2022\\n\\nAbstract: In six-degrees-of-freedom light-field (LF) experiences, the viewer's freedom is limited by the extent to which the plenoptic function was sampled. Existing LF datasets represent only small portions of the plenoptic function, such that they either cover a small volume, or they have limited field of view. Therefore, we propose a new LF image dataset \\\"SILVR\\\" that allows for six-degrees-of-freedom navigation in much larger volumes while maintaining full panoramic field of view. We rendered three different virtual scenes in various configurations, where the number of views ranges from 642 to 2226. One of these scenes (called Zen Garden) is a novel scene, and is made publicly available. We chose to position the virtual cameras closely together in large cuboid and spherical organisations ($2.2m^3$ to $48m^3$), equipped with 180{\\\\deg} fish-eye lenses. Every view is rendered to a color image and depth map of 2048px $\\\\times$ 2048px. Additionally, we present the software used to automate the multi-view rendering process, as well as a lens-reprojection tool that converts between images with panoramic or fish-eye projection to a standard rectilinear (i.e., perspective) projection. Finally, we demonstrate how the proposed dataset and software can be used to evaluate LF coding/rendering techniques(in this case for training NeRFs with instant-ngp). As such, we provide the first publicly-available LF dataset for large volumes of light with full panoramic field of view\",\n \"Title: On the Link Between Subjective Score Prediction and Disagreement of Video Quality Metrics\\nYear: 2021\\n\\nAbstract: Several video quality metrics (VQMs) have been proposed in many publications to predict how humans perceive video quality. It is common to observe significant disagreements amongst the quality predictions of these VQMs for the same video sequence. Following an extensive literature search, we found no publicised work that has investigated if such disagreements convey useful information on the accuracy of VQMs. Herein, a measure for quantifying the disagreement between VQMs is proposed. A small-scale subjective study is carried out to assess the effectiveness of our proposal. In particular, the proposed disagreement measure is shown to be extremely effective in determining whether the quality of any given processed video sequence (PVS) can be accurately predicted by the VQMs. This type of information is particularly useful for identifying video sequences that are likely to degrade the end-user's quality of experience (QoE). Our proposal is also useful in selecting the most effective PVSs to be employed in a subjective test. We show that the proposed disagreement measure can be effectively predicted from bitstream features. This establishes a link between the capability to accurately assess the quality of a PVS and the way it is encoded. In addition, an analysis is conducted to compare the performances of some well-known and widely used open-source metrics and two proprietary metrics. The two proprietary metrics are used by a large media company for enhancing its delivery pipeline. The outcome of this comparison highlights the suitability of the open-source VQM, Video Multi-method Assessment Fusion (VMAF), as a good benchmark quality measure for both the industrial and academic environments.\",\n \"Title: Subjective Evaluation of Visual Quality and Simulator Sickness of Short 360 degrees Videos: ITU-T Rec. P.919\\nYear: 2022\\n\\nAbstract: Recently an impressive development in immersive technologies, such as Augmented Reality (AR), Virtual Reality (VR) and 360 degrees video, has been witnessed. However, methods for quality assessment have not been keeping up. This paper studies quality assessment of 360 degrees video from the cross-lab tests (involving ten laboratories and more than 300 participants) carried out by the Immersive Media Group (IMG) of the Video Quality Experts Group (VQEG). These tests were addressed to assess and validate subjective evaluation methodologies for 360 degrees video. Audiovisual quality, simulator sickness symptoms, and exploration behavior were evaluated with short (from 10 seconds to 30 seconds) 360 degrees sequences. The following factors' influences were also analyzed: assessment methodology, sequence duration, Head-Mounted Display (HMD) device, uniform and non-uniform coding degradations, and simulator sickness assessment methods. The obtained results have demonstrated the validity of Absolute Category Rating (ACR) and Degradation Category Rating (DCR) for subjective tests with 360 degrees videos, the possibility of using 10-second videos (with or without audio) when addressing quality evaluation of coding artifacts, as well as any commercial HMD (satisfying minimum requirements). Also, more efficient methods than the long Simulator Sickness Questionnaire (SSQ) have been proposed to evaluate related symptoms with 360 degrees videos. These results have been instrumental for the development of the ITU-T Recommendation P.919. Finally, the annotated dataset from the tests is made publicly available for the research community.\",\n \"Title: Keyframe Insertion for Random Access and Packet-Loss Repair in H.264/AVC, H.265/HEVC, and H.266/VVC\\nYear: 2022\\n\\nAbstract: Sending low-delay live video over error-prone channels comes with packet-loss-repair and random-access challenges. Existing solutions have a negative impact on end-users with reliable connections or users that do not switch channels. To minimize this impact, the keyframe-insertion technique extends a compression-efficient normal stream (NS) with a companion stream (CS) solely consisting of keyframes [1].\",\n \"Title: Linking the cognitive load induced by route instruction types and building configuration during indoor route guidance, a usability study in VR\\nYear: 2022\\n\\nAbstract: Every route instruction type (e.g. map, symbol, photo) induces a specific cognitive load. However, when these types are used at different decision points in a building, the building configuration of these points also influences the induced cognitive load. Therefore, the process of route guidance results in an interaction between the instruction type and the decision point, which determines the induced cognitive load. One way of reducing cognitive load during route guidance is by using adaptive systems that show specific route instruction types at specific decision points. Therefore, in this VR experiment, the usability of such an adaptive indoor route guidance system is tested by tracking the wayfinding and gaze behavior of the users. First, the difference in wayfinding and gaze behavior between all route instruction types is compared. Next, the building configuration at the decision points is quantified through the architectural theory of space syntax, and the correlation with the wayfinding and gaze behavior is determined. Our findings indicate that adapting the route instruction type does make a difference for the user.\",\n \"Title: Watermarking for Large-Scale Video Distribution through Out-of-the-Loop Frame Replacement\\nYear: 2022\\n\\nAbstract: Forensic watermarking is used in large-scale video-distribution applications to track digital pirates after they illegally leak videos. Existing methods either have a high embedding complexity or have troubles providing imperceptibility and robustness. Therefore, this paper proposes a novel watermarking method that has a low embedding complexity, as well as sufficient imperceptibility and robustness. The main novelty is that frames are replaced out of the loop, which has a very low complexity. This is inspired by A/B watermarking, yet applied on frame level rather than video-segment level. The resulting drift-error artifacts are used for robust watermark detection. Additionally, a faster watermark detection method is proposed at the cost of a decrease in embedding capacity. We show that the drift errors caused by frame replacement are imperceptible, and that our method has a negligible impact on the video bitrate. Moreover, we demonstrate a high level of robustness. These interesting properties come at the cost of a non-blind detection with a relatively high complexity, that is partially solved by the proposed fast detection method. Additionally, a high detection complexity is not necessarily a problem since detection occurs infrequently and recent speed-up solutions exist. In conclusion, the proposed method is a novel and scalable solution that enables secure large-scale video distribution.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":60,"string":"60"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13254,"string":"13,254"}}},{"rowIdx":13255,"cells":{"question_id":{"kind":"string","value":"Gabriela Ochoa"},"question":{"kind":"string","value":"Generate an abstract for the title \"On Funnel Depths and Acceptance Criteria in Stochastic Local Search\" using the following items: 1. Fitness landscape funnels\n2. Local Optima Networks (LONs)\n3. Iterated local search (ILS) acceptance strategies\n4. Funnel depth and search difficulty"},"answer":{"kind":"string","value":"We propose looking at the phenomenon of fitness landscape funnels in terms of their depth. In particular, we examine how the depth of funnels in Local Optima Networks (LONs) of benchmark Quadratic Assignment Problem instances relate to metaheuristic performance. Three distinct iterated local search (ILS) acceptance strategies are considered: better-or-equal (standard), annealing-like, and restart. Funnel measurements are analysed for their connection to ILS performance on the underlying combinatorial problems. We communicate the findings through hierarchical clustering of LONs, network visualisations, subgroup analysis, correlation analysis, and Random Forest regression models. The results show that funnel depth is associated with search difficulty, and that there is an interplay between funnel structure and acceptance strategy. Standard and annealing acceptance work better than restart on both deep-funnel and shallow-funnel problems; standard acceptance is the best strategy when optimal funnel(s) are deep, while annealing acceptance is superior when they are shallow. Regression models including funnel depth measurements could explain up to 96% of ILS runtime variance (with annealing-like acceptance). The runtime of ILS with restarts was less explainable using funnel features."},"correct_answer":{"kind":"string","value":"We propose looking at the phenomenon of fitness landscape funnels in terms of their depth. In particular, we examine how the depth of funnels in Local Optima Networks (LONs) of benchmark Quadratic Assignment Problem instances relate to metaheuristic performance. Three distinct iterated local search (ILS) acceptance strategies are considered: better-or-equal (standard), annealing-like, and restart. Funnel measurements are analysed for their connection to ILS performance on the underlying combinatorial problems. We communicate the findings through hierarchical clustering of LONs, network visualisations, subgroup analysis, correlation analysis, and Random Forest regression models. The results show that funnel depth is associated with search difficulty, and that there is an interplay between funnel structure and acceptance strategy. Standard and annealing acceptance work better than restart on both deep-funnel and shallow-funnel problems; standard acceptance is the best strategy when optimal funnel(s) are deep, while annealing acceptance is superior when they are shallow. Regression models including funnel depth measurements could explain up to 96% of ILS runtime variance (with annealing-like acceptance). The runtime of ILS with restarts was less explainable using funnel features."},"documents":{"kind":"list like","value":["Title: Automated heuristic design\nYear: 2011\n\nAbstract: This tutorial will discuss state-of-the-art techniques for automating the design of heuristic search methods, in order to remove or reduce the need for a human expert in the process of designing an effective algorithm to solve a search problem. Using machine learning or meta-level search, several approaches have been proposed in computer science, artificial intelligence and operational research. The aim is to develop methodologies which can adapt to different environments without manually having to customise the search, or its parameters, for each particular problem domain. This can be seen as one of the drawbacks of many current metaheuristic and evolutionary implementations, which tend to have to be customised for a particular class of problems or even specific problem instances. We have identified two main types of approaches to this challenge: heuristic selection, and heuristic generation. In heuristic selection the idea is to automatically combine fixed pre-existing simple heuristics or neighbourhood structures to solve the problem at hand; whereas in heuristic generation the idea is to automatically create new heuristics (or heuristic components) suited to a given problem or class of problems. This latter approach is typically achieved by combining, through the use of genetic programming for example, components or building-blocks of human designed heuristics. This tutorial will go over the intellectual roots and origins of both automated heuristic selection and generation, before discussing work carried out to date in these two directions and then focusing on some observations and promising research directions.","Title: Local optima networks with escape edges\nYear: 2011\n\nAbstract: This paper proposes an alternative definition of edges (escape edges) for the recently introduced network-based model of combinatorial landscapes: Local Optima Networks (LON). The model compresses the information given by the whole search space into a smaller mathematical object that is the graph having as vertices the local optima and as edges the possible weighted transitions between them. The original definition of edges accounted for the notion of transitions between the basins of attraction of local optima. This definition, although informative, produced densely connected networks and required the exhaustive sampling of the basins of attraction. The alternative escape edges proposed here do not require a full computation of the basins. Instead, they account for the chances of escaping a local optima after a controlled mutation (e.g. 1 or 2 bit-flips) followed by hill-climbing. A statistical analysis comparing the two LON models for a set of NK landscapes, is presented and discussed. Moreover, a preliminary study is presented, which aims at validating the LON models as a tool for analyzing the dynamics of stochastic local search in combinatorial optimization.","Title: A PSO/ACO approach to knowledge discovery in a pharmacovigilance context\nYear: 2009\n\nAbstract: We propose and evaluate the use of a Particle Swarm Optimization/Ant Colony Optimization (PSO/ACO) methodology for classification and rule discovery in the context of medication postmarketing surveillance or pharmacovigilance. Our study considers a large data set of diabetic patients on two widely used antidiabetic drugs (rosiglitazone and pioglitazone), and the risk of myocardial infarction as an adverse effect. The goal is to determine the presence of previously undetected causal relationships between therapeutics, patient characteristics, and adverse medication outcomes. Since the proposed approach is able to discover classification rules, the elicited knowledge may suggest new hypotheses regarding associations between risk factors and an adverse event. Our classification results show high accuracy. Furthermore, several medication-related rules were discovered and analyzed. The elicited rules support previous studies from the medical literature. Moreover, one of the studied antidiabetic drugs (rosiglitazone) was found to have a significant higher risk of an adverse event on diabetic, hypertensive patients, as compared to the other drug. This last finding suggests that pioglitazone may have a protective effect against myocardial infarction on diabetic, hypertensive patients.","Title: Vehicle routing and adaptive iterated local search within the hyflex hyper-heuristic framework\nYear: 2012\n\nAbstract: HyFlex (Hyper-heuristic Flexible framework) [15] is a software framework enabling the development of domain independent search heuristics (hyper-heuristics), and testing across multiple problem domains. This framework was used as a base for the first Cross-domain Heuristic Search Challenge, a research competition that attracted significant international attention. In this paper, we present one of the problems that was used as a hidden domain in the competition, namely, the capacitated vehicle routing problem with time windows. The domain implements a data structure and objective function for the vehicle routing problem, as well as many state-of- the-art low-level heuristics (search operators) of several types. The domain is tested using two adaptive variants of a multiple-neighborhood iterated local search algorithm that operate in a domain independent fashion, and therefore can be considered as hyper-heuristics. Our results confirm that adding adaptation mechanisms improve the performance of hyper-heuristics. It is our hope that this new and challenging problem domain can be used to promote research within hyper-heuristics, adaptive operator selection, adaptive multi-meme algorithms and autonomous control for search algorithms.","Title: Assortative mating drastically alters the magnitude of error thresholds\nYear: 2006\n\nAbstract: The error threshold of replication is an important notion of the quasispecies evolution model; it is a critical mutation rate (error rate) beyond which structures obtained by an evolutionary process are destroyed more frequently than selection can reproduce them. With mutation rates above this critical value, an error catastrophe occurs and the genomic information is irretrievably lost. Recombination has been found to reduce the magnitude of the error threshold in evolving viral quasispecies. Here, through a simulation model based on genetic algorithms, we incorporate assortative mating and explore its effect on the magnitude of error thresholds. We found, consistently on four fitness landscapes, and across a range of evolutionary parameter values, that assortative mating overcomes the shift toward lower error threshold magnitudes induced by recombination, on the other hand, dissortative mating drastically reduces the error threshold magnitude. These results have implications to both natural and artificial evolution: First, they support the hypothesis that assortative mating by itself may overcome some of the evolutionary disadvantages of sex in nature. Second, they suggest a critical interaction between mutation rates and mating strategies in evolutionary algorithms.","Title: Setting The Mutation Rate: Scope And Limitations Of The 1/L Heuristic\nYear: 2002\n\nAbstract: An important decision to make when design- ing a GA is how to set the evolutionary pa- rameters. Among these parameters, the mu- tation rate has been acknowledged as the most sensitive one. All approaches so far for a near-optimal setting of the mutation rate have intrinsic limitations. A promising guideline is, however, the heuristic suggest- ing pm =1 /L where L is the string length. This paper is a first attempt to explore the scope and limitations of this heuristic on GAs with bit-string representation. Specifically, we select two real-world domains as test prob- lems and explore (i) whether optimal mu- tation rates change with time; and (ii) the interactions between the mutation rate and other evolutionary parameters. Results sug- gest that a constant mutation rate of 1/L is useful for a GA with a controlled 'moderate' selection pressure. It should be, however, re- vised for a weak or extremely strong selection pressure, and for a small population size.","Title: Analyzing the landscape of a graph based hyper-heuristic for timetabling problems\nYear: 2009\n\nAbstract: Hyper-heuristics can be thought of as \"heuristics to choose heuristics\". They are concerned with adaptively finding solution methods, rather than directly producing a solution for the particular problem at hand. Hence, an important feature of hyper-heuristics is that they operate on a search space of heuristics rather than directly on a search space of problem solutions. A motivating aim is to build systems which are fundamentally more generic than is possible today. Understanding the structure of these heuristic search spaces is therefore, a research direction worth exploring. In this paper, we use the notion of fitness landscapes in the context of constructive hyper-heuristics. We conduct a landscape analysis on a heuristic search space conformed by sequences of graph coloring heuristics for timetabling. Our study reveals that these landscapes have a high level of neutrality and positional bias. Furthermore, although rugged, they have the encouraging feature of a globally convex or big valley structure, which indicates that an optimal solution would not be isolated but surrounded by many local minima. We suggest that using search methodologies that explicitly exploit these features may enhance the performance of constructive hyper-heuristics.","Title: The Connectivity of NK Landscapes' Basins: A Network Analysis\nYear: 2008\n\nAbstract: We propose a network characterization of combinatorial fitness landscapes by adapting the notion of inherent networks proposed for energy surfaces. We use the well-known family of NK landscapes as an example. In our case the inherent network is the graph where the vertices represent the local maxima in the landscape, and the edges account for the transition probabilities between their corresponding basins of attraction. We exhaustively extracted such networks on representative small NK landscape instances, and performed a statistical characterization of their properties. We found that most of these network properties can be related to the search difficulty on the underlying NK landscapes with varying values of K.","Title: A Reinforcement Learning-Great-Deluge Hyper-Heuristic for Examination Timetabling\nYear: 2010\n\nAbstract: Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solution goes through two successive processes heuristic selection and move acceptance until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems with different characteristics. Some previous studies show that different combinations of heuristic selection and move acceptance as hyper-heuristic components might yield different performances. This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.","Title: Dispatching rules for production scheduling: a hyper-heuristic landscape analysis\nYear: 2009\n\nAbstract: Hyper-heuristics or \"heuristics to chose heuristics\" are an emergent search methodology that seeks to automate the process of selecting or combining simpler heuristics in order to solve hard computational search problems. The distinguishing feature of hyper-heuristics, as compared to other heuristic search algorithms, is that they operate on a search space of heuristics rather than directly on the search space of solutions to the underlying problem. Therefore, a detailed understanding of the properties of these heuristic search spaces is of utmost importance for understanding the behaviour and improving the design of hyper-heuristic methods. Heuristics search spaces can be studied using the metaphor of fitness landscapes. This paper formalises the notion of hyper-heuristic landscapes and performs a landscape analysis of the heuristic search space induced by a dispatching-rule-based hyper-heuristic for production scheduling. The studied hyper-heuristic spaces are found to be \"easy\" to search. They also exhibit some special features such as positional bias and neutrality. It is argued that search methods that exploit these features may enhance the performance of hyper-heuristics.","Title: A critical view of the evolutionary design of self-assembling systems\nYear: 2005\n\nAbstract: The automated design of systems which self-assemble is a fundamental cornerstone of nanotechnology. In this paper we review some work in which we have applied Evolutionary Algorithms (EAs) for the automated design of systems self-assembly. We will focus in three important minimalist self-assembly problems and we discuss the difficulties encountered while applying EAs to these test cases. We also suggest some promising lines of work that could possibly help overcome current limitations in the evolutionary design of self-assembling systems.","Title: Adaptive iterated local search for cross-domain optimisation\nYear: 2011\n\nAbstract: We propose two adaptive variants of a multiple neighborhood iterated local search algorithm. These variants employ online learning techniques, also called adaptive operation selection, in order to select which perturbation to apply at each iteration step from a set of available move operators. Using a common software interface (the HyFlex framework), the proposed algorithms are tested across four hard combinatorial optimisation problems: permutation flow shop, 1D bin packing, maximum satisfiability, and personnel scheduling (including instance data from real-world industrial applications). Using the HyFlex framework, exactly the same high level search strategy can be applied to all the domains and instances. Our results confirm that the adaptive variants outperform a baseline iterated local search with uniform random selection of the move operators. We argue that the adaptive algorithms proposed are general yet powerful, and contribute to the goal of increasing the generality and applicability of heuristic search.","Title: The cross-domain heuristic search challenge – an international research competition\nYear: 2011\n\nAbstract: The first Cross-domain Heuristic Search Challenge (CHeSC 2011) seeks to bring together practitioners from operational research, computer science and artificial intelligence who are interested in developing more generally applicable search methodologies. The challenge is to design a search algorithm that works well, not only across different instances of the same problem, but also across different problem domains. This article overviews the main features of this challenge.","Title: Population-based optimization of cytostatic/cytotoxic combination cancer chemotherapy\nYear: 2013\n\nAbstract: This article studies the suitability of modern population based algorithms for designing combination cancer chemotherapies. The problem of designing chemotherapy schedules is expressed as an optimization problem (an optimal control problem) where the objective is to minimize the tumor size without compromising the patient's health. Given the complexity of the underlying mathematical model describing the tumor's progression (considering two types of drugs, the cell cycle and the immune system response), analytical and classical optimization methods are not suitable, instead, stochastic heuristic optimization methods are the right tool to solve the optimal control problem. Considering several solution quality and performance metrics, we compared three powerful heuristic algorithms for real-parameter optimization, namely, CMA evolution strategy, differential evolution, and particle swarm pattern search method. The three algorithms were able to successfully solve the posed problem. However, differential evolution outperformed its counterparts both in quality of the obtained solutions and efficiency of search.","Title: On the automatic discovery of variants of the NEH procedure for flow shop scheduling using genetic programming\nYear: 2011\n\nAbstract: We use genetic programming to find variants of the well-known Nawaz, En-score and Ham (NEH) heuristic for the permutation flow shop problem. Each variant uses a different ranking function to prioritize operations during schedule construction. We have tested our ideas on problems where jobs have release times, due dates, and weights and have considered five objective functions: makespan, sum of tardiness, sum of weighted tardiness, sum of completion times and sum of weighted completion times. The implemented genetic programming system has been carefully tuned and used to generate one variant of NEH for each objective function. The new NEHs, obtained with genetic programming, have been compared with the original NEH and randomized NEH versions on a large set of benchmark problems. Our results indicate that the NEH variants discovered by genetic programming are superior to the original NEH and its stochastic version on most of the problems investigated. Journal of the Operational Research Society (2011) 62, 381-396. doi:10.1057/jors.2010.132 Published online 25 August 2010","Title: Local optima networks, landscape autocorrelation and heuristic search performance\nYear: 2012\n\nAbstract: Recent developments in fitness landscape analysis include the study of Local Optima Networks (LON) and applications of the Elementary Landscapes theory. This paper represents a first step at combining these two tools to explore their ability to forecast the performance of search algorithms. We base our analysis on the Quadratic Assignment Problem (QAP) and conduct a large statistical study over 600 generated instances of different types. Our results reveal interesting links between the network measures, the autocorrelation measures and the performance of heuristic search algorithms.","Title: Fitness Landscapes And The Andrews-Curtis Conjecture\nYear: 2012\n\nAbstract: Attempts have been made to eliminate some potential counterexamples to the Andrews-Curtis conjecture using the combinatorial optimization methods of blind-search and the genetic algorithms meta-heuristic. Breadth-first search with secondary storage is currently the most successful method, which raises questions regarding the inferior performance of heuristic search. In order to understand the underlying reasons we obtain fitness landscape metrics for a number of balanced presentations and draw conclusions regarding the likely effectiveness of other meta-heuristics.","Title: Adaptive evolutionary algorithms and extensions to the hyflex hyper-heuristic framework\nYear: 2012\n\nAbstract: HyFlex is a recently proposed software framework for implementing hyper-heuristics and domain-independent heuristic optimisation algorithms [13]. Although it was originally designed to implement hyper-heuristics, it provides a population and a set of move operators of different types. This enable the implementation of adaptive versions of other heuristics such as evolutionary algorithms and iterated local search. The contributions of this article are twofold. First, a number of extensions to the HyFlex framework are proposed and implemented that enable the design of more effective adaptive heuristics. Second, it is demonstrated that adaptive evolutionary algorithms can be implemented within the framework, and that the use of crossover and a diversity metric produced improved results, including a new best-known solution, on the studied vehicle routing problem.","Title: Hyper-heuristics and cross-domain optimization\nYear: 2012\n\nAbstract: Hyper-heuristics comprise a set of approaches which are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was firstly used in 2000 to describe 'heuristics to choose heuristics' in the context of combinatorial optimization. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s and 1970s, and a number of related research areas can be identified. The definition of hyper-heuristics has been recently extended to refer to 'a search method or learning mechanism for selecting or generating heuristics to solve computational search problems'. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This tutorial will discuss the motivation, definition and classification of hyper-heuristics. The tutorial will also cover a recent international research competition: the first 'Cross-domain Heuristic Search Challenge'(CHeSC 2011) (http://www.asap.cs.nott.ac.uk/chesc2011). The challenge was to design a search algorithm that works well, not only across different instances of the same problem, but also across different problem domains. Using a sport metaphor, it is the 'Decathlon' challenge of search heuristics. To support the competition, and to promote research in this area, a software framework (HyFlex) was developed, which features a common interface for dealing with different optimization problems. The tutorial will summarize the main features of HyFlex and the competition; and will present an analysis of the results, highlighting the design principles of the most successful cross-domain hyper-heuristics.","Title: Partial neighborhoods of the traveling salesman problem\nYear: 2011\n\nAbstract: The Traveling Salesman Problem (TSP) is known to display an elementary landscape under all k-opt move operators. Previous work has also shown that partial neighborhoods may exist that retain some properties characteristic of elementary landscapes. For a tour of n cities, we show that the 2-opt neighborhood can be decomposed into n/2-1 partial neighborhoods. While this paper focuses on the TSP, it also introduces a more formal treatment of partial neighborhoods which applies to all elementary landscapes. Tracking partial neighborhood averages in elementary landscapes requires partitioning the cost matrix. After every move in the search space, the relevant partitions must be updated. However, just as the evaluation function allows a partial update for the TSP, there also exists a partial update for the cost matrix partitions. By only looking at a subset of the partial neighborhoods we can further reduce the cost of updating the cost matrix partitions.","Title: Local Optima Networks: A New Model of Combinatorial Fitness Landscapes.\nYear: 2014\n\nAbstract: This chapter overviews a recently introduced network-based model of combinatorial landscapes: Local Optima Networks (LON). The model compresses the information given by the whole search space into a smaller mathematical object that is a graph having as vertices the local optima and as edges the possible weighted transitions between them. Two definitions of edges have been proposed: basin-transition and escape-edges, which capture relevant topological features of the underlying search spaces. This network model brings a new set of metrics to characterize the structure of combinatorial landscapes, those associated with the science of complex networks. These metrics are described, and results are presented of local optima network extraction and analysis for two selected combinatorial landscapes: NK landscapes and the quadratic assignment problem. Network features are found to correlate with and even predict the performance of heuristic search algorithms operating on these problems.","Title: Optimal Mutation Rates and Selection Pressure in Genetic Algorithms\nYear: 2000\n\nAbstract: Abstract: It has been argued that optimal per-locusmutation rates in GAs are proportional toselection pressure and the reciprocal of genotypelength. In this paper we suggest thatthe notion of error threshold, borrowed frommolecular evolution, sheds new light on thisargument. We show empirically the existenceof error thresholds in GAs running on a simpleabstract landscape; and then investigatea real-world industrial problem, demonstratingcomparable phenomena in a practical application.","Title: An effective heuristic for the two-dimensional irregular bin packing problem.\nYear: 2013\n\nAbstract: This paper proposes an adaptation, to the two-dimensional irregular bin packing problem of the Djang and Finch heuristic (DJD), originally designed for the one-dimensional bin packing problem. In the two-dimensional case, not only is it the case that the piece’s size is important but its shape also has a significant influence. Therefore, DJD as a selection heuristic has to be paired with a placement heuristic to completely construct a solution to the underlying packing problem. A successful adaptation of the DJD requires a routine to reduce computational costs, which is also proposed and successfully tested in this paper. Results, on a wide variety of instance types with convex polygons, are found to be significantly better than those produced by more conventional selection heuristics.","Title: HyFlex: a benchmark framework for cross-domain heuristic search\nYear: 2012\n\nAbstract: This paper presents HyFlex, a software framework for the development of cross-domain search methodologies. The framework features a common software interface for dealing with different combinatorial optimisation problems and provides the algorithm components that are problem specific. In this way, the algorithm designer does not require a detailed knowledge of the problem domains and thus can concentrate his/her efforts on designing adaptive general-purpose optimisation algorithms. Six hard combinatorial problems are fully implemented: maximum satisfiability, one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing. Each domain contains a varied set of instances, including real-world industrial data and an extensive set of state-of-the-art problem specific heuristics and search operators. HyFlex represents a valuable new benchmark of heuristic search generality, with which adaptive cross-domain algorithms are being easily developed and reliably compared.This article serves both as a tutorial and a as survey of the research achievements and publications so far using HyFlex.","Title: Modeling and optimization of combined cytostatic and cytotoxic cancer chemotherapy.\nYear: 2010\n\nAbstract: This study extends a previous mathematical model of cancer cytotoxic chemotherapy, which considered cycling tumor cells and interactions with the immune system, by incorporating a different type of drug: a cytostatic agent. The effect of a cytostatic drug is to arrest cells in a phase of their cycle. In consequence, once tumor cells are arrested and synchronized they can be targeted with a cytotoxic agent, thus maximizing cell kill fraction and minimizing normal cell killing. The goal is to incorporate the new drug into the chemotherapy protocol and devise optimal delivery schedules.The problem of designing efficient combined chemotherapies is formulated as an optimal control problem and tackled using a state-of-the-art evolutionary algorithm for real-valued encoding, namely the covariance matrix adaptation evolution strategy. Alternative solution representations and three formulations of the underlying objective function are proposed and compared.The optimization problem was successfully solved by the proposed approach. The encoding that enforced non-overlapping (simultaneous) application of the two types of drugs produced competitive protocols with significant less amount of toxic drug, thus achieving better immune system health. When compared to treatment protocols that only consider a cytotoxic agent, the incorporation of a cytostatic drug dramatically improved the outcome and performance of the overall treatment, confirming in silico that the combination of a cytostatic with a cytotoxic agent improves the efficacy and efficiency of the chemotherapy.We conclude that the proposed approach can serve as a valuable decision support tool for the medical practitioner facing the complex problem of designing efficient combined chemotherapies.","Title: Towards the decathlon challenge of search heuristics\nYear: 2009\n\nAbstract: We present an object oriented framework for designing and evaluating heuristic search algorithms that achieve a high level of generality and work well on a wide range of combinatorial optimization problems. Our framework, named HyFlex, differs from most software tools for meta-heuristics and evolutionary computation in that it provides the algorithm components that are problem-specific instead of those which are problem-independent. In this way, we simultaneously liberate algorithm designers from needing to know the details of the problem domains; and prevent them from incorporating additional problem specific information in their algorithms. The efforts need instead to be focused on designing high-level strategies to intelligently combine the provided problem specific algorithmic components. We plan to propose a challenge, based on our framework, where the winners will be those algorithms with a better overall performance across all of the different domains. Using an Olympic metaphor, we are not solely focussed on the 100 meters race, but instead on the decathlon of modern search methodologies.","Title: Search methodologies in real-world software engineering\nYear: 2013\n\nAbstract: One of the aims of software engineering is to reduce overall software costs. Optimisation is, therefore, relevant to the process of software development. This article describes recent case studies on the application of modern search methodologies to challenging real-world problems in software engineering. It also describes a recent research initiative: Dynamic Adaptive Automated Software Engineering (DAASE), whose goal is to embed optimisation into deployed software to create self-optimising adaptive systems. The article accompanies an invited talk for the Workshop on Bridging the Gap between Industry and Academia in Optimisation to be held as part of GECCO 2013.","Title: Local optima networks and the performance of iterated local search\nYear: 2012\n\nAbstract: Local Optima Networks (LONs) have been recently proposed as an alternative model of combinatorial fitness landscapes. The model compresses the information given by the whole search space into a smaller mathematical object that is the graph having as vertices the local optima and as edges the possible weighted transitions between them. A new set of metrics can be derived from this model that capture the distribution and connectivity of the local optima in the underlying configuration space. This paper departs from the descriptive analysis of local optima networks, and actively studies the correlation between network features and the performance of a local search heuristic. The NK family of landscapes and the Iterated Local Search metaheuristic are considered. With a statistically-sound approach based on multiple linear regression, it is shown that some LONs' features strongly influence and can even partly predict the performance of a heuristic search algorithm. This study validates the expressive power of LONs as a model of combinatorial fitness landscapes.","Title: A study of NK landscapes' basins and local optima networks\nYear: 2008\n\nAbstract: We propose a network characterization of combinatorial fitness landscapes by adapting the notion of inherent networks proposed for energy surfaces (Doye, 2002). We use the well-known family of $NK$ landscapes as an example. In our case the inherent network is the graph where the vertices are all the local maxima and edges mean basin adjacency between two maxima. We exhaustively extract such networks on representative small NK landscape instances, and show that they are 'small-worlds'. However, the maxima graphs are not random, since their clustering coefficients are much larger than those of corresponding random graphs. Furthermore, the degree distributions are close to exponential instead of Poissonian. We also describe the nature of the basins of attraction and their relationship with the local maxima network.","Title: Clustering of local optima in combinatorial fitness landscapes\nYear: 2011\n\nAbstract: Using the recently proposed model of combinatorial landscapes: local optima networks, we study the distribution of local optima in two classes of instances of the quadratic assignment problem. Our results indicate that the two problem instance classes give rise to very different configuration spaces. For the so-called real-like class, the optima networks possess a clear modular structure, while the networks belonging to the class of random uniform instances are less well partitionable into clusters. We briefly discuss the consequences of the findings for heuristically searching the corresponding problem spaces.","Title: Consensus Sequence Plots and Error Thresholds: Tools for Visualising the Structure of Fitness Landscapes\nYear: 2000\n\nAbstract: This paper investigates the occurrence of error thresholds in genetic algorithms (GAs) running on a wide range of fitness landscape structures. The error threshold, a notion from molecular evolution, is a critical mutation rate beyond which the evolutionary dynamics of a population changes drastically. The paper also introduces Consensus sequence plots, an empirical tool for locating error thresholds on complex landscapes. This plots were borrowed and adapted from theoretical biology. Results suggest that error thresholds occur in GAs but only on landscapes of certain degree of ruggedness or complexity. Moreover, consensus sequence plots can be useful for predicting some features of a landscape such as ruggedness and \"step-ness\". We argue that error thresholds and consensus sequence plots, may become useful tools for analyzing evolutionary algorithms and visualising the structure of fitness landscapes.","Title: The cooperative royal road: avoiding hitchhiking\nYear: 2007\n\nAbstract: We propose using the so called Royal Road functions astest functions for cooperative co-evolutionary algorithms (CCEAs). TheRoyal Road functions were created in the early 90's with the aim ofdemonstrating the superiority of genetic algorithms over local searchmethods. Unexpectedly, the opposite was found to be true. The researchdeepened our understanding of the phenomenon of hitchhiking whereunfavorable alleles may become established in the population followingan early association with an instance of a highly fit schema. Here, wetake advantage of the modular and hierarchical structure of the RoyalRoad functions to adapt them to a co-evolutionary setting. Using a multiplepopulation approach, we show that a CCEA easily outperforms astandard genetic algorithm on the Royal Road functions, by naturallyovercoming the hitchhiking effect. Moreover, we found that the optimalnumber of sub-populations for the CCEA is not the same as the numberof components that the function can be linearly separated into, andpropose an explanation for this behavior. We argue that this class offunctions may serve in foundational studies of cooperative co-evolution.","Title: Evolving l-systems to capture protein structure native conformations\nYear: 2005\n\nAbstract: A protein is a linear chain of amino acids that folds into a unique functional structure, called its native state. In this state, proteins show repeated substructures like alpha helices and beta sheets. This suggests that native structures may be captured by the formalism known as Lindenmayer systems (L-systems). In this paper an evolutionary approach is used as the inference procedure for folded structures on simple lattice models. The algorithm searches the space of L-systems which are then executed to obtain the phenotype, thus our approach is close to Grammatical Evolution. The problem is to find a set of rewriting rules that represents a target native structure on the lattice model. The proposed approach has produced promising results for short sequences. Thus the foundations are set for a novel encoding based on L-systems for evolutionary approaches to both the Protein Structure Prediction and Inverse Folding Problems.","Title: Cheating for problem solving: a genetic algorithm with social interactions\nYear: 2009\n\nAbstract: We propose a variation of the standard genetic algorithm that incorporates social interaction between the individuals in the population. Our goal is to understand the evolutionary role of social systems and its possible application as a non-genetic new step in evolutionary algorithms. In biological populations, i.e. animals, even human beings and microorganisms, social interactions often affect the fitness of individuals. It is conceivable that the perturbation of the fitness via social interactions is an evolutionary strategy to avoid trapping into local optimum, thus avoiding a fast convergence of the population. We model the social interactions according to Game Theory. The population is, therefore, composed by cooperator and defector individuals whose interactions produce payoffs according to well known game models (prisoner's dilemma, chicken game, and others). Our results on Knapsack problems show, for some game models, a significant performance improvement as compared to a standard genetic algorithm.","Title: Error Thresholds and Their Relation to Optimal Mutation Rates\nYear: 1999\n\nAbstract: The error threshold -- a notion from molecular evolution -- is the critical mutation rate beyond which structures obtained by the evolutionary process are destroyed more frequently than selection can reproduce them. We argue that this notion is closely related to the more familiar notion of optimal mutation rates in Evolutionary Algorithms (EAs). This correspondence has been intuitively perceived before ([9], [11]). However, no previous study, to our knowledge, has been aimed at explicitly testing the hypothesis of such a relationship. Here we propose a methodology for doing so. Results on a restricted range of fitness landscapes suggest that these two notions are indeed correlated. There is not, however, a critically precise optimal mutation rate but rather a range of values producing similar near-optimal performance. When recombination is used, both error thresholds and optimal mutation ranges are lower than in the asexual case. This knowledge may have both theoretical relevance in understanding EA behavior, and practical implications for setting optimal values of evolutionary parameters.","Title: On Genetic Algorithms and Lindenmayer Systems\nYear: 1998\n\nAbstract: This paper describes a system for simulating the evolution of artificial 2D plant morphologies. Virtual plant genotypes are inspired by the mathemati- cal formalism known as Lindenmayer systems (L-systems). The phenotypes are the branching structures resulting from the derivation and graphic interpretation of the genotypes. Evolution is simulated using a genetic algorithm with a fitness function inspired by current evolutionary hypotheses concerning the factors that have had the greatest effect on plant evolution. The system also provides inter- active selection, allowing the user to d irect simulated evolution towards pre- ferred ph enotypes. Simulation results demonstrate many interesting structures, suggesting that artificial evolution constitutes a powerful tool for (1) exploring the large, complex space of branching structures found in nature, and (2) gen- erating novel ones. Finally, we emphasize that Lindenmayer systems constitute a highly suitable encoding for artificial evolution studies.","Title: First-improvement vs. best-improvement local optima networks of NK landscapes\nYear: 2012\n\nAbstract: This paper extends a recently proposed model for combinatorial landscapes: Local Optima Networks (LON), to incorporate a first-improvement (greedy-ascent) hill-climbing algorithm, instead of a best-improvement (steepestascent) one, for the definition and extraction of the basins of attraction of the landscape optima. A statistical analysis comparing best and first improvement network models for a set of NK landscapes, is presented and discussed. Our results suggest structural differences between the two models with respect to both the network connectivity, and the nature of the basins of attraction. The impact of these differences in the behavior of search heuristics based on first and best improvement local search is thoroughly discussed.","Title: Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms\nYear: 2014\n\nAbstract: The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to promote awareness and cross-fertilisation of ideas across the (by and large, non-overlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.","Title: An evolutionary approach to cancer chemotherapy scheduling\nYear: 2007\n\nAbstract: In this paper, we investigate the employment of evolutionary algorithms as a search mechanism in a decision support system for designing chemotherapy schedules. Chemotherapy involves using powerful anti-cancer drugs to help eliminate cancerous cells and cure the condition. It is given in cycles of treatment alternating with rest periods to allow the body to recover from toxic side-effects. The number and duration of these cycles would depend on many factors, and the oncologist would schedule a treatment for each patient's condition. The design of a chemotherapy schedule can be formulated as an optimal control problem; using an underlying mathematical model of tumour growth (that considers interactions with the immune system and multiple applications of a cycle-phase-specific drug), the objective is to find effective drug schedules that help eradicate the tumour while maintaining the patient health's above an acceptable level. A detailed study on the effects of different objective functions, in the quality and diversity of the solutions, was performed. A term that keeps at a minimum the tumour levels throughout the course of treatment was found to produce more regular treatments, at the expense of imposing a higher strain on the patient's health, and reducing the diversity of the solutions. Moreover, when the number of cycles was incorporated in the problem encoding, and a parsimony pressure added to the objective function, shorter treatments were obtained than those initially found by trial and error.","Title: Complex-network analysis of combinatorial spaces: the NK landscape case.\nYear: 2012\n\nAbstract: We propose a network characterization of combinatorial fitness landscapes by adapting the notion of inherent networks proposed for energy surfaces. We use the well-known family of NK landscapes as an example. In our case the inherent network is the graph whose vertices represent the local maxima in the landscape, and the edges account for the transition probabilities between their corresponding basins of attraction. We exhaustively extracted such networks on representative NK landscape instances, and performed a statistical characterization of their properties. We found that most of these network properties are related to the search difficulty on the underlying NK landscapes with varying values of K.","Title: Assortative mating in genetic algorithms for dynamic problems\nYear: 2005\n\nAbstract: Non-random mating seems to be the norm in nature among sexual organisms. A common mating criteria among animals is assortative mating, where individuals mate according to their phenotype similarities (or dissimilarities). This paper explores the effect of including assortative mating in genetic algorithms for dynamic problems. A wide range of mutation rates was explored, since comparative results were found to change drastically for different mutation rates. The strategy for selecting mates was found to interact with the mutation rate value: low mutation rates were the best choice for dissortative mating, medium mutation values for the standard GA, and higher mutation rates for assortative mating. Thus, GA efficiency is related to mate selection strategies in connection with mutation values. For low mutation rates typically used in GA, dissortative mating was shown to be a robust and promising strategy for dynamic problems.","Title: Communities of minima in local optima networks of combinatorial spaces\nYear: 2012\n\nAbstract: In this work, we present a new methodology to study the structure of the configuration spaces of hard combinatorial problems. It consists in building the network that has as nodes the locally optimal configurations and as edges the weighted oriented transitions between their basins of attraction. We apply the approach to the detection of communities in the optima networks produced by two different classes of instances of a hard combinatorial optimization problem: the quadratic assignment problem (QAP). We provide evidence indicating that the two problem instance classes give rise to very different configuration spaces. For the so-called real-like class, the networks possess a clear modular structure, while the optima networks belonging to the class of random uniform instances are less well partitionable into clusters. This is convincingly supported by using several statistical tests. Finally, we briefly discuss the consequences of the findings for heuristically searching the corresponding problem spaces.","Title: The component model for elementary landscapes and partial neighborhoods\nYear: 2014\n\nAbstract: Local search algorithms exploit moves on an adjacency graph of the search space. An ''elementary landscape'' exists if the objective function f is an eigenfunction of the Laplacian of the graph induced by the neighborhood operator; this allows various statistics about the neighborhood to be computed in closed form. A new component based model makes it relatively simple to prove that certain types of landscapes are elementary. The traveling salesperson problem, weighted graph (vertex) coloring and the minimum graph bisection problem yield elementary landscapes under commonly used local search operators. The component model is then used to efficiently compute the mean objective function value over partial neighborhoods for these same problems. For a traveling salesperson problem over n cities, the 2-opt neighborhood can be decomposed into @?n/[email protected]? partial neighborhoods. For graph coloring and the minimum graph bisection problem, partial neighborhoods can be used to focus search on those moves that are capable of producing a solution with a strictly improving objective function value.","Title: Effective learning hyper-heuristics for the course timetabling problem.\nYear: 2014\n\nAbstract: •Course timetabling constraints are converted into generic structures.•Two types of learning for operator selection are contrasted: online and offline.•An empirical approach for selecting the best operators from a pool is proposed.•Online learning shows competitive results, even producing new best-known solutions.•The proposed method achieves a balance between generality and effectiveness.","Title: A unified hyper-heuristic framework for solving bin packing problems\nYear: 2014\n\nAbstract: One- and two-dimensional packing and cutting problems occur in many commercial contexts, and it is often important to be able to get good-quality solutions quickly. Fairly simple deterministic heuristics are often used for this purpose, but such heuristics typically find excellent solutions for some problems and only mediocre ones for others. Trying several different heuristics on a problem adds to the cost. This paper describes a hyper-heuristic methodology that can generate a fast, deterministic algorithm capable of producing results comparable to that of using the best problem-specific heuristic, and sometimes even better, but without the cost of trying all the heuristics. The generated algorithm handles both one- and two-dimensional problems, including two-dimensional problems that involve irregular concave polygons. The approach is validated using a large set of 1417 such problems, including a new benchmark set of 480 problems that include concave polygons.","Title: Local Optima Networks Of The Permutation Flow-Shop Problem\nYear: 2013\n\nAbstract: This article extracts and analyzes local optima networks for the permutation flow-shop problem. Two widely used move operators for permutation representations, namely, swap and insertion, are incorporated into the network landscape model. The performance of a heuristic search algorithm on this problem is also analyzed. In particular, we study the correlation between local optima network features and the performance of an iterated local search heuristic. Our analysis reveals that network features can explain and predict problem difficulty. The evidence confirms the superiority of the insertion operator for this problem.","Title: Evolvability metrics in adaptive operator selection.\nYear: 2014\n\nAbstract: Evolvability metrics gauge the potential for fitness of an individual rather than fitness itself. They measure the local characteristics of the fitness landscape surrounding a solution. In adaptive operator selection the goal is to dynamically select from a given pool the operator to apply next during the search process. An important component of these adaptive schemes is credit assignment, whereby operators are rewarded according to their observed performance. This article brings the notion of evolvability to adaptive operator selection, by proposing an autonomous search algorithm that rewards operators according to their potential for fitness rather than their immediate fitness improvement. The approach is tested within an evolutionary algorithm framework featuring several mutation operators on binary strings. Three benchmark problems of increasing difficulty, Onemax, Royal Staircase and Multiple Knapsack are considered. Experiments reveal that evolvability metrics significantly improve the performance of adaptive operator selection, when compared against standard fitness improvement metrics. The main contribution is to effectively use fitness landscape metrics to guide a self-configuring algorithm.","Title: Generalized asymmetric partition crossover (GAPX) for the asymmetric TSP\nYear: 2014\n\nAbstract: The Generalized Partition Crossover (GPX) constructs new solutions for the Traveling Salesman Problem (TSP) by finding recombining partitions with one entry and one exit in the graph composed by the union of two parent solutions. If there are k recombining partitions in the union graph, 2^k-2 solutions are simultaneously exploited by GPX. Generalized Asymmetric Partition Crossover (GAPX) is introduced; it finds more recombining partitions and can also find partitions for the asymmetric TSP. GAPX does this by locating partitions that cut vertices of degree 4 in the union graph and by finding partitions with multiple entry and exit points, both in O(n) time. GAPX can improve the quality of solutions generated by the Lin-Kernighan-Helsgaun heuristic and improve the state of the art for the asymmetric TSP.","Title: Local Optima Networks of the Quadratic Assignment Problem\nYear: 2011\n\nAbstract: Using a recently proposed model for combinatorial landscapes, Local Optima Networks (LON), we conduct a thorough analysis of two types of instances of the Quadratic Assignment Problem (QAP). This network model is a reduction of the landscape in which the nodes correspond to the local optima, and the edges account for the notion of adjacency between their basins of attraction. The model was inspired by the notion of `inherent network' of potential energy surfaces proposed in physical-chemistry. The local optima networks extracted from the so called uniform and real-like QAP instances, show features clearly distinguishing these two types of instances. Apart from a clear confirmation that the search difficulty increases with the problem dimension, the analysis provides new confirming evidence explaining why the real-like instances are easier to solve exactly using heuristic search, while the uniform instances are easier to solve approximately. Although the local optima network model is still under development, we argue that it provides a novel view of combinatorial landscapes, opening up the possibilities for new analytical tools and understanding of problem difficulty in combinatorial optimization.","Title: Error Thresholds in Genetic Algorithms\nYear: 2006\n\nAbstract: The error threshold of replication is an important notion in the quasispecies evolution model; it is a critical mutation rate (error rate) beyond which structures obtained by an evolutionary process are destroyed more frequently than selection can reproduce them. With mutation rates above this critical value, an error catastrophe occurs and the genomic information is irretrievably lost. Therefore, studying the factors that alter this magnitude has important implications in the study of evolution. Here we use a genetic algorithm, instead of the quasispecies model, as the underlying model of evolution, and explore whether the phenomenon of error thresholds is found on finite populations of bit strings evolving on complex landscapes. Our empirical results verify the occurrence of error thresholds in genetic algorithms. In this way, this notion is brought from molecular evolution to evolutionary computation. We also study the effect of modifying the most prominent evolutionary parameters on the magnitude of this critical value, and found that error thresholds depend mainly on the selection pressure and genotype length.","Title: Local Optima Networks of NK Landscapes With Neutrality\nYear: 2011\n\nAbstract: In previous work, we have introduced a network based model that abstracts many details of the underlying landscape and compresses the landscape information into a weighted, oriented graph which we call the local optima network. The vertices of this graph are the local optima of the given fitness landscape, while the arcs are transition probabilities between local optima basins. Here, we extend this formalism to neutral fitness landscapes, which are common in difficult combinatorial search spaces. The study is based on two neutral variants of the well-known NK family of landscapes (where N stands for the chromosome length, and K for the number of gene epistatic interactions within the chromosome). By using these two NK variants, probabilistic (NKp), and quantified NK (NKq), in which the amount of neutrality can be tuned by a parameter, we show that our new definitions of the optima networks and the associated basins are consistent with the previous definitions for the non-neutral case. Moreover, our empirical study and statistical analysis show that the features of neutral landscapes interpolate smoothly between landscapes with maximum neutrality and non-neutral ones. We found some unknown structural differences between the two studied families of neutral landscapes. But overall, the network features studied confirmed that neutrality, in landscapes with percolating neutral networks, may enhance heuristic search. Our current methodology requires the exhaustive enumeration of the underlying search space. Therefore, sampling techniques should be developed before this analysis can have practical implications. We argue, however, that the proposed model offers a new perspective into the problem difficulty of combinatorial optimization problems and may inspire the design of more effective search heuristics.","Title: An Integer Linear Programming approach to the single and bi-objective Next Release Problem\nYear: 2015\n\nAbstract: ContextThe Next Release Problem involves determining the set of requirements to implement in the next release of a software project. When the problem was first formulated in 2001, Integer Linear Programming, an exact method, was found to be impractical because of large execution times. Since then, the problem has mainly been addressed by employing metaheuristic techniques. ObjectiveIn this paper, we investigate if the single-objective and bi-objective Next Release Problem can be solved exactly and how to better approximate the results when exact resolution is costly. MethodsWe revisit Integer Linear Programming for the single-objective version of the problem. In addition, we integrate it within the Epsilon-constraint method to address the bi-objective problem. We also investigate how the Pareto front of the bi-objective problem can be approximated through an anytime deterministic Integer Linear Programming-based algorithm when results are required within strict runtime constraints. Comparisons are carried out against NSGA-II. Experiments are performed on a combination of synthetic and real-world datasets. FindingsWe show that a modern Integer Linear Programming solver is now a viable method for this problem. Large single objective instances and small bi-objective instances can be solved exactly very quickly. On large bi-objective instances, execution times can be significant when calculating the complete Pareto front. However, good approximations can be found effectively. ConclusionThis study suggests that (1) approximation algorithms can be discarded in favor of the exact method for the single-objective instances and small bi-objective instances, (2) the Integer Linear Programming-based approximate algorithm outperforms the NSGA-II genetic approach on large bi-objective instances, and (3) the run times for both methods are low enough to be used in real-world situations.","Title: The Multi-Funnel Structure of TSP Fitness Landscapes: A Visual Exploration.\nYear: 2015\n\nAbstract: We use the Local Optima Network model to study the structure of symmetric TSP fitness landscapes. The 'big-valley' hypothesis holds that for TSP and other combinatorial problems, local optima are not randomly distributed, instead they tend to be clustered around the global optimum. However, a recent study has observed that, for solutions close in evaluation to the global optimum, this structure breaks down into multiple valleys, forming what has been called 'multiple funnels'. The multiple funnel concept implies that local optima are organised into clusters, so that a particular local optimum largely belongs to a particular funnel. Our study is the first to extract and visualise local optima networks for TSP and is based on a sampling methodology relying on the Chained Lin-Kernighan algorithm. We confirm the existence of multiple funnels on two selected TSP instances, finding additional funnels in a previously studied instance. Our results suggests that transitions among funnels are possible using operators such as 'double-bridge'. However, for consistently escaping sub-optimal funnels, more robust escaping mechanisms are required.","Title: Combining Mutation and Recombination to Improve a Distributed Model of Adaptive Operator Selection.\nYear: 2015\n\nAbstract: We present evidence indicating that adding a crossover island greatly improves the performance of a Dynamic Island Model for Adaptive Operator Selection. Two combinatorial optimisation problems are considered: the Onemax benchmark, to prove the concept; and a real-world formulation of the course timetabling problem to test practical relevance. Crossover is added to the recently proposed dynamic island adaptive model for operator selection which considered mutation only. When comparing the models with and without a recombination, we found that having a crossover island significantly improves the performance. Our experiments also provide compelling evidence of the dynamic role of crossover during search: it is a useful operator across the whole search process. The idea of combining different type of operators in a distributed adaptive search model is worth further investigation.","Title: Tunnelling Crossover Networks\nYear: 2015\n\nAbstract: Local optima networks are a recent model of fitness landscapes. They compress the landscape by representing local optima as nodes, and search transitions among them as edges. Previous local optima networks considered transitions based on mutation; this study looks instead at transitions based on deterministic recombination. We define and analyse networks based on the recently proposed partition crossover for k-bounded pseudo-Boolean functions, using NKq landscapes as a case study. Partition crossover was initially proposed for the travelling salesman problem, where it was found to ``tunnel\\\" between local optima, i.e., jump from local optimum to local optimum. Our network analysis shows that this also happens for NK landscapes: local optima are densely connected via partition crossover. We found marked differences between the adjacent and random interaction NK models. Surprisingly, with the random model, instances have a lower number of local optima on average, but their networks are more sparse and decompose into several clusters. There is also large variability in the size and pattern of connectivity of instances coming from the same landscape parameter values. These network features offer new insight informing why some instances are harder to solve than others.","Title: Deconstructing the Big Valley Search Space Hypothesis.\nYear: 2016\n\nAbstract: The big valley hypothesis suggests that, in combinatorial optimisation, local optima of good quality are clustered and surround the global optimum. We show here that the idea of a single valley does not always hold. Instead the big valley seems to de-construct into several valleys, also called ‘funnels’ in theoretical chemistry. We use the local optima networks model and propose an effective procedure for extracting the network data. We conduct a detailed study on four selected TSP instances of moderate size and observe that the big valley decomposes into a number of sub-valleys of different sizes and fitness distributions. Sometimes the global optimum is located in the largest valley, which suggests an easy to search landscape, but this is not generally the case. The global optimum might be located in a small valley, which offers a clear and visual explanation of the increased search difficulty in these cases. Our study opens up new possibilities for analysing and visualising combinatorial landscapes as complex networks.","Title: A Benchmark Set Extension And Comparative Study For The Hyflex Framework\nYear: 2015\n\nAbstract: In this work we conduct a comparative study of several publicly available, state-of-the-art hyper-heuristics for HyFlex in order to assess their generality across domains. To this purpose we extend the HyFlex benchmark set with 3 new problem domains: The 0-1 Knap Sack, Quadratic Assignment and Max-Cut Problem. To our knowledge, this is the first public extension of the benchmark since the CHeSC 2011 competition. In addition, this is the first study testing the Fair-Share Iterated Local Search (FS-ILS) method, designed in prior research, using a semi-automated design approach, on new unseen problem domains. We show that, of the methods compared, Adap-HH (CHeSC 2011 winner) clearly perfoms the most consistently, overall. In addition, we identify a weakness of, as well as a way to further simplify the FS-ILS method. Finally, we found that, overall, the state-of-the-art methods compared, generalized much better than a naive baseline.","Title: Communities of Local Optima as Funnels in Fitness Landscapes.\nYear: 2016\n\nAbstract: We conduct an analysis of local optima networks extracted from fitness landscapes of the Kauffman NK model under iterated local search. Applying the Markov Cluster Algorithm for community detection to the local optima networks, we find that the landscapes consist of multiple clusters. This result complements recent findings in the literature that landscapes often decompose into multiple funnels, which increases their difficulty for iterated local search. Our results suggest that the number of clusters as well as the size of the cluster in which the global optimum is located are correlated to the search difficulty of landscapes. We conclude that clusters found by community detection in local optima networks offer a new way to characterize the multi-funnel structure of fitness landscapes.","Title: Additional Dimensions to the Study of Funnels in Combinatorial Landscapes.\nYear: 2016\n\nAbstract: The global structure of travelling salesman's fitness landscapes has recently revealed the presence of multiple 'funnels'. This implies that local optima are organised into several clusters, so that a particular local optimum largely belongs to a particular funnel. Such a global structure can increase search difficulty, especially, when the global optimum is located in a deep, narrow funnel. Our study brings more precision (and dimensions) to the notion of funnels with a data-driven approach using Local Optima Networks and the Chained Lin-Kernighan heuristic. We start by exploring the funnel `floors', characterising them using the notion of communities from complex networks. We then analyse the more complex funnel `basins'. Since their depth is relevant to search, we visualise them in 3D. Our study, across a set of TSP instances, reveals a multi-funnel structure in most of them. However, the specific topology varies across instances and relates to search difficulty. Finally, including a stronger perturbation into Chained Lin-Kernighan proved to smooth the funnel structure, reducing the number of funnels and enlarging the valley leading to global optima.","Title: Evolutionary Algorithms with Linkage Information for Feature Selection in Brain Computer Interfaces.\nYear: 2016\n\nAbstract: Brain Computer Interfaces are an essential technology for the advancement of prosthetic limbs, but current signal acquisition methods are hindered by a number of factors, not least, noise. In this context, Feature Selection is required to choose the important signal features and improve classifier accuracy. Evolutionary algorithms have proven to outperform filtering methods (in terms of accuracy) for Feature Selection. This paper applies a single-point heuristic search method, Iterated Local Search (ILS), and compares it to a genetic algorithm (GA) and a memetic algorithm (MA). It then further attempts to utilise Linkage between features to guide search operators in the algorithms stated. The GA was found to outperform ILS. Counter-intuitively, linkage-guided algorithms resulted in higher classification error rates than their unguided alternatives. Explanations for this are explored.","Title: The effect of landscape funnels in QAPLIB instances\nYear: 2017\n\nAbstract: The effectiveness of common metaheuristics on combinatorial optimisation problems can be limited by certain characteristics of the fitness landscape. We use the local optima network model to compress the 'inherent structure' of a problem space into a network whose structure relates to the empirical hardness of the underlying landscape. Monotonic sequences axe used on the local optima networks of a benchmark set of QAP instances (QAPLIB) to expose landscape funnels. The results suggest links between features of these structures and lowered metaheuristic performance.","Title: A methodology for determining an effective subset of heuristics in selection hyper-heuristics.\nYear: 2017\n\nAbstract: •A methodology for determining an effective subset of heuristics in hyper-heuristics.•Use of fitness landscape metrics and non-parametric statistics to rank heuristics.•New adaptive iterated local search hyper-heuristic using dynamic multi-armed bandit.•State-of-the-art results in course timetabling including new best-known solutions.","Title: Shaping communities of local optima by perturbation strength.\nYear: 2017\n\nAbstract: Recent work discovered that fitness landscapes induced by Iterated Local Search (ILS) may consist of multiple clusters, denoted as funnels or communities of local optima. Such studies exist only for perturbation operators (kicks) with low strength. We examine how different strengths of the ILS perturbation operator affect the number and size of clusters. We present an empirical study based on local optima networks from NK fitness landscapes. Our results show that a properly selected perturbation strength can help overcome the effect of ILS getting trapped in clusters of local optima. This has implications for designing effective ILS approaches in practice, where traditionally only small perturbations or complete restarts are applied, with the middle ground of intermediate perturbation strengths largely unexplored.","Title: Comparing communities of optima with funnels in combinatorial fitness landscapes.\nYear: 2017\n\nAbstract: The existence of sub-optimal funnels in combinatorial fitness landscapes has been linked to search difficulty. The exact nature of these structures --- and how commonly they appear --- is not yet fully understood. Improving our understanding of funnels could help with designing effective diversification mechanisms for a 'smoothing' effect, making optimisation easier. We model fitness landscapes as local optima networks. The relationship between communities of local optima found by network clustering algorithms and funnels is explored. Funnels are identified using the notion of monotonic sequences from the study of energy landscapes in theoretical chemistry. NK Landscapes and the Quadratic Assignment Problem are used as case studies. Our results show that communities are linked to funnels. The analysis exhibits relationships between these landscape structures and the performance of trajectory-based metaheuristics such as Simulated Annealing (SA) and Iterated Local Search (ILS). In particular, ILS gets trapped in funnels, and modular communities of optima slow it down. The funnels contribute to lower success for SA. We show that increasing the strength of ILS perturbation helps to 'smooth' the funnels and improves performance in multi-funnel landscapes.","Title: Modelling genetic improvement landscapes with local optima networks.\nYear: 2017\n\nAbstract: Local optima networks are a compact representation of the global structure of a search space. They can be used for analysis and visualisation. This paper provides one of the first analyses of program search spaces using local optima networks. These are generated by sampling the search space by recording the progress of an Iterated Local Search algorithm. Source code mutations in comparison and Boolean operators are considered. The search spaces of two small benchmark programs, the triangle and TCAS programs, are analysed and visualised. Results show a high level of neutrality i.e. connected test-equivalent mutants. It is also generally relatively easy to find a path from a random mutant to a mutant that passes all test cases.","Title: Optimizing one million variable NK landscapes by hybridizing deterministic recombination and local search.\nYear: 2017\n\nAbstract: In gray-box optimization, the search algorithms have access to the variable interaction graph (VIG) of the optimization problem. For Mk Landscapes (and NK Landscapes) we can use the VIG to identify an improving solution in the Hamming neighborhood in constant time. In addition, using the VIG, deterministic Partition Crossover is able to explore an exponential number of solutions in a time that is linear in the size of the problem. Both methods have been used in isolation in previous search algorithms. We present two new gray-box algorithms that combine Partition Crossover with highly efficient local search. The best algorithms are able to locate the global optimum on Adjacent NK Landscape instances with one million variables. The algorithms are compared with a state-of-the-art algorithm for pseudo-Boolean optimization: Gray-Box Parameterless Population Pyramid. The results show that the best algorithm is always one combining Partition Crossover and highly efficient local search. But the results also illustrate that the best optimizer differs on Adjacent and Random NK Landscapes.","Title: Understanding Phase Transitions with Local Optima Networks: Number Partitioning as a Case Study.\nYear: 2017\n\nAbstract: Phase transitions play an important role in understanding search difficulty in combinatorial optimisation. However, previous attempts have not revealed a clear link between fitness landscape properties and the phase transition. We explore whether the global landscape structure of the number partitioning problem changes with the phase transition. Using the local optima network model, we analyse a number of instances before, during, and after the phase transition. We compute relevant network and neutrality metrics; and importantly, identify and visualise the funnel structure with an approach (monotonic sequences) inspired by theoretical chemistry. While most metrics remain oblivious to the phase transition, our results reveal that the funnel structure clearly changes. Easy instances feature a single or a small number of dominant funnels leading to global optima; hard instances have a large number of suboptimal funnels attracting the search. Our study brings new insights and tools to the study of phase transitions in combinatorial optimisation.","Title: Visualising The Search Landscape Of The Triangle Program\nYear: 2017\n\nAbstract: High order mutation analysis of a software engineering benchmark, including schema and local optima networks, suggests program improvements may not be as hard to find as is often assumed. (1) Bit-wise genetic building blocks are not deceptive and can lead to all global optima. (2) There are many neutral networks, plateaux and local optima, nevertheless in most cases near the human written C source code there are hill climbing routes including neutral moves to solutions.","Title: Evolving Training Sets for Improved Transfer Learning in Brain Computer Interfaces.\nYear: 2017\n\nAbstract: A new proof-of-concept method for optimising the performance of Brain Computer Interfaces (BCI) while minimising the quantity of required training data is introduced. This is achieved by using an evolutionary approach to rearrange the distribution of training instances, prior to the construction of an Ensemble Learning Generic Information (ELGI) model. The training data from a population was optimised to emphasise generality of the models derived from it, prior to a recombination with participant-specific data via the ELGI approach, and training of classifiers. Evidence is given to support the adoption of this approach in the more difficult BCI conditions: smaller training sets, and those suffering from temporal drift. This paper serves as a case study to lay the groundwork for further exploration of this approach.","Title: Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces.\nYear: 2018\n\nAbstract: Brain Computer Interfaces provide a very challenging classification task due to small numbers of instances, large numbers of features, non-stationary problems, and low signal-to-noise ratios. Feature selection (FS) is a promising solution to help mitigate these effects. Wrapper FS methods are typically found to outperform filter FS methods, but reliance on cross-validation accuracies can be misleading due to overfitting. This paper proposes a filter-wrapper hybrid based on Iterated Local Search and Mutual Information, and shows that it can provide more reliable solutions, where the solutions are more able to generalise to unseen data. This study further contributes comparisons over multiple datasets, something that has been uncommon in the literature.","Title: On the Fractal Nature of Local Optima Networks.\nYear: 2018\n\nAbstract: A Local Optima Network represents fitness landscape connectivity within the space of local optima as a mathematical graph. In certain other complex networks or graphs there have been recent observations made about inherent self-similarity. An object is said to be self-similar if it shows the same patterns when measured at different scales; another word used to convey self-similarity is fractal. The fractal dimension of an object captures how the detail observed changes with the scale at which it is measured, with a high fractal dimension being associated with complexity. We conduct a detailed study on the fractal nature of the local optima networks of a benchmark combinatorial optimisation problem (NK Landscapes). The results draw connections between fractal characteristics and performance by three prominent metaheuristics: Iterated Local Search, Simulated Annealing, and Tabu Search.","Title: How Perturbation Strength Shapes the Global Structure of TSP Fitness Landscapes.\nYear: 2018\n\nAbstract: Local optima networks are a valuable tool used to analyse and visualise the global structure of combinatorial search spaces; in particular, the existence and distribution of multiple funnels in the landscape. We extract and analyse the networks induced by Chained-LK, a powerful iterated local search for the TSP, on a large set of randomly generated (Uniform and Clustered) instances. Results indicate that increasing the perturbation strength employed by Chained-LK modifies the landscape's global structure, with the effect being markedly different for the two classes of instances. Our quantitative analysis shows that several funnel metrics have stronger correlations with Chained-LK success rate than the number of local optima, indicating that global structure clearly impacts search performance.","Title: Mapping the global structure of TSP fitness landscapes.\nYear: 2018\n\nAbstract: The global structure of combinatorial landscapes is not fully understood, yet it is known to impact the performance of heuristic search methods. We use a so-called local optima network model to characterise and visualise the global structure of travelling salesperson fitness landscapes of different classes, including random and structured real-world instances of realistic size. Our study brings rigour to the characterisation of so-called funnels, and proposes an intensive and effective sampling procedure for extracting the networks. We propose enhanced visualisation techniques, including 3D plots and the incorporation of colour, sizes and widths, to reflect relevant aspects of the search process. This brings an almost tangible new perspective to the landscape and funnel metaphors. Our results reveal a much richer global structure than the suggestion of a ‘big-valley’ structure. Most landscapes of the tested instances have multiple valleys or funnels; and the number, disposition and interaction of these funnels seem to relate to search difficulty on the studied landscapes. We also find that the structured TSP instances feature high levels of neutrality, an observation not previously reported in the literature. We then propose ways of analysing and visualising these neutral landscapes.","Title: PageRank centrality for performance prediction: the impact of the local optima network model.\nYear: 2018\n\nAbstract: A local optima network (LON) compresses relevant features of fitness landscapes in a complex network, where nodes are local optima and edges represent transition probabilities between different basins of attraction. Previous work has found that the PageRank centrality of local optima can be used to predict the success rate and average fitness achieved by local search based metaheuristics. Results are available for LONs where edges describe either basin transition probabilities or escape edges. This paper studies the interplay between the type of LON edges and the ability of the PageRank centrality for the resulting LON to predict the performance of local search based metaheuristics. It finds that LONs are stochastic models of the search heuristic. Thus, to achieve an accurate prediction, the definition of the LON edges must properly reflect the type of diversification steps used in the metaheuristic. LONs with edges representing basin transition probabilities capture well the diversification mechanism of simulated annealing which sometimes also accepts worse solutions that allow the search process to pass between basins. In contrast, LONs with escape edges capture well the diversification step of iterated local search, which escapes from local optima by applying a larger perturbation step.","Title: An Empirical Study of Meta- and Hyper-Heuristic Search for Multi-Objective Release Planning.\nYear: 2018\n\nAbstract: A variety of meta-heuristic search algorithms have been introduced for optimising software release planning. However, there has been no comprehensive empirical study of different search algorithms across multiple different real-world datasets. In this article, we present an empirical study of global, local, and hybrid meta- and hyper-heuristic search-based algorithms on 10 real-world datasets. We find that the hyper-heuristics are particularly effective. For example, the hyper-heuristic genetic algorithm significantly outperformed the other six approaches (and with high effect size) for solution quality 85% of the time, and was also faster than all others 70% of the time. Furthermore, correlation analysis reveals that it scales well as the number of requirements increases.","Title: Search-maps: visualising and exploiting the global structure of computational search spaces.\nYear: 2018\n\nAbstract: No abstract available.","Title: Enhancing partition crossover with articulation points analysis.\nYear: 2018\n\nAbstract: Partition Crossover is a recombination operator for pseudo-Boolean optimization with the ability to explore an exponential number of solutions in linear or square time. It decomposes the objective function as a sum of subfunctions, each one depending on a different set of variables. The decomposition makes it possible to select the best parent for each subfunction independently and the operator provides the best out of 2q solutions, where q is the number of sub-functions in the decomposition. These subfunctions are defined over the connected components of the recombination graph: a subgraph of the objective function variable interaction graph containing only the differing variables in the two parents. In this paper, we advance further and propose a new way to increase the number of linearly independent subfunctions by analyzing the articulation points of the recombination graph. These points correspond to variables that, once flipped, increase the number of connected components. The presence of a connected component with an articulation point increases the number of explored solutions by a factor of, at least, 4. We evaluate the new operator using Iterated Local Search combined with Partition Crossover to solve NK Landscapes and MAX-SAT.","Title: Multifractality and dimensional determinism in local optima networks.\nYear: 2018\n\nAbstract: We conduct a study of local optima networks (LONs) in a search space using fractal dimensions. The fractal dimension (FD) of these networks is a complexity index which assigns a non-integer dimension to an object. We propose a fine-grained approach to obtaining the FD of LONs, using the probabilistic search transitions encoded in LON edge weights. We then apply multi-fractal calculations to LONs for the first time, comparing with mono-fractal analysis. For complex systems such as LONs, the dimensionality may be different between two sub-systems and multi-fractal analysis is needed. Here we focus on the Quadratic Assignment Problem (QAP), conducting fractal analyses on sampled LONs of reasonable size for the first time. We also include fully enumerated LONs of smaller size. Our results show that local optima spaces can be multi-fractal and that valuable information regarding probabilistic self-similarity is encoded in the edge weights of local optima networks. Links are drawn between these phenomena and the performance of two competitive metaheuristic algorithms.","Title: Tutorials at PPSN 2018.\nYear: 2018\n\nAbstract: PPSN 2018 features a total of 23 free tutorials covering a broad range of topics in evolutionary computation and related areas. From theory and methods to applications and computer implementations, and from introductory to advanced, the PPSN 2018 tutorial program offers participants the opportunity to learn more about both well-established and ongoing research in this field.","Title: Visualising the global structure of search landscapes: genetic improvement as a case study.\nYear: 2018\n\nAbstract: The search landscape is a common metaphor to describe the structure of computational search spaces. Different landscape metrics can be computed and used to predict search difficulty. Yet, the metaphor falls short in visualisation terms because it is hard to represent complex landscapes, both in terms of size and dimensionality. This paper combines local optima networks, as a compact representation of the global structure of a search space, and dimensionality reduction, using the t-distributed stochastic neighbour embedding algorithm, in order to both bring the metaphor to life and convey new insight into the search process. As a case study, two benchmark programs, under a genetic improvement bug-fixing scenario, are analysed and visualised using the proposed method. Local optima networks for both iterated local search and a hybrid genetic algorithm, across different neighbourhoods, are compared, highlighting the differences in how the landscape is explored.","Title: Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis\nYear: 2018\n\nAbstract: Big Data Analytics have become an integral part of Health Informatics over the past years, with the analysis of Internet data being all the more popular in health assessment in various topics. In this study, we first examine the geographical distribution of the online behavioral variations towards Chlamydia, Gonorrhea, Syphilis, Tuberculosis, and Hepatitis in the United States by year from 2004 to 2017. Next, we examine the correlations between Google Trends data and official health data from the ‘Centers for Disease Control and Prevention’ (CDC) on said diseases, followed by estimating linear regressions for the respective relationships. The results show that Infoveillance can assist with exploring public awareness and accurately measure the behavioral changes towards said diseases. The correlations between Google Trends data and CDC data on Chlamydia cases are statistically significant at a national level and in most of the states, while the forecasting exhibits good performing results in many states. For Hepatitis, significant correlations are observed for several US States, while forecasting also exhibits promising results. On the contrary, several factors can affect the applicability of this forecasting method, as in the cases of Gonorrhea, Syphilis, and Tuberculosis, where the correlations are statistically significant in fewer states. Thus this study highlights that the analysis of Google Trends data should be done with caution in order for the results to be robust. In addition, we suggest that the applicability of this method is not that trivial or universal, and that several factors need to be taken into account when using online data in this line of research. However, this study also supports previous findings suggesting that the analysis of real-time online data is important in health assessment, as it tackles the long procedure of data collection and analysis in traditional survey methods, and provides us with information that could not be accessible otherwise.","Title: Incorporating knowledge of secondary structures in a l-system-based encoding for protein folding\nYear: 2005\n\nAbstract: An encoding scheme for protein folding on lattice models, inspired by parametric L-systems, was proposed. The encoding incorporates problem domain knowledge in the form of predesigned production rules that capture commonly known secondary structures: α-helices and β-sheets. The ability of this encoding to capture protein native conformations was tested using an evolutionary algorithm as the inference procedure for discovering L-systems. Results confirmed the suitability of the proposed representation. It appears that the occurrence of motifs and sub-structures is an important component in protein folding, and these sub-structures may be captured by a grammar-based encoding. This line of research suggests novel and compact encoding schemes for protein folding that may have practical implications in solving meaningful problems in biotechnology such as structure prediction and protein folding.","Title: Forecasting AIDS prevalence in the United States using online search traffic data\nYear: 2018\n\nAbstract: Over the past decade and with the increasing use of the Internet, the assessment of health issues using online search traffic data has become an integral part of Health Informatics. Internet data in general and from Google Trends in particular have been shown to be valid and valuable in predictions, forecastings, and nowcastings; and in detecting, tracking, and monitoring diseases’ outbreaks and epidemics. Empirical relationships have been shown to exist between Google Trends’ data and official data in several health topics, with the science of infodemiology using the vast amount of information available online for the assessment of public health and policy matters. The aim of this study is to provide a method of forecasting AIDS prevalence in the US using online search traffic data from Google Trends on AIDS related terms. The results at first show that significant correlations between Google Trends’ data and official health data on AIDS prevalence (2004–2015) exist in several States, while the estimated forecasting models for AIDS prevalence show that official health data and Google Trends data on AIDS follow a logarithmic relationship. Overall, the results of this study support previous work on the subject suggesting that Google data are valid and valuable for the analysis and forecasting of human behavior towards health topics, and could further assist with Health Assessment in the US and in other countries and regions with valid available official health data.","Title: A New Generalized Partition Crossover for the Traveling Salesman Problem: Tunneling Between Local Optima.\nYear: 2020\n\nAbstract: Generalized Partition Crossover (GPX) is a deterministic recombination operator developed for the Traveling Salesman Problem. Partition crossover operators return the best of 2k reachable offspring, where k is the number of recombining components. This article introduces a new GPX2 operator, which finds more recombining components than GPX or Iterative Partial Transcription (IPT). We also show that GPX2 has O(n) runtime complexity, while also introducing new enhancements to reduce the execution time of GPX2. Finally, we experimentally demonstrate the efficiency of GPX2 when it is used to improve solutions found by the multitrial Lin-Kernighan-Helsgaum (LKH) algorithm. Significant improvements in performance are documented on large (n>5000) and very large (n=100,000) instances of the Traveling Salesman Problem.","Title: Hyper-heuristics: a survey of the state of the art.\nYear: 2013\n\nAbstract: Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyperheuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.","Title: Local optima networks for continuous fitness landscapes.\nYear: 2019\n\nAbstract: Local Optima Networks (LONs) have been proposed as a coarsegrained model of discrete (combinatorial) fitness landscapes, where nodes are local optima and edges are search transitions based on an exploration search operator. This paper presents one of the first complex network analysis of continuous fitness landscapes. We use benchmark functions with well-known global structure, and an existing implementation of a Basin-Hopping algorithm to extract the networks. We also explore the impact of varying the Basin-Hopping perturbation step-size. Our results suggest that the landscape's connectivity pattern (global structure) strongly varies with the perturbation step-size, with extreme values of this parameter being detrimental to search and fragmenting the global structure. Our LON visualisations strikingly illustrate the landscape's global (funnel) structure, indicating that LONs serve as a tool for visualising high-dimensional functions.","Title: Local optima network analysis for MAX-SAT.\nYear: 2019\n\nAbstract: Local Optima Networks (LONs) are a valuable tool to understand fitness landscapes of optimization problems observed from the perspective of a search algorithm. Local optima of the optimization problem are linked by an edge in LONs when an operation in the search algorithm allows one of them to be reached from the other. Previous work analyzed several combinatorial optimization problems using LONs and provided a visual guide to understand why the instances are difficult or easy for the search algorithms. In this work we analyze for the first time the MAX-SAT problem. Given a Boolean formula in Conjunctive Normal Form, the goal of the MAX-SAT problem is to find an assignment maximizing the number of satistified clauses. Several random and industrial instances of MAX-SAT are analyzed using Iterated Local Search to sample the search space.","Title: Recent advances in fitness landscape analysis.\nYear: 2019\n\nAbstract: No abstract available.","Title: Real-like MAX-SAT instances and the landscape structure across the phase transition\nYear: 2021\n\nAbstract: ABSTRACTIn contrast with random uniform instances, industrial SAT instances of large size are solvable today by state-of-the-art algorithms. It is believed that this is the consequence of the non-random structure of the distribution of variables into clauses. In order to produce benchmark instances resembling those of real-world formulas with a given structure, generative models have been proposed. In this paper we study the MAX-3SAT problem with model-generated instances having a power-law distribution. Specifically, we target the regions in which computational difficulty undergoes an easy/hard phase transition as a function of clause density and of the power-law exponent. Our approach makes use of a sampling technique to build a graph model (a local optima network) in which nodes are local optima and directed edges are transitions between optima basins. The objective is to relate the structure of the instance fitness landscape with problem difficulty through the transition. We succeed in associating the transition with straightforward network metrics, thus providing a novel and original fitness landscape view of the computational features of the power-law model and its phase transition.","Title: Local search pivoting rules and the landscape global structure\nYear: 2021\n\nAbstract: ABSTRACTIn local search algorithms, the pivoting rule determines which neighboring solution to select and thus strongly influences the behavior of the algorithm and its capacity to sample good-quality local optima. The classical pivoting rules are first and best improvement, with alternative rules such as worst improvement and maximum expansion recently studied on hill-climbing algorithms. This article conducts a thorough empirical comparison of five pivoting rules (best, first, worst, approximated worst and maximum expansion) on two benchmark combinatorial problems, NK landscapes and the unconstrained binary quadratic problem (UBQP), with varied sizes and ruggedness. We present both a performance analysis of the alternative pivoting rules within an iterated local search (ILS) framework and a fitness landscape analysis and visualization using local optima networks. Our results reveal that the performance of the pivoting rules within an ILS framework may differ from their performance as single climbers and that worst improvement and maximum expansion can outperform classical pivoting rules.","Title: Partition crossover for continuous optimization: ePX\nYear: 2021\n\nAbstract: ABSTRACTPartition crossover (PX) is an efficient recombination operator for gray-box optimization. PX is applied in problems where the objective function can be written as a sum of subfunctions fl(.). In PX, the variable interaction graph (VIG) is decomposed by removing vertices with common variables. Parent variables are inherited together during recombination if they are part of the same connected recombining component of the decomposed VIG. A new way of generating the recombination graph is proposed here. The VIG is decomposed by removing edges associated with subfunctions fl(.) that have similar evaluation for combinations of variables inherited from the parents. By doing so, the partial evaluations of fl(.) are taken into account when decomposing the VIG. This allows the use of partition crossover in continuous optimization. Results of experiments where local optima are recombined indicate that more recombining components are found. When the proposed epsilon-PX (ePX) is compared with other recombination operators in Genetic Algorithms and Differential Evolution, better performance is obtained when the epistasis degree is low.","Title: Search trajectory networks: A tool for analysing and visualising the behaviour of metaheuristics\nYear: 2021\n\nAbstract: A large number of metaheuristics inspired by natural and social phenomena have been proposed in the last few decades, each trying to be more powerful and innovative than others. However, there is a lack of accessible tools to analyse, contrast and visualise the behaviour of metaheuristics when solving optimisation problems. When the metaphors are stripped away, are these algorithms different in their behaviour? To help to answer this question, we propose a data-driven, graph-based model, search trajectory networks (STNs) in order to analyse, visualise and directly contrast the behaviour of different types of metaheuristics. One strength of our approach is that it does not require any additional sampling or algorithmic methods. Instead, the models are constructed from data gathered while the metaheuristics are solving the optimisation problems. We present our methodology, and consider in detail two case studies covering both continuous and combinatorial optimisation. In terms of metaheuristics, our case studies cover the main current paradigms: evolutionary, swarm, and stochastic local search approaches. (c) 2021 Elsevier B.V. All rights reserved. <comment>Superscript/Subscript Available</comment","Title: Understanding parameter spaces using local optima networks: a case study on particle swarm optimization\nYear: 2021\n\nAbstract: ABSTRACTA major challenge with utilizing a metaheuristic is finding optimal or near optimal parameters for a given problem instance. It is well known that the best performing control parameters are often problem dependent, with poorly chosen parameters even leading to algorithm failure. What is not obvious is how strongly the complexity of the parameter landscape itself is coupled with the underlying objective function the metaheuristic is attempting to solve. In this paper local optima networks (LONs) are utilized to visualize and analyze the parameter landscapes of particle swarm optimization (PSO) over an array of objective functions. It was found that the structure of the parameter landscape is affected by the underlying objective function, and in some cases by a considerable degree across multiple metrics. Furthermore, despite PSO's parameter landscape having a relatively simple macro structure, the LONs demonstrate that there is actually a considerable amount of complexity at a micro level; making parameter tuning harder for PSO than would have been initially thought. Apart from the PSO specific findings this paper also provides a formalism of parameter landscapes and demonstrates that LONs can be used as an effective tool in the analysis and visualization of parameter landscapes of metaheuristics.","Title: Dynastic Potential Crossover Operator\nYear: 2022\n\nAbstract: An optimal recombination operator for two-parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of an optimal recombination operator is optimal in the smallest hyperplane containing the two parent solutions. Exploring this hyperplane is computationally costly, in general, requiring exponential time in the worst case. However, when the variable interaction graph of the objective function is sparse, exploration can be done in polynomial time. In this article, we present a recombination operator, called Dynastic Potential Crossover (DPX), that runs in polynomial time and behaves like an optimal recombination operator for low-epistasis combinatorial problems. We compare this operator, both theoretically and experimentally, with traditional crossover operators, like uniform crossover and network crossover, and with two recently defined efficient recombination operators: partition crossover and articulation points partition crossover. The empirical comparison uses NKQ Landscapes and MAX-SAT instances. DPX outperforms the other crossover operators in terms of quality of the offspring and provides better results included in a trajectory and a population-based metaheuristic, but it requires more time and memory to compute the offspring.","Title: Search Trajectories Networks of Multiobjective Evolutionary Algorithms.\nYear: 2022\n\nAbstract: Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, search trajectory networks (STNs), to model the behavior of MOEAs. Our approach uses the idea of decomposition, where a multiobjective problem is transformed into several single-objective problems. We show that STNs can be used to model and distinguish the search behavior of two popular multiobjective algorithms, MOEA/D and NSGA-II, using 10 continuous benchmark problems with 2 and 3 objectives. Our findings suggest that we can improve our understanding of MOEAs using STNs for algorithm analysis.","Title: Component-wise Analysis of Automatically Designed Multiobjective Algorithms on Constrained Problems\nYear: 2022\n\nAbstract: The performance of multiobjective algorithms varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there has been an increasing interest in their automatic design from component parts. These automatically designed metaheuristics can outperform their human-developed counterparts. However, it is still uncertain what are the most influential components leading to their performance improvement. This study introduces a new methodology to investigate the effects of the final configuration of an automatically designed algorithm. We apply this methodology to a well-performing Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) designed by the irace package on nine constrained problems. We then contrast the impact of the algorithm components in terms of their Search Trajectory Networks (STNs), the diversity of the population, and the hypervolume. Our results indicate that the most influential components were the restart and update strategies, with higher increments in performance and more distinct metric values. Also, their relative influence depends on the problem difficulty: not using the restart strategy was more influential in problems where MOEA/D performs better; while the update strategy was more influential in problems where MOEA/D performs the worst.","Title: Search Trajectory Networks Applied to the Cyclic Bandwidth Sum Problem\nYear: 2021\n\nAbstract: Search trajectory networks (STNs) were proposed as a tool to analyze the behavior of metaheuristics in relation to their exploration ability and the search space regions they traverse. The technique derives from the study of fitness landscapes using local optima networks (LONs). STNs are related to LONs in that both are built as graphs, modelling the transitions among solutions or group of solutions in the search space. The key difference is that STN nodes can represent solutions or groups of solutions that are not necessarily locally optimal. This work presents an STN-based study for a particular combinatorial optimization problem, the cyclic bandwidth sum minimization. STNs were employed to analyze the two leading algorithms for this problem: a memetic algorithm and a hyperheuristic memetic algorithm. We also propose a novel grouping method for STNs that can be generally applied to both continuous and combinatorial spaces.","Title: Fitness landscape analysis of convolutional neural network architectures for image classification\nYear: 2022\n\nAbstract: •Novel application of local optima network analysis to neural architecture landscapes.•Findings on a reduced search space were found to still hold on a larger search space.•Neural architecture search landscapes have relatively few local optima.","Title: Optima Organize into Lattices Under Recombination: An example using the Traveling Salesman Problem\nYear: 2022\n\nAbstract: Local optima networks (LONs) model the global distribution and connectivity pattern of local optima under given search operators. Recent research has looked at how recombination operators can jump from a pair of parents that are locally optimal to a new child that is either a local optimum, or is guaranteed to be in a new basin of attraction. Recombination can therefore also induce a local optima network which maps how crossover moves between local optima. In this paper, we prove that recombination induces a LON which is actually a network of overlapping hypercube lattices. Given two or more samples from any lattice, we can also infer the existence of additional local optima that have not previously been reached by sampling. We prove that these lattices can be exponentially large. Finally, we prove that there exists TSP instances can be solved in polynomial time by exploiting Partition Crossover; these same instances are not solved by local search."],"string":"[\n \"Title: Automated heuristic design\\nYear: 2011\\n\\nAbstract: This tutorial will discuss state-of-the-art techniques for automating the design of heuristic search methods, in order to remove or reduce the need for a human expert in the process of designing an effective algorithm to solve a search problem. Using machine learning or meta-level search, several approaches have been proposed in computer science, artificial intelligence and operational research. The aim is to develop methodologies which can adapt to different environments without manually having to customise the search, or its parameters, for each particular problem domain. This can be seen as one of the drawbacks of many current metaheuristic and evolutionary implementations, which tend to have to be customised for a particular class of problems or even specific problem instances. We have identified two main types of approaches to this challenge: heuristic selection, and heuristic generation. In heuristic selection the idea is to automatically combine fixed pre-existing simple heuristics or neighbourhood structures to solve the problem at hand; whereas in heuristic generation the idea is to automatically create new heuristics (or heuristic components) suited to a given problem or class of problems. This latter approach is typically achieved by combining, through the use of genetic programming for example, components or building-blocks of human designed heuristics. This tutorial will go over the intellectual roots and origins of both automated heuristic selection and generation, before discussing work carried out to date in these two directions and then focusing on some observations and promising research directions.\",\n \"Title: Local optima networks with escape edges\\nYear: 2011\\n\\nAbstract: This paper proposes an alternative definition of edges (escape edges) for the recently introduced network-based model of combinatorial landscapes: Local Optima Networks (LON). The model compresses the information given by the whole search space into a smaller mathematical object that is the graph having as vertices the local optima and as edges the possible weighted transitions between them. The original definition of edges accounted for the notion of transitions between the basins of attraction of local optima. This definition, although informative, produced densely connected networks and required the exhaustive sampling of the basins of attraction. The alternative escape edges proposed here do not require a full computation of the basins. Instead, they account for the chances of escaping a local optima after a controlled mutation (e.g. 1 or 2 bit-flips) followed by hill-climbing. A statistical analysis comparing the two LON models for a set of NK landscapes, is presented and discussed. Moreover, a preliminary study is presented, which aims at validating the LON models as a tool for analyzing the dynamics of stochastic local search in combinatorial optimization.\",\n \"Title: A PSO/ACO approach to knowledge discovery in a pharmacovigilance context\\nYear: 2009\\n\\nAbstract: We propose and evaluate the use of a Particle Swarm Optimization/Ant Colony Optimization (PSO/ACO) methodology for classification and rule discovery in the context of medication postmarketing surveillance or pharmacovigilance. Our study considers a large data set of diabetic patients on two widely used antidiabetic drugs (rosiglitazone and pioglitazone), and the risk of myocardial infarction as an adverse effect. The goal is to determine the presence of previously undetected causal relationships between therapeutics, patient characteristics, and adverse medication outcomes. Since the proposed approach is able to discover classification rules, the elicited knowledge may suggest new hypotheses regarding associations between risk factors and an adverse event. Our classification results show high accuracy. Furthermore, several medication-related rules were discovered and analyzed. The elicited rules support previous studies from the medical literature. Moreover, one of the studied antidiabetic drugs (rosiglitazone) was found to have a significant higher risk of an adverse event on diabetic, hypertensive patients, as compared to the other drug. This last finding suggests that pioglitazone may have a protective effect against myocardial infarction on diabetic, hypertensive patients.\",\n \"Title: Vehicle routing and adaptive iterated local search within the hyflex hyper-heuristic framework\\nYear: 2012\\n\\nAbstract: HyFlex (Hyper-heuristic Flexible framework) [15] is a software framework enabling the development of domain independent search heuristics (hyper-heuristics), and testing across multiple problem domains. This framework was used as a base for the first Cross-domain Heuristic Search Challenge, a research competition that attracted significant international attention. In this paper, we present one of the problems that was used as a hidden domain in the competition, namely, the capacitated vehicle routing problem with time windows. The domain implements a data structure and objective function for the vehicle routing problem, as well as many state-of- the-art low-level heuristics (search operators) of several types. The domain is tested using two adaptive variants of a multiple-neighborhood iterated local search algorithm that operate in a domain independent fashion, and therefore can be considered as hyper-heuristics. Our results confirm that adding adaptation mechanisms improve the performance of hyper-heuristics. It is our hope that this new and challenging problem domain can be used to promote research within hyper-heuristics, adaptive operator selection, adaptive multi-meme algorithms and autonomous control for search algorithms.\",\n \"Title: Assortative mating drastically alters the magnitude of error thresholds\\nYear: 2006\\n\\nAbstract: The error threshold of replication is an important notion of the quasispecies evolution model; it is a critical mutation rate (error rate) beyond which structures obtained by an evolutionary process are destroyed more frequently than selection can reproduce them. With mutation rates above this critical value, an error catastrophe occurs and the genomic information is irretrievably lost. Recombination has been found to reduce the magnitude of the error threshold in evolving viral quasispecies. Here, through a simulation model based on genetic algorithms, we incorporate assortative mating and explore its effect on the magnitude of error thresholds. We found, consistently on four fitness landscapes, and across a range of evolutionary parameter values, that assortative mating overcomes the shift toward lower error threshold magnitudes induced by recombination, on the other hand, dissortative mating drastically reduces the error threshold magnitude. These results have implications to both natural and artificial evolution: First, they support the hypothesis that assortative mating by itself may overcome some of the evolutionary disadvantages of sex in nature. Second, they suggest a critical interaction between mutation rates and mating strategies in evolutionary algorithms.\",\n \"Title: Setting The Mutation Rate: Scope And Limitations Of The 1/L Heuristic\\nYear: 2002\\n\\nAbstract: An important decision to make when design- ing a GA is how to set the evolutionary pa- rameters. Among these parameters, the mu- tation rate has been acknowledged as the most sensitive one. All approaches so far for a near-optimal setting of the mutation rate have intrinsic limitations. A promising guideline is, however, the heuristic suggest- ing pm =1 /L where L is the string length. This paper is a first attempt to explore the scope and limitations of this heuristic on GAs with bit-string representation. Specifically, we select two real-world domains as test prob- lems and explore (i) whether optimal mu- tation rates change with time; and (ii) the interactions between the mutation rate and other evolutionary parameters. Results sug- gest that a constant mutation rate of 1/L is useful for a GA with a controlled 'moderate' selection pressure. It should be, however, re- vised for a weak or extremely strong selection pressure, and for a small population size.\",\n \"Title: Analyzing the landscape of a graph based hyper-heuristic for timetabling problems\\nYear: 2009\\n\\nAbstract: Hyper-heuristics can be thought of as \\\"heuristics to choose heuristics\\\". They are concerned with adaptively finding solution methods, rather than directly producing a solution for the particular problem at hand. Hence, an important feature of hyper-heuristics is that they operate on a search space of heuristics rather than directly on a search space of problem solutions. A motivating aim is to build systems which are fundamentally more generic than is possible today. Understanding the structure of these heuristic search spaces is therefore, a research direction worth exploring. In this paper, we use the notion of fitness landscapes in the context of constructive hyper-heuristics. We conduct a landscape analysis on a heuristic search space conformed by sequences of graph coloring heuristics for timetabling. Our study reveals that these landscapes have a high level of neutrality and positional bias. Furthermore, although rugged, they have the encouraging feature of a globally convex or big valley structure, which indicates that an optimal solution would not be isolated but surrounded by many local minima. We suggest that using search methodologies that explicitly exploit these features may enhance the performance of constructive hyper-heuristics.\",\n \"Title: The Connectivity of NK Landscapes' Basins: A Network Analysis\\nYear: 2008\\n\\nAbstract: We propose a network characterization of combinatorial fitness landscapes by adapting the notion of inherent networks proposed for energy surfaces. We use the well-known family of NK landscapes as an example. In our case the inherent network is the graph where the vertices represent the local maxima in the landscape, and the edges account for the transition probabilities between their corresponding basins of attraction. We exhaustively extracted such networks on representative small NK landscape instances, and performed a statistical characterization of their properties. We found that most of these network properties can be related to the search difficulty on the underlying NK landscapes with varying values of K.\",\n \"Title: A Reinforcement Learning-Great-Deluge Hyper-Heuristic for Examination Timetabling\\nYear: 2010\\n\\nAbstract: Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solution goes through two successive processes heuristic selection and move acceptance until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems with different characteristics. Some previous studies show that different combinations of heuristic selection and move acceptance as hyper-heuristic components might yield different performances. This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.\",\n \"Title: Dispatching rules for production scheduling: a hyper-heuristic landscape analysis\\nYear: 2009\\n\\nAbstract: Hyper-heuristics or \\\"heuristics to chose heuristics\\\" are an emergent search methodology that seeks to automate the process of selecting or combining simpler heuristics in order to solve hard computational search problems. The distinguishing feature of hyper-heuristics, as compared to other heuristic search algorithms, is that they operate on a search space of heuristics rather than directly on the search space of solutions to the underlying problem. Therefore, a detailed understanding of the properties of these heuristic search spaces is of utmost importance for understanding the behaviour and improving the design of hyper-heuristic methods. Heuristics search spaces can be studied using the metaphor of fitness landscapes. This paper formalises the notion of hyper-heuristic landscapes and performs a landscape analysis of the heuristic search space induced by a dispatching-rule-based hyper-heuristic for production scheduling. The studied hyper-heuristic spaces are found to be \\\"easy\\\" to search. They also exhibit some special features such as positional bias and neutrality. It is argued that search methods that exploit these features may enhance the performance of hyper-heuristics.\",\n \"Title: A critical view of the evolutionary design of self-assembling systems\\nYear: 2005\\n\\nAbstract: The automated design of systems which self-assemble is a fundamental cornerstone of nanotechnology. In this paper we review some work in which we have applied Evolutionary Algorithms (EAs) for the automated design of systems self-assembly. We will focus in three important minimalist self-assembly problems and we discuss the difficulties encountered while applying EAs to these test cases. We also suggest some promising lines of work that could possibly help overcome current limitations in the evolutionary design of self-assembling systems.\",\n \"Title: Adaptive iterated local search for cross-domain optimisation\\nYear: 2011\\n\\nAbstract: We propose two adaptive variants of a multiple neighborhood iterated local search algorithm. These variants employ online learning techniques, also called adaptive operation selection, in order to select which perturbation to apply at each iteration step from a set of available move operators. Using a common software interface (the HyFlex framework), the proposed algorithms are tested across four hard combinatorial optimisation problems: permutation flow shop, 1D bin packing, maximum satisfiability, and personnel scheduling (including instance data from real-world industrial applications). Using the HyFlex framework, exactly the same high level search strategy can be applied to all the domains and instances. Our results confirm that the adaptive variants outperform a baseline iterated local search with uniform random selection of the move operators. We argue that the adaptive algorithms proposed are general yet powerful, and contribute to the goal of increasing the generality and applicability of heuristic search.\",\n \"Title: The cross-domain heuristic search challenge – an international research competition\\nYear: 2011\\n\\nAbstract: The first Cross-domain Heuristic Search Challenge (CHeSC 2011) seeks to bring together practitioners from operational research, computer science and artificial intelligence who are interested in developing more generally applicable search methodologies. The challenge is to design a search algorithm that works well, not only across different instances of the same problem, but also across different problem domains. This article overviews the main features of this challenge.\",\n \"Title: Population-based optimization of cytostatic/cytotoxic combination cancer chemotherapy\\nYear: 2013\\n\\nAbstract: This article studies the suitability of modern population based algorithms for designing combination cancer chemotherapies. The problem of designing chemotherapy schedules is expressed as an optimization problem (an optimal control problem) where the objective is to minimize the tumor size without compromising the patient's health. Given the complexity of the underlying mathematical model describing the tumor's progression (considering two types of drugs, the cell cycle and the immune system response), analytical and classical optimization methods are not suitable, instead, stochastic heuristic optimization methods are the right tool to solve the optimal control problem. Considering several solution quality and performance metrics, we compared three powerful heuristic algorithms for real-parameter optimization, namely, CMA evolution strategy, differential evolution, and particle swarm pattern search method. The three algorithms were able to successfully solve the posed problem. However, differential evolution outperformed its counterparts both in quality of the obtained solutions and efficiency of search.\",\n \"Title: On the automatic discovery of variants of the NEH procedure for flow shop scheduling using genetic programming\\nYear: 2011\\n\\nAbstract: We use genetic programming to find variants of the well-known Nawaz, En-score and Ham (NEH) heuristic for the permutation flow shop problem. Each variant uses a different ranking function to prioritize operations during schedule construction. We have tested our ideas on problems where jobs have release times, due dates, and weights and have considered five objective functions: makespan, sum of tardiness, sum of weighted tardiness, sum of completion times and sum of weighted completion times. The implemented genetic programming system has been carefully tuned and used to generate one variant of NEH for each objective function. The new NEHs, obtained with genetic programming, have been compared with the original NEH and randomized NEH versions on a large set of benchmark problems. Our results indicate that the NEH variants discovered by genetic programming are superior to the original NEH and its stochastic version on most of the problems investigated. Journal of the Operational Research Society (2011) 62, 381-396. doi:10.1057/jors.2010.132 Published online 25 August 2010\",\n \"Title: Local optima networks, landscape autocorrelation and heuristic search performance\\nYear: 2012\\n\\nAbstract: Recent developments in fitness landscape analysis include the study of Local Optima Networks (LON) and applications of the Elementary Landscapes theory. This paper represents a first step at combining these two tools to explore their ability to forecast the performance of search algorithms. We base our analysis on the Quadratic Assignment Problem (QAP) and conduct a large statistical study over 600 generated instances of different types. Our results reveal interesting links between the network measures, the autocorrelation measures and the performance of heuristic search algorithms.\",\n \"Title: Fitness Landscapes And The Andrews-Curtis Conjecture\\nYear: 2012\\n\\nAbstract: Attempts have been made to eliminate some potential counterexamples to the Andrews-Curtis conjecture using the combinatorial optimization methods of blind-search and the genetic algorithms meta-heuristic. Breadth-first search with secondary storage is currently the most successful method, which raises questions regarding the inferior performance of heuristic search. In order to understand the underlying reasons we obtain fitness landscape metrics for a number of balanced presentations and draw conclusions regarding the likely effectiveness of other meta-heuristics.\",\n \"Title: Adaptive evolutionary algorithms and extensions to the hyflex hyper-heuristic framework\\nYear: 2012\\n\\nAbstract: HyFlex is a recently proposed software framework for implementing hyper-heuristics and domain-independent heuristic optimisation algorithms [13]. Although it was originally designed to implement hyper-heuristics, it provides a population and a set of move operators of different types. This enable the implementation of adaptive versions of other heuristics such as evolutionary algorithms and iterated local search. The contributions of this article are twofold. First, a number of extensions to the HyFlex framework are proposed and implemented that enable the design of more effective adaptive heuristics. Second, it is demonstrated that adaptive evolutionary algorithms can be implemented within the framework, and that the use of crossover and a diversity metric produced improved results, including a new best-known solution, on the studied vehicle routing problem.\",\n \"Title: Hyper-heuristics and cross-domain optimization\\nYear: 2012\\n\\nAbstract: Hyper-heuristics comprise a set of approaches which are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was firstly used in 2000 to describe 'heuristics to choose heuristics' in the context of combinatorial optimization. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s and 1970s, and a number of related research areas can be identified. The definition of hyper-heuristics has been recently extended to refer to 'a search method or learning mechanism for selecting or generating heuristics to solve computational search problems'. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This tutorial will discuss the motivation, definition and classification of hyper-heuristics. The tutorial will also cover a recent international research competition: the first 'Cross-domain Heuristic Search Challenge'(CHeSC 2011) (http://www.asap.cs.nott.ac.uk/chesc2011). The challenge was to design a search algorithm that works well, not only across different instances of the same problem, but also across different problem domains. Using a sport metaphor, it is the 'Decathlon' challenge of search heuristics. To support the competition, and to promote research in this area, a software framework (HyFlex) was developed, which features a common interface for dealing with different optimization problems. The tutorial will summarize the main features of HyFlex and the competition; and will present an analysis of the results, highlighting the design principles of the most successful cross-domain hyper-heuristics.\",\n \"Title: Partial neighborhoods of the traveling salesman problem\\nYear: 2011\\n\\nAbstract: The Traveling Salesman Problem (TSP) is known to display an elementary landscape under all k-opt move operators. Previous work has also shown that partial neighborhoods may exist that retain some properties characteristic of elementary landscapes. For a tour of n cities, we show that the 2-opt neighborhood can be decomposed into n/2-1 partial neighborhoods. While this paper focuses on the TSP, it also introduces a more formal treatment of partial neighborhoods which applies to all elementary landscapes. Tracking partial neighborhood averages in elementary landscapes requires partitioning the cost matrix. After every move in the search space, the relevant partitions must be updated. However, just as the evaluation function allows a partial update for the TSP, there also exists a partial update for the cost matrix partitions. By only looking at a subset of the partial neighborhoods we can further reduce the cost of updating the cost matrix partitions.\",\n \"Title: Local Optima Networks: A New Model of Combinatorial Fitness Landscapes.\\nYear: 2014\\n\\nAbstract: This chapter overviews a recently introduced network-based model of combinatorial landscapes: Local Optima Networks (LON). The model compresses the information given by the whole search space into a smaller mathematical object that is a graph having as vertices the local optima and as edges the possible weighted transitions between them. Two definitions of edges have been proposed: basin-transition and escape-edges, which capture relevant topological features of the underlying search spaces. This network model brings a new set of metrics to characterize the structure of combinatorial landscapes, those associated with the science of complex networks. These metrics are described, and results are presented of local optima network extraction and analysis for two selected combinatorial landscapes: NK landscapes and the quadratic assignment problem. Network features are found to correlate with and even predict the performance of heuristic search algorithms operating on these problems.\",\n \"Title: Optimal Mutation Rates and Selection Pressure in Genetic Algorithms\\nYear: 2000\\n\\nAbstract: Abstract: It has been argued that optimal per-locusmutation rates in GAs are proportional toselection pressure and the reciprocal of genotypelength. In this paper we suggest thatthe notion of error threshold, borrowed frommolecular evolution, sheds new light on thisargument. We show empirically the existenceof error thresholds in GAs running on a simpleabstract landscape; and then investigatea real-world industrial problem, demonstratingcomparable phenomena in a practical application.\",\n \"Title: An effective heuristic for the two-dimensional irregular bin packing problem.\\nYear: 2013\\n\\nAbstract: This paper proposes an adaptation, to the two-dimensional irregular bin packing problem of the Djang and Finch heuristic (DJD), originally designed for the one-dimensional bin packing problem. In the two-dimensional case, not only is it the case that the piece’s size is important but its shape also has a significant influence. Therefore, DJD as a selection heuristic has to be paired with a placement heuristic to completely construct a solution to the underlying packing problem. A successful adaptation of the DJD requires a routine to reduce computational costs, which is also proposed and successfully tested in this paper. Results, on a wide variety of instance types with convex polygons, are found to be significantly better than those produced by more conventional selection heuristics.\",\n \"Title: HyFlex: a benchmark framework for cross-domain heuristic search\\nYear: 2012\\n\\nAbstract: This paper presents HyFlex, a software framework for the development of cross-domain search methodologies. The framework features a common software interface for dealing with different combinatorial optimisation problems and provides the algorithm components that are problem specific. In this way, the algorithm designer does not require a detailed knowledge of the problem domains and thus can concentrate his/her efforts on designing adaptive general-purpose optimisation algorithms. Six hard combinatorial problems are fully implemented: maximum satisfiability, one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing. Each domain contains a varied set of instances, including real-world industrial data and an extensive set of state-of-the-art problem specific heuristics and search operators. HyFlex represents a valuable new benchmark of heuristic search generality, with which adaptive cross-domain algorithms are being easily developed and reliably compared.This article serves both as a tutorial and a as survey of the research achievements and publications so far using HyFlex.\",\n \"Title: Modeling and optimization of combined cytostatic and cytotoxic cancer chemotherapy.\\nYear: 2010\\n\\nAbstract: This study extends a previous mathematical model of cancer cytotoxic chemotherapy, which considered cycling tumor cells and interactions with the immune system, by incorporating a different type of drug: a cytostatic agent. The effect of a cytostatic drug is to arrest cells in a phase of their cycle. In consequence, once tumor cells are arrested and synchronized they can be targeted with a cytotoxic agent, thus maximizing cell kill fraction and minimizing normal cell killing. The goal is to incorporate the new drug into the chemotherapy protocol and devise optimal delivery schedules.The problem of designing efficient combined chemotherapies is formulated as an optimal control problem and tackled using a state-of-the-art evolutionary algorithm for real-valued encoding, namely the covariance matrix adaptation evolution strategy. Alternative solution representations and three formulations of the underlying objective function are proposed and compared.The optimization problem was successfully solved by the proposed approach. The encoding that enforced non-overlapping (simultaneous) application of the two types of drugs produced competitive protocols with significant less amount of toxic drug, thus achieving better immune system health. When compared to treatment protocols that only consider a cytotoxic agent, the incorporation of a cytostatic drug dramatically improved the outcome and performance of the overall treatment, confirming in silico that the combination of a cytostatic with a cytotoxic agent improves the efficacy and efficiency of the chemotherapy.We conclude that the proposed approach can serve as a valuable decision support tool for the medical practitioner facing the complex problem of designing efficient combined chemotherapies.\",\n \"Title: Towards the decathlon challenge of search heuristics\\nYear: 2009\\n\\nAbstract: We present an object oriented framework for designing and evaluating heuristic search algorithms that achieve a high level of generality and work well on a wide range of combinatorial optimization problems. Our framework, named HyFlex, differs from most software tools for meta-heuristics and evolutionary computation in that it provides the algorithm components that are problem-specific instead of those which are problem-independent. In this way, we simultaneously liberate algorithm designers from needing to know the details of the problem domains; and prevent them from incorporating additional problem specific information in their algorithms. The efforts need instead to be focused on designing high-level strategies to intelligently combine the provided problem specific algorithmic components. We plan to propose a challenge, based on our framework, where the winners will be those algorithms with a better overall performance across all of the different domains. Using an Olympic metaphor, we are not solely focussed on the 100 meters race, but instead on the decathlon of modern search methodologies.\",\n \"Title: Search methodologies in real-world software engineering\\nYear: 2013\\n\\nAbstract: One of the aims of software engineering is to reduce overall software costs. Optimisation is, therefore, relevant to the process of software development. This article describes recent case studies on the application of modern search methodologies to challenging real-world problems in software engineering. It also describes a recent research initiative: Dynamic Adaptive Automated Software Engineering (DAASE), whose goal is to embed optimisation into deployed software to create self-optimising adaptive systems. The article accompanies an invited talk for the Workshop on Bridging the Gap between Industry and Academia in Optimisation to be held as part of GECCO 2013.\",\n \"Title: Local optima networks and the performance of iterated local search\\nYear: 2012\\n\\nAbstract: Local Optima Networks (LONs) have been recently proposed as an alternative model of combinatorial fitness landscapes. The model compresses the information given by the whole search space into a smaller mathematical object that is the graph having as vertices the local optima and as edges the possible weighted transitions between them. A new set of metrics can be derived from this model that capture the distribution and connectivity of the local optima in the underlying configuration space. This paper departs from the descriptive analysis of local optima networks, and actively studies the correlation between network features and the performance of a local search heuristic. The NK family of landscapes and the Iterated Local Search metaheuristic are considered. With a statistically-sound approach based on multiple linear regression, it is shown that some LONs' features strongly influence and can even partly predict the performance of a heuristic search algorithm. This study validates the expressive power of LONs as a model of combinatorial fitness landscapes.\",\n \"Title: A study of NK landscapes' basins and local optima networks\\nYear: 2008\\n\\nAbstract: We propose a network characterization of combinatorial fitness landscapes by adapting the notion of inherent networks proposed for energy surfaces (Doye, 2002). We use the well-known family of $NK$ landscapes as an example. In our case the inherent network is the graph where the vertices are all the local maxima and edges mean basin adjacency between two maxima. We exhaustively extract such networks on representative small NK landscape instances, and show that they are 'small-worlds'. However, the maxima graphs are not random, since their clustering coefficients are much larger than those of corresponding random graphs. Furthermore, the degree distributions are close to exponential instead of Poissonian. We also describe the nature of the basins of attraction and their relationship with the local maxima network.\",\n \"Title: Clustering of local optima in combinatorial fitness landscapes\\nYear: 2011\\n\\nAbstract: Using the recently proposed model of combinatorial landscapes: local optima networks, we study the distribution of local optima in two classes of instances of the quadratic assignment problem. Our results indicate that the two problem instance classes give rise to very different configuration spaces. For the so-called real-like class, the optima networks possess a clear modular structure, while the networks belonging to the class of random uniform instances are less well partitionable into clusters. We briefly discuss the consequences of the findings for heuristically searching the corresponding problem spaces.\",\n \"Title: Consensus Sequence Plots and Error Thresholds: Tools for Visualising the Structure of Fitness Landscapes\\nYear: 2000\\n\\nAbstract: This paper investigates the occurrence of error thresholds in genetic algorithms (GAs) running on a wide range of fitness landscape structures. The error threshold, a notion from molecular evolution, is a critical mutation rate beyond which the evolutionary dynamics of a population changes drastically. The paper also introduces Consensus sequence plots, an empirical tool for locating error thresholds on complex landscapes. This plots were borrowed and adapted from theoretical biology. Results suggest that error thresholds occur in GAs but only on landscapes of certain degree of ruggedness or complexity. Moreover, consensus sequence plots can be useful for predicting some features of a landscape such as ruggedness and \\\"step-ness\\\". We argue that error thresholds and consensus sequence plots, may become useful tools for analyzing evolutionary algorithms and visualising the structure of fitness landscapes.\",\n \"Title: The cooperative royal road: avoiding hitchhiking\\nYear: 2007\\n\\nAbstract: We propose using the so called Royal Road functions astest functions for cooperative co-evolutionary algorithms (CCEAs). TheRoyal Road functions were created in the early 90's with the aim ofdemonstrating the superiority of genetic algorithms over local searchmethods. Unexpectedly, the opposite was found to be true. The researchdeepened our understanding of the phenomenon of hitchhiking whereunfavorable alleles may become established in the population followingan early association with an instance of a highly fit schema. Here, wetake advantage of the modular and hierarchical structure of the RoyalRoad functions to adapt them to a co-evolutionary setting. Using a multiplepopulation approach, we show that a CCEA easily outperforms astandard genetic algorithm on the Royal Road functions, by naturallyovercoming the hitchhiking effect. Moreover, we found that the optimalnumber of sub-populations for the CCEA is not the same as the numberof components that the function can be linearly separated into, andpropose an explanation for this behavior. We argue that this class offunctions may serve in foundational studies of cooperative co-evolution.\",\n \"Title: Evolving l-systems to capture protein structure native conformations\\nYear: 2005\\n\\nAbstract: A protein is a linear chain of amino acids that folds into a unique functional structure, called its native state. In this state, proteins show repeated substructures like alpha helices and beta sheets. This suggests that native structures may be captured by the formalism known as Lindenmayer systems (L-systems). In this paper an evolutionary approach is used as the inference procedure for folded structures on simple lattice models. The algorithm searches the space of L-systems which are then executed to obtain the phenotype, thus our approach is close to Grammatical Evolution. The problem is to find a set of rewriting rules that represents a target native structure on the lattice model. The proposed approach has produced promising results for short sequences. Thus the foundations are set for a novel encoding based on L-systems for evolutionary approaches to both the Protein Structure Prediction and Inverse Folding Problems.\",\n \"Title: Cheating for problem solving: a genetic algorithm with social interactions\\nYear: 2009\\n\\nAbstract: We propose a variation of the standard genetic algorithm that incorporates social interaction between the individuals in the population. Our goal is to understand the evolutionary role of social systems and its possible application as a non-genetic new step in evolutionary algorithms. In biological populations, i.e. animals, even human beings and microorganisms, social interactions often affect the fitness of individuals. It is conceivable that the perturbation of the fitness via social interactions is an evolutionary strategy to avoid trapping into local optimum, thus avoiding a fast convergence of the population. We model the social interactions according to Game Theory. The population is, therefore, composed by cooperator and defector individuals whose interactions produce payoffs according to well known game models (prisoner's dilemma, chicken game, and others). Our results on Knapsack problems show, for some game models, a significant performance improvement as compared to a standard genetic algorithm.\",\n \"Title: Error Thresholds and Their Relation to Optimal Mutation Rates\\nYear: 1999\\n\\nAbstract: The error threshold -- a notion from molecular evolution -- is the critical mutation rate beyond which structures obtained by the evolutionary process are destroyed more frequently than selection can reproduce them. We argue that this notion is closely related to the more familiar notion of optimal mutation rates in Evolutionary Algorithms (EAs). This correspondence has been intuitively perceived before ([9], [11]). However, no previous study, to our knowledge, has been aimed at explicitly testing the hypothesis of such a relationship. Here we propose a methodology for doing so. Results on a restricted range of fitness landscapes suggest that these two notions are indeed correlated. There is not, however, a critically precise optimal mutation rate but rather a range of values producing similar near-optimal performance. When recombination is used, both error thresholds and optimal mutation ranges are lower than in the asexual case. This knowledge may have both theoretical relevance in understanding EA behavior, and practical implications for setting optimal values of evolutionary parameters.\",\n \"Title: On Genetic Algorithms and Lindenmayer Systems\\nYear: 1998\\n\\nAbstract: This paper describes a system for simulating the evolution of artificial 2D plant morphologies. Virtual plant genotypes are inspired by the mathemati- cal formalism known as Lindenmayer systems (L-systems). The phenotypes are the branching structures resulting from the derivation and graphic interpretation of the genotypes. Evolution is simulated using a genetic algorithm with a fitness function inspired by current evolutionary hypotheses concerning the factors that have had the greatest effect on plant evolution. The system also provides inter- active selection, allowing the user to d irect simulated evolution towards pre- ferred ph enotypes. Simulation results demonstrate many interesting structures, suggesting that artificial evolution constitutes a powerful tool for (1) exploring the large, complex space of branching structures found in nature, and (2) gen- erating novel ones. Finally, we emphasize that Lindenmayer systems constitute a highly suitable encoding for artificial evolution studies.\",\n \"Title: First-improvement vs. best-improvement local optima networks of NK landscapes\\nYear: 2012\\n\\nAbstract: This paper extends a recently proposed model for combinatorial landscapes: Local Optima Networks (LON), to incorporate a first-improvement (greedy-ascent) hill-climbing algorithm, instead of a best-improvement (steepestascent) one, for the definition and extraction of the basins of attraction of the landscape optima. A statistical analysis comparing best and first improvement network models for a set of NK landscapes, is presented and discussed. Our results suggest structural differences between the two models with respect to both the network connectivity, and the nature of the basins of attraction. The impact of these differences in the behavior of search heuristics based on first and best improvement local search is thoroughly discussed.\",\n \"Title: Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms\\nYear: 2014\\n\\nAbstract: The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to promote awareness and cross-fertilisation of ideas across the (by and large, non-overlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.\",\n \"Title: An evolutionary approach to cancer chemotherapy scheduling\\nYear: 2007\\n\\nAbstract: In this paper, we investigate the employment of evolutionary algorithms as a search mechanism in a decision support system for designing chemotherapy schedules. Chemotherapy involves using powerful anti-cancer drugs to help eliminate cancerous cells and cure the condition. It is given in cycles of treatment alternating with rest periods to allow the body to recover from toxic side-effects. The number and duration of these cycles would depend on many factors, and the oncologist would schedule a treatment for each patient's condition. The design of a chemotherapy schedule can be formulated as an optimal control problem; using an underlying mathematical model of tumour growth (that considers interactions with the immune system and multiple applications of a cycle-phase-specific drug), the objective is to find effective drug schedules that help eradicate the tumour while maintaining the patient health's above an acceptable level. A detailed study on the effects of different objective functions, in the quality and diversity of the solutions, was performed. A term that keeps at a minimum the tumour levels throughout the course of treatment was found to produce more regular treatments, at the expense of imposing a higher strain on the patient's health, and reducing the diversity of the solutions. Moreover, when the number of cycles was incorporated in the problem encoding, and a parsimony pressure added to the objective function, shorter treatments were obtained than those initially found by trial and error.\",\n \"Title: Complex-network analysis of combinatorial spaces: the NK landscape case.\\nYear: 2012\\n\\nAbstract: We propose a network characterization of combinatorial fitness landscapes by adapting the notion of inherent networks proposed for energy surfaces. We use the well-known family of NK landscapes as an example. In our case the inherent network is the graph whose vertices represent the local maxima in the landscape, and the edges account for the transition probabilities between their corresponding basins of attraction. We exhaustively extracted such networks on representative NK landscape instances, and performed a statistical characterization of their properties. We found that most of these network properties are related to the search difficulty on the underlying NK landscapes with varying values of K.\",\n \"Title: Assortative mating in genetic algorithms for dynamic problems\\nYear: 2005\\n\\nAbstract: Non-random mating seems to be the norm in nature among sexual organisms. A common mating criteria among animals is assortative mating, where individuals mate according to their phenotype similarities (or dissimilarities). This paper explores the effect of including assortative mating in genetic algorithms for dynamic problems. A wide range of mutation rates was explored, since comparative results were found to change drastically for different mutation rates. The strategy for selecting mates was found to interact with the mutation rate value: low mutation rates were the best choice for dissortative mating, medium mutation values for the standard GA, and higher mutation rates for assortative mating. Thus, GA efficiency is related to mate selection strategies in connection with mutation values. For low mutation rates typically used in GA, dissortative mating was shown to be a robust and promising strategy for dynamic problems.\",\n \"Title: Communities of minima in local optima networks of combinatorial spaces\\nYear: 2012\\n\\nAbstract: In this work, we present a new methodology to study the structure of the configuration spaces of hard combinatorial problems. It consists in building the network that has as nodes the locally optimal configurations and as edges the weighted oriented transitions between their basins of attraction. We apply the approach to the detection of communities in the optima networks produced by two different classes of instances of a hard combinatorial optimization problem: the quadratic assignment problem (QAP). We provide evidence indicating that the two problem instance classes give rise to very different configuration spaces. For the so-called real-like class, the networks possess a clear modular structure, while the optima networks belonging to the class of random uniform instances are less well partitionable into clusters. This is convincingly supported by using several statistical tests. Finally, we briefly discuss the consequences of the findings for heuristically searching the corresponding problem spaces.\",\n \"Title: The component model for elementary landscapes and partial neighborhoods\\nYear: 2014\\n\\nAbstract: Local search algorithms exploit moves on an adjacency graph of the search space. An ''elementary landscape'' exists if the objective function f is an eigenfunction of the Laplacian of the graph induced by the neighborhood operator; this allows various statistics about the neighborhood to be computed in closed form. A new component based model makes it relatively simple to prove that certain types of landscapes are elementary. The traveling salesperson problem, weighted graph (vertex) coloring and the minimum graph bisection problem yield elementary landscapes under commonly used local search operators. The component model is then used to efficiently compute the mean objective function value over partial neighborhoods for these same problems. For a traveling salesperson problem over n cities, the 2-opt neighborhood can be decomposed into @?n/[email protected]? partial neighborhoods. For graph coloring and the minimum graph bisection problem, partial neighborhoods can be used to focus search on those moves that are capable of producing a solution with a strictly improving objective function value.\",\n \"Title: Effective learning hyper-heuristics for the course timetabling problem.\\nYear: 2014\\n\\nAbstract: •Course timetabling constraints are converted into generic structures.•Two types of learning for operator selection are contrasted: online and offline.•An empirical approach for selecting the best operators from a pool is proposed.•Online learning shows competitive results, even producing new best-known solutions.•The proposed method achieves a balance between generality and effectiveness.\",\n \"Title: A unified hyper-heuristic framework for solving bin packing problems\\nYear: 2014\\n\\nAbstract: One- and two-dimensional packing and cutting problems occur in many commercial contexts, and it is often important to be able to get good-quality solutions quickly. Fairly simple deterministic heuristics are often used for this purpose, but such heuristics typically find excellent solutions for some problems and only mediocre ones for others. Trying several different heuristics on a problem adds to the cost. This paper describes a hyper-heuristic methodology that can generate a fast, deterministic algorithm capable of producing results comparable to that of using the best problem-specific heuristic, and sometimes even better, but without the cost of trying all the heuristics. The generated algorithm handles both one- and two-dimensional problems, including two-dimensional problems that involve irregular concave polygons. The approach is validated using a large set of 1417 such problems, including a new benchmark set of 480 problems that include concave polygons.\",\n \"Title: Local Optima Networks Of The Permutation Flow-Shop Problem\\nYear: 2013\\n\\nAbstract: This article extracts and analyzes local optima networks for the permutation flow-shop problem. Two widely used move operators for permutation representations, namely, swap and insertion, are incorporated into the network landscape model. The performance of a heuristic search algorithm on this problem is also analyzed. In particular, we study the correlation between local optima network features and the performance of an iterated local search heuristic. Our analysis reveals that network features can explain and predict problem difficulty. The evidence confirms the superiority of the insertion operator for this problem.\",\n \"Title: Evolvability metrics in adaptive operator selection.\\nYear: 2014\\n\\nAbstract: Evolvability metrics gauge the potential for fitness of an individual rather than fitness itself. They measure the local characteristics of the fitness landscape surrounding a solution. In adaptive operator selection the goal is to dynamically select from a given pool the operator to apply next during the search process. An important component of these adaptive schemes is credit assignment, whereby operators are rewarded according to their observed performance. This article brings the notion of evolvability to adaptive operator selection, by proposing an autonomous search algorithm that rewards operators according to their potential for fitness rather than their immediate fitness improvement. The approach is tested within an evolutionary algorithm framework featuring several mutation operators on binary strings. Three benchmark problems of increasing difficulty, Onemax, Royal Staircase and Multiple Knapsack are considered. Experiments reveal that evolvability metrics significantly improve the performance of adaptive operator selection, when compared against standard fitness improvement metrics. The main contribution is to effectively use fitness landscape metrics to guide a self-configuring algorithm.\",\n \"Title: Generalized asymmetric partition crossover (GAPX) for the asymmetric TSP\\nYear: 2014\\n\\nAbstract: The Generalized Partition Crossover (GPX) constructs new solutions for the Traveling Salesman Problem (TSP) by finding recombining partitions with one entry and one exit in the graph composed by the union of two parent solutions. If there are k recombining partitions in the union graph, 2^k-2 solutions are simultaneously exploited by GPX. Generalized Asymmetric Partition Crossover (GAPX) is introduced; it finds more recombining partitions and can also find partitions for the asymmetric TSP. GAPX does this by locating partitions that cut vertices of degree 4 in the union graph and by finding partitions with multiple entry and exit points, both in O(n) time. GAPX can improve the quality of solutions generated by the Lin-Kernighan-Helsgaun heuristic and improve the state of the art for the asymmetric TSP.\",\n \"Title: Local Optima Networks of the Quadratic Assignment Problem\\nYear: 2011\\n\\nAbstract: Using a recently proposed model for combinatorial landscapes, Local Optima Networks (LON), we conduct a thorough analysis of two types of instances of the Quadratic Assignment Problem (QAP). This network model is a reduction of the landscape in which the nodes correspond to the local optima, and the edges account for the notion of adjacency between their basins of attraction. The model was inspired by the notion of `inherent network' of potential energy surfaces proposed in physical-chemistry. The local optima networks extracted from the so called uniform and real-like QAP instances, show features clearly distinguishing these two types of instances. Apart from a clear confirmation that the search difficulty increases with the problem dimension, the analysis provides new confirming evidence explaining why the real-like instances are easier to solve exactly using heuristic search, while the uniform instances are easier to solve approximately. Although the local optima network model is still under development, we argue that it provides a novel view of combinatorial landscapes, opening up the possibilities for new analytical tools and understanding of problem difficulty in combinatorial optimization.\",\n \"Title: Error Thresholds in Genetic Algorithms\\nYear: 2006\\n\\nAbstract: The error threshold of replication is an important notion in the quasispecies evolution model; it is a critical mutation rate (error rate) beyond which structures obtained by an evolutionary process are destroyed more frequently than selection can reproduce them. With mutation rates above this critical value, an error catastrophe occurs and the genomic information is irretrievably lost. Therefore, studying the factors that alter this magnitude has important implications in the study of evolution. Here we use a genetic algorithm, instead of the quasispecies model, as the underlying model of evolution, and explore whether the phenomenon of error thresholds is found on finite populations of bit strings evolving on complex landscapes. Our empirical results verify the occurrence of error thresholds in genetic algorithms. In this way, this notion is brought from molecular evolution to evolutionary computation. We also study the effect of modifying the most prominent evolutionary parameters on the magnitude of this critical value, and found that error thresholds depend mainly on the selection pressure and genotype length.\",\n \"Title: Local Optima Networks of NK Landscapes With Neutrality\\nYear: 2011\\n\\nAbstract: In previous work, we have introduced a network based model that abstracts many details of the underlying landscape and compresses the landscape information into a weighted, oriented graph which we call the local optima network. The vertices of this graph are the local optima of the given fitness landscape, while the arcs are transition probabilities between local optima basins. Here, we extend this formalism to neutral fitness landscapes, which are common in difficult combinatorial search spaces. The study is based on two neutral variants of the well-known NK family of landscapes (where N stands for the chromosome length, and K for the number of gene epistatic interactions within the chromosome). By using these two NK variants, probabilistic (NKp), and quantified NK (NKq), in which the amount of neutrality can be tuned by a parameter, we show that our new definitions of the optima networks and the associated basins are consistent with the previous definitions for the non-neutral case. Moreover, our empirical study and statistical analysis show that the features of neutral landscapes interpolate smoothly between landscapes with maximum neutrality and non-neutral ones. We found some unknown structural differences between the two studied families of neutral landscapes. But overall, the network features studied confirmed that neutrality, in landscapes with percolating neutral networks, may enhance heuristic search. Our current methodology requires the exhaustive enumeration of the underlying search space. Therefore, sampling techniques should be developed before this analysis can have practical implications. We argue, however, that the proposed model offers a new perspective into the problem difficulty of combinatorial optimization problems and may inspire the design of more effective search heuristics.\",\n \"Title: An Integer Linear Programming approach to the single and bi-objective Next Release Problem\\nYear: 2015\\n\\nAbstract: ContextThe Next Release Problem involves determining the set of requirements to implement in the next release of a software project. When the problem was first formulated in 2001, Integer Linear Programming, an exact method, was found to be impractical because of large execution times. Since then, the problem has mainly been addressed by employing metaheuristic techniques. ObjectiveIn this paper, we investigate if the single-objective and bi-objective Next Release Problem can be solved exactly and how to better approximate the results when exact resolution is costly. MethodsWe revisit Integer Linear Programming for the single-objective version of the problem. In addition, we integrate it within the Epsilon-constraint method to address the bi-objective problem. We also investigate how the Pareto front of the bi-objective problem can be approximated through an anytime deterministic Integer Linear Programming-based algorithm when results are required within strict runtime constraints. Comparisons are carried out against NSGA-II. Experiments are performed on a combination of synthetic and real-world datasets. FindingsWe show that a modern Integer Linear Programming solver is now a viable method for this problem. Large single objective instances and small bi-objective instances can be solved exactly very quickly. On large bi-objective instances, execution times can be significant when calculating the complete Pareto front. However, good approximations can be found effectively. ConclusionThis study suggests that (1) approximation algorithms can be discarded in favor of the exact method for the single-objective instances and small bi-objective instances, (2) the Integer Linear Programming-based approximate algorithm outperforms the NSGA-II genetic approach on large bi-objective instances, and (3) the run times for both methods are low enough to be used in real-world situations.\",\n \"Title: The Multi-Funnel Structure of TSP Fitness Landscapes: A Visual Exploration.\\nYear: 2015\\n\\nAbstract: We use the Local Optima Network model to study the structure of symmetric TSP fitness landscapes. The 'big-valley' hypothesis holds that for TSP and other combinatorial problems, local optima are not randomly distributed, instead they tend to be clustered around the global optimum. However, a recent study has observed that, for solutions close in evaluation to the global optimum, this structure breaks down into multiple valleys, forming what has been called 'multiple funnels'. The multiple funnel concept implies that local optima are organised into clusters, so that a particular local optimum largely belongs to a particular funnel. Our study is the first to extract and visualise local optima networks for TSP and is based on a sampling methodology relying on the Chained Lin-Kernighan algorithm. We confirm the existence of multiple funnels on two selected TSP instances, finding additional funnels in a previously studied instance. Our results suggests that transitions among funnels are possible using operators such as 'double-bridge'. However, for consistently escaping sub-optimal funnels, more robust escaping mechanisms are required.\",\n \"Title: Combining Mutation and Recombination to Improve a Distributed Model of Adaptive Operator Selection.\\nYear: 2015\\n\\nAbstract: We present evidence indicating that adding a crossover island greatly improves the performance of a Dynamic Island Model for Adaptive Operator Selection. Two combinatorial optimisation problems are considered: the Onemax benchmark, to prove the concept; and a real-world formulation of the course timetabling problem to test practical relevance. Crossover is added to the recently proposed dynamic island adaptive model for operator selection which considered mutation only. When comparing the models with and without a recombination, we found that having a crossover island significantly improves the performance. Our experiments also provide compelling evidence of the dynamic role of crossover during search: it is a useful operator across the whole search process. The idea of combining different type of operators in a distributed adaptive search model is worth further investigation.\",\n \"Title: Tunnelling Crossover Networks\\nYear: 2015\\n\\nAbstract: Local optima networks are a recent model of fitness landscapes. They compress the landscape by representing local optima as nodes, and search transitions among them as edges. Previous local optima networks considered transitions based on mutation; this study looks instead at transitions based on deterministic recombination. We define and analyse networks based on the recently proposed partition crossover for k-bounded pseudo-Boolean functions, using NKq landscapes as a case study. Partition crossover was initially proposed for the travelling salesman problem, where it was found to ``tunnel\\\\\\\" between local optima, i.e., jump from local optimum to local optimum. Our network analysis shows that this also happens for NK landscapes: local optima are densely connected via partition crossover. We found marked differences between the adjacent and random interaction NK models. Surprisingly, with the random model, instances have a lower number of local optima on average, but their networks are more sparse and decompose into several clusters. There is also large variability in the size and pattern of connectivity of instances coming from the same landscape parameter values. These network features offer new insight informing why some instances are harder to solve than others.\",\n \"Title: Deconstructing the Big Valley Search Space Hypothesis.\\nYear: 2016\\n\\nAbstract: The big valley hypothesis suggests that, in combinatorial optimisation, local optima of good quality are clustered and surround the global optimum. We show here that the idea of a single valley does not always hold. Instead the big valley seems to de-construct into several valleys, also called ‘funnels’ in theoretical chemistry. We use the local optima networks model and propose an effective procedure for extracting the network data. We conduct a detailed study on four selected TSP instances of moderate size and observe that the big valley decomposes into a number of sub-valleys of different sizes and fitness distributions. Sometimes the global optimum is located in the largest valley, which suggests an easy to search landscape, but this is not generally the case. The global optimum might be located in a small valley, which offers a clear and visual explanation of the increased search difficulty in these cases. Our study opens up new possibilities for analysing and visualising combinatorial landscapes as complex networks.\",\n \"Title: A Benchmark Set Extension And Comparative Study For The Hyflex Framework\\nYear: 2015\\n\\nAbstract: In this work we conduct a comparative study of several publicly available, state-of-the-art hyper-heuristics for HyFlex in order to assess their generality across domains. To this purpose we extend the HyFlex benchmark set with 3 new problem domains: The 0-1 Knap Sack, Quadratic Assignment and Max-Cut Problem. To our knowledge, this is the first public extension of the benchmark since the CHeSC 2011 competition. In addition, this is the first study testing the Fair-Share Iterated Local Search (FS-ILS) method, designed in prior research, using a semi-automated design approach, on new unseen problem domains. We show that, of the methods compared, Adap-HH (CHeSC 2011 winner) clearly perfoms the most consistently, overall. In addition, we identify a weakness of, as well as a way to further simplify the FS-ILS method. Finally, we found that, overall, the state-of-the-art methods compared, generalized much better than a naive baseline.\",\n \"Title: Communities of Local Optima as Funnels in Fitness Landscapes.\\nYear: 2016\\n\\nAbstract: We conduct an analysis of local optima networks extracted from fitness landscapes of the Kauffman NK model under iterated local search. Applying the Markov Cluster Algorithm for community detection to the local optima networks, we find that the landscapes consist of multiple clusters. This result complements recent findings in the literature that landscapes often decompose into multiple funnels, which increases their difficulty for iterated local search. Our results suggest that the number of clusters as well as the size of the cluster in which the global optimum is located are correlated to the search difficulty of landscapes. We conclude that clusters found by community detection in local optima networks offer a new way to characterize the multi-funnel structure of fitness landscapes.\",\n \"Title: Additional Dimensions to the Study of Funnels in Combinatorial Landscapes.\\nYear: 2016\\n\\nAbstract: The global structure of travelling salesman's fitness landscapes has recently revealed the presence of multiple 'funnels'. This implies that local optima are organised into several clusters, so that a particular local optimum largely belongs to a particular funnel. Such a global structure can increase search difficulty, especially, when the global optimum is located in a deep, narrow funnel. Our study brings more precision (and dimensions) to the notion of funnels with a data-driven approach using Local Optima Networks and the Chained Lin-Kernighan heuristic. We start by exploring the funnel `floors', characterising them using the notion of communities from complex networks. We then analyse the more complex funnel `basins'. Since their depth is relevant to search, we visualise them in 3D. Our study, across a set of TSP instances, reveals a multi-funnel structure in most of them. However, the specific topology varies across instances and relates to search difficulty. Finally, including a stronger perturbation into Chained Lin-Kernighan proved to smooth the funnel structure, reducing the number of funnels and enlarging the valley leading to global optima.\",\n \"Title: Evolutionary Algorithms with Linkage Information for Feature Selection in Brain Computer Interfaces.\\nYear: 2016\\n\\nAbstract: Brain Computer Interfaces are an essential technology for the advancement of prosthetic limbs, but current signal acquisition methods are hindered by a number of factors, not least, noise. In this context, Feature Selection is required to choose the important signal features and improve classifier accuracy. Evolutionary algorithms have proven to outperform filtering methods (in terms of accuracy) for Feature Selection. This paper applies a single-point heuristic search method, Iterated Local Search (ILS), and compares it to a genetic algorithm (GA) and a memetic algorithm (MA). It then further attempts to utilise Linkage between features to guide search operators in the algorithms stated. The GA was found to outperform ILS. Counter-intuitively, linkage-guided algorithms resulted in higher classification error rates than their unguided alternatives. Explanations for this are explored.\",\n \"Title: The effect of landscape funnels in QAPLIB instances\\nYear: 2017\\n\\nAbstract: The effectiveness of common metaheuristics on combinatorial optimisation problems can be limited by certain characteristics of the fitness landscape. We use the local optima network model to compress the 'inherent structure' of a problem space into a network whose structure relates to the empirical hardness of the underlying landscape. Monotonic sequences axe used on the local optima networks of a benchmark set of QAP instances (QAPLIB) to expose landscape funnels. The results suggest links between features of these structures and lowered metaheuristic performance.\",\n \"Title: A methodology for determining an effective subset of heuristics in selection hyper-heuristics.\\nYear: 2017\\n\\nAbstract: •A methodology for determining an effective subset of heuristics in hyper-heuristics.•Use of fitness landscape metrics and non-parametric statistics to rank heuristics.•New adaptive iterated local search hyper-heuristic using dynamic multi-armed bandit.•State-of-the-art results in course timetabling including new best-known solutions.\",\n \"Title: Shaping communities of local optima by perturbation strength.\\nYear: 2017\\n\\nAbstract: Recent work discovered that fitness landscapes induced by Iterated Local Search (ILS) may consist of multiple clusters, denoted as funnels or communities of local optima. Such studies exist only for perturbation operators (kicks) with low strength. We examine how different strengths of the ILS perturbation operator affect the number and size of clusters. We present an empirical study based on local optima networks from NK fitness landscapes. Our results show that a properly selected perturbation strength can help overcome the effect of ILS getting trapped in clusters of local optima. This has implications for designing effective ILS approaches in practice, where traditionally only small perturbations or complete restarts are applied, with the middle ground of intermediate perturbation strengths largely unexplored.\",\n \"Title: Comparing communities of optima with funnels in combinatorial fitness landscapes.\\nYear: 2017\\n\\nAbstract: The existence of sub-optimal funnels in combinatorial fitness landscapes has been linked to search difficulty. The exact nature of these structures --- and how commonly they appear --- is not yet fully understood. Improving our understanding of funnels could help with designing effective diversification mechanisms for a 'smoothing' effect, making optimisation easier. We model fitness landscapes as local optima networks. The relationship between communities of local optima found by network clustering algorithms and funnels is explored. Funnels are identified using the notion of monotonic sequences from the study of energy landscapes in theoretical chemistry. NK Landscapes and the Quadratic Assignment Problem are used as case studies. Our results show that communities are linked to funnels. The analysis exhibits relationships between these landscape structures and the performance of trajectory-based metaheuristics such as Simulated Annealing (SA) and Iterated Local Search (ILS). In particular, ILS gets trapped in funnels, and modular communities of optima slow it down. The funnels contribute to lower success for SA. We show that increasing the strength of ILS perturbation helps to 'smooth' the funnels and improves performance in multi-funnel landscapes.\",\n \"Title: Modelling genetic improvement landscapes with local optima networks.\\nYear: 2017\\n\\nAbstract: Local optima networks are a compact representation of the global structure of a search space. They can be used for analysis and visualisation. This paper provides one of the first analyses of program search spaces using local optima networks. These are generated by sampling the search space by recording the progress of an Iterated Local Search algorithm. Source code mutations in comparison and Boolean operators are considered. The search spaces of two small benchmark programs, the triangle and TCAS programs, are analysed and visualised. Results show a high level of neutrality i.e. connected test-equivalent mutants. It is also generally relatively easy to find a path from a random mutant to a mutant that passes all test cases.\",\n \"Title: Optimizing one million variable NK landscapes by hybridizing deterministic recombination and local search.\\nYear: 2017\\n\\nAbstract: In gray-box optimization, the search algorithms have access to the variable interaction graph (VIG) of the optimization problem. For Mk Landscapes (and NK Landscapes) we can use the VIG to identify an improving solution in the Hamming neighborhood in constant time. In addition, using the VIG, deterministic Partition Crossover is able to explore an exponential number of solutions in a time that is linear in the size of the problem. Both methods have been used in isolation in previous search algorithms. We present two new gray-box algorithms that combine Partition Crossover with highly efficient local search. The best algorithms are able to locate the global optimum on Adjacent NK Landscape instances with one million variables. The algorithms are compared with a state-of-the-art algorithm for pseudo-Boolean optimization: Gray-Box Parameterless Population Pyramid. The results show that the best algorithm is always one combining Partition Crossover and highly efficient local search. But the results also illustrate that the best optimizer differs on Adjacent and Random NK Landscapes.\",\n \"Title: Understanding Phase Transitions with Local Optima Networks: Number Partitioning as a Case Study.\\nYear: 2017\\n\\nAbstract: Phase transitions play an important role in understanding search difficulty in combinatorial optimisation. However, previous attempts have not revealed a clear link between fitness landscape properties and the phase transition. We explore whether the global landscape structure of the number partitioning problem changes with the phase transition. Using the local optima network model, we analyse a number of instances before, during, and after the phase transition. We compute relevant network and neutrality metrics; and importantly, identify and visualise the funnel structure with an approach (monotonic sequences) inspired by theoretical chemistry. While most metrics remain oblivious to the phase transition, our results reveal that the funnel structure clearly changes. Easy instances feature a single or a small number of dominant funnels leading to global optima; hard instances have a large number of suboptimal funnels attracting the search. Our study brings new insights and tools to the study of phase transitions in combinatorial optimisation.\",\n \"Title: Visualising The Search Landscape Of The Triangle Program\\nYear: 2017\\n\\nAbstract: High order mutation analysis of a software engineering benchmark, including schema and local optima networks, suggests program improvements may not be as hard to find as is often assumed. (1) Bit-wise genetic building blocks are not deceptive and can lead to all global optima. (2) There are many neutral networks, plateaux and local optima, nevertheless in most cases near the human written C source code there are hill climbing routes including neutral moves to solutions.\",\n \"Title: Evolving Training Sets for Improved Transfer Learning in Brain Computer Interfaces.\\nYear: 2017\\n\\nAbstract: A new proof-of-concept method for optimising the performance of Brain Computer Interfaces (BCI) while minimising the quantity of required training data is introduced. This is achieved by using an evolutionary approach to rearrange the distribution of training instances, prior to the construction of an Ensemble Learning Generic Information (ELGI) model. The training data from a population was optimised to emphasise generality of the models derived from it, prior to a recombination with participant-specific data via the ELGI approach, and training of classifiers. Evidence is given to support the adoption of this approach in the more difficult BCI conditions: smaller training sets, and those suffering from temporal drift. This paper serves as a case study to lay the groundwork for further exploration of this approach.\",\n \"Title: Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces.\\nYear: 2018\\n\\nAbstract: Brain Computer Interfaces provide a very challenging classification task due to small numbers of instances, large numbers of features, non-stationary problems, and low signal-to-noise ratios. Feature selection (FS) is a promising solution to help mitigate these effects. Wrapper FS methods are typically found to outperform filter FS methods, but reliance on cross-validation accuracies can be misleading due to overfitting. This paper proposes a filter-wrapper hybrid based on Iterated Local Search and Mutual Information, and shows that it can provide more reliable solutions, where the solutions are more able to generalise to unseen data. This study further contributes comparisons over multiple datasets, something that has been uncommon in the literature.\",\n \"Title: On the Fractal Nature of Local Optima Networks.\\nYear: 2018\\n\\nAbstract: A Local Optima Network represents fitness landscape connectivity within the space of local optima as a mathematical graph. In certain other complex networks or graphs there have been recent observations made about inherent self-similarity. An object is said to be self-similar if it shows the same patterns when measured at different scales; another word used to convey self-similarity is fractal. The fractal dimension of an object captures how the detail observed changes with the scale at which it is measured, with a high fractal dimension being associated with complexity. We conduct a detailed study on the fractal nature of the local optima networks of a benchmark combinatorial optimisation problem (NK Landscapes). The results draw connections between fractal characteristics and performance by three prominent metaheuristics: Iterated Local Search, Simulated Annealing, and Tabu Search.\",\n \"Title: How Perturbation Strength Shapes the Global Structure of TSP Fitness Landscapes.\\nYear: 2018\\n\\nAbstract: Local optima networks are a valuable tool used to analyse and visualise the global structure of combinatorial search spaces; in particular, the existence and distribution of multiple funnels in the landscape. We extract and analyse the networks induced by Chained-LK, a powerful iterated local search for the TSP, on a large set of randomly generated (Uniform and Clustered) instances. Results indicate that increasing the perturbation strength employed by Chained-LK modifies the landscape's global structure, with the effect being markedly different for the two classes of instances. Our quantitative analysis shows that several funnel metrics have stronger correlations with Chained-LK success rate than the number of local optima, indicating that global structure clearly impacts search performance.\",\n \"Title: Mapping the global structure of TSP fitness landscapes.\\nYear: 2018\\n\\nAbstract: The global structure of combinatorial landscapes is not fully understood, yet it is known to impact the performance of heuristic search methods. We use a so-called local optima network model to characterise and visualise the global structure of travelling salesperson fitness landscapes of different classes, including random and structured real-world instances of realistic size. Our study brings rigour to the characterisation of so-called funnels, and proposes an intensive and effective sampling procedure for extracting the networks. We propose enhanced visualisation techniques, including 3D plots and the incorporation of colour, sizes and widths, to reflect relevant aspects of the search process. This brings an almost tangible new perspective to the landscape and funnel metaphors. Our results reveal a much richer global structure than the suggestion of a ‘big-valley’ structure. Most landscapes of the tested instances have multiple valleys or funnels; and the number, disposition and interaction of these funnels seem to relate to search difficulty on the studied landscapes. We also find that the structured TSP instances feature high levels of neutrality, an observation not previously reported in the literature. We then propose ways of analysing and visualising these neutral landscapes.\",\n \"Title: PageRank centrality for performance prediction: the impact of the local optima network model.\\nYear: 2018\\n\\nAbstract: A local optima network (LON) compresses relevant features of fitness landscapes in a complex network, where nodes are local optima and edges represent transition probabilities between different basins of attraction. Previous work has found that the PageRank centrality of local optima can be used to predict the success rate and average fitness achieved by local search based metaheuristics. Results are available for LONs where edges describe either basin transition probabilities or escape edges. This paper studies the interplay between the type of LON edges and the ability of the PageRank centrality for the resulting LON to predict the performance of local search based metaheuristics. It finds that LONs are stochastic models of the search heuristic. Thus, to achieve an accurate prediction, the definition of the LON edges must properly reflect the type of diversification steps used in the metaheuristic. LONs with edges representing basin transition probabilities capture well the diversification mechanism of simulated annealing which sometimes also accepts worse solutions that allow the search process to pass between basins. In contrast, LONs with escape edges capture well the diversification step of iterated local search, which escapes from local optima by applying a larger perturbation step.\",\n \"Title: An Empirical Study of Meta- and Hyper-Heuristic Search for Multi-Objective Release Planning.\\nYear: 2018\\n\\nAbstract: A variety of meta-heuristic search algorithms have been introduced for optimising software release planning. However, there has been no comprehensive empirical study of different search algorithms across multiple different real-world datasets. In this article, we present an empirical study of global, local, and hybrid meta- and hyper-heuristic search-based algorithms on 10 real-world datasets. We find that the hyper-heuristics are particularly effective. For example, the hyper-heuristic genetic algorithm significantly outperformed the other six approaches (and with high effect size) for solution quality 85% of the time, and was also faster than all others 70% of the time. Furthermore, correlation analysis reveals that it scales well as the number of requirements increases.\",\n \"Title: Search-maps: visualising and exploiting the global structure of computational search spaces.\\nYear: 2018\\n\\nAbstract: No abstract available.\",\n \"Title: Enhancing partition crossover with articulation points analysis.\\nYear: 2018\\n\\nAbstract: Partition Crossover is a recombination operator for pseudo-Boolean optimization with the ability to explore an exponential number of solutions in linear or square time. It decomposes the objective function as a sum of subfunctions, each one depending on a different set of variables. The decomposition makes it possible to select the best parent for each subfunction independently and the operator provides the best out of 2q solutions, where q is the number of sub-functions in the decomposition. These subfunctions are defined over the connected components of the recombination graph: a subgraph of the objective function variable interaction graph containing only the differing variables in the two parents. In this paper, we advance further and propose a new way to increase the number of linearly independent subfunctions by analyzing the articulation points of the recombination graph. These points correspond to variables that, once flipped, increase the number of connected components. The presence of a connected component with an articulation point increases the number of explored solutions by a factor of, at least, 4. We evaluate the new operator using Iterated Local Search combined with Partition Crossover to solve NK Landscapes and MAX-SAT.\",\n \"Title: Multifractality and dimensional determinism in local optima networks.\\nYear: 2018\\n\\nAbstract: We conduct a study of local optima networks (LONs) in a search space using fractal dimensions. The fractal dimension (FD) of these networks is a complexity index which assigns a non-integer dimension to an object. We propose a fine-grained approach to obtaining the FD of LONs, using the probabilistic search transitions encoded in LON edge weights. We then apply multi-fractal calculations to LONs for the first time, comparing with mono-fractal analysis. For complex systems such as LONs, the dimensionality may be different between two sub-systems and multi-fractal analysis is needed. Here we focus on the Quadratic Assignment Problem (QAP), conducting fractal analyses on sampled LONs of reasonable size for the first time. We also include fully enumerated LONs of smaller size. Our results show that local optima spaces can be multi-fractal and that valuable information regarding probabilistic self-similarity is encoded in the edge weights of local optima networks. Links are drawn between these phenomena and the performance of two competitive metaheuristic algorithms.\",\n \"Title: Tutorials at PPSN 2018.\\nYear: 2018\\n\\nAbstract: PPSN 2018 features a total of 23 free tutorials covering a broad range of topics in evolutionary computation and related areas. From theory and methods to applications and computer implementations, and from introductory to advanced, the PPSN 2018 tutorial program offers participants the opportunity to learn more about both well-established and ongoing research in this field.\",\n \"Title: Visualising the global structure of search landscapes: genetic improvement as a case study.\\nYear: 2018\\n\\nAbstract: The search landscape is a common metaphor to describe the structure of computational search spaces. Different landscape metrics can be computed and used to predict search difficulty. Yet, the metaphor falls short in visualisation terms because it is hard to represent complex landscapes, both in terms of size and dimensionality. This paper combines local optima networks, as a compact representation of the global structure of a search space, and dimensionality reduction, using the t-distributed stochastic neighbour embedding algorithm, in order to both bring the metaphor to life and convey new insight into the search process. As a case study, two benchmark programs, under a genetic improvement bug-fixing scenario, are analysed and visualised using the proposed method. Local optima networks for both iterated local search and a hybrid genetic algorithm, across different neighbourhoods, are compared, highlighting the differences in how the landscape is explored.\",\n \"Title: Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis\\nYear: 2018\\n\\nAbstract: Big Data Analytics have become an integral part of Health Informatics over the past years, with the analysis of Internet data being all the more popular in health assessment in various topics. In this study, we first examine the geographical distribution of the online behavioral variations towards Chlamydia, Gonorrhea, Syphilis, Tuberculosis, and Hepatitis in the United States by year from 2004 to 2017. Next, we examine the correlations between Google Trends data and official health data from the ‘Centers for Disease Control and Prevention’ (CDC) on said diseases, followed by estimating linear regressions for the respective relationships. The results show that Infoveillance can assist with exploring public awareness and accurately measure the behavioral changes towards said diseases. The correlations between Google Trends data and CDC data on Chlamydia cases are statistically significant at a national level and in most of the states, while the forecasting exhibits good performing results in many states. For Hepatitis, significant correlations are observed for several US States, while forecasting also exhibits promising results. On the contrary, several factors can affect the applicability of this forecasting method, as in the cases of Gonorrhea, Syphilis, and Tuberculosis, where the correlations are statistically significant in fewer states. Thus this study highlights that the analysis of Google Trends data should be done with caution in order for the results to be robust. In addition, we suggest that the applicability of this method is not that trivial or universal, and that several factors need to be taken into account when using online data in this line of research. However, this study also supports previous findings suggesting that the analysis of real-time online data is important in health assessment, as it tackles the long procedure of data collection and analysis in traditional survey methods, and provides us with information that could not be accessible otherwise.\",\n \"Title: Incorporating knowledge of secondary structures in a l-system-based encoding for protein folding\\nYear: 2005\\n\\nAbstract: An encoding scheme for protein folding on lattice models, inspired by parametric L-systems, was proposed. The encoding incorporates problem domain knowledge in the form of predesigned production rules that capture commonly known secondary structures: α-helices and β-sheets. The ability of this encoding to capture protein native conformations was tested using an evolutionary algorithm as the inference procedure for discovering L-systems. Results confirmed the suitability of the proposed representation. It appears that the occurrence of motifs and sub-structures is an important component in protein folding, and these sub-structures may be captured by a grammar-based encoding. This line of research suggests novel and compact encoding schemes for protein folding that may have practical implications in solving meaningful problems in biotechnology such as structure prediction and protein folding.\",\n \"Title: Forecasting AIDS prevalence in the United States using online search traffic data\\nYear: 2018\\n\\nAbstract: Over the past decade and with the increasing use of the Internet, the assessment of health issues using online search traffic data has become an integral part of Health Informatics. Internet data in general and from Google Trends in particular have been shown to be valid and valuable in predictions, forecastings, and nowcastings; and in detecting, tracking, and monitoring diseases’ outbreaks and epidemics. Empirical relationships have been shown to exist between Google Trends’ data and official data in several health topics, with the science of infodemiology using the vast amount of information available online for the assessment of public health and policy matters. The aim of this study is to provide a method of forecasting AIDS prevalence in the US using online search traffic data from Google Trends on AIDS related terms. The results at first show that significant correlations between Google Trends’ data and official health data on AIDS prevalence (2004–2015) exist in several States, while the estimated forecasting models for AIDS prevalence show that official health data and Google Trends data on AIDS follow a logarithmic relationship. Overall, the results of this study support previous work on the subject suggesting that Google data are valid and valuable for the analysis and forecasting of human behavior towards health topics, and could further assist with Health Assessment in the US and in other countries and regions with valid available official health data.\",\n \"Title: A New Generalized Partition Crossover for the Traveling Salesman Problem: Tunneling Between Local Optima.\\nYear: 2020\\n\\nAbstract: Generalized Partition Crossover (GPX) is a deterministic recombination operator developed for the Traveling Salesman Problem. Partition crossover operators return the best of 2k reachable offspring, where k is the number of recombining components. This article introduces a new GPX2 operator, which finds more recombining components than GPX or Iterative Partial Transcription (IPT). We also show that GPX2 has O(n) runtime complexity, while also introducing new enhancements to reduce the execution time of GPX2. Finally, we experimentally demonstrate the efficiency of GPX2 when it is used to improve solutions found by the multitrial Lin-Kernighan-Helsgaum (LKH) algorithm. Significant improvements in performance are documented on large (n>5000) and very large (n=100,000) instances of the Traveling Salesman Problem.\",\n \"Title: Hyper-heuristics: a survey of the state of the art.\\nYear: 2013\\n\\nAbstract: Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyperheuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.\",\n \"Title: Local optima networks for continuous fitness landscapes.\\nYear: 2019\\n\\nAbstract: Local Optima Networks (LONs) have been proposed as a coarsegrained model of discrete (combinatorial) fitness landscapes, where nodes are local optima and edges are search transitions based on an exploration search operator. This paper presents one of the first complex network analysis of continuous fitness landscapes. We use benchmark functions with well-known global structure, and an existing implementation of a Basin-Hopping algorithm to extract the networks. We also explore the impact of varying the Basin-Hopping perturbation step-size. Our results suggest that the landscape's connectivity pattern (global structure) strongly varies with the perturbation step-size, with extreme values of this parameter being detrimental to search and fragmenting the global structure. Our LON visualisations strikingly illustrate the landscape's global (funnel) structure, indicating that LONs serve as a tool for visualising high-dimensional functions.\",\n \"Title: Local optima network analysis for MAX-SAT.\\nYear: 2019\\n\\nAbstract: Local Optima Networks (LONs) are a valuable tool to understand fitness landscapes of optimization problems observed from the perspective of a search algorithm. Local optima of the optimization problem are linked by an edge in LONs when an operation in the search algorithm allows one of them to be reached from the other. Previous work analyzed several combinatorial optimization problems using LONs and provided a visual guide to understand why the instances are difficult or easy for the search algorithms. In this work we analyze for the first time the MAX-SAT problem. Given a Boolean formula in Conjunctive Normal Form, the goal of the MAX-SAT problem is to find an assignment maximizing the number of satistified clauses. Several random and industrial instances of MAX-SAT are analyzed using Iterated Local Search to sample the search space.\",\n \"Title: Recent advances in fitness landscape analysis.\\nYear: 2019\\n\\nAbstract: No abstract available.\",\n \"Title: Real-like MAX-SAT instances and the landscape structure across the phase transition\\nYear: 2021\\n\\nAbstract: ABSTRACTIn contrast with random uniform instances, industrial SAT instances of large size are solvable today by state-of-the-art algorithms. It is believed that this is the consequence of the non-random structure of the distribution of variables into clauses. In order to produce benchmark instances resembling those of real-world formulas with a given structure, generative models have been proposed. In this paper we study the MAX-3SAT problem with model-generated instances having a power-law distribution. Specifically, we target the regions in which computational difficulty undergoes an easy/hard phase transition as a function of clause density and of the power-law exponent. Our approach makes use of a sampling technique to build a graph model (a local optima network) in which nodes are local optima and directed edges are transitions between optima basins. The objective is to relate the structure of the instance fitness landscape with problem difficulty through the transition. We succeed in associating the transition with straightforward network metrics, thus providing a novel and original fitness landscape view of the computational features of the power-law model and its phase transition.\",\n \"Title: Local search pivoting rules and the landscape global structure\\nYear: 2021\\n\\nAbstract: ABSTRACTIn local search algorithms, the pivoting rule determines which neighboring solution to select and thus strongly influences the behavior of the algorithm and its capacity to sample good-quality local optima. The classical pivoting rules are first and best improvement, with alternative rules such as worst improvement and maximum expansion recently studied on hill-climbing algorithms. This article conducts a thorough empirical comparison of five pivoting rules (best, first, worst, approximated worst and maximum expansion) on two benchmark combinatorial problems, NK landscapes and the unconstrained binary quadratic problem (UBQP), with varied sizes and ruggedness. We present both a performance analysis of the alternative pivoting rules within an iterated local search (ILS) framework and a fitness landscape analysis and visualization using local optima networks. Our results reveal that the performance of the pivoting rules within an ILS framework may differ from their performance as single climbers and that worst improvement and maximum expansion can outperform classical pivoting rules.\",\n \"Title: Partition crossover for continuous optimization: ePX\\nYear: 2021\\n\\nAbstract: ABSTRACTPartition crossover (PX) is an efficient recombination operator for gray-box optimization. PX is applied in problems where the objective function can be written as a sum of subfunctions fl(.). In PX, the variable interaction graph (VIG) is decomposed by removing vertices with common variables. Parent variables are inherited together during recombination if they are part of the same connected recombining component of the decomposed VIG. A new way of generating the recombination graph is proposed here. The VIG is decomposed by removing edges associated with subfunctions fl(.) that have similar evaluation for combinations of variables inherited from the parents. By doing so, the partial evaluations of fl(.) are taken into account when decomposing the VIG. This allows the use of partition crossover in continuous optimization. Results of experiments where local optima are recombined indicate that more recombining components are found. When the proposed epsilon-PX (ePX) is compared with other recombination operators in Genetic Algorithms and Differential Evolution, better performance is obtained when the epistasis degree is low.\",\n \"Title: Search trajectory networks: A tool for analysing and visualising the behaviour of metaheuristics\\nYear: 2021\\n\\nAbstract: A large number of metaheuristics inspired by natural and social phenomena have been proposed in the last few decades, each trying to be more powerful and innovative than others. However, there is a lack of accessible tools to analyse, contrast and visualise the behaviour of metaheuristics when solving optimisation problems. When the metaphors are stripped away, are these algorithms different in their behaviour? To help to answer this question, we propose a data-driven, graph-based model, search trajectory networks (STNs) in order to analyse, visualise and directly contrast the behaviour of different types of metaheuristics. One strength of our approach is that it does not require any additional sampling or algorithmic methods. Instead, the models are constructed from data gathered while the metaheuristics are solving the optimisation problems. We present our methodology, and consider in detail two case studies covering both continuous and combinatorial optimisation. In terms of metaheuristics, our case studies cover the main current paradigms: evolutionary, swarm, and stochastic local search approaches. (c) 2021 Elsevier B.V. All rights reserved. <comment>Superscript/Subscript Available</comment\",\n \"Title: Understanding parameter spaces using local optima networks: a case study on particle swarm optimization\\nYear: 2021\\n\\nAbstract: ABSTRACTA major challenge with utilizing a metaheuristic is finding optimal or near optimal parameters for a given problem instance. It is well known that the best performing control parameters are often problem dependent, with poorly chosen parameters even leading to algorithm failure. What is not obvious is how strongly the complexity of the parameter landscape itself is coupled with the underlying objective function the metaheuristic is attempting to solve. In this paper local optima networks (LONs) are utilized to visualize and analyze the parameter landscapes of particle swarm optimization (PSO) over an array of objective functions. It was found that the structure of the parameter landscape is affected by the underlying objective function, and in some cases by a considerable degree across multiple metrics. Furthermore, despite PSO's parameter landscape having a relatively simple macro structure, the LONs demonstrate that there is actually a considerable amount of complexity at a micro level; making parameter tuning harder for PSO than would have been initially thought. Apart from the PSO specific findings this paper also provides a formalism of parameter landscapes and demonstrates that LONs can be used as an effective tool in the analysis and visualization of parameter landscapes of metaheuristics.\",\n \"Title: Dynastic Potential Crossover Operator\\nYear: 2022\\n\\nAbstract: An optimal recombination operator for two-parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of an optimal recombination operator is optimal in the smallest hyperplane containing the two parent solutions. Exploring this hyperplane is computationally costly, in general, requiring exponential time in the worst case. However, when the variable interaction graph of the objective function is sparse, exploration can be done in polynomial time. In this article, we present a recombination operator, called Dynastic Potential Crossover (DPX), that runs in polynomial time and behaves like an optimal recombination operator for low-epistasis combinatorial problems. We compare this operator, both theoretically and experimentally, with traditional crossover operators, like uniform crossover and network crossover, and with two recently defined efficient recombination operators: partition crossover and articulation points partition crossover. The empirical comparison uses NKQ Landscapes and MAX-SAT instances. DPX outperforms the other crossover operators in terms of quality of the offspring and provides better results included in a trajectory and a population-based metaheuristic, but it requires more time and memory to compute the offspring.\",\n \"Title: Search Trajectories Networks of Multiobjective Evolutionary Algorithms.\\nYear: 2022\\n\\nAbstract: Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, search trajectory networks (STNs), to model the behavior of MOEAs. Our approach uses the idea of decomposition, where a multiobjective problem is transformed into several single-objective problems. We show that STNs can be used to model and distinguish the search behavior of two popular multiobjective algorithms, MOEA/D and NSGA-II, using 10 continuous benchmark problems with 2 and 3 objectives. Our findings suggest that we can improve our understanding of MOEAs using STNs for algorithm analysis.\",\n \"Title: Component-wise Analysis of Automatically Designed Multiobjective Algorithms on Constrained Problems\\nYear: 2022\\n\\nAbstract: The performance of multiobjective algorithms varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there has been an increasing interest in their automatic design from component parts. These automatically designed metaheuristics can outperform their human-developed counterparts. However, it is still uncertain what are the most influential components leading to their performance improvement. This study introduces a new methodology to investigate the effects of the final configuration of an automatically designed algorithm. We apply this methodology to a well-performing Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) designed by the irace package on nine constrained problems. We then contrast the impact of the algorithm components in terms of their Search Trajectory Networks (STNs), the diversity of the population, and the hypervolume. Our results indicate that the most influential components were the restart and update strategies, with higher increments in performance and more distinct metric values. Also, their relative influence depends on the problem difficulty: not using the restart strategy was more influential in problems where MOEA/D performs better; while the update strategy was more influential in problems where MOEA/D performs the worst.\",\n \"Title: Search Trajectory Networks Applied to the Cyclic Bandwidth Sum Problem\\nYear: 2021\\n\\nAbstract: Search trajectory networks (STNs) were proposed as a tool to analyze the behavior of metaheuristics in relation to their exploration ability and the search space regions they traverse. The technique derives from the study of fitness landscapes using local optima networks (LONs). STNs are related to LONs in that both are built as graphs, modelling the transitions among solutions or group of solutions in the search space. The key difference is that STN nodes can represent solutions or groups of solutions that are not necessarily locally optimal. This work presents an STN-based study for a particular combinatorial optimization problem, the cyclic bandwidth sum minimization. STNs were employed to analyze the two leading algorithms for this problem: a memetic algorithm and a hyperheuristic memetic algorithm. We also propose a novel grouping method for STNs that can be generally applied to both continuous and combinatorial spaces.\",\n \"Title: Fitness landscape analysis of convolutional neural network architectures for image classification\\nYear: 2022\\n\\nAbstract: •Novel application of local optima network analysis to neural architecture landscapes.•Findings on a reduced search space were found to still hold on a larger search space.•Neural architecture search landscapes have relatively few local optima.\",\n \"Title: Optima Organize into Lattices Under Recombination: An example using the Traveling Salesman Problem\\nYear: 2022\\n\\nAbstract: Local optima networks (LONs) model the global distribution and connectivity pattern of local optima under given search operators. Recent research has looked at how recombination operators can jump from a pair of parents that are locally optimal to a new child that is either a local optimum, or is guaranteed to be in a new basin of attraction. Recombination can therefore also induce a local optima network which maps how crossover moves between local optima. In this paper, we prove that recombination induces a LON which is actually a network of overlapping hypercube lattices. Given two or more samples from any lattice, we can also infer the existence of additional local optima that have not previously been reached by sampling. We prove that these lattices can be exponentially large. Finally, we prove that there exists TSP instances can be solved in polynomial time by exploiting Partition Crossover; these same instances are not solved by local search.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":99,"string":"99"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13255,"string":"13,255"}}},{"rowIdx":13256,"cells":{"question_id":{"kind":"string","value":"Malcolm I Heywood"},"question":{"kind":"string","value":"Generate an abstract for the title \"Evolving dota 2 shadow fiend bots using genetic programming with external memory\" using the following items: genetic programming, Dota 2, reinforcement learning, competitive play, external memory"},"answer":{"kind":"string","value":"ABSTRACTThe capacity of genetic programming (GP) to evolve a 'hero' character in the Dota 2 video game is investigated. A reinforcement learning context is assumed in which the only input is a 320-dimensional state vector and performance is expressed in terms of kills and net worth. Minimal assumptions are made to initialize the GP game playing agents - evolution from a tabula rasa starting point - implying that: 1) the instruction set is not task specific; 2) end of game performance feedback reflects quantitive properties a player experiences; 3) no attempt is made to impart game specific knowledge into GP, such as heuristics for improving navigation, minimizing partial observability, improving team work or prioritizing the protection of specific strategically important structures. In short, GP has to actively develop its own strategies for all aspects of the game. We are able to demonstrate competitive play with the built in game opponents assuming 1-on-1 competitions using the 'Shadow Fiend' hero. The single most important contributing factor to this result is the provision of external memory to GP. Without this, the resulting Dota 2 bots are not able to identify strategies that match those of the built-in game bot."},"correct_answer":{"kind":"string","value":"ABSTRACTThe capacity of genetic programming (GP) to evolve a 'hero' character in the Dota 2 video game is investigated. A reinforcement learning context is assumed in which the only input is a 320-dimensional state vector and performance is expressed in terms of kills and net worth. Minimal assumptions are made to initialize the GP game playing agents - evolution from a tabula rasa starting point - implying that: 1) the instruction set is not task specific; 2) end of game performance feedback reflects quantitive properties a player experiences; 3) no attempt is made to impart game specific knowledge into GP, such as heuristics for improving navigation, minimizing partial observability, improving team work or prioritizing the protection of specific strategically important structures. In short, GP has to actively develop its own strategies for all aspects of the game. We are able to demonstrate competitive play with the built in game opponents assuming 1-on-1 competitions using the 'Shadow Fiend' hero. The single most important contributing factor to this result is the provision of external memory to GP. Without this, the resulting Dota 2 bots are not able to identify strategies that match those of the built-in game bot."},"documents":{"kind":"list like","value":["Title: Comparing dimension reduction techniques for document clustering\nYear: 2005\n\nAbstract: In this research, a systematic study is conducted of four dimension reduction techniques for the text clustering problem, using five benchmark data sets Of the four methods – Independent Component Analysis (ICA), Latent Semantic Indexing (LSI), Document Frequency (DF) and Random Projection (RP) – ICA and LSI are clearly superior when the k-means clustering algorithm is applied, irrespective of the data sets Random projection consistently returns the worst results, where this appears to be due to the noise distribution characterizing the document clustering task.","Title: An analysis of clustering objectives for feature selection applied to encrypted traffic identification\nYear: 2010\n\nAbstract: This work explores the use of clustering objectives in a Multi-Objective Genetic Algorithm (MOGA) for both, feature selection and cluster count optimization, under the application of flow based encrypted traffic identification. We first explore whether it is possible to achieve the performance of a gold standard model (i.e., classification objectives), using a MOGA based on clustering objectives. Then, we explore the performance gain (if it exists) of applying a logarithmic transformation to the data prior to running the MOGA. Results show that MOGA trained with clustering objectives can closely reproduce the behavior of a gold standard model, not only in terms of the selected features, but also in terms of the achieved detection rate and false positives rate, above 90% and less than 1% respectively. On the other hand, no gain was observed by applying logarithmic transformation to the data.","Title: Benchmarking coevolutionary teaming under classification problems with large attribute spaces\nYear: 2009\n\nAbstract: Benchmarking of a team based model of Genetic Programming demonstrates that the naturally embedded style of feature selection is usefully extended by the teaming metaphor to provide solutions in terms of exceptionally low attribute counts. To take this concept to its logical conclusion the teaming model must be able to build teams with a non-overlapping behavioral trait, from a single population. The Symbiotic Bid-Based (SBB) algorithm is demonstrated to fit this purpose under an evaluation utilizing data sets with 650 to 5,000 attributes. The resulting solutions are one to two orders simpler than solutions identified under the alternative embedded paradigms of C4.5 and MaxEnt.","Title: One-Class Genetic Programming\nYear: 2009\n\nAbstract: One-class classification naturally only provides one-class of exemplars, the target class, from which to construct the classification\n model. The one-class approach is constructed from artificial data combined with the known in-class exemplars. A multi-objective\n fitness function in combination with a local membership function is then used to encourage a co-operative coevolutionary decomposition\n of the original problem under a novelty detection model of classification. Learners are therefore associated with different\n subsets of the target class data and encouraged to tradeoff detection versus false positive performance; where this is equivalent\n to assessing the misclassification of artificial exemplars versus detection of subsets of the target class. Finally, the architecture\n makes extensive use of active learning to reinforce the scalability of the overall approach.","Title: Network protocol discovery and analysis via live interaction\nYear: 2012\n\nAbstract: In this work, we explore the use of evolutionary computing toward protocol analysis. The ability to discover, analyse, and experiment with unknown protocols is paramount within the realm of network security; our approach to this crucial analysis is to interact with a network service, discovering sequences of commands that do not result in error messages. In so doing, our work investigates the real-life responses of a service, allowing for exploration and analysis of the protocol in question. Our system initiates sequences of commands randomly, interacts with and learns from the responses, and modifies its next set of sequences accordingly. Such an exploration results in a set of command sequences that reflect correct uses of the service in testing. These discovered sequences can then be used to identify the service, unforeseen uses of the service, and, most importantly, potential weaknesses.","Title: Symbiotic Evolutionary Subspace Clustering\nYear: 2012\n\nAbstract: New emerging high-dimensional data sets have made traditional clustering algorithms increasingly inefficient. More sophisticated approaches are required to cope with the increasing dimensionality and cardinality of such data sets. Feature selection methods are proposed as a solution to deal with this problem, however they fail for data sets where the attribute support for different clusters is not the same. For this category of data sets subspace clustering algorithms have been introduced over the past decade. We approach this problem from the perspective of Genetic Algorithms by adopting a hierarchical data structure deployed in three stages. 1) a traditional clustering algorithm is applied independently to each attribute of the data set, thus defining a grid of potential 1-d cluster centroids. 2) representing multi-dimensional cluster centroids by indexing 1-d cluster centroids. 3) converting the problem of finding the best combination of cluster centroids into that of discrete optimization and applying a multi-objective evolutionary algorithm, which uses group fitness evaluation to give a fitness to a group of clusters, as defined by process 2. Synthetic data sets with different characteristics are generated as the ground truth to evaluate the resulting algorithm for Evolutionary Subspace Clustering (ESC) as well as benchmark against alternative subspace and full-space clustering algorithms. ESC returns competitive accuracy and while typically utilizing less attributes and scaling as attribute count increases.","Title: Using code bloat to obfuscate evolved network traffic\nYear: 2010\n\nAbstract: In this work, we investigate the ability of genetic programming techniques to evolve valid network patterns, while avoiding detectability by obfuscating the intent of the traffic. In order to validate our system's capabilities, we choose to evolve a port scan attack while running the packets through an Intrusion Detection System (IDS). In turn, the evolutionary process uses feedback such that it minimizes the alarms raised while port scanning across a network range. Results build off of previous work allow us to further analyze and understand what the role of introns, code bloat, play in the systems ability to reduce the detectability of it malicious behaviour.","Title: Use of a genetic algorithm in brill's transformation-based part-of-speech tagger\nYear: 2005\n\nAbstract: The tagging problem in natural language processing is to find a way to label every word in a text as a particular part of speech, e.g., proper noun. An effective way of solving this problem with high accuracy is the transformation-based or \"Brill\" tagger. In Brill's system, a number of transformation templates are specified a priori that are instantiated and ranked during a greedy search-based algorithm. This paper describes a variant of Brill's implementation that instead uses a genetic algorithm to generate the instantiated rules and provide an adaptive ranking. Based on tagging accuracy, the new system provides a better hybrid evolutionary computation solution to the part-of-speech (POS) problem than the previous attempt. Although not able to make up for the use of a priori knowledge utilized by Brill, the method appears to point the way for an improved solution to the tagging problem.","Title: On the impact of streaming interface heuristics on GP trading agents: an FX benchmarking study\nYear: 2013\n\nAbstract: Most research into frameworks for evolving trading agents emphasize aspects associated with the evolution of technical indicators and decision trees / rules. One of the factors that drives the development of such frameworks is the non-stationary, streaming nature of the task. However, it is the heuristics used to interface the evolutionary framework to the streaming data which potentially have most impact on the quality of the resulting trading agents. We demonstrate that including a validation partition has a significant impact on determining the overall success of the trading agents. Moreover, rather than conduct evolution on a continuous basis, only retraining when changes in trading quality are detected also yields significant advantages. Neither of these heuristics are widely recognized by research in evolving trading agent frameworks, although both are relatively easy to add to current frameworks. Benchmarking over a 3 year period of the EURUSD foreign exchange supports these findings.","Title: Coevolutionary bid-based genetic programming for problem decomposition in classification\nYear: 2008\n\nAbstract: In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains such as classification. This represents a significant departure from models where each individual constructs a direct input-outcome map, for example, from the set of exemplars to the set of class labels as is typical under the classification domain. In contrast, the proposed model focuses on learning a bidding strategy based on the exemplar feature vectors; each individual is associated with a single discrete action and the individual with the maximum bid `wins' the right to suggest its action. Thus, the number of individuals associated with each action is a function of the intra-action bidding behaviour. Credit assignment is designed to reward correct but unique bidding strategies relative to the target actions. An advantage of the model over other teaming methods is its ability to automatically determine the number of and interaction between cooperative team members. The resulting model shares several traits with learning classifier systems and as such both approaches are benchmarked on nine large classification problems. Moreover, both of the evolutionary models are compared against the deterministic Support Vector Machine classification algorithm. Performance assessment considers the computational, classification, and complexity characteristics of the resulting solutions. The bid-based model is found to provide simple yet effective solutions that are robust to wide variations in the class representation. Support Vector Machines and classifier systems tend to perform better under balanced datasets albeit resulting in black-box solutions.","Title: Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces\nYear: 2012\n\nAbstract: Classification under large attribute spaces represents a dual learning problem in which attribute subspaces need to be identified at the same time as the classifier design is established. Embedded as opposed to filter or wrapper methodologies address both tasks simultaneously. The motivation for this work stems from the observation that team based approaches to Genetic Programming (GP) have the potential to design multiple classifiers per class--each with a potentially unique attribute subspace--without recourse to filter or wrapper style preprocessing steps. Specifically, competitive coevolution provides the basis for scaling the algorithm to data sets with large instance counts; whereas cooperative coevolution provides a framework for problem decomposition under a bid-based model for establishing program context. Symbiosis is used to separate the tasks of team/ensemble composition from the design of specific team members. Team composition is specified in terms of a combinatorial search performed by a Genetic Algorithm (GA); whereas the properties of individual team members and therefore subspace identification is established under an independent GP population. Teaming implies that the members of the resulting ensemble of classifiers should have explicitly non-overlapping behaviour. Performance evaluation is conducted over data sets taken from the UCI repository with 649---102,660 attributes and 2---10 classes. The resulting teams identify attribute spaces 1---4 orders of magnitude smaller than under the original data set. Moreover, team members generally consist of less than 10 instructions; thus, small attribute subspaces are not being traded for opaque models.","Title: Evolving recurrent models using linear GP\nYear: 2005\n\nAbstract: Turing complete Genetic Programming (GP) models introduce the concept of internal state, and therefore have the capacity for identifying interesting temporal properties. Surprisingly, there is little evidence of the application of such models to problems for prediction. An empirical evaluation is made of a simple recurrent linear GP model over standard prediction problems.","Title: Growing recurrent self organizing map\nYear: 2007\n\nAbstract: Abstract—The growing Recurrent Self-Organizing Map (GRSOM) is embedded,into a standard,Self-Organizing Map (SOM) hierarchy. To do so, the KDD benchmark dataset from the International Knowledge,Discovery and Data Mining Tools Competition is employed. This dataset consists of 500,000 training patterns and 41 features,for each,pattern. Unlike most of the previous methods, only 6 of the basic features are employed.,The resulting model,has,a capability of detection (false positive) rate of 89.6% (5.66%), where this is as good as the data-mining approaches,that uses all 41 features and,twice as faster than a similar hierarchical SOM architecture.","Title: MOGE: GP classification problem decomposition using multi-objective optimization\nYear: 2006\n\nAbstract: A novel approach to classification is proposed in which a Pareto-based ranking of individuals is used to encourage multiple individuals to participate in the solution. To do so, the classification problem is re-expressed as a cluster consistency problem, thus allowing utilization of techniques from multi-objective optimization. Such a formulation enables classification problems to be automatically decomposed and solved by several specialist classifiers rather than by a single 'super' individual. In this paper, we demonstrate the proposed approach to two benchmark binary problems and recommend a natural extension to multi-class problems. Results indicate the general appropriateness of the approach.","Title: Negotiating Agents in Manufacturing Decision Making Processes\nYear: 2001\n\nAbstract: Real-time decision making for on-the-spot orders is considered using a novel distributed agent architecture. The order agents will have the ability to perform multi-stage negotiation using continuous double auction market-based model representing resources as producers and orders as customers. Qualitative information of resources and order requirements is captured in a Quality Function Definition (QFD) decision table. The QFD decision table also provides the interface for the agent to evaluate objectives, attributes and user preferences relating to resources and order requirements. This directs the negotiation against the most applicable resources at each stage through the market. In particular, the agents can learn from experience and adapt to the market change. The operation of such a system is illustrated within a garment industry application context.","Title: Symbiosis, complexification and simplicity under GP\nYear: 2010\n\nAbstract: Models of Genetic Programming (GP) frequently reflect a neo-Darwinian view to evolution in which inheritance is based on a process of gradual refinement and the resulting solutions take the form of single monolithic programs. Conversely, introducing an explicitly symbiotic model of inheritance makes a divide-and-conquer metaphor for problem decomposition central to evolution. Benchmarking gradualist versus symbiotic models of evolution under a common evolutionary framework illustrates that not only does symbiosis result in more accurate solutions, but the solutions are also much simpler in terms of instruction and attribute count over a wide range of classification problem domains.","Title: Evolving TCP/IP packets: A case study of port scans.\nYear: 2009\n\nAbstract: In this work, we investigate the ability of genetic programming techniques to evolve valid network packets, including all relevant header values, towards a specific goal. We see this as a first step in building a fuzzing system that can learn to adapt for vulnerability analysis. By developing a system that learns the packets that are required to be transmitted towards targets, using feedback from an external network source, we make a step towards having a system that can intelligently explore the capabilities of a given security system. In order to validate our system's capabilities we evolve a variety of port scan patterns while running the packets through an IDS, with the goal to minimizes the alarms raised during the scanning process. Results show that the system not only successfully evolves valid TCP packets, but also remains stealthy in its activity.","Title: Towards Efficient Training on Large Datasets for Genetic Programming\nYear: 2004\n\nAbstract: Genetic programming (GP) has the potential to provide unique solutions to a wide range of supervised learning problems. The technique, however, does suffer from a widely acknowledged computational overhead. As a consequence applications of GP are often confined to datasets consisting of hundreds of training exemplars as opposed to tens of thousands of exemplars, thus limiting the widespread applicability of the approach. In this work we propose and thoroughly investigate a data sub-sampling algorithm - hierarchical dynamic subset selection - that filters the initial training dataset in parallel with the learning process. The motivation being to focus the GP training on the most difficult or least recently visited exemplars. To do so, we build on the dynamic sub-set selection algorithm of Gathercole and extend it into a hierarchy of subset selections, thus matching the concept of a memory hierarchy supported in modem computers. Such an approach provides for the training of GP solutions to data sets with hundreds of thousands of exemplars in tens of minutes whilst matching the classification accuracies of more classical approaches.","Title: Hierarchical task decomposition through symbiosis in reinforcement learning\nYear: 2012\n\nAbstract: Adopting a symbiotic model of evolution separates context for deploying an action from the action itself. Such a separation provides a mechanism for task decomposition in temporal sequence learning. Moreover, previously learned policies are taken to be synonymous with meta actions (actions that are themselves policies). Should solutions to the task not be forthcoming in an initial round of evolution, then solutions from the earlier round represent the 'meta' actions for a new round of evolution. This provides the basis for evolving policy trees. A benchmarking study is performed using the Acrobot handstand task. Solutions to date from reinforcement learning have not been able to approach the performance of those established 14 years ago using an A* search and a priori knowledge regarding the Acrobot energy equations. The proposed symbiotic approach is able to match and, for the first time, better these results. Moreover, unlike previous work, solutions are tested under a broad range of Acrobot initial conditions, with hierarchical solutions providing significantly better generalization performance.","Title: Malicious automatically generated domain name detection using Stateful-SBB\nYear: 2013\n\nAbstract: This work investigates the detection of Botnet Command and Control (C&C) activity by monitoring Domain Name System (DNS) traffic. Detection signatures are automatically generated using evolutionary computation technique based on Stateful-SBB. The evaluation performed shows that the proposed system can work on raw variable length domain name strings with very high accuracy.","Title: Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings\nYear: 2007\n\nAbstract: Developmental Genetic Programming (DGP) algorithms have explicitly required the search space for a problem to be divided into genotypes and corresponding phenotypes. The two search spaces are often connected with a genotype-phenotype mapping (GPM) intended to model the biological genetic code, where current implementations of this concept involve evolution of the mappings along with evolution of the genotype solutions. This work presents the Probabilistic Adaptive Mapping DGP (PAM DGP), a new developmental implementation that involves research contributions in the areas of GPMs and coevolution. The algorithm component of PAM DGP is demonstrated to overcome coevolutionary performance problems that are identified and empirically benchmarked against the latest competing algorithm that adapts similar GPMs. An adaptive redundant mapping encoding is then incorporated into PAM DGP for further performance enhancement. PAM DGP with two mapping types are compared to the competing Adaptive Mapping algorithm and Traditional GP in two medical classification domains, where PAM DGP with redundant encodings is found to provide superior fitness performance over the other algorithms through it's ability to explicitly decrease the size of the function set during evolution.","Title: GP classifier problem decomposition using first-price and second-price auctions\nYear: 2007\n\nAbstract: This work details an auction-based model for problem decomposition in Genetic Programming classification. The approach builds on the population-based methodology of Genetic Programming to evolve individuals that bid high for patterns that they can correctly classify. The model returns a set of individuals that decompose the problem by way of this bidding process and is directly applicable to multi-class domains. An investigation of two auction types emphasizes the effect of auction design on the properties of the resulting solution. The work demonstrates that auctions are an effective mechanism for problem decomposition in classification problems and that Genetic Programming is an effective means of evolving the underlying bidding behaviour.","Title: Speeding up the Self-Organizing Feature Map Using Dynamic Subset Selection\nYear: 2005\n\nAbstract: An active learning algorithm is devised for training Self-Organizing Feature Maps on large data sets. Active learning algorithms recognize that not all exemplars are created equal. Thus, the concepts of exemplar age and difficulty are used to filter the original data set such that training epochs are only conducted over a small subset of the original data set. The ensuing Hierarchical Dynamic Subset Selection algorithm introduces definitions for exemplar difficulty suitable to an unsupervised learning context and therefore appropriate Self-organizing map (SOM) stopping criteria. The algorithm is benchmarked on several real world data sets with training set exemplar counts in the region of 30--500 thousand. Cluster accuracy is demonstrated to be at least as good as that from the original SOM algorithm while requiring a fraction of the computational overhead.","Title: Probabilistic adaptive mapping developmental genetic programming (PAM DGP): a new developmental approach\nYear: 2006\n\nAbstract: Developmental Genetic Programming (DGP) algorithms have been introduced where the search space for a problem is divided into genotypes and corresponding phenotypes that are connected by a mapping (or “genetic code”). Current implementations of this concept involve evolution of the mappings in addition to the traditional evolution of genotypes. We introduce the latest version of Probabilistic Adaptive Mapping DGP (PAM DGP), a robust and highly customizable algorithm that overcomes performance problems identified for the latest competing adaptive mapping algorithm. PAM DGP is then shown to outperform the competing algorithm on two non-trivial regression benchmarks.","Title: Evolutionary clustering with arbitrary subspaces\nYear: 2009\n\nAbstract: Subspace clustering algorithms in their most general form attempt to describe data with clusters that are not constrained to index a common set of attributes. Previous evolutionary approaches to this problem have assumed a weaker model in which clusters are built in a common subset. Moreover, a filter method is generally assumed in which a classical clustering algorithm is employed in the inner loop. Needless to say, this presents a considerable computational overhead. In this work we recognize the utility of assuming a `bottom-up' approach to subspace clustering. Specifically, we apply a classical clustering algorithm to each attribute to establish 1-d clusters that are then indexed by a MOGA to design a population of subspace clusters. The ensuing search is entirely in terms of a combinatorial optimization problem, thus computationally very efficient. A final single objective GA is then applied to search the set of subspace clusters identified under the MOGA for the most suitable combination.","Title: Training binary GP classifiers efficiently: a Pareto-coevolutionary approach\nYear: 2007\n\nAbstract: The conversion and extension of the Incremental Pareto-Coevolution Archive algorithm (IPCA) into the domain of Genetic Programming classification is presented. In particular, the coevolutionary aspect of the IPCA algorithm is utilized to simultaneously evolve a subset of the training data that provides distinctions between candidate classifiers. Empirical results indicate that such a scheme significantly reduces the computational overhead of fitness evaluation on large binary classification data sets. Moreover, unlike the performance of GP classifiers trained using alternative subset selection algorithms, the proposed Pareto-coevolutionary approach is able to match or better the classification performance of GP trained over all training exemplars. Finally, problem decomposition appears as a natural consequence of assuming a Pareto model for coevolution. In order to make use of this property a voting scheme is used to integrate the results of all classifiers from the Pareto front, post training.","Title: On Run Time Libraries And Hierarchical Symbiosis\nYear: 2012\n\nAbstract: Run time libraries (RTL) in genetic programming (GP) represent a scenario in which individuals evolved under an earlier independent evolutionary run can be potentially incorporated into a following GP run. To date, schemes for exploiting the RTL metaphor have emphasized syntactic over behavioural approaches. Thus, instructions are added to the later run such that the previous code can be explicitly indexed. In this work we demonstrate how the RTL concept is naturally supported by adopting a symbiotic framework for coevolution. Under the Pinball reinforcement learning task, we demonstrate how the initial RTL can be coevolved within a simpler formulation of the task and then used as the basis for providing solutions to a more difficult target task under the same domain. The resulting solutions are stronger than an RTL as coevolved against the target task alone or symbiosis as evolved without support for RTL.","Title: Toward Co-Evolutionary Training Of A Multi-Class Classifier\nYear: 2005\n\nAbstract: In this work the multi-class classification capabilities of Genetic Programming (GP) are explored in the context of a competitive co-evolutionary system, in which a population of GP classifiers is trained against an evolving population of trainers (exemplar selectors) with the goal of reducing GP training time for large multi-class classification problems. Moreover, the niche-enabling mechanisms established in the Genetic Algorithm (GA) literature, known as crowding and sharing, are implemented for the classifier population in order to provide multi-class solutions from a single population in the same trial. The results as presented in the paper indicate the appropriateness of the competitive co-evolutionary training approach under GP multi-class classification.","Title: Label free change detection on streaming data with cooperative multi-objective genetic programming\nYear: 2013\n\nAbstract: Classification under streaming data conditions requires that the machine learning (ML) approach operate interactively with the stream content. Thus, given some initial ML classification capability, it is not possible to assume that stream content will be stationary. It is therefore necessary to first detect when the stream content changes. Only after detecting a change, can classifier retraining be triggered. Current methods for change detection tend to assume an entropy filter approach, where class labels are necessary. In practice, labeling the stream would be extremely expensive. This work proposes an approach in which the behaviour of GP individuals is used to detect change without the use of labels. Only after detecting a change is label information requested. Benchmarking under a computer network traffic analysis scenario demonstrates that the proposed approach performs at least as well as the filter method, while retaining the advantage of requiring no labels.","Title: A hierarchical SOM-based intrusion detection system\nYear: 2007\n\nAbstract: Purely based on a hierarchy of self-organizing feature maps (SOMs), an approach to network intrusion detection is investigated. Our principle interest is to establish just how far such an approach can be taken in practice. To do so, the KDD benchmark data set from the International Knowledge Discovery and Data Mining Tools Competition is employed. Extensive analysis is conducted in order to assess the significance of the features employed, the partitioning of training data and the complexity of the architecture. Contributions that follow from such a holistic evaluation of the SOM include recognizing that (1) best performance is achieved using a two-layer SOM hierarchy, based on all 41-features from the KDD data set. (2) Only 40% of the original training data is sufficient for training purposes. (3) The 'Protocol' feature provides the basis for a switching parameter, thus supporting modular solutions to the detection problem. The ensuing detector provides false positive and detection rates of 1.38% and 90.4% under test conditions; where this represents the best performance to date of a detector based on an unsupervised learning algorithm.","Title: Testing Detector Parameterization Using Evolutionary Exploit Generation\nYear: 2009\n\nAbstract: The testing of anomaly detectors is considered from the perspective of a Multi-objective Evolutionary Exploit Generator (EEG). Such a framework provides users of anomaly detection systems two capabilities. Firstly, no knowledge of protected data structures need be assumed. Secondly, the evolved exploits are then able to demonstrate weaknesses in the ensuing detector parameterization. In this work we focus on the parameterization of the second generation anomaly detector `pH' and demonstrate how use of an EEG may identify weak parameterization of the detector.","Title: On evolutionary subspace clustering with symbiosis.\nYear: 2014\n\nAbstract: Subspace clustering identifies the attribute support for each cluster as well as identifying the location and number of clusters. In the most general case, attributes associated with each cluster could be unique. A multi-objective evolutionary method is proposed to identify the unique attribute support of each cluster while detecting its data instances. The proposed algorithm, symbiotic evolutionary subspace clustering (S-ESC) borrows from ‘symbiosis’ in the sense that each clustering solution is defined in terms of a host (single member of the host population) and a number of coevolved cluster centroids (or symbionts in an independent symbiont population). Symbionts define clusters and therefore attribute subspaces, whereas hosts define sets of clusters to constitute a non-degenerate solution. The symbiotic representation of S-ESC is the key to making it scalable to high-dimensional datasets, while an integrated subsampling process makes it scalable to tasks with a large number of data items. Benchmarking is performed against a test suite of 59 subspace clustering tasks with four well known comparator algorithms from both the full-dimensional and subspace clustering literature: EM, MINECLUS, PROCLUS, STATPC. Performance of the S-ESC algorithm was found to be robust across a wide cross-section of properties with a common parameterization utilized throughout. This was not the case for the comparator algorithms. Specifically, performance could be sensitive to the particular data distribution or parameter sweeps might be necessary to provide comparable performance. An additional evaluation is performed against a non-symbiotic GA, with S-ESC still returning superior clustering solutions.","Title: Evolutionary computation as an artificial attacker: generating evasion attacks for detector vulnerability testing.\nYear: 2011\n\nAbstract: Abstract Intrusion detection systems protect our infrastructures by monitoring for signs of intrusions. However, intrusion detection systems are themselves susceptible to vulnerabilities, which the attackers take advantage of to evade detection. In particular, we focus on evasion attacks in which the attacker aims to generate a stealthy attack that eliminates or minimizes the likelihood of detection. Attackers achieve stealth by mimicking normal behaviour while achieving the attack goals, hence bypassing the detector. Previous work focused on generating evasion attacks using the internal knowledge of the detectors, hence adopting a ‘white-box’ access to the detector. On the other hand, we adopt a ‘black-box’ approach and propose an evolutionary attacker based on Genetic Programming. The access of our ‘black-box’ approach is limited to the feedback of the detector such as anomaly rates and delays. We compare our ‘black-box’ approach with various ‘white-box’ approaches to investigate its effectiveness. In doing so, the impact of anomalies from the break-in stage of the attacks and the delays based on locality frame counts are also discussed. This is particularly important if the performance comparison is to reflect the real capabilities of detectors.","Title: Improving GP classifier generalization using a cluster separation metric\nYear: 2006\n\nAbstract: Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited in previous work. Here, we revisit the design of fitness functions for genetic programming by explicitly considering the contribution of the wrapper and cost function. Within the context of supervised learning, as applied to classification problems, a clustering methodology is introduced using cost functions which encourage maximization of separation between in and out of class exemplars. Through a series of empirical investigations of the nature of these functions, we demonstrate that classifier performance is much more dependable than previously the case under the genetic programming paradigm.","Title: Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification\nYear: 2007\n\nAbstract: A bid-based approach for coevolving Genetic Programming classifiers is presented. The approach coevolves a population of learners thatdecompose the instance space by way of their aggregate bidding behaviour. To reduce computation overhead, a small, relevant, subsetof training exemplars is (competitively) coevolved alongside the learners. The approach solves multi-class problems using a single population and is evaluated on three large datasets. It is found tobe competitive, especially compared to classifier systems, whilesignificantly reducing the computation overhead associated withtraining.","Title: Flat vs. symbiotic evolutionary subspace clusterings\nYear: 2013\n\nAbstract: Subspace clustering coevolves the attribute space supporting clusters at the same time as parameterizing the cluster location and combination. Typically, a 'flat' representation is pursued in which individuals describe both the property of individual clusters as well as the combination of clusters used to define the overall solution; hereafter F-ESC. Conversely, a symbiotic approach was recently proposed in which candidate clusters and the combination of clusters are coevolved from independent populations; hereafter S-ESC. In this work a common framework is pursued in order for flat and symbiotic evolutionary subspace clustering to be compared directly. We show that F-ESC might match S-ESC results for data sets with high proportions of cluster support, however, the gap between the two algorithm increases as cluster support decreases.","Title: On the utility of trading criteria based retraining in forex markets\nYear: 2013\n\nAbstract: This research investigates the ability of genetic programming (GP) to build profitable trading strategies for the Foreign Exchange Market (FX) of three major currency pairs (EURUSD, USDCHF and EURCHF) using one hour prices from 2008 to 2011. We recognize that such environments are likely to be non-stationary. Thus, we do not require a single training partition to capture all likely future behaviours. We address this by detecting poor trading behaviours and use this to trigger retraining. In addition the task of evolving good technical indicators (TI) and the rules for deploying trading actions is explicitly separated. Thus, separate GP populations are used to coevolve TI and trading behaviours under a mutualistic symbiotic association. The results of 100 simulations demonstrate that an adaptive retraining algorithm significantly outperforms a single-strategy approach (population evolved once) and generates profitable solutions with a high probability.","Title: Optimizing anomaly detector deployment under evolutionary black-box vulnerability testing\nYear: 2009\n\nAbstract: This work focuses on testing anomaly detectors from the perspective of a Multi-objective Evolutionary Exploit Generator (EEG). Such a framework provides users of anomaly detection systems two capabilities. Firstly, no knowledge of protected data structures need to be assumed (i.e. the detector is a black-box), where the time, knowledge and availability of tools to perform such an analysis might not be generally available. Secondly, the evolved exploits are then able to demonstrate weaknesses in the ensuing detector parameterization. Therefore, the system administrator can identify the suitable parameters for the effective operation of the detector. EEG is employed against two second generation anomaly detectors, namely pH and pH with schema mask, on four UNIX applications in order to perform a vulnerability assessment and make a comparison between the two detectors.","Title: Classification as clustering: A pareto cooperative-competitive gp approach\nYear: 2011\n\nAbstract: Intuitively population based algorithms such as genetic programming provide a natural environment for supporting solutions that learn to decompose the overall task between multiple individuals, or a team. This work presents a framework for evolving teams without recourse to prespecifying the number of cooperating individuals. To do so, each individual evolves a mapping to a distribution of outcomes that, following clustering, establishes the parameterization of a (Gaussian) local membership function. This gives individuals the opportunity to represent subsets of tasks, where the overall task is that of classification under the supervised learning domain. Thus, rather than each team member representing an entire class, individuals are free to identify unique subsets of the overall classification task. The framework is supported by techniques from evolutionary multiobjective optimization (EMO) and Pareto competitive coevolution. EMO establishes the basis for encouraging individuals to provide accurate yet nonoverlaping behaviors; whereas competitive coevolution provides the mechanism for scaling to potentially large unbalanced datasets. Benchmarking is performed against recent examples of nonlinear SVM classifiers over 12 UCI datasets with between 150 and 200,000 training instances. Solutions from the proposed coevolutionary multiobjective GP framework appear to provide a good balance between classification performance and model complexity, especially as the dataset instance count increases.","Title: Real-time digital–optical correlator-systems design\nYear: 1999\n\nAbstract: The systems design of a real-time hybrid digital–optical correlator is detailed. Emphasis is on: partitioning of the digital and optical systems such that the efficiency of the optical image train is maximised; and the parallelisation of tasks within the digital sub-system to maximise throughput. The significance of communication bottlenecks are emphasised in the digital system and the constraints this enforces on the initial selection of DSP devices.","Title: Register Based Genetic Programming on FPGA Computing Platforms\nYear: 2000\n\nAbstract: The use of FPGA based custom computing platforms is proposed for implementing linearly structured Genetic Programs. Such a context enables consideration of micro architectural and instruction design issues not normally possible when using classical Von Neumann machines. More importantly, the desirability of minimising memory management overheads results in the imposition of additional constraints to the crossover operator. Specifically, individuals are described in terms of the number of pages and page length, where the page length is common across individuals of the population. Pairwise crossover therefore results in the swapping of equal length pages, hence minimising memory overheads. Simulation of the approach demonstrates that the method warrants further study.","Title: Symbiogenesis as a mechanism for building complex adaptive systems: a review\nYear: 2010\n\nAbstract: In 1996 Daida et al. reviewed the case for using symbiosis as the basis for evolving complex adaptive systems [6]. Specific observations included the impact of different philosophical views taken by biologists as to what constituted a symbiotic relationship and whether symbiosis represented an operator or a state. The case was made for symbiosis as an operator. Thus, although specific cost benefit characterizations may vary, the underlying process of symbiosis is the same, supporting the operator based perspective. Symbiosis provides an additional mechanism for adaption/ complexification than available under Mendelian genetics with which Evolutionary Computation (EC) is most widely associated. In the following we review the case for symbiosis in EC. In particular, symbiosis appears to represent a much more effective mechanism for automatic hierarchical model building and therefore scaling EC methods to more difficult problem domains than through Mendelian genetics alone.","Title: An Investigation of Multi-objective Genetic Algorithms for Encrypted Traffic Identification.\nYear: 2009\n\nAbstract: The increasing use of encrypted traffic combined with non-standard port associations makes the task of traffic identification increasingly difficult. This work adopts a multi-objective clustering approach to the problem in which a Genetic Algorithm performs both feature selection and cluster count optimization under a flow based representation. Solutions do not use port numbers, IP address or payload. Performance of the resulting model provides 90% detection 0.8% false positive rates with 13 clusters supported by 14 of the original 38 features.","Title: Managing team-based problem solving with symbiotic bid-based genetic programming\nYear: 2008\n\nAbstract: Bid-based Genetic Programming (GP) provides an elegant mechanism for facilitating cooperative problem decomposition without an a priori specification of the number of team members. This is in contrast to existing teaming approaches where individuals learn a direct input-output map (e.g., from exemplars to class labels), allowing the approach to scale to problems with multiple outcomes (classes), while at the same time providing a mechanism for choosing an outcome from those suggested by team members. This paper proposes a symbiotic relationship that continues to support the cooperative bid-based process for problem decomposition while making the credit assignment process much clearer. Specifically, team membership is defined by a team population indexing combinations of GP individuals in a separate team member population. A Pareto-based competitive coevolutionary component enables the approach to scale to large problems by evolving informative test points in a third population. The ensuing Symbiotic Bid-Based (SBB) model is evaluated on three large classification problems and compared to the XCS learning classifier system (LCS) formulation and to the support vector machine (SVM) implementation LIBSVM. On two of the three problems investigated the overall accuracy of the SBB classifiers was found to be competitive with the XCS and SVM results. At the same time, on all problems, the SBB classifiers were able to detect instances of all classes whereas the XCS and SVM models often ignored exemplars of minor classes. Moreover, this was achieved with a level of model complexity significantly lower than that identified by the SVM and XCS solutions.","Title: Intelligent Packets For Dynamic Network Routing Using Distributed Genetic Algorithm\nYear: 2002\n\nAbstract: A distributed GA is designed for the packet switched network routing problem under minimal information. The requirements of such a problem mean that agents are required to possess more intelligence than was previously the case. To this end a distributed GA approach is developed and benchmarked against the AntNet algorithm under the same information constraints.","Title: Cooperative problem decomposition in Pareto competitive classifier models of coevolution\nYear: 2008\n\nAbstract: Pareto competitive models of coevolution have the potential to provide a number of distinct advantages over the canonical approach to training under the Genetic Programming (GP) classifier domain. Recent work has specifically focused on the reformulation of training as a two-population competition, that is learners versus training exemplars. Such a scheme affords, for example, the capacity to decouple the fitness evaluation overhead from the data set size through sub sampling while naturally encouraging 'teams' or composite solutions as opposed to solutions based on a single individual alone. One outstanding question with respect to the latter characteristic is with regards to the nature of the team (archive) behavior in terms of pattern coverage. That is to say, which models are used when, and what are the implications for solution modularity as it relates, for example, to the assignment of exemplars to solution participants. The current work investigates two Pareto competitive approaches to classification under GP, with one configured to employ an explicitly cooperative multi-objective cost function based and the other employing the classical (error-based) cost function. We empirically demonstrate a critical distinction between the two with regards to problem decomposition, with the capacity to provide a decomposition into unique behaviors being much more prevalent when co-operative mechanisms are explicitly supported.","Title: GP under streaming data constraints: a case for pareto archiving?\nYear: 2012\n\nAbstract: Classification as applied to streaming data implies that only a small number of new training instances appear at each generation and are never explicitly reintroduced by the stream. Pareto competitive coevolution provides a potential framework for archiving useful training instances between generations under an archive of finite size. Such a coevolutionary framework is defined for the online evolution of classifiers under genetic programming. Benchmarking is performed under multi-class data sets with class imbalance and training partitions with between 1,000's to 100,000's of instances. The impact of enforcing different constraints for accessing the stream are investigated. The role of online adaptation is explicitly documented and tests made on the relative impact of label error on the quality of streaming classifier results.","Title: Novelty-Based fitness: an evaluation under the santa fe trail\nYear: 2010\n\nAbstract: We present an empirical analysis of the effects of incorporating novelty-based fitness (phenotypic behavioral diversity) into Genetic Programming with respect to training, test and generalization performance. Three novelty-based approaches are considered: novelty comparison against a finite archive of behavioral archetypes, novelty comparison against all previously seen behaviors, and a simple linear combination of the first method with a standard fitness measure. Performance is evaluated on the Santa Fe Trail, a well known GP benchmark selected for its deceptiveness and established generalization test procedures. Results are compared to a standard quality-based fitness function (count of food eaten). Ultimately, the quality style objective provided better overall performance, however, solutions identified under novelty based fitness functions generally provided much better test performance than their corresponding training performance. This is interpreted as representing a requirement for layered learning/ symbiosis when assuming novelty based fitness functions in order to more quickly achieve the integration of diverse behaviors into a single cohesive strategy.","Title: Casgp: Building Cascaded Hierarchical Models Using Niching\nYear: 2005\n\nAbstract: A Cascaded model is introduced for mining large datasets using Genetic Programming without recourse to specialist hardware. Such an algorithm satisfies the seeming conflicting requirements of scalability and accuracy on large datasets by incrementally building GP classifiers through the use of a hierarchical Dynamic Subset Selection algorithm. Models are built incrementally with each layer of the cascade receiving as ingot the original feature vector, plus the output from the previous layer(s). In order to encourage each layer to explicitly solve new aspects of the problem a combination of Sum Square Error and Niching is utilized. Thus, previous layers of the model are considered a niche, and the cost function is a shared error metric.","Title: Learning recursive programs with cooperative coevolution of genetic code mapping and genotype\nYear: 2007\n\nAbstract: The Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP) algorithm that cooperatively coevolves a population of adaptive mappings and associated genotypes is used to learn recursive solutions given a function set consisting of general (not implicitly recursive) machine-language instructions. PAM DGP using redundant encodings to model the evolution of the biological genetic code is found to more efficiently learn 2nd and 3rd order recursive Fibonacci functions than related developmental systems and traditional linear GP. PAM DGP using redundant encoding is also demonstrated to produce the semantically highest quality solutions for all three recursive functions considered (Factorial, 2nd and 3rd order Fibonacci). PAM DGP is then shown to have produced such solutions by evolving redundant mappings to select and emphasize appropriate subsets of the function set useful for producing the naturally recursive solutions.","Title: On GPU based fitness evaluation with decoupled training partition cardinality\nYear: 2013\n\nAbstract: GPU acceleration of increasingly complex variants of evolutionary frameworks typically assume that all the training data used during evolution resides on the GPU. Such an assumption places limits on the style of application to which evolutionary computation can be applied. Conversely, several coevolutionary frameworks explicitly decouple fitness evaluation from the size of the training partition. Thus, a subset of training exemplars is coevolved with the population of evolved individuals. In this work we articulate the design decisions necessary to support Pareto archiving for Genetic Programming under a commodity GPU platform. Benchmarking of corresponding CPU and GPU implementations demonstrates that the GPU platform is still capable of providing a times ten reduction in computation time.","Title: Heterogeneous Digital Library Query Platform Using A Truly Distributed Multi-Agent Search\nYear: 2000\n\nAbstract: A platform for performing multi-agent searches in heterogeneous digital libraries is proposed. This differs significantly from previous approaches by completely removing the concept of a centralized search engine. Specifically, the organization of information held on domain index servers is constrained to conform to a virtual tree representation based on facets and global keyword concept schema particular to the set of information providers associated with the domain of interest (e.g. preparatory intranet). Simulation studies are used to compare this platform against a digital library platform presently in use, which employs the traditional central server scheme. Improvements in terms of query service time and robustness are demonstrated.","Title: Benchmarking pareto archiving heuristics in the presence of concept drift: diversity versus age\nYear: 2013\n\nAbstract: A framework for coevolving genetic programming teams with Pareto archiving is benchmarked under two representative tasks for non-stationary streaming environments. The specific interest lies in determining the relative contribution of diversity and aging heuristics to the maintenance of the Pareto archive. Pareto archiving, in turn, is responsible for targeting data (and therefore champion individuals) as appropriate for retention beyond the limiting scope of the sliding window interface to the data stream. Fitness sharing alone is considered most effective under a non-stationary stream characterized by continuous (incremental) changes. Fitness sharing with an aging heuristic acts as the preferred heuristic when the stream is characterized by non-stationary stepwise changes.","Title: GP classification under imbalanced data sets: active sub-sampling and AUC approximation\nYear: 2008\n\nAbstract: The problem of evolving binary classification models under increasingly unbalanced data sets is approached by proposing a strategy consisting of two components: Sub-sampling and 'robust' fitness function design. In particular, recent work in the wider machine learning literature has recognized that maintaining the original distribution of exemplars during training is often not appropriate for designing classifiers that are robust to degenerate classifier behavior. To this end we propose a 'Simple Active Learning Heuristic' (SALH) in which a subset of exemplars is sampled with uniform probability under a class balance enforcing rule for fitness evaluation. In addition, an efficient estimator for the Area Under the Curve (AUC) performance metric is assumed in the form of a modified Wilcoxon-Mann-Whitney (WMW) statistic. Performance is evaluated in terms of six representative UCI data sets and benchmarked against: canonical GP, SALH based GP, SALH and the modified WMW statistic, and deterministic classifiers (Naive Bayes and C4.5). The resulting SALH-WMW model is demonstrated to be both efficient and effective at providing solutions maximizing performance assessed in terms of AUC.","Title: Generating Mimicry Attacks Using Genetic Programming: A Benchmarking Study\nYear: 2009\n\nAbstract: Mimicry attacks have been the focus of detector research where the objective of the attacker is to generate multiple attacks satisfying the same generic exploit goals for a given vulnerability. In this work, multi-objective Genetic programming is used to establish a \"black-box\" approach to mimicry attack generation. No knowledge is made of internal data structures of the target anomaly detector, only the anomaly rate reported by the detector. Such a \"black box\" methodology enables a vulnerability testing approach where both open-source and commodity anomaly detection systems can be tested. The approach successfully identifies exploits when benchmarked over four detectors and four applications.","Title: On the application of GP to streaming data classification tasks with label budgets\nYear: 2014\n\nAbstract: A framework is introduced for applying GP to streaming data classification tasks under label budgets. This is a fundamental requirement if GP is going to adapt to the challenge of streaming data environments. The framework proposes three elements: a sampling policy, a data subset and a data archiving policy. The sampling policy establishes on what basis data is sampled from the stream, and therefore when label information is requested. The data subset is used to define what GP individuals evolve against. The composition of such a subset is a mixture of data forwarded under the sampling policy and historical data identified through the data archiving policy. The combination of sampling policy and the data subset achieve a decoupling between the rate at which the stream passes and the rate at which evolution commences. Benchmarking is performed on two artificial data sets with specific forms of sudden shift and gradual drift as well as a well known real-world data set.","Title: On botnet behaviour analysis using GP and C4.5\nYear: 2014\n\nAbstract: Botnets represent a destructive cyber security threat that aim to hide their malicious activities within legitimate Internet traffic. Part of what makes botnets so affective is that they often upgrade themselves over time, hence reacting to improved detection mechanisms. In addition, Internet common communication protocols (i.e. HTTP) are used for the purposes of constructing subversive communication channels. This work employs machine learning algorithms (genetic programming and decision trees) to detect distinct behaviours in various botnets. That is to say, botnets mimic legitimate HTTP traffic while actually serving botnet purposes. To this end, two different feature sets are employed and analyzed to see how differences between three botnets - Zeus, Conficker and Torpig - can be distinguished. Specific recommendations are then made regarding the utility of different feature sets and machine learning algorithms for detecting each type of botnet.","Title: On Evolving Multi-agent FX Traders.\nYear: 2014\n\nAbstract: Current frameworks for identifying trading agents using machine learning are able to simultaneously address the characterization of both technical indicator and decision tree. Moreover, multi-agent frameworks have also been proposed with the goal of improving the reliability and trust in the agent policy identified. Such advances need weighing against the computational overhead of assuming such flexibility. In this work a framework for evolutionary multi-agent trading is introduced and systematically benchmarked for FX currency trading; including the impact of FX trading spread. It is demonstrated that simplifications can be made to the 'base' trading agent that do not impact on the quality of solutions, but provide considerable computational speedups. The resulting evolutionary multi-agent architecture is demonstrated to provide significant benefits to the profitability and improve the reliability with which profitable policies are returned.","Title: Cascaded GP models for data mining\nYear: 2004\n\nAbstract: The cascade architecture for incremental learning is demonstrated within the context of genetic programming. Such a scheme provides the basis for building steadily more complex models until a desired degree of accuracy is reached. The architecture is demonstrated for several data mining datasets. Efficient training on standard computing platforms is retained using the RSS-DSS algorithm for stochastically sampling datasets in proportion to exemplar 'difficulty' and 'age'. Finally, the ensuing empirical study provides the basis for recommending the utility of sum square cost functions in the datasets considered.","Title: Probabilistic (Genotype) Adaptive Mapping Combinations for Developmental Genetic Programming\nYear: 2006\n\nAbstract: In development genetic programming (DGP) approaches where the search space is divided into genotypes and phenotypes, a mapping (or \"genetic code\") is needed to connect the two spaces. This model has subsequently been extended so that mappings evolve, and recently an implementation was proposed that co-evolves a genotype population and a population of adaptive mappings. Here, the authors identify and investigate performance obstacles for this recent implementation. They then introduce a new probabilistic adaptive mapping DGP that avoids those performance problems and explores a greater search space of genotype-mapping combinations without significant computational expense. The algorithm is shown to be more robust and to outperform the comparison adaptive mapping algorithm on challenging settings of the chosen test benchmark.","Title: Automatically Evading IDS Using GP Authored Attacks\nYear: 2007\n\nAbstract: A mimicry attack is a type of attack where the basic steps of a minimalist 'core' attack are used to design multiple attacks achieving the same objective from the same application. Research in mimicry attacks is valuable in determining and eliminating weaknesses of detectors. In this work, we provide a genetic programming based automated process for designing all components of a mimicry attack relative to the Stide detector under a vulnerable Traceroute application. Results indicate that the automatic process is able to generate mimicry attacks that reduce the alarm rate from ~65% of the original attack, to ~2.7%, effectively making the attack indistinguishable from normal behaviors","Title: Scaling Genetic Programming to Large Datasets Using Hierarchical Dynamic Subset Selection\nYear: 2007\n\nAbstract: The computational overhead of genetic programming (GP) may be directly addressed without recourse to hardware solutions using active learning algorithms based on the random or dynamic subset selection heuristics (RSS or DSS). This correspondence begins by presenting a family of hierarchical DSS algorithms: RSS-DSS, cascaded RSS-DSS, and the balanced block DSS algorithm, where the latter has not been previously introduced. Extensive benchmarking over four unbalanced real-world binary classification problems with 30000-500000 training exemplars demonstrates that both the cascade and balanced block algorithms are able to reduce the likelihood of degenerates while providing a significant improvement in classification accuracy relative to the original RSS-DSS algorithm. Moreover, comparison with GP trained without an active learning algorithm indicates that classification performance is not compromised, while training is completed in minutes as opposed to half a day.","Title: One-class learning with multi-objective genetic programming\nYear: 2007\n\nAbstract: One-class classification naturally only provides one class of exemplars on which to construct the classification model. In this work, multi- objective genetic programming (GP) allows the one-class learning problem to be decomposed by multiple GP classifiers, each attempting to identify only a subset of the target data to classify. In order for GP to identify appropriate subsets of the one-class data, artificial outclass data is gener- ated in and around the provided inclass data. A local Gaussian wrapper is employed where this reinforces a novelty detection as opposed to a dis- crimination approach to classification. Furthermore, a hierarchical subset selection strategy is used to deal with the necessarily large number of generated outclass exemplars. The proposed approach is demonstrated on three one-class classification datasets and was found to be competitive with a one-class SVM classifier and a binary SVM classifier.","Title: Multi-objective competitive coevolution for efficient GP classifier problem decomposition\nYear: 2007\n\nAbstract: A novel approach to the classification of large and unbalanced multi-class data sets is presented where the widely acknowledged issues of scalability, solution transparency, and problem decomposition are addressed simultaneously within the context of the genetic programming (GP) paradigm. A cooperative coevolutionary training environment that employs multi-objective evaluation provides the basis for problem decomposition and reduced solution complexity, while scalability is achieved through a Pareto competitive coevolutionary framework, allowing the system to be applied to large data sets (tens or hundreds of thousands of exemplars) without recourse to hardware-specific speedups. Moreover, a key departure from the canonical GP approach to classification is utilized in which the output of GP is expressed in terms of a non-binary, local membership function (e.g. a Gaussian), where it is no longer necessary for an expression to represent an entire class. Decomposition is then achieved through reformulating the classification problem as one of cluster consistency, where an appropriate subset of the training patterns can be associated with each individual such that problems are solved by several specialist classifiers rather than by a single 'super' individual.","Title: Bottom-up evolutionary subspace clustering\nYear: 2010\n\nAbstract: The ultimate goal of subspace clustering algorithms is to identify both the subset of attributes supporting a cluster and the location of the cluster in the subspace. In this work a generic evolutionary approach to bottom-up subspace clustering is proposed consisting of three steps. The first applies a non-evolutionary clustering algorithm attribute-wise to establish the lattice from which subspace clusters will be designed. In the second step a multi-objective Genetic Algorithm (MOGA) is used to evolve good candidate subspace clusters (CSC) through a combinatorial search w.r.t. the attribute-wise lattice from step 1. The third step then searches in the space of CSC from the population of the the first MOGA to find the best combination of subspace clusters, again under a MOGA formulation. Important properties of the approach are that a standard clustering algorithm is deployed in step one to build the initial lattice of attribute-wise clusters. This helps to decouple the computational expense of clustering using Evolutionary Computation, with the MOGA applied in steps 2 and 3 building clusters through a combinatorial search relative to the original lattice parameters. Benchmarking on data sets with tens to hundreds of attributes illustrates the feasibility of the approach.","Title: Genetic optimization and hierarchical clustering applied to encrypted traffic identification\nYear: 2011\n\nAbstract: An important part of network management requires the accurate identification and classification of network traffic for decisions regarding bandwidth management, quality of service, and security. This work explores the use of a Multi-Objective Genetic Algorithm (MOGA) for both, feature selection and cluster count optimization, for an unsupervised machine learning technique, K-Means, applied to encrypted traffic identification. Specifically, a hierarchical K-Means algorithm is employed, comparing its performance to the MOGA with a non-hierarchical (flat) K-Means algorithm. The latter has already been benchmarked against common unsupervised techniques found in the literature, where results have favored the proposed MOGA. The purpose of this paper is to explore the gains, if any, obtained by increasing cluster purity in the proposed model by means of a second layer of clusters. In this work, SSH is chosen as an example of an encrypted application. However, nothing prevents the proposed model to work with other types of encrypted traffic, such as SSL or Skype. Results show that with the hierarchical MOGA, significant gains are observed in terms of the classification performance of the system.","Title: Revisiting the Acrobot ‘height’ task: An example of efficient evolutionary policy search under an episodic goal seeking task\nYear: 2011\n\nAbstract: Evolutionary methods for addressing the temporal sequence learning problem generally fall into policy search as opposed to value function optimization approaches. Various re cent results have made the claim that the policy search approach is at best inefficient at solving episodic 'goal seeking' tasks i.e., tasks under which the reward is limited to describing properties associated with a successful outcome have no qualification for degrees of failure. This work demonstrates that such a conclusion is due to a lack of diversity in the training scenarios. We therefore return to the Acrobot 'height' task domain originally used to demonstrate complete failure in evolutionary policy search. This time a very simple stochastic sampling heuristic for defining a population of training configurations is introduced. Benchmarking two recent evolutionary policy search algorithms - Neural Evolution of Augmented Topologies (NEAT) and Symbiotic Bid-Based (SBB) Genetic Programming - under this condition demonstrates solutions as effective as those returned by advanced value function methods. Moreover this is achieved while remaining within the evaluation limit imposed by the original study.","Title: Evolutionary model building under streaming data for classification tasks: opportunities and challenges\nYear: 2015\n\nAbstract: Streaming data analysis potentially represents a significant shift in emphasis from schemes historically pursued for offline (batch) approaches to the classification task. In particular, a streaming data application implies that: (1) the data itself has no formal `start' or `end'; (2) the properties of the process generating the data are non-stationary, thus models that function correctly for some part(s) of a stream may be ineffective elsewhere; (3) constraints on the time to produce a response, potentially implying an anytime operational requirement; and (4) given the prohibitive cost of employing an oracle to label a stream, a finite labelling budget is necessary. The scope of this article is to provide a survey of developments for model building under streaming environments from the perspective of both evolutionary and non-evolutionary frameworks. In doing so, we bring attention to the challenges and opportunities that developing solutions to streaming data classification tasks are likely to face using evolutionary approaches.","Title: Systems control for a micro-stereolithography prototype\nYear: 1998\n\nAbstract: The system control of a micro-stereolithographic fabrication system is detailed, with specific attention given to the opto-electronic and electro-mechanical interfaces. The application of the National Instrument LabVEIW© environment is demonstrated to provide a good basis for rapid application development of the necessary control structures. In particular, this provides the basis for a highly flexible control system, typical activities of which include: low-level interface formats; icon-based systems design; and support for good human–computer interfacing. The resulting system has the capability to provide two orders of magnitude improvement above that currently available.","Title: Solving Complex Problems with Coevolutionary Algorithms\nYear: 2015\n\nAbstract: No abstract available.","Title: Knowledge Transfer from Keepaway Soccer to Half-field Offense through Program Symbiosis: Building Simple Programs for a Complex Task\nYear: 2015\n\nAbstract: Half-field Offense (HFO) is a sub-task of Robocup 2D Simulated Soccer. HFO is a challenging, multi-agent machine learning problem in which a team of offense players attempt to manoeuvre the ball past a defending team and around the goalie in order to score. The agent's sensors and actuators are noisy, making the problem highly stochastic and partially observable. These same real-world characteristics have made Keepaway soccer, which represents one sub-task of HFO, a popular testbed in the reinforcement learning and task-transfer literature in particular. We demonstrate how policies initially evolved for Keepaway can be reused within a symbiotic framework for coevolving policies in genetic programming (GP), with no additional training or transfer function, in order to improve learning in the HFO task. Moreover, the highly modular policies discovered by GP are shown to be significantly less complex than solutions based on traditional value-function optimization while achieving the same level of play in HFO.","Title: On the Impact of Class Imbalance in GP Streaming Classification with Label Budgets.\nYear: 2016\n\nAbstract: Streaming data scenarios introduce a set of requirements that do not exist under supervised learning paradigms typically employed for classification. Specific examples include, anytime operation, non-stationary processes, and limited label budgets. From the perspective of class imbalance, this implies that it is not even possible to guarantee that all classes are present in the samples of data used to construct a model. Moreover, when decisions are made regarding what subset of data to sample, no label information is available. Only after sampling is label information provided. This represents a more challenging task than encountered under non-streaming (offline) scenarios because the training partition contains label information. In this work, we investigate the utility of different protocols for sampling from the stream under the above constraints. Adopting a uniform sampling protocol was previously shown to be reasonably effective under both evolutionary and non-evolutionary streaming classifiers. In this work, we introduce a scheme for using the current ‘champion’ classifier to bias the sampling of training instances during the course of the stream. The resulting streaming framework for genetic programming is more effective at sampling minor classes and therefore reacting to changes in the underlying process responsible for generating the data stream.","Title: Better Trade Exits For Foreign Exchange Currency Trading Using Fxgp\nYear: 2015\n\nAbstract: Retracement is the tendency of markets to move between upper 'resistance' and lower 'support' price levels. Human traders frequently make use of visual tools to help identify these resistance and support levels so that they can by used in their trading decisions. These decision can be put into trading strategies composed of rules designed to mitigate losses after a trade is started, often called 'stop loss' orders, or to take profit at a near optimal time, often called 'take profit' orders. However, identifying such resistance and support levels is notoriously difficult given market volatility. Indeed, the levels need recalculating on a continuous basis, and only hold to an approximate degree. In this work we describe an approach for evolving buy-stay-sell currency trading rules using genetic programming. These rules are explicitly linked to technical indicators that incorporate features characterizing retracement. Benchmarking is then performed using the most recent three years of data from the EURUSD foreign exchange market with three different methods of identifying retracement based on moving average, pivot points and Fibonacci ratios. Investment strategies employing Fibonacci ratios and found to provide superior performance among the strategies examined.","Title: Benchmarking Stream Clustering For Churn Detection In Dynamic Networks\nYear: 2015\n\nAbstract: Retaining users and customers is one of the most important challenges for the service industry from mobile communications to online gaming. As the users of these services form dynamic networks that grow in size, predicting 'churners' becomes harder and harder. In this work, we explore the use of anomaly detection for churn prediction. To this end, we evaluate bio-inspired and deterministic online clustering algorithms on both cell phone and online gaming data sets. We discuss the results of each technique from the perspective of: feature identification, sensitivity analysis of the parameters as well as their capacity to detect churn.","Title: Feature selection for robust backscatter DDoS detection\nYear: 2015\n\nAbstract: This paper analyzes the effect of using different feature selection algorithms for robust backscatter DDoS detection. To achieve this, we analyzed four different training sets with four different feature sets. We employed two well-known feature selection algorithms, namely Chi-Square and Symmetrical Uncertainty, together with the Decision Tree classifier. All the datasets employed are publicly available and provided by CAIDA. Our experimental results show that it is possible to develop a robust detection system that can generalize well to the changing backscatter DDoS behaviours over time using a small number of selected features.","Title: Initiating a Moving Target Network Defense with a Real-time Neuro-evolutionary Detector.\nYear: 2016\n\nAbstract: The moving network target defense (MTD) based approach to security aims to design and develop capabilities to dynamically change the attack surfaces to make it more difficult for attackers to strike. One such capability is to dynamically change the IP addresses of subnetworks in unpredictable ways in an attempt to disrupt the ability of an attacker to collect the necessary reconnaissance information to launch successful attacks. In particular, Denial of Service (DoS) and worms represent examples of distributed attacks that can potentially propagate through networks very quickly, but could also be disrupted by MTD. Conversely, MTD are also disruptive to regular users. For example, when IP addresses are changed dynamically it is no longer effective to use DNS caches for IP address resolutions before any communication can be performed. In this work we take another approach. We note that the deployment of MTD could be triggered through the use of light-weight intrusion detection. We demonstrate that the neuro-evolution of augmented topologies algorithm (NEAT) has the capacity to construct detectors that operate on packet data and produce sparse topologies, hence are real-time in operation. Benchmarking under examples of DoS and worm attacks indicates that NEAT detectors can be constructed from relatively small amounts of data and detect attacks approx. 90% accuracy. Additional experiments with the open-ended evolution of code modules through genetic program teams provided detection rates approaching 100%. We believe that adopting such an approach to MTB a more specific deployment strategy that is less invasive to legitimate users, while disrupting the actions of malicious users.","Title: Discovering Rubik's Cube Subgroups using Coevolutionary GP: A Five Twist Experiment.\nYear: 2016\n\nAbstract: This work reports on an approach to direct policy discovery (a form of reinforcement learning) using genetic programming (GP) for the 3 by 3 by 3 Rubik's Cube. Specifically, a synthesis of two approaches is proposed: 1) a previous group theoretic formulation is used to suggest a sequence of objectives for developing solutions to different stages of the overall task; and 2) a hierarchical formulation of GP policy search is utilized in which policies adapted for an earlier objective are explicitly transferred to aid the construction of policies for the next objective. The resulting hierarchical organization of policies explicitly demonstrates task decomposition and policy reuse. Algorithmically, the process makes use of a recursive call to a common approach for maintaining a diverse population of GP individuals and then learns how to reuse subsets of programs (policies) developed against the earlier objective. Other than the two objectives, we do not explicitly identify how to decompose the task or mark specific policies for reuse. Moreover, at the end of evolution we return a population solving 100% of 17,675,698 different initial Cubes for the two objectives currently in use.","Title: Solving Complex Problems with Coevolutionary Algorithms.\nYear: 2017\n\nAbstract: No abstract available.","Title: On Synergies between Diversity and Task Decomposition in Constructing Complex Systems with GP.\nYear: 2016\n\nAbstract: Complexity in genetic programming is unfortunately often associated with undesirable properties such as code bloat. In this work, we review developments in which complex systems are promoted through: 1) the evolution of teams of programs, and then 2) the context specific reuse of previously evolved code. To do so, two classes of diversity are identified: intra-team diversity and inter-team diversity. Intra-team diversity promotes task decomposition/cooperative coevolution between multiple programs, i.e. teams of programs. A fundamental requirement is that programs can learn context. Inter-team diversity is promoted through maintaining model and task diversity during evolution. The combination of both result in the ability to identify teams of programs and associate them with specific contexts, and then organize teams of programs hierarchically so solve multiple tasks. Finally, the concept of cumulative population wide performance is used to illustrate how inter model diversity in particular introduces useful biases into the types of solutions evolved.","Title: Coevolving deep hierarchies of programs to solve complex tasks.\nYear: 2017\n\nAbstract: Scaling genetic programming to organize large complex combinations of programs remains an under investigated topic in general. This work revisits the issue by first demonstrating the respective contributions of coevolution and diversity maintenance. Competitive coevolution is employed to organize a task in such a way that the most informative training cases are retained. Cooperative coevolution helps discover modularity in the solutions discovered and, in this work, is fundamental to constructing complex structures of programs that still execute efficiently (the policy tree). The role of coevolution and diversity maintenance is first independently established under the task of discovering reinforcement learning policies for solving Rubik's Cubes scrambled with 5-twists. With this established, a combined approach is then adopted for building large organizations of code for representing policies that solve 5 to 8-twist combinations of the Cube. The resulting 'deep' policy tree organizes hundreds of programs to provide optimal solutions to tens of millions of test cube configurations.","Title: Multi-task learning in Atari video games with emergent tangled program graphs.\nYear: 2017\n\nAbstract: The Atari 2600 video game console provides an environment for investigating the ability to build artificial agent behaviours for a variety of games using a common interface. Such a task has received attention for addressing issues such as: 1) operation directly from a high-dimensional game screen; and 2) partial observability of state. However, a general theme has been to assume a common machine learning algorithm, but completely retrain the model for each game title. Success in this respect implies that agent behaviours can be identified without hand crafting game specific attributes/actions. This work advances current state-of-the-art by evolving solutions to play multiple titles from the same run. We demonstrate that in evolving solutions to multiple game titles, agent behaviours for an individual game as well as single agents capable of playing all games emerge from the same evolutionary run. Moreover, the computational cost is no more than that used for building solutions for a single title. Finally, while generally matching the skill level of controllers from neuro-evolution/deep learning, the genetic programming solutions evolved here are several orders of magnitude simpler, resulting in real-time operation at a fraction of the cost.","Title: Properties of a GP active learning framework for streaming data with class imbalance.\nYear: 2017\n\nAbstract: Active learning algorithms attempt to interactively develop a subset of data from which fitness evaluation is performed. Moreover, the distribution of labeled content within the data subset may adapt over time as genetic programming (GP) individuals improve. The basic goal is therefore to identify the most meaningful subset of data to improve the current model. Under a streaming data context additional challenges exist relative to the non-streaming scenario: non-stationary processes, partial observability anytime operation. This means that it is not possible to guarantee that the content of the data subset even provides exemplars for each class that could appear in the stream (i.e., different classes appear/disappear at different parts of the stream). With this in mind, an investigation is performed into the impact of adopting different policies for controlling the development of data subset content. To do so, a generic framework is defined in terms of sampling and archiving policies. The resulting evaluation under several large multi-class datasets with class imbalance indicates that adopting random sampling with a biased archiving policy is sufficient for evolving GP classifiers that match or better the current state-of-the-art, particularly when detecting minor classes.","Title: Return-oriented programme evolution with ROPER: a proof of concept.\nYear: 2017\n\nAbstract: Return-orientated programming (ROP) identifies code snippets ending in a return instruction (gadgets) and chains them together to construct exploits. Gadgets are already present in executable memory, thus avoiding the need to explicitly inject new code. As such ROP represents one of the most difficult exploit mechanisms to mitigate. ROP design is essentially driven by the skill of human hacker, limiting the ability of exploit mitigation to reacting to attacks. In this work we describe an evolutionary approach to ROP design, thus potentially pointing to the automatic detection of vulnerabilities before application code is released.","Title: Emergent Tangled Graph Representations For Atari Game Playing Agents\nYear: 2017\n\nAbstract: Organizing code into coherent programs and relating different programs to each other represents an underlying requirement for scaling genetic programming to more difficult task domains. Assuming a model in which policies are defined by teams of programs, in which team and program are represented using independent populations and coevolved, has previously been shown to support the development of variable sized teams. In this work, we generalize the approach to provide a complete framework for organizing multiple teams into arbitrarily deep/wide structures through a process of continuous evolution; hereafter the Tangled Program Graph (TPG). Benchmarking is conducted using a subset of 20 games from the Arcade Learning Environment (ALE), an Atari 2600 video game emulator. The games considered here correspond to those in which deep learning was unable to reach a threshold of play consistent with that of a human. Information provided to the learning agent is limited to that which a human would experience. That is, screen capture sensory input, Atari joystick actions, and game score. The performance of the proposed approach exceeds that of deep learning in 15 of the 20 games, with 7 of the 15 also exceeding that associated with a human level of competence. Moreover, in contrast to solutions from deep learning, solutions discovered by TPG are also very 'sparse'. Rather than assuming that all of the state space contributes to every decision, each action in TPG is resolved following execution of a subset of an individual's graph. This results in significantly lower computational requirements for model building than presently the case for deep learning.","Title: Scaling Tangled Program Graphs to Visual Reinforcement Learning in ViZDoom.\nYear: 2018\n\nAbstract: A tangled program graph framework (TPG) was recently proposed as an emergent process for decomposing tasks and simultaneously composing solutions by organizing code into graphs of teams of programs. The initial evaluation assessed the ability of TPG to discover agents capable of playing Atari game titles under the Arcade Learning Environment. This is an example of ‘visual’ reinforcement learning, i.e. agents are evolved directly from the frame buffer without recourse to hand designed features. TPG was able to evolve solutions competitive with state-of-the-art deep reinforcement learning solutions, but at a fraction of the complexity. One simplification assumed was that the visual input could be down sampled from a (210 times 160) resolution to (42 times 32). In this work, we consider the challenging 3D first person shooter environment of ViZDoom and require that agents be evolved at the original visual resolution of (320 times 240) pixels. In addition, we address issues for developing agents capable of operating in multi-task ViZDoom environments simultaneously. The resulting TPG solutions retain all the emergent properties of the original work as well as the computational efficiency. Moreover, solutions appear to generalize across multiple task scenarios, whereas equivalent solutions from deep reinforcement learning have focused on single task scenarios alone.","Title: On botnet detection with genetic programming under streaming data label budgets and class imbalance.\nYear: 2018\n\nAbstract: Algorithms for constructing models of classification under streaming data scenarios are becoming increasingly important. In order for such algorithms to be applicable under ‘real-world’ contexts we adopt the following objectives: 1) operate under label budgets, 2) make label requests without recourse to true label information, and 3) robustness to class imbalance. Specifically, we assume that model building is only performed using the content of a Data Subset (as in active learning). Thus, the principle design decisions are with regard to the definitions employed for sampling and archiving policies. Moreover, these policies should operate without prior information regarding the distribution of classes, as this varies over the course of the stream. A team formulation for genetic programming (GP) is assumed as the generic model for classification in order to support incremental changes to classifier content. Benchmarking is conducted with thirteen real-world Botnet datasets with label budgets of the order of 0.5–5% and significant amounts of class imbalance. Specific recommendations are made for detecting the costly minor classes under these conditions. Comparison with current approaches to streaming data under label budgets supports the significance of these findings.","Title: Streaming Botnet traffic analysis using bio-inspired active learning.\nYear: 2018\n\nAbstract: Non-stationary network traffic, together with stealth occurrences of malicious behaviors, make analyzing network traffic challenging. In this research, a machine learning framework is used to incrementally learn the network behavior and adapt to the changes in the traffic. This framework works under two main constraints: 1) label budget, 2) class imbalance; which makes it suitable for real-world network scenarios. Evaluations are performed on a public dataset with multiple Botnet scenarios under 0.5% and 5% label budgets; only around 2.2% of traffic is Botnet. Our results demonstrate the significance of the proposed Stream Genetic Programming solution and a general robustness to factors such as long latencies between instances of the same Botnet.","Title: Benchmarking evolutionary computation approaches to insider threat detection.\nYear: 2018\n\nAbstract: Insider threat detection represents a challenging problem to companies and organizations where malicious actions are performed by authorized users. This is a highly skewed data problem, where the huge class imbalance makes the adaptation of learning algorithms to the real world context very difficult. In this work, applications of genetic programming (GP) and stream active learning are evaluated for insider threat detection. Linear GP with lexicase/multi-objective selection is employed to address the problem under a stationary data assumption. Moreover, streaming GP is employed to address the problem under a non-stationary data assumption. Experiments conducted on a publicly available corporate data set show the capability of the approaches in dealing with extreme class imbalance, stream learning and adaptation to the real world context.","Title: A genetic algorithm for dynamic controller placement in software defined networking.\nYear: 2018\n\nAbstract: The Software Defined Networking paradigm has enabled dynamic configuration and control of large networks. Although the division of the control and data planes on networks has lead to dynamic reconfigurability of large networks, finding the minimal and optimal set of controllers that can adapt to the changes in the network has proven to be a challenging problem. Recent research tends to favor small solution sets with a focus on either propagation latency or controller load distribution, and struggles to find large balanced solution sets. In this paper, we propose a multi-objective genetic algorithm based approach to the controller placement problem that minimizes inter-controller latency, load distribution and the number of controllers with fitness sharing. We demonstrate that the proposed approach provides diverse and adaptive solutions to real network architectures such as the United States backbone and Japanese backbone networks. We further discuss the relevance and application of a diversity focused genetic algorithm for a moving target defense security model.","Title: Discovering Agent Behaviors Through Code Reuse: Examples From Half-Field Offense and Ms. Pac-Man.\nYear: 2018\n\nAbstract: This paper demonstrates how code reuse allows genetic programming (GP) to discover strategies for difficult gaming scenarios while maintaining relatively low model complexity. Critical factors in the proposed approach are illustrated through an in-depth study in two challenging task domains: RoboCup soccer and Ms. Pac-Man. In RoboCup, we demonstrate how policies initially evolved for simple subtas...","Title: Emergent Solutions to High-Dimensional Multitask Reinforcement Learning.\nYear: 2018\n\nAbstract: Algorithms that learn through environmental interaction and delayed rewards, or reinforcement learning, increasingly face the challenge of scaling to dynamic, highdimensional, and partially observable environments. Significant attention is being paid to frameworks from deep learning, which scale to high-dimensional data by decomposing the task through multi-layered neural networks. While effective, the representation is complex and computationally demanding. In this work we propose a framework based on Genetic Programming which adaptively complexifies policies through interaction with the task. We make a direct comparison with several deep reinforcement learning frameworks in the challenging Atari video game environment as well as more traditional reinforcement learning frameworks based on a priori engineered features. Results indicate that the proposed approach matches the quality of deep learning while being a minimum of three orders of magnitude simpler with respect to model complexity. This results in real-time operation of the champion RL agent without recourse to specialized hardware support. Moreover, the approach is capable of evolving solutions to multiple game titles simultaneously with no additional computational cost. In this case, agent behaviours for an individual game as well as single agents capable of playing all games emerge from the same evolutionary run.","Title: Network Analytics For Streaming Traffic Analysis\nYear: 2019\n\nAbstract: Network traffic analytics has always been an important field for cybersecurity and network operations. The analysis could be done on the stream of data (network traffic) in an on-line mode for real-time network operations or on the captured traffic data in an off-line mode for forensic analysis. In this paper, we aim to shed light into using machine learning based network analytics on traffic flows both for network operations and forensic analysis purposes. In doing so, our goal is to design and develop systems that could assist the human expert in analyzing \"unknown background\" traffic.","Title: Benchmarking genetic programming in dynamic insider threat detection.\nYear: 2019\n\nAbstract: In real world applications, variation in deployment environments, such as changes in data collection techniques, can affect the effectiveness and/or efficiency of machine learning (ML) systems. In this work, we investigate techniques to allow a previously trained population of Linear Genetic Programming (LGP) insider threat detectors to adapt to an expanded feature space. Experiments show that appropriate methods can be adopted to enable LGP to incorporate the new data efficiently, hence reducing computation requirements and expediting deployment under the new conditions.","Title: Evolving Simple Solutions to the CIFAR-10 Benchmark using Tangled Program Graphs\nYear: 2021\n\nAbstract: The goal of the CIFAR-10 benchmark is recast from the perspective of discovering light-weight as well as accurate solutions. Specifically, the image data, on which CIFAR-10 is based, requires multiple practical issues to be addressed that are not often considered collectively when applying genetic programming to classification problems. Issues of particular interest include cardinality, multi-class classification and diversity maintenance. We demonstrate that diversity maintenance and cardinality can be approached simultaneously by adopting a data subset to compose pools of exemplars for lexicase selection. The issues of multi-class classification and solution simplicity are addressed by adopting the tangled program graph (TPG) approach to emergent modularity. In addition, the mutation operator is modified to ensure that class labels do not 'die out' during evolution. The resulting benchmarking study demonstrates solutions that are significantly more accurate than AutoML while providing comparable accuracies with solutions from unsupervised feature discovery, i.e. 70% accuracy. However, unlike the latter TPG solutions are several orders of magnitude simpler.","Title: Log Abstraction for Information Security: Heuristics and Reproducibility\nYear: 2021\n\nAbstract: The collection of log messages regarding the operation of deployed services and application is an integral component to the forensic analysis for the identification and understanding of security incidents. Approaches for parsing and abstraction of such logs, despite widespread use and study, do not directly account for the individualities of the domain of information security. This, in return, limits their applicability on the field. In this work, we analyze the state-of-the-art log parsing and abstraction algorithms from the perspective of information security. First, we reproduce/replicate previous analysis of such algorithms from the literature. Then, we evaluate their ability for parsing and abstraction of log files for forensic analysis purposes. Our study demonstrates that while the state-of-the-art techniques are accurate in log parsing, improvements are necessary in terms of achieving a holistic view to aid in forensic analysis for the identification and understanding of security incidents.","Title: On the impact of tangled program graph marking schemes under the atari reinforcement learning benchmark\nYear: 2021\n\nAbstract: ABSTRACTTangled program graphs (TPG) support emergent modularity by first identifying subsets of programs that can usefully coexist (a team/ graph node) and then identifying the circumstance under which to reference other teams (arc adaptation). Variation operators manipulate the content of teams and arcs. This introduces cycles into the TPG structures. Previously, this effect was eradicated at run time by marking nodes while evaluating TPG individuals. In this work, a new marking heuristic is introduced, that of arc (learner) marking. This means that nodes can be revisited, but not the same arcs. We investigate the impact of this through 18 titles from the Arcade Learning Environment. The performance and complexity of the policies appear to be similar, but with specific tasks (game titles) resulting in preferences for one scheme over another.","Title: Training regime influences to semi-supervised learning for insider threat detection\nYear: 2021\n\nAbstract: A malicious insider is one of the most damaging threats to any organization from industry to government agencies. Many challenges from insider threat detection come from the fact that the ground truth is very limited and costly to acquire. This paper presents a semi-supervised learning approach to insider threat detection. We employ three machine learning methods under different real-world conditions. These include obtaining the initial ground truth training data randomly or via a certain type of insider malicious behavior or by anomaly detection system scores. Evaluation results show that the approach allows learning from very limited data for insider threat detection at high precision. 90% of malicious data instances are detected under 1% false positive rate.","Title: Emergent Tangled Program Graphs in Partially Observable Recursive Forecasting and ViZDoom Navigation Tasks\nYear: 2021\n\nAbstract: AbstractModularity represents a recurring theme in the attempt to scale evolution to the design of complex systems. However, modularity rarely forms the central theme of an artificial approach to evolution. In this work, we report on progress with the recently proposed Tangled Program Graph (TPG) framework in which programs are modules. The combination of the TPG representation and its variation operators enable both teams of programs and graphs of teams of programs to appear in an emergent process. The original development of TPG was limited to tasks with, for the most part, complete information. This work details two recent approaches for scaling TPG to tasks that are dominated by partially observable sources of information using different formulations of indexed memory. One formulation emphasizes the incremental construction of memory, again as an emergent process, resulting in a distributed view of state. The second formulation assumes a single global instance of memory and develops it as a communication medium, thus a single global view of state. The resulting empirical evaluation demonstrates that TPG equipped with memory is able to solve multi-task recursive time-series forecasting problems and visual navigation tasks expressed in two levels of a commercial first-person shooter environment."],"string":"[\n \"Title: Comparing dimension reduction techniques for document clustering\\nYear: 2005\\n\\nAbstract: In this research, a systematic study is conducted of four dimension reduction techniques for the text clustering problem, using five benchmark data sets Of the four methods – Independent Component Analysis (ICA), Latent Semantic Indexing (LSI), Document Frequency (DF) and Random Projection (RP) – ICA and LSI are clearly superior when the k-means clustering algorithm is applied, irrespective of the data sets Random projection consistently returns the worst results, where this appears to be due to the noise distribution characterizing the document clustering task.\",\n \"Title: An analysis of clustering objectives for feature selection applied to encrypted traffic identification\\nYear: 2010\\n\\nAbstract: This work explores the use of clustering objectives in a Multi-Objective Genetic Algorithm (MOGA) for both, feature selection and cluster count optimization, under the application of flow based encrypted traffic identification. We first explore whether it is possible to achieve the performance of a gold standard model (i.e., classification objectives), using a MOGA based on clustering objectives. Then, we explore the performance gain (if it exists) of applying a logarithmic transformation to the data prior to running the MOGA. Results show that MOGA trained with clustering objectives can closely reproduce the behavior of a gold standard model, not only in terms of the selected features, but also in terms of the achieved detection rate and false positives rate, above 90% and less than 1% respectively. On the other hand, no gain was observed by applying logarithmic transformation to the data.\",\n \"Title: Benchmarking coevolutionary teaming under classification problems with large attribute spaces\\nYear: 2009\\n\\nAbstract: Benchmarking of a team based model of Genetic Programming demonstrates that the naturally embedded style of feature selection is usefully extended by the teaming metaphor to provide solutions in terms of exceptionally low attribute counts. To take this concept to its logical conclusion the teaming model must be able to build teams with a non-overlapping behavioral trait, from a single population. The Symbiotic Bid-Based (SBB) algorithm is demonstrated to fit this purpose under an evaluation utilizing data sets with 650 to 5,000 attributes. The resulting solutions are one to two orders simpler than solutions identified under the alternative embedded paradigms of C4.5 and MaxEnt.\",\n \"Title: One-Class Genetic Programming\\nYear: 2009\\n\\nAbstract: One-class classification naturally only provides one-class of exemplars, the target class, from which to construct the classification\\n model. The one-class approach is constructed from artificial data combined with the known in-class exemplars. A multi-objective\\n fitness function in combination with a local membership function is then used to encourage a co-operative coevolutionary decomposition\\n of the original problem under a novelty detection model of classification. Learners are therefore associated with different\\n subsets of the target class data and encouraged to tradeoff detection versus false positive performance; where this is equivalent\\n to assessing the misclassification of artificial exemplars versus detection of subsets of the target class. Finally, the architecture\\n makes extensive use of active learning to reinforce the scalability of the overall approach.\",\n \"Title: Network protocol discovery and analysis via live interaction\\nYear: 2012\\n\\nAbstract: In this work, we explore the use of evolutionary computing toward protocol analysis. The ability to discover, analyse, and experiment with unknown protocols is paramount within the realm of network security; our approach to this crucial analysis is to interact with a network service, discovering sequences of commands that do not result in error messages. In so doing, our work investigates the real-life responses of a service, allowing for exploration and analysis of the protocol in question. Our system initiates sequences of commands randomly, interacts with and learns from the responses, and modifies its next set of sequences accordingly. Such an exploration results in a set of command sequences that reflect correct uses of the service in testing. These discovered sequences can then be used to identify the service, unforeseen uses of the service, and, most importantly, potential weaknesses.\",\n \"Title: Symbiotic Evolutionary Subspace Clustering\\nYear: 2012\\n\\nAbstract: New emerging high-dimensional data sets have made traditional clustering algorithms increasingly inefficient. More sophisticated approaches are required to cope with the increasing dimensionality and cardinality of such data sets. Feature selection methods are proposed as a solution to deal with this problem, however they fail for data sets where the attribute support for different clusters is not the same. For this category of data sets subspace clustering algorithms have been introduced over the past decade. We approach this problem from the perspective of Genetic Algorithms by adopting a hierarchical data structure deployed in three stages. 1) a traditional clustering algorithm is applied independently to each attribute of the data set, thus defining a grid of potential 1-d cluster centroids. 2) representing multi-dimensional cluster centroids by indexing 1-d cluster centroids. 3) converting the problem of finding the best combination of cluster centroids into that of discrete optimization and applying a multi-objective evolutionary algorithm, which uses group fitness evaluation to give a fitness to a group of clusters, as defined by process 2. Synthetic data sets with different characteristics are generated as the ground truth to evaluate the resulting algorithm for Evolutionary Subspace Clustering (ESC) as well as benchmark against alternative subspace and full-space clustering algorithms. ESC returns competitive accuracy and while typically utilizing less attributes and scaling as attribute count increases.\",\n \"Title: Using code bloat to obfuscate evolved network traffic\\nYear: 2010\\n\\nAbstract: In this work, we investigate the ability of genetic programming techniques to evolve valid network patterns, while avoiding detectability by obfuscating the intent of the traffic. In order to validate our system's capabilities, we choose to evolve a port scan attack while running the packets through an Intrusion Detection System (IDS). In turn, the evolutionary process uses feedback such that it minimizes the alarms raised while port scanning across a network range. Results build off of previous work allow us to further analyze and understand what the role of introns, code bloat, play in the systems ability to reduce the detectability of it malicious behaviour.\",\n \"Title: Use of a genetic algorithm in brill's transformation-based part-of-speech tagger\\nYear: 2005\\n\\nAbstract: The tagging problem in natural language processing is to find a way to label every word in a text as a particular part of speech, e.g., proper noun. An effective way of solving this problem with high accuracy is the transformation-based or \\\"Brill\\\" tagger. In Brill's system, a number of transformation templates are specified a priori that are instantiated and ranked during a greedy search-based algorithm. This paper describes a variant of Brill's implementation that instead uses a genetic algorithm to generate the instantiated rules and provide an adaptive ranking. Based on tagging accuracy, the new system provides a better hybrid evolutionary computation solution to the part-of-speech (POS) problem than the previous attempt. Although not able to make up for the use of a priori knowledge utilized by Brill, the method appears to point the way for an improved solution to the tagging problem.\",\n \"Title: On the impact of streaming interface heuristics on GP trading agents: an FX benchmarking study\\nYear: 2013\\n\\nAbstract: Most research into frameworks for evolving trading agents emphasize aspects associated with the evolution of technical indicators and decision trees / rules. One of the factors that drives the development of such frameworks is the non-stationary, streaming nature of the task. However, it is the heuristics used to interface the evolutionary framework to the streaming data which potentially have most impact on the quality of the resulting trading agents. We demonstrate that including a validation partition has a significant impact on determining the overall success of the trading agents. Moreover, rather than conduct evolution on a continuous basis, only retraining when changes in trading quality are detected also yields significant advantages. Neither of these heuristics are widely recognized by research in evolving trading agent frameworks, although both are relatively easy to add to current frameworks. Benchmarking over a 3 year period of the EURUSD foreign exchange supports these findings.\",\n \"Title: Coevolutionary bid-based genetic programming for problem decomposition in classification\\nYear: 2008\\n\\nAbstract: In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains such as classification. This represents a significant departure from models where each individual constructs a direct input-outcome map, for example, from the set of exemplars to the set of class labels as is typical under the classification domain. In contrast, the proposed model focuses on learning a bidding strategy based on the exemplar feature vectors; each individual is associated with a single discrete action and the individual with the maximum bid `wins' the right to suggest its action. Thus, the number of individuals associated with each action is a function of the intra-action bidding behaviour. Credit assignment is designed to reward correct but unique bidding strategies relative to the target actions. An advantage of the model over other teaming methods is its ability to automatically determine the number of and interaction between cooperative team members. The resulting model shares several traits with learning classifier systems and as such both approaches are benchmarked on nine large classification problems. Moreover, both of the evolutionary models are compared against the deterministic Support Vector Machine classification algorithm. Performance assessment considers the computational, classification, and complexity characteristics of the resulting solutions. The bid-based model is found to provide simple yet effective solutions that are robust to wide variations in the class representation. Support Vector Machines and classifier systems tend to perform better under balanced datasets albeit resulting in black-box solutions.\",\n \"Title: Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces\\nYear: 2012\\n\\nAbstract: Classification under large attribute spaces represents a dual learning problem in which attribute subspaces need to be identified at the same time as the classifier design is established. Embedded as opposed to filter or wrapper methodologies address both tasks simultaneously. The motivation for this work stems from the observation that team based approaches to Genetic Programming (GP) have the potential to design multiple classifiers per class--each with a potentially unique attribute subspace--without recourse to filter or wrapper style preprocessing steps. Specifically, competitive coevolution provides the basis for scaling the algorithm to data sets with large instance counts; whereas cooperative coevolution provides a framework for problem decomposition under a bid-based model for establishing program context. Symbiosis is used to separate the tasks of team/ensemble composition from the design of specific team members. Team composition is specified in terms of a combinatorial search performed by a Genetic Algorithm (GA); whereas the properties of individual team members and therefore subspace identification is established under an independent GP population. Teaming implies that the members of the resulting ensemble of classifiers should have explicitly non-overlapping behaviour. Performance evaluation is conducted over data sets taken from the UCI repository with 649---102,660 attributes and 2---10 classes. The resulting teams identify attribute spaces 1---4 orders of magnitude smaller than under the original data set. Moreover, team members generally consist of less than 10 instructions; thus, small attribute subspaces are not being traded for opaque models.\",\n \"Title: Evolving recurrent models using linear GP\\nYear: 2005\\n\\nAbstract: Turing complete Genetic Programming (GP) models introduce the concept of internal state, and therefore have the capacity for identifying interesting temporal properties. Surprisingly, there is little evidence of the application of such models to problems for prediction. An empirical evaluation is made of a simple recurrent linear GP model over standard prediction problems.\",\n \"Title: Growing recurrent self organizing map\\nYear: 2007\\n\\nAbstract: Abstract—The growing Recurrent Self-Organizing Map (GRSOM) is embedded,into a standard,Self-Organizing Map (SOM) hierarchy. To do so, the KDD benchmark dataset from the International Knowledge,Discovery and Data Mining Tools Competition is employed. This dataset consists of 500,000 training patterns and 41 features,for each,pattern. Unlike most of the previous methods, only 6 of the basic features are employed.,The resulting model,has,a capability of detection (false positive) rate of 89.6% (5.66%), where this is as good as the data-mining approaches,that uses all 41 features and,twice as faster than a similar hierarchical SOM architecture.\",\n \"Title: MOGE: GP classification problem decomposition using multi-objective optimization\\nYear: 2006\\n\\nAbstract: A novel approach to classification is proposed in which a Pareto-based ranking of individuals is used to encourage multiple individuals to participate in the solution. To do so, the classification problem is re-expressed as a cluster consistency problem, thus allowing utilization of techniques from multi-objective optimization. Such a formulation enables classification problems to be automatically decomposed and solved by several specialist classifiers rather than by a single 'super' individual. In this paper, we demonstrate the proposed approach to two benchmark binary problems and recommend a natural extension to multi-class problems. Results indicate the general appropriateness of the approach.\",\n \"Title: Negotiating Agents in Manufacturing Decision Making Processes\\nYear: 2001\\n\\nAbstract: Real-time decision making for on-the-spot orders is considered using a novel distributed agent architecture. The order agents will have the ability to perform multi-stage negotiation using continuous double auction market-based model representing resources as producers and orders as customers. Qualitative information of resources and order requirements is captured in a Quality Function Definition (QFD) decision table. The QFD decision table also provides the interface for the agent to evaluate objectives, attributes and user preferences relating to resources and order requirements. This directs the negotiation against the most applicable resources at each stage through the market. In particular, the agents can learn from experience and adapt to the market change. The operation of such a system is illustrated within a garment industry application context.\",\n \"Title: Symbiosis, complexification and simplicity under GP\\nYear: 2010\\n\\nAbstract: Models of Genetic Programming (GP) frequently reflect a neo-Darwinian view to evolution in which inheritance is based on a process of gradual refinement and the resulting solutions take the form of single monolithic programs. Conversely, introducing an explicitly symbiotic model of inheritance makes a divide-and-conquer metaphor for problem decomposition central to evolution. Benchmarking gradualist versus symbiotic models of evolution under a common evolutionary framework illustrates that not only does symbiosis result in more accurate solutions, but the solutions are also much simpler in terms of instruction and attribute count over a wide range of classification problem domains.\",\n \"Title: Evolving TCP/IP packets: A case study of port scans.\\nYear: 2009\\n\\nAbstract: In this work, we investigate the ability of genetic programming techniques to evolve valid network packets, including all relevant header values, towards a specific goal. We see this as a first step in building a fuzzing system that can learn to adapt for vulnerability analysis. By developing a system that learns the packets that are required to be transmitted towards targets, using feedback from an external network source, we make a step towards having a system that can intelligently explore the capabilities of a given security system. In order to validate our system's capabilities we evolve a variety of port scan patterns while running the packets through an IDS, with the goal to minimizes the alarms raised during the scanning process. Results show that the system not only successfully evolves valid TCP packets, but also remains stealthy in its activity.\",\n \"Title: Towards Efficient Training on Large Datasets for Genetic Programming\\nYear: 2004\\n\\nAbstract: Genetic programming (GP) has the potential to provide unique solutions to a wide range of supervised learning problems. The technique, however, does suffer from a widely acknowledged computational overhead. As a consequence applications of GP are often confined to datasets consisting of hundreds of training exemplars as opposed to tens of thousands of exemplars, thus limiting the widespread applicability of the approach. In this work we propose and thoroughly investigate a data sub-sampling algorithm - hierarchical dynamic subset selection - that filters the initial training dataset in parallel with the learning process. The motivation being to focus the GP training on the most difficult or least recently visited exemplars. To do so, we build on the dynamic sub-set selection algorithm of Gathercole and extend it into a hierarchy of subset selections, thus matching the concept of a memory hierarchy supported in modem computers. Such an approach provides for the training of GP solutions to data sets with hundreds of thousands of exemplars in tens of minutes whilst matching the classification accuracies of more classical approaches.\",\n \"Title: Hierarchical task decomposition through symbiosis in reinforcement learning\\nYear: 2012\\n\\nAbstract: Adopting a symbiotic model of evolution separates context for deploying an action from the action itself. Such a separation provides a mechanism for task decomposition in temporal sequence learning. Moreover, previously learned policies are taken to be synonymous with meta actions (actions that are themselves policies). Should solutions to the task not be forthcoming in an initial round of evolution, then solutions from the earlier round represent the 'meta' actions for a new round of evolution. This provides the basis for evolving policy trees. A benchmarking study is performed using the Acrobot handstand task. Solutions to date from reinforcement learning have not been able to approach the performance of those established 14 years ago using an A* search and a priori knowledge regarding the Acrobot energy equations. The proposed symbiotic approach is able to match and, for the first time, better these results. Moreover, unlike previous work, solutions are tested under a broad range of Acrobot initial conditions, with hierarchical solutions providing significantly better generalization performance.\",\n \"Title: Malicious automatically generated domain name detection using Stateful-SBB\\nYear: 2013\\n\\nAbstract: This work investigates the detection of Botnet Command and Control (C&C) activity by monitoring Domain Name System (DNS) traffic. Detection signatures are automatically generated using evolutionary computation technique based on Stateful-SBB. The evaluation performed shows that the proposed system can work on raw variable length domain name strings with very high accuracy.\",\n \"Title: Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings\\nYear: 2007\\n\\nAbstract: Developmental Genetic Programming (DGP) algorithms have explicitly required the search space for a problem to be divided into genotypes and corresponding phenotypes. The two search spaces are often connected with a genotype-phenotype mapping (GPM) intended to model the biological genetic code, where current implementations of this concept involve evolution of the mappings along with evolution of the genotype solutions. This work presents the Probabilistic Adaptive Mapping DGP (PAM DGP), a new developmental implementation that involves research contributions in the areas of GPMs and coevolution. The algorithm component of PAM DGP is demonstrated to overcome coevolutionary performance problems that are identified and empirically benchmarked against the latest competing algorithm that adapts similar GPMs. An adaptive redundant mapping encoding is then incorporated into PAM DGP for further performance enhancement. PAM DGP with two mapping types are compared to the competing Adaptive Mapping algorithm and Traditional GP in two medical classification domains, where PAM DGP with redundant encodings is found to provide superior fitness performance over the other algorithms through it's ability to explicitly decrease the size of the function set during evolution.\",\n \"Title: GP classifier problem decomposition using first-price and second-price auctions\\nYear: 2007\\n\\nAbstract: This work details an auction-based model for problem decomposition in Genetic Programming classification. The approach builds on the population-based methodology of Genetic Programming to evolve individuals that bid high for patterns that they can correctly classify. The model returns a set of individuals that decompose the problem by way of this bidding process and is directly applicable to multi-class domains. An investigation of two auction types emphasizes the effect of auction design on the properties of the resulting solution. The work demonstrates that auctions are an effective mechanism for problem decomposition in classification problems and that Genetic Programming is an effective means of evolving the underlying bidding behaviour.\",\n \"Title: Speeding up the Self-Organizing Feature Map Using Dynamic Subset Selection\\nYear: 2005\\n\\nAbstract: An active learning algorithm is devised for training Self-Organizing Feature Maps on large data sets. Active learning algorithms recognize that not all exemplars are created equal. Thus, the concepts of exemplar age and difficulty are used to filter the original data set such that training epochs are only conducted over a small subset of the original data set. The ensuing Hierarchical Dynamic Subset Selection algorithm introduces definitions for exemplar difficulty suitable to an unsupervised learning context and therefore appropriate Self-organizing map (SOM) stopping criteria. The algorithm is benchmarked on several real world data sets with training set exemplar counts in the region of 30--500 thousand. Cluster accuracy is demonstrated to be at least as good as that from the original SOM algorithm while requiring a fraction of the computational overhead.\",\n \"Title: Probabilistic adaptive mapping developmental genetic programming (PAM DGP): a new developmental approach\\nYear: 2006\\n\\nAbstract: Developmental Genetic Programming (DGP) algorithms have been introduced where the search space for a problem is divided into genotypes and corresponding phenotypes that are connected by a mapping (or “genetic code”). Current implementations of this concept involve evolution of the mappings in addition to the traditional evolution of genotypes. We introduce the latest version of Probabilistic Adaptive Mapping DGP (PAM DGP), a robust and highly customizable algorithm that overcomes performance problems identified for the latest competing adaptive mapping algorithm. PAM DGP is then shown to outperform the competing algorithm on two non-trivial regression benchmarks.\",\n \"Title: Evolutionary clustering with arbitrary subspaces\\nYear: 2009\\n\\nAbstract: Subspace clustering algorithms in their most general form attempt to describe data with clusters that are not constrained to index a common set of attributes. Previous evolutionary approaches to this problem have assumed a weaker model in which clusters are built in a common subset. Moreover, a filter method is generally assumed in which a classical clustering algorithm is employed in the inner loop. Needless to say, this presents a considerable computational overhead. In this work we recognize the utility of assuming a `bottom-up' approach to subspace clustering. Specifically, we apply a classical clustering algorithm to each attribute to establish 1-d clusters that are then indexed by a MOGA to design a population of subspace clusters. The ensuing search is entirely in terms of a combinatorial optimization problem, thus computationally very efficient. A final single objective GA is then applied to search the set of subspace clusters identified under the MOGA for the most suitable combination.\",\n \"Title: Training binary GP classifiers efficiently: a Pareto-coevolutionary approach\\nYear: 2007\\n\\nAbstract: The conversion and extension of the Incremental Pareto-Coevolution Archive algorithm (IPCA) into the domain of Genetic Programming classification is presented. In particular, the coevolutionary aspect of the IPCA algorithm is utilized to simultaneously evolve a subset of the training data that provides distinctions between candidate classifiers. Empirical results indicate that such a scheme significantly reduces the computational overhead of fitness evaluation on large binary classification data sets. Moreover, unlike the performance of GP classifiers trained using alternative subset selection algorithms, the proposed Pareto-coevolutionary approach is able to match or better the classification performance of GP trained over all training exemplars. Finally, problem decomposition appears as a natural consequence of assuming a Pareto model for coevolution. In order to make use of this property a voting scheme is used to integrate the results of all classifiers from the Pareto front, post training.\",\n \"Title: On Run Time Libraries And Hierarchical Symbiosis\\nYear: 2012\\n\\nAbstract: Run time libraries (RTL) in genetic programming (GP) represent a scenario in which individuals evolved under an earlier independent evolutionary run can be potentially incorporated into a following GP run. To date, schemes for exploiting the RTL metaphor have emphasized syntactic over behavioural approaches. Thus, instructions are added to the later run such that the previous code can be explicitly indexed. In this work we demonstrate how the RTL concept is naturally supported by adopting a symbiotic framework for coevolution. Under the Pinball reinforcement learning task, we demonstrate how the initial RTL can be coevolved within a simpler formulation of the task and then used as the basis for providing solutions to a more difficult target task under the same domain. The resulting solutions are stronger than an RTL as coevolved against the target task alone or symbiosis as evolved without support for RTL.\",\n \"Title: Toward Co-Evolutionary Training Of A Multi-Class Classifier\\nYear: 2005\\n\\nAbstract: In this work the multi-class classification capabilities of Genetic Programming (GP) are explored in the context of a competitive co-evolutionary system, in which a population of GP classifiers is trained against an evolving population of trainers (exemplar selectors) with the goal of reducing GP training time for large multi-class classification problems. Moreover, the niche-enabling mechanisms established in the Genetic Algorithm (GA) literature, known as crowding and sharing, are implemented for the classifier population in order to provide multi-class solutions from a single population in the same trial. The results as presented in the paper indicate the appropriateness of the competitive co-evolutionary training approach under GP multi-class classification.\",\n \"Title: Label free change detection on streaming data with cooperative multi-objective genetic programming\\nYear: 2013\\n\\nAbstract: Classification under streaming data conditions requires that the machine learning (ML) approach operate interactively with the stream content. Thus, given some initial ML classification capability, it is not possible to assume that stream content will be stationary. It is therefore necessary to first detect when the stream content changes. Only after detecting a change, can classifier retraining be triggered. Current methods for change detection tend to assume an entropy filter approach, where class labels are necessary. In practice, labeling the stream would be extremely expensive. This work proposes an approach in which the behaviour of GP individuals is used to detect change without the use of labels. Only after detecting a change is label information requested. Benchmarking under a computer network traffic analysis scenario demonstrates that the proposed approach performs at least as well as the filter method, while retaining the advantage of requiring no labels.\",\n \"Title: A hierarchical SOM-based intrusion detection system\\nYear: 2007\\n\\nAbstract: Purely based on a hierarchy of self-organizing feature maps (SOMs), an approach to network intrusion detection is investigated. Our principle interest is to establish just how far such an approach can be taken in practice. To do so, the KDD benchmark data set from the International Knowledge Discovery and Data Mining Tools Competition is employed. Extensive analysis is conducted in order to assess the significance of the features employed, the partitioning of training data and the complexity of the architecture. Contributions that follow from such a holistic evaluation of the SOM include recognizing that (1) best performance is achieved using a two-layer SOM hierarchy, based on all 41-features from the KDD data set. (2) Only 40% of the original training data is sufficient for training purposes. (3) The 'Protocol' feature provides the basis for a switching parameter, thus supporting modular solutions to the detection problem. The ensuing detector provides false positive and detection rates of 1.38% and 90.4% under test conditions; where this represents the best performance to date of a detector based on an unsupervised learning algorithm.\",\n \"Title: Testing Detector Parameterization Using Evolutionary Exploit Generation\\nYear: 2009\\n\\nAbstract: The testing of anomaly detectors is considered from the perspective of a Multi-objective Evolutionary Exploit Generator (EEG). Such a framework provides users of anomaly detection systems two capabilities. Firstly, no knowledge of protected data structures need be assumed. Secondly, the evolved exploits are then able to demonstrate weaknesses in the ensuing detector parameterization. In this work we focus on the parameterization of the second generation anomaly detector `pH' and demonstrate how use of an EEG may identify weak parameterization of the detector.\",\n \"Title: On evolutionary subspace clustering with symbiosis.\\nYear: 2014\\n\\nAbstract: Subspace clustering identifies the attribute support for each cluster as well as identifying the location and number of clusters. In the most general case, attributes associated with each cluster could be unique. A multi-objective evolutionary method is proposed to identify the unique attribute support of each cluster while detecting its data instances. The proposed algorithm, symbiotic evolutionary subspace clustering (S-ESC) borrows from ‘symbiosis’ in the sense that each clustering solution is defined in terms of a host (single member of the host population) and a number of coevolved cluster centroids (or symbionts in an independent symbiont population). Symbionts define clusters and therefore attribute subspaces, whereas hosts define sets of clusters to constitute a non-degenerate solution. The symbiotic representation of S-ESC is the key to making it scalable to high-dimensional datasets, while an integrated subsampling process makes it scalable to tasks with a large number of data items. Benchmarking is performed against a test suite of 59 subspace clustering tasks with four well known comparator algorithms from both the full-dimensional and subspace clustering literature: EM, MINECLUS, PROCLUS, STATPC. Performance of the S-ESC algorithm was found to be robust across a wide cross-section of properties with a common parameterization utilized throughout. This was not the case for the comparator algorithms. Specifically, performance could be sensitive to the particular data distribution or parameter sweeps might be necessary to provide comparable performance. An additional evaluation is performed against a non-symbiotic GA, with S-ESC still returning superior clustering solutions.\",\n \"Title: Evolutionary computation as an artificial attacker: generating evasion attacks for detector vulnerability testing.\\nYear: 2011\\n\\nAbstract: Abstract Intrusion detection systems protect our infrastructures by monitoring for signs of intrusions. However, intrusion detection systems are themselves susceptible to vulnerabilities, which the attackers take advantage of to evade detection. In particular, we focus on evasion attacks in which the attacker aims to generate a stealthy attack that eliminates or minimizes the likelihood of detection. Attackers achieve stealth by mimicking normal behaviour while achieving the attack goals, hence bypassing the detector. Previous work focused on generating evasion attacks using the internal knowledge of the detectors, hence adopting a ‘white-box’ access to the detector. On the other hand, we adopt a ‘black-box’ approach and propose an evolutionary attacker based on Genetic Programming. The access of our ‘black-box’ approach is limited to the feedback of the detector such as anomaly rates and delays. We compare our ‘black-box’ approach with various ‘white-box’ approaches to investigate its effectiveness. In doing so, the impact of anomalies from the break-in stage of the attacks and the delays based on locality frame counts are also discussed. This is particularly important if the performance comparison is to reflect the real capabilities of detectors.\",\n \"Title: Improving GP classifier generalization using a cluster separation metric\\nYear: 2006\\n\\nAbstract: Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited in previous work. Here, we revisit the design of fitness functions for genetic programming by explicitly considering the contribution of the wrapper and cost function. Within the context of supervised learning, as applied to classification problems, a clustering methodology is introduced using cost functions which encourage maximization of separation between in and out of class exemplars. Through a series of empirical investigations of the nature of these functions, we demonstrate that classifier performance is much more dependable than previously the case under the genetic programming paradigm.\",\n \"Title: Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification\\nYear: 2007\\n\\nAbstract: A bid-based approach for coevolving Genetic Programming classifiers is presented. The approach coevolves a population of learners thatdecompose the instance space by way of their aggregate bidding behaviour. To reduce computation overhead, a small, relevant, subsetof training exemplars is (competitively) coevolved alongside the learners. The approach solves multi-class problems using a single population and is evaluated on three large datasets. It is found tobe competitive, especially compared to classifier systems, whilesignificantly reducing the computation overhead associated withtraining.\",\n \"Title: Flat vs. symbiotic evolutionary subspace clusterings\\nYear: 2013\\n\\nAbstract: Subspace clustering coevolves the attribute space supporting clusters at the same time as parameterizing the cluster location and combination. Typically, a 'flat' representation is pursued in which individuals describe both the property of individual clusters as well as the combination of clusters used to define the overall solution; hereafter F-ESC. Conversely, a symbiotic approach was recently proposed in which candidate clusters and the combination of clusters are coevolved from independent populations; hereafter S-ESC. In this work a common framework is pursued in order for flat and symbiotic evolutionary subspace clustering to be compared directly. We show that F-ESC might match S-ESC results for data sets with high proportions of cluster support, however, the gap between the two algorithm increases as cluster support decreases.\",\n \"Title: On the utility of trading criteria based retraining in forex markets\\nYear: 2013\\n\\nAbstract: This research investigates the ability of genetic programming (GP) to build profitable trading strategies for the Foreign Exchange Market (FX) of three major currency pairs (EURUSD, USDCHF and EURCHF) using one hour prices from 2008 to 2011. We recognize that such environments are likely to be non-stationary. Thus, we do not require a single training partition to capture all likely future behaviours. We address this by detecting poor trading behaviours and use this to trigger retraining. In addition the task of evolving good technical indicators (TI) and the rules for deploying trading actions is explicitly separated. Thus, separate GP populations are used to coevolve TI and trading behaviours under a mutualistic symbiotic association. The results of 100 simulations demonstrate that an adaptive retraining algorithm significantly outperforms a single-strategy approach (population evolved once) and generates profitable solutions with a high probability.\",\n \"Title: Optimizing anomaly detector deployment under evolutionary black-box vulnerability testing\\nYear: 2009\\n\\nAbstract: This work focuses on testing anomaly detectors from the perspective of a Multi-objective Evolutionary Exploit Generator (EEG). Such a framework provides users of anomaly detection systems two capabilities. Firstly, no knowledge of protected data structures need to be assumed (i.e. the detector is a black-box), where the time, knowledge and availability of tools to perform such an analysis might not be generally available. Secondly, the evolved exploits are then able to demonstrate weaknesses in the ensuing detector parameterization. Therefore, the system administrator can identify the suitable parameters for the effective operation of the detector. EEG is employed against two second generation anomaly detectors, namely pH and pH with schema mask, on four UNIX applications in order to perform a vulnerability assessment and make a comparison between the two detectors.\",\n \"Title: Classification as clustering: A pareto cooperative-competitive gp approach\\nYear: 2011\\n\\nAbstract: Intuitively population based algorithms such as genetic programming provide a natural environment for supporting solutions that learn to decompose the overall task between multiple individuals, or a team. This work presents a framework for evolving teams without recourse to prespecifying the number of cooperating individuals. To do so, each individual evolves a mapping to a distribution of outcomes that, following clustering, establishes the parameterization of a (Gaussian) local membership function. This gives individuals the opportunity to represent subsets of tasks, where the overall task is that of classification under the supervised learning domain. Thus, rather than each team member representing an entire class, individuals are free to identify unique subsets of the overall classification task. The framework is supported by techniques from evolutionary multiobjective optimization (EMO) and Pareto competitive coevolution. EMO establishes the basis for encouraging individuals to provide accurate yet nonoverlaping behaviors; whereas competitive coevolution provides the mechanism for scaling to potentially large unbalanced datasets. Benchmarking is performed against recent examples of nonlinear SVM classifiers over 12 UCI datasets with between 150 and 200,000 training instances. Solutions from the proposed coevolutionary multiobjective GP framework appear to provide a good balance between classification performance and model complexity, especially as the dataset instance count increases.\",\n \"Title: Real-time digital–optical correlator-systems design\\nYear: 1999\\n\\nAbstract: The systems design of a real-time hybrid digital–optical correlator is detailed. Emphasis is on: partitioning of the digital and optical systems such that the efficiency of the optical image train is maximised; and the parallelisation of tasks within the digital sub-system to maximise throughput. The significance of communication bottlenecks are emphasised in the digital system and the constraints this enforces on the initial selection of DSP devices.\",\n \"Title: Register Based Genetic Programming on FPGA Computing Platforms\\nYear: 2000\\n\\nAbstract: The use of FPGA based custom computing platforms is proposed for implementing linearly structured Genetic Programs. Such a context enables consideration of micro architectural and instruction design issues not normally possible when using classical Von Neumann machines. More importantly, the desirability of minimising memory management overheads results in the imposition of additional constraints to the crossover operator. Specifically, individuals are described in terms of the number of pages and page length, where the page length is common across individuals of the population. Pairwise crossover therefore results in the swapping of equal length pages, hence minimising memory overheads. Simulation of the approach demonstrates that the method warrants further study.\",\n \"Title: Symbiogenesis as a mechanism for building complex adaptive systems: a review\\nYear: 2010\\n\\nAbstract: In 1996 Daida et al. reviewed the case for using symbiosis as the basis for evolving complex adaptive systems [6]. Specific observations included the impact of different philosophical views taken by biologists as to what constituted a symbiotic relationship and whether symbiosis represented an operator or a state. The case was made for symbiosis as an operator. Thus, although specific cost benefit characterizations may vary, the underlying process of symbiosis is the same, supporting the operator based perspective. Symbiosis provides an additional mechanism for adaption/ complexification than available under Mendelian genetics with which Evolutionary Computation (EC) is most widely associated. In the following we review the case for symbiosis in EC. In particular, symbiosis appears to represent a much more effective mechanism for automatic hierarchical model building and therefore scaling EC methods to more difficult problem domains than through Mendelian genetics alone.\",\n \"Title: An Investigation of Multi-objective Genetic Algorithms for Encrypted Traffic Identification.\\nYear: 2009\\n\\nAbstract: The increasing use of encrypted traffic combined with non-standard port associations makes the task of traffic identification increasingly difficult. This work adopts a multi-objective clustering approach to the problem in which a Genetic Algorithm performs both feature selection and cluster count optimization under a flow based representation. Solutions do not use port numbers, IP address or payload. Performance of the resulting model provides 90% detection 0.8% false positive rates with 13 clusters supported by 14 of the original 38 features.\",\n \"Title: Managing team-based problem solving with symbiotic bid-based genetic programming\\nYear: 2008\\n\\nAbstract: Bid-based Genetic Programming (GP) provides an elegant mechanism for facilitating cooperative problem decomposition without an a priori specification of the number of team members. This is in contrast to existing teaming approaches where individuals learn a direct input-output map (e.g., from exemplars to class labels), allowing the approach to scale to problems with multiple outcomes (classes), while at the same time providing a mechanism for choosing an outcome from those suggested by team members. This paper proposes a symbiotic relationship that continues to support the cooperative bid-based process for problem decomposition while making the credit assignment process much clearer. Specifically, team membership is defined by a team population indexing combinations of GP individuals in a separate team member population. A Pareto-based competitive coevolutionary component enables the approach to scale to large problems by evolving informative test points in a third population. The ensuing Symbiotic Bid-Based (SBB) model is evaluated on three large classification problems and compared to the XCS learning classifier system (LCS) formulation and to the support vector machine (SVM) implementation LIBSVM. On two of the three problems investigated the overall accuracy of the SBB classifiers was found to be competitive with the XCS and SVM results. At the same time, on all problems, the SBB classifiers were able to detect instances of all classes whereas the XCS and SVM models often ignored exemplars of minor classes. Moreover, this was achieved with a level of model complexity significantly lower than that identified by the SVM and XCS solutions.\",\n \"Title: Intelligent Packets For Dynamic Network Routing Using Distributed Genetic Algorithm\\nYear: 2002\\n\\nAbstract: A distributed GA is designed for the packet switched network routing problem under minimal information. The requirements of such a problem mean that agents are required to possess more intelligence than was previously the case. To this end a distributed GA approach is developed and benchmarked against the AntNet algorithm under the same information constraints.\",\n \"Title: Cooperative problem decomposition in Pareto competitive classifier models of coevolution\\nYear: 2008\\n\\nAbstract: Pareto competitive models of coevolution have the potential to provide a number of distinct advantages over the canonical approach to training under the Genetic Programming (GP) classifier domain. Recent work has specifically focused on the reformulation of training as a two-population competition, that is learners versus training exemplars. Such a scheme affords, for example, the capacity to decouple the fitness evaluation overhead from the data set size through sub sampling while naturally encouraging 'teams' or composite solutions as opposed to solutions based on a single individual alone. One outstanding question with respect to the latter characteristic is with regards to the nature of the team (archive) behavior in terms of pattern coverage. That is to say, which models are used when, and what are the implications for solution modularity as it relates, for example, to the assignment of exemplars to solution participants. The current work investigates two Pareto competitive approaches to classification under GP, with one configured to employ an explicitly cooperative multi-objective cost function based and the other employing the classical (error-based) cost function. We empirically demonstrate a critical distinction between the two with regards to problem decomposition, with the capacity to provide a decomposition into unique behaviors being much more prevalent when co-operative mechanisms are explicitly supported.\",\n \"Title: GP under streaming data constraints: a case for pareto archiving?\\nYear: 2012\\n\\nAbstract: Classification as applied to streaming data implies that only a small number of new training instances appear at each generation and are never explicitly reintroduced by the stream. Pareto competitive coevolution provides a potential framework for archiving useful training instances between generations under an archive of finite size. Such a coevolutionary framework is defined for the online evolution of classifiers under genetic programming. Benchmarking is performed under multi-class data sets with class imbalance and training partitions with between 1,000's to 100,000's of instances. The impact of enforcing different constraints for accessing the stream are investigated. The role of online adaptation is explicitly documented and tests made on the relative impact of label error on the quality of streaming classifier results.\",\n \"Title: Novelty-Based fitness: an evaluation under the santa fe trail\\nYear: 2010\\n\\nAbstract: We present an empirical analysis of the effects of incorporating novelty-based fitness (phenotypic behavioral diversity) into Genetic Programming with respect to training, test and generalization performance. Three novelty-based approaches are considered: novelty comparison against a finite archive of behavioral archetypes, novelty comparison against all previously seen behaviors, and a simple linear combination of the first method with a standard fitness measure. Performance is evaluated on the Santa Fe Trail, a well known GP benchmark selected for its deceptiveness and established generalization test procedures. Results are compared to a standard quality-based fitness function (count of food eaten). Ultimately, the quality style objective provided better overall performance, however, solutions identified under novelty based fitness functions generally provided much better test performance than their corresponding training performance. This is interpreted as representing a requirement for layered learning/ symbiosis when assuming novelty based fitness functions in order to more quickly achieve the integration of diverse behaviors into a single cohesive strategy.\",\n \"Title: Casgp: Building Cascaded Hierarchical Models Using Niching\\nYear: 2005\\n\\nAbstract: A Cascaded model is introduced for mining large datasets using Genetic Programming without recourse to specialist hardware. Such an algorithm satisfies the seeming conflicting requirements of scalability and accuracy on large datasets by incrementally building GP classifiers through the use of a hierarchical Dynamic Subset Selection algorithm. Models are built incrementally with each layer of the cascade receiving as ingot the original feature vector, plus the output from the previous layer(s). In order to encourage each layer to explicitly solve new aspects of the problem a combination of Sum Square Error and Niching is utilized. Thus, previous layers of the model are considered a niche, and the cost function is a shared error metric.\",\n \"Title: Learning recursive programs with cooperative coevolution of genetic code mapping and genotype\\nYear: 2007\\n\\nAbstract: The Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP) algorithm that cooperatively coevolves a population of adaptive mappings and associated genotypes is used to learn recursive solutions given a function set consisting of general (not implicitly recursive) machine-language instructions. PAM DGP using redundant encodings to model the evolution of the biological genetic code is found to more efficiently learn 2nd and 3rd order recursive Fibonacci functions than related developmental systems and traditional linear GP. PAM DGP using redundant encoding is also demonstrated to produce the semantically highest quality solutions for all three recursive functions considered (Factorial, 2nd and 3rd order Fibonacci). PAM DGP is then shown to have produced such solutions by evolving redundant mappings to select and emphasize appropriate subsets of the function set useful for producing the naturally recursive solutions.\",\n \"Title: On GPU based fitness evaluation with decoupled training partition cardinality\\nYear: 2013\\n\\nAbstract: GPU acceleration of increasingly complex variants of evolutionary frameworks typically assume that all the training data used during evolution resides on the GPU. Such an assumption places limits on the style of application to which evolutionary computation can be applied. Conversely, several coevolutionary frameworks explicitly decouple fitness evaluation from the size of the training partition. Thus, a subset of training exemplars is coevolved with the population of evolved individuals. In this work we articulate the design decisions necessary to support Pareto archiving for Genetic Programming under a commodity GPU platform. Benchmarking of corresponding CPU and GPU implementations demonstrates that the GPU platform is still capable of providing a times ten reduction in computation time.\",\n \"Title: Heterogeneous Digital Library Query Platform Using A Truly Distributed Multi-Agent Search\\nYear: 2000\\n\\nAbstract: A platform for performing multi-agent searches in heterogeneous digital libraries is proposed. This differs significantly from previous approaches by completely removing the concept of a centralized search engine. Specifically, the organization of information held on domain index servers is constrained to conform to a virtual tree representation based on facets and global keyword concept schema particular to the set of information providers associated with the domain of interest (e.g. preparatory intranet). Simulation studies are used to compare this platform against a digital library platform presently in use, which employs the traditional central server scheme. Improvements in terms of query service time and robustness are demonstrated.\",\n \"Title: Benchmarking pareto archiving heuristics in the presence of concept drift: diversity versus age\\nYear: 2013\\n\\nAbstract: A framework for coevolving genetic programming teams with Pareto archiving is benchmarked under two representative tasks for non-stationary streaming environments. The specific interest lies in determining the relative contribution of diversity and aging heuristics to the maintenance of the Pareto archive. Pareto archiving, in turn, is responsible for targeting data (and therefore champion individuals) as appropriate for retention beyond the limiting scope of the sliding window interface to the data stream. Fitness sharing alone is considered most effective under a non-stationary stream characterized by continuous (incremental) changes. Fitness sharing with an aging heuristic acts as the preferred heuristic when the stream is characterized by non-stationary stepwise changes.\",\n \"Title: GP classification under imbalanced data sets: active sub-sampling and AUC approximation\\nYear: 2008\\n\\nAbstract: The problem of evolving binary classification models under increasingly unbalanced data sets is approached by proposing a strategy consisting of two components: Sub-sampling and 'robust' fitness function design. In particular, recent work in the wider machine learning literature has recognized that maintaining the original distribution of exemplars during training is often not appropriate for designing classifiers that are robust to degenerate classifier behavior. To this end we propose a 'Simple Active Learning Heuristic' (SALH) in which a subset of exemplars is sampled with uniform probability under a class balance enforcing rule for fitness evaluation. In addition, an efficient estimator for the Area Under the Curve (AUC) performance metric is assumed in the form of a modified Wilcoxon-Mann-Whitney (WMW) statistic. Performance is evaluated in terms of six representative UCI data sets and benchmarked against: canonical GP, SALH based GP, SALH and the modified WMW statistic, and deterministic classifiers (Naive Bayes and C4.5). The resulting SALH-WMW model is demonstrated to be both efficient and effective at providing solutions maximizing performance assessed in terms of AUC.\",\n \"Title: Generating Mimicry Attacks Using Genetic Programming: A Benchmarking Study\\nYear: 2009\\n\\nAbstract: Mimicry attacks have been the focus of detector research where the objective of the attacker is to generate multiple attacks satisfying the same generic exploit goals for a given vulnerability. In this work, multi-objective Genetic programming is used to establish a \\\"black-box\\\" approach to mimicry attack generation. No knowledge is made of internal data structures of the target anomaly detector, only the anomaly rate reported by the detector. Such a \\\"black box\\\" methodology enables a vulnerability testing approach where both open-source and commodity anomaly detection systems can be tested. The approach successfully identifies exploits when benchmarked over four detectors and four applications.\",\n \"Title: On the application of GP to streaming data classification tasks with label budgets\\nYear: 2014\\n\\nAbstract: A framework is introduced for applying GP to streaming data classification tasks under label budgets. This is a fundamental requirement if GP is going to adapt to the challenge of streaming data environments. The framework proposes three elements: a sampling policy, a data subset and a data archiving policy. The sampling policy establishes on what basis data is sampled from the stream, and therefore when label information is requested. The data subset is used to define what GP individuals evolve against. The composition of such a subset is a mixture of data forwarded under the sampling policy and historical data identified through the data archiving policy. The combination of sampling policy and the data subset achieve a decoupling between the rate at which the stream passes and the rate at which evolution commences. Benchmarking is performed on two artificial data sets with specific forms of sudden shift and gradual drift as well as a well known real-world data set.\",\n \"Title: On botnet behaviour analysis using GP and C4.5\\nYear: 2014\\n\\nAbstract: Botnets represent a destructive cyber security threat that aim to hide their malicious activities within legitimate Internet traffic. Part of what makes botnets so affective is that they often upgrade themselves over time, hence reacting to improved detection mechanisms. In addition, Internet common communication protocols (i.e. HTTP) are used for the purposes of constructing subversive communication channels. This work employs machine learning algorithms (genetic programming and decision trees) to detect distinct behaviours in various botnets. That is to say, botnets mimic legitimate HTTP traffic while actually serving botnet purposes. To this end, two different feature sets are employed and analyzed to see how differences between three botnets - Zeus, Conficker and Torpig - can be distinguished. Specific recommendations are then made regarding the utility of different feature sets and machine learning algorithms for detecting each type of botnet.\",\n \"Title: On Evolving Multi-agent FX Traders.\\nYear: 2014\\n\\nAbstract: Current frameworks for identifying trading agents using machine learning are able to simultaneously address the characterization of both technical indicator and decision tree. Moreover, multi-agent frameworks have also been proposed with the goal of improving the reliability and trust in the agent policy identified. Such advances need weighing against the computational overhead of assuming such flexibility. In this work a framework for evolutionary multi-agent trading is introduced and systematically benchmarked for FX currency trading; including the impact of FX trading spread. It is demonstrated that simplifications can be made to the 'base' trading agent that do not impact on the quality of solutions, but provide considerable computational speedups. The resulting evolutionary multi-agent architecture is demonstrated to provide significant benefits to the profitability and improve the reliability with which profitable policies are returned.\",\n \"Title: Cascaded GP models for data mining\\nYear: 2004\\n\\nAbstract: The cascade architecture for incremental learning is demonstrated within the context of genetic programming. Such a scheme provides the basis for building steadily more complex models until a desired degree of accuracy is reached. The architecture is demonstrated for several data mining datasets. Efficient training on standard computing platforms is retained using the RSS-DSS algorithm for stochastically sampling datasets in proportion to exemplar 'difficulty' and 'age'. Finally, the ensuing empirical study provides the basis for recommending the utility of sum square cost functions in the datasets considered.\",\n \"Title: Probabilistic (Genotype) Adaptive Mapping Combinations for Developmental Genetic Programming\\nYear: 2006\\n\\nAbstract: In development genetic programming (DGP) approaches where the search space is divided into genotypes and phenotypes, a mapping (or \\\"genetic code\\\") is needed to connect the two spaces. This model has subsequently been extended so that mappings evolve, and recently an implementation was proposed that co-evolves a genotype population and a population of adaptive mappings. Here, the authors identify and investigate performance obstacles for this recent implementation. They then introduce a new probabilistic adaptive mapping DGP that avoids those performance problems and explores a greater search space of genotype-mapping combinations without significant computational expense. The algorithm is shown to be more robust and to outperform the comparison adaptive mapping algorithm on challenging settings of the chosen test benchmark.\",\n \"Title: Automatically Evading IDS Using GP Authored Attacks\\nYear: 2007\\n\\nAbstract: A mimicry attack is a type of attack where the basic steps of a minimalist 'core' attack are used to design multiple attacks achieving the same objective from the same application. Research in mimicry attacks is valuable in determining and eliminating weaknesses of detectors. In this work, we provide a genetic programming based automated process for designing all components of a mimicry attack relative to the Stide detector under a vulnerable Traceroute application. Results indicate that the automatic process is able to generate mimicry attacks that reduce the alarm rate from ~65% of the original attack, to ~2.7%, effectively making the attack indistinguishable from normal behaviors\",\n \"Title: Scaling Genetic Programming to Large Datasets Using Hierarchical Dynamic Subset Selection\\nYear: 2007\\n\\nAbstract: The computational overhead of genetic programming (GP) may be directly addressed without recourse to hardware solutions using active learning algorithms based on the random or dynamic subset selection heuristics (RSS or DSS). This correspondence begins by presenting a family of hierarchical DSS algorithms: RSS-DSS, cascaded RSS-DSS, and the balanced block DSS algorithm, where the latter has not been previously introduced. Extensive benchmarking over four unbalanced real-world binary classification problems with 30000-500000 training exemplars demonstrates that both the cascade and balanced block algorithms are able to reduce the likelihood of degenerates while providing a significant improvement in classification accuracy relative to the original RSS-DSS algorithm. Moreover, comparison with GP trained without an active learning algorithm indicates that classification performance is not compromised, while training is completed in minutes as opposed to half a day.\",\n \"Title: One-class learning with multi-objective genetic programming\\nYear: 2007\\n\\nAbstract: One-class classification naturally only provides one class of exemplars on which to construct the classification model. In this work, multi- objective genetic programming (GP) allows the one-class learning problem to be decomposed by multiple GP classifiers, each attempting to identify only a subset of the target data to classify. In order for GP to identify appropriate subsets of the one-class data, artificial outclass data is gener- ated in and around the provided inclass data. A local Gaussian wrapper is employed where this reinforces a novelty detection as opposed to a dis- crimination approach to classification. Furthermore, a hierarchical subset selection strategy is used to deal with the necessarily large number of generated outclass exemplars. The proposed approach is demonstrated on three one-class classification datasets and was found to be competitive with a one-class SVM classifier and a binary SVM classifier.\",\n \"Title: Multi-objective competitive coevolution for efficient GP classifier problem decomposition\\nYear: 2007\\n\\nAbstract: A novel approach to the classification of large and unbalanced multi-class data sets is presented where the widely acknowledged issues of scalability, solution transparency, and problem decomposition are addressed simultaneously within the context of the genetic programming (GP) paradigm. A cooperative coevolutionary training environment that employs multi-objective evaluation provides the basis for problem decomposition and reduced solution complexity, while scalability is achieved through a Pareto competitive coevolutionary framework, allowing the system to be applied to large data sets (tens or hundreds of thousands of exemplars) without recourse to hardware-specific speedups. Moreover, a key departure from the canonical GP approach to classification is utilized in which the output of GP is expressed in terms of a non-binary, local membership function (e.g. a Gaussian), where it is no longer necessary for an expression to represent an entire class. Decomposition is then achieved through reformulating the classification problem as one of cluster consistency, where an appropriate subset of the training patterns can be associated with each individual such that problems are solved by several specialist classifiers rather than by a single 'super' individual.\",\n \"Title: Bottom-up evolutionary subspace clustering\\nYear: 2010\\n\\nAbstract: The ultimate goal of subspace clustering algorithms is to identify both the subset of attributes supporting a cluster and the location of the cluster in the subspace. In this work a generic evolutionary approach to bottom-up subspace clustering is proposed consisting of three steps. The first applies a non-evolutionary clustering algorithm attribute-wise to establish the lattice from which subspace clusters will be designed. In the second step a multi-objective Genetic Algorithm (MOGA) is used to evolve good candidate subspace clusters (CSC) through a combinatorial search w.r.t. the attribute-wise lattice from step 1. The third step then searches in the space of CSC from the population of the the first MOGA to find the best combination of subspace clusters, again under a MOGA formulation. Important properties of the approach are that a standard clustering algorithm is deployed in step one to build the initial lattice of attribute-wise clusters. This helps to decouple the computational expense of clustering using Evolutionary Computation, with the MOGA applied in steps 2 and 3 building clusters through a combinatorial search relative to the original lattice parameters. Benchmarking on data sets with tens to hundreds of attributes illustrates the feasibility of the approach.\",\n \"Title: Genetic optimization and hierarchical clustering applied to encrypted traffic identification\\nYear: 2011\\n\\nAbstract: An important part of network management requires the accurate identification and classification of network traffic for decisions regarding bandwidth management, quality of service, and security. This work explores the use of a Multi-Objective Genetic Algorithm (MOGA) for both, feature selection and cluster count optimization, for an unsupervised machine learning technique, K-Means, applied to encrypted traffic identification. Specifically, a hierarchical K-Means algorithm is employed, comparing its performance to the MOGA with a non-hierarchical (flat) K-Means algorithm. The latter has already been benchmarked against common unsupervised techniques found in the literature, where results have favored the proposed MOGA. The purpose of this paper is to explore the gains, if any, obtained by increasing cluster purity in the proposed model by means of a second layer of clusters. In this work, SSH is chosen as an example of an encrypted application. However, nothing prevents the proposed model to work with other types of encrypted traffic, such as SSL or Skype. Results show that with the hierarchical MOGA, significant gains are observed in terms of the classification performance of the system.\",\n \"Title: Revisiting the Acrobot ‘height’ task: An example of efficient evolutionary policy search under an episodic goal seeking task\\nYear: 2011\\n\\nAbstract: Evolutionary methods for addressing the temporal sequence learning problem generally fall into policy search as opposed to value function optimization approaches. Various re cent results have made the claim that the policy search approach is at best inefficient at solving episodic 'goal seeking' tasks i.e., tasks under which the reward is limited to describing properties associated with a successful outcome have no qualification for degrees of failure. This work demonstrates that such a conclusion is due to a lack of diversity in the training scenarios. We therefore return to the Acrobot 'height' task domain originally used to demonstrate complete failure in evolutionary policy search. This time a very simple stochastic sampling heuristic for defining a population of training configurations is introduced. Benchmarking two recent evolutionary policy search algorithms - Neural Evolution of Augmented Topologies (NEAT) and Symbiotic Bid-Based (SBB) Genetic Programming - under this condition demonstrates solutions as effective as those returned by advanced value function methods. Moreover this is achieved while remaining within the evaluation limit imposed by the original study.\",\n \"Title: Evolutionary model building under streaming data for classification tasks: opportunities and challenges\\nYear: 2015\\n\\nAbstract: Streaming data analysis potentially represents a significant shift in emphasis from schemes historically pursued for offline (batch) approaches to the classification task. In particular, a streaming data application implies that: (1) the data itself has no formal `start' or `end'; (2) the properties of the process generating the data are non-stationary, thus models that function correctly for some part(s) of a stream may be ineffective elsewhere; (3) constraints on the time to produce a response, potentially implying an anytime operational requirement; and (4) given the prohibitive cost of employing an oracle to label a stream, a finite labelling budget is necessary. The scope of this article is to provide a survey of developments for model building under streaming environments from the perspective of both evolutionary and non-evolutionary frameworks. In doing so, we bring attention to the challenges and opportunities that developing solutions to streaming data classification tasks are likely to face using evolutionary approaches.\",\n \"Title: Systems control for a micro-stereolithography prototype\\nYear: 1998\\n\\nAbstract: The system control of a micro-stereolithographic fabrication system is detailed, with specific attention given to the opto-electronic and electro-mechanical interfaces. The application of the National Instrument LabVEIW© environment is demonstrated to provide a good basis for rapid application development of the necessary control structures. In particular, this provides the basis for a highly flexible control system, typical activities of which include: low-level interface formats; icon-based systems design; and support for good human–computer interfacing. The resulting system has the capability to provide two orders of magnitude improvement above that currently available.\",\n \"Title: Solving Complex Problems with Coevolutionary Algorithms\\nYear: 2015\\n\\nAbstract: No abstract available.\",\n \"Title: Knowledge Transfer from Keepaway Soccer to Half-field Offense through Program Symbiosis: Building Simple Programs for a Complex Task\\nYear: 2015\\n\\nAbstract: Half-field Offense (HFO) is a sub-task of Robocup 2D Simulated Soccer. HFO is a challenging, multi-agent machine learning problem in which a team of offense players attempt to manoeuvre the ball past a defending team and around the goalie in order to score. The agent's sensors and actuators are noisy, making the problem highly stochastic and partially observable. These same real-world characteristics have made Keepaway soccer, which represents one sub-task of HFO, a popular testbed in the reinforcement learning and task-transfer literature in particular. We demonstrate how policies initially evolved for Keepaway can be reused within a symbiotic framework for coevolving policies in genetic programming (GP), with no additional training or transfer function, in order to improve learning in the HFO task. Moreover, the highly modular policies discovered by GP are shown to be significantly less complex than solutions based on traditional value-function optimization while achieving the same level of play in HFO.\",\n \"Title: On the Impact of Class Imbalance in GP Streaming Classification with Label Budgets.\\nYear: 2016\\n\\nAbstract: Streaming data scenarios introduce a set of requirements that do not exist under supervised learning paradigms typically employed for classification. Specific examples include, anytime operation, non-stationary processes, and limited label budgets. From the perspective of class imbalance, this implies that it is not even possible to guarantee that all classes are present in the samples of data used to construct a model. Moreover, when decisions are made regarding what subset of data to sample, no label information is available. Only after sampling is label information provided. This represents a more challenging task than encountered under non-streaming (offline) scenarios because the training partition contains label information. In this work, we investigate the utility of different protocols for sampling from the stream under the above constraints. Adopting a uniform sampling protocol was previously shown to be reasonably effective under both evolutionary and non-evolutionary streaming classifiers. In this work, we introduce a scheme for using the current ‘champion’ classifier to bias the sampling of training instances during the course of the stream. The resulting streaming framework for genetic programming is more effective at sampling minor classes and therefore reacting to changes in the underlying process responsible for generating the data stream.\",\n \"Title: Better Trade Exits For Foreign Exchange Currency Trading Using Fxgp\\nYear: 2015\\n\\nAbstract: Retracement is the tendency of markets to move between upper 'resistance' and lower 'support' price levels. Human traders frequently make use of visual tools to help identify these resistance and support levels so that they can by used in their trading decisions. These decision can be put into trading strategies composed of rules designed to mitigate losses after a trade is started, often called 'stop loss' orders, or to take profit at a near optimal time, often called 'take profit' orders. However, identifying such resistance and support levels is notoriously difficult given market volatility. Indeed, the levels need recalculating on a continuous basis, and only hold to an approximate degree. In this work we describe an approach for evolving buy-stay-sell currency trading rules using genetic programming. These rules are explicitly linked to technical indicators that incorporate features characterizing retracement. Benchmarking is then performed using the most recent three years of data from the EURUSD foreign exchange market with three different methods of identifying retracement based on moving average, pivot points and Fibonacci ratios. Investment strategies employing Fibonacci ratios and found to provide superior performance among the strategies examined.\",\n \"Title: Benchmarking Stream Clustering For Churn Detection In Dynamic Networks\\nYear: 2015\\n\\nAbstract: Retaining users and customers is one of the most important challenges for the service industry from mobile communications to online gaming. As the users of these services form dynamic networks that grow in size, predicting 'churners' becomes harder and harder. In this work, we explore the use of anomaly detection for churn prediction. To this end, we evaluate bio-inspired and deterministic online clustering algorithms on both cell phone and online gaming data sets. We discuss the results of each technique from the perspective of: feature identification, sensitivity analysis of the parameters as well as their capacity to detect churn.\",\n \"Title: Feature selection for robust backscatter DDoS detection\\nYear: 2015\\n\\nAbstract: This paper analyzes the effect of using different feature selection algorithms for robust backscatter DDoS detection. To achieve this, we analyzed four different training sets with four different feature sets. We employed two well-known feature selection algorithms, namely Chi-Square and Symmetrical Uncertainty, together with the Decision Tree classifier. All the datasets employed are publicly available and provided by CAIDA. Our experimental results show that it is possible to develop a robust detection system that can generalize well to the changing backscatter DDoS behaviours over time using a small number of selected features.\",\n \"Title: Initiating a Moving Target Network Defense with a Real-time Neuro-evolutionary Detector.\\nYear: 2016\\n\\nAbstract: The moving network target defense (MTD) based approach to security aims to design and develop capabilities to dynamically change the attack surfaces to make it more difficult for attackers to strike. One such capability is to dynamically change the IP addresses of subnetworks in unpredictable ways in an attempt to disrupt the ability of an attacker to collect the necessary reconnaissance information to launch successful attacks. In particular, Denial of Service (DoS) and worms represent examples of distributed attacks that can potentially propagate through networks very quickly, but could also be disrupted by MTD. Conversely, MTD are also disruptive to regular users. For example, when IP addresses are changed dynamically it is no longer effective to use DNS caches for IP address resolutions before any communication can be performed. In this work we take another approach. We note that the deployment of MTD could be triggered through the use of light-weight intrusion detection. We demonstrate that the neuro-evolution of augmented topologies algorithm (NEAT) has the capacity to construct detectors that operate on packet data and produce sparse topologies, hence are real-time in operation. Benchmarking under examples of DoS and worm attacks indicates that NEAT detectors can be constructed from relatively small amounts of data and detect attacks approx. 90% accuracy. Additional experiments with the open-ended evolution of code modules through genetic program teams provided detection rates approaching 100%. We believe that adopting such an approach to MTB a more specific deployment strategy that is less invasive to legitimate users, while disrupting the actions of malicious users.\",\n \"Title: Discovering Rubik's Cube Subgroups using Coevolutionary GP: A Five Twist Experiment.\\nYear: 2016\\n\\nAbstract: This work reports on an approach to direct policy discovery (a form of reinforcement learning) using genetic programming (GP) for the 3 by 3 by 3 Rubik's Cube. Specifically, a synthesis of two approaches is proposed: 1) a previous group theoretic formulation is used to suggest a sequence of objectives for developing solutions to different stages of the overall task; and 2) a hierarchical formulation of GP policy search is utilized in which policies adapted for an earlier objective are explicitly transferred to aid the construction of policies for the next objective. The resulting hierarchical organization of policies explicitly demonstrates task decomposition and policy reuse. Algorithmically, the process makes use of a recursive call to a common approach for maintaining a diverse population of GP individuals and then learns how to reuse subsets of programs (policies) developed against the earlier objective. Other than the two objectives, we do not explicitly identify how to decompose the task or mark specific policies for reuse. Moreover, at the end of evolution we return a population solving 100% of 17,675,698 different initial Cubes for the two objectives currently in use.\",\n \"Title: Solving Complex Problems with Coevolutionary Algorithms.\\nYear: 2017\\n\\nAbstract: No abstract available.\",\n \"Title: On Synergies between Diversity and Task Decomposition in Constructing Complex Systems with GP.\\nYear: 2016\\n\\nAbstract: Complexity in genetic programming is unfortunately often associated with undesirable properties such as code bloat. In this work, we review developments in which complex systems are promoted through: 1) the evolution of teams of programs, and then 2) the context specific reuse of previously evolved code. To do so, two classes of diversity are identified: intra-team diversity and inter-team diversity. Intra-team diversity promotes task decomposition/cooperative coevolution between multiple programs, i.e. teams of programs. A fundamental requirement is that programs can learn context. Inter-team diversity is promoted through maintaining model and task diversity during evolution. The combination of both result in the ability to identify teams of programs and associate them with specific contexts, and then organize teams of programs hierarchically so solve multiple tasks. Finally, the concept of cumulative population wide performance is used to illustrate how inter model diversity in particular introduces useful biases into the types of solutions evolved.\",\n \"Title: Coevolving deep hierarchies of programs to solve complex tasks.\\nYear: 2017\\n\\nAbstract: Scaling genetic programming to organize large complex combinations of programs remains an under investigated topic in general. This work revisits the issue by first demonstrating the respective contributions of coevolution and diversity maintenance. Competitive coevolution is employed to organize a task in such a way that the most informative training cases are retained. Cooperative coevolution helps discover modularity in the solutions discovered and, in this work, is fundamental to constructing complex structures of programs that still execute efficiently (the policy tree). The role of coevolution and diversity maintenance is first independently established under the task of discovering reinforcement learning policies for solving Rubik's Cubes scrambled with 5-twists. With this established, a combined approach is then adopted for building large organizations of code for representing policies that solve 5 to 8-twist combinations of the Cube. The resulting 'deep' policy tree organizes hundreds of programs to provide optimal solutions to tens of millions of test cube configurations.\",\n \"Title: Multi-task learning in Atari video games with emergent tangled program graphs.\\nYear: 2017\\n\\nAbstract: The Atari 2600 video game console provides an environment for investigating the ability to build artificial agent behaviours for a variety of games using a common interface. Such a task has received attention for addressing issues such as: 1) operation directly from a high-dimensional game screen; and 2) partial observability of state. However, a general theme has been to assume a common machine learning algorithm, but completely retrain the model for each game title. Success in this respect implies that agent behaviours can be identified without hand crafting game specific attributes/actions. This work advances current state-of-the-art by evolving solutions to play multiple titles from the same run. We demonstrate that in evolving solutions to multiple game titles, agent behaviours for an individual game as well as single agents capable of playing all games emerge from the same evolutionary run. Moreover, the computational cost is no more than that used for building solutions for a single title. Finally, while generally matching the skill level of controllers from neuro-evolution/deep learning, the genetic programming solutions evolved here are several orders of magnitude simpler, resulting in real-time operation at a fraction of the cost.\",\n \"Title: Properties of a GP active learning framework for streaming data with class imbalance.\\nYear: 2017\\n\\nAbstract: Active learning algorithms attempt to interactively develop a subset of data from which fitness evaluation is performed. Moreover, the distribution of labeled content within the data subset may adapt over time as genetic programming (GP) individuals improve. The basic goal is therefore to identify the most meaningful subset of data to improve the current model. Under a streaming data context additional challenges exist relative to the non-streaming scenario: non-stationary processes, partial observability anytime operation. This means that it is not possible to guarantee that the content of the data subset even provides exemplars for each class that could appear in the stream (i.e., different classes appear/disappear at different parts of the stream). With this in mind, an investigation is performed into the impact of adopting different policies for controlling the development of data subset content. To do so, a generic framework is defined in terms of sampling and archiving policies. The resulting evaluation under several large multi-class datasets with class imbalance indicates that adopting random sampling with a biased archiving policy is sufficient for evolving GP classifiers that match or better the current state-of-the-art, particularly when detecting minor classes.\",\n \"Title: Return-oriented programme evolution with ROPER: a proof of concept.\\nYear: 2017\\n\\nAbstract: Return-orientated programming (ROP) identifies code snippets ending in a return instruction (gadgets) and chains them together to construct exploits. Gadgets are already present in executable memory, thus avoiding the need to explicitly inject new code. As such ROP represents one of the most difficult exploit mechanisms to mitigate. ROP design is essentially driven by the skill of human hacker, limiting the ability of exploit mitigation to reacting to attacks. In this work we describe an evolutionary approach to ROP design, thus potentially pointing to the automatic detection of vulnerabilities before application code is released.\",\n \"Title: Emergent Tangled Graph Representations For Atari Game Playing Agents\\nYear: 2017\\n\\nAbstract: Organizing code into coherent programs and relating different programs to each other represents an underlying requirement for scaling genetic programming to more difficult task domains. Assuming a model in which policies are defined by teams of programs, in which team and program are represented using independent populations and coevolved, has previously been shown to support the development of variable sized teams. In this work, we generalize the approach to provide a complete framework for organizing multiple teams into arbitrarily deep/wide structures through a process of continuous evolution; hereafter the Tangled Program Graph (TPG). Benchmarking is conducted using a subset of 20 games from the Arcade Learning Environment (ALE), an Atari 2600 video game emulator. The games considered here correspond to those in which deep learning was unable to reach a threshold of play consistent with that of a human. Information provided to the learning agent is limited to that which a human would experience. That is, screen capture sensory input, Atari joystick actions, and game score. The performance of the proposed approach exceeds that of deep learning in 15 of the 20 games, with 7 of the 15 also exceeding that associated with a human level of competence. Moreover, in contrast to solutions from deep learning, solutions discovered by TPG are also very 'sparse'. Rather than assuming that all of the state space contributes to every decision, each action in TPG is resolved following execution of a subset of an individual's graph. This results in significantly lower computational requirements for model building than presently the case for deep learning.\",\n \"Title: Scaling Tangled Program Graphs to Visual Reinforcement Learning in ViZDoom.\\nYear: 2018\\n\\nAbstract: A tangled program graph framework (TPG) was recently proposed as an emergent process for decomposing tasks and simultaneously composing solutions by organizing code into graphs of teams of programs. The initial evaluation assessed the ability of TPG to discover agents capable of playing Atari game titles under the Arcade Learning Environment. This is an example of ‘visual’ reinforcement learning, i.e. agents are evolved directly from the frame buffer without recourse to hand designed features. TPG was able to evolve solutions competitive with state-of-the-art deep reinforcement learning solutions, but at a fraction of the complexity. One simplification assumed was that the visual input could be down sampled from a (210 times 160) resolution to (42 times 32). In this work, we consider the challenging 3D first person shooter environment of ViZDoom and require that agents be evolved at the original visual resolution of (320 times 240) pixels. In addition, we address issues for developing agents capable of operating in multi-task ViZDoom environments simultaneously. The resulting TPG solutions retain all the emergent properties of the original work as well as the computational efficiency. Moreover, solutions appear to generalize across multiple task scenarios, whereas equivalent solutions from deep reinforcement learning have focused on single task scenarios alone.\",\n \"Title: On botnet detection with genetic programming under streaming data label budgets and class imbalance.\\nYear: 2018\\n\\nAbstract: Algorithms for constructing models of classification under streaming data scenarios are becoming increasingly important. In order for such algorithms to be applicable under ‘real-world’ contexts we adopt the following objectives: 1) operate under label budgets, 2) make label requests without recourse to true label information, and 3) robustness to class imbalance. Specifically, we assume that model building is only performed using the content of a Data Subset (as in active learning). Thus, the principle design decisions are with regard to the definitions employed for sampling and archiving policies. Moreover, these policies should operate without prior information regarding the distribution of classes, as this varies over the course of the stream. A team formulation for genetic programming (GP) is assumed as the generic model for classification in order to support incremental changes to classifier content. Benchmarking is conducted with thirteen real-world Botnet datasets with label budgets of the order of 0.5–5% and significant amounts of class imbalance. Specific recommendations are made for detecting the costly minor classes under these conditions. Comparison with current approaches to streaming data under label budgets supports the significance of these findings.\",\n \"Title: Streaming Botnet traffic analysis using bio-inspired active learning.\\nYear: 2018\\n\\nAbstract: Non-stationary network traffic, together with stealth occurrences of malicious behaviors, make analyzing network traffic challenging. In this research, a machine learning framework is used to incrementally learn the network behavior and adapt to the changes in the traffic. This framework works under two main constraints: 1) label budget, 2) class imbalance; which makes it suitable for real-world network scenarios. Evaluations are performed on a public dataset with multiple Botnet scenarios under 0.5% and 5% label budgets; only around 2.2% of traffic is Botnet. Our results demonstrate the significance of the proposed Stream Genetic Programming solution and a general robustness to factors such as long latencies between instances of the same Botnet.\",\n \"Title: Benchmarking evolutionary computation approaches to insider threat detection.\\nYear: 2018\\n\\nAbstract: Insider threat detection represents a challenging problem to companies and organizations where malicious actions are performed by authorized users. This is a highly skewed data problem, where the huge class imbalance makes the adaptation of learning algorithms to the real world context very difficult. In this work, applications of genetic programming (GP) and stream active learning are evaluated for insider threat detection. Linear GP with lexicase/multi-objective selection is employed to address the problem under a stationary data assumption. Moreover, streaming GP is employed to address the problem under a non-stationary data assumption. Experiments conducted on a publicly available corporate data set show the capability of the approaches in dealing with extreme class imbalance, stream learning and adaptation to the real world context.\",\n \"Title: A genetic algorithm for dynamic controller placement in software defined networking.\\nYear: 2018\\n\\nAbstract: The Software Defined Networking paradigm has enabled dynamic configuration and control of large networks. Although the division of the control and data planes on networks has lead to dynamic reconfigurability of large networks, finding the minimal and optimal set of controllers that can adapt to the changes in the network has proven to be a challenging problem. Recent research tends to favor small solution sets with a focus on either propagation latency or controller load distribution, and struggles to find large balanced solution sets. In this paper, we propose a multi-objective genetic algorithm based approach to the controller placement problem that minimizes inter-controller latency, load distribution and the number of controllers with fitness sharing. We demonstrate that the proposed approach provides diverse and adaptive solutions to real network architectures such as the United States backbone and Japanese backbone networks. We further discuss the relevance and application of a diversity focused genetic algorithm for a moving target defense security model.\",\n \"Title: Discovering Agent Behaviors Through Code Reuse: Examples From Half-Field Offense and Ms. Pac-Man.\\nYear: 2018\\n\\nAbstract: This paper demonstrates how code reuse allows genetic programming (GP) to discover strategies for difficult gaming scenarios while maintaining relatively low model complexity. Critical factors in the proposed approach are illustrated through an in-depth study in two challenging task domains: RoboCup soccer and Ms. Pac-Man. In RoboCup, we demonstrate how policies initially evolved for simple subtas...\",\n \"Title: Emergent Solutions to High-Dimensional Multitask Reinforcement Learning.\\nYear: 2018\\n\\nAbstract: Algorithms that learn through environmental interaction and delayed rewards, or reinforcement learning, increasingly face the challenge of scaling to dynamic, highdimensional, and partially observable environments. Significant attention is being paid to frameworks from deep learning, which scale to high-dimensional data by decomposing the task through multi-layered neural networks. While effective, the representation is complex and computationally demanding. In this work we propose a framework based on Genetic Programming which adaptively complexifies policies through interaction with the task. We make a direct comparison with several deep reinforcement learning frameworks in the challenging Atari video game environment as well as more traditional reinforcement learning frameworks based on a priori engineered features. Results indicate that the proposed approach matches the quality of deep learning while being a minimum of three orders of magnitude simpler with respect to model complexity. This results in real-time operation of the champion RL agent without recourse to specialized hardware support. Moreover, the approach is capable of evolving solutions to multiple game titles simultaneously with no additional computational cost. In this case, agent behaviours for an individual game as well as single agents capable of playing all games emerge from the same evolutionary run.\",\n \"Title: Network Analytics For Streaming Traffic Analysis\\nYear: 2019\\n\\nAbstract: Network traffic analytics has always been an important field for cybersecurity and network operations. The analysis could be done on the stream of data (network traffic) in an on-line mode for real-time network operations or on the captured traffic data in an off-line mode for forensic analysis. In this paper, we aim to shed light into using machine learning based network analytics on traffic flows both for network operations and forensic analysis purposes. In doing so, our goal is to design and develop systems that could assist the human expert in analyzing \\\"unknown background\\\" traffic.\",\n \"Title: Benchmarking genetic programming in dynamic insider threat detection.\\nYear: 2019\\n\\nAbstract: In real world applications, variation in deployment environments, such as changes in data collection techniques, can affect the effectiveness and/or efficiency of machine learning (ML) systems. In this work, we investigate techniques to allow a previously trained population of Linear Genetic Programming (LGP) insider threat detectors to adapt to an expanded feature space. Experiments show that appropriate methods can be adopted to enable LGP to incorporate the new data efficiently, hence reducing computation requirements and expediting deployment under the new conditions.\",\n \"Title: Evolving Simple Solutions to the CIFAR-10 Benchmark using Tangled Program Graphs\\nYear: 2021\\n\\nAbstract: The goal of the CIFAR-10 benchmark is recast from the perspective of discovering light-weight as well as accurate solutions. Specifically, the image data, on which CIFAR-10 is based, requires multiple practical issues to be addressed that are not often considered collectively when applying genetic programming to classification problems. Issues of particular interest include cardinality, multi-class classification and diversity maintenance. We demonstrate that diversity maintenance and cardinality can be approached simultaneously by adopting a data subset to compose pools of exemplars for lexicase selection. The issues of multi-class classification and solution simplicity are addressed by adopting the tangled program graph (TPG) approach to emergent modularity. In addition, the mutation operator is modified to ensure that class labels do not 'die out' during evolution. The resulting benchmarking study demonstrates solutions that are significantly more accurate than AutoML while providing comparable accuracies with solutions from unsupervised feature discovery, i.e. 70% accuracy. However, unlike the latter TPG solutions are several orders of magnitude simpler.\",\n \"Title: Log Abstraction for Information Security: Heuristics and Reproducibility\\nYear: 2021\\n\\nAbstract: The collection of log messages regarding the operation of deployed services and application is an integral component to the forensic analysis for the identification and understanding of security incidents. Approaches for parsing and abstraction of such logs, despite widespread use and study, do not directly account for the individualities of the domain of information security. This, in return, limits their applicability on the field. In this work, we analyze the state-of-the-art log parsing and abstraction algorithms from the perspective of information security. First, we reproduce/replicate previous analysis of such algorithms from the literature. Then, we evaluate their ability for parsing and abstraction of log files for forensic analysis purposes. Our study demonstrates that while the state-of-the-art techniques are accurate in log parsing, improvements are necessary in terms of achieving a holistic view to aid in forensic analysis for the identification and understanding of security incidents.\",\n \"Title: On the impact of tangled program graph marking schemes under the atari reinforcement learning benchmark\\nYear: 2021\\n\\nAbstract: ABSTRACTTangled program graphs (TPG) support emergent modularity by first identifying subsets of programs that can usefully coexist (a team/ graph node) and then identifying the circumstance under which to reference other teams (arc adaptation). Variation operators manipulate the content of teams and arcs. This introduces cycles into the TPG structures. Previously, this effect was eradicated at run time by marking nodes while evaluating TPG individuals. In this work, a new marking heuristic is introduced, that of arc (learner) marking. This means that nodes can be revisited, but not the same arcs. We investigate the impact of this through 18 titles from the Arcade Learning Environment. The performance and complexity of the policies appear to be similar, but with specific tasks (game titles) resulting in preferences for one scheme over another.\",\n \"Title: Training regime influences to semi-supervised learning for insider threat detection\\nYear: 2021\\n\\nAbstract: A malicious insider is one of the most damaging threats to any organization from industry to government agencies. Many challenges from insider threat detection come from the fact that the ground truth is very limited and costly to acquire. This paper presents a semi-supervised learning approach to insider threat detection. We employ three machine learning methods under different real-world conditions. These include obtaining the initial ground truth training data randomly or via a certain type of insider malicious behavior or by anomaly detection system scores. Evaluation results show that the approach allows learning from very limited data for insider threat detection at high precision. 90% of malicious data instances are detected under 1% false positive rate.\",\n \"Title: Emergent Tangled Program Graphs in Partially Observable Recursive Forecasting and ViZDoom Navigation Tasks\\nYear: 2021\\n\\nAbstract: AbstractModularity represents a recurring theme in the attempt to scale evolution to the design of complex systems. However, modularity rarely forms the central theme of an artificial approach to evolution. In this work, we report on progress with the recently proposed Tangled Program Graph (TPG) framework in which programs are modules. The combination of the TPG representation and its variation operators enable both teams of programs and graphs of teams of programs to appear in an emergent process. The original development of TPG was limited to tasks with, for the most part, complete information. This work details two recent approaches for scaling TPG to tasks that are dominated by partially observable sources of information using different formulations of indexed memory. One formulation emphasizes the incremental construction of memory, again as an emergent process, resulting in a distributed view of state. The second formulation assumes a single global instance of memory and develops it as a communication medium, thus a single global view of state. The resulting empirical evaluation demonstrates that TPG equipped with memory is able to solve multi-task recursive time-series forecasting problems and visual navigation tasks expressed in two levels of a commercial first-person shooter environment.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":98,"string":"98"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13256,"string":"13,256"}}},{"rowIdx":13257,"cells":{"question_id":{"kind":"string","value":"Marc Noy"},"question":{"kind":"string","value":"Generate an abstract for the title \"On The Number Of Labeled Graphs Of Bounded Treewidth\" using the following items: graphs, treewidth, labeled, upper bound, lower bound."},"answer":{"kind":"string","value":"Let T-n,T-k be the number of labeled graphs on n vertices and treewidth at most k (equivalently, the number of labeled partial k-trees). We show that(c k 2(k) n/log k)(n) 2(-) k(k+3)/2 k -2k-2 <= T-n,T-k <= (k 2(k) n)(n) 2(-) k(k+1)/2 k(-k),for k > 1 and some explicit absolute constant c > 0. Disregarding lower-order terms, the gap between the lower and upper bound is of order (log k)(n). The upper bound is a direct consequence of the well-known formula for the number of labeled k-trees, while the lower bound is obtained from an explicit construction. It follows from this construction that both bounds also apply to graphs of pathwidth and proper-pathwidth at most k."},"correct_answer":{"kind":"string","value":"Let T-n,T-k be the number of labeled graphs on n vertices and treewidth at most k (equivalently, the number of labeled partial k-trees). We show that(c k 2(k) n/log k)(n) 2(-) k(k+3)/2 k -2k-2 <= T-n,T-k <= (k 2(k) n)(n) 2(-) k(k+1)/2 k(-k),for k > 1 and some explicit absolute constant c > 0. Disregarding lower-order terms, the gap between the lower and upper bound is of order (log k)(n). The upper bound is a direct consequence of the well-known formula for the number of labeled k-trees, while the lower bound is obtained from an explicit construction. It follows from this construction that both bounds also apply to graphs of pathwidth and proper-pathwidth at most k."},"documents":{"kind":"list like","value":["Title: Stage-graph representations\nYear: 1997\n\nAbstract: We consider graph applications of the well-known paradigm “killing two birds with one stone”. In the plane, this gives rise to a stage graph as follows: vertices are the points, and u , v is an edge if and only if the (infinite, straight) line segment joining u to v intersects the stage. Such graphs are shown to be comparability graphs of ordered sets of dimension 2. Similar graphs can be constructed when we have a fixed number k of stages on the plane. In this case, u , v is an edge if and only if the (straight) line segment uv intersects one of the k stages. In this paper, we study stage representations of stage graphs and give upper and lower bounds on the number of stages needed to represent a graph.","Title: Preface: Volume 10\nYear: 2001\n\nAbstract: The http://www-mat.upc.es/limda/comb01/wwgrafs/principalx.html Euroconference on Combinatorics, Graph Theory and Applications was held in the http://www.crm.esCentre de Recerca Matemàtica near Barcelona, Spain, on September 12-14 2001.","Title: Graphs of Non-Crossing Perfect Matchings\nYear: 2002\n\nAbstract: Let Pn be a set of n = 2m points that are the vertices of a convex polygon, and let Mmbe the graph having as vertices all the perfect matchings in the point set Pn whose edgesare straight line segments and do not cross, and edges joining two perfect matchings M1and M2 if M2 = M1 \\Gamma (a; b) \\Gamma (c; d) + (a; d) + (b; c) for some points a; b; c; d of Pn . Weprove the following results about Mm : its diameter is m \\Gamma 1; it is bipartite for every m;the connectivity is equal to m...","Title: Identities for non-crossing graphs and multigraphs\nYear: 2008\n\nAbstract: We refine an identity between the numbers of certain non-crossing graphs and multigraphs, by modifying a bijection found by P. Podbrdský [A bijective proof of an identity for noncrossing graphs, Discrete Math. 260 (2003) 249–253]. We also prove a new identity between the number of acyclic non-crossing graphs with n vertices and k edges (isolated vertices allowed and no multiple edges allowed), and the number of non-crossing connected graphs with n edges and k vertices (multiple edges allowed and no isolated vertices allowed).","Title: A lower bound on the number of triangulations of planar point sets\nYear: 2004\n\nAbstract: We show that the number of straight-edge triangulations exhibited by any set of n points in general position in the plane is bounded from below by Ω(2.33n).","Title: Counting Triangulations Of Almost-Convex Polygons\nYear: 1997\n\nAbstract: We define an almost-convex polygon as a non convex polygon in which any two vertices see each other inside the polygon unless they are not adjacent and belong to a chain of consecutive concave vertices. Using inclusion-exclusion techniques, we find formulas for the number of triangulations of almost-convex polygons in terms of the number and position of the concave vertices. We translate these formulas into the language of generating functions and provide several simple asymptotic estimates. We also prove that certain balanced configurations yield the maximum number of triangulations.","Title: Consecutive patterns in permutations\nYear: 2003\n\nAbstract: In this paper we study the distribution of the number of occurrences of a permutation σ as a subword among all permutations in Sn. We solve the problem in several cases depending on the shape of σ by obtaining the corresponding bivariate exponential generating functions as solutions of certain linear differential equations with polynomial coefficients. Our method is based on the representation of permutations as increasing binary trees and on symbolic methods.","Title: On matroids determined by their Tutte polynomials\nYear: 2005\n\nAbstract: A matroid is T-unique if it is determined up to isomorphism by its Tutte polynomial. Known T-unique matroids include projective and affine geometries of rank at least four, wheels, whirls, free and binary spikes, and certain generalizations of these matroids. In this paper we survey this work and give three new results. Namely, we prove the T-uniqueness of M(K\\\"m\\\",\\\"n) and of the truncations of M(K\\\"n), and we show the existence of exponentially large families of T-unique matroids.","Title: Asymptotic enumeration of orientations\nYear: 2010\n\nAbstract: We find the asymptotic number of 2-orientations of quadrangulations with n inner faces, and of 3-orientations of triangulations with n inner vertices. We also find the asymptotic number of prime 2-orientations (no separating quadrangle) and prime 3-orientations (no separating triangle). The estimates we find are of the form c . n(-alpha)gamma(n), for suitable constants c, alpha, gamma with alpha = 4 for 2-orientations and alpha = 5 for 3-orientations. The proofs are based on singularity analysis of D-finite generating functions, using the Fuchsian theory of complex linear differential equations.","Title: Visualizing Objects With Mirrors\nYear: 2004\n\nAbstract: We present an automatic camera and mirrors placement method for visualizing 3D scenes such that complete and nonredundant visibility is guaranteed, as well as highest image resolution. We provide algorithms and geometries for optimal object-independent mirrors and viewpoint placement for all objects in a specific class. We also briefly consider object-dependent algorithmic problems, where mirrors and viewpoints are positioned as a function of the object.","Title: Enumeration and limit laws for series-parallel graphs\nYear: 2007\n\nAbstract: We show that the number g\"n of labelled series-parallel graphs on n vertices is asymptotically g\"n~g@?n^-^5^/^2@c^nn!, where @c and g are explicit computable constants. We show that the number of edges in random series-parallel graphs is asymptotically normal with linear mean and variance, and that it is sharply concentrated around its expected value. Similar results are proved for labelled outerplanar graphs and for graphs not containing K\"2\",\"3 as a minor.","Title: On the number of triangulations every planar point set must have\nYear: 2001\n\nAbstract: We show that the number of straight line triangulations exhibited by anyset of n points in general position in the plane is bounded from below by45 + ")n) for some " > 0. To the knowledge of the authors this is therst non-trivial lower bound.1","Title: Growth constants of minor-closed classes of graphs\nYear: 2010\n\nAbstract: A minor-closed class of graphs is a set of labelled graphs which is closed under isomorphism and under taking minors. For a minor-closed class G, let g\"n be the number of graphs in G which have n vertices. The growth constant of G is @c=lim@?sup(g\"n/n!)^1^/^n. We study the properties of the set @C of growth constants of minor-closed classes of graphs. Among other results, we show that @C does not contain any number in the interval [0,2], besides 0, 1, @x and 2, where @x~1.76. An infinity of further gaps is found by determining all the possible growth constants between 2 and @d~2.25159. Our results give in fact a complete characterization of all the minor-closed classes with growth constant at most @d in terms of their excluded minors.","Title: Properties of Random Triangulations and Trees\nYear: 1999\n\nAbstract: Let Tn denote the set of triangulations of a convex polygon K with n sides. We study functions that measure very natural \"geometric\" features of a triangulation¿ 2 Tn, for example,1n.¿/ which counts the maximal number of diagonals in¿ incident to a single vertex of K. It is familiar that Tn is bijectively equivalent to Bn, the set of rooted binary trees with n¡ 2 internal nodes, and also to Pn, the set of nonnegative lattice paths that start at 0, make 2n¡ 4 steps Xi of size§1, and end at X1C¢¢¢CX2n¡4D 0.1n and the other functions translate into interesting properties of trees in Bn, and paths in Pn, that seem not to have been studied before. We treat these functions as random variables under the uniform probability on Tn and can describe their behavior quite precisely. A main result is that 1n is very close to log n (all logs are base 2). Finally we describe efficient algorithms to generate triangulations in Tn uniformly, and in certain interesting subsets.","Title: Diagonally convex directed polyominoes and even trees: a bijection and related issues\nYear: 2002\n\nAbstract: We present a simple bijection between diagonally convex directed (DCD) polyominoes with n diagonals and plane trees with 2n edges in which every vertex has even degree (even trees), which specializes to a bijection between parallelogram polyominoes and full binary trees. Next we consider a natural definition of symmetry for DCD-polyominoes, even trees, ternary trees, and non-crossing trees, and show that the number of symmetric objects of a given size is the same in all four cases.","Title: Analytic combinatorics of non-crossing configurations\nYear: 1999\n\nAbstract: This paper describes a systematic approach to the enumeration of ‘non-crossing’ geometric configurations built on vertices of a convex n -gon in the plane. It relies on generating functions, symbolic methods, singularity analysis, and singularity perturbation. Consequences are both exact and asymptotic counting results for trees, forests, graphs, connected graphs, dissections, and partitions. Limit laws of the Gaussian type are also established in this framework; they concern a variety of parameters like number of leaves in trees, number of components or edges in graphs, etc.","Title: A characterization of P4-indifference graphs\nYear: 1999\n\nAbstract: A graph is a P4-indifference graph if itadmits a linear ordering $\\prec$ on its vertices such that everychordless path with vertices a, b, c, d and edges ab, bc,cd has either a $\\prec$ b $\\prec$ c$\\prec$ d or d $\\prec$ c $\\prec$ b$\\prec$ a. P4-indifference graphsgeneralize indifference graphs and are perfectly orderable. We givea characterization of P4-indifference graphs byforbidden induced subgraphs. © 1999 John Wiley & Sons,Inc. J Graph Theory 31: 155-162, 1999","Title: On the Complexity of Computing the Tutte Polynomial of Bicircular Matroids\nYear: 2006\n\nAbstract: We show that evaluating the Tutte polynomial for the class of bicircular matroids is #P-hard at every point $(x,y)$ except those in the hyperbola $(x-1)(y-1)=1$ and possibly those on the lines $x=0$ and $x=-1$. Since bicircular matroids form a rather restricted subclass of transversal matroids, our results can be seen as a partial strengthening of a result by Colbourn, Provan and Vertigan, namely that the evaluation of the Tutte polynomial for the class of transversal matroids is #P-hard for all points except those in the hyperbola $(x-1)(y-1)=1$.","Title: Hamiltonicity and colorings of arrangement graphs\nYear: 2006\n\nAbstract: We study connectivity, Hamilton path and Hamilton cycle decomposition, 4-edge and 3-vertex coloring for geometric graphs arising from pseudoline (affine or projective) and pseudocircle (spherical) arrangements. While arrangements as geometric objects are well studied in discrete and computational geometry, their graph theoretical properties seem to have received little attention so far. In this paper we show that they provide well-structured examples of families of planar and projective-planar graphs with very interesting properties. Most prominently, spherical arrangements admit decompositions into two Hamilton cycles; this is a new addition to the relatively few families of 4-regular graphs that are known to have Hamiltonian decompositions. Other classes of arrangements have interesting properties as well: 4-connectivity, 3-vertex coloring or Hamilton paths and cycles. We show a number of negative results as well: there are projective arrangements which cannot be 3-vertex colored. A number of conjectures and open questions accompany our results.","Title: Lower bounds on the number of crossing-free subgraphs of KN\nYear: 2000\n\nAbstract: We improve previous lower bounds on the number of simple polygonizations, and other kinds of crossing-free subgraphs, of a set of N points in the plane by analyzing a suitable configuration. We also prove that the number of crossing-free perfect matchings and spanning trees is minimum when the points are in convex position.","Title: Asymptotic enumeration and limit laws for graphs of fixed genus\nYear: 2011\n\nAbstract: It is shown that the number of labelled graphs with n vertices that can be embedded in the orientable surface S\"g of genus g grows asymptotically likec^(^g^)n^5^(^g^-^1^)^/^2^-^1@c^nn! where c^(^g^)0, and @c~27.23 is the exponential growth rate of planar graphs. This generalizes the result for the planar case g=0, obtained by Gimenez and Noy. An analogous result for non-orientable surfaces is obtained. In addition, it is proved that several parameters of interest behave asymptotically as in the planar case. It follows, in particular, that a random graph embeddable in S\"g has a unique 2-connected component of linear size with high probability.","Title: Bijections for Baxter families and related objects\nYear: 2011\n\nAbstract: The Baxter number B\"n can be written as B\"n=@?\"k\"=\"0^n@Q\"k\",\"n\"-\"k\"-\"1 with@Q\"k\",\"@?=2(k+1)^2(k+2)(k+@?k)(k+@?+1k)(k+@?+2k). These numbers have first appeared in the enumeration of so-called Baxter permutations; B\"n is the number of Baxter permutations of size n, and @Q\"k\",\"@? is the number of Baxter permutations with k descents and @? rises. With a series of bijections we identify several families of combinatorial objects counted by the numbers @Q\"k\",\"@?. Apart from Baxter permutations, these include plane bipolar orientations with k+2 vertices and @?+2 faces, 2-orientations of planar quadrangulations with k+2 white and @?+2 black vertices, certain pairs of binary trees with k+1 left and @?+1 right leaves, and a family of triples of non-intersecting lattice paths. This last family allows us to determine the value of @Q\"k\",\"@? as an application of the lemma of Lindstrom Gessel-Viennot. The approach also allows us to count certain other subfamilies, e.g., alternating Baxter permutations, objects with symmetries and, via a bijection with a class of plane bipolar orientations, also Schnyder woods of triangulations. Most of the enumerative results and some of the bijections are not new. Our contribution is mainly in the simplified and unifying presentation of this beautiful piece of combinatorics.","Title: On the number of self-dual rooted maps\nYear: 2014\n\nAbstract: We compute the number of self-dual rooted maps with n edges. We also compute the number of 2-connected and 3-connected self-dual rooted maps, and show that the latter are counted by the Fine numbers.","Title: On the minimum size of visibility graphs\nYear: 2002\n\nAbstract: In this paper we give tight lower bounds on the size of the visibility graph, the contracted visibility graph, and the bar-visibility graph of n disjoint line segments in the plane, according to their vertex-connectivity.","Title: Lattice path matroids: enumerative aspects and Tutte polynomials\nYear: 2003\n\nAbstract: Fix two lattice paths P and Q from (0,0) to (m, r) that use East and North steps with P never going above Q. We show that the lattice paths that go from (0,0) to (m, r) and that remain in the region bounded by P and Q can be identified with the bases of a particular type of transversal matroid, which we call a lattice path matroid. We consider a variety of enumerative aspects of these matroids and we study three important matroid invariants, namely the Tutte polynomial and, for special types of lattice path matroids, the characteristic polynomial and the β invariant. In particular, we show that the Tutte polynomial is the generating function for two basic lattice path statistics and we show that certain sequences of lattice path matroids give rise to sequences of Tutte polynomials for which there are relatively simple generating functions. We show that Tutte polynomials of lattice path matroids can be computed in polynomial time. Also, we obtain a new result about lattice paths from an analysis of the β invariant of certain lattice path matroids.","Title: Partitions of a finite Boolean lattice into intervals\nYear: 2009\n\nAbstract: Related to activities in matroids, J.E. Dawson introduced a construction that leads to partitions of the Boolean lattice of parts of a set into intervals. In this paper we characterize explicitly the partitions of a Boolean lattice into intervals that arise from this construction, and we prove that the construction is essentially unique.","Title: Graphs determined by polynomial invariants\nYear: 2003\n\nAbstract: Many polynomials have been defined associated to graphs, like the characteristic, matchings, chromatic and Tutte polynomials. Besides their intrinsic interest, they encode useful combinatorial information about the given graph. It is natural then to ask to what extent any of these polynomials determines a graph and, in particular, whether one can find graphs that can be uniquely determined by a given polynomial. In this paper we survey known results in this area and, at the same time, we present some new results.","Title: Gray codes for non-crossing partitions and dissections of a convex polygon\nYear: 2009\n\nAbstract: In this paper we develop Gray codes for two families of geometric objects: non-crossing partitions and dissections of a convex polygon by means of non-crossing diagonals.","Title: Triangle-Free Planar Graphs and Segment Intersection Graphs\nYear: 1999\n\nAbstract: We prove that every triangle-free planar graph is the intersection graph of a set of segments in the plane. Moreover, the segments can be chosen in only three directions (horizontal, vertical and oblique) and in such a way that no two segments cross, i.e., intersect in a common interior point. This particular class of intersection graphs is also known as contact graphs.","Title: Clusters, generating functions and asymptotics for consecutive patterns in permutations\nYear: 2012\n\nAbstract: We use the cluster method to enumerate permutations avoiding consecutive patterns. We reprove and generalize in a unified way several known results and obtain new ones, including some patterns of lengths 4 and 5, as well as some infinite families of patterns of a given shape. By enumerating linear extensions of certain posets, we find a differential equation satisfied by the inverse of the exponential generating function counting occurrences of the pattern. We prove that for a large class of patterns, this inverse is always an entire function.We also complete the classification of consecutive patterns of length up to 6 into equivalence classes, proving a conjecture of Nakamura. Finally, we show that the monotone pattern asymptotically dominates (in the sense that it is easiest to avoid) all non-overlapping patterns of the same length, thus proving a conjecture of Elizalde and Noy for a positive fraction of all patterns.","Title: Enumeration of noncrossing trees on a circle\nYear: 1998\n\nAbstract: We consider several enumerative problems concerning labelled trees whose vertices lie on a circle and whose edges are rectilinear and do not cross.","Title: Graph classes with given 3-connected components: Asymptotic enumeration and random graphs.\nYear: 2013\n\nAbstract: Consider a family T of 3-connected graphs of moderate growth, and let G be the class of graphs whose 3-connected components are graphs in T. We present a general framework for analyzing such graphs classes based on singularity analysis of generating functions, which generalizes previously studied cases such as planar graphs and series-parallel graphs. We provide a general result for the asymptotic number of graphs in G, based on the singularities of the exponential generating function associated to T. We derive limit laws, which are either normal or Poisson, for several basic parameters, including the number of edges, number of blocks and number of components. For the size of the largest block we find a fundamental dichotomy: classes similar to planar graphs have almost surely a unique block of linear size, while classes similar to series-parallel graphs have only sublinear blocks. This dichotomy was already observed by Panagiotou and Steger [25], and we provide a finer description. For some classes under study both regimes occur, because of a critical phenomenon as the edge density in the class varies. Finally, we analyze the size of the largest 3-connected component in random planar graphs. (C) 2012 Wiley Periodicals, Inc.","Title: Graph classes with given 3-connected components: asymptotic counting and critical phenomena\nYear: 2007\n\nAbstract: Fix a family T of 3-connected graphs, and let G be the class of graphs whose 3-connected components are the graphs in T. We present a general framework for analyzing such graph classes based on singularity analysis of generating functions. This generalizes previously studied cases such as planar graphs and series-parallel graphs. We provide a general theorem for the asymptotic number of graphs in G, based on the singularities of the exponential generating function associated to T. For some of the classes under study we show the presence of critical phenomena as the edge density in the class varies.","Title: On the maximum number of cycles in outerplanar and series-parallel graphs\nYear: 2009\n\nAbstract: Let c(n) be the maximum number of cycles in an outerplanar graph with n vertices. We show that lim c(n)1/n exists and equals β = 1.502837 . . ., where β is a constant related to the recurrence $${x_{n+1} = 1 + x_n^2, \\, x_0=1}$$. The same result holds for the larger class of series–parallel graphs.","Title: Packing trees into planar graphs\nYear: 2002\n\nAbstract: The main problem considered in this paper is the following: given two trees both with n vertices, whether it is possible to draw them on the plane with the same set of vertices without crossings and duplicated edges. We formulate this problem in terms of packing graphs and give a solution in several situations. We also solve some related problems on drawing trees and cycles.","Title: Separating objects in the plane by wedges and strips\nYear: 2001\n\nAbstract: In this paper we study the separability of two disjoint sets of objects in the plane according to two criteria: wedge separability and strip separability . We give algorithms for computing all the separating wedges and strips, the wedges with the maximum and minimum angle, and the narrowest and the widest strip. The objects we consider are points, segments, polygons and circles. As applications, we improve the computation of all the largest circles separating two sets of line segments by a log n factor, and we generalize the algorithm for computing the minimum polygonal separator of two sets of points to two sets of line segments with the same running time.","Title: Recursively constructible families of graphs\nYear: 2004\n\nAbstract: An infinite sequence of graphs {G\"n}\"n\"=\"0 is called recursive if the Tutte polynomials T(G\"n;x,y) satisfy a linear recurrence relation whose coefficients are polynomials in x and y. In this paper we introduce a general method based on transfer matrices for proving that a family is recursive that covers all examples known to us. As an application we show that, for fixed s, the sequence of complete bipartite graphs {K\"s\",\"n} is recursive and satisfies a linear recurrence whose degree is the number of partitions of the integer s.","Title: Computing the Tutte Polynomial on Graphs of Bounded Clique-Width\nYear: 2006\n\nAbstract: The Tutte polynomial is a notoriously hard graph invariant, and efficient algorithms for it are known only for a few special graph classes, like for those of bounded tree-width. The notion of clique-width extends the definition of cographs (graphs without induced $P_4$), and it is a more general notion than that of tree-width. We show a subexponential algorithm (running in time $\\exp{O(n^{1-\\varepsilon})}\\,$) for computing the Tutte polynomial on graphs of bounded clique-width. In fact, our algorithm computes the more general $U$-polynomial.","Title: Optimal Shooting: Characterizations and Applications\nYear: 1995\n\nAbstract: We have introduced the archer's problem and shown that its solution leads to the intersting class of stage graphs which we characterized to be permutation graphs. The characterization which leads to the solution for the archer's problem allowed for the development of improved algorithms for matching in permutation graphs, for a class of two-processor scheduling problems, and for several geometric problems. We answer the natural question of how the archer's problem generalizes to multiple stages and to three-dimensions. In two dimensions we establish upper and lower bounds on the number of stages required to represent graphs. In three dimensions we give characterization results and establish the NP-completeness of the recognition problem already for triangular stages.","Title: Degree distribution in random planar graphs\nYear: 2011\n\nAbstract: We prove that for each k=0, the probability that a root vertex in a random planar graph has degree k tends to a computable constant d\"k, so that the expected number of vertices of degree k is asymptotically d\"kn, and moreover that @?\"kd\"k=1. The proof uses the tools developed by Gimenez and Noy in their solution to the problem of the asymptotic enumeration of planar graphs, and is based on a detailed analysis of the generating functions involved in counting planar graphs. However, in order to keep track of the degree of the root, new technical difficulties arise. We obtain explicit, although quite involved expressions, for the coefficients in the singular expansions of the generating functions of interest, which allow us to use transfer theorems in order to get an explicit expression for the probability generating function p(w)=@?\"kd\"kw^k. From this we can compute the d\"k to any degree of accuracy, and derive the asymptotic estimate d\"k~c@?k^-^1^/^2q^k for large values of k, where q~0.67 is a constant defined analytically.","Title: Improved Bounds for the Number of Forests and Acyclic Orientations in the Square Lattice\nYear: 2003\n\nAbstract: In a recent paper Merino and Welsh (1999) studied several counting problems on the square lattice L-n. There the authors gave the following bounds for the asymptotics of f(n), the number of forests of L-n, and alpha(n), the number of acyclic orientations of L-n:3.209912 less than or equal to lim(n-->infinity) f(n)(1/n 2) less than or equal to 3.84161 and 22/7 less than or equal to lim(n-->infinity) alpha(n)(1/n 2) less than or equal to 3.70925. In this paper we improve these bounds as follows: 3.64497 less than or equal to lim(n-->infinity) f(n)(1/n 2) less than or equal to 3.74101 and 3.41358 less than or equal to lim(n-->infinity) alpha(n)(1/n 2) less than or equal to 3.55449. We obtain this by developing a method for computing the Tutte polynomial of the square lattice and other related graphs based on transfer matrices.","Title: On Graphs Determined by Their Tutte Polynomials\nYear: 2004\n\nAbstract: We say that a graph G is T-unique if any other graph having the same Tutte polynomial as G is necessarily isomorphic to G. In this paper we show that several well-known families of graphs are T-unique: wheels, squares of cycles, complete multipartite graphs, ladders, Möbius ladders, and hypercubes. In order to prove these results, we show that several parameters of a graph, like the number of cycles of length 3, 4 and 5, and the edge-connectivity are determined by its Tutte polynomial.","Title: Vertices of given degree in series-parallel graphs\nYear: 2010\n\nAbstract: We show that the numbers of vertices of a given degree k ≥ 1 in several kinds of series-parallel labeled graphs of size n satisfy a central limit theorem with mean and variance proportional to n, and quadratic exponential tail estimates. We further prove a corresponding theorem for the number of nodes of degree two in labeled planar graphs. The proof method is based on generating functions and singularity analysis. In particular, we need systems of equations for multivariate generating functions and transfer results for singular representations of analytic functions. © 2009 Wiley Periodicals, Inc. Random Struct. Alg., 2010","Title: Geometric tree graphs of points in the plane\nYear: 1998\n\nAbstract: Abstract: this paper we study the geometric treegraph T (P ), defined as the graph having T (P )as vertices and where two non-crossing spanningtrees T 1 and T 2 are adjacent if T 2 = T 1 \\Gamma e + f ,for some edges e and f (Fig. 1). The combinatorialversion of the problem has been largelystudied (see for instance [4])","Title: On the number of visibility graphs of simple polygons\nYear: 2001\n\nAbstract: We present upper and lower bounds for the number of labelled visibility graphs of simple polygons of n vertices. The lower bound is roughly in the order of O (n 2n ) and the upper bound is O (n 3n ) .","Title: A solution to the tennis ball problem\nYear: 2005\n\nAbstract: We present a complete solution to the so-called tennis ball problem, which is equivalent to counting the number of lattice paths in the plane that use North and East steps and lie between certain boundaries. The solution takes the form of explicit expressions for the corresponding generating functions.Our method is based on the properties of Tutte polynomials of matroids associated to lattice paths. We also show how the same method provides a solution to a wide generalization of the problem.","Title: Simultaneous Edge Flipping In Triangulations\nYear: 2003\n\nAbstract: We generalize the operation of flipping an edge in a triangulation to that of flipping several edges simultaneously. Our main result is an optimal upper bound on the number of simultaneous flips that are needed to transform a triangulation into another. Our results hold for triangulations of point sets and for polygons.","Title: Universal exponents and tail estimates in the enumeration of planar maps.\nYear: 2011\n\nAbstract: It has been observed that for most classes of planar maps, the number of maps of size n grows asymptotically like c⋅n−5/2γn, for suitable positive constants c and γ. It has also been observed that, if dk is the limit probability that the root vertex in a random map has degree k, then again for most classes of maps the tail of the distribution is asymptotically of the form dk∼c⋅k1/2qk as k→∞, for positive constants c, q with q<1.","Title: Statistics on non-crossing trees\nYear: 2002\n\nAbstract: A non-crossing tree is a tree drawn on the plane having as vertices a set of points on the boundary of a circle, and whose edges are straight line segments and do not cross. Continuing previous research on non-crossing trees, we study several new statistics: number of end-points and boundary edges, number of vertices of a given degree, maximum degree, height, and path-length. In some cases, we obtain closed formulae while in others we deduce asymptotic estimates. Our approach is based on generating functions and on several bijections between NC-trees and various other combinatorial objects.","Title: The maximum degree of series-parallel graphs\nYear: 2011\n\nAbstract: We prove that the maximum degree Δn of a random series-parallel graph with n vertices satisfies Δn/logn â聠聮 c in probability, and Δn ~ c logn for a computable constant c 0. The same kind of result holds for 2-connected series-parallel graphs, for outerplanar graphs, and for 2-connected outerplanar graphs.","Title: The maximum degree of random planar graphs\nYear: 2012\n\nAbstract: Let Pn denote a graph drawn uniformly at random from the class of all simple planar graphs with n vertices. We show that the maximum degree of a vertex in Pn is with probability 1 − o(1) asymptotically equal to c log n, where c ≈ 2.529 is determined explicitly. A similar result is also true for random 2-connected planar graphs. Our analysis combines two orthogonal methods that complement each other. First, in order to obtain the upper bound, we resort to exact methods, i.e., to generating functions and analytic combinatorics. This allows us to obtain fairly precise asymptotic estimates for the expected number of vertices of any given degree in Pn. On the other hand, for the lower bound we use Boltzmann sampling. In particular, by tracing the execution of an adequate algorithm that generates a random planar graph, we are able to explicitly find vertices of sufficiently high degree in Pn.","Title: Tutte uniqueness of line graphs\nYear: 2005\n\nAbstract: We prove that if a graph H has the same Tutte polynomial as the line graph of a d-regular, d-edge-connected graph, then H is the line graph of a d-regular graph. Using this result, we prove that the line graph of a regular complete t-partite graph is uniquely determined by its Tutte polynomial. We prove the same result for the line graph of any complete bipartite graph.","Title: Stabbing information of a simple polygon\nYear: 1996\n\nAbstract: The purpose of this paper is to investigate a new combinatorial object describing the structure of a simple polygon and compare it to other well-known objects such as the internal and external visibility graphs, the convex hull and the order type of the vertex set. We call the new object the stubbing information . In fact, we define three variations of the stabbing information, strong, weak and labelled, and explore the relationships among them. The main result of the paper is that strong stabbing information is sufficient to recover the convex hull, the internal and external visibility graphs and to determine which vertices are reflex and it is not sufficient to recover the order type of the vertex set. We also show that the labelled stabbing information is equivalent to the order type. We give algorithms for computing each of these new structures.","Title: A class of point-sets with few k-sets\nYear: 2000\n\nAbstract: A k -set of a finite set S of points in the plane is a subset of cardinality k that can be separated from the rest by a straight line. The question of how many k -sets a set of n points can contain is a long-standing open problem where a lower bound of Ω(n log k) and an upper bound of O (nk 1/3 ) are known today. Under certain restrictions on the set S , for example, if all points lie on a convex curve, the number of k -sets is linear . We generalize this observation by showing that if the points of S lie on a constant number of convex curves, the number of k -sets remains linear in n .","Title: Augmenting the Connectivity of Outerplanar Graphs\nYear: 2010\n\nAbstract: We provide an optimal algorithm for the problem of augmenting an outerplanar graph G by adding a minimum number of edges in such a way that the augmented graph G′ is outerplanar and 2-connected. We also solve optimally the same problem when instead we require G′ to be 2-edge-connected.","Title: Extremal statistics on non-crossing configurations.\nYear: 2014\n\nAbstract: We analyze extremal statistics in non-crossing configurations on the n vertices of a convex polygon. We prove that the maximum degree and the largest component are of logarithmic order, and that, suitably scaled, they converge to a well-defined constant. We also prove that the diameter is of order n. The proofs are based on singularity analysis, an application of the first and second moment method, and on the analysis of iterated functions.","Title: Foreword.\nYear: 1994\n\nAbstract: Delivering multifaceted, quality care to women living with metastatic breast cancer (MBC) demands professional competence and an advanced level of practice. The breast cancer nursing community is evolving to meet this need as more nurses are appointed specifically for the advanced disease setting, while nurses who previously worked only in early stage disease are now delivering care across the disease trajectory, fulfilling a 'diagnosis to death' nursing model. The MBC nursing community, linked by UK charity Breast Cancer Care and the Roche Nursing Matters programme, offers forums for learning, and provides ongoing support to this group of nurses. This supplement has been commissioned by Roche Products Ltd to continue supporting nurses who treat patients with MBC by sharing learning and best practice, with a view to encouraging innovation in service delivery.","Title: Random cubic planar graphs revisited\nYear: 2016\n\nAbstract: The goal of our work is to analyze random cubic planar graphs according to the uniform distribution. More precisely, let G be the class of labelled cubic planar graphs and let gn be the number of graphs with n vertices. Then each graph in G with n vertices has the same probability 1/gn. This model was analyzed first by Bodirsky et al. [1], and here we revisit and extend their work. The motivation for this revision is twofold. First, some proofs in [1] where incomplete with respect to the singularity analysis and we aim at providing full proofs. Secondly, we obtain new results that considerably strengthen those in [1] and shed more light on the structure of random cubic planar graphs. We present a selection of our results on asymptotic enumeration and on limit laws for parameters of random graphs.","Title: Isolated cycles of critical random graphs.\nYear: 2017\n\nAbstract: Consider the Erdos-Renyi random graph G(n, M) built with n vertices andM edges uniformly randomly chosen from the set of n2 edges. Let L be a set of positive integers. For any number of edges M 6n/2 + O(n 2/3 ), we compute – via analytic combinatorics – the number of isolated cycles of G(n, M) whose length is in L.","Title: Counting Outerplanar Maps.\nYear: 2017\n\nAbstract: A map is outerplanar if all its vertices lie in the outer face. We enumerate various classes of rooted outerplanar maps with respect to the number of edges and vertices. The proofs involve several bijections with lattice paths. As a consequence of our results, we obtain an efficient scheme for encoding simple outerplanar maps.","Title: Enumeration of labeled 4-regular planar graphs.\nYear: 2017\n\nAbstract: Abstract In this extended abstract, we present the first combinatorial scheme for counting labeled 4-regular planar graphs through a complete recursive decomposition. More precisely, we show that the exponential generating function counting labeled 4-regular planar graphs can be computed effectively as the solution of a system of equations. From here we can extract the coefficients by means of algebraic calculus. As a by-product, we can also compute the algebraic generating function counting labeled 3-connected 4-regular planar maps.","Title: On Zero-One and Convergence Laws for Graphs Embeddable on a Fixed Surface.\nYear: 2018\n\nAbstract: We show that for no surface except for the plane does monadic second-order logic (MSO) have a zero-one-law - and not even a convergence law - on the class of (connected) graphs embeddable on the surface. In addition we show that every rational in [0,1] is the limiting probability of some MSO formula. This strongly refutes a conjecture by Heinig et al. (2014) who proved a convergence law for planar graphs, and a zero-one law for connected planar graphs, and also identified the so-called gaps of [0,1]: the subintervals that are not limiting probabilities of any MSO formula. The proof relies on a combination of methods from structural graph theory, especially large face-width embeddings of graphs on surfaces, analytic combinatorics, and finite model theory, and several parts of the proof may be of independent interest. In particular, we identify precisely the properties that make the zero-one law work on planar graphs but fail for every other surface.","Title: The first order convergence law fails for random perfect graphs.\nYear: 2018\n\nAbstract: We consider first order expressible properties of random perfect graphs. That is, we pick a graph Gn uniformly at random from all (labelled) perfect graphs on n vertices and consider the probability that it satisfies some graph property that can be expressed in the first order language of graphs. We show that there exists such a first order expressible property for which the probability that Gn satisfies it does not converge as n→∞.","Title: Random Planar Maps and Graphs with Minimum Degree Two and Three.\nYear: 2018\n\nAbstract: We find precise asymptotic estimates for the number of planar maps and graphs with a condition on the minimum degree, and properties of random graphs from these classes. In particular we show that the size of the largest tree attached to the core of a random planar graph is of order c log(n) for an explicit constant c. These results provide new information on the structure of random planar graphs.","Title: Flipping Edges in Triangulations\nYear: 1999\n\nAbstract: In this paper we study the problem of flipping edges in triangulations of polygons and point sets. We prove that if a polygon Qn has k reflex vertices, then any triangulation of Qn can be transformed to another triangulation of Qn with at most O(n + k 2 ) flips. We produce examples of polygons with two triangulations T and T © such that to transform T to T © requires O(n2 ) flips. These results are then extended to triangulations of point sets. We also show that any triangulation of an n point set always has n − 4 2 edges that can be flipped."],"string":"[\n \"Title: Stage-graph representations\\nYear: 1997\\n\\nAbstract: We consider graph applications of the well-known paradigm “killing two birds with one stone”. In the plane, this gives rise to a stage graph as follows: vertices are the points, and u , v is an edge if and only if the (infinite, straight) line segment joining u to v intersects the stage. Such graphs are shown to be comparability graphs of ordered sets of dimension 2. Similar graphs can be constructed when we have a fixed number k of stages on the plane. In this case, u , v is an edge if and only if the (straight) line segment uv intersects one of the k stages. In this paper, we study stage representations of stage graphs and give upper and lower bounds on the number of stages needed to represent a graph.\",\n \"Title: Preface: Volume 10\\nYear: 2001\\n\\nAbstract: The http://www-mat.upc.es/limda/comb01/wwgrafs/principalx.html Euroconference on Combinatorics, Graph Theory and Applications was held in the http://www.crm.esCentre de Recerca Matemàtica near Barcelona, Spain, on September 12-14 2001.\",\n \"Title: Graphs of Non-Crossing Perfect Matchings\\nYear: 2002\\n\\nAbstract: Let Pn be a set of n = 2m points that are the vertices of a convex polygon, and let Mmbe the graph having as vertices all the perfect matchings in the point set Pn whose edgesare straight line segments and do not cross, and edges joining two perfect matchings M1and M2 if M2 = M1 \\\\Gamma (a; b) \\\\Gamma (c; d) + (a; d) + (b; c) for some points a; b; c; d of Pn . Weprove the following results about Mm : its diameter is m \\\\Gamma 1; it is bipartite for every m;the connectivity is equal to m...\",\n \"Title: Identities for non-crossing graphs and multigraphs\\nYear: 2008\\n\\nAbstract: We refine an identity between the numbers of certain non-crossing graphs and multigraphs, by modifying a bijection found by P. Podbrdský [A bijective proof of an identity for noncrossing graphs, Discrete Math. 260 (2003) 249–253]. We also prove a new identity between the number of acyclic non-crossing graphs with n vertices and k edges (isolated vertices allowed and no multiple edges allowed), and the number of non-crossing connected graphs with n edges and k vertices (multiple edges allowed and no isolated vertices allowed).\",\n \"Title: A lower bound on the number of triangulations of planar point sets\\nYear: 2004\\n\\nAbstract: We show that the number of straight-edge triangulations exhibited by any set of n points in general position in the plane is bounded from below by Ω(2.33n).\",\n \"Title: Counting Triangulations Of Almost-Convex Polygons\\nYear: 1997\\n\\nAbstract: We define an almost-convex polygon as a non convex polygon in which any two vertices see each other inside the polygon unless they are not adjacent and belong to a chain of consecutive concave vertices. Using inclusion-exclusion techniques, we find formulas for the number of triangulations of almost-convex polygons in terms of the number and position of the concave vertices. We translate these formulas into the language of generating functions and provide several simple asymptotic estimates. We also prove that certain balanced configurations yield the maximum number of triangulations.\",\n \"Title: Consecutive patterns in permutations\\nYear: 2003\\n\\nAbstract: In this paper we study the distribution of the number of occurrences of a permutation σ as a subword among all permutations in Sn. We solve the problem in several cases depending on the shape of σ by obtaining the corresponding bivariate exponential generating functions as solutions of certain linear differential equations with polynomial coefficients. Our method is based on the representation of permutations as increasing binary trees and on symbolic methods.\",\n \"Title: On matroids determined by their Tutte polynomials\\nYear: 2005\\n\\nAbstract: A matroid is T-unique if it is determined up to isomorphism by its Tutte polynomial. Known T-unique matroids include projective and affine geometries of rank at least four, wheels, whirls, free and binary spikes, and certain generalizations of these matroids. In this paper we survey this work and give three new results. Namely, we prove the T-uniqueness of M(K\\\\\\\"m\\\\\\\",\\\\\\\"n) and of the truncations of M(K\\\\\\\"n), and we show the existence of exponentially large families of T-unique matroids.\",\n \"Title: Asymptotic enumeration of orientations\\nYear: 2010\\n\\nAbstract: We find the asymptotic number of 2-orientations of quadrangulations with n inner faces, and of 3-orientations of triangulations with n inner vertices. We also find the asymptotic number of prime 2-orientations (no separating quadrangle) and prime 3-orientations (no separating triangle). The estimates we find are of the form c . n(-alpha)gamma(n), for suitable constants c, alpha, gamma with alpha = 4 for 2-orientations and alpha = 5 for 3-orientations. The proofs are based on singularity analysis of D-finite generating functions, using the Fuchsian theory of complex linear differential equations.\",\n \"Title: Visualizing Objects With Mirrors\\nYear: 2004\\n\\nAbstract: We present an automatic camera and mirrors placement method for visualizing 3D scenes such that complete and nonredundant visibility is guaranteed, as well as highest image resolution. We provide algorithms and geometries for optimal object-independent mirrors and viewpoint placement for all objects in a specific class. We also briefly consider object-dependent algorithmic problems, where mirrors and viewpoints are positioned as a function of the object.\",\n \"Title: Enumeration and limit laws for series-parallel graphs\\nYear: 2007\\n\\nAbstract: We show that the number g\\\"n of labelled series-parallel graphs on n vertices is asymptotically g\\\"n~g@?n^-^5^/^2@c^nn!, where @c and g are explicit computable constants. We show that the number of edges in random series-parallel graphs is asymptotically normal with linear mean and variance, and that it is sharply concentrated around its expected value. Similar results are proved for labelled outerplanar graphs and for graphs not containing K\\\"2\\\",\\\"3 as a minor.\",\n \"Title: On the number of triangulations every planar point set must have\\nYear: 2001\\n\\nAbstract: We show that the number of straight line triangulations exhibited by anyset of n points in general position in the plane is bounded from below by45 + ")n) for some " > 0. To the knowledge of the authors this is therst non-trivial lower bound.1\",\n \"Title: Growth constants of minor-closed classes of graphs\\nYear: 2010\\n\\nAbstract: A minor-closed class of graphs is a set of labelled graphs which is closed under isomorphism and under taking minors. For a minor-closed class G, let g\\\"n be the number of graphs in G which have n vertices. The growth constant of G is @c=lim@?sup(g\\\"n/n!)^1^/^n. We study the properties of the set @C of growth constants of minor-closed classes of graphs. Among other results, we show that @C does not contain any number in the interval [0,2], besides 0, 1, @x and 2, where @x~1.76. An infinity of further gaps is found by determining all the possible growth constants between 2 and @d~2.25159. Our results give in fact a complete characterization of all the minor-closed classes with growth constant at most @d in terms of their excluded minors.\",\n \"Title: Properties of Random Triangulations and Trees\\nYear: 1999\\n\\nAbstract: Let Tn denote the set of triangulations of a convex polygon K with n sides. We study functions that measure very natural \\\"geometric\\\" features of a triangulation¿ 2 Tn, for example,1n.¿/ which counts the maximal number of diagonals in¿ incident to a single vertex of K. It is familiar that Tn is bijectively equivalent to Bn, the set of rooted binary trees with n¡ 2 internal nodes, and also to Pn, the set of nonnegative lattice paths that start at 0, make 2n¡ 4 steps Xi of size§1, and end at X1C¢¢¢CX2n¡4D 0.1n and the other functions translate into interesting properties of trees in Bn, and paths in Pn, that seem not to have been studied before. We treat these functions as random variables under the uniform probability on Tn and can describe their behavior quite precisely. A main result is that 1n is very close to log n (all logs are base 2). Finally we describe efficient algorithms to generate triangulations in Tn uniformly, and in certain interesting subsets.\",\n \"Title: Diagonally convex directed polyominoes and even trees: a bijection and related issues\\nYear: 2002\\n\\nAbstract: We present a simple bijection between diagonally convex directed (DCD) polyominoes with n diagonals and plane trees with 2n edges in which every vertex has even degree (even trees), which specializes to a bijection between parallelogram polyominoes and full binary trees. Next we consider a natural definition of symmetry for DCD-polyominoes, even trees, ternary trees, and non-crossing trees, and show that the number of symmetric objects of a given size is the same in all four cases.\",\n \"Title: Analytic combinatorics of non-crossing configurations\\nYear: 1999\\n\\nAbstract: This paper describes a systematic approach to the enumeration of ‘non-crossing’ geometric configurations built on vertices of a convex n -gon in the plane. It relies on generating functions, symbolic methods, singularity analysis, and singularity perturbation. Consequences are both exact and asymptotic counting results for trees, forests, graphs, connected graphs, dissections, and partitions. Limit laws of the Gaussian type are also established in this framework; they concern a variety of parameters like number of leaves in trees, number of components or edges in graphs, etc.\",\n \"Title: A characterization of P4-indifference graphs\\nYear: 1999\\n\\nAbstract: A graph is a P4-indifference graph if itadmits a linear ordering $\\\\prec$ on its vertices such that everychordless path with vertices a, b, c, d and edges ab, bc,cd has either a $\\\\prec$ b $\\\\prec$ c$\\\\prec$ d or d $\\\\prec$ c $\\\\prec$ b$\\\\prec$ a. P4-indifference graphsgeneralize indifference graphs and are perfectly orderable. We givea characterization of P4-indifference graphs byforbidden induced subgraphs. © 1999 John Wiley & Sons,Inc. J Graph Theory 31: 155-162, 1999\",\n \"Title: On the Complexity of Computing the Tutte Polynomial of Bicircular Matroids\\nYear: 2006\\n\\nAbstract: We show that evaluating the Tutte polynomial for the class of bicircular matroids is #P-hard at every point $(x,y)$ except those in the hyperbola $(x-1)(y-1)=1$ and possibly those on the lines $x=0$ and $x=-1$. Since bicircular matroids form a rather restricted subclass of transversal matroids, our results can be seen as a partial strengthening of a result by Colbourn, Provan and Vertigan, namely that the evaluation of the Tutte polynomial for the class of transversal matroids is #P-hard for all points except those in the hyperbola $(x-1)(y-1)=1$.\",\n \"Title: Hamiltonicity and colorings of arrangement graphs\\nYear: 2006\\n\\nAbstract: We study connectivity, Hamilton path and Hamilton cycle decomposition, 4-edge and 3-vertex coloring for geometric graphs arising from pseudoline (affine or projective) and pseudocircle (spherical) arrangements. While arrangements as geometric objects are well studied in discrete and computational geometry, their graph theoretical properties seem to have received little attention so far. In this paper we show that they provide well-structured examples of families of planar and projective-planar graphs with very interesting properties. Most prominently, spherical arrangements admit decompositions into two Hamilton cycles; this is a new addition to the relatively few families of 4-regular graphs that are known to have Hamiltonian decompositions. Other classes of arrangements have interesting properties as well: 4-connectivity, 3-vertex coloring or Hamilton paths and cycles. We show a number of negative results as well: there are projective arrangements which cannot be 3-vertex colored. A number of conjectures and open questions accompany our results.\",\n \"Title: Lower bounds on the number of crossing-free subgraphs of KN\\nYear: 2000\\n\\nAbstract: We improve previous lower bounds on the number of simple polygonizations, and other kinds of crossing-free subgraphs, of a set of N points in the plane by analyzing a suitable configuration. We also prove that the number of crossing-free perfect matchings and spanning trees is minimum when the points are in convex position.\",\n \"Title: Asymptotic enumeration and limit laws for graphs of fixed genus\\nYear: 2011\\n\\nAbstract: It is shown that the number of labelled graphs with n vertices that can be embedded in the orientable surface S\\\"g of genus g grows asymptotically likec^(^g^)n^5^(^g^-^1^)^/^2^-^1@c^nn! where c^(^g^)0, and @c~27.23 is the exponential growth rate of planar graphs. This generalizes the result for the planar case g=0, obtained by Gimenez and Noy. An analogous result for non-orientable surfaces is obtained. In addition, it is proved that several parameters of interest behave asymptotically as in the planar case. It follows, in particular, that a random graph embeddable in S\\\"g has a unique 2-connected component of linear size with high probability.\",\n \"Title: Bijections for Baxter families and related objects\\nYear: 2011\\n\\nAbstract: The Baxter number B\\\"n can be written as B\\\"n=@?\\\"k\\\"=\\\"0^n@Q\\\"k\\\",\\\"n\\\"-\\\"k\\\"-\\\"1 with@Q\\\"k\\\",\\\"@?=2(k+1)^2(k+2)(k+@?k)(k+@?+1k)(k+@?+2k). These numbers have first appeared in the enumeration of so-called Baxter permutations; B\\\"n is the number of Baxter permutations of size n, and @Q\\\"k\\\",\\\"@? is the number of Baxter permutations with k descents and @? rises. With a series of bijections we identify several families of combinatorial objects counted by the numbers @Q\\\"k\\\",\\\"@?. Apart from Baxter permutations, these include plane bipolar orientations with k+2 vertices and @?+2 faces, 2-orientations of planar quadrangulations with k+2 white and @?+2 black vertices, certain pairs of binary trees with k+1 left and @?+1 right leaves, and a family of triples of non-intersecting lattice paths. This last family allows us to determine the value of @Q\\\"k\\\",\\\"@? as an application of the lemma of Lindstrom Gessel-Viennot. The approach also allows us to count certain other subfamilies, e.g., alternating Baxter permutations, objects with symmetries and, via a bijection with a class of plane bipolar orientations, also Schnyder woods of triangulations. Most of the enumerative results and some of the bijections are not new. Our contribution is mainly in the simplified and unifying presentation of this beautiful piece of combinatorics.\",\n \"Title: On the number of self-dual rooted maps\\nYear: 2014\\n\\nAbstract: We compute the number of self-dual rooted maps with n edges. We also compute the number of 2-connected and 3-connected self-dual rooted maps, and show that the latter are counted by the Fine numbers.\",\n \"Title: On the minimum size of visibility graphs\\nYear: 2002\\n\\nAbstract: In this paper we give tight lower bounds on the size of the visibility graph, the contracted visibility graph, and the bar-visibility graph of n disjoint line segments in the plane, according to their vertex-connectivity.\",\n \"Title: Lattice path matroids: enumerative aspects and Tutte polynomials\\nYear: 2003\\n\\nAbstract: Fix two lattice paths P and Q from (0,0) to (m, r) that use East and North steps with P never going above Q. We show that the lattice paths that go from (0,0) to (m, r) and that remain in the region bounded by P and Q can be identified with the bases of a particular type of transversal matroid, which we call a lattice path matroid. We consider a variety of enumerative aspects of these matroids and we study three important matroid invariants, namely the Tutte polynomial and, for special types of lattice path matroids, the characteristic polynomial and the β invariant. In particular, we show that the Tutte polynomial is the generating function for two basic lattice path statistics and we show that certain sequences of lattice path matroids give rise to sequences of Tutte polynomials for which there are relatively simple generating functions. We show that Tutte polynomials of lattice path matroids can be computed in polynomial time. Also, we obtain a new result about lattice paths from an analysis of the β invariant of certain lattice path matroids.\",\n \"Title: Partitions of a finite Boolean lattice into intervals\\nYear: 2009\\n\\nAbstract: Related to activities in matroids, J.E. Dawson introduced a construction that leads to partitions of the Boolean lattice of parts of a set into intervals. In this paper we characterize explicitly the partitions of a Boolean lattice into intervals that arise from this construction, and we prove that the construction is essentially unique.\",\n \"Title: Graphs determined by polynomial invariants\\nYear: 2003\\n\\nAbstract: Many polynomials have been defined associated to graphs, like the characteristic, matchings, chromatic and Tutte polynomials. Besides their intrinsic interest, they encode useful combinatorial information about the given graph. It is natural then to ask to what extent any of these polynomials determines a graph and, in particular, whether one can find graphs that can be uniquely determined by a given polynomial. In this paper we survey known results in this area and, at the same time, we present some new results.\",\n \"Title: Gray codes for non-crossing partitions and dissections of a convex polygon\\nYear: 2009\\n\\nAbstract: In this paper we develop Gray codes for two families of geometric objects: non-crossing partitions and dissections of a convex polygon by means of non-crossing diagonals.\",\n \"Title: Triangle-Free Planar Graphs and Segment Intersection Graphs\\nYear: 1999\\n\\nAbstract: We prove that every triangle-free planar graph is the intersection graph of a set of segments in the plane. Moreover, the segments can be chosen in only three directions (horizontal, vertical and oblique) and in such a way that no two segments cross, i.e., intersect in a common interior point. This particular class of intersection graphs is also known as contact graphs.\",\n \"Title: Clusters, generating functions and asymptotics for consecutive patterns in permutations\\nYear: 2012\\n\\nAbstract: We use the cluster method to enumerate permutations avoiding consecutive patterns. We reprove and generalize in a unified way several known results and obtain new ones, including some patterns of lengths 4 and 5, as well as some infinite families of patterns of a given shape. By enumerating linear extensions of certain posets, we find a differential equation satisfied by the inverse of the exponential generating function counting occurrences of the pattern. We prove that for a large class of patterns, this inverse is always an entire function.We also complete the classification of consecutive patterns of length up to 6 into equivalence classes, proving a conjecture of Nakamura. Finally, we show that the monotone pattern asymptotically dominates (in the sense that it is easiest to avoid) all non-overlapping patterns of the same length, thus proving a conjecture of Elizalde and Noy for a positive fraction of all patterns.\",\n \"Title: Enumeration of noncrossing trees on a circle\\nYear: 1998\\n\\nAbstract: We consider several enumerative problems concerning labelled trees whose vertices lie on a circle and whose edges are rectilinear and do not cross.\",\n \"Title: Graph classes with given 3-connected components: Asymptotic enumeration and random graphs.\\nYear: 2013\\n\\nAbstract: Consider a family T of 3-connected graphs of moderate growth, and let G be the class of graphs whose 3-connected components are graphs in T. We present a general framework for analyzing such graphs classes based on singularity analysis of generating functions, which generalizes previously studied cases such as planar graphs and series-parallel graphs. We provide a general result for the asymptotic number of graphs in G, based on the singularities of the exponential generating function associated to T. We derive limit laws, which are either normal or Poisson, for several basic parameters, including the number of edges, number of blocks and number of components. For the size of the largest block we find a fundamental dichotomy: classes similar to planar graphs have almost surely a unique block of linear size, while classes similar to series-parallel graphs have only sublinear blocks. This dichotomy was already observed by Panagiotou and Steger [25], and we provide a finer description. For some classes under study both regimes occur, because of a critical phenomenon as the edge density in the class varies. Finally, we analyze the size of the largest 3-connected component in random planar graphs. (C) 2012 Wiley Periodicals, Inc.\",\n \"Title: Graph classes with given 3-connected components: asymptotic counting and critical phenomena\\nYear: 2007\\n\\nAbstract: Fix a family T of 3-connected graphs, and let G be the class of graphs whose 3-connected components are the graphs in T. We present a general framework for analyzing such graph classes based on singularity analysis of generating functions. This generalizes previously studied cases such as planar graphs and series-parallel graphs. We provide a general theorem for the asymptotic number of graphs in G, based on the singularities of the exponential generating function associated to T. For some of the classes under study we show the presence of critical phenomena as the edge density in the class varies.\",\n \"Title: On the maximum number of cycles in outerplanar and series-parallel graphs\\nYear: 2009\\n\\nAbstract: Let c(n) be the maximum number of cycles in an outerplanar graph with n vertices. We show that lim c(n)1/n exists and equals β = 1.502837 . . ., where β is a constant related to the recurrence $${x_{n+1} = 1 + x_n^2, \\\\, x_0=1}$$. The same result holds for the larger class of series–parallel graphs.\",\n \"Title: Packing trees into planar graphs\\nYear: 2002\\n\\nAbstract: The main problem considered in this paper is the following: given two trees both with n vertices, whether it is possible to draw them on the plane with the same set of vertices without crossings and duplicated edges. We formulate this problem in terms of packing graphs and give a solution in several situations. We also solve some related problems on drawing trees and cycles.\",\n \"Title: Separating objects in the plane by wedges and strips\\nYear: 2001\\n\\nAbstract: In this paper we study the separability of two disjoint sets of objects in the plane according to two criteria: wedge separability and strip separability . We give algorithms for computing all the separating wedges and strips, the wedges with the maximum and minimum angle, and the narrowest and the widest strip. The objects we consider are points, segments, polygons and circles. As applications, we improve the computation of all the largest circles separating two sets of line segments by a log n factor, and we generalize the algorithm for computing the minimum polygonal separator of two sets of points to two sets of line segments with the same running time.\",\n \"Title: Recursively constructible families of graphs\\nYear: 2004\\n\\nAbstract: An infinite sequence of graphs {G\\\"n}\\\"n\\\"=\\\"0 is called recursive if the Tutte polynomials T(G\\\"n;x,y) satisfy a linear recurrence relation whose coefficients are polynomials in x and y. In this paper we introduce a general method based on transfer matrices for proving that a family is recursive that covers all examples known to us. As an application we show that, for fixed s, the sequence of complete bipartite graphs {K\\\"s\\\",\\\"n} is recursive and satisfies a linear recurrence whose degree is the number of partitions of the integer s.\",\n \"Title: Computing the Tutte Polynomial on Graphs of Bounded Clique-Width\\nYear: 2006\\n\\nAbstract: The Tutte polynomial is a notoriously hard graph invariant, and efficient algorithms for it are known only for a few special graph classes, like for those of bounded tree-width. The notion of clique-width extends the definition of cographs (graphs without induced $P_4$), and it is a more general notion than that of tree-width. We show a subexponential algorithm (running in time $\\\\exp{O(n^{1-\\\\varepsilon})}\\\\,$) for computing the Tutte polynomial on graphs of bounded clique-width. In fact, our algorithm computes the more general $U$-polynomial.\",\n \"Title: Optimal Shooting: Characterizations and Applications\\nYear: 1995\\n\\nAbstract: We have introduced the archer's problem and shown that its solution leads to the intersting class of stage graphs which we characterized to be permutation graphs. The characterization which leads to the solution for the archer's problem allowed for the development of improved algorithms for matching in permutation graphs, for a class of two-processor scheduling problems, and for several geometric problems. We answer the natural question of how the archer's problem generalizes to multiple stages and to three-dimensions. In two dimensions we establish upper and lower bounds on the number of stages required to represent graphs. In three dimensions we give characterization results and establish the NP-completeness of the recognition problem already for triangular stages.\",\n \"Title: Degree distribution in random planar graphs\\nYear: 2011\\n\\nAbstract: We prove that for each k=0, the probability that a root vertex in a random planar graph has degree k tends to a computable constant d\\\"k, so that the expected number of vertices of degree k is asymptotically d\\\"kn, and moreover that @?\\\"kd\\\"k=1. The proof uses the tools developed by Gimenez and Noy in their solution to the problem of the asymptotic enumeration of planar graphs, and is based on a detailed analysis of the generating functions involved in counting planar graphs. However, in order to keep track of the degree of the root, new technical difficulties arise. We obtain explicit, although quite involved expressions, for the coefficients in the singular expansions of the generating functions of interest, which allow us to use transfer theorems in order to get an explicit expression for the probability generating function p(w)=@?\\\"kd\\\"kw^k. From this we can compute the d\\\"k to any degree of accuracy, and derive the asymptotic estimate d\\\"k~c@?k^-^1^/^2q^k for large values of k, where q~0.67 is a constant defined analytically.\",\n \"Title: Improved Bounds for the Number of Forests and Acyclic Orientations in the Square Lattice\\nYear: 2003\\n\\nAbstract: In a recent paper Merino and Welsh (1999) studied several counting problems on the square lattice L-n. There the authors gave the following bounds for the asymptotics of f(n), the number of forests of L-n, and alpha(n), the number of acyclic orientations of L-n:3.209912 less than or equal to lim(n-->infinity) f(n)(1/n 2) less than or equal to 3.84161 and 22/7 less than or equal to lim(n-->infinity) alpha(n)(1/n 2) less than or equal to 3.70925. In this paper we improve these bounds as follows: 3.64497 less than or equal to lim(n-->infinity) f(n)(1/n 2) less than or equal to 3.74101 and 3.41358 less than or equal to lim(n-->infinity) alpha(n)(1/n 2) less than or equal to 3.55449. We obtain this by developing a method for computing the Tutte polynomial of the square lattice and other related graphs based on transfer matrices.\",\n \"Title: On Graphs Determined by Their Tutte Polynomials\\nYear: 2004\\n\\nAbstract: We say that a graph G is T-unique if any other graph having the same Tutte polynomial as G is necessarily isomorphic to G. In this paper we show that several well-known families of graphs are T-unique: wheels, squares of cycles, complete multipartite graphs, ladders, Möbius ladders, and hypercubes. In order to prove these results, we show that several parameters of a graph, like the number of cycles of length 3, 4 and 5, and the edge-connectivity are determined by its Tutte polynomial.\",\n \"Title: Vertices of given degree in series-parallel graphs\\nYear: 2010\\n\\nAbstract: We show that the numbers of vertices of a given degree k ≥ 1 in several kinds of series-parallel labeled graphs of size n satisfy a central limit theorem with mean and variance proportional to n, and quadratic exponential tail estimates. We further prove a corresponding theorem for the number of nodes of degree two in labeled planar graphs. The proof method is based on generating functions and singularity analysis. In particular, we need systems of equations for multivariate generating functions and transfer results for singular representations of analytic functions. © 2009 Wiley Periodicals, Inc. Random Struct. Alg., 2010\",\n \"Title: Geometric tree graphs of points in the plane\\nYear: 1998\\n\\nAbstract: Abstract: this paper we study the geometric treegraph T (P ), defined as the graph having T (P )as vertices and where two non-crossing spanningtrees T 1 and T 2 are adjacent if T 2 = T 1 \\\\Gamma e + f ,for some edges e and f (Fig. 1). The combinatorialversion of the problem has been largelystudied (see for instance [4])\",\n \"Title: On the number of visibility graphs of simple polygons\\nYear: 2001\\n\\nAbstract: We present upper and lower bounds for the number of labelled visibility graphs of simple polygons of n vertices. The lower bound is roughly in the order of O (n 2n ) and the upper bound is O (n 3n ) .\",\n \"Title: A solution to the tennis ball problem\\nYear: 2005\\n\\nAbstract: We present a complete solution to the so-called tennis ball problem, which is equivalent to counting the number of lattice paths in the plane that use North and East steps and lie between certain boundaries. The solution takes the form of explicit expressions for the corresponding generating functions.Our method is based on the properties of Tutte polynomials of matroids associated to lattice paths. We also show how the same method provides a solution to a wide generalization of the problem.\",\n \"Title: Simultaneous Edge Flipping In Triangulations\\nYear: 2003\\n\\nAbstract: We generalize the operation of flipping an edge in a triangulation to that of flipping several edges simultaneously. Our main result is an optimal upper bound on the number of simultaneous flips that are needed to transform a triangulation into another. Our results hold for triangulations of point sets and for polygons.\",\n \"Title: Universal exponents and tail estimates in the enumeration of planar maps.\\nYear: 2011\\n\\nAbstract: It has been observed that for most classes of planar maps, the number of maps of size n grows asymptotically like c⋅n−5/2γn, for suitable positive constants c and γ. It has also been observed that, if dk is the limit probability that the root vertex in a random map has degree k, then again for most classes of maps the tail of the distribution is asymptotically of the form dk∼c⋅k1/2qk as k→∞, for positive constants c, q with q<1.\",\n \"Title: Statistics on non-crossing trees\\nYear: 2002\\n\\nAbstract: A non-crossing tree is a tree drawn on the plane having as vertices a set of points on the boundary of a circle, and whose edges are straight line segments and do not cross. Continuing previous research on non-crossing trees, we study several new statistics: number of end-points and boundary edges, number of vertices of a given degree, maximum degree, height, and path-length. In some cases, we obtain closed formulae while in others we deduce asymptotic estimates. Our approach is based on generating functions and on several bijections between NC-trees and various other combinatorial objects.\",\n \"Title: The maximum degree of series-parallel graphs\\nYear: 2011\\n\\nAbstract: We prove that the maximum degree Δn of a random series-parallel graph with n vertices satisfies Δn/logn â聠聮 c in probability, and Δn ~ c logn for a computable constant c 0. The same kind of result holds for 2-connected series-parallel graphs, for outerplanar graphs, and for 2-connected outerplanar graphs.\",\n \"Title: The maximum degree of random planar graphs\\nYear: 2012\\n\\nAbstract: Let Pn denote a graph drawn uniformly at random from the class of all simple planar graphs with n vertices. We show that the maximum degree of a vertex in Pn is with probability 1 − o(1) asymptotically equal to c log n, where c ≈ 2.529 is determined explicitly. A similar result is also true for random 2-connected planar graphs. Our analysis combines two orthogonal methods that complement each other. First, in order to obtain the upper bound, we resort to exact methods, i.e., to generating functions and analytic combinatorics. This allows us to obtain fairly precise asymptotic estimates for the expected number of vertices of any given degree in Pn. On the other hand, for the lower bound we use Boltzmann sampling. In particular, by tracing the execution of an adequate algorithm that generates a random planar graph, we are able to explicitly find vertices of sufficiently high degree in Pn.\",\n \"Title: Tutte uniqueness of line graphs\\nYear: 2005\\n\\nAbstract: We prove that if a graph H has the same Tutte polynomial as the line graph of a d-regular, d-edge-connected graph, then H is the line graph of a d-regular graph. Using this result, we prove that the line graph of a regular complete t-partite graph is uniquely determined by its Tutte polynomial. We prove the same result for the line graph of any complete bipartite graph.\",\n \"Title: Stabbing information of a simple polygon\\nYear: 1996\\n\\nAbstract: The purpose of this paper is to investigate a new combinatorial object describing the structure of a simple polygon and compare it to other well-known objects such as the internal and external visibility graphs, the convex hull and the order type of the vertex set. We call the new object the stubbing information . In fact, we define three variations of the stabbing information, strong, weak and labelled, and explore the relationships among them. The main result of the paper is that strong stabbing information is sufficient to recover the convex hull, the internal and external visibility graphs and to determine which vertices are reflex and it is not sufficient to recover the order type of the vertex set. We also show that the labelled stabbing information is equivalent to the order type. We give algorithms for computing each of these new structures.\",\n \"Title: A class of point-sets with few k-sets\\nYear: 2000\\n\\nAbstract: A k -set of a finite set S of points in the plane is a subset of cardinality k that can be separated from the rest by a straight line. The question of how many k -sets a set of n points can contain is a long-standing open problem where a lower bound of Ω(n log k) and an upper bound of O (nk 1/3 ) are known today. Under certain restrictions on the set S , for example, if all points lie on a convex curve, the number of k -sets is linear . We generalize this observation by showing that if the points of S lie on a constant number of convex curves, the number of k -sets remains linear in n .\",\n \"Title: Augmenting the Connectivity of Outerplanar Graphs\\nYear: 2010\\n\\nAbstract: We provide an optimal algorithm for the problem of augmenting an outerplanar graph G by adding a minimum number of edges in such a way that the augmented graph G′ is outerplanar and 2-connected. We also solve optimally the same problem when instead we require G′ to be 2-edge-connected.\",\n \"Title: Extremal statistics on non-crossing configurations.\\nYear: 2014\\n\\nAbstract: We analyze extremal statistics in non-crossing configurations on the n vertices of a convex polygon. We prove that the maximum degree and the largest component are of logarithmic order, and that, suitably scaled, they converge to a well-defined constant. We also prove that the diameter is of order n. The proofs are based on singularity analysis, an application of the first and second moment method, and on the analysis of iterated functions.\",\n \"Title: Foreword.\\nYear: 1994\\n\\nAbstract: Delivering multifaceted, quality care to women living with metastatic breast cancer (MBC) demands professional competence and an advanced level of practice. The breast cancer nursing community is evolving to meet this need as more nurses are appointed specifically for the advanced disease setting, while nurses who previously worked only in early stage disease are now delivering care across the disease trajectory, fulfilling a 'diagnosis to death' nursing model. The MBC nursing community, linked by UK charity Breast Cancer Care and the Roche Nursing Matters programme, offers forums for learning, and provides ongoing support to this group of nurses. This supplement has been commissioned by Roche Products Ltd to continue supporting nurses who treat patients with MBC by sharing learning and best practice, with a view to encouraging innovation in service delivery.\",\n \"Title: Random cubic planar graphs revisited\\nYear: 2016\\n\\nAbstract: The goal of our work is to analyze random cubic planar graphs according to the uniform distribution. More precisely, let G be the class of labelled cubic planar graphs and let gn be the number of graphs with n vertices. Then each graph in G with n vertices has the same probability 1/gn. This model was analyzed first by Bodirsky et al. [1], and here we revisit and extend their work. The motivation for this revision is twofold. First, some proofs in [1] where incomplete with respect to the singularity analysis and we aim at providing full proofs. Secondly, we obtain new results that considerably strengthen those in [1] and shed more light on the structure of random cubic planar graphs. We present a selection of our results on asymptotic enumeration and on limit laws for parameters of random graphs.\",\n \"Title: Isolated cycles of critical random graphs.\\nYear: 2017\\n\\nAbstract: Consider the Erdos-Renyi random graph G(n, M) built with n vertices andM edges uniformly randomly chosen from the set of n2 edges. Let L be a set of positive integers. For any number of edges M 6n/2 + O(n 2/3 ), we compute – via analytic combinatorics – the number of isolated cycles of G(n, M) whose length is in L.\",\n \"Title: Counting Outerplanar Maps.\\nYear: 2017\\n\\nAbstract: A map is outerplanar if all its vertices lie in the outer face. We enumerate various classes of rooted outerplanar maps with respect to the number of edges and vertices. The proofs involve several bijections with lattice paths. As a consequence of our results, we obtain an efficient scheme for encoding simple outerplanar maps.\",\n \"Title: Enumeration of labeled 4-regular planar graphs.\\nYear: 2017\\n\\nAbstract: Abstract In this extended abstract, we present the first combinatorial scheme for counting labeled 4-regular planar graphs through a complete recursive decomposition. More precisely, we show that the exponential generating function counting labeled 4-regular planar graphs can be computed effectively as the solution of a system of equations. From here we can extract the coefficients by means of algebraic calculus. As a by-product, we can also compute the algebraic generating function counting labeled 3-connected 4-regular planar maps.\",\n \"Title: On Zero-One and Convergence Laws for Graphs Embeddable on a Fixed Surface.\\nYear: 2018\\n\\nAbstract: We show that for no surface except for the plane does monadic second-order logic (MSO) have a zero-one-law - and not even a convergence law - on the class of (connected) graphs embeddable on the surface. In addition we show that every rational in [0,1] is the limiting probability of some MSO formula. This strongly refutes a conjecture by Heinig et al. (2014) who proved a convergence law for planar graphs, and a zero-one law for connected planar graphs, and also identified the so-called gaps of [0,1]: the subintervals that are not limiting probabilities of any MSO formula. The proof relies on a combination of methods from structural graph theory, especially large face-width embeddings of graphs on surfaces, analytic combinatorics, and finite model theory, and several parts of the proof may be of independent interest. In particular, we identify precisely the properties that make the zero-one law work on planar graphs but fail for every other surface.\",\n \"Title: The first order convergence law fails for random perfect graphs.\\nYear: 2018\\n\\nAbstract: We consider first order expressible properties of random perfect graphs. That is, we pick a graph Gn uniformly at random from all (labelled) perfect graphs on n vertices and consider the probability that it satisfies some graph property that can be expressed in the first order language of graphs. We show that there exists such a first order expressible property for which the probability that Gn satisfies it does not converge as n→∞.\",\n \"Title: Random Planar Maps and Graphs with Minimum Degree Two and Three.\\nYear: 2018\\n\\nAbstract: We find precise asymptotic estimates for the number of planar maps and graphs with a condition on the minimum degree, and properties of random graphs from these classes. In particular we show that the size of the largest tree attached to the core of a random planar graph is of order c log(n) for an explicit constant c. These results provide new information on the structure of random planar graphs.\",\n \"Title: Flipping Edges in Triangulations\\nYear: 1999\\n\\nAbstract: In this paper we study the problem of flipping edges in triangulations of polygons and point sets. We prove that if a polygon Qn has k reflex vertices, then any triangulation of Qn can be transformed to another triangulation of Qn with at most O(n + k 2 ) flips. We produce examples of polygons with two triangulations T and T © such that to transform T to T © requires O(n2 ) flips. These results are then extended to triangulations of point sets. We also show that any triangulation of an n point set always has n − 4 2 edges that can be flipped.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":65,"string":"65"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13257,"string":"13,257"}}},{"rowIdx":13258,"cells":{"question_id":{"kind":"string","value":"John C S Lui"},"question":{"kind":"string","value":"Generate an abstract for the title \"Designing Approximate and Deployable SRPT Scheduler: A Unified Framework\" using the following items: 1. Scheduling policy \n2. Congestion control \n3. Shortest-Remaining-Processing-Time (SRPT)\n4. Message completion time (MCT)\n5. Difficulty with deployment of"},"answer":{"kind":"string","value":"The scheduling policy installed on switches of datacenters plays a significant role on congestion control. Shortest-Remaining-Processing-Time (SRPT) achieves the near-optimal average message completion time (MCT) in various scenarios, but is difficult to deploy as viewed by the industry. The reasons are two-fold: 1) many commodity switches only provide FIFO queues, and 2) the information of remain..."},"correct_answer":{"kind":"string","value":"The scheduling policy installed on switches of datacenters plays a significant role on congestion control. Shortest-Remaining-Processing-Time (SRPT) achieves the near-optimal average message completion time (MCT) in various scenarios, but is difficult to deploy as viewed by the industry. The reasons are two-fold: 1) many commodity switches only provide FIFO queues, and 2) the information of remain..."},"documents":{"kind":"list like","value":["Title: Handling high-bandwidth traffic aggregates by receiver-driven feedback control\nYear: 2005\n\nAbstract: High-bandwidth traffic aggregates may occur during times of flooding-based distributed denial-of-service attacks or flash crowds. Congestion control of these traffic aggregates is important to avoid congestion collapse of network services. This paper presents a class of feedback-control algorithms that proactively protect a network server from overload by installing rate throttles in a set of upstream routers. A control-theoretical framework is proposed to optimize the control setting such that throttling can be achieved in a distributed and fair manner. We develop control-theoretic algorithms that (1) are highly adaptive by avoiding the configuration of unnecessary control parameters, (2) provide max-min fairness for any number of throttling routers, (3) respond very quickly to network changes, (4) are extremely robust against extrinsic factors beyond the system control, and (5) are stable under given delay bounds.","Title: On the Access Pricing Issues of Wireless Mesh Networks\nYear: 2006\n\nAbstract: This paper studies the use of pricing as an incentive mechanism to encourage private, self-interested nodes to participate in a public wireless mesh network and cooperate in the packet forwarding service. Our focus is on the \"economic behavior\" of the network nodes the pricing and purchasing strategies of the access point, wireless relaying nodes, and clients. We use a \"game theoretic approach\" to analyze their interactions from one-hop to multihop network and when the network has an unlimited or limited channel capacity. We show that the access point and relaying wireless nodes will adopt a simple, yet optimal, fixed-rate pricing strategy in a multi-hop network with an unlimited capacity. Yet, the fixed-rate pricing strategy fails to be optimal in the limited capacity case. To this end, we focus on the access point adopting a more practical \"fixedrate, non-interrupted service\" model and propose an algorithm based on the Markovian decision theory to devise the optimal pricing strategy.","Title: Design of Scalable Continuous Media Servers\nYear: 2002\n\nAbstract: Multimedia applications place high demands for quality-of-service (QoS), performance, and reliability on systems. These stringent requirements make design of cost-effective and scalable systems difficult. Therefore efficient adaptive and dynamic resource management techniques in conjunction with data placement techniques can be of great help in improving performance, scalability, and reliability of such systems. This is the focus of our paper.","Title: A Secure Cloud Backup System with Assured Deletion and Version Control\nYear: 2011\n\nAbstract: Cloud storage is an emerging service model that enables individuals and enterprises to outsource the storage of data backups to remote cloud providers at a low cost. However, cloud clients must enforce security guarantees of their outsourced data backups. We present Fade Version, a secure cloud backup system that serves as a security layer on top of today's cloud storage services. Fade Version follows the standard version-controlled backup design, which eliminates the storage of redundant data across different versions of backups. On top of this, Fade Version applies cryptographic protection to data backups. Specifically, it enables fine-grained assured deletion, that is, cloud clients can assuredly delete particular backup versions or files on the cloud and make them permanently inaccessible to anyone, while other versions that share the common data of the deleted versions or files will remain unaffected. We implement a proof-of-concept prototype of Fade Version and conduct empirical evaluation atop Amazon S3. We show that Fade Version only adds minimal performance overhead over a traditional cloud backup service that does not support assured deletion.","Title: Carbon-Aware Load Balancing for Geo-distributed Cloud Services\nYear: 2013\n\nAbstract: Recently, data center carbon emission has become an emerging concern for the cloud service providers. Previous works are limited on cutting down the power consumption of the data centers to defuse such a concern. In this paper, we show how the spatial and temporal variabilities of the electricity carbon footprint can be fully exploited to further green the cloud running on top of geographically distributed data centers. We jointly consider the electricity cost, service level agreement (SLA) requirement, and emission reduction budget. To navigate such a three-way tradeoff, we take advantage of Lyapunov optimization techniques to design and analyze a carbon-aware control framework, which makes online decisions on geographical load balancing, capacity right-sizing, and server speed scaling. Results from rigorous mathematical analyses and real-world trace-driven empirical evaluation demonstrate its effectiveness in both minimizing electricity cost and reducing carbon emission.","Title: Performance modeling on the interaction of ISPs\nYear: 2006\n\nAbstract: In this paper, we consider interactions of Internet Service Providers (ISPs) and how these interactions can affect the overall traffic and resource allocation between ISPs. In particular, we consider a simplified two-level hierarchical model wherein there are a single tier-1 ISP and N 1 tier-2 ISPs. Each tier-2 ISP needs to pay the tier-1 ISP for the connectivity service. At the same time, a tier-2 ISP can also arrange to have “private peering” links with other tier-2 ISPs. Therefore, each tier-2 ISP can optimize its utility by deciding on the proper traffic routing of transmitting traffic, either via the tier-1 ISP link, or via the private peering link with other tier-2 ISPs. The tier-1 ISP, on the other hand, needs to decide on the proper resource allocation for all its tier-2 peers so as to avoid monopolization of its resource by a single peer (i.e., to achieve customer diversity). We investigate a distributed framework wherein a tier-1 ISP can achieve customer diversity while tier-2 peers can perform their utility maximization in terms of traffic routing. We also explore other important issues such as sensitivity and convergency. Extensive simulations are carried out to quantify the merits of the proposed distributed framework.","Title: Stochastic Analysis and File Availability Enhancement for BT-like File Sharing Systems\nYear: 2006\n\nAbstract: In this paper, we present the mathematical analysis of two important performance measures for a BitTorrent (BT) like P2P file sharing system, namely, average file downloading time and file availability. For the file downloading time, we develop a model using the \"stochastic differential equation\" approach, which can capture the system more accurately than some previous approach and can capture various network settings and peers behavior. We study the steady-state behavior and obtain the closed-form solutions for performance measures which allow us to carry sensitivity analysis on various performance measures for various system parameters. We then extend this model to consider multiclass peers wherein some peers are behind firewalls which may impede the uploading service. We also present the mathematical model to study the file availability of a BT-like system. The model helps us gain the understanding of why the \"rarest-first\" chunk selection policy is used in today's BT protocol. We propose a novel chunk selection algorithm to enhance the overall system file availability. Extensive simulations are carried to validate our analysis","Title: Stochastic analysis of a randomized detection algorithm for pollution attack in P2P live streaming systems\nYear: 2010\n\nAbstract: Pollution attack is known to have a disastrous effect on existing P2P infrastructures: it can reduce the number of legitimate P2P users by as much as 85%, and it generates abundant bogus data which may deplete the communication bandwidth. We propose a distributed defense and detection mechanism to resolve pollution attacks. The mechanism is composed of a set of ''randomized'' and ''fully distributed'' algorithms that can be executed by any legitimate peer. We present the analytical framework to quantify (a) the probability of false negative, (b) the probability of false positive, and (c) the distribution of time needed for detection. In our detection algorithm and analysis, we consider the case of (1) single attacker within the neighborhood, (2) multiple attackers within the neighborhood. Furthermore, we show how to ''optimize'' the system parameters so as to quickly discover and eliminate malicious peers from the system.","Title: Incentive P2P networks: a protocol to encourage information sharing and contribution\nYear: 2003\n\nAbstract: In this paper, we explore a scheduling policy for file transfer service such that we can provide incentive to the P2P nodes. The assigned transfer capacity for each request is a function of the requesting node's connection type, its utility function, and its relative contribution compared with the other requesting nodes. Our objective is to make efficient use of P2P network resources and to provide both fairness and incentive to all nodes in the P2P community.","Title: Distributed Collaborative Key Agreement Protocols for Dynamic Peer Groups\nYear: 2002\n\nAbstract: We consider several distributed collaborative key agreement protocols for dynamic peer groups. This problem has several important characteristics which make it different from traditional secure group communication. They are (1) distributed nature in which there is no centralized key server, (2) collaborative nature in which the group key is contributory; i.e., each group member will collaboratively contribute its part to the global group key, and (3) dynamic nature in which existing members can leave the group while new members may join. Instead of performing individual rekey operations, i.e., recomputing the group key after every join or leave request, we consider an interval-based approach of rekeying. In particular, we consider three distributed algorithms for updating the group key: (1) the Rebuild algorithm, (2) the Batch algorithm, and (3) the Queue-batch algorithm. Performance of these distributed algorithms under different settings, such as different join and leave probabilities, is analyzed. We show that these three distributed algorithms significantly outperform the individual rekey algorithm, and that the Queue-batch algorithm performs the best among the three distributed algorithms. Moreover, the Queue-batch algorithm has the intrinsic property of balancing the computation/communication workload such that the dynamic peer group can quickly begin secure group communication. This provides a fundamental understanding about establishinga collaborative group key for a distributed dynamic peer group.","Title: A case for a multi-key secure video proxy: theory, design, and implementation\nYear: 2002\n\nAbstract: Because of limited server and network capacities in multimedia streaming, proxies are commonly used to cache multimedia objects such that, by accessing nearby proxies, clients can enjoy smaller start-up latencies and reduced packet loss and delay jitters for their requests. However, the use of video proxies increases the risk that multimedia data are exposed to unauthorized access by intruders. In this paper, we present a framework for implementing a secure video proxy or, more generally, a secure proxy architecture. The framework employs a notion of asymmetric reversible parametric sequences to provide the following security properties: (1) data confidentiality during transmission, (2) end-to-end data confidentiality, (3) data confidentiality against proxy intruders, and (4) data confidentiality against member collusion. Our framework is grounded on a multi-key RSA technique such that system resilience against attacks is provably strong given standard computability assumptions. We also propose the use of a set of encryption configuration parameters to trade off proxy encryption throughput against the viewing quality of video by unauthorized parties. Implementation results on a Pentium III/800 MHz machine show that our techniques can simultaneously achieve high encryption throughput and extremely low video quality (in terms of both PSNR and the visual quality of decoded frames) during unauthorized viewing.","Title: A panoramic view of 3g data/control-plane traffic: mobile device perspective\nYear: 2012\n\nAbstract: Users can access the Internet via 3G/4G cellular data networks using various types of user devices (e.g., smartphones, tablets, datacards). We conduct a detailed measurement study on the impact of different device types on the data/control-plane performance of a commercial, city-wide 3G cellular data network in China. We present a methodology that correlates different data/control-plane datasets collected at different points in the network core, and identify more than 60K devices of different types per day on average. For the devices we identify, we investigate how their commonly used Internet applications and internal heartbeat mechanisms lead to distinct data/control-plane behaviors. For example, we observe that datacard devices contribute a large volume of IP traffic in the data plane, while smartphones introduce significant resource overhead in the signaling control plane. Our measurement study provides insights for network operators to strategize pricing and resource allocation for the data/control planes of their cellular data networks with regard to the market penetrations of various device types.","Title: Distributed mechanism in detecting and defending against the low-rate TCP attack\nYear: 2006\n\nAbstract: In this paper, we consider a distributed mechanism to detect and to defend against the low-rate TCP attack. The low-rate TCP attack is a recently discovered attack. In essence, it is a periodic short burst that exploits the homogeneity of the minimum retransmission timeout (RTO) of TCP flows and forces all affected TCP flows to backoff and enter the retransmission timeout state. When these affected TCP flows timeout and retransmit their packets, the low-rate attack will again send a short burst to force these affected TCP flows to enter RTO again. Therefore these affected TCP flows may be entitled to zero or very low transmission bandwidth. This sort of attack is difficult to identify due to a large family of attack patterns. We propose a distributed detection mechanism to identify the low-rate attack. In particular, we use the dynamic time warping approach to robustly and accurately identify the existence of the low-rate attack. Once the attack is detected, we use a fair resource allocation mechanism to schedule all packets so that (1) the number of affected TCP flows is minimized and, (2) provide sufficient resource protection to those affected TCP flows. Experiments are carried out to quantify the robustness and accuracy of the proposed distributed detection mechanism. In particular, one can achieve a very low false positive/negative when compare to legitimate Internet traffic. Our experiments also illustrate the the efficiency of the defense mechanism across different attack patterns and network topologies.","Title: Providing Throughput Differentiation for TCP Flows Using Adaptive TwoColor Marking and Multi-Level AQM\nYear: 2002\n\nAbstract: In this paper we propose a new paradigm for a DifferentialService (DiffServ) network consisting of two-color marking at the edges ofthe network using token buckets coupled with differential treatment in thecore. Using fluid-flow modelling, we present existence conditions for tokenbucketrates and differential marking probabilities at the core that resultin all edges receiving at least their minimum guaranteed rates. We thenpresent an integrated DiffServ architecture comprising of an active ...","Title: Security adoption in heterogeneous networks: the influence of cyber-insurance market\nYear: 2012\n\nAbstract: Hosts (or nodes) in the Internet often face epidemic risks such as virus and worms attack. Despite the awareness of these risks and the availability of anti-virus software, investment in security protection is still scare, and hence epidemic risk is still prevalent. Deciding whether to invest in security protection is an interdependent process : security investment decision made by one node can affect the security risk of others, and therefore affect their decisions also. The first contribution of this paper is to provide a fundamental understanding on how \"network externality \" effect with \"nodes heterogeneity \" may affect security adoption. We characterize it as a Bayesian network game in which nodes only have the local information, e.g., the number of neighbors, as well as minimum common information, e.g., degree distribution of the network. Our second contribution is in analyzing a new form of risk management called cyber-insurance . We investigate how the presence of competitive insurance market can affect the security adoption.","Title: Analytical Models for Mixed Workload Multimedia Storage Servers\nYear: 1999\n\nAbstract: Efficient resource management is essential in providing scalability in large multimedia information systems serving a variety of applications. In order to explore design tradeoffs in such systems, what is needed are accurate analytical models of mixed workload servers with tractable solutions. Motivated by the need for mixed workload storage servers as well as the need for performance studies of such systems, we first present a family of scheduling algorithm for mixed workload storage servers. We then develop a set of corresponding analytical non-Markovian models. We show how our analytical solution methodology applies to this entire family of models and how it can be used to obtain performance measures of interest for different classes of workloads. Lastly, we illustrate through numerical examples how such models can be used to study performance tradeoffs and facilitate the making of design choices in mixed workload storage servers. (C) 1999 Published by Elsevier Science B.V. All rights reserved.","Title: Design of Efficient Sampling Methods on Hybrid Social-Affiliation Networks.\nYear: 2014\n\nAbstract: Graph sampling via crawling has become increasingly popular and important in the study of measuring various characteristics of large scale complex networks. While powerful, it is known to be challenging when the graph is loosely connected or disconnected which slows down the convergence of random walks and can cause poor estimation accuracy. In this work, we observe that the graph under study, or called target graph, usually does not exist in isolation. In many situations, the target graph is related to an auxiliary graph and an affiliation graph, and the target graph becomes well connected when we view it from the perspective of these three graphs together, or called a hybrid social-affiliation graph in this paper. When directly sampling the target graph is difficult or inefficient, we can indirectly sample it efficiently with the assistances of the other two graphs. We design three sampling methods on such a hybrid social-affiliation network. Experiments conducted on both synthetic and real datasets demonstrate the effectiveness of our proposed methods.","Title: Sampling Content Distributed Over Graphs.\nYear: 2013\n\nAbstract: Despite recent effort to estimate topology characteristics of large graphs (i.e., online social networks and peer-to-peer networks), little attention has been given to develop a formal methodology to characterize the vast amount of content distributed over these networks. Due to the large scale nature of these networks, exhaustive enumeration of this content is computationally prohibitive. In this paper, we show how one can obtain content properties by sampling only a small fraction of vertices. We first show that when sampling is naively applied, this can produce a huge bias in content statistics (i.e., average number of content duplications). To remove this bias, one may use maximum likelihood estimation to estimate content characteristics. However our experimental results show that one needs to sample most vertices in the graph to obtain accurate statistics using such a method. To address this challenge, we propose two efficient estimators: special copy estimator (SCE) and weighted copy estimator (WCE) to measure content characteristics using available information in sampled contents. SCE uses the special content copy indicator to compute the estimate, while WCE derives the estimate based on meta-information in sampled vertices. We perform experiments to show WCE and SCE are cost effective and also ``{\\em asymptotically unbiased}''. Our methodology provides a new tool for researchers to efficiently query content distributed in large scale networks.","Title: A Cost-effective Near-line Storage Server for Multimedia System\nYear: 1995\n\nAbstract: We consider a storage server architecture for multimedia information systems. While most other works on multimedia storage servers assume on-line disk storage, we consider a two-tier storage architecture with a robotic tape library as the vast near-line storage and on-line disks as the front-line storage. Magnetic tapes are cheaper, more robust, and have a larger capacity; hence they are more cost effective for large scale storage systems (e.g., video on demand (VOD) systems may store tens of thousands of videos). We study in detail the design issues of the tape subsystem and propose some novel tape scheduling algorithms which give faster response and require less disk buffering.","Title: Understanding the paradoxical effects of power control on the capacity of wireless networks\nYear: 2009\n\nAbstract: Recent works show conflicting results: network capacity may increase or decrease with higher transmission power under different scenarios. In this work, we want to understand this paradox. Specifically, we address the following questions: (1)Theoretically, should we increase or decrease transmission power to maximize network capacity? (2) Theoretically, how much network capacity gain can we achieve by power control? (3) Under realistic situations, how do power control, link scheduling and routing interact with each other? Under which scenarios can we expect a large capacity gain by using higher transmission power? To answer these questions, firstly, we prove that the optimal network capacity is a non-decreasing function of transmission power. Secondly, we prove that the optimal network capacity can be increased unlimitedly by higher transmission power in some network configurations. However, when nodes are distributed uniformly, the gain of optimal network capacity by higher transmission power is upper-bounded by a positive constant. Thirdly, we discuss why network capacity may increase or decrease with higher transmission power under different scenarios using carrier sensing and the minimum hop-count routing. Extensive simulations verify our analysis.","Title: Balancing Workload and Communication Cost for a Distributed Virtual Environment\nYear: 1998\n\nAbstract: Recent advancement in computer graphics and networking technologies enable researchers to experiment and build the distributed virtual environment (DVE) system. In general, a DVE system allows many clients to simultaneously explore a virtual world and at the same time, users in this virtual environment can interact with each other and can manipulate the objects in the virtual world. To implement such a system, there are many challenges that researchers have to face so as to build an efficient DVE system that can represent a large virtual world and at the same time, support large number of concurrent users. In this work, we present some of these important issues, such as the necessity to balance the workload among different servers as well as minimizing the inter-server communication. We show that the problem, in general, is NP-complete and we proposed several algorithms so as to find the partitioning policy efficiently.","Title: A Hybrid Approach to Failed Disk Recovery Using RAID-6 Codes: Algorithms and Performance Evaluation\nYear: 2011\n\nAbstract: The current parallel storage systems use thousands of inexpensive disks to meet the storage requirement of applications. Data redundancy and/or coding are used to enhance data availability, for instance, Row-diagonal parity (RDP) and EVENODD codes, which are widely used in RAID-6 storage systems, provide data availability with up to two disk failures. To reduce the probability of data unavailability, whenever a single disk fails, disk recovery will be carried out. We find that the conventional recovery schemes of RDP and EVENODD codes for a single failed disk only use one parity disk. However, there are two parity disks in the system, and both can be used for single disk failure recovery. In this article, we propose a hybrid recovery approach that uses both parities for single disk failure recovery, and we design efficient recovery schemes for RDP code (RDOR-RDP) and EVENODD code (RDOR-EVENODD). Our recovery scheme has the following attractive properties: (1) “read optimality” in the sense that our scheme issues the smallest number of disk reads to recover a single failed disk and it reduces approximately 1/4 of disk reads compared with conventional schemes; (2) “load balancing property” in that all surviving disks will be subjected to the same (or almost the same) amount of additional workload in rebuilding the failed disk. We carry out performance evaluation to quantify the merits of RDOR-RDP and RDOR-EVENODD on some widely used disks with DiskSim. The offline experimental results show that RDOR-RDP and RDOR-EVENODD outperform the conventional recovery schemes of RDP and EVENODD codes in terms of total recovery time and recovery workload on individual surviving disk. However, the improvements are less than the theoretical value (approximately 25%), as RDOR-RDP and RDOR-EVENODD change the disk access pattern from purely sequential to a more random one compared with their conventional schemes.","Title: Computing bounds on steady state availability of repairable computer systems\nYear: 1994\n\nAbstract: One of the most important performance measures for computer system designers is system availability. Most often, Markov models are used in representing systems for dependability/availability analysis. Due to complex interactions between components and complex repair policies, the Markov model often has an irregular structure, and closed-form solutions are extremely difficult to obtain. Also, a realistic system model often has an unmanageably large state space and it quickly becomes impractical to even generate the entire transition rate matrix. In this paper, we present a methodology that can (i) bound the system steady state availability and at the same time, (ii) drastically reduce the state space of the model that must be solved. The bounding algorithm is iterative and generates a part of the transition matrix at each step. At each step, tighter bounds on system availability are obtained. The algorithm also allows the size of the submodel, to be solved at each step, to be chosen so as to accommodate memory limitations. This general bounding methodology provides an efficient way to evaluate dependability models with very large state spaces without ever generating the entire transition rate matrix.","Title: Determining the end-to-end throughput capacity in multi-hop networks: methodology and applications\nYear: 2006\n\nAbstract: In this paper, we present a methodology to analytically compute the throughput capacity, or the maximum end-to-end throughput of a given source and destination pair in a multi-hop wireless network. The end-to-end throughput capacity is computed by considering the interference due to neighboring nodes, as well as various modes of hidden node interference. Knowing the throughput capacity is important because it facilitates the design of routing policy, admission control for realtime traffic, as well as load control for wireless networks. We model location-dependent neighboring interference and we use a contention graph to represent these interference relationships. Based on the contention graph, we formulate the individual link capacity as a set of fixed point equations. The end-to-end throughput capacity can then be determined once these link capacities are obtained. To illustrate the utility of our proposed methodology, we present two important applications: (a) route optimization to determine the path with the maximum end-to-end throughput capacity and, (b) optimal offered load control for a given path so that the maximum end-to-end capacity can be achieved. Extensive simulations are carried out to verify and validate the proposed analytical methodology.","Title: Characterizing the capacity gain of stream control scheduling in MIMO wireless mesh networks\nYear: 2007\n\nAbstract: Stream control has recently attracted attentions in the research of MIMO wireless networks as a potential way to improve network capacity. How- ever, inappropriate use of stream control may significantly degrade network ca- pacity as well. In this paper, we provide the first formal study on stream control scheduling in MIMO wireless mesh networks. We derive the theoretical upper bound on network capacity gain of stream control scheduling. We also provide an efficient scheduling algorithm and show that its achieved network capacity gain is close to its theoretical upper bound. Moreover, we point out the poor performance of a previous stream control scheduling algorithm SCMA under the general set- tings of wireless mesh networks. This formal characterization provides a deeper understanding of steam control scheduling in MIMO wireless mesh networks.","Title: A Novel Video-On-Demand Storage Architecture for Supporting Constant Frame Rate with Variable Bit Rate Retrieval\nYear: 1995\n\nAbstract: One of the quality of service (QOS) factors in video-on-demand (VOD) applications is to provide high resolution quality to end users. One way to achieve this is to provide a constant display frame rate (e.g., 30 frames/sec) at the display station. However, due to the nature of video files and compression technique applied, video frame sizes vary significantly from frame to frame. Therefore, although the display frame rate is fixed, data retrieval is a variable bit rate process. Conventional VOD storage servers assume a peak rate retrieval of video files. Therefore, the number of concurrent requests to the VOD server cannot be maximized. In this paper, we consider a VOD storage server which can support a fixed frame rate at the display and at the same time, variable bit rates retrieval of compressed video files. We describe 1) video files layout strategy, 2) request scheduling algorithm, 3) buffering issues and, 4) various VCR features support such that the number of conclurent requests can be maximized.","Title: Location Updates and Probabilistic Tracking Algorithms for Mobile Cellular Networks\nYear: 1999\n\nAbstract: In this work, we develop a novel mathematical model to analyze different location update protocols for mobile cellular network. Our model can capture many important features of user movements, such as geographically dependent movement, directional movement and non-exponential distribution of cell dwell times. We propose to use the transient method to predict the location of a mobile user. This method is efficient and, more importantly, able to address the temporal change in the current location of a mobile user. Therefore, the accuracy of the prediction of the location of a user can be enhanced.We also propose a new location update protocol which can attain a lower tracking cost and also adapt to different mobility patterns. Transient performance analysis is also carried out and the results are compared with other protocols.","Title: Multi-path streaming: is it worth the trouble?\nYear: 2002\n\nAbstract: Quality of service (QoS) in streaming of continuous media over the Internet is still poor and inconsistent. The degradation in quality of continuous media applications, involving delivery of video and audio, is partly due to variations in delays as well as losses experi- enced by packets sent through wide-area networks. Although many such applications can tolerate some degree of missing information, significant losses degrade an application's quality of service. One approach to providing QoS for continuous media applica- tions over the Internet is to use the IntServ model for signaling (e.g., RSVP) and resource reservation in all routers along the streaming path. However, this approach suffers from scalability and deploy- ment problems. In contrast, in our work we investigate the potential benefits of providing QoS guarantees in continuous media delivery through the exploitation of multiple paths existing in the network between a set of senders and a receiver. One advantage of this ap- proach is that the complexity of QoS provision can be pushed to the network edge and hence improve the scalability and deploy- ment characteristics while at the same time provide a certain level of QoS guarantees. Our focus in this work is on providing a fun- damental understanding of the benefits of using multiple paths to deliver continuous media data (such as video) destined for a par- ticular receiver, i.e., this data is fragmented into packets and the different packets take alternate routes to the receiver. We note that such paths do not have to be completely disjoint, i.e., it is sufficient for them to have disjoint points of congestion or bottlenecks. Existence of multiple paths with disjoint bottlenecks includes the following potential benefits: (a) reduction in correlation between consecutive packet losses which we believe will lead to improve- ments in the quality of delivered data, (b) increased throughput, and (c) ability to adjust to variations in congestion patterns on dif- ferent parts of the network. In this work, our focus is on providing the fundamental understanding and on characterizing the benefits of the multi-path approach to streaming of pre-stored continuous media data over wide-area networks (under the setup described be- low). More specifically, we focus on loss characteristics as they are an indication of the resulting quality of the delivered data stream. We believe that the understanding of loss characteristics under a multi-path approach is non-trivial and deserves further attention. We also believe that this work is a step in the right direction. In general, the use of multiple paths in designing of distributed (over best-effort wide-area networks) continuous media applica- tions requires consideration of a number of issues (please refer to (5) for details). However, in this work we limit the scope by focus- ing on (a) delivery of pre-stored video, (in contrast to delivery of This work was partly done while the author was with the Univer- sity of Maryland. \"live\" data), (b) application-level schemes (which are deployable today over the current Internet), where the path used between any pair of hosts is determined by a network-level routing algorithm (we note that our system does not require specific knowledge of the paths, only the ability to determine whether two paths share a point of congestion, e.g., using (7)); and (c) accomplishment of multiple paths to the same receiver by distributing servers across wide-area networks and streaming data from multiple senders simultaneously. Our system is depicted in Figure 1, where any server can send any fraction of the continuous media data. More specifically, server","Title: A Distributed Throttling Approach for Handling High Bandwidth Aggregates\nYear: 2007\n\nAbstract: Public-access networks need to handle persistent congestion and overload caused by high bandwidth aggregates that may occur during times of flooding-based DDoS attacks or flash crowds. The often unpredictable nature of these two activities can severely degrade server performance. Legitimate user requests also suffer considerably when traffic from many different sources aggregates inside the network and causes congestion. This paper studies a family of algorithms that \"proactively” protect a server from overload by installing rate throttles in a set of upstream routers. Based on an optimal control setting, we propose algorithms that achieve throttling in a distributed and fair manner by taking important performance metrics into consideration, such as minimizing overall load variations. Using ns-2 simulations, we show that our proposed algorithms 1) are highly adaptive by avoiding unnecessary parameter configuration, 2) provide max-min fairness for any number of throttling routers, 3) respond very quickly to network changes, 4) are extremely robust against extrinsic factors beyond the system control, and 5) are stable under given delay bounds.","Title: Secure Overlay Cloud Storage with Access Control and Assured Deletion\nYear: 2012\n\nAbstract: We can now outsource data backups off-site to third-party cloud storage services so as to reduce data management costs. However, we must provide security guarantees for the outsourced data, which is now maintained by third parties. We design and implement FADE, a secure overlay cloud storage system that achieves fine-grained, policy-based access control and file assured deletion. It associates outsourced files with file access policies, and assuredly deletes files to make them unrecoverable to anyone upon revocations of file access policies. To achieve such security goals, FADE is built upon a set of cryptographic key operations that are self-maintained by a quorum of key managers that are independent of third-party clouds. In particular, FADE acts as an overlay system that works seamlessly atop today's cloud storage services. We implement a proof-of-concept prototype of FADE atop Amazon S3, one of today's cloud storage services. We conduct extensive empirical studies, and demonstrate that FADE provides security protection for outsourced data, while introducing only minimal performance and monetary cost overhead. Our work provides insights of how to incorporate value-added security features into today's cloud storage services.","Title: End system multicast: an architectural infrastructure and topological optimization\nYear: 2004\n\nAbstract: Although IP multicast has been proposed and investigated for years, there are major problems inherent in the IP multicasting technique, e.g. difficulty to scale up the system, difficulty in allocating a globally unique multicast address, complexity in supporting higher level features such as reliable data transfer and congestion/flow control, more importantly, difficulty to deploy on the current Internet infrastructure due to necessity to change many core routers. Recently, end-system multicast (ESM) has been proposed as an alternative solution so that multicasting services can be quickly deployed. In this paper, we consider the'architectural' and 'optimization' issues on designing an ESM-tree. Specifically, we present a distributed algorithm on how to create and maintain an ESM-tree. We propose a distributed algorithm to perform tree optimization (TO) so that an ESM-tree can dynamically adapt to the changing network condition (e.g. drop in transfer bandwidth) so that the nodes within an ESM-tree can receive data more efficiently. The distributed algorithm has the important theoretical properties that at all times, a tree topology can be maintained and any node joining, leaving, as well as any TO operation will not 'partition' the underlying ESM-tree. Therefore, our work can be used to provide an efficient architectural infrastructure for ESM services. We have implemented a prototype ESM system and carried out experiments to illustrate the effectiveness and the performance gains of our ESM optimization protocol.","Title: Modeling Multi-state Diffusion Process in Complex Networks: Theory and Applications\nYear: 2013\n\nAbstract: There is a growing interest to understand the fundamental principles of how epidemic, ideas or information spread over large networks (e.g., the Internet or online social networks). Conventional approach is to use SIS models (or its derivatives). However, these models usually are over-simplified and may not be applicable in realistic situations. In this paper, we propose a generalization of the SIS model by allowing intermediate states between susceptible and infected states. To analyze the diffusion process on large graphs, we use the ``mean-field analysis technique'' to determine which initial condition leads to or prevents information or virus outbreak. Numerical results show our methodology can accurately predict the behavior of the phase-transition process for various large graphs (e.g., complete graphs, random graphs or power-law graphs). We also extend our generalized SIS model to consider the interaction of two competing sources (i.e., competing products or virus-antidote modeling). We present the analytical derivation and show experimentally how different factors, i.e., transmission rates, recovery rates, number of states or initial condition, can affect the phase transition process and the final equilibrium. Our models and methodology can serve as an essential tool in understanding information diffusion in large networks.","Title: On Service Replication Strategy for Service Overlay Networks\nYear: 2004\n\nAbstract: The service overlay network (SON) is an effective means to deliver end-to-end QoS guaranteed applications on the current Internet. In [5], authors address the bandwidth provisioning problem on a SON, specifically, in determining the appropriate amount of bandwidth capacity to purchase from various autonomous systems so as to satisfy the QoS requirements of the SON's end users and at the same time, maximizes the total revenue of operating the overlay network. In this paper, we extend the concept of the service overlay network. Since traffic demands are time varying and there may be some unexpected events which can cause a traffic surge, these will significantly increase the probability of QoS violation and will reduce the profit margin of a SON. To overcome these problems, we propose to replicate services on the service gateways so as to dynamically adapt to these traffic surges. We show that the service replication problem, in general, is intractable. We propose an efficient service replication algorithm which replicates services for a subset of traffic flows. Under our replication strategy, one does not need to increase the bandwidth capacity of underlying links and at the same time, be able to increase the average profit for the overlay network. Experiments are carried out to illustrate that replication algorithm provides higher flexibility during traffic fluctuations and can quickly find a near-optimal solution.","Title: Distributed Algorithm for Service Replication in Service Overlay Network\nYear: 2004\n\nAbstract: The service overlay network (SON) is an effective mean to deploy end-to-end QoS guaranteed content delivery services on the current Internet. We model the content delivery service on a SON as a service delivery tree (SDT). Previous studies have addressed the optimal distribution tree formulation issues. In this paper, we focus on the problem of maximizing the total operation profit of the SON. In [1], authors introduced a cost model for optimal bandwidth provisioning in SON. In this paper, we extended this concept and propose an alternative approach to maximize the total effective throughput of SDT as well as to minimize the QoS violation penalty of the SON by service replication. We present both centralized and distributed algorithms for the placement of replicated servers on the SDT. Experiments are carried to quantify the merit, effectiveness and the scalability of the proposed service replication algorithm. In particular, the performance gain is very close to the exhaustive search. The algorithm performs well when we scale up the service overlay networks. Finally, we show that one only needs to perform a small number of replications to attain the optimal gain.","Title: Defending against distributed denial-of-service attacks with max-min fair server-centric router throttles\nYear: 2005\n\nAbstract: Our work targets a network architecture and accompanying algorithms for countering distributed denial-of-service (DDoS) attacks directed at an Internet server. The basic mechanism is for a server under stress to install a router throttle at selected upstream routers. The throttle can be the leaky-bucket rate at which a router can forward packets destined for the server. Hence, before aggressive packets can converge to overwhelm the server, participating routers proactively regulate the contributing packet rates to more moderate levels, thus forestalling an impending attack. In allocating the server capacity among the routers, we propose a notion of level-k max-min fairness. We first present a control-theoretic model to evaluate algorithm convergence under a varitey of system parameters. In addition, we present packet network simulation results using a realistic global network topology, and various models of good user and attacker distributions and behavior. Using a generator model of web requests parameterized by empirical data, we also evaluate the impact of throttling in protecting user access to a web server. First, for aggressive attackers, the throttle mechanism is highly effective in preferentially dropping attacker traffic over good user traffic. In particular, level-k max-min fairness gives better good-user protection than recursive pushback of max-min fair rate limits proposed in the literature. Second, throttling can regulate the experienced server load to below its design limit - in the presence of user dynamics - so that the server can remain operational during a DDoS attack. Lastly, we present implementation results of our prototype on a Pentium III/866 MHz machine. The results show that router throttling has low deployment overhead in time and memory.","Title: Deriving Communication Sub-graph and Optimal Synchronizing Interval for a Distributed Virtual Environment System\nYear: 1999\n\nAbstract: Advances in the multimedia and networking technologies allow the realization of the distributed virtual environment (DVE). DVE is a distributed system which allows many clients who are located in different locations (within a LAN or across a WAN) to concurrently explore and interact with each other in a high resolution, 3-dimensional graphical virtual environment. Some of the potential applications of DVE are: tele-conferencing, distributed games, distributed interactive simulation, collaborative group-ware environment, $\\ldots$, etc. One challenging issue in designing a DVE is to guarantee that each participating client to have the same consistent view of the virtual world. To provide this consistent view, the DVE needs to perform synchronization actions periodically. In this paper, we present a methodology of deriving an optimal synchronizing interval. Our work is divided into two parts. In the first part, algorithms are presented to construct suitable communication sub-graph for the clients. In the second part, we present the outline on how to derive the optimal synchronizing interval based on the proposed communication sub-graph using the Markov chain theory and the fundamental matrix.","Title: Adaptive piggybacking: a novel technique for data sharing in video-on-demand storage servers\nYear: 1996\n\nAbstract: Recent technological advances have made multimedia on-demand services, such as homeentertainment and home-shopping, important to the consumer market. One of the most challengingaspects of this type of service is providing access either instantaneously or within asmall and reasonable latency upon request. In this paper, we consider improvements in performanceof multimedia storage servers through data sharing between requests for popular objects,where I/O bandwidth is assumed to be the...","Title: Quantifying Complexity and Performance Gains of Distributed Caching in a Wireless Mobile Computing Environment\nYear: 1997\n\nAbstract: In a mobile computing system, the wireless communication bandwidth is a scarce resource that needs to be managed carefully. In this paper, we investigate the use of distributed caching as an approach to reduce the wireless bandwidth consumption for data access. We find that conventional caching techniques cannot fully utilize the dissemination feature of the wireless channel. We thus propose a novel distributed caching protocol that can minimize the overall system bandwidth consumption at the cost of CPU processing time at the server side. This protocol allows the server to select data items into a broadcast set, based on a performance gain parameter called the bandwidth gain, and then send the broadcast set to all the mobile computers within the server's cell. We show that in general, this selection process is NP-hard, and therefore we propose a heuristic algorithm that can attain a near-optimal performance. We also propose an analytical model for the protocol and derive closed-form performance measures, such as the bandwidth utilization and the expected response time of data access by mobile computers. Experiments show that our distributed caching protocol can greatly reduce the bandwidth consumption so that the wireless network environment can accommodate more users and, at the same time, vastly improve the expected response time for data access by mobile computers.","Title: On incentivizing caching for P2P-VoD systems\nYear: 2012\n\nAbstract: Peer-to-peer (P2P) systems consist of selfish peers which would like to receive services from others but would not like to contribute their own resources by default. To conquer this problem, proper incentive schemes are needed so as to stimulate the peers' contributions. This issue is particularly important for P2P Video-on-Demand (VoD) services. Content providers of P2P-VoD services want to incentivize peers to cache video data so that the peers can transfer data to one another and alleviate the workload of the content server. In this paper, we design a practical mechanism under which the peers are rewarded based on the popularity of the video they cache. We characterize the impact of this incentive scheme on peers' caching behaviors. In particular, we formulate an optimization framework to decide the optimal reward price for each video so as to keep enough replicas and minimize the content provider's operational cost. Via extensive simulations, we validate the effectiveness and efficiency of our incentive scheme.","Title: ADAM: an automatic and extensible platform to stress test android anti-virus systems\nYear: 2012\n\nAbstract: With the rising threat of smartphone malware, both academic community and commercial anti-virus companies proposed many methodologies and products to defend against smartphone malware. Thus, how to assess the effectiveness of these defense mechanisms against existing and unknown malware becomes important. We propose ADAM, an automated and extensible system that can evaluate, via large-scale stress tests, the effectiveness of anti-virus systems against a variety of malware samples for the Android platform. Specifically, ADAM can automatically transform an original malware sample to different variants via repackaging and obfuscation techniques in order to evaluate the robustness of different anti-virus systems against malware mutation. The transformation and evaluation processes of ADAM are fully automatic, generic, and extensible for different types of malware, anti-virus systems, and malware transformation techniques. We demonstrate the efficacy of ADAM using 222 Android malware samples that we collected in the wild. Using ADAM, we generate different variants based on our collected malware samples, and evaluate the detection of these variants against commercial anti-virus systems.","Title: Toward Picture-perfect Streaming on the Internet\nYear: 2005\n\nAbstract: Quality of service (QoS) in streaming of continuous media over the Internet is poor, which is partly due to variations in delays, bandwidth limitations, and packet losses. Although continuous media applications can tolerate some missing data, non-recoverable information loss degrades these applications' QoS. Consequently, a number of application areas have backed away from streaming of their content over the Internet. Inability to control the resulting visual and auditory quality of the streamed presentation is an important reason for such a trend. We believe that this trend can be reversed. To this end, this paper gives an overview of our efforts in exploring high quality streaming through the exploitation of multiple paths existing in the network. By high quality, we mean with significant bandwidth requirements, of relatively long duration, and without information loss or hiccups in data delivery. We believe this to be a promising approach.","Title: On the Performance Bounds of Practical Wireless Network Coding\nYear: 2010\n\nAbstract: Network coding is an attracting technology that has been shown to be able to improve the throughput of wireless networks. However, there still lacks fundamental understanding on how network coding works under realistic scenarios. In this paper, we examine the performance of a recently proposed network coding system under a realistic wireless physical layer and practical random access mechanisms. We propose a key performance measure called “encoding number”-the number of packets that can be encoded via network coding in each transmission. We provide an upper bound on the encoding number for the general coding topology, and derive the average encoding number and system throughput for a general class of random access mechanisms. Based on the practical coding system, we also derive a tighter upper bound on the throughput gain for a general wireless network. Our results are of fundamental value for coding-related MAC/Routing protocol design and analysis.","Title: Social Sensor Placement in Large Scale Networks: A Graph Sampling Perspective\nYear: 2013\n\nAbstract: Sensor placement for the purpose of detecting/tracking news outbreak and preventing rumor spreading is a challenging problem in a large scale online social network (OSN). This problem is a kind of subset selection problem: choosing a small set of items from a large population so to maximize some prespecified set function. However, it is known to be NP-complete. Existing heuristics are very costly especially for modern OSNs which usually contain hundreds of millions of users. This paper aims to design methods to find \\emph{good solutions} that can well trade off efficiency and accuracy. We first show that it is possible to obtain a high quality solution with a probabilistic guarantee from a \"{\\em candidate set}\" of the underlying social network. By exploring this candidate set, one can increase the efficiency of placing social sensors. We also present how this candidate set can be obtained using \"{\\em graph sampling}\", which has an advantage over previous methods of not requiring the prior knowledge of the complete network topology. Experiments carried out on two real datasets demonstrate not only the accuracy and efficiency of our approach, but also effectiveness in detecting and predicting news outbreak.","Title: On the evolution of the internet economic ecosystem\nYear: 2012\n\nAbstract: The evolution of the Internet has manifested itself in many ways: the traffic characteristics, the interconnection topologies and the business relationships among the autonomous components. It is important to understand why (and how) this evolution came about, and how the interplay of these dynamics may affect future evolution and services. We propose a network aware, macroscopic model that captures the characteristics and interactions of the application and network providers, and show how it leads to a market equilibrium of the ecosystem. By analyzing the driving forces and the dynamics of the market equilibrium, we obtain some fundamental understandings of the cause and effect of the Internet evolution, which explain why some historical and recent evolutions have happened. Furthermore, by projecting the likely future evolutions, our model can help application and network providers to make informed business decisions so as to succeed in this competitive ecosystem.","Title: On oligopoly spectrum allocation game in cognitive radio networks with capacity constraints\nYear: 2010\n\nAbstract: Dynamic spectrum sharing is a promising technology to improve spectrum utilization in future wireless networks. The flexible spectrum management provides new opportunities for licensed primary user and unlicensed secondary users to reallocate the spectrum resource efficiently. In this paper, we present an oligopoly pricing framework for dynamic spectrum allocation in which the primary users sell excessive spectrum to the secondary users for monetary return. We present two approaches, the strict constraints (type-I) and the QoS penalty (type-II), to model the realistic situation that the primary users have limited capacities. In the oligopoly model with strict constraints, we propose a low-complexity searching method to obtain the Nash Equilibrium and prove its uniqueness. When reduced to a duopoly game, we analytically show the interesting gaps in the leader-follower pricing strategy. In the QoS penalty based oligopoly model, a novel variable transformation method is developed to derive the unique Nash Equilibrium. When the market information is limited, we provide three myopically optimal algorithms ''StrictBEST'', ''StrictBR'' and ''QoSBEST'' that enable price adjustment for duopoly primary users based on the Best Response Function (BRF) and the bounded rationality (BR) principles. Numerical results validate the effectiveness of our analysis and demonstrate the convergence of ''StrictBEST'' as well as ''QoSBEST'' to the Nash Equilibrium. For the ''StrictBR'' algorithm, we reveal the chaotic behaviors of dynamic price adaptation in response to the learning rates.","Title: The Fundamental Role of Hop Distance in IEEE802.11 Multi-Hop Ad Hoc Networks\nYear: 2005\n\nAbstract: In wireless networks, it is well understood what throughput can be achieved by nodes who can hear each other (i.e. nodes within a single cell). The effects of nodes beyond the sensing range (known as hidden nodes) on a sender are complicated and difficult to analyze. Consequently, how to analytically model multi-hop ad-hoc networks, specially networks based on the popular IEEE 802.11 standards remains largely open. In a recent paper, the throughput of a particular wireless network topology (linear network with a given number of hidden nodes) has been derived analytically. In this paper, we unify previous results on single-cell models, and results characterizing different types of hidden node interference and the analysis of C. Ng et al., (2004), to derive a general solution for throughput given a linear network of arbitrary density and transmission distance between source and destination nodes. An important insight from our model is that there is a certain transmission distance, which is less than the maximum transmission distance, that optimizes throughput in such networks. This result is verified using ns-2 simulation with both single as well as multiple flows.","Title: Secure Cache Provision: Provable DDOS Prevention for Randomly Partitioned Services with Replication\nYear: 2013\n\nAbstract: In this paper we show a small but fast popularity-based front-end cache can provide provable DDOS prevention for randomly partitioned cluster services with replication. To achieve this, we first give the best strategy for an adversary to overload the system, and then prove that the cache size is lower bounded by O(n log log n/ log d), where n is the number of back-end nodes and d is the replication factor. Since log log n/ log d <; 2 holds for almost all the current clusters (i.e., the number of back-end nodes n <; 105 and the replication factor d ≥ 3), this result implies an O(n) lower bound on the required cache size. Our analysis and results are well validated through extensive simulations.","Title: Scheduling and data layout policies for a near-line multimedia storage architecture\nYear: 1997\n\nAbstract: Recent advances in computer technologies have made it feasible to provide multimedia services, such as news distribution and entertainment, via high-bandwidth networks. The storage and retrieval of large multimedia objects (e.g., video) becomes a major design issue of the multimedia information system. While most other works on multimedia storage servers assume an on-line disk storage system, we consider a two-tier storage architecture with a robotic tape library as the vast near-line storage and an on-line disk system as the front-line storage. Magnetic tapes are cheaper, more robust, and have a larger capacity; hence, they are more cost effective for large scale storage systems (e.g., videoon-demand (VOD) systems may store tens of thousands of videos). We study in detail the design issues of the tape sub-system and propose some novel tape-scheduling algorithms which give faster response and require less disk buffer space. We also study the disk-striping policy and the data layout on the tape cartridge in order to fully utilize the throughput of the robotic tape system and to minimize the on-line disk storage space.","Title: An Efficient Partitioning Algorithm for Distributed Virtual Environment Systems\nYear: 2002\n\nAbstract: Distributed virtual environment (DVE) systems model and simulate the activities of thousands of entities interacting in a virtual world over a wide area network. Possible applications for DVE systems are multiplayer video games, military and industrial trainings, and collaborative engineering. In general, a DVE system is composed of many servers and each server is responsible to manage multiple clients who want to participate in the virtual world. Each server receives updates from different clients (such as the current position and orientation of each client) and then delivers this information to other clients in the virtual world. The server also needs to perform other tasks, such as object collision detection and synchronization control. A large scale DVE system needs to support many clients and this imposes a heavy requirement on networking resources and computational resources. Therefore, how to meet the growing requirement of bandwidth and computational resources is one of the major challenges in designing a scalable and cost-effective DVE system. In this paper, we propose an efficient partitioning algorithm that addresses the scalability issue of designing a large scale DVE system. The main idea is to dynamically divide the virtual world into different partitions and then efficiently assign these partitions to different servers. This way, each server will process approximately the same amount of workload. Another objective of the partitioning algorithm is to reduce the server-to-server communication overhead. The theoretical foundation of our dynamic partitioning algorithm is based on the linear optimization principle. We also illustrate how one can parallelize the proposed partitioning algorithm so that it can efficiently partition a very large scale DVE system. Lastly, experiments are carried out to illustrate the effectiveness of the proposed partitioning algorithm under various settings of the virtual world.","Title: Sampling node pairs over large graphs\nYear: 2013\n\nAbstract: Characterizing user pair relationships is important for applications such as friend recommendation and interest targeting in online social networks (OSNs). Due to the large scale nature of such networks, it is infeasible to enumerate all user pairs and so sampling is used. In this paper, we show that it is a great challenge even for OSN service providers to characterize user pair relationships even when they possess the complete graph topology. The reason is that when sampling techniques (i.e., uniform vertex sampling (UVS) and random walk (RW)) are naively applied, they can introduce large biases, in particular, for estimating similarity distribution of user pairs with constraints such as existence of mutual neighbors, which is important for applications such as identifying network homophily. Estimating statistics of user pairs is more challenging in the absence of the complete topology information, since an unbiased sampling technique such as UVS is usually not allowed, and exploring the OSN graph topology is expensive. To address these challenges, we present asymptotically unbiased sampling methods to characterize user pair properties based on UVS and RW techniques respectively. We carry out an evaluation of our methods to show their accuracy and efficiency. Finally, we apply our methods to two Chinese OSNs, Doudan and Xiami, and discover significant homophily is present in these two networks.","Title: A Proportional-Delay DiffServ-Enabled Web Server: Admission Control and Dynamic Adaptation\nYear: 2004\n\nAbstract: Abstract--We consider a Web server that can provide differentiated services to clients with different quality of service (QoS) requirements. The Web server can provide N \\geq 1 classes of proportional-delay differentiated services (PDDS) to heterogeneous clients. An operator can specify fixed performance spacings between classes, namely, r_{i,i+1} 1, for i=1,\\ldots,N-1. Requests in class i+1 are guaranteed to have an average waiting time which is 1/r_{i,i+1} of the average waiting time of class i requests. With PDDS, we can provide consistent performance spacings over a wide range of system loading and this simplifies many pricing issues. In addition, each client can specify a maximum average waiting time requirement to be guaranteed by the PDDS-enabled Web server. We show that, in general, the problem of assigning clients to service classes in order to optimize system efficacy is NP-complete. We propose two efficient admission control algorithms so that a Web server can provide the QoS guarantees and, at the same time, classify each client to its \"lowest驴 admissible class, resulting in lowest usage cost for the admitted client. We also consider how to perform end-point dynamic adaptation such that admitted clients can submit requests at a lower class and further reduce their usage costs without violating their QoS requirements. We propose two dynamic adaptation algorithms: one is server-based and the other is client-based. The client-based adaptation is distributed and is based on a noncooperative game technique. We carry out experiments to illustrate the effectiveness of these algorithms under different utility functions and traffic arrival patterns (e.g., Poisson, MMPP, and Pareto). We report extensive experimental results to illustrate the effectiveness of our proposed algorithms.","Title: COPACC: a cooperative proxy-client caching system for on-demand media streaming\nYear: 2005\n\nAbstract: Proxy caching is a key technique to reduce transmission cost for on-demand multimedia streaming. However, its effectiveness is limited by the insufficient storage space and weak cooperations among proxies and their clients. In this paper, we propose COPACC, a novel cooperative proxy-and-client caching system that combines the advantages of both proxy caching and peer-to-peer (P2P) client communications. We propose a comprehensive suite of protocols to facilitate the interactions among different network entities in COPACC. We also develop an efficient cache allocation algorithm to minimize the aggregated transmission cost of the whole system. Simulation results demonstrate that COPACC achieves remarkably lower transmission cost. Moreover, it is much more robust than a pure P2P system in the presence of node failures.","Title: Doubly Exponential Solution for Randomized Load Balancing Models with Markovian Arrival Processes and PH Service Times\nYear: 2010\n\nAbstract: In this paper, we provide a novel matrix-analytic approach for studying\ndoubly exponential solutions of randomized load balancing models (also known as\nsupermarket models) with Markovian arrival processes (MAPs) and phase-type (PH)\nservice times. We describe the supermarket model as a system of differential\nvector equations by means of density dependent jump Markov processes, and\nobtain a closed-form solution with a doubly exponential structure to the fixed\npoint of the system of differential vector equations. Based on this, we show\nthat the fixed point can be decomposed into the product of two factors\ninflecting arrival information and service information, and further find that\nthe doubly exponential solution to the fixed point is not always unique for\nmore general supermarket models. Furthermore, we analyze the exponential\nconvergence of the current location of the supermarket model to its fixed\npoint, and apply the Kurtz Theorem to study density dependent jump Markov\nprocess given in the supermarket model with MAPs and PH service times, which\nleads to the Lipschitz condition under which the fraction measure of the\nsupermarket model weakly converges the system of differential vector equations.\nThis paper gains a new understanding of how workload probing can help in load\nbalancing jobs with non-Poisson arrivals and non-exponential service times.","Title: Secure Real-Time Streaming Protocol (RTSP) for Hierarchical Proxy Caching\nYear: 2008\n\nAbstract: Proxies are commonly used to cache objects, especially multimedia objects, so that clients can enjoy a better quality-of-service (QoS) guarantees such as smaller start- up latency and lower loss rate. But the use of multime- dia proxies increases the risk that data are exposed to unauthorized access by intruders. In this paper, we pro- pose an enhancement of the Internet IETF's Real-time Streaming Protocol (RTSP) which employs a notion of \"asymmetric reversible parametric sequence\" (ARPS) to provide the following security properties: (i) data confi- dentiality during transmission, (ii) end-to-end data confi- dentiality, (iii) data confidentiality against proxy intrud- ers, and (iv) data confidentiality against member collu- sion. We present the Secure Multimedia Library (SML) which is based on ARPS and then realize these security features on a production video streaming server: Apple's Darwin Streaming Server. Our framework guarantees the system resilience against attacks is provably strong given the standard computability assumptions. To reduce the computation demand on the receiving client, our scheme only requires the client to perform a \"single decryption operation\" to recover the original data even though the data packets have been encrypted by multiple proxies along the delivery path. To tradeoff between degree of confidentiality and computational overhead, we also pro- pose the use of a set of \"encryption configuration param- eters\" (ECP) to trade off proxy encryption throughput against the presentation quality of audio/video obtained by unauthorized parties. Our implementation prototype shows that one can simultaneously achieve high encryp- tion throughput and extremely low audio/video quality (in terms of audio fidelity, and peak signal-to-noise ratio and visual quality of decoded video frames) for unautho- rized access.","Title: An Eclectic Survey of Bounding Methods for Markov Chain Models\nYear: 1995\n\nAbstract: Markov models are often used for performance modeling. However most models do not have closed form solutions, and numerical solutions are often not feasible due to the large (or even infinite) state space of models of practical interest. One can sometimes take advantage of special structures such as nearly completely decomposable models or models with matrix analytic solutions. This paper presents a brief survey of some work over the past few years on finding bounds on performance measures for models that are otherwise intractable. The bounds are found by modifying the original model such that: (a) the modified model is efficiently solvable and (b) the modified model is proven to provide an upper (lower) bound on the value of the performance measure in the original model. We have successfully applied this methodology in a number of studies which are briefly surveyed here. This is not meant to be a broad, inclusive survey but rather we concentrate on an approach that the authors have been exploring.","Title: On the Practical and Security Issues of Batch Content Distribution Via Network Coding\nYear: 2006\n\nAbstract: File distribution via network coding has received a lot of attention lately. However, direct application of network codingmay have security problems. In particular, attackers can inject \"faked\" packets into the file distribution process to slowdown the information dispersal or even deplete the network resource. Therefore, content verification is an important and practicalissue when network coding is employed. When network coding is used, it is infeasible for the source of the content to provideall the hash values or signatures required for verification, and hence the traditional \"hash-and-sign\" methods are no longerapplicable. Recently, a new on-the-fly verification technique is proposed by Krohn et al. for rateless erasure codes [1].However, their scheme requires a large number of hash values to be distributed in advance, and all of them are needed to verifyeven for a single packet. We propose a new batch delivery and verification scheme that is similar to the classical scenariowhere the authentication information of a message is embedded with the message and is sufficient for the verification purpose.We investigate how our technique can be applied when random linear network coding is employed, and show that both the computationaland the bandwidth overhead can be greatly reduced by using a variant of the random network coding. We further show by simulationthat this variant is sufficiently effective in practice.","Title: SEAL: A secure communication library for building dynamic group key agreement applications\nYear: 2007\n\nAbstract: We present the SEcure communicAtion Library (SEAL) [source can be downloaded from: http://www.cse.cuhk.edu.hk/~cslui/ANSRlab/software/SEAL/], a Linux-based C language application programming interface (API) library that implements secure group key agreement algorithms that allow a communication group to periodically renew a common secret group key for secure and private communication. The group key agreement protocols satisfy several important characteristics: distributed property (i.e., no centralized key server is needed), collaborative property (i.e., every group member contributes to the group key), and dynamic property (i.e., group members can join or leave the group without impairing the efficiency of the group key generation). Using SEAL, we developed a testing tool termed Gauger to evaluate the performance of the group key agreement algorithms in both wired and wireless LANs according to different levels of membership dynamics. We show that our implementation achieves robustness when there are group members leaving the communication group in the middle of a rekeying operation. We also developed a secure chat-room application termed Chatter to illustrate the usage of SEAL. Our SEAL implementation demonstrates the effectiveness of group key agreement in real network settings.","Title: AppATP: An Energy Conserving Adaptive Mobile-Cloud Transmission Protocol\nYear: 2015\n\nAbstract: Many mobile applications require frequent wireless transmissions between the content provider and mobile devices, consuming much energy in mobile devices. Motivated by the popularity of prefetch-friendly or delay-tolerant apps (e.g., social networking, app updates, cloud storage), we design and implement an application-layer transmission protocol, AppATP, which leverages cloud computing to manage data transmissions for mobile apps, transferring data to and from mobile devices in an energy-efficient manner. Measurements show that significantly amount of energy is consumed by mobile devices during poor connectivity. Based on this observation, AppATP adaptively seizes periods of good bandwidth condition to prefetch frequently used data with minimum energy consumption, while deferring delay-tolerant data during poor network connectivity. Using the stochastic control framework, AppATP only relies on the current network information and data queue sizes to make an online decision on transmission scheduling, and performs well under unpredictable wireless network conditions. We implement AppATP on Samsung Note 2 smartphones and Amazon EC2. Results from both trace-driven simulations and extensive real-world experiments show that AppATP can be applied to a variety of application scenarios while achieving 30%-50% energy savings for mobile devices.","Title: Evolution of the Internet Economic Ecosystem\nYear: 2015\n\nAbstract: The evolution of the Internet has manifested itself in many ways: the traffic characteristics, the interconnection topologies, and the business relationships among the autonomous components. It is important to understand why (and how) this evolution came about, and how the interplay of these dynamics may affect future evolution and services. We propose a network-aware, macroscopic model that captures the characteristics and interactions of the application and network providers, and show how it leads to a market equilibrium of the ecosystem. By analyzing the driving forces and the dynamics of the market equilibrium, we obtain some fundamental understandings of the cause and effect of the Internet evolution, which explain why some historical and recent evolutions have happened. Furthermore, by projecting the likely future evolutions, our model can help application and network providers to make informed business decisions so as to succeed in this competitive ecosystem.","Title: Whom to follow: Efficient followee selection for cascading outbreak detection on online social networks.\nYear: 2014\n\nAbstract: Online social networks (OSNs), such as Twitter and Sina Weibo, have become important platforms for generating and spreading information on the Internet. On these OSNs, the “follow model” has become a popular way to discover information; i.e., a user subscribes to content generated by others by following them as information sources. The content producers are called followees. Due to human beings’ limited attention capacity and the constraints imposed by OSNs, a user can only follow a few followees. The question then arises: which subset of followees shall we follow so that we can discover the most information in an OSN in a timely fashion? To solve this problem, we present a randomized method that does not require complete OSN data and is well suited for third parties who do not own OSN data. Our method is based on the birthday paradox and is mathematically tractable for analysing its solution quality and computational efficiency. Moreover, we find that the power-law structure of real-world OSNs can further improve the solution quality of our method. Experiments conducted on two real datasets demonstrate that our method can create a good trade-off between solution quality and computational efficiency.","Title: Unbiased Characterization of Node Pairs over Large Graphs\nYear: 2015\n\nAbstract: Characterizing user pair relationships is important for applications such as friend recommendation and interest targeting in online social networks (OSNs). Due to the large-scale nature of such networks, it is infeasible to enumerate all user pairs and thus sampling is used. In this article, we show that it is a great challenge for OSN service providers to characterize user pair relationships, even when they possess the complete graph topology. The reason is that when sampling techniques (i.e., uniform vertex sampling (UVS) and random walk (RW)) are naively applied, they can introduce large biases, particularly for estimating similarity distribution of user pairs with constraints like existence of mutual neighbors, which is important for applications such as identifying network homophily. Estimating statistics of user pairs is more challenging in the absence of the complete topology information, as an unbiased sampling technique like UVS is usually not allowed and exploring the OSN graph topology is expensive. To address these challenges, we present unbiased sampling methods to characterize user pair properties based on UVS and RW techniques. We carry out an evaluation of our methods to show their accuracy and efficiency. Finally, we apply our methods to three OSNs—Foursquare, Douban, and Xiami—and discover that significant homophily is present in these networks.","Title: Efficiently Estimating Motif Statistics of Large Networks\nYear: 2014\n\nAbstract: Exploring statistics of locally connected subgraph patterns (also known as network motifs) has helped researchers better understand the structure and function of biological and Online Social Networks (OSNs). Nowadays, the massive size of some critical networks—often stored in already overloaded relational databases—effectively limits the rate at which nodes and edges can be explored, making it a challenge to accurately discover subgraph statistics. In this work, we propose sampling methods to accurately estimate subgraph statistics from as few queried nodes as possible. We present sampling algorithms that efficiently and accurately estimate subgraph properties of massive networks. Our algorithms require no precomputation or complete network topology information. At the same time, we provide theoretical guarantees of convergence. We perform experiments using widely known datasets and show that, for the same accuracy, our algorithms require an order of magnitude less queries (samples) than the current state-of-the-art algorithms.","Title: Accurate DNS query characteristics estimation via active probing.\nYear: 2015\n\nAbstract: As the hidden backbone of today׳s Internet, the Domain Name System (DNS) provides name resolution service for almost every networked application. To exploit the rich DNS query information for traffic engineering or user behavior analysis, both passive capturing and active probing techniques have been proposed in recent years. Despite its full visibility of DNS behaviors, the passive capturing technique suffers from prohibitive management cost and results in tremendous privacy concerns towards its large-scale and collaborative deployment. Comparatively, the active probing technique overcomes these limitations, providing broad-view and privacy-preserving DNS query analysis at the cost of constrained visibility of fine-grained DNS behavior. This paper aims to accurately estimate DNS query characteristics based on DNS cache activities, which can be acquired via active probing on a large scale at negligible management cost and minimized privacy concerns. Specifically, we have made three contributions: (1) we propose a novel solution, which integrates the renewal theory-based DNS caching formulation and the hyper-exponential distribution model. The solution offers great flexibility to model various domains; (2) we perform a large-scale real-world DNS trace measurement, and demonstrate that our solution significantly improves the estimation accuracy; (3) we apply our solution to estimate the malware-infected host population in remote management networks. The experimental results have demonstrated that our solution can achieve high estimation accuracy and outperforms the existing method.","Title: Mathematical Modeling of Insurance Mechanisms for E-commerce Systems.\nYear: 2015\n\nAbstract: Electronic commerce (a.k.a. E-commerce) systems such as eBay and Taobao of Alibaba are becoming increasingly popular. Having an effective reputation system is critical to this type of internet service because it can assist buyers to evaluate the trustworthiness of sellers, and it can also improve the revenue for reputable sellers and E-commerce operators. We formulate a stochastic model to analyze an eBay-like reputation system and propose four measures to quantify its effectiveness: (1) new seller ramp up time, (2) new seller drop out probability, (3) long term profit gains for sellers, and (4) average per seller transaction gains for the E-commerce operator. Through our analysis, we identify key factors which influence these four measures. We propose a new insurance mechanism which consists of an insurance protocol and a transaction mechanism to improve the above four measures. We show that our insurance mechanism can reduce the ramp up time by around 87.2%, and guarantee new sellers ramp up before the deadline $T_w$ with a high probability (close to 1.0). It also increases the long term profit gains and average per seller transaction gains by at least 95.3%.","Title: Block-Structured Supermarket Models.\nYear: 2014\n\nAbstract: Supermarket models are a class of parallel queueing networks with an adaptive control scheme that play a key role in the study of resource management of, such as, computer networks, manufacturing systems and transportation networks. When the arrival processes are non-Poisson and the service times are non-exponential, analysis of such a supermarket model is always limited, interesting, and challenging.This paper describes a supermarket model with non-Poisson inputs: Markovian Arrival Processes (MAPs) and with non-exponential service times: Phase-type (PH) distributions, and provides a generalized matrix-analytic method which is first combined with the operator semigroup and the mean-field limit. When discussing such a more general supermarket model, this paper makes some new results and advances as follows: (1) Providing a detailed probability analysis for setting up an infinite-dimensional system of differential vector equations satisfied by the expected fraction vector, where the invariance of environment factors is given as an important result. (2) Introducing the phase-type structure to the operator semigroup and to the mean-field limit, and a Lipschitz condition can be established by means of a unified matrix-differential algorithm. (3) The matrix-analytic method is used to compute the fixed point which leads to performance computation of this system. Finally, we use some numerical examples to illustrate how the performance measures of this supermarket model depend on the non-Poisson inputs and on the non-exponential service times. Thus the results of this paper give new highlight on understanding influence of non-Poisson inputs and of non-exponential service times on performance measures of more general supermarket models.","Title: Algorithmic Design for Competitive Influence Maximization Problems.\nYear: 2014\n\nAbstract: Given the popularity of the viral marketing campaign in online social networks, finding an effective method to identify a set of most influential nodes so to compete well with other viral marketing competitors is of upmost importance. We propose a \"General Competitive Independent Cascade (GCIC)\" model to describe the general influence propagation of two competing sources in the same network. We formulate the \"Competitive Influence Maximization (CIM)\" problem as follows: Under a prespecified influence propagation model and that the competitor's seed set is known, how to find a seed set of $k$ nodes so as to trigger the largest influence cascade? We propose a general algorithmic framework TCIM for the CIM problem under the GCIC model. TCIM returns a $(1-1/e-\\epsilon)$-approximate solution with probability at least $1-n^{-\\ell}$, and has an efficient time complexity of $O(c(k+\\ell)(m+n)\\log n/\\epsilon^2)$, where $c$ depends on specific propagation model and may also depend on $k$ and underlying network $G$. To the best of our knowledge, this is the first general algorithmic framework that has both $(1-1/e-\\epsilon)$ performance guarantee and practical efficiency. We conduct extensive experiments on real-world datasets under three specific influence propagation models, and show the efficiency and accuracy of our framework. In particular, we achieve up to four orders of magnitude speedup as compared to the previous state-of-the-art algorithms with the approximate guarantee.","Title: Friends or Foes: Distributed and Randomized Algorithms to Determine Dishonest Recommenders in Online Social Networks\nYear: 2014\n\nAbstract: Viral marketing is becoming important due to the popularity of online social networks (OSNs). Companies may provide incentives (e.g., via free samples of a product) to a small group of users in an OSN, and these users provide recommendations to their friends, which eventually increases the overall sales of a given product. Nevertheless, this also opens a door for malicious behaviors: dishonest users may intentionally give misleading recommendations to their friends so as to distort the normal sales distribution. In this paper, we propose a detection framework to identify dishonest users in the OSNs. In particular, we present a set of fully distributed and randomized algorithms, and also quantify the performance of the algorithms by deriving probability of false positive, probability of false negative, and the distribution of number of detection rounds. Extensive simulations are also carried out to illustrate the impact of misleading recommendations and the effectiveness of our detection algorithms. The methodology we present here will enhance the security level of viral marketing in the OSNs.","Title: Minfer: Inferring Motif Statistics From Sampled Edges.\nYear: 2015\n\nAbstract: Characterizing motif (i.e., locally connected subgraph patterns) statistics is important for understanding complex networks such as online social networks and communication networks. Previous work made the strong assumption that the graph topology of interest is known, and that the dataset either fits into main memory or stored on disks such that it is not expensive to obtain all neighbors of any given node. In practice, researchers have to deal with the situation where the graph topology is unknown, either because the graph is dynamic, or because it is expensive to collect and store all topological and meta information on disk. Hence, what is available to researchers is only a snapshot of the graph generated by sampling edges from the graph at random, which we called a \"RESampled graph\". Clearly, a RESampled graph's motif statistics may be quite different from the underlying original graph. To solve this challenge, we propose a framework and implement a system called Minfer, which can take the given RESampled graph and accurately infer the underlying graph's motif statistics. We also use Fisher information to bound the error of our estimates. Experiments using large scale datasets show that our method to be accurate.","Title: Mathematical modeling of group product recommendation with partial information: How many ratings do we need?\nYear: 2014\n\nAbstract: Product recommendation is one of the most important services in the Internet. In this paper, we consider a product recommendation system which recommends products to a group of users. The recommendation system only has partial preference information on this group of users: a user only indicates his preference to a small subset of products in the form of ratings. This partial preference information makes it a challenge to produce an accurate recommendation. In this work, we explore a number of fundamental questions. What is the desired number of ratings per product so to guarantee an accurate recommendation? What are some effective voting rules in summarizing ratings? How users’ misbehavior such as cheating, in product rating may affect the recommendation accuracy? What are some efficient rating schemes? To answer these questions, we present a formal mathematical model of a group recommendation system. We formally analyze the model. Through this analysis we gain the insight to develop a randomized algorithm which is both computationally efficient and asymptotically accurate in evaluating the recommendation accuracy under a very general setting. We propose a novel and efficient heterogeneous rating scheme which requires equal or less rating workload, but can improve over a homogeneous rating scheme by as much as 30%. We carry out experiments on both synthetic data and real-world data from TripAdvisor. Not only we validate our model, but also we obtain a number of interesting observations, i.e., a small of misbehaving users can decrease the recommendation accuracy remarkably. For TripAdvisor, one hundred ratings per product is sufficient to guarantee a high accuracy recommendation. We believe our model and methodology are important building blocks to refine and improve applications of group recommendation systems.","Title: SAND: A Fault-Tolerant Streaming Architecture for Network Traffic Analytics\nYear: 2014\n\nAbstract: Many long-running network analytics applications impose a high-throughput and high reliability requirements on stream processing systems. However, previous stream processing systems cannot sustain high-speed traffic at the core router level. Furthermore, their fault-tolerant schemes cannot provide strong consistency which is essential for network analytics. In this paper, we present the design and implementation of SAND, a fault-tolerant distributed stream processing system for network analytics. SAND is designed to operate under high-speed network traffic, and it uses a novel check pointing protocol which can perform failure recovery based on upstream backup and check pointing. We prove our fault-tolerant scheme provides strong consistency even under multiple node failure. We implement several real-world network analytics applications on SAND, evaluate their performance using network traffic captured from commercial cellular core networks, and demonstrate that SAND can sustain high-speed network traffic and that our fault-tolerant scheme is efficient.","Title: Impact of Data Locality on Garbage Collection in SSDs: A General Analytical Study\nYear: 2015\n\nAbstract: Solid-state drives (SSDs) necessitate garbage collection (GC) to erase data blocks and reclaim the space of invalidated data, and GC inevitably introduces additional writes due to data relocation. The performance of GC, which is quantified by cleaning cost or write amplification, is critical to the overall performance of SSDs. However, characterizing GC performance is complicated by the general implementations of GC algorithms and the complex data locality characteristics of real-world workloads. This paper presents a general analytical study to characterize the performance impact of data locality on a general family of GC algorithms. We develop probabilistic models to address two fundamental issues: (1) What is the impact of data locality on the performance of locality-oblivious GC? (2) How can data locality be leveraged to improve the performance in locality-aware GC? We further conduct extensive trace-driven simulations on real-world workloads to validate the findings of our models.","Title: DroidTrace: A ptrace based Android dynamic analysis system with forward execution capability\nYear: 2014\n\nAbstract: Android, being an open source smartphone operating system, enjoys a large community of developers who create new mobile services and applications. However, it also attracts malware writers to exploit Android devices in order to distribute malicious apps in the wild. In fact, Android malware are becoming more sophisticated and they use advanced “dynamic loading” techniques like Java reflection or native code execution to bypass security detection. To detect dynamic loading, one has to use dynamic analysis. Currently, there are only a handful of Android dynamic analysis tools available, and they all have shortcomings in detecting dynamic loading. The aim of this paper is to design and implement a dynamic analysis system which allows analysts to perform systematic analysis of dynamic payloads with malicious behaviors. We propose “DroidTrace”, a ptrace based dynamic analysis system with forward execution capability. Our system uses ptrace to monitor selected system calls of the target process which is running the dynamic payloads, and classifies the payloads behaviors through the system call sequence, e.g., behaviors such as file access, network connection, inter-process communication and even privilege escalation. Also, DroidTrace performs “physical modification” to trigger different dynamic loading behaviors within an app. Using DroidTrace, we carry out a large scale analysis on 36,170 dynamic payloads in 50,000 apps and 294 malware in 10 families (four of them are zero-day) with various dynamic loading behaviors.","Title: On efficient bandwidth allocation for traffic variability in datacenters\nYear: 2014\n\nAbstract: Datacenter networks suffer unpredictable performance due to a lack of application level bandwidth guarantees. A lot of attentions have been drawn to solve this problem such as how to provide bandwidth guarantees for Virtualized Machines (VMs), proportional bandwidth share among tenants, and high network utilization under peak traffic. However, existing solutions fail to cope with highly dynamic traffic in datacenter networks. In this paper, we consider the effects of large numbers of short flows and massive bursty traffic in the datacenter, and design a novel distributed rate allocation algorithm based on the Logistic model under the control-theoretic framework. The theoretical analysis and experimental results using OpenFlow show that our algorithm not only guarantees the bandwidth for VMs, but also provides fast convergence to efficiency and fairness, and smooth response to bursty traffic.","Title: Exploring bundling sale strategy in online service markets with network effects\nYear: 2014\n\nAbstract: In recent years, we have witnessed a growing trend for online service companies to offer “bundling sales” to increase revenue. Bundling sale means that a company groups a set of its products/services and charges this bundle at a fixed price, which is usually less than the total price of individual items. In this paper, our goal is to understand the underlying dynamics of bundling, in particular, what is the optimal bundling sale strategy and under what situations it will be more attractive than the separate sales. We focus on online service markets that exhibit network effect. We provide mathematical models to capture the interactions between buyers and sellers, analyze the market equilibrium and its stability, and formulate an optimization framework to determine the optimal sale strategy for the service provider. We analyze the impact of the key factors, including the network effects and operating costs, on the profitability of bundling. We show that bundling is more profitable than separate sale in most cases; however, the heterogeneity of services and the asymmetry of operating costs reduce the advantage of bundling. These findings provide important insights in designing proper sale strategies for online services.","Title: Enpublic Apps: Security Threats Using iOS Enterprise and Developer Certificates\nYear: 2015\n\nAbstract: Compared with Android, the conventional wisdom is that iOS is more secure. However, both jailbroken and non-jailbroken iOS devices have number of vulnerabilities. For iOS, apps need to interact with the underlying system using Application Programming Interfaces (APIs). Some of these APIs remain undocumented and Apple forbids apps in App Store from using them. These APIs, also known as \\\"private APIs\\\", provide powerful features to developers and yet they may have serious security consequences if misused. Furthermore, apps which use private APIs can bypass the App Store and use the \\\"Apple's Enterprise/Developer Certificates\\\" for distribution. This poses a significant threat to the iOS ecosystem. So far, there is no formal study to understand these apps and how private APIs are being encapsulated. We call these iOS apps which distribute to the public using enterprise certificates as \\\"enpublic\\\" apps. In this paper, we present the design and implementation of iAnalytics, which can automatically analyze \\\"enpublic\\\" apps' private API usages and vulnerabilities. Using iAnalytics, we crawled and analyzed 1,408 enpublic iOS apps. We discovered that: 844 (60%) out of the 1408 apps do use private APIs, 14 (1%) apps contain URL scheme vulnerabilities, 901 (64%) enpublic apps transport sensitive information through unencrypted channel or store the information in plaintext on the phone. In addition, we summarized 25 private APIs which are crucial and security sensitive on iOS 6/7/8, and we have filed one CVE (Common Vulnerabilities and Exposures) for iOS devices.","Title: DroidRay: a security evaluation system for customized android firmwares\nYear: 2014\n\nAbstract: Android mobile devices are enjoying a lion's market share in smartphones and mobile devices. This also attracts malware writers to target the Android platform. Recently, we have discovered a new Android malware distribution channel: releasing malicious firmwares with pre-installed malware to the wild. This poses significant risk since users of mobile devices cannot change the content of the malicious firmwares. Furthermore, pre-installed applications have \\\" more permissions\\\" (i.e., silent installation) than other legitimate mobile apps, so they can download more malware or access users' confidential information. To understand and address this new form of malware distribution channel, we design and implement \\\"DroidRay\\\": a security evaluation system for customized Android firmwares. DroidRay uses both static and dynamic analyses to evaluate the firmware security on both the application and system levels. To understand the impact of this new malware distribution channel, we analyze 250 Android firmwares and 24,009 pre-installed applications. We reveal how the malicious firmware and pre-installed malware are injected, and discovered 1,947 (8.1%) pre-installed applications have signature vulnerability and 19 (7.6%) firmwares contain pre-installed malware. In addition, 142 (56.8%) firmwares have the default signature vulnerability, five (2.0%) firmwares contain malicious hosts file, at most 40 (16.0%) firmwares have the native level privilege escalation vulnerability and at least 249 (99.6%) firmwares have the Java level privilege escalation vulnerability. Lastly, we investigate a real-world case of a pre-installed zero-day malware known as CEPlugnew, which involves 348,018 infected Android smartphones, and we show its degree and geographical penetration. This shows the significance of this new malware distribution channel, and DroidRay is an effective tool to combat this new form of malware spreading.","Title: Mathematical Modeling of Crowdsourcing Systems: Incentive Mechanism and Rating System Design\nYear: 2014\n\nAbstract: Crowd sourcing systems like Yahoo! Answers, Amazon Mechanical Turk, and Google Helpouts, etc., have seen an increasing prevalence in the past few years. The participation of users, high quality solutions, and a fair rating system are critical to the revenue of a crowd sourcing system. In this paper, we design a class of simple but effective incentive mechanisms to attract users participating, and providing high quality solutions. Our incentive mechanism consists of a task bundling scheme and a rating system, and pay workers according to solution ratings from requesters. We also propose a probabilistic model to capture various human factors like biases in rating, and we quantify its impact on the incentive mechanism, which is shown to be highly robust. We develop a model to characterize the design space of a class of commonly used rating systems - threshold based rating systems. We quantify the impact of such rating systems and the bundling scheme on the incentive mechanism.","Title: A provable algorithmic approach to product selection problems for market entry and sustainability\nYear: 2014\n\nAbstract: Given the globalized economy, how to process the heterogeneous web data so to extract customers' purchase behavior is crucial to manufacturers who want to enter or sustain in a competitive market. To maximize the sales, manufacturers not only need to decide what products to produce so to meet diverse customers' requirements, but at the same time, compete with competitors' products. In this paper, we present a general framework for the following product selection problems: (1) k-BSP problem, which is for a manufacturer to enter a competitive market, and (2) k-BBP problem, which is for a manufacturer to sustain in a competitive market. We propose several product adoption models to describe the complex purchase behavior of customers, and formally show that these problems are NP-hard in general. To tackle these problems, we propose computationally efficient greedy-based approximation algorithms. Based on the submodularity analysis, we prove that our algorithms can guarantee a (1--1/e)-approximation ratio as compared to the optimal solutions. We perform large scale data analysis to show the efficiency and accuracy of our framework. In our experiments, we observe 1,300 to 250,000 times speedup as compared to the exhaustive algorithms, and our solutions can achieve on average 96% of solution quality as compared to the optimal solutions. Finally, we apply our algorithms on web dataset to show the impact of customers' different purchase behavior on the results of product selection.","Title: Keynote speakers: Big data science and social networks — Accelerating insights and building value\nYear: 2014\n\nAbstract: These keynote discusses the following: Big Data Science and Social Networks - Accelerating Insights and Building Value; Sampling Theory of Large Networks; Mining Online Social Networks: Opportunities and Privacy Issues.","Title: Automatic recovery from disk failure in continuous-media servers\nYear: 2002\n\nAbstract: Continuous-media (CM) servers have been around for some years. Apart from server capacity, another important issue in the deployment of CM servers is reliability. This study investigates rebuild algorithms for automatically rebuilding data stored in a failed disk into a spare disk. Specifically, a block-based rebuild algorithm is studied with the rebuild time and buffer requirement modeled. A buffer-sharing scheme is then proposed to eliminate the additional buffers needed by the rebuild process. To further improve rebuild performance, a track-based rebuild algorithm that rebuilds lost data in tracks is proposed and analyzed. Results show that track-based rebuild, while it substantially outperforms block-based rebuild, requires significantly more buffers (17-135 percent more) even with buffer sharing. To tackle this problem, a novel pipelined rebuild algorithm is proposed to take advantage of the sequential property of track retrievals to pipeline the reading and writing processes. This pipelined rebuild algorithm achieves the same rebuild performance as track-based rebuild, but reduces the extra buffer requirement to insignificant levels (0.7-1.9 percent). Numerical results computed using models of five commercial disk drives demonstrate that automatic rebuild of a failed disk can be done in a reasonable amount of time, even at relatively high server utilization (e.g., less than 1.5 hours at 90 percent utilization).","Title: Bounding the mean response time of the minimum expected delay routing policy: an algorithmic approach\nYear: 1995\n\nAbstract: Balancing loads in a multi-server system can have a significant impact on performance. In this paper, we model such a system as a heterogeneous multi-server queueing system. We study the behavior of such a system operating under the minimum expected delay (MED) routing policy, i.e., an arriving customer is assigned to the queue which has the minimal expected value of unfinished work. This routing discipline can be viewed as a generalization of the join-the-shortest queue (SQ) discipline for homogeneous servers. There is no closed-form solution for this class of queueing problem. In this paper, we provide a methodology to compute upper and lower bounds on the mean response time of the system. This methodology allows one to tradeoff the tightness of the bounds and computational cost. Applications and numerical examples are presented which show how to use this methodology for deriving performance measures and also illustrating that the excellent accuracy of the computational algorithm which is achievable with modest computational cost","Title: Buffer and I/O resource pre-allocation for implementing batching and buffering techniques for Video-on-Demand systems\nYear: 1997\n\nAbstract: To design a cost effective VOD server, it is important to carefully manage the system resources so that the number of concurrent viewers can be maximized. Previous research results use data sharing techniques, such as batching, buffering and piggybacking, to reduce the demand for I/O resources in a VOD system. However, these techniques still suffer from the problem that additional I/O resources are needed in the system for providing VCR functionality --- without careful resource management, the benefits of these data sharing techniques can be lost. In this paper, we first introduce a model for determining the amount of resources required for supporting both normal playback and VCR functionality to satisfy predefined performance characteristics. Consequently, this model allows us to maximize the benefits of data sharing techniques. Furthermore, one important application of this model is its use in making system sizing decisions. Proper system sizing will result in a more cost-effective VOD system.","Title: On the robustness of soft state protocols\nYear: 2004\n\nAbstract: Soft state has been a mantra of Internet protocol design for the past decade. System designers build protocols that implement soft state mechanisms based on intuition or on qualitative arguments that the design is \"better\", yet there has never been a formal performance evaluation study that draws the same conclusion. In fact, previous attempts [P. Ji et al., 2003 and S. Raman et al., 1999] to build such a quantitative argument have found that pure soft state protocols significantly under-perform their hard state counterparts, and that only soft-hard hybrids can match hard state protocol performance. In this paper, we argue otherwise. We develop models that provide a performance-oriented explanation and justification of the Internet designer's intuition. The novel observation is that, if network conditions are known, a hard state protocol can always be configured to outperform its soft state counterpart. However, in reality, network conditions are unpredictable, and that soft state protocols are much more resilient to unanticipated fluctuations in these conditions.","Title: A multikey secure multimedia proxy using asymmetric reversible parametric sequences: theory, design, and implementation\nYear: 2005\n\nAbstract: Because of limited server and network capacities for streaming applications, multimedia proxies are commonly used to cache multimedia objects such that, by accessing nearby proxies, clients can enjoy a smaller start-up latency and receive a better quality-of-service (QoS) guarantee - for example, reduced packet loss and delay jitters for their requests. However, the use of multimedia proxies increases the risk that multimedia data are exposed to unauthorized access by intruders. In this paper, we present a framework for implementing a secure multimedia proxy system for audio and video streaming applications. The framework employs a notion of asymmetric reversible parametric sequence (ARPS) to provide the following security properties: (i) data confidentiality during transmission, (ii) end-to-end data confidentiality, (iii) data confidentiality against proxy intruders, and (iv) data confidentiality against member collusion. Our framework is grounded on a multi-key RSA technique such that system resilience against attacks is provably strong given standard computability assumptions. One important feature of our proposed scheme is that clients only need to perform a single decryption operation to recover the original data even though the data packets may have been encrypted by multiple proxies along the delivery path. We also propose the use of a set of encryption configuration parameters (ECP) to trade off proxy encryption throughput against the presentation quality of audio/video obtained by unauthorized parties. Implementation results show that we can simultaneously achieve high encryption throughput and extremely low video quality (in terms of peak signal-to-noise ratio and visual quality of decoded video frames) for unauthorized access.","Title: Constructing communication subgraphs and deriving an optimal synchronization interval for distributed virtual environment systems\nYear: 2001\n\nAbstract: In this paper, we consider problems of constructing a communication subgraph and deriving an optimal synchronization interval for a distributed virtual environment system (DVE). In general, a DVE system is a distributed system which allows many clients who are located in different parts of the network to concurrently explore and interact with each other under a high resolution, 3D, graphical virtual environment. Each client in a DVE system is represented by an avatar in the virtual environment, and each avatar can move and interact freely in the virtual environment. There are many challenging issues in designing a cost-effective DVE system. In this work, we address two important design issues, namely: 1) how to construct a communication subgraph which can efficiently carry traffic generated by all clients in a DVE system, and 2) how to guarantee that each participating client has the same consistent view of the virtual world. In other words, if there is an action taken by an avatar or if there is any change in the state of an object in the virtual world, every participating client will be able to view the change. To provide this consistent view, a DVE system needs to perform synchronization actions periodically. We present several algorithms for constructing a communication subgraph. In the subgraph construction, we try to reduce the consumption of network bandwidth resources or reduce the maximum delay between any two clients in a DVE system. Based on a given communication subgraph, we then derive the optimal synchronization interval so as to guarantee the view consistency among all participating clients. The derivation of the optimal synchronization interval is based on the theory of Markov chains and the fundamental matrix.","Title: Analysis of Adaptive Incentive Protocols for P2P Networks\nYear: 2009\n\nAbstract: Incentive protocols play a crucial role to encourage cooperation among nodes in networking applications. The aim of this paper is to provide a general analytical framework to analyze and design a large family of incentive protocols. We consider a class of incentive protocols wherein peers can distributively learn and adapt their actions. Using our analytical framework, one can evaluate the expected performance gain and system robustness of a given incentive protocol. To illustrate the framework, we present three incentive policies and two learning (or adaptive) models. We show under what conditions the network may collapse (e.g., no cooperation in the system) or the incentive protocol can guarantee a high degree of cooperation. In particular, we formally show the connection between evaluating incentive protocols and evolutionary game theory so to identify robustness characteristics of an incentive policy.","Title: A Mathematical Framework for Analyzing Adaptive Incentive Protocols in P2P Networks\nYear: 2012\n\nAbstract: In peer-to-peer (P2P) networks, incentive protocol is used to encourage cooperation among end-nodes so as to deliver a scalable and robust service. However, the design and analysis of incentive protocols have been ad hoc and heuristic at best. The objective of this paper is to provide a simple yet general framework to analyze and design incentive protocols. We consider a class of incentive protocols that can learn and adapt to other end-nodes' strategies. Based on our analytical framework, one can evaluate the expected performance gain and, more importantly, the system robustness of a given incentive protocol. To illustrate the framework, we present two adaptive learning models and three incentive policies and show the conditions in which the P2P networks may collapse and the conditions in which the P2P networks can guarantee a high degree of cooperation. We also show the connection between evaluating incentive protocol and evolutionary game theory so one can easily identify robustness characteristics of a given policy. Using our framework, one can gain the understanding on the price of altruism and system stability, as well as the correctness of the adaptive incentive policy.","Title: Performance Analysis Of A New Updating Rule For Td(Lambda) Learning In Feedforward Networks For Position Evaluation In Go Game\nYear: 1996\n\nAbstract: In this paper, a new updating rule for applyingTemporal Difference (TD) learningtomultilayerfeedforward networks is derived. Networks are trained toevaluateGoboardpositions byTD() learningwith differentvalues of .Performance of eachnetworkisestimated bylettingitplay against other networks. Resultsshowthat non-zero gives better learning for thenetworkandstatistically, larger gives better performance.1. IntroductionIn reinforcement learning, thereisnoteacher available to givethe...","Title: Walking in the Cloud: Parallel SimRank at Scale.\nYear: 2015\n\nAbstract: Despite its popularity, SimRank is computationally costly, in both time and space. In particular, its recursive nature poses a great challenge in using modern distributed computing power, and also prevents querying similarities individually. Existing solutions suffer greatly from these practical issues. In this paper, we break such dependency for maximum efficiency possible. Our method consists of offline and online phases. In offline phase, a length-n indexing vector is derived by solving a linear system in parallel. At online query time, the similarities are computed instantly from the index vector. Throughout, the Monte Carlo method is used to maximally reduce time and space. Our algorithm, called CloudWalker, is highly parallelizable, with only linear time and space. Remarkably, it responses to both single-pair and single-source queries in constant time. CloudWalker is orders of magnitude more efficient and scalable than existing solutions for large-scale problems. Implemented on Spark with 10 machines and tested on the web-scale clue-web graph with 1 billion nodes and 43 billion edges, it takes 110 hours for offline indexing, 64 seconds for a single-pair query, and 188 seconds for a single-source query. To the best of our knowledge, our work is the first to report results on clue-web, which is 10x larger than the largest graph ever reported for SimRank computation.","Title: A General-Purpose Query-Centric Framework for Querying Big Graphs.\nYear: 2016\n\nAbstract: Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems employ a user-friendly \\\"think like a vertex\\\" programming model, and exhibit good scalability for tasks where the majority of graph vertices participate in computation. However, the design of these systems can seriously under-utilize the resources in a cluster for processing light-workload graph queries, where only a small fraction of vertices need to be accessed. In this work, we develop a new open-source system, called Quegel, for querying big graphs. Quegel treats queries as first-class citizens in its design: users only need to specify the Pregel-like algorithm for a generic query, and Quegel processes light-workload graph queries on demand, using a novel superstep-sharing execution model to effectively utilize the cluster resources. Quegel further provides a convenient interface for constructing graph indexes, which significantly improve query performance but are not supported by existing graph-parallel systems. Our experiments verified that Quegel is highly efficient in answering various types of graph queries and is up to orders of magnitude faster than existing systems.","Title: Mathematical Modeling and Analysis of Product Rating with Partial Information\nYear: 2015\n\nAbstract: Many Web services like Amazon, Epinions, and TripAdvisor provide historical product ratings so that users can evaluate the quality of products. Product ratings are important because they affect how well a product will be adopted by the market. The challenge is that we only have partial information on these ratings: each user assigns ratings to only a small subset of products. Under this partial information setting, we explore a number of fundamental questions. What is the minimum number of ratings a product needs so that one can make a reliable evaluation of its quality? How may users’ misbehavior, such as cheating in product rating, affect the evaluation result? To answer these questions, we present a probabilistic model to capture various important factors (e.g., rating aggregation rules, rating behavior) that may influence the product quality assessment under the partial information setting. We derive the minimum number of ratings needed to produce a reliable indicator on the quality of a product. We extend our model to accommodate users’ misbehavior in product rating. We derive the maximum fraction of misbehaving users that a rating aggregation rule can tolerate and the minimum number of ratings needed to compensate. We carry out experiments using both synthetic and real-world data (from Amazon and TripAdvisor). We not only validate our model but also show that the “average rating rule” produces more reliable and robust product quality assessments than the “majority rating rule” and the “median rating rule” in aggregating product ratings. Last, we perform experiments on two movie rating datasets (from Flixster and Netflix) to demonstrate how to apply our framework to improve the applications of recommender systems.","Title: Quegel: A General-Purpose Query-Centric Framework for Querying Big Graphs.\nYear: 2016\n\nAbstract: Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems expose the user-friendly \"think like a vertex\" programming interface to users, and exhibit good horizontal scalability. However, these systems are designed for tasks where the majority of graph vertices participate in computation, but are not suitable for processing light-workload graph queries where only a small fraction of vertices need to be accessed. The programming paradigm adopted by these systems can seriously under-utilize the resources in a cluster for graph query processing. In this work, we develop a new open-source system, called Quegel, for querying big graphs, which treats queries as first-class citizens in the design of its computing model. Users only need to specify the Pregel-like algorithm for a generic query, and Quegel processes light-workload graph queries on demand using a novel superstep-sharing execution model to effectively utilize the cluster resources. Quegel further provides a convenient interface for constructing graph indexes, which significantly improve query performance but are not supported by existing graph-parallel systems. Our experiments verified that Quegel is highly efficient in answering various types of graph queries and is up to orders of magnitude faster than existing systems.","Title: Impact of Online Activities on Influence Maximization: A Random Walk Approach.\nYear: 2016\n\nAbstract: With the popularity of OSNs, finding a set of most influential users so as to trigger the largest influence cascade is of significance. This task is usually modeled as an influence maximization problem, and it has been widely studied in the past decade. However, considering that users in OSNs may participate in various kinds of online activities, e.g., giving ratings to products, joining discussion groups, etc., influence diffusion through online activities becomes even more significant. Thus, it necessitates to revisit the influence maximization problem by taking online activities into consideration. In this paper, we study the impact of online activities by formulating the influence maximization problem for social-activity networks (SANs) containing both users and online activities. To address the computation challenge, we define an influence centrality via random walks on hypergraph to measure influence, then use the Monte Carlo framework to efficiently estimate the centrality in SANs, and use the Hoeffding inequality to bound the approximation error. Furthermore, we develop a greedy-based algorithm with two novel optimization techniques to find the most influential users. By conducting extensive experiments with real-world datasets, we show that compared to the state-of-the-art algorithm IMM [20], our approach improves the accuracy of selecting the top k most influential users in SANs by up to an order of magnitude, and our approach has a comparable computation time even though it needs to handle a large amount of online activities. Furthermore, our approach is also general enough to work under different diffusion models, e.g., IC and LT models, as well as heterogeneous systems, e.g., multiple types of users and online activities in SANs.","Title: A General Framework for Estimating Graphlet Statistics via Random Walk.\nYear: 2016\n\nAbstract: Graphlets are induced subgraph patterns and have been frequently applied to characterize the local topology structures of graphs across various domains, e.g., online social networks (OSNs) and biological networks. Discovering and computing graphlet statistics are highly challenging. First, the massive size of real-world graphs makes the exact computation of graphlets extremely expensive. Secondly, the graph topology may not be readily available so one has to resort to web crawling using the available application programming interfaces (APIs). In this work, we propose a general and novel framework to estimate graphlet statistics of \\\"any size.\\\" Our framework is based on collecting samples through consecutive steps of random walks. We derive an analytical bound on the sample size (via the Chernoff-Hoeffding technique) to guarantee the convergence of our unbiased estimator. To further improve the accuracy, we introduce two novel optimization techniques to reduce the lower bound on the sample size. Experimental evaluations demonstrate that our methods outperform the state-of-the-art method up to an order of magnitude both in terms of accuracy and time cost.","Title: A Unified Framework for Information Consumption Based on Markov Chains.\nYear: 2016\n\nAbstract: This paper establishes a Markov chain model as a unified framework for understanding information consumption processes in complex networks, with clear implications to the Internet and big-data technologies. In particular, the proposed model is the first one to address the formation mechanism of the trichotomy in observed probability density functions from empirical data of various social and technical networks. Both simulation and experimental results demonstrate a good match of the proposed model with real datasets, showing its superiority over the classical power-law models.","Title: Analysis of Reliability Dynamics of SSD RAID.\nYear: 2016\n\nAbstract: Solid-state drives (SSDs) have been widely deployed in desktops and data centers. However, SSDs suffer from bit errors, and the bit error rate is time dependent since it increases as an SSD wears down. Traditional storage systems mainly use parity-based RAID to provide reliability guarantees by striping redundancy across multiple devices, but the effectiveness of traditional RAID schemes in SSDs remains debatable. In particular, an open problem is how different parity distributions over multiple devices influence the reliability of an SSD RAID array. That is, should we evenly distribute patsaverties as suggested by conventional wisdom, or unevenly distribute parties as recently proposed for SSD RAID? To address this fundamental problem, we propose the first analytical model to quantify the reliability dynamics of an SSD RAID array as it ages. Specifically, we develop a “non-homogeneous” continuous time Markov chain model, and derive the transient reliability solution. We validate our model via trace-driven simulation and conduct numerical analysis to analyze the reliability dynamics of SSD RAID arrays subject to different parity distributions, error rates, and SSD array configurations. Our model enables system practitioners to decide the appropriate parity distribution based on their reliability requirements.","Title: Analyzing competitive influence maximization problems with partial information: An approximation algorithmic framework\nYear: 2015\n\nAbstract: Given the popularity of the viral marketing campaign in online social networks, finding a computationally efficient method to identify a set of most influential nodes so as to compete well with others is of the utmost importance. In this paper, we propose a general model to describe the influence propagation of multiple competing sources in the same network. We formulate the Competitive Influence Maximization with Partial information (CIMP) problem: given an influence propagation model and the probability of a node being in the competitor’s seed set, how to find a set of k seeds so to trigger the largest expected influence cascade under the presence of other competitors? We propose a general algorithmic framework, Two-phase Competitive Influence Maximization (TCIM), to address the CIMP problem. TCIM returns a (1−1/e−ϵ)-approximate solution with probability of at least 1−n−ℓ, where ℓ≥1/2 is a parameter controlling the trade-off between the success probability and the computational efficiency. TCIM has an efficient expected time complexity of O(c(k+ℓ)(m+n)logn/ϵ2), where n and m are the number of nodes and edges in the network, and c is a function of the given propagation model (which may depend on k and the underlying network). To the best of our knowledge, this is the first work which provides a general framework for the competitive influence maximization problem where the seeds of the competitor could be given as an explicit set of seed nodes or a probability distribution of seed nodes. Moreover, our algorithmic framework provides both quality guarantee of solution and practical computational efficiency. We conduct extensive experiments on real-world datasets under three specific influence propagation models, and show the efficiency and accuracy of our framework. In particular, for the case where the seed set of the competitor is given explicitly, we achieve up to four orders of magnitude speedup as compared to previous algorithms with the same quality guarantee. When the competitor’s seed set is not given explicitly, running TCIM using the probability distribution of the competitor’s seeds returns nodes with higher expected influence than those nodes returned by TCIM using an explicit guess of the competitor’s seeds.","Title: Modeling eBay-like reputation systems: Analysis, characterization and insurance mechanism design\nYear: 2015\n\nAbstract: E-commerce systems like eBay are becoming increasingly popular. Having an effective reputation system is critical because it can assist buyers to evaluate the trustworthiness of sellers, and improve the revenue for reputable sellers and E-commerce operators. We formulate a stochastic model to analyze an eBay-like reputation system and propose four measures to quantify its effectiveness: (1) new seller ramp up time, (2) new seller drop out probability, (3) long term profit gains for sellers, and (4) average per seller transaction gains for E-commerce operators. By analyzing a dataset from eBay, we discover that eBay suffers a long ramp up time, low long term profit gains and low average per seller transaction gains. We design a novel insurance mechanism consisting of an insurance protocol and a transaction mechanism to improve the above four measures. We formulate an optimization framework to select appropriate parameters for our insurance mechanism. We conduct experiments on an eBay’s dataset and show that our insurance mechanism reduces ramp up time by 91%, improves both the long term profit gains and the average per seller transaction gains by 26.66%. It also guarantees that new sellers drop out with a small probability (close to 0).","Title: Tracking Triadic Cardinality Distributions for Burst Detection in Social Activity Streams.\nYear: 2015\n\nAbstract: In everyday life, we often observe unusually frequent interactions among people before or during important events, e.g., we receive/send more greetings from/to our friends on Christmas Day, than usual. We also observe that some videos suddenly go viral through people's sharing in online social networks (OSNs). Do these seemingly different phenomena share a common structure? All these phenomena are associated with sudden surges of user activities in networks, which we call \"bursts\" in this work. We find that the emergence of a burst is accompanied with the formation of triangles in networks. This finding motivates us to propose a new method to detect bursts in OSNs. We first introduce a new measure, \"triadic cardinality distribution\", corresponding to the fractions of nodes with different numbers of triangles, i.e., triadic cardinalities, within a network. We demonstrate that this distribution changes when a burst occurs, and is naturally immunized against spamming social-bot attacks. Hence, by tracking triadic cardinality distributions, we can reliably detect bursts in OSNs. To avoid handling massive activity data generated by OSN users, we design an efficient sample-estimate solution to estimate the triadic cardinality distribution from sampled data. Extensive experiments conducted on real data demonstrate the usefulness of this triadic cardinality distribution and the effectiveness of our sample-estimate solution.","Title: DroidEagle: seamless detection of visually similar Android apps\nYear: 2015\n\nAbstract: Repackaged malware and phishing malware consist 86% [35] of all Android malware, and they significantly affect the Android ecosystem. Previous work use disassembled Dalvik bytecode and hashing approaches to detect repackaged malware, but these approaches are vulnerable to obfuscation attacks and they demand large computational resources on mobile devices. In this work, we propose a novel methodology which uses the layout resources within an app to detect apps which are \\\"visually similar\\\", a common characteristic in repackaged apps and phishing malware. To detect visually similar apps, we design and implement DroidEagle which consists of two sub-systems: RepoEagle and HostEagle. RepoEagle is to perform large scale detection on apps repositories (e.g., apps markets), and HostEagle is a lightweight mobile app which can help users to quickly detect visually similar Android app upon download. We demonstrate the high accuracy and efficiency of DroidEagle: Within 3 hours RepoEagle can detect 1298 visually similar apps from 99 626 apps in a repository. In less than one second, HostEagle can help an Android user to determine whether a downloaded mobile app is a repackaged apps or a phishing malware. This is the first work which provides both speed and scalability in discovering repackaged apps and phishing malware in Android system.","Title: A Data Driven Approach to Uncover Deficiencies in Online Reputation Systems\nYear: 2015\n\nAbstract: Online reputation systems serve as core building blocks in various Internet services such as E-commerce (e.g. eBay) and crowdsourcing (e.g., oDesk). The flaws of real-world online reputation systems were reported extensively. Users who are frustrated about the system will eventually abandon such service. However, no formal studies have explored such flaws. This paper presents the first attempt, which develops a novel data analytical framework to uncover online reputation system deficiencies from data. We develop a novel measure to quantify the efficiency of online reputation systems, i.e., ramp up time of a new service provider. We first show that inherent preferences or personal biases in assigning feedbacks (or ratings) cause the computational infeasibility in evaluating online reputation systems from data. We develop a computationally efficient randomized algorithm with theoretical performance guarantees to address this computational challenge. We apply our methodology to real-life datasets (from eBay and Google Helpouts), we discover that the ramp up time in eBay and Google Helpouts are around 791 and 1,327 days respectively. Around 78.7% sellers have ramped up in eBay and only 1.5% workers have ramped up in Google Helpouts. This small fraction and the long ramp up time (1,327 days) explain why Google Helpouts was eventually shut down in April 2015.","Title: A Mechanism Approach To Reduce New Seller Ramp Up Time In Ebay-Like Reputation Systems\nYear: 2015\n\nAbstract: We present a novel insurance mechanism consisting of an insurance protocol and a transaction mechanism to reduce new seller ramp up time in eBay-like reputation mechanisms. We conduct experiments on an eBay's dataset and show that our insurance mechanism reduces ramp up time by 90%.","Title: A tale of three graphs: Sampling design on hybrid social-affiliation networks\nYear: 2015\n\nAbstract: Random walk-based graph sampling methods have become increasingly popular and important for characterizing large-scale complex networks. While powerful, they are known to exhibit problems when the graph is loosely connected, which slows down the convergence of a random walk and can result in poor estimation accuracy. In this work, we observe that many graphs under study, called target graphs, usually do not exist in isolation. In many situations, a target graph is often related to an auxiliary graph and an affiliation graph, and the target graph becomes better connected when viewed from these three graphs as a whole, or what we called a hybrid social-affiliation network. This viewpoint brings extra benefits to the graph sampling framework, e.g., when directly sampling a target graph is difficult or inefficient, we can efficiently sample it with the assistance of auxiliary and affiliation graphs. We propose three sampling methods on such a hybrid social-affiliation network to estimate target graph characteristics, and conduct extensive experiments on both synthetic and real datasets, to demonstrate the effectiveness of these new sampling methods.","Title: VENUS: Vertex-Centric Streamlined Graph Computation on a Single PC\nYear: 2015\n\nAbstract: Recent studies show that disk-based graph computation on just a single PC can be as highly competitive as cluster-based computing systems on large-scale problems. Inspired by this remarkable progress, we develop VENUS, a disk-based graph computation system which is able to handle billion-scale problems efficiently on a commodity PC. VENUS adopts a novel computing architecture that features vertex-centric \" streamlined \" processing – the graph is sequentially loaded and the update functions are executed in parallel on the fly. VENUS deliberately avoids loading batch edge data by separating read-only structure data from mutable vertex data on disk. Furthermore, it minimizes random IOs by caching vertex data in main memory. The streamlined processing is realized with efficient sequential scan over massive structure data and fast feeding a large number of update functions. Extensive evaluation on large real-world and synthetic graphs has demonstrated the efficiency of VENUS. For example, VENUS takes just 8 minutes with hard disk for PageRank on the Twitter graph with 1.5 billion edges. In contrast, Spark takes 8.1 minutes with 50 machines and 100 CPUs, and GraphChi takes 13 minutes using fast SSD drive.","Title: I/O Efficient Algorithms for Exact Distance Queries on Disk-Resident Dynamic Graphs\nYear: 2015\n\nAbstract: Point-to-point shortest distance queries are fundamental to large graph analytics. Motivated by the need for low-latency distance queries in large-scale \\\"dynamic\\\" graphs, we consider the problem of answering exact shortest distance queries on disk-resident scale-free dynamic graphs. Our query processing uses the canonical labeling method, which is a special 2-hop distance labeling for fast distance queries. In this paper, we propose two I/O efficient algorithms to update the canonical labeling. To the best of our knowledge, our proposed methods are the first practical disk-based methods to \\\"incrementally update\\\" the canonical labeling on dynamic graphs. We also show how to answer distance queries on the latest network based on outdated labels and new edges. Extensive experiments demonstrate the efficiency of our methods. Our update methods are an order of magnitude faster than reconstructing the canonical labeling. When the number of new edges is small, say less than 1% of the previous number of edges, our query algorithm based on outdated labels provides exact shortest distance and the query time is comparable to other query algorithms using latest labels.","Title: Diversified Temporal Subgraph Pattern Mining\nYear: 2016\n\nAbstract: Many graphs in real-world applications, such as telecommunications networks, social-interaction graphs and co-authorship graphs, contain temporal information. However, existing graph mining algorithms fail to exploit these temporal information and the resulting subgraph patterns do not contain any temporal attribute. In this paper, we study the problem of mining a set of diversified temporal subgraph patterns from a temporal graph, where each subgraph is associated with the time interval that the pattern spans. This problem motivates important applications such as finding social trends in social networks, or detecting temporal hotspots in telecommunications networks. We propose a divide-and-conquer algorithm along with effective pruning techniques, and our approach runs 2 to 3 orders of magnitude faster than a baseline algorithm and obtains high-quality temporal subgraph patterns in real temporal graphs.","Title: Taming Energy Cost of Disk Encryption Software on Data-Intensive Mobile Devices.\nYear: 2016\n\nAbstract: Disk encryption is frequently used to secure confidential data on mobile devices. However, the high energy cost of disk encryption poses a heavy burden on those devices with limited battery capacity especially when a large amount of data needs to be protected by disk encryption. To address the challenge, we develop a new kernel-level disk encryption software, Populus. Almost 98% of Populus's encryption/decryption computation is not related with the input plaintext/ciphertext, so we accomplish the computation in advance during initialization when a consistent power supply is available. We conduct cryptanalysis on Populus and finally conclude that state-of-the-art cryptanalysis techniques fail to break Populus in reasonable computational complexity. We also conduct energy consumption experiments on Populus and dm-crypt, a famous disk encryption software for Android and Linux mobile devices. The experimental results demonstrate that Populus consumes 50%-70% less energy than dm-crypt.","Title: Stochastic Modeling of Hybrid Cache Systems\nYear: 2016\n\nAbstract: In recent years, there is an increasing demand of big memory systems so to perform large scale data analytics. Since DRAM memories are expensive, some researchers are suggesting to use other memory systems such as non-volatile memory (NVM) technology to build large-memory computing systems. However, whether the NVM technology can be a viable alternative (either economically and technically) to DRAM remains an open question. To answer this question, it is important to consider how to design a memory system from a \"system perspective\",that is, incorporating different performance characteristics andprice ratios from hybrid memory devices. This paper presents an analytical model of a \"hybrid page cache system\" so to understand the diverse design space and performance impact of a hybrid cache system. We consider (1) various architectural choices, (2) design strategies, and (3) configuration of different memory devices. Using this model, we provide guidelines on how to design hybrid page cache to reach a good trade-off between high system throughput (in I/O per sec or IOPS) and fast cache reactivity which is defined by the time to fill the cache. We also show how one can configure the DRAM capacity and NVM capacity under a fixed budget. We pick PCM as an example for NVM and conduct numerical analysis. Our analysis indicates that incorporating PCM in a page cache system significantly improves the system performance, and it also shows larger benefit to allocate more PCM in page cache in some cases. Besides, for the common setting of performance-price ratio of PCM, \"flat architecture\" offers as a better choice, but \"layered architecture\" outperforms if PCM write performance can be significantly improved in the future.","Title: VENUS: A System for Streamlined Graph Computation on a Single PC.\nYear: 2016\n\nAbstract: Recent studies show that disk-based graph computation systems on just a single PC can be as highly competitive as cluster-based systems on large-scale problems. Inspired by this remarkable progress, we develop VENUS, a disk-based graph computation system which is able to handle billion-scale graphs efficiently on a commodity PC. VENUS adopts a novel computing architecture that features vertex-cent...","Title: Trading Discount for Reputation?: On the Design and Analysis of E-Commerce Discount Mechanisms.\nYear: 2016\n\nAbstract: We develop an optimization framework to trade short-term profits for reputation (i.e., reducing ramp-up time). We apply the stochastic bandits framework to design an online discounting mechanism which infers the optimal discount from a seller's historical transaction data. We conduct experiments on an eBay's dataset and show that our online discounting mechanism can trade 60% of the shortterm profits for reducing the ramp-up time by 40%.","Title: Minfer: A Method Of Inferring Motif Statistics From Sampled Edges\nYear: 2016\n\nAbstract: Characterizing motif (i.e., locally connected subgraph patterns) statistics is important for understanding complex networks such as online social networks and communication networks. Previous work made the strong assumption that the graph topology of interest is known in advance. In practice, sometimes researchers have to deal with the situation where the graph topology is unknown because it is expensive to collect and store all topological and meta information. Hence, typically what is available to researchers is only a snapshot of the graph, i.e., a subgraph of the graph. Crawling methods such as breadth first sampling can be used to generate the snapshot. However, these methods fail to sample a streaming graph represented as a high speed stream of edges. Therefore, graph mining applications such as network traffic monitoring use random edge sampling (i.e., sample each edge with a fixed probability) to collect edges and generate a sampled graph, which we called a \" RE Sampled graph \". Clearly, a RESampled graph's motif statistics may be quite different from those of the underlying original graph. To resolve this, we propose a framework and implement a system called Minfer, which takes the given RESampled graph and accurately infers the underlying graph's motif statistics. We also apply Fisher information to bound the errors of our estimates. Experiments using large scale datasets show the accuracy and efficiency of our method.","Title: PowerWalk: Scalable Personalized PageRank via Random Walks with Vertex-Centric Decomposition\nYear: 2016\n\nAbstract: Most methods for Personalized PageRank (PPR) precompute and store all accurate\nPPR vectors, and at query time, return the ones of interest directly. However,\nthe storage and computation of all accurate PPR vectors can be prohibitive for\nlarge graphs, especially in caching them in memory for real-time online\nquerying. In this paper, we propose a distributed framework that strikes a\ngood balance between offline indexing and online querying. The offline\nindexing computes a fingerprint of the PPR vector of each vertex by performing\nbillions of ``short'' random walks in parallel across a cluster of machines.\nWe prove that our indexing method has an exponential convergence, achieving the\nsame precision with previous methods using a much smaller number of random\nwalks. At query time, the new PPR vector is composed by a linear combination\nof related fingerprints, in a highly efficient vertex-centric decomposition\nmanner. Interestingly, the resulting PPR vector is much more accurate than its\noffline counterpart because it actually uses more random walks in its\nestimation. More importantly, we show that such decomposition for a batch of\nqueries can be very efficiently processed using a shared decomposition. Our\nimplementation, PowerWalk, takes advantage of advanced distributed graph\nengines and it outperforms the state-of-the-art algorithms by orders of\nmagnitude. Particularly, it responses to tens of thousands of queries on\ngraphs with billions of edges in just a few seconds.","Title: Generalization of Information Spreading Forensics via Sequential Dependent Snapshots.\nYear: 2016\n\nAbstract: Learning the characteristics of information spreading in networks is crucial in communication studies, social network sentiment analysis and epidemic investigation. Previous work on information spreading has been focused on the information source detection using either a single observation, or multiple but \\\"independent\\\" observations of the underlying network while assuming information spreads at a \\\"uniform spreading rate\\\". In this paper, we conduct the first theoretical and experimental study on information spreading forensics, and propose a framework for estimating information spreading rates, information source start time and location of information source by utilizing \\\"multiple sequential and dependent snapshots\\\" where information can spread at different rates. We prove that our estimation framework generalizes the rumor centrality [1], and we allow heterogeneous information spreading rates on different branches in d-regular tree networks. We further show that our framework can provide highly accurate estimates for the information spreading rates on different branches, the source start time, and more accurate estimate for the location of information source than rumor centrality and Jordan center in both synthetic networks and real-world networks (i.e., Twitter).","Title: Design and Analysis of Incentive and Reputation Mechanisms for Online Crowdsourcing Systems.\nYear: 2016\n\nAbstract: Today, online crowdsourcing services like Amazon Mechanical Turk, UpWork, and Yahoo! Answers are gaining in popularity. For such online services, it is important to attract “workers” to provide high-quality solutions to the “tasks” outsourced by “requesters.” The challenge is that workers have different skill sets and can provide different amounts of effort. In this article, we design a class of incentive and reputation mechanisms to solicit high-quality solutions from workers. Our incentive mechanism allows multiple workers to solve a task, splits the reward among workers based on requester evaluations of the solution quality, and guarantees that high-skilled workers provide high-quality solutions. However, our incentive mechanism suffers the potential risk that a requester will eventually collects low-quality solutions due to fundamental limitations in task assigning accuracy. Our reputation mechanism ensures that low-skilled workers do not provide low-quality solutions by tracking workers’ historical contributions and penalizing those workers having poor reputations. We show that by coupling our reputation mechanism with our incentive mechanism, a requester can collect at least one high-quality solution. We present an optimization framework to select parameters for our reputation mechanism. We show that there is a trade-off between system efficiency (i.e., the number of tasks that can be solved for a given reward) and revenue (i.e., the amount of transaction fees), and we present the optimal trade-off curve between system efficiency and revenue. We demonstrate the applicability and effectiveness of our mechanisms through experiments using a real-world dataset from UpWork. We infer model parameters from this data, use them to determine proper rewards, and select the parameters of our incentive and reputation mechanisms for UpWork. Experimental results show that our incentive and reputation mechanisms achieve 98.82% of the maximum system efficiency while only sacrificing 4% of revenue.","Title: TaintART: A Practical Multi-level Information-Flow Tracking System for Android RunTime.\nYear: 2016\n\nAbstract: Mobile operating systems like Android failed to provide sufficient protection on personal data, and privacy leakage becomes a major concern. To understand the security risks and privacy leakage, analysts have to carry out data-flow analysis. In 2014, Android upgraded with a fundamentally new design known as Android RunTime (ART) environment in Android 5.0. ART adopts ahead-of-time compilation strategy and replaces previous virtual-machine-based Dalvik. Unfortunately, many data-flow analysis systems like TaintDroid were designed for the legacy Dalvik environment. This makes data-flow analysis of new apps and malware infeasible. We design a multi-level information-flow tracking system for the new Android system called TaintART. TaintART employs a multi-level taint analysis technique to minimize the taint tag storage. Therefore, taint tags can be stored in processor registers to provide efficient taint propagation operations. We also customize the ART compiler to maximize performance gains of the ahead-of-time compilation optimizations. Based on the general design of TaintART, we also implement a multi-level privacy enforcement to prevent sensitive data leakage. We demonstrate that TaintART only incurs less than 15% overheads on a CPU-bound microbenchmark and negligible overhead on built-in or third-party applications. Compared to legacy Dalvik environment in Android 4.4, TaintART achieves about 99.7% faster performance for Java runtime benchmark.","Title: BLOW-UP: Toward Distributed and Scalable Space Filling Curve Construction in 3D Volumetric WSNs.\nYear: 2016\n\nAbstract: In wireless sensor networks (WSNs), a space filling curve (SFC) refers to a path passing through all nodes in the network, with each node visited at least once. By enforcing a linear order of the sensor nodes through an SFC, many applications in WSNs concerning serial operations on both sensor nodes and sensor data can be performed, with examples including serial data fusion and path planning of mobile nodes. Although a few studies have made efforts to find such SFCs in WSNs, they primarily target 2D planar or 3D surface settings and cannot be directly applied to 3D volumetric WSNs due to considerably more complex geometric features and topology shapes that the 3D volumetric settings introduce. This article presents BLOW-UP, a distributed, scalable, and connectivity-based algorithm to construct an SFC for a 3D volumetric WSN (or alternatively to linearize the 3D volumetric network). The main idea of BLOW-UP is to decompose the given 3D volumetric network into a series of connected and closed layers, and the nodes are traversed layer by layer, incrementally from the innermost to the outermost, yielding an SFC covering the entire network, provably at least once and at most a constant number of times. To the best of our knowledge, BLOW-UP is the first algorithm that realizes linearization in 3D volumetric WSNs. It does not require advance knowledge of location or distance information. It is also scalable with a nearly constant per-node storage cost and message cost. Extensive simulations under various networks demonstrate its effectiveness on nodes’ covered times, coverage rate, and covering speed.","Title: Monet: A User-oriented Behavior-based Malware Variants Detection System for Android.\nYear: 2017\n\nAbstract: Android, the most popular mobile OS, has around 78% of the mobile market share. Due to its popularity, it attracts many malware attacks. In fact, people have discovered around 1 million new malware samples per quarter, and it was reported that over 98% of these new malware samples are in fact “derivatives” (or variants) from existing malware families. In this paper, we first show that runtime beha...","Title: Accurate Learning or Fast Mixing? Dynamic Adaptability of Caching Algorithms.\nYear: 2018\n\nAbstract: Typical analysis of content caching algorithms using the metric of steady state hit probability under a stationary request process does not account for performance loss under a variable request arrival process. In this paper, we instead conceptualize caching algorithms as complexity-limited online distribution learning algorithms and use this vantage point to study their adaptability from two pers...","Title: eBA: Efficient Bandwidth Guarantee Under Traffic Variability in Datacenters.\nYear: 2017\n\nAbstract: Datacenter networks suffer unpredictable performance due to a lack of application level bandwidth guarantees. A lot of attention has been drawn to solve this problem such as how to provide bandwidth guarantees for virtualized machines VMs, proportional bandwidth share among tenants, and high network utilization under peak traffic. However, existing solutions fail to cope with highly dynamic traffic in datacenter networks. In this paper, we propose eBA, an efficient solution to bandwidth allocation that provides end-to-end bandwidth guarantee for VMs under large numbers of short flows and massive bursty traffic in datacenters. eBA leverages a novel distributed VM-to-VM rate control algorithm based on the logistic model under the control-theoretic framework. eBA’s implementation requires no changes to hardware or applications and can be deployed in standard protocol stack. The theoretical analysis and the experimental results show that eBA not only guarantees the bandwidth for VMs, but also provides fast convergence to efficiency and fairness, as well as smooth response to bursty traffic.","Title: Mining Graphlet Counts in Online Social Networks.\nYear: 2018\n\nAbstract: Counting subgraphs is a fundamental analysis task for online social networks (OSNs). Given the sheer size and restricted access of OSN, efficient computation of subgraph counts is highly challenging. Although a number of algorithms have been proposed to estimate the relative counts of subgraphs in OSNs with restricted access, there are only few works which try to solve a more general problem, i.e., counting subgraph frequencies. In this article, we propose an efficient random walk-based framework to estimate the subgraph counts. Our framework generates samples by leveraging consecutive steps of the random walk as well as by observing neighbors of visited nodes. Using the importance sampling technique, we derive unbiased estimators of the subgraph counts. To make better use of the degree information of visited nodes, we also design improved estimators, which increases the accuracy of the estimation with no additional cost. We conduct extensive experimental evaluation on real-world OSNs to confirm our theoretical claims. The experiment results show that our estimators are unbiased, accurate, efficient, and better than the state-of-the-art algorithms. For the Weibo graph with more than 58 million nodes, our method produces estimate of triangle count with an error less than 5% using only 20,000 sampled nodes. Detailed comparison with the state-of-the-art methods demonstrates that our algorithm is 2--10 times more accurate.","Title: I/O-efficient calculation of H-group closeness centrality over disk-resident graphs.\nYear: 2017\n\nAbstract: We introduce H-group closeness centrality in this work. H-group closeness centrality of a group of nodes measures how close this node group is to other nodes in a graph, and can be used in numerous applications such as measuring the importance and influence of a group of users in a social network. When a large graph contains billions of edges that cannot reside entirely in a computers main memory, computing and maximizing H-group closeness centrality both become challenging. In this work, we present a systematic solution for efficiently computing and maximizing H-group closeness centrality in large disk-resident graphs, only using a common PC. Our solution leverages a probabilistic counting method to efficiently estimate H-group closeness with high accuracy, rather than exhaustively computing it in an exact fashion. Furthermore, we design an I/O-efficient greedy algorithm to find a node group that approximately maximizes H-group closeness centrality in a graph. Our algorithm exploits several appealing properties of the defined H-group closeness measure to reduce the computational cost of handling a disk-resident graph. Extensive evaluation on large real-world and synthetic graphs demonstrates the efficiency of our proposed method. For example, our proposed I/O-efficient greedy algorithm is about 300 times faster than a simple multi-pass method on the Twitter graph with 1.4 billion edges. This reduces the running time of identifying one group member from nearly an hour to less than 20s on average.","Title: Workload-Aware Elastic Striping With Hot Data Identification for SSD RAID Arrays.\nYear: 2017\n\nAbstract: Redundant array of independent disk (RAID) offers a good option to provide device-level fault tolerance for solid-state drives (SSDs). However, parity update with either read–modify–write or read–reconstruct–write may introduce a lot of extra I/Os and thus significantly degrades SSD RAID performance. To reduce the parity update cost, elastic striping chooses to reconstruct new stripes with only the newly updated data chunks instead of directly updating parity chunks. However, it necessitates an RAID-level garbage collection (GC) process, which may incur a very high cost due to the mixture of hot and cold data chunks. To address this problem, we follow the idea of elastic striping and propose a workload-aware scheme (WAS) to reduce the RAID-level GC cost so as to improve the performance and endurance of SSD RAID. In particular, we first develop a novel lightweight hot data identification scheme which requires only a very small computation time and memory cost, then propose a hotness-aware elastic striping approach to separately write data chunks with different hotness to different regions in SSD RAID. To evaluate the effectiveness and efficiency of our WAS, we implement a prototype system on RAID-5 and RAID-6 arrays composed of commercial SSDs. Experimental results show that compared to original elastic striping, our scheme reduces 30.0%–70.6% (and 23.9%–63.2%) of chunk writes under the RAID-5 (and RAID-6) settings, and also reduces the average response time by 60.9%–79.3% (and 56.8%–80.9%) for RAID-5 (and RAID-6), respectively. Besides, our scheme also improves the endurance and reliability of SSD RAID compared to original elastic striping.","Title: A Fast Sampling Method of Exploring Graphlet Degrees of Large Directed and Undirected Graphs.\nYear: 2016\n\nAbstract: Exploring small connected and induced subgraph patterns (CIS patterns, or graphlets) has recently attracted considerable attention. Despite recent efforts on computing the number of instances a specific graphlet appears in a large graph (i.e., the total number of CISes isomorphic to the graphlet), little attention has been paid to characterizing a nodeu0027s graphlet degree, i.e., the number of CISes isomorphic to the graphlet that include the node, which is an important metric for analyzing complex networks such as social and biological networks. Similar to global graphlet counting, it is challenging to compute node graphlet degrees for a large graph due to the combinatorial nature of the problem. Unfortunately, previous methods of computing global graphlet counts are not suited to solve this problem. In this paper we propose sampling methods to estimate node graphlet degrees for undirected and directed graphs, and analyze the error of our estimates. To the best of our knowledge, we are the first to study this problem and give a fast scalable solution. We conduct experiments on a variety of real-word datasets that demonstrate that our methods accurately and efficiently estimate node graphlet degrees for graphs with millions of edges.","Title: Towards Efficient Node Discoverability Optimization on Large Networks.\nYear: 2017\n\nAbstract: Many people dream to become famous, YouTube video makers also wish their videos to have a large audience, and product retailers always hope to expose their products to customers as many as possible. Do these seemingly different phenomena share a common structure? We find that fame, popularity, or exposure, could be modeled as a nodeu0027s discoverability in some properly defined network, and all of the previously mentioned phenomena can be stated as: a target node wants to be discovered easily by existing nodes in the network. In this work, we explicitly define the node discoverability in a network, and formulate a general node discoverability optimization problem. While the optimization problem is NP-hard, we find that the defined discoverability measures satisfy good properties that enable us to use a greedy algorithm to find provably near-optimal solutions. The computational complexity of a greedy algorithm is dominated by the time cost of an oracle call, i.e., calculating the marginal gain of a given node. To scale up the oracle call over large networks, we propose an estimation-and-refinement approach, that provides a good trade-off between estimation accuracy and computational efficiency. Experiments conducted on real graphs demonstrate that our method is hundreds of times faster than an exact method using dynamic programming, thereby allowing us to solve the optimization problem on large networks.","Title: Cocoa: Dynamic Container-Based Group Buying Strategies for Cloud Computing.\nYear: 2017\n\nAbstract: Although the Infrastructure-as-a-Service (IaaS) cloud offers diverse instance types to users, a significant portion of cloud users, especially those with small and short demands, cannot find an instance type that exactly fits their needs or fully utilize purchased instance-hours. In the meantime, cloud service providers are also faced with the challenge to consolidate small, short jobs, which exhibit strong dynamics, to effectively improve resource utilization. To handle such inefficiencies and improve cloud resource utilization, we propose Cocoa (COmputing in COntAiners), a novel group buying mechanism that organizes jobs with complementary resource demands into groups and allocates them to group buying deals predefined by cloud providers. Each group buying deal offers a resource pool for all the jobs in the deal, which can be implemented as either a virtual machine or a physical server. By running each user job on a virtualized container, our mechanism allows flexible resource sharing among different users in the same group buying deal, while improving resource utilization for cloud providers. To organize jobs with varied resource demands and durations into groups, we model the initial static group organization as a variable-sized vector bin packing problem, and the subsequent dynamic group organization problem as an online multidimensional knapsack problem. Through extensive simulations driven by a large amount of real usage traces from a Google cluster, we evaluate the potential cost reduction achieved by Cocoa. We show that through the effective combination and interaction of the proposed static and dynamic group organization strategies, Cocoa greatly outperforms the existing cloud workload consolidation mechanism, substantiating the feasibility of group buying in cloud computing.","Title: On the Feasibility of Inter-Domain Routing via a Small Broker Set.\nYear: 2019\n\nAbstract: The Internet is a gigantic distributed system where the end-to-end (E2E) quality-of-service (QoS) plays an important role. Yet the current inter-domain routing protocol, namely, the Border Gateway Protocol (BGP), cannot provide E2E QoS guarantees. The main reason is that an autonomous system (AS) can only receive guarantees from its first-hop ASes via service level agreements (SLAs). But beyond the first-hop, QoS along the path from a source AS to a destination AS is not within the source AS's control regime. This makes it difficult to provide high quality-of-experience services to many Internet users even when many content providers are willing to pay for such high quality E2E guarantees. In this paper, we investigate the feasibility of providing high QoS-guaranteed E2E transit services by utilizing a (small) set of ASes/IXPs to serve as “\n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">brokers</italic>\n” to provide supervision, control and resource negotiation. Finding an optimal set of ASes as brokers can be formulated as a Maximum Coverage with \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math notation=\"LaTeX\">$B-$</tex-math><alternatives><inline-graphic xlink:href=\"lui-ieq1-2865572.gif\"/></alternatives></inline-formula>\ndominating path Guarantee (MCBG) problem, and we show that it is in fact NP-hard. To address this problem, we design a \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math notation=\"LaTeX\">$(\\frac{1{-}e^{-\\!1}}{2}){-}$</tex-math><alternatives><inline-graphic xlink:href=\"lui-ieq2-2865572.gif\"/></alternatives></inline-formula>\napproximation algorithm and also an efficient heuristic algorithm when additional constraints (e.g., the path length) are considered. We further analyze the APX-hardness of the MCBG problem to reveal the existence of the best approximation ratio. Based on the current Internet topology, we demonstrate that it is indeed feasible to provide high QoS guarantees for most E2E connections with only a small broker set: with only 0.19, 1.9 or 6.8 percent ASes/IXPs serving as brokers, 53.13, 85.41 or 99.29 percent of all global E2E connections can receive high QoS-guaranteed services. Finally, we provide an economic model to study the behaviours of ASes when cooperating our brokerage scheme with the BGP protocol, and show that there are incentives to form and maintain such a brokerage coalition.","Title: Joint Optimization of Chain Placement and Request Scheduling for Network Function Virtualization\nYear: 2017\n\nAbstract: Compared with executing Network Functions (NFs) on dedicated hardwares, the recent trend of Network Function Virtualization (NFV) holds the promise for operators to flexibly deploy software-based NFs on commodity servers. However, virtual NFs (VNFs) are normally \"chained\" together to provide a specific network service. Thus, an efficient scheme is needed to place the VNF chains across the network and effectively schedule requests to service instances, which can maximize the average resource utilization of each node in service and simultaneously minimize the average response latency of each request. To this end, we formulate first VNF chains placement problem as a variant of bin-packing problem, which is NP-hard, and we model request scheduling problem based on the key concepts from open Jackson network. To jointly optimize the performance of NFV, we propose a priority-driven weighted algorithm to improve resource utilization and a heuristic algorithm to reduce response latency. Through extensive trace-driven simulations, we show that our methods can indeed enhance performance in diverse scenarios. In particular, we can improve the average resource utilization by 33.4% and can reduce the average total latency by 19.9% as compared with the state-of-the-art methods.","Title: Smartmd: A High Performance Deduplication Engine With Mixed Pages\nYear: 2017\n\nAbstract: In hypervisor-based virtualization environments, translation lookaside buffers (TLBs) misses may induce two-dimensional page table walks, which may incur a long access latency, and this issue becomes worse with ever increasing memory capacity. To reduce the overhead of TLB misses, large pages (e.g., 2M-pages) are widely supported in modern hardware platforms to reduce the number of page table entries. However, memory management with large pages can be inefficient in deduplication, leading to low utilization of memory, which is a precious resource for a variety of applications.To simultaneously enjoy benefits of high performance by accessing memory with large pages (e.g., 2M-pages) and high deduplication rate by managing memory with base pages (e.g., 4K-pages), we propose Smart Memory Deduplciation, or SmartMD in short, which is an adaptive and efficient management scheme for mixed-page memory. Specifically, we propose two lightweight schemes to accurately monitor pages' access frequency and repetition rate, and present a dynamic and adaptive conversion scheme to selectively split or reconstruct large pages. We implement a prototype system and conduct extensive experiments with various workloads. Experiment results show that SmartMD can simultaneously achieve high access performance similar to systems using large pages, and achieves a deduplication rate similar to that applying aggressive deduplication scheme (i.e., KSM) at the same time on base pages.","Title: Pricing Intra-Datacenter Networks With Over-Committed Bandwidth Guarantee\nYear: 2017\n\nAbstract: Current IaaS clouds provide performance guarantee on CPU and memory but no quantitative network performance for VM instances. Our measurements from three production IaaS clouds show that for the VMs with same CPU and memory, or similar pricing, the difference in bandwidth performance can be as much as 16x, which reveals a severe price-performance anomaly due to a lack of pricing for bandwidth guarantee. Considering the low network utilization in cloud-scale datacenters, we address this by presenting SoftBW, a system that enables pricing bandwidth with over commitment on bandwidth guarantee. SoftBW leverages usage-based charging to guarantee price-performance consistency among tenants, and implements a fulfillment based scheduling to provide bandwidth/fairness guarantee under bandwidth over commitment. Both testbed experiments and large-scale simulation results validate Soft13Ws ability of providing efficient bandwidth guarantee, and show that by using bandwidth over commitment, SoftBW increases 3.9x network utilization while incurring less than 5% guarantee failure.","Title: Multi-dimensional network security game: How do attacker and defender battle on parallel targets?\nYear: 2014\n\nAbstract: In this paper, we consider a new network security game wherein an attacker and a defender are battling over “multiple” targets. This type of game is appropriate to model many current network security conflicts such as Internet phishing, mobile malware or network intrusions. In such attacks, the attacker and the defender need to decide how to allocate resources on each target so as to maximize their utilities within their resource limits. We model such a multidimensional network security game as a constrained nonzero sum game. Two security breaching models, the product-form and the proportion-form, are considered. For each breaching model, we prove the existence of a unique Nash equilibrium (NE) based on Rosen's theorem and propose efficient algorithms to find the NE when the games are strictly concave. Furthermore, we show the existence of multiple NEs in the product-form breaching model when the strict concavity does not hold. Our study sheds light on the strategic behaviors of the attacker and the defender, in particular, on how they allocate resources to the targets which have different weights, and how their utilities as well as strategies are influenced by the resource constraints.","Title: Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations\nYear: 2018\n\nAbstract: The unbiasedness of online product ratings, an important property to ensure that users' ratings indeed reflect their true evaluations to products, is vital both in shaping consumer purchase decisions and providing reliable recommendations. Recent experimental studies showed that distortions from historical ratings would ruin the unbiasedness of subsequent ratings. How to \\\"discover\\\" the distortions from historical ratings in each single rating (or at the micro-level), and perform the \\\"debiasing operations\\\" in real rating systems are the main objectives of this work. Using 42 million real customer ratings, we first show that users either \\\"assimilate\\\" or \\\"contrast\\\" to historical ratings under different scenarios: users conform to historical ratings if historical ratings are not far from the product quality (assimilation), while users deviate from historical ratings if historical ratings are significantly different from the product quality (contrast). This phenomenon can be explained by the well-known psychological argument: the \\\"Assimilate-Contrast\\\" theory. However, none of the existing works on modeling historical ratings' influence have taken this into account, and this motivates us to propose the Historical Influence Aware Latent Factor Model (HIALF), the first model for real rating systems to capture and mitigate historical distortions in each single rating. HIALF also allows us to study the influence patterns of historical ratings from a modeling perspective, and it perfectly matches the assimilation and contrast effects we previously observed. Also, HIALF achieves significant improvements in predicting subsequent ratings, and accurately predicts the relationships revealed in previous empirical measurements on real ratings. Finally, we show that HIALF can contribute to better recommendations by decoupling users' real preference from distorted ratings, and reveal the intrinsic product quality for wiser consumer purchase decisions.","Title: The Economics of the Cloud\nYear: 2017\n\nAbstract: This article proposes a model to study the interaction of price competition and congestion in the cloud computing marketplace. Specifically, we propose a three-tier market model that captures a marketplace with users purchasing services from Software-as-a-Service (SaaS) providers, which in turn purchase computing resources from either Provider-as-a-Service (PaaS) or Infrastructure-as-a-Service (IaaS) providers. Within each level, we define and characterize market equilibria. Further, we use these characterizations to understand the relative profitability of SaaSs and PaaSs/IaaSs and to understand the impact of price competition on the user experienced performance, that is, the “price of anarchy” of the cloud marketplace. Our results highlight that both of these depend fundamentally on the degree to which congestion results from shared or dedicated resources in the cloud.","Title: A Unified Framework to Estimate Global and Local Graphlet Counts for Streaming Graphs.\nYear: 2017\n\nAbstract: Counting small connected subgraph patterns called graphlets is emerging as a powerful tool for exploring topological structure of networks and for analysis of roles of individual nodes. Graphlets have numerous applications ranging from biology to network science. Computing graphlet counts for \"dynamic graphs\" is highly challenging due to the streaming nature of the input, sheer size of the graphs, and superlinear time complexity of the problem. Few practical results are known under the massive streaming graphs setting. In this work, we propose a \"unified framework\" to estimate the graphlet counts of the whole graph as well as the graphlet counts of individual nodes under the streaming graph setting. Our framework subsumes previous methods and provides more flexible and accurate estimation of the graphlet counts. We propose a general unbiased estimator which can be applied to any k-node graphlets. Furthermore, efficient implementation is provided for the 3, 4-node graphlets. We perform detailed empirical study on real-world graphs, and show that our framework produces estimation of graphlet count for streaming graphs with 1.7 to 170.8 times smaller error compared with other state-of-the-art methods. Our framework also achieves high accuracy on the estimation of graphlets for each individual node which previous works could not achieve.","Title: Tracking Triadic Cardinality Distributions for Burst Detection in High-Speed Multigraph Streams.\nYear: 2017\n\nAbstract: In everyday life, we often observe unusually frequent interactions among people before or during important events, e.g., people send/receive more greetings to/from their friends on holidays than regular days. We also observe that some videos or hashtags suddenly go viral through people's sharing on online social networks (OSNs). Do these seemingly different phenomena share a common structure? All these phenomena are associated with sudden surges of user interactions in networks, which we call \"bursts\" in this work. We uncover that the emergence of a burst is accompanied with the formation of triangles in some properly defined networks. This finding motivates us to propose a new and robust method to detect bursts on OSNs. We first introduce a new measure, \"triadic cardinality distribution\", corresponding to the fractions of nodes with different numbers of triangles, i.e., triadic cardinalities, in a network. We show that this distribution not only changes when a burst occurs, but it also has a robustness property that it is immunized against common spamming social-bot attacks. Hence, by tracking triadic cardinality distributions, we can more reliably detect bursts than simply counting interactions on OSNs. To avoid handling massive activity data generated by OSN users during the triadic tracking, we design an efficient \"sample-estimate\" framework to provide maximum likelihood estimate on the triadic cardinality distribution. We propose several sampling methods, and provide insights about their performance difference, through both theoretical analysis and empirical experiments on real world networks.","Title: G-thinker: Big Graph Mining Made Easier and Faster.\nYear: 2017\n\nAbstract: This paper proposes a general system for compute-intensive graph mining tasks that find from a big graph all subgraphs that satisfy certain requirements (e.g., graph matching and community detection). Due to the broad range of applications of such tasks, many single-threaded algorithms have been proposed. However, graphs such as online social networks and knowledge graphs often have billions of vertices and edges, which require distributed processing in order to scale. Unfortunately, existing distributed graph processing systems such as Pregel and GraphLab are designed for data-intensive analytics, and are inefficient for compute-intensive graph mining tasks since computation over any data is coupled with the datau0027s access that involves network transmission. We propose a distributed graph mining framework, called G-thinker, which is designed for compute-intensive graph mining workloads. G-thinker provides an intuitive graph-exploration API for the convenient implementation of various graph mining algorithms, and the runtime engine provides efficient execution with bounded memory consumption, light network communication, and parallelism between computation and communication. Extensive experiments were conducted, which demonstrate that G-thinker is orders of magnitude faster than existing solution, and can scale to graphs that are two orders of magnitude larger given the same hardware resources.","Title: Multi-level Feedback Web Links Selection Problem: Learning and Optimization\nYear: 2017\n\nAbstract: Selecting the right web links for a website is important because appropriate links not only can provide high attractiveness but can also increase the website's revenue. In this work, we first show that web links have an intrinsic multi-level feedback structure. For example, consider a 2-level feedback web link: the 1st level feedback provides the Click-Through Rate (CTR) and the 2nd level feedback provides the potential revenue, which collectively produce the compound 2-level revenue. We consider the context-free links selection problem of selecting links for a homepage so as to maximize the total compound 2-level revenue while keeping the total 1st level feedback above a preset threshold. We further generalize the problem to links with n (n ≥ 2)-level feedback structure. The key challenge is that the links' multi-level feedback structures are unobservable unless the links are selected on the homepage. To our best knowledge, we are the first to model the links selection problem as a constrained multi-armed bandit problem and design an effective links selection algorithm by learning the links' multi-level structure with provable sub-linear regret and violation bounds. We uncover the multi-level feedback structures of web links in two real-world datasets. We also conduct extensive experiments on the datasets to compare our proposed LExp algorithm with two state-of-the-art context-free bandit algorithms and demonstrate that LExp algorithm is the most effective in links selection while satisfying the constraint.","Title: Joint Cache Resource Allocation and Request Routing for In-network Caching Services.\nYear: 2018\n\nAbstract: In-network caching is recognized as an effective solution to offload content servers and the network. A cache service provider (SP) always has incentives to better utilize its cache resources by taking into account diverse roles that content providers (CPs) play, e.g., their business models, traffic characteristics, preferences. In this paper, we study the cache resource allocation problem in a Multi-Cache Multi-CP environment. We propose a cache partitioning approach, where each cache can be partitioned into slices with each slice dedicated to a content provider. We propose a content-oblivious request routing algorithm, to be used by individual caches, that optimizes the routing strategy for each CP. We associate with each content provider a utility that is a function of its content delivery performance, and formulate an optimization problem with the objective to maximize the sum of utilities over all content providers. We establish the biconvexity of the problem, and develop decentralized (online) algorithms based on convexity of the subproblem. The proposed model is further extended to bandwidth-constrained and minimum-delay scenarios, for which we prove fundamental properties, and develop efficient algorithms. Finally, we present numerical results to show the efficacy of our mechanism and the convergence of our algorithms.","Title: Sampling Online Social Networks by Random Walk with Indirect Jumps.\nYear: 2019\n\nAbstract: Random walk-based sampling methods are gaining popularity and importance in characterizing large networks. While powerful, they suffer from the slow mixing problem when the graph is loosely connected, which results in poor estimation accuracy. Random walk with jumps (RWwJ) can address the slow mixing problem but it is inapplicable if the graph does not support uniform vertex sampling (UNI). In this work, we develop methods that can efficiently sample a graph without the necessity of UNI but still enjoy the similar benefits as RWwJ. We observe that many graphs under study, called target graphs, do not exist in isolation. In many situations, a target graph is related to an auxiliary graph and a bipartite graph, and they together form a better connected two-layered network structure. This new viewpoint brings extra benefits to graph sampling: if directly sampling a target graph is difficult, we can sample it indirectly with the assistance of the other two graphs. We propose a series of new graph sampling techniques by exploiting such a two-layered network structure to estimate target graph characteristics. Experiments conducted on both synthetic and real-world networks demonstrate the effectiveness and usefulness of these new techniques.","Title: Mining Customer Valuations to Optimize Product Bundling Strategy\nYear: 2017\n\nAbstract: Product bundling is widely adopted for information goods and online services because it can increase profit for companies. For example, cable companies often bundle Internet access and video streaming services together. However, it is challenging to obtain an optimal bundling strategy, not only because it is computationally expensive, but also that customers’ private information (e.g., valuations for products) is needed for the decision, and we need to infer it from accessible datasets. As customers’ purchasing data are getting richer due to the popularity of online shopping, doors are open for us to infer this information. This paper aims to address: (1) How to infer customers’ valuations from the purchasing data? (2) How to determine the optimal product bundle to maximize the profit? We first formulate a profit maximization framework to select the optimal bundle set. We show that finding the optimal bundle set is NPhard. We then identify key factors that impact the profitability of product bundling. These findings give us insights to develop a computationally efficient algorithm to approximate the optimal product bundle with a provable performance guarantee. To obtain the input of the bundling algorithm, we infer the distribution of customers’ valuations from their purchasing data, based on which we run our bundling algorithm and conduct experiments on an Amazon co-purchasing dataset. We extensively evaluate the accuracy of our inference and the bundling algorithm. Our results reveal conditions under which bundling is highly profitable and provide insights to guide the deployment of product bundling.","Title: MOSS-5: A Fast Method of Approximating Counts of 5-Node Graphlets in Large Graphs.\nYear: 2018\n\nAbstract: Counting 3-, 4-, and 5-node graphlets in graphs is important for graph mining applications such as discovering abnormal/ evolution patterns in social and biology networks. In addition, it is recently widely used for computing similarities between graphs and graph classification applications such as protein function prediction and malware detection. However, it is challenging to compute these graph...","Title: Incentive Mechanism and Rating System Design for Crowdsourcing Systems: Analysis, Tradeoffs and Inference.\nYear: 2018\n\nAbstract: Macrotasking crowdsourcing systems like Elance and Fiverr serve as efficient platforms for requesters to outsource challenging and innovative tasks that require special skills to workers. It is widely practiced in such systems that requesters reward workers based on requesters' assessment on solution quality. The challenge is that requesters' assessment may not be accurate to reflect the intrinsic...","Title: On the Theory of Function Placement and Chaining for Network Function Virtualization.\nYear: 2018\n\nAbstract: Network function virtualization (NFV) can significantly reduce the operation cost and speed up the deployment for network services to markets. Under NFV, a network service is composed by a chain of ordered virtual functions, or we call a \"network function chain.\" A fundamental question is when given a number of network function chains, on which servers should we place these functions and how should we form a chain on these functions? This is challenging due to the intricate dependency relationship of functions and the intrinsic complex nature of the optimization. In this paper, we formulate the function placement and chaining problem as an integer optimization, where each variable is an indicator whether one service chain can be deployed on a configuration (or a possible function placement of a service chain). While this problem is generally NP-hard, our contribution is to show that it can be mapped to an exponential number of min-cost flow problems. Instead of solving all the min-cost problems, one can select a small number of mapped min-cost problems, which are likely to have a low cost. To achieve this, we relax the integer problem into a fractional linear problem, and theoretically prove that the fractional solutions possess some desirable properties, i.e., the number and the utilization of selected configurations can be upper and lower bounded, respectively. Based on such properties, we determine some \"good\" configurations selected from the fractional solution and determine the mapped min-cost flow problem, and this helps us to develop efficient algorithms for network function placement and chaining. Via extensive simulations, we show that our algorithms significantly outperform state-of-art algorithms and achieve near-optimal performance.","Title: Enhancing Reputation via Price Discounts in E-Commerce Systems: A Data-Driven Approach.\nYear: 2018\n\nAbstract: Reputation systems have become an indispensable component of modern E-commerce systems, as they help buyers make informed decisions in choosing trustworthy sellers. To attract buyers and increase the transaction volume, sellers need to earn reasonably high reputation scores. This process usually takes a substantial amount of time. To accelerate this process, sellers can provide price discounts to attract users, but the underlying difficulty is that sellers have no prior knowledge on buyers’ preferences over price discounts. In this article, we develop an online algorithm to infer the optimal discount rate from data. We first formulate an optimization framework to select the optimal discount rate given buyers’ discount preferences, which is a tradeoff between the short-term profit and the ramp-up time (for reputation). We then derive the closed-form optimal discount rate, which gives us key insights in applying a stochastic bandits framework to infer the optimal discount rate from the transaction data with regret upper bounds. We show that the computational complexity of evaluating the performance metrics is infeasibly high, and therefore, we develop efficient randomized algorithms with guaranteed performance to approximate them. Finally, we conduct experiments on a dataset crawled from eBay. Experimental results show that our framework can trade 60% of the short-term profit for reducing the ramp-up time by 40%. This reduction in the ramp-up time can increase the long-term profit of a seller by at least 20%.","Title: Beyond the Click-Through Rate: Web Link Selection with Multi-level Feedback.\nYear: 2018\n\nAbstract: The web link selection problem is to select a small subset of web links from a large web link pool, and to place the selected links on a web page that can only accommodate a limited number of links, e.g., advertisements, recommendations, or news feeds. Despite the long concerned click-through rate which reflects the attractiveness of the link itself, the revenue can only be obtained from user actions after clicks, e.g., purchasing after being directed to the product pages by recommendation links. Thus, the web links have an intrinsic emph{multi-level feedback structure}. With this observation, we consider the context-free web link selection problem, where the objective is to maximize revenue while ensuring that the attractiveness is no less than a preset threshold. The key challenge of the problem is that each linku0027s multi-level feedbacks are stochastic, and unobservable unless the link is selected. We model this problem with a constrained stochastic multi-armed bandit formulation, and design an efficient link selection algorithm, called Constrained Upper Confidence Bound algorithm (textbf{Con-UCB}), and prove $O(sqrt{Tln T})$ bounds on both the regret and the violation of the attractiveness constraint. We conduct extensive experiments on three real-world datasets, and show that textbf{Con-UCB} outperforms state-of-the-art context-free bandit algorithms concerning the multi-level feedback structure.","Title: Enabling Relay-Assisted D2D Communication for Cellular Networks: Algorithm and Protocols.\nYear: 2018\n\nAbstract: Recently, there is a growing emphasis on device-to-device (D2D) communication, which is the key component of the Internet-of-Things ecosystem. D2D communication can operate on the licensed spectrum of cellular networks so as to improve the spectrum utilization. In this paper, we focus on the resource allocation problem for general multihop D2D communication and introduce users' mobility into D2D c...","Title: An Online Learning Approach to Network Application Optimization with Guarantee.\nYear: 2018\n\nAbstract: Network application optimization is essential for improving the performance of the application as well as its user experience. The network application parameters are crucial in making proper decisions for network application optimizations. However, many works are impractical by assuming a priori knowledge of the parameters which are usually unknown and need to be estimated. There have been studies that consider optimizing network application in an online learning context using multi-armed bandit models. However, existing frameworks are problematic as they only consider to find the optimal decisions to minimize the regret, but neglect the constraints (or guarantee) requirements which may be excessively violated. in this paper, we propose a novel online learning framework for network application optimizations with guarantee. To the best of our knowledge, we are the first to formulate the stochastic constrained multi-armed bandit model with time-varying \"multi-level rewards\" by taking both \"regret\" and \"violation\" into consideration. We are also the first to design a constrained bandit policy, Learning with Minimum Guarantee (LMG), with provable sub-linear regret and violation bounds. We illustrate how our framework can he applied to several emerging network application optimizations, namely, (1) opportunistic multichannel selection, (2) data-guaranteed crowdsensing, and (3) stability guaranteed crowdsourced transcoding To show the effectiveness of LMG in optimizing these applications with different minimum requirements, we also conduct extensive simulations by comparing LMG with existing state-of-the-art policies.","Title: Hybrid Circuit/Packet Network Scheduling with Multiple Composite Paths.\nYear: 2018\n\nAbstract: A switching/forwarding fabric with high-bandwidth one-to-one and low-bandwidth many-to-many data forwarding can be achieved by combing electronic packet switches (EPS) and optical circuit switches (OCS). This hybrid solution scales well but it is not suitable for cloud computing/datacenter applications, which typically rely on one-to-many and many-to-one communications. Recently, composite-path switching (cp-switching), which adds paths between EPS and OCS, is introduced to deal with this skewed traffic pattern. The state-of-the-art scheduling algorithm for cp-switches reduces, by heuristics, a cp-switching problem with one composite path to a hybrid switching problem without the composite path, and leverages existing scheduling techniques to tackle the latter problem. Unfortunately, the approach provides neither performance guarantee nor the support for multiple composite paths. In this paper, we systematically study the shortest time cp-switch scheduling problem with multiple composite paths and show that the problem can be expressed as an optimization problem with sparsity constraints. An LP-based algorithm is derived accordingly which supports multiple composite paths and more importantly, it provides performance guarantee. Simulations demonstrate that adding more composite paths can help shorten the schedule, and our approach outperforms existing methods by 30% to 70%.","Title: Tracking Influential Nodes in Time-Decaying Dynamic Interaction Networks.\nYear: 2018\n\nAbstract: Identifying influential nodes that can jointly trigger the maximum influence spread in networks is a fundamental problem in many applications such as viral marketing, online advertising, and disease control. Most existing studies assume that social influence is static and they fail to capture the dynamics of influence in reality. In this work, we address the dynamic influence challenge by designing efficient streaming methods that can identify influential nodes from highly dynamic node interaction streams. We first propose a general time-decaying dynamic interaction network (TDN) model to model node interaction streams with the ability to smoothly discard outdated data. Based on the TDN model, we design three algorithms, i.e., SieveADN, BasicReduction, and HistApprox. SieveADN identifies influential nodes from a special kind of TDNs with efficiency. BasicReduction uses SieveADN as a basic building block to identify influential nodes from general TDNs. HistApprox significantly improves the efficiency of BasicReduction. More importantly, we theoretically show that all three algorithms enjoy constant factor approximation guarantees. Experiments conducted on various real interaction datasets demonstrate that our approach finds near-optimal solutions with speed at least 5 to 15 times faster than baseline methods.","Title: Graphlet Count Estimation via Convolutional Neural Networks.\nYear: 2018\n\nAbstract: Graphlets are defined as k-node connected induced subgraph patterns. For instance, for an undirected graph, 3-node graphlets include close triangle and open triangle. The number each graphlet, called graphlet count, a signature which characterizes the local network structure a given graph. Graphlet count plays a prominent role in network analysis many fields, most notably bioinformatics and social science. However, computing exact graphlet count is inherently difficult and computational expensive because the number graphlets growsexponentially large as the graph size and/or graphlet size grow. To deal with this difficulty, many sampling methods were proposed to estimate graphlet count with bounded error. Nevertheless, these methods require large number of samples to be statistically reliable, which is still computationally demanding. Intuitively, learning from historic graphs can make estimation more accurate and avoid many repetitive counting to reduce computational cost. Based on this idea, we propose a convolutional neural network (CNN) framework and two preprocessing techniques to estimate graphlet count. Extensive experiments on two types random graphs and real world biochemistry graphs show that our framework can offer substantial speedup on estimating graphlet count new graphs with high accuracy.","Title: Sybil Detection in Social-Activity Networks: Modeling, Algorithms and Evaluations\nYear: 2018\n\nAbstract: Detecting fake accounts (sybils) in online social networks (OSNs) is vital to protect OSN operators and their users from various malicious activities. Typical graph-based sybil detection (a mainstream methodology) assumes that sybils can make friends with only a limited (or small) number of honest users. However, recent evidences showed that this assumption does not hold in real-world OSNs, leading to low detection accuracy. To address this challenge, we explore users' activities to assist sybil detection. The intuition is that honest users are much more selective in choosing who to interact with than to befriend with. We first develop the social and activity network (SAN), a two-layer hyper-graph that unifies users' friendships and their activities, to fully utilize users' activities. We also propose a more practical sybil attack model, where sybils can launch both friendship attacks and activity attacks. We then design Sybil SAN to detect sybils via coupling three random walk-based algorithms on the SAN, and prove the convergence of Sybil SAN. We develop an efficient iterative algorithm to compute the detection metric for Sybil SAN, and derive the number of rounds needed to guarantee the convergence. We use \"matrix perturbation theory\" to bound the detection error when sybils launch many friendship attacks and activity attacks. Extensive experiments on both synthetic and real-world datasets show that Sybil SAN is highly robust against sybil attacks, and can detect sybils accurately under practical scenarios, where current state-of-art sybil defenses have low accuracy.","Title: Submodular Optimization Over Streams with Inhomogeneous Decays.\nYear: 2018\n\nAbstract: Cardinality constrained submodular function maximization, which aims to select a subset of size at most k to maximize a monotone submodular utility function, is the key in many data mining and machine learning applications such as data summarization and maximum coverage problems. When data is given as a stream, streaming submodular optimization (SSO) techniques are desired. Existing SSO techniques can only apply to insertion-only streams where each element has an infinite lifespan, and sliding-window streams where each element has a same lifespan (i.e., window size). However, elements in some data streams may have arbitrary different lifespans, and this requires addressing SSO over streams with inhomogeneous-decays (SSO-ID). This work formulates the SSO-ID problem and presents three algorithms: BASIC-STREAMING is a basic streaming algorithm that achieves an (1/2 - epsilon) approximation factor; HISTAPPROX improves the efficiency significantly and achieves an (1/3 - epsilon) approximation factor; HISTSTREAMING is a streaming version of HISTAPPROX and uses heuristics to further improve the efficiency. Experiments conducted on real data demonstrate that HISTSTREAMING can find high quality solutions and is up to two orders of magnitude faster than the naive GREEDY algorithm.","Title: P-LOC: A Privacy-Preserving Paradigm-Driven Framework for Indoor Localization.\nYear: 2018\n\nAbstract: Indoor localization plays an important role as the basis for a variety of mobile applications, such as navigating, tracking, and monitoring in indoor environments. However, many such systems cause potential privacy leakage in data transmission between mobile users and the localization server (LS). Unfortunately, there has been little research done on privacy issue, and the existing privacy-preserv...","Title: Inferring Higher-Order Structure Statistics of Large Networks from Sampled Edges.\nYear: 2019\n\nAbstract: Recently exploring locally connected subgraphs (also known as motifs or graphlets) of complex networks attracts a lot of attention. Previous work made the strong assumption that the graph topology of interest is known in advance. In practice, sometimes researchers have to deal with the situation where the graph topology is unknown because it is expensive to collect and store all topological inform...","Title: Optimizing node discovery on networks: Problem definitions, fast algorithms, and observations.\nYear: 2019\n\nAbstract: We study a general node discoverability optimization problem on networks, where the goal is to create a few edges to a target node so that the target node can be easily discovered by the other nodes in the network. For instance, a jobseeker may want to connect with some members in LinkedIn so that recruiters can easily find him. We first propose two definitions of node discoverability. Then, we prove that the node discoverability optimization problem is NP-hard. We show that a greedy algorithm can be used to find near-optimal solutions. To scale up the algorithm on large networks, we design three methods: (1) an exact method based on dynamic programming, which is accurate but computationally inefficient; (2) an estimation method based on the framework of random walk, which is efficient but may be inaccurate; (3) an estimation-and-refinement method, which combines the previous two methods and we show that it is both accurate and efficient. Experiments conducted on real networks demonstrate that the estimation-and-refinement method can provide a good trade-off between solution accuracy and computational efficiency, and achieve speedup of up to three orders of magnitude over the exact method.","Title: Fast crawling methods of exploring content distributed over large graphs\nYear: 2019\n\nAbstract: Despite recent effort to estimate topology characteristics of large graphs (e.g., online social networks and peer-to-peer networks), little attention has been given to develop a formal crawling methodology to characterize the vast amount of content distributed over these networks. Due to the large-scale nature of these networks and a limited query rate imposed by network service providers, exhaustively crawling and enumerating content maintained by each vertex is computationally prohibitive. In this paper, we show how one can obtain content properties by crawling only a small fraction of vertices and collecting their content. We first show that when sampling is naively applied, this can produce a huge bias in content statistics (i.e., average number of content replicas). To remove this bias, one may use maximum likelihood estimation to estimate content characteristics. However, our experimental results show that this straightforward method requires to sample most vertices to obtain accurate estimates. To address this challenge, we propose two efficient estimators: special copy estimator (SCE) and weighted copy estimator (WCE) to estimate content characteristics using available information in sampled content. SCE uses the special content copy indicator to compute the estimate, while WCE derives the estimate based on meta-information in sampled vertices. We conduct experiments on a variety of real-word and synthetic datasets, and the results show that WCE and SCE are cost effective and also “asymptotically unbiased”. Our methodology provides a new tool for researchers to efficiently query content distributed in large-scale networks.","Title: T-thinker - a task-centric distributed framework for compute-intensive divide-and-conquer algorithms.\nYear: 2019\n\nAbstract: Many computationally expensive problems are solved by a divide-and-conquer algorithm: a problem over a big dataset can be recursively divided into independent tasks over smaller subsets of the dataset. We present a distributed general-purpose framework called T-thinker which effectively utilizes the CPU cores in a cluster by properly decomposing an expensive problem into smaller independent tasks for parallel computation. T-thinker well overlaps CPU processing with network communication, and its superior performance is verified over a re-engineered graph mining system G-thinker available at http://cs.uab.edu/yanda/gthinker/.","Title: A Markov Decision Process Approach to Analyze Discount & Reputation Trade-offs in E-commerce Systems\nYear: 2018\n\nAbstract: E-commerce systems, e.g., Amazon, eBay and Taobao, are becoming increasingly popular. We consider eBay (or Taobao) like E-commerce systems, where a large number of sellers and buyers transact online. To reflect the trustworthiness of sellers, a reputation system is maintained. In particular, the feedback-based reputation system [4] is the most widely deployed. Sellers of such systems are initialized with a low reputation and they must obtain a sufficiently large number of positive feedbacks from buyers to earn a reputable label. For example, eBay and Taobao use three-level feedbacks, i.e., (-1 (Negative), 0 (Neutral), 1 (Positive). Each seller is initialized with a reputation score of zero. A positive (or negative) rating increases (or decreases) the reputation score by one, while a neutral rating does not change the reputation score.","Title: Mining Deficiencies of Online Reputation Systems: Methodologies, Experiments and Implications\nYear: 2020\n\nAbstract: Online reputation systems serve as core building blocks in various Internet services such as E-commerce (e.g., eBay) and crowdsourcing (e.g., oDesk). The flaws and deficiencies of real-world online reputation systems have been reported extensively. Users who are frustrated about the system will eventually abandon such service. However, there is no systematic and formal studies which examine such deficiencies. This paper presents the first attempt, which develops a novel data analytical framework to uncover online reputation system deficiencies from data. We develop two novel measures to quantify the efficiency of online reputation systems: (1) ramp up time of a new service provider, (2) long term profit gains for a service provider. We present a new data analytical framework to evaluate these two measures from data. We show that inherent preferences or personal biases in expressing feedbacks (or ratings) cause the computational infeasibility in evaluating the ramp up time and the long term profit gains from data. We develop two computationally efficient randomized algorithms with theoretical performance guarantees to address this computational challenge. We apply our methodology to analyze real-life datasets (from eBay, Google Helpouts, Amazon and TripAdvisor). We extensively validate our model and we uncover the deficiencies of online reputation systems. Our experimental results uncovers insights on why Google Helpouts was eventually shut down in April 2015 and why eBay is losing some sellers heavily.","Title: Information Spreading Forensics via Sequential Dependent Snapshots.\nYear: 2018\n\nAbstract: Mining the characteristics of information spreading in networks is crucial in communication studies, network security management, epidemic investigations, etc. Previous works are restrictive because they mainly focused on the information source detection using either a single observation, or multiple but independent observations of the underlying network while assuming a homogeneous information sp...","Title: Quantifying Deployability and Evolvability of Future Internet Architectures via Economic Models\nYear: 2018\n\nAbstract: Emerging new applications demand the current Internet to provide new functionalities. Although many future Internet architectures and protocols have been proposed to fulfill such needs, ISPs have been reluctant to deploy many of these architectures. We believe technical issues are not the main reasons as many of these new proposals are technically sound. In this paper, we take an economic perspective and seek to answer: \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Why do most new Internet architectures fail to be deployed? How can the deployability of a new architecture be enhanced?</italic>\n We develop a game-theoretic model to characterize the outcome of an architecture’s deployment through the \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">equilibrium</italic>\n of ISPs’ decisions. This model enables us to: (1) analyze several key factors of the deployability of a new architecture such as the number of critical ISPs and the change of routing path; (2) explain the deploying outcomes of some previously proposed architectures/protocols such as IPv6, DiffServ, CDN, etc., and shed light on the \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">“Internet flattening phenomenon”</italic>\n; (3) predict the deployability of a new architecture such as NDN, and compare its deployability with competing architectures. Our study suggests that the difficulty to deploy a new Internet architecture comes from the \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">“coordination”</italic>\n of distributed ISPs. Finally, we design a mechanism to enhance the deployability of new architectures.","Title: An Online Learning Multi-path Selection Framework for Multi-path Transmission Protocols\nYear: 2019\n\nAbstract: In the last decade, we have witnessed a tremendous growth of inter-connectivity among hosts in networks. Many new data transmission protocols have been developed to enable multi-path data transmissions between two hosts. However, the existing multi-path transmission protocol designs are limited as they neglect the stochastic nature of the metrics of the paths, e.g., latency, available bandwidth, and packet loss. Moreover, there are different design requirements in the applications, such as low latency, bandwidth throttling, and low loss rate in data delivery. In this paper, we propose a flexible online learning multi-path selection (OLMPS) framework to select multiple paths by learning the stochastic metrics of the paths and meeting the design requirements of the applications. Specifically, we design a set of novel online learning algorithms in the OLMPS framework for three different applications, maxRTT constrained, bandwidth constrained, and loss rate constrained, multi-path selection, to select paths and satisfy the requirements. We prove that the algorithms can provide theoretical guarantees on both sublinear regret and sublinear violation in our OLMPS framework.","Title: On the Profitability of Bundling Sale Strategy for Online Service Markets With Network Effects\nYear: 2019\n\nAbstract: In recent years, we have witnessed a growing trend for online service companies to offer “bundling sales” to increase revenue. Bundling sale means that a company groups a set of products/services and charges this bundle at a fixed price, which is usually less than the total price of individual items in the bundle. In this work, our aim is to understand the underlying dynamics of bundling, particularly what is the optimal bundling sale strategy and under what situations it will be more attractive than the separate sales. We focus on online service markets that exhibit network effects. We formulate mathematical models to capture the interactions between buyers and sellers, analyze the market equilibrium and its stability, and provide an optimization framework to determine the optimal sale strategy for a service provider. We analyze the impact of various factors on the profitability of bundling, including the network effects, operating costs, and variance and correlation of customers’ valuations toward these services. We show that bundling is more profitable when the variance of customers’ valuations and the operational cost of the services are small. In addition, a positive network effect and a negative correlation among customers’ valuation on services increase the profitability of bundling, whereas the heterogeneity of services and the asymmetry of operating costs reduce its advantage.","Title: A Fast Approach To Scale Up Disk Arrays With Parity Declustered Data Layout By Minimizing Data Migration\nYear: 2019\n\nAbstract: Parity declustering is widely deployed in erasure-coded storage systems so as to provide fast recovery and high data availability. However, to perform scaling on such redundant array of inexpensive disks (RAIDs), it is necessary to preserve parity declustered data layout so as to preserve the properties after scaling. Unfortunately, existing scaling algorithms fail to achieve this goal so they cannot be applied for scaling RAIDs with parity declustering. To address this challenge, we develop an efficient online scaling scheme called parity declustering scaling (PDS), which employs an auxiliary balanced incomplete block design to define the data migration so as to preserve parity declustered data layout. Furthermore, PDS can also be applied to scale RAIDs for improving reliability and/or storage efficiency as options by allocating more parity blocks and/or data blocks in stripes. We provide theoretical proofs to formally show that PDS preserves parity declustered data layout, and achieves uniform distributions of data and parity blocks after scaling while requiring only the minimal data migration. We implement PDS in Linux kernel 3.14.72 and evaluate its performance with real-world traces. The results show that PDS can reduce 82.37 percent of scaling time and 18.25 percent of user response time during scaling on average, compared with \"moving-everything\" round-robin approach adapted to achieve parity declustered data layout after scaling.","Title: FAVE: A fast and efficient network Flow AVailability Estimation method with bounded relative error.\nYear: 2019\n\nAbstract: This paper focuses on helping network providers to carry out network capacity planning and sales projection by answering the question: For a given topology and capacity, whether the network can serve current flow demands with high probabilities? We name such probability as “ flow availability” and present the -flow availability estimation (FAVE) problem, which is a generalisation of network connectivity or maximum flow reliability estimations. Realistic networks are often large and dynamic, so flow availabilities cannot be evaluated analytically and simulation is often used. However, naive Monte Carlo (MC) or importance sampling (IS) techniques take an excessive amount of time. To quickly estimate flow availabilities, we utilize the correlations among link and flow failures to figure out the importance of roles played by different links in flow failures, and design three “sequential importance sampling” (SIS) methods which achieve “bounded or even vanishing relative error” with linear computational complexities. When applying to a realistic network, our method reduces the flow availability estimation cost by 900 and 130 times compared with MC and baseline IS methods, respectively. Our method can also facilitate capacity planning by providing better flow availability guarantees, compared with traditional methods.","Title: Optimizing Discount & Reputation Trade-offs in E-commerce Systems: Characterization and Online Learning\nYear: 2019\n\nAbstract: Feedback-based reputation systems are widely deployed in E-commerce systems. Evidences showed that earning a reputable label (for sellers of such systems) may take a substantial amount of time and this implies a reduction of profit. We propose to enhance sellers' reputation via price discounts. However, the challenges are: (1) The demands from buyers depend on both the discount and reputation; (2) The demands are unknown to the seller. To address these challenges, we first formulate a profit maximization problem via a semiMarkov decision process (SMDP) to explore the optimal trade-offs in selecting price discounts. We prove the monotonicity of the optimal profit and optimal discount. Based on the monotonicity, we design a QLFP (Q-learning with forward projection) algorithm, which infers the optimal discount from historical transaction data. We conduct experiments on a dataset from to show that our QLFP algorithm improves the profit by as high as 50% over both the classical Q-learning and speedy Q-learning algorithm. Our QLFP algorithm also improves the profit by as high as four times over the case of not providing any price discount.","Title: Understanding Persuasion Cascades in Online Product Rating Systems\nYear: 2019\n\nAbstract: Online product rating systems have become an indispensable component for numerous web services such as Amazon, eBay, Google play store and TripAdvisor. One functionality of such systems is to uncover the product quality via product ratings (or reviews) contributed by consumers. However, a well-known psychological phenomenon called \"message-based persuasion\" lead to \"biased\" product ratings in a cascading manner (we call this the persuasion cascade). This paper investigates: (1) How does the persuasion cascade influence the product quality estimation accuracy? (2) Given a real-world product rating dataset, how to infer the persuasion cascade and analyze it to draw practical insights? We first develop a mathematical model to capture key factors of a persuasion cascade. We formulate a high-order Markov chain to characterize the opinion dynamics of a persuasion cascade and prove the convergence of opinions. We further bound the product quality estimation error for a class of rating aggregation rules including the averaging scoring rule, via the matrix perturbation theory and the Chernoff bound. We also design a maximum likelihood algorithm to infer parameters of the persuasion cascade. We conduct experiments on the data from Amazon and TripAdvisor, and show that persuasion cascades notably exist, but the average scoring rule has a small product quality estimation error under practical scenarios.","Title: Exploiting Non-Uniform Program Execution Time to Evade Record/Replay Forensic Analysis\nYear: 2020\n\nAbstract: Record/replay system is an essential and widely used module in forensic analysis, as it can help forensic analysts to reconstruct programs’ behaviors. However, the security implication of record/replay systems (i.e., whether record/replay systems can faithfully reproduce all behaviors of a program) has not been thoroughly studied. This paper is the first work which investigates and explores the security limitations of record/replay systems from the perspective of software forensics. In particular, we reveal a type of vulnerability in record/replay systems caused by non-uniform program execution time. A program can exploit this vulnerability to prevent its malicious behavior from being replayed. We conduct a series of experiments on three platforms (i.e., web browser, mobile operating system and virtualized sandbox) to illustrate the wide footprints of the vulnerability. Finally, we discuss possible methods to mitigate the vulnerability. The goal of this work is to study the inherent security limitations of record/replay systems, discover the vulnerability and explore potential mitigation methods, from which forensic analysts can be informed and cautious when applying record/replay systems to software forensics.","Title: A Memory-Efficient Sketch Method for Estimating High Similarities in Streaming Sets.\nYear: 2019\n\nAbstract: Estimating set similarity and detecting highly similar sets are fundamental problems in areas such as databases, machine learning, and information retrieval. MinHash is a well-known technique for approximating Jaccard similarity of sets and has been successfully used for many applications such as similarity search and large scale learning. Its two compressed versions, b-bit MinHash and Odd Sketch, can significantly reduce the memory usage of the original MinHash method, especially for estimating high similarities (i.e., similarities around 1). Although MinHash can be applied to static sets as well as streaming sets, of which elements are given in a streaming fashion and cardinality is unknown or even infinite, unfortunately, b-bit MinHash and Odd Sketch fail to deal with streaming data. To solve this problem, we design a memory efficient sketch method, MaxLogHash, to accurately estimate Jaccard similarities in streaming sets. Compared to MinHash, our method uses smaller sized registers (each register consists of less than 7 bits) to build a compact sketch for each set. We also provide a simple yet accurate estimator for inferring Jaccard similarity from MaxLogHash sketches. In addition, we derive formulas for bounding the estimation error and determine the smallest necessary memory usage (i.e., the number of registers used for a MaxLogHash sketch) for the desired accuracy. We conduct experiments on a variety of datasets, and experimental results show that our method MaxLogHash is about 5 times more memory efficient than MinHash with the same accuracy and computational cost for estimating high similarities.","Title: Walking with Perception: Efficient Random Walk Sampling via Common Neighbor Awareness\nYear: 2019\n\nAbstract: Random walk is widely applied to sample large-scale graphs due to its simplicity of implementation and solid theoretical foundations of bias analysis. However, its computational efficiency is heavily limited by the slow convergence rate (a.k.a. long burn-in period). To address this issue, we propose a common neighbor aware random walk framework called CNARW, which leverages weighted walking by differentiating the next-hop candidate nodes to speed up the convergence. Specifically, CNARW takes into consideration the common neighbors between previously visited nodes and next-hop candidate nodes in each walking step. Based on CNARW, we further develop two efficient \"unbiased sampling\" schemes. Experimental results on real-world network datasets show that our approach converges remarkably faster than the state-of-the-art random walk sampling algorithms. Furthermore, to achieve the same estimation accuracy, our approach reduces the query cost (a measure of sampling budget) significantly. Lastly, we also use two case studies to demonstrate the effectiveness of our sampling framework in solving large-scale graph analysis tasks.","Title: Toward Building Conversational Recommender Systems: A Contextual Bandit Approach.\nYear: 2019\n\nAbstract: Contextual bandit algorithms have gained increasing popularity in recommender systems, because they can learn to adapt recommendations by making exploration-exploitation trade-off. Recommender systems equipped with traditional contextual bandit algorithms are usually trained with behavioral feedback (e.g., clicks) from users on items. The learning speed can be slow because behavioral feedback by nature does not carry sufficient information. As a result, extensive exploration has to be performed. To address the problem, we propose conversational recommendation in which the system occasionally asks questions to the user about her interest. We first generalize contextual bandit to leverage not only behavioral feedback (arm-level feedback), but also verbal feedback (users' interest on categories, topics, etc.). We then propose a new UCB- based algorithm, and theoretically prove that the new algorithm can indeed reduce the amount of exploration in learning. We also design several strategies for asking questions to further optimize the speed of learning. Experiments on synthetic data, Yelp data, and news recommendation data from Toutiao demonstrate the efficacy of the proposed algorithm.","Title: Unifying Offline Causal Inference and Online Bandit Learning for Data Driven Decision\nYear: 2021\n\nAbstract: ABSTRACT A fundamental question for companies with large amount of logged data is: How to use such logged data together with incoming streaming data to make good decisions? Many companies currently make decisions via online A/B tests, but wrong decisions during testing hurt users’ experiences and cause irreversible damage. A typical alternative is offline causal inference, which analyzes logged data alone to make decisions. However, these decisions are not adaptive to the new incoming data, and so a wrong decision will continuously hurt users’ experiences. To overcome the aforementioned limitations, we propose a framework to unify offline causal inference algorithms (e.g., weighting, matching) and online learning algorithms (e.g., UCB, LinUCB). We propose novel algorithms and derive bounds on the decision accuracy via the notion of “regret”. We derive the first upper regret bound for forest-based online bandit algorithms. Experiments on two real datasets show that our algorithms outperform other algorithms that use only logged data or online feedbacks, or algorithms that do not use the data properly.","Title: A Reinforcement Learning Approach to Price Cloud Resources With Provable Convergence Guarantees\nYear: 2022\n\nAbstract: How to generate more revenues is crucial to cloud providers. Evidences from the Amazon cloud system indicate that “dynamic pricing” would be more profitable than “static pricing.” The challenges are: How to set the price in real-time so to maximize revenues? How to estimate the price dependent demand so to optimize the pricing decision? We first design a discrete-time based dynamic pricing scheme and formulate a Markov decision process to characterize the evolving dynamics of the price-dependent demand. We formulate a revenue maximization framework to determine the optimal price and theoretically characterize the “structure” of the optimal revenue and optimal price. We apply the \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$Q$ </tex-math></inline-formula>\n-learning to infer the optimal price from historical transaction data and derive sufficient conditions on the model to guarantee its convergence to the optimal price, but it converges slowly. To speed up the convergence, we incorporate the structure of the optimal revenue that we obtained earlier, leading to the VpQ-learning (\n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$Q$ </tex-math></inline-formula>\n-learning with value projection) algorithm. We derive sufficient conditions, under which the VpQ-learning algorithm converges to the optimal policy. Experiments on a real-world dataset show that the VpQ-learning algorithm outperforms a variety of baselines, i.e., improves the revenue by as high as 50% over the \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$Q$ </tex-math></inline-formula>\n-learning, speedy \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$Q$ </tex-math></inline-formula>\n-learning, and adaptive real-time dynamic programming (ARTDP), and by as high as 20% over the fixed pricing scheme.","Title: A Bootstrapping Approach to Optimize Random Walk Based Statistical Estimation over Graphs\nYear: 2021\n\nAbstract: Graphs are commonly used in various applications such as online social networks (OSNs), E-commerce systems and social recommender systems. Random walk sampling is often used to conduct statistical estimation over such graphs. This paper develops an algorithmic framework to reduce the mean square error of such statistical estimation. Our algorithmic framework is inspired by that the mean square err...","Title: Pricing social visibility service in online social networks: modeling and algorithms\nYear: 2021\n\nAbstract: ABSTRACTIn online social networks (OSNs), users may want to enhance their social visibility, as it can make their contents, i.e., opinions, videos, pictures, etc., attract attention from more users. Motivated by this, we propose a mechanism, where the OSN operator provides a \"social visibility boosting service\" to incentivize \"transactions\" between requesters (users who seek to enhance their social visibility via adding new \"neighbors\") and suppliers (users who are willing to be added as a new \"neighbor\" of any requester when certain \"rewards\" is provided). We design a posted pricing scheme for the OSN provider to charge the requesters who use such boosting service, and reward the suppliers who contribute to such boosting service. The OSN operator keeps a fraction of the payment from requesters and distributes the remaining part to participating suppliers \"fairly\" via a scheme based on the Shapley value. The objective of the OSN provider is to select the price and supplier set to maximize the revenue under the budget constraint of requesters. We first show that the revenue maximization problem is not simpler than an NP-hard problem. We then decompose it into two subroutines, prove the hardness of each sub-routine, and eventually design computationally efficient approximation algorithms to solve the revenue maximization problem. We conduct extensive experiments to evaluate our proposed algorithms.","Title: Heterogeneous Information Assisted Bandit Learning: Theory And Application\nYear: 2021\n\nAbstract: Contextual bandit serves as an invaluable tool to balance the exploration vs. exploitation trade-off in various applications like online recommendation. In many applications, heterogeneous information network (HIN) can be derived to provide rich side information for contextual bandits, such as different types of attributes and relationships among users and items. In this paper, we propose the first HIN-assisted contextual bandit framework, which utilizes a given HIN to assist contextual bandit learning. The proposed framework uses meta-paths in HIN to extract rich relations among users and items for the contextual bandit. The main challenge is how to leverage these relations, since users' preference over items, the target of our online learning, are closely related to users' preference over meta-paths, however it is unknown which meta-path a user prefers more. We propose the HUCB algorithm to address such a challenge. For each meta-path, the HUCB algorithm employs an independent base bandit algorithm to handle online item recommendation by leveraging the relationship captured in this meta-path. The bandit master is then employed to learn users' preference over meta-paths to dynamically combine base bandit algorithms with a balance of exploration-exploitation trade-off. Experimental results on real datasets from LastFM and Yelp demonstrate the efficacy of the HUCB algorithm.","Title: Social Visibility Optimization In Osns With Anonymity Guarantees: Modeling, Algorithms And Applications\nYear: 2021\n\nAbstract: Online social network (OSN) is an ideal venue to enhance one's visibility. This paper considers how a user (called requester) in an OSN selects a small number of available users and invites them as new friends/followers so as to maximize his \"social visibility\". More importantly, the requester has to do this under the anonymity setting, which means he is not allowed to know the neighborhood information of these available users in the OSN. In this paper, we first develop a mathematical model to quantify the social visibility and formulate the problem of visibility maximization with anonymity guarantee, abbreviated as \"VisMAX-A\". Then we design an algorithmic framework named as \"AdaExp\", which adaptively expands the requester's visibility in multiple rounds. In each round of the expansion, AdaExp uses a query oracle with anonymity guarantee to select only one available user. By using probabilistic data structures like the k-minimum values (KMV) sketch, we design an efficient query oracle with anonymity guarantees. We also conduct experiments on real-world social networks and validate the effectiveness of our algorithms.","Title: Mobile Data Traffic Prediction by Exploiting Time-Evolving User Mobility Patterns\nYear: 2022\n\nAbstract: Understanding mobile data traffic and forecasting future traffic trend is beneficial to wireless carriers and service providers who need to perform resource allocation and energy saving management. However, predicting wireless traffic accurately at large-scale and fine-granularity is particularly challenging due to the following two factors: the spatial correlations between the network units (i.e., a cell tower or an access point) introduced by user arbitrary movements, and the time-evolving nature of user movements which frequently changes with time. In this paper, we use a time-evolving graph to formulate the time-evolving nature of user movements, and propose a model Graph-based Temporal Convolutional Network (GTCN) to predict the future traffic of each network unit in a wireless network. GTCN can bring significant benefits to two aspects. (1) GTCN can effectively learn intra- and inter-time spatial correlations between network units in a time-evolving graph through a node aggregation method. (2) GTCN can efficiently model the temporal dynamics of the mobile traffic trend from different network units through a temporal convolutional layer. Experimental results on two real-world datasets demonstrate the efficiency and efficacy of our method. Compared with state-of-the-art methods, the improvement of the prediction performance of our GTCN is 3.2 to 10.2 percent for different prediction horizons. GTCN also achieves 8.4× faster on prediction time.","Title: On Modeling Influence Maximization in Social Activity Networks under General Settings\nYear: 2021\n\nAbstract: AbstractFinding the set of most influential users in online social networks (OSNs) to trigger the largest influence cascade is meaningful, e.g., companies may leverage the “word-of-mouth” effect to trigger a large cascade of purchases by offering free samples/discounts to those most influential users. This task is usually modeled as an influence maximization problem, and it has been widely studied in the past decade. However, considering that users in OSNs may participate in various online activities, e.g., joining discussion groups and commenting on same pages or products, influence diffusion through online activities becomes even more significant. In this article, we study the impact of online activities by formulating social-activity networks which contain both users and online activities, and thus induce two types of weighted edges, i.e., edges between users and edges between users and activities. To address the computation challenge, we define an influence centrality via random walks, and use the Monte Carlo framework to efficiently estimate the centrality. Furthermore, we develop a greedy-based algorithm with novel optimizations to find the most influential users for node recommendation. Experiments on real-world datasets show that our approach is very computationally efficient under different influence models, and also achieves larger influence spread by considering online activities.","Title: An Online Mean Field Approach for Hybrid Edge Server Provision\nYear: 2021\n\nAbstract: ABSTRACTThe performance of an edge computing system primarily depends on the edge server provision mode, the task migration scheme, and the computing resource configuration. This paper studies how to perform dynamic resource configuration for hybrid edge server provision under two decentralized task migration schemes. We formulate the dynamic resource configuration as a multi-period online cost minimization problem, aiming to jointly minimize the performance degradation (i.e., execution latency) and the operation expenditure. Due to the stochastic nature, one can only observe the system performance for the currently installed configuration, which is also known as the partial feedback. To overcome this challenge, we derive a deterministic mean field model to approximate the large-scale stochastic edge computing system. We then propose an online mean field aided resource configuration policy, and show that the proposed policy performs asymptotically as good as the offline optimal configuration. Numerical results show that the mean field model can significantly improve the convergence speed in the online resource configuration problem. Moreover, our proposed policy under the two decentralized task migration schemes considerably reduces the operating cost (by 23%) and incurs little communication overhead.","Title: Topology-Theoretic Approach To Address Attribute Linkage Attacks In Differential Privacy\nYear: 2021\n\nAbstract: Differential Privacy (DP) is well-known for its strong privacy guarantee. In this paper, we show that when there are correlations among attributes in the dataset, only relying on DP is not sufficient to defend against the "attribute linkage attack", which is a well-known privacy attack aiming at deducing participant’s attribute information. Our contributions are ① we show that the attribute linkag...","Title: A Truthful Procurement Auction for Incentivizing Heterogeneous Clients in Federated Learning\nYear: 2021\n\nAbstract: Federated Learning (FL) is a new distributed machine learning (ML) approach which enables thousands of mobile devices to collaboratively train artificial intelligence (AI) models using local data without compromising user privacy. Although FL represents a promising computing paradigm, such training process can not be fully realized without an appropriate economic mechanism that incentivizes the pa...","Title: Dynamic online convex optimization with long-term constraints via virtual queue\nYear: 2021\n\nAbstract: In this paper, we investigate the online convex optimization (OCO) with long-term constraints which is widely used in various resource allocations and recommendation systems. Different from the most existing works, our work adopts a dynamic benchmark to analyze the optimization performance since the dynamic benchmark is more common than the static benchmark in practical applications. Moreover, compared with many constrained OCO works ignoring the Slater condition, we study the effect of the Slater condition on the constraint violation bounds and obtain the better performance of the constraint violations when the Slater condition holds. More importantly, we propose a novel iterative optimization algorithm based on the virtual queues to achieve sublinear regret and constraint violations. Finally, we apply our dynamic OCO model to a resource allocation problem in cloud computing and the results of the experiments validate the effectiveness of our algorithm.","Title: MirChecker: Detecting Bugs in Rust Programs via Static Analysis\nYear: 2021\n\nAbstract: ABSTRACTSafe system programming is often a crucial requirement due to its critical role in system software engineering. Conventional low-level programming languages such as C and assembly are efficient, but their inherent unsafe nature makes it undesirable for security-critical scenarios. Recently, Rust has become a promising alternative for safe system-level programming. While giving programmers fine-grained hardware control, its strong type system enforces many security properties including memory safety. However, Rust's security guarantee is not a silver bullet. Runtime crashes and memory-safety errors still harass Rust developers, causing damaging exploitable vulnerabilities, as reported by numerous studies. In this paper, we present and evaluate MirChecker, a fully automated bug detection framework for Rust programs by performing static analysis on Rust's Mid-level Intermediate Representation (MIR). Based on the observation of existing bugs found in Rust codebases, our approach keeps track of both numerical and symbolic information, detects potential runtime crashes and memory-safety errors by using constraint solving techniques, and outputs informative diagnostics to users. We evaluate MirChecker on both buggy code snippets extracted from existing Common Vulnerabilities and Exposures (CVE) and real-world Rust codebases. Our experiments show that MirChecker can detect all the issues in our code snippets, and is capable of performing bug finding in real-world scenarios, where it detected a total of 33 previously unknown bugs including 16 memory-safety issues from 12 Rust packages (crates) with an acceptable false-positive rate.","Title: Cooperative Stochastic Bandits with Asynchronous Agents and Constrained Feedback.\nYear: 2021\n\nAbstract: Use the \"Report an Issue\" link to request a name change.","Title: Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach\nYear: 2022\n\nAbstract: Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements. Previous works mainly explore task offloading when system-side information is given (e.g., server processing speed, cellular data rate), or centralized offloading under system uncertainty. But both generally fall short of handling task placement involving many coexisting users in a dynamic and uncertain environment. In this paper, we develop a multi-user offloading framework considering unknown yet stochastic system-side information to enable a decentralized user-initiated service placement. Specifically, we formulate the dynamic task placement as an online multi-user multi-armed bandit process, and propose a decentralized epoch based offloading (DEBO) to optimize user rewards which are subject to the network delay. We show that DEBO can deduce the optimal user-server assignment, thereby achieving a close-to-optimal service performance and tight O(log T ) offloading regret. Moreover, we generalize DEBO to various common scenarios such as unknown reward gap, dynamic entering or leaving of clients, and fair reward distribution, while further exploring when users’ offloaded tasks require heterogeneous computing resources. Particularly, we accomplish a sub-linear regret for each of these instances. Real measurements based evaluations corroborate the superiority of our offloading schemes over state-of-the-art approaches in optimizing delay-sensitive rewards.","Title: Topology-theoretic approach to address attribute linkage attacks in differential privacy\nYear: 2022\n\nAbstract: Differential Privacy (DP) is well-known for its strong privacy guarantee. Briefly speaking, DP algorithms guarantee that the statistical information of the data is roughly preserved, and at the same time, individual privacy is protected with guarantees. However, when there are correlations among the attribute in the dataset, only relying on DP is not sufficient to defend against the attribute linkage attack, which is a well-known privacy attack aiming at deducing individuals' private information. In the attribute linkage attack, the adversary can leverage prior knowledge about the victim, combined with accessing the published dataset, to infer sensitive information about a victim. In this paper, we study the attribute linkage attack in DP settings, and argue that enhancing DP can give users a higher level of privacy guarantees. Our contributions are (1) we show that the attribute linkage attack can be initiated with high probability under the protection of DP, (2) we propose a variant of DP called APL-Free epsilon-DP to provide a higher level of privacy guarantees, (3) we design an algorithm APLKiller which satisfies the APL-Free epsilon-DP. Finally, experiments show that our algorithm not only eliminates the attribute linkage attack, and at the same time, it has a better ability to extract useful information from the data. (C) 2021 Elsevier Ltd. All rights reserved.","Title: Inferring Hidden IoT Devices and User Interactions via Spatial-Temporal Traffic Fingerprinting\nYear: 2022\n\nAbstract: With the popularization of Internet of Things (IoT) devices in smart home and industry fields, a huge number of IoT devices are connected to the Internet. However, what devices are connected to a network may not be known by the Internet Service Provider (ISP), since many IoT devices are placed within small networks (e.g., home networks) and are hidden behind network address translation (NAT). With...","Title: Towards Personalized Privacy-Preserving Truth Discovery Over Crowdsourced Data Streams\nYear: 2022\n\nAbstract: Truth discovery is an effective paradigm which could reveal the truth from crowdsouced data with conflicts, enabling data-driven decision-making systems to make quick and smart decisions. The increasing privacy concern promotes users to perturb or encrypt their private data before outsourcing, which poses significant challenges for truth discovery. Although several privacy-preserving truth discove...","Title: Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications.\nYear: 2022\n\nAbstract: Multi-player multi-armed bandits (MMAB) study how decentralized players cooperatively play the same multi-armed bandit so as to maximize their total cumulative rewards. Existing MMAB models mostly assume when more than one player pulls the same arm, they either have a collision and obtain zero rewards, or have no collision and gain independent rewards, both of which are usually too restrictive in practical scenarios. In this paper, we propose an MMAB with shareable resources as an extension to the collision and non-collision settings. Each shareable arm has finite shareable resources and a \"per-load\" reward random variable, both of which are unknown to players. The reward from a shareable arm is equal to the \"per-load\" reward multiplied by the minimum between the number of players pulling the arm and the arm's maximal shareable resources. We consider two types of feedback: sharing demand information (SDI) and sharing demand awareness (SDA), each of which provides different signals of resource sharing. We design the DPE-SDI and SIC-SDA algorithms to address the shareable arm problem under these two cases of feedback respectively and prove that both algorithms have logarithmic regrets that are tight in the number of rounds. We conduct simulations to validate both algorithms' performance and show their utilities in wireless networking and edge computing.","Title: Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms.\nYear: 2022\n\nAbstract: We generalize the multiple-play multi-armed bandits (MP-MAB) problem with a shareable arms setting, in which several plays can share the same arm. Furthermore, each shareable arm has a finite reward capacity and a “per-load” reward distribution, both of which are unknown to the learner. The reward from a shareable arm is load-dependent, which is the “per-load” reward multiplying either the number of plays pulling the arm, or its reward capacity when the number of plays exceeds the capacity limit. When the “per-load” reward follows a Gaussian distribution, we prove a sample complexity lower bound of learning the capacity from load-dependent rewards and also a regret lower bound of this new MP-MAB problem. We devise a capacity estimator whose sample complexity upper bound matches the lower bound in terms of reward means and capacities. We also propose an online learning algorithm to address the problem and prove its regret upper bound. This regret upper bound’s first term is the same as regret lower bound’s, and its second and third terms also evidently correspond to lower bound’s. Extensive experiments validate our algorithm’s performance and also its gain in 5G & 4G base station selection.","Title: Optimizing Social Visibility in OSNs With Anonymity Guarantees: Efficient Algorithms and Applications\nYear: 2022\n\nAbstract: Online social network (OSN) is an ideal venue to enhance one's visibility. This paper considers how a user (called requester) in an OSN selects a small number of users and invites them as new friends/followers so as to maximize her “\n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">social visibility</i>\n”. More importantly, the requester has to do this under the anonymity setting, which means she is not allowed to know the neighborhood information of other users in the OSN. In this paper, we first develop a mathematical model to quantify the social visibility and formulate the problem of visibility maximization with anonymity guarantee, abbreviated as “VisMAX-A”. We prove that the VisMAX-A problem is NP-hard even with an “perfect query oracle” which knows users’ neighborhood information. Then we design an algorithmic framework named as “AdaExp,” which adaptively expands the requester's visible set in multiple rounds. In each round of the expansion, the algorithmic framework uses a query oracle with anonymity guarantee to select one user to increase the requester's visibility. We prove the theoretical guarantees of our algorithmic framework, which quantify how the query oracle may influence the solution guarantee. By using probabilistic data structures like the k-minimum values (KMV) sketch and online learning methods like multi-armed bandit (MAB), we design an efficient query oracle with anonymity guarantees to estimate the best candidate to increase requester's visibility. We also conduct experiments on real-world social networks and validate the effectiveness of our algorithms. Lastly, we demonstrate applications of our algorithm on real-world data and show that one can increase her social visibility significantly by only adding a few (e.g., one or two) friends/followers in the OSNs.","Title: Approximate and Deployable Shortest Remaining Processing Time Scheduler\nYear: 2022\n\nAbstract: The scheduling policy installed on switches of datacenters plays a significant role on congestion control. Shortest-Remaining-Processing-Time (SRPT) achieves the near-optimal average message completion time (MCT) in various scenarios, but is difficult to deploy as viewed by the industry. The reasons are two-fold: 1) many commodity switches only provide FIFO queues, and 2) the information of remaining message size is not available. Recently, the idea of emulating SRPT using only a few FIFO queues and the original message size has been coined as the approximate and deployable SRPT (ADS) design. In this paper, we provide the first theoretical study on the optimal ADS design. Specifically, we first characterize a wide range of feasible ADS scheduling policies via a unified framework, and then derive the steady-state MCT, slowdown, and impoliteness in the M/G/1 setting. Hence we formulate the optimal ADS design as a non-linear combinatorial optimization problem, which aims to minimize the average MCT given the available FIFO queues. We also take into account the proportional fairness and temporal fairness constraints based on the maximal slowdown and impoliteness, respectively. The optimal ADS design problem is NP-hard in general, and does not exhibit monotonicity or sub-modularity. We leverage its decomposable structure and devise an efficient algorithm to solve the optimal ADS policy. We carry out extensive flow-level simulations and packet-level experiments to evaluate the proposed optimal ADS design. Results show that the optimal ADS policy installed on eight FIFO queues is capable of emulating the true SRPT.","Title: Partial-Quasi-Newton Methods: Efficient Algorithms for Minimax Optimization Problems with Unbalanced Dimensionality\nYear: 2022\n\nAbstract: This paper studies the strongly-convex-strongly-concave minimax optimization with unbalanced dimensionality. Such problems contain several popular applications in data science such as few shot learning and fairness-aware machine learning task. The design of conventional iterative algorithm for minimax optimization typically focuses on reducing the total number of oracle calls, which ignores the unbalanced computational cost for accessing the information from two different variables in minimax. We propose a novel second-order optimization algorithm, called Partial-Quasi-Newton (PQN) method, which takes the advantage of unbalanced structure in the problem to establish the Hessian estimate efficiently. We theoretically prove our PQN method converges to the saddle point faster than existing minimax optimization algorithms. The numerical experiments on real-world applications show the proposed PQN performs significantly better than the state-of-the-art methods.","Title: Federated online clustering of bandits.\nYear: 2022\n\nAbstract: Contextual multi-armed bandit (MAB) is an important sequential decision-making problem in recommendation systems. A line of works, called the clustering of bandits (CLUB), utilize the collaborative effect over users and dramatically improve the recommendation quality. Owing to the increasing application scale and public concerns about privacy, there is a growing demand to keep user data decentralized and push bandit learning to the local server side. Existing CLUB algorithms, however, are designed under the centralized setting where data are available at a central server. We focus on studying the federated online clustering of bandit (FCLUB) problem, which aims to minimize the total regret while satisfying privacy and communication considerations. We design a new phase-based scheme for cluster detection and a novel asynchronous communication protocol for cooperative bandit learning for this problem. To protect users’ privacy, previous differential privacy (DP) definitions are not very suitable, and we propose a new DP notion that acts on the user cluster level. We provide rigorous proofs to show that our algorithm simultaneously achieves (clustered) DP, sublinear communication complexity and sublinear regret. Finally, experimental evaluations show our superior performance compared with benchmark algorithms.","Title: Toward Large-Scale Hybrid Edge Server Provision: An Online Mean Field Learning Approach\nYear: 2022\n\nAbstract: The efficiency of a large-scale edge computing system primarily depends on three aspects: i) edge server provision, ii) task migration, and iii) computing resource configuration. In this paper, we study the dynamic resource configuration for hybrid edge server provision under two decentralized task migration schemes. We formulate the dynamic resource configuration as an online cost minimization problem, aiming to jointly minimize performance degradation and operation expenditure. Due to the stochastic nature, it is an online learning problem with partial feedback. To address it, we derive a deterministic mean field model to approximate the stochastic edge computing system. We show that the mean field model provides the increasingly accurate full feedback as the system scales. We then propose a learning policy based on the mean field model, and show that our proposed policy performs asymptotically as well as the offline optimal configuration. We provide two ways of setting the policy parameters, which achieve a constant competitive ratio (under certain mild conditions) and a sub-linear regret, respectively. Numerical results show that the mean field model significantly improves the convergence speed. Moreover, our proposed policy under the decentralized task migration schemes considerably reduces the operating cost (by 23%) and incurs little communication overhead.","Title: PupilRec: Leveraging Pupil Morphology for Recommending on Smartphones\nYear: 2022\n\nAbstract: As mobile shopping has gradually become the mainstream shopping mode, recommendation systems are gaining an increasingly wide adoption. Existing recommendation systems are mainly based on explicit and implicit user behaviors. However, these user behaviors may not directly indicate users’ inner feelings, causing erroneous user preference estimation and thus leading to inaccurate recommendations. Inspired by our key observation on the correlation between pupil size and users’ inner feelings, we consider using the change of pupil size when browsing to model users’ preferences, so as to achieve targeted recommendations. To this end, we propose PupilRec as a computer-vision-based recommendation framework involving a mobile terminal and a server side. On the mobile terminal, PupilRec collects users’ pupil size change information through the front camera of smartphones; it then preprocesses the raw pupil size data before transmitting them to the server. On the server side, PupilRec utilizes the Tsfresh package and Random Forest algorithm to figure out the key time-series features directly implying user preferences. PupilRec then trains a neural network to fit a user preference model. Using this model, PupilRec predicts user preference to obtain a user–product matrix and further simplifies it by singular value decomposition. Finally, the real-time recommendation is achieved by a collaborative filtering module that retrieves recommended contents to users smartphones. We prototype PupilRec and conduct both experiments and field studies to comprehensively evaluate the effectiveness of PupilRec by recruiting 67 volunteers. The overall results show that PupilRec can accurately estimate users’ preference and can recommend products users interested in.","Title: A Control-Theoretic and Online Learning Approach to Self-Tuning Queue Management\nYear: 2022\n\nAbstract: There is a growing trend that network applications not only require higher throughput, but also impose stricter delay requirements. The current Internet congestion control, which is driven by active queue management (AQM) algorithms interacting with the Transmission Control Protocol (TCP), has been playing an important role in supporting network applications. However, it still exhibits many open issues. Most of AQM algorithms only deploy a single-queue structure that cannot differentiate flows and easily leads to unfairness. Moreover, the parameter settings of AQM are often static, making them difficult to adapt to the dynamic network environments. In this paper, we propose a general framework for designing \"self-tuning\" queue management (SQM), which is adaptive to the changing environments and provides fair congestion control among flows. We first present a general architecture of SQM with fair queueing and propose a general fluid model to analyze it. To adapt to the stochastic environments, we formulate a stochastic network utility maximization (SNUM) problem, and utilize online convex optimization (OCO) and control theory to develop a distributed SQM algorithm which can self-tune different queue weights and control parameters. Numerical and packet-level simulation results show that our SQM algorithm significantly improves queueing delay and fairness among flows.","Title: Achieving efficiency via fairness in online resource allocation\nYear: 2022\n\nAbstract: ABSTRACTThe classic utility maximization framework studies the fairness-efficiency tradeoff in various resource allocation problems (e.g., bandwidth allocation). The weighted alpha-fair utility is a common utilitarian metric. However, this classic framework cannot tackle those allocation problems with the online decision-making requirement (e.g., caching capacity allocation under unknown requests). Existing studies on these online allocation problems largely follow the online learning approaches, thus inevitably overlook the allocation fairness. In this paper, we propose a novel utility maximization framework accommodating the online setting. The major challenge of designing this framework lies in the tight coupling between the desirable fairness guarantee and the unknown allocation efficiency. To tackle this, we integrate the weighted alpha-fair utility with the learning rationale, by properly devising the merit-based weights and the increasing fairness levels. Under our proposed framework, the utility-maximizing allocation in each time slot is weighted alpha-fair. Our framework also performs asymptotically as well as the offline optimal/efficient outcome. We demonstrate how this framework functions in two networking applications. In size-based scheduling, it enables network switches to prioritize short flows and avoid flow starvation without the prior flow size information. In file caching, our framework outperforms several state-of-the-art caching policies up to 21% in terms of cache-hit-ratio.","Title: Improving Bandit Learning Via Heterogeneous Information Networks: Algorithms and Applications\nYear: 2022\n\nAbstract: ontextual bandit serves as an invaluable tool to balance the exploration vs. exploitation tradeoff in various applications such as online recommendation. In many applications, heterogeneous information networks (HINs) provide rich side information for contextual bandits, such as different types of attributes and relationships among users and items. In this article, we propose the first HIN-assisted contextual bandit framework, which utilizes a given HIN to assist contextual bandit learning. The proposed framework uses meta-paths in HIN to extract rich relations among users and items for the contextual bandit. The main challenge is how to leverage these relations, since users’ preference over items, the target of our online learning, are closely related to users’ preference over meta-paths. However, it is unknown which meta-path a user prefers more. Thus, both preferences are needed to be learned in an online fashion with exploration vs. exploitation tradeoff balanced. We propose the HIN-assisted upper confidence bound (HUCB) algorithm to address such a challenge. For each meta-path, the HUCB algorithm employs an independent base bandit algorithm to handle online item recommendations by leveraging the relationship captured in this meta-path. A bandit master is then employed to learn users’ preference over meta-paths to dynamically combine base bandit algorithms with a balance of exploration vs. exploitation tradeoff. We theoretically prove that the HUCB algorithm can achieve similar performance compared with the optimal algorithm where each user is served according to his true preference over meta-paths (assuming the optimal algorithm knows the preference). Moreover, we prove that the HUCB algorithm benefits from leveraging HIN in achieving a smaller regret upper bound than the baseline algorithm without leveraging HIN. Experimental results on a synthetic dataset, as well as real datasets from LastFM and Yelp demonstrate the fast learning speed of the HUCB algorithm.","Title: Detecting Cross-language Memory Management Issues in Rust\nYear: 2022\n\nAbstract: Rust is a promising system-level programming language that can prevent memory corruption bugs using its strong type system and ownership-based memory management scheme. In practice, programmers usually write Rust code in conjunction with other languages such as C/C++ through Foreign Function Interface (FFI). For example, many notable projects are developed using Rust and other programming languages, such as Firefox, Google Fuchsia OS, and the Linux kernel. Although it is widely believed that gradually re-implementing security-critical components in Rust is a way of enhancing software security, however, using FFI is inherently unsafe. In this paper, we show that memory management across the FFI boundaries is error-prone. Any incorrect use of FFI may corrupt Rust's ownership system, leading to memory safety issues. To tackle this problem, we design and build FFIChecker, an automated static analysis and bug detection tool dedicated to memory management issues across the Rust/C FFI. We evaluate our tool by checking 987 Rust packages crawled from the official package registry and reveal 34 bugs in 12 packages. Our experiments show that FFIChecker is a useful tool to detect real-world cross-language memory management issues with a reasonable amount of computational resources."],"string":"[\n \"Title: Handling high-bandwidth traffic aggregates by receiver-driven feedback control\\nYear: 2005\\n\\nAbstract: High-bandwidth traffic aggregates may occur during times of flooding-based distributed denial-of-service attacks or flash crowds. Congestion control of these traffic aggregates is important to avoid congestion collapse of network services. This paper presents a class of feedback-control algorithms that proactively protect a network server from overload by installing rate throttles in a set of upstream routers. A control-theoretical framework is proposed to optimize the control setting such that throttling can be achieved in a distributed and fair manner. We develop control-theoretic algorithms that (1) are highly adaptive by avoiding the configuration of unnecessary control parameters, (2) provide max-min fairness for any number of throttling routers, (3) respond very quickly to network changes, (4) are extremely robust against extrinsic factors beyond the system control, and (5) are stable under given delay bounds.\",\n \"Title: On the Access Pricing Issues of Wireless Mesh Networks\\nYear: 2006\\n\\nAbstract: This paper studies the use of pricing as an incentive mechanism to encourage private, self-interested nodes to participate in a public wireless mesh network and cooperate in the packet forwarding service. Our focus is on the \\\"economic behavior\\\" of the network nodes the pricing and purchasing strategies of the access point, wireless relaying nodes, and clients. We use a \\\"game theoretic approach\\\" to analyze their interactions from one-hop to multihop network and when the network has an unlimited or limited channel capacity. We show that the access point and relaying wireless nodes will adopt a simple, yet optimal, fixed-rate pricing strategy in a multi-hop network with an unlimited capacity. Yet, the fixed-rate pricing strategy fails to be optimal in the limited capacity case. To this end, we focus on the access point adopting a more practical \\\"fixedrate, non-interrupted service\\\" model and propose an algorithm based on the Markovian decision theory to devise the optimal pricing strategy.\",\n \"Title: Design of Scalable Continuous Media Servers\\nYear: 2002\\n\\nAbstract: Multimedia applications place high demands for quality-of-service (QoS), performance, and reliability on systems. These stringent requirements make design of cost-effective and scalable systems difficult. Therefore efficient adaptive and dynamic resource management techniques in conjunction with data placement techniques can be of great help in improving performance, scalability, and reliability of such systems. This is the focus of our paper.\",\n \"Title: A Secure Cloud Backup System with Assured Deletion and Version Control\\nYear: 2011\\n\\nAbstract: Cloud storage is an emerging service model that enables individuals and enterprises to outsource the storage of data backups to remote cloud providers at a low cost. However, cloud clients must enforce security guarantees of their outsourced data backups. We present Fade Version, a secure cloud backup system that serves as a security layer on top of today's cloud storage services. Fade Version follows the standard version-controlled backup design, which eliminates the storage of redundant data across different versions of backups. On top of this, Fade Version applies cryptographic protection to data backups. Specifically, it enables fine-grained assured deletion, that is, cloud clients can assuredly delete particular backup versions or files on the cloud and make them permanently inaccessible to anyone, while other versions that share the common data of the deleted versions or files will remain unaffected. We implement a proof-of-concept prototype of Fade Version and conduct empirical evaluation atop Amazon S3. We show that Fade Version only adds minimal performance overhead over a traditional cloud backup service that does not support assured deletion.\",\n \"Title: Carbon-Aware Load Balancing for Geo-distributed Cloud Services\\nYear: 2013\\n\\nAbstract: Recently, data center carbon emission has become an emerging concern for the cloud service providers. Previous works are limited on cutting down the power consumption of the data centers to defuse such a concern. In this paper, we show how the spatial and temporal variabilities of the electricity carbon footprint can be fully exploited to further green the cloud running on top of geographically distributed data centers. We jointly consider the electricity cost, service level agreement (SLA) requirement, and emission reduction budget. To navigate such a three-way tradeoff, we take advantage of Lyapunov optimization techniques to design and analyze a carbon-aware control framework, which makes online decisions on geographical load balancing, capacity right-sizing, and server speed scaling. Results from rigorous mathematical analyses and real-world trace-driven empirical evaluation demonstrate its effectiveness in both minimizing electricity cost and reducing carbon emission.\",\n \"Title: Performance modeling on the interaction of ISPs\\nYear: 2006\\n\\nAbstract: In this paper, we consider interactions of Internet Service Providers (ISPs) and how these interactions can affect the overall traffic and resource allocation between ISPs. In particular, we consider a simplified two-level hierarchical model wherein there are a single tier-1 ISP and N 1 tier-2 ISPs. Each tier-2 ISP needs to pay the tier-1 ISP for the connectivity service. At the same time, a tier-2 ISP can also arrange to have “private peering” links with other tier-2 ISPs. Therefore, each tier-2 ISP can optimize its utility by deciding on the proper traffic routing of transmitting traffic, either via the tier-1 ISP link, or via the private peering link with other tier-2 ISPs. The tier-1 ISP, on the other hand, needs to decide on the proper resource allocation for all its tier-2 peers so as to avoid monopolization of its resource by a single peer (i.e., to achieve customer diversity). We investigate a distributed framework wherein a tier-1 ISP can achieve customer diversity while tier-2 peers can perform their utility maximization in terms of traffic routing. We also explore other important issues such as sensitivity and convergency. Extensive simulations are carried out to quantify the merits of the proposed distributed framework.\",\n \"Title: Stochastic Analysis and File Availability Enhancement for BT-like File Sharing Systems\\nYear: 2006\\n\\nAbstract: In this paper, we present the mathematical analysis of two important performance measures for a BitTorrent (BT) like P2P file sharing system, namely, average file downloading time and file availability. For the file downloading time, we develop a model using the \\\"stochastic differential equation\\\" approach, which can capture the system more accurately than some previous approach and can capture various network settings and peers behavior. We study the steady-state behavior and obtain the closed-form solutions for performance measures which allow us to carry sensitivity analysis on various performance measures for various system parameters. We then extend this model to consider multiclass peers wherein some peers are behind firewalls which may impede the uploading service. We also present the mathematical model to study the file availability of a BT-like system. The model helps us gain the understanding of why the \\\"rarest-first\\\" chunk selection policy is used in today's BT protocol. We propose a novel chunk selection algorithm to enhance the overall system file availability. Extensive simulations are carried to validate our analysis\",\n \"Title: Stochastic analysis of a randomized detection algorithm for pollution attack in P2P live streaming systems\\nYear: 2010\\n\\nAbstract: Pollution attack is known to have a disastrous effect on existing P2P infrastructures: it can reduce the number of legitimate P2P users by as much as 85%, and it generates abundant bogus data which may deplete the communication bandwidth. We propose a distributed defense and detection mechanism to resolve pollution attacks. The mechanism is composed of a set of ''randomized'' and ''fully distributed'' algorithms that can be executed by any legitimate peer. We present the analytical framework to quantify (a) the probability of false negative, (b) the probability of false positive, and (c) the distribution of time needed for detection. In our detection algorithm and analysis, we consider the case of (1) single attacker within the neighborhood, (2) multiple attackers within the neighborhood. Furthermore, we show how to ''optimize'' the system parameters so as to quickly discover and eliminate malicious peers from the system.\",\n \"Title: Incentive P2P networks: a protocol to encourage information sharing and contribution\\nYear: 2003\\n\\nAbstract: In this paper, we explore a scheduling policy for file transfer service such that we can provide incentive to the P2P nodes. The assigned transfer capacity for each request is a function of the requesting node's connection type, its utility function, and its relative contribution compared with the other requesting nodes. Our objective is to make efficient use of P2P network resources and to provide both fairness and incentive to all nodes in the P2P community.\",\n \"Title: Distributed Collaborative Key Agreement Protocols for Dynamic Peer Groups\\nYear: 2002\\n\\nAbstract: We consider several distributed collaborative key agreement protocols for dynamic peer groups. This problem has several important characteristics which make it different from traditional secure group communication. They are (1) distributed nature in which there is no centralized key server, (2) collaborative nature in which the group key is contributory; i.e., each group member will collaboratively contribute its part to the global group key, and (3) dynamic nature in which existing members can leave the group while new members may join. Instead of performing individual rekey operations, i.e., recomputing the group key after every join or leave request, we consider an interval-based approach of rekeying. In particular, we consider three distributed algorithms for updating the group key: (1) the Rebuild algorithm, (2) the Batch algorithm, and (3) the Queue-batch algorithm. Performance of these distributed algorithms under different settings, such as different join and leave probabilities, is analyzed. We show that these three distributed algorithms significantly outperform the individual rekey algorithm, and that the Queue-batch algorithm performs the best among the three distributed algorithms. Moreover, the Queue-batch algorithm has the intrinsic property of balancing the computation/communication workload such that the dynamic peer group can quickly begin secure group communication. This provides a fundamental understanding about establishinga collaborative group key for a distributed dynamic peer group.\",\n \"Title: A case for a multi-key secure video proxy: theory, design, and implementation\\nYear: 2002\\n\\nAbstract: Because of limited server and network capacities in multimedia streaming, proxies are commonly used to cache multimedia objects such that, by accessing nearby proxies, clients can enjoy smaller start-up latencies and reduced packet loss and delay jitters for their requests. However, the use of video proxies increases the risk that multimedia data are exposed to unauthorized access by intruders. In this paper, we present a framework for implementing a secure video proxy or, more generally, a secure proxy architecture. The framework employs a notion of asymmetric reversible parametric sequences to provide the following security properties: (1) data confidentiality during transmission, (2) end-to-end data confidentiality, (3) data confidentiality against proxy intruders, and (4) data confidentiality against member collusion. Our framework is grounded on a multi-key RSA technique such that system resilience against attacks is provably strong given standard computability assumptions. We also propose the use of a set of encryption configuration parameters to trade off proxy encryption throughput against the viewing quality of video by unauthorized parties. Implementation results on a Pentium III/800 MHz machine show that our techniques can simultaneously achieve high encryption throughput and extremely low video quality (in terms of both PSNR and the visual quality of decoded frames) during unauthorized viewing.\",\n \"Title: A panoramic view of 3g data/control-plane traffic: mobile device perspective\\nYear: 2012\\n\\nAbstract: Users can access the Internet via 3G/4G cellular data networks using various types of user devices (e.g., smartphones, tablets, datacards). We conduct a detailed measurement study on the impact of different device types on the data/control-plane performance of a commercial, city-wide 3G cellular data network in China. We present a methodology that correlates different data/control-plane datasets collected at different points in the network core, and identify more than 60K devices of different types per day on average. For the devices we identify, we investigate how their commonly used Internet applications and internal heartbeat mechanisms lead to distinct data/control-plane behaviors. For example, we observe that datacard devices contribute a large volume of IP traffic in the data plane, while smartphones introduce significant resource overhead in the signaling control plane. Our measurement study provides insights for network operators to strategize pricing and resource allocation for the data/control planes of their cellular data networks with regard to the market penetrations of various device types.\",\n \"Title: Distributed mechanism in detecting and defending against the low-rate TCP attack\\nYear: 2006\\n\\nAbstract: In this paper, we consider a distributed mechanism to detect and to defend against the low-rate TCP attack. The low-rate TCP attack is a recently discovered attack. In essence, it is a periodic short burst that exploits the homogeneity of the minimum retransmission timeout (RTO) of TCP flows and forces all affected TCP flows to backoff and enter the retransmission timeout state. When these affected TCP flows timeout and retransmit their packets, the low-rate attack will again send a short burst to force these affected TCP flows to enter RTO again. Therefore these affected TCP flows may be entitled to zero or very low transmission bandwidth. This sort of attack is difficult to identify due to a large family of attack patterns. We propose a distributed detection mechanism to identify the low-rate attack. In particular, we use the dynamic time warping approach to robustly and accurately identify the existence of the low-rate attack. Once the attack is detected, we use a fair resource allocation mechanism to schedule all packets so that (1) the number of affected TCP flows is minimized and, (2) provide sufficient resource protection to those affected TCP flows. Experiments are carried out to quantify the robustness and accuracy of the proposed distributed detection mechanism. In particular, one can achieve a very low false positive/negative when compare to legitimate Internet traffic. Our experiments also illustrate the the efficiency of the defense mechanism across different attack patterns and network topologies.\",\n \"Title: Providing Throughput Differentiation for TCP Flows Using Adaptive TwoColor Marking and Multi-Level AQM\\nYear: 2002\\n\\nAbstract: In this paper we propose a new paradigm for a DifferentialService (DiffServ) network consisting of two-color marking at the edges ofthe network using token buckets coupled with differential treatment in thecore. Using fluid-flow modelling, we present existence conditions for tokenbucketrates and differential marking probabilities at the core that resultin all edges receiving at least their minimum guaranteed rates. We thenpresent an integrated DiffServ architecture comprising of an active ...\",\n \"Title: Security adoption in heterogeneous networks: the influence of cyber-insurance market\\nYear: 2012\\n\\nAbstract: Hosts (or nodes) in the Internet often face epidemic risks such as virus and worms attack. Despite the awareness of these risks and the availability of anti-virus software, investment in security protection is still scare, and hence epidemic risk is still prevalent. Deciding whether to invest in security protection is an interdependent process : security investment decision made by one node can affect the security risk of others, and therefore affect their decisions also. The first contribution of this paper is to provide a fundamental understanding on how \\\"network externality \\\" effect with \\\"nodes heterogeneity \\\" may affect security adoption. We characterize it as a Bayesian network game in which nodes only have the local information, e.g., the number of neighbors, as well as minimum common information, e.g., degree distribution of the network. Our second contribution is in analyzing a new form of risk management called cyber-insurance . We investigate how the presence of competitive insurance market can affect the security adoption.\",\n \"Title: Analytical Models for Mixed Workload Multimedia Storage Servers\\nYear: 1999\\n\\nAbstract: Efficient resource management is essential in providing scalability in large multimedia information systems serving a variety of applications. In order to explore design tradeoffs in such systems, what is needed are accurate analytical models of mixed workload servers with tractable solutions. Motivated by the need for mixed workload storage servers as well as the need for performance studies of such systems, we first present a family of scheduling algorithm for mixed workload storage servers. We then develop a set of corresponding analytical non-Markovian models. We show how our analytical solution methodology applies to this entire family of models and how it can be used to obtain performance measures of interest for different classes of workloads. Lastly, we illustrate through numerical examples how such models can be used to study performance tradeoffs and facilitate the making of design choices in mixed workload storage servers. (C) 1999 Published by Elsevier Science B.V. All rights reserved.\",\n \"Title: Design of Efficient Sampling Methods on Hybrid Social-Affiliation Networks.\\nYear: 2014\\n\\nAbstract: Graph sampling via crawling has become increasingly popular and important in the study of measuring various characteristics of large scale complex networks. While powerful, it is known to be challenging when the graph is loosely connected or disconnected which slows down the convergence of random walks and can cause poor estimation accuracy. In this work, we observe that the graph under study, or called target graph, usually does not exist in isolation. In many situations, the target graph is related to an auxiliary graph and an affiliation graph, and the target graph becomes well connected when we view it from the perspective of these three graphs together, or called a hybrid social-affiliation graph in this paper. When directly sampling the target graph is difficult or inefficient, we can indirectly sample it efficiently with the assistances of the other two graphs. We design three sampling methods on such a hybrid social-affiliation network. Experiments conducted on both synthetic and real datasets demonstrate the effectiveness of our proposed methods.\",\n \"Title: Sampling Content Distributed Over Graphs.\\nYear: 2013\\n\\nAbstract: Despite recent effort to estimate topology characteristics of large graphs (i.e., online social networks and peer-to-peer networks), little attention has been given to develop a formal methodology to characterize the vast amount of content distributed over these networks. Due to the large scale nature of these networks, exhaustive enumeration of this content is computationally prohibitive. In this paper, we show how one can obtain content properties by sampling only a small fraction of vertices. We first show that when sampling is naively applied, this can produce a huge bias in content statistics (i.e., average number of content duplications). To remove this bias, one may use maximum likelihood estimation to estimate content characteristics. However our experimental results show that one needs to sample most vertices in the graph to obtain accurate statistics using such a method. To address this challenge, we propose two efficient estimators: special copy estimator (SCE) and weighted copy estimator (WCE) to measure content characteristics using available information in sampled contents. SCE uses the special content copy indicator to compute the estimate, while WCE derives the estimate based on meta-information in sampled vertices. We perform experiments to show WCE and SCE are cost effective and also ``{\\\\em asymptotically unbiased}''. Our methodology provides a new tool for researchers to efficiently query content distributed in large scale networks.\",\n \"Title: A Cost-effective Near-line Storage Server for Multimedia System\\nYear: 1995\\n\\nAbstract: We consider a storage server architecture for multimedia information systems. While most other works on multimedia storage servers assume on-line disk storage, we consider a two-tier storage architecture with a robotic tape library as the vast near-line storage and on-line disks as the front-line storage. Magnetic tapes are cheaper, more robust, and have a larger capacity; hence they are more cost effective for large scale storage systems (e.g., video on demand (VOD) systems may store tens of thousands of videos). We study in detail the design issues of the tape subsystem and propose some novel tape scheduling algorithms which give faster response and require less disk buffering.\",\n \"Title: Understanding the paradoxical effects of power control on the capacity of wireless networks\\nYear: 2009\\n\\nAbstract: Recent works show conflicting results: network capacity may increase or decrease with higher transmission power under different scenarios. In this work, we want to understand this paradox. Specifically, we address the following questions: (1)Theoretically, should we increase or decrease transmission power to maximize network capacity? (2) Theoretically, how much network capacity gain can we achieve by power control? (3) Under realistic situations, how do power control, link scheduling and routing interact with each other? Under which scenarios can we expect a large capacity gain by using higher transmission power? To answer these questions, firstly, we prove that the optimal network capacity is a non-decreasing function of transmission power. Secondly, we prove that the optimal network capacity can be increased unlimitedly by higher transmission power in some network configurations. However, when nodes are distributed uniformly, the gain of optimal network capacity by higher transmission power is upper-bounded by a positive constant. Thirdly, we discuss why network capacity may increase or decrease with higher transmission power under different scenarios using carrier sensing and the minimum hop-count routing. Extensive simulations verify our analysis.\",\n \"Title: Balancing Workload and Communication Cost for a Distributed Virtual Environment\\nYear: 1998\\n\\nAbstract: Recent advancement in computer graphics and networking technologies enable researchers to experiment and build the distributed virtual environment (DVE) system. In general, a DVE system allows many clients to simultaneously explore a virtual world and at the same time, users in this virtual environment can interact with each other and can manipulate the objects in the virtual world. To implement such a system, there are many challenges that researchers have to face so as to build an efficient DVE system that can represent a large virtual world and at the same time, support large number of concurrent users. In this work, we present some of these important issues, such as the necessity to balance the workload among different servers as well as minimizing the inter-server communication. We show that the problem, in general, is NP-complete and we proposed several algorithms so as to find the partitioning policy efficiently.\",\n \"Title: A Hybrid Approach to Failed Disk Recovery Using RAID-6 Codes: Algorithms and Performance Evaluation\\nYear: 2011\\n\\nAbstract: The current parallel storage systems use thousands of inexpensive disks to meet the storage requirement of applications. Data redundancy and/or coding are used to enhance data availability, for instance, Row-diagonal parity (RDP) and EVENODD codes, which are widely used in RAID-6 storage systems, provide data availability with up to two disk failures. To reduce the probability of data unavailability, whenever a single disk fails, disk recovery will be carried out. We find that the conventional recovery schemes of RDP and EVENODD codes for a single failed disk only use one parity disk. However, there are two parity disks in the system, and both can be used for single disk failure recovery. In this article, we propose a hybrid recovery approach that uses both parities for single disk failure recovery, and we design efficient recovery schemes for RDP code (RDOR-RDP) and EVENODD code (RDOR-EVENODD). Our recovery scheme has the following attractive properties: (1) “read optimality” in the sense that our scheme issues the smallest number of disk reads to recover a single failed disk and it reduces approximately 1/4 of disk reads compared with conventional schemes; (2) “load balancing property” in that all surviving disks will be subjected to the same (or almost the same) amount of additional workload in rebuilding the failed disk. We carry out performance evaluation to quantify the merits of RDOR-RDP and RDOR-EVENODD on some widely used disks with DiskSim. The offline experimental results show that RDOR-RDP and RDOR-EVENODD outperform the conventional recovery schemes of RDP and EVENODD codes in terms of total recovery time and recovery workload on individual surviving disk. However, the improvements are less than the theoretical value (approximately 25%), as RDOR-RDP and RDOR-EVENODD change the disk access pattern from purely sequential to a more random one compared with their conventional schemes.\",\n \"Title: Computing bounds on steady state availability of repairable computer systems\\nYear: 1994\\n\\nAbstract: One of the most important performance measures for computer system designers is system availability. Most often, Markov models are used in representing systems for dependability/availability analysis. Due to complex interactions between components and complex repair policies, the Markov model often has an irregular structure, and closed-form solutions are extremely difficult to obtain. Also, a realistic system model often has an unmanageably large state space and it quickly becomes impractical to even generate the entire transition rate matrix. In this paper, we present a methodology that can (i) bound the system steady state availability and at the same time, (ii) drastically reduce the state space of the model that must be solved. The bounding algorithm is iterative and generates a part of the transition matrix at each step. At each step, tighter bounds on system availability are obtained. The algorithm also allows the size of the submodel, to be solved at each step, to be chosen so as to accommodate memory limitations. This general bounding methodology provides an efficient way to evaluate dependability models with very large state spaces without ever generating the entire transition rate matrix.\",\n \"Title: Determining the end-to-end throughput capacity in multi-hop networks: methodology and applications\\nYear: 2006\\n\\nAbstract: In this paper, we present a methodology to analytically compute the throughput capacity, or the maximum end-to-end throughput of a given source and destination pair in a multi-hop wireless network. The end-to-end throughput capacity is computed by considering the interference due to neighboring nodes, as well as various modes of hidden node interference. Knowing the throughput capacity is important because it facilitates the design of routing policy, admission control for realtime traffic, as well as load control for wireless networks. We model location-dependent neighboring interference and we use a contention graph to represent these interference relationships. Based on the contention graph, we formulate the individual link capacity as a set of fixed point equations. The end-to-end throughput capacity can then be determined once these link capacities are obtained. To illustrate the utility of our proposed methodology, we present two important applications: (a) route optimization to determine the path with the maximum end-to-end throughput capacity and, (b) optimal offered load control for a given path so that the maximum end-to-end capacity can be achieved. Extensive simulations are carried out to verify and validate the proposed analytical methodology.\",\n \"Title: Characterizing the capacity gain of stream control scheduling in MIMO wireless mesh networks\\nYear: 2007\\n\\nAbstract: Stream control has recently attracted attentions in the research of MIMO wireless networks as a potential way to improve network capacity. How- ever, inappropriate use of stream control may significantly degrade network ca- pacity as well. In this paper, we provide the first formal study on stream control scheduling in MIMO wireless mesh networks. We derive the theoretical upper bound on network capacity gain of stream control scheduling. We also provide an efficient scheduling algorithm and show that its achieved network capacity gain is close to its theoretical upper bound. Moreover, we point out the poor performance of a previous stream control scheduling algorithm SCMA under the general set- tings of wireless mesh networks. This formal characterization provides a deeper understanding of steam control scheduling in MIMO wireless mesh networks.\",\n \"Title: A Novel Video-On-Demand Storage Architecture for Supporting Constant Frame Rate with Variable Bit Rate Retrieval\\nYear: 1995\\n\\nAbstract: One of the quality of service (QOS) factors in video-on-demand (VOD) applications is to provide high resolution quality to end users. One way to achieve this is to provide a constant display frame rate (e.g., 30 frames/sec) at the display station. However, due to the nature of video files and compression technique applied, video frame sizes vary significantly from frame to frame. Therefore, although the display frame rate is fixed, data retrieval is a variable bit rate process. Conventional VOD storage servers assume a peak rate retrieval of video files. Therefore, the number of concurrent requests to the VOD server cannot be maximized. In this paper, we consider a VOD storage server which can support a fixed frame rate at the display and at the same time, variable bit rates retrieval of compressed video files. We describe 1) video files layout strategy, 2) request scheduling algorithm, 3) buffering issues and, 4) various VCR features support such that the number of conclurent requests can be maximized.\",\n \"Title: Location Updates and Probabilistic Tracking Algorithms for Mobile Cellular Networks\\nYear: 1999\\n\\nAbstract: In this work, we develop a novel mathematical model to analyze different location update protocols for mobile cellular network. Our model can capture many important features of user movements, such as geographically dependent movement, directional movement and non-exponential distribution of cell dwell times. We propose to use the transient method to predict the location of a mobile user. This method is efficient and, more importantly, able to address the temporal change in the current location of a mobile user. Therefore, the accuracy of the prediction of the location of a user can be enhanced.We also propose a new location update protocol which can attain a lower tracking cost and also adapt to different mobility patterns. Transient performance analysis is also carried out and the results are compared with other protocols.\",\n \"Title: Multi-path streaming: is it worth the trouble?\\nYear: 2002\\n\\nAbstract: Quality of service (QoS) in streaming of continuous media over the Internet is still poor and inconsistent. The degradation in quality of continuous media applications, involving delivery of video and audio, is partly due to variations in delays as well as losses experi- enced by packets sent through wide-area networks. Although many such applications can tolerate some degree of missing information, significant losses degrade an application's quality of service. One approach to providing QoS for continuous media applica- tions over the Internet is to use the IntServ model for signaling (e.g., RSVP) and resource reservation in all routers along the streaming path. However, this approach suffers from scalability and deploy- ment problems. In contrast, in our work we investigate the potential benefits of providing QoS guarantees in continuous media delivery through the exploitation of multiple paths existing in the network between a set of senders and a receiver. One advantage of this ap- proach is that the complexity of QoS provision can be pushed to the network edge and hence improve the scalability and deploy- ment characteristics while at the same time provide a certain level of QoS guarantees. Our focus in this work is on providing a fun- damental understanding of the benefits of using multiple paths to deliver continuous media data (such as video) destined for a par- ticular receiver, i.e., this data is fragmented into packets and the different packets take alternate routes to the receiver. We note that such paths do not have to be completely disjoint, i.e., it is sufficient for them to have disjoint points of congestion or bottlenecks. Existence of multiple paths with disjoint bottlenecks includes the following potential benefits: (a) reduction in correlation between consecutive packet losses which we believe will lead to improve- ments in the quality of delivered data, (b) increased throughput, and (c) ability to adjust to variations in congestion patterns on dif- ferent parts of the network. In this work, our focus is on providing the fundamental understanding and on characterizing the benefits of the multi-path approach to streaming of pre-stored continuous media data over wide-area networks (under the setup described be- low). More specifically, we focus on loss characteristics as they are an indication of the resulting quality of the delivered data stream. We believe that the understanding of loss characteristics under a multi-path approach is non-trivial and deserves further attention. We also believe that this work is a step in the right direction. In general, the use of multiple paths in designing of distributed (over best-effort wide-area networks) continuous media applica- tions requires consideration of a number of issues (please refer to (5) for details). However, in this work we limit the scope by focus- ing on (a) delivery of pre-stored video, (in contrast to delivery of This work was partly done while the author was with the Univer- sity of Maryland. \\\"live\\\" data), (b) application-level schemes (which are deployable today over the current Internet), where the path used between any pair of hosts is determined by a network-level routing algorithm (we note that our system does not require specific knowledge of the paths, only the ability to determine whether two paths share a point of congestion, e.g., using (7)); and (c) accomplishment of multiple paths to the same receiver by distributing servers across wide-area networks and streaming data from multiple senders simultaneously. Our system is depicted in Figure 1, where any server can send any fraction of the continuous media data. More specifically, server\",\n \"Title: A Distributed Throttling Approach for Handling High Bandwidth Aggregates\\nYear: 2007\\n\\nAbstract: Public-access networks need to handle persistent congestion and overload caused by high bandwidth aggregates that may occur during times of flooding-based DDoS attacks or flash crowds. The often unpredictable nature of these two activities can severely degrade server performance. Legitimate user requests also suffer considerably when traffic from many different sources aggregates inside the network and causes congestion. This paper studies a family of algorithms that \\\"proactively” protect a server from overload by installing rate throttles in a set of upstream routers. Based on an optimal control setting, we propose algorithms that achieve throttling in a distributed and fair manner by taking important performance metrics into consideration, such as minimizing overall load variations. Using ns-2 simulations, we show that our proposed algorithms 1) are highly adaptive by avoiding unnecessary parameter configuration, 2) provide max-min fairness for any number of throttling routers, 3) respond very quickly to network changes, 4) are extremely robust against extrinsic factors beyond the system control, and 5) are stable under given delay bounds.\",\n \"Title: Secure Overlay Cloud Storage with Access Control and Assured Deletion\\nYear: 2012\\n\\nAbstract: We can now outsource data backups off-site to third-party cloud storage services so as to reduce data management costs. However, we must provide security guarantees for the outsourced data, which is now maintained by third parties. We design and implement FADE, a secure overlay cloud storage system that achieves fine-grained, policy-based access control and file assured deletion. It associates outsourced files with file access policies, and assuredly deletes files to make them unrecoverable to anyone upon revocations of file access policies. To achieve such security goals, FADE is built upon a set of cryptographic key operations that are self-maintained by a quorum of key managers that are independent of third-party clouds. In particular, FADE acts as an overlay system that works seamlessly atop today's cloud storage services. We implement a proof-of-concept prototype of FADE atop Amazon S3, one of today's cloud storage services. We conduct extensive empirical studies, and demonstrate that FADE provides security protection for outsourced data, while introducing only minimal performance and monetary cost overhead. Our work provides insights of how to incorporate value-added security features into today's cloud storage services.\",\n \"Title: End system multicast: an architectural infrastructure and topological optimization\\nYear: 2004\\n\\nAbstract: Although IP multicast has been proposed and investigated for years, there are major problems inherent in the IP multicasting technique, e.g. difficulty to scale up the system, difficulty in allocating a globally unique multicast address, complexity in supporting higher level features such as reliable data transfer and congestion/flow control, more importantly, difficulty to deploy on the current Internet infrastructure due to necessity to change many core routers. Recently, end-system multicast (ESM) has been proposed as an alternative solution so that multicasting services can be quickly deployed. In this paper, we consider the'architectural' and 'optimization' issues on designing an ESM-tree. Specifically, we present a distributed algorithm on how to create and maintain an ESM-tree. We propose a distributed algorithm to perform tree optimization (TO) so that an ESM-tree can dynamically adapt to the changing network condition (e.g. drop in transfer bandwidth) so that the nodes within an ESM-tree can receive data more efficiently. The distributed algorithm has the important theoretical properties that at all times, a tree topology can be maintained and any node joining, leaving, as well as any TO operation will not 'partition' the underlying ESM-tree. Therefore, our work can be used to provide an efficient architectural infrastructure for ESM services. We have implemented a prototype ESM system and carried out experiments to illustrate the effectiveness and the performance gains of our ESM optimization protocol.\",\n \"Title: Modeling Multi-state Diffusion Process in Complex Networks: Theory and Applications\\nYear: 2013\\n\\nAbstract: There is a growing interest to understand the fundamental principles of how epidemic, ideas or information spread over large networks (e.g., the Internet or online social networks). Conventional approach is to use SIS models (or its derivatives). However, these models usually are over-simplified and may not be applicable in realistic situations. In this paper, we propose a generalization of the SIS model by allowing intermediate states between susceptible and infected states. To analyze the diffusion process on large graphs, we use the ``mean-field analysis technique'' to determine which initial condition leads to or prevents information or virus outbreak. Numerical results show our methodology can accurately predict the behavior of the phase-transition process for various large graphs (e.g., complete graphs, random graphs or power-law graphs). We also extend our generalized SIS model to consider the interaction of two competing sources (i.e., competing products or virus-antidote modeling). We present the analytical derivation and show experimentally how different factors, i.e., transmission rates, recovery rates, number of states or initial condition, can affect the phase transition process and the final equilibrium. Our models and methodology can serve as an essential tool in understanding information diffusion in large networks.\",\n \"Title: On Service Replication Strategy for Service Overlay Networks\\nYear: 2004\\n\\nAbstract: The service overlay network (SON) is an effective means to deliver end-to-end QoS guaranteed applications on the current Internet. In [5], authors address the bandwidth provisioning problem on a SON, specifically, in determining the appropriate amount of bandwidth capacity to purchase from various autonomous systems so as to satisfy the QoS requirements of the SON's end users and at the same time, maximizes the total revenue of operating the overlay network. In this paper, we extend the concept of the service overlay network. Since traffic demands are time varying and there may be some unexpected events which can cause a traffic surge, these will significantly increase the probability of QoS violation and will reduce the profit margin of a SON. To overcome these problems, we propose to replicate services on the service gateways so as to dynamically adapt to these traffic surges. We show that the service replication problem, in general, is intractable. We propose an efficient service replication algorithm which replicates services for a subset of traffic flows. Under our replication strategy, one does not need to increase the bandwidth capacity of underlying links and at the same time, be able to increase the average profit for the overlay network. Experiments are carried out to illustrate that replication algorithm provides higher flexibility during traffic fluctuations and can quickly find a near-optimal solution.\",\n \"Title: Distributed Algorithm for Service Replication in Service Overlay Network\\nYear: 2004\\n\\nAbstract: The service overlay network (SON) is an effective mean to deploy end-to-end QoS guaranteed content delivery services on the current Internet. We model the content delivery service on a SON as a service delivery tree (SDT). Previous studies have addressed the optimal distribution tree formulation issues. In this paper, we focus on the problem of maximizing the total operation profit of the SON. In [1], authors introduced a cost model for optimal bandwidth provisioning in SON. In this paper, we extended this concept and propose an alternative approach to maximize the total effective throughput of SDT as well as to minimize the QoS violation penalty of the SON by service replication. We present both centralized and distributed algorithms for the placement of replicated servers on the SDT. Experiments are carried to quantify the merit, effectiveness and the scalability of the proposed service replication algorithm. In particular, the performance gain is very close to the exhaustive search. The algorithm performs well when we scale up the service overlay networks. Finally, we show that one only needs to perform a small number of replications to attain the optimal gain.\",\n \"Title: Defending against distributed denial-of-service attacks with max-min fair server-centric router throttles\\nYear: 2005\\n\\nAbstract: Our work targets a network architecture and accompanying algorithms for countering distributed denial-of-service (DDoS) attacks directed at an Internet server. The basic mechanism is for a server under stress to install a router throttle at selected upstream routers. The throttle can be the leaky-bucket rate at which a router can forward packets destined for the server. Hence, before aggressive packets can converge to overwhelm the server, participating routers proactively regulate the contributing packet rates to more moderate levels, thus forestalling an impending attack. In allocating the server capacity among the routers, we propose a notion of level-k max-min fairness. We first present a control-theoretic model to evaluate algorithm convergence under a varitey of system parameters. In addition, we present packet network simulation results using a realistic global network topology, and various models of good user and attacker distributions and behavior. Using a generator model of web requests parameterized by empirical data, we also evaluate the impact of throttling in protecting user access to a web server. First, for aggressive attackers, the throttle mechanism is highly effective in preferentially dropping attacker traffic over good user traffic. In particular, level-k max-min fairness gives better good-user protection than recursive pushback of max-min fair rate limits proposed in the literature. Second, throttling can regulate the experienced server load to below its design limit - in the presence of user dynamics - so that the server can remain operational during a DDoS attack. Lastly, we present implementation results of our prototype on a Pentium III/866 MHz machine. The results show that router throttling has low deployment overhead in time and memory.\",\n \"Title: Deriving Communication Sub-graph and Optimal Synchronizing Interval for a Distributed Virtual Environment System\\nYear: 1999\\n\\nAbstract: Advances in the multimedia and networking technologies allow the realization of the distributed virtual environment (DVE). DVE is a distributed system which allows many clients who are located in different locations (within a LAN or across a WAN) to concurrently explore and interact with each other in a high resolution, 3-dimensional graphical virtual environment. Some of the potential applications of DVE are: tele-conferencing, distributed games, distributed interactive simulation, collaborative group-ware environment, $\\\\ldots$, etc. One challenging issue in designing a DVE is to guarantee that each participating client to have the same consistent view of the virtual world. To provide this consistent view, the DVE needs to perform synchronization actions periodically. In this paper, we present a methodology of deriving an optimal synchronizing interval. Our work is divided into two parts. In the first part, algorithms are presented to construct suitable communication sub-graph for the clients. In the second part, we present the outline on how to derive the optimal synchronizing interval based on the proposed communication sub-graph using the Markov chain theory and the fundamental matrix.\",\n \"Title: Adaptive piggybacking: a novel technique for data sharing in video-on-demand storage servers\\nYear: 1996\\n\\nAbstract: Recent technological advances have made multimedia on-demand services, such as homeentertainment and home-shopping, important to the consumer market. One of the most challengingaspects of this type of service is providing access either instantaneously or within asmall and reasonable latency upon request. In this paper, we consider improvements in performanceof multimedia storage servers through data sharing between requests for popular objects,where I/O bandwidth is assumed to be the...\",\n \"Title: Quantifying Complexity and Performance Gains of Distributed Caching in a Wireless Mobile Computing Environment\\nYear: 1997\\n\\nAbstract: In a mobile computing system, the wireless communication bandwidth is a scarce resource that needs to be managed carefully. In this paper, we investigate the use of distributed caching as an approach to reduce the wireless bandwidth consumption for data access. We find that conventional caching techniques cannot fully utilize the dissemination feature of the wireless channel. We thus propose a novel distributed caching protocol that can minimize the overall system bandwidth consumption at the cost of CPU processing time at the server side. This protocol allows the server to select data items into a broadcast set, based on a performance gain parameter called the bandwidth gain, and then send the broadcast set to all the mobile computers within the server's cell. We show that in general, this selection process is NP-hard, and therefore we propose a heuristic algorithm that can attain a near-optimal performance. We also propose an analytical model for the protocol and derive closed-form performance measures, such as the bandwidth utilization and the expected response time of data access by mobile computers. Experiments show that our distributed caching protocol can greatly reduce the bandwidth consumption so that the wireless network environment can accommodate more users and, at the same time, vastly improve the expected response time for data access by mobile computers.\",\n \"Title: On incentivizing caching for P2P-VoD systems\\nYear: 2012\\n\\nAbstract: Peer-to-peer (P2P) systems consist of selfish peers which would like to receive services from others but would not like to contribute their own resources by default. To conquer this problem, proper incentive schemes are needed so as to stimulate the peers' contributions. This issue is particularly important for P2P Video-on-Demand (VoD) services. Content providers of P2P-VoD services want to incentivize peers to cache video data so that the peers can transfer data to one another and alleviate the workload of the content server. In this paper, we design a practical mechanism under which the peers are rewarded based on the popularity of the video they cache. We characterize the impact of this incentive scheme on peers' caching behaviors. In particular, we formulate an optimization framework to decide the optimal reward price for each video so as to keep enough replicas and minimize the content provider's operational cost. Via extensive simulations, we validate the effectiveness and efficiency of our incentive scheme.\",\n \"Title: ADAM: an automatic and extensible platform to stress test android anti-virus systems\\nYear: 2012\\n\\nAbstract: With the rising threat of smartphone malware, both academic community and commercial anti-virus companies proposed many methodologies and products to defend against smartphone malware. Thus, how to assess the effectiveness of these defense mechanisms against existing and unknown malware becomes important. We propose ADAM, an automated and extensible system that can evaluate, via large-scale stress tests, the effectiveness of anti-virus systems against a variety of malware samples for the Android platform. Specifically, ADAM can automatically transform an original malware sample to different variants via repackaging and obfuscation techniques in order to evaluate the robustness of different anti-virus systems against malware mutation. The transformation and evaluation processes of ADAM are fully automatic, generic, and extensible for different types of malware, anti-virus systems, and malware transformation techniques. We demonstrate the efficacy of ADAM using 222 Android malware samples that we collected in the wild. Using ADAM, we generate different variants based on our collected malware samples, and evaluate the detection of these variants against commercial anti-virus systems.\",\n \"Title: Toward Picture-perfect Streaming on the Internet\\nYear: 2005\\n\\nAbstract: Quality of service (QoS) in streaming of continuous media over the Internet is poor, which is partly due to variations in delays, bandwidth limitations, and packet losses. Although continuous media applications can tolerate some missing data, non-recoverable information loss degrades these applications' QoS. Consequently, a number of application areas have backed away from streaming of their content over the Internet. Inability to control the resulting visual and auditory quality of the streamed presentation is an important reason for such a trend. We believe that this trend can be reversed. To this end, this paper gives an overview of our efforts in exploring high quality streaming through the exploitation of multiple paths existing in the network. By high quality, we mean with significant bandwidth requirements, of relatively long duration, and without information loss or hiccups in data delivery. We believe this to be a promising approach.\",\n \"Title: On the Performance Bounds of Practical Wireless Network Coding\\nYear: 2010\\n\\nAbstract: Network coding is an attracting technology that has been shown to be able to improve the throughput of wireless networks. However, there still lacks fundamental understanding on how network coding works under realistic scenarios. In this paper, we examine the performance of a recently proposed network coding system under a realistic wireless physical layer and practical random access mechanisms. We propose a key performance measure called “encoding number”-the number of packets that can be encoded via network coding in each transmission. We provide an upper bound on the encoding number for the general coding topology, and derive the average encoding number and system throughput for a general class of random access mechanisms. Based on the practical coding system, we also derive a tighter upper bound on the throughput gain for a general wireless network. Our results are of fundamental value for coding-related MAC/Routing protocol design and analysis.\",\n \"Title: Social Sensor Placement in Large Scale Networks: A Graph Sampling Perspective\\nYear: 2013\\n\\nAbstract: Sensor placement for the purpose of detecting/tracking news outbreak and preventing rumor spreading is a challenging problem in a large scale online social network (OSN). This problem is a kind of subset selection problem: choosing a small set of items from a large population so to maximize some prespecified set function. However, it is known to be NP-complete. Existing heuristics are very costly especially for modern OSNs which usually contain hundreds of millions of users. This paper aims to design methods to find \\\\emph{good solutions} that can well trade off efficiency and accuracy. We first show that it is possible to obtain a high quality solution with a probabilistic guarantee from a \\\"{\\\\em candidate set}\\\" of the underlying social network. By exploring this candidate set, one can increase the efficiency of placing social sensors. We also present how this candidate set can be obtained using \\\"{\\\\em graph sampling}\\\", which has an advantage over previous methods of not requiring the prior knowledge of the complete network topology. Experiments carried out on two real datasets demonstrate not only the accuracy and efficiency of our approach, but also effectiveness in detecting and predicting news outbreak.\",\n \"Title: On the evolution of the internet economic ecosystem\\nYear: 2012\\n\\nAbstract: The evolution of the Internet has manifested itself in many ways: the traffic characteristics, the interconnection topologies and the business relationships among the autonomous components. It is important to understand why (and how) this evolution came about, and how the interplay of these dynamics may affect future evolution and services. We propose a network aware, macroscopic model that captures the characteristics and interactions of the application and network providers, and show how it leads to a market equilibrium of the ecosystem. By analyzing the driving forces and the dynamics of the market equilibrium, we obtain some fundamental understandings of the cause and effect of the Internet evolution, which explain why some historical and recent evolutions have happened. Furthermore, by projecting the likely future evolutions, our model can help application and network providers to make informed business decisions so as to succeed in this competitive ecosystem.\",\n \"Title: On oligopoly spectrum allocation game in cognitive radio networks with capacity constraints\\nYear: 2010\\n\\nAbstract: Dynamic spectrum sharing is a promising technology to improve spectrum utilization in future wireless networks. The flexible spectrum management provides new opportunities for licensed primary user and unlicensed secondary users to reallocate the spectrum resource efficiently. In this paper, we present an oligopoly pricing framework for dynamic spectrum allocation in which the primary users sell excessive spectrum to the secondary users for monetary return. We present two approaches, the strict constraints (type-I) and the QoS penalty (type-II), to model the realistic situation that the primary users have limited capacities. In the oligopoly model with strict constraints, we propose a low-complexity searching method to obtain the Nash Equilibrium and prove its uniqueness. When reduced to a duopoly game, we analytically show the interesting gaps in the leader-follower pricing strategy. In the QoS penalty based oligopoly model, a novel variable transformation method is developed to derive the unique Nash Equilibrium. When the market information is limited, we provide three myopically optimal algorithms ''StrictBEST'', ''StrictBR'' and ''QoSBEST'' that enable price adjustment for duopoly primary users based on the Best Response Function (BRF) and the bounded rationality (BR) principles. Numerical results validate the effectiveness of our analysis and demonstrate the convergence of ''StrictBEST'' as well as ''QoSBEST'' to the Nash Equilibrium. For the ''StrictBR'' algorithm, we reveal the chaotic behaviors of dynamic price adaptation in response to the learning rates.\",\n \"Title: The Fundamental Role of Hop Distance in IEEE802.11 Multi-Hop Ad Hoc Networks\\nYear: 2005\\n\\nAbstract: In wireless networks, it is well understood what throughput can be achieved by nodes who can hear each other (i.e. nodes within a single cell). The effects of nodes beyond the sensing range (known as hidden nodes) on a sender are complicated and difficult to analyze. Consequently, how to analytically model multi-hop ad-hoc networks, specially networks based on the popular IEEE 802.11 standards remains largely open. In a recent paper, the throughput of a particular wireless network topology (linear network with a given number of hidden nodes) has been derived analytically. In this paper, we unify previous results on single-cell models, and results characterizing different types of hidden node interference and the analysis of C. Ng et al., (2004), to derive a general solution for throughput given a linear network of arbitrary density and transmission distance between source and destination nodes. An important insight from our model is that there is a certain transmission distance, which is less than the maximum transmission distance, that optimizes throughput in such networks. This result is verified using ns-2 simulation with both single as well as multiple flows.\",\n \"Title: Secure Cache Provision: Provable DDOS Prevention for Randomly Partitioned Services with Replication\\nYear: 2013\\n\\nAbstract: In this paper we show a small but fast popularity-based front-end cache can provide provable DDOS prevention for randomly partitioned cluster services with replication. To achieve this, we first give the best strategy for an adversary to overload the system, and then prove that the cache size is lower bounded by O(n log log n/ log d), where n is the number of back-end nodes and d is the replication factor. Since log log n/ log d <; 2 holds for almost all the current clusters (i.e., the number of back-end nodes n <; 105 and the replication factor d ≥ 3), this result implies an O(n) lower bound on the required cache size. Our analysis and results are well validated through extensive simulations.\",\n \"Title: Scheduling and data layout policies for a near-line multimedia storage architecture\\nYear: 1997\\n\\nAbstract: Recent advances in computer technologies have made it feasible to provide multimedia services, such as news distribution and entertainment, via high-bandwidth networks. The storage and retrieval of large multimedia objects (e.g., video) becomes a major design issue of the multimedia information system. While most other works on multimedia storage servers assume an on-line disk storage system, we consider a two-tier storage architecture with a robotic tape library as the vast near-line storage and an on-line disk system as the front-line storage. Magnetic tapes are cheaper, more robust, and have a larger capacity; hence, they are more cost effective for large scale storage systems (e.g., videoon-demand (VOD) systems may store tens of thousands of videos). We study in detail the design issues of the tape sub-system and propose some novel tape-scheduling algorithms which give faster response and require less disk buffer space. We also study the disk-striping policy and the data layout on the tape cartridge in order to fully utilize the throughput of the robotic tape system and to minimize the on-line disk storage space.\",\n \"Title: An Efficient Partitioning Algorithm for Distributed Virtual Environment Systems\\nYear: 2002\\n\\nAbstract: Distributed virtual environment (DVE) systems model and simulate the activities of thousands of entities interacting in a virtual world over a wide area network. Possible applications for DVE systems are multiplayer video games, military and industrial trainings, and collaborative engineering. In general, a DVE system is composed of many servers and each server is responsible to manage multiple clients who want to participate in the virtual world. Each server receives updates from different clients (such as the current position and orientation of each client) and then delivers this information to other clients in the virtual world. The server also needs to perform other tasks, such as object collision detection and synchronization control. A large scale DVE system needs to support many clients and this imposes a heavy requirement on networking resources and computational resources. Therefore, how to meet the growing requirement of bandwidth and computational resources is one of the major challenges in designing a scalable and cost-effective DVE system. In this paper, we propose an efficient partitioning algorithm that addresses the scalability issue of designing a large scale DVE system. The main idea is to dynamically divide the virtual world into different partitions and then efficiently assign these partitions to different servers. This way, each server will process approximately the same amount of workload. Another objective of the partitioning algorithm is to reduce the server-to-server communication overhead. The theoretical foundation of our dynamic partitioning algorithm is based on the linear optimization principle. We also illustrate how one can parallelize the proposed partitioning algorithm so that it can efficiently partition a very large scale DVE system. Lastly, experiments are carried out to illustrate the effectiveness of the proposed partitioning algorithm under various settings of the virtual world.\",\n \"Title: Sampling node pairs over large graphs\\nYear: 2013\\n\\nAbstract: Characterizing user pair relationships is important for applications such as friend recommendation and interest targeting in online social networks (OSNs). Due to the large scale nature of such networks, it is infeasible to enumerate all user pairs and so sampling is used. In this paper, we show that it is a great challenge even for OSN service providers to characterize user pair relationships even when they possess the complete graph topology. The reason is that when sampling techniques (i.e., uniform vertex sampling (UVS) and random walk (RW)) are naively applied, they can introduce large biases, in particular, for estimating similarity distribution of user pairs with constraints such as existence of mutual neighbors, which is important for applications such as identifying network homophily. Estimating statistics of user pairs is more challenging in the absence of the complete topology information, since an unbiased sampling technique such as UVS is usually not allowed, and exploring the OSN graph topology is expensive. To address these challenges, we present asymptotically unbiased sampling methods to characterize user pair properties based on UVS and RW techniques respectively. We carry out an evaluation of our methods to show their accuracy and efficiency. Finally, we apply our methods to two Chinese OSNs, Doudan and Xiami, and discover significant homophily is present in these two networks.\",\n \"Title: A Proportional-Delay DiffServ-Enabled Web Server: Admission Control and Dynamic Adaptation\\nYear: 2004\\n\\nAbstract: Abstract--We consider a Web server that can provide differentiated services to clients with different quality of service (QoS) requirements. The Web server can provide N \\\\geq 1 classes of proportional-delay differentiated services (PDDS) to heterogeneous clients. An operator can specify fixed performance spacings between classes, namely, r_{i,i+1} 1, for i=1,\\\\ldots,N-1. Requests in class i+1 are guaranteed to have an average waiting time which is 1/r_{i,i+1} of the average waiting time of class i requests. With PDDS, we can provide consistent performance spacings over a wide range of system loading and this simplifies many pricing issues. In addition, each client can specify a maximum average waiting time requirement to be guaranteed by the PDDS-enabled Web server. We show that, in general, the problem of assigning clients to service classes in order to optimize system efficacy is NP-complete. We propose two efficient admission control algorithms so that a Web server can provide the QoS guarantees and, at the same time, classify each client to its \\\"lowest驴 admissible class, resulting in lowest usage cost for the admitted client. We also consider how to perform end-point dynamic adaptation such that admitted clients can submit requests at a lower class and further reduce their usage costs without violating their QoS requirements. We propose two dynamic adaptation algorithms: one is server-based and the other is client-based. The client-based adaptation is distributed and is based on a noncooperative game technique. We carry out experiments to illustrate the effectiveness of these algorithms under different utility functions and traffic arrival patterns (e.g., Poisson, MMPP, and Pareto). We report extensive experimental results to illustrate the effectiveness of our proposed algorithms.\",\n \"Title: COPACC: a cooperative proxy-client caching system for on-demand media streaming\\nYear: 2005\\n\\nAbstract: Proxy caching is a key technique to reduce transmission cost for on-demand multimedia streaming. However, its effectiveness is limited by the insufficient storage space and weak cooperations among proxies and their clients. In this paper, we propose COPACC, a novel cooperative proxy-and-client caching system that combines the advantages of both proxy caching and peer-to-peer (P2P) client communications. We propose a comprehensive suite of protocols to facilitate the interactions among different network entities in COPACC. We also develop an efficient cache allocation algorithm to minimize the aggregated transmission cost of the whole system. Simulation results demonstrate that COPACC achieves remarkably lower transmission cost. Moreover, it is much more robust than a pure P2P system in the presence of node failures.\",\n \"Title: Doubly Exponential Solution for Randomized Load Balancing Models with Markovian Arrival Processes and PH Service Times\\nYear: 2010\\n\\nAbstract: In this paper, we provide a novel matrix-analytic approach for studying\\ndoubly exponential solutions of randomized load balancing models (also known as\\nsupermarket models) with Markovian arrival processes (MAPs) and phase-type (PH)\\nservice times. We describe the supermarket model as a system of differential\\nvector equations by means of density dependent jump Markov processes, and\\nobtain a closed-form solution with a doubly exponential structure to the fixed\\npoint of the system of differential vector equations. Based on this, we show\\nthat the fixed point can be decomposed into the product of two factors\\ninflecting arrival information and service information, and further find that\\nthe doubly exponential solution to the fixed point is not always unique for\\nmore general supermarket models. Furthermore, we analyze the exponential\\nconvergence of the current location of the supermarket model to its fixed\\npoint, and apply the Kurtz Theorem to study density dependent jump Markov\\nprocess given in the supermarket model with MAPs and PH service times, which\\nleads to the Lipschitz condition under which the fraction measure of the\\nsupermarket model weakly converges the system of differential vector equations.\\nThis paper gains a new understanding of how workload probing can help in load\\nbalancing jobs with non-Poisson arrivals and non-exponential service times.\",\n \"Title: Secure Real-Time Streaming Protocol (RTSP) for Hierarchical Proxy Caching\\nYear: 2008\\n\\nAbstract: Proxies are commonly used to cache objects, especially multimedia objects, so that clients can enjoy a better quality-of-service (QoS) guarantees such as smaller start- up latency and lower loss rate. But the use of multime- dia proxies increases the risk that data are exposed to unauthorized access by intruders. In this paper, we pro- pose an enhancement of the Internet IETF's Real-time Streaming Protocol (RTSP) which employs a notion of \\\"asymmetric reversible parametric sequence\\\" (ARPS) to provide the following security properties: (i) data confi- dentiality during transmission, (ii) end-to-end data confi- dentiality, (iii) data confidentiality against proxy intrud- ers, and (iv) data confidentiality against member collu- sion. We present the Secure Multimedia Library (SML) which is based on ARPS and then realize these security features on a production video streaming server: Apple's Darwin Streaming Server. Our framework guarantees the system resilience against attacks is provably strong given the standard computability assumptions. To reduce the computation demand on the receiving client, our scheme only requires the client to perform a \\\"single decryption operation\\\" to recover the original data even though the data packets have been encrypted by multiple proxies along the delivery path. To tradeoff between degree of confidentiality and computational overhead, we also pro- pose the use of a set of \\\"encryption configuration param- eters\\\" (ECP) to trade off proxy encryption throughput against the presentation quality of audio/video obtained by unauthorized parties. Our implementation prototype shows that one can simultaneously achieve high encryp- tion throughput and extremely low audio/video quality (in terms of audio fidelity, and peak signal-to-noise ratio and visual quality of decoded video frames) for unautho- rized access.\",\n \"Title: An Eclectic Survey of Bounding Methods for Markov Chain Models\\nYear: 1995\\n\\nAbstract: Markov models are often used for performance modeling. However most models do not have closed form solutions, and numerical solutions are often not feasible due to the large (or even infinite) state space of models of practical interest. One can sometimes take advantage of special structures such as nearly completely decomposable models or models with matrix analytic solutions. This paper presents a brief survey of some work over the past few years on finding bounds on performance measures for models that are otherwise intractable. The bounds are found by modifying the original model such that: (a) the modified model is efficiently solvable and (b) the modified model is proven to provide an upper (lower) bound on the value of the performance measure in the original model. We have successfully applied this methodology in a number of studies which are briefly surveyed here. This is not meant to be a broad, inclusive survey but rather we concentrate on an approach that the authors have been exploring.\",\n \"Title: On the Practical and Security Issues of Batch Content Distribution Via Network Coding\\nYear: 2006\\n\\nAbstract: File distribution via network coding has received a lot of attention lately. However, direct application of network codingmay have security problems. In particular, attackers can inject \\\"faked\\\" packets into the file distribution process to slowdown the information dispersal or even deplete the network resource. Therefore, content verification is an important and practicalissue when network coding is employed. When network coding is used, it is infeasible for the source of the content to provideall the hash values or signatures required for verification, and hence the traditional \\\"hash-and-sign\\\" methods are no longerapplicable. Recently, a new on-the-fly verification technique is proposed by Krohn et al. for rateless erasure codes [1].However, their scheme requires a large number of hash values to be distributed in advance, and all of them are needed to verifyeven for a single packet. We propose a new batch delivery and verification scheme that is similar to the classical scenariowhere the authentication information of a message is embedded with the message and is sufficient for the verification purpose.We investigate how our technique can be applied when random linear network coding is employed, and show that both the computationaland the bandwidth overhead can be greatly reduced by using a variant of the random network coding. We further show by simulationthat this variant is sufficiently effective in practice.\",\n \"Title: SEAL: A secure communication library for building dynamic group key agreement applications\\nYear: 2007\\n\\nAbstract: We present the SEcure communicAtion Library (SEAL) [source can be downloaded from: http://www.cse.cuhk.edu.hk/~cslui/ANSRlab/software/SEAL/], a Linux-based C language application programming interface (API) library that implements secure group key agreement algorithms that allow a communication group to periodically renew a common secret group key for secure and private communication. The group key agreement protocols satisfy several important characteristics: distributed property (i.e., no centralized key server is needed), collaborative property (i.e., every group member contributes to the group key), and dynamic property (i.e., group members can join or leave the group without impairing the efficiency of the group key generation). Using SEAL, we developed a testing tool termed Gauger to evaluate the performance of the group key agreement algorithms in both wired and wireless LANs according to different levels of membership dynamics. We show that our implementation achieves robustness when there are group members leaving the communication group in the middle of a rekeying operation. We also developed a secure chat-room application termed Chatter to illustrate the usage of SEAL. Our SEAL implementation demonstrates the effectiveness of group key agreement in real network settings.\",\n \"Title: AppATP: An Energy Conserving Adaptive Mobile-Cloud Transmission Protocol\\nYear: 2015\\n\\nAbstract: Many mobile applications require frequent wireless transmissions between the content provider and mobile devices, consuming much energy in mobile devices. Motivated by the popularity of prefetch-friendly or delay-tolerant apps (e.g., social networking, app updates, cloud storage), we design and implement an application-layer transmission protocol, AppATP, which leverages cloud computing to manage data transmissions for mobile apps, transferring data to and from mobile devices in an energy-efficient manner. Measurements show that significantly amount of energy is consumed by mobile devices during poor connectivity. Based on this observation, AppATP adaptively seizes periods of good bandwidth condition to prefetch frequently used data with minimum energy consumption, while deferring delay-tolerant data during poor network connectivity. Using the stochastic control framework, AppATP only relies on the current network information and data queue sizes to make an online decision on transmission scheduling, and performs well under unpredictable wireless network conditions. We implement AppATP on Samsung Note 2 smartphones and Amazon EC2. Results from both trace-driven simulations and extensive real-world experiments show that AppATP can be applied to a variety of application scenarios while achieving 30%-50% energy savings for mobile devices.\",\n \"Title: Evolution of the Internet Economic Ecosystem\\nYear: 2015\\n\\nAbstract: The evolution of the Internet has manifested itself in many ways: the traffic characteristics, the interconnection topologies, and the business relationships among the autonomous components. It is important to understand why (and how) this evolution came about, and how the interplay of these dynamics may affect future evolution and services. We propose a network-aware, macroscopic model that captures the characteristics and interactions of the application and network providers, and show how it leads to a market equilibrium of the ecosystem. By analyzing the driving forces and the dynamics of the market equilibrium, we obtain some fundamental understandings of the cause and effect of the Internet evolution, which explain why some historical and recent evolutions have happened. Furthermore, by projecting the likely future evolutions, our model can help application and network providers to make informed business decisions so as to succeed in this competitive ecosystem.\",\n \"Title: Whom to follow: Efficient followee selection for cascading outbreak detection on online social networks.\\nYear: 2014\\n\\nAbstract: Online social networks (OSNs), such as Twitter and Sina Weibo, have become important platforms for generating and spreading information on the Internet. On these OSNs, the “follow model” has become a popular way to discover information; i.e., a user subscribes to content generated by others by following them as information sources. The content producers are called followees. Due to human beings’ limited attention capacity and the constraints imposed by OSNs, a user can only follow a few followees. The question then arises: which subset of followees shall we follow so that we can discover the most information in an OSN in a timely fashion? To solve this problem, we present a randomized method that does not require complete OSN data and is well suited for third parties who do not own OSN data. Our method is based on the birthday paradox and is mathematically tractable for analysing its solution quality and computational efficiency. Moreover, we find that the power-law structure of real-world OSNs can further improve the solution quality of our method. Experiments conducted on two real datasets demonstrate that our method can create a good trade-off between solution quality and computational efficiency.\",\n \"Title: Unbiased Characterization of Node Pairs over Large Graphs\\nYear: 2015\\n\\nAbstract: Characterizing user pair relationships is important for applications such as friend recommendation and interest targeting in online social networks (OSNs). Due to the large-scale nature of such networks, it is infeasible to enumerate all user pairs and thus sampling is used. In this article, we show that it is a great challenge for OSN service providers to characterize user pair relationships, even when they possess the complete graph topology. The reason is that when sampling techniques (i.e., uniform vertex sampling (UVS) and random walk (RW)) are naively applied, they can introduce large biases, particularly for estimating similarity distribution of user pairs with constraints like existence of mutual neighbors, which is important for applications such as identifying network homophily. Estimating statistics of user pairs is more challenging in the absence of the complete topology information, as an unbiased sampling technique like UVS is usually not allowed and exploring the OSN graph topology is expensive. To address these challenges, we present unbiased sampling methods to characterize user pair properties based on UVS and RW techniques. We carry out an evaluation of our methods to show their accuracy and efficiency. Finally, we apply our methods to three OSNs—Foursquare, Douban, and Xiami—and discover that significant homophily is present in these networks.\",\n \"Title: Efficiently Estimating Motif Statistics of Large Networks\\nYear: 2014\\n\\nAbstract: Exploring statistics of locally connected subgraph patterns (also known as network motifs) has helped researchers better understand the structure and function of biological and Online Social Networks (OSNs). Nowadays, the massive size of some critical networks—often stored in already overloaded relational databases—effectively limits the rate at which nodes and edges can be explored, making it a challenge to accurately discover subgraph statistics. In this work, we propose sampling methods to accurately estimate subgraph statistics from as few queried nodes as possible. We present sampling algorithms that efficiently and accurately estimate subgraph properties of massive networks. Our algorithms require no precomputation or complete network topology information. At the same time, we provide theoretical guarantees of convergence. We perform experiments using widely known datasets and show that, for the same accuracy, our algorithms require an order of magnitude less queries (samples) than the current state-of-the-art algorithms.\",\n \"Title: Accurate DNS query characteristics estimation via active probing.\\nYear: 2015\\n\\nAbstract: As the hidden backbone of today׳s Internet, the Domain Name System (DNS) provides name resolution service for almost every networked application. To exploit the rich DNS query information for traffic engineering or user behavior analysis, both passive capturing and active probing techniques have been proposed in recent years. Despite its full visibility of DNS behaviors, the passive capturing technique suffers from prohibitive management cost and results in tremendous privacy concerns towards its large-scale and collaborative deployment. Comparatively, the active probing technique overcomes these limitations, providing broad-view and privacy-preserving DNS query analysis at the cost of constrained visibility of fine-grained DNS behavior. This paper aims to accurately estimate DNS query characteristics based on DNS cache activities, which can be acquired via active probing on a large scale at negligible management cost and minimized privacy concerns. Specifically, we have made three contributions: (1) we propose a novel solution, which integrates the renewal theory-based DNS caching formulation and the hyper-exponential distribution model. The solution offers great flexibility to model various domains; (2) we perform a large-scale real-world DNS trace measurement, and demonstrate that our solution significantly improves the estimation accuracy; (3) we apply our solution to estimate the malware-infected host population in remote management networks. The experimental results have demonstrated that our solution can achieve high estimation accuracy and outperforms the existing method.\",\n \"Title: Mathematical Modeling of Insurance Mechanisms for E-commerce Systems.\\nYear: 2015\\n\\nAbstract: Electronic commerce (a.k.a. E-commerce) systems such as eBay and Taobao of Alibaba are becoming increasingly popular. Having an effective reputation system is critical to this type of internet service because it can assist buyers to evaluate the trustworthiness of sellers, and it can also improve the revenue for reputable sellers and E-commerce operators. We formulate a stochastic model to analyze an eBay-like reputation system and propose four measures to quantify its effectiveness: (1) new seller ramp up time, (2) new seller drop out probability, (3) long term profit gains for sellers, and (4) average per seller transaction gains for the E-commerce operator. Through our analysis, we identify key factors which influence these four measures. We propose a new insurance mechanism which consists of an insurance protocol and a transaction mechanism to improve the above four measures. We show that our insurance mechanism can reduce the ramp up time by around 87.2%, and guarantee new sellers ramp up before the deadline $T_w$ with a high probability (close to 1.0). It also increases the long term profit gains and average per seller transaction gains by at least 95.3%.\",\n \"Title: Block-Structured Supermarket Models.\\nYear: 2014\\n\\nAbstract: Supermarket models are a class of parallel queueing networks with an adaptive control scheme that play a key role in the study of resource management of, such as, computer networks, manufacturing systems and transportation networks. When the arrival processes are non-Poisson and the service times are non-exponential, analysis of such a supermarket model is always limited, interesting, and challenging.This paper describes a supermarket model with non-Poisson inputs: Markovian Arrival Processes (MAPs) and with non-exponential service times: Phase-type (PH) distributions, and provides a generalized matrix-analytic method which is first combined with the operator semigroup and the mean-field limit. When discussing such a more general supermarket model, this paper makes some new results and advances as follows: (1) Providing a detailed probability analysis for setting up an infinite-dimensional system of differential vector equations satisfied by the expected fraction vector, where the invariance of environment factors is given as an important result. (2) Introducing the phase-type structure to the operator semigroup and to the mean-field limit, and a Lipschitz condition can be established by means of a unified matrix-differential algorithm. (3) The matrix-analytic method is used to compute the fixed point which leads to performance computation of this system. Finally, we use some numerical examples to illustrate how the performance measures of this supermarket model depend on the non-Poisson inputs and on the non-exponential service times. Thus the results of this paper give new highlight on understanding influence of non-Poisson inputs and of non-exponential service times on performance measures of more general supermarket models.\",\n \"Title: Algorithmic Design for Competitive Influence Maximization Problems.\\nYear: 2014\\n\\nAbstract: Given the popularity of the viral marketing campaign in online social networks, finding an effective method to identify a set of most influential nodes so to compete well with other viral marketing competitors is of upmost importance. We propose a \\\"General Competitive Independent Cascade (GCIC)\\\" model to describe the general influence propagation of two competing sources in the same network. We formulate the \\\"Competitive Influence Maximization (CIM)\\\" problem as follows: Under a prespecified influence propagation model and that the competitor's seed set is known, how to find a seed set of $k$ nodes so as to trigger the largest influence cascade? We propose a general algorithmic framework TCIM for the CIM problem under the GCIC model. TCIM returns a $(1-1/e-\\\\epsilon)$-approximate solution with probability at least $1-n^{-\\\\ell}$, and has an efficient time complexity of $O(c(k+\\\\ell)(m+n)\\\\log n/\\\\epsilon^2)$, where $c$ depends on specific propagation model and may also depend on $k$ and underlying network $G$. To the best of our knowledge, this is the first general algorithmic framework that has both $(1-1/e-\\\\epsilon)$ performance guarantee and practical efficiency. We conduct extensive experiments on real-world datasets under three specific influence propagation models, and show the efficiency and accuracy of our framework. In particular, we achieve up to four orders of magnitude speedup as compared to the previous state-of-the-art algorithms with the approximate guarantee.\",\n \"Title: Friends or Foes: Distributed and Randomized Algorithms to Determine Dishonest Recommenders in Online Social Networks\\nYear: 2014\\n\\nAbstract: Viral marketing is becoming important due to the popularity of online social networks (OSNs). Companies may provide incentives (e.g., via free samples of a product) to a small group of users in an OSN, and these users provide recommendations to their friends, which eventually increases the overall sales of a given product. Nevertheless, this also opens a door for malicious behaviors: dishonest users may intentionally give misleading recommendations to their friends so as to distort the normal sales distribution. In this paper, we propose a detection framework to identify dishonest users in the OSNs. In particular, we present a set of fully distributed and randomized algorithms, and also quantify the performance of the algorithms by deriving probability of false positive, probability of false negative, and the distribution of number of detection rounds. Extensive simulations are also carried out to illustrate the impact of misleading recommendations and the effectiveness of our detection algorithms. The methodology we present here will enhance the security level of viral marketing in the OSNs.\",\n \"Title: Minfer: Inferring Motif Statistics From Sampled Edges.\\nYear: 2015\\n\\nAbstract: Characterizing motif (i.e., locally connected subgraph patterns) statistics is important for understanding complex networks such as online social networks and communication networks. Previous work made the strong assumption that the graph topology of interest is known, and that the dataset either fits into main memory or stored on disks such that it is not expensive to obtain all neighbors of any given node. In practice, researchers have to deal with the situation where the graph topology is unknown, either because the graph is dynamic, or because it is expensive to collect and store all topological and meta information on disk. Hence, what is available to researchers is only a snapshot of the graph generated by sampling edges from the graph at random, which we called a \\\"RESampled graph\\\". Clearly, a RESampled graph's motif statistics may be quite different from the underlying original graph. To solve this challenge, we propose a framework and implement a system called Minfer, which can take the given RESampled graph and accurately infer the underlying graph's motif statistics. We also use Fisher information to bound the error of our estimates. Experiments using large scale datasets show that our method to be accurate.\",\n \"Title: Mathematical modeling of group product recommendation with partial information: How many ratings do we need?\\nYear: 2014\\n\\nAbstract: Product recommendation is one of the most important services in the Internet. In this paper, we consider a product recommendation system which recommends products to a group of users. The recommendation system only has partial preference information on this group of users: a user only indicates his preference to a small subset of products in the form of ratings. This partial preference information makes it a challenge to produce an accurate recommendation. In this work, we explore a number of fundamental questions. What is the desired number of ratings per product so to guarantee an accurate recommendation? What are some effective voting rules in summarizing ratings? How users’ misbehavior such as cheating, in product rating may affect the recommendation accuracy? What are some efficient rating schemes? To answer these questions, we present a formal mathematical model of a group recommendation system. We formally analyze the model. Through this analysis we gain the insight to develop a randomized algorithm which is both computationally efficient and asymptotically accurate in evaluating the recommendation accuracy under a very general setting. We propose a novel and efficient heterogeneous rating scheme which requires equal or less rating workload, but can improve over a homogeneous rating scheme by as much as 30%. We carry out experiments on both synthetic data and real-world data from TripAdvisor. Not only we validate our model, but also we obtain a number of interesting observations, i.e., a small of misbehaving users can decrease the recommendation accuracy remarkably. For TripAdvisor, one hundred ratings per product is sufficient to guarantee a high accuracy recommendation. We believe our model and methodology are important building blocks to refine and improve applications of group recommendation systems.\",\n \"Title: SAND: A Fault-Tolerant Streaming Architecture for Network Traffic Analytics\\nYear: 2014\\n\\nAbstract: Many long-running network analytics applications impose a high-throughput and high reliability requirements on stream processing systems. However, previous stream processing systems cannot sustain high-speed traffic at the core router level. Furthermore, their fault-tolerant schemes cannot provide strong consistency which is essential for network analytics. In this paper, we present the design and implementation of SAND, a fault-tolerant distributed stream processing system for network analytics. SAND is designed to operate under high-speed network traffic, and it uses a novel check pointing protocol which can perform failure recovery based on upstream backup and check pointing. We prove our fault-tolerant scheme provides strong consistency even under multiple node failure. We implement several real-world network analytics applications on SAND, evaluate their performance using network traffic captured from commercial cellular core networks, and demonstrate that SAND can sustain high-speed network traffic and that our fault-tolerant scheme is efficient.\",\n \"Title: Impact of Data Locality on Garbage Collection in SSDs: A General Analytical Study\\nYear: 2015\\n\\nAbstract: Solid-state drives (SSDs) necessitate garbage collection (GC) to erase data blocks and reclaim the space of invalidated data, and GC inevitably introduces additional writes due to data relocation. The performance of GC, which is quantified by cleaning cost or write amplification, is critical to the overall performance of SSDs. However, characterizing GC performance is complicated by the general implementations of GC algorithms and the complex data locality characteristics of real-world workloads. This paper presents a general analytical study to characterize the performance impact of data locality on a general family of GC algorithms. We develop probabilistic models to address two fundamental issues: (1) What is the impact of data locality on the performance of locality-oblivious GC? (2) How can data locality be leveraged to improve the performance in locality-aware GC? We further conduct extensive trace-driven simulations on real-world workloads to validate the findings of our models.\",\n \"Title: DroidTrace: A ptrace based Android dynamic analysis system with forward execution capability\\nYear: 2014\\n\\nAbstract: Android, being an open source smartphone operating system, enjoys a large community of developers who create new mobile services and applications. However, it also attracts malware writers to exploit Android devices in order to distribute malicious apps in the wild. In fact, Android malware are becoming more sophisticated and they use advanced “dynamic loading” techniques like Java reflection or native code execution to bypass security detection. To detect dynamic loading, one has to use dynamic analysis. Currently, there are only a handful of Android dynamic analysis tools available, and they all have shortcomings in detecting dynamic loading. The aim of this paper is to design and implement a dynamic analysis system which allows analysts to perform systematic analysis of dynamic payloads with malicious behaviors. We propose “DroidTrace”, a ptrace based dynamic analysis system with forward execution capability. Our system uses ptrace to monitor selected system calls of the target process which is running the dynamic payloads, and classifies the payloads behaviors through the system call sequence, e.g., behaviors such as file access, network connection, inter-process communication and even privilege escalation. Also, DroidTrace performs “physical modification” to trigger different dynamic loading behaviors within an app. Using DroidTrace, we carry out a large scale analysis on 36,170 dynamic payloads in 50,000 apps and 294 malware in 10 families (four of them are zero-day) with various dynamic loading behaviors.\",\n \"Title: On efficient bandwidth allocation for traffic variability in datacenters\\nYear: 2014\\n\\nAbstract: Datacenter networks suffer unpredictable performance due to a lack of application level bandwidth guarantees. A lot of attentions have been drawn to solve this problem such as how to provide bandwidth guarantees for Virtualized Machines (VMs), proportional bandwidth share among tenants, and high network utilization under peak traffic. However, existing solutions fail to cope with highly dynamic traffic in datacenter networks. In this paper, we consider the effects of large numbers of short flows and massive bursty traffic in the datacenter, and design a novel distributed rate allocation algorithm based on the Logistic model under the control-theoretic framework. The theoretical analysis and experimental results using OpenFlow show that our algorithm not only guarantees the bandwidth for VMs, but also provides fast convergence to efficiency and fairness, and smooth response to bursty traffic.\",\n \"Title: Exploring bundling sale strategy in online service markets with network effects\\nYear: 2014\\n\\nAbstract: In recent years, we have witnessed a growing trend for online service companies to offer “bundling sales” to increase revenue. Bundling sale means that a company groups a set of its products/services and charges this bundle at a fixed price, which is usually less than the total price of individual items. In this paper, our goal is to understand the underlying dynamics of bundling, in particular, what is the optimal bundling sale strategy and under what situations it will be more attractive than the separate sales. We focus on online service markets that exhibit network effect. We provide mathematical models to capture the interactions between buyers and sellers, analyze the market equilibrium and its stability, and formulate an optimization framework to determine the optimal sale strategy for the service provider. We analyze the impact of the key factors, including the network effects and operating costs, on the profitability of bundling. We show that bundling is more profitable than separate sale in most cases; however, the heterogeneity of services and the asymmetry of operating costs reduce the advantage of bundling. These findings provide important insights in designing proper sale strategies for online services.\",\n \"Title: Enpublic Apps: Security Threats Using iOS Enterprise and Developer Certificates\\nYear: 2015\\n\\nAbstract: Compared with Android, the conventional wisdom is that iOS is more secure. However, both jailbroken and non-jailbroken iOS devices have number of vulnerabilities. For iOS, apps need to interact with the underlying system using Application Programming Interfaces (APIs). Some of these APIs remain undocumented and Apple forbids apps in App Store from using them. These APIs, also known as \\\\\\\"private APIs\\\\\\\", provide powerful features to developers and yet they may have serious security consequences if misused. Furthermore, apps which use private APIs can bypass the App Store and use the \\\\\\\"Apple's Enterprise/Developer Certificates\\\\\\\" for distribution. This poses a significant threat to the iOS ecosystem. So far, there is no formal study to understand these apps and how private APIs are being encapsulated. We call these iOS apps which distribute to the public using enterprise certificates as \\\\\\\"enpublic\\\\\\\" apps. In this paper, we present the design and implementation of iAnalytics, which can automatically analyze \\\\\\\"enpublic\\\\\\\" apps' private API usages and vulnerabilities. Using iAnalytics, we crawled and analyzed 1,408 enpublic iOS apps. We discovered that: 844 (60%) out of the 1408 apps do use private APIs, 14 (1%) apps contain URL scheme vulnerabilities, 901 (64%) enpublic apps transport sensitive information through unencrypted channel or store the information in plaintext on the phone. In addition, we summarized 25 private APIs which are crucial and security sensitive on iOS 6/7/8, and we have filed one CVE (Common Vulnerabilities and Exposures) for iOS devices.\",\n \"Title: DroidRay: a security evaluation system for customized android firmwares\\nYear: 2014\\n\\nAbstract: Android mobile devices are enjoying a lion's market share in smartphones and mobile devices. This also attracts malware writers to target the Android platform. Recently, we have discovered a new Android malware distribution channel: releasing malicious firmwares with pre-installed malware to the wild. This poses significant risk since users of mobile devices cannot change the content of the malicious firmwares. Furthermore, pre-installed applications have \\\\\\\" more permissions\\\\\\\" (i.e., silent installation) than other legitimate mobile apps, so they can download more malware or access users' confidential information. To understand and address this new form of malware distribution channel, we design and implement \\\\\\\"DroidRay\\\\\\\": a security evaluation system for customized Android firmwares. DroidRay uses both static and dynamic analyses to evaluate the firmware security on both the application and system levels. To understand the impact of this new malware distribution channel, we analyze 250 Android firmwares and 24,009 pre-installed applications. We reveal how the malicious firmware and pre-installed malware are injected, and discovered 1,947 (8.1%) pre-installed applications have signature vulnerability and 19 (7.6%) firmwares contain pre-installed malware. In addition, 142 (56.8%) firmwares have the default signature vulnerability, five (2.0%) firmwares contain malicious hosts file, at most 40 (16.0%) firmwares have the native level privilege escalation vulnerability and at least 249 (99.6%) firmwares have the Java level privilege escalation vulnerability. Lastly, we investigate a real-world case of a pre-installed zero-day malware known as CEPlugnew, which involves 348,018 infected Android smartphones, and we show its degree and geographical penetration. This shows the significance of this new malware distribution channel, and DroidRay is an effective tool to combat this new form of malware spreading.\",\n \"Title: Mathematical Modeling of Crowdsourcing Systems: Incentive Mechanism and Rating System Design\\nYear: 2014\\n\\nAbstract: Crowd sourcing systems like Yahoo! Answers, Amazon Mechanical Turk, and Google Helpouts, etc., have seen an increasing prevalence in the past few years. The participation of users, high quality solutions, and a fair rating system are critical to the revenue of a crowd sourcing system. In this paper, we design a class of simple but effective incentive mechanisms to attract users participating, and providing high quality solutions. Our incentive mechanism consists of a task bundling scheme and a rating system, and pay workers according to solution ratings from requesters. We also propose a probabilistic model to capture various human factors like biases in rating, and we quantify its impact on the incentive mechanism, which is shown to be highly robust. We develop a model to characterize the design space of a class of commonly used rating systems - threshold based rating systems. We quantify the impact of such rating systems and the bundling scheme on the incentive mechanism.\",\n \"Title: A provable algorithmic approach to product selection problems for market entry and sustainability\\nYear: 2014\\n\\nAbstract: Given the globalized economy, how to process the heterogeneous web data so to extract customers' purchase behavior is crucial to manufacturers who want to enter or sustain in a competitive market. To maximize the sales, manufacturers not only need to decide what products to produce so to meet diverse customers' requirements, but at the same time, compete with competitors' products. In this paper, we present a general framework for the following product selection problems: (1) k-BSP problem, which is for a manufacturer to enter a competitive market, and (2) k-BBP problem, which is for a manufacturer to sustain in a competitive market. We propose several product adoption models to describe the complex purchase behavior of customers, and formally show that these problems are NP-hard in general. To tackle these problems, we propose computationally efficient greedy-based approximation algorithms. Based on the submodularity analysis, we prove that our algorithms can guarantee a (1--1/e)-approximation ratio as compared to the optimal solutions. We perform large scale data analysis to show the efficiency and accuracy of our framework. In our experiments, we observe 1,300 to 250,000 times speedup as compared to the exhaustive algorithms, and our solutions can achieve on average 96% of solution quality as compared to the optimal solutions. Finally, we apply our algorithms on web dataset to show the impact of customers' different purchase behavior on the results of product selection.\",\n \"Title: Keynote speakers: Big data science and social networks — Accelerating insights and building value\\nYear: 2014\\n\\nAbstract: These keynote discusses the following: Big Data Science and Social Networks - Accelerating Insights and Building Value; Sampling Theory of Large Networks; Mining Online Social Networks: Opportunities and Privacy Issues.\",\n \"Title: Automatic recovery from disk failure in continuous-media servers\\nYear: 2002\\n\\nAbstract: Continuous-media (CM) servers have been around for some years. Apart from server capacity, another important issue in the deployment of CM servers is reliability. This study investigates rebuild algorithms for automatically rebuilding data stored in a failed disk into a spare disk. Specifically, a block-based rebuild algorithm is studied with the rebuild time and buffer requirement modeled. A buffer-sharing scheme is then proposed to eliminate the additional buffers needed by the rebuild process. To further improve rebuild performance, a track-based rebuild algorithm that rebuilds lost data in tracks is proposed and analyzed. Results show that track-based rebuild, while it substantially outperforms block-based rebuild, requires significantly more buffers (17-135 percent more) even with buffer sharing. To tackle this problem, a novel pipelined rebuild algorithm is proposed to take advantage of the sequential property of track retrievals to pipeline the reading and writing processes. This pipelined rebuild algorithm achieves the same rebuild performance as track-based rebuild, but reduces the extra buffer requirement to insignificant levels (0.7-1.9 percent). Numerical results computed using models of five commercial disk drives demonstrate that automatic rebuild of a failed disk can be done in a reasonable amount of time, even at relatively high server utilization (e.g., less than 1.5 hours at 90 percent utilization).\",\n \"Title: Bounding the mean response time of the minimum expected delay routing policy: an algorithmic approach\\nYear: 1995\\n\\nAbstract: Balancing loads in a multi-server system can have a significant impact on performance. In this paper, we model such a system as a heterogeneous multi-server queueing system. We study the behavior of such a system operating under the minimum expected delay (MED) routing policy, i.e., an arriving customer is assigned to the queue which has the minimal expected value of unfinished work. This routing discipline can be viewed as a generalization of the join-the-shortest queue (SQ) discipline for homogeneous servers. There is no closed-form solution for this class of queueing problem. In this paper, we provide a methodology to compute upper and lower bounds on the mean response time of the system. This methodology allows one to tradeoff the tightness of the bounds and computational cost. Applications and numerical examples are presented which show how to use this methodology for deriving performance measures and also illustrating that the excellent accuracy of the computational algorithm which is achievable with modest computational cost\",\n \"Title: Buffer and I/O resource pre-allocation for implementing batching and buffering techniques for Video-on-Demand systems\\nYear: 1997\\n\\nAbstract: To design a cost effective VOD server, it is important to carefully manage the system resources so that the number of concurrent viewers can be maximized. Previous research results use data sharing techniques, such as batching, buffering and piggybacking, to reduce the demand for I/O resources in a VOD system. However, these techniques still suffer from the problem that additional I/O resources are needed in the system for providing VCR functionality --- without careful resource management, the benefits of these data sharing techniques can be lost. In this paper, we first introduce a model for determining the amount of resources required for supporting both normal playback and VCR functionality to satisfy predefined performance characteristics. Consequently, this model allows us to maximize the benefits of data sharing techniques. Furthermore, one important application of this model is its use in making system sizing decisions. Proper system sizing will result in a more cost-effective VOD system.\",\n \"Title: On the robustness of soft state protocols\\nYear: 2004\\n\\nAbstract: Soft state has been a mantra of Internet protocol design for the past decade. System designers build protocols that implement soft state mechanisms based on intuition or on qualitative arguments that the design is \\\"better\\\", yet there has never been a formal performance evaluation study that draws the same conclusion. In fact, previous attempts [P. Ji et al., 2003 and S. Raman et al., 1999] to build such a quantitative argument have found that pure soft state protocols significantly under-perform their hard state counterparts, and that only soft-hard hybrids can match hard state protocol performance. In this paper, we argue otherwise. We develop models that provide a performance-oriented explanation and justification of the Internet designer's intuition. The novel observation is that, if network conditions are known, a hard state protocol can always be configured to outperform its soft state counterpart. However, in reality, network conditions are unpredictable, and that soft state protocols are much more resilient to unanticipated fluctuations in these conditions.\",\n \"Title: A multikey secure multimedia proxy using asymmetric reversible parametric sequences: theory, design, and implementation\\nYear: 2005\\n\\nAbstract: Because of limited server and network capacities for streaming applications, multimedia proxies are commonly used to cache multimedia objects such that, by accessing nearby proxies, clients can enjoy a smaller start-up latency and receive a better quality-of-service (QoS) guarantee - for example, reduced packet loss and delay jitters for their requests. However, the use of multimedia proxies increases the risk that multimedia data are exposed to unauthorized access by intruders. In this paper, we present a framework for implementing a secure multimedia proxy system for audio and video streaming applications. The framework employs a notion of asymmetric reversible parametric sequence (ARPS) to provide the following security properties: (i) data confidentiality during transmission, (ii) end-to-end data confidentiality, (iii) data confidentiality against proxy intruders, and (iv) data confidentiality against member collusion. Our framework is grounded on a multi-key RSA technique such that system resilience against attacks is provably strong given standard computability assumptions. One important feature of our proposed scheme is that clients only need to perform a single decryption operation to recover the original data even though the data packets may have been encrypted by multiple proxies along the delivery path. We also propose the use of a set of encryption configuration parameters (ECP) to trade off proxy encryption throughput against the presentation quality of audio/video obtained by unauthorized parties. Implementation results show that we can simultaneously achieve high encryption throughput and extremely low video quality (in terms of peak signal-to-noise ratio and visual quality of decoded video frames) for unauthorized access.\",\n \"Title: Constructing communication subgraphs and deriving an optimal synchronization interval for distributed virtual environment systems\\nYear: 2001\\n\\nAbstract: In this paper, we consider problems of constructing a communication subgraph and deriving an optimal synchronization interval for a distributed virtual environment system (DVE). In general, a DVE system is a distributed system which allows many clients who are located in different parts of the network to concurrently explore and interact with each other under a high resolution, 3D, graphical virtual environment. Each client in a DVE system is represented by an avatar in the virtual environment, and each avatar can move and interact freely in the virtual environment. There are many challenging issues in designing a cost-effective DVE system. In this work, we address two important design issues, namely: 1) how to construct a communication subgraph which can efficiently carry traffic generated by all clients in a DVE system, and 2) how to guarantee that each participating client has the same consistent view of the virtual world. In other words, if there is an action taken by an avatar or if there is any change in the state of an object in the virtual world, every participating client will be able to view the change. To provide this consistent view, a DVE system needs to perform synchronization actions periodically. We present several algorithms for constructing a communication subgraph. In the subgraph construction, we try to reduce the consumption of network bandwidth resources or reduce the maximum delay between any two clients in a DVE system. Based on a given communication subgraph, we then derive the optimal synchronization interval so as to guarantee the view consistency among all participating clients. The derivation of the optimal synchronization interval is based on the theory of Markov chains and the fundamental matrix.\",\n \"Title: Analysis of Adaptive Incentive Protocols for P2P Networks\\nYear: 2009\\n\\nAbstract: Incentive protocols play a crucial role to encourage cooperation among nodes in networking applications. The aim of this paper is to provide a general analytical framework to analyze and design a large family of incentive protocols. We consider a class of incentive protocols wherein peers can distributively learn and adapt their actions. Using our analytical framework, one can evaluate the expected performance gain and system robustness of a given incentive protocol. To illustrate the framework, we present three incentive policies and two learning (or adaptive) models. We show under what conditions the network may collapse (e.g., no cooperation in the system) or the incentive protocol can guarantee a high degree of cooperation. In particular, we formally show the connection between evaluating incentive protocols and evolutionary game theory so to identify robustness characteristics of an incentive policy.\",\n \"Title: A Mathematical Framework for Analyzing Adaptive Incentive Protocols in P2P Networks\\nYear: 2012\\n\\nAbstract: In peer-to-peer (P2P) networks, incentive protocol is used to encourage cooperation among end-nodes so as to deliver a scalable and robust service. However, the design and analysis of incentive protocols have been ad hoc and heuristic at best. The objective of this paper is to provide a simple yet general framework to analyze and design incentive protocols. We consider a class of incentive protocols that can learn and adapt to other end-nodes' strategies. Based on our analytical framework, one can evaluate the expected performance gain and, more importantly, the system robustness of a given incentive protocol. To illustrate the framework, we present two adaptive learning models and three incentive policies and show the conditions in which the P2P networks may collapse and the conditions in which the P2P networks can guarantee a high degree of cooperation. We also show the connection between evaluating incentive protocol and evolutionary game theory so one can easily identify robustness characteristics of a given policy. Using our framework, one can gain the understanding on the price of altruism and system stability, as well as the correctness of the adaptive incentive policy.\",\n \"Title: Performance Analysis Of A New Updating Rule For Td(Lambda) Learning In Feedforward Networks For Position Evaluation In Go Game\\nYear: 1996\\n\\nAbstract: In this paper, a new updating rule for applyingTemporal Difference (TD) learningtomultilayerfeedforward networks is derived. Networks are trained toevaluateGoboardpositions byTD() learningwith differentvalues of .Performance of eachnetworkisestimated bylettingitplay against other networks. Resultsshowthat non-zero gives better learning for thenetworkandstatistically, larger gives better performance.1. IntroductionIn reinforcement learning, thereisnoteacher available to givethe...\",\n \"Title: Walking in the Cloud: Parallel SimRank at Scale.\\nYear: 2015\\n\\nAbstract: Despite its popularity, SimRank is computationally costly, in both time and space. In particular, its recursive nature poses a great challenge in using modern distributed computing power, and also prevents querying similarities individually. Existing solutions suffer greatly from these practical issues. In this paper, we break such dependency for maximum efficiency possible. Our method consists of offline and online phases. In offline phase, a length-n indexing vector is derived by solving a linear system in parallel. At online query time, the similarities are computed instantly from the index vector. Throughout, the Monte Carlo method is used to maximally reduce time and space. Our algorithm, called CloudWalker, is highly parallelizable, with only linear time and space. Remarkably, it responses to both single-pair and single-source queries in constant time. CloudWalker is orders of magnitude more efficient and scalable than existing solutions for large-scale problems. Implemented on Spark with 10 machines and tested on the web-scale clue-web graph with 1 billion nodes and 43 billion edges, it takes 110 hours for offline indexing, 64 seconds for a single-pair query, and 188 seconds for a single-source query. To the best of our knowledge, our work is the first to report results on clue-web, which is 10x larger than the largest graph ever reported for SimRank computation.\",\n \"Title: A General-Purpose Query-Centric Framework for Querying Big Graphs.\\nYear: 2016\\n\\nAbstract: Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems employ a user-friendly \\\\\\\"think like a vertex\\\\\\\" programming model, and exhibit good scalability for tasks where the majority of graph vertices participate in computation. However, the design of these systems can seriously under-utilize the resources in a cluster for processing light-workload graph queries, where only a small fraction of vertices need to be accessed. In this work, we develop a new open-source system, called Quegel, for querying big graphs. Quegel treats queries as first-class citizens in its design: users only need to specify the Pregel-like algorithm for a generic query, and Quegel processes light-workload graph queries on demand, using a novel superstep-sharing execution model to effectively utilize the cluster resources. Quegel further provides a convenient interface for constructing graph indexes, which significantly improve query performance but are not supported by existing graph-parallel systems. Our experiments verified that Quegel is highly efficient in answering various types of graph queries and is up to orders of magnitude faster than existing systems.\",\n \"Title: Mathematical Modeling and Analysis of Product Rating with Partial Information\\nYear: 2015\\n\\nAbstract: Many Web services like Amazon, Epinions, and TripAdvisor provide historical product ratings so that users can evaluate the quality of products. Product ratings are important because they affect how well a product will be adopted by the market. The challenge is that we only have partial information on these ratings: each user assigns ratings to only a small subset of products. Under this partial information setting, we explore a number of fundamental questions. What is the minimum number of ratings a product needs so that one can make a reliable evaluation of its quality? How may users’ misbehavior, such as cheating in product rating, affect the evaluation result? To answer these questions, we present a probabilistic model to capture various important factors (e.g., rating aggregation rules, rating behavior) that may influence the product quality assessment under the partial information setting. We derive the minimum number of ratings needed to produce a reliable indicator on the quality of a product. We extend our model to accommodate users’ misbehavior in product rating. We derive the maximum fraction of misbehaving users that a rating aggregation rule can tolerate and the minimum number of ratings needed to compensate. We carry out experiments using both synthetic and real-world data (from Amazon and TripAdvisor). We not only validate our model but also show that the “average rating rule” produces more reliable and robust product quality assessments than the “majority rating rule” and the “median rating rule” in aggregating product ratings. Last, we perform experiments on two movie rating datasets (from Flixster and Netflix) to demonstrate how to apply our framework to improve the applications of recommender systems.\",\n \"Title: Quegel: A General-Purpose Query-Centric Framework for Querying Big Graphs.\\nYear: 2016\\n\\nAbstract: Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems expose the user-friendly \\\"think like a vertex\\\" programming interface to users, and exhibit good horizontal scalability. However, these systems are designed for tasks where the majority of graph vertices participate in computation, but are not suitable for processing light-workload graph queries where only a small fraction of vertices need to be accessed. The programming paradigm adopted by these systems can seriously under-utilize the resources in a cluster for graph query processing. In this work, we develop a new open-source system, called Quegel, for querying big graphs, which treats queries as first-class citizens in the design of its computing model. Users only need to specify the Pregel-like algorithm for a generic query, and Quegel processes light-workload graph queries on demand using a novel superstep-sharing execution model to effectively utilize the cluster resources. Quegel further provides a convenient interface for constructing graph indexes, which significantly improve query performance but are not supported by existing graph-parallel systems. Our experiments verified that Quegel is highly efficient in answering various types of graph queries and is up to orders of magnitude faster than existing systems.\",\n \"Title: Impact of Online Activities on Influence Maximization: A Random Walk Approach.\\nYear: 2016\\n\\nAbstract: With the popularity of OSNs, finding a set of most influential users so as to trigger the largest influence cascade is of significance. This task is usually modeled as an influence maximization problem, and it has been widely studied in the past decade. However, considering that users in OSNs may participate in various kinds of online activities, e.g., giving ratings to products, joining discussion groups, etc., influence diffusion through online activities becomes even more significant. Thus, it necessitates to revisit the influence maximization problem by taking online activities into consideration. In this paper, we study the impact of online activities by formulating the influence maximization problem for social-activity networks (SANs) containing both users and online activities. To address the computation challenge, we define an influence centrality via random walks on hypergraph to measure influence, then use the Monte Carlo framework to efficiently estimate the centrality in SANs, and use the Hoeffding inequality to bound the approximation error. Furthermore, we develop a greedy-based algorithm with two novel optimization techniques to find the most influential users. By conducting extensive experiments with real-world datasets, we show that compared to the state-of-the-art algorithm IMM [20], our approach improves the accuracy of selecting the top k most influential users in SANs by up to an order of magnitude, and our approach has a comparable computation time even though it needs to handle a large amount of online activities. Furthermore, our approach is also general enough to work under different diffusion models, e.g., IC and LT models, as well as heterogeneous systems, e.g., multiple types of users and online activities in SANs.\",\n \"Title: A General Framework for Estimating Graphlet Statistics via Random Walk.\\nYear: 2016\\n\\nAbstract: Graphlets are induced subgraph patterns and have been frequently applied to characterize the local topology structures of graphs across various domains, e.g., online social networks (OSNs) and biological networks. Discovering and computing graphlet statistics are highly challenging. First, the massive size of real-world graphs makes the exact computation of graphlets extremely expensive. Secondly, the graph topology may not be readily available so one has to resort to web crawling using the available application programming interfaces (APIs). In this work, we propose a general and novel framework to estimate graphlet statistics of \\\\\\\"any size.\\\\\\\" Our framework is based on collecting samples through consecutive steps of random walks. We derive an analytical bound on the sample size (via the Chernoff-Hoeffding technique) to guarantee the convergence of our unbiased estimator. To further improve the accuracy, we introduce two novel optimization techniques to reduce the lower bound on the sample size. Experimental evaluations demonstrate that our methods outperform the state-of-the-art method up to an order of magnitude both in terms of accuracy and time cost.\",\n \"Title: A Unified Framework for Information Consumption Based on Markov Chains.\\nYear: 2016\\n\\nAbstract: This paper establishes a Markov chain model as a unified framework for understanding information consumption processes in complex networks, with clear implications to the Internet and big-data technologies. In particular, the proposed model is the first one to address the formation mechanism of the trichotomy in observed probability density functions from empirical data of various social and technical networks. Both simulation and experimental results demonstrate a good match of the proposed model with real datasets, showing its superiority over the classical power-law models.\",\n \"Title: Analysis of Reliability Dynamics of SSD RAID.\\nYear: 2016\\n\\nAbstract: Solid-state drives (SSDs) have been widely deployed in desktops and data centers. However, SSDs suffer from bit errors, and the bit error rate is time dependent since it increases as an SSD wears down. Traditional storage systems mainly use parity-based RAID to provide reliability guarantees by striping redundancy across multiple devices, but the effectiveness of traditional RAID schemes in SSDs remains debatable. In particular, an open problem is how different parity distributions over multiple devices influence the reliability of an SSD RAID array. That is, should we evenly distribute patsaverties as suggested by conventional wisdom, or unevenly distribute parties as recently proposed for SSD RAID? To address this fundamental problem, we propose the first analytical model to quantify the reliability dynamics of an SSD RAID array as it ages. Specifically, we develop a “non-homogeneous” continuous time Markov chain model, and derive the transient reliability solution. We validate our model via trace-driven simulation and conduct numerical analysis to analyze the reliability dynamics of SSD RAID arrays subject to different parity distributions, error rates, and SSD array configurations. Our model enables system practitioners to decide the appropriate parity distribution based on their reliability requirements.\",\n \"Title: Analyzing competitive influence maximization problems with partial information: An approximation algorithmic framework\\nYear: 2015\\n\\nAbstract: Given the popularity of the viral marketing campaign in online social networks, finding a computationally efficient method to identify a set of most influential nodes so as to compete well with others is of the utmost importance. In this paper, we propose a general model to describe the influence propagation of multiple competing sources in the same network. We formulate the Competitive Influence Maximization with Partial information (CIMP) problem: given an influence propagation model and the probability of a node being in the competitor’s seed set, how to find a set of k seeds so to trigger the largest expected influence cascade under the presence of other competitors? We propose a general algorithmic framework, Two-phase Competitive Influence Maximization (TCIM), to address the CIMP problem. TCIM returns a (1−1/e−ϵ)-approximate solution with probability of at least 1−n−ℓ, where ℓ≥1/2 is a parameter controlling the trade-off between the success probability and the computational efficiency. TCIM has an efficient expected time complexity of O(c(k+ℓ)(m+n)logn/ϵ2), where n and m are the number of nodes and edges in the network, and c is a function of the given propagation model (which may depend on k and the underlying network). To the best of our knowledge, this is the first work which provides a general framework for the competitive influence maximization problem where the seeds of the competitor could be given as an explicit set of seed nodes or a probability distribution of seed nodes. Moreover, our algorithmic framework provides both quality guarantee of solution and practical computational efficiency. We conduct extensive experiments on real-world datasets under three specific influence propagation models, and show the efficiency and accuracy of our framework. In particular, for the case where the seed set of the competitor is given explicitly, we achieve up to four orders of magnitude speedup as compared to previous algorithms with the same quality guarantee. When the competitor’s seed set is not given explicitly, running TCIM using the probability distribution of the competitor’s seeds returns nodes with higher expected influence than those nodes returned by TCIM using an explicit guess of the competitor’s seeds.\",\n \"Title: Modeling eBay-like reputation systems: Analysis, characterization and insurance mechanism design\\nYear: 2015\\n\\nAbstract: E-commerce systems like eBay are becoming increasingly popular. Having an effective reputation system is critical because it can assist buyers to evaluate the trustworthiness of sellers, and improve the revenue for reputable sellers and E-commerce operators. We formulate a stochastic model to analyze an eBay-like reputation system and propose four measures to quantify its effectiveness: (1) new seller ramp up time, (2) new seller drop out probability, (3) long term profit gains for sellers, and (4) average per seller transaction gains for E-commerce operators. By analyzing a dataset from eBay, we discover that eBay suffers a long ramp up time, low long term profit gains and low average per seller transaction gains. We design a novel insurance mechanism consisting of an insurance protocol and a transaction mechanism to improve the above four measures. We formulate an optimization framework to select appropriate parameters for our insurance mechanism. We conduct experiments on an eBay’s dataset and show that our insurance mechanism reduces ramp up time by 91%, improves both the long term profit gains and the average per seller transaction gains by 26.66%. It also guarantees that new sellers drop out with a small probability (close to 0).\",\n \"Title: Tracking Triadic Cardinality Distributions for Burst Detection in Social Activity Streams.\\nYear: 2015\\n\\nAbstract: In everyday life, we often observe unusually frequent interactions among people before or during important events, e.g., we receive/send more greetings from/to our friends on Christmas Day, than usual. We also observe that some videos suddenly go viral through people's sharing in online social networks (OSNs). Do these seemingly different phenomena share a common structure? All these phenomena are associated with sudden surges of user activities in networks, which we call \\\"bursts\\\" in this work. We find that the emergence of a burst is accompanied with the formation of triangles in networks. This finding motivates us to propose a new method to detect bursts in OSNs. We first introduce a new measure, \\\"triadic cardinality distribution\\\", corresponding to the fractions of nodes with different numbers of triangles, i.e., triadic cardinalities, within a network. We demonstrate that this distribution changes when a burst occurs, and is naturally immunized against spamming social-bot attacks. Hence, by tracking triadic cardinality distributions, we can reliably detect bursts in OSNs. To avoid handling massive activity data generated by OSN users, we design an efficient sample-estimate solution to estimate the triadic cardinality distribution from sampled data. Extensive experiments conducted on real data demonstrate the usefulness of this triadic cardinality distribution and the effectiveness of our sample-estimate solution.\",\n \"Title: DroidEagle: seamless detection of visually similar Android apps\\nYear: 2015\\n\\nAbstract: Repackaged malware and phishing malware consist 86% [35] of all Android malware, and they significantly affect the Android ecosystem. Previous work use disassembled Dalvik bytecode and hashing approaches to detect repackaged malware, but these approaches are vulnerable to obfuscation attacks and they demand large computational resources on mobile devices. In this work, we propose a novel methodology which uses the layout resources within an app to detect apps which are \\\\\\\"visually similar\\\\\\\", a common characteristic in repackaged apps and phishing malware. To detect visually similar apps, we design and implement DroidEagle which consists of two sub-systems: RepoEagle and HostEagle. RepoEagle is to perform large scale detection on apps repositories (e.g., apps markets), and HostEagle is a lightweight mobile app which can help users to quickly detect visually similar Android app upon download. We demonstrate the high accuracy and efficiency of DroidEagle: Within 3 hours RepoEagle can detect 1298 visually similar apps from 99 626 apps in a repository. In less than one second, HostEagle can help an Android user to determine whether a downloaded mobile app is a repackaged apps or a phishing malware. This is the first work which provides both speed and scalability in discovering repackaged apps and phishing malware in Android system.\",\n \"Title: A Data Driven Approach to Uncover Deficiencies in Online Reputation Systems\\nYear: 2015\\n\\nAbstract: Online reputation systems serve as core building blocks in various Internet services such as E-commerce (e.g. eBay) and crowdsourcing (e.g., oDesk). The flaws of real-world online reputation systems were reported extensively. Users who are frustrated about the system will eventually abandon such service. However, no formal studies have explored such flaws. This paper presents the first attempt, which develops a novel data analytical framework to uncover online reputation system deficiencies from data. We develop a novel measure to quantify the efficiency of online reputation systems, i.e., ramp up time of a new service provider. We first show that inherent preferences or personal biases in assigning feedbacks (or ratings) cause the computational infeasibility in evaluating online reputation systems from data. We develop a computationally efficient randomized algorithm with theoretical performance guarantees to address this computational challenge. We apply our methodology to real-life datasets (from eBay and Google Helpouts), we discover that the ramp up time in eBay and Google Helpouts are around 791 and 1,327 days respectively. Around 78.7% sellers have ramped up in eBay and only 1.5% workers have ramped up in Google Helpouts. This small fraction and the long ramp up time (1,327 days) explain why Google Helpouts was eventually shut down in April 2015.\",\n \"Title: A Mechanism Approach To Reduce New Seller Ramp Up Time In Ebay-Like Reputation Systems\\nYear: 2015\\n\\nAbstract: We present a novel insurance mechanism consisting of an insurance protocol and a transaction mechanism to reduce new seller ramp up time in eBay-like reputation mechanisms. We conduct experiments on an eBay's dataset and show that our insurance mechanism reduces ramp up time by 90%.\",\n \"Title: A tale of three graphs: Sampling design on hybrid social-affiliation networks\\nYear: 2015\\n\\nAbstract: Random walk-based graph sampling methods have become increasingly popular and important for characterizing large-scale complex networks. While powerful, they are known to exhibit problems when the graph is loosely connected, which slows down the convergence of a random walk and can result in poor estimation accuracy. In this work, we observe that many graphs under study, called target graphs, usually do not exist in isolation. In many situations, a target graph is often related to an auxiliary graph and an affiliation graph, and the target graph becomes better connected when viewed from these three graphs as a whole, or what we called a hybrid social-affiliation network. This viewpoint brings extra benefits to the graph sampling framework, e.g., when directly sampling a target graph is difficult or inefficient, we can efficiently sample it with the assistance of auxiliary and affiliation graphs. We propose three sampling methods on such a hybrid social-affiliation network to estimate target graph characteristics, and conduct extensive experiments on both synthetic and real datasets, to demonstrate the effectiveness of these new sampling methods.\",\n \"Title: VENUS: Vertex-Centric Streamlined Graph Computation on a Single PC\\nYear: 2015\\n\\nAbstract: Recent studies show that disk-based graph computation on just a single PC can be as highly competitive as cluster-based computing systems on large-scale problems. Inspired by this remarkable progress, we develop VENUS, a disk-based graph computation system which is able to handle billion-scale problems efficiently on a commodity PC. VENUS adopts a novel computing architecture that features vertex-centric \\\" streamlined \\\" processing – the graph is sequentially loaded and the update functions are executed in parallel on the fly. VENUS deliberately avoids loading batch edge data by separating read-only structure data from mutable vertex data on disk. Furthermore, it minimizes random IOs by caching vertex data in main memory. The streamlined processing is realized with efficient sequential scan over massive structure data and fast feeding a large number of update functions. Extensive evaluation on large real-world and synthetic graphs has demonstrated the efficiency of VENUS. For example, VENUS takes just 8 minutes with hard disk for PageRank on the Twitter graph with 1.5 billion edges. In contrast, Spark takes 8.1 minutes with 50 machines and 100 CPUs, and GraphChi takes 13 minutes using fast SSD drive.\",\n \"Title: I/O Efficient Algorithms for Exact Distance Queries on Disk-Resident Dynamic Graphs\\nYear: 2015\\n\\nAbstract: Point-to-point shortest distance queries are fundamental to large graph analytics. Motivated by the need for low-latency distance queries in large-scale \\\\\\\"dynamic\\\\\\\" graphs, we consider the problem of answering exact shortest distance queries on disk-resident scale-free dynamic graphs. Our query processing uses the canonical labeling method, which is a special 2-hop distance labeling for fast distance queries. In this paper, we propose two I/O efficient algorithms to update the canonical labeling. To the best of our knowledge, our proposed methods are the first practical disk-based methods to \\\\\\\"incrementally update\\\\\\\" the canonical labeling on dynamic graphs. We also show how to answer distance queries on the latest network based on outdated labels and new edges. Extensive experiments demonstrate the efficiency of our methods. Our update methods are an order of magnitude faster than reconstructing the canonical labeling. When the number of new edges is small, say less than 1% of the previous number of edges, our query algorithm based on outdated labels provides exact shortest distance and the query time is comparable to other query algorithms using latest labels.\",\n \"Title: Diversified Temporal Subgraph Pattern Mining\\nYear: 2016\\n\\nAbstract: Many graphs in real-world applications, such as telecommunications networks, social-interaction graphs and co-authorship graphs, contain temporal information. However, existing graph mining algorithms fail to exploit these temporal information and the resulting subgraph patterns do not contain any temporal attribute. In this paper, we study the problem of mining a set of diversified temporal subgraph patterns from a temporal graph, where each subgraph is associated with the time interval that the pattern spans. This problem motivates important applications such as finding social trends in social networks, or detecting temporal hotspots in telecommunications networks. We propose a divide-and-conquer algorithm along with effective pruning techniques, and our approach runs 2 to 3 orders of magnitude faster than a baseline algorithm and obtains high-quality temporal subgraph patterns in real temporal graphs.\",\n \"Title: Taming Energy Cost of Disk Encryption Software on Data-Intensive Mobile Devices.\\nYear: 2016\\n\\nAbstract: Disk encryption is frequently used to secure confidential data on mobile devices. However, the high energy cost of disk encryption poses a heavy burden on those devices with limited battery capacity especially when a large amount of data needs to be protected by disk encryption. To address the challenge, we develop a new kernel-level disk encryption software, Populus. Almost 98% of Populus's encryption/decryption computation is not related with the input plaintext/ciphertext, so we accomplish the computation in advance during initialization when a consistent power supply is available. We conduct cryptanalysis on Populus and finally conclude that state-of-the-art cryptanalysis techniques fail to break Populus in reasonable computational complexity. We also conduct energy consumption experiments on Populus and dm-crypt, a famous disk encryption software for Android and Linux mobile devices. The experimental results demonstrate that Populus consumes 50%-70% less energy than dm-crypt.\",\n \"Title: Stochastic Modeling of Hybrid Cache Systems\\nYear: 2016\\n\\nAbstract: In recent years, there is an increasing demand of big memory systems so to perform large scale data analytics. Since DRAM memories are expensive, some researchers are suggesting to use other memory systems such as non-volatile memory (NVM) technology to build large-memory computing systems. However, whether the NVM technology can be a viable alternative (either economically and technically) to DRAM remains an open question. To answer this question, it is important to consider how to design a memory system from a \\\"system perspective\\\",that is, incorporating different performance characteristics andprice ratios from hybrid memory devices. This paper presents an analytical model of a \\\"hybrid page cache system\\\" so to understand the diverse design space and performance impact of a hybrid cache system. We consider (1) various architectural choices, (2) design strategies, and (3) configuration of different memory devices. Using this model, we provide guidelines on how to design hybrid page cache to reach a good trade-off between high system throughput (in I/O per sec or IOPS) and fast cache reactivity which is defined by the time to fill the cache. We also show how one can configure the DRAM capacity and NVM capacity under a fixed budget. We pick PCM as an example for NVM and conduct numerical analysis. Our analysis indicates that incorporating PCM in a page cache system significantly improves the system performance, and it also shows larger benefit to allocate more PCM in page cache in some cases. Besides, for the common setting of performance-price ratio of PCM, \\\"flat architecture\\\" offers as a better choice, but \\\"layered architecture\\\" outperforms if PCM write performance can be significantly improved in the future.\",\n \"Title: VENUS: A System for Streamlined Graph Computation on a Single PC.\\nYear: 2016\\n\\nAbstract: Recent studies show that disk-based graph computation systems on just a single PC can be as highly competitive as cluster-based systems on large-scale problems. Inspired by this remarkable progress, we develop VENUS, a disk-based graph computation system which is able to handle billion-scale graphs efficiently on a commodity PC. VENUS adopts a novel computing architecture that features vertex-cent...\",\n \"Title: Trading Discount for Reputation?: On the Design and Analysis of E-Commerce Discount Mechanisms.\\nYear: 2016\\n\\nAbstract: We develop an optimization framework to trade short-term profits for reputation (i.e., reducing ramp-up time). We apply the stochastic bandits framework to design an online discounting mechanism which infers the optimal discount from a seller's historical transaction data. We conduct experiments on an eBay's dataset and show that our online discounting mechanism can trade 60% of the shortterm profits for reducing the ramp-up time by 40%.\",\n \"Title: Minfer: A Method Of Inferring Motif Statistics From Sampled Edges\\nYear: 2016\\n\\nAbstract: Characterizing motif (i.e., locally connected subgraph patterns) statistics is important for understanding complex networks such as online social networks and communication networks. Previous work made the strong assumption that the graph topology of interest is known in advance. In practice, sometimes researchers have to deal with the situation where the graph topology is unknown because it is expensive to collect and store all topological and meta information. Hence, typically what is available to researchers is only a snapshot of the graph, i.e., a subgraph of the graph. Crawling methods such as breadth first sampling can be used to generate the snapshot. However, these methods fail to sample a streaming graph represented as a high speed stream of edges. Therefore, graph mining applications such as network traffic monitoring use random edge sampling (i.e., sample each edge with a fixed probability) to collect edges and generate a sampled graph, which we called a \\\" RE Sampled graph \\\". Clearly, a RESampled graph's motif statistics may be quite different from those of the underlying original graph. To resolve this, we propose a framework and implement a system called Minfer, which takes the given RESampled graph and accurately infers the underlying graph's motif statistics. We also apply Fisher information to bound the errors of our estimates. Experiments using large scale datasets show the accuracy and efficiency of our method.\",\n \"Title: PowerWalk: Scalable Personalized PageRank via Random Walks with Vertex-Centric Decomposition\\nYear: 2016\\n\\nAbstract: Most methods for Personalized PageRank (PPR) precompute and store all accurate\\nPPR vectors, and at query time, return the ones of interest directly. However,\\nthe storage and computation of all accurate PPR vectors can be prohibitive for\\nlarge graphs, especially in caching them in memory for real-time online\\nquerying. In this paper, we propose a distributed framework that strikes a\\ngood balance between offline indexing and online querying. The offline\\nindexing computes a fingerprint of the PPR vector of each vertex by performing\\nbillions of ``short'' random walks in parallel across a cluster of machines.\\nWe prove that our indexing method has an exponential convergence, achieving the\\nsame precision with previous methods using a much smaller number of random\\nwalks. At query time, the new PPR vector is composed by a linear combination\\nof related fingerprints, in a highly efficient vertex-centric decomposition\\nmanner. Interestingly, the resulting PPR vector is much more accurate than its\\noffline counterpart because it actually uses more random walks in its\\nestimation. More importantly, we show that such decomposition for a batch of\\nqueries can be very efficiently processed using a shared decomposition. Our\\nimplementation, PowerWalk, takes advantage of advanced distributed graph\\nengines and it outperforms the state-of-the-art algorithms by orders of\\nmagnitude. Particularly, it responses to tens of thousands of queries on\\ngraphs with billions of edges in just a few seconds.\",\n \"Title: Generalization of Information Spreading Forensics via Sequential Dependent Snapshots.\\nYear: 2016\\n\\nAbstract: Learning the characteristics of information spreading in networks is crucial in communication studies, social network sentiment analysis and epidemic investigation. Previous work on information spreading has been focused on the information source detection using either a single observation, or multiple but \\\\\\\"independent\\\\\\\" observations of the underlying network while assuming information spreads at a \\\\\\\"uniform spreading rate\\\\\\\". In this paper, we conduct the first theoretical and experimental study on information spreading forensics, and propose a framework for estimating information spreading rates, information source start time and location of information source by utilizing \\\\\\\"multiple sequential and dependent snapshots\\\\\\\" where information can spread at different rates. We prove that our estimation framework generalizes the rumor centrality [1], and we allow heterogeneous information spreading rates on different branches in d-regular tree networks. We further show that our framework can provide highly accurate estimates for the information spreading rates on different branches, the source start time, and more accurate estimate for the location of information source than rumor centrality and Jordan center in both synthetic networks and real-world networks (i.e., Twitter).\",\n \"Title: Design and Analysis of Incentive and Reputation Mechanisms for Online Crowdsourcing Systems.\\nYear: 2016\\n\\nAbstract: Today, online crowdsourcing services like Amazon Mechanical Turk, UpWork, and Yahoo! Answers are gaining in popularity. For such online services, it is important to attract “workers” to provide high-quality solutions to the “tasks” outsourced by “requesters.” The challenge is that workers have different skill sets and can provide different amounts of effort. In this article, we design a class of incentive and reputation mechanisms to solicit high-quality solutions from workers. Our incentive mechanism allows multiple workers to solve a task, splits the reward among workers based on requester evaluations of the solution quality, and guarantees that high-skilled workers provide high-quality solutions. However, our incentive mechanism suffers the potential risk that a requester will eventually collects low-quality solutions due to fundamental limitations in task assigning accuracy. Our reputation mechanism ensures that low-skilled workers do not provide low-quality solutions by tracking workers’ historical contributions and penalizing those workers having poor reputations. We show that by coupling our reputation mechanism with our incentive mechanism, a requester can collect at least one high-quality solution. We present an optimization framework to select parameters for our reputation mechanism. We show that there is a trade-off between system efficiency (i.e., the number of tasks that can be solved for a given reward) and revenue (i.e., the amount of transaction fees), and we present the optimal trade-off curve between system efficiency and revenue. We demonstrate the applicability and effectiveness of our mechanisms through experiments using a real-world dataset from UpWork. We infer model parameters from this data, use them to determine proper rewards, and select the parameters of our incentive and reputation mechanisms for UpWork. Experimental results show that our incentive and reputation mechanisms achieve 98.82% of the maximum system efficiency while only sacrificing 4% of revenue.\",\n \"Title: TaintART: A Practical Multi-level Information-Flow Tracking System for Android RunTime.\\nYear: 2016\\n\\nAbstract: Mobile operating systems like Android failed to provide sufficient protection on personal data, and privacy leakage becomes a major concern. To understand the security risks and privacy leakage, analysts have to carry out data-flow analysis. In 2014, Android upgraded with a fundamentally new design known as Android RunTime (ART) environment in Android 5.0. ART adopts ahead-of-time compilation strategy and replaces previous virtual-machine-based Dalvik. Unfortunately, many data-flow analysis systems like TaintDroid were designed for the legacy Dalvik environment. This makes data-flow analysis of new apps and malware infeasible. We design a multi-level information-flow tracking system for the new Android system called TaintART. TaintART employs a multi-level taint analysis technique to minimize the taint tag storage. Therefore, taint tags can be stored in processor registers to provide efficient taint propagation operations. We also customize the ART compiler to maximize performance gains of the ahead-of-time compilation optimizations. Based on the general design of TaintART, we also implement a multi-level privacy enforcement to prevent sensitive data leakage. We demonstrate that TaintART only incurs less than 15% overheads on a CPU-bound microbenchmark and negligible overhead on built-in or third-party applications. Compared to legacy Dalvik environment in Android 4.4, TaintART achieves about 99.7% faster performance for Java runtime benchmark.\",\n \"Title: BLOW-UP: Toward Distributed and Scalable Space Filling Curve Construction in 3D Volumetric WSNs.\\nYear: 2016\\n\\nAbstract: In wireless sensor networks (WSNs), a space filling curve (SFC) refers to a path passing through all nodes in the network, with each node visited at least once. By enforcing a linear order of the sensor nodes through an SFC, many applications in WSNs concerning serial operations on both sensor nodes and sensor data can be performed, with examples including serial data fusion and path planning of mobile nodes. Although a few studies have made efforts to find such SFCs in WSNs, they primarily target 2D planar or 3D surface settings and cannot be directly applied to 3D volumetric WSNs due to considerably more complex geometric features and topology shapes that the 3D volumetric settings introduce. This article presents BLOW-UP, a distributed, scalable, and connectivity-based algorithm to construct an SFC for a 3D volumetric WSN (or alternatively to linearize the 3D volumetric network). The main idea of BLOW-UP is to decompose the given 3D volumetric network into a series of connected and closed layers, and the nodes are traversed layer by layer, incrementally from the innermost to the outermost, yielding an SFC covering the entire network, provably at least once and at most a constant number of times. To the best of our knowledge, BLOW-UP is the first algorithm that realizes linearization in 3D volumetric WSNs. It does not require advance knowledge of location or distance information. It is also scalable with a nearly constant per-node storage cost and message cost. Extensive simulations under various networks demonstrate its effectiveness on nodes’ covered times, coverage rate, and covering speed.\",\n \"Title: Monet: A User-oriented Behavior-based Malware Variants Detection System for Android.\\nYear: 2017\\n\\nAbstract: Android, the most popular mobile OS, has around 78% of the mobile market share. Due to its popularity, it attracts many malware attacks. In fact, people have discovered around 1 million new malware samples per quarter, and it was reported that over 98% of these new malware samples are in fact “derivatives” (or variants) from existing malware families. In this paper, we first show that runtime beha...\",\n \"Title: Accurate Learning or Fast Mixing? Dynamic Adaptability of Caching Algorithms.\\nYear: 2018\\n\\nAbstract: Typical analysis of content caching algorithms using the metric of steady state hit probability under a stationary request process does not account for performance loss under a variable request arrival process. In this paper, we instead conceptualize caching algorithms as complexity-limited online distribution learning algorithms and use this vantage point to study their adaptability from two pers...\",\n \"Title: eBA: Efficient Bandwidth Guarantee Under Traffic Variability in Datacenters.\\nYear: 2017\\n\\nAbstract: Datacenter networks suffer unpredictable performance due to a lack of application level bandwidth guarantees. A lot of attention has been drawn to solve this problem such as how to provide bandwidth guarantees for virtualized machines VMs, proportional bandwidth share among tenants, and high network utilization under peak traffic. However, existing solutions fail to cope with highly dynamic traffic in datacenter networks. In this paper, we propose eBA, an efficient solution to bandwidth allocation that provides end-to-end bandwidth guarantee for VMs under large numbers of short flows and massive bursty traffic in datacenters. eBA leverages a novel distributed VM-to-VM rate control algorithm based on the logistic model under the control-theoretic framework. eBA’s implementation requires no changes to hardware or applications and can be deployed in standard protocol stack. The theoretical analysis and the experimental results show that eBA not only guarantees the bandwidth for VMs, but also provides fast convergence to efficiency and fairness, as well as smooth response to bursty traffic.\",\n \"Title: Mining Graphlet Counts in Online Social Networks.\\nYear: 2018\\n\\nAbstract: Counting subgraphs is a fundamental analysis task for online social networks (OSNs). Given the sheer size and restricted access of OSN, efficient computation of subgraph counts is highly challenging. Although a number of algorithms have been proposed to estimate the relative counts of subgraphs in OSNs with restricted access, there are only few works which try to solve a more general problem, i.e., counting subgraph frequencies. In this article, we propose an efficient random walk-based framework to estimate the subgraph counts. Our framework generates samples by leveraging consecutive steps of the random walk as well as by observing neighbors of visited nodes. Using the importance sampling technique, we derive unbiased estimators of the subgraph counts. To make better use of the degree information of visited nodes, we also design improved estimators, which increases the accuracy of the estimation with no additional cost. We conduct extensive experimental evaluation on real-world OSNs to confirm our theoretical claims. The experiment results show that our estimators are unbiased, accurate, efficient, and better than the state-of-the-art algorithms. For the Weibo graph with more than 58 million nodes, our method produces estimate of triangle count with an error less than 5% using only 20,000 sampled nodes. Detailed comparison with the state-of-the-art methods demonstrates that our algorithm is 2--10 times more accurate.\",\n \"Title: I/O-efficient calculation of H-group closeness centrality over disk-resident graphs.\\nYear: 2017\\n\\nAbstract: We introduce H-group closeness centrality in this work. H-group closeness centrality of a group of nodes measures how close this node group is to other nodes in a graph, and can be used in numerous applications such as measuring the importance and influence of a group of users in a social network. When a large graph contains billions of edges that cannot reside entirely in a computers main memory, computing and maximizing H-group closeness centrality both become challenging. In this work, we present a systematic solution for efficiently computing and maximizing H-group closeness centrality in large disk-resident graphs, only using a common PC. Our solution leverages a probabilistic counting method to efficiently estimate H-group closeness with high accuracy, rather than exhaustively computing it in an exact fashion. Furthermore, we design an I/O-efficient greedy algorithm to find a node group that approximately maximizes H-group closeness centrality in a graph. Our algorithm exploits several appealing properties of the defined H-group closeness measure to reduce the computational cost of handling a disk-resident graph. Extensive evaluation on large real-world and synthetic graphs demonstrates the efficiency of our proposed method. For example, our proposed I/O-efficient greedy algorithm is about 300 times faster than a simple multi-pass method on the Twitter graph with 1.4 billion edges. This reduces the running time of identifying one group member from nearly an hour to less than 20s on average.\",\n \"Title: Workload-Aware Elastic Striping With Hot Data Identification for SSD RAID Arrays.\\nYear: 2017\\n\\nAbstract: Redundant array of independent disk (RAID) offers a good option to provide device-level fault tolerance for solid-state drives (SSDs). However, parity update with either read–modify–write or read–reconstruct–write may introduce a lot of extra I/Os and thus significantly degrades SSD RAID performance. To reduce the parity update cost, elastic striping chooses to reconstruct new stripes with only the newly updated data chunks instead of directly updating parity chunks. However, it necessitates an RAID-level garbage collection (GC) process, which may incur a very high cost due to the mixture of hot and cold data chunks. To address this problem, we follow the idea of elastic striping and propose a workload-aware scheme (WAS) to reduce the RAID-level GC cost so as to improve the performance and endurance of SSD RAID. In particular, we first develop a novel lightweight hot data identification scheme which requires only a very small computation time and memory cost, then propose a hotness-aware elastic striping approach to separately write data chunks with different hotness to different regions in SSD RAID. To evaluate the effectiveness and efficiency of our WAS, we implement a prototype system on RAID-5 and RAID-6 arrays composed of commercial SSDs. Experimental results show that compared to original elastic striping, our scheme reduces 30.0%–70.6% (and 23.9%–63.2%) of chunk writes under the RAID-5 (and RAID-6) settings, and also reduces the average response time by 60.9%–79.3% (and 56.8%–80.9%) for RAID-5 (and RAID-6), respectively. Besides, our scheme also improves the endurance and reliability of SSD RAID compared to original elastic striping.\",\n \"Title: A Fast Sampling Method of Exploring Graphlet Degrees of Large Directed and Undirected Graphs.\\nYear: 2016\\n\\nAbstract: Exploring small connected and induced subgraph patterns (CIS patterns, or graphlets) has recently attracted considerable attention. Despite recent efforts on computing the number of instances a specific graphlet appears in a large graph (i.e., the total number of CISes isomorphic to the graphlet), little attention has been paid to characterizing a nodeu0027s graphlet degree, i.e., the number of CISes isomorphic to the graphlet that include the node, which is an important metric for analyzing complex networks such as social and biological networks. Similar to global graphlet counting, it is challenging to compute node graphlet degrees for a large graph due to the combinatorial nature of the problem. Unfortunately, previous methods of computing global graphlet counts are not suited to solve this problem. In this paper we propose sampling methods to estimate node graphlet degrees for undirected and directed graphs, and analyze the error of our estimates. To the best of our knowledge, we are the first to study this problem and give a fast scalable solution. We conduct experiments on a variety of real-word datasets that demonstrate that our methods accurately and efficiently estimate node graphlet degrees for graphs with millions of edges.\",\n \"Title: Towards Efficient Node Discoverability Optimization on Large Networks.\\nYear: 2017\\n\\nAbstract: Many people dream to become famous, YouTube video makers also wish their videos to have a large audience, and product retailers always hope to expose their products to customers as many as possible. Do these seemingly different phenomena share a common structure? We find that fame, popularity, or exposure, could be modeled as a nodeu0027s discoverability in some properly defined network, and all of the previously mentioned phenomena can be stated as: a target node wants to be discovered easily by existing nodes in the network. In this work, we explicitly define the node discoverability in a network, and formulate a general node discoverability optimization problem. While the optimization problem is NP-hard, we find that the defined discoverability measures satisfy good properties that enable us to use a greedy algorithm to find provably near-optimal solutions. The computational complexity of a greedy algorithm is dominated by the time cost of an oracle call, i.e., calculating the marginal gain of a given node. To scale up the oracle call over large networks, we propose an estimation-and-refinement approach, that provides a good trade-off between estimation accuracy and computational efficiency. Experiments conducted on real graphs demonstrate that our method is hundreds of times faster than an exact method using dynamic programming, thereby allowing us to solve the optimization problem on large networks.\",\n \"Title: Cocoa: Dynamic Container-Based Group Buying Strategies for Cloud Computing.\\nYear: 2017\\n\\nAbstract: Although the Infrastructure-as-a-Service (IaaS) cloud offers diverse instance types to users, a significant portion of cloud users, especially those with small and short demands, cannot find an instance type that exactly fits their needs or fully utilize purchased instance-hours. In the meantime, cloud service providers are also faced with the challenge to consolidate small, short jobs, which exhibit strong dynamics, to effectively improve resource utilization. To handle such inefficiencies and improve cloud resource utilization, we propose Cocoa (COmputing in COntAiners), a novel group buying mechanism that organizes jobs with complementary resource demands into groups and allocates them to group buying deals predefined by cloud providers. Each group buying deal offers a resource pool for all the jobs in the deal, which can be implemented as either a virtual machine or a physical server. By running each user job on a virtualized container, our mechanism allows flexible resource sharing among different users in the same group buying deal, while improving resource utilization for cloud providers. To organize jobs with varied resource demands and durations into groups, we model the initial static group organization as a variable-sized vector bin packing problem, and the subsequent dynamic group organization problem as an online multidimensional knapsack problem. Through extensive simulations driven by a large amount of real usage traces from a Google cluster, we evaluate the potential cost reduction achieved by Cocoa. We show that through the effective combination and interaction of the proposed static and dynamic group organization strategies, Cocoa greatly outperforms the existing cloud workload consolidation mechanism, substantiating the feasibility of group buying in cloud computing.\",\n \"Title: On the Feasibility of Inter-Domain Routing via a Small Broker Set.\\nYear: 2019\\n\\nAbstract: The Internet is a gigantic distributed system where the end-to-end (E2E) quality-of-service (QoS) plays an important role. Yet the current inter-domain routing protocol, namely, the Border Gateway Protocol (BGP), cannot provide E2E QoS guarantees. The main reason is that an autonomous system (AS) can only receive guarantees from its first-hop ASes via service level agreements (SLAs). But beyond the first-hop, QoS along the path from a source AS to a destination AS is not within the source AS's control regime. This makes it difficult to provide high quality-of-experience services to many Internet users even when many content providers are willing to pay for such high quality E2E guarantees. In this paper, we investigate the feasibility of providing high QoS-guaranteed E2E transit services by utilizing a (small) set of ASes/IXPs to serve as “\\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">brokers</italic>\\n” to provide supervision, control and resource negotiation. Finding an optimal set of ASes as brokers can be formulated as a Maximum Coverage with \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"><tex-math notation=\\\"LaTeX\\\">$B-$</tex-math><alternatives><inline-graphic xlink:href=\\\"lui-ieq1-2865572.gif\\\"/></alternatives></inline-formula>\\ndominating path Guarantee (MCBG) problem, and we show that it is in fact NP-hard. To address this problem, we design a \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"><tex-math notation=\\\"LaTeX\\\">$(\\\\frac{1{-}e^{-\\\\!1}}{2}){-}$</tex-math><alternatives><inline-graphic xlink:href=\\\"lui-ieq2-2865572.gif\\\"/></alternatives></inline-formula>\\napproximation algorithm and also an efficient heuristic algorithm when additional constraints (e.g., the path length) are considered. We further analyze the APX-hardness of the MCBG problem to reveal the existence of the best approximation ratio. Based on the current Internet topology, we demonstrate that it is indeed feasible to provide high QoS guarantees for most E2E connections with only a small broker set: with only 0.19, 1.9 or 6.8 percent ASes/IXPs serving as brokers, 53.13, 85.41 or 99.29 percent of all global E2E connections can receive high QoS-guaranteed services. Finally, we provide an economic model to study the behaviours of ASes when cooperating our brokerage scheme with the BGP protocol, and show that there are incentives to form and maintain such a brokerage coalition.\",\n \"Title: Joint Optimization of Chain Placement and Request Scheduling for Network Function Virtualization\\nYear: 2017\\n\\nAbstract: Compared with executing Network Functions (NFs) on dedicated hardwares, the recent trend of Network Function Virtualization (NFV) holds the promise for operators to flexibly deploy software-based NFs on commodity servers. However, virtual NFs (VNFs) are normally \\\"chained\\\" together to provide a specific network service. Thus, an efficient scheme is needed to place the VNF chains across the network and effectively schedule requests to service instances, which can maximize the average resource utilization of each node in service and simultaneously minimize the average response latency of each request. To this end, we formulate first VNF chains placement problem as a variant of bin-packing problem, which is NP-hard, and we model request scheduling problem based on the key concepts from open Jackson network. To jointly optimize the performance of NFV, we propose a priority-driven weighted algorithm to improve resource utilization and a heuristic algorithm to reduce response latency. Through extensive trace-driven simulations, we show that our methods can indeed enhance performance in diverse scenarios. In particular, we can improve the average resource utilization by 33.4% and can reduce the average total latency by 19.9% as compared with the state-of-the-art methods.\",\n \"Title: Smartmd: A High Performance Deduplication Engine With Mixed Pages\\nYear: 2017\\n\\nAbstract: In hypervisor-based virtualization environments, translation lookaside buffers (TLBs) misses may induce two-dimensional page table walks, which may incur a long access latency, and this issue becomes worse with ever increasing memory capacity. To reduce the overhead of TLB misses, large pages (e.g., 2M-pages) are widely supported in modern hardware platforms to reduce the number of page table entries. However, memory management with large pages can be inefficient in deduplication, leading to low utilization of memory, which is a precious resource for a variety of applications.To simultaneously enjoy benefits of high performance by accessing memory with large pages (e.g., 2M-pages) and high deduplication rate by managing memory with base pages (e.g., 4K-pages), we propose Smart Memory Deduplciation, or SmartMD in short, which is an adaptive and efficient management scheme for mixed-page memory. Specifically, we propose two lightweight schemes to accurately monitor pages' access frequency and repetition rate, and present a dynamic and adaptive conversion scheme to selectively split or reconstruct large pages. We implement a prototype system and conduct extensive experiments with various workloads. Experiment results show that SmartMD can simultaneously achieve high access performance similar to systems using large pages, and achieves a deduplication rate similar to that applying aggressive deduplication scheme (i.e., KSM) at the same time on base pages.\",\n \"Title: Pricing Intra-Datacenter Networks With Over-Committed Bandwidth Guarantee\\nYear: 2017\\n\\nAbstract: Current IaaS clouds provide performance guarantee on CPU and memory but no quantitative network performance for VM instances. Our measurements from three production IaaS clouds show that for the VMs with same CPU and memory, or similar pricing, the difference in bandwidth performance can be as much as 16x, which reveals a severe price-performance anomaly due to a lack of pricing for bandwidth guarantee. Considering the low network utilization in cloud-scale datacenters, we address this by presenting SoftBW, a system that enables pricing bandwidth with over commitment on bandwidth guarantee. SoftBW leverages usage-based charging to guarantee price-performance consistency among tenants, and implements a fulfillment based scheduling to provide bandwidth/fairness guarantee under bandwidth over commitment. Both testbed experiments and large-scale simulation results validate Soft13Ws ability of providing efficient bandwidth guarantee, and show that by using bandwidth over commitment, SoftBW increases 3.9x network utilization while incurring less than 5% guarantee failure.\",\n \"Title: Multi-dimensional network security game: How do attacker and defender battle on parallel targets?\\nYear: 2014\\n\\nAbstract: In this paper, we consider a new network security game wherein an attacker and a defender are battling over “multiple” targets. This type of game is appropriate to model many current network security conflicts such as Internet phishing, mobile malware or network intrusions. In such attacks, the attacker and the defender need to decide how to allocate resources on each target so as to maximize their utilities within their resource limits. We model such a multidimensional network security game as a constrained nonzero sum game. Two security breaching models, the product-form and the proportion-form, are considered. For each breaching model, we prove the existence of a unique Nash equilibrium (NE) based on Rosen's theorem and propose efficient algorithms to find the NE when the games are strictly concave. Furthermore, we show the existence of multiple NEs in the product-form breaching model when the strict concavity does not hold. Our study sheds light on the strategic behaviors of the attacker and the defender, in particular, on how they allocate resources to the targets which have different weights, and how their utilities as well as strategies are influenced by the resource constraints.\",\n \"Title: Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations\\nYear: 2018\\n\\nAbstract: The unbiasedness of online product ratings, an important property to ensure that users' ratings indeed reflect their true evaluations to products, is vital both in shaping consumer purchase decisions and providing reliable recommendations. Recent experimental studies showed that distortions from historical ratings would ruin the unbiasedness of subsequent ratings. How to \\\\\\\"discover\\\\\\\" the distortions from historical ratings in each single rating (or at the micro-level), and perform the \\\\\\\"debiasing operations\\\\\\\" in real rating systems are the main objectives of this work. Using 42 million real customer ratings, we first show that users either \\\\\\\"assimilate\\\\\\\" or \\\\\\\"contrast\\\\\\\" to historical ratings under different scenarios: users conform to historical ratings if historical ratings are not far from the product quality (assimilation), while users deviate from historical ratings if historical ratings are significantly different from the product quality (contrast). This phenomenon can be explained by the well-known psychological argument: the \\\\\\\"Assimilate-Contrast\\\\\\\" theory. However, none of the existing works on modeling historical ratings' influence have taken this into account, and this motivates us to propose the Historical Influence Aware Latent Factor Model (HIALF), the first model for real rating systems to capture and mitigate historical distortions in each single rating. HIALF also allows us to study the influence patterns of historical ratings from a modeling perspective, and it perfectly matches the assimilation and contrast effects we previously observed. Also, HIALF achieves significant improvements in predicting subsequent ratings, and accurately predicts the relationships revealed in previous empirical measurements on real ratings. Finally, we show that HIALF can contribute to better recommendations by decoupling users' real preference from distorted ratings, and reveal the intrinsic product quality for wiser consumer purchase decisions.\",\n \"Title: The Economics of the Cloud\\nYear: 2017\\n\\nAbstract: This article proposes a model to study the interaction of price competition and congestion in the cloud computing marketplace. Specifically, we propose a three-tier market model that captures a marketplace with users purchasing services from Software-as-a-Service (SaaS) providers, which in turn purchase computing resources from either Provider-as-a-Service (PaaS) or Infrastructure-as-a-Service (IaaS) providers. Within each level, we define and characterize market equilibria. Further, we use these characterizations to understand the relative profitability of SaaSs and PaaSs/IaaSs and to understand the impact of price competition on the user experienced performance, that is, the “price of anarchy” of the cloud marketplace. Our results highlight that both of these depend fundamentally on the degree to which congestion results from shared or dedicated resources in the cloud.\",\n \"Title: A Unified Framework to Estimate Global and Local Graphlet Counts for Streaming Graphs.\\nYear: 2017\\n\\nAbstract: Counting small connected subgraph patterns called graphlets is emerging as a powerful tool for exploring topological structure of networks and for analysis of roles of individual nodes. Graphlets have numerous applications ranging from biology to network science. Computing graphlet counts for \\\"dynamic graphs\\\" is highly challenging due to the streaming nature of the input, sheer size of the graphs, and superlinear time complexity of the problem. Few practical results are known under the massive streaming graphs setting. In this work, we propose a \\\"unified framework\\\" to estimate the graphlet counts of the whole graph as well as the graphlet counts of individual nodes under the streaming graph setting. Our framework subsumes previous methods and provides more flexible and accurate estimation of the graphlet counts. We propose a general unbiased estimator which can be applied to any k-node graphlets. Furthermore, efficient implementation is provided for the 3, 4-node graphlets. We perform detailed empirical study on real-world graphs, and show that our framework produces estimation of graphlet count for streaming graphs with 1.7 to 170.8 times smaller error compared with other state-of-the-art methods. Our framework also achieves high accuracy on the estimation of graphlets for each individual node which previous works could not achieve.\",\n \"Title: Tracking Triadic Cardinality Distributions for Burst Detection in High-Speed Multigraph Streams.\\nYear: 2017\\n\\nAbstract: In everyday life, we often observe unusually frequent interactions among people before or during important events, e.g., people send/receive more greetings to/from their friends on holidays than regular days. We also observe that some videos or hashtags suddenly go viral through people's sharing on online social networks (OSNs). Do these seemingly different phenomena share a common structure? All these phenomena are associated with sudden surges of user interactions in networks, which we call \\\"bursts\\\" in this work. We uncover that the emergence of a burst is accompanied with the formation of triangles in some properly defined networks. This finding motivates us to propose a new and robust method to detect bursts on OSNs. We first introduce a new measure, \\\"triadic cardinality distribution\\\", corresponding to the fractions of nodes with different numbers of triangles, i.e., triadic cardinalities, in a network. We show that this distribution not only changes when a burst occurs, but it also has a robustness property that it is immunized against common spamming social-bot attacks. Hence, by tracking triadic cardinality distributions, we can more reliably detect bursts than simply counting interactions on OSNs. To avoid handling massive activity data generated by OSN users during the triadic tracking, we design an efficient \\\"sample-estimate\\\" framework to provide maximum likelihood estimate on the triadic cardinality distribution. We propose several sampling methods, and provide insights about their performance difference, through both theoretical analysis and empirical experiments on real world networks.\",\n \"Title: G-thinker: Big Graph Mining Made Easier and Faster.\\nYear: 2017\\n\\nAbstract: This paper proposes a general system for compute-intensive graph mining tasks that find from a big graph all subgraphs that satisfy certain requirements (e.g., graph matching and community detection). Due to the broad range of applications of such tasks, many single-threaded algorithms have been proposed. However, graphs such as online social networks and knowledge graphs often have billions of vertices and edges, which require distributed processing in order to scale. Unfortunately, existing distributed graph processing systems such as Pregel and GraphLab are designed for data-intensive analytics, and are inefficient for compute-intensive graph mining tasks since computation over any data is coupled with the datau0027s access that involves network transmission. We propose a distributed graph mining framework, called G-thinker, which is designed for compute-intensive graph mining workloads. G-thinker provides an intuitive graph-exploration API for the convenient implementation of various graph mining algorithms, and the runtime engine provides efficient execution with bounded memory consumption, light network communication, and parallelism between computation and communication. Extensive experiments were conducted, which demonstrate that G-thinker is orders of magnitude faster than existing solution, and can scale to graphs that are two orders of magnitude larger given the same hardware resources.\",\n \"Title: Multi-level Feedback Web Links Selection Problem: Learning and Optimization\\nYear: 2017\\n\\nAbstract: Selecting the right web links for a website is important because appropriate links not only can provide high attractiveness but can also increase the website's revenue. In this work, we first show that web links have an intrinsic multi-level feedback structure. For example, consider a 2-level feedback web link: the 1st level feedback provides the Click-Through Rate (CTR) and the 2nd level feedback provides the potential revenue, which collectively produce the compound 2-level revenue. We consider the context-free links selection problem of selecting links for a homepage so as to maximize the total compound 2-level revenue while keeping the total 1st level feedback above a preset threshold. We further generalize the problem to links with n (n ≥ 2)-level feedback structure. The key challenge is that the links' multi-level feedback structures are unobservable unless the links are selected on the homepage. To our best knowledge, we are the first to model the links selection problem as a constrained multi-armed bandit problem and design an effective links selection algorithm by learning the links' multi-level structure with provable sub-linear regret and violation bounds. We uncover the multi-level feedback structures of web links in two real-world datasets. We also conduct extensive experiments on the datasets to compare our proposed LExp algorithm with two state-of-the-art context-free bandit algorithms and demonstrate that LExp algorithm is the most effective in links selection while satisfying the constraint.\",\n \"Title: Joint Cache Resource Allocation and Request Routing for In-network Caching Services.\\nYear: 2018\\n\\nAbstract: In-network caching is recognized as an effective solution to offload content servers and the network. A cache service provider (SP) always has incentives to better utilize its cache resources by taking into account diverse roles that content providers (CPs) play, e.g., their business models, traffic characteristics, preferences. In this paper, we study the cache resource allocation problem in a Multi-Cache Multi-CP environment. We propose a cache partitioning approach, where each cache can be partitioned into slices with each slice dedicated to a content provider. We propose a content-oblivious request routing algorithm, to be used by individual caches, that optimizes the routing strategy for each CP. We associate with each content provider a utility that is a function of its content delivery performance, and formulate an optimization problem with the objective to maximize the sum of utilities over all content providers. We establish the biconvexity of the problem, and develop decentralized (online) algorithms based on convexity of the subproblem. The proposed model is further extended to bandwidth-constrained and minimum-delay scenarios, for which we prove fundamental properties, and develop efficient algorithms. Finally, we present numerical results to show the efficacy of our mechanism and the convergence of our algorithms.\",\n \"Title: Sampling Online Social Networks by Random Walk with Indirect Jumps.\\nYear: 2019\\n\\nAbstract: Random walk-based sampling methods are gaining popularity and importance in characterizing large networks. While powerful, they suffer from the slow mixing problem when the graph is loosely connected, which results in poor estimation accuracy. Random walk with jumps (RWwJ) can address the slow mixing problem but it is inapplicable if the graph does not support uniform vertex sampling (UNI). In this work, we develop methods that can efficiently sample a graph without the necessity of UNI but still enjoy the similar benefits as RWwJ. We observe that many graphs under study, called target graphs, do not exist in isolation. In many situations, a target graph is related to an auxiliary graph and a bipartite graph, and they together form a better connected two-layered network structure. This new viewpoint brings extra benefits to graph sampling: if directly sampling a target graph is difficult, we can sample it indirectly with the assistance of the other two graphs. We propose a series of new graph sampling techniques by exploiting such a two-layered network structure to estimate target graph characteristics. Experiments conducted on both synthetic and real-world networks demonstrate the effectiveness and usefulness of these new techniques.\",\n \"Title: Mining Customer Valuations to Optimize Product Bundling Strategy\\nYear: 2017\\n\\nAbstract: Product bundling is widely adopted for information goods and online services because it can increase profit for companies. For example, cable companies often bundle Internet access and video streaming services together. However, it is challenging to obtain an optimal bundling strategy, not only because it is computationally expensive, but also that customers’ private information (e.g., valuations for products) is needed for the decision, and we need to infer it from accessible datasets. As customers’ purchasing data are getting richer due to the popularity of online shopping, doors are open for us to infer this information. This paper aims to address: (1) How to infer customers’ valuations from the purchasing data? (2) How to determine the optimal product bundle to maximize the profit? We first formulate a profit maximization framework to select the optimal bundle set. We show that finding the optimal bundle set is NPhard. We then identify key factors that impact the profitability of product bundling. These findings give us insights to develop a computationally efficient algorithm to approximate the optimal product bundle with a provable performance guarantee. To obtain the input of the bundling algorithm, we infer the distribution of customers’ valuations from their purchasing data, based on which we run our bundling algorithm and conduct experiments on an Amazon co-purchasing dataset. We extensively evaluate the accuracy of our inference and the bundling algorithm. Our results reveal conditions under which bundling is highly profitable and provide insights to guide the deployment of product bundling.\",\n \"Title: MOSS-5: A Fast Method of Approximating Counts of 5-Node Graphlets in Large Graphs.\\nYear: 2018\\n\\nAbstract: Counting 3-, 4-, and 5-node graphlets in graphs is important for graph mining applications such as discovering abnormal/ evolution patterns in social and biology networks. In addition, it is recently widely used for computing similarities between graphs and graph classification applications such as protein function prediction and malware detection. However, it is challenging to compute these graph...\",\n \"Title: Incentive Mechanism and Rating System Design for Crowdsourcing Systems: Analysis, Tradeoffs and Inference.\\nYear: 2018\\n\\nAbstract: Macrotasking crowdsourcing systems like Elance and Fiverr serve as efficient platforms for requesters to outsource challenging and innovative tasks that require special skills to workers. It is widely practiced in such systems that requesters reward workers based on requesters' assessment on solution quality. The challenge is that requesters' assessment may not be accurate to reflect the intrinsic...\",\n \"Title: On the Theory of Function Placement and Chaining for Network Function Virtualization.\\nYear: 2018\\n\\nAbstract: Network function virtualization (NFV) can significantly reduce the operation cost and speed up the deployment for network services to markets. Under NFV, a network service is composed by a chain of ordered virtual functions, or we call a \\\"network function chain.\\\" A fundamental question is when given a number of network function chains, on which servers should we place these functions and how should we form a chain on these functions? This is challenging due to the intricate dependency relationship of functions and the intrinsic complex nature of the optimization. In this paper, we formulate the function placement and chaining problem as an integer optimization, where each variable is an indicator whether one service chain can be deployed on a configuration (or a possible function placement of a service chain). While this problem is generally NP-hard, our contribution is to show that it can be mapped to an exponential number of min-cost flow problems. Instead of solving all the min-cost problems, one can select a small number of mapped min-cost problems, which are likely to have a low cost. To achieve this, we relax the integer problem into a fractional linear problem, and theoretically prove that the fractional solutions possess some desirable properties, i.e., the number and the utilization of selected configurations can be upper and lower bounded, respectively. Based on such properties, we determine some \\\"good\\\" configurations selected from the fractional solution and determine the mapped min-cost flow problem, and this helps us to develop efficient algorithms for network function placement and chaining. Via extensive simulations, we show that our algorithms significantly outperform state-of-art algorithms and achieve near-optimal performance.\",\n \"Title: Enhancing Reputation via Price Discounts in E-Commerce Systems: A Data-Driven Approach.\\nYear: 2018\\n\\nAbstract: Reputation systems have become an indispensable component of modern E-commerce systems, as they help buyers make informed decisions in choosing trustworthy sellers. To attract buyers and increase the transaction volume, sellers need to earn reasonably high reputation scores. This process usually takes a substantial amount of time. To accelerate this process, sellers can provide price discounts to attract users, but the underlying difficulty is that sellers have no prior knowledge on buyers’ preferences over price discounts. In this article, we develop an online algorithm to infer the optimal discount rate from data. We first formulate an optimization framework to select the optimal discount rate given buyers’ discount preferences, which is a tradeoff between the short-term profit and the ramp-up time (for reputation). We then derive the closed-form optimal discount rate, which gives us key insights in applying a stochastic bandits framework to infer the optimal discount rate from the transaction data with regret upper bounds. We show that the computational complexity of evaluating the performance metrics is infeasibly high, and therefore, we develop efficient randomized algorithms with guaranteed performance to approximate them. Finally, we conduct experiments on a dataset crawled from eBay. Experimental results show that our framework can trade 60% of the short-term profit for reducing the ramp-up time by 40%. This reduction in the ramp-up time can increase the long-term profit of a seller by at least 20%.\",\n \"Title: Beyond the Click-Through Rate: Web Link Selection with Multi-level Feedback.\\nYear: 2018\\n\\nAbstract: The web link selection problem is to select a small subset of web links from a large web link pool, and to place the selected links on a web page that can only accommodate a limited number of links, e.g., advertisements, recommendations, or news feeds. Despite the long concerned click-through rate which reflects the attractiveness of the link itself, the revenue can only be obtained from user actions after clicks, e.g., purchasing after being directed to the product pages by recommendation links. Thus, the web links have an intrinsic emph{multi-level feedback structure}. With this observation, we consider the context-free web link selection problem, where the objective is to maximize revenue while ensuring that the attractiveness is no less than a preset threshold. The key challenge of the problem is that each linku0027s multi-level feedbacks are stochastic, and unobservable unless the link is selected. We model this problem with a constrained stochastic multi-armed bandit formulation, and design an efficient link selection algorithm, called Constrained Upper Confidence Bound algorithm (textbf{Con-UCB}), and prove $O(sqrt{Tln T})$ bounds on both the regret and the violation of the attractiveness constraint. We conduct extensive experiments on three real-world datasets, and show that textbf{Con-UCB} outperforms state-of-the-art context-free bandit algorithms concerning the multi-level feedback structure.\",\n \"Title: Enabling Relay-Assisted D2D Communication for Cellular Networks: Algorithm and Protocols.\\nYear: 2018\\n\\nAbstract: Recently, there is a growing emphasis on device-to-device (D2D) communication, which is the key component of the Internet-of-Things ecosystem. D2D communication can operate on the licensed spectrum of cellular networks so as to improve the spectrum utilization. In this paper, we focus on the resource allocation problem for general multihop D2D communication and introduce users' mobility into D2D c...\",\n \"Title: An Online Learning Approach to Network Application Optimization with Guarantee.\\nYear: 2018\\n\\nAbstract: Network application optimization is essential for improving the performance of the application as well as its user experience. The network application parameters are crucial in making proper decisions for network application optimizations. However, many works are impractical by assuming a priori knowledge of the parameters which are usually unknown and need to be estimated. There have been studies that consider optimizing network application in an online learning context using multi-armed bandit models. However, existing frameworks are problematic as they only consider to find the optimal decisions to minimize the regret, but neglect the constraints (or guarantee) requirements which may be excessively violated. in this paper, we propose a novel online learning framework for network application optimizations with guarantee. To the best of our knowledge, we are the first to formulate the stochastic constrained multi-armed bandit model with time-varying \\\"multi-level rewards\\\" by taking both \\\"regret\\\" and \\\"violation\\\" into consideration. We are also the first to design a constrained bandit policy, Learning with Minimum Guarantee (LMG), with provable sub-linear regret and violation bounds. We illustrate how our framework can he applied to several emerging network application optimizations, namely, (1) opportunistic multichannel selection, (2) data-guaranteed crowdsensing, and (3) stability guaranteed crowdsourced transcoding To show the effectiveness of LMG in optimizing these applications with different minimum requirements, we also conduct extensive simulations by comparing LMG with existing state-of-the-art policies.\",\n \"Title: Hybrid Circuit/Packet Network Scheduling with Multiple Composite Paths.\\nYear: 2018\\n\\nAbstract: A switching/forwarding fabric with high-bandwidth one-to-one and low-bandwidth many-to-many data forwarding can be achieved by combing electronic packet switches (EPS) and optical circuit switches (OCS). This hybrid solution scales well but it is not suitable for cloud computing/datacenter applications, which typically rely on one-to-many and many-to-one communications. Recently, composite-path switching (cp-switching), which adds paths between EPS and OCS, is introduced to deal with this skewed traffic pattern. The state-of-the-art scheduling algorithm for cp-switches reduces, by heuristics, a cp-switching problem with one composite path to a hybrid switching problem without the composite path, and leverages existing scheduling techniques to tackle the latter problem. Unfortunately, the approach provides neither performance guarantee nor the support for multiple composite paths. In this paper, we systematically study the shortest time cp-switch scheduling problem with multiple composite paths and show that the problem can be expressed as an optimization problem with sparsity constraints. An LP-based algorithm is derived accordingly which supports multiple composite paths and more importantly, it provides performance guarantee. Simulations demonstrate that adding more composite paths can help shorten the schedule, and our approach outperforms existing methods by 30% to 70%.\",\n \"Title: Tracking Influential Nodes in Time-Decaying Dynamic Interaction Networks.\\nYear: 2018\\n\\nAbstract: Identifying influential nodes that can jointly trigger the maximum influence spread in networks is a fundamental problem in many applications such as viral marketing, online advertising, and disease control. Most existing studies assume that social influence is static and they fail to capture the dynamics of influence in reality. In this work, we address the dynamic influence challenge by designing efficient streaming methods that can identify influential nodes from highly dynamic node interaction streams. We first propose a general time-decaying dynamic interaction network (TDN) model to model node interaction streams with the ability to smoothly discard outdated data. Based on the TDN model, we design three algorithms, i.e., SieveADN, BasicReduction, and HistApprox. SieveADN identifies influential nodes from a special kind of TDNs with efficiency. BasicReduction uses SieveADN as a basic building block to identify influential nodes from general TDNs. HistApprox significantly improves the efficiency of BasicReduction. More importantly, we theoretically show that all three algorithms enjoy constant factor approximation guarantees. Experiments conducted on various real interaction datasets demonstrate that our approach finds near-optimal solutions with speed at least 5 to 15 times faster than baseline methods.\",\n \"Title: Graphlet Count Estimation via Convolutional Neural Networks.\\nYear: 2018\\n\\nAbstract: Graphlets are defined as k-node connected induced subgraph patterns. For instance, for an undirected graph, 3-node graphlets include close triangle and open triangle. The number each graphlet, called graphlet count, a signature which characterizes the local network structure a given graph. Graphlet count plays a prominent role in network analysis many fields, most notably bioinformatics and social science. However, computing exact graphlet count is inherently difficult and computational expensive because the number graphlets growsexponentially large as the graph size and/or graphlet size grow. To deal with this difficulty, many sampling methods were proposed to estimate graphlet count with bounded error. Nevertheless, these methods require large number of samples to be statistically reliable, which is still computationally demanding. Intuitively, learning from historic graphs can make estimation more accurate and avoid many repetitive counting to reduce computational cost. Based on this idea, we propose a convolutional neural network (CNN) framework and two preprocessing techniques to estimate graphlet count. Extensive experiments on two types random graphs and real world biochemistry graphs show that our framework can offer substantial speedup on estimating graphlet count new graphs with high accuracy.\",\n \"Title: Sybil Detection in Social-Activity Networks: Modeling, Algorithms and Evaluations\\nYear: 2018\\n\\nAbstract: Detecting fake accounts (sybils) in online social networks (OSNs) is vital to protect OSN operators and their users from various malicious activities. Typical graph-based sybil detection (a mainstream methodology) assumes that sybils can make friends with only a limited (or small) number of honest users. However, recent evidences showed that this assumption does not hold in real-world OSNs, leading to low detection accuracy. To address this challenge, we explore users' activities to assist sybil detection. The intuition is that honest users are much more selective in choosing who to interact with than to befriend with. We first develop the social and activity network (SAN), a two-layer hyper-graph that unifies users' friendships and their activities, to fully utilize users' activities. We also propose a more practical sybil attack model, where sybils can launch both friendship attacks and activity attacks. We then design Sybil SAN to detect sybils via coupling three random walk-based algorithms on the SAN, and prove the convergence of Sybil SAN. We develop an efficient iterative algorithm to compute the detection metric for Sybil SAN, and derive the number of rounds needed to guarantee the convergence. We use \\\"matrix perturbation theory\\\" to bound the detection error when sybils launch many friendship attacks and activity attacks. Extensive experiments on both synthetic and real-world datasets show that Sybil SAN is highly robust against sybil attacks, and can detect sybils accurately under practical scenarios, where current state-of-art sybil defenses have low accuracy.\",\n \"Title: Submodular Optimization Over Streams with Inhomogeneous Decays.\\nYear: 2018\\n\\nAbstract: Cardinality constrained submodular function maximization, which aims to select a subset of size at most k to maximize a monotone submodular utility function, is the key in many data mining and machine learning applications such as data summarization and maximum coverage problems. When data is given as a stream, streaming submodular optimization (SSO) techniques are desired. Existing SSO techniques can only apply to insertion-only streams where each element has an infinite lifespan, and sliding-window streams where each element has a same lifespan (i.e., window size). However, elements in some data streams may have arbitrary different lifespans, and this requires addressing SSO over streams with inhomogeneous-decays (SSO-ID). This work formulates the SSO-ID problem and presents three algorithms: BASIC-STREAMING is a basic streaming algorithm that achieves an (1/2 - epsilon) approximation factor; HISTAPPROX improves the efficiency significantly and achieves an (1/3 - epsilon) approximation factor; HISTSTREAMING is a streaming version of HISTAPPROX and uses heuristics to further improve the efficiency. Experiments conducted on real data demonstrate that HISTSTREAMING can find high quality solutions and is up to two orders of magnitude faster than the naive GREEDY algorithm.\",\n \"Title: P-LOC: A Privacy-Preserving Paradigm-Driven Framework for Indoor Localization.\\nYear: 2018\\n\\nAbstract: Indoor localization plays an important role as the basis for a variety of mobile applications, such as navigating, tracking, and monitoring in indoor environments. However, many such systems cause potential privacy leakage in data transmission between mobile users and the localization server (LS). Unfortunately, there has been little research done on privacy issue, and the existing privacy-preserv...\",\n \"Title: Inferring Higher-Order Structure Statistics of Large Networks from Sampled Edges.\\nYear: 2019\\n\\nAbstract: Recently exploring locally connected subgraphs (also known as motifs or graphlets) of complex networks attracts a lot of attention. Previous work made the strong assumption that the graph topology of interest is known in advance. In practice, sometimes researchers have to deal with the situation where the graph topology is unknown because it is expensive to collect and store all topological inform...\",\n \"Title: Optimizing node discovery on networks: Problem definitions, fast algorithms, and observations.\\nYear: 2019\\n\\nAbstract: We study a general node discoverability optimization problem on networks, where the goal is to create a few edges to a target node so that the target node can be easily discovered by the other nodes in the network. For instance, a jobseeker may want to connect with some members in LinkedIn so that recruiters can easily find him. We first propose two definitions of node discoverability. Then, we prove that the node discoverability optimization problem is NP-hard. We show that a greedy algorithm can be used to find near-optimal solutions. To scale up the algorithm on large networks, we design three methods: (1) an exact method based on dynamic programming, which is accurate but computationally inefficient; (2) an estimation method based on the framework of random walk, which is efficient but may be inaccurate; (3) an estimation-and-refinement method, which combines the previous two methods and we show that it is both accurate and efficient. Experiments conducted on real networks demonstrate that the estimation-and-refinement method can provide a good trade-off between solution accuracy and computational efficiency, and achieve speedup of up to three orders of magnitude over the exact method.\",\n \"Title: Fast crawling methods of exploring content distributed over large graphs\\nYear: 2019\\n\\nAbstract: Despite recent effort to estimate topology characteristics of large graphs (e.g., online social networks and peer-to-peer networks), little attention has been given to develop a formal crawling methodology to characterize the vast amount of content distributed over these networks. Due to the large-scale nature of these networks and a limited query rate imposed by network service providers, exhaustively crawling and enumerating content maintained by each vertex is computationally prohibitive. In this paper, we show how one can obtain content properties by crawling only a small fraction of vertices and collecting their content. We first show that when sampling is naively applied, this can produce a huge bias in content statistics (i.e., average number of content replicas). To remove this bias, one may use maximum likelihood estimation to estimate content characteristics. However, our experimental results show that this straightforward method requires to sample most vertices to obtain accurate estimates. To address this challenge, we propose two efficient estimators: special copy estimator (SCE) and weighted copy estimator (WCE) to estimate content characteristics using available information in sampled content. SCE uses the special content copy indicator to compute the estimate, while WCE derives the estimate based on meta-information in sampled vertices. We conduct experiments on a variety of real-word and synthetic datasets, and the results show that WCE and SCE are cost effective and also “asymptotically unbiased”. Our methodology provides a new tool for researchers to efficiently query content distributed in large-scale networks.\",\n \"Title: T-thinker - a task-centric distributed framework for compute-intensive divide-and-conquer algorithms.\\nYear: 2019\\n\\nAbstract: Many computationally expensive problems are solved by a divide-and-conquer algorithm: a problem over a big dataset can be recursively divided into independent tasks over smaller subsets of the dataset. We present a distributed general-purpose framework called T-thinker which effectively utilizes the CPU cores in a cluster by properly decomposing an expensive problem into smaller independent tasks for parallel computation. T-thinker well overlaps CPU processing with network communication, and its superior performance is verified over a re-engineered graph mining system G-thinker available at http://cs.uab.edu/yanda/gthinker/.\",\n \"Title: A Markov Decision Process Approach to Analyze Discount & Reputation Trade-offs in E-commerce Systems\\nYear: 2018\\n\\nAbstract: E-commerce systems, e.g., Amazon, eBay and Taobao, are becoming increasingly popular. We consider eBay (or Taobao) like E-commerce systems, where a large number of sellers and buyers transact online. To reflect the trustworthiness of sellers, a reputation system is maintained. In particular, the feedback-based reputation system [4] is the most widely deployed. Sellers of such systems are initialized with a low reputation and they must obtain a sufficiently large number of positive feedbacks from buyers to earn a reputable label. For example, eBay and Taobao use three-level feedbacks, i.e., (-1 (Negative), 0 (Neutral), 1 (Positive). Each seller is initialized with a reputation score of zero. A positive (or negative) rating increases (or decreases) the reputation score by one, while a neutral rating does not change the reputation score.\",\n \"Title: Mining Deficiencies of Online Reputation Systems: Methodologies, Experiments and Implications\\nYear: 2020\\n\\nAbstract: Online reputation systems serve as core building blocks in various Internet services such as E-commerce (e.g., eBay) and crowdsourcing (e.g., oDesk). The flaws and deficiencies of real-world online reputation systems have been reported extensively. Users who are frustrated about the system will eventually abandon such service. However, there is no systematic and formal studies which examine such deficiencies. This paper presents the first attempt, which develops a novel data analytical framework to uncover online reputation system deficiencies from data. We develop two novel measures to quantify the efficiency of online reputation systems: (1) ramp up time of a new service provider, (2) long term profit gains for a service provider. We present a new data analytical framework to evaluate these two measures from data. We show that inherent preferences or personal biases in expressing feedbacks (or ratings) cause the computational infeasibility in evaluating the ramp up time and the long term profit gains from data. We develop two computationally efficient randomized algorithms with theoretical performance guarantees to address this computational challenge. We apply our methodology to analyze real-life datasets (from eBay, Google Helpouts, Amazon and TripAdvisor). We extensively validate our model and we uncover the deficiencies of online reputation systems. Our experimental results uncovers insights on why Google Helpouts was eventually shut down in April 2015 and why eBay is losing some sellers heavily.\",\n \"Title: Information Spreading Forensics via Sequential Dependent Snapshots.\\nYear: 2018\\n\\nAbstract: Mining the characteristics of information spreading in networks is crucial in communication studies, network security management, epidemic investigations, etc. Previous works are restrictive because they mainly focused on the information source detection using either a single observation, or multiple but independent observations of the underlying network while assuming a homogeneous information sp...\",\n \"Title: Quantifying Deployability and Evolvability of Future Internet Architectures via Economic Models\\nYear: 2018\\n\\nAbstract: Emerging new applications demand the current Internet to provide new functionalities. Although many future Internet architectures and protocols have been proposed to fulfill such needs, ISPs have been reluctant to deploy many of these architectures. We believe technical issues are not the main reasons as many of these new proposals are technically sound. In this paper, we take an economic perspective and seek to answer: \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">Why do most new Internet architectures fail to be deployed? How can the deployability of a new architecture be enhanced?</italic>\\n We develop a game-theoretic model to characterize the outcome of an architecture’s deployment through the \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">equilibrium</italic>\\n of ISPs’ decisions. This model enables us to: (1) analyze several key factors of the deployability of a new architecture such as the number of critical ISPs and the change of routing path; (2) explain the deploying outcomes of some previously proposed architectures/protocols such as IPv6, DiffServ, CDN, etc., and shed light on the \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">“Internet flattening phenomenon”</italic>\\n; (3) predict the deployability of a new architecture such as NDN, and compare its deployability with competing architectures. Our study suggests that the difficulty to deploy a new Internet architecture comes from the \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">“coordination”</italic>\\n of distributed ISPs. Finally, we design a mechanism to enhance the deployability of new architectures.\",\n \"Title: An Online Learning Multi-path Selection Framework for Multi-path Transmission Protocols\\nYear: 2019\\n\\nAbstract: In the last decade, we have witnessed a tremendous growth of inter-connectivity among hosts in networks. Many new data transmission protocols have been developed to enable multi-path data transmissions between two hosts. However, the existing multi-path transmission protocol designs are limited as they neglect the stochastic nature of the metrics of the paths, e.g., latency, available bandwidth, and packet loss. Moreover, there are different design requirements in the applications, such as low latency, bandwidth throttling, and low loss rate in data delivery. In this paper, we propose a flexible online learning multi-path selection (OLMPS) framework to select multiple paths by learning the stochastic metrics of the paths and meeting the design requirements of the applications. Specifically, we design a set of novel online learning algorithms in the OLMPS framework for three different applications, maxRTT constrained, bandwidth constrained, and loss rate constrained, multi-path selection, to select paths and satisfy the requirements. We prove that the algorithms can provide theoretical guarantees on both sublinear regret and sublinear violation in our OLMPS framework.\",\n \"Title: On the Profitability of Bundling Sale Strategy for Online Service Markets With Network Effects\\nYear: 2019\\n\\nAbstract: In recent years, we have witnessed a growing trend for online service companies to offer “bundling sales” to increase revenue. Bundling sale means that a company groups a set of products/services and charges this bundle at a fixed price, which is usually less than the total price of individual items in the bundle. In this work, our aim is to understand the underlying dynamics of bundling, particularly what is the optimal bundling sale strategy and under what situations it will be more attractive than the separate sales. We focus on online service markets that exhibit network effects. We formulate mathematical models to capture the interactions between buyers and sellers, analyze the market equilibrium and its stability, and provide an optimization framework to determine the optimal sale strategy for a service provider. We analyze the impact of various factors on the profitability of bundling, including the network effects, operating costs, and variance and correlation of customers’ valuations toward these services. We show that bundling is more profitable when the variance of customers’ valuations and the operational cost of the services are small. In addition, a positive network effect and a negative correlation among customers’ valuation on services increase the profitability of bundling, whereas the heterogeneity of services and the asymmetry of operating costs reduce its advantage.\",\n \"Title: A Fast Approach To Scale Up Disk Arrays With Parity Declustered Data Layout By Minimizing Data Migration\\nYear: 2019\\n\\nAbstract: Parity declustering is widely deployed in erasure-coded storage systems so as to provide fast recovery and high data availability. However, to perform scaling on such redundant array of inexpensive disks (RAIDs), it is necessary to preserve parity declustered data layout so as to preserve the properties after scaling. Unfortunately, existing scaling algorithms fail to achieve this goal so they cannot be applied for scaling RAIDs with parity declustering. To address this challenge, we develop an efficient online scaling scheme called parity declustering scaling (PDS), which employs an auxiliary balanced incomplete block design to define the data migration so as to preserve parity declustered data layout. Furthermore, PDS can also be applied to scale RAIDs for improving reliability and/or storage efficiency as options by allocating more parity blocks and/or data blocks in stripes. We provide theoretical proofs to formally show that PDS preserves parity declustered data layout, and achieves uniform distributions of data and parity blocks after scaling while requiring only the minimal data migration. We implement PDS in Linux kernel 3.14.72 and evaluate its performance with real-world traces. The results show that PDS can reduce 82.37 percent of scaling time and 18.25 percent of user response time during scaling on average, compared with \\\"moving-everything\\\" round-robin approach adapted to achieve parity declustered data layout after scaling.\",\n \"Title: FAVE: A fast and efficient network Flow AVailability Estimation method with bounded relative error.\\nYear: 2019\\n\\nAbstract: This paper focuses on helping network providers to carry out network capacity planning and sales projection by answering the question: For a given topology and capacity, whether the network can serve current flow demands with high probabilities? We name such probability as “ flow availability” and present the -flow availability estimation (FAVE) problem, which is a generalisation of network connectivity or maximum flow reliability estimations. Realistic networks are often large and dynamic, so flow availabilities cannot be evaluated analytically and simulation is often used. However, naive Monte Carlo (MC) or importance sampling (IS) techniques take an excessive amount of time. To quickly estimate flow availabilities, we utilize the correlations among link and flow failures to figure out the importance of roles played by different links in flow failures, and design three “sequential importance sampling” (SIS) methods which achieve “bounded or even vanishing relative error” with linear computational complexities. When applying to a realistic network, our method reduces the flow availability estimation cost by 900 and 130 times compared with MC and baseline IS methods, respectively. Our method can also facilitate capacity planning by providing better flow availability guarantees, compared with traditional methods.\",\n \"Title: Optimizing Discount & Reputation Trade-offs in E-commerce Systems: Characterization and Online Learning\\nYear: 2019\\n\\nAbstract: Feedback-based reputation systems are widely deployed in E-commerce systems. Evidences showed that earning a reputable label (for sellers of such systems) may take a substantial amount of time and this implies a reduction of profit. We propose to enhance sellers' reputation via price discounts. However, the challenges are: (1) The demands from buyers depend on both the discount and reputation; (2) The demands are unknown to the seller. To address these challenges, we first formulate a profit maximization problem via a semiMarkov decision process (SMDP) to explore the optimal trade-offs in selecting price discounts. We prove the monotonicity of the optimal profit and optimal discount. Based on the monotonicity, we design a QLFP (Q-learning with forward projection) algorithm, which infers the optimal discount from historical transaction data. We conduct experiments on a dataset from to show that our QLFP algorithm improves the profit by as high as 50% over both the classical Q-learning and speedy Q-learning algorithm. Our QLFP algorithm also improves the profit by as high as four times over the case of not providing any price discount.\",\n \"Title: Understanding Persuasion Cascades in Online Product Rating Systems\\nYear: 2019\\n\\nAbstract: Online product rating systems have become an indispensable component for numerous web services such as Amazon, eBay, Google play store and TripAdvisor. One functionality of such systems is to uncover the product quality via product ratings (or reviews) contributed by consumers. However, a well-known psychological phenomenon called \\\"message-based persuasion\\\" lead to \\\"biased\\\" product ratings in a cascading manner (we call this the persuasion cascade). This paper investigates: (1) How does the persuasion cascade influence the product quality estimation accuracy? (2) Given a real-world product rating dataset, how to infer the persuasion cascade and analyze it to draw practical insights? We first develop a mathematical model to capture key factors of a persuasion cascade. We formulate a high-order Markov chain to characterize the opinion dynamics of a persuasion cascade and prove the convergence of opinions. We further bound the product quality estimation error for a class of rating aggregation rules including the averaging scoring rule, via the matrix perturbation theory and the Chernoff bound. We also design a maximum likelihood algorithm to infer parameters of the persuasion cascade. We conduct experiments on the data from Amazon and TripAdvisor, and show that persuasion cascades notably exist, but the average scoring rule has a small product quality estimation error under practical scenarios.\",\n \"Title: Exploiting Non-Uniform Program Execution Time to Evade Record/Replay Forensic Analysis\\nYear: 2020\\n\\nAbstract: Record/replay system is an essential and widely used module in forensic analysis, as it can help forensic analysts to reconstruct programs’ behaviors. However, the security implication of record/replay systems (i.e., whether record/replay systems can faithfully reproduce all behaviors of a program) has not been thoroughly studied. This paper is the first work which investigates and explores the security limitations of record/replay systems from the perspective of software forensics. In particular, we reveal a type of vulnerability in record/replay systems caused by non-uniform program execution time. A program can exploit this vulnerability to prevent its malicious behavior from being replayed. We conduct a series of experiments on three platforms (i.e., web browser, mobile operating system and virtualized sandbox) to illustrate the wide footprints of the vulnerability. Finally, we discuss possible methods to mitigate the vulnerability. The goal of this work is to study the inherent security limitations of record/replay systems, discover the vulnerability and explore potential mitigation methods, from which forensic analysts can be informed and cautious when applying record/replay systems to software forensics.\",\n \"Title: A Memory-Efficient Sketch Method for Estimating High Similarities in Streaming Sets.\\nYear: 2019\\n\\nAbstract: Estimating set similarity and detecting highly similar sets are fundamental problems in areas such as databases, machine learning, and information retrieval. MinHash is a well-known technique for approximating Jaccard similarity of sets and has been successfully used for many applications such as similarity search and large scale learning. Its two compressed versions, b-bit MinHash and Odd Sketch, can significantly reduce the memory usage of the original MinHash method, especially for estimating high similarities (i.e., similarities around 1). Although MinHash can be applied to static sets as well as streaming sets, of which elements are given in a streaming fashion and cardinality is unknown or even infinite, unfortunately, b-bit MinHash and Odd Sketch fail to deal with streaming data. To solve this problem, we design a memory efficient sketch method, MaxLogHash, to accurately estimate Jaccard similarities in streaming sets. Compared to MinHash, our method uses smaller sized registers (each register consists of less than 7 bits) to build a compact sketch for each set. We also provide a simple yet accurate estimator for inferring Jaccard similarity from MaxLogHash sketches. In addition, we derive formulas for bounding the estimation error and determine the smallest necessary memory usage (i.e., the number of registers used for a MaxLogHash sketch) for the desired accuracy. We conduct experiments on a variety of datasets, and experimental results show that our method MaxLogHash is about 5 times more memory efficient than MinHash with the same accuracy and computational cost for estimating high similarities.\",\n \"Title: Walking with Perception: Efficient Random Walk Sampling via Common Neighbor Awareness\\nYear: 2019\\n\\nAbstract: Random walk is widely applied to sample large-scale graphs due to its simplicity of implementation and solid theoretical foundations of bias analysis. However, its computational efficiency is heavily limited by the slow convergence rate (a.k.a. long burn-in period). To address this issue, we propose a common neighbor aware random walk framework called CNARW, which leverages weighted walking by differentiating the next-hop candidate nodes to speed up the convergence. Specifically, CNARW takes into consideration the common neighbors between previously visited nodes and next-hop candidate nodes in each walking step. Based on CNARW, we further develop two efficient \\\"unbiased sampling\\\" schemes. Experimental results on real-world network datasets show that our approach converges remarkably faster than the state-of-the-art random walk sampling algorithms. Furthermore, to achieve the same estimation accuracy, our approach reduces the query cost (a measure of sampling budget) significantly. Lastly, we also use two case studies to demonstrate the effectiveness of our sampling framework in solving large-scale graph analysis tasks.\",\n \"Title: Toward Building Conversational Recommender Systems: A Contextual Bandit Approach.\\nYear: 2019\\n\\nAbstract: Contextual bandit algorithms have gained increasing popularity in recommender systems, because they can learn to adapt recommendations by making exploration-exploitation trade-off. Recommender systems equipped with traditional contextual bandit algorithms are usually trained with behavioral feedback (e.g., clicks) from users on items. The learning speed can be slow because behavioral feedback by nature does not carry sufficient information. As a result, extensive exploration has to be performed. To address the problem, we propose conversational recommendation in which the system occasionally asks questions to the user about her interest. We first generalize contextual bandit to leverage not only behavioral feedback (arm-level feedback), but also verbal feedback (users' interest on categories, topics, etc.). We then propose a new UCB- based algorithm, and theoretically prove that the new algorithm can indeed reduce the amount of exploration in learning. We also design several strategies for asking questions to further optimize the speed of learning. Experiments on synthetic data, Yelp data, and news recommendation data from Toutiao demonstrate the efficacy of the proposed algorithm.\",\n \"Title: Unifying Offline Causal Inference and Online Bandit Learning for Data Driven Decision\\nYear: 2021\\n\\nAbstract: ABSTRACT A fundamental question for companies with large amount of logged data is: How to use such logged data together with incoming streaming data to make good decisions? Many companies currently make decisions via online A/B tests, but wrong decisions during testing hurt users’ experiences and cause irreversible damage. A typical alternative is offline causal inference, which analyzes logged data alone to make decisions. However, these decisions are not adaptive to the new incoming data, and so a wrong decision will continuously hurt users’ experiences. To overcome the aforementioned limitations, we propose a framework to unify offline causal inference algorithms (e.g., weighting, matching) and online learning algorithms (e.g., UCB, LinUCB). We propose novel algorithms and derive bounds on the decision accuracy via the notion of “regret”. We derive the first upper regret bound for forest-based online bandit algorithms. Experiments on two real datasets show that our algorithms outperform other algorithms that use only logged data or online feedbacks, or algorithms that do not use the data properly.\",\n \"Title: A Reinforcement Learning Approach to Price Cloud Resources With Provable Convergence Guarantees\\nYear: 2022\\n\\nAbstract: How to generate more revenues is crucial to cloud providers. Evidences from the Amazon cloud system indicate that “dynamic pricing” would be more profitable than “static pricing.” The challenges are: How to set the price in real-time so to maximize revenues? How to estimate the price dependent demand so to optimize the pricing decision? We first design a discrete-time based dynamic pricing scheme and formulate a Markov decision process to characterize the evolving dynamics of the price-dependent demand. We formulate a revenue maximization framework to determine the optimal price and theoretically characterize the “structure” of the optimal revenue and optimal price. We apply the \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$Q$ </tex-math></inline-formula>\\n-learning to infer the optimal price from historical transaction data and derive sufficient conditions on the model to guarantee its convergence to the optimal price, but it converges slowly. To speed up the convergence, we incorporate the structure of the optimal revenue that we obtained earlier, leading to the VpQ-learning (\\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$Q$ </tex-math></inline-formula>\\n-learning with value projection) algorithm. We derive sufficient conditions, under which the VpQ-learning algorithm converges to the optimal policy. Experiments on a real-world dataset show that the VpQ-learning algorithm outperforms a variety of baselines, i.e., improves the revenue by as high as 50% over the \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$Q$ </tex-math></inline-formula>\\n-learning, speedy \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$Q$ </tex-math></inline-formula>\\n-learning, and adaptive real-time dynamic programming (ARTDP), and by as high as 20% over the fixed pricing scheme.\",\n \"Title: A Bootstrapping Approach to Optimize Random Walk Based Statistical Estimation over Graphs\\nYear: 2021\\n\\nAbstract: Graphs are commonly used in various applications such as online social networks (OSNs), E-commerce systems and social recommender systems. Random walk sampling is often used to conduct statistical estimation over such graphs. This paper develops an algorithmic framework to reduce the mean square error of such statistical estimation. Our algorithmic framework is inspired by that the mean square err...\",\n \"Title: Pricing social visibility service in online social networks: modeling and algorithms\\nYear: 2021\\n\\nAbstract: ABSTRACTIn online social networks (OSNs), users may want to enhance their social visibility, as it can make their contents, i.e., opinions, videos, pictures, etc., attract attention from more users. Motivated by this, we propose a mechanism, where the OSN operator provides a \\\"social visibility boosting service\\\" to incentivize \\\"transactions\\\" between requesters (users who seek to enhance their social visibility via adding new \\\"neighbors\\\") and suppliers (users who are willing to be added as a new \\\"neighbor\\\" of any requester when certain \\\"rewards\\\" is provided). We design a posted pricing scheme for the OSN provider to charge the requesters who use such boosting service, and reward the suppliers who contribute to such boosting service. The OSN operator keeps a fraction of the payment from requesters and distributes the remaining part to participating suppliers \\\"fairly\\\" via a scheme based on the Shapley value. The objective of the OSN provider is to select the price and supplier set to maximize the revenue under the budget constraint of requesters. We first show that the revenue maximization problem is not simpler than an NP-hard problem. We then decompose it into two subroutines, prove the hardness of each sub-routine, and eventually design computationally efficient approximation algorithms to solve the revenue maximization problem. We conduct extensive experiments to evaluate our proposed algorithms.\",\n \"Title: Heterogeneous Information Assisted Bandit Learning: Theory And Application\\nYear: 2021\\n\\nAbstract: Contextual bandit serves as an invaluable tool to balance the exploration vs. exploitation trade-off in various applications like online recommendation. In many applications, heterogeneous information network (HIN) can be derived to provide rich side information for contextual bandits, such as different types of attributes and relationships among users and items. In this paper, we propose the first HIN-assisted contextual bandit framework, which utilizes a given HIN to assist contextual bandit learning. The proposed framework uses meta-paths in HIN to extract rich relations among users and items for the contextual bandit. The main challenge is how to leverage these relations, since users' preference over items, the target of our online learning, are closely related to users' preference over meta-paths, however it is unknown which meta-path a user prefers more. We propose the HUCB algorithm to address such a challenge. For each meta-path, the HUCB algorithm employs an independent base bandit algorithm to handle online item recommendation by leveraging the relationship captured in this meta-path. The bandit master is then employed to learn users' preference over meta-paths to dynamically combine base bandit algorithms with a balance of exploration-exploitation trade-off. Experimental results on real datasets from LastFM and Yelp demonstrate the efficacy of the HUCB algorithm.\",\n \"Title: Social Visibility Optimization In Osns With Anonymity Guarantees: Modeling, Algorithms And Applications\\nYear: 2021\\n\\nAbstract: Online social network (OSN) is an ideal venue to enhance one's visibility. This paper considers how a user (called requester) in an OSN selects a small number of available users and invites them as new friends/followers so as to maximize his \\\"social visibility\\\". More importantly, the requester has to do this under the anonymity setting, which means he is not allowed to know the neighborhood information of these available users in the OSN. In this paper, we first develop a mathematical model to quantify the social visibility and formulate the problem of visibility maximization with anonymity guarantee, abbreviated as \\\"VisMAX-A\\\". Then we design an algorithmic framework named as \\\"AdaExp\\\", which adaptively expands the requester's visibility in multiple rounds. In each round of the expansion, AdaExp uses a query oracle with anonymity guarantee to select only one available user. By using probabilistic data structures like the k-minimum values (KMV) sketch, we design an efficient query oracle with anonymity guarantees. We also conduct experiments on real-world social networks and validate the effectiveness of our algorithms.\",\n \"Title: Mobile Data Traffic Prediction by Exploiting Time-Evolving User Mobility Patterns\\nYear: 2022\\n\\nAbstract: Understanding mobile data traffic and forecasting future traffic trend is beneficial to wireless carriers and service providers who need to perform resource allocation and energy saving management. However, predicting wireless traffic accurately at large-scale and fine-granularity is particularly challenging due to the following two factors: the spatial correlations between the network units (i.e., a cell tower or an access point) introduced by user arbitrary movements, and the time-evolving nature of user movements which frequently changes with time. In this paper, we use a time-evolving graph to formulate the time-evolving nature of user movements, and propose a model Graph-based Temporal Convolutional Network (GTCN) to predict the future traffic of each network unit in a wireless network. GTCN can bring significant benefits to two aspects. (1) GTCN can effectively learn intra- and inter-time spatial correlations between network units in a time-evolving graph through a node aggregation method. (2) GTCN can efficiently model the temporal dynamics of the mobile traffic trend from different network units through a temporal convolutional layer. Experimental results on two real-world datasets demonstrate the efficiency and efficacy of our method. Compared with state-of-the-art methods, the improvement of the prediction performance of our GTCN is 3.2 to 10.2 percent for different prediction horizons. GTCN also achieves 8.4× faster on prediction time.\",\n \"Title: On Modeling Influence Maximization in Social Activity Networks under General Settings\\nYear: 2021\\n\\nAbstract: AbstractFinding the set of most influential users in online social networks (OSNs) to trigger the largest influence cascade is meaningful, e.g., companies may leverage the “word-of-mouth” effect to trigger a large cascade of purchases by offering free samples/discounts to those most influential users. This task is usually modeled as an influence maximization problem, and it has been widely studied in the past decade. However, considering that users in OSNs may participate in various online activities, e.g., joining discussion groups and commenting on same pages or products, influence diffusion through online activities becomes even more significant. In this article, we study the impact of online activities by formulating social-activity networks which contain both users and online activities, and thus induce two types of weighted edges, i.e., edges between users and edges between users and activities. To address the computation challenge, we define an influence centrality via random walks, and use the Monte Carlo framework to efficiently estimate the centrality. Furthermore, we develop a greedy-based algorithm with novel optimizations to find the most influential users for node recommendation. Experiments on real-world datasets show that our approach is very computationally efficient under different influence models, and also achieves larger influence spread by considering online activities.\",\n \"Title: An Online Mean Field Approach for Hybrid Edge Server Provision\\nYear: 2021\\n\\nAbstract: ABSTRACTThe performance of an edge computing system primarily depends on the edge server provision mode, the task migration scheme, and the computing resource configuration. This paper studies how to perform dynamic resource configuration for hybrid edge server provision under two decentralized task migration schemes. We formulate the dynamic resource configuration as a multi-period online cost minimization problem, aiming to jointly minimize the performance degradation (i.e., execution latency) and the operation expenditure. Due to the stochastic nature, one can only observe the system performance for the currently installed configuration, which is also known as the partial feedback. To overcome this challenge, we derive a deterministic mean field model to approximate the large-scale stochastic edge computing system. We then propose an online mean field aided resource configuration policy, and show that the proposed policy performs asymptotically as good as the offline optimal configuration. Numerical results show that the mean field model can significantly improve the convergence speed in the online resource configuration problem. Moreover, our proposed policy under the two decentralized task migration schemes considerably reduces the operating cost (by 23%) and incurs little communication overhead.\",\n \"Title: Topology-Theoretic Approach To Address Attribute Linkage Attacks In Differential Privacy\\nYear: 2021\\n\\nAbstract: Differential Privacy (DP) is well-known for its strong privacy guarantee. In this paper, we show that when there are correlations among attributes in the dataset, only relying on DP is not sufficient to defend against the "attribute linkage attack", which is a well-known privacy attack aiming at deducing participant’s attribute information. Our contributions are ① we show that the attribute linkag...\",\n \"Title: A Truthful Procurement Auction for Incentivizing Heterogeneous Clients in Federated Learning\\nYear: 2021\\n\\nAbstract: Federated Learning (FL) is a new distributed machine learning (ML) approach which enables thousands of mobile devices to collaboratively train artificial intelligence (AI) models using local data without compromising user privacy. Although FL represents a promising computing paradigm, such training process can not be fully realized without an appropriate economic mechanism that incentivizes the pa...\",\n \"Title: Dynamic online convex optimization with long-term constraints via virtual queue\\nYear: 2021\\n\\nAbstract: In this paper, we investigate the online convex optimization (OCO) with long-term constraints which is widely used in various resource allocations and recommendation systems. Different from the most existing works, our work adopts a dynamic benchmark to analyze the optimization performance since the dynamic benchmark is more common than the static benchmark in practical applications. Moreover, compared with many constrained OCO works ignoring the Slater condition, we study the effect of the Slater condition on the constraint violation bounds and obtain the better performance of the constraint violations when the Slater condition holds. More importantly, we propose a novel iterative optimization algorithm based on the virtual queues to achieve sublinear regret and constraint violations. Finally, we apply our dynamic OCO model to a resource allocation problem in cloud computing and the results of the experiments validate the effectiveness of our algorithm.\",\n \"Title: MirChecker: Detecting Bugs in Rust Programs via Static Analysis\\nYear: 2021\\n\\nAbstract: ABSTRACTSafe system programming is often a crucial requirement due to its critical role in system software engineering. Conventional low-level programming languages such as C and assembly are efficient, but their inherent unsafe nature makes it undesirable for security-critical scenarios. Recently, Rust has become a promising alternative for safe system-level programming. While giving programmers fine-grained hardware control, its strong type system enforces many security properties including memory safety. However, Rust's security guarantee is not a silver bullet. Runtime crashes and memory-safety errors still harass Rust developers, causing damaging exploitable vulnerabilities, as reported by numerous studies. In this paper, we present and evaluate MirChecker, a fully automated bug detection framework for Rust programs by performing static analysis on Rust's Mid-level Intermediate Representation (MIR). Based on the observation of existing bugs found in Rust codebases, our approach keeps track of both numerical and symbolic information, detects potential runtime crashes and memory-safety errors by using constraint solving techniques, and outputs informative diagnostics to users. We evaluate MirChecker on both buggy code snippets extracted from existing Common Vulnerabilities and Exposures (CVE) and real-world Rust codebases. Our experiments show that MirChecker can detect all the issues in our code snippets, and is capable of performing bug finding in real-world scenarios, where it detected a total of 33 previously unknown bugs including 16 memory-safety issues from 12 Rust packages (crates) with an acceptable false-positive rate.\",\n \"Title: Cooperative Stochastic Bandits with Asynchronous Agents and Constrained Feedback.\\nYear: 2021\\n\\nAbstract: Use the \\\"Report an Issue\\\" link to request a name change.\",\n \"Title: Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach\\nYear: 2022\\n\\nAbstract: Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements. Previous works mainly explore task offloading when system-side information is given (e.g., server processing speed, cellular data rate), or centralized offloading under system uncertainty. But both generally fall short of handling task placement involving many coexisting users in a dynamic and uncertain environment. In this paper, we develop a multi-user offloading framework considering unknown yet stochastic system-side information to enable a decentralized user-initiated service placement. Specifically, we formulate the dynamic task placement as an online multi-user multi-armed bandit process, and propose a decentralized epoch based offloading (DEBO) to optimize user rewards which are subject to the network delay. We show that DEBO can deduce the optimal user-server assignment, thereby achieving a close-to-optimal service performance and tight O(log T ) offloading regret. Moreover, we generalize DEBO to various common scenarios such as unknown reward gap, dynamic entering or leaving of clients, and fair reward distribution, while further exploring when users’ offloaded tasks require heterogeneous computing resources. Particularly, we accomplish a sub-linear regret for each of these instances. Real measurements based evaluations corroborate the superiority of our offloading schemes over state-of-the-art approaches in optimizing delay-sensitive rewards.\",\n \"Title: Topology-theoretic approach to address attribute linkage attacks in differential privacy\\nYear: 2022\\n\\nAbstract: Differential Privacy (DP) is well-known for its strong privacy guarantee. Briefly speaking, DP algorithms guarantee that the statistical information of the data is roughly preserved, and at the same time, individual privacy is protected with guarantees. However, when there are correlations among the attribute in the dataset, only relying on DP is not sufficient to defend against the attribute linkage attack, which is a well-known privacy attack aiming at deducing individuals' private information. In the attribute linkage attack, the adversary can leverage prior knowledge about the victim, combined with accessing the published dataset, to infer sensitive information about a victim. In this paper, we study the attribute linkage attack in DP settings, and argue that enhancing DP can give users a higher level of privacy guarantees. Our contributions are (1) we show that the attribute linkage attack can be initiated with high probability under the protection of DP, (2) we propose a variant of DP called APL-Free epsilon-DP to provide a higher level of privacy guarantees, (3) we design an algorithm APLKiller which satisfies the APL-Free epsilon-DP. Finally, experiments show that our algorithm not only eliminates the attribute linkage attack, and at the same time, it has a better ability to extract useful information from the data. (C) 2021 Elsevier Ltd. All rights reserved.\",\n \"Title: Inferring Hidden IoT Devices and User Interactions via Spatial-Temporal Traffic Fingerprinting\\nYear: 2022\\n\\nAbstract: With the popularization of Internet of Things (IoT) devices in smart home and industry fields, a huge number of IoT devices are connected to the Internet. However, what devices are connected to a network may not be known by the Internet Service Provider (ISP), since many IoT devices are placed within small networks (e.g., home networks) and are hidden behind network address translation (NAT). With...\",\n \"Title: Towards Personalized Privacy-Preserving Truth Discovery Over Crowdsourced Data Streams\\nYear: 2022\\n\\nAbstract: Truth discovery is an effective paradigm which could reveal the truth from crowdsouced data with conflicts, enabling data-driven decision-making systems to make quick and smart decisions. The increasing privacy concern promotes users to perturb or encrypt their private data before outsourcing, which poses significant challenges for truth discovery. Although several privacy-preserving truth discove...\",\n \"Title: Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications.\\nYear: 2022\\n\\nAbstract: Multi-player multi-armed bandits (MMAB) study how decentralized players cooperatively play the same multi-armed bandit so as to maximize their total cumulative rewards. Existing MMAB models mostly assume when more than one player pulls the same arm, they either have a collision and obtain zero rewards, or have no collision and gain independent rewards, both of which are usually too restrictive in practical scenarios. In this paper, we propose an MMAB with shareable resources as an extension to the collision and non-collision settings. Each shareable arm has finite shareable resources and a \\\"per-load\\\" reward random variable, both of which are unknown to players. The reward from a shareable arm is equal to the \\\"per-load\\\" reward multiplied by the minimum between the number of players pulling the arm and the arm's maximal shareable resources. We consider two types of feedback: sharing demand information (SDI) and sharing demand awareness (SDA), each of which provides different signals of resource sharing. We design the DPE-SDI and SIC-SDA algorithms to address the shareable arm problem under these two cases of feedback respectively and prove that both algorithms have logarithmic regrets that are tight in the number of rounds. We conduct simulations to validate both algorithms' performance and show their utilities in wireless networking and edge computing.\",\n \"Title: Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms.\\nYear: 2022\\n\\nAbstract: We generalize the multiple-play multi-armed bandits (MP-MAB) problem with a shareable arms setting, in which several plays can share the same arm. Furthermore, each shareable arm has a finite reward capacity and a “per-load” reward distribution, both of which are unknown to the learner. The reward from a shareable arm is load-dependent, which is the “per-load” reward multiplying either the number of plays pulling the arm, or its reward capacity when the number of plays exceeds the capacity limit. When the “per-load” reward follows a Gaussian distribution, we prove a sample complexity lower bound of learning the capacity from load-dependent rewards and also a regret lower bound of this new MP-MAB problem. We devise a capacity estimator whose sample complexity upper bound matches the lower bound in terms of reward means and capacities. We also propose an online learning algorithm to address the problem and prove its regret upper bound. This regret upper bound’s first term is the same as regret lower bound’s, and its second and third terms also evidently correspond to lower bound’s. Extensive experiments validate our algorithm’s performance and also its gain in 5G & 4G base station selection.\",\n \"Title: Optimizing Social Visibility in OSNs With Anonymity Guarantees: Efficient Algorithms and Applications\\nYear: 2022\\n\\nAbstract: Online social network (OSN) is an ideal venue to enhance one's visibility. This paper considers how a user (called requester) in an OSN selects a small number of users and invites them as new friends/followers so as to maximize her “\\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">social visibility</i>\\n”. More importantly, the requester has to do this under the anonymity setting, which means she is not allowed to know the neighborhood information of other users in the OSN. In this paper, we first develop a mathematical model to quantify the social visibility and formulate the problem of visibility maximization with anonymity guarantee, abbreviated as “VisMAX-A”. We prove that the VisMAX-A problem is NP-hard even with an “perfect query oracle” which knows users’ neighborhood information. Then we design an algorithmic framework named as “AdaExp,” which adaptively expands the requester's visible set in multiple rounds. In each round of the expansion, the algorithmic framework uses a query oracle with anonymity guarantee to select one user to increase the requester's visibility. We prove the theoretical guarantees of our algorithmic framework, which quantify how the query oracle may influence the solution guarantee. By using probabilistic data structures like the k-minimum values (KMV) sketch and online learning methods like multi-armed bandit (MAB), we design an efficient query oracle with anonymity guarantees to estimate the best candidate to increase requester's visibility. We also conduct experiments on real-world social networks and validate the effectiveness of our algorithms. Lastly, we demonstrate applications of our algorithm on real-world data and show that one can increase her social visibility significantly by only adding a few (e.g., one or two) friends/followers in the OSNs.\",\n \"Title: Approximate and Deployable Shortest Remaining Processing Time Scheduler\\nYear: 2022\\n\\nAbstract: The scheduling policy installed on switches of datacenters plays a significant role on congestion control. Shortest-Remaining-Processing-Time (SRPT) achieves the near-optimal average message completion time (MCT) in various scenarios, but is difficult to deploy as viewed by the industry. The reasons are two-fold: 1) many commodity switches only provide FIFO queues, and 2) the information of remaining message size is not available. Recently, the idea of emulating SRPT using only a few FIFO queues and the original message size has been coined as the approximate and deployable SRPT (ADS) design. In this paper, we provide the first theoretical study on the optimal ADS design. Specifically, we first characterize a wide range of feasible ADS scheduling policies via a unified framework, and then derive the steady-state MCT, slowdown, and impoliteness in the M/G/1 setting. Hence we formulate the optimal ADS design as a non-linear combinatorial optimization problem, which aims to minimize the average MCT given the available FIFO queues. We also take into account the proportional fairness and temporal fairness constraints based on the maximal slowdown and impoliteness, respectively. The optimal ADS design problem is NP-hard in general, and does not exhibit monotonicity or sub-modularity. We leverage its decomposable structure and devise an efficient algorithm to solve the optimal ADS policy. We carry out extensive flow-level simulations and packet-level experiments to evaluate the proposed optimal ADS design. Results show that the optimal ADS policy installed on eight FIFO queues is capable of emulating the true SRPT.\",\n \"Title: Partial-Quasi-Newton Methods: Efficient Algorithms for Minimax Optimization Problems with Unbalanced Dimensionality\\nYear: 2022\\n\\nAbstract: This paper studies the strongly-convex-strongly-concave minimax optimization with unbalanced dimensionality. Such problems contain several popular applications in data science such as few shot learning and fairness-aware machine learning task. The design of conventional iterative algorithm for minimax optimization typically focuses on reducing the total number of oracle calls, which ignores the unbalanced computational cost for accessing the information from two different variables in minimax. We propose a novel second-order optimization algorithm, called Partial-Quasi-Newton (PQN) method, which takes the advantage of unbalanced structure in the problem to establish the Hessian estimate efficiently. We theoretically prove our PQN method converges to the saddle point faster than existing minimax optimization algorithms. The numerical experiments on real-world applications show the proposed PQN performs significantly better than the state-of-the-art methods.\",\n \"Title: Federated online clustering of bandits.\\nYear: 2022\\n\\nAbstract: Contextual multi-armed bandit (MAB) is an important sequential decision-making problem in recommendation systems. A line of works, called the clustering of bandits (CLUB), utilize the collaborative effect over users and dramatically improve the recommendation quality. Owing to the increasing application scale and public concerns about privacy, there is a growing demand to keep user data decentralized and push bandit learning to the local server side. Existing CLUB algorithms, however, are designed under the centralized setting where data are available at a central server. We focus on studying the federated online clustering of bandit (FCLUB) problem, which aims to minimize the total regret while satisfying privacy and communication considerations. We design a new phase-based scheme for cluster detection and a novel asynchronous communication protocol for cooperative bandit learning for this problem. To protect users’ privacy, previous differential privacy (DP) definitions are not very suitable, and we propose a new DP notion that acts on the user cluster level. We provide rigorous proofs to show that our algorithm simultaneously achieves (clustered) DP, sublinear communication complexity and sublinear regret. Finally, experimental evaluations show our superior performance compared with benchmark algorithms.\",\n \"Title: Toward Large-Scale Hybrid Edge Server Provision: An Online Mean Field Learning Approach\\nYear: 2022\\n\\nAbstract: The efficiency of a large-scale edge computing system primarily depends on three aspects: i) edge server provision, ii) task migration, and iii) computing resource configuration. In this paper, we study the dynamic resource configuration for hybrid edge server provision under two decentralized task migration schemes. We formulate the dynamic resource configuration as an online cost minimization problem, aiming to jointly minimize performance degradation and operation expenditure. Due to the stochastic nature, it is an online learning problem with partial feedback. To address it, we derive a deterministic mean field model to approximate the stochastic edge computing system. We show that the mean field model provides the increasingly accurate full feedback as the system scales. We then propose a learning policy based on the mean field model, and show that our proposed policy performs asymptotically as well as the offline optimal configuration. We provide two ways of setting the policy parameters, which achieve a constant competitive ratio (under certain mild conditions) and a sub-linear regret, respectively. Numerical results show that the mean field model significantly improves the convergence speed. Moreover, our proposed policy under the decentralized task migration schemes considerably reduces the operating cost (by 23%) and incurs little communication overhead.\",\n \"Title: PupilRec: Leveraging Pupil Morphology for Recommending on Smartphones\\nYear: 2022\\n\\nAbstract: As mobile shopping has gradually become the mainstream shopping mode, recommendation systems are gaining an increasingly wide adoption. Existing recommendation systems are mainly based on explicit and implicit user behaviors. However, these user behaviors may not directly indicate users’ inner feelings, causing erroneous user preference estimation and thus leading to inaccurate recommendations. Inspired by our key observation on the correlation between pupil size and users’ inner feelings, we consider using the change of pupil size when browsing to model users’ preferences, so as to achieve targeted recommendations. To this end, we propose PupilRec as a computer-vision-based recommendation framework involving a mobile terminal and a server side. On the mobile terminal, PupilRec collects users’ pupil size change information through the front camera of smartphones; it then preprocesses the raw pupil size data before transmitting them to the server. On the server side, PupilRec utilizes the Tsfresh package and Random Forest algorithm to figure out the key time-series features directly implying user preferences. PupilRec then trains a neural network to fit a user preference model. Using this model, PupilRec predicts user preference to obtain a user–product matrix and further simplifies it by singular value decomposition. Finally, the real-time recommendation is achieved by a collaborative filtering module that retrieves recommended contents to users smartphones. We prototype PupilRec and conduct both experiments and field studies to comprehensively evaluate the effectiveness of PupilRec by recruiting 67 volunteers. The overall results show that PupilRec can accurately estimate users’ preference and can recommend products users interested in.\",\n \"Title: A Control-Theoretic and Online Learning Approach to Self-Tuning Queue Management\\nYear: 2022\\n\\nAbstract: There is a growing trend that network applications not only require higher throughput, but also impose stricter delay requirements. The current Internet congestion control, which is driven by active queue management (AQM) algorithms interacting with the Transmission Control Protocol (TCP), has been playing an important role in supporting network applications. However, it still exhibits many open issues. Most of AQM algorithms only deploy a single-queue structure that cannot differentiate flows and easily leads to unfairness. Moreover, the parameter settings of AQM are often static, making them difficult to adapt to the dynamic network environments. In this paper, we propose a general framework for designing \\\"self-tuning\\\" queue management (SQM), which is adaptive to the changing environments and provides fair congestion control among flows. We first present a general architecture of SQM with fair queueing and propose a general fluid model to analyze it. To adapt to the stochastic environments, we formulate a stochastic network utility maximization (SNUM) problem, and utilize online convex optimization (OCO) and control theory to develop a distributed SQM algorithm which can self-tune different queue weights and control parameters. Numerical and packet-level simulation results show that our SQM algorithm significantly improves queueing delay and fairness among flows.\",\n \"Title: Achieving efficiency via fairness in online resource allocation\\nYear: 2022\\n\\nAbstract: ABSTRACTThe classic utility maximization framework studies the fairness-efficiency tradeoff in various resource allocation problems (e.g., bandwidth allocation). The weighted alpha-fair utility is a common utilitarian metric. However, this classic framework cannot tackle those allocation problems with the online decision-making requirement (e.g., caching capacity allocation under unknown requests). Existing studies on these online allocation problems largely follow the online learning approaches, thus inevitably overlook the allocation fairness. In this paper, we propose a novel utility maximization framework accommodating the online setting. The major challenge of designing this framework lies in the tight coupling between the desirable fairness guarantee and the unknown allocation efficiency. To tackle this, we integrate the weighted alpha-fair utility with the learning rationale, by properly devising the merit-based weights and the increasing fairness levels. Under our proposed framework, the utility-maximizing allocation in each time slot is weighted alpha-fair. Our framework also performs asymptotically as well as the offline optimal/efficient outcome. We demonstrate how this framework functions in two networking applications. In size-based scheduling, it enables network switches to prioritize short flows and avoid flow starvation without the prior flow size information. In file caching, our framework outperforms several state-of-the-art caching policies up to 21% in terms of cache-hit-ratio.\",\n \"Title: Improving Bandit Learning Via Heterogeneous Information Networks: Algorithms and Applications\\nYear: 2022\\n\\nAbstract: ontextual bandit serves as an invaluable tool to balance the exploration vs. exploitation tradeoff in various applications such as online recommendation. In many applications, heterogeneous information networks (HINs) provide rich side information for contextual bandits, such as different types of attributes and relationships among users and items. In this article, we propose the first HIN-assisted contextual bandit framework, which utilizes a given HIN to assist contextual bandit learning. The proposed framework uses meta-paths in HIN to extract rich relations among users and items for the contextual bandit. The main challenge is how to leverage these relations, since users’ preference over items, the target of our online learning, are closely related to users’ preference over meta-paths. However, it is unknown which meta-path a user prefers more. Thus, both preferences are needed to be learned in an online fashion with exploration vs. exploitation tradeoff balanced. We propose the HIN-assisted upper confidence bound (HUCB) algorithm to address such a challenge. For each meta-path, the HUCB algorithm employs an independent base bandit algorithm to handle online item recommendations by leveraging the relationship captured in this meta-path. A bandit master is then employed to learn users’ preference over meta-paths to dynamically combine base bandit algorithms with a balance of exploration vs. exploitation tradeoff. We theoretically prove that the HUCB algorithm can achieve similar performance compared with the optimal algorithm where each user is served according to his true preference over meta-paths (assuming the optimal algorithm knows the preference). Moreover, we prove that the HUCB algorithm benefits from leveraging HIN in achieving a smaller regret upper bound than the baseline algorithm without leveraging HIN. Experimental results on a synthetic dataset, as well as real datasets from LastFM and Yelp demonstrate the fast learning speed of the HUCB algorithm.\",\n \"Title: Detecting Cross-language Memory Management Issues in Rust\\nYear: 2022\\n\\nAbstract: Rust is a promising system-level programming language that can prevent memory corruption bugs using its strong type system and ownership-based memory management scheme. In practice, programmers usually write Rust code in conjunction with other languages such as C/C++ through Foreign Function Interface (FFI). For example, many notable projects are developed using Rust and other programming languages, such as Firefox, Google Fuchsia OS, and the Linux kernel. Although it is widely believed that gradually re-implementing security-critical components in Rust is a way of enhancing software security, however, using FFI is inherently unsafe. In this paper, we show that memory management across the FFI boundaries is error-prone. Any incorrect use of FFI may corrupt Rust's ownership system, leading to memory safety issues. To tackle this problem, we design and build FFIChecker, an automated static analysis and bug detection tool dedicated to memory management issues across the Rust/C FFI. We evaluate our tool by checking 987 Rust packages crawled from the official package registry and reveal 34 bugs in 12 packages. Our experiments show that FFIChecker is a useful tool to detect real-world cross-language memory management issues with a reasonable amount of computational resources.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":200,"string":"200"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13258,"string":"13,258"}}},{"rowIdx":13259,"cells":{"question_id":{"kind":"string","value":"Tianzhu Zhang"},"question":{"kind":"string","value":"Generate an abstract for the title \"I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs\" using the following items: 1. Zero-shot action recognition \n2. Structured knowledge graph \n3. Two-Stream Graph Convolutional Network \n4. Semantic-embedding vectors \n5. Favorable performance"},"answer":{"kind":"string","value":"Recently, with the ever-growing action categories, zero-shot action recognition (ZSAR) has been achieved by automatically mining the underlying concepts (e.g., actions, attributes) in videos. However, most existing methods only exploit the visual cues of these concepts but ignore external knowledge information for modeling explicit relationships between them. In fact, humans have remarkable ability to transfer knowledge learned from familiar classes to recognize unfamiliar classes. To narrow the knowledge gap between existing methods and humans, we propose an end-to-end ZSAR framework based on a structured knowledge graph, which can jointly model the relationships between action-attribute, action-action, and attribute-attribute. To effectively leverage the knowledge graph, we design a novel Two-Stream Graph Convolutional Network (TS-GCN) consisting of a classifier branch and an instance branch. Specifically, the classifier branch takes the semantic-embedding vectors of all the concepts as input, then generates the classifiers for action categories. The instance branch maps the attribute embeddings and scores of each video instance into an attribute-feature space. Finally, the generated classifiers are evaluated on the attribute features of each video, and a classification loss is adopted for optimizing the whole network. In addition, a self-attention module is utilized to model the temporal information of videos. Extensive experimental results on three realistic action benchmarks Olympic Sports, HMDB51 and UCF101 demonstrate the favorable performance of our proposed framework."},"correct_answer":{"kind":"string","value":"Recently, with the ever-growing action categories, zero-shot action recognition (ZSAR) has been achieved by automatically mining the underlying concepts (e.g., actions, attributes) in videos. However, most existing methods only exploit the visual cues of these concepts but ignore external knowledge information for modeling explicit relationships between them. In fact, humans have remarkable ability to transfer knowledge learned from familiar classes to recognize unfamiliar classes. To narrow the knowledge gap between existing methods and humans, we propose an end-to-end ZSAR framework based on a structured knowledge graph, which can jointly model the relationships between action-attribute, action-action, and attribute-attribute. To effectively leverage the knowledge graph, we design a novel Two-Stream Graph Convolutional Network (TS-GCN) consisting of a classifier branch and an instance branch. Specifically, the classifier branch takes the semantic-embedding vectors of all the concepts as input, then generates the classifiers for action categories. The instance branch maps the attribute embeddings and scores of each video instance into an attribute-feature space. Finally, the generated classifiers are evaluated on the attribute features of each video, and a classification loss is adopted for optimizing the whole network. In addition, a self-attention module is utilized to model the temporal information of videos. Extensive experimental results on three realistic action benchmarks Olympic Sports, HMDB51 and UCF101 demonstrate the favorable performance of our proposed framework."},"documents":{"kind":"list like","value":["Title: Sustainable Urban Solid Waste Management from both Life Cycle and Urban Metabolism Perspectives\nYear: 2012\n\nAbstract: Urban solid waste management is an important component of urban sustainability. The concept of evaluating technologies from both life cycle and urban metabolism perspectives is proposed in this study. The analysis from life cycle perspective (using physical input-output life cycle assessment model) provides support for determining the priority of technologies, and the analysis from urban metabolism perspective (using physical input-output model) provides support for identifying the acceptability of technologies. Suzhou City in China is taken as an example. From urban metabolism perspective, sludge recycling is regarded as an accepted method, while current fly ash recycling method is unsatisfying. Technical levels of scrap tire recycling and food waste recycling should be improved to reduce their negative effects on Suzhou's urban metabolism. From life cycle perspective, sludge recycling has the smallest environmental impacts, and scrap tire recycling has larger environmental impacts than food waste recycling does. Thus, more concerns should be paid to technical improvements of scrap tire recycling than to that of food waste recycling. The concept of evaluating technologies from both life cycle and urban metabolism perspectives in this study provides foundations for evaluating technologies in other countries and cities.","Title: Low-rank sparse learning for robust visual tracking\nYear: 2012\n\nAbstract: In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1].","Title: Discriminative exemplar coding for sign language recognition with Kinect.\nYear: 2013\n\nAbstract: Sign language recognition is a growing research area in the field of computer vision. A challenge within it is to model various signs, varying with time resolution, visual manual appearance, and so on. In this paper, we propose a discriminative exemplar coding (DEC) approach, as well as utilizing Kinect sensor, to model various signs. The proposed DEC method can be summarized as three steps. First, a quantity of class-specific candidate exemplars are learned from sign language videos in each sign category by considering their discrimination. Then, every video of all signs is described as a set of similarities between frames within it and the candidate exemplars. Instead of simply using a heuristic distance measure, the similarities are decided by a set of exemplar-based classifiers through the multiple instance learning, in which a positive (or negative) video is treated as a positive (or negative) bag and those frames similar to the given exemplar in Euclidean space as instances. Finally, we formulate the selection of the most discriminative exemplars into a framework and simultaneously produce a sign video classifier to recognize sign. To evaluate our method, we collect an American sign language dataset, which includes approximately 2000 phrases, while each phrase is captured by Kinect sensor with color, depth, and skeleton information. Experimental results on our dataset demonstrate the feasibility and effectiveness of the proposed approach for sign language recognition.","Title: Robust multi-object tracking via cross-domain contextual information for sports video analysis\nYear: 2012\n\nAbstract: Multiple player tracking is one of the main building blocks needed in a sports video analysis system. In an uncalibrated camera setting, robust mutli-object tracking can be very difficult due to a number of reasons including the presence of noise, occlusion, fast camera motion, low-resolution image capture, varying viewpoints and illumination changes. To address the problem of multi-object tracking in sports videos, we go beyond the video frame domain and make use of information in a homography transform domain that is denoted the homography field domain. We propose a novel particle filter based tracking algorithm that uses both object appearance information (e.g. color and shape) in the image domain and cross-domain contextual information in the field domain to improve object tracking. In the field domain, the effect of fast camera motion is significantly alleviated since the underlying homography transform from each frame to the field domain can be accurately estimated. We use contextual trajectory information (intra-trajectory and inter-trajectory context) to further improve the prediction of object states within an particle filter framework. Here, intra-trajectory contextual information is based on history tracking results in the field domain, while inter-trajectory contextual information is extracted from a compiled trajectory dataset based on tracks computed from videos depicting the same sport. Experimental results on real world sports data show that our system is able to effectively and robustly track a variable number of targets regardless of background clutter, camera motion and frequent mutual occlusion between targets.","Title: Graph-Guided Fusion Penalty Based Sparse Coding for Image Classification.\nYear: 2013\n\nAbstract: In image classification, conventional sparse coding only encodes local features independently. As a result, the similar local features may be encoded into code vectors with large discrepancy. This sensitiveness has became the bottleneck of the traditional sparse coding based image classification methods. In this paper, we propose a novel graph-guided fusion penalty based sparse coding method. To alleviate the sensitiveness of the traditional sparse coding, our approach constrains that the similar local features are encoded into similar code vectors. To achieve this goal, we add the popular graph-guided fusion penalty term into the traditional l1-regularized sparse coding formulation. Finally, we adopt the multi-task form of the smoothing proximal gradient method to solve our optimization problem efficiently. Experimental results on 3 benchmark datasets demonstrate the effectiveness of our improved sparse coding method in image classification. © Springer International Publishing Switzerland 2013.","Title: Multi-cue Based Multi-target Tracking with Boosted MHT.\nYear: 2013\n\nAbstract: Tracking multiple objects is critical to automatic video content analysis and virtual reality. The major problem is how to solve data association problem when ambiguous observations are caused by objects in close proximity or occlusion. To tackle this problem, we propose a boosted multiple hypotheses tracking (BMHT) algorithm for multiobject tracking. Here, on-line boosting learning is adopted to enhance the discriminative property and enlarge search space of the generative tracker MHT. To make the tracker be more reliable, a multi-cue integration strategy is adopted to consider different kinds of features under the on-line boosting framework. In this paper, we integrate both appearance and motion pattern information. For simplicity, Haar-like features and optical flow are adopted. We test our BMHT tracker on several challenging video sequences that involve heavy occlusion and pose variations. Experimental results show that the proposed BMHT achieves good performance. © Springer International Publishing Switzerland 2013.","Title: M4L: Maximum margin Multi-instance Multi-cluster Learning for scene modeling\nYear: 2013\n\nAbstract: Automatically learning and grouping key motion patterns in a traffic scene captured by a static camera is a fundamental and challenging task for intelligent video surveillance. To learn motion patterns, trajectory obtained by object tracking is parameterized, and scene image is spatially and evenly divided into multiple regular cell blocks which potentially contain several primary motion patterns. Then, for each block, Gaussian Mixture Model (GMM) is adopted to learn its motion patterns based on the parameters of trajectories. Grouping motion pattern can be done by clustering blocks indirectly, and each cluster of blocks corresponds to a certain motion pattern. For one particular block, each of its motion pattern (Gaussian component) can be viewed as an instance, and all motion patterns (Gaussian components) constitute a bag which can correspond to multiple semantic clusters. Therefore, blocks can be grouped as a Multi-instance Multi-cluster Learning (MIMCL) problem, and a novel Maximum Margin Multi-instance Multi-cluster Learning (M^4L) algorithm is proposed. To avoid processing a difficult optimization problem, M^4L is further relaxed and solved by making use of a combination of the Cutting Plane method and Constrained Concave-Convex Procedure (CCCP). Extensive experiments are conducted on multiple real world video sequences containing various patterns and the results validate the effectiveness of our proposed approach.","Title: A generic framework for event detection in various video domains\nYear: 2010\n\nAbstract: Event detection is essential for the extensively studied video analysis and understanding area. Although various approaches have been proposed for event detection, there is a lack of a generic event detection framework that can be applied to various video domains (e.g. sports, news, movies, surveillance). In this paper, we present a generic event detection approach based on semi-supervised learning and Internet vision. Concretely, a Graph-based Semi-Supervised Multiple Instance Learning (GSSMIL) algorithm is proposed to jointly explore small-scale expert labeled videos and large-scale unlabeled videos to train the event models to detect video event boundaries. The expert labeled videos are obtained from the analysis and alignment of well-structured video related text (e.g. movie scripts, web-casting text, close caption). The unlabeled data are obtained by querying related events from the video search engine (e.g. YouTube) in order to give more distributive information for event modeling. A critical issue of GSSMIL in constructing a graph is the weight assignment, where the weight of an edge specifies the similarity between two data points. To tackle this problem, we propose a novel Multiple Instance Learning Induced Similarity (MILIS) measure by learning instance sensitive classifiers. We perform the thorough experiments in three popular video domains: movies, sports and news. The results compared with the state-of-the-arts are promising and demonstrate our proposed approach is performance-effective.","Title: Robust Visual Tracking via Structured Multi-Task Sparse Learning\nYear: 2013\n\nAbstract: In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing $$\\ell _{p,q}$$ mixed norms $$(\\text{ specifically} p\\in \\{2,\\infty \\}$$ and $$q=1),$$ we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular $$L_1$$ tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259---2272, 2011) is a special case of our MTT formulation (denoted as the $$L_{11}$$ tracker) when $$p=q=1.$$ Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers.","Title: Mining Semantic Context Information for Intelligent Video Surveillance of Traffic Scenes\nYear: 2013\n\nAbstract: Automated visual surveillance systems are attracting extensive interest due to public security. In this paper, we attempt to mine semantic context information including object-specific context information and scene-specific context information (learned from object-specific context information) to build an intelligent system with robust object detection, tracking, and classification and abnormal event detection. By means of object-specific context information, a cotrained classifier, which takes advantage of the multiview information of objects and reduces the number of labeling training samples, is learned to classify objects into pedestrians or vehicles with high object classification performance. For each kind of object, we learn its corresponding semantic scene-specific context information: motion pattern, width distribution, paths, and entry/exist points. Based on this information, it is efficient to improve object detection and tracking and abnormal event detection. Experimental results demonstrate the effectiveness of our semantic context features for multiple real-world traffic scenes.","Title: Learning semantic scene models by object classification and trajectory clustering\nYear: 2009\n\nAbstract: Activity analysis is a basic task in video surveillance and has become an active research area. However, due to the diversity of moving objects category and their motion patterns, developing robust semantic scene models for activity analysis remains a challenging problem in traffic scenarios. This paper proposes a novel framework to learn semantic scene models. In this framework, the detected moving objects are first classified as pedestrians or vehicles via a co-trained classifier which takes advantage of the multiview information of objects. As a result, the framework can automatically learn motion patterns respectively for pedestrians and vehicles. Then, a graph is proposed to learn and cluster the motion patterns. To this end, trajectory is parameterized and the image is cut into multiple blocks which are taken as the nodes in the graph. Based on the parameters of trajectories, the primary motion patterns in each node (block) are extracted via Gaussian mixture model (GMM), and supplied to this graph. The graph cut algorithm is finally employed to group the motion patterns together, and trajectories are clustered to learn semantic scene models. Experimental results and applications to real world scenes show the validity of our proposed method.","Title: Boosted multi-class semi-supervised learning for human action recognition\nYear: 2011\n\nAbstract: Human action recognition is a challenging task due to significant intra-class variations, occlusion, and background clutter. Most of the existing work use the action models based on statistic learning algorithms for classification. To achieve good performance on recognition, a large amount of the labeled samples are therefore required to train the sophisticated action models. However, collecting labeled samples is labor-intensive. To tackle this problem, we propose a boosted multi-class semi-supervised learning algorithm in which the co-EM algorithm is adopted to leverage the information from unlabeled data. Three key issues are addressed in this paper. Firstly, we formulate the action recognition in a multi-class semi-supervised learning problem to deal with the insufficient labeled data and high computational expense. Secondly, boosted co-EM is employed for the semi-supervised model construction. To overcome the high dimensional feature space, weighted multiple discriminant analysis (WMDA) is used to project the features into low dimensional subspaces in which the Gaussian mixture models (GMM) are trained and boosting scheme is used to integrate the subspace models. Thirdly, we present the upper bound of the training error in multi-class framework, which is able to guide the novel classifier construction. In theory, the proposed solution is proved to minimize this upper error bound. Experimental results have shown good performance on public datasets.","Title: Weakly Supervised Graph Propagation Towards Collective Image Parsing\nYear: 2012\n\nAbstract: In this work, we propose a weakly supervised graph propagation method to automatically assign the annotated labels at image level to those contextually derived semantic regions. The graph is constructed with the over-segmented patches of the image collection as nodes. Image-level labels are imposed on the graph as weak supervision information over subgraphs, each of which corresponds to all patches of one image, and the contextual information across different images at patch level are then mined to assist the process of label propagation from images to their descendent regions. The ultimate optimization problem is efficiently solved by Convex Concave Programming (CCCP). Extensive experiments on four benchmark datasets clearly demonstrate the effectiveness of our proposed method for the task of collective image parsing. Two extensions including image annotation and concept map based image retrieval demonstrate the proposed image parsing algorithm can effectively aid other vision tasks.","Title: A Generic Framework for Video Annotation via Semi-Supervised Learning\nYear: 2012\n\nAbstract: Learning-based video annotation is essential for video analysis and understanding, and many various approaches have been proposed to avoid the intensive labor costs of purely manual annotation. However, there lacks a generic framework due to several difficulties, such as dependence of domain knowledge, insufficiency of training data, no precise localization and inefficacy for large-scale video dataset. In this paper, we propose a novel approach based on semi-supervised learning by means of information from the Internet for interesting event annotation in videos. Concretely, a Fast Graph-based Semi-Supervised Multiple Instance Learning (FGSSMIL) algorithm, which aims to simultaneously tackle these difficulties in a generic framework for various video domains (e.g., sports, news, and movies), is proposed to jointly explore small-scale expert labeled videos and large-scale unlabeled videos to train the models. The expert labeled videos are obtained from the analysis and alignment of well-structured video related text (e.g., movie scripts, web-casting text, close caption). The unlabeled data are obtained by querying related events from the video search engine (e.g., YouTube, Google) in order to give more distributive information for event modeling. Two critical issues of FGSSMIL are: 1) how to calculate the weight assignment for a graph construction, where the weight of an edge specifies the similarity between two data points. To tackle this problem, we propose a novel Multiple Instance Learning Induced Similarity (MILIS) measure by learning instance sensitive classifiers; 2) how to solve the algorithm efficiently for large-scale dataset through an optimization approach. To address this issue, Concave-Convex Procedure (CCCP) and nonnegative multiplicative updating rule are adopted. We perform the extensive experiments in three popular video domains: movies, sports, and news. The results compared with the state-of-the-arts are promising and demonstrate the effectiveness and efficiency of our proposed approach.","Title: Hierarchical object representations for visual recognition via weakly supervised learning\nYear: 2012\n\nAbstract: In this paper, we propose a weakly supervised approach to learn hierarchical object representations for visual recognition. The learning process is carried out in a bottom-up manner to discover latent visual patterns in multiple scales. To relieve the disturbance of complex backgrounds in natural images, bounding boxes of foreground objects are adopted as weak knowledge in the learning stage to promote those visual patterns which are more related to the target objects. The difference between the patterns of foreground objects and backgrounds is relatively vague at low-levels, but becomes more distinct along with the feature transformations to high-levels. In the test stage, an input image is verified against the learnt patterns level-by-level, and the responses at each level construct a hierarchy of representations which indicates the occurring possibilities of the target object at various scales. Experiments on two PASCAL datasets showed encouraging results for visual recognition.","Title: Human action recognition in videos using hybrid motion features\nYear: 2010\n\nAbstract: In this paper, we present hybrid motion features to promote action recognition in videos. The features are composed of two complementary components from different views of motion information. On one hand, the period feature is extracted to capture global motion in time-domain. On the other hand, the enhanced histograms of motion words (EHOM) are proposed to describe local motion information. Each word is represented by optical flow of a frame and the correlations between words are encoded into the transition matrix of a Markov process, and then its stationary distribution is extracted as the final EHOM. Compared to traditional Bags of Words representation, EHOM preserves not only relationships between words but also temporary information in videos to some extent. We show that by integrating local and global features, we get improved recognition rates on a variety of standard datasets.","Title: Human action recognition via multi-view learning.\nYear: 2010\n\nAbstract: In this paper, we propose a novel approach to automatically learn a compact and yet discriminative representation for humane action recognition. Considering the static visual information and motion information, each frame is represented in two feature subsets (views) and Gaussian Mixture Model (GMM) is adopted to model the distributions of those features. In order to complement the strengths of the different features (views), a Co-EM based multiview learning framework is introduced to estimate the parameters of GMM instead of conventional single view based EM. Then Gaussian components are considered as video words to describe videos with different time resolutions. Compared with the traditional method to recognize action, there are several advantages with the proposed method using Co-EM strategy. First, complex actions are efficiently modeled by GMM, and the number of its component is automatically determined with the Minimum Description Length (MDL). Second, because the imperfectness of single view can be compensated by the other view in the Co-EM, the resulting bag of video words are superior to that formed by any single view. To the best of our knowledge, we are the first to try the Co-EM based multi-view learning method for action recognition and obtain significantly better results. We extensively verify our proposed approach on two publicly available challenging datasets: the KTH dataset and Weizmann dataset. The experimental results show the validity of our proposed method.","Title: Hi, magic closet, tell me what to wear!\nYear: 2012\n\nAbstract: In this paper, we aim at a practical system, magic closet, for automatic occasion-oriented clothing recommendation. Given a user-input occasion, e.g., wedding, shopping or dating, magic closet intelligently suggests the most suitable clothing from the user's own clothing photo album, or automatically pairs the user-specified reference clothing (upper-body or lower-body) with the most suitable one from online shops. Two key criteria are explicitly considered for the magic closet system. One criterion is to wear properly, e.g., compared to suit pants, it is more decent to wear a cocktail dress for a banquet occasion. The other criterion is to wear aesthetically, e.g., a red T-shirt matches better white pants than green pants. To narrow the semantic gap between the low-level features of clothing and the high-level occasion categories, we adopt middle-level clothing attributes (e.g., clothing category, color, pattern) as a bridge. More specifically, the clothing attributes are treated as latent variables in our proposed latent Support Vector Machine (SVM) based recommendation model. The wearing properly criterion is described in the model through a feature-occasion potential and an attribute-occasion potential, while the wearing aesthetically criterion is expressed by an attribute-attribute potential. To learn a generalize-well model and comprehensively evaluate it, we collect a large clothing What-to-Wear (WoW) dataset, and thoroughly annotate the whole dataset with 7 multi-value clothing attributes and 10 occasion categories via Amazon Mechanic Turk. Extensive experiments on the WoW dataset demonstrate the effectiveness of the magic closet system for both occasion-oriented clothing recommendation and pairing.","Title: Locality discriminative coding for image classification\nYear: 2013\n\nAbstract: The Bag-of-Words (BOW) based methods are widely used in image classification. However, huge number of visual information is omitted inevitably in the quantization step of the BOW. Recently, NBNN and its improved methods like Local NBNN were proposed to solve this problem. Nevertheless, these methods do not perform better than the state-of-the-art BOW based methods. In this paper, based on the advantages of BOW and Local NBNN, we introduce a novel locality discriminative coding (LDC) method. We convert each low level local feature, such as SIFT, into code vector using the Local Feature-to-Class distance other than by k-means quantization. Extensive experimental results on 4 challenging benchmark datasets show that our LDC method outperforms 6 state-of-the-art image classification methods (3 based on NBNN, 3 based on BOW).","Title: Object Tracking by Occlusion Detection via Structured Sparse Learning\nYear: 2013\n\nAbstract: Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object's track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art.","Title: Low-Rank Sparse Coding for Image Classification\nYear: 2013\n\nAbstract: In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.","Title: Latent support vector machine for sign language recognition with Kinect\nYear: 2013\n\nAbstract: In this paper, we propose a novel algorithm to model and recognize sign language with Kinect sensor. We assume that in a sign language video, some frames are expected to be both discriminative and representative. Under this assumption, each frame in training videos is assigned a binary latent variable indicating its discriminative capability. A Latent Support Vector Machine model is then developed to classify the signs, as well as localize the discriminative and representative frames in videos. In addition, we utilize the depth map together with color image captured by Kinect sensor to obtain more effective and accurate feature to enhance the recognition accuracy. To evaluate our approach, we collected an American Sign Language (ASL) dataset which included approximately 2000 phrases, while each phrase was captured by Kinect sensor and hence included color, depth and skeleton information. Experiments on our dataset demonstrate the effectiveness of the proposed method for sign language recognition.","Title: Boosted Exemplar Learning for Action Recognition and Annotation\nYear: 2011\n\nAbstract: Human action recognition and annotation is an active research topic in computer vision. How to model various actions, varying with time resolution, visual appearance, and others, is a challenging task. In this paper, we propose a boosted exemplar learning (BEL) approach to model various actions in a weakly supervised manner, i.e., only action bag-level labels are provided but action instance level ones are not. The proposed BEL method can be summarized as three steps. First, for each action category, amount of class-specific candidate exemplars are learned through an optimization formulation considering their discrimination and co-occurrence. Second, each action bag is described as a set of similarities between its instances and candidate exemplars. Instead of simply using a heuristic distance measure, the similarities are decided by the exemplar-based classifiers through the multiple instance learning, in which a positive (or negative) video or image set is deemed as a positive (or negative) action bag and those frames similar to the given exemplar in Euclidean Space as action instances. Third, we formulate the selection of the most discriminative exemplars into a boosted feature selection framework and simultaneously obtain an action bag-based detector. Experimental results on two publicly available datasets: the KTH dataset and Weizmann dataset, demonstrate the validity and effectiveness of the proposed approach for action recognition. We also apply BEL to learn representations of actions by using images collected from the Web and use this knowledge to automatically annotate action in YouTube videos. Results are very impressive, which proves that the proposed algorithm is also practical in unconstraint environments.","Title: Robust Visual Tracking Via Consistent Low-Rank Sparse Learning\nYear: 2015\n\nAbstract: Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against $$14$$ 14 state-of-the-art tracking methods on a set of $$25$$ 25 challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.","Title: A new discriminative coding method for image classification\nYear: 2015\n\nAbstract: The bag-of-words (BOW) based methods are widely used in image classification. However, huge number of visual information is omitted inevitably in the quantization step of the BOW. Recently, NBNN and its improved methods like Local NBNN were proposed to solve this problem. Nevertheless, these methods do not perform better than the state-of-the-art BOW based methods. In this paper, based on the advantages of BOW and Local NBNN, we introduce a novel locality discriminative coding (LDC) method. We convert each low level local feature, such as SIFT, into code vector using the Local Feature-to-Class distance other than by k-means quantization. After coding, sum-pooling combined with SPM is used to construct a single feature representation vector for each image. Extensive experimental results on several challenging benchmark datasets show that our LDC method outperforms six state-of-the-art image classification methods.","Title: Automatic Visual Concept Learning for Social Event Understanding\nYear: 2015\n\nAbstract: Vision-based event analysis is extremely difficult due to the various concepts (object, action, and scene) contained in videos. Though visual concept-based event analysis has achieved significant progress, it has two disadvantages: visual concept is defined manually, and has only one corresponding classifier in traditional methods. To deal with these issues, we propose a novel automatic visual concept learning algorithm for social event understanding in videos. First, instead of defining visual concept manually, we propose an effective automatic concept mining algorithm with the help of Wikipedia, N-gram Web services, and Flickr. Then, based on the learned visual concept, we propose a novel boosting concept learning algorithm to iteratively learn multiple classifiers for each concept to enhance its representative discriminability. The extensive experimental evaluations on the collected dataset well demonstrate the effectiveness of the proposed algorithm for social event understanding.","Title: Cross-Domain Feature Learning in Multimedia\nYear: 2015\n\nAbstract: In the Web 2.0 era, a huge number of media data, such as text, image/video, and social interaction information, have been generated on the social media sites (e.g., Facebook, Google, Flickr, and YouTube). These media data can be effectively adopted for many applications (e.g., image/video annotation, image/video retrieval, and event classification) in multimedia. However, it is difficult to design an effective feature representation to describe these data because they have multi-modal property (e.g., text, image, video, and audio) and multi-domain property (e.g., Flickr, Google, and YouTube). To deal with these issues, we propose a novel cross-domain feature learning (CDFL) algorithm based on stacked denoising auto-encoders. By introducing the modal correlation constraint and the cross-domain constraint in conventional auto-encoder, our CDFL can maximize the correlations among different modalities and extract domain invariant semantic features simultaneously. To evaluate our CDFL algorithm , we apply it to three important applications: sentiment classification, spam filtering, and event classification. Comprehensive evaluations demonstrate the encouraging performance of the proposed approach.","Title: Robust Visual Tracking via Exclusive Context Modeling.\nYear: 2016\n\nAbstract: In this paper, we formulate particle filter-based object tracking as an exclusive sparse learning problem that exploits contextual information. To achieve this goal, we propose the context-aware exclusive sparse tracker (CEST) to model particle appearances as linear combinations of dictionary templates that are updated dynamically. Learning the representation of each particle is formulated as an exclusive sparse representation problem, where the overall dictionary is composed of multiple {group} dictionaries that can contain contextual information. With context, CEST is less prone to tracker drift. Interestingly, we show that the popular L₁ tracker [1] is a special case of our CEST formulation. The proposed learning problem is efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. To make the tracker much faster, we reduce the number of learning problems to be solved by using the dual problem to quickly and systematically rank and prune particles in each frame. We test our CEST tracker on challenging benchmark sequences that involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that CEST consistently outperforms state-of-the-art trackers.","Title: Social Event Classification via Boosted Multimodal Supervised Latent Dirichlet Allocation\nYear: 2014\n\nAbstract: With the rapidly increasing popularity of social media sites (e.g., Flickr, YouTube, and Facebook), it is convenient for users to share their own comments on many social events, which successfully facilitates social event generation, sharing and propagation and results in a large amount of user-contributed media data (e.g., images, videos, and text) for a wide variety of real-world events of different types and scales. As a consequence, it has become more and more difficult to exactly find the interesting events from massive social media data, which is useful to browse, search and monitor social events by users or governments. To deal with these issues, we propose a novel boosted multimodal supervised Latent Dirichlet Allocation (BMM-SLDA) for social event classification by integrating a supervised topic model, denoted as multi-modal supervised Latent Dirichlet Allocation (mm-SLDA), in the boosting framework. Our proposed BMM-SLDA has a number of advantages. (1) Our mm-SLDA can effectively exploit the multimodality and the multiclass property of social events jointly, and make use of the supervised category label information to classify multiclass social event directly. (2) It is suitable for large-scale data analysis by utilizing boosting weighted sampling strategy to iteratively select a small subset of data to efficiently train the corresponding topic models. (3) It effectively exploits social event structure by the document weight distribution with classification error and can iteratively learn new topic model to correct the previously misclassified event documents. We evaluate our BMM-SLDA on a real world dataset and show extensive experimental results, which demonstrate that our model outperforms state-of-the-art methods.","Title: Cross-Domain Multi-Event Tracking via CO-PMHT\nYear: 2014\n\nAbstract: With the massive growth of events on the Internet, efficient organization and monitoring of events becomes a practical challenge. To deal with this problem, we propose a novel CO-PMHT (CO-Probabilistic Multi-Hypothesis Tracking) algorithm for cross-domain multi-event tracking to obtain their informative summary details and evolutionary trends over time. We collect a large-scale dataset by searching keywords on two domains (Gooogle News and Flickr) and downloading both images and textual content for an event. Given the input data, our algorithm can track multiple events in the two domains collaboratively and boost the tracking performance. Specifically, the bridge between two domains is a semantic posterior probability, that avoids the domain gap. After tracking, we can visualize the whole evolutionary process of the event over time and mine the semantic topics of each event for deep understanding and event prediction. The extensive experimental evaluations on the collected dataset well demonstrate the effectiveness of the proposed algorithm for cross-domain multi-event tracking.","Title: Boosted Multifeature Learning for Cross-Domain Transfer\nYear: 2015\n\nAbstract: Conventional learning algorithm assumes that the training data and test data share a common distribution. However, this assumption will greatly hinder the practical application of the learned model for cross-domain data analysis in multimedia. To deal with this issue, transfer learning based technology should be adopted. As a typical version of transfer learning, domain adaption has been extensively studied recently due to its theoretical value and practical interest. In this article, we propose a boosted multifeature learning (BMFL) approach to iteratively learn multiple representations within a boosting procedure for unsupervised domain adaption. The proposed BMFL method has a number of properties. (1) It reuses all instances with different weights assigned by the previous boosting iteration and avoids discarding labeled instances as in conventional methods. (2) It models the instance weight distribution effectively by considering the classification error and the domain similarity, which facilitates learning new feature representation to correct the previously misclassified instances. (3) It learns multiple different feature representations to effectively bridge the source and target domains. We evaluate the BMFL by comparing its performance on three applications: image classification, sentiment classification and spam filtering. Extensive experimental results demonstrate that the proposed BMFL algorithm performs favorably against state-of-the-art domain adaption methods.","Title: Partial Occlusion Handling for Visual Tracking via Robust Part Matching\nYear: 2014\n\nAbstract: Part-based visual tracking is advantageous due to its robustness against partial occlusion. However, how to effectively exploit the confidence scores of individual parts to construct a robust tracker is still a challenging problem. In this paper, we address this problem by simultaneously matching parts in each of multiple frames, which is realized by a locality-constrained low-rank sparse learning method that establishes multi-frame part correspondences through optimization of partial permutation matrices. The proposed part matching tracker (PMT) has a number of attractive properties. (1) It exploits the spatial-temporal localityconstrained property for robust part matching. (2) It matches local parts from multiple frames jointly by considering their low-rank and sparse structure information, which can effectively handle part appearance variations due to occlusion or noise. (3) The proposed PMT model has the inbuilt mechanism of leveraging multi-mode target templates, so that the dilemma of template updating when encountering occlusion in tracking can be better handled. This contrasts with existing methods that only do part matching between a pair of frames. We evaluate PMT and compare with 10 popular state-of-the-art methods on challenging benchmarks. Experimental results show that PMT consistently outperform these existing trackers.","Title: Boosted Multi-modal Supervised Latent Dirichlet Allocation for Social Event Classification\nYear: 2014\n\nAbstract: With the rapidly increasing popularity of Social Media sites (e.g., Flickr, YouTube, and Facebook), it is convenient for users to share their own comments on many social events, which successfully facilitates social event generation, sharing and propagation and results in a large amount of user-contributed media data (e.g., images, videos, and texts) for a wide variety of real-world events of different types and scales. As a consequence, it has become more and more difficult to find exactly the interesting events from massive social media data, which is useful to browse, search and monitor social events by users or governments. To deal with these issues, we propose a novel boosted multi-modal supervised Latent Dirichlet Allocation (BMM-SLDA) for social event classification. Our BMM-SLDA has a number of advantages. (1) It can effectively exploit the multi-modality and the supervised information of social events jointly. (2) It is suitable to large-scale data analysis by utilizing boosting weighted sampling strategy to iteratively select a small subset data to efficiently train the corresponding topic models. (3) It effectively exploits boosting document weight distribution by classification error, and can iteratively learn new topic model to correct the previously misclassified documents. We evaluate our BMM-SLDA on a real-world dataset and show extensive results, which show that our model outperforms state-of-the-art methods.","Title: Multi-Modal Supervised Latent Dirichlet Allocation for Event Classification in Social Media\nYear: 2014\n\nAbstract: In social media, many existing websites (e.g., Flickr, YouTube, and Facebook) are for users to share their own interests and opinions of many popular events, and successfully facilitate the event generation, sharing and propagation. As a result, there are substantial amounts of user-contributed media data (e.g., images, videos, and textual content) for a wide variety of real-world events of different types and scales. The aim of this paper is to automatically identify the interesting events from massive social media data, which are useful to browse, search and monitor social events by users or governments. To achieve this goal, we propose a novel multi-modal supervised latent dirichlet allocation (mm-SLDA) for social event classification. Our proposed mm-SLDA has a number of advantages. (1) It can effectively exploit the multi-modality and the multi-class property of social events jointly. (2) It makes use of the supervised social event category label information and is able to classify multi-class social event directly. We evaluate our proposed mm-SLDA on a real world dataset and show extensive experimental results, which demonstrate that our model outperforms state-of-the-art methods.","Title: Human action recognition in videos using motion impression image\nYear: 2009\n\nAbstract: Human action recognition in surveillance has become a hot topic in computer vision. In this paper, we develope a new method to recognize human action using motion information in video. Video sequence is compressed along time axis into a Motion Impression Image (MII), which is combined with two types of impression images from different views. One is a Period Impression Image (PII) by exploring the characteristics of the motion frequency. The other is an Optical Flow Impression Image (OFII) obtained from the analysis of motion mode. The proposed MII is a compact and time-invariant representation. Furthermore, it is simple and efficient to implement. After quantizing the combined MIIs, we feed them into a spatial pyramid matching kernel (SPMK) based classifier to recognize various human actions. At last, experiments on a known benchmark dataset demonstrate the better performance of the proposed approach against the state-of-the-art algorithms.","Title: Multi-object tracking via MHT with multiple information fusion in surveillance video\nYear: 2015\n\nAbstract: Tracking multiple objects is critical to automatic video content analysis and virtual reality. The major problem is how to solve data association problem when ambiguous measurements are caused by objects in close proximity. To tackle this problem, we propose a multiple information fusion-based multiple hypotheses tracking algorithm integrated with appearance feature, local motion pattern feature and repulsion---inertia model for multi-object tracking. Appearance model based on HSV---local binary patterns histogram and local motion pattern based on optical flow are adopted to describe objects. A likelihood calculation framework is proposed to incorporate the similarities of appearance, dynamic process and local motion pattern. To consider the changes in appearance and motion pattern over time, we make use of an effective template updating strategy for each object. In addition, a repulsion---inertia model is adopted to explore more useful information from ambiguous detections. Experimental results show that the proposed approach generates better trajectories with less missing objects and identity switches.","Title: Context-aware learning for automatic sports highlight recognition\nYear: 2012\n\nAbstract: Video highlight recognition is the procedure in which a long video sequence is summarized into a shorter video clip that depicts the most “salient” parts of the sequence. It is an important technique for content delivery systems and search systems which create multimedia content tailored to their users' needs. This paper deals specifically with capturing highlights inherent to sports videos, especially for American football. Our proposed system exploits the multimodal nature of sports videos (i.e. visual, audio, and text cues) to detect the most important segments among them. The optimal combination of these cues is learned in a data-driven fashion using user preferences (expert input) as ground truth. Unlike most highlight recognition systems in the literature that define a highlight to be salient only in its own right (globally salient), we also consider the context of each video segment w.r.t. the video sequence it belongs to (locally salient). To validate our method, we compile a large dataset of broadcast American football videos, acquire their ground truth highlights, and evaluate the performance of our learning approach.","Title: Load disaggregation using harmonic analysis and regularized optimization\nYear: 2012\n\nAbstract: In this paper, we present a load disaggregation technique that uses regularized optimization together with harmonic frequency signatures of appliances. The benefits of our technique are twofold: 1) The regularized optimization is faster than integer programming; and 2) The harmonic frequency signatures allow us to disaggregate the loads using as few as 10 cycles (equaling as little as 200 milliseconds) of samples, instead of having to wait for state changes from appliances or weekly usage pattern to emerge. We test our proposed technique in proof-of-concept experiments and show that our technique returns accurate disaggregation results.","Title: Multi-Modal Event Topic Model for Social Event Analysis\nYear: 2016\n\nAbstract: With the massive growth of social events in Internet , it has become more and more difficult to exactly find and organize the interesting events from massive social media data, which is useful to browse, search, and monitor social events by users or governments . To deal with this problem, we propose a novel multi-modal social event tracking and evolution framework to not only effectively capture multi-modal topics of social events, but also obtain the evolutionary trends of social events and generate effective event summary details over time. To achieve this goal, we propose a novel multi-modal event topic model (mmETM), which can effectively model social media documents, including long text with related images, and learn the correlations between textual and visual modalities to separate the visual-representative topics and non-visual-representative topics. To apply the mmETM model to social event tracking, we adopt an incremental learning strategy denoted as incremental mmETM, which can obtain informative textual and visual topics of social events over time to help understand these events and their evolutionary trends. To evaluate the effectiveness of our proposed algorithm, we collect a real-world dataset to conduct various experiments. Both qualitative and quantitative evaluations demonstrate that the proposed mmETM algorithm performs favorably against several state-of-the-art methods.","Title: Latent Support Vector Machine Modeling for Sign Language Recognition with Kinect\nYear: 2015\n\nAbstract: Vision-based sign language recognition has attracted more and more interest from researchers in the computer vision field. In this article, we propose a novel algorithm to model and recognize sign language performed in front of a Microsoft Kinect sensor. Under the assumption that some frames are expected to be both discriminative and representative in a sign language video, we first assign a binary latent variable to each frame in training videos for indicating its discriminative capability, then develop a latent support vector machine model to classify the signs, as well as localize the discriminative and representative frames in each video. In addition, we utilize the depth map together with the color image captured by the Kinect sensor to obtain a more effective and accurate feature to enhance the recognition accuracy. To evaluate our approach, we conducted experiments on both word-level sign language and sentence-level sign language. An American Sign Language dataset including approximately 2,000 word-level sign language phrases and 2,000 sentence-level sign language phrases was collected using the Kinect sensor, and each phrase contains color, depth, and skeleton information. Experiments on our dataset demonstrate the effectiveness of the proposed method for sign language recognition.","Title: Cross-Domain Collaborative Learning in Social Multimedia\nYear: 2015\n\nAbstract: Cross-domain data analysis is one of the most important tasks in social multimedia. It has a wide range of real-world applications, including cross-platform event analysis, cross-domain multi-event tracking, cross-domain video recommendation, etc. It is also very challenging because the data have multi-modal and multi-domain properties, and there are no explicit correlations to link different domains. To deal with these issues, we propose a generic Cross-Domain Collaborative Learning (CDCL) framework based on non-parametric Bayesian dictionary learning model for cross-domain data analysis. In the proposed CDCL model, it can make use of the shared domain priors and modality priors to collaboratively learn the data's representations by considering the domain discrepancy and the multi-modal property. As a result, our CDCL model can effectively explore the virtues of different information sources to complement and enhance each other for cross-domain data analysis. To evaluate the proposed model, we apply it for two different applications: cross-platform event recognition and cross-network video recommendation. The extensive experimental evaluations well demonstrate the effectiveness of the proposed algorithm for cross-domain data analysis.","Title: Structural Sparse Tracking\nYear: 2015\n\nAbstract: Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.","Title: The Role of Inter-Controller Traffic for Placement of Distributed SDN Controllers.\nYear: 2016\n\nAbstract: We consider a distributed Software Defined Networking (SDN) architecture adopting a cluster of multiple controllers to improve network performance and reliability. Besides the Openflow control traffic exchanged between controllers and switches, we focus on the control traffic exchanged among the controllers in the cluster, needed to run coordination and consensus algorithms to keep the controllers synchronized. We estimate the effect of the inter-controller communications on the reaction time perceived by the switches depending on the data-ownership model adopted in the cluster. The model is accurately validated in an operational Software Defined WAN (SDWAN). We advocate a careful placement of the controllers, that should take into account both the above kinds of control traffic. We evaluate, for some real ISP network topologies, the delay tradeoffs for the controllers placement problem and we propose a novel evolutionary algorithm to find the corresponding Pareto frontier. Our work provides novel quantitative tools to optimize the planning and the design of the network supporting the control plane of SDN networks, especially when the network is very large and in-band control plane is adopted. We also show that for operational distributed controllers (e.g. OpenDaylight and ONOS), the location of the controller which acts as a leader in the consensus algorithm has a strong impact on the reactivity perceived by switches.","Title: Deep Relative Attributes.\nYear: 2016\n\nAbstract: Relative attribute (RA) learning aims to learn the ranking function describing the relative strength of the attribute. Most of current learning approaches learn a linear ranking function for each attribute by use of the hand-crafted visual features. Different from the existing study, in this paper, we propose a novel deep relative attributes (DRA) algorithm to learn visual features and the effective nonlinear ranking function to describe the RA of image pairs in a unified framework. Here, visual features and the ranking function are learned jointly, and they can benefit each other. The proposed DRA model is comprised of five convolutional neural layers, five fully connected layers, and a relative loss function which contains the contrastive constraint and the similar constraint corresponding to the ordered image pairs and the unordered image pairs, respectively. To train the DRA model effectively, we make use of the transferred knowledge from the large scale visual recognition on ImageNet [1] to the RA learning task. We evaluate the proposed DRA model on three widely used datasets. Extensive experimental results demonstrate that the proposed DRA model consistently and significantly outperforms the state-of-the-art RA learning methods. On the public OSR, PubFig, and Shoes datasets, compared with the previous RA learning results [2], the average ranking accuracies have been significantly improved by about 8%, 9%, and 14%, respectively.","Title: Semantic Feature Mining for Video Event Understanding.\nYear: 2016\n\nAbstract: Content-based video understanding is extremely difficult due to the semantic gap between low-level vision signals and the various semantic concepts (object, action, and scene) in videos. Though feature extraction from videos has achieved significant progress, most of the previous methods rely only on low-level features, such as the appearance and motion features. Recently, visual-feature extraction has been improved significantly with machine-learning algorithms, especially deep learning. However, there is still not enough work focusing on extracting semantic features from videos directly. The goal of this article is to adopt unlabeled videos with the help of text descriptions to learn an embedding function, which can be used to extract more effective semantic features from videos when only a few labeled samples are available for video recognition. To achieve this goal, we propose a novel embedding convolutional neural network (ECNN). We evaluate our algorithm by comparing its performance on three challenging benchmarks with several popular state-of-the-art methods. Extensive experimental results show that the proposed ECNN consistently and significantly outperforms the existing methods.","Title: Multi-modal Multi-view Topic-opinion Mining for Social Event Analysis.\nYear: 2016\n\nAbstract: In this paper, we propose a novel multi-modal multi-view topic-opinion mining (MMTOM) model for social event analysis in multiple collection sources. Compared with existing topic-opinion mining methods, our proposed model has several advantages: (1) The proposed MMTOM can effectively take into account multi-modal and multi-view properties jointly in a unified and principled way for social event modeling. (2) Our model is general and can be applied to many other applications in multimedia, such as opinion mining and sentiment analysis, multi-view association visualization, and topic-opinion mining for movie review. (3) The proposed MMTOM is able to not only discover multi-modal common topics from all collections as well as summarize the similarities and differences of these collections along each specific topic, but also automatically mine multi-view opinions on the learned topics across different collections. (4) Our topic-opinion mining results can be effectively applied to many applications including multi-modal multi-view topic-opinion retrieval and visualization, which achieve much better performance than existing methods. To evaluate the proposed model, we collect a real-world dataset for research on multi-modal multi-view social event analysis, and will release it for academic use. We have conducted extensive experiments, and both qualitative and quantitative evaluation results have demonstrated the effectiveness of the proposed MMTOM.","Title: Abnormal Event Discovery in User Generated Photos.\nYear: 2016\n\nAbstract: Vision based event analysis plays a very critical role in automatically organizing user generated photos. As one of the important tasks in event analysis, abnormal event discovery still does not obtain much attentions. It is difficult because only few samples can be used for event pattern learning. In this paper, by considering the photo taken time, we propose a novel one-class structured event modeling (OSEM) where we explore the temporal event patterns in negative photos of the event using the continuous conditional random field (CRF). With the estimated piecewise training of CRF, the proposed OSEM can be efficiently solved using stochastic gradients descent (SGD) in an end-to-end form. The extensive experimental results on a collected abnormal event dataset demonstrate the effectiveness of the proposed OSEM.","Title: Temporal Restricted Visual Tracking Via Reverse-Low-Rank Sparse Learning.\nYear: 2017\n\nAbstract: An effective representation model, which aims to mine the most meaningful information in the data, plays an important role in visual tracking. Some recent particle-filter-based trackers achieve promising results by introducing the low-rank assumption into the representation model. However, their assumed low-rank structure of candidates limits the robustness when facing severe challenges such as ab...","Title: 3d Part-Based Sparse Tracker With Automatic Synchronization And Registration\nYear: 2016\n\nAbstract: In this paper, we present a part-based sparse tracker in a particle filter framework where both the motion and appearance model are formulated in 3D. The motion model is adaptive and directed according to a simple yet powerful occlusion handling paradigm, which is intrinsically fused in the motion model. Also, since 3D trackers are sensitive to synchronization and registration noise in the RGB and depth streams, we propose automated methods to solve these two issues. Extensive experiments are conducted on a popular RGBD tracking benchmark, which demonstrate that our tracker can achieve superior results, outperforming many other recent and state-of-the-art RGBD trackers.","Title: Structural Correlation Filter For Robust Visual Tracking\nYear: 2016\n\nAbstract: In this paper, we propose a novel structural correlation filter (SCF) model for robust visual tracking. The proposed SCF model takes part-based tracking strategies into account in a correlation filter tracker, and exploits circular shifts of all parts for their motion modeling to preserve target object structure. Compared with existing correlation filter trackers, our proposed tracker has several advantages: (1) Due to the part strategy, the learned structural correlation filters are less sensitive to partial occlusion, and have computational efficiency and robustness. (2) The learned filters are able to not only distinguish the parts from the background as the traditional correlation filters, but also exploit the intrinsic relationship among local parts via spatial constraints to preserve object structure. (3) The learned correlation filters not only make most parts share similar motion, but also tolerate outlier parts that have different motion. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SCF tracking algorithm performs favorably against several state-of-the-art methods.","Title: Deep Relative Tracking.\nYear: 2017\n\nAbstract: Most existing tracking methods are direct trackers, which directly exploit foreground or/and background information for object appearance modeling and decide whether an image patch is target object or not. As a result, these trackers cannot perform well when target appearance changes heavily and becomes different from its model. To deal with this issue, we propose a novel relative tracker, which can effectively exploit the relative relationship among image patches from both foreground and background for object appearance modeling. Different from direct trackers, the proposed relative tracker is robust to localize target object by use of the best image patch with the highest relative score to the target appearance model. To model relative relationship among large-scale image patch pairs, we propose a novel and effective deep relative learning algorithm through the convolutional neural network. We test the proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our method consistently outperforms the state-of-the-art trackers due to the powerful capacity of the proposed deep relative model.","Title: The role of inter-controller traffic in SDN controllers placement\nYear: 2016\n\nAbstract: We consider a distributed Software Defined Networking (SDN) architecture adopting a cluster of multiple controllers to improve network performance and reliability. Differently from previous work, we focus on the control traffic exchanged among the controllers, in addition to the Openflow control traffic exchanged between controllers and switches. We develop an analytical model to estimate the reaction time perceived at the switches due to the inter-controller communications, based on the data-ownership model adopted in the cluster. We advocate a careful placement of the controllers, taking into account the two above kinds of control traffic. We evaluate, for some real ISP network topologies, the possible delay tradeoffs for the controllers placement problem.","Title: On-The-Fly Traffic Classification And Control With A Stateful Sdn Approach\nYear: 2017\n\nAbstract: The novel \"stateful\" approach in Software Defined Networking (SDN) provides programmable processing capabilities within the switches to reduce the interaction with the SDN controller and thus improve the scalability and the performance of the network. In our work we consider specifically the stateful extension of OpenFlow that was recently proposed, called OpenState, that allows to program simple state machines in almoststandard OpenFlow switches.We consider a reactive traffic control application that reacts to the traffic flows which are identified in real-time by a generic traffic classification engine. We devise an architecture in which an OpenState-enabled switch sends the minimum number of packets to the traffic classifier, in order to minimize the load on the classifier and improve the scalability of the approach. We design two stateful approaches to minimize the memory occupancy in the flow tables of the switches. Finally, we validate experimentally our solutions and estimate the required memory for the flow tables.","Title: A Unified Personalized Video Recommendation via Dynamic Recurrent Neural Networks.\nYear: 2017\n\nAbstract: Personalized video recommender systems play an essential role in bridging users and videos. However, most existing video recommendation methods assume that user profiles (interests) are static. In fact, the static assumption is inadequate to reflect users' dynamic interests as time goes by, especially in the online video recommendation scenarios with dramatic changes of video contents and frequent drift of users' interests over different topics. To overcome the above issue, we propose a dynamic recurrent neural network to model users' dynamic interests over time in a unified framework for personalized video recommendation. Furthermore, to build a much more comprehensive recommendation system, the proposed model is designed to exploit video semantic embedding, user interest modeling, and user relevance mining jointly to model users' preferences. By considering these three factors, the RNN model becomes an interest network which can capture users' high level interests effectively. Extensive experimental results on both single-network and cross-network video recommendation scenarios demonstrate the superior performance of the proposed model compared with other state-of-the-art algorithms.","Title: The role of the inter-controller consensus in the placement of distributed SDN controllers.\nYear: 2017\n\nAbstract: We consider a distributed Software Defined Networking (SDN) architecture adopting a cluster of controllers to improve network scalability and reliability. Different from previous works that focused solely on the control traffic exchanged between controllers and switches, we additionally consider the control traffic exchanged among the controllers (e.g., the control traffic to keep the shared data structures synchronized). We advocate a careful placement of the controllers, which should take into account both the two kinds of control traffic. We evaluate, for some real ISP network topologies, the delay tradeoffs for the controller placement problem and we propose a novel evolutionary algorithm to find the corresponding Pareto frontier. Furthermore, we develop a simple model to estimate the reaction time perceived by the switches, which is accurately validated in an operational Software Defined WAN (SDWAN). We also formalize the optimization problem to minimize the reaction time and devise a novel approximation algorithm, whose performance is assessed against the optimal solver in real ISP network topologies.","Title: Dealing with Misbehaving Controllers in SDN Networks.\nYear: 2017\n\nAbstract: The logical centralized approach in the control of SDN networks allows an unprecedented level of programmability in the network, but also implies the vulnerability in the case of misbehavior of the controller, due for example to software bugs, hardware problems or hacker attacks. In our work we propose to exploit the diversity offered by multiple controllers to manage the network switches and detect misbehaviors whenever one controller issues different OpenFlow instructions for the data plane with respect to the others. We design a behavioral checker, denoted as BeCheck, that acts as a transparent relay in the interaction between the network switches and the controllers. We propose and investigate different policies to relay the messages and to detect the controller misbehavior. We implement and validate our approach in a simple testbed, showing the possible tradeoff between detection reliability and controller reactivity perceived at the switches.","Title: Correlation Particle Filter for Visual Tracking.\nYear: 2018\n\nAbstract: In this paper, we propose a novel correlation particle filter (CPF) for robust visual tracking. Instead of a simple combination of a correlation filter and a particle filter, we exploit and complement the strength of each one. Compared with existing tracking methods based on correlation filters and particle filters, the proposed tracker has four major advantages: 1) it is robust to partial and tot...","Title: Cross-Domain Collaborative Learning via Discriminative Nonparametric Bayesian Model.\nYear: 2018\n\nAbstract: Cross-domain data analysis has been becoming more and more important, and can be effectively adopted for many applications. However, it is difficult to propose a unified cross-domain collaborative learning framework for cross-domain analysis in social multimedia, because cross-domain data have multidomain, multimodal, sparse, and supervised properties. In this paper, we propose a generic cross-dom...","Title: Deep-Structured Event Modeling for User-Generated Photos.\nYear: 2018\n\nAbstract: Vision-based event analysis is difficult because of the following challenges. The first challenge is intraclass variation. Photos uploaded by users are sparsely sampled visual appearances of an event over time. Thus, each photo may only capture a single object or scene of a specific complex event. The second challenge is interclass confusion. Photos related to different events may contain similar ...","Title: High-speed per-flow software monitoring with limited resources.\nYear: 2018\n\nAbstract: No abstract available.","Title: Text2Video: An End-to-end Learning Framework for Expressing Text With Videos.\nYear: 2018\n\nAbstract: Video creation is a challenging and highly profession-al task that generally involves substantial manual efforts. To ease this burden, a better approach is to automatically produce new videos based on clips from the massive amount of existing videos according to arbitrary text. In this paper, we formulate video creation as a problem of retrieving a sequence of videos for a sentence stream. To achi...","Title: Learning semantic topics for domain-adapted textual knowledge transfer.\nYear: 2018\n\nAbstract: Traditional text classification methods make a basic assumption: the training and test data are homologous, while this naive assumption may not hold in the real world. Hence, this paper studies the problem of domain-adapted news text classification, hereby a model is trained on labeled data from one source domain and is able to be deployed on the other. To realize the cross-domain text classification, we propose a domain-adapted text classification method based on topic model LDA and TextCNN model, named TextLDACNN. Specifically, our work calculates the topic similarity between source and target domain, which is severed as an effective constraint to regularize the training process and hence improve the generalization of the source model to the target domain. Text classifier trained with unsupervised topic feature representation clearly outperforms the baseline TextCNN model. The result shows that our method achieves an approximately 4.0% improvement compared to the state-of-the-art method.","Title: Online Multimodal Multiexpert Learning for Social Event Tracking.\nYear: 2018\n\nAbstract: In this paper, we aim to automatically identify and track the interesting social event from vast amounts of social media data. However, there are two existing challenges: 1) how to model multimodal social event data over time and visualize the topic evolution and 2) how to alleviate the tracking drift problem to boost social event tracking accuracy. We propose a novel online multimodal multiexpert...","Title: FlowMon-DPDK: Parsimonious Per-Flow Software Monitoring at Line Rate\nYear: 2018\n\nAbstract: Testing experimental network devices requires deep performance analysis, which is usually performed with expensive, not flexible, hardware equipment. With the advent of highspeed packet I/O frameworks, general purpose equipments have narrowed the performance gap in respect of dedicated hardware and a variety of software-based solutions have emerged for handling traffic at very high speed. While the literature abounds with software traffic generators, existing monitoring solutions do not target worst-case scenarios (i.e., 64B packets at line rate) that are particularly relevant for stress-testing high-speed network functions, or occupy too many resources. In this paper we first analyse the design space for high-speed traffic monitoring that leads us to specific choices characterizing FlowMon-DPDK, a DPDK-based software traffic monitor that we release as an open source project. In a nutshell, FlowMon-DPDK provides tunable fine-grained statistics at both packet and flow levels. Experimental results demonstrate that our traffic monitor is able to provide per-flow statistics with 5-nines precision at high-speed (14.88 Mpps) using an exiguous amount of resources. Finally, we showcase FlowMon-DPDK usage by testing two open source prototypes for stateful flow-level end-host and in-network packet processing.","Title: Watch, Think and Attend: End-to-End Video Classification via Dynamic Knowledge Evolution Modeling.\nYear: 2018\n\nAbstract: Video classification has been achieved by automatically mining the underlying concepts (\\eg actions, events) in videos, which plays an essential role in intelligent video analysis. However, most existing algorithms only exploit the visual cues of these concepts but ignore external knowledge information for modeling their relationships during the evolution of videos. In fact, humans have remarkable ability to utilize acquired knowledge to reason about the dynamically changing world. To narrow the knowledge gap between existing methods and humans, we propose an end-to-end video classification framework based on a structured knowledge graph, which can model the dynamic knowledge evolution in videos overtime. Here, we map the concepts of videos to the nodes of the knowledge graph. To effectively leverage the knowledge graph, we adopt a graph convLSTM model to not only identify local knowledge structures in each video shot but also model dynamic patterns of knowledge evolution across these shots. Furthermore, a novel knowledge-based attention model is designed by considering the importance of each video shot and relationships between concepts. We show that by using knowledge graphs, our framework is able to improve the performance of various existing methods. Extensive experimental results on two video classification benchmarks UCF101 and Youtube-8M demonstrate the favorable performance of the proposed framework.","Title: Facial Expression Recognition in the Wild: A Cycle-Consistent Adversarial Attention Transfer Approach.\nYear: 2018\n\nAbstract: Facial expression recognition (FER) is a very challenging problem due to different expressions under arbitrary poses. Most conventional approaches mainly perform FER under laboratory controlled environment. Different from existing methods, in this paper, we formulate the FER in the wild as a domain adaptation problem, and propose a novel auxiliary domain guided Cycle-consistent adversarial Attention Transfer model (CycleAT) for simultaneous facial image synthesis and facial expression recognition in the wild. The proposed model utilizes large-scale unlabeled web facial images as an auxiliary domain to reduce the gap between source domain and target domain based on generative adversarial networks (GAN) embedded with an effective attention transfer module, which enjoys several merits. First, the GAN-based method can automatically generate labeled facial images in the wild through harnessing information from labeled facial images in source domain and unlabeled web facial images in auxiliary domain. Second, the class-discriminative spatial attention maps from the classifier in source domain are leveraged to boost the performance of the classifier in target domain. Third, it can effectively preserve the structural consistency of local pixels and global attributes in the synthesized facial images through pixel cycle-consistency and discriminative loss. Quantitative and qualitative evaluations on two challenging in-the-wild datasets demonstrate that the proposed model performs favorably against state-of-the-art methods.","Title: Describe and Attend to Track: Learning Natural Language guided Structural Representation and Visual Attention for Object Tracking.\nYear: 2018\n\nAbstract: The tracking-by-detection framework requires a set of positive and negative training samples to learn robust tracking models for precise localization of target objects. However, existing tracking models mostly treat different samples independently while ignores the relationship information among them. In this paper, we propose a novel structure-aware deep neural network to overcome such limitations. In particular, we construct a graph to represent the pairwise relationships among training samples, and additionally take the natural language as the supervised information to learn both feature representations and classifiers robustly. To refine the states of the target and re-track the target when it is back to view from heavy occlusion and out of view, we elaborately design a novel subnetwork to learn the target-driven visual attentions from the guidance of both visual and natural language cues. Extensive experiments on five tracking benchmark datasets validated the effectiveness of our proposed method.","Title: Learning Multi-task Correlation Particle Filters for Visual Tracking.\nYear: 2019\n\nAbstract: We propose a multi-task correlation particle filter (MCPF) for robust visual tracking. We first present the multi-task correlation filter (MCF) that takes the interdependencies among different object parts and features into account to learn the correlation filters jointly. The proposed MCPF is introduced to exploit and complement the strength of a MCF and a particle filter. Compared with existing tracking methods based on correlation filters and particle filters, the proposed MCPF enjoys several merits. First, it exploits the interdependencies among different features to derive the correlation filters jointly, and makes the learned filters complement and enhance each other to obtain consistent responses. Second, it handles partial occlusion via a part-based representation, and exploits the intrinsic relationship among local parts via spatial constraints to preserve object structure and learn the correlation filters jointly. Third, it effectively handles large scale variation via a sampling scheme by drawing particles at different scales for target object state estimation. Fourth, it shepherds the sampled particles toward the modes of the target state distribution via the MCF, and effectively covers object states well using fewer particles than conventional particle filters. Extensive experimental results odemonstrate that the proposed MCPF tracking algorithm performs favorably against the state-of-the-art methods.","Title: Multi-modal max-margin supervised topic model for social event analysis.\nYear: 2019\n\nAbstract: In this paper, we proposed a novel multi-modal max-margin supervised topic model (MMSTM) for social event analysis by jointly learning the representation together with the classifier in a unified framework. Compared with existing methods, the proposed MMSTM model has several advantages. (1) The proposed model can utilize the classifier as the regularization term of our model to jointly learn the parameters in the generative model and max-margin classifier, and use the Gibbs sampling to learn parameters of the representation model and max-margin classifier by minimizing the expected loss function. (2) The proposed model is able to not only effectively mine the multi-modal property by jointly learning the latent topic relevance among multiple modalities for social event representation, but also exploit the supervised information by considering a discriminative max-margin classifier for event classification to boost the classification performance. (3) In order to validate the effectiveness of the proposed model, we collect a large-scale real-world dataset for social event analysis, and both qualitative and quantitative evaluation results have demonstrated the effectiveness of the proposed MMSTM.","Title: Boosted Exemplar Learning for human action recognition\nYear: 2009\n\nAbstract: Human action recognition has been an active research topic in computer vision. How to model all kinds of actions, varying with time resolution, visual appearance, etc., is quite a challenging task for recognition. In this paper, we propose a Boosted Exemplar Learning (BEL) approach to recognize various actions in a weakly supervised manner, i.e., only video-based labels are provided but frame-based ones are not. First, for a given action, each video is described as a set of similarities between its frames and some candidate ones (called as exemplars), which are selected from training videos belonging to the action. Instead of simply using a heuristic distance measure, the similarities are decided by the exemplar-based classifiers through the Multiple Instance Learning (MIL), in which a positive (or negative) video is deemed as a positive (or negative) bag and those similar frames to the given exemplar in Euclidean Space as instances. Second, we formulate the selection of the most discriminative exemplars into a boosted feature selection framework and simultaneously obtain a video-based action detector in the boosted learning process. Experimental results on two publicly available challenging datasets: the KTH dataset and Weizmann dataset demonstrate the validity and effectiveness of the proposed approach.","Title: In Defense Of Sparse Tracking: Circulant Sparse Tracker\nYear: 2016\n\nAbstract: Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.","Title: Robust Target Tracking by Online Random Forests and Superpixels.\nYear: 2018\n\nAbstract: This paper presents a robust joint discriminative appearance model-based tracking method using online random forests and mid-level feature (superpixels). To achieve superpixel-wise discriminative ability, we propose a joint appearance model that consists of two random forest-based models, i.e., the background-target discriminative model (BTM) and the distractor-target discriminative model (DTM). M...","Title: A Generic Framework for Social Event Analysis.\nYear: 2017\n\nAbstract: Social event is something that occurs at specific place and time associated with some specific actions, and it consists of many stories over time. With the explosion of Web 2.0 platforms, a popular social event that is happening around us and around the world can spread very fast. As a result, social event analysis becomes more and more important for users to understand the whole evolutionary trend of social event over time. However, it is very challenging to do social event analysis because social event data from different social media sites have multi-modal, multi-domain, and large-scale properties. The goal of our research is to design advanced multimedia techniques to deal with the above issues and establish an effective and robust social event analysis framework for social event representation, detection, tracking and evolution analysis. (1) For social event representation, we propose a novel cross-domain collaborative learning algorithm based on non-parametric Bayesian dictionary learning model. It can make use of the shared domain priors and modality priors to collaboratively learn the data's representations by considering the domain discrepancy and the multi-modal property.(2) For social event detection, we propose a boosted multi-modal supervised Latent Dirichlet Allocation model. It can effectively exploit multi-modality information and utilize boosting weighted sampling strategy for large-scale data processing. (3) For social event tracking, we propose a novel multi-modal event topic model, which can effectively model the correlations between textual and visual modalities, and obtain their topics over time. (4) For social event evolution analysis, we propose a novel multi-modal multi-view topic-opinion mining model to conduct fined-grained topic and opinion analysis for social events from multiple social media sites collaboratively. It can discover multi-modal topics and the corresponding opinions over time to understand the evolutionary processes of social event. Extensive experimental results show that the proposed algorithms perform favorably against state-of-the-art methods for social event analysis.","Title: SMART: Joint Sampling and Regression for Visual Tracking.\nYear: 2019\n\nAbstract: Most existing trackers are either sampling-based or regression-based methods. Sampling-based methods estimate the target state by sampling many target candidates. Although these methods achieve significant performance, they often suffer from a high computational burden. Regression-based methods often learn a computationally efficient regression function to directly predict the geometric distortion...","Title: Robust Structural Sparse Tracking.\nYear: 2019\n\nAbstract: Sparse representations have been applied to visual tracking by finding the best candidate region with minimal reconstruction error based on a set of target templates. However, most existing sparse trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidate regions, thereby making them less effective when similar objects appear at close proximity or under occlusion. In this paper, we propose a novel structural sparse representation, which not only exploits the intrinsic relationships among target candidate regions and local patches to learn their representations jointly, but also preserves the spatial structure among the local patches inside each target candidate region. For robust visual tracking, we take outliers resulting from occlusion and noise into account when searching for the best target region. Constructed within a Bayesian filtering framework, we show that the proposed algorithm accommodates most existing sparse trackers with respective merits. The formulated problem can be efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. Qualitative and quantitative evaluations on challenging benchmark datasets demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.","Title: P2T: Part-to-Target Tracking via Deep Regression Learning.\nYear: 2018\n\nAbstract: Most existing part-based tracking methods are part-to-part trackers, which usually have two separated steps including the part matching and target localization. Different from existing methods, in this paper, we propose a novel part-to-target (P2T) tracker in a unified fashion by inferring target location from parts directly. To achieve this goal, we propose a novel deep regression model for P2T r...","Title: Three-Dimensional Attention-Based Deep Ranking Model for Video Highlight Detection.\nYear: 2018\n\nAbstract: The video highlight detection task is to localize key elements (moments of user's major or special interest) in a video. Most of existing highlight detection approaches extract features from the video segment as a whole without considering the difference of local features both temporally and spatially. Due to the complexity of video content, this kind of mixed features will impact the final highli...","Title: TAME: An Efficient Task Allocation Algorithm for Integrated Mobile Gaming\nYear: 2019\n\nAbstract: We consider an integrated mobile gaming platform, in which the mobile device (e.g., smartphone) of a player can offload some game tasks toward a server as well as some neighboring mobile devices. The advantages of such a platform are manyfold: it can lead to an improved game experience, to a better use of energy resources, and, while offloading tasks to other mobile users, to the exploitation of the unused computing and storage resources of the mobile equipment, thus reducing the bandwidth and computing costs of the overall system. In this context, we formulate the problem of offloading the game computational tasks as an optimization problem that minimizes the maximum energy consumption across a set of mobile devices, under the constraints of a maximum response time and a limited availability of computation, communication, and storage resources. In light of the problem complexity, we then propose a heuristic, called Task Allocation with Minimal Energy cost (TAME), which is shown to closely approximate the optimal solution in all scenarios we considered. TAME also outperforms state-of-the-art algorithms under both synthetic and real scenarios, which have been devised based on a realistic and detailed energy consumption model for computation and communication resources. Our results, although tailored to mobile gaming, could be extended to other applications, where it may be beneficial to offload computational and storage tasks through device-to-device communications, as enabled by Wi-Fi, Bluetooth, or the upcoming 5G technology.","Title: A Unified Generative Adversarial Framework for Image Generation and Person Re-identification.\nYear: 2018\n\nAbstract: Person re-identification (re-id) aims to match a certain person across multiple non-overlapping cameras. It is a challenging task because the same person's appearance can be very different across camera views due to the presence of large pose variations. To overcome this issue, in this paper, we propose a novel unified person re-id framework by exploiting person poses and identities jointly for simultaneous person image synthesis under arbitrary poses and pose-invariant person re-identification. The framework is composed of a GAN based network and two Feature Extraction Networks (FEN), and enjoys following merits. First, it is a unified generative adversarial model for person image generation and person re-identification. Second, a pose estimator is utilized into the generator as a supervisor in the training process, which can effectively help pose transfer and guide the image generation with any desired pose. As a result, the proposed model can automatically generate a person image under an arbitrary pose. Third, the identity-sensitive representation is explicitly disentangled from pose variations through the person identity and pose embedding. Fourth, the learned re-id model can have better generalizability on a new person re-id dataset by using the synthesized images as auxiliary samples. Extensive experimental results on four standard benchmarks including Market-1501 [69], DukeMTMC-reID [40], CUHK03 [23], and CUHK01 [22] demonstrate that the proposed model can perform favorably against state-of-the-art methods.","Title: Video Highlight Detection Via Region-Based Deep Ranking Model\nYear: 2019\n\nAbstract: The video highlight detection task is to localize key elements (moments of user's major or special interest) in a video. Most of the existing highlight detection approaches extract features from the video segment as a whole without considering the difference of local features spatially. In spatial extent, not all regions are worth watching because some of them only contain the background of the environment without human or other moving objects, especially when there is lots of clutter in the background. To deal with this issue, we propose a novel region-based model which can automatically localize the key elements in a video without any extra supervised annotations. Specifically, the proposed model produces position-sensitive score maps for local regions in the spatial dimension of the video segment, and then aggregates all position-wise scores with position-pooling operation. The regions with higher response values will be extracted as key elements. Thus more effective features of the video segment are obtained to predict the highlight score. The proposed position-sensitive scheme can be easily integrated into an endto-end fully convolutional network which aims to update parameters via stochastic gradient descent method in the backward propagation to improve the robustness of the model. Extensive experimental results on the YouTube and SumMe datasets demonstrate that the proposed approach achieves significant improvement over state-of-the-art methods.","Title: Deep Multi-Modality Adversarial Networks for Unsupervised Domain Adaptation\nYear: 2019\n\nAbstract: Unsupervised Domain Adaptation (UDA) aims to transfer domain knowledge from existing well-defined tasks to new ones where labels are unavailable. In the real world applications, domain discrepancy is usually uncontrollable especially for multi-modality data. Therefore, it is significantly motivated to deal with multi-modality domain adaptation task. As labels are unavailable in target domain, how to learn semantic multimodality representations and how to successfully adapt the classifier from source to target domain, remain open challenges in multi-modality domain adaptation task. To deal with these issues, we propose a Multi-Modality Adversarial Network (MMAN), which applies stacked attention to learn semantic multi-modality representations and reduces domain discrepancy via adversarial training. Unlike the previous domain adaptation methods which cannot make full use of source domain categories information, multi-channel constraint is employed to capture fine-grained categories knowledge that could enhance the discrimination of target samples and boost target performance on single-modality and multi-modality domain adaptation problem. We apply the proposed MMAN to two applications including cross domain object recognition and cross domain social event recognition. The extensive experimental evaluations demonstrate the effectiveness of the proposed model for unsupervised domain adaptation.","Title: FloWatcher-DPDK: lightweight line-rate flow-level monitoring in software\nYear: 2019\n\nAbstract: In the last few years, several software-based solutions have been proved to be very efficient for high-speed packet processing, traffic generation, and monitoring, and can be considered valid alternatives to expensive and non-flexible hardware-based solutions. In this paper, we first benchmark heterogeneous design choices for software-based packet monitoring systems in terms of achievable performance and required resources (i.e., the number of CPU cores). Building on this extensive analysis we design FloWatcher-DPDK, a DPDK-based high-speed software traffic monitor we provide to the community as an open source project. In a nutshell, FloWatcher-DPDK provides tunable fine-grained statistics at packet and flow levels. Experimental results demonstrate that FloWatcher-DPDK sustains per-flow statistics with 5-nines precision at high-speed (e.g., 14.88 Mpps) using a limited amount of resources. Finally, we showcase the usage of FloWatcher-DPDK by configuring it to analyze the performance of two open source prototypes for stateful flow-level end-host and in-network packet processing.","Title: Geometry Uncertainty Projection Network for Monocular 3D Object Detection.\nYear: 2021\n\nAbstract: Geometry Projection is a powerful depth estimation method in monocular 3D object detection. It estimates depth dependent on heights, which introduces mathematical priors into the deep model. But projection process also introduces the error amplification problem, in which the error of the estimated height will be amplified and reflected greatly at the output depth. This property leads to uncontrollable depth inferences and also damages the training efficiency. In this paper, we propose a Geometry Uncertainty Projection Network (GUP Net) to tackle the error amplification problem at both inference and training stages. Specifically, a GUP module is proposed to obtains the geometry-guided uncertainty of the inferred depth, which not only provides high reliable confidence for each depth but also benefits depth learning. Furthermore, at the training stage, we propose a Hierarchical Task Learning strategy to reduce the instability caused by error amplification. This learning algorithm monitors the learning situation of each task by a proposed indicator and adaptively assigns the proper loss weights for different tasks according to their pre-tasks situation. Based on that, each task starts learning only when its pre-tasks are learned well, which can significantly improve the stability and efficiency of the training process. Extensive experiments demonstrate the effectiveness of the proposed method. The overall model can infer more reliable object depth than existing methods and outperforms the state-of-the-art image-based monocular 3D detectors by 3.74% and 4.7% AP40 of the car and pedestrian categories on the KITTI benchmark.","Title: Object Tracking via Spatial-Temporal Memory Network\nYear: 2022\n\nAbstract: Temporal and spatial contexts, characterizing target appearance variations and target-background differences, respectively, are crucial for improving the online adaptive ability and instance-level discriminative ability of object tracking. However, most existing trackers focus on either the temporal context or the spatial context during tracking and have not exploited these contexts simultaneously...","Title: Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer\nYear: 2021\n\nAbstract: Occluded person re-identification (Re-ID) is a challenging task as persons are frequently occluded by various obstacles or other persons, especially in the crowd scenario. To address these issues, we propose a novel end-to-end Part-Aware Transformer (PAT) for occluded person Re-ID through diverse part discovery via a transformer encoder-decoder architecture, including a pixel context based transformer encoder and a part prototype based transformer decoder. The proposed PAT model enjoys several merits. First, to the best of our knowledge, this is the first work to exploit the transformer encoder-decoder architecture for occluded person Re-ID in a unified deep model. Second, to learn part prototypes well with only identity labels, we design two effective mechanisms including part diversity and part discriminability. Consequently, we can achieve diverse part discovery for occluded person Re-ID in a weakly supervised manner. Extensive experimental results on six challenging benchmarks for three tasks (occluded, partial and holistic Re-ID) demonstrate that our proposed PAT performs favorably against stat-of-the-art methods.","Title: Lesion-Aware Transformers for Diabetic Retinopathy Grading\nYear: 2021\n\nAbstract: Diabetic retinopathy (DR) is the leading cause of permanent blindness in the working-age population. And automatic DR diagnosis can assist ophthalmologists to design tailored treatments for patients, including DR grading and lesion discovery. However, most of existing methods treat DR grading and lesion discovery as two independent tasks, which require lesion annotations as a learning guidance and limits the actual deployment. To alleviate this problem, we propose a novel lesion-aware transformer (LAT) for DR grading and lesion discovery jointly in a unified deep model via an encoder-decoder structure including a pixel relation based encoder and a lesion filter based decoder. The proposed LAT enjoys several merits. First, to the best of our knowledge, this is the first work to formulate lesion discovery as a weakly supervised lesion localization problem via a transformer decoder. Second, to learn lesion filters well with only image-level labels, we design two effective mechanisms including lesion region importance and lesion region diversity for identifying diverse lesion regions. Extensive experimental results on three challenging benchmarks including Messidor-1, Messidor-2 and EyePACS demonstrate that the proposed LAT performs favorably against state-of-the-art DR grading and lesion discovery methods.","Title: Uncertainty Guided Collaborative Training for Weakly Supervised Temporal Action Detection\nYear: 2021\n\nAbstract: Weakly supervised temporal action detection aims to localize temporal boundaries of actions and identify their categories simultaneously with only video-level category labels during training. Among existing methods, attention based methods have achieved superior performance by separating action and non-action segments. However, without the segment-level ground-truth supervision, the quality of the...","Title: Meta-Attack - Class-agnostic and Model-agnostic Physical Adversarial Attack.\nYear: 2021\n\nAbstract: Modern deep neural networks are often vulnerable to adversarial examples. Most exist attack methods focus on crafting adversarial examples in the digital domain, while only limited works study physical adversarial attack. However, it is more challenging to generate effective adversarial examples in the physical world due to many uncontrollable physical dynamics. Most current physical attack methods aim to generate robust physical adversarial examples by simulating all possible physical dynamics. When attacking new images or new DNN models, they require expensive manually efforts for simulating physical dynamics and considerable time for iteratively optimizing for each image. To tackle these issues, we propose a class-agnostic and model-agnostic physical adversarial attack model (Meta-Attack), which is able to not only generate robust physical adversarial examples by simulating color and shape distortions, but also generalize to attacking novel images and novel DNN models by accessing a few digital and physical images. To the best of our knowledge, this is the first work to formulate the physical attack as a few-shot learning problem. Here, the training task is redefined as the composition of a support set, a query set, and a target DNN model. Under the few- shot setting, we design a novel class-agnostic and model-agnostic meta-learning algorithm to enhance the generalization ability of our method. Extensive experimental results on two benchmark datasets with four challenging experimental settings verify the superior robustness and generalization of our method by comparing to state-of-the-art physical attack methods.","Title: Task-aware Part Mining Network for Few-Shot Learning.\nYear: 2021\n\nAbstract: Few-Shot Learning (FSL) aims at classifying samples into new unseen classes with only a handful of labeled samples available. However, most of the existing methods are based on the image-level pooled representation, yet ignore considerable local clues that are transferable across tasks. To address this issue, we propose an end-to-end Task-aware Part Mining Network (TPMN) by integrating an automatic part mining process into the metric-based model for FSL. The proposed TPMN model enjoys several merits. First, we design a meta filter learner to generate task-aware part filters based on the task embedding in a meta-learning way. The task-aware part filters can adapt to any individual task and automatically mine task-related local parts even for an unseen task. Second, an adaptive importance generator is proposed to identify key local parts and assign adaptive importance weights to different parts. To the best of our knowledge, this is the first work to automatically exploit the task-aware local parts in a meta-learning way for FSL. Extensive experimental results on four standard benchmarks demonstrate that the proposed model performs favorably against state-of-the-art FSL methods.","Title: Foreground Activation Maps for Weakly Supervised Object Localization.\nYear: 2021\n\nAbstract: Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels, which has better scalability and practicability than fully supervised methods in the actual deployment. However, with only image-level labels, learning object classification models tends to activate object parts and ignore the whole object, while expanding object parts into the whole object may deteriorate classification performance. To alleviate this problem, we propose foreground activation maps (FAM), whose aim is to optimize object localization and classification jointly via an object-aware attention module and a part-aware attention module in a unified model, where the two tasks can complement and enhance each other. To the best of our knowledge, this is the first work that can achieve remarkable performance for both tasks by optimizing them jointly via FAM for WSOL. Besides, the designed two modules can effectively highlight foreground objects for localization and discover discriminative parts for classification. Extensive experiments with four backbones on two standard benchmarks demonstrate that our FAM performs favorably against state-of-the-art WSOL methods.","Title: Inference of virtual network functions’ state via analysis of the CPU behavior\nYear: 2021\n\nAbstract: The on-going process of softwarization of IT networks promises to reduce the operational and management costs of network infrastructures by replacing hardware middleboxes with equivalent pieces of code executed on general-purpose servers. Alongside the benefits from the operator’s perspective, new strategies to provide the network’s resources to users are arising. Following the principle of "every...","Title: Diverse Complementary Part Mining for Weakly Supervised Object Localization\nYear: 2022\n\nAbstract: Weakly Supervised Object Localization (WSOL) aims to localize objects with only image-level labels, which has better scalability and practicability than fully supervised methods in the actual deployment. However, a common limitation for available techniques based on classification networks is that they only highlight the most discriminative part of the object, not the entire object. To alleviate this problem, we propose a novel end-to-end part discovery model (PDM) to learn multiple discriminative object parts in a unified network for accurate object localization and classification. The proposed PDM enjoys several merits. First, to the best of our knowledge, it is the first work to directly model diverse and robust object parts by exploiting part diversity, compactness, and importance jointly for WSOL. Second, three effective mechanisms including diversity, compactness, and importance learning mechanisms are designed to learn robust object parts. Therefore, our model can exploit complementary spatial information and local details from the learned object parts, which help to produce precise bounding boxes and discriminate different object categories. Extensive experiments on two standard benchmarks demonstrate that our PDM performs favorably against state-of-the-art WSOL approaches.","Title: Interpreting AI for Networking: Where We Are and Where We Are Going\nYear: 2022\n\nAbstract: In recent years, artificial intelligence (AI) techniques have been increasingly adopted to tackle networking problems. Although AI algorithms can deliver high-quality solutions, most of them are inherently intricate and erratic for human cognition. This lack of interpretability tremendously hinders the commercial success of AI-based solutions in practice. To cope with this challenge, networking re...","Title: A Keypoint-based Global Association Network for Lane Detection\nYear: 2022\n\nAbstract: Lane detection is a challenging task that requires predicting complex topology shapes of lane lines and distinguishing different types of lanes simultaneously. Earlier works follow a top-down roadmap to regress predefined anchors into various shapes of lane lines, which lacks enough flexibility to fit complex shapes of lanes due to the fixed anchor shapes. Lately, some works propose to formulate lane detection as a keypoint estimation problem to describe the shapes of lane lines more flexibly and gradually group adjacent keypoints belonging to the same lane line in a point-by-point manner, which is inefficient and time-consuming during postprocessing. In this paper, we propose a Global Association Network (GANet) to formulate the lane detection problem from a new perspective, where each keypoint is directly regressed to the starting point of the lane line instead of point-by-point extension. Concretely, the association of keypoints to their belonged lane line is conducted by predicting their offsets to the corresponding starting points of lanes globally without dependence on each other, which could be done in parallel to greatly improve efficiency. In addition, we further propose a Lane-aware Feature Aggregator (LFA), which adaptively captures the local correlations between adjacent keypoints to supplement local information to the global association. Extensive experiments on two popular lane detection benchmarks show that our method outperforms previous methods with F1 score of 79.63% on CULane and 97.71% on Tusimple dataset with high FPS.","Title: Correlation filters based on spatial-temporal Gaussion scale mixture modelling for visual tracking\nYear: 2022\n\nAbstract: Correlation Filters (CFs) have shown outstanding performance in tracking, but are subject to unwanted boundary effects. Spatial regularization (SR) is widely used as an efficient method to alleviate the boundary effects. However, spatial regularization is almost handcrafted and fixed during tracking process, which cannot handle the diversity of objects and the complexity of motion. Furthermore, the rich spatio-temporal correlations among multiple targets of interest cannot be fully exploited. Herein, we propose a spatio-temporal Gaussian scale mixture model (ST-GSM) for correlation-filter-based visual tracking. In our Gaussian scale mixture (GSM) model, each correlation filter coefficient is decomposed into the product of a positive scalar multiplier with sparsity and a Gaussian random variable. The reliable components of the Gaussian random variable can be adaptively selected based on the positive multipliers, aiming at alleviating the notorious boundary effects. To exploit the temporal consistency between adjacent frames, nonzero-means GSM models are developed to characterize the temporal correlations. Specifically, the filter coefficient obtained in the previous frame is used as the mean prior for the current frame. The spatial correlations among filter coefficients have been considered in the structured GSM model, thereby further improving the tracking performance. Experimental results show that the proposed model can significantly improve the performance of CF-based trackers.","Title: Bayesian Correlation Filter Learning With Gaussian Scale Mixture Model for Visual Tracking\nYear: 2022\n\nAbstract: Correlation filters (CF), a popular tool for visual tracking, suffer from unwanted boundary effects due to the periodic assumption needed for FFT implementation. To address this issue, spatially regularized discriminative correlation filters (SRDCF) have been proposed by introducing a weighting matrix to the regularization term. However, the existing design of spatial weighting matrix is often heu...","Title: Motion-modulated Temporal Fragment Alignment Network For Few-Shot Action Recognition\nYear: 2022\n\nAbstract: While the majority of FSL models focus on image classification, the extension to action recognition is rather challenging due to the additional temporal dimension in videos. To address this issue, we propose an end-to-end Motion-modulated Temporal Fragment Alignment Network (MT-FAN) by jointly exploring the task-specific motion modulation and the multi-level temporal fragment alignment for Few-Shot Action Recognition (FSAR). The proposed MT-FAN model enjoys several merits. First, we design a motion modulator conditioned on the learned task-specific motion embeddings, which can activate the channels related to the task-shared motion patterns for each frame. Second, a segment attention mechanism is proposed to automatically discover the higher-level segments for multi-level temporal fragment alignment, which encompasses the frame-to-frame, segment-to-segment, and segment-to-frame alignments. To the best of our knowledge, this is the first work to exploit task-specific motion modulation for FSAR. Extensive experimental results on four standard benchmarks demonstrate that the proposed model performs favorably against the state-of-the-art FSAR methods.","Title: Adversarial Transformers for Weakly Supervised Object Localization\nYear: 2022\n\nAbstract: Weakly supervised object localization (WSOL) aims at localizing objects with only image-level labels, which has better scalability and practicability than fully supervised methods. However, without pixel-level supervision, existing methods tend to generate rough localization maps, which hinders localization performance. To alleviate this problem, we propose an adversarial transformer network (ATNet), which aims to obtain a well-learned localization model with pixel-level pseudo labels. The proposed ATNet enjoys several merits. First, we design an object transformer ( $G$ ) that can generate localization maps and pseudo labels effectively and dynamically, and a part transformer ( $D$ ) to accurately discriminate detailed local differences between localization maps and pseudo labels. Second, we propose to train $G$ and $D$ via an adversarial process, where $G$ can generate more accurate localization maps approaching pseudo labels to fool $D$ . To the best of our knowledge, this is the first work to explore transformers with adversarial training to obtain a well-learned localization model for WSOL. Extensive experiments with four backbones on two standard benchmarks demonstrate that our ATNet achieves favorable performance against state-of-the-art WSOL methods. Besides, our adversarial training can provide higher robustness against adversarial attacks.","Title: Visible-Infrared Person Re-Identification With Modality-Specific Memory Network\nYear: 2022\n\nAbstract: Visible-infrared person re-identification (VI-ReID) is challenging due to the large modality discrepancy between visible and infrared images. Existing methods mainly focus on learning modality-shared representations by embedding images from different modalities into a common feature space, in which some discriminative modality information is discarded. Different from these methods, in this paper, we propose a novel Modality-Specific Memory Network (MSMNet) to complete the missing modality information and aggregate visible and infrared modality features into a unified feature space for the VI-ReID task. The proposed model enjoys several merits. First, it can exploit the missing modality information to alleviate the modality discrepancy when only the single-modality input is provided. To the best of our knowledge, this is the first work to exploit the missing modality information completion and alleviate the modality discrepancy with the memory network. Second, to guide the learning process of the memory network, we design three effective learning strategies, including feature consistency, memory representativeness and structural alignment. By incorporating these learning strategies in a unified model, the memory network can be well learned to propagate identity-related information between modalities and boost the VI-ReID performance. Extensive experimental results on two standard benchmarks (SYSU-MM01 and RegDB) demonstrate that the proposed MSMNet performs favorably against state-of-the-art methods."],"string":"[\n \"Title: Sustainable Urban Solid Waste Management from both Life Cycle and Urban Metabolism Perspectives\\nYear: 2012\\n\\nAbstract: Urban solid waste management is an important component of urban sustainability. The concept of evaluating technologies from both life cycle and urban metabolism perspectives is proposed in this study. The analysis from life cycle perspective (using physical input-output life cycle assessment model) provides support for determining the priority of technologies, and the analysis from urban metabolism perspective (using physical input-output model) provides support for identifying the acceptability of technologies. Suzhou City in China is taken as an example. From urban metabolism perspective, sludge recycling is regarded as an accepted method, while current fly ash recycling method is unsatisfying. Technical levels of scrap tire recycling and food waste recycling should be improved to reduce their negative effects on Suzhou's urban metabolism. From life cycle perspective, sludge recycling has the smallest environmental impacts, and scrap tire recycling has larger environmental impacts than food waste recycling does. Thus, more concerns should be paid to technical improvements of scrap tire recycling than to that of food waste recycling. The concept of evaluating technologies from both life cycle and urban metabolism perspectives in this study provides foundations for evaluating technologies in other countries and cities.\",\n \"Title: Low-rank sparse learning for robust visual tracking\\nYear: 2012\\n\\nAbstract: In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1].\",\n \"Title: Discriminative exemplar coding for sign language recognition with Kinect.\\nYear: 2013\\n\\nAbstract: Sign language recognition is a growing research area in the field of computer vision. A challenge within it is to model various signs, varying with time resolution, visual manual appearance, and so on. In this paper, we propose a discriminative exemplar coding (DEC) approach, as well as utilizing Kinect sensor, to model various signs. The proposed DEC method can be summarized as three steps. First, a quantity of class-specific candidate exemplars are learned from sign language videos in each sign category by considering their discrimination. Then, every video of all signs is described as a set of similarities between frames within it and the candidate exemplars. Instead of simply using a heuristic distance measure, the similarities are decided by a set of exemplar-based classifiers through the multiple instance learning, in which a positive (or negative) video is treated as a positive (or negative) bag and those frames similar to the given exemplar in Euclidean space as instances. Finally, we formulate the selection of the most discriminative exemplars into a framework and simultaneously produce a sign video classifier to recognize sign. To evaluate our method, we collect an American sign language dataset, which includes approximately 2000 phrases, while each phrase is captured by Kinect sensor with color, depth, and skeleton information. Experimental results on our dataset demonstrate the feasibility and effectiveness of the proposed approach for sign language recognition.\",\n \"Title: Robust multi-object tracking via cross-domain contextual information for sports video analysis\\nYear: 2012\\n\\nAbstract: Multiple player tracking is one of the main building blocks needed in a sports video analysis system. In an uncalibrated camera setting, robust mutli-object tracking can be very difficult due to a number of reasons including the presence of noise, occlusion, fast camera motion, low-resolution image capture, varying viewpoints and illumination changes. To address the problem of multi-object tracking in sports videos, we go beyond the video frame domain and make use of information in a homography transform domain that is denoted the homography field domain. We propose a novel particle filter based tracking algorithm that uses both object appearance information (e.g. color and shape) in the image domain and cross-domain contextual information in the field domain to improve object tracking. In the field domain, the effect of fast camera motion is significantly alleviated since the underlying homography transform from each frame to the field domain can be accurately estimated. We use contextual trajectory information (intra-trajectory and inter-trajectory context) to further improve the prediction of object states within an particle filter framework. Here, intra-trajectory contextual information is based on history tracking results in the field domain, while inter-trajectory contextual information is extracted from a compiled trajectory dataset based on tracks computed from videos depicting the same sport. Experimental results on real world sports data show that our system is able to effectively and robustly track a variable number of targets regardless of background clutter, camera motion and frequent mutual occlusion between targets.\",\n \"Title: Graph-Guided Fusion Penalty Based Sparse Coding for Image Classification.\\nYear: 2013\\n\\nAbstract: In image classification, conventional sparse coding only encodes local features independently. As a result, the similar local features may be encoded into code vectors with large discrepancy. This sensitiveness has became the bottleneck of the traditional sparse coding based image classification methods. In this paper, we propose a novel graph-guided fusion penalty based sparse coding method. To alleviate the sensitiveness of the traditional sparse coding, our approach constrains that the similar local features are encoded into similar code vectors. To achieve this goal, we add the popular graph-guided fusion penalty term into the traditional l1-regularized sparse coding formulation. Finally, we adopt the multi-task form of the smoothing proximal gradient method to solve our optimization problem efficiently. Experimental results on 3 benchmark datasets demonstrate the effectiveness of our improved sparse coding method in image classification. © Springer International Publishing Switzerland 2013.\",\n \"Title: Multi-cue Based Multi-target Tracking with Boosted MHT.\\nYear: 2013\\n\\nAbstract: Tracking multiple objects is critical to automatic video content analysis and virtual reality. The major problem is how to solve data association problem when ambiguous observations are caused by objects in close proximity or occlusion. To tackle this problem, we propose a boosted multiple hypotheses tracking (BMHT) algorithm for multiobject tracking. Here, on-line boosting learning is adopted to enhance the discriminative property and enlarge search space of the generative tracker MHT. To make the tracker be more reliable, a multi-cue integration strategy is adopted to consider different kinds of features under the on-line boosting framework. In this paper, we integrate both appearance and motion pattern information. For simplicity, Haar-like features and optical flow are adopted. We test our BMHT tracker on several challenging video sequences that involve heavy occlusion and pose variations. Experimental results show that the proposed BMHT achieves good performance. © Springer International Publishing Switzerland 2013.\",\n \"Title: M4L: Maximum margin Multi-instance Multi-cluster Learning for scene modeling\\nYear: 2013\\n\\nAbstract: Automatically learning and grouping key motion patterns in a traffic scene captured by a static camera is a fundamental and challenging task for intelligent video surveillance. To learn motion patterns, trajectory obtained by object tracking is parameterized, and scene image is spatially and evenly divided into multiple regular cell blocks which potentially contain several primary motion patterns. Then, for each block, Gaussian Mixture Model (GMM) is adopted to learn its motion patterns based on the parameters of trajectories. Grouping motion pattern can be done by clustering blocks indirectly, and each cluster of blocks corresponds to a certain motion pattern. For one particular block, each of its motion pattern (Gaussian component) can be viewed as an instance, and all motion patterns (Gaussian components) constitute a bag which can correspond to multiple semantic clusters. Therefore, blocks can be grouped as a Multi-instance Multi-cluster Learning (MIMCL) problem, and a novel Maximum Margin Multi-instance Multi-cluster Learning (M^4L) algorithm is proposed. To avoid processing a difficult optimization problem, M^4L is further relaxed and solved by making use of a combination of the Cutting Plane method and Constrained Concave-Convex Procedure (CCCP). Extensive experiments are conducted on multiple real world video sequences containing various patterns and the results validate the effectiveness of our proposed approach.\",\n \"Title: A generic framework for event detection in various video domains\\nYear: 2010\\n\\nAbstract: Event detection is essential for the extensively studied video analysis and understanding area. Although various approaches have been proposed for event detection, there is a lack of a generic event detection framework that can be applied to various video domains (e.g. sports, news, movies, surveillance). In this paper, we present a generic event detection approach based on semi-supervised learning and Internet vision. Concretely, a Graph-based Semi-Supervised Multiple Instance Learning (GSSMIL) algorithm is proposed to jointly explore small-scale expert labeled videos and large-scale unlabeled videos to train the event models to detect video event boundaries. The expert labeled videos are obtained from the analysis and alignment of well-structured video related text (e.g. movie scripts, web-casting text, close caption). The unlabeled data are obtained by querying related events from the video search engine (e.g. YouTube) in order to give more distributive information for event modeling. A critical issue of GSSMIL in constructing a graph is the weight assignment, where the weight of an edge specifies the similarity between two data points. To tackle this problem, we propose a novel Multiple Instance Learning Induced Similarity (MILIS) measure by learning instance sensitive classifiers. We perform the thorough experiments in three popular video domains: movies, sports and news. The results compared with the state-of-the-arts are promising and demonstrate our proposed approach is performance-effective.\",\n \"Title: Robust Visual Tracking via Structured Multi-Task Sparse Learning\\nYear: 2013\\n\\nAbstract: In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing $$\\\\ell _{p,q}$$ mixed norms $$(\\\\text{ specifically} p\\\\in \\\\{2,\\\\infty \\\\}$$ and $$q=1),$$ we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular $$L_1$$ tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259---2272, 2011) is a special case of our MTT formulation (denoted as the $$L_{11}$$ tracker) when $$p=q=1.$$ Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers.\",\n \"Title: Mining Semantic Context Information for Intelligent Video Surveillance of Traffic Scenes\\nYear: 2013\\n\\nAbstract: Automated visual surveillance systems are attracting extensive interest due to public security. In this paper, we attempt to mine semantic context information including object-specific context information and scene-specific context information (learned from object-specific context information) to build an intelligent system with robust object detection, tracking, and classification and abnormal event detection. By means of object-specific context information, a cotrained classifier, which takes advantage of the multiview information of objects and reduces the number of labeling training samples, is learned to classify objects into pedestrians or vehicles with high object classification performance. For each kind of object, we learn its corresponding semantic scene-specific context information: motion pattern, width distribution, paths, and entry/exist points. Based on this information, it is efficient to improve object detection and tracking and abnormal event detection. Experimental results demonstrate the effectiveness of our semantic context features for multiple real-world traffic scenes.\",\n \"Title: Learning semantic scene models by object classification and trajectory clustering\\nYear: 2009\\n\\nAbstract: Activity analysis is a basic task in video surveillance and has become an active research area. However, due to the diversity of moving objects category and their motion patterns, developing robust semantic scene models for activity analysis remains a challenging problem in traffic scenarios. This paper proposes a novel framework to learn semantic scene models. In this framework, the detected moving objects are first classified as pedestrians or vehicles via a co-trained classifier which takes advantage of the multiview information of objects. As a result, the framework can automatically learn motion patterns respectively for pedestrians and vehicles. Then, a graph is proposed to learn and cluster the motion patterns. To this end, trajectory is parameterized and the image is cut into multiple blocks which are taken as the nodes in the graph. Based on the parameters of trajectories, the primary motion patterns in each node (block) are extracted via Gaussian mixture model (GMM), and supplied to this graph. The graph cut algorithm is finally employed to group the motion patterns together, and trajectories are clustered to learn semantic scene models. Experimental results and applications to real world scenes show the validity of our proposed method.\",\n \"Title: Boosted multi-class semi-supervised learning for human action recognition\\nYear: 2011\\n\\nAbstract: Human action recognition is a challenging task due to significant intra-class variations, occlusion, and background clutter. Most of the existing work use the action models based on statistic learning algorithms for classification. To achieve good performance on recognition, a large amount of the labeled samples are therefore required to train the sophisticated action models. However, collecting labeled samples is labor-intensive. To tackle this problem, we propose a boosted multi-class semi-supervised learning algorithm in which the co-EM algorithm is adopted to leverage the information from unlabeled data. Three key issues are addressed in this paper. Firstly, we formulate the action recognition in a multi-class semi-supervised learning problem to deal with the insufficient labeled data and high computational expense. Secondly, boosted co-EM is employed for the semi-supervised model construction. To overcome the high dimensional feature space, weighted multiple discriminant analysis (WMDA) is used to project the features into low dimensional subspaces in which the Gaussian mixture models (GMM) are trained and boosting scheme is used to integrate the subspace models. Thirdly, we present the upper bound of the training error in multi-class framework, which is able to guide the novel classifier construction. In theory, the proposed solution is proved to minimize this upper error bound. Experimental results have shown good performance on public datasets.\",\n \"Title: Weakly Supervised Graph Propagation Towards Collective Image Parsing\\nYear: 2012\\n\\nAbstract: In this work, we propose a weakly supervised graph propagation method to automatically assign the annotated labels at image level to those contextually derived semantic regions. The graph is constructed with the over-segmented patches of the image collection as nodes. Image-level labels are imposed on the graph as weak supervision information over subgraphs, each of which corresponds to all patches of one image, and the contextual information across different images at patch level are then mined to assist the process of label propagation from images to their descendent regions. The ultimate optimization problem is efficiently solved by Convex Concave Programming (CCCP). Extensive experiments on four benchmark datasets clearly demonstrate the effectiveness of our proposed method for the task of collective image parsing. Two extensions including image annotation and concept map based image retrieval demonstrate the proposed image parsing algorithm can effectively aid other vision tasks.\",\n \"Title: A Generic Framework for Video Annotation via Semi-Supervised Learning\\nYear: 2012\\n\\nAbstract: Learning-based video annotation is essential for video analysis and understanding, and many various approaches have been proposed to avoid the intensive labor costs of purely manual annotation. However, there lacks a generic framework due to several difficulties, such as dependence of domain knowledge, insufficiency of training data, no precise localization and inefficacy for large-scale video dataset. In this paper, we propose a novel approach based on semi-supervised learning by means of information from the Internet for interesting event annotation in videos. Concretely, a Fast Graph-based Semi-Supervised Multiple Instance Learning (FGSSMIL) algorithm, which aims to simultaneously tackle these difficulties in a generic framework for various video domains (e.g., sports, news, and movies), is proposed to jointly explore small-scale expert labeled videos and large-scale unlabeled videos to train the models. The expert labeled videos are obtained from the analysis and alignment of well-structured video related text (e.g., movie scripts, web-casting text, close caption). The unlabeled data are obtained by querying related events from the video search engine (e.g., YouTube, Google) in order to give more distributive information for event modeling. Two critical issues of FGSSMIL are: 1) how to calculate the weight assignment for a graph construction, where the weight of an edge specifies the similarity between two data points. To tackle this problem, we propose a novel Multiple Instance Learning Induced Similarity (MILIS) measure by learning instance sensitive classifiers; 2) how to solve the algorithm efficiently for large-scale dataset through an optimization approach. To address this issue, Concave-Convex Procedure (CCCP) and nonnegative multiplicative updating rule are adopted. We perform the extensive experiments in three popular video domains: movies, sports, and news. The results compared with the state-of-the-arts are promising and demonstrate the effectiveness and efficiency of our proposed approach.\",\n \"Title: Hierarchical object representations for visual recognition via weakly supervised learning\\nYear: 2012\\n\\nAbstract: In this paper, we propose a weakly supervised approach to learn hierarchical object representations for visual recognition. The learning process is carried out in a bottom-up manner to discover latent visual patterns in multiple scales. To relieve the disturbance of complex backgrounds in natural images, bounding boxes of foreground objects are adopted as weak knowledge in the learning stage to promote those visual patterns which are more related to the target objects. The difference between the patterns of foreground objects and backgrounds is relatively vague at low-levels, but becomes more distinct along with the feature transformations to high-levels. In the test stage, an input image is verified against the learnt patterns level-by-level, and the responses at each level construct a hierarchy of representations which indicates the occurring possibilities of the target object at various scales. Experiments on two PASCAL datasets showed encouraging results for visual recognition.\",\n \"Title: Human action recognition in videos using hybrid motion features\\nYear: 2010\\n\\nAbstract: In this paper, we present hybrid motion features to promote action recognition in videos. The features are composed of two complementary components from different views of motion information. On one hand, the period feature is extracted to capture global motion in time-domain. On the other hand, the enhanced histograms of motion words (EHOM) are proposed to describe local motion information. Each word is represented by optical flow of a frame and the correlations between words are encoded into the transition matrix of a Markov process, and then its stationary distribution is extracted as the final EHOM. Compared to traditional Bags of Words representation, EHOM preserves not only relationships between words but also temporary information in videos to some extent. We show that by integrating local and global features, we get improved recognition rates on a variety of standard datasets.\",\n \"Title: Human action recognition via multi-view learning.\\nYear: 2010\\n\\nAbstract: In this paper, we propose a novel approach to automatically learn a compact and yet discriminative representation for humane action recognition. Considering the static visual information and motion information, each frame is represented in two feature subsets (views) and Gaussian Mixture Model (GMM) is adopted to model the distributions of those features. In order to complement the strengths of the different features (views), a Co-EM based multiview learning framework is introduced to estimate the parameters of GMM instead of conventional single view based EM. Then Gaussian components are considered as video words to describe videos with different time resolutions. Compared with the traditional method to recognize action, there are several advantages with the proposed method using Co-EM strategy. First, complex actions are efficiently modeled by GMM, and the number of its component is automatically determined with the Minimum Description Length (MDL). Second, because the imperfectness of single view can be compensated by the other view in the Co-EM, the resulting bag of video words are superior to that formed by any single view. To the best of our knowledge, we are the first to try the Co-EM based multi-view learning method for action recognition and obtain significantly better results. We extensively verify our proposed approach on two publicly available challenging datasets: the KTH dataset and Weizmann dataset. The experimental results show the validity of our proposed method.\",\n \"Title: Hi, magic closet, tell me what to wear!\\nYear: 2012\\n\\nAbstract: In this paper, we aim at a practical system, magic closet, for automatic occasion-oriented clothing recommendation. Given a user-input occasion, e.g., wedding, shopping or dating, magic closet intelligently suggests the most suitable clothing from the user's own clothing photo album, or automatically pairs the user-specified reference clothing (upper-body or lower-body) with the most suitable one from online shops. Two key criteria are explicitly considered for the magic closet system. One criterion is to wear properly, e.g., compared to suit pants, it is more decent to wear a cocktail dress for a banquet occasion. The other criterion is to wear aesthetically, e.g., a red T-shirt matches better white pants than green pants. To narrow the semantic gap between the low-level features of clothing and the high-level occasion categories, we adopt middle-level clothing attributes (e.g., clothing category, color, pattern) as a bridge. More specifically, the clothing attributes are treated as latent variables in our proposed latent Support Vector Machine (SVM) based recommendation model. The wearing properly criterion is described in the model through a feature-occasion potential and an attribute-occasion potential, while the wearing aesthetically criterion is expressed by an attribute-attribute potential. To learn a generalize-well model and comprehensively evaluate it, we collect a large clothing What-to-Wear (WoW) dataset, and thoroughly annotate the whole dataset with 7 multi-value clothing attributes and 10 occasion categories via Amazon Mechanic Turk. Extensive experiments on the WoW dataset demonstrate the effectiveness of the magic closet system for both occasion-oriented clothing recommendation and pairing.\",\n \"Title: Locality discriminative coding for image classification\\nYear: 2013\\n\\nAbstract: The Bag-of-Words (BOW) based methods are widely used in image classification. However, huge number of visual information is omitted inevitably in the quantization step of the BOW. Recently, NBNN and its improved methods like Local NBNN were proposed to solve this problem. Nevertheless, these methods do not perform better than the state-of-the-art BOW based methods. In this paper, based on the advantages of BOW and Local NBNN, we introduce a novel locality discriminative coding (LDC) method. We convert each low level local feature, such as SIFT, into code vector using the Local Feature-to-Class distance other than by k-means quantization. Extensive experimental results on 4 challenging benchmark datasets show that our LDC method outperforms 6 state-of-the-art image classification methods (3 based on NBNN, 3 based on BOW).\",\n \"Title: Object Tracking by Occlusion Detection via Structured Sparse Learning\\nYear: 2013\\n\\nAbstract: Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object's track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art.\",\n \"Title: Low-Rank Sparse Coding for Image Classification\\nYear: 2013\\n\\nAbstract: In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.\",\n \"Title: Latent support vector machine for sign language recognition with Kinect\\nYear: 2013\\n\\nAbstract: In this paper, we propose a novel algorithm to model and recognize sign language with Kinect sensor. We assume that in a sign language video, some frames are expected to be both discriminative and representative. Under this assumption, each frame in training videos is assigned a binary latent variable indicating its discriminative capability. A Latent Support Vector Machine model is then developed to classify the signs, as well as localize the discriminative and representative frames in videos. In addition, we utilize the depth map together with color image captured by Kinect sensor to obtain more effective and accurate feature to enhance the recognition accuracy. To evaluate our approach, we collected an American Sign Language (ASL) dataset which included approximately 2000 phrases, while each phrase was captured by Kinect sensor and hence included color, depth and skeleton information. Experiments on our dataset demonstrate the effectiveness of the proposed method for sign language recognition.\",\n \"Title: Boosted Exemplar Learning for Action Recognition and Annotation\\nYear: 2011\\n\\nAbstract: Human action recognition and annotation is an active research topic in computer vision. How to model various actions, varying with time resolution, visual appearance, and others, is a challenging task. In this paper, we propose a boosted exemplar learning (BEL) approach to model various actions in a weakly supervised manner, i.e., only action bag-level labels are provided but action instance level ones are not. The proposed BEL method can be summarized as three steps. First, for each action category, amount of class-specific candidate exemplars are learned through an optimization formulation considering their discrimination and co-occurrence. Second, each action bag is described as a set of similarities between its instances and candidate exemplars. Instead of simply using a heuristic distance measure, the similarities are decided by the exemplar-based classifiers through the multiple instance learning, in which a positive (or negative) video or image set is deemed as a positive (or negative) action bag and those frames similar to the given exemplar in Euclidean Space as action instances. Third, we formulate the selection of the most discriminative exemplars into a boosted feature selection framework and simultaneously obtain an action bag-based detector. Experimental results on two publicly available datasets: the KTH dataset and Weizmann dataset, demonstrate the validity and effectiveness of the proposed approach for action recognition. We also apply BEL to learn representations of actions by using images collected from the Web and use this knowledge to automatically annotate action in YouTube videos. Results are very impressive, which proves that the proposed algorithm is also practical in unconstraint environments.\",\n \"Title: Robust Visual Tracking Via Consistent Low-Rank Sparse Learning\\nYear: 2015\\n\\nAbstract: Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against $$14$$ 14 state-of-the-art tracking methods on a set of $$25$$ 25 challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.\",\n \"Title: A new discriminative coding method for image classification\\nYear: 2015\\n\\nAbstract: The bag-of-words (BOW) based methods are widely used in image classification. However, huge number of visual information is omitted inevitably in the quantization step of the BOW. Recently, NBNN and its improved methods like Local NBNN were proposed to solve this problem. Nevertheless, these methods do not perform better than the state-of-the-art BOW based methods. In this paper, based on the advantages of BOW and Local NBNN, we introduce a novel locality discriminative coding (LDC) method. We convert each low level local feature, such as SIFT, into code vector using the Local Feature-to-Class distance other than by k-means quantization. After coding, sum-pooling combined with SPM is used to construct a single feature representation vector for each image. Extensive experimental results on several challenging benchmark datasets show that our LDC method outperforms six state-of-the-art image classification methods.\",\n \"Title: Automatic Visual Concept Learning for Social Event Understanding\\nYear: 2015\\n\\nAbstract: Vision-based event analysis is extremely difficult due to the various concepts (object, action, and scene) contained in videos. Though visual concept-based event analysis has achieved significant progress, it has two disadvantages: visual concept is defined manually, and has only one corresponding classifier in traditional methods. To deal with these issues, we propose a novel automatic visual concept learning algorithm for social event understanding in videos. First, instead of defining visual concept manually, we propose an effective automatic concept mining algorithm with the help of Wikipedia, N-gram Web services, and Flickr. Then, based on the learned visual concept, we propose a novel boosting concept learning algorithm to iteratively learn multiple classifiers for each concept to enhance its representative discriminability. The extensive experimental evaluations on the collected dataset well demonstrate the effectiveness of the proposed algorithm for social event understanding.\",\n \"Title: Cross-Domain Feature Learning in Multimedia\\nYear: 2015\\n\\nAbstract: In the Web 2.0 era, a huge number of media data, such as text, image/video, and social interaction information, have been generated on the social media sites (e.g., Facebook, Google, Flickr, and YouTube). These media data can be effectively adopted for many applications (e.g., image/video annotation, image/video retrieval, and event classification) in multimedia. However, it is difficult to design an effective feature representation to describe these data because they have multi-modal property (e.g., text, image, video, and audio) and multi-domain property (e.g., Flickr, Google, and YouTube). To deal with these issues, we propose a novel cross-domain feature learning (CDFL) algorithm based on stacked denoising auto-encoders. By introducing the modal correlation constraint and the cross-domain constraint in conventional auto-encoder, our CDFL can maximize the correlations among different modalities and extract domain invariant semantic features simultaneously. To evaluate our CDFL algorithm , we apply it to three important applications: sentiment classification, spam filtering, and event classification. Comprehensive evaluations demonstrate the encouraging performance of the proposed approach.\",\n \"Title: Robust Visual Tracking via Exclusive Context Modeling.\\nYear: 2016\\n\\nAbstract: In this paper, we formulate particle filter-based object tracking as an exclusive sparse learning problem that exploits contextual information. To achieve this goal, we propose the context-aware exclusive sparse tracker (CEST) to model particle appearances as linear combinations of dictionary templates that are updated dynamically. Learning the representation of each particle is formulated as an exclusive sparse representation problem, where the overall dictionary is composed of multiple {group} dictionaries that can contain contextual information. With context, CEST is less prone to tracker drift. Interestingly, we show that the popular L₁ tracker [1] is a special case of our CEST formulation. The proposed learning problem is efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. To make the tracker much faster, we reduce the number of learning problems to be solved by using the dual problem to quickly and systematically rank and prune particles in each frame. We test our CEST tracker on challenging benchmark sequences that involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that CEST consistently outperforms state-of-the-art trackers.\",\n \"Title: Social Event Classification via Boosted Multimodal Supervised Latent Dirichlet Allocation\\nYear: 2014\\n\\nAbstract: With the rapidly increasing popularity of social media sites (e.g., Flickr, YouTube, and Facebook), it is convenient for users to share their own comments on many social events, which successfully facilitates social event generation, sharing and propagation and results in a large amount of user-contributed media data (e.g., images, videos, and text) for a wide variety of real-world events of different types and scales. As a consequence, it has become more and more difficult to exactly find the interesting events from massive social media data, which is useful to browse, search and monitor social events by users or governments. To deal with these issues, we propose a novel boosted multimodal supervised Latent Dirichlet Allocation (BMM-SLDA) for social event classification by integrating a supervised topic model, denoted as multi-modal supervised Latent Dirichlet Allocation (mm-SLDA), in the boosting framework. Our proposed BMM-SLDA has a number of advantages. (1) Our mm-SLDA can effectively exploit the multimodality and the multiclass property of social events jointly, and make use of the supervised category label information to classify multiclass social event directly. (2) It is suitable for large-scale data analysis by utilizing boosting weighted sampling strategy to iteratively select a small subset of data to efficiently train the corresponding topic models. (3) It effectively exploits social event structure by the document weight distribution with classification error and can iteratively learn new topic model to correct the previously misclassified event documents. We evaluate our BMM-SLDA on a real world dataset and show extensive experimental results, which demonstrate that our model outperforms state-of-the-art methods.\",\n \"Title: Cross-Domain Multi-Event Tracking via CO-PMHT\\nYear: 2014\\n\\nAbstract: With the massive growth of events on the Internet, efficient organization and monitoring of events becomes a practical challenge. To deal with this problem, we propose a novel CO-PMHT (CO-Probabilistic Multi-Hypothesis Tracking) algorithm for cross-domain multi-event tracking to obtain their informative summary details and evolutionary trends over time. We collect a large-scale dataset by searching keywords on two domains (Gooogle News and Flickr) and downloading both images and textual content for an event. Given the input data, our algorithm can track multiple events in the two domains collaboratively and boost the tracking performance. Specifically, the bridge between two domains is a semantic posterior probability, that avoids the domain gap. After tracking, we can visualize the whole evolutionary process of the event over time and mine the semantic topics of each event for deep understanding and event prediction. The extensive experimental evaluations on the collected dataset well demonstrate the effectiveness of the proposed algorithm for cross-domain multi-event tracking.\",\n \"Title: Boosted Multifeature Learning for Cross-Domain Transfer\\nYear: 2015\\n\\nAbstract: Conventional learning algorithm assumes that the training data and test data share a common distribution. However, this assumption will greatly hinder the practical application of the learned model for cross-domain data analysis in multimedia. To deal with this issue, transfer learning based technology should be adopted. As a typical version of transfer learning, domain adaption has been extensively studied recently due to its theoretical value and practical interest. In this article, we propose a boosted multifeature learning (BMFL) approach to iteratively learn multiple representations within a boosting procedure for unsupervised domain adaption. The proposed BMFL method has a number of properties. (1) It reuses all instances with different weights assigned by the previous boosting iteration and avoids discarding labeled instances as in conventional methods. (2) It models the instance weight distribution effectively by considering the classification error and the domain similarity, which facilitates learning new feature representation to correct the previously misclassified instances. (3) It learns multiple different feature representations to effectively bridge the source and target domains. We evaluate the BMFL by comparing its performance on three applications: image classification, sentiment classification and spam filtering. Extensive experimental results demonstrate that the proposed BMFL algorithm performs favorably against state-of-the-art domain adaption methods.\",\n \"Title: Partial Occlusion Handling for Visual Tracking via Robust Part Matching\\nYear: 2014\\n\\nAbstract: Part-based visual tracking is advantageous due to its robustness against partial occlusion. However, how to effectively exploit the confidence scores of individual parts to construct a robust tracker is still a challenging problem. In this paper, we address this problem by simultaneously matching parts in each of multiple frames, which is realized by a locality-constrained low-rank sparse learning method that establishes multi-frame part correspondences through optimization of partial permutation matrices. The proposed part matching tracker (PMT) has a number of attractive properties. (1) It exploits the spatial-temporal localityconstrained property for robust part matching. (2) It matches local parts from multiple frames jointly by considering their low-rank and sparse structure information, which can effectively handle part appearance variations due to occlusion or noise. (3) The proposed PMT model has the inbuilt mechanism of leveraging multi-mode target templates, so that the dilemma of template updating when encountering occlusion in tracking can be better handled. This contrasts with existing methods that only do part matching between a pair of frames. We evaluate PMT and compare with 10 popular state-of-the-art methods on challenging benchmarks. Experimental results show that PMT consistently outperform these existing trackers.\",\n \"Title: Boosted Multi-modal Supervised Latent Dirichlet Allocation for Social Event Classification\\nYear: 2014\\n\\nAbstract: With the rapidly increasing popularity of Social Media sites (e.g., Flickr, YouTube, and Facebook), it is convenient for users to share their own comments on many social events, which successfully facilitates social event generation, sharing and propagation and results in a large amount of user-contributed media data (e.g., images, videos, and texts) for a wide variety of real-world events of different types and scales. As a consequence, it has become more and more difficult to find exactly the interesting events from massive social media data, which is useful to browse, search and monitor social events by users or governments. To deal with these issues, we propose a novel boosted multi-modal supervised Latent Dirichlet Allocation (BMM-SLDA) for social event classification. Our BMM-SLDA has a number of advantages. (1) It can effectively exploit the multi-modality and the supervised information of social events jointly. (2) It is suitable to large-scale data analysis by utilizing boosting weighted sampling strategy to iteratively select a small subset data to efficiently train the corresponding topic models. (3) It effectively exploits boosting document weight distribution by classification error, and can iteratively learn new topic model to correct the previously misclassified documents. We evaluate our BMM-SLDA on a real-world dataset and show extensive results, which show that our model outperforms state-of-the-art methods.\",\n \"Title: Multi-Modal Supervised Latent Dirichlet Allocation for Event Classification in Social Media\\nYear: 2014\\n\\nAbstract: In social media, many existing websites (e.g., Flickr, YouTube, and Facebook) are for users to share their own interests and opinions of many popular events, and successfully facilitate the event generation, sharing and propagation. As a result, there are substantial amounts of user-contributed media data (e.g., images, videos, and textual content) for a wide variety of real-world events of different types and scales. The aim of this paper is to automatically identify the interesting events from massive social media data, which are useful to browse, search and monitor social events by users or governments. To achieve this goal, we propose a novel multi-modal supervised latent dirichlet allocation (mm-SLDA) for social event classification. Our proposed mm-SLDA has a number of advantages. (1) It can effectively exploit the multi-modality and the multi-class property of social events jointly. (2) It makes use of the supervised social event category label information and is able to classify multi-class social event directly. We evaluate our proposed mm-SLDA on a real world dataset and show extensive experimental results, which demonstrate that our model outperforms state-of-the-art methods.\",\n \"Title: Human action recognition in videos using motion impression image\\nYear: 2009\\n\\nAbstract: Human action recognition in surveillance has become a hot topic in computer vision. In this paper, we develope a new method to recognize human action using motion information in video. Video sequence is compressed along time axis into a Motion Impression Image (MII), which is combined with two types of impression images from different views. One is a Period Impression Image (PII) by exploring the characteristics of the motion frequency. The other is an Optical Flow Impression Image (OFII) obtained from the analysis of motion mode. The proposed MII is a compact and time-invariant representation. Furthermore, it is simple and efficient to implement. After quantizing the combined MIIs, we feed them into a spatial pyramid matching kernel (SPMK) based classifier to recognize various human actions. At last, experiments on a known benchmark dataset demonstrate the better performance of the proposed approach against the state-of-the-art algorithms.\",\n \"Title: Multi-object tracking via MHT with multiple information fusion in surveillance video\\nYear: 2015\\n\\nAbstract: Tracking multiple objects is critical to automatic video content analysis and virtual reality. The major problem is how to solve data association problem when ambiguous measurements are caused by objects in close proximity. To tackle this problem, we propose a multiple information fusion-based multiple hypotheses tracking algorithm integrated with appearance feature, local motion pattern feature and repulsion---inertia model for multi-object tracking. Appearance model based on HSV---local binary patterns histogram and local motion pattern based on optical flow are adopted to describe objects. A likelihood calculation framework is proposed to incorporate the similarities of appearance, dynamic process and local motion pattern. To consider the changes in appearance and motion pattern over time, we make use of an effective template updating strategy for each object. In addition, a repulsion---inertia model is adopted to explore more useful information from ambiguous detections. Experimental results show that the proposed approach generates better trajectories with less missing objects and identity switches.\",\n \"Title: Context-aware learning for automatic sports highlight recognition\\nYear: 2012\\n\\nAbstract: Video highlight recognition is the procedure in which a long video sequence is summarized into a shorter video clip that depicts the most “salient” parts of the sequence. It is an important technique for content delivery systems and search systems which create multimedia content tailored to their users' needs. This paper deals specifically with capturing highlights inherent to sports videos, especially for American football. Our proposed system exploits the multimodal nature of sports videos (i.e. visual, audio, and text cues) to detect the most important segments among them. The optimal combination of these cues is learned in a data-driven fashion using user preferences (expert input) as ground truth. Unlike most highlight recognition systems in the literature that define a highlight to be salient only in its own right (globally salient), we also consider the context of each video segment w.r.t. the video sequence it belongs to (locally salient). To validate our method, we compile a large dataset of broadcast American football videos, acquire their ground truth highlights, and evaluate the performance of our learning approach.\",\n \"Title: Load disaggregation using harmonic analysis and regularized optimization\\nYear: 2012\\n\\nAbstract: In this paper, we present a load disaggregation technique that uses regularized optimization together with harmonic frequency signatures of appliances. The benefits of our technique are twofold: 1) The regularized optimization is faster than integer programming; and 2) The harmonic frequency signatures allow us to disaggregate the loads using as few as 10 cycles (equaling as little as 200 milliseconds) of samples, instead of having to wait for state changes from appliances or weekly usage pattern to emerge. We test our proposed technique in proof-of-concept experiments and show that our technique returns accurate disaggregation results.\",\n \"Title: Multi-Modal Event Topic Model for Social Event Analysis\\nYear: 2016\\n\\nAbstract: With the massive growth of social events in Internet , it has become more and more difficult to exactly find and organize the interesting events from massive social media data, which is useful to browse, search, and monitor social events by users or governments . To deal with this problem, we propose a novel multi-modal social event tracking and evolution framework to not only effectively capture multi-modal topics of social events, but also obtain the evolutionary trends of social events and generate effective event summary details over time. To achieve this goal, we propose a novel multi-modal event topic model (mmETM), which can effectively model social media documents, including long text with related images, and learn the correlations between textual and visual modalities to separate the visual-representative topics and non-visual-representative topics. To apply the mmETM model to social event tracking, we adopt an incremental learning strategy denoted as incremental mmETM, which can obtain informative textual and visual topics of social events over time to help understand these events and their evolutionary trends. To evaluate the effectiveness of our proposed algorithm, we collect a real-world dataset to conduct various experiments. Both qualitative and quantitative evaluations demonstrate that the proposed mmETM algorithm performs favorably against several state-of-the-art methods.\",\n \"Title: Latent Support Vector Machine Modeling for Sign Language Recognition with Kinect\\nYear: 2015\\n\\nAbstract: Vision-based sign language recognition has attracted more and more interest from researchers in the computer vision field. In this article, we propose a novel algorithm to model and recognize sign language performed in front of a Microsoft Kinect sensor. Under the assumption that some frames are expected to be both discriminative and representative in a sign language video, we first assign a binary latent variable to each frame in training videos for indicating its discriminative capability, then develop a latent support vector machine model to classify the signs, as well as localize the discriminative and representative frames in each video. In addition, we utilize the depth map together with the color image captured by the Kinect sensor to obtain a more effective and accurate feature to enhance the recognition accuracy. To evaluate our approach, we conducted experiments on both word-level sign language and sentence-level sign language. An American Sign Language dataset including approximately 2,000 word-level sign language phrases and 2,000 sentence-level sign language phrases was collected using the Kinect sensor, and each phrase contains color, depth, and skeleton information. Experiments on our dataset demonstrate the effectiveness of the proposed method for sign language recognition.\",\n \"Title: Cross-Domain Collaborative Learning in Social Multimedia\\nYear: 2015\\n\\nAbstract: Cross-domain data analysis is one of the most important tasks in social multimedia. It has a wide range of real-world applications, including cross-platform event analysis, cross-domain multi-event tracking, cross-domain video recommendation, etc. It is also very challenging because the data have multi-modal and multi-domain properties, and there are no explicit correlations to link different domains. To deal with these issues, we propose a generic Cross-Domain Collaborative Learning (CDCL) framework based on non-parametric Bayesian dictionary learning model for cross-domain data analysis. In the proposed CDCL model, it can make use of the shared domain priors and modality priors to collaboratively learn the data's representations by considering the domain discrepancy and the multi-modal property. As a result, our CDCL model can effectively explore the virtues of different information sources to complement and enhance each other for cross-domain data analysis. To evaluate the proposed model, we apply it for two different applications: cross-platform event recognition and cross-network video recommendation. The extensive experimental evaluations well demonstrate the effectiveness of the proposed algorithm for cross-domain data analysis.\",\n \"Title: Structural Sparse Tracking\\nYear: 2015\\n\\nAbstract: Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.\",\n \"Title: The Role of Inter-Controller Traffic for Placement of Distributed SDN Controllers.\\nYear: 2016\\n\\nAbstract: We consider a distributed Software Defined Networking (SDN) architecture adopting a cluster of multiple controllers to improve network performance and reliability. Besides the Openflow control traffic exchanged between controllers and switches, we focus on the control traffic exchanged among the controllers in the cluster, needed to run coordination and consensus algorithms to keep the controllers synchronized. We estimate the effect of the inter-controller communications on the reaction time perceived by the switches depending on the data-ownership model adopted in the cluster. The model is accurately validated in an operational Software Defined WAN (SDWAN). We advocate a careful placement of the controllers, that should take into account both the above kinds of control traffic. We evaluate, for some real ISP network topologies, the delay tradeoffs for the controllers placement problem and we propose a novel evolutionary algorithm to find the corresponding Pareto frontier. Our work provides novel quantitative tools to optimize the planning and the design of the network supporting the control plane of SDN networks, especially when the network is very large and in-band control plane is adopted. We also show that for operational distributed controllers (e.g. OpenDaylight and ONOS), the location of the controller which acts as a leader in the consensus algorithm has a strong impact on the reactivity perceived by switches.\",\n \"Title: Deep Relative Attributes.\\nYear: 2016\\n\\nAbstract: Relative attribute (RA) learning aims to learn the ranking function describing the relative strength of the attribute. Most of current learning approaches learn a linear ranking function for each attribute by use of the hand-crafted visual features. Different from the existing study, in this paper, we propose a novel deep relative attributes (DRA) algorithm to learn visual features and the effective nonlinear ranking function to describe the RA of image pairs in a unified framework. Here, visual features and the ranking function are learned jointly, and they can benefit each other. The proposed DRA model is comprised of five convolutional neural layers, five fully connected layers, and a relative loss function which contains the contrastive constraint and the similar constraint corresponding to the ordered image pairs and the unordered image pairs, respectively. To train the DRA model effectively, we make use of the transferred knowledge from the large scale visual recognition on ImageNet [1] to the RA learning task. We evaluate the proposed DRA model on three widely used datasets. Extensive experimental results demonstrate that the proposed DRA model consistently and significantly outperforms the state-of-the-art RA learning methods. On the public OSR, PubFig, and Shoes datasets, compared with the previous RA learning results [2], the average ranking accuracies have been significantly improved by about 8%, 9%, and 14%, respectively.\",\n \"Title: Semantic Feature Mining for Video Event Understanding.\\nYear: 2016\\n\\nAbstract: Content-based video understanding is extremely difficult due to the semantic gap between low-level vision signals and the various semantic concepts (object, action, and scene) in videos. Though feature extraction from videos has achieved significant progress, most of the previous methods rely only on low-level features, such as the appearance and motion features. Recently, visual-feature extraction has been improved significantly with machine-learning algorithms, especially deep learning. However, there is still not enough work focusing on extracting semantic features from videos directly. The goal of this article is to adopt unlabeled videos with the help of text descriptions to learn an embedding function, which can be used to extract more effective semantic features from videos when only a few labeled samples are available for video recognition. To achieve this goal, we propose a novel embedding convolutional neural network (ECNN). We evaluate our algorithm by comparing its performance on three challenging benchmarks with several popular state-of-the-art methods. Extensive experimental results show that the proposed ECNN consistently and significantly outperforms the existing methods.\",\n \"Title: Multi-modal Multi-view Topic-opinion Mining for Social Event Analysis.\\nYear: 2016\\n\\nAbstract: In this paper, we propose a novel multi-modal multi-view topic-opinion mining (MMTOM) model for social event analysis in multiple collection sources. Compared with existing topic-opinion mining methods, our proposed model has several advantages: (1) The proposed MMTOM can effectively take into account multi-modal and multi-view properties jointly in a unified and principled way for social event modeling. (2) Our model is general and can be applied to many other applications in multimedia, such as opinion mining and sentiment analysis, multi-view association visualization, and topic-opinion mining for movie review. (3) The proposed MMTOM is able to not only discover multi-modal common topics from all collections as well as summarize the similarities and differences of these collections along each specific topic, but also automatically mine multi-view opinions on the learned topics across different collections. (4) Our topic-opinion mining results can be effectively applied to many applications including multi-modal multi-view topic-opinion retrieval and visualization, which achieve much better performance than existing methods. To evaluate the proposed model, we collect a real-world dataset for research on multi-modal multi-view social event analysis, and will release it for academic use. We have conducted extensive experiments, and both qualitative and quantitative evaluation results have demonstrated the effectiveness of the proposed MMTOM.\",\n \"Title: Abnormal Event Discovery in User Generated Photos.\\nYear: 2016\\n\\nAbstract: Vision based event analysis plays a very critical role in automatically organizing user generated photos. As one of the important tasks in event analysis, abnormal event discovery still does not obtain much attentions. It is difficult because only few samples can be used for event pattern learning. In this paper, by considering the photo taken time, we propose a novel one-class structured event modeling (OSEM) where we explore the temporal event patterns in negative photos of the event using the continuous conditional random field (CRF). With the estimated piecewise training of CRF, the proposed OSEM can be efficiently solved using stochastic gradients descent (SGD) in an end-to-end form. The extensive experimental results on a collected abnormal event dataset demonstrate the effectiveness of the proposed OSEM.\",\n \"Title: Temporal Restricted Visual Tracking Via Reverse-Low-Rank Sparse Learning.\\nYear: 2017\\n\\nAbstract: An effective representation model, which aims to mine the most meaningful information in the data, plays an important role in visual tracking. Some recent particle-filter-based trackers achieve promising results by introducing the low-rank assumption into the representation model. However, their assumed low-rank structure of candidates limits the robustness when facing severe challenges such as ab...\",\n \"Title: 3d Part-Based Sparse Tracker With Automatic Synchronization And Registration\\nYear: 2016\\n\\nAbstract: In this paper, we present a part-based sparse tracker in a particle filter framework where both the motion and appearance model are formulated in 3D. The motion model is adaptive and directed according to a simple yet powerful occlusion handling paradigm, which is intrinsically fused in the motion model. Also, since 3D trackers are sensitive to synchronization and registration noise in the RGB and depth streams, we propose automated methods to solve these two issues. Extensive experiments are conducted on a popular RGBD tracking benchmark, which demonstrate that our tracker can achieve superior results, outperforming many other recent and state-of-the-art RGBD trackers.\",\n \"Title: Structural Correlation Filter For Robust Visual Tracking\\nYear: 2016\\n\\nAbstract: In this paper, we propose a novel structural correlation filter (SCF) model for robust visual tracking. The proposed SCF model takes part-based tracking strategies into account in a correlation filter tracker, and exploits circular shifts of all parts for their motion modeling to preserve target object structure. Compared with existing correlation filter trackers, our proposed tracker has several advantages: (1) Due to the part strategy, the learned structural correlation filters are less sensitive to partial occlusion, and have computational efficiency and robustness. (2) The learned filters are able to not only distinguish the parts from the background as the traditional correlation filters, but also exploit the intrinsic relationship among local parts via spatial constraints to preserve object structure. (3) The learned correlation filters not only make most parts share similar motion, but also tolerate outlier parts that have different motion. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SCF tracking algorithm performs favorably against several state-of-the-art methods.\",\n \"Title: Deep Relative Tracking.\\nYear: 2017\\n\\nAbstract: Most existing tracking methods are direct trackers, which directly exploit foreground or/and background information for object appearance modeling and decide whether an image patch is target object or not. As a result, these trackers cannot perform well when target appearance changes heavily and becomes different from its model. To deal with this issue, we propose a novel relative tracker, which can effectively exploit the relative relationship among image patches from both foreground and background for object appearance modeling. Different from direct trackers, the proposed relative tracker is robust to localize target object by use of the best image patch with the highest relative score to the target appearance model. To model relative relationship among large-scale image patch pairs, we propose a novel and effective deep relative learning algorithm through the convolutional neural network. We test the proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our method consistently outperforms the state-of-the-art trackers due to the powerful capacity of the proposed deep relative model.\",\n \"Title: The role of inter-controller traffic in SDN controllers placement\\nYear: 2016\\n\\nAbstract: We consider a distributed Software Defined Networking (SDN) architecture adopting a cluster of multiple controllers to improve network performance and reliability. Differently from previous work, we focus on the control traffic exchanged among the controllers, in addition to the Openflow control traffic exchanged between controllers and switches. We develop an analytical model to estimate the reaction time perceived at the switches due to the inter-controller communications, based on the data-ownership model adopted in the cluster. We advocate a careful placement of the controllers, taking into account the two above kinds of control traffic. We evaluate, for some real ISP network topologies, the possible delay tradeoffs for the controllers placement problem.\",\n \"Title: On-The-Fly Traffic Classification And Control With A Stateful Sdn Approach\\nYear: 2017\\n\\nAbstract: The novel \\\"stateful\\\" approach in Software Defined Networking (SDN) provides programmable processing capabilities within the switches to reduce the interaction with the SDN controller and thus improve the scalability and the performance of the network. In our work we consider specifically the stateful extension of OpenFlow that was recently proposed, called OpenState, that allows to program simple state machines in almoststandard OpenFlow switches.We consider a reactive traffic control application that reacts to the traffic flows which are identified in real-time by a generic traffic classification engine. We devise an architecture in which an OpenState-enabled switch sends the minimum number of packets to the traffic classifier, in order to minimize the load on the classifier and improve the scalability of the approach. We design two stateful approaches to minimize the memory occupancy in the flow tables of the switches. Finally, we validate experimentally our solutions and estimate the required memory for the flow tables.\",\n \"Title: A Unified Personalized Video Recommendation via Dynamic Recurrent Neural Networks.\\nYear: 2017\\n\\nAbstract: Personalized video recommender systems play an essential role in bridging users and videos. However, most existing video recommendation methods assume that user profiles (interests) are static. In fact, the static assumption is inadequate to reflect users' dynamic interests as time goes by, especially in the online video recommendation scenarios with dramatic changes of video contents and frequent drift of users' interests over different topics. To overcome the above issue, we propose a dynamic recurrent neural network to model users' dynamic interests over time in a unified framework for personalized video recommendation. Furthermore, to build a much more comprehensive recommendation system, the proposed model is designed to exploit video semantic embedding, user interest modeling, and user relevance mining jointly to model users' preferences. By considering these three factors, the RNN model becomes an interest network which can capture users' high level interests effectively. Extensive experimental results on both single-network and cross-network video recommendation scenarios demonstrate the superior performance of the proposed model compared with other state-of-the-art algorithms.\",\n \"Title: The role of the inter-controller consensus in the placement of distributed SDN controllers.\\nYear: 2017\\n\\nAbstract: We consider a distributed Software Defined Networking (SDN) architecture adopting a cluster of controllers to improve network scalability and reliability. Different from previous works that focused solely on the control traffic exchanged between controllers and switches, we additionally consider the control traffic exchanged among the controllers (e.g., the control traffic to keep the shared data structures synchronized). We advocate a careful placement of the controllers, which should take into account both the two kinds of control traffic. We evaluate, for some real ISP network topologies, the delay tradeoffs for the controller placement problem and we propose a novel evolutionary algorithm to find the corresponding Pareto frontier. Furthermore, we develop a simple model to estimate the reaction time perceived by the switches, which is accurately validated in an operational Software Defined WAN (SDWAN). We also formalize the optimization problem to minimize the reaction time and devise a novel approximation algorithm, whose performance is assessed against the optimal solver in real ISP network topologies.\",\n \"Title: Dealing with Misbehaving Controllers in SDN Networks.\\nYear: 2017\\n\\nAbstract: The logical centralized approach in the control of SDN networks allows an unprecedented level of programmability in the network, but also implies the vulnerability in the case of misbehavior of the controller, due for example to software bugs, hardware problems or hacker attacks. In our work we propose to exploit the diversity offered by multiple controllers to manage the network switches and detect misbehaviors whenever one controller issues different OpenFlow instructions for the data plane with respect to the others. We design a behavioral checker, denoted as BeCheck, that acts as a transparent relay in the interaction between the network switches and the controllers. We propose and investigate different policies to relay the messages and to detect the controller misbehavior. We implement and validate our approach in a simple testbed, showing the possible tradeoff between detection reliability and controller reactivity perceived at the switches.\",\n \"Title: Correlation Particle Filter for Visual Tracking.\\nYear: 2018\\n\\nAbstract: In this paper, we propose a novel correlation particle filter (CPF) for robust visual tracking. Instead of a simple combination of a correlation filter and a particle filter, we exploit and complement the strength of each one. Compared with existing tracking methods based on correlation filters and particle filters, the proposed tracker has four major advantages: 1) it is robust to partial and tot...\",\n \"Title: Cross-Domain Collaborative Learning via Discriminative Nonparametric Bayesian Model.\\nYear: 2018\\n\\nAbstract: Cross-domain data analysis has been becoming more and more important, and can be effectively adopted for many applications. However, it is difficult to propose a unified cross-domain collaborative learning framework for cross-domain analysis in social multimedia, because cross-domain data have multidomain, multimodal, sparse, and supervised properties. In this paper, we propose a generic cross-dom...\",\n \"Title: Deep-Structured Event Modeling for User-Generated Photos.\\nYear: 2018\\n\\nAbstract: Vision-based event analysis is difficult because of the following challenges. The first challenge is intraclass variation. Photos uploaded by users are sparsely sampled visual appearances of an event over time. Thus, each photo may only capture a single object or scene of a specific complex event. The second challenge is interclass confusion. Photos related to different events may contain similar ...\",\n \"Title: High-speed per-flow software monitoring with limited resources.\\nYear: 2018\\n\\nAbstract: No abstract available.\",\n \"Title: Text2Video: An End-to-end Learning Framework for Expressing Text With Videos.\\nYear: 2018\\n\\nAbstract: Video creation is a challenging and highly profession-al task that generally involves substantial manual efforts. To ease this burden, a better approach is to automatically produce new videos based on clips from the massive amount of existing videos according to arbitrary text. In this paper, we formulate video creation as a problem of retrieving a sequence of videos for a sentence stream. To achi...\",\n \"Title: Learning semantic topics for domain-adapted textual knowledge transfer.\\nYear: 2018\\n\\nAbstract: Traditional text classification methods make a basic assumption: the training and test data are homologous, while this naive assumption may not hold in the real world. Hence, this paper studies the problem of domain-adapted news text classification, hereby a model is trained on labeled data from one source domain and is able to be deployed on the other. To realize the cross-domain text classification, we propose a domain-adapted text classification method based on topic model LDA and TextCNN model, named TextLDACNN. Specifically, our work calculates the topic similarity between source and target domain, which is severed as an effective constraint to regularize the training process and hence improve the generalization of the source model to the target domain. Text classifier trained with unsupervised topic feature representation clearly outperforms the baseline TextCNN model. The result shows that our method achieves an approximately 4.0% improvement compared to the state-of-the-art method.\",\n \"Title: Online Multimodal Multiexpert Learning for Social Event Tracking.\\nYear: 2018\\n\\nAbstract: In this paper, we aim to automatically identify and track the interesting social event from vast amounts of social media data. However, there are two existing challenges: 1) how to model multimodal social event data over time and visualize the topic evolution and 2) how to alleviate the tracking drift problem to boost social event tracking accuracy. We propose a novel online multimodal multiexpert...\",\n \"Title: FlowMon-DPDK: Parsimonious Per-Flow Software Monitoring at Line Rate\\nYear: 2018\\n\\nAbstract: Testing experimental network devices requires deep performance analysis, which is usually performed with expensive, not flexible, hardware equipment. With the advent of highspeed packet I/O frameworks, general purpose equipments have narrowed the performance gap in respect of dedicated hardware and a variety of software-based solutions have emerged for handling traffic at very high speed. While the literature abounds with software traffic generators, existing monitoring solutions do not target worst-case scenarios (i.e., 64B packets at line rate) that are particularly relevant for stress-testing high-speed network functions, or occupy too many resources. In this paper we first analyse the design space for high-speed traffic monitoring that leads us to specific choices characterizing FlowMon-DPDK, a DPDK-based software traffic monitor that we release as an open source project. In a nutshell, FlowMon-DPDK provides tunable fine-grained statistics at both packet and flow levels. Experimental results demonstrate that our traffic monitor is able to provide per-flow statistics with 5-nines precision at high-speed (14.88 Mpps) using an exiguous amount of resources. Finally, we showcase FlowMon-DPDK usage by testing two open source prototypes for stateful flow-level end-host and in-network packet processing.\",\n \"Title: Watch, Think and Attend: End-to-End Video Classification via Dynamic Knowledge Evolution Modeling.\\nYear: 2018\\n\\nAbstract: Video classification has been achieved by automatically mining the underlying concepts (\\\\eg actions, events) in videos, which plays an essential role in intelligent video analysis. However, most existing algorithms only exploit the visual cues of these concepts but ignore external knowledge information for modeling their relationships during the evolution of videos. In fact, humans have remarkable ability to utilize acquired knowledge to reason about the dynamically changing world. To narrow the knowledge gap between existing methods and humans, we propose an end-to-end video classification framework based on a structured knowledge graph, which can model the dynamic knowledge evolution in videos overtime. Here, we map the concepts of videos to the nodes of the knowledge graph. To effectively leverage the knowledge graph, we adopt a graph convLSTM model to not only identify local knowledge structures in each video shot but also model dynamic patterns of knowledge evolution across these shots. Furthermore, a novel knowledge-based attention model is designed by considering the importance of each video shot and relationships between concepts. We show that by using knowledge graphs, our framework is able to improve the performance of various existing methods. Extensive experimental results on two video classification benchmarks UCF101 and Youtube-8M demonstrate the favorable performance of the proposed framework.\",\n \"Title: Facial Expression Recognition in the Wild: A Cycle-Consistent Adversarial Attention Transfer Approach.\\nYear: 2018\\n\\nAbstract: Facial expression recognition (FER) is a very challenging problem due to different expressions under arbitrary poses. Most conventional approaches mainly perform FER under laboratory controlled environment. Different from existing methods, in this paper, we formulate the FER in the wild as a domain adaptation problem, and propose a novel auxiliary domain guided Cycle-consistent adversarial Attention Transfer model (CycleAT) for simultaneous facial image synthesis and facial expression recognition in the wild. The proposed model utilizes large-scale unlabeled web facial images as an auxiliary domain to reduce the gap between source domain and target domain based on generative adversarial networks (GAN) embedded with an effective attention transfer module, which enjoys several merits. First, the GAN-based method can automatically generate labeled facial images in the wild through harnessing information from labeled facial images in source domain and unlabeled web facial images in auxiliary domain. Second, the class-discriminative spatial attention maps from the classifier in source domain are leveraged to boost the performance of the classifier in target domain. Third, it can effectively preserve the structural consistency of local pixels and global attributes in the synthesized facial images through pixel cycle-consistency and discriminative loss. Quantitative and qualitative evaluations on two challenging in-the-wild datasets demonstrate that the proposed model performs favorably against state-of-the-art methods.\",\n \"Title: Describe and Attend to Track: Learning Natural Language guided Structural Representation and Visual Attention for Object Tracking.\\nYear: 2018\\n\\nAbstract: The tracking-by-detection framework requires a set of positive and negative training samples to learn robust tracking models for precise localization of target objects. However, existing tracking models mostly treat different samples independently while ignores the relationship information among them. In this paper, we propose a novel structure-aware deep neural network to overcome such limitations. In particular, we construct a graph to represent the pairwise relationships among training samples, and additionally take the natural language as the supervised information to learn both feature representations and classifiers robustly. To refine the states of the target and re-track the target when it is back to view from heavy occlusion and out of view, we elaborately design a novel subnetwork to learn the target-driven visual attentions from the guidance of both visual and natural language cues. Extensive experiments on five tracking benchmark datasets validated the effectiveness of our proposed method.\",\n \"Title: Learning Multi-task Correlation Particle Filters for Visual Tracking.\\nYear: 2019\\n\\nAbstract: We propose a multi-task correlation particle filter (MCPF) for robust visual tracking. We first present the multi-task correlation filter (MCF) that takes the interdependencies among different object parts and features into account to learn the correlation filters jointly. The proposed MCPF is introduced to exploit and complement the strength of a MCF and a particle filter. Compared with existing tracking methods based on correlation filters and particle filters, the proposed MCPF enjoys several merits. First, it exploits the interdependencies among different features to derive the correlation filters jointly, and makes the learned filters complement and enhance each other to obtain consistent responses. Second, it handles partial occlusion via a part-based representation, and exploits the intrinsic relationship among local parts via spatial constraints to preserve object structure and learn the correlation filters jointly. Third, it effectively handles large scale variation via a sampling scheme by drawing particles at different scales for target object state estimation. Fourth, it shepherds the sampled particles toward the modes of the target state distribution via the MCF, and effectively covers object states well using fewer particles than conventional particle filters. Extensive experimental results odemonstrate that the proposed MCPF tracking algorithm performs favorably against the state-of-the-art methods.\",\n \"Title: Multi-modal max-margin supervised topic model for social event analysis.\\nYear: 2019\\n\\nAbstract: In this paper, we proposed a novel multi-modal max-margin supervised topic model (MMSTM) for social event analysis by jointly learning the representation together with the classifier in a unified framework. Compared with existing methods, the proposed MMSTM model has several advantages. (1) The proposed model can utilize the classifier as the regularization term of our model to jointly learn the parameters in the generative model and max-margin classifier, and use the Gibbs sampling to learn parameters of the representation model and max-margin classifier by minimizing the expected loss function. (2) The proposed model is able to not only effectively mine the multi-modal property by jointly learning the latent topic relevance among multiple modalities for social event representation, but also exploit the supervised information by considering a discriminative max-margin classifier for event classification to boost the classification performance. (3) In order to validate the effectiveness of the proposed model, we collect a large-scale real-world dataset for social event analysis, and both qualitative and quantitative evaluation results have demonstrated the effectiveness of the proposed MMSTM.\",\n \"Title: Boosted Exemplar Learning for human action recognition\\nYear: 2009\\n\\nAbstract: Human action recognition has been an active research topic in computer vision. How to model all kinds of actions, varying with time resolution, visual appearance, etc., is quite a challenging task for recognition. In this paper, we propose a Boosted Exemplar Learning (BEL) approach to recognize various actions in a weakly supervised manner, i.e., only video-based labels are provided but frame-based ones are not. First, for a given action, each video is described as a set of similarities between its frames and some candidate ones (called as exemplars), which are selected from training videos belonging to the action. Instead of simply using a heuristic distance measure, the similarities are decided by the exemplar-based classifiers through the Multiple Instance Learning (MIL), in which a positive (or negative) video is deemed as a positive (or negative) bag and those similar frames to the given exemplar in Euclidean Space as instances. Second, we formulate the selection of the most discriminative exemplars into a boosted feature selection framework and simultaneously obtain a video-based action detector in the boosted learning process. Experimental results on two publicly available challenging datasets: the KTH dataset and Weizmann dataset demonstrate the validity and effectiveness of the proposed approach.\",\n \"Title: In Defense Of Sparse Tracking: Circulant Sparse Tracker\\nYear: 2016\\n\\nAbstract: Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.\",\n \"Title: Robust Target Tracking by Online Random Forests and Superpixels.\\nYear: 2018\\n\\nAbstract: This paper presents a robust joint discriminative appearance model-based tracking method using online random forests and mid-level feature (superpixels). To achieve superpixel-wise discriminative ability, we propose a joint appearance model that consists of two random forest-based models, i.e., the background-target discriminative model (BTM) and the distractor-target discriminative model (DTM). M...\",\n \"Title: A Generic Framework for Social Event Analysis.\\nYear: 2017\\n\\nAbstract: Social event is something that occurs at specific place and time associated with some specific actions, and it consists of many stories over time. With the explosion of Web 2.0 platforms, a popular social event that is happening around us and around the world can spread very fast. As a result, social event analysis becomes more and more important for users to understand the whole evolutionary trend of social event over time. However, it is very challenging to do social event analysis because social event data from different social media sites have multi-modal, multi-domain, and large-scale properties. The goal of our research is to design advanced multimedia techniques to deal with the above issues and establish an effective and robust social event analysis framework for social event representation, detection, tracking and evolution analysis. (1) For social event representation, we propose a novel cross-domain collaborative learning algorithm based on non-parametric Bayesian dictionary learning model. It can make use of the shared domain priors and modality priors to collaboratively learn the data's representations by considering the domain discrepancy and the multi-modal property.(2) For social event detection, we propose a boosted multi-modal supervised Latent Dirichlet Allocation model. It can effectively exploit multi-modality information and utilize boosting weighted sampling strategy for large-scale data processing. (3) For social event tracking, we propose a novel multi-modal event topic model, which can effectively model the correlations between textual and visual modalities, and obtain their topics over time. (4) For social event evolution analysis, we propose a novel multi-modal multi-view topic-opinion mining model to conduct fined-grained topic and opinion analysis for social events from multiple social media sites collaboratively. It can discover multi-modal topics and the corresponding opinions over time to understand the evolutionary processes of social event. Extensive experimental results show that the proposed algorithms perform favorably against state-of-the-art methods for social event analysis.\",\n \"Title: SMART: Joint Sampling and Regression for Visual Tracking.\\nYear: 2019\\n\\nAbstract: Most existing trackers are either sampling-based or regression-based methods. Sampling-based methods estimate the target state by sampling many target candidates. Although these methods achieve significant performance, they often suffer from a high computational burden. Regression-based methods often learn a computationally efficient regression function to directly predict the geometric distortion...\",\n \"Title: Robust Structural Sparse Tracking.\\nYear: 2019\\n\\nAbstract: Sparse representations have been applied to visual tracking by finding the best candidate region with minimal reconstruction error based on a set of target templates. However, most existing sparse trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidate regions, thereby making them less effective when similar objects appear at close proximity or under occlusion. In this paper, we propose a novel structural sparse representation, which not only exploits the intrinsic relationships among target candidate regions and local patches to learn their representations jointly, but also preserves the spatial structure among the local patches inside each target candidate region. For robust visual tracking, we take outliers resulting from occlusion and noise into account when searching for the best target region. Constructed within a Bayesian filtering framework, we show that the proposed algorithm accommodates most existing sparse trackers with respective merits. The formulated problem can be efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. Qualitative and quantitative evaluations on challenging benchmark datasets demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.\",\n \"Title: P2T: Part-to-Target Tracking via Deep Regression Learning.\\nYear: 2018\\n\\nAbstract: Most existing part-based tracking methods are part-to-part trackers, which usually have two separated steps including the part matching and target localization. Different from existing methods, in this paper, we propose a novel part-to-target (P2T) tracker in a unified fashion by inferring target location from parts directly. To achieve this goal, we propose a novel deep regression model for P2T r...\",\n \"Title: Three-Dimensional Attention-Based Deep Ranking Model for Video Highlight Detection.\\nYear: 2018\\n\\nAbstract: The video highlight detection task is to localize key elements (moments of user's major or special interest) in a video. Most of existing highlight detection approaches extract features from the video segment as a whole without considering the difference of local features both temporally and spatially. Due to the complexity of video content, this kind of mixed features will impact the final highli...\",\n \"Title: TAME: An Efficient Task Allocation Algorithm for Integrated Mobile Gaming\\nYear: 2019\\n\\nAbstract: We consider an integrated mobile gaming platform, in which the mobile device (e.g., smartphone) of a player can offload some game tasks toward a server as well as some neighboring mobile devices. The advantages of such a platform are manyfold: it can lead to an improved game experience, to a better use of energy resources, and, while offloading tasks to other mobile users, to the exploitation of the unused computing and storage resources of the mobile equipment, thus reducing the bandwidth and computing costs of the overall system. In this context, we formulate the problem of offloading the game computational tasks as an optimization problem that minimizes the maximum energy consumption across a set of mobile devices, under the constraints of a maximum response time and a limited availability of computation, communication, and storage resources. In light of the problem complexity, we then propose a heuristic, called Task Allocation with Minimal Energy cost (TAME), which is shown to closely approximate the optimal solution in all scenarios we considered. TAME also outperforms state-of-the-art algorithms under both synthetic and real scenarios, which have been devised based on a realistic and detailed energy consumption model for computation and communication resources. Our results, although tailored to mobile gaming, could be extended to other applications, where it may be beneficial to offload computational and storage tasks through device-to-device communications, as enabled by Wi-Fi, Bluetooth, or the upcoming 5G technology.\",\n \"Title: A Unified Generative Adversarial Framework for Image Generation and Person Re-identification.\\nYear: 2018\\n\\nAbstract: Person re-identification (re-id) aims to match a certain person across multiple non-overlapping cameras. It is a challenging task because the same person's appearance can be very different across camera views due to the presence of large pose variations. To overcome this issue, in this paper, we propose a novel unified person re-id framework by exploiting person poses and identities jointly for simultaneous person image synthesis under arbitrary poses and pose-invariant person re-identification. The framework is composed of a GAN based network and two Feature Extraction Networks (FEN), and enjoys following merits. First, it is a unified generative adversarial model for person image generation and person re-identification. Second, a pose estimator is utilized into the generator as a supervisor in the training process, which can effectively help pose transfer and guide the image generation with any desired pose. As a result, the proposed model can automatically generate a person image under an arbitrary pose. Third, the identity-sensitive representation is explicitly disentangled from pose variations through the person identity and pose embedding. Fourth, the learned re-id model can have better generalizability on a new person re-id dataset by using the synthesized images as auxiliary samples. Extensive experimental results on four standard benchmarks including Market-1501 [69], DukeMTMC-reID [40], CUHK03 [23], and CUHK01 [22] demonstrate that the proposed model can perform favorably against state-of-the-art methods.\",\n \"Title: Video Highlight Detection Via Region-Based Deep Ranking Model\\nYear: 2019\\n\\nAbstract: The video highlight detection task is to localize key elements (moments of user's major or special interest) in a video. Most of the existing highlight detection approaches extract features from the video segment as a whole without considering the difference of local features spatially. In spatial extent, not all regions are worth watching because some of them only contain the background of the environment without human or other moving objects, especially when there is lots of clutter in the background. To deal with this issue, we propose a novel region-based model which can automatically localize the key elements in a video without any extra supervised annotations. Specifically, the proposed model produces position-sensitive score maps for local regions in the spatial dimension of the video segment, and then aggregates all position-wise scores with position-pooling operation. The regions with higher response values will be extracted as key elements. Thus more effective features of the video segment are obtained to predict the highlight score. The proposed position-sensitive scheme can be easily integrated into an endto-end fully convolutional network which aims to update parameters via stochastic gradient descent method in the backward propagation to improve the robustness of the model. Extensive experimental results on the YouTube and SumMe datasets demonstrate that the proposed approach achieves significant improvement over state-of-the-art methods.\",\n \"Title: Deep Multi-Modality Adversarial Networks for Unsupervised Domain Adaptation\\nYear: 2019\\n\\nAbstract: Unsupervised Domain Adaptation (UDA) aims to transfer domain knowledge from existing well-defined tasks to new ones where labels are unavailable. In the real world applications, domain discrepancy is usually uncontrollable especially for multi-modality data. Therefore, it is significantly motivated to deal with multi-modality domain adaptation task. As labels are unavailable in target domain, how to learn semantic multimodality representations and how to successfully adapt the classifier from source to target domain, remain open challenges in multi-modality domain adaptation task. To deal with these issues, we propose a Multi-Modality Adversarial Network (MMAN), which applies stacked attention to learn semantic multi-modality representations and reduces domain discrepancy via adversarial training. Unlike the previous domain adaptation methods which cannot make full use of source domain categories information, multi-channel constraint is employed to capture fine-grained categories knowledge that could enhance the discrimination of target samples and boost target performance on single-modality and multi-modality domain adaptation problem. We apply the proposed MMAN to two applications including cross domain object recognition and cross domain social event recognition. The extensive experimental evaluations demonstrate the effectiveness of the proposed model for unsupervised domain adaptation.\",\n \"Title: FloWatcher-DPDK: lightweight line-rate flow-level monitoring in software\\nYear: 2019\\n\\nAbstract: In the last few years, several software-based solutions have been proved to be very efficient for high-speed packet processing, traffic generation, and monitoring, and can be considered valid alternatives to expensive and non-flexible hardware-based solutions. In this paper, we first benchmark heterogeneous design choices for software-based packet monitoring systems in terms of achievable performance and required resources (i.e., the number of CPU cores). Building on this extensive analysis we design FloWatcher-DPDK, a DPDK-based high-speed software traffic monitor we provide to the community as an open source project. In a nutshell, FloWatcher-DPDK provides tunable fine-grained statistics at packet and flow levels. Experimental results demonstrate that FloWatcher-DPDK sustains per-flow statistics with 5-nines precision at high-speed (e.g., 14.88 Mpps) using a limited amount of resources. Finally, we showcase the usage of FloWatcher-DPDK by configuring it to analyze the performance of two open source prototypes for stateful flow-level end-host and in-network packet processing.\",\n \"Title: Geometry Uncertainty Projection Network for Monocular 3D Object Detection.\\nYear: 2021\\n\\nAbstract: Geometry Projection is a powerful depth estimation method in monocular 3D object detection. It estimates depth dependent on heights, which introduces mathematical priors into the deep model. But projection process also introduces the error amplification problem, in which the error of the estimated height will be amplified and reflected greatly at the output depth. This property leads to uncontrollable depth inferences and also damages the training efficiency. In this paper, we propose a Geometry Uncertainty Projection Network (GUP Net) to tackle the error amplification problem at both inference and training stages. Specifically, a GUP module is proposed to obtains the geometry-guided uncertainty of the inferred depth, which not only provides high reliable confidence for each depth but also benefits depth learning. Furthermore, at the training stage, we propose a Hierarchical Task Learning strategy to reduce the instability caused by error amplification. This learning algorithm monitors the learning situation of each task by a proposed indicator and adaptively assigns the proper loss weights for different tasks according to their pre-tasks situation. Based on that, each task starts learning only when its pre-tasks are learned well, which can significantly improve the stability and efficiency of the training process. Extensive experiments demonstrate the effectiveness of the proposed method. The overall model can infer more reliable object depth than existing methods and outperforms the state-of-the-art image-based monocular 3D detectors by 3.74% and 4.7% AP40 of the car and pedestrian categories on the KITTI benchmark.\",\n \"Title: Object Tracking via Spatial-Temporal Memory Network\\nYear: 2022\\n\\nAbstract: Temporal and spatial contexts, characterizing target appearance variations and target-background differences, respectively, are crucial for improving the online adaptive ability and instance-level discriminative ability of object tracking. However, most existing trackers focus on either the temporal context or the spatial context during tracking and have not exploited these contexts simultaneously...\",\n \"Title: Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer\\nYear: 2021\\n\\nAbstract: Occluded person re-identification (Re-ID) is a challenging task as persons are frequently occluded by various obstacles or other persons, especially in the crowd scenario. To address these issues, we propose a novel end-to-end Part-Aware Transformer (PAT) for occluded person Re-ID through diverse part discovery via a transformer encoder-decoder architecture, including a pixel context based transformer encoder and a part prototype based transformer decoder. The proposed PAT model enjoys several merits. First, to the best of our knowledge, this is the first work to exploit the transformer encoder-decoder architecture for occluded person Re-ID in a unified deep model. Second, to learn part prototypes well with only identity labels, we design two effective mechanisms including part diversity and part discriminability. Consequently, we can achieve diverse part discovery for occluded person Re-ID in a weakly supervised manner. Extensive experimental results on six challenging benchmarks for three tasks (occluded, partial and holistic Re-ID) demonstrate that our proposed PAT performs favorably against stat-of-the-art methods.\",\n \"Title: Lesion-Aware Transformers for Diabetic Retinopathy Grading\\nYear: 2021\\n\\nAbstract: Diabetic retinopathy (DR) is the leading cause of permanent blindness in the working-age population. And automatic DR diagnosis can assist ophthalmologists to design tailored treatments for patients, including DR grading and lesion discovery. However, most of existing methods treat DR grading and lesion discovery as two independent tasks, which require lesion annotations as a learning guidance and limits the actual deployment. To alleviate this problem, we propose a novel lesion-aware transformer (LAT) for DR grading and lesion discovery jointly in a unified deep model via an encoder-decoder structure including a pixel relation based encoder and a lesion filter based decoder. The proposed LAT enjoys several merits. First, to the best of our knowledge, this is the first work to formulate lesion discovery as a weakly supervised lesion localization problem via a transformer decoder. Second, to learn lesion filters well with only image-level labels, we design two effective mechanisms including lesion region importance and lesion region diversity for identifying diverse lesion regions. Extensive experimental results on three challenging benchmarks including Messidor-1, Messidor-2 and EyePACS demonstrate that the proposed LAT performs favorably against state-of-the-art DR grading and lesion discovery methods.\",\n \"Title: Uncertainty Guided Collaborative Training for Weakly Supervised Temporal Action Detection\\nYear: 2021\\n\\nAbstract: Weakly supervised temporal action detection aims to localize temporal boundaries of actions and identify their categories simultaneously with only video-level category labels during training. Among existing methods, attention based methods have achieved superior performance by separating action and non-action segments. However, without the segment-level ground-truth supervision, the quality of the...\",\n \"Title: Meta-Attack - Class-agnostic and Model-agnostic Physical Adversarial Attack.\\nYear: 2021\\n\\nAbstract: Modern deep neural networks are often vulnerable to adversarial examples. Most exist attack methods focus on crafting adversarial examples in the digital domain, while only limited works study physical adversarial attack. However, it is more challenging to generate effective adversarial examples in the physical world due to many uncontrollable physical dynamics. Most current physical attack methods aim to generate robust physical adversarial examples by simulating all possible physical dynamics. When attacking new images or new DNN models, they require expensive manually efforts for simulating physical dynamics and considerable time for iteratively optimizing for each image. To tackle these issues, we propose a class-agnostic and model-agnostic physical adversarial attack model (Meta-Attack), which is able to not only generate robust physical adversarial examples by simulating color and shape distortions, but also generalize to attacking novel images and novel DNN models by accessing a few digital and physical images. To the best of our knowledge, this is the first work to formulate the physical attack as a few-shot learning problem. Here, the training task is redefined as the composition of a support set, a query set, and a target DNN model. Under the few- shot setting, we design a novel class-agnostic and model-agnostic meta-learning algorithm to enhance the generalization ability of our method. Extensive experimental results on two benchmark datasets with four challenging experimental settings verify the superior robustness and generalization of our method by comparing to state-of-the-art physical attack methods.\",\n \"Title: Task-aware Part Mining Network for Few-Shot Learning.\\nYear: 2021\\n\\nAbstract: Few-Shot Learning (FSL) aims at classifying samples into new unseen classes with only a handful of labeled samples available. However, most of the existing methods are based on the image-level pooled representation, yet ignore considerable local clues that are transferable across tasks. To address this issue, we propose an end-to-end Task-aware Part Mining Network (TPMN) by integrating an automatic part mining process into the metric-based model for FSL. The proposed TPMN model enjoys several merits. First, we design a meta filter learner to generate task-aware part filters based on the task embedding in a meta-learning way. The task-aware part filters can adapt to any individual task and automatically mine task-related local parts even for an unseen task. Second, an adaptive importance generator is proposed to identify key local parts and assign adaptive importance weights to different parts. To the best of our knowledge, this is the first work to automatically exploit the task-aware local parts in a meta-learning way for FSL. Extensive experimental results on four standard benchmarks demonstrate that the proposed model performs favorably against state-of-the-art FSL methods.\",\n \"Title: Foreground Activation Maps for Weakly Supervised Object Localization.\\nYear: 2021\\n\\nAbstract: Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels, which has better scalability and practicability than fully supervised methods in the actual deployment. However, with only image-level labels, learning object classification models tends to activate object parts and ignore the whole object, while expanding object parts into the whole object may deteriorate classification performance. To alleviate this problem, we propose foreground activation maps (FAM), whose aim is to optimize object localization and classification jointly via an object-aware attention module and a part-aware attention module in a unified model, where the two tasks can complement and enhance each other. To the best of our knowledge, this is the first work that can achieve remarkable performance for both tasks by optimizing them jointly via FAM for WSOL. Besides, the designed two modules can effectively highlight foreground objects for localization and discover discriminative parts for classification. Extensive experiments with four backbones on two standard benchmarks demonstrate that our FAM performs favorably against state-of-the-art WSOL methods.\",\n \"Title: Inference of virtual network functions’ state via analysis of the CPU behavior\\nYear: 2021\\n\\nAbstract: The on-going process of softwarization of IT networks promises to reduce the operational and management costs of network infrastructures by replacing hardware middleboxes with equivalent pieces of code executed on general-purpose servers. Alongside the benefits from the operator’s perspective, new strategies to provide the network’s resources to users are arising. Following the principle of "every...\",\n \"Title: Diverse Complementary Part Mining for Weakly Supervised Object Localization\\nYear: 2022\\n\\nAbstract: Weakly Supervised Object Localization (WSOL) aims to localize objects with only image-level labels, which has better scalability and practicability than fully supervised methods in the actual deployment. However, a common limitation for available techniques based on classification networks is that they only highlight the most discriminative part of the object, not the entire object. To alleviate this problem, we propose a novel end-to-end part discovery model (PDM) to learn multiple discriminative object parts in a unified network for accurate object localization and classification. The proposed PDM enjoys several merits. First, to the best of our knowledge, it is the first work to directly model diverse and robust object parts by exploiting part diversity, compactness, and importance jointly for WSOL. Second, three effective mechanisms including diversity, compactness, and importance learning mechanisms are designed to learn robust object parts. Therefore, our model can exploit complementary spatial information and local details from the learned object parts, which help to produce precise bounding boxes and discriminate different object categories. Extensive experiments on two standard benchmarks demonstrate that our PDM performs favorably against state-of-the-art WSOL approaches.\",\n \"Title: Interpreting AI for Networking: Where We Are and Where We Are Going\\nYear: 2022\\n\\nAbstract: In recent years, artificial intelligence (AI) techniques have been increasingly adopted to tackle networking problems. Although AI algorithms can deliver high-quality solutions, most of them are inherently intricate and erratic for human cognition. This lack of interpretability tremendously hinders the commercial success of AI-based solutions in practice. To cope with this challenge, networking re...\",\n \"Title: A Keypoint-based Global Association Network for Lane Detection\\nYear: 2022\\n\\nAbstract: Lane detection is a challenging task that requires predicting complex topology shapes of lane lines and distinguishing different types of lanes simultaneously. Earlier works follow a top-down roadmap to regress predefined anchors into various shapes of lane lines, which lacks enough flexibility to fit complex shapes of lanes due to the fixed anchor shapes. Lately, some works propose to formulate lane detection as a keypoint estimation problem to describe the shapes of lane lines more flexibly and gradually group adjacent keypoints belonging to the same lane line in a point-by-point manner, which is inefficient and time-consuming during postprocessing. In this paper, we propose a Global Association Network (GANet) to formulate the lane detection problem from a new perspective, where each keypoint is directly regressed to the starting point of the lane line instead of point-by-point extension. Concretely, the association of keypoints to their belonged lane line is conducted by predicting their offsets to the corresponding starting points of lanes globally without dependence on each other, which could be done in parallel to greatly improve efficiency. In addition, we further propose a Lane-aware Feature Aggregator (LFA), which adaptively captures the local correlations between adjacent keypoints to supplement local information to the global association. Extensive experiments on two popular lane detection benchmarks show that our method outperforms previous methods with F1 score of 79.63% on CULane and 97.71% on Tusimple dataset with high FPS.\",\n \"Title: Correlation filters based on spatial-temporal Gaussion scale mixture modelling for visual tracking\\nYear: 2022\\n\\nAbstract: Correlation Filters (CFs) have shown outstanding performance in tracking, but are subject to unwanted boundary effects. Spatial regularization (SR) is widely used as an efficient method to alleviate the boundary effects. However, spatial regularization is almost handcrafted and fixed during tracking process, which cannot handle the diversity of objects and the complexity of motion. Furthermore, the rich spatio-temporal correlations among multiple targets of interest cannot be fully exploited. Herein, we propose a spatio-temporal Gaussian scale mixture model (ST-GSM) for correlation-filter-based visual tracking. In our Gaussian scale mixture (GSM) model, each correlation filter coefficient is decomposed into the product of a positive scalar multiplier with sparsity and a Gaussian random variable. The reliable components of the Gaussian random variable can be adaptively selected based on the positive multipliers, aiming at alleviating the notorious boundary effects. To exploit the temporal consistency between adjacent frames, nonzero-means GSM models are developed to characterize the temporal correlations. Specifically, the filter coefficient obtained in the previous frame is used as the mean prior for the current frame. The spatial correlations among filter coefficients have been considered in the structured GSM model, thereby further improving the tracking performance. Experimental results show that the proposed model can significantly improve the performance of CF-based trackers.\",\n \"Title: Bayesian Correlation Filter Learning With Gaussian Scale Mixture Model for Visual Tracking\\nYear: 2022\\n\\nAbstract: Correlation filters (CF), a popular tool for visual tracking, suffer from unwanted boundary effects due to the periodic assumption needed for FFT implementation. To address this issue, spatially regularized discriminative correlation filters (SRDCF) have been proposed by introducing a weighting matrix to the regularization term. However, the existing design of spatial weighting matrix is often heu...\",\n \"Title: Motion-modulated Temporal Fragment Alignment Network For Few-Shot Action Recognition\\nYear: 2022\\n\\nAbstract: While the majority of FSL models focus on image classification, the extension to action recognition is rather challenging due to the additional temporal dimension in videos. To address this issue, we propose an end-to-end Motion-modulated Temporal Fragment Alignment Network (MT-FAN) by jointly exploring the task-specific motion modulation and the multi-level temporal fragment alignment for Few-Shot Action Recognition (FSAR). The proposed MT-FAN model enjoys several merits. First, we design a motion modulator conditioned on the learned task-specific motion embeddings, which can activate the channels related to the task-shared motion patterns for each frame. Second, a segment attention mechanism is proposed to automatically discover the higher-level segments for multi-level temporal fragment alignment, which encompasses the frame-to-frame, segment-to-segment, and segment-to-frame alignments. To the best of our knowledge, this is the first work to exploit task-specific motion modulation for FSAR. Extensive experimental results on four standard benchmarks demonstrate that the proposed model performs favorably against the state-of-the-art FSAR methods.\",\n \"Title: Adversarial Transformers for Weakly Supervised Object Localization\\nYear: 2022\\n\\nAbstract: Weakly supervised object localization (WSOL) aims at localizing objects with only image-level labels, which has better scalability and practicability than fully supervised methods. However, without pixel-level supervision, existing methods tend to generate rough localization maps, which hinders localization performance. To alleviate this problem, we propose an adversarial transformer network (ATNet), which aims to obtain a well-learned localization model with pixel-level pseudo labels. The proposed ATNet enjoys several merits. First, we design an object transformer ( $G$ ) that can generate localization maps and pseudo labels effectively and dynamically, and a part transformer ( $D$ ) to accurately discriminate detailed local differences between localization maps and pseudo labels. Second, we propose to train $G$ and $D$ via an adversarial process, where $G$ can generate more accurate localization maps approaching pseudo labels to fool $D$ . To the best of our knowledge, this is the first work to explore transformers with adversarial training to obtain a well-learned localization model for WSOL. Extensive experiments with four backbones on two standard benchmarks demonstrate that our ATNet achieves favorable performance against state-of-the-art WSOL methods. Besides, our adversarial training can provide higher robustness against adversarial attacks.\",\n \"Title: Visible-Infrared Person Re-Identification With Modality-Specific Memory Network\\nYear: 2022\\n\\nAbstract: Visible-infrared person re-identification (VI-ReID) is challenging due to the large modality discrepancy between visible and infrared images. Existing methods mainly focus on learning modality-shared representations by embedding images from different modalities into a common feature space, in which some discriminative modality information is discarded. Different from these methods, in this paper, we propose a novel Modality-Specific Memory Network (MSMNet) to complete the missing modality information and aggregate visible and infrared modality features into a unified feature space for the VI-ReID task. The proposed model enjoys several merits. First, it can exploit the missing modality information to alleviate the modality discrepancy when only the single-modality input is provided. To the best of our knowledge, this is the first work to exploit the missing modality information completion and alleviate the modality discrepancy with the memory network. Second, to guide the learning process of the memory network, we design three effective learning strategies, including feature consistency, memory representativeness and structural alignment. By incorporating these learning strategies in a unified model, the memory network can be well learned to propagate identity-related information between modalities and boost the VI-ReID performance. Extensive experimental results on two standard benchmarks (SYSU-MM01 and RegDB) demonstrate that the proposed MSMNet performs favorably against state-of-the-art methods.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":99,"string":"99"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13259,"string":"13,259"}}},{"rowIdx":13260,"cells":{"question_id":{"kind":"string","value":"Francesca Arcelli Fontana"},"question":{"kind":"string","value":"Generate an abstract for the title \"Using Design Pattern Clues To Improve The Precision Of Design Pattern Detection Tools\" using the following items: 1. Design pattern detection \n2. Reverse engineering \n3. Program comprehension \n4. Design recovery \n5. Micro structures detection"},"answer":{"kind":"string","value":"Design pattern detection, or rather the detection of structures that match design patterns, is useful for reverse engineering, program comprehension and for design recovery as well as for re-documenting object-oriented systems. Finding design patterns inside the code gives hints to software engineers about the methodologies adopted and the problems found during its design phases, and helps the engineers to evolve and maintain the system. In this paper, we present the results provided by four different design pattern detection tools on the analysis of JHotDraw 6.0b1, a well-known Java GUI framework. We show that the tools generally provide different results, even while evaluating the same system. From this observation, we introduce an approach based on micro structures detection that aims to discard the false positives from the detected results, hence improving the precision of the analyzed tools results. For this purpose we exploit a set of micro structures called design pattern clues, which give useful hints for the detection of design patterns."},"correct_answer":{"kind":"string","value":"Design pattern detection, or rather the detection of structures that match design patterns, is useful for reverse engineering, program comprehension and for design recovery as well as for re-documenting object-oriented systems. Finding design patterns inside the code gives hints to software engineers about the methodologies adopted and the problems found during its design phases, and helps the engineers to evolve and maintain the system. In this paper, we present the results provided by four different design pattern detection tools on the analysis of JHotDraw 6.0b1, a well-known Java GUI framework. We show that the tools generally provide different results, even while evaluating the same system. From this observation, we introduce an approach based on micro structures detection that aims to discard the false positives from the detected results, hence improving the precision of the analyzed tools results. For this purpose we exploit a set of micro structures called design pattern clues, which give useful hints for the detection of design patterns."},"documents":{"kind":"list like","value":["Title: Exploiting Analogy for Design Pattern Recognition and Classification\nYear: 2001\n\nAbstract: Analogy has been used in several domains and at different levels; if we consider the field of software engineering, analogy has been exploited for example for design reuse [12] and for specification and code reuse [13], where analogy is used to determine the degree of closeness, as a weak form of matching to then derive some similarities-based metrics.Here, we consider analogy based on similarity, where similarity is can be a fuzzy relation satisfying some given properties. We investigate in the paper the field of design patterns recognition and classification from several points of views, with the aim to give the principal hints in developing a system of support to integrate design patterns during the design phase, and to enhance in this way this phase.","Title: Information-Driven Collective Intelligences\nYear: 2010\n\nAbstract: Information-driven collective intelligences derive from the connection and the interaction of multiple, distributed, independent\n agents that produce and process information, and eventually turn it into knowledge, meant in the broad sense of capability\n of conceptual representation. For instance, the World-wide Web can be viewed as an information-driven collective intelligence\n emerging from the digital network known as the Internet. The point here is how to extend such a capability for knowledge generation\n from the participating agents to the collective intelligence itself.\n \n \n In this paper we show how this can be obtained with graph-based algorithms for the detection of communities of agents so as\n to support a dynamic, self-organized form of concept-discovery and concept-incarnation. In particular, we show how to strengthen\n community ties around concepts in order to increase their level of socialization and, consequently, of “fertility” in the\n generation of new concepts. Since there exists a direct relationship between concept discovery and innovation in human intelligences,\n we point out how analogous innovation capabilities can now be supported within information-driven collective intelligences,\n with direct applications to product innovation.","Title: Elemental Design Patterns Recognition In Java\nYear: 2005\n\nAbstract: With the advent of UML and MDA, models play an increasingly important role in software development. Hence, the management of the quality of models is of key importance for completing projects succesfully. However, existing approaches towards software ...","Title: DPB: A Benchmark for Design Pattern Detection Tools\nYear: 2012\n\nAbstract: Many activities can be done to support software evolution and reverse engineering of a system. Design pattern detection is one of these activities. It is useful to gain knowledge on the design issues of an existing system, on its architecture and design quality, improving the comprehension of the system and hence its maintainability and evolution. Several tools for design pattern detection have been developed in the literature, but they usually provide different results when analyzing the same systems. Some works have been proposed in the literature to compare these results, but a standard and widely-accepted benchmark is not yet available. In this work we propose our benchmark web application for design pattern detection, based on a community driven evaluation.","Title: User adaptive models based on similarity\nYear: 2000\n\nAbstract: In this work, we try to give a more precise meaning to the notion of \"context\" and to the knowledge associated with it, in order to build models representing a context able to adapt themselves to the user needs. We propose similarities as a tool to represent contexts. To do so, we consider a parametric fanfily of sunilarities and supply some methods for trying to \"guess\" the value of the parameter that best fits the context where the similarity shall be applied.","Title: Code Smell Detection: Towards a Machine Learning-Based Approach\nYear: 2013\n\nAbstract: Several code smells detection tools have been developed providing different results, because smells can be subjectively interpreted and hence detected in different ways. Usually the detection techniques are based on the computation of different kinds of metrics, and other aspects related to the domain of the system under analysis, its size and other design features are not taken into account. In this paper we propose an approach we are studying based on machine learning techniques. We outline some common problems faced for smells detection and we describe the different steps of our approach and the algorithms we use for the classification.","Title: Using Web Services for a Workflow Engine\nYear: 2004\n\nAbstract: The aim of this paper is to describe an architecture developed to integrate solutions for the management and organization of a generic market. This represents an interesting case study for Web Service deployment. We call this architecture Workflow Engine. The actors involved in the market are characterized by a specific role. We describe a document centric approach, in which the evolution of each single business process can be reconstructed through the documents exchanged through messages. We will observe how the message-oriented approach is fundamental to completely exploit the Web Services available technology.","Title: A Distributed Document Management Approach for Workflow Support in the DBSA Architecture\nYear: 2003\n\nAbstract: Large and distributed organizations have to face a com- plex management problem: keeping track of state informa- tion of every \"entity\" (person, property, project, etc.). A tra- ditional centralized database is not well suited if the com- munication between parts of the organization is not reli- able or not \"always-on'. In this paper we present an archi- tecture, largely based on the distribution of formally struc- tured documents, designed to support the tracking of \"state changes\" inside the organization.","Title: Code Smells, Micro Patterns And Their Relations\nYear: 2012\n\nAbstract: Code smells are an example of a pattern-oriented trend applied to software evolution. Due to their abstract and informal nature, the detection process requires support from lower-level indicators like individual metric values, history of code changes, knowledge about program structure or results of program execution. We examine how the presence of program structures, called micro patterns, is correlated with the code smells and how micro patterns can be exploited for the purpose of smell detection.","Title: Equalizing the Structures of Web Communities in Ontology Development Tools\nYear: 2009\n\nAbstract: In this paper we face some relevant issues on the relations between web communities and ontologies. We build an operator that constructs a weak Web Community, according to the definition given in [16],starting from a seed of web sites. The necessity of such an operator is derived from a problem arisen in the model developed in [3], in which some relevant concepts in automotive oriented ontology were not given a corresponding Web community. This fact –if not considered- can bring automatic ontology development ([9,18]) to some non-correct results. In this work we define and analyze a new operator, called Com, with the tools furnished by the method of parametrization ([8,15]) and we find that, given a seed S and the induced graph I(S), the community generated by our operator is monotonic with respect to clustering and is denser than the original graph I(S).","Title: Using fuzzy grammars for the evaluation of SGML -based documents\nYear: 1997\n\nAbstract: 1 ABSTRACT In this paper we apply the Fuzzy Grammars or L-Grammars to the evaluation of documents based on the standard nonproprietary format SGML. Given a document type definition (DTD), we extend its associated language by adding a judgement label By selecting a particular label and by taking the start symbol of the grammar associated to the DTD, we can generate any DTD-compliant document with a fuzzy degree of membership derived from the judgement label. In this way we can fit the computational model underlying the L-languages to the process of collecting different evaluations of the same document.","Title: JADEPT: Dynamic Analysis for Behavioral Design Pattern Detection\nYear: 2009\n\nAbstract: In the context of reverse engineering, the recognition of design patterns provides additional information related to the rationale behind the design. This paper presents our approach to the recognition of design patterns based on dynamic analysis of Java software. The idea behind our approach is to identify a set of rules capturing information necessary to identify a design pattern instance. Rules are characterized by weights indicating their importance in the detection of a specific design pattern. The core behavior of each design pattern may be described through a subset of these rules forming a macrorule. Macro-rules define the main traits of a pattern. JADEPT (JAva DEsign Pattern deTector) is our software for design pattern identification based on this idea. It captures static and dynamic aspects through a dynamic analysis of the software by exploiting the JPDA (Java Platform Debugger Architecture). The extracted information is stored in a database. Queries to the database implement the rules defined to recognize design patterns. The tool has been validated with positive results on different academic implementations of design patterns and on systems as JADEPT itself.","Title: A tool for design pattern detection and software architecture reconstruction\nYear: 2011\n\nAbstract: It is well known that software maintenance and evolution are expensive activities, both in terms of invested time and money. Reverse engineering activities support the obtainment of abstractions and views from a target system that should help the engineers to maintain, evolve and eventually re-engineer it. Two important tasks pursued by reverse engineering are design pattern detection and software architecture reconstruction, whose main objectives are the identification of the design patterns that have been used in the implementation of a system as well as the generation of views placed at different levels of abstractions, which let the practitioners focus on the overall architecture of the system without worrying about the programming details it has been implemented with. In this context we propose an Eclipse plug-in called MARPLE (Metrics and Architecture Reconstruction Plug-in for Eclipse), which supports both the detection of design patterns and software architecture reconstruction activities through the use of basic elements and metrics that are mechanically extracted from the source code. The development of this platform is mainly based on the exploitation of the Eclipse framework and plug-ins as well as of different Java libraries for data access and graph management and visualization. In this paper we focus our attention on the design pattern detection process.","Title: An Agent Based Internet Infrastructure for Learning Commerce\nYear: 2000\n\nAbstract: Among the incredible amount of applications brought on the wave by the diffusion of the Internet, the possibility of an Electronic Commercialization of Learning assumes a relevant role. Thousands of people potentially acting as Learning Material Sellers, Buyers and Brokers are present on the Net asking for and efficient and effective infrastructure allowing them to play their role in this new market. Motivations are present and mature to investigate how to build such an infrastructure.In this paper we exploit agents and their mobility feature to develop a system for the electronic commerce of learning materials available on the Web. We tackle the aspects related to the negotiation, sometimes needed, to conclude a transaction and the aspects related to the best ways to save and classify the materials, through the learning object metadata approach and the related standardization efforts. A prototype architecture and its implementation is presented and discussed and directions for future studies are set.","Title: Ontologies and Communities Co-evolution in Information Systems .\nYear: 2010\n\nAbstract: Communities and ontologies are both concepts that have acquired strong momentum since the coming of age of new media such as Internet and the Web. They have become more relevant in a situation where growing communities and creating information categorizable through ontologies is made much easier and faster compared to what was possible before. In spite of this concomitance, the roles they have played in this information-rich environment have been so far not only different but also largely antithetic. The one played by communities is dynamic, and views information as something which is constantly changed and re-created by the agents that produce it. By contrast, the one played by ontologies views information in terms of its management at the meta-level through categories and concepts hierarchies, and it assumes that the ontology remains static, or changes very slowly as a consequence of decisions taken by the domain experts that control it. Given that information change is generally community-driven and this brings the clear necessity to make communities and ontologies interact. We propose to pursue this goal through a knowledge management approach, where the interaction between communities and ontologies is implemented as a knowledge life-cycle that leads to the creation of new concepts in the ontology as a consequence of the evolution of the information spaces constantly extended and re-created by the communities.","Title: Multimedia Distributed Learning Environments: Evolution towards Intelligent Communications\nYear: 2002\n\nAbstract: In this paper we describe the realization of a Multimedia Distributed Learning System developed by means of the Java language in a Intranet environment. In particular, we focus our discussion on the definition of the existing scenario, on the requirements specification and on the description of our overall system, by providing also first experimental results.We decided to add to our system a Software Agents based Intelligent Communication Support to manage both high level and low level communication needs coming from the nature of the application. Such communication needs could reasonably be considered as shared by a large family of distributed applications based on Intranet technologies. We present the main characteristics of our agents and describe how to use them to better achieve defined goals in terms of flexibility and scalability and to add network-awareness capabilities to the application.","Title: Software aging assessment through a specialization of the SQuaRE quality model\nYear: 2009\n\nAbstract: In the last years the software application portfolio has become a key asset for almost all companies. During their lives, applications undergo lots of changes to add new functionalities or to refactor older ones; these changes tend to reduce the quality of the applications themselves, causing the phenomenon known as software aging. Monitoring of software aging is very important for companies, but up to now there are no standard approaches to perform this task. In addition many of the suggested models assess software aging basing on few software features, whereas this phenomenon affects all of the software aspects. In 2005 ISO/IEC released the SQuaRE quality model which covers several elements of software quality assessment, but some issues make SQuaRE usage quite difficult. The purpose of this paper is to suggest an approach to software aging monitoring that considers the software product in its wholeness and to provide a specialization of the SQuaRE quality model which allows to perform this task.","Title: A System For The Discovery And Selection Of Floss Projects\nYear: 2014\n\nAbstract: Developing software systems by reusing components is a common practice. FLOSS (Free, Libre and Open Source Software) components represent a significant part of the reusable components available. The selection of suitable FLOSS components raises important issues both for software companies and research institutions. RepoFinder supports a keywordbased discovery process for FLOSS components, and applies well-known software metrics and analyses to compare the structural aspects of different components.","Title: Impact of refactoring on quality code evaluation.\nYear: 2011\n\nAbstract: Code smells are characteristics of the software that may indicate a code or design problem that can make software hard to understand, to evolve and maintain. Detecting code smells in the code and consequently applying the right refactoring steps, when necessary, is very important for improving the quality of the code. In this paper, according to well known metrics proposed to evaluate the code and design quality of a system, we analyze the impact of refactoring, applied to remove code smells, on the quality evaluation of the system.","Title: A Similarity-Based Resolution Rule\nYear: 2002\n\nAbstract: We propose an extension of the resolution rule as the core of a logic programming language based on similarity. Starting from a fuzzy unification algorithm described in Ref. 2 and then extended in Ref. 10, we introduce a fuzzy resolution rule, based on an extended most general unifier supplied by the extended unification algorithm. In our approach, unification fades into a unification degree because of a similarity introduced in a first-order language. Intuitively, the unification degree of a set of first-order terms is the cost one has to pay to consider these terms as equal. For this reason, our extension of the resolution is more structured than its classic counterpart; that is, when the empty clause is reached, in addition to a computed answer, a set of conditions is also determined. We give both the operational and fixed-point semantics of our extended logic programming language, and we prove their equivalence. (C) 2002 Wiley Periodicals, Inc.","Title: A software architecture for distributed organization management\nYear: 2006\n\nAbstract: In large organizations, management of large amounts of knowledge is a common problem. This knowledge is usually available in a distributed environment, in structured or non-structured form, and often is not exactly known where it is located and how to retrieve it in flexible ways. This paper describes an architecture to manage typical activities for an organization such as our University. During system analysis and specification, we had to collect a lot of information about structure and content of our organization, information available in various formats and media and not always automatically collectable. Hence, we designed a Document-based Software Architecture to support systems where formalization of information repositories, standardization of information location and management of every aspect involved in distributed contexts are crucial needs. We discuss if the configuration of a central-knowledge centered organization is the right solution or if it is better the choice of a distributed one. Since, all the documents managed in any organization are usually available in a structured way, we foresee a strong usage of XML documents and metadata standard definitions.","Title: Computational Models for Information Reuse\nYear: 1999\n\nAbstract: Techniques that optimize computations by reusing partial results have a long tradition in computer science. Seen from the point of view of sequential computations, all these techniques share the common execution strategy of storing partial results in a centralized data structure, while they differ as to how the results are computed, e.g. in a data-driven or constraint-driven fashion. Concurrent sy...","Title: On Investigating Code Smells Correlations\nYear: 2011\n\nAbstract: Code smells are characteristics of the software that may indicate a code or design problem that can make software hard to evolve and maintain. Detecting and removing code smells, when necessary, improves the quality and maintainability of a system. Code smells have been defined in [5], and different detection tools have been developed, each one characterized by particular features and providing often different results. Usually detection techniques are based on the computation of a particular set of combined metrics, or standard object-oriented metrics [8] or metrics defined ad hoc for the smell detection. As outlined in [3] there is the need for a clearer research strategy on smells identification and measurement. Other knowledge has to be exploited for their detection. In this work we are interested to investigate the direct and indirect correlations existing between smells. Moreover we propose to analyze if other relations exist between code smell and another kind of micro structure, called micro pattern[6]. We started this research since we think that the knowledge on these relations between smells could be exploited by code smell detection tools to improve their results. If one code smell exists, this can imply the existence of another code smell, or if one smell exists, another one cannot be there, or perhaps we could observe that some code smells tend to go together.","Title: Design patterns: a survey on their micro-structures.\nYear: 2013\n\nAbstract: Design patterns play a significant role in reverse engineering by providing information not only on how but also on why a solution has been implemented in a specific way because of their semantics. The application of design patterns leads to their personalization to a specific context, hence to the generation of variants. This makes their recognition a challenging task, which may be addressed through the understanding and detection of the micro-structures design patterns are made of. This is very useful for the detection as well as for the application of design patterns. The principal aim of this paper is to present a survey on these micro-structures and a comparison among them in the perspective of reverse engineering. Because of their less complex structure and behavior, as well as closer link to the source code, the recognition of these micro-structures may be automated, which can be considered a step towards the automatic recognition of the more complex design patterns. In this paper, we consider four of the most significant types of micro-structures: elemental design patterns, clues, sub-patterns, and micro patterns. To analyze the role of the micro-structures in the design pattern detection process, we make a comparison among these four types of micro-structures and among the micro-structures of various types in order to identify the relations among them. Copyright (C) 2011 John Wiley & Sons, Ltd.","Title: Modeling QoS through Architectural Reflection\nYear: 2005\n\nAbstract: In the context of the MAIS' project, we designed a reflective architecture to be used for Multichannel Adaptive Information Systems. Architectural reflection is an emerging research topic that enables information systems to observe and control their own structure and behaviour. One of the main issues raised by architectural reflection is related to the understanding of the reflective knowledge and to its proper design, implementation, and usage. In our approach the reflective knowledge is quality of service (QoS) oriented in that it captures the QoS features exhibited by the system components. lit this paper, we describe the different layers of our architecture focusing on how to model and to exploit QoS to achieve dynamic adaptability in a reflective architecture.","Title: Metrics and Antipatterns for Software Quality Evaluation\nYear: 2011\n\nAbstract: In the context of software evolution, many activities are involved and are very useful, like being able to evaluate the design quality of an evolving system, both to locate the parts that need particular refactoring or reengineering efforts, and to evaluate parts that are well designed. This paper aims to give support hints for the evaluation of the code and design quality of a system and in particular we suggest to use metrics computation and antipatterns detection together. We propose metrics computation based on particular kinds of micro-structures and the detection of structural and object-oriented antipatterns with the aim of identifying areas of design improvements. We can evaluate the quality of a system according to different issues, for example by understanding its global complexity, analyzing the cohesion and coupling of system modules and locating the most critical and complex components that need particular refactoring or maintenance.","Title: Automatic Detection Of Bad Smells In Code: An Experimental Assessment\nYear: 2012\n\nAbstract: Code smells are structural characteristics of software that may indicate a code or design problem that makes software hard to evolve and maintain, and may trigger refactoring of code. Recent research is active in defining automatic detection tools to help humans in finding smells when code size becomes unmanageable for manual review. Since the definitions of code smells are informal and subjective, assessing how effective code smell detection tools are is both important and hard to achieve. This paper reviews the current panorama of the tools for automatic code smell detection. It defines research questions about the consistency of their responses, their ability to expose the regions of code most affected by structural decay, and the relevance of their responses with respect to future software evolution. It gives answers to them by analyzing the output of four representative code smell detectors applied to six different versions of GanttProject, an open source system written in Java. The results of these experiments cast light on what current code smell detection tools are able to do and what the relevant areas for further improvement are.","Title: Design Pattern Detection for Reverse Engineering\nYear: 2006\n\nAbstract: The main goal of the workshop is to address the issues related to design patterns identification for design recovery focusing on the role of the reverse engineering in identifying the sub-elements of the design patterns that can improve their detection.","Title: An Eclipse Plug-in for the Java PathFinder Runtime Verification System\nYear: 2006\n\nAbstract: Java PathFinder (JPF) is an explicit state model checker developed by the Automated Software Engineering Group of NASA of the AMES Research Center (California). Eclipse is probably the most important and used Java Integrated Developing Environment (IDE) and not only; it is a framework/environment that can be easily extended with new functionalities by exploiting its plug-in mechanism. Through a JPF plug-in for Eclipse it is possible to integrate powerful model checking and testing capabilities into the development environment. This paper presents the re-design of the standalone version of JPF towards an Eclipse plug-in exploiting and outlining in this way the advantages of an open source development.","Title: Designing an Architecture for Multichannel Adaptive Information Systems\nYear: 2004\n\nAbstract: Multichannel Adaptive Information Systems aim at enabling users/clients to access software applications through various types of devices (i.e., PCs, laptops, palmtops, PDAs, mobile phones, etc) and networks (i.e., local, wireless, bluetooth, etc) by adapting their functionalities/performances to the specific features of each device/network. Further, such systems should enable a smooth switch among different channels (devices and networks) according to the current requirements of, the users. Multichannel Adaptive Information Systems raise interesting research challenges that are related to the development of architectures, methodologies, environments, tools for such systems, and concern issues as adaptivity, flexibility, transactionality, security, etc. This paper introduces the research efforts towards the design of MAIS software architecture for this kind of systems, as specified within the Italian MURST-FIRB Project MAIS(1) \"Multichannel Adaptive Information System\".","Title: A Specialisation Of The Square Quality Model For The Evaluation Of The Software Evolution And Maintenance Activity\nYear: 2008\n\nAbstract: One of the activities where companies invest the most today is software evolution and maintenance; in spite of this big effort, companies do not have a reliable and agreed model to evaluate the quality of maintenance activity and to monitor how their software evolves as a result of maintenance.Although the ISO/IEC SQuaRE quality model is well-known in industry and covers also maintenance issues, it is too general and too abstract to be directly applied; moreover some of the metrics that the model suggests are very difficult and expensive to apply.The purpose of this paper is to specialise the ISO/IEC SQuaRE quality model in order to make it easier to use for a company.","Title: Evolution of an Adaptive Middleware Exploiting Architectural Reflection\nYear: 2006\n\nAbstract: Nowadays information systems are required to adapt themselves dynamically to the ever changing environment and requirements. Architectural reflection represents a principled means to address adaptivity. It also represents an emerging approach to deal with the software evolution issues. In this paper we aim to point out how systems exploiting architectural reflection to achieve adaptivity evolve in an organized, linear manner controlling easier their growth and complexity than systems based on ad hoc solutions. To sustain this affirmation we present the possible evolution improvements we gain through our Adaptive and Reflective Middleware (ARM).","Title: Boosting concept discovery in collective intelligences\nYear: 2009\n\nAbstract: Collective intelligence derives from the connection and the interaction of multiple, distributed, independent intelligent units via a network, such as, typically, a digital data network. As collective intelligences are effectively making their way into reality in consequence of ubiquitous digital communication, the opportunity and the challenge arise of extending their basic architecture in order to support higher thought-processes analogous to those characterizing human intelligences. We address here specifically the process of conceptual abstraction, namely the discovery of new concepts and ideas, and, to this purpose, we introduce the general functional notion of cognitive prosthesis supporting the implementation of a given thought-process in a collective intelligence. Since there exists a direct relationship between concept discovery and innovation in human intelligences, we point out how analogous innovation capabilities can now be supported for collective intelligences, with direct applications to Web-based innovation of products and services.","Title: Evaluation of SGML-based information through fuzzy techniques\nYear: 2001\n\nAbstract: The large amount of information available and the difficulty on processing it has made knowledge management a promising area of research. Several topics are related to it, for example distributed and intelligent information retrieval, information filtering and information evaluation, which became crucial. In this paper, we focus our attention on the knowledge evaluation problem. With the aim of evaluating information coded in the standard non-proprietary format SGML (as also in XML), we propose some evaluation methods based on L-grammars which are fuzzy grammars. In particular we apply these methods to the evaluation of documents in SGML-format and to the evaluation of HTML-pages in the World Wide Web. L-grammars generate recursively enumerable L-languages, as it has been proved in Gerla ((1991), Information Sciences 53), and so they can be used to generate fuzzy languages based on extensions of the document type definitions (DTD) involved by SGML. Given a DTD, we extend its associated language by adding a judgement label . By selecting a particular label and by taking the start symbol of the grammar associated to the DTD, we can generate any DTD-compliant document with a fuzzy degree of membership derived from the judgement label. In this way we fit the computational model underlying the recursively enumerable L-languages to the process of collecting different evaluations of the same document. Finally, we outline how the generalization of these methods of evaluation can be applied in different contexts and for different roles, as for example for information filtering.","Title: On the parallel implementation of the higher order logic language lambda Prolog\nYear: 1994\n\nAbstract: Higher-order logic languages seem particularly suitable to be implemented on parallelarchitectures for at lest two reasons. On one hand, they offer more opportunitiesfor parallelization than first-order logic languages. On the other hand, the greatercomputational costs of higher-order unification of typed -terms should allow the exploitationof parallelism with a larger granularity, possibly leading to higher efficiency.In spite of their potential interest, there are a few...","Title: An Experience Report on Using Code Smells Detection Tools\nYear: 2011\n\nAbstract: Detecting code smells in the code and consequently applying the right refactoring steps when necessary is very important to improve the quality of the code. Different tools have been proposed for code smell detection, each one characterized by particular features. The aim of this paper is to describe our experience on using different tools for code smell detection. We outline the main differences among them and the different results we obtained.","Title: Integrating Concepts and Knowledge in Large Content Networks.\nYear: 2014\n\nAbstract: Large content networks like the World Wide Web contain huge amounts of information that have the potential of being integrated because their components fit within common concepts and/or are connected through hidden, implicit relationships. One attempt at such an integration is the program called the “Web of Data,” which is an evolution of the Semantic Web. It targets semi-structured information sources such as Wikipedia and turns them into fully structured ones in the form of Web-based databases like DBpedia and then integrates them with other public databases such as Geonames. On the other hand, the vast majority of the information residing on the Web is still totally unstructured, which is the starting point for our approach that aims to integrate unstructured information sources. For this purpose, we exploit techniques from Probabilistic Topic Modeling, in order to cluster Web pages into concepts (topics), which are then related through higher-level concept networks; we also make implicit semantic relationships emerge between single Web pages. The approach has been tested through a number of case studies that are here described. While the applicative focus of the research reported here is on knowledge integration on the specific and relevant case of the WWW, the wider aim is to provide a framework for integration generally applicable to all complex content networks where information propagates from multiple sources.","Title: On applying machine learning techniques for design pattern detection.\nYear: 2015\n\nAbstract: We apply machine learning to detect design patterns in software systems.We exploit a specific design pattern model to apply machine learning techniques.We compare the performances of several machine learning algorithms.We provide a large dataset containing manually checked design pattern instances. The detection of design patterns is a useful activity giving support to the comprehension and maintenance of software systems. Many approaches and tools have been proposed in the literature providing different results. In this paper, we extend a previous work regarding the application of machine learning techniques for design pattern detection, by adding a more extensive experimentation and enhancements in the analysis method. Here we exploit a combination of graph matching and machine learning techniques, implemented in a tool we developed, called MARPLE-DPD. Our approach allows the application of machine learning techniques, leveraging a modeling of design patterns that is able to represent pattern instances composed of a variable number of classes. We describe the experimentations for the detection of five design patterns on 10 open source software systems, compare the performances obtained by different learning models with respect to a baseline, and discuss the encountered issues.","Title: A Refactoring Suggestion Tool For Removing Clones In Java Code\nYear: 2014\n\nAbstract: Code duplication is considered a widespread code smell, a symptom of bad code development practices or potential design issues. Code smells are also considered to be indicators of poor software maintainability. The refactoring cost associated with removing code clones can be very high, partly because of the number of different decisions that must be made regarding the kind of refactoring steps to apply. Here, we describe a tool that has been developed to suggest the best refactoring steps that could be taken to remove clones in Java code. Our approach is based on the classification of clones, in terms of their location in a class hierarchy, so that decisions can be made from a restricted set of refactorings that have been evaluated using multiple criteria.","Title: Pattern detection for conceptual schema recovery in data-intensive systems\nYear: 2014\n\nAbstract: AbstractIn this paper, an approach for information systems reverse engineering is proposed and applied. The aim is to support a unified perspective to the reverse engineering process of both data and software. At the state of the art, indeed, many methods, techniques, and tools for software reverse engineering have been proposed to support program comprehension, software maintenance, and software evolution. Other approaches and tools have been proposed for data reverse engineering, with the aim, for example, to provide complete and up-to-date documentation of legacy databases. However, the two engineering communities often worked independently, and very few approaches addressed the reverse engineering of both data and software as information system's constituencies. Hence, a higher integration is needed to support a better co-evolution of databases and programs, in an environment often characterized by high availability of data and volatility of information flows. Accordingly, the approach we propose leverages the detection of object-relational mapping design patterns to build a conceptual schema of the software under analysis. Then, the conceptual schema is mapped to the domain model of the system, to support the design of the evolution of the information system itself. The approach is evaluated on two large-scale open-source enterprise applications. Copyright © 2014 John Wiley & Sons, Ltd.","Title: Capturing Software Evolution and Change through Code Repository Smells.\nYear: 2014\n\nAbstract: In the last years we have seen the rise and the fall of many version control systems. These systems collect a large amount of data spanning from the path of the files involved in changes to the exact text changed in every file. This data can be exploited to produce an overview about how the system changed over time and evolved. We have developed a tool, called VCS-Analyzer, to use this information, both for data retrieval and analysis tasks. Currently, VCS-Analyzer implements six different analyses: two based on source code for the computation of metrics and the detection of code smells, and four original analysis based on repositories metadata, which are based on the concepts of Repository Metrics and Code Repository Smells. In this paper, we describe one smell and two metrics we have defined for source code repositories analysis.","Title: Tracking line changes in source code repositories\nYear: 2014\n\nAbstract: Previous research determined that the analysis of file changes in software repositories is useful for maintenance activities, like defect prediction. Changes rarely modify the entire file contents, but are usually localized in specific code regions.","Title: A comparison of reverse engineering tools based on design pattern decomposition\nYear: 2005\n\nAbstract: The usefulness of design patterns in forward engineering is already well-known and several tools provide support for their application in the development of software systems. While the role of design patterns in reverse engineering is still argued primarily due to their informal definition which leads to various possible implementations of each pattern. One of the most discussed aspects related to design patterns is about the need of their formalization according to the drawbacks this can represent. Formalization leads to the identification of the so-called sub-patterns, which are the recurring fundamental elements design patterns are composed of. In this paper we analyze the role sub-patterns play in two reverse engineering tools: FUJABA and SPQR. Attention is focused on how sub-patterns are exploited to define and to detect design patterns. To emphasize the similarities and differences between the two approaches, the composite design pattern is considered as example.","Title: Software agents for computer vision: a preliminary discussion\nYear: 1998\n\nAbstract: In this paper, we start a preliminary discussion about the development of software agents developed for Computer Vision. Such agents must be heterogeneous and adaptive in nature to satisfy increasing requests for integration-cooperation capability exhibited by current Computer Vision applications (e.g., biomedical imaging and image retrieval on the Web). We give a brief introduction to the overall architecture design and we focus on lowest level agents, called W-bots (Working Software Robots). We define the nature of such agents and describe their ways of interaction, and their adaptive capabilities. Finally, we present an example of the proposed architecture utilizing VDM++ to specify agents as active objects.","Title: Investigating the impact of code smells debt on quality code evaluation\nYear: 2012\n\nAbstract: Different forms of technical debt exist that have to be carefully managed. In this paper we focus our attention on design debt, represented by code smells. We consider three smells that we detect in open source systems of different domains. Our principal aim is to give advice on which design debt has to be paid first, according to the three smells we have analyzed. Moreover, we discuss if the detection of these smells could be tailored to the specific application domain of a system.","Title: Fuzzy XML queries via context-based choice of aggregations.\nYear: 2000\n\nAbstract: A flexible query model is presented for semi-structured information stored in well-formed XML documents, modeled as XML fuzzy graphs by computing estimates of the importance of the information associated to XML elements and attributes. The notion of fuzzy graph closure with threshold is then used to obtain a fuzzy extension of the XML fuzzy graphs' topological structure. Weights associated to closure arcs are computed as a conjunction of the importance values of the underlying arcs in the original graph, via a context-based choice of conjunctions. Query results are subgraphs of the resulting fuzzy closure graph, presented as a ranked list according to their degree of matching to the user query.","Title: A context-aware style of software design\nYear: 2015\n\nAbstract: Contemporary large software systems rely on complex software ecosystems for managing infrastructural tasks. While these ecosystems facilitate software development, the software architect must put care in not relying on assumptions on behaviors and policies of the ecosystem that may change with platform evolution. Based on our experience with developing analyses within MARPLE, a framework for software comprehension and architecture reconstruction, we propose an abstract, context-aware style for specifying software. In the spirit of decoupling computation from coordination, this style decouples the specification of the computations to be performed from the specification of the contexts where they must take place. Software described in this way exposes its primitives at a level of abstraction closer to that of the framework, enabling better reasoning on the features of the design, easing correct implementation, and fostering a better interaction between the software and the framework it relies on.","Title: Towards Assessing Software Architecture Quality by Exploiting Code Smell Relations\nYear: 2015\n\nAbstract: We can evaluate software architecture quality using a plethora of metrics proposed in the literature, but interpreting and exploiting in the right way these metrics is not always a simple task. This is true for both fixing the right metric threshold values and determining the actions to be taken to improve the quality of the system. Instead of metrics, we can detect code or architectural anomalies that give us useful hints on the possible architecture degradation. In this paper, we focus our attention on the detection of code smells and in particular on their relations and co-occurrences, with the aim to evaluate tecnical debt in an architectural context. We start from the assumption that certain patterns of code anomalies tend to be better indicators of architectural degradation than simple metrics evaluation.","Title: Automatic Metric Thresholds Derivation for Code Smell Detection\nYear: 2015\n\nAbstract: Code smells are archetypes of design shortcomings in the code that can potentially cause problems during maintenance. One known approach for detecting code smells is via detection rules: a combination of different object-oriented metrics with pre-defined threshold values. The usage of inadequate thresholds when using this approach could lead to either having too few observations (too many false negatives) or too many observations (too many false positives). Furthermore, without a clear methodology for deriving thresholds, one is left with those suggested in literature (or by the tool vendors), which may not necessarily be suitable to the context of analysis. In this paper, we propose a data-driven (i.e., benchmark-based) method to derive threshold values for code metrics, which can be used for implementing detection rules for code smells. Our method is transparent, repeatable and enables the extraction of thresholds that respect the statistical properties of the metric in question (such as scale and distribution). Thus, our approach enables the calibration of code smell detection rules by selecting relevant systems as benchmark data. To illustrate our approach, we generated a benchmark dataset based on 74 systems of the Qualitas Corpus, and extracted the thresholds for five smell detection rules.","Title: A Duplicated Code Refactoring Advisor\nYear: 2015\n\nAbstract: Refactoring is one of the key practices in Extreme Programming and other agile methods. Duplicated code is one of the most pervasive and pungent smells to remove from source code through refactoring. Duplicated code has been largely studied in the literature, and different types of duplications, or \"clones\", have been identified. Some studies analyzed in details the problems caused by clones in the code, others outlined also the difficulties in removing clones, and the cases in which it could be better not removing them. The refactoring cost for removing clones can be very high, also due to the different choices on the possible refactoring steps. In this paper, we describe our approach and tool developed with the aim to suggest the best refactorings to remove clones in Java code. Our approach is based on the classification of the clones in terms of their location in a class hierarchy, and allows to choose among a restricted set of refactorings, which are then evaluated using multiple criteria. We provide a validation of the effectiveness of the approach.","Title: Including structural factors into the metrics-based code smells detection\nYear: 2015\n\nAbstract: Code smells help to discover and describe deeper problems in software design. Several automated methods of smell detection are based the analysis of a combination of code-related metrics relevant for a given flaw. However, some smells reflect more complex issues and require a holistic perspective that woudl cover a number of different sources of data. In this paper we experimentally verify the usefulness of including structural factors into a metrics-based detection of God Class and Brain Class code smells.","Title: On experimenting refactoring tools to remove code smells\nYear: 2015\n\nAbstract: When we develop a software project of a certain complexity, source code maintainability could become a problem, in particular if developers do not use a consolidate development process that simplifies the management of the entire project. When source code becomes very complex, it is difficult for developers to share and modify it. We can improve internal software qualities such as reusability, maintainability and readability through refactoring. Refactoring can be applied to remove possible problems in the code, as code smells. Identifying code smells and removing them through refactoring results in better code maintainability, but it can be an overwhelming task. In this paper, we describe our experimentation on using four refactoring tools to remove code smells in four systems, with the aim to outline advantages and disadvantages of the tools with respect to the accomplishment of this task, and to identify the smells easier to be removed among the ones we considered in this paper.","Title: Inter-smell relations in industrial and open source systems: A replication and comparative analysis\nYear: 2015\n\nAbstract: The presence of anti-patterns and code smells can affect adversely software evolution and quality. Recent work has shown that code smells that appear together in the same file (i.e., collocated smells) can interact with each other, leading to various types of maintenance issues and/or to the intensification of negative effects. It has also been found that code smell interactions can occur across coupled files (i.e., coupled smells), with comparable negative effects as the interaction of same-file (collocated) smells. Different inter-smell relations have been described in previous work, yet only few studies have evaluated them empirically. This study attempts to replicate the findings from previous work on inter-smell relations by analyzing larger systems, and by including both industrial and open source ones. We also include the analysis of coupled smells in addition to collocated smells, to achieve a more complete picture of inter-smell relations. Our results suggest that if coupled smells are not considered, one may risk increasing the number of false negatives when analysing inter-smells. A major finding is that patterns of inter-smell relations vary between open source and industrial systems, suggesting that contextual variables should be considered in further studies on code smells.","Title: Towards a prioritization of code debt: A code smell Intensity Index\nYear: 2015\n\nAbstract: Code smells can be used to capture symptoms of code decay and potential maintenance problems that can be avoided by applying the right refactoring. They can be seen as a source of technical debt. However, tools for code smell detection often provide far too many and different results, and identify many false positive code smell instances. In fact, these tools are rooted on initial and rather informal code smell definitions. This represents a challenge to interpret their results in different ways. In this paper, we provide an Intensity Index, to be used as an estimator to determine the most critical instances, prioritizing the examination of smells and, potentially, their removal. We apply Intensity on the detection of six well known and common smells and we report their Intensity distribution from an analysis performed on 74 systems of the Qualitas Corpus, showing how Intensity could be used to prioritize code smells inspection.","Title: Antipattern and Code Smell False Positives: Preliminary Conceptualization and Classification\nYear: 2016\n\nAbstract: Anti-patterns and code smells are archetypes used for describing software design shortcomings that can negatively affect software quality, in particular maintainability. Tools, metrics and methodologies have been developed to identify these archetypes, based on the assumption that they can point at problematic code. However, recent empirical studies have shown that some of these archetypes are ubiquitous in real world programs, and many of them are found not to be as detrimental to quality as previously conjectured. We are therefore interested in revisiting common anti-patterns and code smells, and building a catalogue of cases that constitute candidates for \"false positives\". We propose a preliminary classification of such false positives with the aim of facilitating a better understanding of the effects of anti-patterns and code smells in practice. We hope that the development and further refinement of such a classification can support researchers and tool vendors in their endeavour to develop more pragmatic, context-relevant detection and analysis tools for anti-patterns and code smells.","Title: An Experience Report on Detecting and Repairing Software Architecture Erosion\nYear: 2016\n\nAbstract: Architecture erosion constitutes the most visible effect of the degradation of design. It is a major reason to address the design debt, often caused by architectural mismatch problems. Today, the identification of design erosion is a major concern for designers and software maintainers. Adequate tools are necessary to identify and repair the debt. This paper describes our experience on identifying architectural erosion problems on existing open source software applications through the support of two well known tools. Moreover, we outline if the tools provide useful hints in repairing some of the detected problems.","Title: Tool support for evaluating architectural debt of an existing system: an experience report.\nYear: 2016\n\nAbstract: During software development, maintenance and evolution, we can assess the quality of code, design and architecture to capture hints of possible problems and face them as soon as possible. We can capture signals of architecture erosion in different ways by exploiting several tools with the aim to identify architectural violations, architectural smells or other relevant features. In this paper, we provide our experience report in using three known tools to capture information that can be useful to identify and evaluate the architectural debt of an application. We outline the main differences among these tools and the results they produce.","Title: On evaluating the impact of the refactoring of architectural problems on software quality.\nYear: 2016\n\nAbstract: We can improve software quality in different ways and by removing different kinds of problems. In this paper, we focus our attention on architectural problems, as architectural smells or antipatterns represent, we remove some of these problems through refactoring steps and we check the impact that the refactoring has on different quality metrics. In particular, we focus our attention on some Quality Indexes computed by four tools. These tools are used also for the detection of the architectural problems. We present the results and outline different issues related to the impact of the refactoring of these architectural problems on the Quality Indexes and the difficulties in the choice of the problems to be refactored.","Title: Smells Like Teen Spirit: Improving Bug Prediction Performance Using the Intensity of Code Smells\nYear: 2016\n\nAbstract: Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes. Specifically, we evaluate the contribution of a measure of the severity of code smells (i.e., code smell intensity) by adding it to existing bug prediction models and comparing the results of the new model against the baseline model. Results indicate that the accuracy of a bug prediction model increases by adding the code smell intensity as predictor. We also evaluate the actual gain provided by the intensity index with respect to the other metrics in the model, including the ones used to compute the code smell intensity. We observe that the intensity index is much more important as compared to other metrics used for predicting the buggyness of smelly classes.","Title: Technical Debt Indexes Provided by Tools: A Preliminary Discussion\nYear: 2016\n\nAbstract: In software maintenance and evolution, it is important to assess both code and architecture in order to identify issues to be solved to improve software quality. Different tools provide some kind of index giving us an overall evaluation of a project to be used when managing its technical debt. In this paper, we outline how the indexes, that we call in general Technical Debt Indexes, provided by five different tools are computed. We describe their principal features and differences, what aspects they are missing, and we outline if (and how) the indexes take into account architectural problems that could have a major impact on the architectural debt. We show that the indexes rely on different information sources and measure different quantities.","Title: Code smell severity classification using machine learning techniques.\nYear: 2017\n\nAbstract: Several code smells detection tools have been developed providing different results, because smells can be subjectively interpreted and hence detected in different ways. Machine learning techniques have been used for different topics in software engineering, e.g., design pattern detection, code smell detection, bug prediction, recommending systems. In this paper, we focus our attention on the classification of code smell severity through the use of machine learning techniques in different experiments. The severity of code smells is an important factor to take into consideration when reporting code smell detection results, since it allows the prioritization of refactoring efforts. In fact, code smells with high severity can be particularly large and complex, and create larger issues to the maintainability of software a system. In our experiments, we apply several machine learning models, spanning from multinomial classification to regression, plus a method to apply binary classifiers for ordinal classification. In fact, we model code smell severity as an ordinal variable. We take the baseline models from previous work, where we applied binary classification models for code smell detection with good results. We report and compare the performance of the models according to their accuracy and four different performance measures used for the evaluation of ordinal classification techniques. From our results, while the accuracy of the classification of severity is not high as in the binary classification of absence or presence of code smells, the ranking correlation of the actual and predicted severity for the best models reaches 0.880.96, measured through Spearmans .","Title: Students' Feedback in Using GitHub in a Project Development for a Software Engineering Course.\nYear: 2017\n\nAbstract: GitHub is a platform used for the development of software projects. It provides a traceable project repository and a social meeting place for communities of practices. This poster presents the students' feedback on using GitHub as a development platform for software projects counting as an exam for a 3rd-year undergraduate software engineering course on software design. Students worked in teams and their feedback is positive overall.","Title: Change Prediction through Coding Rules Violations.\nYear: 2017\n\nAbstract: Static source code analysis is an increasingly important activity to manage software project quality, and is often found as a part of the development process. A widely adopted way of checking code quality is through the detection of violations to specific sets of rules addressing good programming practices. SonarQube is a platform able to detect these violations, called Issues. In this paper we described an empirical study performend on two industrial projects, where we used Issues extracted on different versions of the projects to predict changes in code through a set of machine learning models. We achieved good detection performances, especially when predicting changes in the next version. This result paves the way for future investigations of the interest in an industrial setting towards the prioritization of Issues management according to their impact on change-proneness.","Title: Arcan: A Tool for Architectural Smells Detection\nYear: 2017\n\nAbstract: Code smells are sub-optimal coding circumstances such as blob classes or spaghetti code - they have received much attention and tooling in recent software engineering research. Higher-up in the abstraction level, architectural smells are problems or sub-optimal architectural patterns or other design-level characteristics. These have received significantly less attention even though they are usually considered more critical than code smells, and harder to detect, remove, and refactor. This paper describes an open-source tool called Arcan developed for the detection of architectural smells through an evaluation of several different architecture dependency issues. The detection techniques inside Arcan exploit graph database technology, allowing for high scalability in smells detection and better management of large amounts of dependencies of multiple kinds. In the scope of this paper, we focus on the evaluation of Arcan results carried out with real-life software developers to check if the architectural smells detected by Arcan are really perceived as problems and to get an overall usefulness evaluation of the tool.","Title: Ninth International Workshop on Managing Technical Debt: report on the MTD 2017 workshop\nYear: 2017\n\nAbstract: Welcome to the Ninth International Workshop on Managing Technical Debt, collocated with the 18th International Conference on Agile Software Development (XP 2017) in Cologne. The technical debt research community continues to expand through collaborations of industry, tool vendors, and academia. The main topic of this year's workshop was on the impact of agile development approaches towards the management of technical debt.","Title: Model-Driven Reverse Engineering Approaches: A Systematic Literature Review.\nYear: 2017\n\nAbstract: This paper explores and describes the state of the art for what concerns the model-driven approaches proposed in the literature to support reverse engineering. We conducted a systematic literature review on this topic with the aim to answer three research questions. We focus on various solutions developed for model-driven reverse engineering, outlining in particular the models they use and the transformations applied to the models. We also consider the tools used for model definition, extraction, and transformation and the level of automation reached by the available tools. The model-driven reverse engineering approaches are also analyzed based on various features such as genericity, extensibility, automation of the reverse engineering process, and coverage of the full or partial source artifacts. We describe in detail and compare fifteen approaches applying model-driven reverse engineering. Based on this analysis, we identify and indicate some hints on choosing a model-driven reverse engineering approach from the available ones, and we outline open issues concerning the model-driven reverse engineering approaches.","Title: Evaluation Of Self-Adaptive Systems: A Women Perspective\nYear: 2017\n\nAbstract: Statistically, the number of women in computer science is significantly lower than the number of men working in the same domain. This applies also to the subarea of software architectures. We would like to outline that there may be also exceptions: there are more women than men leading and working in the evaluation of autonomic and self-adaptive issues in software systems. In this paper, we introduce seven research approaches involving women in the evaluation of self-adaptive systems. Further, we discuss the heterogeneity of these evaluation approaches from various points of view (e.g., types of systems addressed, types of evaluation, qualitative vs quantitative or static vs dynamic). The paper ends with our thoughts and perspectives on future needs and trends in the evaluation of self-adaptive systems.","Title: Alternatives To The Knowledge Discovery Metamodel: An Investigation\nYear: 2017\n\nAbstract: To better understand and exploit the knowledge necessary to comprehend and evolve an existing system, different models can be extracted from it. Models represent the extracted information at various abstraction levels, and are useful to document, maintain, and reengineer the system. The Knowledge Discovery Metamodel (KDM) has been defined by the object management group as a meta-model supporting a large share of reverse engineering activities. Its specification has also been adopted by the ISO in 2012. This paper explores and describes alternative meta-models proposed in the literature to support reverse engineering, program comprehension, and software evolution activities. We focus on the similarity and differences of the alternative meta-models with KDM, trying to understand the potentials of reciprocal information interchange. We describe KDM and other five meta-models, plus their extensions available in the literature and their diffusion in the reverse engineering community. We also investigate the approaches using KDM and the five meta-models. In the paper, we underline the limited reuse of models for reverse engineering, and identify potential directions for future related research, to enhance the existing models and ease the exchange of information among them.","Title: Technical Debt in Agile Development: Report on the Ninth Workshop on Managing Technical Debt (MTD 2017).\nYear: 2017\n\nAbstract: We report on the Ninth International Workshop on Managing Technical Debt, collocated with the 18th International Conference on Agile Software Development (XP 2017) in Cologne. The technical debt research community continues to expand through collaborations of industry, tool vendors, and academia. The theme of this year's workshop was on technical debt in agile development. Presentations and discussion centered on the topics: technical debt at the code level, architectural technical debt assessment, agile approaches and their impact on technical debt management, and selling the business case of technical debt management.","Title: How do community smells influence code smells?\nYear: 2018\n\nAbstract: Code smells reflect sub-optimal patterns of code that often lead to critical software flaws or failure. In the same way, community smells reflect sub-optimal organisational and socio-technical patterns in the organisational structure of the software community.\n\nTo understand the relation between the community smells and code smells we start by surveying 162 developers of nine open-source systems. Then we look deeper into this connection by conducting an empirical study of 117 releases from these systems.\n\nOur results indicate that community-related factors are intuitively perceived by most developers as causes of the persistence of code smells. Inspired by this observation we design a community-aware prediction model for code smells and show that it outperforms a model that does not consider community factors.","Title: Machine learning based code smell detection through WekaNose.\nYear: 2018\n\nAbstract: Code smells can be subjectively interpreted, the results provided by detectors are usually different, the agreement in the results is scarce, and a benchmark for the comparison of these results is not yet available. The main approaches used to detect code smells are based on the computation of a set of metrics. However code smell detectors often use different metrics and/or different thresholds, according to their detection rules. As result of this inconsistency the number of detected smells can increase or decrease accordingly, and this makes hard to understand when, for a specific software, a certain characteristic identifies a code smell or not. In this work, we introduce WekaNose, a tool that allows to perform an experiment to study code smell detection through machine learning techniques. The experiment's purpose is to select rules, and/or obtain trained algorithms, that can classify an instance (method or class) as affected or not by a code smell. These rules have the main advantage of being extracted through an example-based approach, rather then a heuristic-based one.","Title: Support for architectural smell refactoring.\nYear: 2018\n\nAbstract: To preserve high quality of a project, it is necessary to perform many refactoring steps during the development and maintenance phases. Code refactoring received a great attention in the literature, often directed to the refactoring of code smells. While at the architectural level, architectural smells received less attention. One of the most common architectural smell which may affect many parts of a software application is the Cyclic Dependency smell. This position paper presents a tool prototype that suggests which path a developer could follow to remove Cyclic Dependency smells in Java applications. The tool has been developed as an extension of the Arcan tool for architectural smells detection. In this paper we describe how the tool identifies the path to follow for the refactoring of Cyclic Dependency on real projects. The tool has been thought only to suggest a possible refactoring approach which a developer could considerate to resolve Cyclic Dependency.","Title: Identifying and Prioritizing Architectural Debt Through Architectural Smells: A Case Study in a Large Software Company.\nYear: 2018\n\nAbstract: Architectural technical debt can have a huge impact on software maintainability and evolution. Hence, different architectural violations, detected as architectural smells, need to be identified and refactored. In this paper, we conducted a multiple case-study on several architectural smells detected in four industrial projects. We conducted an in-depth investigation with a questionnaire, interviews and thorough inspection of the code with the practitioners. We evaluated the negative impact of the technical debt detected by the architectural smells, their difficulty to be refactored and the usefulness of the detection tool. The results show that practitioners appreciated the help of automatic detection, and that they prioritize refactoring architectural debt that causes more negative impact despite the higher refactoring effort.","Title: Collaborative and teamwork software development in an undergraduate software engineering course.\nYear: 2018\n\nAbstract: •Collaboration and teamwork through the GitHub platform.•Software quality assessment through SonarQube.•Project management tasks through Microsoft Project.•Evaluation based on student feedback after assignments and a team project.•Identification of future improvements for the teaching/learning process.","Title: Code smells and their collocations: A large-scale experiment on open-source systems.\nYear: 2018\n\nAbstract: Code smells indicate possible flaws in software design, that could negatively affect system’s maintainability. Interactions among smells located in the same classes (i.e., collocated smells) have even more detrimental effect on quality. Extracted frequent patterns of collocated smells could help to understand practical consequences of collocations. In this paper we identify and empirically validate frequent collocations of 14 code smells detected in 92 Java systems, using three approaches: pairwise correlation analysis, PCA and associative rules. To cross-validate the results, we used up to 6 detectors for each smell. Additionally, we examine and compare techniques used to extract the relationships. The contribution is three-fold: (1) we identify and empirically validate relationships among the examined code smells on a large dataset that we made publicly available, (2) we discuss how the choice of code smell detectors affects results, and (3) we analyze the impact of software domain on existence of the smell collocations. Additionally, we found that analytical methods we used to discover collocations, are complementary. Smells collocations display recurring patterns that could help prioritizing the classes affected by code smells to be refactored and developing or enhancing detectors exploiting information about collocations. They can also help the developers focusing on classes deserving more maintenance effort.","Title: Towards an Architectural Debt Index\nYear: 2018\n\nAbstract: Different indexes have been proposed to evaluate software quality and technical debt. Usually these indexes take into account different code level issues and several metrics, well known software metrics or new ones defined ad hoc for a specific purpose. In this paper we propose and define a new index, more oriented to the evaluation of architectural violations. We describe in detail the index, called Architectural Debt Index, that we integrated in a tool developed for architectural smell detection. The index is based on the detection of architectural smells, their criticality and their history. Currently only dependency architectural smells have been considered, but other architectural debt indicators can be considered and integrated in the index computation.","Title: An Architectural Smells Detection Tool for C and C++ Projects\nYear: 2018\n\nAbstract: Architectural smells gained great attention in the past few years since they directly affect software quality and increase architectural technical debt. However, while it is straightforward to understand why they are important, it is more difficult to find techniques and tools to detect and remove architectural smells. The purpose of this paper is to introduce an open-source tool for automatic architectural smells detection for C/C++ projects, by creating an abstraction of the project and defining the concept of dependency between elements belonging to the project in order to identify architectural smells. The tool has been validated on some open-source projects with promising results.","Title: [Research Paper] Automatic Detection of Sources and Sinks in Arbitrary Java Libraries.\nYear: 2018\n\nAbstract: In the last decade, data security has become a primary concern for an increasing amount of companies around the world. Protecting the customeru0027s privacy is now at the core of many businesses operating in any kind of market. Thus, the demand for new technologies to safeguard user data and prevent data breaches has increased accordingly. In this work, we investigate a machine learning-based approach to automatically extract sources and sinks from arbitrary Java libraries. Our method exploits several different features based on semantic, syntactic, intra-procedural dataflow and class-hierarchy traits embedded into the bytecode to distinguish sources and sinks. The performed experiments show that, under certain conditions and after some preprocessing, sources and sinks across different libraries share common characteristics that allow a machine learning model to distinguish them from the other library methods. The prototype model achieved remarkable results of 86% accuracy and 81% F-measure on our validation set of roughly 600 methods.","Title: Toward a Smell-Aware Bug Prediction Model\nYear: 2019\n\nAbstract: Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper, we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes. Specifically, we evaluate the contribution of a measure of the severity of code smells (i.e., code smell intensity) by adding it to existing bug prediction models based on both product and process metrics, and comparing the results of the new model against the baseline models. Results indicate that the accuracy of a bug prediction model increases by adding the code smell intensity as predictor. We also compare the results achieved by the proposed model with the ones of an alternative technique which considers metrics about the history of code smells in files, finding that our model works generally better. However, we observed interesting complementarities between the set of \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">buggy and smelly</italic>\n classes correctly classified by the two models. By evaluating the actual information gain provided by the intensity index with respect to the other metrics in the model, we found that the intensity index is a relevant feature for both product and process metrics-based models. At the same time, the metric counting the average number of code smells in previous versions of a class considered by the alternative model is also able to reduce the entropy of the model. On the basis of this result, we devise and evaluate a \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">smell-aware</italic>\n combined bug prediction model that included product, process, and smell-related features. We demonstrate how such model classifies bug-prone code components with an F-Measure at least 13 percent higher than the existing state-of-the-art models.","Title: Automatic Detection of Instability Architectural Smells\nYear: 2016\n\nAbstract: Code smells represent well known symptoms of problems at code level, and architectural smells can be seen as their counterpart at architecture level. If identified in a system, they are usually considered more critical than code smells, for their effect on maintainability issues. In this paper, we introduce a tool for the detection of architectural smells that could have an impact on the stability of a system. The detection techniques are based on the analysis of dependency graphs extracted from compiled Java projects and stored in a graph database. The results combine the information gathered from dependency and instability metrics to identify flaws hidden in the software architecture. We also propose some filters trying to avoid possible false positives.","Title: Technical Debt Prioritization: State of the Art. A Systematic Literature Review\nYear: 2019\n\nAbstract: Background. Software companies need to manage and refactor Technical Debt issues. Therefore, it is necessary to understand if and when refactoring Technical Debt should be prioritized with respect to developing features or fixing bugs. Objective. The goal of this study is to investigate the existing body of knowledge in software engineering to understand what Technical Debt prioritization approaches have been proposed in research and industry. Method. We conducted a Systematic Literature Review among 384 unique papers published until 2018, following a consolidated methodology applied in Software Engineering. We included 38 primary studies. Results. Different approaches have been proposed for Technical Debt prioritization, all having different goals and optimizing on different criteria. The proposed measures capture only a small part of the plethora of factors used to prioritize Technical Debt qualitatively in practice. We report an impact map of such factors. However, there is a lack of empirical and validated set of tools. Conclusion. We observed that technical Debt prioritization research is preliminary and there is no consensus on what are the important factors and how to measure them. Consequently, we cannot consider current research conclusive and in this paper, we outline different directions for necessary future investigations.","Title: Are architectural smells independent from code smells? An empirical study\nYear: 2019\n\nAbstract: •Case study analyzing the correlations among code smells, groups of code smells and architectural smells.•Architectural smells are not correlated with code smells.•Architectural smells cannot be derived from code smells.•Architectural smells deserve special attention by researchers, who should investigate their actual harmfulness.","Title: DebtHunter: A Machine Learning-based Approach for Detecting Self-Admitted Technical Debt\nYear: 2021\n\nAbstract: Due to limited time, budget or resources, a team is prone to introduce code that does not follow the best software development practices. This code that introduces instability in the software projects is known as Technical Debt (TD). Often, TD intentionally manifests in source code, which is known as Self-Admitted Technical Debt (SATD). This paper presents DebtHunter, a natural language processing (NLP)- and machine learning (ML)- based approach for identifying and classifying SATD in source code comments. The proposed classification approach combines two classification phases for differentiating between the multiple debt types. Evaluations over 10 open source systems, containing more than 259k comments, showed that the approach was able to improve the performance of others in the literature. The presented approach is supported by a tool that can help developers to effectively manage SATD. The tool complements the analysis over Java source code by allowing developers to also examine the associated issue tracker. DebtHunter can be used in a continuous evolution environment to monitor the development process and make developers aware of how and where SATD is introduced, thus helping them to manage and resolve it.","Title: Impact of Opportunistic Reuse Practices to Technical Debt\nYear: 2021\n\nAbstract: Technical debt (TD) has been recognized as an important quality problem for both software architecture and code. The evolution of TD techniques over the past years has led to a number of research and commercial tools. In addition, the increasing trend of opportunistic reuse (as opposed to systematic reuse), where developers reuse code assets in popular repositories, is changing the way components ...","Title: Evaluating the Architectural Debt of IoT Projects\nYear: 2021\n\nAbstract: We observed a great and increasing interest in the last few years towards the evaluation of technical debt of software projects, in particular in the direction of code and architectural debt evaluation. This kind of analysis has not yet been performed for IoT projects. Hence, in this paper we start this exploration through the analysis of four Open Source IoT projects. We focus our attention on ar...","Title: The Perception of Architectural Smells in Industrial Practice\nYear: 2021\n\nAbstract: Architectural technical debt (TD) is the most significant type of TD in industrial practice. Our goals in this study were to better understand the phenomenon of architectural smells, help practitioners better manage them, and offer relevant support to researchers.","Title: On the relation between architectural smells and source code changes\nYear: 2022\n\nAbstract: Although architectural smells are one of the most studied type of architectural technical debt, their impact on maintenance effort has not been thoroughly investigated. Studying this impact would help to understand how much technical debt interest is being paid due to the existence of architecture smells and how this interest can be calculated. This work is a first attempt to address this issue by investigating the relation between architecture smells and source code changes. Specifically, we study whether the frequency and size of changes are correlated with the presence of a selected set of architectural smells. We detect architectural smells using the Arcan tool, which detects architectural smells by building a dependency graph of the system analyzed and then looking for the typical structures of the architectural smells. The findings, based on a case study of 31 open-source Java systems, show that 87% of the analyzed commits present more changes in artifacts with at least one smell, and the likelihood of changing increases with the number of smells. Moreover, there is also evidence to confirm that change frequency increases after the introduction of a smell and that the size of changes is also larger in smelly artifacts. These findings hold true especially in Medium-Large and Large artifacts."],"string":"[\n \"Title: Exploiting Analogy for Design Pattern Recognition and Classification\\nYear: 2001\\n\\nAbstract: Analogy has been used in several domains and at different levels; if we consider the field of software engineering, analogy has been exploited for example for design reuse [12] and for specification and code reuse [13], where analogy is used to determine the degree of closeness, as a weak form of matching to then derive some similarities-based metrics.Here, we consider analogy based on similarity, where similarity is can be a fuzzy relation satisfying some given properties. We investigate in the paper the field of design patterns recognition and classification from several points of views, with the aim to give the principal hints in developing a system of support to integrate design patterns during the design phase, and to enhance in this way this phase.\",\n \"Title: Information-Driven Collective Intelligences\\nYear: 2010\\n\\nAbstract: Information-driven collective intelligences derive from the connection and the interaction of multiple, distributed, independent\\n agents that produce and process information, and eventually turn it into knowledge, meant in the broad sense of capability\\n of conceptual representation. For instance, the World-wide Web can be viewed as an information-driven collective intelligence\\n emerging from the digital network known as the Internet. The point here is how to extend such a capability for knowledge generation\\n from the participating agents to the collective intelligence itself.\\n \\n \\n In this paper we show how this can be obtained with graph-based algorithms for the detection of communities of agents so as\\n to support a dynamic, self-organized form of concept-discovery and concept-incarnation. In particular, we show how to strengthen\\n community ties around concepts in order to increase their level of socialization and, consequently, of “fertility” in the\\n generation of new concepts. Since there exists a direct relationship between concept discovery and innovation in human intelligences,\\n we point out how analogous innovation capabilities can now be supported within information-driven collective intelligences,\\n with direct applications to product innovation.\",\n \"Title: Elemental Design Patterns Recognition In Java\\nYear: 2005\\n\\nAbstract: With the advent of UML and MDA, models play an increasingly important role in software development. Hence, the management of the quality of models is of key importance for completing projects succesfully. However, existing approaches towards software ...\",\n \"Title: DPB: A Benchmark for Design Pattern Detection Tools\\nYear: 2012\\n\\nAbstract: Many activities can be done to support software evolution and reverse engineering of a system. Design pattern detection is one of these activities. It is useful to gain knowledge on the design issues of an existing system, on its architecture and design quality, improving the comprehension of the system and hence its maintainability and evolution. Several tools for design pattern detection have been developed in the literature, but they usually provide different results when analyzing the same systems. Some works have been proposed in the literature to compare these results, but a standard and widely-accepted benchmark is not yet available. In this work we propose our benchmark web application for design pattern detection, based on a community driven evaluation.\",\n \"Title: User adaptive models based on similarity\\nYear: 2000\\n\\nAbstract: In this work, we try to give a more precise meaning to the notion of \\\"context\\\" and to the knowledge associated with it, in order to build models representing a context able to adapt themselves to the user needs. We propose similarities as a tool to represent contexts. To do so, we consider a parametric fanfily of sunilarities and supply some methods for trying to \\\"guess\\\" the value of the parameter that best fits the context where the similarity shall be applied.\",\n \"Title: Code Smell Detection: Towards a Machine Learning-Based Approach\\nYear: 2013\\n\\nAbstract: Several code smells detection tools have been developed providing different results, because smells can be subjectively interpreted and hence detected in different ways. Usually the detection techniques are based on the computation of different kinds of metrics, and other aspects related to the domain of the system under analysis, its size and other design features are not taken into account. In this paper we propose an approach we are studying based on machine learning techniques. We outline some common problems faced for smells detection and we describe the different steps of our approach and the algorithms we use for the classification.\",\n \"Title: Using Web Services for a Workflow Engine\\nYear: 2004\\n\\nAbstract: The aim of this paper is to describe an architecture developed to integrate solutions for the management and organization of a generic market. This represents an interesting case study for Web Service deployment. We call this architecture Workflow Engine. The actors involved in the market are characterized by a specific role. We describe a document centric approach, in which the evolution of each single business process can be reconstructed through the documents exchanged through messages. We will observe how the message-oriented approach is fundamental to completely exploit the Web Services available technology.\",\n \"Title: A Distributed Document Management Approach for Workflow Support in the DBSA Architecture\\nYear: 2003\\n\\nAbstract: Large and distributed organizations have to face a com- plex management problem: keeping track of state informa- tion of every \\\"entity\\\" (person, property, project, etc.). A tra- ditional centralized database is not well suited if the com- munication between parts of the organization is not reli- able or not \\\"always-on'. In this paper we present an archi- tecture, largely based on the distribution of formally struc- tured documents, designed to support the tracking of \\\"state changes\\\" inside the organization.\",\n \"Title: Code Smells, Micro Patterns And Their Relations\\nYear: 2012\\n\\nAbstract: Code smells are an example of a pattern-oriented trend applied to software evolution. Due to their abstract and informal nature, the detection process requires support from lower-level indicators like individual metric values, history of code changes, knowledge about program structure or results of program execution. We examine how the presence of program structures, called micro patterns, is correlated with the code smells and how micro patterns can be exploited for the purpose of smell detection.\",\n \"Title: Equalizing the Structures of Web Communities in Ontology Development Tools\\nYear: 2009\\n\\nAbstract: In this paper we face some relevant issues on the relations between web communities and ontologies. We build an operator that constructs a weak Web Community, according to the definition given in [16],starting from a seed of web sites. The necessity of such an operator is derived from a problem arisen in the model developed in [3], in which some relevant concepts in automotive oriented ontology were not given a corresponding Web community. This fact –if not considered- can bring automatic ontology development ([9,18]) to some non-correct results. In this work we define and analyze a new operator, called Com, with the tools furnished by the method of parametrization ([8,15]) and we find that, given a seed S and the induced graph I(S), the community generated by our operator is monotonic with respect to clustering and is denser than the original graph I(S).\",\n \"Title: Using fuzzy grammars for the evaluation of SGML -based documents\\nYear: 1997\\n\\nAbstract: 1 ABSTRACT In this paper we apply the Fuzzy Grammars or L-Grammars to the evaluation of documents based on the standard nonproprietary format SGML. Given a document type definition (DTD), we extend its associated language by adding a judgement label By selecting a particular label and by taking the start symbol of the grammar associated to the DTD, we can generate any DTD-compliant document with a fuzzy degree of membership derived from the judgement label. In this way we can fit the computational model underlying the L-languages to the process of collecting different evaluations of the same document.\",\n \"Title: JADEPT: Dynamic Analysis for Behavioral Design Pattern Detection\\nYear: 2009\\n\\nAbstract: In the context of reverse engineering, the recognition of design patterns provides additional information related to the rationale behind the design. This paper presents our approach to the recognition of design patterns based on dynamic analysis of Java software. The idea behind our approach is to identify a set of rules capturing information necessary to identify a design pattern instance. Rules are characterized by weights indicating their importance in the detection of a specific design pattern. The core behavior of each design pattern may be described through a subset of these rules forming a macrorule. Macro-rules define the main traits of a pattern. JADEPT (JAva DEsign Pattern deTector) is our software for design pattern identification based on this idea. It captures static and dynamic aspects through a dynamic analysis of the software by exploiting the JPDA (Java Platform Debugger Architecture). The extracted information is stored in a database. Queries to the database implement the rules defined to recognize design patterns. The tool has been validated with positive results on different academic implementations of design patterns and on systems as JADEPT itself.\",\n \"Title: A tool for design pattern detection and software architecture reconstruction\\nYear: 2011\\n\\nAbstract: It is well known that software maintenance and evolution are expensive activities, both in terms of invested time and money. Reverse engineering activities support the obtainment of abstractions and views from a target system that should help the engineers to maintain, evolve and eventually re-engineer it. Two important tasks pursued by reverse engineering are design pattern detection and software architecture reconstruction, whose main objectives are the identification of the design patterns that have been used in the implementation of a system as well as the generation of views placed at different levels of abstractions, which let the practitioners focus on the overall architecture of the system without worrying about the programming details it has been implemented with. In this context we propose an Eclipse plug-in called MARPLE (Metrics and Architecture Reconstruction Plug-in for Eclipse), which supports both the detection of design patterns and software architecture reconstruction activities through the use of basic elements and metrics that are mechanically extracted from the source code. The development of this platform is mainly based on the exploitation of the Eclipse framework and plug-ins as well as of different Java libraries for data access and graph management and visualization. In this paper we focus our attention on the design pattern detection process.\",\n \"Title: An Agent Based Internet Infrastructure for Learning Commerce\\nYear: 2000\\n\\nAbstract: Among the incredible amount of applications brought on the wave by the diffusion of the Internet, the possibility of an Electronic Commercialization of Learning assumes a relevant role. Thousands of people potentially acting as Learning Material Sellers, Buyers and Brokers are present on the Net asking for and efficient and effective infrastructure allowing them to play their role in this new market. Motivations are present and mature to investigate how to build such an infrastructure.In this paper we exploit agents and their mobility feature to develop a system for the electronic commerce of learning materials available on the Web. We tackle the aspects related to the negotiation, sometimes needed, to conclude a transaction and the aspects related to the best ways to save and classify the materials, through the learning object metadata approach and the related standardization efforts. A prototype architecture and its implementation is presented and discussed and directions for future studies are set.\",\n \"Title: Ontologies and Communities Co-evolution in Information Systems .\\nYear: 2010\\n\\nAbstract: Communities and ontologies are both concepts that have acquired strong momentum since the coming of age of new media such as Internet and the Web. They have become more relevant in a situation where growing communities and creating information categorizable through ontologies is made much easier and faster compared to what was possible before. In spite of this concomitance, the roles they have played in this information-rich environment have been so far not only different but also largely antithetic. The one played by communities is dynamic, and views information as something which is constantly changed and re-created by the agents that produce it. By contrast, the one played by ontologies views information in terms of its management at the meta-level through categories and concepts hierarchies, and it assumes that the ontology remains static, or changes very slowly as a consequence of decisions taken by the domain experts that control it. Given that information change is generally community-driven and this brings the clear necessity to make communities and ontologies interact. We propose to pursue this goal through a knowledge management approach, where the interaction between communities and ontologies is implemented as a knowledge life-cycle that leads to the creation of new concepts in the ontology as a consequence of the evolution of the information spaces constantly extended and re-created by the communities.\",\n \"Title: Multimedia Distributed Learning Environments: Evolution towards Intelligent Communications\\nYear: 2002\\n\\nAbstract: In this paper we describe the realization of a Multimedia Distributed Learning System developed by means of the Java language in a Intranet environment. In particular, we focus our discussion on the definition of the existing scenario, on the requirements specification and on the description of our overall system, by providing also first experimental results.We decided to add to our system a Software Agents based Intelligent Communication Support to manage both high level and low level communication needs coming from the nature of the application. Such communication needs could reasonably be considered as shared by a large family of distributed applications based on Intranet technologies. We present the main characteristics of our agents and describe how to use them to better achieve defined goals in terms of flexibility and scalability and to add network-awareness capabilities to the application.\",\n \"Title: Software aging assessment through a specialization of the SQuaRE quality model\\nYear: 2009\\n\\nAbstract: In the last years the software application portfolio has become a key asset for almost all companies. During their lives, applications undergo lots of changes to add new functionalities or to refactor older ones; these changes tend to reduce the quality of the applications themselves, causing the phenomenon known as software aging. Monitoring of software aging is very important for companies, but up to now there are no standard approaches to perform this task. In addition many of the suggested models assess software aging basing on few software features, whereas this phenomenon affects all of the software aspects. In 2005 ISO/IEC released the SQuaRE quality model which covers several elements of software quality assessment, but some issues make SQuaRE usage quite difficult. The purpose of this paper is to suggest an approach to software aging monitoring that considers the software product in its wholeness and to provide a specialization of the SQuaRE quality model which allows to perform this task.\",\n \"Title: A System For The Discovery And Selection Of Floss Projects\\nYear: 2014\\n\\nAbstract: Developing software systems by reusing components is a common practice. FLOSS (Free, Libre and Open Source Software) components represent a significant part of the reusable components available. The selection of suitable FLOSS components raises important issues both for software companies and research institutions. RepoFinder supports a keywordbased discovery process for FLOSS components, and applies well-known software metrics and analyses to compare the structural aspects of different components.\",\n \"Title: Impact of refactoring on quality code evaluation.\\nYear: 2011\\n\\nAbstract: Code smells are characteristics of the software that may indicate a code or design problem that can make software hard to understand, to evolve and maintain. Detecting code smells in the code and consequently applying the right refactoring steps, when necessary, is very important for improving the quality of the code. In this paper, according to well known metrics proposed to evaluate the code and design quality of a system, we analyze the impact of refactoring, applied to remove code smells, on the quality evaluation of the system.\",\n \"Title: A Similarity-Based Resolution Rule\\nYear: 2002\\n\\nAbstract: We propose an extension of the resolution rule as the core of a logic programming language based on similarity. Starting from a fuzzy unification algorithm described in Ref. 2 and then extended in Ref. 10, we introduce a fuzzy resolution rule, based on an extended most general unifier supplied by the extended unification algorithm. In our approach, unification fades into a unification degree because of a similarity introduced in a first-order language. Intuitively, the unification degree of a set of first-order terms is the cost one has to pay to consider these terms as equal. For this reason, our extension of the resolution is more structured than its classic counterpart; that is, when the empty clause is reached, in addition to a computed answer, a set of conditions is also determined. We give both the operational and fixed-point semantics of our extended logic programming language, and we prove their equivalence. (C) 2002 Wiley Periodicals, Inc.\",\n \"Title: A software architecture for distributed organization management\\nYear: 2006\\n\\nAbstract: In large organizations, management of large amounts of knowledge is a common problem. This knowledge is usually available in a distributed environment, in structured or non-structured form, and often is not exactly known where it is located and how to retrieve it in flexible ways. This paper describes an architecture to manage typical activities for an organization such as our University. During system analysis and specification, we had to collect a lot of information about structure and content of our organization, information available in various formats and media and not always automatically collectable. Hence, we designed a Document-based Software Architecture to support systems where formalization of information repositories, standardization of information location and management of every aspect involved in distributed contexts are crucial needs. We discuss if the configuration of a central-knowledge centered organization is the right solution or if it is better the choice of a distributed one. Since, all the documents managed in any organization are usually available in a structured way, we foresee a strong usage of XML documents and metadata standard definitions.\",\n \"Title: Computational Models for Information Reuse\\nYear: 1999\\n\\nAbstract: Techniques that optimize computations by reusing partial results have a long tradition in computer science. Seen from the point of view of sequential computations, all these techniques share the common execution strategy of storing partial results in a centralized data structure, while they differ as to how the results are computed, e.g. in a data-driven or constraint-driven fashion. Concurrent sy...\",\n \"Title: On Investigating Code Smells Correlations\\nYear: 2011\\n\\nAbstract: Code smells are characteristics of the software that may indicate a code or design problem that can make software hard to evolve and maintain. Detecting and removing code smells, when necessary, improves the quality and maintainability of a system. Code smells have been defined in [5], and different detection tools have been developed, each one characterized by particular features and providing often different results. Usually detection techniques are based on the computation of a particular set of combined metrics, or standard object-oriented metrics [8] or metrics defined ad hoc for the smell detection. As outlined in [3] there is the need for a clearer research strategy on smells identification and measurement. Other knowledge has to be exploited for their detection. In this work we are interested to investigate the direct and indirect correlations existing between smells. Moreover we propose to analyze if other relations exist between code smell and another kind of micro structure, called micro pattern[6]. We started this research since we think that the knowledge on these relations between smells could be exploited by code smell detection tools to improve their results. If one code smell exists, this can imply the existence of another code smell, or if one smell exists, another one cannot be there, or perhaps we could observe that some code smells tend to go together.\",\n \"Title: Design patterns: a survey on their micro-structures.\\nYear: 2013\\n\\nAbstract: Design patterns play a significant role in reverse engineering by providing information not only on how but also on why a solution has been implemented in a specific way because of their semantics. The application of design patterns leads to their personalization to a specific context, hence to the generation of variants. This makes their recognition a challenging task, which may be addressed through the understanding and detection of the micro-structures design patterns are made of. This is very useful for the detection as well as for the application of design patterns. The principal aim of this paper is to present a survey on these micro-structures and a comparison among them in the perspective of reverse engineering. Because of their less complex structure and behavior, as well as closer link to the source code, the recognition of these micro-structures may be automated, which can be considered a step towards the automatic recognition of the more complex design patterns. In this paper, we consider four of the most significant types of micro-structures: elemental design patterns, clues, sub-patterns, and micro patterns. To analyze the role of the micro-structures in the design pattern detection process, we make a comparison among these four types of micro-structures and among the micro-structures of various types in order to identify the relations among them. Copyright (C) 2011 John Wiley & Sons, Ltd.\",\n \"Title: Modeling QoS through Architectural Reflection\\nYear: 2005\\n\\nAbstract: In the context of the MAIS' project, we designed a reflective architecture to be used for Multichannel Adaptive Information Systems. Architectural reflection is an emerging research topic that enables information systems to observe and control their own structure and behaviour. One of the main issues raised by architectural reflection is related to the understanding of the reflective knowledge and to its proper design, implementation, and usage. In our approach the reflective knowledge is quality of service (QoS) oriented in that it captures the QoS features exhibited by the system components. lit this paper, we describe the different layers of our architecture focusing on how to model and to exploit QoS to achieve dynamic adaptability in a reflective architecture.\",\n \"Title: Metrics and Antipatterns for Software Quality Evaluation\\nYear: 2011\\n\\nAbstract: In the context of software evolution, many activities are involved and are very useful, like being able to evaluate the design quality of an evolving system, both to locate the parts that need particular refactoring or reengineering efforts, and to evaluate parts that are well designed. This paper aims to give support hints for the evaluation of the code and design quality of a system and in particular we suggest to use metrics computation and antipatterns detection together. We propose metrics computation based on particular kinds of micro-structures and the detection of structural and object-oriented antipatterns with the aim of identifying areas of design improvements. We can evaluate the quality of a system according to different issues, for example by understanding its global complexity, analyzing the cohesion and coupling of system modules and locating the most critical and complex components that need particular refactoring or maintenance.\",\n \"Title: Automatic Detection Of Bad Smells In Code: An Experimental Assessment\\nYear: 2012\\n\\nAbstract: Code smells are structural characteristics of software that may indicate a code or design problem that makes software hard to evolve and maintain, and may trigger refactoring of code. Recent research is active in defining automatic detection tools to help humans in finding smells when code size becomes unmanageable for manual review. Since the definitions of code smells are informal and subjective, assessing how effective code smell detection tools are is both important and hard to achieve. This paper reviews the current panorama of the tools for automatic code smell detection. It defines research questions about the consistency of their responses, their ability to expose the regions of code most affected by structural decay, and the relevance of their responses with respect to future software evolution. It gives answers to them by analyzing the output of four representative code smell detectors applied to six different versions of GanttProject, an open source system written in Java. The results of these experiments cast light on what current code smell detection tools are able to do and what the relevant areas for further improvement are.\",\n \"Title: Design Pattern Detection for Reverse Engineering\\nYear: 2006\\n\\nAbstract: The main goal of the workshop is to address the issues related to design patterns identification for design recovery focusing on the role of the reverse engineering in identifying the sub-elements of the design patterns that can improve their detection.\",\n \"Title: An Eclipse Plug-in for the Java PathFinder Runtime Verification System\\nYear: 2006\\n\\nAbstract: Java PathFinder (JPF) is an explicit state model checker developed by the Automated Software Engineering Group of NASA of the AMES Research Center (California). Eclipse is probably the most important and used Java Integrated Developing Environment (IDE) and not only; it is a framework/environment that can be easily extended with new functionalities by exploiting its plug-in mechanism. Through a JPF plug-in for Eclipse it is possible to integrate powerful model checking and testing capabilities into the development environment. This paper presents the re-design of the standalone version of JPF towards an Eclipse plug-in exploiting and outlining in this way the advantages of an open source development.\",\n \"Title: Designing an Architecture for Multichannel Adaptive Information Systems\\nYear: 2004\\n\\nAbstract: Multichannel Adaptive Information Systems aim at enabling users/clients to access software applications through various types of devices (i.e., PCs, laptops, palmtops, PDAs, mobile phones, etc) and networks (i.e., local, wireless, bluetooth, etc) by adapting their functionalities/performances to the specific features of each device/network. Further, such systems should enable a smooth switch among different channels (devices and networks) according to the current requirements of, the users. Multichannel Adaptive Information Systems raise interesting research challenges that are related to the development of architectures, methodologies, environments, tools for such systems, and concern issues as adaptivity, flexibility, transactionality, security, etc. This paper introduces the research efforts towards the design of MAIS software architecture for this kind of systems, as specified within the Italian MURST-FIRB Project MAIS(1) \\\"Multichannel Adaptive Information System\\\".\",\n \"Title: A Specialisation Of The Square Quality Model For The Evaluation Of The Software Evolution And Maintenance Activity\\nYear: 2008\\n\\nAbstract: One of the activities where companies invest the most today is software evolution and maintenance; in spite of this big effort, companies do not have a reliable and agreed model to evaluate the quality of maintenance activity and to monitor how their software evolves as a result of maintenance.Although the ISO/IEC SQuaRE quality model is well-known in industry and covers also maintenance issues, it is too general and too abstract to be directly applied; moreover some of the metrics that the model suggests are very difficult and expensive to apply.The purpose of this paper is to specialise the ISO/IEC SQuaRE quality model in order to make it easier to use for a company.\",\n \"Title: Evolution of an Adaptive Middleware Exploiting Architectural Reflection\\nYear: 2006\\n\\nAbstract: Nowadays information systems are required to adapt themselves dynamically to the ever changing environment and requirements. Architectural reflection represents a principled means to address adaptivity. It also represents an emerging approach to deal with the software evolution issues. In this paper we aim to point out how systems exploiting architectural reflection to achieve adaptivity evolve in an organized, linear manner controlling easier their growth and complexity than systems based on ad hoc solutions. To sustain this affirmation we present the possible evolution improvements we gain through our Adaptive and Reflective Middleware (ARM).\",\n \"Title: Boosting concept discovery in collective intelligences\\nYear: 2009\\n\\nAbstract: Collective intelligence derives from the connection and the interaction of multiple, distributed, independent intelligent units via a network, such as, typically, a digital data network. As collective intelligences are effectively making their way into reality in consequence of ubiquitous digital communication, the opportunity and the challenge arise of extending their basic architecture in order to support higher thought-processes analogous to those characterizing human intelligences. We address here specifically the process of conceptual abstraction, namely the discovery of new concepts and ideas, and, to this purpose, we introduce the general functional notion of cognitive prosthesis supporting the implementation of a given thought-process in a collective intelligence. Since there exists a direct relationship between concept discovery and innovation in human intelligences, we point out how analogous innovation capabilities can now be supported for collective intelligences, with direct applications to Web-based innovation of products and services.\",\n \"Title: Evaluation of SGML-based information through fuzzy techniques\\nYear: 2001\\n\\nAbstract: The large amount of information available and the difficulty on processing it has made knowledge management a promising area of research. Several topics are related to it, for example distributed and intelligent information retrieval, information filtering and information evaluation, which became crucial. In this paper, we focus our attention on the knowledge evaluation problem. With the aim of evaluating information coded in the standard non-proprietary format SGML (as also in XML), we propose some evaluation methods based on L-grammars which are fuzzy grammars. In particular we apply these methods to the evaluation of documents in SGML-format and to the evaluation of HTML-pages in the World Wide Web. L-grammars generate recursively enumerable L-languages, as it has been proved in Gerla ((1991), Information Sciences 53), and so they can be used to generate fuzzy languages based on extensions of the document type definitions (DTD) involved by SGML. Given a DTD, we extend its associated language by adding a judgement label . By selecting a particular label and by taking the start symbol of the grammar associated to the DTD, we can generate any DTD-compliant document with a fuzzy degree of membership derived from the judgement label. In this way we fit the computational model underlying the recursively enumerable L-languages to the process of collecting different evaluations of the same document. Finally, we outline how the generalization of these methods of evaluation can be applied in different contexts and for different roles, as for example for information filtering.\",\n \"Title: On the parallel implementation of the higher order logic language lambda Prolog\\nYear: 1994\\n\\nAbstract: Higher-order logic languages seem particularly suitable to be implemented on parallelarchitectures for at lest two reasons. On one hand, they offer more opportunitiesfor parallelization than first-order logic languages. On the other hand, the greatercomputational costs of higher-order unification of typed -terms should allow the exploitationof parallelism with a larger granularity, possibly leading to higher efficiency.In spite of their potential interest, there are a few...\",\n \"Title: An Experience Report on Using Code Smells Detection Tools\\nYear: 2011\\n\\nAbstract: Detecting code smells in the code and consequently applying the right refactoring steps when necessary is very important to improve the quality of the code. Different tools have been proposed for code smell detection, each one characterized by particular features. The aim of this paper is to describe our experience on using different tools for code smell detection. We outline the main differences among them and the different results we obtained.\",\n \"Title: Integrating Concepts and Knowledge in Large Content Networks.\\nYear: 2014\\n\\nAbstract: Large content networks like the World Wide Web contain huge amounts of information that have the potential of being integrated because their components fit within common concepts and/or are connected through hidden, implicit relationships. One attempt at such an integration is the program called the “Web of Data,” which is an evolution of the Semantic Web. It targets semi-structured information sources such as Wikipedia and turns them into fully structured ones in the form of Web-based databases like DBpedia and then integrates them with other public databases such as Geonames. On the other hand, the vast majority of the information residing on the Web is still totally unstructured, which is the starting point for our approach that aims to integrate unstructured information sources. For this purpose, we exploit techniques from Probabilistic Topic Modeling, in order to cluster Web pages into concepts (topics), which are then related through higher-level concept networks; we also make implicit semantic relationships emerge between single Web pages. The approach has been tested through a number of case studies that are here described. While the applicative focus of the research reported here is on knowledge integration on the specific and relevant case of the WWW, the wider aim is to provide a framework for integration generally applicable to all complex content networks where information propagates from multiple sources.\",\n \"Title: On applying machine learning techniques for design pattern detection.\\nYear: 2015\\n\\nAbstract: We apply machine learning to detect design patterns in software systems.We exploit a specific design pattern model to apply machine learning techniques.We compare the performances of several machine learning algorithms.We provide a large dataset containing manually checked design pattern instances. The detection of design patterns is a useful activity giving support to the comprehension and maintenance of software systems. Many approaches and tools have been proposed in the literature providing different results. In this paper, we extend a previous work regarding the application of machine learning techniques for design pattern detection, by adding a more extensive experimentation and enhancements in the analysis method. Here we exploit a combination of graph matching and machine learning techniques, implemented in a tool we developed, called MARPLE-DPD. Our approach allows the application of machine learning techniques, leveraging a modeling of design patterns that is able to represent pattern instances composed of a variable number of classes. We describe the experimentations for the detection of five design patterns on 10 open source software systems, compare the performances obtained by different learning models with respect to a baseline, and discuss the encountered issues.\",\n \"Title: A Refactoring Suggestion Tool For Removing Clones In Java Code\\nYear: 2014\\n\\nAbstract: Code duplication is considered a widespread code smell, a symptom of bad code development practices or potential design issues. Code smells are also considered to be indicators of poor software maintainability. The refactoring cost associated with removing code clones can be very high, partly because of the number of different decisions that must be made regarding the kind of refactoring steps to apply. Here, we describe a tool that has been developed to suggest the best refactoring steps that could be taken to remove clones in Java code. Our approach is based on the classification of clones, in terms of their location in a class hierarchy, so that decisions can be made from a restricted set of refactorings that have been evaluated using multiple criteria.\",\n \"Title: Pattern detection for conceptual schema recovery in data-intensive systems\\nYear: 2014\\n\\nAbstract: AbstractIn this paper, an approach for information systems reverse engineering is proposed and applied. The aim is to support a unified perspective to the reverse engineering process of both data and software. At the state of the art, indeed, many methods, techniques, and tools for software reverse engineering have been proposed to support program comprehension, software maintenance, and software evolution. Other approaches and tools have been proposed for data reverse engineering, with the aim, for example, to provide complete and up-to-date documentation of legacy databases. However, the two engineering communities often worked independently, and very few approaches addressed the reverse engineering of both data and software as information system's constituencies. Hence, a higher integration is needed to support a better co-evolution of databases and programs, in an environment often characterized by high availability of data and volatility of information flows. Accordingly, the approach we propose leverages the detection of object-relational mapping design patterns to build a conceptual schema of the software under analysis. Then, the conceptual schema is mapped to the domain model of the system, to support the design of the evolution of the information system itself. The approach is evaluated on two large-scale open-source enterprise applications. Copyright © 2014 John Wiley & Sons, Ltd.\",\n \"Title: Capturing Software Evolution and Change through Code Repository Smells.\\nYear: 2014\\n\\nAbstract: In the last years we have seen the rise and the fall of many version control systems. These systems collect a large amount of data spanning from the path of the files involved in changes to the exact text changed in every file. This data can be exploited to produce an overview about how the system changed over time and evolved. We have developed a tool, called VCS-Analyzer, to use this information, both for data retrieval and analysis tasks. Currently, VCS-Analyzer implements six different analyses: two based on source code for the computation of metrics and the detection of code smells, and four original analysis based on repositories metadata, which are based on the concepts of Repository Metrics and Code Repository Smells. In this paper, we describe one smell and two metrics we have defined for source code repositories analysis.\",\n \"Title: Tracking line changes in source code repositories\\nYear: 2014\\n\\nAbstract: Previous research determined that the analysis of file changes in software repositories is useful for maintenance activities, like defect prediction. Changes rarely modify the entire file contents, but are usually localized in specific code regions.\",\n \"Title: A comparison of reverse engineering tools based on design pattern decomposition\\nYear: 2005\\n\\nAbstract: The usefulness of design patterns in forward engineering is already well-known and several tools provide support for their application in the development of software systems. While the role of design patterns in reverse engineering is still argued primarily due to their informal definition which leads to various possible implementations of each pattern. One of the most discussed aspects related to design patterns is about the need of their formalization according to the drawbacks this can represent. Formalization leads to the identification of the so-called sub-patterns, which are the recurring fundamental elements design patterns are composed of. In this paper we analyze the role sub-patterns play in two reverse engineering tools: FUJABA and SPQR. Attention is focused on how sub-patterns are exploited to define and to detect design patterns. To emphasize the similarities and differences between the two approaches, the composite design pattern is considered as example.\",\n \"Title: Software agents for computer vision: a preliminary discussion\\nYear: 1998\\n\\nAbstract: In this paper, we start a preliminary discussion about the development of software agents developed for Computer Vision. Such agents must be heterogeneous and adaptive in nature to satisfy increasing requests for integration-cooperation capability exhibited by current Computer Vision applications (e.g., biomedical imaging and image retrieval on the Web). We give a brief introduction to the overall architecture design and we focus on lowest level agents, called W-bots (Working Software Robots). We define the nature of such agents and describe their ways of interaction, and their adaptive capabilities. Finally, we present an example of the proposed architecture utilizing VDM++ to specify agents as active objects.\",\n \"Title: Investigating the impact of code smells debt on quality code evaluation\\nYear: 2012\\n\\nAbstract: Different forms of technical debt exist that have to be carefully managed. In this paper we focus our attention on design debt, represented by code smells. We consider three smells that we detect in open source systems of different domains. Our principal aim is to give advice on which design debt has to be paid first, according to the three smells we have analyzed. Moreover, we discuss if the detection of these smells could be tailored to the specific application domain of a system.\",\n \"Title: Fuzzy XML queries via context-based choice of aggregations.\\nYear: 2000\\n\\nAbstract: A flexible query model is presented for semi-structured information stored in well-formed XML documents, modeled as XML fuzzy graphs by computing estimates of the importance of the information associated to XML elements and attributes. The notion of fuzzy graph closure with threshold is then used to obtain a fuzzy extension of the XML fuzzy graphs' topological structure. Weights associated to closure arcs are computed as a conjunction of the importance values of the underlying arcs in the original graph, via a context-based choice of conjunctions. Query results are subgraphs of the resulting fuzzy closure graph, presented as a ranked list according to their degree of matching to the user query.\",\n \"Title: A context-aware style of software design\\nYear: 2015\\n\\nAbstract: Contemporary large software systems rely on complex software ecosystems for managing infrastructural tasks. While these ecosystems facilitate software development, the software architect must put care in not relying on assumptions on behaviors and policies of the ecosystem that may change with platform evolution. Based on our experience with developing analyses within MARPLE, a framework for software comprehension and architecture reconstruction, we propose an abstract, context-aware style for specifying software. In the spirit of decoupling computation from coordination, this style decouples the specification of the computations to be performed from the specification of the contexts where they must take place. Software described in this way exposes its primitives at a level of abstraction closer to that of the framework, enabling better reasoning on the features of the design, easing correct implementation, and fostering a better interaction between the software and the framework it relies on.\",\n \"Title: Towards Assessing Software Architecture Quality by Exploiting Code Smell Relations\\nYear: 2015\\n\\nAbstract: We can evaluate software architecture quality using a plethora of metrics proposed in the literature, but interpreting and exploiting in the right way these metrics is not always a simple task. This is true for both fixing the right metric threshold values and determining the actions to be taken to improve the quality of the system. Instead of metrics, we can detect code or architectural anomalies that give us useful hints on the possible architecture degradation. In this paper, we focus our attention on the detection of code smells and in particular on their relations and co-occurrences, with the aim to evaluate tecnical debt in an architectural context. We start from the assumption that certain patterns of code anomalies tend to be better indicators of architectural degradation than simple metrics evaluation.\",\n \"Title: Automatic Metric Thresholds Derivation for Code Smell Detection\\nYear: 2015\\n\\nAbstract: Code smells are archetypes of design shortcomings in the code that can potentially cause problems during maintenance. One known approach for detecting code smells is via detection rules: a combination of different object-oriented metrics with pre-defined threshold values. The usage of inadequate thresholds when using this approach could lead to either having too few observations (too many false negatives) or too many observations (too many false positives). Furthermore, without a clear methodology for deriving thresholds, one is left with those suggested in literature (or by the tool vendors), which may not necessarily be suitable to the context of analysis. In this paper, we propose a data-driven (i.e., benchmark-based) method to derive threshold values for code metrics, which can be used for implementing detection rules for code smells. Our method is transparent, repeatable and enables the extraction of thresholds that respect the statistical properties of the metric in question (such as scale and distribution). Thus, our approach enables the calibration of code smell detection rules by selecting relevant systems as benchmark data. To illustrate our approach, we generated a benchmark dataset based on 74 systems of the Qualitas Corpus, and extracted the thresholds for five smell detection rules.\",\n \"Title: A Duplicated Code Refactoring Advisor\\nYear: 2015\\n\\nAbstract: Refactoring is one of the key practices in Extreme Programming and other agile methods. Duplicated code is one of the most pervasive and pungent smells to remove from source code through refactoring. Duplicated code has been largely studied in the literature, and different types of duplications, or \\\"clones\\\", have been identified. Some studies analyzed in details the problems caused by clones in the code, others outlined also the difficulties in removing clones, and the cases in which it could be better not removing them. The refactoring cost for removing clones can be very high, also due to the different choices on the possible refactoring steps. In this paper, we describe our approach and tool developed with the aim to suggest the best refactorings to remove clones in Java code. Our approach is based on the classification of the clones in terms of their location in a class hierarchy, and allows to choose among a restricted set of refactorings, which are then evaluated using multiple criteria. We provide a validation of the effectiveness of the approach.\",\n \"Title: Including structural factors into the metrics-based code smells detection\\nYear: 2015\\n\\nAbstract: Code smells help to discover and describe deeper problems in software design. Several automated methods of smell detection are based the analysis of a combination of code-related metrics relevant for a given flaw. However, some smells reflect more complex issues and require a holistic perspective that woudl cover a number of different sources of data. In this paper we experimentally verify the usefulness of including structural factors into a metrics-based detection of God Class and Brain Class code smells.\",\n \"Title: On experimenting refactoring tools to remove code smells\\nYear: 2015\\n\\nAbstract: When we develop a software project of a certain complexity, source code maintainability could become a problem, in particular if developers do not use a consolidate development process that simplifies the management of the entire project. When source code becomes very complex, it is difficult for developers to share and modify it. We can improve internal software qualities such as reusability, maintainability and readability through refactoring. Refactoring can be applied to remove possible problems in the code, as code smells. Identifying code smells and removing them through refactoring results in better code maintainability, but it can be an overwhelming task. In this paper, we describe our experimentation on using four refactoring tools to remove code smells in four systems, with the aim to outline advantages and disadvantages of the tools with respect to the accomplishment of this task, and to identify the smells easier to be removed among the ones we considered in this paper.\",\n \"Title: Inter-smell relations in industrial and open source systems: A replication and comparative analysis\\nYear: 2015\\n\\nAbstract: The presence of anti-patterns and code smells can affect adversely software evolution and quality. Recent work has shown that code smells that appear together in the same file (i.e., collocated smells) can interact with each other, leading to various types of maintenance issues and/or to the intensification of negative effects. It has also been found that code smell interactions can occur across coupled files (i.e., coupled smells), with comparable negative effects as the interaction of same-file (collocated) smells. Different inter-smell relations have been described in previous work, yet only few studies have evaluated them empirically. This study attempts to replicate the findings from previous work on inter-smell relations by analyzing larger systems, and by including both industrial and open source ones. We also include the analysis of coupled smells in addition to collocated smells, to achieve a more complete picture of inter-smell relations. Our results suggest that if coupled smells are not considered, one may risk increasing the number of false negatives when analysing inter-smells. A major finding is that patterns of inter-smell relations vary between open source and industrial systems, suggesting that contextual variables should be considered in further studies on code smells.\",\n \"Title: Towards a prioritization of code debt: A code smell Intensity Index\\nYear: 2015\\n\\nAbstract: Code smells can be used to capture symptoms of code decay and potential maintenance problems that can be avoided by applying the right refactoring. They can be seen as a source of technical debt. However, tools for code smell detection often provide far too many and different results, and identify many false positive code smell instances. In fact, these tools are rooted on initial and rather informal code smell definitions. This represents a challenge to interpret their results in different ways. In this paper, we provide an Intensity Index, to be used as an estimator to determine the most critical instances, prioritizing the examination of smells and, potentially, their removal. We apply Intensity on the detection of six well known and common smells and we report their Intensity distribution from an analysis performed on 74 systems of the Qualitas Corpus, showing how Intensity could be used to prioritize code smells inspection.\",\n \"Title: Antipattern and Code Smell False Positives: Preliminary Conceptualization and Classification\\nYear: 2016\\n\\nAbstract: Anti-patterns and code smells are archetypes used for describing software design shortcomings that can negatively affect software quality, in particular maintainability. Tools, metrics and methodologies have been developed to identify these archetypes, based on the assumption that they can point at problematic code. However, recent empirical studies have shown that some of these archetypes are ubiquitous in real world programs, and many of them are found not to be as detrimental to quality as previously conjectured. We are therefore interested in revisiting common anti-patterns and code smells, and building a catalogue of cases that constitute candidates for \\\"false positives\\\". We propose a preliminary classification of such false positives with the aim of facilitating a better understanding of the effects of anti-patterns and code smells in practice. We hope that the development and further refinement of such a classification can support researchers and tool vendors in their endeavour to develop more pragmatic, context-relevant detection and analysis tools for anti-patterns and code smells.\",\n \"Title: An Experience Report on Detecting and Repairing Software Architecture Erosion\\nYear: 2016\\n\\nAbstract: Architecture erosion constitutes the most visible effect of the degradation of design. It is a major reason to address the design debt, often caused by architectural mismatch problems. Today, the identification of design erosion is a major concern for designers and software maintainers. Adequate tools are necessary to identify and repair the debt. This paper describes our experience on identifying architectural erosion problems on existing open source software applications through the support of two well known tools. Moreover, we outline if the tools provide useful hints in repairing some of the detected problems.\",\n \"Title: Tool support for evaluating architectural debt of an existing system: an experience report.\\nYear: 2016\\n\\nAbstract: During software development, maintenance and evolution, we can assess the quality of code, design and architecture to capture hints of possible problems and face them as soon as possible. We can capture signals of architecture erosion in different ways by exploiting several tools with the aim to identify architectural violations, architectural smells or other relevant features. In this paper, we provide our experience report in using three known tools to capture information that can be useful to identify and evaluate the architectural debt of an application. We outline the main differences among these tools and the results they produce.\",\n \"Title: On evaluating the impact of the refactoring of architectural problems on software quality.\\nYear: 2016\\n\\nAbstract: We can improve software quality in different ways and by removing different kinds of problems. In this paper, we focus our attention on architectural problems, as architectural smells or antipatterns represent, we remove some of these problems through refactoring steps and we check the impact that the refactoring has on different quality metrics. In particular, we focus our attention on some Quality Indexes computed by four tools. These tools are used also for the detection of the architectural problems. We present the results and outline different issues related to the impact of the refactoring of these architectural problems on the Quality Indexes and the difficulties in the choice of the problems to be refactored.\",\n \"Title: Smells Like Teen Spirit: Improving Bug Prediction Performance Using the Intensity of Code Smells\\nYear: 2016\\n\\nAbstract: Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes. Specifically, we evaluate the contribution of a measure of the severity of code smells (i.e., code smell intensity) by adding it to existing bug prediction models and comparing the results of the new model against the baseline model. Results indicate that the accuracy of a bug prediction model increases by adding the code smell intensity as predictor. We also evaluate the actual gain provided by the intensity index with respect to the other metrics in the model, including the ones used to compute the code smell intensity. We observe that the intensity index is much more important as compared to other metrics used for predicting the buggyness of smelly classes.\",\n \"Title: Technical Debt Indexes Provided by Tools: A Preliminary Discussion\\nYear: 2016\\n\\nAbstract: In software maintenance and evolution, it is important to assess both code and architecture in order to identify issues to be solved to improve software quality. Different tools provide some kind of index giving us an overall evaluation of a project to be used when managing its technical debt. In this paper, we outline how the indexes, that we call in general Technical Debt Indexes, provided by five different tools are computed. We describe their principal features and differences, what aspects they are missing, and we outline if (and how) the indexes take into account architectural problems that could have a major impact on the architectural debt. We show that the indexes rely on different information sources and measure different quantities.\",\n \"Title: Code smell severity classification using machine learning techniques.\\nYear: 2017\\n\\nAbstract: Several code smells detection tools have been developed providing different results, because smells can be subjectively interpreted and hence detected in different ways. Machine learning techniques have been used for different topics in software engineering, e.g., design pattern detection, code smell detection, bug prediction, recommending systems. In this paper, we focus our attention on the classification of code smell severity through the use of machine learning techniques in different experiments. The severity of code smells is an important factor to take into consideration when reporting code smell detection results, since it allows the prioritization of refactoring efforts. In fact, code smells with high severity can be particularly large and complex, and create larger issues to the maintainability of software a system. In our experiments, we apply several machine learning models, spanning from multinomial classification to regression, plus a method to apply binary classifiers for ordinal classification. In fact, we model code smell severity as an ordinal variable. We take the baseline models from previous work, where we applied binary classification models for code smell detection with good results. We report and compare the performance of the models according to their accuracy and four different performance measures used for the evaluation of ordinal classification techniques. From our results, while the accuracy of the classification of severity is not high as in the binary classification of absence or presence of code smells, the ranking correlation of the actual and predicted severity for the best models reaches 0.880.96, measured through Spearmans .\",\n \"Title: Students' Feedback in Using GitHub in a Project Development for a Software Engineering Course.\\nYear: 2017\\n\\nAbstract: GitHub is a platform used for the development of software projects. It provides a traceable project repository and a social meeting place for communities of practices. This poster presents the students' feedback on using GitHub as a development platform for software projects counting as an exam for a 3rd-year undergraduate software engineering course on software design. Students worked in teams and their feedback is positive overall.\",\n \"Title: Change Prediction through Coding Rules Violations.\\nYear: 2017\\n\\nAbstract: Static source code analysis is an increasingly important activity to manage software project quality, and is often found as a part of the development process. A widely adopted way of checking code quality is through the detection of violations to specific sets of rules addressing good programming practices. SonarQube is a platform able to detect these violations, called Issues. In this paper we described an empirical study performend on two industrial projects, where we used Issues extracted on different versions of the projects to predict changes in code through a set of machine learning models. We achieved good detection performances, especially when predicting changes in the next version. This result paves the way for future investigations of the interest in an industrial setting towards the prioritization of Issues management according to their impact on change-proneness.\",\n \"Title: Arcan: A Tool for Architectural Smells Detection\\nYear: 2017\\n\\nAbstract: Code smells are sub-optimal coding circumstances such as blob classes or spaghetti code - they have received much attention and tooling in recent software engineering research. Higher-up in the abstraction level, architectural smells are problems or sub-optimal architectural patterns or other design-level characteristics. These have received significantly less attention even though they are usually considered more critical than code smells, and harder to detect, remove, and refactor. This paper describes an open-source tool called Arcan developed for the detection of architectural smells through an evaluation of several different architecture dependency issues. The detection techniques inside Arcan exploit graph database technology, allowing for high scalability in smells detection and better management of large amounts of dependencies of multiple kinds. In the scope of this paper, we focus on the evaluation of Arcan results carried out with real-life software developers to check if the architectural smells detected by Arcan are really perceived as problems and to get an overall usefulness evaluation of the tool.\",\n \"Title: Ninth International Workshop on Managing Technical Debt: report on the MTD 2017 workshop\\nYear: 2017\\n\\nAbstract: Welcome to the Ninth International Workshop on Managing Technical Debt, collocated with the 18th International Conference on Agile Software Development (XP 2017) in Cologne. The technical debt research community continues to expand through collaborations of industry, tool vendors, and academia. The main topic of this year's workshop was on the impact of agile development approaches towards the management of technical debt.\",\n \"Title: Model-Driven Reverse Engineering Approaches: A Systematic Literature Review.\\nYear: 2017\\n\\nAbstract: This paper explores and describes the state of the art for what concerns the model-driven approaches proposed in the literature to support reverse engineering. We conducted a systematic literature review on this topic with the aim to answer three research questions. We focus on various solutions developed for model-driven reverse engineering, outlining in particular the models they use and the transformations applied to the models. We also consider the tools used for model definition, extraction, and transformation and the level of automation reached by the available tools. The model-driven reverse engineering approaches are also analyzed based on various features such as genericity, extensibility, automation of the reverse engineering process, and coverage of the full or partial source artifacts. We describe in detail and compare fifteen approaches applying model-driven reverse engineering. Based on this analysis, we identify and indicate some hints on choosing a model-driven reverse engineering approach from the available ones, and we outline open issues concerning the model-driven reverse engineering approaches.\",\n \"Title: Evaluation Of Self-Adaptive Systems: A Women Perspective\\nYear: 2017\\n\\nAbstract: Statistically, the number of women in computer science is significantly lower than the number of men working in the same domain. This applies also to the subarea of software architectures. We would like to outline that there may be also exceptions: there are more women than men leading and working in the evaluation of autonomic and self-adaptive issues in software systems. In this paper, we introduce seven research approaches involving women in the evaluation of self-adaptive systems. Further, we discuss the heterogeneity of these evaluation approaches from various points of view (e.g., types of systems addressed, types of evaluation, qualitative vs quantitative or static vs dynamic). The paper ends with our thoughts and perspectives on future needs and trends in the evaluation of self-adaptive systems.\",\n \"Title: Alternatives To The Knowledge Discovery Metamodel: An Investigation\\nYear: 2017\\n\\nAbstract: To better understand and exploit the knowledge necessary to comprehend and evolve an existing system, different models can be extracted from it. Models represent the extracted information at various abstraction levels, and are useful to document, maintain, and reengineer the system. The Knowledge Discovery Metamodel (KDM) has been defined by the object management group as a meta-model supporting a large share of reverse engineering activities. Its specification has also been adopted by the ISO in 2012. This paper explores and describes alternative meta-models proposed in the literature to support reverse engineering, program comprehension, and software evolution activities. We focus on the similarity and differences of the alternative meta-models with KDM, trying to understand the potentials of reciprocal information interchange. We describe KDM and other five meta-models, plus their extensions available in the literature and their diffusion in the reverse engineering community. We also investigate the approaches using KDM and the five meta-models. In the paper, we underline the limited reuse of models for reverse engineering, and identify potential directions for future related research, to enhance the existing models and ease the exchange of information among them.\",\n \"Title: Technical Debt in Agile Development: Report on the Ninth Workshop on Managing Technical Debt (MTD 2017).\\nYear: 2017\\n\\nAbstract: We report on the Ninth International Workshop on Managing Technical Debt, collocated with the 18th International Conference on Agile Software Development (XP 2017) in Cologne. The technical debt research community continues to expand through collaborations of industry, tool vendors, and academia. The theme of this year's workshop was on technical debt in agile development. Presentations and discussion centered on the topics: technical debt at the code level, architectural technical debt assessment, agile approaches and their impact on technical debt management, and selling the business case of technical debt management.\",\n \"Title: How do community smells influence code smells?\\nYear: 2018\\n\\nAbstract: Code smells reflect sub-optimal patterns of code that often lead to critical software flaws or failure. In the same way, community smells reflect sub-optimal organisational and socio-technical patterns in the organisational structure of the software community.\\n\\nTo understand the relation between the community smells and code smells we start by surveying 162 developers of nine open-source systems. Then we look deeper into this connection by conducting an empirical study of 117 releases from these systems.\\n\\nOur results indicate that community-related factors are intuitively perceived by most developers as causes of the persistence of code smells. Inspired by this observation we design a community-aware prediction model for code smells and show that it outperforms a model that does not consider community factors.\",\n \"Title: Machine learning based code smell detection through WekaNose.\\nYear: 2018\\n\\nAbstract: Code smells can be subjectively interpreted, the results provided by detectors are usually different, the agreement in the results is scarce, and a benchmark for the comparison of these results is not yet available. The main approaches used to detect code smells are based on the computation of a set of metrics. However code smell detectors often use different metrics and/or different thresholds, according to their detection rules. As result of this inconsistency the number of detected smells can increase or decrease accordingly, and this makes hard to understand when, for a specific software, a certain characteristic identifies a code smell or not. In this work, we introduce WekaNose, a tool that allows to perform an experiment to study code smell detection through machine learning techniques. The experiment's purpose is to select rules, and/or obtain trained algorithms, that can classify an instance (method or class) as affected or not by a code smell. These rules have the main advantage of being extracted through an example-based approach, rather then a heuristic-based one.\",\n \"Title: Support for architectural smell refactoring.\\nYear: 2018\\n\\nAbstract: To preserve high quality of a project, it is necessary to perform many refactoring steps during the development and maintenance phases. Code refactoring received a great attention in the literature, often directed to the refactoring of code smells. While at the architectural level, architectural smells received less attention. One of the most common architectural smell which may affect many parts of a software application is the Cyclic Dependency smell. This position paper presents a tool prototype that suggests which path a developer could follow to remove Cyclic Dependency smells in Java applications. The tool has been developed as an extension of the Arcan tool for architectural smells detection. In this paper we describe how the tool identifies the path to follow for the refactoring of Cyclic Dependency on real projects. The tool has been thought only to suggest a possible refactoring approach which a developer could considerate to resolve Cyclic Dependency.\",\n \"Title: Identifying and Prioritizing Architectural Debt Through Architectural Smells: A Case Study in a Large Software Company.\\nYear: 2018\\n\\nAbstract: Architectural technical debt can have a huge impact on software maintainability and evolution. Hence, different architectural violations, detected as architectural smells, need to be identified and refactored. In this paper, we conducted a multiple case-study on several architectural smells detected in four industrial projects. We conducted an in-depth investigation with a questionnaire, interviews and thorough inspection of the code with the practitioners. We evaluated the negative impact of the technical debt detected by the architectural smells, their difficulty to be refactored and the usefulness of the detection tool. The results show that practitioners appreciated the help of automatic detection, and that they prioritize refactoring architectural debt that causes more negative impact despite the higher refactoring effort.\",\n \"Title: Collaborative and teamwork software development in an undergraduate software engineering course.\\nYear: 2018\\n\\nAbstract: •Collaboration and teamwork through the GitHub platform.•Software quality assessment through SonarQube.•Project management tasks through Microsoft Project.•Evaluation based on student feedback after assignments and a team project.•Identification of future improvements for the teaching/learning process.\",\n \"Title: Code smells and their collocations: A large-scale experiment on open-source systems.\\nYear: 2018\\n\\nAbstract: Code smells indicate possible flaws in software design, that could negatively affect system’s maintainability. Interactions among smells located in the same classes (i.e., collocated smells) have even more detrimental effect on quality. Extracted frequent patterns of collocated smells could help to understand practical consequences of collocations. In this paper we identify and empirically validate frequent collocations of 14 code smells detected in 92 Java systems, using three approaches: pairwise correlation analysis, PCA and associative rules. To cross-validate the results, we used up to 6 detectors for each smell. Additionally, we examine and compare techniques used to extract the relationships. The contribution is three-fold: (1) we identify and empirically validate relationships among the examined code smells on a large dataset that we made publicly available, (2) we discuss how the choice of code smell detectors affects results, and (3) we analyze the impact of software domain on existence of the smell collocations. Additionally, we found that analytical methods we used to discover collocations, are complementary. Smells collocations display recurring patterns that could help prioritizing the classes affected by code smells to be refactored and developing or enhancing detectors exploiting information about collocations. They can also help the developers focusing on classes deserving more maintenance effort.\",\n \"Title: Towards an Architectural Debt Index\\nYear: 2018\\n\\nAbstract: Different indexes have been proposed to evaluate software quality and technical debt. Usually these indexes take into account different code level issues and several metrics, well known software metrics or new ones defined ad hoc for a specific purpose. In this paper we propose and define a new index, more oriented to the evaluation of architectural violations. We describe in detail the index, called Architectural Debt Index, that we integrated in a tool developed for architectural smell detection. The index is based on the detection of architectural smells, their criticality and their history. Currently only dependency architectural smells have been considered, but other architectural debt indicators can be considered and integrated in the index computation.\",\n \"Title: An Architectural Smells Detection Tool for C and C++ Projects\\nYear: 2018\\n\\nAbstract: Architectural smells gained great attention in the past few years since they directly affect software quality and increase architectural technical debt. However, while it is straightforward to understand why they are important, it is more difficult to find techniques and tools to detect and remove architectural smells. The purpose of this paper is to introduce an open-source tool for automatic architectural smells detection for C/C++ projects, by creating an abstraction of the project and defining the concept of dependency between elements belonging to the project in order to identify architectural smells. The tool has been validated on some open-source projects with promising results.\",\n \"Title: [Research Paper] Automatic Detection of Sources and Sinks in Arbitrary Java Libraries.\\nYear: 2018\\n\\nAbstract: In the last decade, data security has become a primary concern for an increasing amount of companies around the world. Protecting the customeru0027s privacy is now at the core of many businesses operating in any kind of market. Thus, the demand for new technologies to safeguard user data and prevent data breaches has increased accordingly. In this work, we investigate a machine learning-based approach to automatically extract sources and sinks from arbitrary Java libraries. Our method exploits several different features based on semantic, syntactic, intra-procedural dataflow and class-hierarchy traits embedded into the bytecode to distinguish sources and sinks. The performed experiments show that, under certain conditions and after some preprocessing, sources and sinks across different libraries share common characteristics that allow a machine learning model to distinguish them from the other library methods. The prototype model achieved remarkable results of 86% accuracy and 81% F-measure on our validation set of roughly 600 methods.\",\n \"Title: Toward a Smell-Aware Bug Prediction Model\\nYear: 2019\\n\\nAbstract: Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper, we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes. Specifically, we evaluate the contribution of a measure of the severity of code smells (i.e., code smell intensity) by adding it to existing bug prediction models based on both product and process metrics, and comparing the results of the new model against the baseline models. Results indicate that the accuracy of a bug prediction model increases by adding the code smell intensity as predictor. We also compare the results achieved by the proposed model with the ones of an alternative technique which considers metrics about the history of code smells in files, finding that our model works generally better. However, we observed interesting complementarities between the set of \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">buggy and smelly</italic>\\n classes correctly classified by the two models. By evaluating the actual information gain provided by the intensity index with respect to the other metrics in the model, we found that the intensity index is a relevant feature for both product and process metrics-based models. At the same time, the metric counting the average number of code smells in previous versions of a class considered by the alternative model is also able to reduce the entropy of the model. On the basis of this result, we devise and evaluate a \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">smell-aware</italic>\\n combined bug prediction model that included product, process, and smell-related features. We demonstrate how such model classifies bug-prone code components with an F-Measure at least 13 percent higher than the existing state-of-the-art models.\",\n \"Title: Automatic Detection of Instability Architectural Smells\\nYear: 2016\\n\\nAbstract: Code smells represent well known symptoms of problems at code level, and architectural smells can be seen as their counterpart at architecture level. If identified in a system, they are usually considered more critical than code smells, for their effect on maintainability issues. In this paper, we introduce a tool for the detection of architectural smells that could have an impact on the stability of a system. The detection techniques are based on the analysis of dependency graphs extracted from compiled Java projects and stored in a graph database. The results combine the information gathered from dependency and instability metrics to identify flaws hidden in the software architecture. We also propose some filters trying to avoid possible false positives.\",\n \"Title: Technical Debt Prioritization: State of the Art. A Systematic Literature Review\\nYear: 2019\\n\\nAbstract: Background. Software companies need to manage and refactor Technical Debt issues. Therefore, it is necessary to understand if and when refactoring Technical Debt should be prioritized with respect to developing features or fixing bugs. Objective. The goal of this study is to investigate the existing body of knowledge in software engineering to understand what Technical Debt prioritization approaches have been proposed in research and industry. Method. We conducted a Systematic Literature Review among 384 unique papers published until 2018, following a consolidated methodology applied in Software Engineering. We included 38 primary studies. Results. Different approaches have been proposed for Technical Debt prioritization, all having different goals and optimizing on different criteria. The proposed measures capture only a small part of the plethora of factors used to prioritize Technical Debt qualitatively in practice. We report an impact map of such factors. However, there is a lack of empirical and validated set of tools. Conclusion. We observed that technical Debt prioritization research is preliminary and there is no consensus on what are the important factors and how to measure them. Consequently, we cannot consider current research conclusive and in this paper, we outline different directions for necessary future investigations.\",\n \"Title: Are architectural smells independent from code smells? An empirical study\\nYear: 2019\\n\\nAbstract: •Case study analyzing the correlations among code smells, groups of code smells and architectural smells.•Architectural smells are not correlated with code smells.•Architectural smells cannot be derived from code smells.•Architectural smells deserve special attention by researchers, who should investigate their actual harmfulness.\",\n \"Title: DebtHunter: A Machine Learning-based Approach for Detecting Self-Admitted Technical Debt\\nYear: 2021\\n\\nAbstract: Due to limited time, budget or resources, a team is prone to introduce code that does not follow the best software development practices. This code that introduces instability in the software projects is known as Technical Debt (TD). Often, TD intentionally manifests in source code, which is known as Self-Admitted Technical Debt (SATD). This paper presents DebtHunter, a natural language processing (NLP)- and machine learning (ML)- based approach for identifying and classifying SATD in source code comments. The proposed classification approach combines two classification phases for differentiating between the multiple debt types. Evaluations over 10 open source systems, containing more than 259k comments, showed that the approach was able to improve the performance of others in the literature. The presented approach is supported by a tool that can help developers to effectively manage SATD. The tool complements the analysis over Java source code by allowing developers to also examine the associated issue tracker. DebtHunter can be used in a continuous evolution environment to monitor the development process and make developers aware of how and where SATD is introduced, thus helping them to manage and resolve it.\",\n \"Title: Impact of Opportunistic Reuse Practices to Technical Debt\\nYear: 2021\\n\\nAbstract: Technical debt (TD) has been recognized as an important quality problem for both software architecture and code. The evolution of TD techniques over the past years has led to a number of research and commercial tools. In addition, the increasing trend of opportunistic reuse (as opposed to systematic reuse), where developers reuse code assets in popular repositories, is changing the way components ...\",\n \"Title: Evaluating the Architectural Debt of IoT Projects\\nYear: 2021\\n\\nAbstract: We observed a great and increasing interest in the last few years towards the evaluation of technical debt of software projects, in particular in the direction of code and architectural debt evaluation. This kind of analysis has not yet been performed for IoT projects. Hence, in this paper we start this exploration through the analysis of four Open Source IoT projects. We focus our attention on ar...\",\n \"Title: The Perception of Architectural Smells in Industrial Practice\\nYear: 2021\\n\\nAbstract: Architectural technical debt (TD) is the most significant type of TD in industrial practice. Our goals in this study were to better understand the phenomenon of architectural smells, help practitioners better manage them, and offer relevant support to researchers.\",\n \"Title: On the relation between architectural smells and source code changes\\nYear: 2022\\n\\nAbstract: Although architectural smells are one of the most studied type of architectural technical debt, their impact on maintenance effort has not been thoroughly investigated. Studying this impact would help to understand how much technical debt interest is being paid due to the existence of architecture smells and how this interest can be calculated. This work is a first attempt to address this issue by investigating the relation between architecture smells and source code changes. Specifically, we study whether the frequency and size of changes are correlated with the presence of a selected set of architectural smells. We detect architectural smells using the Arcan tool, which detects architectural smells by building a dependency graph of the system analyzed and then looking for the typical structures of the architectural smells. The findings, based on a case study of 31 open-source Java systems, show that 87% of the analyzed commits present more changes in artifacts with at least one smell, and the likelihood of changing increases with the number of smells. Moreover, there is also evidence to confirm that change frequency increases after the introduction of a smell and that the size of changes is also larger in smelly artifacts. These findings hold true especially in Medium-Large and Large artifacts.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":87,"string":"87"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13260,"string":"13,260"}}},{"rowIdx":13261,"cells":{"question_id":{"kind":"string","value":"Ning Li"},"question":{"kind":"string","value":"Generate an abstract for the title \"On the total {k}-domination number of Cartesian products of graphs\" using the following items: gamma t, k-domination number, Cartesian product, isolated vertices, conjecture."},"answer":{"kind":"string","value":"Let γ\n \n t\n {k}(G) denote the total {k}-domination number of graph G, and let \n \n denote the Cartesian product of graphs G and H. In this paper, we show that for any graphs G and H without isolated vertices, \n \n . As a corollary of this result, we have \n \n for all graphs G and H without isolated vertices, which is given by Pak Tung Ho (Util. Math., 2008, to appear) and first appeared as a conjecture proposed by Henning and Rall (Graph. Comb. 21:63–69, 2005)."},"correct_answer":{"kind":"string","value":"Let γ\n \n t\n {k}(G) denote the total {k}-domination number of graph G, and let \n \n denote the Cartesian product of graphs G and H. In this paper, we show that for any graphs G and H without isolated vertices, \n \n . As a corollary of this result, we have \n \n for all graphs G and H without isolated vertices, which is given by Pak Tung Ho (Util. Math., 2008, to appear) and first appeared as a conjecture proposed by Henning and Rall (Graph. Comb. 21:63–69, 2005)."},"documents":{"kind":"list like","value":["Title: Sparse Normalized Least Mean Absolute Deviation Algorithm Based on Unbiasedness Criterion for System Identification With Noisy Input.\nYear: 2018\n\nAbstract: In order to solve the system identification problems, the normalized least mean absolute deviation (NLMAD) algorithm was developed as an effective and robust method. In this paper, aiming at the system identification problems with sparsity characteristic, and taking the advantage of the NLMAD algorithm to suppress impulsive output measurement noise interference, we introduce the L-1-norm as a sparse penalty constraint into the NLMAD algorithm to design a robust sparse adaptive filtering algorithm. Furthermore, considering the biased estimation caused by the input noise, we employ an unbiasedness criterion to derive an effective bias-compensated vector which can compensate the bias efficiently for the proposed sparse NLMAD algorithm. The desirable performance of the new method is measured with simulations of two stages. The proposed bias-compensated sparse NLMAD algorithm achieves better performance compared to other existing methods in both stages. Simulation results demonstrate the excellent performance of the proposed algorithm in solving sparse system identification problems. The promising results in this paper suggest that the bias-compensated sparse NLMAD algorithm may become a useful tool for system identification with noisy input and impulsive output noise.","Title: Biopanning data bank 2018: hugging next generation phage display.\nYear: 2018\n\nAbstract: The 2018 update of the biopanning data bank (BDB) stores phage display data sequenced by Sanger sequencing and next generation sequencing technologies. In this work, we upgraded the database with more biopanning data sets and several new features, including (i) incorporation of next generation biopanning data and the unselected population where the target is not determined and the round of screening is zero; (ii) addition of sequencing information; (iii) improvement of browsing and searching systems and 3D chemical structure viewer; (iv) integration of standalone tools for target-unrelated peptides analysis within conventional phage display and next generation phage display (NGPD) data. In the current version of BDB (released on 19 January 2018), the database houses 3291 sets of biopanning data collected from 1540 published articles, including 95 NGPD data sets and 3196 traditional biopanning data sets. The BDB database serves as an important and comprehensive resource for developing peptide ligands.","Title: Raw Data-Based Motion Compensation for High-Resolution Sliding Spotlight Synthetic Aperture Radar.\nYear: 2018\n\nAbstract: For accurate motion compensation (MOCO) in airborne synthetic aperture radar (SAR) imaging, a high-precision inertial navigation system (INS) is required. However, an INS is not always precise enough or is sometimes not even included in airborne SAR systems. In this paper, a new, raw, data-based range-invariant motion compensation approach, which can effectively extract the displacements in the line-of-sight (LOS) direction, is proposed for high-resolution sliding spotlight SAR mode. In this approach, the sub-aperture radial accelerations of the airborne platform are estimated via a well-developed weighted total least square (WTLS) method considering the time-varying beam direction. The effectiveness of the proposed approach is validated by two airborne sliding spotlight C band SAR raw datasets containing different types of terrain, with a high spatial resolution of about 0.15 m in azimuth.","Title: Words alignment based on association rules for cross-domain sentiment classification.\nYear: 2018\n\nAbstract: Automatic classification of sentiment data (e.g., reviews, blogs) has many applications in enterprise user management systems, and can help us understand people’s attitudes about products or services. However, it is difficult to train an accurate sentiment classifier for different domains. One of the major reasons is that people often use different words to express the same sentiment in different domains, and we cannot easily find a direct mapping relationship between them to reduce the differences between domains. So, the accuracy of the sentiment classifier will decline sharply when we apply a classifier trained in one domain to other domains. In this paper, we propose a novel approach called words alignment based on association rules (WAAR) for cross-domain sentiment classification, which can establish an indirect mapping relationship between domain-specific words in different domains by learning the strong association rules between domain-shared words and domain-specific words in the same domain. In this way, the differences between the source domain and target domain can be reduced to some extent, and a more accurate cross-domain classifier can be trained. Experimental results on Amazon® datasets show the effectiveness of our approach on improving the performance of cross-domain sentiment classification.","Title: Maximum correntropy unscented Kalman and information filters for non-Gaussian measurement noise.\nYear: 2017\n\nAbstract: In this paper, we investigate the state estimation problem of nonlinear systems with non-Gaussian measurement noise. Based on a newly defined cost function which is obtained by a combination of weighted least square (WLS) and maximum correntropy criterion (MCC), we derive our maximum correntropy unscented Kalman filter (MCUKF) and the corresponding maximum correntropy unscented information filter (MCUIF). Comparing with existing MCUKF, our MCUKF avoids the numerical problem occurred when the measurements contain large outliers, and can obtain similar or even better estimation results. When the kernel bandwidth goes infinity, we prove that our MCUKF and MCUIF will converge to UKF and UIF, respectively, while existing MCUIF will not in this case and it generally has poor estimation accuracy as well. Two typical nonlinear models are used to illustrate the advantages of our proposed algorithms.","Title: Dielectric Characteristics of Unsaturated Loess and the Safety Detection of the Road Subgrade Based on GPR.\nYear: 2018\n\nAbstract: We present a moisture content and permittivity model to simultaneously detect and estimate defects in loess subgrade. Based on the ground-penetrating radar (GPR) method, the dielectric properties of loess in the northwest of China and the imaging feature of the moisture content of different strata were studied. The relative permittivity of loess with different moisture contents was experimented in the laboratory. It was found that the relative permittivity of unsaturated loess was positively related to moisture content. The relationship between relative permittivity and moisture content in different antenna frequencies of GPR was analyzed. Electromagnetic wave reflection rules in the loess interface were studied using the numerical method with different moisture contents. With the increase in moisture content, the amplitude of GPR was increased. When the above conclusions were applied in the engineering practices, there are good effects to detect the defects of the road subgrade. It is a significant guidance for determining the qualitative research of defects in the roadbed.","Title: Inversion Model of GPR Imaging Characteristics of Point Objects and Fracture Detection of Heritage Building.\nYear: 2018\n\nAbstract: There are often many hidden structural defects in heritage buildings. As a convenient and effective nondestructive detecting method, ground-penetrating radar (GPR) has a technical advantage in detecting and protecting heritage buildings depending on the advanced image interpretation. The analytic relationship between buried depth and radius of point object and long and short axis of hyperbolic equation was established according to derivations of formulas. The image characteristics of hyperbolic curves with different depth and radius were studied by finite-difference time-domain method (FDTD). And then, inversion models of buried depth and radius of point object were established. The buried depth and radius can be accurately deduced by long and short axis of hyperbolic image. This result was applied in the detection of pedestal defects of the heritage building, and the depth and distribution range of hidden fracture can be accurately interpreted. It provides an effective and fast method to detect hidden defects in civil engineering.","Title: Bridging effort-aware prediction and strong classification: a just-in-time software defect prediction study.\nYear: 2018\n\nAbstract: Context: Most research into software defect prediction ignores the differing amount of effort entailed in searching for defects between software components. The result is sub-optimal solutions in terms of allocating testing resources. Recently effort-aware (EA) defect prediction has sought to redress this deficiency. However, there is a gap between previous classification research and EA prediction.\n\nObjective: We seek to transfer strong defect classification capability to efficient effort-aware software defect prediction.\n\nMethod: We study the relationship between classification performance and the cost-effectiveness curve experimentally (using six open-source software data sets).\n\nResults: We observe extremely skewed distributions of change size which contributes to the lack of relationship between classification performance and the ability to find efficient test orderings for defect detection. Trimming allows all effort-aware approaches bridging high classification capability to efficient effort-aware performance.\n\nConclusion: Effort distributions dominate effort-aware models. Trimming is a practical method to handle this problem.","Title: ForkTail: a black-box fork-join tail latency prediction model for user-facing datacenter workloads.\nYear: 2018\n\nAbstract: The workflows of the predominant user-facing datacenter services, including web searching and social networking, are underlaid by various Fork-Join structures. Due to the lack of understanding the performance of Fork-Join structures in general, today's datacenters often resort to resource overprovisioning, operating under low resource utilization, to meet stringent tail-latency service level objectives (SLOs) for such services. Hence, to achieve high resource utilization, while meeting stringent tail-latency SLOs, it is of paramount importance to be able to accurately predict the tail latency for a broad range of Fork-Join structures of practical interests.\n\nIn this paper, we propose ForkTail, a black-box Fork-Join tail latency prediction model that covers a wide range of Fork-Join structures. In ForkTail, all Fork nodes are treated as black boxes, admitting both homogeneous and inhomogeneous cases, and different requests in the request flow are allowed to spawn different numbers of tasks forked to different numbers of Fork nodes. On the basis of the central limit theorem for queuing models under heavy load, we are able to arrive at a highly computational effective, empirical expression for the tail latency as a function of the means and variances of the task response times. Since this expression can be applied to request sub-flows at any granularities, it can be used for tail-latency prediction for services in a consolidated environment, where different services and applications may share the same datacenter cluster resources. Our extensive testing results based on model-based and trace-driven simulations, as well as a real-world case study in a cloud environment demonstrate that the expression can consistently predict the tail latency within 20% and 15% prediction errors at 80% and 90% load levels, respectively. Moreover, our sensitivity analysis demonstrates that such errors can be well compensated for with no more than 5% and 3% resource overprovisioning at these two load levels, respectively. This, together with its extremely low computational complexity, makes ForkTail a viable tool for both offline and online job scheduling and resource provisioning for user-facing datacenter applications.","Title: Robust H∞ control of uncertain linear system with interval time-varying delays by using Wirtinger inequality.\nYear: 2018\n\nAbstract: This paper is concerned with the robust H∞ control problem of uncertain linear system with interval time-varying delays. Firstly, by using Wirtinger inequality approach, after constructing a new Lyapunov functional, the novel robust delay-dependent stability and stabilization criteria are developed. These improved results are expected to have less conservatism, due to the utilization of Wirtinger inequality and consideration of the information of the lower bound of time delay. Secondly, based on the obtained criteria, the memoryless H∞ state feedback controller design scheme is presented, thus robust H∞ controller design and performance analysis for uncertain linear system are developed. For a prescribed H∞ performance level, the designed robust H∞ controller can guarantee the uncertain linear system to be robustly stable and endure a larger delay. At last, numerical examples are provided to illustrate the effectiveness and superiority of the developed results.","Title: Distributed Joint Offloading Decision and Resource Allocation for Multi-User Mobile Edge Computing: A Game Theory Approach.\nYear: 2018\n\nAbstract: Due to the spectrum reuse in small cell network, the inter-cell interference has great effect on MECu0027s performance. In this paper, for reducing the energy consumption and latency of MEC, we propose a game theory based jointing offloading decision and resource allocation algorithm for multi-user MEC. In this algorithm, the transmission power, offloading decision, and mobile useru0027s CPU capability are determined jointly. We prove that this game is an exact potential game and the NE of this game exists and is unique. For reaching the NE, the best response dynamic is applied. We calculate the best responses of these three variables. Moreover, we investigate the properties of this algorithm, including the convergence, the computation complexity, and the price of anarchy. The theoretical analysis shows that the inter-cell interference has great effect on the performance of MEC. The NE of this game is Pareto efficiency and also the global optimal solution of the proposed optimal issue. Finally, we evaluate the performance of this algorithm by simulation. The simulation results illustrates that this algorithm is effective on improving the performance of the multi-user MEC system.","Title: Benchmarking the HLA typing performance of Polysolver and Optitype in 50 Danish parental trios.\nYear: 2018\n\nAbstract: Although current computational methods for HLA typing generally provide satisfactory results, our benchmark - using data with ultra-high sequencing depth - demonstrates the incompleteness of current reference databases, and highlights the importance of providing genomic databases addressing current sequencing standards, a problem yet to be resolved before benefiting fully from personalised medicine approaches HLA phenotyping is essential.","Title: The total {}-domatic number of wheels and complete graphs\nYear: 2012\n\nAbstract: Let k be a positive integer and let G be a graph with vertex set V(G). The total {k}-dominating function (T{k}DF) of a graph G is a function f from V(G) to the set {0,1,2,驴,k}, such that for each vertex v驴V(G), the sum of the values of all its neighbors assigned by f is at least k. A set {f 1,f 2,驴,f d } of pairwise different T{k}DFs of G with the property that $\\sum_{i=1}^{d}f_{i}(v)\\leq k$ for each v驴V(G), is called a total {k}-dominating family (T{k}D family) of G. The total {k}-domatic number of a graph G, denoted by $d_{t}^{\\{k\\}}(G)$ , is the maximum number of functions in a T{k}D family. In this paper, we determine the exact values of the total {k}-domatic numbers of wheels and complete graphs, which answers an open problem of Sheikholeslami and Volkmann (J. Comb. Optim., 2010) and completes a result in the same paper.","Title: A 1.5-GS/s 5-bit interpolating ADC with offset averaging and interpolating sharing resistors network\nYear: 2015\n\nAbstract: In this paper, a 1.5-GS/s 5-bit interpolating analog-to-digital converter (ADC) with offset averaging and interpolating sharing resistors network (OAISRN) is presented. The proposed OAISRN is based on conventional flash ADC and the concept of zero crossing points in folding and interpolating architecture. In order to reduce power dissipation yet to make a high performance, it removes half of preamplifiers and ensures matching by using offset averaging and 2x-interpolation sharing resistors network behind the initial zero crossing points generators array. Implementation of the OAISRN is explained in detail and its impact on signal bandwidth is discussed. The interpolating ADC in TSMC 65 nm process achieves SNDR of 27.8 dB and SFDR of 37.7 dB for 745 MHz input frequency located at 1.5 GS/s in postsimulation. The power consumption is 9.6 mW under a supply voltage of 1.2 V.","Title: A 30-GHz to 39-GHz mm-Wave low-power injection-locked frequency divider in 65nm CMOS\nYear: 2015\n\nAbstract: In this paper, a wide locking range and low power Injection-Locked Frequency Divider (ILFD) is presented. Compared to conventional structure, 2-b digital controlled capacitor array is introduced to extend its locking range. Meanwhile, the tail current source is eliminated to reduce the supply voltage and enlarge the output voltage swing. The post-simulations show that the total locking-range is from 29.7GHz to 39.2GHz when input power is 0dBm. While operating at highest frequency band, the output power is 1.3dBm, and phase noise at 1-MHz offset are -156.9dBc/Hz. The divider core draws a RMS current of 8.5mA under a supply voltage of 0.65V. The total divider with 50Ω output buffer takes up 540μm×205μm without probe pad in TSMC 65-nm CMOS process.","Title: Deep Joint Semantic-Embedding Hashing.\nYear: 2018\n\nAbstract: Hashing has been widely deployed to large-scale image retrieval due to its low storage cost and fast query speed. Almost all deep hashing methods do not sufficiently discover semantic correlation from label information, which results in the learned hash codes less discriminative. In this paper, we propose a novel Deep Joint Semantic-Embedding Hashing (DSEH) approach that consists of LabNet and ImgNet. Specifically, LabNet is explored to capture abundant semantic correlation between sample pairs and supervise ImgNet from both semantic level and hash codes level, which is conductive to the generated hash codes being more discriminative and similarity-preserving. Extensive experiments on three benchmark datasets show that the proposed model outperforms current state-of-the-art methods.","Title: An Autofocus Cartesian Factorized Backprojection Algorithm for Spotlight Synthetic Aperture Radar Imaging.\nYear: 2018\n\nAbstract: A backprojection (BP) algorithm is recognized as an ideal method for high-resolution synthetic aperture radar (SAR) imaging. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. The Cartesian factorized BP (CFBP) algorithm is proposed recently to avoid massive interpolations and improve the performance. However, integrating autofocus techniques with the CFBP...","Title: Dynamic Similar Sub-Series Selection Method for Time Series Forecasting.\nYear: 2018\n\nAbstract: Accumulation of influencing factors during several consecutive time periods makes the variation of target parameters lag behind the variation of their influencing factors. This important phenomenon, known as the cumulative effect, would lead to relatively large forecasting errors. In this paper, the dynamic similar sub-series method is proposed to take cumulative effect into consideration. The similar sub-series for the forecasting parameter sub-series are selected based on similarities of target parameter sub-series and influencing factors sub-series. The internal variations of target parameter sub-series and each influencing factor sub-series are innovatively integrated into the selection rules for the dynamic similar sub-series. The corresponding forecasting algorithm is designed, and the forecasting parameters are deduced and forecasted according to the variation of the dynamic similar sub-series. The proposed method is compared with a variety of representative methods under the short-term daily average load forecasting, the electricity price forecasting and the global horizontal irradiance forecasting scenarios to demonstrate its effectiveness.","Title: Compressive Sensing Based Multiple Source Localization in the Presence of Sensor Position Uncertainty and Nonuniform Noise.\nYear: 2018\n\nAbstract: Source localization is an important field of research and the received signal strength (RSS)-based method is of particular interest. Further, by exploiting the sparsity of localization problem, the compressive sensing (CS) can be applied to considerably decrease the number of RSS measurements, especially in multi-source scenario. However, most existing CS-based localization methods usually neglect some practical issues. In particular, the sensor positions are assumed be known exactly, while in practice they may not be accurate. Additionally, a uniform Gaussian noise assumption is made that the noise variances of sensors are identical, but in practice the noise should be nonuniform. When these assumptions are violated, the localization performance will deteriorate dramatically. To address such issues, in this paper, we formulate the source localization based on superimposed RSS measurements as a sparse signal recovery problem. Moreover, by regarding the sensor positions as adjustable parameters, the inaccurate sensor positions can be refined through the adjustment of parameters. Following this idea, we develop a novel iterative algorithm for joint signal recovery and parameter optimization based on the variational expectation-maximization algorithm. Consequently, the sensor position uncertainty can be alleviated and thus the signal recovery performance will be improved greatly. Meanwhile, it is also capable of learning the variance of nonuniform noise. Extensive simulation results demonstrate the superiority of the proposed method.","Title: Performance Comparison of Saliency Detection.\nYear: 2018\n\nAbstract: Saliency detection has attracted significant attention in the field of computer vision technology over years. At present, more than 100 saliency detection models have been proposed. In this paper, a relatively more detailed classification is proposed. Furthermore, we selected 25 models and evaluated their performance using four public image datasets. We also discussed common problems, such as the influence to model performance by prior information and multiple objects. Finally, we offered future research directions.","Title: A 5-to-8-GHz Wideband Miniaturized Dielectric Spectroscopy Chip With $I/Q$ Mismatch Calibration in 65-nm CMOS.\nYear: 2018\n\nAbstract: This paper presents a fully integrated broadband biosensor chip for miniaturized dielectric spectroscopy (DS). The proposed biosensor chip consists of wideband exciting signal source and high sensitivity receiver. The exciting signal source consists of a wideband phase-locked loop (PLL) and output drivers. The wideband receiver is composed of low noise amplifier, mixer, and output buffers. The pro...","Title: Cross-Layer Balanced Relay Node Selection Algorithm for Opportunistic Routing in Underwater Ad-Hoc Networks\nYear: 2018\n\nAbstract: Due to the different transmission media, the underwater environment poses a more severe situation for routing algorithm design than that of the terrestrial environment. In this paper, we propose a weight based fuzzy logic (WBFL) relay node selection algorithm for the underwater Ad-hoc network, which can get the most balanced result than the previous algorithms without increasing the computation complexity. In this algorithm, the parameter scatters instead of the parameter values are inputted into the fuzzy logic inference system. By this innovation, the algorithm can take as much cross-layer parameters into account as possible during the relay node selection. Moreover, taking the node mobility into account, we propose a geographic based link lifetime prediction algorithm for underwater Ad-hoc network. The simulation results show that the WBFL algorithm can improve the network throughput at most 50% compare with the ExOR algorithm; moreover, the WBFL is effective and accurate on selecting relay nodes and the computation complexity is less than the previous algorithms.","Title: Secrecy Performance Analysis of Artificial-Noise-Aided Spatial Modulation in the Presence of Imperfect CSI.\nYear: 2018\n\nAbstract: The security of information over wireless channels has always been an important aspect of wireless communication research. In this paper, the secrecy performance of a spatial modulated system with an artificial noise is investigated over the Rayleigh channel, and the corresponding secrecy rate is analyzed in the presence of imperfect channel state information (CSI). Based on the secrecy performance analysis, the ergodic rates and the corresponding lower bounds of the legitimate receiver and eavesdropper are, respectively, derived. As a result, the closed-form lower bound of ergodic rate of the legitimate receiver is obtained, and the lower bound of ergodic rate of the eavesdropper and the corresponding ergodic secrecy rate (ESR) can be achieved by calculating the mean values. To avoid the mean operation and reduce the complexity, the closed-form approximated expression of ESR is also derived. This theoretical ESR can include the one under perfect CSI as a special case and has the value close to the corresponding simulation. Thus, it can provide good performance evaluation for the artificial-noise-aided spatial modulation system with imperfect CSI. Simulation results verify the effectiveness of the theoretical analysis. The system with imperfect CSI is found to have low ESR as compared to the one with perfect CSI due to the estimation error. Moreover, initially, the ESR increases with increasing power of artificial noise, but then, due to the total power constraint, it starts decreasing.","Title: An Optimized Symmetric WENO Method-Based Numerical Simulation of Intense Sound Field Generated by Underwater Plasma Sound Source.\nYear: 2018\n\nAbstract: The intensive pulse sound wave can be generated by the underwater plasma sound source (UPSS) based on the discharge of the underwater high voltage. The distribution of the sound field is prominently nonlinear. In this paper, the sound field of the intensive UPSS is described by the integral two-dimensional axisymmetric unsteady Euler equations firstly. In order to solve the Euler equations numerically, an optimized fifth-order symmetric WENO (weighted essentially nonoscillatory) method based on the three templates is proposed which is called WENO-SYM3. Without increasing the number of candidate templates, a new symmetric template structure can be obtained by expanding the second template and shifting the third one backwards for one space. The method is validated through numerical examples and experiments, and the results show that WENO-SYM3 has a high distinguished accuracy; meanwhile, its nonphysical oscillations are not obvious. The experimental results are basically the same as the calculation results, and the maximum error is around 3%.","Title: Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China.\nYear: 2018\n\nAbstract: Although optical remote sensing can intuitively detect algal bloom, it is limited by the weather conditions. Synthetic aperture radar (SAR) is not affected by inadequate weather conditions. According to visual interpretation of SAR images and comparisons of quasi-synchronized optical images, the gathering areas of algal bloom present as dark regions on SAR images. It is shown that using SAR to monitor the water surface is workable. However, dark regions may also be caused by other factors, such as low wind speeds. This challenges with SAR monitoring of algal bloom on the water surface. In this study, an improved K-means algorithm, combined with multi-Otsu thresholding algorithm, was proposed to segment the dark regions. After feature analysis and extraction of Sentinel-1A images, an algal bloom recognition model with a support vector machine (SVM) was applied to discriminate the algal bloom dark regions from the low wind dark regions. According the experimental results, the overall accuracy achieved 74.00% in Taihu Lake. Additionally, this method was also validated in Chaohu Lake and Danjiangkou Reservoir. Therefore, it can be concluded that SAR can provide a new technical means for monitoring algal bloom of inland lakes, particularly when it is cloudy and unsuitable for optical remote sensing. To obtain more information about algal bloom, multi-band and multi-polarization SAR images can be considered for future.","Title: Probability Prediction-Based Reliable and Efficient Opportunistic Routing Algorithm for VANETs.\nYear: 2018\n\nAbstract: In the vehicular ad hoc networks (VANETs), due to the high mobility of vehicles, the network parameters change frequently and the information that the sender maintains may outdate when it wants to transmit data packet to the receiver, so for improving the routing efficiency and reliability, we propose the probability prediction-based reliable and efficient opportunistic routing (PRO) algorithm for...","Title: Global dissipativity analysis of quaternion-valued memristor-based neural networks with proportional delay.\nYear: 2018\n\nAbstract: In this paper, the global dissipativity problem is investigated for the quaternion-valued memristor-based neural networks (QVMNNs) with proportional delay. Firstly, the mathematical model of QVMNNs is proposed, which is a challenging model due to the unbounded delay and the quaternion field. Secondly, based on matrix measure method and Lyapunov theorem, combing with modulus inequality technique and generalized Halanay inequality technique, some sufficient conditions guaranteeing global dissipativity of QVMNNs with proportional delay are derived. Moreover, the global attractive set of QVMNNs can also be obtained. Finally, three numerical examples are presented to demonstrate the effectiveness of the theoretical results.","Title: An Efficient Missing Data Prediction Method Based on Kronecker Compressive Sensing in Multivariable Time Series.\nYear: 2018\n\nAbstract: The existence of missing data severely affects the establishment of correct data mining model from the raw data. Unfortunately, most of the existing missing data prediction approaches are inefficient to predict missing data from multivariable time series due to the low accuracy and poor stability property. To address this issue, we propose an efficient method using the novel Kronecker compressive sensing theory. First, we exploit the spatial and temporal properties of the multivariable time series to construct the sparse representation basis and design the measurement matrix according to the location of missing data. Accordingly, the missing data prediction problem is modeled as a sparse vector recovery problem. Then, we verify the validity of the model from two aspects: whether the sparse representation vector is sufficiently sparse and the sensing matrix satisfies the restricted isometry property of compressive sensing. Finally, we investigate the sparse recovery algorithms to find the best suited one in our application scenario. Simulation results indicate that the proposed method is highly efficient in predicting the missing data of multivariable time series.","Title: Synchronization criteria for inertial memristor-based neural networks with linear coupling.\nYear: 2018\n\nAbstract: This paper is concerned with the synchronization problem for an array of memristive neural networks with inertial term, linear coupling and time-varying delay. Since parameters in the connection weight matrices are state-dependent, that is to say, the connection weight matrices jump in certain intervals, the mathematical model of the coupled inertial memristive neural networks can be considered as an interval parametric uncertain system. Based on the interval parametric uncertainty theory, two different synchronization criteria for memristor-based neural networks are obtained by applying the p-matrix measure (p=1,2,∞,ω), Halanay inequality and constructing suitable Lyapunov–Krasovskii functionals. Two simulation examples with fully-connected and nearest neighboring topology are presented to demonstrate the efficiency of the obtained theoretical results.","Title: Exploiting Fine-Grained Subcarrier Information for Device-Free Localization in Wireless Sensor Networks.\nYear: 2018\n\nAbstract: Device-free localization (DFL) that aims to localize targets without carrying any electronic devices is addressed as an emerging and promising research topic. DFL techniques estimate the locations of transceiver-free targets by analyzing their shadowing effects on the radio signals that travel through the area of interest. Recently, compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements by exploiting the inherent spatial sparsity of target locations. In this paper, we propose a novel CS-based multi-target DFL method to leverage the frequency diversity of fine-grained subcarrier information. Specifically, we build the dictionaries of multiple channels based on the saddle surface model and formulate the multi-target DFL as a joint sparse recovery problem. To estimate the location vector, an iterative location vector estimation algorithm is developed under the multitask Bayesian compressive sensing (MBCS) framework. Compared with the state-of-the-art CS-based multi-target DFL approaches, simulation results validate the superiority of the proposed algorithm.","Title: An autonomous dynamic trust management system with uncertainty analysis.\nYear: 2018\n\nAbstract: In order to help users select a reliable service in the open and complicated network, an autonomous dynamic trust management system is put forward based on collaborative recommendation mechanism and uncertainty analysis of satisfaction data. Any user in the network can store and update autonomously his trust table which contains direct interaction experience, find easily the reliable referrers according to collaborative recommendation, and collect valuable trust data and calculate the satisfaction and its reliability based on uncertainty analysis. Then the direct, recommended and self-recommended trusts can be calculated and finally be used to compute the global trust which will help the user to make a trust decision. A complicated network environment which includes quite a part of malicious nodes is built for investigating the robustness and effectiveness of the trust system. The experimental results show that the quality of service selection can be improved with the increase of interaction; the system can keep stable even though there are 50% malicious nodes initially in the network or a large number of malicious nodes join the network after stability; the system can resist the collusion attack more effectively and has lower computation and storage overhead than contrastive models.","Title: Multi-strategy monarch butterfly optimization algorithm for discounted {0-1} knapsack problem.\nYear: 2018\n\nAbstract: As an expanded classical 0-1 knapsack problem (0-1 KP), the discounted {0-1} knapsack problem (DKP) is proposed based on the concept of discount in the commercial world. The DKP contains a set of item groups where each group includes three items, whereas no more than one item in each group can be packed in the knapsack, which makes it more complex and challenging than 0-1 KP. At present, the main two algorithms for solving the DKP include exact algorithms and approximate algorithms. However, there are some topics which need to be further discussed, i.e., the improvement of the solution quality. In this paper, a novel multi-strategy monarch butterfly optimization (MMBO) algorithm for DKP is proposed. In MMBO, two effective strategies, neighborhood mutation with crowding and Gaussian perturbation, are introduced into MMBO. Experimental analyses show that the first strategy can enhance the global search ability, while the second strategy can strengthen local search ability and prevent premature convergence during the evolution process. Based on this, MBO is combined with each strategy, denoted as NCMBO and GMMBO, respectively. We compared MMBO with other six methods, including NCMBO, GMMBO, MBO, FirEGA, SecEGA and elephant herding optimization. The experimental results on three types of large-scale DKP instances show that NCMBO, GMMBO and MMBO are all suitable for solving DKP. In addition, MMBO outperforms other six methods and can achieve a good approximate solution with its approximation ratio close to 1 on almost all the DKP instances.","Title: Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises.\nYear: 2018\n\nAbstract: In this paper, a novel robust particle filter is proposed to address the measurement outliers occurring in the multiple autonomous underwater vehicles (AUVs) based cooperative navigation (CN). As compared with the classic unscented particle filter (UPF) based on Gaussian assumption of measurement noise, the proposed robust particle filter based on the maximum correntropy criterion (MCC) exhibits better robustness against heavy-tailed measurement noises that are often induced by measurement outliers in CN systems. Furthermore, the proposed robust particle filter is computationally much more efficient than existing robust UPF due to the use of a Kullback-Leibler distance-resampling to adjust the number of particles online. Experimental results based on actual lake trial show that the proposed maximum correntropy based unscented particle filter (MCUPF) has better estimation accuracy than existing state-of-the-art robust filters for CN systems with heavy-tailed measurement noises, and the proposed MCUPF has lower computational complexity than existing robust particle filters.","Title: Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks.\nYear: 2018\n\nAbstract: The existing public key-based en-route filtering schemes are vulnerable to report disruption attacks or selective forwarding attacks, and they fail to consider any measure to detect and punish the malicious nodes. The authors propose a series of public key-based security mechanisms for wireless sensor networks (WSNs) in this paper, including a mechanism for verifying the partial signatures, a substitution mechanism, an effective report forwarding protocol, and a trust value-based mechanism to identify and punish the malicious nodes. Finally, the authors develop a public key-based authentication and en-route filtering scheme (PKAEF), which can resist false data injection attacks, report disruption attacks and selective forwarding attacks, and can mitigate the impact of malicious nodes. Detailed performance analysis and evaluation show that, in most cases, PKAEF outperforms previous works in terms of safety, filtering efficiency, and data availability.","Title: MRI-compatible injection system for magnetic microparticle embolization.\nYear: 2019\n\nAbstract: This system has the capability to operate within the strong magnetic field of a clinical 3T MRI, to form proper particle aggregates and to automatically inject these aggregates into the MRI bore. Moreover, the versatility of the proposed injector renders it suitable for selective injections of MDEBs during MR-guided embolization procedures.","Title: Scene invariant crowd counting using multi-scales head detection in video surveillance.\nYear: 2018\n\nAbstract: With a soaring increase in the application of video surveillance in daily life, the estimation of crowd density has already become a hot field. Crowd counting has a very close relationship with traffic planning, pedestrian analysing and emergency warning. Here, a novel crowd counting method based on multi-scales head detection is proposed. The authors' approach first uses gradients difference to e...","Title: Self-organizing weighted incremental probabilistic latent semantic analysis.\nYear: 2018\n\nAbstract: PLSA (Probabilistic Latent Semantic Analysis) is a popular topic modeling technique which has been widely applied to text mining applications to discover the underlying topics embedded in the data corpus. However, due to the variability of increasing data, it is necessary to discover the dynamic topics and process the large dataset incrementally. Moreover, PLSA models suffer from the problem of inferencing new documents. To overcome these problems, in this paper, we propose a novel Weighted Incremental PLSA algorithm called WIPLSA to dynamically discover topics and incrementally learn the topics from new documents. The experiments verify that the proposed WIPLSA could capture the dynamic topics hidden in the dynamic updating data corpus. Compared with PLSA, MAP PLSA and QB PLSA, WIPLSA performs better in perspexity on large dataset, which make it applicable for big data mining. In addition, WIPLSA has good performance in the application of document categorization.","Title: A New High-Efficiency Multilevel Frequency-Modulation Different Chaos Shift Keying Communication System.\nYear: 2018\n\nAbstract: Considering the large number of delay lines and orthogonality Walsh code sequences in the traditional multilevel frequency-modulation different chaos shift keying (FM-DCSK), in this paper, we propose a new multilevel FM-DCSK, named multilevel M-ary FM-DCSK (MFM-DCSK). The MFM-DCSK transmits multilevel symbols directly in the communication channel without using orthogonality Walsh code sequence. Ba...","Title: Sensitivity-Compensated Micro-Pressure Flexible Sensor for Aerospace Vehicle.\nYear: 2019\n\nAbstract: When flight vehicles (e.g., aerospace vehicles, Low Earth Orbit (LEO) satellites, near-space aircrafts, Unmanned Aerial Vehicles (UAVs) and drones) fly at high speed, their surfaces suffer the micro-pressure from high-altitude thin air. The long-term effect of this pressure causes the surface components of flight vehicle to deform or fall off, which can lead to a serious accident. To solve this problem, this paper proposes a sensitivity-compensated micro-pressure flexible sensor based on hyper-elastic plastic material and plate parallel capacitance. The sensor is able to measure a range of 0-6 kPa micro-pressure suffered by the flight vehicle's surface with high sensitivity and flexible devices. In this paper, we propose the principle, structure design and fabrication of the sensitivity-compensated micro-pressure flexible sensor. We carried out experiments to obtain the static characteristic curve between micro-pressure and the output capacitance of the sensor devices, and investigated the relationship between sensitivity and geometric parameters. We also compared the performance of the flexible sensor before and after sensitivity compensation. The result shows that the sensor can measure a range of 0-2 kPa and 2-6 kPa with a sensitivity of 0.27 kPa(-1) and 0.021 kPa(-1), which are 80% and 141.38% higher than the sensor before compensation; a linearity of 1.39% and 2.88%, which are 51.7% and 13.1% higher than the sensor before compensation; and a hysteresis and repeatability of 4.95% and 2.38%, respectively. The sensor has potential applications in flight vehicles to measure the micro-pressure with high sensitivity and flexibility.","Title: Controller Synthesis For Switched Positive Linear Systems By Output Feedback\nYear: 2018\n\nAbstract: The output-feedback controller synthesis issue for a class of continuous-time switched linear systems via average dwell time switching with constrained controls and output is addressed. First, we focus on the output-feedback stabilisation by decomposing control gain matrix. Second, the stabilisation problem with sign-restricted controls while imposing positivity and stability on closed-loop system is solved, then the results extend to asymmetrically bounded controls and constrained outputs. All the derived conditions are formulated as linear programming. Finally, numerical examples are given to show the applicability of the proposed methods.","Title: Energy-efficient power allocation for massive M2M communication over LTE-A cellular uplink\nYear: 2018\n\nAbstract: In order to enable massive machine-to-machine (M2M) communication in Long Term Evolution-Advanced (LTE-A) cellular network, a machine-type communication gateway (MTCG) is introduced to solve the issue of control signaling overheads. In this paper, a power allocation algorithm based on the method of Lagrange multipliers is proposed for massive M2M communication over LTE-A cellular uplink. The proposed algorithm can decrease the signaling overheads by enabling the machine-type communication device (MTCD) to access the base station (BS) via a MTCG that collects the data from a group of M2M devices and forward it to the BS. The core objective of the proposed algorithm is to maximize the total energy efficiency of a group of M2M devices, while satisfying the time delay of the M2M devices, by coordinating the power transmission of the MTCDs and the MTCG. The simulation results demonstrate that the proposed algorithm outperforms the heuristic algorithm and has relatively close performance to the optimal design.","Title: A novel approach for missing data prediction in coevolving time series\nYear: 2019\n\nAbstract: Although various innovative sensing technologies have been widely employed, data missing in collections of time series occurs frequently, which turns out to be a major menace to precise data analysis. However, many existing missing data prediction approaches either might be infeasible or could be inefficient to predict missing data from multiple time series. To solve this problem, we proposed a novel approach based on the compressive sensing theory and sparse Bayesian learning theory for missing data prediction in coevolving time series. First, we model the problem by designing the corresponding sparse representation basis and measurement matrix. Then, the missing data prediction problem is formulated as the multiple sparse vectors recovery problem. Many simultaneous sparse estimation approaches focus on joint estimation of multiple sparse vectors with a common support from given linear observations, which is however too strict in some real applications. In this paper, largely utilizing the interior patterns of coevolving time series, we design a tuning parameter-free algorithm based on the sparse Bayesian learning, which can simultaneously solve multiple sparse estimation takes without the requirement of auxiliary information. Simulation results demonstrate that our approach can recover the entire time series efficiently using only those data that are not missing, even if, a high ratio of collected data are missing.","Title: Demonstration of Dual-Channel TOPS SAR Imaging With Airborne C-Band Data.\nYear: 2017\n\nAbstract: Multichannel in azimuth synthetic aperture radar (SAR) operating in the terrain observation by progressive scans (TOPS) acquisition mode has attracted much attention recently for its capability to achieve ultrawide-swath imaging with a high spatial resolution. In order to verify the feasibility and operability of this newly developed remote sensing concept, a C-band airborne azimuth dual-channel T...","Title: Aggregating Multidimensional Wireless Link Information For Device-Free Localization\nYear: 2018\n\nAbstract: Device-free localization (DFL) is an emerging and promising technique, which can realize target localization without the requirement of attaching any wireless devices to targets. By analyzing the shadowing loss caused by targets on wireless links, we can estimate the target locations. However, for existing DFL approaches, a large number of wireless links is required to guarantee a certain localization precision, which may lead to high hardware cost. In this paper, we propose a novel multi-target device-free localization method with multidimensional wireless link information (MDMI). Unlike previous works that measure RSS only on a single transmission power level, MDMI collects RSS measurements from multiple transmission power levels to enrich the measurement information. Furthermore, the compressive sensing (CS) theory is applied by exploiting the inherent spatial sparsity of DFL. We model the DFL problem as a joint sparse recovery problem and adopt the multiple sparse Bayesian learning (M-SBL) algorithm to reconstruct the sparse vectors of different transmission power levels. Numerical simulation results demonstrate the outstanding performance of the proposed method.","Title: Securing Communication Data in Pervasive Social Networking Based on Trust with KP-ABE.\nYear: 2019\n\nAbstract: Pervasive Social Networking (PSN) intends to support instant social activities in a pervasive way at any time and anywhere. In order to protect crucial social activities, ensure communication dependability, and enhance user privacy, securing pervasive social communications becomes especially important. However, neither centralized nor distributed solutions can protect PSN communications as expected. How to automatically control data access in a trustworthy and efficient way is an important security issue. In this article, we propose a scheme to guarantee communication data security in PSN based on two dimensions of trust in a flexible manner on the basis of Key-Policy Attribute-Based Encryption (KP-ABE). Its advantages and performance are justified and evaluated through extensive analysis on security, computation complexity, communication cost, scalability, and flexibility, as well as scheme implementation. In addition, we develop a demo system based on Android mobile devices to test our scheme in practice. The results demonstrate its efficiency and effectiveness. Comparison with our previous work based on CP-ABE (Yan and Wang 2017) further shows its feasibility to be applied to PSN.","Title: AET-IAA: A Novel Approach to Enhance Azimuth Resolution for ScanSAR Interferometry.\nYear: 2017\n\nAbstract: Scanning synthetic aperture radar (ScanSAR) obtains wide swath images at the expense of azimuth resolution by sweeping the antenna beam periodically. Interferometric applications benefit from the wide swath of ScanSAR data. As a result, ScanSAR interferometry can efficiently acquire large scale geophysical information. However, the low azimuth resolution of ScanSAR images limits some applications ...","Title: Force Point Transfer Method to Solve the Structure of Soft Exoskeleton Robot Deformation due to the Driving Force\nYear: 2018\n\nAbstract: The structure of the human body is the result of biological evolution, which can support us to complete the action demand of daily life work well. But for high strength manual workers, soldiers and disabled patients with motor dysfunction, they need use external auxiliary devices to assist them in completing their actions. The exoskeleton robot is the product of man-machine fusion, which can provide wearer with an auxiliary power. The soft exoskeleton robot has the characteristics of adaptability and flexibility in movement, which is flourish. But the soft exoskeleton has the disadvantage of easy deformation of the soft material, which will affect the motion effect and the control precision. In this paper, the force position point transfer method is proposed to solve the easy deformation of the tensile force of the soft materials. The pulling force is transferred from the soft material to the hard backboard, which can solve the problem of the deformation of the soft materials. It is of great significance to improve the force effect and the precision of the robot position control.","Title: Fault-Tolerant and Self-Adaptive Market-Based Coordination Using Hoplites Framework for Multi-Robot Patrolling Tasks\nYear: 2018\n\nAbstract: An autonomous robot team can be employed for continuous coverage of a dynamic environment. In this paper, we propose a novel approach for creating multi-robot patrolling policies, which is fault-tolerant and self-adaptive. A dynamic priority queue and time-out replanning mechanism are maintained by each robot to schedule the tasks fault-tolerantly in the context of the market-based method. Hoplites framework is adapted by introducing a self-adaptive threshold adjustment and sharing mechanism to provide a high-level coordination. This work is demonstrated by a multi-robot patrolling task implemented on Robot Operating System (ROS). A flexible tool, Stage, is leveraged to provide the simulated environment. The experimental results validate the effectiveness and availability.","Title: Real-time indoor scene reconstruction with Manhattan assumption.\nYear: 2019\n\nAbstract: This paper presents a novel end-to-end system for real-time indoor scene reconstruction, which outperforms traditional image feature point-based method and dense geometry correspondence-based method in handling indoor scenes with less texture and geometry features. In our method, we fully explore the Manhattan assumption, i.e. scenes are majorly consisted with planar surfaces with orthogonal normal directions. Given an input depth frame, we first extract dominant axes coordinates via principle component analysis which involves the orthogonal prior and reduce the influence of noise. Then we calculate the coordinates of dominant planes (such as walls, floor and ceiling) in the coordinates using mean shift. Finally, we compute the camera orientation and reconstruct the scene by proposing a fast scheme based on matching the dominant axes and planes to the previous frame. We have tested our approach on several datasets and demonstrated that it outperforms some well known existing methods in these experiments. The performance of our method is also able to meet the requirement of real-time with an unoptimized CPU implementation.","Title: A Novel Adaptive Kalman Filter With Colored Measurement Noise.\nYear: 2018\n\nAbstract: In this paper, a novel variational Bayesian-based adaptive Kalman filter (VBAKF) is proposed to solve the problem of a linear state-space model with colored measurement noise and inaccurate noise covariance matrices. The filter problem of a linear state-space model with colored measurement noise and inaccurate noise covariance matrices is transformed into the filtering problem of a linear state-space model with white measurement noise and inaccurate noise covariance matrices using measurement differencing method and state augmentation approach. The augmentation state vector, corresponding predicted error covariance matrix and covariance matrix of white measurement noise are jointly estimated based on the variational Bayesian approach. The ability of addressing the colored measurement and inaccurate noise covariance matrices is demonstrated in the simulation of a target tracking example.","Title: A statistic approach for power analysis of integrated GPU.\nYear: 2019\n\nAbstract: As datasets grow, high performance computing has gradually become an important tool for artificial intelligence, particularly due to the powerful and efficient parallel computing provided by GPUs. However, it has been a general concern that the rising performance of GPUs usually consumes high power. In this work, we investigate the study of evaluating the power consumption of AMD’s integrated GPU (iGPU). Particularly, by adopting the linear regression method on the collecting data of performance counters, we model the power of iGPU using real hardware measurements. Unfortunately, the profiling tool CodeXL cannot be straightforwardly used for sampling power data and as a countermeasure we propose a mechanism called kernel extension to enable the system data sampling for model evaluation. Experimental results indicate that the median absolute error of our model is less than 3%. Furthermore, we simplify our statistical model for lower latency without significantly reducing the accuracy and stability.","Title: Radial Basis Function Kernel Parameter Optimization Algorithm in Support Vector Machine Based on Segmented Dichotomy\nYear: 2018\n\nAbstract: By analyzing the influences of kernel parameter and penalty factor for generalization performance on Support Vector Machine (SVM), a novel parameter optimization algorithm based on segmented dichotomy is proposed for Radial Basis Function (RBF) kernel. Combine with Segmented Dichotomy(SD) and Gird Searching(GS) method, a composite parameter selection, SD-GS algorithm, is structured for rapid optimization of kernel parameter and penalty factor. UCI Machine Learning database is used to test our proposed method. Experimental results have shown that performance on parameter selection is better than traversal exponential grid searching. Thus, the optimized parameter combination of SD-GS algorithm enables RBF kernel in SVM to have higher generalization performance.","Title: Vehicle Detection Under UAV Based on Optimal Dense YOLO Method\nYear: 2018\n\nAbstract: In this paper, a deep neural network model based on small target detection under UAV platform is designed. Due to the One-stage detection model like YOLO having novel structure and great industrial application potential, this paper proposes a new model of detection based on YOLOv2 structure. Faced with missed detection problem of small target, a series of improved schemes are proposed, which are suitable for small vehicles' detection under aerial view angle, and can achieve real-time detection, including dense topology and optimal pooling strategy.","Title: Human Detection Under UAV: an Improved Faster R-CNN Approach\nYear: 2018\n\nAbstract: Although Faster R-CNN has excellent performance in object detection, it still has some difficulties in detecting small targets and slightly overlapped targets in UAV (Unmanned Aerial Vehicle) images. Based on Faster R-CNN, this paper uses ResNet101 as a feature extractor. We increase the number of anchors from 9 to 15 in RPN so that the small targets can match more anchors and get sufficient training. Due to the increasement of anchors, this paper introduces a 1×1 convolution layer to integrate features and reduce the feature map channels. We also apply RoIAlign to avoid the misalignment caused by RoIPool. The improved model effectively increases the detection rate of small targets and slightly overlapped targets so that it can be applied to human detection under UAV. The improved model can detect small targets with a size of about 30×80 pixels on aerial images with resolution of 3840×2160 pixels. Compared with Faster R-CNN, the improved model increases AP (Average Precision) from 74.31% to 79.77% on the WILDTRACK dataset.","Title: Storage Sharing Optimization Under Constraints of SLO Compliance and Performance Variability.\nYear: 2019\n\nAbstract: SLO enforcement with the required strong SLO compliance and the desired low level of performance variability is necessary to ensure QoS for user applications with precisely differentiated service levels. However, for the cloud consolidating a large number of VMs rented by users, it is a great challenge to formulate an IO capacity allocation among consolidated VMs under the user-customized QoS constraints of SLO compliance and performance fluctuation for consolidated VMs. To address this challenge, we propose SASLO, an end-to-end VM-oriented control framework that supports users in customizing SLO targets and QoS constraints for each VM. SASLO can dynamically coordinate the throughput target and IO size limit for each VM adapting to the status of SLO enforcement so as to maximize the IO capacity allocation among consolidated VMs under QoS constraints. To accurately enforce time-varying throughput target, SASLO establishes a proportional-integral IO controller for each individual VM to converge the actual throughput to the target with an expected settling time. Our extensive evaluation driven by representative benchmarks demonstrates that SASLO is able to formulate a satisfactory IO capacity allocation plan for consolidated VMs under the constraints of SLO compliance and performance variability.","Title: Age-invariant face verification based on Local Classifier Ensemble\nYear: 2012\n\nAbstract: Human faces undergo considerable amount of variations across ages. This paper proposes an age-invariant face verification method by using a Local Classifier Ensemble Model (LCEM). First, reference points are located based on an extended Active Shape Model and faces are aligned afterwards. Second, a face is grouped into several non-overlapping patches and each group is further divided into several overlapping sub-patches. Local classifiers are then trained for each sub-patch and integrated to build an ensemble classifier under a Semi-Naive Bayesian framework. Finally, the proposed approach is evaluated on the MORPH database and experimental results show that such local classifier ensemble achieves significant and robust performance.","Title: Security uniform office format specification and API design based on the java platform\nYear: 2012\n\nAbstract: \"Uniform Office Format\" (UOF) is the Chinese national standard for office document format. It aims to solve the problem that the office document format is not unified. It is helpful for information sharing and document exchange. Moreover, it improves the Chinese office software's compatibility, and establishes the foundation for the document information exchange. Through the study of Specifications for the Syntax and Processing of UOF and XML Encryption/ Signature, we proposed a set of APIs which ensure the confidentiality, integrity, and authentication of the UOF documents. The API is implemented by Java Cryptography Architecture, Java Cryptography Extension, Dom4j, and XPath technologies on Java platform and is confirmed to XML Digital Signature APIs (JSR105) and XML Digital Encryption APIs (JSR106) Specifications. Every interface and class is designed according to the rules that should keep high cohesion inside module and low coupling between modules. It also implements a special set of APIs for security UOF documents. Furthermore, a set of test cases together with a GUI program are developed to verify the availability and correctness of the system. It is believed that this project will contribute a lot to the research and adoption of the Specification for the Syntax and Processing of UOF Encryption/Signature.","Title: Design of observer-based event-driven controllers for a class of state-dependent nonlinear systems.\nYear: 2016\n\nAbstract: The design problems of the observer-based event-driven controllers are investigated for the state-dependent nonlinear systems in this paper. An event-driven criterion is proposed to determine whether the newly sampled states of the designed state-dependent observer should be sent out to the controller. As a result, the communication resources can be saved significantly while the burden of the network communication can be reduced. Influenced by the event-driven controller, the closed-loop system is rewritten as the delayed system. The state-dependent integral function is introduced to be the Lyapunov function candidate to obtain less conservative asymptotic stability conditions and preserve the desired H∞ performance for the closed-loop system. The observer gain matrix, the controller gain matrix and the event-driven parameters are co-designed and co-obtained in terms of solution to a set of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed method in this paper is illustrated by the numerical examples and the tunnel diode circuit systems.","Title: Type-2 T-S fuzzy modeling for the dynamic systems with measurement noise.\nYear: 2008\n\nAbstract: In actual industrial processes,the measurement data always contain noise.Therefore,this situation will affect the accuracy of modeling.Compared with the type-1 fuzzy sets,the membership functions in type-2 fuzzy sets include the primary membership function and the secondary membership function.They provide the additional degrees of freedom that make it possible to model uncertainties brought by the noises.In this paper,a type-2 T-S fuzzy model is presented to minimize the effect of measurement noises.Furthermore,the influence of the initial conditions is considered in the algorithm.The primary membership function is gained through an improved nearest-neighborhood clustering algorithm,and the secondary membership function is determined through GMM based on the sufficient statistics.The orthogonal least-squared algorithm is used to identify the consequent of the fuzzy rules.Finally,the simulation results are compared with those obtained from a type-1 T-S fuzzy modeling results and the superiority of the proposed approach is highlighted.","Title: Reference Samples Padding for Intra-Frame Coding of Omnidirectional Video.\nYear: 2018\n\nAbstract: Due to lack of video coding standard tailored specially to spherical or omnidirectional videos, which are important in Virtual Reality applications, omnidirectional video frames have to be projected onto the 2-Dimension(2D) plane, using the EquiRectangular Projection(ERP) for example, to utilize existing video compression techniques. As there is no information available for prediction outside the left or right border after projection, unexpected artefacts that are generated at the right border of frames degrade the subjective visual quality. Utilizing continuity of the content in the omnidirectional video based on ERP, in this work, an efficient intra-coding method is proposed based on adaptively padding the reference samples at the right border for omnidirectional videos. Using the state of art video encoder, the proposed method can reduce the BD-rate by 0.24% in average for the coding tree units (CTUs) at the right border.","Title: An Effective Tap-Length NLMS Algorithm for Network Echo Cancellers.\nYear: 2017\n\nAbstract: A novel normalized least mean square (NLMS)-based algorithm is proposed to locate the active taps of the unknown impulse response in the application of network echo cancellation. Starting with the full length of the unknown echo path, the start-tap and end-tap estimates of the active region adapt, respectively, in the form of fractional tap. The effective tap-length estimate is defined by the length between the start-tap and end-tap estimates. The filter tap-weight vector is then updated with this effective tap-length estimate following the NLMS algorithm, while the tap weights located beyond the effective tap-length estimate are directly set to zero. In case that the active region of the unknown echo path shifts in a large scale, the start-tap and end-tap estimates are reset to the full length when the effective tap-length estimate is lower than a predetermined threshold value. Simulation results demonstrate that the proposed fractional tap-based algorithm can efficiently locate and track the effective tap length of the unknown echo path when the active coefficients change abruptly in locations and magnitudes, outperforming the existing stochastic tap-based schemes.","Title: Mass-Driven Topology-Aware Curve Skeleton Extraction from Incomplete Point Clouds\nYear: 2020\n\nAbstract: We introduce a mass-driven curve skeleton as a curve skeleton representation for 3D point cloud data. The mass-driven curve skeleton presents geometric properties and mass distribution of a curve skeleton simultaneously. The computation of the mass-driven curve skeleton is formulated as a minimization of Wasserstein distance, with an entropic regularization term, between mass distributions of point clouds and curve skeletons. Assuming that the mass of one sampling point should be transported to a line-like structure, a topology-aware rough curve skeleton is extracted via the optimal transport plan. A Dirichlet energy regularization term is then used to obtain a smooth curve skeleton via geometric optimization. Given that rough curve skeleton extraction does not depend on complete point clouds, our algorithm can be directly applied to curve skeleton extraction from incomplete point clouds. We demonstrate that a mass-driven curve skeleton can be directly applied to an unoriented raw point scan with significant noise, outliers and large areas of missing data. In comparison with state-of-the-art methods on curve skeleton extraction, the performance of the proposed mass-driven curve skeleton is more robust in terms of extracting a correct topology.","Title: Hybrid consensus sigma point approximation nonlinear filter using statistical linearization\nYear: 2018\n\nAbstract: In this paper, a hybrid consensus sigma point approximation nonlinear filter is proposed for state estimation in collaborative sensor network, where hybrid consensus of both measurement and information is utilised. Statistical linearization of nonlinear functions is used in sigma point filters, that is, unscented Kalman filter (UKF), cubature Kalman filter (CKF), and central difference Kalman filter (CDKF). Stability of the proposed algorithm is also analysed with the help of linearization operation and some conservative assumptions. Two typical target tracking examples are used to demonstrate the effectiveness of the proposed algorithms. Simulation results show that the proposed algorithms are more stable than existing algorithms, and among our proposed algorithms, CKF- and CDKF-based algorithms are more accurate and stable than the UKF-based one.","Title: Graph cut based automatic aorta segmentation with an adaptive smoothness constraint in 3D abdominal CT images.\nYear: 2018\n\nAbstract: Aorta segmentation is clinically important as it is a necessary step towards accurate assessments of some aorta disease. In the paper, we present a graph cut based method for automated aorta segmentation. In our method, a discriminative integrated feature (DIF) and a novel adaptive smoothness constraint are designed. DIF consists of low level features and other discriminative features including eigenvalues of Hessian and local self-similarity descriptor. DIF and random forests (RFs) are used to generate the probability maps. The probability maps containing learning information from RFs are more accurate than traditional probability maps generated based on intensities directly. The negative logarithm of the probability maps serves as the penalty term in a cost function. Additionally, a novel adaptive smoothness constraint is imposed to ensure a smooth solution. The adaptive smoothness term is constructed by DIFs and data-driven weights. Two kinds of data-driven weights are developed based on the idea that the discontinuity of two neighboring voxels with different labels should be distinct with two neighboring voxels with the same label. The final segmentation is obtained by optimizing the cost function using graph cuts. We evaluate the proposed method through challenging task of abdominal aorta segmentation in 3D CT images. With average dice metric (DM) > 0.9690 on the test set, our experimental results demonstrate that our method achieves higher aorta segmentation accuracy than existing methods.","Title: Distributed Power Control for Interference-Aware Multi-User Mobile Edge Computing: A Game Theory Approach\nYear: 2018\n\nAbstract: The computation task offloading and resource management in mobile edge computing (MEC) become attractive in recent years. Many algorithms have been proposed to improve the performance of the MEC system. However, the research on power control in MEC systems is just starting. The power control in the single-user and an interference-free multi-user MEC systems has been investigated; but in the interference-aware multi-user MEC systems, this issue has not been learned in detail. Therefore, a game theory-based power control approach for the interference-aware multi-user MEC system is proposed in this paper. In this algorithm, both the interference and the multi-user scenario are considered. Moreover, the existence and uniqueness of the Nash Equilibrium (NE) of this game are proved, and the performance of this algorithm is evaluated via theoretical analysis and numerical simulation. The convergence, the computation complexity and the price of anarchy in terms of the system-wide computation overhead are investigated in detail. The performance of this algorithm has been compared with the traditional localized optimal algorithm by simulation. The simulation results demonstrate that the proposed algorithm has more advantages than the traditional one.","Title: Robust Diffusion Estimation Algorithm Via Genearlized Cauchy Loss Function Over Distributed Network\nYear: 2017\n\nAbstract: The cost function used in distributed estimation methods is in general the minimum mean square error (MMSE) criterion which can achieve optimal performance under Gaussian assumption. While the performance will degrade severely when they suffer from the non-Gaussian situations, such as the impulsive-noise case. In this paper, we extend the outstanding Cauchy Loss (CL) to generalized CL (GCL) which can achieve a better performance than MMSE methods since it takes into account unique distribution characteristics and robustness to outliers. In addition, we use the GCL to develop a novel robust diffusion estimation method over distributed network in impulse noise environments. Simulation results show that the proposed diffusion GCL algorithm outperforms other outstanding robust diffusion adaptive filtering algorithms in impulsive noise environments.","Title: Efficient OLAP algorithms on GPU-accelerated Hadoop clusters\nYear: 2019\n\nAbstract: In the time of big data, on-line analytical processing (OLAP) is an important method to process massive data. In order to realize a system with the capacity of both high storage and high computing power, Hadoop and GPU are both applied in OLAP. In general, three cores of OLAP determines the efficiency of OLAP analysis, which are aggregation of multi-dimensional data, pre-calculation of multi-dimensional data set (Cube) and connection of dimension table and fact table. For the purpose of boosting efficiency, this paper presents optimizing algorithms for each core. Beginning with aggregation on single machine, this paper firstly designs the GPU-based aggregation algorithm. Then, GPU-based Cube algorithm is introduced to accelerate pre-calculation, using inverted index to shrink computation amount. Finally, with new-designed dimension table connecting algorithm and query algorithm, GPU-based OLAP analysis algorithm is presented. Along with corresponding experiments and results, each algorithm shows their ability of boosting efficiency, optimizing GPU-based OLAP analysis on Hadoop.","Title: Time-Frequency Squeezing and Generalized Demodulation Combined for Variable Speed Bearing Fault Diagnosis\nYear: 2019\n\nAbstract: High-resolution time-frequency representation (TFR) method is effective for signal analysis and feature detection. However, for variable speed bearing vibration signal, conventional TFR method is prone to blur and affect the accuracy of the instantaneous frequency estimation. Moreover, the traditional order tracking, relying on equi-angular resampling, usually suffers from interpolation error. To solve such problems, we propose a joint time-frequency (TF) squeezing method and generalized demodulation (GD) to realize variable speed bearing fault diagnosis. The method can represent the time-varying fault characteristic frequency precisely and be free from resampling. First, using fast spectral kurtosis to select the optimal-frequency band which is sensitive to rolling bearing fault, and extracting envelope by Hilbert transform within the selected optimal frequency band. Next, a high-quality TF clustering method based on short-time Fourier transform is applied to the TF analysis of the envelope to get a clear TFR, from which the frequency information for GD is obtained. Finally, processing the basic demodulator via the peak search through the TF analysis results in the TFR for GD to gain a resampling-free-order spectrum. Based on the more precise TF information from the clearer TFR, the bearing fault can be diagnosed via GD without tachometer or any resampling involved, avoiding the amplitude error and low computational efficiency of resampling. Simulation study and experimental signal analysis validate that the proposed method has better performance than those methods based on conventional TF analysis and resampling.","Title: Energy Efficiency Optimization for D2D Communication Underlaying Distributed Antenna System\nYear: 2019\n\nAbstract: In this paper, the energy efficiency (EE) performance of a downlink distributed antenna system with device-to-device (D2D) underlay communication is investigated over a composite Nakagami fading channel. The optimal power allocation (PA) schemes are developed for improving the EE performance. Under the minimum rate constraint of cellular user (CU), the maximum power constraints of CU and D2D pair, an optimization problem for maximizing the system EE is formulated. The Karush–Kuhn–Tucker conditions are used to solve the optimization problem. Based on the 1-D search method and the bisection method, two schemes and corresponding algorithms are, respectively, proposed to obtain the optimal solutions of PA. For a given transmit power of D2D pair, the closed-form PA of remote antennas (RAs) and the number of active RAs are derived, which greatly simplifies the optimization. It is shown that the proposed two schemes can obtain the same EE performance, and the scheme with the bisection method has lower complexity than that with the 1-D search method. Computer simulation verifies the effectiveness of these two schemes, and superior EE performance can be achieved.","Title: Incorporation Of Faulty Prior Knowledge In Multi-Target Device-Free Localization\nYear: 2019\n\nAbstract: As an emerging and promising technique, device-free localization (DFL) has drawn considerable attention in recent years. By exploiting the inherent spatial sparsity of target localization, the compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements. In practical scenarios, a prior knowledge about target locations is usually available, which can be obtained by coarse localization or tracking techniques. Among existing CS-based DFL approaches, however, few works consider the utilization of prior knowledge. To make use of the prior knowledge that is partly or erroneous, this paper proposes a novel faulty prior knowledge aided multi-target device-free localization (FPK-DFL) method. It first incorporates the faulty prior knowledge into a three-layer hierarchical prior model. Then, it estimates location vector and learns model parameters under a variational Bayesian inference (VBI) framework. Simulation results show that the proposed method can improve the localization accuracy by taking advantage of the faulty prior knowledge.","Title: DF -FS based path planning algorithm with sparse waypoints in unstructured terrain.\nYear: 2018\n\nAbstract: For autonomous navigation in unstructured terrain, itu0027s difficult to plan the path with traditional algorithms based on searching guidance waypoints such as ARA* and Field D* as a result of the poor positioning signal and the environmental uncertainty especially when dense waypoints are not given. Because of irregular road boundaries, there are also problems for path planning based road detection methods. To solve these problems, the depth first forward search (DF-FS) based path planning algorithm which combines forward searching along the road and model-based prediction is proposed. DF-FS based planner takes road boundary constraint, road direction and heading of vehicle as the basis of path planning, while using model prediction optimizes planned path to improve navigation stability. The experiment results show that in unstructured terrain with sparse waypoints or even without waypoints in the one-way road, the autonomous navigation can be guaranteed effectively. Meanwhile the proposed algorithm has a strong robustness to the uncertainty of positioning and environmental perception, so it is suitable for autonomous navigation in rough terrain.","Title: The research about radiometric technology of two-dimensional rotary table based on image gray level.\nYear: 2019\n\nAbstract: It is difficult to measure infrared radiation quantitatively for fast dynamic targets such as aircraft and missile. At present, there are many problems such as low measurement accuracy, feedback delay, and complex measurement system. In this paper, a radiation measurement scheme based on image grayscale is proposed. The scheme adopts a two-dimensional turntable structure, which can measure the radiation quantity rapidly and accurately. Based on the image method, this method is used to calibrate the gray value of the target point and compare it with that of the absolute standard radiator, so as to obtain the radiation value of the target indirectly. The key of this method is to establish an accurate radiation measurement model. The proposed model based on dynamic calibration is about 50% more accurate than the traditional model. The final measurement accuracy of this model is 75%. Through the infrared radiation measurement of specific dynamic objects in two-dimensional space, it is found that the dynamic calibration model based on this measurement method has higher measurement precision.","Title: DF-CSPG: A Potential Game Approach for Device-Free Localization Exploiting Joint Sparsity.\nYear: 2019\n\nAbstract: Device-free localization (DFL) plays an increasingly dominant role in some security and military applications, aiming at locating targets without carrying any extra devices. Most research utilize received signal strength, causing poor accuracy due to insufficient information. In this letter, we propose DF-CSPG, a new DFL algorithm using compressive sensing (CS) theory and potential game (PG) appro...","Title: Ensemble Time-Stepping Algorithm for the Convection-Diffusion Equation with Random Diffusivity.\nYear: 2019\n\nAbstract: In this paper, we develop two ensemble time-stepping algorithms to solve the convection-diffusion equation with random diffusion coefficients, forcing terms and initial conditions based on the pseudo-spectral stochastic collocation method. The key step of the pseudo-spectral stochastic collocation method is to solve a number of deterministic problems derived from the original stochastic convection-diffusion equation. In general, a common way to solve the set of deterministic problems is by using the backward differentiation formula, which requires us to store the coefficient matrix and right-hand-side vector multiple times, and solve them one by one. However, the proposed algorithm only need to solve a single linear system with one shared coefficient matrix and multiple right-hand-side vectors, reducing both storage required and computational cost of the solution process. The stability and error analysis of the first- and second-order ensemble time-stepping algorithms are provided. Several numerical experiments are presented to confirm the theoretical analyses and verify the feasibility and effectiveness of the proposed method.","Title: Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel Diversity.\nYear: 2019\n\nAbstract: As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings are vulnerable to environmental dynamics. The deviation between runtime RSS variations and the data in a fixed dictionary can significantly deteriorate the performance of DFL. In this paper, we introduce ComDec, a novel CS-based DFL method using channel state information (CSI) to enhance localization accuracy and robustness. To exploit the channel diversity of CSI measurements, the DFL problem is formulated as a joint sparse recovery problem that recovers multiple sparse vectors with common support. To solve this problem, we develop a joint sparse recovery algorithm under the variational Bayesian inference framework. In this algorithm, dictionaries are parameterized based on the saddle surface model. To adapt to the environmental changes and different channel characteristics, dictionary parameters are modelled as tunable parameters. Simulation results verified the superior performance of ComDec as compared with other state-of-the-art CS-based DFL methods.","Title: A 60-GHz 3.0-Gb/s Spectrum Efficient BPOOK Transceiver for Low-Power Short-Range Wireless in 65-nm CMOS\nYear: 2019\n\nAbstract: This paper presents a 60-GHz transceiver for low-power high-speed short-range wireless using the proposed binary-phase on-off keying (BPOOK) modulation scheme. The proposed BPOOK wireless transceiver transmits radio frequency (RF) signal with amplitude modulated on and off by input baseband data, and meanwhile, the phase is changing between 0° and 180°. The BPOOK transceiver achieves doubled spectral efficiency compared with OOK modulation and binary-phase-shift keying (BPSK) modulation. It also cancels the intrinsic local-oscillator feed through (LOFT) issue in the OOK modulation. The BPOOK RF signal can be demodulated by employing low-power square-law envelope detector incoherently. The transceiver is fabricated in a standard 65-nm CMOS technology. A data rate of 3.0 Gb/s is achieved while consuming a power of 100 mW from 1-V supply. The incoherent receiver has a sensitivity of −46 dBm. The core area of the transceiver is 1.56 mm\n<sup xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>\n.","Title: A 50.1-Gb/s 60-GHz CMOS Transceiver for IEEE 802.11ay With Calibration of LO Feedthrough and I/Q Imbalance\nYear: 2019\n\nAbstract: This paper presents a 60-GHz CMOS transceiver targeting the IEEE 802.11ay standard. A calibration block for local oscillator feedthrough (LOFT) and I/Q imbalance featuring high accuracy and low power consumption is integrated with the transceiver. With the help of the proposed calibration, this paper is capable of boosting the data rate with higher order modulation scheme and wider channel-bonding bandwidth, which are demanded by IEEE 802.11ay. At the same time, it maintains the compatibility with the existing IEEE 802.11ad standard. This paper reports a two-channel-bonding data rate of 24.64 Gb/s in 128 quadrature amplitude modulation (QAM). The corresponding TX-to-RX error vector magnitude (EVM) is −26.1 dB. Furthermore, a four-channel-bonding data rate of 42.24 Gb/s in 64 QAM is realized with a single-element transceiver. The measured maximum data rate is 50.1 Gb/s in 64 QAM, which is the highest data rate achieved in the 60-GHz band. The power consumption is only 169 mW in the transmitting mode and 139 mW in the receiving mode.","Title: ACT: an ACTNet for visual tracking.\nYear: 2019\n\nAbstract: Owing to convolutional neural network (CNN) models' success in various fields of computer vision, the authors proposed an advanced convolutional network (ACTNet) to enhance the accuracy of visual tracking. Different from prior methods, they regard a CNN as not only a semantic feature map extractor but also a position predictor. Rectified Linear Unit (RLU) and sigmoid are both used in ACTNet for fe...","Title: Passivity Analysis for Quaternion-Valued Memristor-Based Neural Networks With Time-Varying Delay.\nYear: 2020\n\nAbstract: This paper is concerned with the problem of global exponential passivity for quaternion-valued memristor-based neural networks (QVMNNs) with time-varying delay. The QVMNNs can be seen as a switched system due to the memristor parameters are switching according to the states of the network. This is the first time that the global exponential passivity of QVMNNs with time-varying delay is investigate...","Title: Correlation of Gut Microbiome Between ASD Children and Mothers and Potential Biomarkers for Risk Assessment.\nYear: 2019\n\nAbstract: Variation of maternal gut microbiota may increase the risk of autism spectrum disorders (ASDs) in offspring. Animal studies have indicated that maternal gut microbiota is related to neurodevelopmental abnormalities in mouse offspring, while it is unclear whether there is a correlation between gut microbiota of ASD children and their mothers. We examined the relationships between gut microbiome profiles of ASD children and those of their mothers, and evaluated the clinical discriminatory power of discovered bacterial biomarkers. Gut microbiome was profiled and evaluated by 16S ribosomal RNA gene sequencing in stool samples of 59 mother–child pairs of ASD children and 30 matched mother–child pairs of healthy children. Significant differences were observed in the gut microbiome composition between ASD and healthy children in our Chinese cohort. Several unique bacterial biomarkers, such as Alcaligenaceae and Acinetobacter, were identified. Mothers of ASD children had more Proteobacteria, Alphaproteobacteria, Moraxellaceae, and Acinetobacter than mothers of healthy children. There was a clear correlation between gut microbiome profiles of children and their mothers; however, children with ASD still had unique bacterial biomarkers, such as Alcaligenaceae, Enterobacteriaceae, and Clostridium. Candidate biomarkers discovered in this study had remarkable discriminatory power. The identified patterns of mother–child gut microbiome profiles may be important for assessing risks during the early stage and planning of personalized treatment and prevention of ASD via microbiota modulation.","Title: How Do Your Neighbors Disclose Your Information: Social-Aware Time Series Imputation\nYear: 2019\n\nAbstract: Different time series is measured in almost all fields including biology, economics and sociology. A common challenge for using such data is the imputation of the missing values with reasonable ones. Most of existing approaches to data imputation assume that individual's observations are independent to each other, which is rarely the case in real-world. In this paper, we study the social-aware time series imputation problem. Given a social network that represents social relations between individuals, we propose a sequential encoder-decoder-based framework and build a connection between the missing observations and the social context. In particular, the proposed model employs the attention mechanism to incorporate social context and temporal context into the imputation task. Experimental results based on two real-world datasets demonstrate that our approach outperforms 11 different baseline methods.","Title: L-Seg: An end-to-end unified framework for multi-lesion segmentation of fundus images.\nYear: 2019\n\nAbstract: Diabetic retinopathy and diabetic macular edema are the two leading causes for blindness in working-age people, and the quantitative and qualitative diagnosis of these two diseases usually depends on the presence and areas of lesions in fundus images. The main related lesions include soft exudates, hard exudates, microaneurysms, and haemorrhages. However, segmentation of these four kinds of lesions is difficult due to their uncertainty in size, contrast, and high interclass similarity. Therefore, we aim to design a multi-lesion segmentation model. We have designed the first small object segmentation network (L-Seg) that can segment the four kinds of lesions simultaneously. Taking into account that small lesion regions could not response at high level of network, we propose a multi-scale feature fusion method to handle this problem. In addition, when considering the cases of both class-imbalance and loss-imbalance problems, we propose a multi-channel bin loss. We have evaluated L-Seg on three fundus datasets including two publicly available datasets - IDRiD and e-ophtha and one private dataset - DDR. Extensive experiments have demonstrated that L-Seg achieves better performance in small lesion segmentation than other deep learning models and traditional methods. Specially, the mAUC score of L-Seg is over 16.8%, 1.51% and 3.11% higher than that of DeepLab v3+ on IDRiD, e-ophtha and DDR datasets, respectively. Moreover, our framework shows competitive performance compared with top-3 teams in IDRiD challenge. The source code of L-Seg is available at: https://github.com/guomugong/L-Seg.","Title: DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale.\nYear: 2019\n\nAbstract: Fine morphological reconstruction of individual neurons across the entire brain is essential for mapping brain circuits. Inference of presynaptic axonal boutons, as a key part of single-neuron fine reconstruction, is critical for interpreting the patterns of neural circuit wiring schemes. However, automated bouton identification remains challenging for current neuron reconstruction tools, as they focus mainly on neurite skeleton drawing and have difficulties accurately quantifying bouton morphology. Here, we developed an automated method for recognizing single-neuron axonal boutons in whole-brain fluorescence microscopy datasets. The method is based on deep convolutional neural networks and density-peak clustering. High-dimensional feature representations of bouton morphology can be learned adaptively through convolutional networks and used for bouton recognition and subtype classification. We demonstrate that the approach is effective for detecting single-neuron boutons at the brain-wide scale for both long-range pyramidal projection neurons and local interneurons.","Title: Risk-Based Multi-Objective Optimization of Distributed Generation Based on GPSO-BFA Algorithm.\nYear: 2019\n\nAbstract: With the rapid evolution of technology, growing accessibility, and environmental appeal of wind and solar electric systems, distributed generation (DG) has been pushed from the fringe to a mainstream factor in the grid. However, due to the randomness and uncertainty of environmental and operational conditions, DG also brings many risks and may adversely affect the reliability and safety of the power grid when connected to the distribution network. Therefore, it is necessary to introduce the risk theory into the allocation and placement of DGs. This paper establishes a comprehensive set of risk and economic indexes by modeling the randomness and uncertainty of DG outputs. In addition, islanded operation, which is a promising development direction of microgrids, is explicitly studied and the related indexes are modeled. Putting them together, we propose a risk-based multi-objective optimal allocation model to optimize the placement and configuration of DGs and provide a reliable and cost-effective system. We solve the formulated multi-objective optimization problem by combining the gradient particle swarm optimization algorithm and the bacterial foraging algorithm. We demonstrate the validity and rationality of the proposed method through the analyses of the American PG&E 69-node system.","Title: A Coarse TF Ridge-Guided Multi-Band Feature Extraction Method for Bearing Fault Diagnosis Under Varying Speed Conditions.\nYear: 2019\n\nAbstract: Currently, most of the vibration signal analysis methods for bearing fault diagnosis under varying speed conditions are based on the resampling technology with shaft rotational frequency (SRF). However, the SRF obtained by a fixed tachometer or time-frequency (TF) ridge detection reduces measuring flexibility or introduces errors inevitably. In this paper, a multi-band feature extraction method using coarse IF ridge-guided variational nonlinear chirp mode decomposition (VNCMD) is proposed for bearing fault diagnosis under varying speed conditions. Specifically, the proposed method is conducted as follows. First, the low-frequency component (LFC) and resonance component are extracted by the low-pass filtering and the fast kurtogram method, respectively, to alleviate the noise interference. Second, the coarse TF ridges are identified by a tractable ridge estimation method that is based on the TF representation for preliminary selection of the initial instantaneous frequency. Third, the coarse TF ridge-guided VNCMD is constructed to track the SRF and instantaneous fault characteristic frequency (IFCF) from the envelope signals of the LFC and the resonance component, respectively. Finally, the characteristic frequency ratio is computed on the basis of the values of SRF and IFCF to determine the fault type of ball bearing without resampling. The simulation studies and experimental verifications confirm that the proposed method can accurately locate bearing defect types and outperforms some existing methods.","Title: Online health assessment of wind turbine based on operational condition recognition\nYear: 2019\n\nAbstract: To reduce the operation and maintenance (O&M) costs, the health assessment of wind turbine has received more and more attention in recent years. However, it is difficult to evaluate the health condition of wind turbine due to the complex and non-stationary operation environment. This paper proposes a data-driven approach for online health assessment of wind turbine based on operational condition recognition. First, the operational condition parameters are selected by analyzing the monitoring data of wind turbine. Considering the time-varying of operation environment, the operational conditions are divided into four subspaces utilizing the K-means clustering algorithm. Then, using the historical state parameters data under normal operation, a health benchmark model is constructed in each operational condition space based on Gaussian Mixture Model (GMM). Further, a Softmax model is trained according to the results of operational condition classification, which is used to identify the online operational condition of wind turbine. Moreover, an overall health index (HI) based on Mahalanobis distance is developed to assess the health condition of wind turbine. Finally, the method is verified by the actual supervisory control and data acquisition (SCADA) data of a wind field in northwestern China. The test results show that the proposed approach can track the running state of the wind turbine accurately and play a good role in early fault warning.","Title: A Modified Cartesian Factorized Back-Projection Algorithm for Highly Squint Spotlight Synthetic Aperture Radar Imaging\nYear: 2019\n\nAbstract: Highly squint synthetic aperture radar (SAR) increases the flexibility and aspect-information of observation but poses several challenges to frequency-domain algorithms. The back-projection (BP) algorithm is recognized to produce the best results for highly squint mode but at high computational expense. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. Th...","Title: Adaptive compressed sensing of color images based on salient region detection\nYear: 2020\n\nAbstract: We propose a novel algorithm for color image compressed sensing (CS). Our method involves the adaptive measurement and reconstruction of color images based on visual saliency detection. First, we divide the image into blocks and transform the RGB channel into the YUV channel. Secondly, we use statistical texture distinctiveness to calculate the saliency of each block and normalize energy, thereby establishing an adaptive measurement rate and measurement matrix. Thirdly, we adaptively measure the Y channel according to block prominence and preserve the information of the UV channel. During reconstruction, we utilize adaptive block measurement rate to re-estimate block saliency and then reconstruct the objective function of the weighted reconstruction model according to the re-estimated block saliency. Finally, we combine the reconstructed Y channel with the reserved UV channel to obtain the final image. Experimental results show that compared with other state-of-the-art approaches, the proposed algorithm can not only provide good subjective visual quality but can also present higher peak signal to noise ratio (PSNR) under the same sampling rate.","Title: Co-Saliency Detection Via Local Prediction And Global Refinement\nYear: 2019\n\nAbstract: This paper presents a novel model in the field of image co-saliency detection. Previous works simply design low level handcrafted features or extract deep features based on image patches for co-saliency calculation, which neglect the entire object perception properties. Besides, they also neglect the problem of visual similar region's mismatching when designing co-saliency calculation model. To solve these problems, we propose a novel strategy by considering both local prediction and global refinement (LPGR). In the local prediction stage, we train a deep convolutional saliency detection network in an end-to-end manner which only use the fully convolutional layers for saliency map prediction to capture the entire object perception properties and reduce feature redundancy. In the global refinement stage, we construct a unified co-saliency refinement model by integrating global appearance similarity into a co-saliency diffusion function, realizing the propagation and optimization of local saliency values in the context of entire image group. To overcome the adverse effects of visual similar regions' mismatching, we innovatively incorporates the inter-images saliency spread constraint (ISC) term into our co-saliency calculation function. Experimental results on public datasets demonstrate consistent performance gains of the proposed model over the state-of-the-art methods.","Title: A New Robust Kalman Filter for SINS/DVL Integrated Navigation System\nYear: 2019\n\nAbstract: A new robust strap-down inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation algorithm are proposed in this paper with a focus on suppressing the process uncertainty and measurement outliers induced by severe manoeuvering. In the proposed algorithm, the one-step predicted probability density function is modeled as Student's t-distribution to deal with the heavy-tailed process noise, and hierarchical Gaussian state-space model for SINS/DVL integrated navigation algorithm is constructed. To detect and eliminate the measurement outliers, each measurement is marked by a binary indicator variable modeled as a beta-Bernoulli distribution. The variational Bayesian approach is used to jointly estimate state vector, auxiliary random variable, scale matrix, Bernoulli variable, and beta variable. The experimental results illustrate that the proposed algorithm has better robustness and navigation accuracy to deal with process uncertainty and measurement outliers than existing state-of-the-art algorithms.","Title: Block Variational Bayesian Algorithm For Multiple Target Localization With Unknown And Time-Varying Transmit Powers In Wsns\nYear: 2019\n\nAbstract: Target localization is an important research field in wireless sensor networks and the received signal strength (RSS)-based method is of particular interest. Since traditional RSS-based method is invalid for simultaneously localizing multiple targets, the compressive sensing theory has been applied to develop an effective localization framework in recent years. However, most existing works implicitly assume the transmit powers of targets are known and constant, which contradicts the practical application scenario as the power might change with time and usually difficult to be known. In this paper, we consider the challenging problem of multiple target localization with unknown and time-varying transmit powers by exploiting multiple measurement vectors (MMVs). Since the conventional MMV formulation is incapable of the time-varying environment, we develop a novel MMV framework, in which the temporal correlation of varying powers can be further exploited. In this way, the problem is transformed to reconstruct a block-sparse signal and learn its inner correlation structure. To address this, a block-sparse reconstruction algorithm is designed, based on the novel application of the variational Bayesian approximation and the expectation-maximization methodology. The proposed algorithm can jointly estimate locations and varying powers of multiple targets with high accuracy. The extensive simulation results demonstrate the superiority of the proposed method in comparison with the state-of-the-art algorithms.","Title: GPU-based efficient join algorithms on Hadoop\nYear: 2019\n\nAbstract: The growing data have brought tremendous pressure for query processing and storage, so there are many studies that focus on using GPU to accelerate join operation, which is one of the most important operations in modern database systems. However, existing GPU acceleration join operation researches are not very suitable for the join operation on big data. Based on this, this paper speeds up nested loop join, hash join and theta join, combining Hadoop with GPU, which is also the first to use GPU to accelerate theta join. At the same time, after the data pre-filtering and pre-processing, using MapReduce and HDFS in Hadoop proposed in this paper, the larger data table can be handled, compared to existing GPU acceleration methods. Also with MapReduce in Hadoop, the algorithm proposed in this paper can estimate the number of results more accurately and allocate the appropriate storage space without unnecessary costs, making it more efficient. Experimental results show that comparing with GPU-based approach without Hadoop, our approach increases the speed by 1.5–2 times, and comparing with the Hadoop-based approaches without GPU, our approach increases the speed by 1.3–2 times.","Title: Fair Deployment Of An Unmanned Aerial Vehicle Base Station For Maximal Coverage\nYear: 2019\n\nAbstract: Unmanned aerial vehicle mounted base stations (UAV-BSs) can provide wireless cellular service to ground users in a variety of scenarios. The efficient deployment of such UAV-BSs while optimizing the coverage area is one of the key challenges. We investigate the deployment of UAV-BS to maximize the coverage of ground users, and further analyzes the impact of the deployment of UAV-BS on the fairness of ground users. In this paper, we first calculated the location of the UAV-BS according to the QoS requirements of the ground users, and then the fairness of ground users is taken into account by calculating three different fairness indexes. The performance of two genetic algorithms, namely Standard Genetic Algorithm (SGA) and Multi-Population Genetic Algorithm (MPGA) are compared to solve the optimization problem of UAV-BS deployment. The simulations are presented showing that the performance of the two algorithms, and the fairness performance of the ground users is also given.","Title: Variational Bayesian Inference-Based Multiple Target Localization in WSNs With Quantized Received Signal Strength.\nYear: 2019\n\nAbstract: The received signal strength (RSS)-based target localization is an important field of research with numerous applications in wireless sensor networks. By exploiting the sparsity of localization, the compressive sensing (CS) can be applied to develop an effective localization framework for multiple targets. However, the existing CS-based method considers localization directly from real-valued sensor measurements, which implicitly assume that the measurements have infinite bit precision. When the assumption is violated, the localization performance will deteriorate dramatically, especially at low quantization bit rates. In this paper, we first design a quantizer for efficiently processing the raw RSS measurements and then develop a novel Bayesian CS framework for estimating target locations from the quantized measurements. By modeling the quantization errors as independent random variables, the non-linear localization problem is formulated in a linear CS observation model. Following this idea, we solve the problem from a Bayesian perspective and design some sophisticated prior distributions to guarantee the performance. To address this, we resort to the variational Bayesian inference methodology and propose a novel iterative algorithm for jointly estimating target locations and dealing with quantization errors. The extensive simulation results demonstrate the superiority of the proposed algorithm in comparison with the state-of-the-art methods.","Title: Combining fine texture and coarse color features for color texture classification.\nYear: 2017\n\nAbstract: Color texture classification plays an important role in computer vision applications because texture and color are two fundamental visual features. To classify the color texture via extracting discriminative color texture features in real time, we present an approach of combining the fine texture and coarse color features for color texture classification. First, the input image is transformed from RGB to HSV color space to separate texture and color information. Second, the scale-selective completed local binary count (CLBC) algorithm is introduced to extract the fine texture feature from the V component in HSV color space. Third, both H and S components are quantized at an optimal coarse level. Furthermore, the joint histogram of H and S components is calculated, which is considered as the coarse color feature. Finally, the fine texture and coarse color features are combined as the final descriptor and the nearest subspace classifier is used for classification. Experimental results on CUReT, KTH-TIPS, and New-BarkTex databases demonstrate that the proposed method achieves state-of-the-art classification performance. Moreover, the proposed method is fast enough for real-time applications. (C) 2017 SPIE and IS&T","Title: SA-M-SBL: An Algorithm for CSI-Based Device-Free Localization With Faulty Prior Information.\nYear: 2019\n\nAbstract: As an emerging technique, device-free localization (DFL) is promising to localize the target without attaching any transceivers. Recently, the benefits of channel state information (CSI) on DFL have been revealed in this paper. Motivated by this, in this paper, we propose to exploit the channel diversity of CSI measurements for multi-target DFL under the compressive sensing (CS) framework. The CSI-based multi-target DFL problem is formulated as a joint sparse recovery problem which reconstructs the unknown sparse vectors of multiple channels. Moreover, in practice, some faulty prior information (e.g., coarse positions) is usually available. To take advantage of this information for joint sparse recovery, novel support knowledge-aided multiple sparse Bayesian learning (SA-M-SBL) algorithm is introduced, which incorporates the prior information into a three-layer hierarchical prior model. With this model, the joint sparsity of the sparse vectors can be induced, and their values can be estimated via the variational Bayesian inference (VBI). The numerical simulation results demonstrate the outstanding performance of the proposed method compared with the state-of-the-art CS-based multi-target DFL methods.","Title: Robust passive adaptive fault tolerant control for stochastic wing flutter via delay control\nYear: 2019\n\nAbstract: •The model of this manuscript is a more general LOE–LIP flutter model and covers several common fault tolerant situations.•The designed observer of this manuscript can compensate both the nonlinear uncertainty and the Itô differential item.•The robust passive adaptive fault tolerant controller based on the observer is constructed to solve the wing flutter problem.","Title: Data-Driven Adaptive Iterative Learning Method for Active Vibration Control Based on Imprecise Probability\nYear: 2019\n\nAbstract: A data-driven adaptive iterative learning (IL) method is proposed for the active control of structural vibration. Considering the repeatability of structural dynamic responses in the vibration process, the time-varying proportional-type iterative learning (P-type IL) method was applied for the design of feedback controllers. The model-free adaptive (MFA) control, a data-driven method, was used to self-tune the time-varying learning gains of the P-type IL method for improving the control precision of the system and the learning speed of the controllers. By using multi-source information, the state of the controlled system was detected and identified. The square root values of feedback gains can be considered as characteristic parameters and the theory of imprecise probability was investigated as a tool for designing the stopping criteria. The motion equation was driven from dynamic finite element (FE) formulation of piezoelectric material, and then was linearized and transformed properly to design the MFA controller. The proposed method was numerically and experimentally tested for a piezoelectric cantilever plate. The results demonstrate that the proposed method performs excellent in vibration suppression and the controllers had fast learning speeds.","Title: Aco-Rr: Ant Colony Optimization Ridge Regression In Reuse Of Smart City System\nYear: 2019\n\nAbstract: With the rapid development of artificial intelligence, governments of different countries have been focusing on building smart cities. To build a smart city is a system construction process which not only requires a lot of human and material resources, but also takes a long period of time. Due to the lack of enough human and material resources, it is a key challenge for lots of small and medium-sized cities to develop the intelligent construction, compared with the large cities with abundant resources. Reusing the existing smart city system to assist the intelligent construction of the small and medium-sizes cities is a reasonable way to solve this challenge. Following this idea, we propose a model of Ant Colony Optimization Ridge Regression (ACO-RR), which is a smart city evaluation method based on the ridge regression. The model helps small and medium-sized cities to select and reuse the existing smart city systems according to their personalized characteristics from different successful stories. Furthermore, the proposed model tackles the limitation of ridge parameters' selection affecting the stability and generalization ability, because the parameters of the traditional ridge regression is manually random selected. To evaluate our model performance, we conduct experiments on real-world smart city data set. The experimental results demonstrate that our model outperforms the baseline methods, such as support vector machine and neural network.","Title: Fatigue Crack Width Detection Based on the Active Sensing Method: A Feasibility Study\nYear: 2019\n\nAbstract: Across multiple industries, metal fatigue damage is one of the most common failure types, and it will induce great loss if the fatigue damage cannot be detected in a timely manner. To predict the fatigue life of metal structures under practical service status, the fatigue test of metal materials is necessary. Current fatigue tests only focus on the total number of cycles before fatigue failure; however, the occurrence and expansion speed of cracks have not been studied before. Therefore, in this paper, based on the active sensing method, a feasibility study is developed to make crack growth detection of standard metal specimen during the fatigue testing. Two PZT patches are bonded on both sides of the crack on a specimen, which is caused by cyclic tension-compression loads. One patch works as the actuator, and the other one is used as the sensor. Based on the wavelet packet signal energy, a damage index is proposed to quantify the received signals and then represent the degree of crack width. The experimental results demonstrate that the received signal wavelet package energy decreases monotonically with enlarging crack width, and thus the effectiveness of the proposed method is verified.","Title: Compressive Sensing Based Device-Free Multi-Target Localization Using Quantized Measurement.\nYear: 2019\n\nAbstract: Device-free localization (DFL), requiring no extra devices equipped with a target, is an important field of research on the Internet of Thing (IoT). Energy efficiency issue is essential for the development of the IoT, but seldom of the existing papers are focus on it. So we investigate this issue with quantized data of only several bits under the compressive sensing (CS) framework, which can both reduce the required wireless link number and the bit number in the DFL scheme. First, through exploiting the discrete property of CS theory, we calculate the discrete measurement probability bypass computing the complex or uncalculated measurement probability density function (pdf), which can well represent the measurement distribution characteristic. Second, we design a unique quantization scheme for each wireless link according to their measurement probability and build a novel DFL model by analyzing the quantization error. Third, a new DF-QVBI algorithm is proposed to recover the target location, which can make great use of the quantization error. Finally, numerical simulations show the superiority and robustness of the proposed method.","Title: Mention Recommendation With Context-Aware Probabilistic Matrix Factorization\nYear: 2019\n\nAbstract: Mention as a key feature on social networks can break through the effect of structural trapping and expand the visibility of a message. Although existing works usually use rank learning as implementation strategy before performing mention recommendation, these approaches may interfere with the influening factor exploration and cause some biases. In this paper, we propose a novel Context-aware Mention recommendation model based on Probabilistic Matrix Factorization (CMPMF). This model considers four important mention contextual factors including topic relevance, mention affinity, user profile similarity and message semantic similarity to measure the relevance score from users and messages dimensions. We fuse these mention contextual factors in latent spaces into the framework of probabilistic matrix factorization to improve the performance of mention recommendation. Through evaluation on a real-world dataset from Weibo, the empirically study demonstrates the effectiveness of discovered mention contextual factors. We also observe that topic relevance and mention affinity play a much significant role in the mention recommendation task. The results demonstrate our proposed method outperforms the state-of-the-art algorithms.","Title: Stable Controller Design for T-S Fuzzy Control Systems with Piecewise Multi-linear Interpolations into Membership Functions.\nYear: 2019\n\nAbstract: This paper focuses on stabilization of T–S fuzzy control systems. We use the information of the premises of the T–S fuzzy control systems to reduce the conservativeness of the stabilization conditions. First, the membership functions (MFs) in the premises are approximated with their piecewise multi-linear interpolations. In this way, different types of MFs can be tackled in a unified approach. We use the errors between the T–S fuzzy systems and the interpolated systems as feedbacks to ensure that the errors tend to zero. Then, we design stable controllers for the fuzzy control systems based on the obtained systems with piecewise multi-linear interpolations and express our results as a group of linear matrix inequalities. It is proved that when the MFs are both single-variate and multi-variate, our results can stabilize the T–S fuzzy control systems. Finally, several simulation examples are utilized to illustrate the merits of the proposed method with both PDC and non-PDC in this paper.","Title: GRADI: Towards Group Recommendation Using Attentive Dual Top-Down and Bottom-Up Influences\nYear: 2019\n\nAbstract: Most of current group recommenders only consider the bottom-up influences, i.e., the preference of a group is greatly affected by the members in the group. For example, children usually dominate the preference of a family while senior experts often lead the preference of a professional group. However, in reality there also exist the top-down influences, i.e., a group inherently affects its every member, because a group often has some distinct themes which limit the preferences of all the members in the group. For instance, the members in a group for sports may prefer hiking and rock climbing, whereas the members in a group for entertainment would like to watch movies and play games. In other words, the influences between a group and its members are dual. To this end, this paper proposes a new model for Group Recommendation using Attentive Dual Influences (GRADI) that simultaneously explores both the bottom-up and top-down influences between a group and its members. The preference of a member in a group is represented as the group-specific member embedding by modeling the top-down influences from the group to the member. In addition, the preference of a group on a target item is represented as the item-specific group representation by considering the bottom-up influences from all the members to the group, where an attentive mechanism is developed to aggregate the preferences of all the members on a target item. Furthermore, GRADI investigates the interactions between groups and items with neural collaborative filtering. Results of extensive experiments conducted on two real-world datasets show that GRADI outperforms other state-of-the-art models.","Title: A High-performance Inference Accelerator Exploiting Patterned Sparsity in CNNs\nYear: 2020\n\nAbstract: Convolutional neural networks (CNNs) have emerged as the critical technology for deep learning with significantly growing computation and memory demands. Model compression has been widely acknowledged as an effective way to achieve acceleration on CNNs. However, most proposed architectures of FPGA are inefficient for compressed models that contain a large amount of zero operations. In this work, we propose a sparse CNNs inference accelerator on FPGA utilizing uniform sparsity introduced by pattern pruning to achieve high energy efficiency. Our architecture maintains the sparse weights in a compressed format to reduce the storage demands and displays a flexible kernel-stationary dataflow to enable the extensive data reusing. In addition, we design flexible computing arrays which can be dynamically reconfigured to balance workload with low overheads. Specially, the on-chip memory applies a novel data buffering structure with slightly rearranged sequences to address the challenge of access conflict. The experiments show that our accelerator can achieve $316 .4 {GOP/s }\\sim 343 .5 {GOP/s }$ for VGG16 and ResNet-50.","Title: A Novel Vortex Synthetic Aperture Radar Imaging System: Decreasing the Pulse Repetition Frequency Without Increasing the Antenna Aperture\nYear: 2022\n\nAbstract: Synthetic aperture radar (SAR) is an advanced ground-observing remote sensing technology, and high-resolution wide-swath (HRWS) imaging has always been the goal of SAR. In general, an improved azimuth resolution requires a large pulse repetition frequency (PRF), resulting in high system requirements. To overcome this difficulty, azimuth multichannel technology has gradually developed, achieving HRWS imaging and decreasing the PRF by increasing the spatial sampling, i.e., increasing the number of antenna subapertures in the azimuth. This article proposes a theoretical architecture that generates multiple virtual receiving apertures in the azimuth rather than real apertures. The virtual receiving apertures are formed by multiplying the azimuth signals by linear phase histories provided by vortex beams carrying different orbital angular momentum (OAM) modes. Vortex beams with different OAM modes have different oblique phase wavefronts, so virtual receiving positions are generated in the along-track direction. This approach aims to reduce the PRF without increasing the azimuth real receiving aperture. Simulation results demonstrate the effectiveness and limitations of the method. Finally, to overcome the inherent limitations of the method, two possible implementation schemes are proposed.","Title: Ray-tracing Simulation and Hybrid Channel Modeling for Low-Terahertz UAV Communications\nYear: 2021\n\nAbstract: Unmanned Aerial Vehicles (UAVs) are currently playing an important role in both military and commercial applications. While UAVs possess potential to enhance network coverage with their high flexibility, combining with the Terahertz (THz) band is promising to enhance the wireless data rate with abundant spectrum resources and ultra-high bandwidth. As a fundamental challenge, air-ground (AG) channel modeling and analysis in the THz band are investigated in this paper, based on ray-tracing simulation and hybrid modeling approaches. The channel statistics are extracted and analyzed based on ray-tracing simulated data, including path loss, excess delay, among others. In particular, the height dependence of statistical parameters is thoroughly analyzed, which shows a clear trend of increasing channel sparsity with high-altitude UAVs. In light of the simulated data and analysis, a hybrid channel model including the line-of-sight, ground-reflection path, and stochastically generated multi-path, is proposed to characterize the THz AG propagation, for which the fading characteristics are validated.","Title: Anti-Jamming Trajectory and Power Design for Cognitive UAV Communications\nYear: 2021\n\nAbstract: This paper studies the anti-jamming issue for cognitive unmanned aerial vehicle (UAV) communication networks. We aim to maximize the throughput of information collection in sensitive areas or unmanned areas of military borders by optimizing the UAV trajectory and the secondary transmitters (STs)' transmit power, in the presence of malicious jammers with imperfect location information. However, the optimization problem is challenging to solve due to the non-convexity. To overcome this difficulty, we propose a throughput maximization algorithm with the aid of block coordinate descent (BCD) method, successive convex approximation (SCA) technique, and S-procedure. Numerical results show that our proposed algorithm significantly improves the system throughput compared with the benchmark algorithms, and simultaneously achieves effective anti-jamming.","Title: Rom: A Requirement Opinions Mining Method Preliminary Try Based On Software Review Data\nYear: 2020\n\nAbstract: Requirement opinion mining aims to mine user opinions that can be used to help the mining of software requirements from various data sources. However, in the development of social network systems, software application platforms or stores and other data sources, the massive, noisy, non-standard data, makes the mining of effective requirement opinions more difficult. Therefore, there is less work in software requirements mining based on the data of software review in development social media or application market. This paper attempts to provide some knowledge support for requirement user story establishing in RE based on the opinion mining and clustering of massively software review data. First of all, this paper combines the requirements of the requirements engineering field to define the requirement opinions, functional requirement opinions and non-functional requirements opinions. Secondly, using the deep learning model to classify the functional requirement reviews and non-functional requirements reviews included in the reviews; Based on the differences between functional data and non-functional data, this paper defines three categories in the description of software functional data, and chooses to use sequence labeling methods to identify functional requirements. Then use the K-means clustering method based on word vector to cluster the review data, and combine TF-IDF and syntactic analysis to extract the aspect and aspect requirements or specific requirements of the requirement opinion respectively, so as to realize the requirement opinion mining of software review data. Finally, this article will give a case study based on the user review data of the mobile phone application service platform 360 mobile assistants.","Title: A Surface And Interior Material Identification Technology Based On Dual-Mode Sensor\nYear: 2021\n\nAbstract: In this paper, a novel hybrid-sensor integrating TENG and inductive sensor is proposed to acquire the triboelectric and inductive signals from different objects. A matching CNN algorithm was used to process the above signals in order to achieving highly accurate recognition. The identification accuracy of different solid surfaces reached 98.89% and the identification accuracy of different solution reached 95%. As a demonstration, we designed a real-time identification system for effective sorting of different items.","Title: Time-Domain Notch Filtering Method for Pulse RFI Mitigation in Synthetic Aperture Radar\nYear: 2022\n\nAbstract: Synthetic aperture radar (SAR) often shares spectrum with other systems, such as radio, TV, cellular network, and so on, which are likely to produce radio frequency interference (RFI). Pulse RFI, which hinders SAR signal processing and image interpretation severely, is a common form of RFI and cannot be neglected. The simple and easy-to-implement frequency-domain notch filtering (FNF) method has been widely used to mitigate narrowband pulse RFIs. However, a well-known problem with abnormal sidelobe effect, which is caused by missing spectrum gaps due to the notch operation, is aroused when using FNF. In this letter, a novel time-domain notch filtering (TNF) is proposed. In the proposed method, pulse RFI occurrences are detected and notched by a simple log-ratio operator in a pulse by pulse manner. Then, missing-data iterative adaptive approach (MIAA) is performed to recover the notched signal to avoid ghosts. Experimental results via simulated and real L-band airborne SAR raw data validate the performance of the proposed method.","Title: Object Tracking In Hyperspectral-Oriented Video With Fast Spatial-Spectral Features\nYear: 2021\n\nAbstract: This paper presents a correlation filter object tracker based on fast spatial-spectral features (FSSF) to realize robust, real-time object tracking in hyperspectral surveillance video. Traditional object tracking in surveillance video based only on appearance information often fails in the presence of background clutter, low resolution, and appearance changes. Hyperspectral imaging uses unique spectral properties as well as spatial information to improve tracking accuracy in such challenging environments. However, the high-dimensionality of hyperspectral images causes high computational costs and difficulties for discriminative feature extraction. In FSSF, the real-time spatial-spectral convolution (RSSC) kernel is updated in real time in the Fourier transform domain without offline training to quickly extract discriminative spatial-spectral features. The spatial-spectral features are integrated into correlation filters to complete the hyperspectral tracking. To validate the proposed scheme, we collected a hyperspectral surveillance video (HSSV) dataset consisting of 70 sequences in 25 bands. Extensive experiments confirm the advantages and the efficiency of the proposed FSSF for object tracking in hyperspectral video tracking in challenging conditions of background clutter, low resolution, and appearance changes.","Title: New Sets Of Quadriphase Cross Z-Complementary Pairs For Preamble Design In Spatial Modulation\nYear: 2021\n\nAbstract: Cross Z-complementary pairs (CZCPs) are a special kind of Z-complementary pairs having zero autocorrelation sums around the in-phase position and end-shift position, also having zero cross-correlation sums around the end-shift position. Recent results have shown that CZCPs are very efficient in designing pilot sequences for spatial modulation enabled multiple-input multiple-output (MIMO) systems. In this paper, we propose systematic constructions of binary and quadriphase CZCPs with new lengths of the form 2M, where even-length binary Z-complementary pairs of length M exists. Interestingly, the proposed binary and quadriphase CZCPs have large CZC(ratio) (i.e. the ratio of achieved zero correlation zone width over maximum zero correlation zone width). One of the proposed construction can also construct optimal quadriphase CZCPs using quadriphase GCPs.","Title: Anomaly Detection Via Self-Organizing Map.\nYear: 2021\n\nAbstract: Anomaly detection plays a key role in industrial manufacturing for product quality control. Traditional methods for anomaly detection are rule-based with limited generalization ability. Recent methods based on supervised deep learning are more powerful but require large-scale annotated datasets for training. In practice, abnormal products are rare thus it is very difficult to train a deep model in a fully supervised way. In this paper, we propose a novel unsupervised anomaly detection approach based on Self-organizing Map (SOM). Our method, Self-organizing Map for Anomaly Detection (SOMAD) maintains normal characteristics by using topological memory based on multi-scale features. SOMAD achieves state-of the-art performance on unsupervised anomaly detection and localization on the MVTec dataset.","Title: Erratum to “Evolver: a Deep Learning Processor With On-Device Quantization-Voltage-Frequency Tuning”\nYear: 2021\n\nAbstract: In the above article <xref ref-type=\"bibr\" rid=\"ref1\">[1]</xref>, there are some references that were cited incorrectly in the text. The corrections are as follows:","Title: Thermal Image Super-Resolution Challenge - PBVS 2021\nYear: 2021\n\nAbstract: This paper presents results from the second Thermal Image Super-Resolution (TISR) challenge organized in the framework of the Perception Beyond the Visible Spectrum (PBVS) 2021 workshop. For this second edition, the same thermal image dataset considered during the first challenge has been used; only mid-resolution (MR) and high-resolution (HR) sets have been considered. The dataset consists of 951 training images and 50 testing images for each resolution. A set of 20 images for each resolution is kept aside for evaluation. The two evaluation methodologies proposed for the first challenge are also considered in this opportunity. The first evaluation task consists of measuring the PSNR and SSIM between the obtained SR image and the corresponding ground truth (i.e., the HR thermal image downsampled by four). The second evaluation also consists of measuring the PSNR and SSIM, but in this case, considers the x2 SR obtained from the given MR thermal image; this evaluation is performed between the SR image with respect to the semi-registered HR image, which has been acquired with another camera. The results out-performed those from the first challenge, thus showing an improvement in both evaluation metrics.","Title: Jmcmprsk: An R Package For Joint Modelling Of Longitudinal And Survival Data With Competing Risks\nYear: 2021\n\nAbstract: In this paper, we describe an R package named JMcmprsk, for joint modelling of longitudinal and survival data with competing risks. The package in its current version implements two joint models of longitudinal and survival data proposed to handle competing risks survival data together with continuous and ordinal longitudinal outcomes respectively (Elashoff et al., 2008; Li et al., 2010). The corresponding R implementations are further illustrated with real examples. The package also provides simulation functions to simulate datasets for joint modelling with continuous or ordinal outcomes under the competing risks scenario, which provide useful tools to validate and evaluate new joint modelling methods.","Title: Fusion of 3D lung CT and serum biomarkers for diagnosis of multiple pathological types on pulmonary nodules\nYear: 2021\n\nAbstract: Background and Objective: : Current researches on pulmonary nodules mainly focused on the binary-classification of benign and malignant pulmonary nodules. However, in clinical applications, it is not enough to judge whether pulmonary nodules are benign or malignant. In this paper, we proposed a fusion model based on the Lung Information Dataset Containing 3D CT Images and Serum Biomarkers (LIDC -CISB) we constructed to accurately diagnose the types of pulmonary nodules in squamous cell car-cinoma, adenocarcinoma, inflammation and other benign diseases. Methods: : Using single modal information of lung 3D CT images and single modal information of Lung Tumor Biomarkers (LTBs) in LIDC -CISB, a Multi-resolution 3D Multi-classification deep learning model (Mr-Mc) and a Multi-Layer Perceptron machine learning model (MLP) were constructed for diagnosing multiple pathological types of pulmonary nodules, respectively. To comprehensively use the double modal information of CT images and LTBs, we used transfer learning to fuse Mr-Mc and MLP, and constructed a multimodal information fusion model that could classify multiple pathological types of benign and malignant pulmonary nodules. Results: : Experiments showed that the constructed Mr-Mc model can achieve an average accuracy of 0.805 and MLP model can achieve an average accuracy of 0.887. The fusion model was verified on a dataset containing 64 samples, and achieved an average accuracy of 0.906. Conclusions: : This is the first study to simultaneously use CT images and LTBs to diagnose multiple pathological types of benign and malignant pulmonary nodules, and experiments showed that our re-search was more advanced and more suitable for practical clinical applications. (c) 2021 Elsevier B.V. All rights reserved.","Title: Characterization of gastric cancer stem-like molecular features, immune and pharmacogenomic landscapes\nYear: 2022\n\nAbstract: Cancer stem cells (CSCs) actively reprogram their tumor microenvironment (TME) to sustain a supportive niche, which may have a dramatic impact on prognosis and immunotherapy. However, our knowledge of the landscape of the gastric cancer stem-like cell (GCSC) microenvironment needs to be further improved. A multi-step process of machine learning approaches was performed to develop and validate the prognostic and predictive potential of the GCSC-related score (GCScore). The high GCScore subgroup was not only associated with stem cell characteristics, but also with a potential immune escape mechanism. Furthermore, we experimentally demonstrated the upregulated infiltration of CD206(+) tumor-associated macrophages (TAMs) in the invasive margin region, which in turn maintained the stem cell properties of tumor cells. Finally, we proposed that the GCScore showed a robust capacity for prediction for immunotherapy, and investigated potential therapeutic targets and compounds for patients with a high GCScore. The results indicate that the proposed GCScore can be a promising predictor of prognosis and responses to immunotherapy, which provides new strategies for the precision treatment of GCSCs.","Title: DA-BERT: Enhancing Knowledge Selection in Dialog via Domain Adapted BERT with Dynamic Masking Probability\nYear: 2021\n\nAbstract: One of the most challenging tasks in Knowledge-grounded Task-oriented Dialog Systems (KTDS) is the knowledge selection task, which aims to find the proper knowledge snippets to handle user requests. This paper proposes DA-BERT to employ pre-trained BERT with domain adaptive training and newly proposed dynamic masking probability to deal with knowledge selection in KTDS. Domain adaptive training mi...","Title: An Overview on Supervised Semi-structured Data Classification\nYear: 2021\n\nAbstract: Many collaboratively building resources, such as Wikipedia, Weibo and Quora, exist in the form of semi-structured data. The semi-structured data has been widely used in areas such as data integration, data distribution, data storage, data management, information retrieval and knowledge management. For large volumes of semi-structured data on the Web, semi-structured data classification technique c...","Title: Shock Wave Energy Limit Analysis of Underwater Plasma Sound Source System\nYear: 2021\n\nAbstract: The analysis and prediction of the energy limit of a single underwater plasma sound source (UPSS) system can provide theoretical guidance for the design of UPSS and array bunching. In this paper, the influence factors of shock wave energy produced by high voltage pulse discharge are studied by experiment. The relationship between the peak pressure of the shock wave and the discharge voltage, the c...","Title: Toolkit for Dynamic Control Rapid Prototype Simulation System of Robots Applied in Space Experimental Cabin\nYear: 2021\n\nAbstract: As the space environment is difficult to be recreated on the earth, it is essential to develop a software toolkit to validate the parameters designing for space robots. In this paper, a toolkit addressed for dynamic control rapid prototype simulation system of the space robot was presented. Based on the real-time control technology, the toolkit was consisted of real-time dynamic computing, redundant dual-arm control system, visual imaging and measurement and teleoperation. Specifically, the collision problem involved in the manipulator end effector and the target could be analyzed by contact dynamics models in the real-time dynamic computing module. Furthermore, a peg-in-hole (multi cores) assembly experiment involving the disassembly for the space electrical connectors was carried out, which was adopted to evaluate the simulation effect of toolkit. Our results showed that the connection between the space plug and socket with multi cores was well built up in the simulation environment. Moreover, the toolkit can be used to make simulations of on orbit operational missions by multiple of human-computer interaction, which is the key for the evaluation of control algorithm applied on the space robot.","Title: Real-time 3D-Lidar, MMW Radar and GPS/IMU fusion based vehicle detection and tracking in unstructured environment\nYear: 2021\n\nAbstract: To solve the problem of unmanned ground vehicle leader-follower formation transportation in unstructured environment, we propose a novel target detection and tracking method based on multi-sensor fusion perception. Combined with 3D-Lidar, millimeter wave Radar and GPS/IMU, the proposed method can achieve stable target detection and continuous tracking of both static and dynamic vehicles. First, 3D-Lidar is used to detect the geometric model of the leader vehicle to complete the initialization of tracking target and it can also be assisted for target tracking. Then during the movement, the dynamic leader is mainly tracked through millimeter wave Radar as this sensor can keep tracking the same target with a constant index and effectively distinguish dynamic vehicle from other static obstacles according to relative speed estimation. In addition, by using GPS/IMU based integrated navigation, the movement trend of the leader can be derived according to the echo vehicle pose information and the relative position relationship. This is helpful to reduce the region of interest for target tracking and improve the real-time performance. In different unstructured environments, we perform the leader-follower formation transportation experiments for hundreds of kilometers. In rough terrain, the maximum tracking speed can still reach 40km/h and the maximum tracking distance can be up to 100 meters. Experiments show that the proposed method is suitable for vehicle target detection and tracking in unstructured environment. It has good robustness and high real-time performance with an average processing frame rate of 20Hz. The proposed method can be used for the formation transportation of unmanned ground vehicles to reduce labor costs.","Title: Recovering Stress Distribution on Deformable Tissue for a Magnetic Actuated Insertable Laparoscopic Surgical Camera\nYear: 2021\n\nAbstract: Fully insertable laparoscopic cameras represent a promising future of minimally invasive surgery. The most characteristic technology adopted on these devices is transabdominal anchoring and actuation based on magnetic coupling. However, few have paid adequate attention to the safety concerns. As the camera is anchored against the interior abdominal wall without any force feedback, the patient is being exposed to a high risk of getting injured by inappropriate stress on the tissue. We have recovered the camera-tissue interaction force via a non-invasive approach in our previous work. Aiming to access the stress distribution, this paper presents a viscoelastic camera-tissue interaction model, which establishes explicit relations between the contact force and the stress distribution on the tissue. For the first time, a geometric constraint between the contact angle and the tissue indentation is introduced, which helps make the multivariable model solvable. Ex-vivo experiments on porcine abdomen tissue facilitated by non-invasive force measurement validate effectiveness of the model. This work lays foundation for improving control and surgical safety of using a magnetic actuated insertable laparoscopic surgical camera.","Title: No-Reference Physics-Based Quality Assessment of Polarization Images and Its Application to Demosaicking\nYear: 2021\n\nAbstract: Assessing the quality of polarization images is of significance for recovering reliable polarization information. Widely used quality assessment methods including peak signal-to-noise ratio and structural similarity index require reference data that is usually not available in practice. We introduce a simple and effective physics-based quality assessment method for polarization images that does not require any reference. This metric, based on the self-consistency of redundant linear polarization measurements, can thus be used to evaluate the quality of polarization images degraded by noise, misalignment, or demosaicking errors even in the absence of ground-truth. Based on this new metric, we propose a novel processing algorithm that significantly improves demosaicking of division-of-focal-plane polarization images by enabling efficient fusion between demosaicking algorithms and edge-preserving image filtering. Experimental results obtained on public databases and homemade polarization images show the effectiveness of the proposed method.","Title: Radar based obstacle detection in unstructured scene\nYear: 2021\n\nAbstract: To solve the problem of obstacle detection in unstructured scene, we propose a novel obstacle detection method based on Radar. Compared with other sensors like 3D-Lidar and color camera, Radar has higher environmental robustness especially for sandy road with dust. According to the relative velocity information of detected obstacles, obstacles are divided into static ones and dynamic ones in the proposed method. For static obstacles, map reconstruction based on Bayesian probability model is used to overcome the problem of sparse obstacle detection result in single frame and obstacle retention in the blind area. The integrated navigation system of GNSS and IMU is used to provide position and posture information during map reconstruction. For dynamic obstacles, the historical trajectory of the same target is recorded in the global coordinate frame, then the Kalman predictor based trajectory tracking and prediction is used to predict the movement trend of moving obstacles. The proposed method has been verified in a variety of unstructured scene. The experimental results show that the proposed method can provide useful obstacle perception result and meet the autonomous navigation application of UGV in unstructured scene. We also compare the proposed method with that based on 3D-Lidar in terms of the same scene. And it has obvious advantages in environmental robustness.","Title: Detecting unknown dams from high-resolution remote sensing images: A deep learning and spatial analysis approach\nYear: 2021\n\nAbstract: •Automatic detecting unknown dams from high-resolution remote sensing images in broad areas.•Multi object detection models improves the recall rate of dam detection.•Geographical analysis and comprehensive discrimination methods can effectively improve the precision rate of dam detection.•A comprehensive application case proves the advantages of the proposed framework.","Title: Predicting Stock Movements: Using Multiresolution Wavelet Reconstruction and Deep Learning in Neural Networks\nYear: 2021\n\nAbstract: Stock movement prediction is important in the financial world because investors want to observe trends in stock prices before making investment decisions. However, given the non-linear non-stationary financial time series characteristics of stock prices, this remains an extremely challenging task. A wavelet is a mathematical function used to divide a given function or continuous-time signal into different scale components. Wavelet analysis has good time-frequency local characteristics and good zooming capability for non-stationary random signals. However, the application of the wavelet theory is generally limited to a small scale. The neural networks method is a powerful tool to deal with large-scale problems. Therefore, the combination of neural networks and wavelet analysis becomes more applicable for stock behavior prediction. To rebuild the signals in multiple scales, and filter the measurement noise, a forecasting model based on a stock price time series was provided, employing multiresolution analysis (MRA). Then, the deep learning in the neural network method was used to train and test the empirical data. To explain the fundamental concepts, a conceptual analysis of similar algorithms was performed. The data set for the experiment was chosen to capture a wide range of stock movements from 1 January 2009 to 31 December 2017. Comparison analyses between the algorithms and industries were conducted to show that the method is stable and reliable. This study focused on medium-term stock predictions to predict future stock behavior over 11 days of horizons. Our test results showed a 75% hit rate, on average, for all industries, in terms of US stocks on FORTUNE Global 500. We confirmed the effectiveness of our model and method based on the findings of the empirical research. This study's primary contribution is to demonstrate the reconstruction model of the stock time series and to perform recurrent neural networks using the deep learning method. Our findings fill an academic research gap, by demonstrating that deep learning can be used to predict stock movement.</p>","Title: An Incast-Coflow-Aware Minimum-Rate-Guaranteed Congestion Control Protocol for Datacenter Applications\nYear: 2021\n\nAbstract: Today s datacenters need to meet service level objectives (SLOs) for applications, which can be translated into deadlines for (co)flows running between job execution stages. As a result, meeting (co)flow deadlines with high probabilities is essential to attract and retain customers and hence, generate high revenue. To fill the lack of a transport protocol that can facilitate low (co)flow deadline ...","Title: A Novel Secure Diffusion Kalman Filter Algorithm Against False Data Injection Attacks\nYear: 2021\n\nAbstract: This paper proposes a novel secure diffusion Kalman filter (dKF) algorithm to improve the estimation performance crippled by false data injection attacks on sensors in wireless sensor networks (WSNs). Different from the conventional dKF, each adjacent node in the WSNs is detected to ascertain its trustworthiness before local estimate fusion, so as to form a new secure network topology. Then the combination step is performed to fuse the information collected from the secure topology. The proposed secure dKF algorithm, having a better estimation performance, is robust to false data injection attacks on multiple sensors and partial elements of measurements. For the proposed secure dKF algorithm, its mean and mean-square performance are derived, based on which its convergence is analysed. Additionally, the estimating and tracking problem of projectile position is investigated to confirm the effectiveness of the proposed secure dKF algorithm. It is shown by simulations that the proposed secure dKF algorithm achieves a significant estimation performance gain.","Title: A 24 Db Gain 51-68 Ghz Common Source Low Noise Amplifier Using Asymmetric-Layout Transistors\nYear: 2012\n\nAbstract: At mm-wave frequency, the layout of CMOS transistors has a larger effect on the device performance than ever before in low frequency. In this work, the distance between the gate and drain contact (D-gd) has been enlarged to obtain a better maximum available gain (MAG). By using the asymmetric-layout transistor, a 0.6 dB MAG improvement is realized when Dgd changes from 60 nm to 200 mm A four-stage common-source low noise amplifier is implemented in a 65 nm CMOS process. A measured peak power gain of 24 dB is achieved with a power dissipation of 30 mW from a 1.2-V power supply. An 18 dB variable gain is also realized by adjusting the bias voltage. The measured 3-dB bandwidth is about 17 GHz from 51 GHz to 68 GHz, and noise figure (NF) is from 4.0 dB to 7.6 dB.","Title: Localization of Multiple RF Sources via Priori Knowledge-Aided Bayesian Compressive Sensing in UAV-Based WSN\nYear: 2021\n\nAbstract: In the unmanned aerial vehicle-based wireless sensor network, the compressive sensing approach can simultaneously locate multiple ground radio frequency sources, while the existing algorithms’ localization accuracies would deteriorate confronting the heavy noise. Hence, we exploit the potential priori knowledge and propose a robust localization algorithm named priori knowledge-aided Bayesian compr...","Title: EMS-Net: Enhanced Multi-Scale Network for Polyp Segmentation\nYear: 2021\n\nAbstract: In recent years, polyp segmentation plays an important role in the diagnosis and treatment of colorectal cancer. Accurate segmentation of polyps is very challenging due to different sizes, shapes, and unclear boundaries. Making full use of multi-scale contextual information to segment polyps may bring better results. In this paper, we propose an enhanced multi-scale network for accurate polyp segmentation. It is composed of a multi-scale connected baseline (U-Net+++), a multi-scale backbone (Res2Net), three Receptive Field Block (RFB) modules, and four Local Context Attention (LCA) modules. Specifically, the baseline's multi-scale skip connections can aggregate features in both low-level and high-level layers. We have evaluated our model on three publicly available and challenging datasets (EndoScene, CVC-ClinicDB, Kvasir-SEG). Compared with other methods, our model achieves SOTA performance. It is noteworthy that our model is the only network that has achieved over 0.900 mean Dice on EndoScene and CVC-ClinicDB.","Title: Characterizing Ancient Channel of the Yellow River From Spaceborne SAR: Case Study of Chinese Gaofen-3 Satellite\nYear: 2022\n\nAbstract: The lower reaches of the ancient Yellow River (AYR) migrated very frequently, like a loong swinging its tail on the land of China. As the origin of Chinese civilization, AYR provided natural conditions for the people to thrive. However, the sediment carried by AYR still has a negative impact on local agricultural production. This letter, based on Gaofen-3, extracted scattering characteristics of a...","Title: FKTAN: Fusion Keystroke Time-Textual Attention Networks for Continuous Authentication\nYear: 2021\n\nAbstract: With the rapid development of computer technology, the traditional Internet data security and information privacy issues are gradually expanding to all aspects of society as a whole. As the first line of defense for information security, identity authentication technology becomes crucial. Among the many authentication technologies, continuous authentication technology has gained increasing attenti...","Title: An improved sine–cosine algorithm with dynamic selection pressure\nYear: 2021\n\nAbstract: Sine cosine algorithm (SCA) is a newly developed optimization tool that has received increasing attention in the field of computational intelligence. Nevertheless, SCA has a combination of strong exploration and weak exploitation abilities, which undermines the performance of the algorithm. A dynamic selection strategy (DSS) has been proposed in this paper to overcome this limitation. The central idea is that the selection pressure of the parent is dynamically regulated during the course of evolution, and the parent with higher fitness have higher selection probability. Accordingly, the weak exploitation ability of SCA can be enhanced. This approach can be readily applied to other SCA variants as well. Results from systematic experiments conducted on CEC2014 competition benchmark suite show that the proposed DSS method can significantly improve the performance of the SCAs. In addition, the proposed DSS-SCA algorithm has been successfully applied to six engineering design problems, which shows that the algorithm has the potential to solve other practical problems.","Title: The Trapezoidal Sketch for Frequency Estimation in Network Flow\nYear: 2021\n\nAbstract: The sketch is one of the typical and widely-used data structures for estimating the frequencies of items in data streams. However, since the counter sizes in traditional rectangular sketch (r-sketch) are the same, it is hard to achieve small space usage, high capacity (i.e., the maximum frequency can be recorded), and high estimated accuracy simultaneously. Moreover, when considering the high skew...","Title: Anomaly Detection in Unstructured Logs Using Attention-based Bi-LSTM Network\nYear: 2021\n\nAbstract: System logs record valuable information about the runtime status of IT systems. Therefore, system logs are a naturally excellent source of information for anomaly detection. Most of the existing studies on log-based anomaly detection construct a detection model to identify anomalous logs. Generally, the model treats historical logs as natural language sequences and learns the normal patterns from ...","Title: A Novel Method for Layover Detection in Mountainous Areas with SAR Images\nYear: 2021\n\nAbstract: It is well known that there are geometric distortions in synthetic aperture radar (SAR) images when the terrain undulates. Layover is the most common one, which brings challenges to the application of SAR remote sensing. This study proposes a novel detection method that is mainly aimed at the layover caused by mountains and can be performed with only medium-resolution SAR images and no other auxiliary data. The detection includes the following four stages: initial processing, difference image calculation and rough and fine layover detection. Initial processing mainly obtains the potential layover areas, which are mixed with the built-up areas after classification. Additionally, according to the analysis of the backscatter coefficient (BC) of various ground objects with different polarization images, the layover areas are detected step-by-step from the mixed areas, in which the region-based FCM segmentation algorithm and spatial relationship criteria are used. Taking the Danjiangkou Reservoir area as the study area, the relevant experiments with Sentinel-1A SAR images were conducted. The quantitative analysis of detection results adopted the figure of merit (FoM), and the highest accuracy was up to 87.6% of one selected validation region. Experiments in the South Taihang area also showed the satisfactory effect of layover detection, and the values of FoM were all above 85%. These results show that the proposed method can do well in the layover detection caused by mountains. Its simplicity and effectiveness are helpful in removing the influence of layover on SAR image applications to a certain extent and improving the development of SAR remote sensing technology.","Title: An Azimuth Signal-Reconstruction Method Based on Two-Step Projection Technology for Spaceborne Azimuth Multi-Channel High-Resolution and Wide-Swath SAR\nYear: 2021\n\nAbstract: Azimuth non-uniform signal-reconstruction is a critical step for azimuth multi-channel high-resolution wide-swath (HRWS) synthetic aperture radar (SAR) data processing. However, the received non-uniform signal has noise in the actual azimuth multi-channel SAR (MCSAR) operation, which leads to the serious reduction in the signal-to-noise ratio (SNR) of the results processed by a traditional reconstruction algorithm. Aiming to address the problem of reducing the SNR of the traditional reconstruction algorithm in the reconstruction of non-uniform signal with noise, a novel signal-reconstruction algorithm based on two-step projection technology (TSPT) for the MCSAR system is proposed in this paper. The key part of the TSPT algorithm consists of a two-step projection. The first projection is to project the given signal into the selected intermediate subspace, spanned by the integer conversion of the compact support kernel function. This process generates a set of sparse equations, which can be solved efficiently by using the sparse equation solver. The second key projection is to project the first projection result into the subspace of the known sampled signal. The secondary projection can be achieved with a digital linear translation invariant (LSI) filter and generate a uniformly spaced signal. As a result, compared with the traditional azimuth MCSAR signal-reconstruction algorithm, the proposed algorithm can improve SNR and reduce the azimuth ambiguity-signal-ratio (AASR). The processing results of simulated data and real raw data verify the effectiveness of the proposed algorithm.","Title: Design and Development of Real-Time Barrage System for College Class\nYear: 2021\n\nAbstract: In order to activate classroom atmosphere, enhance interaction between teachers and students, and to facilitate teachers to answer questions raised by students in a timely manner, a new real-time barrage class system was designed. To complete overall system design, C/S and B/S mixed-mode and MVC architecture were adopted. Our system is divided into a PC terminal function management module and a mobile terminal function management module. Through TCP protocol, mobile terminal data are communicated with server in form of JSON string. In addition to basic functions of each module, Highcharts technology was used on mobile terminal to realize visual display and analysis function of barrage data. Combined with web crawler technology and the DFA (Deterministic Finite Automaton) algorithm, a sensitive vocabulary filtering function was realized by us. Practice has proved that our real-time barrage class system can strengthen interaction between teachers and students in college classes, stimulate students' enthusiasm for learning, and improve students' participation and concentration in teaching process. Therefore, our system has feasibility and promotion value.","Title: Estimation Performance of Cyber-Physical Systems Attacked by False Data Injection\nYear: 2021\n\nAbstract: This paper investigates the state estimation error covariances of cyber-physical systems (CPSs) when its physical system and transmission channels are attacked by false data injection (FDI). Specifically, sensors in CPSs measure the physical systems' state and then send these measurement information to a fusion center through transmission channels. Due to easy deployment and maintenance of sensors as well as unprotected wireless transmission medium, CPSs in sensing and communication are vulnerable to malicious attacks. To maliciously degrade the system estimation performance, an intruder attempts to modify the state and measurements by launching FDI attacks. To seek an optimal attack strategy from the perspective of an attacker, the corresponding estimate error covariances of CPSs are calculated. Based on this, we compare the estimation error covariances of different attacks on the physical system and transmission channels. Simulations demonstrate the effectiveness of the theoretical results.","Title: Quantification and 3D Localization of Magnetically Navigated Superparamagnetic Particles Using MRI in Phantom and Swine Chemoembolization Models\nYear: 2022\n\nAbstract: <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Objective:</i>\n Superparamagnetic nanoparticles (SPIONs) can be combined with tumor chemoembolization agents to form magnetic drug-eluting beads (MDEBs), which are navigated magnetically in the MRI scanner through the vascular system. We aim to develop a method to accurately quantify and localize these particles and to validate the method in phantoms and swine models. \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Methods:</i>\n MDEBs were made of Fe\n<sub xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">3</sub>\nO\n<sub xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">4</sub>\n SPIONs. After injected known numbers of MDEBs, susceptibility artifacts in three-dimensional (3D) volumetric interpolated breath-hold examination (VIBE) sequences were acquired in glass and Polyvinyl alcohol (PVA) phantoms, and two living swine. Image processing of VIBE images provided the volume relationship between MDEBs and their artifact at different VIBE acquisitions and post-processing parameters. Simulated hepatic-artery embolization was performed \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">in vivo</i>\n with an MRI-conditional magnetic-injection system, using the volume relationship to locate and quantify MDEB distribution. \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Results:</i>\n Individual MDEBs were spatially identified, and their artifacts quantified, showing no correlation with magnetic-field orientation or sequence bandwidth, but exhibiting a relationship with echo time and providing a linear volume relationship. Two MDEB aggregates were magnetically steered into desired liver regions while the other 19 had no steering, and 25 aggregates were injected into another swine without steering. The MDEBs were spatially identified and the volume relationship showed accuracy in assessing the number of the MDEBs, with small errors (≤ 8.8%). \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Conclusion and Significance:</i>\n MDEBs were able to be steered into desired body regions and then localized using 3D VIBE sequences. The resulting volume relationship was linear, robust, and allowed for quantitative analysis of the MDEB distribution.","Title: The Identification of ECG Signals Using WT-UKF and IPSO-SVM\nYear: 2022\n\nAbstract: The biometric identification method is a current research hotspot in the pattern recognition field. Due to the advantages of electrocardiogram (ECG) signals, which are difficult to replicate and easy to obtain, ECG-based identity identification has become a new direction in biometric recognition research. In order to improve the accuracy of ECG signal identification, this paper proposes an ECG identification method based on a multi-scale wavelet transform combined with the unscented Kalman filter (WT-UKF) algorithm and the improved particle swarm optimization-support vector machine (IPSO-SVM). First, the WT-UKF algorithm can effectively eliminate the noise components and preserve the characteristics of ECG signals when denoising the ECG data. Then, the wavelet positioning method is used to detect the feature points of the denoised signals, and the obtained feature points are combined with multiple feature vectors to characterize the ECG signals, thus reducing the data dimension in identity identification. Finally, SVM is used for ECG signal identification, and the improved particle swarm optimization (IPSO) algorithm is used for parameter optimization in SVM. According to the analysis of simulation experiments, compared with the traditional WT denoising, the WT-UKF method proposed in this paper improves the accuracy of feature point detection and increases the final recognition rate by 1.5%. The highest recognition accuracy of a single individual in the entire ECG identification system achieves 100%, and the average recognition accuracy can reach 95.17%.","Title: Spatio-Temporal Estimation of Rice Height Using Time Series Sentinel-1 Images\nYear: 2022\n\nAbstract: Rice height, as the fundamental biophysical attribute, is a controlling factor in crop phenology estimation and yield estimation. The aim of this study was to use time series Sentinel-1A images to estimate the spatio-temporal distribution of rice height. In this study, a particle filter (PF) was applied for the real-time estimation of rice height compared with a simplified water cloud model (SWCM) on the basis of rice mapping and transplanting date. It was found that the VH backscatter (sigma(vh)o) can potentially be applied to accurately estimate rice height compared with VV backscatter (sigma(o)(vv)), the sigma(vh)o/sigma(0)(vv) ratio, and the Radar Vegetation Index (RVI, 4* sigma(o)(vh)/(sigma(o)(vh)+sigma(o)(vv))). The results show that the rice height estimation by PF generated a better result with a root-mean-square error (RMSE) equal to 7.36 cm and a determination factor (R-2) of 0.95 compared with SWCM (RMSE = 12.59 cm and R-2 = 0.86). Moreover, rice height in the south and east of the study area was higher than in the north and west. The reason for this is that the south and east are near to the South China Sea, and there are higher temperatures and earlier transplanting. Altogether, our results demonstrate the potential of PF and sigma(o)(vh) to study the spatio-temporal distribution of crop height estimation. As a result, the PF method can contribute greatly to improvements in crop monitoring.","Title: A Novel High-Squint Spotlight SAR Raw Data Simulation Scheme in 2-D Frequency Domain\nYear: 2022\n\nAbstract: Raw data simulation is the front-end work of synthetic aperture radar (SAR), which is of great significance. For high-squint spotlight SAR, the frequency domain simulation algorithm is invalid because of the range-azimuth coupling effect. In order to realize high-squint spotlight SAR raw data simulation in the frequency domain, an algorithm based on coordinate transformation and non-uniform fast Fourier transform (NUFFT) is proposed. This algorithm generates broadside raw data using a two-dimensional (2-D) frequency simulation algorithm; then, coordinate transformation is used by analyzing the characteristics of broadside and high-squint spotlight SAR. After coordinate transformation, NUFFT is carried out to realize the coupling relation in the 2-D frequency domain. Since the coordinate transformation ignores the influence of range walk, the range walk is compensated after NUFFT. As a result, compared with the traditional squint spotlight SAR frequency domain simulation algorithm, the proposed algorithm can improve the accuracy of point and distributed target imaging results, and the efficiency of the proposed algorithm can be significantly improved in contrast the traditional time domain algorithm.","Title: Optimal False Data Injection Attacks on MTC\nYear: 2022\n\nAbstract: This paper proposes a convex optimization problem, based on which the optimal strategy of false data injection (FDI) attacks is obtained to intrude machine-type-communications (MTC) networks from the perspective of an attacker, aiming to seek effective defensive measures based on a good understanding of attackers’ behaviour. We consider a target tracking example, which is a typical application of ...","Title: 3D multi-resolution deep learning model for diagnosis of multiple pathological types on pulmonary nodules\nYear: 2022\n\nAbstract: To accurately diagnose multiple pathological types of pulmonary nodules based on lung computed tomography (CT) images, a multi-resolution three-dimensional (3D) multi-classification deep learning model (Mr-Mc) was proposed. The Mr-Mc model was constructed by using our own constructed lung CT image dataset of pulmonary nodules with clinical pathological information (LCID-CPI), which can accurately diagnose inflammation, squamous cell carcinoma, adenocarcinoma, and other benign diseases. In order to process nodules with different sizes, a multi-resolution extraction method was proposed to extract 3D volume data with different resolutions from lung CT images. The Mr-Mc was composed of three different resolution networks, each of which has input volume data of a specific resolution. Experiments showed that the constructed Mr-Mc model can achieve an average accuracy of 0.81 on LCID-CPI. Besides, the Mr-Mc model can also achieve a high accuracy of 0.87 on the Lung Image Database Consortium and Image Database Resource Initiative dataset.","Title: s-CAM: An Untethered Insertable Laparoscopic Surgical Camera Robot with Non-Contact Actuation\nYear: 2022\n\nAbstract: Fully insertable robotic imaging devices represent a promising future of minimally invasive laparoscopic vision. Emerging research efforts in this field have resulted in several proof-of-concept prototypes. One common drawback of these designs derives from their clumsy tethering wires which not only cause operational interference but also reduce camera mobility. In this paper, a tetherless insertable surgical camera (s-CAM) robot with non-contact transabdominal actuation is presented for single-incision laparoscopic vision. Wireless video transmission and control communication using onboard power help eliminate cumbersome tethering wires. Furthermore, magnetic based camera actuation gets rid of intrinsic physical constraints of mechanical driving mechanisms, thereby improving camera mobility and reducing operational interference. In addition, a custom Bluetooth low energy (BLE) application profile and a real-time operating system (RTOS) based multitask programming framework are also proposed to facilitate embedded software design for insertable medical devices. Initial ex vivo test results of the s-CAM design have demonstrated technical feasibility of a tetherless insertable laparoscopic camera. Effective imaging is confirmed at as low as 500 lx illumination. Wireless laparoscopic vision is accessible within a distance of more than 10 m. Transabdominal BLE communication is stable at over -52 dBm and shows its potential for wireless control of insertable medical devices. RTOS based sfotware event response is bounded within 1 ms while the CPU usage is at 3 similar to 5%. The device is able to work for 50 min with its onboard power. For the mobility, the robot can translate against the interior abdominal wall to reach full abdomen quadrants, tilt between -180 degrees and +180 degrees, and pan in the range of 0 degrees similar to 360 degrees. The s-CAM has brought robotic laparoscopic imaging one step further toward less invasiveness and more dexterity.","Title: Optimal Time Selection for ISAR Imaging of Ship Targets Based on Time-Frequency Analysis of Multiple Scatterers\nYear: 2022\n\nAbstract: The equivalent rotation vector synthesized by 3-D nonuniform rotation of ship target leads to the time-varying characteristic of Doppler frequency of ship echo data, which will cause the image to be defocused in azimuth. In order to obtain high-resolution inverse synthetic aperture radar (ISAR) images of ship targets, we developed an optimal time selection algorithm based on time-frequency analysis of multiple scatterers. In this letter, we addressed the challenges of the serious cross-term interference in time-frequency analysis and measuring the stability of Doppler frequency. The time interval with a minimum variation of Doppler frequency was determined by using time-frequency analysis of multiple isolated scatterers and root mean squared error (RMSE). The effectiveness and robustness of the proposed algorithm were evaluated by both simulated and real ISAR data.","Title: Oriented Gaussian Function-Based Box Boundary-Aware Vectors for Oriented Ship Detection in Multiresolution SAR Imagery\nYear: 2022\n\nAbstract: As an important remote sensing means, synthetic aperture radar (SAR) has many superiorities to other sensors. How to effectively detect and locate ships in SAR images is also a popular field. In previous ship detection research, most algorithms focus on detecting the horizontal bounding box of ship targets, which ignore the rotation angle of each ships. Thus, too much background noise in the horizontal detection results makes them difficult to describe each ship accurately. Inspired by the powerful feature representation ability of convolutional neural networks (CNNs), a novel anchor-free and keypoint-based deep learning method is proposed for oriented ship detection in multiresolution SAR images. Our detector first extracts multilevel features from the input SAR image with a backbone network and feature pyramid network. Next, considering multiscale ships in multiresolution SAR images, we detect different sizes of ships on different levels of feature maps with identical head network structures. In each head network, the classification subnetwork determines each pixel in feature maps as the central pixel of this ship or not, and the regression subnetwork regresses the oriented bounding box for each ship. In the training process, the proposed oriented nonnormalized Gaussian function is used to describe the center point of ship targets, while the nonuniform weighting of the different level loss functions is used to suppress the imbalanced sample distribution. Experimental results on two authoritative SAR-oriented ship detection datasets and two Gaofen-3 images demonstrate the effectiveness and robustness of the proposed methods.","Title: Water-Body Segmentation for SAR Images: Past, Current, and Future\nYear: 2022\n\nAbstract: Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 years in the field of water body extraction in SAR images, and makes some proposals that the community working with SAR image waterbody extraction should consider. Firstly, this review focuses on the main ideas and characteristics of traditional water body extraction on SAR images, mainly focusing on traditional Machine Learning (ML) methods. Secondly, how Deep Learning (DL) methods are applied and optimized in the task of water-body segmentation for SAR images is summarized from the two levels of pixel and image. We also pay more attention to the most popular networks, such as U-Net and its modified models, and novel networks, such as the Cascaded Fully-Convolutional Network (CFCN) and River-Net. In the end, an in-depth discussion is presented, along with conclusions and future trends, on the limitations and challenges of DL for water-body segmentation.","Title: Joint User Association and Resource Allocation for MmWave and THz Coexistence Networks\nYear: 2021\n\nAbstract: With the development of information technology and the continuous increase of user demand, how to cope with the limitation of spectrum resources becomes a tough problem. Meanwhile, terahertz communication technology has become a key candidate technology to solve the above challenges. Taking advantage of the small propagation range and high bandwidth of terahertz, the coexistence network of millimeter wave and terahertz can effectively increase the communication rate of users in theory. So this paper considers the joint user association and power allocation of millimeter wave and terahertz, and solves the objective function through Lagrangian duality and gradient iteration algorithm. The simulation results show that the coexistence of millimeter wave and terahertz can greatly improve the system throughput and the achievable transmission rate of users.","Title: Blind Zone Performance Enhancement with THz Communication\nYear: 2021\n\nAbstract: Blind zone is a long standing issue in microwave networks. THz communication is regarded as a promising technology in 6G era to cope with these problems due to its wide bandwidth and short transmission distances. However, THz band causes great loss of non-line-of-sight path due to its molecular absorption properties and the communication distance is limited. In order to reduce the communication blind zone and ensure the coverage of the network, we combine THz band and microwave band to form a dual-band network. The object function is formulated to maximize the data rate of the system through joint bandwidth and power allocation scheduling, which is NP-hard. Hence, we decompose the original problem into two subproblems: bandwidth allocation problem and power allocation problem. The bandwidth allocation problem is solved by the convex optimization tool, and we introduce a difference of convex (DC) programming to solve the power allocation problem. Simulation results verify that the THz base stations can improve the performance of microwave network.","Title: An Iterative Algorithm for the Generalized Reflexive Solution Group of a System of Quaternion Matrix Equations\nYear: 2022\n\nAbstract: In the present paper, an iterative algorithm is proposed for solving the generalized (P, Q)- reflexive solution group of a system of quaternion matrix equations Sigma(M)(l=1) (A(ls)X(l)B(ls) + C-ls(X) over tilde D-l(ls)) = F-s, s = 1, 2, ..., N. A generalized (P, Q)-reflexive solution group, as well as the least Frobenius norm generalized (P, Q)-reflexive solution group, can be derived by choosing appropriate initial matrices, respectively. Moreover, the optimal approximate generalized (P, Q)-reflexive solution group to a given matrix group can be derived by computing the least Frobenius norm generalized (P, Q)-reflexive solution group of a reestablished system of matrix equations. Finally, some numerical examples are given to illustrate the effectiveness of the algorithm.","Title: TIM: threat context-enhanced TTP intelligence mining on unstructured threat data\nYear: 2022\n\nAbstract: TTPs (Tactics, Techniques, and Procedures), which represent an attacker's goals and methods, are the long period and essential feature of the attacker. Defenders can use TTP intelligence to perform the penetration test and compensate for defense deficiency. However, most TTP intelligence is described in unstructured threat data, such as APT analysis reports. Manually converting natural language TTPs descriptions to standard TTP names, such as ATT&CK TTP names and IDs, is time-consuming and requires deep expertise. In this paper, we define the TTP classification task as a sentence classification task. We annotate a new sentence-level TTP dataset with 6 categories and 6061 TTP descriptions from 10761 security analysis reports. We construct a threat context-enhanced TTP intelligence mining (TIM) framework to mine TTP intelligence from unstructured threat data. The TIM framework uses TCENet (Threat Context Enhanced Network) to find and classify TTP descriptions, which we define as three continuous sentences, from textual data. Meanwhile, we use the element features of TTP in the descriptions to enhance the TTPs classification accuracy of TCENet. The evaluation result shows that the average classification accuracy of our proposed method on the 6 TTP categories reaches 0.941. The evaluation results also show that adding TTP element features can improve our classification accuracy compared to using only text features. TCENet also achieved the best results compared to the previous document-level TTP classification works and other popular text classification methods, even in the case of few-shot training samples. Finally, the TIM framework organizes TTP descriptions and TTP elements into STIX 2.1 format as final TTP intelligence for sharing the long-period and essential attack behavior characteristics of attackers. In addition, we transform TTP intelligence into sigma detection rules for attack behavior detection. Such TTP intelligence and rules can help defenders deploy long-term effective threat detection and perform more realistic attack simulations to strengthen defense.","Title: Online health assessment and fault prediction for wind turbine generator\nYear: 2022\n\nAbstract: A health assessment and fault prediction method for wind turbine generators is proposed in this article. In health assessment module, considering generator status transferring along with environment and wind turbine-self operating, variables under wind turbine normal working are divided into two parameter spaces and recognized, namely operating conditions and status parameters. Then generator health benchmark models based on Gaussian mixture model are established in different operating condition sub-spaces after the data imbalance problem solved. For online health assessment, health deterioration index based on condition recognition models is calculated and a dual-threshold alarm scheme is proposed. When an alarm is raised by degraded health deterioration index, the program could access fault prediction module, where the generator rear bearing temperature trend and fault remaining time can be predicted through weights redistribution and hyper-parameter optimized support vector regression. In experiments, the proposed health assessment and fault prediction was verified in a real wind farm, and results showed this method could assess generator condition accurately and improve special fault prediction performance.","Title: Clutter Suppression for Wideband Radar STAP\nYear: 2022\n\nAbstract: Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or ``zero-bandwidth,'' where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets.","Title: TriCTI: an actionable cyber threat intelligence discovery system via trigger-enhanced neural network\nYear: 2022\n\nAbstract: The cybersecurity report provides unstructured actionable cyber threat intelligence (CTI) with detailed threat attack procedures and indicators of compromise (IOCs), e.g., malware hash or URL (uniform resource locator) of command and control server. The actionable CTI, integrated into intrusion detection systems, can not only prioritize the most urgent threats based on the campaign stages of attack vectors (i.e., IOCs) but also take appropriate mitigation measures based on contextual information of the alerts. However, the dramatic growth in the number of cybersecurity reports makes it nearly impossible for security professionals to find an efficient way to use these massive amounts of threat intelligence. In this paper, we propose a trigger-enhanced actionable CTI discovery system (TriCTI) to portray a relationship between IOCs and campaign stages and generate actionable CTI from cybersecurity reports through natural language processing (NLP) technology. Specifically, we introduce the “campaign trigger” for an effective explanation of the campaign stages to improve the performance of the classification model. The campaign trigger phrases are the keywords in the sentence that imply the campaign stage. The trained final trigger vectors have similar space representations with the keywords in the unseen sentence and will help correct classification by increasing the weight of the keywords. We also meticulously devise a data augmentation specifically for cybersecurity training sets to cope with the challenge of the scarcity of annotation data sets. Compared with state-of-the-art text classification models, such as BERT, the trigger-enhanced classification model has better performance with accuracy (86.99%) and F1 score (87.02%). We run TriCTI on more than 29k cybersecurity reports, from which we automatically and efficiently collect 113,543 actionable CTI. In particular, we verify the actionability of discovered CTI by using large-scale field data from VirusTotal (VT). The results demonstrate that the threat intelligence provided by VT lacks a part of the threat context for IOCs, such as the Actions on Objectives campaign stage. As a comparison, our proposed method can completely identify the actionable CTI in all campaign stages. Accordingly, cyber threats can be identified and resisted at any campaign stage with the discovered actionable CTI.","Title: Integrated Online Prediction Model for IoT Data\nYear: 2021\n\nAbstract: Data prediction research is very important for applications based on the Internet of things (IoT). However, the large-scale, high-dimension, and the inevitable missing values seriously hinder accurate prediction result. To meet this challenge, an integrated online prediction model is proposed, which can solve future values prediction and missing values imputation simultaneously in the low-dimensional space. Specifically, we embed the classic prediction models into the objective function of matrix factorization (MF) to obtain low-dimensional embedding parameters, which are then used to construct the prediction model. Furthermore, a rolling prediction mechanism is proposed to update the prediction model when new sampled data comes, based on the online MF with time-varying parameters. Simulation results on four UCI public data sets show the superior performance of the proposed method.","Title: Pulse RFI Mitigation in Synthetic Aperture Radar Data via a Three-Step Approach: Location, Notch, and Recovery\nYear: 2022\n\nAbstract: In complex electromagnetic environments, synthetic aperture radar (SAR) is severely affected by radio frequency interference (RFI) from other systems, such as ground-based radar, cellular networks, and global positioning systems, and this interference cannot be neglected. Pulse RFI (PRFI), a common form of RFI, can hinder SAR signal processing and image interpretation to varying degrees. The time-domain notch filtering method designed for mitigating PRFI can locate and mitigate the evident PRFI covered in SAR echo data, but it is helpless against PRFI hidden in a strong echo signal. In this article, a three-step approach is proposed to tackle the PRFI problem. In the proposed approach, the first step is to detect and locate PRFI; this is based on eigenvalue decomposition (EVD) and the short-time Fourier transform (STFT). The second step is to notch PRFI, and this is based on a time-domain notch filter. The third step is to recover the notched signal using a novel matrix completion strategy, which integrates with a robust low-rank matrix completion (LRMC) technique-i.e.,the singular value thresholding (SVT) algorithm-and a well-known Lagrange interpolation technique. Experimental results via simulated SAR data, Sentinel-1 level-0 raw data, and L-band airborne SAR raw data demonstrate the performance of the proposed approach.","Title: Improving Dialog System Grounded with Unstructured Knowledge by Domain Adaptive Pre-Training and Post-Ranking\nYear: 2021\n\nAbstract: Linguistic intelligence and the ability to converse with human are important and indispensable parts of humanoid robots. One of the most challenging tasks in knowledge-grounded task-oriented dialog systems (KTDS) is the knowledge selection task, which aims to find the proper knowledge snippets to respond to user dialog requests. In this paper, we first propose domain adapted-BERT (DA-BERT) which employs pre-trained bidirectional encoder representations from transformers (BERT) with domain adaptive training and dynamic masking probability for knowledge selection in KTDS. Domain adaptive training can minimize the domain gap between the general text data that BERT is pre-trained on and the dialog-knowledge joint data while dynamic masking probability enhances the training in an easy-to-hard manner. After knowledge selection, the next task in KTDS is knowledge-grounded generation. To improve the performance in knowledge-grounded generation, we propose GPT-PR to employ post-ranking on the generator's outputs. Post-ranking eliminates the possibility of generating hallucination response by a large portion during the sampling-based decoding process and thus can improve the quality of the generated response. Experimental results on the benchmark dataset show that our proposed pre-training and post-ranking methods, DA-BERT and GPT-PR, respectively, outperform the state-of-the-art models with large margins across all the evaluation metrics. Moreover, in the experiments, we also analyze the bad cases of DA-BERT and GPT-PR and do visualizations to facilitate further research in this direction.","Title: Identification of Crop Type Based on C-AENN Using Time Series Sentinel-1A SAR Data\nYear: 2022\n\nAbstract: Crop type identification is the initial stage and an important part of the agricultural monitoring system. It is well known that synthetic aperture radar (SAR) Sentinel-1A imagery provides a reliable data source for crop type identification. However, a single-temporal SAR image does not contain enough features, and the unique physical characteristics of radar images are relatively lacking, which limits its potential in crop mapping. In addition, current methods may not be applicable for time-series SAR data. To address the above issues, a new crop type identification method was proposed. Specifically, a farmland mask was firstly generated by the object Markov random field (OMRF) model to remove the interference of non-farmland factors. Then, the features of the standard backscatter coefficient, Sigma-naught (sigma(0)), and the normalized backscatter coefficient by the incident angle, Gamma-naught (gamma(0)), were extracted for each type of crop, and the optimal feature combination was found from time-series SAR images by means of Jeffries-Matusita (J-M) distance analysis. Finally, to make efficient utilization of optimal multi-temporal feature combination, a new network, the convolutional-autoencoder neural network (C-AENN), was developed for the crop type identification task. In order to prove the effectiveness of the method, several classical machine learning methods such as support vector machine (SVM), random forest (RF), etc., and deep learning methods such as one dimensional convolutional neural network (1D-CNN) and stacked auto-encoder (SAE), etc., were used for comparison. In terms of quantitative assessment, the proposed method achieved the highest accuracy, with a macro-F1 score of 0.9825, an overall accuracy (OA) score of 0.9794, and a Kappa coefficient (Kappa) score of 0.9705. In terms of qualitative assessment, four typical regions were chosen for intuitive comparison with the sample maps, and the identification result covering the study area was compared with a contemporaneous optical image, which indicated the high accuracy of the proposed method. In short, this study enables the effective identification of crop types, which demonstrates the importance of multi-temporal radar images in feature combination and the necessity of deep learning networks to extract complex features.","Title: Soft Exoskeleton With Fully Actuated Thumb Movements for Grasping Assistance\nYear: 2022\n\nAbstract: It has been clinically proven that exoskeletons are effective self-training rehabilitation or daily living assistance devices for patients with hand dysfunctions. However, exoskeleton-assisted hand exercises with high degrees-of-freedom are considered as challenging tasks because the digit space, especially the thumb, cannot accommodate enough actuators. In this article, we report a tendon-driven soft hand exoskeleton with a hybrid configuration for thumb actuation. The soft hand exoskeleton system uses the least number of actuators to realize full degrees-of-freedom actuation for all digits. It is tested on a stroke patient with hemiplegia and a healthy subject. The experimental results show that the hand exoskeleton could assist the stroke patient to accomplish various training tasks, such as thumb encircling, grasping, pinching, releasing, and writing. It was found that digit trajectories and joint angle changes of the stroke patient were close to those of the healthy subject. Especially, the range of motion of the stroke patient shows significant improvement with the hand exoskeleton assistance compared to that without the hand exoskeleton assistance. The research in this article paves the way to develop fully actuated soft hand exoskeleton that can be eventually integrated with an electroencephalogram or electromyography for self-training rehabilitation or daily living assistance.","Title: The Identification of ECG Signals Using Wavelet Transform and WOA-PNN\nYear: 2022\n\nAbstract: Electrocardiogram (ECG) signal identification technology is rapidly replacing traditional fingerprint, face, iris and other recognition technologies, avoiding the vulnerability of traditional recognition technologies. This paper proposes an ECG signal identification method based on the wavelet transform algorithm and the probabilistic neural network by whale optimization algorithm (WOA-PNN). Firstly, Q, R and S waves are detected by wavelet transform, and the P and T waves are detected by local windowed wavelet transform. The characteristic values are constructed by the detected time points, and the ECG data dimension is smaller than that of the non-reference detection. Secondly, combined with the probabilistic neural network, the mean impact value algorithm is used to screen the characteristic values, the characteristic values with low influence are eliminated, and the input and complexity of the model are simplified. Finally, a WOA-PNN combined classification method is proposed to intelligently optimize the hyper parameters in the probabilistic neural network algorithm to improve the model accuracy. According to the simulation verification on three databases, ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia, the identification accuracy of a single ECG cycle is 96.97%, and the identification accuracy of three ECG cycles is 99.43%.","Title: In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory\nYear: 2022\n\nAbstract: Continually learning new classes from few training examples without forgetting previous old classes demands a flexible architecture with an inevitably growing portion of storage, in which new examples and classes can be incrementally stored and efficiently retrieved. One viable architectural solution is to tightly couple a stationary deep neural network to a dynamically evolving explicit memory (EM). As the centerpiece of this architecture, we propose an EM unit that leverages energy-efficient in-memory compute (IMC) cores during the course of continual learning operations. We demonstrate for the first time how the EM unit can physically superpose multiple training examples, expand to accommodate unseen classes, and perform similarity search during inference, using operations on an IMC core based on phase-change memory (PCM). Specifically, the physical superposition of few encoded training examples is realized via in-situ progressive crystallization of PCM devices. The classification accuracy achieved on the IMC core remains within a range of 1.28%-2.5% compared to that of the state-of-the-art full-precision baseline software model on both the CIFAR-100 and miniImageNet datasets when continually learning 40 novel classes (from only five examples per class) on top of 60 old classes.","Title: EfiLoc: large-scale visual indoor localization with efficient correlation between sparse features and 3D points\nYear: 2022\n\nAbstract: Important location information of a query image can be obtained directly through indoor 3D points. However, the 3D model-based indoor positioning is still an open issue to be addressed, especially in large-scale dynamic environments. We design and realize the positioning system for large indoor scenes called the EfiLoc. First, we develop a lightweight network model, which can quickly extract discriminative global deep features to improve the discrimination of similar scenes. Another property is that the generated sparser main global descriptors can greatly reduce the retrieval time of multi-dimensional features. Second, we innovatively implement the efficient association of 3D point with the 2D features generated by its projection regions. Preserving the associations of the pixels in some key areas of the image, the precise and quick large-scale indoor localization can be realized. The experimental results show that EfiLoc can achieve good positioning accuracy and is of better robustness to the environment of weak textures and similar scenes compared with current state-of-the-art vision-based solutions.","Title: Design of Uniform Magnetic Field Coil by Quasi-Elliptic Function Fitting Method With Multiple Optimizations in Miniature Atomic Sensors\nYear: 2022\n\nAbstract: This article presents a design method of cylindrical uniform field coil based on quasi-elliptic function fitting (EFF) method with multiple optimizations. The EFF method is a type of discrete wire techniques aiming at simplifying the coil structure. Combing with particle swarm optimization (PSO) algorithm, the optimized parameters of EFF coil are obtained for highly uniform magnetic field. To further improve the uniformity of magnetic field in the central space, a new regional points selection optimization method is proposed. The calculation results show that after PSO optimization, the absolute maximum relative error in the target region is optimized to 0.098% by EFF coil comparing with modified single saddle coil (3.08%), nest coil (2.4%), and target field method (TFM) coil (0.11%). After regional points selection optimization, the volume proportion of absolute magnetic field relative error less than \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math notation=\"LaTeX\">$1\\times 10^{-4}$</tex-math></inline-formula>\n increases from 35.24% to 54.38% by EFF coil comparing with TFM coil. In addition, the simple EFF coil structure is verified to be easy to process and keep high performance through experiments. As the EFF coil’s high uniformity and simple structure, the new method has important significance for the miniaturization of atomic sensors.","Title: Time-Domain Autofocus for Ultrahigh Resolution SAR Based on Azimuth Scaling Transformation\nYear: 2022\n\nAbstract: For ultrahigh resolution synthetic aperture radar (SAR), azimuth spectrum aliasing limits the application of frequency-domain autofocus algorithms. Therefore, time-domain autofocus algorithms are often used for ultrahigh resolution SAR imaging. However, the azimuth deramping operation in current time-domain autofocus algorithms may introduce an additional azimuth-dependent phase. This phase can be regarded as a part of the phase error, which significantly reduces the estimation accuracy of the phase error. To address this issue, this article proposes a new time-domain autofocus algorithm based on azimuth scaling transformation (AST) for ultrahigh resolution SAR. In this algorithm, we first adopt the azimuth scaling operation to avoid the azimuth-dependent phase so that the estimation accuracy of error can be greatly improved. Then, for the azimuth-dependent shifts caused by the azimuth scaling operation, we adopt the alignment processing to remove them in azimuth-time domain. Finally, we can estimate the error accurately from the aligned signal. The simulation and measured data were processed to verify the effectiveness of the algorithm.","Title: A novel neural prescribed performance control design for constrained flexible hypersonic vehicles with compensation system\nYear: 2022\n\nAbstract: In this study, a novel prescribed performance function and auxiliary system are investigated for the constrained flexible hypersonic vehicles. The non-affine model of hypersonic vehicles is decomposed into velocity and altitude dynamic systems. For the velocity one, the adaptive neural prescribed controller is devised to guarantee that the tracking error converges to an arbitrary small compact. Then, the altitude dynamics subsystem is delivered as a pure feedback non-affine pattern, which gets the controller out of the dependence on precise affine model. On this premise, the exploited prescribed performance mechanism can not only avoid obtaining the initial sign of tracking error in advance but also can be employed in solving the problem that the conventional prescribed performance function could not cover the tracking error when the altitude subsystem is input constraint. And a Nussbaum-type function is employed to handle the control direction. Meanwhile, the controller utilizes a new auxiliary compensation algorithm to pursue the stable situation for the input restricted system. In order to cut down the computational burden, neural networks employ the min-learning parameter theory to approximate uncertainties in the subsystems. Finally, the simulation results convey that the presented approach has a superior dynamic and steady-state performance.","Title: Observation and Mitigation of Mutual RFI Between SAR Satellites: A Case Study Between Chinese GaoFen-3 and European Sentinel-1A\nYear: 2022\n\nAbstract: In synthetic aperture radar (SAR) data processing, radio frequency interference (RFI) has been recognized as a challenging issue, which can significantly degrade the image quality, including amplitude, phase, geometry, and so on. Most of the RFI sources are direct waves transmitted from the ground. Recently, a new form of RFI, namely, the mutual RFI (MRFI), which is a kind of scatter-wave RFI originating from the nearby ground area simultaneously illuminated by different SAR satellites, has been reported. In this article, the signatures of MRFI are characterized, and an example case study between Chinese GaoFen-3 (GF-3) and European Sentinel-1A (S-1A) is analyzed. In order to remove the artifacts caused by MRFI on SAR images, a novel RFI detector based on spectrum energy cancellation (SEC), which has the capability of detecting MRFI, is developed. Two methods, i.e., the traditional notch filtering method and an improved eigen-subspace projection (ESP) method proposed in this article, are employed to mitigate MRFI. The former method has robust mitigation performance with a fast processing speed, whereas the latter has an improved mitigation accuracy and lower sidelobes for strong scatterers. The methods are designed for cases in which the MRFI has a different radar center frequency than the useful signal, and some of the data are free from MRFI. The conditions required for the proper use of the approach are discussed. In contrast to the conventional mitigating methods performed on the raw data domain, the proposed approach begins with the focused single-look complex (SLC) SAR data. The effectiveness of the proposed approach is demonstrated on several GF-3 SLC SAR images, which were contaminated by MRFI from the S-1A.","Title: A novel deep framework for dynamic malware detection based on API sequence intrinsic features\nYear: 2022\n\nAbstract: Dynamic malware detection executes the software in a secured virtual environment and monitors its run-time behavior. This technique widely uses API sequence analysis to identify whether the running software is malicious or not. However, existing solutions typically only consider the API name or frequency of API usage, and the feature mining of API sequence is not sufficient, which leads some malware to escape from being detected. In this paper, we propose a novel malware detection framework using deep learning models to capture and combine more meaningful features which are called intrinsic features of the API sequence. Specifically, we first apply embedding and convolutional layers to conduct a joint representation of multiple APIs to represent the software behavior. Secondly, we use the category, action, and operation object of the API to represent the semantic information of each API call. Finally, we use the Bi-LSTM module to mine the relationship information between APIs. Our proposed model achieves an accuracy of 0.9731 and an F1-score of 0.9724 on a large real dataset, which outperforms baselines significantly. We also conduct ablation studies to prove the effectiveness of our intrinsic features.","Title: An Improved Hybrid PSO-TS Algorithm for Solving Nonlinear Equations of SHEPWM in Multilevel Inverters\nYear: 2022\n\nAbstract: Selective Harmonic Elimination Pulse Width Modulation (SHEWM) can eliminate selected low order harmonics by solving nonlinear transcendental equations to obtain the switching angles in advance and achieve precise harmonics control. However, when solving nonlinear equations, the convergence rate and solution accuracy of the existing intelligent algorithms for multilevel inverter SHEPWM will decrease with the increase of the number of switching angles. In this paper, an improved hybrid PSO-TS algorithm (IPSO-TS) is proposed for switching angle selection in PWM inverter. In the proposed IPSO-TS algorithm, both the PSO algorithm and TS algorithm are improved respectively to enhance their convergence when calculating multiple switching angles. Also, these two improved algorithms are combined to obtain the hybrid algorithm IPSO-TS, in which PSO is used to provide global search capabilities, and TS is used to increases the accuracy of solutions. Simulations are carried out using MATLAB/SIMULINK environment to demonstrate the better convergence of the proposed algorithm compared with the other existing methods. Moreover, the simulation results are further verified by experimental results.","Title: BETA: a comprehensive benchmark for computational drug-target prediction\nYear: 2022\n\nAbstract: Internal validation is the most popular evaluation strategy used for drug-target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug-drug and protein-protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.","Title: Calibration of Radar RCS Measurement Errors by Observing the Luneburg Lens Onboard the LEO Satellite\nYear: 2022\n\nAbstract: Accurate radar RCS measurements are critical to the feature recognition of spatial targets. A calibration method for radar RCS measurement errors is proposed for the first time in the context of special target tracking by observing the Luneburg Lens onboard the LEO satellite. The Luneburg Lens has favorable RCS scattering properties for the radar microwave. Thus, the laboratory RCS measurements of the Luneburg Lens, with some fixed incident frequency and with different incident orientations for the radar microwave, will be implemented in order to build a database. The incident orientation for the radar microwave in the satellite body frame will be calculated by taking advantage of the precise orbit parameters, with errors only at the magnitude of several centimeters and within the actual satellite attitude parameters. According to the incident orientation, the referenced RCS measurements can be effectively obtained by the bilinear interpolation in the database. The errors of actual RCS measurements can thus be calibrated by comparing the referenced and the actual RCS measurements. In the RCS measurement experiment, which lasts less than 400 s, the actual RCS measurement errors of the Luneburg Lens are nearly less than 0 dBsm, which indicates that the RCS measurement errors of the spatial targets can be effectively calculated by the proposed calibration method. After the elaborated calibration, the RCS measurements of the spatial targets can be accurately obtained by radar tracking.","Title: Simultaneous Screening and Detection of RFI From Massive SAR Images: A Case Study on European Sentinel-1\nYear: 2022\n\nAbstract: Currently, the spaceborne synthetic aperture radar (SAR) system transmits a great deal of data to the ground processing station and generates massive images daily, only a tiny fraction of which contains radio frequency interference (RFI). However, most of the existing RFI detection methods are based on the prior conditions in which the known image contains interference. In fact, it is difficult to learn whether SAR images contain RFIs without prescreening, so it is of great significance to the rapid and real-time screening and detection of RFI in SAR images. This article proposes a method to screen and detect RFI from massive SAR images simultaneously. 1) We construct an approximate RFI-free background image by using the preprocessed time-series SAR images acquired in the past. 2) We generate difference images based on the image change detection method and analyze them by using an adaptive threshold, then calculate the entropy of all difference images to complete the preliminary screening of the RFI-containing). According to the preliminary results, we remove the RFI-containing parts; after reconstructing the background, we repeat step 2 with the images to be detected and obtain the final screening and detection results. Massive experimental results based on Sentinel-1 images validate the performance of the proposed method.","Title: Time Series Forecasting of Motor Bearing Vibration Based on Informer\nYear: 2022\n\nAbstract: Electric energy, as an economical and clean energy, plays a significant role in the development of science and technology and the economy. The motor is the core equipment of the power station; therefore, monitoring the motor vibration and predicting time series of the bearing vibration can effectively avoid hazards such as bearing heating and reduce energy consumption. Time series forecasting methods of motor bearing vibration based on sliding window forecasting, such as CNN, LSTM, etc., have the problem of error accumulation, and the longer the time-series forecasting, the larger the error. In order to solve the problem of error accumulation caused by the conventional methods of time series forecasting of motor bearing vibration, this paper innovatively introduces Informer into time series forecasting of motor bearing vibration. Based on Transformer, Informer introduces ProbSparse self-attention and self-attention distilling, and applies random search to optimize the model parameters to reduce the error accumulation in forecasting, achieve the optimization of time and space complexity and improve the model forecasting. Comparing the forecasting results of Informer and those of other forecasting models in three publicly available datasets, it is verified that Informer has excellent performance in time series forecasting of motor bearing vibration and the forecasting results reach 10(-2)similar to 10(-6).","Title: An Autoselection Strategy of Multiobjective Evolutionary Algorithms Based on Performance Indicator and its Application\nYear: 2022\n\nAbstract: The use of ensemble approaches in the single-objective evolutionary algorithms is ubiquitous, but ensembles of multiobjective evolutionary algorithms (MOEAs) have achieved relatively little attention. On the other hand, manually selecting a suitable MOEA to solve an actual multiobjective optimization problem (MOP) is time-consuming and challenging. Therefore, developing a multiobjective hyperheuristic to allocate computational resources for multiple MOEAs in an intelligent approach is beneficial. In this work, an autoselection strategy of MOEAs based on the performance indicator (MOEAS-PI) is introduced to alleviate the abovementioned problem. In the MOEAS-PI, the performance of each constituent MOEA in the pool is assessed according to a real-time and comprehensive performance indicator, which contains both the current and future performances. The MOEAS-PI is able to easily choose the best performing MOEA during the evolutionary process. Also, it can enhance the robustness of MOEAs and reduce the application risk. The effectiveness of the MOEAS-PI is carefully evaluated on 23 MOPs. Simulation results demonstrate that the MOEAS-PI is an effective and efficient method to integrate the advantages of each individual algorithm. Finally, the MOEAS-PI is utilized to solve a translation control problem of an immersed tunnel element under current flow. Experimental results reveal that the MOEAS-PI is a reliable and effective optimization approach to solve actual MOPs. \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Note to Practitioners</i>\n—Multiobjective optimization problems (MOPs) have been commonly found in various fields. However, a single MOEA cannot guarantee its sufficient robustness and adaptability in solving MOPs. Therefore, this study aims to propose a multiobjective hyperheuristic algorithm to improve the robustness of MOEAs. The performance of the proposed algorithm is tested on benchmark test functions and an actual MOP. The results show that the proposed approach can select a suitable MOEA to solve a particular type of MOPs during the evolutionary process and provide a solution set for decision-makers to control the translation of an immersed tunnel element under different objectives/operator environments.","Title: Research status and development trend of image camouflage effect evaluation\nYear: 2022\n\nAbstract: In today’s military operations, camouflage generally uses feature reduction techniques such as camouflage net, low emission coating and camouflage pattern to minimize its detectability and optimize the survivability of high-value assets. However, there is no internationally recognized standard method and procedure to evaluate camouflage equipment and techniques. This paper briefly introduces the existing subjective evaluation methods such as professional evaluation, image-based interpretation, discovery probability equation, and objective evaluation methods such as linear weighted model, neural network model, human visual attention mechanism and multi-attribute decision-making. The application scenarios, advantages and disadvantages of each method are analyzed, and the future development trend of camouflage evaluation model in deep learning, dynamic target camouflage evaluation and hyperspectral field is pointed out.","Title: VISEL: A visual and magnetic fusion-based large-scale indoor localization system with improved high-precision semantic maps\nYear: 2022\n\nAbstract: Multisource fusion localization is a mainstream scheme for acquiring accurate locations in complex indoor scenes. To overcome the interference of indoor structures on radio and illumination variation on visual features, the semantic maps provide an effective way for multisource fusion localization. However, due to the lack of visual depth information, solutions of indoor semantic maps suffer from large semantic segmentation errors for similar objects, which leads to the unstable performance of localization systems. To overcome the issue in semantic and fusion localization, we develop a localization system to demonstrate the use of restudy semantic map and self-adapting fusion localization would achieve centimeter-level positioning accuracy, termed VISEL. VISEL uses the proposed spatial attention-aware semantic model to enhance the discrimination of semantic features for capturing accurate semantic maps. On the basis of high-precision semantic maps, VISEL completes an enhanced particle filter fusion localization module with adaptive reassign weight to different localization modules, which successfully improves accuracy through complementary advantages between different signals while overcoming the drawbacks of each signal and interference of complex environment. The extensive experimental results show that VISEL outperforms current state-of-the-art positioning systems and achieves an average positioning accuracy of 0.4 m. VISEL utilizes semantic maps with depth features and enhanced particle filter to reduce the fusion localization error by 38%, which suggests the high-precision semantic maps with depth features could provide a robust solution for the fusion localization system for indoor complex scenes.","Title: Infrared Small-Target Detection Based on Radiation Characteristics with a Multimodal Feature Fusion Network\nYear: 2022\n\nAbstract: Infrared small-target detection has widespread influences on anti-missile warning, precise weapon guidance, infrared stealth and anti-stealth, military reconnaissance, and other national defense fields. However, small targets are easily submerged in background clutter noise and have fewer pixels and shape features. Furthermore, random target positions and irregular motion can lead to target detection being carried out in the whole space-time domain. This could result in a large amount of calculation, and the accuracy and real-time performance are difficult to be guaranteed. Therefore, infrared small-target detection is still a challenging and far-reaching research hotspot. To solve the above problem, a novel multimodal feature fusion network (MFFN) is proposed, based on morphological characteristics, infrared radiation, and motion characteristics, which could compensate for the deficiency in the description of single modal characteristics of small targets and improve the recognition precision. Our innovations introduced in the paper are addressed in the following three aspects: Firstly, in the morphological domain, we propose a network with the skip-connected feature pyramid network (SCFPN) and dilated convolutional block attention module integrated with Resblock (DAMR) introduced to the backbone, which is designed to improve the feature extraction ability for infrared small targets. Secondly, in the radiation characteristic domain, we propose a prediction model of atmospheric transmittance based on deep neural networks (DNNs), which predicts the atmospheric transmittance effectively without being limited by the complex environment to improve the measurement accuracy of radiation characteristics. Finally, the dilated convolutional-network-based bidirectional encoder representation from a transformers (DC-BERT) structure combined with an attention mechanism is proposed for the feature extraction of radiation and motion characteristics. Finally, experiments on our self-established optoelectronic equipment detected dataset (OEDD) show that our method is superior to eight state-of-the-art algorithms in terms of the accuracy and robustness of infrared small-target detection. The comparative experimental results of four kinds of target sequences indicate that the average recognition rate P-avg is 92.64%, the mean average precision (mAP) is 92.01%, and the F1 score is 90.52%.","Title: Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data\nYear: 2022\n\nAbstract: Crop classification is an important part of crop management and yield estimation. In recent years, neural networks have made great progress in synthetic aperture radar (SAR) crop classification. However, the insufficient number of labeled samples limits the classification performance of neural networks. In order to solve this problem, a new crop classification method combining geodesic distance spectral similarity measurement and a one-dimensional convolutional neural network (GDSSM-CNN) is proposed in this study. The method consisted of: (1) the geodesic distance spectral similarity method (GDSSM) for obtaining similarity and (2) the one-dimensional convolutional neural network model for crop classification. Thereinto, a large number of training data are extracted by GDSSM and the generalized volume scattering model which is based on radar vegetation index (GRVI), and then classified by 1D-CNN. In order to prove the effectiveness of the GDSSM-CNN method, the GDSSM method and 1D-CNN method are compared in the case of a limited sample. In terms of evaluation and verification of methods, the GDSSM-CNN method has the highest accuracy, with an accuracy rate of 91.2%, which is 19.94% and 23.91% higher than the GDSSM method and the 1D-CNN method, respectively. In general, the GDSSM-CNN method uses a small number of ground measurement samples, and it uses the rich polarity information in multi-temporal fully polarized SAR data to obtain a large number of training samples, which can quickly improve the accuracy of classification in a short time, which has more new inspiration for crop classification.","Title: Framework for Runway's True Heading Extraction in Remote Sensing Images Based on Deep Learning and Semantic Constraints\nYear: 2022\n\nAbstract: Runway heading is a crucial runway attribute that closely affects aircraft' takeoff and landing safely while having a large number of missing in existing runway thematic databases, limiting the evaluation and analysis globally. This article proposes an automated runway heading extraction method, considering various runway surface materials and spatial structure differences encountered in wide-area detection, promoting a quick and reliable broad investigation. First, multiscale detection is performed based on the mask regional convolutional neural network runway detection model to locate airport runways, using the mask nonmaximum suppression mechanism algorithm to distinguish runways with highly overlapping candidate results. Second, abnormality judgment and mask selection according to semantic constraints were applied to the identified runway area to reserve high-confidence masks to accurately extract the runway heading. We tested our framework results on 1483 runways in the United States with complex runway materials and structures combination. The runway detection recall was 99.12%, and 98.9% of the runway heading errors were limited to 2 degrees, showing that the overall frame for extracting the true runway heading is reliable. In addition, we complement the true headings of 892 runways in OurAirports, promoting research on the completion, verification, and updating of runway semantics in related databases.","Title: An Improved RFI Mitigation Approach for SAR Based on Low-Rank Sparse Decomposition: From the Perspective of Useful Signal Protection\nYear: 2022\n\nAbstract: As an open system, synthetic aperture radar (SAR) inevitably receives radio frequency interference (RFI) generated by electromagnetic equipment in the same band. The existence of RFI seriously affects SAR signal processing and image interpretation. In recent years, many algorithms and models related to RFI mitigation have been proposed. However, most of that focus on effectively mitigating the RFI is insufficient to protect the useful signals. This article proposes a mitigation method of RFI with a signal-protected capability. (1) The kurtosis coefficient is used to detect RFI pulse-by-pulse, and the echoes containing RFI are stored in matrix form. (2) The preliminary extraction of RFI is complete by low-rank sparse decomposition of the echo matrix containing RFI. (3) For the secondary separation of RFI, the accurate position of RFI in the preliminary extraction results is located by the fuzzy C-means clustering; then, we separate the RFI and the remaining useful signals again and reconstruct the useful signals to complete the mitigation work. The proposed method can further protect useful signals while effectively removing interference through the secondary separation of RFI. Experimental results based on simulated and measured data verify the performance and potential of the proposed method.","Title: A Novel Method for SAR Ship Detection Based on Eigensubspace Projection\nYear: 2022\n\nAbstract: Synthetic Aperture Radar (SAR) is a high-resolution radar that operates all day and in all weather conditions, so it has been widely used in various fields of science and technology. Ship detection using SAR images has become important research in marine applications. However, in complex scenes, ships are easily submerged in sea clutter, which cause missed detection. Due to this, strong sidelobes in SAR images generate false targets and reduce the detection accuracy. To solve these problems, a ship detection method based on eigensubspace projection (ESSP) in SAR images is proposed. First, the image is reconstructed into a new observation matrix along the azimuth direction, and the phase space matrix of the reconstructed image is constructed by using the Hankel characteristic, which preliminarily determines the approximate position of the ship. Then, the autocorrelation matrix of the reconstructed image is decomposed by eigenvalue decomposition (EVD). According to the size of the eigenvalues, the corresponding eigenvectors are divided into two parts, which constitute the basis of the ship subspace and the clutter subspace. Finally, the original image is projected into the ship subspace, and the ship data in the ship subspace are rearranged to obtain the precise position of the ship with significantly suppressed clutter. To verify the effectiveness of the proposed method, the ESSP method is compared with other detection methods on four images at different sea conditions. The results show that the detection accuracy of the ESSP method reaches 89.87% in complex scenes. Compared with other methods, the proposed method can extract ship targets from sea clutter more accurately and reduce the number of false alarms, which has obvious advantages in terms of detection accuracy and timeliness.","Title: A Novel Group Paging Control Method for Massive MTC Accesses in LTE Networks\nYear: 2022\n\nAbstract: The fifth-generation (5G) systems have to deal with massive deployment of machine-type-communication (MTC) devices. System overload may occur during a random access procedure under a limited number of preamble resources and physical uplink shared channel resources especially when there exist massive MTC devices in a cell. In order to resolve the system overload (caused by the massive MTC deployment), the 3GPP proposed the adopted a group paging (GP)-based uplink access technique. But its performances dramatically decrease as the number of MTC devices increases. In this paper we propose a novel method, named ACB-based group paging overload control method (AGO). To reduce the number of simultaneous access MTC devices, AGO first scatters the MTC devices over a GP interval, and then automatically adjusts ACB parameters according to the load conditions. By doing so, AGO achieves high-channel access probability for MTC devices. Simulation results show that this method is superior to the GP and Pre-backoff mechanisms in terms of success and collision probability, average access latency and resource utilization rate.","Title: Human–Computer Interaction Cognitive Behavior Modeling of Command and Control Systems\nYear: 2022\n\nAbstract: Human–computer interaction cognitive behavior (HCICB) modeling faces four deficiencies: 1) lack of a standard framework model; 2) large simulation error; 3) single simulation dimension; and 4) lack of a simulation software. To solve these deficiencies, we have carried out work in four aspects. First, we construct an HCICB model with the user, system device, and environment as the core elements, which provides a unified framework for the subsequent HCICB modeling in the Military Internet of Things (MIoT) command and control (C2) system. Second, we correct visual and motion parameters in the adaptive control of thought rational module of the Cogtool model by the commander in the loop (CIL) experiment. Third, we construct a mental workload (MW) prediction model of the MIoT C2 system based on improved visual auditory cognitive psychomotor, which realizes fast, high-precision, and quantitative MW prediction. It is added as a simulation dimension for the HCICB. Fourth, we develop MwCogtool, an HCICB prediction software that can rapidly simulate typical tasks at the design and usage stages of the MIoT C2 system, and also can output six parameters, including task completion time (TCT), MW, eye movement prepare time, eye movement execution time, motion time, and cognitive time in the whole process quickly and visually. In addition, we select 20 real users and 9 typical tasks of the MIoT C2 system to carry out the CIL verification experiment. Compared with Cogtool, MwCogtool reduces the maximum simulation error in TCT of the C2 system from 45.00% to 5.58%. The consistency of simulation results with real user data reaches 0.99. The results of the MW prediction model can significantly and negatively predict the change of real users’ eye movement, and can accurately predict the trend of MW change. Simultaneously, we build a fitting model between the mean MW prediction value and eye movement parameters.","Title: Lexical semantics enhanced neural word embeddings\nYear: 2022\n\nAbstract: Current breakthroughs in natural language processing have benefited dramatically from- neural language models, through which distributional semantics can leverage neural data representations to facilitate downstream applications. Since neural embeddings use context prediction on word co-occurrences to yield dense vectors, they are inevitably prone to capture more semantic association than semantic similarity. To improve vector space models in deriving semantic similarity, we post-process neural word embeddings through deep metric learning, through which we can inject lexical-semantic relations, including syn/antonymy and hypo/hypernymy, into a distributional space. We introduce hierarchy-fitting, a novel semantic specialization approach to modelling semantic similarity nuances inherently stored in the IS-A hierarchies. Hierarchy-fitting attains state-of-the-art results on the common- and rare-word benchmark datasets for deriving semantic similarity from neural word embeddings. It also incorporates an asymmetric distance function to specialize hypernymy’s directionality explicitly, through which it significantly improves vanilla embeddings in multiple evaluation tasks of detecting hypernymy and directionality without negative impacts on semantic similarity judgement. The results demonstrate the efficacy of hierarchy-fitting in specializing neural embeddings with semantic relations in late fusion, potentially expanding its applicability to aggregating heterogeneous data and various knowledge resources for learning multimodal semantic spaces.","Title: DMalNet: Dynamic malware analysis based on API feature engineering and graph learning\nYear: 2022\n\nAbstract: Application Programming Interfaces (APIs) are widely considered a useful data source for dynamic malware analysis to understand the behavioral characteristics of malware. However, the accuracy of API-based malware analysis is limited for two reasons. (1) Existing solutions often only consider the API names while ignoring the API arguments, or cannot fully exploit the semantic information from different types of arguments. (2) The relationship between API calls is important to describe the software behavior but is difficult to capture. To overcome the above limitations, we propose DMalNet, a novel malware analysis framework for accurate malware detection and classification. Specifically, we first present a hybrid feature encoder to extract semantic features from API names and arguments. Then, we derive an API call graph from the API call sequence to convert the relationship between API calls into the structural information of the graph. Finally, we design a graph neural network to implement the malware detection and type classification. To evaluate our approach, we use datasets of over 20k benign and 18k malicious samples belonging to 8 malware types. DMalNet achieves 98.43% and 91.42% accuracy on malware detection and malware type classification, respectively. We also conduct ablation studies to assess the impact of API feature engineering and the graph learning model. Further experiments show that DMalNet can effectively detect malware.","Title: Switching pinning control for memristive neural networks system with Markovian switching topologies\nYear: 2022\n\nAbstract: This work concentrates on the issue of leader-following bipartite synchronization of multiple memristive neural networks with Markovian jump topology. In contrast to conventional coupled neural network systems, the coupled neural network model under consideration possesses both cooperative and competitive connections among neuron nodes. Specifically, the interaction between neighbors’ nodes is described by a signed graph, in which a positive weight represents an alliance relationship between two neuron nodes while a negative weight represents an adversarial relationship between two neuron nodes. By designing a pinning discontinuous controller that makes full use of the mode information, some effective criteria that ensure the stability of bipartite synchronization error states are obtained. All network nodes can synchronize the target node state bipartitely. Finally, two simulation examples are provided to demonstrate the viability of the suggested bipartite synchronization control approach.","Title: DGGCN: Dictionary based DGA detection method based on DomainGraph and GCN\nYear: 2022\n\nAbstract: Nowadays, malware uses Algorithmically Generated Domains (AGDs) to establish communication with Command and Control (C&C) servers. Dictionary based Domain Generation Algorithm (DGA) selects words from the frequently changed dictionaries to generate AGDs similar to benign domains, which degrades the accuracy of string based detection method. To combat this, we propose a DGA detection method based on DomainGraph and GCN (Graph Convolutional Network) which detects cross-dictionary AGDs based on the association relation between domains instead of lexical features. Starting from the association relation between domains rather than the lexical features of the domain itself, we can detect the unknown AGDs from a known AGD, regardless of the DGA dictionary they use. The proposed method exploits the fact that string association of benign domains is weak, while AGDs' association is strong. DGGCN composes a domain segmentation method, constructs a graph composed of domains (DomainGraph) based on segmentations and adopts GCN to detect AGDs. We conduct the experiments on public datasets under three settings: detecting AGDs generated by familiar dictionaries, unfamiliar dictionaries and confusing dictionaries. The results reveal that DGGCN can detect cross-dictionary AGDs similar to benign domains more accurately and robustly.","Title: Research on Two-dimensional Vector Magnetic Field Sensing Characteristics of All-fiber Magnetic Field Sensor Based on the Magnetostrictive Effect\nYear: 2022\n\nAbstract: Based on the Giant Magnetostrictive Material (GMM) Terfenol-D and fiber Bragg grating, the optical fiber magnetic field temperature sensor for magnetic field measurement is proposed. Its magnetic field and temperature sensing characteristics are theoretically analyzed and experimentally verified, and a sensitivity matrix is established to overcome the cross-sensitivity of magnetic field and temperature. In addition, the vector response of the magnetic field in three orthogonal planes is experimentally tested. It is worth noting that the magnetic field vector response of the sensor in three orthogonal planes is experimentally tested, and it is found that the magnetic field vector response of the sensor in the three orthogonal planes is different. The sensor has the advantages of low cost, easy integration, and good vector properties. It has an important application significance for marine magnetic network detection. Based on the sensing of cascaded Fiber Bragg Gratings (FBGs), an array sensing integration scheme for magnetic field detection is proposed.","Title: Verification of Dam Spatial Location in Open Datasets Based on Geographic Knowledge and Deep Learning\nYear: 2022\n\nAbstract: Dams are important water conservation hubs; however, dam locations provided by open datasets are often unreliable. The aim of this article is to provide a single, geographic location-reliable dataset of dams for the scientific community by fusing existing dam datasets and verifying these locations. Using Southeast Asia as the case study, we propose an efficient and automatic method to verify dam locations and developed a process framework. First, the possible location of a dam was obtained by analyzing its geographic location characteristics. Then, the deep learning method was used to detect dams. Finally, a variety of geographic knowledge was applied to comprehensively verify the detection results to obtain accurate and reliable dam location information. The fused dam dataset we produced includes the locations of 4493 dams in Southeast Asia, which were verified using the proposed framework. The verification results were then evaluated via manual visual inspection. The verification accuracy of the framework was 86.7%. The experimental results show that the proposed framework can quickly and reliably verify dam spatial locations and provide solutions for the spatial location verification of other remote sensing objects.","Title: Compact Closed-loop EEG/fNIRS Recording and TMS Neuromodulation System\nYear: 2022\n\nAbstract: Transcranial magnetic stimulation (TMS) is a clinic-proven neuromodulation technique to treat various neurological and psychological diseases. However, existing TMS devices are bulky, expensive, and open loop. Therefore, they can only be used in fixed locations, and the modulation effects cannot be measured. In this paper, a wearable close-loop neuromodulation system that integrates real-time EEG/fNIRS recording and TMS is proposed. The system’s real-time signal monitoring and analysis function allows the inducing of magnetic stimulation and helps measure its effects after stimulation. The proposed system can measure and analyze 23 channel EEG and 64 channel fNIRS signals simultaneously in real-time. The dimensions of the fabricated TMS device are 330mm × 148mm × 112mm, with peak voltage and current values of 1100 V and 2400A, respectively, resulting in peak magnetic stimulation of 620 mTesla and bidirectional current pulse width of 311 μs.","Title: Extension and Evaluation of SSC for Removing Wideband RFI in SLC SAR Images\nYear: 2022\n\nAbstract: Synthetic aperture radar (SAR), as a wideband radar system, is easily contaminated by radio frequency interference (RFI), which affects the imaging quality of SAR. The subband spectral cancellation (SSC) method and its modifications utilize the SAR single-look complex (SLC) image to realize RFI extraction and mitigation by subtracting between sub-images, which are robust and efficient for engineering applications. In the past, the traditional SSC was often applied to narrowband interference (NBI) mitigation. However, when it was used for wideband interference (WBI) mitigation, it would cause the mitigated image to lose much of its useful information. In contrast, this paper proposes an improved SSC method based on successive cancellation and data accumulation (SSC-SCDA) for WBI mitigation. First, the fast Fourier transform (FFT) is used to characterize the SAR SLC data in the frequency domain, and the average range spectrum algorithm is used to detect whether there are interference components in the SAR SLC data. Then, according to the carrier frequency and bandwidth of the RFI in the frequency domain, the subbands are divided, and a cancellation strategy is formulated. Finally, based on the successive cancellation and data accumulation technology, WBIs can be removed by using only a small percentage of the clean subbands. Based on the simulated experiments, the interference mitigation performance of the proposed method is analyzed when the interference-to-signal bandwidth ratio (ISBR) varies from 20% to 80% under different signal-to-interference-to-noise ratios (SINR). The experimental results based on WBI-contaminated European Space Agency (ESA) Sentinel-1A SAR SLC data demonstrate the effectiveness of the proposed method in WBI mitigation.","Title: ESP-LRSMD: A Two-Step Detector for Ship Detection Using SLC SAR Imagery\nYear: 2022\n\nAbstract: Synthetic aperture radar (SAR), an active microwave remote sensing equipment, can provide all-day, all-weather, and high-resolution images for Earth observation. Ship monitoring using SAR is significant to fishery application, marine transportation, maritime security, and so on. Most traditional SAR ship detection methods use the amplitude information of the SAR imagery and employ the constant false alarm rate (CFAR), deep learning, and other technologies to detect the ship targets. However, in addition to the amplitude image, the SAR single-look complex (SLC) data can also be utilized for ship detection since its complex-valued information is beneficial to characterize the ship's feature in the transforming domains. In this article, the strong scattering feature of ships in the image domain and low-rank feature in the transforming domain is studied, and on this basis, a two-step detector is proposed to detect ship targets in SAR SLC image. The first step is to identify the presence of ships, which is based on the eigen-subspace projection (ESP) technology to detect the occurrences of the ship targets in azimuth cells of the SAR data. The second step is to locate the position of ships, which is based on the low-rank and sparse matrix decomposition (LRSMD) to obtain the instantaneous frequency information of the ship targets in the time-frequency domain. Finally, the ship targets can be located in a line-by-line manner in the azimuth direction. Experimental results via European Sentinel-1A and Chinese Gaofen-3 single-pol SAR SLC data demonstrate the performance of the proposed detector.","Title: Multistep-Ahead Prediction of Ocean SSTA Based on Hybrid Empirical Mode Decomposition and Gated Recurrent Unit Model\nYear: 2022\n\nAbstract: The prediction of sea surface temperature anomalies (SSTA) is vital to the study of marine ecosystems and global climate. The SSTA can be accurately forecasted one step ahead by numerical and statistical methods. However, multistep-ahead forecasting for SSTA is greatly challenging since the nonlinearity and nonstationarity of SSTA and the lag problem of prediction. Therefore, in this article, a multistep-ahead SSTA forecasting method based on the hybrid empirical mode decomposition (EMD) and gated recurrent unit (GRU) model is proposed considering that EMD has the advantage of reducing data complexity and GRU is good at long-term prediction of data. First, the EMD algorithm is applied to obtain several intrinsic modal mode functions that are more stationary than the original data. Then, GRU is used for multistep forecasting, in which three multistep forecasting strategies (recursive, direct, and multioutput) are compared. The proposed hybrid model is validated by multistep forecasting for monthly average SSTA at the Nino3.4 region. The experimental results show that using reconstruction error as part of the prediction effectively improves the prediction accuracy of the EEMD-GRU model and outperforms other EMD algorithms combined with GRU. Compared with traditional models, the EEMD-GRU model can better predict future multimonth trends of SSTA and effectively alleviate the problem of prediction lag of the traditional model. Finally, this model is applied to the Nino3.4 regional SSTA prediction, and the results show that this model can provide a reference for ocean research.","Title: A Repeater-Type SAR Deceptive Jamming Method Based on Joint Encoding of Amplitude and Phase in the Intra-Pulse and Inter-Pulse\nYear: 2022\n\nAbstract: Due to advantages such as low power consumption and high concealment, deceptive jamming against synthetic aperture radar (SAR) has received extensive attention in electronic countermeasures. However, the false targets generated by most of the deceptive jamming methods still have limitations, such as poor controllability and strong regularity. Inspired by the idea of waveform coding, this paper proposed a repeater-type SAR deceptive jamming method through the joint encoding of amplitude and phase in intra-pulse and inter-pulse, which can generate a two-dimensional controllable deceptive jamming effect. Specifically, the proposed method mainly includes two parts, i.e., grouping and encoding. The number of groups determines the number of false targets, and the presence of the phase encoding produces false targets. The amplitude encoding affects the amplitude of the false targets. For the intra-pulse cases, the proposed method first samples the intercepted SAR signal. Meanwhile, the sampling points are grouped in turn. For the inter-pulse cases, the grouped objects are the pulses. Subsequently, the joint encoding of amplitude and phase is performed on each group, which generates jamming signals with deceptive effects. In this paper, the imaging effect of the generated jamming signals is analyzed in detail, and the characteristics of false targets, including numbers, position, and amplitude, are derived. The simulation and experimental results verify the correctness of the theoretical analysis. In addition, the superiority of the proposed method is verified by comparing it with other methods.","Title: Towards Intelligent Noninvasive Closed-loop Neuromodulation Systems\nYear: 2022\n\nAbstract: Neuromodulation is an effective method to treat various kinds of brain diseases. It has drawn significant attention in academic research and the medical market and has become a rapidly developing area of medicine. However, current neuromodulation technologies face bottlenecks such as high cost, bulky, and lack of intelligent closed-loop for patient-specific treatments. These bottlenecks further limit the widespread use of neuromodulation technologies. Fortunately, these challenges, in turn, drive the advancement in integrated circuits and systems designs for biomedical applications. This paper presents recent research on IC development that is relevant to addressing the abovementioned challenges, emphasizing closing the loop with on-chip artificial intelligence (AI). Our current research will be presented as a case study to demonstrate how dedicated IC designs and AI can benefit the development of an intelligent and miniaturized closed-loop neuromodulation system. The challenges and emerging directions for the development of circuits and systems for neuromodulation technologies are summarized in the concluding remarks."],"string":"[\n \"Title: Sparse Normalized Least Mean Absolute Deviation Algorithm Based on Unbiasedness Criterion for System Identification With Noisy Input.\\nYear: 2018\\n\\nAbstract: In order to solve the system identification problems, the normalized least mean absolute deviation (NLMAD) algorithm was developed as an effective and robust method. In this paper, aiming at the system identification problems with sparsity characteristic, and taking the advantage of the NLMAD algorithm to suppress impulsive output measurement noise interference, we introduce the L-1-norm as a sparse penalty constraint into the NLMAD algorithm to design a robust sparse adaptive filtering algorithm. Furthermore, considering the biased estimation caused by the input noise, we employ an unbiasedness criterion to derive an effective bias-compensated vector which can compensate the bias efficiently for the proposed sparse NLMAD algorithm. The desirable performance of the new method is measured with simulations of two stages. The proposed bias-compensated sparse NLMAD algorithm achieves better performance compared to other existing methods in both stages. Simulation results demonstrate the excellent performance of the proposed algorithm in solving sparse system identification problems. The promising results in this paper suggest that the bias-compensated sparse NLMAD algorithm may become a useful tool for system identification with noisy input and impulsive output noise.\",\n \"Title: Biopanning data bank 2018: hugging next generation phage display.\\nYear: 2018\\n\\nAbstract: The 2018 update of the biopanning data bank (BDB) stores phage display data sequenced by Sanger sequencing and next generation sequencing technologies. In this work, we upgraded the database with more biopanning data sets and several new features, including (i) incorporation of next generation biopanning data and the unselected population where the target is not determined and the round of screening is zero; (ii) addition of sequencing information; (iii) improvement of browsing and searching systems and 3D chemical structure viewer; (iv) integration of standalone tools for target-unrelated peptides analysis within conventional phage display and next generation phage display (NGPD) data. In the current version of BDB (released on 19 January 2018), the database houses 3291 sets of biopanning data collected from 1540 published articles, including 95 NGPD data sets and 3196 traditional biopanning data sets. The BDB database serves as an important and comprehensive resource for developing peptide ligands.\",\n \"Title: Raw Data-Based Motion Compensation for High-Resolution Sliding Spotlight Synthetic Aperture Radar.\\nYear: 2018\\n\\nAbstract: For accurate motion compensation (MOCO) in airborne synthetic aperture radar (SAR) imaging, a high-precision inertial navigation system (INS) is required. However, an INS is not always precise enough or is sometimes not even included in airborne SAR systems. In this paper, a new, raw, data-based range-invariant motion compensation approach, which can effectively extract the displacements in the line-of-sight (LOS) direction, is proposed for high-resolution sliding spotlight SAR mode. In this approach, the sub-aperture radial accelerations of the airborne platform are estimated via a well-developed weighted total least square (WTLS) method considering the time-varying beam direction. The effectiveness of the proposed approach is validated by two airborne sliding spotlight C band SAR raw datasets containing different types of terrain, with a high spatial resolution of about 0.15 m in azimuth.\",\n \"Title: Words alignment based on association rules for cross-domain sentiment classification.\\nYear: 2018\\n\\nAbstract: Automatic classification of sentiment data (e.g., reviews, blogs) has many applications in enterprise user management systems, and can help us understand people’s attitudes about products or services. However, it is difficult to train an accurate sentiment classifier for different domains. One of the major reasons is that people often use different words to express the same sentiment in different domains, and we cannot easily find a direct mapping relationship between them to reduce the differences between domains. So, the accuracy of the sentiment classifier will decline sharply when we apply a classifier trained in one domain to other domains. In this paper, we propose a novel approach called words alignment based on association rules (WAAR) for cross-domain sentiment classification, which can establish an indirect mapping relationship between domain-specific words in different domains by learning the strong association rules between domain-shared words and domain-specific words in the same domain. In this way, the differences between the source domain and target domain can be reduced to some extent, and a more accurate cross-domain classifier can be trained. Experimental results on Amazon® datasets show the effectiveness of our approach on improving the performance of cross-domain sentiment classification.\",\n \"Title: Maximum correntropy unscented Kalman and information filters for non-Gaussian measurement noise.\\nYear: 2017\\n\\nAbstract: In this paper, we investigate the state estimation problem of nonlinear systems with non-Gaussian measurement noise. Based on a newly defined cost function which is obtained by a combination of weighted least square (WLS) and maximum correntropy criterion (MCC), we derive our maximum correntropy unscented Kalman filter (MCUKF) and the corresponding maximum correntropy unscented information filter (MCUIF). Comparing with existing MCUKF, our MCUKF avoids the numerical problem occurred when the measurements contain large outliers, and can obtain similar or even better estimation results. When the kernel bandwidth goes infinity, we prove that our MCUKF and MCUIF will converge to UKF and UIF, respectively, while existing MCUIF will not in this case and it generally has poor estimation accuracy as well. Two typical nonlinear models are used to illustrate the advantages of our proposed algorithms.\",\n \"Title: Dielectric Characteristics of Unsaturated Loess and the Safety Detection of the Road Subgrade Based on GPR.\\nYear: 2018\\n\\nAbstract: We present a moisture content and permittivity model to simultaneously detect and estimate defects in loess subgrade. Based on the ground-penetrating radar (GPR) method, the dielectric properties of loess in the northwest of China and the imaging feature of the moisture content of different strata were studied. The relative permittivity of loess with different moisture contents was experimented in the laboratory. It was found that the relative permittivity of unsaturated loess was positively related to moisture content. The relationship between relative permittivity and moisture content in different antenna frequencies of GPR was analyzed. Electromagnetic wave reflection rules in the loess interface were studied using the numerical method with different moisture contents. With the increase in moisture content, the amplitude of GPR was increased. When the above conclusions were applied in the engineering practices, there are good effects to detect the defects of the road subgrade. It is a significant guidance for determining the qualitative research of defects in the roadbed.\",\n \"Title: Inversion Model of GPR Imaging Characteristics of Point Objects and Fracture Detection of Heritage Building.\\nYear: 2018\\n\\nAbstract: There are often many hidden structural defects in heritage buildings. As a convenient and effective nondestructive detecting method, ground-penetrating radar (GPR) has a technical advantage in detecting and protecting heritage buildings depending on the advanced image interpretation. The analytic relationship between buried depth and radius of point object and long and short axis of hyperbolic equation was established according to derivations of formulas. The image characteristics of hyperbolic curves with different depth and radius were studied by finite-difference time-domain method (FDTD). And then, inversion models of buried depth and radius of point object were established. The buried depth and radius can be accurately deduced by long and short axis of hyperbolic image. This result was applied in the detection of pedestal defects of the heritage building, and the depth and distribution range of hidden fracture can be accurately interpreted. It provides an effective and fast method to detect hidden defects in civil engineering.\",\n \"Title: Bridging effort-aware prediction and strong classification: a just-in-time software defect prediction study.\\nYear: 2018\\n\\nAbstract: Context: Most research into software defect prediction ignores the differing amount of effort entailed in searching for defects between software components. The result is sub-optimal solutions in terms of allocating testing resources. Recently effort-aware (EA) defect prediction has sought to redress this deficiency. However, there is a gap between previous classification research and EA prediction.\\n\\nObjective: We seek to transfer strong defect classification capability to efficient effort-aware software defect prediction.\\n\\nMethod: We study the relationship between classification performance and the cost-effectiveness curve experimentally (using six open-source software data sets).\\n\\nResults: We observe extremely skewed distributions of change size which contributes to the lack of relationship between classification performance and the ability to find efficient test orderings for defect detection. Trimming allows all effort-aware approaches bridging high classification capability to efficient effort-aware performance.\\n\\nConclusion: Effort distributions dominate effort-aware models. Trimming is a practical method to handle this problem.\",\n \"Title: ForkTail: a black-box fork-join tail latency prediction model for user-facing datacenter workloads.\\nYear: 2018\\n\\nAbstract: The workflows of the predominant user-facing datacenter services, including web searching and social networking, are underlaid by various Fork-Join structures. Due to the lack of understanding the performance of Fork-Join structures in general, today's datacenters often resort to resource overprovisioning, operating under low resource utilization, to meet stringent tail-latency service level objectives (SLOs) for such services. Hence, to achieve high resource utilization, while meeting stringent tail-latency SLOs, it is of paramount importance to be able to accurately predict the tail latency for a broad range of Fork-Join structures of practical interests.\\n\\nIn this paper, we propose ForkTail, a black-box Fork-Join tail latency prediction model that covers a wide range of Fork-Join structures. In ForkTail, all Fork nodes are treated as black boxes, admitting both homogeneous and inhomogeneous cases, and different requests in the request flow are allowed to spawn different numbers of tasks forked to different numbers of Fork nodes. On the basis of the central limit theorem for queuing models under heavy load, we are able to arrive at a highly computational effective, empirical expression for the tail latency as a function of the means and variances of the task response times. Since this expression can be applied to request sub-flows at any granularities, it can be used for tail-latency prediction for services in a consolidated environment, where different services and applications may share the same datacenter cluster resources. Our extensive testing results based on model-based and trace-driven simulations, as well as a real-world case study in a cloud environment demonstrate that the expression can consistently predict the tail latency within 20% and 15% prediction errors at 80% and 90% load levels, respectively. Moreover, our sensitivity analysis demonstrates that such errors can be well compensated for with no more than 5% and 3% resource overprovisioning at these two load levels, respectively. This, together with its extremely low computational complexity, makes ForkTail a viable tool for both offline and online job scheduling and resource provisioning for user-facing datacenter applications.\",\n \"Title: Robust H∞ control of uncertain linear system with interval time-varying delays by using Wirtinger inequality.\\nYear: 2018\\n\\nAbstract: This paper is concerned with the robust H∞ control problem of uncertain linear system with interval time-varying delays. Firstly, by using Wirtinger inequality approach, after constructing a new Lyapunov functional, the novel robust delay-dependent stability and stabilization criteria are developed. These improved results are expected to have less conservatism, due to the utilization of Wirtinger inequality and consideration of the information of the lower bound of time delay. Secondly, based on the obtained criteria, the memoryless H∞ state feedback controller design scheme is presented, thus robust H∞ controller design and performance analysis for uncertain linear system are developed. For a prescribed H∞ performance level, the designed robust H∞ controller can guarantee the uncertain linear system to be robustly stable and endure a larger delay. At last, numerical examples are provided to illustrate the effectiveness and superiority of the developed results.\",\n \"Title: Distributed Joint Offloading Decision and Resource Allocation for Multi-User Mobile Edge Computing: A Game Theory Approach.\\nYear: 2018\\n\\nAbstract: Due to the spectrum reuse in small cell network, the inter-cell interference has great effect on MECu0027s performance. In this paper, for reducing the energy consumption and latency of MEC, we propose a game theory based jointing offloading decision and resource allocation algorithm for multi-user MEC. In this algorithm, the transmission power, offloading decision, and mobile useru0027s CPU capability are determined jointly. We prove that this game is an exact potential game and the NE of this game exists and is unique. For reaching the NE, the best response dynamic is applied. We calculate the best responses of these three variables. Moreover, we investigate the properties of this algorithm, including the convergence, the computation complexity, and the price of anarchy. The theoretical analysis shows that the inter-cell interference has great effect on the performance of MEC. The NE of this game is Pareto efficiency and also the global optimal solution of the proposed optimal issue. Finally, we evaluate the performance of this algorithm by simulation. The simulation results illustrates that this algorithm is effective on improving the performance of the multi-user MEC system.\",\n \"Title: Benchmarking the HLA typing performance of Polysolver and Optitype in 50 Danish parental trios.\\nYear: 2018\\n\\nAbstract: Although current computational methods for HLA typing generally provide satisfactory results, our benchmark - using data with ultra-high sequencing depth - demonstrates the incompleteness of current reference databases, and highlights the importance of providing genomic databases addressing current sequencing standards, a problem yet to be resolved before benefiting fully from personalised medicine approaches HLA phenotyping is essential.\",\n \"Title: The total {}-domatic number of wheels and complete graphs\\nYear: 2012\\n\\nAbstract: Let k be a positive integer and let G be a graph with vertex set V(G). The total {k}-dominating function (T{k}DF) of a graph G is a function f from V(G) to the set {0,1,2,驴,k}, such that for each vertex v驴V(G), the sum of the values of all its neighbors assigned by f is at least k. A set {f 1,f 2,驴,f d } of pairwise different T{k}DFs of G with the property that $\\\\sum_{i=1}^{d}f_{i}(v)\\\\leq k$ for each v驴V(G), is called a total {k}-dominating family (T{k}D family) of G. The total {k}-domatic number of a graph G, denoted by $d_{t}^{\\\\{k\\\\}}(G)$ , is the maximum number of functions in a T{k}D family. In this paper, we determine the exact values of the total {k}-domatic numbers of wheels and complete graphs, which answers an open problem of Sheikholeslami and Volkmann (J. Comb. Optim., 2010) and completes a result in the same paper.\",\n \"Title: A 1.5-GS/s 5-bit interpolating ADC with offset averaging and interpolating sharing resistors network\\nYear: 2015\\n\\nAbstract: In this paper, a 1.5-GS/s 5-bit interpolating analog-to-digital converter (ADC) with offset averaging and interpolating sharing resistors network (OAISRN) is presented. The proposed OAISRN is based on conventional flash ADC and the concept of zero crossing points in folding and interpolating architecture. In order to reduce power dissipation yet to make a high performance, it removes half of preamplifiers and ensures matching by using offset averaging and 2x-interpolation sharing resistors network behind the initial zero crossing points generators array. Implementation of the OAISRN is explained in detail and its impact on signal bandwidth is discussed. The interpolating ADC in TSMC 65 nm process achieves SNDR of 27.8 dB and SFDR of 37.7 dB for 745 MHz input frequency located at 1.5 GS/s in postsimulation. The power consumption is 9.6 mW under a supply voltage of 1.2 V.\",\n \"Title: A 30-GHz to 39-GHz mm-Wave low-power injection-locked frequency divider in 65nm CMOS\\nYear: 2015\\n\\nAbstract: In this paper, a wide locking range and low power Injection-Locked Frequency Divider (ILFD) is presented. Compared to conventional structure, 2-b digital controlled capacitor array is introduced to extend its locking range. Meanwhile, the tail current source is eliminated to reduce the supply voltage and enlarge the output voltage swing. The post-simulations show that the total locking-range is from 29.7GHz to 39.2GHz when input power is 0dBm. While operating at highest frequency band, the output power is 1.3dBm, and phase noise at 1-MHz offset are -156.9dBc/Hz. The divider core draws a RMS current of 8.5mA under a supply voltage of 0.65V. The total divider with 50Ω output buffer takes up 540μm×205μm without probe pad in TSMC 65-nm CMOS process.\",\n \"Title: Deep Joint Semantic-Embedding Hashing.\\nYear: 2018\\n\\nAbstract: Hashing has been widely deployed to large-scale image retrieval due to its low storage cost and fast query speed. Almost all deep hashing methods do not sufficiently discover semantic correlation from label information, which results in the learned hash codes less discriminative. In this paper, we propose a novel Deep Joint Semantic-Embedding Hashing (DSEH) approach that consists of LabNet and ImgNet. Specifically, LabNet is explored to capture abundant semantic correlation between sample pairs and supervise ImgNet from both semantic level and hash codes level, which is conductive to the generated hash codes being more discriminative and similarity-preserving. Extensive experiments on three benchmark datasets show that the proposed model outperforms current state-of-the-art methods.\",\n \"Title: An Autofocus Cartesian Factorized Backprojection Algorithm for Spotlight Synthetic Aperture Radar Imaging.\\nYear: 2018\\n\\nAbstract: A backprojection (BP) algorithm is recognized as an ideal method for high-resolution synthetic aperture radar (SAR) imaging. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. The Cartesian factorized BP (CFBP) algorithm is proposed recently to avoid massive interpolations and improve the performance. However, integrating autofocus techniques with the CFBP...\",\n \"Title: Dynamic Similar Sub-Series Selection Method for Time Series Forecasting.\\nYear: 2018\\n\\nAbstract: Accumulation of influencing factors during several consecutive time periods makes the variation of target parameters lag behind the variation of their influencing factors. This important phenomenon, known as the cumulative effect, would lead to relatively large forecasting errors. In this paper, the dynamic similar sub-series method is proposed to take cumulative effect into consideration. The similar sub-series for the forecasting parameter sub-series are selected based on similarities of target parameter sub-series and influencing factors sub-series. The internal variations of target parameter sub-series and each influencing factor sub-series are innovatively integrated into the selection rules for the dynamic similar sub-series. The corresponding forecasting algorithm is designed, and the forecasting parameters are deduced and forecasted according to the variation of the dynamic similar sub-series. The proposed method is compared with a variety of representative methods under the short-term daily average load forecasting, the electricity price forecasting and the global horizontal irradiance forecasting scenarios to demonstrate its effectiveness.\",\n \"Title: Compressive Sensing Based Multiple Source Localization in the Presence of Sensor Position Uncertainty and Nonuniform Noise.\\nYear: 2018\\n\\nAbstract: Source localization is an important field of research and the received signal strength (RSS)-based method is of particular interest. Further, by exploiting the sparsity of localization problem, the compressive sensing (CS) can be applied to considerably decrease the number of RSS measurements, especially in multi-source scenario. However, most existing CS-based localization methods usually neglect some practical issues. In particular, the sensor positions are assumed be known exactly, while in practice they may not be accurate. Additionally, a uniform Gaussian noise assumption is made that the noise variances of sensors are identical, but in practice the noise should be nonuniform. When these assumptions are violated, the localization performance will deteriorate dramatically. To address such issues, in this paper, we formulate the source localization based on superimposed RSS measurements as a sparse signal recovery problem. Moreover, by regarding the sensor positions as adjustable parameters, the inaccurate sensor positions can be refined through the adjustment of parameters. Following this idea, we develop a novel iterative algorithm for joint signal recovery and parameter optimization based on the variational expectation-maximization algorithm. Consequently, the sensor position uncertainty can be alleviated and thus the signal recovery performance will be improved greatly. Meanwhile, it is also capable of learning the variance of nonuniform noise. Extensive simulation results demonstrate the superiority of the proposed method.\",\n \"Title: Performance Comparison of Saliency Detection.\\nYear: 2018\\n\\nAbstract: Saliency detection has attracted significant attention in the field of computer vision technology over years. At present, more than 100 saliency detection models have been proposed. In this paper, a relatively more detailed classification is proposed. Furthermore, we selected 25 models and evaluated their performance using four public image datasets. We also discussed common problems, such as the influence to model performance by prior information and multiple objects. Finally, we offered future research directions.\",\n \"Title: A 5-to-8-GHz Wideband Miniaturized Dielectric Spectroscopy Chip With $I/Q$ Mismatch Calibration in 65-nm CMOS.\\nYear: 2018\\n\\nAbstract: This paper presents a fully integrated broadband biosensor chip for miniaturized dielectric spectroscopy (DS). The proposed biosensor chip consists of wideband exciting signal source and high sensitivity receiver. The exciting signal source consists of a wideband phase-locked loop (PLL) and output drivers. The wideband receiver is composed of low noise amplifier, mixer, and output buffers. The pro...\",\n \"Title: Cross-Layer Balanced Relay Node Selection Algorithm for Opportunistic Routing in Underwater Ad-Hoc Networks\\nYear: 2018\\n\\nAbstract: Due to the different transmission media, the underwater environment poses a more severe situation for routing algorithm design than that of the terrestrial environment. In this paper, we propose a weight based fuzzy logic (WBFL) relay node selection algorithm for the underwater Ad-hoc network, which can get the most balanced result than the previous algorithms without increasing the computation complexity. In this algorithm, the parameter scatters instead of the parameter values are inputted into the fuzzy logic inference system. By this innovation, the algorithm can take as much cross-layer parameters into account as possible during the relay node selection. Moreover, taking the node mobility into account, we propose a geographic based link lifetime prediction algorithm for underwater Ad-hoc network. The simulation results show that the WBFL algorithm can improve the network throughput at most 50% compare with the ExOR algorithm; moreover, the WBFL is effective and accurate on selecting relay nodes and the computation complexity is less than the previous algorithms.\",\n \"Title: Secrecy Performance Analysis of Artificial-Noise-Aided Spatial Modulation in the Presence of Imperfect CSI.\\nYear: 2018\\n\\nAbstract: The security of information over wireless channels has always been an important aspect of wireless communication research. In this paper, the secrecy performance of a spatial modulated system with an artificial noise is investigated over the Rayleigh channel, and the corresponding secrecy rate is analyzed in the presence of imperfect channel state information (CSI). Based on the secrecy performance analysis, the ergodic rates and the corresponding lower bounds of the legitimate receiver and eavesdropper are, respectively, derived. As a result, the closed-form lower bound of ergodic rate of the legitimate receiver is obtained, and the lower bound of ergodic rate of the eavesdropper and the corresponding ergodic secrecy rate (ESR) can be achieved by calculating the mean values. To avoid the mean operation and reduce the complexity, the closed-form approximated expression of ESR is also derived. This theoretical ESR can include the one under perfect CSI as a special case and has the value close to the corresponding simulation. Thus, it can provide good performance evaluation for the artificial-noise-aided spatial modulation system with imperfect CSI. Simulation results verify the effectiveness of the theoretical analysis. The system with imperfect CSI is found to have low ESR as compared to the one with perfect CSI due to the estimation error. Moreover, initially, the ESR increases with increasing power of artificial noise, but then, due to the total power constraint, it starts decreasing.\",\n \"Title: An Optimized Symmetric WENO Method-Based Numerical Simulation of Intense Sound Field Generated by Underwater Plasma Sound Source.\\nYear: 2018\\n\\nAbstract: The intensive pulse sound wave can be generated by the underwater plasma sound source (UPSS) based on the discharge of the underwater high voltage. The distribution of the sound field is prominently nonlinear. In this paper, the sound field of the intensive UPSS is described by the integral two-dimensional axisymmetric unsteady Euler equations firstly. In order to solve the Euler equations numerically, an optimized fifth-order symmetric WENO (weighted essentially nonoscillatory) method based on the three templates is proposed which is called WENO-SYM3. Without increasing the number of candidate templates, a new symmetric template structure can be obtained by expanding the second template and shifting the third one backwards for one space. The method is validated through numerical examples and experiments, and the results show that WENO-SYM3 has a high distinguished accuracy; meanwhile, its nonphysical oscillations are not obvious. The experimental results are basically the same as the calculation results, and the maximum error is around 3%.\",\n \"Title: Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China.\\nYear: 2018\\n\\nAbstract: Although optical remote sensing can intuitively detect algal bloom, it is limited by the weather conditions. Synthetic aperture radar (SAR) is not affected by inadequate weather conditions. According to visual interpretation of SAR images and comparisons of quasi-synchronized optical images, the gathering areas of algal bloom present as dark regions on SAR images. It is shown that using SAR to monitor the water surface is workable. However, dark regions may also be caused by other factors, such as low wind speeds. This challenges with SAR monitoring of algal bloom on the water surface. In this study, an improved K-means algorithm, combined with multi-Otsu thresholding algorithm, was proposed to segment the dark regions. After feature analysis and extraction of Sentinel-1A images, an algal bloom recognition model with a support vector machine (SVM) was applied to discriminate the algal bloom dark regions from the low wind dark regions. According the experimental results, the overall accuracy achieved 74.00% in Taihu Lake. Additionally, this method was also validated in Chaohu Lake and Danjiangkou Reservoir. Therefore, it can be concluded that SAR can provide a new technical means for monitoring algal bloom of inland lakes, particularly when it is cloudy and unsuitable for optical remote sensing. To obtain more information about algal bloom, multi-band and multi-polarization SAR images can be considered for future.\",\n \"Title: Probability Prediction-Based Reliable and Efficient Opportunistic Routing Algorithm for VANETs.\\nYear: 2018\\n\\nAbstract: In the vehicular ad hoc networks (VANETs), due to the high mobility of vehicles, the network parameters change frequently and the information that the sender maintains may outdate when it wants to transmit data packet to the receiver, so for improving the routing efficiency and reliability, we propose the probability prediction-based reliable and efficient opportunistic routing (PRO) algorithm for...\",\n \"Title: Global dissipativity analysis of quaternion-valued memristor-based neural networks with proportional delay.\\nYear: 2018\\n\\nAbstract: In this paper, the global dissipativity problem is investigated for the quaternion-valued memristor-based neural networks (QVMNNs) with proportional delay. Firstly, the mathematical model of QVMNNs is proposed, which is a challenging model due to the unbounded delay and the quaternion field. Secondly, based on matrix measure method and Lyapunov theorem, combing with modulus inequality technique and generalized Halanay inequality technique, some sufficient conditions guaranteeing global dissipativity of QVMNNs with proportional delay are derived. Moreover, the global attractive set of QVMNNs can also be obtained. Finally, three numerical examples are presented to demonstrate the effectiveness of the theoretical results.\",\n \"Title: An Efficient Missing Data Prediction Method Based on Kronecker Compressive Sensing in Multivariable Time Series.\\nYear: 2018\\n\\nAbstract: The existence of missing data severely affects the establishment of correct data mining model from the raw data. Unfortunately, most of the existing missing data prediction approaches are inefficient to predict missing data from multivariable time series due to the low accuracy and poor stability property. To address this issue, we propose an efficient method using the novel Kronecker compressive sensing theory. First, we exploit the spatial and temporal properties of the multivariable time series to construct the sparse representation basis and design the measurement matrix according to the location of missing data. Accordingly, the missing data prediction problem is modeled as a sparse vector recovery problem. Then, we verify the validity of the model from two aspects: whether the sparse representation vector is sufficiently sparse and the sensing matrix satisfies the restricted isometry property of compressive sensing. Finally, we investigate the sparse recovery algorithms to find the best suited one in our application scenario. Simulation results indicate that the proposed method is highly efficient in predicting the missing data of multivariable time series.\",\n \"Title: Synchronization criteria for inertial memristor-based neural networks with linear coupling.\\nYear: 2018\\n\\nAbstract: This paper is concerned with the synchronization problem for an array of memristive neural networks with inertial term, linear coupling and time-varying delay. Since parameters in the connection weight matrices are state-dependent, that is to say, the connection weight matrices jump in certain intervals, the mathematical model of the coupled inertial memristive neural networks can be considered as an interval parametric uncertain system. Based on the interval parametric uncertainty theory, two different synchronization criteria for memristor-based neural networks are obtained by applying the p-matrix measure (p=1,2,∞,ω), Halanay inequality and constructing suitable Lyapunov–Krasovskii functionals. Two simulation examples with fully-connected and nearest neighboring topology are presented to demonstrate the efficiency of the obtained theoretical results.\",\n \"Title: Exploiting Fine-Grained Subcarrier Information for Device-Free Localization in Wireless Sensor Networks.\\nYear: 2018\\n\\nAbstract: Device-free localization (DFL) that aims to localize targets without carrying any electronic devices is addressed as an emerging and promising research topic. DFL techniques estimate the locations of transceiver-free targets by analyzing their shadowing effects on the radio signals that travel through the area of interest. Recently, compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements by exploiting the inherent spatial sparsity of target locations. In this paper, we propose a novel CS-based multi-target DFL method to leverage the frequency diversity of fine-grained subcarrier information. Specifically, we build the dictionaries of multiple channels based on the saddle surface model and formulate the multi-target DFL as a joint sparse recovery problem. To estimate the location vector, an iterative location vector estimation algorithm is developed under the multitask Bayesian compressive sensing (MBCS) framework. Compared with the state-of-the-art CS-based multi-target DFL approaches, simulation results validate the superiority of the proposed algorithm.\",\n \"Title: An autonomous dynamic trust management system with uncertainty analysis.\\nYear: 2018\\n\\nAbstract: In order to help users select a reliable service in the open and complicated network, an autonomous dynamic trust management system is put forward based on collaborative recommendation mechanism and uncertainty analysis of satisfaction data. Any user in the network can store and update autonomously his trust table which contains direct interaction experience, find easily the reliable referrers according to collaborative recommendation, and collect valuable trust data and calculate the satisfaction and its reliability based on uncertainty analysis. Then the direct, recommended and self-recommended trusts can be calculated and finally be used to compute the global trust which will help the user to make a trust decision. A complicated network environment which includes quite a part of malicious nodes is built for investigating the robustness and effectiveness of the trust system. The experimental results show that the quality of service selection can be improved with the increase of interaction; the system can keep stable even though there are 50% malicious nodes initially in the network or a large number of malicious nodes join the network after stability; the system can resist the collusion attack more effectively and has lower computation and storage overhead than contrastive models.\",\n \"Title: Multi-strategy monarch butterfly optimization algorithm for discounted {0-1} knapsack problem.\\nYear: 2018\\n\\nAbstract: As an expanded classical 0-1 knapsack problem (0-1 KP), the discounted {0-1} knapsack problem (DKP) is proposed based on the concept of discount in the commercial world. The DKP contains a set of item groups where each group includes three items, whereas no more than one item in each group can be packed in the knapsack, which makes it more complex and challenging than 0-1 KP. At present, the main two algorithms for solving the DKP include exact algorithms and approximate algorithms. However, there are some topics which need to be further discussed, i.e., the improvement of the solution quality. In this paper, a novel multi-strategy monarch butterfly optimization (MMBO) algorithm for DKP is proposed. In MMBO, two effective strategies, neighborhood mutation with crowding and Gaussian perturbation, are introduced into MMBO. Experimental analyses show that the first strategy can enhance the global search ability, while the second strategy can strengthen local search ability and prevent premature convergence during the evolution process. Based on this, MBO is combined with each strategy, denoted as NCMBO and GMMBO, respectively. We compared MMBO with other six methods, including NCMBO, GMMBO, MBO, FirEGA, SecEGA and elephant herding optimization. The experimental results on three types of large-scale DKP instances show that NCMBO, GMMBO and MMBO are all suitable for solving DKP. In addition, MMBO outperforms other six methods and can achieve a good approximate solution with its approximation ratio close to 1 on almost all the DKP instances.\",\n \"Title: Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises.\\nYear: 2018\\n\\nAbstract: In this paper, a novel robust particle filter is proposed to address the measurement outliers occurring in the multiple autonomous underwater vehicles (AUVs) based cooperative navigation (CN). As compared with the classic unscented particle filter (UPF) based on Gaussian assumption of measurement noise, the proposed robust particle filter based on the maximum correntropy criterion (MCC) exhibits better robustness against heavy-tailed measurement noises that are often induced by measurement outliers in CN systems. Furthermore, the proposed robust particle filter is computationally much more efficient than existing robust UPF due to the use of a Kullback-Leibler distance-resampling to adjust the number of particles online. Experimental results based on actual lake trial show that the proposed maximum correntropy based unscented particle filter (MCUPF) has better estimation accuracy than existing state-of-the-art robust filters for CN systems with heavy-tailed measurement noises, and the proposed MCUPF has lower computational complexity than existing robust particle filters.\",\n \"Title: Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks.\\nYear: 2018\\n\\nAbstract: The existing public key-based en-route filtering schemes are vulnerable to report disruption attacks or selective forwarding attacks, and they fail to consider any measure to detect and punish the malicious nodes. The authors propose a series of public key-based security mechanisms for wireless sensor networks (WSNs) in this paper, including a mechanism for verifying the partial signatures, a substitution mechanism, an effective report forwarding protocol, and a trust value-based mechanism to identify and punish the malicious nodes. Finally, the authors develop a public key-based authentication and en-route filtering scheme (PKAEF), which can resist false data injection attacks, report disruption attacks and selective forwarding attacks, and can mitigate the impact of malicious nodes. Detailed performance analysis and evaluation show that, in most cases, PKAEF outperforms previous works in terms of safety, filtering efficiency, and data availability.\",\n \"Title: MRI-compatible injection system for magnetic microparticle embolization.\\nYear: 2019\\n\\nAbstract: This system has the capability to operate within the strong magnetic field of a clinical 3T MRI, to form proper particle aggregates and to automatically inject these aggregates into the MRI bore. Moreover, the versatility of the proposed injector renders it suitable for selective injections of MDEBs during MR-guided embolization procedures.\",\n \"Title: Scene invariant crowd counting using multi-scales head detection in video surveillance.\\nYear: 2018\\n\\nAbstract: With a soaring increase in the application of video surveillance in daily life, the estimation of crowd density has already become a hot field. Crowd counting has a very close relationship with traffic planning, pedestrian analysing and emergency warning. Here, a novel crowd counting method based on multi-scales head detection is proposed. The authors' approach first uses gradients difference to e...\",\n \"Title: Self-organizing weighted incremental probabilistic latent semantic analysis.\\nYear: 2018\\n\\nAbstract: PLSA (Probabilistic Latent Semantic Analysis) is a popular topic modeling technique which has been widely applied to text mining applications to discover the underlying topics embedded in the data corpus. However, due to the variability of increasing data, it is necessary to discover the dynamic topics and process the large dataset incrementally. Moreover, PLSA models suffer from the problem of inferencing new documents. To overcome these problems, in this paper, we propose a novel Weighted Incremental PLSA algorithm called WIPLSA to dynamically discover topics and incrementally learn the topics from new documents. The experiments verify that the proposed WIPLSA could capture the dynamic topics hidden in the dynamic updating data corpus. Compared with PLSA, MAP PLSA and QB PLSA, WIPLSA performs better in perspexity on large dataset, which make it applicable for big data mining. In addition, WIPLSA has good performance in the application of document categorization.\",\n \"Title: A New High-Efficiency Multilevel Frequency-Modulation Different Chaos Shift Keying Communication System.\\nYear: 2018\\n\\nAbstract: Considering the large number of delay lines and orthogonality Walsh code sequences in the traditional multilevel frequency-modulation different chaos shift keying (FM-DCSK), in this paper, we propose a new multilevel FM-DCSK, named multilevel M-ary FM-DCSK (MFM-DCSK). The MFM-DCSK transmits multilevel symbols directly in the communication channel without using orthogonality Walsh code sequence. Ba...\",\n \"Title: Sensitivity-Compensated Micro-Pressure Flexible Sensor for Aerospace Vehicle.\\nYear: 2019\\n\\nAbstract: When flight vehicles (e.g., aerospace vehicles, Low Earth Orbit (LEO) satellites, near-space aircrafts, Unmanned Aerial Vehicles (UAVs) and drones) fly at high speed, their surfaces suffer the micro-pressure from high-altitude thin air. The long-term effect of this pressure causes the surface components of flight vehicle to deform or fall off, which can lead to a serious accident. To solve this problem, this paper proposes a sensitivity-compensated micro-pressure flexible sensor based on hyper-elastic plastic material and plate parallel capacitance. The sensor is able to measure a range of 0-6 kPa micro-pressure suffered by the flight vehicle's surface with high sensitivity and flexible devices. In this paper, we propose the principle, structure design and fabrication of the sensitivity-compensated micro-pressure flexible sensor. We carried out experiments to obtain the static characteristic curve between micro-pressure and the output capacitance of the sensor devices, and investigated the relationship between sensitivity and geometric parameters. We also compared the performance of the flexible sensor before and after sensitivity compensation. The result shows that the sensor can measure a range of 0-2 kPa and 2-6 kPa with a sensitivity of 0.27 kPa(-1) and 0.021 kPa(-1), which are 80% and 141.38% higher than the sensor before compensation; a linearity of 1.39% and 2.88%, which are 51.7% and 13.1% higher than the sensor before compensation; and a hysteresis and repeatability of 4.95% and 2.38%, respectively. The sensor has potential applications in flight vehicles to measure the micro-pressure with high sensitivity and flexibility.\",\n \"Title: Controller Synthesis For Switched Positive Linear Systems By Output Feedback\\nYear: 2018\\n\\nAbstract: The output-feedback controller synthesis issue for a class of continuous-time switched linear systems via average dwell time switching with constrained controls and output is addressed. First, we focus on the output-feedback stabilisation by decomposing control gain matrix. Second, the stabilisation problem with sign-restricted controls while imposing positivity and stability on closed-loop system is solved, then the results extend to asymmetrically bounded controls and constrained outputs. All the derived conditions are formulated as linear programming. Finally, numerical examples are given to show the applicability of the proposed methods.\",\n \"Title: Energy-efficient power allocation for massive M2M communication over LTE-A cellular uplink\\nYear: 2018\\n\\nAbstract: In order to enable massive machine-to-machine (M2M) communication in Long Term Evolution-Advanced (LTE-A) cellular network, a machine-type communication gateway (MTCG) is introduced to solve the issue of control signaling overheads. In this paper, a power allocation algorithm based on the method of Lagrange multipliers is proposed for massive M2M communication over LTE-A cellular uplink. The proposed algorithm can decrease the signaling overheads by enabling the machine-type communication device (MTCD) to access the base station (BS) via a MTCG that collects the data from a group of M2M devices and forward it to the BS. The core objective of the proposed algorithm is to maximize the total energy efficiency of a group of M2M devices, while satisfying the time delay of the M2M devices, by coordinating the power transmission of the MTCDs and the MTCG. The simulation results demonstrate that the proposed algorithm outperforms the heuristic algorithm and has relatively close performance to the optimal design.\",\n \"Title: A novel approach for missing data prediction in coevolving time series\\nYear: 2019\\n\\nAbstract: Although various innovative sensing technologies have been widely employed, data missing in collections of time series occurs frequently, which turns out to be a major menace to precise data analysis. However, many existing missing data prediction approaches either might be infeasible or could be inefficient to predict missing data from multiple time series. To solve this problem, we proposed a novel approach based on the compressive sensing theory and sparse Bayesian learning theory for missing data prediction in coevolving time series. First, we model the problem by designing the corresponding sparse representation basis and measurement matrix. Then, the missing data prediction problem is formulated as the multiple sparse vectors recovery problem. Many simultaneous sparse estimation approaches focus on joint estimation of multiple sparse vectors with a common support from given linear observations, which is however too strict in some real applications. In this paper, largely utilizing the interior patterns of coevolving time series, we design a tuning parameter-free algorithm based on the sparse Bayesian learning, which can simultaneously solve multiple sparse estimation takes without the requirement of auxiliary information. Simulation results demonstrate that our approach can recover the entire time series efficiently using only those data that are not missing, even if, a high ratio of collected data are missing.\",\n \"Title: Demonstration of Dual-Channel TOPS SAR Imaging With Airborne C-Band Data.\\nYear: 2017\\n\\nAbstract: Multichannel in azimuth synthetic aperture radar (SAR) operating in the terrain observation by progressive scans (TOPS) acquisition mode has attracted much attention recently for its capability to achieve ultrawide-swath imaging with a high spatial resolution. In order to verify the feasibility and operability of this newly developed remote sensing concept, a C-band airborne azimuth dual-channel T...\",\n \"Title: Aggregating Multidimensional Wireless Link Information For Device-Free Localization\\nYear: 2018\\n\\nAbstract: Device-free localization (DFL) is an emerging and promising technique, which can realize target localization without the requirement of attaching any wireless devices to targets. By analyzing the shadowing loss caused by targets on wireless links, we can estimate the target locations. However, for existing DFL approaches, a large number of wireless links is required to guarantee a certain localization precision, which may lead to high hardware cost. In this paper, we propose a novel multi-target device-free localization method with multidimensional wireless link information (MDMI). Unlike previous works that measure RSS only on a single transmission power level, MDMI collects RSS measurements from multiple transmission power levels to enrich the measurement information. Furthermore, the compressive sensing (CS) theory is applied by exploiting the inherent spatial sparsity of DFL. We model the DFL problem as a joint sparse recovery problem and adopt the multiple sparse Bayesian learning (M-SBL) algorithm to reconstruct the sparse vectors of different transmission power levels. Numerical simulation results demonstrate the outstanding performance of the proposed method.\",\n \"Title: Securing Communication Data in Pervasive Social Networking Based on Trust with KP-ABE.\\nYear: 2019\\n\\nAbstract: Pervasive Social Networking (PSN) intends to support instant social activities in a pervasive way at any time and anywhere. In order to protect crucial social activities, ensure communication dependability, and enhance user privacy, securing pervasive social communications becomes especially important. However, neither centralized nor distributed solutions can protect PSN communications as expected. How to automatically control data access in a trustworthy and efficient way is an important security issue. In this article, we propose a scheme to guarantee communication data security in PSN based on two dimensions of trust in a flexible manner on the basis of Key-Policy Attribute-Based Encryption (KP-ABE). Its advantages and performance are justified and evaluated through extensive analysis on security, computation complexity, communication cost, scalability, and flexibility, as well as scheme implementation. In addition, we develop a demo system based on Android mobile devices to test our scheme in practice. The results demonstrate its efficiency and effectiveness. Comparison with our previous work based on CP-ABE (Yan and Wang 2017) further shows its feasibility to be applied to PSN.\",\n \"Title: AET-IAA: A Novel Approach to Enhance Azimuth Resolution for ScanSAR Interferometry.\\nYear: 2017\\n\\nAbstract: Scanning synthetic aperture radar (ScanSAR) obtains wide swath images at the expense of azimuth resolution by sweeping the antenna beam periodically. Interferometric applications benefit from the wide swath of ScanSAR data. As a result, ScanSAR interferometry can efficiently acquire large scale geophysical information. However, the low azimuth resolution of ScanSAR images limits some applications ...\",\n \"Title: Force Point Transfer Method to Solve the Structure of Soft Exoskeleton Robot Deformation due to the Driving Force\\nYear: 2018\\n\\nAbstract: The structure of the human body is the result of biological evolution, which can support us to complete the action demand of daily life work well. But for high strength manual workers, soldiers and disabled patients with motor dysfunction, they need use external auxiliary devices to assist them in completing their actions. The exoskeleton robot is the product of man-machine fusion, which can provide wearer with an auxiliary power. The soft exoskeleton robot has the characteristics of adaptability and flexibility in movement, which is flourish. But the soft exoskeleton has the disadvantage of easy deformation of the soft material, which will affect the motion effect and the control precision. In this paper, the force position point transfer method is proposed to solve the easy deformation of the tensile force of the soft materials. The pulling force is transferred from the soft material to the hard backboard, which can solve the problem of the deformation of the soft materials. It is of great significance to improve the force effect and the precision of the robot position control.\",\n \"Title: Fault-Tolerant and Self-Adaptive Market-Based Coordination Using Hoplites Framework for Multi-Robot Patrolling Tasks\\nYear: 2018\\n\\nAbstract: An autonomous robot team can be employed for continuous coverage of a dynamic environment. In this paper, we propose a novel approach for creating multi-robot patrolling policies, which is fault-tolerant and self-adaptive. A dynamic priority queue and time-out replanning mechanism are maintained by each robot to schedule the tasks fault-tolerantly in the context of the market-based method. Hoplites framework is adapted by introducing a self-adaptive threshold adjustment and sharing mechanism to provide a high-level coordination. This work is demonstrated by a multi-robot patrolling task implemented on Robot Operating System (ROS). A flexible tool, Stage, is leveraged to provide the simulated environment. The experimental results validate the effectiveness and availability.\",\n \"Title: Real-time indoor scene reconstruction with Manhattan assumption.\\nYear: 2019\\n\\nAbstract: This paper presents a novel end-to-end system for real-time indoor scene reconstruction, which outperforms traditional image feature point-based method and dense geometry correspondence-based method in handling indoor scenes with less texture and geometry features. In our method, we fully explore the Manhattan assumption, i.e. scenes are majorly consisted with planar surfaces with orthogonal normal directions. Given an input depth frame, we first extract dominant axes coordinates via principle component analysis which involves the orthogonal prior and reduce the influence of noise. Then we calculate the coordinates of dominant planes (such as walls, floor and ceiling) in the coordinates using mean shift. Finally, we compute the camera orientation and reconstruct the scene by proposing a fast scheme based on matching the dominant axes and planes to the previous frame. We have tested our approach on several datasets and demonstrated that it outperforms some well known existing methods in these experiments. The performance of our method is also able to meet the requirement of real-time with an unoptimized CPU implementation.\",\n \"Title: A Novel Adaptive Kalman Filter With Colored Measurement Noise.\\nYear: 2018\\n\\nAbstract: In this paper, a novel variational Bayesian-based adaptive Kalman filter (VBAKF) is proposed to solve the problem of a linear state-space model with colored measurement noise and inaccurate noise covariance matrices. The filter problem of a linear state-space model with colored measurement noise and inaccurate noise covariance matrices is transformed into the filtering problem of a linear state-space model with white measurement noise and inaccurate noise covariance matrices using measurement differencing method and state augmentation approach. The augmentation state vector, corresponding predicted error covariance matrix and covariance matrix of white measurement noise are jointly estimated based on the variational Bayesian approach. The ability of addressing the colored measurement and inaccurate noise covariance matrices is demonstrated in the simulation of a target tracking example.\",\n \"Title: A statistic approach for power analysis of integrated GPU.\\nYear: 2019\\n\\nAbstract: As datasets grow, high performance computing has gradually become an important tool for artificial intelligence, particularly due to the powerful and efficient parallel computing provided by GPUs. However, it has been a general concern that the rising performance of GPUs usually consumes high power. In this work, we investigate the study of evaluating the power consumption of AMD’s integrated GPU (iGPU). Particularly, by adopting the linear regression method on the collecting data of performance counters, we model the power of iGPU using real hardware measurements. Unfortunately, the profiling tool CodeXL cannot be straightforwardly used for sampling power data and as a countermeasure we propose a mechanism called kernel extension to enable the system data sampling for model evaluation. Experimental results indicate that the median absolute error of our model is less than 3%. Furthermore, we simplify our statistical model for lower latency without significantly reducing the accuracy and stability.\",\n \"Title: Radial Basis Function Kernel Parameter Optimization Algorithm in Support Vector Machine Based on Segmented Dichotomy\\nYear: 2018\\n\\nAbstract: By analyzing the influences of kernel parameter and penalty factor for generalization performance on Support Vector Machine (SVM), a novel parameter optimization algorithm based on segmented dichotomy is proposed for Radial Basis Function (RBF) kernel. Combine with Segmented Dichotomy(SD) and Gird Searching(GS) method, a composite parameter selection, SD-GS algorithm, is structured for rapid optimization of kernel parameter and penalty factor. UCI Machine Learning database is used to test our proposed method. Experimental results have shown that performance on parameter selection is better than traversal exponential grid searching. Thus, the optimized parameter combination of SD-GS algorithm enables RBF kernel in SVM to have higher generalization performance.\",\n \"Title: Vehicle Detection Under UAV Based on Optimal Dense YOLO Method\\nYear: 2018\\n\\nAbstract: In this paper, a deep neural network model based on small target detection under UAV platform is designed. Due to the One-stage detection model like YOLO having novel structure and great industrial application potential, this paper proposes a new model of detection based on YOLOv2 structure. Faced with missed detection problem of small target, a series of improved schemes are proposed, which are suitable for small vehicles' detection under aerial view angle, and can achieve real-time detection, including dense topology and optimal pooling strategy.\",\n \"Title: Human Detection Under UAV: an Improved Faster R-CNN Approach\\nYear: 2018\\n\\nAbstract: Although Faster R-CNN has excellent performance in object detection, it still has some difficulties in detecting small targets and slightly overlapped targets in UAV (Unmanned Aerial Vehicle) images. Based on Faster R-CNN, this paper uses ResNet101 as a feature extractor. We increase the number of anchors from 9 to 15 in RPN so that the small targets can match more anchors and get sufficient training. Due to the increasement of anchors, this paper introduces a 1×1 convolution layer to integrate features and reduce the feature map channels. We also apply RoIAlign to avoid the misalignment caused by RoIPool. The improved model effectively increases the detection rate of small targets and slightly overlapped targets so that it can be applied to human detection under UAV. The improved model can detect small targets with a size of about 30×80 pixels on aerial images with resolution of 3840×2160 pixels. Compared with Faster R-CNN, the improved model increases AP (Average Precision) from 74.31% to 79.77% on the WILDTRACK dataset.\",\n \"Title: Storage Sharing Optimization Under Constraints of SLO Compliance and Performance Variability.\\nYear: 2019\\n\\nAbstract: SLO enforcement with the required strong SLO compliance and the desired low level of performance variability is necessary to ensure QoS for user applications with precisely differentiated service levels. However, for the cloud consolidating a large number of VMs rented by users, it is a great challenge to formulate an IO capacity allocation among consolidated VMs under the user-customized QoS constraints of SLO compliance and performance fluctuation for consolidated VMs. To address this challenge, we propose SASLO, an end-to-end VM-oriented control framework that supports users in customizing SLO targets and QoS constraints for each VM. SASLO can dynamically coordinate the throughput target and IO size limit for each VM adapting to the status of SLO enforcement so as to maximize the IO capacity allocation among consolidated VMs under QoS constraints. To accurately enforce time-varying throughput target, SASLO establishes a proportional-integral IO controller for each individual VM to converge the actual throughput to the target with an expected settling time. Our extensive evaluation driven by representative benchmarks demonstrates that SASLO is able to formulate a satisfactory IO capacity allocation plan for consolidated VMs under the constraints of SLO compliance and performance variability.\",\n \"Title: Age-invariant face verification based on Local Classifier Ensemble\\nYear: 2012\\n\\nAbstract: Human faces undergo considerable amount of variations across ages. This paper proposes an age-invariant face verification method by using a Local Classifier Ensemble Model (LCEM). First, reference points are located based on an extended Active Shape Model and faces are aligned afterwards. Second, a face is grouped into several non-overlapping patches and each group is further divided into several overlapping sub-patches. Local classifiers are then trained for each sub-patch and integrated to build an ensemble classifier under a Semi-Naive Bayesian framework. Finally, the proposed approach is evaluated on the MORPH database and experimental results show that such local classifier ensemble achieves significant and robust performance.\",\n \"Title: Security uniform office format specification and API design based on the java platform\\nYear: 2012\\n\\nAbstract: \\\"Uniform Office Format\\\" (UOF) is the Chinese national standard for office document format. It aims to solve the problem that the office document format is not unified. It is helpful for information sharing and document exchange. Moreover, it improves the Chinese office software's compatibility, and establishes the foundation for the document information exchange. Through the study of Specifications for the Syntax and Processing of UOF and XML Encryption/ Signature, we proposed a set of APIs which ensure the confidentiality, integrity, and authentication of the UOF documents. The API is implemented by Java Cryptography Architecture, Java Cryptography Extension, Dom4j, and XPath technologies on Java platform and is confirmed to XML Digital Signature APIs (JSR105) and XML Digital Encryption APIs (JSR106) Specifications. Every interface and class is designed according to the rules that should keep high cohesion inside module and low coupling between modules. It also implements a special set of APIs for security UOF documents. Furthermore, a set of test cases together with a GUI program are developed to verify the availability and correctness of the system. It is believed that this project will contribute a lot to the research and adoption of the Specification for the Syntax and Processing of UOF Encryption/Signature.\",\n \"Title: Design of observer-based event-driven controllers for a class of state-dependent nonlinear systems.\\nYear: 2016\\n\\nAbstract: The design problems of the observer-based event-driven controllers are investigated for the state-dependent nonlinear systems in this paper. An event-driven criterion is proposed to determine whether the newly sampled states of the designed state-dependent observer should be sent out to the controller. As a result, the communication resources can be saved significantly while the burden of the network communication can be reduced. Influenced by the event-driven controller, the closed-loop system is rewritten as the delayed system. The state-dependent integral function is introduced to be the Lyapunov function candidate to obtain less conservative asymptotic stability conditions and preserve the desired H∞ performance for the closed-loop system. The observer gain matrix, the controller gain matrix and the event-driven parameters are co-designed and co-obtained in terms of solution to a set of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed method in this paper is illustrated by the numerical examples and the tunnel diode circuit systems.\",\n \"Title: Type-2 T-S fuzzy modeling for the dynamic systems with measurement noise.\\nYear: 2008\\n\\nAbstract: In actual industrial processes,the measurement data always contain noise.Therefore,this situation will affect the accuracy of modeling.Compared with the type-1 fuzzy sets,the membership functions in type-2 fuzzy sets include the primary membership function and the secondary membership function.They provide the additional degrees of freedom that make it possible to model uncertainties brought by the noises.In this paper,a type-2 T-S fuzzy model is presented to minimize the effect of measurement noises.Furthermore,the influence of the initial conditions is considered in the algorithm.The primary membership function is gained through an improved nearest-neighborhood clustering algorithm,and the secondary membership function is determined through GMM based on the sufficient statistics.The orthogonal least-squared algorithm is used to identify the consequent of the fuzzy rules.Finally,the simulation results are compared with those obtained from a type-1 T-S fuzzy modeling results and the superiority of the proposed approach is highlighted.\",\n \"Title: Reference Samples Padding for Intra-Frame Coding of Omnidirectional Video.\\nYear: 2018\\n\\nAbstract: Due to lack of video coding standard tailored specially to spherical or omnidirectional videos, which are important in Virtual Reality applications, omnidirectional video frames have to be projected onto the 2-Dimension(2D) plane, using the EquiRectangular Projection(ERP) for example, to utilize existing video compression techniques. As there is no information available for prediction outside the left or right border after projection, unexpected artefacts that are generated at the right border of frames degrade the subjective visual quality. Utilizing continuity of the content in the omnidirectional video based on ERP, in this work, an efficient intra-coding method is proposed based on adaptively padding the reference samples at the right border for omnidirectional videos. Using the state of art video encoder, the proposed method can reduce the BD-rate by 0.24% in average for the coding tree units (CTUs) at the right border.\",\n \"Title: An Effective Tap-Length NLMS Algorithm for Network Echo Cancellers.\\nYear: 2017\\n\\nAbstract: A novel normalized least mean square (NLMS)-based algorithm is proposed to locate the active taps of the unknown impulse response in the application of network echo cancellation. Starting with the full length of the unknown echo path, the start-tap and end-tap estimates of the active region adapt, respectively, in the form of fractional tap. The effective tap-length estimate is defined by the length between the start-tap and end-tap estimates. The filter tap-weight vector is then updated with this effective tap-length estimate following the NLMS algorithm, while the tap weights located beyond the effective tap-length estimate are directly set to zero. In case that the active region of the unknown echo path shifts in a large scale, the start-tap and end-tap estimates are reset to the full length when the effective tap-length estimate is lower than a predetermined threshold value. Simulation results demonstrate that the proposed fractional tap-based algorithm can efficiently locate and track the effective tap length of the unknown echo path when the active coefficients change abruptly in locations and magnitudes, outperforming the existing stochastic tap-based schemes.\",\n \"Title: Mass-Driven Topology-Aware Curve Skeleton Extraction from Incomplete Point Clouds\\nYear: 2020\\n\\nAbstract: We introduce a mass-driven curve skeleton as a curve skeleton representation for 3D point cloud data. The mass-driven curve skeleton presents geometric properties and mass distribution of a curve skeleton simultaneously. The computation of the mass-driven curve skeleton is formulated as a minimization of Wasserstein distance, with an entropic regularization term, between mass distributions of point clouds and curve skeletons. Assuming that the mass of one sampling point should be transported to a line-like structure, a topology-aware rough curve skeleton is extracted via the optimal transport plan. A Dirichlet energy regularization term is then used to obtain a smooth curve skeleton via geometric optimization. Given that rough curve skeleton extraction does not depend on complete point clouds, our algorithm can be directly applied to curve skeleton extraction from incomplete point clouds. We demonstrate that a mass-driven curve skeleton can be directly applied to an unoriented raw point scan with significant noise, outliers and large areas of missing data. In comparison with state-of-the-art methods on curve skeleton extraction, the performance of the proposed mass-driven curve skeleton is more robust in terms of extracting a correct topology.\",\n \"Title: Hybrid consensus sigma point approximation nonlinear filter using statistical linearization\\nYear: 2018\\n\\nAbstract: In this paper, a hybrid consensus sigma point approximation nonlinear filter is proposed for state estimation in collaborative sensor network, where hybrid consensus of both measurement and information is utilised. Statistical linearization of nonlinear functions is used in sigma point filters, that is, unscented Kalman filter (UKF), cubature Kalman filter (CKF), and central difference Kalman filter (CDKF). Stability of the proposed algorithm is also analysed with the help of linearization operation and some conservative assumptions. Two typical target tracking examples are used to demonstrate the effectiveness of the proposed algorithms. Simulation results show that the proposed algorithms are more stable than existing algorithms, and among our proposed algorithms, CKF- and CDKF-based algorithms are more accurate and stable than the UKF-based one.\",\n \"Title: Graph cut based automatic aorta segmentation with an adaptive smoothness constraint in 3D abdominal CT images.\\nYear: 2018\\n\\nAbstract: Aorta segmentation is clinically important as it is a necessary step towards accurate assessments of some aorta disease. In the paper, we present a graph cut based method for automated aorta segmentation. In our method, a discriminative integrated feature (DIF) and a novel adaptive smoothness constraint are designed. DIF consists of low level features and other discriminative features including eigenvalues of Hessian and local self-similarity descriptor. DIF and random forests (RFs) are used to generate the probability maps. The probability maps containing learning information from RFs are more accurate than traditional probability maps generated based on intensities directly. The negative logarithm of the probability maps serves as the penalty term in a cost function. Additionally, a novel adaptive smoothness constraint is imposed to ensure a smooth solution. The adaptive smoothness term is constructed by DIFs and data-driven weights. Two kinds of data-driven weights are developed based on the idea that the discontinuity of two neighboring voxels with different labels should be distinct with two neighboring voxels with the same label. The final segmentation is obtained by optimizing the cost function using graph cuts. We evaluate the proposed method through challenging task of abdominal aorta segmentation in 3D CT images. With average dice metric (DM) > 0.9690 on the test set, our experimental results demonstrate that our method achieves higher aorta segmentation accuracy than existing methods.\",\n \"Title: Distributed Power Control for Interference-Aware Multi-User Mobile Edge Computing: A Game Theory Approach\\nYear: 2018\\n\\nAbstract: The computation task offloading and resource management in mobile edge computing (MEC) become attractive in recent years. Many algorithms have been proposed to improve the performance of the MEC system. However, the research on power control in MEC systems is just starting. The power control in the single-user and an interference-free multi-user MEC systems has been investigated; but in the interference-aware multi-user MEC systems, this issue has not been learned in detail. Therefore, a game theory-based power control approach for the interference-aware multi-user MEC system is proposed in this paper. In this algorithm, both the interference and the multi-user scenario are considered. Moreover, the existence and uniqueness of the Nash Equilibrium (NE) of this game are proved, and the performance of this algorithm is evaluated via theoretical analysis and numerical simulation. The convergence, the computation complexity and the price of anarchy in terms of the system-wide computation overhead are investigated in detail. The performance of this algorithm has been compared with the traditional localized optimal algorithm by simulation. The simulation results demonstrate that the proposed algorithm has more advantages than the traditional one.\",\n \"Title: Robust Diffusion Estimation Algorithm Via Genearlized Cauchy Loss Function Over Distributed Network\\nYear: 2017\\n\\nAbstract: The cost function used in distributed estimation methods is in general the minimum mean square error (MMSE) criterion which can achieve optimal performance under Gaussian assumption. While the performance will degrade severely when they suffer from the non-Gaussian situations, such as the impulsive-noise case. In this paper, we extend the outstanding Cauchy Loss (CL) to generalized CL (GCL) which can achieve a better performance than MMSE methods since it takes into account unique distribution characteristics and robustness to outliers. In addition, we use the GCL to develop a novel robust diffusion estimation method over distributed network in impulse noise environments. Simulation results show that the proposed diffusion GCL algorithm outperforms other outstanding robust diffusion adaptive filtering algorithms in impulsive noise environments.\",\n \"Title: Efficient OLAP algorithms on GPU-accelerated Hadoop clusters\\nYear: 2019\\n\\nAbstract: In the time of big data, on-line analytical processing (OLAP) is an important method to process massive data. In order to realize a system with the capacity of both high storage and high computing power, Hadoop and GPU are both applied in OLAP. In general, three cores of OLAP determines the efficiency of OLAP analysis, which are aggregation of multi-dimensional data, pre-calculation of multi-dimensional data set (Cube) and connection of dimension table and fact table. For the purpose of boosting efficiency, this paper presents optimizing algorithms for each core. Beginning with aggregation on single machine, this paper firstly designs the GPU-based aggregation algorithm. Then, GPU-based Cube algorithm is introduced to accelerate pre-calculation, using inverted index to shrink computation amount. Finally, with new-designed dimension table connecting algorithm and query algorithm, GPU-based OLAP analysis algorithm is presented. Along with corresponding experiments and results, each algorithm shows their ability of boosting efficiency, optimizing GPU-based OLAP analysis on Hadoop.\",\n \"Title: Time-Frequency Squeezing and Generalized Demodulation Combined for Variable Speed Bearing Fault Diagnosis\\nYear: 2019\\n\\nAbstract: High-resolution time-frequency representation (TFR) method is effective for signal analysis and feature detection. However, for variable speed bearing vibration signal, conventional TFR method is prone to blur and affect the accuracy of the instantaneous frequency estimation. Moreover, the traditional order tracking, relying on equi-angular resampling, usually suffers from interpolation error. To solve such problems, we propose a joint time-frequency (TF) squeezing method and generalized demodulation (GD) to realize variable speed bearing fault diagnosis. The method can represent the time-varying fault characteristic frequency precisely and be free from resampling. First, using fast spectral kurtosis to select the optimal-frequency band which is sensitive to rolling bearing fault, and extracting envelope by Hilbert transform within the selected optimal frequency band. Next, a high-quality TF clustering method based on short-time Fourier transform is applied to the TF analysis of the envelope to get a clear TFR, from which the frequency information for GD is obtained. Finally, processing the basic demodulator via the peak search through the TF analysis results in the TFR for GD to gain a resampling-free-order spectrum. Based on the more precise TF information from the clearer TFR, the bearing fault can be diagnosed via GD without tachometer or any resampling involved, avoiding the amplitude error and low computational efficiency of resampling. Simulation study and experimental signal analysis validate that the proposed method has better performance than those methods based on conventional TF analysis and resampling.\",\n \"Title: Energy Efficiency Optimization for D2D Communication Underlaying Distributed Antenna System\\nYear: 2019\\n\\nAbstract: In this paper, the energy efficiency (EE) performance of a downlink distributed antenna system with device-to-device (D2D) underlay communication is investigated over a composite Nakagami fading channel. The optimal power allocation (PA) schemes are developed for improving the EE performance. Under the minimum rate constraint of cellular user (CU), the maximum power constraints of CU and D2D pair, an optimization problem for maximizing the system EE is formulated. The Karush–Kuhn–Tucker conditions are used to solve the optimization problem. Based on the 1-D search method and the bisection method, two schemes and corresponding algorithms are, respectively, proposed to obtain the optimal solutions of PA. For a given transmit power of D2D pair, the closed-form PA of remote antennas (RAs) and the number of active RAs are derived, which greatly simplifies the optimization. It is shown that the proposed two schemes can obtain the same EE performance, and the scheme with the bisection method has lower complexity than that with the 1-D search method. Computer simulation verifies the effectiveness of these two schemes, and superior EE performance can be achieved.\",\n \"Title: Incorporation Of Faulty Prior Knowledge In Multi-Target Device-Free Localization\\nYear: 2019\\n\\nAbstract: As an emerging and promising technique, device-free localization (DFL) has drawn considerable attention in recent years. By exploiting the inherent spatial sparsity of target localization, the compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements. In practical scenarios, a prior knowledge about target locations is usually available, which can be obtained by coarse localization or tracking techniques. Among existing CS-based DFL approaches, however, few works consider the utilization of prior knowledge. To make use of the prior knowledge that is partly or erroneous, this paper proposes a novel faulty prior knowledge aided multi-target device-free localization (FPK-DFL) method. It first incorporates the faulty prior knowledge into a three-layer hierarchical prior model. Then, it estimates location vector and learns model parameters under a variational Bayesian inference (VBI) framework. Simulation results show that the proposed method can improve the localization accuracy by taking advantage of the faulty prior knowledge.\",\n \"Title: DF -FS based path planning algorithm with sparse waypoints in unstructured terrain.\\nYear: 2018\\n\\nAbstract: For autonomous navigation in unstructured terrain, itu0027s difficult to plan the path with traditional algorithms based on searching guidance waypoints such as ARA* and Field D* as a result of the poor positioning signal and the environmental uncertainty especially when dense waypoints are not given. Because of irregular road boundaries, there are also problems for path planning based road detection methods. To solve these problems, the depth first forward search (DF-FS) based path planning algorithm which combines forward searching along the road and model-based prediction is proposed. DF-FS based planner takes road boundary constraint, road direction and heading of vehicle as the basis of path planning, while using model prediction optimizes planned path to improve navigation stability. The experiment results show that in unstructured terrain with sparse waypoints or even without waypoints in the one-way road, the autonomous navigation can be guaranteed effectively. Meanwhile the proposed algorithm has a strong robustness to the uncertainty of positioning and environmental perception, so it is suitable for autonomous navigation in rough terrain.\",\n \"Title: The research about radiometric technology of two-dimensional rotary table based on image gray level.\\nYear: 2019\\n\\nAbstract: It is difficult to measure infrared radiation quantitatively for fast dynamic targets such as aircraft and missile. At present, there are many problems such as low measurement accuracy, feedback delay, and complex measurement system. In this paper, a radiation measurement scheme based on image grayscale is proposed. The scheme adopts a two-dimensional turntable structure, which can measure the radiation quantity rapidly and accurately. Based on the image method, this method is used to calibrate the gray value of the target point and compare it with that of the absolute standard radiator, so as to obtain the radiation value of the target indirectly. The key of this method is to establish an accurate radiation measurement model. The proposed model based on dynamic calibration is about 50% more accurate than the traditional model. The final measurement accuracy of this model is 75%. Through the infrared radiation measurement of specific dynamic objects in two-dimensional space, it is found that the dynamic calibration model based on this measurement method has higher measurement precision.\",\n \"Title: DF-CSPG: A Potential Game Approach for Device-Free Localization Exploiting Joint Sparsity.\\nYear: 2019\\n\\nAbstract: Device-free localization (DFL) plays an increasingly dominant role in some security and military applications, aiming at locating targets without carrying any extra devices. Most research utilize received signal strength, causing poor accuracy due to insufficient information. In this letter, we propose DF-CSPG, a new DFL algorithm using compressive sensing (CS) theory and potential game (PG) appro...\",\n \"Title: Ensemble Time-Stepping Algorithm for the Convection-Diffusion Equation with Random Diffusivity.\\nYear: 2019\\n\\nAbstract: In this paper, we develop two ensemble time-stepping algorithms to solve the convection-diffusion equation with random diffusion coefficients, forcing terms and initial conditions based on the pseudo-spectral stochastic collocation method. The key step of the pseudo-spectral stochastic collocation method is to solve a number of deterministic problems derived from the original stochastic convection-diffusion equation. In general, a common way to solve the set of deterministic problems is by using the backward differentiation formula, which requires us to store the coefficient matrix and right-hand-side vector multiple times, and solve them one by one. However, the proposed algorithm only need to solve a single linear system with one shared coefficient matrix and multiple right-hand-side vectors, reducing both storage required and computational cost of the solution process. The stability and error analysis of the first- and second-order ensemble time-stepping algorithms are provided. Several numerical experiments are presented to confirm the theoretical analyses and verify the feasibility and effectiveness of the proposed method.\",\n \"Title: Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel Diversity.\\nYear: 2019\\n\\nAbstract: As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings are vulnerable to environmental dynamics. The deviation between runtime RSS variations and the data in a fixed dictionary can significantly deteriorate the performance of DFL. In this paper, we introduce ComDec, a novel CS-based DFL method using channel state information (CSI) to enhance localization accuracy and robustness. To exploit the channel diversity of CSI measurements, the DFL problem is formulated as a joint sparse recovery problem that recovers multiple sparse vectors with common support. To solve this problem, we develop a joint sparse recovery algorithm under the variational Bayesian inference framework. In this algorithm, dictionaries are parameterized based on the saddle surface model. To adapt to the environmental changes and different channel characteristics, dictionary parameters are modelled as tunable parameters. Simulation results verified the superior performance of ComDec as compared with other state-of-the-art CS-based DFL methods.\",\n \"Title: A 60-GHz 3.0-Gb/s Spectrum Efficient BPOOK Transceiver for Low-Power Short-Range Wireless in 65-nm CMOS\\nYear: 2019\\n\\nAbstract: This paper presents a 60-GHz transceiver for low-power high-speed short-range wireless using the proposed binary-phase on-off keying (BPOOK) modulation scheme. The proposed BPOOK wireless transceiver transmits radio frequency (RF) signal with amplitude modulated on and off by input baseband data, and meanwhile, the phase is changing between 0° and 180°. The BPOOK transceiver achieves doubled spectral efficiency compared with OOK modulation and binary-phase-shift keying (BPSK) modulation. It also cancels the intrinsic local-oscillator feed through (LOFT) issue in the OOK modulation. The BPOOK RF signal can be demodulated by employing low-power square-law envelope detector incoherently. The transceiver is fabricated in a standard 65-nm CMOS technology. A data rate of 3.0 Gb/s is achieved while consuming a power of 100 mW from 1-V supply. The incoherent receiver has a sensitivity of −46 dBm. The core area of the transceiver is 1.56 mm\\n<sup xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">2</sup>\\n.\",\n \"Title: A 50.1-Gb/s 60-GHz CMOS Transceiver for IEEE 802.11ay With Calibration of LO Feedthrough and I/Q Imbalance\\nYear: 2019\\n\\nAbstract: This paper presents a 60-GHz CMOS transceiver targeting the IEEE 802.11ay standard. A calibration block for local oscillator feedthrough (LOFT) and I/Q imbalance featuring high accuracy and low power consumption is integrated with the transceiver. With the help of the proposed calibration, this paper is capable of boosting the data rate with higher order modulation scheme and wider channel-bonding bandwidth, which are demanded by IEEE 802.11ay. At the same time, it maintains the compatibility with the existing IEEE 802.11ad standard. This paper reports a two-channel-bonding data rate of 24.64 Gb/s in 128 quadrature amplitude modulation (QAM). The corresponding TX-to-RX error vector magnitude (EVM) is −26.1 dB. Furthermore, a four-channel-bonding data rate of 42.24 Gb/s in 64 QAM is realized with a single-element transceiver. The measured maximum data rate is 50.1 Gb/s in 64 QAM, which is the highest data rate achieved in the 60-GHz band. The power consumption is only 169 mW in the transmitting mode and 139 mW in the receiving mode.\",\n \"Title: ACT: an ACTNet for visual tracking.\\nYear: 2019\\n\\nAbstract: Owing to convolutional neural network (CNN) models' success in various fields of computer vision, the authors proposed an advanced convolutional network (ACTNet) to enhance the accuracy of visual tracking. Different from prior methods, they regard a CNN as not only a semantic feature map extractor but also a position predictor. Rectified Linear Unit (RLU) and sigmoid are both used in ACTNet for fe...\",\n \"Title: Passivity Analysis for Quaternion-Valued Memristor-Based Neural Networks With Time-Varying Delay.\\nYear: 2020\\n\\nAbstract: This paper is concerned with the problem of global exponential passivity for quaternion-valued memristor-based neural networks (QVMNNs) with time-varying delay. The QVMNNs can be seen as a switched system due to the memristor parameters are switching according to the states of the network. This is the first time that the global exponential passivity of QVMNNs with time-varying delay is investigate...\",\n \"Title: Correlation of Gut Microbiome Between ASD Children and Mothers and Potential Biomarkers for Risk Assessment.\\nYear: 2019\\n\\nAbstract: Variation of maternal gut microbiota may increase the risk of autism spectrum disorders (ASDs) in offspring. Animal studies have indicated that maternal gut microbiota is related to neurodevelopmental abnormalities in mouse offspring, while it is unclear whether there is a correlation between gut microbiota of ASD children and their mothers. We examined the relationships between gut microbiome profiles of ASD children and those of their mothers, and evaluated the clinical discriminatory power of discovered bacterial biomarkers. Gut microbiome was profiled and evaluated by 16S ribosomal RNA gene sequencing in stool samples of 59 mother–child pairs of ASD children and 30 matched mother–child pairs of healthy children. Significant differences were observed in the gut microbiome composition between ASD and healthy children in our Chinese cohort. Several unique bacterial biomarkers, such as Alcaligenaceae and Acinetobacter, were identified. Mothers of ASD children had more Proteobacteria, Alphaproteobacteria, Moraxellaceae, and Acinetobacter than mothers of healthy children. There was a clear correlation between gut microbiome profiles of children and their mothers; however, children with ASD still had unique bacterial biomarkers, such as Alcaligenaceae, Enterobacteriaceae, and Clostridium. Candidate biomarkers discovered in this study had remarkable discriminatory power. The identified patterns of mother–child gut microbiome profiles may be important for assessing risks during the early stage and planning of personalized treatment and prevention of ASD via microbiota modulation.\",\n \"Title: How Do Your Neighbors Disclose Your Information: Social-Aware Time Series Imputation\\nYear: 2019\\n\\nAbstract: Different time series is measured in almost all fields including biology, economics and sociology. A common challenge for using such data is the imputation of the missing values with reasonable ones. Most of existing approaches to data imputation assume that individual's observations are independent to each other, which is rarely the case in real-world. In this paper, we study the social-aware time series imputation problem. Given a social network that represents social relations between individuals, we propose a sequential encoder-decoder-based framework and build a connection between the missing observations and the social context. In particular, the proposed model employs the attention mechanism to incorporate social context and temporal context into the imputation task. Experimental results based on two real-world datasets demonstrate that our approach outperforms 11 different baseline methods.\",\n \"Title: L-Seg: An end-to-end unified framework for multi-lesion segmentation of fundus images.\\nYear: 2019\\n\\nAbstract: Diabetic retinopathy and diabetic macular edema are the two leading causes for blindness in working-age people, and the quantitative and qualitative diagnosis of these two diseases usually depends on the presence and areas of lesions in fundus images. The main related lesions include soft exudates, hard exudates, microaneurysms, and haemorrhages. However, segmentation of these four kinds of lesions is difficult due to their uncertainty in size, contrast, and high interclass similarity. Therefore, we aim to design a multi-lesion segmentation model. We have designed the first small object segmentation network (L-Seg) that can segment the four kinds of lesions simultaneously. Taking into account that small lesion regions could not response at high level of network, we propose a multi-scale feature fusion method to handle this problem. In addition, when considering the cases of both class-imbalance and loss-imbalance problems, we propose a multi-channel bin loss. We have evaluated L-Seg on three fundus datasets including two publicly available datasets - IDRiD and e-ophtha and one private dataset - DDR. Extensive experiments have demonstrated that L-Seg achieves better performance in small lesion segmentation than other deep learning models and traditional methods. Specially, the mAUC score of L-Seg is over 16.8%, 1.51% and 3.11% higher than that of DeepLab v3+ on IDRiD, e-ophtha and DDR datasets, respectively. Moreover, our framework shows competitive performance compared with top-3 teams in IDRiD challenge. The source code of L-Seg is available at: https://github.com/guomugong/L-Seg.\",\n \"Title: DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale.\\nYear: 2019\\n\\nAbstract: Fine morphological reconstruction of individual neurons across the entire brain is essential for mapping brain circuits. Inference of presynaptic axonal boutons, as a key part of single-neuron fine reconstruction, is critical for interpreting the patterns of neural circuit wiring schemes. However, automated bouton identification remains challenging for current neuron reconstruction tools, as they focus mainly on neurite skeleton drawing and have difficulties accurately quantifying bouton morphology. Here, we developed an automated method for recognizing single-neuron axonal boutons in whole-brain fluorescence microscopy datasets. The method is based on deep convolutional neural networks and density-peak clustering. High-dimensional feature representations of bouton morphology can be learned adaptively through convolutional networks and used for bouton recognition and subtype classification. We demonstrate that the approach is effective for detecting single-neuron boutons at the brain-wide scale for both long-range pyramidal projection neurons and local interneurons.\",\n \"Title: Risk-Based Multi-Objective Optimization of Distributed Generation Based on GPSO-BFA Algorithm.\\nYear: 2019\\n\\nAbstract: With the rapid evolution of technology, growing accessibility, and environmental appeal of wind and solar electric systems, distributed generation (DG) has been pushed from the fringe to a mainstream factor in the grid. However, due to the randomness and uncertainty of environmental and operational conditions, DG also brings many risks and may adversely affect the reliability and safety of the power grid when connected to the distribution network. Therefore, it is necessary to introduce the risk theory into the allocation and placement of DGs. This paper establishes a comprehensive set of risk and economic indexes by modeling the randomness and uncertainty of DG outputs. In addition, islanded operation, which is a promising development direction of microgrids, is explicitly studied and the related indexes are modeled. Putting them together, we propose a risk-based multi-objective optimal allocation model to optimize the placement and configuration of DGs and provide a reliable and cost-effective system. We solve the formulated multi-objective optimization problem by combining the gradient particle swarm optimization algorithm and the bacterial foraging algorithm. We demonstrate the validity and rationality of the proposed method through the analyses of the American PG&E 69-node system.\",\n \"Title: A Coarse TF Ridge-Guided Multi-Band Feature Extraction Method for Bearing Fault Diagnosis Under Varying Speed Conditions.\\nYear: 2019\\n\\nAbstract: Currently, most of the vibration signal analysis methods for bearing fault diagnosis under varying speed conditions are based on the resampling technology with shaft rotational frequency (SRF). However, the SRF obtained by a fixed tachometer or time-frequency (TF) ridge detection reduces measuring flexibility or introduces errors inevitably. In this paper, a multi-band feature extraction method using coarse IF ridge-guided variational nonlinear chirp mode decomposition (VNCMD) is proposed for bearing fault diagnosis under varying speed conditions. Specifically, the proposed method is conducted as follows. First, the low-frequency component (LFC) and resonance component are extracted by the low-pass filtering and the fast kurtogram method, respectively, to alleviate the noise interference. Second, the coarse TF ridges are identified by a tractable ridge estimation method that is based on the TF representation for preliminary selection of the initial instantaneous frequency. Third, the coarse TF ridge-guided VNCMD is constructed to track the SRF and instantaneous fault characteristic frequency (IFCF) from the envelope signals of the LFC and the resonance component, respectively. Finally, the characteristic frequency ratio is computed on the basis of the values of SRF and IFCF to determine the fault type of ball bearing without resampling. The simulation studies and experimental verifications confirm that the proposed method can accurately locate bearing defect types and outperforms some existing methods.\",\n \"Title: Online health assessment of wind turbine based on operational condition recognition\\nYear: 2019\\n\\nAbstract: To reduce the operation and maintenance (O&M) costs, the health assessment of wind turbine has received more and more attention in recent years. However, it is difficult to evaluate the health condition of wind turbine due to the complex and non-stationary operation environment. This paper proposes a data-driven approach for online health assessment of wind turbine based on operational condition recognition. First, the operational condition parameters are selected by analyzing the monitoring data of wind turbine. Considering the time-varying of operation environment, the operational conditions are divided into four subspaces utilizing the K-means clustering algorithm. Then, using the historical state parameters data under normal operation, a health benchmark model is constructed in each operational condition space based on Gaussian Mixture Model (GMM). Further, a Softmax model is trained according to the results of operational condition classification, which is used to identify the online operational condition of wind turbine. Moreover, an overall health index (HI) based on Mahalanobis distance is developed to assess the health condition of wind turbine. Finally, the method is verified by the actual supervisory control and data acquisition (SCADA) data of a wind field in northwestern China. The test results show that the proposed approach can track the running state of the wind turbine accurately and play a good role in early fault warning.\",\n \"Title: A Modified Cartesian Factorized Back-Projection Algorithm for Highly Squint Spotlight Synthetic Aperture Radar Imaging\\nYear: 2019\\n\\nAbstract: Highly squint synthetic aperture radar (SAR) increases the flexibility and aspect-information of observation but poses several challenges to frequency-domain algorithms. The back-projection (BP) algorithm is recognized to produce the best results for highly squint mode but at high computational expense. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. Th...\",\n \"Title: Adaptive compressed sensing of color images based on salient region detection\\nYear: 2020\\n\\nAbstract: We propose a novel algorithm for color image compressed sensing (CS). Our method involves the adaptive measurement and reconstruction of color images based on visual saliency detection. First, we divide the image into blocks and transform the RGB channel into the YUV channel. Secondly, we use statistical texture distinctiveness to calculate the saliency of each block and normalize energy, thereby establishing an adaptive measurement rate and measurement matrix. Thirdly, we adaptively measure the Y channel according to block prominence and preserve the information of the UV channel. During reconstruction, we utilize adaptive block measurement rate to re-estimate block saliency and then reconstruct the objective function of the weighted reconstruction model according to the re-estimated block saliency. Finally, we combine the reconstructed Y channel with the reserved UV channel to obtain the final image. Experimental results show that compared with other state-of-the-art approaches, the proposed algorithm can not only provide good subjective visual quality but can also present higher peak signal to noise ratio (PSNR) under the same sampling rate.\",\n \"Title: Co-Saliency Detection Via Local Prediction And Global Refinement\\nYear: 2019\\n\\nAbstract: This paper presents a novel model in the field of image co-saliency detection. Previous works simply design low level handcrafted features or extract deep features based on image patches for co-saliency calculation, which neglect the entire object perception properties. Besides, they also neglect the problem of visual similar region's mismatching when designing co-saliency calculation model. To solve these problems, we propose a novel strategy by considering both local prediction and global refinement (LPGR). In the local prediction stage, we train a deep convolutional saliency detection network in an end-to-end manner which only use the fully convolutional layers for saliency map prediction to capture the entire object perception properties and reduce feature redundancy. In the global refinement stage, we construct a unified co-saliency refinement model by integrating global appearance similarity into a co-saliency diffusion function, realizing the propagation and optimization of local saliency values in the context of entire image group. To overcome the adverse effects of visual similar regions' mismatching, we innovatively incorporates the inter-images saliency spread constraint (ISC) term into our co-saliency calculation function. Experimental results on public datasets demonstrate consistent performance gains of the proposed model over the state-of-the-art methods.\",\n \"Title: A New Robust Kalman Filter for SINS/DVL Integrated Navigation System\\nYear: 2019\\n\\nAbstract: A new robust strap-down inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation algorithm are proposed in this paper with a focus on suppressing the process uncertainty and measurement outliers induced by severe manoeuvering. In the proposed algorithm, the one-step predicted probability density function is modeled as Student's t-distribution to deal with the heavy-tailed process noise, and hierarchical Gaussian state-space model for SINS/DVL integrated navigation algorithm is constructed. To detect and eliminate the measurement outliers, each measurement is marked by a binary indicator variable modeled as a beta-Bernoulli distribution. The variational Bayesian approach is used to jointly estimate state vector, auxiliary random variable, scale matrix, Bernoulli variable, and beta variable. The experimental results illustrate that the proposed algorithm has better robustness and navigation accuracy to deal with process uncertainty and measurement outliers than existing state-of-the-art algorithms.\",\n \"Title: Block Variational Bayesian Algorithm For Multiple Target Localization With Unknown And Time-Varying Transmit Powers In Wsns\\nYear: 2019\\n\\nAbstract: Target localization is an important research field in wireless sensor networks and the received signal strength (RSS)-based method is of particular interest. Since traditional RSS-based method is invalid for simultaneously localizing multiple targets, the compressive sensing theory has been applied to develop an effective localization framework in recent years. However, most existing works implicitly assume the transmit powers of targets are known and constant, which contradicts the practical application scenario as the power might change with time and usually difficult to be known. In this paper, we consider the challenging problem of multiple target localization with unknown and time-varying transmit powers by exploiting multiple measurement vectors (MMVs). Since the conventional MMV formulation is incapable of the time-varying environment, we develop a novel MMV framework, in which the temporal correlation of varying powers can be further exploited. In this way, the problem is transformed to reconstruct a block-sparse signal and learn its inner correlation structure. To address this, a block-sparse reconstruction algorithm is designed, based on the novel application of the variational Bayesian approximation and the expectation-maximization methodology. The proposed algorithm can jointly estimate locations and varying powers of multiple targets with high accuracy. The extensive simulation results demonstrate the superiority of the proposed method in comparison with the state-of-the-art algorithms.\",\n \"Title: GPU-based efficient join algorithms on Hadoop\\nYear: 2019\\n\\nAbstract: The growing data have brought tremendous pressure for query processing and storage, so there are many studies that focus on using GPU to accelerate join operation, which is one of the most important operations in modern database systems. However, existing GPU acceleration join operation researches are not very suitable for the join operation on big data. Based on this, this paper speeds up nested loop join, hash join and theta join, combining Hadoop with GPU, which is also the first to use GPU to accelerate theta join. At the same time, after the data pre-filtering and pre-processing, using MapReduce and HDFS in Hadoop proposed in this paper, the larger data table can be handled, compared to existing GPU acceleration methods. Also with MapReduce in Hadoop, the algorithm proposed in this paper can estimate the number of results more accurately and allocate the appropriate storage space without unnecessary costs, making it more efficient. Experimental results show that comparing with GPU-based approach without Hadoop, our approach increases the speed by 1.5–2 times, and comparing with the Hadoop-based approaches without GPU, our approach increases the speed by 1.3–2 times.\",\n \"Title: Fair Deployment Of An Unmanned Aerial Vehicle Base Station For Maximal Coverage\\nYear: 2019\\n\\nAbstract: Unmanned aerial vehicle mounted base stations (UAV-BSs) can provide wireless cellular service to ground users in a variety of scenarios. The efficient deployment of such UAV-BSs while optimizing the coverage area is one of the key challenges. We investigate the deployment of UAV-BS to maximize the coverage of ground users, and further analyzes the impact of the deployment of UAV-BS on the fairness of ground users. In this paper, we first calculated the location of the UAV-BS according to the QoS requirements of the ground users, and then the fairness of ground users is taken into account by calculating three different fairness indexes. The performance of two genetic algorithms, namely Standard Genetic Algorithm (SGA) and Multi-Population Genetic Algorithm (MPGA) are compared to solve the optimization problem of UAV-BS deployment. The simulations are presented showing that the performance of the two algorithms, and the fairness performance of the ground users is also given.\",\n \"Title: Variational Bayesian Inference-Based Multiple Target Localization in WSNs With Quantized Received Signal Strength.\\nYear: 2019\\n\\nAbstract: The received signal strength (RSS)-based target localization is an important field of research with numerous applications in wireless sensor networks. By exploiting the sparsity of localization, the compressive sensing (CS) can be applied to develop an effective localization framework for multiple targets. However, the existing CS-based method considers localization directly from real-valued sensor measurements, which implicitly assume that the measurements have infinite bit precision. When the assumption is violated, the localization performance will deteriorate dramatically, especially at low quantization bit rates. In this paper, we first design a quantizer for efficiently processing the raw RSS measurements and then develop a novel Bayesian CS framework for estimating target locations from the quantized measurements. By modeling the quantization errors as independent random variables, the non-linear localization problem is formulated in a linear CS observation model. Following this idea, we solve the problem from a Bayesian perspective and design some sophisticated prior distributions to guarantee the performance. To address this, we resort to the variational Bayesian inference methodology and propose a novel iterative algorithm for jointly estimating target locations and dealing with quantization errors. The extensive simulation results demonstrate the superiority of the proposed algorithm in comparison with the state-of-the-art methods.\",\n \"Title: Combining fine texture and coarse color features for color texture classification.\\nYear: 2017\\n\\nAbstract: Color texture classification plays an important role in computer vision applications because texture and color are two fundamental visual features. To classify the color texture via extracting discriminative color texture features in real time, we present an approach of combining the fine texture and coarse color features for color texture classification. First, the input image is transformed from RGB to HSV color space to separate texture and color information. Second, the scale-selective completed local binary count (CLBC) algorithm is introduced to extract the fine texture feature from the V component in HSV color space. Third, both H and S components are quantized at an optimal coarse level. Furthermore, the joint histogram of H and S components is calculated, which is considered as the coarse color feature. Finally, the fine texture and coarse color features are combined as the final descriptor and the nearest subspace classifier is used for classification. Experimental results on CUReT, KTH-TIPS, and New-BarkTex databases demonstrate that the proposed method achieves state-of-the-art classification performance. Moreover, the proposed method is fast enough for real-time applications. (C) 2017 SPIE and IS&T\",\n \"Title: SA-M-SBL: An Algorithm for CSI-Based Device-Free Localization With Faulty Prior Information.\\nYear: 2019\\n\\nAbstract: As an emerging technique, device-free localization (DFL) is promising to localize the target without attaching any transceivers. Recently, the benefits of channel state information (CSI) on DFL have been revealed in this paper. Motivated by this, in this paper, we propose to exploit the channel diversity of CSI measurements for multi-target DFL under the compressive sensing (CS) framework. The CSI-based multi-target DFL problem is formulated as a joint sparse recovery problem which reconstructs the unknown sparse vectors of multiple channels. Moreover, in practice, some faulty prior information (e.g., coarse positions) is usually available. To take advantage of this information for joint sparse recovery, novel support knowledge-aided multiple sparse Bayesian learning (SA-M-SBL) algorithm is introduced, which incorporates the prior information into a three-layer hierarchical prior model. With this model, the joint sparsity of the sparse vectors can be induced, and their values can be estimated via the variational Bayesian inference (VBI). The numerical simulation results demonstrate the outstanding performance of the proposed method compared with the state-of-the-art CS-based multi-target DFL methods.\",\n \"Title: Robust passive adaptive fault tolerant control for stochastic wing flutter via delay control\\nYear: 2019\\n\\nAbstract: •The model of this manuscript is a more general LOE–LIP flutter model and covers several common fault tolerant situations.•The designed observer of this manuscript can compensate both the nonlinear uncertainty and the Itô differential item.•The robust passive adaptive fault tolerant controller based on the observer is constructed to solve the wing flutter problem.\",\n \"Title: Data-Driven Adaptive Iterative Learning Method for Active Vibration Control Based on Imprecise Probability\\nYear: 2019\\n\\nAbstract: A data-driven adaptive iterative learning (IL) method is proposed for the active control of structural vibration. Considering the repeatability of structural dynamic responses in the vibration process, the time-varying proportional-type iterative learning (P-type IL) method was applied for the design of feedback controllers. The model-free adaptive (MFA) control, a data-driven method, was used to self-tune the time-varying learning gains of the P-type IL method for improving the control precision of the system and the learning speed of the controllers. By using multi-source information, the state of the controlled system was detected and identified. The square root values of feedback gains can be considered as characteristic parameters and the theory of imprecise probability was investigated as a tool for designing the stopping criteria. The motion equation was driven from dynamic finite element (FE) formulation of piezoelectric material, and then was linearized and transformed properly to design the MFA controller. The proposed method was numerically and experimentally tested for a piezoelectric cantilever plate. The results demonstrate that the proposed method performs excellent in vibration suppression and the controllers had fast learning speeds.\",\n \"Title: Aco-Rr: Ant Colony Optimization Ridge Regression In Reuse Of Smart City System\\nYear: 2019\\n\\nAbstract: With the rapid development of artificial intelligence, governments of different countries have been focusing on building smart cities. To build a smart city is a system construction process which not only requires a lot of human and material resources, but also takes a long period of time. Due to the lack of enough human and material resources, it is a key challenge for lots of small and medium-sized cities to develop the intelligent construction, compared with the large cities with abundant resources. Reusing the existing smart city system to assist the intelligent construction of the small and medium-sizes cities is a reasonable way to solve this challenge. Following this idea, we propose a model of Ant Colony Optimization Ridge Regression (ACO-RR), which is a smart city evaluation method based on the ridge regression. The model helps small and medium-sized cities to select and reuse the existing smart city systems according to their personalized characteristics from different successful stories. Furthermore, the proposed model tackles the limitation of ridge parameters' selection affecting the stability and generalization ability, because the parameters of the traditional ridge regression is manually random selected. To evaluate our model performance, we conduct experiments on real-world smart city data set. The experimental results demonstrate that our model outperforms the baseline methods, such as support vector machine and neural network.\",\n \"Title: Fatigue Crack Width Detection Based on the Active Sensing Method: A Feasibility Study\\nYear: 2019\\n\\nAbstract: Across multiple industries, metal fatigue damage is one of the most common failure types, and it will induce great loss if the fatigue damage cannot be detected in a timely manner. To predict the fatigue life of metal structures under practical service status, the fatigue test of metal materials is necessary. Current fatigue tests only focus on the total number of cycles before fatigue failure; however, the occurrence and expansion speed of cracks have not been studied before. Therefore, in this paper, based on the active sensing method, a feasibility study is developed to make crack growth detection of standard metal specimen during the fatigue testing. Two PZT patches are bonded on both sides of the crack on a specimen, which is caused by cyclic tension-compression loads. One patch works as the actuator, and the other one is used as the sensor. Based on the wavelet packet signal energy, a damage index is proposed to quantify the received signals and then represent the degree of crack width. The experimental results demonstrate that the received signal wavelet package energy decreases monotonically with enlarging crack width, and thus the effectiveness of the proposed method is verified.\",\n \"Title: Compressive Sensing Based Device-Free Multi-Target Localization Using Quantized Measurement.\\nYear: 2019\\n\\nAbstract: Device-free localization (DFL), requiring no extra devices equipped with a target, is an important field of research on the Internet of Thing (IoT). Energy efficiency issue is essential for the development of the IoT, but seldom of the existing papers are focus on it. So we investigate this issue with quantized data of only several bits under the compressive sensing (CS) framework, which can both reduce the required wireless link number and the bit number in the DFL scheme. First, through exploiting the discrete property of CS theory, we calculate the discrete measurement probability bypass computing the complex or uncalculated measurement probability density function (pdf), which can well represent the measurement distribution characteristic. Second, we design a unique quantization scheme for each wireless link according to their measurement probability and build a novel DFL model by analyzing the quantization error. Third, a new DF-QVBI algorithm is proposed to recover the target location, which can make great use of the quantization error. Finally, numerical simulations show the superiority and robustness of the proposed method.\",\n \"Title: Mention Recommendation With Context-Aware Probabilistic Matrix Factorization\\nYear: 2019\\n\\nAbstract: Mention as a key feature on social networks can break through the effect of structural trapping and expand the visibility of a message. Although existing works usually use rank learning as implementation strategy before performing mention recommendation, these approaches may interfere with the influening factor exploration and cause some biases. In this paper, we propose a novel Context-aware Mention recommendation model based on Probabilistic Matrix Factorization (CMPMF). This model considers four important mention contextual factors including topic relevance, mention affinity, user profile similarity and message semantic similarity to measure the relevance score from users and messages dimensions. We fuse these mention contextual factors in latent spaces into the framework of probabilistic matrix factorization to improve the performance of mention recommendation. Through evaluation on a real-world dataset from Weibo, the empirically study demonstrates the effectiveness of discovered mention contextual factors. We also observe that topic relevance and mention affinity play a much significant role in the mention recommendation task. The results demonstrate our proposed method outperforms the state-of-the-art algorithms.\",\n \"Title: Stable Controller Design for T-S Fuzzy Control Systems with Piecewise Multi-linear Interpolations into Membership Functions.\\nYear: 2019\\n\\nAbstract: This paper focuses on stabilization of T–S fuzzy control systems. We use the information of the premises of the T–S fuzzy control systems to reduce the conservativeness of the stabilization conditions. First, the membership functions (MFs) in the premises are approximated with their piecewise multi-linear interpolations. In this way, different types of MFs can be tackled in a unified approach. We use the errors between the T–S fuzzy systems and the interpolated systems as feedbacks to ensure that the errors tend to zero. Then, we design stable controllers for the fuzzy control systems based on the obtained systems with piecewise multi-linear interpolations and express our results as a group of linear matrix inequalities. It is proved that when the MFs are both single-variate and multi-variate, our results can stabilize the T–S fuzzy control systems. Finally, several simulation examples are utilized to illustrate the merits of the proposed method with both PDC and non-PDC in this paper.\",\n \"Title: GRADI: Towards Group Recommendation Using Attentive Dual Top-Down and Bottom-Up Influences\\nYear: 2019\\n\\nAbstract: Most of current group recommenders only consider the bottom-up influences, i.e., the preference of a group is greatly affected by the members in the group. For example, children usually dominate the preference of a family while senior experts often lead the preference of a professional group. However, in reality there also exist the top-down influences, i.e., a group inherently affects its every member, because a group often has some distinct themes which limit the preferences of all the members in the group. For instance, the members in a group for sports may prefer hiking and rock climbing, whereas the members in a group for entertainment would like to watch movies and play games. In other words, the influences between a group and its members are dual. To this end, this paper proposes a new model for Group Recommendation using Attentive Dual Influences (GRADI) that simultaneously explores both the bottom-up and top-down influences between a group and its members. The preference of a member in a group is represented as the group-specific member embedding by modeling the top-down influences from the group to the member. In addition, the preference of a group on a target item is represented as the item-specific group representation by considering the bottom-up influences from all the members to the group, where an attentive mechanism is developed to aggregate the preferences of all the members on a target item. Furthermore, GRADI investigates the interactions between groups and items with neural collaborative filtering. Results of extensive experiments conducted on two real-world datasets show that GRADI outperforms other state-of-the-art models.\",\n \"Title: A High-performance Inference Accelerator Exploiting Patterned Sparsity in CNNs\\nYear: 2020\\n\\nAbstract: Convolutional neural networks (CNNs) have emerged as the critical technology for deep learning with significantly growing computation and memory demands. Model compression has been widely acknowledged as an effective way to achieve acceleration on CNNs. However, most proposed architectures of FPGA are inefficient for compressed models that contain a large amount of zero operations. In this work, we propose a sparse CNNs inference accelerator on FPGA utilizing uniform sparsity introduced by pattern pruning to achieve high energy efficiency. Our architecture maintains the sparse weights in a compressed format to reduce the storage demands and displays a flexible kernel-stationary dataflow to enable the extensive data reusing. In addition, we design flexible computing arrays which can be dynamically reconfigured to balance workload with low overheads. Specially, the on-chip memory applies a novel data buffering structure with slightly rearranged sequences to address the challenge of access conflict. The experiments show that our accelerator can achieve $316 .4 {GOP/s }\\\\sim 343 .5 {GOP/s }$ for VGG16 and ResNet-50.\",\n \"Title: A Novel Vortex Synthetic Aperture Radar Imaging System: Decreasing the Pulse Repetition Frequency Without Increasing the Antenna Aperture\\nYear: 2022\\n\\nAbstract: Synthetic aperture radar (SAR) is an advanced ground-observing remote sensing technology, and high-resolution wide-swath (HRWS) imaging has always been the goal of SAR. In general, an improved azimuth resolution requires a large pulse repetition frequency (PRF), resulting in high system requirements. To overcome this difficulty, azimuth multichannel technology has gradually developed, achieving HRWS imaging and decreasing the PRF by increasing the spatial sampling, i.e., increasing the number of antenna subapertures in the azimuth. This article proposes a theoretical architecture that generates multiple virtual receiving apertures in the azimuth rather than real apertures. The virtual receiving apertures are formed by multiplying the azimuth signals by linear phase histories provided by vortex beams carrying different orbital angular momentum (OAM) modes. Vortex beams with different OAM modes have different oblique phase wavefronts, so virtual receiving positions are generated in the along-track direction. This approach aims to reduce the PRF without increasing the azimuth real receiving aperture. Simulation results demonstrate the effectiveness and limitations of the method. Finally, to overcome the inherent limitations of the method, two possible implementation schemes are proposed.\",\n \"Title: Ray-tracing Simulation and Hybrid Channel Modeling for Low-Terahertz UAV Communications\\nYear: 2021\\n\\nAbstract: Unmanned Aerial Vehicles (UAVs) are currently playing an important role in both military and commercial applications. While UAVs possess potential to enhance network coverage with their high flexibility, combining with the Terahertz (THz) band is promising to enhance the wireless data rate with abundant spectrum resources and ultra-high bandwidth. As a fundamental challenge, air-ground (AG) channel modeling and analysis in the THz band are investigated in this paper, based on ray-tracing simulation and hybrid modeling approaches. The channel statistics are extracted and analyzed based on ray-tracing simulated data, including path loss, excess delay, among others. In particular, the height dependence of statistical parameters is thoroughly analyzed, which shows a clear trend of increasing channel sparsity with high-altitude UAVs. In light of the simulated data and analysis, a hybrid channel model including the line-of-sight, ground-reflection path, and stochastically generated multi-path, is proposed to characterize the THz AG propagation, for which the fading characteristics are validated.\",\n \"Title: Anti-Jamming Trajectory and Power Design for Cognitive UAV Communications\\nYear: 2021\\n\\nAbstract: This paper studies the anti-jamming issue for cognitive unmanned aerial vehicle (UAV) communication networks. We aim to maximize the throughput of information collection in sensitive areas or unmanned areas of military borders by optimizing the UAV trajectory and the secondary transmitters (STs)' transmit power, in the presence of malicious jammers with imperfect location information. However, the optimization problem is challenging to solve due to the non-convexity. To overcome this difficulty, we propose a throughput maximization algorithm with the aid of block coordinate descent (BCD) method, successive convex approximation (SCA) technique, and S-procedure. Numerical results show that our proposed algorithm significantly improves the system throughput compared with the benchmark algorithms, and simultaneously achieves effective anti-jamming.\",\n \"Title: Rom: A Requirement Opinions Mining Method Preliminary Try Based On Software Review Data\\nYear: 2020\\n\\nAbstract: Requirement opinion mining aims to mine user opinions that can be used to help the mining of software requirements from various data sources. However, in the development of social network systems, software application platforms or stores and other data sources, the massive, noisy, non-standard data, makes the mining of effective requirement opinions more difficult. Therefore, there is less work in software requirements mining based on the data of software review in development social media or application market. This paper attempts to provide some knowledge support for requirement user story establishing in RE based on the opinion mining and clustering of massively software review data. First of all, this paper combines the requirements of the requirements engineering field to define the requirement opinions, functional requirement opinions and non-functional requirements opinions. Secondly, using the deep learning model to classify the functional requirement reviews and non-functional requirements reviews included in the reviews; Based on the differences between functional data and non-functional data, this paper defines three categories in the description of software functional data, and chooses to use sequence labeling methods to identify functional requirements. Then use the K-means clustering method based on word vector to cluster the review data, and combine TF-IDF and syntactic analysis to extract the aspect and aspect requirements or specific requirements of the requirement opinion respectively, so as to realize the requirement opinion mining of software review data. Finally, this article will give a case study based on the user review data of the mobile phone application service platform 360 mobile assistants.\",\n \"Title: A Surface And Interior Material Identification Technology Based On Dual-Mode Sensor\\nYear: 2021\\n\\nAbstract: In this paper, a novel hybrid-sensor integrating TENG and inductive sensor is proposed to acquire the triboelectric and inductive signals from different objects. A matching CNN algorithm was used to process the above signals in order to achieving highly accurate recognition. The identification accuracy of different solid surfaces reached 98.89% and the identification accuracy of different solution reached 95%. As a demonstration, we designed a real-time identification system for effective sorting of different items.\",\n \"Title: Time-Domain Notch Filtering Method for Pulse RFI Mitigation in Synthetic Aperture Radar\\nYear: 2022\\n\\nAbstract: Synthetic aperture radar (SAR) often shares spectrum with other systems, such as radio, TV, cellular network, and so on, which are likely to produce radio frequency interference (RFI). Pulse RFI, which hinders SAR signal processing and image interpretation severely, is a common form of RFI and cannot be neglected. The simple and easy-to-implement frequency-domain notch filtering (FNF) method has been widely used to mitigate narrowband pulse RFIs. However, a well-known problem with abnormal sidelobe effect, which is caused by missing spectrum gaps due to the notch operation, is aroused when using FNF. In this letter, a novel time-domain notch filtering (TNF) is proposed. In the proposed method, pulse RFI occurrences are detected and notched by a simple log-ratio operator in a pulse by pulse manner. Then, missing-data iterative adaptive approach (MIAA) is performed to recover the notched signal to avoid ghosts. Experimental results via simulated and real L-band airborne SAR raw data validate the performance of the proposed method.\",\n \"Title: Object Tracking In Hyperspectral-Oriented Video With Fast Spatial-Spectral Features\\nYear: 2021\\n\\nAbstract: This paper presents a correlation filter object tracker based on fast spatial-spectral features (FSSF) to realize robust, real-time object tracking in hyperspectral surveillance video. Traditional object tracking in surveillance video based only on appearance information often fails in the presence of background clutter, low resolution, and appearance changes. Hyperspectral imaging uses unique spectral properties as well as spatial information to improve tracking accuracy in such challenging environments. However, the high-dimensionality of hyperspectral images causes high computational costs and difficulties for discriminative feature extraction. In FSSF, the real-time spatial-spectral convolution (RSSC) kernel is updated in real time in the Fourier transform domain without offline training to quickly extract discriminative spatial-spectral features. The spatial-spectral features are integrated into correlation filters to complete the hyperspectral tracking. To validate the proposed scheme, we collected a hyperspectral surveillance video (HSSV) dataset consisting of 70 sequences in 25 bands. Extensive experiments confirm the advantages and the efficiency of the proposed FSSF for object tracking in hyperspectral video tracking in challenging conditions of background clutter, low resolution, and appearance changes.\",\n \"Title: New Sets Of Quadriphase Cross Z-Complementary Pairs For Preamble Design In Spatial Modulation\\nYear: 2021\\n\\nAbstract: Cross Z-complementary pairs (CZCPs) are a special kind of Z-complementary pairs having zero autocorrelation sums around the in-phase position and end-shift position, also having zero cross-correlation sums around the end-shift position. Recent results have shown that CZCPs are very efficient in designing pilot sequences for spatial modulation enabled multiple-input multiple-output (MIMO) systems. In this paper, we propose systematic constructions of binary and quadriphase CZCPs with new lengths of the form 2M, where even-length binary Z-complementary pairs of length M exists. Interestingly, the proposed binary and quadriphase CZCPs have large CZC(ratio) (i.e. the ratio of achieved zero correlation zone width over maximum zero correlation zone width). One of the proposed construction can also construct optimal quadriphase CZCPs using quadriphase GCPs.\",\n \"Title: Anomaly Detection Via Self-Organizing Map.\\nYear: 2021\\n\\nAbstract: Anomaly detection plays a key role in industrial manufacturing for product quality control. Traditional methods for anomaly detection are rule-based with limited generalization ability. Recent methods based on supervised deep learning are more powerful but require large-scale annotated datasets for training. In practice, abnormal products are rare thus it is very difficult to train a deep model in a fully supervised way. In this paper, we propose a novel unsupervised anomaly detection approach based on Self-organizing Map (SOM). Our method, Self-organizing Map for Anomaly Detection (SOMAD) maintains normal characteristics by using topological memory based on multi-scale features. SOMAD achieves state-of the-art performance on unsupervised anomaly detection and localization on the MVTec dataset.\",\n \"Title: Erratum to “Evolver: a Deep Learning Processor With On-Device Quantization-Voltage-Frequency Tuning”\\nYear: 2021\\n\\nAbstract: In the above article <xref ref-type=\\\"bibr\\\" rid=\\\"ref1\\\">[1]</xref>, there are some references that were cited incorrectly in the text. The corrections are as follows:\",\n \"Title: Thermal Image Super-Resolution Challenge - PBVS 2021\\nYear: 2021\\n\\nAbstract: This paper presents results from the second Thermal Image Super-Resolution (TISR) challenge organized in the framework of the Perception Beyond the Visible Spectrum (PBVS) 2021 workshop. For this second edition, the same thermal image dataset considered during the first challenge has been used; only mid-resolution (MR) and high-resolution (HR) sets have been considered. The dataset consists of 951 training images and 50 testing images for each resolution. A set of 20 images for each resolution is kept aside for evaluation. The two evaluation methodologies proposed for the first challenge are also considered in this opportunity. The first evaluation task consists of measuring the PSNR and SSIM between the obtained SR image and the corresponding ground truth (i.e., the HR thermal image downsampled by four). The second evaluation also consists of measuring the PSNR and SSIM, but in this case, considers the x2 SR obtained from the given MR thermal image; this evaluation is performed between the SR image with respect to the semi-registered HR image, which has been acquired with another camera. The results out-performed those from the first challenge, thus showing an improvement in both evaluation metrics.\",\n \"Title: Jmcmprsk: An R Package For Joint Modelling Of Longitudinal And Survival Data With Competing Risks\\nYear: 2021\\n\\nAbstract: In this paper, we describe an R package named JMcmprsk, for joint modelling of longitudinal and survival data with competing risks. The package in its current version implements two joint models of longitudinal and survival data proposed to handle competing risks survival data together with continuous and ordinal longitudinal outcomes respectively (Elashoff et al., 2008; Li et al., 2010). The corresponding R implementations are further illustrated with real examples. The package also provides simulation functions to simulate datasets for joint modelling with continuous or ordinal outcomes under the competing risks scenario, which provide useful tools to validate and evaluate new joint modelling methods.\",\n \"Title: Fusion of 3D lung CT and serum biomarkers for diagnosis of multiple pathological types on pulmonary nodules\\nYear: 2021\\n\\nAbstract: Background and Objective: : Current researches on pulmonary nodules mainly focused on the binary-classification of benign and malignant pulmonary nodules. However, in clinical applications, it is not enough to judge whether pulmonary nodules are benign or malignant. In this paper, we proposed a fusion model based on the Lung Information Dataset Containing 3D CT Images and Serum Biomarkers (LIDC -CISB) we constructed to accurately diagnose the types of pulmonary nodules in squamous cell car-cinoma, adenocarcinoma, inflammation and other benign diseases. Methods: : Using single modal information of lung 3D CT images and single modal information of Lung Tumor Biomarkers (LTBs) in LIDC -CISB, a Multi-resolution 3D Multi-classification deep learning model (Mr-Mc) and a Multi-Layer Perceptron machine learning model (MLP) were constructed for diagnosing multiple pathological types of pulmonary nodules, respectively. To comprehensively use the double modal information of CT images and LTBs, we used transfer learning to fuse Mr-Mc and MLP, and constructed a multimodal information fusion model that could classify multiple pathological types of benign and malignant pulmonary nodules. Results: : Experiments showed that the constructed Mr-Mc model can achieve an average accuracy of 0.805 and MLP model can achieve an average accuracy of 0.887. The fusion model was verified on a dataset containing 64 samples, and achieved an average accuracy of 0.906. Conclusions: : This is the first study to simultaneously use CT images and LTBs to diagnose multiple pathological types of benign and malignant pulmonary nodules, and experiments showed that our re-search was more advanced and more suitable for practical clinical applications. (c) 2021 Elsevier B.V. All rights reserved.\",\n \"Title: Characterization of gastric cancer stem-like molecular features, immune and pharmacogenomic landscapes\\nYear: 2022\\n\\nAbstract: Cancer stem cells (CSCs) actively reprogram their tumor microenvironment (TME) to sustain a supportive niche, which may have a dramatic impact on prognosis and immunotherapy. However, our knowledge of the landscape of the gastric cancer stem-like cell (GCSC) microenvironment needs to be further improved. A multi-step process of machine learning approaches was performed to develop and validate the prognostic and predictive potential of the GCSC-related score (GCScore). The high GCScore subgroup was not only associated with stem cell characteristics, but also with a potential immune escape mechanism. Furthermore, we experimentally demonstrated the upregulated infiltration of CD206(+) tumor-associated macrophages (TAMs) in the invasive margin region, which in turn maintained the stem cell properties of tumor cells. Finally, we proposed that the GCScore showed a robust capacity for prediction for immunotherapy, and investigated potential therapeutic targets and compounds for patients with a high GCScore. The results indicate that the proposed GCScore can be a promising predictor of prognosis and responses to immunotherapy, which provides new strategies for the precision treatment of GCSCs.\",\n \"Title: DA-BERT: Enhancing Knowledge Selection in Dialog via Domain Adapted BERT with Dynamic Masking Probability\\nYear: 2021\\n\\nAbstract: One of the most challenging tasks in Knowledge-grounded Task-oriented Dialog Systems (KTDS) is the knowledge selection task, which aims to find the proper knowledge snippets to handle user requests. This paper proposes DA-BERT to employ pre-trained BERT with domain adaptive training and newly proposed dynamic masking probability to deal with knowledge selection in KTDS. Domain adaptive training mi...\",\n \"Title: An Overview on Supervised Semi-structured Data Classification\\nYear: 2021\\n\\nAbstract: Many collaboratively building resources, such as Wikipedia, Weibo and Quora, exist in the form of semi-structured data. The semi-structured data has been widely used in areas such as data integration, data distribution, data storage, data management, information retrieval and knowledge management. For large volumes of semi-structured data on the Web, semi-structured data classification technique c...\",\n \"Title: Shock Wave Energy Limit Analysis of Underwater Plasma Sound Source System\\nYear: 2021\\n\\nAbstract: The analysis and prediction of the energy limit of a single underwater plasma sound source (UPSS) system can provide theoretical guidance for the design of UPSS and array bunching. In this paper, the influence factors of shock wave energy produced by high voltage pulse discharge are studied by experiment. The relationship between the peak pressure of the shock wave and the discharge voltage, the c...\",\n \"Title: Toolkit for Dynamic Control Rapid Prototype Simulation System of Robots Applied in Space Experimental Cabin\\nYear: 2021\\n\\nAbstract: As the space environment is difficult to be recreated on the earth, it is essential to develop a software toolkit to validate the parameters designing for space robots. In this paper, a toolkit addressed for dynamic control rapid prototype simulation system of the space robot was presented. Based on the real-time control technology, the toolkit was consisted of real-time dynamic computing, redundant dual-arm control system, visual imaging and measurement and teleoperation. Specifically, the collision problem involved in the manipulator end effector and the target could be analyzed by contact dynamics models in the real-time dynamic computing module. Furthermore, a peg-in-hole (multi cores) assembly experiment involving the disassembly for the space electrical connectors was carried out, which was adopted to evaluate the simulation effect of toolkit. Our results showed that the connection between the space plug and socket with multi cores was well built up in the simulation environment. Moreover, the toolkit can be used to make simulations of on orbit operational missions by multiple of human-computer interaction, which is the key for the evaluation of control algorithm applied on the space robot.\",\n \"Title: Real-time 3D-Lidar, MMW Radar and GPS/IMU fusion based vehicle detection and tracking in unstructured environment\\nYear: 2021\\n\\nAbstract: To solve the problem of unmanned ground vehicle leader-follower formation transportation in unstructured environment, we propose a novel target detection and tracking method based on multi-sensor fusion perception. Combined with 3D-Lidar, millimeter wave Radar and GPS/IMU, the proposed method can achieve stable target detection and continuous tracking of both static and dynamic vehicles. First, 3D-Lidar is used to detect the geometric model of the leader vehicle to complete the initialization of tracking target and it can also be assisted for target tracking. Then during the movement, the dynamic leader is mainly tracked through millimeter wave Radar as this sensor can keep tracking the same target with a constant index and effectively distinguish dynamic vehicle from other static obstacles according to relative speed estimation. In addition, by using GPS/IMU based integrated navigation, the movement trend of the leader can be derived according to the echo vehicle pose information and the relative position relationship. This is helpful to reduce the region of interest for target tracking and improve the real-time performance. In different unstructured environments, we perform the leader-follower formation transportation experiments for hundreds of kilometers. In rough terrain, the maximum tracking speed can still reach 40km/h and the maximum tracking distance can be up to 100 meters. Experiments show that the proposed method is suitable for vehicle target detection and tracking in unstructured environment. It has good robustness and high real-time performance with an average processing frame rate of 20Hz. The proposed method can be used for the formation transportation of unmanned ground vehicles to reduce labor costs.\",\n \"Title: Recovering Stress Distribution on Deformable Tissue for a Magnetic Actuated Insertable Laparoscopic Surgical Camera\\nYear: 2021\\n\\nAbstract: Fully insertable laparoscopic cameras represent a promising future of minimally invasive surgery. The most characteristic technology adopted on these devices is transabdominal anchoring and actuation based on magnetic coupling. However, few have paid adequate attention to the safety concerns. As the camera is anchored against the interior abdominal wall without any force feedback, the patient is being exposed to a high risk of getting injured by inappropriate stress on the tissue. We have recovered the camera-tissue interaction force via a non-invasive approach in our previous work. Aiming to access the stress distribution, this paper presents a viscoelastic camera-tissue interaction model, which establishes explicit relations between the contact force and the stress distribution on the tissue. For the first time, a geometric constraint between the contact angle and the tissue indentation is introduced, which helps make the multivariable model solvable. Ex-vivo experiments on porcine abdomen tissue facilitated by non-invasive force measurement validate effectiveness of the model. This work lays foundation for improving control and surgical safety of using a magnetic actuated insertable laparoscopic surgical camera.\",\n \"Title: No-Reference Physics-Based Quality Assessment of Polarization Images and Its Application to Demosaicking\\nYear: 2021\\n\\nAbstract: Assessing the quality of polarization images is of significance for recovering reliable polarization information. Widely used quality assessment methods including peak signal-to-noise ratio and structural similarity index require reference data that is usually not available in practice. We introduce a simple and effective physics-based quality assessment method for polarization images that does not require any reference. This metric, based on the self-consistency of redundant linear polarization measurements, can thus be used to evaluate the quality of polarization images degraded by noise, misalignment, or demosaicking errors even in the absence of ground-truth. Based on this new metric, we propose a novel processing algorithm that significantly improves demosaicking of division-of-focal-plane polarization images by enabling efficient fusion between demosaicking algorithms and edge-preserving image filtering. Experimental results obtained on public databases and homemade polarization images show the effectiveness of the proposed method.\",\n \"Title: Radar based obstacle detection in unstructured scene\\nYear: 2021\\n\\nAbstract: To solve the problem of obstacle detection in unstructured scene, we propose a novel obstacle detection method based on Radar. Compared with other sensors like 3D-Lidar and color camera, Radar has higher environmental robustness especially for sandy road with dust. According to the relative velocity information of detected obstacles, obstacles are divided into static ones and dynamic ones in the proposed method. For static obstacles, map reconstruction based on Bayesian probability model is used to overcome the problem of sparse obstacle detection result in single frame and obstacle retention in the blind area. The integrated navigation system of GNSS and IMU is used to provide position and posture information during map reconstruction. For dynamic obstacles, the historical trajectory of the same target is recorded in the global coordinate frame, then the Kalman predictor based trajectory tracking and prediction is used to predict the movement trend of moving obstacles. The proposed method has been verified in a variety of unstructured scene. The experimental results show that the proposed method can provide useful obstacle perception result and meet the autonomous navigation application of UGV in unstructured scene. We also compare the proposed method with that based on 3D-Lidar in terms of the same scene. And it has obvious advantages in environmental robustness.\",\n \"Title: Detecting unknown dams from high-resolution remote sensing images: A deep learning and spatial analysis approach\\nYear: 2021\\n\\nAbstract: •Automatic detecting unknown dams from high-resolution remote sensing images in broad areas.•Multi object detection models improves the recall rate of dam detection.•Geographical analysis and comprehensive discrimination methods can effectively improve the precision rate of dam detection.•A comprehensive application case proves the advantages of the proposed framework.\",\n \"Title: Predicting Stock Movements: Using Multiresolution Wavelet Reconstruction and Deep Learning in Neural Networks\\nYear: 2021\\n\\nAbstract: Stock movement prediction is important in the financial world because investors want to observe trends in stock prices before making investment decisions. However, given the non-linear non-stationary financial time series characteristics of stock prices, this remains an extremely challenging task. A wavelet is a mathematical function used to divide a given function or continuous-time signal into different scale components. Wavelet analysis has good time-frequency local characteristics and good zooming capability for non-stationary random signals. However, the application of the wavelet theory is generally limited to a small scale. The neural networks method is a powerful tool to deal with large-scale problems. Therefore, the combination of neural networks and wavelet analysis becomes more applicable for stock behavior prediction. To rebuild the signals in multiple scales, and filter the measurement noise, a forecasting model based on a stock price time series was provided, employing multiresolution analysis (MRA). Then, the deep learning in the neural network method was used to train and test the empirical data. To explain the fundamental concepts, a conceptual analysis of similar algorithms was performed. The data set for the experiment was chosen to capture a wide range of stock movements from 1 January 2009 to 31 December 2017. Comparison analyses between the algorithms and industries were conducted to show that the method is stable and reliable. This study focused on medium-term stock predictions to predict future stock behavior over 11 days of horizons. Our test results showed a 75% hit rate, on average, for all industries, in terms of US stocks on FORTUNE Global 500. We confirmed the effectiveness of our model and method based on the findings of the empirical research. This study's primary contribution is to demonstrate the reconstruction model of the stock time series and to perform recurrent neural networks using the deep learning method. Our findings fill an academic research gap, by demonstrating that deep learning can be used to predict stock movement.</p>\",\n \"Title: An Incast-Coflow-Aware Minimum-Rate-Guaranteed Congestion Control Protocol for Datacenter Applications\\nYear: 2021\\n\\nAbstract: Today s datacenters need to meet service level objectives (SLOs) for applications, which can be translated into deadlines for (co)flows running between job execution stages. As a result, meeting (co)flow deadlines with high probabilities is essential to attract and retain customers and hence, generate high revenue. To fill the lack of a transport protocol that can facilitate low (co)flow deadline ...\",\n \"Title: A Novel Secure Diffusion Kalman Filter Algorithm Against False Data Injection Attacks\\nYear: 2021\\n\\nAbstract: This paper proposes a novel secure diffusion Kalman filter (dKF) algorithm to improve the estimation performance crippled by false data injection attacks on sensors in wireless sensor networks (WSNs). Different from the conventional dKF, each adjacent node in the WSNs is detected to ascertain its trustworthiness before local estimate fusion, so as to form a new secure network topology. Then the combination step is performed to fuse the information collected from the secure topology. The proposed secure dKF algorithm, having a better estimation performance, is robust to false data injection attacks on multiple sensors and partial elements of measurements. For the proposed secure dKF algorithm, its mean and mean-square performance are derived, based on which its convergence is analysed. Additionally, the estimating and tracking problem of projectile position is investigated to confirm the effectiveness of the proposed secure dKF algorithm. It is shown by simulations that the proposed secure dKF algorithm achieves a significant estimation performance gain.\",\n \"Title: A 24 Db Gain 51-68 Ghz Common Source Low Noise Amplifier Using Asymmetric-Layout Transistors\\nYear: 2012\\n\\nAbstract: At mm-wave frequency, the layout of CMOS transistors has a larger effect on the device performance than ever before in low frequency. In this work, the distance between the gate and drain contact (D-gd) has been enlarged to obtain a better maximum available gain (MAG). By using the asymmetric-layout transistor, a 0.6 dB MAG improvement is realized when Dgd changes from 60 nm to 200 mm A four-stage common-source low noise amplifier is implemented in a 65 nm CMOS process. A measured peak power gain of 24 dB is achieved with a power dissipation of 30 mW from a 1.2-V power supply. An 18 dB variable gain is also realized by adjusting the bias voltage. The measured 3-dB bandwidth is about 17 GHz from 51 GHz to 68 GHz, and noise figure (NF) is from 4.0 dB to 7.6 dB.\",\n \"Title: Localization of Multiple RF Sources via Priori Knowledge-Aided Bayesian Compressive Sensing in UAV-Based WSN\\nYear: 2021\\n\\nAbstract: In the unmanned aerial vehicle-based wireless sensor network, the compressive sensing approach can simultaneously locate multiple ground radio frequency sources, while the existing algorithms’ localization accuracies would deteriorate confronting the heavy noise. Hence, we exploit the potential priori knowledge and propose a robust localization algorithm named priori knowledge-aided Bayesian compr...\",\n \"Title: EMS-Net: Enhanced Multi-Scale Network for Polyp Segmentation\\nYear: 2021\\n\\nAbstract: In recent years, polyp segmentation plays an important role in the diagnosis and treatment of colorectal cancer. Accurate segmentation of polyps is very challenging due to different sizes, shapes, and unclear boundaries. Making full use of multi-scale contextual information to segment polyps may bring better results. In this paper, we propose an enhanced multi-scale network for accurate polyp segmentation. It is composed of a multi-scale connected baseline (U-Net+++), a multi-scale backbone (Res2Net), three Receptive Field Block (RFB) modules, and four Local Context Attention (LCA) modules. Specifically, the baseline's multi-scale skip connections can aggregate features in both low-level and high-level layers. We have evaluated our model on three publicly available and challenging datasets (EndoScene, CVC-ClinicDB, Kvasir-SEG). Compared with other methods, our model achieves SOTA performance. It is noteworthy that our model is the only network that has achieved over 0.900 mean Dice on EndoScene and CVC-ClinicDB.\",\n \"Title: Characterizing Ancient Channel of the Yellow River From Spaceborne SAR: Case Study of Chinese Gaofen-3 Satellite\\nYear: 2022\\n\\nAbstract: The lower reaches of the ancient Yellow River (AYR) migrated very frequently, like a loong swinging its tail on the land of China. As the origin of Chinese civilization, AYR provided natural conditions for the people to thrive. However, the sediment carried by AYR still has a negative impact on local agricultural production. This letter, based on Gaofen-3, extracted scattering characteristics of a...\",\n \"Title: FKTAN: Fusion Keystroke Time-Textual Attention Networks for Continuous Authentication\\nYear: 2021\\n\\nAbstract: With the rapid development of computer technology, the traditional Internet data security and information privacy issues are gradually expanding to all aspects of society as a whole. As the first line of defense for information security, identity authentication technology becomes crucial. Among the many authentication technologies, continuous authentication technology has gained increasing attenti...\",\n \"Title: An improved sine–cosine algorithm with dynamic selection pressure\\nYear: 2021\\n\\nAbstract: Sine cosine algorithm (SCA) is a newly developed optimization tool that has received increasing attention in the field of computational intelligence. Nevertheless, SCA has a combination of strong exploration and weak exploitation abilities, which undermines the performance of the algorithm. A dynamic selection strategy (DSS) has been proposed in this paper to overcome this limitation. The central idea is that the selection pressure of the parent is dynamically regulated during the course of evolution, and the parent with higher fitness have higher selection probability. Accordingly, the weak exploitation ability of SCA can be enhanced. This approach can be readily applied to other SCA variants as well. Results from systematic experiments conducted on CEC2014 competition benchmark suite show that the proposed DSS method can significantly improve the performance of the SCAs. In addition, the proposed DSS-SCA algorithm has been successfully applied to six engineering design problems, which shows that the algorithm has the potential to solve other practical problems.\",\n \"Title: The Trapezoidal Sketch for Frequency Estimation in Network Flow\\nYear: 2021\\n\\nAbstract: The sketch is one of the typical and widely-used data structures for estimating the frequencies of items in data streams. However, since the counter sizes in traditional rectangular sketch (r-sketch) are the same, it is hard to achieve small space usage, high capacity (i.e., the maximum frequency can be recorded), and high estimated accuracy simultaneously. Moreover, when considering the high skew...\",\n \"Title: Anomaly Detection in Unstructured Logs Using Attention-based Bi-LSTM Network\\nYear: 2021\\n\\nAbstract: System logs record valuable information about the runtime status of IT systems. Therefore, system logs are a naturally excellent source of information for anomaly detection. Most of the existing studies on log-based anomaly detection construct a detection model to identify anomalous logs. Generally, the model treats historical logs as natural language sequences and learns the normal patterns from ...\",\n \"Title: A Novel Method for Layover Detection in Mountainous Areas with SAR Images\\nYear: 2021\\n\\nAbstract: It is well known that there are geometric distortions in synthetic aperture radar (SAR) images when the terrain undulates. Layover is the most common one, which brings challenges to the application of SAR remote sensing. This study proposes a novel detection method that is mainly aimed at the layover caused by mountains and can be performed with only medium-resolution SAR images and no other auxiliary data. The detection includes the following four stages: initial processing, difference image calculation and rough and fine layover detection. Initial processing mainly obtains the potential layover areas, which are mixed with the built-up areas after classification. Additionally, according to the analysis of the backscatter coefficient (BC) of various ground objects with different polarization images, the layover areas are detected step-by-step from the mixed areas, in which the region-based FCM segmentation algorithm and spatial relationship criteria are used. Taking the Danjiangkou Reservoir area as the study area, the relevant experiments with Sentinel-1A SAR images were conducted. The quantitative analysis of detection results adopted the figure of merit (FoM), and the highest accuracy was up to 87.6% of one selected validation region. Experiments in the South Taihang area also showed the satisfactory effect of layover detection, and the values of FoM were all above 85%. These results show that the proposed method can do well in the layover detection caused by mountains. Its simplicity and effectiveness are helpful in removing the influence of layover on SAR image applications to a certain extent and improving the development of SAR remote sensing technology.\",\n \"Title: An Azimuth Signal-Reconstruction Method Based on Two-Step Projection Technology for Spaceborne Azimuth Multi-Channel High-Resolution and Wide-Swath SAR\\nYear: 2021\\n\\nAbstract: Azimuth non-uniform signal-reconstruction is a critical step for azimuth multi-channel high-resolution wide-swath (HRWS) synthetic aperture radar (SAR) data processing. However, the received non-uniform signal has noise in the actual azimuth multi-channel SAR (MCSAR) operation, which leads to the serious reduction in the signal-to-noise ratio (SNR) of the results processed by a traditional reconstruction algorithm. Aiming to address the problem of reducing the SNR of the traditional reconstruction algorithm in the reconstruction of non-uniform signal with noise, a novel signal-reconstruction algorithm based on two-step projection technology (TSPT) for the MCSAR system is proposed in this paper. The key part of the TSPT algorithm consists of a two-step projection. The first projection is to project the given signal into the selected intermediate subspace, spanned by the integer conversion of the compact support kernel function. This process generates a set of sparse equations, which can be solved efficiently by using the sparse equation solver. The second key projection is to project the first projection result into the subspace of the known sampled signal. The secondary projection can be achieved with a digital linear translation invariant (LSI) filter and generate a uniformly spaced signal. As a result, compared with the traditional azimuth MCSAR signal-reconstruction algorithm, the proposed algorithm can improve SNR and reduce the azimuth ambiguity-signal-ratio (AASR). The processing results of simulated data and real raw data verify the effectiveness of the proposed algorithm.\",\n \"Title: Design and Development of Real-Time Barrage System for College Class\\nYear: 2021\\n\\nAbstract: In order to activate classroom atmosphere, enhance interaction between teachers and students, and to facilitate teachers to answer questions raised by students in a timely manner, a new real-time barrage class system was designed. To complete overall system design, C/S and B/S mixed-mode and MVC architecture were adopted. Our system is divided into a PC terminal function management module and a mobile terminal function management module. Through TCP protocol, mobile terminal data are communicated with server in form of JSON string. In addition to basic functions of each module, Highcharts technology was used on mobile terminal to realize visual display and analysis function of barrage data. Combined with web crawler technology and the DFA (Deterministic Finite Automaton) algorithm, a sensitive vocabulary filtering function was realized by us. Practice has proved that our real-time barrage class system can strengthen interaction between teachers and students in college classes, stimulate students' enthusiasm for learning, and improve students' participation and concentration in teaching process. Therefore, our system has feasibility and promotion value.\",\n \"Title: Estimation Performance of Cyber-Physical Systems Attacked by False Data Injection\\nYear: 2021\\n\\nAbstract: This paper investigates the state estimation error covariances of cyber-physical systems (CPSs) when its physical system and transmission channels are attacked by false data injection (FDI). Specifically, sensors in CPSs measure the physical systems' state and then send these measurement information to a fusion center through transmission channels. Due to easy deployment and maintenance of sensors as well as unprotected wireless transmission medium, CPSs in sensing and communication are vulnerable to malicious attacks. To maliciously degrade the system estimation performance, an intruder attempts to modify the state and measurements by launching FDI attacks. To seek an optimal attack strategy from the perspective of an attacker, the corresponding estimate error covariances of CPSs are calculated. Based on this, we compare the estimation error covariances of different attacks on the physical system and transmission channels. Simulations demonstrate the effectiveness of the theoretical results.\",\n \"Title: Quantification and 3D Localization of Magnetically Navigated Superparamagnetic Particles Using MRI in Phantom and Swine Chemoembolization Models\\nYear: 2022\\n\\nAbstract: <italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">Objective:</i>\\n Superparamagnetic nanoparticles (SPIONs) can be combined with tumor chemoembolization agents to form magnetic drug-eluting beads (MDEBs), which are navigated magnetically in the MRI scanner through the vascular system. We aim to develop a method to accurately quantify and localize these particles and to validate the method in phantoms and swine models. \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">Methods:</i>\\n MDEBs were made of Fe\\n<sub xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">3</sub>\\nO\\n<sub xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">4</sub>\\n SPIONs. After injected known numbers of MDEBs, susceptibility artifacts in three-dimensional (3D) volumetric interpolated breath-hold examination (VIBE) sequences were acquired in glass and Polyvinyl alcohol (PVA) phantoms, and two living swine. Image processing of VIBE images provided the volume relationship between MDEBs and their artifact at different VIBE acquisitions and post-processing parameters. Simulated hepatic-artery embolization was performed \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">in vivo</i>\\n with an MRI-conditional magnetic-injection system, using the volume relationship to locate and quantify MDEB distribution. \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">Results:</i>\\n Individual MDEBs were spatially identified, and their artifacts quantified, showing no correlation with magnetic-field orientation or sequence bandwidth, but exhibiting a relationship with echo time and providing a linear volume relationship. Two MDEB aggregates were magnetically steered into desired liver regions while the other 19 had no steering, and 25 aggregates were injected into another swine without steering. The MDEBs were spatially identified and the volume relationship showed accuracy in assessing the number of the MDEBs, with small errors (≤ 8.8%). \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">Conclusion and Significance:</i>\\n MDEBs were able to be steered into desired body regions and then localized using 3D VIBE sequences. The resulting volume relationship was linear, robust, and allowed for quantitative analysis of the MDEB distribution.\",\n \"Title: The Identification of ECG Signals Using WT-UKF and IPSO-SVM\\nYear: 2022\\n\\nAbstract: The biometric identification method is a current research hotspot in the pattern recognition field. Due to the advantages of electrocardiogram (ECG) signals, which are difficult to replicate and easy to obtain, ECG-based identity identification has become a new direction in biometric recognition research. In order to improve the accuracy of ECG signal identification, this paper proposes an ECG identification method based on a multi-scale wavelet transform combined with the unscented Kalman filter (WT-UKF) algorithm and the improved particle swarm optimization-support vector machine (IPSO-SVM). First, the WT-UKF algorithm can effectively eliminate the noise components and preserve the characteristics of ECG signals when denoising the ECG data. Then, the wavelet positioning method is used to detect the feature points of the denoised signals, and the obtained feature points are combined with multiple feature vectors to characterize the ECG signals, thus reducing the data dimension in identity identification. Finally, SVM is used for ECG signal identification, and the improved particle swarm optimization (IPSO) algorithm is used for parameter optimization in SVM. According to the analysis of simulation experiments, compared with the traditional WT denoising, the WT-UKF method proposed in this paper improves the accuracy of feature point detection and increases the final recognition rate by 1.5%. The highest recognition accuracy of a single individual in the entire ECG identification system achieves 100%, and the average recognition accuracy can reach 95.17%.\",\n \"Title: Spatio-Temporal Estimation of Rice Height Using Time Series Sentinel-1 Images\\nYear: 2022\\n\\nAbstract: Rice height, as the fundamental biophysical attribute, is a controlling factor in crop phenology estimation and yield estimation. The aim of this study was to use time series Sentinel-1A images to estimate the spatio-temporal distribution of rice height. In this study, a particle filter (PF) was applied for the real-time estimation of rice height compared with a simplified water cloud model (SWCM) on the basis of rice mapping and transplanting date. It was found that the VH backscatter (sigma(vh)o) can potentially be applied to accurately estimate rice height compared with VV backscatter (sigma(o)(vv)), the sigma(vh)o/sigma(0)(vv) ratio, and the Radar Vegetation Index (RVI, 4* sigma(o)(vh)/(sigma(o)(vh)+sigma(o)(vv))). The results show that the rice height estimation by PF generated a better result with a root-mean-square error (RMSE) equal to 7.36 cm and a determination factor (R-2) of 0.95 compared with SWCM (RMSE = 12.59 cm and R-2 = 0.86). Moreover, rice height in the south and east of the study area was higher than in the north and west. The reason for this is that the south and east are near to the South China Sea, and there are higher temperatures and earlier transplanting. Altogether, our results demonstrate the potential of PF and sigma(o)(vh) to study the spatio-temporal distribution of crop height estimation. As a result, the PF method can contribute greatly to improvements in crop monitoring.\",\n \"Title: A Novel High-Squint Spotlight SAR Raw Data Simulation Scheme in 2-D Frequency Domain\\nYear: 2022\\n\\nAbstract: Raw data simulation is the front-end work of synthetic aperture radar (SAR), which is of great significance. For high-squint spotlight SAR, the frequency domain simulation algorithm is invalid because of the range-azimuth coupling effect. In order to realize high-squint spotlight SAR raw data simulation in the frequency domain, an algorithm based on coordinate transformation and non-uniform fast Fourier transform (NUFFT) is proposed. This algorithm generates broadside raw data using a two-dimensional (2-D) frequency simulation algorithm; then, coordinate transformation is used by analyzing the characteristics of broadside and high-squint spotlight SAR. After coordinate transformation, NUFFT is carried out to realize the coupling relation in the 2-D frequency domain. Since the coordinate transformation ignores the influence of range walk, the range walk is compensated after NUFFT. As a result, compared with the traditional squint spotlight SAR frequency domain simulation algorithm, the proposed algorithm can improve the accuracy of point and distributed target imaging results, and the efficiency of the proposed algorithm can be significantly improved in contrast the traditional time domain algorithm.\",\n \"Title: Optimal False Data Injection Attacks on MTC\\nYear: 2022\\n\\nAbstract: This paper proposes a convex optimization problem, based on which the optimal strategy of false data injection (FDI) attacks is obtained to intrude machine-type-communications (MTC) networks from the perspective of an attacker, aiming to seek effective defensive measures based on a good understanding of attackers’ behaviour. We consider a target tracking example, which is a typical application of ...\",\n \"Title: 3D multi-resolution deep learning model for diagnosis of multiple pathological types on pulmonary nodules\\nYear: 2022\\n\\nAbstract: To accurately diagnose multiple pathological types of pulmonary nodules based on lung computed tomography (CT) images, a multi-resolution three-dimensional (3D) multi-classification deep learning model (Mr-Mc) was proposed. The Mr-Mc model was constructed by using our own constructed lung CT image dataset of pulmonary nodules with clinical pathological information (LCID-CPI), which can accurately diagnose inflammation, squamous cell carcinoma, adenocarcinoma, and other benign diseases. In order to process nodules with different sizes, a multi-resolution extraction method was proposed to extract 3D volume data with different resolutions from lung CT images. The Mr-Mc was composed of three different resolution networks, each of which has input volume data of a specific resolution. Experiments showed that the constructed Mr-Mc model can achieve an average accuracy of 0.81 on LCID-CPI. Besides, the Mr-Mc model can also achieve a high accuracy of 0.87 on the Lung Image Database Consortium and Image Database Resource Initiative dataset.\",\n \"Title: s-CAM: An Untethered Insertable Laparoscopic Surgical Camera Robot with Non-Contact Actuation\\nYear: 2022\\n\\nAbstract: Fully insertable robotic imaging devices represent a promising future of minimally invasive laparoscopic vision. Emerging research efforts in this field have resulted in several proof-of-concept prototypes. One common drawback of these designs derives from their clumsy tethering wires which not only cause operational interference but also reduce camera mobility. In this paper, a tetherless insertable surgical camera (s-CAM) robot with non-contact transabdominal actuation is presented for single-incision laparoscopic vision. Wireless video transmission and control communication using onboard power help eliminate cumbersome tethering wires. Furthermore, magnetic based camera actuation gets rid of intrinsic physical constraints of mechanical driving mechanisms, thereby improving camera mobility and reducing operational interference. In addition, a custom Bluetooth low energy (BLE) application profile and a real-time operating system (RTOS) based multitask programming framework are also proposed to facilitate embedded software design for insertable medical devices. Initial ex vivo test results of the s-CAM design have demonstrated technical feasibility of a tetherless insertable laparoscopic camera. Effective imaging is confirmed at as low as 500 lx illumination. Wireless laparoscopic vision is accessible within a distance of more than 10 m. Transabdominal BLE communication is stable at over -52 dBm and shows its potential for wireless control of insertable medical devices. RTOS based sfotware event response is bounded within 1 ms while the CPU usage is at 3 similar to 5%. The device is able to work for 50 min with its onboard power. For the mobility, the robot can translate against the interior abdominal wall to reach full abdomen quadrants, tilt between -180 degrees and +180 degrees, and pan in the range of 0 degrees similar to 360 degrees. The s-CAM has brought robotic laparoscopic imaging one step further toward less invasiveness and more dexterity.\",\n \"Title: Optimal Time Selection for ISAR Imaging of Ship Targets Based on Time-Frequency Analysis of Multiple Scatterers\\nYear: 2022\\n\\nAbstract: The equivalent rotation vector synthesized by 3-D nonuniform rotation of ship target leads to the time-varying characteristic of Doppler frequency of ship echo data, which will cause the image to be defocused in azimuth. In order to obtain high-resolution inverse synthetic aperture radar (ISAR) images of ship targets, we developed an optimal time selection algorithm based on time-frequency analysis of multiple scatterers. In this letter, we addressed the challenges of the serious cross-term interference in time-frequency analysis and measuring the stability of Doppler frequency. The time interval with a minimum variation of Doppler frequency was determined by using time-frequency analysis of multiple isolated scatterers and root mean squared error (RMSE). The effectiveness and robustness of the proposed algorithm were evaluated by both simulated and real ISAR data.\",\n \"Title: Oriented Gaussian Function-Based Box Boundary-Aware Vectors for Oriented Ship Detection in Multiresolution SAR Imagery\\nYear: 2022\\n\\nAbstract: As an important remote sensing means, synthetic aperture radar (SAR) has many superiorities to other sensors. How to effectively detect and locate ships in SAR images is also a popular field. In previous ship detection research, most algorithms focus on detecting the horizontal bounding box of ship targets, which ignore the rotation angle of each ships. Thus, too much background noise in the horizontal detection results makes them difficult to describe each ship accurately. Inspired by the powerful feature representation ability of convolutional neural networks (CNNs), a novel anchor-free and keypoint-based deep learning method is proposed for oriented ship detection in multiresolution SAR images. Our detector first extracts multilevel features from the input SAR image with a backbone network and feature pyramid network. Next, considering multiscale ships in multiresolution SAR images, we detect different sizes of ships on different levels of feature maps with identical head network structures. In each head network, the classification subnetwork determines each pixel in feature maps as the central pixel of this ship or not, and the regression subnetwork regresses the oriented bounding box for each ship. In the training process, the proposed oriented nonnormalized Gaussian function is used to describe the center point of ship targets, while the nonuniform weighting of the different level loss functions is used to suppress the imbalanced sample distribution. Experimental results on two authoritative SAR-oriented ship detection datasets and two Gaofen-3 images demonstrate the effectiveness and robustness of the proposed methods.\",\n \"Title: Water-Body Segmentation for SAR Images: Past, Current, and Future\\nYear: 2022\\n\\nAbstract: Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 years in the field of water body extraction in SAR images, and makes some proposals that the community working with SAR image waterbody extraction should consider. Firstly, this review focuses on the main ideas and characteristics of traditional water body extraction on SAR images, mainly focusing on traditional Machine Learning (ML) methods. Secondly, how Deep Learning (DL) methods are applied and optimized in the task of water-body segmentation for SAR images is summarized from the two levels of pixel and image. We also pay more attention to the most popular networks, such as U-Net and its modified models, and novel networks, such as the Cascaded Fully-Convolutional Network (CFCN) and River-Net. In the end, an in-depth discussion is presented, along with conclusions and future trends, on the limitations and challenges of DL for water-body segmentation.\",\n \"Title: Joint User Association and Resource Allocation for MmWave and THz Coexistence Networks\\nYear: 2021\\n\\nAbstract: With the development of information technology and the continuous increase of user demand, how to cope with the limitation of spectrum resources becomes a tough problem. Meanwhile, terahertz communication technology has become a key candidate technology to solve the above challenges. Taking advantage of the small propagation range and high bandwidth of terahertz, the coexistence network of millimeter wave and terahertz can effectively increase the communication rate of users in theory. So this paper considers the joint user association and power allocation of millimeter wave and terahertz, and solves the objective function through Lagrangian duality and gradient iteration algorithm. The simulation results show that the coexistence of millimeter wave and terahertz can greatly improve the system throughput and the achievable transmission rate of users.\",\n \"Title: Blind Zone Performance Enhancement with THz Communication\\nYear: 2021\\n\\nAbstract: Blind zone is a long standing issue in microwave networks. THz communication is regarded as a promising technology in 6G era to cope with these problems due to its wide bandwidth and short transmission distances. However, THz band causes great loss of non-line-of-sight path due to its molecular absorption properties and the communication distance is limited. In order to reduce the communication blind zone and ensure the coverage of the network, we combine THz band and microwave band to form a dual-band network. The object function is formulated to maximize the data rate of the system through joint bandwidth and power allocation scheduling, which is NP-hard. Hence, we decompose the original problem into two subproblems: bandwidth allocation problem and power allocation problem. The bandwidth allocation problem is solved by the convex optimization tool, and we introduce a difference of convex (DC) programming to solve the power allocation problem. Simulation results verify that the THz base stations can improve the performance of microwave network.\",\n \"Title: An Iterative Algorithm for the Generalized Reflexive Solution Group of a System of Quaternion Matrix Equations\\nYear: 2022\\n\\nAbstract: In the present paper, an iterative algorithm is proposed for solving the generalized (P, Q)- reflexive solution group of a system of quaternion matrix equations Sigma(M)(l=1) (A(ls)X(l)B(ls) + C-ls(X) over tilde D-l(ls)) = F-s, s = 1, 2, ..., N. A generalized (P, Q)-reflexive solution group, as well as the least Frobenius norm generalized (P, Q)-reflexive solution group, can be derived by choosing appropriate initial matrices, respectively. Moreover, the optimal approximate generalized (P, Q)-reflexive solution group to a given matrix group can be derived by computing the least Frobenius norm generalized (P, Q)-reflexive solution group of a reestablished system of matrix equations. Finally, some numerical examples are given to illustrate the effectiveness of the algorithm.\",\n \"Title: TIM: threat context-enhanced TTP intelligence mining on unstructured threat data\\nYear: 2022\\n\\nAbstract: TTPs (Tactics, Techniques, and Procedures), which represent an attacker's goals and methods, are the long period and essential feature of the attacker. Defenders can use TTP intelligence to perform the penetration test and compensate for defense deficiency. However, most TTP intelligence is described in unstructured threat data, such as APT analysis reports. Manually converting natural language TTPs descriptions to standard TTP names, such as ATT&CK TTP names and IDs, is time-consuming and requires deep expertise. In this paper, we define the TTP classification task as a sentence classification task. We annotate a new sentence-level TTP dataset with 6 categories and 6061 TTP descriptions from 10761 security analysis reports. We construct a threat context-enhanced TTP intelligence mining (TIM) framework to mine TTP intelligence from unstructured threat data. The TIM framework uses TCENet (Threat Context Enhanced Network) to find and classify TTP descriptions, which we define as three continuous sentences, from textual data. Meanwhile, we use the element features of TTP in the descriptions to enhance the TTPs classification accuracy of TCENet. The evaluation result shows that the average classification accuracy of our proposed method on the 6 TTP categories reaches 0.941. The evaluation results also show that adding TTP element features can improve our classification accuracy compared to using only text features. TCENet also achieved the best results compared to the previous document-level TTP classification works and other popular text classification methods, even in the case of few-shot training samples. Finally, the TIM framework organizes TTP descriptions and TTP elements into STIX 2.1 format as final TTP intelligence for sharing the long-period and essential attack behavior characteristics of attackers. In addition, we transform TTP intelligence into sigma detection rules for attack behavior detection. Such TTP intelligence and rules can help defenders deploy long-term effective threat detection and perform more realistic attack simulations to strengthen defense.\",\n \"Title: Online health assessment and fault prediction for wind turbine generator\\nYear: 2022\\n\\nAbstract: A health assessment and fault prediction method for wind turbine generators is proposed in this article. In health assessment module, considering generator status transferring along with environment and wind turbine-self operating, variables under wind turbine normal working are divided into two parameter spaces and recognized, namely operating conditions and status parameters. Then generator health benchmark models based on Gaussian mixture model are established in different operating condition sub-spaces after the data imbalance problem solved. For online health assessment, health deterioration index based on condition recognition models is calculated and a dual-threshold alarm scheme is proposed. When an alarm is raised by degraded health deterioration index, the program could access fault prediction module, where the generator rear bearing temperature trend and fault remaining time can be predicted through weights redistribution and hyper-parameter optimized support vector regression. In experiments, the proposed health assessment and fault prediction was verified in a real wind farm, and results showed this method could assess generator condition accurately and improve special fault prediction performance.\",\n \"Title: Clutter Suppression for Wideband Radar STAP\\nYear: 2022\\n\\nAbstract: Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or ``zero-bandwidth,'' where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets.\",\n \"Title: TriCTI: an actionable cyber threat intelligence discovery system via trigger-enhanced neural network\\nYear: 2022\\n\\nAbstract: The cybersecurity report provides unstructured actionable cyber threat intelligence (CTI) with detailed threat attack procedures and indicators of compromise (IOCs), e.g., malware hash or URL (uniform resource locator) of command and control server. The actionable CTI, integrated into intrusion detection systems, can not only prioritize the most urgent threats based on the campaign stages of attack vectors (i.e., IOCs) but also take appropriate mitigation measures based on contextual information of the alerts. However, the dramatic growth in the number of cybersecurity reports makes it nearly impossible for security professionals to find an efficient way to use these massive amounts of threat intelligence. In this paper, we propose a trigger-enhanced actionable CTI discovery system (TriCTI) to portray a relationship between IOCs and campaign stages and generate actionable CTI from cybersecurity reports through natural language processing (NLP) technology. Specifically, we introduce the “campaign trigger” for an effective explanation of the campaign stages to improve the performance of the classification model. The campaign trigger phrases are the keywords in the sentence that imply the campaign stage. The trained final trigger vectors have similar space representations with the keywords in the unseen sentence and will help correct classification by increasing the weight of the keywords. We also meticulously devise a data augmentation specifically for cybersecurity training sets to cope with the challenge of the scarcity of annotation data sets. Compared with state-of-the-art text classification models, such as BERT, the trigger-enhanced classification model has better performance with accuracy (86.99%) and F1 score (87.02%). We run TriCTI on more than 29k cybersecurity reports, from which we automatically and efficiently collect 113,543 actionable CTI. In particular, we verify the actionability of discovered CTI by using large-scale field data from VirusTotal (VT). The results demonstrate that the threat intelligence provided by VT lacks a part of the threat context for IOCs, such as the Actions on Objectives campaign stage. As a comparison, our proposed method can completely identify the actionable CTI in all campaign stages. Accordingly, cyber threats can be identified and resisted at any campaign stage with the discovered actionable CTI.\",\n \"Title: Integrated Online Prediction Model for IoT Data\\nYear: 2021\\n\\nAbstract: Data prediction research is very important for applications based on the Internet of things (IoT). However, the large-scale, high-dimension, and the inevitable missing values seriously hinder accurate prediction result. To meet this challenge, an integrated online prediction model is proposed, which can solve future values prediction and missing values imputation simultaneously in the low-dimensional space. Specifically, we embed the classic prediction models into the objective function of matrix factorization (MF) to obtain low-dimensional embedding parameters, which are then used to construct the prediction model. Furthermore, a rolling prediction mechanism is proposed to update the prediction model when new sampled data comes, based on the online MF with time-varying parameters. Simulation results on four UCI public data sets show the superior performance of the proposed method.\",\n \"Title: Pulse RFI Mitigation in Synthetic Aperture Radar Data via a Three-Step Approach: Location, Notch, and Recovery\\nYear: 2022\\n\\nAbstract: In complex electromagnetic environments, synthetic aperture radar (SAR) is severely affected by radio frequency interference (RFI) from other systems, such as ground-based radar, cellular networks, and global positioning systems, and this interference cannot be neglected. Pulse RFI (PRFI), a common form of RFI, can hinder SAR signal processing and image interpretation to varying degrees. The time-domain notch filtering method designed for mitigating PRFI can locate and mitigate the evident PRFI covered in SAR echo data, but it is helpless against PRFI hidden in a strong echo signal. In this article, a three-step approach is proposed to tackle the PRFI problem. In the proposed approach, the first step is to detect and locate PRFI; this is based on eigenvalue decomposition (EVD) and the short-time Fourier transform (STFT). The second step is to notch PRFI, and this is based on a time-domain notch filter. The third step is to recover the notched signal using a novel matrix completion strategy, which integrates with a robust low-rank matrix completion (LRMC) technique-i.e.,the singular value thresholding (SVT) algorithm-and a well-known Lagrange interpolation technique. Experimental results via simulated SAR data, Sentinel-1 level-0 raw data, and L-band airborne SAR raw data demonstrate the performance of the proposed approach.\",\n \"Title: Improving Dialog System Grounded with Unstructured Knowledge by Domain Adaptive Pre-Training and Post-Ranking\\nYear: 2021\\n\\nAbstract: Linguistic intelligence and the ability to converse with human are important and indispensable parts of humanoid robots. One of the most challenging tasks in knowledge-grounded task-oriented dialog systems (KTDS) is the knowledge selection task, which aims to find the proper knowledge snippets to respond to user dialog requests. In this paper, we first propose domain adapted-BERT (DA-BERT) which employs pre-trained bidirectional encoder representations from transformers (BERT) with domain adaptive training and dynamic masking probability for knowledge selection in KTDS. Domain adaptive training can minimize the domain gap between the general text data that BERT is pre-trained on and the dialog-knowledge joint data while dynamic masking probability enhances the training in an easy-to-hard manner. After knowledge selection, the next task in KTDS is knowledge-grounded generation. To improve the performance in knowledge-grounded generation, we propose GPT-PR to employ post-ranking on the generator's outputs. Post-ranking eliminates the possibility of generating hallucination response by a large portion during the sampling-based decoding process and thus can improve the quality of the generated response. Experimental results on the benchmark dataset show that our proposed pre-training and post-ranking methods, DA-BERT and GPT-PR, respectively, outperform the state-of-the-art models with large margins across all the evaluation metrics. Moreover, in the experiments, we also analyze the bad cases of DA-BERT and GPT-PR and do visualizations to facilitate further research in this direction.\",\n \"Title: Identification of Crop Type Based on C-AENN Using Time Series Sentinel-1A SAR Data\\nYear: 2022\\n\\nAbstract: Crop type identification is the initial stage and an important part of the agricultural monitoring system. It is well known that synthetic aperture radar (SAR) Sentinel-1A imagery provides a reliable data source for crop type identification. However, a single-temporal SAR image does not contain enough features, and the unique physical characteristics of radar images are relatively lacking, which limits its potential in crop mapping. In addition, current methods may not be applicable for time-series SAR data. To address the above issues, a new crop type identification method was proposed. Specifically, a farmland mask was firstly generated by the object Markov random field (OMRF) model to remove the interference of non-farmland factors. Then, the features of the standard backscatter coefficient, Sigma-naught (sigma(0)), and the normalized backscatter coefficient by the incident angle, Gamma-naught (gamma(0)), were extracted for each type of crop, and the optimal feature combination was found from time-series SAR images by means of Jeffries-Matusita (J-M) distance analysis. Finally, to make efficient utilization of optimal multi-temporal feature combination, a new network, the convolutional-autoencoder neural network (C-AENN), was developed for the crop type identification task. In order to prove the effectiveness of the method, several classical machine learning methods such as support vector machine (SVM), random forest (RF), etc., and deep learning methods such as one dimensional convolutional neural network (1D-CNN) and stacked auto-encoder (SAE), etc., were used for comparison. In terms of quantitative assessment, the proposed method achieved the highest accuracy, with a macro-F1 score of 0.9825, an overall accuracy (OA) score of 0.9794, and a Kappa coefficient (Kappa) score of 0.9705. In terms of qualitative assessment, four typical regions were chosen for intuitive comparison with the sample maps, and the identification result covering the study area was compared with a contemporaneous optical image, which indicated the high accuracy of the proposed method. In short, this study enables the effective identification of crop types, which demonstrates the importance of multi-temporal radar images in feature combination and the necessity of deep learning networks to extract complex features.\",\n \"Title: Soft Exoskeleton With Fully Actuated Thumb Movements for Grasping Assistance\\nYear: 2022\\n\\nAbstract: It has been clinically proven that exoskeletons are effective self-training rehabilitation or daily living assistance devices for patients with hand dysfunctions. However, exoskeleton-assisted hand exercises with high degrees-of-freedom are considered as challenging tasks because the digit space, especially the thumb, cannot accommodate enough actuators. In this article, we report a tendon-driven soft hand exoskeleton with a hybrid configuration for thumb actuation. The soft hand exoskeleton system uses the least number of actuators to realize full degrees-of-freedom actuation for all digits. It is tested on a stroke patient with hemiplegia and a healthy subject. The experimental results show that the hand exoskeleton could assist the stroke patient to accomplish various training tasks, such as thumb encircling, grasping, pinching, releasing, and writing. It was found that digit trajectories and joint angle changes of the stroke patient were close to those of the healthy subject. Especially, the range of motion of the stroke patient shows significant improvement with the hand exoskeleton assistance compared to that without the hand exoskeleton assistance. The research in this article paves the way to develop fully actuated soft hand exoskeleton that can be eventually integrated with an electroencephalogram or electromyography for self-training rehabilitation or daily living assistance.\",\n \"Title: The Identification of ECG Signals Using Wavelet Transform and WOA-PNN\\nYear: 2022\\n\\nAbstract: Electrocardiogram (ECG) signal identification technology is rapidly replacing traditional fingerprint, face, iris and other recognition technologies, avoiding the vulnerability of traditional recognition technologies. This paper proposes an ECG signal identification method based on the wavelet transform algorithm and the probabilistic neural network by whale optimization algorithm (WOA-PNN). Firstly, Q, R and S waves are detected by wavelet transform, and the P and T waves are detected by local windowed wavelet transform. The characteristic values are constructed by the detected time points, and the ECG data dimension is smaller than that of the non-reference detection. Secondly, combined with the probabilistic neural network, the mean impact value algorithm is used to screen the characteristic values, the characteristic values with low influence are eliminated, and the input and complexity of the model are simplified. Finally, a WOA-PNN combined classification method is proposed to intelligently optimize the hyper parameters in the probabilistic neural network algorithm to improve the model accuracy. According to the simulation verification on three databases, ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia, the identification accuracy of a single ECG cycle is 96.97%, and the identification accuracy of three ECG cycles is 99.43%.\",\n \"Title: In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory\\nYear: 2022\\n\\nAbstract: Continually learning new classes from few training examples without forgetting previous old classes demands a flexible architecture with an inevitably growing portion of storage, in which new examples and classes can be incrementally stored and efficiently retrieved. One viable architectural solution is to tightly couple a stationary deep neural network to a dynamically evolving explicit memory (EM). As the centerpiece of this architecture, we propose an EM unit that leverages energy-efficient in-memory compute (IMC) cores during the course of continual learning operations. We demonstrate for the first time how the EM unit can physically superpose multiple training examples, expand to accommodate unseen classes, and perform similarity search during inference, using operations on an IMC core based on phase-change memory (PCM). Specifically, the physical superposition of few encoded training examples is realized via in-situ progressive crystallization of PCM devices. The classification accuracy achieved on the IMC core remains within a range of 1.28%-2.5% compared to that of the state-of-the-art full-precision baseline software model on both the CIFAR-100 and miniImageNet datasets when continually learning 40 novel classes (from only five examples per class) on top of 60 old classes.\",\n \"Title: EfiLoc: large-scale visual indoor localization with efficient correlation between sparse features and 3D points\\nYear: 2022\\n\\nAbstract: Important location information of a query image can be obtained directly through indoor 3D points. However, the 3D model-based indoor positioning is still an open issue to be addressed, especially in large-scale dynamic environments. We design and realize the positioning system for large indoor scenes called the EfiLoc. First, we develop a lightweight network model, which can quickly extract discriminative global deep features to improve the discrimination of similar scenes. Another property is that the generated sparser main global descriptors can greatly reduce the retrieval time of multi-dimensional features. Second, we innovatively implement the efficient association of 3D point with the 2D features generated by its projection regions. Preserving the associations of the pixels in some key areas of the image, the precise and quick large-scale indoor localization can be realized. The experimental results show that EfiLoc can achieve good positioning accuracy and is of better robustness to the environment of weak textures and similar scenes compared with current state-of-the-art vision-based solutions.\",\n \"Title: Design of Uniform Magnetic Field Coil by Quasi-Elliptic Function Fitting Method With Multiple Optimizations in Miniature Atomic Sensors\\nYear: 2022\\n\\nAbstract: This article presents a design method of cylindrical uniform field coil based on quasi-elliptic function fitting (EFF) method with multiple optimizations. The EFF method is a type of discrete wire techniques aiming at simplifying the coil structure. Combing with particle swarm optimization (PSO) algorithm, the optimized parameters of EFF coil are obtained for highly uniform magnetic field. To further improve the uniformity of magnetic field in the central space, a new regional points selection optimization method is proposed. The calculation results show that after PSO optimization, the absolute maximum relative error in the target region is optimized to 0.098% by EFF coil comparing with modified single saddle coil (3.08%), nest coil (2.4%), and target field method (TFM) coil (0.11%). After regional points selection optimization, the volume proportion of absolute magnetic field relative error less than \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"><tex-math notation=\\\"LaTeX\\\">$1\\\\times 10^{-4}$</tex-math></inline-formula>\\n increases from 35.24% to 54.38% by EFF coil comparing with TFM coil. In addition, the simple EFF coil structure is verified to be easy to process and keep high performance through experiments. As the EFF coil’s high uniformity and simple structure, the new method has important significance for the miniaturization of atomic sensors.\",\n \"Title: Time-Domain Autofocus for Ultrahigh Resolution SAR Based on Azimuth Scaling Transformation\\nYear: 2022\\n\\nAbstract: For ultrahigh resolution synthetic aperture radar (SAR), azimuth spectrum aliasing limits the application of frequency-domain autofocus algorithms. Therefore, time-domain autofocus algorithms are often used for ultrahigh resolution SAR imaging. However, the azimuth deramping operation in current time-domain autofocus algorithms may introduce an additional azimuth-dependent phase. This phase can be regarded as a part of the phase error, which significantly reduces the estimation accuracy of the phase error. To address this issue, this article proposes a new time-domain autofocus algorithm based on azimuth scaling transformation (AST) for ultrahigh resolution SAR. In this algorithm, we first adopt the azimuth scaling operation to avoid the azimuth-dependent phase so that the estimation accuracy of error can be greatly improved. Then, for the azimuth-dependent shifts caused by the azimuth scaling operation, we adopt the alignment processing to remove them in azimuth-time domain. Finally, we can estimate the error accurately from the aligned signal. The simulation and measured data were processed to verify the effectiveness of the algorithm.\",\n \"Title: A novel neural prescribed performance control design for constrained flexible hypersonic vehicles with compensation system\\nYear: 2022\\n\\nAbstract: In this study, a novel prescribed performance function and auxiliary system are investigated for the constrained flexible hypersonic vehicles. The non-affine model of hypersonic vehicles is decomposed into velocity and altitude dynamic systems. For the velocity one, the adaptive neural prescribed controller is devised to guarantee that the tracking error converges to an arbitrary small compact. Then, the altitude dynamics subsystem is delivered as a pure feedback non-affine pattern, which gets the controller out of the dependence on precise affine model. On this premise, the exploited prescribed performance mechanism can not only avoid obtaining the initial sign of tracking error in advance but also can be employed in solving the problem that the conventional prescribed performance function could not cover the tracking error when the altitude subsystem is input constraint. And a Nussbaum-type function is employed to handle the control direction. Meanwhile, the controller utilizes a new auxiliary compensation algorithm to pursue the stable situation for the input restricted system. In order to cut down the computational burden, neural networks employ the min-learning parameter theory to approximate uncertainties in the subsystems. Finally, the simulation results convey that the presented approach has a superior dynamic and steady-state performance.\",\n \"Title: Observation and Mitigation of Mutual RFI Between SAR Satellites: A Case Study Between Chinese GaoFen-3 and European Sentinel-1A\\nYear: 2022\\n\\nAbstract: In synthetic aperture radar (SAR) data processing, radio frequency interference (RFI) has been recognized as a challenging issue, which can significantly degrade the image quality, including amplitude, phase, geometry, and so on. Most of the RFI sources are direct waves transmitted from the ground. Recently, a new form of RFI, namely, the mutual RFI (MRFI), which is a kind of scatter-wave RFI originating from the nearby ground area simultaneously illuminated by different SAR satellites, has been reported. In this article, the signatures of MRFI are characterized, and an example case study between Chinese GaoFen-3 (GF-3) and European Sentinel-1A (S-1A) is analyzed. In order to remove the artifacts caused by MRFI on SAR images, a novel RFI detector based on spectrum energy cancellation (SEC), which has the capability of detecting MRFI, is developed. Two methods, i.e., the traditional notch filtering method and an improved eigen-subspace projection (ESP) method proposed in this article, are employed to mitigate MRFI. The former method has robust mitigation performance with a fast processing speed, whereas the latter has an improved mitigation accuracy and lower sidelobes for strong scatterers. The methods are designed for cases in which the MRFI has a different radar center frequency than the useful signal, and some of the data are free from MRFI. The conditions required for the proper use of the approach are discussed. In contrast to the conventional mitigating methods performed on the raw data domain, the proposed approach begins with the focused single-look complex (SLC) SAR data. The effectiveness of the proposed approach is demonstrated on several GF-3 SLC SAR images, which were contaminated by MRFI from the S-1A.\",\n \"Title: A novel deep framework for dynamic malware detection based on API sequence intrinsic features\\nYear: 2022\\n\\nAbstract: Dynamic malware detection executes the software in a secured virtual environment and monitors its run-time behavior. This technique widely uses API sequence analysis to identify whether the running software is malicious or not. However, existing solutions typically only consider the API name or frequency of API usage, and the feature mining of API sequence is not sufficient, which leads some malware to escape from being detected. In this paper, we propose a novel malware detection framework using deep learning models to capture and combine more meaningful features which are called intrinsic features of the API sequence. Specifically, we first apply embedding and convolutional layers to conduct a joint representation of multiple APIs to represent the software behavior. Secondly, we use the category, action, and operation object of the API to represent the semantic information of each API call. Finally, we use the Bi-LSTM module to mine the relationship information between APIs. Our proposed model achieves an accuracy of 0.9731 and an F1-score of 0.9724 on a large real dataset, which outperforms baselines significantly. We also conduct ablation studies to prove the effectiveness of our intrinsic features.\",\n \"Title: An Improved Hybrid PSO-TS Algorithm for Solving Nonlinear Equations of SHEPWM in Multilevel Inverters\\nYear: 2022\\n\\nAbstract: Selective Harmonic Elimination Pulse Width Modulation (SHEWM) can eliminate selected low order harmonics by solving nonlinear transcendental equations to obtain the switching angles in advance and achieve precise harmonics control. However, when solving nonlinear equations, the convergence rate and solution accuracy of the existing intelligent algorithms for multilevel inverter SHEPWM will decrease with the increase of the number of switching angles. In this paper, an improved hybrid PSO-TS algorithm (IPSO-TS) is proposed for switching angle selection in PWM inverter. In the proposed IPSO-TS algorithm, both the PSO algorithm and TS algorithm are improved respectively to enhance their convergence when calculating multiple switching angles. Also, these two improved algorithms are combined to obtain the hybrid algorithm IPSO-TS, in which PSO is used to provide global search capabilities, and TS is used to increases the accuracy of solutions. Simulations are carried out using MATLAB/SIMULINK environment to demonstrate the better convergence of the proposed algorithm compared with the other existing methods. Moreover, the simulation results are further verified by experimental results.\",\n \"Title: BETA: a comprehensive benchmark for computational drug-target prediction\\nYear: 2022\\n\\nAbstract: Internal validation is the most popular evaluation strategy used for drug-target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug-drug and protein-protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.\",\n \"Title: Calibration of Radar RCS Measurement Errors by Observing the Luneburg Lens Onboard the LEO Satellite\\nYear: 2022\\n\\nAbstract: Accurate radar RCS measurements are critical to the feature recognition of spatial targets. A calibration method for radar RCS measurement errors is proposed for the first time in the context of special target tracking by observing the Luneburg Lens onboard the LEO satellite. The Luneburg Lens has favorable RCS scattering properties for the radar microwave. Thus, the laboratory RCS measurements of the Luneburg Lens, with some fixed incident frequency and with different incident orientations for the radar microwave, will be implemented in order to build a database. The incident orientation for the radar microwave in the satellite body frame will be calculated by taking advantage of the precise orbit parameters, with errors only at the magnitude of several centimeters and within the actual satellite attitude parameters. According to the incident orientation, the referenced RCS measurements can be effectively obtained by the bilinear interpolation in the database. The errors of actual RCS measurements can thus be calibrated by comparing the referenced and the actual RCS measurements. In the RCS measurement experiment, which lasts less than 400 s, the actual RCS measurement errors of the Luneburg Lens are nearly less than 0 dBsm, which indicates that the RCS measurement errors of the spatial targets can be effectively calculated by the proposed calibration method. After the elaborated calibration, the RCS measurements of the spatial targets can be accurately obtained by radar tracking.\",\n \"Title: Simultaneous Screening and Detection of RFI From Massive SAR Images: A Case Study on European Sentinel-1\\nYear: 2022\\n\\nAbstract: Currently, the spaceborne synthetic aperture radar (SAR) system transmits a great deal of data to the ground processing station and generates massive images daily, only a tiny fraction of which contains radio frequency interference (RFI). However, most of the existing RFI detection methods are based on the prior conditions in which the known image contains interference. In fact, it is difficult to learn whether SAR images contain RFIs without prescreening, so it is of great significance to the rapid and real-time screening and detection of RFI in SAR images. This article proposes a method to screen and detect RFI from massive SAR images simultaneously. 1) We construct an approximate RFI-free background image by using the preprocessed time-series SAR images acquired in the past. 2) We generate difference images based on the image change detection method and analyze them by using an adaptive threshold, then calculate the entropy of all difference images to complete the preliminary screening of the RFI-containing). According to the preliminary results, we remove the RFI-containing parts; after reconstructing the background, we repeat step 2 with the images to be detected and obtain the final screening and detection results. Massive experimental results based on Sentinel-1 images validate the performance of the proposed method.\",\n \"Title: Time Series Forecasting of Motor Bearing Vibration Based on Informer\\nYear: 2022\\n\\nAbstract: Electric energy, as an economical and clean energy, plays a significant role in the development of science and technology and the economy. The motor is the core equipment of the power station; therefore, monitoring the motor vibration and predicting time series of the bearing vibration can effectively avoid hazards such as bearing heating and reduce energy consumption. Time series forecasting methods of motor bearing vibration based on sliding window forecasting, such as CNN, LSTM, etc., have the problem of error accumulation, and the longer the time-series forecasting, the larger the error. In order to solve the problem of error accumulation caused by the conventional methods of time series forecasting of motor bearing vibration, this paper innovatively introduces Informer into time series forecasting of motor bearing vibration. Based on Transformer, Informer introduces ProbSparse self-attention and self-attention distilling, and applies random search to optimize the model parameters to reduce the error accumulation in forecasting, achieve the optimization of time and space complexity and improve the model forecasting. Comparing the forecasting results of Informer and those of other forecasting models in three publicly available datasets, it is verified that Informer has excellent performance in time series forecasting of motor bearing vibration and the forecasting results reach 10(-2)similar to 10(-6).\",\n \"Title: An Autoselection Strategy of Multiobjective Evolutionary Algorithms Based on Performance Indicator and its Application\\nYear: 2022\\n\\nAbstract: The use of ensemble approaches in the single-objective evolutionary algorithms is ubiquitous, but ensembles of multiobjective evolutionary algorithms (MOEAs) have achieved relatively little attention. On the other hand, manually selecting a suitable MOEA to solve an actual multiobjective optimization problem (MOP) is time-consuming and challenging. Therefore, developing a multiobjective hyperheuristic to allocate computational resources for multiple MOEAs in an intelligent approach is beneficial. In this work, an autoselection strategy of MOEAs based on the performance indicator (MOEAS-PI) is introduced to alleviate the abovementioned problem. In the MOEAS-PI, the performance of each constituent MOEA in the pool is assessed according to a real-time and comprehensive performance indicator, which contains both the current and future performances. The MOEAS-PI is able to easily choose the best performing MOEA during the evolutionary process. Also, it can enhance the robustness of MOEAs and reduce the application risk. The effectiveness of the MOEAS-PI is carefully evaluated on 23 MOPs. Simulation results demonstrate that the MOEAS-PI is an effective and efficient method to integrate the advantages of each individual algorithm. Finally, the MOEAS-PI is utilized to solve a translation control problem of an immersed tunnel element under current flow. Experimental results reveal that the MOEAS-PI is a reliable and effective optimization approach to solve actual MOPs. \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">Note to Practitioners</i>\\n—Multiobjective optimization problems (MOPs) have been commonly found in various fields. However, a single MOEA cannot guarantee its sufficient robustness and adaptability in solving MOPs. Therefore, this study aims to propose a multiobjective hyperheuristic algorithm to improve the robustness of MOEAs. The performance of the proposed algorithm is tested on benchmark test functions and an actual MOP. The results show that the proposed approach can select a suitable MOEA to solve a particular type of MOPs during the evolutionary process and provide a solution set for decision-makers to control the translation of an immersed tunnel element under different objectives/operator environments.\",\n \"Title: Research status and development trend of image camouflage effect evaluation\\nYear: 2022\\n\\nAbstract: In today’s military operations, camouflage generally uses feature reduction techniques such as camouflage net, low emission coating and camouflage pattern to minimize its detectability and optimize the survivability of high-value assets. However, there is no internationally recognized standard method and procedure to evaluate camouflage equipment and techniques. This paper briefly introduces the existing subjective evaluation methods such as professional evaluation, image-based interpretation, discovery probability equation, and objective evaluation methods such as linear weighted model, neural network model, human visual attention mechanism and multi-attribute decision-making. The application scenarios, advantages and disadvantages of each method are analyzed, and the future development trend of camouflage evaluation model in deep learning, dynamic target camouflage evaluation and hyperspectral field is pointed out.\",\n \"Title: VISEL: A visual and magnetic fusion-based large-scale indoor localization system with improved high-precision semantic maps\\nYear: 2022\\n\\nAbstract: Multisource fusion localization is a mainstream scheme for acquiring accurate locations in complex indoor scenes. To overcome the interference of indoor structures on radio and illumination variation on visual features, the semantic maps provide an effective way for multisource fusion localization. However, due to the lack of visual depth information, solutions of indoor semantic maps suffer from large semantic segmentation errors for similar objects, which leads to the unstable performance of localization systems. To overcome the issue in semantic and fusion localization, we develop a localization system to demonstrate the use of restudy semantic map and self-adapting fusion localization would achieve centimeter-level positioning accuracy, termed VISEL. VISEL uses the proposed spatial attention-aware semantic model to enhance the discrimination of semantic features for capturing accurate semantic maps. On the basis of high-precision semantic maps, VISEL completes an enhanced particle filter fusion localization module with adaptive reassign weight to different localization modules, which successfully improves accuracy through complementary advantages between different signals while overcoming the drawbacks of each signal and interference of complex environment. The extensive experimental results show that VISEL outperforms current state-of-the-art positioning systems and achieves an average positioning accuracy of 0.4 m. VISEL utilizes semantic maps with depth features and enhanced particle filter to reduce the fusion localization error by 38%, which suggests the high-precision semantic maps with depth features could provide a robust solution for the fusion localization system for indoor complex scenes.\",\n \"Title: Infrared Small-Target Detection Based on Radiation Characteristics with a Multimodal Feature Fusion Network\\nYear: 2022\\n\\nAbstract: Infrared small-target detection has widespread influences on anti-missile warning, precise weapon guidance, infrared stealth and anti-stealth, military reconnaissance, and other national defense fields. However, small targets are easily submerged in background clutter noise and have fewer pixels and shape features. Furthermore, random target positions and irregular motion can lead to target detection being carried out in the whole space-time domain. This could result in a large amount of calculation, and the accuracy and real-time performance are difficult to be guaranteed. Therefore, infrared small-target detection is still a challenging and far-reaching research hotspot. To solve the above problem, a novel multimodal feature fusion network (MFFN) is proposed, based on morphological characteristics, infrared radiation, and motion characteristics, which could compensate for the deficiency in the description of single modal characteristics of small targets and improve the recognition precision. Our innovations introduced in the paper are addressed in the following three aspects: Firstly, in the morphological domain, we propose a network with the skip-connected feature pyramid network (SCFPN) and dilated convolutional block attention module integrated with Resblock (DAMR) introduced to the backbone, which is designed to improve the feature extraction ability for infrared small targets. Secondly, in the radiation characteristic domain, we propose a prediction model of atmospheric transmittance based on deep neural networks (DNNs), which predicts the atmospheric transmittance effectively without being limited by the complex environment to improve the measurement accuracy of radiation characteristics. Finally, the dilated convolutional-network-based bidirectional encoder representation from a transformers (DC-BERT) structure combined with an attention mechanism is proposed for the feature extraction of radiation and motion characteristics. Finally, experiments on our self-established optoelectronic equipment detected dataset (OEDD) show that our method is superior to eight state-of-the-art algorithms in terms of the accuracy and robustness of infrared small-target detection. The comparative experimental results of four kinds of target sequences indicate that the average recognition rate P-avg is 92.64%, the mean average precision (mAP) is 92.01%, and the F1 score is 90.52%.\",\n \"Title: Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data\\nYear: 2022\\n\\nAbstract: Crop classification is an important part of crop management and yield estimation. In recent years, neural networks have made great progress in synthetic aperture radar (SAR) crop classification. However, the insufficient number of labeled samples limits the classification performance of neural networks. In order to solve this problem, a new crop classification method combining geodesic distance spectral similarity measurement and a one-dimensional convolutional neural network (GDSSM-CNN) is proposed in this study. The method consisted of: (1) the geodesic distance spectral similarity method (GDSSM) for obtaining similarity and (2) the one-dimensional convolutional neural network model for crop classification. Thereinto, a large number of training data are extracted by GDSSM and the generalized volume scattering model which is based on radar vegetation index (GRVI), and then classified by 1D-CNN. In order to prove the effectiveness of the GDSSM-CNN method, the GDSSM method and 1D-CNN method are compared in the case of a limited sample. In terms of evaluation and verification of methods, the GDSSM-CNN method has the highest accuracy, with an accuracy rate of 91.2%, which is 19.94% and 23.91% higher than the GDSSM method and the 1D-CNN method, respectively. In general, the GDSSM-CNN method uses a small number of ground measurement samples, and it uses the rich polarity information in multi-temporal fully polarized SAR data to obtain a large number of training samples, which can quickly improve the accuracy of classification in a short time, which has more new inspiration for crop classification.\",\n \"Title: Framework for Runway's True Heading Extraction in Remote Sensing Images Based on Deep Learning and Semantic Constraints\\nYear: 2022\\n\\nAbstract: Runway heading is a crucial runway attribute that closely affects aircraft' takeoff and landing safely while having a large number of missing in existing runway thematic databases, limiting the evaluation and analysis globally. This article proposes an automated runway heading extraction method, considering various runway surface materials and spatial structure differences encountered in wide-area detection, promoting a quick and reliable broad investigation. First, multiscale detection is performed based on the mask regional convolutional neural network runway detection model to locate airport runways, using the mask nonmaximum suppression mechanism algorithm to distinguish runways with highly overlapping candidate results. Second, abnormality judgment and mask selection according to semantic constraints were applied to the identified runway area to reserve high-confidence masks to accurately extract the runway heading. We tested our framework results on 1483 runways in the United States with complex runway materials and structures combination. The runway detection recall was 99.12%, and 98.9% of the runway heading errors were limited to 2 degrees, showing that the overall frame for extracting the true runway heading is reliable. In addition, we complement the true headings of 892 runways in OurAirports, promoting research on the completion, verification, and updating of runway semantics in related databases.\",\n \"Title: An Improved RFI Mitigation Approach for SAR Based on Low-Rank Sparse Decomposition: From the Perspective of Useful Signal Protection\\nYear: 2022\\n\\nAbstract: As an open system, synthetic aperture radar (SAR) inevitably receives radio frequency interference (RFI) generated by electromagnetic equipment in the same band. The existence of RFI seriously affects SAR signal processing and image interpretation. In recent years, many algorithms and models related to RFI mitigation have been proposed. However, most of that focus on effectively mitigating the RFI is insufficient to protect the useful signals. This article proposes a mitigation method of RFI with a signal-protected capability. (1) The kurtosis coefficient is used to detect RFI pulse-by-pulse, and the echoes containing RFI are stored in matrix form. (2) The preliminary extraction of RFI is complete by low-rank sparse decomposition of the echo matrix containing RFI. (3) For the secondary separation of RFI, the accurate position of RFI in the preliminary extraction results is located by the fuzzy C-means clustering; then, we separate the RFI and the remaining useful signals again and reconstruct the useful signals to complete the mitigation work. The proposed method can further protect useful signals while effectively removing interference through the secondary separation of RFI. Experimental results based on simulated and measured data verify the performance and potential of the proposed method.\",\n \"Title: A Novel Method for SAR Ship Detection Based on Eigensubspace Projection\\nYear: 2022\\n\\nAbstract: Synthetic Aperture Radar (SAR) is a high-resolution radar that operates all day and in all weather conditions, so it has been widely used in various fields of science and technology. Ship detection using SAR images has become important research in marine applications. However, in complex scenes, ships are easily submerged in sea clutter, which cause missed detection. Due to this, strong sidelobes in SAR images generate false targets and reduce the detection accuracy. To solve these problems, a ship detection method based on eigensubspace projection (ESSP) in SAR images is proposed. First, the image is reconstructed into a new observation matrix along the azimuth direction, and the phase space matrix of the reconstructed image is constructed by using the Hankel characteristic, which preliminarily determines the approximate position of the ship. Then, the autocorrelation matrix of the reconstructed image is decomposed by eigenvalue decomposition (EVD). According to the size of the eigenvalues, the corresponding eigenvectors are divided into two parts, which constitute the basis of the ship subspace and the clutter subspace. Finally, the original image is projected into the ship subspace, and the ship data in the ship subspace are rearranged to obtain the precise position of the ship with significantly suppressed clutter. To verify the effectiveness of the proposed method, the ESSP method is compared with other detection methods on four images at different sea conditions. The results show that the detection accuracy of the ESSP method reaches 89.87% in complex scenes. Compared with other methods, the proposed method can extract ship targets from sea clutter more accurately and reduce the number of false alarms, which has obvious advantages in terms of detection accuracy and timeliness.\",\n \"Title: A Novel Group Paging Control Method for Massive MTC Accesses in LTE Networks\\nYear: 2022\\n\\nAbstract: The fifth-generation (5G) systems have to deal with massive deployment of machine-type-communication (MTC) devices. System overload may occur during a random access procedure under a limited number of preamble resources and physical uplink shared channel resources especially when there exist massive MTC devices in a cell. In order to resolve the system overload (caused by the massive MTC deployment), the 3GPP proposed the adopted a group paging (GP)-based uplink access technique. But its performances dramatically decrease as the number of MTC devices increases. In this paper we propose a novel method, named ACB-based group paging overload control method (AGO). To reduce the number of simultaneous access MTC devices, AGO first scatters the MTC devices over a GP interval, and then automatically adjusts ACB parameters according to the load conditions. By doing so, AGO achieves high-channel access probability for MTC devices. Simulation results show that this method is superior to the GP and Pre-backoff mechanisms in terms of success and collision probability, average access latency and resource utilization rate.\",\n \"Title: Human–Computer Interaction Cognitive Behavior Modeling of Command and Control Systems\\nYear: 2022\\n\\nAbstract: Human–computer interaction cognitive behavior (HCICB) modeling faces four deficiencies: 1) lack of a standard framework model; 2) large simulation error; 3) single simulation dimension; and 4) lack of a simulation software. To solve these deficiencies, we have carried out work in four aspects. First, we construct an HCICB model with the user, system device, and environment as the core elements, which provides a unified framework for the subsequent HCICB modeling in the Military Internet of Things (MIoT) command and control (C2) system. Second, we correct visual and motion parameters in the adaptive control of thought rational module of the Cogtool model by the commander in the loop (CIL) experiment. Third, we construct a mental workload (MW) prediction model of the MIoT C2 system based on improved visual auditory cognitive psychomotor, which realizes fast, high-precision, and quantitative MW prediction. It is added as a simulation dimension for the HCICB. Fourth, we develop MwCogtool, an HCICB prediction software that can rapidly simulate typical tasks at the design and usage stages of the MIoT C2 system, and also can output six parameters, including task completion time (TCT), MW, eye movement prepare time, eye movement execution time, motion time, and cognitive time in the whole process quickly and visually. In addition, we select 20 real users and 9 typical tasks of the MIoT C2 system to carry out the CIL verification experiment. Compared with Cogtool, MwCogtool reduces the maximum simulation error in TCT of the C2 system from 45.00% to 5.58%. The consistency of simulation results with real user data reaches 0.99. The results of the MW prediction model can significantly and negatively predict the change of real users’ eye movement, and can accurately predict the trend of MW change. Simultaneously, we build a fitting model between the mean MW prediction value and eye movement parameters.\",\n \"Title: Lexical semantics enhanced neural word embeddings\\nYear: 2022\\n\\nAbstract: Current breakthroughs in natural language processing have benefited dramatically from- neural language models, through which distributional semantics can leverage neural data representations to facilitate downstream applications. Since neural embeddings use context prediction on word co-occurrences to yield dense vectors, they are inevitably prone to capture more semantic association than semantic similarity. To improve vector space models in deriving semantic similarity, we post-process neural word embeddings through deep metric learning, through which we can inject lexical-semantic relations, including syn/antonymy and hypo/hypernymy, into a distributional space. We introduce hierarchy-fitting, a novel semantic specialization approach to modelling semantic similarity nuances inherently stored in the IS-A hierarchies. Hierarchy-fitting attains state-of-the-art results on the common- and rare-word benchmark datasets for deriving semantic similarity from neural word embeddings. It also incorporates an asymmetric distance function to specialize hypernymy’s directionality explicitly, through which it significantly improves vanilla embeddings in multiple evaluation tasks of detecting hypernymy and directionality without negative impacts on semantic similarity judgement. The results demonstrate the efficacy of hierarchy-fitting in specializing neural embeddings with semantic relations in late fusion, potentially expanding its applicability to aggregating heterogeneous data and various knowledge resources for learning multimodal semantic spaces.\",\n \"Title: DMalNet: Dynamic malware analysis based on API feature engineering and graph learning\\nYear: 2022\\n\\nAbstract: Application Programming Interfaces (APIs) are widely considered a useful data source for dynamic malware analysis to understand the behavioral characteristics of malware. However, the accuracy of API-based malware analysis is limited for two reasons. (1) Existing solutions often only consider the API names while ignoring the API arguments, or cannot fully exploit the semantic information from different types of arguments. (2) The relationship between API calls is important to describe the software behavior but is difficult to capture. To overcome the above limitations, we propose DMalNet, a novel malware analysis framework for accurate malware detection and classification. Specifically, we first present a hybrid feature encoder to extract semantic features from API names and arguments. Then, we derive an API call graph from the API call sequence to convert the relationship between API calls into the structural information of the graph. Finally, we design a graph neural network to implement the malware detection and type classification. To evaluate our approach, we use datasets of over 20k benign and 18k malicious samples belonging to 8 malware types. DMalNet achieves 98.43% and 91.42% accuracy on malware detection and malware type classification, respectively. We also conduct ablation studies to assess the impact of API feature engineering and the graph learning model. Further experiments show that DMalNet can effectively detect malware.\",\n \"Title: Switching pinning control for memristive neural networks system with Markovian switching topologies\\nYear: 2022\\n\\nAbstract: This work concentrates on the issue of leader-following bipartite synchronization of multiple memristive neural networks with Markovian jump topology. In contrast to conventional coupled neural network systems, the coupled neural network model under consideration possesses both cooperative and competitive connections among neuron nodes. Specifically, the interaction between neighbors’ nodes is described by a signed graph, in which a positive weight represents an alliance relationship between two neuron nodes while a negative weight represents an adversarial relationship between two neuron nodes. By designing a pinning discontinuous controller that makes full use of the mode information, some effective criteria that ensure the stability of bipartite synchronization error states are obtained. All network nodes can synchronize the target node state bipartitely. Finally, two simulation examples are provided to demonstrate the viability of the suggested bipartite synchronization control approach.\",\n \"Title: DGGCN: Dictionary based DGA detection method based on DomainGraph and GCN\\nYear: 2022\\n\\nAbstract: Nowadays, malware uses Algorithmically Generated Domains (AGDs) to establish communication with Command and Control (C&C) servers. Dictionary based Domain Generation Algorithm (DGA) selects words from the frequently changed dictionaries to generate AGDs similar to benign domains, which degrades the accuracy of string based detection method. To combat this, we propose a DGA detection method based on DomainGraph and GCN (Graph Convolutional Network) which detects cross-dictionary AGDs based on the association relation between domains instead of lexical features. Starting from the association relation between domains rather than the lexical features of the domain itself, we can detect the unknown AGDs from a known AGD, regardless of the DGA dictionary they use. The proposed method exploits the fact that string association of benign domains is weak, while AGDs' association is strong. DGGCN composes a domain segmentation method, constructs a graph composed of domains (DomainGraph) based on segmentations and adopts GCN to detect AGDs. We conduct the experiments on public datasets under three settings: detecting AGDs generated by familiar dictionaries, unfamiliar dictionaries and confusing dictionaries. The results reveal that DGGCN can detect cross-dictionary AGDs similar to benign domains more accurately and robustly.\",\n \"Title: Research on Two-dimensional Vector Magnetic Field Sensing Characteristics of All-fiber Magnetic Field Sensor Based on the Magnetostrictive Effect\\nYear: 2022\\n\\nAbstract: Based on the Giant Magnetostrictive Material (GMM) Terfenol-D and fiber Bragg grating, the optical fiber magnetic field temperature sensor for magnetic field measurement is proposed. Its magnetic field and temperature sensing characteristics are theoretically analyzed and experimentally verified, and a sensitivity matrix is established to overcome the cross-sensitivity of magnetic field and temperature. In addition, the vector response of the magnetic field in three orthogonal planes is experimentally tested. It is worth noting that the magnetic field vector response of the sensor in three orthogonal planes is experimentally tested, and it is found that the magnetic field vector response of the sensor in the three orthogonal planes is different. The sensor has the advantages of low cost, easy integration, and good vector properties. It has an important application significance for marine magnetic network detection. Based on the sensing of cascaded Fiber Bragg Gratings (FBGs), an array sensing integration scheme for magnetic field detection is proposed.\",\n \"Title: Verification of Dam Spatial Location in Open Datasets Based on Geographic Knowledge and Deep Learning\\nYear: 2022\\n\\nAbstract: Dams are important water conservation hubs; however, dam locations provided by open datasets are often unreliable. The aim of this article is to provide a single, geographic location-reliable dataset of dams for the scientific community by fusing existing dam datasets and verifying these locations. Using Southeast Asia as the case study, we propose an efficient and automatic method to verify dam locations and developed a process framework. First, the possible location of a dam was obtained by analyzing its geographic location characteristics. Then, the deep learning method was used to detect dams. Finally, a variety of geographic knowledge was applied to comprehensively verify the detection results to obtain accurate and reliable dam location information. The fused dam dataset we produced includes the locations of 4493 dams in Southeast Asia, which were verified using the proposed framework. The verification results were then evaluated via manual visual inspection. The verification accuracy of the framework was 86.7%. The experimental results show that the proposed framework can quickly and reliably verify dam spatial locations and provide solutions for the spatial location verification of other remote sensing objects.\",\n \"Title: Compact Closed-loop EEG/fNIRS Recording and TMS Neuromodulation System\\nYear: 2022\\n\\nAbstract: Transcranial magnetic stimulation (TMS) is a clinic-proven neuromodulation technique to treat various neurological and psychological diseases. However, existing TMS devices are bulky, expensive, and open loop. Therefore, they can only be used in fixed locations, and the modulation effects cannot be measured. In this paper, a wearable close-loop neuromodulation system that integrates real-time EEG/fNIRS recording and TMS is proposed. The system’s real-time signal monitoring and analysis function allows the inducing of magnetic stimulation and helps measure its effects after stimulation. The proposed system can measure and analyze 23 channel EEG and 64 channel fNIRS signals simultaneously in real-time. The dimensions of the fabricated TMS device are 330mm × 148mm × 112mm, with peak voltage and current values of 1100 V and 2400A, respectively, resulting in peak magnetic stimulation of 620 mTesla and bidirectional current pulse width of 311 μs.\",\n \"Title: Extension and Evaluation of SSC for Removing Wideband RFI in SLC SAR Images\\nYear: 2022\\n\\nAbstract: Synthetic aperture radar (SAR), as a wideband radar system, is easily contaminated by radio frequency interference (RFI), which affects the imaging quality of SAR. The subband spectral cancellation (SSC) method and its modifications utilize the SAR single-look complex (SLC) image to realize RFI extraction and mitigation by subtracting between sub-images, which are robust and efficient for engineering applications. In the past, the traditional SSC was often applied to narrowband interference (NBI) mitigation. However, when it was used for wideband interference (WBI) mitigation, it would cause the mitigated image to lose much of its useful information. In contrast, this paper proposes an improved SSC method based on successive cancellation and data accumulation (SSC-SCDA) for WBI mitigation. First, the fast Fourier transform (FFT) is used to characterize the SAR SLC data in the frequency domain, and the average range spectrum algorithm is used to detect whether there are interference components in the SAR SLC data. Then, according to the carrier frequency and bandwidth of the RFI in the frequency domain, the subbands are divided, and a cancellation strategy is formulated. Finally, based on the successive cancellation and data accumulation technology, WBIs can be removed by using only a small percentage of the clean subbands. Based on the simulated experiments, the interference mitigation performance of the proposed method is analyzed when the interference-to-signal bandwidth ratio (ISBR) varies from 20% to 80% under different signal-to-interference-to-noise ratios (SINR). The experimental results based on WBI-contaminated European Space Agency (ESA) Sentinel-1A SAR SLC data demonstrate the effectiveness of the proposed method in WBI mitigation.\",\n \"Title: ESP-LRSMD: A Two-Step Detector for Ship Detection Using SLC SAR Imagery\\nYear: 2022\\n\\nAbstract: Synthetic aperture radar (SAR), an active microwave remote sensing equipment, can provide all-day, all-weather, and high-resolution images for Earth observation. Ship monitoring using SAR is significant to fishery application, marine transportation, maritime security, and so on. Most traditional SAR ship detection methods use the amplitude information of the SAR imagery and employ the constant false alarm rate (CFAR), deep learning, and other technologies to detect the ship targets. However, in addition to the amplitude image, the SAR single-look complex (SLC) data can also be utilized for ship detection since its complex-valued information is beneficial to characterize the ship's feature in the transforming domains. In this article, the strong scattering feature of ships in the image domain and low-rank feature in the transforming domain is studied, and on this basis, a two-step detector is proposed to detect ship targets in SAR SLC image. The first step is to identify the presence of ships, which is based on the eigen-subspace projection (ESP) technology to detect the occurrences of the ship targets in azimuth cells of the SAR data. The second step is to locate the position of ships, which is based on the low-rank and sparse matrix decomposition (LRSMD) to obtain the instantaneous frequency information of the ship targets in the time-frequency domain. Finally, the ship targets can be located in a line-by-line manner in the azimuth direction. Experimental results via European Sentinel-1A and Chinese Gaofen-3 single-pol SAR SLC data demonstrate the performance of the proposed detector.\",\n \"Title: Multistep-Ahead Prediction of Ocean SSTA Based on Hybrid Empirical Mode Decomposition and Gated Recurrent Unit Model\\nYear: 2022\\n\\nAbstract: The prediction of sea surface temperature anomalies (SSTA) is vital to the study of marine ecosystems and global climate. The SSTA can be accurately forecasted one step ahead by numerical and statistical methods. However, multistep-ahead forecasting for SSTA is greatly challenging since the nonlinearity and nonstationarity of SSTA and the lag problem of prediction. Therefore, in this article, a multistep-ahead SSTA forecasting method based on the hybrid empirical mode decomposition (EMD) and gated recurrent unit (GRU) model is proposed considering that EMD has the advantage of reducing data complexity and GRU is good at long-term prediction of data. First, the EMD algorithm is applied to obtain several intrinsic modal mode functions that are more stationary than the original data. Then, GRU is used for multistep forecasting, in which three multistep forecasting strategies (recursive, direct, and multioutput) are compared. The proposed hybrid model is validated by multistep forecasting for monthly average SSTA at the Nino3.4 region. The experimental results show that using reconstruction error as part of the prediction effectively improves the prediction accuracy of the EEMD-GRU model and outperforms other EMD algorithms combined with GRU. Compared with traditional models, the EEMD-GRU model can better predict future multimonth trends of SSTA and effectively alleviate the problem of prediction lag of the traditional model. Finally, this model is applied to the Nino3.4 regional SSTA prediction, and the results show that this model can provide a reference for ocean research.\",\n \"Title: A Repeater-Type SAR Deceptive Jamming Method Based on Joint Encoding of Amplitude and Phase in the Intra-Pulse and Inter-Pulse\\nYear: 2022\\n\\nAbstract: Due to advantages such as low power consumption and high concealment, deceptive jamming against synthetic aperture radar (SAR) has received extensive attention in electronic countermeasures. However, the false targets generated by most of the deceptive jamming methods still have limitations, such as poor controllability and strong regularity. Inspired by the idea of waveform coding, this paper proposed a repeater-type SAR deceptive jamming method through the joint encoding of amplitude and phase in intra-pulse and inter-pulse, which can generate a two-dimensional controllable deceptive jamming effect. Specifically, the proposed method mainly includes two parts, i.e., grouping and encoding. The number of groups determines the number of false targets, and the presence of the phase encoding produces false targets. The amplitude encoding affects the amplitude of the false targets. For the intra-pulse cases, the proposed method first samples the intercepted SAR signal. Meanwhile, the sampling points are grouped in turn. For the inter-pulse cases, the grouped objects are the pulses. Subsequently, the joint encoding of amplitude and phase is performed on each group, which generates jamming signals with deceptive effects. In this paper, the imaging effect of the generated jamming signals is analyzed in detail, and the characteristics of false targets, including numbers, position, and amplitude, are derived. The simulation and experimental results verify the correctness of the theoretical analysis. In addition, the superiority of the proposed method is verified by comparing it with other methods.\",\n \"Title: Towards Intelligent Noninvasive Closed-loop Neuromodulation Systems\\nYear: 2022\\n\\nAbstract: Neuromodulation is an effective method to treat various kinds of brain diseases. It has drawn significant attention in academic research and the medical market and has become a rapidly developing area of medicine. However, current neuromodulation technologies face bottlenecks such as high cost, bulky, and lack of intelligent closed-loop for patient-specific treatments. These bottlenecks further limit the widespread use of neuromodulation technologies. Fortunately, these challenges, in turn, drive the advancement in integrated circuits and systems designs for biomedical applications. This paper presents recent research on IC development that is relevant to addressing the abovementioned challenges, emphasizing closing the loop with on-chip artificial intelligence (AI). Our current research will be presented as a case study to demonstrate how dedicated IC designs and AI can benefit the development of an intelligent and miniaturized closed-loop neuromodulation system. The challenges and emerging directions for the development of circuits and systems for neuromodulation technologies are summarized in the concluding remarks.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":200,"string":"200"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13261,"string":"13,261"}}},{"rowIdx":13262,"cells":{"question_id":{"kind":"string","value":"Luca Aceto"},"question":{"kind":"string","value":"Generate an abstract for the title \"Equational Axioms for Probabilistic Bisimilarity\" using the following items: equational axiomatization, probabilistic bisimulation equivalence, finite-state agents, iteration theories, iteration algebras."},"answer":{"kind":"string","value":"This paper gives an equational axiomatization of probabilistic bisimulation equivalence for a class of finite-state agents previously studied by Stark and Smolka ((2000) Proof, Language, and Interaction: Essays in Honour of Robin Milner, pp. 571-595). The axiomatization is obtained by extending the general axioms of iteration theories (or iteration algebras), which characterize, among others, the equational properties of the fixed point operator on (驴-)continuous or monotonic functions, with three axiom schemas that express laws that are specific to probabilistic bisimilarity. Hence probabilistic bisimilarity (over finite-state agents) has an equational axiomatization relative to iteration algebras."},"correct_answer":{"kind":"string","value":"This paper gives an equational axiomatization of probabilistic bisimulation equivalence for a class of finite-state agents previously studied by Stark and Smolka ((2000) Proof, Language, and Interaction: Essays in Honour of Robin Milner, pp. 571-595). The axiomatization is obtained by extending the general axioms of iteration theories (or iteration algebras), which characterize, among others, the equational properties of the fixed point operator on (驴-)continuous or monotonic functions, with three axiom schemas that express laws that are specific to probabilistic bisimilarity. Hence probabilistic bisimilarity (over finite-state agents) has an equational axiomatization relative to iteration algebras."},"documents":{"kind":"list like","value":["Title: Axiomatizing Tropical Semirings\nYear: 2001\n\nAbstract: This paper studies the equational theory of various exotic semirings presented in the literature. Exotic semirings are semirings\n whose underlying carrier set is some subset of the set of real numbers equipped with binary operations of minimum or maximum\n as sum, and addition as product. Two prime examples of such structures are the (max, +) semiring and the tropical semiring. It is shown that none of the exotic semirings commonly considered in the literature has a finite basis for its equations,\n and that similar results hold for the commutative idempotent weak semirings that underlie them. For each of these commutative\n idempotent weak semirings, the paper offers characterizations of the equations that hold in them, explicit descriptions of\n the free algebras in the varieties they generate, and relative axiomatization results.","Title: Axiomatizing prefix iteration with silent steps\nYear: 1996\n\nAbstract: Preflx iteration is a variation on the original binary version of the Kleene star operation P⁄Q, obtained by restricting the flrst argument to be an atomic action. The interaction of preflx iteration with silent steps is studied in the setting of Milner's basic CCS. Complete equational ax- iomatizations are given for four notions of behavioural congruence over basicCCSwithpreflxiteration,viz.branchingcongruence, ·-congruence, delay congruence and weak congruence. The completeness proofs for ·-, delay,andweakcongruenceareobtainedbyreductiontothecompleteness theorem for branching congruence. It is also argued that the use of the completeness result for branching congruence in obtaining the complete- nessresultforweakcongruenceleadstoaconsiderablesimpliflcationwith respect to the only direct proof presented in the literature. The prelimi- naries and the completeness proofs focus on open terms, i.e., terms that maycontainprocessvariables. Asabyproduct,the !-completenessofthe axiomatizationsisobtainedaswellastheircompletenessforclosedterms.","Title: A menagerie of non-finitely based process semantics over BPA* - from ready simulation to completed traces\nYear: 1998\n\nAbstract: Fokkink and Zantema (Fokkink and Zantema 1994) have shown that bisimulation equivalence has a finite equational axiomatization over the language of Basic Process Algebra with the binary Kleene star operation (BPA*). In light of this positive result on the mathematical tractability of bisimulation equivalence over BPA*, a natural question to ask is whether any other (pre)congruence relation in van Glabbeek's linear time/branching time spectrum is finitely (in)equationally axiomatizable over it. In this paper, we prove that, unlike bisimulation equivalence, none of the preorders and equivalences in van Glabbeek's linear time/branching time spectrum, whose discriminating power lies in between that of ready simulation and that of completed traces, has a finite equational axiomatization. This we achieve by exhibiting a family of (in)equivalences that holds in ready simulation semantics (which is the finest semantics that we consider) and whose instances cannot all be proved by means of any finite set of (in)equations that is sound in completed trace semantics (which is the coarsest semantics that is appropriate for the language BPA*). To this end, for every finite collection of (in)equations that are sound in completed trace semantics, we build a model in which some of the (in)equivalences of the family under consideration fail. The construction of the model mimics the one used by Conway (Conway 1971, p. 105) in his proof of a result, originally due to Redko, to the effect that infinitely many equations are needed to axiomatize equality of regular expressions.Our non-finite axiomatizability results apply to the language BPA* over an arbitrary non-empty set of actions. In particular, we show that completed trace equivalence is not finitely based over BPA* even when the set of actions is a singleton. Our proof of this result may be adapted to the standard language of regular expressions to yield a solution to an open problem posed by Salomaa (Salomaa 1969, p. 143).Another semantics that is usually considered in process theory is trace semantics. Trace semantics is, in general, not preserved by sequential composition, and is therefore inappropriate for the language BPA*. We show that, if the set of actions is a singleton, trace equivalence and preorder are preserved by all the operators in the signature of BPA*, and coincide with simulation equivalence and preorder, respectively. In that case, unlike all the other semantics considered in this paper, trace semantics have finite, complete equational axiomatizations over closed terms.","Title: A Rule Format for Unit Elements\nYear: 2010\n\nAbstract: This paper offers a meta-theorem for languages with a Structural Operational Semantics (SOS) in the style of Plotkin. Namely, it proposes a generic rule format for SOS guaranteeing that certain constants act as left- or right-unit elements for a set of binary operators. We show the generality of our format by applying it to a wide range of operators from the literature on process calculi.","Title: A Complete Classification of the Expressiveness of Interval Logics of Allen's Relations over Dense Linear Orders\nYear: 2013\n\nAbstract: Interval temporal logics are temporal logics that take time intervals, instead of time instants, as their primitive temporal entities. One of the most studied interval temporal logics is Halpern and Shoham's modal logic of time intervals (HS), which has a distinct modality for each binary relation between intervals over a linear order. As HS turns out to be undecidable over most classes of linear orders, the study of HS fragments, featuring a proper subset of HS modalities, is a major item in the research agenda for interval temporal logics. A characterization of HS fragments in terms of their relative expressive power has been given for the class of all linear orders. Unfortunately, there is no easy way to directly transfer such a result to other meaningful classes of linear orders. In this paper, we provide a complete classification of the expressiveness of HS fragments over the class of (all) dense linear orders.","Title: Complete and ready simulation semantics are not finitely based over BCCSP, even with a singleton alphabet\nYear: 2011\n\nAbstract: This note shows that the complete and the ready simulation preorders do not have a finite inequational basis over the language BCCSP when the set of actions is a singleton. Moreover, the equivalences induced by those preorders do not have a finite (in)equational axiomatization either. These results are in contrast with a claim of finite axiomatizability for those semantics in the literature, which was based on the erroneous assumption that they coincide with complete trace semantics in the presence of a singleton set of actions.","Title: Axiomatizing Gsos With Predicates\nYear: 2011\n\nAbstract: In this paper, we introduce an extension of the GSOS rule format with predicates such as termination, convergence and divergence. For this format we generalize the technique proposed by Aceto, Bloom and Vaandrager for the automatic generation of ground-complete axiomatizations of bisimilarity over GSOS systems. Our procedure is implemented in a tool that receives SOS specifications as input and derives the corresponding axiomatizations automatically. This paves the way to checking strong bisimilarity over process terms by means of theorem-proving techniques.","Title: Termination, deadlock, and divergence\nYear: 1992\n\nAbstract: In this paper, a process algebra that incorporates explicit representations of successful termination, deadlock, and divergence is introduced and its semantic theory is analyzed. Both an operational and a denotational semantics for the language is given and it is shown that they agree. The operational theory is based upon a suitable adaptation of the notion of bisimulation preorder. The denotational semantics for the language is given in terms of the initial continuous algebra that satisfies a set of equations E, CIE. It is shown that CIE is fully abstract with respect to our choice of behavioral preorder. Several results of independent interest are obtained; namely, the finite approximability of the behavioral preorder and a partial completeness result for the set of equations E with respect to the preorder.","Title: Rule formats for distributivity\nYear: 2012\n\nAbstract: This paper proposes rule formats for Structural Operational Semantics guaranteeing that certain binary operators are left distributive with respect to a set of binary operators. Examples of left-distributivity laws from the literature are shown to be instances of the provided formats.","Title: Graphical Representation Of Covariant-Contravariant Modal Formulae\nYear: 2011\n\nAbstract: Covariant-contravariant simulation is a combination of standard (covariant) simulation, its contravariant counterpart and bisimulation. We have previously studied its logical characterization by means of the covariant-contravariant modal logic. Moreover, we have investigated the relationships between this model and that of modal transition systems, where two kinds of transitions ( the so-called may and must transitions) were combined in order to obtain a simple framework to express a notion of refinement over state-transition models. In a classic paper, Boudol and Larsen established a precise connection between the graphical approach, by means of modal transition systems, and the logical approach, based on Hennessy-Milner logic without negation, to system specification. They obtained a (graphical) representation theorem proving that a formula can be represented by a term if, and only if, it is consistent and prime. We show in this paper that the formulae from the covariant-contravariant modal logic that admit a \"graphical\" representation by means of processes, modulo the covariant-contravariant simulation preorder, are also the consistent and prime ones. In order to obtain the desired graphical representation result, we first restrict ourselves to the case of covariantcontravariant systems without bivariant actions. Bivariant actions can be incorporated later by means of an encoding that splits each bivariant action into its covariant and its contravariant parts.","Title: Impossibility Results in the Equational Logic of Processes\nYear: 2007\n\nAbstract: This talk offers a survey of negative results on the existence of finite equational axiomatizations for bisimulation equivalence over fragments of algebraic process calculi.","Title: A Fully Equational Proof of Parikh's Theorem\nYear: 2002\n\nAbstract: We show that the validity of Parikh's theorem for context free languages depends only on a few equational properties of least pre-fixed points. Moreover, we exhibit an infinite basis of mu-term equations of continuous commutative idempotent semirings.","Title: Axiomatizing weak ready simulation semantics over BCCSP\nYear: 2011\n\nAbstract: Ready simulation has proven to be one of the most significant semantics in process theory. It is at the heart of a number of general results that pave the way to a comprehensive understanding of the spectrum of process semantics. Since its original definition by Bloom, Istrail and Meyer in 1995, several authors have proposed generalizations of ready simulation to deal with internal actions. However, a thorough study of the (non-)existence of finite (in)equational bases for weak ready simulation semantics is still missing in the literature. This paper presents a complete account of positive and negative results on the axiomatizability of weak ready simulation semantics over the language BCCSP. In addition, this study offers a thorough analysis of the axiomatizability properties of weak simulation semantics.","Title: CPO models for compact GSOS languages\nYear: 1996\n\nAbstract: In this paper, we present a general way of giving denotational semantics to a class of languages equipped with an operational semantics that fits the GSOS format of Bloom, Istrail, and Meyer. The canonical model used for this purpose will be Abramsky's domain of synchronization trees, and the denotational semantics automatically generated by our methods will be guaranteed to be fully abstract with respect to the finitely observable part of the bisimulation preorder. In the process of establishing the full abstraction result, we also obtain several general results on the bisimulation preorder (including a complete axiomatization for it), and give a novel operational interpretation of GSOS languages.","Title: Model Checking via Reachability Testing for Timed Automata\nYear: 1998\n\nAbstract: In this paper we develop an approach to model-checking for timed automata via reachability testing. As our specification formalism, we consider a dense-time property language with clocks. This property language may be used to express safety and bounded liveness properties of real-time systems. We show how to automatically synthesize, for every formula phi, a test automaton T-phi in such a way that checking whether a system S satisfies the property phi can be reduced to a reachability question over the system obtained by making T-phi interact with S.","Title: Modelling and Simulation of Asynchronous Real-Time Systems using Timed Rebeca\nYear: 2011\n\nAbstract: In this paper we propose Timed Rebeca as an extension of the Rebeca language that can be used to model distributed and asynchronous systems with timing constraints. Timed Rebeca restricts the modeller to a pure asynchronous actor-based paradigm, where the structure of the model represents the service oriented architecture, while the computational model matches the network infrastructure. The modeller can specify both computational and network delay, and assign deadlines for serving a request. We provide the formal semantics of the language using Structural Operational Semantics, and show its expressiveness by means of examples. We developed a tool for automated translation from Timed Rebeca to the Erlang language, which provides a first implementation of Timed Rebeca. We can use the tool to set the parameters of Timed Rebeca models, which represent the environment and component variables, and use McErlang to run multiple simulations for different settings. The results of the simulations can then be employed to select the most appropriate values for the parameters in the model. Simulation is shown to be an effective analysis support, specially where model checking faces almost immediate state explosion in an asynchronous setting.","Title: Sigma algebras in probabilistic epistemic dynamics\nYear: 2011\n\nAbstract: This paper extends probabilistic dynamic epistemic logic from a finite setting to an infinite setting, by introducing σ-algebras to the probability spaces in the models. This may extend the applicability of the logic to a real world setting with infinitely many possible measurements. It is shown that the dynamics preserves desirable properties of measurability and that completeness of the proof system holds with the extended semantics.","Title: The power of reachability testing for timed automata\nYear: 2003\n\nAbstract: The computational engine of the verification tool UPPALL consists of a collection of efficient algorithms for the analysis of teachability properties of systems. Model-checking of properties other than plain reachability ones may currently be carried out in such a tool as follows. Given a property φ to model-check, the user must provide a test automaton Tφ for it. This test automaton must be such that the original system S has the property expressed by φ precisely when none of the distinguished reject states of Tφ can be reached in the synchronized parallel composition of S with Tφ. This raises the question of which properties may be analysed by UPPAAL in such a way. This paper gives an answer to this question by providing a complete characterization of the class of properties for which model-checking can be reduced to reachability testing in the sense outlined above. This result is obtained as a corollary of a stronger statement pertaining to the compositionality of the property language considered in this study. In particular, it is shown that our language is the least expressive compositional language that can express a simple safety property stating that no reject state can ever be reached.Finally, the property language characterizing the power of reachability testing is used to provide a definition of characteristics properties with respect to a timed version of the ready simulation preorder, for nodes of τ-free, deterministic timed automata.","Title: Proving the validity of equations in gsos languages using rule-matching bisimilarity\nYear: 2012\n\nAbstract: This paper presents a bisimulation-based method for establishing the soundness of equations between terms constructed using operations whose semantics are specified by rules in the GSOS format of Bloom, Istrail and Meyer. The method is inspired by de Simone's FH-bisimilarity and uses transition rules as schematic transitions in a bisimulation-like relation between open terms. The soundness of the method is proved and examples showing its applicability are provided. The proposed bisimulation-based proof method is incomplete, but we do offer some completeness results for restricted classes of GSOS specifications. An extension of the proof method to the setting of GSOS languages with predicates is also offered.","Title: Characteristic Formulae: From Automata to Logic\nYear: 2007\n\nAbstract: This paper discusses the classic notion of characteristic formulae for pro- cesses using variations on Hennessy-Milner logic as the underlying logical specification language. It is shown how to characterize logically (states of) finite labelled transition systems modulo bisimilarity using a single formula in Hennessy-Milner logic with recursion. Moreover, characteristic formulae for timed automata with respect to timed bisimilarity and the faster-than pre- order of Moller and Tofts are o ered in terms of the logic L of Laroussinie, Larsen and Weise.","Title: On the Ill-Timed but Well-Caused\nYear: 1993\n\nAbstract: There has been considerable controversy in concurrency theory between the ‘interleaving’ and ‘true concurrency’ schools. The former school advocates associating a transition system with a process which captures concurrent execution via the interleaving of occurrences: the latter adopts more complex semantic structures to avoid reducing concurrency to interleaving.","Title: Resource bisimilarity and graded bisimilarity coincide\nYear: 2010\n\nAbstract: Resource bisimilarity has been proposed in the literature on concurrency theory as a notion of bisimilarity over labeled transition systems that takes into account the number of choices that a system has. Independently, g-bisimilarity has been defined over Kripke models as a suitable notion of bisimilarity for graded modal logic. This note shows that these two notions of bisimilarity coincide over image-finite Kripke frames.","Title: Conservative extension in structural operational semantics\nYear: 1999\n\nAbstract: IntroductionStructural operational semantics (SOS) [44] provides a framework to givean operational semantics to programming and specification languages. Inparticular, because of its intuitive appeal and flexibility, SOS has found considerableapplication in the study of the semantics of concurrent processes.SOS generates a labelled transition system, whose states are the closed termsover an algebraic signature, and whose transitions are supplied with labels.The transitions between states ...","Title: The equational theory of prebisimilarity over basic CCS with divergence\nYear: 2008\n\nAbstract: This paper studies the equational theory of prebisimilarity, a bisimulation-based preorder introduced by Hennessy and Milner in the early 1980s, over basic CCS with the divergent process @W. It is well known that prebisimilarity affords a finite ground-complete axiomatization over this language; this study proves that this ground-complete axiomatization is also complete in the presence of an infinite set of actions. Moreover, in sharp contrast to this positive result, it is shown that prebisimilarity is not finitely based over basic CCS with the divergent process @W when the set of actions is finite and nonempty.","Title: On Rule Formats for Zero and Unit Elements\nYear: 2010\n\nAbstract: This paper proposes a rule format for Structural Operational Semantics guaranteeing that certain constants act as left or right zero elements for a set of binary operators. Our design approach is also applied to reformulate an earlier rule format for unit elements developed by some of the authors. Examples of left and right zero, as well as unit, elements from the literature are shown to be checkable using the provided formats.","Title: Rule formats for determinism and idempotence\nYear: 2012\n\nAbstract: Determinism is a semantic property of (a fragment of) a language that specifies that a program cannot evolve operationally in several different ways. Idempotence is a property of binary composition operators requiring that the composition of two identical specifications or programs will result in a piece of specification or program that is equivalent to the original components. In this paper, we propose (related) meta-theorems for guaranteeing the determinism and idempotence of binary operators. These meta-theorems are formulated in terms of syntactic templates for operational semantics, called rule formats. In order to obtain a powerful rule format for idempotence, we make use of the determinism of certain transition relations in the definition of the format for idempotence. We show the applicability of our formats by applying them to various operational semantics from the literature.","Title: A Bisimulation-Based Method For Proving The Validity Of Equations In Gsos Languages\nYear: 2009\n\nAbstract: This paper presents a bisimulation-based method for establishing the soundness of equations between terms constructed using operations whose semantics is specified by rules in the GSOS format of Bloom, Istrail and Meyer. The method is inspired by de Simone's FH-bisimilarity and uses transition rules as schematic transitions in a bisimulation-like relation between open terms. The soundness of the method is proven and examples showing its applicability are provided. The proposed bisimulation-based proof method is incomplete, but the article offers some completeness results for restricted classes of GSOS specifications.","Title: Relating modal refinements, covariant-contravariant simulations and partial bisimulations\nYear: 2011\n\nAbstract: This paper studies the relationships between three notions of behavioural preorder that have been proposed in the literature: refinement over modal transition systems, and the covariant-contravariant simulation and the partial bisimulation preorders over labelled transition systems. It is shown that there are mutual translations between modal transition systems and labelled transition systems that preserve, and reflect, refinement and the covariant-contravariant simulation preorder. The translations are also shown to preserve the modal properties that can be expressed in the logics that characterize those preorders. A translation from labelled transition systems modulo the partial bisimulation preorder into the same model modulo the covariant-contravariant simulation preorder is also offered, together with some evidence that the former model is less expressive than the latter. In order to gain more insight into the relationships between modal transition systems modulo refinement and labelled transition systems modulo the covariant-contravariant simulation preorder, their connections are also phrased and studied in the context of institutions.","Title: The max-plus algebra of the natural numbers has no finite equational basis.\nYear: 2003\n\nAbstract: This paper shows that the collection of identities which hold in the algebra N of the natural numbers with constant zero, and binary operations of sum and maximum is not finitely based. Moreover, it is proven that, for every n, the equations in at most n variables that hold in N do not form an equational basis. As a stepping stone in the proof of these facts, several results of independent interest are obtained. In particular, explicit descriptions of the free algebras in the variety generated by N are offered. Such descriptions are based upon a geometric characterization of the equations that hold in N, which also yields that the equational theory of N is decidable in exponential time.","Title: A note on an expressiveness hierarchy for multi-exit iteration\nYear: 2003\n\nAbstract: Multi-exit iteration is a generalization of the standard binary Kleene star operation. The addition of this construct to Basic Process Algebra (BPA) yields a more expressive language than that obtained by augmenting BPA with the standard binary Kleene star. This note offers an expressiveness hierarchy, modulo bisimulation equivalence, for the family of multi-exit iteration operators proposed by Bergstra, Bethke and Ponse.","Title: On a question of A. Salomaa the equational theory of regular expressions over a singleton alphabet is not finitely based\nYear: 1998\n\nAbstract: Salomaa (1969, p. 143) asked whether the equational theory of regular expressions over a singleton alphabet has a finite equational base. In this paper, we provide a negative answer to this long-standing question. The proof of our main result rests upon a model-theoretic argument. For every finite collection of equations, that are sound in the algebra of regular expressions over a singleton alphabet, we build a model in which some valid regular equation fails. The construction of the model mimics the one used by Conway (1971, p. 105) in his proof of a result, originally due to Redko, to the effect that infinitely many equations are needed to axiomatize equality of regular expressions. Our analysis of the model, however, needs to be more refined than the one provided by Conway (1971). (C) 1998 Elsevier Science B.V. All rights reserved.","Title: Full abstraction for series-parallel pomsets\nYear: 1991\n\nAbstract: In this paper, we provide a behavioural characterization of the class of finite, seriesparallel pomsets by showing that this simple model based on partial orders is fully-abstract with respect to the behavioural equivalence obtained by applying Abramsky's testing scenario for bisimulation equivalence, [Ab87], in all refinement contexts, [AH89]. This casts the observability of series-parallel pomsets in a purely interleaving framework. Moreover, we prove that the order structure of a series-parallel pomset is completely revealed by its set of ST-traces, [G190], and provide a complete axiomatization of ST-trace equivalence over the class of series-parallel pomsets.","Title: The saga of the axiomatization of parallel composition\nYear: 2007\n\nAbstract: This paper surveys some classic and recent results on the finite axiomatizability of bisimilarity over CCS-like languages. It focuses, in particular, on non-finite axiomatizability results stemming from the semantic interplay between parallel composition and nondeterministic choice. The paper also highlights the role that auxiliary operators, such as Bergstra and Klop's left and communication merge and Hennessy's merge operator, play in the search for a finite, equational axiomatization of parallel composition both for classic process algebras and for their real-time extensions.","Title: Relating semantic models for the object calculus\nYear: 1997\n\nAbstract: Abadi and Cardelli have investigated several versions of the ç-calculus, a calculus for describing central features of object-oriented programs, with particular emphasis on various type systems. In this paper we study the properties of a denotational semantics due to Abadi and Cardelli vis-à-vis the notion of observational congruence for the calculus Ob 1<:μ . In particular, we prove that the denotational semantics based on partial equivalence relations is correct with respect to observational congruence. By means of a counter-example, we argue that the denotational model is not fully abstract with respect to observational congruence. In fact, the model is able to distinguish objects that have the same behaviour in every Ob 1<:μ -context.","Title: A characterization of finitary bisimulation\nYear: 1997\n\nAbstract: This document in subdirectory RS/97/26/, Department of Computer Science, Aalborg UniversityFredrik Bajersvej 7E, 9220 Aalborg , DenmarkEmail: luca@cs.auc.dkAnna Ing'olfsd'ottirDipartimento di Sistemi ed Informatica, Universit`a di FirenzeVia Lombroso 6/17, 50134 Firenze, ItalyEmail: annai@dsi2.ing.unifi.itKeywords and Phrases: Concurrency, labelled transition system with divergence,bisimulation preorder, finitary relation","Title: Algebraic synchronization trees and processes\nYear: 2012\n\nAbstract: We study algebraic synchronization trees, i.e., initial solutions of algebraic recursion schemes over the continuous categorical algebra of synchronization trees. In particular, we investigate the relative expressive power of algebraic recursion schemes over two signatures, which are based on those for Basic CCS and Basic Process Algebra, as a means for defining synchronization trees up to isomorphism as well as modulo bisimilarity and language equivalence. The expressiveness of algebraic recursion schemes is also compared to that of the low levels in the Caucal hierarchy.","Title: A Static View of Localities\nYear: 1994\n\nAbstract: This paper proposes alternative, effective characterizations for nets of automata of the location equivalence and preorder presented by Boudol et al. in the companion paper [BCHK]. Contrary to the technical development in the above given reference, where locations are dynamically associated to the subparts of a process in the operational semantics, the equivalence and preorder we propose are based on a static association of locations to the parallel components of a net. Following this static approach, it is possible to give these “distributed nets” a standard operational semantics which associates with each net a finite labelled transition system. Using this operational semantics for distributed nets, we introduce effective notions of equivalence and preorder which are shown to coincide with those proposed in [BCHK].","Title: Characteristic formulae for timed automata\nYear: 2000\n\nAbstract: This paper offers characteristic formula constructions in the real-time logic L-nu for several behavioural relations between (states of) timed automata. The behavioural relations studied in this work are timed (bi)similarity, timed ready simulation, faster-than bisimilarity and timed trace inclusion. The characteristic formulae delivered by our constructions have size which is linear in that of the timed automaton they logically describe. This also applies to the characteristic formula for timed bisimulation equivalence, for which an exponential space construction was previously offered by Laroussinie, Larsen and Weise.","Title: SOS rule formats for idempotent terms and idempotent unary operators.\nYear: 2014\n\nAbstract: A unary operator f is idempotent if the equation f(x)=f(f(x)) holds. On the other end, an element a of an algebra is said to be an idempotent for a binary operator ⊙ if a=a⊙a. This paper presents a rule format for Structural Operational Semantics that guarantees that a unary operator be idempotent modulo bisimilarity. The proposed rule format relies on a companion one ensuring that certain terms are idempotent with respect to some binary operator. This study also offers a variety of examples showing the applicability of both formats.","Title: Failures Semantics for a Simple Process Language with Refinement\nYear: 1991\n\nAbstract: We study a suitable semantic theory based on the standard failures preorder [BHR84] for a simple process algebra which includes operators for the refinement of actions by processes. We present a model-theoretic and a behavioural characterization of the largest precongruence associated with failures preorder, \nF\nc\n, over the language we consider.\n The model-theoretic characterization is in terms of a pomset failures model which is shown to be fully abstract with respect to \nF\nc\n. Pomset failures (labelled partial orders with refusal sets) allow us to express the nonsequential as well as the nondeterministic characteristics of \nF\nc\n. The behavioural characterization is in terms of a variation on the standard failures preorder based on ideas of ST-semantics, [Gla90].","Title: On \"Axiomatising Finite Concurrent Processes\"\nYear: 1994\n\nAbstract: In his pioneering paper [Axiomatising finite concurrent processes, SIAM J. Comput., 17 (1988), pp. 997--1017], Hennessy gave complete axiomatizations of Milner's observational congruence and of t-observational congruence which made use of an auxiliary operation to axiomatize parallel composition. Unfortunately, those axiomatizations turn out to be flawed due to the subtle interplay between Hennessy's auxiliary parallel operator and synchronization. The aim of this paper is to present correct versions of the equational characterizations given in Hennessy's paper. Some of the problems which arise in giving operational semantics to the auxiliary operators used by Bergstra and Klop and Hennessy in the theory of congruences like Milner's observational congruence are also discussed.","Title: A finite equational base for CCS with left merge and communication merge\nYear: 2006\n\nAbstract: Using the left merge and the communication merge from ACP, we present an equational base (i.e., a ground-complete and ω-complete set of valid equations) for the fragment of CCS without recursion, restriction and relabeling modulo (strong) bisimilarity. Our equational base is finite if the set of actions is finite.","Title: On Relating Concurency and Nondeterminism\nYear: 1991\n\nAbstract: We present an intuitive preorder for a simple CCS-like language whose semantic theory allows us to relate concurrency and nondeterminism without reducing the former to the latter. The preorder over processes is induced by using an equationally defined preorder over computations in a bisimulation-like protocol. The relationships of the proposed preorder with pomset bisimulation and standard strong bisimulation equivalence are studied in detail. Moreover, we give an axiomatisation of the preorder over recursion-free processes.","Title: On the Axiomatizability of Priority\nYear: 2006\n\nAbstract: This paper studies the equational theory of bisimulation equivalence over the process algebra BCCSP extended with the priority operator of Baeten, Bergstra and Klop. It is proven that, in the presence of an infinite set of actions, bisimulation equivalence has no finite, sound, ground-complete equational axiomatization over that language. This negative result applies even if the syntax is extended with an arbitrary collection of auxiliary operators, and motivates the study of axiomatizations using equations with action predicates as conditions. In the presence of an infinite set of actions, it is shown that, in general, bisimulation equivalence has no finite, sound, ground-complete axiomatization consisting of equations with action predicates as conditions over the language studied in this paper. Finally, sucient conditions on the priority structure","Title: On the axiomatisability of priority\nYear: 2008\n\nAbstract: This paper studies the equational theory of bisimulation equivalence over the process algebra BCCSP extended with the priority operator of Baeten, Bergstra and Klop. We prove that, in the presence of an infinite set of actions, bisimulation equivalence has no finite, sound, ground-complete equational axiomatisation over that language. This negative result applies even if the syntax is extended with an arbitrary collection of auxiliary operators, and motivates the study of axiomatisations using equations with action predicates as conditions. In the presence of an infinite set of actions, it is shown that, in general, bisimulation equivalence has no finite, sound, ground-complete axiomatisation consisting of equations with action predicates as conditions over the language studied in this paper. Finally, sufficient conditions on the priority structure over actions are identified that lead to a finite, ground-complete axiomatisation of bisimulation equivalence using equations with action predicates as conditions.","Title: On the expressibility of priority\nYear: 2008\n\nAbstract: This short note offers results confirming that the priority operator of Baeten, Bergstra and Klop cannot be expressed using positive rule formats for operational semantics. Although expected, this inexpressibility result does not seem to have appeared before in the literature.","Title: 2-Nested Simulation Is Not Finitely Equationally Axiomatizable\nYear: 2001\n\nAbstract: 2-nested simulation was introduced by Groote and Vaandrager [10] as the coarsest equivalence included in completed trace equivalence for which the tyft/tyxt format is a congruence format. In the linear time-branching time spectrum of van Glabbeek [8], 2-nested simulation is one of the few equivalences for which no finite equational axiomatization is presented. In this paper we prove that such an axiomatization does not exist for 2-nested simulation.","Title: Split-2 Bisimilarity has a Finite Axiomatization over CCS with Hennessy's Merge\nYear: 2005\n\nAbstract: This note shows that split-2 bisimulation equivalence (also known as timed equivalence) affords a finite equational axiomatization over the process algebra obtained by adding an auxiliary operation proposed by Hennessy in 1981 to the recursion, relabelling and restriction free fragment of Milner's Calculus of Communicating Systems. Thus the addition of a single binary operation, viz. Hennessy's merge, is sufficient for the finite equational axiomatization of parallel composition modulo this non-interleaving equivalence. This result is in sharp contrast to a theorem previously obtained by the same authors to the effect that the same language is not finitely based modulo bisimulation equivalence.","Title: Relating distributed, temporal and causal observations of simple processes\nYear: 1992\n\nAbstract: The noninterleaving equivalences that have been proposed in the literature may be roughlydivided into three classes, depending on the kind of observational scenarios underlying them.Some of them distinguish parallelism from nondeterminism by admitting explicit observationsof the causal structure of systems; some others are based on the observation of distribution andstill others on the assumption that actions have duration.In general, these three observational scenarios give rise to...","Title: The Complexity of Checking Consistency of Pedigree Information and Related Problems\nYear: 2004\n\nAbstract: Consistency checking is a fundamental computational problem in ge- netics. Given a pedigree and information on the genotypes (of some) of the indi- viduals in it, the aim of consistency checking is to determine whether these data are consistent with the classic Mendelian laws of inheritance. This problem arose originally from the geneticists' need to filter their input data from erroneous in- formation, and is well motivated from both a biological and a sociological view- point. This paper shows that consistency checking is NP-complete, even with focus on a single gene and in the presence of three alleles. Several other results on the computational complexity of problems from genetics that are related to consistency checking are also offered. In particular, it is shown that checking the consistency of pedigrees over two alleles, and of pedigrees without loops, can be done in polynomial time.","Title: On the axiomatizability of priority II\nYear: 2011\n\nAbstract: This paper contributes to the study of the equational theory of the priority operator of Baeten, Bergstra and Klop in the setting of the process algebra BCCSP. It is shown that, in the presence of at least two actions, the collection of process equations over BCCSP with the priority operator that are valid modulo bisimilarity, irrespective of the chosen priority order over actions, is not finitely based. This holds true even if one restricts oneself to the collection of valid process equations that do not contain occurrences of process variables.","Title: Timing and causality in process algebra\nYear: 1996\n\nAbstract: There has been considerable controversy in concurrency theory between the ‘interleaving’ and ‘true concurrency’ schools. The\n former school advocates associating a transition system with a process which captures concurrent execution via the interleaving\n of occurrences; the latter adopts more complex semantic structures to avoid reducing concurrency to interleaving.\n \n In this paper we show that the two approaches are not irreconcilable. We define a timed process algebra where occurrences\n are associated with intervals of time, and give it a transition system semantics. This semantics has many of the advantages\n of the interleaving approach; the algebra admits an expansion theorem, and bisimulation semantics can be used as usual. Our\n transition systems, however, incorporate timing information, and this enables us to express concurrency: merely adding timing\n appropriately generalises transition systems to asynchronous transition systems, showing that time gives a link between true\n concurrency and interleaving. Moreover, we can provide a complete axiomatisation of bisimulation for our algebra; a result\n that is often problematic in a timed setting.\n \n \n \n Another advantage of incorporating timing information into the calculus is that it allows a particularly simple definition\n of action refinement; this we present. The paper concludes with a comparison of the equivalence we present with those in the\n literature, and an example system specification in our formalism.","Title: Meta Sos - A Maude Based Sos Meta-Theory Framework\nYear: 2013\n\nAbstract: Meta SOS is a software framework designed to integrate the results from the meta-theory of structural operational semantics (SOS). These results include deriving semantic properties of language constructs just by syntactically analyzing their rule-based definition, as well as automatically deriving sound and ground-complete axiomatizations for languages, when considering a notion of behavioural equivalence. This paper describes the Meta SOS framework by blending aspects from the meta-theory of SOS, details on their implementation in Maude, and running examples.","Title: CCS with Hennessy's merge has no finite-equational axiomatization\nYear: 2005\n\nAbstract: This paper confirms a conjecture of Bergstra and Klop's from 1984 by establishing that the process algebra obtained by adding an auxiliary operator proposed by Hennessy in 1981 to the recursion free fragment of Milner's Calculus of Communicating Systems is not finitely based modulo bisimulation equivalence. Thus, Hennessy's merge cannot replace the left merge and communication merge operators proposed by Bergstra and Klop, at least if a finite axiomatization of parallel composition modulo bisimulation equivalence is desired.","Title: PREG axiomatizer: a ground bisimilarity checker for GSOS with predicates\nYear: 2011\n\nAbstract: PREG Axiomatizer is a tool used for proving strong bisimilarity between ground terms consisting of operations in the GSOS format extended with predicates. It automatically derives sound and ground-complete axiomatizations using a technique proposed by the authors of this paper. These axiomatizations are provided as input to the Maude system, which, in turn, is used as a reduction engine for provided ground terms. These terms are bisimilar if and only if their normal forms obtained in this fashion are equal. The motivation of this tool is the optimized handling of equivalence checking between complex ground terms within automated provers and checkers.","Title: On the specification of modal systems: A comparison of three frameworks.\nYear: 2013\n\nAbstract: This paper studies the relationships between three notions of behavioural preorder that have been proposed in the literature: refinement over modal transition systems, and the covariant–contravariant simulation and the partial bisimulation preorders over labelled transition systems. It is shown that there are mutual translations between modal transition systems and labelled transition systems that preserve, and reflect, refinement and the covariant–contravariant simulation preorder. The translations are also shown to preserve the modal properties that can be expressed in the logics that characterize those preorders. A translation from labelled transition systems modulo the partial bisimulation preorder into the same model modulo the covariant–contravariant simulation preorder is also offered, together with some evidence that the former model is less expressive than the latter. In order to gain more insight into the relationships between modal transition systems modulo refinement and labelled transition systems modulo the covariant–contravariant simulation preorder, their connections are also phrased and studied in the context of institutions.","Title: Finite Equational Bases for Fragments of CCS with Restriction and Relabelling\nYear: 2008\n\nAbstract: We investigate the equational theory of several fragments of CCS modulo (strong) bisimilarity with special attention to restriction and relabelling. The largest fragment we consider includes action prefixing, choice, parallel composition without communication, restriction and relabelling. We present a finite equational base (i.e., a finite ground-complete and omega-complete axiomatisation) for it, including the left merge from ACP as auxiliary operation to facilitate the axiomatisation of parallel composition.","Title: An Equational Axiomatization of Observation Congruence for Prefix Iteration\nYear: 1996\n\nAbstract: Fokkink ((1994) Inf. Process. Lett.\n52: 333–337) has recently proposed a complete equational axiomatization of strong bisimulation equivalence for MPA\n\n*\n(A), i.e., the language obtained by extending Milner's basic CCS with prefix iteration. Prefix iteration is a variation on the original binary version of the Kleene star operation p\n\n*\n\nq obtained by restricting the first argument to be an atomic action. In this paper, we extend Fokkink's results to a setting with the unobservable action by giving a complete equational axiomatization of Milner's observation congruence over MPA\n\n*\n(A). The axiomatization is obtained by extending Fokkink's axiom system with two of Milner's standard -laws, and three equations that describe the interplay between the silent nature of and prefix iteration. Using a technique due to Groote, we also show that the resulting axiomatization is -complete, i.e., complete for equality of open terms.","Title: The equational theory of weak complete simulation semantics over BCCSP\nYear: 2012\n\nAbstract: This paper presents a complete account of positive and negative results on the finite axiomatizability of weak complete simulation semantics over the language BCCSP. We offer finite (un)conditional ground-complete axiomatizations for the weak complete simulation precongruence. In sharp contrast to this positive result, we prove that, in the presence of at least one observable action, the (in)equational theory of the weak complete simulation precongruence over BCCSP does not have a finite (in)equational basis. In fact, the set of (in)equations in at most one variable that hold in weak complete simulation semantics over BCCSP does not have an (in)equational basis of ‘bounded depth', let alone a finite one.","Title: Teaching Concurrency: Theory in Practice\nYear: 2009\n\nAbstract: Teaching courses that rely on sound mathematical principles is nowadays a challenging task at many universities. On the one hand there is an increased demand for educating students in these areas, on the other hand there are more and more students being accepted with less adequate skills in mathematics. We report here on our experiences in teaching concurrency theory over the last twenty years or so to students ranging from mathsphobic bachelor students to sophisticated doctoral students. The contents of the courses, the material on which they are based and the pedagogical philosophy underlying them are described, as well as some of the lessons that we have learned over the years.","Title: Testing equivalences for event structures\nYear: 1986\n\nAbstract: A flexible abstraction mechanism for models of concurrency, which allows systems which look the same to be considered equivalent, is proposed. Using three classes of atomic observations (sequences of actions, sequences of multisets of actions and partial orderings of actions) different information on the causal and temporal structure of Event Structures, a basic model of parallelism, is captured. As a result, three different semantic models for concurrent systems are obtained. These models can be used as the basis for defining interleaving, multisets or partial ordering semantics of concurrent systems. The common framework used to define the models allows us to study the relationship between these three traditional approaches to the semantics of concurrent communicating systems.","Title: Testing Hennessy-Milner Logic with Recursion\nYear: 1999\n\nAbstract: This study offers a characterization of the collection of properties expressible in Hennessy-Milner Logic (HML) with recursion that can be tested using finite LTSs. In addition to actions used to probe the behaviour of the tested system, the LTSs that we use as tests will be able to perform a distinguished action nok to signal their dissatisfaction during the interaction with the tested process. A process s passes the test T iff T does not perform the action nok when it interacts with s. A test T tests for a property Φ in HML with recursion iff it is passed by exactly the states that satisfy Φ. The paper gives an expressive completeness result offering a characterization of the collection of properties in HML with recursion that are testable in the above sense.","Title: Turning SOS rules into equations\nYear: 1992\n\nAbstract: A procedure is given for extracting from a GSOS specification of an arbitrary process algebra a complete axiom system for bisimulation equivalence (equational, except for possibly one conditional equation). The methods apply to almost all SOSs for process algebras that have appeared in the literature, and the axiomatizations compare reasonably well with most axioms that have been presented. In particular, they discover the L characterization of parallel composition. It is noted that completeness results for equational axiomatizations are tedious and have become rather standard in many cases. A generalization of extant completeness results shows that in principle this burden can be completely removed if one gives a GSOS description of a process algebra","Title: Characteristic Formulae For Relations With Nested Fixed Points\nYear: 2012\n\nAbstract: A general framework for the connection between characteristic formulae and behavioral semantics is described in [2]. This approach does not suitably cover semantics defined by nested fixed points, such as the n-nested simulation semantics for n greater than 2. In this study we address this deficiency and give a description of nested fixed points that extends the approach for single fixed points in an intuitive and comprehensive way.","Title: Nested Semantics over Finite Trees are Equationally Hard: Invited Talk\nYear: 2004\n\nAbstract: One of the criteria that has been put forward for studying the mathematical tractability of the behavioural equivalences in van Glabbeek's linear time- banching time spectrum is that they afford elegant, finite equational axiomatizations over fragments of process algebraic languages. A review of existing complete equational axiomatizations for many of the behavioural semantics in van Glabbeek's spectrum is offered in his encyclopedic chapter in the [Handbook of Process Algebra, Jan A. Bergstra, Alban Ponse, and Scott A. Smolka, Editors. North-Holland, Amsterdam, 2001]. The equational axiomatizations offered ibidem are over the language BCCSP, a common fragment of Milner's CCS and Hoare's CSP suitable for describing finite synchronization trees, and characterize the differences between behavioural semantics in terms of a few revealing axioms.","Title: A complete axiomatization of timed bisimulation for a class of timed regular behaviours\nYear: 1995\n\nAbstract: One of the most satisfactory results in process theory is Milner's axiomatization of strong bisimulation for regular CCS. This result holds for open terms with finite-state recursion. Wang has shown that timed bisimulation can also be axiomatized, but only for closed terms without recursion. In this paper, we provide an axiomatization for timed bisimulation of open terms with finite-state recursion.","Title: CPO Models for a Class of GSOS Languages\nYear: 1995\n\nAbstract: In this paper, we present a general way of giving denotational semantics to a class of languages equipped with an operational semantics that fits the GSOS format of Bloom, Istrail and Meyer. The canonical model used for this purpose will be Abramsky's domain of synchronization trees, and the denotational semantics automatically generated by our methods will be guaranteed to be fully abstract with respect to the finitely observable part of the bisimulation preorder. In the process of establishing the full abstraction result, we also obtain several general results on the bisimulation preorder (including a complete axiomatization for it), and give a novel operational interpretation of GSOS languages.","Title: Impossibility results for the equational theory of timed CCS\nYear: 2007\n\nAbstract: We study the equational theory of Timed CCS as proposed by Wang Yi in CONCUR'90. Common to Wang Yi's paper, we particularly focus on a class of linearly-ordered time domains exemplified by the positive real or rational numbers. We show that, even when the set of basic actions is a singleton, there are parallel Timed CCS processes that do not have any sequential equivalent and thus improve on the Gap Theorem for Timed CCS presented by Godskesen and Larsen in FSTTCS'92. Furthermore, we show that timed bisimilarity is not finitely based both for single-sorted and two-sorted presentations of Timed CCS. We further strengthen this result by showing that, unlike in some other process algebras, adding the untimed or the timed left-merge operator to the syntax and semantics of Timed CCS does not solve the axiomatizability problem.","Title: The Quest for Equational Axiomatizations of Parallel Composition: Status and Open Problems\nYear: 2006\n\nAbstract: This essay recounts the story of the quest for equational axiomatizations of parallel composition operators in process description languages, and of similar results in the classic field of formal language theory. Some of the outstanding open problems are also mentioned.","Title: Finite equational bases in process algebra: results and open questions\nYear: 2005\n\nAbstract: Van Glabbeek (1990) presented the linear time/branching time spectrum of behavioral equivalences for finitely branching, concrete, sequential processes. He studied these semantics in the setting of the basic process algebra BCCSP, and tried to give finite complete axiomatizations for them. Obtaining such axiomatizations in concurrency theory often turns out to be difficult, even in the setting of simple languages like BCCSP. This has raised a host of open questions that have been the subject of intensive research in recent years. Most of these questions have been settled over BCCSP, either positively by giving a finite complete axiomatization, or negatively by proving that such an axiomatization does not exist. Still some open questions remain. This paper reports on these results, and on the state-of-the-art in axiomatizations for richer process algebras with constructs like sequential and parallel composition.","Title: Characteristic formulae for fixed-point semantics: A general framework\nYear: 2009\n\nAbstract: The concurrency theory literature offers a wealth of examples of characteristic-formula constructions for various behavioural relations over finite labelled transition systems and Kripke structures that are defined in terms of fixed points of suitable functions. Such constructions and their proofs of correctness have been developed independently, but have a common underlying structure. This paper provides a general view of characteristic formulae that are expressed in terms of logics that have a facility for the recursive definition of formulae. We show how several examples of characteristic-formula constructions in the literature can be recovered as instances of the proposed general framework, and how the framework can be used to yield novel constructions. The paper also offers general results pertaining to the definition of co-characteristic formulae and of characteristic formulae expressed in terms of infinitary modal logics.","Title: Is Your Model Checker on Time? On the Complexity of Model Checking for Timed Modal Logics\nYear: 1999\n\nAbstract: This paper studies the structural complexity of model checking for (variations on) the specification formalisms used in the tools CMC and Uppaal, and fragments of a timed alternation-free |ì-calculus. For each of the logics we study, we characterize the computational complexity of model checking, as well as its specification and program complexity, using timed automata as our system model.","Title: GSOS and finite labelled transition systems\nYear: 1994\n\nAbstract: Recently there has been considerable interest in studying formats of Plotkin style inference rules which ensure that the induced labelled transition system semantics have certain properties. In this note, I shall give a contribution to this line of research by giving a restricted version of Bloom, Istrail and Meyer's GSOS format Bloom et al. (1988), Bloom (1989) which induces finite labelled transition systems.","Title: Foreword: Special issue in memory of nadia busi\nYear: 2009\n\nAbstract: We still recall very vividly the announcement of Nadia Busi's death. It was Wednesday, 5 September 2007, and we were enjoying the half-day excursion at CONCUR 2007, a conference where just before lunch on that very day some of Nadia's latest work had been presented. The weather was splendid and we were enjoying the gorgeous view from the Arrábida Convent, basking in the glorious sunlight and admiring the blue sea in the distance. Everything was a celebration of life until death struck. Mario Bravetti, one of our colleagues from Bologna, received a phone call and broke the news to us that ‘Nadia has passed away’. After receiving this message, a cloud came over all the CONCUR participants who had known her.","Title: SOS rule formats for zero and unit elements\nYear: 2011\n\nAbstract: This paper proposes rule formats for Structural Operational Semantics guaranteeing that certain constants act as left or right unit/zero elements for a set of binary operators. Examples of left and right zero, as well as unit, elements from the literature are shown to fit the rule formats offered in this study.","Title: A Theory of Testing for ACP\nYear: 1991\n\nAbstract: This paper introduces a process algebra which incorporates the auxiliary operators of ACP, [BK85], and is tailored towards algebraic verifications in the theory of testing equivalence. The process algebra we consider is essentially a version of ACP with the empty process in which the nondeterministic choice operators familiar from TCSP, [BHR84], and TCCS, [DH87], are used in lieu of the internal action and the single choice operator favoured by CCS, [Mil89], and ACP. We present a behavioural semantics for the language based upon a natural notion of testing equivalence, [DH84], and show that, contrary to what happens in a setting with the internal action , the left-merge operator is compatible with it. A complete equational characterization of the behavioural semantics is given for finite processes, thus providing an algebraic theory supporting the use of the auxiliary operators of ACP in algebraic verifications for testing equivalence. Finally we give a fully-abstract denotational model for finite processes with respect to the testing preorder based on a variation on Hennessy's Acceptance Trees suitable for our language.","Title: Some of My Favourite Results in Classic Process Algebra\nYear: 2003\n\nAbstract: Abstract: this paper has generated a veritable industry of results onthe meta-theory of SOS and process algebras. (See [10] for a mention of some ofthese achievements and pointers to the original literature.) The proof techniquesused in these results were extremely ingenious, and have paved the way to manysimilar developments. Again, the role played by the modal characterizations ofbehavioural equivalences in the proof of the characterizations of the largestcongruences is remarkable","Title: A complete equational axiomatization for MPA with string iteration\nYear: 1999\n\nAbstract: We study equational axiomatizations of bisimulation equivalence for the language obtained by extending Milner's basic CCS with string iteration. String iteration is a variation on the original binary version of the Kleene star operation p ∗ q obtained by restricting the first argument to be a non-empty sequence of atomic actions. We show that, for every positive integer k , bisimulation equivalence over the set of processes in this language with loops of length at most k is finitely axiomatizable, provided that the set of actions is finite. We also offer an infinite equational theory that completely axiomatizes bisimulation equivalence over the whole language. We prove that this result cannot be improved upon by showing that no finite equational axiomatization of bisimulation equivalence over basic CCS with string iteration can exist, unless the set of actions is empty.","Title: Nested semantics over finite trees are equationally hard\nYear: 2004\n\nAbstract: This paper studies nested simulation and nested trace semantics over the language BCCSP, a basic formalism to express finite process behaviour. It is shown that none of these semantics affords finite (in)equational axiomatizations over BCCSP. In particular, for each of the nested semantics studied in this paper, the collection of sound, closed (in)equations over a singleton action set is not finitely based.","Title: A Cook's Tour of Equational Axiomatizations for Prefix Iteration\nYear: 1998\n\nAbstract: this paper, we continue this research programme bystudying axiomatic characterizations for more abstract semantics over this languagethan those based on variations of bisimulation. More precisely, we consider readysimulation, simulation, readiness, trace and language semantics, and provide complete(in)equational axiomatizations for each of these notions over BCCS with prefixiteration. All of the axiom systems we present are finite, if so is the set of atomicactions under consideration....","Title: Adding action refinement to a finite process algebra\nYear: 1994\n\nAbstract: Without Abstract","Title: Decompositional reasoning about the history of parallel processes\nYear: 2011\n\nAbstract: This paper presents a decomposition technique for Hennessy-Milner logic with past and its extension with recursively defined formulae. In order to highlight the main ideas and technical tools, processes are described using a subset of CCS with parallel composition, nondeterministic choice, action prefixing and the inaction constant. The study focuses on developing decompositional reasoning techniques for parallel contexts in that language.","Title: On the Expressiveness of the Interval Logic of Allen's Relations Over Finite and Discrete Linear Orders.\nYear: 2014\n\nAbstract: Interval temporal logics take time intervals, instead of time instants, as their primitive temporal entities. One of the most studied interval temporal logics is Halpern and Shoham's modal logic of time intervals HS, which associates a modal operator with each binary relation between intervals over a linear order (the so-called Allen's interval relations). A complete classification of all HS fragments with respect to their relative expressive power has been recently given for the classes of all linear orders and of all dense linear orders. The cases of discrete and finite linear orders turn out to be much more involved. In this paper, we make a significant step towards solving the classification problem over those classes of linear orders. First, we illustrate various non-trivial temporal properties that can be expressed by HS fragments when interpreted over finite and discrete linear orders; then, we provide a complete set of definabilities for the HS modalities corresponding to the Allen's relations meets, later, begins, finishes, and during, as well as the ones corresponding to their inverse relations. The only missing cases are those of the relations overlaps and overlapped by.","Title: Axiomatizing weak simulation semantics over BCCSP.\nYear: 2014\n\nAbstract: This paper is devoted to the study of the (in)equational theory of the largest (pre)congruences over the language BCCSP induced by variations on the classic simulation preorder and equivalence that abstract from internal steps in process behaviours. In particular, the article focuses on the (pre)congruences associated with the weak simulation, the weak complete simulation and the weak ready simulation preorders. We present results on the (non)existence of finite (ground-)complete (in)equational axiomatizations for each of these behavioural semantics. The axiomatization of those semantics using conditional equations is also discussed in some detail.","Title: A ground-complete axiomatization of stateless bisimilarity over Linda.\nYear: 2015\n\nAbstract: This paper offers a finite, ground-complete axiomatization of stateless bisimilarity over the tuple-space-based coordination language Linda. As stepping stones towards that result, axiomatizations of stateless bisimilarity over the sequential fragment of Linda without the nask primitive, and over the full sequential sub-language are given. It is also shown that stateless bisimilarity coincides with standard bisimilarity over the sequential fragment of Linda without the nask primitive.","Title: A Cancellation Theorem for BCCSP\nYear: 2008\n\nAbstract: This paper presents a cancellation theorem for the preorders in van Glabbeek's linear time-branching time spectrum over BCCSP. Apart from having some intrinsic interest, the proven cancellation result plays a crucial role in the study of the cover equations, in the sense of Fokkink and Nain, that characterize the studied semantics. The techniques used in the proof of the cancellation theorem may also have some independent interest.","Title: A complete classification of the expressiveness of interval logics of Allen's relations: the general and the dense cases.\nYear: 2016\n\nAbstract: Interval temporal logics take time intervals, instead of time points, as their primitive temporal entities. One of the most studied interval temporal logics is Halpern and Shoham’s modal logic of time intervals HS, which associates a modal operator with each binary relation between intervals over a linear order (the so-called Allen’s interval relations). In this paper, we compare and classify the expressiveness of all fragments of HS on the class of all linear orders and on the subclass of all dense linear orders. For each of these classes, we identify a complete set of definabilities between HS modalities, valid in that class, thus obtaining a complete classification of the family of all 4096 fragments of HS with respect to their expressiveness. We show that on the class of all linear orders there are exactly 1347 expressively different fragments of HS, while on the class of dense linear orders there are exactly 966 such expressively different fragments.","Title: Report on The EATCS Council Meeting\nYear: 2015\n\nAbstract: The EATCS Council meeting took place over lunch on the 12th and the 13th of July 2016 during ICALP 2016 in Rome This piece provides a brief and informal report on the discussions that took place at the meeting for the benefit of our members.","Title: Report on The EATCS General Assembly\nYear: 2015\n\nAbstract: The annual general assembly of the EATCS was held at ICALP 2016 on Thursday, 14 July, from 16:30 till 18:15.","Title: A Cost/Reward Method for Optimal Infinite Scheduling in Mobile Cloud Computing.\nYear: 2015\n\nAbstract: Computation offloading is a key concept in Mobile Cloud Computing: it concerns the capability of moving application components from a mobile device to the cloud. This technique, in general, improves the efficiency of a system, although sometimes it can lead to a performance degradation. To decide when and what to offload, we propose the use of a method for determining an optimal infinite scheduler, which is able to manage the resource assignment of components with the aim of improving the system efficiency in terms of battery consumption and time. In particular, in this paper we define a cost/reward horizon method for Mobile Cloud Computing systems specified in the language MobiCa. By means of the model checker UPPAAL, we synthesize an optimal infinite scheduler for a given system specification. We assess our approach through a case study, which highlights the importance of a scheduler for reducing energy consumption and improving system performance.","Title: Rule Formats for Bounded Nondeterminism in Structural Operational Semantics.\nYear: 2016\n\nAbstract: We present rule formats for structural operational semantics that guarantee that the associated labelled transition system has each of the three following finiteness properties: finite branching, initials finiteness and image finiteness.","Title: Decision Support for Mobile Cloud Computing Applications via Model Checking\nYear: 2015\n\nAbstract: Deciding whether to off load some computation is a crucial issue in Mobile Cloud Computing systems. This paper proposes a new methodology, whose goal is to provide runtime support for off loading decisions, based on a formal framework. By means of a domain specific language (MobiCa), a developer can define both system and application structure. Using the diagnostic trace generated at runtime by the well-known model checker UPPAAL, driven by some query verified on the timed automata model associated with the MobiCa specification, the framework decides which application fragments should be remotely executed. The proposed approach is exemplified on a navigator case study, probably one of the most used applications on mobile devices.","Title: On Verifying Hennessy-Milner Logic With Recursion At Runtime\nYear: 2015\n\nAbstract: We study mu HML (a branching-time logic with least and greatest fixpoints) from a runtime verification perspective. We establish which subset of the logic can be verified at runtime and define correct monitor-synthesis algorithms for this subset. We also prove completeness results wrt. these logical subsets that show that no other properties apart from those identified can be verified at runtime.","Title: When Are Prime Formulae Characteristic?\nYear: 2015\n\nAbstract: In the setting of the modal logic that characterizes modal refinement over modal transition systems, Boudol and Larsen showed that the formulae for which model checking can be reduced to preorder checking, that is, the characteristic formulae, are exactly the consistent and prime ones. This paper presents general, sufficient conditions guaranteeing that characteristic formulae are exactly the consistent and prime ones. It is shown that the given conditions apply to the logics characterizing all the semantics in van Glabbeek's branching-time spectrum.","Title: Model-Based Testing Of Mobile Systems - An Empirical Study On Quizup Android App\nYear: 2016\n\nAbstract: We present an empirical study in which model-based testing (MBT) was applied to a mobile system: the Android client of QuizUp, the largest mobile trivia game in the world. The study shows that traditional MBT approaches based on extended finite-state machines can be used to test a mobile app in an effective and efficient way. Non-trivial defects were detected on a deployed system that has millions of users and was already well tested. The duration of the overall testing effort was of three months, including the construction of the models. Maintaining a single behavioral model for the app was key in order to test it in an efficient way.","Title: Spotlight on Marta Kwiatkowska.\nYear: 2017\n\nAbstract: Marta Kwiatkowska is a Professor of Computing Systems and a Fellow of Trinity College at the University of Oxford. Before joining the University of Oxford, she was Professor in the School of Computer Science at the University of Birmingham, Lecturer at the University of Leicester and Assistant Professor at the Jagiellonian University in Cracow, Poland. She holds a BSc/MSc in Computer Science from the Jagiellonian University, an MA from Oxford and a PhD from the University of Leicester. In 2014, she was awarded an honorary doctorate from KTH Royal Institute of Technology in Stockholm and she became an ACM Fellow in 2016. Marta Kwiatkowska has been a pioneer in the development of model checking for probabilistic and quantitative systems. She was one the proposers of the use of multiterminal binary decision diagrams for the symbolic analysis of probabilistic systems and has done fundamental work on many of the models of quantitative systems that have been studied in the literature, such as continuous time Markov chains, Markov decision processes and stochastic games, amongst others. A hallmark of Marta Kwiatkowska's research career has been the synthesis of innovative and rigorous theoretical work with significant real-world applications. In addition, she has the ability to forge successful research connections with scientists from other sciences. She has led the development of the highly influential PRISM model checker1, recognised through the HVC 2016 Award, and has identified applications of quantitative verification to a wide range of areas, including security protocols, nanotechnology designs, power management, planning, and systems biology. In the past few years she has focused on applying quantitative techniques to the verification of pervasive and ubiquitous computing through her ERC Advanced Grant VERIWARE \\\"From software verification to everyware verification\\\" In addition to her outstanding research achievements, Marta Kwiatkowska has served the theoretical computer science community by sitting on prestigious award committees, organizing conferences and workshops and, perhaps even more importantly, by acting as an inspiring mentor for young researchers and as a role model for female researchers in computer science, to whom she has provided support and guidance. To wit, Marta Kwiatkowska has been the Senior Member of OxWoCS (Oxford Women in Computer Science Society) since its foundation in 2013 and has been a source of continued support and encouragement for that student-led society, providing its leadership with advice and suggestions for events as well as mentoring OxWoCS Committee members. Another interesting initiative for women with which she was involved is Suffrage Science2, which deserves to be advertised more broadly (not least because it involves jewellery designs). In addition to being named as ACM Fellow for \\\"contributions to the theory and practice of probabilistic verification\\\", Marta Kwiatkowska is a member of Academia Europea and a Fellow of the British Computer Society. She is a true stalwart of the Logic in Computer Science research community and a great example for young researchers. It is therefore fitting to spotlight her contribution in this column of ACM SIGLOG News.","Title: Logical Characterisations and Compositionality of Input-Output Conformance Simulation.\nYear: 2017\n\nAbstract: Input-output conformance simulation (iocos) has been proposed by Gregorio-Rodr\\iguez, Llana and Martinez-Torres as a simulation-based behavioural preorder underlying model-based testing. This relation is inspired by Tretman's classic ioco relation, but has better worst-case complexity than ioco and supports stepwise refinement. The goal of this paper is to develop the theory of iocos by studying logical characterisations of this relation and its compositionality. More specifically, this article presents characterisations of iocos in terms of modal logics and compares them with an existing logical characterisation for ioco proposed by Beohar and Mousavi. A precongruence rule format for iocos and a rule format ensuring that operations take quiescence properly into account are also given. Both rule formats are based on the GSOS format by Bloom, Istrail and Meyer.","Title: Trace Simulation Semantics is not Finitely Based over BCCSP.\nYear: 2017\n\nAbstract: This note shows that the trace simulation preorder does not have a finite inequational basis over the language BCCSP. Indeed, no collection of sound inequations of bounded depth is ground-complete with respect to the trace simulation preorder over BCCSP even over a singleton set of actions.","Title: INTERVIEW WITH PAUL SPIRAKIS PRESIDENT OF THE EATCS\nYear: 2016\n\nAbstract: During its annual meeting at ICALP 2016 in Rome, the Council of the EATCS elected Paul Spirakis (University of Liverpool, UK, and University of Patras, Greece) as its new president. Paul is a well-known figure in the theoreticalcomputer- science community and truly needs no introduction. However, I felt that it might be a good idea to interview him briefly in order to give him the opportunity to present himself to the community and to discuss briefly some of his plans for his mandate as president of the EATCS.","Title: EATCS Fellows' Advice to the Young Theoretical Computer Scientist.\nYear: 2016\n\nAbstract: I have always enjoyed reading articles, interviews, blog posts and books in which top-class scientists share their experience with, and provide advice to, young researchers. In fact, despite not being young any more, alas, I feel that I invariably learn something new by reading those pieces, which, at the very least, remind me of the things that I should be doing, and that perhaps I am not doing, to uphold high standards in my job. Based on my partiality for scientific advice and stories, it is not overly surprising that I was struck by the thought that it would be interesting to ask the EATCS Fellows for","Title: RULE FORMATS FOR NOMINAL PROCESS CALCULI\nYear: 2018\n\nAbstract: The nominal transition systems (NTSs) of Parrow et al. describe the operational semantics of nominal process calculi. We study NTSs in terms of the nominal residual transition systems (NRTSs) that we introduce. We provide rule formats for the specifications of NRTSs that ensure that the associated NRTS is an NTS and apply them to the operational specifications of the early and late pi-calculus. We also explore alternative specifications of the NTSs in which we allow residuals of abstraction sort, and introduce translations between the systems with and without residuals of abstraction sort. Our study stems from the Nominal SOS of Cimini et al. and from earlier works in nominal sets and nominal logic by Gabbay, Pitts and their collaborators.","Title: eAOP: an aspect oriented programming framework for Erlang.\nYear: 2017\n\nAbstract: Aspect oriented programming (AOP) is a paradigm ideal for defining cross-cutting concerns within an existing application. Although several AOP frameworks exist for more renowned languages such as Java and C#, little to no frameworks have been developed for actor oriented languages such as Erlang. We thus present eAOP, a new AOP framework specifically designed to instrument actor-oriented constructs in Erlang such as message sends and receives, along with other traditional constructs such as function calls.","Title: A Survey Of Runtime Monitoring Instrumentation Techniques\nYear: 2017\n\nAbstract: Runtime Monitoring is a lightweight and dynamic verification technique that involves observing the internal operations of a software system and/or its interactions with other external entities, with the aim of determining whether the system satisfies or violates a correctness specification. Compilation techniques employed in Runtime Monitoring tools allow monitors to be automatically derived from high-level correctness specifications (aka. properties). This allows the same property to be converted into different types of monitors, which may apply different instrumentation techniques for checking whether the property was satisfied or not. In this paper we compare and contrast the various types of monitoring methodologies found in the current literature, and classify them into a spectrum of monitoring instrumentation techniques, ranging from completely asynchronous monitoring on the one end to completely synchronous monitoring on the other.","Title: Monitoring for Silent Actions.\nYear: 2017\n\nAbstract: Silent actions are an essential mechanism for system modelling and specification. They are used to abstractly report the occurrence of computation steps without divulging their precise details, thereby enabling the description of important aspects such as the branching structure of a system. Yet, their use rarely features in specification logics used in runtime verification. We study monitorability aspects of a branching-time logic that employs silent actions, identifying which formulas are monitorable for a number of instrumentation setups. We also consider defective instrumentation setups that imprecisely report silent events, and establish monitorability results for tolerating these imperfections.","Title: A Framework for Parameterized Monitorability.\nYear: 2018\n\nAbstract: We introduce a general framework for Runtime Verification, parameterized with respect to a set of conditions. These conditions are encoded in the trace generated by a monitored process, which a monitor can observe. We present this parameterized framework in its general form and prove that it corresponds to a fragment of HML with recursion, extended with these conditions. We then show how this framework can be applied to a number of instantiations of the set of conditions.","Title: Developing Theoretical Foundations for Runtime Enforcement.\nYear: 2018\n\nAbstract: The ubiquitous reliance on software systems increases the need for ensuring that systems behave correctly and are well protected against security risks. Runtime enforcement is a dynamic analysis technique that utilizes software monitors to check the runtime behaviour of a software system with respect to a correctness specification. Whenever the runtime behaviour of the monitored system is about to deviate from the specification (either due to a programming bug or a security hijack attack), the monitors apply enforcement techniques to prevent this deviation. Current Runtime Enforcement techniques require that the correctness specification defines the behaviour of the enforcement monitor itself. This burdens the specifier with not only having to define property that needs to be enforced, but also with having to specify how this should be enforced at runtime; we thus relieve the specifier from this burden by resorting to a highly expressive logic. Using a logic we allow the specifier to define the correctness specification as a logic formula from which we can automatically synthesise the appropriate enforcement monitor. Highly expressive logics can, however, permit for defining a wide variety of formulae, some of which cannot actually be enforced correctly at runtime. We thus study the enforceability of Hennessy Milner Logic with Recursion for which we identify a subset that allows for defining enforceable formulae. This allows us to define a synthesis function that translates enforceable formulae into enforcement monitors. As our monitors are meant to ensure the correct behaviour of the monitored system, it is imperative that they work correctly themselves. We thus study formal definitions that allow us to ensure that our enforcement monitors behave correctly.","Title: Monitorability for the Hennessy–Milner logic with recursion\nYear: 2017\n\nAbstract: We study (mu ) HML, a branching-time logic with least and greatest fixpoints, from a runtime verification perspective. The logic may be used to specify properties of programs whose behaviour may be expressed as a labelled transition system. We establish which subset of this logic can be monitored for at runtime by merely observing the runtime execution of a program. A monitor-synthesis algorithm is defined for this subset, where it is shown that the resulting synthesised monitors correctly perform the required analysis from the observed behaviour. We also prove completeness results wrt. this logical subset that show that, up to logical equivalence, no other properties apart from those identified can be monitored for and verified at runtime.","Title: Adventures in monitorability: from branching to linear time and back again.\nYear: 2019\n\nAbstract: This paper establishes a comprehensive theory of runtime monitorability for Hennessy-Milner logic with recursion, a very expressive variant of the modal µ-calculus. It investigates the monitorability of that logic with a linear-time semantics and then compares the obtained results with ones that were previously presented in the literature for a branching-time setting. Our work establishes an expressiveness hierarchy of monitorable fragments of Hennessy-Milner logic with recursion in a linear-time setting and exactly identifies what kinds of guarantees can be given using runtime monitors for each fragment in the hierarchy. Each fragment is shown to be complete, in the sense that it can express all properties that can be monitored under the corresponding guarantees. The study is carried out using a principled approach to monitoring that connects the semantics of the logic and the operational semantics of monitors. The proposed framework supports the automatic, compositional synthesis of correct monitors from monitorable properties.","Title: Deriving Complete Inference Systems for a Class of GSOS Languages Generation Regular Behaviours\nYear: 1994\n\nAbstract: In this paper I characterize a class of infinitasy GSOS specifications, obtained by relaxing some of the finiteness constraints of the original format of Bloom, Istrail and\n Meyer, which generate regular processes. I then show how the techniques of Aceto, Bloom and Vaandrager can be adapted to give\n a procedure for converting any such language definition to a complete equational axiom system for strong bisimulation of processes\n which does not use infinitary proof rules. Equalities between recursive, regular processes can be established in the resulting\n inference systems by means of standard axioms to unwind recursive definitions, and the so-called Recursive Specification Principle (RSP).","Title: An equational axiomatization for multi-exit iteration\nYear: 1997\n\nAbstract: This paper presents an equational axiomatization of bisimulation equivalence over the language of Basic Process Algebra (BPA) with multi-exit iteration. Multi-exit iteration is a generalization of the standard binary Kleene star operation that allows for the specification of agents that, up to bisimulation equivalence, are solutions of systems of recursion equations of the form X 1 = def P 1 X 2 +Q 1 ⋮ X n = def P n X 1 +Q n , where n is a positive integer and the P i and the Q i are process terms. The addition of multi-exit iteration to BPA yields a more expressive language than that obtained by augmenting BPA with the standard binary Kleene star (BPA*). As a consequence, the proof of completeness of the proposed equational axiomatization for this language, although standard in its general structure, is much more involved than that for BPA*. An expressiveness hierarchy for the family of k -exit iteration operators proposed by Bergstra, Bethke, and Ponse is also offered.","Title: Equational theories of tropical semirings\nYear: 2003\n\nAbstract: This paper studies the equational theories of various exotic semirings presented in the literature. Exotic semirings are semirings whose underlying carrier set is some subset of the set of real numbers equipped with binary operations of minimum or maximum as sum, and addition as product. Two prime examples of such structures are the (max,+) semiring and the tropical semiring. It is shown that none of the exotic semirings commonly considered in the literature has a finite basis for its equations, and that similar results hold for the commutative idempotent weak semirings that underlie them. For each of these commutative idempotent weak semirings, the paper offers characterizations of the equations that hold in them, decidability results for their equational theories, explicit descriptions of the free algebras in the varieties they generate, and relative axiomatization results.","Title: A unified rule format for bounded nondeterminism in SOS with terms as labels.\nYear: 2017\n\nAbstract: We present a unified rule format for structural operational semantics with terms as labels that guarantees that the associated labelled transition system has some bounded-nondeterminism property. The properties we consider include finite branching, initials finiteness and image finiteness.","Title: On the Complexity of Determinizing Monitors.\nYear: 2017\n\nAbstract: We examine the determinization of monitors. We demonstrate that every monitor is equivalent to a deterministic one, which is at most doubly exponential in size with respect to the original monitor. When monitors are described as CCS-like processes, this doubly-exponential bound is optimal. When (deterministic) monitors are described as finite automata (as their LTS), then they can be exponentially more succinct than their CCS process form.","Title: A Foundation For Runtime Monitoring\nYear: 2017\n\nAbstract: Runtime Verification is a lightweight technique that complements other verification methods in an effort to ensure software correctness. The technique poses novel questions to software engineers: it is not easy to identify which specifications are amenable to runtime monitoring, nor is it clear which monitors effect the required runtime analysis correctly. This exposition targets a foundational understanding of these questions. Particularly, it considers an expressive specification logic (a syntactic variant of the modal mu- calculus) that is agnostic of the verification method used, together with an elemental framework providing an operational semantics for the runtime analysis performed by monitors. The correspondence between the property satisfactions in the logic on the one hand, and the verdicts reached by the monitors performing the analysis on the other, is a central theme of the study. Such a correspondence underpins the concept of monitorability, used to identify the subsets of the logic that can be adequately monitored for by RV. Another theme of the study is that of understanding what should be expected of a monitor in order for the verification process to be correct. We show how the monitor framework considered can constitute a basis whereby various notions of monitor correctness may be defined and investigated.","Title: Ready to preorder: get your BCCSP axiomatization for free!\nYear: 2007\n\nAbstract: This paper contributes to the study of the equational theory of the semantics in van Glabbeek's linear time - branching time spectrum over the language BCCSP, a basic process algebra for the description of finite synchronization trees. It offers an algorithm for producing a complete (respectively, ground-complete) equational axiomatization of any behavioral congruence lying between ready simulation equivalence and partial traces equivalence from a complete (respectively, ground-complete) inequational axiomatization of its underlying precongruence--that is, of the precongruence whose kernel is the equivalence. The algorithm preserves finiteness of the axiomatization when the set of actions is finite.","Title: On Runtime Enforcement via Suppressions.\nYear: 2018\n\nAbstract: Runtime enforcement is a dynamic analysis technique that uses monitors to enforce the behaviour specified by some correctness property on an executing system. The enforceability of a logic captures the extent to which the properties expressible via the logic can be enforced at runtime. We study the enforceability of Hennessy-Milner Logic with Recursion (muHML) with respect to suppression enforcement. We develop an operational framework for enforcement which we then use to formalise when a monitor enforces a muHML property. We also show that the safety syntactic fragment of the logic, sHML, is enforceable by providing an automated synthesis function that generates correct suppression monitors from sHML formulas.","Title: The Cost of Monitoring Alone.\nYear: 2019\n\nAbstract: We compare the succinctness of two monitoring systems for properties of infinite traces, namely parallel and regular monitors. Although a parallel monitor can be turned into an equivalent regular monitor, the cost of this transformation is a double-exponential blowup in the syntactic size of the monitors, and a triple-exponential blowup when the goal is a deterministic monitor. We show that these bounds are tight and that they also hold for translations between corresponding fragments of Hennessy-Milner logic with recursion over infinite traces.","Title: Logical characterisations, rule formats and compositionality for input-output conformance simulation\nYear: 2019\n\nAbstract: Input-output conformance simulation (iocos_ ) has been proposed by Gregorio-Rodríguez, Llana and Martínez-Torres as a simulation-based behavioural preorder underlying model-based testing. This relation is inspired by Tretmans' classic ioco relation, but has better worst-case complexity than ioco and supports stepwise refinement. The goal of this paper is to develop the theory of iocos_ by studying logical characterisations of this relation, rule formats for it and its compositionality. More specifically, this article presents characterisations of iocos_ in terms of modal logics and compares them with an existing logical characterisation for ioco proposed by Beohar and Mousavi. It also offers a characteristic-formula construction for iocos_ over finite processes in an extension of the proposed modal logics with greatest fixed points. A precongruence rule format for iocos_ and a rule format ensuring that operations take quiescence properly into account are also given. Both rule formats are based on the GSOS format by Bloom, Istrail and Meyer. The general modal decomposition methodology of Fokkink and van Glabbeek is used to show how to check the satisfaction of properties expressed in the logic for iocos_ in a compositional way for operations specified by rules in the precongruence rule format for iocos_ .","Title: An Operational Guide to Monitorability.\nYear: 2019\n\nAbstract: Monitorability delineates what properties can be verified at runtime. Although many monitorability definitions exist, few are defined explicitly in terms of the guarantees provided by monitors, i.e., the computational entities carrying out the verification. We view monitorability as a spectrum: the fewer monitor guarantees that are required, the more properties become monitorable. We present a monitorability hierarchy and provide operational and syntactic characterisations for its levels. Existing monitorability definitions are mapped into our hierarchy, providing a unified framework that makes the operational assumptions and guarantees of each definition explicit. This provides a rigorous foundation that can inform design choices and correctness claims for runtime verification tools.","Title: Comparing Controlled System Synthesis And Suppression Enforcement\nYear: 2021\n\nAbstract: Runtime enforcement and control system synthesis are two verification techniques that automate the process of transforming an erroneous system into a valid one. As both techniques can modify the behaviour of a system to prevent erroneous executions, they are both ideal for ensuring safety. In this paper, we investigate the interplay between these two techniques and identify control system synthesis as being the static counterpart to suppression-based runtime enforcement, in the context of safety properties.","Title: Monitoring Hyperproperties with Circuits\nYear: 2022\n\nAbstract: This paper presents an extension of the safety fragment of Hennessy-Milner Logic with recursion over sets of traces, in the spirit of Hyper-LTL. It then introduces a novel monitoring setup that employs circuit-like structures to combine verdicts from regular monitors. The main contribution of this study is the monitors and their semantics themselves, as well as a monitor-synthesis procedure from formulae in the logic that yields `circuit-like monitors' of this type that are sound and violation complete over a finite set of infinite traces.","Title: On the Axiomatisation of Branching Bisimulation Congruence over CCS\nYear: 2022\n\nAbstract: In this paper we investigate the equational theory of (the restriction, relabelling, and recursion free fragment of) CCS modulo rooted branching bisimilarity, which is a classic, bisimulation-based notion of equivalence that abstracts from internal computational steps in process behaviour. Firstly, we show that CCS is not finitely based modulo the considered congruence. As a key step of independent interest in the proof of that negative result, we prove that each CCS process has a unique parallel decomposition into indecomposable processes modulo branching bisimilarity. As a second main contribution, we show that, when the set of actions is finite, rooted branching bisimilarity has a finite equational basis over CCS enriched with the left merge and communication merge operators from ACP.","Title: Axiomatizing recursion-free, regular monitors\nYear: 2022\n\nAbstract: Monitors are a key tool in the field of runtime verification, where they are used to verify system properties by analyzing execution traces generated by processes. Work on runtime monitoring carried out in a series of papers by Aceto et al. has specified monitors using a variation on the regular fragment of Milner's CCS and studied two trace-based notions of equivalence over monitors, namely verdict and ω-verdict equivalence. This article is devoted to the study of the equational logic of monitors modulo those two notions of equivalence. It presents complete equational axiomatizations of verdict and ω-verdict equivalence for closed and open terms over recursion-free monitors. It is also shown that verdict equivalence has no finite equational axiomatization over open monitors when the set of actions is finite and contains at least two actions.","Title: Complexity through Translations for Modal Logic with Recursion.\nYear: 2022\n\nAbstract: This paper studies the complexity of classical modal logics and of their extension with fixed-point operators, using translations to transfer results across logics. In particular, we show several complexity results for multi-agent logics via translations to and from the mu-calculus and modal logic, which allow us to transfer known upper and lower bounds. We also use these translations to introduce a terminating tableau system for the logics we study, based on Kozen's tableau for the mu-calculus, and the one of Fitting and Massacci for modal logic.","Title: A Monitoring Tool for Linear-Time mu HML\nYear: 2022\n\nAbstract: We present the implementation of a prototype tool that run-time checks specifications written in a maximally-expressive safety fragment of the linear-time modal mu-calculus called maxHML. Our technical development is founded on previous results that give a compositional synthesis procedure for generating monitors from maxHML formulae. This paper instantiates this synthesis to a first-order setting, where systems produce executions containing events that carry data. We augment the logic with predicates over data, and extend the synthesis procedure to generate executable monitors for Erlang, a general-purpose programming language. These monitors are instrumented via inlining to induce minimal runtime overhead. Our monitoring algorithm also maintains information, which it uses to explain how verdicts are reached."],"string":"[\n \"Title: Axiomatizing Tropical Semirings\\nYear: 2001\\n\\nAbstract: This paper studies the equational theory of various exotic semirings presented in the literature. Exotic semirings are semirings\\n whose underlying carrier set is some subset of the set of real numbers equipped with binary operations of minimum or maximum\\n as sum, and addition as product. Two prime examples of such structures are the (max, +) semiring and the tropical semiring. It is shown that none of the exotic semirings commonly considered in the literature has a finite basis for its equations,\\n and that similar results hold for the commutative idempotent weak semirings that underlie them. For each of these commutative\\n idempotent weak semirings, the paper offers characterizations of the equations that hold in them, explicit descriptions of\\n the free algebras in the varieties they generate, and relative axiomatization results.\",\n \"Title: Axiomatizing prefix iteration with silent steps\\nYear: 1996\\n\\nAbstract: Preflx iteration is a variation on the original binary version of the Kleene star operation P⁄Q, obtained by restricting the flrst argument to be an atomic action. The interaction of preflx iteration with silent steps is studied in the setting of Milner's basic CCS. Complete equational ax- iomatizations are given for four notions of behavioural congruence over basicCCSwithpreflxiteration,viz.branchingcongruence, ·-congruence, delay congruence and weak congruence. The completeness proofs for ·-, delay,andweakcongruenceareobtainedbyreductiontothecompleteness theorem for branching congruence. It is also argued that the use of the completeness result for branching congruence in obtaining the complete- nessresultforweakcongruenceleadstoaconsiderablesimpliflcationwith respect to the only direct proof presented in the literature. The prelimi- naries and the completeness proofs focus on open terms, i.e., terms that maycontainprocessvariables. Asabyproduct,the !-completenessofthe axiomatizationsisobtainedaswellastheircompletenessforclosedterms.\",\n \"Title: A menagerie of non-finitely based process semantics over BPA* - from ready simulation to completed traces\\nYear: 1998\\n\\nAbstract: Fokkink and Zantema (Fokkink and Zantema 1994) have shown that bisimulation equivalence has a finite equational axiomatization over the language of Basic Process Algebra with the binary Kleene star operation (BPA*). In light of this positive result on the mathematical tractability of bisimulation equivalence over BPA*, a natural question to ask is whether any other (pre)congruence relation in van Glabbeek's linear time/branching time spectrum is finitely (in)equationally axiomatizable over it. In this paper, we prove that, unlike bisimulation equivalence, none of the preorders and equivalences in van Glabbeek's linear time/branching time spectrum, whose discriminating power lies in between that of ready simulation and that of completed traces, has a finite equational axiomatization. This we achieve by exhibiting a family of (in)equivalences that holds in ready simulation semantics (which is the finest semantics that we consider) and whose instances cannot all be proved by means of any finite set of (in)equations that is sound in completed trace semantics (which is the coarsest semantics that is appropriate for the language BPA*). To this end, for every finite collection of (in)equations that are sound in completed trace semantics, we build a model in which some of the (in)equivalences of the family under consideration fail. The construction of the model mimics the one used by Conway (Conway 1971, p. 105) in his proof of a result, originally due to Redko, to the effect that infinitely many equations are needed to axiomatize equality of regular expressions.Our non-finite axiomatizability results apply to the language BPA* over an arbitrary non-empty set of actions. In particular, we show that completed trace equivalence is not finitely based over BPA* even when the set of actions is a singleton. Our proof of this result may be adapted to the standard language of regular expressions to yield a solution to an open problem posed by Salomaa (Salomaa 1969, p. 143).Another semantics that is usually considered in process theory is trace semantics. Trace semantics is, in general, not preserved by sequential composition, and is therefore inappropriate for the language BPA*. We show that, if the set of actions is a singleton, trace equivalence and preorder are preserved by all the operators in the signature of BPA*, and coincide with simulation equivalence and preorder, respectively. In that case, unlike all the other semantics considered in this paper, trace semantics have finite, complete equational axiomatizations over closed terms.\",\n \"Title: A Rule Format for Unit Elements\\nYear: 2010\\n\\nAbstract: This paper offers a meta-theorem for languages with a Structural Operational Semantics (SOS) in the style of Plotkin. Namely, it proposes a generic rule format for SOS guaranteeing that certain constants act as left- or right-unit elements for a set of binary operators. We show the generality of our format by applying it to a wide range of operators from the literature on process calculi.\",\n \"Title: A Complete Classification of the Expressiveness of Interval Logics of Allen's Relations over Dense Linear Orders\\nYear: 2013\\n\\nAbstract: Interval temporal logics are temporal logics that take time intervals, instead of time instants, as their primitive temporal entities. One of the most studied interval temporal logics is Halpern and Shoham's modal logic of time intervals (HS), which has a distinct modality for each binary relation between intervals over a linear order. As HS turns out to be undecidable over most classes of linear orders, the study of HS fragments, featuring a proper subset of HS modalities, is a major item in the research agenda for interval temporal logics. A characterization of HS fragments in terms of their relative expressive power has been given for the class of all linear orders. Unfortunately, there is no easy way to directly transfer such a result to other meaningful classes of linear orders. In this paper, we provide a complete classification of the expressiveness of HS fragments over the class of (all) dense linear orders.\",\n \"Title: Complete and ready simulation semantics are not finitely based over BCCSP, even with a singleton alphabet\\nYear: 2011\\n\\nAbstract: This note shows that the complete and the ready simulation preorders do not have a finite inequational basis over the language BCCSP when the set of actions is a singleton. Moreover, the equivalences induced by those preorders do not have a finite (in)equational axiomatization either. These results are in contrast with a claim of finite axiomatizability for those semantics in the literature, which was based on the erroneous assumption that they coincide with complete trace semantics in the presence of a singleton set of actions.\",\n \"Title: Axiomatizing Gsos With Predicates\\nYear: 2011\\n\\nAbstract: In this paper, we introduce an extension of the GSOS rule format with predicates such as termination, convergence and divergence. For this format we generalize the technique proposed by Aceto, Bloom and Vaandrager for the automatic generation of ground-complete axiomatizations of bisimilarity over GSOS systems. Our procedure is implemented in a tool that receives SOS specifications as input and derives the corresponding axiomatizations automatically. This paves the way to checking strong bisimilarity over process terms by means of theorem-proving techniques.\",\n \"Title: Termination, deadlock, and divergence\\nYear: 1992\\n\\nAbstract: In this paper, a process algebra that incorporates explicit representations of successful termination, deadlock, and divergence is introduced and its semantic theory is analyzed. Both an operational and a denotational semantics for the language is given and it is shown that they agree. The operational theory is based upon a suitable adaptation of the notion of bisimulation preorder. The denotational semantics for the language is given in terms of the initial continuous algebra that satisfies a set of equations E, CIE. It is shown that CIE is fully abstract with respect to our choice of behavioral preorder. Several results of independent interest are obtained; namely, the finite approximability of the behavioral preorder and a partial completeness result for the set of equations E with respect to the preorder.\",\n \"Title: Rule formats for distributivity\\nYear: 2012\\n\\nAbstract: This paper proposes rule formats for Structural Operational Semantics guaranteeing that certain binary operators are left distributive with respect to a set of binary operators. Examples of left-distributivity laws from the literature are shown to be instances of the provided formats.\",\n \"Title: Graphical Representation Of Covariant-Contravariant Modal Formulae\\nYear: 2011\\n\\nAbstract: Covariant-contravariant simulation is a combination of standard (covariant) simulation, its contravariant counterpart and bisimulation. We have previously studied its logical characterization by means of the covariant-contravariant modal logic. Moreover, we have investigated the relationships between this model and that of modal transition systems, where two kinds of transitions ( the so-called may and must transitions) were combined in order to obtain a simple framework to express a notion of refinement over state-transition models. In a classic paper, Boudol and Larsen established a precise connection between the graphical approach, by means of modal transition systems, and the logical approach, based on Hennessy-Milner logic without negation, to system specification. They obtained a (graphical) representation theorem proving that a formula can be represented by a term if, and only if, it is consistent and prime. We show in this paper that the formulae from the covariant-contravariant modal logic that admit a \\\"graphical\\\" representation by means of processes, modulo the covariant-contravariant simulation preorder, are also the consistent and prime ones. In order to obtain the desired graphical representation result, we first restrict ourselves to the case of covariantcontravariant systems without bivariant actions. Bivariant actions can be incorporated later by means of an encoding that splits each bivariant action into its covariant and its contravariant parts.\",\n \"Title: Impossibility Results in the Equational Logic of Processes\\nYear: 2007\\n\\nAbstract: This talk offers a survey of negative results on the existence of finite equational axiomatizations for bisimulation equivalence over fragments of algebraic process calculi.\",\n \"Title: A Fully Equational Proof of Parikh's Theorem\\nYear: 2002\\n\\nAbstract: We show that the validity of Parikh's theorem for context free languages depends only on a few equational properties of least pre-fixed points. Moreover, we exhibit an infinite basis of mu-term equations of continuous commutative idempotent semirings.\",\n \"Title: Axiomatizing weak ready simulation semantics over BCCSP\\nYear: 2011\\n\\nAbstract: Ready simulation has proven to be one of the most significant semantics in process theory. It is at the heart of a number of general results that pave the way to a comprehensive understanding of the spectrum of process semantics. Since its original definition by Bloom, Istrail and Meyer in 1995, several authors have proposed generalizations of ready simulation to deal with internal actions. However, a thorough study of the (non-)existence of finite (in)equational bases for weak ready simulation semantics is still missing in the literature. This paper presents a complete account of positive and negative results on the axiomatizability of weak ready simulation semantics over the language BCCSP. In addition, this study offers a thorough analysis of the axiomatizability properties of weak simulation semantics.\",\n \"Title: CPO models for compact GSOS languages\\nYear: 1996\\n\\nAbstract: In this paper, we present a general way of giving denotational semantics to a class of languages equipped with an operational semantics that fits the GSOS format of Bloom, Istrail, and Meyer. The canonical model used for this purpose will be Abramsky's domain of synchronization trees, and the denotational semantics automatically generated by our methods will be guaranteed to be fully abstract with respect to the finitely observable part of the bisimulation preorder. In the process of establishing the full abstraction result, we also obtain several general results on the bisimulation preorder (including a complete axiomatization for it), and give a novel operational interpretation of GSOS languages.\",\n \"Title: Model Checking via Reachability Testing for Timed Automata\\nYear: 1998\\n\\nAbstract: In this paper we develop an approach to model-checking for timed automata via reachability testing. As our specification formalism, we consider a dense-time property language with clocks. This property language may be used to express safety and bounded liveness properties of real-time systems. We show how to automatically synthesize, for every formula phi, a test automaton T-phi in such a way that checking whether a system S satisfies the property phi can be reduced to a reachability question over the system obtained by making T-phi interact with S.\",\n \"Title: Modelling and Simulation of Asynchronous Real-Time Systems using Timed Rebeca\\nYear: 2011\\n\\nAbstract: In this paper we propose Timed Rebeca as an extension of the Rebeca language that can be used to model distributed and asynchronous systems with timing constraints. Timed Rebeca restricts the modeller to a pure asynchronous actor-based paradigm, where the structure of the model represents the service oriented architecture, while the computational model matches the network infrastructure. The modeller can specify both computational and network delay, and assign deadlines for serving a request. We provide the formal semantics of the language using Structural Operational Semantics, and show its expressiveness by means of examples. We developed a tool for automated translation from Timed Rebeca to the Erlang language, which provides a first implementation of Timed Rebeca. We can use the tool to set the parameters of Timed Rebeca models, which represent the environment and component variables, and use McErlang to run multiple simulations for different settings. The results of the simulations can then be employed to select the most appropriate values for the parameters in the model. Simulation is shown to be an effective analysis support, specially where model checking faces almost immediate state explosion in an asynchronous setting.\",\n \"Title: Sigma algebras in probabilistic epistemic dynamics\\nYear: 2011\\n\\nAbstract: This paper extends probabilistic dynamic epistemic logic from a finite setting to an infinite setting, by introducing σ-algebras to the probability spaces in the models. This may extend the applicability of the logic to a real world setting with infinitely many possible measurements. It is shown that the dynamics preserves desirable properties of measurability and that completeness of the proof system holds with the extended semantics.\",\n \"Title: The power of reachability testing for timed automata\\nYear: 2003\\n\\nAbstract: The computational engine of the verification tool UPPALL consists of a collection of efficient algorithms for the analysis of teachability properties of systems. Model-checking of properties other than plain reachability ones may currently be carried out in such a tool as follows. Given a property φ to model-check, the user must provide a test automaton Tφ for it. This test automaton must be such that the original system S has the property expressed by φ precisely when none of the distinguished reject states of Tφ can be reached in the synchronized parallel composition of S with Tφ. This raises the question of which properties may be analysed by UPPAAL in such a way. This paper gives an answer to this question by providing a complete characterization of the class of properties for which model-checking can be reduced to reachability testing in the sense outlined above. This result is obtained as a corollary of a stronger statement pertaining to the compositionality of the property language considered in this study. In particular, it is shown that our language is the least expressive compositional language that can express a simple safety property stating that no reject state can ever be reached.Finally, the property language characterizing the power of reachability testing is used to provide a definition of characteristics properties with respect to a timed version of the ready simulation preorder, for nodes of τ-free, deterministic timed automata.\",\n \"Title: Proving the validity of equations in gsos languages using rule-matching bisimilarity\\nYear: 2012\\n\\nAbstract: This paper presents a bisimulation-based method for establishing the soundness of equations between terms constructed using operations whose semantics are specified by rules in the GSOS format of Bloom, Istrail and Meyer. The method is inspired by de Simone's FH-bisimilarity and uses transition rules as schematic transitions in a bisimulation-like relation between open terms. The soundness of the method is proved and examples showing its applicability are provided. The proposed bisimulation-based proof method is incomplete, but we do offer some completeness results for restricted classes of GSOS specifications. An extension of the proof method to the setting of GSOS languages with predicates is also offered.\",\n \"Title: Characteristic Formulae: From Automata to Logic\\nYear: 2007\\n\\nAbstract: This paper discusses the classic notion of characteristic formulae for pro- cesses using variations on Hennessy-Milner logic as the underlying logical specification language. It is shown how to characterize logically (states of) finite labelled transition systems modulo bisimilarity using a single formula in Hennessy-Milner logic with recursion. Moreover, characteristic formulae for timed automata with respect to timed bisimilarity and the faster-than pre- order of Moller and Tofts are o ered in terms of the logic L of Laroussinie, Larsen and Weise.\",\n \"Title: On the Ill-Timed but Well-Caused\\nYear: 1993\\n\\nAbstract: There has been considerable controversy in concurrency theory between the ‘interleaving’ and ‘true concurrency’ schools. The former school advocates associating a transition system with a process which captures concurrent execution via the interleaving of occurrences: the latter adopts more complex semantic structures to avoid reducing concurrency to interleaving.\",\n \"Title: Resource bisimilarity and graded bisimilarity coincide\\nYear: 2010\\n\\nAbstract: Resource bisimilarity has been proposed in the literature on concurrency theory as a notion of bisimilarity over labeled transition systems that takes into account the number of choices that a system has. Independently, g-bisimilarity has been defined over Kripke models as a suitable notion of bisimilarity for graded modal logic. This note shows that these two notions of bisimilarity coincide over image-finite Kripke frames.\",\n \"Title: Conservative extension in structural operational semantics\\nYear: 1999\\n\\nAbstract: IntroductionStructural operational semantics (SOS) [44] provides a framework to givean operational semantics to programming and specification languages. Inparticular, because of its intuitive appeal and flexibility, SOS has found considerableapplication in the study of the semantics of concurrent processes.SOS generates a labelled transition system, whose states are the closed termsover an algebraic signature, and whose transitions are supplied with labels.The transitions between states ...\",\n \"Title: The equational theory of prebisimilarity over basic CCS with divergence\\nYear: 2008\\n\\nAbstract: This paper studies the equational theory of prebisimilarity, a bisimulation-based preorder introduced by Hennessy and Milner in the early 1980s, over basic CCS with the divergent process @W. It is well known that prebisimilarity affords a finite ground-complete axiomatization over this language; this study proves that this ground-complete axiomatization is also complete in the presence of an infinite set of actions. Moreover, in sharp contrast to this positive result, it is shown that prebisimilarity is not finitely based over basic CCS with the divergent process @W when the set of actions is finite and nonempty.\",\n \"Title: On Rule Formats for Zero and Unit Elements\\nYear: 2010\\n\\nAbstract: This paper proposes a rule format for Structural Operational Semantics guaranteeing that certain constants act as left or right zero elements for a set of binary operators. Our design approach is also applied to reformulate an earlier rule format for unit elements developed by some of the authors. Examples of left and right zero, as well as unit, elements from the literature are shown to be checkable using the provided formats.\",\n \"Title: Rule formats for determinism and idempotence\\nYear: 2012\\n\\nAbstract: Determinism is a semantic property of (a fragment of) a language that specifies that a program cannot evolve operationally in several different ways. Idempotence is a property of binary composition operators requiring that the composition of two identical specifications or programs will result in a piece of specification or program that is equivalent to the original components. In this paper, we propose (related) meta-theorems for guaranteeing the determinism and idempotence of binary operators. These meta-theorems are formulated in terms of syntactic templates for operational semantics, called rule formats. In order to obtain a powerful rule format for idempotence, we make use of the determinism of certain transition relations in the definition of the format for idempotence. We show the applicability of our formats by applying them to various operational semantics from the literature.\",\n \"Title: A Bisimulation-Based Method For Proving The Validity Of Equations In Gsos Languages\\nYear: 2009\\n\\nAbstract: This paper presents a bisimulation-based method for establishing the soundness of equations between terms constructed using operations whose semantics is specified by rules in the GSOS format of Bloom, Istrail and Meyer. The method is inspired by de Simone's FH-bisimilarity and uses transition rules as schematic transitions in a bisimulation-like relation between open terms. The soundness of the method is proven and examples showing its applicability are provided. The proposed bisimulation-based proof method is incomplete, but the article offers some completeness results for restricted classes of GSOS specifications.\",\n \"Title: Relating modal refinements, covariant-contravariant simulations and partial bisimulations\\nYear: 2011\\n\\nAbstract: This paper studies the relationships between three notions of behavioural preorder that have been proposed in the literature: refinement over modal transition systems, and the covariant-contravariant simulation and the partial bisimulation preorders over labelled transition systems. It is shown that there are mutual translations between modal transition systems and labelled transition systems that preserve, and reflect, refinement and the covariant-contravariant simulation preorder. The translations are also shown to preserve the modal properties that can be expressed in the logics that characterize those preorders. A translation from labelled transition systems modulo the partial bisimulation preorder into the same model modulo the covariant-contravariant simulation preorder is also offered, together with some evidence that the former model is less expressive than the latter. In order to gain more insight into the relationships between modal transition systems modulo refinement and labelled transition systems modulo the covariant-contravariant simulation preorder, their connections are also phrased and studied in the context of institutions.\",\n \"Title: The max-plus algebra of the natural numbers has no finite equational basis.\\nYear: 2003\\n\\nAbstract: This paper shows that the collection of identities which hold in the algebra N of the natural numbers with constant zero, and binary operations of sum and maximum is not finitely based. Moreover, it is proven that, for every n, the equations in at most n variables that hold in N do not form an equational basis. As a stepping stone in the proof of these facts, several results of independent interest are obtained. In particular, explicit descriptions of the free algebras in the variety generated by N are offered. Such descriptions are based upon a geometric characterization of the equations that hold in N, which also yields that the equational theory of N is decidable in exponential time.\",\n \"Title: A note on an expressiveness hierarchy for multi-exit iteration\\nYear: 2003\\n\\nAbstract: Multi-exit iteration is a generalization of the standard binary Kleene star operation. The addition of this construct to Basic Process Algebra (BPA) yields a more expressive language than that obtained by augmenting BPA with the standard binary Kleene star. This note offers an expressiveness hierarchy, modulo bisimulation equivalence, for the family of multi-exit iteration operators proposed by Bergstra, Bethke and Ponse.\",\n \"Title: On a question of A. Salomaa the equational theory of regular expressions over a singleton alphabet is not finitely based\\nYear: 1998\\n\\nAbstract: Salomaa (1969, p. 143) asked whether the equational theory of regular expressions over a singleton alphabet has a finite equational base. In this paper, we provide a negative answer to this long-standing question. The proof of our main result rests upon a model-theoretic argument. For every finite collection of equations, that are sound in the algebra of regular expressions over a singleton alphabet, we build a model in which some valid regular equation fails. The construction of the model mimics the one used by Conway (1971, p. 105) in his proof of a result, originally due to Redko, to the effect that infinitely many equations are needed to axiomatize equality of regular expressions. Our analysis of the model, however, needs to be more refined than the one provided by Conway (1971). (C) 1998 Elsevier Science B.V. All rights reserved.\",\n \"Title: Full abstraction for series-parallel pomsets\\nYear: 1991\\n\\nAbstract: In this paper, we provide a behavioural characterization of the class of finite, seriesparallel pomsets by showing that this simple model based on partial orders is fully-abstract with respect to the behavioural equivalence obtained by applying Abramsky's testing scenario for bisimulation equivalence, [Ab87], in all refinement contexts, [AH89]. This casts the observability of series-parallel pomsets in a purely interleaving framework. Moreover, we prove that the order structure of a series-parallel pomset is completely revealed by its set of ST-traces, [G190], and provide a complete axiomatization of ST-trace equivalence over the class of series-parallel pomsets.\",\n \"Title: The saga of the axiomatization of parallel composition\\nYear: 2007\\n\\nAbstract: This paper surveys some classic and recent results on the finite axiomatizability of bisimilarity over CCS-like languages. It focuses, in particular, on non-finite axiomatizability results stemming from the semantic interplay between parallel composition and nondeterministic choice. The paper also highlights the role that auxiliary operators, such as Bergstra and Klop's left and communication merge and Hennessy's merge operator, play in the search for a finite, equational axiomatization of parallel composition both for classic process algebras and for their real-time extensions.\",\n \"Title: Relating semantic models for the object calculus\\nYear: 1997\\n\\nAbstract: Abadi and Cardelli have investigated several versions of the ç-calculus, a calculus for describing central features of object-oriented programs, with particular emphasis on various type systems. In this paper we study the properties of a denotational semantics due to Abadi and Cardelli vis-à-vis the notion of observational congruence for the calculus Ob 1<:μ . In particular, we prove that the denotational semantics based on partial equivalence relations is correct with respect to observational congruence. By means of a counter-example, we argue that the denotational model is not fully abstract with respect to observational congruence. In fact, the model is able to distinguish objects that have the same behaviour in every Ob 1<:μ -context.\",\n \"Title: A characterization of finitary bisimulation\\nYear: 1997\\n\\nAbstract: This document in subdirectory RS/97/26/, Department of Computer Science, Aalborg UniversityFredrik Bajersvej 7E, 9220 Aalborg , DenmarkEmail: luca@cs.auc.dkAnna Ing'olfsd'ottirDipartimento di Sistemi ed Informatica, Universit`a di FirenzeVia Lombroso 6/17, 50134 Firenze, ItalyEmail: annai@dsi2.ing.unifi.itKeywords and Phrases: Concurrency, labelled transition system with divergence,bisimulation preorder, finitary relation\",\n \"Title: Algebraic synchronization trees and processes\\nYear: 2012\\n\\nAbstract: We study algebraic synchronization trees, i.e., initial solutions of algebraic recursion schemes over the continuous categorical algebra of synchronization trees. In particular, we investigate the relative expressive power of algebraic recursion schemes over two signatures, which are based on those for Basic CCS and Basic Process Algebra, as a means for defining synchronization trees up to isomorphism as well as modulo bisimilarity and language equivalence. The expressiveness of algebraic recursion schemes is also compared to that of the low levels in the Caucal hierarchy.\",\n \"Title: A Static View of Localities\\nYear: 1994\\n\\nAbstract: This paper proposes alternative, effective characterizations for nets of automata of the location equivalence and preorder presented by Boudol et al. in the companion paper [BCHK]. Contrary to the technical development in the above given reference, where locations are dynamically associated to the subparts of a process in the operational semantics, the equivalence and preorder we propose are based on a static association of locations to the parallel components of a net. Following this static approach, it is possible to give these “distributed nets” a standard operational semantics which associates with each net a finite labelled transition system. Using this operational semantics for distributed nets, we introduce effective notions of equivalence and preorder which are shown to coincide with those proposed in [BCHK].\",\n \"Title: Characteristic formulae for timed automata\\nYear: 2000\\n\\nAbstract: This paper offers characteristic formula constructions in the real-time logic L-nu for several behavioural relations between (states of) timed automata. The behavioural relations studied in this work are timed (bi)similarity, timed ready simulation, faster-than bisimilarity and timed trace inclusion. The characteristic formulae delivered by our constructions have size which is linear in that of the timed automaton they logically describe. This also applies to the characteristic formula for timed bisimulation equivalence, for which an exponential space construction was previously offered by Laroussinie, Larsen and Weise.\",\n \"Title: SOS rule formats for idempotent terms and idempotent unary operators.\\nYear: 2014\\n\\nAbstract: A unary operator f is idempotent if the equation f(x)=f(f(x)) holds. On the other end, an element a of an algebra is said to be an idempotent for a binary operator ⊙ if a=a⊙a. This paper presents a rule format for Structural Operational Semantics that guarantees that a unary operator be idempotent modulo bisimilarity. The proposed rule format relies on a companion one ensuring that certain terms are idempotent with respect to some binary operator. This study also offers a variety of examples showing the applicability of both formats.\",\n \"Title: Failures Semantics for a Simple Process Language with Refinement\\nYear: 1991\\n\\nAbstract: We study a suitable semantic theory based on the standard failures preorder [BHR84] for a simple process algebra which includes operators for the refinement of actions by processes. We present a model-theoretic and a behavioural characterization of the largest precongruence associated with failures preorder, \\nF\\nc\\n, over the language we consider.\\n The model-theoretic characterization is in terms of a pomset failures model which is shown to be fully abstract with respect to \\nF\\nc\\n. Pomset failures (labelled partial orders with refusal sets) allow us to express the nonsequential as well as the nondeterministic characteristics of \\nF\\nc\\n. The behavioural characterization is in terms of a variation on the standard failures preorder based on ideas of ST-semantics, [Gla90].\",\n \"Title: On \\\"Axiomatising Finite Concurrent Processes\\\"\\nYear: 1994\\n\\nAbstract: In his pioneering paper [Axiomatising finite concurrent processes, SIAM J. Comput., 17 (1988), pp. 997--1017], Hennessy gave complete axiomatizations of Milner's observational congruence and of t-observational congruence which made use of an auxiliary operation to axiomatize parallel composition. Unfortunately, those axiomatizations turn out to be flawed due to the subtle interplay between Hennessy's auxiliary parallel operator and synchronization. The aim of this paper is to present correct versions of the equational characterizations given in Hennessy's paper. Some of the problems which arise in giving operational semantics to the auxiliary operators used by Bergstra and Klop and Hennessy in the theory of congruences like Milner's observational congruence are also discussed.\",\n \"Title: A finite equational base for CCS with left merge and communication merge\\nYear: 2006\\n\\nAbstract: Using the left merge and the communication merge from ACP, we present an equational base (i.e., a ground-complete and ω-complete set of valid equations) for the fragment of CCS without recursion, restriction and relabeling modulo (strong) bisimilarity. Our equational base is finite if the set of actions is finite.\",\n \"Title: On Relating Concurency and Nondeterminism\\nYear: 1991\\n\\nAbstract: We present an intuitive preorder for a simple CCS-like language whose semantic theory allows us to relate concurrency and nondeterminism without reducing the former to the latter. The preorder over processes is induced by using an equationally defined preorder over computations in a bisimulation-like protocol. The relationships of the proposed preorder with pomset bisimulation and standard strong bisimulation equivalence are studied in detail. Moreover, we give an axiomatisation of the preorder over recursion-free processes.\",\n \"Title: On the Axiomatizability of Priority\\nYear: 2006\\n\\nAbstract: This paper studies the equational theory of bisimulation equivalence over the process algebra BCCSP extended with the priority operator of Baeten, Bergstra and Klop. It is proven that, in the presence of an infinite set of actions, bisimulation equivalence has no finite, sound, ground-complete equational axiomatization over that language. This negative result applies even if the syntax is extended with an arbitrary collection of auxiliary operators, and motivates the study of axiomatizations using equations with action predicates as conditions. In the presence of an infinite set of actions, it is shown that, in general, bisimulation equivalence has no finite, sound, ground-complete axiomatization consisting of equations with action predicates as conditions over the language studied in this paper. Finally, sucient conditions on the priority structure\",\n \"Title: On the axiomatisability of priority\\nYear: 2008\\n\\nAbstract: This paper studies the equational theory of bisimulation equivalence over the process algebra BCCSP extended with the priority operator of Baeten, Bergstra and Klop. We prove that, in the presence of an infinite set of actions, bisimulation equivalence has no finite, sound, ground-complete equational axiomatisation over that language. This negative result applies even if the syntax is extended with an arbitrary collection of auxiliary operators, and motivates the study of axiomatisations using equations with action predicates as conditions. In the presence of an infinite set of actions, it is shown that, in general, bisimulation equivalence has no finite, sound, ground-complete axiomatisation consisting of equations with action predicates as conditions over the language studied in this paper. Finally, sufficient conditions on the priority structure over actions are identified that lead to a finite, ground-complete axiomatisation of bisimulation equivalence using equations with action predicates as conditions.\",\n \"Title: On the expressibility of priority\\nYear: 2008\\n\\nAbstract: This short note offers results confirming that the priority operator of Baeten, Bergstra and Klop cannot be expressed using positive rule formats for operational semantics. Although expected, this inexpressibility result does not seem to have appeared before in the literature.\",\n \"Title: 2-Nested Simulation Is Not Finitely Equationally Axiomatizable\\nYear: 2001\\n\\nAbstract: 2-nested simulation was introduced by Groote and Vaandrager [10] as the coarsest equivalence included in completed trace equivalence for which the tyft/tyxt format is a congruence format. In the linear time-branching time spectrum of van Glabbeek [8], 2-nested simulation is one of the few equivalences for which no finite equational axiomatization is presented. In this paper we prove that such an axiomatization does not exist for 2-nested simulation.\",\n \"Title: Split-2 Bisimilarity has a Finite Axiomatization over CCS with Hennessy's Merge\\nYear: 2005\\n\\nAbstract: This note shows that split-2 bisimulation equivalence (also known as timed equivalence) affords a finite equational axiomatization over the process algebra obtained by adding an auxiliary operation proposed by Hennessy in 1981 to the recursion, relabelling and restriction free fragment of Milner's Calculus of Communicating Systems. Thus the addition of a single binary operation, viz. Hennessy's merge, is sufficient for the finite equational axiomatization of parallel composition modulo this non-interleaving equivalence. This result is in sharp contrast to a theorem previously obtained by the same authors to the effect that the same language is not finitely based modulo bisimulation equivalence.\",\n \"Title: Relating distributed, temporal and causal observations of simple processes\\nYear: 1992\\n\\nAbstract: The noninterleaving equivalences that have been proposed in the literature may be roughlydivided into three classes, depending on the kind of observational scenarios underlying them.Some of them distinguish parallelism from nondeterminism by admitting explicit observationsof the causal structure of systems; some others are based on the observation of distribution andstill others on the assumption that actions have duration.In general, these three observational scenarios give rise to...\",\n \"Title: The Complexity of Checking Consistency of Pedigree Information and Related Problems\\nYear: 2004\\n\\nAbstract: Consistency checking is a fundamental computational problem in ge- netics. Given a pedigree and information on the genotypes (of some) of the indi- viduals in it, the aim of consistency checking is to determine whether these data are consistent with the classic Mendelian laws of inheritance. This problem arose originally from the geneticists' need to filter their input data from erroneous in- formation, and is well motivated from both a biological and a sociological view- point. This paper shows that consistency checking is NP-complete, even with focus on a single gene and in the presence of three alleles. Several other results on the computational complexity of problems from genetics that are related to consistency checking are also offered. In particular, it is shown that checking the consistency of pedigrees over two alleles, and of pedigrees without loops, can be done in polynomial time.\",\n \"Title: On the axiomatizability of priority II\\nYear: 2011\\n\\nAbstract: This paper contributes to the study of the equational theory of the priority operator of Baeten, Bergstra and Klop in the setting of the process algebra BCCSP. It is shown that, in the presence of at least two actions, the collection of process equations over BCCSP with the priority operator that are valid modulo bisimilarity, irrespective of the chosen priority order over actions, is not finitely based. This holds true even if one restricts oneself to the collection of valid process equations that do not contain occurrences of process variables.\",\n \"Title: Timing and causality in process algebra\\nYear: 1996\\n\\nAbstract: There has been considerable controversy in concurrency theory between the ‘interleaving’ and ‘true concurrency’ schools. The\\n former school advocates associating a transition system with a process which captures concurrent execution via the interleaving\\n of occurrences; the latter adopts more complex semantic structures to avoid reducing concurrency to interleaving.\\n \\n In this paper we show that the two approaches are not irreconcilable. We define a timed process algebra where occurrences\\n are associated with intervals of time, and give it a transition system semantics. This semantics has many of the advantages\\n of the interleaving approach; the algebra admits an expansion theorem, and bisimulation semantics can be used as usual. Our\\n transition systems, however, incorporate timing information, and this enables us to express concurrency: merely adding timing\\n appropriately generalises transition systems to asynchronous transition systems, showing that time gives a link between true\\n concurrency and interleaving. Moreover, we can provide a complete axiomatisation of bisimulation for our algebra; a result\\n that is often problematic in a timed setting.\\n \\n \\n \\n Another advantage of incorporating timing information into the calculus is that it allows a particularly simple definition\\n of action refinement; this we present. The paper concludes with a comparison of the equivalence we present with those in the\\n literature, and an example system specification in our formalism.\",\n \"Title: Meta Sos - A Maude Based Sos Meta-Theory Framework\\nYear: 2013\\n\\nAbstract: Meta SOS is a software framework designed to integrate the results from the meta-theory of structural operational semantics (SOS). These results include deriving semantic properties of language constructs just by syntactically analyzing their rule-based definition, as well as automatically deriving sound and ground-complete axiomatizations for languages, when considering a notion of behavioural equivalence. This paper describes the Meta SOS framework by blending aspects from the meta-theory of SOS, details on their implementation in Maude, and running examples.\",\n \"Title: CCS with Hennessy's merge has no finite-equational axiomatization\\nYear: 2005\\n\\nAbstract: This paper confirms a conjecture of Bergstra and Klop's from 1984 by establishing that the process algebra obtained by adding an auxiliary operator proposed by Hennessy in 1981 to the recursion free fragment of Milner's Calculus of Communicating Systems is not finitely based modulo bisimulation equivalence. Thus, Hennessy's merge cannot replace the left merge and communication merge operators proposed by Bergstra and Klop, at least if a finite axiomatization of parallel composition modulo bisimulation equivalence is desired.\",\n \"Title: PREG axiomatizer: a ground bisimilarity checker for GSOS with predicates\\nYear: 2011\\n\\nAbstract: PREG Axiomatizer is a tool used for proving strong bisimilarity between ground terms consisting of operations in the GSOS format extended with predicates. It automatically derives sound and ground-complete axiomatizations using a technique proposed by the authors of this paper. These axiomatizations are provided as input to the Maude system, which, in turn, is used as a reduction engine for provided ground terms. These terms are bisimilar if and only if their normal forms obtained in this fashion are equal. The motivation of this tool is the optimized handling of equivalence checking between complex ground terms within automated provers and checkers.\",\n \"Title: On the specification of modal systems: A comparison of three frameworks.\\nYear: 2013\\n\\nAbstract: This paper studies the relationships between three notions of behavioural preorder that have been proposed in the literature: refinement over modal transition systems, and the covariant–contravariant simulation and the partial bisimulation preorders over labelled transition systems. It is shown that there are mutual translations between modal transition systems and labelled transition systems that preserve, and reflect, refinement and the covariant–contravariant simulation preorder. The translations are also shown to preserve the modal properties that can be expressed in the logics that characterize those preorders. A translation from labelled transition systems modulo the partial bisimulation preorder into the same model modulo the covariant–contravariant simulation preorder is also offered, together with some evidence that the former model is less expressive than the latter. In order to gain more insight into the relationships between modal transition systems modulo refinement and labelled transition systems modulo the covariant–contravariant simulation preorder, their connections are also phrased and studied in the context of institutions.\",\n \"Title: Finite Equational Bases for Fragments of CCS with Restriction and Relabelling\\nYear: 2008\\n\\nAbstract: We investigate the equational theory of several fragments of CCS modulo (strong) bisimilarity with special attention to restriction and relabelling. The largest fragment we consider includes action prefixing, choice, parallel composition without communication, restriction and relabelling. We present a finite equational base (i.e., a finite ground-complete and omega-complete axiomatisation) for it, including the left merge from ACP as auxiliary operation to facilitate the axiomatisation of parallel composition.\",\n \"Title: An Equational Axiomatization of Observation Congruence for Prefix Iteration\\nYear: 1996\\n\\nAbstract: Fokkink ((1994) Inf. Process. Lett.\\n52: 333–337) has recently proposed a complete equational axiomatization of strong bisimulation equivalence for MPA\\n\\n*\\n(A), i.e., the language obtained by extending Milner's basic CCS with prefix iteration. Prefix iteration is a variation on the original binary version of the Kleene star operation p\\n\\n*\\n\\nq obtained by restricting the first argument to be an atomic action. In this paper, we extend Fokkink's results to a setting with the unobservable action by giving a complete equational axiomatization of Milner's observation congruence over MPA\\n\\n*\\n(A). The axiomatization is obtained by extending Fokkink's axiom system with two of Milner's standard -laws, and three equations that describe the interplay between the silent nature of and prefix iteration. Using a technique due to Groote, we also show that the resulting axiomatization is -complete, i.e., complete for equality of open terms.\",\n \"Title: The equational theory of weak complete simulation semantics over BCCSP\\nYear: 2012\\n\\nAbstract: This paper presents a complete account of positive and negative results on the finite axiomatizability of weak complete simulation semantics over the language BCCSP. We offer finite (un)conditional ground-complete axiomatizations for the weak complete simulation precongruence. In sharp contrast to this positive result, we prove that, in the presence of at least one observable action, the (in)equational theory of the weak complete simulation precongruence over BCCSP does not have a finite (in)equational basis. In fact, the set of (in)equations in at most one variable that hold in weak complete simulation semantics over BCCSP does not have an (in)equational basis of ‘bounded depth', let alone a finite one.\",\n \"Title: Teaching Concurrency: Theory in Practice\\nYear: 2009\\n\\nAbstract: Teaching courses that rely on sound mathematical principles is nowadays a challenging task at many universities. On the one hand there is an increased demand for educating students in these areas, on the other hand there are more and more students being accepted with less adequate skills in mathematics. We report here on our experiences in teaching concurrency theory over the last twenty years or so to students ranging from mathsphobic bachelor students to sophisticated doctoral students. The contents of the courses, the material on which they are based and the pedagogical philosophy underlying them are described, as well as some of the lessons that we have learned over the years.\",\n \"Title: Testing equivalences for event structures\\nYear: 1986\\n\\nAbstract: A flexible abstraction mechanism for models of concurrency, which allows systems which look the same to be considered equivalent, is proposed. Using three classes of atomic observations (sequences of actions, sequences of multisets of actions and partial orderings of actions) different information on the causal and temporal structure of Event Structures, a basic model of parallelism, is captured. As a result, three different semantic models for concurrent systems are obtained. These models can be used as the basis for defining interleaving, multisets or partial ordering semantics of concurrent systems. The common framework used to define the models allows us to study the relationship between these three traditional approaches to the semantics of concurrent communicating systems.\",\n \"Title: Testing Hennessy-Milner Logic with Recursion\\nYear: 1999\\n\\nAbstract: This study offers a characterization of the collection of properties expressible in Hennessy-Milner Logic (HML) with recursion that can be tested using finite LTSs. In addition to actions used to probe the behaviour of the tested system, the LTSs that we use as tests will be able to perform a distinguished action nok to signal their dissatisfaction during the interaction with the tested process. A process s passes the test T iff T does not perform the action nok when it interacts with s. A test T tests for a property Φ in HML with recursion iff it is passed by exactly the states that satisfy Φ. The paper gives an expressive completeness result offering a characterization of the collection of properties in HML with recursion that are testable in the above sense.\",\n \"Title: Turning SOS rules into equations\\nYear: 1992\\n\\nAbstract: A procedure is given for extracting from a GSOS specification of an arbitrary process algebra a complete axiom system for bisimulation equivalence (equational, except for possibly one conditional equation). The methods apply to almost all SOSs for process algebras that have appeared in the literature, and the axiomatizations compare reasonably well with most axioms that have been presented. In particular, they discover the L characterization of parallel composition. It is noted that completeness results for equational axiomatizations are tedious and have become rather standard in many cases. A generalization of extant completeness results shows that in principle this burden can be completely removed if one gives a GSOS description of a process algebra\",\n \"Title: Characteristic Formulae For Relations With Nested Fixed Points\\nYear: 2012\\n\\nAbstract: A general framework for the connection between characteristic formulae and behavioral semantics is described in [2]. This approach does not suitably cover semantics defined by nested fixed points, such as the n-nested simulation semantics for n greater than 2. In this study we address this deficiency and give a description of nested fixed points that extends the approach for single fixed points in an intuitive and comprehensive way.\",\n \"Title: Nested Semantics over Finite Trees are Equationally Hard: Invited Talk\\nYear: 2004\\n\\nAbstract: One of the criteria that has been put forward for studying the mathematical tractability of the behavioural equivalences in van Glabbeek's linear time- banching time spectrum is that they afford elegant, finite equational axiomatizations over fragments of process algebraic languages. A review of existing complete equational axiomatizations for many of the behavioural semantics in van Glabbeek's spectrum is offered in his encyclopedic chapter in the [Handbook of Process Algebra, Jan A. Bergstra, Alban Ponse, and Scott A. Smolka, Editors. North-Holland, Amsterdam, 2001]. The equational axiomatizations offered ibidem are over the language BCCSP, a common fragment of Milner's CCS and Hoare's CSP suitable for describing finite synchronization trees, and characterize the differences between behavioural semantics in terms of a few revealing axioms.\",\n \"Title: A complete axiomatization of timed bisimulation for a class of timed regular behaviours\\nYear: 1995\\n\\nAbstract: One of the most satisfactory results in process theory is Milner's axiomatization of strong bisimulation for regular CCS. This result holds for open terms with finite-state recursion. Wang has shown that timed bisimulation can also be axiomatized, but only for closed terms without recursion. In this paper, we provide an axiomatization for timed bisimulation of open terms with finite-state recursion.\",\n \"Title: CPO Models for a Class of GSOS Languages\\nYear: 1995\\n\\nAbstract: In this paper, we present a general way of giving denotational semantics to a class of languages equipped with an operational semantics that fits the GSOS format of Bloom, Istrail and Meyer. The canonical model used for this purpose will be Abramsky's domain of synchronization trees, and the denotational semantics automatically generated by our methods will be guaranteed to be fully abstract with respect to the finitely observable part of the bisimulation preorder. In the process of establishing the full abstraction result, we also obtain several general results on the bisimulation preorder (including a complete axiomatization for it), and give a novel operational interpretation of GSOS languages.\",\n \"Title: Impossibility results for the equational theory of timed CCS\\nYear: 2007\\n\\nAbstract: We study the equational theory of Timed CCS as proposed by Wang Yi in CONCUR'90. Common to Wang Yi's paper, we particularly focus on a class of linearly-ordered time domains exemplified by the positive real or rational numbers. We show that, even when the set of basic actions is a singleton, there are parallel Timed CCS processes that do not have any sequential equivalent and thus improve on the Gap Theorem for Timed CCS presented by Godskesen and Larsen in FSTTCS'92. Furthermore, we show that timed bisimilarity is not finitely based both for single-sorted and two-sorted presentations of Timed CCS. We further strengthen this result by showing that, unlike in some other process algebras, adding the untimed or the timed left-merge operator to the syntax and semantics of Timed CCS does not solve the axiomatizability problem.\",\n \"Title: The Quest for Equational Axiomatizations of Parallel Composition: Status and Open Problems\\nYear: 2006\\n\\nAbstract: This essay recounts the story of the quest for equational axiomatizations of parallel composition operators in process description languages, and of similar results in the classic field of formal language theory. Some of the outstanding open problems are also mentioned.\",\n \"Title: Finite equational bases in process algebra: results and open questions\\nYear: 2005\\n\\nAbstract: Van Glabbeek (1990) presented the linear time/branching time spectrum of behavioral equivalences for finitely branching, concrete, sequential processes. He studied these semantics in the setting of the basic process algebra BCCSP, and tried to give finite complete axiomatizations for them. Obtaining such axiomatizations in concurrency theory often turns out to be difficult, even in the setting of simple languages like BCCSP. This has raised a host of open questions that have been the subject of intensive research in recent years. Most of these questions have been settled over BCCSP, either positively by giving a finite complete axiomatization, or negatively by proving that such an axiomatization does not exist. Still some open questions remain. This paper reports on these results, and on the state-of-the-art in axiomatizations for richer process algebras with constructs like sequential and parallel composition.\",\n \"Title: Characteristic formulae for fixed-point semantics: A general framework\\nYear: 2009\\n\\nAbstract: The concurrency theory literature offers a wealth of examples of characteristic-formula constructions for various behavioural relations over finite labelled transition systems and Kripke structures that are defined in terms of fixed points of suitable functions. Such constructions and their proofs of correctness have been developed independently, but have a common underlying structure. This paper provides a general view of characteristic formulae that are expressed in terms of logics that have a facility for the recursive definition of formulae. We show how several examples of characteristic-formula constructions in the literature can be recovered as instances of the proposed general framework, and how the framework can be used to yield novel constructions. The paper also offers general results pertaining to the definition of co-characteristic formulae and of characteristic formulae expressed in terms of infinitary modal logics.\",\n \"Title: Is Your Model Checker on Time? On the Complexity of Model Checking for Timed Modal Logics\\nYear: 1999\\n\\nAbstract: This paper studies the structural complexity of model checking for (variations on) the specification formalisms used in the tools CMC and Uppaal, and fragments of a timed alternation-free |ì-calculus. For each of the logics we study, we characterize the computational complexity of model checking, as well as its specification and program complexity, using timed automata as our system model.\",\n \"Title: GSOS and finite labelled transition systems\\nYear: 1994\\n\\nAbstract: Recently there has been considerable interest in studying formats of Plotkin style inference rules which ensure that the induced labelled transition system semantics have certain properties. In this note, I shall give a contribution to this line of research by giving a restricted version of Bloom, Istrail and Meyer's GSOS format Bloom et al. (1988), Bloom (1989) which induces finite labelled transition systems.\",\n \"Title: Foreword: Special issue in memory of nadia busi\\nYear: 2009\\n\\nAbstract: We still recall very vividly the announcement of Nadia Busi's death. It was Wednesday, 5 September 2007, and we were enjoying the half-day excursion at CONCUR 2007, a conference where just before lunch on that very day some of Nadia's latest work had been presented. The weather was splendid and we were enjoying the gorgeous view from the Arrábida Convent, basking in the glorious sunlight and admiring the blue sea in the distance. Everything was a celebration of life until death struck. Mario Bravetti, one of our colleagues from Bologna, received a phone call and broke the news to us that ‘Nadia has passed away’. After receiving this message, a cloud came over all the CONCUR participants who had known her.\",\n \"Title: SOS rule formats for zero and unit elements\\nYear: 2011\\n\\nAbstract: This paper proposes rule formats for Structural Operational Semantics guaranteeing that certain constants act as left or right unit/zero elements for a set of binary operators. Examples of left and right zero, as well as unit, elements from the literature are shown to fit the rule formats offered in this study.\",\n \"Title: A Theory of Testing for ACP\\nYear: 1991\\n\\nAbstract: This paper introduces a process algebra which incorporates the auxiliary operators of ACP, [BK85], and is tailored towards algebraic verifications in the theory of testing equivalence. The process algebra we consider is essentially a version of ACP with the empty process in which the nondeterministic choice operators familiar from TCSP, [BHR84], and TCCS, [DH87], are used in lieu of the internal action and the single choice operator favoured by CCS, [Mil89], and ACP. We present a behavioural semantics for the language based upon a natural notion of testing equivalence, [DH84], and show that, contrary to what happens in a setting with the internal action , the left-merge operator is compatible with it. A complete equational characterization of the behavioural semantics is given for finite processes, thus providing an algebraic theory supporting the use of the auxiliary operators of ACP in algebraic verifications for testing equivalence. Finally we give a fully-abstract denotational model for finite processes with respect to the testing preorder based on a variation on Hennessy's Acceptance Trees suitable for our language.\",\n \"Title: Some of My Favourite Results in Classic Process Algebra\\nYear: 2003\\n\\nAbstract: Abstract: this paper has generated a veritable industry of results onthe meta-theory of SOS and process algebras. (See [10] for a mention of some ofthese achievements and pointers to the original literature.) The proof techniquesused in these results were extremely ingenious, and have paved the way to manysimilar developments. Again, the role played by the modal characterizations ofbehavioural equivalences in the proof of the characterizations of the largestcongruences is remarkable\",\n \"Title: A complete equational axiomatization for MPA with string iteration\\nYear: 1999\\n\\nAbstract: We study equational axiomatizations of bisimulation equivalence for the language obtained by extending Milner's basic CCS with string iteration. String iteration is a variation on the original binary version of the Kleene star operation p ∗ q obtained by restricting the first argument to be a non-empty sequence of atomic actions. We show that, for every positive integer k , bisimulation equivalence over the set of processes in this language with loops of length at most k is finitely axiomatizable, provided that the set of actions is finite. We also offer an infinite equational theory that completely axiomatizes bisimulation equivalence over the whole language. We prove that this result cannot be improved upon by showing that no finite equational axiomatization of bisimulation equivalence over basic CCS with string iteration can exist, unless the set of actions is empty.\",\n \"Title: Nested semantics over finite trees are equationally hard\\nYear: 2004\\n\\nAbstract: This paper studies nested simulation and nested trace semantics over the language BCCSP, a basic formalism to express finite process behaviour. It is shown that none of these semantics affords finite (in)equational axiomatizations over BCCSP. In particular, for each of the nested semantics studied in this paper, the collection of sound, closed (in)equations over a singleton action set is not finitely based.\",\n \"Title: A Cook's Tour of Equational Axiomatizations for Prefix Iteration\\nYear: 1998\\n\\nAbstract: this paper, we continue this research programme bystudying axiomatic characterizations for more abstract semantics over this languagethan those based on variations of bisimulation. More precisely, we consider readysimulation, simulation, readiness, trace and language semantics, and provide complete(in)equational axiomatizations for each of these notions over BCCS with prefixiteration. All of the axiom systems we present are finite, if so is the set of atomicactions under consideration....\",\n \"Title: Adding action refinement to a finite process algebra\\nYear: 1994\\n\\nAbstract: Without Abstract\",\n \"Title: Decompositional reasoning about the history of parallel processes\\nYear: 2011\\n\\nAbstract: This paper presents a decomposition technique for Hennessy-Milner logic with past and its extension with recursively defined formulae. In order to highlight the main ideas and technical tools, processes are described using a subset of CCS with parallel composition, nondeterministic choice, action prefixing and the inaction constant. The study focuses on developing decompositional reasoning techniques for parallel contexts in that language.\",\n \"Title: On the Expressiveness of the Interval Logic of Allen's Relations Over Finite and Discrete Linear Orders.\\nYear: 2014\\n\\nAbstract: Interval temporal logics take time intervals, instead of time instants, as their primitive temporal entities. One of the most studied interval temporal logics is Halpern and Shoham's modal logic of time intervals HS, which associates a modal operator with each binary relation between intervals over a linear order (the so-called Allen's interval relations). A complete classification of all HS fragments with respect to their relative expressive power has been recently given for the classes of all linear orders and of all dense linear orders. The cases of discrete and finite linear orders turn out to be much more involved. In this paper, we make a significant step towards solving the classification problem over those classes of linear orders. First, we illustrate various non-trivial temporal properties that can be expressed by HS fragments when interpreted over finite and discrete linear orders; then, we provide a complete set of definabilities for the HS modalities corresponding to the Allen's relations meets, later, begins, finishes, and during, as well as the ones corresponding to their inverse relations. The only missing cases are those of the relations overlaps and overlapped by.\",\n \"Title: Axiomatizing weak simulation semantics over BCCSP.\\nYear: 2014\\n\\nAbstract: This paper is devoted to the study of the (in)equational theory of the largest (pre)congruences over the language BCCSP induced by variations on the classic simulation preorder and equivalence that abstract from internal steps in process behaviours. In particular, the article focuses on the (pre)congruences associated with the weak simulation, the weak complete simulation and the weak ready simulation preorders. We present results on the (non)existence of finite (ground-)complete (in)equational axiomatizations for each of these behavioural semantics. The axiomatization of those semantics using conditional equations is also discussed in some detail.\",\n \"Title: A ground-complete axiomatization of stateless bisimilarity over Linda.\\nYear: 2015\\n\\nAbstract: This paper offers a finite, ground-complete axiomatization of stateless bisimilarity over the tuple-space-based coordination language Linda. As stepping stones towards that result, axiomatizations of stateless bisimilarity over the sequential fragment of Linda without the nask primitive, and over the full sequential sub-language are given. It is also shown that stateless bisimilarity coincides with standard bisimilarity over the sequential fragment of Linda without the nask primitive.\",\n \"Title: A Cancellation Theorem for BCCSP\\nYear: 2008\\n\\nAbstract: This paper presents a cancellation theorem for the preorders in van Glabbeek's linear time-branching time spectrum over BCCSP. Apart from having some intrinsic interest, the proven cancellation result plays a crucial role in the study of the cover equations, in the sense of Fokkink and Nain, that characterize the studied semantics. The techniques used in the proof of the cancellation theorem may also have some independent interest.\",\n \"Title: A complete classification of the expressiveness of interval logics of Allen's relations: the general and the dense cases.\\nYear: 2016\\n\\nAbstract: Interval temporal logics take time intervals, instead of time points, as their primitive temporal entities. One of the most studied interval temporal logics is Halpern and Shoham’s modal logic of time intervals HS, which associates a modal operator with each binary relation between intervals over a linear order (the so-called Allen’s interval relations). In this paper, we compare and classify the expressiveness of all fragments of HS on the class of all linear orders and on the subclass of all dense linear orders. For each of these classes, we identify a complete set of definabilities between HS modalities, valid in that class, thus obtaining a complete classification of the family of all 4096 fragments of HS with respect to their expressiveness. We show that on the class of all linear orders there are exactly 1347 expressively different fragments of HS, while on the class of dense linear orders there are exactly 966 such expressively different fragments.\",\n \"Title: Report on The EATCS Council Meeting\\nYear: 2015\\n\\nAbstract: The EATCS Council meeting took place over lunch on the 12th and the 13th of July 2016 during ICALP 2016 in Rome This piece provides a brief and informal report on the discussions that took place at the meeting for the benefit of our members.\",\n \"Title: Report on The EATCS General Assembly\\nYear: 2015\\n\\nAbstract: The annual general assembly of the EATCS was held at ICALP 2016 on Thursday, 14 July, from 16:30 till 18:15.\",\n \"Title: A Cost/Reward Method for Optimal Infinite Scheduling in Mobile Cloud Computing.\\nYear: 2015\\n\\nAbstract: Computation offloading is a key concept in Mobile Cloud Computing: it concerns the capability of moving application components from a mobile device to the cloud. This technique, in general, improves the efficiency of a system, although sometimes it can lead to a performance degradation. To decide when and what to offload, we propose the use of a method for determining an optimal infinite scheduler, which is able to manage the resource assignment of components with the aim of improving the system efficiency in terms of battery consumption and time. In particular, in this paper we define a cost/reward horizon method for Mobile Cloud Computing systems specified in the language MobiCa. By means of the model checker UPPAAL, we synthesize an optimal infinite scheduler for a given system specification. We assess our approach through a case study, which highlights the importance of a scheduler for reducing energy consumption and improving system performance.\",\n \"Title: Rule Formats for Bounded Nondeterminism in Structural Operational Semantics.\\nYear: 2016\\n\\nAbstract: We present rule formats for structural operational semantics that guarantee that the associated labelled transition system has each of the three following finiteness properties: finite branching, initials finiteness and image finiteness.\",\n \"Title: Decision Support for Mobile Cloud Computing Applications via Model Checking\\nYear: 2015\\n\\nAbstract: Deciding whether to off load some computation is a crucial issue in Mobile Cloud Computing systems. This paper proposes a new methodology, whose goal is to provide runtime support for off loading decisions, based on a formal framework. By means of a domain specific language (MobiCa), a developer can define both system and application structure. Using the diagnostic trace generated at runtime by the well-known model checker UPPAAL, driven by some query verified on the timed automata model associated with the MobiCa specification, the framework decides which application fragments should be remotely executed. The proposed approach is exemplified on a navigator case study, probably one of the most used applications on mobile devices.\",\n \"Title: On Verifying Hennessy-Milner Logic With Recursion At Runtime\\nYear: 2015\\n\\nAbstract: We study mu HML (a branching-time logic with least and greatest fixpoints) from a runtime verification perspective. We establish which subset of the logic can be verified at runtime and define correct monitor-synthesis algorithms for this subset. We also prove completeness results wrt. these logical subsets that show that no other properties apart from those identified can be verified at runtime.\",\n \"Title: When Are Prime Formulae Characteristic?\\nYear: 2015\\n\\nAbstract: In the setting of the modal logic that characterizes modal refinement over modal transition systems, Boudol and Larsen showed that the formulae for which model checking can be reduced to preorder checking, that is, the characteristic formulae, are exactly the consistent and prime ones. This paper presents general, sufficient conditions guaranteeing that characteristic formulae are exactly the consistent and prime ones. It is shown that the given conditions apply to the logics characterizing all the semantics in van Glabbeek's branching-time spectrum.\",\n \"Title: Model-Based Testing Of Mobile Systems - An Empirical Study On Quizup Android App\\nYear: 2016\\n\\nAbstract: We present an empirical study in which model-based testing (MBT) was applied to a mobile system: the Android client of QuizUp, the largest mobile trivia game in the world. The study shows that traditional MBT approaches based on extended finite-state machines can be used to test a mobile app in an effective and efficient way. Non-trivial defects were detected on a deployed system that has millions of users and was already well tested. The duration of the overall testing effort was of three months, including the construction of the models. Maintaining a single behavioral model for the app was key in order to test it in an efficient way.\",\n \"Title: Spotlight on Marta Kwiatkowska.\\nYear: 2017\\n\\nAbstract: Marta Kwiatkowska is a Professor of Computing Systems and a Fellow of Trinity College at the University of Oxford. Before joining the University of Oxford, she was Professor in the School of Computer Science at the University of Birmingham, Lecturer at the University of Leicester and Assistant Professor at the Jagiellonian University in Cracow, Poland. She holds a BSc/MSc in Computer Science from the Jagiellonian University, an MA from Oxford and a PhD from the University of Leicester. In 2014, she was awarded an honorary doctorate from KTH Royal Institute of Technology in Stockholm and she became an ACM Fellow in 2016. Marta Kwiatkowska has been a pioneer in the development of model checking for probabilistic and quantitative systems. She was one the proposers of the use of multiterminal binary decision diagrams for the symbolic analysis of probabilistic systems and has done fundamental work on many of the models of quantitative systems that have been studied in the literature, such as continuous time Markov chains, Markov decision processes and stochastic games, amongst others. A hallmark of Marta Kwiatkowska's research career has been the synthesis of innovative and rigorous theoretical work with significant real-world applications. In addition, she has the ability to forge successful research connections with scientists from other sciences. She has led the development of the highly influential PRISM model checker1, recognised through the HVC 2016 Award, and has identified applications of quantitative verification to a wide range of areas, including security protocols, nanotechnology designs, power management, planning, and systems biology. In the past few years she has focused on applying quantitative techniques to the verification of pervasive and ubiquitous computing through her ERC Advanced Grant VERIWARE \\\\\\\"From software verification to everyware verification\\\\\\\" In addition to her outstanding research achievements, Marta Kwiatkowska has served the theoretical computer science community by sitting on prestigious award committees, organizing conferences and workshops and, perhaps even more importantly, by acting as an inspiring mentor for young researchers and as a role model for female researchers in computer science, to whom she has provided support and guidance. To wit, Marta Kwiatkowska has been the Senior Member of OxWoCS (Oxford Women in Computer Science Society) since its foundation in 2013 and has been a source of continued support and encouragement for that student-led society, providing its leadership with advice and suggestions for events as well as mentoring OxWoCS Committee members. Another interesting initiative for women with which she was involved is Suffrage Science2, which deserves to be advertised more broadly (not least because it involves jewellery designs). In addition to being named as ACM Fellow for \\\\\\\"contributions to the theory and practice of probabilistic verification\\\\\\\", Marta Kwiatkowska is a member of Academia Europea and a Fellow of the British Computer Society. She is a true stalwart of the Logic in Computer Science research community and a great example for young researchers. It is therefore fitting to spotlight her contribution in this column of ACM SIGLOG News.\",\n \"Title: Logical Characterisations and Compositionality of Input-Output Conformance Simulation.\\nYear: 2017\\n\\nAbstract: Input-output conformance simulation (iocos) has been proposed by Gregorio-Rodr\\\\iguez, Llana and Martinez-Torres as a simulation-based behavioural preorder underlying model-based testing. This relation is inspired by Tretman's classic ioco relation, but has better worst-case complexity than ioco and supports stepwise refinement. The goal of this paper is to develop the theory of iocos by studying logical characterisations of this relation and its compositionality. More specifically, this article presents characterisations of iocos in terms of modal logics and compares them with an existing logical characterisation for ioco proposed by Beohar and Mousavi. A precongruence rule format for iocos and a rule format ensuring that operations take quiescence properly into account are also given. Both rule formats are based on the GSOS format by Bloom, Istrail and Meyer.\",\n \"Title: Trace Simulation Semantics is not Finitely Based over BCCSP.\\nYear: 2017\\n\\nAbstract: This note shows that the trace simulation preorder does not have a finite inequational basis over the language BCCSP. Indeed, no collection of sound inequations of bounded depth is ground-complete with respect to the trace simulation preorder over BCCSP even over a singleton set of actions.\",\n \"Title: INTERVIEW WITH PAUL SPIRAKIS PRESIDENT OF THE EATCS\\nYear: 2016\\n\\nAbstract: During its annual meeting at ICALP 2016 in Rome, the Council of the EATCS elected Paul Spirakis (University of Liverpool, UK, and University of Patras, Greece) as its new president. Paul is a well-known figure in the theoreticalcomputer- science community and truly needs no introduction. However, I felt that it might be a good idea to interview him briefly in order to give him the opportunity to present himself to the community and to discuss briefly some of his plans for his mandate as president of the EATCS.\",\n \"Title: EATCS Fellows' Advice to the Young Theoretical Computer Scientist.\\nYear: 2016\\n\\nAbstract: I have always enjoyed reading articles, interviews, blog posts and books in which top-class scientists share their experience with, and provide advice to, young researchers. In fact, despite not being young any more, alas, I feel that I invariably learn something new by reading those pieces, which, at the very least, remind me of the things that I should be doing, and that perhaps I am not doing, to uphold high standards in my job. Based on my partiality for scientific advice and stories, it is not overly surprising that I was struck by the thought that it would be interesting to ask the EATCS Fellows for\",\n \"Title: RULE FORMATS FOR NOMINAL PROCESS CALCULI\\nYear: 2018\\n\\nAbstract: The nominal transition systems (NTSs) of Parrow et al. describe the operational semantics of nominal process calculi. We study NTSs in terms of the nominal residual transition systems (NRTSs) that we introduce. We provide rule formats for the specifications of NRTSs that ensure that the associated NRTS is an NTS and apply them to the operational specifications of the early and late pi-calculus. We also explore alternative specifications of the NTSs in which we allow residuals of abstraction sort, and introduce translations between the systems with and without residuals of abstraction sort. Our study stems from the Nominal SOS of Cimini et al. and from earlier works in nominal sets and nominal logic by Gabbay, Pitts and their collaborators.\",\n \"Title: eAOP: an aspect oriented programming framework for Erlang.\\nYear: 2017\\n\\nAbstract: Aspect oriented programming (AOP) is a paradigm ideal for defining cross-cutting concerns within an existing application. Although several AOP frameworks exist for more renowned languages such as Java and C#, little to no frameworks have been developed for actor oriented languages such as Erlang. We thus present eAOP, a new AOP framework specifically designed to instrument actor-oriented constructs in Erlang such as message sends and receives, along with other traditional constructs such as function calls.\",\n \"Title: A Survey Of Runtime Monitoring Instrumentation Techniques\\nYear: 2017\\n\\nAbstract: Runtime Monitoring is a lightweight and dynamic verification technique that involves observing the internal operations of a software system and/or its interactions with other external entities, with the aim of determining whether the system satisfies or violates a correctness specification. Compilation techniques employed in Runtime Monitoring tools allow monitors to be automatically derived from high-level correctness specifications (aka. properties). This allows the same property to be converted into different types of monitors, which may apply different instrumentation techniques for checking whether the property was satisfied or not. In this paper we compare and contrast the various types of monitoring methodologies found in the current literature, and classify them into a spectrum of monitoring instrumentation techniques, ranging from completely asynchronous monitoring on the one end to completely synchronous monitoring on the other.\",\n \"Title: Monitoring for Silent Actions.\\nYear: 2017\\n\\nAbstract: Silent actions are an essential mechanism for system modelling and specification. They are used to abstractly report the occurrence of computation steps without divulging their precise details, thereby enabling the description of important aspects such as the branching structure of a system. Yet, their use rarely features in specification logics used in runtime verification. We study monitorability aspects of a branching-time logic that employs silent actions, identifying which formulas are monitorable for a number of instrumentation setups. We also consider defective instrumentation setups that imprecisely report silent events, and establish monitorability results for tolerating these imperfections.\",\n \"Title: A Framework for Parameterized Monitorability.\\nYear: 2018\\n\\nAbstract: We introduce a general framework for Runtime Verification, parameterized with respect to a set of conditions. These conditions are encoded in the trace generated by a monitored process, which a monitor can observe. We present this parameterized framework in its general form and prove that it corresponds to a fragment of HML with recursion, extended with these conditions. We then show how this framework can be applied to a number of instantiations of the set of conditions.\",\n \"Title: Developing Theoretical Foundations for Runtime Enforcement.\\nYear: 2018\\n\\nAbstract: The ubiquitous reliance on software systems increases the need for ensuring that systems behave correctly and are well protected against security risks. Runtime enforcement is a dynamic analysis technique that utilizes software monitors to check the runtime behaviour of a software system with respect to a correctness specification. Whenever the runtime behaviour of the monitored system is about to deviate from the specification (either due to a programming bug or a security hijack attack), the monitors apply enforcement techniques to prevent this deviation. Current Runtime Enforcement techniques require that the correctness specification defines the behaviour of the enforcement monitor itself. This burdens the specifier with not only having to define property that needs to be enforced, but also with having to specify how this should be enforced at runtime; we thus relieve the specifier from this burden by resorting to a highly expressive logic. Using a logic we allow the specifier to define the correctness specification as a logic formula from which we can automatically synthesise the appropriate enforcement monitor. Highly expressive logics can, however, permit for defining a wide variety of formulae, some of which cannot actually be enforced correctly at runtime. We thus study the enforceability of Hennessy Milner Logic with Recursion for which we identify a subset that allows for defining enforceable formulae. This allows us to define a synthesis function that translates enforceable formulae into enforcement monitors. As our monitors are meant to ensure the correct behaviour of the monitored system, it is imperative that they work correctly themselves. We thus study formal definitions that allow us to ensure that our enforcement monitors behave correctly.\",\n \"Title: Monitorability for the Hennessy–Milner logic with recursion\\nYear: 2017\\n\\nAbstract: We study (mu ) HML, a branching-time logic with least and greatest fixpoints, from a runtime verification perspective. The logic may be used to specify properties of programs whose behaviour may be expressed as a labelled transition system. We establish which subset of this logic can be monitored for at runtime by merely observing the runtime execution of a program. A monitor-synthesis algorithm is defined for this subset, where it is shown that the resulting synthesised monitors correctly perform the required analysis from the observed behaviour. We also prove completeness results wrt. this logical subset that show that, up to logical equivalence, no other properties apart from those identified can be monitored for and verified at runtime.\",\n \"Title: Adventures in monitorability: from branching to linear time and back again.\\nYear: 2019\\n\\nAbstract: This paper establishes a comprehensive theory of runtime monitorability for Hennessy-Milner logic with recursion, a very expressive variant of the modal µ-calculus. It investigates the monitorability of that logic with a linear-time semantics and then compares the obtained results with ones that were previously presented in the literature for a branching-time setting. Our work establishes an expressiveness hierarchy of monitorable fragments of Hennessy-Milner logic with recursion in a linear-time setting and exactly identifies what kinds of guarantees can be given using runtime monitors for each fragment in the hierarchy. Each fragment is shown to be complete, in the sense that it can express all properties that can be monitored under the corresponding guarantees. The study is carried out using a principled approach to monitoring that connects the semantics of the logic and the operational semantics of monitors. The proposed framework supports the automatic, compositional synthesis of correct monitors from monitorable properties.\",\n \"Title: Deriving Complete Inference Systems for a Class of GSOS Languages Generation Regular Behaviours\\nYear: 1994\\n\\nAbstract: In this paper I characterize a class of infinitasy GSOS specifications, obtained by relaxing some of the finiteness constraints of the original format of Bloom, Istrail and\\n Meyer, which generate regular processes. I then show how the techniques of Aceto, Bloom and Vaandrager can be adapted to give\\n a procedure for converting any such language definition to a complete equational axiom system for strong bisimulation of processes\\n which does not use infinitary proof rules. Equalities between recursive, regular processes can be established in the resulting\\n inference systems by means of standard axioms to unwind recursive definitions, and the so-called Recursive Specification Principle (RSP).\",\n \"Title: An equational axiomatization for multi-exit iteration\\nYear: 1997\\n\\nAbstract: This paper presents an equational axiomatization of bisimulation equivalence over the language of Basic Process Algebra (BPA) with multi-exit iteration. Multi-exit iteration is a generalization of the standard binary Kleene star operation that allows for the specification of agents that, up to bisimulation equivalence, are solutions of systems of recursion equations of the form X 1 = def P 1 X 2 +Q 1 ⋮ X n = def P n X 1 +Q n , where n is a positive integer and the P i and the Q i are process terms. The addition of multi-exit iteration to BPA yields a more expressive language than that obtained by augmenting BPA with the standard binary Kleene star (BPA*). As a consequence, the proof of completeness of the proposed equational axiomatization for this language, although standard in its general structure, is much more involved than that for BPA*. An expressiveness hierarchy for the family of k -exit iteration operators proposed by Bergstra, Bethke, and Ponse is also offered.\",\n \"Title: Equational theories of tropical semirings\\nYear: 2003\\n\\nAbstract: This paper studies the equational theories of various exotic semirings presented in the literature. Exotic semirings are semirings whose underlying carrier set is some subset of the set of real numbers equipped with binary operations of minimum or maximum as sum, and addition as product. Two prime examples of such structures are the (max,+) semiring and the tropical semiring. It is shown that none of the exotic semirings commonly considered in the literature has a finite basis for its equations, and that similar results hold for the commutative idempotent weak semirings that underlie them. For each of these commutative idempotent weak semirings, the paper offers characterizations of the equations that hold in them, decidability results for their equational theories, explicit descriptions of the free algebras in the varieties they generate, and relative axiomatization results.\",\n \"Title: A unified rule format for bounded nondeterminism in SOS with terms as labels.\\nYear: 2017\\n\\nAbstract: We present a unified rule format for structural operational semantics with terms as labels that guarantees that the associated labelled transition system has some bounded-nondeterminism property. The properties we consider include finite branching, initials finiteness and image finiteness.\",\n \"Title: On the Complexity of Determinizing Monitors.\\nYear: 2017\\n\\nAbstract: We examine the determinization of monitors. We demonstrate that every monitor is equivalent to a deterministic one, which is at most doubly exponential in size with respect to the original monitor. When monitors are described as CCS-like processes, this doubly-exponential bound is optimal. When (deterministic) monitors are described as finite automata (as their LTS), then they can be exponentially more succinct than their CCS process form.\",\n \"Title: A Foundation For Runtime Monitoring\\nYear: 2017\\n\\nAbstract: Runtime Verification is a lightweight technique that complements other verification methods in an effort to ensure software correctness. The technique poses novel questions to software engineers: it is not easy to identify which specifications are amenable to runtime monitoring, nor is it clear which monitors effect the required runtime analysis correctly. This exposition targets a foundational understanding of these questions. Particularly, it considers an expressive specification logic (a syntactic variant of the modal mu- calculus) that is agnostic of the verification method used, together with an elemental framework providing an operational semantics for the runtime analysis performed by monitors. The correspondence between the property satisfactions in the logic on the one hand, and the verdicts reached by the monitors performing the analysis on the other, is a central theme of the study. Such a correspondence underpins the concept of monitorability, used to identify the subsets of the logic that can be adequately monitored for by RV. Another theme of the study is that of understanding what should be expected of a monitor in order for the verification process to be correct. We show how the monitor framework considered can constitute a basis whereby various notions of monitor correctness may be defined and investigated.\",\n \"Title: Ready to preorder: get your BCCSP axiomatization for free!\\nYear: 2007\\n\\nAbstract: This paper contributes to the study of the equational theory of the semantics in van Glabbeek's linear time - branching time spectrum over the language BCCSP, a basic process algebra for the description of finite synchronization trees. It offers an algorithm for producing a complete (respectively, ground-complete) equational axiomatization of any behavioral congruence lying between ready simulation equivalence and partial traces equivalence from a complete (respectively, ground-complete) inequational axiomatization of its underlying precongruence--that is, of the precongruence whose kernel is the equivalence. The algorithm preserves finiteness of the axiomatization when the set of actions is finite.\",\n \"Title: On Runtime Enforcement via Suppressions.\\nYear: 2018\\n\\nAbstract: Runtime enforcement is a dynamic analysis technique that uses monitors to enforce the behaviour specified by some correctness property on an executing system. The enforceability of a logic captures the extent to which the properties expressible via the logic can be enforced at runtime. We study the enforceability of Hennessy-Milner Logic with Recursion (muHML) with respect to suppression enforcement. We develop an operational framework for enforcement which we then use to formalise when a monitor enforces a muHML property. We also show that the safety syntactic fragment of the logic, sHML, is enforceable by providing an automated synthesis function that generates correct suppression monitors from sHML formulas.\",\n \"Title: The Cost of Monitoring Alone.\\nYear: 2019\\n\\nAbstract: We compare the succinctness of two monitoring systems for properties of infinite traces, namely parallel and regular monitors. Although a parallel monitor can be turned into an equivalent regular monitor, the cost of this transformation is a double-exponential blowup in the syntactic size of the monitors, and a triple-exponential blowup when the goal is a deterministic monitor. We show that these bounds are tight and that they also hold for translations between corresponding fragments of Hennessy-Milner logic with recursion over infinite traces.\",\n \"Title: Logical characterisations, rule formats and compositionality for input-output conformance simulation\\nYear: 2019\\n\\nAbstract: Input-output conformance simulation (iocos_ ) has been proposed by Gregorio-Rodríguez, Llana and Martínez-Torres as a simulation-based behavioural preorder underlying model-based testing. This relation is inspired by Tretmans' classic ioco relation, but has better worst-case complexity than ioco and supports stepwise refinement. The goal of this paper is to develop the theory of iocos_ by studying logical characterisations of this relation, rule formats for it and its compositionality. More specifically, this article presents characterisations of iocos_ in terms of modal logics and compares them with an existing logical characterisation for ioco proposed by Beohar and Mousavi. It also offers a characteristic-formula construction for iocos_ over finite processes in an extension of the proposed modal logics with greatest fixed points. A precongruence rule format for iocos_ and a rule format ensuring that operations take quiescence properly into account are also given. Both rule formats are based on the GSOS format by Bloom, Istrail and Meyer. The general modal decomposition methodology of Fokkink and van Glabbeek is used to show how to check the satisfaction of properties expressed in the logic for iocos_ in a compositional way for operations specified by rules in the precongruence rule format for iocos_ .\",\n \"Title: An Operational Guide to Monitorability.\\nYear: 2019\\n\\nAbstract: Monitorability delineates what properties can be verified at runtime. Although many monitorability definitions exist, few are defined explicitly in terms of the guarantees provided by monitors, i.e., the computational entities carrying out the verification. We view monitorability as a spectrum: the fewer monitor guarantees that are required, the more properties become monitorable. We present a monitorability hierarchy and provide operational and syntactic characterisations for its levels. Existing monitorability definitions are mapped into our hierarchy, providing a unified framework that makes the operational assumptions and guarantees of each definition explicit. This provides a rigorous foundation that can inform design choices and correctness claims for runtime verification tools.\",\n \"Title: Comparing Controlled System Synthesis And Suppression Enforcement\\nYear: 2021\\n\\nAbstract: Runtime enforcement and control system synthesis are two verification techniques that automate the process of transforming an erroneous system into a valid one. As both techniques can modify the behaviour of a system to prevent erroneous executions, they are both ideal for ensuring safety. In this paper, we investigate the interplay between these two techniques and identify control system synthesis as being the static counterpart to suppression-based runtime enforcement, in the context of safety properties.\",\n \"Title: Monitoring Hyperproperties with Circuits\\nYear: 2022\\n\\nAbstract: This paper presents an extension of the safety fragment of Hennessy-Milner Logic with recursion over sets of traces, in the spirit of Hyper-LTL. It then introduces a novel monitoring setup that employs circuit-like structures to combine verdicts from regular monitors. The main contribution of this study is the monitors and their semantics themselves, as well as a monitor-synthesis procedure from formulae in the logic that yields `circuit-like monitors' of this type that are sound and violation complete over a finite set of infinite traces.\",\n \"Title: On the Axiomatisation of Branching Bisimulation Congruence over CCS\\nYear: 2022\\n\\nAbstract: In this paper we investigate the equational theory of (the restriction, relabelling, and recursion free fragment of) CCS modulo rooted branching bisimilarity, which is a classic, bisimulation-based notion of equivalence that abstracts from internal computational steps in process behaviour. Firstly, we show that CCS is not finitely based modulo the considered congruence. As a key step of independent interest in the proof of that negative result, we prove that each CCS process has a unique parallel decomposition into indecomposable processes modulo branching bisimilarity. As a second main contribution, we show that, when the set of actions is finite, rooted branching bisimilarity has a finite equational basis over CCS enriched with the left merge and communication merge operators from ACP.\",\n \"Title: Axiomatizing recursion-free, regular monitors\\nYear: 2022\\n\\nAbstract: Monitors are a key tool in the field of runtime verification, where they are used to verify system properties by analyzing execution traces generated by processes. Work on runtime monitoring carried out in a series of papers by Aceto et al. has specified monitors using a variation on the regular fragment of Milner's CCS and studied two trace-based notions of equivalence over monitors, namely verdict and ω-verdict equivalence. This article is devoted to the study of the equational logic of monitors modulo those two notions of equivalence. It presents complete equational axiomatizations of verdict and ω-verdict equivalence for closed and open terms over recursion-free monitors. It is also shown that verdict equivalence has no finite equational axiomatization over open monitors when the set of actions is finite and contains at least two actions.\",\n \"Title: Complexity through Translations for Modal Logic with Recursion.\\nYear: 2022\\n\\nAbstract: This paper studies the complexity of classical modal logics and of their extension with fixed-point operators, using translations to transfer results across logics. In particular, we show several complexity results for multi-agent logics via translations to and from the mu-calculus and modal logic, which allow us to transfer known upper and lower bounds. We also use these translations to introduce a terminating tableau system for the logics we study, based on Kozen's tableau for the mu-calculus, and the one of Fitting and Massacci for modal logic.\",\n \"Title: A Monitoring Tool for Linear-Time mu HML\\nYear: 2022\\n\\nAbstract: We present the implementation of a prototype tool that run-time checks specifications written in a maximally-expressive safety fragment of the linear-time modal mu-calculus called maxHML. Our technical development is founded on previous results that give a compositional synthesis procedure for generating monitors from maxHML formulae. This paper instantiates this synthesis to a first-order setting, where systems produce executions containing events that carry data. We augment the logic with predicates over data, and extend the synthesis procedure to generate executable monitors for Erlang, a general-purpose programming language. These monitors are instrumented via inlining to induce minimal runtime overhead. Our monitoring algorithm also maintains information, which it uses to explain how verdicts are reached.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":125,"string":"125"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13262,"string":"13,262"}}},{"rowIdx":13263,"cells":{"question_id":{"kind":"string","value":"Andreas Steininger"},"question":{"kind":"string","value":"Generate an abstract for the title \"Measuring SET pulsewidths in logic gates using digital infrastructure\" using the following items: digital infrastructure, SET pulsewidths, radiation sensitivity, radiation tolerance, analog simulation."},"answer":{"kind":"string","value":"We present a purely digital infrastructure for measuring SET pulsewidths in logic gates. Such a facility is crucial for experimentally studying radiation sensitivity and SET propagation of a circuit. Our digital-only implementation facilitates measurement within a standard-cell CMOS chip, without the need of any analog or customized circuitry on-chip. Besides high resolution and area efficiency, a fundamental requirement guiding the development of our solution was radiation tolerance, as it shall be employed on a test chip that is fully exposed to radiation in an experimental study. We validate our architecture, for various primary radiation target circuits, by analog simulation, injecting SETs of varying strength using the standard double-exponential current model."},"correct_answer":{"kind":"string","value":"We present a purely digital infrastructure for measuring SET pulsewidths in logic gates. Such a facility is crucial for experimentally studying radiation sensitivity and SET propagation of a circuit. Our digital-only implementation facilitates measurement within a standard-cell CMOS chip, without the need of any analog or customized circuitry on-chip. Besides high resolution and area efficiency, a fundamental requirement guiding the development of our solution was radiation tolerance, as it shall be employed on a test chip that is fully exposed to radiation in an experimental study. We validate our architecture, for various primary radiation target circuits, by analog simulation, injecting SETs of varying strength using the standard double-exponential current model."},"documents":{"kind":"list like","value":["Title: FIT-IT-Projekt DARTS: dezentrale fehlertolerante Taktgenerierung\nYear: 2007\n\nAbstract: Dieser Artikel beschreibt Ziele und Lsungsansatz des prmierten FIT-IT-\"Embedded Systems\"-Projekts \"Distributed Algorithms for Robust Tick Synchronization\" (DARTS), das der Entwicklung einer fehlertoleranten Alternative zur herkmmlichen Taktgenerierung in VLSI-Chips, Systems-on-Chip und anderen Hardwaresystemen gewidmet ist. Anstatt das Taktsignal eines einzelnen Oszillators mit Hilfe eines aufwndig balancierten Clock-Trees systemweit zu verteilen, wird in DARTS der Takt jeder Funktionseinheit mit Hilfe eines lokalen Taktgenerators erzeugt. Damit wird der \"Single Point of Failure\" eliminiert, den ein zentraler Oszillator zwangslufig darstellt. Im Gegensatz zum klassischen \"Globally Asynchronous Locally Synchronous\" (GALS)-Ansatz sind die lokalen Taktgeneratoren beim DARTS-Ansatz jedoch keine Oszillatoren, sondern Instanzen eines verteilten Algorithmus zur fehlertoleranten Generierungsynchronisierterlokaler Taktsignale.","Title: How Does Resource Utilization Affect Fault Tolerance?\nYear: 2000\n\nAbstract: Much fault-tolerant architecture are based on the single-fault assumption, hence accumulation of dormant faults represents a potential reliability hazard. Based on the example of the fail-silent 驴Time-Triggered Architecture驴 we study sources and effects of dormant faults. We identify software as being more prone to dormant faults than hardware. By means of modeling, we reveal a high sensitivity of the MTTF to the existence of even a small amount of irregularly used resources. We propose on-line testing as a means of coping with dormant faults and sketch an appropriate test strategy.","Title: A Reconfigurable Generic Dual-Core Architecture\nYear: 2006\n\nAbstract: In this paper we propose a generic frame for the implementation of a dual-core processor with two modes of operation. One is the safety mode that allows to run the two cores in lock step in a classical master/checker fashion. A clock delay of 1.5 clock cycles between master and checker establishes the temporal redundancy to minimize the potential for common mode faults. The second operation mode allows a parallel execution of different instruction streams on the two cores in a multiprocessor fashion. The possibility to dynamically switch between the two modes allows for an efficient utilization of the duplicated core. We propose an implementation of such a generic frame that can be applied in conjunction with virtually any standard processor core. Also we perform a systematic failure analysis for the safety mode and the mode switching procedure. Experimental fault injection confirms that our reconfigurable architecture indeed provides the same fail safe properties as the classical master/checker architecture.","Title: Automated Testing of FlexRay Clusters for System Inconsistencies in Automotive Networks\nYear: 2008\n\nAbstract: Most innovations in the automotive domain are nowadays enabled by networked embedded systems. In this context a new communication subsystem termed FlexRay was recently introduced. Relying on the time-triggered paradigm it promises to provide the bandwidth and reliability required for future applications. For building reliable systems with FlexRay, verification and testing are of utmost importance. In this paper a new system test approach that focuses on the communication subsystem is presented. With the help of a dedicated FPGA-based star-coupler we show that inconsistent system states (Slightly-Off-Specification failures) can occur in real life systems, how they can be found using our solution and how one can avoid them.","Title: Runtime verification of microcontroller binary code\nYear: 2014\n\nAbstract: Runtime verification bridges the gap between formal verification and testing by providing techniques and tools that connect executions of a software to its specification without trying to prove the absence of errors. This article presents a framework for runtime verification of microcontroller binary code, which provides the above mentioned link in a non-intrusive fashion: the framework neither requires code instrumentation nor does it affect the execution of the analyzed program. This is achieved using a dedicated hardware unit that runs on a field programmable gate array in parallel to the analyzed microcontroller program. Different instances of this framework are discussed, with varying degrees of expressiveness of the supported specification languages and complexity in the hardware design. These instances range from invariant checkers for a restricted class of linear template constraints to a programmable processor that supports past-time linear temporal logic with timing constraints.","Title: How to Tune the MTTF of a Fail-Silent System\nYear: 2001\n\nAbstract: Color digital imagers contain Red, Green and Blue subpixels within each color pixel. Defects that develop either at fabrication time or due to environmentally induced errors over time can cause a single color subpixel (e.g., R) to fail, while leaving ...","Title: On the role of the power supply as an entry for common cause faults—An experimental analysis\nYear: 2009\n\nAbstract: With the advanced scaling of CMOS technology in the nanometer range, highly integrated mixed-signal systems can be designed. The use of nanometer CMOS, however, poses many challenges. This keynote presentation gives an overview of problems due to increased ...","Title: Rigorously modeling self-stabilizing fault-tolerant circuits: An ultra-robust clocking scheme for systems-on-chip\nYear: 2014\n\nAbstract: We present the first implementation of a distributed clock generation scheme for Systems-on-Chip that recovers from an unbounded number of arbitrary transient faults despite a large number of arbitrary permanent faults. We devise self-stabilizing hardware building blocks and a hybrid synchronous/asynchronous state machine enabling metastability-free transitions of the algorithm's states. We provide a comprehensive modeling approach that permits to prove, given correctness of the constructed low-level building blocks, the high-level properties of the synchronization algorithm (which have been established in a more abstract model). We believe this approach to be of interest in its own right, since this is the first technique permitting to mathematically verify, at manageable complexity, high-level properties of a fault-prone system in terms of its very basic components. We evaluate a prototype implementation, which has been designed in VHDL, using the Petrify tool in conjunction with some extensions, and synthesized for an Altera Cyclone FPGA.","Title: On the Risk of Fault Coupling over the Chip Substrate\nYear: 2009\n\nAbstract: Duplication and comparison has proven to be an efficient method for error detection. Based on this generic principle dual core processor architectures with output comparison are being proposed for safety critical applications. Placing two instances of the same (arbitrary) processor on one die yields a very cost efficient \"single chip\" implementation of this principle. At the same time, however, the physical coupling of the two replica creates the potential for certain types of faults to affect both cores in the same way, such that the mutual checking will fail. The key question here is how this type of coverage leakage relates to other imperfections of the duplication and comparison approach that would also be found using two cores on separate dies (such as coupling over a common power supply or clock). In this paper we analyze several of the relevant physical coupling mechanisms and elaborate a model to decompose the genesis of a common cause fault into several steps. We present an experimental study showing that a very tight local and temporal coincidence of the fault effect in both replica is a crucial prerequisite for a common cause fault. Based on this quantitative input we can conclude from our decomposition model that the risk of common cause faults is low for physical coupling mechanisms with relatively slow propagation speed, such as thermal and mechanical effects. The role of asymmetry for mitigating common cause faults is discussed in the light of these findings.","Title: Solving the Fundamental Problem of Digital Design - A Systematic Review of Design Methods\nYear: 2006\n\nAbstract: During the last decade various asynchronous circuit structures and design methods have been proposed that seem to be quite different. In essence, however, all these methods contribute to solving the same fundamental design problem in one way or another. In this paper we use a simple communication model to figure out what this fundamental design problem actually is and to highlight its roots. We show how each of the related sub-problems can be conceptually solved in the time domain and in the information domain. Having this model in mind we finally develop a common framework to classify the most popular asynchronous design methods and figure out which sub-problem they actually solve and in which respect they differ from each other.","Title: A Fail-Silent Reconfigurable Superscalar Processor\nYear: 2007\n\nAbstract: We propose a reconfigurable superscalar processor with two modes of operation: In safety mode the two pipelines run in lock step, executing the same instruction sequence, thus allowing to detect hardware failures. In performance mode different instruction streams are executed in parallel, just like in a standard superscalar processor. Considering that many embedded applications comprise a mixture of safety-critical and non safety-critical functions, the ability to dynamically switch between the two modes allows an efficient utilization of the duplicated pipeline. To complement the error detection enabled by the duplicated pipeline, non-duplicated components such as the register file are secured by parity. A systematic failure analysis shows that the proposed implementation can indeed detect all single faults in safety mode and that the ability to switch modes does not compromise the fail safe property. These encouraging results are finally confirmed by extensive fault injection experiments.","Title: A Generic Dual Core Architecture With Error Containment\nYear: 2004\n\nAbstract: The dual core strategy allows to construct a fail-silent processor from two instances (master/checker) of any arbitrary standard processor. Its main drawbacks are its vulnerability with respect to common mode failures and the existence of residual single points of failure. In this paper we propose a generic frame that systematically eliminates these drawbacks. First, we employ temporal redundancy to cope with common mode failures. Unlike similar approaches we can ensure error containment even if - as a result of the temporal redundancy - the comparison by the checker core is delayed. We attain this by introducing a specific delay element for outgoing data. Second, we perform a systematic analysis of potential single points of failure and eliminate these by careful layout, self-checking circuits and similar methods. We finally validate our approach by means of exhaustive fault injection experiments. The results indicate a 100% self-checking coverage for stuck-at faults and complete error containment. Since the proposed framework has been kept generic in the sense that the individual standard processor cores are treated as black boxes, these results are valid independent of the core actually used.","Title: Hardware implementation of an SAD based stereo vision algorithm\nYear: 2007\n\nAbstract: This paper presents the hardware implementation of a stereo vision core algorithm, that runs in real-time and is targeted at automotive applications. The algorithm is based on the sum of absolute differences (SAD) and computes the disparity map using 320 times 240 input images with a maximum disparity of 100 pixels. The hardware operates at a frequency of 65 MHz and achieves a frame rate of 425 fps by calculating the data highly parallel and pipelined. Thus an implemented and basically optimized software solution, running on an Intel Pentium 4 with 3 GHz clock frequency is 166 times outperformed.","Title: Architecture and Design Analysis of a Digital Single-Event Transient/Upset Measurement Chip\nYear: 2012\n\nAbstract: This paper presents the architecture and a detailed design analysis of a digital measurement chip which facilitates long-term irradiation experiments of basic asynchronous circuits. It combines radiation targets like Muller C-elements and elastic pipelines as well as standard combinational gates and flip-fops with an elaborate on-chip measurement infrastructure. Major architectural challenges result from the fact that the latter must operate reliably under the same radiation conditions the target circuits are exposed to, without wasting precious die area for a rad-hard design. A measurement architecture based on multiple non-rad-hard counters is used, which we show to be resilient against double faults, as well as many triple and even higher-multiplicity faults. The analysis is done by means of comprehensive fault injection experiments, which are based on detailed Spice models of the circuits in conjunction with a standard double-exponential current injection model for single-event transients. We also provide probabilistic calculations of the sustainable particle flow rates, based on the results of a detailed area analysis in conjunction with experimentally determined cross section data for the ASIC implementation technology used. The results confirm that the overall architecture indeed supports significant target hit rates, without exceeding the resilience bound of the measurement infrastructure.","Title: Remote measurement of local oscillator drifts in FlexRay networks\nYear: 2009\n\nAbstract: Distributed systems, especially time-triggered ones, are implementing clock synchronization algorithms to provide and maintain a common view of time among the different nodes. Such architectures heavily rely on the nodes' local oscillators to remain within given accuracy bounds. However, measuring the oscillator frequencies (e.g., for maintenance or diagnosis) is usually difficult to perform since it requires physical access to each single node and may interfere with the running application. Moreover, clock synchronization features tend to mask clock deviations. In this work, we propose a non-intrusive method for remote measurement of the individual oscillator drifts within a distributed system. Our approach is based on a tester that sends carefully aligned messages to stimulate the clock synchronization service and records the resulting bus traffic for an analysis of the nodes' synchronization behavior. This tester needs access to the communication bus only. We focus our work to FlexRay and validate our approach by experiments.","Title: Soft Error Tolerant Asynchronous Circuits Based on Dual Redundant Four State Logic\nYear: 2009\n\nAbstract: The continuing downscaling of integrated circuits makes modern devices more susceptible to soft errors. This paper investigates the possibility of using Four-State Logic (FSL) to improve the fault tolerance of digital circuits. FSL is a possible implementation of asynchronous Quasi Delay Insensitive (QDI) logic using a more efficient encoding and handshake protocol. The behavior of asynchronous circuits designed with FSL when subjected to transient faults is analyzed. We present methods based on dual redundancy that allow to detect as well as correct soft errors autonomously. The concept is demonstrated by fault injection experiments.","Title: Implementation of self-healing asynchronous circuits at the example of a video-processing algorithm\nYear: 2010\n\nAbstract: This paper presents a self-healing, asynchronous implementation of a small part of the hardware pre-processing to be used in the video processing unit of GAIA, a scientific mission of the European Space Agency (ESA). With the applied concept the circuit is able to detect permanent faults during runtime and to recover from them by an autonomous reconfiguration. The work describes how to transform a standard FSL circuit into a self-healing FSL design and presents first results of hardware experiments.","Title: Metastability Characterization For Muller C-Elements\nYear: 2013\n\nAbstract: We(1) present an approach for experimental metastability characterization of Muller C-elements. It is based on the late transition detection scheme known from flip flop characterization. Substantial additional challenges arise from the facts that with the Muller C-element the input transition to use as a reference for the output delay may change from case to case, and the error flags of the detector need to be reliably synchronized into the other timing domain. Our solution strategy involves taking measurements concurrently and sorting out irrelevant results later on. This is done based on detailed information about type and relative position of input transitions as well as type and polarity of the output transition, for the collection of all of which we propose efficient means. An example study on an FPGA platform proves the applicability and correct operation of our approach.","Title: Economic Online Self-Test in the Time-Triggered Architecture\nYear: 1999\n\nAbstract: Fault-tolerant, distributed, real-time architectures are often based on a restrictive fault model and special assumptions of the node behavior in the event of faults. The Time-Triggered Architecture (TTA) typifies such an architecture. Using the TTA, this article analyzes the implications of the single-fault assumption and the fail-silence assumption on error detection and testing. Preventing fault accumulation within the communication controllers is of utmost importance in guaranteeing system fault tolerance. The methods for on-line self-testing traditionally applied to attain this goal compromise the responsiveness of the system or result in costly solutions. The strategy presented in this article is cost-efficient, since it fully utilizes the properties offered by the TTA for testing.","Title: An Approach for Efficient Metastability Characterization of FPGAs through the Designer\nYear: 2013\n\nAbstract: The efficient design of a synchronizer for a given MTBF limit heavily depends on the availability of an accurate metastability characterization of the bistables in the target technology. We propose a measurement approach for FPGAs that comes along without any specific measurement infrastructure and can hence be performed by the designer with relatively low efforts, but is yet very accurate. Our concept comprises the use of the FPGA-internal digital clock manager (DCM), calibration measurements for the latter, averaging over several parallel measurement runs, and separated analysis of different metastability cases. To demonstrate the power of our approach we present detailed measurement results for a Xilinx Virtex-4 FPGA that even show slave metastability. Furthermore, we discuss how diverse constraints can be considered to make the measurement more accurate and time efficient.","Title: FATAL+: A Self-Stabilizing Byzantine Fault-tolerant Clocking Scheme for SoCs\nYear: 2012\n\nAbstract: We present concept and implementation of a self-stabilizing Byzantine fault-tolerant distributed clock generation scheme for multi-synchronous GALS architectures in critical applications. It combines a variant of a recently introduced self-stabilizing algorithm for generating low-frequency, low-accuracy synchronized pulses with a simple non-stabilizing high-frequency, high-accuracy clock synchronization algorithm. We provide thorough correctness proofs and a performance analysis, which use methods from fault-tolerant distributed computing research but also addresses hardware-related issues like metastability. The algorithm, which consists of several concurrent communicating asynchronous state machines, has been implemented in VHDL using Petrify in conjunction with some extensions, and synthetisized for an Altera Cyclone FPGA. An experimental validation of this prototype has been carried out to confirm the skew and clock frequency bounds predicted by the theoretical analysis, as well as the very short stabilization times (required for recovering after excessively many transient failures) achievable in practice.","Title: On the Necessity of On-line-BIST in Safety-Critical Applications - A Case-Study\nYear: 1999\n\nAbstract: This paper analyzes the effect of dormant faults on the mean time to failure (MTTF) of highly reliable systems. The analysis is performed by means of Markov models that allow quantifying the effect of dormant faults and other vital reliability parameters. It turns out that the presence of dormant faults can drastically reduce the MTTF of a system, particularly if the operating system allows a sporadic (\"event-driven\") change from a regular mode of operation to another mode. Virtually every practical system involves such a change, at least in case of emergency.It is demonstrated that on-line built-in self-test (BIST) is an effective means to overcome the deteriorating effect of dormant faults and re-establish a high MTTF. A very moderate test period may already be sufficient. The analysis is performed for the example of a fail-silent communication system for safety-critical real-time applications.","Title: Flexible hardware-based stereo matching\nYear: 2008\n\nAbstract: To enable adaptive stereo vision for hardware-based embedded stereo vision systems, we propose a novel technique for implementing a flexible block size, disparity range, and frame rate. By reusing existing resources of a static architecture, rather than dynamic reconfiguration, our technique is compatible with application specific integrated circuit (ASIC) as well as field programmable gate array (FPGA) implementations. We present the corresponding block diagrams and their implementation in our hardware-based stereo matching architecture. Furthermore, we show the impact of flexible stereo matching on the generated disparity maps for the sum of absolute differences (SADs), rank, and census transform algorithms. Finally, we discuss the resource usage and achievable performance when synthesized for an Altera Stratix II FPGA.","Title: Built-In Fault Injectors - The Logical Continuation of BIST?\nYear: 2003\n\nAbstract: With the increasing number of embedded computer systems being used in safety critical applications the testing and assessment of a system's fault tolerance properties become a crucial issue. Experimental fault injection plays an important role in the process of fault tolerance assessment. The restricted accessibility of nodes within a chip or system-on-a-chip, however, make such experiments extremely dif- ficult. The idea we want to promote in this paper is to add a fault injector on to the silicon that can perform fault injection automatically on demand during several stages of design. In a first step we discuss the usefulness of such an infrastructure IP module. Then, starting from an FPGA-based fault-injection toolset that we have already developed, we investigate the feasibility of designing such a module under the given tight economic constraints.","Title: An investigation of the clique problem in FlexRay.\nYear: 2008\n\nAbstract: Distributed, time-triggered communication based on FlexRay is likely to become an enabler for future safety related applications in the automotive domain. Prior to series deployment, however, confidence has to be established that all mechanisms work as expected. One particular issue in this regard is the clique problem. Cliques are groups of nodes connected to the same bus which are only able to communicate with the members of the same clique, but not with members from another one. In time-triggered systems cliques might be caused, for instance, by Byzantine faults leading to a different view of the global time.In constrast to other hard real-time systems, FlexRay lacks explicit protocol support for detecting and resolving cliques. Thus, in this paper we investigate the clique problem in a real FlexRay system. We explain the different types of cliques and give examples how they might emerge and look like. One focus is the experimental study of the behavior of a real FlexRay cluster with existing cliques. In particular, we investigate the integration behavior of nodes into a system with present cliques as well as the behavior of the system when it is in a clique state. After a presentation of the results, we conclude by giving some directions about some protocol features which might be used for implementing clique avoidance or clique detection mechanisms.","Title: Designing FlexRay-based automotive architectures: a holistic OEM approach\nYear: 2012\n\nAbstract: FlexRay is likely to become the de-facto standard for upcoming in-vehicle communication. Efficient scheduling of the static and dynamic segment of the communication cycle in combination with the determination of more than 60 parameters that are part of the FlexRay protocol is a challenging task. This paper provides a formal analysis for interdependencies between the parameters as well as a scheduling approach for the static and dynamic segment. Experimental results give evidence of a significant interdependency between the subtasks such that a holistic scheduling approach becomes mandatory to provide high-quality FlexRay schedules. As a solution, this work introduces a complete functional FlexRay scheduling approach that takes parameter selection, allocation of messages to the static and dynamic segment, and concurrent scheduling into account. A real-world case study from the automotive domain gives evidence of efficiency and applicability of the proposed approach.","Title: Processor Support For Temporal Predictability The Spear Design Example\nYear: 2003\n\nAbstract: The demand for predictable timing behavior is characteristic for real-time applications. Experience has shown that this property cannot be achieved by software alone but rather requires support from the processor This situation is analyzed and mapped to a design rationale for SPEAR (Scalable Processor for Embedded Applications in Real-time Environments), a processor that has been designed to meet the specific temporal demands of real-time systems. At the hardware level, SPEAR guarantees interrupt response with minimum temporal jitter and minimum delay. Furthermore, the processor provides an instruction set that only has constant-time instructions. At the software level, SPEAR supports the implementation of temporally predictable code according to the single-path programming paradigm. Altogether these features support writing of code with minimal jitter and provide the basis for exact temporal predictability. Experimental results show that SPEAR indeed exhibits the anticipated highly predictable timing behavior.","Title: Safely Stimulating the Clock Synchronization Algorithm in Time-Triggered Systems - a Combined Formal & Experimental Approach\nYear: 2009\n\nAbstract: Deterministic replay is used during testing to reproduce a scenario and drive the system under test to a given state. In this work, we replay an a priori defined bus traffic to influence the clock synchronization mechanism. Beyond testing this distributed mechanism itself, our aim is to draw conclusions on the nodes' bus receiver operation. Since these replay activities are part of a transparent o...","Title: A Metastability-Free Multi-synchronous Communication Scheme for SoCs\nYear: 2009\n\nAbstract: We propose a communication scheme for GALS systems with independent but approximately synchronized clock sources, which guarantees high-speed metastability-free communication between any two peers via bounded-size FIFO buffers. The proposed approach can be used atop of any multi-synchronous clocking system that guarantees a synchronization precision in the order of several clock cycles, like our fault-tolerant DARTS clocks. We determine detailed formulas for the required communication buffer size, and prove that this choice indeed guarantees metastability-free communication between correct peers, at maximum clock speed. We also describe a fast and efficient implementation of our scheme, and calculate the required buffer size for a sample test scenario. Experimental results confirm that the size lower bounds provided by our formulas are tight in this setting.","Title: Digital Late-Transition Metastability Simulation Model\nYear: 2013\n\nAbstract: As modern systems-on-chip contain increasingly more clock domain crossings, the associated metastability effects become much more pronounced. To maintain the reliability of the design, efficient means for metastability analysis become mandatory. We propose an approach that allows simulation of late transitions, the most dominant effect of metastability in modern VLSI chips. While this approach is purely digital and hence very fast, it allows a precise treatment in the time domain. These properties make it attractive for modeling even complex circuit structures. The core of our approach is a timing model that carefully maps the relative times of setting the latch opaque and the arrival of new data at the input to the appropriate data-input/output delay. Unlike with existing models this mapping is not simply linear and hence restricted to a very narrow scope, it rather works for arbitrary inter-arrival times between data and enable. We systematically elaborate an analytic description of this input/output delay and illustrate how the respective model parameters can be derived from an initial circuit simulation, either automatically or with manual support. Once calibrated, the model allows fast and precise simulation, which we illustrate in a case study.","Title: Mapping a Fault-Tolerant Distributed Algorithm to Systems on Chip\nYear: 2008\n\nAbstract: Systems on chip (SoC) have much in common with traditional (networked) distributed systems in that they consist of largely independent components with dedicated communication interfaces. Therefore the adoption of classic distributed algorithms for SoCs suggests itself. The implementation complexity of these algorithms, however, significantly depends on the underlying failure models. In traditional software-based solutions this is normally not an issue, such that the most unconstrained, namely the Byzantine, failure model is often applied here. Our case study of a hardware implemented tick synchronization algorithm shows, however, that in an SoC-implementation substantial hardware savings can result from restricting the failure model to benign failures (omissions, crashes). On the downside, it turns out that such restricted failure models have a fairly poor coverage with respect to the hardware faults occurring in practice, and that additional measures to enforce these restrictions may entail an implementation overhead that outweighs the gain obtained in the implementation of a simpler algorithm. As a remedy we investigate the potential of failure transformation in this context and show that this technique may indeed yield an optimized overall solution.","Title: Automated test-trace inspection for microcontroller binary code\nYear: 2011\n\nAbstract: This paper presents a non-intrusive framework for runtime verification of executable microcontroller code. A dedicated hardware unit is attached to a microcontroller, which executes the program under scrutiny, to track atomic propositions stated as assertions over program variables. The truth verdicts over the assertions are the inputs to a custom-designed μ CPU unit that evaluates past-time LTL specifications in parallel to program execution. To achieve this, the instruction set of the μ CPU is tailored to determining satisfaction of specifications.","Title: Past time LTL runtime verification for microcontroller binary code\nYear: 2011\n\nAbstract: This paper presents a method for runtime verification of microcontroller binary code based on past time linear temporal logic (ptLTL). We show how to implement a framework that, owing to a dedicated hardware unit, does not require code instrumentation, thus, allowing the program under scrutiny to remain unchanged. Furthermore, we demonstrate techniques for synthesizing the hardware and software units required to monitor the validity of ptLTL specifications.","Title: Testing and build-in self-test - a survey\nYear: 2000\n\nAbstract: As the density of VLSI circuits increases it becomes attractive to integrate dedicated test logic on a chip. This built-in self-test (BIST) approach not only offers economic benefits but also interesting technical opportunities with respect to hierarchical testing and the reuse of test logic during the application of the circuit. Starting with an overview of test problems, test applications and terminology this survey reviews common test methods and analyzes the basic test procedure. The concept of BIST is introduced and discussed, BIST strategies for random logic as well as for structured logic are shown.","Title: Protecting an Asynchronous NoC against Transient Channel Faults\nYear: 2012\n\nAbstract: Considering the increasing rates of transient faults predicted for future technology nodes, we investigate options for protecting the communication channels within an NoC from those. Our target is an asynchronous NoC, as its data-driven activity emanates from a more energy aware communication concept. We compare different redundancy schemes with the aim of providing a reliable communication service where error detection and correction is transparent to the higher system levels, and we also propose a new scheme. Our comparison is supported by results from simulation and synthesis. While all solutions are designed to withstand at least one transient fault, they largely differ in their area and delay penalty as well as potential extra coverage that some of them offer.","Title: An SET Tolerant Tree Arbiter Cell\nYear: 2013\n\nAbstract: Due to their inherently in deterministic behavior arbiters cannot simply be made fault tolerant by replication. We present an in-depth analysis of a tree arbiter cell with respect to possible faults and failure modes. Based on these results we devise a fault tolerant implementation of this cell that carefully avoids all single points of failure and can hence withstand transient faults as well as bit flips in its stateful elements. We verify the fault tolerance of our implementation by means of model checking and compare its overheads and performance penalties with a TMR-based solution. While the validation confirms that our approach is indeed suitable for use within an overall fault tolerance concept, the penalties turn out to be lower than for a comparable TMR approach.","Title: Is Asynchronous Logic More Robust Than Synchronous Logic?\nYear: 2009\n\nAbstract: With clock rates beyond 1 GHz, the model of a systemwide synchronous clock is becoming difficult to maintain; therefore, asynchronous design styles are increasingly receiving attention. While the traditional synchronous design style is well-proven and backed up by a rich field experience, comparatively little is known about the properties of asynchronous circuits in practical application. In the face of increased transient fault rates, robustness is a crucial property, and from a conceptual view, the so-called “delay-insensitive” asynchronous design approaches promise to be more robust than synchronous ones, since their operation does not depend on tight timing margins, and data are two-rail coded. A practical assessment of asynchronous designs in fault-injection (FI) studies, however, can rarely be found, and there is a lack of adequate methods and tools in this particular domain. Therefore, the objective of this work is 1) to provide a common approach for efficient and accurate FI in synchronous and in asynchronous designs, and 2) to experimentally compare the robustness of both synchronous and asynchronous designs. To this end, a synchronous 16-bit processor as well as its asynchronous (delay insensitive) equivalent are subjected to signal flips and delay faults. The results of over 489 million experiments are summarized and discussed, and a detailed discussion on the specific properties of the chosen asynchronous design style is given.","Title: Towards an optimal combination of error detection mechanisms\nYear: 1991\n\nAbstract: Self checking components are widely used to realize highly reliable systems. The design of a computer-board with self checking capability requires the employment of multiple mechanisms for the detection of faults and errors to achieve short error detection latency and high error detection coverage. Literature suggests a lot of such mechanisms and describes their effectiveness. However, without a good strategy their combination will very likely result in a system where some types of error are detected by several mechanisms simultaneously, whereas other types remain undetected. Moreover, the permissible number of mechanisms is limited by cost and complexity of the system.","Title: An infrastructure for accurate characterization of single-event transients in digital circuits\nYear: 2013\n\nAbstract: We present the architecture and a detailed pre-fabrication analysis of a digital measurement ASIC facilitating long-term irradiation experiments of basic asynchronous circuits, which also demonstrates the suitability of the general approach for obtaining accurate radiation failure models developed in our FATAL project. Our ASIC design combines radiation targets like Muller C-elements and elastic pipelines as well as standard combinational gates and flip-flops with an elaborate on-chip measurement infrastructure. Major architectural challenges result from the fact that the latter must operate reliably under the same radiation conditions the target circuits are exposed to, without wasting precious die area for a rad-hard design. A measurement architecture based on multiple non-rad-hard counters is used, which we show to be resilient against double faults, as well as many triple and even higher-multiplicity faults. The design evaluation is done by means of comprehensive fault injection experiments, which are based on detailed Spice models of the target circuits in conjunction with a standard double-exponential current injection model for single-event transients (SET). To be as accurate as possible, the parameters of this current model have been aligned with results obtained from 3D device simulation models, which have in turn been validated and calibrated using micro-beam radiation experiments at the GSI in Darmstadt, Germany. For the latter, target circuits instrumented with high-speed sense amplifiers have been used for analog SET recording. Together with a probabilistic analysis of the sustainable particle flow rates, based on a detailed area analysis and experimental cross-section data, we can conclude that the proposed architecture will indeed sustain significant target hit rates, without exceeding the resilience bound of the measurement infrastructure.","Title: Efficient Stimulus Generation For Testing Embedded Distributed Systems - The Flexray Example\nYear: 2005\n\nAbstract: Embedded electronic communication systems play a vital role in the future development of automotive systems. For successful application developments new test and diagnosis solutions for these distributed systems are required This paper presents solutions for the stimulus generation of test systems based on a remote test under the stringent constraints of the automotive industry. We elaborate a flexible and accurate method that enables a systematic and comprehensive test of data link layer related communication services. Furthermore, we discuss how this solution can be applied for various different test purposes (e.g. for verification, robustness, interopembility or maintenance tests) and demonstrate its application by use of an example that varies one fundamental protocol parameter of FlexRay.","Title: VLSI implementation of a distributed algorithm for fault-tolerant clock generation\nYear: 2011\n\nAbstract: We present a novel approach for the on-chip generation of a fault-tolerant clock. Our method is based on the hardware implementation of a tick synchronization algorithm from the distributed systems community. We discuss the selection of an appropriate algorithm, present the refinement steps necessary to facilitate its efficient mapping to hardware, and elaborate on the key challenges we had to overcome in our actual ASIC implementation. Our measurement results confirm that the approach is indeed capable of creating a globally synchronized clock in a distributed fashion that is tolerant to a (configurable) number of arbitrary faults. This property facilitates eliminating the clock as a single point of failure. Our solution is based on purely asynchronous design, obviating the need for crystal oscillators. It is capable of adapting to parameter variations as well as changes in temperature and power supply-properties that are considered highly desirable for future technology nodes.","Title: Towards a Systematic Test for Embedded Automotive Communication Systems\nYear: 2008\n\nAbstract: The introduction of computer-controlled intelligent safety and comfort features has turned cars into complex distributed computing systems. In such a system the proper operation of the communication backbone as well as the proper interaction of components from different vendors must be ensured for all configurations and operating conditions. This system-level test goes far beyond the (isolated) te...","Title: Architecture for monitoring SET propagation in 16-bit Sklansky adder\nYear: 2014\n\nAbstract: We propose a measurement architecture that allows to trace generation and propagation of single-event transients in a combinational target circuit that will be subjected to radiation in an experimental study. We choose the Sklansky adder as a target circuit, since it exhibits both properties we are interested in, namely different amounts of fanout and a carry propagation chain. The problem of devising a suitable on-chip measurement infrastructure lies in the partly contradictory requirements, like constrained area, radiation tolerance and good resolution of the location and propagation path of particle hits. Our proposed architecture is based on linear feedback shift registers that can be used as lean and robust counter implementations. These counters are attached at selected locations within the target adder circuit, and we show by means of a simulation study as well as a fault dictionary that this architecture indeed comes up to our expectations.","Title: Equivalence of clock gating and synchronization with applicability to GALS communication\nYear: 2014\n\nAbstract: Pausible clocking is a very popular approach for clock domain interfacing in GALS systems. However, accuracy and stability of the ring oscillator that is central to this principle are bad. This suggests to use gated crystal oscillators instead. In this paper we will formally show that the problem of clock gating is equivalent to the synchronization problem. We will present a fundamental block diagram for a gated clock, comprising an AND gate and a synchronizer for the control input, and will give evidence that the related circuits proposed so far in the literature are instantiations of this principle. According to our equivalence proof none of these circuits can hence be free from a residual risk of metastability; typically the MTBF is determined by the synchronizer block. This stands in contrast to the pausible clocking where the arbiter can safely prevent metastable outputs. We further argue that a handshake based data transfer (without clock stopping) yields essentially the same properties wrt. MTBF and performance, while causing more localized effects in case of a metastable upset. In conclusion the use of clock gating does not seem to provide any advantages over the alternative schemes and can hence not be recommended.","Title: A tree arbiter cell for high speed resource sharing in asynchronous environments\nYear: 2014\n\nAbstract: We present a novel tree arbiter cell that allows a pipelined processing of asynchronous requests. In this way it can achieve significantly lower delay in the critical case of frequent requests coming from different clients. We elaborate the necessary extension to facilitate a cascaded use of this cell in a tree-like fashion, and we show by theoretical analysis that in this configuration our cell provides better fairness than the standard approach. We implement our approach and quantitatively compare its performance properties with related work in a gate-level simulation. In our sample asynchronous Networks-on-Chip application our new cell proves to increase the throughput of three different designs available in literature by approximately 61.28%, 69.24%, and 186.85% respectively.","Title: Protection of Muller-Pipelines from transient faults\nYear: 2014\n\nAbstract: While it is well understood how to efficiently protect the data path in an asynchronous transmission channel against transient faults, much less is known about protecting the handshake signals along with their associated logic - mostly a Muller Pipeline - although these are equally critical for the proper function. In this paper we analyze the possible failure scenarios in the handshake of a 4-phase bundled data protocol that can arise from transient faults and systematically elaborate mitigation techniques for the resulting effects, namely single event transients and single event upsets. By simulated fault injection we show the effectiveness of the proposed extensions for protecting the channel. We take care to make these extensions themselves immune against transient faults, and we prove their proper and deadlock-free operation under fault conditions by means of model checking. Finally we show that, while providing superior coverage, our approach is in line with comparable approaches with respect to the area overhead.","Title: Online test vector insertion: A concurrent built-in self-testing (CBIST) approach for asynchronous logic\nYear: 2014\n\nAbstract: Complementing concurrent checking with online testing is crucial for preventing fault accumulation in fault-tolerant systems with long mission times. While implementing a non-intrusive online test is cumbersome in a synchronous environment, this task becomes even more challenging in asynchronous designs. The latter receive increasing attention, mainly due to their elastic timing behaviour; however the issues related with their testing remain a key obstacle for their wide adoption. In this paper we present a novel approach for testing of asynchronous circuits that leverages the redundancy present in the conventional 4-phase protocol for implementing a fully transparent and fully concurrent test procedure. The key idea is to use the protocol's unproductive NULL phase for processing test vectors, thus effectively interleaving the incoming 4-phase data stream with a test data stream in a 2-phase fashion. We present implementation templates for the fundamental building blocks required and give a proof-of-concept by an example application that also serves as a platform for evaluating the overheads of our solution which turn out to be moderate.","Title: Recovery Mechanisms for Dual Core Architectures\nYear: 2006\n\nAbstract: Dual core architectures are commonly used to establish fault tolerance on the node level. Since comparison is usually performed for the outputs only, no precise diagnostic information is available, and error handling comes down to a reset of both cores. The strategy proposed in this paper allows a more fine-grained error handling. It is based on the following steps: (1) Identification of those registers that are actually relevant for recovering the last known correct core state. (2) Protection of these registers by additional comparators. (3) Use of the trap mechanism for recovering a consistent state of the complete core. (4) (Optional) provision of rollback capability for the relevant registers in order to relax the critical path constraints. In the paper these individual steps was discussed and motivated, and put them into context. In many cases the speed-up that was gained for the recovery was sufficient for using a dual core as a fail-operational instead of a fail-silent component with respect to transient faults. Rather than being restricted to a specific processor design our mechanisms can be employed in a wide variety of dual-core architectures","Title: The effect of quartz drift on convergence-average based clock synchronization\nYear: 2007\n\nAbstract: The aim of this work is to study how the temporal behavior of time-triggered distributed systems is influenced by a drift of their underlying quartz. More especially, we focus on convergence-average clock synchronization used e.g. for TTP/C and FlexRay. We show that, for a fault free system in a stable environment, the global time converges to an interval spanned by the accuracies of the physical clocks independently of the system history. Although this result seems quite intuitive, it is not self-evident, given the complex behavior of distributed clock synchronization. Moreover, this proof allows to derive diagnosis information about the underlying quartz and is the enabler of remote system test.","Title: On the Threat of Metastability in an Asynchronous Fault-Tolerant Clock Generation Scheme\nYear: 2009\n\nAbstract: Due to their handshake-based flow control, asynchronous circuits generally do not suffer from metastability issues as much as synchronous circuits do. We will show, however, that fault effects like single-event transients can force (sequential) asynchronous building blocks such as Muller C-Elements into a metastable state. At the example of a fault-tolerant clock generation scheme, we will illustrate that metastability could overcome conventional error containment boundaries, and that, ultimately, a single metastable upset could cause even a multiple Byzantine fault-tolerant system to fail. In order to quantify this threat, we performed analytic modeling and simulation of the elastic pipelines, which are at the heart of our physical implementation of the fault-tolerant clocks. Our analysis results reveal that only transient pulses of some very specific width can trigger metastable behavior. So even without consideration of other masking effects the probability of a metastable upset to propagate through a pipeline is fairly small. Still, however, a thorough metastability analysis is mandatory for circuits employed in high-dependability applications.","Title: Power supply induced common cause faults-experimental assessment of potential countermeasures\nYear: 2009\n\nAbstract: Fault-tolerant architectures based on physical replication of components are vulnerable to faults that cause the same effect in all replica. Short outages in a power supply shared by all replica are a prominent example for such common cause faults. For systems in which the provision of a replicated power supply would cause prohibitive efforts the identification of reliable countermeasures against these effects is vital to maintain the required dependability level. In this paper we propose several of such countermeasures, namely parity protection, voltage monitoring and time diversity of the replica. We perform extensive fault injection experiments on three fault-tolerant dual core processor designs, one FPGA based and two commercial ASICs. These experiments provide evidence for the vulnerability of a completely unprotected dual core solution, while time diversity and voltage monitoring in combination with increased timing margins turn out particularly effective for eliminating common cause effects.","Title: Classifying Virtual Channel Access Control Schemes for Asynchronous NoCs\nYear: 2013\n\nAbstract: Several asynchronous NoC implementations have been proposed in the literature so far, providing different properties based on different assumptions and flow control strategies. Most often they are presented as monolithic solutions, making it difficult to associate the different implementation details with the achieved properties, or to identify interdependencies of choices. The key contribution of this paper will be to elaborate a generic framework for requirements on and properties of asynchronous NoCs with virtual channels that shall allow to isolate specific choices and ease the comparison between existing solutions. We will demonstrate this by aligning some of the solutions from the literature to our framework. To illustrate the usefulness of our scheme we will apply it for pinpointing a deficiency in an existing protocol and systematically elaborating a way to fix this issue.","Title: SET propagation in micropipelines\nYear: 2013\n\nAbstract: Radiation-induced Single Event Transients (SETs) have the potential to create metastability in asynchronous circuits, as they are much shorter than the typical handshake cycle and do not respect the timing closure. Micropipelines have been shown to be effective in filtering those pulses. In this paper1 we investigate the propagation of pulses of critical width through a micropipeline in SPICE simulations. We study how the pipeline implementation, especially the output buffer design (matched threshold, high threshold, Schmitt-trigger) influences the propagation behavior. For the solutions with single sided thresholds we observe a considerable propagation potential of critical pulses that strongly depends, however, on the degree of threshold matching. The Schmitt trigger output, in contrast, reliably filters all pulses which are shorter than a certain threshold while propagating all others securely. At the same time our respective analysis reveals that the cost of the Schmitt trigger stage in terms of performance overheads is also significant, so the choice needs to be carefully balanced with the application requirements.","Title: A deterministic approach for hardware fault injection in asynchronous QDI logic\nYear: 2010\n\nAbstract: This paper presents a new approach for hardware based fault injection in Quasi Delay Insensitive (QDI) asynchronous circuits. Configurable saboteurs are placed at points of interest in the circuit and allow to inject various types of faults on an arbitrary number of signals. These saboteurs not only redefine the logic value of a faulty signal but also the exact moment of the fault occurrence. In asynchronous logic, signal events rather than time are used to trigger on a circuit's state. Our concept allows to precisely control the order of concurrent signal events. It can be shown that the fault sensitivity highly depends on that event ordering. Thereby a deterministic and reproducible investigation of QDI circuits in the presence of transient and permanent faults in hardware is obtained. The work is evaluated by fault injection experiments on different circuits.","Title: A Multi-Credit Flow Control scheme for asynchronous NoCs\nYear: 2013\n\nAbstract: Credit schemes are used to establish flow control in NoCs without blocking the communication channel. In traditional implementations one credit is transmitted per data flit, so the credit channel conveys as many messages as the data channel. Our proposed multi-credit scheme transmits credits in bundles of M, yielding one credit transmission per M flits. This saves transitions on the credit channel and promises a slower, more energy efficient implementation. We investigate requirements, options and benefits of this approach; first in theory, and then in a concrete application example, in which we propose a specifically beneficial implementation. Our study confirms that, with a negligible increase in area, our scheme can reduce dynamic energy as well as bandwidth requirements for the credit channel.","Title: Containment of Metastable Voltages in FPGAs\nYear: 2015\n\nAbstract: The significant PVT variations seen with modern technologies make synchronous design inefficient. Asynchronous design with its flexible timing is a promising alternative, but prototyping is difficult on the available FPGA platforms which are clock centric and do not provide the required functional primitives like mutual exclusion or Muller C-elements. The solutions proposed in the literature work nicely in principle, but cannot safely handle metastability issues that are inevitable at interfaces even in asynchronous designs. In this paper we propose a reliable implementation of a Schmitt-trigger, which allows to safely convert potential intermediate voltage levels that result from metastability into late transitions that can be reliably handled in the asynchronous domain. Beyond the actual circuit we also discuss the associated routing constraints to make the circuit work properly in spite of the uncertain routing within FPGAs. Furthermore we propose a procedure for an \"in situ reliability assessment\" of the specific Schmitt-trigger element under consideration, which also applies to metastability containment with high-or low-threshold inverters only. Our proof of concept is based on experimental results for both Xilinx and Altera FPGA platforms.","Title: On the Appropriate Handling of Metastable Voltages in FPGAs.\nYear: 2016\n\nAbstract: The significant process, voltage and temperature (PVT) variations seen with modern technologies make strictly synchronous design inefficient. Asynchronous design with its flexible timing is a promising alternative, but prototyping is difficult on the available FPGA platforms which are clock centric and do not provide the required functional primitives like mutual exclusion or Muller C-elements. The solutions proposed in the literature so far work nicely in principle but cannot safely handle metastability issues that are inevitable even at some interfaces in asynchronous designs. In this paper, we propose reliable implementations of the fundamental function blocks required to safely convert potential intermediate voltage levels that result from metastability into late transitions that can be reliably handled in the asynchronous domain. These are high- and low-threshold buffers as well as a Schmitt-trigger. We give elaborate background analysis for the proposed circuits and also present the associated routing constraints to make the Schmitt-trigger circuit work properly in spite of the uncertain routing within FPGAs. Furthermore, we propose a procedure for an \"in situ reliability assessment\" of the specific Schmitt-trigger element under consideration, which also applies to metastability containment with high- or low-threshold buffers only. Our proof of concept is based on experimental results for both Xilinx and Altera FPGA platforms.","Title: A composable real-time architecture for replicated railway applications\nYear: 2015\n\nAbstract: Triple-modular-redundant applications are widely used for fault-tolerant safety-critical computation. They have strict timing requirements for correct operation. We present an architecture which provides composability and mixed-criticality to support integration and to ease certification of such safety-critical applications. In this architecture, an additional layer is required for the sharing/partitioning of resources. This potentially jeopardizes the synchronization necessary for the triple-modular-redundant applications.We investigate the effects of different (unsynchronized) scheduling methods for the resource-sharing layer in this architecture and conclude that an out-of-the-box solution, which guarantees the technical separation between applications with fast reaction time requirements is only feasible when executing at most one instance of a triple-modular-redundant application per CPU-core for single and multi-core CPUs. Only when accepting changes in the applications or the applications' synchronization mechanisms, are more flexible solutions with good performance and resource utilization available.","Title: Building reliable systems-on-chip in nanoscale technologies\nYear: 2015\n\nAbstract: Modern application-specific integrated circuits (ASICs) contain complete systems on a single die, composed of many processing elements that communicate over a dedicated router-based on-chip network. As systems-on-chip comprise billions of transistors with feature sizes in the range of 10 nm, reliable operation cannot be established without carefully engineered support at all levels, from technology to the circuit- and the system-layer. This article surveys contributions of research groups at TU Wien to this field. At lower levels of abstraction, they range from the generation of fault models for simulation that closely match reality and are at the same time efficient to use, to circuit-level radiation-tolerance techniques. At the level of on-chip networks, novel fault-tolerant routing algorithms are being developed together with architectural techniques to isolate faulty parts while keeping the healthy parts connected and active.","Title: How to Synchronize a Pausible Clock to a Reference\nYear: 2015\n\nAbstract: Pausible clocking is an efficient means for metastability free communication in heterochronous GALS systems. State-of-the-art implementations are based on ring oscillators, which suffer from bad frequency stability and accuracy. Building a stable pausible clock by simply switching a crystal based clock source, on the other hand, has been shown to incur metastability issues, thus rendering pausible clocking no better than a conventional synchronizer approach. In this paper we propose a solution that provides the stability and accuracy of a crystal based clock while still being free from metastable upsets. It is based on a ring oscillator that can be paused and resumed just like in the traditional approach, but is synchronized with a crystal clock during its stable phases of operation. We will present an appropriate circuit implementation and carefully analyze its operation. We will give evidence that the circuit switches on and off as fast as the traditional one, and that the clock output does not suffer from metastable voltage or spurious pulses at any time.","Title: Methods for analysing and improving the fault resilience of delay-insensitive codes\nYear: 2015\n\nAbstract: Delay-insensitive (DI) codes are usually prone to transient faults occurring during an ongoing transmission. For most DI codewords even a single transient can turn an incomplete transmission into a complete codeword, which is different from the originally sent codeword. Unless further redundant information is provided, the receiver has no means to detect such a transmission fault. In this paper we therefore propose two methods to systematically increase redundancy, either by i) building resilient subcodes, or by ii) using a two-step data encoding where error detecting codes are appropriately combined with delay-insensitive codes. In contrast to existing approaches we carefully avoid the introduction of timing assumptions to mask faults. Both methods are generic and can be used for any 4-phase DI code. In this paper we apply them to m-of-n codes, Berger and Zero-Sum codes and thoroughly analyse the efficiency of the resulting coding schemes.","Title: A versatile and reliable glitch filter for clocks\nYear: 2015\n\nAbstract: In today's complex system-on-chip architectures the protection of the clock(s) against glitches introduced by environmental disturbances, attackers, or gating measures is becoming increasingly important. Glitch protection is a delicate issue in the digital domain, as it is inherently coupled with metastability issues. The circuit we propose in this paper outputs a clock that strictly follows an input reference clock in the regular case, but guarantees a minimum output pulse width even in case of arbitrary behavior of the reference. We will give a thorough analysis showing that, unlike most existing solutions, our circuit can handle metastability without any residual risk of upsets. Still its implementation is very simple. Our theoretical claims will be supported by simulation results. Furthermore, we will give some examples on possible use cases for such a circuit, like clock gating, clock self-repair, or defense against clock attacks.","Title: A pausible clock with crystal oscillator accuracy\nYear: 2015\n\nAbstract: Pausible clocking is an efficient means of establishing communication between different timing domains without suffering from the risk of metastable upsets, as seen with synchronizer based solutions. On the downside, pausible clocks are usually ring-oscillator based and hence exhibit relatively bad accuracy and stability. In this paper we extend an existing solution to pausible clocking that allows to synchronize the ring oscillator to a high precision reference like a crystal clock. In that basic solution, the generated clock will run out of sync with the reference during a synchronization phase whenever it is paused for longer than a clock period. Our goal here is to avoid irregular periods of the generated clock and to keep it in sync with the reference at all possible times. We achieve this by (a) decoupling the communication interface in a way that allows a data transfer without actually pausing the clock, and (b) carefully aligning the signal timings. This finally yields a clock source that combines the benefits of being stable like a crystal clock and allowing hazard-free communication, like a ring-oscillator based pausible clock.","Title: Measuring the Distribution of Metastable Upsets over Time\nYear: 2015\n\nAbstract: As modern ASICs comprise an increasing number of independently clocked subsystems that need to interact, the accurate reliability assessment of synchronizers becomes crucial. Traditionally the reliability of a synchronizer is characterized by the mean time between upsets (MTBU), and the relevant flip-flop parameters are specified in a way to support MTBU calculation. In this paper we claim that actually a deeper insight into the distribution of upsets over time is needed in order to make a reasonable prediction in the range of the high reliability values that are generally targeted. We present a measurement concept that appropriately extends state-of-the-art approaches so as to allow for an experimental assessment of the upset distribution over time. In this way the distribution function can be studied, and in particular the probability of upsets with low temporal distance--which is the relevant one for high reliability--can be identified. We implement our concept on three different FPGA platforms and present the selected results. The distribution function we obtain indicates that the assumption of a uniform or standard normal distribution, which one might be tempted to imply for lack of better information, is definitely not generally useful.","Title: Reliable and Continuous Measurement of SET Pulse Widths\nYear: 2015\n\nAbstract: Gaining better insights into propagation and masking of radiation induced faults at some point requires the conduction of physical experiments. In our attempt to design a target ASIC for such experiments we elaborated an on-chip infrastructure that allows recording the pulse width of radiation induced voltage transients. Its unique properties are the ability to record more than one of these measurement values before having to be read out, and to be radiation tolerant. By careful circuit design and an unconventional redundancy architecture we obtained an infrastructure that provides the required features while being extremely area efficient. We motivate and present our proposed circuit architecture and evaluate it by means of a fault dictionary as well as simulation results.","Title: Enhanced Metastability Characterization Based on AC Analysis\nYear: 2015\n\nAbstract: The common way of characterizing the metastability properties of a circuit is by its metastability resolution constant tau and the aperture window. This approach is based on a model that represents the storage cell as a pair of crosscoupled inverters, each of which is, in turn, modeled by a constant-gain amplifier with a first-order low-pass filter at the output. The former reflects the inverter's signal regeneration capability, while the latter approximates its dynamic behavior. In this simple model there is no natural way of expressing, e.g., the load dependence of tau, therefore each change of the element's load capacitance requires a full recalibration. In this paper we propose decomposing the inverter into its constituent transistors and using their small-signal equivalent circuits for modeling. Metastability characterization is now based on a Spice AC analysis which yields a higher-order dynamic model of the circuit. Once the relevant parameters are known for a given element, the load dependence of tau can be expressed analytically, thus elegantly avoiding recalibration. We compare our approach with the extended nose short simulation (ENSS) method from literature and show that the results deviate by no more than 1 -- 2%.","Title: The Metastable Behavior of a Schmitt-Trigger\nYear: 2016\n\nAbstract: Schmitt-Trigger circuits are the method of choice for converting general signal shapes into clean, well-behaved digital ones. In this context these circuits are often used for metastability handling, as well. However, like any other positive feedback circuit, a Schmitt-Trigger can become metastable itself. Therefore, its own metastable behavior must be well understood, in particular the conditions that may cause its metastability. In this paper we will build on existing results from Marino to show that (a) a monotonic input signal can cause late transitions but never leads to a non-digital voltage at the Schmitt-Trigger output, and (b) a non-monotonic input can pin the Schmitt-Trigger output to a constant voltage at any desired (also non-digital) level for an arbitrary duration. In fact, the output can even be driven to any waveform within the dynamic limits of the system. We will base our analysis on a mathematical model of a Schmitt-Trigger's dynamic behavior and perform SPICE simulations to support our theory and confirm its validity for modern CMOS implementations. Furthermore, we will discuss several use cases of a Schmitt-Trigger in the light of our results.","Title: A new coding scheme for fault tolerant 4-phase delay-insensitive codes\nYear: 2016\n\nAbstract: Delay-insensitive (DI) codes are usually prone to transient faults occurring during an ongoing transmission. For most DI code words even a single transient can turn an incomplete transmission into a complete code word, which is different from the originally sent one. In this paper we therefore propose a novel two-step data encoding scheme that combines DI and error detecting codes. Our solution exploits the inherent fault resilience of DI codes to achieve a low coding overhead. For analyzing this fault resilience we use methods from graph theory. In contrast to existing approaches we carefully avoid the introduction of timing assumptions to mask faults. The presented coding scheme is generic and can, in principle, be used with any 4-phase DI code. We show how to apply it to m-of-n codes and analyze the resulting coding efficiency.","Title: Does Cascading Schmitt-Trigger Stages Improve the Metastable Behavior?\nYear: 2016\n\nAbstract: Schmitt-Trigger stages are the method of choice for robust discretization of input voltages with excessive transition times or significant noise. However, they may suffer from metastability. Based on the experience that the cascading of flip-flop stages yields a dramatic improvement of their overall metastability hardness, in this paper we elaborate on the question whether the cascading of Schmitt-Trigger stages can obtain a similar gain. We perform a theoretic analysis that is backed up by an existing metastability model for a single Schmitt-Trigger stage and elaborate some claims about the behavior of a Schmitt-Trigger cascade. These claims suggest that the occurrence of metastability is indeed reduced from the first stage to the second which suggests an improvement. On the downside, however, it becomes clear that metastability can still not be completely ruled out, and in some cases the behavior of the cascade may be less beneficial for a given application, e.g. by introducing seemingly acausal transitions. We validate our findings by extensive HSPICE simulations in which we directly cover our most important claims.","Title: A Model for the Metastability Delay of Sequential Elements.\nYear: 2017\n\nAbstract: It is well known that every sequential element may become metastable when provided with marginal inputs, such as input transitions occurring too close or input voltage not reaching a defined HI or LO level. In this case the sequential element requires extra time to decide which digital output level to finally present, which is perceived as an output delay. The amount of this delay depends on how close the elements state is to the balance point, at which the delay may, theoretically, become infinite. While metastability can be safely avoided within a closed timing domain, it cannot be completely ruled out at timing domain boundaries. Therefore it is important to quantify its effect. Traditionally this is done by means of a \"mean time between upsets\" (MTBU) which gives the expected interval between two metastable upsets. The latter is defined as the event of latching the still undecided output of one sequential element by a subsequent one. However, such a definition only makes sense in a time-safe environment like a synchronous design. In this paper we will extend the scope to so-called value-safe environments, in which a sequential element can safely finalize its decision, since the subsequent one waits for completion before capturing its output. Here metastability is not a matter of \"failure\" but a performance issue, and hence characterization by MTBU is not intuitive. Therefore we will put the focus on the delay aspect and derive a suitable model. This model extends existing approaches by also including the area of very weak metastability and thus providing complete coverage. We will show its validity through comparison with transistor-level simulation results for the most popular sequential elements in different implementations, point out its relation to the traditional MTBU model parameters, namely tau and T-0, and show how to use it for calculating the performance penalty in a value-safe environment.","Title: Measuring metastability using a time-to-digital converter\nYear: 2017\n\nAbstract: In view of the numerous clock domain crossings found in modern systems-on-chip and multicore architectures precise metastability characterization is a fundamental task. We propose a conceptually novel approach for the experimental assessment of upset rate over resolution time that is usually employed to extract the relevant characteristics. Our method is based on connecting a time-to-digital converter to the output of the flip flop under test, rather than using a phase shifted clock, as conventionally done. We present the details of an FPGA implementation of our approach and show its feasibility through an experimental evaluation, whose results favorably match those obtained by the conventional method. The benefits of the novel scheme are the ability to perform a calibration for the delay steps, a speed-up of the measurement process, and the availability of a more comprehensive and ordered measurement data set.","Title: Setup for an Experimental Study of Radiation Effects in 65nm CMOS.\nYear: 2017\n\nAbstract: Physical radiation experiments are a vital means for calibrating simulation models targeted to studying the impact of ionizing particles on VLSI circuits. However, their conduction requires special care and a very specific setup. In this paper we give an overview of such an experimental setup, and highlight some specific details. Beyond showing the context overarching the objectives of the experiments, the envisioned radiation sources, as well as design and architecture of a specific target ASIC, we will put specific emphasis on the communication infrastructure, namely an FPGA that controls the data exchange between some preprocessing infrastructure located on the target ASIC on one side and the host PC running the data analysis on the other. Finally, the physical arrangement comprising carrier PCB for the target ASIC, and cabling, which need to adhere specific requirements, will receive some attention as well.","Title: A versatile architecture for long-term monitoring of single-event transient durations.\nYear: 2017\n\nAbstract: We present design and analysis of an on-chip measurement infrastructure, which facilitates long-term monitoring of single-event transient durations in digital VLSI circuits exposed to uncontrollable radiation. Unlike the known oscilloscope-based methods, our approach is all-digital: SET durations are measured by the SET-gated counting of pulses generated by a high-frequency ring oscillator, and stored in an up/down-counter array organized in a ring. We carefully elaborate a comprehensive concept for making our infrastructure SEU tolerant, with the main challenge being to attain a sufficiently high probability of recording useful hits in the target before exhausting the SEU tolerance of the infrastructure. Our key contribution here concerns the protection of the counter array: Rather than resorting to radiation hardening or explicit triple modular redundancy (TMR), we save area by using a novel redundant duplex counter architecture: For a small number of recorded SETs, our architecture implicitly implements TMR, albeit in a way that degrades gracefully for larger numbers of recorded SETs. Besides standard functional and timing verification, we use Spice-based SET injection for verifying the effectiveness of our SEU-tolerant architecture, and some cross section-based probabilistic analysis for confirming that our measurement infrastructure based on it indeed achieves its purpose.","Title: A Critical Charge Model for Estimating the SET and SEU Sensitivity: A Muller C-Element Case Study\nYear: 2017\n\nAbstract: This paper presents a critical charge model for estimating the SET and SEU robustness. The proposed model has been derived by analytic fitting of SPICE results, using a Muller C-element designed in 65 and 130 nm bulk CMOS technologies as the target device. The critical charge is expressed in terms of the size of C-element, size of load inverter, supply voltage and temperature, for constant timing parameters of the SET/SEU current pulse. The proposed model could be utilized to calculate the critical charge causing a SET, for both analyzed technologies, with the accuracy comparable to SPICE simulations. The critical charge for SEU was higher than for SET, but the dependencies obtained for SET response were qualitatively similar to those for SEU. This implies that the proposed critical charge model may be applicable for optimizing the SET/SEU robustness evaluation of the circuits involving the Muller C-element. Moreover, the model may also serve as a basis for evaluating the SET/SEU robustness of other standard cells and other technologies, and thus also for analysis of the SET/SEU robustness of complex circuits.","Title: Refined metastability characterization using a time-to-digital converter.\nYear: 2018\n\nAbstract: In view of the numerous clock domain crossings found in modern systems-on-chip and multicore architectures precise metastability characterization is a fundamental task. We propose a conceptually novel approach for the experimental assessment of upset rate over resolution time that is usually employed to extract the relevant characteristics. Our method is based on connecting a time-to-digital converter to the output of the flip flop under test, rather than using a phase shifted clock, as conventionally done. We present the details of an FPGA implementation of our approach and show its feasibility through an experimental evaluation, whose results favorably match those obtained by the conventional method. The benefits of the novel scheme are the ability to perform a calibration for the delay steps, a speed-up of the measurement process, and the availability of a more comprehensive and ordered measurement data set. Especially the latter can be of crucial importance when (even multiple) glitches or oscillation are suspected to result from metastability, or when the temporal distribution of upsets matters (bursts of upsets, e.g.).","Title: Using a Duplex Time-to-Digital Converter for Metastability Characterization of an FPGA\nYear: 2018\n\nAbstract: In view of the increasing number of clock domains found in modern ASICs, the precise characterization of metastability at their boundaries becomes crucial. In some cases, the conventional approach does not provide a sufficient level of detail information. As an alternative approach, the use of a time-to-digital converter based on a tapped delay line has been proposed. In this paper we extend the latter by an additional tapped delay line thus allowing to further refine the concept. We present the underlying concept, its implementation, as well as experimental measurements on an FPGA platform that reveals significant variations in the metastable behavior of different FPGA boards of the same type.","Title: Partially Systematic Constant-Weight Codes for Delay-Insensitive Communication\nYear: 2018\n\nAbstract: The increasing complexity and modularity of contemporary systems makes the availability of flexible and robust, yet efficient, module-level interconnect instrumental. Delay-insensitive codes are very attractive in this context. In this paper we explore the idea of composing hybrid coding schemes that combine the benefits of constant-weight codes, namely simple completion detection, with those of systematic codes, namely zero-effort decoding. Apart from showing how to systematically construct efficient hybrid codes, we elaborate in great detail on the cost-efficient and orphan-free implementation of completion detectors, as well as that of suitable encoders and decoders. A comparison of various concrete solutions in terms of coding efficiency as well as hardware overhead gives evidence for the benefits of our proposed approach, and at the same time serves as a repository for ready-made optimized solutions.","Title: State Recovery for Coarse-Grain TMR Designs in FPGAs Using Partial Reconfiguration\nYear: 2018\n\nAbstract: The operation of field-programmable gate arrays (FPGAs) in harsh environments like space entails the need for suitable fault-tolerance techniques of which Triple-Modular Redundancy (TMR) is most commonly deployed. While TMR is undoubtedly effective in masking faults, state recovery remains a problematic issue: Fine-grain TMR allows safe recovery, but incurs prohibitive area and performance penalties. In contrast, coarse-grain TMR has little overhead, but cannot safely provide recovery without roll-back or reset. We use the dynamic reconfiguration feature of modern FPGAs to augment an initially coarse-grain TMR with the ability of temporarily loading a fine-grain TMR design for forward-state-recovery. Therefore, we can seamlessly resume correct (fully redundant) operation in case of data-as well as configuration faults that occurred in the FPGA. As a proof of concept, the paper presents a showcase design and discusses distinctive properties of this new approach.","Title: Measuring Metastability with Free-Running Clocks\nYear: 2017\n\nAbstract: With the increasing number of clock domain crossings in modern VLSI circuits, the area, power and performance overheads introduced by synchronization have a rising impact on the overall system parameters. To minimize these overheads while still reaching the targeted system reliability, it is very important to precisely know the parameters of the circuit elements used for synchronization regarding metastability. While it is well understood, what parameters are relevant and how they can be derived from circuit models, obtained from simulation or even measured, the state of the art measurement approaches require precisely timed clock inputs. These are typically provided with expensive test equipment, on-chip clock management blocks or calibrated delay lines. This paper proposes an approach for measuring metastability parameters using uncorrelated free-running clocks only at the expense of a more challenging post-processing. The principle is closely related to sampling oscilloscopes with the same fundamental property: the measurement resolution is theoretically unbounded but proportional to the measurement time. Apart from the description of the circuit, this paper includes an analysis of the effect of clock uncertainty (jitter) on the measurement result and an experimental evaluation.","Title: An Experimental Study of Metastability-Induced Glitching Behavior\nYear: 2019\n\nAbstract: The increasing number of clock domain crossings in modern systems-on-chip makes the careful consideration of metastability paramount. However, the manifestation of metastability at a flip-flop output is often unduly reduced to late transitions only, while glitches are hardly ever accounted for. In this paper we study the occurrence of glitches resulting from metastability in detail. To this end we propose a measurement circuit whose principle substantially differs from the conventional approach, and by that allows to reliably detect glitches. By means of experimental measurements on an FPGA target we can clearly identify late transitions, single glitches and double glitches as possible manifestations of metastability. Some of these behaviors are unexpected as they do not follow from the traditional modeling theory. We also study the dependence of metastable behavior on supply voltage. Beyond confirming that, as reported in previous literature, the metastable decay constant tau is voltage-dependent, we also produce strong evidence that the relative occurrence of glitches is not voltage-dependent.","Title: An Automated Setup for Large-Scale Simulation-Based Fault-Injection Experiments on Asynchronous Digital Circuits\nYear: 2021\n\nAbstract: Experimental fault injection is an essential tool in the assessment and verification of fault-tolerance properties. Often, in these experiments it is impossible to reasonably cover the huge parameter space spanned by target state and fault parameters, and compromises or restrictions must be made. This is even more pronounced for asynchronous circuits where a convenient discretization of time throu...","Title: Towards Explaining the Fault Sensitivity of Different QDI Pipeline Styles\nYear: 2021\n\nAbstract: Asynchronous circuits, specifically those using a quasi delay-insensitive (QDI) implementation are known for their high resilience against timing uncertainties. However, their event-based operation principle impedes their temporal masking capability, making them more susceptible to fault-induced transitions caused by single event transients. While synchronous circuits obtain high resilience throug...","Title: Simulation-Based Approaches for Comprehensive Schmitt-Trigger Analyses\nYear: 2022\n\nAbstract: Schmitt-Triggers (S/Ts) are often utilized to clean noisy analog signals at intermediate voltage values in digital circuits. However, they are vulnerable to metastability, which may cause the same undesired non-digital output behavior that was supposed to be removed in the first place. To enable an efficient characterization of static and dynamic metastability properties of S/Ts (e.g., the metasta...","Title: On SAT-Based Model Checking of Speed-Independent Circuits\nYear: 2022\n\nAbstract: Formal verification plays an important role in the quality assurance of digital circuits. Apart from the now standard equivalence checking between design steps, functional correctness can be proven with model checking. In one approach, a Boolean satisfiability (SAT) problem describing the circuit’s implementation and expected properties is generated for each of a bounded number of time steps and f...","Title: AµFLIPS: An Asynchronous Microprocessor With FLexIbly-timed Pipeline Stages\nYear: 2022\n\nAbstract: AµFLIPS is an asynchronous microprocessor with novel pipeline register organization to resolve data and control hazards using synchronous hazard resolving schemes. Existing works claim that mechanisms for handling data and control hazards in synchronous systems are not directly applicable to asynchronous pipelined processors, because of distributed control nature of the latter. As a result of that...","Title: enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks.\nYear: 2022\n\nAbstract: Research on Deep Neural Networks (DNNs) has focused on improving performance and accuracy for real-world deployments, leading to new models, such as Spiking Neural Networks (SNNs), and optimization techniques, e.g., quantization and pruning for compressed networks. However, the deployment of these innovative models and optimization techniques introduces possible reliability issues, which is a pillar for DNNs to be widely used in safety-critical applications, e.g., autonomous driving. Moreover, scaling technology nodes have the associated risk of multiple faults happening at the same time, a possibility not addressed in state-of-the-art resiliency analyses. Towards better reliability analysis for DNNs, we present enpheeph, a Fault Injection Framework for Spiking and Compressed DNNs. The enpheeph framework enables optimized execution on specialized hardware devices, e.g., GPUs, while providing complete customizability to investigate different fault models, emulating various reliability constraints and use-cases. Hence, the faults can be executed on SNNs as well as compressed networks with minimal-to-none modifications to the underlying code, a feat that is not achievable by other state-of-the-art tools. To evaluate our enpheeph framework, we analyze the resiliency of different DNN and SNN models, with different compression techniques. By injecting a random and increasing number of faults, we show that DNNs can show a reduction in accuracy with a fault rate as low as 7 x 10 ^ (-7) faults per parameter, with an accuracy drop higher than 40%. Run-time overhead when executing enpheeph is less than 20% of the baseline execution time when executing 100 000 faults concurrently, at least 10x lower than state-of-the-art frameworks, making enpheeph future-proof for complex fault injection scenarios. We release enpheeph at https://github.com/Alexei95/enpheeph."],"string":"[\n \"Title: FIT-IT-Projekt DARTS: dezentrale fehlertolerante Taktgenerierung\\nYear: 2007\\n\\nAbstract: Dieser Artikel beschreibt Ziele und Lsungsansatz des prmierten FIT-IT-\\\"Embedded Systems\\\"-Projekts \\\"Distributed Algorithms for Robust Tick Synchronization\\\" (DARTS), das der Entwicklung einer fehlertoleranten Alternative zur herkmmlichen Taktgenerierung in VLSI-Chips, Systems-on-Chip und anderen Hardwaresystemen gewidmet ist. Anstatt das Taktsignal eines einzelnen Oszillators mit Hilfe eines aufwndig balancierten Clock-Trees systemweit zu verteilen, wird in DARTS der Takt jeder Funktionseinheit mit Hilfe eines lokalen Taktgenerators erzeugt. Damit wird der \\\"Single Point of Failure\\\" eliminiert, den ein zentraler Oszillator zwangslufig darstellt. Im Gegensatz zum klassischen \\\"Globally Asynchronous Locally Synchronous\\\" (GALS)-Ansatz sind die lokalen Taktgeneratoren beim DARTS-Ansatz jedoch keine Oszillatoren, sondern Instanzen eines verteilten Algorithmus zur fehlertoleranten Generierungsynchronisierterlokaler Taktsignale.\",\n \"Title: How Does Resource Utilization Affect Fault Tolerance?\\nYear: 2000\\n\\nAbstract: Much fault-tolerant architecture are based on the single-fault assumption, hence accumulation of dormant faults represents a potential reliability hazard. Based on the example of the fail-silent 驴Time-Triggered Architecture驴 we study sources and effects of dormant faults. We identify software as being more prone to dormant faults than hardware. By means of modeling, we reveal a high sensitivity of the MTTF to the existence of even a small amount of irregularly used resources. We propose on-line testing as a means of coping with dormant faults and sketch an appropriate test strategy.\",\n \"Title: A Reconfigurable Generic Dual-Core Architecture\\nYear: 2006\\n\\nAbstract: In this paper we propose a generic frame for the implementation of a dual-core processor with two modes of operation. One is the safety mode that allows to run the two cores in lock step in a classical master/checker fashion. A clock delay of 1.5 clock cycles between master and checker establishes the temporal redundancy to minimize the potential for common mode faults. The second operation mode allows a parallel execution of different instruction streams on the two cores in a multiprocessor fashion. The possibility to dynamically switch between the two modes allows for an efficient utilization of the duplicated core. We propose an implementation of such a generic frame that can be applied in conjunction with virtually any standard processor core. Also we perform a systematic failure analysis for the safety mode and the mode switching procedure. Experimental fault injection confirms that our reconfigurable architecture indeed provides the same fail safe properties as the classical master/checker architecture.\",\n \"Title: Automated Testing of FlexRay Clusters for System Inconsistencies in Automotive Networks\\nYear: 2008\\n\\nAbstract: Most innovations in the automotive domain are nowadays enabled by networked embedded systems. In this context a new communication subsystem termed FlexRay was recently introduced. Relying on the time-triggered paradigm it promises to provide the bandwidth and reliability required for future applications. For building reliable systems with FlexRay, verification and testing are of utmost importance. In this paper a new system test approach that focuses on the communication subsystem is presented. With the help of a dedicated FPGA-based star-coupler we show that inconsistent system states (Slightly-Off-Specification failures) can occur in real life systems, how they can be found using our solution and how one can avoid them.\",\n \"Title: Runtime verification of microcontroller binary code\\nYear: 2014\\n\\nAbstract: Runtime verification bridges the gap between formal verification and testing by providing techniques and tools that connect executions of a software to its specification without trying to prove the absence of errors. This article presents a framework for runtime verification of microcontroller binary code, which provides the above mentioned link in a non-intrusive fashion: the framework neither requires code instrumentation nor does it affect the execution of the analyzed program. This is achieved using a dedicated hardware unit that runs on a field programmable gate array in parallel to the analyzed microcontroller program. Different instances of this framework are discussed, with varying degrees of expressiveness of the supported specification languages and complexity in the hardware design. These instances range from invariant checkers for a restricted class of linear template constraints to a programmable processor that supports past-time linear temporal logic with timing constraints.\",\n \"Title: How to Tune the MTTF of a Fail-Silent System\\nYear: 2001\\n\\nAbstract: Color digital imagers contain Red, Green and Blue subpixels within each color pixel. Defects that develop either at fabrication time or due to environmentally induced errors over time can cause a single color subpixel (e.g., R) to fail, while leaving ...\",\n \"Title: On the role of the power supply as an entry for common cause faults—An experimental analysis\\nYear: 2009\\n\\nAbstract: With the advanced scaling of CMOS technology in the nanometer range, highly integrated mixed-signal systems can be designed. The use of nanometer CMOS, however, poses many challenges. This keynote presentation gives an overview of problems due to increased ...\",\n \"Title: Rigorously modeling self-stabilizing fault-tolerant circuits: An ultra-robust clocking scheme for systems-on-chip\\nYear: 2014\\n\\nAbstract: We present the first implementation of a distributed clock generation scheme for Systems-on-Chip that recovers from an unbounded number of arbitrary transient faults despite a large number of arbitrary permanent faults. We devise self-stabilizing hardware building blocks and a hybrid synchronous/asynchronous state machine enabling metastability-free transitions of the algorithm's states. We provide a comprehensive modeling approach that permits to prove, given correctness of the constructed low-level building blocks, the high-level properties of the synchronization algorithm (which have been established in a more abstract model). We believe this approach to be of interest in its own right, since this is the first technique permitting to mathematically verify, at manageable complexity, high-level properties of a fault-prone system in terms of its very basic components. We evaluate a prototype implementation, which has been designed in VHDL, using the Petrify tool in conjunction with some extensions, and synthesized for an Altera Cyclone FPGA.\",\n \"Title: On the Risk of Fault Coupling over the Chip Substrate\\nYear: 2009\\n\\nAbstract: Duplication and comparison has proven to be an efficient method for error detection. Based on this generic principle dual core processor architectures with output comparison are being proposed for safety critical applications. Placing two instances of the same (arbitrary) processor on one die yields a very cost efficient \\\"single chip\\\" implementation of this principle. At the same time, however, the physical coupling of the two replica creates the potential for certain types of faults to affect both cores in the same way, such that the mutual checking will fail. The key question here is how this type of coverage leakage relates to other imperfections of the duplication and comparison approach that would also be found using two cores on separate dies (such as coupling over a common power supply or clock). In this paper we analyze several of the relevant physical coupling mechanisms and elaborate a model to decompose the genesis of a common cause fault into several steps. We present an experimental study showing that a very tight local and temporal coincidence of the fault effect in both replica is a crucial prerequisite for a common cause fault. Based on this quantitative input we can conclude from our decomposition model that the risk of common cause faults is low for physical coupling mechanisms with relatively slow propagation speed, such as thermal and mechanical effects. The role of asymmetry for mitigating common cause faults is discussed in the light of these findings.\",\n \"Title: Solving the Fundamental Problem of Digital Design - A Systematic Review of Design Methods\\nYear: 2006\\n\\nAbstract: During the last decade various asynchronous circuit structures and design methods have been proposed that seem to be quite different. In essence, however, all these methods contribute to solving the same fundamental design problem in one way or another. In this paper we use a simple communication model to figure out what this fundamental design problem actually is and to highlight its roots. We show how each of the related sub-problems can be conceptually solved in the time domain and in the information domain. Having this model in mind we finally develop a common framework to classify the most popular asynchronous design methods and figure out which sub-problem they actually solve and in which respect they differ from each other.\",\n \"Title: A Fail-Silent Reconfigurable Superscalar Processor\\nYear: 2007\\n\\nAbstract: We propose a reconfigurable superscalar processor with two modes of operation: In safety mode the two pipelines run in lock step, executing the same instruction sequence, thus allowing to detect hardware failures. In performance mode different instruction streams are executed in parallel, just like in a standard superscalar processor. Considering that many embedded applications comprise a mixture of safety-critical and non safety-critical functions, the ability to dynamically switch between the two modes allows an efficient utilization of the duplicated pipeline. To complement the error detection enabled by the duplicated pipeline, non-duplicated components such as the register file are secured by parity. A systematic failure analysis shows that the proposed implementation can indeed detect all single faults in safety mode and that the ability to switch modes does not compromise the fail safe property. These encouraging results are finally confirmed by extensive fault injection experiments.\",\n \"Title: A Generic Dual Core Architecture With Error Containment\\nYear: 2004\\n\\nAbstract: The dual core strategy allows to construct a fail-silent processor from two instances (master/checker) of any arbitrary standard processor. Its main drawbacks are its vulnerability with respect to common mode failures and the existence of residual single points of failure. In this paper we propose a generic frame that systematically eliminates these drawbacks. First, we employ temporal redundancy to cope with common mode failures. Unlike similar approaches we can ensure error containment even if - as a result of the temporal redundancy - the comparison by the checker core is delayed. We attain this by introducing a specific delay element for outgoing data. Second, we perform a systematic analysis of potential single points of failure and eliminate these by careful layout, self-checking circuits and similar methods. We finally validate our approach by means of exhaustive fault injection experiments. The results indicate a 100% self-checking coverage for stuck-at faults and complete error containment. Since the proposed framework has been kept generic in the sense that the individual standard processor cores are treated as black boxes, these results are valid independent of the core actually used.\",\n \"Title: Hardware implementation of an SAD based stereo vision algorithm\\nYear: 2007\\n\\nAbstract: This paper presents the hardware implementation of a stereo vision core algorithm, that runs in real-time and is targeted at automotive applications. The algorithm is based on the sum of absolute differences (SAD) and computes the disparity map using 320 times 240 input images with a maximum disparity of 100 pixels. The hardware operates at a frequency of 65 MHz and achieves a frame rate of 425 fps by calculating the data highly parallel and pipelined. Thus an implemented and basically optimized software solution, running on an Intel Pentium 4 with 3 GHz clock frequency is 166 times outperformed.\",\n \"Title: Architecture and Design Analysis of a Digital Single-Event Transient/Upset Measurement Chip\\nYear: 2012\\n\\nAbstract: This paper presents the architecture and a detailed design analysis of a digital measurement chip which facilitates long-term irradiation experiments of basic asynchronous circuits. It combines radiation targets like Muller C-elements and elastic pipelines as well as standard combinational gates and flip-fops with an elaborate on-chip measurement infrastructure. Major architectural challenges result from the fact that the latter must operate reliably under the same radiation conditions the target circuits are exposed to, without wasting precious die area for a rad-hard design. A measurement architecture based on multiple non-rad-hard counters is used, which we show to be resilient against double faults, as well as many triple and even higher-multiplicity faults. The analysis is done by means of comprehensive fault injection experiments, which are based on detailed Spice models of the circuits in conjunction with a standard double-exponential current injection model for single-event transients. We also provide probabilistic calculations of the sustainable particle flow rates, based on the results of a detailed area analysis in conjunction with experimentally determined cross section data for the ASIC implementation technology used. The results confirm that the overall architecture indeed supports significant target hit rates, without exceeding the resilience bound of the measurement infrastructure.\",\n \"Title: Remote measurement of local oscillator drifts in FlexRay networks\\nYear: 2009\\n\\nAbstract: Distributed systems, especially time-triggered ones, are implementing clock synchronization algorithms to provide and maintain a common view of time among the different nodes. Such architectures heavily rely on the nodes' local oscillators to remain within given accuracy bounds. However, measuring the oscillator frequencies (e.g., for maintenance or diagnosis) is usually difficult to perform since it requires physical access to each single node and may interfere with the running application. Moreover, clock synchronization features tend to mask clock deviations. In this work, we propose a non-intrusive method for remote measurement of the individual oscillator drifts within a distributed system. Our approach is based on a tester that sends carefully aligned messages to stimulate the clock synchronization service and records the resulting bus traffic for an analysis of the nodes' synchronization behavior. This tester needs access to the communication bus only. We focus our work to FlexRay and validate our approach by experiments.\",\n \"Title: Soft Error Tolerant Asynchronous Circuits Based on Dual Redundant Four State Logic\\nYear: 2009\\n\\nAbstract: The continuing downscaling of integrated circuits makes modern devices more susceptible to soft errors. This paper investigates the possibility of using Four-State Logic (FSL) to improve the fault tolerance of digital circuits. FSL is a possible implementation of asynchronous Quasi Delay Insensitive (QDI) logic using a more efficient encoding and handshake protocol. The behavior of asynchronous circuits designed with FSL when subjected to transient faults is analyzed. We present methods based on dual redundancy that allow to detect as well as correct soft errors autonomously. The concept is demonstrated by fault injection experiments.\",\n \"Title: Implementation of self-healing asynchronous circuits at the example of a video-processing algorithm\\nYear: 2010\\n\\nAbstract: This paper presents a self-healing, asynchronous implementation of a small part of the hardware pre-processing to be used in the video processing unit of GAIA, a scientific mission of the European Space Agency (ESA). With the applied concept the circuit is able to detect permanent faults during runtime and to recover from them by an autonomous reconfiguration. The work describes how to transform a standard FSL circuit into a self-healing FSL design and presents first results of hardware experiments.\",\n \"Title: Metastability Characterization For Muller C-Elements\\nYear: 2013\\n\\nAbstract: We(1) present an approach for experimental metastability characterization of Muller C-elements. It is based on the late transition detection scheme known from flip flop characterization. Substantial additional challenges arise from the facts that with the Muller C-element the input transition to use as a reference for the output delay may change from case to case, and the error flags of the detector need to be reliably synchronized into the other timing domain. Our solution strategy involves taking measurements concurrently and sorting out irrelevant results later on. This is done based on detailed information about type and relative position of input transitions as well as type and polarity of the output transition, for the collection of all of which we propose efficient means. An example study on an FPGA platform proves the applicability and correct operation of our approach.\",\n \"Title: Economic Online Self-Test in the Time-Triggered Architecture\\nYear: 1999\\n\\nAbstract: Fault-tolerant, distributed, real-time architectures are often based on a restrictive fault model and special assumptions of the node behavior in the event of faults. The Time-Triggered Architecture (TTA) typifies such an architecture. Using the TTA, this article analyzes the implications of the single-fault assumption and the fail-silence assumption on error detection and testing. Preventing fault accumulation within the communication controllers is of utmost importance in guaranteeing system fault tolerance. The methods for on-line self-testing traditionally applied to attain this goal compromise the responsiveness of the system or result in costly solutions. The strategy presented in this article is cost-efficient, since it fully utilizes the properties offered by the TTA for testing.\",\n \"Title: An Approach for Efficient Metastability Characterization of FPGAs through the Designer\\nYear: 2013\\n\\nAbstract: The efficient design of a synchronizer for a given MTBF limit heavily depends on the availability of an accurate metastability characterization of the bistables in the target technology. We propose a measurement approach for FPGAs that comes along without any specific measurement infrastructure and can hence be performed by the designer with relatively low efforts, but is yet very accurate. Our concept comprises the use of the FPGA-internal digital clock manager (DCM), calibration measurements for the latter, averaging over several parallel measurement runs, and separated analysis of different metastability cases. To demonstrate the power of our approach we present detailed measurement results for a Xilinx Virtex-4 FPGA that even show slave metastability. Furthermore, we discuss how diverse constraints can be considered to make the measurement more accurate and time efficient.\",\n \"Title: FATAL+: A Self-Stabilizing Byzantine Fault-tolerant Clocking Scheme for SoCs\\nYear: 2012\\n\\nAbstract: We present concept and implementation of a self-stabilizing Byzantine fault-tolerant distributed clock generation scheme for multi-synchronous GALS architectures in critical applications. It combines a variant of a recently introduced self-stabilizing algorithm for generating low-frequency, low-accuracy synchronized pulses with a simple non-stabilizing high-frequency, high-accuracy clock synchronization algorithm. We provide thorough correctness proofs and a performance analysis, which use methods from fault-tolerant distributed computing research but also addresses hardware-related issues like metastability. The algorithm, which consists of several concurrent communicating asynchronous state machines, has been implemented in VHDL using Petrify in conjunction with some extensions, and synthetisized for an Altera Cyclone FPGA. An experimental validation of this prototype has been carried out to confirm the skew and clock frequency bounds predicted by the theoretical analysis, as well as the very short stabilization times (required for recovering after excessively many transient failures) achievable in practice.\",\n \"Title: On the Necessity of On-line-BIST in Safety-Critical Applications - A Case-Study\\nYear: 1999\\n\\nAbstract: This paper analyzes the effect of dormant faults on the mean time to failure (MTTF) of highly reliable systems. The analysis is performed by means of Markov models that allow quantifying the effect of dormant faults and other vital reliability parameters. It turns out that the presence of dormant faults can drastically reduce the MTTF of a system, particularly if the operating system allows a sporadic (\\\"event-driven\\\") change from a regular mode of operation to another mode. Virtually every practical system involves such a change, at least in case of emergency.It is demonstrated that on-line built-in self-test (BIST) is an effective means to overcome the deteriorating effect of dormant faults and re-establish a high MTTF. A very moderate test period may already be sufficient. The analysis is performed for the example of a fail-silent communication system for safety-critical real-time applications.\",\n \"Title: Flexible hardware-based stereo matching\\nYear: 2008\\n\\nAbstract: To enable adaptive stereo vision for hardware-based embedded stereo vision systems, we propose a novel technique for implementing a flexible block size, disparity range, and frame rate. By reusing existing resources of a static architecture, rather than dynamic reconfiguration, our technique is compatible with application specific integrated circuit (ASIC) as well as field programmable gate array (FPGA) implementations. We present the corresponding block diagrams and their implementation in our hardware-based stereo matching architecture. Furthermore, we show the impact of flexible stereo matching on the generated disparity maps for the sum of absolute differences (SADs), rank, and census transform algorithms. Finally, we discuss the resource usage and achievable performance when synthesized for an Altera Stratix II FPGA.\",\n \"Title: Built-In Fault Injectors - The Logical Continuation of BIST?\\nYear: 2003\\n\\nAbstract: With the increasing number of embedded computer systems being used in safety critical applications the testing and assessment of a system's fault tolerance properties become a crucial issue. Experimental fault injection plays an important role in the process of fault tolerance assessment. The restricted accessibility of nodes within a chip or system-on-a-chip, however, make such experiments extremely dif- ficult. The idea we want to promote in this paper is to add a fault injector on to the silicon that can perform fault injection automatically on demand during several stages of design. In a first step we discuss the usefulness of such an infrastructure IP module. Then, starting from an FPGA-based fault-injection toolset that we have already developed, we investigate the feasibility of designing such a module under the given tight economic constraints.\",\n \"Title: An investigation of the clique problem in FlexRay.\\nYear: 2008\\n\\nAbstract: Distributed, time-triggered communication based on FlexRay is likely to become an enabler for future safety related applications in the automotive domain. Prior to series deployment, however, confidence has to be established that all mechanisms work as expected. One particular issue in this regard is the clique problem. Cliques are groups of nodes connected to the same bus which are only able to communicate with the members of the same clique, but not with members from another one. In time-triggered systems cliques might be caused, for instance, by Byzantine faults leading to a different view of the global time.In constrast to other hard real-time systems, FlexRay lacks explicit protocol support for detecting and resolving cliques. Thus, in this paper we investigate the clique problem in a real FlexRay system. We explain the different types of cliques and give examples how they might emerge and look like. One focus is the experimental study of the behavior of a real FlexRay cluster with existing cliques. In particular, we investigate the integration behavior of nodes into a system with present cliques as well as the behavior of the system when it is in a clique state. After a presentation of the results, we conclude by giving some directions about some protocol features which might be used for implementing clique avoidance or clique detection mechanisms.\",\n \"Title: Designing FlexRay-based automotive architectures: a holistic OEM approach\\nYear: 2012\\n\\nAbstract: FlexRay is likely to become the de-facto standard for upcoming in-vehicle communication. Efficient scheduling of the static and dynamic segment of the communication cycle in combination with the determination of more than 60 parameters that are part of the FlexRay protocol is a challenging task. This paper provides a formal analysis for interdependencies between the parameters as well as a scheduling approach for the static and dynamic segment. Experimental results give evidence of a significant interdependency between the subtasks such that a holistic scheduling approach becomes mandatory to provide high-quality FlexRay schedules. As a solution, this work introduces a complete functional FlexRay scheduling approach that takes parameter selection, allocation of messages to the static and dynamic segment, and concurrent scheduling into account. A real-world case study from the automotive domain gives evidence of efficiency and applicability of the proposed approach.\",\n \"Title: Processor Support For Temporal Predictability The Spear Design Example\\nYear: 2003\\n\\nAbstract: The demand for predictable timing behavior is characteristic for real-time applications. Experience has shown that this property cannot be achieved by software alone but rather requires support from the processor This situation is analyzed and mapped to a design rationale for SPEAR (Scalable Processor for Embedded Applications in Real-time Environments), a processor that has been designed to meet the specific temporal demands of real-time systems. At the hardware level, SPEAR guarantees interrupt response with minimum temporal jitter and minimum delay. Furthermore, the processor provides an instruction set that only has constant-time instructions. At the software level, SPEAR supports the implementation of temporally predictable code according to the single-path programming paradigm. Altogether these features support writing of code with minimal jitter and provide the basis for exact temporal predictability. Experimental results show that SPEAR indeed exhibits the anticipated highly predictable timing behavior.\",\n \"Title: Safely Stimulating the Clock Synchronization Algorithm in Time-Triggered Systems - a Combined Formal & Experimental Approach\\nYear: 2009\\n\\nAbstract: Deterministic replay is used during testing to reproduce a scenario and drive the system under test to a given state. In this work, we replay an a priori defined bus traffic to influence the clock synchronization mechanism. Beyond testing this distributed mechanism itself, our aim is to draw conclusions on the nodes' bus receiver operation. Since these replay activities are part of a transparent o...\",\n \"Title: A Metastability-Free Multi-synchronous Communication Scheme for SoCs\\nYear: 2009\\n\\nAbstract: We propose a communication scheme for GALS systems with independent but approximately synchronized clock sources, which guarantees high-speed metastability-free communication between any two peers via bounded-size FIFO buffers. The proposed approach can be used atop of any multi-synchronous clocking system that guarantees a synchronization precision in the order of several clock cycles, like our fault-tolerant DARTS clocks. We determine detailed formulas for the required communication buffer size, and prove that this choice indeed guarantees metastability-free communication between correct peers, at maximum clock speed. We also describe a fast and efficient implementation of our scheme, and calculate the required buffer size for a sample test scenario. Experimental results confirm that the size lower bounds provided by our formulas are tight in this setting.\",\n \"Title: Digital Late-Transition Metastability Simulation Model\\nYear: 2013\\n\\nAbstract: As modern systems-on-chip contain increasingly more clock domain crossings, the associated metastability effects become much more pronounced. To maintain the reliability of the design, efficient means for metastability analysis become mandatory. We propose an approach that allows simulation of late transitions, the most dominant effect of metastability in modern VLSI chips. While this approach is purely digital and hence very fast, it allows a precise treatment in the time domain. These properties make it attractive for modeling even complex circuit structures. The core of our approach is a timing model that carefully maps the relative times of setting the latch opaque and the arrival of new data at the input to the appropriate data-input/output delay. Unlike with existing models this mapping is not simply linear and hence restricted to a very narrow scope, it rather works for arbitrary inter-arrival times between data and enable. We systematically elaborate an analytic description of this input/output delay and illustrate how the respective model parameters can be derived from an initial circuit simulation, either automatically or with manual support. Once calibrated, the model allows fast and precise simulation, which we illustrate in a case study.\",\n \"Title: Mapping a Fault-Tolerant Distributed Algorithm to Systems on Chip\\nYear: 2008\\n\\nAbstract: Systems on chip (SoC) have much in common with traditional (networked) distributed systems in that they consist of largely independent components with dedicated communication interfaces. Therefore the adoption of classic distributed algorithms for SoCs suggests itself. The implementation complexity of these algorithms, however, significantly depends on the underlying failure models. In traditional software-based solutions this is normally not an issue, such that the most unconstrained, namely the Byzantine, failure model is often applied here. Our case study of a hardware implemented tick synchronization algorithm shows, however, that in an SoC-implementation substantial hardware savings can result from restricting the failure model to benign failures (omissions, crashes). On the downside, it turns out that such restricted failure models have a fairly poor coverage with respect to the hardware faults occurring in practice, and that additional measures to enforce these restrictions may entail an implementation overhead that outweighs the gain obtained in the implementation of a simpler algorithm. As a remedy we investigate the potential of failure transformation in this context and show that this technique may indeed yield an optimized overall solution.\",\n \"Title: Automated test-trace inspection for microcontroller binary code\\nYear: 2011\\n\\nAbstract: This paper presents a non-intrusive framework for runtime verification of executable microcontroller code. A dedicated hardware unit is attached to a microcontroller, which executes the program under scrutiny, to track atomic propositions stated as assertions over program variables. The truth verdicts over the assertions are the inputs to a custom-designed μ CPU unit that evaluates past-time LTL specifications in parallel to program execution. To achieve this, the instruction set of the μ CPU is tailored to determining satisfaction of specifications.\",\n \"Title: Past time LTL runtime verification for microcontroller binary code\\nYear: 2011\\n\\nAbstract: This paper presents a method for runtime verification of microcontroller binary code based on past time linear temporal logic (ptLTL). We show how to implement a framework that, owing to a dedicated hardware unit, does not require code instrumentation, thus, allowing the program under scrutiny to remain unchanged. Furthermore, we demonstrate techniques for synthesizing the hardware and software units required to monitor the validity of ptLTL specifications.\",\n \"Title: Testing and build-in self-test - a survey\\nYear: 2000\\n\\nAbstract: As the density of VLSI circuits increases it becomes attractive to integrate dedicated test logic on a chip. This built-in self-test (BIST) approach not only offers economic benefits but also interesting technical opportunities with respect to hierarchical testing and the reuse of test logic during the application of the circuit. Starting with an overview of test problems, test applications and terminology this survey reviews common test methods and analyzes the basic test procedure. The concept of BIST is introduced and discussed, BIST strategies for random logic as well as for structured logic are shown.\",\n \"Title: Protecting an Asynchronous NoC against Transient Channel Faults\\nYear: 2012\\n\\nAbstract: Considering the increasing rates of transient faults predicted for future technology nodes, we investigate options for protecting the communication channels within an NoC from those. Our target is an asynchronous NoC, as its data-driven activity emanates from a more energy aware communication concept. We compare different redundancy schemes with the aim of providing a reliable communication service where error detection and correction is transparent to the higher system levels, and we also propose a new scheme. Our comparison is supported by results from simulation and synthesis. While all solutions are designed to withstand at least one transient fault, they largely differ in their area and delay penalty as well as potential extra coverage that some of them offer.\",\n \"Title: An SET Tolerant Tree Arbiter Cell\\nYear: 2013\\n\\nAbstract: Due to their inherently in deterministic behavior arbiters cannot simply be made fault tolerant by replication. We present an in-depth analysis of a tree arbiter cell with respect to possible faults and failure modes. Based on these results we devise a fault tolerant implementation of this cell that carefully avoids all single points of failure and can hence withstand transient faults as well as bit flips in its stateful elements. We verify the fault tolerance of our implementation by means of model checking and compare its overheads and performance penalties with a TMR-based solution. While the validation confirms that our approach is indeed suitable for use within an overall fault tolerance concept, the penalties turn out to be lower than for a comparable TMR approach.\",\n \"Title: Is Asynchronous Logic More Robust Than Synchronous Logic?\\nYear: 2009\\n\\nAbstract: With clock rates beyond 1 GHz, the model of a systemwide synchronous clock is becoming difficult to maintain; therefore, asynchronous design styles are increasingly receiving attention. While the traditional synchronous design style is well-proven and backed up by a rich field experience, comparatively little is known about the properties of asynchronous circuits in practical application. In the face of increased transient fault rates, robustness is a crucial property, and from a conceptual view, the so-called “delay-insensitive” asynchronous design approaches promise to be more robust than synchronous ones, since their operation does not depend on tight timing margins, and data are two-rail coded. A practical assessment of asynchronous designs in fault-injection (FI) studies, however, can rarely be found, and there is a lack of adequate methods and tools in this particular domain. Therefore, the objective of this work is 1) to provide a common approach for efficient and accurate FI in synchronous and in asynchronous designs, and 2) to experimentally compare the robustness of both synchronous and asynchronous designs. To this end, a synchronous 16-bit processor as well as its asynchronous (delay insensitive) equivalent are subjected to signal flips and delay faults. The results of over 489 million experiments are summarized and discussed, and a detailed discussion on the specific properties of the chosen asynchronous design style is given.\",\n \"Title: Towards an optimal combination of error detection mechanisms\\nYear: 1991\\n\\nAbstract: Self checking components are widely used to realize highly reliable systems. The design of a computer-board with self checking capability requires the employment of multiple mechanisms for the detection of faults and errors to achieve short error detection latency and high error detection coverage. Literature suggests a lot of such mechanisms and describes their effectiveness. However, without a good strategy their combination will very likely result in a system where some types of error are detected by several mechanisms simultaneously, whereas other types remain undetected. Moreover, the permissible number of mechanisms is limited by cost and complexity of the system.\",\n \"Title: An infrastructure for accurate characterization of single-event transients in digital circuits\\nYear: 2013\\n\\nAbstract: We present the architecture and a detailed pre-fabrication analysis of a digital measurement ASIC facilitating long-term irradiation experiments of basic asynchronous circuits, which also demonstrates the suitability of the general approach for obtaining accurate radiation failure models developed in our FATAL project. Our ASIC design combines radiation targets like Muller C-elements and elastic pipelines as well as standard combinational gates and flip-flops with an elaborate on-chip measurement infrastructure. Major architectural challenges result from the fact that the latter must operate reliably under the same radiation conditions the target circuits are exposed to, without wasting precious die area for a rad-hard design. A measurement architecture based on multiple non-rad-hard counters is used, which we show to be resilient against double faults, as well as many triple and even higher-multiplicity faults. The design evaluation is done by means of comprehensive fault injection experiments, which are based on detailed Spice models of the target circuits in conjunction with a standard double-exponential current injection model for single-event transients (SET). To be as accurate as possible, the parameters of this current model have been aligned with results obtained from 3D device simulation models, which have in turn been validated and calibrated using micro-beam radiation experiments at the GSI in Darmstadt, Germany. For the latter, target circuits instrumented with high-speed sense amplifiers have been used for analog SET recording. Together with a probabilistic analysis of the sustainable particle flow rates, based on a detailed area analysis and experimental cross-section data, we can conclude that the proposed architecture will indeed sustain significant target hit rates, without exceeding the resilience bound of the measurement infrastructure.\",\n \"Title: Efficient Stimulus Generation For Testing Embedded Distributed Systems - The Flexray Example\\nYear: 2005\\n\\nAbstract: Embedded electronic communication systems play a vital role in the future development of automotive systems. For successful application developments new test and diagnosis solutions for these distributed systems are required This paper presents solutions for the stimulus generation of test systems based on a remote test under the stringent constraints of the automotive industry. We elaborate a flexible and accurate method that enables a systematic and comprehensive test of data link layer related communication services. Furthermore, we discuss how this solution can be applied for various different test purposes (e.g. for verification, robustness, interopembility or maintenance tests) and demonstrate its application by use of an example that varies one fundamental protocol parameter of FlexRay.\",\n \"Title: VLSI implementation of a distributed algorithm for fault-tolerant clock generation\\nYear: 2011\\n\\nAbstract: We present a novel approach for the on-chip generation of a fault-tolerant clock. Our method is based on the hardware implementation of a tick synchronization algorithm from the distributed systems community. We discuss the selection of an appropriate algorithm, present the refinement steps necessary to facilitate its efficient mapping to hardware, and elaborate on the key challenges we had to overcome in our actual ASIC implementation. Our measurement results confirm that the approach is indeed capable of creating a globally synchronized clock in a distributed fashion that is tolerant to a (configurable) number of arbitrary faults. This property facilitates eliminating the clock as a single point of failure. Our solution is based on purely asynchronous design, obviating the need for crystal oscillators. It is capable of adapting to parameter variations as well as changes in temperature and power supply-properties that are considered highly desirable for future technology nodes.\",\n \"Title: Towards a Systematic Test for Embedded Automotive Communication Systems\\nYear: 2008\\n\\nAbstract: The introduction of computer-controlled intelligent safety and comfort features has turned cars into complex distributed computing systems. In such a system the proper operation of the communication backbone as well as the proper interaction of components from different vendors must be ensured for all configurations and operating conditions. This system-level test goes far beyond the (isolated) te...\",\n \"Title: Architecture for monitoring SET propagation in 16-bit Sklansky adder\\nYear: 2014\\n\\nAbstract: We propose a measurement architecture that allows to trace generation and propagation of single-event transients in a combinational target circuit that will be subjected to radiation in an experimental study. We choose the Sklansky adder as a target circuit, since it exhibits both properties we are interested in, namely different amounts of fanout and a carry propagation chain. The problem of devising a suitable on-chip measurement infrastructure lies in the partly contradictory requirements, like constrained area, radiation tolerance and good resolution of the location and propagation path of particle hits. Our proposed architecture is based on linear feedback shift registers that can be used as lean and robust counter implementations. These counters are attached at selected locations within the target adder circuit, and we show by means of a simulation study as well as a fault dictionary that this architecture indeed comes up to our expectations.\",\n \"Title: Equivalence of clock gating and synchronization with applicability to GALS communication\\nYear: 2014\\n\\nAbstract: Pausible clocking is a very popular approach for clock domain interfacing in GALS systems. However, accuracy and stability of the ring oscillator that is central to this principle are bad. This suggests to use gated crystal oscillators instead. In this paper we will formally show that the problem of clock gating is equivalent to the synchronization problem. We will present a fundamental block diagram for a gated clock, comprising an AND gate and a synchronizer for the control input, and will give evidence that the related circuits proposed so far in the literature are instantiations of this principle. According to our equivalence proof none of these circuits can hence be free from a residual risk of metastability; typically the MTBF is determined by the synchronizer block. This stands in contrast to the pausible clocking where the arbiter can safely prevent metastable outputs. We further argue that a handshake based data transfer (without clock stopping) yields essentially the same properties wrt. MTBF and performance, while causing more localized effects in case of a metastable upset. In conclusion the use of clock gating does not seem to provide any advantages over the alternative schemes and can hence not be recommended.\",\n \"Title: A tree arbiter cell for high speed resource sharing in asynchronous environments\\nYear: 2014\\n\\nAbstract: We present a novel tree arbiter cell that allows a pipelined processing of asynchronous requests. In this way it can achieve significantly lower delay in the critical case of frequent requests coming from different clients. We elaborate the necessary extension to facilitate a cascaded use of this cell in a tree-like fashion, and we show by theoretical analysis that in this configuration our cell provides better fairness than the standard approach. We implement our approach and quantitatively compare its performance properties with related work in a gate-level simulation. In our sample asynchronous Networks-on-Chip application our new cell proves to increase the throughput of three different designs available in literature by approximately 61.28%, 69.24%, and 186.85% respectively.\",\n \"Title: Protection of Muller-Pipelines from transient faults\\nYear: 2014\\n\\nAbstract: While it is well understood how to efficiently protect the data path in an asynchronous transmission channel against transient faults, much less is known about protecting the handshake signals along with their associated logic - mostly a Muller Pipeline - although these are equally critical for the proper function. In this paper we analyze the possible failure scenarios in the handshake of a 4-phase bundled data protocol that can arise from transient faults and systematically elaborate mitigation techniques for the resulting effects, namely single event transients and single event upsets. By simulated fault injection we show the effectiveness of the proposed extensions for protecting the channel. We take care to make these extensions themselves immune against transient faults, and we prove their proper and deadlock-free operation under fault conditions by means of model checking. Finally we show that, while providing superior coverage, our approach is in line with comparable approaches with respect to the area overhead.\",\n \"Title: Online test vector insertion: A concurrent built-in self-testing (CBIST) approach for asynchronous logic\\nYear: 2014\\n\\nAbstract: Complementing concurrent checking with online testing is crucial for preventing fault accumulation in fault-tolerant systems with long mission times. While implementing a non-intrusive online test is cumbersome in a synchronous environment, this task becomes even more challenging in asynchronous designs. The latter receive increasing attention, mainly due to their elastic timing behaviour; however the issues related with their testing remain a key obstacle for their wide adoption. In this paper we present a novel approach for testing of asynchronous circuits that leverages the redundancy present in the conventional 4-phase protocol for implementing a fully transparent and fully concurrent test procedure. The key idea is to use the protocol's unproductive NULL phase for processing test vectors, thus effectively interleaving the incoming 4-phase data stream with a test data stream in a 2-phase fashion. We present implementation templates for the fundamental building blocks required and give a proof-of-concept by an example application that also serves as a platform for evaluating the overheads of our solution which turn out to be moderate.\",\n \"Title: Recovery Mechanisms for Dual Core Architectures\\nYear: 2006\\n\\nAbstract: Dual core architectures are commonly used to establish fault tolerance on the node level. Since comparison is usually performed for the outputs only, no precise diagnostic information is available, and error handling comes down to a reset of both cores. The strategy proposed in this paper allows a more fine-grained error handling. It is based on the following steps: (1) Identification of those registers that are actually relevant for recovering the last known correct core state. (2) Protection of these registers by additional comparators. (3) Use of the trap mechanism for recovering a consistent state of the complete core. (4) (Optional) provision of rollback capability for the relevant registers in order to relax the critical path constraints. In the paper these individual steps was discussed and motivated, and put them into context. In many cases the speed-up that was gained for the recovery was sufficient for using a dual core as a fail-operational instead of a fail-silent component with respect to transient faults. Rather than being restricted to a specific processor design our mechanisms can be employed in a wide variety of dual-core architectures\",\n \"Title: The effect of quartz drift on convergence-average based clock synchronization\\nYear: 2007\\n\\nAbstract: The aim of this work is to study how the temporal behavior of time-triggered distributed systems is influenced by a drift of their underlying quartz. More especially, we focus on convergence-average clock synchronization used e.g. for TTP/C and FlexRay. We show that, for a fault free system in a stable environment, the global time converges to an interval spanned by the accuracies of the physical clocks independently of the system history. Although this result seems quite intuitive, it is not self-evident, given the complex behavior of distributed clock synchronization. Moreover, this proof allows to derive diagnosis information about the underlying quartz and is the enabler of remote system test.\",\n \"Title: On the Threat of Metastability in an Asynchronous Fault-Tolerant Clock Generation Scheme\\nYear: 2009\\n\\nAbstract: Due to their handshake-based flow control, asynchronous circuits generally do not suffer from metastability issues as much as synchronous circuits do. We will show, however, that fault effects like single-event transients can force (sequential) asynchronous building blocks such as Muller C-Elements into a metastable state. At the example of a fault-tolerant clock generation scheme, we will illustrate that metastability could overcome conventional error containment boundaries, and that, ultimately, a single metastable upset could cause even a multiple Byzantine fault-tolerant system to fail. In order to quantify this threat, we performed analytic modeling and simulation of the elastic pipelines, which are at the heart of our physical implementation of the fault-tolerant clocks. Our analysis results reveal that only transient pulses of some very specific width can trigger metastable behavior. So even without consideration of other masking effects the probability of a metastable upset to propagate through a pipeline is fairly small. Still, however, a thorough metastability analysis is mandatory for circuits employed in high-dependability applications.\",\n \"Title: Power supply induced common cause faults-experimental assessment of potential countermeasures\\nYear: 2009\\n\\nAbstract: Fault-tolerant architectures based on physical replication of components are vulnerable to faults that cause the same effect in all replica. Short outages in a power supply shared by all replica are a prominent example for such common cause faults. For systems in which the provision of a replicated power supply would cause prohibitive efforts the identification of reliable countermeasures against these effects is vital to maintain the required dependability level. In this paper we propose several of such countermeasures, namely parity protection, voltage monitoring and time diversity of the replica. We perform extensive fault injection experiments on three fault-tolerant dual core processor designs, one FPGA based and two commercial ASICs. These experiments provide evidence for the vulnerability of a completely unprotected dual core solution, while time diversity and voltage monitoring in combination with increased timing margins turn out particularly effective for eliminating common cause effects.\",\n \"Title: Classifying Virtual Channel Access Control Schemes for Asynchronous NoCs\\nYear: 2013\\n\\nAbstract: Several asynchronous NoC implementations have been proposed in the literature so far, providing different properties based on different assumptions and flow control strategies. Most often they are presented as monolithic solutions, making it difficult to associate the different implementation details with the achieved properties, or to identify interdependencies of choices. The key contribution of this paper will be to elaborate a generic framework for requirements on and properties of asynchronous NoCs with virtual channels that shall allow to isolate specific choices and ease the comparison between existing solutions. We will demonstrate this by aligning some of the solutions from the literature to our framework. To illustrate the usefulness of our scheme we will apply it for pinpointing a deficiency in an existing protocol and systematically elaborating a way to fix this issue.\",\n \"Title: SET propagation in micropipelines\\nYear: 2013\\n\\nAbstract: Radiation-induced Single Event Transients (SETs) have the potential to create metastability in asynchronous circuits, as they are much shorter than the typical handshake cycle and do not respect the timing closure. Micropipelines have been shown to be effective in filtering those pulses. In this paper1 we investigate the propagation of pulses of critical width through a micropipeline in SPICE simulations. We study how the pipeline implementation, especially the output buffer design (matched threshold, high threshold, Schmitt-trigger) influences the propagation behavior. For the solutions with single sided thresholds we observe a considerable propagation potential of critical pulses that strongly depends, however, on the degree of threshold matching. The Schmitt trigger output, in contrast, reliably filters all pulses which are shorter than a certain threshold while propagating all others securely. At the same time our respective analysis reveals that the cost of the Schmitt trigger stage in terms of performance overheads is also significant, so the choice needs to be carefully balanced with the application requirements.\",\n \"Title: A deterministic approach for hardware fault injection in asynchronous QDI logic\\nYear: 2010\\n\\nAbstract: This paper presents a new approach for hardware based fault injection in Quasi Delay Insensitive (QDI) asynchronous circuits. Configurable saboteurs are placed at points of interest in the circuit and allow to inject various types of faults on an arbitrary number of signals. These saboteurs not only redefine the logic value of a faulty signal but also the exact moment of the fault occurrence. In asynchronous logic, signal events rather than time are used to trigger on a circuit's state. Our concept allows to precisely control the order of concurrent signal events. It can be shown that the fault sensitivity highly depends on that event ordering. Thereby a deterministic and reproducible investigation of QDI circuits in the presence of transient and permanent faults in hardware is obtained. The work is evaluated by fault injection experiments on different circuits.\",\n \"Title: A Multi-Credit Flow Control scheme for asynchronous NoCs\\nYear: 2013\\n\\nAbstract: Credit schemes are used to establish flow control in NoCs without blocking the communication channel. In traditional implementations one credit is transmitted per data flit, so the credit channel conveys as many messages as the data channel. Our proposed multi-credit scheme transmits credits in bundles of M, yielding one credit transmission per M flits. This saves transitions on the credit channel and promises a slower, more energy efficient implementation. We investigate requirements, options and benefits of this approach; first in theory, and then in a concrete application example, in which we propose a specifically beneficial implementation. Our study confirms that, with a negligible increase in area, our scheme can reduce dynamic energy as well as bandwidth requirements for the credit channel.\",\n \"Title: Containment of Metastable Voltages in FPGAs\\nYear: 2015\\n\\nAbstract: The significant PVT variations seen with modern technologies make synchronous design inefficient. Asynchronous design with its flexible timing is a promising alternative, but prototyping is difficult on the available FPGA platforms which are clock centric and do not provide the required functional primitives like mutual exclusion or Muller C-elements. The solutions proposed in the literature work nicely in principle, but cannot safely handle metastability issues that are inevitable at interfaces even in asynchronous designs. In this paper we propose a reliable implementation of a Schmitt-trigger, which allows to safely convert potential intermediate voltage levels that result from metastability into late transitions that can be reliably handled in the asynchronous domain. Beyond the actual circuit we also discuss the associated routing constraints to make the circuit work properly in spite of the uncertain routing within FPGAs. Furthermore we propose a procedure for an \\\"in situ reliability assessment\\\" of the specific Schmitt-trigger element under consideration, which also applies to metastability containment with high-or low-threshold inverters only. Our proof of concept is based on experimental results for both Xilinx and Altera FPGA platforms.\",\n \"Title: On the Appropriate Handling of Metastable Voltages in FPGAs.\\nYear: 2016\\n\\nAbstract: The significant process, voltage and temperature (PVT) variations seen with modern technologies make strictly synchronous design inefficient. Asynchronous design with its flexible timing is a promising alternative, but prototyping is difficult on the available FPGA platforms which are clock centric and do not provide the required functional primitives like mutual exclusion or Muller C-elements. The solutions proposed in the literature so far work nicely in principle but cannot safely handle metastability issues that are inevitable even at some interfaces in asynchronous designs. In this paper, we propose reliable implementations of the fundamental function blocks required to safely convert potential intermediate voltage levels that result from metastability into late transitions that can be reliably handled in the asynchronous domain. These are high- and low-threshold buffers as well as a Schmitt-trigger. We give elaborate background analysis for the proposed circuits and also present the associated routing constraints to make the Schmitt-trigger circuit work properly in spite of the uncertain routing within FPGAs. Furthermore, we propose a procedure for an \\\"in situ reliability assessment\\\" of the specific Schmitt-trigger element under consideration, which also applies to metastability containment with high- or low-threshold buffers only. Our proof of concept is based on experimental results for both Xilinx and Altera FPGA platforms.\",\n \"Title: A composable real-time architecture for replicated railway applications\\nYear: 2015\\n\\nAbstract: Triple-modular-redundant applications are widely used for fault-tolerant safety-critical computation. They have strict timing requirements for correct operation. We present an architecture which provides composability and mixed-criticality to support integration and to ease certification of such safety-critical applications. In this architecture, an additional layer is required for the sharing/partitioning of resources. This potentially jeopardizes the synchronization necessary for the triple-modular-redundant applications.We investigate the effects of different (unsynchronized) scheduling methods for the resource-sharing layer in this architecture and conclude that an out-of-the-box solution, which guarantees the technical separation between applications with fast reaction time requirements is only feasible when executing at most one instance of a triple-modular-redundant application per CPU-core for single and multi-core CPUs. Only when accepting changes in the applications or the applications' synchronization mechanisms, are more flexible solutions with good performance and resource utilization available.\",\n \"Title: Building reliable systems-on-chip in nanoscale technologies\\nYear: 2015\\n\\nAbstract: Modern application-specific integrated circuits (ASICs) contain complete systems on a single die, composed of many processing elements that communicate over a dedicated router-based on-chip network. As systems-on-chip comprise billions of transistors with feature sizes in the range of 10 nm, reliable operation cannot be established without carefully engineered support at all levels, from technology to the circuit- and the system-layer. This article surveys contributions of research groups at TU Wien to this field. At lower levels of abstraction, they range from the generation of fault models for simulation that closely match reality and are at the same time efficient to use, to circuit-level radiation-tolerance techniques. At the level of on-chip networks, novel fault-tolerant routing algorithms are being developed together with architectural techniques to isolate faulty parts while keeping the healthy parts connected and active.\",\n \"Title: How to Synchronize a Pausible Clock to a Reference\\nYear: 2015\\n\\nAbstract: Pausible clocking is an efficient means for metastability free communication in heterochronous GALS systems. State-of-the-art implementations are based on ring oscillators, which suffer from bad frequency stability and accuracy. Building a stable pausible clock by simply switching a crystal based clock source, on the other hand, has been shown to incur metastability issues, thus rendering pausible clocking no better than a conventional synchronizer approach. In this paper we propose a solution that provides the stability and accuracy of a crystal based clock while still being free from metastable upsets. It is based on a ring oscillator that can be paused and resumed just like in the traditional approach, but is synchronized with a crystal clock during its stable phases of operation. We will present an appropriate circuit implementation and carefully analyze its operation. We will give evidence that the circuit switches on and off as fast as the traditional one, and that the clock output does not suffer from metastable voltage or spurious pulses at any time.\",\n \"Title: Methods for analysing and improving the fault resilience of delay-insensitive codes\\nYear: 2015\\n\\nAbstract: Delay-insensitive (DI) codes are usually prone to transient faults occurring during an ongoing transmission. For most DI codewords even a single transient can turn an incomplete transmission into a complete codeword, which is different from the originally sent codeword. Unless further redundant information is provided, the receiver has no means to detect such a transmission fault. In this paper we therefore propose two methods to systematically increase redundancy, either by i) building resilient subcodes, or by ii) using a two-step data encoding where error detecting codes are appropriately combined with delay-insensitive codes. In contrast to existing approaches we carefully avoid the introduction of timing assumptions to mask faults. Both methods are generic and can be used for any 4-phase DI code. In this paper we apply them to m-of-n codes, Berger and Zero-Sum codes and thoroughly analyse the efficiency of the resulting coding schemes.\",\n \"Title: A versatile and reliable glitch filter for clocks\\nYear: 2015\\n\\nAbstract: In today's complex system-on-chip architectures the protection of the clock(s) against glitches introduced by environmental disturbances, attackers, or gating measures is becoming increasingly important. Glitch protection is a delicate issue in the digital domain, as it is inherently coupled with metastability issues. The circuit we propose in this paper outputs a clock that strictly follows an input reference clock in the regular case, but guarantees a minimum output pulse width even in case of arbitrary behavior of the reference. We will give a thorough analysis showing that, unlike most existing solutions, our circuit can handle metastability without any residual risk of upsets. Still its implementation is very simple. Our theoretical claims will be supported by simulation results. Furthermore, we will give some examples on possible use cases for such a circuit, like clock gating, clock self-repair, or defense against clock attacks.\",\n \"Title: A pausible clock with crystal oscillator accuracy\\nYear: 2015\\n\\nAbstract: Pausible clocking is an efficient means of establishing communication between different timing domains without suffering from the risk of metastable upsets, as seen with synchronizer based solutions. On the downside, pausible clocks are usually ring-oscillator based and hence exhibit relatively bad accuracy and stability. In this paper we extend an existing solution to pausible clocking that allows to synchronize the ring oscillator to a high precision reference like a crystal clock. In that basic solution, the generated clock will run out of sync with the reference during a synchronization phase whenever it is paused for longer than a clock period. Our goal here is to avoid irregular periods of the generated clock and to keep it in sync with the reference at all possible times. We achieve this by (a) decoupling the communication interface in a way that allows a data transfer without actually pausing the clock, and (b) carefully aligning the signal timings. This finally yields a clock source that combines the benefits of being stable like a crystal clock and allowing hazard-free communication, like a ring-oscillator based pausible clock.\",\n \"Title: Measuring the Distribution of Metastable Upsets over Time\\nYear: 2015\\n\\nAbstract: As modern ASICs comprise an increasing number of independently clocked subsystems that need to interact, the accurate reliability assessment of synchronizers becomes crucial. Traditionally the reliability of a synchronizer is characterized by the mean time between upsets (MTBU), and the relevant flip-flop parameters are specified in a way to support MTBU calculation. In this paper we claim that actually a deeper insight into the distribution of upsets over time is needed in order to make a reasonable prediction in the range of the high reliability values that are generally targeted. We present a measurement concept that appropriately extends state-of-the-art approaches so as to allow for an experimental assessment of the upset distribution over time. In this way the distribution function can be studied, and in particular the probability of upsets with low temporal distance--which is the relevant one for high reliability--can be identified. We implement our concept on three different FPGA platforms and present the selected results. The distribution function we obtain indicates that the assumption of a uniform or standard normal distribution, which one might be tempted to imply for lack of better information, is definitely not generally useful.\",\n \"Title: Reliable and Continuous Measurement of SET Pulse Widths\\nYear: 2015\\n\\nAbstract: Gaining better insights into propagation and masking of radiation induced faults at some point requires the conduction of physical experiments. In our attempt to design a target ASIC for such experiments we elaborated an on-chip infrastructure that allows recording the pulse width of radiation induced voltage transients. Its unique properties are the ability to record more than one of these measurement values before having to be read out, and to be radiation tolerant. By careful circuit design and an unconventional redundancy architecture we obtained an infrastructure that provides the required features while being extremely area efficient. We motivate and present our proposed circuit architecture and evaluate it by means of a fault dictionary as well as simulation results.\",\n \"Title: Enhanced Metastability Characterization Based on AC Analysis\\nYear: 2015\\n\\nAbstract: The common way of characterizing the metastability properties of a circuit is by its metastability resolution constant tau and the aperture window. This approach is based on a model that represents the storage cell as a pair of crosscoupled inverters, each of which is, in turn, modeled by a constant-gain amplifier with a first-order low-pass filter at the output. The former reflects the inverter's signal regeneration capability, while the latter approximates its dynamic behavior. In this simple model there is no natural way of expressing, e.g., the load dependence of tau, therefore each change of the element's load capacitance requires a full recalibration. In this paper we propose decomposing the inverter into its constituent transistors and using their small-signal equivalent circuits for modeling. Metastability characterization is now based on a Spice AC analysis which yields a higher-order dynamic model of the circuit. Once the relevant parameters are known for a given element, the load dependence of tau can be expressed analytically, thus elegantly avoiding recalibration. We compare our approach with the extended nose short simulation (ENSS) method from literature and show that the results deviate by no more than 1 -- 2%.\",\n \"Title: The Metastable Behavior of a Schmitt-Trigger\\nYear: 2016\\n\\nAbstract: Schmitt-Trigger circuits are the method of choice for converting general signal shapes into clean, well-behaved digital ones. In this context these circuits are often used for metastability handling, as well. However, like any other positive feedback circuit, a Schmitt-Trigger can become metastable itself. Therefore, its own metastable behavior must be well understood, in particular the conditions that may cause its metastability. In this paper we will build on existing results from Marino to show that (a) a monotonic input signal can cause late transitions but never leads to a non-digital voltage at the Schmitt-Trigger output, and (b) a non-monotonic input can pin the Schmitt-Trigger output to a constant voltage at any desired (also non-digital) level for an arbitrary duration. In fact, the output can even be driven to any waveform within the dynamic limits of the system. We will base our analysis on a mathematical model of a Schmitt-Trigger's dynamic behavior and perform SPICE simulations to support our theory and confirm its validity for modern CMOS implementations. Furthermore, we will discuss several use cases of a Schmitt-Trigger in the light of our results.\",\n \"Title: A new coding scheme for fault tolerant 4-phase delay-insensitive codes\\nYear: 2016\\n\\nAbstract: Delay-insensitive (DI) codes are usually prone to transient faults occurring during an ongoing transmission. For most DI code words even a single transient can turn an incomplete transmission into a complete code word, which is different from the originally sent one. In this paper we therefore propose a novel two-step data encoding scheme that combines DI and error detecting codes. Our solution exploits the inherent fault resilience of DI codes to achieve a low coding overhead. For analyzing this fault resilience we use methods from graph theory. In contrast to existing approaches we carefully avoid the introduction of timing assumptions to mask faults. The presented coding scheme is generic and can, in principle, be used with any 4-phase DI code. We show how to apply it to m-of-n codes and analyze the resulting coding efficiency.\",\n \"Title: Does Cascading Schmitt-Trigger Stages Improve the Metastable Behavior?\\nYear: 2016\\n\\nAbstract: Schmitt-Trigger stages are the method of choice for robust discretization of input voltages with excessive transition times or significant noise. However, they may suffer from metastability. Based on the experience that the cascading of flip-flop stages yields a dramatic improvement of their overall metastability hardness, in this paper we elaborate on the question whether the cascading of Schmitt-Trigger stages can obtain a similar gain. We perform a theoretic analysis that is backed up by an existing metastability model for a single Schmitt-Trigger stage and elaborate some claims about the behavior of a Schmitt-Trigger cascade. These claims suggest that the occurrence of metastability is indeed reduced from the first stage to the second which suggests an improvement. On the downside, however, it becomes clear that metastability can still not be completely ruled out, and in some cases the behavior of the cascade may be less beneficial for a given application, e.g. by introducing seemingly acausal transitions. We validate our findings by extensive HSPICE simulations in which we directly cover our most important claims.\",\n \"Title: A Model for the Metastability Delay of Sequential Elements.\\nYear: 2017\\n\\nAbstract: It is well known that every sequential element may become metastable when provided with marginal inputs, such as input transitions occurring too close or input voltage not reaching a defined HI or LO level. In this case the sequential element requires extra time to decide which digital output level to finally present, which is perceived as an output delay. The amount of this delay depends on how close the elements state is to the balance point, at which the delay may, theoretically, become infinite. While metastability can be safely avoided within a closed timing domain, it cannot be completely ruled out at timing domain boundaries. Therefore it is important to quantify its effect. Traditionally this is done by means of a \\\"mean time between upsets\\\" (MTBU) which gives the expected interval between two metastable upsets. The latter is defined as the event of latching the still undecided output of one sequential element by a subsequent one. However, such a definition only makes sense in a time-safe environment like a synchronous design. In this paper we will extend the scope to so-called value-safe environments, in which a sequential element can safely finalize its decision, since the subsequent one waits for completion before capturing its output. Here metastability is not a matter of \\\"failure\\\" but a performance issue, and hence characterization by MTBU is not intuitive. Therefore we will put the focus on the delay aspect and derive a suitable model. This model extends existing approaches by also including the area of very weak metastability and thus providing complete coverage. We will show its validity through comparison with transistor-level simulation results for the most popular sequential elements in different implementations, point out its relation to the traditional MTBU model parameters, namely tau and T-0, and show how to use it for calculating the performance penalty in a value-safe environment.\",\n \"Title: Measuring metastability using a time-to-digital converter\\nYear: 2017\\n\\nAbstract: In view of the numerous clock domain crossings found in modern systems-on-chip and multicore architectures precise metastability characterization is a fundamental task. We propose a conceptually novel approach for the experimental assessment of upset rate over resolution time that is usually employed to extract the relevant characteristics. Our method is based on connecting a time-to-digital converter to the output of the flip flop under test, rather than using a phase shifted clock, as conventionally done. We present the details of an FPGA implementation of our approach and show its feasibility through an experimental evaluation, whose results favorably match those obtained by the conventional method. The benefits of the novel scheme are the ability to perform a calibration for the delay steps, a speed-up of the measurement process, and the availability of a more comprehensive and ordered measurement data set.\",\n \"Title: Setup for an Experimental Study of Radiation Effects in 65nm CMOS.\\nYear: 2017\\n\\nAbstract: Physical radiation experiments are a vital means for calibrating simulation models targeted to studying the impact of ionizing particles on VLSI circuits. However, their conduction requires special care and a very specific setup. In this paper we give an overview of such an experimental setup, and highlight some specific details. Beyond showing the context overarching the objectives of the experiments, the envisioned radiation sources, as well as design and architecture of a specific target ASIC, we will put specific emphasis on the communication infrastructure, namely an FPGA that controls the data exchange between some preprocessing infrastructure located on the target ASIC on one side and the host PC running the data analysis on the other. Finally, the physical arrangement comprising carrier PCB for the target ASIC, and cabling, which need to adhere specific requirements, will receive some attention as well.\",\n \"Title: A versatile architecture for long-term monitoring of single-event transient durations.\\nYear: 2017\\n\\nAbstract: We present design and analysis of an on-chip measurement infrastructure, which facilitates long-term monitoring of single-event transient durations in digital VLSI circuits exposed to uncontrollable radiation. Unlike the known oscilloscope-based methods, our approach is all-digital: SET durations are measured by the SET-gated counting of pulses generated by a high-frequency ring oscillator, and stored in an up/down-counter array organized in a ring. We carefully elaborate a comprehensive concept for making our infrastructure SEU tolerant, with the main challenge being to attain a sufficiently high probability of recording useful hits in the target before exhausting the SEU tolerance of the infrastructure. Our key contribution here concerns the protection of the counter array: Rather than resorting to radiation hardening or explicit triple modular redundancy (TMR), we save area by using a novel redundant duplex counter architecture: For a small number of recorded SETs, our architecture implicitly implements TMR, albeit in a way that degrades gracefully for larger numbers of recorded SETs. Besides standard functional and timing verification, we use Spice-based SET injection for verifying the effectiveness of our SEU-tolerant architecture, and some cross section-based probabilistic analysis for confirming that our measurement infrastructure based on it indeed achieves its purpose.\",\n \"Title: A Critical Charge Model for Estimating the SET and SEU Sensitivity: A Muller C-Element Case Study\\nYear: 2017\\n\\nAbstract: This paper presents a critical charge model for estimating the SET and SEU robustness. The proposed model has been derived by analytic fitting of SPICE results, using a Muller C-element designed in 65 and 130 nm bulk CMOS technologies as the target device. The critical charge is expressed in terms of the size of C-element, size of load inverter, supply voltage and temperature, for constant timing parameters of the SET/SEU current pulse. The proposed model could be utilized to calculate the critical charge causing a SET, for both analyzed technologies, with the accuracy comparable to SPICE simulations. The critical charge for SEU was higher than for SET, but the dependencies obtained for SET response were qualitatively similar to those for SEU. This implies that the proposed critical charge model may be applicable for optimizing the SET/SEU robustness evaluation of the circuits involving the Muller C-element. Moreover, the model may also serve as a basis for evaluating the SET/SEU robustness of other standard cells and other technologies, and thus also for analysis of the SET/SEU robustness of complex circuits.\",\n \"Title: Refined metastability characterization using a time-to-digital converter.\\nYear: 2018\\n\\nAbstract: In view of the numerous clock domain crossings found in modern systems-on-chip and multicore architectures precise metastability characterization is a fundamental task. We propose a conceptually novel approach for the experimental assessment of upset rate over resolution time that is usually employed to extract the relevant characteristics. Our method is based on connecting a time-to-digital converter to the output of the flip flop under test, rather than using a phase shifted clock, as conventionally done. We present the details of an FPGA implementation of our approach and show its feasibility through an experimental evaluation, whose results favorably match those obtained by the conventional method. The benefits of the novel scheme are the ability to perform a calibration for the delay steps, a speed-up of the measurement process, and the availability of a more comprehensive and ordered measurement data set. Especially the latter can be of crucial importance when (even multiple) glitches or oscillation are suspected to result from metastability, or when the temporal distribution of upsets matters (bursts of upsets, e.g.).\",\n \"Title: Using a Duplex Time-to-Digital Converter for Metastability Characterization of an FPGA\\nYear: 2018\\n\\nAbstract: In view of the increasing number of clock domains found in modern ASICs, the precise characterization of metastability at their boundaries becomes crucial. In some cases, the conventional approach does not provide a sufficient level of detail information. As an alternative approach, the use of a time-to-digital converter based on a tapped delay line has been proposed. In this paper we extend the latter by an additional tapped delay line thus allowing to further refine the concept. We present the underlying concept, its implementation, as well as experimental measurements on an FPGA platform that reveals significant variations in the metastable behavior of different FPGA boards of the same type.\",\n \"Title: Partially Systematic Constant-Weight Codes for Delay-Insensitive Communication\\nYear: 2018\\n\\nAbstract: The increasing complexity and modularity of contemporary systems makes the availability of flexible and robust, yet efficient, module-level interconnect instrumental. Delay-insensitive codes are very attractive in this context. In this paper we explore the idea of composing hybrid coding schemes that combine the benefits of constant-weight codes, namely simple completion detection, with those of systematic codes, namely zero-effort decoding. Apart from showing how to systematically construct efficient hybrid codes, we elaborate in great detail on the cost-efficient and orphan-free implementation of completion detectors, as well as that of suitable encoders and decoders. A comparison of various concrete solutions in terms of coding efficiency as well as hardware overhead gives evidence for the benefits of our proposed approach, and at the same time serves as a repository for ready-made optimized solutions.\",\n \"Title: State Recovery for Coarse-Grain TMR Designs in FPGAs Using Partial Reconfiguration\\nYear: 2018\\n\\nAbstract: The operation of field-programmable gate arrays (FPGAs) in harsh environments like space entails the need for suitable fault-tolerance techniques of which Triple-Modular Redundancy (TMR) is most commonly deployed. While TMR is undoubtedly effective in masking faults, state recovery remains a problematic issue: Fine-grain TMR allows safe recovery, but incurs prohibitive area and performance penalties. In contrast, coarse-grain TMR has little overhead, but cannot safely provide recovery without roll-back or reset. We use the dynamic reconfiguration feature of modern FPGAs to augment an initially coarse-grain TMR with the ability of temporarily loading a fine-grain TMR design for forward-state-recovery. Therefore, we can seamlessly resume correct (fully redundant) operation in case of data-as well as configuration faults that occurred in the FPGA. As a proof of concept, the paper presents a showcase design and discusses distinctive properties of this new approach.\",\n \"Title: Measuring Metastability with Free-Running Clocks\\nYear: 2017\\n\\nAbstract: With the increasing number of clock domain crossings in modern VLSI circuits, the area, power and performance overheads introduced by synchronization have a rising impact on the overall system parameters. To minimize these overheads while still reaching the targeted system reliability, it is very important to precisely know the parameters of the circuit elements used for synchronization regarding metastability. While it is well understood, what parameters are relevant and how they can be derived from circuit models, obtained from simulation or even measured, the state of the art measurement approaches require precisely timed clock inputs. These are typically provided with expensive test equipment, on-chip clock management blocks or calibrated delay lines. This paper proposes an approach for measuring metastability parameters using uncorrelated free-running clocks only at the expense of a more challenging post-processing. The principle is closely related to sampling oscilloscopes with the same fundamental property: the measurement resolution is theoretically unbounded but proportional to the measurement time. Apart from the description of the circuit, this paper includes an analysis of the effect of clock uncertainty (jitter) on the measurement result and an experimental evaluation.\",\n \"Title: An Experimental Study of Metastability-Induced Glitching Behavior\\nYear: 2019\\n\\nAbstract: The increasing number of clock domain crossings in modern systems-on-chip makes the careful consideration of metastability paramount. However, the manifestation of metastability at a flip-flop output is often unduly reduced to late transitions only, while glitches are hardly ever accounted for. In this paper we study the occurrence of glitches resulting from metastability in detail. To this end we propose a measurement circuit whose principle substantially differs from the conventional approach, and by that allows to reliably detect glitches. By means of experimental measurements on an FPGA target we can clearly identify late transitions, single glitches and double glitches as possible manifestations of metastability. Some of these behaviors are unexpected as they do not follow from the traditional modeling theory. We also study the dependence of metastable behavior on supply voltage. Beyond confirming that, as reported in previous literature, the metastable decay constant tau is voltage-dependent, we also produce strong evidence that the relative occurrence of glitches is not voltage-dependent.\",\n \"Title: An Automated Setup for Large-Scale Simulation-Based Fault-Injection Experiments on Asynchronous Digital Circuits\\nYear: 2021\\n\\nAbstract: Experimental fault injection is an essential tool in the assessment and verification of fault-tolerance properties. Often, in these experiments it is impossible to reasonably cover the huge parameter space spanned by target state and fault parameters, and compromises or restrictions must be made. This is even more pronounced for asynchronous circuits where a convenient discretization of time throu...\",\n \"Title: Towards Explaining the Fault Sensitivity of Different QDI Pipeline Styles\\nYear: 2021\\n\\nAbstract: Asynchronous circuits, specifically those using a quasi delay-insensitive (QDI) implementation are known for their high resilience against timing uncertainties. However, their event-based operation principle impedes their temporal masking capability, making them more susceptible to fault-induced transitions caused by single event transients. While synchronous circuits obtain high resilience throug...\",\n \"Title: Simulation-Based Approaches for Comprehensive Schmitt-Trigger Analyses\\nYear: 2022\\n\\nAbstract: Schmitt-Triggers (S/Ts) are often utilized to clean noisy analog signals at intermediate voltage values in digital circuits. However, they are vulnerable to metastability, which may cause the same undesired non-digital output behavior that was supposed to be removed in the first place. To enable an efficient characterization of static and dynamic metastability properties of S/Ts (e.g., the metasta...\",\n \"Title: On SAT-Based Model Checking of Speed-Independent Circuits\\nYear: 2022\\n\\nAbstract: Formal verification plays an important role in the quality assurance of digital circuits. Apart from the now standard equivalence checking between design steps, functional correctness can be proven with model checking. In one approach, a Boolean satisfiability (SAT) problem describing the circuit’s implementation and expected properties is generated for each of a bounded number of time steps and f...\",\n \"Title: AµFLIPS: An Asynchronous Microprocessor With FLexIbly-timed Pipeline Stages\\nYear: 2022\\n\\nAbstract: AµFLIPS is an asynchronous microprocessor with novel pipeline register organization to resolve data and control hazards using synchronous hazard resolving schemes. Existing works claim that mechanisms for handling data and control hazards in synchronous systems are not directly applicable to asynchronous pipelined processors, because of distributed control nature of the latter. As a result of that...\",\n \"Title: enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks.\\nYear: 2022\\n\\nAbstract: Research on Deep Neural Networks (DNNs) has focused on improving performance and accuracy for real-world deployments, leading to new models, such as Spiking Neural Networks (SNNs), and optimization techniques, e.g., quantization and pruning for compressed networks. However, the deployment of these innovative models and optimization techniques introduces possible reliability issues, which is a pillar for DNNs to be widely used in safety-critical applications, e.g., autonomous driving. Moreover, scaling technology nodes have the associated risk of multiple faults happening at the same time, a possibility not addressed in state-of-the-art resiliency analyses. Towards better reliability analysis for DNNs, we present enpheeph, a Fault Injection Framework for Spiking and Compressed DNNs. The enpheeph framework enables optimized execution on specialized hardware devices, e.g., GPUs, while providing complete customizability to investigate different fault models, emulating various reliability constraints and use-cases. Hence, the faults can be executed on SNNs as well as compressed networks with minimal-to-none modifications to the underlying code, a feat that is not achievable by other state-of-the-art tools. To evaluate our enpheeph framework, we analyze the resiliency of different DNN and SNN models, with different compression techniques. By injecting a random and increasing number of faults, we show that DNNs can show a reduction in accuracy with a fault rate as low as 7 x 10 ^ (-7) faults per parameter, with an accuracy drop higher than 40%. Run-time overhead when executing enpheeph is less than 20% of the baseline execution time when executing 100 000 faults concurrently, at least 10x lower than state-of-the-art frameworks, making enpheeph future-proof for complex fault injection scenarios. We release enpheeph at https://github.com/Alexei95/enpheeph.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":86,"string":"86"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13263,"string":"13,263"}}},{"rowIdx":13264,"cells":{"question_id":{"kind":"string","value":"Hongxiang Li"},"question":{"kind":"string","value":"Generate an abstract for the title \"Ofdma Resource Allocation And Qos Provision In Hybrid Wireless Network\" using the following items: OFDMA, Resource allocation, QoS routing, Hybrid wireless network, Throughput."},"answer":{"kind":"string","value":"This paper studies the joint OFDMA resource allocation and QoS routing problem in a hybrid wireless network, which includes an OFDMA based cellular network and an ad-hoc network with many sensor nodes. In such application, each sensor node generates certain amount of data that need to be sent to cellular base station (BS). The cellular nodes act as gateway to forward all the uplink traffic. We formulate the optimization problem to find the best OFDMA resource allocation and QoS routing scheme that maximizes the overall network throughput. We also provide algorithms and simulation results to illustrate our solutions for different network configurations."},"correct_answer":{"kind":"string","value":"This paper studies the joint OFDMA resource allocation and QoS routing problem in a hybrid wireless network, which includes an OFDMA based cellular network and an ad-hoc network with many sensor nodes. In such application, each sensor node generates certain amount of data that need to be sent to cellular base station (BS). The cellular nodes act as gateway to forward all the uplink traffic. We formulate the optimization problem to find the best OFDMA resource allocation and QoS routing scheme that maximizes the overall network throughput. We also provide algorithms and simulation results to illustrate our solutions for different network configurations."},"documents":{"kind":"list like","value":["Title: A new global-based video enhancement algorithm by fusing features of multiple region-of-interests\nYear: 2011\n\nAbstract: Video enhancement plays an important role in various video applications. It is desirable to achieve high visual quality of the entire picture where multiple region-of-interests (ROIs) within the frame can be adaptively and simultaneously enhanced. In this paper, a new global-based video enhancement algorithm is proposed. The proposed algorithm first analyzes features from different ROIs. Then, a 'global' tone mapping curve is created for the entire picture which can adaptively enhance different regions at the same time. According to the statistics of ROIs, two fusion strategies, i.e., piecewise-based and factor-based fusions, are proposed for creating the global tone mapping curve. Experimental results show that the proposed algorithm can obtain more appealing perceptual quality than the state-of-the-art algorithms.","Title: A survey on resource allocation in high performance distributed computing systems.\nYear: 2013\n\nAbstract: •Classification of high performance computing (HPC) systems is provided.•Current HPC paradigms and industrial application suites are discussed.•State of the art in HPC resource allocation is reported.•Hardware and software solutions are discussed for optimized HPC systems.","Title: Ofdma Optimality Analysis In Mimo Uplink Channels\nYear: 2006\n\nAbstract: This paper studies the optimality of OFDMA in MIMO enabled multiuser multicarrier uplink network. We present the necessary and sufficient conditions for OFDMA being optimal on any boundary point of the capacity region. In the low SNR case, we further derive the probabilities of these conditions. For the general MIMO case, we provide an example with which we quantify OFDMA optimality and show the probability of OFDMA being optimal is non-travial regardless of the SNR regions. To reach the the MISO sum capacity limit, our results show that the number of shared subchannels under the optimal solution should be less than the number of total users, thus the performance gap between the OFDMA scheme and the optimal solution is negligible when the number of subchannels is sufficiently large.","Title: Adaptive scheduling for multicasting hard deadline constrained prioritized data via network coding.\nYear: 2012\n\nAbstract: Network coding offers a promising platform for multicast transmission by approaching its min-cut capacity. However, pushing the network throughput toward this upper bound comes with a sacrifice in delivery delay due to the decoding procedure that requires performing batch of coded packets. Further, in some transmission scenarios where the receivers experience deep fading or unable to collect a full set of the transmitted data, no useful information is recovered. The effect is more severe in the networks where the transmitted information has priority structure with hard deadline constraint due to the limited delivery time and data interdependencies. In this paper, we consider single-hop wireless networks where the transmitter wishes to multicast hard deadline constrained prioritized data to many receivers over lossy channels. We first study the network performance of a variety of transmission techniques, depending on how the transmitter schedules transmission in each time slot. We then propose an adaptive encoding and scheduling technique to maximize the network throughput. To find the optimal transmission scheduling at the presence of the network dynamics, we cast the problem in the framework of Markov Decision Processes (MDP) and use backward induction method to find an optimal solution. We further propose simulation-based algorithm and greedy scheduling technique that obtain high performance with much lower time complexity. Both analytical and simulation results have been provided to corroborate the effectiveness of the proposed techniques.","Title: Distributed optimal power control for multicarrier cognitive systems\nYear: 2012\n\nAbstract: In this paper, the power optimization of the multicarrier cognitive system underlying the primary network is investigated. We consider the interference coupled cognitive network under individual secondary user's power constraint and primary user's rate constraint. A multicarrier discrete distributed (MCDD) algorithm based on Gibbs sampler is proposed. Although the problem is nonconcave, MCDD is proved to converge to the global optimal solution. To reduce the computational complexity and convergence time, the Gibbs sampler based Lagrangian algorithm (GSLA) is proposed to get a near optimal solution. We also provide simulation results to show the effectiveness of the proposed algorithms.","Title: Optimal Interference Pre-Cancellation Order In Dpc-Based Broadcast And Unicast Hybrid Network\nYear: 2013\n\nAbstract: It is well known that the Dirty Paper Coding (DPC) achieves the channel capacity for multiuser degraded channels. However, the optimality of DPC remains unknown for non-degraded channel. In this paper, we derive the optimal interference pre-cancellation order for a DPC based broadcast and unicast hybrid network. Specifically, we study different DPC cancellation schemes to maximize the hybrid capacity region. The conditions for each scheme being optimal are analytically derived. Our results show that the optimal interference pre-cancellation order varies with SNR and broadcast and unicast channel conditions.","Title: A new package-group-transmission-based algorithm for human activity recognition in videos.\nYear: 2011\n\nAbstract: In this paper, a new package-group-transmission-based algorithm is proposed for human activity recognition in videos. The proposed algorithm first models the entire scene as a network where each node in the network corresponds to a segmentation of the scene. Based on this network, we further model people in the scene as groups of packages. Thus, various human activities can be modeled as the process of \"package group transmission\" in the network and these activities can be efficiently recognized by suitably analyzing the \"package transmission\" process. Our proposed algorithm can not only detect activities under the challenging multiple camera scenario, but also be able to recognize various complex group activities among people. Experimental results demonstrate the effectiveness of our proposed algorithm. © 2011 IEEE.","Title: Network Coverage in Multicarrier Broadcast System\nYear: 2010\n\nAbstract: In this paper, we discuss the network coverage of the generic multicarrier (or OFDM) based broadcast system by exploiting the frequency diversity over subcarriers. We first define the performance metric and discuss the relationship among broadcast rate, coverage area and the outage probability. To calculate the actual broadcast coverage area, we provide analytical results for both the low SNR and high SNR cases. In addition, we extend our discussion to the multicarrier multicell broadcast network. Simulation results show that multicell cooperation (MCC) has the best performance.","Title: An Application of Markov Jump Process Model for Activity-Based Indoor Mobility Prediction in Wireless Networks\nYear: 2011\n\nAbstract: One of the most important objectives of a wireless network is to facilitate a prediction of users mobility regardless of their point of attachment to the network. In indoor environments the effective users motion prediction system and wireless localization technology play an important role in all aspects of peoples daily lives. In this paper we propose an activity-based continuous-time Markov model to define and predict the human movement patterns. This model is a simple extension of an Activity based Mobility Prediction algorithm using Markov modeling (AMPuMM) technique. Both models are experimentally evaluated in realistic small university campus scenario. The obtained results show us the high efficiency of the jump methodology in the prediction of the students activities in the indoor campus environment.","Title: Saliency-based visualization for image search\nYear: 2011\n\nAbstract: In this paper, we propose a novel algorithm for improving and visualizing image search results. The proposed algorithm improves user's image search experience by three steps: (1) re-rank the initial image search results by the random walk refinement based on visual consistency and saliency cues, (2) project the re-ranked images into a 2-dimentional panel according to their saliency information and correlations, (3) detect and extract the saliency regions in each image for final visualization. To evaluate the performance of our algorithm, user study has been conducted. Experimental results demonstrate that our visualization algorithm provides more pleasing image search experience than the conventional image search methods.","Title: Facial expression mapping based on elastic and muscle-distribution-based models\nYear: 2012\n\nAbstract: In this paper, a new algorithm is proposed for facial expression mapping. The proposed algorithm first introduces a new elastic model to balance the global and local warping effects such that the impacts from facial feature differences between people can be avoided, thus more reasonable geometric warping results can be created. Furthermore, a muscle-distribution-based (MD) model is also proposed. The proposed MD model utilizes the muscle distribution information of the human face to evaluate and strengthen the facial illumination details. By this way, the impacts from human face difference as well as the effects of unsuitable noise filtering can be effectively alleviated. Experimental results show that our proposed algorithm can create obviously better facial expression results than the existing methods.","Title: Adaptive resource allocation for heterogeneous traffic over heterogeneous relay networks\nYear: 2013\n\nAbstract: Future wireless communication networks will face the dual challenge of supporting large traffic volumes while providing reliable service for heterogeneous traffic types. In order to meet the ever increasing traffic demand, heterogeneous relay network is introduced as an enabling technology for the fourth generation (4G) mobile broadband networks. In this paper, we will investigate optimal power and subcarrier allocation strategies for heterogeneous relay networks under statistical quality of service (QoS) constraints. To be specific, we will characterize the effective capacity of a wireless relay system under QoS constraints. The properties of the optimal resource and subcarrier allocation strategies for delay-sensitive traffic over heterogeneous relay networks will also be identified. Two low complexity resource and subcarrier allocation algorithms will be introduced to optimize the effective capacity. Our results suggest that the optimal power and subcarrier allocation strategies depend heavily on the underlying QoS constraint. In the low signal-to-interference-plus-noise (SINR) regime, when there is no QoS constraint, both base stations and relay nodes will allocate all the transmit power to the best subcarrier. However, as the QoS requirement becomes more stringent, both base stations and relay nodes will spread their transmit power over multiple subcarriers.","Title: An Analysis of Uplink OFDMA Optimality\nYear: 2006\n\nAbstract: Motivated by the increasing popularity of OFDMA, this paper studies the sum-rate optimality of OFDMA as an uplink multicarrier multiple-access scheme. We address the relationship between OFDMA and the optimal multicarrier multiple- access schemes. More specifically, we are interested in answering the following three questions: (i) what are the conditions under which OFDMA is sum-rate optimal? (ii) what is the probability of OFDMA being sum-rate optimal? and (iii) in the case OFDMA is suboptimal, what is the performance gap between OFDMA and the optimal multicarrier multiple access solution? Within a generic discrete multicarrier uplink framework, we present the necessary and sufficient conditions for OFDMA optimality and derive the probabilities of these conditions for both low and high SNR regions. Our results show that the number of shared subchannels under the optimal solution should be less than the number of total users, thus the performance gap between the OFDMA scheme and the optimal solution is negligible when the number of subchannels is large.","Title: The existence of infinitely many primary trees\nYear: 1994\n\nAbstract: As an analogue of the basis elements in a vector space, the primal and primary graphs are studied by Dewdney and other authors. It may be called the binary structure that a tree with diameter ⩽2 is a primary iff it is a star with 2 n leaves. This paper further investigates an infinite family of primary trees with binary structures by means of an algorithm related to the matching theory of bipartite graphs.","Title: Quantitative comparisons of the state-of-the-art data center architectures.\nYear: 2013\n\nAbstract: Data centers are experiencing a remarkable growth in the number of interconnected servers. Being one of the foremost data center design concerns, network infrastructure plays a pivotal role in the initial capital investment and ascertaining the performance parameters for the data center. Legacy data center network (DCN) infrastructure lacks the inherent capability to meet the data centers growth trend and aggregate bandwidth demands. Deployment of even the highest-end enterprise network equipment only delivers around 50% of the aggregate bandwidth at the edge of network. The vital challenges faced by the legacy DCN architecture trigger the need for new DCN architectures, to accommodate the growing demands of the cloud computing' paradigm. We have implemented and simulated the state of the art DCN models in this paper, namely: (a) legacy DCN architecture, (b) switch-based, and (c) hybrid models, and compared their effectiveness by monitoring the network: (a) throughput and (b) average packet delay. The presented analysis may be perceived as a background benchmarking study for the further research on the simulation and implementation of the DCN-customized topologies and customized addressing protocols in the large-scale data centers. We have performed extensive simulations under various network traffic patterns to ascertain the strengths and inadequacies of the different DCN architectures. Moreover, we provide a firm foundation for further research and enhancement in DCN architectures. Copyright (c) 2012 John Wiley & Sons, Ltd.","Title: A Low-Complexity Decoding Algorithm For Hierarchically Modulated Signals In Sfn\nYear: 2013\n\nAbstract: In this paper, the hierarchical modulation (HM) technique is adopted in a single frequency network (SFN) to provide both global and local information. In order to mitigate the inter-layer interference (ILI) and inter-cell interference (ICI), we develop a low-complexity successive interference cancellation (SIC) algorithm for the coded HM signals in the SFN. The proposed decoding algorithm can be applied to different soft-decision channel coding schemes (e. g., Turbo codes, LDPC codes) under various channel profiles. We analyzed the decoding complexity of the proposed algorithm, and evaluated the bit error rate (BER) performance. The simulations show that the new decoding algorithm can offer up to 0.7 dB carrier to noise ratio (C/N) gain compared with the traditional SIC approach under different channel models, while providing the comparable performance (up to 95% decoding complexity savings) with the multi-layer iterative decoding approach. The performance evaluation and decoding complexity comparisons indicate that the proposed structured SIC approach offers a good performance-complexity trade-off, especially for the HM-based SFN scenarios.","Title: Energy-efficient cooperative geographic routing in wireless sensor networks\nYear: 2012\n\nAbstract: In this paper, we propose an energy-efficient cooperative geographic routing (ECGR) to reach energy-efficient routing in wireless sensor networks(WSN). ECGR fully takes advantage of cooperative diversity and geographic routing. At physical layer, the sensor nodes cooperatively transmit the same packet to achieve farther transmission distance of each hop. While at the network layer, ECGR adaptively selects the appropriate cooperative nodes to form forwarding clusters to forward the packet when we consider energy consumption and geographic information of the sensor nodes. In order to fully assess the energy consumption, we take circuit energy consumption of the sensor nodes into consideration. According to the description above, we increase the transmitting distance of each hop but decrease the hop number. Meanwhile, the overall network energy consumption is reduced and balanced among nodes to extend the life of the network.","Title: Kernel Density Estimation For Wireless Fading Channels Using Slepian Semi-Wavelets\nYear: 2004\n\nAbstract: Efficient and rapid prediction of the future values of the fading envelop is important in fading compensation techniques. This paper is devoted to the discussion of modeling fading channels using kernel density estimation. Our kernel density estimators are non parametric density estimators. Although it will be assumed that the distribution has a probability density function, the data will be allowed to speak for themselves! We are mainly interested in a new kernel density estimator based on Slepian semi-wavelets introduced in a previous paper [9]. Constructed from Slepian semi-wavelets, this kernel density estimator possesses a number of elegant properties. It has higher convergence rate compared to non-wavelet density estimators. It also has adaptive positivity and hence eliminates the Gibbs phenomenon displayed by conventional wavelet estimators. A er a brief introduction to some necessary background materials, we demonstrate a numerical algorithm to compute the Slepian kernel density estimator. To test its capability, the kernel density estimator is used to model wireless fading channels. A numerical experiment is performed using simulated data. Comparison of Slepian kernel estimators and other popular kernel density estimators are also discussed.","Title: A new heat-map-based algorithm for human group activity recognition\nYear: 2012\n\nAbstract: In this paper, a new heat-map-based (HMB) algorithm is proposed for human group activity recognition. The proposed algorithm first models people trajectories as series of \"heat sources\" and then applies a thermal diffusion process to create a heat map (HM) for representing the group activities. Based on this heat map, a new surface-fitting (SF) method is also proposed for recognizing human group activities. Our proposed HM feature can efficiently keep the temporal motion information of the group activities while the proposed SF method can effectively catch the characteristics of the heat map for activity recognition. Experimental results demonstrate the effectiveness of our proposed algorithm.","Title: Macroblock Classification Method for Video Applications Involving Motions.\nYear: 2012\n\nAbstract: In this paper, a macroblock classification method is proposed for various video processing applications involving motions. Based on the analysis of the Motion Vector field in the compressed video, we propose to classify Macroblocks of each video frame into different classes and use this class information to describe the frame content. We demonstrate that this low-computation-complexity method can ...","Title: Doa Estimation Error And Resolution Loss In Linear Sensor Array\nYear: 2013\n\nAbstract: In order to estimate the direction of signal arrival (DOA) precisely, array of sensors is widely used to enhance resolution and accuracy of the results. For subspace-based algorithms, array sensors' position error may severely influence the estimation result since it changes the array in manifold. In this paper, we provide an improved DOA estimation method for general linear sensor array so that the effect of position error is minimized. We analytically present the relationship between sensor position error and the array's minimum resolution. Simulation results show that the new method does enhance the accuracy of the DOA estimation and the array resolution is determined by both the number of array sensors and the position error.","Title: Energy-Effective Relay Selection by Utilizing Spacial Diversity for Random Wireless Sensor Networks\nYear: 2013\n\nAbstract: In this letter, we propose a new energy-efficient metric for relay node selection in multi-hop random wireless sensor networks (WSN) where the sensor nodes are distributed randomly and the node circuit energy consumption is also taken into consideration. During the routing, the relay node is selected in a forwarding area, which is defined by two parameters: a forwarding area radius and forwarding area angle. Under the guide of the new metric, we maximize the effective transmitting distance per unit energy consumption by selecting the appropriate forwarding area radius and angle. In this way, we provide a new point of view to achieve energy-effective relay node selection.","Title: Performance analysis and optimization for energy-efficient cooperative transmission in random wireless sensor network.\nYear: 2013\n\nAbstract: This paper considers random wireless sensor networks where nodes are distributed randomly and form clusters to transmit the packets to relay clusters using cooperative multi-input-multi-output (CMIMO) technique. First, we study CMIMO energy consumption and divide it into intra-cluster energy consumption and inter-cluster energy consumption. Then we focus on analyzing the system performance in term...","Title: Capacity Of Multicarrier Multilayer Broadcast And Unicast Hybrid Cellular System With Independent Channel Coding Over Subcarriers\nYear: 2011\n\nAbstract: In this paper, we discuss the hybrid capacity region of a generic multicarrier multilayer broadcast and unicast cellular system with independent channel coding over subcarriers. In particular, we analytically derive the capacity region and provide conditions to achieve its boundary. The simulation results show that the hybrid capacity regions are considerably higher than those of the traditional time division multiplexing scheme.","Title: An overview of achieving energy efficiency in on-chip networks\nYear: 2010\n\nAbstract: Due to the increasing bandwidth demand for the network-on-chip (NoC), interconnection networks become a dominant source of energy consumption in systems-on-chip (SoCs) and chip multi processors (CMPs). Therefore, energy efficient NoC is key to a successful SoC development. This paper presents an overview of different techniques to achieve energy efficiency at the different levels of NoC design including: a) component level where dynamic voltage scaling (DVS) and dynamic link shutdown (DLS) techniques are reviewed; b) circuit level, e.g., voltage swinging of signals; c) architectural level, where specialised tools, such as Wattch and Orion are discussed. We also summarise research on thermal optimisation issues. To the best of our knowledge, this is the first survey of recent research results on the area.","Title: Spectrum Optimization For Ofdma Based Hybrid Wireless Networks\nYear: 2011\n\nAbstract: This paper proposes a new cooperative resource allocation scheme for OFDMA based hybrid network. Different from the traditional cognitive radio networks that aim to utilize the spectrum holes in an uncoordinated way, we study the joint resource allocation for both the primary users and the secondary users. Results show that our approach can achieve higher spectral efficiency and provide satisfactory admission control and QoS for all users.","Title: On The Boundary Inequality For Bandwidth Of Graphs\nYear: 1996\n\nAbstract: The quantity B(G) = min max {\\f(u)-f(v)\\: (u, v) is an element of E(G)} is called the bandwidth of a graph G = (V(G), E(G)) where min is taken over all bijections f: V(G) --> {1,2,..., \\V(G)\\} called labelings. L.H. Harper presented an important inequality related to the boundary partial derivative S of subsets S subset of or equal to V(G). This paper gives a refinement of Harper's inequality which will be more powerful in determining bandwidths for several classes of graphs.","Title: An overview of energy efficiency techniques in cluster computing systems\nYear: 2013\n\nAbstract: Two major constraints demand more consideration for energy efficiency in cluster computing: (a) operational costs, and (b) system reliability. Increasing energy efficiency in cluster systems will reduce energy consumption, excess heat, lower operational costs, and improve system reliability. Based on the energy-power relationship, and the fact that energy consumption can be reduced with strategic power management, we focus in this survey on the characteristic of two main power management technologies: (a) static power management (SPM) systems that utilize low-power components to save the energy, and (b) dynamic power management (DPM) systems that utilize software and power-scalable components to optimize the energy consumption. We present the current state of the art in both of the SPM and DPM techniques, citing representative examples. The survey is concluded with a brief discussion and some assumptions about the possible future directions that could be explored to improve the energy efficiency in cluster computing.","Title: A new Temporal-Constraint-Based algorithm by handling temporal qualities for video enhancement\nYear: 2011\n\nAbstract: Video enhancement has played very important roles in many applications. However, most existing enhancement methods only focus on the spatial quality within a frame while the temporal qualities of the enhanced video are often unguaranteed. In this paper, a new algorithm is proposed for video enhancement. The proposed algorithm introduces new temporal constraints and combines them with the spatial constraints such that both the spatial and temporal qualities of the video can be improved. Two strategies are proposed for including the temporal constraints. Experimental results demonstrate the effectiveness of the proposed algorithm.","Title: A New Cooperative Spectrum Sensing Scheme for Cognitive Ad-Hoc Networks.\nYear: 2011\n\nAbstract: As the radio spectrum is becoming more and more crowded, cognitive radio has recently become a hot research topic to improve the spectrum utilization efficiency. It is well known that the success of cognitive radio depends heavily on fast and efficient spectrum sensing that is very difficult in practice. Toward this end, this paper introduces a new guard-resident cooperative spectrum sensing scheme for a cognitive ad-hoc network. In particular, we classify cognitive nodes as either or based on the spectrum neighbor decision and distributed boundary search. The guard nodes sense the spectrum and then inform the resident nodes that are greatly relieved from spectrum sensing about the radio environmental changes. The analysis and simulation results show that the proposed algorithm can significantly reduce the total spectrum sensing load without sacrificing the sensing accuracy.","Title: OFDMA Capacity Analysis in MIMO Channels\nYear: 2010\n\nAbstract: In this paper, the performance of orthogonal frequency division multiple access orthogonal frequency division multiple access (OFDMA) is studied in the context of the general multiuser multicarrier multiple-input multiple-output (MIMO) (M3) system for both uplink (UP) and downlink (DL) channels. The relationship between OFDMA and the optimal M3 solution is investigated from an information theoretical point of view. Specifically, the necessary and sufficient conditions of OFDMA being the capacity-maximizing M3 transmission scheme is derived. In addition, OFDMA optimality is explicitly analyzed in some special cases.","Title: DPC-Based Hierarchical Broadcasting: Design and Implementation\nYear: 2008\n\nAbstract: This paper discusses interference precancellation in digital hierarchical broadcasting (HB). In particular, we present the principles and implementation of structured dirty paper coding (SDPC) that approaches the capacity limit of dirty paper coding in multilayer broadcasting. As an alternative to Tomlinson-Harashima precoding (THP), SDPC eliminates the significant performance loss suffered by THP...","Title: Secure network-coded wireless multicast for delay-sensitive data\nYear: 2012\n\nAbstract: Wireless multicast for delay-sensitive data is challenging because different receivers may experience different packet losses. Network coding offers significant advantages over the traditional Automatic Repeat-reQuest (ARQ) protocols in that it mitigates the need for retransmission and has the potential to approach the min-cut capacity. Network-coded multicast would be, however, vulnerable to false packet injection attacks, in which the adversary injects bogus packets to prevent receivers from correctly decoding the original data. Without a right defense in place, even a single bogus packet can completely change the decoding outcome. Existing solutions either incur high computation cost or cannot withstand high packet loss. In this paper, we propose a novel scheme to defend against false packet injection attacks on network-coded multicast for delay-sensitive data. Specifically, we propose an efficient authentication mechanism based on null space properties of coded packets, aiming to enable receivers to detect any bogus packets with high probability. We further design an adaptive scheduling algorithm based on Markov Decision Processes (MDP) to maximize the number of authenticated packets that can be received within a given time constraint. Both analytical and simulation results have been provided to demonstrate the efficacy and efficiency of our proposed scheme.","Title: A patch-based framework for detecting abnormal activities with a PTZ camera\nYear: 2012\n\nAbstract: In this paper, a novel patch-based (PB) framework is proposed for detecting abnormal activities using a Pan-Tilt-Zoom (PTZ) camera. We first propose a new scene-patch-based (SSB) algorithm which can efficiently extract the target object's global trajectory from the PTZ camera. Furthermore, we propose an extended network-based (ENB) algorithm for detecting abnormal activities. The proposed ENB algorithm models the entire scene as a network where each node in the network corresponds to a patch of the scene and each edge between nodes corresponds to the activity correlation between the scene patchs. Based on this network, a recursive training strategy is proposed to train the edge weights in the network such that abnormal activities can be effectively detected through these trained edge weights. Experimental results demonstrate the effectiveness of our proposed framework.","Title: Secure Wireless Multicast for Delay-Sensitive Data via Network Coding\nYear: 2013\n\nAbstract: Wireless multicast for delay-sensitive data is challenging because of the heterogeneity effect where each receiver may experience different packet losses. Fortunately, network coding, a new advanced routing protocol, offers significant advantages over the traditional Automatic Repeat reQuest (ARQ) protocols in that it mitigates the need for retransmission and has the potential to approach the min-cut capacity. Network-coded multicast would be, however, vulnerable to false packet injection attacks, in which the adversary injects bogus packets to prevent receivers from correctly decoding the original data. Without a right defense in place, even a single bogus packet can completely change the decoding outcome. Existing solutions either incur high computation cost or cannot withstand high packet loss. In this paper, we propose a novel scheme to defend against false packet injection attacks on network-coded multicast for delay-sensitive data. Specifically, we propose an efficient authentication mechanism based on null space properties of coded packets, aiming to enable receivers to detect any bogus packets with high probability. We further design an adaptive scheduling algorithm based on the Markov Decision Processes (MDP) to maximize the number of authenticated packets received within a given time constraint. Both analytical and simulation results have been provided to demonstrate the efficacy and efficiency of our proposed scheme.","Title: A Low-Complexity Decoding Algorithm for Coded Hierarchical Modulation in Single Frequency Networks\nYear: 2014\n\nAbstract: In this paper, the hierarchical modulation (HM) technique is adopted in a single frequency network (SFN) to provide both global and local information. In order to mitigate the interlayer interference and intercell interference, we develop a low-complexity successive interference cancellation (SIC) algorithm for the coded HM signals in the SFN. The proposed decoding algorithm can be applied to different soft-decision channel coding schemes (e.g., Turbo codes, LDPC codes) under various channel profiles. We analyzed the decoding complexity of the proposed algorithm, and evaluated the bit error rate performance. The simulations show that the new decoding algorithm can offer up to 0.7 dB carrier to noise ratio ((C/N)) gain compared with the traditional SIC approach under different channel models, while providing the comparable performance (up to 95% decoding complexity savings) with the multilayer iterative decoding approach. The performance evaluation and decoding complexity comparisons indicate that the proposed structured SIC approach offers a good performance-complexity trade-off, especially for the HM-based SFN scenarios.","Title: A Fast Sub-Pixel Motion Estimation Algorithm for H.264/AVC Video Coding\nYear: 2011\n\nAbstract: Motion estimation (ME) is one of the most time-consuming parts in video coding. The use of multiple partition sizes in H.264/AVC makes it even more complicated when compared to ME in conventional video coding standards. It is important to develop fast and effective sub-pixel ME algorithms since: 1) the computation overhead by sub-pixel ME has become relatively significant while the complexity of integer-pixel search has been greatly reduced by fast algorithms, and 2) reducing sub-pixel search points can greatly save the computation for sub-pixel interpolation. In this letter, a novel fast sub-pixel ME algorithm is proposed which performs a “rough” sub-pixel search before the partition selection, and performs a “precise” sub-pixel search for the best partition. By reducing the searching load for the large number of non-best partitions, the computation complexity for sub-pixel search can be greatly decreased. Experimental results show that our method can reduce the sub-pixel search points by more than 50% compared to existing fast sub-pixel ME methods with negligible quality degradation.","Title: A New Network-Based Algorithm for Human Activity Recognition in Videos\nYear: 2014\n\nAbstract: In this paper, a new network-transmission-based (NTB) algorithm is proposed for human activity recognition in videos. The proposed NTB algorithm models the entire scene as an error-free network. In this network, each node corresponds to a patch of the scene and each edge represents the activity correlation between the corresponding patches. Based on this network, we further model people in the scene as packages, while human activities can be modeled as the process of package transmission in the network. By analyzing these specific package transmission processes, various activities can be effectively detected. The implementation of our NTB algorithm into abnormal activity detection and group activity recognition are described in detail in this paper. Experimental results demonstrate the effectiveness of our proposed algorithm.","Title: A new shot change detection method using information from motion estimation\nYear: 2010\n\nAbstract: In many video coding related applications, both shot change detection and motion estimation need to be performed. In this paper, a new shot change detection method is proposed for such applications, which uses the information from motion estimation. We first propose to classify Macroblocks of each frame into different classes based on the information obtained in the motion estimation process. Each class can capture some specific characteristics of the frame. The shot changes can then be detected based on the extracted class information. The proposed method has low computation complexity. It can easily be implemented into most existing video coding systems without extra cost. Experimental results show that the proposed method can detect shot changes effectively. Some extension applications of our class information are also discussed in the paper.","Title: A new network-based algorithm for multi-camera abnormal activity detection\nYear: 2011\n\nAbstract: In this paper, a new abnormal activity detection algorithm is proposed for multi-camera surveillance applications. The proposed algorithm models the entire scene covered by the multi-camera system as a network. In this network, each node corresponds to a segmentation of the entire scene and each edge represents the activity correlation between the corresponding segmentations. Based on this network, the proposed algorithm further models human activities as the signal transmission process in the network. Thus, abnormal activities can be detected if their 'network transmission energy' is obviously larger than the normal case. Compared with the previous methods, the proposed algorithm is more general and is flexible to handle various multi-camera scenarios and configurations. Experimental results demonstrate the effectiveness of the proposed algorithm.","Title: Performance analysis of joint decode-and-forward scheme for two-way relay channels\nYear: 2013\n\nAbstract: This paper addresses the performance of the joint decode-and-forward (JDF) strategy for two-way relay systems over Rayleigh fading channels. We show that the JDF enjoys a lower bit error rate (BER) in the first time slot, which translates into higher capacity for broadcast in the second time slot. Specifically, we present the optimal decision rules for JDF and partial decoding-and-forward (PDF) and derive their corresponding BER union bounds for BPSK signals. Furthermore, we propose a novel scheme, termed JDF-JM, to re-encode the messages at the relay node for unmatched data rates from the two sources. The achievable rates of JDF and PDF for the second time slot are then analyzed. Simulation results are provided to illustrate the performance of JDF over DF-XOR under different channel coding and modulation settings.","Title: Heuristics-based nominal channels allocation in cellular networks\nYear: 2010\n\nAbstract: In this paper, we develop four heuristics to deal with the nominal channel allocation in cellular networks. We provide the theoretical bound on the number of channels for voice and data services. The four heuristics are compared using varying traffic distributions.","Title: Transmission Schemes for Multicarrier Broadcast and Unicast Hybrid Systems\nYear: 2008\n\nAbstract: Wireless infrastructures can be conveniently categorized into two types: those that provide point-to-point dasiaunicastpsila connectivity (e.g., cellular) and others that are dasiabroadcastpsila in nature (such as TV and radio) and are inherently point-to-multipoint. This paper introduces a transmission scheme for multicarrier hybrid system that supports both broadcasting and unicasting on a single-frequency platform. In particular, we present a dirty-paper coding (DPC) based overlay architecture to enable interference-free reception of unicast signals. The fundamental limits of hybrid communications are investigated, along with power allocation algorithms that approach the hybrid capacity. Our results show that OFDMA for unicast offers the best hybrid performance and considerable gains over orthogonal multiplexing (e.g., TDM/FDM) can be achieved through interference-free overlay. In addition, we describe approaches to further improve the spectral efficiency and the broadcast coverage through multicell collaboration.","Title: Dynamic light trail routing in WDM optical networks\nYear: 2011\n\nAbstract: Fiber optics have replaced copper as the primary transmission medium. Wavelength Division Multiplexing (WDM) networks effectively increase single-link bandwidth from 10 Mbps to over 160 Gbps, and have been considered as a promising candidate for the next-generation backbone network. All optical circuits each on a separate wavelength called lightpaths represent the first major method for optical communication. The granularity provided between a source and destination node is that of a complete wavelength. Once a lightpath is set up, the entire wavelength is used exclusively by the connection’s source and destination node-pair. No sub-wavelength sharing between nodes along the lightpath is allowed. However, it is often observed that the bandwidth requirement in today’s network is often dynamically varying and does not justify the need for allocating an entire wavelength. Therefore, the wavelength capacity may be underutilized. A new technology termed light trail was proposed to avoid the inability of intermediate nodes to use a connection wavelength, and the constant reconfiguration of switches. In this article, we study dynamic light trail routing in a WDM optical network. We present an efficient algorithm for establishing a light trail routing for a new connection request, while using minimum network resources. We also study survivable network routing using the proposed light trail technology. We present an efficient heuristic for computing a pair of working and protection light trails for a dynamic incoming connection request. Simulation results are presented which demonstrate the advantages of our routing schemes.","Title: Robust Power Control for Amplify-and-Forward Relaying Scheme\nYear: 2015\n\nAbstract: Power control problem is investigated for amplify-and-forward cellular relay network in this letter. Since the wireless channels vary with time, we aim at a power allocation scheme that is robust to the fluctuations. An optimization problem is formulated which targets at utility maximization subject to a pre-defined outage probability threshold and limited power at relay station. With that, a distributed iterative algorithm is proposed to obtain the optimal power. Numerical results show the effectiveness of the algorithm, and also present the robustness of this system.","Title: Distributed Energy Efficiency Optimization for MIMO Cognitive Radio Network\nYear: 2015\n\nAbstract: This paper considers a multiple-input-multipleoutput (MIMO) cognitive radio network, where secondary users (SUs) equipped with multiple antennas coexist with a primary user (PU). Joint power allocation and transmission beamforming design is proposed to optimize the network energy efficiency under power constraint and interference temperature constraint. Distributed algorithm that maximizes energy efficiency of each individual node is derived through fractional programming. Simulation results are presented to validate the convergence and effectiveness of the proposed algorithm.","Title: A New Cross-Level Decoding Scheme for LT Codes\nYear: 2015\n\nAbstract: In this letter, we propose a new cross-level decoding method for LT codes, where the results of packet-level decoding are fed back to bit-level decoding to improve the overall decoding performance. Another advantage of our method is that it keeps the original LT decoder structure and does not require extra real field computation. We provide the revised And-Or Tree analysis for the proposed method and validate it with numerical simulations.","Title: An integrative approach for measuring semantic similarities using gene ontology.\nYear: 2014\n\nAbstract: Gene Ontology (GO) provides rich information and a convenient way to study gene functional similarity, which has been successfully used in various applications. However, the existing GO based similarity measurements have limited functions for only a subset of GO information is considered in each measure. An appropriate integration of the existing measures to take into account more information in GO is demanding.We propose a novel integrative measure called InteGO2 to automatically select appropriate seed measures and then to integrate them using a metaheuristic search method. The experiment results show that InteGO2 significantly improves the performance of gene similarity in human, Arabidopsis and yeast on both molecular function and biological process GO categories.InteGO2 computes gene-to-gene similarities more accurately than tested existing measures and has high robustness. The supplementary document and software are available at http://mlg.hit.edu.cn:8082/.","Title: A survey on text mining in social networks.\nYear: 2015\n\nAbstract: In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.","Title: Energy Efficiency of Hybrid Cellular With Heterogeneous QoS Provisions\nYear: 2014\n\nAbstract: In this letter, we consider a hybrid broadcast and unicast cellular network (hybrid cellular for short) with heterogeneous quality of service (QoS) constraints in the low-power regime. In particular, we consider the unicast user with delay constraint and the broadcast user with outage constraint. The transmission energy efficiency of the dirty paper coding (DPC) and time division multiple access (TDMA) schemes are analyzed and compared from information theoretic point of view. Specifically, the minimum energy per bit and the wideband slope region are derived in closed forms. While both DPC and TDMA schemes require the same minimum energy per bit, DPC outperforms TDMA with regard to the wideband slope region. We also obtain the critical points for both schemes beyond which the wideband slope decreases for both unicast and broadcast users. Finally, we show how the heterogeneous QoS constraints affect the energy efficiency in hybrid cellular.","Title: Achievable Degrees of Freedom of MIMO Y Channel With Delayed CSIT.\nYear: 2014\n\nAbstract: In this letter, we study the degrees of freedom (DOF) for multiple-input-multiple-output (MIMO) Y channel in frequency division duplex (FDD) systems. Assuming each node has delayed channel state information at the transmitter (CSIT), we propose a physical layer network coding multi-stage transmission (PLNC-MST) scheme during broadcast (BC) phase. The results show that PLNC offers significant DOF gain in MIMO Y channel.","Title: Cross-Layer Design of Joint Beamforming and Random Network Coding in Wireless Multicast Networks\nYear: 2014\n\nAbstract: In this letter, we consider a wireless network where the transmitter, such as a base station, multicasts hard-deadline-constrained prioritized data to users over fading channels. We propose a new cross-layer transmission scheme that involves simultaneous smart antenna beamforming at physical layer and random network coding at network layer to maximize the overall network throughput. In this joint design, beamforming is used for dynamic transmitting power allocation to achieve power gain; while the random network coding is adopted to achieve coding gain. The simulation results show the effectiveness of the proposed algorithm.","Title: Resource Allocation for Delay-Sensitive Traffic over LTE-Advanced Relay Networks\nYear: 2015\n\nAbstract: Future wireless networks will face the dual challenge of supporting large traffic volumes while providing reliable service for delay-sensitive traffic. To meet the challenge, relay network has been introduced as a new network architecture for the fourth generation (4G) LTE-Advanced (LTE-A) networks. In this paper, we investigate resource allocation including subcarrier and power allocation for LTE-A relay networks under statistical quality of service (QoS) constraints. By dual decomposition, we derive the optimal subcarrier and power allocation strategies to maximize the effective capacity (EC) of the underlying LTEA relay systems. Characteristics of optimal resource allocation strategies are identified, and a low-complexity suboptimal scheme is developed through optimizing the subcarrier and power allocation individually. Our result suggests that the optimal subcarrier and power allocation strategies depend heavily on the underlying QoS constraint. For example, in the low signal-to-interference-plus-noise (SINR) regime, when there are less stringent QoS constraints, base stations and relay stations tend to allocate all the power to the best available subcarrier. However, as QoS requirements become more stringent, both base stations and relay stations will spread their power over available subcarriers. On the other hand, in the high SINR regime, regardless of the QoS constraints, base stations and relay stations tend to equally allocate power among available subcarriers.","Title: Transmission Schemes for Multicasting Hard Deadline Constrained Prioritized Data in Wireless Multimedia Streaming.\nYear: 2016\n\nAbstract: In wireless multicast multimedia streaming, every data block (i.e., a group of packets) needs to be delivered within a hard deadline. On the other hand, to provide multilevel services, the widely adopted scalable video coding (SVC) prioritizes a video bit stream into a base layer and several enhancement layers. In this paper, we combine SVC with the transmission delay constraint and focus on multi...","Title: A web tool for measuring gene semantic similarities by combining gene ontology and gene co-function networks\nYear: 2015\n\nAbstract: Gene Ontology(GO) is one of the most popular bioinformatic resource to study functional relationships between genes. However, the existing tools for calculating the semantic similarity are limited since they only rely on GO annotations and structure. We provide a working demonstration of a web tool that calculates functional similarities between GO terms by integrating gene co-function networks and information from GO. We demonstrate the function of the web tool by comparing remote GO terms.","Title: Physical-Layer Secrecy Performance in Finite Blocklength Case.\nYear: 2015\n\nAbstract: For physical-layer secrecy performance, existing studies only focus on coding schemes that can achieve the secrecy capacity. However, finite blocklength penalty could be fatal for security of private message. In this paper, we explore the impact of finite blocklength on secrecy for the wiretap channel model. We propose secrecy performance matrices for finite blocklength analysis and provide analytical expressions to evaluate the secrecy performance. We demonstrate trade-off between secrecy and reliability, and also provide an upper bound of secret information rate in finite blocklength case. The results are also applied to indicate asymptotic performance.","Title: Queue Stability Analysis in Network Coded Wireless Multicast Network.\nYear: 2016\n\nAbstract: This letter considers a single hop wireless multicast network. We first introduce a new two-level queuing system consisting of a main queue and a virtual queue, where each packet in the virtual queue is associated with a user index set. Then, we propose a network coding based packet scheduling method to maximize the system input rate under the queue stability constraint. Our analytical and simulation results demonstrate the effectiveness of the proposed solution.","Title: Achievable degrees of freedom of MIMO two-way X relay channel with delayed CSIT.\nYear: 2016\n\nAbstract: The previous work on interference alignment for multiple-input-multiple-output (MIMO) two-way X relay channel assumes perfect channel state information at the transmitter (CSIT), which is reasonable in slow fading channel. However, in fast fading scenario, this assumption is impractical. In this paper, assuming that each node has delayed CSIT, we study the achievable degrees of freedom (DOF) for MIMO two-way X relay channel in frequency division duplex (FDD) systems. Specifically, in the broadcast (BC) phase, we propose a new multiple-stage transmission (MST) scheme, which utilizes retrospective interference alignment for physical layer network coding (PLNC). We show that MST can achieve significant DOF gain and tremendous power gain over other schemes. When the number of antennas for each user, N, is smaller than the number of the relays, M, the time division multiple access (TDMA) scheme can only achieve an ergodic sum-rate increase by N bps/Hz for every increasing of 3 dB of signal-to-noise power ratio (SNR), while the proposed MST scheme can achieve an ergodic sum-rate increase by M bps/Hz.","Title: A New Wireless Multicast Queuing Design Using Network Coding and Data-Flow Model.\nYear: 2016\n\nAbstract: A queue stability analysis in a wireless multicast network with packet erasure channels is studied in this letter. In particular, a new queuing model consisting of several sub-queues is introduced, where each packet in a sub-queue is associated with an index set indicating its intended users. Our objective is to maximize the input rate under the queue stability constraints. We formulate it as a linear programming problem and propose a network coding-based packet scheduling scheme that finds the optimal solution. Finally, the simulation results corroborate our method.","Title: Adaptive Random Network Coding for Multicasting Hard-Deadline-Constrained Prioritized Data.\nYear: 2016\n\nAbstract: Random network coding (RNC) is a promising solution for multicast services over wireless networks. In this paper, we consider the problem of deadline-constrained multimedia multicast from a single transmitter to multiple users over wireless erasure channels. A novel transmission scheduling strategy based on adaptive RNC is developed to maximize the throughput of multicast sessions under a determin...","Title: Optimize the spectrum sensing and decision making strategies under uncertainty for SATCOM.\nYear: 2016\n\nAbstract: The ability to provide accurate spectrum sensing and decision making under an uncertainty environment is proving useful for the military SATCOM to increases the spectrum utilization. Dynamic spectrum access(DSA) allows a secondary user to access the spectrum holes that are not occupied by the primary users. However, the spectrum sensing for DSA is normally performed in a complex SATCOM environment under uncertainty, caused by the high GEO/LEO mobility, weak signals after a long distance of propagation, the high interference and jamming in an adversarial environment, etc. The uncertainty results in a high error probability in the spectrum sensing. In such a case, DSA requires a decision-making process to optimally determine which channels to sense and access. In this paper, we propose an approach for optimal spectrum sensing and decision making that mathematically models the uncertainty in the SATCOM while the whole system throughput is maximized. Specifically, we model the DSA with decision making as a Partially Observable Markov Decision Process (POMDP) problem. Optimal DSA strategy has been discussed by an optimization process. Monte Carlo simulations are carried out and our simulation results demonstrate the efficiency of the proposed DSA strategy.","Title: Enabling cyber-physical communication in 5G cellular networks: challenges, spatial spectrum sensing, and cyber-security\nYear: 2017\n\nAbstract: Cyber-physical systems (CPS) help create new services and applications by revolutionising our world in different fields through their tight interactions and automated decisions. This is especially true with the ongoing increase in the number of physical things (sensors, actuators, smartphones, tablets, and so on) along with the explosive increase in the usage of online networking services and appl...","Title: An AMP based decoder for massive MU-MIMO-OFDM with low-resolution ADCs\nYear: 2017\n\nAbstract: In massive MIMO systems, multiple high-speed high-resolution ADCs will be a major obstacle for real applications because of the hardware complexity and power consumption. One solution to this problem is to adopt low-cost low-resolution ADCs instead. Note that highly quantized signals will destroy the orthogonality of OFDM design, the key point remains to find computational decoding methods while maintaining acceptable performances. To address this issue, we develop a low complexity algorithm designed for low-resolution ADCs RF-frontend using iterative decoding based on Approximate-Message-Passing (AMP). The proposed algorithms can provide adequate performance in low-resolution ADC systems compared to approximate LMMSE solution.","Title: Iterative IQ Imbalance Compensation Receiver for Single Carrier Transmission.\nYear: 2017\n\nAbstract: It is well known that low-cost RF front-end implementation such as direct conversion transmitter suffers from serious in-phase/quadrature phase (IQ) imbalance. Most existing works in single carrier transmission employ linear compensation methods, such as Least Square and minimum mean square error, to combat the interference caused by IQ imbalance. Actually, for single carrier transmission, it is p...","Title: Enabling jamming-resistant communications in wireless MIMO networks.\nYear: 2017\n\nAbstract: In this paper, we propose a practical anti-jamming solution for wireless MIMO networks to enable their legitimate communications in the presence of multiple high-power and broadband radio jamming attacks, and evaluate the proposed solution using real-world implementations in a WiFi network. We first develop a blind jamming mitigation (BJM) algorithm, which can cancel jamming signals from multiple unknown radio jammers and, at the same time, equalize channel to recover the signals from the legitimate sender. Unlike the existing jamming mitigation algorithms, the BJM algorithm does not need any channel information for jamming mitigation and signal recovery. Based on the BJM algorithm, we develop a jamming-resistant receiver (termed JrRx) and a holistic anti-jamming scheme to salvage legitimate communications in the face of jamming attacks. We have built a prototype of JrRx and evaluated its performance in a WiFi network. Experimental results show that, as long as JrRx has more antennas than the jammers, it can successfully decode the signals from the sender, even if the jamming signals are 20 dB stronger than the signals of interest.","Title: Uplink MU-MIMO in Asynchronous Wireless LANs.\nYear: 2018\n\nAbstract: In wireless LANs (WLANs), network-wide time and frequency synchronization among user devices is widely regarded as a necessity for uplink MU-MIMO. Therefore, to enable uplink MU-MIMO in 802.11ax, dedicated MAC protocols (e.g., trigger frame and timing advance mechanism) have been proposed to synchronize user devices in the time and frequency domains. Such MAC protocols increase not only network complexity but also communication overhead. In this paper, we show that the time and frequency synchronization among user devices is not a necessity for uplink MU-MIMO. We propose a practical uplink MU-MIMO solution which does not require time and frequency alignments of the signals from user devices. The key component in our solution is a new PHY design for AP's receiver, which can decode the concurrent signals from multiple asynchronous user devices. We have built a prototype of our uplink MU-MIMO solution on USPR2-GNURadio testbed. Experimental results show that, using the new PHY, an M-antenna AP can successfully decode the concurrent signals from M asynchronous user devices (2 ≤ M ≤ 4).","Title: Lyapunov Scheduling and Optimization in Network Coded Wireless Multicast Network.\nYear: 2018\n\nAbstract: In this paper, we investigate network coding based scheduling in a single-hop wireless multicast network under queueing stability constraint. Specifically, Lyapunov optimization model is adopted and an optimal network coding scheduling algorithm is derived, which significantly reduces the computational complexity and queue length compared to existing solutions. Moreover, two scheduling policies ba...","Title: Real-Time Graph Partition and Embedding of Large Network\nYear: 2018\n\nAbstract: Recently, large-scale networks attract significant attention to analyze and extract the hidden information of big data. Toward this end, graph embedding is a method to embed a high dimensional graph into a much lower dimensional vector space while maximally preserving the structural information of the original network. However, effective graph embedding is particularly challenging when massive graph data are generated and processed for real-time applications. In this paper, we address this challenge and propose a new real-time and distributed graph embedding algorithm (RTDGE) that is capable of distributively embedding a large-scale graph in a streaming fashion. Specifically, our RTDGE consists of the following components: (1) a graph partition scheme that divides all edges into distinct subgraphs, where vertices are associated with edges and may belong to several subgraphs; (2) a dynamic negative sampling (DNS) method that updates the embedded vectors in real-time; and (3) an unsupervised global aggregation scheme that combines all locally embedded vectors into a global vector space. Furthermore, we also build a real-time distributed graph embedding platform based on Apache Kafka and Apache Storm. Extensive experimental results show that RTDGE outperforms existing solutions in terms of graph embedding efficiency and accuracy.","Title: A Robust Power Allocation Scheme for Relay Communication Networks Based on Seller–Buyer Game\nYear: 2017\n\nAbstract: A robust power allocation algorithm is proposed in this paper based on robust game theory. Considering that the channel gains are usually changing with the dynamic environment in practice, the uncertainty will cause the estimation errors of channel gains and deteriorate the quality of services of users. A probability threshold method is used to deal with the uncertainty of the channel gains. To jointly consider the maximum utilities of source node and relay node, a seller–buyer game is introduced and it is proved that the game converges to the unique equilibrium point. Besides, a distributed iteration algorithm is proposed to reduce the information overhead. Numerical results show the effectiveness and robustness of the proposed power algorithm.","Title: Hybrid Multicast and Device-to-Device Communications Based on Adaptive Random Network Coding\nYear: 2019\n\nAbstract: Random network coding (RNC) is an efficient coding scheme to improve the performance of wireless multicast networks, on the premise that a receiver is able to collect a full set of network-coded packets. However, in resource (time, bandwidth, and so on) constrained communications, a receiver may only receive a partial set of network coded packets, leading to poor system performance. On the other hand, device-to-device (D2D) communications utilize user proximity and spatial diversity to improve the communication efficiency. In this paper, we propose a hybrid multicast and D2D transmission scheme based on adaptive RNC (ARNC) to increase network throughput under packet erasure channels. In the proposed scheme, the packet encoding structure is optimized adaptively according to the network status, such that even if only a partial set of the coded packets are received, the user equipments (UEs) can still decode useful information and regenerate new encoded packet for D2D communications. In particular, the multicast mode and D2D mode are switched dynamically during a scheduling session according to the status of each UE. Under this hybrid mode, we can effectively overcome the effect of erasure channel and improve the overall network throughput. By comparing our scheme with other scheduling methods, we provide simulation results to corroborate the effectiveness of the proposed techniques.","Title: Coordinated Beamforming for Multicell Multicast Secret Communications\nYear: 2018\n\nAbstract: In this paper, we study the optimal transmitter beamforming design in a coordinated multicell multicast network under the wiretap channel model, where each cell serves a group of users and a single-antenna eavesdropper tries to overhear the confidential information. We study the problem of finding the optimal beamforming strategy at each base station to minimize the total transmit power of the multicell network while guaranteeing secrecy and QoS requirements of each user. The resulting non-convex optimization problem is solved through a sequence of convex semi-definite relaxations. The effectiveness of the proposed algorithm is validated via numerical simulations.","Title: Secure Transmission Algorithm Based on Subcarrier Sorting and XOR Operation in OFDM Systems\nYear: 2018\n\nAbstract: Considering an OFDM modulation based system including one pair of legitimate transceivers and one eavesdropping node, we design a physical layer security transmission algorithm, which first sorts the subcarriers in terms of the legitimate channel’s quality and then sends one half of original information bits over the first half better subcarriers while the XOR results of two halves of information bits over the rest subcarriers with poorer channel quality. This algorithm can achieve perfect security without reducing the spectrum utilization of the system. Further, in order to improve bit-error-rate (BER) performance of the legitimate receiver, the subcarrier power is optimally allocated to minimize the BER of the legitimate receiver. The simulation results show that the proposed scheme could obtain better performance at the legitimate receiver under the condition of perfect security when the ideal channel reciprocity at both ends of the legitimate transmission is assumed. The designed power distribution scheme can further reduce the legitimate receiver’s BER effectively.","Title: An Energy-efficient Metric for Relay Selection in Large-Scale Multi-hop Wireless Networks\nYear: 2019\n\nAbstract: This paper considers a large-scale multi-hop wireless networks where the nodes are distributed randomly with high density in a 2-Dimension plane and the source node transmits the packets to the remote destination in a multi-hop way. Thus, relay selection becomes the key technology during the routing. Accordingly, a new energy-effective metric is proposed for the relay selection in the large-scale multi-hop networks. During the routing, the forwarding area is predefined dynamically, in which the relay is selected opportunistically according to the metric. Meanwhile, to fully evaluate the system performance, the node circuit energy consumption is also taken into consideration. Under our metric, the average forwarding distance per unit energy consumption of each hop is maximized by selecting the optimal parameters of predefined forwarding area. In this way, the number of nodes that participated into the routing are optimized for the purpose of energy saving.","Title: A Practical Underlay Spectrum Sharing Scheme for Cognitive Radio Networks\nYear: 2019\n\nAbstract: As the proliferation of mobile devices has led to an ever-growing demand for wireless Internet services, the spectrum shortage issue becomes increasingly severe and spectrum sharing is regarded as a promising approach to addressing the spectrum shortage issue. In this paper, we propose a practical underlay spectrum sharing scheme for cognitive radio networks (CRNs) where the primary users are oblivious to the secondary users. The key components of our scheme are two MIMO-based interference cancellation (IC) techniques to handle cross-network interference on the secondary network side. The first one is a blind beamforming technique for secondary transmitters. This IC technique allows a secondary transmitter to nullify its generated interference for primary users without requiring channel state information (CSI). The second one is a blind interference cancellation (BIC) technique for secondary receivers. This IC technique enables a secondary receiver to decode its desired signal in the presence of strong unknown interference from primary transmitters. Based on these two MIMO-based IC techniques, we develop a MAC protocol for the secondary network to enable underlay spectrum sharing in CRNs. We have implemented the proposed underlay spectrum sharing scheme on a GNURadio-USRP2 wireless testbed. Experimental results show that the secondary users can achieve an average of 1 bit/s/Hz spectrum efficiency without degrading the performance of the primary users in a real-world office building environment.","Title: Research On A Rice Counting Algorithm Based On An Improved Mcnn And A Density Map\nYear: 2021\n\nAbstract: The thousand grain weight is an index of size, fullness and quality in crop seed detection and is an important basis for field yield prediction. To detect the thousand grain weight of rice requires the accurate counting of rice. We collected a total of 5670 images of three different types of rice seeds with different qualities to construct a model. Considering the different shapes of different types of rice, this study used an adaptive Gaussian kernel to convolve with the rice coordinate function to obtain a more accurate density map, which was used as an important basis for determining the results of subsequent experiments. A Multi-Column Convolutional Neural Network was used to extract the features of different sizes of rice, and the features were fused by the fusion network to learn the mapping relationship from the original map features to the density map features. An advanced prior step was added to the original algorithm to estimate the density level of the image, which weakened the effect of the rice adhesion condition on the counting results. Extensive comparison experiments show that the proposed method is more accurate than the original MCNN algorithm.","Title: Plane Net: an efficient local feature extraction network\nYear: 2021\n\nAbstract: Due to memory and computing resources limitations, deploying convolutional neural networks on embedded and mobile devices is challenging. However, the redundant use of the 1 x 1 convolution in traditional light-weight networks, such as MobileNetV1, has increased the computing time. By utilizing the 1 x 1 convolution that plays a vital role in extracting local features more effectively, a new lightweight network, named PlaneNet, is introduced. PlaneNet can improve the accuracy and reduce the numbers of parameters and multiply-accumulate operations (Madds). Our model is evaluated on classification and semantic segmentation tasks. In the classification tasks, the CIFAR-10, Caltech-101, and ImageNet2012 datasets are used. In the semantic segmentation task, PlaneNet is tested on the VOC2012 datasets. The experimental results demonstrate that PlaneNet (74.48%) can obtain higher accuracy than MobileNetV3-Large (73.99%) and GhostNet (72.87%) and achieves state-of-the-art performance with fewer network parameters in both tasks. In addition, compared with the existing models, it has reached the practical application level on mobile devices. The code of PlaneNet on GitHub:https://github.om/LinB203/Planenet."],"string":"[\n \"Title: A new global-based video enhancement algorithm by fusing features of multiple region-of-interests\\nYear: 2011\\n\\nAbstract: Video enhancement plays an important role in various video applications. It is desirable to achieve high visual quality of the entire picture where multiple region-of-interests (ROIs) within the frame can be adaptively and simultaneously enhanced. In this paper, a new global-based video enhancement algorithm is proposed. The proposed algorithm first analyzes features from different ROIs. Then, a 'global' tone mapping curve is created for the entire picture which can adaptively enhance different regions at the same time. According to the statistics of ROIs, two fusion strategies, i.e., piecewise-based and factor-based fusions, are proposed for creating the global tone mapping curve. Experimental results show that the proposed algorithm can obtain more appealing perceptual quality than the state-of-the-art algorithms.\",\n \"Title: A survey on resource allocation in high performance distributed computing systems.\\nYear: 2013\\n\\nAbstract: •Classification of high performance computing (HPC) systems is provided.•Current HPC paradigms and industrial application suites are discussed.•State of the art in HPC resource allocation is reported.•Hardware and software solutions are discussed for optimized HPC systems.\",\n \"Title: Ofdma Optimality Analysis In Mimo Uplink Channels\\nYear: 2006\\n\\nAbstract: This paper studies the optimality of OFDMA in MIMO enabled multiuser multicarrier uplink network. We present the necessary and sufficient conditions for OFDMA being optimal on any boundary point of the capacity region. In the low SNR case, we further derive the probabilities of these conditions. For the general MIMO case, we provide an example with which we quantify OFDMA optimality and show the probability of OFDMA being optimal is non-travial regardless of the SNR regions. To reach the the MISO sum capacity limit, our results show that the number of shared subchannels under the optimal solution should be less than the number of total users, thus the performance gap between the OFDMA scheme and the optimal solution is negligible when the number of subchannels is sufficiently large.\",\n \"Title: Adaptive scheduling for multicasting hard deadline constrained prioritized data via network coding.\\nYear: 2012\\n\\nAbstract: Network coding offers a promising platform for multicast transmission by approaching its min-cut capacity. However, pushing the network throughput toward this upper bound comes with a sacrifice in delivery delay due to the decoding procedure that requires performing batch of coded packets. Further, in some transmission scenarios where the receivers experience deep fading or unable to collect a full set of the transmitted data, no useful information is recovered. The effect is more severe in the networks where the transmitted information has priority structure with hard deadline constraint due to the limited delivery time and data interdependencies. In this paper, we consider single-hop wireless networks where the transmitter wishes to multicast hard deadline constrained prioritized data to many receivers over lossy channels. We first study the network performance of a variety of transmission techniques, depending on how the transmitter schedules transmission in each time slot. We then propose an adaptive encoding and scheduling technique to maximize the network throughput. To find the optimal transmission scheduling at the presence of the network dynamics, we cast the problem in the framework of Markov Decision Processes (MDP) and use backward induction method to find an optimal solution. We further propose simulation-based algorithm and greedy scheduling technique that obtain high performance with much lower time complexity. Both analytical and simulation results have been provided to corroborate the effectiveness of the proposed techniques.\",\n \"Title: Distributed optimal power control for multicarrier cognitive systems\\nYear: 2012\\n\\nAbstract: In this paper, the power optimization of the multicarrier cognitive system underlying the primary network is investigated. We consider the interference coupled cognitive network under individual secondary user's power constraint and primary user's rate constraint. A multicarrier discrete distributed (MCDD) algorithm based on Gibbs sampler is proposed. Although the problem is nonconcave, MCDD is proved to converge to the global optimal solution. To reduce the computational complexity and convergence time, the Gibbs sampler based Lagrangian algorithm (GSLA) is proposed to get a near optimal solution. We also provide simulation results to show the effectiveness of the proposed algorithms.\",\n \"Title: Optimal Interference Pre-Cancellation Order In Dpc-Based Broadcast And Unicast Hybrid Network\\nYear: 2013\\n\\nAbstract: It is well known that the Dirty Paper Coding (DPC) achieves the channel capacity for multiuser degraded channels. However, the optimality of DPC remains unknown for non-degraded channel. In this paper, we derive the optimal interference pre-cancellation order for a DPC based broadcast and unicast hybrid network. Specifically, we study different DPC cancellation schemes to maximize the hybrid capacity region. The conditions for each scheme being optimal are analytically derived. Our results show that the optimal interference pre-cancellation order varies with SNR and broadcast and unicast channel conditions.\",\n \"Title: A new package-group-transmission-based algorithm for human activity recognition in videos.\\nYear: 2011\\n\\nAbstract: In this paper, a new package-group-transmission-based algorithm is proposed for human activity recognition in videos. The proposed algorithm first models the entire scene as a network where each node in the network corresponds to a segmentation of the scene. Based on this network, we further model people in the scene as groups of packages. Thus, various human activities can be modeled as the process of \\\"package group transmission\\\" in the network and these activities can be efficiently recognized by suitably analyzing the \\\"package transmission\\\" process. Our proposed algorithm can not only detect activities under the challenging multiple camera scenario, but also be able to recognize various complex group activities among people. Experimental results demonstrate the effectiveness of our proposed algorithm. © 2011 IEEE.\",\n \"Title: Network Coverage in Multicarrier Broadcast System\\nYear: 2010\\n\\nAbstract: In this paper, we discuss the network coverage of the generic multicarrier (or OFDM) based broadcast system by exploiting the frequency diversity over subcarriers. We first define the performance metric and discuss the relationship among broadcast rate, coverage area and the outage probability. To calculate the actual broadcast coverage area, we provide analytical results for both the low SNR and high SNR cases. In addition, we extend our discussion to the multicarrier multicell broadcast network. Simulation results show that multicell cooperation (MCC) has the best performance.\",\n \"Title: An Application of Markov Jump Process Model for Activity-Based Indoor Mobility Prediction in Wireless Networks\\nYear: 2011\\n\\nAbstract: One of the most important objectives of a wireless network is to facilitate a prediction of users mobility regardless of their point of attachment to the network. In indoor environments the effective users motion prediction system and wireless localization technology play an important role in all aspects of peoples daily lives. In this paper we propose an activity-based continuous-time Markov model to define and predict the human movement patterns. This model is a simple extension of an Activity based Mobility Prediction algorithm using Markov modeling (AMPuMM) technique. Both models are experimentally evaluated in realistic small university campus scenario. The obtained results show us the high efficiency of the jump methodology in the prediction of the students activities in the indoor campus environment.\",\n \"Title: Saliency-based visualization for image search\\nYear: 2011\\n\\nAbstract: In this paper, we propose a novel algorithm for improving and visualizing image search results. The proposed algorithm improves user's image search experience by three steps: (1) re-rank the initial image search results by the random walk refinement based on visual consistency and saliency cues, (2) project the re-ranked images into a 2-dimentional panel according to their saliency information and correlations, (3) detect and extract the saliency regions in each image for final visualization. To evaluate the performance of our algorithm, user study has been conducted. Experimental results demonstrate that our visualization algorithm provides more pleasing image search experience than the conventional image search methods.\",\n \"Title: Facial expression mapping based on elastic and muscle-distribution-based models\\nYear: 2012\\n\\nAbstract: In this paper, a new algorithm is proposed for facial expression mapping. The proposed algorithm first introduces a new elastic model to balance the global and local warping effects such that the impacts from facial feature differences between people can be avoided, thus more reasonable geometric warping results can be created. Furthermore, a muscle-distribution-based (MD) model is also proposed. The proposed MD model utilizes the muscle distribution information of the human face to evaluate and strengthen the facial illumination details. By this way, the impacts from human face difference as well as the effects of unsuitable noise filtering can be effectively alleviated. Experimental results show that our proposed algorithm can create obviously better facial expression results than the existing methods.\",\n \"Title: Adaptive resource allocation for heterogeneous traffic over heterogeneous relay networks\\nYear: 2013\\n\\nAbstract: Future wireless communication networks will face the dual challenge of supporting large traffic volumes while providing reliable service for heterogeneous traffic types. In order to meet the ever increasing traffic demand, heterogeneous relay network is introduced as an enabling technology for the fourth generation (4G) mobile broadband networks. In this paper, we will investigate optimal power and subcarrier allocation strategies for heterogeneous relay networks under statistical quality of service (QoS) constraints. To be specific, we will characterize the effective capacity of a wireless relay system under QoS constraints. The properties of the optimal resource and subcarrier allocation strategies for delay-sensitive traffic over heterogeneous relay networks will also be identified. Two low complexity resource and subcarrier allocation algorithms will be introduced to optimize the effective capacity. Our results suggest that the optimal power and subcarrier allocation strategies depend heavily on the underlying QoS constraint. In the low signal-to-interference-plus-noise (SINR) regime, when there is no QoS constraint, both base stations and relay nodes will allocate all the transmit power to the best subcarrier. However, as the QoS requirement becomes more stringent, both base stations and relay nodes will spread their transmit power over multiple subcarriers.\",\n \"Title: An Analysis of Uplink OFDMA Optimality\\nYear: 2006\\n\\nAbstract: Motivated by the increasing popularity of OFDMA, this paper studies the sum-rate optimality of OFDMA as an uplink multicarrier multiple-access scheme. We address the relationship between OFDMA and the optimal multicarrier multiple- access schemes. More specifically, we are interested in answering the following three questions: (i) what are the conditions under which OFDMA is sum-rate optimal? (ii) what is the probability of OFDMA being sum-rate optimal? and (iii) in the case OFDMA is suboptimal, what is the performance gap between OFDMA and the optimal multicarrier multiple access solution? Within a generic discrete multicarrier uplink framework, we present the necessary and sufficient conditions for OFDMA optimality and derive the probabilities of these conditions for both low and high SNR regions. Our results show that the number of shared subchannels under the optimal solution should be less than the number of total users, thus the performance gap between the OFDMA scheme and the optimal solution is negligible when the number of subchannels is large.\",\n \"Title: The existence of infinitely many primary trees\\nYear: 1994\\n\\nAbstract: As an analogue of the basis elements in a vector space, the primal and primary graphs are studied by Dewdney and other authors. It may be called the binary structure that a tree with diameter ⩽2 is a primary iff it is a star with 2 n leaves. This paper further investigates an infinite family of primary trees with binary structures by means of an algorithm related to the matching theory of bipartite graphs.\",\n \"Title: Quantitative comparisons of the state-of-the-art data center architectures.\\nYear: 2013\\n\\nAbstract: Data centers are experiencing a remarkable growth in the number of interconnected servers. Being one of the foremost data center design concerns, network infrastructure plays a pivotal role in the initial capital investment and ascertaining the performance parameters for the data center. Legacy data center network (DCN) infrastructure lacks the inherent capability to meet the data centers growth trend and aggregate bandwidth demands. Deployment of even the highest-end enterprise network equipment only delivers around 50% of the aggregate bandwidth at the edge of network. The vital challenges faced by the legacy DCN architecture trigger the need for new DCN architectures, to accommodate the growing demands of the cloud computing' paradigm. We have implemented and simulated the state of the art DCN models in this paper, namely: (a) legacy DCN architecture, (b) switch-based, and (c) hybrid models, and compared their effectiveness by monitoring the network: (a) throughput and (b) average packet delay. The presented analysis may be perceived as a background benchmarking study for the further research on the simulation and implementation of the DCN-customized topologies and customized addressing protocols in the large-scale data centers. We have performed extensive simulations under various network traffic patterns to ascertain the strengths and inadequacies of the different DCN architectures. Moreover, we provide a firm foundation for further research and enhancement in DCN architectures. Copyright (c) 2012 John Wiley & Sons, Ltd.\",\n \"Title: A Low-Complexity Decoding Algorithm For Hierarchically Modulated Signals In Sfn\\nYear: 2013\\n\\nAbstract: In this paper, the hierarchical modulation (HM) technique is adopted in a single frequency network (SFN) to provide both global and local information. In order to mitigate the inter-layer interference (ILI) and inter-cell interference (ICI), we develop a low-complexity successive interference cancellation (SIC) algorithm for the coded HM signals in the SFN. The proposed decoding algorithm can be applied to different soft-decision channel coding schemes (e. g., Turbo codes, LDPC codes) under various channel profiles. We analyzed the decoding complexity of the proposed algorithm, and evaluated the bit error rate (BER) performance. The simulations show that the new decoding algorithm can offer up to 0.7 dB carrier to noise ratio (C/N) gain compared with the traditional SIC approach under different channel models, while providing the comparable performance (up to 95% decoding complexity savings) with the multi-layer iterative decoding approach. The performance evaluation and decoding complexity comparisons indicate that the proposed structured SIC approach offers a good performance-complexity trade-off, especially for the HM-based SFN scenarios.\",\n \"Title: Energy-efficient cooperative geographic routing in wireless sensor networks\\nYear: 2012\\n\\nAbstract: In this paper, we propose an energy-efficient cooperative geographic routing (ECGR) to reach energy-efficient routing in wireless sensor networks(WSN). ECGR fully takes advantage of cooperative diversity and geographic routing. At physical layer, the sensor nodes cooperatively transmit the same packet to achieve farther transmission distance of each hop. While at the network layer, ECGR adaptively selects the appropriate cooperative nodes to form forwarding clusters to forward the packet when we consider energy consumption and geographic information of the sensor nodes. In order to fully assess the energy consumption, we take circuit energy consumption of the sensor nodes into consideration. According to the description above, we increase the transmitting distance of each hop but decrease the hop number. Meanwhile, the overall network energy consumption is reduced and balanced among nodes to extend the life of the network.\",\n \"Title: Kernel Density Estimation For Wireless Fading Channels Using Slepian Semi-Wavelets\\nYear: 2004\\n\\nAbstract: Efficient and rapid prediction of the future values of the fading envelop is important in fading compensation techniques. This paper is devoted to the discussion of modeling fading channels using kernel density estimation. Our kernel density estimators are non parametric density estimators. Although it will be assumed that the distribution has a probability density function, the data will be allowed to speak for themselves! We are mainly interested in a new kernel density estimator based on Slepian semi-wavelets introduced in a previous paper [9]. Constructed from Slepian semi-wavelets, this kernel density estimator possesses a number of elegant properties. It has higher convergence rate compared to non-wavelet density estimators. It also has adaptive positivity and hence eliminates the Gibbs phenomenon displayed by conventional wavelet estimators. A er a brief introduction to some necessary background materials, we demonstrate a numerical algorithm to compute the Slepian kernel density estimator. To test its capability, the kernel density estimator is used to model wireless fading channels. A numerical experiment is performed using simulated data. Comparison of Slepian kernel estimators and other popular kernel density estimators are also discussed.\",\n \"Title: A new heat-map-based algorithm for human group activity recognition\\nYear: 2012\\n\\nAbstract: In this paper, a new heat-map-based (HMB) algorithm is proposed for human group activity recognition. The proposed algorithm first models people trajectories as series of \\\"heat sources\\\" and then applies a thermal diffusion process to create a heat map (HM) for representing the group activities. Based on this heat map, a new surface-fitting (SF) method is also proposed for recognizing human group activities. Our proposed HM feature can efficiently keep the temporal motion information of the group activities while the proposed SF method can effectively catch the characteristics of the heat map for activity recognition. Experimental results demonstrate the effectiveness of our proposed algorithm.\",\n \"Title: Macroblock Classification Method for Video Applications Involving Motions.\\nYear: 2012\\n\\nAbstract: In this paper, a macroblock classification method is proposed for various video processing applications involving motions. Based on the analysis of the Motion Vector field in the compressed video, we propose to classify Macroblocks of each video frame into different classes and use this class information to describe the frame content. We demonstrate that this low-computation-complexity method can ...\",\n \"Title: Doa Estimation Error And Resolution Loss In Linear Sensor Array\\nYear: 2013\\n\\nAbstract: In order to estimate the direction of signal arrival (DOA) precisely, array of sensors is widely used to enhance resolution and accuracy of the results. For subspace-based algorithms, array sensors' position error may severely influence the estimation result since it changes the array in manifold. In this paper, we provide an improved DOA estimation method for general linear sensor array so that the effect of position error is minimized. We analytically present the relationship between sensor position error and the array's minimum resolution. Simulation results show that the new method does enhance the accuracy of the DOA estimation and the array resolution is determined by both the number of array sensors and the position error.\",\n \"Title: Energy-Effective Relay Selection by Utilizing Spacial Diversity for Random Wireless Sensor Networks\\nYear: 2013\\n\\nAbstract: In this letter, we propose a new energy-efficient metric for relay node selection in multi-hop random wireless sensor networks (WSN) where the sensor nodes are distributed randomly and the node circuit energy consumption is also taken into consideration. During the routing, the relay node is selected in a forwarding area, which is defined by two parameters: a forwarding area radius and forwarding area angle. Under the guide of the new metric, we maximize the effective transmitting distance per unit energy consumption by selecting the appropriate forwarding area radius and angle. In this way, we provide a new point of view to achieve energy-effective relay node selection.\",\n \"Title: Performance analysis and optimization for energy-efficient cooperative transmission in random wireless sensor network.\\nYear: 2013\\n\\nAbstract: This paper considers random wireless sensor networks where nodes are distributed randomly and form clusters to transmit the packets to relay clusters using cooperative multi-input-multi-output (CMIMO) technique. First, we study CMIMO energy consumption and divide it into intra-cluster energy consumption and inter-cluster energy consumption. Then we focus on analyzing the system performance in term...\",\n \"Title: Capacity Of Multicarrier Multilayer Broadcast And Unicast Hybrid Cellular System With Independent Channel Coding Over Subcarriers\\nYear: 2011\\n\\nAbstract: In this paper, we discuss the hybrid capacity region of a generic multicarrier multilayer broadcast and unicast cellular system with independent channel coding over subcarriers. In particular, we analytically derive the capacity region and provide conditions to achieve its boundary. The simulation results show that the hybrid capacity regions are considerably higher than those of the traditional time division multiplexing scheme.\",\n \"Title: An overview of achieving energy efficiency in on-chip networks\\nYear: 2010\\n\\nAbstract: Due to the increasing bandwidth demand for the network-on-chip (NoC), interconnection networks become a dominant source of energy consumption in systems-on-chip (SoCs) and chip multi processors (CMPs). Therefore, energy efficient NoC is key to a successful SoC development. This paper presents an overview of different techniques to achieve energy efficiency at the different levels of NoC design including: a) component level where dynamic voltage scaling (DVS) and dynamic link shutdown (DLS) techniques are reviewed; b) circuit level, e.g., voltage swinging of signals; c) architectural level, where specialised tools, such as Wattch and Orion are discussed. We also summarise research on thermal optimisation issues. To the best of our knowledge, this is the first survey of recent research results on the area.\",\n \"Title: Spectrum Optimization For Ofdma Based Hybrid Wireless Networks\\nYear: 2011\\n\\nAbstract: This paper proposes a new cooperative resource allocation scheme for OFDMA based hybrid network. Different from the traditional cognitive radio networks that aim to utilize the spectrum holes in an uncoordinated way, we study the joint resource allocation for both the primary users and the secondary users. Results show that our approach can achieve higher spectral efficiency and provide satisfactory admission control and QoS for all users.\",\n \"Title: On The Boundary Inequality For Bandwidth Of Graphs\\nYear: 1996\\n\\nAbstract: The quantity B(G) = min max {\\\\f(u)-f(v)\\\\: (u, v) is an element of E(G)} is called the bandwidth of a graph G = (V(G), E(G)) where min is taken over all bijections f: V(G) --> {1,2,..., \\\\V(G)\\\\} called labelings. L.H. Harper presented an important inequality related to the boundary partial derivative S of subsets S subset of or equal to V(G). This paper gives a refinement of Harper's inequality which will be more powerful in determining bandwidths for several classes of graphs.\",\n \"Title: An overview of energy efficiency techniques in cluster computing systems\\nYear: 2013\\n\\nAbstract: Two major constraints demand more consideration for energy efficiency in cluster computing: (a) operational costs, and (b) system reliability. Increasing energy efficiency in cluster systems will reduce energy consumption, excess heat, lower operational costs, and improve system reliability. Based on the energy-power relationship, and the fact that energy consumption can be reduced with strategic power management, we focus in this survey on the characteristic of two main power management technologies: (a) static power management (SPM) systems that utilize low-power components to save the energy, and (b) dynamic power management (DPM) systems that utilize software and power-scalable components to optimize the energy consumption. We present the current state of the art in both of the SPM and DPM techniques, citing representative examples. The survey is concluded with a brief discussion and some assumptions about the possible future directions that could be explored to improve the energy efficiency in cluster computing.\",\n \"Title: A new Temporal-Constraint-Based algorithm by handling temporal qualities for video enhancement\\nYear: 2011\\n\\nAbstract: Video enhancement has played very important roles in many applications. However, most existing enhancement methods only focus on the spatial quality within a frame while the temporal qualities of the enhanced video are often unguaranteed. In this paper, a new algorithm is proposed for video enhancement. The proposed algorithm introduces new temporal constraints and combines them with the spatial constraints such that both the spatial and temporal qualities of the video can be improved. Two strategies are proposed for including the temporal constraints. Experimental results demonstrate the effectiveness of the proposed algorithm.\",\n \"Title: A New Cooperative Spectrum Sensing Scheme for Cognitive Ad-Hoc Networks.\\nYear: 2011\\n\\nAbstract: As the radio spectrum is becoming more and more crowded, cognitive radio has recently become a hot research topic to improve the spectrum utilization efficiency. It is well known that the success of cognitive radio depends heavily on fast and efficient spectrum sensing that is very difficult in practice. Toward this end, this paper introduces a new guard-resident cooperative spectrum sensing scheme for a cognitive ad-hoc network. In particular, we classify cognitive nodes as either or based on the spectrum neighbor decision and distributed boundary search. The guard nodes sense the spectrum and then inform the resident nodes that are greatly relieved from spectrum sensing about the radio environmental changes. The analysis and simulation results show that the proposed algorithm can significantly reduce the total spectrum sensing load without sacrificing the sensing accuracy.\",\n \"Title: OFDMA Capacity Analysis in MIMO Channels\\nYear: 2010\\n\\nAbstract: In this paper, the performance of orthogonal frequency division multiple access orthogonal frequency division multiple access (OFDMA) is studied in the context of the general multiuser multicarrier multiple-input multiple-output (MIMO) (M3) system for both uplink (UP) and downlink (DL) channels. The relationship between OFDMA and the optimal M3 solution is investigated from an information theoretical point of view. Specifically, the necessary and sufficient conditions of OFDMA being the capacity-maximizing M3 transmission scheme is derived. In addition, OFDMA optimality is explicitly analyzed in some special cases.\",\n \"Title: DPC-Based Hierarchical Broadcasting: Design and Implementation\\nYear: 2008\\n\\nAbstract: This paper discusses interference precancellation in digital hierarchical broadcasting (HB). In particular, we present the principles and implementation of structured dirty paper coding (SDPC) that approaches the capacity limit of dirty paper coding in multilayer broadcasting. As an alternative to Tomlinson-Harashima precoding (THP), SDPC eliminates the significant performance loss suffered by THP...\",\n \"Title: Secure network-coded wireless multicast for delay-sensitive data\\nYear: 2012\\n\\nAbstract: Wireless multicast for delay-sensitive data is challenging because different receivers may experience different packet losses. Network coding offers significant advantages over the traditional Automatic Repeat-reQuest (ARQ) protocols in that it mitigates the need for retransmission and has the potential to approach the min-cut capacity. Network-coded multicast would be, however, vulnerable to false packet injection attacks, in which the adversary injects bogus packets to prevent receivers from correctly decoding the original data. Without a right defense in place, even a single bogus packet can completely change the decoding outcome. Existing solutions either incur high computation cost or cannot withstand high packet loss. In this paper, we propose a novel scheme to defend against false packet injection attacks on network-coded multicast for delay-sensitive data. Specifically, we propose an efficient authentication mechanism based on null space properties of coded packets, aiming to enable receivers to detect any bogus packets with high probability. We further design an adaptive scheduling algorithm based on Markov Decision Processes (MDP) to maximize the number of authenticated packets that can be received within a given time constraint. Both analytical and simulation results have been provided to demonstrate the efficacy and efficiency of our proposed scheme.\",\n \"Title: A patch-based framework for detecting abnormal activities with a PTZ camera\\nYear: 2012\\n\\nAbstract: In this paper, a novel patch-based (PB) framework is proposed for detecting abnormal activities using a Pan-Tilt-Zoom (PTZ) camera. We first propose a new scene-patch-based (SSB) algorithm which can efficiently extract the target object's global trajectory from the PTZ camera. Furthermore, we propose an extended network-based (ENB) algorithm for detecting abnormal activities. The proposed ENB algorithm models the entire scene as a network where each node in the network corresponds to a patch of the scene and each edge between nodes corresponds to the activity correlation between the scene patchs. Based on this network, a recursive training strategy is proposed to train the edge weights in the network such that abnormal activities can be effectively detected through these trained edge weights. Experimental results demonstrate the effectiveness of our proposed framework.\",\n \"Title: Secure Wireless Multicast for Delay-Sensitive Data via Network Coding\\nYear: 2013\\n\\nAbstract: Wireless multicast for delay-sensitive data is challenging because of the heterogeneity effect where each receiver may experience different packet losses. Fortunately, network coding, a new advanced routing protocol, offers significant advantages over the traditional Automatic Repeat reQuest (ARQ) protocols in that it mitigates the need for retransmission and has the potential to approach the min-cut capacity. Network-coded multicast would be, however, vulnerable to false packet injection attacks, in which the adversary injects bogus packets to prevent receivers from correctly decoding the original data. Without a right defense in place, even a single bogus packet can completely change the decoding outcome. Existing solutions either incur high computation cost or cannot withstand high packet loss. In this paper, we propose a novel scheme to defend against false packet injection attacks on network-coded multicast for delay-sensitive data. Specifically, we propose an efficient authentication mechanism based on null space properties of coded packets, aiming to enable receivers to detect any bogus packets with high probability. We further design an adaptive scheduling algorithm based on the Markov Decision Processes (MDP) to maximize the number of authenticated packets received within a given time constraint. Both analytical and simulation results have been provided to demonstrate the efficacy and efficiency of our proposed scheme.\",\n \"Title: A Low-Complexity Decoding Algorithm for Coded Hierarchical Modulation in Single Frequency Networks\\nYear: 2014\\n\\nAbstract: In this paper, the hierarchical modulation (HM) technique is adopted in a single frequency network (SFN) to provide both global and local information. In order to mitigate the interlayer interference and intercell interference, we develop a low-complexity successive interference cancellation (SIC) algorithm for the coded HM signals in the SFN. The proposed decoding algorithm can be applied to different soft-decision channel coding schemes (e.g., Turbo codes, LDPC codes) under various channel profiles. We analyzed the decoding complexity of the proposed algorithm, and evaluated the bit error rate performance. The simulations show that the new decoding algorithm can offer up to 0.7 dB carrier to noise ratio ((C/N)) gain compared with the traditional SIC approach under different channel models, while providing the comparable performance (up to 95% decoding complexity savings) with the multilayer iterative decoding approach. The performance evaluation and decoding complexity comparisons indicate that the proposed structured SIC approach offers a good performance-complexity trade-off, especially for the HM-based SFN scenarios.\",\n \"Title: A Fast Sub-Pixel Motion Estimation Algorithm for H.264/AVC Video Coding\\nYear: 2011\\n\\nAbstract: Motion estimation (ME) is one of the most time-consuming parts in video coding. The use of multiple partition sizes in H.264/AVC makes it even more complicated when compared to ME in conventional video coding standards. It is important to develop fast and effective sub-pixel ME algorithms since: 1) the computation overhead by sub-pixel ME has become relatively significant while the complexity of integer-pixel search has been greatly reduced by fast algorithms, and 2) reducing sub-pixel search points can greatly save the computation for sub-pixel interpolation. In this letter, a novel fast sub-pixel ME algorithm is proposed which performs a “rough” sub-pixel search before the partition selection, and performs a “precise” sub-pixel search for the best partition. By reducing the searching load for the large number of non-best partitions, the computation complexity for sub-pixel search can be greatly decreased. Experimental results show that our method can reduce the sub-pixel search points by more than 50% compared to existing fast sub-pixel ME methods with negligible quality degradation.\",\n \"Title: A New Network-Based Algorithm for Human Activity Recognition in Videos\\nYear: 2014\\n\\nAbstract: In this paper, a new network-transmission-based (NTB) algorithm is proposed for human activity recognition in videos. The proposed NTB algorithm models the entire scene as an error-free network. In this network, each node corresponds to a patch of the scene and each edge represents the activity correlation between the corresponding patches. Based on this network, we further model people in the scene as packages, while human activities can be modeled as the process of package transmission in the network. By analyzing these specific package transmission processes, various activities can be effectively detected. The implementation of our NTB algorithm into abnormal activity detection and group activity recognition are described in detail in this paper. Experimental results demonstrate the effectiveness of our proposed algorithm.\",\n \"Title: A new shot change detection method using information from motion estimation\\nYear: 2010\\n\\nAbstract: In many video coding related applications, both shot change detection and motion estimation need to be performed. In this paper, a new shot change detection method is proposed for such applications, which uses the information from motion estimation. We first propose to classify Macroblocks of each frame into different classes based on the information obtained in the motion estimation process. Each class can capture some specific characteristics of the frame. The shot changes can then be detected based on the extracted class information. The proposed method has low computation complexity. It can easily be implemented into most existing video coding systems without extra cost. Experimental results show that the proposed method can detect shot changes effectively. Some extension applications of our class information are also discussed in the paper.\",\n \"Title: A new network-based algorithm for multi-camera abnormal activity detection\\nYear: 2011\\n\\nAbstract: In this paper, a new abnormal activity detection algorithm is proposed for multi-camera surveillance applications. The proposed algorithm models the entire scene covered by the multi-camera system as a network. In this network, each node corresponds to a segmentation of the entire scene and each edge represents the activity correlation between the corresponding segmentations. Based on this network, the proposed algorithm further models human activities as the signal transmission process in the network. Thus, abnormal activities can be detected if their 'network transmission energy' is obviously larger than the normal case. Compared with the previous methods, the proposed algorithm is more general and is flexible to handle various multi-camera scenarios and configurations. Experimental results demonstrate the effectiveness of the proposed algorithm.\",\n \"Title: Performance analysis of joint decode-and-forward scheme for two-way relay channels\\nYear: 2013\\n\\nAbstract: This paper addresses the performance of the joint decode-and-forward (JDF) strategy for two-way relay systems over Rayleigh fading channels. We show that the JDF enjoys a lower bit error rate (BER) in the first time slot, which translates into higher capacity for broadcast in the second time slot. Specifically, we present the optimal decision rules for JDF and partial decoding-and-forward (PDF) and derive their corresponding BER union bounds for BPSK signals. Furthermore, we propose a novel scheme, termed JDF-JM, to re-encode the messages at the relay node for unmatched data rates from the two sources. The achievable rates of JDF and PDF for the second time slot are then analyzed. Simulation results are provided to illustrate the performance of JDF over DF-XOR under different channel coding and modulation settings.\",\n \"Title: Heuristics-based nominal channels allocation in cellular networks\\nYear: 2010\\n\\nAbstract: In this paper, we develop four heuristics to deal with the nominal channel allocation in cellular networks. We provide the theoretical bound on the number of channels for voice and data services. The four heuristics are compared using varying traffic distributions.\",\n \"Title: Transmission Schemes for Multicarrier Broadcast and Unicast Hybrid Systems\\nYear: 2008\\n\\nAbstract: Wireless infrastructures can be conveniently categorized into two types: those that provide point-to-point dasiaunicastpsila connectivity (e.g., cellular) and others that are dasiabroadcastpsila in nature (such as TV and radio) and are inherently point-to-multipoint. This paper introduces a transmission scheme for multicarrier hybrid system that supports both broadcasting and unicasting on a single-frequency platform. In particular, we present a dirty-paper coding (DPC) based overlay architecture to enable interference-free reception of unicast signals. The fundamental limits of hybrid communications are investigated, along with power allocation algorithms that approach the hybrid capacity. Our results show that OFDMA for unicast offers the best hybrid performance and considerable gains over orthogonal multiplexing (e.g., TDM/FDM) can be achieved through interference-free overlay. In addition, we describe approaches to further improve the spectral efficiency and the broadcast coverage through multicell collaboration.\",\n \"Title: Dynamic light trail routing in WDM optical networks\\nYear: 2011\\n\\nAbstract: Fiber optics have replaced copper as the primary transmission medium. Wavelength Division Multiplexing (WDM) networks effectively increase single-link bandwidth from 10 Mbps to over 160 Gbps, and have been considered as a promising candidate for the next-generation backbone network. All optical circuits each on a separate wavelength called lightpaths represent the first major method for optical communication. The granularity provided between a source and destination node is that of a complete wavelength. Once a lightpath is set up, the entire wavelength is used exclusively by the connection’s source and destination node-pair. No sub-wavelength sharing between nodes along the lightpath is allowed. However, it is often observed that the bandwidth requirement in today’s network is often dynamically varying and does not justify the need for allocating an entire wavelength. Therefore, the wavelength capacity may be underutilized. A new technology termed light trail was proposed to avoid the inability of intermediate nodes to use a connection wavelength, and the constant reconfiguration of switches. In this article, we study dynamic light trail routing in a WDM optical network. We present an efficient algorithm for establishing a light trail routing for a new connection request, while using minimum network resources. We also study survivable network routing using the proposed light trail technology. We present an efficient heuristic for computing a pair of working and protection light trails for a dynamic incoming connection request. Simulation results are presented which demonstrate the advantages of our routing schemes.\",\n \"Title: Robust Power Control for Amplify-and-Forward Relaying Scheme\\nYear: 2015\\n\\nAbstract: Power control problem is investigated for amplify-and-forward cellular relay network in this letter. Since the wireless channels vary with time, we aim at a power allocation scheme that is robust to the fluctuations. An optimization problem is formulated which targets at utility maximization subject to a pre-defined outage probability threshold and limited power at relay station. With that, a distributed iterative algorithm is proposed to obtain the optimal power. Numerical results show the effectiveness of the algorithm, and also present the robustness of this system.\",\n \"Title: Distributed Energy Efficiency Optimization for MIMO Cognitive Radio Network\\nYear: 2015\\n\\nAbstract: This paper considers a multiple-input-multipleoutput (MIMO) cognitive radio network, where secondary users (SUs) equipped with multiple antennas coexist with a primary user (PU). Joint power allocation and transmission beamforming design is proposed to optimize the network energy efficiency under power constraint and interference temperature constraint. Distributed algorithm that maximizes energy efficiency of each individual node is derived through fractional programming. Simulation results are presented to validate the convergence and effectiveness of the proposed algorithm.\",\n \"Title: A New Cross-Level Decoding Scheme for LT Codes\\nYear: 2015\\n\\nAbstract: In this letter, we propose a new cross-level decoding method for LT codes, where the results of packet-level decoding are fed back to bit-level decoding to improve the overall decoding performance. Another advantage of our method is that it keeps the original LT decoder structure and does not require extra real field computation. We provide the revised And-Or Tree analysis for the proposed method and validate it with numerical simulations.\",\n \"Title: An integrative approach for measuring semantic similarities using gene ontology.\\nYear: 2014\\n\\nAbstract: Gene Ontology (GO) provides rich information and a convenient way to study gene functional similarity, which has been successfully used in various applications. However, the existing GO based similarity measurements have limited functions for only a subset of GO information is considered in each measure. An appropriate integration of the existing measures to take into account more information in GO is demanding.We propose a novel integrative measure called InteGO2 to automatically select appropriate seed measures and then to integrate them using a metaheuristic search method. The experiment results show that InteGO2 significantly improves the performance of gene similarity in human, Arabidopsis and yeast on both molecular function and biological process GO categories.InteGO2 computes gene-to-gene similarities more accurately than tested existing measures and has high robustness. The supplementary document and software are available at http://mlg.hit.edu.cn:8082/.\",\n \"Title: A survey on text mining in social networks.\\nYear: 2015\\n\\nAbstract: In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.\",\n \"Title: Energy Efficiency of Hybrid Cellular With Heterogeneous QoS Provisions\\nYear: 2014\\n\\nAbstract: In this letter, we consider a hybrid broadcast and unicast cellular network (hybrid cellular for short) with heterogeneous quality of service (QoS) constraints in the low-power regime. In particular, we consider the unicast user with delay constraint and the broadcast user with outage constraint. The transmission energy efficiency of the dirty paper coding (DPC) and time division multiple access (TDMA) schemes are analyzed and compared from information theoretic point of view. Specifically, the minimum energy per bit and the wideband slope region are derived in closed forms. While both DPC and TDMA schemes require the same minimum energy per bit, DPC outperforms TDMA with regard to the wideband slope region. We also obtain the critical points for both schemes beyond which the wideband slope decreases for both unicast and broadcast users. Finally, we show how the heterogeneous QoS constraints affect the energy efficiency in hybrid cellular.\",\n \"Title: Achievable Degrees of Freedom of MIMO Y Channel With Delayed CSIT.\\nYear: 2014\\n\\nAbstract: In this letter, we study the degrees of freedom (DOF) for multiple-input-multiple-output (MIMO) Y channel in frequency division duplex (FDD) systems. Assuming each node has delayed channel state information at the transmitter (CSIT), we propose a physical layer network coding multi-stage transmission (PLNC-MST) scheme during broadcast (BC) phase. The results show that PLNC offers significant DOF gain in MIMO Y channel.\",\n \"Title: Cross-Layer Design of Joint Beamforming and Random Network Coding in Wireless Multicast Networks\\nYear: 2014\\n\\nAbstract: In this letter, we consider a wireless network where the transmitter, such as a base station, multicasts hard-deadline-constrained prioritized data to users over fading channels. We propose a new cross-layer transmission scheme that involves simultaneous smart antenna beamforming at physical layer and random network coding at network layer to maximize the overall network throughput. In this joint design, beamforming is used for dynamic transmitting power allocation to achieve power gain; while the random network coding is adopted to achieve coding gain. The simulation results show the effectiveness of the proposed algorithm.\",\n \"Title: Resource Allocation for Delay-Sensitive Traffic over LTE-Advanced Relay Networks\\nYear: 2015\\n\\nAbstract: Future wireless networks will face the dual challenge of supporting large traffic volumes while providing reliable service for delay-sensitive traffic. To meet the challenge, relay network has been introduced as a new network architecture for the fourth generation (4G) LTE-Advanced (LTE-A) networks. In this paper, we investigate resource allocation including subcarrier and power allocation for LTE-A relay networks under statistical quality of service (QoS) constraints. By dual decomposition, we derive the optimal subcarrier and power allocation strategies to maximize the effective capacity (EC) of the underlying LTEA relay systems. Characteristics of optimal resource allocation strategies are identified, and a low-complexity suboptimal scheme is developed through optimizing the subcarrier and power allocation individually. Our result suggests that the optimal subcarrier and power allocation strategies depend heavily on the underlying QoS constraint. For example, in the low signal-to-interference-plus-noise (SINR) regime, when there are less stringent QoS constraints, base stations and relay stations tend to allocate all the power to the best available subcarrier. However, as QoS requirements become more stringent, both base stations and relay stations will spread their power over available subcarriers. On the other hand, in the high SINR regime, regardless of the QoS constraints, base stations and relay stations tend to equally allocate power among available subcarriers.\",\n \"Title: Transmission Schemes for Multicasting Hard Deadline Constrained Prioritized Data in Wireless Multimedia Streaming.\\nYear: 2016\\n\\nAbstract: In wireless multicast multimedia streaming, every data block (i.e., a group of packets) needs to be delivered within a hard deadline. On the other hand, to provide multilevel services, the widely adopted scalable video coding (SVC) prioritizes a video bit stream into a base layer and several enhancement layers. In this paper, we combine SVC with the transmission delay constraint and focus on multi...\",\n \"Title: A web tool for measuring gene semantic similarities by combining gene ontology and gene co-function networks\\nYear: 2015\\n\\nAbstract: Gene Ontology(GO) is one of the most popular bioinformatic resource to study functional relationships between genes. However, the existing tools for calculating the semantic similarity are limited since they only rely on GO annotations and structure. We provide a working demonstration of a web tool that calculates functional similarities between GO terms by integrating gene co-function networks and information from GO. We demonstrate the function of the web tool by comparing remote GO terms.\",\n \"Title: Physical-Layer Secrecy Performance in Finite Blocklength Case.\\nYear: 2015\\n\\nAbstract: For physical-layer secrecy performance, existing studies only focus on coding schemes that can achieve the secrecy capacity. However, finite blocklength penalty could be fatal for security of private message. In this paper, we explore the impact of finite blocklength on secrecy for the wiretap channel model. We propose secrecy performance matrices for finite blocklength analysis and provide analytical expressions to evaluate the secrecy performance. We demonstrate trade-off between secrecy and reliability, and also provide an upper bound of secret information rate in finite blocklength case. The results are also applied to indicate asymptotic performance.\",\n \"Title: Queue Stability Analysis in Network Coded Wireless Multicast Network.\\nYear: 2016\\n\\nAbstract: This letter considers a single hop wireless multicast network. We first introduce a new two-level queuing system consisting of a main queue and a virtual queue, where each packet in the virtual queue is associated with a user index set. Then, we propose a network coding based packet scheduling method to maximize the system input rate under the queue stability constraint. Our analytical and simulation results demonstrate the effectiveness of the proposed solution.\",\n \"Title: Achievable degrees of freedom of MIMO two-way X relay channel with delayed CSIT.\\nYear: 2016\\n\\nAbstract: The previous work on interference alignment for multiple-input-multiple-output (MIMO) two-way X relay channel assumes perfect channel state information at the transmitter (CSIT), which is reasonable in slow fading channel. However, in fast fading scenario, this assumption is impractical. In this paper, assuming that each node has delayed CSIT, we study the achievable degrees of freedom (DOF) for MIMO two-way X relay channel in frequency division duplex (FDD) systems. Specifically, in the broadcast (BC) phase, we propose a new multiple-stage transmission (MST) scheme, which utilizes retrospective interference alignment for physical layer network coding (PLNC). We show that MST can achieve significant DOF gain and tremendous power gain over other schemes. When the number of antennas for each user, N, is smaller than the number of the relays, M, the time division multiple access (TDMA) scheme can only achieve an ergodic sum-rate increase by N bps/Hz for every increasing of 3 dB of signal-to-noise power ratio (SNR), while the proposed MST scheme can achieve an ergodic sum-rate increase by M bps/Hz.\",\n \"Title: A New Wireless Multicast Queuing Design Using Network Coding and Data-Flow Model.\\nYear: 2016\\n\\nAbstract: A queue stability analysis in a wireless multicast network with packet erasure channels is studied in this letter. In particular, a new queuing model consisting of several sub-queues is introduced, where each packet in a sub-queue is associated with an index set indicating its intended users. Our objective is to maximize the input rate under the queue stability constraints. We formulate it as a linear programming problem and propose a network coding-based packet scheduling scheme that finds the optimal solution. Finally, the simulation results corroborate our method.\",\n \"Title: Adaptive Random Network Coding for Multicasting Hard-Deadline-Constrained Prioritized Data.\\nYear: 2016\\n\\nAbstract: Random network coding (RNC) is a promising solution for multicast services over wireless networks. In this paper, we consider the problem of deadline-constrained multimedia multicast from a single transmitter to multiple users over wireless erasure channels. A novel transmission scheduling strategy based on adaptive RNC is developed to maximize the throughput of multicast sessions under a determin...\",\n \"Title: Optimize the spectrum sensing and decision making strategies under uncertainty for SATCOM.\\nYear: 2016\\n\\nAbstract: The ability to provide accurate spectrum sensing and decision making under an uncertainty environment is proving useful for the military SATCOM to increases the spectrum utilization. Dynamic spectrum access(DSA) allows a secondary user to access the spectrum holes that are not occupied by the primary users. However, the spectrum sensing for DSA is normally performed in a complex SATCOM environment under uncertainty, caused by the high GEO/LEO mobility, weak signals after a long distance of propagation, the high interference and jamming in an adversarial environment, etc. The uncertainty results in a high error probability in the spectrum sensing. In such a case, DSA requires a decision-making process to optimally determine which channels to sense and access. In this paper, we propose an approach for optimal spectrum sensing and decision making that mathematically models the uncertainty in the SATCOM while the whole system throughput is maximized. Specifically, we model the DSA with decision making as a Partially Observable Markov Decision Process (POMDP) problem. Optimal DSA strategy has been discussed by an optimization process. Monte Carlo simulations are carried out and our simulation results demonstrate the efficiency of the proposed DSA strategy.\",\n \"Title: Enabling cyber-physical communication in 5G cellular networks: challenges, spatial spectrum sensing, and cyber-security\\nYear: 2017\\n\\nAbstract: Cyber-physical systems (CPS) help create new services and applications by revolutionising our world in different fields through their tight interactions and automated decisions. This is especially true with the ongoing increase in the number of physical things (sensors, actuators, smartphones, tablets, and so on) along with the explosive increase in the usage of online networking services and appl...\",\n \"Title: An AMP based decoder for massive MU-MIMO-OFDM with low-resolution ADCs\\nYear: 2017\\n\\nAbstract: In massive MIMO systems, multiple high-speed high-resolution ADCs will be a major obstacle for real applications because of the hardware complexity and power consumption. One solution to this problem is to adopt low-cost low-resolution ADCs instead. Note that highly quantized signals will destroy the orthogonality of OFDM design, the key point remains to find computational decoding methods while maintaining acceptable performances. To address this issue, we develop a low complexity algorithm designed for low-resolution ADCs RF-frontend using iterative decoding based on Approximate-Message-Passing (AMP). The proposed algorithms can provide adequate performance in low-resolution ADC systems compared to approximate LMMSE solution.\",\n \"Title: Iterative IQ Imbalance Compensation Receiver for Single Carrier Transmission.\\nYear: 2017\\n\\nAbstract: It is well known that low-cost RF front-end implementation such as direct conversion transmitter suffers from serious in-phase/quadrature phase (IQ) imbalance. Most existing works in single carrier transmission employ linear compensation methods, such as Least Square and minimum mean square error, to combat the interference caused by IQ imbalance. Actually, for single carrier transmission, it is p...\",\n \"Title: Enabling jamming-resistant communications in wireless MIMO networks.\\nYear: 2017\\n\\nAbstract: In this paper, we propose a practical anti-jamming solution for wireless MIMO networks to enable their legitimate communications in the presence of multiple high-power and broadband radio jamming attacks, and evaluate the proposed solution using real-world implementations in a WiFi network. We first develop a blind jamming mitigation (BJM) algorithm, which can cancel jamming signals from multiple unknown radio jammers and, at the same time, equalize channel to recover the signals from the legitimate sender. Unlike the existing jamming mitigation algorithms, the BJM algorithm does not need any channel information for jamming mitigation and signal recovery. Based on the BJM algorithm, we develop a jamming-resistant receiver (termed JrRx) and a holistic anti-jamming scheme to salvage legitimate communications in the face of jamming attacks. We have built a prototype of JrRx and evaluated its performance in a WiFi network. Experimental results show that, as long as JrRx has more antennas than the jammers, it can successfully decode the signals from the sender, even if the jamming signals are 20 dB stronger than the signals of interest.\",\n \"Title: Uplink MU-MIMO in Asynchronous Wireless LANs.\\nYear: 2018\\n\\nAbstract: In wireless LANs (WLANs), network-wide time and frequency synchronization among user devices is widely regarded as a necessity for uplink MU-MIMO. Therefore, to enable uplink MU-MIMO in 802.11ax, dedicated MAC protocols (e.g., trigger frame and timing advance mechanism) have been proposed to synchronize user devices in the time and frequency domains. Such MAC protocols increase not only network complexity but also communication overhead. In this paper, we show that the time and frequency synchronization among user devices is not a necessity for uplink MU-MIMO. We propose a practical uplink MU-MIMO solution which does not require time and frequency alignments of the signals from user devices. The key component in our solution is a new PHY design for AP's receiver, which can decode the concurrent signals from multiple asynchronous user devices. We have built a prototype of our uplink MU-MIMO solution on USPR2-GNURadio testbed. Experimental results show that, using the new PHY, an M-antenna AP can successfully decode the concurrent signals from M asynchronous user devices (2 ≤ M ≤ 4).\",\n \"Title: Lyapunov Scheduling and Optimization in Network Coded Wireless Multicast Network.\\nYear: 2018\\n\\nAbstract: In this paper, we investigate network coding based scheduling in a single-hop wireless multicast network under queueing stability constraint. Specifically, Lyapunov optimization model is adopted and an optimal network coding scheduling algorithm is derived, which significantly reduces the computational complexity and queue length compared to existing solutions. Moreover, two scheduling policies ba...\",\n \"Title: Real-Time Graph Partition and Embedding of Large Network\\nYear: 2018\\n\\nAbstract: Recently, large-scale networks attract significant attention to analyze and extract the hidden information of big data. Toward this end, graph embedding is a method to embed a high dimensional graph into a much lower dimensional vector space while maximally preserving the structural information of the original network. However, effective graph embedding is particularly challenging when massive graph data are generated and processed for real-time applications. In this paper, we address this challenge and propose a new real-time and distributed graph embedding algorithm (RTDGE) that is capable of distributively embedding a large-scale graph in a streaming fashion. Specifically, our RTDGE consists of the following components: (1) a graph partition scheme that divides all edges into distinct subgraphs, where vertices are associated with edges and may belong to several subgraphs; (2) a dynamic negative sampling (DNS) method that updates the embedded vectors in real-time; and (3) an unsupervised global aggregation scheme that combines all locally embedded vectors into a global vector space. Furthermore, we also build a real-time distributed graph embedding platform based on Apache Kafka and Apache Storm. Extensive experimental results show that RTDGE outperforms existing solutions in terms of graph embedding efficiency and accuracy.\",\n \"Title: A Robust Power Allocation Scheme for Relay Communication Networks Based on Seller–Buyer Game\\nYear: 2017\\n\\nAbstract: A robust power allocation algorithm is proposed in this paper based on robust game theory. Considering that the channel gains are usually changing with the dynamic environment in practice, the uncertainty will cause the estimation errors of channel gains and deteriorate the quality of services of users. A probability threshold method is used to deal with the uncertainty of the channel gains. To jointly consider the maximum utilities of source node and relay node, a seller–buyer game is introduced and it is proved that the game converges to the unique equilibrium point. Besides, a distributed iteration algorithm is proposed to reduce the information overhead. Numerical results show the effectiveness and robustness of the proposed power algorithm.\",\n \"Title: Hybrid Multicast and Device-to-Device Communications Based on Adaptive Random Network Coding\\nYear: 2019\\n\\nAbstract: Random network coding (RNC) is an efficient coding scheme to improve the performance of wireless multicast networks, on the premise that a receiver is able to collect a full set of network-coded packets. However, in resource (time, bandwidth, and so on) constrained communications, a receiver may only receive a partial set of network coded packets, leading to poor system performance. On the other hand, device-to-device (D2D) communications utilize user proximity and spatial diversity to improve the communication efficiency. In this paper, we propose a hybrid multicast and D2D transmission scheme based on adaptive RNC (ARNC) to increase network throughput under packet erasure channels. In the proposed scheme, the packet encoding structure is optimized adaptively according to the network status, such that even if only a partial set of the coded packets are received, the user equipments (UEs) can still decode useful information and regenerate new encoded packet for D2D communications. In particular, the multicast mode and D2D mode are switched dynamically during a scheduling session according to the status of each UE. Under this hybrid mode, we can effectively overcome the effect of erasure channel and improve the overall network throughput. By comparing our scheme with other scheduling methods, we provide simulation results to corroborate the effectiveness of the proposed techniques.\",\n \"Title: Coordinated Beamforming for Multicell Multicast Secret Communications\\nYear: 2018\\n\\nAbstract: In this paper, we study the optimal transmitter beamforming design in a coordinated multicell multicast network under the wiretap channel model, where each cell serves a group of users and a single-antenna eavesdropper tries to overhear the confidential information. We study the problem of finding the optimal beamforming strategy at each base station to minimize the total transmit power of the multicell network while guaranteeing secrecy and QoS requirements of each user. The resulting non-convex optimization problem is solved through a sequence of convex semi-definite relaxations. The effectiveness of the proposed algorithm is validated via numerical simulations.\",\n \"Title: Secure Transmission Algorithm Based on Subcarrier Sorting and XOR Operation in OFDM Systems\\nYear: 2018\\n\\nAbstract: Considering an OFDM modulation based system including one pair of legitimate transceivers and one eavesdropping node, we design a physical layer security transmission algorithm, which first sorts the subcarriers in terms of the legitimate channel’s quality and then sends one half of original information bits over the first half better subcarriers while the XOR results of two halves of information bits over the rest subcarriers with poorer channel quality. This algorithm can achieve perfect security without reducing the spectrum utilization of the system. Further, in order to improve bit-error-rate (BER) performance of the legitimate receiver, the subcarrier power is optimally allocated to minimize the BER of the legitimate receiver. The simulation results show that the proposed scheme could obtain better performance at the legitimate receiver under the condition of perfect security when the ideal channel reciprocity at both ends of the legitimate transmission is assumed. The designed power distribution scheme can further reduce the legitimate receiver’s BER effectively.\",\n \"Title: An Energy-efficient Metric for Relay Selection in Large-Scale Multi-hop Wireless Networks\\nYear: 2019\\n\\nAbstract: This paper considers a large-scale multi-hop wireless networks where the nodes are distributed randomly with high density in a 2-Dimension plane and the source node transmits the packets to the remote destination in a multi-hop way. Thus, relay selection becomes the key technology during the routing. Accordingly, a new energy-effective metric is proposed for the relay selection in the large-scale multi-hop networks. During the routing, the forwarding area is predefined dynamically, in which the relay is selected opportunistically according to the metric. Meanwhile, to fully evaluate the system performance, the node circuit energy consumption is also taken into consideration. Under our metric, the average forwarding distance per unit energy consumption of each hop is maximized by selecting the optimal parameters of predefined forwarding area. In this way, the number of nodes that participated into the routing are optimized for the purpose of energy saving.\",\n \"Title: A Practical Underlay Spectrum Sharing Scheme for Cognitive Radio Networks\\nYear: 2019\\n\\nAbstract: As the proliferation of mobile devices has led to an ever-growing demand for wireless Internet services, the spectrum shortage issue becomes increasingly severe and spectrum sharing is regarded as a promising approach to addressing the spectrum shortage issue. In this paper, we propose a practical underlay spectrum sharing scheme for cognitive radio networks (CRNs) where the primary users are oblivious to the secondary users. The key components of our scheme are two MIMO-based interference cancellation (IC) techniques to handle cross-network interference on the secondary network side. The first one is a blind beamforming technique for secondary transmitters. This IC technique allows a secondary transmitter to nullify its generated interference for primary users without requiring channel state information (CSI). The second one is a blind interference cancellation (BIC) technique for secondary receivers. This IC technique enables a secondary receiver to decode its desired signal in the presence of strong unknown interference from primary transmitters. Based on these two MIMO-based IC techniques, we develop a MAC protocol for the secondary network to enable underlay spectrum sharing in CRNs. We have implemented the proposed underlay spectrum sharing scheme on a GNURadio-USRP2 wireless testbed. Experimental results show that the secondary users can achieve an average of 1 bit/s/Hz spectrum efficiency without degrading the performance of the primary users in a real-world office building environment.\",\n \"Title: Research On A Rice Counting Algorithm Based On An Improved Mcnn And A Density Map\\nYear: 2021\\n\\nAbstract: The thousand grain weight is an index of size, fullness and quality in crop seed detection and is an important basis for field yield prediction. To detect the thousand grain weight of rice requires the accurate counting of rice. We collected a total of 5670 images of three different types of rice seeds with different qualities to construct a model. Considering the different shapes of different types of rice, this study used an adaptive Gaussian kernel to convolve with the rice coordinate function to obtain a more accurate density map, which was used as an important basis for determining the results of subsequent experiments. A Multi-Column Convolutional Neural Network was used to extract the features of different sizes of rice, and the features were fused by the fusion network to learn the mapping relationship from the original map features to the density map features. An advanced prior step was added to the original algorithm to estimate the density level of the image, which weakened the effect of the rice adhesion condition on the counting results. Extensive comparison experiments show that the proposed method is more accurate than the original MCNN algorithm.\",\n \"Title: Plane Net: an efficient local feature extraction network\\nYear: 2021\\n\\nAbstract: Due to memory and computing resources limitations, deploying convolutional neural networks on embedded and mobile devices is challenging. However, the redundant use of the 1 x 1 convolution in traditional light-weight networks, such as MobileNetV1, has increased the computing time. By utilizing the 1 x 1 convolution that plays a vital role in extracting local features more effectively, a new lightweight network, named PlaneNet, is introduced. PlaneNet can improve the accuracy and reduce the numbers of parameters and multiply-accumulate operations (Madds). Our model is evaluated on classification and semantic segmentation tasks. In the classification tasks, the CIFAR-10, Caltech-101, and ImageNet2012 datasets are used. In the semantic segmentation task, PlaneNet is tested on the VOC2012 datasets. The experimental results demonstrate that PlaneNet (74.48%) can obtain higher accuracy than MobileNetV3-Large (73.99%) and GhostNet (72.87%) and achieves state-of-the-art performance with fewer network parameters in both tasks. In addition, compared with the existing models, it has reached the practical application level on mobile devices. The code of PlaneNet on GitHub:https://github.om/LinB203/Planenet.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":76,"string":"76"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13264,"string":"13,264"}}},{"rowIdx":13265,"cells":{"question_id":{"kind":"string","value":"Zhikui Chen"},"question":{"kind":"string","value":"Generate an abstract for the title \"A WSN Routing Protocol Based on the Context-Aware Technology\" using the following items: Wireless sensor networks, context-aware technology, CATRP, routing protocol, pervasive computing."},"answer":{"kind":"string","value":"With the rapid progress of the whole computer technology, the wireless sensor networks gestated by the MEMS, wireless communication and low-consumption embedded technology construct the blueprint of the pervasive computing gradually for the people. This paper syncretizes the core thought of the context-aware technology and the WSN routing protocol with bringing forward the protocol CATRP adapting it through the research on the context-aware technology which belongs to the kernel technologies of the pervasive computing. And this paper designs the whole directions of the protocol in the work flow, data structure and quantitative algorithm and so on. Finally, the simulation experiment will be given to analyze the work performance indexes of the CATRP with the congeneric classical protocols in the contrast and prove the results in energy efficient, theory validity and high reliability. This paper enriches the theory base and makes some contribution for the WSNs technology transiting to the pervasive computing in the future in this research phase."},"correct_answer":{"kind":"string","value":"With the rapid progress of the whole computer technology, the wireless sensor networks gestated by the MEMS, wireless communication and low-consumption embedded technology construct the blueprint of the pervasive computing gradually for the people. This paper syncretizes the core thought of the context-aware technology and the WSN routing protocol with bringing forward the protocol CATRP adapting it through the research on the context-aware technology which belongs to the kernel technologies of the pervasive computing. And this paper designs the whole directions of the protocol in the work flow, data structure and quantitative algorithm and so on. Finally, the simulation experiment will be given to analyze the work performance indexes of the CATRP with the congeneric classical protocols in the contrast and prove the results in energy efficient, theory validity and high reliability. This paper enriches the theory base and makes some contribution for the WSNs technology transiting to the pervasive computing in the future in this research phase."},"documents":{"kind":"list like","value":["Title: An attribute-based scheme for service recommendation using association rules and ant colony algorithm\nYear: 2010\n\nAbstract: With the rapid development of m-commerce, predicting user's navigation and making the service recommendation become more and more important. Most researches focus on predicting user's navigation using context history and user preferences. But, the influence of the attributes of a service has been ignored. Simultaneously, some attributes are variable, so the recommendations are changeable. Therefore, the paper proposes an attribute-based scheme for service recommendation based on CASUP (context-aware system considering user preference). In proposed approach, the services are classified into several service clusters, and the service recommendations are carried out using Apriori algorithm and ant colony algorithm. Finally, the proposed model is validated by several simulation experiments, which demonstrate the effects of the service attributes in m-commerce.","Title: A Practical Localization Algorithm Based on Wireless Sensor Networks.\nYear: 2010\n\nAbstract: Many localization algorithms and systems have been developed by means of wireless sensor networks for both indoor and outdoor environments. To achieve higher localization accuracy, extra hardware equipments are utilized by most of the existing localization algorithms, which increase the cost and greatly limit the range of location-based applications. In this paper we present a method which can effectively meet different localization accuracy requirements of most indoor and outdoor location services in realistic applications. Our algorithm is composed of two phases: partition phase, in which the target region is split into small grids and localization refinement phase in which a higher accuracy location can be generated by applying a trick algorithm. A realistic demo system using our algorithm has been developed to illustrate its feasibility and availability. The results show that our algorithm can improve the localization accuracy.","Title: iCare: A Mobile Health Monitoring System for the Elderly\nYear: 2010\n\nAbstract: This paper describes a mobile health monitoring system called iCare for the elderly. We use wireless body sensors and smart phones to monitor the well being of the elderly. It can offer remote monitoring for the elderly anytime anywhere and provide tailored services for each person based on their personal health condition. When detecting an emergency, the smart phone will automatically alert pre-assigned people who could be the old people's family and friends, and call the ambulance of the emergency centre. It also acts as the personal health information system and the medical guidance which offers one communication platform and the medical knowledge database so that the family and friends of the served people can cooperate with doctors to take care of him/her. The system also features some unique functions that cater to the living demands of the elderly, including regular reminder, quick alarm, medical guidance, etc. iCare is not only a real-time health monitoring system for the elderly, but also a living assistant which can make their lives more convenient and comfortable.","Title: An Architecture of Data Processing Using Deluge Computing in Internet of Things.\nYear: 2011\n\nAbstract: Internet of Things (IoT) is an upcoming network technology well studied by many researchers. Mass applications and terminal nodes with heterogenous network connection make the tremendous scale of data process extremely difficult. A combined architecture with Cloud Computing and Sea Computing is proposed. Deluge Computing, a new concept, using Adaptive Coding and Modulation and Granular Computing aims to release the pressure of data process when data deluge bursts. Granular Computing is emphasized in this architecture for data reduction.","Title: A Cognitive QoS Method Based on Parameter Sensitivity.\nYear: 2010\n\nAbstract: Different applications in the network have different sensitivity for the certain QoS parameters. The existing mechanisms cannot modify the packet loss policy exactly according to the needs of QoS parameters. Therefore, the network will not maximize its overall efficiency. This paper proposes a novel cognitive approach for QoS. It classifies the applications by various combinations of the QoS parameters. And it uses a cognitive layer to modify the loss probability of the packet. Simulation results prove that this approach can reduce the account of useless packets sent when the congestion occurs in the network. The overall efficiency of the useful data transmission can be improved as well. The method can be used by Internet of Things to improve its message processing and sensor data fusion to reduce energy consumption.","Title: Quasi Real-Time Evaluation System for Seismic Disaster Based on Internet of Things\nYear: 2011\n\nAbstract: In recent years, disastrous earthquake assault the human frequently. The evaluation and estimate of the loss and damage in seismic disaster is an important factor for earthquake emergence and post-earthquake reconstruction decision-making. This paper synthetically designs a set of disaster real-time evaluation system based on the seismic networking of things. This paper firstly collects real-time data through the internet of things, then accesses forecasts the economic loss in earthquake by the technology of the geographic information system (GIS), earthquake loss estimation theory and the powerful computing functions of high-performance computer group. This paper also completes the deployment of IOT and the client software. The system can improve our ability to respond after the earthquake disaster and provide information for the emergency rescue command.","Title: A Scenario for Identity Management in Daidalos\nYear: 2007\n\nAbstract: Digital Identity corresponds to the electronic information associated generally with an individual in a particular identity system. This is used by online service providers to authenticate and authorize users for services protected by access policies. Having good identity systems can enable individuals to use effectively and extensively electronic transactions in a secure yet privacy preserving manner. In order to match the above purpose, this paper introduces an identity scenario, a user-centric identity management, in terms of VID - virtual identity, which concept is designed to protect a user's privacy and secure communication data, which contemplates the multitude of identities and roles we take on each time we turn on our computer, mobile phone or PDA.. Simultaneously, the associated identity management system with its related infrastructure is described. A key component, the ID broker, of the proposed infrastructure is also detailed including some interfaces and their related components. Furthermore, a method that allows a user to manage his/her service via the VID is introduced.","Title: An energy efficient medium access control protocol for target tracking based on dynamic convey tree collaboration in wireless sensor networks\nYear: 2012\n\nAbstract: Collisions and interferences among nodes pose a challenge for data aggregation in many applications, such as target tracking by adopting dynamic convey tree-based collaboration (DCTC). Because coordination with a time division multiple access (TDMA) medium access control (MAC) might provide an opportunity for better interference control, in this paper, we refine slot allocation to nodes in a dynamic convey tree and design an energy efficient MAC protocol called dynamic-time division multiple access (D-TDMA). The D-TDMA protocol avoids collisions and interferences and allocates contiguous active slots to nodes as far as possible during data aggregation from leaf nodes to a root node. As a result, energy consumption in switching from sleep to active state is saved. In comparison with Always-On scheme, theoretical analysis results show that the proposed protocol D-TDMA improves energy efficiency by up to 28.3% during one data aggregation. Furthermore, simulation results show that D-TDMA does not suffer from collisions and interferences among nodes in a dynamic convey tree and performs similar throughput to that of Always-On scheme. Because of its advantage of parallel and continuous scheduling among node pairs in the convey tree, D-TDMA outperforms efficient slot reservation in both energy efficiency and low delay because of to slot saving. Copyright © 2012 John Wiley & Sons, Ltd.","Title: Principal-Agent Theory Model in Dynamic Spectrum Sharing\nYear: 2011\n\nAbstract: This paper points to the dynamic spectrum sharing problem in cognitive radio network and analyzes the behavior of primary user (PU) and secondary user (SU). The relationship between PU and SU can be treated as principal and agent. Then, a principal-agent theory based two-step game model is proposed with the consideration of cooperation and price coeffcient which effect the SU's choice and PU's profit. Further, some incomplete information is assumed as random variables so that certainty equivalent is introduced in the model. Both cooperation and price effect are simulated at the end.","Title: User Preference Heterogeneous Network Selection in Less Subjective Ways\nYear: 2014\n\nAbstract: Heterogeneous network selection is a popular topic with the rapid development of telecommunication technologies. Literatures employed different ways for solving the issue with drawbacks more or less especially neglecting the user preference and using much subjective configurations. This paper proposes a user preference scheme including realtime or non-realtime pursuit, money and battery consumption concern and non-specific requirement. For front four modes, a technique for order preference by similarity to ideal solution based method with vote which reduces the subjectiveness is introduced. Then, a function-less and totally unsubjective method called directional distance function based data envelopment analysis method faces the non-specific requirement. Several examples test these two methods and the results support them.","Title: Element Retrieval Using Namespace Based on Keyword Search over XML Documents.\nYear: 2010\n\nAbstract: Querying over XML elements using keyword search is steadily gaining popularity. The traditional similarity measure is widely employed in order to effectively retrieve various XML documents. A number of authors have already proposed different similarity-measure methods that take advantage of the structure and content of XML documents. They do not, however, consider the similarity between latent semantic information of element texts and that of keywords in a query. Although many algorithms on XML element search are available, some of them have the high computational complexity due to searching a huge number of elements. In this paper, we propose a new algorithm that makes use of the semantic similarity between elements instead of between entire XML documents, considering not only the structure and content of an XML document, but also semantic information of namespaces in elements. We compare our algorithm with the three other algorithms by testing on the real datasets. The experiments have demonstrated that our proposed method is able to improve the query accuracy, as well as to reduce the running time.","Title: A customizable QoS strategy for convergent heterogeneous wireless communications\nYear: 2007\n\nAbstract: This article proposes a general customizable QoS scenario for heterogeneous wireless access network technologies. The scenario provides robust and flexible QoS support by adapting the amount of resources reserved for the characteristics of the wireless channel, as well as automatic reconfiguration mechanisms of the multimedia encoding rate and error-resilient techniques to adapt to the dynamic mobile communication environment. Two new scenarios, adaptive FEC and MAC-Lite, are introduced to the proposed scheme. These enable a maximizing of the overall performance of the wireless network. The appropriate interfaces within the core network are described for performing end-to-end QoS, which deliver network meters and exchange QoS parameters with the proposed intra and inter cross-layer approaches, including context transfer during handover. Each part, extended by the proposed method can be configured by the application and be independently implemented. To evaluate the proposed scheme, the QoS components were integrated into a trial platform that was built and simulations were then performed. The results demonstrate that the communication quality was significantly improved","Title: A Universal Storage Architecture for Big Data in Cloud Environment\nYear: 2013\n\nAbstract: With the rapid development of the Internet of Things and Electronic Commerce, we have entered the era of big data. The characteristics, such as great amount and heterogeneousity, of big data bring the challenge to the storage and analytics. The paper presented a universal storage architecture for big data in cloud environment. We use clustering analysis to divide the cloud nodes into multiple clusters according to the communication cost between different nodes. The cluster with the strongest computing power is selected to provide the universal storage and query interface for users. Each of other clusters is responsible for storing the data of a particular model, such as relational data, key-value data, and document data and so on. Experiments show that our architecture can store all kinds of heterogeneous big data and provide users with unified storage and query interface for big data easily and quickly.","Title: D-TDMA: An Approach of Dynamic TDMA Scheduling for Target Tracking in Wireless Sensor Networks.\nYear: 2010\n\nAbstract: In the application of target tracking based on dynamic convey tree-based collaboration (DCTC) in wireless sensor networks, collisions and interferences among nodes pose a challenge for data aggregation. In this paper, we refine slot allocation to the nodes in dynamic convey tree and design an energy efficient MAC protocol called D-TDMA, which avoids collisions and interferences and allocates contiguous active slots to nodes as far as possible during data aggregation from leaf nodes to a root node. As a result, the energy consumption in switching from sleeping to active state is saved. Simulation results show that D-TDMA not only gets rid of the impact of collisions and interferences among nodes in dynamic convey tree, but also performs similar throughput to that of CSMA-like mechanism where nodes work without active/sleep scheduling strategy. Our protocol outperforms ESR in both energy efficiency and low delay.","Title: An Algorithm of Weighted Monte Carlo Localization Based on Smallest Enclosing Circle.\nYear: 2011\n\nAbstract: To meet the needs of node mobility of Internet of thing (IOT), a localization algorithm named Weighted Monte Carlo Localization based on Smallest Enclosing Circle is proposed, which is based on the classic Monte Carlo Localization algorithm, aiming to solve the localization problem of mobile nodes. The algorithm uses the hops of anchor nodes and generates the smallest enclosing circle of anchor nodes to assist localizing, thus effectively inhibited the unevenness of anchor nodes caused by the Monte Carlo localization algorithm and reflects impact of the anchor nodes on unknown nodes. The simulation results show that the algorithm effectively reduces the sampling area and the sampling frequency, eventually increase the accuracy of localized nodes.","Title: A localization method for the Internet of Things\nYear: 2012\n\nAbstract: Many localization algorithms and systems have been developed by means of wireless sensor networks for both indoor and outdoor environments. To achieve higher localization accuracy, extra hardware equipments are utilized by most of the existing localization solutions, which increase the cost and considerably limit the location-based applications. The Internet of Things (IOT) integrates many technologies, such as Internet, Zigbee, Bluetooth, infrared, WiFi, GPRS, 3G, etc., which can enable different ways to obtain the location information of various objects. Location-based service is a primary service of the IOT, while localization accuracy is a key issue. In this paper, a higher accuracy localization scheme is proposed which can effectively satisfy diverse requirements for many indoor and outdoor location services. The proposed scheme composes of two phases: (1) the partition phase, in which the target region is split into small grids; (2) the localization refinement phase, in which a higher accuracy of localization can be obtained by applying an algorithm designed in the paper. A trial system is set up to verify correctness of the proposed scheme and furthermore to illustrate its feasibility and availability. The experimental results show that the proposed scheme can improve the localization accuracy.","Title: A clustering approximation mechanism based on data spatial correlation in wireless sensor networks\nYear: 2010\n\nAbstract: In wireless sensor networks (WSNs), the sensor nodes that locate near often sense the similar data, however, transmitting the repeated or redundant data often cause unnecessary energy consumption. Aiming at this point, this paper firstly proposes a gridbased spatial correlation clustering (GSCC) method which clusters the sensor nodes according to data correlation. According to GSCC, in the same cluster the member nodes have high similarity. Based on GSCC, then this paper proposes a spatial correlation clustering approximation framework (SCCAF). SCCAF can largely save networks' energy by which the cluster head estimates the data of its member nodes provided that approximation value is in the allowable error range. Experiments prove that not only SCCAF based on GSCC method can prolong the lifetime of the sensor networks compared with LEACH but also SCCAF guarantees more accuracy than CASA (clustering-based approximate scheme for data aggregation) which is a previous approximation scheme.","Title: Multi-node Scheduling Algorithm Based on Clustering Analysis and Data Partitioning in Emergency Management Cloud.\nYear: 2013\n\nAbstract: Real-time processing is a key problem for big data analysis and processing, especially in emergency management. Strongly promoted by the leading industrial companies, cloud computing becomes increasingly popular tool for emergency management, that is emergency management cloud. How to make optimal deployment of emergency management cloud applications is a challenging research problem. The paper proposes a multi-node scheduling algorithm based on clustering analysis and data partitioning in emergency management cloud. First, the presented method divides the cloud nodes into clusters according to the communication cost between different nodes, and then selects a cluster for the big data analysis services. Second, the load balancing theory is used to dispatch big data analysis to these computing nodes in a way to enable synchronized completion at best-effort performance. At last, to improve the real-time of big data analysis, the paper presents a multi-node scheduling algorithm based on game theory to find optimal scheduling strategy for each scheduling node. Experimental results show the effectiveness of our scheduling algorithm for big data analytics in emergency management. © 2013 Springer-Verlag.","Title: Distributed Compressed Sensing Mri Using Volume Array Coil\nYear: 2013\n\nAbstract: The volume array coil in the magnetic resonance imaging (MRI) system is a typical application of the distributed sensor network in the biomedical area. Each coil provides a large coverage of the imaged object, and the signals are largely overlapped during the data acquisition. The intercoil image similarities can be explored for the distributed compressed sensing (CS) based image reconstruction. In this work, a singular value decomposition (SVD) based sparsity basis was developed for the CS-MRI with a volume array coil configuration. In this novel imaging method, the spatial correlation both of intracoil and intercoil exploited. The experimental results showed that is with eightfold undersampled k-space data acquisition, the target images could still be faithfully reconstructed using the proposed method, which offered a better imaging performance compared to conventional CS schemes.","Title: A Trust Model Based on Service Classification in Mobile Services\nYear: 2010\n\nAbstract: Internet of Things (IoT) and B3G/4G communication are promoting the pervasive mobile services with its advanced features. However, security problems are also baffled the development. This paper proposes a trust model to protect the user's security. The billing or trust operator works as an agent to provide a trust authentication for all the service providers. The services are classified by sensitive value calculation. With the value, the user's trustiness for corresponding service can be obtained. For decision, three trust regions are divided, which is referred to three ranks: high, medium and low. The trust region tells the customer, with his calculated trust value, which rank he has got and which authentication methods should be used for access. Authentication history and penalty are also involved with reasons.","Title: A Mobile Service Recommendation System Using Multi-Criteria Ratings\nYear: 2010\n\nAbstract: With the rapid advancement of wireless technologies and mobile devices, mobile services offer great convenience and huge opportunities for service creation. However, information overload make service recommendation become a crucial issue in mobile services. Although traditional single-criteria recommendation systems have been successful in a number of personalization applications, obviously individual criterion cannot satisfy consumers' demands. Relying on multi-criteria ratings, this paper presents a novel recommendation system using the multi-agent technology. In this system, the ratings with respect to the three criteria are aggregated into an overall service ranking list by a rank aggregation algorithm. Furthermore, all of the services are classified into several clusters to reduce information overload further. Finally, Based on multi-criteria rank aggregation, the prototype of a recommendation system is implemented. Successful applications of this recommendation system have demonstrated the efficiency of the proposed approach.","Title: A Distributed Weighted Possibilistic C-Means Algorithm For Clustering Incomplete Big Sensor Data\nYear: 2014\n\nAbstract: Possibilistic c-means clustering algorithm(PCM) has emerged as an important technique for pattern recognition and data analysis. Owning to the existence of many missing values, PCM is difficult to produce a good clustering result in real time. The paper proposes a distributed weighted possibillistic c-means clustering algorithm (DWPCM), which works in three steps. First the paper applies the partial distance strategy to PCM (PDPCM) for calculating the distance between any two objects in the incomplete data set. Further, a weighted PDPCM algorithm (WPCM) is designed to reduce the corruption of missing values by assigning low weight values to incomplete data objects. Finally, to improve the cluster speed of WPCM, the cloud computing technology is used to optimize the WPCM algorithm by designing the distributed weighted possibilistic c-means clustering algorithm (DWPCM) based on MapReduce. The experimental results demonstrate that the proposed algorithms can produce an appropriate partition efficiently for incomplete big sensor data.","Title: IoT-SVKSearch: a real-time multimodal search engine mechanism for the internet of things\nYear: 2014\n\nAbstract: AbstractRecent advances on the Internet of Things IoT have posed great challenges to the search engine community. IoT systems manage huge numbers of heterogeneous sensors and/or monitoring devices, which continuously monitor the states of real-world objects, and most data are generated automatically through sampling. The sampling data are dynamically changing so that the IoT search engine should support real-time retrieval. Additionally, the IoT search involves not only keyword matches but also spatial-temporal searches and value-based approximate searches, as IoT sampling data are generally from spatial-temporal scenario. To meet these challenges, we propose a 'Hybrid Real-time Search Engine Framework for the Internet of Things based on Spatial-Temporal, Value-based, and Keyword-based Conditions' 'IoT-SVK Search Engine' or simply 'IoT-SVKSearch' for short in this paper. The experiments show that the IoT-SVK search engine has satisfactory performances in supporting real-time, multi-modal retrieval of massive sensor sampling data in the IoT. Copyright © 2013 John Wiley & Sons, Ltd.","Title: Risk computing based on capacity of risk-absorbing in virtual community environment\nYear: 2014\n\nAbstract: AbstractThe security issues are extremely popular in the virtual community VC environment because of the uncertainties. Many works use trust mechanisms as the enhancement. However, trust has flaws in some aspects, making it uncontrollable. Discussed in this paper is the reason that risk is chosen to evaluate individual's credit. Then, risk in VC environment has several properties that are helpful to understand the relationship between VC and members. A new concept is that the capacity of risk absorbing is introduced to make the model more reasonable for usual scenario. In order to compute the capacity of risk absorbing problem, shapley value approach is utilized to equalize the risk of individuals who join the game in series. The evaluation result can provide VC computed evidence to control the individual's behavior. Copyright © 2014 John Wiley & Sons, Ltd.","Title: A nodes scheduling model based on Markov chain prediction for big streaming data analysis\nYear: 2015\n\nAbstract: AbstractStreaming data analysis is an important part of big data processing. However, streaming data is difficult to be analyzed and processed in real time because of the rapid data arriving speed and huge size of data set in stream model. The paper proposes a nodes scheduling model based on Markov chain prediction for analyzing big streaming data in real time by following three steps: i construct data state transition graph using Markov chain to predict the varying trend of big streaming data; ii choose appropriate cloud computing nodes to process big streaming data depending on the predicted result of the data state transition graph; and iii assign big streaming data to these computing nodes using the load balancing theory, which ensures that all subtasks are accomplished synchronously. Experiments demonstrate that the proposed scheduling algorithm can fast process big streaming data effectively. Copyright © 2014 John Wiley & Sons, Ltd.","Title: A weighted kernel possibilistic c-means algorithm based on cloud computing for clustering big data\nYear: 2014\n\nAbstract: AbstractPossibilistic c-means PCM cluster algorithm has emerged as an important tool for data preprocessing widely used in data mining and knowledge discovery. Owning to the huge amount of data, high computational complexity, and noise-corrupted data, the PCM algorithms scaled for big data find it difficult to produce a good result in real time. The paper proposes a weighted kernel PCM wkPCM algorithm to cluster data objects in appropriate groups. The proposed algorithm introduces weights to define the relative importance of each object in the kernel clustering solution, which reduces the corruption caused by noisy data. In order to improve the real time of the proposed algorithm, cloud computing technology is used to optimize wkPCM to propose a distributed wkPCM algorithm based on MapReduce, which can provide significant computation speed. Experiment demonstrates that the proposed possibilistic clustering algorithms can cluster big data in appropriate groups in real time. Copyright © 2014 John Wiley & Sons, Ltd.","Title: Multiple layer design for mass data transmission against channel congestion in IoT\nYear: 2014\n\nAbstract: AbstractInternet of Things IoT is well studied from many aspects; however, data transmission in a large-scale constructed IoT network is still an open topic. In this paper, the problems of channel congestion caused by mass data transmission are discussed respectively from different perspectives. Then, a multiple layer solution is proposed, pointing to each layer including data processing architecture, data dimension reduction, data abandon protocol, and spectrum sharing. In the architecture layer, a combined scheme with cloud computing and sea computing is introduced. Context awareness and granular computing is exploited to implement the data dimension reduction. And cognitive protocol is involved with type of service, which drops certain data to guarantee the entire network connectivity. Then, a principal-agent theory based two-step game model is proposed with the consideration of cooperation and price coefficient, which affect the secondary user's choice and primary user's profit. Some incomplete information is assumed as random variables so that certainty equivalent is introduced in the model. A simple scenario shows how the data dimension reduction works and how simulations for data abandon protocol and spectrum sharing test the two parts, respectively. Copyright © 2012 John Wiley & Sons, Ltd.","Title: Poster: CIS: a community-based incentive scheme for socially-aware networking\nYear: 2014\n\nAbstract: To address selfishness in socially-aware networking, we propose a Community-based Incentive Scheme (CIS). CIS utilizes community to stimulate cooperation among selfish nodes and allows all nodes to behave selfishly to imitate the realistic condition. The simulation results show that CIS effectively stimulates selfish nodes to cooperate, achieves higher delivery ratio while not increasing latency dramatically.","Title: Poster: bacteria inspired mitigation of selfish users in ad-hoc social networks\nYear: 2014\n\nAbstract: In data management protocols for Ad-hoc Social Networks (ASNETs), involvement of selfish users can pose a serious threat to network performance and fairness. Therefore, it is essential to detect and mitigate their effects on other well behaving users. We contribute to this line of research by combining the benefits of users' social behavior (social tie) with a biologically inspired approach in ASNETs. We designed a bio-inspired scheme (BoDMaS) to detect and mitigate selfish users in replication operations. Its goals include providing greater accessibility and effective detection of selfish users. The proposed scheme not only guarantees accessibility and effective detection rate, but also ensures the reliability of replica allocation operations.","Title: Poster: reliable TCP for popular data in socially-aware ad-hoc networks\nYear: 2014\n\nAbstract: Reliable social connectivity and transmission of data for popular nodes are vital in multihop Ad-hoc Social Networks (ASNETs). However, congestion may occur and hence popular nodes might not achieve required bandwidth when multiple senders share data with a single receiver. The traditional Transport Control Protocol (TCP) might not be able to perform efficiently in ASNETs. Therefore, we propose a Reliable TCP for Popular Data in Socially-aware Ad-hoc Networks called RTPS. RTPS employs a popularity level approach for every single node which partitions the bandwidth among users. The reliability of popular data is ensured since bandwidth will firstly assigned to the popular sender. Preliminary results show that by delaying the acknowledgment and improving the performance of TCP, RTPS reduces collision loss in multihop ASNETs.","Title: M2C: Energy efficient mobile cloud system for deep learning\nYear: 2014\n\nAbstract: With the number increasing of applications and services that are available on mobile devices, mobile cloud computing has drawn a substantial amount of attention by academia and industry in the past several years. When facing the most exciting machine learning applications such as deep learning, the computing requirement is intensive. For the purpose of improving energy efficiency of mobile device and enhancing the performance of applications through reducing execution time, M2C offloads computation of its machine learning application to the cloud side. We propose the prototype of M2C with the mobile side on Android, iPad and with the cloud side on the open source cloud: Spark, a part of the Berkeley Data Analytics Stack with NVIDA GPU. M2C's distinct set of varying computational tools and mobile nodes allows for thorough implementing distributed machine learning algorithm and innovative wireless protocols with energy efficiency, verifying the theoretical research and bringing the user extremely fast experience.","Title: A strategy for differentiated access service selection based on application in WLANs\nYear: 2014\n\nAbstract: The service quality of a station in IEEE 802.11 wLAN (wireless Local Area Networks) is strongly influenced by which access point it associates with. Therefore, a key challenge is how to select an appropriate access point from multiple available ones. Conventional association protocols have been proved to be not effective. In this paper a strategy for differentiated access service selection based on users' running application is presented. The goals of the proposed strategy are: to select and dynamically reselect access point based on user's actual QoS requirements on bandwidth and delay leading to differentiated service quality provision; and to redistribute network load across access points. The effectiveness of our strategy is evaluated through simulation study, confirming better performance compared with conventional ways.","Title: Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning\nYear: 2016\n\nAbstract: To improve the efficiency of big data feature learning, the paper proposes a privacy preserving deep computation model by offloading the expensive operations to the cloud. Privacy concerns become evident because there are a large number of private data by various applications in the smart city, such as sensitive data of governments or proprietary information of enterprises. To protect the private data, the proposed model uses the BGV encryption scheme to encrypt the private data and employs cloud servers to perform the high-order back-propagation algorithm on the encrypted data efficiently for deep computation model training. Furthermore, the proposed scheme approximates the Sigmoid function as a polynomial function to support the secure computation of the activation function with the BGV encryption. In our scheme, only the encryption operations and the decryption operations are performed by the client while all the computation tasks are performed on the cloud. Experimental results show that our scheme is improved by approximately 2.5 times in the training efficiency compared to the conventional deep computation model without disclosing the private data using the cloud computing including ten nodes. More importantly, our scheme is highly scalable by employing more cloud servers, which is particularly suitable for big data.","Title: CITS 2017 general chairs' message\nYear: 2017\n\nAbstract: Welcome to the 2017 International Conference on Computer, Information and Telecommunication Systems (CITS 2017). This year's conference marks the sixth CITS, which is being held on annual basis.","Title: Distributed fuzzy c-means algorithms for big sensor data based on cloud computing\nYear: 2015\n\nAbstract: Fuzzy c-means FCM clustering has emerged as one important technique for pattern recognition, image processing and data analysis. Owing to the huge amount of data and computational complexity, FCM is difficult to cluster big sensor data in real time. The paper proposes three distributed FCM algorithms, namely distributed FCM algorithm based on MapReduce DFCM, distributed online FCM algorithm based on sampled affinity propagation AP clustering DOFCM and distributed kernel FCM algorithm based on MapReduce DKFCM. The proposed algorithms use cloud computing technology to improve the efficiency for clustering big sensor data. Experiments prove the superiority of the proposed methods through the clustering accuracy and clustering speed.","Title: Deep Computation Model for Unsupervised Feature Learning on Big Data.\nYear: 2016\n\nAbstract: Deep learning has been successfully applied to feature learning in speech recognition, image classification and language processing. However, current deep learning models work in the vector space, resulting in the failure to learn features for big data since a vector cannot model the highly non-linear distribution of big data, especially heterogeneous data. This paper proposes a deep computation m...","Title: Integration of scheduling and network coding in multi-rate wireless mesh networks: Optimization models and algorithms\nYear: 2016\n\nAbstract: In order to fully utilize spectrum resource in wireless mesh networks (WMNs), we propose a combination of some popular communication techniques, including link scheduling, spatial reuse, power and rate adaptation and network coding (NC), to activate as many transmission links as possible during one scheduling period, so that the total scheduling length can be minimized and network throughput can be maximized. Different from previous studies, we consider the interplay among these techniques and present an optimal NC-aware link scheduling mechanism in multi-rate WMNs, which relies on the enumeration of all possible schedules. Due to the high computational complexity of our proposed model, we utilize a column generation (CG)-based method to resolve the optimization problem and decompose it into a master problem (MP) and a pricing problem (PP). Furthermore, we present a distributed power control algorithm for PP, by which the computational complexity of the CG-based scheme can be largely reduced. Simulation results demonstrate the superiority of our method under various network situations.","Title: A Hybrid Method for Incomplete Data Imputation\nYear: 2015\n\nAbstract: With the explosive increase of data volume, the research of data quality and data usability draws extensive attention. In this work, we focus on one aspect of data usability -- incomplete data imputation, and present a novel missing value imputation method using stacked auto-encoder and incremental clustering (SAICI). Specifically, SAICI's functionality rests on four pillars: (i) a distinctive value assigned to impute missing values initially, (ii) the stacked auto-encoder(SAE) applied to locate principal features, (iii) a new incremental clustering utilized to partition incomplete data set, and (iv) the top nearest neighbors' weighted values designed to refill the missing values. Most importantly, stages (ii)~(iv) iterate until convergence condition is satisfied. Experimental results demonstrate that the proposed scheme not only imputes the missing data values effectively, but also has better time performance. Moreover, this work is suitable for distributed data processing framework, which can be applied to the imputation of incomplete big data.","Title: BRDPHHC: A Balance RDF Data Partitioning Algorithm Based on Hybrid Hierarchical Clustering\nYear: 2015\n\nAbstract: Data partitioning is a fundamental step to achieve effective storage and query of RDF big data. This paper presents a balance RDF data partitioning algorithm based on hybrid hierarchical clustering (BRDPHHC), which combines AP and K-means clustering. BRDPHHC's functionality includes three aspects: (i) a pre-processing step combining nodes compression and nodes remove to reduce the scale of raw data points, (ii) AP clustering algorithm is used to coarsen the RDF graph step by step and produce data blocks, and (iii) K-means algorithm is used for data partitioning finally. Experiments on benchmark datasets demonstrate the effectiveness of the proposed scheme.","Title: A Trust-Based User Assignment Scheme in Ad Hoc Social Networks\nYear: 2015\n\nAbstract: Although cooperation among individuals plays a key factor in the commercial development of wireless networks, trust is an important factor due to the uncertainty and uncontrollability caused by the self-organizing character of different entities. In this paper, we present a trust-based user assignment scheme by considering node sociality, the reason behind is that effective user assignment should not only build a reliable system basing on the behavior of network individuals, but also encourage selfish nodes to forward packets for one another. At first, a model for trustworthiness management is built up by considering social relationship. Then, user assignment for each transmission is decided by a double auction-based mechanism. Simulation result demonstrates that our scheme is able to obtain better network performance than the existing method in link connectivity and social welfare.","Title: Privacy Preserving Back-Propagation Based on BGV on Cloud\nYear: 2015\n\nAbstract: Back-propagation is the most effective algorithm for training deep learning models that are proved to have a great ability for big data feature learning. However, back-propagation is of high time complexity, leading to a low efficiency of big data learning. Aiming at this problem, the paper proposes a privacy preserving back-propagation algorithm based on the BGV encryption scheme on cloud. The proposed algorithm improved the efficiency of back-propagation learning by offloading the expensive operations on the cloud. Furthermore, the BGV encryption scheme is used to protect the private data during the learning process using the power of the cloud computing. Experiments show that our proposed scheme is secure and efficient.","Title: A Layered Intersection Scan Algorithm Based on Semantic Context\nYear: 2010\n\nAbstract: Along with the rapid growth of the xml data quantity on the Internet, the xml data retrieval research has attracted more and more attention. The searching algorithm based on key words is a research hotspot in this field. We present a context-based layered intersection scan algorithm (CLISA), which uses the context semantic of key words to filter large amount of redundant information, different from the approach of using key words to get SLCA. Based on LISA, CLISA operates LCAs to get SLCAs of different semantics, and then chooses the SLCA that best fits user隆炉s need. Through experiments we can see that CLISA can be more in accordance with the user's demand on condition that it expends more time than LISA.","Title: A Data Imputation Method Based On Deep Belief Network\nYear: 2015\n\nAbstract: Accurately filling missing values is an important step to enhance the usability of Big Data. However current incomplete data imputation algorithms are of high time complexity and low accuracy. To address this problem, we propose a novel algorithm to impute incomplete data. Firstly, a deep belief network model with denoising is designed to remove the noise brought by incomplete data and extract high quality features. Then, we utilize softmax for data classification. Finally, according to the classification results, partial distance and sequence imputation strategies are proposed to measure the correlation between records and improve filling accuracy, respectively. Compared with different algorithms, the experimental results confirm the effectiveness and efficiency of the proposed method in data imputation.","Title: Social-Oriented Resource Management in Cloud-Based Mobile Networks.\nYear: 2016\n\nAbstract: One of the predominant challenges for cloud-based services is the coordination of resources in the application and network layers to provide adaptive service delivery and an acceptable user experience. This article surveys resource management in cloud-based networks. The authors suggest that consideration of users' social associations can improve link connectivity and service delivery, and that us...","Title: Toward Efficient 5G Transmission: SER Performance Analysis for Asynchronous Physical-Layer Network Coding.\nYear: 2016\n\nAbstract: People's pursuit of higher quality communication has never ceased, which challenges the layout of 5G networks. Physical-layer network coding (PNC), as a key technology for 5G, supplies a powerful platform through leveraging the broadcast nature of wireless media. However, the symbol error rate (SER) of PNC is not well investigated, which would seriously influence user's quality of experience due to packet loss in wireless environment. In this paper, considering both phase and symbol misalignments, we perform analysis on SER of asynchronous PNC. By assuming part of information is known to the relay, and we derive the lower bound for SER. Afterward, through applying the concept of error vector and eliminating redundant terms, we derive the upper bound for SER. Both the lower and upper bounds are applicable to either multiuser detection-based network coding or belief propagation-based maximum a posteriori decoding. Finally, Monte Carlo simulation verifies our results and demonstrates that the bounds are relatively tight. The analytical results derived in this paper can facilitate future studies of practical and theoretical issues on PNC.","Title: Icfs: An Improved Fast Search And Find Of Density Peaks Clustering Algorithm\nYear: 2016\n\nAbstract: Clustering is a fundamental and important technique under many circumstances including data mining, pattern recognition, image processing and other industrial applications. During the past decades, many clustering algorithms have been developed, such as DBSCAN, AP and CFS. As the latest clustering algorithm proposed in Science magazine in 2014, clustering by fast search and find of density peaks, named as CFS, is a simple and outstanding algorithm for its promising performance on data sets of arbitrary shape. However, CFS's performance is usually affected by the cutoff distance d(c), the density peaks, the selection of cluster centers and the allocation strategy of data points. In this paper, we propose an improved algorithm (ICFS) to deal with the several weaknesses of it. Unlike CFS, the proposed algorithm designs a formula for the cutoff distance calculation and a method for cluster centers selection to improve its robustness. Moreover, a new non-center point's allocation strategy and the cluster merging and splitting processes are developed to adapt to the density peaks and adjust the clusters dynamically, which can improve the clustering accuracy and scalability. The ICFS method is evaluated on several datasets by comparison with the original CFS algorithm. Results demonstrate the effectiveness of the proposed method.","Title: PPHOCFS: Privacy Preserving High-Order CFS Algorithm on the Cloud for Clustering Multimedia Data.\nYear: 2016\n\nAbstract: Clustering is a commonly used technique for multimedia data analysis and management. In this article, we propose a high-order clustering algorithm by fast search and find of density peaks (HOCFS) by extending the traditional clustering scheme by fast search and find of density peaks (CFS) algorithm from the vector space to the tensor space for multimedia data clustering. Furthermore, we propose a privacy preserving HOCFS algorithm (PPHOCFS) which improves the efficiency of the HOCFS algorithm by using the cloud computing to perform most of the clustering operations. To protect the private data in the multimedia data sets during the clustering process on the cloud, the raw data is encrypted by the Brakerski-Gentry-Vaikun-tanathan (BGV) strategy before being uploaded to the cloud for performing the HOCFS clustering algorithm efficiently. In the proposed method, the client is required to only execute the encryption/decryption operations and the cloud servers are employed to perform all the computing operations. Finally, the performance of our scheme is evaluated on two representative multimedia data sets, namely NUS-WIDE and SNAE2, in terms of clustering accuracy, execution time, and speedup in the experiments. The results demonstrate that the proposed PPHOCFS scheme can save at least 40% running time compared with HOCFS, without disclosing the private data on the cloud, making our scheme securely suitable for multimedia big data clustering.","Title: A privacy-preserving high-order neuro-fuzzy c-means algorithm with cloud computing.\nYear: 2017\n\nAbstract: Currently, massive heterogeneous data is generating from the Internet of Things (IoT). Heterogeneous data processing with the neuro-fuzzy technology has become a hot topic for IoT. In this work, we propose a privacy-preserving high-order neuro-fuzzy c-means algorithm for clustering heterogeneous data (PPHOFCM) on cloud computing. PPHOFCM clusters the heterogeneous data set by representing each heterogeneous data object as a tensor and uses the tensor distance to capture the correlations in the high-order tensor space. Furthermore, the cloud computing is employed to improve the clustering efficiency for massive heterogeneous data from IoT. The BGV encryption scheme is used to protect the private data when performing the high-order neuro-fuzzy c-means algorithm on cloud computing. Experiments are conducted on two real IoT datasets to verify the proposed algorithm.","Title: STLIS: A Scalable Two-Level Index Scheme for Big Data in IoT.\nYear: 2016\n\nAbstract: The rapid development of the Internet of Things causes the dramatic growth of data, which poses an important challenge on the storage and quick retrieval of big data. As an effective representation model, RDF receives the most attention. More and more storage and index schemes have been developed for RDF model. For the large-scale RDF data, most of them suffer from a large number of self-joins, high storage cost, and many intermediate results. In this paper, we propose a scalable two-level index scheme (STLIS) for RDF data. In the first level, we devise a compressed path template tree (CPTT) index based on S-tree to retrieve the candidate sets of full path. In the second level, we create a hierarchical edge index (HEI) and a node-predicate (NP) index to accelerate the match. Extensive experiments are executed on two representative RDF benchmarks and one real RDF dataset in IoT by comparison with three representative index schemes, that is, RDF-3X, Bitmat, and TripleBit. Results demonstrate that our proposed scheme can respond to the complex query in real time and save much storage space compared with RDF-3X and Bitmat.","Title: A High-Order CFS Algorithm for Clustering Big Data.\nYear: 2016\n\nAbstract: With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i) an adaptive dropout deep learning model to learn features from each type of data, (ii) a feature tensor model to capture the correlations of heterogeneous data, and (iii) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.","Title: BRGP: a balanced RDF graph partitioning algorithm for cloud storage.\nYear: 2017\n\nAbstract: The continuous growth of resource description framework (RDF) data poses an important challenge on RDF data partitioning that is a vital technique for effective cloud storage. Recently, many partitioning algorithms for large RDF data have been developed, and most of them are based on graph partitioning. However, existing graph partitioning methods could not partition asymmetric RDF data effectively, resulting in a lower performance for cloud storage. This paper proposes a balanced RDF graph partitioning algorithm for storing massive RDF data on cloud. We first devise a modularity-based multi-level label propagation algorithm (MMLP) to partition RDF graph roughly and then use a balanced K-mediods clustering algorithm for final k-way partitioning. Balanced RDF graph partitioning algorithm designs an effective label update rule and a balanced modification strategy to achieve a high quality coarsening result and make the partition as equilibrium as possible. Experiments are carried on two representative RDF benchmarks and one real RDF dataset by comparison with two representative graph partitioning methods, that is, METIS and MLP+METIS. Results demonstrate that our proposed scheme can produce a high-quality partition for massive RDF data storage on cloud. Copyright (C) 2016 John Wiley & Sons, Ltd.","Title: An Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things.\nYear: 2017\n\nAbstract: With the rapid advances of sensing technologies and wireless communications, large amounts of dynamic data pertaining to industrial production are being collected from many sensor nodes deployed in the industrial Internet of Things. Analyzing those data effectively can help to improve the industrial services and mitigate the system unprepared breakdowns. As an important technique of data analysis,...","Title: Social-Oriented Adaptive Transmission in Opportunistic Internet of Smartphones.\nYear: 2017\n\nAbstract: Stable and reliable wireless communication is one of the critical demands for smart cities to connect people and devices. Although intelligent terminals can be leveraged to deliver and exchange data through Internet, poor network coverage and expensive network access challenge the deployment of network infrastructure. In this paper, we propose a social-oriented smartphone-based adaptive transmissi...","Title: A Tucker Deep Computation Model for Mobile Multimedia Feature Learning\nYear: 2017\n\nAbstract: Recently, the deep computation model, as a tensor deep learning model, has achieved super performance for multimedia feature learning. However, the conventional deep computation model involves a large number of parameters. Typically, training a deep computation model with millions of parameters needs high-performance servers with large-scale memory and powerful computing units, limiting the growth of the model size for multimedia feature learning on common devices such as portable CPUs and conventional desktops. To tackle this problem, this article proposes a Tucker deep computation model by using the Tucker decomposition to compress the weight tensors in the full-connected layers for multimedia feature learning. Furthermore, a learning algorithm based on the back-propagation strategy is devised to train the parameters of the Tucker deep computation model. Finally, the performance of the Tucker deep computation model is evaluated by comparing with the conventional deep computation model on two representative multimedia datasets, that is, CUAVE and SNAE2, in terms of accuracy drop, parameter reduction, and speedup in the experiments. Results imply that the Tucker deep computation model can achieve a large-parameter reduction and speedup with a small accuracy drop for multimedia feature learning.","Title: Parameter-Free Incremental Co-Clustering for Multi-Modal Data in Cyber-Physical-Social Systems.\nYear: 2017\n\nAbstract: With the rapid advances of cyber-physical-social systems (CPSS), large amounts of dynamic multi-modal data are being generated and collected. Analyzing those data effectively and efficiently can help to promote the development and improve the service quality of CPSS applications. As an important technique of multi-modal data analysis, co-clustering, designed to identify groupings of multi-dimensional data based on cross-modality fusion, is often exploited. Unfortunately, most existing co-clustering methods that mainly focus on dealing with static data become infeasible to fuse huge volume of multi-modal data in dynamic CPSS environments. To tackle this problem, this paper proposes a parameter-free incremental co-clustering method to deal with multi-modal data dynamically. In the proposed method, the single-modality similarity measure is extended to multiple modalities and three operations, namely, cluster creating, cluster merging, and instance partitioning, are defined to incrementally integrate new arriving objects to current clustering patterns without introducing additive parameters. Moreover, an adaptive weight scheme is designed to measure the importance of feature modalities based on the intra-cluster scatters. Extensive experiments on three real-world multi-modal datasets collected from CPSS demonstrate that the proposed method outperforms the compared state-of-the-art methods in terms of effectiveness and efficiency, thus it is promising for clustering dynamic multi-modal data in cyber-physical-social systems.","Title: A Hierarchical Routing Algorithm for Satellite Networks Considering FSO Communication\nYear: 2017\n\nAbstract: Insufficient ground stations promote the emergence of inter-satellite links (ISLs). Nowadays, adopting free space optical (FSO) ISLs has been a new trend for data communications. However, FSO ISLs across the atmosphere may perform worse in adverse weather such as fog or snow. In addition, using ordinary flat routing causes an excessive processing overhead, and diverse quality of experiences (QoEs) cannot be well satisfied. To solve these problems, we propose a Hierarchical Routing protocol for low/medium earth orbit (LEO/MEO) Satellite RF( radio frequency)/FSO Communication Networks (HR-SFCN). The entire network is divided into several LEO-satellite clusters, and each cluster treats the MEO satellite as the manager. To satisfy diverse QoE requirements, we propose the optimization algorithm for data interactions within the cluster. To reduce the processing overhead, we utilize MEO satellites as relay nodes for inter-cluster communications. To guarantee the link availability, we also perform adaptive RF/FSO link switching according to weather conditions. Given a STK(satellite tool kit)-based satellite communication network model, simulation results demonstrate that our HR-SFCN achieves a good trade-off among packet loss rate, path cost and end-to-end delay. Our RF/FSO hybrid system also increases the link availability up to 99.999%.","Title: Incomplete multi-view clustering via deep semantic mapping.\nYear: 2018\n\nAbstract: Multi-view clustering, which explores complementary information between multiple feature sets by consensus grouping, has benefited many data analytic applications. The majority of previous multi-view clustering studies usually assume that all feature sets appear in complete. In real-world applications, however, it is often the case that some views could suffer from the missing of examples, resulting in incomplete feature sets. The incompleteness of views makes it difficult to synthesize all feature sets and achieve a comprehensive description of data samples. In this paper, we develop a novel incomplete multi-view clustering method, which projects all incomplete multi-view data to a complete and unified representation in a common subspace. Different from existing researches which exploit shallow learning to identify the common subspace, a deep incomplete multi-view clustering (DIMC) incorporating with the constraint of intrinsic geometric structure is proposed here to couple incomplete multi-view samples. To bridge the gap between each view and the common representation, the multi-view deep coupled networks are trained to map high-level semantic features. Besides, to preserve the local invariance for each view, an affinity graph based regularizer is constructed to encode geometrical information. Therefore, a new objective function is developed and the optimization processes are presented. Extensive experiments on several real-world datasets demonstrate that the proposed DIMC is superior to the state-of-the-art incomplete multi-view clustering methods. (c) 2017 Published by Elsevier B.V.","Title: Unsupervised Multiview Nonnegative Correlated Feature Learning for Data Clustering.\nYear: 2018\n\nAbstract: Multiview data, which provide complementary information for consensus grouping, are very common in real-world applications. However, synthesizing multiple heterogeneous features to learn a comprehensive description of the data samples is challenging. To tackle this problem, many methods explore the correlations among various features across different views by the assumption that all views share th...","Title: Deep Convolutional Computation Model for Feature Learning on Big Data in Internet of Things.\nYear: 2018\n\nAbstract: Currently, a large number of industrial data, usually referred to big data, are collected from Internet of Things (IoT). Big data are typically heterogeneous, i.e., each object in big datasets is multimodal, posing a challenging issue on the convolutional neural network (CNN) that is one of the most representative deep learning models. In this paper, a deep convolutional computation model (DCCM) i...","Title: A survey on deep learning for big data.\nYear: 2018\n\nAbstract: •We review the most typical four deep learning models and their variants.•We provide a survey on deep learning models for big data feature learning.•We discuss the remaining challenges and future trends of big data deep learning.","Title: Incremental CFS clustering on large data.\nYear: 2017\n\nAbstract: As a popular data mining tool, clustering focuses on revealing underlying patterns embedded in data. However, most existing clustering methods mainly deal with static data, which may not be suitable for analyzing large data in dynamic environments. To tackle this problem, this paper proposes an incremental clustering method based on the CFS, clustering by fast search and find of density peaks, to process large dynamic data. In the proposed method, multiple representatives are identified for each cluster to integrate new objects into previous clustering patterns at first. Then the convex hull theory is employed to modify the representatives accordingly. To further improve the generality and effectiveness, one-time cluster adjustment strategy is explored. Extensive experiments on several real-world image datasets demonstrate that the proposed method outperforms state-of-the-art methods for clustering large data.","Title: MMHG: Multi-modal Hypergraph Learning for Overall Survival After D2 Gastrectomy for Gastric Cancer\nYear: 2017\n\nAbstract: Overall survival (OS) prediction has been a central topic of oncology. Most existing OS prediction models are based on traditional statistical methods which becomes a limitation when confronted with high dimensional dataset as well complicated internal relation among features in practice. In this paper, we weaken this limitation by exploring the application of multi-modal hypergraph (MMHG) learning framework to improve the accuracy of prediction. More specifically, the proposed hypergraph model unites the demographics, pathologic characteristics and physiological indicators simultaneously to predict the overall survival after D2 gastrectomy for gastric cancer. Experiments are carried out on a real data set of West China Hospital of Sichuan University with 939 patients to evaluate the proposed approach by comparison with random forest (RF) and support vector machine (SVM). Results demonstrate that our scheme outperforms the baseline methods in overall survival classification performance.","Title: Semantic Clustering-Based Deep Hypergraph Model for Online Reviews Semantic Classification in Cyber-Physical-Social Systems.\nYear: 2018\n\nAbstract: Sentiment classification of online reviews is playing an increasingly important role for both consumers and businesses in cyber-physical-social systems. However, existing works ignore the semantic correlation among different reviews, causing the ineffectiveness for sentiment classification. In this paper, a word embedding clustering-based deep hypergraph model (ECDHG) is proposed for the sentiment analysis of online reviews. The ECDHG introduces external knowledge by employing the pre-training word embeddings to express reviews. Then, semantic units are detected under the supervision of semantic cliques discovered by an improved hierarchical fast clustering algorithm. Convolutional neural networks are connected to extract the high-order textual and semantic features of reviews. Finally, the hypergraph can be constructed based on high-order relations of samples for the sentiment classification of reviews. Experiments are performed on five-domain data sets including movie, book, DVD, kitchen, and electronic to assess the performance of the proposed model compared with other seven models. The results validate that our model outperforms the compared methods in classification accuracy.","Title: A Collaborative Filtering Recommendation-Based Scheme for WLANs With Differentiated Access Service.\nYear: 2018\n\nAbstract: As the deployment of WLANs becomes extensive, users commonly have to select one access point (AP) for association among multiple available APs. However, the existing AP selection policies are not sufficient enough due to their drawbacks (e.g., imbalanced load and time consuming for association). In this paper, we present a collaborative filtering (CF) recommendation-based AP selection strategy in ...","Title: Multiple Kernel Learning Based on Weak Learner for Automatic Image Annotation.\nYear: 2017\n\nAbstract: Image annotation is a challenging problem, which has attracted intensive attention recently due to the semantic gap between images and corresponding tags. However, most existing works neglect the imbalance distribution of different classes and the internal correlations across modalities. To address these issues, we propose a multiple kernel learning method based on weak learner for image annotation, which can acquire the semantic correlations to predict tags of a given image. More specifically, we first employ the convolutional neural network to extract the semantic features of images, and take advantage of the over-sampling technique to generate new samples of minority classes which can solve the imbalance problem. Further, our proposed multiple kernel learning method is applied to obtain the internal correlations between images and tags. In order to further improve the prediction performance, we combine the boosting procedure with the multiple kernel learning to enhance the performance of classifier. We evaluate the proposed method on two benchmark datasets. The experimental results demonstrate that our method is superior to several state-of-the-art methods.","Title: TCMHG: Topic-Based Cross-Modal Hypergraph Learning for Online Service Recommendations.\nYear: 2018\n\nAbstract: Online product reviews sentiment classification plays an important role on service recommendation, yet most of current researches on it only focus on single-modal information ignoring the complementary information, that results in unsatisfied accuracy of sentiment classification. This paper proposes a cross-modal hypergraph model to capture textual information and sentimental information simultaneously for sentiment classification of reviews. Furthermore, a mixture model by coupling the latent Dirichlet allocation topic model with the proposed cross-modal hypergraph is designed to mitigate the ambiguity of some specific words, which may express opposite polarity in different contexts. Experiments are carried out on four-domain data sets (books, DVD, electronics, and kitchen) to evaluate the proposed approaches by comparison with lexicon-based method, Naive Bayes, maximum entropy, and support vector machine. Results demonstrate that our schemes outperform the baseline methods in sentiment classification accuracy.","Title: Local Similarity Imputation Based on Fast Clustering for Incomplete Data in Cyber-Physical Systems.\nYear: 2018\n\nAbstract: Missing values are common in cyber-physical systems (CPS) for a variety of reasons, such as sensor faults, communication malfunctions, environmental interferences, and human errors. An accurate missing value imputation is crucial to promote the data quality for data mining and statistical analysis tasks. Unfortunately, most of the existing methods take use of the whole data set to impute a missing...","Title: Multimedia Feature Mapping and Correlation Learning for Cross-Modal Retrieval\nYear: 2018\n\nAbstract: AbstractThis article describes how with the rapid increasing of multimedia content on the Internet, the need for effective cross-modal retrieval has attracted much attention recently. Many related works ignore the latent semantic correlations of modalities in the non-linear space and the extraction of high-level modality features, which only focuses on the semantic mapping of modalities in linear space and the use of low-level artificial features as modality feature representation. To solve these issues, the authors first utilizes convolutional neural networks and topic modal to obtain a high-level semantic feature of various modalities. Sequentially, they propose a supervised learning algorithm based on a kernel with partial least squares that can capture semantic correlations across modalities. Finally, the joint model of different modalities is learnt by the training set. Extensive experiments are conducted on three benchmark datasets that include Wikipedia, Pascal and MIRFlickr. The results show that the proposed approach achieves better retrieval performance over several state-of-the-art approaches.","Title: A Hybrid Imputation Method Based on Denoising Restricted Boltzmann Machine\nYear: 2018\n\nAbstract: AbstractData imputation is an important issue in data processing and analysis which has serious impact on the results of data mining and learning. Most of the existing algorithms are either utilizing whole data sets for imputation or only considering the correlation among records. Aiming at these problems, the article proposes a hybrid method to fill incomplete data. In order to reduce interference and computation, denoising restricted Boltzmann machine model is developed for robust feature extraction from incomplete data and clustering. Then, the article proposes partial-distance and co-occurrence matrix strategies to measure correlation between records and attributes, respectively. Finally, quantifiable correlation is converted to weights for imputation. Compared with different algorithms, the experimental results confirm the effectiveness and efficiency of the proposed method in data imputation.","Title: A Weighted Fuzzy c-Means Clustering Algorithm for Incomplete Big Sensor Data.\nYear: 2017\n\nAbstract: Sensor data processing plays an important role on the development of the wireless sensor networks in the big data era. Owning to the existence of a large number of incomplete data in wireless sensor networks, fuzzy c-means clustering algorithm (FCM) finds it difficult to produce an appropriate cluster result. The paper proposes a distributed weighted fuzzy c-means algorithm based on incomplete data imputation for big sensor data (DWFCM). DWFCM improves Affinity Propagation (AP) clustering algorithm by designing a new similarity metrics for imputing incomplete sensor data, and then proposes a weighted FCM (wFCM) by assigning a lower weighted value to the incomplete data object for improving the cluster accuracy. Finally, we validate the proposed weighted FCM algorithm on the dataset collected from the smart WSN lab. Experiments demonstrate that the weighted FCM algorithm could fill the missing values very accurately and improve the clustering results effectively.","Title: A Partitioning and Index Algorithm for RDF Data of Cloud-Based Robotic Systems.\nYear: 2018\n\nAbstract: Robotic systems generally employ resource description framework (RDF) to express heterogeneous data coming from different sensors. With the access of more terminals, the RDF volume in robotic systems is becoming larger and larger, posing new significant challenges to the storage and retrieval of RDF data. This paper proposes a star-based partitioning and index algorithm for RDF data of robotic systems. First, we construct a two-hop star structure by MapReduce and HDFS, and get a coarsened weighted graph. Next, a balance partitioning algorithm is used to divide the weighted graph. After partitioning, a compressed and linked S-tree index is proposed to improve the query efficiency. Experiments are executed on benchmark and real data sets to evaluate the studied partitioning and index methods. Results show that our partitioning method has a lower replication ratio, and a better load balancing performance, so our method is efficient for star query and competitive in complex query.","Title: Supervised Intra- and Inter-Modality Similarity Preserving Hashing for Cross-Modal Retrieval.\nYear: 2018\n\nAbstract: Cross-modal hashing has drawn considerable interest in multimodal retrieval due to the explosive growth of big data on multimedia. However, the existing methods mainly focus on unified hash codes learning and investigate the local geometric structure in the original space, resulting in low-discriminative power hash code of out-of-sample instances. To address this important problem, this paper is dedicated to investigate the hashing functions learning by considering the modality correlation preserving in the expected low-dimensional common space. A cross-modal hashing method based on supervised collective matrix factorization is proposed by taking intra-modality and inter-modality similarity preserving into account. For more flexible hashing functions, label information is embedded into the hashing functions learning procedure. Specifically, we explore the intra-modality similarity preserving in the expected low-dimensional common space. In addition, a supervised shrinking scheme is used to enhance the local geometric consistency in each modality. The proposed method learns unified hash codes as well as hashing functions for different modalities; the overall objective function, consisting of collective matrix factorization and intra- and inter-modality similarity embedding, is solved using an alternative optimization in an iterative scheme. Extensive experiments on three benchmark data sets demonstrate that the proposed method is more flexible to new coming data and can achieve superior performance to the state-of-the-art supervised cross-modal hashing approaches in most of the cases.","Title: Distributed Feature Selection for Efficient Economic Big Data Analysis.\nYear: 2018\n\nAbstract: With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although such data offers super opportunities for economic analysis, its low-quality, high-dimensionality and huge-volume pose great challenges on efficient analysis of economic big data. The existing methods have primarily analyzed economic data from the perspective of econometrics, ...","Title: A Tensor-Train Deep Computation Model for Industry Informatics Big Data Feature Learning.\nYear: 2018\n\nAbstract: The deep computation model has been proved to be effective for big data hierarchical feature and representation learning in the tensor space. However, it requires expensively computational resources including high-performance computing units and large memory to train a deep computation model with a large number of parameters, limiting its effectiveness and efficiency for industry informatics big d...","Title: An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics.\nYear: 2018\n\nAbstract: Deep learning, as the most important architecture of current computational intelligence, achieves super performance to predict the cloud workload for industry informatics. However, it is a nontrivial task to train a deep learning model efficiently since the deep learning model often includes a great number of parameters. In this paper, an efficient deep learning model based on the canonical polyad...","Title: Combinative hypergraph learning in subspace for cross-modal ranking.\nYear: 2018\n\nAbstract: Recent years have witnessed a surge of interests in cross-modal ranking. To bridge the gap between heterogeneous modalities, many projection based methods have been studied to learn common subspace where the correlation across different modalities can be directly measured. However, these methods generally consider pair-wise relationship merely, while ignoring the high-order relationship. In this paper, a combinative hypergraph learning in subspace for cross-modal ranking (CHLS) is proposed to enhance the performance of cross-modal ranking by capturing high-order relationship. We formulate the cross-modal ranking as a hypergraph learning problem in latent subspace where the high-order relationship among ranking instances can be captured. Furthermore, we propose a combinative hypergraph based on fused similarity information to encode both the intra-similarity in each modality and the inter-similarity across different modalities into the compact subspace representation, which can further enhance the performance of cross-modal ranking. Experiments on three representative cross-modal datasets show the effectiveness of the proposed method for cross-modal ranking. Furthermore, the ranking results achieved by the proposed CHLS can recall 80% of the relevant cross-modal instances at a much earlier stage compared against state-of-the-art methods for both cross-modal ranking tasks, i.e. image query text and text query image.","Title: Privacy-Preserving Double-Projection Deep Computation Model With Crowdsourcing on Cloud for Big Data Feature Learning.\nYear: 2018\n\nAbstract: Recent years have witness a considerable advance of Internet of Things with the tremendous progress of communication theories and sensing technologies. A large number of data, usually referring to big data, have been generated from Internet of Things. In this paper, we present a double-projection deep computation model (DPDCM) for big data feature learning, which projects the raw input into two se...","Title: A Cooperative Quality-Aware Service Access System for Social Internet of Vehicles.\nYear: 2018\n\nAbstract: Because of the enormous potential to guarantee road safety and improve driving experience, social Internet of Vehicle (SIoV) is becoming a hot research topic in both academic and industrial circles. As the ever-increasing variety, quantity, and intelligence of on-board equipment, along with the evergrowing demand for service quality of automobiles, the way to provide users with a range of security...","Title: Deep Discrete Cross-Modal Hashing for Cross-Media Retrieval.\nYear: 2018\n\nAbstract: •A framework of cross-modal deep neural networks is proposed for cross- media retrieval.•Intra-modality and inter-modality similarity preserving are considered in binary codes learning.•Binary constraint is addressed by discrete cyclic coordinate descent (DCC) algorithm without relaxation.•Experimental results on four datasets demonstrate the effectiveness of the proposed DDCMH, which is significantly superior to state-of-the-art cross-modal hashing approaches.","Title: A Dropconnect Deep Computation Model for Highly Heterogeneous Data Feature Learning in Mobile Sensing Networks.\nYear: 2018\n\nAbstract: Deep computation model, as a tensor deep learning model, outperforms multi-modal deep learning models for feature learning on heterogenous data. However, deep computation model is limited in generalization to small heterogeneous data sets since it typically requires many training objects to learn the parameters. In this article, we propose a dropconnect deep computation model (DDCM) for highly het...","Title: Novel Framework of Risk-Aware Virtual Network Embedding in Optical Data Center Networks.\nYear: 2018\n\nAbstract: The traffic between geographically distributed data centers (DCs) becomes bandwidth hungry. Since the optical interconnection has a high capacity, the optical data center network (ODCN)-where DCs are located at the edge of the optical backbone-emerges. By virtualization, the virtual networks-representing service requirements-are embedded onto the same part of the substrate ODCN. Each virtual netwo...","Title: An Improved Deep Computation Model Based on Canonical Polyadic Decomposition.\nYear: 2018\n\nAbstract: Deep computation models achieve super performance for big data feature learning. However, training a deep computation model poses a significant challenge since a deep computation model typically involves a large number of parameters. Specially, it needs a high-performance computing server with a large-scale memory and a powerful computing unit to train a deep computation model, making it difficult...","Title: Heterogeneous visual features integration for image recognition optimization in internet of things.\nYear: 2018\n\nAbstract: •A multimodal deep learning approach for image recognition optimization is proposed.•A back propagation optimization algorithm with shared weights is designed.•Results show our scheme has superior performance on image recognition.","Title: Cross-Entropy Pruning for Compressing Convolutional Neural Networks.\nYear: 2018\n\nAbstract: The success of CNNs is accompanied by deep models and heavy storage costs. For compressing CNNs, we propose an efficient and robust pruning approach, cross-entropy pruning (CEP). Given a trained CNN model, connections were divided into groups in a group-wise way according to their corresponding output neurons. All connections with their cross-entropy errors below a grouping threshold were then rem...","Title: ICFS Clustering With Multiple Representatives for Large Data.\nYear: 2019\n\nAbstract: With the prevailing development of Cyber-physical-social systems and Internet of Things, large-scale data have been collected consistently. Mining large data effectively and efficiently becomes increasingly important to promote the development and improve the service quality of these applications. Clustering, a popular data mining technique, aims to identify underlying patterns hidden in the data....","Title: An Improved Stacked Auto-Encoder for Network Traffic Flow Classification.\nYear: 2018\n\nAbstract: Network flow classification plays a very important role in various network applications and is a fundamental task in network flow control. However, the innovations in the multi-source network application and the elastic network architecture with the network flows of high volume, velocity, variety, and veracity pose unprecedented challenges on accurate network flow classification. In this article, ...","Title: Dependable Deep Computation Model for Feature Learning on Big Data in Cyber-Physical Systems.\nYear: 2019\n\nAbstract: With the ongoing development of sensor devices and network techniques, big data are being generated from the cyber-physical systems. Because of sensor equipment occasional failure and network transmission unreliability, a large number of low-quality data, such as noisy data and incomplete data, is collected from the cyber-physical systems. Low-quality data pose a remarkable challenge on deep learning models for big data feature learning. As a novel deep learning model, the deep computation model achieves superior performance for big data feature learning. However, it is difficult for the deep computation model to learn dependable features for low-quality data, since it uses the nonlinear function as the encoder. In this article, a dependable deep computation model is proposed for feature learning on low-quality big data in cyber-physical systems. Specially, a regularity is added into the objective function of the deep computation model to obtain reliable features in the intermediate-level representation space. Furthermore, a learning algorithm based on the back-propagation strategy is devised to train the parameters of the proposed model. Finally, experiments are conducted on three representative datasets and a real dataset to evaluate the effectiveness of the dependable deep computation model for low-quality big data feature learning. Results show that the proposed model achieves a remarkable result for the tasks of classification, restoration, and prediction, proving the potential of this work for practical applications in cyber-physical systems.","Title: Dual Graph-Regularized Multi-view Feature Learning\nYear: 2018\n\nAbstract: Real-world datasets often describe data instances in different views that complement information for each other. Unfortunately, synthesizing these views for learning a comprehensive description of data items is challenging. To tackle it, many approaches have been studied to explore correlations between various features by assuming that all views can be projected into a same semantic subspace. Following this idea, we propose a novel semi-supervised method, namely dual graphregularized multi-view feature learning (DGMFL), for data representation in this paper. The core idea is to generate a latent subspace among different views. Our approach utilizes dual graph regularization to capture semantic relationships among data items on both multi-view features and label information, as well as locates view-specific features for each view to reduce the effects of uncorrelated items. In this way, DGMFL could achieve more comprehensive representations hidden in multi-view datasets. Extensive experiments demonstrate that DGMFL model is superior to state-of-the-art multi-view learning methods on real-world datasets.","Title: Deep Semantic Mapping for Heterogeneous Multimedia Transfer Learning Using Co-Occurrence Data\nYear: 2019\n\nAbstract: Transfer learning, which focuses on finding a favorable representation for instances of different domains based on auxiliary data, can mitigate the divergence between domains through knowledge transfer. Recently, increasing efforts on transfer learning have employed deep neural networks (DNN) to learn more robust and higher level feature representations to better tackle cross-media disparities. However, only a few articles consider the correction and semantic matching between multi-layer heterogeneous domain networks. In this article, we propose a deep semantic mapping model for heterogeneous multimedia transfer learning (DHTL) using co-occurrence data. More specifically, we integrate the DNN with canonical correlation analysis (CCA) to derive a deep correlation subspace as the joint semantic representation for associating data across different domains. In the proposed DHTL, a multi-layer correlation matching network across domains is constructed, in which the CCA is combined to bridge each pair of domain-specific hidden layers. To train the network, a joint objective function is defined and the optimization processes are presented. When the deep semantic representation is achieved, the shared features of the source domain are transferred for task learning in the target domain. Extensive experiments for three multimedia recognition applications demonstrate that the proposed DHTL can effectively find deep semantic representations for heterogeneous domains, and it is superior to the several existing state-of-the-art methods for deep transfer learning.","Title: Smart Chinese medicine for hypertension treatment with a deep learning model.\nYear: 2019\n\nAbstract: As one important component of smart world, smart health is drawing more and more attention. Over the years, hypertension has become a high incident disease and it is continuing to threaten human health seriously. Unfortunately, no effective way has been found to cure hypertension at present. Many medical experiences have demonstrated the curative effect of traditional Chinese medicine in the hypertension treatment. In this paper, we explore smart Chinese medicine for hypertension treatment by combining deep learning and the traditional Chinese medicine theory. First, we present a potential idea to categorize the clinical cases into different groups based on symptom with the stacked auto-encoder model. Furthermore, we discuss and summarize the curative effect of classical prescriptions and herbs for each type of symptom in hypertension. Finally, we review the related work about deep learning models in medical application. The explored smart Chinese medicine is expected to guide doctors to treat hypertension diseases in clinical, which is helpful to build smart world, especially smart health.","Title: Robust And Graph Regularised Non-Negative Matrix Factorisation For Heterogeneous Co-Transfer Clustering\nYear: 2019\n\nAbstract: Transferring learning is proposed to tackle the problem where target instances are scarce to train an accurate model. Most existing transferring learning algorithms are designed for supervised learning and cannot obtain transferring results on multiple heterogeneous domains simultaneously. Moreover, the performance of transfer learning can be seriously degraded with the appearance of noises and corruptions. In this paper, a robust non-negative collective matrix factorisation model is proposed for heterogeneous co-transfer clustering which introduces the error matrices to capture the sparsely distributed noises. The heterogeneous clustering tasks are handled simultaneously and the graph regularisation is enforced on the collective matrix factorisation model to keep the intrinsic geometric structure of different domains. Experiment results on the real-world dataset show the proposed algorithm outperforms the baselines.","Title: An integrated scheme based on service classification in pervasive mobile services\nYear: 2012\n\nAbstract: Internet of Things and broadband communication are promoting the pervasive mobile services with their advanced features. However, security problems baffled the development. This paper proposes a trust model to protect the user's security. The billing or trust operator works as an agent to provide a trust authentication for all the service providers. The services are classified by sensitive value calculation. With the combined location-aware and identity-aware information and authentication history, the user's trustiness for corresponding service can be obtained. For decision, three trust regions are divided and referred to three ranks as follows: high, medium and low. The trust region tells the customer which authentication methods should be used for access. Penalty coefficient is also involved for forbidding force-crack. Copyright © 2012 John Wiley & Sons, Ltd.","Title: An Incremental Deep Convolutional Computation Model for Feature Learning on Industrial Big Data\nYear: 2019\n\nAbstract: The deep convolutional computation model (DCCM) enabled remarkable progress in feature learning of industrial big data in Internet of Things. However, as a typical static deep learning model, it is difficult to learn features for incremental industrial big data. To solve this problem, we propose an incremental DCCM by developing two incremental algorithms, i.e., parameter-incremental algorithm and structure-incremental algorithm. The parameter-incremental algorithm aims to incrementally train the fully connected layers together with fine tuning for incorporating the new knowledge into the prior one. Then, the structure-incremental algorithm is used to transfer the previous knowledge by introducing an updating rule of the tensor convolutional, pooling, and fully connected layers. Furthermore, the dropout strategy is extended into the tensor fully connected layer to improve the robustness of the proposed model. Finally, extensive experiments are carried out on the representative datasets including CIFRA and CUAVE to justify the proposed model in terms of adaption, preservation, and convergence efficiency.","Title: Energy-Efficient Scheduling for Real-Time Systems Based on Deep Q-Learning Model\nYear: 2019\n\nAbstract: Energy saving is a critical and challenging issue for real-time systems in embedded devices because of their limited energy supply. To reduce the energy consumption, a hybrid dynamic voltage and frequency scaling (DVFS) scheduling based on Q-learning (QL-HDS) was proposed by combining energy-efficient DVFS techniques. However, QL-HDS discretizes the system state parameters with a certain step size, resulting in a poor distinction of the system states. More importantly, it is difficult for QL-HDS to learn a system for various task sets with a Q-table and limited training sets. In this paper, an energy-efficient scheduling scheme based on deep Q-learning model is proposed for periodic tasks in real-time systems (DQL-EES). Specially, a deep Q-learning model is designed by combining a stacked auto-encoder and a Q-learning model. In the deep Q-learning model, the stacked auto-encoder is used to replace the Q-function for learning the Q-value of each DVFS technology for any system state. Furthermore, a training strategy is devised to learn the parameters of the deep Q-learning model based on the experience replay scheme. Finally, the performance of the proposed scheme is evaluated by comparison with QL-HDS on different simulation task sets. Results demonstrated that the proposed algorithm can save average \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math notation=\"LaTeX\">$4.2\\%$</tex-math><alternatives><inline-graphic xlink:href=\"yang-ieq1-2743704.gif\"/></alternatives></inline-formula>\n energy than QL-HDS.","Title: High-order possibilistic c-means algorithms based on tensor decompositions for big data in IoT.\nYear: 2018\n\nAbstract: •A CP-HOPCM algorithm based on canonical polyadic decomposition is proposed.•The canonical polyadic decomposition in CP-HOPCM is used to compress the attributes.•A TT-HOPCM algorithm based on the tensor-network is proposed.•The tensor-network in TT-HOPCM is used to compress the attributes.•The proposed schemes compress the objects greatly without a high accuracy drop.","Title: PPHOPCM: Privacy-Preserving High-Order Possibilistic c-Means Algorithm for Big Data Clustering with Cloud Computing\nYear: 2022\n\nAbstract: As one important technique of fuzzy clustering in data mining and pattern recognition, the possibilistic c-means algorithm (PCM) has been widely used in image analysis and knowledge discovery. However, it is difficult for PCM to produce a good result for clustering big data, especially for heterogenous data, since it is initially designed for only small structured dataset. To tackle this problem, ...","Title: An Adaptive Droupout Deep Computation Model for Industrial IoT Big Data Learning with Crowdsourcing to Cloud Computing\nYear: 2019\n\nAbstract: Deep computation, as an advanced machine learning model, has achieved the state-of-the-art performance for feature learning on big data in industrial Internet of Things (IoT). However, the current deep computation model usually suffers from overfitting due to the lack of public available labeled training samples, limiting its performance for big data feature learning. Motivated by the idea of active learning, an adaptive dropout deep computation model (ADDCM) with crowdsourcing to cloud is proposed for industrial IoT big data feature learning in this paper. First, a distribution function is designed to set the dropout rate for each hidden layer to prevent overfitting for the deep computation model. Furthermore, the outsourcing selection algorithm based on the maximum entropy is employed to choose appropriate samples from the training set to crowdsource on the cloud platform. Finally, an improved supervised learning from multiple experts scheme is presented to aggregate answers given by human workers and to update the parameters of the ADDCM simultaneously. Extensive experiments are conducted to evaluate the performance of the presented model by comparing with the dropout deep computation model and other state-of-the-art crowdsourcing algorithms. The results demonstrate that the proposed model can prevent overfitting effectively and aggregate the labeled samples to train the parameters of the deep computation model with crowdsouring for industrial IoT big data feature learning.","Title: Secure weighted possibilistic c-means algorithm on cloud for clustering big data.\nYear: 2019\n\nAbstract: The weighted possibilistic c-means algorithm is an important soft clustering technique for big data analytics with cloud computing. However, the private data will be disclosed when the raw data is directly uploaded to cloud for efficient clustering. In this paper, a secure weighted possibilistic c-means algorithm based on the BGV encryption scheme is proposed for big data clustering on cloud. Specially, BGV is used to encrypt the raw data for the privacy preservation on cloud. Furthermore, the Taylor theorem is used to approximate the functions for calculating the weight value of each object and updating the membership matrix and the cluster centers as the polynomial functions which only include addition and multiplication operations such that the weighed possibilistic c-means algorithm can be securely and correctly performed on the encrypted data in cloud. Finally, the presented scheme is estimated on two big datasets, i.e., eGSAD and sWSN, by comparing with the traditional weighted possibilistic c-means method in terms of effectiveness, efficiency and scalability. The results show that the presented scheme performs more efficiently than the traditional weighted possiblistic c-means algorithm and it achieves a good scalability on cloud for big data clustering.","Title: Integration of Image Feature and Word Relevance: Toward Automatic Image Annotation in Cyber-Physical-Social Systems.\nYear: 2018\n\nAbstract: Image annotation is challenging due to the explosive increase of image data in cyber-physical-social systems. Because of the semantic gap between images and corresponding labels, it has attracted extensive attentions in recent years. However, most existing research neglects the imbalanced distribution of different classes and the internal relevance of image labels. Besides, the weak image labeling affects the annotation performance to some extent. To address these issues, we propose a learning model for image annotation through integrating deep features and label relevance of images. Specifically, we first employ a convolutional neural-network approach to extract the deep features of images and utilize the synthetic minority oversampling technique to deal with the problem of class imbalance. Furthermore, we exploit the correlations, including symbiotic and semantic relationships of labels, to compute the relevance of label sets. Then, we incorporate this relevance into one classifier to reconstruct the complete label sets, and learn the mapping from image features to the reconstructed label sets by the other classifier. In addition, a joint convex loss function is proposed, which combines the two classifiers via co-regularization and compels them to be consistent. We evaluate the proposed method on two benchmark data sets. The experimental results demonstrate that our method outperforms several state-of-the-art solutions.","Title: A Double Deep Q-learning Model for Energy-efficient Edge Scheduling\nYear: 2019\n\nAbstract: Reducing consumption is a vital and challenging problem for the edge computing devices since they are always energy-limited. To tackle this problem, a deep Q-learning model with multiple DVFS algorithms was proposed for energy-efficient scheduling (DQL-EES). However, DQL-EES is highly unstable when using a single stacked auto-encoder to approximate the Q-function. Additionally, it cannot distinguish the continuous system states well since it depends on a Q-table to generate the target values for training parameters. In this paper, a double deep Q-learning model is proposed for energy-efficient edge scheduling (DDQ-EES). Specially, the proposed double deep Q-learning model includes a generated network for producing the Q-value for each DVFS algorithm and a target network for producing the target Q-values to train the parameters. Furthermore, the rectified linear units (ReLU) function is used as the activation function in the double deep Q-learning model, instead of the Sigmoid function in QDL-EES, to avoid gradient vanishing. Finally, a learning algorithm based on experience replay is developed to train the parameters of the proposed model. The proposed model is compared with DQL-EES on EdgeCloudSim in terms of saving and training time. Results indicate that our proposed model can save average $2%-2.4%$ energy and achieve a higher training efficiency than QQL-EES, proving its potential for energy-efficient edge scheduling.","Title: A Unified Smart Chinese Medicine Framework for Healthcare and Medical Services.\nYear: 2021\n\nAbstract: Smart Chinese medicine has emerged to contribute to the evolution of healthcare and medical services by applying machine learning together with advanced computing techniques like cloud computing to computer-aided diagnosis and treatment in the health engineering and informatics. Specifically, smart Chinese medicine is considered to have the potential to treat difficult and complicated diseases suc...","Title: Deep Learning Models For Diagnosing Spleen And Stomach Diseases In Smart Chinese Medicine With Cloud Computing\nYear: 2021\n\nAbstract: Cloud computing is significantly contributing to the development of smart Chinese medicine. The diagnosis and treatment of spleen and stomach diseases has been arousing great interest in smart Chinese medicine with cloud computing since many persons are suffering from spleen and stomach diseases. Currently, spleen and stomach diseases present some new characteristics with the dramatic changes in natural climate, social environment, and human living habits. Recently, deep learning, together with cloud computing techniques, has successfully used in medical image analysis and therefore it is the most promising model for diagnosing spleen and stomach disease in smart Chinese medicine. In this paper, we present a survey on deep learning models in medical image analysis for computer-aided diagnosis in modern medicine. Afterwards, we summarize the syndrome types of spleen and stomach diseases and furthermore analyze the causes and pathogenesis for each syndrome. Finally, we discuss the open challenges and research directions of deep learning models applicable to the computer-aided diagnosis of spleen and stomach diseases, which is expected to contribute to the development of smart Chinese medicine with cloud computing.","Title: A Deep CFS Model for Text Clustering\nYear: 2018\n\nAbstract: With the fast development of the Internet technology, the court text information is collected from various fields at an unprecedented speed, such as Weibo and Wechat. This big court text information of high volume poses a vast challenge for the judge making reasonable decisions based on the vast cases. To cluster the reasonable assistant cases from the vast cases, we propose a deep CFS model for the text clustering, which can cluster the court text effectively, in this paper. In the proposed model, a robust deep text feature extractor is designed to improve the cluster accuracy, in which an ensemble of deep learning models are used to learn the deep features of the text. Furthermore, the CFS algorithm is conducted on the extracted deep text features, to discover the non-spherical clusters with the automatic find of the cluster centers. Finally, the proposed deep cluster model is evaluated on two typical datasets and the results show it can perform better than compared models in terms of the cluster accuracy.","Title: A New Deep Transfer Learning Model for Judicial Data Classification\nYear: 2018\n\nAbstract: For judicial analysis, usually there are enough labeled instances for one domain, but there are few or even no labeled instances in target domain. Therefore, to bridge the gap between these two domains and use the sufficient source information for target analysis is important. In this paper, we focus on developing a new deep transfer learning model to translate the source domain information for target data classification. The proposed model integrates the deep neural network (DNN) with canonical correlation analysis (CCA) to derive a deep correlation subspace for associating data across different domains. Moreover, a new objective is designed to train the whole network jointly. When the deep semantic representation is achieved, the shared features of the source domain are transferred for instance classification in the target domain. Experiments on several datasets present that the proposed method is superior to the state-of-the-art methods for deep transfer learning, which is promising for judicial data classification.","Title: A canonical polyadic deep convolutional computation model for big data feature learning in Internet of Things\nYear: 2019\n\nAbstract: In recent years, the Internet of Things is more widely deployed with increasing amounts of data gathered. These data are of high volume, velocity, veracity and variety, posing a vast challenge on the data analysis, especially with respect to variety and velocity. To address this challenge, a canonical polyadic deep convolutional computation model is introduced to efficiently and effectively capture the hierarchical representation of the big data by employing the canonical polyadic decomposition to factorize the deep convolutional computation. In particular, to speed up the learning of local topologies hidden in the big data, a canonical polyadic convolutional kernel is devised by compacting the tensor convolutional kernel into the linear combination of the principle rank-1 tensors. Furthermore, the canonical polyadic tensor fully-connected weight is used to efficiently map the correlation in the fully-connected layer. After that, the canonical polyadic high-order back-propagation is devised to train the canonical polyadic deep convolutional computation model. At last, detailed experiments are carried out on two well-known datasets. And results illustrate that the introduced model achieves higher performance than a competing model.","Title: Unsupervised multi-view non-negative for law data feature learning with dual graph-regularization in smart Internet of Things\nYear: 2019\n\nAbstract: In the real world, the law data in the smart Internet of Things usually consists of heterogeneous information with some noises. Non-negative matrix factorization is a popular tool for multi-view learning, which can be employed to represent and learn heterogeneous law features comprehensively. However, current NMF-based techniques generally use clean multi-view datasets to generate common subspace, while in practice, they often contain some noises or unrelated items so that the performance of the algorithms may be severely degraded. In this paper, we propose to develop a novel subspace learning model, called Adaptive Dual Graph-regularized Multi-View Non-Negative Feature Learning (ADMFL), for multi-view data representation. We utilize the geometric structures of both data and feature manifold to model the distribution of data points in the common subspace. Meanwhile, we lift the effect of unrelated features down through separating the view-specific features for each view. Moreover, we introduce a weight factor for all views and maintain the sparsity of the latent common representation. An effective objective function is thus designed and iteratively updated until convergence. Experiments on standard datasets demonstrate that the proposed ADMFL method outperforms other compared methods in the paper.","Title: MULTIPLE-INPUT MULTIPLE-OUTPUT FUSION NETWORK FOR GENERALIZED ZERO-SHOT LEARNING\nYear: 2021\n\nAbstract: Generalized zero-shot learning (GZSL) has attracted considerable attention recently, which trains models with data from seen classes and tests on data from both seen and unseen classes. Most of the existing methods attempt to find a mapping from visual space to semantic space, such mapping can easily result in the domain shift problem. To address this issue, we propose a Multiple-Input Multiple-Output Fusion Network to GZSL. It can generate similar common semantic representation to paired inputs even with only the class semantic embeddings. This makes it possible to synthesize pseudo samples from attributes of unseen classes. Extensive experiments carried out on three benchmark datasets show the effectiveness of the proposed model.","Title: Fake Reviewer Group Detection in Online Review Systems\nYear: 2021\n\nAbstract: Online review systems are important components in influencing customers' purchase decisions. To manipulate a product's reputation, many stores hire large numbers of people to produce fake reviews to mislead customers. Previous methods tackle this problem by detecting malicious individuals, ignoring the fact that the spam activities are often formed in groups, where individuals work collectively to write fake reviews. Fake reviewer group detection, however, is more challenging due to the difficulties in capturing the underlying relationships in groups. In this work, we present an unsupervised and end-to-end approach for fake reviewer group detection in online reviews. Specifically, our method can be summarized into two procedures. First, cohensive groups are detected with modularity-based graph convolutional networks. Then the suspiciousness of each group is measured by several anomaly indicators from both individual and group levels. The fake reviewer groups can be finally detected through suspiciousness. Extensive experiments are conducted on real-world datasets, and the results show that our proposed method is effective in detecting fake reviewer groups compared with the state-of-the-art baselines.","Title: LSTM-MFCN: A time series classifier based on multi-scale spatial–temporal features\nYear: 2022\n\nAbstract: Time series classification (TSC) task attracts huge interests, since they correspond to the real-world problems in a wide variety of fields, such as industry monitoring. Deep learning methods, especially CNN and FCN, shows competitive performance in TSC task by their virtue of good adaption for raw time series and self-adapting extraction of features. Then various variants of CNN are proposed so as to make further breakthrough by the better perception to characteristics of data. Among them, LSTM-FCN and GRU-FCN who learn spatial and temporal features simultaneously are the most remarkable ones, achieving state of the art results. Therefore, inspired by their success and in consideration of the discriminative features implied in time series are diverse in size, a multimodal network LSTM-MFCN composed of multi-scale FCN (MFCN) and LSTM are proposed in this work. The gate-based network LSTM naturally fits to various terms time dependencies, and FCN with multi-scale sets of filters are capable to perceive spatial features of different range from time series curves. Besides, dilation convolution is deployed to build multi-scale receptive fields in larger level without increasing the parameters to be trained. The full perception of large multi-scale spatial–temporal features lead LSTM-MFCN to possess comprehensive and thorough grasp to time series, thus achieve even better accuracies. Finally, two representative architectures are presented specifically and their experiments on UCR datasets reveals the effectiveness and superiority of proposed LSTM-MFCN.","Title: Incremental multi-view correlated feature learning based on non-negative matrix factorisation\nYear: 2021\n\nAbstract: In real-world applications, large amounts of data from multiple sources come in the form of streams. This makes multi-view feature learning cost much time when new instances rise incrementally. Dealing with these growing multi-view data becomes a challenging problem. Some single-view methods focus on processing the data dynamically, but they are not suitable for multi-view data. Some online multi-view methods are proposed to tackle it, but they ignore the influence of uncorrelated items in each view. Therefore, in this study, the authors propose a new algorithm, called Incremental Multi-view Correlated Feature Learning (IMCFL) based on non-negative matrix factorisation, to learn the common feature across views. By separating uncorrelated items of new instances and constructing incremental joint learning of correlated and uncorrelated features, the proposed IMCFL can eliminate the influence of uncorrelated information in the individual view and improve the effectiveness of incremental multi-view common feature learning. Extensive experiments on real-world datasets confirm its superiority by comparing it with other state-of-the-art incremental and non-incremental methods.","Title: A two-stage deep transfer learning model and its application for medical image processing in Traditional Chinese Medicine\nYear: 2022\n\nAbstract: People are deepening the study on model explainability as well as performance to better understand models’ decisions from the human perspective. However, the lack of rare clinical diagnosis data always limits the power of the emerging data-driven deep diagnosis methods, and the traditional deep transfer learning (DTL) applicable for the case is often insufficient to learn specific features in medical image processing, leading to poor explainability. To address those challenges, a two-stage deep transfer learning model is proposed and applied to assist in the Traditional Chinese Medicine (TCM) tongue diagnosis. Especially, a two-stage transfer learning training strategy is designed to loose the data dependence of deep learning on the domain data, which is composed of the imitate stage that discovers shared basic source features and the transfer stage to relearn target patterns, with good explainability. The corresponding deep squeeze-and-excitation convolutional network is proposed to learn the clinical patterns of tongue symptoms, in which a three-layer feature pyramid network fuses the multi-scale tongue features. Extensive experiments are conducted on the real clinical dataset in terms of classification accuracy and learning efficiency. The resulting accuracy of the proposed model proves its performance advantage with the recognition time achieving real-time performance.","Title: Dual Alignment Self-Supervised Incomplete Multi-View Subspace Clustering Network\nYear: 2021\n\nAbstract: Incomplete multi-view clustering has attracted much attention in decade years. To date, most of the remarkable achievements, however, exploit shallow models to learn shared feature representations based on incomplete views. Although some deep learning methods have been proposed to solve this issue, the existing ones still have the following problems: 1) The consistency between views is ignored, which will have serious negative impacts on incomplete multi-view learning. 2) The learned features do not have sufficient cluster-friendliness, that is, the tightness within clusters and the repulsiveness between clusters are not fully considered. To tackle the above shortcomings, we propose a Dual Alignment Self-supervised Incomplete Multi-view Subspace Clustering network (DASIMSC) in this paper. Specifically, the manifold alignment constraint and consistency alignment constraint are integrated with the autoencoder to preserve the compact inherent local structure within the view and the consistency semantics between incomplete views, respectively. Moreover, a self-expression layer coupled with a spectral clustering module is designed to naturally separate different types of data, leveraging the current clustering results to supervise subspace learning, which excludes inter-cluster. Experimental results on several datasets show that our algorithm outperforms all compared state-of-the-arts.","Title: Multilabel Aerial Image Classification With Unsupervised Domain Adaptation\nYear: 2022\n\nAbstract: Deep learning (DL) methods are promising for the multilabel aerial image classification (MAIC) task. However, current DL methods face a common problem: the need for large multilabeled datasets. Collecting and annotating raw aerial image datasets can be extremely time- and labor-consuming. To address this concern in MAIC, domain adaptation (DA) provides a novel solution by transferring the knowledge learned from a label-rich dataset (i.e., the source domain) to a label-scarce dataset (i.e., the target domain), while current DA models are mainly designed for single-labeled tasks. In this article, we propose a novel end-to-end MAIC model based on DA techniques, named DA-MAIC. To the best of our knowledge, this article for the first time integrates DA to tackle the label scarcity problem in the MAIC task. Specifically, the proposed DA-MAIC is composed of two main parts: the image classifier and the domain classifier. The image classifier captures task-discriminative features based on the graph convolutional network (GCN) to predict multiple image labels; and the domain classifier extracts domain-invariant representations, which mitigates the domain shift between two underlying distributions. We extensively evaluate the proposed DA-MAIC from different perspectives on three benchmark datasets, including the commonly used UCM dataset, the high-resolution AID dataset, and the recently proposed DFC15 dataset. Both quantitative and qualitative results support that the proposed DA-MAIC can generalize the source domain knowledge to new scenarios and substantially improve the classification performance on the target domain task.","Title: Multi-View Representation Learning via Dual Optimal Transportation\nYear: 2021\n\nAbstract: Recently, multi-view representation learning has gained rapid growth in various fields. Most of previous multi-view learning methods rely on strong notions of distances that often provide no useful gradients in deep network training, which greatly degrades the performance in merging complementary information of views. To address this challenge, a multi-view representation learning network with dual optimal transportation (MDOT-Net) is proposed to capture fusion representations embedded in a common manifold with the weak topology. In MDOT-Net, the multi-view representation learning is modelled as an optimal transportation (OT) problem in manifold fitting, which is further decomposed into the intra-view OT and the inter-view OT. The intra-view OT is implemented by a view-specific adversarial variational network, which accurately captures local manifold structures within views by leveraging fusion knowledge. The inter-view OT is implemented by a view-fusion adversarial inference network, which models fusion representations compatible with diversities of sub-manifolds by utilizing view-specific knowledge. The two OTs boost mutually by providing prior knowledge to each other in multi-view representation learning. Finally, numerous experiments are conducted on four benchmark datasets, and the results demonstrate MDOT-Net is competitive against state-of-the-art algorithms.","Title: LEGAN: A Light and Effective Generative Adversarial Network for medical image synthesis\nYear: 2022\n\nAbstract: Medical image synthesis plays an important role in clinical diagnosis by providing auxiliary pathological information. However, previous methods usually utilize the one-step strategy designed for wild image synthesis, which are not sensitive to local details of tissues within medical images. In addition, these methods consume a great number of computing resources in generating medical images, which seriously limits their applicability in clinical diagnosis. To address the above issues, a Light and Effective Generative Adversarial Network (LEGAN) is proposed to generate high-fidelity medical images in a lightweight manner. In particular, a coarse-to-fine paradigm is designed to imitate the painting process of humans for medical image synthesis within a two-stage generative adversarial network, which guarantees the sensitivity to local information of medical images. Furthermore, a low-rank convolutional layer is introduced to construct LEGAN for lightweight medical image synthesis, which utilizes principal components of full-rank convolutional kernels to reduce model redundancy. Additionally, a multi-stage mutual information distillation is devised to maximize dependencies of distributions between generated and real medical images in model training. Finally, extensive experiments are conducted in two typical tasks, i.e., retinal fundus image synthesis and proton density weighted MR image synthesis. The results demonstrate that LEGAN outperforms the comparison methods by a significant margin in terms of Fréchet inception distance (FID) and Number of parameters (NoP)."],"string":"[\n \"Title: An attribute-based scheme for service recommendation using association rules and ant colony algorithm\\nYear: 2010\\n\\nAbstract: With the rapid development of m-commerce, predicting user's navigation and making the service recommendation become more and more important. Most researches focus on predicting user's navigation using context history and user preferences. But, the influence of the attributes of a service has been ignored. Simultaneously, some attributes are variable, so the recommendations are changeable. Therefore, the paper proposes an attribute-based scheme for service recommendation based on CASUP (context-aware system considering user preference). In proposed approach, the services are classified into several service clusters, and the service recommendations are carried out using Apriori algorithm and ant colony algorithm. Finally, the proposed model is validated by several simulation experiments, which demonstrate the effects of the service attributes in m-commerce.\",\n \"Title: A Practical Localization Algorithm Based on Wireless Sensor Networks.\\nYear: 2010\\n\\nAbstract: Many localization algorithms and systems have been developed by means of wireless sensor networks for both indoor and outdoor environments. To achieve higher localization accuracy, extra hardware equipments are utilized by most of the existing localization algorithms, which increase the cost and greatly limit the range of location-based applications. In this paper we present a method which can effectively meet different localization accuracy requirements of most indoor and outdoor location services in realistic applications. Our algorithm is composed of two phases: partition phase, in which the target region is split into small grids and localization refinement phase in which a higher accuracy location can be generated by applying a trick algorithm. A realistic demo system using our algorithm has been developed to illustrate its feasibility and availability. The results show that our algorithm can improve the localization accuracy.\",\n \"Title: iCare: A Mobile Health Monitoring System for the Elderly\\nYear: 2010\\n\\nAbstract: This paper describes a mobile health monitoring system called iCare for the elderly. We use wireless body sensors and smart phones to monitor the well being of the elderly. It can offer remote monitoring for the elderly anytime anywhere and provide tailored services for each person based on their personal health condition. When detecting an emergency, the smart phone will automatically alert pre-assigned people who could be the old people's family and friends, and call the ambulance of the emergency centre. It also acts as the personal health information system and the medical guidance which offers one communication platform and the medical knowledge database so that the family and friends of the served people can cooperate with doctors to take care of him/her. The system also features some unique functions that cater to the living demands of the elderly, including regular reminder, quick alarm, medical guidance, etc. iCare is not only a real-time health monitoring system for the elderly, but also a living assistant which can make their lives more convenient and comfortable.\",\n \"Title: An Architecture of Data Processing Using Deluge Computing in Internet of Things.\\nYear: 2011\\n\\nAbstract: Internet of Things (IoT) is an upcoming network technology well studied by many researchers. Mass applications and terminal nodes with heterogenous network connection make the tremendous scale of data process extremely difficult. A combined architecture with Cloud Computing and Sea Computing is proposed. Deluge Computing, a new concept, using Adaptive Coding and Modulation and Granular Computing aims to release the pressure of data process when data deluge bursts. Granular Computing is emphasized in this architecture for data reduction.\",\n \"Title: A Cognitive QoS Method Based on Parameter Sensitivity.\\nYear: 2010\\n\\nAbstract: Different applications in the network have different sensitivity for the certain QoS parameters. The existing mechanisms cannot modify the packet loss policy exactly according to the needs of QoS parameters. Therefore, the network will not maximize its overall efficiency. This paper proposes a novel cognitive approach for QoS. It classifies the applications by various combinations of the QoS parameters. And it uses a cognitive layer to modify the loss probability of the packet. Simulation results prove that this approach can reduce the account of useless packets sent when the congestion occurs in the network. The overall efficiency of the useful data transmission can be improved as well. The method can be used by Internet of Things to improve its message processing and sensor data fusion to reduce energy consumption.\",\n \"Title: Quasi Real-Time Evaluation System for Seismic Disaster Based on Internet of Things\\nYear: 2011\\n\\nAbstract: In recent years, disastrous earthquake assault the human frequently. The evaluation and estimate of the loss and damage in seismic disaster is an important factor for earthquake emergence and post-earthquake reconstruction decision-making. This paper synthetically designs a set of disaster real-time evaluation system based on the seismic networking of things. This paper firstly collects real-time data through the internet of things, then accesses forecasts the economic loss in earthquake by the technology of the geographic information system (GIS), earthquake loss estimation theory and the powerful computing functions of high-performance computer group. This paper also completes the deployment of IOT and the client software. The system can improve our ability to respond after the earthquake disaster and provide information for the emergency rescue command.\",\n \"Title: A Scenario for Identity Management in Daidalos\\nYear: 2007\\n\\nAbstract: Digital Identity corresponds to the electronic information associated generally with an individual in a particular identity system. This is used by online service providers to authenticate and authorize users for services protected by access policies. Having good identity systems can enable individuals to use effectively and extensively electronic transactions in a secure yet privacy preserving manner. In order to match the above purpose, this paper introduces an identity scenario, a user-centric identity management, in terms of VID - virtual identity, which concept is designed to protect a user's privacy and secure communication data, which contemplates the multitude of identities and roles we take on each time we turn on our computer, mobile phone or PDA.. Simultaneously, the associated identity management system with its related infrastructure is described. A key component, the ID broker, of the proposed infrastructure is also detailed including some interfaces and their related components. Furthermore, a method that allows a user to manage his/her service via the VID is introduced.\",\n \"Title: An energy efficient medium access control protocol for target tracking based on dynamic convey tree collaboration in wireless sensor networks\\nYear: 2012\\n\\nAbstract: Collisions and interferences among nodes pose a challenge for data aggregation in many applications, such as target tracking by adopting dynamic convey tree-based collaboration (DCTC). Because coordination with a time division multiple access (TDMA) medium access control (MAC) might provide an opportunity for better interference control, in this paper, we refine slot allocation to nodes in a dynamic convey tree and design an energy efficient MAC protocol called dynamic-time division multiple access (D-TDMA). The D-TDMA protocol avoids collisions and interferences and allocates contiguous active slots to nodes as far as possible during data aggregation from leaf nodes to a root node. As a result, energy consumption in switching from sleep to active state is saved. In comparison with Always-On scheme, theoretical analysis results show that the proposed protocol D-TDMA improves energy efficiency by up to 28.3% during one data aggregation. Furthermore, simulation results show that D-TDMA does not suffer from collisions and interferences among nodes in a dynamic convey tree and performs similar throughput to that of Always-On scheme. Because of its advantage of parallel and continuous scheduling among node pairs in the convey tree, D-TDMA outperforms efficient slot reservation in both energy efficiency and low delay because of to slot saving. Copyright © 2012 John Wiley & Sons, Ltd.\",\n \"Title: Principal-Agent Theory Model in Dynamic Spectrum Sharing\\nYear: 2011\\n\\nAbstract: This paper points to the dynamic spectrum sharing problem in cognitive radio network and analyzes the behavior of primary user (PU) and secondary user (SU). The relationship between PU and SU can be treated as principal and agent. Then, a principal-agent theory based two-step game model is proposed with the consideration of cooperation and price coeffcient which effect the SU's choice and PU's profit. Further, some incomplete information is assumed as random variables so that certainty equivalent is introduced in the model. Both cooperation and price effect are simulated at the end.\",\n \"Title: User Preference Heterogeneous Network Selection in Less Subjective Ways\\nYear: 2014\\n\\nAbstract: Heterogeneous network selection is a popular topic with the rapid development of telecommunication technologies. Literatures employed different ways for solving the issue with drawbacks more or less especially neglecting the user preference and using much subjective configurations. This paper proposes a user preference scheme including realtime or non-realtime pursuit, money and battery consumption concern and non-specific requirement. For front four modes, a technique for order preference by similarity to ideal solution based method with vote which reduces the subjectiveness is introduced. Then, a function-less and totally unsubjective method called directional distance function based data envelopment analysis method faces the non-specific requirement. Several examples test these two methods and the results support them.\",\n \"Title: Element Retrieval Using Namespace Based on Keyword Search over XML Documents.\\nYear: 2010\\n\\nAbstract: Querying over XML elements using keyword search is steadily gaining popularity. The traditional similarity measure is widely employed in order to effectively retrieve various XML documents. A number of authors have already proposed different similarity-measure methods that take advantage of the structure and content of XML documents. They do not, however, consider the similarity between latent semantic information of element texts and that of keywords in a query. Although many algorithms on XML element search are available, some of them have the high computational complexity due to searching a huge number of elements. In this paper, we propose a new algorithm that makes use of the semantic similarity between elements instead of between entire XML documents, considering not only the structure and content of an XML document, but also semantic information of namespaces in elements. We compare our algorithm with the three other algorithms by testing on the real datasets. The experiments have demonstrated that our proposed method is able to improve the query accuracy, as well as to reduce the running time.\",\n \"Title: A customizable QoS strategy for convergent heterogeneous wireless communications\\nYear: 2007\\n\\nAbstract: This article proposes a general customizable QoS scenario for heterogeneous wireless access network technologies. The scenario provides robust and flexible QoS support by adapting the amount of resources reserved for the characteristics of the wireless channel, as well as automatic reconfiguration mechanisms of the multimedia encoding rate and error-resilient techniques to adapt to the dynamic mobile communication environment. Two new scenarios, adaptive FEC and MAC-Lite, are introduced to the proposed scheme. These enable a maximizing of the overall performance of the wireless network. The appropriate interfaces within the core network are described for performing end-to-end QoS, which deliver network meters and exchange QoS parameters with the proposed intra and inter cross-layer approaches, including context transfer during handover. Each part, extended by the proposed method can be configured by the application and be independently implemented. To evaluate the proposed scheme, the QoS components were integrated into a trial platform that was built and simulations were then performed. The results demonstrate that the communication quality was significantly improved\",\n \"Title: A Universal Storage Architecture for Big Data in Cloud Environment\\nYear: 2013\\n\\nAbstract: With the rapid development of the Internet of Things and Electronic Commerce, we have entered the era of big data. The characteristics, such as great amount and heterogeneousity, of big data bring the challenge to the storage and analytics. The paper presented a universal storage architecture for big data in cloud environment. We use clustering analysis to divide the cloud nodes into multiple clusters according to the communication cost between different nodes. The cluster with the strongest computing power is selected to provide the universal storage and query interface for users. Each of other clusters is responsible for storing the data of a particular model, such as relational data, key-value data, and document data and so on. Experiments show that our architecture can store all kinds of heterogeneous big data and provide users with unified storage and query interface for big data easily and quickly.\",\n \"Title: D-TDMA: An Approach of Dynamic TDMA Scheduling for Target Tracking in Wireless Sensor Networks.\\nYear: 2010\\n\\nAbstract: In the application of target tracking based on dynamic convey tree-based collaboration (DCTC) in wireless sensor networks, collisions and interferences among nodes pose a challenge for data aggregation. In this paper, we refine slot allocation to the nodes in dynamic convey tree and design an energy efficient MAC protocol called D-TDMA, which avoids collisions and interferences and allocates contiguous active slots to nodes as far as possible during data aggregation from leaf nodes to a root node. As a result, the energy consumption in switching from sleeping to active state is saved. Simulation results show that D-TDMA not only gets rid of the impact of collisions and interferences among nodes in dynamic convey tree, but also performs similar throughput to that of CSMA-like mechanism where nodes work without active/sleep scheduling strategy. Our protocol outperforms ESR in both energy efficiency and low delay.\",\n \"Title: An Algorithm of Weighted Monte Carlo Localization Based on Smallest Enclosing Circle.\\nYear: 2011\\n\\nAbstract: To meet the needs of node mobility of Internet of thing (IOT), a localization algorithm named Weighted Monte Carlo Localization based on Smallest Enclosing Circle is proposed, which is based on the classic Monte Carlo Localization algorithm, aiming to solve the localization problem of mobile nodes. The algorithm uses the hops of anchor nodes and generates the smallest enclosing circle of anchor nodes to assist localizing, thus effectively inhibited the unevenness of anchor nodes caused by the Monte Carlo localization algorithm and reflects impact of the anchor nodes on unknown nodes. The simulation results show that the algorithm effectively reduces the sampling area and the sampling frequency, eventually increase the accuracy of localized nodes.\",\n \"Title: A localization method for the Internet of Things\\nYear: 2012\\n\\nAbstract: Many localization algorithms and systems have been developed by means of wireless sensor networks for both indoor and outdoor environments. To achieve higher localization accuracy, extra hardware equipments are utilized by most of the existing localization solutions, which increase the cost and considerably limit the location-based applications. The Internet of Things (IOT) integrates many technologies, such as Internet, Zigbee, Bluetooth, infrared, WiFi, GPRS, 3G, etc., which can enable different ways to obtain the location information of various objects. Location-based service is a primary service of the IOT, while localization accuracy is a key issue. In this paper, a higher accuracy localization scheme is proposed which can effectively satisfy diverse requirements for many indoor and outdoor location services. The proposed scheme composes of two phases: (1) the partition phase, in which the target region is split into small grids; (2) the localization refinement phase, in which a higher accuracy of localization can be obtained by applying an algorithm designed in the paper. A trial system is set up to verify correctness of the proposed scheme and furthermore to illustrate its feasibility and availability. The experimental results show that the proposed scheme can improve the localization accuracy.\",\n \"Title: A clustering approximation mechanism based on data spatial correlation in wireless sensor networks\\nYear: 2010\\n\\nAbstract: In wireless sensor networks (WSNs), the sensor nodes that locate near often sense the similar data, however, transmitting the repeated or redundant data often cause unnecessary energy consumption. Aiming at this point, this paper firstly proposes a gridbased spatial correlation clustering (GSCC) method which clusters the sensor nodes according to data correlation. According to GSCC, in the same cluster the member nodes have high similarity. Based on GSCC, then this paper proposes a spatial correlation clustering approximation framework (SCCAF). SCCAF can largely save networks' energy by which the cluster head estimates the data of its member nodes provided that approximation value is in the allowable error range. Experiments prove that not only SCCAF based on GSCC method can prolong the lifetime of the sensor networks compared with LEACH but also SCCAF guarantees more accuracy than CASA (clustering-based approximate scheme for data aggregation) which is a previous approximation scheme.\",\n \"Title: Multi-node Scheduling Algorithm Based on Clustering Analysis and Data Partitioning in Emergency Management Cloud.\\nYear: 2013\\n\\nAbstract: Real-time processing is a key problem for big data analysis and processing, especially in emergency management. Strongly promoted by the leading industrial companies, cloud computing becomes increasingly popular tool for emergency management, that is emergency management cloud. How to make optimal deployment of emergency management cloud applications is a challenging research problem. The paper proposes a multi-node scheduling algorithm based on clustering analysis and data partitioning in emergency management cloud. First, the presented method divides the cloud nodes into clusters according to the communication cost between different nodes, and then selects a cluster for the big data analysis services. Second, the load balancing theory is used to dispatch big data analysis to these computing nodes in a way to enable synchronized completion at best-effort performance. At last, to improve the real-time of big data analysis, the paper presents a multi-node scheduling algorithm based on game theory to find optimal scheduling strategy for each scheduling node. Experimental results show the effectiveness of our scheduling algorithm for big data analytics in emergency management. © 2013 Springer-Verlag.\",\n \"Title: Distributed Compressed Sensing Mri Using Volume Array Coil\\nYear: 2013\\n\\nAbstract: The volume array coil in the magnetic resonance imaging (MRI) system is a typical application of the distributed sensor network in the biomedical area. Each coil provides a large coverage of the imaged object, and the signals are largely overlapped during the data acquisition. The intercoil image similarities can be explored for the distributed compressed sensing (CS) based image reconstruction. In this work, a singular value decomposition (SVD) based sparsity basis was developed for the CS-MRI with a volume array coil configuration. In this novel imaging method, the spatial correlation both of intracoil and intercoil exploited. The experimental results showed that is with eightfold undersampled k-space data acquisition, the target images could still be faithfully reconstructed using the proposed method, which offered a better imaging performance compared to conventional CS schemes.\",\n \"Title: A Trust Model Based on Service Classification in Mobile Services\\nYear: 2010\\n\\nAbstract: Internet of Things (IoT) and B3G/4G communication are promoting the pervasive mobile services with its advanced features. However, security problems are also baffled the development. This paper proposes a trust model to protect the user's security. The billing or trust operator works as an agent to provide a trust authentication for all the service providers. The services are classified by sensitive value calculation. With the value, the user's trustiness for corresponding service can be obtained. For decision, three trust regions are divided, which is referred to three ranks: high, medium and low. The trust region tells the customer, with his calculated trust value, which rank he has got and which authentication methods should be used for access. Authentication history and penalty are also involved with reasons.\",\n \"Title: A Mobile Service Recommendation System Using Multi-Criteria Ratings\\nYear: 2010\\n\\nAbstract: With the rapid advancement of wireless technologies and mobile devices, mobile services offer great convenience and huge opportunities for service creation. However, information overload make service recommendation become a crucial issue in mobile services. Although traditional single-criteria recommendation systems have been successful in a number of personalization applications, obviously individual criterion cannot satisfy consumers' demands. Relying on multi-criteria ratings, this paper presents a novel recommendation system using the multi-agent technology. In this system, the ratings with respect to the three criteria are aggregated into an overall service ranking list by a rank aggregation algorithm. Furthermore, all of the services are classified into several clusters to reduce information overload further. Finally, Based on multi-criteria rank aggregation, the prototype of a recommendation system is implemented. Successful applications of this recommendation system have demonstrated the efficiency of the proposed approach.\",\n \"Title: A Distributed Weighted Possibilistic C-Means Algorithm For Clustering Incomplete Big Sensor Data\\nYear: 2014\\n\\nAbstract: Possibilistic c-means clustering algorithm(PCM) has emerged as an important technique for pattern recognition and data analysis. Owning to the existence of many missing values, PCM is difficult to produce a good clustering result in real time. The paper proposes a distributed weighted possibillistic c-means clustering algorithm (DWPCM), which works in three steps. First the paper applies the partial distance strategy to PCM (PDPCM) for calculating the distance between any two objects in the incomplete data set. Further, a weighted PDPCM algorithm (WPCM) is designed to reduce the corruption of missing values by assigning low weight values to incomplete data objects. Finally, to improve the cluster speed of WPCM, the cloud computing technology is used to optimize the WPCM algorithm by designing the distributed weighted possibilistic c-means clustering algorithm (DWPCM) based on MapReduce. The experimental results demonstrate that the proposed algorithms can produce an appropriate partition efficiently for incomplete big sensor data.\",\n \"Title: IoT-SVKSearch: a real-time multimodal search engine mechanism for the internet of things\\nYear: 2014\\n\\nAbstract: AbstractRecent advances on the Internet of Things IoT have posed great challenges to the search engine community. IoT systems manage huge numbers of heterogeneous sensors and/or monitoring devices, which continuously monitor the states of real-world objects, and most data are generated automatically through sampling. The sampling data are dynamically changing so that the IoT search engine should support real-time retrieval. Additionally, the IoT search involves not only keyword matches but also spatial-temporal searches and value-based approximate searches, as IoT sampling data are generally from spatial-temporal scenario. To meet these challenges, we propose a 'Hybrid Real-time Search Engine Framework for the Internet of Things based on Spatial-Temporal, Value-based, and Keyword-based Conditions' 'IoT-SVK Search Engine' or simply 'IoT-SVKSearch' for short in this paper. The experiments show that the IoT-SVK search engine has satisfactory performances in supporting real-time, multi-modal retrieval of massive sensor sampling data in the IoT. Copyright © 2013 John Wiley & Sons, Ltd.\",\n \"Title: Risk computing based on capacity of risk-absorbing in virtual community environment\\nYear: 2014\\n\\nAbstract: AbstractThe security issues are extremely popular in the virtual community VC environment because of the uncertainties. Many works use trust mechanisms as the enhancement. However, trust has flaws in some aspects, making it uncontrollable. Discussed in this paper is the reason that risk is chosen to evaluate individual's credit. Then, risk in VC environment has several properties that are helpful to understand the relationship between VC and members. A new concept is that the capacity of risk absorbing is introduced to make the model more reasonable for usual scenario. In order to compute the capacity of risk absorbing problem, shapley value approach is utilized to equalize the risk of individuals who join the game in series. The evaluation result can provide VC computed evidence to control the individual's behavior. Copyright © 2014 John Wiley & Sons, Ltd.\",\n \"Title: A nodes scheduling model based on Markov chain prediction for big streaming data analysis\\nYear: 2015\\n\\nAbstract: AbstractStreaming data analysis is an important part of big data processing. However, streaming data is difficult to be analyzed and processed in real time because of the rapid data arriving speed and huge size of data set in stream model. The paper proposes a nodes scheduling model based on Markov chain prediction for analyzing big streaming data in real time by following three steps: i construct data state transition graph using Markov chain to predict the varying trend of big streaming data; ii choose appropriate cloud computing nodes to process big streaming data depending on the predicted result of the data state transition graph; and iii assign big streaming data to these computing nodes using the load balancing theory, which ensures that all subtasks are accomplished synchronously. Experiments demonstrate that the proposed scheduling algorithm can fast process big streaming data effectively. Copyright © 2014 John Wiley & Sons, Ltd.\",\n \"Title: A weighted kernel possibilistic c-means algorithm based on cloud computing for clustering big data\\nYear: 2014\\n\\nAbstract: AbstractPossibilistic c-means PCM cluster algorithm has emerged as an important tool for data preprocessing widely used in data mining and knowledge discovery. Owning to the huge amount of data, high computational complexity, and noise-corrupted data, the PCM algorithms scaled for big data find it difficult to produce a good result in real time. The paper proposes a weighted kernel PCM wkPCM algorithm to cluster data objects in appropriate groups. The proposed algorithm introduces weights to define the relative importance of each object in the kernel clustering solution, which reduces the corruption caused by noisy data. In order to improve the real time of the proposed algorithm, cloud computing technology is used to optimize wkPCM to propose a distributed wkPCM algorithm based on MapReduce, which can provide significant computation speed. Experiment demonstrates that the proposed possibilistic clustering algorithms can cluster big data in appropriate groups in real time. Copyright © 2014 John Wiley & Sons, Ltd.\",\n \"Title: Multiple layer design for mass data transmission against channel congestion in IoT\\nYear: 2014\\n\\nAbstract: AbstractInternet of Things IoT is well studied from many aspects; however, data transmission in a large-scale constructed IoT network is still an open topic. In this paper, the problems of channel congestion caused by mass data transmission are discussed respectively from different perspectives. Then, a multiple layer solution is proposed, pointing to each layer including data processing architecture, data dimension reduction, data abandon protocol, and spectrum sharing. In the architecture layer, a combined scheme with cloud computing and sea computing is introduced. Context awareness and granular computing is exploited to implement the data dimension reduction. And cognitive protocol is involved with type of service, which drops certain data to guarantee the entire network connectivity. Then, a principal-agent theory based two-step game model is proposed with the consideration of cooperation and price coefficient, which affect the secondary user's choice and primary user's profit. Some incomplete information is assumed as random variables so that certainty equivalent is introduced in the model. A simple scenario shows how the data dimension reduction works and how simulations for data abandon protocol and spectrum sharing test the two parts, respectively. Copyright © 2012 John Wiley & Sons, Ltd.\",\n \"Title: Poster: CIS: a community-based incentive scheme for socially-aware networking\\nYear: 2014\\n\\nAbstract: To address selfishness in socially-aware networking, we propose a Community-based Incentive Scheme (CIS). CIS utilizes community to stimulate cooperation among selfish nodes and allows all nodes to behave selfishly to imitate the realistic condition. The simulation results show that CIS effectively stimulates selfish nodes to cooperate, achieves higher delivery ratio while not increasing latency dramatically.\",\n \"Title: Poster: bacteria inspired mitigation of selfish users in ad-hoc social networks\\nYear: 2014\\n\\nAbstract: In data management protocols for Ad-hoc Social Networks (ASNETs), involvement of selfish users can pose a serious threat to network performance and fairness. Therefore, it is essential to detect and mitigate their effects on other well behaving users. We contribute to this line of research by combining the benefits of users' social behavior (social tie) with a biologically inspired approach in ASNETs. We designed a bio-inspired scheme (BoDMaS) to detect and mitigate selfish users in replication operations. Its goals include providing greater accessibility and effective detection of selfish users. The proposed scheme not only guarantees accessibility and effective detection rate, but also ensures the reliability of replica allocation operations.\",\n \"Title: Poster: reliable TCP for popular data in socially-aware ad-hoc networks\\nYear: 2014\\n\\nAbstract: Reliable social connectivity and transmission of data for popular nodes are vital in multihop Ad-hoc Social Networks (ASNETs). However, congestion may occur and hence popular nodes might not achieve required bandwidth when multiple senders share data with a single receiver. The traditional Transport Control Protocol (TCP) might not be able to perform efficiently in ASNETs. Therefore, we propose a Reliable TCP for Popular Data in Socially-aware Ad-hoc Networks called RTPS. RTPS employs a popularity level approach for every single node which partitions the bandwidth among users. The reliability of popular data is ensured since bandwidth will firstly assigned to the popular sender. Preliminary results show that by delaying the acknowledgment and improving the performance of TCP, RTPS reduces collision loss in multihop ASNETs.\",\n \"Title: M2C: Energy efficient mobile cloud system for deep learning\\nYear: 2014\\n\\nAbstract: With the number increasing of applications and services that are available on mobile devices, mobile cloud computing has drawn a substantial amount of attention by academia and industry in the past several years. When facing the most exciting machine learning applications such as deep learning, the computing requirement is intensive. For the purpose of improving energy efficiency of mobile device and enhancing the performance of applications through reducing execution time, M2C offloads computation of its machine learning application to the cloud side. We propose the prototype of M2C with the mobile side on Android, iPad and with the cloud side on the open source cloud: Spark, a part of the Berkeley Data Analytics Stack with NVIDA GPU. M2C's distinct set of varying computational tools and mobile nodes allows for thorough implementing distributed machine learning algorithm and innovative wireless protocols with energy efficiency, verifying the theoretical research and bringing the user extremely fast experience.\",\n \"Title: A strategy for differentiated access service selection based on application in WLANs\\nYear: 2014\\n\\nAbstract: The service quality of a station in IEEE 802.11 wLAN (wireless Local Area Networks) is strongly influenced by which access point it associates with. Therefore, a key challenge is how to select an appropriate access point from multiple available ones. Conventional association protocols have been proved to be not effective. In this paper a strategy for differentiated access service selection based on users' running application is presented. The goals of the proposed strategy are: to select and dynamically reselect access point based on user's actual QoS requirements on bandwidth and delay leading to differentiated service quality provision; and to redistribute network load across access points. The effectiveness of our strategy is evaluated through simulation study, confirming better performance compared with conventional ways.\",\n \"Title: Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning\\nYear: 2016\\n\\nAbstract: To improve the efficiency of big data feature learning, the paper proposes a privacy preserving deep computation model by offloading the expensive operations to the cloud. Privacy concerns become evident because there are a large number of private data by various applications in the smart city, such as sensitive data of governments or proprietary information of enterprises. To protect the private data, the proposed model uses the BGV encryption scheme to encrypt the private data and employs cloud servers to perform the high-order back-propagation algorithm on the encrypted data efficiently for deep computation model training. Furthermore, the proposed scheme approximates the Sigmoid function as a polynomial function to support the secure computation of the activation function with the BGV encryption. In our scheme, only the encryption operations and the decryption operations are performed by the client while all the computation tasks are performed on the cloud. Experimental results show that our scheme is improved by approximately 2.5 times in the training efficiency compared to the conventional deep computation model without disclosing the private data using the cloud computing including ten nodes. More importantly, our scheme is highly scalable by employing more cloud servers, which is particularly suitable for big data.\",\n \"Title: CITS 2017 general chairs' message\\nYear: 2017\\n\\nAbstract: Welcome to the 2017 International Conference on Computer, Information and Telecommunication Systems (CITS 2017). This year's conference marks the sixth CITS, which is being held on annual basis.\",\n \"Title: Distributed fuzzy c-means algorithms for big sensor data based on cloud computing\\nYear: 2015\\n\\nAbstract: Fuzzy c-means FCM clustering has emerged as one important technique for pattern recognition, image processing and data analysis. Owing to the huge amount of data and computational complexity, FCM is difficult to cluster big sensor data in real time. The paper proposes three distributed FCM algorithms, namely distributed FCM algorithm based on MapReduce DFCM, distributed online FCM algorithm based on sampled affinity propagation AP clustering DOFCM and distributed kernel FCM algorithm based on MapReduce DKFCM. The proposed algorithms use cloud computing technology to improve the efficiency for clustering big sensor data. Experiments prove the superiority of the proposed methods through the clustering accuracy and clustering speed.\",\n \"Title: Deep Computation Model for Unsupervised Feature Learning on Big Data.\\nYear: 2016\\n\\nAbstract: Deep learning has been successfully applied to feature learning in speech recognition, image classification and language processing. However, current deep learning models work in the vector space, resulting in the failure to learn features for big data since a vector cannot model the highly non-linear distribution of big data, especially heterogeneous data. This paper proposes a deep computation m...\",\n \"Title: Integration of scheduling and network coding in multi-rate wireless mesh networks: Optimization models and algorithms\\nYear: 2016\\n\\nAbstract: In order to fully utilize spectrum resource in wireless mesh networks (WMNs), we propose a combination of some popular communication techniques, including link scheduling, spatial reuse, power and rate adaptation and network coding (NC), to activate as many transmission links as possible during one scheduling period, so that the total scheduling length can be minimized and network throughput can be maximized. Different from previous studies, we consider the interplay among these techniques and present an optimal NC-aware link scheduling mechanism in multi-rate WMNs, which relies on the enumeration of all possible schedules. Due to the high computational complexity of our proposed model, we utilize a column generation (CG)-based method to resolve the optimization problem and decompose it into a master problem (MP) and a pricing problem (PP). Furthermore, we present a distributed power control algorithm for PP, by which the computational complexity of the CG-based scheme can be largely reduced. Simulation results demonstrate the superiority of our method under various network situations.\",\n \"Title: A Hybrid Method for Incomplete Data Imputation\\nYear: 2015\\n\\nAbstract: With the explosive increase of data volume, the research of data quality and data usability draws extensive attention. In this work, we focus on one aspect of data usability -- incomplete data imputation, and present a novel missing value imputation method using stacked auto-encoder and incremental clustering (SAICI). Specifically, SAICI's functionality rests on four pillars: (i) a distinctive value assigned to impute missing values initially, (ii) the stacked auto-encoder(SAE) applied to locate principal features, (iii) a new incremental clustering utilized to partition incomplete data set, and (iv) the top nearest neighbors' weighted values designed to refill the missing values. Most importantly, stages (ii)~(iv) iterate until convergence condition is satisfied. Experimental results demonstrate that the proposed scheme not only imputes the missing data values effectively, but also has better time performance. Moreover, this work is suitable for distributed data processing framework, which can be applied to the imputation of incomplete big data.\",\n \"Title: BRDPHHC: A Balance RDF Data Partitioning Algorithm Based on Hybrid Hierarchical Clustering\\nYear: 2015\\n\\nAbstract: Data partitioning is a fundamental step to achieve effective storage and query of RDF big data. This paper presents a balance RDF data partitioning algorithm based on hybrid hierarchical clustering (BRDPHHC), which combines AP and K-means clustering. BRDPHHC's functionality includes three aspects: (i) a pre-processing step combining nodes compression and nodes remove to reduce the scale of raw data points, (ii) AP clustering algorithm is used to coarsen the RDF graph step by step and produce data blocks, and (iii) K-means algorithm is used for data partitioning finally. Experiments on benchmark datasets demonstrate the effectiveness of the proposed scheme.\",\n \"Title: A Trust-Based User Assignment Scheme in Ad Hoc Social Networks\\nYear: 2015\\n\\nAbstract: Although cooperation among individuals plays a key factor in the commercial development of wireless networks, trust is an important factor due to the uncertainty and uncontrollability caused by the self-organizing character of different entities. In this paper, we present a trust-based user assignment scheme by considering node sociality, the reason behind is that effective user assignment should not only build a reliable system basing on the behavior of network individuals, but also encourage selfish nodes to forward packets for one another. At first, a model for trustworthiness management is built up by considering social relationship. Then, user assignment for each transmission is decided by a double auction-based mechanism. Simulation result demonstrates that our scheme is able to obtain better network performance than the existing method in link connectivity and social welfare.\",\n \"Title: Privacy Preserving Back-Propagation Based on BGV on Cloud\\nYear: 2015\\n\\nAbstract: Back-propagation is the most effective algorithm for training deep learning models that are proved to have a great ability for big data feature learning. However, back-propagation is of high time complexity, leading to a low efficiency of big data learning. Aiming at this problem, the paper proposes a privacy preserving back-propagation algorithm based on the BGV encryption scheme on cloud. The proposed algorithm improved the efficiency of back-propagation learning by offloading the expensive operations on the cloud. Furthermore, the BGV encryption scheme is used to protect the private data during the learning process using the power of the cloud computing. Experiments show that our proposed scheme is secure and efficient.\",\n \"Title: A Layered Intersection Scan Algorithm Based on Semantic Context\\nYear: 2010\\n\\nAbstract: Along with the rapid growth of the xml data quantity on the Internet, the xml data retrieval research has attracted more and more attention. The searching algorithm based on key words is a research hotspot in this field. We present a context-based layered intersection scan algorithm (CLISA), which uses the context semantic of key words to filter large amount of redundant information, different from the approach of using key words to get SLCA. Based on LISA, CLISA operates LCAs to get SLCAs of different semantics, and then chooses the SLCA that best fits user隆炉s need. Through experiments we can see that CLISA can be more in accordance with the user's demand on condition that it expends more time than LISA.\",\n \"Title: A Data Imputation Method Based On Deep Belief Network\\nYear: 2015\\n\\nAbstract: Accurately filling missing values is an important step to enhance the usability of Big Data. However current incomplete data imputation algorithms are of high time complexity and low accuracy. To address this problem, we propose a novel algorithm to impute incomplete data. Firstly, a deep belief network model with denoising is designed to remove the noise brought by incomplete data and extract high quality features. Then, we utilize softmax for data classification. Finally, according to the classification results, partial distance and sequence imputation strategies are proposed to measure the correlation between records and improve filling accuracy, respectively. Compared with different algorithms, the experimental results confirm the effectiveness and efficiency of the proposed method in data imputation.\",\n \"Title: Social-Oriented Resource Management in Cloud-Based Mobile Networks.\\nYear: 2016\\n\\nAbstract: One of the predominant challenges for cloud-based services is the coordination of resources in the application and network layers to provide adaptive service delivery and an acceptable user experience. This article surveys resource management in cloud-based networks. The authors suggest that consideration of users' social associations can improve link connectivity and service delivery, and that us...\",\n \"Title: Toward Efficient 5G Transmission: SER Performance Analysis for Asynchronous Physical-Layer Network Coding.\\nYear: 2016\\n\\nAbstract: People's pursuit of higher quality communication has never ceased, which challenges the layout of 5G networks. Physical-layer network coding (PNC), as a key technology for 5G, supplies a powerful platform through leveraging the broadcast nature of wireless media. However, the symbol error rate (SER) of PNC is not well investigated, which would seriously influence user's quality of experience due to packet loss in wireless environment. In this paper, considering both phase and symbol misalignments, we perform analysis on SER of asynchronous PNC. By assuming part of information is known to the relay, and we derive the lower bound for SER. Afterward, through applying the concept of error vector and eliminating redundant terms, we derive the upper bound for SER. Both the lower and upper bounds are applicable to either multiuser detection-based network coding or belief propagation-based maximum a posteriori decoding. Finally, Monte Carlo simulation verifies our results and demonstrates that the bounds are relatively tight. The analytical results derived in this paper can facilitate future studies of practical and theoretical issues on PNC.\",\n \"Title: Icfs: An Improved Fast Search And Find Of Density Peaks Clustering Algorithm\\nYear: 2016\\n\\nAbstract: Clustering is a fundamental and important technique under many circumstances including data mining, pattern recognition, image processing and other industrial applications. During the past decades, many clustering algorithms have been developed, such as DBSCAN, AP and CFS. As the latest clustering algorithm proposed in Science magazine in 2014, clustering by fast search and find of density peaks, named as CFS, is a simple and outstanding algorithm for its promising performance on data sets of arbitrary shape. However, CFS's performance is usually affected by the cutoff distance d(c), the density peaks, the selection of cluster centers and the allocation strategy of data points. In this paper, we propose an improved algorithm (ICFS) to deal with the several weaknesses of it. Unlike CFS, the proposed algorithm designs a formula for the cutoff distance calculation and a method for cluster centers selection to improve its robustness. Moreover, a new non-center point's allocation strategy and the cluster merging and splitting processes are developed to adapt to the density peaks and adjust the clusters dynamically, which can improve the clustering accuracy and scalability. The ICFS method is evaluated on several datasets by comparison with the original CFS algorithm. Results demonstrate the effectiveness of the proposed method.\",\n \"Title: PPHOCFS: Privacy Preserving High-Order CFS Algorithm on the Cloud for Clustering Multimedia Data.\\nYear: 2016\\n\\nAbstract: Clustering is a commonly used technique for multimedia data analysis and management. In this article, we propose a high-order clustering algorithm by fast search and find of density peaks (HOCFS) by extending the traditional clustering scheme by fast search and find of density peaks (CFS) algorithm from the vector space to the tensor space for multimedia data clustering. Furthermore, we propose a privacy preserving HOCFS algorithm (PPHOCFS) which improves the efficiency of the HOCFS algorithm by using the cloud computing to perform most of the clustering operations. To protect the private data in the multimedia data sets during the clustering process on the cloud, the raw data is encrypted by the Brakerski-Gentry-Vaikun-tanathan (BGV) strategy before being uploaded to the cloud for performing the HOCFS clustering algorithm efficiently. In the proposed method, the client is required to only execute the encryption/decryption operations and the cloud servers are employed to perform all the computing operations. Finally, the performance of our scheme is evaluated on two representative multimedia data sets, namely NUS-WIDE and SNAE2, in terms of clustering accuracy, execution time, and speedup in the experiments. The results demonstrate that the proposed PPHOCFS scheme can save at least 40% running time compared with HOCFS, without disclosing the private data on the cloud, making our scheme securely suitable for multimedia big data clustering.\",\n \"Title: A privacy-preserving high-order neuro-fuzzy c-means algorithm with cloud computing.\\nYear: 2017\\n\\nAbstract: Currently, massive heterogeneous data is generating from the Internet of Things (IoT). Heterogeneous data processing with the neuro-fuzzy technology has become a hot topic for IoT. In this work, we propose a privacy-preserving high-order neuro-fuzzy c-means algorithm for clustering heterogeneous data (PPHOFCM) on cloud computing. PPHOFCM clusters the heterogeneous data set by representing each heterogeneous data object as a tensor and uses the tensor distance to capture the correlations in the high-order tensor space. Furthermore, the cloud computing is employed to improve the clustering efficiency for massive heterogeneous data from IoT. The BGV encryption scheme is used to protect the private data when performing the high-order neuro-fuzzy c-means algorithm on cloud computing. Experiments are conducted on two real IoT datasets to verify the proposed algorithm.\",\n \"Title: STLIS: A Scalable Two-Level Index Scheme for Big Data in IoT.\\nYear: 2016\\n\\nAbstract: The rapid development of the Internet of Things causes the dramatic growth of data, which poses an important challenge on the storage and quick retrieval of big data. As an effective representation model, RDF receives the most attention. More and more storage and index schemes have been developed for RDF model. For the large-scale RDF data, most of them suffer from a large number of self-joins, high storage cost, and many intermediate results. In this paper, we propose a scalable two-level index scheme (STLIS) for RDF data. In the first level, we devise a compressed path template tree (CPTT) index based on S-tree to retrieve the candidate sets of full path. In the second level, we create a hierarchical edge index (HEI) and a node-predicate (NP) index to accelerate the match. Extensive experiments are executed on two representative RDF benchmarks and one real RDF dataset in IoT by comparison with three representative index schemes, that is, RDF-3X, Bitmat, and TripleBit. Results demonstrate that our proposed scheme can respond to the complex query in real time and save much storage space compared with RDF-3X and Bitmat.\",\n \"Title: A High-Order CFS Algorithm for Clustering Big Data.\\nYear: 2016\\n\\nAbstract: With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i) an adaptive dropout deep learning model to learn features from each type of data, (ii) a feature tensor model to capture the correlations of heterogeneous data, and (iii) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.\",\n \"Title: BRGP: a balanced RDF graph partitioning algorithm for cloud storage.\\nYear: 2017\\n\\nAbstract: The continuous growth of resource description framework (RDF) data poses an important challenge on RDF data partitioning that is a vital technique for effective cloud storage. Recently, many partitioning algorithms for large RDF data have been developed, and most of them are based on graph partitioning. However, existing graph partitioning methods could not partition asymmetric RDF data effectively, resulting in a lower performance for cloud storage. This paper proposes a balanced RDF graph partitioning algorithm for storing massive RDF data on cloud. We first devise a modularity-based multi-level label propagation algorithm (MMLP) to partition RDF graph roughly and then use a balanced K-mediods clustering algorithm for final k-way partitioning. Balanced RDF graph partitioning algorithm designs an effective label update rule and a balanced modification strategy to achieve a high quality coarsening result and make the partition as equilibrium as possible. Experiments are carried on two representative RDF benchmarks and one real RDF dataset by comparison with two representative graph partitioning methods, that is, METIS and MLP+METIS. Results demonstrate that our proposed scheme can produce a high-quality partition for massive RDF data storage on cloud. Copyright (C) 2016 John Wiley & Sons, Ltd.\",\n \"Title: An Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things.\\nYear: 2017\\n\\nAbstract: With the rapid advances of sensing technologies and wireless communications, large amounts of dynamic data pertaining to industrial production are being collected from many sensor nodes deployed in the industrial Internet of Things. Analyzing those data effectively can help to improve the industrial services and mitigate the system unprepared breakdowns. As an important technique of data analysis,...\",\n \"Title: Social-Oriented Adaptive Transmission in Opportunistic Internet of Smartphones.\\nYear: 2017\\n\\nAbstract: Stable and reliable wireless communication is one of the critical demands for smart cities to connect people and devices. Although intelligent terminals can be leveraged to deliver and exchange data through Internet, poor network coverage and expensive network access challenge the deployment of network infrastructure. In this paper, we propose a social-oriented smartphone-based adaptive transmissi...\",\n \"Title: A Tucker Deep Computation Model for Mobile Multimedia Feature Learning\\nYear: 2017\\n\\nAbstract: Recently, the deep computation model, as a tensor deep learning model, has achieved super performance for multimedia feature learning. However, the conventional deep computation model involves a large number of parameters. Typically, training a deep computation model with millions of parameters needs high-performance servers with large-scale memory and powerful computing units, limiting the growth of the model size for multimedia feature learning on common devices such as portable CPUs and conventional desktops. To tackle this problem, this article proposes a Tucker deep computation model by using the Tucker decomposition to compress the weight tensors in the full-connected layers for multimedia feature learning. Furthermore, a learning algorithm based on the back-propagation strategy is devised to train the parameters of the Tucker deep computation model. Finally, the performance of the Tucker deep computation model is evaluated by comparing with the conventional deep computation model on two representative multimedia datasets, that is, CUAVE and SNAE2, in terms of accuracy drop, parameter reduction, and speedup in the experiments. Results imply that the Tucker deep computation model can achieve a large-parameter reduction and speedup with a small accuracy drop for multimedia feature learning.\",\n \"Title: Parameter-Free Incremental Co-Clustering for Multi-Modal Data in Cyber-Physical-Social Systems.\\nYear: 2017\\n\\nAbstract: With the rapid advances of cyber-physical-social systems (CPSS), large amounts of dynamic multi-modal data are being generated and collected. Analyzing those data effectively and efficiently can help to promote the development and improve the service quality of CPSS applications. As an important technique of multi-modal data analysis, co-clustering, designed to identify groupings of multi-dimensional data based on cross-modality fusion, is often exploited. Unfortunately, most existing co-clustering methods that mainly focus on dealing with static data become infeasible to fuse huge volume of multi-modal data in dynamic CPSS environments. To tackle this problem, this paper proposes a parameter-free incremental co-clustering method to deal with multi-modal data dynamically. In the proposed method, the single-modality similarity measure is extended to multiple modalities and three operations, namely, cluster creating, cluster merging, and instance partitioning, are defined to incrementally integrate new arriving objects to current clustering patterns without introducing additive parameters. Moreover, an adaptive weight scheme is designed to measure the importance of feature modalities based on the intra-cluster scatters. Extensive experiments on three real-world multi-modal datasets collected from CPSS demonstrate that the proposed method outperforms the compared state-of-the-art methods in terms of effectiveness and efficiency, thus it is promising for clustering dynamic multi-modal data in cyber-physical-social systems.\",\n \"Title: A Hierarchical Routing Algorithm for Satellite Networks Considering FSO Communication\\nYear: 2017\\n\\nAbstract: Insufficient ground stations promote the emergence of inter-satellite links (ISLs). Nowadays, adopting free space optical (FSO) ISLs has been a new trend for data communications. However, FSO ISLs across the atmosphere may perform worse in adverse weather such as fog or snow. In addition, using ordinary flat routing causes an excessive processing overhead, and diverse quality of experiences (QoEs) cannot be well satisfied. To solve these problems, we propose a Hierarchical Routing protocol for low/medium earth orbit (LEO/MEO) Satellite RF( radio frequency)/FSO Communication Networks (HR-SFCN). The entire network is divided into several LEO-satellite clusters, and each cluster treats the MEO satellite as the manager. To satisfy diverse QoE requirements, we propose the optimization algorithm for data interactions within the cluster. To reduce the processing overhead, we utilize MEO satellites as relay nodes for inter-cluster communications. To guarantee the link availability, we also perform adaptive RF/FSO link switching according to weather conditions. Given a STK(satellite tool kit)-based satellite communication network model, simulation results demonstrate that our HR-SFCN achieves a good trade-off among packet loss rate, path cost and end-to-end delay. Our RF/FSO hybrid system also increases the link availability up to 99.999%.\",\n \"Title: Incomplete multi-view clustering via deep semantic mapping.\\nYear: 2018\\n\\nAbstract: Multi-view clustering, which explores complementary information between multiple feature sets by consensus grouping, has benefited many data analytic applications. The majority of previous multi-view clustering studies usually assume that all feature sets appear in complete. In real-world applications, however, it is often the case that some views could suffer from the missing of examples, resulting in incomplete feature sets. The incompleteness of views makes it difficult to synthesize all feature sets and achieve a comprehensive description of data samples. In this paper, we develop a novel incomplete multi-view clustering method, which projects all incomplete multi-view data to a complete and unified representation in a common subspace. Different from existing researches which exploit shallow learning to identify the common subspace, a deep incomplete multi-view clustering (DIMC) incorporating with the constraint of intrinsic geometric structure is proposed here to couple incomplete multi-view samples. To bridge the gap between each view and the common representation, the multi-view deep coupled networks are trained to map high-level semantic features. Besides, to preserve the local invariance for each view, an affinity graph based regularizer is constructed to encode geometrical information. Therefore, a new objective function is developed and the optimization processes are presented. Extensive experiments on several real-world datasets demonstrate that the proposed DIMC is superior to the state-of-the-art incomplete multi-view clustering methods. (c) 2017 Published by Elsevier B.V.\",\n \"Title: Unsupervised Multiview Nonnegative Correlated Feature Learning for Data Clustering.\\nYear: 2018\\n\\nAbstract: Multiview data, which provide complementary information for consensus grouping, are very common in real-world applications. However, synthesizing multiple heterogeneous features to learn a comprehensive description of the data samples is challenging. To tackle this problem, many methods explore the correlations among various features across different views by the assumption that all views share th...\",\n \"Title: Deep Convolutional Computation Model for Feature Learning on Big Data in Internet of Things.\\nYear: 2018\\n\\nAbstract: Currently, a large number of industrial data, usually referred to big data, are collected from Internet of Things (IoT). Big data are typically heterogeneous, i.e., each object in big datasets is multimodal, posing a challenging issue on the convolutional neural network (CNN) that is one of the most representative deep learning models. In this paper, a deep convolutional computation model (DCCM) i...\",\n \"Title: A survey on deep learning for big data.\\nYear: 2018\\n\\nAbstract: •We review the most typical four deep learning models and their variants.•We provide a survey on deep learning models for big data feature learning.•We discuss the remaining challenges and future trends of big data deep learning.\",\n \"Title: Incremental CFS clustering on large data.\\nYear: 2017\\n\\nAbstract: As a popular data mining tool, clustering focuses on revealing underlying patterns embedded in data. However, most existing clustering methods mainly deal with static data, which may not be suitable for analyzing large data in dynamic environments. To tackle this problem, this paper proposes an incremental clustering method based on the CFS, clustering by fast search and find of density peaks, to process large dynamic data. In the proposed method, multiple representatives are identified for each cluster to integrate new objects into previous clustering patterns at first. Then the convex hull theory is employed to modify the representatives accordingly. To further improve the generality and effectiveness, one-time cluster adjustment strategy is explored. Extensive experiments on several real-world image datasets demonstrate that the proposed method outperforms state-of-the-art methods for clustering large data.\",\n \"Title: MMHG: Multi-modal Hypergraph Learning for Overall Survival After D2 Gastrectomy for Gastric Cancer\\nYear: 2017\\n\\nAbstract: Overall survival (OS) prediction has been a central topic of oncology. Most existing OS prediction models are based on traditional statistical methods which becomes a limitation when confronted with high dimensional dataset as well complicated internal relation among features in practice. In this paper, we weaken this limitation by exploring the application of multi-modal hypergraph (MMHG) learning framework to improve the accuracy of prediction. More specifically, the proposed hypergraph model unites the demographics, pathologic characteristics and physiological indicators simultaneously to predict the overall survival after D2 gastrectomy for gastric cancer. Experiments are carried out on a real data set of West China Hospital of Sichuan University with 939 patients to evaluate the proposed approach by comparison with random forest (RF) and support vector machine (SVM). Results demonstrate that our scheme outperforms the baseline methods in overall survival classification performance.\",\n \"Title: Semantic Clustering-Based Deep Hypergraph Model for Online Reviews Semantic Classification in Cyber-Physical-Social Systems.\\nYear: 2018\\n\\nAbstract: Sentiment classification of online reviews is playing an increasingly important role for both consumers and businesses in cyber-physical-social systems. However, existing works ignore the semantic correlation among different reviews, causing the ineffectiveness for sentiment classification. In this paper, a word embedding clustering-based deep hypergraph model (ECDHG) is proposed for the sentiment analysis of online reviews. The ECDHG introduces external knowledge by employing the pre-training word embeddings to express reviews. Then, semantic units are detected under the supervision of semantic cliques discovered by an improved hierarchical fast clustering algorithm. Convolutional neural networks are connected to extract the high-order textual and semantic features of reviews. Finally, the hypergraph can be constructed based on high-order relations of samples for the sentiment classification of reviews. Experiments are performed on five-domain data sets including movie, book, DVD, kitchen, and electronic to assess the performance of the proposed model compared with other seven models. The results validate that our model outperforms the compared methods in classification accuracy.\",\n \"Title: A Collaborative Filtering Recommendation-Based Scheme for WLANs With Differentiated Access Service.\\nYear: 2018\\n\\nAbstract: As the deployment of WLANs becomes extensive, users commonly have to select one access point (AP) for association among multiple available APs. However, the existing AP selection policies are not sufficient enough due to their drawbacks (e.g., imbalanced load and time consuming for association). In this paper, we present a collaborative filtering (CF) recommendation-based AP selection strategy in ...\",\n \"Title: Multiple Kernel Learning Based on Weak Learner for Automatic Image Annotation.\\nYear: 2017\\n\\nAbstract: Image annotation is a challenging problem, which has attracted intensive attention recently due to the semantic gap between images and corresponding tags. However, most existing works neglect the imbalance distribution of different classes and the internal correlations across modalities. To address these issues, we propose a multiple kernel learning method based on weak learner for image annotation, which can acquire the semantic correlations to predict tags of a given image. More specifically, we first employ the convolutional neural network to extract the semantic features of images, and take advantage of the over-sampling technique to generate new samples of minority classes which can solve the imbalance problem. Further, our proposed multiple kernel learning method is applied to obtain the internal correlations between images and tags. In order to further improve the prediction performance, we combine the boosting procedure with the multiple kernel learning to enhance the performance of classifier. We evaluate the proposed method on two benchmark datasets. The experimental results demonstrate that our method is superior to several state-of-the-art methods.\",\n \"Title: TCMHG: Topic-Based Cross-Modal Hypergraph Learning for Online Service Recommendations.\\nYear: 2018\\n\\nAbstract: Online product reviews sentiment classification plays an important role on service recommendation, yet most of current researches on it only focus on single-modal information ignoring the complementary information, that results in unsatisfied accuracy of sentiment classification. This paper proposes a cross-modal hypergraph model to capture textual information and sentimental information simultaneously for sentiment classification of reviews. Furthermore, a mixture model by coupling the latent Dirichlet allocation topic model with the proposed cross-modal hypergraph is designed to mitigate the ambiguity of some specific words, which may express opposite polarity in different contexts. Experiments are carried out on four-domain data sets (books, DVD, electronics, and kitchen) to evaluate the proposed approaches by comparison with lexicon-based method, Naive Bayes, maximum entropy, and support vector machine. Results demonstrate that our schemes outperform the baseline methods in sentiment classification accuracy.\",\n \"Title: Local Similarity Imputation Based on Fast Clustering for Incomplete Data in Cyber-Physical Systems.\\nYear: 2018\\n\\nAbstract: Missing values are common in cyber-physical systems (CPS) for a variety of reasons, such as sensor faults, communication malfunctions, environmental interferences, and human errors. An accurate missing value imputation is crucial to promote the data quality for data mining and statistical analysis tasks. Unfortunately, most of the existing methods take use of the whole data set to impute a missing...\",\n \"Title: Multimedia Feature Mapping and Correlation Learning for Cross-Modal Retrieval\\nYear: 2018\\n\\nAbstract: AbstractThis article describes how with the rapid increasing of multimedia content on the Internet, the need for effective cross-modal retrieval has attracted much attention recently. Many related works ignore the latent semantic correlations of modalities in the non-linear space and the extraction of high-level modality features, which only focuses on the semantic mapping of modalities in linear space and the use of low-level artificial features as modality feature representation. To solve these issues, the authors first utilizes convolutional neural networks and topic modal to obtain a high-level semantic feature of various modalities. Sequentially, they propose a supervised learning algorithm based on a kernel with partial least squares that can capture semantic correlations across modalities. Finally, the joint model of different modalities is learnt by the training set. Extensive experiments are conducted on three benchmark datasets that include Wikipedia, Pascal and MIRFlickr. The results show that the proposed approach achieves better retrieval performance over several state-of-the-art approaches.\",\n \"Title: A Hybrid Imputation Method Based on Denoising Restricted Boltzmann Machine\\nYear: 2018\\n\\nAbstract: AbstractData imputation is an important issue in data processing and analysis which has serious impact on the results of data mining and learning. Most of the existing algorithms are either utilizing whole data sets for imputation or only considering the correlation among records. Aiming at these problems, the article proposes a hybrid method to fill incomplete data. In order to reduce interference and computation, denoising restricted Boltzmann machine model is developed for robust feature extraction from incomplete data and clustering. Then, the article proposes partial-distance and co-occurrence matrix strategies to measure correlation between records and attributes, respectively. Finally, quantifiable correlation is converted to weights for imputation. Compared with different algorithms, the experimental results confirm the effectiveness and efficiency of the proposed method in data imputation.\",\n \"Title: A Weighted Fuzzy c-Means Clustering Algorithm for Incomplete Big Sensor Data.\\nYear: 2017\\n\\nAbstract: Sensor data processing plays an important role on the development of the wireless sensor networks in the big data era. Owning to the existence of a large number of incomplete data in wireless sensor networks, fuzzy c-means clustering algorithm (FCM) finds it difficult to produce an appropriate cluster result. The paper proposes a distributed weighted fuzzy c-means algorithm based on incomplete data imputation for big sensor data (DWFCM). DWFCM improves Affinity Propagation (AP) clustering algorithm by designing a new similarity metrics for imputing incomplete sensor data, and then proposes a weighted FCM (wFCM) by assigning a lower weighted value to the incomplete data object for improving the cluster accuracy. Finally, we validate the proposed weighted FCM algorithm on the dataset collected from the smart WSN lab. Experiments demonstrate that the weighted FCM algorithm could fill the missing values very accurately and improve the clustering results effectively.\",\n \"Title: A Partitioning and Index Algorithm for RDF Data of Cloud-Based Robotic Systems.\\nYear: 2018\\n\\nAbstract: Robotic systems generally employ resource description framework (RDF) to express heterogeneous data coming from different sensors. With the access of more terminals, the RDF volume in robotic systems is becoming larger and larger, posing new significant challenges to the storage and retrieval of RDF data. This paper proposes a star-based partitioning and index algorithm for RDF data of robotic systems. First, we construct a two-hop star structure by MapReduce and HDFS, and get a coarsened weighted graph. Next, a balance partitioning algorithm is used to divide the weighted graph. After partitioning, a compressed and linked S-tree index is proposed to improve the query efficiency. Experiments are executed on benchmark and real data sets to evaluate the studied partitioning and index methods. Results show that our partitioning method has a lower replication ratio, and a better load balancing performance, so our method is efficient for star query and competitive in complex query.\",\n \"Title: Supervised Intra- and Inter-Modality Similarity Preserving Hashing for Cross-Modal Retrieval.\\nYear: 2018\\n\\nAbstract: Cross-modal hashing has drawn considerable interest in multimodal retrieval due to the explosive growth of big data on multimedia. However, the existing methods mainly focus on unified hash codes learning and investigate the local geometric structure in the original space, resulting in low-discriminative power hash code of out-of-sample instances. To address this important problem, this paper is dedicated to investigate the hashing functions learning by considering the modality correlation preserving in the expected low-dimensional common space. A cross-modal hashing method based on supervised collective matrix factorization is proposed by taking intra-modality and inter-modality similarity preserving into account. For more flexible hashing functions, label information is embedded into the hashing functions learning procedure. Specifically, we explore the intra-modality similarity preserving in the expected low-dimensional common space. In addition, a supervised shrinking scheme is used to enhance the local geometric consistency in each modality. The proposed method learns unified hash codes as well as hashing functions for different modalities; the overall objective function, consisting of collective matrix factorization and intra- and inter-modality similarity embedding, is solved using an alternative optimization in an iterative scheme. Extensive experiments on three benchmark data sets demonstrate that the proposed method is more flexible to new coming data and can achieve superior performance to the state-of-the-art supervised cross-modal hashing approaches in most of the cases.\",\n \"Title: Distributed Feature Selection for Efficient Economic Big Data Analysis.\\nYear: 2018\\n\\nAbstract: With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although such data offers super opportunities for economic analysis, its low-quality, high-dimensionality and huge-volume pose great challenges on efficient analysis of economic big data. The existing methods have primarily analyzed economic data from the perspective of econometrics, ...\",\n \"Title: A Tensor-Train Deep Computation Model for Industry Informatics Big Data Feature Learning.\\nYear: 2018\\n\\nAbstract: The deep computation model has been proved to be effective for big data hierarchical feature and representation learning in the tensor space. However, it requires expensively computational resources including high-performance computing units and large memory to train a deep computation model with a large number of parameters, limiting its effectiveness and efficiency for industry informatics big d...\",\n \"Title: An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics.\\nYear: 2018\\n\\nAbstract: Deep learning, as the most important architecture of current computational intelligence, achieves super performance to predict the cloud workload for industry informatics. However, it is a nontrivial task to train a deep learning model efficiently since the deep learning model often includes a great number of parameters. In this paper, an efficient deep learning model based on the canonical polyad...\",\n \"Title: Combinative hypergraph learning in subspace for cross-modal ranking.\\nYear: 2018\\n\\nAbstract: Recent years have witnessed a surge of interests in cross-modal ranking. To bridge the gap between heterogeneous modalities, many projection based methods have been studied to learn common subspace where the correlation across different modalities can be directly measured. However, these methods generally consider pair-wise relationship merely, while ignoring the high-order relationship. In this paper, a combinative hypergraph learning in subspace for cross-modal ranking (CHLS) is proposed to enhance the performance of cross-modal ranking by capturing high-order relationship. We formulate the cross-modal ranking as a hypergraph learning problem in latent subspace where the high-order relationship among ranking instances can be captured. Furthermore, we propose a combinative hypergraph based on fused similarity information to encode both the intra-similarity in each modality and the inter-similarity across different modalities into the compact subspace representation, which can further enhance the performance of cross-modal ranking. Experiments on three representative cross-modal datasets show the effectiveness of the proposed method for cross-modal ranking. Furthermore, the ranking results achieved by the proposed CHLS can recall 80% of the relevant cross-modal instances at a much earlier stage compared against state-of-the-art methods for both cross-modal ranking tasks, i.e. image query text and text query image.\",\n \"Title: Privacy-Preserving Double-Projection Deep Computation Model With Crowdsourcing on Cloud for Big Data Feature Learning.\\nYear: 2018\\n\\nAbstract: Recent years have witness a considerable advance of Internet of Things with the tremendous progress of communication theories and sensing technologies. A large number of data, usually referring to big data, have been generated from Internet of Things. In this paper, we present a double-projection deep computation model (DPDCM) for big data feature learning, which projects the raw input into two se...\",\n \"Title: A Cooperative Quality-Aware Service Access System for Social Internet of Vehicles.\\nYear: 2018\\n\\nAbstract: Because of the enormous potential to guarantee road safety and improve driving experience, social Internet of Vehicle (SIoV) is becoming a hot research topic in both academic and industrial circles. As the ever-increasing variety, quantity, and intelligence of on-board equipment, along with the evergrowing demand for service quality of automobiles, the way to provide users with a range of security...\",\n \"Title: Deep Discrete Cross-Modal Hashing for Cross-Media Retrieval.\\nYear: 2018\\n\\nAbstract: •A framework of cross-modal deep neural networks is proposed for cross- media retrieval.•Intra-modality and inter-modality similarity preserving are considered in binary codes learning.•Binary constraint is addressed by discrete cyclic coordinate descent (DCC) algorithm without relaxation.•Experimental results on four datasets demonstrate the effectiveness of the proposed DDCMH, which is significantly superior to state-of-the-art cross-modal hashing approaches.\",\n \"Title: A Dropconnect Deep Computation Model for Highly Heterogeneous Data Feature Learning in Mobile Sensing Networks.\\nYear: 2018\\n\\nAbstract: Deep computation model, as a tensor deep learning model, outperforms multi-modal deep learning models for feature learning on heterogenous data. However, deep computation model is limited in generalization to small heterogeneous data sets since it typically requires many training objects to learn the parameters. In this article, we propose a dropconnect deep computation model (DDCM) for highly het...\",\n \"Title: Novel Framework of Risk-Aware Virtual Network Embedding in Optical Data Center Networks.\\nYear: 2018\\n\\nAbstract: The traffic between geographically distributed data centers (DCs) becomes bandwidth hungry. Since the optical interconnection has a high capacity, the optical data center network (ODCN)-where DCs are located at the edge of the optical backbone-emerges. By virtualization, the virtual networks-representing service requirements-are embedded onto the same part of the substrate ODCN. Each virtual netwo...\",\n \"Title: An Improved Deep Computation Model Based on Canonical Polyadic Decomposition.\\nYear: 2018\\n\\nAbstract: Deep computation models achieve super performance for big data feature learning. However, training a deep computation model poses a significant challenge since a deep computation model typically involves a large number of parameters. Specially, it needs a high-performance computing server with a large-scale memory and a powerful computing unit to train a deep computation model, making it difficult...\",\n \"Title: Heterogeneous visual features integration for image recognition optimization in internet of things.\\nYear: 2018\\n\\nAbstract: •A multimodal deep learning approach for image recognition optimization is proposed.•A back propagation optimization algorithm with shared weights is designed.•Results show our scheme has superior performance on image recognition.\",\n \"Title: Cross-Entropy Pruning for Compressing Convolutional Neural Networks.\\nYear: 2018\\n\\nAbstract: The success of CNNs is accompanied by deep models and heavy storage costs. For compressing CNNs, we propose an efficient and robust pruning approach, cross-entropy pruning (CEP). Given a trained CNN model, connections were divided into groups in a group-wise way according to their corresponding output neurons. All connections with their cross-entropy errors below a grouping threshold were then rem...\",\n \"Title: ICFS Clustering With Multiple Representatives for Large Data.\\nYear: 2019\\n\\nAbstract: With the prevailing development of Cyber-physical-social systems and Internet of Things, large-scale data have been collected consistently. Mining large data effectively and efficiently becomes increasingly important to promote the development and improve the service quality of these applications. Clustering, a popular data mining technique, aims to identify underlying patterns hidden in the data....\",\n \"Title: An Improved Stacked Auto-Encoder for Network Traffic Flow Classification.\\nYear: 2018\\n\\nAbstract: Network flow classification plays a very important role in various network applications and is a fundamental task in network flow control. However, the innovations in the multi-source network application and the elastic network architecture with the network flows of high volume, velocity, variety, and veracity pose unprecedented challenges on accurate network flow classification. In this article, ...\",\n \"Title: Dependable Deep Computation Model for Feature Learning on Big Data in Cyber-Physical Systems.\\nYear: 2019\\n\\nAbstract: With the ongoing development of sensor devices and network techniques, big data are being generated from the cyber-physical systems. Because of sensor equipment occasional failure and network transmission unreliability, a large number of low-quality data, such as noisy data and incomplete data, is collected from the cyber-physical systems. Low-quality data pose a remarkable challenge on deep learning models for big data feature learning. As a novel deep learning model, the deep computation model achieves superior performance for big data feature learning. However, it is difficult for the deep computation model to learn dependable features for low-quality data, since it uses the nonlinear function as the encoder. In this article, a dependable deep computation model is proposed for feature learning on low-quality big data in cyber-physical systems. Specially, a regularity is added into the objective function of the deep computation model to obtain reliable features in the intermediate-level representation space. Furthermore, a learning algorithm based on the back-propagation strategy is devised to train the parameters of the proposed model. Finally, experiments are conducted on three representative datasets and a real dataset to evaluate the effectiveness of the dependable deep computation model for low-quality big data feature learning. Results show that the proposed model achieves a remarkable result for the tasks of classification, restoration, and prediction, proving the potential of this work for practical applications in cyber-physical systems.\",\n \"Title: Dual Graph-Regularized Multi-view Feature Learning\\nYear: 2018\\n\\nAbstract: Real-world datasets often describe data instances in different views that complement information for each other. Unfortunately, synthesizing these views for learning a comprehensive description of data items is challenging. To tackle it, many approaches have been studied to explore correlations between various features by assuming that all views can be projected into a same semantic subspace. Following this idea, we propose a novel semi-supervised method, namely dual graphregularized multi-view feature learning (DGMFL), for data representation in this paper. The core idea is to generate a latent subspace among different views. Our approach utilizes dual graph regularization to capture semantic relationships among data items on both multi-view features and label information, as well as locates view-specific features for each view to reduce the effects of uncorrelated items. In this way, DGMFL could achieve more comprehensive representations hidden in multi-view datasets. Extensive experiments demonstrate that DGMFL model is superior to state-of-the-art multi-view learning methods on real-world datasets.\",\n \"Title: Deep Semantic Mapping for Heterogeneous Multimedia Transfer Learning Using Co-Occurrence Data\\nYear: 2019\\n\\nAbstract: Transfer learning, which focuses on finding a favorable representation for instances of different domains based on auxiliary data, can mitigate the divergence between domains through knowledge transfer. Recently, increasing efforts on transfer learning have employed deep neural networks (DNN) to learn more robust and higher level feature representations to better tackle cross-media disparities. However, only a few articles consider the correction and semantic matching between multi-layer heterogeneous domain networks. In this article, we propose a deep semantic mapping model for heterogeneous multimedia transfer learning (DHTL) using co-occurrence data. More specifically, we integrate the DNN with canonical correlation analysis (CCA) to derive a deep correlation subspace as the joint semantic representation for associating data across different domains. In the proposed DHTL, a multi-layer correlation matching network across domains is constructed, in which the CCA is combined to bridge each pair of domain-specific hidden layers. To train the network, a joint objective function is defined and the optimization processes are presented. When the deep semantic representation is achieved, the shared features of the source domain are transferred for task learning in the target domain. Extensive experiments for three multimedia recognition applications demonstrate that the proposed DHTL can effectively find deep semantic representations for heterogeneous domains, and it is superior to the several existing state-of-the-art methods for deep transfer learning.\",\n \"Title: Smart Chinese medicine for hypertension treatment with a deep learning model.\\nYear: 2019\\n\\nAbstract: As one important component of smart world, smart health is drawing more and more attention. Over the years, hypertension has become a high incident disease and it is continuing to threaten human health seriously. Unfortunately, no effective way has been found to cure hypertension at present. Many medical experiences have demonstrated the curative effect of traditional Chinese medicine in the hypertension treatment. In this paper, we explore smart Chinese medicine for hypertension treatment by combining deep learning and the traditional Chinese medicine theory. First, we present a potential idea to categorize the clinical cases into different groups based on symptom with the stacked auto-encoder model. Furthermore, we discuss and summarize the curative effect of classical prescriptions and herbs for each type of symptom in hypertension. Finally, we review the related work about deep learning models in medical application. The explored smart Chinese medicine is expected to guide doctors to treat hypertension diseases in clinical, which is helpful to build smart world, especially smart health.\",\n \"Title: Robust And Graph Regularised Non-Negative Matrix Factorisation For Heterogeneous Co-Transfer Clustering\\nYear: 2019\\n\\nAbstract: Transferring learning is proposed to tackle the problem where target instances are scarce to train an accurate model. Most existing transferring learning algorithms are designed for supervised learning and cannot obtain transferring results on multiple heterogeneous domains simultaneously. Moreover, the performance of transfer learning can be seriously degraded with the appearance of noises and corruptions. In this paper, a robust non-negative collective matrix factorisation model is proposed for heterogeneous co-transfer clustering which introduces the error matrices to capture the sparsely distributed noises. The heterogeneous clustering tasks are handled simultaneously and the graph regularisation is enforced on the collective matrix factorisation model to keep the intrinsic geometric structure of different domains. Experiment results on the real-world dataset show the proposed algorithm outperforms the baselines.\",\n \"Title: An integrated scheme based on service classification in pervasive mobile services\\nYear: 2012\\n\\nAbstract: Internet of Things and broadband communication are promoting the pervasive mobile services with their advanced features. However, security problems baffled the development. This paper proposes a trust model to protect the user's security. The billing or trust operator works as an agent to provide a trust authentication for all the service providers. The services are classified by sensitive value calculation. With the combined location-aware and identity-aware information and authentication history, the user's trustiness for corresponding service can be obtained. For decision, three trust regions are divided and referred to three ranks as follows: high, medium and low. The trust region tells the customer which authentication methods should be used for access. Penalty coefficient is also involved for forbidding force-crack. Copyright © 2012 John Wiley & Sons, Ltd.\",\n \"Title: An Incremental Deep Convolutional Computation Model for Feature Learning on Industrial Big Data\\nYear: 2019\\n\\nAbstract: The deep convolutional computation model (DCCM) enabled remarkable progress in feature learning of industrial big data in Internet of Things. However, as a typical static deep learning model, it is difficult to learn features for incremental industrial big data. To solve this problem, we propose an incremental DCCM by developing two incremental algorithms, i.e., parameter-incremental algorithm and structure-incremental algorithm. The parameter-incremental algorithm aims to incrementally train the fully connected layers together with fine tuning for incorporating the new knowledge into the prior one. Then, the structure-incremental algorithm is used to transfer the previous knowledge by introducing an updating rule of the tensor convolutional, pooling, and fully connected layers. Furthermore, the dropout strategy is extended into the tensor fully connected layer to improve the robustness of the proposed model. Finally, extensive experiments are carried out on the representative datasets including CIFRA and CUAVE to justify the proposed model in terms of adaption, preservation, and convergence efficiency.\",\n \"Title: Energy-Efficient Scheduling for Real-Time Systems Based on Deep Q-Learning Model\\nYear: 2019\\n\\nAbstract: Energy saving is a critical and challenging issue for real-time systems in embedded devices because of their limited energy supply. To reduce the energy consumption, a hybrid dynamic voltage and frequency scaling (DVFS) scheduling based on Q-learning (QL-HDS) was proposed by combining energy-efficient DVFS techniques. However, QL-HDS discretizes the system state parameters with a certain step size, resulting in a poor distinction of the system states. More importantly, it is difficult for QL-HDS to learn a system for various task sets with a Q-table and limited training sets. In this paper, an energy-efficient scheduling scheme based on deep Q-learning model is proposed for periodic tasks in real-time systems (DQL-EES). Specially, a deep Q-learning model is designed by combining a stacked auto-encoder and a Q-learning model. In the deep Q-learning model, the stacked auto-encoder is used to replace the Q-function for learning the Q-value of each DVFS technology for any system state. Furthermore, a training strategy is devised to learn the parameters of the deep Q-learning model based on the experience replay scheme. Finally, the performance of the proposed scheme is evaluated by comparison with QL-HDS on different simulation task sets. Results demonstrated that the proposed algorithm can save average \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"><tex-math notation=\\\"LaTeX\\\">$4.2\\\\%$</tex-math><alternatives><inline-graphic xlink:href=\\\"yang-ieq1-2743704.gif\\\"/></alternatives></inline-formula>\\n energy than QL-HDS.\",\n \"Title: High-order possibilistic c-means algorithms based on tensor decompositions for big data in IoT.\\nYear: 2018\\n\\nAbstract: •A CP-HOPCM algorithm based on canonical polyadic decomposition is proposed.•The canonical polyadic decomposition in CP-HOPCM is used to compress the attributes.•A TT-HOPCM algorithm based on the tensor-network is proposed.•The tensor-network in TT-HOPCM is used to compress the attributes.•The proposed schemes compress the objects greatly without a high accuracy drop.\",\n \"Title: PPHOPCM: Privacy-Preserving High-Order Possibilistic c-Means Algorithm for Big Data Clustering with Cloud Computing\\nYear: 2022\\n\\nAbstract: As one important technique of fuzzy clustering in data mining and pattern recognition, the possibilistic c-means algorithm (PCM) has been widely used in image analysis and knowledge discovery. However, it is difficult for PCM to produce a good result for clustering big data, especially for heterogenous data, since it is initially designed for only small structured dataset. To tackle this problem, ...\",\n \"Title: An Adaptive Droupout Deep Computation Model for Industrial IoT Big Data Learning with Crowdsourcing to Cloud Computing\\nYear: 2019\\n\\nAbstract: Deep computation, as an advanced machine learning model, has achieved the state-of-the-art performance for feature learning on big data in industrial Internet of Things (IoT). However, the current deep computation model usually suffers from overfitting due to the lack of public available labeled training samples, limiting its performance for big data feature learning. Motivated by the idea of active learning, an adaptive dropout deep computation model (ADDCM) with crowdsourcing to cloud is proposed for industrial IoT big data feature learning in this paper. First, a distribution function is designed to set the dropout rate for each hidden layer to prevent overfitting for the deep computation model. Furthermore, the outsourcing selection algorithm based on the maximum entropy is employed to choose appropriate samples from the training set to crowdsource on the cloud platform. Finally, an improved supervised learning from multiple experts scheme is presented to aggregate answers given by human workers and to update the parameters of the ADDCM simultaneously. Extensive experiments are conducted to evaluate the performance of the presented model by comparing with the dropout deep computation model and other state-of-the-art crowdsourcing algorithms. The results demonstrate that the proposed model can prevent overfitting effectively and aggregate the labeled samples to train the parameters of the deep computation model with crowdsouring for industrial IoT big data feature learning.\",\n \"Title: Secure weighted possibilistic c-means algorithm on cloud for clustering big data.\\nYear: 2019\\n\\nAbstract: The weighted possibilistic c-means algorithm is an important soft clustering technique for big data analytics with cloud computing. However, the private data will be disclosed when the raw data is directly uploaded to cloud for efficient clustering. In this paper, a secure weighted possibilistic c-means algorithm based on the BGV encryption scheme is proposed for big data clustering on cloud. Specially, BGV is used to encrypt the raw data for the privacy preservation on cloud. Furthermore, the Taylor theorem is used to approximate the functions for calculating the weight value of each object and updating the membership matrix and the cluster centers as the polynomial functions which only include addition and multiplication operations such that the weighed possibilistic c-means algorithm can be securely and correctly performed on the encrypted data in cloud. Finally, the presented scheme is estimated on two big datasets, i.e., eGSAD and sWSN, by comparing with the traditional weighted possibilistic c-means method in terms of effectiveness, efficiency and scalability. The results show that the presented scheme performs more efficiently than the traditional weighted possiblistic c-means algorithm and it achieves a good scalability on cloud for big data clustering.\",\n \"Title: Integration of Image Feature and Word Relevance: Toward Automatic Image Annotation in Cyber-Physical-Social Systems.\\nYear: 2018\\n\\nAbstract: Image annotation is challenging due to the explosive increase of image data in cyber-physical-social systems. Because of the semantic gap between images and corresponding labels, it has attracted extensive attentions in recent years. However, most existing research neglects the imbalanced distribution of different classes and the internal relevance of image labels. Besides, the weak image labeling affects the annotation performance to some extent. To address these issues, we propose a learning model for image annotation through integrating deep features and label relevance of images. Specifically, we first employ a convolutional neural-network approach to extract the deep features of images and utilize the synthetic minority oversampling technique to deal with the problem of class imbalance. Furthermore, we exploit the correlations, including symbiotic and semantic relationships of labels, to compute the relevance of label sets. Then, we incorporate this relevance into one classifier to reconstruct the complete label sets, and learn the mapping from image features to the reconstructed label sets by the other classifier. In addition, a joint convex loss function is proposed, which combines the two classifiers via co-regularization and compels them to be consistent. We evaluate the proposed method on two benchmark data sets. The experimental results demonstrate that our method outperforms several state-of-the-art solutions.\",\n \"Title: A Double Deep Q-learning Model for Energy-efficient Edge Scheduling\\nYear: 2019\\n\\nAbstract: Reducing consumption is a vital and challenging problem for the edge computing devices since they are always energy-limited. To tackle this problem, a deep Q-learning model with multiple DVFS algorithms was proposed for energy-efficient scheduling (DQL-EES). However, DQL-EES is highly unstable when using a single stacked auto-encoder to approximate the Q-function. Additionally, it cannot distinguish the continuous system states well since it depends on a Q-table to generate the target values for training parameters. In this paper, a double deep Q-learning model is proposed for energy-efficient edge scheduling (DDQ-EES). Specially, the proposed double deep Q-learning model includes a generated network for producing the Q-value for each DVFS algorithm and a target network for producing the target Q-values to train the parameters. Furthermore, the rectified linear units (ReLU) function is used as the activation function in the double deep Q-learning model, instead of the Sigmoid function in QDL-EES, to avoid gradient vanishing. Finally, a learning algorithm based on experience replay is developed to train the parameters of the proposed model. The proposed model is compared with DQL-EES on EdgeCloudSim in terms of saving and training time. Results indicate that our proposed model can save average $2%-2.4%$ energy and achieve a higher training efficiency than QQL-EES, proving its potential for energy-efficient edge scheduling.\",\n \"Title: A Unified Smart Chinese Medicine Framework for Healthcare and Medical Services.\\nYear: 2021\\n\\nAbstract: Smart Chinese medicine has emerged to contribute to the evolution of healthcare and medical services by applying machine learning together with advanced computing techniques like cloud computing to computer-aided diagnosis and treatment in the health engineering and informatics. Specifically, smart Chinese medicine is considered to have the potential to treat difficult and complicated diseases suc...\",\n \"Title: Deep Learning Models For Diagnosing Spleen And Stomach Diseases In Smart Chinese Medicine With Cloud Computing\\nYear: 2021\\n\\nAbstract: Cloud computing is significantly contributing to the development of smart Chinese medicine. The diagnosis and treatment of spleen and stomach diseases has been arousing great interest in smart Chinese medicine with cloud computing since many persons are suffering from spleen and stomach diseases. Currently, spleen and stomach diseases present some new characteristics with the dramatic changes in natural climate, social environment, and human living habits. Recently, deep learning, together with cloud computing techniques, has successfully used in medical image analysis and therefore it is the most promising model for diagnosing spleen and stomach disease in smart Chinese medicine. In this paper, we present a survey on deep learning models in medical image analysis for computer-aided diagnosis in modern medicine. Afterwards, we summarize the syndrome types of spleen and stomach diseases and furthermore analyze the causes and pathogenesis for each syndrome. Finally, we discuss the open challenges and research directions of deep learning models applicable to the computer-aided diagnosis of spleen and stomach diseases, which is expected to contribute to the development of smart Chinese medicine with cloud computing.\",\n \"Title: A Deep CFS Model for Text Clustering\\nYear: 2018\\n\\nAbstract: With the fast development of the Internet technology, the court text information is collected from various fields at an unprecedented speed, such as Weibo and Wechat. This big court text information of high volume poses a vast challenge for the judge making reasonable decisions based on the vast cases. To cluster the reasonable assistant cases from the vast cases, we propose a deep CFS model for the text clustering, which can cluster the court text effectively, in this paper. In the proposed model, a robust deep text feature extractor is designed to improve the cluster accuracy, in which an ensemble of deep learning models are used to learn the deep features of the text. Furthermore, the CFS algorithm is conducted on the extracted deep text features, to discover the non-spherical clusters with the automatic find of the cluster centers. Finally, the proposed deep cluster model is evaluated on two typical datasets and the results show it can perform better than compared models in terms of the cluster accuracy.\",\n \"Title: A New Deep Transfer Learning Model for Judicial Data Classification\\nYear: 2018\\n\\nAbstract: For judicial analysis, usually there are enough labeled instances for one domain, but there are few or even no labeled instances in target domain. Therefore, to bridge the gap between these two domains and use the sufficient source information for target analysis is important. In this paper, we focus on developing a new deep transfer learning model to translate the source domain information for target data classification. The proposed model integrates the deep neural network (DNN) with canonical correlation analysis (CCA) to derive a deep correlation subspace for associating data across different domains. Moreover, a new objective is designed to train the whole network jointly. When the deep semantic representation is achieved, the shared features of the source domain are transferred for instance classification in the target domain. Experiments on several datasets present that the proposed method is superior to the state-of-the-art methods for deep transfer learning, which is promising for judicial data classification.\",\n \"Title: A canonical polyadic deep convolutional computation model for big data feature learning in Internet of Things\\nYear: 2019\\n\\nAbstract: In recent years, the Internet of Things is more widely deployed with increasing amounts of data gathered. These data are of high volume, velocity, veracity and variety, posing a vast challenge on the data analysis, especially with respect to variety and velocity. To address this challenge, a canonical polyadic deep convolutional computation model is introduced to efficiently and effectively capture the hierarchical representation of the big data by employing the canonical polyadic decomposition to factorize the deep convolutional computation. In particular, to speed up the learning of local topologies hidden in the big data, a canonical polyadic convolutional kernel is devised by compacting the tensor convolutional kernel into the linear combination of the principle rank-1 tensors. Furthermore, the canonical polyadic tensor fully-connected weight is used to efficiently map the correlation in the fully-connected layer. After that, the canonical polyadic high-order back-propagation is devised to train the canonical polyadic deep convolutional computation model. At last, detailed experiments are carried out on two well-known datasets. And results illustrate that the introduced model achieves higher performance than a competing model.\",\n \"Title: Unsupervised multi-view non-negative for law data feature learning with dual graph-regularization in smart Internet of Things\\nYear: 2019\\n\\nAbstract: In the real world, the law data in the smart Internet of Things usually consists of heterogeneous information with some noises. Non-negative matrix factorization is a popular tool for multi-view learning, which can be employed to represent and learn heterogeneous law features comprehensively. However, current NMF-based techniques generally use clean multi-view datasets to generate common subspace, while in practice, they often contain some noises or unrelated items so that the performance of the algorithms may be severely degraded. In this paper, we propose to develop a novel subspace learning model, called Adaptive Dual Graph-regularized Multi-View Non-Negative Feature Learning (ADMFL), for multi-view data representation. We utilize the geometric structures of both data and feature manifold to model the distribution of data points in the common subspace. Meanwhile, we lift the effect of unrelated features down through separating the view-specific features for each view. Moreover, we introduce a weight factor for all views and maintain the sparsity of the latent common representation. An effective objective function is thus designed and iteratively updated until convergence. Experiments on standard datasets demonstrate that the proposed ADMFL method outperforms other compared methods in the paper.\",\n \"Title: MULTIPLE-INPUT MULTIPLE-OUTPUT FUSION NETWORK FOR GENERALIZED ZERO-SHOT LEARNING\\nYear: 2021\\n\\nAbstract: Generalized zero-shot learning (GZSL) has attracted considerable attention recently, which trains models with data from seen classes and tests on data from both seen and unseen classes. Most of the existing methods attempt to find a mapping from visual space to semantic space, such mapping can easily result in the domain shift problem. To address this issue, we propose a Multiple-Input Multiple-Output Fusion Network to GZSL. It can generate similar common semantic representation to paired inputs even with only the class semantic embeddings. This makes it possible to synthesize pseudo samples from attributes of unseen classes. Extensive experiments carried out on three benchmark datasets show the effectiveness of the proposed model.\",\n \"Title: Fake Reviewer Group Detection in Online Review Systems\\nYear: 2021\\n\\nAbstract: Online review systems are important components in influencing customers' purchase decisions. To manipulate a product's reputation, many stores hire large numbers of people to produce fake reviews to mislead customers. Previous methods tackle this problem by detecting malicious individuals, ignoring the fact that the spam activities are often formed in groups, where individuals work collectively to write fake reviews. Fake reviewer group detection, however, is more challenging due to the difficulties in capturing the underlying relationships in groups. In this work, we present an unsupervised and end-to-end approach for fake reviewer group detection in online reviews. Specifically, our method can be summarized into two procedures. First, cohensive groups are detected with modularity-based graph convolutional networks. Then the suspiciousness of each group is measured by several anomaly indicators from both individual and group levels. The fake reviewer groups can be finally detected through suspiciousness. Extensive experiments are conducted on real-world datasets, and the results show that our proposed method is effective in detecting fake reviewer groups compared with the state-of-the-art baselines.\",\n \"Title: LSTM-MFCN: A time series classifier based on multi-scale spatial–temporal features\\nYear: 2022\\n\\nAbstract: Time series classification (TSC) task attracts huge interests, since they correspond to the real-world problems in a wide variety of fields, such as industry monitoring. Deep learning methods, especially CNN and FCN, shows competitive performance in TSC task by their virtue of good adaption for raw time series and self-adapting extraction of features. Then various variants of CNN are proposed so as to make further breakthrough by the better perception to characteristics of data. Among them, LSTM-FCN and GRU-FCN who learn spatial and temporal features simultaneously are the most remarkable ones, achieving state of the art results. Therefore, inspired by their success and in consideration of the discriminative features implied in time series are diverse in size, a multimodal network LSTM-MFCN composed of multi-scale FCN (MFCN) and LSTM are proposed in this work. The gate-based network LSTM naturally fits to various terms time dependencies, and FCN with multi-scale sets of filters are capable to perceive spatial features of different range from time series curves. Besides, dilation convolution is deployed to build multi-scale receptive fields in larger level without increasing the parameters to be trained. The full perception of large multi-scale spatial–temporal features lead LSTM-MFCN to possess comprehensive and thorough grasp to time series, thus achieve even better accuracies. Finally, two representative architectures are presented specifically and their experiments on UCR datasets reveals the effectiveness and superiority of proposed LSTM-MFCN.\",\n \"Title: Incremental multi-view correlated feature learning based on non-negative matrix factorisation\\nYear: 2021\\n\\nAbstract: In real-world applications, large amounts of data from multiple sources come in the form of streams. This makes multi-view feature learning cost much time when new instances rise incrementally. Dealing with these growing multi-view data becomes a challenging problem. Some single-view methods focus on processing the data dynamically, but they are not suitable for multi-view data. Some online multi-view methods are proposed to tackle it, but they ignore the influence of uncorrelated items in each view. Therefore, in this study, the authors propose a new algorithm, called Incremental Multi-view Correlated Feature Learning (IMCFL) based on non-negative matrix factorisation, to learn the common feature across views. By separating uncorrelated items of new instances and constructing incremental joint learning of correlated and uncorrelated features, the proposed IMCFL can eliminate the influence of uncorrelated information in the individual view and improve the effectiveness of incremental multi-view common feature learning. Extensive experiments on real-world datasets confirm its superiority by comparing it with other state-of-the-art incremental and non-incremental methods.\",\n \"Title: A two-stage deep transfer learning model and its application for medical image processing in Traditional Chinese Medicine\\nYear: 2022\\n\\nAbstract: People are deepening the study on model explainability as well as performance to better understand models’ decisions from the human perspective. However, the lack of rare clinical diagnosis data always limits the power of the emerging data-driven deep diagnosis methods, and the traditional deep transfer learning (DTL) applicable for the case is often insufficient to learn specific features in medical image processing, leading to poor explainability. To address those challenges, a two-stage deep transfer learning model is proposed and applied to assist in the Traditional Chinese Medicine (TCM) tongue diagnosis. Especially, a two-stage transfer learning training strategy is designed to loose the data dependence of deep learning on the domain data, which is composed of the imitate stage that discovers shared basic source features and the transfer stage to relearn target patterns, with good explainability. The corresponding deep squeeze-and-excitation convolutional network is proposed to learn the clinical patterns of tongue symptoms, in which a three-layer feature pyramid network fuses the multi-scale tongue features. Extensive experiments are conducted on the real clinical dataset in terms of classification accuracy and learning efficiency. The resulting accuracy of the proposed model proves its performance advantage with the recognition time achieving real-time performance.\",\n \"Title: Dual Alignment Self-Supervised Incomplete Multi-View Subspace Clustering Network\\nYear: 2021\\n\\nAbstract: Incomplete multi-view clustering has attracted much attention in decade years. To date, most of the remarkable achievements, however, exploit shallow models to learn shared feature representations based on incomplete views. Although some deep learning methods have been proposed to solve this issue, the existing ones still have the following problems: 1) The consistency between views is ignored, which will have serious negative impacts on incomplete multi-view learning. 2) The learned features do not have sufficient cluster-friendliness, that is, the tightness within clusters and the repulsiveness between clusters are not fully considered. To tackle the above shortcomings, we propose a Dual Alignment Self-supervised Incomplete Multi-view Subspace Clustering network (DASIMSC) in this paper. Specifically, the manifold alignment constraint and consistency alignment constraint are integrated with the autoencoder to preserve the compact inherent local structure within the view and the consistency semantics between incomplete views, respectively. Moreover, a self-expression layer coupled with a spectral clustering module is designed to naturally separate different types of data, leveraging the current clustering results to supervise subspace learning, which excludes inter-cluster. Experimental results on several datasets show that our algorithm outperforms all compared state-of-the-arts.\",\n \"Title: Multilabel Aerial Image Classification With Unsupervised Domain Adaptation\\nYear: 2022\\n\\nAbstract: Deep learning (DL) methods are promising for the multilabel aerial image classification (MAIC) task. However, current DL methods face a common problem: the need for large multilabeled datasets. Collecting and annotating raw aerial image datasets can be extremely time- and labor-consuming. To address this concern in MAIC, domain adaptation (DA) provides a novel solution by transferring the knowledge learned from a label-rich dataset (i.e., the source domain) to a label-scarce dataset (i.e., the target domain), while current DA models are mainly designed for single-labeled tasks. In this article, we propose a novel end-to-end MAIC model based on DA techniques, named DA-MAIC. To the best of our knowledge, this article for the first time integrates DA to tackle the label scarcity problem in the MAIC task. Specifically, the proposed DA-MAIC is composed of two main parts: the image classifier and the domain classifier. The image classifier captures task-discriminative features based on the graph convolutional network (GCN) to predict multiple image labels; and the domain classifier extracts domain-invariant representations, which mitigates the domain shift between two underlying distributions. We extensively evaluate the proposed DA-MAIC from different perspectives on three benchmark datasets, including the commonly used UCM dataset, the high-resolution AID dataset, and the recently proposed DFC15 dataset. Both quantitative and qualitative results support that the proposed DA-MAIC can generalize the source domain knowledge to new scenarios and substantially improve the classification performance on the target domain task.\",\n \"Title: Multi-View Representation Learning via Dual Optimal Transportation\\nYear: 2021\\n\\nAbstract: Recently, multi-view representation learning has gained rapid growth in various fields. Most of previous multi-view learning methods rely on strong notions of distances that often provide no useful gradients in deep network training, which greatly degrades the performance in merging complementary information of views. To address this challenge, a multi-view representation learning network with dual optimal transportation (MDOT-Net) is proposed to capture fusion representations embedded in a common manifold with the weak topology. In MDOT-Net, the multi-view representation learning is modelled as an optimal transportation (OT) problem in manifold fitting, which is further decomposed into the intra-view OT and the inter-view OT. The intra-view OT is implemented by a view-specific adversarial variational network, which accurately captures local manifold structures within views by leveraging fusion knowledge. The inter-view OT is implemented by a view-fusion adversarial inference network, which models fusion representations compatible with diversities of sub-manifolds by utilizing view-specific knowledge. The two OTs boost mutually by providing prior knowledge to each other in multi-view representation learning. Finally, numerous experiments are conducted on four benchmark datasets, and the results demonstrate MDOT-Net is competitive against state-of-the-art algorithms.\",\n \"Title: LEGAN: A Light and Effective Generative Adversarial Network for medical image synthesis\\nYear: 2022\\n\\nAbstract: Medical image synthesis plays an important role in clinical diagnosis by providing auxiliary pathological information. However, previous methods usually utilize the one-step strategy designed for wild image synthesis, which are not sensitive to local details of tissues within medical images. In addition, these methods consume a great number of computing resources in generating medical images, which seriously limits their applicability in clinical diagnosis. To address the above issues, a Light and Effective Generative Adversarial Network (LEGAN) is proposed to generate high-fidelity medical images in a lightweight manner. In particular, a coarse-to-fine paradigm is designed to imitate the painting process of humans for medical image synthesis within a two-stage generative adversarial network, which guarantees the sensitivity to local information of medical images. Furthermore, a low-rank convolutional layer is introduced to construct LEGAN for lightweight medical image synthesis, which utilizes principal components of full-rank convolutional kernels to reduce model redundancy. Additionally, a multi-stage mutual information distillation is devised to maximize dependencies of distributions between generated and real medical images in model training. Finally, extensive experiments are conducted in two typical tasks, i.e., retinal fundus image synthesis and proton density weighted MR image synthesis. The results demonstrate that LEGAN outperforms the comparison methods by a significant margin in terms of Fréchet inception distance (FID) and Number of parameters (NoP).\"\n]","isConversational":false},"profile_size":{"kind":"number","value":115,"string":"115"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13265,"string":"13,265"}}},{"rowIdx":13266,"cells":{"question_id":{"kind":"string","value":"Yun Sing Koh"},"question":{"kind":"string","value":"Generate an abstract for the title \"Detecting online auction shilling frauds using supervised learning\" using the following items: 1. Online auction fraud\n2. Supervised learning\n3. Synthetic data generator\n4. Fraud detection models\n5. Shilling fraud"},"answer":{"kind":"string","value":"Online auction sites are a target for fraud due to their anonymity, number of potential targets and low likelihood of identification. Researchers have developed methods for identifying fraud. However, these methods must be individually tailored for each type of fraud, since each differs in the characteristics important for their identification. Using supervised learning methods, it is possible to produce classifiers for specific types of fraud by providing a dataset where instances with behaviours of interest are assigned to a separate class. However this requires multiple labelled datasets: one for each fraud type of interest. It is difficult to use real-world datasets for this purpose since they are difficult to label, often limited in size, and contain zero or multiple suspicious behaviours that may or may not be under investigation. The aims of this work are to: (1) demonstrate the approach of using supervised learning together with a validated synthetic data generator to create fraud detection models that are experimentally more accurate than existing methods and that is effective over real data, and (2) to evaluate a set of features for use in general fraud detection is shown to further improve the performance of the created detection models. The approach is as follows: the data generator is an agent-based simulation modelled on users in commercial online auction data. The simulation is extended using fraud agents which model a known type of online auction fraud called competitive shilling. These agents are added to the simulation to produce the synthetic datasets. Features extracted from this data are used as training data for supervised learning. Using this approach, we optimise an existing fraud detection algorithm, and produce classifiers capable of detecting shilling fraud. Experimental results with synthetic data show the new models have significant improvements in detection accuracy. Results with commercial data show the models identify users with suspicious behaviour."},"correct_answer":{"kind":"string","value":"Online auction sites are a target for fraud due to their anonymity, number of potential targets and low likelihood of identification. Researchers have developed methods for identifying fraud. However, these methods must be individually tailored for each type of fraud, since each differs in the characteristics important for their identification. Using supervised learning methods, it is possible to produce classifiers for specific types of fraud by providing a dataset where instances with behaviours of interest are assigned to a separate class. However this requires multiple labelled datasets: one for each fraud type of interest. It is difficult to use real-world datasets for this purpose since they are difficult to label, often limited in size, and contain zero or multiple suspicious behaviours that may or may not be under investigation. The aims of this work are to: (1) demonstrate the approach of using supervised learning together with a validated synthetic data generator to create fraud detection models that are experimentally more accurate than existing methods and that is effective over real data, and (2) to evaluate a set of features for use in general fraud detection is shown to further improve the performance of the created detection models. The approach is as follows: the data generator is an agent-based simulation modelled on users in commercial online auction data. The simulation is extended using fraud agents which model a known type of online auction fraud called competitive shilling. These agents are added to the simulation to produce the synthetic datasets. Features extracted from this data are used as training data for supervised learning. Using this approach, we optimise an existing fraud detection algorithm, and produce classifiers capable of detecting shilling fraud. Experimental results with synthetic data show the new models have significant improvements in detection accuracy. Results with commercial data show the models identify users with suspicious behaviour."},"documents":{"kind":"list like","value":["Title: Automatic assignment of item weights for pattern mining on data streams\nYear: 2011\n\nAbstract: Research inWeighted Association Rule Mining (WARM) has largely concentrated on mining traditional static transactional datasets. Whilst there have been a few attempts at researching WARM in a data stream environment, none have addressed the problem of assigning and adapting weights in the presence of concept drift, which often occurs in a data stream environment. In this research we experiment with two methods of adapting weights; firstly, a simplistic method that recomputes the entire set of weights at fixed intervals, and secondly a method that relies on a distance function that assesses the extent of change in the stream and only updates those items that have had significant change in their patterns of interaction. We show that the latter method is able to maintain good accuracy whilst being several times faster than the former.","Title: RP-Tree: rare pattern tree mining\nYear: 2011\n\nAbstract: Most association rule mining techniques concentrate on finding frequent rules. However, rare association rules are in some cases more interesting than frequent association rules since rare rules represent unexpected or unknown associations. All current algorithms for rare association rule mining use an Apriori level-wise approach which has computationally expensive candidate generation and pruning steps. We propose RP-Tree, a method for mining a subset of rare association rules using a tree structure, and an information gain component that helps to identify the more interesting association rules. Empirical evaluation using a range of real world datasets shows that RP-Tree itemset and rule generation is more time efficient than modified versions of FP-Growth and ARIMA, and discovers 92-100% of all the interesting rare association rules.","Title: Finding Maximal Overlapping Communities.\nYear: 2013\n\nAbstract: Social networks play an important role in everyday life. Nowadays there is various research that concentrates on detecting communities within these networks. Traditionally most of the community detection algorithms focus on detecting disjoint networks. However there is a need for overlapping community detection. In recent years there have been some attempts at detecting overlapping communities. Most of these techniques concentrate on just detecting these communities, none of this research tries to detect the maximal set of these communities which gives more stability. In this paper we propose a new method called Maximal-DSHRINK that allows us to detect the maximal set of overlapping communities within a social network. We show that the maximal set provides us with better quality in terms of modularity gain.","Title: Weighted association rule mining using particle swarm optimization\nYear: 2011\n\nAbstract: Association rule mining is an important data mining task that discovers relationships among items in a transaction database. Most approaches to association rule mining assume that the items within the dataset have a uniform distribution. Therefore, weighted association rule mining (WARM) was introduced to provide a notion of importance to individual items. In previous work most of these approaches require users to assign weights for each item. This is infeasible when we have millions of items in a dataset. In this paper we propose a novel method, Weighted Association Rule Mining using Particle Swarm Optimization (WARM SWARM), which uses particle swarm optimization to assign meaningful item weights for association rule mining.","Title: Efficiently finding negative association rules without support threshold\nYear: 2007\n\nAbstract: Typically association rule mining only considers positive frequent itemsets in rule generation, where rules involving only the presence of items are generated. In this paper we consider the complementary problem of negative association rule mining, which generates rules describing the absence of itemsets from transactions. We describe a new approach called MINR (Mining Interesting Negative Rules) to efficiently find all interesting negative association rules. Here we only consider the presence or absence of itemsets that are strongly associated. Our approach does not require a user defined support threshold, and is based on pruning items that occur together by coincidence. For every individual itemset we calculate two custom thresholds based on their support: the positive and negative chance thresholds. We compared our implementation against Pearson φ correlation.","Title: CBDT: A Concept Based Approach to Data Stream Mining\nYear: 2009\n\nAbstract: Data Stream mining presents unique challenges compared to traditional mining on a random sample drawn from a stationary statistical distribution. Data from real-world data streams are subject to concept drift due to changes that take place continuously in the underlying data generation mechanism. Concept drift complicates the process of mining data as models that are learnt need to be updated continuously to reflect recent changes in the data while retaining relevant information that has been learnt from the past. In this paper, we describe a Concept Based Decision Tree (CBDT) learner and compare it with the CVDFT algorithm, which uses a sliding time window. Our experimental results show that CBDT outperforms CVFDT in terms of both classification accuracy and memory consumption.","Title: Kernel-Tree: mining frequent patterns in a data stream based on forecast support\nYear: 2012\n\nAbstract: Although frequent pattern mining techniques have been extensively studied, the extension of their application onto data streams has been challenging. Due to data streams being continuous and unbounded, an efficient algorithm that avoids multiple scans of data is needed. In this paper we propose Kernel-Tree (KerTree), a single pass tree structured technique that mines frequent patterns in a data stream based on forecasting the support of current items in the future state. Unlike previous techniques that build a tree based on the support of items in the previous block, KerTree performs an estimation of item support in the next block and builds the tree based on the estimation. By building the tree on an estimated future state, KerTree effectively reduces the need to restructure for every block and thus results in a better performance and mines the complete set of frequent patterns from the stream while maintaining a compact structure.","Title: Transaction clustering using a seeds based approach\nYear: 2008\n\nAbstract: Transaction clustering has received a great deal of attention in the past few years. Its functionality extends well beyond traditional clustering algorithms which basically perform a near-neighbourhood search for locating groups of similar instances. The basic concept underlying transaction clustering stems from the concept of large items as defined by association rule mining algorithms. Clusters formed on the basis of large items that are shared between instances offer an attractive alternative to association rule mining systems. Currently, none of the techniques proposed offer a good solution to scenarios where large items overlap across clusters. In this paper we overcome the aforementioned limitations by using cluster seeds that represent initial centroids. Seeds are generated from sets of transaction items that occur together above a certain threshold and such seeds may overlap in their itemsets across clusters.","Title: Rare association rule mining via transaction clustering\nYear: 2008\n\nAbstract: Rare association rule mining has received a great deal of attention in the recent past. In this research, we use transaction clustering as a pre-processing mechanism to generate rare association rules. The basic concept underlying transaction clustering stems from the concept of large items as defined by traditional association rule mining algorithms. We make use of an approach proposed by Koh & Pears (2008) to cluster transactions prior to mining for association rules. We show that pre-processing the dataset by clustering will enable each cluster to express their own associations without interference or contamination from other sub groupings that have different patterns of relationships. Our results show that the rare rules produced by each cluster are more informative than rules found from direct association rule mining on the unpartitioned dataset.","Title: WeightTransmitter: weighted association rule mining using landmark weights\nYear: 2012\n\nAbstract: Weighted Association Rule Mining (WARM) is a technique that is commonly used to overcome the well-known limitations of the classical Association Rule Mining approach. The assignment of high weights to important items enables rules that express relationships between high weight items to be ranked ahead of rules that only feature less important items. Most previous research to weight assignment has used subjective measures to assign weights and are reliant on domain specific information. Whilst there have been a few approaches that automatically deduce weights from patterns of interaction between items, none of them take advantage of the situation where weights of only a subset of items are known in advance. We propose a model, WeightTransmitter, that interpolates the unknown weights from a known subset of weights.","Title: Automatic Item Weight Generation for Pattern Mining and its Application\nYear: 2011\n\nAbstract: Association rule mining discovers relationships among items in a transactional database. Most approaches assume that all items within a dataset have a uniform distribution with respect to support. However, this is not always the case, and weighted association rule mining WARM was introduced to provide importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. In certain cases, it is difficult to provide weights to all items within a dataset. In this paper, the authors propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. The authors experiment shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared with a miner that does not employ a weighting scheme. The authors applied the model in a real world application to mine text from a given collection of documents. The use of item weighting enabled the authors to attach more importance to terms that are distinctive. The results demonstrate that keyword discrimination via item weighting leads to informative rules.","Title: Generating realistic online auction data\nYear: 2012\n\nAbstract: To combat online auction fraud, researchers have developed fraud detection and prevention methods. However, it is difficult to effectively evaluate these methods using commercial or synthetic auction data. For commercial data, it is not possible to accurately identify cases of fraud. For synthetic auction data, the conclusions drawn may not extend to the real world. The availability of realistic synthetic auction data, which models real auction data, will be invaluable for effective evaluation of fraud detection algorithms. We present an agent-based simulator that is capable of generating realistic English auction data. The agents and model are based on data collected from the TradeMe online auction site. We evaluate the generated data in two ways to show that it is similar to the TradeMe data. Evaluation of individual features show that correlation is greater than 0.9 for 8 of the 10 features, and evaluation using multiple features gives a median accuracy of 0.87.","Title: Rare pattern mining on data streams\nYear: 2012\n\nAbstract: There has been some research in the area of rare pattern mining where the researchers try to capture patterns involving events that are unusual in a dataset. These patterns are considered more useful than frequent patterns in some domain, including detection of computer attacks, or fraudulent credit transactions. Until now, most of the research in this area concentrates only on finding rare rules in a static dataset. There is a proliferation of applications which generate data streams, such as network logs and banking transactions. Applying techniques for static datasets is not practical for data streams. In this paper we propose a novel approach called Streaming Rare Pattern Tree (SRP-Tree), which finds rare rules in a data stream environment using a sliding window, and show that it is faster than current approaches.","Title: Discriminatory confidence analysis in pattern mining\nYear: 2011\n\nAbstract: The field of association rule mining has long been dominated by algorithms that search for patterns based on their frequency of occurrence in a given dataset. The birth of weighted association rule mining caused a fundamental paradigm shift in the way that patterns are identified. Consideration was given to the \"importance\" of an item in addition to its frequency of occurrence. In this research we propose a novel measure which we term Discriminatory Confidence that identifies the extent to which a given item can segment a dataset in a meaningful manner. We devise an efficient algorithm which is driven by an Information Scoring model that identifies items with high discriminatory power. We compare our results with the classical approach to association rule mining and show that the Information Scoring model produces widely divergent results. Our research reveals that mining on the basis of frequency alone tends to exclude some of the most informative patterns that are discovered using discriminatory power.","Title: Finding sporadic rules in the diagnosis of the Erythemato-Squamous diseases\nYear: 2008\n\nAbstract: Most previous research into association rule data mining has focused on finding frequent rules; rules with high support and high confidence. However detecting rare or sporadic association rules, which have low support and high confidence, is a worthwhile task as well, as they represent rare, but potentially interesting and important associations. Mining rare or sporadic rules is a difficult data mining problem, and most previous approaches use an Apriori [1] like method [2-9]. However in order for Apriori to find rare rules, minimum support must be set very low, which results in a large amount of redundant rules and a long runtime. We previously proposed the Apriori-Inverse [10] and MIISR [11] algorithms to find sporadic rules quickly and efficiently. This paper provides an insight into the qualitative results produced by our proposed algorithms. We explore a specific real-world case study in more detail to get a qualitative understanding, namely the Dermatology dataset, for the diagnosis of the Erythemato-Squamous diseases. We show that traditional unmodified Apriori is not well suited to this task, and that our proposed algorithms are capable of producing interesting sporadic rules without having any expert domain knowledge.","Title: Mining interesting imperfectly sporadic rules\nYear: 2008\n\nAbstract: Detecting association rules with low support but high confidence is a difficult data mining problem. To find such rules using approaches like the Apriori algorithm, minimum support must be set very low, which results in a large number of redundant rules. We are interested in sporadic rules; i.e. those that fall below a maximum support level but above the level of support expected from random coincidence. There are two types of sporadic rules: perfectly sporadic and imperfectly sporadic. Here we are more concerned about finding imperfectly sporadic rules, where the support of the antecedent as a whole falls below maximum support, but where items may have quite high support individually. In this paper, we introduce an algorithm called Mining Interesting Imperfectly Sporadic Rules (MIISR) to find imperfectly sporadic rules efficiently, e.g. fever, headache, stiff neck → meningitis. Our proposed method uses item constraints and coincidence pruning to discover these rules in reasonable time. This paper is an expanded version of Koh et al. [Advances in knowledge discovery and data mining: 10th Pacific-Asia Conference (PAKDD 2006), Singapore. Lecture Notes in Computer Science 3918, Springer, Berlin, pp 473–482].","Title: Finding sporadic rules using apriori-inverse\nYear: 2005\n\nAbstract: We define sporadic rules as those with low support but high confidence: for example, a rare association of two symptoms indicating a rare disease. To find such rules using the well-known Apriori algorithm, minimum support has to be set very low, producing a large number of trivial frequent itemsets. We propose “Apriori-Inverse”, a method of discovering sporadic rules by ignoring all candidate itemsets above a maximum support threshold. We define two classes of sporadic rule: perfectly sporadic rules (those that consist only of items falling below maximum support) and imperfectly sporadic rules (those that may contain items over the maximum support threshold). We show that Apriori-Inverse finds all perfectly sporadic rules much more quickly than Apriori. We also propose extensions to Apriori-Inverse to allow us to find some (but not necessarily all) imperfectly sporadic rules.","Title: EWgen: automatic generation of item weights for weighted association rule mining\nYear: 2010\n\nAbstract: Association Rule Mining is an important data mining technique that has been widely used as an automatic rule generation method. While having outstanding success in many different application domains, it also has the potential to generate a vast number of rules, many of which are of little interest to the user. Weighted Association Rule Mining (WARM) overcomes this problem by assigning weights to items thus enabling interesting rules to be ranked ahead of less interesting ones and making it easier for the user to determine which rules are the most useful. Past research on WARM assumes that users have the necessary knowledge to supply item weights. In this research we relax this assumption by deriving item weights based on interactions between items. Our experimentation shows that the rule bases produced by our scheme produces more compact rule bases with a higher information content than standard rule generation methods.","Title: Weighted association rule mining via a graph based connectivity model\nYear: 2013\n\nAbstract: Association rule mining is an important data mining task that discovers relationships among items in a transaction database. Classical association rule mining approaches make the implicit assumption that an item's importance is determined by its support. In contrast, Weighted Association Rule Mining (WARM) attempts to provide a notion of importance, or weight to individual items that are not based solely on item support. Previous approaches to Weighted Association Rule Mining assign item weights in a subjective manner, based on a user's specialized knowledge of the underlying domain that is involved. Such approaches are infeasible when millions of items are present in a dataset, or when domain knowledge is unavailable. Furthermore, even when such domain information is available, a weight assignment based on subjective information constrains the knowledge discovered to fit with the weights assigned, thus inhibiting the discovery of new trends in the data. In this research we automate the process of weight assignment by formulating a linear model that captures relationships between items. This approach extends prior research based on the Valency model. We extend the Valency model by expanding the field of interaction beyond immediate neighborhoods and show that this leads to significant improvements in performance on a number of different metrics that we use.","Title: Efficient negative association rule mining based on chance thresholds\nYear: 2014\n\nAbstract: Typically association rule mining only considers positive frequent itemsets in rule generation, where rules involving only the presence of items are generated. In this paper we consider the complementary problem of negative association rule mining, which generates rules describing the absence of itemsets from transactions. We describe a new approach called MINR Mining Interesting Negative Rules to efficiently find all interesting negative association rules. In our approach, we only consider the presence or absence of itemsets that are strongly associated. Our approach does not require a user defined support threshold, and is based on pruning coincidental itemsets. For every individual itemset we calculate two custom thresholds based on their support: the positive and negative chance thresholds. Itemsets whose support falls above their positive chance threshold are considered positively associated whereas itemsets whose support falls below its negative chance threshold are considered negatively associated. We compared our implementation against Pearson Φ correlation and we note that MINR was able to generate sets of rules which are more interesting.","Title: Evaluating Fraud Detection Algorithms Using an Auction Data Generator\nYear: 2012\n\nAbstract: Online auction sites are a target for fraud. Researchers have developed fraud detection and prevention methods. However, there are difficulties when using either commercial or synthetic auction data to evaluate the effectiveness of these methods. When using commercial data, it is not possible to accurately identify cases of fraud. Using synthetic data, the conclusions drawn may not extend to the real world. The availability of realistic synthetic auction data, which models real auction data, will be invaluable for effective evaluation of fraud detection algorithms. We present an agent-based simulator that is capable of generating realistic English auction data. The agents and model are based on data collected from the Trade Me online auction site. We evaluate the generated data in two ways to show that it is similar to the Trade Me auction data we have collected. In addition, we demonstrate that the simulator can have additional agents added to simulate fraudulent behaviour, and be used to evaluate fraud detection algorithms: we implement three different fraud behaviours and three detection algorithms, and using the simulator, compare the ability of the detection algorithms to correctly identify fraudulent agents.","Title: Mining non-coincidental rules without a user defined support threshold\nYear: 2008\n\nAbstract: Traditional association rulemining techniques employ the support and confidence framework. However, specifying minimum support of the mined rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system. Here we propose replacing Apriori's user-defined minimum support threshold with the more meaningful MinAbsSup function. This calculates a custom minimum support for each itemset based on the probability of chance collision of its items, as derived from the inverse of Fisher's exact test. We will introduce the notion of coincidental itemsets; given a transaction dataset there is a chance that two independent items are appearing together by random coincidence. Rules generated from these itemsets do not denote a meaningful association, and are not useful.","Title: Valency based weighted association rule mining\nYear: 2010\n\nAbstract: Association rule mining is an important data mining task that discovers relationships among items in a transaction database. Most approaches to association rule mining assume that all items within a dataset have a uniform distribution with respect to support. Therefore, weighted association rule mining (WARM) was introduced to provide a notion of importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. This is infeasible when millions of items are present in a dataset. In this paper we propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. Our experimentation shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared to a miner that does not employ such a weighting scheme.","Title: Verb selection using semantic role labeling for citation classification\nYear: 2013\n\nAbstract: Citation classification is the task of assigning a category to a reference or citation. The current sets of categories or classes proposed in the literature vary in size and they are based on the analysis of a small sample of citation sentences. We are developing a process to automatically generate such categories and base them on the analysis of a large corpus of papers. Part of the generation process involves selecting the main verb relevant to the reference being cited in the sentence. In this paper we present our recently developed technique that automatically identifies the relevant verb in a citation sentence. The technique uses heuristic rules, which are dependent on the results of a semantic role labeler. Four test sets were collected, and the common annotations of the test sets annotated by three people were used to assess the accuracy of the rules. Through experimentation we show that the average accuracy achieved using our technique that automatically extracts verbs from citation sentences across the four test sets is reasonable at 75%.","Title: SPO-Tree: efficient single pass ordered incremental pattern mining\nYear: 2011\n\nAbstract: Since the introduction of FP-growth using FP-tree there has been a lot of research into extending its usage to data stream or incremental mining. Most incremental mining adapts the Apriori algorithm. However, we believe that using a tree based approach would increase performance as compared to the candidate generation and testing mechanism used in Apriori. Despite this FP-tree still requires two scans through a dataset. In this paper we present a novel tree structure called Single Pass Ordered Tree SPO-Tree that captures information with a single scan for incremental mining. All items in a transaction are inserted/sorted based on their frequency. The tree is reorganized dynamically when necessary. SPO-Tree allows for easy maintenance in an incremental or data stream environment.","Title: Web usage mining based recommender systems using implicit heterogeneous data: A Particle Swarm Optimization based clustering approach\nYear: 2014\n\nAbstract: Recommender systems have become one of the necessary tools to help a web user find a potentially interesting resource based on their preferences. In implicit recommender systems, the recommendations are made based on the implicit information of the web users i.e. data collected from web logs or cookies without knowing users preferences. Developing such a recommender system is complex due to the huge amount of anonymous noisy data. In this paper we present a Particle Swarm Optimization (PSO) based clustering approach called Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering) for building a recommender system based on implicit web usage data. The approach mimics multi-agent properties of the particles of a swarm and divide the problem space into smaller sub-spaces i.e. clusters. Each cluster represents a particular group of user with similar interests. Later, the K-nearest neighbours of the most relevant cluster are generated as recommendations for a web user and ranked based on their distance. We performed different experiments for preprocessing, to assess the quality of clusters, and for the accuracy of recommendations. An overall accuracy of 65% to 95% was achieved for different scenarios, while in some cases the accuracy touched 100 precent when the selection was made from the top-5 recommendations.","Title: SPAN: Finding collaborative frauds in online auctions.\nYear: 2014\n\nAbstract: Fraud is an ongoing concern for online auction websites. Current methods to detect or prevent fraud have been limited in several ways, making them difficult to apply in real world settings. Firstly, existing methods cannot adapt to changes in the behaviour of fraudulent users over time: new models must be continuously constructed as they gradually lose accuracy. In addition, each method can only be used to detect a specific type of fraud. Secondly, existing methods are generally poor at identifying collaborative frauds. And thirdly, method training and evaluation has been limited by the quality of available datasets. We propose an algorithm named SPAN (Score Propagation over an Auction Network), for detecting users committing collaborative fraud that addresses these problems. SPAN is a two phase method that first applies anomaly detection on multiple 2-dimensional feature subspaces to generate an initial anomaly score for each user, then applies belief propagation to revise those scores to identify suspicious groups of users. We report extensive experimental results using synthetic data which shows that SPAN performs well across three different types of fraud, and outperforms a previous approach for collaborative fraud detection called 2-Level Fraud Spotting. Experiments on a real dataset shows that SPAN behaves reasonably, and can identify groups of users that appear anomalous.","Title: Research on particle swarm optimization based clustering: A systematic review of literature and techniques.\nYear: 2014\n\nAbstract: Optimization based pattern discovery has emerged as an important field in knowledge discovery and data mining (KDD), and has been used to enhance the efficiency and accuracy of clustering, classification, association rules and outlier detection. Cluster analysis, which identifies groups of similar data items in large datasets, is one of its recent beneficiaries. The increasing complexity and large amounts of data in the datasets have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. Swarm intelligence (SI) is one such optimization technique whose algorithms have successfully been demonstrated as solutions for different data clustering domains. In this paper we investigate the growth of literature in SI and its algorithms, particularly Particle Swarm Optimization (PSO). This paper makes two major contributions. Firstly, it provides a thorough literature overview focusing on some of the most cited techniques that have been used for PSO-based data clustering. Secondly, we analyze the reported results and highlight the performance of different techniques against contemporary clustering techniques. We also provide an brief overview of our PSO-based hierarchical clustering approach (HPSO-clustering) and compare the results with traditional hierarchical agglomerative clustering (HAC), K-means, and PSO clustering.","Title: A graph based approach to inferring item weights for pattern mining.\nYear: 2015\n\nAbstract: •We infer unknown items weights from a small subset of known landmark weights.•Domain specific knowledge is used to guide the weight inference process.•A graph model is used to model items and their interactions.•Significant improvements in Precision and Recall over previous research.•The novel graph partitioning algorithm shows significant gains in execution time.","Title: Detection of abnormal profiles on group attacks in recommender systems\nYear: 2014\n\nAbstract: Recommender systems using Collaborative Filtering techniques are capable of make personalized predictions. However, these systems are highly vulnerable to profile injection attacks. Group attacks are attacks that target a group of items instead of one, and there are common attributes among these items. Such profiles will have a good probability of being similar to a large number of user profiles, making them hard to detect. We propose a novel technique for identifying group attack profiles which uses an improved metric based on Degree of Similarity with Top Neighbors (DegSim) and Rating Deviation from Mean Agreement (RDMA). We also extend our work with a detailed analysis of target item rating patterns. Experiments show that the combined methods can improve detection rates in user-based recommender systems.","Title: Attack detection in recommender systems based on target item analysis\nYear: 2014\n\nAbstract: Recommender systems are highly vulnerable to attacks. Attackers who introduce biased ratings in order to affect recommendations, have been shown to be effective against collaborative filtering algorithms. In this paper, we study the use of statistical metrics to detect rating patterns of attackers. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analysing rating patterns between malicious profiles and genuine profiles in shilling attacks. Building upon this, we propose and evaluate an algorithm for detecting shilling attacks in recommender systems using a statistical approach. We look at two attack models: random attack and average attack. The experimental results show that our detection technique based on target item analysis is an effective approach in detecting shilling attacks for both the random and average attack model.","Title: Web bots detection using Particle Swarm Optimization based clustering\nYear: 2014\n\nAbstract: Optimization based techniques have emerged as important methods to tackle the problems of efficiency and accuracy in data mining. One of the current application areas is outlier detection that has not been fully explored yet but has enormous potential. Web bots are an example of outliers, which can be found in the web usage analysis process. Web bot requests are different from a genuine web user as web bots crawl large numbers of pages in a very short time. If web bots remains undetected they can skew the analysis process which can result in incorrect patterns that can cause wrong decisions. In this paper we use one of the popular Swarm Intelligence (SI) based techniques called Particle Swarm Optimization (PSO) to detect web bots among genuine user requests. We use our Particle Swarm Optimization (PSO) based clustering algorithm, Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering) to cluster the web-usage data and detect the abnormal behaviour caused by the web bots. We present the results of detection which shows that our proposed approach is capable of detecting such abnormal behaviour. We then compare the characteristics of the detected web bots with genuine web-users using cross validation.","Title: Detecting Volatility Shift in Data Streams\nYear: 2014\n\nAbstract: Current drift detection techniques detect a change in distribution within a stream. However, there are no current techniques that analyze the change in the rate of these detected changes. We coin the term stream volatility, to describe the rate of changes in a stream. A stream has a high volatility if changes are detected frequently and has a low volatility if changes are detected infrequently. We are particularly interested in a volatility shift which is a change in the rate of change (e.g. From high volatility to low volatility). We introduce and define the concept of stream volatility, and propose a novel technique to detect volatility on data streams in the presence of concept drifts. In the experiments we show our algorithm to be both fast and efficient. We also propose a new algorithm for drift detection called SEED that is faster and more memory efficient than the existing state-of-the-art drift detection approach. A faster drift detection algorithm has a flow-on benefit to the subsequent volatility detection stage because both algorithms run concurrently on the data stream.","Title: Hierarchical PSO clustering based recommender system\nYear: 2012\n\nAbstract: Due to a marked increase in the number of web users and their activities, many application areas that use patterns generated from their activities has been proposed. Web-based implicit recommender systems are one such application. An implicit recommender system is a tool that helps guide a user to a particular web resource based on implicit data. Implicit data comes from the web users activities without their active participation. Building such a system is a complex process due to two reasons, there is a huge amount of data and the quality of the data is poor. In this research, we tackle the first problem of generating patterns efficiently for recommender system by proposing Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering). HPSO-clustering is a clustering approach based on Particle Swarm Optimization which combines both the properties of hierarchical and partitional clustering. We grouped the users' session into different clusters. Recommendations for an active user are generated from these clusters. In this paper we report the results of accuracy of recommendations. We achieved an overall 60% to 65% of precision for an active user, while in some clusters the precision achieved was 100% when top 5 ranked recommendations were selected.","Title: Clustering heterogeneous web usage data using Hierarchical Particle Swarm Optimization\nYear: 2013\n\nAbstract: Data clustering aims to group data based on similarities between the data elements. Recently, due to the increasing complexity and amount of heterogenous data, modeling of such data for clustering has become a serious challenge. In this paper we tackle the problem of modeling heterogeneous web usage data for clustering. The main contribution is a new similarity measure which we propose to cluster heterogeneous web usage data. We then use this similarity measure in our Particle Swarm Optimization (PSO) based clustering algorithm, Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering). HPSO-clustering combines the qualities of hierarchical and partitional clustering to cluster data in a hierarchical agglomerative manner. We present the clustering results and explain the effects of the new similarity measure on inter-cluster and intra-cluster distances. These measures verify the applicability of the proposed similarity measure on web usage data.","Title: Rare Pattern Mining from Data Streams Using SRP-Tree and Its Variants.\nYear: 2015\n\nAbstract: There has been some research in the area of rare pattern mining where the researchers try to capture patterns involving events that are unusual in a dataset. These patterns are considered more useful than frequent patterns in some domains, including detection of computer attacks, or fraudulent credit transactions. Until now, most of the research in this area concentrates only on finding rare rules in a static dataset. There is a proliferation of applications which generate data streams, such as network logs and banking transactions, and applying techniques that mine static datasets is not practical for data streams. We propose a novel approach called Streaming Rare Pattern Tree (SRP-Tree) and its variations, which finds rare rules in a data stream environment using a sliding window, and show that it both finds the complete set of itemsets and runs with fast execution time.","Title: Non-redundant rare itemset generation\nYear: 2009\n\nAbstract: Rare itemsets are likely to be of great interest because they often relate to high-impact transactions which may give rise to rules of great practical significance. Research into the rare association rule mining problem has gained momentum in the recent past. In this paper, we propose a novel approach that captures such rare rules while ensuring that redundant rules are eliminated. Extensive testing on real-world datasets from the UCI repository confirm that our approach outperforms both the Apriori-Inverse(Koh et al. 2006) and Relative Support (Yun et al. 2003) algorithms.","Title: Indirect weighted association rules mining for academic network collaboration recommendations\nYear: 2012\n\nAbstract: Collaborative research is increasingly important and popular in academic circles. However for young researchers identifying new research collaborators to form joint research and analyzing the level of cooperation of the current partners can be a very complex task. Thus recommendation of new collaborations would be important for young researchers. This paper presents a new approach to recommend collaborators in an academic social network using the co-authorship network. We propose a weighted indirect rule mining approach using a novel weighting mechanism called sociability.","Title: Tracking Drift Severity in Data Streams\nYear: 2015\n\nAbstract: The evolution of data or concept drift is a common phenomena in data streams. Currently most drift detection methods are able to locate the point of drift, but are unable to provide important information on the characteristics of change such as the magnitude of change which we refer to as drift severity. Monitoring drift severity provides crucial information to users allowing them to formulate a more adaptive response. In this paper, we propose a drift detector, MagSeed, which is capable of tracking drift severity with a high rate of true positives and a low rate of false positives. We evaluate MagSeed on synthetic and real world data, and compare it to state of the art drift detectors ADWIN2 and DDM.","Title: Drift Detection Using Stream Volatility\nYear: 2015\n\nAbstract: Current methods in data streams that detect concept drifts in the underlying distribution of data look at the distribution difference using statistical measures based on mean and variance. Existing methods are unable to proactively approximate the probability of a concept drift occurring and predict future drift points. We extend the current drift detection design by proposing the use of historical drift trends to estimate the probability of expecting a drift at different points across the stream, which we term the expected drift probability. We offer empirical evidence that applying our expected drift probability with the state-of-the-art drift detector, ADWIN, we can improve the detection performance of ADWIN by significantly reducing the false positive rate. To the best of our knowledge, this is the first work that investigates this idea. We also show that our overall concept can be easily incorporated back onto incremental classifiers such as VFDT and demonstrate that the performance of the classifier is further improved.","Title: StreamXM: An Adaptive Partitional Clustering Solution for Evolving Data Streams.\nYear: 2015\n\nAbstract: A challenge imposed by continuously arriving data streams is to analyze them and to modify the models that explain them as new data arrives. We propose StreamXM, a stream clustering technique that does not require an arbitrary selection of number of clusters, repeated and expensive heuristics or in-depth prior knowledge of the data to create an informed clustering that relates to the data. It allows a clustering that can adapt its number of classes to those present in the underlying distribution. In this paper, we propose two different variants of StreamXM and compare them against a current, state-of-the-art technique, StreamKM. We evaluate our proposed techniques using both synthetic and real world datasets. From our results, we show StreamXM and StreamKM run in similar time and with similar accuracy when running with similar numbers of clusters. We show our algorithms can provide superior stream clustering if true clusters are not known or if emerging or disappearing concepts will exist within the data stream.","Title: HI-Tree: Mining High Influence Patterns Using External and Internal Utility Values\nYear: 2015\n\nAbstract: We propose an efficient algorithm, called HI-Tree, for mining high influence patterns for an incremental dataset. In traditional pattern mining, one would find the complete set of patterns and then apply a post-pruning step to it. The size of the complete mining results is typically prohibitively large, despite the fact that only a small percentage of high utility patterns are interesting. Thus it is inefficient to wait for the mining algorithm to complete and then apply feature selection to post-process the large number of resulting patterns. Instead of generating the complete set of frequent patterns we are able to directly mine patterns with high utility values in an incremental manner. In this paper we propose a novel utility measure called an influence factor using the concepts of external utility and internal utility of an item. The influence factor for an item takes into consideration its connectivity with its neighborhood as well as its importance within a transaction. The measure is especially useful in problem domains utilizing network or interaction characteristics amongst items such as in a social network or web click-stream data. We compared our technique against state of the art incremental mining techniques and show that our technique has better rule generation and runtime performance.","Title: Unsupervised Semantic and Syntactic Based Classification of Scientific Citations\nYear: 2015\n\nAbstract: In the recent years, the number of scientific publications has increased substantially. A way to measure the impact of a publication is to count the number of citations to the paper. Thus, citations are being used as a proxy for a researcher's contribution and influence in a field. Citation classification can provide context to the citations. To perform citation classification, supervised techniques are normally used. To the best of our knowledge there are no research that performs this task in a unsupervised manner. In this paper we present two techniques to cluster citations automatically without human intervention. This paper presents two novel techniques to cluster citations according to their contents (semantic) and the citation sentence styles (syntactic). The techniques are validated using external test sets from existing supervised citation classification studies.","Title: Unsupervised Rare Pattern Mining: A Survey.\nYear: 2016\n\nAbstract: Association rule mining was first introduced to examine patterns among frequent items. The original motivation for seeking these rules arose from need to examine customer purchasing behaviour in supermarket transaction data. It seeks to identify combinations of items or itemsets, whose presence in a transaction affects the likelihood of the presence of another specific item or itemsets. In recent years, there has been an increasing demand for rare association rule mining. Detecting rare patterns in data is a vital task, with numerous high-impact applications including medical, finance, and security. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for rare pattern mining. We investigate the problems in finding rare rules using traditional association rule mining. As rare association rule mining has not been well explored, there is still specific groundwork that needs to be established. We will discuss some of the major issues in rare association rule mining and also look at current algorithms. As a contribution, we give a general framework for categorizing algorithms: Apriori and Tree based. We highlight the differences between these methods. Finally, we present several real-world application using rare pattern mining in diverse domains. We conclude our survey with a discussion on open and practical challenges in the field.","Title: Phishing Detection on Twitter Streams.\nYear: 2016\n\nAbstract: With the prevalence of cutting-edge technology, the social media network is gaining popularity and is becoming a worldwide phenomenon. Twitter is one of the most widely used social media sites, with over 500 million users all around the world. Along with its rapidly growing number of users, it has also attracted unwanted users such as scammers, spammers and phishers. Research has already been conducted to prevent such issues using network or contextual features with supervised learning. However, these methods are not robust to changes, such as temporal changes or changes in phishing trends. Current techniques also use additional network information. However, these techniques cannot be used before spammers form a particular number of user relationships. We propose an unsupervised technique that detects phishing in Twitter using a 2-phase unsupervised learning algorithm called PDT Phishing Detector for Twitter. From the experiments we show that our technique has high accuracy ranging between 0.88 and 0.99.","Title: Effect of verbal comprehension skill and self-reported features on reliability of crowdsourced relevance judgments.\nYear: 2016\n\nAbstract: Test collection is extensively used to evaluate information retrieval systems in laboratory-based evaluation experimentation. In a classic setting of a test collection, human assessors involve relevance judgments, which are both a costly and time-consuming task, and scales poorly. Researchers are still being challenged in performing reliable and low-cost evaluation of retrieval systems. Crowdsourcing as a novel method provides a cost-effective and quick solution for creating relevance judgments. However, crowdsourcing comes with the risk of a heterogeneous mass of potential workers who create the relevance judgments with varied levels of accuracy. It is, therefore, essential to understand the factors that affect the reliability of crowdsourced judgments. In this article, we measured various cognitive characteristics of workers, and explored the effects of these characteristics on judgment reliability, in comparison with a human gold standard. We discovered a significant correlation between judgment reliability and the level of verbal comprehension skill. This association conveys an idea for improving the reliability of judgments by discriminating workers into various groups according to their cognitive abilities and to filter out (or to include) certain group(s) of workers. Aside from that, we also discovered a significant association between reliability of judgments and self-reported difficulty of judgment as well as confidence in the task. Exploring the reliability of using crowdsourcing to create relevance judgments.Investigating effects of cognitive skills on the reliability of judgments.Investigating effects of cognitive skills on system rankings.Investigating effects of self-reported competence on the reliability of judgments.","Title: CPF: Concept Profiling Framework for Recurring Drifts in Data Streams.\nYear: 2016\n\nAbstract: We propose the Concept Profiling Framework (CPF), a meta-learner that uses a concept drift detector and a collection of classification models to perform effective classification on data streams with recurrent concept drifts, through relating models by similarity of their classifying behaviour. We introduce a memory-efficient version of our framework and show that it can operate faster and with less memory than a naive implementation while achieving similar accuracy. We compare this memory-efficient version of CPF to a state-of-the-art meta-learner made to handle recurrent drift and show that we can regularly achieve improved classification accuracy along with runtime and memory use. We provide results from testing on synthetic and real-world datasets to prove CPF’s value in classifying data streams with recurrent concepts.","Title: Effects Of Objective And Subjective Competence On The Reliability Of Crowdsourced Relevance Judgments\nYear: 2017\n\nAbstract: Introduction. Despite the popularity of crowdsourcing, the reliability of crowdsourced output has been questioned since crowdsourced workers display varied degrees of attention, ability and accuracy. It is important, therefore, to understand the factors that affect the reliability of crowdsourcing. In the context of producing relevance judgments, crowdsourcing has been recently proposed as an alternative approach to traditional methods of information retrieval evaluation, which are mostly expensive and scale poorly.Aim. The purpose of this study is to measure various cognitive characteristics of crowdsourced workers, and explore the effect that these characteristics have upon judgment reliability, as measured against a human gold standard.Method. The authors examined whether workers with high verbal comprehension skill could outperform workers with low verbal comprehension skill in terms of judgment reliability in crowdsourcing.Results. A significant correlation was found between judgment reliability and measured verbal comprehension skill, as well as with self-reported difficulty of judgment and confidence in the task. Surprisingly, however, there is no correlation between level of self-reported topic knowledge and reliability.Conclusions. Our findings show that verbal comprehension skill influences the accuracy of the relevance judgments created by the crowdsourced workers.","Title: mHUIMiner: A Fast High Utility Itemset Mining Algorithm for Sparse Datasets.\nYear: 2017\n\nAbstract: High utility itemset mining is the problem of finding sets of items whose utilities are higher than or equal to a specific threshold. We propose a novel technique called mHUIMiner, which utilises a tree structure to guide the itemset expansion process to avoid considering itemsets that are nonexistent in the database. Unlike current techniques, it does not have a complex pruning strategy that requires expensive computation overhead. Extensive experiments have been done to compare mHUIMiner to other state-of-the-art algorithms. The experimental results show that our technique outperforms the state-of-the-art algorithms in terms of running time for sparse datasets.","Title: Volatility Adaptive Classifier System.\nYear: 2017\n\nAbstract: A data stream’s concept may evolve over time, which is known as the concept drift. Concept drifts affect the prediction accuracy of the learning model and are required to be handled to maintain the model quality. In most cases, there is a trade-off between maintaining prediction quality and learning efficiency. We present a novel framework known as the Volatility-Adaptive Classifier System (VACS) to balance the trade-off. The system contains an adaptive classifier and a non-adaptive classifier. The former can maintain a higher prediction quality but requires additional computational overhead, and the latter requires less computational overhead but its prediction quality may be susceptible to concept drifts. VACS automatically applies the adaptive classifier when the concept drifts are frequent, and switches to the non-adaptive classifier when drifts are infrequent. As a result, VACS can maintain a relatively low computational cost while still maintaining a high enough overall prediction quality. To the best of our knowledge, this is the first data stream mining framework that applies different learners to reduce the learning overheads.","Title: Vehicle emission prediction using remote sensing data and machine learning techniques.\nYear: 2018\n\nAbstract: More and more researchers are using remote sensing technology to measure real-world, on-road automobile emissions of nitric oxide (NO), one of the most important and frequently studied pollutants. Partnered with the National Institute of Water and Atmospheric Research (NIWA) in New Zealand, we aim to establish a robust NO emission factor prediction model using remote sensing data to forecast future emissions. This model can be extremely useful to local transport authorities for monitoring urban pollution levels and validating the effectiveness of existing traffic control policies. We have conducted this research using real-world data that were collected over a 10-year span between 2005 and 2015. The experimental results have shown that the vehicle emission patterns are continuously changing and the relevance of remote sensing data for future predictions decays as they get older.\n\nWe propose a 3-step machine learning approach to establish this model. Most notably we use quantile regression forest (a variation of random forest) as the base algorithm and use random forest's variable importance measure to validate and interpret the features. The model yields error rates that compare favourably to linear model based recursive partitioning and the original random forest model.","Title: Discovering High Utility Change Points in Customer Transaction Data.\nYear: 2018\n\nAbstract: High Utility Itemset Mining (HUIM) consists of identifying all sets of items (itemsets) that have a high utility (e.g. have a high profit) in a database of customer transactions. Important limitations of traditional HUIM algorithms is that they do not consider that the utility of itemsets varies as time passes and that itemsets may not have a high utility in the whole database, but in some specific time periods. To overcome these drawbacks, this paper defines the novel problem of discovering change points of high utility itemsets, that is to find the time points where the utility of an itemset is changing considerably. An efficient algorithms named HUCP-Miner is proposed to mine these change points. Experimental results show that the proposed algorithm has excellent performance and can discover interesting patterns that are not identified by traditional HUIM algorithms.","Title: Evaluating the effectiveness of information retrieval systems using effort-based relevance judgment.\nYear: 2018\n\nAbstract: Purpose The effort in addition to relevance is a major factor for satisfaction and utility of the document to the actual user. The purpose of this paper is to propose a method in generating relevance judgments that incorporate effort without human judges' involvement. Then the study determines the variation in system rankings due to low effort relevance judgment in evaluating retrieval systems at different depth of evaluation. Design/methodology/approach Effort-based relevance judgments are generated using a proposed boxplot approach for simple document features, HTML features and readability features. The boxplot approach is a simple yet repeatable approach in classifying documents' effort while ensuring outlier scores do not skew the grading of the entire set of documents. Findings The retrieval systems evaluation using low effort relevance judgments has a stronger influence on shallow depth of evaluation compared to deeper depth. It is proved that difference in the system rankings is due to low effort documents and not the number of relevant documents. Originality/value Hence, it is crucial to evaluate retrieval systems at shallow depth using low effort relevance judgments.","Title: Predicting Air Quality from Low-Cost Sensor Measurements.\nYear: 2018\n\nAbstract: Urban air pollution poses a significant global health risk, but due to the high expense of measuring air quality, the amount of available data on pollutant exposure has generally been wanting. In recent years this has motivated the development of several cheap, portable air quality monitoring instruments. However, these instruments also tend to be unreliable, and thus the raw measurements require preprocessing to make accurate predictions of actual air quality conditions, making them an apt target for machine learning techniques. In this paper we use a dataset of measurements from a low cost air-quality instrument—the ODIN-SD—to examine which techniques are most appropriate, and the limitations of such an approach. From theoretical and experimental considerations, we conclude that a robust linear regression over measurements of air quality metrics, as well as relative humidity and temperature measurements produces the model with greatest accuracy. We also discuss issues of concept drift which occur in this context, and quantify how much training data is required to strike the right balance between predictive accuracy and efficient data collection.","Title: Interpreting Intermittent Bugs in Mozilla Applications Using Change Angle.\nYear: 2018\n\nAbstract: Learning in evolving environments involves learning from data where the statistical characteristics can change over time. Current change detection algorithms that are used online for data streams detect whether a change has occurred in the data but there is always a detection delay. None of the existing online techniques can accurately pin-point the exact location of when the change starts to occur, which can be critical. We present a novel method Change Angle and we show, for the first time, how to pin-point online the location at which change starts to occur. We apply our Change Angle method in the application area of software revision control using Mozilla data, where it is important to detect not only the presence of change but also to pin-point accurately the location of when change starts to occur.","Title: Extrapolation prefix tree for data stream mining using a landmark model\nYear: 2012\n\nAbstract: Since the introduction of FP-growth there has been extensive research into extending its usage to data streams or incremental mining. This task is particularly challenging in the data stream environment because of the unbounded nature of a data stream and the need for avoiding multiple scans of the data. In this paper, we propose an algorithm, Extrapolation Prefix Tree that extracts frequent itemsets using a landmark windowing scheme. The algorithm uses a prefix tree structure to store arriving transactions, but unlike previous approaches estimates the structure of the tree in the next block of data based on the arrival pattern of items appearing in transactions that arrive in the current block. Our experimentation shows that Extrapolation-Tree significantly outperforms the CP-Tree, both in terms of the number of updates and the execution time required to keep the tree current while maintaining a compact tree.","Title: Identifying Top- Nodes in Social Networks: A Survey.\nYear: 2019\n\nAbstract: Top-k nodes are the important actors for a subjectively determined topic in a social network. To some extent, a topic is taken as a ranking criteria for identifying top-k nodes. Within a viral marketing network, subjectively selected topics can include the following: Who can promote a new product to the largest number of people, and who are the highest spending customers? Based on these questions, there has been a growing interest in top-k nodes research to effectively identify key players. In this article, we review and classify existing literature on top-k nodes identification into two major categories: top-k influential nodes and top-k significant nodes. We survey both theoretical and applied work in the field and describe promising research directions based on our review. This research area has proven to be beneficial for data analysis on online social networks as well as practical applications on real-life networks.","Title: Items2Data: Generating Synthetic Boolean Datasets from Itemsets\nYear: 2019\n\nAbstract: Boolean data is a core data type in machine learning. It is used to represent categorical and transactional data. Unlike real valued data, it is notoriously difficult to efficiently design boolean datasets that satisfy particular constraints. Inverse Frequent Itemset Mining (IFM) is the problem of constructing a boolean dataset, satisfying given support constraints for some itemsets. Previous work mainly focuses on the theoretical complexity of IFM and practical solutions scale poorly or do not satisfy all the constraints. We propose Items2Data, a practical algorithm for generating boolean datasets which is efficient under specific conditions. We introduce global closure to describe the condition which a dataset can be efficiently constructed. We evaluate Items2Data and its use in designing synthetic datasets and to analyze its accuracy, scalability and speed on real world datasets. The results indicate Items2Data is practical and efficient for generating synthetic boolean data when pre-defined itemsets are globally closed.","Title: Recurring Concept Meta-learning for Evolving Data Streams.\nYear: 2019\n\nAbstract: •A fast, accurate classification framework for data streams that reuses classifiers.•More accurate than a state-of-the-art classifier reuse framework on synthetic data.•Five times faster and as accurate as this framework on real-world benchmarks.•More accurate than a state-of-the-art ensemble technique on synthetic datasets.•Six times faster and as accurate as this framework on real-world benchmarks.","Title: Network Embedding and Change Modeling in Dynamic Heterogeneous Networks\nYear: 2019\n\nAbstract: Network embedding learns the vector representations of nodes. Most real world networks are heterogeneous and evolve over time. There are, however, no network embedding approaches designed for dynamic heterogeneous networks so far. Addressing this research gap is beneficial for analyzing and mining real world networks. We develop a novel representation learning method, change2vec, which considers a dynamic heterogeneous network as snapshots of networks with different time stamps. Instead of processing the whole network at each time stamp, change2vec models changes between two consecutive static networks by capturing newly-added and deleted nodes with their neighbour nodes as well as newly-formed or deleted edges that caused core structural changes known as triad closure or open processes. Change2vec leverages metapath based node embedding and change modeling to preserve both heterogeneous and dynamic features of a network. Experimental results show that change2vec outperforms two state-of-the-art methods in terms of clustering performance and efficiency.","Title: Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information\nYear: 2021\n\nAbstract: Streaming sources of data are becoming more common as the ability to collect data in real-time grows. A major concern in dealing with data streams is concept drift, a change in the distribution of data over time, for example, due to changes in environmental conditions. Representing concepts (stationary periods featuring similar behaviour) is a key idea in adapting to concept drift. By testing the ...","Title: Analyzing and repairing concept drift adaptation in data stream classification\nYear: 2022\n\nAbstract: Data collected over time often exhibit changes in distribution, or concept drift, caused by changes in factors relevant to the classification task, e.g. weather conditions. Incorporating all relevant factors into the model may be able to capture these changes, however, this is usually not practical. Data stream based methods, which instead explicitly detect concept drift, have been shown to retain performance under unknown changing conditions. These methods adapt to concept drift by training a model to classify each distinct data distribution. However, we hypothesize that existing methods do not robustly handle real-world tasks, leading to adaptation errors where context is misidentified. Adaptation errors may cause a system to use a model which does not fit the current data, reducing performance. We propose a novel repair algorithm to identify and correct errors in concept drift adaptation. Evaluation on synthetic data shows that our proposed AiRStream system has higher performance than baseline methods, while is also better at capturing the dynamics of the stream. Evaluation on an air quality inference task shows AiRStream provides increased real-world performance compared to eight baseline methods. A case study shows that AiRStream is able to build a robust model of environmental conditions over this task, allowing the adaptions made to concept drift to be analysed and related to changes in weather. We discovered a strong predictive link between the adaptions made by AiRStream and changes in meteorological conditions.","Title: Continual Correction of Errors Using Smart Memory Replay\nYear: 2021\n\nAbstract: Catastrophic forgetting is an issue for all continual learning models with neural network architectures, however, research in this area often concentrates on prevention of catastrophic forgetting and not on the potential to improve existing knowledge with new information. In this paper, we explore using smart memory replay to augment a state-of-the-art regularization model. Our method continually corrects model errors for learned tasks, without compromising the learning of new tasks. We demonstrate how our method can achieve higher accuracy of predictions than a standard regularization model on multiple datasets, while incurring similar computational costs. In our experiments we show how using our method gains between five to ten percent accuracy on a large image classification dataset. We also illustrate how our method is able to regain task knowledge after catastrophic forgetting occurs.","Title: A Meta-Learning Approach for Automated Hyperparameter Tuning in Evolving Data Streams\nYear: 2021\n\nAbstract: Most machine learning algorithms rely on a set of hyperparameters, which are tuned either by domain experts or using automated methods. In the context of data streams, the complexity of this task is compounded by the need for tuning every time a concept drift occurs. The problem of automated tuning may lead to classification performance improvements. Current methods optimise the hyperparameters from scratch every time concept drift is detected. Additionally, meta-learning techniques developed for static machine learning tasks allow pre-built knowledge to adapt faster to new tasks. We propose a meta-learning approach for automated tuning in evolving data streams, specifically for tuning parameters in Adaptive Random Forest (ARF) and parameters for several drift detectors. We compare our approach against non-adaptive methods on both synthetic and real-world datasets. Empirical results show that our approach makes a compromises between predictive performance and resource consumption for ARF, and between true positives and false positives for drift detectors.","Title: Nacre: Proactive Recurrent Concept Drift Detection in Data Streams\nYear: 2021\n\nAbstract: Concept drift detection is used to signal to a learning algorithm that there has been a change in the underlying distribution of the data stream. However, there is a delay in detecting the actual drifts, leading to performance loss between the start of the drift and the detection point. There are two major challenges in reducing such performance loss, specifically the difficulty in anticipating the location of the next drift point and determining the exact concept that will appear for timely concept adaptation. In this research, we leverage concept recurrences in data streams. We proposed a framework called Nacre, which can perform proactive drift detection and online updates to allow for smooth adaptation of concept drifts. We present a novel technique, called drift coordinator, that anticipates the next drift point and assesses the incoming concept. This will ultimately increase accuracy in the classification performance. We demonstrate that our method is able to learn and predict drift trends in streams with recurring drifts. This allows the anticipation of future changes which enables users and detection methods to be more proactive. We empirically show that our technique outperforms baselines in terms of accuracy, kappa, accuracy gain per drift and cumulative accuracy gain on both synthetic and real-world datasets.","Title: Analyzing and Repairing Concept Drift Adaptation in Data Stream Classification\nYear: 2021\n\nAbstract: Data collected over time often exhibit changes in distribution, or concept drift, caused by changes in hidden context relevant to the classification task, e.g. weather conditions. Adaptive learning methods are able to retain performance in changing conditions by explicitly detecting concept drift and changing the classifier used to make predictions. However, in realworld conditions, existing metho...","Title: Evaluating Unsupervised Text Embeddings on Software User Feedback\nYear: 2021\n\nAbstract: User feedback on software products has been shown to be useful for development and can be exceedingly abundant online. Many approaches have been developed to elicit requirements in different ways from this large volume of feedback, including the use of unsupervised clustering, underpinned by text embeddings. Methods for embedding text can vary significantly within the literature, highlighting the ...","Title: Probabilistic exact adaptive random forest for recurrent concepts in data streams\nYear: 2022\n\nAbstract: In order to adapt random forests to the dynamic nature of data streams, the state-of-the-art technique discards trained trees and grows new trees when concept drifts are detected. This is particularly wasteful when recurrent patterns exist. In this work, we introduce a novel framework called PEARL, which uses both an exact technique and a probabilistic graphical model with Lossy Counting, to replace drifted trees with relevant trees built in the past. The exact technique utilizes pattern matching to find the set of drifted trees that co-occurred in predictions in the past. Meanwhile, a probabilistic graphical model is being built to capture the tree replacements among recurrent concept drifts. Once the graphical model becomes stable, it replaces the exact technique and finds relevant trees in a probabilistic fashion. Further, Lossy Counting is applied to the graphical model which brings an added theoretical guarantee for both error rate and space complexity. We empirically show our technique outperforms baselines in terms of accuracy and kappa on both synthetic and real-world datasets.","Title: Semi-supervised Conditional Density Estimation with Wasserstein Laplacian Regularisation.\nYear: 2022\n\nAbstract: Conditional Density Estimation (CDE) has wide-reaching applicability to various real-world problems, such as spatial density estimation and environmental modelling. CDE estimates the probability density of a random variable rather than a single value and can thus model uncertainty and inverse problems. This task is inherently more complex than regression, and many algorithms suffer from overfitting, particularly when modelled with few labelled data points. For applications where unlabelled data is abundant but labelled data is scarce, we propose Wasserstein Laplacian Regularisation, a semi-supervised learning framework that allows CDE algorithms to leverage these unlabelled data. The framework minimises an objective function which ensures that the learned model is smooth along the manifold of the underlying data, as measured by Wasserstein distance. When applying our framework to Mixture Density Networks, the resulting semi-supervised algorithm can achieve similar performance to a supervised model with up to three times as many labelled data points on baseline datasets. We additionally apply our technique to the problem of remote sensing for chlorophyll-a estimation in inland waters.","Title: Measuring Drift Severity by Tree Structure Classifiers\nYear: 2022\n\nAbstract: Streaming data has become more common as our ability to collect data in real-time increases. A primary concern in dealing with data streams is concept drift, which describes changes in the underlying distribution of streaming data. Measuring drift severity is crucial for model adaptation. Drift severity can be a proxy in choosing concept drift adaptation strategies. Current methods measure drift severity by monitoring the changes in the learner performance or measuring the difference between data distributions. However, these methods cannot measure the drift severity if the ground truth labels are unavailable. Specifically, performance-based methods cannot measure marginal drift, and distribution-based methods cannot measure conditional drift. We propose a novel framework named Tree-based Drift Measurement (TDM) that measures both marginal and conditional drift without revisiting historical data. TDM measures the difference between tree classifiers by transforming them into sets of binary vectors. An experiment shows that TDM achieves similar performance to the state-of-the-art methods and provides the best trade-off between runtime and memory usage. A case study shows that the online learner performance can be improved by adapting different drift adaptation strategies based on the drift severity."],"string":"[\n \"Title: Automatic assignment of item weights for pattern mining on data streams\\nYear: 2011\\n\\nAbstract: Research inWeighted Association Rule Mining (WARM) has largely concentrated on mining traditional static transactional datasets. Whilst there have been a few attempts at researching WARM in a data stream environment, none have addressed the problem of assigning and adapting weights in the presence of concept drift, which often occurs in a data stream environment. In this research we experiment with two methods of adapting weights; firstly, a simplistic method that recomputes the entire set of weights at fixed intervals, and secondly a method that relies on a distance function that assesses the extent of change in the stream and only updates those items that have had significant change in their patterns of interaction. We show that the latter method is able to maintain good accuracy whilst being several times faster than the former.\",\n \"Title: RP-Tree: rare pattern tree mining\\nYear: 2011\\n\\nAbstract: Most association rule mining techniques concentrate on finding frequent rules. However, rare association rules are in some cases more interesting than frequent association rules since rare rules represent unexpected or unknown associations. All current algorithms for rare association rule mining use an Apriori level-wise approach which has computationally expensive candidate generation and pruning steps. We propose RP-Tree, a method for mining a subset of rare association rules using a tree structure, and an information gain component that helps to identify the more interesting association rules. Empirical evaluation using a range of real world datasets shows that RP-Tree itemset and rule generation is more time efficient than modified versions of FP-Growth and ARIMA, and discovers 92-100% of all the interesting rare association rules.\",\n \"Title: Finding Maximal Overlapping Communities.\\nYear: 2013\\n\\nAbstract: Social networks play an important role in everyday life. Nowadays there is various research that concentrates on detecting communities within these networks. Traditionally most of the community detection algorithms focus on detecting disjoint networks. However there is a need for overlapping community detection. In recent years there have been some attempts at detecting overlapping communities. Most of these techniques concentrate on just detecting these communities, none of this research tries to detect the maximal set of these communities which gives more stability. In this paper we propose a new method called Maximal-DSHRINK that allows us to detect the maximal set of overlapping communities within a social network. We show that the maximal set provides us with better quality in terms of modularity gain.\",\n \"Title: Weighted association rule mining using particle swarm optimization\\nYear: 2011\\n\\nAbstract: Association rule mining is an important data mining task that discovers relationships among items in a transaction database. Most approaches to association rule mining assume that the items within the dataset have a uniform distribution. Therefore, weighted association rule mining (WARM) was introduced to provide a notion of importance to individual items. In previous work most of these approaches require users to assign weights for each item. This is infeasible when we have millions of items in a dataset. In this paper we propose a novel method, Weighted Association Rule Mining using Particle Swarm Optimization (WARM SWARM), which uses particle swarm optimization to assign meaningful item weights for association rule mining.\",\n \"Title: Efficiently finding negative association rules without support threshold\\nYear: 2007\\n\\nAbstract: Typically association rule mining only considers positive frequent itemsets in rule generation, where rules involving only the presence of items are generated. In this paper we consider the complementary problem of negative association rule mining, which generates rules describing the absence of itemsets from transactions. We describe a new approach called MINR (Mining Interesting Negative Rules) to efficiently find all interesting negative association rules. Here we only consider the presence or absence of itemsets that are strongly associated. Our approach does not require a user defined support threshold, and is based on pruning items that occur together by coincidence. For every individual itemset we calculate two custom thresholds based on their support: the positive and negative chance thresholds. We compared our implementation against Pearson φ correlation.\",\n \"Title: CBDT: A Concept Based Approach to Data Stream Mining\\nYear: 2009\\n\\nAbstract: Data Stream mining presents unique challenges compared to traditional mining on a random sample drawn from a stationary statistical distribution. Data from real-world data streams are subject to concept drift due to changes that take place continuously in the underlying data generation mechanism. Concept drift complicates the process of mining data as models that are learnt need to be updated continuously to reflect recent changes in the data while retaining relevant information that has been learnt from the past. In this paper, we describe a Concept Based Decision Tree (CBDT) learner and compare it with the CVDFT algorithm, which uses a sliding time window. Our experimental results show that CBDT outperforms CVFDT in terms of both classification accuracy and memory consumption.\",\n \"Title: Kernel-Tree: mining frequent patterns in a data stream based on forecast support\\nYear: 2012\\n\\nAbstract: Although frequent pattern mining techniques have been extensively studied, the extension of their application onto data streams has been challenging. Due to data streams being continuous and unbounded, an efficient algorithm that avoids multiple scans of data is needed. In this paper we propose Kernel-Tree (KerTree), a single pass tree structured technique that mines frequent patterns in a data stream based on forecasting the support of current items in the future state. Unlike previous techniques that build a tree based on the support of items in the previous block, KerTree performs an estimation of item support in the next block and builds the tree based on the estimation. By building the tree on an estimated future state, KerTree effectively reduces the need to restructure for every block and thus results in a better performance and mines the complete set of frequent patterns from the stream while maintaining a compact structure.\",\n \"Title: Transaction clustering using a seeds based approach\\nYear: 2008\\n\\nAbstract: Transaction clustering has received a great deal of attention in the past few years. Its functionality extends well beyond traditional clustering algorithms which basically perform a near-neighbourhood search for locating groups of similar instances. The basic concept underlying transaction clustering stems from the concept of large items as defined by association rule mining algorithms. Clusters formed on the basis of large items that are shared between instances offer an attractive alternative to association rule mining systems. Currently, none of the techniques proposed offer a good solution to scenarios where large items overlap across clusters. In this paper we overcome the aforementioned limitations by using cluster seeds that represent initial centroids. Seeds are generated from sets of transaction items that occur together above a certain threshold and such seeds may overlap in their itemsets across clusters.\",\n \"Title: Rare association rule mining via transaction clustering\\nYear: 2008\\n\\nAbstract: Rare association rule mining has received a great deal of attention in the recent past. In this research, we use transaction clustering as a pre-processing mechanism to generate rare association rules. The basic concept underlying transaction clustering stems from the concept of large items as defined by traditional association rule mining algorithms. We make use of an approach proposed by Koh & Pears (2008) to cluster transactions prior to mining for association rules. We show that pre-processing the dataset by clustering will enable each cluster to express their own associations without interference or contamination from other sub groupings that have different patterns of relationships. Our results show that the rare rules produced by each cluster are more informative than rules found from direct association rule mining on the unpartitioned dataset.\",\n \"Title: WeightTransmitter: weighted association rule mining using landmark weights\\nYear: 2012\\n\\nAbstract: Weighted Association Rule Mining (WARM) is a technique that is commonly used to overcome the well-known limitations of the classical Association Rule Mining approach. The assignment of high weights to important items enables rules that express relationships between high weight items to be ranked ahead of rules that only feature less important items. Most previous research to weight assignment has used subjective measures to assign weights and are reliant on domain specific information. Whilst there have been a few approaches that automatically deduce weights from patterns of interaction between items, none of them take advantage of the situation where weights of only a subset of items are known in advance. We propose a model, WeightTransmitter, that interpolates the unknown weights from a known subset of weights.\",\n \"Title: Automatic Item Weight Generation for Pattern Mining and its Application\\nYear: 2011\\n\\nAbstract: Association rule mining discovers relationships among items in a transactional database. Most approaches assume that all items within a dataset have a uniform distribution with respect to support. However, this is not always the case, and weighted association rule mining WARM was introduced to provide importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. In certain cases, it is difficult to provide weights to all items within a dataset. In this paper, the authors propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. The authors experiment shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared with a miner that does not employ a weighting scheme. The authors applied the model in a real world application to mine text from a given collection of documents. The use of item weighting enabled the authors to attach more importance to terms that are distinctive. The results demonstrate that keyword discrimination via item weighting leads to informative rules.\",\n \"Title: Generating realistic online auction data\\nYear: 2012\\n\\nAbstract: To combat online auction fraud, researchers have developed fraud detection and prevention methods. However, it is difficult to effectively evaluate these methods using commercial or synthetic auction data. For commercial data, it is not possible to accurately identify cases of fraud. For synthetic auction data, the conclusions drawn may not extend to the real world. The availability of realistic synthetic auction data, which models real auction data, will be invaluable for effective evaluation of fraud detection algorithms. We present an agent-based simulator that is capable of generating realistic English auction data. The agents and model are based on data collected from the TradeMe online auction site. We evaluate the generated data in two ways to show that it is similar to the TradeMe data. Evaluation of individual features show that correlation is greater than 0.9 for 8 of the 10 features, and evaluation using multiple features gives a median accuracy of 0.87.\",\n \"Title: Rare pattern mining on data streams\\nYear: 2012\\n\\nAbstract: There has been some research in the area of rare pattern mining where the researchers try to capture patterns involving events that are unusual in a dataset. These patterns are considered more useful than frequent patterns in some domain, including detection of computer attacks, or fraudulent credit transactions. Until now, most of the research in this area concentrates only on finding rare rules in a static dataset. There is a proliferation of applications which generate data streams, such as network logs and banking transactions. Applying techniques for static datasets is not practical for data streams. In this paper we propose a novel approach called Streaming Rare Pattern Tree (SRP-Tree), which finds rare rules in a data stream environment using a sliding window, and show that it is faster than current approaches.\",\n \"Title: Discriminatory confidence analysis in pattern mining\\nYear: 2011\\n\\nAbstract: The field of association rule mining has long been dominated by algorithms that search for patterns based on their frequency of occurrence in a given dataset. The birth of weighted association rule mining caused a fundamental paradigm shift in the way that patterns are identified. Consideration was given to the \\\"importance\\\" of an item in addition to its frequency of occurrence. In this research we propose a novel measure which we term Discriminatory Confidence that identifies the extent to which a given item can segment a dataset in a meaningful manner. We devise an efficient algorithm which is driven by an Information Scoring model that identifies items with high discriminatory power. We compare our results with the classical approach to association rule mining and show that the Information Scoring model produces widely divergent results. Our research reveals that mining on the basis of frequency alone tends to exclude some of the most informative patterns that are discovered using discriminatory power.\",\n \"Title: Finding sporadic rules in the diagnosis of the Erythemato-Squamous diseases\\nYear: 2008\\n\\nAbstract: Most previous research into association rule data mining has focused on finding frequent rules; rules with high support and high confidence. However detecting rare or sporadic association rules, which have low support and high confidence, is a worthwhile task as well, as they represent rare, but potentially interesting and important associations. Mining rare or sporadic rules is a difficult data mining problem, and most previous approaches use an Apriori [1] like method [2-9]. However in order for Apriori to find rare rules, minimum support must be set very low, which results in a large amount of redundant rules and a long runtime. We previously proposed the Apriori-Inverse [10] and MIISR [11] algorithms to find sporadic rules quickly and efficiently. This paper provides an insight into the qualitative results produced by our proposed algorithms. We explore a specific real-world case study in more detail to get a qualitative understanding, namely the Dermatology dataset, for the diagnosis of the Erythemato-Squamous diseases. We show that traditional unmodified Apriori is not well suited to this task, and that our proposed algorithms are capable of producing interesting sporadic rules without having any expert domain knowledge.\",\n \"Title: Mining interesting imperfectly sporadic rules\\nYear: 2008\\n\\nAbstract: Detecting association rules with low support but high confidence is a difficult data mining problem. To find such rules using approaches like the Apriori algorithm, minimum support must be set very low, which results in a large number of redundant rules. We are interested in sporadic rules; i.e. those that fall below a maximum support level but above the level of support expected from random coincidence. There are two types of sporadic rules: perfectly sporadic and imperfectly sporadic. Here we are more concerned about finding imperfectly sporadic rules, where the support of the antecedent as a whole falls below maximum support, but where items may have quite high support individually. In this paper, we introduce an algorithm called Mining Interesting Imperfectly Sporadic Rules (MIISR) to find imperfectly sporadic rules efficiently, e.g. fever, headache, stiff neck → meningitis. Our proposed method uses item constraints and coincidence pruning to discover these rules in reasonable time. This paper is an expanded version of Koh et al. [Advances in knowledge discovery and data mining: 10th Pacific-Asia Conference (PAKDD 2006), Singapore. Lecture Notes in Computer Science 3918, Springer, Berlin, pp 473–482].\",\n \"Title: Finding sporadic rules using apriori-inverse\\nYear: 2005\\n\\nAbstract: We define sporadic rules as those with low support but high confidence: for example, a rare association of two symptoms indicating a rare disease. To find such rules using the well-known Apriori algorithm, minimum support has to be set very low, producing a large number of trivial frequent itemsets. We propose “Apriori-Inverse”, a method of discovering sporadic rules by ignoring all candidate itemsets above a maximum support threshold. We define two classes of sporadic rule: perfectly sporadic rules (those that consist only of items falling below maximum support) and imperfectly sporadic rules (those that may contain items over the maximum support threshold). We show that Apriori-Inverse finds all perfectly sporadic rules much more quickly than Apriori. We also propose extensions to Apriori-Inverse to allow us to find some (but not necessarily all) imperfectly sporadic rules.\",\n \"Title: EWgen: automatic generation of item weights for weighted association rule mining\\nYear: 2010\\n\\nAbstract: Association Rule Mining is an important data mining technique that has been widely used as an automatic rule generation method. While having outstanding success in many different application domains, it also has the potential to generate a vast number of rules, many of which are of little interest to the user. Weighted Association Rule Mining (WARM) overcomes this problem by assigning weights to items thus enabling interesting rules to be ranked ahead of less interesting ones and making it easier for the user to determine which rules are the most useful. Past research on WARM assumes that users have the necessary knowledge to supply item weights. In this research we relax this assumption by deriving item weights based on interactions between items. Our experimentation shows that the rule bases produced by our scheme produces more compact rule bases with a higher information content than standard rule generation methods.\",\n \"Title: Weighted association rule mining via a graph based connectivity model\\nYear: 2013\\n\\nAbstract: Association rule mining is an important data mining task that discovers relationships among items in a transaction database. Classical association rule mining approaches make the implicit assumption that an item's importance is determined by its support. In contrast, Weighted Association Rule Mining (WARM) attempts to provide a notion of importance, or weight to individual items that are not based solely on item support. Previous approaches to Weighted Association Rule Mining assign item weights in a subjective manner, based on a user's specialized knowledge of the underlying domain that is involved. Such approaches are infeasible when millions of items are present in a dataset, or when domain knowledge is unavailable. Furthermore, even when such domain information is available, a weight assignment based on subjective information constrains the knowledge discovered to fit with the weights assigned, thus inhibiting the discovery of new trends in the data. In this research we automate the process of weight assignment by formulating a linear model that captures relationships between items. This approach extends prior research based on the Valency model. We extend the Valency model by expanding the field of interaction beyond immediate neighborhoods and show that this leads to significant improvements in performance on a number of different metrics that we use.\",\n \"Title: Efficient negative association rule mining based on chance thresholds\\nYear: 2014\\n\\nAbstract: Typically association rule mining only considers positive frequent itemsets in rule generation, where rules involving only the presence of items are generated. In this paper we consider the complementary problem of negative association rule mining, which generates rules describing the absence of itemsets from transactions. We describe a new approach called MINR Mining Interesting Negative Rules to efficiently find all interesting negative association rules. In our approach, we only consider the presence or absence of itemsets that are strongly associated. Our approach does not require a user defined support threshold, and is based on pruning coincidental itemsets. For every individual itemset we calculate two custom thresholds based on their support: the positive and negative chance thresholds. Itemsets whose support falls above their positive chance threshold are considered positively associated whereas itemsets whose support falls below its negative chance threshold are considered negatively associated. We compared our implementation against Pearson Φ correlation and we note that MINR was able to generate sets of rules which are more interesting.\",\n \"Title: Evaluating Fraud Detection Algorithms Using an Auction Data Generator\\nYear: 2012\\n\\nAbstract: Online auction sites are a target for fraud. Researchers have developed fraud detection and prevention methods. However, there are difficulties when using either commercial or synthetic auction data to evaluate the effectiveness of these methods. When using commercial data, it is not possible to accurately identify cases of fraud. Using synthetic data, the conclusions drawn may not extend to the real world. The availability of realistic synthetic auction data, which models real auction data, will be invaluable for effective evaluation of fraud detection algorithms. We present an agent-based simulator that is capable of generating realistic English auction data. The agents and model are based on data collected from the Trade Me online auction site. We evaluate the generated data in two ways to show that it is similar to the Trade Me auction data we have collected. In addition, we demonstrate that the simulator can have additional agents added to simulate fraudulent behaviour, and be used to evaluate fraud detection algorithms: we implement three different fraud behaviours and three detection algorithms, and using the simulator, compare the ability of the detection algorithms to correctly identify fraudulent agents.\",\n \"Title: Mining non-coincidental rules without a user defined support threshold\\nYear: 2008\\n\\nAbstract: Traditional association rulemining techniques employ the support and confidence framework. However, specifying minimum support of the mined rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system. Here we propose replacing Apriori's user-defined minimum support threshold with the more meaningful MinAbsSup function. This calculates a custom minimum support for each itemset based on the probability of chance collision of its items, as derived from the inverse of Fisher's exact test. We will introduce the notion of coincidental itemsets; given a transaction dataset there is a chance that two independent items are appearing together by random coincidence. Rules generated from these itemsets do not denote a meaningful association, and are not useful.\",\n \"Title: Valency based weighted association rule mining\\nYear: 2010\\n\\nAbstract: Association rule mining is an important data mining task that discovers relationships among items in a transaction database. Most approaches to association rule mining assume that all items within a dataset have a uniform distribution with respect to support. Therefore, weighted association rule mining (WARM) was introduced to provide a notion of importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. This is infeasible when millions of items are present in a dataset. In this paper we propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. Our experimentation shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared to a miner that does not employ such a weighting scheme.\",\n \"Title: Verb selection using semantic role labeling for citation classification\\nYear: 2013\\n\\nAbstract: Citation classification is the task of assigning a category to a reference or citation. The current sets of categories or classes proposed in the literature vary in size and they are based on the analysis of a small sample of citation sentences. We are developing a process to automatically generate such categories and base them on the analysis of a large corpus of papers. Part of the generation process involves selecting the main verb relevant to the reference being cited in the sentence. In this paper we present our recently developed technique that automatically identifies the relevant verb in a citation sentence. The technique uses heuristic rules, which are dependent on the results of a semantic role labeler. Four test sets were collected, and the common annotations of the test sets annotated by three people were used to assess the accuracy of the rules. Through experimentation we show that the average accuracy achieved using our technique that automatically extracts verbs from citation sentences across the four test sets is reasonable at 75%.\",\n \"Title: SPO-Tree: efficient single pass ordered incremental pattern mining\\nYear: 2011\\n\\nAbstract: Since the introduction of FP-growth using FP-tree there has been a lot of research into extending its usage to data stream or incremental mining. Most incremental mining adapts the Apriori algorithm. However, we believe that using a tree based approach would increase performance as compared to the candidate generation and testing mechanism used in Apriori. Despite this FP-tree still requires two scans through a dataset. In this paper we present a novel tree structure called Single Pass Ordered Tree SPO-Tree that captures information with a single scan for incremental mining. All items in a transaction are inserted/sorted based on their frequency. The tree is reorganized dynamically when necessary. SPO-Tree allows for easy maintenance in an incremental or data stream environment.\",\n \"Title: Web usage mining based recommender systems using implicit heterogeneous data: A Particle Swarm Optimization based clustering approach\\nYear: 2014\\n\\nAbstract: Recommender systems have become one of the necessary tools to help a web user find a potentially interesting resource based on their preferences. In implicit recommender systems, the recommendations are made based on the implicit information of the web users i.e. data collected from web logs or cookies without knowing users preferences. Developing such a recommender system is complex due to the huge amount of anonymous noisy data. In this paper we present a Particle Swarm Optimization (PSO) based clustering approach called Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering) for building a recommender system based on implicit web usage data. The approach mimics multi-agent properties of the particles of a swarm and divide the problem space into smaller sub-spaces i.e. clusters. Each cluster represents a particular group of user with similar interests. Later, the K-nearest neighbours of the most relevant cluster are generated as recommendations for a web user and ranked based on their distance. We performed different experiments for preprocessing, to assess the quality of clusters, and for the accuracy of recommendations. An overall accuracy of 65% to 95% was achieved for different scenarios, while in some cases the accuracy touched 100 precent when the selection was made from the top-5 recommendations.\",\n \"Title: SPAN: Finding collaborative frauds in online auctions.\\nYear: 2014\\n\\nAbstract: Fraud is an ongoing concern for online auction websites. Current methods to detect or prevent fraud have been limited in several ways, making them difficult to apply in real world settings. Firstly, existing methods cannot adapt to changes in the behaviour of fraudulent users over time: new models must be continuously constructed as they gradually lose accuracy. In addition, each method can only be used to detect a specific type of fraud. Secondly, existing methods are generally poor at identifying collaborative frauds. And thirdly, method training and evaluation has been limited by the quality of available datasets. We propose an algorithm named SPAN (Score Propagation over an Auction Network), for detecting users committing collaborative fraud that addresses these problems. SPAN is a two phase method that first applies anomaly detection on multiple 2-dimensional feature subspaces to generate an initial anomaly score for each user, then applies belief propagation to revise those scores to identify suspicious groups of users. We report extensive experimental results using synthetic data which shows that SPAN performs well across three different types of fraud, and outperforms a previous approach for collaborative fraud detection called 2-Level Fraud Spotting. Experiments on a real dataset shows that SPAN behaves reasonably, and can identify groups of users that appear anomalous.\",\n \"Title: Research on particle swarm optimization based clustering: A systematic review of literature and techniques.\\nYear: 2014\\n\\nAbstract: Optimization based pattern discovery has emerged as an important field in knowledge discovery and data mining (KDD), and has been used to enhance the efficiency and accuracy of clustering, classification, association rules and outlier detection. Cluster analysis, which identifies groups of similar data items in large datasets, is one of its recent beneficiaries. The increasing complexity and large amounts of data in the datasets have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. Swarm intelligence (SI) is one such optimization technique whose algorithms have successfully been demonstrated as solutions for different data clustering domains. In this paper we investigate the growth of literature in SI and its algorithms, particularly Particle Swarm Optimization (PSO). This paper makes two major contributions. Firstly, it provides a thorough literature overview focusing on some of the most cited techniques that have been used for PSO-based data clustering. Secondly, we analyze the reported results and highlight the performance of different techniques against contemporary clustering techniques. We also provide an brief overview of our PSO-based hierarchical clustering approach (HPSO-clustering) and compare the results with traditional hierarchical agglomerative clustering (HAC), K-means, and PSO clustering.\",\n \"Title: A graph based approach to inferring item weights for pattern mining.\\nYear: 2015\\n\\nAbstract: •We infer unknown items weights from a small subset of known landmark weights.•Domain specific knowledge is used to guide the weight inference process.•A graph model is used to model items and their interactions.•Significant improvements in Precision and Recall over previous research.•The novel graph partitioning algorithm shows significant gains in execution time.\",\n \"Title: Detection of abnormal profiles on group attacks in recommender systems\\nYear: 2014\\n\\nAbstract: Recommender systems using Collaborative Filtering techniques are capable of make personalized predictions. However, these systems are highly vulnerable to profile injection attacks. Group attacks are attacks that target a group of items instead of one, and there are common attributes among these items. Such profiles will have a good probability of being similar to a large number of user profiles, making them hard to detect. We propose a novel technique for identifying group attack profiles which uses an improved metric based on Degree of Similarity with Top Neighbors (DegSim) and Rating Deviation from Mean Agreement (RDMA). We also extend our work with a detailed analysis of target item rating patterns. Experiments show that the combined methods can improve detection rates in user-based recommender systems.\",\n \"Title: Attack detection in recommender systems based on target item analysis\\nYear: 2014\\n\\nAbstract: Recommender systems are highly vulnerable to attacks. Attackers who introduce biased ratings in order to affect recommendations, have been shown to be effective against collaborative filtering algorithms. In this paper, we study the use of statistical metrics to detect rating patterns of attackers. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analysing rating patterns between malicious profiles and genuine profiles in shilling attacks. Building upon this, we propose and evaluate an algorithm for detecting shilling attacks in recommender systems using a statistical approach. We look at two attack models: random attack and average attack. The experimental results show that our detection technique based on target item analysis is an effective approach in detecting shilling attacks for both the random and average attack model.\",\n \"Title: Web bots detection using Particle Swarm Optimization based clustering\\nYear: 2014\\n\\nAbstract: Optimization based techniques have emerged as important methods to tackle the problems of efficiency and accuracy in data mining. One of the current application areas is outlier detection that has not been fully explored yet but has enormous potential. Web bots are an example of outliers, which can be found in the web usage analysis process. Web bot requests are different from a genuine web user as web bots crawl large numbers of pages in a very short time. If web bots remains undetected they can skew the analysis process which can result in incorrect patterns that can cause wrong decisions. In this paper we use one of the popular Swarm Intelligence (SI) based techniques called Particle Swarm Optimization (PSO) to detect web bots among genuine user requests. We use our Particle Swarm Optimization (PSO) based clustering algorithm, Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering) to cluster the web-usage data and detect the abnormal behaviour caused by the web bots. We present the results of detection which shows that our proposed approach is capable of detecting such abnormal behaviour. We then compare the characteristics of the detected web bots with genuine web-users using cross validation.\",\n \"Title: Detecting Volatility Shift in Data Streams\\nYear: 2014\\n\\nAbstract: Current drift detection techniques detect a change in distribution within a stream. However, there are no current techniques that analyze the change in the rate of these detected changes. We coin the term stream volatility, to describe the rate of changes in a stream. A stream has a high volatility if changes are detected frequently and has a low volatility if changes are detected infrequently. We are particularly interested in a volatility shift which is a change in the rate of change (e.g. From high volatility to low volatility). We introduce and define the concept of stream volatility, and propose a novel technique to detect volatility on data streams in the presence of concept drifts. In the experiments we show our algorithm to be both fast and efficient. We also propose a new algorithm for drift detection called SEED that is faster and more memory efficient than the existing state-of-the-art drift detection approach. A faster drift detection algorithm has a flow-on benefit to the subsequent volatility detection stage because both algorithms run concurrently on the data stream.\",\n \"Title: Hierarchical PSO clustering based recommender system\\nYear: 2012\\n\\nAbstract: Due to a marked increase in the number of web users and their activities, many application areas that use patterns generated from their activities has been proposed. Web-based implicit recommender systems are one such application. An implicit recommender system is a tool that helps guide a user to a particular web resource based on implicit data. Implicit data comes from the web users activities without their active participation. Building such a system is a complex process due to two reasons, there is a huge amount of data and the quality of the data is poor. In this research, we tackle the first problem of generating patterns efficiently for recommender system by proposing Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering). HPSO-clustering is a clustering approach based on Particle Swarm Optimization which combines both the properties of hierarchical and partitional clustering. We grouped the users' session into different clusters. Recommendations for an active user are generated from these clusters. In this paper we report the results of accuracy of recommendations. We achieved an overall 60% to 65% of precision for an active user, while in some clusters the precision achieved was 100% when top 5 ranked recommendations were selected.\",\n \"Title: Clustering heterogeneous web usage data using Hierarchical Particle Swarm Optimization\\nYear: 2013\\n\\nAbstract: Data clustering aims to group data based on similarities between the data elements. Recently, due to the increasing complexity and amount of heterogenous data, modeling of such data for clustering has become a serious challenge. In this paper we tackle the problem of modeling heterogeneous web usage data for clustering. The main contribution is a new similarity measure which we propose to cluster heterogeneous web usage data. We then use this similarity measure in our Particle Swarm Optimization (PSO) based clustering algorithm, Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering). HPSO-clustering combines the qualities of hierarchical and partitional clustering to cluster data in a hierarchical agglomerative manner. We present the clustering results and explain the effects of the new similarity measure on inter-cluster and intra-cluster distances. These measures verify the applicability of the proposed similarity measure on web usage data.\",\n \"Title: Rare Pattern Mining from Data Streams Using SRP-Tree and Its Variants.\\nYear: 2015\\n\\nAbstract: There has been some research in the area of rare pattern mining where the researchers try to capture patterns involving events that are unusual in a dataset. These patterns are considered more useful than frequent patterns in some domains, including detection of computer attacks, or fraudulent credit transactions. Until now, most of the research in this area concentrates only on finding rare rules in a static dataset. There is a proliferation of applications which generate data streams, such as network logs and banking transactions, and applying techniques that mine static datasets is not practical for data streams. We propose a novel approach called Streaming Rare Pattern Tree (SRP-Tree) and its variations, which finds rare rules in a data stream environment using a sliding window, and show that it both finds the complete set of itemsets and runs with fast execution time.\",\n \"Title: Non-redundant rare itemset generation\\nYear: 2009\\n\\nAbstract: Rare itemsets are likely to be of great interest because they often relate to high-impact transactions which may give rise to rules of great practical significance. Research into the rare association rule mining problem has gained momentum in the recent past. In this paper, we propose a novel approach that captures such rare rules while ensuring that redundant rules are eliminated. Extensive testing on real-world datasets from the UCI repository confirm that our approach outperforms both the Apriori-Inverse(Koh et al. 2006) and Relative Support (Yun et al. 2003) algorithms.\",\n \"Title: Indirect weighted association rules mining for academic network collaboration recommendations\\nYear: 2012\\n\\nAbstract: Collaborative research is increasingly important and popular in academic circles. However for young researchers identifying new research collaborators to form joint research and analyzing the level of cooperation of the current partners can be a very complex task. Thus recommendation of new collaborations would be important for young researchers. This paper presents a new approach to recommend collaborators in an academic social network using the co-authorship network. We propose a weighted indirect rule mining approach using a novel weighting mechanism called sociability.\",\n \"Title: Tracking Drift Severity in Data Streams\\nYear: 2015\\n\\nAbstract: The evolution of data or concept drift is a common phenomena in data streams. Currently most drift detection methods are able to locate the point of drift, but are unable to provide important information on the characteristics of change such as the magnitude of change which we refer to as drift severity. Monitoring drift severity provides crucial information to users allowing them to formulate a more adaptive response. In this paper, we propose a drift detector, MagSeed, which is capable of tracking drift severity with a high rate of true positives and a low rate of false positives. We evaluate MagSeed on synthetic and real world data, and compare it to state of the art drift detectors ADWIN2 and DDM.\",\n \"Title: Drift Detection Using Stream Volatility\\nYear: 2015\\n\\nAbstract: Current methods in data streams that detect concept drifts in the underlying distribution of data look at the distribution difference using statistical measures based on mean and variance. Existing methods are unable to proactively approximate the probability of a concept drift occurring and predict future drift points. We extend the current drift detection design by proposing the use of historical drift trends to estimate the probability of expecting a drift at different points across the stream, which we term the expected drift probability. We offer empirical evidence that applying our expected drift probability with the state-of-the-art drift detector, ADWIN, we can improve the detection performance of ADWIN by significantly reducing the false positive rate. To the best of our knowledge, this is the first work that investigates this idea. We also show that our overall concept can be easily incorporated back onto incremental classifiers such as VFDT and demonstrate that the performance of the classifier is further improved.\",\n \"Title: StreamXM: An Adaptive Partitional Clustering Solution for Evolving Data Streams.\\nYear: 2015\\n\\nAbstract: A challenge imposed by continuously arriving data streams is to analyze them and to modify the models that explain them as new data arrives. We propose StreamXM, a stream clustering technique that does not require an arbitrary selection of number of clusters, repeated and expensive heuristics or in-depth prior knowledge of the data to create an informed clustering that relates to the data. It allows a clustering that can adapt its number of classes to those present in the underlying distribution. In this paper, we propose two different variants of StreamXM and compare them against a current, state-of-the-art technique, StreamKM. We evaluate our proposed techniques using both synthetic and real world datasets. From our results, we show StreamXM and StreamKM run in similar time and with similar accuracy when running with similar numbers of clusters. We show our algorithms can provide superior stream clustering if true clusters are not known or if emerging or disappearing concepts will exist within the data stream.\",\n \"Title: HI-Tree: Mining High Influence Patterns Using External and Internal Utility Values\\nYear: 2015\\n\\nAbstract: We propose an efficient algorithm, called HI-Tree, for mining high influence patterns for an incremental dataset. In traditional pattern mining, one would find the complete set of patterns and then apply a post-pruning step to it. The size of the complete mining results is typically prohibitively large, despite the fact that only a small percentage of high utility patterns are interesting. Thus it is inefficient to wait for the mining algorithm to complete and then apply feature selection to post-process the large number of resulting patterns. Instead of generating the complete set of frequent patterns we are able to directly mine patterns with high utility values in an incremental manner. In this paper we propose a novel utility measure called an influence factor using the concepts of external utility and internal utility of an item. The influence factor for an item takes into consideration its connectivity with its neighborhood as well as its importance within a transaction. The measure is especially useful in problem domains utilizing network or interaction characteristics amongst items such as in a social network or web click-stream data. We compared our technique against state of the art incremental mining techniques and show that our technique has better rule generation and runtime performance.\",\n \"Title: Unsupervised Semantic and Syntactic Based Classification of Scientific Citations\\nYear: 2015\\n\\nAbstract: In the recent years, the number of scientific publications has increased substantially. A way to measure the impact of a publication is to count the number of citations to the paper. Thus, citations are being used as a proxy for a researcher's contribution and influence in a field. Citation classification can provide context to the citations. To perform citation classification, supervised techniques are normally used. To the best of our knowledge there are no research that performs this task in a unsupervised manner. In this paper we present two techniques to cluster citations automatically without human intervention. This paper presents two novel techniques to cluster citations according to their contents (semantic) and the citation sentence styles (syntactic). The techniques are validated using external test sets from existing supervised citation classification studies.\",\n \"Title: Unsupervised Rare Pattern Mining: A Survey.\\nYear: 2016\\n\\nAbstract: Association rule mining was first introduced to examine patterns among frequent items. The original motivation for seeking these rules arose from need to examine customer purchasing behaviour in supermarket transaction data. It seeks to identify combinations of items or itemsets, whose presence in a transaction affects the likelihood of the presence of another specific item or itemsets. In recent years, there has been an increasing demand for rare association rule mining. Detecting rare patterns in data is a vital task, with numerous high-impact applications including medical, finance, and security. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for rare pattern mining. We investigate the problems in finding rare rules using traditional association rule mining. As rare association rule mining has not been well explored, there is still specific groundwork that needs to be established. We will discuss some of the major issues in rare association rule mining and also look at current algorithms. As a contribution, we give a general framework for categorizing algorithms: Apriori and Tree based. We highlight the differences between these methods. Finally, we present several real-world application using rare pattern mining in diverse domains. We conclude our survey with a discussion on open and practical challenges in the field.\",\n \"Title: Phishing Detection on Twitter Streams.\\nYear: 2016\\n\\nAbstract: With the prevalence of cutting-edge technology, the social media network is gaining popularity and is becoming a worldwide phenomenon. Twitter is one of the most widely used social media sites, with over 500 million users all around the world. Along with its rapidly growing number of users, it has also attracted unwanted users such as scammers, spammers and phishers. Research has already been conducted to prevent such issues using network or contextual features with supervised learning. However, these methods are not robust to changes, such as temporal changes or changes in phishing trends. Current techniques also use additional network information. However, these techniques cannot be used before spammers form a particular number of user relationships. We propose an unsupervised technique that detects phishing in Twitter using a 2-phase unsupervised learning algorithm called PDT Phishing Detector for Twitter. From the experiments we show that our technique has high accuracy ranging between 0.88 and 0.99.\",\n \"Title: Effect of verbal comprehension skill and self-reported features on reliability of crowdsourced relevance judgments.\\nYear: 2016\\n\\nAbstract: Test collection is extensively used to evaluate information retrieval systems in laboratory-based evaluation experimentation. In a classic setting of a test collection, human assessors involve relevance judgments, which are both a costly and time-consuming task, and scales poorly. Researchers are still being challenged in performing reliable and low-cost evaluation of retrieval systems. Crowdsourcing as a novel method provides a cost-effective and quick solution for creating relevance judgments. However, crowdsourcing comes with the risk of a heterogeneous mass of potential workers who create the relevance judgments with varied levels of accuracy. It is, therefore, essential to understand the factors that affect the reliability of crowdsourced judgments. In this article, we measured various cognitive characteristics of workers, and explored the effects of these characteristics on judgment reliability, in comparison with a human gold standard. We discovered a significant correlation between judgment reliability and the level of verbal comprehension skill. This association conveys an idea for improving the reliability of judgments by discriminating workers into various groups according to their cognitive abilities and to filter out (or to include) certain group(s) of workers. Aside from that, we also discovered a significant association between reliability of judgments and self-reported difficulty of judgment as well as confidence in the task. Exploring the reliability of using crowdsourcing to create relevance judgments.Investigating effects of cognitive skills on the reliability of judgments.Investigating effects of cognitive skills on system rankings.Investigating effects of self-reported competence on the reliability of judgments.\",\n \"Title: CPF: Concept Profiling Framework for Recurring Drifts in Data Streams.\\nYear: 2016\\n\\nAbstract: We propose the Concept Profiling Framework (CPF), a meta-learner that uses a concept drift detector and a collection of classification models to perform effective classification on data streams with recurrent concept drifts, through relating models by similarity of their classifying behaviour. We introduce a memory-efficient version of our framework and show that it can operate faster and with less memory than a naive implementation while achieving similar accuracy. We compare this memory-efficient version of CPF to a state-of-the-art meta-learner made to handle recurrent drift and show that we can regularly achieve improved classification accuracy along with runtime and memory use. We provide results from testing on synthetic and real-world datasets to prove CPF’s value in classifying data streams with recurrent concepts.\",\n \"Title: Effects Of Objective And Subjective Competence On The Reliability Of Crowdsourced Relevance Judgments\\nYear: 2017\\n\\nAbstract: Introduction. Despite the popularity of crowdsourcing, the reliability of crowdsourced output has been questioned since crowdsourced workers display varied degrees of attention, ability and accuracy. It is important, therefore, to understand the factors that affect the reliability of crowdsourcing. In the context of producing relevance judgments, crowdsourcing has been recently proposed as an alternative approach to traditional methods of information retrieval evaluation, which are mostly expensive and scale poorly.Aim. The purpose of this study is to measure various cognitive characteristics of crowdsourced workers, and explore the effect that these characteristics have upon judgment reliability, as measured against a human gold standard.Method. The authors examined whether workers with high verbal comprehension skill could outperform workers with low verbal comprehension skill in terms of judgment reliability in crowdsourcing.Results. A significant correlation was found between judgment reliability and measured verbal comprehension skill, as well as with self-reported difficulty of judgment and confidence in the task. Surprisingly, however, there is no correlation between level of self-reported topic knowledge and reliability.Conclusions. Our findings show that verbal comprehension skill influences the accuracy of the relevance judgments created by the crowdsourced workers.\",\n \"Title: mHUIMiner: A Fast High Utility Itemset Mining Algorithm for Sparse Datasets.\\nYear: 2017\\n\\nAbstract: High utility itemset mining is the problem of finding sets of items whose utilities are higher than or equal to a specific threshold. We propose a novel technique called mHUIMiner, which utilises a tree structure to guide the itemset expansion process to avoid considering itemsets that are nonexistent in the database. Unlike current techniques, it does not have a complex pruning strategy that requires expensive computation overhead. Extensive experiments have been done to compare mHUIMiner to other state-of-the-art algorithms. The experimental results show that our technique outperforms the state-of-the-art algorithms in terms of running time for sparse datasets.\",\n \"Title: Volatility Adaptive Classifier System.\\nYear: 2017\\n\\nAbstract: A data stream’s concept may evolve over time, which is known as the concept drift. Concept drifts affect the prediction accuracy of the learning model and are required to be handled to maintain the model quality. In most cases, there is a trade-off between maintaining prediction quality and learning efficiency. We present a novel framework known as the Volatility-Adaptive Classifier System (VACS) to balance the trade-off. The system contains an adaptive classifier and a non-adaptive classifier. The former can maintain a higher prediction quality but requires additional computational overhead, and the latter requires less computational overhead but its prediction quality may be susceptible to concept drifts. VACS automatically applies the adaptive classifier when the concept drifts are frequent, and switches to the non-adaptive classifier when drifts are infrequent. As a result, VACS can maintain a relatively low computational cost while still maintaining a high enough overall prediction quality. To the best of our knowledge, this is the first data stream mining framework that applies different learners to reduce the learning overheads.\",\n \"Title: Vehicle emission prediction using remote sensing data and machine learning techniques.\\nYear: 2018\\n\\nAbstract: More and more researchers are using remote sensing technology to measure real-world, on-road automobile emissions of nitric oxide (NO), one of the most important and frequently studied pollutants. Partnered with the National Institute of Water and Atmospheric Research (NIWA) in New Zealand, we aim to establish a robust NO emission factor prediction model using remote sensing data to forecast future emissions. This model can be extremely useful to local transport authorities for monitoring urban pollution levels and validating the effectiveness of existing traffic control policies. We have conducted this research using real-world data that were collected over a 10-year span between 2005 and 2015. The experimental results have shown that the vehicle emission patterns are continuously changing and the relevance of remote sensing data for future predictions decays as they get older.\\n\\nWe propose a 3-step machine learning approach to establish this model. Most notably we use quantile regression forest (a variation of random forest) as the base algorithm and use random forest's variable importance measure to validate and interpret the features. The model yields error rates that compare favourably to linear model based recursive partitioning and the original random forest model.\",\n \"Title: Discovering High Utility Change Points in Customer Transaction Data.\\nYear: 2018\\n\\nAbstract: High Utility Itemset Mining (HUIM) consists of identifying all sets of items (itemsets) that have a high utility (e.g. have a high profit) in a database of customer transactions. Important limitations of traditional HUIM algorithms is that they do not consider that the utility of itemsets varies as time passes and that itemsets may not have a high utility in the whole database, but in some specific time periods. To overcome these drawbacks, this paper defines the novel problem of discovering change points of high utility itemsets, that is to find the time points where the utility of an itemset is changing considerably. An efficient algorithms named HUCP-Miner is proposed to mine these change points. Experimental results show that the proposed algorithm has excellent performance and can discover interesting patterns that are not identified by traditional HUIM algorithms.\",\n \"Title: Evaluating the effectiveness of information retrieval systems using effort-based relevance judgment.\\nYear: 2018\\n\\nAbstract: Purpose The effort in addition to relevance is a major factor for satisfaction and utility of the document to the actual user. The purpose of this paper is to propose a method in generating relevance judgments that incorporate effort without human judges' involvement. Then the study determines the variation in system rankings due to low effort relevance judgment in evaluating retrieval systems at different depth of evaluation. Design/methodology/approach Effort-based relevance judgments are generated using a proposed boxplot approach for simple document features, HTML features and readability features. The boxplot approach is a simple yet repeatable approach in classifying documents' effort while ensuring outlier scores do not skew the grading of the entire set of documents. Findings The retrieval systems evaluation using low effort relevance judgments has a stronger influence on shallow depth of evaluation compared to deeper depth. It is proved that difference in the system rankings is due to low effort documents and not the number of relevant documents. Originality/value Hence, it is crucial to evaluate retrieval systems at shallow depth using low effort relevance judgments.\",\n \"Title: Predicting Air Quality from Low-Cost Sensor Measurements.\\nYear: 2018\\n\\nAbstract: Urban air pollution poses a significant global health risk, but due to the high expense of measuring air quality, the amount of available data on pollutant exposure has generally been wanting. In recent years this has motivated the development of several cheap, portable air quality monitoring instruments. However, these instruments also tend to be unreliable, and thus the raw measurements require preprocessing to make accurate predictions of actual air quality conditions, making them an apt target for machine learning techniques. In this paper we use a dataset of measurements from a low cost air-quality instrument—the ODIN-SD—to examine which techniques are most appropriate, and the limitations of such an approach. From theoretical and experimental considerations, we conclude that a robust linear regression over measurements of air quality metrics, as well as relative humidity and temperature measurements produces the model with greatest accuracy. We also discuss issues of concept drift which occur in this context, and quantify how much training data is required to strike the right balance between predictive accuracy and efficient data collection.\",\n \"Title: Interpreting Intermittent Bugs in Mozilla Applications Using Change Angle.\\nYear: 2018\\n\\nAbstract: Learning in evolving environments involves learning from data where the statistical characteristics can change over time. Current change detection algorithms that are used online for data streams detect whether a change has occurred in the data but there is always a detection delay. None of the existing online techniques can accurately pin-point the exact location of when the change starts to occur, which can be critical. We present a novel method Change Angle and we show, for the first time, how to pin-point online the location at which change starts to occur. We apply our Change Angle method in the application area of software revision control using Mozilla data, where it is important to detect not only the presence of change but also to pin-point accurately the location of when change starts to occur.\",\n \"Title: Extrapolation prefix tree for data stream mining using a landmark model\\nYear: 2012\\n\\nAbstract: Since the introduction of FP-growth there has been extensive research into extending its usage to data streams or incremental mining. This task is particularly challenging in the data stream environment because of the unbounded nature of a data stream and the need for avoiding multiple scans of the data. In this paper, we propose an algorithm, Extrapolation Prefix Tree that extracts frequent itemsets using a landmark windowing scheme. The algorithm uses a prefix tree structure to store arriving transactions, but unlike previous approaches estimates the structure of the tree in the next block of data based on the arrival pattern of items appearing in transactions that arrive in the current block. Our experimentation shows that Extrapolation-Tree significantly outperforms the CP-Tree, both in terms of the number of updates and the execution time required to keep the tree current while maintaining a compact tree.\",\n \"Title: Identifying Top- Nodes in Social Networks: A Survey.\\nYear: 2019\\n\\nAbstract: Top-k nodes are the important actors for a subjectively determined topic in a social network. To some extent, a topic is taken as a ranking criteria for identifying top-k nodes. Within a viral marketing network, subjectively selected topics can include the following: Who can promote a new product to the largest number of people, and who are the highest spending customers? Based on these questions, there has been a growing interest in top-k nodes research to effectively identify key players. In this article, we review and classify existing literature on top-k nodes identification into two major categories: top-k influential nodes and top-k significant nodes. We survey both theoretical and applied work in the field and describe promising research directions based on our review. This research area has proven to be beneficial for data analysis on online social networks as well as practical applications on real-life networks.\",\n \"Title: Items2Data: Generating Synthetic Boolean Datasets from Itemsets\\nYear: 2019\\n\\nAbstract: Boolean data is a core data type in machine learning. It is used to represent categorical and transactional data. Unlike real valued data, it is notoriously difficult to efficiently design boolean datasets that satisfy particular constraints. Inverse Frequent Itemset Mining (IFM) is the problem of constructing a boolean dataset, satisfying given support constraints for some itemsets. Previous work mainly focuses on the theoretical complexity of IFM and practical solutions scale poorly or do not satisfy all the constraints. We propose Items2Data, a practical algorithm for generating boolean datasets which is efficient under specific conditions. We introduce global closure to describe the condition which a dataset can be efficiently constructed. We evaluate Items2Data and its use in designing synthetic datasets and to analyze its accuracy, scalability and speed on real world datasets. The results indicate Items2Data is practical and efficient for generating synthetic boolean data when pre-defined itemsets are globally closed.\",\n \"Title: Recurring Concept Meta-learning for Evolving Data Streams.\\nYear: 2019\\n\\nAbstract: •A fast, accurate classification framework for data streams that reuses classifiers.•More accurate than a state-of-the-art classifier reuse framework on synthetic data.•Five times faster and as accurate as this framework on real-world benchmarks.•More accurate than a state-of-the-art ensemble technique on synthetic datasets.•Six times faster and as accurate as this framework on real-world benchmarks.\",\n \"Title: Network Embedding and Change Modeling in Dynamic Heterogeneous Networks\\nYear: 2019\\n\\nAbstract: Network embedding learns the vector representations of nodes. Most real world networks are heterogeneous and evolve over time. There are, however, no network embedding approaches designed for dynamic heterogeneous networks so far. Addressing this research gap is beneficial for analyzing and mining real world networks. We develop a novel representation learning method, change2vec, which considers a dynamic heterogeneous network as snapshots of networks with different time stamps. Instead of processing the whole network at each time stamp, change2vec models changes between two consecutive static networks by capturing newly-added and deleted nodes with their neighbour nodes as well as newly-formed or deleted edges that caused core structural changes known as triad closure or open processes. Change2vec leverages metapath based node embedding and change modeling to preserve both heterogeneous and dynamic features of a network. Experimental results show that change2vec outperforms two state-of-the-art methods in terms of clustering performance and efficiency.\",\n \"Title: Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information\\nYear: 2021\\n\\nAbstract: Streaming sources of data are becoming more common as the ability to collect data in real-time grows. A major concern in dealing with data streams is concept drift, a change in the distribution of data over time, for example, due to changes in environmental conditions. Representing concepts (stationary periods featuring similar behaviour) is a key idea in adapting to concept drift. By testing the ...\",\n \"Title: Analyzing and repairing concept drift adaptation in data stream classification\\nYear: 2022\\n\\nAbstract: Data collected over time often exhibit changes in distribution, or concept drift, caused by changes in factors relevant to the classification task, e.g. weather conditions. Incorporating all relevant factors into the model may be able to capture these changes, however, this is usually not practical. Data stream based methods, which instead explicitly detect concept drift, have been shown to retain performance under unknown changing conditions. These methods adapt to concept drift by training a model to classify each distinct data distribution. However, we hypothesize that existing methods do not robustly handle real-world tasks, leading to adaptation errors where context is misidentified. Adaptation errors may cause a system to use a model which does not fit the current data, reducing performance. We propose a novel repair algorithm to identify and correct errors in concept drift adaptation. Evaluation on synthetic data shows that our proposed AiRStream system has higher performance than baseline methods, while is also better at capturing the dynamics of the stream. Evaluation on an air quality inference task shows AiRStream provides increased real-world performance compared to eight baseline methods. A case study shows that AiRStream is able to build a robust model of environmental conditions over this task, allowing the adaptions made to concept drift to be analysed and related to changes in weather. We discovered a strong predictive link between the adaptions made by AiRStream and changes in meteorological conditions.\",\n \"Title: Continual Correction of Errors Using Smart Memory Replay\\nYear: 2021\\n\\nAbstract: Catastrophic forgetting is an issue for all continual learning models with neural network architectures, however, research in this area often concentrates on prevention of catastrophic forgetting and not on the potential to improve existing knowledge with new information. In this paper, we explore using smart memory replay to augment a state-of-the-art regularization model. Our method continually corrects model errors for learned tasks, without compromising the learning of new tasks. We demonstrate how our method can achieve higher accuracy of predictions than a standard regularization model on multiple datasets, while incurring similar computational costs. In our experiments we show how using our method gains between five to ten percent accuracy on a large image classification dataset. We also illustrate how our method is able to regain task knowledge after catastrophic forgetting occurs.\",\n \"Title: A Meta-Learning Approach for Automated Hyperparameter Tuning in Evolving Data Streams\\nYear: 2021\\n\\nAbstract: Most machine learning algorithms rely on a set of hyperparameters, which are tuned either by domain experts or using automated methods. In the context of data streams, the complexity of this task is compounded by the need for tuning every time a concept drift occurs. The problem of automated tuning may lead to classification performance improvements. Current methods optimise the hyperparameters from scratch every time concept drift is detected. Additionally, meta-learning techniques developed for static machine learning tasks allow pre-built knowledge to adapt faster to new tasks. We propose a meta-learning approach for automated tuning in evolving data streams, specifically for tuning parameters in Adaptive Random Forest (ARF) and parameters for several drift detectors. We compare our approach against non-adaptive methods on both synthetic and real-world datasets. Empirical results show that our approach makes a compromises between predictive performance and resource consumption for ARF, and between true positives and false positives for drift detectors.\",\n \"Title: Nacre: Proactive Recurrent Concept Drift Detection in Data Streams\\nYear: 2021\\n\\nAbstract: Concept drift detection is used to signal to a learning algorithm that there has been a change in the underlying distribution of the data stream. However, there is a delay in detecting the actual drifts, leading to performance loss between the start of the drift and the detection point. There are two major challenges in reducing such performance loss, specifically the difficulty in anticipating the location of the next drift point and determining the exact concept that will appear for timely concept adaptation. In this research, we leverage concept recurrences in data streams. We proposed a framework called Nacre, which can perform proactive drift detection and online updates to allow for smooth adaptation of concept drifts. We present a novel technique, called drift coordinator, that anticipates the next drift point and assesses the incoming concept. This will ultimately increase accuracy in the classification performance. We demonstrate that our method is able to learn and predict drift trends in streams with recurring drifts. This allows the anticipation of future changes which enables users and detection methods to be more proactive. We empirically show that our technique outperforms baselines in terms of accuracy, kappa, accuracy gain per drift and cumulative accuracy gain on both synthetic and real-world datasets.\",\n \"Title: Analyzing and Repairing Concept Drift Adaptation in Data Stream Classification\\nYear: 2021\\n\\nAbstract: Data collected over time often exhibit changes in distribution, or concept drift, caused by changes in hidden context relevant to the classification task, e.g. weather conditions. Adaptive learning methods are able to retain performance in changing conditions by explicitly detecting concept drift and changing the classifier used to make predictions. However, in realworld conditions, existing metho...\",\n \"Title: Evaluating Unsupervised Text Embeddings on Software User Feedback\\nYear: 2021\\n\\nAbstract: User feedback on software products has been shown to be useful for development and can be exceedingly abundant online. Many approaches have been developed to elicit requirements in different ways from this large volume of feedback, including the use of unsupervised clustering, underpinned by text embeddings. Methods for embedding text can vary significantly within the literature, highlighting the ...\",\n \"Title: Probabilistic exact adaptive random forest for recurrent concepts in data streams\\nYear: 2022\\n\\nAbstract: In order to adapt random forests to the dynamic nature of data streams, the state-of-the-art technique discards trained trees and grows new trees when concept drifts are detected. This is particularly wasteful when recurrent patterns exist. In this work, we introduce a novel framework called PEARL, which uses both an exact technique and a probabilistic graphical model with Lossy Counting, to replace drifted trees with relevant trees built in the past. The exact technique utilizes pattern matching to find the set of drifted trees that co-occurred in predictions in the past. Meanwhile, a probabilistic graphical model is being built to capture the tree replacements among recurrent concept drifts. Once the graphical model becomes stable, it replaces the exact technique and finds relevant trees in a probabilistic fashion. Further, Lossy Counting is applied to the graphical model which brings an added theoretical guarantee for both error rate and space complexity. We empirically show our technique outperforms baselines in terms of accuracy and kappa on both synthetic and real-world datasets.\",\n \"Title: Semi-supervised Conditional Density Estimation with Wasserstein Laplacian Regularisation.\\nYear: 2022\\n\\nAbstract: Conditional Density Estimation (CDE) has wide-reaching applicability to various real-world problems, such as spatial density estimation and environmental modelling. CDE estimates the probability density of a random variable rather than a single value and can thus model uncertainty and inverse problems. This task is inherently more complex than regression, and many algorithms suffer from overfitting, particularly when modelled with few labelled data points. For applications where unlabelled data is abundant but labelled data is scarce, we propose Wasserstein Laplacian Regularisation, a semi-supervised learning framework that allows CDE algorithms to leverage these unlabelled data. The framework minimises an objective function which ensures that the learned model is smooth along the manifold of the underlying data, as measured by Wasserstein distance. When applying our framework to Mixture Density Networks, the resulting semi-supervised algorithm can achieve similar performance to a supervised model with up to three times as many labelled data points on baseline datasets. We additionally apply our technique to the problem of remote sensing for chlorophyll-a estimation in inland waters.\",\n \"Title: Measuring Drift Severity by Tree Structure Classifiers\\nYear: 2022\\n\\nAbstract: Streaming data has become more common as our ability to collect data in real-time increases. A primary concern in dealing with data streams is concept drift, which describes changes in the underlying distribution of streaming data. Measuring drift severity is crucial for model adaptation. Drift severity can be a proxy in choosing concept drift adaptation strategies. Current methods measure drift severity by monitoring the changes in the learner performance or measuring the difference between data distributions. However, these methods cannot measure the drift severity if the ground truth labels are unavailable. Specifically, performance-based methods cannot measure marginal drift, and distribution-based methods cannot measure conditional drift. We propose a novel framework named Tree-based Drift Measurement (TDM) that measures both marginal and conditional drift without revisiting historical data. TDM measures the difference between tree classifiers by transforming them into sets of binary vectors. An experiment shows that TDM achieves similar performance to the state-of-the-art methods and provides the best trade-off between runtime and memory usage. A case study shows that the online learner performance can be improved by adapting different drift adaptation strategies based on the drift severity.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":70,"string":"70"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13266,"string":"13,266"}}},{"rowIdx":13267,"cells":{"question_id":{"kind":"string","value":"Michael Zakharyaschev"},"question":{"kind":"string","value":"Generate an abstract for the title \"Semi-qualitative Reasoning about Distances: A Preliminary Report\" using the following items: family of languages, metric spaces, computational complexity, satisfiability problem, undecidability"},"answer":{"kind":"string","value":"We introduce a family of languages intended for representing knowledge and reasoning about metric (and more general distance) spaces. While the simplest language can speak only about distances between individual objects and Boolean relations between sets, the more expressive ones are capable of capturing notions such as 'somewhere in (or somewhere out of) the sphere of a certain radius', 'everywhere in a certain ring', etc. The computational complexity of the satisfiability problem for formulas in our languages ranges from NP-completeness to undecidability and depends on the class of distance spaces in which they are interpreted. Besides the class of all metric spaces, we consider, for example, the spaces R × R and N × N with their natural metrics."},"correct_answer":{"kind":"string","value":"We introduce a family of languages intended for representing knowledge and reasoning about metric (and more general distance) spaces. While the simplest language can speak only about distances between individual objects and Boolean relations between sets, the more expressive ones are capable of capturing notions such as 'somewhere in (or somewhere out of) the sphere of a certain radius', 'everywhere in a certain ring', etc. The computational complexity of the satisfiability problem for formulas in our languages ranges from NP-completeness to undecidability and depends on the class of distance spaces in which they are interpreted. Besides the class of all metric spaces, we consider, for example, the spaces R × R and N × N with their natural metrics."},"documents":{"kind":"list like","value":["Title: On the Computational Complexity of Spatio-Temporal Logics.\nYear: 2003\n\nAbstract: Recently, a hierarchy of spatio-temporal logics based on the propositional temporal logic PTL and the spatial languages RCC-8, BRCC-8 and S4u has been introduced. Although a number of results on their computational properties were obtained, the most important questions were left open. In this paper, we solve almost all of these problems and provide a clear picture of the balance between expressiveness and 'computational realisability' within the hierarchy.","Title: Temporalising Tableaux\nYear: 2004\n\nAbstract: As a remedy for the bad computational behaviour of first-order temporal logic (FOTL), it has recently been proposed to restrict the application of temporal opera- tors to formulas with at most one free variable thereby obtaining so-called monodic fragments of FOTL. In this paper, we are concerned with constructing tableau al- gorithms for monodic fragments based on decidable fragments of fist-order logic like the two-variable fragment or the guarded fragment. We present a general framework that shows how existing decision procedures for first-order fragments can be used for constructing a tableau algorithm for the corresponding monodic fragment of FOTL. Some example instantiations of the framework are presented.","Title: Axiomatizing Distance Logics\nYear: 2002\n\nAbstract: In [8, 6] we introduced a family of `modal\" languages intended for talking about distances. These languages are interpreted in `distance spaces\" which satisfy some (or all) of the standard axioms of metric spaces. Among other things, we singled out decidable logics of distance spaces and proved expressive completeness results relating classical and modal languages. The aim of this paper is to axiomatize the modal fragments of the semantically defined distance logics of [6] and give a new proof...","Title: Reasoning about distances\nYear: 2003\n\nAbstract: The paper presents a novel expressive logic-based formalism intended for reasoning about numerical distances. We investigate its computational prop erties (in particular, show that it is EXPTIME- complete) and devise a tableau-based satisfiability- checking algorithm. To be able to express knowl edge about implicit or unknown distances, we then extend the language with variables ranging over distances and prove that the resulting logic is de- cidable as well.","Title: Decidable and Undecidable Fragments of First-Order Branching Temporal Logics\nYear: 2002\n\nAbstract: In this paper we analyze the decision problem for fragments of first-order extensions of branching time temporal logics such as computational tree logics CTL and CTL * or Prior's Ockhamist logic of historical necessity. On the one hand, we show that the one-variable fragments of logics like first-order CTL * 驴 such as the product of propositional CTL * with simple propositional modal logic S5, or even the one-variable bundled first-order temporal logic with sole temporal operator some time in the future' 驴 are undecidable. On the other hand, it is proved that by restricting applications of first-order quantifiers to state (i.e.,path-independent) formulas, and applications of temporal operators and path quantifiers to formulas with at most one free variable, we can obtain decidable fragments. The positive decidability results can serve as a unifying framework for devising expressive and effective time-dependent knowledge representation formalisms, e.g., temporal description or spatio-temporal logics.","Title: Automated reasoning about metric and topology\nYear: 2006\n\nAbstract: In this paper we compare two approaches to automated reasoning about metric and topology in the framework of the logic $\\mathcal{MT}$ introduced in [10]. $\\mathcal{MT}$-formulas are built from set variablesp1,p2,... (for arbitrary subsets of a metric space) using the Booleans ∧, ∨, →, and ¬, the distance operators∃a and ∃≤a, for $a\\in {\\mathbb Q}^{ 0}$, and the topological interior and closure operatorsI and C. Intended models for this logic are of the form $\\mathfrak I=(\\Delta,d,p_{1}^{\\mathfrak I},p_{2}^{\\mathfrak I},\\dots)$ where (Δ,d) is a metric space and $p_{i}^{\\mathfrak I} \\subseteq \\Delta$. The extension$\\varphi^{\\mathfrak I} \\subseteq \\Delta$ of an $\\mathcal{MT}$-formula ϕ in $\\mathfrak I$ is defined inductively in the usual way, with I and C being interpreted as the interior and closure operators induced by the metric, and $(\\exists^{a-neighbourhood of $\\varphi^{\\mathfrak I}$, and $(\\exists^{\\le a}\\varphi)^{\\mathfrak I}$ is the closed one. A formula ϕ is satisfiable if there is a model ${\\mathfrak I}$ such that $\\varphi^{\\mathfrak I} \\ne \\emptyset$; ϕ is valid if ¬ϕ is not satisfiable.","Title: A Note on Concepts and Distances\nYear: 2001\n\nAbstract: We combine the description logic ALC with the metric logics dened in (15). Entities that are conceived of as abstract points in the realm of ALC are given a spatial extension via an 'extension relation,' connecting abstract points in the domain of an ALC-model to points in a metric space. Conversely, regions in the metric space are connected via the converse 'extension relation' to certain points in the ALC-model. We prove the decidability of the satisabilit y problem for the resulting hybrid formalism, give a few examples, and discuss further extensions of the ideas introduced.","Title: 'Closer' representation and reasoning\nYear: 2005\n\nAbstract: We argue that orthodox tools for defining concepts in the framework of description logic should often be augmented with constructors that could allow definitions in terms of similarity (or closeness). We present a corres- ponding logical formalism with the binary operator 'more similar or closer to X than to Y ' and investigate its computational behaviour in dierent distance (or similarity) spaces. The concept satisfiability problem turns out to be ExpTime-complete for many classes of distances spaces no mat- ter whether they are required to be symmetric and/or satisfy the triangle inequality. Moreover, the complexity remains the same if we extend the language with the operators 'somewhere in the neighbourhood of radius a' where a is a non-negative rational number. However, for various natural subspaces of the real line R (and Euclidean spaces of higher dimensions) even the similarity logic with the sole 'closer' operator turns out to be undecidable. This quite unexpected result is proved by reduction of the solvability problem for Diophantine equations (Hilbert's 10th problem).","Title: Monodic Epistemic Predicate Logic\nYear: 2000\n\nAbstract: We consider the monodic formulas of common knowledge predicate logic, which allow applications of epistemic operators to formulas with at most one free variable. We provide finite axiomatizations of the monodic fragment of the most important common knowledge predicate logics (the full logics are known to be not recursively enumerable) and single out a number of their decidable fragments. On the other hand, it is proved that the addition of the equality symbol to the monodic fragment makes it not recursively enumerable.","Title: Multi-Dimensional Description Logics\nYear: 1999\n\nAbstract: In this paper, we construct a new concept description language intended for representing dynamic and intensional knowledge. The most important feature distinguishing this language from its predecessors in the literature is that it allows applications of modal operators to all kinds of syntactic terms: concepts, roles and formulas. Moreover, the language may contain both local (i.e., state-dependent) and global (i.e., state-independent) concepts, roles and objects. All this provides us with the most complete and natural means for reflecting the dynamic and intensional behaviour of application domains. We construct a satisfiability checking (mosaic-type) algorithm for this language (based on ALC) in (i) arbitrary multimodal frames, (ii) frames with universal accessibility relations (for knowledge) and (iii) frames with transitive, symmetrical and euclidean relations (for beliefs). On the other hand, it is shown that the satisfaction problem becomes undecidable if the underlying frames are arbitrary linear orders or the language contains the common knowledge operator for n ≥ 2 agents.","Title: Modularity in DL-Lite\nYear: 2007\n\nAbstract: We develop a formal framework for modular ontologies by analysing four notions of conservative extension and their applications in refining, re-using, merging, and segment- ing ontologies. For two members of the DL-Lite family of description logics, we prove important meta-properties of these notions such as robustness under joins, vocabulary extensions, and iterated import of ontologies. The computational complexity of the cor- responding reasoning tasks is investigated.","Title: Logics of metric spaces\nYear: 2003\n\nAbstract: We investigate the expressive power and computational properties of two different types of languages intended for speaking about distances. First, we consider a first-order language FM the two-variable fragment of which turns out to be undecidable in the class of distance spaces validating the triangular inequality as well as in the class of all metric spaces. Yet, this two-variable fragment is decidable in various weaker classes of distance spaces. Second, we introduce a variable-free modal language MS that, when interpreted in metric spaces, has the same expressive power as the two-variable fragment of FM. We determine natural and expressive fragments of MS which are decidable in various classes of distance spaces validating the triangular inequality, in particular, the class of all metric spaces.","Title: A Temporal Description Logic for Reasoning over Conceptual Schemas and Queries\nYear: 2002\n\nAbstract: This paper introduces a new logical formalism, intended for temporal conceptual modelling, as a natural combination of the well-known description logic DLR and point-based linear temporal logic with Since and Until. We define a query language (where queries are non-recursive Datalog programs and atoms are complex DLRUS expressions) and investigate the problem of checking query containment under the constraints defined by DLRUS conceptual schemas--i.e., DLRUS knowledge bases--as well as the problems of schema satisfiability and logical implication.","Title: A Logic for Concepts and Similarity\nYear: 2007\n\nAbstract: Categorization of objects into classes is currently supported by (at least) two `orthogonal' methods. In logic-based approaches, classifications are defined through ontologies or knowledge bases which describe the existing relationships among terms. Description logic (DL) has become one of the most successful formalisms for representing such knowledge bases, in particular because theoretically wel...","Title: Ontology-Based Data Access: Ontop of Databases\nYear: 2013\n\nAbstract: We present the architecture and technologies underpinning the OBDA system Ontop and taking full advantage of storing data in relational databases. We discuss the theoretical foundations of Ontop: the tree-witness query rewriting, <InlineEquation ID=\\\"IEq1\\\" <EquationSource Format=\\\"TEX\\\"$\\\\mathcal{T}$</EquationSource> </InlineEquation>-mappings and optimisations based on database integrity constraints and SQL features. We analyse the performance of Ontop in a series of experiments and demonstrate that, for standard ontologies, queries and data stored in relational databases, Ontop is fast, efficient and produces SQL rewritings of high quality.","Title: A logic for metric and topology\nYear: 2005\n\nAbstract: We propose a logic for reasoning about metric spaces with the induced topologies. It combines the 'qualitative' interior and closure operators with 'quantitative' operators 'somewhere in the sphere of radius r,' including or excluding the boundary We supply the logic with both the intended metric space semantics and a natural relational semantics. and show that the latter (i) provides finite partial representations of(in general) infinite metric models and (ii) reduces the standard 'epsilon-definitions' of closure and interior to simple constraints on relations. These features of the relational semantics suggest a finite axiomatisation of the logic and provide means to prove its EXPTIME-completeness (even if the rational numerical parameters are coded in binary). An extension with metric variables satisfying linear rational (in)equalities is proved to be decidable as well. Our logic can be regarded as a 'well-behaved' common denominator of logical systems constructed in temporal, spatial. and similarity-based quantitative and qualitative representation and reasoning. Interpreted on the real line (with its Euclidean metric), it is a natural fragment of decidable temporal logics for specification and verification of real-time systems. On the real plane, it is closely related to quantitative and qualitative formalisms for spatial representation and reasoning. but this time the logic becomes undecidable.","Title: Comparative similarity, tree automata, and diophantine equations\nYear: 2005\n\nAbstract: The notion of comparative similarity ‘X is more similar or closer to Y than to Z’ has been investigated in both foundational and applied areas of knowledge representation and reasoning, e.g., in concept formation, similarity-based reasoning and areas of bioinformatics such as protein sequence alignment. In this paper we analyse the computational behaviour of the ‘propositional’ logic with the binary operator ‘closer to a set τ1 than to a set τ2’ and nominals interpreted over various classes of distance (or similarity) spaces. In particular, using a reduction to the emptiness problem for certain tree automata, we show that the satisfiability problem for this logic is ExpTime-complete for the classes of all finite symmetric and all finite (possibly non-symmetric) distance spaces. For finite subspaces of the real line (and higher dimensional Euclidean spaces) we prove the undecidability of satisfiability by a reduction of the solvability problem for Diophantine equations. As our ‘closer’ operator has the same expressive power as the standard operator of conditional logic, these results may have interesting implications for conditional logic as well.","Title: On Non-Local Propositional and Local One-Variable Quantified CTL*\nYear: 2002\n\nAbstract: We prove decidability of non-local' propositional CTL*, where truth values of atoms may depend on the branch of evaluation. This result is then used to show decidability of the weak' one-variable fragment of first-order (local) CTL*, in which all temporal operators and path quantifiers except tomorrow' are applicable only to sentences. Various spatio-temporal logics based on combinations of CTL* and RCC-8 can be embedded into this fragment, and so are decidable.","Title: From topology to metric: modal logic and quantification in metric spaces\nYear: 2006\n\nAbstract: We propose a framework for comparing the expressive power and computational behaviour of modal logics designed for reasoning about qualitative aspects of metric spaces. Within this framework we can compare such well-known logics as S4 (for the topology induced by the metric), wK4 (for the derivation operator of the topology), variants of conditional logic, as well as logics of comparative similarity. One of the main problems for the new family of logics is to delimit the borders between 'decidable' and 'undecidable.' As a first step in this direction, we consider the modal logic with the operator 'closer to a set 0 than to a set 1' interpreted in metric spaces. This logic contains S4 with the universal modality and corresponds to a very natural language within our framework. We prove that over arbitrary metric spaces this logic is ExpTime-complete. Recall that over R, Q, and Z, as well as their finite subspaces, this logic is undecidable.","Title: Can You Tell the Difference Between DL-Lite Ontologies?\nYear: 2008\n\nAbstract: We develop a formal framework for comparing differ- ent versions of DL-Lite ontologies. Four notions of difference and entailment between ontologies are intro- duced and their applications in ontology development and maintenance discussed. These notions are obtained by distinguishing between differences that can be ob- served among concept inclusions, answers to queries over ABoxes, and by taking into account additional con- text ontologies. We compare these notions, study their meta-properties, and determine the computational com- plexity of the corresponding reasoning tasks. Moreover, we show that checking difference and entailment can be automated by means of encoding into QBF satisfiability and using off-the-shelf QBF solvers. Finally, we ex- plore the relationship between the notion of forgetting (or uniform interpolation) and our notions of difference between ontologies.","Title: On the Succinctness of Query Rewriting over OWL 2 QL Ontologies with Shallow Chases.\nYear: 2014\n\nAbstract: We investigate the size of first-order rewritings of conjunctive queries over OWL 2 QL ontologies of depth 1 and 2 by means of hypergraph programs computing Boolean functions. Both positive and negative results are obtained. Conjunctive queries over ontologies of depth 1 have polynomial-size nonrecursive datalog rewritings; tree-shaped queries have polynomial positive existential rewritings; however, in the worst case, positive existential rewritings can only be of superpolynomial size. Positive existential and nonrecursive datalog rewritings of queries over ontologies of depth 2 suffer an exponential blowup in the worst case, while first-order rewritings are superpolynomial unless $\\text{NP} \\subseteq \\text{P}/\\text{poly}$. We also analyse rewritings of tree-shaped queries over arbitrary ontologies and observe that the query entailment problem for such queries is fixed-parameter tractable.","Title: The Complexity of Clausal Fragments of LTL.\nYear: 2013\n\nAbstract: We introduce and investigate a number of fragments of propo- sitional temporal logic LTL over the flow of time (Z, <). The fragments are defined in terms of the available temporal operators and the structure of the clausal normal form of the temporal formulas. We determine the computational complexity of the satisfiability problem for each of the fragments, which ranges from NLogSpace to PTime, NP and PSpace.","Title: Query Inseparability and Module Extraction in OWL 2 QL.\nYear: 2011\n\nAbstract: The OWL 2 QL profile of OWL, based on the DL family of description logics, is emerging as a major language for developing new ontologies and approximating existing ones. Its main application is ontology-based data access, where ontologies are used to provide background knowledge for answering queries over data. We give a survey of recent results on the computational complexity of checking query inseparability for OWL 2 QL ontologies and analyse the impact of various OWL 2 QL constructs in the context of query inseparability. We also discuss practical query inseparability checking and minimal module extraction algorithms, as well as experimental results.","Title: Spatial Reasoning in RCC-8 with Boolean Region Terms\nYear: 2000\n\nAbstract: We extend the expressive power of the region connection calculus RCC-8 by allowing applications of the 8 binary relations of RCC-8 not only to atomic regions but also to Boolean combinations of them. It is shown that the statisfiability problem for the extended language in arbitrary topological spaces is still in NP; however, it becomes PSPACE-complete if only the Euclidean spaces R-n, n > 0, are regarded as possible interpretations. In particular, in contrast to pure RCC-8, the new language is capable of distinguishing between connected and non-connected topological spaces.","Title: Modal Logics for Metric Spaces: Open Problems\nYear: 2005\n\nAbstract: Modal logics and their models have been used to speak about and represent topological spaces since the 1940s [22, 23, 16, 17]. Examples include Tarski’s programme of algebraisation of topology (“of creating an algebraic apparatus for the treatment of portions of point-set topology,” to be more precise) which involved modal logic S4 [17], and the use of the extension of S4 with the universal modality (and its fragments) for spatial representation and reasoning; see, e.g., [5, 18, 7, 8, 1, 9] and references therein. Metric spaces are even more important mathematical structures that are fundamental for many areas of mathematics and computer science (recent examples include classification in bioinformatics, linguistics, botany, etc. using various similarity measures). A natural research programme is then to find out to which extent modal-like formalisms can be useful for speaking about metric spaces. Such a programme was launched in 2000 [21, 13, 14]. The aim of this note is to attract attention to the most important open problems and new directions of research in this exciting and promising area.","Title: On the Computational Complexity of Spatial Logics with Connectedness Constraints\nYear: 2008\n\nAbstract: We investigate the computational complexity of spatial logics extended with the means to represent topological connectedness and restrict the number of connected components. In particular, we show that the connectedness constraints can increase complexity from NP to PSpace , ExpTime and, if component counting is allowed, to NExpTime .","Title: Spatio-temporal representation and reasoning based on RCC-8\nYear: 2000\n\nAbstract: this paper is to introduce a hierarchy of languages intendedfor qualitative spatio-temporal representation and reasoning, provide these languageswith topological temporal semantics, construct effective reasoning algorithms,and estimate their computational complexity.","Title: Axiomatizing the monodic fragment of first-order temporal logic\nYear: 2002\n\nAbstract: It is known that even seemingly small fragments of the first-order temporal logic over the natural numbers are not recursively enumerable. In this paper we show that the monodic (not monadic, where this result does not hold) fragment is an exception by constructing its finite Hilbert-style axiomatization. We also show that the monodic fragment with equality is not recursively axiomatizable.","Title: The Greatest Extension of S4 into which Intuitionistic Logic is Embeddable\nYear: 1997\n\nAbstract: This paper gives a characterization of those quasi-normal extensions of the modal system S4 into which intuitionistic propositional logic Int is embeddable by the Gödel translation. It is shown that, as in the normal case, the set of quasi-normal modal companions of Int contains the greatest logic, M*, for which, however, the analog of the Blok-Esakia theorem does not hold. M* is proved to be decidable and Halldén-complete; it has the disjunction property but does not have the finite model property.","Title: The price of query rewriting in ontology-based data access.\nYear: 2014\n\nAbstract: We give a solution to the succinctness problem for the size of first-order rewritings of conjunctive queries in ontology-based data access with ontology languages such as OWL 2 QL, linear Datalog± and sticky Datalog±. We show that positive existential and nonrecursive datalog rewritings, which do not use extra non-logical symbols (except for intensional predicates in the case of datalog rewritings), suffer an exponential blowup in the worst case, while first-order rewritings can grow superpolynomially unless NP⊆P/poly. We also prove that nonrecursive datalog rewritings are in general exponentially more succinct than positive existential rewritings, while first-order rewritings can be superpolynomially more succinct than positive existential rewritings. On the other hand, we construct polynomial-size positive existential and nonrecursive datalog rewritings under the assumption that any data instance contains two fixed constants.","Title: Temporal description logic for ontology-based data access\nYear: 2013\n\nAbstract: Our aim is to investigate ontology-based data access over temporal data with validity time and ontologies capable of temporal conceptual modelling. To this end, we design a temporal description logic, TQL, that extends the standard ontology language OWL2QL, provides basic means for temporal conceptual modelling and ensures first-order rewritability of conjunctive queries for suitably defined data instances with validity time.","Title: Topological Logics with Connectedness over Euclidean Spaces\nYear: 2011\n\nAbstract: We consider the quantifier-free languages, Bc and Bc°, obtained by augmenting the signature of Boolean algebras with a unary predicate representing, respectively, the property of being connected, and the property of having a connected interior. These languages are interpreted over the regular closed sets of Rn (n ≥ 2) and, additionally, over the regular closed semilinear sets of Rn. The resulting logics are examples of formalisms that have recently been proposed in the Artificial Intelligence literature under the rubric Qualitative Spatial Reasoning. We prove that the satisfiability problem for Bc is undecidable over the regular closed semilinear sets in all dimensions greater than 1, and that the satisfiability problem for Bc and Bc° is undecidable over both the regular closed sets and the regular closed semilinear sets in the Euclidean plane. However, we also prove that the satisfiability problem for Bc° is NP-complete over the regular closed sets in all dimensions greater than 2, while the corresponding problem for the regular closed semilinear sets is ExpTime-complete. Our results show, in particular, that spatial reasoning is much harder over Euclidean spaces than over arbitrary topological spaces.","Title: Polynomial conjunctive query rewriting under unary inclusion dependencies\nYear: 2011\n\nAbstract: Ontology-based data access (OBDA) is widely accepted as an important ingredient of the new generation of information systems. In the OBDA paradigm, potentially incomplete relational data is enriched by means of ontologies, representing intensional knowledge of the application domain. We consider the problem of conjunctive query answering in OBDA. Certain ontology languages have been identified as FO-rewritable (e.g., DL-Lite and sticky-join sets of TGDs), which means that the ontology can be incorporated into the user's query, thus reducing OBDA to standard relational query evaluation. However, all known query rewriting techniques produce queries that are exponentially large in the size of the user's query, which can be a serious issue for standard relational database engines. In this paper, we present a polynomial query rewriting for conjunctive queries under unary inclusion dependencies. On the other hand, we show that binary inclusion dependencies do not admit polynomial query rewriting algorithms.","Title: Temporal Description Logic for Ontology-Based Data Access (Extended Version)\nYear: 2013\n\nAbstract: Our aim is to investigate ontology-based data access over temporal data with validity time and ontologies capable of temporal conceptual modelling. To this end, we design a temporal description logic, TQL, that extends the standard ontology language OWL 2 QL, provides basic means for temporal conceptual modelling and ensures first-order rewritability of conjunctive queries for suitably defined data instances with validity time.","Title: A modal logic framework for reasoning about comparative distances and topology\nYear: 2010\n\nAbstract: We propose and investigate a uniform modal logic framework for reasoning about topology and relative distance in metric and more general distance spaces, thus enabling the comparison and combination of logics from distinct research traditions such as Tarski’s S4 for topological closure and interior, conditional logics, and logics of comparative similarity. This framework is obtained by decomposing the underlying modal-like operators into first-order quantifier patterns. We then show that quite a powerful and natural fragment of the resulting first-order logic can be captured by one binary operator comparing distances between sets and one unary operator distinguishing between realised and limit distances (i.e., between minimum and infimum). Due to its greater expressive power, this logic turns out to behave quite differently from both S4 and conditional logics. We provide finite (Hilbert-style) axiomatisations and ExpTime-completeness proofs for the logics of various classes of distance spaces, in particular metric spaces. But we also show that the logic of the real line (and various other important metric spaces) is not recursively enumerable. This result is proved by an encoding of Diophantine equations.","Title: Speaking about Transitive Frames in Propositional Languages\nYear: 1998\n\nAbstract: This paper is a comparative study of the propositional intuitionistic(non-modal) and classical modal languages interpreted in the standard wayon transitive frames. It shows that, when talking about these framesrather than conventional quasi-orders, the intuitionistic languagedisplays some unusual features: its expressive power becomes weaker thanthat of the modal language, the induced consequence relation does not havea deduction theorem and is not protoalgebraic. Nevertheless, the paperdevelops a manageable model theory for this consequence and its extensionswhich also reveals some unexpected phenomena. The balance between theintuitionistic and modal languages is restored by adding to the former onemore implication.","Title: Monodic fragments of first-order temporal logics: 2000-2001 A.D\nYear: 2001\n\nAbstract: The aim of this paper is to summarize and analyze some results obtained in 2000-2001 about decidable and undecidable fragments of various first-order temporal logics, give some applications in the field of knowledge representation and reasoning, and attract the attention of the 'temporal community' to a number of interesting open problems.","Title: A Tableau Algorithm for Reasoning about Concepts and Similarity\nYear: 2003\n\nAbstract: We present a tableau-based decision procedure for the fusion (independent join) of the expressive description logic ALCQO and the logic MS for reasoning about distances and similarities. The resulting 'hybrid' logic allows both precise and approximate representation of and reasoning about concepts. The tableau algorithm combines the existing tableaux for the components and shows that the tableau technique can be fruitfully applied to fusions of logics with nominals-the case in which no general decidability transfer results for fusions axe available.","Title: DL - Lite and Role Inclusions\nYear: 2008\n\nAbstract: We give a classification of the complexity of DL-Lite logics extended with role inclusion axioms. We show that the data complexity of instance checking becomes P -hard in the presence of functionality constraints, and coNP -hard if arbitrary number restrictions are allowed, even with primitive concept inclusions. The combined complexity of satisfiability in this case jumps to ExpTime . On the other side, the combined complexity for the logics without number restrictions depends only on the form of concept inclusions and can range from NLogSpace and P to NP; the data complexity for such logics stays in LogSpace .","Title: The Combined Approach to Query Answering in DL-Lite\nYear: 2010\n\nAbstract: Databases and related information systems can bene- fit from the use of ontologies to enrich the data with general background knowledge. The DL-Lite family of ontology languages was specifically tailored towards such ontology-based data access, enabling an imple- mentation in a relational database management system (RDBMS) based on a query rewriting approach. In this paper, we propose an alternative approach to im- plementing ontology-based data access in DL-Lite with the distinguishing feature of allowing to rewrite both the query and the data. We show that, in contrast to the ex- isting approaches, no exponential blowup is produced by the rewritings. Based on experiments with a number of real-world ontologies, we demonstrate that query ex- ecution in the proposed approach is often more efficient than in existing approaches, especially for large ontolo- gies. We also show how to seamlessly integrate the data rewriting step of our approach into an RDBMS us- ing views (which solves the update problem) and make an interesting observation regarding the succinctness of queries in the original query rewriting approach.","Title: Conservative extensions in modal logic\nYear: 2006\n\nAbstract: Every normal modal logic L gives rise to the consequence relation ' |=L which holds if, and only if, is true in a world of an L-model whenever ' is true in that world. We consider the following al- gorithmic problem for L. Given two modal formulas '1 and '2, decide whether '1^'2 is a conservative extension of'1 in the sense that whenever '1 ^'2 |=L and does not contain propositional variables not occurring in '1, then '1 |=L. We first prove that the conservativeness problem is coNExpTime-hard for all modal logics of unbounded width (which have rooted frames with more than N successors of the root, for any N < !). Then we show that this problem is (i) coNExpTime-complete for S5 and K, (ii) in ExpSpace for S4 and (iii) ExpSpace-complete for GL.3 (the logic of finite strict linear orders). The proofs for S5 and K use the fact that these logics have uniform interpolants of exponential size.","Title: Reasoning over extended ER models\nYear: 2007\n\nAbstract: We investigate the computational complexity of reasoning over various fragments of the Extended Entity-Relationship (EER) language, which includes a number of constructs: ISA between entities and relationships, disjointness and covering of entities and relationships, cardinality constraints for entities in relationships and their refinements as well as multiplicity constraints for attributes. We extend the known EXPTIME-completeness result for UML class diagrams [5] and show that reasoning over EER diagrams with ISA between relationships is EXPTIME-complete even without relationship covering. Surprisingly, reasoning becomes NP-complete when we drop ISA between relationships (while still allowing all types of constraints on entities). If we further omit disjointness and covering over entities, reasoning becomes polynomial. Our lower complexity bound results are proved by direct reductions, while the upper bounds follow from the correspondences with expressive variants of the description logic DL-Lite, which we establish in this paper. These correspondences also show the usefulness of DL-Lite as a language for reasoning over conceptual models and ontologies.","Title: A Cookbook for Temporal Conceptual Data Modelling with Description Logics\nYear: 2012\n\nAbstract: We design temporal description logics (TDLs) suitable for reasoning about temporal conceptual data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of concept inclusions (ranging from atomic concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (ℤ, <), satisfying the constant domain assumption. Concept and role inclusions of the TBox hold at all moments of time (globally), and data assertions of the ABox hold at specified moments of time. To express temporal constraints of conceptual data models, the languages are equipped with flexible and rigid roles, standard future and past temporal operators on concepts, and operators “always” and “sometime” on roles. The most expressive of our TDLs (which can capture lifespan cardinalities and either qualitative or quantitative evolution constraints) turns out to be undecidable. However, by omitting some of the temporal operators on concepts/roles or by restricting the form of concept inclusions, we construct logics whose complexity ranges between NLogSpace and PSpace. These positive results are obtained by reduction to various clausal fragments of propositional temporal logic, which opens a way to employ propositional or first-order temporal provers for reasoning about temporal data models.","Title: DL-Lite with temporalised concepts, rigid axioms and roles\nYear: 2009\n\nAbstract: We investigate the temporal extension of the description logic DL-Litebool(RN) with the until operator on concepts, rigid (time-independent) and local (time-dependent) roles, and rigid TBox axioms. Using an embedding into the one-variable fragment of first-order temporal logic and the quasimodel technique, we prove that (i) the satisfiability problem for the resulting logic is PSpace-complete, and that (ii) by weakening until to sometime in the future we obtain an NP-complete logic, which matches the complexities of the propositional linear-time temporal logics with the corresponding temporal operators.","Title: Resasoning about Concepts and Similarity\nYear: 2003\n\nAbstract: Abstract: this paper still hold true. This applies, for example, to the similaritymeasures considered in [15, 9, 4] which satisfy the first two axioms of metricspaces only","Title: Products of 'transitive' modal logics\nYear: 2005\n\nAbstract: We solve a major open problem concerning algorithmic properties of products of 'transitive' modal logics by showing that products and commutators of such standard logics as K4. S4. S4. 1, K4.3. GL. or Grz are undecidable and do not have the finite model property. More generally. we prow that no Kripke complete extension of the commutator [K4. K41 with product frames of arbitrary finite or infinite depth (with respect to both accessibility relations) can be decidable. In particular, if C-1 and C-2 are classes of transitive frames such that their depth cannot be bounded by any fixed n < omega then the logic of the class {F-1, x F-2 \\ F-1 is an element of C-1, F-2 is an element of C-2} is undecidable. (On the contrary, the product of, say, K4 and the logic of all transitive Kripke frames of depth <= n. for some fixed n < omega, is decidable.) The complexity of these undecidable logics ranges from r.e. to co-r.e. and Pi \\-complete. As a consequence. we give the first known examples of Kripke incomplete commutators of Kripke complete logics.","Title: Undecidability of the unification and admissibility problems for modal and description logics\nYear: 2006\n\nAbstract: We show that the unification problem “is there a substitution instance of a given formula that is provable in a given logic?” is undecidable for basic modal logics K and K4 extended with the universal modality. It follows that the admissibility problem for inference rules is undecidable for these logics as well. These are the first examples of standard decidable modal logics for which the unification and admissibility problems are undecidable. We also prove undecidability of the unification and admissibility problems for K and K4 with at least two modal operators and nominals (instead of the universal modality), thereby showing that these problems are undecidable for basic hybrid logics. Recently, unification has been introduced as an important reasoning service for description logics. The undecidability proof for K with nominals can be used to show the undecidability of unification for Boolean description logics with nominals (such as ALCO and SHIQO). The undecidability proof for K with the universal modality can be used to show that the unification problem relative to role boxes is undecidable for Boolean description logics with transitive roles, inverse roles, and role hierarchies (such as SHI and SHIQ).","Title: Modal description logics: modalizing roles\nYear: 1999\n\nAbstract: We construct a new concept description language intended for representing dynamic and intensional knowledge. The most important feature distinguishing this language from its predecessors in the literature is that it allows applications of modal operators to all kinds of syntactic terms: concepts, roles and formulas. Moreover, the language may contain both local (i.e., state-dependent) and global (i.e., state-independent) concepts, roles and objects. All this provides us with the most complete and natural means for reflecting the dynamic and intensional behaviour of application domains. We construct a satisfiability checking (mosaic-type) algorithm for this language (based on 𝒜ℒ𝒞) in (i) arbitrary multimodal frames, (ii) frames with universal accessibility relations (for knowledge) and (iii) frames with transitive, symmetric and euclidean relations (for beliefs). On the other hand, it is shown that the satisfaction problem becomes undecidable if the underlying frames are arbitrary strict linear orders, 〈$$\\mathbb{N}$$,","Title: Query Answering in Expressive Variants of DL-Lite\nYear: 2007\n\nAbstract: The use of ontologies in various application domains, such as Data Integration, the Semantic Web, or ontology-based data man- agement, where ontologies provide the access to large amounts of data, is posing challenging requirements w.r.t. the trade-o between the ex- pressive power of a Description Logic and the eciency of reasoning. The logics of the DL-Lite family were specifically designed to meet such requirements and optimized w.r.t. the data complexity of answering com- plex types of queries. In this paper, we propose DL-Litebool, an extension of DL-Lite with full Booleans and number restrictions, and study the complexity of reasoning in DL-Litebool and its significant sub-logics. We obtain our results, together with useful insights into the properties of the studied logics, by a novel reduction to the one-variable fragment of first- order logic. We study the computational complexity of satisfiability and subsumption, and the data complexity of answering positive existential queries (which extend unions of conjunctive queries). Notably, we extend the LogSpace upper bound for the data complexity of answering unions of conjunctive queries in DL-Lite to positive queries and to the possibility of expressing also number restrictions, hence local functionality.","Title: Which Kind of Module Should I Extract?\nYear: 2009\n\nAbstract: There are various techniques for specifying a module of an on- tology that covers all knowledge about a given set of terms. These dier with respect to the size of the module, the complexity of its computa- tion, and certain robustness properties. In this paper, we survey existing logic-based approaches, focus on syntactic approximations, and compare dierent kinds of modules with respect to their properties. This is in- tended to give guidelines on how to choose \"the right kind of module\".","Title: Tailoring temporal description logics for reasoning over temporal conceptual models\nYear: 2011\n\nAbstract: Temporal data models have been used to describe how data can evolve in the context of temporal databases. Both the Extended Entity-Relationship (EER) model and the Unified Modelling Language (UML) have been temporally extended to design temporal databases. To automatically check quality properties of conceptual schemas various encoding to Description Logics (DLs) have been proposed in the literature. On the other hand, reasoning on temporally extended DLs turn out to be too complex for effective reasoning ranging from 2EXPTIME up to undecidable languages. We propose here to temporalize the 'light-weight' DL-Lite logics obtaining nice computational results while still being able to represent various constraints of temporal conceptual models. In particular, we consider temporal extensions of DL-LiteboolN, which was shown to be adequate for capturing non-temporal conceptual models without relationship inclusion, and its fragment DL-LitecoreN with most primitive concept inclusions, which are nevertheless enough to represent almost all types of atemporal constraints (apart from covering).","Title: The DL-lite family and relations\nYear: 2014\n\nAbstract: The recently introduced series of description logics under the common moniker 'DL-Lite' has attracted attention of the description logic and semantic web communities due to the low computational complexity of inference, on the one hand, and the ability to represent conceptual modeling formalisms, on the other. The main aim of this article is to carry out a thorough and systematic investigation of inference in extensions of the original DL-Lite logics along five axes: by (i) adding the Boolean connectives and (ii) number restrictions to concept constructs, (iii) allowing role hierarchies, (iv) allowing role disjointness, symmetry, asymmetry, reflexivity, irreflexivity and transitivity constraints, and (v) adopting or dropping the unique name assumption. We analyze the combined complexity of satisfiability for the resulting logics, as well as the data complexity of instance checking and answering positive existential queries. Our approach is based on embedding DL-Lite logics in suitable fragments of the one-variable first-order logic, which provides useful insights into their properties and, in particular, computational behavior.","Title: On the Products of Linear Modal Logics\nYear: 2001\n\nAbstract: We study two‐dimensional Cartesian products of modal logics determined by infinite or arbitrarily long finite linear orders and prove a general theorem showing that in many cases these products are undecidable, in particular, such are the squares of standard linear logics like K4.3, S4.3, GL.3, Grz.3, or the logic determined by the Cartesian square of any infinite linear order. This theorem solves...","Title: On Dynamic Topological and Metric Logics\nYear: 2006\n\nAbstract: The first result of this paper shows that some dynamic topological logics interpreted in various topological spaces with homeomorphisms are not recursively enumerable (and so are not recurs- ively axiomatisable). This gives a 'negative' solution to a conjecture of Kremer and Mints (11). Second, we prove the non-elementary decidability of the dynamic metric logic with distance op- erators of the form 'somewhere in the ball of radius a,' for a2 Q+, interpreted in arbitrary metric spaces with distance preserving automorphisms.","Title: Ontology-based data access with databases: a short course\nYear: 2013\n\nAbstract: Ontology-based data access (OBDA) is regarded as a key ingredient of the new generation of information systems. In the OBDA paradigm, an ontology defines a high-level global schema of (already existing) data sources and provides a vocabulary for user queries. An OBDA system rewrites such queries and ontologies into the vocabulary of the data sources and then delegates the actual query evaluation to a suitable query answering system such as a relational database management system or a datalog engine. In this chapter, we mainly focus on OBDA with the ontology language OWL 2QL, one of the three profiles of the W3C standard Web Ontology Language OWL 2, and relational databases, although other possible languages will also be discussed. We consider different types of conjunctive query rewriting and their succinctness, different architectures of OBDA systems, and give an overview of the OBDA system Ontop.","Title: Dynamic topological logics over spaces with continuous functions\nYear: 2006\n\nAbstract: Dynamic topological logics are combinations of topological and temporal modal logics that are used for reasoning about dynamical systems consisting of a topological space and a continuous function on it. Here we partially solve a major open problem in the field by showing (by reduction of the !-reachability problem for lossy channel systems) that the dynamic topological logic over arbitrary topological spaces as well as those over Rn, for each n 1, are undecidable. Actually, we prove this result for the natural and expressive fragment of the full dynamic topological language where the topological operators cannot be applied to formulas containing the temporal eventuality. Using Kruskal's tree theorem we also show that the formulas of this fragment that are valid in arbitrary topological spaces with continuous functions are recursively enumerable, which is not the case for spaces with homeomorphisms.","Title: Willem Blok and Modal Logic\nYear: 2006\n\nAbstract: Abstract. We present our personal view on W.J. Blok’s contribution to modal logic.","Title: Spatial Logics with Connectedness Predicates\nYear: 2010\n\nAbstract: We consider quantifier-free spatial logics, designed for qualitative spatial representation and reasoning in AI, and extend them with the means to represent topological connectedness of regions and restrict the number of their connected components. We investigate the computational complexity of these logics and show that the connectedness constraints can increase complexity from NP to PSPACE, EXPTIME and, if component counting is allowed, to NEXPTIME.","Title: All Finitely Axiomatizable Normal Extensions Of K4.3 Are Decidable\nYear: 1995\n\nAbstract: We use the apparatus of the canonical formulas introduced by Zakharyaschev [10] to prove that all finitely axiomatizable normal modal logics containing K4.3 are decidable, though possibly not characterized by classes of finite frames. Our method is purely frame-theoretic. Roughly, given a normal logic L above K4.3, we enumerate effectively a class of (possibly infinite) frames with respect to which L is complete, show how to check effectively whether a frame in the class validates a given formula, and then apply a Harrop-style argument to establish the decidability of L, provided of course that it has finitely many axioms.","Title: Temporalising Logics: Fifteen Years After\nYear: 2007\n\nAbstract: A straightforward way of adding a temporal dimension to a logical system is to combine it with a suitable temporal logic. Typical examples are first-order temporal logic [3], temporal description logics [1] or spatio-temporal logics [4]. In 1992, Finger and Gabbay [2] started an investigation of possible ways of temporalising abstract logical systems.","Title: Temporal logics over transitive states\nYear: 2005\n\nAbstract: We investigate the computational behaviour of ‘two-dimensional' propositional temporal logics over (ℕ,","Title: Topology, connectedness, and modal logic\nYear: 2008\n\nAbstract: This paper presents a survey of topological spatial logics, taking as its point of departure the interpretation of the modal logic S4 due to McKinsey and Tarski. We consider the effect of extending this logic with the means to represent topological connectedness, focusing principally on the issue of computational complexity. In particular, we draw attention to the special problems which arise when the logics are interpreted not over arbitrary topological spaces, but over (low-dimensional) Euclidean spaces.","Title: The DLRUS Temporal Description Logic\nYear: 2001\n\nAbstract: This paper introduces a new logical formalism, intended for temporal con- ceptual modelling, as a natural combination of the well-known description logic DLR and point-based linear temporal logic with Since and Until. We de- fine a query language (where queries are non-recursive Datalog pr ograms and atoms are complex DLRUS expressions) and investigate the problem of check- ing query containment under the constraints defined by DLRUS conceptual schemas, as well as the problems of schema satisfiability and logical implication. Although it is shown that reasoning in full DLRUS is undecidable, we identify the decidable (in a sense, maximal) fragment DLRUS by allowing applications of temporal operators to formulas and entities only (but not to relation expre s- sions). We obtain the following hierarchy of complexity results: (a) reason ing in DLRUS with atomic formulas is EXPTIME-complete, (b) satisfiability and logical implication of arbitrary DLRUS formulas is EXPSPACE-complete, and (c) the problem of checking query containment of non-recursive Datalog queries under DLRUS constraints is decidable in 2EXPTIME.","Title: Temporal Description Logics: A Survey\nYear: 2008\n\nAbstract: We survey temporal description logics that are based on standard temporal logics such as LTL and CTL. In particular, we concentrate on the computational complexity of the satisfiability problem and algorithms for deciding it.","Title: Complexity of reasoning over temporal data models\nYear: 2010\n\nAbstract: We investigate the computational complexity of reasoning over temporal extensions of conceptual data models. The temporal conceptual models we analyse include the standard UML/EER constructs, such as ISA between entities and relationships, disjointness and covering, cardinality constraints and their refinements, multiplicity and key constraints; in the temporal dimension, we have timestamping, evolution, transition and lifespan cardinality constraints. We give a nearly comprehensive picture of the impact of these constructs on the complexity of reasoning, which can range from NLOGSPACE to undecidability.","Title: Decidable fragments of first-order temporal logics\nYear: 2000\n\nAbstract: In this paper, we introduce a new fragment of the first-order temporal language, called the monodic fragment, in which all formulas beginning with a temporal operator (Since or Until) have at most one free variable. We show that the satisfiability problem for monodic formulas in various linear time structures can be reduced to the satisfiability problem for a certain fragment of classical first-order logic. This reduction is then used to single out a number of decidable fragments of first-order temporal logics and of two-sorted first-order logics in which one sort is intended for temporal reasoning. Besides standard first-order time structures, we consider also those that have only finite first-order domains, and extend the results mentioned above to temporal logics of finite domains. We prove decidability in three different ways: using decidability of monadic second-order logic over the intended flows of time, by an explicit analysis of structures with natural numbers time, and by a composition method that builds a model from pieces in finitely many steps.","Title: Combining spatial and temporal logics: expressiveness vs. complexity\nYear: 2011\n\nAbstract: In this paper, we construct and investigate a hierarchy of spatio-temporal formalisms that result from various combinations of propositional spatial and temporal logics such as the propositional temporal logic PT L, the spatial logics RCC-8, BRCC-8, S4u and their fragments. The obtained results give a clear picture of the trade-off between expressiveness and 'computational realisability' within the hierarchy. We demonstrate how different combining principles as well as spatial and temporal primitives can produce NP-, PSPACE-, EXPSPACE-, 2EXPSPACE-complete, and even undecidable spatio-temporal logics out of components that are at most NP- or PSPACE-complete.","Title: Interpreting Topological Logics over Euclidean Spaces\nYear: 2010\n\nAbstract: Topological logics are a family of languages for repre- senting and reasoning about topological data. In this paper, we consider propositional topological logics able to express the property of connectedness. The satisfia- bility problem for such logics is shown to depend not only on the spaces they are interpreted in, but also on the subsets of those spaces over which their variables are allowed to range. We identify the crucial notion of tameness, and chart the surprising patterns of sen- sitivity to the presence of non-tame regions exhibited by a range of topological logics in low-dimensional Eu- clidean spaces.","Title: A Note on Relativised Products of Modal Logics\nYear: 2002\n\nAbstract: this paper.each frame of the class.) For example, Kis the logic of all n-ary product frames. It is nothard to see that S5is the logic of all n-ary products of universal frames having the sameworlds, that is, frames hU; R i i with R i = U U . We refer to product frames of this kindas cubic universal product S5-frames. Note that the `i-reduct\" FU 1 U n ; R iof F 1 F n is a union of n disjoint copies of F i . Thus, Fand F i validate the same...","Title: On the decidability of connectedness constraints in 2D and 3D Euclidean spaces\nYear: 2011\n\nAbstract: We investigate (quantifier-free) spatial constraint languages with equality, contact and connectedness predicates, as well as Boolean operations on regions, interpreted over low-dimensional Euclidean spaces. We show that the complexity of reasoning varies dramatically depending on the dimension of the space and on the type of regions considered. For example, the logic with the interior-connectedness predicate (and without contact) is undecidable over polygons or regular closed sets in R2, EXPTIME-complete over polyhedra in R3, and NP-complete over regular closed sets in R3.","Title: A Tableau Decision Algorithm for Modalized ALC with Constant Domains\nYear: 2002\n\nAbstract: The aim of this paper is to construct a tableau decision algorithm for the modal description logic K ALC with constant domains. More precisely, we present a tableau procedure that is capable of deciding, given an ALC-formula ϕ with extra modal operators (which are applied only to concepts and TBox axioms, but not to roles), whether ϕ is satisfiable in a model with constant domains and arbitrary accessibility relations. Tableau-based algorithms have been shown to be 'practical' even for logics of rather high complexity. This gives us grounds to believe that, although the satisfiability problem for K ALC is known to be NEXPTIME-complete, by providing a tableau decision algorithm we demonstrate that highly expressive description logics with modal operators have a chance to be implementable. The paper gives a solution to an open problem of Baader and Laux [5].","Title: Logic-based ontology comparison and module extraction, with an application to DL-Lite\nYear: 2010\n\nAbstract: We develop a formal framework for comparing different versions of ontologies, and apply it to ontologies formulated in terms of DL-Lite, a family of 'lightweight' description logics designed for data-intensive applications. The main feature of our approach is that we take into account the vocabulary (=signature) with respect to which one wants to compare ontologies. Five variants of difference and inseparability relations between ontologies are introduced and their respective applications for ontology development and maintenance discussed. These variants are obtained by generalising the notion of conservative extension from mathematical logic and by distinguishing between differences that can be observed among concept inclusions, answers to queries over ABoxes, by taking into account additional context ontologies, and by considering a model-theoretic, language-independent notion of difference. We compare these variants, study their meta-properties, determine the computational complexity of the corresponding reasoning tasks, and present decision algorithms. Moreover, we show that checking inseparability can be automated by means of encoding into QBF satisfiability and using off-the-shelf general purpose QBF solvers. Inseparability relations between ontologies are then used to develop a formal framework for (minimal) module extraction. We demonstrate that different types of minimal modules induced by these inseparability relations can be automatically extracted from real-world medium-size DL-Lite ontologies by composing the known tractable syntactic locality-based module extraction algorithm with our non-tractable extraction algorithms and using the multi-engine QBF solver aqme. Finally, we explore the relationship between uniform interpolation (or forgetting) and inseparability.","Title: On the Computational Complexity of Decidable Fragments of First-Order Linear Temporal Logics\nYear: 2003\n\nAbstract: We study the complexity of some fragments of first-order temporal logic over natural numbers time. The one-variable fragment of linear first-order temporal logic even with sole temporal operator El is EXPSPACE-complete (this solves an open problem of [10]). So are the one-variable, two-variable and monadic monodic fragments with Until and Since. If we add the operators circle(n), with n given in binary, the fragments become 2EXPSPACE-complete. The packed monodic fragment has the same complexity as its pure first-order part - 2EXPTIME-complete. Over an), class of flows of time containing one with an infinite ascending sequence - e.g., rationals and real numbers time, and arbitrary strict linear orders - we obtain EXPSPACE lower bounds (which solves an open problem of [16]). Our results continue to hold if we restrict to models with finite first-order domains.","Title: Reasoning over Conceptual Schemas and Queries in Temporal Databases\nYear: 2001\n\nAbstract: This paper introduces a new logical formalism, intended for temporal conceptual modelling, as a natural combination of the well-known description logic and point-based linear temporal logic with Since and Until. The expressive power of the resulting logic is illustrated by provid- ing a systematic formalisation of the most important temporal entity-relationship data models appeared in the literature. We define a query language (where queries a re non-recursive Datalog programs and atoms are complex expressions) and investigate the problem of checking query containment under the constraints defined by conceptual schemas, as well as the problems of schema sat- isfiability and logical implication. Although it is shown th at reasoning in full is undecidable, we identify the decidable (in a sense, maximal) fragment by allowing applications of temporal operators to formulas and entities only (but not to relation expressions). We obtain the following hierar- chy of complexity results: (a) reasoning in with atomic formulas is EXPTIME-complete, (b) satisfiability and logical implication of arbitrary formulas is EXPSPACE-complete, and (c) the problem of checking query containment of non-recursive Datalog queries under constraints is decidable in 2EXPTIME.","Title: Tableaux for Temporal Description Logic with Constant Domains\nYear: 2001\n\nAbstract: We show how to combine the standard tableau system for the basic description logic ALC and Wolper's tableau calculus for propositional temporal logic PTL (with the temporal operators 'next-time' and 'until') in order to design a terminating sound and complete tableau-based satisfiability-checking algorithm for the temporal description logic PTLALC of [20] interpreted in models with constant domains. We use the method of quasimodels [18,16] to represent models with infinite domains, and the technique of minimal types [11] to maintain these domains constant. The combination is flexible and can be extended to more expressive description logics or even to decidable fragments of first-order temporal logics.","Title: A decidable extension of SROIQ with complex role chains and unions\nYear: 2013\n\nAbstract: We design a decidable extension of the description logic SROIQ underlying the Web Ontology Language OWL2. The new logic, called SR+OIQ, supports a controlled use of role axioms whose right-hand side may contain role chains or role unions. We give a tableau algorithm for checking concept satisfiability with respect to SR+OIQ ontologies and prove its soundness, completeness and termination.","Title: Undecidability of first-order intuitionistic and modal logics with two variables\nYear: 2005\n\nAbstract: We prove that the two-variable fragment of first-order intuitionistic logic is undecidable, even without constants and equality We also show that the two-variable fragment of a quantified modal logic L with expanding first-order domains is undecidable whenever there is a Kripke frame for L with a point having infinitely many successors (such are, in particular, the first-order extensions of practically all standard modal logics like K. K4, GL. S4 S5 K4.1, S4.2, GL.3 etc.). For many quantified modal logics. including those in the standard nomenclature above. even the monadic two-variable fragments turn out to be undecidable.","Title: Checking DL-Lite Modularity with QBF Solvers\nYear: 2008\n\nAbstract: We show how the reasoning tasks of checking various versions of conservativity for the description logic DL-Litebool can be reduced to satisability of quantied Boolean formulas and how o-the-shelf QBF solvers perform on a number of typical DL-Litebool ontologies.","Title: Logic in the Time of WWW: An OWL View\nYear: 2011\n\nAbstract: This paper analyses the classical automated reasoning problem-given a theory T\\mathcal{T}, a set A\\mathcal{A} of ground atoms and a formula ϕ, decide whether \n\n\n\n\n\n(T, A) \\models j</font\n>(\\mathcal{T}, \\mathcal{A}) \\models \\varphi–in the context of the OWL 2 Web Ontology Language and ontology-based data access (OBDA). In a typical OBDA scenario, T\\mathcal{T} is an OWL 2 ‘ontology’ providing a user-oriented view of raw data A\\mathcal{A} and ϕ(x) is a query with answer variables x. Unlike classical automated reasoning, an important requirement for OBDA is that it should scale to large amounts of data\n and preferably be as efficient as standard relational database management systems. There are various ways of formalising this\n requirement, which give rise to different fragments of firstorder logic suitable for OBDA. For example, according to the query-rewriting approach of [1], given T, A\\mathcal{T}, \\mathcal{A} and ϕ(x), one has to compute a new query ϕ′(x), independently of A\\mathcal{A}, such that, for any tuple a, \n\n\n\n\n\n(T, A) \\models j</font\n>(a)(\\mathcal{T}, \\mathcal{A}) \\models \\varphi(a) iff \n\n\n\n\n\nA \\models j</font\n>¢</font\n>(a)\\mathcal{A} \\models \\varphi'(a). As a result, this approach can only be applicable to the languages for which query-answering belongs to the class AC0 for data complexity (that is, if only A\\mathcal{A} is regarded as input, whereas both T\\mathcal{T} and ϕ are regarded as fixed). In the combined approach to OBDA [4,2], given T\\mathcal{T} , A\\mathcal{A} and ϕ(x), one has to compute new (i) A¢</font\n> Ê</font\n> A\\mathcal{A}' \\supseteq \\mathcal{A} in polynomial time in T\\mathcal{T} and A\\mathcal{A}, and (ii) ϕ′(x) in polynomial tome in T\\mathcal{T} and ϕ such that, for any tuple a, \n\n\n\n\n\n(T, A) \\models j</font\n>(a)(\\mathcal{T}, \\mathcal{A}) \\models \\varphi(a) iff \n\n\n\n\n\nA¢</font\n>\\models j</font\n>¢</font\n>(a)\\mathcal{A}' \\models \\varphi'(a). The combined approach can be used for languages with polynomial queryanswering.","Title: Exponential lower bounds and separation for query rewriting\nYear: 2012\n\nAbstract: We establish connections between the size of circuits and formulas computing monotone Boolean functions and the size of first-order and nonrecursive Datalog rewritings for conjunctive queries over OWL 2 QL ontologies. We use known lower bounds and separation results from circuit complexity to prove similar results for the size of rewritings that do not use non-signature constants. For example, we show that, in the worst case, positive existential and nonrecursive Datalog rewritings are exponentially longer than the original queries; nonrecursive Datalog rewritings are in general exponentially more succinct than positive existential rewritings; while first-order rewritings can be superpolynomially more succinct than positive existential rewritings.","Title: Multi-Dimensional Modal Logic as a Framework for Spatio-Temporal Reasoning\nYear: 2002\n\nAbstract: In this paper we advocate the use of multi-dimensional modal logics as a framework for knowledge representation and, in particular, for representing spatio-temporal information. We construct a two-dimensional logic capable of describing topological relationships that change over time. This logic, called PSTL (Propositional Spatio-Temporal Logic) is the Cartesian product of the well-known temporal logic PTL and the modal logic S4u, which is the Lewis system S4 augmented with the universal modality. Although it is an open problem whether the full PSTL is decidable, we show that it contains decidable fragments into which various temporal extensions (both point-based and interval based) of the spatial logic RCC-8 can be embedded. We consider known decidability and complexity results that are relevant to computation with multi-dimensional formalisms and discuss possible directions for further research.","Title: Spatial reasoning with RCC8 and connectedness constraints in Euclidean spaces.\nYear: 2014\n\nAbstract: The language RCC8 is a widely-studied formalism for describing topological arrangements of spatial regions. The variables of this language range over the collection of non-empty, regular closed sets of n-dimensional Euclidean space, here denoted RC+(Rn), and its non-logical primitives allow us to specify how the interiors, exteriors and boundaries of these sets intersect. The key question is the satisfiability problem: given a finite set of atomic RCC8-constraints in m variables, determine whether there exists an m-tuple of elements of RC+(Rn) satisfying them. These problems are known to coincide for all n≥1, so that RCC8-satisfiability is independent of dimension. This common satisfiability problem is NLogSpace-complete. Unfortunately, RCC8 lacks the means to say that a spatial region comprises a ‘single piece’, and the present article investigates what happens when this facility is added. We consider two extensions of RCC8: RCC8c, in which we can state that a region is connected, and RCC8c∘, in which we can instead state that a region has a connected interior. The satisfiability problems for both these languages are easily seen to depend on the dimension n, for n≤3. Furthermore, in the case of RCC8c∘, we show that there exist finite sets of constraints that are satisfiable over RC+(R2), but only by ‘wild’ regions having no possible physical meaning. This prompts us to consider interpretations over the more restrictive domain of non-empty, regular closed, polyhedral sets, RCP+(Rn). We show that (a) the satisfiability problems for RCC8c (equivalently, RCC8c∘) over RC+(R) and RCP+(R) are distinct and both NP-complete; (b) the satisfiability problems for RCC8c over RC+(R2) and RCP+(R2) are identical and NP-complete; (c) the satisfiability problems for RCC8c∘ over RC+(R2) and RCP+(R2) are distinct, and the latter is NP-complete. Decidability of the satisfiability problem for RCC8c∘ over RC+(R2) is open. For n≥3, RCC8c and RCC8c∘ are not interestingly different from RCC8. We finish by answering the following question: given that a set of RCC8c- or RCC8c∘-constraints is satisfiable over RC+(Rn) or RCP+(Rn), how complex is the simplest satisfying assignment? In particular, we exhibit, for both languages, a sequence of constraints Φn, satisfiable over RCP+(R2), such that the size of Φn grows polynomially in n, while the smallest configuration of polygons satisfying Φn cuts the plane into a number of pieces that grows exponentially. We further show that, over RC+(R2), RCC8c again requires exponentially large satisfying diagrams, while RCC8c∘ can force regions in satisfying configurations to have infinitely many components.","Title: An Introduction To Description Logics And Query Rewriting\nYear: 2014\n\nAbstract: This chapter gives an overview of the description logics underlying the OWL 2 Web Ontology Language and its three tractable profiles, OWL 2 RL, OWL 2 EL and OWL 2 QL. We consider the syntax and semantics of these description logics as well as main reasoning tasks and their computational complexity. We also discuss the semantical foundations for first-order and datalog rewritings of conjunctive queries over knowledge bases given in the OWL2 profiles, and outline the architecture of the ontology-based data access system Ontop.","Title: On the succinctness of query rewriting over shallow ontologies\nYear: 2014\n\nAbstract: We investigate the succinctness problem for conjunctive query rewritings over OWL 2QL ontologies of depth 1 and 2 by means of hypergraph programs computing Boolean functions. Both positive and negative results are obtained. We show that, over ontologies of depth 1, conjunctive queries have polynomial-size nonrecursive datalog rewritings; tree-shaped queries have polynomial positive existential rewritings; however, in the worst case, positive existential rewritings can be superpolynomial. Over ontologies of depth 2, positive existential and nonrecursive datalog rewritings of conjunctive queries can suffer an exponential blowup, while first-order rewritings can be superpolynomial unless NP ⊆ P/poly. We also analyse rewritings of tree-shaped queries over arbitrary ontologies and note that query entailment for such queries is fixed-parameter tractable.","Title: Answering SPARQL Queries over Databases under OWL 2 QL Entailment Regime\nYear: 2014\n\nAbstract: We present an extension of the ontology-based data access platform Ontop that supports answering SPARQL queries under the OWL 2 QL direct semantics entailment regime for data instances stored in relational databases. On the theoretical side, we show how any input SPARQL query, OWL 2 QL ontology and R2RML mappings can be rewritten to an equivalent SQL query solely over the data. On the practical side, we present initial experimental results demonstrating that by applying the Ontop technologiesthe tree-witness query rewriting, <InlineEquation ID=\\\"IEq1\\\" <EquationSource Format=\\\"TEX\\\"$\\\\mathcal{T}$</EquationSource> </InlineEquation>-mappings compiling R2RML mappings with ontology hierarchies, and <InlineEquation ID=\\\"IEq2\\\" <EquationSource Format=\\\"TEX\\\"$\\\\mathcal{T}$</EquationSource> </InlineEquation>-mapping optimisations using SQL expressivity and database integrity constraintsthe system produces scalable SQL queries.","Title: Tractable Interval Temporal Propositional And Description Logics\nYear: 2015\n\nAbstract: We design a tractable Horn fragment of the Halpern-Shoham temporal logic and extend it to interval-based temporal description logics, instance checking in which is P-complete for both combined and data complexity.","Title: Games for query inseparability of description logic knowledge bases.\nYear: 2016\n\nAbstract: We consider conjunctive query inseparability of description logic knowledge bases with respect to a given signature—a fundamental problem in knowledge base versioning, module extraction, forgetting and knowledge exchange. We give a uniform game-theoretic characterisation of knowledge base conjunctive query inseparability and develop worst-case optimal decision algorithms for fragments of Horn-ALCHI, including the description logics underpinning OWL 2 QL and OWL 2 EL. We also determine the data and combined complexity of deciding query inseparability. While query inseparability for all of these logics is P-complete for data complexity, the combined complexity ranges from P- to ExpTime- to 2ExpTime-completeness. We use these results to resolve two major open problems for OWL 2 QL by showing that TBox query inseparability and the membership problem for universal conjunctive query solutions in knowledge exchange are both ExpTime-complete for combined complexity. Finally, we introduce a more flexible notion of inseparability which compares answers to conjunctive queries in a given signature over a given set of individuals. In this case, checking query inseparability becomes NP-complete for data complexity, but the ExpTime- and 2ExpTime-completeness combined complexity results are preserved.","Title: Conservative Rewritability of Description Logic TBoxes: First Results.\nYear: 2015\n\nAbstract: We want to understand when a given TBox T in a description logic L can be rewritten into a TBox T′ in a weaker description logic L′. Two notions of rewritability are considered: model-conservative rewritability (T′ entails T and all models of T can be expanded to models of T′) and L-conservative rewritability (T′ entails T and every L-consequence of T′ in the signature of T is a consequence of T ) and investigate rewritability of TBoxes in ALCI to ALC, ALCQ to ALC, ALC to EL⊥, and ALCI to DL-Litehorn. We compare conservative rewritability with equivalent rewritability, give model-theoretic characterizations of conservative rewritability, prove complexity results for deciding rewritability, and provide some rewriting algorithms.","Title: Theoretically Optimal Datalog Rewritings for OWL 2 QL Ontology-Mediated Queries.\nYear: 2016\n\nAbstract: We show that, for OWL2QL ontology-mediated queries with (i) ontologies of bounded depth and conjunctive queries of bounded treewidth, (ii) ontologies of bounded depth and bounded-leaf tree-shaped conjunctive queries, and (iii) arbitrary ontologies and bounded-leaf tree-shaped conjunctive queries, one can construct and evaluate nonrecursive datalog rewritings by, respectively, LOGCFL, NL and LOGCFL algorithms, which matches the optimal combined complexity.","Title: Interval Temporal Description Logics.\nYear: 2015\n\nAbstract: In this paper, we construct a combination HS-Litehorn of the Halpern-Shoham interval temporal logic HS [15] with the description logic DL-Litehorn [12, 1], which is a Horn extension of the standard language OWL 2 QL. The temporal operators of HS are of the form 〈R〉 (‘diamond’) and [R] (‘box’), where R is one of Allen’s interval relations After, Begins, Ends, During, Later, Overlaps and their inverses (Ā, B, Ē, D, L, Ō). The propositional variables of HS are interpreted by sets of closed intervals [i, j] of some flow of time (e.g., Z, R), and a formula 〈R〉φ ([R]φ) is regarded to be true in [i, j] iff φ is true in some (respectively, all) interval(s) [i′, j′] such that [i, j]R[i′, j′] in Allen’s interval algebra. In HS-Litehorn, we represent temporal data by means of assertions such as SummerSchool(RW, t1, t2) and teaches(US,DL, s1, s2), which say that RW is a summer school that takes place in the time interval [t1, t2] and US teaches DL in the time interval [s1, s2]. Note that temporal databases store data in a similar format [17]. Temporal concept and role inclusions are used to impose constraints on the data and introduce new concepts and roles. For example, AdvCourseu〈D〉MorningSession v ⊥ says that advanced courses are not given in the morning sessions described by 〈B〉LectureDayu〈A〉Lunch v MorningSession; teaches v [D]teaches claims that the role teaches is downward hereditary (or stative) in the sense that if it holds in some interval then it also holds in all of its sub-intervals; [D](〈O〉teaches t 〈D〉teaches) u 〈B〉teaches u 〈E〉teaches v teaches, on the contrary, states that teaches is coalesced (or upward hereditary). The inclusions teaches v [D]teaches and [D](〈O〉teaches t 〈D〉teaches) v teaches ensure that teaches is both upward and downward hereditary. On the other hand, ‘rising stock market’ and ‘high average speed’ are typical examples of concepts that are not downward hereditary; for a discussion of these notions see [6, 21, 18]. Although the complexity of full HS-Litehorn remains unknown, in this paper we define two fragments, HS-LiteH/flat horn and HS-Lite H[G] horn , where satisfiability and instance checking are P-complete for both combined and data complexity. Our interest in tractable description logics with interval temporal operators is motivated by possible applications in ontology-based data access (OBDA) [12] to temporal databases. In this context, we naturally require reasonably expressive yet tractable ontology and query languages with temporal constructs (although","Title: First-order rewritability of temporal ontology-mediated queries\nYear: 2015\n\nAbstract: Aiming at ontology-based data access over temporal, in particular streaming data, we design a language of ontology-mediated queries by extending OWL2 QL and SPARQL with temporal operators, and investigate rewritability of these queries into two-sorted first-order logic with < and PLUS over time.","Title: When are description logic knowledge bases indistinguishable?\nYear: 2015\n\nAbstract: Deciding inseparability of description logic knowledge bases (KBs) with respect to conjunctive queries is fundamental for many KB engineering and maintenance tasks including versioning, module extraction, knowledge exchange and forgetting. We study the combined and data complexity of this inseparability problem for fragments of Horn-ALCHI, including the description logics underpinning OWL 2 QL and OWL 2 EL.","Title: Ontology-Mediated Queries: Combined Complexity and Succinctness of Rewritings via Circuit Complexity\nYear: 2018\n\nAbstract: AbstractWe give solutions to two fundamental computational problems in ontology-based data access with the W3C standard ontology language OWL 2 QL: the succinctness problem for first-order rewritings of ontology-mediated queries (OMQs) and the complexity problem for OMQ answering. We classify OMQs according to the shape of their conjunctive queries (treewidth, the number of leaves) and the existential depth of their ontologies. For each of these classes, we determine the combined complexity of OMQ answering and whether all OMQs in the class have polynomial-size first-order, positive existential, and nonrecursive datalog rewritings. We obtain the succinctness results using hypergraph programs, a new computational model for Boolean functions, which makes it possible to connect the size of OMQ rewritings and circuit complexity.","Title: Horn Fragments of the Halpern-Shoham Interval Temporal Logic (Technical Report).\nYear: 2016\n\nAbstract: We investigate the satisfiability problem for Horn fragments of the Halpern-Shoham interval temporal logic depending on the type (box or diamond) of the interval modal operators, the type of the underlying linear order (discrete or dense), and the type of semantics for the interval relations (reflexive or irreflexive). For example, we show that satisfiability of Horn formulas with diamonds is undecidable for any type of linear orders and semantics. On the contrary, satisfiability of Horn formulas with boxes is tractable over both discrete and dense orders under the reflexive semantics and over dense orders under the irreflexive semantics, but becomes undecidable over discrete orders under the irreflexive semantics. Satisfiability of binary Horn formulas with both boxes and diamonds is always undecidable under the irreflexive semantics.","Title: Query-Based Entailment and Inseparability for ALC Ontologies (Full Version).\nYear: 2016\n\nAbstract: We investigate the problem whether two ALC knowledge bases are indistinguishable by queries over a given vocabulary. We give model-theoretic criteria in terms of (partial) homomorphisms and products and prove that this problem is undecidable for conjunctive queries (CQs) but 2EXPTIME-complete for UCQs (unions of CQs). The same results hold if CQs are replaced by rooted CQs. We also consider the problem whether two ALC TBoxes give the same answers to any query in a given vocabulary over all ABoxes, and show that for CQs this problem is undecidable, too, but becomes decidable and 2EXPTIME-complete in Horn-ALC, and even EXPTIME-complete in Horn-ALC when restricted to (unions of) rooted CQs.","Title: Temporal and Spatial OBDA with Many-Dimensional Halpern-Shoham Logic.\nYear: 2016\n\nAbstract: We design an extension datalogHSn□ of datalog with hyperrectangle generalisations of Halpern-Shoham's modal operators on intervals and a corresponding query language. We prove that, over n-dimensional spaces comprised of Z and R, finding certain answers to datalogHSn□ queries can be reduced to standard datalog query answering. We present experimental results showing the expressivity and efficiency of datalogHSn□ on historical data.","Title: Query-Based Entailment and Inseparability for ALC Ontologies.\nYear: 2016\n\nAbstract: We investigate the problem whether two ALC knowledge bases are indistinguishable by queries over a given vocabulary. We give model-theoretic criteria and prove that this problem is undecidable for conjunctive queries (CQs) but decidable in 2EXPTIME for unions of rooted CQs. We also consider the problem whether two ALC TBoxes give the same answers to any query in a given vocabulary over all ABoxes, and show that for CQs this problem is undecidable, too, but becomes decidable and 2EXPTIME-complete in Horn-ALC, and even EXPTIME-complete in Horn-ALC when restricted to (unions of) rooted CQs.","Title: Conservative Rewritability of Description Logic TBoxes.\nYear: 2016\n\nAbstract: We investigate the problem of conservative rewritability of a TBox T in a description logic (DL) L into a TBox T′ in a weaker DL L′. We focus on model-conservative rewritability (T′ entails T and all models of T are expandable to models of T′), subsumption-conservative rewritability (T′ entails T and all subsumptions in the signature of T entailed by T′ are entailed by T), and standard DLs between ALC and ALCQI. We give model-theoretic characterizations of conservative rewritability via bisimulations, inverse p-morphisms and generated subinterpretations, and use them to obtain a few rewriting algorithms and complexity results for deciding rewritability.","Title: The Complexity of Ontology-Based Data Access with OWL 2 QL and Bounded Treewidth Queries.\nYear: 2017\n\nAbstract: Our concern is the overhead of answering OWL 2 QL ontology-mediated queries (OMQs) in ontology-based data access compared to evaluating their underlying tree-shaped and, more generally, bounded treewidth conjunctive queries (CQs). We show that OMQs with bounded depth ontologies have nonrecursive datalog (NDL) rewritings that can be constructed and evaluated in LOGCFL for combined complexity, and even in NL if their CQs are tree-shaped with a bounded number of leaves. Thus, such OMQs incur no overhead in complexity-theoretic terms. For OMQs with arbitrary ontologies and bounded-leaf tree-shaped CQs, NDL-rewritings are constructed and evaluated in LOGCFL. We experimentally demonstrate feasibility and scalability of our rewritings compared to previously proposed NDL-rewritings. On the negative side, we prove that answering OMQs with tree-shaped CQs is not fixed-parameter tractable if the ontology depth or the number of leaves in the CQs is regarded as the parameter, and that answering OMQs with a fixed ontology (of infinite depth) is NP-complete for tree-shaped CQs and LOGCFL-complete for bounded-leaf CQs.","Title: Metric Temporal Logic for Ontology-Based Data Access over Log Data.\nYear: 2017\n\nAbstract: We present a new metric temporal logic HornMTL over dense time and its datalog extension datalogMTL. The use of datalogMTL is demonstrated in the context of ontology-based data access over meteorological data. We show decidability of answering ontology-mediated queries for a practically relevant non-recursive fragment of datalogMTL. Finally, we discuss directions of the future work, including the potential use-cases in analyzing log data of engines and devices.","Title: Inseparability and Conservative Extensions of Description Logic Ontologies: A Survey.\nYear: 2018\n\nAbstract: The question whether an ontology can safely be replaced by another, possibly simpler, one is fundamental for many ontology engineering and maintenance tasks. It underpins, for example, ontology versioning, ontology modularization, forgetting, and knowledge exchange. What ‘safe replacement’ means depends on the intended application of the ontology. If, for example, it is used to query data, then the answers to any relevant ontology-mediated query should be the same over any relevant data set; if, in contrast, the ontology is used for conceptual reasoning, then the entailed subsumptions between concept expressions should coincide. This gives rise to different notions of ontology inseparability such as query inseparability and concept inseparability, which generalize corresponding notions of conservative extensions. In this chapter, we survey results on various notions of inseparability in the context of description logic ontologies, discussing their applications, useful model-theoretic characterizations, algorithms for determining whether two ontologies are inseparable (and, sometimes, for computing the difference between them if they are not), and the computational complexity of this problem.","Title: Querying log data with metric temporal logic\nYear: 2018\n\nAbstract: AbstractWe propose a novel framework for ontology-based access to temporal log data using a datalog extension datalogMTL of the Horn fragment of the metric temporal logic MTL. We show that datalogMTL is EXPSPACE-complete even with punctual intervals, in which case full MTL is known to be undecidable. We also prove that nonrecursive datalogMTL is PSPACE-complete for combined complexity and in AC0 for data complexity. We demonstrate by two real-world use cases that nonrecursive datalogMTL programs can express complex temporal concepts from typical user queries and thereby facilitate access to temporal log data. Our experiments with Siemens turbine data and MesoWest weather data show that datalogMTL ontology-mediated queries are efficient and scale on large datasets.","Title: On the Data Complexity of Ontology-Mediated Queries with a Covering Axiom.\nYear: 2017\n\nAbstract: This paper reports on our ongoing work that aims at a classification of conjunctive queries q according to the data complexity of answering ontologymediated queries ({A T F}; q). We give examples of queries from the complexity classes ϵ {AC0; L; NL; P; CONP}, and obtain a few syntactical conditions for -membership and -hardness.","Title: Optimal Nonrecursive Datalog Rewritings of Linear TGDs and Bounded (Hyper)Tree-Width Queries.\nYear: 2017\n\nAbstract: Our concern is answering ontology-mediated queries (O, q), where O is a set of linear tgds and q a conjunctive query (CQ) of bounded hypertree width. Assuming that the arity of predicates is bounded, we show that polynomial-size nonrecursive Datalog rewritings can be constructed and executed in (i) LOGCFL for OMQs with ontologies of bounded existential depth; (ii) NL for OMQs with ontologies of bounded depth and CQs whose hypertree decompositions have a bounded number of leaves; (iii) LOGCFL for OMQs with acyclic CQs whose join trees have a bounded number of leaves.","Title: Horn Fragments of the Halpern-Shoham Interval Temporal Logic\nYear: 2017\n\nAbstract: We investigate the satisfiability problem for Horn fragments of the Halpern-Shoham interval temporal logic depending on the type (box or diamond) of the interval modal operators, the type of the underlying linear order (discrete or dense), and the type of semantics for the interval relations (reflexive or irreflexive). For example, we show that satisfiability of Horn formulas with diamonds is undecidable for any type of linear orders and semantics. On the contrary, satisfiability of Horn formulas with boxes is tractable over both discrete and dense orders under the reflexive semantics and over dense orders under the irreflexive semantics but becomes undecidable over discrete orders under the irreflexive semantics. Satisfiability of binary Horn formulas with both boxes and diamonds is always undecidable under the irreflexive semantics.","Title: Ontology-Mediated Query Answering over Temporal Data: A Survey (Invited Talk).\nYear: 2017\n\nAbstract: We discuss the use of various temporal knowledge representation formalisms for ontology-mediated query answering over temporal data. In particular, we analyse ontology and query languages based on the linear temporal logic LTL, the multi-dimensional Halpern-Shoham interval temporal logic HS_n, as well as the metric temporal logic MTL. Our main focus is on the data complexity of answering temporal ontology-mediated queries and their rewritability into standard first-order and datalog queries.","Title: A Framework for Temporal Ontology-Based Data Access: A Proposal.\nYear: 2017\n\nAbstract: Predictive analysis gradually gains importance in industry. For instance, service engineers at Siemens diagnostic centres unveil hidden knowledge in huge amounts of historical sensor data and use this knowledge to improve the predictive systems analysing live data. Currently, the analysis is usually done using data-dependent rules that are specific to individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. One solution to this problem is to employ ontology-based data access (OBDA) that provides a conceptual view of data via an ontology. However, classical OBDA systems do not support access to temporal data and reasoning over it. To address this issue, we propose a framework of temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in RDF format, classical ontology and rule languages to reflect static information, as well as a temporal rule language to describe events. We also propose a SPARQL-based query language for retrieving temporal information and, finally, an architecture of system implementation extending the state-of-the-art OBDA platform Ontop.","Title: Ontology-Based Data Access to Slegge.\nYear: 2017\n\nAbstract: We report on our experience in ontology-based data access to the Slegge database at Statoil and share the resources employed in this use case: end-user information needs (in natural language), their translations into SPARQL, the Subsurface Exploration Ontology, the schema of the Slegge database with integrity constraints, and the mappings connecting the ontology and the schema.","Title: Ontology-Based Data Access: A Survey.\nYear: 2018\n\nAbstract: We present the framework of ontology-based data access, a semantic paradigm for providing a convenient and user-friendly access to data repositories, which has been actively developed and studied in the past decade. Focusing on relational data sources, we discuss the main ingredients of ontology-based data access, key theoretical results, techniques, applications and future challenges.","Title: Minimal module extraction from DL-lite ontologies using QBF solvers\nYear: 2009\n\nAbstract: We present a formal framework for (minimal) module extraction based on an abstract notion of inseparability w.r.t. a signature between ontologies. Two instances of this framework are discussed in detail for DL-Lite ontologies: concept inseparability, when ontologies imply the same complex concept inclusions over the signature, and query inseparability, when they give the same answers to existential queries for any instance data over the signature. We demonstrate that different types of corresponding minimal modules for these inseparability relations can be automatically extracted from large-scale DL-Lite ontologies by composing the tractable syntactic locality-based module extraction algorithm with intractable extraction algorithms using the multi-engine QBF solver AQME. The extracted minimal modules are compared with those obtained using non-logic-based approaches.","Title: On Non-local Propositional and Weak Monodic Quantified CTL*\nYear: 2004\n\nAbstract: In this paper we prove decidability of two kinds of branching time temporal logics. First we show that the non-local version of propositional PCTL*, in which truth values of atoms may depend on the branch of evaluation, is decidable. Then we use this result to establish decidability of various fragments of quantified PCTL*, where the next-time operator can be applied only to formulas with at most one free variable, all other temporal operators and path quantifiers are applicable only to sentences, and the first-order constructs follow the pattern of any of several decidable fragments of first-order logic.","Title: Ontology-Based Data Access with a Horn Fragment of Metric Temporal Logic.\nYear: 2017\n\nAbstract: We advocate datalogMTL, a datalog extension of a Horn fragment of the metric temporal logic MTL, as a language for ontology-based access to temporal log data. We show that datalogMTL is EXPSPACE-complete even with punctual intervals, in which case MTL is known to be undecidable. Non-recursive datalogMTL turns out to be PSPACE-complete for combined complexity and in AC(0) for data complexity. We demonstrate by two real-world use cases that nonrecursive datalogMTL programs can express complex temporal concepts from typical user queries and thereby facilitate access to log data. Our experiments with Siemens turbine data and MesoWest weather data show that datalogMTL ontology-mediated queries are efficient and scale on large datasets of up to 11GB.","Title: On the Parametrised Complexity of Tree-Shaped Ontology-Mediated Queries in OWL 2 QL.\nYear: 2017\n\nAbstract: We discuss the parameterised complexity of answering tree-shaped ontology-mediated queries (OMQs) in OWL 2 QL under various restrictions on their ontologies and conjunctive queries (CQs). In particular, we construct an ontology T such that answering OMQs (T , q) with tree-shaped CQs q is W[1]-hard if the number of leaves in q is regarded as the parameter. The number of leaves has previously been identified as an important characteristic of CQs as bounding it leads to tractable OMQ answering. Our result shows that treating it as a parameter does not make the problem fixed-parameter tractable, even for a fixed ontology.","Title: Model Comparison Games for Horn Description Logics\nYear: 2019\n\nAbstract: Horn description logics are syntactically defined fragments of standard description logics that fall within the Horn fragment of first-order logic and for which ontology-mediated query answering is in PTime for data complexity. They were independently introduced in modal logic to capture the intersection of Horn first-order logic with modal logic. In this paper, we introduce model comparison games for the basic Horn description logic hornALC (corresponding to the basic Horn modal logic) and use them to obtain an Ehrenfeucht-Fraïssé type definability result and a van Benthem style expressive completeness result for hornALC. We also establish a finite model theory version of the latter. The Ehrenfeucht-Fraïssé type definability result is used to show that checking hornALC indistinguishability of models is ExpTime-complete, which is in sharp contrast to ALC indistinguishability (i.e., bisimulation equivalence) checkable in PTime. In addition, we explore the behavior of Horn fragments of more expressive description and modal logics by defining a Horn guarded fragment of first-order logic and introducing model comparison games for it.","Title: Query Inseparability for ALC Ontologies.\nYear: 2019\n\nAbstract: We investigate the problem whether two ALC ontologies are indistinguishable (or inseparable) by means of queries in a given signature, which is fundamental for ontology engineering tasks such as ontology versioning, modularisation, update, and forgetting. We consider both knowledge base (KB) and TBox inseparability. For KBs, we give model-theoretic criteria in terms of (finite partial) homomorphisms and products and prove that this problem is undecidable for conjunctive queries (CQs), but 2ExpTime-complete for unions of CQs (UCQs). The same results hold if (U)CQs are replaced by rooted (U)CQs, where every variable is connected to an answer variable. We also show that inseparability by CQs is still undecidable if one KB is given in the lightweight DL EL and if no restrictions are imposed on the signature of the CQs. We also consider the problem whether two ALC TBoxes give the same answers to any query over any ABox in a given signature and show that, for CQs, this problem is undecidable, too. We then develop model-theoretic criteria for HornALC TBoxes and show using tree automata that, in contrast, inseparability becomes decidable and 2ExpTime-complete, even ExpTime-complete when restricted to (unions of) rooted CQs.","Title: Ontology-Based Access To Temporal Data With Ontop: A Framework Proposal\nYear: 2019\n\nAbstract: Predictive analysis gradually gains importance in industry. For instance, service engineers at Siemens diagnostic centres unveil hidden knowledge in huge amounts of historical sensor data and use it to improve the predictive systems analysing live data. Currently, the analysis is usually done using data-dependent rules that are specific to individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. One solution to this problem is to employ ontology-based data access (OBDA), which provides a conceptual view of data via an ontology. However, classical OBDA systems do not support access to temporal data and reasoning over it. To address this issue, we propose a framework for temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in the RDF format, classical ontology and rule languages to reflect static information, as well as a temporal rule language to describe events. We also propose a SPARQL-based query language for retrieving temporal information and, finally, an architecture of system implementation extending the state-of-the-art OBDA platform Ontop.","Title: Data Complexity and Rewritability of Ontology-Mediated Queries in Metric Temporal Logic under the Event-Based Semantics (Full Version).\nYear: 2019\n\nAbstract: We investigate the data complexity of answering queries mediated by metric temporal logic ontologies under the event-based semantics assuming that data instances are finite timed words timestamped with binary fractions. We identify classes of ontology-mediated queries answering which can be done in AC0, NC1, L, NL, P, and coNP for data complexity, provide their rewritings to first-order logic and its extensions with primitive recursion, transitive closure or datalog, and establish lower complexity bounds.","Title: Deciding Boundedness of Monadic Sirups\nYear: 2021\n\nAbstract: ABSTRACTWe show that deciding boundedness (aka FO-rewritability) of monadic single rule datalog programs (sirups) is 2\\Exp-hard, which matches the upper bound known since 1988 and finally settles a long-standing open problem. We obtain this result as a byproduct of an attempt to classify monadic 'disjunctive sirups'---Boolean conjunctive queries $\\q$ with unary and binary predicates mediated by a disjunctive rule $T(x) łor F(x) łeftarrow A(x)$---according to the data complexity of their evaluation. Apart from establishing that deciding FO-rewritability of disjunctive sirups with a dag-shaped $\\q$ is also 2\\Exp-hard, we make substantial progress towards obtaining a complete FO/Ł-hardness dichotomy of disjunctive sirups with ditree-shaped $\\q$.","Title: First-Order Rewritability and Complexity of Two-Dimensional Temporal Ontology-Mediated Queries.\nYear: 2022\n\nAbstract: Aiming at ontology-based data access to temporal data, we design two-dimensional temporal ontology and query languages by combining logics from the (extended) DL-Lite family with linear temporal logic LTL over discrete time (Z,<). Our main concern is first-order rewritability of ontology-mediated queries (OMQs) that consist of a 2D ontology and a positive temporal instance query. Our target languages for FO-rewritings are two-sorted FO(<) - first-order logic with sorts for time instants ordered by the built-in precedence relation < and for the domain of individuals - its extension FOE with the standard congruence predicates t \\equiv 0 mod n, for any fixed n > 1, and FO(RPR) that admits relational primitive recursion. In terms of circuit complexity, FOE- and FO(RPR)-rewritability guarantee answering OMQs in uniform AC0 and NC1, respectively. We proceed in three steps. First, we define a hierarchy of 2D DL-Lite/LTL ontology languages and investigate the FO-rewritability of OMQs with atomic queries by constructing projections onto 1D LTL OMQs and employing recent results on the FO-rewritability of propositional LTL OMQs. As the projections involve deciding consistency of ontologies and data, we also consider the consistency problem for our languages. While the undecidability of consistency for 2D ontology languages with expressive Boolean role inclusions might be expected, we also show that, rather surprisingly, the restriction to Krom and Horn role inclusions leads to decidability (and ExpSpace-completeness), even if one admits full Booleans on concepts. As a final step, we lift some of the rewritability results for atomic OMQs to OMQs with expressive positive temporal instance queries. The lifting results are based on an in-depth study of the canonical models and only concern Horn ontologies.","Title: Unique Characterisability and Learnability of Temporal Instance Queries.\nYear: 2022\n\nAbstract: We aim to determine which temporal instance queries can be uniquely characterised by a (polynomial-size) set of positive and negative temporal data examples. We start by considering queries formulated in fragments of propositional linear temporal logic LTL that correspond to conjunctive queries (CQs) or extensions thereof induced by the until operator. Not all of these queries admit polynomial characterisations but by restricting them further to path-shaped queries we identify natural classes that do. We then investigate how far the obtained characterisations can be lifted to temporal knowledge graphs queried by 2D languages combining LTL with concepts in description logics EL or ELI (i.e., tree-shaped CQs). While temporal operators in the scope of description logic constructors can destroy polynomial characterisability, we obtain general transfer results for the case when description logic constructors are within the scope of temporal operators. Finally, we apply our characterisations to establish (polynomial) learnability of temporal instance queries using membership queries in the active learning framework.","Title: On the First-Order Rewritability of Ontology-Mediated Queries in Linear Temporal Logic (Extended Abstract).\nYear: 2022\n\nAbstract: We argue that linear temporal logic LTL in tandem with monadic first-order logic can be used as a ba- sic language for ontology-based access to tempo- ral data and obtain a classification of the resulting ontology-mediated queries according to the type of standard first-order queries they can be rewritten to."],"string":"[\n \"Title: On the Computational Complexity of Spatio-Temporal Logics.\\nYear: 2003\\n\\nAbstract: Recently, a hierarchy of spatio-temporal logics based on the propositional temporal logic PTL and the spatial languages RCC-8, BRCC-8 and S4u has been introduced. Although a number of results on their computational properties were obtained, the most important questions were left open. In this paper, we solve almost all of these problems and provide a clear picture of the balance between expressiveness and 'computational realisability' within the hierarchy.\",\n \"Title: Temporalising Tableaux\\nYear: 2004\\n\\nAbstract: As a remedy for the bad computational behaviour of first-order temporal logic (FOTL), it has recently been proposed to restrict the application of temporal opera- tors to formulas with at most one free variable thereby obtaining so-called monodic fragments of FOTL. In this paper, we are concerned with constructing tableau al- gorithms for monodic fragments based on decidable fragments of fist-order logic like the two-variable fragment or the guarded fragment. We present a general framework that shows how existing decision procedures for first-order fragments can be used for constructing a tableau algorithm for the corresponding monodic fragment of FOTL. Some example instantiations of the framework are presented.\",\n \"Title: Axiomatizing Distance Logics\\nYear: 2002\\n\\nAbstract: In [8, 6] we introduced a family of `modal\\\" languages intended for talking about distances. These languages are interpreted in `distance spaces\\\" which satisfy some (or all) of the standard axioms of metric spaces. Among other things, we singled out decidable logics of distance spaces and proved expressive completeness results relating classical and modal languages. The aim of this paper is to axiomatize the modal fragments of the semantically defined distance logics of [6] and give a new proof...\",\n \"Title: Reasoning about distances\\nYear: 2003\\n\\nAbstract: The paper presents a novel expressive logic-based formalism intended for reasoning about numerical distances. We investigate its computational prop erties (in particular, show that it is EXPTIME- complete) and devise a tableau-based satisfiability- checking algorithm. To be able to express knowl edge about implicit or unknown distances, we then extend the language with variables ranging over distances and prove that the resulting logic is de- cidable as well.\",\n \"Title: Decidable and Undecidable Fragments of First-Order Branching Temporal Logics\\nYear: 2002\\n\\nAbstract: In this paper we analyze the decision problem for fragments of first-order extensions of branching time temporal logics such as computational tree logics CTL and CTL * or Prior's Ockhamist logic of historical necessity. On the one hand, we show that the one-variable fragments of logics like first-order CTL * 驴 such as the product of propositional CTL * with simple propositional modal logic S5, or even the one-variable bundled first-order temporal logic with sole temporal operator some time in the future' 驴 are undecidable. On the other hand, it is proved that by restricting applications of first-order quantifiers to state (i.e.,path-independent) formulas, and applications of temporal operators and path quantifiers to formulas with at most one free variable, we can obtain decidable fragments. The positive decidability results can serve as a unifying framework for devising expressive and effective time-dependent knowledge representation formalisms, e.g., temporal description or spatio-temporal logics.\",\n \"Title: Automated reasoning about metric and topology\\nYear: 2006\\n\\nAbstract: In this paper we compare two approaches to automated reasoning about metric and topology in the framework of the logic $\\\\mathcal{MT}$ introduced in [10]. $\\\\mathcal{MT}$-formulas are built from set variablesp1,p2,... (for arbitrary subsets of a metric space) using the Booleans ∧, ∨, →, and ¬, the distance operators∃a and ∃≤a, for $a\\\\in {\\\\mathbb Q}^{ 0}$, and the topological interior and closure operatorsI and C. Intended models for this logic are of the form $\\\\mathfrak I=(\\\\Delta,d,p_{1}^{\\\\mathfrak I},p_{2}^{\\\\mathfrak I},\\\\dots)$ where (Δ,d) is a metric space and $p_{i}^{\\\\mathfrak I} \\\\subseteq \\\\Delta$. The extension$\\\\varphi^{\\\\mathfrak I} \\\\subseteq \\\\Delta$ of an $\\\\mathcal{MT}$-formula ϕ in $\\\\mathfrak I$ is defined inductively in the usual way, with I and C being interpreted as the interior and closure operators induced by the metric, and $(\\\\exists^{a-neighbourhood of $\\\\varphi^{\\\\mathfrak I}$, and $(\\\\exists^{\\\\le a}\\\\varphi)^{\\\\mathfrak I}$ is the closed one. A formula ϕ is satisfiable if there is a model ${\\\\mathfrak I}$ such that $\\\\varphi^{\\\\mathfrak I} \\\\ne \\\\emptyset$; ϕ is valid if ¬ϕ is not satisfiable.\",\n \"Title: A Note on Concepts and Distances\\nYear: 2001\\n\\nAbstract: We combine the description logic ALC with the metric logics dened in (15). Entities that are conceived of as abstract points in the realm of ALC are given a spatial extension via an 'extension relation,' connecting abstract points in the domain of an ALC-model to points in a metric space. Conversely, regions in the metric space are connected via the converse 'extension relation' to certain points in the ALC-model. We prove the decidability of the satisabilit y problem for the resulting hybrid formalism, give a few examples, and discuss further extensions of the ideas introduced.\",\n \"Title: 'Closer' representation and reasoning\\nYear: 2005\\n\\nAbstract: We argue that orthodox tools for defining concepts in the framework of description logic should often be augmented with constructors that could allow definitions in terms of similarity (or closeness). We present a corres- ponding logical formalism with the binary operator 'more similar or closer to X than to Y ' and investigate its computational behaviour in dierent distance (or similarity) spaces. The concept satisfiability problem turns out to be ExpTime-complete for many classes of distances spaces no mat- ter whether they are required to be symmetric and/or satisfy the triangle inequality. Moreover, the complexity remains the same if we extend the language with the operators 'somewhere in the neighbourhood of radius a' where a is a non-negative rational number. However, for various natural subspaces of the real line R (and Euclidean spaces of higher dimensions) even the similarity logic with the sole 'closer' operator turns out to be undecidable. This quite unexpected result is proved by reduction of the solvability problem for Diophantine equations (Hilbert's 10th problem).\",\n \"Title: Monodic Epistemic Predicate Logic\\nYear: 2000\\n\\nAbstract: We consider the monodic formulas of common knowledge predicate logic, which allow applications of epistemic operators to formulas with at most one free variable. We provide finite axiomatizations of the monodic fragment of the most important common knowledge predicate logics (the full logics are known to be not recursively enumerable) and single out a number of their decidable fragments. On the other hand, it is proved that the addition of the equality symbol to the monodic fragment makes it not recursively enumerable.\",\n \"Title: Multi-Dimensional Description Logics\\nYear: 1999\\n\\nAbstract: In this paper, we construct a new concept description language intended for representing dynamic and intensional knowledge. The most important feature distinguishing this language from its predecessors in the literature is that it allows applications of modal operators to all kinds of syntactic terms: concepts, roles and formulas. Moreover, the language may contain both local (i.e., state-dependent) and global (i.e., state-independent) concepts, roles and objects. All this provides us with the most complete and natural means for reflecting the dynamic and intensional behaviour of application domains. We construct a satisfiability checking (mosaic-type) algorithm for this language (based on ALC) in (i) arbitrary multimodal frames, (ii) frames with universal accessibility relations (for knowledge) and (iii) frames with transitive, symmetrical and euclidean relations (for beliefs). On the other hand, it is shown that the satisfaction problem becomes undecidable if the underlying frames are arbitrary linear orders or the language contains the common knowledge operator for n ≥ 2 agents.\",\n \"Title: Modularity in DL-Lite\\nYear: 2007\\n\\nAbstract: We develop a formal framework for modular ontologies by analysing four notions of conservative extension and their applications in refining, re-using, merging, and segment- ing ontologies. For two members of the DL-Lite family of description logics, we prove important meta-properties of these notions such as robustness under joins, vocabulary extensions, and iterated import of ontologies. The computational complexity of the cor- responding reasoning tasks is investigated.\",\n \"Title: Logics of metric spaces\\nYear: 2003\\n\\nAbstract: We investigate the expressive power and computational properties of two different types of languages intended for speaking about distances. First, we consider a first-order language FM the two-variable fragment of which turns out to be undecidable in the class of distance spaces validating the triangular inequality as well as in the class of all metric spaces. Yet, this two-variable fragment is decidable in various weaker classes of distance spaces. Second, we introduce a variable-free modal language MS that, when interpreted in metric spaces, has the same expressive power as the two-variable fragment of FM. We determine natural and expressive fragments of MS which are decidable in various classes of distance spaces validating the triangular inequality, in particular, the class of all metric spaces.\",\n \"Title: A Temporal Description Logic for Reasoning over Conceptual Schemas and Queries\\nYear: 2002\\n\\nAbstract: This paper introduces a new logical formalism, intended for temporal conceptual modelling, as a natural combination of the well-known description logic DLR and point-based linear temporal logic with Since and Until. We define a query language (where queries are non-recursive Datalog programs and atoms are complex DLRUS expressions) and investigate the problem of checking query containment under the constraints defined by DLRUS conceptual schemas--i.e., DLRUS knowledge bases--as well as the problems of schema satisfiability and logical implication.\",\n \"Title: A Logic for Concepts and Similarity\\nYear: 2007\\n\\nAbstract: Categorization of objects into classes is currently supported by (at least) two `orthogonal' methods. In logic-based approaches, classifications are defined through ontologies or knowledge bases which describe the existing relationships among terms. Description logic (DL) has become one of the most successful formalisms for representing such knowledge bases, in particular because theoretically wel...\",\n \"Title: Ontology-Based Data Access: Ontop of Databases\\nYear: 2013\\n\\nAbstract: We present the architecture and technologies underpinning the OBDA system Ontop and taking full advantage of storing data in relational databases. We discuss the theoretical foundations of Ontop: the tree-witness query rewriting, <InlineEquation ID=\\\\\\\"IEq1\\\\\\\" <EquationSource Format=\\\\\\\"TEX\\\\\\\"$\\\\\\\\mathcal{T}$</EquationSource> </InlineEquation>-mappings and optimisations based on database integrity constraints and SQL features. We analyse the performance of Ontop in a series of experiments and demonstrate that, for standard ontologies, queries and data stored in relational databases, Ontop is fast, efficient and produces SQL rewritings of high quality.\",\n \"Title: A logic for metric and topology\\nYear: 2005\\n\\nAbstract: We propose a logic for reasoning about metric spaces with the induced topologies. It combines the 'qualitative' interior and closure operators with 'quantitative' operators 'somewhere in the sphere of radius r,' including or excluding the boundary We supply the logic with both the intended metric space semantics and a natural relational semantics. and show that the latter (i) provides finite partial representations of(in general) infinite metric models and (ii) reduces the standard 'epsilon-definitions' of closure and interior to simple constraints on relations. These features of the relational semantics suggest a finite axiomatisation of the logic and provide means to prove its EXPTIME-completeness (even if the rational numerical parameters are coded in binary). An extension with metric variables satisfying linear rational (in)equalities is proved to be decidable as well. Our logic can be regarded as a 'well-behaved' common denominator of logical systems constructed in temporal, spatial. and similarity-based quantitative and qualitative representation and reasoning. Interpreted on the real line (with its Euclidean metric), it is a natural fragment of decidable temporal logics for specification and verification of real-time systems. On the real plane, it is closely related to quantitative and qualitative formalisms for spatial representation and reasoning. but this time the logic becomes undecidable.\",\n \"Title: Comparative similarity, tree automata, and diophantine equations\\nYear: 2005\\n\\nAbstract: The notion of comparative similarity ‘X is more similar or closer to Y than to Z’ has been investigated in both foundational and applied areas of knowledge representation and reasoning, e.g., in concept formation, similarity-based reasoning and areas of bioinformatics such as protein sequence alignment. In this paper we analyse the computational behaviour of the ‘propositional’ logic with the binary operator ‘closer to a set τ1 than to a set τ2’ and nominals interpreted over various classes of distance (or similarity) spaces. In particular, using a reduction to the emptiness problem for certain tree automata, we show that the satisfiability problem for this logic is ExpTime-complete for the classes of all finite symmetric and all finite (possibly non-symmetric) distance spaces. For finite subspaces of the real line (and higher dimensional Euclidean spaces) we prove the undecidability of satisfiability by a reduction of the solvability problem for Diophantine equations. As our ‘closer’ operator has the same expressive power as the standard operator of conditional logic, these results may have interesting implications for conditional logic as well.\",\n \"Title: On Non-Local Propositional and Local One-Variable Quantified CTL*\\nYear: 2002\\n\\nAbstract: We prove decidability of non-local' propositional CTL*, where truth values of atoms may depend on the branch of evaluation. This result is then used to show decidability of the weak' one-variable fragment of first-order (local) CTL*, in which all temporal operators and path quantifiers except tomorrow' are applicable only to sentences. Various spatio-temporal logics based on combinations of CTL* and RCC-8 can be embedded into this fragment, and so are decidable.\",\n \"Title: From topology to metric: modal logic and quantification in metric spaces\\nYear: 2006\\n\\nAbstract: We propose a framework for comparing the expressive power and computational behaviour of modal logics designed for reasoning about qualitative aspects of metric spaces. Within this framework we can compare such well-known logics as S4 (for the topology induced by the metric), wK4 (for the derivation operator of the topology), variants of conditional logic, as well as logics of comparative similarity. One of the main problems for the new family of logics is to delimit the borders between 'decidable' and 'undecidable.' As a first step in this direction, we consider the modal logic with the operator 'closer to a set 0 than to a set 1' interpreted in metric spaces. This logic contains S4 with the universal modality and corresponds to a very natural language within our framework. We prove that over arbitrary metric spaces this logic is ExpTime-complete. Recall that over R, Q, and Z, as well as their finite subspaces, this logic is undecidable.\",\n \"Title: Can You Tell the Difference Between DL-Lite Ontologies?\\nYear: 2008\\n\\nAbstract: We develop a formal framework for comparing differ- ent versions of DL-Lite ontologies. Four notions of difference and entailment between ontologies are intro- duced and their applications in ontology development and maintenance discussed. These notions are obtained by distinguishing between differences that can be ob- served among concept inclusions, answers to queries over ABoxes, and by taking into account additional con- text ontologies. We compare these notions, study their meta-properties, and determine the computational com- plexity of the corresponding reasoning tasks. Moreover, we show that checking difference and entailment can be automated by means of encoding into QBF satisfiability and using off-the-shelf QBF solvers. Finally, we ex- plore the relationship between the notion of forgetting (or uniform interpolation) and our notions of difference between ontologies.\",\n \"Title: On the Succinctness of Query Rewriting over OWL 2 QL Ontologies with Shallow Chases.\\nYear: 2014\\n\\nAbstract: We investigate the size of first-order rewritings of conjunctive queries over OWL 2 QL ontologies of depth 1 and 2 by means of hypergraph programs computing Boolean functions. Both positive and negative results are obtained. Conjunctive queries over ontologies of depth 1 have polynomial-size nonrecursive datalog rewritings; tree-shaped queries have polynomial positive existential rewritings; however, in the worst case, positive existential rewritings can only be of superpolynomial size. Positive existential and nonrecursive datalog rewritings of queries over ontologies of depth 2 suffer an exponential blowup in the worst case, while first-order rewritings are superpolynomial unless $\\\\text{NP} \\\\subseteq \\\\text{P}/\\\\text{poly}$. We also analyse rewritings of tree-shaped queries over arbitrary ontologies and observe that the query entailment problem for such queries is fixed-parameter tractable.\",\n \"Title: The Complexity of Clausal Fragments of LTL.\\nYear: 2013\\n\\nAbstract: We introduce and investigate a number of fragments of propo- sitional temporal logic LTL over the flow of time (Z, <). The fragments are defined in terms of the available temporal operators and the structure of the clausal normal form of the temporal formulas. We determine the computational complexity of the satisfiability problem for each of the fragments, which ranges from NLogSpace to PTime, NP and PSpace.\",\n \"Title: Query Inseparability and Module Extraction in OWL 2 QL.\\nYear: 2011\\n\\nAbstract: The OWL 2 QL profile of OWL, based on the DL family of description logics, is emerging as a major language for developing new ontologies and approximating existing ones. Its main application is ontology-based data access, where ontologies are used to provide background knowledge for answering queries over data. We give a survey of recent results on the computational complexity of checking query inseparability for OWL 2 QL ontologies and analyse the impact of various OWL 2 QL constructs in the context of query inseparability. We also discuss practical query inseparability checking and minimal module extraction algorithms, as well as experimental results.\",\n \"Title: Spatial Reasoning in RCC-8 with Boolean Region Terms\\nYear: 2000\\n\\nAbstract: We extend the expressive power of the region connection calculus RCC-8 by allowing applications of the 8 binary relations of RCC-8 not only to atomic regions but also to Boolean combinations of them. It is shown that the statisfiability problem for the extended language in arbitrary topological spaces is still in NP; however, it becomes PSPACE-complete if only the Euclidean spaces R-n, n > 0, are regarded as possible interpretations. In particular, in contrast to pure RCC-8, the new language is capable of distinguishing between connected and non-connected topological spaces.\",\n \"Title: Modal Logics for Metric Spaces: Open Problems\\nYear: 2005\\n\\nAbstract: Modal logics and their models have been used to speak about and represent topological spaces since the 1940s [22, 23, 16, 17]. Examples include Tarski’s programme of algebraisation of topology (“of creating an algebraic apparatus for the treatment of portions of point-set topology,” to be more precise) which involved modal logic S4 [17], and the use of the extension of S4 with the universal modality (and its fragments) for spatial representation and reasoning; see, e.g., [5, 18, 7, 8, 1, 9] and references therein. Metric spaces are even more important mathematical structures that are fundamental for many areas of mathematics and computer science (recent examples include classification in bioinformatics, linguistics, botany, etc. using various similarity measures). A natural research programme is then to find out to which extent modal-like formalisms can be useful for speaking about metric spaces. Such a programme was launched in 2000 [21, 13, 14]. The aim of this note is to attract attention to the most important open problems and new directions of research in this exciting and promising area.\",\n \"Title: On the Computational Complexity of Spatial Logics with Connectedness Constraints\\nYear: 2008\\n\\nAbstract: We investigate the computational complexity of spatial logics extended with the means to represent topological connectedness and restrict the number of connected components. In particular, we show that the connectedness constraints can increase complexity from NP to PSpace , ExpTime and, if component counting is allowed, to NExpTime .\",\n \"Title: Spatio-temporal representation and reasoning based on RCC-8\\nYear: 2000\\n\\nAbstract: this paper is to introduce a hierarchy of languages intendedfor qualitative spatio-temporal representation and reasoning, provide these languageswith topological temporal semantics, construct effective reasoning algorithms,and estimate their computational complexity.\",\n \"Title: Axiomatizing the monodic fragment of first-order temporal logic\\nYear: 2002\\n\\nAbstract: It is known that even seemingly small fragments of the first-order temporal logic over the natural numbers are not recursively enumerable. In this paper we show that the monodic (not monadic, where this result does not hold) fragment is an exception by constructing its finite Hilbert-style axiomatization. We also show that the monodic fragment with equality is not recursively axiomatizable.\",\n \"Title: The Greatest Extension of S4 into which Intuitionistic Logic is Embeddable\\nYear: 1997\\n\\nAbstract: This paper gives a characterization of those quasi-normal extensions of the modal system S4 into which intuitionistic propositional logic Int is embeddable by the Gödel translation. It is shown that, as in the normal case, the set of quasi-normal modal companions of Int contains the greatest logic, M*, for which, however, the analog of the Blok-Esakia theorem does not hold. M* is proved to be decidable and Halldén-complete; it has the disjunction property but does not have the finite model property.\",\n \"Title: The price of query rewriting in ontology-based data access.\\nYear: 2014\\n\\nAbstract: We give a solution to the succinctness problem for the size of first-order rewritings of conjunctive queries in ontology-based data access with ontology languages such as OWL 2 QL, linear Datalog± and sticky Datalog±. We show that positive existential and nonrecursive datalog rewritings, which do not use extra non-logical symbols (except for intensional predicates in the case of datalog rewritings), suffer an exponential blowup in the worst case, while first-order rewritings can grow superpolynomially unless NP⊆P/poly. We also prove that nonrecursive datalog rewritings are in general exponentially more succinct than positive existential rewritings, while first-order rewritings can be superpolynomially more succinct than positive existential rewritings. On the other hand, we construct polynomial-size positive existential and nonrecursive datalog rewritings under the assumption that any data instance contains two fixed constants.\",\n \"Title: Temporal description logic for ontology-based data access\\nYear: 2013\\n\\nAbstract: Our aim is to investigate ontology-based data access over temporal data with validity time and ontologies capable of temporal conceptual modelling. To this end, we design a temporal description logic, TQL, that extends the standard ontology language OWL2QL, provides basic means for temporal conceptual modelling and ensures first-order rewritability of conjunctive queries for suitably defined data instances with validity time.\",\n \"Title: Topological Logics with Connectedness over Euclidean Spaces\\nYear: 2011\\n\\nAbstract: We consider the quantifier-free languages, Bc and Bc°, obtained by augmenting the signature of Boolean algebras with a unary predicate representing, respectively, the property of being connected, and the property of having a connected interior. These languages are interpreted over the regular closed sets of Rn (n ≥ 2) and, additionally, over the regular closed semilinear sets of Rn. The resulting logics are examples of formalisms that have recently been proposed in the Artificial Intelligence literature under the rubric Qualitative Spatial Reasoning. We prove that the satisfiability problem for Bc is undecidable over the regular closed semilinear sets in all dimensions greater than 1, and that the satisfiability problem for Bc and Bc° is undecidable over both the regular closed sets and the regular closed semilinear sets in the Euclidean plane. However, we also prove that the satisfiability problem for Bc° is NP-complete over the regular closed sets in all dimensions greater than 2, while the corresponding problem for the regular closed semilinear sets is ExpTime-complete. Our results show, in particular, that spatial reasoning is much harder over Euclidean spaces than over arbitrary topological spaces.\",\n \"Title: Polynomial conjunctive query rewriting under unary inclusion dependencies\\nYear: 2011\\n\\nAbstract: Ontology-based data access (OBDA) is widely accepted as an important ingredient of the new generation of information systems. In the OBDA paradigm, potentially incomplete relational data is enriched by means of ontologies, representing intensional knowledge of the application domain. We consider the problem of conjunctive query answering in OBDA. Certain ontology languages have been identified as FO-rewritable (e.g., DL-Lite and sticky-join sets of TGDs), which means that the ontology can be incorporated into the user's query, thus reducing OBDA to standard relational query evaluation. However, all known query rewriting techniques produce queries that are exponentially large in the size of the user's query, which can be a serious issue for standard relational database engines. In this paper, we present a polynomial query rewriting for conjunctive queries under unary inclusion dependencies. On the other hand, we show that binary inclusion dependencies do not admit polynomial query rewriting algorithms.\",\n \"Title: Temporal Description Logic for Ontology-Based Data Access (Extended Version)\\nYear: 2013\\n\\nAbstract: Our aim is to investigate ontology-based data access over temporal data with validity time and ontologies capable of temporal conceptual modelling. To this end, we design a temporal description logic, TQL, that extends the standard ontology language OWL 2 QL, provides basic means for temporal conceptual modelling and ensures first-order rewritability of conjunctive queries for suitably defined data instances with validity time.\",\n \"Title: A modal logic framework for reasoning about comparative distances and topology\\nYear: 2010\\n\\nAbstract: We propose and investigate a uniform modal logic framework for reasoning about topology and relative distance in metric and more general distance spaces, thus enabling the comparison and combination of logics from distinct research traditions such as Tarski’s S4 for topological closure and interior, conditional logics, and logics of comparative similarity. This framework is obtained by decomposing the underlying modal-like operators into first-order quantifier patterns. We then show that quite a powerful and natural fragment of the resulting first-order logic can be captured by one binary operator comparing distances between sets and one unary operator distinguishing between realised and limit distances (i.e., between minimum and infimum). Due to its greater expressive power, this logic turns out to behave quite differently from both S4 and conditional logics. We provide finite (Hilbert-style) axiomatisations and ExpTime-completeness proofs for the logics of various classes of distance spaces, in particular metric spaces. But we also show that the logic of the real line (and various other important metric spaces) is not recursively enumerable. This result is proved by an encoding of Diophantine equations.\",\n \"Title: Speaking about Transitive Frames in Propositional Languages\\nYear: 1998\\n\\nAbstract: This paper is a comparative study of the propositional intuitionistic(non-modal) and classical modal languages interpreted in the standard wayon transitive frames. It shows that, when talking about these framesrather than conventional quasi-orders, the intuitionistic languagedisplays some unusual features: its expressive power becomes weaker thanthat of the modal language, the induced consequence relation does not havea deduction theorem and is not protoalgebraic. Nevertheless, the paperdevelops a manageable model theory for this consequence and its extensionswhich also reveals some unexpected phenomena. The balance between theintuitionistic and modal languages is restored by adding to the former onemore implication.\",\n \"Title: Monodic fragments of first-order temporal logics: 2000-2001 A.D\\nYear: 2001\\n\\nAbstract: The aim of this paper is to summarize and analyze some results obtained in 2000-2001 about decidable and undecidable fragments of various first-order temporal logics, give some applications in the field of knowledge representation and reasoning, and attract the attention of the 'temporal community' to a number of interesting open problems.\",\n \"Title: A Tableau Algorithm for Reasoning about Concepts and Similarity\\nYear: 2003\\n\\nAbstract: We present a tableau-based decision procedure for the fusion (independent join) of the expressive description logic ALCQO and the logic MS for reasoning about distances and similarities. The resulting 'hybrid' logic allows both precise and approximate representation of and reasoning about concepts. The tableau algorithm combines the existing tableaux for the components and shows that the tableau technique can be fruitfully applied to fusions of logics with nominals-the case in which no general decidability transfer results for fusions axe available.\",\n \"Title: DL - Lite and Role Inclusions\\nYear: 2008\\n\\nAbstract: We give a classification of the complexity of DL-Lite logics extended with role inclusion axioms. We show that the data complexity of instance checking becomes P -hard in the presence of functionality constraints, and coNP -hard if arbitrary number restrictions are allowed, even with primitive concept inclusions. The combined complexity of satisfiability in this case jumps to ExpTime . On the other side, the combined complexity for the logics without number restrictions depends only on the form of concept inclusions and can range from NLogSpace and P to NP; the data complexity for such logics stays in LogSpace .\",\n \"Title: The Combined Approach to Query Answering in DL-Lite\\nYear: 2010\\n\\nAbstract: Databases and related information systems can bene- fit from the use of ontologies to enrich the data with general background knowledge. The DL-Lite family of ontology languages was specifically tailored towards such ontology-based data access, enabling an imple- mentation in a relational database management system (RDBMS) based on a query rewriting approach. In this paper, we propose an alternative approach to im- plementing ontology-based data access in DL-Lite with the distinguishing feature of allowing to rewrite both the query and the data. We show that, in contrast to the ex- isting approaches, no exponential blowup is produced by the rewritings. Based on experiments with a number of real-world ontologies, we demonstrate that query ex- ecution in the proposed approach is often more efficient than in existing approaches, especially for large ontolo- gies. We also show how to seamlessly integrate the data rewriting step of our approach into an RDBMS us- ing views (which solves the update problem) and make an interesting observation regarding the succinctness of queries in the original query rewriting approach.\",\n \"Title: Conservative extensions in modal logic\\nYear: 2006\\n\\nAbstract: Every normal modal logic L gives rise to the consequence relation ' |=L which holds if, and only if, is true in a world of an L-model whenever ' is true in that world. We consider the following al- gorithmic problem for L. Given two modal formulas '1 and '2, decide whether '1^'2 is a conservative extension of'1 in the sense that whenever '1 ^'2 |=L and does not contain propositional variables not occurring in '1, then '1 |=L. We first prove that the conservativeness problem is coNExpTime-hard for all modal logics of unbounded width (which have rooted frames with more than N successors of the root, for any N < !). Then we show that this problem is (i) coNExpTime-complete for S5 and K, (ii) in ExpSpace for S4 and (iii) ExpSpace-complete for GL.3 (the logic of finite strict linear orders). The proofs for S5 and K use the fact that these logics have uniform interpolants of exponential size.\",\n \"Title: Reasoning over extended ER models\\nYear: 2007\\n\\nAbstract: We investigate the computational complexity of reasoning over various fragments of the Extended Entity-Relationship (EER) language, which includes a number of constructs: ISA between entities and relationships, disjointness and covering of entities and relationships, cardinality constraints for entities in relationships and their refinements as well as multiplicity constraints for attributes. We extend the known EXPTIME-completeness result for UML class diagrams [5] and show that reasoning over EER diagrams with ISA between relationships is EXPTIME-complete even without relationship covering. Surprisingly, reasoning becomes NP-complete when we drop ISA between relationships (while still allowing all types of constraints on entities). If we further omit disjointness and covering over entities, reasoning becomes polynomial. Our lower complexity bound results are proved by direct reductions, while the upper bounds follow from the correspondences with expressive variants of the description logic DL-Lite, which we establish in this paper. These correspondences also show the usefulness of DL-Lite as a language for reasoning over conceptual models and ontologies.\",\n \"Title: A Cookbook for Temporal Conceptual Data Modelling with Description Logics\\nYear: 2012\\n\\nAbstract: We design temporal description logics (TDLs) suitable for reasoning about temporal conceptual data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of concept inclusions (ranging from atomic concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (ℤ, <), satisfying the constant domain assumption. Concept and role inclusions of the TBox hold at all moments of time (globally), and data assertions of the ABox hold at specified moments of time. To express temporal constraints of conceptual data models, the languages are equipped with flexible and rigid roles, standard future and past temporal operators on concepts, and operators “always” and “sometime” on roles. The most expressive of our TDLs (which can capture lifespan cardinalities and either qualitative or quantitative evolution constraints) turns out to be undecidable. However, by omitting some of the temporal operators on concepts/roles or by restricting the form of concept inclusions, we construct logics whose complexity ranges between NLogSpace and PSpace. These positive results are obtained by reduction to various clausal fragments of propositional temporal logic, which opens a way to employ propositional or first-order temporal provers for reasoning about temporal data models.\",\n \"Title: DL-Lite with temporalised concepts, rigid axioms and roles\\nYear: 2009\\n\\nAbstract: We investigate the temporal extension of the description logic DL-Litebool(RN) with the until operator on concepts, rigid (time-independent) and local (time-dependent) roles, and rigid TBox axioms. Using an embedding into the one-variable fragment of first-order temporal logic and the quasimodel technique, we prove that (i) the satisfiability problem for the resulting logic is PSpace-complete, and that (ii) by weakening until to sometime in the future we obtain an NP-complete logic, which matches the complexities of the propositional linear-time temporal logics with the corresponding temporal operators.\",\n \"Title: Resasoning about Concepts and Similarity\\nYear: 2003\\n\\nAbstract: Abstract: this paper still hold true. This applies, for example, to the similaritymeasures considered in [15, 9, 4] which satisfy the first two axioms of metricspaces only\",\n \"Title: Products of 'transitive' modal logics\\nYear: 2005\\n\\nAbstract: We solve a major open problem concerning algorithmic properties of products of 'transitive' modal logics by showing that products and commutators of such standard logics as K4. S4. S4. 1, K4.3. GL. or Grz are undecidable and do not have the finite model property. More generally. we prow that no Kripke complete extension of the commutator [K4. K41 with product frames of arbitrary finite or infinite depth (with respect to both accessibility relations) can be decidable. In particular, if C-1 and C-2 are classes of transitive frames such that their depth cannot be bounded by any fixed n < omega then the logic of the class {F-1, x F-2 \\\\ F-1 is an element of C-1, F-2 is an element of C-2} is undecidable. (On the contrary, the product of, say, K4 and the logic of all transitive Kripke frames of depth <= n. for some fixed n < omega, is decidable.) The complexity of these undecidable logics ranges from r.e. to co-r.e. and Pi \\\\-complete. As a consequence. we give the first known examples of Kripke incomplete commutators of Kripke complete logics.\",\n \"Title: Undecidability of the unification and admissibility problems for modal and description logics\\nYear: 2006\\n\\nAbstract: We show that the unification problem “is there a substitution instance of a given formula that is provable in a given logic?” is undecidable for basic modal logics K and K4 extended with the universal modality. It follows that the admissibility problem for inference rules is undecidable for these logics as well. These are the first examples of standard decidable modal logics for which the unification and admissibility problems are undecidable. We also prove undecidability of the unification and admissibility problems for K and K4 with at least two modal operators and nominals (instead of the universal modality), thereby showing that these problems are undecidable for basic hybrid logics. Recently, unification has been introduced as an important reasoning service for description logics. The undecidability proof for K with nominals can be used to show the undecidability of unification for Boolean description logics with nominals (such as ALCO and SHIQO). The undecidability proof for K with the universal modality can be used to show that the unification problem relative to role boxes is undecidable for Boolean description logics with transitive roles, inverse roles, and role hierarchies (such as SHI and SHIQ).\",\n \"Title: Modal description logics: modalizing roles\\nYear: 1999\\n\\nAbstract: We construct a new concept description language intended for representing dynamic and intensional knowledge. The most important feature distinguishing this language from its predecessors in the literature is that it allows applications of modal operators to all kinds of syntactic terms: concepts, roles and formulas. Moreover, the language may contain both local (i.e., state-dependent) and global (i.e., state-independent) concepts, roles and objects. All this provides us with the most complete and natural means for reflecting the dynamic and intensional behaviour of application domains. We construct a satisfiability checking (mosaic-type) algorithm for this language (based on 𝒜ℒ𝒞) in (i) arbitrary multimodal frames, (ii) frames with universal accessibility relations (for knowledge) and (iii) frames with transitive, symmetric and euclidean relations (for beliefs). On the other hand, it is shown that the satisfaction problem becomes undecidable if the underlying frames are arbitrary strict linear orders, 〈$$\\\\mathbb{N}$$,\",\n \"Title: Query Answering in Expressive Variants of DL-Lite\\nYear: 2007\\n\\nAbstract: The use of ontologies in various application domains, such as Data Integration, the Semantic Web, or ontology-based data man- agement, where ontologies provide the access to large amounts of data, is posing challenging requirements w.r.t. the trade-o between the ex- pressive power of a Description Logic and the eciency of reasoning. The logics of the DL-Lite family were specifically designed to meet such requirements and optimized w.r.t. the data complexity of answering com- plex types of queries. In this paper, we propose DL-Litebool, an extension of DL-Lite with full Booleans and number restrictions, and study the complexity of reasoning in DL-Litebool and its significant sub-logics. We obtain our results, together with useful insights into the properties of the studied logics, by a novel reduction to the one-variable fragment of first- order logic. We study the computational complexity of satisfiability and subsumption, and the data complexity of answering positive existential queries (which extend unions of conjunctive queries). Notably, we extend the LogSpace upper bound for the data complexity of answering unions of conjunctive queries in DL-Lite to positive queries and to the possibility of expressing also number restrictions, hence local functionality.\",\n \"Title: Which Kind of Module Should I Extract?\\nYear: 2009\\n\\nAbstract: There are various techniques for specifying a module of an on- tology that covers all knowledge about a given set of terms. These dier with respect to the size of the module, the complexity of its computa- tion, and certain robustness properties. In this paper, we survey existing logic-based approaches, focus on syntactic approximations, and compare dierent kinds of modules with respect to their properties. This is in- tended to give guidelines on how to choose \\\"the right kind of module\\\".\",\n \"Title: Tailoring temporal description logics for reasoning over temporal conceptual models\\nYear: 2011\\n\\nAbstract: Temporal data models have been used to describe how data can evolve in the context of temporal databases. Both the Extended Entity-Relationship (EER) model and the Unified Modelling Language (UML) have been temporally extended to design temporal databases. To automatically check quality properties of conceptual schemas various encoding to Description Logics (DLs) have been proposed in the literature. On the other hand, reasoning on temporally extended DLs turn out to be too complex for effective reasoning ranging from 2EXPTIME up to undecidable languages. We propose here to temporalize the 'light-weight' DL-Lite logics obtaining nice computational results while still being able to represent various constraints of temporal conceptual models. In particular, we consider temporal extensions of DL-LiteboolN, which was shown to be adequate for capturing non-temporal conceptual models without relationship inclusion, and its fragment DL-LitecoreN with most primitive concept inclusions, which are nevertheless enough to represent almost all types of atemporal constraints (apart from covering).\",\n \"Title: The DL-lite family and relations\\nYear: 2014\\n\\nAbstract: The recently introduced series of description logics under the common moniker 'DL-Lite' has attracted attention of the description logic and semantic web communities due to the low computational complexity of inference, on the one hand, and the ability to represent conceptual modeling formalisms, on the other. The main aim of this article is to carry out a thorough and systematic investigation of inference in extensions of the original DL-Lite logics along five axes: by (i) adding the Boolean connectives and (ii) number restrictions to concept constructs, (iii) allowing role hierarchies, (iv) allowing role disjointness, symmetry, asymmetry, reflexivity, irreflexivity and transitivity constraints, and (v) adopting or dropping the unique name assumption. We analyze the combined complexity of satisfiability for the resulting logics, as well as the data complexity of instance checking and answering positive existential queries. Our approach is based on embedding DL-Lite logics in suitable fragments of the one-variable first-order logic, which provides useful insights into their properties and, in particular, computational behavior.\",\n \"Title: On the Products of Linear Modal Logics\\nYear: 2001\\n\\nAbstract: We study two‐dimensional Cartesian products of modal logics determined by infinite or arbitrarily long finite linear orders and prove a general theorem showing that in many cases these products are undecidable, in particular, such are the squares of standard linear logics like K4.3, S4.3, GL.3, Grz.3, or the logic determined by the Cartesian square of any infinite linear order. This theorem solves...\",\n \"Title: On Dynamic Topological and Metric Logics\\nYear: 2006\\n\\nAbstract: The first result of this paper shows that some dynamic topological logics interpreted in various topological spaces with homeomorphisms are not recursively enumerable (and so are not recurs- ively axiomatisable). This gives a 'negative' solution to a conjecture of Kremer and Mints (11). Second, we prove the non-elementary decidability of the dynamic metric logic with distance op- erators of the form 'somewhere in the ball of radius a,' for a2 Q+, interpreted in arbitrary metric spaces with distance preserving automorphisms.\",\n \"Title: Ontology-based data access with databases: a short course\\nYear: 2013\\n\\nAbstract: Ontology-based data access (OBDA) is regarded as a key ingredient of the new generation of information systems. In the OBDA paradigm, an ontology defines a high-level global schema of (already existing) data sources and provides a vocabulary for user queries. An OBDA system rewrites such queries and ontologies into the vocabulary of the data sources and then delegates the actual query evaluation to a suitable query answering system such as a relational database management system or a datalog engine. In this chapter, we mainly focus on OBDA with the ontology language OWL 2QL, one of the three profiles of the W3C standard Web Ontology Language OWL 2, and relational databases, although other possible languages will also be discussed. We consider different types of conjunctive query rewriting and their succinctness, different architectures of OBDA systems, and give an overview of the OBDA system Ontop.\",\n \"Title: Dynamic topological logics over spaces with continuous functions\\nYear: 2006\\n\\nAbstract: Dynamic topological logics are combinations of topological and temporal modal logics that are used for reasoning about dynamical systems consisting of a topological space and a continuous function on it. Here we partially solve a major open problem in the field by showing (by reduction of the !-reachability problem for lossy channel systems) that the dynamic topological logic over arbitrary topological spaces as well as those over Rn, for each n 1, are undecidable. Actually, we prove this result for the natural and expressive fragment of the full dynamic topological language where the topological operators cannot be applied to formulas containing the temporal eventuality. Using Kruskal's tree theorem we also show that the formulas of this fragment that are valid in arbitrary topological spaces with continuous functions are recursively enumerable, which is not the case for spaces with homeomorphisms.\",\n \"Title: Willem Blok and Modal Logic\\nYear: 2006\\n\\nAbstract: Abstract. We present our personal view on W.J. Blok’s contribution to modal logic.\",\n \"Title: Spatial Logics with Connectedness Predicates\\nYear: 2010\\n\\nAbstract: We consider quantifier-free spatial logics, designed for qualitative spatial representation and reasoning in AI, and extend them with the means to represent topological connectedness of regions and restrict the number of their connected components. We investigate the computational complexity of these logics and show that the connectedness constraints can increase complexity from NP to PSPACE, EXPTIME and, if component counting is allowed, to NEXPTIME.\",\n \"Title: All Finitely Axiomatizable Normal Extensions Of K4.3 Are Decidable\\nYear: 1995\\n\\nAbstract: We use the apparatus of the canonical formulas introduced by Zakharyaschev [10] to prove that all finitely axiomatizable normal modal logics containing K4.3 are decidable, though possibly not characterized by classes of finite frames. Our method is purely frame-theoretic. Roughly, given a normal logic L above K4.3, we enumerate effectively a class of (possibly infinite) frames with respect to which L is complete, show how to check effectively whether a frame in the class validates a given formula, and then apply a Harrop-style argument to establish the decidability of L, provided of course that it has finitely many axioms.\",\n \"Title: Temporalising Logics: Fifteen Years After\\nYear: 2007\\n\\nAbstract: A straightforward way of adding a temporal dimension to a logical system is to combine it with a suitable temporal logic. Typical examples are first-order temporal logic [3], temporal description logics [1] or spatio-temporal logics [4]. In 1992, Finger and Gabbay [2] started an investigation of possible ways of temporalising abstract logical systems.\",\n \"Title: Temporal logics over transitive states\\nYear: 2005\\n\\nAbstract: We investigate the computational behaviour of ‘two-dimensional' propositional temporal logics over (ℕ,\",\n \"Title: Topology, connectedness, and modal logic\\nYear: 2008\\n\\nAbstract: This paper presents a survey of topological spatial logics, taking as its point of departure the interpretation of the modal logic S4 due to McKinsey and Tarski. We consider the effect of extending this logic with the means to represent topological connectedness, focusing principally on the issue of computational complexity. In particular, we draw attention to the special problems which arise when the logics are interpreted not over arbitrary topological spaces, but over (low-dimensional) Euclidean spaces.\",\n \"Title: The DLRUS Temporal Description Logic\\nYear: 2001\\n\\nAbstract: This paper introduces a new logical formalism, intended for temporal con- ceptual modelling, as a natural combination of the well-known description logic DLR and point-based linear temporal logic with Since and Until. We de- fine a query language (where queries are non-recursive Datalog pr ograms and atoms are complex DLRUS expressions) and investigate the problem of check- ing query containment under the constraints defined by DLRUS conceptual schemas, as well as the problems of schema satisfiability and logical implication. Although it is shown that reasoning in full DLRUS is undecidable, we identify the decidable (in a sense, maximal) fragment DLRUS by allowing applications of temporal operators to formulas and entities only (but not to relation expre s- sions). We obtain the following hierarchy of complexity results: (a) reason ing in DLRUS with atomic formulas is EXPTIME-complete, (b) satisfiability and logical implication of arbitrary DLRUS formulas is EXPSPACE-complete, and (c) the problem of checking query containment of non-recursive Datalog queries under DLRUS constraints is decidable in 2EXPTIME.\",\n \"Title: Temporal Description Logics: A Survey\\nYear: 2008\\n\\nAbstract: We survey temporal description logics that are based on standard temporal logics such as LTL and CTL. In particular, we concentrate on the computational complexity of the satisfiability problem and algorithms for deciding it.\",\n \"Title: Complexity of reasoning over temporal data models\\nYear: 2010\\n\\nAbstract: We investigate the computational complexity of reasoning over temporal extensions of conceptual data models. The temporal conceptual models we analyse include the standard UML/EER constructs, such as ISA between entities and relationships, disjointness and covering, cardinality constraints and their refinements, multiplicity and key constraints; in the temporal dimension, we have timestamping, evolution, transition and lifespan cardinality constraints. We give a nearly comprehensive picture of the impact of these constructs on the complexity of reasoning, which can range from NLOGSPACE to undecidability.\",\n \"Title: Decidable fragments of first-order temporal logics\\nYear: 2000\\n\\nAbstract: In this paper, we introduce a new fragment of the first-order temporal language, called the monodic fragment, in which all formulas beginning with a temporal operator (Since or Until) have at most one free variable. We show that the satisfiability problem for monodic formulas in various linear time structures can be reduced to the satisfiability problem for a certain fragment of classical first-order logic. This reduction is then used to single out a number of decidable fragments of first-order temporal logics and of two-sorted first-order logics in which one sort is intended for temporal reasoning. Besides standard first-order time structures, we consider also those that have only finite first-order domains, and extend the results mentioned above to temporal logics of finite domains. We prove decidability in three different ways: using decidability of monadic second-order logic over the intended flows of time, by an explicit analysis of structures with natural numbers time, and by a composition method that builds a model from pieces in finitely many steps.\",\n \"Title: Combining spatial and temporal logics: expressiveness vs. complexity\\nYear: 2011\\n\\nAbstract: In this paper, we construct and investigate a hierarchy of spatio-temporal formalisms that result from various combinations of propositional spatial and temporal logics such as the propositional temporal logic PT L, the spatial logics RCC-8, BRCC-8, S4u and their fragments. The obtained results give a clear picture of the trade-off between expressiveness and 'computational realisability' within the hierarchy. We demonstrate how different combining principles as well as spatial and temporal primitives can produce NP-, PSPACE-, EXPSPACE-, 2EXPSPACE-complete, and even undecidable spatio-temporal logics out of components that are at most NP- or PSPACE-complete.\",\n \"Title: Interpreting Topological Logics over Euclidean Spaces\\nYear: 2010\\n\\nAbstract: Topological logics are a family of languages for repre- senting and reasoning about topological data. In this paper, we consider propositional topological logics able to express the property of connectedness. The satisfia- bility problem for such logics is shown to depend not only on the spaces they are interpreted in, but also on the subsets of those spaces over which their variables are allowed to range. We identify the crucial notion of tameness, and chart the surprising patterns of sen- sitivity to the presence of non-tame regions exhibited by a range of topological logics in low-dimensional Eu- clidean spaces.\",\n \"Title: A Note on Relativised Products of Modal Logics\\nYear: 2002\\n\\nAbstract: this paper.each frame of the class.) For example, Kis the logic of all n-ary product frames. It is nothard to see that S5is the logic of all n-ary products of universal frames having the sameworlds, that is, frames hU; R i i with R i = U U . We refer to product frames of this kindas cubic universal product S5-frames. Note that the `i-reduct\\\" FU 1 U n ; R iof F 1 F n is a union of n disjoint copies of F i . Thus, Fand F i validate the same...\",\n \"Title: On the decidability of connectedness constraints in 2D and 3D Euclidean spaces\\nYear: 2011\\n\\nAbstract: We investigate (quantifier-free) spatial constraint languages with equality, contact and connectedness predicates, as well as Boolean operations on regions, interpreted over low-dimensional Euclidean spaces. We show that the complexity of reasoning varies dramatically depending on the dimension of the space and on the type of regions considered. For example, the logic with the interior-connectedness predicate (and without contact) is undecidable over polygons or regular closed sets in R2, EXPTIME-complete over polyhedra in R3, and NP-complete over regular closed sets in R3.\",\n \"Title: A Tableau Decision Algorithm for Modalized ALC with Constant Domains\\nYear: 2002\\n\\nAbstract: The aim of this paper is to construct a tableau decision algorithm for the modal description logic K ALC with constant domains. More precisely, we present a tableau procedure that is capable of deciding, given an ALC-formula ϕ with extra modal operators (which are applied only to concepts and TBox axioms, but not to roles), whether ϕ is satisfiable in a model with constant domains and arbitrary accessibility relations. Tableau-based algorithms have been shown to be 'practical' even for logics of rather high complexity. This gives us grounds to believe that, although the satisfiability problem for K ALC is known to be NEXPTIME-complete, by providing a tableau decision algorithm we demonstrate that highly expressive description logics with modal operators have a chance to be implementable. The paper gives a solution to an open problem of Baader and Laux [5].\",\n \"Title: Logic-based ontology comparison and module extraction, with an application to DL-Lite\\nYear: 2010\\n\\nAbstract: We develop a formal framework for comparing different versions of ontologies, and apply it to ontologies formulated in terms of DL-Lite, a family of 'lightweight' description logics designed for data-intensive applications. The main feature of our approach is that we take into account the vocabulary (=signature) with respect to which one wants to compare ontologies. Five variants of difference and inseparability relations between ontologies are introduced and their respective applications for ontology development and maintenance discussed. These variants are obtained by generalising the notion of conservative extension from mathematical logic and by distinguishing between differences that can be observed among concept inclusions, answers to queries over ABoxes, by taking into account additional context ontologies, and by considering a model-theoretic, language-independent notion of difference. We compare these variants, study their meta-properties, determine the computational complexity of the corresponding reasoning tasks, and present decision algorithms. Moreover, we show that checking inseparability can be automated by means of encoding into QBF satisfiability and using off-the-shelf general purpose QBF solvers. Inseparability relations between ontologies are then used to develop a formal framework for (minimal) module extraction. We demonstrate that different types of minimal modules induced by these inseparability relations can be automatically extracted from real-world medium-size DL-Lite ontologies by composing the known tractable syntactic locality-based module extraction algorithm with our non-tractable extraction algorithms and using the multi-engine QBF solver aqme. Finally, we explore the relationship between uniform interpolation (or forgetting) and inseparability.\",\n \"Title: On the Computational Complexity of Decidable Fragments of First-Order Linear Temporal Logics\\nYear: 2003\\n\\nAbstract: We study the complexity of some fragments of first-order temporal logic over natural numbers time. The one-variable fragment of linear first-order temporal logic even with sole temporal operator El is EXPSPACE-complete (this solves an open problem of [10]). So are the one-variable, two-variable and monadic monodic fragments with Until and Since. If we add the operators circle(n), with n given in binary, the fragments become 2EXPSPACE-complete. The packed monodic fragment has the same complexity as its pure first-order part - 2EXPTIME-complete. Over an), class of flows of time containing one with an infinite ascending sequence - e.g., rationals and real numbers time, and arbitrary strict linear orders - we obtain EXPSPACE lower bounds (which solves an open problem of [16]). Our results continue to hold if we restrict to models with finite first-order domains.\",\n \"Title: Reasoning over Conceptual Schemas and Queries in Temporal Databases\\nYear: 2001\\n\\nAbstract: This paper introduces a new logical formalism, intended for temporal conceptual modelling, as a natural combination of the well-known description logic and point-based linear temporal logic with Since and Until. The expressive power of the resulting logic is illustrated by provid- ing a systematic formalisation of the most important temporal entity-relationship data models appeared in the literature. We define a query language (where queries a re non-recursive Datalog programs and atoms are complex expressions) and investigate the problem of checking query containment under the constraints defined by conceptual schemas, as well as the problems of schema sat- isfiability and logical implication. Although it is shown th at reasoning in full is undecidable, we identify the decidable (in a sense, maximal) fragment by allowing applications of temporal operators to formulas and entities only (but not to relation expressions). We obtain the following hierar- chy of complexity results: (a) reasoning in with atomic formulas is EXPTIME-complete, (b) satisfiability and logical implication of arbitrary formulas is EXPSPACE-complete, and (c) the problem of checking query containment of non-recursive Datalog queries under constraints is decidable in 2EXPTIME.\",\n \"Title: Tableaux for Temporal Description Logic with Constant Domains\\nYear: 2001\\n\\nAbstract: We show how to combine the standard tableau system for the basic description logic ALC and Wolper's tableau calculus for propositional temporal logic PTL (with the temporal operators 'next-time' and 'until') in order to design a terminating sound and complete tableau-based satisfiability-checking algorithm for the temporal description logic PTLALC of [20] interpreted in models with constant domains. We use the method of quasimodels [18,16] to represent models with infinite domains, and the technique of minimal types [11] to maintain these domains constant. The combination is flexible and can be extended to more expressive description logics or even to decidable fragments of first-order temporal logics.\",\n \"Title: A decidable extension of SROIQ with complex role chains and unions\\nYear: 2013\\n\\nAbstract: We design a decidable extension of the description logic SROIQ underlying the Web Ontology Language OWL2. The new logic, called SR+OIQ, supports a controlled use of role axioms whose right-hand side may contain role chains or role unions. We give a tableau algorithm for checking concept satisfiability with respect to SR+OIQ ontologies and prove its soundness, completeness and termination.\",\n \"Title: Undecidability of first-order intuitionistic and modal logics with two variables\\nYear: 2005\\n\\nAbstract: We prove that the two-variable fragment of first-order intuitionistic logic is undecidable, even without constants and equality We also show that the two-variable fragment of a quantified modal logic L with expanding first-order domains is undecidable whenever there is a Kripke frame for L with a point having infinitely many successors (such are, in particular, the first-order extensions of practically all standard modal logics like K. K4, GL. S4 S5 K4.1, S4.2, GL.3 etc.). For many quantified modal logics. including those in the standard nomenclature above. even the monadic two-variable fragments turn out to be undecidable.\",\n \"Title: Checking DL-Lite Modularity with QBF Solvers\\nYear: 2008\\n\\nAbstract: We show how the reasoning tasks of checking various versions of conservativity for the description logic DL-Litebool can be reduced to satisability of quantied Boolean formulas and how o-the-shelf QBF solvers perform on a number of typical DL-Litebool ontologies.\",\n \"Title: Logic in the Time of WWW: An OWL View\\nYear: 2011\\n\\nAbstract: This paper analyses the classical automated reasoning problem-given a theory T\\\\mathcal{T}, a set A\\\\mathcal{A} of ground atoms and a formula ϕ, decide whether \\n\\n\\n\\n\\n\\n(T, A) \\\\models j</font\\n>(\\\\mathcal{T}, \\\\mathcal{A}) \\\\models \\\\varphi–in the context of the OWL 2 Web Ontology Language and ontology-based data access (OBDA). In a typical OBDA scenario, T\\\\mathcal{T} is an OWL 2 ‘ontology’ providing a user-oriented view of raw data A\\\\mathcal{A} and ϕ(x) is a query with answer variables x. Unlike classical automated reasoning, an important requirement for OBDA is that it should scale to large amounts of data\\n and preferably be as efficient as standard relational database management systems. There are various ways of formalising this\\n requirement, which give rise to different fragments of firstorder logic suitable for OBDA. For example, according to the query-rewriting approach of [1], given T, A\\\\mathcal{T}, \\\\mathcal{A} and ϕ(x), one has to compute a new query ϕ′(x), independently of A\\\\mathcal{A}, such that, for any tuple a, \\n\\n\\n\\n\\n\\n(T, A) \\\\models j</font\\n>(a)(\\\\mathcal{T}, \\\\mathcal{A}) \\\\models \\\\varphi(a) iff \\n\\n\\n\\n\\n\\nA \\\\models j</font\\n>¢</font\\n>(a)\\\\mathcal{A} \\\\models \\\\varphi'(a). As a result, this approach can only be applicable to the languages for which query-answering belongs to the class AC0 for data complexity (that is, if only A\\\\mathcal{A} is regarded as input, whereas both T\\\\mathcal{T} and ϕ are regarded as fixed). In the combined approach to OBDA [4,2], given T\\\\mathcal{T} , A\\\\mathcal{A} and ϕ(x), one has to compute new (i) A¢</font\\n> Ê</font\\n> A\\\\mathcal{A}' \\\\supseteq \\\\mathcal{A} in polynomial time in T\\\\mathcal{T} and A\\\\mathcal{A}, and (ii) ϕ′(x) in polynomial tome in T\\\\mathcal{T} and ϕ such that, for any tuple a, \\n\\n\\n\\n\\n\\n(T, A) \\\\models j</font\\n>(a)(\\\\mathcal{T}, \\\\mathcal{A}) \\\\models \\\\varphi(a) iff \\n\\n\\n\\n\\n\\nA¢</font\\n>\\\\models j</font\\n>¢</font\\n>(a)\\\\mathcal{A}' \\\\models \\\\varphi'(a). The combined approach can be used for languages with polynomial queryanswering.\",\n \"Title: Exponential lower bounds and separation for query rewriting\\nYear: 2012\\n\\nAbstract: We establish connections between the size of circuits and formulas computing monotone Boolean functions and the size of first-order and nonrecursive Datalog rewritings for conjunctive queries over OWL 2 QL ontologies. We use known lower bounds and separation results from circuit complexity to prove similar results for the size of rewritings that do not use non-signature constants. For example, we show that, in the worst case, positive existential and nonrecursive Datalog rewritings are exponentially longer than the original queries; nonrecursive Datalog rewritings are in general exponentially more succinct than positive existential rewritings; while first-order rewritings can be superpolynomially more succinct than positive existential rewritings.\",\n \"Title: Multi-Dimensional Modal Logic as a Framework for Spatio-Temporal Reasoning\\nYear: 2002\\n\\nAbstract: In this paper we advocate the use of multi-dimensional modal logics as a framework for knowledge representation and, in particular, for representing spatio-temporal information. We construct a two-dimensional logic capable of describing topological relationships that change over time. This logic, called PSTL (Propositional Spatio-Temporal Logic) is the Cartesian product of the well-known temporal logic PTL and the modal logic S4u, which is the Lewis system S4 augmented with the universal modality. Although it is an open problem whether the full PSTL is decidable, we show that it contains decidable fragments into which various temporal extensions (both point-based and interval based) of the spatial logic RCC-8 can be embedded. We consider known decidability and complexity results that are relevant to computation with multi-dimensional formalisms and discuss possible directions for further research.\",\n \"Title: Spatial reasoning with RCC8 and connectedness constraints in Euclidean spaces.\\nYear: 2014\\n\\nAbstract: The language RCC8 is a widely-studied formalism for describing topological arrangements of spatial regions. The variables of this language range over the collection of non-empty, regular closed sets of n-dimensional Euclidean space, here denoted RC+(Rn), and its non-logical primitives allow us to specify how the interiors, exteriors and boundaries of these sets intersect. The key question is the satisfiability problem: given a finite set of atomic RCC8-constraints in m variables, determine whether there exists an m-tuple of elements of RC+(Rn) satisfying them. These problems are known to coincide for all n≥1, so that RCC8-satisfiability is independent of dimension. This common satisfiability problem is NLogSpace-complete. Unfortunately, RCC8 lacks the means to say that a spatial region comprises a ‘single piece’, and the present article investigates what happens when this facility is added. We consider two extensions of RCC8: RCC8c, in which we can state that a region is connected, and RCC8c∘, in which we can instead state that a region has a connected interior. The satisfiability problems for both these languages are easily seen to depend on the dimension n, for n≤3. Furthermore, in the case of RCC8c∘, we show that there exist finite sets of constraints that are satisfiable over RC+(R2), but only by ‘wild’ regions having no possible physical meaning. This prompts us to consider interpretations over the more restrictive domain of non-empty, regular closed, polyhedral sets, RCP+(Rn). We show that (a) the satisfiability problems for RCC8c (equivalently, RCC8c∘) over RC+(R) and RCP+(R) are distinct and both NP-complete; (b) the satisfiability problems for RCC8c over RC+(R2) and RCP+(R2) are identical and NP-complete; (c) the satisfiability problems for RCC8c∘ over RC+(R2) and RCP+(R2) are distinct, and the latter is NP-complete. Decidability of the satisfiability problem for RCC8c∘ over RC+(R2) is open. For n≥3, RCC8c and RCC8c∘ are not interestingly different from RCC8. We finish by answering the following question: given that a set of RCC8c- or RCC8c∘-constraints is satisfiable over RC+(Rn) or RCP+(Rn), how complex is the simplest satisfying assignment? In particular, we exhibit, for both languages, a sequence of constraints Φn, satisfiable over RCP+(R2), such that the size of Φn grows polynomially in n, while the smallest configuration of polygons satisfying Φn cuts the plane into a number of pieces that grows exponentially. We further show that, over RC+(R2), RCC8c again requires exponentially large satisfying diagrams, while RCC8c∘ can force regions in satisfying configurations to have infinitely many components.\",\n \"Title: An Introduction To Description Logics And Query Rewriting\\nYear: 2014\\n\\nAbstract: This chapter gives an overview of the description logics underlying the OWL 2 Web Ontology Language and its three tractable profiles, OWL 2 RL, OWL 2 EL and OWL 2 QL. We consider the syntax and semantics of these description logics as well as main reasoning tasks and their computational complexity. We also discuss the semantical foundations for first-order and datalog rewritings of conjunctive queries over knowledge bases given in the OWL2 profiles, and outline the architecture of the ontology-based data access system Ontop.\",\n \"Title: On the succinctness of query rewriting over shallow ontologies\\nYear: 2014\\n\\nAbstract: We investigate the succinctness problem for conjunctive query rewritings over OWL 2QL ontologies of depth 1 and 2 by means of hypergraph programs computing Boolean functions. Both positive and negative results are obtained. We show that, over ontologies of depth 1, conjunctive queries have polynomial-size nonrecursive datalog rewritings; tree-shaped queries have polynomial positive existential rewritings; however, in the worst case, positive existential rewritings can be superpolynomial. Over ontologies of depth 2, positive existential and nonrecursive datalog rewritings of conjunctive queries can suffer an exponential blowup, while first-order rewritings can be superpolynomial unless NP ⊆ P/poly. We also analyse rewritings of tree-shaped queries over arbitrary ontologies and note that query entailment for such queries is fixed-parameter tractable.\",\n \"Title: Answering SPARQL Queries over Databases under OWL 2 QL Entailment Regime\\nYear: 2014\\n\\nAbstract: We present an extension of the ontology-based data access platform Ontop that supports answering SPARQL queries under the OWL 2 QL direct semantics entailment regime for data instances stored in relational databases. On the theoretical side, we show how any input SPARQL query, OWL 2 QL ontology and R2RML mappings can be rewritten to an equivalent SQL query solely over the data. On the practical side, we present initial experimental results demonstrating that by applying the Ontop technologiesthe tree-witness query rewriting, <InlineEquation ID=\\\\\\\"IEq1\\\\\\\" <EquationSource Format=\\\\\\\"TEX\\\\\\\"$\\\\\\\\mathcal{T}$</EquationSource> </InlineEquation>-mappings compiling R2RML mappings with ontology hierarchies, and <InlineEquation ID=\\\\\\\"IEq2\\\\\\\" <EquationSource Format=\\\\\\\"TEX\\\\\\\"$\\\\\\\\mathcal{T}$</EquationSource> </InlineEquation>-mapping optimisations using SQL expressivity and database integrity constraintsthe system produces scalable SQL queries.\",\n \"Title: Tractable Interval Temporal Propositional And Description Logics\\nYear: 2015\\n\\nAbstract: We design a tractable Horn fragment of the Halpern-Shoham temporal logic and extend it to interval-based temporal description logics, instance checking in which is P-complete for both combined and data complexity.\",\n \"Title: Games for query inseparability of description logic knowledge bases.\\nYear: 2016\\n\\nAbstract: We consider conjunctive query inseparability of description logic knowledge bases with respect to a given signature—a fundamental problem in knowledge base versioning, module extraction, forgetting and knowledge exchange. We give a uniform game-theoretic characterisation of knowledge base conjunctive query inseparability and develop worst-case optimal decision algorithms for fragments of Horn-ALCHI, including the description logics underpinning OWL 2 QL and OWL 2 EL. We also determine the data and combined complexity of deciding query inseparability. While query inseparability for all of these logics is P-complete for data complexity, the combined complexity ranges from P- to ExpTime- to 2ExpTime-completeness. We use these results to resolve two major open problems for OWL 2 QL by showing that TBox query inseparability and the membership problem for universal conjunctive query solutions in knowledge exchange are both ExpTime-complete for combined complexity. Finally, we introduce a more flexible notion of inseparability which compares answers to conjunctive queries in a given signature over a given set of individuals. In this case, checking query inseparability becomes NP-complete for data complexity, but the ExpTime- and 2ExpTime-completeness combined complexity results are preserved.\",\n \"Title: Conservative Rewritability of Description Logic TBoxes: First Results.\\nYear: 2015\\n\\nAbstract: We want to understand when a given TBox T in a description logic L can be rewritten into a TBox T′ in a weaker description logic L′. Two notions of rewritability are considered: model-conservative rewritability (T′ entails T and all models of T can be expanded to models of T′) and L-conservative rewritability (T′ entails T and every L-consequence of T′ in the signature of T is a consequence of T ) and investigate rewritability of TBoxes in ALCI to ALC, ALCQ to ALC, ALC to EL⊥, and ALCI to DL-Litehorn. We compare conservative rewritability with equivalent rewritability, give model-theoretic characterizations of conservative rewritability, prove complexity results for deciding rewritability, and provide some rewriting algorithms.\",\n \"Title: Theoretically Optimal Datalog Rewritings for OWL 2 QL Ontology-Mediated Queries.\\nYear: 2016\\n\\nAbstract: We show that, for OWL2QL ontology-mediated queries with (i) ontologies of bounded depth and conjunctive queries of bounded treewidth, (ii) ontologies of bounded depth and bounded-leaf tree-shaped conjunctive queries, and (iii) arbitrary ontologies and bounded-leaf tree-shaped conjunctive queries, one can construct and evaluate nonrecursive datalog rewritings by, respectively, LOGCFL, NL and LOGCFL algorithms, which matches the optimal combined complexity.\",\n \"Title: Interval Temporal Description Logics.\\nYear: 2015\\n\\nAbstract: In this paper, we construct a combination HS-Litehorn of the Halpern-Shoham interval temporal logic HS [15] with the description logic DL-Litehorn [12, 1], which is a Horn extension of the standard language OWL 2 QL. The temporal operators of HS are of the form 〈R〉 (‘diamond’) and [R] (‘box’), where R is one of Allen’s interval relations After, Begins, Ends, During, Later, Overlaps and their inverses (Ā, B, Ē, D, L, Ō). The propositional variables of HS are interpreted by sets of closed intervals [i, j] of some flow of time (e.g., Z, R), and a formula 〈R〉φ ([R]φ) is regarded to be true in [i, j] iff φ is true in some (respectively, all) interval(s) [i′, j′] such that [i, j]R[i′, j′] in Allen’s interval algebra. In HS-Litehorn, we represent temporal data by means of assertions such as SummerSchool(RW, t1, t2) and teaches(US,DL, s1, s2), which say that RW is a summer school that takes place in the time interval [t1, t2] and US teaches DL in the time interval [s1, s2]. Note that temporal databases store data in a similar format [17]. Temporal concept and role inclusions are used to impose constraints on the data and introduce new concepts and roles. For example, AdvCourseu〈D〉MorningSession v ⊥ says that advanced courses are not given in the morning sessions described by 〈B〉LectureDayu〈A〉Lunch v MorningSession; teaches v [D]teaches claims that the role teaches is downward hereditary (or stative) in the sense that if it holds in some interval then it also holds in all of its sub-intervals; [D](〈O〉teaches t 〈D〉teaches) u 〈B〉teaches u 〈E〉teaches v teaches, on the contrary, states that teaches is coalesced (or upward hereditary). The inclusions teaches v [D]teaches and [D](〈O〉teaches t 〈D〉teaches) v teaches ensure that teaches is both upward and downward hereditary. On the other hand, ‘rising stock market’ and ‘high average speed’ are typical examples of concepts that are not downward hereditary; for a discussion of these notions see [6, 21, 18]. Although the complexity of full HS-Litehorn remains unknown, in this paper we define two fragments, HS-LiteH/flat horn and HS-Lite H[G] horn , where satisfiability and instance checking are P-complete for both combined and data complexity. Our interest in tractable description logics with interval temporal operators is motivated by possible applications in ontology-based data access (OBDA) [12] to temporal databases. In this context, we naturally require reasonably expressive yet tractable ontology and query languages with temporal constructs (although\",\n \"Title: First-order rewritability of temporal ontology-mediated queries\\nYear: 2015\\n\\nAbstract: Aiming at ontology-based data access over temporal, in particular streaming data, we design a language of ontology-mediated queries by extending OWL2 QL and SPARQL with temporal operators, and investigate rewritability of these queries into two-sorted first-order logic with < and PLUS over time.\",\n \"Title: When are description logic knowledge bases indistinguishable?\\nYear: 2015\\n\\nAbstract: Deciding inseparability of description logic knowledge bases (KBs) with respect to conjunctive queries is fundamental for many KB engineering and maintenance tasks including versioning, module extraction, knowledge exchange and forgetting. We study the combined and data complexity of this inseparability problem for fragments of Horn-ALCHI, including the description logics underpinning OWL 2 QL and OWL 2 EL.\",\n \"Title: Ontology-Mediated Queries: Combined Complexity and Succinctness of Rewritings via Circuit Complexity\\nYear: 2018\\n\\nAbstract: AbstractWe give solutions to two fundamental computational problems in ontology-based data access with the W3C standard ontology language OWL 2 QL: the succinctness problem for first-order rewritings of ontology-mediated queries (OMQs) and the complexity problem for OMQ answering. We classify OMQs according to the shape of their conjunctive queries (treewidth, the number of leaves) and the existential depth of their ontologies. For each of these classes, we determine the combined complexity of OMQ answering and whether all OMQs in the class have polynomial-size first-order, positive existential, and nonrecursive datalog rewritings. We obtain the succinctness results using hypergraph programs, a new computational model for Boolean functions, which makes it possible to connect the size of OMQ rewritings and circuit complexity.\",\n \"Title: Horn Fragments of the Halpern-Shoham Interval Temporal Logic (Technical Report).\\nYear: 2016\\n\\nAbstract: We investigate the satisfiability problem for Horn fragments of the Halpern-Shoham interval temporal logic depending on the type (box or diamond) of the interval modal operators, the type of the underlying linear order (discrete or dense), and the type of semantics for the interval relations (reflexive or irreflexive). For example, we show that satisfiability of Horn formulas with diamonds is undecidable for any type of linear orders and semantics. On the contrary, satisfiability of Horn formulas with boxes is tractable over both discrete and dense orders under the reflexive semantics and over dense orders under the irreflexive semantics, but becomes undecidable over discrete orders under the irreflexive semantics. Satisfiability of binary Horn formulas with both boxes and diamonds is always undecidable under the irreflexive semantics.\",\n \"Title: Query-Based Entailment and Inseparability for ALC Ontologies (Full Version).\\nYear: 2016\\n\\nAbstract: We investigate the problem whether two ALC knowledge bases are indistinguishable by queries over a given vocabulary. We give model-theoretic criteria in terms of (partial) homomorphisms and products and prove that this problem is undecidable for conjunctive queries (CQs) but 2EXPTIME-complete for UCQs (unions of CQs). The same results hold if CQs are replaced by rooted CQs. We also consider the problem whether two ALC TBoxes give the same answers to any query in a given vocabulary over all ABoxes, and show that for CQs this problem is undecidable, too, but becomes decidable and 2EXPTIME-complete in Horn-ALC, and even EXPTIME-complete in Horn-ALC when restricted to (unions of) rooted CQs.\",\n \"Title: Temporal and Spatial OBDA with Many-Dimensional Halpern-Shoham Logic.\\nYear: 2016\\n\\nAbstract: We design an extension datalogHSn□ of datalog with hyperrectangle generalisations of Halpern-Shoham's modal operators on intervals and a corresponding query language. We prove that, over n-dimensional spaces comprised of Z and R, finding certain answers to datalogHSn□ queries can be reduced to standard datalog query answering. We present experimental results showing the expressivity and efficiency of datalogHSn□ on historical data.\",\n \"Title: Query-Based Entailment and Inseparability for ALC Ontologies.\\nYear: 2016\\n\\nAbstract: We investigate the problem whether two ALC knowledge bases are indistinguishable by queries over a given vocabulary. We give model-theoretic criteria and prove that this problem is undecidable for conjunctive queries (CQs) but decidable in 2EXPTIME for unions of rooted CQs. We also consider the problem whether two ALC TBoxes give the same answers to any query in a given vocabulary over all ABoxes, and show that for CQs this problem is undecidable, too, but becomes decidable and 2EXPTIME-complete in Horn-ALC, and even EXPTIME-complete in Horn-ALC when restricted to (unions of) rooted CQs.\",\n \"Title: Conservative Rewritability of Description Logic TBoxes.\\nYear: 2016\\n\\nAbstract: We investigate the problem of conservative rewritability of a TBox T in a description logic (DL) L into a TBox T′ in a weaker DL L′. We focus on model-conservative rewritability (T′ entails T and all models of T are expandable to models of T′), subsumption-conservative rewritability (T′ entails T and all subsumptions in the signature of T entailed by T′ are entailed by T), and standard DLs between ALC and ALCQI. We give model-theoretic characterizations of conservative rewritability via bisimulations, inverse p-morphisms and generated subinterpretations, and use them to obtain a few rewriting algorithms and complexity results for deciding rewritability.\",\n \"Title: The Complexity of Ontology-Based Data Access with OWL 2 QL and Bounded Treewidth Queries.\\nYear: 2017\\n\\nAbstract: Our concern is the overhead of answering OWL 2 QL ontology-mediated queries (OMQs) in ontology-based data access compared to evaluating their underlying tree-shaped and, more generally, bounded treewidth conjunctive queries (CQs). We show that OMQs with bounded depth ontologies have nonrecursive datalog (NDL) rewritings that can be constructed and evaluated in LOGCFL for combined complexity, and even in NL if their CQs are tree-shaped with a bounded number of leaves. Thus, such OMQs incur no overhead in complexity-theoretic terms. For OMQs with arbitrary ontologies and bounded-leaf tree-shaped CQs, NDL-rewritings are constructed and evaluated in LOGCFL. We experimentally demonstrate feasibility and scalability of our rewritings compared to previously proposed NDL-rewritings. On the negative side, we prove that answering OMQs with tree-shaped CQs is not fixed-parameter tractable if the ontology depth or the number of leaves in the CQs is regarded as the parameter, and that answering OMQs with a fixed ontology (of infinite depth) is NP-complete for tree-shaped CQs and LOGCFL-complete for bounded-leaf CQs.\",\n \"Title: Metric Temporal Logic for Ontology-Based Data Access over Log Data.\\nYear: 2017\\n\\nAbstract: We present a new metric temporal logic HornMTL over dense time and its datalog extension datalogMTL. The use of datalogMTL is demonstrated in the context of ontology-based data access over meteorological data. We show decidability of answering ontology-mediated queries for a practically relevant non-recursive fragment of datalogMTL. Finally, we discuss directions of the future work, including the potential use-cases in analyzing log data of engines and devices.\",\n \"Title: Inseparability and Conservative Extensions of Description Logic Ontologies: A Survey.\\nYear: 2018\\n\\nAbstract: The question whether an ontology can safely be replaced by another, possibly simpler, one is fundamental for many ontology engineering and maintenance tasks. It underpins, for example, ontology versioning, ontology modularization, forgetting, and knowledge exchange. What ‘safe replacement’ means depends on the intended application of the ontology. If, for example, it is used to query data, then the answers to any relevant ontology-mediated query should be the same over any relevant data set; if, in contrast, the ontology is used for conceptual reasoning, then the entailed subsumptions between concept expressions should coincide. This gives rise to different notions of ontology inseparability such as query inseparability and concept inseparability, which generalize corresponding notions of conservative extensions. In this chapter, we survey results on various notions of inseparability in the context of description logic ontologies, discussing their applications, useful model-theoretic characterizations, algorithms for determining whether two ontologies are inseparable (and, sometimes, for computing the difference between them if they are not), and the computational complexity of this problem.\",\n \"Title: Querying log data with metric temporal logic\\nYear: 2018\\n\\nAbstract: AbstractWe propose a novel framework for ontology-based access to temporal log data using a datalog extension datalogMTL of the Horn fragment of the metric temporal logic MTL. We show that datalogMTL is EXPSPACE-complete even with punctual intervals, in which case full MTL is known to be undecidable. We also prove that nonrecursive datalogMTL is PSPACE-complete for combined complexity and in AC0 for data complexity. We demonstrate by two real-world use cases that nonrecursive datalogMTL programs can express complex temporal concepts from typical user queries and thereby facilitate access to temporal log data. Our experiments with Siemens turbine data and MesoWest weather data show that datalogMTL ontology-mediated queries are efficient and scale on large datasets.\",\n \"Title: On the Data Complexity of Ontology-Mediated Queries with a Covering Axiom.\\nYear: 2017\\n\\nAbstract: This paper reports on our ongoing work that aims at a classification of conjunctive queries q according to the data complexity of answering ontologymediated queries ({A T F}; q). We give examples of queries from the complexity classes ϵ {AC0; L; NL; P; CONP}, and obtain a few syntactical conditions for -membership and -hardness.\",\n \"Title: Optimal Nonrecursive Datalog Rewritings of Linear TGDs and Bounded (Hyper)Tree-Width Queries.\\nYear: 2017\\n\\nAbstract: Our concern is answering ontology-mediated queries (O, q), where O is a set of linear tgds and q a conjunctive query (CQ) of bounded hypertree width. Assuming that the arity of predicates is bounded, we show that polynomial-size nonrecursive Datalog rewritings can be constructed and executed in (i) LOGCFL for OMQs with ontologies of bounded existential depth; (ii) NL for OMQs with ontologies of bounded depth and CQs whose hypertree decompositions have a bounded number of leaves; (iii) LOGCFL for OMQs with acyclic CQs whose join trees have a bounded number of leaves.\",\n \"Title: Horn Fragments of the Halpern-Shoham Interval Temporal Logic\\nYear: 2017\\n\\nAbstract: We investigate the satisfiability problem for Horn fragments of the Halpern-Shoham interval temporal logic depending on the type (box or diamond) of the interval modal operators, the type of the underlying linear order (discrete or dense), and the type of semantics for the interval relations (reflexive or irreflexive). For example, we show that satisfiability of Horn formulas with diamonds is undecidable for any type of linear orders and semantics. On the contrary, satisfiability of Horn formulas with boxes is tractable over both discrete and dense orders under the reflexive semantics and over dense orders under the irreflexive semantics but becomes undecidable over discrete orders under the irreflexive semantics. Satisfiability of binary Horn formulas with both boxes and diamonds is always undecidable under the irreflexive semantics.\",\n \"Title: Ontology-Mediated Query Answering over Temporal Data: A Survey (Invited Talk).\\nYear: 2017\\n\\nAbstract: We discuss the use of various temporal knowledge representation formalisms for ontology-mediated query answering over temporal data. In particular, we analyse ontology and query languages based on the linear temporal logic LTL, the multi-dimensional Halpern-Shoham interval temporal logic HS_n, as well as the metric temporal logic MTL. Our main focus is on the data complexity of answering temporal ontology-mediated queries and their rewritability into standard first-order and datalog queries.\",\n \"Title: A Framework for Temporal Ontology-Based Data Access: A Proposal.\\nYear: 2017\\n\\nAbstract: Predictive analysis gradually gains importance in industry. For instance, service engineers at Siemens diagnostic centres unveil hidden knowledge in huge amounts of historical sensor data and use this knowledge to improve the predictive systems analysing live data. Currently, the analysis is usually done using data-dependent rules that are specific to individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. One solution to this problem is to employ ontology-based data access (OBDA) that provides a conceptual view of data via an ontology. However, classical OBDA systems do not support access to temporal data and reasoning over it. To address this issue, we propose a framework of temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in RDF format, classical ontology and rule languages to reflect static information, as well as a temporal rule language to describe events. We also propose a SPARQL-based query language for retrieving temporal information and, finally, an architecture of system implementation extending the state-of-the-art OBDA platform Ontop.\",\n \"Title: Ontology-Based Data Access to Slegge.\\nYear: 2017\\n\\nAbstract: We report on our experience in ontology-based data access to the Slegge database at Statoil and share the resources employed in this use case: end-user information needs (in natural language), their translations into SPARQL, the Subsurface Exploration Ontology, the schema of the Slegge database with integrity constraints, and the mappings connecting the ontology and the schema.\",\n \"Title: Ontology-Based Data Access: A Survey.\\nYear: 2018\\n\\nAbstract: We present the framework of ontology-based data access, a semantic paradigm for providing a convenient and user-friendly access to data repositories, which has been actively developed and studied in the past decade. Focusing on relational data sources, we discuss the main ingredients of ontology-based data access, key theoretical results, techniques, applications and future challenges.\",\n \"Title: Minimal module extraction from DL-lite ontologies using QBF solvers\\nYear: 2009\\n\\nAbstract: We present a formal framework for (minimal) module extraction based on an abstract notion of inseparability w.r.t. a signature between ontologies. Two instances of this framework are discussed in detail for DL-Lite ontologies: concept inseparability, when ontologies imply the same complex concept inclusions over the signature, and query inseparability, when they give the same answers to existential queries for any instance data over the signature. We demonstrate that different types of corresponding minimal modules for these inseparability relations can be automatically extracted from large-scale DL-Lite ontologies by composing the tractable syntactic locality-based module extraction algorithm with intractable extraction algorithms using the multi-engine QBF solver AQME. The extracted minimal modules are compared with those obtained using non-logic-based approaches.\",\n \"Title: On Non-local Propositional and Weak Monodic Quantified CTL*\\nYear: 2004\\n\\nAbstract: In this paper we prove decidability of two kinds of branching time temporal logics. First we show that the non-local version of propositional PCTL*, in which truth values of atoms may depend on the branch of evaluation, is decidable. Then we use this result to establish decidability of various fragments of quantified PCTL*, where the next-time operator can be applied only to formulas with at most one free variable, all other temporal operators and path quantifiers are applicable only to sentences, and the first-order constructs follow the pattern of any of several decidable fragments of first-order logic.\",\n \"Title: Ontology-Based Data Access with a Horn Fragment of Metric Temporal Logic.\\nYear: 2017\\n\\nAbstract: We advocate datalogMTL, a datalog extension of a Horn fragment of the metric temporal logic MTL, as a language for ontology-based access to temporal log data. We show that datalogMTL is EXPSPACE-complete even with punctual intervals, in which case MTL is known to be undecidable. Non-recursive datalogMTL turns out to be PSPACE-complete for combined complexity and in AC(0) for data complexity. We demonstrate by two real-world use cases that nonrecursive datalogMTL programs can express complex temporal concepts from typical user queries and thereby facilitate access to log data. Our experiments with Siemens turbine data and MesoWest weather data show that datalogMTL ontology-mediated queries are efficient and scale on large datasets of up to 11GB.\",\n \"Title: On the Parametrised Complexity of Tree-Shaped Ontology-Mediated Queries in OWL 2 QL.\\nYear: 2017\\n\\nAbstract: We discuss the parameterised complexity of answering tree-shaped ontology-mediated queries (OMQs) in OWL 2 QL under various restrictions on their ontologies and conjunctive queries (CQs). In particular, we construct an ontology T such that answering OMQs (T , q) with tree-shaped CQs q is W[1]-hard if the number of leaves in q is regarded as the parameter. The number of leaves has previously been identified as an important characteristic of CQs as bounding it leads to tractable OMQ answering. Our result shows that treating it as a parameter does not make the problem fixed-parameter tractable, even for a fixed ontology.\",\n \"Title: Model Comparison Games for Horn Description Logics\\nYear: 2019\\n\\nAbstract: Horn description logics are syntactically defined fragments of standard description logics that fall within the Horn fragment of first-order logic and for which ontology-mediated query answering is in PTime for data complexity. They were independently introduced in modal logic to capture the intersection of Horn first-order logic with modal logic. In this paper, we introduce model comparison games for the basic Horn description logic hornALC (corresponding to the basic Horn modal logic) and use them to obtain an Ehrenfeucht-Fraïssé type definability result and a van Benthem style expressive completeness result for hornALC. We also establish a finite model theory version of the latter. The Ehrenfeucht-Fraïssé type definability result is used to show that checking hornALC indistinguishability of models is ExpTime-complete, which is in sharp contrast to ALC indistinguishability (i.e., bisimulation equivalence) checkable in PTime. In addition, we explore the behavior of Horn fragments of more expressive description and modal logics by defining a Horn guarded fragment of first-order logic and introducing model comparison games for it.\",\n \"Title: Query Inseparability for ALC Ontologies.\\nYear: 2019\\n\\nAbstract: We investigate the problem whether two ALC ontologies are indistinguishable (or inseparable) by means of queries in a given signature, which is fundamental for ontology engineering tasks such as ontology versioning, modularisation, update, and forgetting. We consider both knowledge base (KB) and TBox inseparability. For KBs, we give model-theoretic criteria in terms of (finite partial) homomorphisms and products and prove that this problem is undecidable for conjunctive queries (CQs), but 2ExpTime-complete for unions of CQs (UCQs). The same results hold if (U)CQs are replaced by rooted (U)CQs, where every variable is connected to an answer variable. We also show that inseparability by CQs is still undecidable if one KB is given in the lightweight DL EL and if no restrictions are imposed on the signature of the CQs. We also consider the problem whether two ALC TBoxes give the same answers to any query over any ABox in a given signature and show that, for CQs, this problem is undecidable, too. We then develop model-theoretic criteria for HornALC TBoxes and show using tree automata that, in contrast, inseparability becomes decidable and 2ExpTime-complete, even ExpTime-complete when restricted to (unions of) rooted CQs.\",\n \"Title: Ontology-Based Access To Temporal Data With Ontop: A Framework Proposal\\nYear: 2019\\n\\nAbstract: Predictive analysis gradually gains importance in industry. For instance, service engineers at Siemens diagnostic centres unveil hidden knowledge in huge amounts of historical sensor data and use it to improve the predictive systems analysing live data. Currently, the analysis is usually done using data-dependent rules that are specific to individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. One solution to this problem is to employ ontology-based data access (OBDA), which provides a conceptual view of data via an ontology. However, classical OBDA systems do not support access to temporal data and reasoning over it. To address this issue, we propose a framework for temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in the RDF format, classical ontology and rule languages to reflect static information, as well as a temporal rule language to describe events. We also propose a SPARQL-based query language for retrieving temporal information and, finally, an architecture of system implementation extending the state-of-the-art OBDA platform Ontop.\",\n \"Title: Data Complexity and Rewritability of Ontology-Mediated Queries in Metric Temporal Logic under the Event-Based Semantics (Full Version).\\nYear: 2019\\n\\nAbstract: We investigate the data complexity of answering queries mediated by metric temporal logic ontologies under the event-based semantics assuming that data instances are finite timed words timestamped with binary fractions. We identify classes of ontology-mediated queries answering which can be done in AC0, NC1, L, NL, P, and coNP for data complexity, provide their rewritings to first-order logic and its extensions with primitive recursion, transitive closure or datalog, and establish lower complexity bounds.\",\n \"Title: Deciding Boundedness of Monadic Sirups\\nYear: 2021\\n\\nAbstract: ABSTRACTWe show that deciding boundedness (aka FO-rewritability) of monadic single rule datalog programs (sirups) is 2\\\\Exp-hard, which matches the upper bound known since 1988 and finally settles a long-standing open problem. We obtain this result as a byproduct of an attempt to classify monadic 'disjunctive sirups'---Boolean conjunctive queries $\\\\q$ with unary and binary predicates mediated by a disjunctive rule $T(x) łor F(x) łeftarrow A(x)$---according to the data complexity of their evaluation. Apart from establishing that deciding FO-rewritability of disjunctive sirups with a dag-shaped $\\\\q$ is also 2\\\\Exp-hard, we make substantial progress towards obtaining a complete FO/Ł-hardness dichotomy of disjunctive sirups with ditree-shaped $\\\\q$.\",\n \"Title: First-Order Rewritability and Complexity of Two-Dimensional Temporal Ontology-Mediated Queries.\\nYear: 2022\\n\\nAbstract: Aiming at ontology-based data access to temporal data, we design two-dimensional temporal ontology and query languages by combining logics from the (extended) DL-Lite family with linear temporal logic LTL over discrete time (Z,<). Our main concern is first-order rewritability of ontology-mediated queries (OMQs) that consist of a 2D ontology and a positive temporal instance query. Our target languages for FO-rewritings are two-sorted FO(<) - first-order logic with sorts for time instants ordered by the built-in precedence relation < and for the domain of individuals - its extension FOE with the standard congruence predicates t \\\\equiv 0 mod n, for any fixed n > 1, and FO(RPR) that admits relational primitive recursion. In terms of circuit complexity, FOE- and FO(RPR)-rewritability guarantee answering OMQs in uniform AC0 and NC1, respectively. We proceed in three steps. First, we define a hierarchy of 2D DL-Lite/LTL ontology languages and investigate the FO-rewritability of OMQs with atomic queries by constructing projections onto 1D LTL OMQs and employing recent results on the FO-rewritability of propositional LTL OMQs. As the projections involve deciding consistency of ontologies and data, we also consider the consistency problem for our languages. While the undecidability of consistency for 2D ontology languages with expressive Boolean role inclusions might be expected, we also show that, rather surprisingly, the restriction to Krom and Horn role inclusions leads to decidability (and ExpSpace-completeness), even if one admits full Booleans on concepts. As a final step, we lift some of the rewritability results for atomic OMQs to OMQs with expressive positive temporal instance queries. The lifting results are based on an in-depth study of the canonical models and only concern Horn ontologies.\",\n \"Title: Unique Characterisability and Learnability of Temporal Instance Queries.\\nYear: 2022\\n\\nAbstract: We aim to determine which temporal instance queries can be uniquely characterised by a (polynomial-size) set of positive and negative temporal data examples. We start by considering queries formulated in fragments of propositional linear temporal logic LTL that correspond to conjunctive queries (CQs) or extensions thereof induced by the until operator. Not all of these queries admit polynomial characterisations but by restricting them further to path-shaped queries we identify natural classes that do. We then investigate how far the obtained characterisations can be lifted to temporal knowledge graphs queried by 2D languages combining LTL with concepts in description logics EL or ELI (i.e., tree-shaped CQs). While temporal operators in the scope of description logic constructors can destroy polynomial characterisability, we obtain general transfer results for the case when description logic constructors are within the scope of temporal operators. Finally, we apply our characterisations to establish (polynomial) learnability of temporal instance queries using membership queries in the active learning framework.\",\n \"Title: On the First-Order Rewritability of Ontology-Mediated Queries in Linear Temporal Logic (Extended Abstract).\\nYear: 2022\\n\\nAbstract: We argue that linear temporal logic LTL in tandem with monadic first-order logic can be used as a ba- sic language for ontology-based access to tempo- ral data and obtain a classification of the resulting ontology-mediated queries according to the type of standard first-order queries they can be rewritten to.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":121,"string":"121"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13267,"string":"13,267"}}},{"rowIdx":13268,"cells":{"question_id":{"kind":"string","value":"Myra B Cohen"},"question":{"kind":"string","value":"Generate an abstract for the title \"Constructing test suites for interaction testing\" using the following items: Software system faults, pairwise testing, t-way testing, covering array, variable strength covering array."},"answer":{"kind":"string","value":"Software system faults are often caused by unexpected interactions among components. Yet the size of a test suite required to test all possible combinations of interactions can be prohibitive in even a moderately sized project. Instead, we may use pairwise or t-way testing to provide a guarantee that all pairs or t-way combinations of components are tested together. This concept draws on methods used in statistical testing for manufacturing and has been extended to software system testing. A covering array, CA(N; t, k, v), is an N × k array on v symbols such that every N × t sub-array contains all ordered subsets from v symbols of size t at least once. The properties of these objects, however, do not necessarily satisfy real software testing needs. Instead we examine a less studied object, the mixed level covering array and propose a new object, the variable strength covering array, which provides a more robust environment for software interaction testing. Initial results are presented suggesting that heuristic search techniques are more effective than some of the known greedy methods for finding smaller sized test suites. We present a discussion of an integrated approach for finding covering arrays and discuss how application of these techniques can be used to construct variable strength arrays."},"correct_answer":{"kind":"string","value":"Software system faults are often caused by unexpected interactions among components. Yet the size of a test suite required to test all possible combinations of interactions can be prohibitive in even a moderately sized project. Instead, we may use pairwise or t-way testing to provide a guarantee that all pairs or t-way combinations of components are tested together. This concept draws on methods used in statistical testing for manufacturing and has been extended to software system testing. A covering array, CA(N; t, k, v), is an N × k array on v symbols such that every N × t sub-array contains all ordered subsets from v symbols of size t at least once. The properties of these objects, however, do not necessarily satisfy real software testing needs. Instead we examine a less studied object, the mixed level covering array and propose a new object, the variable strength covering array, which provides a more robust environment for software interaction testing. Initial results are presented suggesting that heuristic search techniques are more effective than some of the known greedy methods for finding smaller sized test suites. We present a discussion of an integrated approach for finding covering arrays and discuss how application of these techniques can be used to construct variable strength arrays."},"documents":{"kind":"list like","value":["Title: Testing across configurations: implications for combinatorial testing\nYear: 2006\n\nAbstract: User configurable software systems allow users to customize functionality at run time. In essence, each such system consists of a family of potentially thousands or millions of program instantiations. Testing methods cannot test all of these configurations, therefore some sampling mechanism must be applied. A common approach to providing such a mechanism has been to use combinatorial interaction testing. To date, however, little work has been done to quantify the effects of different configurations on a test suites' operation and effectiveness. In this paper we present a case study that investigates the effects of changing configurations on two types of test suites. Our results show that test coverage and fault detection effectiveness do not vary much across configurations for entire test suites; however, for individual test cases and certain types of faults, configurations matter.","Title: A self-adjusting code cache manager to balance start-up time and memory usage\nYear: 2010\n\nAbstract: In virtual machines for embedded devices that use just-in-time compilation, the management of the code cache can significantly impact performance in terms of both memory usage and start-up time. Although improving memory usage has been a common focus for system designers, start-up time is often overlooked. In systems with constrained resources, however, these two performance metrics are often at odds and must be considered together. In this paper, we present an adaptive self-adjusting code cache manager to improve performance with respect to both start-up time and memory usage. It balances these concerns by detecting changes in method compilation rates, resizing the cache after each pitching event. We conduct experiments to validate our proposed system and quantify the impacts that different code cache management techniques have on memory usage and start-up time through two oracle systems. Our results show that the proposed algorithm yields nearly the same start-up times as a hand-tuned oracle and shorter execution times than those of the SSCLI in eight out of ten applications. It also has lower memory usage over time in all but one application.","Title: A framework of greedy methods for constructing interaction test suites\nYear: 2005\n\nAbstract: Greedy algorithms for the construction of software interaction test suites are studied. A framework is developed to evaluate a large class of greedy methods that build suites one test at a time. Within this framework are many instantiations of greedy methods generalizing those in the literature. Greedy algorithms are popular when the time for test suite construction is of paramount concern. We focus on the size of the test suite produced by each instantiation. Experiments are analyzed using statistical techniques to determine the importance of the implementation decisions within the framework. This framework provides a platform for optimizing the accuracy and speed of \"one-test-at-a-time\" greedy methods.","Title: Human performance regression testing\nYear: 2013\n\nAbstract: As software systems evolve, new interface features such as keyboard shortcuts and toolbars are introduced. While it is common to regression test the new features for functional correctness, there has been less focus on systematic regression testing for usability, due to the effort and time involved in human studies. Cognitive modeling tools such as CogTool provide some help by computing predictions of user performance, but they still require manual effort to describe the user interface and tasks, limiting regression testing efforts. In recent work, we developed CogTool Helper to reduce the effort required to generate human performance models of existing systems. We build on this work by providing task specific test case generation and present our vision for human performance regression testing (HPRT) that generates large numbers of test cases and evaluates a range of human performance predictions for the same task. We examine the feasibility of HPRT on four tasks in LibreOffice, find several regressions, and then discuss how a project team could use this information. We also illustrate that we can increase efficiency with sampling by leveraging an inference algorithm. Samples that take approximately 50% of the runtime lose at most 10% of the performance predictions.","Title: Constructing Interaction Test Suites for Highly-Configurable Systems in the Presence of Constraints: A Greedy Approach\nYear: 2008\n\nAbstract: Researchers have explored the application of combinatorial interaction testing (CIT) methods to construct samples to drive systematic testing of software system configurations. Applying CIT to highly-configurable software systems is complicated by the fact that, in many such systems, there are constraints between specific configuration parameters that render certain combinations invalid. Many CIT algorithms lack a mechanism to avoid these. In recent work, automated constraint solving methods have been combined with search-based CIT construction methods to address the constraint problem with promising results. However, these techniques can incur a non-trivial overhead. In this paper, we build upon our previous work to develop a family of greedy CIT sample generation algorithms that exploit calculations made by modern boolean satisfiability (SAT) solvers to prune the search space of the CIT problem. We perform a comparative evaluation of the cost-effectiveness of these algorithms on four real-world highly-configurable software systems and on a population of synthetic examples that share the characteristics of those systems. In combination our techniques reduce the cost of CIT in the presence of constraints to 30\\% of the cost of widely-used unconstrained CIT methods without sacrificing the quality of the solutions.","Title: Covering arrays for efficient fault characterization in complex configuration spaces\nYear: 2006\n\nAbstract: Testing systems with large configurations spaces that change often is a challenging problem. The cost and complexity of QA explodes because often there isn't just one system, but a multitude of related systems. Bugs may appear in certain configurations, but not in others.The Skoll system and process has been developed to test these types of systems through distributed, continuous quality assurance, leveraging user resources around-the-world, around-the-clock. It has been shown to be effective in automatically characterizing configurations in which failures manifest. The derived information helps developers quickly narrow down the cause of failures which then improves turn around time for fixes. However, this method does not scale well. It requires one to exhaustively test each configuration in the configuration space.In this paper we examine an alternative approach. The idea is to systematically sample the configuration space, test only the selected configurations, and conduct fault characterization on the resulting data. The sampling approach we use is based on calculating a mathematical object called a covering array. We empirically assess the effect of using covering array derived test schedules on the resulting fault characterizations and provide guidelines to practitioners for their use.","Title: Efficiency and early fault detection with lower and higher strength combinatorial interaction testing\nYear: 2013\n\nAbstract: Combinatorial Interaction Testing (CIT) is important because it tests the interactions between the many features and parameters that make up the configuration space of software systems. However, in order to be practically applicable, it must be able to cater for soft and hard real-world constraints and should, ideally, report a test priority order that maximises earliest fault detection. We show that we can achieve the highest strength CIT in 5.65 minutes on average. This was previously thought to be too computationally expensive to be feasible. Furthermore, we show that higher strength suites find more faults, while prioritisations using lower strengths are no worse at achieving early fault revelation.","Title: Interaction testing of highly-configurable systems in the presence of constraints\nYear: 2007\n\nAbstract: Combinatorial interaction testing (CIT) is a method to sample configurations of a software system systematically for testing. Many algorithms have been developed that create CIT samples, however few have considered the practical concerns that arise when adding constraints between combinations of options. In this paper, we survey constraint handling techniques in existing algorithms and discuss the challenges that they present. We examine two highly-configurable software systems to quantify the nature of constraints in real systems. We then present a general constraint representation and solving technique that can be integrated with existing CIT algorithms and compare two constraint-enhanced algorithm implementations with existing CIT tools to demonstrate feasibility.","Title: Cluttered Orderings for the Complete Graph\nYear: 2001\n\nAbstract: In a systematic erasure code for the correction of two simultaneous erasures, each information symbol must have two associated parity symbols. When implemented in a redundant array of independent disks (RAID), performance requirements on the update penalty necessitate that each information symbol be associated with no more parity symbols than the two required. This leads to a simple graph model of the erasure codes, with parity symbols as vertices and information symbols as edges. Ordering the edges so that no more than f check disks (vertices) appear among any set of d consecutive edges is found to optimize access performance of the disk array when d is maximized. These cluttered orderings are examined for the complete graph Kn. The maximum number d of edges is determined precisely when f ≤ 5 and when f = n - 1, and bounds are derived in the remaining cases.","Title: Configuration-aware regression testing: an empirical study of sampling and prioritization\nYear: 2008\n\nAbstract: Configurable software lets users customize applications in many ways, and is becoming increasingly prevalent. Researchers have created techniques for testing configurable software, but to date, only a little research has addressed the problems of regression testing configurable systems as they evolve. Whereas problems such as selective retesting and test prioritization at the test case level have been extensively researched, these problems have rarely been considered at the configuration level. In this paper we address the problem of providing configuration-aware regression testing for evolving software systems. We use combinatorial interaction testing techniques to model and generate configuration samples for use in regression testing. We conduct an empirical study on a non-trivial evolving software system to measure the impact of configurations on testing effectiveness, and to compare the effectiveness of different configuration prioritization techniques on early fault detection during regression testing. Our results show that configurations can have a large impact on fault detection and that prioritization of configurations can be effective.","Title: Analysis, Test and Verification in The Presence of Variability (Dagstuhl Seminar 13091).\nYear: 2013\n\nAbstract: This report documents the program and the outcomes of Dagstuhl Seminar 13091 \"Analysis, Test and Verification in The Presence of Variability\". The seminar had the goal of consolidating and stimulating research on analysis of software models with variability, enabling the design of variability-aware tool chains. We brought together 46 key researchers from three continents, working on quality assurance challenges that arise from introducing variability, and some who do not work with variability, but that are experts in their respective areas in the broader domain of software analysis or testing research. As a result of interactions triggered by sessions of different formats, the participants were able to classify their approaches with respect to a number of dimensions that helped to identify similarities and differences that have already been useful to improve understanding and foster new collaborations among the participants.","Title: Continuous test suite augmentation in software product lines\nYear: 2013\n\nAbstract: Software Product Line (SPL) engineering offers several advantages in the development of families of software products. There is still a need, however, to generate test cases for individual products in product lines more efficiently. In this paper we propose an approach, CONTESA, for generating test cases for SPLs using test suite augmentation. Instead of generating test cases for products independently, our approach generates new test cases for products in an order that allows it to build on test cases created for products tested earlier. In this work, we use a genetic algorithm to generate test cases, targeting branches not yet covered in each product, although other algorithms and coverage criteria could be utilized. We have evaluated CONTESA on two non-trivial SPLs, and have shown that CONTESA is more efficient and effective than an approach that generates test cases for products independently. A further evaluation shows that CONTESA is more effective at achieving coverage, and reveals as many faults as an existing feature-based testing approach.","Title: Coverage and adequacy in software product line testing\nYear: 2006\n\nAbstract: Software product line modeling has received a great deal of attention for its potential in fostering reuse of software artifacts across development phases. Research on the testing phase, has focused on identifying the potential for reuse of test cases across product line instances. While this offers potential reductions in test development effort for a given product line instance, it does not focus on and leverage the fundamental abstraction that is inherent in software product lines - variability.In this paper, we illustrate how rich software product line modeling notations can be mapped onto an underlying relational model that captures variability in the feasible product line instances. This relational model serves as the semantic basis for defining a family of coverage criteria for testing of a product line. These criteria make it possible to accumulate test coverage information for the product line itself over the course of multiple product line instance development efforts. Cumulative coverage, in turn, enables targeted testing efforts for new product line instances. We describe how combinatorial interaction testing methods can be applied to define test configurations that achieve a desired level of coverage and identify challenges to scaling such methods to large, complex software product lines.","Title: Combinatorial Interaction Regression Testing: A Study of Test Case Generation and Prioritization\nYear: 2007\n\nAbstract: Regression testing is an expensive part of the software maintenance process. Effective regression testing techniques select and order (or prioritize) test cases between successive releases of a program. However, selection and prioritization are dependent on the quality of the initial test suite. An effective and cost efficient test generation technique is combinatorial interaction testing, CIT, which systematically samples all t-way combinations of input parameters. Research on CIT, to date, has focused on single version software systems. There has been little work that empirically assesses the use of CIT test generation as the basis for selection or prioritization. In this paper we examine the effectiveness of CIT across multiple versions of two software subjects. Our results show that CIT performs well in finding seeded faults when compared with an exhaustive test set. We examine several CIT prioritization techniques and compare them with a re-generation/prioritization technique. We find that prioritized and re-generated/prioritized CIT test suites may find faults earlier than unordered CIT test suites, although the re-generated/prioritized test suites sometimes exhibit decreased fault detection.","Title: Feedback driven adaptive combinatorial testing\nYear: 2011\n\nAbstract: The configuration spaces of modern software systems are too large to test exhaustively. Combinatorial interaction testing (CIT) approaches, such as covering arrays, systematically sample the configuration space and test only the selected configurations. The basic justification for CIT approaches is that they can cost-effectively exercise all system behaviors caused by the settings of t or fewer options. We conjecture, however, that in practice many such behaviors are not actually tested because of masking effects -- failures that perturb execution so as to prevent some behaviors from being exercised. In this work we present a feedback-driven, adaptive, combinatorial testing approach aimed at detecting and working around masking effects. At each iteration we detect potential masking effects, heuristically isolate their likely causes, and then generate new covering arrays that allow previously masked combinations to be tested in the subsequent iteration. We empirically assess the effectiveness of the proposed approach on two large widely used open source software systems. Our results suggest that masking effects do exist and that our approach provides a promising and efficient way to work around them.","Title: Ladder orderings of pairs and RAID performance\nYear: 2004\n\nAbstract: In a systematic erasure code for the correction of two simultaneous erasures, each information symbol must have two associated parity symbols. When implemented in a redundant array of independent disks (RAID), performance requirements on the update penalty necessitate that each information symbol be associated with no more parity symbols than the two required. This leads to a simple graph model of the erasure codes, with parity symbols as vertices and information symbols as edges. Based on simulations of RAID performance, an ordering of the edges in which every sequence of three consecutive edges in the order induces as few vertices as possible is found to optimize access performance of the disk array. The ladder orderings to optimize performance are shown to exist for the complete graph Kn, except possibly when n ∈ {15, 18, 22}.","Title: Constructing strength three covering arrays with augmented annealing\nYear: 2008\n\nAbstract: A covering arrayCA(N;t,k,v) is an Nxk array such that every Nxt sub-array contains all t-tuples from v symbols at least once, where t is the strength of the array. One application of these objects is to generate software test suites to cover all t-sets of component interactions. Methods for construction of covering arrays for software testing have focused on two main areas. The first is finding new algebraic and combinatorial constructions that produce smaller covering arrays. The second is refining computational search algorithms to find smaller covering arrays more quickly. In this paper, we examine some new cut-and-paste techniques for strength three covering arrays that combine recursive combinatorial constructions with computational search; when simulated annealing is the base method, this is augmented annealing. This method leverages the computational efficiency and optimality of size obtained through combinatorial constructions while benefiting from the generality of a heuristic search. We present a few examples of specific constructions and provide new bounds for some strength three covering arrays.","Title: Incremental covering array failure characterization in large configuration spaces\nYear: 2009\n\nAbstract: The increasing complexity of configurable software systems has created a need for more intelligent sampling mechanisms to detect and characterize failure-inducing dependencies between configurations. Prior work - in idealized environments - has shown that test schedules based on a mathematical object, called a covering array, in combination with classification techniques, can meet this need. Applying this approach in practice, however, is tricky because testing time and resource availability are unpredictable, and because failure characteristics can change from release to release. With current approaches developers must set a key covering array parameter (its strength) based on estimated release times and failure characterizations. This will influence the outcome of their results. In this paper we propose a new approach that incrementally builds covering array schedules. This approach begins at a low strength, and then iteratively increases strength as resources allow. At each stage previously tested configurations are reused, thus avoiding duplication of work. With the incremental approach developers need never commit to a specific covering array strength. Instead, by using progressively stronger covering array schedules, failures due to few configuration dependencies can be found and classified as soon and as cheaply as possibly. Additionally, it eliminates the risks of committing to overly strong test schedules. We evaluate this new approach through a case study on three consecutive releases of MySQL, an open source database. Our results suggest that our approach is as good or better than previous approaches, costing less in most cases, and allowing greater flexibility in environments with unpredictable development constraints.","Title: Feature Interaction Faults Revisited: An Exploratory Study\nYear: 2011\n\nAbstract: While a large body of research is dedicated to testing for feature interactions in configurable software, there has been little work that examines what constitutes such a fault at the code level. In consequence, we do not know how prevalent real interaction faults are in practice, what a typical interaction fault looks like in code, how to seed interaction faults, or whether current interaction testing techniques are effective at finding the faults they aim to detect. We make a first step in this direction, by deriving a white box criterion for an interaction fault. Armed with this criterion, we perform an exploratory study on hundreds of faults from the field in two open source systems. We find that only three of the 28 which appear to be interaction faults are in fact due to features' interactions. We investigate the remaining 25 and find that, although they could have been detected without interaction testing, varying the system configuration amplifies the fault-finding power of a test suite, making these faults easier to expose. Thus, we characterize the benefits of interaction testing in regards to both interaction and non-interaction faults. We end with a discussion of several mutations that can be used to mimic interaction faults based on the faults we see in practice.","Title: Directed test suite augmentation: techniques and tradeoffs\nYear: 2010\n\nAbstract: Test suite augmentation techniques are used in regression testing to identify code elements affected by changes and to generate test cases to cover those elements. Our preliminary work suggests that several factors influence the cost and effectiveness of test suite augmentation techniques. These include the order in which affected elements are considered while generating test cases, the manner in which existing regression test cases and newly generated test cases are used, and the algorithm used to generate test cases. In this work, we present the results of an empirical study examining these factors, considering two test case generation algorithms (concolic and genetic). The results of our experiment show that the primary factor affecting augmentation is the test case generation algorithm utilized; this affects both cost and effectiveness. The manner in which existing and newly generated test cases are utilized also has a substantial effect on efficiency but a lesser effect on effectiveness. The order in which affected elements are considered turns out to have relatively few effects when using concolic test case generation, but more substantial effects when using genetic test case generation.","Title: Interaction Coverage Meets Path Coverage by SMT Constraint Solving\nYear: 2009\n\nAbstract: We present a novel approach for generating interaction combinations based on SMT constraint resolution. Our approach can generate maximal interaction coverage in the presence of general constraints as supported by the underlying solver. It supports seeding with general predicates, which allows us to combine it with path exploration such that both interaction and path coverage goals can be met. Our approach is motivated by the application to behavioral model-based testing in the Spec Explorer tool, where parameter combinations must be generated such that all path conditions of a model action have at least one combination which enables the path. It is applied in a large-scale project for model-based quality assurance of interoperability documentation at Microsoft.","Title: Towards Dynamic Adaptive Automated Test Generation for Graphical User Interfaces\nYear: 2009\n\nAbstract: Graphical user interfaces (GUIs) present an enormous number of potential event sequences to users. During testing it is necessary to cover this space, however the complexity of modern GUIs has made this an increasingly difficult task. Our past work has demonstrated that it is important to incorporate \"context'' into GUI test cases, in terms of event combinations, event sequence length, and by considering all possible starting and ending positions for each event. Despite the use of our most refined modeling techniques, many of the generated test cases remain unexecutable. In this paper, we posit that due to the dynamic state-based nature of GUIs, it is important to incorporate feedback from the execution of tests into test case generation algorithms. We propose the use of an evolutionary algorithm to generate test suites with fewer unexecutable test cases and higher event interaction coverage.","Title: Using feature locality: can we leverage history to avoid failures during reconfiguration?\nYear: 2011\n\nAbstract: Despite the best efforts of software engineers, faults still escape into deployed software. Developers need time to prepare and distribute fixes, and in the interim deployments must either tolerate or avoid failures. Self-adaptive systems, systems that adapt to meet changing requirements in a dynamic environment, have a daunting task if their reconfiguration involves adding or removing functional features, because configurable software is known to suffer from failures that appear only under certain feature combinations. Although configuration-dependent failures may be difficult to provoke, and thus hard to detect in testing, we posit that they also constitute opportunities for reconfiguration to increase system reliability. We further conjecture that the failures that are sensitive to a system configuration depend on similar feature combinations, a phenomenon we call feature-locality, and that this locality can be combined with historical data to predict failure-prone configurations. In a case study on 128 failures reported against released versions of an open source configurable system, we find evidence to support our hypothesis. We show that only a small number of features affect the visibility of these failures, and that over time we can learn these features to avoid future failures.","Title: Variable Strength Interaction Testing of Components\nYear: 2003\n\nAbstract: Complete interaction testing of components is too costlyin all but the smallest systems. Yet component interactionsare likely to cause unexpected faults. Recently, design of experimenttechniques have been applied to software testingto guarantee a minimum coverage of all t-way interactionsacross components.However, t is always fixed.This paperexamines the need to vary the size of t in an individual testsuite and defines a new object, the variable strength coveringarray, that has this property.We present some computationalmethods to find variable strength arrays and provideinitial bounds for a group of these objects.","Title: Ordering disks for double erasure codes\nYear: 2001\n\nAbstract: Dish arrays have been designed with two competing goals in mind, the ability to reconstruct erased disks (reliability), and the speed with which information can be read, written, and reconstructed (performance). The substantial loss in performance of write operations as reliability requirements increase has resulted in an emphasis on performance at the expense of reliability. This has proved acceptable given the relatively small members of disks in current disk arrays. We develop a method for improving the performance of write operations in disk arrays capable of correcting any double erasure, by ordering the columns of the erasure code to minimize the amount of parity information that requires updating. For large disk arrays, this affords a method to support the reliability needed without the generally accepted loss of performance.","Title: An orchestrated survey of methodologies for automated software test case generation\nYear: 2013\n\nAbstract: Test case generation is among the most labour-intensive tasks in software testing. It also has a strong impact on the effectiveness and efficiency of software testing. For these reasons, it has been one of the most active research topics in software testing for several decades, resulting in many different approaches and tools. This paper presents an orchestrated survey of the most prominent techniques for automatic generation of software test cases, reviewed in self-standing sections. The techniques presented include: (a) structural testing using symbolic execution, (b) model-based testing, (c) combinatorial testing, (d) random testing and its variant of adaptive random testing, and (e) search-based testing. Each section is contributed by world-renowned active researchers on the technique, and briefly covers the basic ideas underlying the method, the current state of the art, a discussion of the open research problems, and a perspective of the future development of the approach. As a whole, the paper aims at giving an introductory, up-to-date and (relatively) short overview of research in automatic test case generation, while ensuring a comprehensive and authoritative treatment.","Title: Reducing Field Failures in System Configurable Software: Cost-Based Prioritization\nYear: 2009\n\nAbstract: System testing of configurable software is an expensive and resource constrained process. Insufficient testing often leads to escaped faults in the field where failures impact customers and are costly to repair. Prior work has shown that it is possible to efficiently sample configurations for testing using combinatorial interaction testing, and to prioritize these configurations to increase the rate of early fault detection. The underlying assumption to date has been that there is no added complexity to configuring a system level environment over a user configurable one; i.e. the time required to setup and test each individual configuration is nominal. In this paper we examine prioritization of system configurable software driven not only by fault detection but also by the cost of configuration and setup time that moving between different configurations incurs. We present a case study on two releases of an enterprise software system using failures reported in the field. We examine the most effective prioritization technique and conclude that (1) using failure history of configurations can improve the early fault detection rate, but that (2) we must consider fault detection rate over time, not by the number of configurations tested. It is better to test related configurations which incur minimal setup time than to test fewer, more diverse configurations.","Title: Repairing GUI Test Suites Using a Genetic Algorithm\nYear: 2010\n\nAbstract: Recent advances in automated functional testing of Graphical User Interfaces (GUIs) rely on deriving graph models that approximate all possible sequences of events that may be executed on the GUI, and then use the graphs to generate test cases (event sequences) that achieve a specified coverage goal. However, because these models are only approximations of the actual event flows, the generated test cases may suffer from problems of infeasibility, i.e., some events may not be available for execution causing the test case to terminate prematurely. In this paper we develop a method to automatically repair GUI test suites, generating new test cases that are feasible. We use a genetic algorithm to evolve new test cases that increase our test suite's coverage while avoiding infeasible sequences. We experiment with this algorithm on a set of synthetic programs containing different types of constraints and for test sequences of varying lengths. Our results suggest that we can generate new test cases to cover most of the feasible coverage and that the genetic algorithm outperforms a random algorithm trying to achieve the same goal in almost all cases.","Title: Integration testing of software product lines using compositional symbolic execution\nYear: 2012\n\nAbstract: Software product lines are families of products defined by feature commonality and variability, with a well-managed asset base. Recent work in testing of software product lines has exploited similarities across development phases to reuse shared assets and reduce test effort. The use of feature dependence graphs has also been employed to reduce testing effort, but little work has focused on code level analysis of dataflow between features. In this paper we present a compositional symbolic execution technique that works in concert with a feature dependence graph to extract the set of possible interaction trees in a product family. It composes these to incrementally and symbolically analyze feature interactions. We experiment with two product lines and determine that our technique can reduce the overall number of interactions that must be considered during testing, and requires less time to run than a traditional symbolic execution technique.","Title: Augmenting Simulated Annealing to Build Interaction Test Suites\nYear: 2003\n\nAbstract: Component based software development is prone to unexpectedinteraction faults. The goal is to test as manypotential interactions as is feasible within time and budgetconstraints. Two combinatorial objects, the orthogonalarray and the covering array, can be used to generate testsuites that provide a guarantee for coverage of all t-setsof component interactions in the case when the testing ofall interactions is not possible. Methods for constructionof these types of test suites have focused on two main areas.The first is finding new algebraic constructions that producesmaller test suites. The second is refining computationalsearch algorithms to find smaller test suites more quickly. Inthis paper we explore one method for constructing coveringarrays of strength three that combines algebraic constructionswith computational search. This method leveragesthe computational efficiency and optimality of size obtainedthrough algebraic constructions while benefiting from thegenerality of a heuristic search. We present a few examplesof specific constructions and provide some new bounds forsome strength three covering arrays.","Title: Towards incremental adaptive covering arrays\nYear: 2007\n\nAbstract: The increasing complexity of configurable software systems creates a need for more intelligent sampling mechanisms to detect and locate failure-inducing dependencies between configurations. Prior work shows that test schedules based on a mathematical object, called a covering array, can be used to detect and locate failures in combination with a clas- sification tree analysis. This paper addresses limitations of the earlier approach. First, the previous work requires devel- opers to choose the covering array's strength, even though there is no scientific or historical basis for doing so. Second, if a single covering array is insu!c ient to classify specific failures, the entire process must be rerun from scratch. To address these issues, our new approach incrementally and adaptively builds covering array schedules. It begins with a low strength, and continually increases this as resources allow, or poor classification results require. At each stage, previous tests are reused. This allows failures due to only one or two configurations settings to be found and classified as early as possible, and also limits duplication of work when multiple covering arrays must be used.","Title: Kirkman triple systems of order 21 with nontrivial automorphism group\nYear: 2002\n\nAbstract: There are 50,024 Kirkman triple systems of order 21 admitting an automorphism of order 2. There are 13,280 Kirkman triple systems of order 21 admitting an automorphism of order 3. Together with the 192 known systems and some simple exchange operations, this leads to a collection of 63,745 nonisomorphic Kirkman triple systems of order 21. This includes all KTS(21)s having a nontrivial automorphism group. None of these is doubly resolvable. Four are quadrilateral-free, providing the first examples of such a KTS(21).","Title: A Study in Prioritization for Higher Strength Combinatorial Testing\nYear: 2013\n\nAbstract: Recent studies have shown that combinatorial interaction testing (CIT) is an effective fault detection technique and that early fault detection can be improved by ordering test suites by interaction based prioritization approaches. Despite research that has shown that higher strength CIT improves fault detection, there have been fewer studies that aim to understand the impact of prioritization based on higher strength criteria. In this paper, we aim to understand how interaction based prioritization techniques perform, in terms of early fault detection when we prioritize based on 3-way interactions. We generalize prior work on prioritizing using 2-way interactions to t-way prioritization, and empirically evaluate this on three open source subjects, across multiple versions of each. We examine techniques that prioritize both existing CIT suites as well as generate new ones in prioritized order. We find that early fault detection can be improved when prioritizing 3-way CIT test suites by interactions that cover more code, and to a lesser degree when generating tests in prioritized order. Our techniques that work only from the specification, appear to work best with 2-way generation.","Title: Evaluating improvements to a meta-heuristic search for constrained interaction testing\nYear: 2011\n\nAbstract: Combinatorial interaction testing (CIT) is a cost-effective sampling technique for discovering interaction faults in highly-configurable systems. Constrained CIT extends the technique to situations where some features cannot coexist in a configuration, and is therefore more applicable to real-world software. Recent work on greedy algorithms to build CIT samples now efficiently supports these feature constraints. But when testing a single system configuration is expensive, greedy techniques perform worse than meta-heuristic algorithms, because greedy algorithms generally need larger samples to exercise the same set of interactions. On the other hand, current meta-heuristic algorithms have long run times when feature constraints are present. Neither class of algorithm is suitable when both constraints and the cost of testing configurations are important factors. Therefore, we reformulate one meta-heuristic search algorithm for constructing CIT samples, simulated annealing, to more efficiently incorporate constraints. We identify a set of algorithmic changes and experiment with our modifications on 35 realistic constrained problems and on a set of unconstrained problems from the literature to isolate the factors that improve performance. Our evaluation determines that the optimizations reduce run time by a factor of 90 and accomplish the same coverage objectives with even fewer system configurations. Furthermore, the new version compares favorably with greedy algorithms on real-world problems, and, though our modifications were aimed at constrained problems, it shows similar advantages when feature constraints are absent.","Title: Automated testing of GUI applications: models, tools, and controlling flakiness\nYear: 2013\n\nAbstract: System testing of applications with graphical user interfaces (GUIs) such as web browsers, desktop or mobile apps, is more complex than testing from the command line. Specialized tools are needed to generate and run test cases, models are needed to quantify behavioral coverage, and changes in the environment, such as the operating system, virtual machine or system load, as well as starting states of the executions, impact the repeatability of the outcome of tests making tests appear flaky. In this tutorial, we present an overview of the state of the art in GUI testing, consisting of both lectures and demonstrations on various platforms (desktop, web and mobile applications), using an open source testing tool, GUITAR. We show how to setup a system under test, how to extract models without source code, and how to then use those models to generate and replay test cases. We then present a lecture on the various factors that may cause flakiness in the execution of GUI-centric software, and hence impact the results of analyses and experiments based on such software. We end with a demonstration of a community resource for sharing GUI testing artifacts aimed at controlling these factors. This tutorial targets both researchers who develop techniques for testing GUI software, and practitioners from industry who want to learn more about model-based GUI testing or who run and rerun GUI tests and often find their runs are flaky.","Title: Optimal and pessimal orderings of Steiner triple systems in disk arrays\nYear: 2000\n\nAbstract: Steiner triple systems are well studied combinatorial designs that have been shown to possessproperties desirable for the construction of multiple erasure codes in RAID architectures.The ordering of the columns in the parity check matrices of these codes affects system performance.Combinatorial problems involved in the generation of good and bad column orderingsare defined, and examined for small numbers of accesses to consecutive data blocks in the diskarray.1 BackgroundA Steiner ...","Title: Factors affecting the use of genetic algorithms in test suite augmentation\nYear: 2010\n\nAbstract: Test suite augmentation techniques are used in regression testing to help engineers identify code elements affected by changes, and generate test cases to cover those elements. Researchers have created various approaches to identify affected code elements, but only recently have they considered integrating, with this task, approaches for generating test cases. In this paper we explore the use of genetic algorithms in test suite augmentation. We identify several factors that impact the effectiveness of this approach, and we present the results of a case study exploring the effects of one of these factors: the manner in which existing and newly generated test cases are utilized by the genetic algorithm. Our results reveal several ways in which this factor can influence augmentation results, and reveal open problems that researchers must address if they wish to create augmentation techniques that make use of genetic algorithms.","Title: Configurations everywhere: implications for testing and debugging in practice\nYear: 2014\n\nAbstract: Many industrial systems are highly-configurable, complicating the testing and debugging process. While researchers have developed techniques to statically extract, quantify and manipulate the valid system configurations, we conjecture that many of these techniques will fail in practice. In this paper we analyze a highly-configurable industrial application and two open source applications in order to quantify the true challenges that configurability creates for software testing and debugging. We find that (1) all three applications consist of multiple programming languages, hence static analyses need to cross programming language barriers to work, (2) there are many access points and methods to modify configurations, implying that practitioners need configuration traceability and should gather and merge metadata from more than one source and (3) the configuration state of an application on failure cannot be reliably determined by reading persistent data; a runtime memory dump or other heuristics must be used for accurate debugging. We conclude with a roadmap and lessons learned to help practitioners better handle configurability now, and that may lead to new configuration-aware testing and debugging techniques in the future.","Title: Improving the testing and testability of software product lines\nYear: 2010\n\nAbstract: Software Product Line (SPL) engineering offers several advantages in the development of families of software products. There is still a need, however, for better understanding of testability issues and for testing techniques that can operate cost-effectively on SPLs. In this paper we consider these testability issues and highlight some differences between optional versus alternative features. We then provide a graph based testing approach called the FIG Basis Path method that selects products and features for testing based on a feature dependency graph. We conduct a case study on several non-trivial SPLs and show that for these subjects, the FIG Basis Path method is as effective as testing all products, but tests no more than 24% of the products in the SPL.","Title: Regression testing in Software as a Service: An industrial case study\nYear: 2011\n\nAbstract: Many organizations are moving towards a business model of Software as a Service (SaaS), where customers select and pay for services dynamically via the web. In SaaS, service providers face the challenge of delivering and maintaining high quality software solutions which must continue to work under an enormous number of scenarios; customers can easily subscribe and unsubscribe from services at any point. To date, there has been little research on unique approaches for regression test methodologies for testing in a SaaS environment. In this paper, we present an industrial case study of a regression testing approach to improve test effectiveness and efficiency in SaaS. We model service level use cases from field failures as abstract events and then generate sequences of these for testing to provide a broad coverage of the possible use cases. In subsequent releases of the system we prioritize the tests to improve time to detection of faults in the modified system. We have applied our technique to two releases of a large industrial enterprise level SaaS application and demonstrate that using our approach (1) we could have uncovered escaped faults prior to the system release in both versions of the system; (2) using a priority order we could have improved the efficiency of testing in the first version; and (3) prioritization based on failure history from the first version increases the fault detection rate in the new version, suggesting a correlation between the important sequences in versions that can be leveraged for regression testing.","Title: GUI Interaction Testing: Incorporating Event Context\nYear: 2011\n\nAbstract: Graphical user interfaces (GUIs), due to their event-driven nature, present an enormous and potentially unbounded way for users to interact with software. During testing, it is important to “adequately cover” this interaction space. In this paper, we develop a new family of coverage criteria for GUI testing grounded in combinatorial interaction testing. The key motivation of using combinatorial techniques is that they enable us to incorporate “context” into the criteria in terms of event combinations, sequence length, and by including all possible positions for each event. Our new criteria range in both efficiency (measured by the size of the test suite) and effectiveness (the ability of the test suites to detect faults). In a case study on eight applications, we automatically generate test cases and systematically explore the impact of context, as captured by our new criteria. Our study shows that by increasing the event combinations tested and by controlling the relative positions of events defined by the new criteria, we can detect a large number of faults that were undetectable by earlier techniques.","Title: Practical Combinatorial Interaction Testing: Empirical Findings on Efficiency and Early Fault Detection\nYear: 2015\n\nAbstract: Combinatorial Interaction Testing (CIT) is important because it tests the interactions between the many features and parameters that make up the configuration space of software systems. Simulated Annealing (SA) and Greedy Algorithms have been widely used to find CIT test suites. From the literature, there is a widely-held belief that SA is slower, but produces more effective tests suites than Greedy and that SA cannot scale to higher strength coverage. We evaluated both algorithms on 7 real-world subjects for the well-studied 2-way up to the rarely-studied 6-way interaction strengths. Our findings present evidence to challenge this current orthodoxy: real-world constraints allow SA to achieve higher strengths. Furthermore, there was no evidence that Greedy was less effective (in terms of time to fault revelation) compared to SA; the results for the greedy algorithm are actually slightly superior. However, the results are critically dependent on the approach adopted to constraint handling. Moreover, we have also evaluated a genetic algorithm for constrained CIT test suite generation. This is the first time strengths higher than 3 and constraint handling have been used to evaluate GA. Our results show that GA is competitive only for pairwise testing for subjects with a small number of constraints.","Title: SimLatte: A Framework to Support Testing for Worst-Case Interrupt Latencies in Embedded Software\nYear: 2014\n\nAbstract: Embedded systems tend to be interrupt-driven, yet the presence of interrupts can affect system dependability because there can be delays in servicing interrupts. Such delays can occur when multiple interrupt service routines and interrupts of different priorities compete for resources on a given CPU. For this reason, researchers have sought approaches by which to estimate worst-case interrupt latencies (WCILs) for systems. Most existing approaches, however, are based on static analysis. In this paper, we present SIMLATTE, a testing-based approach for finding WCILs. SIMLATTE uses a genetic algorithm for test case generation that converges on a set of inputs and interrupt arrival points that are likely to expose WCILs. It also uses an opportunistic interrupt invocation approach to invoke interrupts at a variety of feasible locations. Our evaluation of SIMLATTE on several non-trivial embedded systems reveals that it is considerably more effective and efficient than random testing. We also determine that the combination of the genetic algorithm and opportunistic interrupt invocation allows SIMLATTE to perform better than it can when using either one in isolation.","Title: Hybrid Directed Test Suite Augmentation: An Interleaving Framework\nYear: 2014\n\nAbstract: Test suite augmentation techniques generate test cases to cover code missed by existing regression test suites. Various augmentation techniques have been proposed, utilizing several test case generation algorithms. Research has shown that different algorithms have different strengths, and that combining them into a single hybrid approach may be cost-effective. In this paper we present a framework for hybrid test suite augmentation that allows test case generation algorithms to be interleaved dynamically and that can easily incorporate new algorithms, interleaving strategies, and choices of other parameters that influence algorithm performance. We empirically study an implementation of this framework in which we use two test case generation algorithms and several algorithm interleavings. Our results show that specific instantiations of our framework can produce augmentation techniques that are more cost-effective than others, and illustrate tradeoffs between instantiations.","Title: PrefFinder: getting the right preference in configurable software systems\nYear: 2014\n\nAbstract: Highly configurable software, such as web browsers,databases or office applications, have a large number of preferences that the user can customize, but documentation of them may be scarce or distributed. A user, tester or service technician may have to search through hundreds or thousands of choices in multiple documents when trying to identify which preference will modify a particular system behavior. In this paper we present PrefFinder, a natural language framework that finds (and changes) user preferences. It is tied into an application's preference system and static documentation. We have instantiated PrefFinder as a plugin on two open source applications, and as a stand-alone GUI for an industrial application. PrefFinder finds thecorrect answer between 76-96% of the time on more than 175 queries. When compared to asking questions on a help forum or through the company's service center, we can potentially save days or even weeks of time.","Title: Beyond the rainbow: self-adaptive failure avoidance in configurable systems\nYear: 2014\n\nAbstract: Self-adaptive software systems monitor their state and then adapt when certain conditions are met, guided by a global utility function. In prior work we developed algorithms and conducted a post-hoc analysis demonstrating the possibility of adapting to software failures by judiciously changing configurations. In this paper we present the REFRACT framework that realizes this idea in practice by building on the self-adaptive Rainbow architecture. REFRACT extends Rainbow with new components and algorithms targeting failure avoidance. We use REFRACT in a case study running four independently executing Firefox clients with 36 passing test cases and 7 seeded faults. The study show that workarounds for all but one of the seeded faults are found and the one that is not found never fails -- it is guarded from failing by a related workaround. Moreover, REFRACT finds workarounds for eight configuration-related unseeded failures from tests that were expected to pass (and did under the default configuration). Finally, the data show that when a failure and its workaround are found, configuration guards prevent the failure from appearing again. In a simulation lasting 24 hours we see over 150 guard activations and no failures with workarounds remaining beyond 16 hours.","Title: Reducing Masking Effects in CombinatorialInteraction Testing: A Feedback DrivenAdaptive Approach\nYear: 2014\n\nAbstract: The configuration spaces of modern software systems are too large to test exhaustively. Combinatorial interaction testing (CIT) approaches, such as covering arrays, systematically sample the configuration space and test only the selected configurations. The basic justification for CIT approaches is that they can cost-effectively exercise all system behaviors caused by the settings of $t$ or fewer options. We conjecture, however, that in practice some of these behaviors are not actually tested because of unanticipated masking effects – test case failures that perturb system execution so as to prevent some behaviors from being exercised. While prior research has identified this problem, most solutions require knowing the masking effects a priori. In practice this is impractical, if not impossible. In this work, we reduce the harmful consequences of masking effects. First we define a novel interaction testing criterion, which aims to ensure that each test case has a fair chance to test all valid t-way combinations of option settings. We then introduce a feedback driven adaptive combinatorial testing process (FDA-CIT) to materialize this criterion in practice. At each iteration of FDA-CIT, we detect potential masking effects, heuristically isolate their likely causes (i.e., fault characterization), and then generate new samples that allow previously masked combinations to be tested in configurations that avoid the likely failure causes. The iterations end when the new interaction testing criterion has been satisfied. This paper compares two different fault characterization approaches – an integral part of the proposed approach, and empirically assesses their effectiveness and efficiency in removing masking effects on two widely used open source software systems. It also compares FDA-CIT against error locating arrays, a state of the art approach for detecting and locating failures. Furthermore, the scalability of the proposed approach is evaluated by comparing it with perfect test scenarios, in which all masking effects are known a priori. Our results suggest that masking effects do exist in practice, and that our approach provides a promising and efficient way to work around them, without requiring that masking effects be known a priori.","Title: Combinatorial test design in practice\nYear: 2010\n\nAbstract: Combinatorial testing is a specification based sampling technique that provides a systematic way to select combinations of program inputs or features for testing. It has been applied over the years to test input data, configurations, web forms, protocols, graphical user interfaces and for testing software product lines. This tutorial introduces the fundamentals of combinatorial testing, including both practical and theoretical foundations, to provide a comprehensive introduction that is relevant to both test practitioners and software engineering researchers. The tutorial will present an overview of Combinatorial Test Design (CTD) and describe some state of the art research advances and domains where CTD has been applied. It will present the theoretical underpinnings of CTD and explain a few algorithmic techniques used to generate CTD samples, as well as describe recent work on practical extensions to these algorithms that allow for a broader use of CTD. A session devoted to modeling test problems using CTD will follow, with attendees obtaining hands-on experience using several realistic problems.","Title: AutoInSpec: Using Missing Test Coverage to Improve Specifications in GUIs\nYear: 2012\n\nAbstract: Developers of a software's graphical user interface (GUI) often fail to document the interface specifications. Without these, models used for automated test generation and execution remain imperfect and incomplete. This leads to unexpected behavior that creates unrecoverable situations for test harnesses, and missed coverage. In this paper, we present AutoInSpec, a technique to infer an important class of specifications, temporal and state-based invariants between GUI events that have been incorrectly modeled. Unlike existing specification mining approaches that require full execution traces, or source code, and that mine all invariants, we simplify the problem. We guide AutoInSpec with coverage criteria and use a previously developed repair framework that builds coverage-adequate test suites, removing unexecutable sub-sequences from consideration. These failing sub-sequences are input to a logic-based inference engine, armed with known invariant templates, to obtain the missing specifications. We validate AutoInSpec on a set of well studied GUI applications.","Title: Making system user interactive tests repeatable: when and what should we control?\nYear: 2015\n\nAbstract: System testing and invariant detection is usually conducted from the user interface perspective when the goal is to evaluate the behavior of an application as a whole. A large number of tools and techniques have been developed to generate and automate this process, many of which have been evaluated in the literature or internally within companies. Typical metrics for determining effectiveness of these techniques include code coverage and fault detection, however, with the assumption that there is determinism in the resulting outputs. In this paper we examine the extent to which a common set of factors such as the system platform, Java version, application starting state and tool harness configurations impact these metrics. We examine three layers of testing outputs: the code layer, the behavioral (or invariant) layer and the external (or user interaction) layer. In a study using five open source applications across three operating system platforms, manipulating several factors, we observe as many as 184 lines of code coverage difference between runs using the same test cases, and up to 96 percent false positives with respect to fault detection. We also see some a small variation among the invariants inferred. Despite our best efforts, we can reduce, but not completely eliminate all possible variation in the output. We use our findings to provide a set of best practices that should lead to better consistency and smaller differences in test outcomes, allowing more repeatable and reliable testing and experimentation.","Title: Learning combinatorial interaction test generation strategies using hyperheuristic search\nYear: 2015\n\nAbstract: The surge of search based software engineering research has been hampered by the need to develop customized search algorithms for different classes of the same problem. For instance, two decades of bespoke Combinatorial Interaction Testing (CIT) algorithm development, our exemplar problem, has left software engineers with a bewildering choice of CIT techniques, each specialized for a particular task. This paper proposes the use of a single hyperheuristic algorithm that learns search strategies across a broad range of problem instances, providing a single generalist approach. We have developed a Hyperheuristic algorithm for CIT, and report experiments that show that our algorithm competes with known best solutions across constrained and unconstrained problems: For all 26 real-world subjects, it equals or outperforms the best result previously reported in the literature. We also present evidence that our algorithm's strong generic performance results from its unsupervised learning. Hyperheuristic search is thus a promising way to relocate CIT design intelligence from human to machine.","Title: Guided Test Generation for Finding Worst-Case Stack Usage in Embedded Systems\nYear: 2015\n\nAbstract: Embedded systems are challenging to program correctly, because they use an interrupt programming paradigm and run in resource constrained environments. This leads to a class of faults for which we need customized verification techniques. One such class of faults, stack overflows, are caused when the combination of active methods and interrupt invocations on the stack grows too large, and these can lead to data loss and other significant device failures. Developers need to estimate the worst-case stack usage (WCSU) during system design, but determining the actual maximum value is known to be a hard problem. The state of the art for calculating WCSU uses static analysis, however this has a tendency to over approximate the potential stack which can lead to wasted resources. Dynamic techniques such as random testing often under approximate the WCSU. In this paper, we present SIMSTACK, a framework that utilizes a combination of static analysis and a genetic algorithm to search for WCSUs. We perform an empirical study to evaluate the effectiveness of SIMSTACK and show that SIMSTACK is competitive with the WCSU values obtained by static analysis, and improves significantly over a random algorithm. When we use only the genetic algorithm, SIMSTACK performs almost as well as the guided technique, but takes significantly longer to converge on the maximum WCSUs.","Title: GitSonifier: using sound to portray developer conflict history\nYear: 2015\n\nAbstract: There are many tools that help software engineers analyze data about their software, projects, and teams. These tools primarily use visualizations to portray data in a concise and understandable way. However, software engineering tasks are often multi-dimensional and temporal, making some visualizations difficult to understand. An alternative for representing data, which can easily incorporate higher dimensionality and temporal information, is the use of sound. In this paper we propose the use of sonification to help portray collaborative development history. Our approach, GitSonifier, combines sound primitives to represent developers, days, and conflicts over the history of a program's development. In a formative user study on an open source project's data, we find that users can easily extract meaningful information from sound clips and differentiate users, passage of time, and development conflicts, suggesting that sonification has the potential to provide benefit in this context.","Title: Users beware: preference inconsistencies ahead\nYear: 2015\n\nAbstract: The structure of preferences for modern highly-configurable software systems has become extremely complex, usually consisting of multiple layers of access that go from the user interface down to the lowest levels of the source code. This complexity can lead to inconsistencies between layers, especially during software evolution. For example, there may be preferences that users can change through the GUI, but that have no effect on the actual behavior of the system because the related source code is not present or has been removed going from one version to the next. These inconsistencies may result in unexpected program behaviors, which range in severity from mild annoyances to more critical security or performance problems. To address this problem, we present SCIC (Software Configuration Inconsistency Checker), a static analysis technique that can automatically detect these kinds of inconsistencies. Unlike other configuration analysis tools, SCIC can handle software that (1) is written in multiple programming languages and (2) has a complex preference structure. In an empirical evaluation that we performed on 10 years worth of versions of both the widely used Mozilla Core and Firefox, SCIC was able to find 40 real inconsistencies (some determined as severe), whose lifetime spanned multiple versions, and whose detection required the analysis of code written in multiple languages.","Title: Piecing together app behavior from multiple artifacts: A case study\nYear: 2015\n\nAbstract: Recent research in mobile software analysis has begun to combine information extracted from an app's source code and marketplace webpage to identify correlated variables and validate an app's quality properties such as its intended behavior, trust or suspiciousness. Such work typically involves analysis of one or two artifacts such as the GUI text, user ratings, app description keywords, permission requests, and sensitive API calls. However, these studies make assumptions about how the various artifacts are populated and used by developers, which may lead to a gap in the resulting analysis. In this paper, we take a step back and perform an in-depth study of 14 popular apps from the Google Play Store. We have studied a set of 16 different artifacts for each app, and conclude that the output of these must be pieced together to form a complete understanding of the app's true behavior. We show that (1) developers are inconsistent in where and how they provide descriptions; (2) each artifact alone has incomplete information; (3) different artifacts may contain contradictory pieces of information; (4) there is a need for new analyses, such as those that use image processing; and (5) without including analyses of advertisement libraries, the complete behavior of an app is not defined. In addition, we show that the number of downloads and ratings of an app does not appear to be a strong predictor of overall app quality, as these are propagated through versions and are not necessarily indicative of the current app version's behavior.","Title: VDTest: an automated framework to support testing for virtual devices.\nYear: 2016\n\nAbstract: The use of virtual devices in place of physical hardware is increasing in activities such as design, testing and debugging. Yet virtual devices are simply software applications, and like all software they are prone to faults. A full system simulator (FSS), is a class of virtual machine that includes a large set of virtual devices -- enough to run the full target software stack. Defects in an FSS virtual device may have cascading effects as the incorrect behavior can be propagated forward to many different platforms as well as to guest programs. In this work we present VDTest, a novel framework for testing virtual devices within an FSS. VDTest begins by generating a test specification obtained through static analysis. It then employs a two-phase testing approach to test virtual components both individually and in combination. It leverages a differential oracle strategy, taking advantage of the existence of a physical or golden device to eliminate the need for manually generating test oracles. In an empirical study using both open source and commercial FSSs, we found 64 faults, 83% more than random testing.","Title: Why Do We Always Blame The Storage Stack?\nYear: 2016\n\nAbstract: Much research effort has been devoted to improving the performance of the I/O stack in mobile devices, but limited time has been spent evaluating mobile application performance from the user's perspective. In this paper, we try to understand how applications running on the newest devices behave with respect to this metric. We develop a methodology for quantifying user-perceived latency and use it to evaluate four common application benchmarks with I/O stack optimization on two of the latest smartphones. Contrary to our expectation, we find that (i) these applications respond reasonably fast and (ii) the user-perceived latency does not drastically (at most 11:8%) benefit from I/O stack optimizations.","Title: The Assurance Timeline: Building Assurance Cases For Synthetic Biology\nYear: 2016\n\nAbstract: Recent research advances in modifying and controlling DNA have created a booming field of biological engineering called synthetic biology. In synthetic biology engineers manipulate and modify living organisms to change (and produce entirely novel) functionality, which has led to new fuel sources or the ability to mitigate pollution. Synthetic biology research is also expected to lead to methods of intelligent drug delivery. In synthetic biology, designs are first built using biological programming languages and then implemented in a laboratory. These synthetic organisms can be considered living programs that will sense, respond and interact with humans while they persist in the natural environment. We argue that we should view these as safety critical devices which can be both regulated and certified. Since the synthetically engineered organisms follow a regular cycle of reproduction and replication that involves mutations, they will eventually adapt and evolve new behavior over time. In this paper we propose the use of an assurance case for synthetically engineered organisms, and present an orthogonal dimension, an assurance timeline, that can be used to reason about the dynamic, evolving aspects of these systems. We present a case study based on a real application to illustrate our ideas.","Title: Scaling up the Fitness Function for Reverse Engineering Feature Models.\nYear: 2016\n\nAbstract: Recent research on software product line engineering has led to several search-based frameworks for reverse engineering feature models. The most common fitness function utilized maximizes the number of matched products with an oracle set of products. However, to calculate this fitness each product defined by the chromosome has to be enumerated using a SAT solver and this limits scalability to product lines with fewer than 30 features. In this paper we propose SAT(ff), a fitness function that simulates validity by computing the difference between constraints in the chromosome and oracle. In an empirical study on 101 feature models comparing SAT(ff) with two existing fitness functions that use the enumeration technique we find that SAT(ff) shows a significant improvement over one, and no significant difference with the other one. We also find that SAT(ff) requires only 7% of the runtime on average scaling to feature models with as many as 97 features.","Title: Budgeted testing through an algorithmic lens.\nYear: 2016\n\nAbstract: Automated testing has been a focus of research for a long time. As such, we tend to think about this in a coverage centric manner. Testing budgets have also driven research such as prioritization and test selection, but as a secondary concern. As our systems get larger, are more dynamic, and impact more people with each change, we argue that we should switch from a coverage centric view to a budgeted testing centric view. Researchers in other fields have designed approximation algorithms for such budgeted scenarios and these are often simple to implement and run. In this paper we present an exemplar study on combinatorial interaction testing (CIT) to show that a budgeted greedy algorithm, when adapted to our problem for various budgets, does almost as well coverage-wise as a state of the art greedy CIT algorithm, better in some cases than a state of the art simulated annealing, and always improves over random. This suggests that we might benefit from switching our focus in large systems, from coverage to budgets.","Title: The Evolutionary Landscape of SBST: A 10 Year Perspective.\nYear: 2017\n\nAbstract: A key indicator of the health and quality of any evolutionary algorithm is the landscape of its search. By analyzing the landscape one can determine the peaks (local maxima) where significant solutions exist. In this paper we examine the landscape for the history of the International Workshop on Search-Based Software Testing (SBST) within the context of the broader field of search-based software testing. We study the evolution of the field, highlighting key advances during three phases of its ten year history. In 2008 the focus of SBST was inner looking, with advances in existing search techniques, improvements to individual generation techniques, and methods to transform the problem space for search effectiveness. However, diverse seeds of new ideas (such as automated program repair) were already being injected into the population. A few SBST tools existed, but the engineer still required skill and expertise to effectively apply search based approaches. During the middle years, open source tools were created and released, whole test suite generation appeared, and searches hybridized. Tool competitions began and industry started to play a stronger role. As we move to the most recent workshop years and look towards the future, more sophisticated techniques such as those that incorporate hyper-heuristics via learning, and/or balance multiple objectives at once are now common. SBST has become a mainstream topic in the testing community, tools are being commercialized and these tools often hide their inner workings, leading to a future that is optimized towards SBST for all.","Title: BioSIMP: Using Software Testing Techniques for Sampling and Inference in Biological Organisms.\nYear: 2017\n\nAbstract: Years of research in software engineering have given us novel ways to reason about, test, and predict the behavior of complex software systems that contain hundreds of thousands of lines of code. Many of these techniques have been inspired by nature such as genetic algorithms, swarm intelligence, and ant colony optimization. In this paper we reverse the direction and present BioSIMP, a process that models and predicts the behavior of biological organisms to aid in the emerging field of systems biology. It utilizes techniques from testing and modeling of highly-configurable software systems. Using both experimental and simulation data we show that BioSIMP can find important environmental factors in two microbial organisms. However, we learn that in order to fully reason about the complexity of biological systems, we will need to extend existing or create new software engineering techniques.","Title: EventFlowSlicer: goal based test generation for graphical user interfaces.\nYear: 2016\n\nAbstract: Automated test generation techniques for graphical user interfaces include model-based approaches that generate tests from a graph or state machine model, capture-replay methods that require the user to demonstrate each test case, and pattern-based approaches that provide templates for abstract test cases. There has been little work, however, in automated goal-based testing, where the goal is a realistic user task, a function, or an abstract behavior. Recent work in human performance regression testing has shown that there is a need for generating multiple test cases that execute the same user task in different ways, however that work does not have an efficient way to generate tests and only a single type of goal has been considered. In this paper we expand the notion of goal based interface testing to generate tests for a variety of goals. We develop a direct test generation technique, EventFlowSlicer, that is more efficient than that used in human performance regression testing, reducing run times by 92.5","Title: Understanding Git history: a multi-sense view\nYear: 2016\n\nAbstract: Version control systems archive data about the development history of a project, which can be used to analyze and understand different facets of a software project. The project history can be used to evaluate the development process of a team, as an aid in bug fixing, or to help new members get on track with development. However, state of the art techniques for analyzing version control data provide only partial views into this information, and lack an easy way to present all the dimensions of the data. In this paper we present GitVS, a hybrid view that incorporates visualization and sonification to represent the multiple dimensions of version control data - development time line, conflicts, etc. In a formative user study comparing the GitHub Network Graph, GitVS, and a version of GitVS without sound, we show GitVS improves over the GitHub Network Graph and that while sound makes it easier to correctly understand version history for some tasks, it is more difficult for others.","Title: End-To-End Molecular Communication Channels In Cell Metabolism: An Information Theoretic Study\nYear: 2017\n\nAbstract: The opportunity to control and fine-tune the behavior of biological cells is a fascinating possibility for many diverse disciplines, ranging from medicine and ecology, to chemical industry and space exploration. While synthetic biology is providing novel tools to reprogram cell behavior from their genetic code, many challenges need to be solved before it can become a true engineering discipline, such as reliability, safety assurance, reproducibility and stability. This paper aims to understand the limits in the controllability of the behavior of a natural (non-engineered) biological cell. In particular, the focus is on cell metabolism, and its natural regulation mechanisms, and their ability to react and change according to the chemical characteristics of the external environment. To understand the aforementioned limits of this ability, molecular communication is used to abstract biological cells into a series of channels that propagate information on the chemical composition of the extracellular environment to the cell's behavior in terms of uptake and consumption of chemical compounds, and growth rate. This provides an information-theoretic framework to analyze the upper bound limit to the capacity of these channels to propagate information, which is based on a well-known and computationally efficient metabolic simulation technique. A numerical study is performed on two human gut microbes, where the upper bound is estimated for different environmental compounds, showing there is a potential for future practical applications.","Title: EventFlowSlicer: a tool for generating realistic goal-driven GUI tests.\nYear: 2017\n\nAbstract: Most automated testing techniques for graphical user interfaces (GUIs) produce test cases that are only concerned with covering the elements (widgets, menus, etc.) on the interface, or the underlying program code, with little consideration of test case semantics. This is effective for functional testing where the aim is to find as many faults as possible. However, when one wants to mimic a real user for evaluating usability, or when it is necessary to extensively test important end-user tasks of a system, or to generate examples of how to use an interface, this generation approach fails. Capture and replay techniques can be used, however there are often multiple ways to achieve a particular goal, and capturing all of these is usually too time consuming and unrealistic. Prior work on human performance regression testing introduced a constraint based method to filter test cases created by a functional test case generator, however that work did not capture the specifications, or directly generate only the required tests and considered only a single type of test goal. In this paper we present EventFlowSlicer, a tool that allows the GUI tester to specify and generate all realistic test cases relevant to achieve a stated goal. The user first captures relevant events on the interface, then adds constraints to provide restrictions on the task. An event flow graph is extracted containing only the widgets of interest for that goal. Next all test cases are generated for edges in the graph which respect the constraints. The test cases can then be replayed using a modified version of GUITAR. A video demonstration of EventFlowSlicer can be found at https://youtu.be/hw7WYz8WYVU.","Title: EvoIsolator: Evolving Program Slices for Hardware Isolation Based Security.\nYear: 2018\n\nAbstract: To provide strong security support for today's applications, microprocessor manufacturers have introduced hardware isolation, an on-chip mechanism that provides secure accesses to sensitive data. Currently, hardware isolation is still difficult to use by software developers because the process to identify access points to sensitive data is error-prone and can lead to under and over protection of sensitive data. Under protection can lead to security vulnerabilities. Over protection can lead to an increased attack surface and excessive communication overhead. In this paper we describe EvoIsolator, a search-based framework to (i) automatically generate executable minimal slices that include all access points to a set of specified sensitive data; and (ii) automatically optimize (for small code block size and low communication overhead) the code modules for hardware isolation. We demonstrate, through a small feasibility study, the potential impact of our proposed code optimizer.","Title: Navigating the maze: the impact of configurability in bioinformatics software.\nYear: 2018\n\nAbstract: The bioinformatics software domain contains thousands of applications for automating tasks such as the pairwise alignment of DNA sequences, building and reasoning about metabolic models or simulating growth of an organism. Its end users range from sophisticated developers to those with little computational experience. In response to their needs, developers provide many options to customize the way their algorithms are tuned. Yet there is little or no automated help for the user in determining the consequences or impact of the options they choose. In this paper we describe our experience working with configurable bioinformatics tools. We find limited documentation and help for combining and selecting options along with variation in both functionality and performance. We also find previously undetected faults. We summarize our findings with a set of lessons learned, and present a roadmap for creating automated techniques to interact with bioinformatics software. We believe these will generalize to other types of scientific software.","Title: Configurations in Android testing: they matter.\nYear: 2018\n\nAbstract: Android has rocketed to the top of the mobile market thanks in large part to its open source model. Vendors use Android for their devices for free, and companies make customizations to suit their needs. This has resulted in a myriad of configurations that are extant in the user space today. In this paper, we show that differences in configurations, if ignored, can lead to differences in test outputs and code coverage. Consequently, researchers who develop new testing techniques and evaluate them on only one or two configurations are missing a necessary dimension in their experiments and developers who ignore this may release buggy software. In a large study on 18 apps across 88 configurations, we show that only one of the 18 apps studied showed no variation at all. The rest showed variation in either, or both, code coverage and test results. 15% of the 2,000 plus test cases across all of the apps vary, and some of the variation is subtle, i.e. not just a test crash. Our results suggest that configurations in Android testing do matter and that developers need to test using configuration-aware techniques.","Title: An evolutionary approach for analyzing Alloy specifications.\nYear: 2018\n\nAbstract: Formal methods use mathematical notations and logical reasoning to precisely define a program's specifications, from which we can instantiate valid instances of a system. With these techniques we can perform a multitude of tasks to check system dependability. Despite the existence of many automated tools including ones considered lightweight, they still lack a strong adoption in practice. At the crux of this problem, is scalability and applicability to large real world applications. In this paper we show how to relax the completeness guarantee without much loss, since soundness is maintained. We have extended a popular lightweight analysis, Alloy, with a genetic algorithm. Our new tool, EvoAlloy, works at the level of finite relations generated by Kodkod and evolves the chromosomes based on the failed constraints. In a feasibility study we demonstrate that we can find solutions to a set of specifications beyond the scope where traditional Alloy fails. While small specifications take longer with EvoAlloy, the scalability means we can handle larger specifications. Our future vision is that when specifications are small we can maintain both soundness and completeness, but when this fails, EvoAlloy can switch to its genetic algorithm.","Title: The Assurance Recipe: Facilitating Assurance Patterns\nYear: 2018\n\nAbstract: As assurance cases have grown in popularity for safety-critical systems, so too has their complexity and thus the need for methods to systematically build them. Assurance cases can grow too large and too abstract for anyone but the original builders to understand, making reuse difficult. Reuse is important because different systems might have identical or similar components, and a good solution for one system should be applicable to similar systems. Prior research has shown engineers can alleviate some of the complexity issues through modularity and identifying common patterns which are more easily understood for reuse across different systems. However, we believe these patterns are too complicated for users who lack expertise in software engineering or assurance cases. This paper suggests the concept of lower-level patterns which we call recipes. We use the safety-critical field of synthetic biology, as an example discipline to demonstrate how a recipe can be built and applied.","Title: Modeling and testing a family of surgical robots: an experience report.\nYear: 2018\n\nAbstract: Safety-critical applications often use dependability cases to validate that specified properties are invariant, or to demonstrate a counter example showing how that property might be violated. However, most dependability cases are written with a single product in mind. At the same time, software product lines (families of related software products) have been studied with the goal of modeling variability and commonality, and building family based techniques for both analysis and testing. However, there has been little work on building an end to end dependability case for a software product line (where a property is modeled, a counter example is found and then validated as a true positive via testing), and none that we know of in an emerging safety-critical domain, that of robotic surgery. In this paper, we study a family of surgical robots, that combine hardware and software, and are highly configurable, representing over 1300 unique robots. At the same time, they are considered safety-critical and should have associated dependability cases. We perform a case study to understand how we can bring together lightweight formal analysis, feature modeling, and testing to provide an end to end pipeline to find potential violations of important safety properties. In the process, we learned that there are some interesting and open challenges for the research community, which if solved will lead towards more dependable safety-critical cyber-physical systems.","Title: The maturation of search-based software testing: successes and challenges\nYear: 2019\n\nAbstract: In this paper we revisit the field of search-based software testing (SBST) in the context of its technological maturity. We highlight some successes with respect to tools, hybrid approaches, extensions and industry adoption. We then discuss some open challenges that remain for SBST including the need for new approaches to system testing, automated oracle generation, incorporating humans into the search process, and leveraging learning through hyper-heuristic search.","Title: Partial Specifications for Program Repair\nYear: 2021\n\nAbstract: In this paper we argue for using many partial test suites instead of one full test suite during program repair. This may provide a pool of simpler, yet correct patches, addressing both the overfitting and poor repair quality problem. To support this idea, we present some insight obtained running APR partial test suites on the well studied triangle program.","Title: CRNRepair: Automated Program Repair of Chemical Reaction Networks\nYear: 2021\n\nAbstract: Chemical reaction networks (CRNs) are abstractions of distributed networks that form the foundations of many natural phenomena such as biological processes. These can be encoded and/or compiled into DNA and have been shown to be Turing complete. Before CRNs are implemented in a physical environment, they are often simulated in programming environments. Researchers have recently designed a software...","Title: HyperGI: automated detection and repair of information flow leakage\nYear: 2021\n\nAbstract: BSTRACTMaintaining confidential information control in software is a persistent security problem where failure means secrets can be revealed via program behaviors. Information flow control techniques traditionally have been based on static or symbolic analyses --- limited in scalability and specialized to particular languages. When programs do leak secrets there are no approaches to automatically repair them unless the leak causes a functional test to fail. We present our vision for HyperGI, a genetic improvement framework that detects, localizes and repairs information leakage. Key elements of HyperGI include (1) the use of two orthogonal test suites, (2) a dynamic leak detection approach which estimates and localizes potential leaks, and (3) a repair component that produces a candidate patch using genetic improvement. We demonstrate the successful use of HyperGI on several programs with no failing functional test cases. We manually examine the resulting patches and identify trade-offs and future directions for fully realizing our vision.","Title: Inference and Test Generation Using Program Invariants in Chemical Reaction Networks\nYear: 2022\n\nAbstract: Chemical reaction networks (CRNs) are an emerging distributed computational paradigm where programs are encoded as a set of abstract chemical reactions. CRNs can be compiled into DNA strands which perform the computations in vitro, creating a foundation for intelligent nanodevices. Recent research proposed a software testing framework for stochastic CRN programs in simulation, however, it relies on existing program specifications. In practice, specifications are often lacking and when they do exist, transforming them into test cases is time-intensive and can be error prone. In this work, we propose an inference technique called ChemFlow which extracts 3 types of invariants from an existing CRN model. The extracted invariants can then be used for test generation or model validation against program implementations. We applied ChemFlow to 13 CRN programs ranging from toy examples to real biological models with hundreds of reactions. We find that the invariants provide strong fault detection and often exhibit less flakiness than specification derived tests. In the biological models we showed invariants to developers and they confirmed that some of these point to parts of the model that are biologically incorrect or incomplete suggesting we may be able to use ChemFlow to improve model quality."],"string":"[\n \"Title: Testing across configurations: implications for combinatorial testing\\nYear: 2006\\n\\nAbstract: User configurable software systems allow users to customize functionality at run time. In essence, each such system consists of a family of potentially thousands or millions of program instantiations. Testing methods cannot test all of these configurations, therefore some sampling mechanism must be applied. A common approach to providing such a mechanism has been to use combinatorial interaction testing. To date, however, little work has been done to quantify the effects of different configurations on a test suites' operation and effectiveness. In this paper we present a case study that investigates the effects of changing configurations on two types of test suites. Our results show that test coverage and fault detection effectiveness do not vary much across configurations for entire test suites; however, for individual test cases and certain types of faults, configurations matter.\",\n \"Title: A self-adjusting code cache manager to balance start-up time and memory usage\\nYear: 2010\\n\\nAbstract: In virtual machines for embedded devices that use just-in-time compilation, the management of the code cache can significantly impact performance in terms of both memory usage and start-up time. Although improving memory usage has been a common focus for system designers, start-up time is often overlooked. In systems with constrained resources, however, these two performance metrics are often at odds and must be considered together. In this paper, we present an adaptive self-adjusting code cache manager to improve performance with respect to both start-up time and memory usage. It balances these concerns by detecting changes in method compilation rates, resizing the cache after each pitching event. We conduct experiments to validate our proposed system and quantify the impacts that different code cache management techniques have on memory usage and start-up time through two oracle systems. Our results show that the proposed algorithm yields nearly the same start-up times as a hand-tuned oracle and shorter execution times than those of the SSCLI in eight out of ten applications. It also has lower memory usage over time in all but one application.\",\n \"Title: A framework of greedy methods for constructing interaction test suites\\nYear: 2005\\n\\nAbstract: Greedy algorithms for the construction of software interaction test suites are studied. A framework is developed to evaluate a large class of greedy methods that build suites one test at a time. Within this framework are many instantiations of greedy methods generalizing those in the literature. Greedy algorithms are popular when the time for test suite construction is of paramount concern. We focus on the size of the test suite produced by each instantiation. Experiments are analyzed using statistical techniques to determine the importance of the implementation decisions within the framework. This framework provides a platform for optimizing the accuracy and speed of \\\"one-test-at-a-time\\\" greedy methods.\",\n \"Title: Human performance regression testing\\nYear: 2013\\n\\nAbstract: As software systems evolve, new interface features such as keyboard shortcuts and toolbars are introduced. While it is common to regression test the new features for functional correctness, there has been less focus on systematic regression testing for usability, due to the effort and time involved in human studies. Cognitive modeling tools such as CogTool provide some help by computing predictions of user performance, but they still require manual effort to describe the user interface and tasks, limiting regression testing efforts. In recent work, we developed CogTool Helper to reduce the effort required to generate human performance models of existing systems. We build on this work by providing task specific test case generation and present our vision for human performance regression testing (HPRT) that generates large numbers of test cases and evaluates a range of human performance predictions for the same task. We examine the feasibility of HPRT on four tasks in LibreOffice, find several regressions, and then discuss how a project team could use this information. We also illustrate that we can increase efficiency with sampling by leveraging an inference algorithm. Samples that take approximately 50% of the runtime lose at most 10% of the performance predictions.\",\n \"Title: Constructing Interaction Test Suites for Highly-Configurable Systems in the Presence of Constraints: A Greedy Approach\\nYear: 2008\\n\\nAbstract: Researchers have explored the application of combinatorial interaction testing (CIT) methods to construct samples to drive systematic testing of software system configurations. Applying CIT to highly-configurable software systems is complicated by the fact that, in many such systems, there are constraints between specific configuration parameters that render certain combinations invalid. Many CIT algorithms lack a mechanism to avoid these. In recent work, automated constraint solving methods have been combined with search-based CIT construction methods to address the constraint problem with promising results. However, these techniques can incur a non-trivial overhead. In this paper, we build upon our previous work to develop a family of greedy CIT sample generation algorithms that exploit calculations made by modern boolean satisfiability (SAT) solvers to prune the search space of the CIT problem. We perform a comparative evaluation of the cost-effectiveness of these algorithms on four real-world highly-configurable software systems and on a population of synthetic examples that share the characteristics of those systems. In combination our techniques reduce the cost of CIT in the presence of constraints to 30\\\\% of the cost of widely-used unconstrained CIT methods without sacrificing the quality of the solutions.\",\n \"Title: Covering arrays for efficient fault characterization in complex configuration spaces\\nYear: 2006\\n\\nAbstract: Testing systems with large configurations spaces that change often is a challenging problem. The cost and complexity of QA explodes because often there isn't just one system, but a multitude of related systems. Bugs may appear in certain configurations, but not in others.The Skoll system and process has been developed to test these types of systems through distributed, continuous quality assurance, leveraging user resources around-the-world, around-the-clock. It has been shown to be effective in automatically characterizing configurations in which failures manifest. The derived information helps developers quickly narrow down the cause of failures which then improves turn around time for fixes. However, this method does not scale well. It requires one to exhaustively test each configuration in the configuration space.In this paper we examine an alternative approach. The idea is to systematically sample the configuration space, test only the selected configurations, and conduct fault characterization on the resulting data. The sampling approach we use is based on calculating a mathematical object called a covering array. We empirically assess the effect of using covering array derived test schedules on the resulting fault characterizations and provide guidelines to practitioners for their use.\",\n \"Title: Efficiency and early fault detection with lower and higher strength combinatorial interaction testing\\nYear: 2013\\n\\nAbstract: Combinatorial Interaction Testing (CIT) is important because it tests the interactions between the many features and parameters that make up the configuration space of software systems. However, in order to be practically applicable, it must be able to cater for soft and hard real-world constraints and should, ideally, report a test priority order that maximises earliest fault detection. We show that we can achieve the highest strength CIT in 5.65 minutes on average. This was previously thought to be too computationally expensive to be feasible. Furthermore, we show that higher strength suites find more faults, while prioritisations using lower strengths are no worse at achieving early fault revelation.\",\n \"Title: Interaction testing of highly-configurable systems in the presence of constraints\\nYear: 2007\\n\\nAbstract: Combinatorial interaction testing (CIT) is a method to sample configurations of a software system systematically for testing. Many algorithms have been developed that create CIT samples, however few have considered the practical concerns that arise when adding constraints between combinations of options. In this paper, we survey constraint handling techniques in existing algorithms and discuss the challenges that they present. We examine two highly-configurable software systems to quantify the nature of constraints in real systems. We then present a general constraint representation and solving technique that can be integrated with existing CIT algorithms and compare two constraint-enhanced algorithm implementations with existing CIT tools to demonstrate feasibility.\",\n \"Title: Cluttered Orderings for the Complete Graph\\nYear: 2001\\n\\nAbstract: In a systematic erasure code for the correction of two simultaneous erasures, each information symbol must have two associated parity symbols. When implemented in a redundant array of independent disks (RAID), performance requirements on the update penalty necessitate that each information symbol be associated with no more parity symbols than the two required. This leads to a simple graph model of the erasure codes, with parity symbols as vertices and information symbols as edges. Ordering the edges so that no more than f check disks (vertices) appear among any set of d consecutive edges is found to optimize access performance of the disk array when d is maximized. These cluttered orderings are examined for the complete graph Kn. The maximum number d of edges is determined precisely when f ≤ 5 and when f = n - 1, and bounds are derived in the remaining cases.\",\n \"Title: Configuration-aware regression testing: an empirical study of sampling and prioritization\\nYear: 2008\\n\\nAbstract: Configurable software lets users customize applications in many ways, and is becoming increasingly prevalent. Researchers have created techniques for testing configurable software, but to date, only a little research has addressed the problems of regression testing configurable systems as they evolve. Whereas problems such as selective retesting and test prioritization at the test case level have been extensively researched, these problems have rarely been considered at the configuration level. In this paper we address the problem of providing configuration-aware regression testing for evolving software systems. We use combinatorial interaction testing techniques to model and generate configuration samples for use in regression testing. We conduct an empirical study on a non-trivial evolving software system to measure the impact of configurations on testing effectiveness, and to compare the effectiveness of different configuration prioritization techniques on early fault detection during regression testing. Our results show that configurations can have a large impact on fault detection and that prioritization of configurations can be effective.\",\n \"Title: Analysis, Test and Verification in The Presence of Variability (Dagstuhl Seminar 13091).\\nYear: 2013\\n\\nAbstract: This report documents the program and the outcomes of Dagstuhl Seminar 13091 \\\"Analysis, Test and Verification in The Presence of Variability\\\". The seminar had the goal of consolidating and stimulating research on analysis of software models with variability, enabling the design of variability-aware tool chains. We brought together 46 key researchers from three continents, working on quality assurance challenges that arise from introducing variability, and some who do not work with variability, but that are experts in their respective areas in the broader domain of software analysis or testing research. As a result of interactions triggered by sessions of different formats, the participants were able to classify their approaches with respect to a number of dimensions that helped to identify similarities and differences that have already been useful to improve understanding and foster new collaborations among the participants.\",\n \"Title: Continuous test suite augmentation in software product lines\\nYear: 2013\\n\\nAbstract: Software Product Line (SPL) engineering offers several advantages in the development of families of software products. There is still a need, however, to generate test cases for individual products in product lines more efficiently. In this paper we propose an approach, CONTESA, for generating test cases for SPLs using test suite augmentation. Instead of generating test cases for products independently, our approach generates new test cases for products in an order that allows it to build on test cases created for products tested earlier. In this work, we use a genetic algorithm to generate test cases, targeting branches not yet covered in each product, although other algorithms and coverage criteria could be utilized. We have evaluated CONTESA on two non-trivial SPLs, and have shown that CONTESA is more efficient and effective than an approach that generates test cases for products independently. A further evaluation shows that CONTESA is more effective at achieving coverage, and reveals as many faults as an existing feature-based testing approach.\",\n \"Title: Coverage and adequacy in software product line testing\\nYear: 2006\\n\\nAbstract: Software product line modeling has received a great deal of attention for its potential in fostering reuse of software artifacts across development phases. Research on the testing phase, has focused on identifying the potential for reuse of test cases across product line instances. While this offers potential reductions in test development effort for a given product line instance, it does not focus on and leverage the fundamental abstraction that is inherent in software product lines - variability.In this paper, we illustrate how rich software product line modeling notations can be mapped onto an underlying relational model that captures variability in the feasible product line instances. This relational model serves as the semantic basis for defining a family of coverage criteria for testing of a product line. These criteria make it possible to accumulate test coverage information for the product line itself over the course of multiple product line instance development efforts. Cumulative coverage, in turn, enables targeted testing efforts for new product line instances. We describe how combinatorial interaction testing methods can be applied to define test configurations that achieve a desired level of coverage and identify challenges to scaling such methods to large, complex software product lines.\",\n \"Title: Combinatorial Interaction Regression Testing: A Study of Test Case Generation and Prioritization\\nYear: 2007\\n\\nAbstract: Regression testing is an expensive part of the software maintenance process. Effective regression testing techniques select and order (or prioritize) test cases between successive releases of a program. However, selection and prioritization are dependent on the quality of the initial test suite. An effective and cost efficient test generation technique is combinatorial interaction testing, CIT, which systematically samples all t-way combinations of input parameters. Research on CIT, to date, has focused on single version software systems. There has been little work that empirically assesses the use of CIT test generation as the basis for selection or prioritization. In this paper we examine the effectiveness of CIT across multiple versions of two software subjects. Our results show that CIT performs well in finding seeded faults when compared with an exhaustive test set. We examine several CIT prioritization techniques and compare them with a re-generation/prioritization technique. We find that prioritized and re-generated/prioritized CIT test suites may find faults earlier than unordered CIT test suites, although the re-generated/prioritized test suites sometimes exhibit decreased fault detection.\",\n \"Title: Feedback driven adaptive combinatorial testing\\nYear: 2011\\n\\nAbstract: The configuration spaces of modern software systems are too large to test exhaustively. Combinatorial interaction testing (CIT) approaches, such as covering arrays, systematically sample the configuration space and test only the selected configurations. The basic justification for CIT approaches is that they can cost-effectively exercise all system behaviors caused by the settings of t or fewer options. We conjecture, however, that in practice many such behaviors are not actually tested because of masking effects -- failures that perturb execution so as to prevent some behaviors from being exercised. In this work we present a feedback-driven, adaptive, combinatorial testing approach aimed at detecting and working around masking effects. At each iteration we detect potential masking effects, heuristically isolate their likely causes, and then generate new covering arrays that allow previously masked combinations to be tested in the subsequent iteration. We empirically assess the effectiveness of the proposed approach on two large widely used open source software systems. Our results suggest that masking effects do exist and that our approach provides a promising and efficient way to work around them.\",\n \"Title: Ladder orderings of pairs and RAID performance\\nYear: 2004\\n\\nAbstract: In a systematic erasure code for the correction of two simultaneous erasures, each information symbol must have two associated parity symbols. When implemented in a redundant array of independent disks (RAID), performance requirements on the update penalty necessitate that each information symbol be associated with no more parity symbols than the two required. This leads to a simple graph model of the erasure codes, with parity symbols as vertices and information symbols as edges. Based on simulations of RAID performance, an ordering of the edges in which every sequence of three consecutive edges in the order induces as few vertices as possible is found to optimize access performance of the disk array. The ladder orderings to optimize performance are shown to exist for the complete graph Kn, except possibly when n ∈ {15, 18, 22}.\",\n \"Title: Constructing strength three covering arrays with augmented annealing\\nYear: 2008\\n\\nAbstract: A covering arrayCA(N;t,k,v) is an Nxk array such that every Nxt sub-array contains all t-tuples from v symbols at least once, where t is the strength of the array. One application of these objects is to generate software test suites to cover all t-sets of component interactions. Methods for construction of covering arrays for software testing have focused on two main areas. The first is finding new algebraic and combinatorial constructions that produce smaller covering arrays. The second is refining computational search algorithms to find smaller covering arrays more quickly. In this paper, we examine some new cut-and-paste techniques for strength three covering arrays that combine recursive combinatorial constructions with computational search; when simulated annealing is the base method, this is augmented annealing. This method leverages the computational efficiency and optimality of size obtained through combinatorial constructions while benefiting from the generality of a heuristic search. We present a few examples of specific constructions and provide new bounds for some strength three covering arrays.\",\n \"Title: Incremental covering array failure characterization in large configuration spaces\\nYear: 2009\\n\\nAbstract: The increasing complexity of configurable software systems has created a need for more intelligent sampling mechanisms to detect and characterize failure-inducing dependencies between configurations. Prior work - in idealized environments - has shown that test schedules based on a mathematical object, called a covering array, in combination with classification techniques, can meet this need. Applying this approach in practice, however, is tricky because testing time and resource availability are unpredictable, and because failure characteristics can change from release to release. With current approaches developers must set a key covering array parameter (its strength) based on estimated release times and failure characterizations. This will influence the outcome of their results. In this paper we propose a new approach that incrementally builds covering array schedules. This approach begins at a low strength, and then iteratively increases strength as resources allow. At each stage previously tested configurations are reused, thus avoiding duplication of work. With the incremental approach developers need never commit to a specific covering array strength. Instead, by using progressively stronger covering array schedules, failures due to few configuration dependencies can be found and classified as soon and as cheaply as possibly. Additionally, it eliminates the risks of committing to overly strong test schedules. We evaluate this new approach through a case study on three consecutive releases of MySQL, an open source database. Our results suggest that our approach is as good or better than previous approaches, costing less in most cases, and allowing greater flexibility in environments with unpredictable development constraints.\",\n \"Title: Feature Interaction Faults Revisited: An Exploratory Study\\nYear: 2011\\n\\nAbstract: While a large body of research is dedicated to testing for feature interactions in configurable software, there has been little work that examines what constitutes such a fault at the code level. In consequence, we do not know how prevalent real interaction faults are in practice, what a typical interaction fault looks like in code, how to seed interaction faults, or whether current interaction testing techniques are effective at finding the faults they aim to detect. We make a first step in this direction, by deriving a white box criterion for an interaction fault. Armed with this criterion, we perform an exploratory study on hundreds of faults from the field in two open source systems. We find that only three of the 28 which appear to be interaction faults are in fact due to features' interactions. We investigate the remaining 25 and find that, although they could have been detected without interaction testing, varying the system configuration amplifies the fault-finding power of a test suite, making these faults easier to expose. Thus, we characterize the benefits of interaction testing in regards to both interaction and non-interaction faults. We end with a discussion of several mutations that can be used to mimic interaction faults based on the faults we see in practice.\",\n \"Title: Directed test suite augmentation: techniques and tradeoffs\\nYear: 2010\\n\\nAbstract: Test suite augmentation techniques are used in regression testing to identify code elements affected by changes and to generate test cases to cover those elements. Our preliminary work suggests that several factors influence the cost and effectiveness of test suite augmentation techniques. These include the order in which affected elements are considered while generating test cases, the manner in which existing regression test cases and newly generated test cases are used, and the algorithm used to generate test cases. In this work, we present the results of an empirical study examining these factors, considering two test case generation algorithms (concolic and genetic). The results of our experiment show that the primary factor affecting augmentation is the test case generation algorithm utilized; this affects both cost and effectiveness. The manner in which existing and newly generated test cases are utilized also has a substantial effect on efficiency but a lesser effect on effectiveness. The order in which affected elements are considered turns out to have relatively few effects when using concolic test case generation, but more substantial effects when using genetic test case generation.\",\n \"Title: Interaction Coverage Meets Path Coverage by SMT Constraint Solving\\nYear: 2009\\n\\nAbstract: We present a novel approach for generating interaction combinations based on SMT constraint resolution. Our approach can generate maximal interaction coverage in the presence of general constraints as supported by the underlying solver. It supports seeding with general predicates, which allows us to combine it with path exploration such that both interaction and path coverage goals can be met. Our approach is motivated by the application to behavioral model-based testing in the Spec Explorer tool, where parameter combinations must be generated such that all path conditions of a model action have at least one combination which enables the path. It is applied in a large-scale project for model-based quality assurance of interoperability documentation at Microsoft.\",\n \"Title: Towards Dynamic Adaptive Automated Test Generation for Graphical User Interfaces\\nYear: 2009\\n\\nAbstract: Graphical user interfaces (GUIs) present an enormous number of potential event sequences to users. During testing it is necessary to cover this space, however the complexity of modern GUIs has made this an increasingly difficult task. Our past work has demonstrated that it is important to incorporate \\\"context'' into GUI test cases, in terms of event combinations, event sequence length, and by considering all possible starting and ending positions for each event. Despite the use of our most refined modeling techniques, many of the generated test cases remain unexecutable. In this paper, we posit that due to the dynamic state-based nature of GUIs, it is important to incorporate feedback from the execution of tests into test case generation algorithms. We propose the use of an evolutionary algorithm to generate test suites with fewer unexecutable test cases and higher event interaction coverage.\",\n \"Title: Using feature locality: can we leverage history to avoid failures during reconfiguration?\\nYear: 2011\\n\\nAbstract: Despite the best efforts of software engineers, faults still escape into deployed software. Developers need time to prepare and distribute fixes, and in the interim deployments must either tolerate or avoid failures. Self-adaptive systems, systems that adapt to meet changing requirements in a dynamic environment, have a daunting task if their reconfiguration involves adding or removing functional features, because configurable software is known to suffer from failures that appear only under certain feature combinations. Although configuration-dependent failures may be difficult to provoke, and thus hard to detect in testing, we posit that they also constitute opportunities for reconfiguration to increase system reliability. We further conjecture that the failures that are sensitive to a system configuration depend on similar feature combinations, a phenomenon we call feature-locality, and that this locality can be combined with historical data to predict failure-prone configurations. In a case study on 128 failures reported against released versions of an open source configurable system, we find evidence to support our hypothesis. We show that only a small number of features affect the visibility of these failures, and that over time we can learn these features to avoid future failures.\",\n \"Title: Variable Strength Interaction Testing of Components\\nYear: 2003\\n\\nAbstract: Complete interaction testing of components is too costlyin all but the smallest systems. Yet component interactionsare likely to cause unexpected faults. Recently, design of experimenttechniques have been applied to software testingto guarantee a minimum coverage of all t-way interactionsacross components.However, t is always fixed.This paperexamines the need to vary the size of t in an individual testsuite and defines a new object, the variable strength coveringarray, that has this property.We present some computationalmethods to find variable strength arrays and provideinitial bounds for a group of these objects.\",\n \"Title: Ordering disks for double erasure codes\\nYear: 2001\\n\\nAbstract: Dish arrays have been designed with two competing goals in mind, the ability to reconstruct erased disks (reliability), and the speed with which information can be read, written, and reconstructed (performance). The substantial loss in performance of write operations as reliability requirements increase has resulted in an emphasis on performance at the expense of reliability. This has proved acceptable given the relatively small members of disks in current disk arrays. We develop a method for improving the performance of write operations in disk arrays capable of correcting any double erasure, by ordering the columns of the erasure code to minimize the amount of parity information that requires updating. For large disk arrays, this affords a method to support the reliability needed without the generally accepted loss of performance.\",\n \"Title: An orchestrated survey of methodologies for automated software test case generation\\nYear: 2013\\n\\nAbstract: Test case generation is among the most labour-intensive tasks in software testing. It also has a strong impact on the effectiveness and efficiency of software testing. For these reasons, it has been one of the most active research topics in software testing for several decades, resulting in many different approaches and tools. This paper presents an orchestrated survey of the most prominent techniques for automatic generation of software test cases, reviewed in self-standing sections. The techniques presented include: (a) structural testing using symbolic execution, (b) model-based testing, (c) combinatorial testing, (d) random testing and its variant of adaptive random testing, and (e) search-based testing. Each section is contributed by world-renowned active researchers on the technique, and briefly covers the basic ideas underlying the method, the current state of the art, a discussion of the open research problems, and a perspective of the future development of the approach. As a whole, the paper aims at giving an introductory, up-to-date and (relatively) short overview of research in automatic test case generation, while ensuring a comprehensive and authoritative treatment.\",\n \"Title: Reducing Field Failures in System Configurable Software: Cost-Based Prioritization\\nYear: 2009\\n\\nAbstract: System testing of configurable software is an expensive and resource constrained process. Insufficient testing often leads to escaped faults in the field where failures impact customers and are costly to repair. Prior work has shown that it is possible to efficiently sample configurations for testing using combinatorial interaction testing, and to prioritize these configurations to increase the rate of early fault detection. The underlying assumption to date has been that there is no added complexity to configuring a system level environment over a user configurable one; i.e. the time required to setup and test each individual configuration is nominal. In this paper we examine prioritization of system configurable software driven not only by fault detection but also by the cost of configuration and setup time that moving between different configurations incurs. We present a case study on two releases of an enterprise software system using failures reported in the field. We examine the most effective prioritization technique and conclude that (1) using failure history of configurations can improve the early fault detection rate, but that (2) we must consider fault detection rate over time, not by the number of configurations tested. It is better to test related configurations which incur minimal setup time than to test fewer, more diverse configurations.\",\n \"Title: Repairing GUI Test Suites Using a Genetic Algorithm\\nYear: 2010\\n\\nAbstract: Recent advances in automated functional testing of Graphical User Interfaces (GUIs) rely on deriving graph models that approximate all possible sequences of events that may be executed on the GUI, and then use the graphs to generate test cases (event sequences) that achieve a specified coverage goal. However, because these models are only approximations of the actual event flows, the generated test cases may suffer from problems of infeasibility, i.e., some events may not be available for execution causing the test case to terminate prematurely. In this paper we develop a method to automatically repair GUI test suites, generating new test cases that are feasible. We use a genetic algorithm to evolve new test cases that increase our test suite's coverage while avoiding infeasible sequences. We experiment with this algorithm on a set of synthetic programs containing different types of constraints and for test sequences of varying lengths. Our results suggest that we can generate new test cases to cover most of the feasible coverage and that the genetic algorithm outperforms a random algorithm trying to achieve the same goal in almost all cases.\",\n \"Title: Integration testing of software product lines using compositional symbolic execution\\nYear: 2012\\n\\nAbstract: Software product lines are families of products defined by feature commonality and variability, with a well-managed asset base. Recent work in testing of software product lines has exploited similarities across development phases to reuse shared assets and reduce test effort. The use of feature dependence graphs has also been employed to reduce testing effort, but little work has focused on code level analysis of dataflow between features. In this paper we present a compositional symbolic execution technique that works in concert with a feature dependence graph to extract the set of possible interaction trees in a product family. It composes these to incrementally and symbolically analyze feature interactions. We experiment with two product lines and determine that our technique can reduce the overall number of interactions that must be considered during testing, and requires less time to run than a traditional symbolic execution technique.\",\n \"Title: Augmenting Simulated Annealing to Build Interaction Test Suites\\nYear: 2003\\n\\nAbstract: Component based software development is prone to unexpectedinteraction faults. The goal is to test as manypotential interactions as is feasible within time and budgetconstraints. Two combinatorial objects, the orthogonalarray and the covering array, can be used to generate testsuites that provide a guarantee for coverage of all t-setsof component interactions in the case when the testing ofall interactions is not possible. Methods for constructionof these types of test suites have focused on two main areas.The first is finding new algebraic constructions that producesmaller test suites. The second is refining computationalsearch algorithms to find smaller test suites more quickly. Inthis paper we explore one method for constructing coveringarrays of strength three that combines algebraic constructionswith computational search. This method leveragesthe computational efficiency and optimality of size obtainedthrough algebraic constructions while benefiting from thegenerality of a heuristic search. We present a few examplesof specific constructions and provide some new bounds forsome strength three covering arrays.\",\n \"Title: Towards incremental adaptive covering arrays\\nYear: 2007\\n\\nAbstract: The increasing complexity of configurable software systems creates a need for more intelligent sampling mechanisms to detect and locate failure-inducing dependencies between configurations. Prior work shows that test schedules based on a mathematical object, called a covering array, can be used to detect and locate failures in combination with a clas- sification tree analysis. This paper addresses limitations of the earlier approach. First, the previous work requires devel- opers to choose the covering array's strength, even though there is no scientific or historical basis for doing so. Second, if a single covering array is insu!c ient to classify specific failures, the entire process must be rerun from scratch. To address these issues, our new approach incrementally and adaptively builds covering array schedules. It begins with a low strength, and continually increases this as resources allow, or poor classification results require. At each stage, previous tests are reused. This allows failures due to only one or two configurations settings to be found and classified as early as possible, and also limits duplication of work when multiple covering arrays must be used.\",\n \"Title: Kirkman triple systems of order 21 with nontrivial automorphism group\\nYear: 2002\\n\\nAbstract: There are 50,024 Kirkman triple systems of order 21 admitting an automorphism of order 2. There are 13,280 Kirkman triple systems of order 21 admitting an automorphism of order 3. Together with the 192 known systems and some simple exchange operations, this leads to a collection of 63,745 nonisomorphic Kirkman triple systems of order 21. This includes all KTS(21)s having a nontrivial automorphism group. None of these is doubly resolvable. Four are quadrilateral-free, providing the first examples of such a KTS(21).\",\n \"Title: A Study in Prioritization for Higher Strength Combinatorial Testing\\nYear: 2013\\n\\nAbstract: Recent studies have shown that combinatorial interaction testing (CIT) is an effective fault detection technique and that early fault detection can be improved by ordering test suites by interaction based prioritization approaches. Despite research that has shown that higher strength CIT improves fault detection, there have been fewer studies that aim to understand the impact of prioritization based on higher strength criteria. In this paper, we aim to understand how interaction based prioritization techniques perform, in terms of early fault detection when we prioritize based on 3-way interactions. We generalize prior work on prioritizing using 2-way interactions to t-way prioritization, and empirically evaluate this on three open source subjects, across multiple versions of each. We examine techniques that prioritize both existing CIT suites as well as generate new ones in prioritized order. We find that early fault detection can be improved when prioritizing 3-way CIT test suites by interactions that cover more code, and to a lesser degree when generating tests in prioritized order. Our techniques that work only from the specification, appear to work best with 2-way generation.\",\n \"Title: Evaluating improvements to a meta-heuristic search for constrained interaction testing\\nYear: 2011\\n\\nAbstract: Combinatorial interaction testing (CIT) is a cost-effective sampling technique for discovering interaction faults in highly-configurable systems. Constrained CIT extends the technique to situations where some features cannot coexist in a configuration, and is therefore more applicable to real-world software. Recent work on greedy algorithms to build CIT samples now efficiently supports these feature constraints. But when testing a single system configuration is expensive, greedy techniques perform worse than meta-heuristic algorithms, because greedy algorithms generally need larger samples to exercise the same set of interactions. On the other hand, current meta-heuristic algorithms have long run times when feature constraints are present. Neither class of algorithm is suitable when both constraints and the cost of testing configurations are important factors. Therefore, we reformulate one meta-heuristic search algorithm for constructing CIT samples, simulated annealing, to more efficiently incorporate constraints. We identify a set of algorithmic changes and experiment with our modifications on 35 realistic constrained problems and on a set of unconstrained problems from the literature to isolate the factors that improve performance. Our evaluation determines that the optimizations reduce run time by a factor of 90 and accomplish the same coverage objectives with even fewer system configurations. Furthermore, the new version compares favorably with greedy algorithms on real-world problems, and, though our modifications were aimed at constrained problems, it shows similar advantages when feature constraints are absent.\",\n \"Title: Automated testing of GUI applications: models, tools, and controlling flakiness\\nYear: 2013\\n\\nAbstract: System testing of applications with graphical user interfaces (GUIs) such as web browsers, desktop or mobile apps, is more complex than testing from the command line. Specialized tools are needed to generate and run test cases, models are needed to quantify behavioral coverage, and changes in the environment, such as the operating system, virtual machine or system load, as well as starting states of the executions, impact the repeatability of the outcome of tests making tests appear flaky. In this tutorial, we present an overview of the state of the art in GUI testing, consisting of both lectures and demonstrations on various platforms (desktop, web and mobile applications), using an open source testing tool, GUITAR. We show how to setup a system under test, how to extract models without source code, and how to then use those models to generate and replay test cases. We then present a lecture on the various factors that may cause flakiness in the execution of GUI-centric software, and hence impact the results of analyses and experiments based on such software. We end with a demonstration of a community resource for sharing GUI testing artifacts aimed at controlling these factors. This tutorial targets both researchers who develop techniques for testing GUI software, and practitioners from industry who want to learn more about model-based GUI testing or who run and rerun GUI tests and often find their runs are flaky.\",\n \"Title: Optimal and pessimal orderings of Steiner triple systems in disk arrays\\nYear: 2000\\n\\nAbstract: Steiner triple systems are well studied combinatorial designs that have been shown to possessproperties desirable for the construction of multiple erasure codes in RAID architectures.The ordering of the columns in the parity check matrices of these codes affects system performance.Combinatorial problems involved in the generation of good and bad column orderingsare defined, and examined for small numbers of accesses to consecutive data blocks in the diskarray.1 BackgroundA Steiner ...\",\n \"Title: Factors affecting the use of genetic algorithms in test suite augmentation\\nYear: 2010\\n\\nAbstract: Test suite augmentation techniques are used in regression testing to help engineers identify code elements affected by changes, and generate test cases to cover those elements. Researchers have created various approaches to identify affected code elements, but only recently have they considered integrating, with this task, approaches for generating test cases. In this paper we explore the use of genetic algorithms in test suite augmentation. We identify several factors that impact the effectiveness of this approach, and we present the results of a case study exploring the effects of one of these factors: the manner in which existing and newly generated test cases are utilized by the genetic algorithm. Our results reveal several ways in which this factor can influence augmentation results, and reveal open problems that researchers must address if they wish to create augmentation techniques that make use of genetic algorithms.\",\n \"Title: Configurations everywhere: implications for testing and debugging in practice\\nYear: 2014\\n\\nAbstract: Many industrial systems are highly-configurable, complicating the testing and debugging process. While researchers have developed techniques to statically extract, quantify and manipulate the valid system configurations, we conjecture that many of these techniques will fail in practice. In this paper we analyze a highly-configurable industrial application and two open source applications in order to quantify the true challenges that configurability creates for software testing and debugging. We find that (1) all three applications consist of multiple programming languages, hence static analyses need to cross programming language barriers to work, (2) there are many access points and methods to modify configurations, implying that practitioners need configuration traceability and should gather and merge metadata from more than one source and (3) the configuration state of an application on failure cannot be reliably determined by reading persistent data; a runtime memory dump or other heuristics must be used for accurate debugging. We conclude with a roadmap and lessons learned to help practitioners better handle configurability now, and that may lead to new configuration-aware testing and debugging techniques in the future.\",\n \"Title: Improving the testing and testability of software product lines\\nYear: 2010\\n\\nAbstract: Software Product Line (SPL) engineering offers several advantages in the development of families of software products. There is still a need, however, for better understanding of testability issues and for testing techniques that can operate cost-effectively on SPLs. In this paper we consider these testability issues and highlight some differences between optional versus alternative features. We then provide a graph based testing approach called the FIG Basis Path method that selects products and features for testing based on a feature dependency graph. We conduct a case study on several non-trivial SPLs and show that for these subjects, the FIG Basis Path method is as effective as testing all products, but tests no more than 24% of the products in the SPL.\",\n \"Title: Regression testing in Software as a Service: An industrial case study\\nYear: 2011\\n\\nAbstract: Many organizations are moving towards a business model of Software as a Service (SaaS), where customers select and pay for services dynamically via the web. In SaaS, service providers face the challenge of delivering and maintaining high quality software solutions which must continue to work under an enormous number of scenarios; customers can easily subscribe and unsubscribe from services at any point. To date, there has been little research on unique approaches for regression test methodologies for testing in a SaaS environment. In this paper, we present an industrial case study of a regression testing approach to improve test effectiveness and efficiency in SaaS. We model service level use cases from field failures as abstract events and then generate sequences of these for testing to provide a broad coverage of the possible use cases. In subsequent releases of the system we prioritize the tests to improve time to detection of faults in the modified system. We have applied our technique to two releases of a large industrial enterprise level SaaS application and demonstrate that using our approach (1) we could have uncovered escaped faults prior to the system release in both versions of the system; (2) using a priority order we could have improved the efficiency of testing in the first version; and (3) prioritization based on failure history from the first version increases the fault detection rate in the new version, suggesting a correlation between the important sequences in versions that can be leveraged for regression testing.\",\n \"Title: GUI Interaction Testing: Incorporating Event Context\\nYear: 2011\\n\\nAbstract: Graphical user interfaces (GUIs), due to their event-driven nature, present an enormous and potentially unbounded way for users to interact with software. During testing, it is important to “adequately cover” this interaction space. In this paper, we develop a new family of coverage criteria for GUI testing grounded in combinatorial interaction testing. The key motivation of using combinatorial techniques is that they enable us to incorporate “context” into the criteria in terms of event combinations, sequence length, and by including all possible positions for each event. Our new criteria range in both efficiency (measured by the size of the test suite) and effectiveness (the ability of the test suites to detect faults). In a case study on eight applications, we automatically generate test cases and systematically explore the impact of context, as captured by our new criteria. Our study shows that by increasing the event combinations tested and by controlling the relative positions of events defined by the new criteria, we can detect a large number of faults that were undetectable by earlier techniques.\",\n \"Title: Practical Combinatorial Interaction Testing: Empirical Findings on Efficiency and Early Fault Detection\\nYear: 2015\\n\\nAbstract: Combinatorial Interaction Testing (CIT) is important because it tests the interactions between the many features and parameters that make up the configuration space of software systems. Simulated Annealing (SA) and Greedy Algorithms have been widely used to find CIT test suites. From the literature, there is a widely-held belief that SA is slower, but produces more effective tests suites than Greedy and that SA cannot scale to higher strength coverage. We evaluated both algorithms on 7 real-world subjects for the well-studied 2-way up to the rarely-studied 6-way interaction strengths. Our findings present evidence to challenge this current orthodoxy: real-world constraints allow SA to achieve higher strengths. Furthermore, there was no evidence that Greedy was less effective (in terms of time to fault revelation) compared to SA; the results for the greedy algorithm are actually slightly superior. However, the results are critically dependent on the approach adopted to constraint handling. Moreover, we have also evaluated a genetic algorithm for constrained CIT test suite generation. This is the first time strengths higher than 3 and constraint handling have been used to evaluate GA. Our results show that GA is competitive only for pairwise testing for subjects with a small number of constraints.\",\n \"Title: SimLatte: A Framework to Support Testing for Worst-Case Interrupt Latencies in Embedded Software\\nYear: 2014\\n\\nAbstract: Embedded systems tend to be interrupt-driven, yet the presence of interrupts can affect system dependability because there can be delays in servicing interrupts. Such delays can occur when multiple interrupt service routines and interrupts of different priorities compete for resources on a given CPU. For this reason, researchers have sought approaches by which to estimate worst-case interrupt latencies (WCILs) for systems. Most existing approaches, however, are based on static analysis. In this paper, we present SIMLATTE, a testing-based approach for finding WCILs. SIMLATTE uses a genetic algorithm for test case generation that converges on a set of inputs and interrupt arrival points that are likely to expose WCILs. It also uses an opportunistic interrupt invocation approach to invoke interrupts at a variety of feasible locations. Our evaluation of SIMLATTE on several non-trivial embedded systems reveals that it is considerably more effective and efficient than random testing. We also determine that the combination of the genetic algorithm and opportunistic interrupt invocation allows SIMLATTE to perform better than it can when using either one in isolation.\",\n \"Title: Hybrid Directed Test Suite Augmentation: An Interleaving Framework\\nYear: 2014\\n\\nAbstract: Test suite augmentation techniques generate test cases to cover code missed by existing regression test suites. Various augmentation techniques have been proposed, utilizing several test case generation algorithms. Research has shown that different algorithms have different strengths, and that combining them into a single hybrid approach may be cost-effective. In this paper we present a framework for hybrid test suite augmentation that allows test case generation algorithms to be interleaved dynamically and that can easily incorporate new algorithms, interleaving strategies, and choices of other parameters that influence algorithm performance. We empirically study an implementation of this framework in which we use two test case generation algorithms and several algorithm interleavings. Our results show that specific instantiations of our framework can produce augmentation techniques that are more cost-effective than others, and illustrate tradeoffs between instantiations.\",\n \"Title: PrefFinder: getting the right preference in configurable software systems\\nYear: 2014\\n\\nAbstract: Highly configurable software, such as web browsers,databases or office applications, have a large number of preferences that the user can customize, but documentation of them may be scarce or distributed. A user, tester or service technician may have to search through hundreds or thousands of choices in multiple documents when trying to identify which preference will modify a particular system behavior. In this paper we present PrefFinder, a natural language framework that finds (and changes) user preferences. It is tied into an application's preference system and static documentation. We have instantiated PrefFinder as a plugin on two open source applications, and as a stand-alone GUI for an industrial application. PrefFinder finds thecorrect answer between 76-96% of the time on more than 175 queries. When compared to asking questions on a help forum or through the company's service center, we can potentially save days or even weeks of time.\",\n \"Title: Beyond the rainbow: self-adaptive failure avoidance in configurable systems\\nYear: 2014\\n\\nAbstract: Self-adaptive software systems monitor their state and then adapt when certain conditions are met, guided by a global utility function. In prior work we developed algorithms and conducted a post-hoc analysis demonstrating the possibility of adapting to software failures by judiciously changing configurations. In this paper we present the REFRACT framework that realizes this idea in practice by building on the self-adaptive Rainbow architecture. REFRACT extends Rainbow with new components and algorithms targeting failure avoidance. We use REFRACT in a case study running four independently executing Firefox clients with 36 passing test cases and 7 seeded faults. The study show that workarounds for all but one of the seeded faults are found and the one that is not found never fails -- it is guarded from failing by a related workaround. Moreover, REFRACT finds workarounds for eight configuration-related unseeded failures from tests that were expected to pass (and did under the default configuration). Finally, the data show that when a failure and its workaround are found, configuration guards prevent the failure from appearing again. In a simulation lasting 24 hours we see over 150 guard activations and no failures with workarounds remaining beyond 16 hours.\",\n \"Title: Reducing Masking Effects in CombinatorialInteraction Testing: A Feedback DrivenAdaptive Approach\\nYear: 2014\\n\\nAbstract: The configuration spaces of modern software systems are too large to test exhaustively. Combinatorial interaction testing (CIT) approaches, such as covering arrays, systematically sample the configuration space and test only the selected configurations. The basic justification for CIT approaches is that they can cost-effectively exercise all system behaviors caused by the settings of $t$ or fewer options. We conjecture, however, that in practice some of these behaviors are not actually tested because of unanticipated masking effects – test case failures that perturb system execution so as to prevent some behaviors from being exercised. While prior research has identified this problem, most solutions require knowing the masking effects a priori. In practice this is impractical, if not impossible. In this work, we reduce the harmful consequences of masking effects. First we define a novel interaction testing criterion, which aims to ensure that each test case has a fair chance to test all valid t-way combinations of option settings. We then introduce a feedback driven adaptive combinatorial testing process (FDA-CIT) to materialize this criterion in practice. At each iteration of FDA-CIT, we detect potential masking effects, heuristically isolate their likely causes (i.e., fault characterization), and then generate new samples that allow previously masked combinations to be tested in configurations that avoid the likely failure causes. The iterations end when the new interaction testing criterion has been satisfied. This paper compares two different fault characterization approaches – an integral part of the proposed approach, and empirically assesses their effectiveness and efficiency in removing masking effects on two widely used open source software systems. It also compares FDA-CIT against error locating arrays, a state of the art approach for detecting and locating failures. Furthermore, the scalability of the proposed approach is evaluated by comparing it with perfect test scenarios, in which all masking effects are known a priori. Our results suggest that masking effects do exist in practice, and that our approach provides a promising and efficient way to work around them, without requiring that masking effects be known a priori.\",\n \"Title: Combinatorial test design in practice\\nYear: 2010\\n\\nAbstract: Combinatorial testing is a specification based sampling technique that provides a systematic way to select combinations of program inputs or features for testing. It has been applied over the years to test input data, configurations, web forms, protocols, graphical user interfaces and for testing software product lines. This tutorial introduces the fundamentals of combinatorial testing, including both practical and theoretical foundations, to provide a comprehensive introduction that is relevant to both test practitioners and software engineering researchers. The tutorial will present an overview of Combinatorial Test Design (CTD) and describe some state of the art research advances and domains where CTD has been applied. It will present the theoretical underpinnings of CTD and explain a few algorithmic techniques used to generate CTD samples, as well as describe recent work on practical extensions to these algorithms that allow for a broader use of CTD. A session devoted to modeling test problems using CTD will follow, with attendees obtaining hands-on experience using several realistic problems.\",\n \"Title: AutoInSpec: Using Missing Test Coverage to Improve Specifications in GUIs\\nYear: 2012\\n\\nAbstract: Developers of a software's graphical user interface (GUI) often fail to document the interface specifications. Without these, models used for automated test generation and execution remain imperfect and incomplete. This leads to unexpected behavior that creates unrecoverable situations for test harnesses, and missed coverage. In this paper, we present AutoInSpec, a technique to infer an important class of specifications, temporal and state-based invariants between GUI events that have been incorrectly modeled. Unlike existing specification mining approaches that require full execution traces, or source code, and that mine all invariants, we simplify the problem. We guide AutoInSpec with coverage criteria and use a previously developed repair framework that builds coverage-adequate test suites, removing unexecutable sub-sequences from consideration. These failing sub-sequences are input to a logic-based inference engine, armed with known invariant templates, to obtain the missing specifications. We validate AutoInSpec on a set of well studied GUI applications.\",\n \"Title: Making system user interactive tests repeatable: when and what should we control?\\nYear: 2015\\n\\nAbstract: System testing and invariant detection is usually conducted from the user interface perspective when the goal is to evaluate the behavior of an application as a whole. A large number of tools and techniques have been developed to generate and automate this process, many of which have been evaluated in the literature or internally within companies. Typical metrics for determining effectiveness of these techniques include code coverage and fault detection, however, with the assumption that there is determinism in the resulting outputs. In this paper we examine the extent to which a common set of factors such as the system platform, Java version, application starting state and tool harness configurations impact these metrics. We examine three layers of testing outputs: the code layer, the behavioral (or invariant) layer and the external (or user interaction) layer. In a study using five open source applications across three operating system platforms, manipulating several factors, we observe as many as 184 lines of code coverage difference between runs using the same test cases, and up to 96 percent false positives with respect to fault detection. We also see some a small variation among the invariants inferred. Despite our best efforts, we can reduce, but not completely eliminate all possible variation in the output. We use our findings to provide a set of best practices that should lead to better consistency and smaller differences in test outcomes, allowing more repeatable and reliable testing and experimentation.\",\n \"Title: Learning combinatorial interaction test generation strategies using hyperheuristic search\\nYear: 2015\\n\\nAbstract: The surge of search based software engineering research has been hampered by the need to develop customized search algorithms for different classes of the same problem. For instance, two decades of bespoke Combinatorial Interaction Testing (CIT) algorithm development, our exemplar problem, has left software engineers with a bewildering choice of CIT techniques, each specialized for a particular task. This paper proposes the use of a single hyperheuristic algorithm that learns search strategies across a broad range of problem instances, providing a single generalist approach. We have developed a Hyperheuristic algorithm for CIT, and report experiments that show that our algorithm competes with known best solutions across constrained and unconstrained problems: For all 26 real-world subjects, it equals or outperforms the best result previously reported in the literature. We also present evidence that our algorithm's strong generic performance results from its unsupervised learning. Hyperheuristic search is thus a promising way to relocate CIT design intelligence from human to machine.\",\n \"Title: Guided Test Generation for Finding Worst-Case Stack Usage in Embedded Systems\\nYear: 2015\\n\\nAbstract: Embedded systems are challenging to program correctly, because they use an interrupt programming paradigm and run in resource constrained environments. This leads to a class of faults for which we need customized verification techniques. One such class of faults, stack overflows, are caused when the combination of active methods and interrupt invocations on the stack grows too large, and these can lead to data loss and other significant device failures. Developers need to estimate the worst-case stack usage (WCSU) during system design, but determining the actual maximum value is known to be a hard problem. The state of the art for calculating WCSU uses static analysis, however this has a tendency to over approximate the potential stack which can lead to wasted resources. Dynamic techniques such as random testing often under approximate the WCSU. In this paper, we present SIMSTACK, a framework that utilizes a combination of static analysis and a genetic algorithm to search for WCSUs. We perform an empirical study to evaluate the effectiveness of SIMSTACK and show that SIMSTACK is competitive with the WCSU values obtained by static analysis, and improves significantly over a random algorithm. When we use only the genetic algorithm, SIMSTACK performs almost as well as the guided technique, but takes significantly longer to converge on the maximum WCSUs.\",\n \"Title: GitSonifier: using sound to portray developer conflict history\\nYear: 2015\\n\\nAbstract: There are many tools that help software engineers analyze data about their software, projects, and teams. These tools primarily use visualizations to portray data in a concise and understandable way. However, software engineering tasks are often multi-dimensional and temporal, making some visualizations difficult to understand. An alternative for representing data, which can easily incorporate higher dimensionality and temporal information, is the use of sound. In this paper we propose the use of sonification to help portray collaborative development history. Our approach, GitSonifier, combines sound primitives to represent developers, days, and conflicts over the history of a program's development. In a formative user study on an open source project's data, we find that users can easily extract meaningful information from sound clips and differentiate users, passage of time, and development conflicts, suggesting that sonification has the potential to provide benefit in this context.\",\n \"Title: Users beware: preference inconsistencies ahead\\nYear: 2015\\n\\nAbstract: The structure of preferences for modern highly-configurable software systems has become extremely complex, usually consisting of multiple layers of access that go from the user interface down to the lowest levels of the source code. This complexity can lead to inconsistencies between layers, especially during software evolution. For example, there may be preferences that users can change through the GUI, but that have no effect on the actual behavior of the system because the related source code is not present or has been removed going from one version to the next. These inconsistencies may result in unexpected program behaviors, which range in severity from mild annoyances to more critical security or performance problems. To address this problem, we present SCIC (Software Configuration Inconsistency Checker), a static analysis technique that can automatically detect these kinds of inconsistencies. Unlike other configuration analysis tools, SCIC can handle software that (1) is written in multiple programming languages and (2) has a complex preference structure. In an empirical evaluation that we performed on 10 years worth of versions of both the widely used Mozilla Core and Firefox, SCIC was able to find 40 real inconsistencies (some determined as severe), whose lifetime spanned multiple versions, and whose detection required the analysis of code written in multiple languages.\",\n \"Title: Piecing together app behavior from multiple artifacts: A case study\\nYear: 2015\\n\\nAbstract: Recent research in mobile software analysis has begun to combine information extracted from an app's source code and marketplace webpage to identify correlated variables and validate an app's quality properties such as its intended behavior, trust or suspiciousness. Such work typically involves analysis of one or two artifacts such as the GUI text, user ratings, app description keywords, permission requests, and sensitive API calls. However, these studies make assumptions about how the various artifacts are populated and used by developers, which may lead to a gap in the resulting analysis. In this paper, we take a step back and perform an in-depth study of 14 popular apps from the Google Play Store. We have studied a set of 16 different artifacts for each app, and conclude that the output of these must be pieced together to form a complete understanding of the app's true behavior. We show that (1) developers are inconsistent in where and how they provide descriptions; (2) each artifact alone has incomplete information; (3) different artifacts may contain contradictory pieces of information; (4) there is a need for new analyses, such as those that use image processing; and (5) without including analyses of advertisement libraries, the complete behavior of an app is not defined. In addition, we show that the number of downloads and ratings of an app does not appear to be a strong predictor of overall app quality, as these are propagated through versions and are not necessarily indicative of the current app version's behavior.\",\n \"Title: VDTest: an automated framework to support testing for virtual devices.\\nYear: 2016\\n\\nAbstract: The use of virtual devices in place of physical hardware is increasing in activities such as design, testing and debugging. Yet virtual devices are simply software applications, and like all software they are prone to faults. A full system simulator (FSS), is a class of virtual machine that includes a large set of virtual devices -- enough to run the full target software stack. Defects in an FSS virtual device may have cascading effects as the incorrect behavior can be propagated forward to many different platforms as well as to guest programs. In this work we present VDTest, a novel framework for testing virtual devices within an FSS. VDTest begins by generating a test specification obtained through static analysis. It then employs a two-phase testing approach to test virtual components both individually and in combination. It leverages a differential oracle strategy, taking advantage of the existence of a physical or golden device to eliminate the need for manually generating test oracles. In an empirical study using both open source and commercial FSSs, we found 64 faults, 83% more than random testing.\",\n \"Title: Why Do We Always Blame The Storage Stack?\\nYear: 2016\\n\\nAbstract: Much research effort has been devoted to improving the performance of the I/O stack in mobile devices, but limited time has been spent evaluating mobile application performance from the user's perspective. In this paper, we try to understand how applications running on the newest devices behave with respect to this metric. We develop a methodology for quantifying user-perceived latency and use it to evaluate four common application benchmarks with I/O stack optimization on two of the latest smartphones. Contrary to our expectation, we find that (i) these applications respond reasonably fast and (ii) the user-perceived latency does not drastically (at most 11:8%) benefit from I/O stack optimizations.\",\n \"Title: The Assurance Timeline: Building Assurance Cases For Synthetic Biology\\nYear: 2016\\n\\nAbstract: Recent research advances in modifying and controlling DNA have created a booming field of biological engineering called synthetic biology. In synthetic biology engineers manipulate and modify living organisms to change (and produce entirely novel) functionality, which has led to new fuel sources or the ability to mitigate pollution. Synthetic biology research is also expected to lead to methods of intelligent drug delivery. In synthetic biology, designs are first built using biological programming languages and then implemented in a laboratory. These synthetic organisms can be considered living programs that will sense, respond and interact with humans while they persist in the natural environment. We argue that we should view these as safety critical devices which can be both regulated and certified. Since the synthetically engineered organisms follow a regular cycle of reproduction and replication that involves mutations, they will eventually adapt and evolve new behavior over time. In this paper we propose the use of an assurance case for synthetically engineered organisms, and present an orthogonal dimension, an assurance timeline, that can be used to reason about the dynamic, evolving aspects of these systems. We present a case study based on a real application to illustrate our ideas.\",\n \"Title: Scaling up the Fitness Function for Reverse Engineering Feature Models.\\nYear: 2016\\n\\nAbstract: Recent research on software product line engineering has led to several search-based frameworks for reverse engineering feature models. The most common fitness function utilized maximizes the number of matched products with an oracle set of products. However, to calculate this fitness each product defined by the chromosome has to be enumerated using a SAT solver and this limits scalability to product lines with fewer than 30 features. In this paper we propose SAT(ff), a fitness function that simulates validity by computing the difference between constraints in the chromosome and oracle. In an empirical study on 101 feature models comparing SAT(ff) with two existing fitness functions that use the enumeration technique we find that SAT(ff) shows a significant improvement over one, and no significant difference with the other one. We also find that SAT(ff) requires only 7% of the runtime on average scaling to feature models with as many as 97 features.\",\n \"Title: Budgeted testing through an algorithmic lens.\\nYear: 2016\\n\\nAbstract: Automated testing has been a focus of research for a long time. As such, we tend to think about this in a coverage centric manner. Testing budgets have also driven research such as prioritization and test selection, but as a secondary concern. As our systems get larger, are more dynamic, and impact more people with each change, we argue that we should switch from a coverage centric view to a budgeted testing centric view. Researchers in other fields have designed approximation algorithms for such budgeted scenarios and these are often simple to implement and run. In this paper we present an exemplar study on combinatorial interaction testing (CIT) to show that a budgeted greedy algorithm, when adapted to our problem for various budgets, does almost as well coverage-wise as a state of the art greedy CIT algorithm, better in some cases than a state of the art simulated annealing, and always improves over random. This suggests that we might benefit from switching our focus in large systems, from coverage to budgets.\",\n \"Title: The Evolutionary Landscape of SBST: A 10 Year Perspective.\\nYear: 2017\\n\\nAbstract: A key indicator of the health and quality of any evolutionary algorithm is the landscape of its search. By analyzing the landscape one can determine the peaks (local maxima) where significant solutions exist. In this paper we examine the landscape for the history of the International Workshop on Search-Based Software Testing (SBST) within the context of the broader field of search-based software testing. We study the evolution of the field, highlighting key advances during three phases of its ten year history. In 2008 the focus of SBST was inner looking, with advances in existing search techniques, improvements to individual generation techniques, and methods to transform the problem space for search effectiveness. However, diverse seeds of new ideas (such as automated program repair) were already being injected into the population. A few SBST tools existed, but the engineer still required skill and expertise to effectively apply search based approaches. During the middle years, open source tools were created and released, whole test suite generation appeared, and searches hybridized. Tool competitions began and industry started to play a stronger role. As we move to the most recent workshop years and look towards the future, more sophisticated techniques such as those that incorporate hyper-heuristics via learning, and/or balance multiple objectives at once are now common. SBST has become a mainstream topic in the testing community, tools are being commercialized and these tools often hide their inner workings, leading to a future that is optimized towards SBST for all.\",\n \"Title: BioSIMP: Using Software Testing Techniques for Sampling and Inference in Biological Organisms.\\nYear: 2017\\n\\nAbstract: Years of research in software engineering have given us novel ways to reason about, test, and predict the behavior of complex software systems that contain hundreds of thousands of lines of code. Many of these techniques have been inspired by nature such as genetic algorithms, swarm intelligence, and ant colony optimization. In this paper we reverse the direction and present BioSIMP, a process that models and predicts the behavior of biological organisms to aid in the emerging field of systems biology. It utilizes techniques from testing and modeling of highly-configurable software systems. Using both experimental and simulation data we show that BioSIMP can find important environmental factors in two microbial organisms. However, we learn that in order to fully reason about the complexity of biological systems, we will need to extend existing or create new software engineering techniques.\",\n \"Title: EventFlowSlicer: goal based test generation for graphical user interfaces.\\nYear: 2016\\n\\nAbstract: Automated test generation techniques for graphical user interfaces include model-based approaches that generate tests from a graph or state machine model, capture-replay methods that require the user to demonstrate each test case, and pattern-based approaches that provide templates for abstract test cases. There has been little work, however, in automated goal-based testing, where the goal is a realistic user task, a function, or an abstract behavior. Recent work in human performance regression testing has shown that there is a need for generating multiple test cases that execute the same user task in different ways, however that work does not have an efficient way to generate tests and only a single type of goal has been considered. In this paper we expand the notion of goal based interface testing to generate tests for a variety of goals. We develop a direct test generation technique, EventFlowSlicer, that is more efficient than that used in human performance regression testing, reducing run times by 92.5\",\n \"Title: Understanding Git history: a multi-sense view\\nYear: 2016\\n\\nAbstract: Version control systems archive data about the development history of a project, which can be used to analyze and understand different facets of a software project. The project history can be used to evaluate the development process of a team, as an aid in bug fixing, or to help new members get on track with development. However, state of the art techniques for analyzing version control data provide only partial views into this information, and lack an easy way to present all the dimensions of the data. In this paper we present GitVS, a hybrid view that incorporates visualization and sonification to represent the multiple dimensions of version control data - development time line, conflicts, etc. In a formative user study comparing the GitHub Network Graph, GitVS, and a version of GitVS without sound, we show GitVS improves over the GitHub Network Graph and that while sound makes it easier to correctly understand version history for some tasks, it is more difficult for others.\",\n \"Title: End-To-End Molecular Communication Channels In Cell Metabolism: An Information Theoretic Study\\nYear: 2017\\n\\nAbstract: The opportunity to control and fine-tune the behavior of biological cells is a fascinating possibility for many diverse disciplines, ranging from medicine and ecology, to chemical industry and space exploration. While synthetic biology is providing novel tools to reprogram cell behavior from their genetic code, many challenges need to be solved before it can become a true engineering discipline, such as reliability, safety assurance, reproducibility and stability. This paper aims to understand the limits in the controllability of the behavior of a natural (non-engineered) biological cell. In particular, the focus is on cell metabolism, and its natural regulation mechanisms, and their ability to react and change according to the chemical characteristics of the external environment. To understand the aforementioned limits of this ability, molecular communication is used to abstract biological cells into a series of channels that propagate information on the chemical composition of the extracellular environment to the cell's behavior in terms of uptake and consumption of chemical compounds, and growth rate. This provides an information-theoretic framework to analyze the upper bound limit to the capacity of these channels to propagate information, which is based on a well-known and computationally efficient metabolic simulation technique. A numerical study is performed on two human gut microbes, where the upper bound is estimated for different environmental compounds, showing there is a potential for future practical applications.\",\n \"Title: EventFlowSlicer: a tool for generating realistic goal-driven GUI tests.\\nYear: 2017\\n\\nAbstract: Most automated testing techniques for graphical user interfaces (GUIs) produce test cases that are only concerned with covering the elements (widgets, menus, etc.) on the interface, or the underlying program code, with little consideration of test case semantics. This is effective for functional testing where the aim is to find as many faults as possible. However, when one wants to mimic a real user for evaluating usability, or when it is necessary to extensively test important end-user tasks of a system, or to generate examples of how to use an interface, this generation approach fails. Capture and replay techniques can be used, however there are often multiple ways to achieve a particular goal, and capturing all of these is usually too time consuming and unrealistic. Prior work on human performance regression testing introduced a constraint based method to filter test cases created by a functional test case generator, however that work did not capture the specifications, or directly generate only the required tests and considered only a single type of test goal. In this paper we present EventFlowSlicer, a tool that allows the GUI tester to specify and generate all realistic test cases relevant to achieve a stated goal. The user first captures relevant events on the interface, then adds constraints to provide restrictions on the task. An event flow graph is extracted containing only the widgets of interest for that goal. Next all test cases are generated for edges in the graph which respect the constraints. The test cases can then be replayed using a modified version of GUITAR. A video demonstration of EventFlowSlicer can be found at https://youtu.be/hw7WYz8WYVU.\",\n \"Title: EvoIsolator: Evolving Program Slices for Hardware Isolation Based Security.\\nYear: 2018\\n\\nAbstract: To provide strong security support for today's applications, microprocessor manufacturers have introduced hardware isolation, an on-chip mechanism that provides secure accesses to sensitive data. Currently, hardware isolation is still difficult to use by software developers because the process to identify access points to sensitive data is error-prone and can lead to under and over protection of sensitive data. Under protection can lead to security vulnerabilities. Over protection can lead to an increased attack surface and excessive communication overhead. In this paper we describe EvoIsolator, a search-based framework to (i) automatically generate executable minimal slices that include all access points to a set of specified sensitive data; and (ii) automatically optimize (for small code block size and low communication overhead) the code modules for hardware isolation. We demonstrate, through a small feasibility study, the potential impact of our proposed code optimizer.\",\n \"Title: Navigating the maze: the impact of configurability in bioinformatics software.\\nYear: 2018\\n\\nAbstract: The bioinformatics software domain contains thousands of applications for automating tasks such as the pairwise alignment of DNA sequences, building and reasoning about metabolic models or simulating growth of an organism. Its end users range from sophisticated developers to those with little computational experience. In response to their needs, developers provide many options to customize the way their algorithms are tuned. Yet there is little or no automated help for the user in determining the consequences or impact of the options they choose. In this paper we describe our experience working with configurable bioinformatics tools. We find limited documentation and help for combining and selecting options along with variation in both functionality and performance. We also find previously undetected faults. We summarize our findings with a set of lessons learned, and present a roadmap for creating automated techniques to interact with bioinformatics software. We believe these will generalize to other types of scientific software.\",\n \"Title: Configurations in Android testing: they matter.\\nYear: 2018\\n\\nAbstract: Android has rocketed to the top of the mobile market thanks in large part to its open source model. Vendors use Android for their devices for free, and companies make customizations to suit their needs. This has resulted in a myriad of configurations that are extant in the user space today. In this paper, we show that differences in configurations, if ignored, can lead to differences in test outputs and code coverage. Consequently, researchers who develop new testing techniques and evaluate them on only one or two configurations are missing a necessary dimension in their experiments and developers who ignore this may release buggy software. In a large study on 18 apps across 88 configurations, we show that only one of the 18 apps studied showed no variation at all. The rest showed variation in either, or both, code coverage and test results. 15% of the 2,000 plus test cases across all of the apps vary, and some of the variation is subtle, i.e. not just a test crash. Our results suggest that configurations in Android testing do matter and that developers need to test using configuration-aware techniques.\",\n \"Title: An evolutionary approach for analyzing Alloy specifications.\\nYear: 2018\\n\\nAbstract: Formal methods use mathematical notations and logical reasoning to precisely define a program's specifications, from which we can instantiate valid instances of a system. With these techniques we can perform a multitude of tasks to check system dependability. Despite the existence of many automated tools including ones considered lightweight, they still lack a strong adoption in practice. At the crux of this problem, is scalability and applicability to large real world applications. In this paper we show how to relax the completeness guarantee without much loss, since soundness is maintained. We have extended a popular lightweight analysis, Alloy, with a genetic algorithm. Our new tool, EvoAlloy, works at the level of finite relations generated by Kodkod and evolves the chromosomes based on the failed constraints. In a feasibility study we demonstrate that we can find solutions to a set of specifications beyond the scope where traditional Alloy fails. While small specifications take longer with EvoAlloy, the scalability means we can handle larger specifications. Our future vision is that when specifications are small we can maintain both soundness and completeness, but when this fails, EvoAlloy can switch to its genetic algorithm.\",\n \"Title: The Assurance Recipe: Facilitating Assurance Patterns\\nYear: 2018\\n\\nAbstract: As assurance cases have grown in popularity for safety-critical systems, so too has their complexity and thus the need for methods to systematically build them. Assurance cases can grow too large and too abstract for anyone but the original builders to understand, making reuse difficult. Reuse is important because different systems might have identical or similar components, and a good solution for one system should be applicable to similar systems. Prior research has shown engineers can alleviate some of the complexity issues through modularity and identifying common patterns which are more easily understood for reuse across different systems. However, we believe these patterns are too complicated for users who lack expertise in software engineering or assurance cases. This paper suggests the concept of lower-level patterns which we call recipes. We use the safety-critical field of synthetic biology, as an example discipline to demonstrate how a recipe can be built and applied.\",\n \"Title: Modeling and testing a family of surgical robots: an experience report.\\nYear: 2018\\n\\nAbstract: Safety-critical applications often use dependability cases to validate that specified properties are invariant, or to demonstrate a counter example showing how that property might be violated. However, most dependability cases are written with a single product in mind. At the same time, software product lines (families of related software products) have been studied with the goal of modeling variability and commonality, and building family based techniques for both analysis and testing. However, there has been little work on building an end to end dependability case for a software product line (where a property is modeled, a counter example is found and then validated as a true positive via testing), and none that we know of in an emerging safety-critical domain, that of robotic surgery. In this paper, we study a family of surgical robots, that combine hardware and software, and are highly configurable, representing over 1300 unique robots. At the same time, they are considered safety-critical and should have associated dependability cases. We perform a case study to understand how we can bring together lightweight formal analysis, feature modeling, and testing to provide an end to end pipeline to find potential violations of important safety properties. In the process, we learned that there are some interesting and open challenges for the research community, which if solved will lead towards more dependable safety-critical cyber-physical systems.\",\n \"Title: The maturation of search-based software testing: successes and challenges\\nYear: 2019\\n\\nAbstract: In this paper we revisit the field of search-based software testing (SBST) in the context of its technological maturity. We highlight some successes with respect to tools, hybrid approaches, extensions and industry adoption. We then discuss some open challenges that remain for SBST including the need for new approaches to system testing, automated oracle generation, incorporating humans into the search process, and leveraging learning through hyper-heuristic search.\",\n \"Title: Partial Specifications for Program Repair\\nYear: 2021\\n\\nAbstract: In this paper we argue for using many partial test suites instead of one full test suite during program repair. This may provide a pool of simpler, yet correct patches, addressing both the overfitting and poor repair quality problem. To support this idea, we present some insight obtained running APR partial test suites on the well studied triangle program.\",\n \"Title: CRNRepair: Automated Program Repair of Chemical Reaction Networks\\nYear: 2021\\n\\nAbstract: Chemical reaction networks (CRNs) are abstractions of distributed networks that form the foundations of many natural phenomena such as biological processes. These can be encoded and/or compiled into DNA and have been shown to be Turing complete. Before CRNs are implemented in a physical environment, they are often simulated in programming environments. Researchers have recently designed a software...\",\n \"Title: HyperGI: automated detection and repair of information flow leakage\\nYear: 2021\\n\\nAbstract: BSTRACTMaintaining confidential information control in software is a persistent security problem where failure means secrets can be revealed via program behaviors. Information flow control techniques traditionally have been based on static or symbolic analyses --- limited in scalability and specialized to particular languages. When programs do leak secrets there are no approaches to automatically repair them unless the leak causes a functional test to fail. We present our vision for HyperGI, a genetic improvement framework that detects, localizes and repairs information leakage. Key elements of HyperGI include (1) the use of two orthogonal test suites, (2) a dynamic leak detection approach which estimates and localizes potential leaks, and (3) a repair component that produces a candidate patch using genetic improvement. We demonstrate the successful use of HyperGI on several programs with no failing functional test cases. We manually examine the resulting patches and identify trade-offs and future directions for fully realizing our vision.\",\n \"Title: Inference and Test Generation Using Program Invariants in Chemical Reaction Networks\\nYear: 2022\\n\\nAbstract: Chemical reaction networks (CRNs) are an emerging distributed computational paradigm where programs are encoded as a set of abstract chemical reactions. CRNs can be compiled into DNA strands which perform the computations in vitro, creating a foundation for intelligent nanodevices. Recent research proposed a software testing framework for stochastic CRN programs in simulation, however, it relies on existing program specifications. In practice, specifications are often lacking and when they do exist, transforming them into test cases is time-intensive and can be error prone. In this work, we propose an inference technique called ChemFlow which extracts 3 types of invariants from an existing CRN model. The extracted invariants can then be used for test generation or model validation against program implementations. We applied ChemFlow to 13 CRN programs ranging from toy examples to real biological models with hundreds of reactions. We find that the invariants provide strong fault detection and often exhibit less flakiness than specification derived tests. In the biological models we showed invariants to developers and they confirmed that some of these point to parts of the model that are biologically incorrect or incomplete suggesting we may be able to use ChemFlow to improve model quality.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":77,"string":"77"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13268,"string":"13,268"}}},{"rowIdx":13269,"cells":{"question_id":{"kind":"string","value":"Zhengtao Ding"},"question":{"kind":"string","value":"Generate an abstract for the title \"Reduced Order Observer Design for Nonlinear Systems with Control Applications\" using the following items: 1. reduced-order observer\n2. nonlinear dynamic systems\n3. control design\n4. unmeasured system state variables\n5. unstable nonlinear zero dynamics"},"answer":{"kind":"string","value":"This paper presents a reduced-order observer design for nonlinear dynamic systems. The proposed reduced-order observer can be applied to a larger class of nonlinear dynamic systems than the systems with linear observer dynamics. The proposed observer design has also been used for control design of a class of nonlinear dynamic systems with the nonlinearity in the unmeasured system state variables. An example is included to demonstrate that the proposed observer and control design method can be used to stabilize a nonlinear dynamic system with unstable nonlinear zero dynamics."},"correct_answer":{"kind":"string","value":"This paper presents a reduced-order observer design for nonlinear dynamic systems. The proposed reduced-order observer can be applied to a larger class of nonlinear dynamic systems than the systems with linear observer dynamics. The proposed observer design has also been used for control design of a class of nonlinear dynamic systems with the nonlinearity in the unmeasured system state variables. An example is included to demonstrate that the proposed observer and control design method can be used to stabilize a nonlinear dynamic system with unstable nonlinear zero dynamics."},"documents":{"kind":"list like","value":["Title: Brief Global output regulation of uncertain nonlinear systems with exogenous signals\nYear: 2001\n\nAbstract: The paper addresses the global output tracking of a class of uncertain output feedback systems affected by disturbances which are generated from a known exosystem. The system uncertainty is parametrized by a unknown parameter vector, which is absorbed in the parametrization of disturbances. A controller based on the estimation of states and disturbances from the output is designed using backstepping to asymptotically track arbitrary trajectories and to guarantee the boundedness of all other variables. The proposed control algorithm provides an alternative with global tracking to structurally stable regulation of nonlinear output feedback systems. It can also be viewed as a generalization to adaptive control algorithms.","Title: Convergent series observer design for a class of nonlinear systems\nYear: 2011\n\nAbstract: This paper deals with convergence analysis for power series solutions to a partial differential equation for nonlinear observer design with linear observer error dynamics. This power series solution is used to design the gain matrix for a Luenberger-like observer for nonlinear systems. The conditions are identified to guarantee the convergence of the series in l2. The linearized model of the original system is assumed to be anti-stable at the origin for the convenience of presentation. The convergent conditions can provide a guideline for nonlinear observer design with a truncated series for the observer gain.","Title: Asymptotic rejection of general periodic disturbances in output-feedback nonlinear systems\nYear: 2006\n\nAbstract: This note deals with asymptotic rejection of general periodic disturbances in nonlinear systems. A novel approach based on integrations over the half of disturbance period is proposed to estimate disturbances which may not be harmonic or even smooth. The proposed algorithm uses the period and the knowledge of the basic disturbance patterns, while the amplitude and phase of the disturbances are unknown, and they are estimated. Based on the estimated disturbance, the disturbance rejection algorithm is then proposed for linear systems and nonlinear systems in the output feedback format, which asymptotically rejects the periodical disturbances. The proposed algorithm extends asymptotic rejection of unknown disturbances from sinusoidal disturbances to a more general class of periodical disturbances which includes zero-mean square waves, symmetric triangular waves etc.","Title: Asymptotic Rejection Of Unmatched General Periodic Disturbances In A Class Of Non-Minimum-Phase Non-Linear Systems\nYear: 2009\n\nAbstract: This article deals with asymptotic rejection of unmatched general periodic disturbances in nonminimum phase non-linear output feedback systems. The steady state responses are defined for unstable systems subject to general periodic disturbances, and the generalised gain and phase are defined for stable systems subject to general periodic disturbances. Based on the new definitions, new results are obtained for the equivalent input disturbance and disturbance estimation. An Lp-convergent estimate of the equivalent input disturbance is incorporated in the control design to ensure the asymptotic rejection of unmatched general periodic disturbances while maintaining the stability of the non-linear system.","Title: Asymptotic rejection of unknown sinusoidal disturbances in nonlinear systems\nYear: 2007\n\nAbstract: This paper deals with global disturbance rejection of nonlinear systems. The disturbance is assumed to be sinusoidal with completely unknown phases, amplitude, and frequencies, but the number of distinct frequencies or the order of the corresponding unknown linear exosystem is known. Different from the common structural assumptions of nonlinear systems needed in literature for disturbance rejection of nonlinear systems, the proposed method only requires the information of control design with a known Lyapunov function when the system is disturbance-free, and a mild assumption needed for internal model design. The proposed disturbance rejection algorithm extends complete global rejection of unknown sinusoidal disturbances for nonlinear dynamic systems beyond the common nonlinear models such as the strict feedback forms and the output feedback forms.","Title: Asymptotic rejection of finite frequency modes of general periodic disturbances in output feedback nonlinear systems\nYear: 2008\n\nAbstract: This paper deals with asymptotic rejection of individual frequency modes in nonlinear systems under general periodic disturbances. An iterative observer design is proposed for disturbance estimation, and it is shown that the proposed observer design successfully extracts individual frequency modes from general periodic disturbances. The proposed iterative observer design is then applied to asymptotic rejection of nonlinear systems under general periodic disturbances.","Title: Asymptotic rejection of asymmetric periodic disturbances in output-feedback nonlinear systems\nYear: 2007\n\nAbstract: This paper deals with asymptotic rejection of periodic disturbances which may have asymmetric basic wave patterns. This class of disturbances covers asymmetric wave forms in the half-period such as alternating sawtooth wave form, some disturbances which are generated from nonlinear oscillation such as Van de Pol oscillators, as well as disturbances with symmetric half-period wave forms such as sinusoidal disturbances and triangular disturbances etc. The systems considered in this paper can be transformed to the nonlinear output feedback form. The amplitude and phase of the disturbances are unknown. The novel concept of integral phase shift is introduced together with the newly introduced half-period integration operator to investigate the invariant properties of asymmetric periodic disturbances. They are used for the estimation of unknown disturbances in the systems, together with observer design techniques to deal with nonlinearity. The proposed control design with the disturbance estimation asymptotically rejects the unknown disturbance, and ensures the overall stability of the system.","Title: Rejection of Nonharmonic Disturbances in Nonlinear Systems With Semi-Global Stability\nYear: 2008\n\nAbstract: An asymptotic rejection algorithm is proposed for nonlinear dynamic systems under nonharmonic periodic disturbances generated from nonlinear exosystems. The dynamic systems considered in this brief are in the normal form, a larger class of nonlinear dynamic systems than the output feedback systems. A new internal model design method is used to exploit the nonlinearities in the exosystem, together ...","Title: Observer Design in Convergent Series for a Class of Nonlinear Systems.\nYear: 2012\n\nAbstract: This paper deals with convergence analysis for power series solutions to a partial differential equation for nonlinear observer design with linear observer error dynamics. This power series solution is used to design the gain matrix for a Luenberger-like observer for nonlinear systems. An explicit domain of convergence around the origin is identified, which is related to the relative sizes of high-order terms in the original nonlinear system with respect to the linearized model. The convergent conditions can provide a guideline for nonlinear observer design with a truncated series for the observer gain.","Title: Adaptive control of triangular systems with nonlinear parameterization\nYear: 2001\n\nAbstract: The paper deals with adaptive control of uncertain nonlinear triangular systems whose uncertainty is characterized by nonlinear parameterization of unknown constant vectors. Robust adaptive control techniques are incorporated in the backstepping control design with flat zones to tackle the nonlinear parameterization together with a novel smooth projection algorithm for parameter estimation. The proposed algorithm guarantees the global boundedness of all the variables and the convergence of the tracking error to any prespecified small limit.","Title: Adaptive stabilization of a class of nonlinear systems with unstable internal dynamics\nYear: 2002\n\nAbstract: The paper considers global stabilization of a class of uncertain nonlinear output feedback systems with unstable internal dynamics. The coefficients which characterize the internal dynamics are allowed to be functions of system output, and the uncertainty of the system is characterized by a unknown constant parameter vector. The key step in the proposed control design is to estimate the internal state variables and impose control over them. The control design is presented first for systems without unknown parameters, and then for the systems with unknown parameters using adaptive control techniques.","Title: On output regulation of discrete-time T-S fuzzy systems\nYear: 2008\n\nAbstract: The output regulation problem is discussed for a class of discrete-time T-S fuzzy systems under periodic disturbances generated form the so-called exosystems. With the assumption that the subsystem in each rule is of the controllable canonical form, the regulation equations are solvable if and only if the poles of the exosystem are different from those of the fuzzy system. By exploiting the structural information encoded in the fuzzy rules, a piecewise state feedback control law can then be constructed to achieve asymptotic rejecting and/or tracking of the unwanted disturbances or the desired trajectory.","Title: One-Sided Lipschitz Observer Design For Circadian Models\nYear: 2011\n\nAbstract: This paper deals with observer designs to mathematical models of circadian rhythms which exist in every living organism. Two mathematical models are considered, with a 3rd order model for Neurospora, and a 7th order model for Mammals. The observer design is based on systems with Lipschitz nonlinearities. In particular, observers based on onesided Lipschitz condition are investigated, and the observers are then designed for both circadian models. Detailed analysis is performed for nonlinear functions in the models to show the one-sided Lipschitz observers can indeed be applied. Several simulations studies of proposed observers are carried out with the results shown in this paper.","Title: Global stabilization and disturbance suppression of a class of nonlinear systems with uncertain internal model\nYear: 2003\n\nAbstract: This paper deals with global stabilization and disturbance suppression of a class of nonlinear systems using output feedback. The disturbances generated from a unknown linear exosystem are completely compensated. The order of the exosystem is assumed known and the eigenvalues are distinct. No other assumptions are needed in the control design. This means that the proposed control design is able to completely compensate the disturbances without knowing their amplitudes, frequencies and phases, as long as the number of different frequency components in the disturbances is known. A new formulation of state estimation is introduced to ensure the global stabilization and complete disturbance suppression. Adaptive control technique is used to design an adaptive internal model based on a recently introduced formulation of unknown exosystems and the parameters in the adaptive internal model converge to the actual values, from which the unknown disturbance frequencies can be calculated. In the proposed control design, a number of control coefficients are made adaptive so that the result is global with respect to unknown frequencies in the disturbances.","Title: Output Regulation of Uncertain Nonlinear Systems With Nonlinear Exosystems\nYear: 2006\n\nAbstract: An adaptive control algorithm is proposed for output regulation of uncertain nonlinear systems in output feedback form under disturbances generated from nonlinear exosystems. A new nonlinear internal model is proposed to generate the desired input term for suppression of the disturbances. The proposed internal model design is based on boundedness of the disturbance, high gain design and saturation and the designed internal model is capable to tackle disturbances in any specied initial conditions. Some uncertainties in the systems are allowed, and they are tackled using nonlinear dominant functions and an adaptive control coefcient. The proposed control algorithm ensures the global convergence of the state variables to the invariant manifold, which implies that the measurement or the tracking error approaches to zero asymptotically.","Title: A flat-zone modification for robust adaptive control of nonlinear output feedback systems with unknown high-frequency gains\nYear: 2002\n\nAbstract: This note deals with adaptive control of perturbed nonlinear output feedback systems with unknown high-frequency gains. The disturbances in the systems are assumed to be bounded, but the bounds are unknown. A flat-zone modification is proposed to incorporate both the bound estimation and Nussbaum gain design in the nonlinear adaptive control. To ensure the differentiability of stabilizing functions needed for backstepping design, high order terms are introduced in the Lyapunov function candidate with a flat zone around the neighborhood of the origin. The output tracking error converges to an arbitrarily small interval around zero.","Title: Online optimal queue scheme for enhanced transport over wireless links\nYear: 2010\n\nAbstract: In this paper we address the performance corruption in TCP flows over a wireless link by employing an online optimal control scheme. It has been shown that TCP flows exhibit low data rate in wireless scenario due to fading channel, frequent hands off and high bit error. The high bit error rate, which is an intrinsic link characteristic of wireless network, contributes to more packets losses, which cut back the congestion window and decrease the transmission rate. Meanwhile, the unpredictable traffic situations and varying network capacity all lead to dynamics changing of TCP behavior. To overcome these problems, we propose a data-based AQM with an optimality criterion to minimize the fluctuation of the queue length and aggregate rate from link capacity. The adaptive control gain is updated by solving the equivalent Hamiltonian without knowing the systems parameters. The simulation results show that the proposed scheme effectively improves the performance of TCP over wireless link with strong robustness to time-varying scenarios of wireless networks.","Title: Polynomial solutions to regulation equations with nonlinear exosystems\nYear: 2010\n\nAbstract: Solving a regulation equation is the core part of output regulation problem. It remains as a challenge to find a solution for a general case of output regulation with nonlinear exosystems. This paper identifies certain conditions for the existence of a polynomial solution, and propose an iterative method for obtaining polynomial solutions for regulation equations with nonlinear exosystems. The proposed solution is then applied for the internal model design in output regulation of nonlinear systems.","Title: Consensus output regulation without state estimation for a class of nonlinear systems\nYear: 2012\n\nAbstract: This paper deals with consensus output regulation of network connected multi-agent systems. Every agent or subsystem is a nonlinear system in the output feedback form, and has identical dynamics. The subsystem dynamics are influenced by state variables generated from an exosystem. The outputs of the subsystems are required to follow a desired trajectory which is a function of the exosystem state. Only some subsystems have access to the desired trajectory, and other subsystems will have to rely on the exchange of information through the network. In this paper, a consensus control design is proposed to ensure that the outputs of all the subsystems converge to the same desired output trajectory by exploiting the internal model design strategy. The proposed control design only uses the relative outputs of the subsystems, and does not require the estimation of subsystem state variables.","Title: Adaptive consensus output regulation of a class of heterogeneous nonlinear systems\nYear: 2013\n\nAbstract: This paper deals with consensus output regulation of a class of network-connected nonlinear systems with unknown parameters. The subsystem may have different dynamics with uncertainties, and under the disturbances generated from an exosystem, and only some subsystems have access to the desired output, which is also formulated as an output of the exosystem, following the standard formulation of output regulation of nonlinear systems. For the clarity of the presentation, we only show the control design of the systems with 2nd-order dynamics. The proposed design makes use of some latest results on Laplacian matrices, and a new design of the internal model, which is based on known functions with unknown constant parameters. Adaptive backstepping design techniques are integrated with the consensus control design to tackle the nonlinearity and unknown constant parameters in the system, and unknown functions relating to the state of the exosystem. The proposed control inputs and adaptive laws are fully decentralized, and ensure the asymptotic convergence to zero of the regulation errors.","Title: Global adaptive output regulation of a class of nonlinear systems with nonlinear exosystems\nYear: 2007\n\nAbstract: This paper deals with global output regulation with nonlinear exosystems for a class of uncertain nonlinear output feedback systems. The circle criterion is exploited for the internal model design to accommodate the nonlinearities in the exosystems, and the explicit conditions are given for the exosystems such that the proposed internal model design can be applied. The uncertainties of the output feedback systems are in the form of unknown constant parameters, and adaptive control techniques are used to ensure the global stability of the proposed control design for output regulation.","Title: Output regulation of a class of continuous-time Markovian jumping systems\nYear: 2013\n\nAbstract: This paper studies output regulation for continuous-time Markovian jumping systems (MJSs), for which mode-dependent regulation equations are investigated. With the extension of regulation scheme to MJSs by stochastic Lyaponov-Krasovskii functional framework, sufficient conditions are, respectively, obtained for state feedback and error feedback. The resulting closed-loop system is guaranteed to be stochastically stable and the output regulation error almost asymptotically converges to zero. A semi-definite optimization approach via disciplined convex programming is adopted to ensure relaxed solutions of the regulation equations. Finally, two numerical simulations are given to illustrate the performance of the proposed approach.","Title: Global Output Feedback Stabilization of Nonlinear Systems With Nonlinearity of Unmeasured States\nYear: 2009\n\nAbstract: This technical note deals with global stabilization of class of nonlinear dynamic systems using output feedback. The systems considered have nonlinear functions of unmeasured state variables, in addition to nonlinear functions of the system outputs. A nonlinear reduced order observer is proposed to estimate the unknown system states. Estimated state variables are used for control design, together with the system output. Certain conditions are identified for the proposed observer and control design. Under these conditions, the proposed control design ensures the global asymptotic stability of the closed-loop control system.","Title: Nonlinear control design and stability analysis of a small-scale unmanned helicopter\nYear: 2013\n\nAbstract: The main focus of this paper is to design nonlinear controllers and analyse stability for a miniature unmanned helicopter. Our work is to utilize nonlinear control methodology to command dynamics of unmanned helicopter which has been divided into slower translational dynamics (outer-loop) and faster orientation dynamics (inner-loop), thus exhibiting hierarchical structure. The attitude angles and position which separately belong to the inner-loop and the outer-loop can be independently controlled by backstepping control strategy. Stability analysis of the helicopter system including flapping dynamics has been presented after designing controllers.","Title: Optimization of tool change timing in a nut forming process using genetic algorithms\nYear: 2004\n\nAbstract: In this study, we consider an optimization problem arising from tool change scheduling of a nut forming process. The process has nine punches with different life spans. The objective of the study is to optimize the tool change timing of the nine punches in order to maximize the production yield. The punch life spans can be extended by means of Ti coating, but coated punches are more expensive. We also evaluate whether it is worth using coated punches. A cost model is developed for the nut forming process. In the developed cost model, we consider the reliability of the nine punches, the periods taken for changing punches, scrap cost and production yield. The cost function for the process is complex, and is difficult to be solved using conventional optimization methods. Therefore, genetic algorithms are used to solve the problems. We have developed our own genetic algorithms using Java programs and applied the developed genetic algorithms to provide solutions to the optimization problem.","Title: Universal disturbance rejection for nonlinear systems in output feedback form\nYear: 2003\n\nAbstract: This note deals with global disturbance rejection via output feedback of a class of uncertain nonlinear systems subject to a class of unknown disturbances. Both the uncertainty in the system model and the uncertainty in the exosystem are tackled concurrently. The disturbances generated from an unknown linear exosystem are completely rejected. The order of the exosystem is assumed known, and the eigenvalues are distinct. The system is assumed in the format of the minimum-phase output feedback form, with no knowledge of the values of any system parameters, including the high-frequency gain. No other assumptions are needed in the control design. A new set of filters are introduced for state estimation. The stability of the internal model is exploited to design a new auxiliary error, involving both the unknown parameters of the reformatted exosystem and those of the system, which makes it possible to group all the unknown parameters in a format suitable to adaptive control design. A Nussbaum gain is introduced in adaptive control design to tackle the unknown high-frequency gain and a number of control coefficients are also made adaptive so that the disturbance rejection is global with respect to unknown frequencies in the disturbances.","Title: Circadian phase control using observer-based back-stepping design.\nYear: 2011\n\nAbstract: Circadian rhythms exist in most of living species, and control their daily biological activities. Disruption of these rhythms can cause circadian disorders or phase shifts. This paper deals with phase recovery of circadian rhythms. The phase restoration is carried out by synchronization of estimated trajectories of unknown state variables of a controlled system with trajectories of a reference system via back-stepping. The unknown state variables are estimated by using recently developed one-sided Lipschitz observer. Both reference and controlled systems are based on a 3rd order model of Neurospora circadian rhythms. Several simulation studies are carried out to verify the operation of proposed synchronization scheme with results shown in this paper.","Title: Global adaptive output feedback stabilization of nonlinear systems of any relative degree with unknown high-frequency gains\nYear: 1998\n\nAbstract: This paper presents an algorithm for adaptive output feedback stabilization of nonlinear single-input/single-output, minimum phase systems of any relative degree which are linear with respect to input and unknown constant parameter vector. The algorithm does not require the knowledge of the sign of the high-frequency gain","Title: Consensus Output Regulation of a Class of Heterogeneous Nonlinear Systems\nYear: 2013\n\nAbstract: This technical note deals with consensus output regulation of network connected multi-agent systems. Every agent or subsystem is a nonlinear system in the output feedback form with relative degree one, but subsystems may have different dynamics in terms of different nonlinear functions and even different system orders. The subsystem dynamics are influenced by state variables generated from an exosystem. The outputs of the subsystems are required to follow a desired trajectory which is a function of the exosystem state. Only some subsystems have access to the desired trajectory, and the other subsystems will have to rely on the exchange of information through the network. In this technical note, a consensus control design is proposed to ensure that the outputs of all the subsystems converge to the same desired output trajectory by exploiting the internal model design strategy. The proposed control design only uses the relative outputs of the subsystems, and does not require the estimation of subsystem state variables.","Title: Global Output Regulation Of A Class Of Nonlinear Systems With Nonlinear Exosystems\nYear: 2006\n\nAbstract: This paper deals with global output regulation with nonlinear exosystems for a class of uncertain nonlinear output feedback systems. The circle criterion is exploited for the internal model design to accommodate the nonlinearities in the exosystems, and the explicit conditions are given for the exosystems such that proposed internal model design can be applied. The uncertainties of the output feedback systems are in the form of unknown constant parameters, and adaptive control techniques are used to ensure the global stability of the proposed control design for output regulation.","Title: Decentralized Output Regulation For Large-Scale Output-Feedback Nonlinear Systems With Nonlinear Exosystem\nYear: 2007\n\nAbstract: This paper presents for the first time a solution to the problem of global decentralized output regulation for a class of large-scale uncertain nonlinear systems with nonlinear exosystem using error information. The proposed constructive approach does not require any matching conditions on the parametric uncertainties with some growth conditions on the interacting output nonlinearities. Two decentralized error feedback adaptive control strategies have been presented under some assumptions.","Title: Adaptive consensus output regulation of a class of nonlinear systems with unknown high-frequency gain.\nYear: 2015\n\nAbstract: This paper deals with adaptive consensus output regulation of a class of network-connected nonlinear systems with completely unknown parameters, including the high frequency gains of the subsystems. The subsystems may have different dynamics, as long as the relative degrees are the same. A new type of Nussbaum gain is proposed to deal with adaptive consensus control of network-connected systems without any knowledge of the high frequency gains. Adaptive laws and internal models are designed for the subsystems to deal with unknown parameters for tracking trajectories and unknown system parameters. In the control design, only the relative information of subsystem outputs are used, provided that regulation error of one of the subsystems is available. The proposed control inputs and the adaptive laws are decentralized. If the relative degree is one, only the relative subsystem outputs are exchanged. For the case of higher relative degrees, the nonlinear model structure of the subsystems is exploited for backstepping control design, and some variables generated by the subsystem controllers are exchanged among the subsystems in the neighbourhood defined by the connection graph.","Title: Consensus control of a class of nonlinear systems with communication time delay\nYear: 2014\n\nAbstract: This paper investigates the consensus control of a class of Lipchitz nonlinear multi-agent systems with communication time delay. Upon exploring certain features of Laplacian matrix, global consensus control conditions are identified using Lyapunov method in the time domain. The proposed control only uses relative state information of the system. Numerical simulations validate the effectiveness of the proposed controller design and show its performance with different nonlinearities and time delays.","Title: Truncated state prediction for control of Lipschitz nonlinear systems with input delay\nYear: 2014\n\nAbstract: This paper deals with control design for Lipschitz nonlinear systems with input delay. A prediction of the system state over the delay period is approximated by the finite dimensional term of the classical state predictor, with the distributed term dropped. This truncated predictor is then used for the state feedback control. A set of conditions for the stability of the closed-loop system are identified based on the analysis in the framework of Lyapunov-Krasovskii functionals. These conditions are presented in the form of matrix inequalities, and generalize the results for the truncated predictor feedback for linear systems to nonlinear systems. The conditions can be checked using LMIs with a set of iterative scalar parameters, in a way similar to the linear systems counterpart.","Title: State feedback consensus controller with states estimation for a nonlinear class system\nYear: 2014\n\nAbstract: This paper deals with consensus control of a class nonlinear system with Lipschitz nonlinearities. The conditions for consensus controller with state estimation are identified by exploring certain features of the Laplacian matrix. Conditions of stability for both are analyzed through Lyapunov functions. The controller utilizes the state estimation provided by the observer based on the output feedback information. To demonstrate the effectiveness of the designed state estimation controller, a simulation example is given.","Title: Adaptive output regulation of a class of nonlinear systems with completely unknown parameters\nYear: 2003\n\nAbstract: A complete control design is proposed for output regulation of nonlinear systems in the output feedback form under disturbance generated from neutral stable exosystems. Only the structural information of the system and the exosystem is needed, such as the order of the system and the order of the exosystem, the relative degree. No information of any parameters in the system and the exosystem is needed, including the sign of the high frequency gain. The proposed control design using the measurement feedback consists of a filtered transform for the system with relative degree greater than one, an adaptive internal model, a Nussbaum gain and a number of adaptive parameters. The state variables under the proposed control asymptotically converges to the invariant manifold, which implies that the measurement or the tracking error approaches to zero asymptotically.","Title: Unknown sinusoidal disturbance rejection of nonlinear dynamic systems\nYear: 2006\n\nAbstract: This paper deals with global disturbance rejection of nonlinear systems. The disturbance is assumed to be sinusoidal with completely unknown phases, amplitude, and frequencies, but the number of distinct frequencies or the order of the corresponding unknown linear exosystem is known. Different from the common structural assumptions of nonlinear systems needed in literature for disturbance rejection of nonlinear systems, the proposed method only requires the information of control design when the system is disturbance-free, and a mild assumption needed for internal model design. The proposed disturbance rejection algorithm extends complete global rejection of unknown sinusoidal disturbances for nonlinear dynamic systems beyond the common nonlinear models such as the strict feedback forms and the output feedback forms","Title: Asymptotic Rejection of Disturbances with Asymmetric Wave Patterns in Output-feedback Nonlinear Systems\nYear: 2006\n\nAbstract: This paper deals with asymptotic rejection of periodic disturbances which may have asymmetric basic wave patterns. This class of disturbances covers asymmetric wave forms in the half period such as alternating sawtooth wave form, some disturbances which are generated from nonlinear oscillation such as Van de Pol oscillators, as well as disturbances with symmetric half-period wave forms such as sinusoidal disturbances and triangular disturbances etc. The systems considered in this paper can be transformed to the nonlinear output feedback form. The amplitude and phase of the disturbances are unknown. A new delay operator and the newly proposed half-period integration technique are used for estimation of the unknown disturbances in the systems, together with observer design techniques to deal with nonlinearity. The proposed control design with the disturbance estimation asymptotically rejects the unknown disturbance, and ensures the overall stability of the system","Title: Asymptotic Rejection of Unmatched General Periodic Disturbances in Output-feedback Nonlinear Systems\nYear: 2007\n\nAbstract: This paper deals with asymptotic rejection of unmatched general periodic disturbances in nonlinear systems in the output feedback form. The system considered in this paper removes the restriction imposed on the asymptotic rejection of general disturbance in the literature that the disturbance only enters the system through the input channel. The class of disturbances covers asymmetric wave forms in the half period such as alternating sawtooth wave form, some disturbances which are generated from nonlinear oscillation such as Van de Pol oscillators, as well as disturbances with symmetric half-period wave forms such as sinusoidal disturbances and triangular disturbances etc. The amplitude and phase of the disturbances are unknown. The steady state solutions of stable linear systems are shown to be half-period alternating if the inputs are. An equivalent input disturbance is then obtained through a partial state transform of the nonlinear system, and application of the results of the periodic solutions of stable linear systems to the zero-dynamics of the nonlinear system which are linear. With the equivalent input disturbances, the asymptotic rejection of unmatched general disturbances are converted to the matched cases. As the half-period alternating property is persevered in the equivalent input disturbances, recently proposed asymptotic rejection, based on the half-period integration and delay operations, of general periodic disturbances with asymmetric half-period wave forms can be applied, with a careful consideration of the phase offsets in the equivalent input disturbances. The proposed control design with the disturbance estimation asymptotically rejects the unknown disturbance, and ensures the overall stability of the system.","Title: Consensus control of a class of nonlinear systems\nYear: 2013\n\nAbstract: This paper deals with consensus control of a class nonlinear systems with Lipschitz nonlinearities. Certain features of the Laplacian matrix are further explored to identify conditions for global consensus control. Under the identified conditions, consensus control and stability of the proposed control are analyzed in time-domain through Lyapunov functions. The proposed control uses relative state information of the system. A simulation study is included to demonstrate the proposed control designs with some simulation results shown.","Title: Consensus Disturbance Rejection with Disturbance Observers\nYear: 2015\n\nAbstract: This paper deals with consensus disturbance rejection of network-connected dynamic systems using disturbance observers. The control objective of consensus disturbance rejection is to achieve a common state trajectory for the networkconnected subsystems that are under deterministic disturbances. The difference from the existing disturbance rejection methods is that only the relative state information is used for disturbance rejection, and as a consequence of using the relative state information, only the part of the disturbances that affect the common trajectories will be rejected. The conditions for designing disturbance observers for consensus control are identified for networked-connect multi-agent systems. Certain features of the individual subsystems are analysed for possible implementation of disturbance-observer based rejection, and the disturbance observers are designed based on the relative state information obtained from the neighbouring subsystems under different network connections. When the network connectivity is available for the disturbance observer design, consensus disturbance rejection is achieved for a directed network with a spanning tree. A fully distributed consensus disturbance rejection design is presented for an undirected network with the use of adaptive parameters for the estimation of the unknown network connectivity. Disturbance observers are also proposed for disturbance rejection in the leader-follower consensus control.","Title: Decentralized Output Regulation of Large Scale Nonlinear Systems with Delay.\nYear: 2009\n\nAbstract: This paper deals with output regulation of a class of large-scale nonlinear systems with delays. Each of the subsystems is in the output feedback form, with nonlinear functions of the subsystem output and the outputs of other subsystems. The system outputs are subject to unknown constant delays. Both the system dynamics and the measurements are subject to unknown disturbances generated from unknown linear exosystems. Decentralized control design approach is adopted to design local controllers using measurements or regulated errors in each subsystems. It is shown in this paper that delays in the outputs of subsystems do not affect the existence of desired feedforward control input, and the invariant manifolds and the desired feedforward inputs always exist if the nonlinear functions are polynomials. Through a special parameterization of an augmented exosystem, an internal model can be designed for each subsystem, without the involvements of the uncertain parameters. The uncertain parameters affected by the uncertainty of the exosystem are estimated using adaptive control laws, and adaptive coefficients in the control inputs are used to suppress other uncertainties. The proposed decentralized adaptive control strategy ensures the global stability of the entire system, and the convergence to zero of the regulated errors. An example is included to demonstrate the proposed control strategy.","Title: Consensus Control of a Class of Lipschitz Nonlinear Systems With Input Delay\nYear: 2015\n\nAbstract: This paper deals with the consensus control design for Lipschitz nonlinear multi-agent systems with input delay. The Artstein-Kwon-Pearson reduction method is employed to deal with the input delay and the integral term that remains in the transformed system is analyzed by using Krasovskii functional. Upon exploring certain features of the Laplacian matrix, sufficient conditions for global stabilit...","Title: Adaptive Rejection Of Non-Linear Disturbances In Extended Non-Linear Output Feedback Systems\nYear: 2008\n\nAbstract: This paper deals with asymptotic rejection of disturbances generated from non-linear exosystems for uncertain nonlinear systems in an extended output feedback form, which allows the vector field coupled with the system input to have different nonlinear functions of the system output as its elements. A new internal model design is proposed to deal with nonlinear functions of the system output that are coupled with the input and the unknown disturbance. Adaptive control techniques are then used to deal with the uncertainty in the system. The proposed adaptive disturbance rejection algorithm with the new internal model design ensures the asymptotic rejection of the unknown nonlinear disturbance and the boundedness of all the variables.","Title: Asymptotic Rejection Of Unmatched General Periodic Disturbances With Nonlinear Lipschitz Internal Models\nYear: 2013\n\nAbstract: This article deals with asymptotic rejection of general periodic disturbances in a class of nonlinear systems by exploiting observer design with Lipschitz output nonlinearities for the internal model design. It is shown that a class of general periodic distances can be modelled as outputs of linear systems with nonlinear output functions. Based on this observation, a nonlinear observer design with output Lipschitz nonlinearities is investigated and integrated for the internal model design to estimate the phase and amplitude of the desired feedforward input for asymptotic disturbance rejection in a class of nonlinear systems with input-to-state stable zero dynamics. A number of problems on disturbance rejection can be formulated in the disturbance rejection form shown in this article to which the proposed nonlinear Lipschitz internal model can be applied. Two examples are included to demonstrate the proposed design strategies.","Title: Consensus Control Of A Class Of Lipschitz Nonlinear Systems\nYear: 2014\n\nAbstract: This paper deals with consensus control of a class of nonlinear systems with Lipschitz nonlinearities. Certain features of the Laplacian matrix are explored in the real Jordan form to identify conditions for global consensus control. Under the identified conditions, consensus control and stability of the proposed control are analysed in time domain through Lyapunov functions in real domain. The proposed control uses relative state information of the system. A simulation study is included to demonstrate the proposed control designs with some simulation results shown.","Title: Distributed adaptive controllers for cooperative output regulation of heterogeneous agents over directed graphs.\nYear: 2016\n\nAbstract: This paper considers the cooperative output regulation problem for linear multi-agent systems with a directed communication graph, heterogeneous linear subsystems, and an exosystem whose output is available to only a subset of subsystems. Both the cases with nominal and uncertain linear subsystems are studied. For the case with nominal linear subsystems, a distributed adaptive observer-based controller is designed, where the distributed adaptive observer is implemented for the subsystems to estimate the exogenous signal. For the case with uncertain linear subsystems, the proposed distributed observer and the internal model principle are combined to solve the robust cooperative output regulation problem. Compared with the existing works, one main contribution of this paper is that the proposed control schemes can be designed and implemented by each subsystem in a fully distributed fashion for a class of directed graphs.","Title: Truncated Prediction Output Feedback Control of a Class of Lipschitz Nonlinear Systems With Input Delay.\nYear: 2016\n\nAbstract: This brief addresses an output feedback control design for Lipschitz nonlinear systems in the presence of input delay. A nonlinear observer is introduced to estimate the system states. A truncated predictor state feedback, which is the conventional predictor state feedback law with the distributed term dropped, is implemented with the estimated states. Lyapunov-Krasovskii functionals are construct...","Title: Distributed adaptive consensus control of nonlinear output-feedback systems on directed graphs.\nYear: 2016\n\nAbstract: This paper deals with consensus control in leader-follower format of a class of network-connected uncertain nonlinear systems by output feedback. Each subsystem is in the nonlinear output feedback form with unknown parameters, and the connection graph among the subsystems is directed. Distributed adaptive control inputs are designed to achieve the consensus control in the sense that the subsystem states asymptotically follow the subsystem at node 0 with no input, which is also known as the leader. The proposed adaptive control only uses relative output measurements and the local information of the connection to each subsystem, and hence the proposed adaptive control is fully distributed. The proposed scheme is different from the consensus output regulation schemes literature, and the leader plays a similar role as a reference model in the classic model reference adaptive control.","Title: Global output regulation for a class of lower triangular nonlinear systems: A feedback domination approach.\nYear: 2017\n\nAbstract: An alternative but new nonlinear output regulation approach is proposed for a class of lower triangular nonlinear systems by using the tool of adding a power integrator. The invariant manifold is solved and represented in terms of various derivatives of disturbances and references. As such, it is unnecessary to suppose that the external disturbances being governed by certain exosystems anymore. The nonlinearities are not restricted to be sufficiently smooth due the utilization of feedback domination approach. It is shown that the time taken to reach the desired invariant manifold from any initial states under external disturbances is guaranteed to be finite time. The control law is concise in structure for implementation since the feedback domination is implemented in each of the recursive design step.","Title: Adaptive consensus disturbance rejection for multi-agent systems on directed graphs\nYear: 2016\n\nAbstract: In this paper, the adaptive consensus disturbance rejection problem is considered for the liner multi-agent systems under directed graphs. Based on the relative state information of the neighboring agents, the consensus protocols, including a state observer and a disturbance observer, are designed to guarantee that the consensus error goes to zero with the complete disturbance rejection. Furthermore, the state observer is designed in a fully distributed fashion with adaptive coupling gain, which has the advantage that the consensus protocol design is independent of the Laplacian matrix associated with the communication network. Finally, an example is given to verify the effectiveness of the theoretical results.","Title: Continuous Finite-Time Output Regulation of Nonlinear Systems With Unmatched Time-Varying Disturbances.\nYear: 2018\n\nAbstract: In this letter, a new continuous nonlinear controller ensuring finite-time output regulation is proposed for a class of nonlinear systems subject to unmatched time-varying disturbances. The controller is developed in a composite manner which combines invariant manifold principle, feedback domination, and disturbance observation techniques. A key idea is to realize the finite-time estimation of sys...","Title: Distributed Adaptive Consensus Output Regulation of Network-Connected Heterogeneous Unknown Linear Systems on Directed Graphs.\nYear: 2017\n\nAbstract: This technical note deals with output regulation of network connected linear systems under directed connection graphs. The subsystem dynamics are unknown and heterogeneous. The desired output signal, generated by a linear exosystem, is only available to some subsystems, and the others will estimate the exosystem state through the network connections for obtaining regulation errors. The exosystem is parametrised in a specific form such that it has a skew-symmetric system matrix, whose property is further explored in the estimation of exosystem state, and in the estimation of the desired feed-forward control inputs for output regulation. The unknown subsystem parameters are dealt with by the adaptive laws, driven by the estimated regulation errors. The proposed adaptive control strategy is fully distributed, in the sense that the control design only uses the information in the connected neighbourhood, without reference to the eigenstructure of the associated Laplacian matrix, as long as the connection graph is strongly connected.","Title: Truncated Predictor Control of Lipschitz Nonlinear Systems With Time-Varying Input Delay.\nYear: 2017\n\nAbstract: This note deals with control design for Lipschitz nonlinear systems with time-varying input delay. Based on a truncated prediction of the system state over the delay period, both a state and an output feedback control law are constructed. Within the framework of Lyapunov-Krasovskii functionals, a set of conditions are identified under which the closed-loop system under either the state feedback or the output feedback law is globally asymptotically stable at the origin. A numerical example is included to demonstrate the effectiveness of the proposed designs.","Title: Distributed Initialization-Free Cost-Optimal Charging Control of Plug-In Electric Vehicles for Demand Management.\nYear: 2017\n\nAbstract: This paper considers the optimal charging problem of plug-in electric vehicles (PEVs) on demand side management. PEVs provide a promising alternative solution to reduction of environmental pollution and fuel emission. With a large number of PEVs connected to the grid, a well-designed charging coordination approach is needed to release the impacts on the power system. A distributed cooperative cont...","Title: Formation control with disturbance rejection for a class of Lipschitz nonlinear systems.\nYear: 2017\n\nAbstract: In this paper, we consider the leader-follower formation control problem for general multi-agent systems with Lipschitz nonlinearity and unknown disturbances. To deal with the disturbances, a disturbance observer-based control strategy is developed for each follower. Then, a time-varying formation protocol is proposed based on the relative state of the neighbouring agents and sufficient conditions for global stability of the formation control are identified using Lyapunov method in the time domain. The proposed strategy and analysis guarantee that all signals in the closed-loop dynamics are uniformly ultimately bounded and the formation tracking errors converge to an arbitrarily small residual set. Finally, the validity of the proposed controller is demonstrated through a numerical example.","Title: A Truncated Prediction Approach to Consensus Control of Lipschitz Nonlinear Multiagent Systems With Input Delay.\nYear: 2017\n\nAbstract: This paper deals with the consensus control problem for Lipschitz nonlinear multiagent systems with input delay. A prediction of the agent state over the delay period is approximated by the zero input solution of the agent dynamics. The structure of a linear state feedback control algorithm is assumed for each agent based on such approximated state prediction. By transforming the Laplacian matrix ...","Title: Fixed-Time Consensus Tracking for Multiagent Systems With High-Order Integrator Dynamics.\nYear: 2018\n\nAbstract: This paper addresses the fixed-time leader-follower consensus problem for high-order integrator multiagent systems subject to matched external disturbances. A new cascade control structure, based on a fixed-time distributed observer, is developed to achieve the fixed-time consensus tracking control. A simulation example is included to show the efficacy and the performance of the proposed control structure with respect to different initial conditions.","Title: Distributed Adaptive Consensus Disturbance Rejection for Multi-Agent Systems on Directed Graphs.\nYear: 2018\n\nAbstract: This paper considers the distributed consensus disturbance rejection problem for general linear multiagent systems with deterministic disturbances under directed communication graphs. Based on the relative state information of the neighboring agents, the consensus protocols, which consist of two observers, including a state observer and a separate disturbance observer, are designed to guarantee th...","Title: Distributed Finite-Time Optimal Resource Management for Microgrids Based on Multi-Agent Framework.\nYear: 2018\n\nAbstract: This paper investigates the optimal resource management in a microgrid under various operating conditions. A two-level optimization system is proposed for the distributed optimal resource management based on a multi-agent system framework. The proposed strategy generates a reference of the optimal power output at the top level through local communication. This strategy only requires the informatio...","Title: Distributed Adaptive Convex Optimization on Directed Graphs via Continuous-Time Algorithms.\nYear: 2018\n\nAbstract: This note considers the distributed optimization problem on directed graphs with nonconvex local objective functions and the unknown network connectivity. A new adaptive algorithm is proposed to minimize a differentiable global objective function. By introducing dynamic coupling gains and updating the coupling gains using relative information of system states, the nonconvexity of local objective f...","Title: Simultaneous Mean and Covariance Correction Filter for Orbit Estimation.\nYear: 2018\n\nAbstract: This paper proposes a novel filtering design, from a viewpoint of identification instead of the conventional nonlinear estimation schemes (NESs), to improve the performance of orbit state estimation for a space target. First, a nonlinear perturbation is viewed or modeled as an unknown input (UI) coupled with the orbit state, to avoid the intractable nonlinear perturbation integral (INPI) required by NESs. Then, a simultaneous mean and covariance correction filter (SMCCF), based on a two-stage expectation maximization (EM) framework, is proposed to simply and analytically fit or identify the first two moments (FTM) of the perturbation (viewed as UI), instead of directly computing such the INPI in NESs. Orbit estimation performance is greatly improved by utilizing the fit UI-FTM to simultaneously correct the state estimation and its covariance. Third, depending on whether enough information is mined, SMCCF should outperform existing NESs or the standard identification algorithms (which view the UI as a constant independent of the state and only utilize the identified UI-mean to correct the state estimation, regardless of its covariance), since it further incorporates the useful covariance information in addition to the mean of the UI. Finally, our simulations demonstrate the superior performance of SMCCF via an orbit estimation example.","Title: H∞ Control for Discrete-time Linear Systems by Integrating Off-policy Q-learning and Zero-sum Game.\nYear: 2018\n\nAbstract: This paper focuses on solving H∞ control problem by proposing a novel off-policy Q-learning algorithm under the framework of zero-sum game for discrete-time (DT) linear systems, using only the measured data along the system trajectories. Firstly, H∞ control problem is formulated, followed by the standard on-policy Q-learning algorithm in order to learn the H∞ controller gain. Secondly, behavior control policy and behavior disturbance policy are introduced, and an off-policy Q-function based game Bellman equation (OPQ-GBE) is derived. Consequently, an off-policy Q-learning algorithm is developed for the first time for discrete-time linear systems subject to the external disturbance, and the convergence and no bias of solution of OPQ-BE are proven. Finally, a F-16 aircraft autopilot is given to verify the effectiveness of the proposed method.","Title: Distributed Multisensor Fusion With Outlier Clearance Technique for Sensor Networks\nYear: 2018\n\nAbstract: One challenge faced by sensor fusion strategy designed for sensor networks is to real-time achieve high reliability estimated data with the inaccuracy and fault in raw sensor measurements. In this paper, we present an online outlier clearance technique with low computational complexity and memory usage inspired by the nearest neighbor rule that can identify and remove the spurious data in the distributed multisensor fusion process. The proposed technique is fully localized and thus saves communication overhead as well as has a good extension as deployed nodes increasing. In this technique, we define a weighted average distance-based outlier factor criterion to detect the outlier and replace it with estimated data. With the help of contrast simulations, in which we adopt other two typical techniques, it illustrates that the proposed technique provides better performance in real-time clearing outlier during the sensor fusion process without the prior knowledge of outlier.","Title: A Revisit To Gradient-Descent Bearing-Only Formation Control\nYear: 2018\n\nAbstract: This paper addresses the problem of bearing-only formation control of multi-agent systems, where each agent can merely obtain the relative bearing measurements of their neighbor neighbors whereas relative distance or position measurements are unavailable. In particular, we revisit a bearing-only formation control law proposed in [1]. Unlike many other existing ones, this control law is gradient-descent, which is favorable from the stability analysis point of view. It has the potential to be extended to handle more complex agent models and moving target formations. Up to now, this control law has not attracted sufficient attention probably because its stability analysis is based on optimization techniques and challenging to generalize. The contribution of this paper is to present a new stability analysis of this formation control law based on Lyapunov approaches. The new stability analysis reveals some new properties of the control law such as exponential convergence rate and lays a foundation for deriving new bearing-only control laws in the future.","Title: Model Fitting Filter\nYear: 2018\n\nAbstract: In this paper, we present a new nonlinear filter based on variational inference, which we have named the model fitting filter (MFF). The core of MFF is to represent or fit the nonlinear measurement likelihood probability with a linear Gaussian probability (LGP) characterized by some parameters that need to be turned. Further, MFF is analytically established on LGP, which is a little similar to the simple linear system filter. Specifically, the nonlinear filtering performance is well improved by utilizing these turned parameters to correct the state estimation. At the same time, MFF does not require the square root decomposition of covariance matrix, so it avoids the inborn defect of numerical instability, which exists more or less in UKF or DDF. The superior performance of MFF is numerically tested by a target tracking problem with nonlinear models.","Title: Sampled-data synchronization of chaotic Lur'e systems via an adaptive event-triggered approach.\nYear: 2018\n\nAbstract: In this paper, based on nonuniform sampling, the master-slave synchronization in a class of Lur’e systems is studied via using the sampled outputs of error system. Different from existing results, the transmission of control signals is determined by a novel adaptive event-triggered scheme, where the triggering thresholds depend on the dynamic behaviors of controlled systems rather than the predetermined constants as some traditional ones. Through choosing two augmented Lyapunov–Krasovskii functionals (LKFs), some delay-dependent synchronization criteria are formulated and the conservatism can be effectively reduced owing to the utilization of Wirtinger-based inequalities and delay-product-type LKF terms. Especially, the existence of the controller can be easily checked since the derived conditions are presented via LMI forms. Finally, two numerical examples with comparisons and simulations are given to illustrate the proposed results.","Title: Robust control of unmanned helicopters with high-order mismatched disturbances via disturbance-compensation-gain construction approach.\nYear: 2018\n\nAbstract: In this paper, a novel robust control strategy based on disturbance-compensation-gain (DCG) construction approach is proposed for small-scale unmanned helicopters in the presence of high-order mismatched disturbances. The overall control structure consists of two hierarchical layers. The inner-loop controller is to guarantee the stability of the unmanned helicopters subject to high-order mismatched disturbances. With the estimation of the disturbances and their successive derivatives via finite-time disturbance observer (FTDO), by properly designing some disturbance compensation gains, a novel robust controller is developed to remove the high-order mismatched disturbances from the output channels. The outer-loop controller is to produce flight commands for inner-loop system, as well as to track the reference trajectory, which is carried out with the dynamic inversion technique. The simulation results demonstrate that the unmanned helicopters are capable to perform flight missions autonomously with the proposed control strategy.","Title: Modeling and switching control of air-breathing hypersonic vehicle with variable geometry inlet.\nYear: 2018\n\nAbstract: In this paper, a multi-model switching control is developed for air-breathing hypersonic vehicle with variable geometry inlet(AHV-VGI). A variable geometry inlet with the translating cowl is adopted to capture the enough air mass flow for the scramjet engine, which can ensure a more powerful thrust. However, the using of VGI causes the unknown changes of the aerodynamics and thrust, making the model of AHV more complex. Therefore, we firstly analyze the thrust characteristic with the translating cowl and present the conception of optimal elongation distance of translating cowl(EDTC). Consequently, multiple different nonlinear aerodynamic models are constructed by curve fitting for each position of the translating cowl. Then, a switching mechanism dependent on EDTC is proposed and the adaptive RBF neural controllers are designed for velocity subsystem and altitude system of every model. Furthermore, the common Lyapunov functional is constructed to prove the stability of the multi-model switching process. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed control approach for AHV-VGI.","Title: Finite-Time Consensus for Second-Order Multi-Agent Systems With Input Saturation.\nYear: 2018\n\nAbstract: In this brief, a new class of finite-time consensus controller is designed for second-order multi-agent systems subject to input saturation. Only a single saturation function is involved in the proposed controller, which distinguishes it from the previous ones. The proposed controllers achieve finite-time consensus using only relative state measurements. Both leaderless and leader-following consen...","Title: Predictor-Based Extended-State-Observer Design for Consensus of MASs With Delays and Disturbances.\nYear: 2019\n\nAbstract: In this paper, we study output feedback leader-follower consensus problem for multiagent systems subject to external disturbances and time delays in both input and output. First, we consider the linear case and a novel predictor-based extended state observer is designed for each follower with relative output information of the neighboring agents. Then, leader-follower consensus protocols are proposed which can compensate the delays and disturbances efficiently. In particular, the proposed observer and controller do not contain any integral term of the past control input and hence are easy to implement. Consensus analysis is put in the framework of Lyapunov-Krasovskii functionals and sufficient conditions are derived to guarantee that the consensus errors converge to zero asymptotically. Then, the results are extended to nonlinear multiagent systems with nonlinear disturbances. Finally, the validity of the proposed design is demonstrated through a numerical example of network-connected unmanned aerial vehicles.","Title: Decentralized Adaptive Event-Triggered H-Infinity Filtering For A Class Of Networked Nonlinear Interconnected Systems\nYear: 2019\n\nAbstract: This paper focuses on the issue of designing an adaptive event-triggered scheme to the decentralized filtering for a class of networked nonlinear interconnected system. A novel adaptive event-triggered condition is proposed by constructing an adaptive law for the threshold. This new type of threshold mainly depends on the error between the states at the current sampling instant and the latest releasing instant, by which the data release rate is adapted to the variation of the system. The limitation of network bandwidth is alleviated on account of a large amount of \"unnecessary\" packets being dropped out before accessing the network. Sufficient conditions are derived such that the overall filtering error system under the proposed adaptive data-transmitting scheme is asymptotically stable with a prescribed disturbance attenuation level. An example is given to show the effectiveness of the proposed scheme.","Title: Off-Policy Interleaved Q-Learning: Optimal Control For Affine Nonlinear Discrete-Time Systems\nYear: 2019\n\nAbstract: In this paper, a novel off-policy interleaved Q-learning algorithm is presented for solving optimal control problem of affine nonlinear discrete-time (DT) systems, using only the measured data along the system trajectories. Affine nonlinear feature of systems, unknown dynamics, and off-policy learning approach pose tremendous challenges on approximating optimal controllers. To this end, on-policy Q-learning method for optimal control of affine nonlinear DT systems is reviewed first, and its convergence is rigorously proven. The bias of solution to Q-function-based Bellman equation caused by adding probing noises to systems for satisfying persistent excitation is also analyzed when using on-policy Q-learning approach. Then, a behavior control policy is introduced followed by proposing an off-policy Q-learning algorithm. Meanwhile, the convergence of algorithm and no bias of solution to optimal control problem when adding probing noise to systems are investigated. Third, three neural networks run by the interleaved Q-learning approach in the actor-critic framework. Thus, a novel off-policy interleaved Q-learning algorithm is derived, and its convergence is proven. Simulation results are given to verify the effectiveness of the proposed method.","Title: Given-time consensus for stochastic Markov jump networks by dynamic output feedback\nYear: 2018\n\nAbstract: This paper focuses on the given-time consensus of stochastic Markov jump networks with disturbances based on dynamic output feedback control. By exploring the relative output information, a distributed dynamic output feedback protocol is designed to guarantee that the disagreement dynamics of networks keeps within the prescribed bound in a fixed time interval. With the new model transformation approach, the obtained results are more general and lead to less conservativeness. An example of industrial material heating unit verifies the engineering application of the proposed algorithm for networks in the presence of both Markov jump topologies and external disturbances.","Title: Robust Script Capital L-2 Disturbance Attenuation For A Class Of Uncertain Lipschitz Nonlinear Systems With Input Delay\nYear: 2019\n\nAbstract: In this paper, we study a robust disturbance attenuation problem that arises when applying the Artstein-Kwon-Pearson reduction transformation for a class of uncertain Lipschitz nonlinear systems with input delay and external disturbances. A conventional predictor-based feedback controller is adopted with the control gain matrix carefully identified by solving a couple of sufficient conditions in terms of linear matrix inequalities . Lyapunov-Krasovskii functionals are constructed to guarantee that the robust disturbance attenuation problem can be solved by the proposed controller. A numerical example is included to validate the performance of the proposed controller.","Title: Off-Policy Q-Learning: Set-Point Design for Optimizing Dual-Rate Rougher Flotation Operational Processes.\nYear: 2018\n\nAbstract: Rougher flotation, composed of unit processes operating at a fast time scale and economic performance measurements known as operational indices measured at a slower time scale, is very basic and the first concentration stage for flotation plants. Optimizing operational process for rougher flotation circuits is extremely important due to high economic profit arising from the optimality of operation...","Title: Robust Cooperative Guidance Law for Simultaneous Arrival\nYear: 2019\n\nAbstract: In the cooperative simultaneous arrival problem, a group of interceptors are guided to simultaneously engage a stationary target. However, some interceptors may not follow the prescribed guidance law during the engagement, which can lead to interception failures. This brief investigates a new robust cooperative simultaneous arrival problem in the presence of misbehaving interceptors. A robust coop...","Title: Adaptive Output Regulation Of Output Feedback Systems With Unknown Nonlinear Exosystems And Unknown High-Frequency Gain Sign\nYear: 2018\n\nAbstract: This paper addresses the problem of adaptive output regulation for a class of output feedback systems subject to unknown disturbances generated from nonlinear exosystems and no knowledge of high-frequency gain sign. The nonlinear internal model based on circle criterion is proposed to generate the feedforward input term for rejection of the disturbances, and some specific conditions are introduced for exosystems such that the proposed internal model can be applied. In the proposed adaptive control scheme, a type of Nussbaum gain is presented to deal with the unknown high-frequency gain sign and the adaptive backstepping technique is then used to establish the final control input if systems possess high relative degree. The proposed control algorithm ensures the convergence of the parameters in the adaptive internal model to the actual values and the measurement output to zero, and the boundedness of all state variables.","Title: Consensus-based Distributed Optimal Energy Management with Less Communication in a Microgrid\nYear: 2019\n\nAbstract: In this paper, to reduce required capacities for information exchanges in microgrids, a novel distributed event-based algorithm is proposed for optimal energy management in a microgrid. Aiming at optimally scheduling the energy supplier's generation, an objective function is formulated to minimize the total cost of maintaining the supply-demand balance with considering power losses. Regarding each...","Title: Fixed-Time Formation Control of Multi-Robot Systems: Design and Experiments\nYear: 2019\n\nAbstract: Time delays exist in network-connected systems. Especially for vision-based multirobot systems, time delays are diverse and complicated due to the communication network, camera latency, image processing, etc. At the same time, many tasks, such as searching and rescue, have timing requirement. This paper focuses on fixed-time formation control of multirobot systems subject to delay constraints. First, predictor-based state transformation is employed for each robot to deal with the input delay, and the uncertain terms remained in the transformed systems are carefully considered. Then, a couple of nonlinear fixed-time formation protocols are proposed for the multirobot systems with respectively undirected and directed topology, and the corresponding settling time is derived by using the Lyapunov functions. In particular, the upper-bound estimation of the formation settling time is explicitly given irrelevant to the initial conditions. Finally, the protocols are validated through a numerical simulation example and then implemented on an E-puck robots platform. Both simulation and experimental results demonstrate the effectiveness of the proposed formation protocols.","Title: Adaptive Optimal Control for a Class of Nonlinear Systems: The Online Policy Iteration Approach.\nYear: 2020\n\nAbstract: This paper studies the online adaptive optimal controller design for a class of nonlinear systems through a novel policy iteration (PI) algorithm. By using the technique of neural network linear differential inclusion (LDI) to linearize the nonlinear terms in each iteration, the optimal law for controller design can be solved through the relevant algebraic Riccati equation (ARE) without using the ...","Title: Finite-Time Synchronization for a Class of Dynamical Complex Networks with Nonidentical Nodes and Uncertain Disturbance\nYear: 2019\n\nAbstract: This paper investigates the finite-time synchronization for a class of linearly coupled dynamical complex networks with both nonidentical nodes and uncertain disturbance. A set of controllers are designed such that the considered system can be finite-timely synchronized onto the target node. Based on the stability of the error equation, the Lyapunov function method and the linear matrix inequality technique, several sufficient conditions are derived to ensure the finite-time synchronization, and applied to the case of identical nodes and the one without uncertain disturbance. Also the adaptive finite-time synchronization is discussed. A numerical example is given to show the effectiveness of the main results obtained.","Title: Disturbance rejection via iterative learning control with a disturbance observer for active magnetic bearing systems.\nYear: 2019\n\nAbstract: Although standard iterative learning control (ILC) approaches can achieve perfect tracking for active magnetic bearing (AMB) systems under external disturbances, the disturbances are required to be iteration-invariant. In contrast to existing approaches, we address the tracking control problem of AMB systems under iteration-variant disturbances that are in different channels from the control inputs. A disturbance observer based ILC scheme is proposed that consists of a universal extended state observer (ESO) and a classical ILC law. Using only output feedback, the proposed control approach estimates and attenuates the disturbances in every iteration. The convergence of the closed-loop system is guaranteed by analyzing the contraction behavior of the tracking error. Simulation and comparison studies demonstrate the superior tracking performance of the proposed control approach.","Title: Event-Based Resilient Formation Control of Multiagent Systems.\nYear: 2021\n\nAbstract: This paper focuses on the time-varying formation tracking issue for nonlinear multiagent systems (MASs). Based on the explicit characterizations of frequency, duration, and magnitude properties for deception attacks, a hybrid framework is proposed for time-varying formation tracking of nonlinear MASs. To realize the desired formation tracking performance under deception attacks, the distributed ed...","Title: Bearing-Only Formation Tracking Control of Multi-Agent Systems\nYear: 2019\n\nAbstract: This paper studies the problem of bearing-only formation control of multi-agent systems, where the control of each agent merely relies on the relative bearings to its neighbors. Although this problem has received increasing research attention recently, it is still unsolved to a large extent due to its highly nonlinear dynamics. In particular, the existing control approaches are only able to solve the simplest scenario where the target formation is stationary and each agent is modeled as a single integrator. The main contribution of this paper is to propose new bearing-only formation control laws to (i) track moving target formations and (ii) handle a variety of agent models including single-integrator, double-integrator, and unicycle models. These control laws are an important step towards the application of bearing-only formation control in practical tasks. Both numerical simulation and real experimental results are presented to verify the effectiveness of the theoretical results.","Title: Tracking Consensus of General Nonlinear Multi-Agent Systems with External Disturbances Under Directed Networks\nYear: 2019\n\nAbstract: This paper considers the fully distributed tracking consensus problem for general nonlinear multi-agent systems with a leader whose control input is nonzero and bounded. First, a new class of distributed state observer for the leader is proposed without the knowledge of the upper bound of the leaderu0027s input. Then, the situations that followers are affected by disturbances with unknown upper bound or disturbances generated by exosystems are investigated. Specifically, two distributed control protocols based on the distributed state observer, neural networks, and adaptive laws are proposed. Finally, simulation examples are provided to illustrate the theoretical results.","Title: Reinforcement learning and adaptive optimization of a class of Markov jump systems with completely unknown dynamic information\nYear: 2020\n\nAbstract: In this paper, an online adaptive optimal control problem of a class of continuous-time Markov jump linear systems (MJLSs) is investigated by using a parallel reinforcement learning (RL) algorithm with completely unknown dynamics. Before collecting and learning the subsystems information of states and inputs, the exploration noise is firstly added to describe the actual control input. Then, a novel parallel RL algorithm is used to parallelly compute the correspondingNcoupled algebraic Riccati equations by online learning. By this algorithm, we will not need to know the dynamic information of the MJLSs. The convergence of the proposed algorithm is also proved. Finally, the effectiveness and applicability of this novel algorithm is illustrated by two simulation examples.","Title: Distributed Training for Multi-Layer Neural Networks by Consensus.\nYear: 2020\n\nAbstract: Over the past decade, there has been a growing interest in large-scale and privacy-concerned machine learning, especially in the situation where the data cannot be shared due to privacy protection or cannot be centralized due to computational limitations. Parallel computation has been proposed to circumvent these limitations, usually based on the master–slave and decentralized topologies, and the comparison study shows that a decentralized graph could avoid the possible communication jam on the central agent but incur extra communication cost. In this brief, a consensus algorithm is designed to allow all agents over the decentralized graph to converge to each other, and the distributed neural networks with enough consensus steps could have nearly the same performance as the centralized training model. Through the analysis of convergence, it is proved that all agents over an undirected graph could converge to the same optimal model even with only a single consensus step, and this can significantly reduce the communication cost. Simulation studies demonstrate that the proposed distributed training algorithm for multi-layer neural networks without data exchange could exhibit comparable or even better performance than the centralized training model.","Title: Adaptive delay compensation for consensus control under switching topologies by observer–predictor\nYear: 2021\n\nAbstract: AbstractAbstractThis paper considers observer–predictor feedback for delay compensation in consensus control under undirected switching graphs. Each node involved in the network is described by a linear system with an actuator delay of uncertain length, and the connection graph is allowed to be unknown and switch arbitrarily. An observer is proposed to estimate the disagreement state using locally available information, and a delay-adaptive consensus protocol is proposed via observer–predictor feedback. With the employment of a “backstepping” state transformation involving the observer state and the estimation delay state, an explicit Lyapunov function is constructed to prove the consensus and stability properties of the resultant closed-loop system. Numerical simulations are presented to demonstrate the effectiveness of the theoretic results.","Title: Variational Bayesian inference for the identification of FIR systems via quantized output data\nYear: 2021\n\nAbstract: AbstractAbstractFor identifying the parameters in finite impulse response (FIR) systems via the quantized output, existing expectation maximization (EM) methods are involved with the intractable integrals. To overcome the above shortcoming, this paper proposes an approach referred to as explicit EM (EEM). First, we fit the quantized output data by a linear Gaussian regression distribution with some compensation parameters. As a result of the linear Gaussianity, the posterior of the excited output can be explicitly computed, so that EEM has the less computation burden than the existing EMs. Second, through optimizing these compensation parameters by resorting to variational Bayesian inference, the intrinsic distribution of the quantized output can be well fitted with guaranteed identification accuracy. Third, the linear Gaussian regression distribution is independent of the specific quantization function form, which enables EEM applicable for different quantization forms. Finally, the simulation demonstrates the feasibility and effectiveness of the proposed approach.","Title: Surrogate-Assisted Cooperation Control of Network-Connected Doubly Fed Induction Generator Wind Farm With Maximized Reactive Power Capacity\nYear: 2022\n\nAbstract: This article aims to realize a cooperative active power control of doubly fed induction generator (DFIG)-based wind farm (WF) to maximize the total reactive power capacity while maintaining the active power supply-and-demand balance. Difficulties lie in that the accurate PQ-curve expressions of wind turbines therein are unknown and nonuniform, thereby putting an obstacle to distributed optimizatio...","Title: Distributed Optimization for Integrated Frequency Regulation and Economic Dispatch in Microgrids\nYear: 2021\n\nAbstract: In hierarchically controlled microgrids, economic dispatch and corresponding real-time control action are inherently performed at different timescales. Owing to the uncertainty in renewables, running economic dispatch in advance may not adequately guarantee the anticipated outcomes. In this paper, distributed optimization algorithms are developed to manage economic dispatch and frequency regulatio...","Title: On the game-theoretic analysis of distributed generative adversarial networks\nYear: 2022\n\nAbstract: In this paper, a distributed method is proposed for training multiple generative adversarial networks (GANs) with private data sets via a game-theoretic approach. To facilitate the requirement of privacy protection, distributed training algorithms offer a promising solution to learn global models without sample exchanges. Existing studies have mainly concentrated on training neural networks using pure cooperation strategies, which are not suitable for GANs. This paper develops a new framework for distributed GANs, where two groups of discriminators and generators are involved in a zero-sum game. Under connected graphs, such a framework is reformulated as a constrained minmax optimisation problem. Then, a fully distributed training algorithm is proposed without exchanging any private data samples. The convergence of the proposed algorithm is established via advanced consensus and optimisation techniques. Simulation studies are presented to validate the effectiveness of the proposed framework and algorithm.","Title: Collaborative Overtaking of Multi-Vehicle Systems in Dynamic Environments: A Distributed Artificial Potential Field Approach\nYear: 2021\n\nAbstract: Multi-vehicle autonomous driving has attracted significant attention from academia and industries due to its ability to improve traffic efficiency and enhance the safety of drivers and passengers. In this paper, a distributed motion planning strategy based on the artificial potential field method is proposed to achieve overtaking of the autonomous vehicle fleet in dynamic environments. Firstly, a dynamic target tracking control protocol is constructed for an autonomous vehicle fleet using a modified artificial potential field. The target tracking control protocol overcomes the disadvantage of the traditional artificial potential field method (i.e., the dynamic target cannot be reached). Besides, a distributed obstacle avoidance control protocol is also designed to avoid potential collisions during the overtaking process. Finally, simulation experiments are performed to verify the feasibility and effectiveness of the proposed algorithm.","Title: Intrinsic Cramér–Rao bounds for distributed Bayesian estimator\nYear: 2022\n\nAbstract: This paper addresses the intrinsic Cramér–Rao bounds (CRBs) for a distributed Bayesian estimator whose states and measurements are on Riemannian manifolds. As Euclidean-based CRBs for recursive Bayesian estimator are no longer applicable to general Riemannian manifolds, the bounds need redesigning to accommodate the non-zero Riemannian curvature. To derive the intrinsic CRBs, we append a coordination step to the recursive Bayesian procedure, where the proposed sequential steps are prediction, measurement and coordination updates. In the coordination step, the estimator minimises the Kullback–Liebler divergence to obtain the consensus of multiple probability density functions (PDFs). Employing the PDFs from those steps together with the affine connection on manifolds the Fisher Information Matrix (FIM) and the curvature terms of the corresponding intrinsic bounds are derived. Subsequently, the design of a distributed estimator for Riemannian information manifold with Gaussian distribution – referred to as distributed Riemannian Kalman filter – is also presented to exemplify the application of the proposed intrinsic bounds. Finally, simulations utilising the designed filter for a distributed quaternionic estimation problem verifies that the covariance matrices of the filter are never below the formulated intrinsic CRBs.","Title: A distributed deep reinforcement learning method for traffic light control\nYear: 2022\n\nAbstract: This paper proposes a distributed deep reinforcement learning algorithm for the traffic light control problem, which consists of local learning and global consensus. Firstly, the reinforcement learning environment for the traffic light control problem is built by defining the three key elements of state, action, and reward. Then, the CNN-based deep Q-network is designed to process the quantized traffic state information to obtain the state-action values. After locally optimizing the deep Q-networks of multiple traffic light agents based on their experience samples, the consensus algorithm is subsequently applied to globally update these agents that are connected over a decentralized communication topology. In this way, the distributed learning agents learn from their neighbors’ experience to optimize the modeling process without actually sharing experience data samples. Lastly, homogeneous and heterogeneous traffic flow patterns on different intersections are simulated in SUMO to verify the superiority of the proposed distributed deep Q-networks, with the comparison to the fixed-time strategy, local learning and centralized learning algorithms. The simulation study demonstrates that the distributed learning algorithm without a central server shows comparable performance with centralized learning, which is much better than fixed-time strategy and the local learning method in both homogeneous and heterogeneous traffic scenarios.","Title: Probabilistic Transmission Scheme for Distributed Filtering Over Randomly Lossy Sensor Networks in the Presence of Eavesdropper\nYear: 2022\n\nAbstract: This article studies distributed secure estimation in sensor networks with packet losses and an eavesdropper. The message is transmitted through communication channels between sensors, which can be overheard by the eavesdropper with a certain probability. Two types of sensor networks under two cooperative-filtering algorithms are considered, and probabilistic transmission schemes to defend against the eavesdropper are proposed. For collectively detectable sensor networks under a consensus Kalman filter, a sufficient distributed detectability condition on the transmission probabilities is identified to guarantee that the estimation errors of the sensors are statistically bounded. Furthermore, a necessary and sufficient security condition is obtained to guarantee unboundedness of the eavesdropper’s estimation error. For neighborhood-detectable nodes under a standard Kalman filter, a sufficient distributed detectability condition and a necessary security condition are provided on the transmission probabilities. Simulation examples are given to illustrate the results.","Title: Adaptive Backstepping Control of Uncertain Sandwich-Like Nonlinear Systems With Deadzone Nonlinearity\nYear: 2022\n\nAbstract: A systematic differentiator-based adaptive backstepping control methodology is proposed for a class of sandwich-like nonlinear system with unknown state-dependent deadzone nonlinearity and parametric uncertainties. The novelty of our approach is that a high-order sliding mode differentiator is utilized to estimate the nonstrict feedback coupling term resulting from the sandwiched deadzone, and all the outputs of the differentiator are integrated into the backstepping procedure based on Lyapunov functions with flat zone recursively. By this approach, all the unknown parameters are estimated online, the discontinuity of the virtual input caused by bound estimations is avoided. It is shown that the ultimate boundedness of all the closed-loop signals is achieved and the output tracking error converges to a preset set. Simulation is performed to verify the theoretical findings.","Title: Fuzzy-Based Adaptive Optimization of Unknown Discrete-Time Nonlinear Markov Jump Systems With Off-Policy Reinforcement Learning\nYear: 2022\n\nAbstract: This article explores a novel adaptive optimal control strategy for a class of sophisticated discrete-time nonlinear Markov jump systems (DTNMJSs) via Takagi–Sugeno fuzzy models and reinforcement learning (RL) techniques. First, the original nonlinear system model is represented by fuzzy approximation, while the relevant optimal control problem is equivalent to designing fuzzy controllers for linear fuzzy systems with Markov jumping parameters. Subsequently, we derive the fuzzy coupled algebraic Riccati equations for the fuzzy-based discrete-time linear Markov jump systems by using Hamiltonian–Bellman methods. Following this, an online fuzzy optimization algorithm for DTNMJSs as well as the associated equivalence proof is given. Then, a fully model-free off-policy fuzzy RL algorithm is derived with proved convergence for the DTNMJSs without using the information of system dynamics and transition probability. Finally, two simulation examples, respectively, related to the single-link robotic arm and the half-car active suspension are given to verify the effectiveness and good performance of the proposed approach."],"string":"[\n \"Title: Brief Global output regulation of uncertain nonlinear systems with exogenous signals\\nYear: 2001\\n\\nAbstract: The paper addresses the global output tracking of a class of uncertain output feedback systems affected by disturbances which are generated from a known exosystem. The system uncertainty is parametrized by a unknown parameter vector, which is absorbed in the parametrization of disturbances. A controller based on the estimation of states and disturbances from the output is designed using backstepping to asymptotically track arbitrary trajectories and to guarantee the boundedness of all other variables. The proposed control algorithm provides an alternative with global tracking to structurally stable regulation of nonlinear output feedback systems. It can also be viewed as a generalization to adaptive control algorithms.\",\n \"Title: Convergent series observer design for a class of nonlinear systems\\nYear: 2011\\n\\nAbstract: This paper deals with convergence analysis for power series solutions to a partial differential equation for nonlinear observer design with linear observer error dynamics. This power series solution is used to design the gain matrix for a Luenberger-like observer for nonlinear systems. The conditions are identified to guarantee the convergence of the series in l2. The linearized model of the original system is assumed to be anti-stable at the origin for the convenience of presentation. The convergent conditions can provide a guideline for nonlinear observer design with a truncated series for the observer gain.\",\n \"Title: Asymptotic rejection of general periodic disturbances in output-feedback nonlinear systems\\nYear: 2006\\n\\nAbstract: This note deals with asymptotic rejection of general periodic disturbances in nonlinear systems. A novel approach based on integrations over the half of disturbance period is proposed to estimate disturbances which may not be harmonic or even smooth. The proposed algorithm uses the period and the knowledge of the basic disturbance patterns, while the amplitude and phase of the disturbances are unknown, and they are estimated. Based on the estimated disturbance, the disturbance rejection algorithm is then proposed for linear systems and nonlinear systems in the output feedback format, which asymptotically rejects the periodical disturbances. The proposed algorithm extends asymptotic rejection of unknown disturbances from sinusoidal disturbances to a more general class of periodical disturbances which includes zero-mean square waves, symmetric triangular waves etc.\",\n \"Title: Asymptotic Rejection Of Unmatched General Periodic Disturbances In A Class Of Non-Minimum-Phase Non-Linear Systems\\nYear: 2009\\n\\nAbstract: This article deals with asymptotic rejection of unmatched general periodic disturbances in nonminimum phase non-linear output feedback systems. The steady state responses are defined for unstable systems subject to general periodic disturbances, and the generalised gain and phase are defined for stable systems subject to general periodic disturbances. Based on the new definitions, new results are obtained for the equivalent input disturbance and disturbance estimation. An Lp-convergent estimate of the equivalent input disturbance is incorporated in the control design to ensure the asymptotic rejection of unmatched general periodic disturbances while maintaining the stability of the non-linear system.\",\n \"Title: Asymptotic rejection of unknown sinusoidal disturbances in nonlinear systems\\nYear: 2007\\n\\nAbstract: This paper deals with global disturbance rejection of nonlinear systems. The disturbance is assumed to be sinusoidal with completely unknown phases, amplitude, and frequencies, but the number of distinct frequencies or the order of the corresponding unknown linear exosystem is known. Different from the common structural assumptions of nonlinear systems needed in literature for disturbance rejection of nonlinear systems, the proposed method only requires the information of control design with a known Lyapunov function when the system is disturbance-free, and a mild assumption needed for internal model design. The proposed disturbance rejection algorithm extends complete global rejection of unknown sinusoidal disturbances for nonlinear dynamic systems beyond the common nonlinear models such as the strict feedback forms and the output feedback forms.\",\n \"Title: Asymptotic rejection of finite frequency modes of general periodic disturbances in output feedback nonlinear systems\\nYear: 2008\\n\\nAbstract: This paper deals with asymptotic rejection of individual frequency modes in nonlinear systems under general periodic disturbances. An iterative observer design is proposed for disturbance estimation, and it is shown that the proposed observer design successfully extracts individual frequency modes from general periodic disturbances. The proposed iterative observer design is then applied to asymptotic rejection of nonlinear systems under general periodic disturbances.\",\n \"Title: Asymptotic rejection of asymmetric periodic disturbances in output-feedback nonlinear systems\\nYear: 2007\\n\\nAbstract: This paper deals with asymptotic rejection of periodic disturbances which may have asymmetric basic wave patterns. This class of disturbances covers asymmetric wave forms in the half-period such as alternating sawtooth wave form, some disturbances which are generated from nonlinear oscillation such as Van de Pol oscillators, as well as disturbances with symmetric half-period wave forms such as sinusoidal disturbances and triangular disturbances etc. The systems considered in this paper can be transformed to the nonlinear output feedback form. The amplitude and phase of the disturbances are unknown. The novel concept of integral phase shift is introduced together with the newly introduced half-period integration operator to investigate the invariant properties of asymmetric periodic disturbances. They are used for the estimation of unknown disturbances in the systems, together with observer design techniques to deal with nonlinearity. The proposed control design with the disturbance estimation asymptotically rejects the unknown disturbance, and ensures the overall stability of the system.\",\n \"Title: Rejection of Nonharmonic Disturbances in Nonlinear Systems With Semi-Global Stability\\nYear: 2008\\n\\nAbstract: An asymptotic rejection algorithm is proposed for nonlinear dynamic systems under nonharmonic periodic disturbances generated from nonlinear exosystems. The dynamic systems considered in this brief are in the normal form, a larger class of nonlinear dynamic systems than the output feedback systems. A new internal model design method is used to exploit the nonlinearities in the exosystem, together ...\",\n \"Title: Observer Design in Convergent Series for a Class of Nonlinear Systems.\\nYear: 2012\\n\\nAbstract: This paper deals with convergence analysis for power series solutions to a partial differential equation for nonlinear observer design with linear observer error dynamics. This power series solution is used to design the gain matrix for a Luenberger-like observer for nonlinear systems. An explicit domain of convergence around the origin is identified, which is related to the relative sizes of high-order terms in the original nonlinear system with respect to the linearized model. The convergent conditions can provide a guideline for nonlinear observer design with a truncated series for the observer gain.\",\n \"Title: Adaptive control of triangular systems with nonlinear parameterization\\nYear: 2001\\n\\nAbstract: The paper deals with adaptive control of uncertain nonlinear triangular systems whose uncertainty is characterized by nonlinear parameterization of unknown constant vectors. Robust adaptive control techniques are incorporated in the backstepping control design with flat zones to tackle the nonlinear parameterization together with a novel smooth projection algorithm for parameter estimation. The proposed algorithm guarantees the global boundedness of all the variables and the convergence of the tracking error to any prespecified small limit.\",\n \"Title: Adaptive stabilization of a class of nonlinear systems with unstable internal dynamics\\nYear: 2002\\n\\nAbstract: The paper considers global stabilization of a class of uncertain nonlinear output feedback systems with unstable internal dynamics. The coefficients which characterize the internal dynamics are allowed to be functions of system output, and the uncertainty of the system is characterized by a unknown constant parameter vector. The key step in the proposed control design is to estimate the internal state variables and impose control over them. The control design is presented first for systems without unknown parameters, and then for the systems with unknown parameters using adaptive control techniques.\",\n \"Title: On output regulation of discrete-time T-S fuzzy systems\\nYear: 2008\\n\\nAbstract: The output regulation problem is discussed for a class of discrete-time T-S fuzzy systems under periodic disturbances generated form the so-called exosystems. With the assumption that the subsystem in each rule is of the controllable canonical form, the regulation equations are solvable if and only if the poles of the exosystem are different from those of the fuzzy system. By exploiting the structural information encoded in the fuzzy rules, a piecewise state feedback control law can then be constructed to achieve asymptotic rejecting and/or tracking of the unwanted disturbances or the desired trajectory.\",\n \"Title: One-Sided Lipschitz Observer Design For Circadian Models\\nYear: 2011\\n\\nAbstract: This paper deals with observer designs to mathematical models of circadian rhythms which exist in every living organism. Two mathematical models are considered, with a 3rd order model for Neurospora, and a 7th order model for Mammals. The observer design is based on systems with Lipschitz nonlinearities. In particular, observers based on onesided Lipschitz condition are investigated, and the observers are then designed for both circadian models. Detailed analysis is performed for nonlinear functions in the models to show the one-sided Lipschitz observers can indeed be applied. Several simulations studies of proposed observers are carried out with the results shown in this paper.\",\n \"Title: Global stabilization and disturbance suppression of a class of nonlinear systems with uncertain internal model\\nYear: 2003\\n\\nAbstract: This paper deals with global stabilization and disturbance suppression of a class of nonlinear systems using output feedback. The disturbances generated from a unknown linear exosystem are completely compensated. The order of the exosystem is assumed known and the eigenvalues are distinct. No other assumptions are needed in the control design. This means that the proposed control design is able to completely compensate the disturbances without knowing their amplitudes, frequencies and phases, as long as the number of different frequency components in the disturbances is known. A new formulation of state estimation is introduced to ensure the global stabilization and complete disturbance suppression. Adaptive control technique is used to design an adaptive internal model based on a recently introduced formulation of unknown exosystems and the parameters in the adaptive internal model converge to the actual values, from which the unknown disturbance frequencies can be calculated. In the proposed control design, a number of control coefficients are made adaptive so that the result is global with respect to unknown frequencies in the disturbances.\",\n \"Title: Output Regulation of Uncertain Nonlinear Systems With Nonlinear Exosystems\\nYear: 2006\\n\\nAbstract: An adaptive control algorithm is proposed for output regulation of uncertain nonlinear systems in output feedback form under disturbances generated from nonlinear exosystems. A new nonlinear internal model is proposed to generate the desired input term for suppression of the disturbances. The proposed internal model design is based on boundedness of the disturbance, high gain design and saturation and the designed internal model is capable to tackle disturbances in any specied initial conditions. Some uncertainties in the systems are allowed, and they are tackled using nonlinear dominant functions and an adaptive control coefcient. The proposed control algorithm ensures the global convergence of the state variables to the invariant manifold, which implies that the measurement or the tracking error approaches to zero asymptotically.\",\n \"Title: A flat-zone modification for robust adaptive control of nonlinear output feedback systems with unknown high-frequency gains\\nYear: 2002\\n\\nAbstract: This note deals with adaptive control of perturbed nonlinear output feedback systems with unknown high-frequency gains. The disturbances in the systems are assumed to be bounded, but the bounds are unknown. A flat-zone modification is proposed to incorporate both the bound estimation and Nussbaum gain design in the nonlinear adaptive control. To ensure the differentiability of stabilizing functions needed for backstepping design, high order terms are introduced in the Lyapunov function candidate with a flat zone around the neighborhood of the origin. The output tracking error converges to an arbitrarily small interval around zero.\",\n \"Title: Online optimal queue scheme for enhanced transport over wireless links\\nYear: 2010\\n\\nAbstract: In this paper we address the performance corruption in TCP flows over a wireless link by employing an online optimal control scheme. It has been shown that TCP flows exhibit low data rate in wireless scenario due to fading channel, frequent hands off and high bit error. The high bit error rate, which is an intrinsic link characteristic of wireless network, contributes to more packets losses, which cut back the congestion window and decrease the transmission rate. Meanwhile, the unpredictable traffic situations and varying network capacity all lead to dynamics changing of TCP behavior. To overcome these problems, we propose a data-based AQM with an optimality criterion to minimize the fluctuation of the queue length and aggregate rate from link capacity. The adaptive control gain is updated by solving the equivalent Hamiltonian without knowing the systems parameters. The simulation results show that the proposed scheme effectively improves the performance of TCP over wireless link with strong robustness to time-varying scenarios of wireless networks.\",\n \"Title: Polynomial solutions to regulation equations with nonlinear exosystems\\nYear: 2010\\n\\nAbstract: Solving a regulation equation is the core part of output regulation problem. It remains as a challenge to find a solution for a general case of output regulation with nonlinear exosystems. This paper identifies certain conditions for the existence of a polynomial solution, and propose an iterative method for obtaining polynomial solutions for regulation equations with nonlinear exosystems. The proposed solution is then applied for the internal model design in output regulation of nonlinear systems.\",\n \"Title: Consensus output regulation without state estimation for a class of nonlinear systems\\nYear: 2012\\n\\nAbstract: This paper deals with consensus output regulation of network connected multi-agent systems. Every agent or subsystem is a nonlinear system in the output feedback form, and has identical dynamics. The subsystem dynamics are influenced by state variables generated from an exosystem. The outputs of the subsystems are required to follow a desired trajectory which is a function of the exosystem state. Only some subsystems have access to the desired trajectory, and other subsystems will have to rely on the exchange of information through the network. In this paper, a consensus control design is proposed to ensure that the outputs of all the subsystems converge to the same desired output trajectory by exploiting the internal model design strategy. The proposed control design only uses the relative outputs of the subsystems, and does not require the estimation of subsystem state variables.\",\n \"Title: Adaptive consensus output regulation of a class of heterogeneous nonlinear systems\\nYear: 2013\\n\\nAbstract: This paper deals with consensus output regulation of a class of network-connected nonlinear systems with unknown parameters. The subsystem may have different dynamics with uncertainties, and under the disturbances generated from an exosystem, and only some subsystems have access to the desired output, which is also formulated as an output of the exosystem, following the standard formulation of output regulation of nonlinear systems. For the clarity of the presentation, we only show the control design of the systems with 2nd-order dynamics. The proposed design makes use of some latest results on Laplacian matrices, and a new design of the internal model, which is based on known functions with unknown constant parameters. Adaptive backstepping design techniques are integrated with the consensus control design to tackle the nonlinearity and unknown constant parameters in the system, and unknown functions relating to the state of the exosystem. The proposed control inputs and adaptive laws are fully decentralized, and ensure the asymptotic convergence to zero of the regulation errors.\",\n \"Title: Global adaptive output regulation of a class of nonlinear systems with nonlinear exosystems\\nYear: 2007\\n\\nAbstract: This paper deals with global output regulation with nonlinear exosystems for a class of uncertain nonlinear output feedback systems. The circle criterion is exploited for the internal model design to accommodate the nonlinearities in the exosystems, and the explicit conditions are given for the exosystems such that the proposed internal model design can be applied. The uncertainties of the output feedback systems are in the form of unknown constant parameters, and adaptive control techniques are used to ensure the global stability of the proposed control design for output regulation.\",\n \"Title: Output regulation of a class of continuous-time Markovian jumping systems\\nYear: 2013\\n\\nAbstract: This paper studies output regulation for continuous-time Markovian jumping systems (MJSs), for which mode-dependent regulation equations are investigated. With the extension of regulation scheme to MJSs by stochastic Lyaponov-Krasovskii functional framework, sufficient conditions are, respectively, obtained for state feedback and error feedback. The resulting closed-loop system is guaranteed to be stochastically stable and the output regulation error almost asymptotically converges to zero. A semi-definite optimization approach via disciplined convex programming is adopted to ensure relaxed solutions of the regulation equations. Finally, two numerical simulations are given to illustrate the performance of the proposed approach.\",\n \"Title: Global Output Feedback Stabilization of Nonlinear Systems With Nonlinearity of Unmeasured States\\nYear: 2009\\n\\nAbstract: This technical note deals with global stabilization of class of nonlinear dynamic systems using output feedback. The systems considered have nonlinear functions of unmeasured state variables, in addition to nonlinear functions of the system outputs. A nonlinear reduced order observer is proposed to estimate the unknown system states. Estimated state variables are used for control design, together with the system output. Certain conditions are identified for the proposed observer and control design. Under these conditions, the proposed control design ensures the global asymptotic stability of the closed-loop control system.\",\n \"Title: Nonlinear control design and stability analysis of a small-scale unmanned helicopter\\nYear: 2013\\n\\nAbstract: The main focus of this paper is to design nonlinear controllers and analyse stability for a miniature unmanned helicopter. Our work is to utilize nonlinear control methodology to command dynamics of unmanned helicopter which has been divided into slower translational dynamics (outer-loop) and faster orientation dynamics (inner-loop), thus exhibiting hierarchical structure. The attitude angles and position which separately belong to the inner-loop and the outer-loop can be independently controlled by backstepping control strategy. Stability analysis of the helicopter system including flapping dynamics has been presented after designing controllers.\",\n \"Title: Optimization of tool change timing in a nut forming process using genetic algorithms\\nYear: 2004\\n\\nAbstract: In this study, we consider an optimization problem arising from tool change scheduling of a nut forming process. The process has nine punches with different life spans. The objective of the study is to optimize the tool change timing of the nine punches in order to maximize the production yield. The punch life spans can be extended by means of Ti coating, but coated punches are more expensive. We also evaluate whether it is worth using coated punches. A cost model is developed for the nut forming process. In the developed cost model, we consider the reliability of the nine punches, the periods taken for changing punches, scrap cost and production yield. The cost function for the process is complex, and is difficult to be solved using conventional optimization methods. Therefore, genetic algorithms are used to solve the problems. We have developed our own genetic algorithms using Java programs and applied the developed genetic algorithms to provide solutions to the optimization problem.\",\n \"Title: Universal disturbance rejection for nonlinear systems in output feedback form\\nYear: 2003\\n\\nAbstract: This note deals with global disturbance rejection via output feedback of a class of uncertain nonlinear systems subject to a class of unknown disturbances. Both the uncertainty in the system model and the uncertainty in the exosystem are tackled concurrently. The disturbances generated from an unknown linear exosystem are completely rejected. The order of the exosystem is assumed known, and the eigenvalues are distinct. The system is assumed in the format of the minimum-phase output feedback form, with no knowledge of the values of any system parameters, including the high-frequency gain. No other assumptions are needed in the control design. A new set of filters are introduced for state estimation. The stability of the internal model is exploited to design a new auxiliary error, involving both the unknown parameters of the reformatted exosystem and those of the system, which makes it possible to group all the unknown parameters in a format suitable to adaptive control design. A Nussbaum gain is introduced in adaptive control design to tackle the unknown high-frequency gain and a number of control coefficients are also made adaptive so that the disturbance rejection is global with respect to unknown frequencies in the disturbances.\",\n \"Title: Circadian phase control using observer-based back-stepping design.\\nYear: 2011\\n\\nAbstract: Circadian rhythms exist in most of living species, and control their daily biological activities. Disruption of these rhythms can cause circadian disorders or phase shifts. This paper deals with phase recovery of circadian rhythms. The phase restoration is carried out by synchronization of estimated trajectories of unknown state variables of a controlled system with trajectories of a reference system via back-stepping. The unknown state variables are estimated by using recently developed one-sided Lipschitz observer. Both reference and controlled systems are based on a 3rd order model of Neurospora circadian rhythms. Several simulation studies are carried out to verify the operation of proposed synchronization scheme with results shown in this paper.\",\n \"Title: Global adaptive output feedback stabilization of nonlinear systems of any relative degree with unknown high-frequency gains\\nYear: 1998\\n\\nAbstract: This paper presents an algorithm for adaptive output feedback stabilization of nonlinear single-input/single-output, minimum phase systems of any relative degree which are linear with respect to input and unknown constant parameter vector. The algorithm does not require the knowledge of the sign of the high-frequency gain\",\n \"Title: Consensus Output Regulation of a Class of Heterogeneous Nonlinear Systems\\nYear: 2013\\n\\nAbstract: This technical note deals with consensus output regulation of network connected multi-agent systems. Every agent or subsystem is a nonlinear system in the output feedback form with relative degree one, but subsystems may have different dynamics in terms of different nonlinear functions and even different system orders. The subsystem dynamics are influenced by state variables generated from an exosystem. The outputs of the subsystems are required to follow a desired trajectory which is a function of the exosystem state. Only some subsystems have access to the desired trajectory, and the other subsystems will have to rely on the exchange of information through the network. In this technical note, a consensus control design is proposed to ensure that the outputs of all the subsystems converge to the same desired output trajectory by exploiting the internal model design strategy. The proposed control design only uses the relative outputs of the subsystems, and does not require the estimation of subsystem state variables.\",\n \"Title: Global Output Regulation Of A Class Of Nonlinear Systems With Nonlinear Exosystems\\nYear: 2006\\n\\nAbstract: This paper deals with global output regulation with nonlinear exosystems for a class of uncertain nonlinear output feedback systems. The circle criterion is exploited for the internal model design to accommodate the nonlinearities in the exosystems, and the explicit conditions are given for the exosystems such that proposed internal model design can be applied. The uncertainties of the output feedback systems are in the form of unknown constant parameters, and adaptive control techniques are used to ensure the global stability of the proposed control design for output regulation.\",\n \"Title: Decentralized Output Regulation For Large-Scale Output-Feedback Nonlinear Systems With Nonlinear Exosystem\\nYear: 2007\\n\\nAbstract: This paper presents for the first time a solution to the problem of global decentralized output regulation for a class of large-scale uncertain nonlinear systems with nonlinear exosystem using error information. The proposed constructive approach does not require any matching conditions on the parametric uncertainties with some growth conditions on the interacting output nonlinearities. Two decentralized error feedback adaptive control strategies have been presented under some assumptions.\",\n \"Title: Adaptive consensus output regulation of a class of nonlinear systems with unknown high-frequency gain.\\nYear: 2015\\n\\nAbstract: This paper deals with adaptive consensus output regulation of a class of network-connected nonlinear systems with completely unknown parameters, including the high frequency gains of the subsystems. The subsystems may have different dynamics, as long as the relative degrees are the same. A new type of Nussbaum gain is proposed to deal with adaptive consensus control of network-connected systems without any knowledge of the high frequency gains. Adaptive laws and internal models are designed for the subsystems to deal with unknown parameters for tracking trajectories and unknown system parameters. In the control design, only the relative information of subsystem outputs are used, provided that regulation error of one of the subsystems is available. The proposed control inputs and the adaptive laws are decentralized. If the relative degree is one, only the relative subsystem outputs are exchanged. For the case of higher relative degrees, the nonlinear model structure of the subsystems is exploited for backstepping control design, and some variables generated by the subsystem controllers are exchanged among the subsystems in the neighbourhood defined by the connection graph.\",\n \"Title: Consensus control of a class of nonlinear systems with communication time delay\\nYear: 2014\\n\\nAbstract: This paper investigates the consensus control of a class of Lipchitz nonlinear multi-agent systems with communication time delay. Upon exploring certain features of Laplacian matrix, global consensus control conditions are identified using Lyapunov method in the time domain. The proposed control only uses relative state information of the system. Numerical simulations validate the effectiveness of the proposed controller design and show its performance with different nonlinearities and time delays.\",\n \"Title: Truncated state prediction for control of Lipschitz nonlinear systems with input delay\\nYear: 2014\\n\\nAbstract: This paper deals with control design for Lipschitz nonlinear systems with input delay. A prediction of the system state over the delay period is approximated by the finite dimensional term of the classical state predictor, with the distributed term dropped. This truncated predictor is then used for the state feedback control. A set of conditions for the stability of the closed-loop system are identified based on the analysis in the framework of Lyapunov-Krasovskii functionals. These conditions are presented in the form of matrix inequalities, and generalize the results for the truncated predictor feedback for linear systems to nonlinear systems. The conditions can be checked using LMIs with a set of iterative scalar parameters, in a way similar to the linear systems counterpart.\",\n \"Title: State feedback consensus controller with states estimation for a nonlinear class system\\nYear: 2014\\n\\nAbstract: This paper deals with consensus control of a class nonlinear system with Lipschitz nonlinearities. The conditions for consensus controller with state estimation are identified by exploring certain features of the Laplacian matrix. Conditions of stability for both are analyzed through Lyapunov functions. The controller utilizes the state estimation provided by the observer based on the output feedback information. To demonstrate the effectiveness of the designed state estimation controller, a simulation example is given.\",\n \"Title: Adaptive output regulation of a class of nonlinear systems with completely unknown parameters\\nYear: 2003\\n\\nAbstract: A complete control design is proposed for output regulation of nonlinear systems in the output feedback form under disturbance generated from neutral stable exosystems. Only the structural information of the system and the exosystem is needed, such as the order of the system and the order of the exosystem, the relative degree. No information of any parameters in the system and the exosystem is needed, including the sign of the high frequency gain. The proposed control design using the measurement feedback consists of a filtered transform for the system with relative degree greater than one, an adaptive internal model, a Nussbaum gain and a number of adaptive parameters. The state variables under the proposed control asymptotically converges to the invariant manifold, which implies that the measurement or the tracking error approaches to zero asymptotically.\",\n \"Title: Unknown sinusoidal disturbance rejection of nonlinear dynamic systems\\nYear: 2006\\n\\nAbstract: This paper deals with global disturbance rejection of nonlinear systems. The disturbance is assumed to be sinusoidal with completely unknown phases, amplitude, and frequencies, but the number of distinct frequencies or the order of the corresponding unknown linear exosystem is known. Different from the common structural assumptions of nonlinear systems needed in literature for disturbance rejection of nonlinear systems, the proposed method only requires the information of control design when the system is disturbance-free, and a mild assumption needed for internal model design. The proposed disturbance rejection algorithm extends complete global rejection of unknown sinusoidal disturbances for nonlinear dynamic systems beyond the common nonlinear models such as the strict feedback forms and the output feedback forms\",\n \"Title: Asymptotic Rejection of Disturbances with Asymmetric Wave Patterns in Output-feedback Nonlinear Systems\\nYear: 2006\\n\\nAbstract: This paper deals with asymptotic rejection of periodic disturbances which may have asymmetric basic wave patterns. This class of disturbances covers asymmetric wave forms in the half period such as alternating sawtooth wave form, some disturbances which are generated from nonlinear oscillation such as Van de Pol oscillators, as well as disturbances with symmetric half-period wave forms such as sinusoidal disturbances and triangular disturbances etc. The systems considered in this paper can be transformed to the nonlinear output feedback form. The amplitude and phase of the disturbances are unknown. A new delay operator and the newly proposed half-period integration technique are used for estimation of the unknown disturbances in the systems, together with observer design techniques to deal with nonlinearity. The proposed control design with the disturbance estimation asymptotically rejects the unknown disturbance, and ensures the overall stability of the system\",\n \"Title: Asymptotic Rejection of Unmatched General Periodic Disturbances in Output-feedback Nonlinear Systems\\nYear: 2007\\n\\nAbstract: This paper deals with asymptotic rejection of unmatched general periodic disturbances in nonlinear systems in the output feedback form. The system considered in this paper removes the restriction imposed on the asymptotic rejection of general disturbance in the literature that the disturbance only enters the system through the input channel. The class of disturbances covers asymmetric wave forms in the half period such as alternating sawtooth wave form, some disturbances which are generated from nonlinear oscillation such as Van de Pol oscillators, as well as disturbances with symmetric half-period wave forms such as sinusoidal disturbances and triangular disturbances etc. The amplitude and phase of the disturbances are unknown. The steady state solutions of stable linear systems are shown to be half-period alternating if the inputs are. An equivalent input disturbance is then obtained through a partial state transform of the nonlinear system, and application of the results of the periodic solutions of stable linear systems to the zero-dynamics of the nonlinear system which are linear. With the equivalent input disturbances, the asymptotic rejection of unmatched general disturbances are converted to the matched cases. As the half-period alternating property is persevered in the equivalent input disturbances, recently proposed asymptotic rejection, based on the half-period integration and delay operations, of general periodic disturbances with asymmetric half-period wave forms can be applied, with a careful consideration of the phase offsets in the equivalent input disturbances. The proposed control design with the disturbance estimation asymptotically rejects the unknown disturbance, and ensures the overall stability of the system.\",\n \"Title: Consensus control of a class of nonlinear systems\\nYear: 2013\\n\\nAbstract: This paper deals with consensus control of a class nonlinear systems with Lipschitz nonlinearities. Certain features of the Laplacian matrix are further explored to identify conditions for global consensus control. Under the identified conditions, consensus control and stability of the proposed control are analyzed in time-domain through Lyapunov functions. The proposed control uses relative state information of the system. A simulation study is included to demonstrate the proposed control designs with some simulation results shown.\",\n \"Title: Consensus Disturbance Rejection with Disturbance Observers\\nYear: 2015\\n\\nAbstract: This paper deals with consensus disturbance rejection of network-connected dynamic systems using disturbance observers. The control objective of consensus disturbance rejection is to achieve a common state trajectory for the networkconnected subsystems that are under deterministic disturbances. The difference from the existing disturbance rejection methods is that only the relative state information is used for disturbance rejection, and as a consequence of using the relative state information, only the part of the disturbances that affect the common trajectories will be rejected. The conditions for designing disturbance observers for consensus control are identified for networked-connect multi-agent systems. Certain features of the individual subsystems are analysed for possible implementation of disturbance-observer based rejection, and the disturbance observers are designed based on the relative state information obtained from the neighbouring subsystems under different network connections. When the network connectivity is available for the disturbance observer design, consensus disturbance rejection is achieved for a directed network with a spanning tree. A fully distributed consensus disturbance rejection design is presented for an undirected network with the use of adaptive parameters for the estimation of the unknown network connectivity. Disturbance observers are also proposed for disturbance rejection in the leader-follower consensus control.\",\n \"Title: Decentralized Output Regulation of Large Scale Nonlinear Systems with Delay.\\nYear: 2009\\n\\nAbstract: This paper deals with output regulation of a class of large-scale nonlinear systems with delays. Each of the subsystems is in the output feedback form, with nonlinear functions of the subsystem output and the outputs of other subsystems. The system outputs are subject to unknown constant delays. Both the system dynamics and the measurements are subject to unknown disturbances generated from unknown linear exosystems. Decentralized control design approach is adopted to design local controllers using measurements or regulated errors in each subsystems. It is shown in this paper that delays in the outputs of subsystems do not affect the existence of desired feedforward control input, and the invariant manifolds and the desired feedforward inputs always exist if the nonlinear functions are polynomials. Through a special parameterization of an augmented exosystem, an internal model can be designed for each subsystem, without the involvements of the uncertain parameters. The uncertain parameters affected by the uncertainty of the exosystem are estimated using adaptive control laws, and adaptive coefficients in the control inputs are used to suppress other uncertainties. The proposed decentralized adaptive control strategy ensures the global stability of the entire system, and the convergence to zero of the regulated errors. An example is included to demonstrate the proposed control strategy.\",\n \"Title: Consensus Control of a Class of Lipschitz Nonlinear Systems With Input Delay\\nYear: 2015\\n\\nAbstract: This paper deals with the consensus control design for Lipschitz nonlinear multi-agent systems with input delay. The Artstein-Kwon-Pearson reduction method is employed to deal with the input delay and the integral term that remains in the transformed system is analyzed by using Krasovskii functional. Upon exploring certain features of the Laplacian matrix, sufficient conditions for global stabilit...\",\n \"Title: Adaptive Rejection Of Non-Linear Disturbances In Extended Non-Linear Output Feedback Systems\\nYear: 2008\\n\\nAbstract: This paper deals with asymptotic rejection of disturbances generated from non-linear exosystems for uncertain nonlinear systems in an extended output feedback form, which allows the vector field coupled with the system input to have different nonlinear functions of the system output as its elements. A new internal model design is proposed to deal with nonlinear functions of the system output that are coupled with the input and the unknown disturbance. Adaptive control techniques are then used to deal with the uncertainty in the system. The proposed adaptive disturbance rejection algorithm with the new internal model design ensures the asymptotic rejection of the unknown nonlinear disturbance and the boundedness of all the variables.\",\n \"Title: Asymptotic Rejection Of Unmatched General Periodic Disturbances With Nonlinear Lipschitz Internal Models\\nYear: 2013\\n\\nAbstract: This article deals with asymptotic rejection of general periodic disturbances in a class of nonlinear systems by exploiting observer design with Lipschitz output nonlinearities for the internal model design. It is shown that a class of general periodic distances can be modelled as outputs of linear systems with nonlinear output functions. Based on this observation, a nonlinear observer design with output Lipschitz nonlinearities is investigated and integrated for the internal model design to estimate the phase and amplitude of the desired feedforward input for asymptotic disturbance rejection in a class of nonlinear systems with input-to-state stable zero dynamics. A number of problems on disturbance rejection can be formulated in the disturbance rejection form shown in this article to which the proposed nonlinear Lipschitz internal model can be applied. Two examples are included to demonstrate the proposed design strategies.\",\n \"Title: Consensus Control Of A Class Of Lipschitz Nonlinear Systems\\nYear: 2014\\n\\nAbstract: This paper deals with consensus control of a class of nonlinear systems with Lipschitz nonlinearities. Certain features of the Laplacian matrix are explored in the real Jordan form to identify conditions for global consensus control. Under the identified conditions, consensus control and stability of the proposed control are analysed in time domain through Lyapunov functions in real domain. The proposed control uses relative state information of the system. A simulation study is included to demonstrate the proposed control designs with some simulation results shown.\",\n \"Title: Distributed adaptive controllers for cooperative output regulation of heterogeneous agents over directed graphs.\\nYear: 2016\\n\\nAbstract: This paper considers the cooperative output regulation problem for linear multi-agent systems with a directed communication graph, heterogeneous linear subsystems, and an exosystem whose output is available to only a subset of subsystems. Both the cases with nominal and uncertain linear subsystems are studied. For the case with nominal linear subsystems, a distributed adaptive observer-based controller is designed, where the distributed adaptive observer is implemented for the subsystems to estimate the exogenous signal. For the case with uncertain linear subsystems, the proposed distributed observer and the internal model principle are combined to solve the robust cooperative output regulation problem. Compared with the existing works, one main contribution of this paper is that the proposed control schemes can be designed and implemented by each subsystem in a fully distributed fashion for a class of directed graphs.\",\n \"Title: Truncated Prediction Output Feedback Control of a Class of Lipschitz Nonlinear Systems With Input Delay.\\nYear: 2016\\n\\nAbstract: This brief addresses an output feedback control design for Lipschitz nonlinear systems in the presence of input delay. A nonlinear observer is introduced to estimate the system states. A truncated predictor state feedback, which is the conventional predictor state feedback law with the distributed term dropped, is implemented with the estimated states. Lyapunov-Krasovskii functionals are construct...\",\n \"Title: Distributed adaptive consensus control of nonlinear output-feedback systems on directed graphs.\\nYear: 2016\\n\\nAbstract: This paper deals with consensus control in leader-follower format of a class of network-connected uncertain nonlinear systems by output feedback. Each subsystem is in the nonlinear output feedback form with unknown parameters, and the connection graph among the subsystems is directed. Distributed adaptive control inputs are designed to achieve the consensus control in the sense that the subsystem states asymptotically follow the subsystem at node 0 with no input, which is also known as the leader. The proposed adaptive control only uses relative output measurements and the local information of the connection to each subsystem, and hence the proposed adaptive control is fully distributed. The proposed scheme is different from the consensus output regulation schemes literature, and the leader plays a similar role as a reference model in the classic model reference adaptive control.\",\n \"Title: Global output regulation for a class of lower triangular nonlinear systems: A feedback domination approach.\\nYear: 2017\\n\\nAbstract: An alternative but new nonlinear output regulation approach is proposed for a class of lower triangular nonlinear systems by using the tool of adding a power integrator. The invariant manifold is solved and represented in terms of various derivatives of disturbances and references. As such, it is unnecessary to suppose that the external disturbances being governed by certain exosystems anymore. The nonlinearities are not restricted to be sufficiently smooth due the utilization of feedback domination approach. It is shown that the time taken to reach the desired invariant manifold from any initial states under external disturbances is guaranteed to be finite time. The control law is concise in structure for implementation since the feedback domination is implemented in each of the recursive design step.\",\n \"Title: Adaptive consensus disturbance rejection for multi-agent systems on directed graphs\\nYear: 2016\\n\\nAbstract: In this paper, the adaptive consensus disturbance rejection problem is considered for the liner multi-agent systems under directed graphs. Based on the relative state information of the neighboring agents, the consensus protocols, including a state observer and a disturbance observer, are designed to guarantee that the consensus error goes to zero with the complete disturbance rejection. Furthermore, the state observer is designed in a fully distributed fashion with adaptive coupling gain, which has the advantage that the consensus protocol design is independent of the Laplacian matrix associated with the communication network. Finally, an example is given to verify the effectiveness of the theoretical results.\",\n \"Title: Continuous Finite-Time Output Regulation of Nonlinear Systems With Unmatched Time-Varying Disturbances.\\nYear: 2018\\n\\nAbstract: In this letter, a new continuous nonlinear controller ensuring finite-time output regulation is proposed for a class of nonlinear systems subject to unmatched time-varying disturbances. The controller is developed in a composite manner which combines invariant manifold principle, feedback domination, and disturbance observation techniques. A key idea is to realize the finite-time estimation of sys...\",\n \"Title: Distributed Adaptive Consensus Output Regulation of Network-Connected Heterogeneous Unknown Linear Systems on Directed Graphs.\\nYear: 2017\\n\\nAbstract: This technical note deals with output regulation of network connected linear systems under directed connection graphs. The subsystem dynamics are unknown and heterogeneous. The desired output signal, generated by a linear exosystem, is only available to some subsystems, and the others will estimate the exosystem state through the network connections for obtaining regulation errors. The exosystem is parametrised in a specific form such that it has a skew-symmetric system matrix, whose property is further explored in the estimation of exosystem state, and in the estimation of the desired feed-forward control inputs for output regulation. The unknown subsystem parameters are dealt with by the adaptive laws, driven by the estimated regulation errors. The proposed adaptive control strategy is fully distributed, in the sense that the control design only uses the information in the connected neighbourhood, without reference to the eigenstructure of the associated Laplacian matrix, as long as the connection graph is strongly connected.\",\n \"Title: Truncated Predictor Control of Lipschitz Nonlinear Systems With Time-Varying Input Delay.\\nYear: 2017\\n\\nAbstract: This note deals with control design for Lipschitz nonlinear systems with time-varying input delay. Based on a truncated prediction of the system state over the delay period, both a state and an output feedback control law are constructed. Within the framework of Lyapunov-Krasovskii functionals, a set of conditions are identified under which the closed-loop system under either the state feedback or the output feedback law is globally asymptotically stable at the origin. A numerical example is included to demonstrate the effectiveness of the proposed designs.\",\n \"Title: Distributed Initialization-Free Cost-Optimal Charging Control of Plug-In Electric Vehicles for Demand Management.\\nYear: 2017\\n\\nAbstract: This paper considers the optimal charging problem of plug-in electric vehicles (PEVs) on demand side management. PEVs provide a promising alternative solution to reduction of environmental pollution and fuel emission. With a large number of PEVs connected to the grid, a well-designed charging coordination approach is needed to release the impacts on the power system. A distributed cooperative cont...\",\n \"Title: Formation control with disturbance rejection for a class of Lipschitz nonlinear systems.\\nYear: 2017\\n\\nAbstract: In this paper, we consider the leader-follower formation control problem for general multi-agent systems with Lipschitz nonlinearity and unknown disturbances. To deal with the disturbances, a disturbance observer-based control strategy is developed for each follower. Then, a time-varying formation protocol is proposed based on the relative state of the neighbouring agents and sufficient conditions for global stability of the formation control are identified using Lyapunov method in the time domain. The proposed strategy and analysis guarantee that all signals in the closed-loop dynamics are uniformly ultimately bounded and the formation tracking errors converge to an arbitrarily small residual set. Finally, the validity of the proposed controller is demonstrated through a numerical example.\",\n \"Title: A Truncated Prediction Approach to Consensus Control of Lipschitz Nonlinear Multiagent Systems With Input Delay.\\nYear: 2017\\n\\nAbstract: This paper deals with the consensus control problem for Lipschitz nonlinear multiagent systems with input delay. A prediction of the agent state over the delay period is approximated by the zero input solution of the agent dynamics. The structure of a linear state feedback control algorithm is assumed for each agent based on such approximated state prediction. By transforming the Laplacian matrix ...\",\n \"Title: Fixed-Time Consensus Tracking for Multiagent Systems With High-Order Integrator Dynamics.\\nYear: 2018\\n\\nAbstract: This paper addresses the fixed-time leader-follower consensus problem for high-order integrator multiagent systems subject to matched external disturbances. A new cascade control structure, based on a fixed-time distributed observer, is developed to achieve the fixed-time consensus tracking control. A simulation example is included to show the efficacy and the performance of the proposed control structure with respect to different initial conditions.\",\n \"Title: Distributed Adaptive Consensus Disturbance Rejection for Multi-Agent Systems on Directed Graphs.\\nYear: 2018\\n\\nAbstract: This paper considers the distributed consensus disturbance rejection problem for general linear multiagent systems with deterministic disturbances under directed communication graphs. Based on the relative state information of the neighboring agents, the consensus protocols, which consist of two observers, including a state observer and a separate disturbance observer, are designed to guarantee th...\",\n \"Title: Distributed Finite-Time Optimal Resource Management for Microgrids Based on Multi-Agent Framework.\\nYear: 2018\\n\\nAbstract: This paper investigates the optimal resource management in a microgrid under various operating conditions. A two-level optimization system is proposed for the distributed optimal resource management based on a multi-agent system framework. The proposed strategy generates a reference of the optimal power output at the top level through local communication. This strategy only requires the informatio...\",\n \"Title: Distributed Adaptive Convex Optimization on Directed Graphs via Continuous-Time Algorithms.\\nYear: 2018\\n\\nAbstract: This note considers the distributed optimization problem on directed graphs with nonconvex local objective functions and the unknown network connectivity. A new adaptive algorithm is proposed to minimize a differentiable global objective function. By introducing dynamic coupling gains and updating the coupling gains using relative information of system states, the nonconvexity of local objective f...\",\n \"Title: Simultaneous Mean and Covariance Correction Filter for Orbit Estimation.\\nYear: 2018\\n\\nAbstract: This paper proposes a novel filtering design, from a viewpoint of identification instead of the conventional nonlinear estimation schemes (NESs), to improve the performance of orbit state estimation for a space target. First, a nonlinear perturbation is viewed or modeled as an unknown input (UI) coupled with the orbit state, to avoid the intractable nonlinear perturbation integral (INPI) required by NESs. Then, a simultaneous mean and covariance correction filter (SMCCF), based on a two-stage expectation maximization (EM) framework, is proposed to simply and analytically fit or identify the first two moments (FTM) of the perturbation (viewed as UI), instead of directly computing such the INPI in NESs. Orbit estimation performance is greatly improved by utilizing the fit UI-FTM to simultaneously correct the state estimation and its covariance. Third, depending on whether enough information is mined, SMCCF should outperform existing NESs or the standard identification algorithms (which view the UI as a constant independent of the state and only utilize the identified UI-mean to correct the state estimation, regardless of its covariance), since it further incorporates the useful covariance information in addition to the mean of the UI. Finally, our simulations demonstrate the superior performance of SMCCF via an orbit estimation example.\",\n \"Title: H∞ Control for Discrete-time Linear Systems by Integrating Off-policy Q-learning and Zero-sum Game.\\nYear: 2018\\n\\nAbstract: This paper focuses on solving H∞ control problem by proposing a novel off-policy Q-learning algorithm under the framework of zero-sum game for discrete-time (DT) linear systems, using only the measured data along the system trajectories. Firstly, H∞ control problem is formulated, followed by the standard on-policy Q-learning algorithm in order to learn the H∞ controller gain. Secondly, behavior control policy and behavior disturbance policy are introduced, and an off-policy Q-function based game Bellman equation (OPQ-GBE) is derived. Consequently, an off-policy Q-learning algorithm is developed for the first time for discrete-time linear systems subject to the external disturbance, and the convergence and no bias of solution of OPQ-BE are proven. Finally, a F-16 aircraft autopilot is given to verify the effectiveness of the proposed method.\",\n \"Title: Distributed Multisensor Fusion With Outlier Clearance Technique for Sensor Networks\\nYear: 2018\\n\\nAbstract: One challenge faced by sensor fusion strategy designed for sensor networks is to real-time achieve high reliability estimated data with the inaccuracy and fault in raw sensor measurements. In this paper, we present an online outlier clearance technique with low computational complexity and memory usage inspired by the nearest neighbor rule that can identify and remove the spurious data in the distributed multisensor fusion process. The proposed technique is fully localized and thus saves communication overhead as well as has a good extension as deployed nodes increasing. In this technique, we define a weighted average distance-based outlier factor criterion to detect the outlier and replace it with estimated data. With the help of contrast simulations, in which we adopt other two typical techniques, it illustrates that the proposed technique provides better performance in real-time clearing outlier during the sensor fusion process without the prior knowledge of outlier.\",\n \"Title: A Revisit To Gradient-Descent Bearing-Only Formation Control\\nYear: 2018\\n\\nAbstract: This paper addresses the problem of bearing-only formation control of multi-agent systems, where each agent can merely obtain the relative bearing measurements of their neighbor neighbors whereas relative distance or position measurements are unavailable. In particular, we revisit a bearing-only formation control law proposed in [1]. Unlike many other existing ones, this control law is gradient-descent, which is favorable from the stability analysis point of view. It has the potential to be extended to handle more complex agent models and moving target formations. Up to now, this control law has not attracted sufficient attention probably because its stability analysis is based on optimization techniques and challenging to generalize. The contribution of this paper is to present a new stability analysis of this formation control law based on Lyapunov approaches. The new stability analysis reveals some new properties of the control law such as exponential convergence rate and lays a foundation for deriving new bearing-only control laws in the future.\",\n \"Title: Model Fitting Filter\\nYear: 2018\\n\\nAbstract: In this paper, we present a new nonlinear filter based on variational inference, which we have named the model fitting filter (MFF). The core of MFF is to represent or fit the nonlinear measurement likelihood probability with a linear Gaussian probability (LGP) characterized by some parameters that need to be turned. Further, MFF is analytically established on LGP, which is a little similar to the simple linear system filter. Specifically, the nonlinear filtering performance is well improved by utilizing these turned parameters to correct the state estimation. At the same time, MFF does not require the square root decomposition of covariance matrix, so it avoids the inborn defect of numerical instability, which exists more or less in UKF or DDF. The superior performance of MFF is numerically tested by a target tracking problem with nonlinear models.\",\n \"Title: Sampled-data synchronization of chaotic Lur'e systems via an adaptive event-triggered approach.\\nYear: 2018\\n\\nAbstract: In this paper, based on nonuniform sampling, the master-slave synchronization in a class of Lur’e systems is studied via using the sampled outputs of error system. Different from existing results, the transmission of control signals is determined by a novel adaptive event-triggered scheme, where the triggering thresholds depend on the dynamic behaviors of controlled systems rather than the predetermined constants as some traditional ones. Through choosing two augmented Lyapunov–Krasovskii functionals (LKFs), some delay-dependent synchronization criteria are formulated and the conservatism can be effectively reduced owing to the utilization of Wirtinger-based inequalities and delay-product-type LKF terms. Especially, the existence of the controller can be easily checked since the derived conditions are presented via LMI forms. Finally, two numerical examples with comparisons and simulations are given to illustrate the proposed results.\",\n \"Title: Robust control of unmanned helicopters with high-order mismatched disturbances via disturbance-compensation-gain construction approach.\\nYear: 2018\\n\\nAbstract: In this paper, a novel robust control strategy based on disturbance-compensation-gain (DCG) construction approach is proposed for small-scale unmanned helicopters in the presence of high-order mismatched disturbances. The overall control structure consists of two hierarchical layers. The inner-loop controller is to guarantee the stability of the unmanned helicopters subject to high-order mismatched disturbances. With the estimation of the disturbances and their successive derivatives via finite-time disturbance observer (FTDO), by properly designing some disturbance compensation gains, a novel robust controller is developed to remove the high-order mismatched disturbances from the output channels. The outer-loop controller is to produce flight commands for inner-loop system, as well as to track the reference trajectory, which is carried out with the dynamic inversion technique. The simulation results demonstrate that the unmanned helicopters are capable to perform flight missions autonomously with the proposed control strategy.\",\n \"Title: Modeling and switching control of air-breathing hypersonic vehicle with variable geometry inlet.\\nYear: 2018\\n\\nAbstract: In this paper, a multi-model switching control is developed for air-breathing hypersonic vehicle with variable geometry inlet(AHV-VGI). A variable geometry inlet with the translating cowl is adopted to capture the enough air mass flow for the scramjet engine, which can ensure a more powerful thrust. However, the using of VGI causes the unknown changes of the aerodynamics and thrust, making the model of AHV more complex. Therefore, we firstly analyze the thrust characteristic with the translating cowl and present the conception of optimal elongation distance of translating cowl(EDTC). Consequently, multiple different nonlinear aerodynamic models are constructed by curve fitting for each position of the translating cowl. Then, a switching mechanism dependent on EDTC is proposed and the adaptive RBF neural controllers are designed for velocity subsystem and altitude system of every model. Furthermore, the common Lyapunov functional is constructed to prove the stability of the multi-model switching process. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed control approach for AHV-VGI.\",\n \"Title: Finite-Time Consensus for Second-Order Multi-Agent Systems With Input Saturation.\\nYear: 2018\\n\\nAbstract: In this brief, a new class of finite-time consensus controller is designed for second-order multi-agent systems subject to input saturation. Only a single saturation function is involved in the proposed controller, which distinguishes it from the previous ones. The proposed controllers achieve finite-time consensus using only relative state measurements. Both leaderless and leader-following consen...\",\n \"Title: Predictor-Based Extended-State-Observer Design for Consensus of MASs With Delays and Disturbances.\\nYear: 2019\\n\\nAbstract: In this paper, we study output feedback leader-follower consensus problem for multiagent systems subject to external disturbances and time delays in both input and output. First, we consider the linear case and a novel predictor-based extended state observer is designed for each follower with relative output information of the neighboring agents. Then, leader-follower consensus protocols are proposed which can compensate the delays and disturbances efficiently. In particular, the proposed observer and controller do not contain any integral term of the past control input and hence are easy to implement. Consensus analysis is put in the framework of Lyapunov-Krasovskii functionals and sufficient conditions are derived to guarantee that the consensus errors converge to zero asymptotically. Then, the results are extended to nonlinear multiagent systems with nonlinear disturbances. Finally, the validity of the proposed design is demonstrated through a numerical example of network-connected unmanned aerial vehicles.\",\n \"Title: Decentralized Adaptive Event-Triggered H-Infinity Filtering For A Class Of Networked Nonlinear Interconnected Systems\\nYear: 2019\\n\\nAbstract: This paper focuses on the issue of designing an adaptive event-triggered scheme to the decentralized filtering for a class of networked nonlinear interconnected system. A novel adaptive event-triggered condition is proposed by constructing an adaptive law for the threshold. This new type of threshold mainly depends on the error between the states at the current sampling instant and the latest releasing instant, by which the data release rate is adapted to the variation of the system. The limitation of network bandwidth is alleviated on account of a large amount of \\\"unnecessary\\\" packets being dropped out before accessing the network. Sufficient conditions are derived such that the overall filtering error system under the proposed adaptive data-transmitting scheme is asymptotically stable with a prescribed disturbance attenuation level. An example is given to show the effectiveness of the proposed scheme.\",\n \"Title: Off-Policy Interleaved Q-Learning: Optimal Control For Affine Nonlinear Discrete-Time Systems\\nYear: 2019\\n\\nAbstract: In this paper, a novel off-policy interleaved Q-learning algorithm is presented for solving optimal control problem of affine nonlinear discrete-time (DT) systems, using only the measured data along the system trajectories. Affine nonlinear feature of systems, unknown dynamics, and off-policy learning approach pose tremendous challenges on approximating optimal controllers. To this end, on-policy Q-learning method for optimal control of affine nonlinear DT systems is reviewed first, and its convergence is rigorously proven. The bias of solution to Q-function-based Bellman equation caused by adding probing noises to systems for satisfying persistent excitation is also analyzed when using on-policy Q-learning approach. Then, a behavior control policy is introduced followed by proposing an off-policy Q-learning algorithm. Meanwhile, the convergence of algorithm and no bias of solution to optimal control problem when adding probing noise to systems are investigated. Third, three neural networks run by the interleaved Q-learning approach in the actor-critic framework. Thus, a novel off-policy interleaved Q-learning algorithm is derived, and its convergence is proven. Simulation results are given to verify the effectiveness of the proposed method.\",\n \"Title: Given-time consensus for stochastic Markov jump networks by dynamic output feedback\\nYear: 2018\\n\\nAbstract: This paper focuses on the given-time consensus of stochastic Markov jump networks with disturbances based on dynamic output feedback control. By exploring the relative output information, a distributed dynamic output feedback protocol is designed to guarantee that the disagreement dynamics of networks keeps within the prescribed bound in a fixed time interval. With the new model transformation approach, the obtained results are more general and lead to less conservativeness. An example of industrial material heating unit verifies the engineering application of the proposed algorithm for networks in the presence of both Markov jump topologies and external disturbances.\",\n \"Title: Robust Script Capital L-2 Disturbance Attenuation For A Class Of Uncertain Lipschitz Nonlinear Systems With Input Delay\\nYear: 2019\\n\\nAbstract: In this paper, we study a robust disturbance attenuation problem that arises when applying the Artstein-Kwon-Pearson reduction transformation for a class of uncertain Lipschitz nonlinear systems with input delay and external disturbances. A conventional predictor-based feedback controller is adopted with the control gain matrix carefully identified by solving a couple of sufficient conditions in terms of linear matrix inequalities . Lyapunov-Krasovskii functionals are constructed to guarantee that the robust disturbance attenuation problem can be solved by the proposed controller. A numerical example is included to validate the performance of the proposed controller.\",\n \"Title: Off-Policy Q-Learning: Set-Point Design for Optimizing Dual-Rate Rougher Flotation Operational Processes.\\nYear: 2018\\n\\nAbstract: Rougher flotation, composed of unit processes operating at a fast time scale and economic performance measurements known as operational indices measured at a slower time scale, is very basic and the first concentration stage for flotation plants. Optimizing operational process for rougher flotation circuits is extremely important due to high economic profit arising from the optimality of operation...\",\n \"Title: Robust Cooperative Guidance Law for Simultaneous Arrival\\nYear: 2019\\n\\nAbstract: In the cooperative simultaneous arrival problem, a group of interceptors are guided to simultaneously engage a stationary target. However, some interceptors may not follow the prescribed guidance law during the engagement, which can lead to interception failures. This brief investigates a new robust cooperative simultaneous arrival problem in the presence of misbehaving interceptors. A robust coop...\",\n \"Title: Adaptive Output Regulation Of Output Feedback Systems With Unknown Nonlinear Exosystems And Unknown High-Frequency Gain Sign\\nYear: 2018\\n\\nAbstract: This paper addresses the problem of adaptive output regulation for a class of output feedback systems subject to unknown disturbances generated from nonlinear exosystems and no knowledge of high-frequency gain sign. The nonlinear internal model based on circle criterion is proposed to generate the feedforward input term for rejection of the disturbances, and some specific conditions are introduced for exosystems such that the proposed internal model can be applied. In the proposed adaptive control scheme, a type of Nussbaum gain is presented to deal with the unknown high-frequency gain sign and the adaptive backstepping technique is then used to establish the final control input if systems possess high relative degree. The proposed control algorithm ensures the convergence of the parameters in the adaptive internal model to the actual values and the measurement output to zero, and the boundedness of all state variables.\",\n \"Title: Consensus-based Distributed Optimal Energy Management with Less Communication in a Microgrid\\nYear: 2019\\n\\nAbstract: In this paper, to reduce required capacities for information exchanges in microgrids, a novel distributed event-based algorithm is proposed for optimal energy management in a microgrid. Aiming at optimally scheduling the energy supplier's generation, an objective function is formulated to minimize the total cost of maintaining the supply-demand balance with considering power losses. Regarding each...\",\n \"Title: Fixed-Time Formation Control of Multi-Robot Systems: Design and Experiments\\nYear: 2019\\n\\nAbstract: Time delays exist in network-connected systems. Especially for vision-based multirobot systems, time delays are diverse and complicated due to the communication network, camera latency, image processing, etc. At the same time, many tasks, such as searching and rescue, have timing requirement. This paper focuses on fixed-time formation control of multirobot systems subject to delay constraints. First, predictor-based state transformation is employed for each robot to deal with the input delay, and the uncertain terms remained in the transformed systems are carefully considered. Then, a couple of nonlinear fixed-time formation protocols are proposed for the multirobot systems with respectively undirected and directed topology, and the corresponding settling time is derived by using the Lyapunov functions. In particular, the upper-bound estimation of the formation settling time is explicitly given irrelevant to the initial conditions. Finally, the protocols are validated through a numerical simulation example and then implemented on an E-puck robots platform. Both simulation and experimental results demonstrate the effectiveness of the proposed formation protocols.\",\n \"Title: Adaptive Optimal Control for a Class of Nonlinear Systems: The Online Policy Iteration Approach.\\nYear: 2020\\n\\nAbstract: This paper studies the online adaptive optimal controller design for a class of nonlinear systems through a novel policy iteration (PI) algorithm. By using the technique of neural network linear differential inclusion (LDI) to linearize the nonlinear terms in each iteration, the optimal law for controller design can be solved through the relevant algebraic Riccati equation (ARE) without using the ...\",\n \"Title: Finite-Time Synchronization for a Class of Dynamical Complex Networks with Nonidentical Nodes and Uncertain Disturbance\\nYear: 2019\\n\\nAbstract: This paper investigates the finite-time synchronization for a class of linearly coupled dynamical complex networks with both nonidentical nodes and uncertain disturbance. A set of controllers are designed such that the considered system can be finite-timely synchronized onto the target node. Based on the stability of the error equation, the Lyapunov function method and the linear matrix inequality technique, several sufficient conditions are derived to ensure the finite-time synchronization, and applied to the case of identical nodes and the one without uncertain disturbance. Also the adaptive finite-time synchronization is discussed. A numerical example is given to show the effectiveness of the main results obtained.\",\n \"Title: Disturbance rejection via iterative learning control with a disturbance observer for active magnetic bearing systems.\\nYear: 2019\\n\\nAbstract: Although standard iterative learning control (ILC) approaches can achieve perfect tracking for active magnetic bearing (AMB) systems under external disturbances, the disturbances are required to be iteration-invariant. In contrast to existing approaches, we address the tracking control problem of AMB systems under iteration-variant disturbances that are in different channels from the control inputs. A disturbance observer based ILC scheme is proposed that consists of a universal extended state observer (ESO) and a classical ILC law. Using only output feedback, the proposed control approach estimates and attenuates the disturbances in every iteration. The convergence of the closed-loop system is guaranteed by analyzing the contraction behavior of the tracking error. Simulation and comparison studies demonstrate the superior tracking performance of the proposed control approach.\",\n \"Title: Event-Based Resilient Formation Control of Multiagent Systems.\\nYear: 2021\\n\\nAbstract: This paper focuses on the time-varying formation tracking issue for nonlinear multiagent systems (MASs). Based on the explicit characterizations of frequency, duration, and magnitude properties for deception attacks, a hybrid framework is proposed for time-varying formation tracking of nonlinear MASs. To realize the desired formation tracking performance under deception attacks, the distributed ed...\",\n \"Title: Bearing-Only Formation Tracking Control of Multi-Agent Systems\\nYear: 2019\\n\\nAbstract: This paper studies the problem of bearing-only formation control of multi-agent systems, where the control of each agent merely relies on the relative bearings to its neighbors. Although this problem has received increasing research attention recently, it is still unsolved to a large extent due to its highly nonlinear dynamics. In particular, the existing control approaches are only able to solve the simplest scenario where the target formation is stationary and each agent is modeled as a single integrator. The main contribution of this paper is to propose new bearing-only formation control laws to (i) track moving target formations and (ii) handle a variety of agent models including single-integrator, double-integrator, and unicycle models. These control laws are an important step towards the application of bearing-only formation control in practical tasks. Both numerical simulation and real experimental results are presented to verify the effectiveness of the theoretical results.\",\n \"Title: Tracking Consensus of General Nonlinear Multi-Agent Systems with External Disturbances Under Directed Networks\\nYear: 2019\\n\\nAbstract: This paper considers the fully distributed tracking consensus problem for general nonlinear multi-agent systems with a leader whose control input is nonzero and bounded. First, a new class of distributed state observer for the leader is proposed without the knowledge of the upper bound of the leaderu0027s input. Then, the situations that followers are affected by disturbances with unknown upper bound or disturbances generated by exosystems are investigated. Specifically, two distributed control protocols based on the distributed state observer, neural networks, and adaptive laws are proposed. Finally, simulation examples are provided to illustrate the theoretical results.\",\n \"Title: Reinforcement learning and adaptive optimization of a class of Markov jump systems with completely unknown dynamic information\\nYear: 2020\\n\\nAbstract: In this paper, an online adaptive optimal control problem of a class of continuous-time Markov jump linear systems (MJLSs) is investigated by using a parallel reinforcement learning (RL) algorithm with completely unknown dynamics. Before collecting and learning the subsystems information of states and inputs, the exploration noise is firstly added to describe the actual control input. Then, a novel parallel RL algorithm is used to parallelly compute the correspondingNcoupled algebraic Riccati equations by online learning. By this algorithm, we will not need to know the dynamic information of the MJLSs. The convergence of the proposed algorithm is also proved. Finally, the effectiveness and applicability of this novel algorithm is illustrated by two simulation examples.\",\n \"Title: Distributed Training for Multi-Layer Neural Networks by Consensus.\\nYear: 2020\\n\\nAbstract: Over the past decade, there has been a growing interest in large-scale and privacy-concerned machine learning, especially in the situation where the data cannot be shared due to privacy protection or cannot be centralized due to computational limitations. Parallel computation has been proposed to circumvent these limitations, usually based on the master–slave and decentralized topologies, and the comparison study shows that a decentralized graph could avoid the possible communication jam on the central agent but incur extra communication cost. In this brief, a consensus algorithm is designed to allow all agents over the decentralized graph to converge to each other, and the distributed neural networks with enough consensus steps could have nearly the same performance as the centralized training model. Through the analysis of convergence, it is proved that all agents over an undirected graph could converge to the same optimal model even with only a single consensus step, and this can significantly reduce the communication cost. Simulation studies demonstrate that the proposed distributed training algorithm for multi-layer neural networks without data exchange could exhibit comparable or even better performance than the centralized training model.\",\n \"Title: Adaptive delay compensation for consensus control under switching topologies by observer–predictor\\nYear: 2021\\n\\nAbstract: AbstractAbstractThis paper considers observer–predictor feedback for delay compensation in consensus control under undirected switching graphs. Each node involved in the network is described by a linear system with an actuator delay of uncertain length, and the connection graph is allowed to be unknown and switch arbitrarily. An observer is proposed to estimate the disagreement state using locally available information, and a delay-adaptive consensus protocol is proposed via observer–predictor feedback. With the employment of a “backstepping” state transformation involving the observer state and the estimation delay state, an explicit Lyapunov function is constructed to prove the consensus and stability properties of the resultant closed-loop system. Numerical simulations are presented to demonstrate the effectiveness of the theoretic results.\",\n \"Title: Variational Bayesian inference for the identification of FIR systems via quantized output data\\nYear: 2021\\n\\nAbstract: AbstractAbstractFor identifying the parameters in finite impulse response (FIR) systems via the quantized output, existing expectation maximization (EM) methods are involved with the intractable integrals. To overcome the above shortcoming, this paper proposes an approach referred to as explicit EM (EEM). First, we fit the quantized output data by a linear Gaussian regression distribution with some compensation parameters. As a result of the linear Gaussianity, the posterior of the excited output can be explicitly computed, so that EEM has the less computation burden than the existing EMs. Second, through optimizing these compensation parameters by resorting to variational Bayesian inference, the intrinsic distribution of the quantized output can be well fitted with guaranteed identification accuracy. Third, the linear Gaussian regression distribution is independent of the specific quantization function form, which enables EEM applicable for different quantization forms. Finally, the simulation demonstrates the feasibility and effectiveness of the proposed approach.\",\n \"Title: Surrogate-Assisted Cooperation Control of Network-Connected Doubly Fed Induction Generator Wind Farm With Maximized Reactive Power Capacity\\nYear: 2022\\n\\nAbstract: This article aims to realize a cooperative active power control of doubly fed induction generator (DFIG)-based wind farm (WF) to maximize the total reactive power capacity while maintaining the active power supply-and-demand balance. Difficulties lie in that the accurate PQ-curve expressions of wind turbines therein are unknown and nonuniform, thereby putting an obstacle to distributed optimizatio...\",\n \"Title: Distributed Optimization for Integrated Frequency Regulation and Economic Dispatch in Microgrids\\nYear: 2021\\n\\nAbstract: In hierarchically controlled microgrids, economic dispatch and corresponding real-time control action are inherently performed at different timescales. Owing to the uncertainty in renewables, running economic dispatch in advance may not adequately guarantee the anticipated outcomes. In this paper, distributed optimization algorithms are developed to manage economic dispatch and frequency regulatio...\",\n \"Title: On the game-theoretic analysis of distributed generative adversarial networks\\nYear: 2022\\n\\nAbstract: In this paper, a distributed method is proposed for training multiple generative adversarial networks (GANs) with private data sets via a game-theoretic approach. To facilitate the requirement of privacy protection, distributed training algorithms offer a promising solution to learn global models without sample exchanges. Existing studies have mainly concentrated on training neural networks using pure cooperation strategies, which are not suitable for GANs. This paper develops a new framework for distributed GANs, where two groups of discriminators and generators are involved in a zero-sum game. Under connected graphs, such a framework is reformulated as a constrained minmax optimisation problem. Then, a fully distributed training algorithm is proposed without exchanging any private data samples. The convergence of the proposed algorithm is established via advanced consensus and optimisation techniques. Simulation studies are presented to validate the effectiveness of the proposed framework and algorithm.\",\n \"Title: Collaborative Overtaking of Multi-Vehicle Systems in Dynamic Environments: A Distributed Artificial Potential Field Approach\\nYear: 2021\\n\\nAbstract: Multi-vehicle autonomous driving has attracted significant attention from academia and industries due to its ability to improve traffic efficiency and enhance the safety of drivers and passengers. In this paper, a distributed motion planning strategy based on the artificial potential field method is proposed to achieve overtaking of the autonomous vehicle fleet in dynamic environments. Firstly, a dynamic target tracking control protocol is constructed for an autonomous vehicle fleet using a modified artificial potential field. The target tracking control protocol overcomes the disadvantage of the traditional artificial potential field method (i.e., the dynamic target cannot be reached). Besides, a distributed obstacle avoidance control protocol is also designed to avoid potential collisions during the overtaking process. Finally, simulation experiments are performed to verify the feasibility and effectiveness of the proposed algorithm.\",\n \"Title: Intrinsic Cramér–Rao bounds for distributed Bayesian estimator\\nYear: 2022\\n\\nAbstract: This paper addresses the intrinsic Cramér–Rao bounds (CRBs) for a distributed Bayesian estimator whose states and measurements are on Riemannian manifolds. As Euclidean-based CRBs for recursive Bayesian estimator are no longer applicable to general Riemannian manifolds, the bounds need redesigning to accommodate the non-zero Riemannian curvature. To derive the intrinsic CRBs, we append a coordination step to the recursive Bayesian procedure, where the proposed sequential steps are prediction, measurement and coordination updates. In the coordination step, the estimator minimises the Kullback–Liebler divergence to obtain the consensus of multiple probability density functions (PDFs). Employing the PDFs from those steps together with the affine connection on manifolds the Fisher Information Matrix (FIM) and the curvature terms of the corresponding intrinsic bounds are derived. Subsequently, the design of a distributed estimator for Riemannian information manifold with Gaussian distribution – referred to as distributed Riemannian Kalman filter – is also presented to exemplify the application of the proposed intrinsic bounds. Finally, simulations utilising the designed filter for a distributed quaternionic estimation problem verifies that the covariance matrices of the filter are never below the formulated intrinsic CRBs.\",\n \"Title: A distributed deep reinforcement learning method for traffic light control\\nYear: 2022\\n\\nAbstract: This paper proposes a distributed deep reinforcement learning algorithm for the traffic light control problem, which consists of local learning and global consensus. Firstly, the reinforcement learning environment for the traffic light control problem is built by defining the three key elements of state, action, and reward. Then, the CNN-based deep Q-network is designed to process the quantized traffic state information to obtain the state-action values. After locally optimizing the deep Q-networks of multiple traffic light agents based on their experience samples, the consensus algorithm is subsequently applied to globally update these agents that are connected over a decentralized communication topology. In this way, the distributed learning agents learn from their neighbors’ experience to optimize the modeling process without actually sharing experience data samples. Lastly, homogeneous and heterogeneous traffic flow patterns on different intersections are simulated in SUMO to verify the superiority of the proposed distributed deep Q-networks, with the comparison to the fixed-time strategy, local learning and centralized learning algorithms. The simulation study demonstrates that the distributed learning algorithm without a central server shows comparable performance with centralized learning, which is much better than fixed-time strategy and the local learning method in both homogeneous and heterogeneous traffic scenarios.\",\n \"Title: Probabilistic Transmission Scheme for Distributed Filtering Over Randomly Lossy Sensor Networks in the Presence of Eavesdropper\\nYear: 2022\\n\\nAbstract: This article studies distributed secure estimation in sensor networks with packet losses and an eavesdropper. The message is transmitted through communication channels between sensors, which can be overheard by the eavesdropper with a certain probability. Two types of sensor networks under two cooperative-filtering algorithms are considered, and probabilistic transmission schemes to defend against the eavesdropper are proposed. For collectively detectable sensor networks under a consensus Kalman filter, a sufficient distributed detectability condition on the transmission probabilities is identified to guarantee that the estimation errors of the sensors are statistically bounded. Furthermore, a necessary and sufficient security condition is obtained to guarantee unboundedness of the eavesdropper’s estimation error. For neighborhood-detectable nodes under a standard Kalman filter, a sufficient distributed detectability condition and a necessary security condition are provided on the transmission probabilities. Simulation examples are given to illustrate the results.\",\n \"Title: Adaptive Backstepping Control of Uncertain Sandwich-Like Nonlinear Systems With Deadzone Nonlinearity\\nYear: 2022\\n\\nAbstract: A systematic differentiator-based adaptive backstepping control methodology is proposed for a class of sandwich-like nonlinear system with unknown state-dependent deadzone nonlinearity and parametric uncertainties. The novelty of our approach is that a high-order sliding mode differentiator is utilized to estimate the nonstrict feedback coupling term resulting from the sandwiched deadzone, and all the outputs of the differentiator are integrated into the backstepping procedure based on Lyapunov functions with flat zone recursively. By this approach, all the unknown parameters are estimated online, the discontinuity of the virtual input caused by bound estimations is avoided. It is shown that the ultimate boundedness of all the closed-loop signals is achieved and the output tracking error converges to a preset set. Simulation is performed to verify the theoretical findings.\",\n \"Title: Fuzzy-Based Adaptive Optimization of Unknown Discrete-Time Nonlinear Markov Jump Systems With Off-Policy Reinforcement Learning\\nYear: 2022\\n\\nAbstract: This article explores a novel adaptive optimal control strategy for a class of sophisticated discrete-time nonlinear Markov jump systems (DTNMJSs) via Takagi–Sugeno fuzzy models and reinforcement learning (RL) techniques. First, the original nonlinear system model is represented by fuzzy approximation, while the relevant optimal control problem is equivalent to designing fuzzy controllers for linear fuzzy systems with Markov jumping parameters. Subsequently, we derive the fuzzy coupled algebraic Riccati equations for the fuzzy-based discrete-time linear Markov jump systems by using Hamiltonian–Bellman methods. Following this, an online fuzzy optimization algorithm for DTNMJSs as well as the associated equivalence proof is given. Then, a fully model-free off-policy fuzzy RL algorithm is derived with proved convergence for the DTNMJSs without using the information of system dynamics and transition probability. Finally, two simulation examples, respectively, related to the single-link robotic arm and the half-car active suspension are given to verify the effectiveness and good performance of the proposed approach.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":99,"string":"99"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13269,"string":"13,269"}}},{"rowIdx":13270,"cells":{"question_id":{"kind":"string","value":"Kun Zhang"},"question":{"kind":"string","value":"Generate an abstract for the title \"A note on the false discovery rate of novel peptides in proteogenomics\" using the following items: Proteogenomics, False discovery rate, Novel peptides, Annotation completeness ratio, Experimental results."},"answer":{"kind":"string","value":"Motivation: Proteogenomics has been well accepted as a tool to discover novel genes. In most conventional proteogenomic studies, a global false discovery rate is used to filter out false positives for identifying credible novel peptides. However, it has been found that the actual level of false positives in novel peptides is often out of control and behaves differently for different genomes. Results: To quantitatively model this problem, we theoretically analyze the subgroup false discovery rates of annotated and novel peptides. Our analysis shows that the annotation completeness ratio of a genome is the dominant factor influencing the subgroup FDR of novel peptides. Experimental results on two real datasets of Escherichia coli and Mycobacterium tuberculosis support our conjecture."},"correct_answer":{"kind":"string","value":"Motivation: Proteogenomics has been well accepted as a tool to discover novel genes. In most conventional proteogenomic studies, a global false discovery rate is used to filter out false positives for identifying credible novel peptides. However, it has been found that the actual level of false positives in novel peptides is often out of control and behaves differently for different genomes. Results: To quantitatively model this problem, we theoretically analyze the subgroup false discovery rates of annotated and novel peptides. Our analysis shows that the annotation completeness ratio of a genome is the dominant factor influencing the subgroup FDR of novel peptides. Experimental results on two real datasets of Escherichia coli and Mycobacterium tuberculosis support our conjecture."},"documents":{"kind":"list like","value":["Title: Research On The Theory And Application Of Ioii In China\nYear: 2016\n\nAbstract: The integration of informatization and industrialization (IOII) is a policy guidelines proposed by the Chinese Government to promote the production upgrade and the transformation of economic development mode, which is given increasingly attention by academic circles. In this paper, a brief introduction to IOII is presented. The research collections of IOII required to study the theory and application are surveyed as well as the application evaluation. By summarizing the development and the existing concept of IOII, a new concept of IOII is put forward. Then by studying comparatively the process and the application evaluation of IOII, suggestions for future empirical investigation is offered.","Title: Causal Discovery Using Regression-Based Conditional Independence Tests.\nYear: 2017\n\nAbstract: Conditional independence (CI) testing is an important tool in causal discovery. Generally, by using CI tests, a set of Markov equivalence classes w.r.t. the observed data can be estimated by checking whether each pair of variables x and y is d-separated, given a set of variables Z. Due to the curse of dimensionality, CI testing is often difficult to return a reliable result for high-dimensional Z. In this paper, we propose a regression-based CI test to relax the test of x perpendicular to y vertical bar Z to simpler unconditional independence tests of x - f(Z) perpendicular to y - g(Z), and x - f(Z) perpendicular to Z or y - g(Z) perpendicular to Z under the assumption that the data-generating procedure follows additive noise models (ANMs). When the ANM is identifiable, we prove that x - f(Z) perpendicular to y - g(Z) double right arrow x perpendicular to y vertical bar Z. We also show that 1) f and g can be easily estimated by regression, 2) our test is more powerful than the state-of-the-art kernel CI tests, and 3) existing causal learning algorithms can infer much more causal directions by using the proposed method.","Title: A Context-Enriched Neural Network Method for Recognizing Lexical Entailment.\nYear: 2017\n\nAbstract: Recognizing lexical entailment (RLE) always plays an important role in inference of natural language, i.e., identifying whether one word entails another, for example, fox entails animal. In the literature, automatically recognizing lexical entailment for word pairs deeply relies on words' contextual representations. However, as a \"prototype\" vector, a single representation cannot reveal multifaceted aspects of the words due to their homonymy and polysemy. In this paper, we propose a supervised Context-Enriched Neural Network (CENN) method for recognizing lexical entailment. To be specific, we first utilize multiple embedding vectors from different contexts to represent the input word pairs. Then, through different combination methods and attention mechanism, we integrate different embedding vectors and optimize their weights to predict whether there are entailment relations in word pairs. Moreover, our proposed framework is flexible and open to handle different word contexts and entailment perspectives in the text corpus. Extensive experiments on five datasets show that our approach significantly improves the performance of automatic RLE in comparison with several state-of-the-art methods.","Title: CSTG: An Effective Framework for Cost-sensitive Sparse Online Learning.\nYear: 2017\n\nAbstract: Sparse online learning and cost-sensitive learning are two important areas of machine learning and data mining research. Each has been well studied with many interesting algorithms developed. However, very limited published work addresses the joint study of these two fields. In this paper, to tackle the high-dimensional data streams with skewed distributions, we introduce a framework of cost-sensitive sparse online learning. Our proposed framework is a substantial extension of the influential Truncated Gradient (TG) method by formulating a new convex optimization problem, where the two mutual restraint factors, misclassification cost and sparsity, can be simultaneously and favorably balanced. We theoretically analyze the regret and cost bounds of the proposed algorithm, and pinpoint its theoretical merit compared to the existing related approaches. Large-scale empirical comparisons to five baseline methods on eight real-world streaming datasets demonstrate the encouraging performance of the developed method. Algorithm implementation and datasets are available upon request.","Title: CirGRDB: a database for the genome-wide deciphering circadian genes and regulators.\nYear: 2018\n\nAbstract: Circadian rhythms govern various kinds of physiological and behavioral functions of the living organisms, and disruptions of the rhythms are highly detrimental to health. Although several databases have been built for circadian genes, a resource for comprehensive post-transcriptional regulatory information of circadian RNAs and expression patterns of disease-related circadian RNAs is still lacking. Here, we developed CirGRDB (http://cirgrdb.biols.ac.cn) by integrating more than 4936 genome-wide assays, with the aim of fulfilling the growing need to understand the rhythms of life. CirGRDB presents a friendly web interface that allows users to search and browse temporal expression patterns of interested genes in 37 human/mouse tissues or cell lines, and three clinical disorders including sleep disorder, aging and tumor. More importantly, eight kinds of potential transcriptional and post-transcriptional regulators involved in the rhythmic expression of the specific genes, including transcription factors, histone modifications, chromatin accessibility, enhancer RNAs, miRNAs, RNA-binding proteins, RNA editing and RNA methylation, can also be retrieved. Furthermore, a regulatory network could be generated based on the regulatory information. In summary, CirGRDB offers a useful repository for exploring disease-related circadian RNAs, and deciphering the transcriptional and post-transcriptional regulation of circadian rhythms.","Title: Design and Implementation of Fire Safety Education System on Campus based on Virtual Reality Technology.\nYear: 2017\n\nAbstract: Fire safety education is essential to every student on campus. Fire safety knowledge learning and operational practice are both important. There is evidence that the virtual reality (VR) based educational method can be a novel and effective approach to learning and practice. However, the existing VR-based system for fire safety education has some shortcomings such as lack of interactivity and high equipment complexity, resulting in low practicability. In order to improve the effect of fire safety education on campus, this paper establishes the model and architecture of fire safety education system based on VR technology. The framework and various elements of fire safety education system are designed and implemented according to the combination of relevant fire safety education theory and VR technology. Finally the prototype version of fire safety education system based on VR technology is built on the HTC VIVE helmet equipment. Through the usability test and comparative analysis of the application experiment, the experiment results prove the feasibility and effectiveness of the proposed approach.","Title: Causal Discovery From Temporally Aggregated Time Series\nYear: 2017\n\nAbstract: Discovering causal structure of a dynamical system from observed time series is a traditional and important problem. In many practical applications, observed data are obtained by applying subsampling or temporally aggregation to the original causal processes, making it difficult to discover the underlying causal relations. Subsampling refers to the procedure that for every k consecutive observations, one is kept, the rest being skipped, and recently some advances have been made in causal discovery from such data. With temporal aggregation, the local averages or sums of k consecutive, non-overlapping observations in the causal process are computed as new observations, and causal discovery from such data is even harder. In this paper, we investigate how to recover causal relations at the original causal frequency from temporally aggregated data when k is known. Assuming the time series at the causal frequency follows a vector autoregressive (VAR) model, we show that the causal structure at the causal frequency is identifiable from aggregated time series if the noise terms are independent and non-Gaussian and some other technical conditions hold. We then present an estimation method based on non-Gaussian state-space modeling and evaluate its performance on both synthetic and real data.","Title: Research on Cigarette Filter Rod Counting System Based on Machine Vision.\nYear: 2017\n\nAbstract: The traditional method for on-line detection of cigarette filter stick packing is manual sampling, which has low efficiency, high labor cost and can't detect all products. Therefore, it is necessary to establish a set of image processing system [10] based on machine vision. This system uses CCD image sensor to get the filter rod section image, through the image smoothing, edge detection, Binarization and feature extraction, the region of interest is analyzed, and finally get the number of filter rods. The filter rod arrangement on the production line is not completely flat. Using the dynamic area threshold method to calculate the number of filter rod in the flat area. 3D reconstruction of a single image is used to calculate the number of filter rod in the uneven area. The accuracy and practicability of the system are verified by theoretical analysis and experimental comparison.","Title: Automatic Measurement of Blood Vessel Angles in Immunohistochemical Images of Liver Cancer.\nYear: 2017\n\nAbstract: This paper presents a method for automated measurement of vascular angle in immunohistochemical images of liver cancer. Firstly, Colour Decon-volution is used to conduct staining separation on a H&E-stained immunohistochemical image, and then blood vessels are segmented using an improved Otsu algorithm. Then the standard SURF algorithm is used to select feature points of the image, and then these feature points are divided into two equal groups according to the distance between individual feature points and the far left (or right) feature point. Finally, a standard least squares method is used to fit two lines using the two groups of points. When the linear deviation of the fitting result based on the two groups of feature points is significant, it is necessary to adjust the belonging of the points of the two groups, and then the two sets are fitted again respectively till the correlation coefficients of the two fitted lines are greater than the predefined threshold, meaning that the measurement of the blood vessel angle in the immunohistochemical map is completed. Compared with the experts' results, our proposed technique results in better accuracy. It is worthy to point out that, to our knowledge, our system is the first one that conducts automated measurement of blood vessel angle of immunohistochemistry.","Title: A Novel Segmentation Framework Using Sparse Random Feature in Histology Images of Colon Cancer.\nYear: 2017\n\nAbstract: In this paper, we present a novel segmentation framework for glandular structures in Hematoxylin and Eosin stained histology images, choosing poorly differentiated colon tissue as an example. The proposed framework' target is to identify precise epithelial nuclei objects. We start with staining separate to detect all nuclei objects, and deploy multi-resolution morphology operation to map the initial epithelial nuclei positions. We proposed a new bag of words scheme using sparse random feature to classify epithelial nuclei and stroma nuclei objects to adjust the rest nuclei positions. Finally, we can use the boundary of optimized epithelial nuclei objects to segment the glandular structure.","Title: A Novel Memory Gradient Based for Efficient Image Segmentation.\nYear: 2017\n\nAbstract: Image segmentation is a very important phase in automatic image analysis. Of the developed techniques for image segmentation, iterative methods have been proven to be one of the most effective algorithms in the literature. Mean shift algorithms is one of the iterative approaches which have been successfully deployed to many applications. However, despite its promising performance, mean shift has shown its weaknesses in convergence in some of the application areas. In this paper, an improved version of the standard mean-shift algorithm using a memory gradient method is proposed and implemented in order to achieve fast convergence rates by integrating mean shift and memory gradient. Experimental results on real images demonstrate that our proposed algorithm not only improves the efficiency of the classical mean shift algorithm, but also achieves better segmentation results.","Title: Identification of Load Categories in Rotor System Based on Vibration Analysis.\nYear: 2017\n\nAbstract: Rotating machinery is often subjected to variable loads during operation. Thus, monitoring and identifying different load types is important. Here, five typical load types have been qualitatively studied for a rotor system. A novel load category identification method for rotor system based on vibration signals is proposed. This method is a combination of ensemble empirical mode decomposition (EEMD), energy feature extraction, and back propagation (BP) neural network. A dedicated load identification test bench for rotor system was developed. According to loads characteristics and test conditions, an experimental plan was formulated, and loading tests for five loads were conducted. Corresponding vibration signals of the rotor system were collected for each load condition via eddy current displacement sensor. Signals were reconstructed using EEMD, and then features were extracted followed by energy calculations. Finally, characteristics were input to the BP neural network, to identify different load types. Comparison and analysis of identifying data and test data revealed a general identification rate of 94.54%, achieving high identification accuracy and good robustness. This shows that the proposed method is feasible. Due to reliable and experimentally validated theoretical results, this method can be applied to load identification and fault diagnosis for rotor equipment used in engineering applications.","Title: Data-driven adaptive dynamic programming schemes for non-zero-sum games of unknown discrete-time nonlinear systems.\nYear: 2018\n\nAbstract: This paper integrates game theory, optimal control theory and reinforcement learning to deal with the discrete-time (DT) multi-player non-zero-sum game issue. As is known, the solutions to non-zero-sum game problems are the outcomes of coupled Riccati equations or coupled Hamilton–Jacobi ones, which are generally difficult to solve analytically and require the knowledge of accurate system mathematical models. However, for most practical industrial systems, the system dynamics cannot be obtained accurately or even unavailable, and the conventional model-based methods will be invalid. To overcome this deficiency, we develop data-based adaptive dynamic programming (ADP) algorithms for completely unknown multi-player systems. Firstly, the Nash equilibrium and stationarity conditions are used to formulate the DT multi-player non-zero-sum game, and then policy iteration algorithm is applied to approximate optimal solutions successively. Secondly, a novel online ADP algorithm combined with a neural-network-based identification scheme is designed and only requires the system data instead of the real system models. Subsequently, a data-driven action-dependent heuristic dynamic programming approach is presented and circumvents the estimation errors caused by the identification learning procedure. Finally, two simulation examples are provided to illustrate the feasibility of our schemes.","Title: Using an optimization algorithm to establish a network of video surveillance for the protection of Golden Camellia.\nYear: 2017\n\nAbstract: The natural environment is facing increasing human disturbance. Many species of flora are extinct or endangered. To improve the efficiency of ecological management and monitoring, this study proposed to establish a video monitoring network to protect a world-famous rare flora: Golden Camellia, in Fangcheng nature reserve, Guangxi Province, China. Based on the model of LSCP (location set covering problem), we attempted to establish full monitoring coverage of camellias while minimizing the number of video cameras. The model was solved by integer programming. In case of multiple solutions, this study proposed two additional criteria, maximize monitoring area and maximize overlapping count, to eliminate suboptimal solutions. Both of the two optimal solutions included 80 cameras covering a monitoring area of over 55km2. Together, these cameras are able to monitor 97.2% of golden camellia in the reserve. The findings of this study suggest that this location optimization model can be used to improve the conservation effectiveness of rare species.","Title: Causality Refined Diagnostic Prediction.\nYear: 2017\n\nAbstract: Applying machine learning in the health care domain has shown promising results in recent years. Interpretable outputs from learning algorithms are desirable for decision making by health care personnel. In this work, we explore the possibility of utilizing causal relationships to refine diagnostic prediction. We focus on the task of diagnostic prediction using discomfort drawings, and explore two ways to employ causal identification to improve the diagnostic results. Firstly, we use causal identification to infer the causal relationships among diagnostic labels which, by itself, provides interpretable results to aid the decision making and training of health care personnel. Secondly, we suggest a post-processing approach where the inferred causal relationships are used to refine the prediction accuracy of a multi-view probabilistic model. Experimental results show firstly that causal identification is capable of detecting the causal relationships among diagnostic labels correctly, and secondly that there is potential for improving pain diagnostics prediction accuracy using the causal relationships.","Title: Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation\nYear: 2018\n\nAbstract: This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar preferences with their social friends. Which, however, may not hold true due to various motivations of making online friends and dynamics of online social networks. Inspired by recent causal process based recommendations that first model user exposures towards items and then use these exposures to guide rating prediction, we utilize social information to capture user exposures rather than user preferences. We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality. Under this new assumption, in this paper, we present a novel recommendation approach (named SERec) to integrate social exposure into collaborative filtering. We propose two methods to implement SERec, namely social regularization and social boosting, each with different ways to construct social exposures. Experiments on four real-world datasets demonstrate that our methods outperform the state-of-the-art methods on top-N recommendations. Further study compares the robustness and scalability of the two proposed methods.","Title: Portfolio risk measurement via stochastic mesh\nYear: 2017\n\nAbstract: We propose a stochastic mesh approach to portfolio risk measurement under the nested setting in which revaluation of the portfolio value requires simulations. While stochastic mesh was originally proposed as a tool for American option pricing, we are interested in estimating via simulation the risk of the portfolio. We establish asymptotic properties of the stochastic mesh estimator for portfolio risk. In particular, we show that the estimator is asymptotically unbiased and consistent, and its mean squared error (MSE) converges to zero in a rate of Γ-1, where Γ is the effort required to simulate the sample paths. This rate of convergence is the same as that under the non-nested setting. The proposed method allows for path dependence of financial instruments in the portfolio. Preliminary numerical experiments show that the proposed method works reasonably well.","Title: A misspecification test for simulation metamodels\nYear: 2017\n\nAbstract: In this paper we propose a novel misspecification test for simulation metamodels. It is a consistent test that helps to assess the adequacy of simulation metamodels. The test statistic we construct is shown to be asymptotically normally distributed under the null hypothesis that the metamodel is correct, while diverging to infinity at a rate of [EQUATION}, where n is the test sample size if the given metamodel is inadequate. Furthermore, as a by-product, we construct confidence intervals for mean squared errors of the metamodels. Preliminary numerical studies show that the test works quite well and has good finite-sample properties.","Title: Characteristics Analysis and Image Processing for Full-Polarization Synthetic Aperture Radar Based on Electromagnetic Scattering From Flat Horizontal Perfect Electric Conducting Reflector.\nYear: 2018\n\nAbstract: In this paper, the physical optics method is employed to study the problem of characteristics analysis and image processing for full-polarization synthetic aperture radar (SAR), where electromagnetic scattering from a flat horizontal perfect electric conducting (PEC) reflector is involved. The model of the full-polarization SAR echo from a small region of flat horizontal PEC reflector is deduced b...","Title: Varcards: An Integrated Genetic And Clinical Database For Coding Variants In The Human Genome\nYear: 2018\n\nAbstract: A growing number of genomic tools and databases were developed to facilitate the interpretation of genomic variants, particularly in coding regions. However, these tools are separately available in different online websites or databases, making it challenging for general clinicians, geneticists and biologists to obtain the first-hand information regarding some particular variants and genes of interest. Starting with coding regions and splice sties, we artificially generated all possible single nucleotide variants (n = 110 154 363) and cataloged all reported insertion and deletions (n = 1 223 370). We then annotated these variants with respect to functional consequences from more than 60 genomic data sources to develop a database, named VarCards(http://varcards.biols.ac.cn/), by which users can conveniently search, browse and annotate the variant-and gene-level implications of given variants, including the following information: (i) functional effects; (ii) functional consequences through different in silico algorithms; (iii) allele frequencies in different populations; (iv) disease-and phenotyperelated knowledge; (v) generalmeaningful gene-level information; and (vi) drug-gene interactions. As a case study, we successfully employed VarCards in interpretation of de novo mutations in autism spectrum disorders. In conclusion, VarCards provides an intuitive interface of necessary information for researchers to prioritize candidate variations and genes.","Title: Value iteration based integral reinforcement learning approach for H∞ controller design of continuous-time nonlinear systems.\nYear: 2018\n\nAbstract: In this paper, a novel integral reinforcement learning approach is developed based on value iteration (VI) for designing the H∞ controller of continuous-time (CT) nonlinear systems. First, the VI learning mechanism is introduced to solve the zero-sum game problems, which is equivalent to the Hamilton–Jacobi–Isaacs (HJI) equation arising in H∞ control problems. Since the proposed method is based on VI learning mechanism, it does not require the admissible control for the implementation, and thus satisfies a more general initial condition than the works based on policy iteration (PI). The iterative property of the value function is analysed with an arbitrary initial positive function, and the H∞ controller can be derived as the iteration converges. For the implementation of the proposed method, three neural networks are introduced to approximate the iterative value function, the iterative control policy and the iterative disturbance policy, respectively. To verify the effectiveness of the VI based method, a linear case and a nonlinear case are presented, respectively.","Title: An Efficient Visualization Method for Polygonal Data with Dynamic Simplification.\nYear: 2018\n\nAbstract: Polygonal data often require rendering with symbolization and simplification in geovisualization. A common issue in existing methods is that simplification, symbolization and rendering are addressed separately, causing computational and data redundancies that reduce efficiency, especially when handling large complex polygonal data. Here, we present an efficient polygonal data visualization method by organizing the simplification, tessellation and rendering operations into a single mesh generalization process. First, based on the sweep line method, we propose a topology embedded trapezoidal mesh data structure to organize the tessellated polygons. Second, we introduce horizontal and vertical generalization operations to simplify the trapezoidal meshes. Finally, we define a heuristic testing algorithm to efficiently preserve the topological consistency. The method is tested using three OpenStreetMap datasets and compared with the Douglas Peucker algorithm and the Binary Line Generalization tree-based method. The results show that the proposed method improves the rendering efficiency by a factor of six. Efficiency-sensitive mapping applications such as emergency mapping could benefit from this method, which would significantly improve their visualization performances.","Title: Research on the Factors Influencing the Measurement Errors of the Discrete Rogowski Coil.\nYear: 2018\n\nAbstract: An innovative array of magnetic coils (the discrete Rogowski coil-RC) with the advantages of flexible structure, miniaturization and mass producibility is investigated. First, the mutual inductance between the discrete RC and circular and rectangular conductors are calculated using the magnetic vector potential (MVP) method. The results are found to be consistent with those calculated using the finite element method, but the MVP method is simpler and more practical. Then, the influence of conductor section parameters, inclination, and eccentricity on the accuracy of the discrete RC is calculated to provide a reference. Studying the influence of an external current on the discrete RC's interference error reveals optimal values for length, winding density, and position arrangement of the solenoids. It has also found that eccentricity and interference errors decreasing with increasing number of solenoids. Finally, a discrete RC prototype is devised and manufactured. The experimental results show consistent output characteristics, with the calculated sensitivity and mutual inductance of the discrete RC being very close to the experimental results. The influence of an external conductor on the measurement of the discrete RC is analyzed experimentally, and the results show that interference from an external current decreases with increasing distance between the external and measured conductors.","Title: Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks.\nYear: 2018\n\nAbstract: In recent years, the classification of breast cancer has been the topic of interest in the field of Healthcare informatics, because it is the second main cause of cancer-related deaths in women. Breast cancer can be identified using a biopsy where tissue is removed and studied under microscope. The diagnosis is based on the qualification of the histopathologist, who will look for abnormal cells. However, if the histopathologist is not well-trained, this may lead to wrong diagnosis. With the recent advances in image processing and machine learning, there is an interest in attempting to develop a reliable pattern recognition based systems to improve the quality of diagnosis. In this paper, we compare two machine learning approaches for the automatic classification of breast cancer histology images into benign and malignant and into benign and malignant sub-classes. The first approach is based on the extraction of a set of handcrafted features encoded by two coding models (bag of words and locality constrained linear coding) and trained by support vector machines, while the second approach is based on the design of convolutional neural networks. We have also experimentally tested dataset augmentation techniques to enhance the accuracy of the convolutional neural network as well as \"handcrafted features + convolutional neural network\" and \"convolutional neural network features + classifier\" configurations. The results show convolutional neural networks outperformed the handcrafted feature based classifier, where we achieved accuracy between 96.15% and 98.33% for the binary classification and 83.31% and 88.23% for the multi-class classification.","Title: Quantum partial search for uneven distribution of multiple target items.\nYear: 2018\n\nAbstract: Quantum partial search algorithm is an approximate search. It aims to find a target block (which has the target items). It runs a little faster than full Grover search. In this paper, we consider quantum partial search algorithm for multiple target items unevenly distributed in a database (target blocks have different number of target items). The algorithm we describe can locate one of the target blocks. Efficiency of the algorithm is measured by number of queries to the oracle. We optimize the algorithm in order to improve efficiency. By perturbation method, we find that the algorithm runs the fastest when target items are evenly distributed in database.","Title: User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning.\nYear: 2018\n\nAbstract: This paper considers recommendation algorithm ensembles in a user-sensitive manner. Recently researchers have proposed various effective recommendation algorithms, which utilized different aspects of the data and different techniques. However, the skewed prediction problem may exist for almost all recommendation algorithms -- algorithms with best average predictive accuracy may cover up that the algorithms may perform poorly for some part of users, which will lead to biased services in real scenarios. In this paper, we propose a user-sensitive ensemble method named UREC to address this issue. We first cluster users based on the recommendation predictions, then we use multi-task learning to learn the user-sensitive ensemble function for the users. In addition, to alleviate the negative effects of new user problem to clustering users, we propose an approximate approach based on a spectral relaxation. Experiments on real-world datasets demonstrate the superiority of our methods.","Title: Causal Generative Domain Adaptation Networks.\nYear: 2018\n\nAbstract: An essential problem in domain adaptation is to understand and make use of distribution changes across domains. For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains. By explicitly modeling the changes, one can even generate data in new domains using the generating process with new values for the latent variables in G-DAN. In practice, the process to generate all features together may involve high-dimensional latent variables, requiring dealing with distributions in high dimensions and making it difficult to learn domain changes from few source domains. Interestingly, by further making use of the causal representation of joint distributions, we then decompose the joint distribution into separate modules, each of which involves different low-dimensional latent variables and can be learned separately, leading to a Causal G-DAN (CG-DAN). This improves both statistical and computational efficiency of the learning procedure. Finally, by matching the feature distribution in the target domain, we can recover the target-domain joint distribution and derive the learning machine for the target domain. We demonstrate the efficacy of both G-DAN and CG-DAN in domain generation and cross-domain prediction on both synthetic and real data experiments.","Title: Iterative adaptive dynamic programming methods with neural network implementation for multi-player zero-sum games.\nYear: 2018\n\nAbstract: This paper presents novel iterative learning methods along with the neural network implementation for multi-player zero-sum games. Solving zero-sum games depends on the solutions of Hamilton–Jacobi-Isaacs equations, which are nonlinear partial differential equations. These solutions are generally difficult or even impossible to be obtained analytically. To overcome this difficulty, iterative adaptive dynamic programming algorithms are utilized. In the related research works, three-network architecture, i.e., critic-actor-disturbance structure, is used to approximate the value function, control policies and disturbance policies. Different from the previous works, this paper employs single-network architecture, i.e., critic-only structure, to implement the proposed algorithms, which reduces the computation burden and the complexity of design procedure. Finally, two simulation examples are provided to illustrate the effectiveness of our proposed methods.","Title: Lung sounds classification using convolutional neural networks.\nYear: 2018\n\nAbstract: •We have proposed an approach for lung sounds classification using CNNs and compared it with two features-based approaches.•In the first handcrafted features-based approach, we used MFCC’s statistics extracted from the signals.•In the second one, we used local binary patterns extracted from spectrograms.•We also opted for ‘Handcrafted Features + CNN’ and ‘CNN Features + Classifier’ configurations.•The final results show that CNNs outperformed the handcrafted features-based approaches.","Title: Comparing Digital Phase-Locked Loop and Kalman Filter for Clock Tracking in Ultrawideband Location System.\nYear: 2018\n\nAbstract: For timing and synchronization system, digital phase-locked loop (DPLL) and Kalman filter all have been widely used as the clock tracking and clock correction schemes for the similar structure and properties. This paper compares the two schemes used for ultrawideband (UWB) location system. The improved Kalman filter is more immune to interference.","Title: Quantum teleportation and Birman–Murakami–Wenzl algebra\nYear: 2017\n\nAbstract: In this paper, we investigate the relationship of quantum teleportation in quantum information science and the Birman–Murakami–Wenzl (BMW) algebra in low-dimensional topology. For simplicity, we focus on the two spin-1/2 representation of the BMW algebra, which is generated by both the Temperley–Lieb projector and the Yang–Baxter gate. We describe quantum teleportation using the Temperley–Lieb projector and the Yang–Baxter gate, respectively, and study teleportation-based quantum computation using the Yang–Baxter gate. On the other hand, we exploit the extended Temperley–Lieb diagrammatical approach to clearly show that the tangle relations of the BMW algebra have a natural interpretation of quantum teleportation. Inspired by this interpretation, we construct a general representation of the tangle relations of the BMW algebra and obtain interesting representations of the BMW algebra. Therefore, our research sheds a light on a link between quantum information science and low-dimensional topology.","Title: XiaoIce Band: A Melody and Arrangement Generation Framework for Pop Music.\nYear: 2018\n\nAbstract: With the development of knowledge of music composition and the recent increase in demand, an increasing number of companies and research institutes have begun to study the automatic generation of music. However, previous models have limitations when applying to song generation, which requires both the melody and arrangement. Besides, many critical factors related to the quality of a song such as chord progression and rhythm patterns are not well addressed. In particular, the problem of how to ensure the harmony of multi-track music is still underexplored. To this end, we present a focused study on pop music generation, in which we take both chord and rhythm influence of melody generation and the harmony of music arrangement into consideration. We propose an end-to-end melody and arrangement generation framework, called XiaoIce Band, which generates a melody track with several accompany tracks played by several types of instruments. Specifically, we devise a Chord based Rhythm and Melody Cross-Generation Model (CRMCG) to generate melody with chord progressions. Then, we propose a Multi-Instrument Co-Arrangement Model (MICA) using multi-task learning for multi-track music arrangement. Finally, we conduct extensive experiments on a real-world dataset, where the results demonstrate the effectiveness of XiaoIce Band.","Title: Driver gene mutations based clustering of tumors: methods and applications.\nYear: 2018\n\nAbstract: Motivation: Somatic mutations in proto-oncogenes and tumor suppressor genes constitute a major category of causal genetic abnormalities in tumor cells. The mutation spectra of thousands of tumors have been generated by The Cancer Genome Atlas (TCGA) and other whole genome (exome) sequencing projects. A promising approach to utilizing these resources for precision medicine is to identify genetic similarity-based sub-types within a cancer type and relate the pinpointed sub-types to the clinical outcomes and pathologic characteristics of patients. Results: We propose two novel methods, ccpwModel and xGeneModel, for mutation-based clustering of tumors. In the former, binary variables indicating the status of cancer driver genes in tumors and the genes' involvement in the core cancer pathways are treated as the features in the clustering process. In the latter, the functional similarities of putative cancer driver genes and their confidence scores as the 'true' driver genes are integrated with the mutation spectra to calculate the genetic distances between tumors. We apply both methods to the TCGA data of 16 cancer types. Promising results are obtained when these methods are compared to state-of-the-art approaches as to the associations between the determined tumor clusters and patient race ( or survival time). We further extend the analysis to detect mutation-characterized transcriptomic prognostic signatures, which are directly relevant to the etiology of carcinogenesis.","Title: Near-optimal output tracking controller design for nonlinear systems using an event-driven ADP approach.\nYear: 2018\n\nAbstract: In this paper, a novel output tracking control scheme for a class of continuous-time nonlinear system is proposed by using an event-driven adaptive dynamic programming (ADP) approach. Based on the controlled system and the desired reference trajectory, an augmented system is constructed for the optimal tracking control problem (OTCP), where the control law is composed of the steady-state part and the feedback part. According to the standard solution of the OTCP, the feedback part associated to the cost function is to be solved and the steady-state part of the control can be obtained directly. Thus the event-driven near-optimal control method is proposed to solve feedback control of the OTCP, where a single network ADP approach is developed to approximate the optimal cost function. The output tracking error dynamic get converged to zero and the parameters of critic neural network (NN) get converged to the optimal ones by using the designed weight tuning law and event-triggering condition. The stability of the augmented system under the novel control scheme is guaranteed based on Lyapunov theory and two simulation examples are presented to demonstrate the effectiveness of the proposed method.","Title: Causal discovery in the presence of missing data.\nYear: 2018\n\nAbstract: Missing data are ubiquitous in many domains such as healthcare. When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be different from those in the complete data generated by the underlying causal process. Consequently, simply applying existing causal discovery methods to the observed data may lead to wrong conclusions. In this paper, we aim at developing a causal discovery method to recover the underlying causal structure from observed data that are missing under different mechanisms, including missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). With missingness mechanisms represented by missingness graphs (m-graphs), we analyze conditions under which additional correction is needed to derive conditional independence/dependence relations in the complete data. Based on our analysis, we propose Missing Value PC (MVPC), which extends the PC algorithm to incorporate additional corrections. Our proposed MVPC is shown in theory to give asymptotically correct results even on data that are MAR or MNAR. Experimental results on both synthetic data and real healthcare applications illustrate that the proposed algorithm is able to find correct causal relations even in the general case of MNAR.","Title: Network Scalability for Ultra-Wideband Real-Time Location Systems Based on vMISO.\nYear: 2018\n\nAbstract: For time-based location systems, a common time base is important for obtaining the correct position. However, wireless clock synchronization is considerably easier to deploy in certain environments, such as large areas. A single-region positioning network is often restricted to the anchor communication range. Therefore, multiple master anchors form the basis of an extensible location network and provide an effective means of adapting the system to a complex propagation environment. In this article, we consider simultaneous network scalability with the relative clock synchronization and localization. We first establish an extendable UWB location system model through intelligent response across the routing layer and physical layer. Anchors act as master or slave anchors in different cluster areas and advance the clock check packet (CCP) from an initial region to the surrounding area to achieve clock synchronization of the entire network. The intracluster CCP transmission is achieved by broadcasting, and the intercluster CCP transmissions use vMISO and are automatically driven by the broadcast arrival time for better results. Furthermore, the physical-access and clock synchronization algorithms are discussed. Finally, location tests combined with routing simulations are conducted to demonstrate the performance of the proposed scheme.","Title: PCA: Page Correlation Aggregation for Memory Deduplication in Virtualized Environments.\nYear: 2018\n\nAbstract: To intelligently share limited memory across VMs in IaaS cloud, content-based page sharing (CBPS), like KSM, is utilized to greatly reduce the memory footprint of VMs. CBPS merges same-content pages into a single copy. However, it introduces some serious cross-VM covert channel threats. Besides, it has heavy overhead due to vast otiose operations, such as page comparisons and checksum calculations, when detecting page sharing opportunities. In this paper, we propose a novel memory deduplication approach called page correlation aggregation (PCA), which can efficiently reduce otiose operations. Meanwhile defends covert channels. One key idea of PCA is to divide VMs’ pages into several sets, since pages with similar attributes have the greatest possibility with the same content. In PCA, the pages of VMs are firstly divided into different groups according to VMs’ attributes. In each group pages are further separated into different classifications based on their access permissions. Thus page comparisons are restricted to the same classification for sharing. The other is that PCA introduces a dedicated cache to mitigate the latency of COW (Copy- On-Write) used for conducting covert channels. We have conducted a prototype on KSM, one popular CBPS technique. Our experimental results show that PCA reduces otiose operations about 40%, and can effectively resist covert channels.","Title: Online adaptive policy iteration based fault-tolerant control algorithm for continuous-time nonlinear tracking systems with actuator failures.\nYear: 2018\n\nAbstract: In this paper, a novel tracking control scheme for continuous-time nonlinear affine systems with actuator faults is proposed by using a policy iteration (PI) based adaptive control algorithm. According to the controlled system and desired reference trajectory, a novel augmented tracking system is constructed and the tracking control problem is converted to the stabilizing issue of the corresponding error dynamic system. PI algorithm, generally used in optimal control and intelligence technique fields, is an important reinforcement learning method to solve the performance function by critic neural network (NN) approximation, which satisfies the Lyapunov equation. For the augmented tracking error system with actuator faults, an online PI based fault-tolerant control law is proposed, where a new tuning law of the adaptive parameter is designed to tolerate four common kinds of actuator faults. The stability of the tracking error dynamic with actuator faults is guaranteed by using Lyapunov theory, and the tracking errors satisfy uniformly bounded as the adaptive parameters get converged. Finally, the designed fault-tolerant feedback control algorithm for nonlinear tracking system with actuator faults is applied in two cases to track the desired reference trajectory, and the simulation results demonstrate the effectiveness and applicability of the proposed method.","Title: Real-Valued Sparse DOA Estimation for MIMO Array System Under Unknown Nonuniform Noise.\nYear: 2018\n\nAbstract: In this paper, the problem of the direction of arrival (DOA) estimation for the multiple input multiple output (MIMO) array system is considered as a real-valued sparse signal recover procedure under the condition of unknown nonuniform noise. Then, a real-valued covariance vector-based sparse Bayesian learning framework is proposed, in which the reduced dimensional (RD) transformation is utilized to remove the redundant elements of MIMO array system, and a linear transformation is applied to eliminate the influence of unknown non-uniform noise. Then by supposing that the source powers follow an independent prior Gaussian distribution with zero-mean, a real-valued covariance vector-based sparse Bayesian model is formulated. And considering its unknown variance as hyperparameters, they can be estimated by adopting the expectation-maximization algorithm. Finally, the DOA can be achieved according to the spatial spectrum of hyperparameters. Simulation results have demonstrated that our proposed method not only achieves more superior performance but also provides robustness against nonuniform noise, compared with other recently reported sparse signal representation based methods.","Title: Random Occlusion-recovery for Person Re-identification.\nYear: 2018\n\nAbstract: As a basic task of multi-camera surveillance system, person re-identification aims to re-identify a query pedestrian observed from non-overlapping cameras or across different time with a single camera. Recently, Deep learning based person re-identification models have achieved great success in many benchmarks. However, these supervised models require a large amount of labeled image data and the process of manual labeling spends much manpower and time. In this study, we introduce a method to automatically synthesize labeled person images and adopt them to increase the sample number for per identity in datasets. Specifically, we use the block rectangles to occlude the random parts of the persons in the images. Then, a generative adversarial network (GAN) model is proposed to use paired occlusion and original images to synthesize the de-occluded images that similar but not identical to the original images. Afterwards, we annotate the de-occluded images with the same labels of their corresponding raw image and use them to augment the training samples. We use the augmented datasets to train baseline model. The experiment results on Market-1501 and CUHK03 datasets show that the effectiveness of the proposed method.","Title: Distributed leader-following consensus of heterogeneous second-order time-varying nonlinear multi-agent systems under directed switching topology.\nYear: 2019\n\nAbstract: This paper focuses on studying the distributed leader-following consensus problem of heterogeneous second-order time-varying nonlinear multi-agent systems (HSTN_MASs) under directed switching topology. Firstly, based on the relative position and relative velocity measurements among neighborhood agents, a class of distributed tracking protocols are proposed. Then the state transformations are provided to solve the time-varying nonlinearity and translate the leader-following problem into the stabilization control problem. By constructing the topology-dependent Lyapunov function and choosing appropriate time-varying regulation factor and coupling strength, it is shown that the exponential tracking of HSTN_MASs can be achieved. Moreover, this paper discusses the distributed leaderless consensus problem of HSTN_MASs. Finally, several simulation examples are given to illustrate the validity of theoretical results.","Title: A pilot study on evaluating children with autism spectrum disorder using computer games.\nYear: 2019\n\nAbstract: Evaluation of children with Autism Spectrum Disorders (ASD) is crucial to clinical diagnosis and educational intervention. The traditional evaluation methods based on questionnaires and scales rely on the experience and expertise of the evaluator, are time-consuming and clinically demanding. Computer games can provide an objective, motiving and safe way for evaluating and reflecting children's development. Therefore, the study aimed to investigate a technology-based method using computer games to evaluate children with ASD. The performance of 40 children with ASD and 51 aged-matched typically developing (TD) children was compared. We found: 1) The completion ratio for children with ASD was lower than TD children for the tasks in most of the games. 2) Significant differences between the ASD and TD groups, but no significant differences within group. 3) The performance of the TD group was better than ASD and the efficiency of TD group was proportional to age. While more research is needed to confirm its reliability and validity, the findings indicate that computer games have great potential in the field of special education as an evaluation tool to clarify difficulties associated with autism.","Title: Evaluation of Radiation Response in CoCrFeCuNi High-Entropy Alloys.\nYear: 2018\n\nAbstract: CoCrFeCuNi high-entropy alloys (HEAs) prepared by arc melting were irradiated with a 100 keV He+ ion beam. Volume swelling and hardening induced by irradiation were evaluated. When the dose reached 5.0 x 10(17) ions/cm(2), the Cu-rich phases exhibited more severe volume swelling compared with the matrix phases. This result indicated that the Cu-rich phases were favorable sites for the nucleation and gathering of He bubbles. X-ray diffraction indicated that all diffraction peak intensities decreased regularly. This reduction suggested loosening of the irradiated layer, thereby reducing crystallinity, under He+ ion irradiation. The Nix-Gao model was used to fit the measured hardness in order to obtain a hardness value H-0 that excludes the indentation size effect. At ion doses of 2.5 x 10(17) ions/cm(2) and 5.0 x 10(17) ions/cm(2), the HEAs showed obvious hardening, which could be attributed to the formation of large amounts of irradiation defects. At the ion dose of 1.0 x 10(18) ions/cm(2), hardening was reduced, owing to the exfoliation of the original irradiation layer, combined with recovery induced by long-term thermal spike. This study is important to explore the potential uses of HEAs under extreme irradiation conditions.","Title: Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition.\nYear: 2018\n\nAbstract: Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods.","Title: A two-stage social trust network partition model for large-scale group decision-making problems.\nYear: 2019\n\nAbstract: With the development of big data and social computing, large-scale group decision making (LGDM) problems attract much attention and become merging with social networks or behavioral factors. In this paper, by considering the trust of social behavioral factor, a two-stage trust network partition algorithm is proposed to reduce the complex of LGDM problems. The large-scale decision makers (DMs) are classified into some leader–follower sub-networks through a network partition algorithm. And a solution method based on trust relationship is proposed to keep the independency of the sub-networks when one follower belongs to more than one leader. Next, weights of independent sub-networks and their individual members are computed, and are further used to aggregate the comprehensive decision information. Finally, alternatives of LGDM problems are sorted with the comprehensive decision information. An experiment with MovieLens data is given to illustrate the proposed LGDM algorithm. And a comparison analysis with general clustering method is provided to verify the effectiveness and feasibility of the proposed method.","Title: A Practice to Search the Summit of a DEM Using Simulated Annealing Technique\nYear: 2018\n\nAbstract: Simulated annealing (SA) algorithm is based on iterative improvement but with stochastic acceptance of bad moves. This allows the algorithm to escape local optimal solution in early sampling rounds and the progressive refinement into efficient solutions in later sampling rounds. In this paper, we show how this algorithm is used to find the highest peak of a Digital Elevation Model (DEM). In order to search efficiently in the terrain data, we proposed a reformed cost function which used logarithmic transformation. A set of parameters were confirmed after many experiments. The result shows that SA is not guarantee to find the optimal solution. However, there are 16% chances to find the highest peak during 400 experiments. Specifically, the search process is demonstrated by 3D visualization in GIS, which can help understand the mechanism of the algorithm and provide convenience for teaching.","Title: Adaptive Data Sampling Mechanism for Process Object.\nYear: 2018\n\nAbstract: Process object is the abstraction of process. In process object, there are different type of entities and associations. The entities vary dependent on other entities. The performance and evolution of process object are affected by the association between entities. These changes could be reflected in the data collected from the process objects. These data from process object could be regard as big data stream. In the context of big data, how to find appropriate data for process object is a challenge. The data sampling should reflect the performance change of process object, and should be adaptive to the current underlying distribution of data in data stream. For finding appropriate data in big data stream to model process object, an adaptive data sampling mechanism is proposed in this paper. Experiments demonstrate the effectiveness of the proposed adaptive data sampling mechanism for process object.","Title: Task Allocation Method For Multi-Uav Teams With Limited Communication Bandwidth\nYear: 2018\n\nAbstract: Multiple unmanned aerial vehicle (UAV) team often needs to perform some certain tasks cooperatively. In the process of UAVs performing these tasks, the bandwidth of the communication network will affect the task assignment results, then the problem of task allocation for multi-UAV teams under the condition of limited communication bandwidth is considered. For a target, UAVs need to perform reconnaissance, attack and assessment tasks in coordination. The Consensus-Based Bundle Algorithm(CBBA) requires each task is assigned to no more than one UAV, so this paper extends CBBA by duplicating cooperative tasks to modify the task list and adding a judgment mechanism to ensure the uniqueness of the duplicate tasks' allocation in order to achieve the purpose of allocating multiple UAVs to the same task. At the same time, this paper utilizes a bid warping link to improve the algorithm performance and applies CBBA in asynchronous environment to reduce communication burden. Simulation results show that this improved algorithm is feasible and more efficient to improve the task allocation result for multi-UAVs teams with fewer messages transmission and a larger global reward.","Title: Image-Enhanced Multi-level Sentence Representation Net for Natural Language Inference\nYear: 2018\n\nAbstract: Natural Language Inference (NLI) task requires an agent to determine the semantic relation between a premise sentence (p) and a hypothesis sentence (h), which demands sufficient understanding about sentences from lexical knowledge to global semantic. Due to the issues such as polysemy, ambiguity, as well as fuzziness of sentences, fully understanding sentences is still challenging. To this end, we propose an Image-Enhanced Multi-Level Sentence Representation Net (IEMLRN), a novel architecture that is able to utilize the image to enhance the sentence semantic understanding at different scales. To be specific, we introduce the corresponding image of sentences as reference information, which can be helpful for sentence semantic understanding and inference relation evaluation. Since image information might be related to the sentence semantics at different scales, we design a multi-level architecture to understand sentences from different granularity and generate the sentence representation more precisely. Experimental results on the large-scale NLI corpus and real-world NLI alike corpus demonstrate that IEMLRN can simultaneously improve the performance. It is noteworthy that IEMLRN significantly outperforms the state-of-the-art sentence-encoding based models on the challenging hard subset and challenging lexical subset of SNLI corpus.","Title: Causal Discovery With Linear Non-Gaussian Models Under Measurement Error: Structural Identifiability Results\nYear: 2018\n\nAbstract: Causal discovery methods aim to recover the causal process that generated purely observational data. Despite its successes on a number of real problems, the presence of measurement error in the observed data can produce serious mistakes in the output of various causal discovery methods. Given the ubiquity of measurement error caused by instruments or proxies used in the measuring process, this problem is one of the main obstacles to reliable causal discovery. It is still unknown to what extent the causal structure of relevant variables can be identified in principle. This study aims to take a step towards filling that void. We assume that the underlining process or the measurement-error free variables follows a linear, non-Guassian causal model, and show that the so-called ordered group decomposition of the causal model, which contains major causal information, is identifiable. The causal structure identifiability is further improved with different types of sparsity constraints on the causal structure. Finally, we give rather mild conditions under which the whole causal structure is fully identifiable.","Title: Online Density Estimation over Streaming Data: A Local Adaptive Solution\nYear: 2018\n\nAbstract: Accurate online density estimation is crucial to numerous applications that are prevalent with streaming data. Existing online approaches for density estimation somewhat lack prompt adaptability when facing drifting concepts, resulting in delayed or even deteriorated approximations. To alleviate this issue, in this work, we propose an adaptive local online density estimator, i.e. ALoKDE, for real-time density estimation on data streams. Two strategies, a statistical test for concept drift detection and an adaptive weighted local online density estimation when the drift occurs, are tightly integrated into ALoKDE. Specifically, using a weighted form, ALoKDE seeks to provide an unbiased estimation by factoring in the statistical hallmarks of the latest learned distribution and any potential distributional changes that could be introduced by each incoming instance. To ensure a high-precision estimate, ALoKDE integrates three key components: local sampling, optimal bandwidth selection at a temporal basis, and adaptive weighting factor determination. We further analyze the asymptotic properties of ALoKDE and derive its theoretical error bounds regarding bias, variance, MSE and MISE. Extensive comparative studies on various artificial and real-world streaming data demonstrate the efficacy of ALoKDE in online density estimation and real-time classification.","Title: Surface Reconstruction Algorithm Based On Local Data Features\nYear: 2018\n\nAbstract: With the development of reverse engineering device, the point cloud data, as a common and important form, is applied to the surface reconstruction domain. Since the point data is so large, scattered and unordered, the representation of point cloud and surface reconstruction is critical contents in reverse engineering system. In this paper, we redefine the representation of point cloud based on set theory, and derive the rules of neighbour relationship of data. Then, we adopt the KD-tree algorithm and improve its searching approach. In addition, the GeoSurface algorithm is proposed, which uses iterative strategy to mesh surface and uses the depth of KD-tree to control the parameters. Finally, we verify the GeoSurface algorithm. The experimental results show the GeoSurface algorithm is effective, and achieves better results in running time and quality of surface reconstruction compared with greedy and Poisson reconstruction algorithm.","Title: Thermal Stability Optimization of the Luojia 1-01 Nighttime Light Remote-Sensing Camera's Principal Distance.\nYear: 2019\n\nAbstract: The instability of the principal distance of the nighttime light remote-sensing camera of the Luojia 1-01 satellite directly affects the geometric accuracy of images, consequently affecting the results of analysis of nighttime light remote-sensing data. Based on the theory of optical passive athermal design, a mathematical model of optical-passive athermal design for principal distance stabilization is established. Positive and negative lenses of different materials and the mechanical structures of different materials are matched to optimize the optical system. According to the index requirements of the Luojia 1-01 camera, an image-telecentric optical system was designed under the guidance of the established mathematical model. In the temperature range of -20 degrees C to +60 degrees C, the principal distance of the system changes from -0.01 m to +0.28 m. After on-orbit testing, the geometric accuracy of the designed nighttime light remote-sensing camera is better than 0.20 pixels and less than index requirement of 0.3 pixels, which indicating that the principal distance maintains good stability on-orbit and meets the application requirements of nighttime light remote sensing.","Title: Real-Time Feature-Based Video Stabilization on FPGA.\nYear: 2017\n\nAbstract: Digital video stabilization is an important video enhancement technology that aims to remove unwanted camera vibrations from video sequences. Trading off between stabilization performance and real-time hardware implementation feasibility, this paper presents a feature-based full-frame video stabilization method and a novel complete fully pipelined architectural design to implement it on field-programmable gate array (FPGA). In the proposed method, feature points are first extracted with the oriented features from accelerated segment test and rotated binary robust independent elementary features algorithm and matched between consecutive frames. Next, the matched point pairs are fitted to the affine transformation model using a random-sample consensus-based approach to estimate inter-frame motion robustly. Then, the estimated results are accumulated to compute the cumulative motion parameters between the current and reference frames, and the translational components are smoothed by a Kalman filter representing intentional camera movement. Finally, a mosaicked image is constructed based on cumulative motion parameters using an image mosaicking technique, and then a display window is created with the desired frame size according to the computed intentional camera movement to obtain a full motion-compensated frame. Using pipelining and parallel processing strategies, the whole process has been designed using a novel complete fully pipelined architecture and implemented on Altera’s Cyclone III FPGA to build a real-time stabilization system. The experimental results have shown that the proposed system can deal with standard PAL video input including arbitrate translation and rotation and can produce full-frame stabilized output providing a better viewing experience at 22.37 ms/frame, thus achieving real-time processing performance.","Title: A Threat Assessment Method Based On Prioritized Visibility Graph Ordered Weighted Averaging Operator\nYear: 2016\n\nAbstract: Multi-target threat assessment is a considerable issue of air combat tactical decision. As the conventional approaches are deficient and imperfect in the procedure of determining the attribute weights and result display of the threats, a novel threat assessment method based on Prioritized Visibility Graph Ordered Weighted Averaging (PVGOWA) operator is proposed. In this work, the relative threat function and the multi-target assessment model of air combat are established. The VGOWA operator is used to calculate the objective weights, which can fully utilize the information in the combat situation. In order to avoid the randomness and uncertainty of expert scoring in the subjective weight determination process, the POWA operator is used to get the final attribute weights by fusing the objective weights and the priorities of the attributes of each target, which are identified by the decision maker. Besides, the radar-chart model is adopted to display the assessment results. Simulation indicates that the proposed method is more reasonable and intuitional for multi-target threat assessment compared to the conventional method.","Title: Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model.\nYear: 2019\n\nAbstract: Zebrafish embryo fluorescent vessel analysis, which aims to automatically investigate the pathogenesis of diseases, has attracted much attention in medical imaging. Zebrafish vessel segmentation is a fairly challenging task, which requires distinguishing foreground and background vessels from the 3D projection images. Recently, there has been a trend to introduce domain knowledge to deep learning algorithms for handling complex environment segmentation problems with accurate achievements. In this paper, a novel dual deep learning framework called Dual ResUNet is developed to conduct zebrafish embryo fluorescent vessel segmentation. To avoid the loss of spatial and identity information, the U-Net model is extended to a dual model with a new residual unit. To achieve stable and robust segmentation performance, our proposed approach merges domain knowledge with a novel contour term and shape constraint. We compare our method qualitatively and quantitatively with several standard segmentation models. Our experimental results show that the proposed method achieves better results than the state-of-art segmentation methods. By investigating the quality of the vessel segmentation, we come to the conclusion that our Dual ResUNet model can learn the characteristic features in those cases where fluorescent protein is deficient or blood vessels are overlapped and achieves robust performance in complicated environments.","Title: Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination.\nYear: 2017\n\nAbstract: It is commonplace to encounter nonstationary or heterogeneous data, of which the underlying generating process changes over time or across data sets (the data sets may have different experimental conditions or data collection conditions). Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper we develop a principled framework for causal discovery from such data, called Constraint-based causal Discovery from Nonstationary/heterogeneous Data (CD-NOD), which addresses two important questions. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a way to determine causal orientations by making use of independence changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.","Title: Research on adaptive robust guidance law for passive homing missile against maneuvering target\nYear: 2017\n\nAbstract: An improved three dimensional adaptive robust guidance law is proposed for the passive homing guidance in which only line-of-sight angular rate can be measured when target maneuvered. The nonlinear control system of missile is designed according to relative motion model of missile and target, which regards coupling and target acceleration as interference. Then a sliding surface and control law is designed based on approximate parallel approaching principle and variable structure control theory. The self-learning ability of RBF neural network is used to approximate the uncertain nonlinear time-varying functions in the control law online. Finally, the network weights adaptive law is proposed according to Lyapunov theory. This method can assure the excellent adaptive ability and robustness of the system. Simulation results indicate that the guidance law is better than traditional proportional navigation and variable structure guidance law in performance.","Title: Learning Vector Autoregressive Models With Latent Processes.\nYear: 2018\n\nAbstract: We study the problem of learning the support of transition matrix between random processes in a Vector Autoregressive (VAR) model from samples when a subset of the processes are latent. It is well known that ignoring the effect of the latent processes may lead to very different estimates of the influences among observed processes, and we are concerned with identifying the influences among the observed processes, those between the latent ones, and those from the latent to the observed ones. We show that the support of transition matrix among the observed processes and lengths of all latent paths between any two observed processes can be identified successfully under some conditions on the VAR model. From the lengths of latent paths, we reconstruct the latent subgraph (representing the influences among the latent processes) with a minimum number of variables uniquely if its topology is a directed tree. Furthermore, we propose an algorithm that finds all possible minimal latent graphs under some conditions on the lengths of latent paths. Our results apply to both non-Gaussian and Gaussian cases, and experimental results on various synthetic and real-world datasets validate our theoretical results.","Title: A combined GPS UWB and MARG locationing algorithm for indoor and outdoor mixed scenario\nYear: 2019\n\nAbstract: An indoor or outdoor positioning system is hard to position in cross-region and complex environment. In order to make up for the loss of blind zone in scenario change, we proposed a cooperative positioning system based on GPS/UWB/MARG, which can achieve the seamless positioning between buildings in the hybrid scene. In the hybrid scene, using a weighted fusion algorithm to achieve the cooperative positioning of GPS and UWB, and MARG is used to improve the positioning accuracy of GPS. With data optimization and performance analysis of a subsystem, we process GPS/MARG data and UWB data by weighted fusion method. The optimal positioning information in different positioning environments will be output after independent judgment. The results show that, compared with GPS/MARG positioning system, the average positioning accuracy of the cooperative positioning system is improved by 64% in the hybrid scene. In addition, it expands the application scenario of a single positioning system.","Title: Cluster computing data mining based on massive intrusion interference constraints in hybrid networks\nYear: 2019\n\nAbstract: To solve the problem that massive intrusion data in hybrid networks greatly interfere network intrusion detection and cause relatively great difficulty to detection due to their frequency discontinuity, a mining algorithm of massive intrusion cluster computing data in hybrid networks based on spectral feature extraction under fixed constraints of time–frequency window is proposed in this paper. The multi-component cross-detection method is used to collect massive intrusion information in hybrid networks and construct a model of massive intrusion signal in hybrid networks. Cascading notch method is used to suppress intrusion interference under constraints of fixed time–frequency window, and extract fundamental quantity and primary function with locality in massive interference information, and obtain a complete energy distribution spectrum on the time–frequency plane. The energy distribution spectrum is used as guidance function to realize cluster calculating data mining with massive intrusion interference constraints. Simulation results show that, in the intrusion detection process of signal-to-noise ratio from − 15 to − 5 dB, the detection accuracy of the method proposed in this paper is always better than that of others. When the signal-to-noise ratio is − 9 dB, the detection accuracy of this method is over 90%. When the signal-to-noise ratio is −9 dB, the detection accuracy of this method can reach 100%. About the detection time, especially after the Number of intrusion date is 2000, the detection time difference between the three methods is increased, and the detection time of other methods is 2–3 times of paper method. The method proposed can accurately locate mass intrusion distribution sources in hybrid networks under the large frequency oscillation of intrusion data in the hybrid networks, realize network intrusion detection and interference suppression and can filter interference information well, so this method improves intrusion detection probability in hybrid networks.","Title: Dynamic Subspace Models for High-Sulfur Gas Sweetening Process Monitoring\nYear: 2017\n\nAbstract: The acid component in high-sulfur gas (HSG) is as high as 5%-15%, which will causes great security risk and has strong corrosive effect to the sweetening equipment. Therefore, productive process monitoring has important significance to ensure the system normal work and safety. However, due to the time lag shift of the operating parameters, static methods seem powerless. Thus, in this paper, we proposed a dynamic subspace model for HSG sweetening process monitoring. Specifically, we first introduce the operating parameters and time series as input candidate features, and get the dynamic expansion matrix by time-lag order analysis. Then we combined the matrix with static PCA model to achieve the process monitoring. The experiments on the actual data of a HSG sweetening plant show that, the false alarm rate (FAR) and missing alarm rate (MAR) of Hotelling T2 and Q statistic (SPE) in DPCA are much lower than those of PCA. In particular, compared to PCA, the SPE MAR in DPCA reduced by 13.42%, and the T2 MAR in DPCA is 4 times lower than that of PCA. Therefore, the proposed monitoring method for HSG sweetening process is efficient and feasible.","Title: Preference-driven yield-and-quality optimization for high-sulfur gas sweetening process by extreme learning machine model\nYear: 2019\n\nAbstract: Yield and quality, as the two most important output variables of high-sulfur gas (HSG) sweetening process, are affected by the operating parameters. The HSG sweetening process involves more than ten operating parameters, so the relationship between the parameters and output variables is complex, non-linear and strong coupling. This paper tries to use data mining methods to explore this relationship and apply it to optimize the yield and quality. First, a ten (inputs)-to-three (outputs) model is established by extreme learning machine (ELM). Then, a preference-driven multi-objective optimization algorithm is used to maximize the yield while ensuring that the concentration of carbon dioxide (CO2) and the concentration of hydrogen sulfide (H2S) in the treated gas are close to but not exceeding 3% and 4 ppm respectively. The proposed method is validated in a HSG purification plant in southwest China. A set of 3044 production data is collected and randomly divided into 80 and 20% for training and testing. The results show that the established ELM model is in good agreement with the actual operation data. The maximum deviation of mean square error (MSE), mean absolute error (MAE) and average absolute deviation percent (AAD %) of the predictions in three scenarios are 0.2047, 0.3177 and 7.91% respectively. Moreover, the optimization based on the obtained ELM model is also validated. In particular, the H2S concentration and CO2 concentration in the treated gas are significantly higher than those before optimization, but have not exceeding the limits. Thus, the consumptions of energy and amine solvents decreased, while the yield increased.","Title: Interrupt protection control of anti-interference nodes in network based on band sampling decision filter modulation\nYear: 2019\n\nAbstract: In network real-time communication, interrupt protection control needs to be conducted for interference nodes due to the multipath interference in the link layer. However, there have always been some incorrect operations in selecting optimal nodes, such as large errors and non-optimal masking results. Therefore, an interrupt protection method for anti-interference nodes in network real-time communication based on band sampling decision filter modulation is proposed in this paper. In this method, a multipath transmission link model of network real-time communication is constructed; an impulse response function of network communication is established; copy autocorrelation processing for signals of communication node sampling is conducted to realize the transformation from time domain to frequency domain; band sampling decision filter is used to conduct noise suppression for interference nodes; an interrupt decision is conducted for filtering output signals in network real-time communication according to the least mean square error criterion, and whether to conduct interrupt mask for nodes is decided according to the threshold, so as to realize the interrupt protection control for anti-interference nodes in network real-time communication. The simulation results show that by using this method to conduct interrupt protection for communication nodes, the output bit error rate is in general lower than those got by the traditional methods, and the highest bit error rate does not exceed 0.5. It tends to be flat from − 4SNR/dB, and completely unaffected by the interference nodes after 4SNR/dB. The method proposed in this paper restrains the interference nodes, and effectively improves the anti-interference capability and the communication quality.","Title: Real-Time Visual Measurement With Opponent Hitting Behavior for Table Tennis Robot.\nYear: 2018\n\nAbstract: In this paper, a visual-based method is proposed for the table tennis robot to estimate the hitting point from the opponent, which can provide a better understanding of the opponent, and thus improve the responsiveness of the table tennis robot. As an essential manifestation of opponent hitting behavior, the information of hitting point includes not only the motion status variation of the ball bef...","Title: Fault Estimation and Fault-Tolerant Control for Switched Fuzzy Stochastic Systems.\nYear: 2018\n\nAbstract: This paper addresses the problems of fault estimation and fault-tolerant control for switched fuzzy stochastic systems with actuator fault and sensor fault. A novel observer is proposed to estimate the system states, actuator, and sensor faults, simultaneously. The proposed observer can be treated as an extension of the traditional proportional-integral observer. The estimation information is util...","Title: Generalized Score Functions for Causal Discovery.\nYear: 2018\n\nAbstract: Discovery of causal relationships from observational data is a fundamental problem. Roughly speaking, there are two types of methods for causal discovery, constraint-based ones and score-based ones. Score-based methods avoid the multiple testing problem and enjoy certain advantages compared to constraint-based ones. However, most of them need strong assumptions on the functional forms of causal mechanisms, as well as on data distributions, which limit their applicability. In practice the precise information of the underlying model class is usually unknown. If the above assumptions are violated, both spurious and missing edges may result. In this paper, we introduce generalized score functions for causal discovery based on the characterization of general (conditional) independence relationships between random variables, without assuming particular model classes. In particular, we exploit regression in RKHS to capture the dependence in a nonparametric way. The resulting causal discovery approach produces asymptotically correct results in rather general cases, which may have nonlinear causal mechanisms, a wide class of data distributions, mixed continuous and discrete data, and multidimensional variables. Experimental results on both synthetic and real-world data demonstrate the efficacy of our proposed approach.","Title: Counting and Sampling from Markov Equivalent DAGs Using Clique Trees\nYear: 2019\n\nAbstract: A directed acyclic graph (DAG) is the most common graphical model for representing causal relationships among a set of variables. When restricted to using only observational data, the structure of the ground truth DAG is identifiable only up to Markov equivalence, based on conditional independence relations among the variables. Therefore, the number of DAGs equivalent to the ground truth DAG is an indicator of the causal complexity of the underlying structure-roughly speaking, it shows how many interventions or how much additional information is further needed to recover the underlying DAG. In this paper, we propose a new technique for counting the number of DAGs in a Markov equivalence class. Our approach is based on the clique tree representation of chordal graphs. We show that in the case of bounded degree graphs, the proposed algorithm is polynomial time. We further demonstrate that this technique can be utilized for uniform sampling from a Markov equivalence class, which provides a stochastic way to enumerate DAGs in the equivalence class and may be needed for finding the best DAG or for causal inference given the equivalence class as input. We also extend our counting and sampling method to the case where prior knowledge about the underlying DAG is available, and present applications of this extension in causal experiment design and estimating the causal effect of joint interventions.","Title: A fast optimal node locating algorithm for MIMO system communication based on link balance control.\nYear: 2019\n\nAbstract: In order to solve the frequent communication obstacle problem in multiple-input and multiple-output (MIMO) system communication caused by routing conflicts, it is necessary to quickly locate the optimal nodes for communication, so a fast optimal node locating algorithm for MIMO system communication based on link balance control is proposed in this paper. In this algorithm, a MIMO system communication channel model is constructed through the link balance control method, and interference features of signals are separated by analyzing the spectrum of communication signals based on the model. Simulation results show that this algorithm can provide 100% accuracy in optimal node locating under signal-to-noise ratio of 80 dB, an average improvement of 13.8% over that in traditional algorithms. The results show that our algorithm can provide high accuracy in locating the optimal nodes for communication, and can ensure high locating efficiency, which effectively improves the communication transmission quality of MIMO system.","Title: The novel part-based cascaded regression algorithm research combining with pose estimation\nYear: 2019\n\nAbstract: Facial feature location is the key procedure in the process of face image analysis. To increase the localization accuracy and success rate of complex facial images due to lack of shape constraint in robust cascaded pose regression algorithm, the paper had proposed the novel part-based cascaded regression algorithm combining pose estimation. The facial landmarks are divided into several areas and pose estimation has used as shape constraint. To enhance the algorithm’s performance, a priori knowledge has been exerted during sampling shape-indexed features to constrain the distance between pixel’s pair, and the number of regression has reduced. The experimental results of proposed algorithm and others have demonstrated that the proposed algorithm has outperformed in localization accuracy and achieved better robustness for occlusion and complex facial images, the better localization success rate has been obtained, and the proposed method can realize real-time processing standard.","Title: Research on Urban Expansion Methods Based on Lacunarity Index\nYear: 2019\n\nAbstract: The problem of urban expansion is an important part of urban development, the domestic research methods were summarized and analyzed. The study found that existing methods can be divided into exponential changes, land use changes and monitoring simulations. However, the research results of existing methods are homogenized. In this study, the gap index model was established and Kunming and Yuxi were taken as examples to systematically introduce the gap index to calculate urban expansion. The study found that the global gap index can better describe the urban expansion characteristics and can explain the heterogeneity between urban land use by scale shifting; The local clearance index can not only describe the urban expansion characteristics, but also analyze the urban expansion direction and expansion trend in detail to describe the spatial pattern change of urban expansion. The application of the gap index in urban expansion has a very good reference for future urban expansion research.","Title: Multiobjective Weapon-Target Assignment Problem By Two-Stage Evolutionary Multiobjective Particle Swarm Optimization\nYear: 2016\n\nAbstract: This paper presents a two-stage evolutionary multiobjective particle swarm optimization (TSMOPSO) algorithm to solve the multiobjective weapon-target assignment (WTA) problem. In order to improve the convergence and the rate of convergence of the proposed algorithm, a two-stage evolutionary strategy is employed. In the first stage of evolution, the population is evolved by the particle status updating rules defined in particle swarm algorithm (PSO). In the second stage of evolution, a novel evolution operator is designed to update the Pareto front solutions which is obtained in the first stage of evolution. Experiments indicate that TSMOPSO algorithm is an effective method to solve the multiobjective weapon-target assignment problem, and the convergence and the rate of convergence of this algorithm have an obvious improvement.","Title: Tensor-Based Angle and Array Gain-Phase Error Estimation Scheme in Bistatic MIMO Radar.\nYear: 2019\n\nAbstract: This paper investigates the issue of angle and array gain-phase error estimation in multiple-input-multiple-output (MIMO) radar, and a tensor-based angle and gain-phase error estimation scheme is proposed. In our approach, the parallel factor (PARAFAC) decomposition is performed to estimate the transmit and receive direction matrices. Then the estimation of gain error can be obtained according to the relationship between the columns of direction matrices. After that, the linear feature of the phase in the additional well-calibrated array element is utilized to estimate the angles. Finally, by fully using the phase characteristics of all arrays, the phase error can be obtained. Our approach can remove the influence of error accumulation, and thus it has a superior angle and gain-phase error estimation performance, particularly under the condition of low signal-to-noise ratio (SNR). The numerical examples validate the superiority and effectiveness of the proposed scheme.","Title: An Adaptive Spectrum Segmentation Method to Optimize Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis.\nYear: 2019\n\nAbstract: Bearings play an important role in the industrial system. Among all kinds of diagnosis methods, empirical wavelet transform has been widely used for its characteristic of separating empirical modes from the spectrum. Although the empirical wavelet transform restrains the mode aliasing of extracting modal functions from the time domain, it runs slowly and separates a large number of invalid components. In this paper, an adaptive and fast empirical wavelet transform method is proposed. The method extracts the first feature cluster in the Fourier transform of the spectrum to reconstruct the trend spectrum. The minimum points are regarded as the initial boundaries. The key spectral negentropy is proposed to extract the frequency band which may contain main information. This method reduces the number of invalid components and computation time by filtering components before reconstruction. The simulation signal proves that the proposed method is effective and the proposed key spectral negentropy has stronger anti-noise ability than kurtosis. The experimental signals show that the method can successfully extract the fault features of inner or outer rings of bearings, and is suitable for the diagnosis of composite faults.","Title: Causal Discovery from Heterogeneous/Nonstationary Data.\nYear: 2019\n\nAbstract: It is commonplace to encounter heterogeneous or nonstationary data, of which the underlying generating process changes across domains or over time. Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper, we develop a framework for causal discovery from such data, called Constraint-based causal Discovery from heterogeneous/NOnstationary Data (CD-NOD), to find causal skeleton and directions and estimate the properties of mechanism changes. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a method to determine causal orientations by making use of independent changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. After learning the causal structure, next, we investigate how to efficiently estimate the `driving forceu0027 of the nonstationarity of a causal mechanism. That is, we aim to extract from data a low-dimensional representation of changes. The proposed methods are nonparametric, with no hard restrictions on data distributions and causal mechanisms, and do not rely on window segmentation. Furthermore, we find that data heterogeneity benefits causal structure identification even with particular types of confounders. Finally, we show the connection between heterogeneity/nonstationarity and soft intervention in causal discovery. Experimental results on various synthetic and real-world data sets (task-fMRI and stock market data) are presented to demonstrate the efficacy of the proposed methods.","Title: On Learning Invariant Representation for Domain Adaptation.\nYear: 2019\n\nAbstract: Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achieve a small error on the source domain. The hope is that the learnt representation, together with the hypothesis learnt from the source domain, can generalize to the target domain. In this paper, we first construct a simple counterexample showing that, contrary to common belief, the above conditions are not sufficient to guarantee successful domain adaptation. In particular, the counterexample (Fig. 1) exhibits emph{conditional shift}: the class-conditional distributions of input features change between source and target domains. To give a sufficient condition for domain adaptation, we propose a natural and interpretable generalization upper bound that explicitly takes into account the aforementioned shift. Moreover, we shed new light on the problem by proving an information-theoretic lower bound on the joint error of emph{any} domain adaptation method that attempts to learn invariant representations. Our result characterizes a fundamental tradeoff between learning invariant representations and achieving small joint error on both domains when the marginal label distributions differ from source to target. Finally, we conduct experiments on real-world datasets that corroborate our theoretical findings. We believe these insights are helpful in guiding the future design of domain adaptation and representation learning algorithms.","Title: Generative-Discriminative Complementary Learning\nYear: 2019\n\nAbstract: The majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases. In this paper, we study the complementary learning problem. Unlike ordinary labels, complementary labels are easy to obtain because an annotator only needs to provide a yes/no answer to a randomly chosen candidate class for each instance. We propose a generative-discriminative complementary learning method that estimates the ordinary labels by modeling both the conditional (discriminative) and instance (generative) distributions. Our method, we call Complementary Conditional GAN (CCGAN), improves the accuracy of predicting ordinary labels and is able to generate high-quality instances in spite of weak supervision. In addition to the extensive empirical studies, we also theoretically show that our model can retrieve the true conditional distribution from the complementarily-labeled data.","Title: Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA.\nYear: 2019\n\nAbstract: We consider the problem of inferring causal relationships between two or more passively observed variables. While the problem of such causal discovery has been extensively studied especially in the bivariate setting, the majority of current methods assume a linear causal relationship, and the few methods which consider non-linear dependencies usually make the assumption of additive noise. Here, we propose a framework through which we can perform causal discovery in the presence of general non-linear relationships. The proposed method is based on recent progress in non-linear independent component analysis and exploits the non-stationarity of observations in order to recover the underlying sources or latent disturbances. We show rigorously that in the case of bivariate causal discovery, such non-linear ICA can be used to infer the causal direction via a series of independence tests. We further propose an alternative measure of causal direction based on asymptotic approximations to the likelihood ratio, as well as an extension to multivariate causal discovery. We demonstrate the capabilities of the proposed method via a series of simulation studies and conclude with an application to neuroimaging data.","Title: Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data.\nYear: 2019\n\nAbstract: Autism spectrum disorder (ASD) is one of the major developmental disorders affecting children. Recently, it has been hypothesized that ASD is associated with atypical brain connectivities. A substantial body of researches use Pearsonu0027s correlation coefficients, mutual information, or partial correlation to investigate the differences in brain connectivities between ASD and typical controls from functional Magnetic Resonance Imaging (fMRI). However, correlation or partial correlation does not directly reveal causal influences - the information flow - between brain regions. Comparing to correlation, causality pinpoints the key connectivity characteristics and removes redundant features for diagnosis. In this paper, we propose a two-step method for large-scale and cyclic causal discovery from fMRI. It can identify brain causal structures without doing interventional experiments. The learned causal structure, as well as the causal influence strength, provides us the path and effectiveness of information flow. With the recovered causal influence strength as candidate features, we then perform ASD diagnosis by further doing feature selection and classification. We apply our methods to three datasets from Autism Brain Imaging Data Exchange (ABIDE). From experimental results, it shows that with causal connectivities, the diagnostic accuracy largely improves. A closer examination shows that information flows starting from the superior front gyrus to default mode network and posterior areas are largely reduced. Moreover, all enhanced information flows are from posterior to anterior or in local areas. Overall, it shows that long-range influences have a larger proportion of reductions than local ones, while local influences have a larger proportion of increases than long-range ones. By examining the graph properties of brain causal structure, the group of ASD shows reduced small-worldness.","Title: DRr-Net: Dynamic Re-read Network for Sentence Semantic Matching\nYear: 2019\n\nAbstract: Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all matching methods, attention mechanism plays an important role in capturing the semantic relations and properly aligning the elements of two sentences. Previous methods utilized attention mechanism to select important parts of sentences at one time. However, the important parts of the sentence during semantic matching are dynamically changing with the degree of sentence understanding. Selecting the important parts at one time may be insufficient for semantic understanding. To this end, we propose a Dynamic Re-read Network (DRr-Net) approach for sentence semantic matching, which is able to pay close attention to a small region of sentences at each step and re-read the important words for better sentence semantic understanding. To be specific, we first employ Attention Stack-GRU (ASG) unit to model the original sentence repeatedly and preserve all the information from bottom-most word embedding input to up-most recurrent output. Second, we utilize Dynamic Re-read (DRr) unit to pay close attention to one important word at one time with the consideration of learned information and re-read the important words for better sentence semantic understanding. Extensive experiments on three sentence matching benchmark datasets demonstrate that DRr-Net has the ability to model sentence semantic more precisely and significantly improve the performance of sentence semantic matching. In addition, it is very interesting that some of finding in our experiments are consistent with the findings of psychological research.","Title: Adaptive Fuzzy Fault-Tolerant Tracking Control for Partially Unknown Systems with Actuator Faults via Integral Reinforcement Learning Method\nYear: 2019\n\nAbstract: In this paper, the fuzzy reinforcement learning based tracking control algorithm is first proposed for partially unknown systems with actuator faults. Based on Takagi-Sugeno fuzzy model, a novel fuzzy-augmented tracking dynamic is developed and the overall fuzzy control policy with corresponding performance index is designed, where four kinds of actuator faults, including actuator loss of effectiveness and bias fault, are considered. Combining the reinforcement learning technique and fuzzy-augmented model, the new fuzzy integral reinforcement learning based fault-tolerant control algorithm is designed and it runs in real time for the system with actuator faults. The dynamic matrices can be partially unknown and the online algorithm requires less information transmissions or computational load along with the learning process. Under the overall fuzzy fault-tolerant policy, the tracking objective is achieved and the stability is proved by Lyapunov theory. Finally, the applications in the single-link robot arm system and the complex pitch-rate control problem of F-16 fighter aircraft demonstrate the effectiveness of the proposed method.","Title: Robust Optimal Control Scheme for Unknown Constrained-Input Nonlinear Systems via a Plug-n-Play Event-Sampled Critic-Only Algorithm\nYear: 2020\n\nAbstract: In this paper, a novel event-sampled robust optimal controller is proposed for a class of continuous-time constrained-input nonlinear systems with unknown dynamics. In order to solve the robust optimal control problem, an online data-driven identifier is established to construct the system dynamics, and an event-sampled critic-only adaptive dynamic programming method is developed to replace the conventional time-driven actor-critic structure. The designed online identification method runs during the solving process and is not applied as a priori part for the solutions, which simplifies the architecture and reduces computational load. The proposed robust optimal control algorithm tunes the parameters of critic-only neural network (NN) by event-triggering condition and runs in a plug-n-play framework without system functions, where fewer transmissions and less computation are required as all the measurements received simultaneously. Based on the novel design, the stability of system and the convergence of critic NN are demonstrated by Lyapunov theory, where the state is asymptotically stable and weight error is guaranteed to be uniformly ultimately bounded. Finally, the applications in a basic nonlinear system and the complex rotational-translational actuator problem demonstrate the effectiveness of the proposed method.","Title: Context-Aware Dual-Attention Network for Natural Language Inference\nYear: 2019\n\nAbstract: Natural Language Inference (NLI) is a fundamental task in natural language understanding. In spite of the importance of existing research on NLI, the problem of how to exploit the contexts of sentences for more precisely capturing the inference relations (i.e. by addressing the issues such as polysemy and ambiguity) is still much open. In this paper, we introduce the corresponding image into inference process. Along this line, we design a novel Context-Aware Dual-Attention Network (CADAN) for tackling NLI task. To be specific, we first utilize the corresponding images as the Image Attention to construct an enriched representation for sentences. Then, we use the enriched representation as the Sentence Attention to analyze the inference relations from detailed perspectives. Finally, a sentence matching method is designed to determine the inference relation in sentence pairs. Experimental results on large-scale NLI corpora and real-world NLI alike corpus demonstrate the superior effectiveness of our CADAN model.","Title: Photonic Generation and Transmission of Linearly Chirped Microwave Waveform With Increased Time-Bandwidth Product\nYear: 2019\n\nAbstract: Photonic generation and transmission of linearly chirped microwave waveform with the increased time-bandwidth product (TBWP) is proposed and demonstrated by phase-encoding and splitting parabolic waveform. In the approach, dual-polarization Mach-Zehnder modulator (DPol-MZM) along with polarization controller (PC) is employed to generate two orthogonally polarized wavelengths, and polarization modulator is driven by a parabolic signal. After splitting parabolic waveform and convolving with a binary pseudo random sequence, chirped microwave waveform with high bandwidth and large temporal duration is generated, corresponding to an improved TBWP. Dispersion-induced power fading can be effectively avoided in the approach, and chirped signal with constant initial phase can be achieved by adjusting the PC. The proposed approach is verified by simulations. Linearly chirped microwave waveform with the central frequency of 40 GHz, bandwidth of 12.5 GHz, and TBWPof 20 160 is generated. RF spurious suppression ratios of the generated signal are investigated, and pulse compression performances are also analyzed. The system features filter-free, high tunability, compact structure, and large TBWP, which has potential application in the modern radar system.","Title: Causal Discovery with Cascade Nonlinear Additive Noise Models.\nYear: 2019\n\nAbstract: Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class is not transitive--even if each direct causal relation follows this model, indirect causal influences, which result from omitted intermediate causal variables and are frequently encountered in practice, do not necessarily follow the model constraints; as a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured intermediate variables, from data, under the variational auto-encoder framework. Our theoretical results show that with our model, causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.","Title: Negentropy Spectrum Decomposition and Its Application in Compound Fault Diagnosis of Rolling Bearing.\nYear: 2019\n\nAbstract: The rolling bearings often suffer from compound fault in practice. Compared with single fault, compound fault contains multiple fault features that are coupled together and make it difficult to detect and extract all fault features by traditional methods such as Hilbert envelope demodulation, wavelet transform and empirical node decomposition (EMD). In order to realize the compound fault diagnosis of rolling bearings and improve the diagnostic accuracy, we developed negentropy spectrum decomposition (NSD), which is based on fast empirical wavelet transform (FEWT) and spectral negentropy, with cyclic extraction as the extraction method. The infogram is constructed by FEWT combined with spectral negentropy to select the best band center and bandwidth for band-pass filtering. The filtered signal is used as a new measured signal, and the fast empirical wavelet transform combined with spectral negentropy is used to filter the new measured signal again. This operation is repeated to achieve cyclic extraction, until the signal no longer contains obvious fault features. After obtaining the envelope of all extracted components, compound fault diagnosis of rolling bearings can be realized. The analysis of the simulation signal and the experimental signal shows that the method can realize the compound fault diagnosis of rolling bearings, which verifies the feasibility and effectiveness of the method. The method proposed in this paper can detect and extract all the fault features of compound fault completely, and it is more reliable for the diagnosis of compound fault. Therefore, the method has practical significance in rolling bearing compound fault diagnosis.","Title: Tracking control optimization scheme for a class of partially unknown fuzzy systems by using integral reinforcement learning architecture.\nYear: 2019\n\nAbstract: In this paper, a novel fuzzy integral reinforcement learning (RL) based tracking control algorithm is first proposed for partially unknown fuzzy systems. Firstly, by using the precompensation and augmentation techniques, a new augmented fuzzy tracking system is constructed by combining the fuzzy logic model and desired reference trajectory, where the solution of actual working feedback control policy is converted into a virtual optimal control problem. Secondly, to overcome the requirements of exact original system information, the integral RL technique is utilized to learn the fuzzy control solution, which relaxes the repeatedly transmissions of system matrices during the solving process. Thirdly, compared with the existing standard solution, some crucial and strict aforementioned assumptions are removed and the system can be partially unknown by using the designed algorithm. Besides, under the novel fuzzy control policy, the tracking objective is achieved and the stability is guaranteed by Lyapunov theory. Finally, the developed integral RL tracking control algorithm for partially unknown systems is applied in a mechanical system and the simulation results demonstrate the effectiveness of the proposed new method.","Title: Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation.\nYear: 2019\n\nAbstract: Discovery of causal relations from observational data is essential for many disciplines of science and real-world applications. However, unlike other machine learning algorithms, whose development has been greatly fostered by a large amount of available benchmark datasets, causal discovery algorithms are notoriously difficult to be systematically evaluated because few datasets with known ground-truth causal relations are available. In this work, we handle the problem of evaluating causal discovery algorithms by building a flexible simulator in the medical setting. We develop a neuropathic pain diagnosis simulator, inspired by the fact that the biological processes of neuropathic pathophysiology are well studied with well-understood causal influences. Our simulator exploits the causal graph of the neuropathic pain pathology and its parameters in the generator are estimated from real-life patient cases. We show that the data generated from our simulator have similar statistics as real-world data. As a clear advantage, the simulator can produce infinite samples without jeopardizing the privacy of real-world patients. Our simulator provides a natural tool for evaluating various types of causal discovery algorithms, including those to deal with practical issues in causal discovery, such as unknown confounders, selection bias, and missing data. Using our simulator, we have evaluated extensively causal discovery algorithms under various settings.","Title: Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models.\nYear: 2019\n\nAbstract: In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments. In this paper, we study causal discovery and forecasting for nonstationary time series. By exploiting a particular type of state-space model to represent the processes, we show that nonstationarity helps to identify causal structure and that forecasting naturally benefits from learned causal knowledge. Specifically, we allow changes in both causal strengths and noise variances in the nonlinear state-space models, which, interestingly, renders both the causal structure and model parameters identifiable. Given the causal model, we treat forecasting as a problem in Bayesian inference in the causal model, which exploits the timevarying property of the data and adapts to new observations in a principled manner. Experimental results on synthetic and real-world data sets demonstrate the efficacy of the proposed methods.","Title: Study on novel signal processing and simultaneous-fault diagnostic method for wind turbine:\nYear: 2019\n\nAbstract: High-precision fault diagnosis is important for the widely installed complex industrial product, the wind turbine. However, intelligent monitoring is difficult due to the fuzzy boundaries and individual different variations of the unseen single or simultaneous-fault of such intricate equipment. To solve this problem, this study proposes an ensemble fault diagnostic framework for simultaneous and coupling failure. First, this paper develops novel signal processing methods for effective feature learning and mapping from the non-stationary and nonlinear raw vibrational signals. The adapted variational mode decomposition is introduced based on the particle swarm optimization that applies the minimum mean envelope entropy to optimize the parameters settings. Second, the novel ensemble extreme learning machine-based network is proposed to isolate the faults that applies one extreme learning machine network to count the number of fault scenarios, and the other one to identify the specific single or simultaneous-fault labels. With this scheme, the self-adaptive ensemble extreme learning machine-based fault diagnostic framework is more accurate and faster than the prevailing probabilistic classifier-based methods, as the proposed method does not rely on empirically specified decision-making threshold and generates all the candidate fault labels at the same time. Finally, this study builds the test platform and compares the overall results with the existing feature analysis methods and classifiers. The experimental results verify that the proposed framework detects both single and simultaneous-fault accurately and quickly.","Title: Ship Radial Size Estimation In High-Resolution Maritime Surveillance Radars Via Sparse Recovery Using Linear Programming\nYear: 2019\n\nAbstract: For high-resolution maritime surveillance radars operating at scan mode and transmitting long duration pulses, radial size estimation of ships is severely interfered by range sidelobe effect of pulse compression. Due to range sidelobe effect, strong ship returns often cover a range interval wider than its radial size and the range interval covered by detected ship returns is sometimes a severe overestimate of its radial size. An approach to mitigate severe overestimation of ship radial sizes is to find the borders of high-resolution range profile (HRRP) of a ship. Therefore, ship radial size estimation boils down to the recovery of sparse HRRPs of ships from radar returns with heavy-tailed sea clutter in a coherent processing interval (CPI) only containing several pulses. In this paper, a sparse recovery method using linear programming (LP) is proposed to recover sparse HRRPs of ships from radar returns with heavy-tailed sea clutter. As a minor contribution, a full scheme is presented for ship radial size estimation in high-resolution surveillance radars operating at scan mode, which consists of coherent detection along with range cells, annotation of the range interval covered by ship returns, the decision of severe range sidelobe effect, recovery of sparse HRRPs of ships, and radial size computation. The performance of the proposed LP-based method to estimate ship radial size is evaluated by using simulated and real radar data. The experimental results show that it attains more accurate radial size estimates of ships than existing methods.","Title: Fuzzy adaptive dynamic programming-based optimal leader-following consensus for heterogeneous nonlinear multi-agent systems\nYear: 2020\n\nAbstract: In this paper, a novel online iterative scheme, based on fuzzy adaptive dynamic programming, is proposed for distributed optimal leader-following consensus of heterogeneous nonlinear multi-agent systems under directed communication graph. This scheme combines game theory, adaptive dynamic programming together with generalized fuzzy hyperbolic model (GFHM). Firstly, based on precompensation technique, an appropriate model transformation is proposed to convert the error system into augmented error system, and an exquisite performance index function is defined for this system. Secondly, on the basis of Hamilton–Jacobi–Bellman (HJB) equation, the optimal consensus control is designed and a novel policy iteration (PI) algorithm is put forward to learn the solutions of the HJB equation online. Here, the proposed PI algorithm is implemented on account of GFHMs. Compared with dual-network model including critic network and action network, the proposed scheme only requires critic network. Thirdly, the augmented consensus error of each agent and the weight estimation error of each GFHM are proved to be uniformly ultimately bounded, and the stability of our method has been verified. Finally, some numerical examples and application examples are conducted to demonstrate the effectiveness of the theoretical results.","Title: A Fast Non-Parametric Detection Method For Infrared Small Dim Target\nYear: 2019\n\nAbstract: This paper presents a fast non-parametric detection method for multiple infrared small dim targets against the complex sky background. Traditional methods are based on the entire image processing with a high requirement of accuracy and speediness. However, the proposed method divides the detection into regions of interest (ROIs) searching and precise positioning according to the idea that from coarse searching to precise positioning. The former pays more attention to accuracy while the latter is more concentrated on speediness. First, ROIs sequence can be built by stationary wavelet transform (SWT) followed by Sobel direction directors. Through Hough line transformation, target region proposals which are settled as prior information of precise positioning can be extracted from ROIs sequence. Then, exact target positions are located by auto-regression analysis of temporal profile. Only the max size of targets is assumed which dramatically reduces the dependence of the method on target parameters. Results demonstrate that the proposed method can rapidly locate multiple targets simultaneously in real-time.","Title: Domain Generalization via Multidomain Discriminant Analysis.\nYear: 2019\n\nAbstract: Domain generalization (DG) aims to incorporate knowledge from multiple source domains into a single model that could generalize well on unseen target domains. This problem is ubiquitous in practice since the distributions of the target data may rarely be identical to those of the source data. In this paper, we propose Multidomain Discriminant Analysis (MDA) to address DG of classification tasks in general situations. MDA learns a domain-invariant feature transformation that aims to achieve appealing properties, including a minimal divergence among domains within each class, a maximal separability among classes, and overall maximal compactness of all classes. Furthermore, we provide the bounds on excess risk and generalization error by learning theory analysis. Comprehensive experiments on synthetic and real benchmark datasets demonstrate the effectiveness of MDA.","Title: Power Demand Response Incentive Pricing Model\nYear: 2019\n\nAbstract: Power demand response aims to clip the peak and fill the valley of power load by interruptible load management. Its closely related to the safety and economic benefit of the power system. At present, various provinces in China have carried out the pilot work of interruptible load management. The power companies signed contracts with power users to stipulate power users to adjust the power load consumption during peak hours or in emergency situations. The singed contracts should be fair for all power users. At the same time, the specific information about the signed contracts should be protected for privacy preservation, which is a typical federated learning scenario. A fair and privacy-preserving incentive pricing model should be proposed to stimulate power uses to participate in thee interruptible load management. To this end, a power demand response incentive pricing model is proposed. Based on the multi-attribute sealed auction game, the bidding model between the power company and the power users is established. In the single bidding model, the risk of the power user's demand response is evaluated firstly. The power users could be classified into different group according to their power load characteristics. Then a user classification selection algorithm is proposed, which enables both the power company's revenue and power user risk to be considered, and enables the selected users to achieve balanced peak clipping. A fair mechanism based on integrals is proposed to assure the fairness between all power users. The case simulations demonstrate that effectiveness of the proposed incentive pricing model.","Title: Unpaired Data Empowers Association Tests\nYear: 2021\n\nAbstract: Motivation: There is growing interest in the biomedical research community to incorporate retrospective data, available in healthcare systems, to shed light on associations between different biomarkers. Understanding the association between various types of biomedical data, such as genetic, blood biomarkers, imaging, etc. can provide a holistic understanding of human diseases. To formally test a hypothesized association between two types of data in Electronic Health Records (EHRs), one requires a substantial sample size with both data modalities to achieve a reasonable power. Current association test methods only allow using data from individuals who have both data modalities. Hence, researchers cannot take advantage of much larger EHR samples that includes individuals with at least one of the data types, which limits the power of the association test.Results: We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and to improve the power of the association test. We study the properties of the new test theoretically and empirically, through a series of simulations and by applying our method on real studies in the context of Chronic Obstructive Pulmonary Disease. We are able to identify an association between the high-dimensional characterization of Computed Tomography chest images and several blood biomarkers as well as the expression of dozens of genes involved in the immune system.","Title: Ideography Leads Us To The Field Of Cognition: A Radical-Guided Associative Model For Chinese Text Classification\nYear: 2021\n\nAbstract: Cognitive psychology research shows that humans have the instinct for abstract thinking, where association plays an essential role in language comprehension. Especially for Chinese, its ideographic writing system allows radicals to trigger semantic association without the need of phonetics. In fact, subconsciously using the associative information guided by radicals is a key for readers to ensure the robustness of semantic understanding. Fortunately, many basic and extended concepts related to radicals are systematically included in Chinese language dictionaries, which leaves a handy but unexplored way for improving Chinese text representation and classification. To this end, we draw inspirations from cognitive principles between ideography and human associative behavior to propose a novel Radical-guided Associative Model (RAM) for Chinese text classification. RAM comprises two coupled spaces, namely Literal Space and Associative Space, which imitates the real process in people's mind when understanding a Chinese text. To be specific, we first devise a serialized modeling structure in Literal Space to thoroughly capture the sequential information of Chinese text. Then, based on the authoritative information provided by Chinese language dictionaries, we design an association module and put forward a strategy called Radical-Word Association to use ideographic radicals as the medium to associate prior concept words in Associative Space. Afterwards, we design an attention module to imitate people's matching and decision between Literal Space and Associative Space, which can balance the importance of each associative words under specific contexts. Finally, extensive experiments on two real-world datasets prove the effectiveness and rationality of RAM, with good cognitive insights for future language modeling.","Title: Testing Independence Between Linear Combinations For Causal Discovery\nYear: 2021\n\nAbstract: Recently, regression based conditional independence (CI) tests have been employed to solve the problem of causal discovery. These methods provide an alternative way to test for CI by transforming CI to independence between residuals. Generally, it is nontrivial to check for independence when these residuals are linearly uncorrelated. With the ability to represent high-order moments, kernel-based methods are usually used to achieve this goal, but at a cost of considerable time. In this paper, we investigate the independence between two linear combinations under linear non-Gaussian structural equation model (SEM). We show that generally the 1-st to 4-th moments of the two linear combinations contain enough information to infer whether or not they are independent. The proposed method provides a simpler but more effective way to measure CIs, with only calculating the 1-st to 4-th moments of the input variables. When applied to causal discovery, the proposed method outperforms kernel-based methods in terms of both speed and accuracy. which is validated by extensive experiments.","Title: Making The Relation Matters: Relation Of Relation Learning Network For Sentence Semantic Matching\nYear: 2021\n\nAbstract: Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially BERT. Despite their effectiveness, most of these models treat output labels as meaningless one-hot vectors, underestimating the semantic information and guidance of relations that these labels reveal, especially for tasks with a small number of labels. To address this problem, we propose a Relation of Relation Learning Network (R-2-Net) for sentence semantic matching. Specifically, we first employ BERT to encode the input sentences from a global perspective. Then a CNN-based encoder is designed to capture keywords and phrase information from a local perspective. To fully leverage labels for better relation information extraction, we introduce a self-supervised relation of relation classification task for guiding R-2-Net to consider more about relations. Meanwhile, a triplet loss is employed to distinguish the intra-class and inter-class relations in a finer granularity. Empirical experiments on two sentence semantic matching tasks demonstrate the superiority of our proposed model.","Title: Deeptrader: A Deep Reinforcement Learning Approach For Risk-Return Balanced Portfolio Management With Market Conditions Embedding\nYear: 2021\n\nAbstract: Most existing reinforcement learning (RL)-based portfolio management models do not take into account the market conditions, which limits their performance in risk-return balancing. In this paper, we propose DeepTrader, a deep RL method to optimize the investment policy. In particular, to tackle the risk-return balancing problem, our model embeds macro market conditions as an indicator to dynamically adjust the proportion between long and short funds, to lower the risk of market fluctuations, with the negative maximum drawdown as the reward function. Additionally, the model involves a unit to evaluate individual assets, which learns dynamic patterns from historical data with the price rising rate as the reward function. Both temporal and spatial dependencies between assets are captured hierarchically by a specific type of graph structure. Particularly, we find that the estimated causal structure best captures the interrelationships between assets, compared to industry classification and correlation. The two units are complementary and integrated to generate a suitable portfolio which fits the market trend well and strikes a balance between return and risk effectively. Experiments on three well-known stock indexes demonstrate the superiority of DeepTrader in terms of risk-gain criteria.","Title: Improving Causal Discovery By Optimal Bayesian Network Learning\nYear: 2021\n\nAbstract: Many widely-used causal discovery methods such as Greedy Equivalent Search (GES), although with asymptotic correctness guarantees, have been reported to produce sub-optimal solutions on finite data, or when the causal faithfulness condition is violated. The constraint-based procedure with Boolean satisfiability (SAT) solver, and the recently proposed Sparsest Permutation (SP) algorithm have shown superb performance, but currently they do not scale well. In this work, we demonstrate that optimal score-based exhaustive search is remarkably useful for causal discovery: it requires weaker conditions to guarantee asymptotic correctness, and outperforms well-known methods including PC, GES, GSP, and NOTEARS. In order to achieve scalability, we also develop an approximation algorithm for larger systems based on the A* method, which scales up to 60+ variables and obtains better results than existing greedy algorithms such as GES, MMHC, and GSP. Our results illustrate the risk of assuming the faithfulness assumption, the advantages of exhaustive search methods, and the limitations of greedy search methods, and shed light on the computational challenges and techniques in scaling up to larger networks and handling unfaithful data.","Title: Implementation Of Efficient Quantum Search Algorithms On Nisq Computers\nYear: 2021\n\nAbstract: Despite the advent of Grover's algorithm for the unstructured search, its successful implementation on near-term quantum devices is still limited. We apply three strategies to reduce the errors associated with implementing quantum search algorithms. Our improved search algorithms have been implemented on the IBM quantum processors. Using them, we demonstrate three- and four-qubit search algorithm with higher average success probabilities compared to previous works. We present the successful execution of the five-qubit search on the IBM quantum processor for the first time. The results have been benchmarked using degraded ratio, which is the ratio between the experimental and the theoretical success probabilities. The fast decay of the degraded ratio supports our divide-and-conquer strategy. Our proposed strategies are also useful for implementation of quantum search algorithms in the post-NISQ era.","Title: Poorly Differentiated Colorectal Gland Segmentation Approach Based on Internal and External Stress in Histology Images\nYear: 2020\n\nAbstract: Histological assessment of glands is one of the key factors in colon cancer grading. We propose an approach for segmentation of glands in colon cancer histopathology images, which bases on the characteristics of lumens and rough gland boundary. First, we present a U-net for stain separation of H& E image to obtain the H-stain, E-stain, and background stain intensity maps. Subsequently, the epithelial nuclei was identified on the H stain intensity map, and the lumen segmentation was performed on the background intensity map. Then, we employ the axis of least inertia and chain to establish a connection between the lumen and the epithelial nuclei. In the light of connection of the lumen and the epithelial nuclei, more accurately gland segmentation is performed based on the rough gland boundary. The proposed stain separation approach is unsupervised, and the stain separation makes the information contained in the H& E image more independent and easy to identify, so as to deal with the uneven stain intensity and the inconspicuous stain difference. We compare our results on a publicly available data set. The results show that the segmentation approach combining the characteristics of lumens and rough gland boundary has improved the segmentation accuracy.","Title: Batch Target Recognition Count under Complex Conditions Based on Att-Unet\nYear: 2020\n\nAbstract: As we all know, the large number of counts is a challenging and time consuming task subject because of oversized number and complex conditions. However, the development of deep learning makes deep learning models very competitive in image segmentation. In this paper, we take cigarette filter rods as the research object. we first evaluate the standard Unet for the filter rod target recognition to separate target and background. Secondly, we use the focal loss function instead of the traditional cross-entropy function to solve the problem of imbalance between target and background area. Thirdly, we add a self-attention module in the traditional Unet convolutional layer to enhance the convolution effect. Fourth, we propose structural element detection criteria and round tangency matching strategy based on HMM (Hidden Markov Model) for the geometric relationship of filter rod position, which further improves the accuracy of the algorithm. We used Qu's [1], Mask-R-CNN [2], FCN [3], Deep-lab-V1 [4] and this paper's algorithm to test the performance of 30000 images from the industrial site. The performance of this paper's algorithm is completely better than the performance of the above algorithm.","Title: External Thread Measurement Based on ResUnet and HMM\nYear: 2020\n\nAbstract: There are many methods of thread measurement. In addition, these thread measurement methods require manual segmentation of regions of interest (threaded area) and These methods are easily disturbed by the environment (e.g. dust, iron filings, oil stains, etc.), resulting in inaccurate measurement results. This paper proposes an external thread measurement method based on ResUnet and hidden Markov model (HMM). First, we propose a ResUnet-based thread edge recognition method that omits the process of calibrating the threaded area and identify the thread edge in a complex environment. Secondly, we use HMM to classify the thread edge points so that the threaded parts can be placed at any angle during the measurement, simplifying the measurement steps and calculating the thread parameters based on the classification results. Finally, we evaluated our method using our own dataset, and the results showed that the difference between the measured value and the standard value is within 0.01 mm.","Title: DGA-Net - Dynamic Gaussian Attention Network for Sentence Semantic Matching.\nYear: 2021\n\nAbstract: Sentence semantic matching requires an agent to determine the semantic relation between two sentences, where much recent progress has been made by the advancement of representation learning techniques and inspiration of human behaviors. Among all these methods, attention mechanism plays an essential role by selecting important parts effectively. However, current attention methods either focus on all the important parts in a static way or only select one important part at one attention step dynamically, which leaves a large space for further improvement. To this end, in this paper, we design a novel Dynamic Gaussian Attention Network (DGA-Net) to combine the advantages of current static and dynamic attention methods. More specifically, we first leverage pre-trained language model to encode the input sentences and construct semantic representations from a global perspective. Then, we develop a Dynamic Gaussian Attention (DGA) to dynamically capture the important parts and corresponding local contexts from a detailed perspective. Finally, we combine the global information and detailed local information together to decide the semantic relation of sentences comprehensively and precisely. Extensive experiments on two popular sentence semantic matching tasks demonstrate that our proposed DGA-Net is effective in improving the ability of attention mechanism.","Title: Observer-Based Output Feedback Event-Triggered Adaptive Control for Linear Multiagent Systems Under Switching Topologies\nYear: 2022\n\nAbstract: The consensus problem of general linear multiagent systems (MASs) is studied under switching topologies by using observer-based event-triggered control method in this article. On the basis of the output information of agents, two kinds of novel event-triggered adaptive control schemes are designed to achieve the leaderless and leader-follower consensus problems, which do not need to utilize the global information of the communication networks. Finally, two simulation examples are introduced to show that the consensus error converges to zero and Zeno behavior is eliminated in MASs. Compared with the existing output feedback control research, one of the significant advantages of our methods is that the controller protocols and triggering mechanisms do not rely on any global information, are independent of the network scale, and are fully distributed ways.","Title: Limited Sliding Network: Fine-Grained Target Detection On Electrical Infrastructure For Power Transmission Line Surveillance\nYear: 2021\n\nAbstract: Because of its small size, low local contrast, and much interference, the field image of fine-grained equipment taken from power transmission line surveillance is hard to be sustained by the traditional small target detection technique, which requires the manual extraction of features, making it difficult to accurately detect micro-fine-grained equipment. The deep learning-based algorithms have prospective application but require abundant data to guarantee performance and tackle the problem of foreground-background imbalance. This paper develops an effective pipeline, i.e., limited sliding network (LSNet), to detect the small and fine-grained defects on equipment in power transmission line infrastructure. The model firstly performs the regional analysis on the entire image to determine the potential target locations. The feature extraction and classification on the potential location image blocks are further performed by the VGG-style model for the dense target locations, and the nonmaximum suppression method is finally applied to locate the target. On the other hand, a specific training method is also developed to better deal with a wide range imbalances of positive and negative samples. The proposed method achieves the detection mean average precision (mAP) rate of 98.66% on the real datasets, while limiting the computational overhead of hardware.","Title: Adversarial Robustness Through the Lens of Causality.\nYear: 2022\n\nAbstract: The adversarial vulnerability of deep neural networks has attracted significant attention in machine learning. From a causal viewpoint, adversarial attacks can be considered as a specific type of distribution change on natural data. As causal reasoning has an instinct for modeling distribution change, we propose to incorporate causality into mitigating adversarial vulnerability. However, causal formulations of the intuition of adversarial attack and the development of robust DNNs are still lacking in the literature. To bridge this gap, we construct a causal graph to model the generation process of adversarial examples and define the adversarial distribution to formalize the intuition of adversarial attacks. From a causal perspective, we find that the label is spuriously correlated with the style (content-independent) information when an instance is given. The spurious correlation implies that the adversarial distribution is constructed via making the statistical conditional association between style information and labels drastically different from that in natural distribution. Thus, DNNs that fit the spurious correlation are vulnerable to the adversarial distribution. Inspired by the observation, we propose the adversarial distribution alignment method to eliminate the difference between the natural distribution and the adversarial distribution. Extensive experiments demonstrate the efficacy of the proposed method. Our method can be seen as the first attempt to leverage causality for mitigating adversarial vulnerability.","Title: AKG: automatic kernel generation for neural processing units using polyhedral transformations\nYear: 2021\n\nAbstract: ABSTRACTExisting tensor compilers have proven their effectiveness in deploying deep neural networks on general-purpose hardware like CPU and GPU, but optimizing for neural processing units (NPUs) is still challenging due to the heterogeneous compute units and complicated memory hierarchy. In this paper, we present AKG, a tensor compiler for NPUs. AKG first lowers the tensor expression language to a polyhedral representation, which is used to automate the memory management of NPUs. Unlike existing approaches that resort to manually written schedules, AKG leverages polyhedral schedulers to perform a much wider class of transformations, and extends the semantics of the polyhedral representation to combine complex tiling techniques and hierarchical fusion strategies. We also implement the domain-specific optimization of convolution in AKG. Moreover, to achieve the optimal performance, we introduce complementary optimizations in code generation, which is followed by an auto-tuner. We conduct extensive experiments on benchmarks ranging from single operators to end-to-end networks. The experimental results show that AKG can obtain superior performance to both manual scheduling approaches and vendor provided libraries. We believe AKG will cast a light on the follow-up compiler works on NPUs.","Title: Region-aware network: Model human’s Top-Down visual perception mechanism for crowd counting\nYear: 2022\n\nAbstract: Background noise and scale variation are common problems that have been long recognized in crowd counting. Humans glance at a crowd image and instantly know the approximate number of human and where they are through attention the crowd regions and the congestion degree of crowd regions with a global receptive field. Hence, in this paper, we propose a novel feedback network with Region-Aware block called RANet by modeling human’s Top-Down visual perception mechanism. Firstly, we introduce a feedback architecture to generate priority maps that provide prior about candidate crowd regions in input images. The prior enables the RANet pay more attention to crowd regions. Then we design Region-Aware block that could adaptively encode the contextual information into input images through global receptive field. More specifically, we scan the whole input images and its priority maps in the form of column vector to obtain a relevance matrix estimating their similarity. The relevance matrix obtained would be utilized to build global relationships between pixels. Our method outperforms state-of-the-art crowd counting methods on several public datasets.","Title: Progressive Open-Domain Response Generation with Multiple Controllable Attributes.\nYear: 2021\n\nAbstract: It is desirable to include more controllable attributes to enhance the diversity of generated responses in open-domain dialogue systems. However, existing methods can generate responses with only one controllable attribute or lack a flexible way to generate them with multiple controllable attributes. In this paper, we propose a Progressively trained Hierarchical Encoder-Decoder (PHED) to tackle this task. More specifically, PHED deploys Conditional Variational AutoEncoder (CVAE) on Transformer to include one aspect of attributes at one stage. A vital characteristic of the CVAE is to separate the latent variables at each stage into two types: a global variable capturing the common semantic features and a specific variable absorbing the attribute information at that stage. PHED then couples the CVAE latent variables with the Transformer encoder and is trained by minimizing a newly derived ELBO and controlled losses to produce the next stage's input and produce responses as required. Finally, we conduct extensive evaluations to show that PHED significantly outperforms the state-of-the-art neural generation models and produces more diverse responses as expected.","Title: SKIP ATTENTION GAN FOR REMOTE SENSING IMAGE SYNTHESIS\nYear: 2021\n\nAbstract: High-quality remote sensing images are difficult to obtain due to limited conditions and high cost for data acquisition. With the development of machine vision and deep learning, some image generation methods (e.g., GANs) are introduced into this field, but it's still hard to generate images with good texture details and structural dependencies. We establish Skip Attention Mechanism to deal with this problem, which learns dependencies between local points on low-resolution feature maps, and then upsample the attention map and combine it with high-resolution feature maps. With this method, long-range dependencies learned from low-resolution are used for generating remote sensing images with more structural details. We name this method as Skip Attention GAN, which is the first method applying cross-scale attention mechanism for unsupervised remote sensing image generation. Experiments show that our method outperforms previous methods under several metrics. Visual and ablation results of attention layers show that Skip Attention has learned long-distance structural dependencies between similar targets.","Title: AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning\nYear: 2022\n\nAbstract: Most approaches in reinforcement learning (RL) are data-hungry and specific to fixed environments. In this paper, we propose a principled framework for adaptive RL, called AdaRL, that adapts reliably to changes across domains. Specifically, we construct a generative environment model for the structural relationships among variables in the system and embed the changes in a compact way, which provides a clear and interpretable picture for locating what and where the changes are and how to adapt. Based on the environment model, we characterize a minimal set of representations, including both domain-specific factors and domain-shared state representations, that suffice for reliable and low-cost transfer. Moreover, we show that by explicitly leveraging a compact representation to encode changes, we can adapt the policy with only a few samples without further policy optimization in the target domain. We illustrate the efficacy of AdaRL through a series of experiments that allow for changes in different components of Cartpole and Atari games.","Title: A Deep Imputation And Inference Framework For Estimating Personalized And Race-Specific Causal Effects Of Genomic Alterations On Psa\nYear: 2021\n\nAbstract: Prostate Specific Antigen (PSA) level in the serum is one of the most widely used markers in monitoring prostate cancer (PCa) progression, treatment response, and disease relapse. Although significant efforts have been taken to analyze various socioeconomic and cultural factors that contribute to the racial disparities in PCa, limited research has been performed to quantitatively understand how and to what extent molecular alterations may impact differential PSA levels present at varied tumor status between African-American and European-American men. Moreover, missing values among patients add another layer of difficulty in precisely inferring their outcomes. In light of these issues, we propose a data-driven, deep learning-based imputation and inference framework (DIIF). DIIF seamlessly encapsulates two modules: an imputation module driven by a regularized deep autoencoder for imputing critical missing information and an inference module in which two deep variational autoencoders are coupled with a graphical inference model to quantify the personalized and race-specific causal effects. Large-scale empirical studies on the independent sub-cohorts of The Cancer Genome Atlas (TCGA) PCa patients demonstrate the effectiveness of DIIF. We further found that somatic mutations in TP53, ATM, PTEN, FOXA1, and PIK3CA are statistically significant genomic factors that may explain the racial disparities in different PCa features characterized by PSA.","Title: Echo state network-based online optimal control for discrete-time nonlinear systems\nYear: 2021\n\nAbstract: This paper investigates the online optimal control problem of discrete-time nonlinear systems using echo state network (ESN)-based adaptive dynamic programming (ADP) method. An online iterative learning algorithm is proposed to solve the partial differential Hamilton-Jacobi-Bellman (HJB) equation in real time. A novel neural networks (NN) critic-actor architecture is presented using two ESNs to implement the ADP method. Then, two online learning laws of the output weights are designed for searching the optimal cost function and control policy. The stability of system and output weights is analysed using Lyapunov approach. Three simulations are given to show the feasibility and effectiveness of the designed algorithm. (C) 2021 Elsevier Inc. All rights reserved.","Title: DAE-GAN - Dynamic Aspect-aware GAN for Text-to-Image Synthesis.\nYear: 2021\n\nAbstract: Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency. Previous methods usually generate an initial image with sentence embedding and then refine it with fine-grained word embedding. Despite the significant progress, the 'aspect' information (e.g., red eyes) contained in the text, referring to several words rather than a word that depicts 'a particular part or feature of something', is often ignored, which is highly helpful for synthesizing image details. How to make better utilization of aspect information in text-to-image synthesis still remains an unresolved challenge. To address this problem, in this paper, we propose a Dynamic Aspect-awarE GAN (DAE-GAN) that represents text information comprehensively from multiple granularities, including sentence-level, word-level, and aspect-level. Moreover, inspired by human learning behaviors, we develop a novel Aspect-aware Dynamic Re-drawer (ADR) for image refinement, in which an Attended Global Refinement (AGR) module and an Aspect-aware Local Refinement (ALR) module are alternately employed. AGR utilizes word-level embedding to globally enhance the previously generated image, while ALR dynamically employs aspect-level embedding to refine image details from a local perspective. Finally, a corresponding matching loss function is designed to ensure the text-image semantic consistency at different levels. Extensive experiments on two well-studied and publicly available datasets (i.e., CUB-200 and COCO) demonstrate the superiority and rationality of our method.","Title: A New 32-Day Average-Difference Method For Calculating Inter-Sensor Calibration Radiometric Biases Between Snpp And Noaa-20 Instruments Within Icvs Framework\nYear: 2021\n\nAbstract: Two existing double-difference (DD) methods, using either a 3rdSensor or Radiative Transfer Modeling (RTM) as a transfer, are applicable primarily for limited regions and channels, and, thus critical in capturing inter-sensor calibration radiometric bias features. A supplementary method is also desirable for estimating inter-sensor calibration biases at the window and lower sounding channels where the DD methods have non-negligible errors. In this study, using the Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System (JPSS)-1 (alias NOAA-20) as an example, we present a new inter-sensor bias statistical method by calculating 32-day averaged differences (32D-AD) of radiometric measurements between the same instrument onboard two satellites. In the new method, a quality control (QC) scheme using one-sigma (for radiance difference), or two-sigma (for radiance) thresholds are established to remove outliers that are significantly affected by diurnal biases within the 32-day temporal coverage. The performance of the method is assessed by applying it to estimate inter-sensor calibration radiometric biases for four instruments onboard SNPP and NOAA-20, i.e., Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS), Nadir Profiler (NP) within the Ozone Mapping and Profiler Suite (OMPS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Our analyses indicate that the globally-averaged inter-sensor differences using the 32D-AD method agree with those using the existing DD methods for available channels, with margins partially due to remaining diurnal errors. In addition, the new method shows its capability in assessing zonal mean features of inter-sensor calibration biases at upper sounding channels. It also detects the solar intrusion anomaly occurring on NOAA-20 OMPS NP at wavelengths below 300 nm over the Northern Hemisphere. Currently, the new method is being operationally adopted to monitor the long-term trends of (globally-averaged) inter-sensor calibration radiometric biases at all channels for the above sensors in the Integrated Calibration/Validation System (ICVS). It is valuable in demonstrating the quality consistencies of the SDR data at the four instruments between SNPP and NOAA-20 in long-term statistics. The methodology is also applicable for other POES cross-sensor calibration bias assessments with minor changes.","Title: Instance-dependent Label-noise Learning under a Structural Causal Model.\nYear: 2021\n\nAbstract: Use the \"Report an Issue\" link to request a name change.","Title: Multi-Attribute Group Decision-Making Method Based On Time-Series Q-Rung Orthopair Fuzzy Sets\nYear: 2021\n\nAbstract: The q-rung orthopair fuzzy set is a powerful and useful tool to deal with uncertainty, but in actual decision-making process, decision-makers are usually required to analyze the actual problem dynamically. Therefore in this paper, we consider the time-series q-rung orthopair fuzzy decision making First, we introduce the new cosine similarity measure of q-ROFS which combines the cosine similarity measure and the Euclidean distance measure. Then, we combine the advantages of projection method and grey correlation degree, establishing the nonlinear programming model to calculate the weights of attributes. Furthermore, we use the exponential decay model to get the weights formulas of q-ROFS at different times. Then we replace the distance function with grey relational projection and extend TOPSIS method. Based on these, we propose a new MAGDM approach to deal with time-series q-rung orthopair fuzzy problem not only from the point of view of geometry but also from the point of view of algebra. Finally, we give a practical example to illustrate effectiveness and feasibility of the new method.","Title: MSIPA: Multi-Scale Interval Pattern-Aware Network for ICU Transfer Prediction\nYear: 2022\n\nAbstract: AbstractAccurate prediction of patients’ ICU transfer events is of great significance for improving ICU treatment efficiency. ICU transition prediction task based on Electronic Health Records (EHR) is a temporal mining task like many other health informatics mining tasks. In the EHR-based temporal mining task, existing approaches are usually unable to mine and exploit patterns used to improve model performance. This article proposes a network based on Interval Pattern-Aware, Multi-Scale Interval Pattern-Aware (MSIPA) network. MSIPA mines different interval patterns in temporal EHR data according to the short, medium, and long intervals. MSIPA utilizes the Scaled Dot-Product Attention mechanism to query the contexts corresponding to the three scale patterns. Furthermore, Transformer will use all three types of contextual information simultaneously for ICU transfer prediction. Extensive experiments on real-world data demonstrate that an MSIPA network outperforms state-of-the-art methods.","Title: Unaligned Image-to-Image Translation by Learning to Reweight.\nYear: 2021\n\nAbstract: Unsupervised image-to-image translation aims at learning the mapping from the source to target domain without using paired images for training. An essential yet restrictive assumption for unsupervised image translation is that the two domains are aligned, e.g., for the selfie2anime task, the anime (selfie) domain must contain only anime (selfie) face images that can be translated to some images in the other domain. Collecting aligned domains can be laborious and needs lots of attention. In this paper, we consider the task of image translation between two unaligned domains, which may arise for various possible reasons. To solve this problem, we propose to select images based on importance reweighting and develop a method to learn the weights and perform translation simultaneously and automatically. We compare the proposed method with state-of-the-art image translation approaches and present qualitative and quantitative results on different tasks with unaligned domains. Extensive empirical evidence demonstrates the usefulness of the proposed problem formulation and the superiority of our method.","Title: Eigenvector-based centralities for multilayer temporal networks under the framework of tensor computation\nYear: 2021\n\nAbstract: •Using sixth-order tensor to represent multilayer temporal networks.•Cosine similarity is proposed to measure interactions between different layers.•Multilayer temporal eigenvector and PageRank centralities are proposed.•The convergence of the proposed iterative algorithms are established.•The proposed two centrality measures are applied to three networks.","Title: Variable Spectral Segmentation Empirical Wavelet Transform For Noisy Signal Processing\nYear: 2021\n\nAbstract: Empirical wavelet transform (EWT) can successfully decompose a smooth or noise-free simulated signal into several components. This method has encountered difficulties in processing simulated signals containing noise and in applications. In order to improve the shortcomings of EWT and expand its application, this paper proposes a variable spectral segmentation EWT (VEWT) associated with the trend of spectral fluctuation. Different from scale-space representation and other optimized direct segmentation methods, this paper proposes a selective segmentation method. The extreme points of Multi-taper power spectral density (MPSD) are used to estimate the modes. An extended algorithm is proposed on the basis of Levenberg-Marquardt-Fletcher, which uses the extreme points and positions of MPSD to calculate the bandwidth corresponding to each mode. The information in each frequency band will be determined as the final mode. In the process of loop extraction, a set of boundaries associated with the fluctuations of the spectral will be obtained. The proposed method is more advantageous for the decomposition of signals containing noise. Simulated signals are used to verify the effectiveness of the proposed method. In addition, the MIT-BIH Arrhythmia Database is used to verify the applicability of the proposed method. (C) 2021 Elsevier Inc. All rights reserved.","Title: Design and application of facial expression analysis system in empathy ability of children with autism spectrum disorder\nYear: 2021\n\nAbstract: Empathy is an important social ability in the early childhood development. One of the significant characteristics of children with autism spectrum disorder (ASD) is their lack of empathy, which makes it difficult for them to feel and understand other people’s emotions and to judge other people’s behavioral intentions, leading to social disorders. This research designs and implements a facial expre...","Title: Learning Temporally Causal Latent Processes from General Temporal Data.\nYear: 2022\n\nAbstract: Our goal is to recover time-delayed latent causal variables and identify their relations from measured temporal data. Estimating causally-related latent variables from observations is particularly challenging as the latent variables are not uniquely recoverable in the most general case. In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be identified from their nonlinear mixtures. We propose LEAP, a theoretically-grounded architecture that extends Variational Autoencoders (VAEs) by enforcing our conditions through proper constraints in causal process prior. Experimental results on various data sets demonstrate that temporally causal latent processes are reliably identified from observed variables under different dependency structures and that our approach considerably outperforms baselines that do not leverage history or nonstationarity information. This is one of the first works that successfully recover time-delayed latent processes from nonlinear mixtures without using sparsity or minimality assumptions.","Title: Action-Sufficient State Representation Learning for Control with Structural Constraints.\nYear: 2022\n\nAbstract: Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks. In this paper, we focus on partially observable environments and propose to learn a minimal set of state representations that capture sufficient information for decision-making, termed Action-Sufficient state Representations (ASRs). We build a generative environment model for the structural relationships among variables in the system and present a principled way to characterize ASRs based on structural constraints and the goal of maximizing cumulative reward in policy learning. We then develop a structured sequential Variational Auto-Encoder to estimate the environment model and extract ASRs. Our empirical results on CarRacing and VizDoom demonstrate a clear advantage of learning and using ASRs for policy learning. Moreover, the estimated environment model and ASRs allow learning behaviors from imagined outcomes in the compact latent space to improve sample efficiency.","Title: A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction\nYear: 2021\n\nAbstract: A fingerprint-based localization system is an economic way to solve an indoor positioning problem. However, the traditional off-line fingerprint collection stage is a time-consuming and laborious process which limits the use of fingerprint-based localization systems. In this paper, based on ubiquitous Wireless Fidelity (Wi-Fi) equipment and a low-cost Ultra-Wideband (UWB) ranging system (with only one UWB anchor), a ready-to-use indoor localization system is proposed to realize long-term and high-accuracy indoor positioning. More specifically, in this system, it is divided into two stages: (1) an initial stage, and (2) a positioning stage. In the initial stage, an Inertial Measure Unit (IMU) is used to calculate the position using Pedestrian Dead Reckon (PDR) algorithm within a preset number of steps, and the location-related fingerprints are collected to train a Convolutional Neural Network (CNN) regression model; simultaneously, in order to make the UWB ranging system adapt to the Non-Line-of-Sight (NLoS) environment, the increments of acceleration and angular velocity in IMU and the increments of single UWB ranging measures are correlated to pre-train a Supported Vector Regression (SVR). After reaching the threshold of time or step number, the system is changed into a positioning stage, and the CNN predicts the position calibrated by corrected UWB ranging. At last, a series of practical experiments are conducted in the real environment; the experiment results show that, due to the corrected UWB ranging measures calibrating the CNN parameters in every positioning period, this system has stable localization results in a comparative long-term range. Additionally, it has the advantages of stability, low cost, anti-noise, etc.","Title: Towards Federated Bayesian Network Structure Learning with Continuous Optimization\nYear: 2022\n\nAbstract: Traditionally, Bayesian network structure learning is often carried out at a central site, in which all data is gathered. However, in practice, data may be distributed across different parties (e.g., companies, devices) who intend to collectively learn a Bayesian network, but are not willing to disclose information related to their data owing to privacy or security concerns. In this work, we present a federated learning approach to estimate the structure of Bayesian network from data that is horizontally partitioned across different parties. We develop a distributed structure learning method based on continuous optimization, using the alternating direction method of multipliers (ADMM), such that only the model parameters have to be exchanged during the optimization process. We demonstrate the flexibility of our approach by adopting it for both linear and nonlinear cases. Experimental results on synthetic and real datasets show that it achieves an improved performance over the other methods, especially when there is a relatively large number of clients and each has a limited sample size.","Title: State Estimation for Hybrid Wheeled-Legged Robots Performing Mobile Manipulation Tasks\nYear: 2021\n\nAbstract: This paper introduces a general state estimation framework fusing multiple sensor information for hybrid wheeled-legged robots performing mobile manipulation tasks. At the core of the state estimator is a novel unified odometry for hybrid locomotion which can seamlessly maintain tracking and has no need to switch between stepping and rolling modes. To the best of our knowledge, the proposed odometry is the first work in this area. It is calculated based on the robot kinematics and instantaneous contact points of wheels with sensor inputs from IMU, joint encoders, joint torque sensors estimating wheel contact status, as well as RGB-D camera detecting geometric features of the terrain (e.g. elevation and surface normal vector). Subsequently, the odometry output is utilized as the motion model of a 3D Lidar map-based Monte Carlo Localization module for drift-free state estimation. As part of the framework, visual localization is integrated to provide high precision guidance for the robot movement relative to an object of interest. The proposed approach was verified thoroughly by two experiments conducted on the Pholus robot with OptiTrack measurements as ground truth.","Title: Multi-objective Privacy-preserving Text Representation Learning\nYear: 2021\n\nAbstract: BSTRACTPrivate information can either take the form of key phrases that are explicitly contained in the text or be implicit. For example, demographic information about the author of a text can be predicted with above-chance accuracy from linguistic cues in the text itself. Letting alone its explicitness, some of the private information correlates with the output labels and therefore can be learned by a neural network. In such a case, there is a tradeoff between the utility of the representation (measured by the accuracy of the classification network) and its privacy. This problem is inherently a multi-objective problem because these two objectives may conflict, necessitating a trade-off. Thus, we explicitly cast this problem as multi-objective optimization (MOO) with the overall objective of finding a Pareto stationary solution. We, therefore, propose a multiple-gradient descent algorithm (MGDA) that enables the efficient application of the Frank-Wolfe algorithm [10] using the line search. Experimental results on sentiment analysis and part-of-speech (POS) tagging show that MGDA produces higher-performing models than most recent proxy objective approaches, and performs as well as single objective baselines.","Title: Non-Nested Estimators For The Central Moments Of A Conditional Expectation And Their Convergence Properties\nYear: 2021\n\nAbstract: The central moments of a conditional expectation exhibit important statistical properties, and the variance is used in many important applications. This paper proposes three non-nested estimators for the central moments that guarantee the asymptotic mean squared error (MSE) of order Gamma(-1), where Gamma represents the total computational budget. Moreover, the central limit theorems are provided, along with the confidence intervals with a convergence rate of Gamma(-1/2). Examples are used to verify the theoretical results and compare the finite sample performance. (C) 2021 Elsevier B.V. All rights reserved.","Title: Compressed Self-Attention For Deep Metric Learning\nYear: 2020\n\nAbstract: In this paper, we aim to enhance self-attention (SA) mechanism for deep metric learning in visual perception, by capturing richer contextual dependencies in visual data. To this end, we propose a novel module, named compressed self-attention (CSA), which significantly reduces the computation and memory cost with a neglectable decrease in accuracy with respect to the original SA mechanism, thanks to the following two characteristics: i) it only needs to compute a small number of base attention maps for a small number of base feature vectors; and ii) the output at each spatial location can be simply obtained by an adaptive weighted average of the outputs calculated from the base attention maps. The high computational efficiency of CSA enables the application to high-resolution shallow layers in convolutional neural networks with little additional cost. In addition, CSA makes it practical to further partition the feature maps into groups along the channel dimension and compute attention maps for features in each group separately, thus increasing the diversity of long-range dependencies and accordingly boosting the accuracy. We evaluate the performance of CSA via extensive experiments on two metric learning tasks: person re-identification and local descriptor learning. Qualitative and quantitative comparisons with latest methods demonstrate the significance of CSA in this topic.","Title: Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases.\nYear: 2021\n\nAbstract: In causal discovery, linear non-Gaussian acyclic models (LiNGAMs) have been studied extensively. While the causally sufficient case is well understood, in many real problems the observed variables are not causally related. Rather, they are generated by latent variables, such as confounders and mediators, which may themselves be causally related. Existing results on the identification of the causal structure among the latent variables often require very strong graphical assumptions. In this paper, we consider partially observed linear models with either non-Gaussian or heterogeneous errors. In that case we give two graphical conditions which are necessary for identification of the causal structure. These conditions are closely related to sparsity of the causal edges. Together with one additional condition on the coefficients, which holds generically for any graph, the two graphical conditions are also sufficient for identifiability. These new conditions can be satisfied even when there is a large number of latent variables. We demonstrate the validity of our results on synthetic data.","Title: Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions.\nYear: 2021\n\nAbstract: Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness. However, the assumption can be approximately violated in many ways, leading to sub-optimal solutions. Although there is a line of research in Bayesian network structure learning that focuses on weakening the assumption, such as exact search methods with well-defined score functions, they do not scale well to large graphs. In this work, we introduce several strategies to improve the scalability of exact score-based methods in the linear Gaussian setting. In particular, we develop a super-structure estimation method based on the support of inverse covariance matrix which requires assumptions that are strictly weaker than faithfulness, and apply it to restrict the search space of exact search. We also propose a local search strategy that performs exact search on the local clusters formed by each variable and its neighbors within two hops in the super-structure. Numerical experiments validate the efficacy of the proposed procedure, and demonstrate that it scales up to hundreds of nodes with a high accuracy.","Title: Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?\nYear: 2021\n\nAbstract: Use the \"Report an Issue\" link to request a name change.","Title: Brain Functional Connectivity Analysis via Graphical Deep Learning\nYear: 2022\n\nAbstract: Objective: Graphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and complex relationships between different brain regions. Method: In this work, a graph convolutional network (GCN) based framework is propo...","Title: GLaM: Efficient Scaling of Language Models with Mixture-of-Experts.\nYear: 2022\n\nAbstract: Scaling language models with more data, compute and parameters has driven significant progress in natural language processing. For example, thanks to scaling, GPT-3 was able to achieve strong results on in-context learning tasks. However, training these large dense models requires significant amounts of computing resources. In this paper, we propose and develop a family of language models named \\glam (\\textbf{G}eneralist \\textbf{La}nguage \\textbf{M}odel), which uses a sparsely activated mixture-of-experts architecture to scale the model capacity while also incurring substantially less training cost compared to dense variants. The largest \\glam has 1.2 trillion parameters, which is approximately 7x larger than GPT-3. It consumes only 1/3 of the energy used to train GPT-3 and requires half of the computation flops for inference, while still achieving better overall fewshot performance across 29 NLP tasks.","Title: AddrArmor: An Address-based Runtime Code-reuse Attack Mitigation for Shared Objects at the Binary-level\nYear: 2021\n\nAbstract: The widespread adoption of DEP has made most modern attacks follow the same general steps: Attackers try to construct code-reuse attacks by using vulnerable indirect branch instructions in shared objects after successful exploits on memory vulnerabilities. In response to code-reuse attacks, researchers have proposed a large number of defenses. However, most of them require access to source code an...","Title: Indoor Location of UWB-TDOA Based on Genetic Algorithm\nYear: 2021\n\nAbstract: With the rapid change of life style, people have higher and higher requirements for high-tech, from outdoor positioning to indoor environment with higher accuracy. In this paper, based on the time difference of arrival (TDOA) location technology, a robust genetic algorithm with good optimization effect is proposed, and the ranging experiment is completed in the environment with UWB signal, which c...","Title: Recognition and Location Based on Fusion of FAST Algorithm and SIFT Algorithm\nYear: 2021\n\nAbstract: SLAM technology is the core technology of robot positioning and navigation, and its application in indoor positioning is a new popular trend. Starting from the application of SLAM in indoor positioning, this paper focuses on the performance improvement of SLAM front-end. This paper mainly analyzes the front-end feature extraction algorithm of vision SLAM technology, and proposes a hybrid algorithm...","Title: Hybrid Location Algorithm of TDOA / AOA Based on Extended Kalman Filter\nYear: 2021\n\nAbstract: with the development of wireless communication technology, in order to achieve high-precision positioning in indoor environment, this paper proposes a TDOA / AOA hybrid positioning algorithm based on extended Kalman filter. Because NLOS (non line of sight) error is the main error of the system, this paper uses Kalman filter to eliminate NLOS error, so as to reduce the TOA error of NLOS in indoor U...","Title: Indoor Sound Source Localization Algorithm Based on BP Neural Network\nYear: 2021\n\nAbstract: Sound source localization is an important part of the perception of things around. Sound source localization can overcome the shortcomings of visual localization, and can also locate the invisible place. The application of sound source localization in indoor is the latest trend. Starting from the application of deep learning to indoor sound source localization, this paper focuses on the analysis a...","Title: Gated Graph Neural Networks (GG-NNs) for Abstractive Multi-Comment Summarization\nYear: 2021\n\nAbstract: Summarization of long sequences into a concise statement is a core problem in natural language processing, which requires a non-trivial understanding of the weakly structured text. Therefore, integrating crowdsourced multiple users' comments into a concise summary is even harder because (1) it requires transferring the weakly structured comments to structured knowledge. Besides, (2) the users comm...","Title: CSRDA: Cost-sensitive Regularized Dual Averaging for Handling Imbalanced and High-dimensional Streaming Data\nYear: 2021\n\nAbstract: Class-imbalance is one of the most challenging problems in online learning due to its impact on the prediction capability of data stream mining models. Most existing approaches for online learning lack an effective mechanism to handle high-dimensional streaming data with skewed class distributions, resulting in insufficient model interpretation and deterioration of online performance. In this pape...","Title: Optimizing smart grid operations from the demand side\nYear: 2021\n\nAbstract: As demand for electricity grows in China, the existing power grid is coming under increasing pressure. Expansion of power generation and delivery capacities across the country requires years of planning and construction. In the meantime, to ensure safe operation of the power grid, it is important to coordinate and optimize the demand side usage. In this paper, we report on our experience deploying an artificial intelligence (AI)-empowered demand-side management platform - the Power Intelligent Decision Support (PIDS) platform - in Shandong Province, China. It consists of three main components: 1) short-term power consumption gap prediction, 2) fine-grained Demand Response (DR) with optimal power adjustment planning, and 3) Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. PIDS has been deployed since August 2018. It is helping over 400 companies optimize their power usage through DR, while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS due to forced shutdown has been reduced from 16% to 0.56%.","Title: Optimal Transport for Causal Discovery\nYear: 2022\n\nAbstract: Approaches based on Functional Causal Models (FCMs) have been proposed to determine causal direction between two variables, by properly restricting model classes; however, their performance is sensitive to the model assumptions, which makes it difficult for practitioners to use. In this paper, we provide a novel dynamical-system view of FCMs and propose a new framework for identifying causal direction in the bivariate case. We first show the connection between FCMs and optimal transport, and then study optimal transport under the constraints of FCMs. Furthermore, by exploiting the dynamical interpretation of optimal transport under the FCM constraints, we determine the corresponding underlying dynamical process of the static cause-effect pair data under the least action principle. It provides a new dimension for describing static causal discovery tasks, while enjoying more freedom for modeling the quantitative causal influences. In particular, we show that Additive Noise Models (ANMs) correspond to volume-preserving pressureless flows. Consequently, based on their velocity field divergence, we introduce a criterion to determine causal direction. With this criterion, we propose a novel optimal transport-based algorithm for ANMs which is robust to the choice of models and extend it to post-noninear models. Our method demonstrated state-of-the-art results on both synthetic and causal discovery benchmark datasets.","Title: Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models\nYear: 2021\n\nAbstract: AbstractIdentification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class does not allow any confounding or intermediate variables between a cause pair–even if each direct causal relation follows this model. However, omitting the latent causal variables is frequently encountered in practice. After the omission, the model does not necessarily follow the model constraints. As a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a confounding cascade nonlinear additive noise model to represent such causal influences–each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured confounding and intermediate variables, from data under the variational auto-encoder framework. Our theoretical results show that with our model, the causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.","Title: HGATs: hierarchical graph attention networks for multiple comments integration\nYear: 2021\n\nAbstract: ABSTRACTFor decades, research in natural language processing (NLP) has focused on summarization. Sequence-to-sequence models for abstractive summarization have been studied extensively, yet generated summaries commonly suffer from fabricated content, and are often found to be near-extractive. We argue that, to address these issues, summarizers need to acquire the co-references that form multiple types of relations over input sentences, e.g., 1-to-N, N-to-1, and N-to-N relations, since the structured knowledge for text usually appears on these relations. By allowing the decoder to pay different attention to the input sentences for the same entity at different generation states, the structured graph representations generate more informative summaries. In this paper, we propose a hierarchical graph attention networks (HGATs) for abstractive summarization with a topic-sensitive PageRank augmented graph. Specifically, we utilize dual decoders, a sequential sentence decoder, and a graph-structured decoder (which are built hierarchically) to maintain the global context and local characteristics of entities, complementing each other. We further design a greedy heuristic to extract salient users' comments while avoiding redundancy to drive a model to better capture entity interactions. Our experimental results show that our models produce significantly higher ROUGE scores than variants without graph-based attention on both SSECIF and CNN/Daily Mail (CNN/DM) datasets.","Title: Conditional Contrastive Learning with Kernel\nYear: 2022\n\nAbstract: Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables. Fair contrastive learning constructs negative pairs, for example, from the same gender (conditioning on sensitive information), which in turn reduces undesirable information from the learned representations; weakly supervised contrastive learning constructs positive pairs with similar annotative attributes (conditioning on auxiliary information), which in turn are incorporated into the representations. Although conditional contrastive learning enables many applications, the conditional sampling procedure can be challenging if we cannot obtain sufficient data pairs for some values of the conditioning variable. This paper presents Conditional Contrastive Learning with Kernel (CCL-K) that converts existing conditional contrastive objectives into alternative forms that mitigate the insufficient data problem. Instead of sampling data according to the value of the conditioning variable, CCL-K uses the Kernel Conditional Embedding Operator that samples data from all available data and assigns weights to each sampled data given the kernel similarity between the values of the conditioning variable. We conduct experiments using weakly supervised, fair, and hard negatives contrastive learning, showing CCL-K outperforms state-of-the-art baselines.","Title: HyperKRP: A Kernel Runtime Security Architecture with A Tiny Hypervisor on Commodity Hardware\nYear: 2021\n\nAbstract: The large body of kernel code provides broad attack surfaces to exploitable bugs or misconfigurations. Current mitigations are difficult to be integrated together or have a non-trivial performance or code size impact. Thus, systematical protection for the kernel is of critical importance and is required. In this paper, we propose a kernel runtime security architecture, called HyperKRP, to provide systematical protection for kernel code, critical kernel data, and efficient kernel page tables. We have implemented a fully working prototype for a recent Linux kernel running on the Intel x86 processor. Our prototype is compromised of three protection engines based on a small size hypervisor. The evaluation shows that HyperKRP effectively ensures kernel runtime security with acceptable overhead.","Title: Invariant Action Effect Model for Reinforcement Learning.\nYear: 2022\n\nAbstract: Good representations can help RL agents perform concise modeling of their surroundings, and thus support effective decision-making in complex environments. Previous methods learn good representations by imposing extra constraints on dynamics. However, in the causal perspective, the causation between the action and its effect is not fully considered in those methods, which leads to the ignorance of the underlying relations among the action effects on the transitions. Based on the intuition that the same action always causes similar effects among different states, we induce such causation by taking the invariance of action effects among states as the relation. By explicitly utilizing such invariance, in this paper, we show that a better representation can be learned and potentially improves the sample efficiency and the generalization ability of the learned policy. We propose Invariant Action Effect Model (IAEM) to capture the invariance in action effects, where the effect of an action is represented as the residual of representations from neighboring states. IAEM is composed of two parts: (1) a new contrastive-based loss to capture the underlying invariance of action effects; (2) an individual action effect and provides a self-adapted weighting strategy to tackle the corner cases where the invariance does not hold. The extensive experiments on two benchmarks, i.e. Grid-World and Atari, show that the representations learned by IAEM preserve the invariance of action effects. Moreover, with the invariant action effect, IAEM can accelerate the learning process by 1.6x, rapidly generalize to new environments by fine-tuning on a few components, and outperform other dynamics-based representation methods by 1.4x in limited steps.","Title: Show Your Faith: Cross-Modal Confidence-Aware Network for Image-Text Matching.\nYear: 2022\n\nAbstract: Image-text matching bridges vision and language, which is a crucial task in the field of multi-modal intelligence. The key challenge lies in how to measure image-text relevance accurately as matching evidence. Most existing works aggregate the local semantic similarities of matched region-word pairs as the overall relevance, and they typically assume that the matched pairs are equally reliable. However, although a region-word pair is locally matched across modalities, it may be inconsistent/unreliable from the global perspective of image-text, resulting in inaccurate relevance measurement. In this paper, we propose a novel Cross-Modal Confidence-Aware Network to infer the matching confidence that indicates the reliability of matched region-word pairs, which is combined with the local semantic similarities to refine the relevance measurement. Specifically, we first calculate the matching confidence via the relevance between the semantic of image regions and the complete described semantic in the image, with the text as a bridge. Further, to richly express the region semantics, we extend the region to its visual context in the image. Then, local semantic similarities are weighted with the inferred confidence to filter out unreliable matched pairs in aggregating. Comprehensive experiments show that our method achieves state-of-the-art performance on benchmarks Flickr30K and MSCOCO.","Title: Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery.\nYear: 2022\n\nAbstract: Recently, many regression based conditional independence (CI) test methods have been proposed to solve the problem of causal discovery. These methods provide alternatives to test CI by first removing the information of the controlling set from the two target variables, and then testing the independence between the corresponding residuals Res1 and Res2. When the residuals are linearly uncorrelated, the independence test between them is nontrivial. With the ability to calculate inner product in high-dimensional space, kernel-based methods are usually used to achieve this goal, but still consume considerable time. In this paper, we investigate the independence between two linear combinations under linear non-Gaussian structural equation model. We show that the dependence between the two residuals can be captured by the difference between the similarity of (Res1, Res2) and that of (Res1, Res3) (Res3 is generated by random permutation) in high-dimensional space. With this result, we design a new method called SCIT for CI test, where permutation test is performed to control Type I error rate. The proposed method is simpler yet more efficient and effective than the existing ones. When applied to causal discovery, the proposed method outperforms the counterparts in terms of both speed and Type II error rate, especially in the case of small sample size, which is validated by our extensive experiments on various datasets.","Title: Identification of Linear Latent Variable Model with Arbitrary Distribution.\nYear: 2022\n\nAbstract: An important problem across multiple disciplines is to infer and understand meaningful latent variables. One strategy commonly used is to model the measured variables in terms of the latent variables under suitable assumptions on the connectivity from the latents to the measured (known as measurement model). Furthermore, it might be even more interesting to discover the causal relations among the latent variables (known as structural model). Recently, some methods have been proposed to estimate the structural model by assuming that the noise terms in the measured and latent variables are non-Gaussian. However, they are not suitable when some of the noise terms become Gaussian. To bridge this gap, we investigate the problem of identification of the structural model with arbitrary noise distributions. We provide necessary and sufficient condition under which the structural model is identifiable: it is identifiable iff for each pair of adjacent latent variables Lx, Ly, (1) at least one of Lx and Ly has non-Gaussian noise, or (2) at least one of them has a non-Gaussian ancestor and is not d-separated from the non-Gaussian component of this ancestor by the common causes of Lx and Ly. This identifiability result relaxes the non-Gaussianity requirements to only a (hopefully small) subset of variables, and accordingly elegantly extends the application scope of the structural model. Based on the above identifiability result, we further propose a practical algorithm to learn the structural model. We verify the correctness of the identifiability result and the effectiveness of the proposed method through empirical studies.","Title: Research on the Algorithmic Layout optimization of Prefabricated Building’s Exterior Wall Panels\nYear: 2021\n\nAbstract: This research was carried out to solve the problem of inefficiency and material waste caused by the lack of optimization in the layout design and cutting of the exterior wall panels of prefabricated steel high-rise residential buildings in China. Combined with building performance simulation, it first studies the relationship between relevant factors in the process of exterior wall panel layout and establishes corresponding mathematical model. Then, genetic and heuristic algorithm program are used to find the optimal solution of the panel cutting methods, thereby improving work efficiency and reducing environment pollution.","Title: Attainability and Optimality: The Equalized Odds Fairness Revisited.\nYear: 2022\n\nAbstract: Fairness of machine learning algorithms has been of increasing interest. In order to suppress or eliminate discrimination in prediction, various notions as well as approaches have been proposed to impose fairness. Given a notion of fairness, an essential problem is then whether or not it can always be attained, even if with an unlimited amount of data. This issue is, however, not well addressed yet. In this paper, focusing on the Equalized Odds notion of fairness, we consider the attainability of this criterion and, furthermore, if it is attainable, the optimality of the prediction performance under various settings. In particular, for prediction performed by a deterministic function of input features, we give conditions under which Equalized Odds can hold true; if the stochastic prediction is acceptable, we show that under mild assumptions, fair predictors can always be derived. For classification, we further prove that compared to enforcing fairness by post-processing, one can always benefit from exploiting all available features during training and get potentially better prediction performance while remaining fair. Moreover, while stochastic prediction can attain Equalized Odds with theoretical guarantees, we also discuss its limitation and potential negative social impacts.","Title: A Drug Recommendation Model Based on Message Propagation and DDI Gating Mechanism\nYear: 2022\n\nAbstract: Drug recommendation task based on the deep learning model has been widely studied and applied in the health care field in recent years. However, the accuracy of drug recommendation models still needs to be improved. In addition, the existing recommendation models either give only one recommendation (however, there may be a variety of drug combination options in practice) or can not provide the confidence level of the recommended result. To fill these gaps, a Drug Recommendation model based on Message Propagation neural network (denoted as DRMP) is proposed in this paper. Then, the Drug-Drug Interaction (DDI) knowledge is introduced into the proposed model to reduce the DDI rate in recommended drugs. Finally, the proposed model is extended to Bayesian Neural Network (BNN) to realize multiple recommendations and give the confidence of each recommendation result, so as to provide richer information to help doctors make decisions. Experimental results on public data sets show that the proposed model is superior to the best existing models.","Title: A study on spinal level, length, and branch type of the inferior mesenteric artery and the position relationship between the inferior mesenteric artery, left colic artery, and inferior mesenteric vein\nYear: 2022\n\nAbstract: This study was aimed to explore the clinical application of dual-energy computed tomography (DECT) monoenergetic plus (mono+) imaging to evaluate anatomical variations in the inferior mesenteric artery (IMA). The clinical and imaging data of 212 patients who had undergone total abdominal DECT were retrospectively analyzed. The post-processing mono+ technique was used to obtain 40-keV single-level images in the arterial phase. Three-dimensional reconstruction was performed to evaluate the relationship between the IMA root position and the spinal level, IMA length, and IMA branch type, as well as the position of the left colic artery (LCA) and inferior mesenteric vein (IMV) at the IMA root level. The IMA root was located at the L3 level in 78.3% of cases and at the L2/L3 level in 3.3%. The highest vertebral level of IMA origin was L2 (4.2%), and the lowest was L4 (7.1%). The distance from the IMA root to the level of the sacral promontory was 99.58 ± 13.07 mm, which increased with the elevation of the IMA root at the spinal level. Of the patients, 53.8% demonstrated Type I IMA, 23.1% Type II, 20.7% Type III, and 2.4% Type IV. The length of the IMA varied from 13.6 to 66.0 mm. 77.3% of the IMAs belonged to Type A, the adjacent type, and 22.7% to Type B, the distant type. DECT mono+ can preoperatively evaluate the anatomical characteristics of the IMA and the positional relationship between the LCA and IMV at the IMA root level, which would help clinicians plan individualized surgery for patients.","Title: Validation of CrIS Radiometric Performance through Its Comparison to ABI\nYear: 2022\n\nAbstract: Radiometric intercomparison between satellite remote sensing instruments has become an increasingly common practice to monitor the stability and even the accuracy of their radiance products. The assessment also enables the evaluation of calibration improvements made to these products, as well as the identification and resolution of remaining calibration inadequacies. In this paper, the radiance products of the Cross-track Infrared Sounder (CrIS), an interferometer-based hyperspectral IR sounder in low Earth orbit (LEO), is compared with the level-1b (L1b) radiance products of the infrared (IR) bands of the Advanced Baseline Imager (ABI), an imaging radiometer in geostationary (GEO) orbit. Two CrIS instruments are currently in operation on S-NPP and NOAA-20 satellites, respectively, and two ABI instruments are in operation on GOES-16 and GOES-17 satellites, respectively. Radiometric intercomparisons are performed between each CrIS-ABI pair. An established procedure by GSICS for such GEO-LEO instrument comparison is principally followed to emulate the radiance of ABI IR bands from CrIS spectra of the collocated pixels to be compared with the actual ABI radiance. Results show that the long-term time series of CrIS-ABI radiance bias have been stable within 0.2 K for nearly all ABI IR bands within a spectral range from 3.7 mu m to 13.3 mu m, except those with known calibration issues. Miscellaneous calibration events that had occurred to either instrument and altered the biases are identified and explained. While the main goal of the work is to support the on-orbit Cal/Val of CrIS, including the future JPSS-2/3/4 CrIS, such observations can also be referenced to further improve the calibration of ABI.","Title: A Method for Extracting Fault Features Using Variable Multilevel Spectral Segmentation Framework and Harmonic Correlation Index\nYear: 2022\n\nAbstract: Adaptive intelligent fault diagnosis technology can improve the automatic status monitoring capability and the success rate of fault diagnosis. This article proposed an adaptive mode decomposition and noise reduction algorithm (VAMD) that use a variable spectral segmentation framework to optimize analytical mode decomposition (AMD) to automatically decompose the mode information in rotating machinery signals. The framework relies on the variability of the window width and envelope estimation characteristics of the order statistics filter (OSF) to increase the diversity of the center frequencies and bandwidth. A novel harmonic correlation index (HCI) is designed to identify the characteristics of rotating machinery faults from various levels of results and improve the usability in mechanical equipment fault diagnosis. The proposed method has successfully achieved fault diagnosis of rolling bearing.","Title: Relevance attack on detectors\nYear: 2022\n\nAbstract: •We propose a novel Relevance Attack on Detectors (RAD). We extend DNN interpreters to detectors, find out the most suitable nodes to attack by relevance maps, and explore the best update techniques to increase the transferability.•We evaluate RAD on 8 black-box models and find its state-of-the-art transferability, which exceeds existing results by above 20%. Detection and segmentation performance is greatly impaired in various metrics, invalidating the state-of-the-art DNN to a very rudimentary counterpart.•By RAD, we create the first adversarial dataset for object detection and instance segmentation, i.e., AOCO. As a potential benchmark, AOCO is generated from COCO and contains 10K high-transferable samples. AOCO helps to quickly evaluate and improve the robustness of detectors.","Title: Exploiting User Friendship Networks for User Identification across Social Networks\nYear: 2022\n\nAbstract: Identifying offline entities corresponding to multiple virtual accounts of users across social networks is crucial for the development of related fields, such as user recommendation system, network security, and user behavior pattern analysis. The data generated by users on multiple social networks has similarities. Thus, the concept of symmetry can be used to analyze user-generated information for user identification. In this paper, we propose a friendship networks-based user identification across social networks algorithm (FNUI), which performs the similarity of multi-hop neighbor nodes of a user to characterize the information redundancy in the friend networks fully. Subsequently, a gradient descent algorithm is used to optimize the contribution of the user's multi-hop nodes in the user identification process. Ultimately, user identification is achieved in conjunction with the Gale-Shapley matching algorithm. Experimental results show that compared with baselines, such as friend relationship-based user identification (FRUI) and friendship learning-based user identification (FBI): (1) The contribution of single-hop neighbor nodes in the user identification process is higher than other multi-hop neighbor nodes; (2) The redundancy of information contained in multi-hop neighbor nodes has a more significant impact on user identification; (3) The precision rate, recall rate, comprehensive evaluation index (F1), and area under curve (AUC) of user identification have been improved.","Title: Joint extraction of entities and overlapping relations by improved graph convolutional networks\nYear: 2022\n\nAbstract: Joint extraction of entities and relations is to recognize entities and semantic relations simultaneously, which is significant for knowledge graph construction. Recently, many effective joint models use dependency trees to capture the structural information of sentences. However, most dependency-based methods cannot make full use of the dependency information. This is because these methods just consider the connection information of dependency trees and ignore the influence of different nodes on the connected edges. In this paper, we establish a novel model to extract entities and relations simultaneously by using improved Multi-task Graph Convolutional networks, called MGCN. Specifically, considering the importance of node information, we merge both node and edge information into Graph Convolutional networks (GCN). In addition, in order to recognize the overlapping relations, we propose an efficient strategy to map multiple relational labels of a sentence into a unique code. Finally, we evaluate our joint model on two public datasets, and the experimental results show that our model outperforms the state-of-art models.","Title: Mixture quantized error entropy for recursive least squares adaptive filtering\nYear: 2022\n\nAbstract: Error entropy is a well-known learning criterion in information theoretic learning (ITL), and it has been successfully applied in robust signal processing and machine learning. To date, many robust learning algorithms have been devised based on the minimum error entropy (MEE) criterion, and the Gaussian kernel function is always utilized as the default kernel function in these algorithms, which is not always the best option. To further improve learning performance, two concepts using a mixture of two Gaussian functions as kernel functions, called mixture error entropy and mixture quantized error entropy, are proposed in this paper. We further propose two new recursive least-squares algorithms based on mixture minimum error entropy (MMEE) and mixture quantized minimum error entropy (MQMEE) optimization criteria. The convergence analysis, steady-state mean-square performance, and computational complexity of the two proposed algorithms are investigated. In addition, the reason why the mixture mechanism (mixture correntropy and mixture error entropy) can improve the performance of adaptive filtering algorithms is explained. Simulation results show that the proposed new recursive least-squares algorithms outperform other RLS-type algorithms, and the practicality of the proposed algorithms is verified by the electro-encephalography application.","Title: Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation\nYear: 2022\n\nAbstract: Unpaired image-to-image translation (I2I) is an ill-posed problem, as an infinite number of translation functions can map the source domain distribution to the target distribution. Therefore, much effort has been put into designing suitable constraints, e.g., cycle consistency (CycleGAN), geometry consistency (GCGAN), and contrastive learning-based constraints (CUTGAN), that help better pose the problem. However, these well-known constraints have limitations: (1) they are either too restrictive or too weak for specific I2I tasks; (2) these methods result in content distortion when there is a significant spatial variation between the source and target domains. This paper proposes a universal regularization technique called maximum spatial perturbation consistency (MSPC), which enforces a spatial perturbation function $(T)$ and the translation operator $(G)$ to be commutative (i.e., $T\\circ G=G\\circ T)$ . In addition, we introduce two adversarial training components for learning the spatial perturbation function. The first one lets $T$ compete with $G$ to achieve maximum perturbation. The second one lets $G$ and $T$ compete with discriminators to align the spatial variations caused by the change of object size, object distortion, background interruptions, etc. Our method outperforms the state-of-the-art methods on most I2I benchmarks. We also introduce a new benchmark, namely the front face to profile face dataset, to emphasize the underlying challenges of I2I for real-world applications. We finally perform ablation experiments to study the sensitivity of our method to the severity of spatial perturbation and its effectiveness for distribution alignment.","Title: Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis\nYear: 2022\n\nAbstract: Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in the given sentence. While pre-trained language models such as BERT have achieved great success, incorporating dynamic semantic changes into ABSA remains challenging. To this end, in this paper, we propose to address this problem by Dynamic Re-weighting BERT (DR-BERT), a novel method designed to learn dynamic aspect-oriented semantics for ABSA. Specifically, we first take the Stack-BERT layers as a primary encoder to grasp the overall semantic of the sentence and then fine-tune it by incorporating a lightweight Dynamic Reweighting Adapter (DRA). Note that the DRA can pay close attention to a small region of the sentences at each step and re-weigh the vitally important words for better aspect-aware sentiment understanding. Finally, experimental results on three benchmark datasets demonstrate the effectiveness and the rationality of our proposed model and provide good interpretable insights for future semantic modeling.","Title: Driver gene detection through Bayesian network integration of mutation and expression profiles\nYear: 2022\n\nAbstract: Motivation: The identification of mutated driver genes and the corresponding pathways is one of the primary goals in understanding tumorigenesis at the patient level. Integration of multi-dimensional genomic data from existing repositories, e.g., The Cancer Genome Atlas (TCGA), offers an effective way to tackle this issue. In this study, we aimed to leverage the complementary genomic information of individuals and create an integrative framework to identify cancer-related driver genes. Specifically, based on pinpointed differentially expressed genes, variants in somatic mutations and a gene interaction network, we proposed an unsupervised Bayesian network integration (BNI) method to detect driver genes and estimate the disease propagation at the patient and/or cohort levels. This new method first captures inherent structural information to construct a functional gene mutation network and then extracts the driver genes and their controlled downstream modules using the minimum cover subset method. Results: Using other credible sources (e.g. Cancer Gene Census and Network of Cancer Genes), we validated the driver genes predicted by the BNI method in three TCGA pan-cancer cohorts. The proposed method provides an effective approach to address tumor heterogeneity faced by personalized medicine. The pinpointed drivers warrant further wet laboratory validation.","Title: Understanding Dynamics of Pandemic Models to Support Predictions of COVID-19 Transmission: Parameter Sensitivity Analysis of SIR-Type Models\nYear: 2022\n\nAbstract: Despite efforts made to model and predict COVID-19 transmission, large predictive uncertainty remains. Failure to understand the dynamics of the nonlinear pandemic prediction model is an important reason. To this end, local and multiple global sensitivity analysis approaches are synthetically applied to analyze the sensitivities of parameters and initial state variables and community size (N) in susceptible-infected-recovered (SIR) and its variant susceptible-exposed-infected-recovered (SEIR) models and basic reproduction number (\n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">R0</i>\n), aiming to provide prior information for parameter estimation and suggestions for COVID-19 prevention and control measures. We found that N influences both the maximum number of actively infected cases and the date on which the maximum number of actively infected cases is reached. The high effect of N on maximum actively infected cases and peak date suggests the necessity of isolating the infected cases in a small community. The protection rate and average quarantined time are most sensitive to the infected populations, with a summation of their first-order sensitivity indices greater than 0.585, and their interactions are also substantial, being 0.389 and 0.334, respectively. The high sensitivities and interaction between the protection rate and average quarantined time suggest that protection and isolation measures should always be implemented in conjunction and started as early as possible. These findings provide insights into the predictability of the pandemic models by estimating influential parameters and suggest how to effectively prevent and control epidemic transmission.","Title: A Multiple Salient Features-Based User Identification across Social Media\nYear: 2022\n\nAbstract: Identifying users across social media has practical applications in many research areas, such as user behavior prediction, commercial recommendation systems, and information retrieval. In this paper, we propose a multiple salient features-based user identification across social media (MSF-UI), which extracts and fuses the rich redundant features contained in user display name, network topology, and published content. According to the differences between users' different features, a multi-module calculation method is used to obtain the similarity between various redundant features. Finally, the bidirectional stable marriage matching algorithm is used for user identification across social media. Experimental results show that: (1) Compared with single-attribute features, the multi-dimensional information generated by users is integrated to optimize the universality of user identification; (2) Compared with baseline methods such as ranking-based cross-matching (RCM) and random forest confirmation algorithm based on stable marriage matching (RFCA-SMM), this method can effectively improve precision rate, recall rate, and comprehensive evaluation index (F1).","Title: Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models\nYear: 2022\n\nAbstract: This paper investigates the problem of selecting instrumental variables relative to a target causal influence X -> Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders. We propose a necessary condition for detecting variables that cannot serve as instrumental variables. Unlike many existing conditions for continuous variables, i.e., that at least two or more valid instrumental variables are present in the system, our condition is designed with a single instrumental variable. We then characterize the graphical implications of our condition in linear non-Gaussian acyclic causal models. Given that the existing graphical criteria for the instrument validity are not directly testable given observational data, we further show whether and how such graphical criteria can be checked by exploiting our condition. Finally, we develop a method to select the set of candidate instrumental variables given observational data. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method.","Title: A Review-aware Graph Contrastive Learning Framework for Recommendation\nYear: 2022\n\nAbstract: Most modern recommender systems predict users' preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the existing review-based recommendation models enriched user/item embedding learning ability with historical reviews or better modeled user-item interactions with the help of available user-item target reviews. Though significant progress has been made, we argue that current solutions for review-based recommendation suffer from two drawbacks. First, as review-based recommendation can be naturally formed as a user-item bipartite graph with edge features from corresponding user-item reviews, how to better exploit this unique graph structure for recommendation? Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better? To this end, in this paper, we propose a novel Review-aware Graph Contrastive Learning (RGCL) framework for review-based recommendation. Specifically, we first construct a review-aware user-item graph with feature-enhanced edges from reviews, where each edge feature is composed of both the user-item rating and the corresponding review semantics. This graph with feature-enhanced edges can help attentively learn each neighbor node weight for user and item representation learning. After that, we design two additional contrastive learning tasks (i.e., Node Discrimination and Edge Discrimination) to provide self-supervised signals for the two components in recommendation process. Finally, extensive experiments over five benchmark datasets demonstrate the superiority of our proposed RGCL compared to the state-of-the-art baselines.","Title: Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification\nYear: 2022\n\nAbstract: Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models.","Title: Real-time adaptive skin detection using skin color model updating unit in videos\nYear: 2022\n\nAbstract: Skin color plays an important role in color image processing and human–computer interaction. However, factors such as rapidly changing illumination, various color styles, and camera characteristics also make skin detection a challenging task. In particular, the real-time requirement of practical applications is a challenging task in skin detection. In this paper, face detection and alignment are applied to select facial reference points for modeling the skin color distribution. Moreover, we propose the conception and detection approach of skin color model updating unit (SCMUU) according to the fact of skin color distribution remains consistent in a range of frames. The redundant operation of frame by frame updating is avoided using one model in frames of SCMUU. When no reliable faces are detected, two strategies are introduced to remedy and reduce the computational cost. It uses the corresponding model parameters if a similar previous SCMUU is found. Otherwise, we use fixed thresholds instead and increase the interval between two consecutive face detection. Besides, the time-consuming steps are accelerated using a graphic processing unit (GPU) with CUDA in this paper. Experimental results show that, compared with other existing methods, the proposed method has good real time and accuracy for skin detection of various resolution videos under different illumination conditions.","Title: Understanding the Users and Videos by Mining a Novel Danmu Dataset\nYear: 2022\n\nAbstract: Recent years have witnessed a successful rise of the time synchronized gossiping comment, or so-called danmu combined with online videos. This new business mode has enriched communication among users by sending users’ feelings through danmus and sharing these danmus on time synchronized videos. Can danmu communication be helpful for better user behavior modeling or video analyzing? To this ...","Title: Detection of Sea-Surface Small Targets Masked by Range Sidelobes of Large Objects\nYear: 2022\n\nAbstract: Many high-resolution maritime radars for sea-surface small target detection transmit pulses with large time-bandwidth-products to increase transmitter power and to use the pulse compression technique for high range resolution. On the sea surface, besides small targets of interest, there exist many uninteresting large objects such as big ships, reefs, and offshore platforms for oil production. Large objects have too strong radar returns and their range sidelobes often mask wide range intervals. In these range intervals, masked small targets are difficult to be detected directly. In this article, a complete scheme is proposed to find small targets masked by range sidelobes of large objects in high-resolution maritime radars. In this scheme, high-precision reconstruction of radar returns of large objects is crucial because tiny reconstruction errors are enough to mask weak returns of small targets of interest. An algorithm using the linear programming and oversampled high-resolution range profile model of a large object is given to obtain high-precision reconstruction of the strong radar returns of a large object. From residual radar returns removed the reconstructed strong returns, a near-optimal coherent detector under sea clutter modeled by the spherically invariant random vector with inverse Gamma texture plus colored Gaussian noise is constructed to find masked small targets. Simulated experiments and real measured radar data are used to verify the effectiveness of the proposed scheme and method and reveal their limitations. The results show that the proposed scheme and method are all effective unless small targets fall into the range mainlobe regions of large objects.","Title: Recalibration and Assessment of the SNPP CrIS Instrument: A Successful History of Restoration After Midwave Infrared Band Anomaly\nYear: 2022\n\nAbstract: The Suomi National Polar-orbiting Partnership (SNPP) cross-track infrared sounder (CrIS) has provided critical observations for environmental applications for nearly ten years. However, on 26 March 2019, the Joint Polar Satellite System (JPSS) interface data processing segment (IDPS) stopped producing the operational SNPP CrIS sensor data record (SDR) product due to a failure of the midwave infrared (MWIR) band. Following a comprehensive risk assessment, the switch from primary Side-1 to redundant Side-2 electronics was made on 24 June 2019, successfully recovering the full capabilities of the sensor. Comprehensive assessment results demonstrate the high quality of the CrIS SDR product resulting from the sensor recalibration, thus meeting the JPSS Level-1 requirements with margin. The spectral calibration prioritized consistency with the CrIS SDR product prior to the side switch to minimize the impact on users. The results show that the radiometric impact on the CrIS SDR product resulting from the side switch is not significant and is within the calibration radiometric uncertainty. It is demonstrated that after instrument restoration, the SNPP CrIS SDR product recovers the quality needed to be used as radiometric reference for calibration and validation of infrared remote sensing instruments. The recovery of the SNPP CrIS MWIR band is expected to support improvements in numerical weather forecasting by restoring the MWIR band channels sensitive to tropospheric water vapor. This should also help maintain continuity and redundancy of one of the backbone observations of the global observing system.","Title: Simulation of three-dimensional phase field model with LBM method using OpenCL\nYear: 2022\n\nAbstract: Multi-field coupled three-dimensional phase field model is a research hotspot for simulating the growth mechanism of solidification microstructure. Single-CPU has become the main bottleneck in the development of phase field model due to its computing power and storage limitations. Based on this, the PF-LBM model of three-dimensional dendritic growth of supercooled pure substance solution under flow is established in this study. We take the succinonitrile (SCN) as the research object and use OpenCL heterogeneous parallel technology to realize the parallel solution. Firstly, the performance of the phase field model solution process is analyzed to find out the time-consuming modules; then, a parallel solution is proposed, and the preliminary parallelization of the phase field model is realized; finally, the problems existing in the parallel algorithm are optimized. The experimental results show that compared with the serial algorithm on the CPU platform under the same conditions, the speedup ratio reaches the highest when the computing scale is 128*128*128, which is 62.10. And the average speedup is 57.46. The scale after parallel is also expanded from 189*189*189 to 357*357*357, which effectively solves the problems existing in single-CPU simulation.","Title: McHa: a multistage clustering-based hierarchical attention model for knowledge graph-aware recommendation\nYear: 2022\n\nAbstract: Knowledge graph-aware recommendation has become an important research topic in recent years. The user preference representation, which preserves the user’s taste towards items (e.g., movies, books.), is obtained through aggregating the information of entities or attributes in knowledge graphs directly. However, the fine-grained heterogeneity information, which can be derived from the groups of items or entities, remains barely exploited in the process of encoding the user interaction intention for the items. To fill up this gap, we propose a Multistage Clustering-based Hierarchical Attention (McHa) model to capture the user preference representation. In our work, we first group the items and their neighboring entities in the knowledge graph into item clusters and entity clusters (jointly referred to as multistage clusters), respectively. Then, the user preference representation is obtained by hierarchically aggregating the heterogeneity information derived from the multistage clusters with the weights generated by the hierarchical attention layers. We conduct extensive experimental comparisons with baselines and the variants. The experimental results indicate that McHa has achieved state-of-the-art performance on three benchmark datasets in two scenarios.","Title: Analysis, design and preliminary evaluation of an anthropometric self-stabilization passive exoskeleton for enhancing the ability of walking with loads\nYear: 2022\n\nAbstract: This paper introduces the analysis, design and preliminary evaluation of an anthropometric self-stabilization passive exoskeleton (ASPE) with elastic band to increase the load transmission efficiency and protect and strengthen the human body during loaded walking. Firstly, we analyze the working principle of passive exoskeleton, and propose an efficient method of reducing the degrees of freedom in exoskeleton relatively, which contributes to a self-stabilization mechanism for balancing the back torque causing by the loaded backpack and assisting human hip flexion. The design of the ASPE is then introduced in detail. The novelty of the ASPE is that the human–machine interaction forces are significantly reduced by integrating the elastic band into the exoskeleton hip joint and converting load gravity into the elastic potential energy of elastic band to assist hip joint flexion. Furthermore, we analyze the dynamic modeling of the ASPE to preliminarily calculate the transmission efficiency regarding the ratio of plantar pressure of the ASPE to load gravity. Besides, we conduct the experiment of human wearing the ASPE to evaluate the performance of the ASPE regarding the ratio of the difference of human plantar pressure with and without the ASPE to load gravity. The results show that the ASPE can effectively transfer load gravity to the ground and maintain the human natural movement. The ratio of the plantar pressure of the ASPE to load gravity is more than 70% in the simulation, and when the walking speed is 4 km/h, the ASPE reduce the human plantar pressure of 68.9% compared to without wearing the ASPE during human walking with load. The results provide evidence for the efficient transmission of the newly designed ASPE during walking with loads. The application of the ASPE have benefits for subjects walking with loads, such as soldiers, to decrease their injuries and strengthen their ability.","Title: Log-based Anomaly Detection from Multi-view by Associating Anomaly Scores with User Trust\nYear: 2021\n\nAbstract: Logs, prevalent among nearly all computer systems, contain rich information that helps with troubleshooting or root cause analysis. Therefore, logs are excellent information sources for anomaly detection. Since logs are diverse and heterogeneous, to deal with all of them requires maintenance personnel to check detection results one by one, which is troublesome. This paper applies an ensemble metho...","Title: Remote Attestation of Large-scale Virtual Machines in the Cloud Data Center\nYear: 2021\n\nAbstract: With the development of cloud computing, remote attestation of virtual machines has received extensive attention. However, the current schemes mainly concentrate on the single prover, and the attestation of a large-scale virtualization environment will cause TPM bottleneck and network congestion, resulting in low efficiency of attestation. This paper proposes CloudTA, an extensible remote attestat...","Title: Dual-Channel and Hierarchical Graph Convolutional Networks for document-level relation extraction\nYear: 2022\n\nAbstract: Document-level relation extraction aims to infer complex semantic relations among entities in an entire document. Compared with the sentence-level relation extraction, document-level relational facts are expressed by multiple mentions across the sentences in a long-distance, requiring excellent reasoning. In this paper, we propose Dual-Channel and Hierarchical Graph Convolutional Networks (DHGCN), which constructs three graphs in token-level, mention-level, and entity-level to model complex interactions among different semantic representations across the document. Based on the multi-level graphs, we apply the Graph Convolutional Network (GCN) for each level to aggregate the relevant information scattered throughout the document for better inferring the implicit relations. Moreover, we propose a dual-channel encoder to capture structural and contextual information simultaneously, which also supplies the contextual representation for the higher layer to avoid losing low-dimension information. Our DHGCN yields significant improvements over the state-of-the-art methods by 2.75, 5.5, and 3.5 F1 on DocRED, CDR, and GDA, respectively, which are popular document-level relation extraction datasets. Furthermore, to demonstrate the effectiveness of our method, we evaluate DHGCN on a fine gained clinical document-level dataset Symptom-Acupoint Relation (SAR) proposed by ourselves and available at https://github.com/QiSun123/SAR. The experimental results illustrate that DHGCN is able to infer more valuable relations among entities in the document.","Title: Partial disentanglement for domain adaptation.\nYear: 2022\n\nAbstract: Unsupervised domain adaptation is critical to many real-world applications where label information is unavailable in the target domain. In general, without further assumptions, the joint distribution of the features and the label is not identifiable in the target domain. To address this issue, we rely on a property of minimal changes of causal mechanisms across domains to minimize unnecessary influences of domain shift. To encode this property, we first formulate the data generating process using a latent variable model with two partitioned latent subspaces: invariant components whose distributions stay the same across domains, and sparse changing components that vary across domains. We further constrain the domain shift to have a restrictive influence on the changing components. Under mild conditions, we show that the latent variables are partially identifiable, from which it follows that the joint distribution of data and labels in the target domain is also identifiable. Given the theoretical insights, we propose a practical domain adaptation framework, called iMSDA. Extensive experimental results reveal that iMSDA outperforms state-of-the-art domain adaptation algorithms on benchmark datasets, demonstrating the effectiveness of our framework.","Title: Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint\nYear: 2022\n\nAbstract: Unsupervised image-to-image (I21) translation aims to learn a domain mapping function that can preserve the semantics of the input images without paired data. However, because the underlying semantics distributions in the source and target domains are often mismatched, current distribution matching-based methods may distort the semantics when matching distributions, resulting in the inconsistency between the input and translated images, which is known as the semantics distortion problem. In this paper, we focus on the low-level I21 translation, where the structure of images is highly related to their semantics. To alleviate semantic distortions in such translation tasks without paired supervision, we propose a novel I21 translation constraint, called Structure Consistency Constraint (SCC), to promote the consistency of image structures by reducing the randomness of color transformation in the translation process. To facilitate estimation and maximization of SCC, we propose an approximate representation of mutual information called relative Squared-loss Mutual Information (rSMI) that enjoys efficient analytic solutions. Our SCC can be easily incorporated into most existing translation models. Quantitative and qualitative comparisons on a range of low-level I21 translation tasks show that translation models with SCC outperform the original models by a significant margin with little additional computational and memory costs.","Title: Negative-Aware Attention Framework for Image-Text Matching\nYear: 2022\n\nAbstract: Image-text matching, as a fundamental task, bridges the gap between vision and language. The key of this task is to accurately measure similarity between these two modalities. Prior work measuring this similarity mainly based on matched fragments (i.e., word/region with high relevance), while underestimating or even ignoring the effect of mismatched fragments (i.e., word/region with low relevance), e.g., via a typical LeaklyReLU or ReLU operation that forces negative scores close or exact to zero in attention. This work argues that mismatched textual fragments, which contain rich mismatching clues, are also crucial for image-text matching. We thereby propose a novel Negative-Aware Attention Framework (NAAF), which explicitly exploits both the positive effect of matched fragments and the negative effect of mismatched fragments to jointly infer image-text similarity. NAAF (1) delicately designs an iterative optimization method to maximally mine the mismatched fragments, facilitating more discriminative and robust negative effects, and (2) devises the two-branch matching mechanism to precisely calculate similarity/dissimilarity degrees for matched/mismatched fragments with different masks. Extensive experiments on two benchmark datasets, i.e., Flickr30K and MSCOCO, demonstrate the superior effectiveness of our NAAF, achieving state-of-the-art performance. Code will be released at: https://github.com/CrossmodalGroup/NAAF.","Title: Soft Subspace Fuzzy Clustering with Dimension Affinity Constraint\nYear: 2022\n\nAbstract: For high-dimensional data, the cluster structure often exists in a feature subset instead of the whole feature space. Soft subspace clustering can efficiently extract the important subspace by allocating a weight to each dimension on the basis of the contribution of this dimension to the cluster identification. However, this kind of method does not consider the correlations between data dimensions in the clustering process. In high-dimensional data, when two dimensions are closely correlated, they should have similar weight assignments, and vice versa. Inspired by the way of clustering with graph embedding technique, we present a novel soft subspace clustering algorithm with considering the correlations between data dimensions. In this method, a novel dimension affinity regularization term is included into the objective function to further highlight those correlated dimensions that are important to the formation of clusters and compress the feature subspaces. Moreover, the alternating direction method of multipliers is adopted to solve the linear optimization problem regarding the dimension weight lasso regularization. In addition, as an extension, the kernelized version is explored to address the non-linear data clustering. Experiments on the real-world datasets demonstrate the efficiency of the presented algorithms in comparison with the conventional clustering methods.","Title: Video emotion analysis enhanced by recognizing emotion in video comments\nYear: 2022\n\nAbstract: Video emotion analysis is one of the hottest topics in the video understanding community to cognize the emotion in videos for affective computing, video recommendation, and so on. Currently, many studies tend to employ different deep structures to model video contents for this task. In fact, audiences responses (e.g., physiological signals and comments) are also important since they are directly related to video emotional content and can reflect the emotions in videos. But current work did not fully explore the potential of viewers’ responses due to the following possible reasons: collecting users’ reactions are time-consuming, and importing collection devices will affect viewers’ normal reactions. Fortunately, as a kind of available users’ reactions on many video websites, user-generated comments have strong connections with users’ reactions to video content, and can indirectly reflect video emotions. To this end, we focus on user-generated comments emotion recognition for video emotion analysis. To overcome the shortcoming that user-generate comments are usually informal and related to plots, we design a novel Visual Enhanced Comments Emotion Recognition Model (VECERM) that leverages the related visual information to enhance comments context and identify comments emotions. Based on this, we can leverage the recognized emotion of comments to enhance the emotion analysis of videos via the temporal association between users’ comments and video emotional contents. Many experiments demonstrate our work has promising results for comments emotion recognition and can analyze video emotion with high efficiency and low cost.","Title: On the shortest path problem of uncertain random digraphs\nYear: 2022\n\nAbstract: In the field of graph theory, the shortest path problem is one of the most significant problems. However, since varieties of indeterminated factors appear in complex networks, determining of the shortest path from one vertex to another in complex networks may be a lot more complicated than the cases in deterministic networks. To illustrate this problem, the model of uncertain random digraph will be proposed via chance theory, in which some arcs exist with degrees in probability measure and others exist with degrees in uncertain measure. The main focus of this paper is to investigate the main properties of the shortest path in uncertain random digraph. Methods and algorithms are designed to calculate the distribution of shortest path more efficiently. Besides, some numerical examples are presented to show the efficiency of these methods and algorithms.","Title: A Novel PDF Shape Control Approach for Nonlinear Stochastic Systems\nYear: 2022\n\nAbstract: In this work, a novel shape control approach of the probability density function (PDF) for nonlinear stochastic systems is presented. First, we provide the formula for the PDF shape controller without devising the control law of the controller. Then, based on the exact analytical solution of the Fokker-Planck-Kolmogorov (FPK) equation, the product function of the polynomial and the exponential polynomial is regarded as the stationary PDF of the state response. To validate the performance of the proposed control approach, we compared it with the exponential polynomial method and the multi-Gaussian closure method by implementing comparative simulation experiments. The results show that the novel PDF shape control approach is effective and feasible. Using an equal number of parameters, our method can achieve a similar or better control effect as the exponential polynomial method. By comparison with the multi-Gaussian closure method, our method has clear advantages in PDF shape control performance. For all cases, the integral of squared error and the errors of first four moments of our proposed method were very small, indicating superior performance and promising good overall control effects of our method. The approach presented in this study provides an alternative for PDF shape control in nonlinear stochastic systems.","Title: Enhanced graph convolutional network based on node importance for document-level relation extraction\nYear: 2022\n\nAbstract: Document-level relation extraction aims to reason complex semantic relations among entities expressed by multiple associated mentions in a document. Existing methods construct document-level graphs to model interactions between entities. However, these methods only pay attention to the connection relationship of nodes, yet ignore the importance of nodes decided by topological structure. In this paper, we propose a novel method, named Enhanced Graph Convolutional Network (EGCN), to extract document-level relations. Unlike previous methods that only model the connection relationship between two nodes, we further exploit the global topological structural information by measuring node importance. We merge these non-local relationship into a Graph Convolutional Network to aggregate relevant information. In addition, to model semantic and syntactic interactions in documents, we design a novel strategy to construct document-level heterogeneous graphs with different types of edges. Experimental results demonstrate that our EGCN outperforms the previous models by 5.54%, 1.7%, and 2.9% \n \n \n \n $$F_1$$\n \n on three public document-level relation extraction datasets.","Title: Optical Turbulence Characteristics in the Upper Troposphere-Lower Stratosphere over the Lhasa within the Asian Summer Monsoon Anticyclone\nYear: 2022\n\nAbstract: The high elevation, complex topography, and unique atmospheric circulations of the Tibetan Plateau (TP) make its optical turbulence characteristics different from those in low-elevation regions. In this study, the characteristics of the atmospheric refractive index structure constant (C-n(2)) profiles in the Lhasa area at different strength states of the Asian summer monsoon anticyclone (ASMA) are analyzed based on precious in situ sounding data measured over the Lhasa in August 2018. C-n(2) in the upper troposphere-lower stratosphere fluctuates significantly within a few days during the ASMA, particularly in the upper troposphere. The effect of the ASMA on C-n(2) varies among the upper troposphere, tropopause, and lower stratosphere. The stronger and closer the ASMA is to Lhasa, the more pronounced is the \"upper highs and lower lows\" pressure field structure, which is beneficial for decreasing the potential temperature lapse rate. The decrease in static stability is an important condition for developing optical turbulence, elevating the tropopause height, and reducing the tropopause temperature. However, if strong high-pressure activity occurs at the lower pressure layer, such as at 500 hPa, an \"upper highs and lower highs\" pressure field structure forms over the Lhasa, increasing the potential temperature lapse rate and suppressing the convective intensity. Being almost unaffected by low-level atmospheric high-pressure activities, the ASMA, as the main influencing factor, mainly inhibits C-n(2) in the tropopause and lower stratosphere. The variations of turbulence intensity in UTLS caused by ASMA activities also have a great influence on astronomical parameters, which will have certain guiding significance for astronomical site testing and observations.","Title: An immune algorithm for solving the optimization problem of locating the battery swapping stations\nYear: 2022\n\nAbstract: Compared with fuel-powered vehicles, new energy electric vehicles have less carbon emissions. The application of new energy electric vehicles in logistics transportation is very significant to reduce vehicle-emitted greenhouse gases and improve environmental protection. In the logistics network using electric transport vehicles, the reasonable layout of battery swapping stations not only improves the distribution efficiency but also saves the distribution cost. In this paper, an integer programming problem model was formulated for reasonably locating battery swapping stations, where the total costs (including construction costs, operation and maintenance costs, and travel costs) were minimized. To solve this problem, we proposed a solution strategy based on an immune optimization algorithm, two crossover operators, and variable crossover and mutation rates. The effectiveness of the presented model and the performance of the proposed solution approach were evaluated using a case study. Compared with the commercial software CPLEX, the numerical results showed that the proposed algorithm can provide the optimal solution with less computing time.","Title: Multi-objective two-level medical facility location problem and tabu search algorithm\nYear: 2022\n\nAbstract: The medical facility location problem has attracted increasing attention in recent years because the rational layout of medical facilities plays an important role in implementing the hierarchical diagnosis and treatment system in many countries. To promote the implementation of a hierarchical diagnosis and treatment system, a two-level medical facility location problem with multiple patient flows between demand points and medical facilities is studied in this paper. We use a direct interpretation of the two-level medical facility location problem as a multi-objective problem to effectively balance different conflicting objectives. Based on the multi-objective tabu search algorithm and the fast construction method of the Pareto non-dominated solution, we also propose a solution approach to yield a set of solutions that can represent the trade-offs among conflicting objectives. The applicability and validation of the presented model and performance of the proposed optimization approach are evaluated using a real case. The experimental results not only indicate that the proposed solution approach can effectively solve the two-level medical facility location problem, but also suggest that the presented model can provide significant results in determining locations for medical facilities.","Title: Evaluation and Optimization on Virtualization Performance Cost under Semantic Gap\nYear: 2022\n\nAbstract: Virtualization is a key enabling technology in modern data centers. While it provides numerous benefits, it also creates new problems. Virtualization requires the hypervisor to treat the virtual machine as a black box, limiting the ability of information exchange between the hypervisor and the virtual machine, bringing a problem known as the semantic gap. Currently, much research on the semantic gap mainly focuses on bridging the semantic gap. The evaluation of the semantic gap, on the other hand, is a neglected but crucial problem, and relevant research is currently lacking. Therefore, this paper proposes a corresponding virtualization performance cost model to better evaluate the semantic gap. Based on this cost model, we summarize solutions that can be used to alleviate the semantic gap. Furthermore, we propose a novel evaluation method for the CPU double scheduling semantic gap. Finally, we propose an effective virtio-balloon based dynamic memory tuning strategy to alleviate the memory semantic gap. The experiments show that for 400.perlbench, our strategy saves 551MB of memory on average during running and reclaims 990MB of memory after running, with a performance cost of only 1.1%. For 429.mcf, our strategy saves 815MB of memory on average during running and reclaims 2130MB of memory after running, although with the performance cost of 27.6%, it prevents performance cliff-like drop caused by memory shortage.","Title: Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases.\nYear: 2022\n\nAbstract: Complex question generation over knowledge bases (KB) aims to generate natural language questions involving multiple KB relations or functional constraints. Existing methods train one encoder-decoder-based model to fit all questions. However, such a one-size-fits-all strategy may not perform well since complex questions exhibit an uneven distribution in many dimensions, such as question types, involved KB relations, and query structures, resulting in insufficient learning for long-tailed samples under different dimensions. To address this problem, we propose a meta-learning framework for complex question generation. The meta-trained generator can acquire universal and transferable meta-knowledge and quickly adapt to long-tailed samples through a few most related training samples. To retrieve similar samples for each input query, we design a self-supervised graph retriever to learn distributed representations for samples, and contrastive learning is leveraged to improve the learned representations. We conduct experiments on both WebQuestionsSP and ComplexWebQuestion, and results on long-tailed samples of different dimensions have been significantly improved, which demonstrates the effectiveness of the proposed framework.","Title: Research on Intelligent Adaptive Control Method for Waste Heat Power Generation Stability Problem\nYear: 2022\n\nAbstract: The effective recovery and utilization of waste heat is an important part of the energy industry chain, but the gas source of waste heat has large fluctuations and strong random frequency changes, which easily affects the quality of power generation. In order to obtain more continuous and stable electricity in the process of waste heat power generation, this paper proposes an intelligent adaptive control method. This control method decouples the variables for the characteristics of strong coupling of the waste heat power generation system, which improves the anti-disturbance performance of the system on the one hand and reduces the steady-state tracking error of the system on the other hand. Experiments show that when the gas source fluctuates, the system fluctuation rate is stable within 7%; when the set value changes, the adjustment time is within 7 s, and the overshoot is small, so the performance is better than the traditional PID control method. The intelligent adaptive control method proposed in this paper is of great significance for the future development of intelligent grid-connected power generation.","Title: Adaptive Backstepping Fuzzy Control for Elevation Equilibrator Electro-Hydraulic Servo System\nYear: 2022\n\nAbstract: An ABFC (adaptive backstepping fuzzy control) strategy is presented for one elevation equilibrator electro-hydraulic servo system of vehicle-mounted artillery with nonmatching uncertainty and parameters time-varying. Method of backstepping is used to overcome the problem of nonmatching uncertainty, adaption laws are adopted to eliminate the unknown parameters online. Fuzzy control is adopted to reduce the switching chattering phenomenon. Co-simulation results show that the ABFC strategy tracks the desired tracking trajectory well. The designed controller has nicer robustness and lower steady-state errors.","Title: Reconfigurable Bit-Serial Operation Using Toggle SOT-MRAM for High-Performance Computing in Memory Architecture\nYear: 2022\n\nAbstract: Computing in memory (CIM) is a promising candidate for high throughput and energy-efficient data-driven applications, which mitigates the well-known memory bottleneck in Von Neumann architecture. In this paper, we present a reconfigurable bit-serial operation using toggle spin-orbit torque magnetic random access memory (TSOT-MRAM) to perform the computation completely in the bit-cell array instead of in a peripheral circuit. This bit-serial CIM (BSCIM) scheme achieves higher throughput and energy efficiency in CIM. First, basic Boolean logic operations are realized by utilizing the feature of TSOT device. A bit-cell array that implements the bit-serial operation is then built to provide the communication between column and row necessary for arithmetic operations, such as the carry propagation of addition and multiplication. Finally, we analyze the reliability of BSCIM scheme and demonstrate the performance advantage by performing convolution operations for \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$28\\times 28$ </tex-math></inline-formula>\n handwritten digit images in a BSCIM architecture. The results show that the delay and energy of BSCIM architecture are respectively reduced by 1.16-5.49 times and 1.12-1.43 times compared with the existing digital CIM architectures. Besides, its throughput and energy efficiency are also enhanced to 51.2 GOPS and 9.9 TOPS/W respectively.","Title: Causal Narrative Comprehension: A New Perspective for Emotion Cause Extraction\nYear: 2022\n\nAbstract: Emotion Cause Extraction (ECE) aims to reveal the cause clauses behind a given emotion expressed in a text, which has become an emerging topic in broad research communities, such as affective computing and natural language processing. Despite the fact that current methods about the ECE task have made great progress in text semantic understanding from lexicon- and sentence-level, they always ignore the certain causal narratives of emotion text. Significantly, these causal narratives are presented in the form of semantic structure and highly helpful for structure-level emotion cause understanding. Nevertheless, causal narrative is just an abstract narratological concept and its involving semantics is quite different from the common sequential information. Thus, how to properly model and utilize such particular narrative information to boost the ECE performance still remains an unresolved challenge. To this end, in this paper, we propose a novel Causal Narrative Comprehension Model (CNCM) for emotion cause extraction, which learns and leverages causal narrative information smartly to address the above problem. Specifically, we develop a Narrative-aware Causal Association (NCA) unit, which mines the narrative cue about emotional results and uses the semantic correlation between causes and results to model causal narratives of documents. Besides, we design a Result-aware Emotion Attention (REA) unit to make full use of the known result of causal narrative for multiple understanding about emotional causal associations. Through the ingenious combination and collaborative utilization of these two units, we could better identify the emotion cause in the text with causal narrative comprehension. Extensive experiments on the public English and Chinese benchmark datasets of ECE task have validated the effectiveness of CNCM with significant margin by comparing with the state-of-the-art baselines, which demonstrates the potential of narrative information in long text understanding."],"string":"[\n \"Title: Research On The Theory And Application Of Ioii In China\\nYear: 2016\\n\\nAbstract: The integration of informatization and industrialization (IOII) is a policy guidelines proposed by the Chinese Government to promote the production upgrade and the transformation of economic development mode, which is given increasingly attention by academic circles. In this paper, a brief introduction to IOII is presented. The research collections of IOII required to study the theory and application are surveyed as well as the application evaluation. By summarizing the development and the existing concept of IOII, a new concept of IOII is put forward. Then by studying comparatively the process and the application evaluation of IOII, suggestions for future empirical investigation is offered.\",\n \"Title: Causal Discovery Using Regression-Based Conditional Independence Tests.\\nYear: 2017\\n\\nAbstract: Conditional independence (CI) testing is an important tool in causal discovery. Generally, by using CI tests, a set of Markov equivalence classes w.r.t. the observed data can be estimated by checking whether each pair of variables x and y is d-separated, given a set of variables Z. Due to the curse of dimensionality, CI testing is often difficult to return a reliable result for high-dimensional Z. In this paper, we propose a regression-based CI test to relax the test of x perpendicular to y vertical bar Z to simpler unconditional independence tests of x - f(Z) perpendicular to y - g(Z), and x - f(Z) perpendicular to Z or y - g(Z) perpendicular to Z under the assumption that the data-generating procedure follows additive noise models (ANMs). When the ANM is identifiable, we prove that x - f(Z) perpendicular to y - g(Z) double right arrow x perpendicular to y vertical bar Z. We also show that 1) f and g can be easily estimated by regression, 2) our test is more powerful than the state-of-the-art kernel CI tests, and 3) existing causal learning algorithms can infer much more causal directions by using the proposed method.\",\n \"Title: A Context-Enriched Neural Network Method for Recognizing Lexical Entailment.\\nYear: 2017\\n\\nAbstract: Recognizing lexical entailment (RLE) always plays an important role in inference of natural language, i.e., identifying whether one word entails another, for example, fox entails animal. In the literature, automatically recognizing lexical entailment for word pairs deeply relies on words' contextual representations. However, as a \\\"prototype\\\" vector, a single representation cannot reveal multifaceted aspects of the words due to their homonymy and polysemy. In this paper, we propose a supervised Context-Enriched Neural Network (CENN) method for recognizing lexical entailment. To be specific, we first utilize multiple embedding vectors from different contexts to represent the input word pairs. Then, through different combination methods and attention mechanism, we integrate different embedding vectors and optimize their weights to predict whether there are entailment relations in word pairs. Moreover, our proposed framework is flexible and open to handle different word contexts and entailment perspectives in the text corpus. Extensive experiments on five datasets show that our approach significantly improves the performance of automatic RLE in comparison with several state-of-the-art methods.\",\n \"Title: CSTG: An Effective Framework for Cost-sensitive Sparse Online Learning.\\nYear: 2017\\n\\nAbstract: Sparse online learning and cost-sensitive learning are two important areas of machine learning and data mining research. Each has been well studied with many interesting algorithms developed. However, very limited published work addresses the joint study of these two fields. In this paper, to tackle the high-dimensional data streams with skewed distributions, we introduce a framework of cost-sensitive sparse online learning. Our proposed framework is a substantial extension of the influential Truncated Gradient (TG) method by formulating a new convex optimization problem, where the two mutual restraint factors, misclassification cost and sparsity, can be simultaneously and favorably balanced. We theoretically analyze the regret and cost bounds of the proposed algorithm, and pinpoint its theoretical merit compared to the existing related approaches. Large-scale empirical comparisons to five baseline methods on eight real-world streaming datasets demonstrate the encouraging performance of the developed method. Algorithm implementation and datasets are available upon request.\",\n \"Title: CirGRDB: a database for the genome-wide deciphering circadian genes and regulators.\\nYear: 2018\\n\\nAbstract: Circadian rhythms govern various kinds of physiological and behavioral functions of the living organisms, and disruptions of the rhythms are highly detrimental to health. Although several databases have been built for circadian genes, a resource for comprehensive post-transcriptional regulatory information of circadian RNAs and expression patterns of disease-related circadian RNAs is still lacking. Here, we developed CirGRDB (http://cirgrdb.biols.ac.cn) by integrating more than 4936 genome-wide assays, with the aim of fulfilling the growing need to understand the rhythms of life. CirGRDB presents a friendly web interface that allows users to search and browse temporal expression patterns of interested genes in 37 human/mouse tissues or cell lines, and three clinical disorders including sleep disorder, aging and tumor. More importantly, eight kinds of potential transcriptional and post-transcriptional regulators involved in the rhythmic expression of the specific genes, including transcription factors, histone modifications, chromatin accessibility, enhancer RNAs, miRNAs, RNA-binding proteins, RNA editing and RNA methylation, can also be retrieved. Furthermore, a regulatory network could be generated based on the regulatory information. In summary, CirGRDB offers a useful repository for exploring disease-related circadian RNAs, and deciphering the transcriptional and post-transcriptional regulation of circadian rhythms.\",\n \"Title: Design and Implementation of Fire Safety Education System on Campus based on Virtual Reality Technology.\\nYear: 2017\\n\\nAbstract: Fire safety education is essential to every student on campus. Fire safety knowledge learning and operational practice are both important. There is evidence that the virtual reality (VR) based educational method can be a novel and effective approach to learning and practice. However, the existing VR-based system for fire safety education has some shortcomings such as lack of interactivity and high equipment complexity, resulting in low practicability. In order to improve the effect of fire safety education on campus, this paper establishes the model and architecture of fire safety education system based on VR technology. The framework and various elements of fire safety education system are designed and implemented according to the combination of relevant fire safety education theory and VR technology. Finally the prototype version of fire safety education system based on VR technology is built on the HTC VIVE helmet equipment. Through the usability test and comparative analysis of the application experiment, the experiment results prove the feasibility and effectiveness of the proposed approach.\",\n \"Title: Causal Discovery From Temporally Aggregated Time Series\\nYear: 2017\\n\\nAbstract: Discovering causal structure of a dynamical system from observed time series is a traditional and important problem. In many practical applications, observed data are obtained by applying subsampling or temporally aggregation to the original causal processes, making it difficult to discover the underlying causal relations. Subsampling refers to the procedure that for every k consecutive observations, one is kept, the rest being skipped, and recently some advances have been made in causal discovery from such data. With temporal aggregation, the local averages or sums of k consecutive, non-overlapping observations in the causal process are computed as new observations, and causal discovery from such data is even harder. In this paper, we investigate how to recover causal relations at the original causal frequency from temporally aggregated data when k is known. Assuming the time series at the causal frequency follows a vector autoregressive (VAR) model, we show that the causal structure at the causal frequency is identifiable from aggregated time series if the noise terms are independent and non-Gaussian and some other technical conditions hold. We then present an estimation method based on non-Gaussian state-space modeling and evaluate its performance on both synthetic and real data.\",\n \"Title: Research on Cigarette Filter Rod Counting System Based on Machine Vision.\\nYear: 2017\\n\\nAbstract: The traditional method for on-line detection of cigarette filter stick packing is manual sampling, which has low efficiency, high labor cost and can't detect all products. Therefore, it is necessary to establish a set of image processing system [10] based on machine vision. This system uses CCD image sensor to get the filter rod section image, through the image smoothing, edge detection, Binarization and feature extraction, the region of interest is analyzed, and finally get the number of filter rods. The filter rod arrangement on the production line is not completely flat. Using the dynamic area threshold method to calculate the number of filter rod in the flat area. 3D reconstruction of a single image is used to calculate the number of filter rod in the uneven area. The accuracy and practicability of the system are verified by theoretical analysis and experimental comparison.\",\n \"Title: Automatic Measurement of Blood Vessel Angles in Immunohistochemical Images of Liver Cancer.\\nYear: 2017\\n\\nAbstract: This paper presents a method for automated measurement of vascular angle in immunohistochemical images of liver cancer. Firstly, Colour Decon-volution is used to conduct staining separation on a H&E-stained immunohistochemical image, and then blood vessels are segmented using an improved Otsu algorithm. Then the standard SURF algorithm is used to select feature points of the image, and then these feature points are divided into two equal groups according to the distance between individual feature points and the far left (or right) feature point. Finally, a standard least squares method is used to fit two lines using the two groups of points. When the linear deviation of the fitting result based on the two groups of feature points is significant, it is necessary to adjust the belonging of the points of the two groups, and then the two sets are fitted again respectively till the correlation coefficients of the two fitted lines are greater than the predefined threshold, meaning that the measurement of the blood vessel angle in the immunohistochemical map is completed. Compared with the experts' results, our proposed technique results in better accuracy. It is worthy to point out that, to our knowledge, our system is the first one that conducts automated measurement of blood vessel angle of immunohistochemistry.\",\n \"Title: A Novel Segmentation Framework Using Sparse Random Feature in Histology Images of Colon Cancer.\\nYear: 2017\\n\\nAbstract: In this paper, we present a novel segmentation framework for glandular structures in Hematoxylin and Eosin stained histology images, choosing poorly differentiated colon tissue as an example. The proposed framework' target is to identify precise epithelial nuclei objects. We start with staining separate to detect all nuclei objects, and deploy multi-resolution morphology operation to map the initial epithelial nuclei positions. We proposed a new bag of words scheme using sparse random feature to classify epithelial nuclei and stroma nuclei objects to adjust the rest nuclei positions. Finally, we can use the boundary of optimized epithelial nuclei objects to segment the glandular structure.\",\n \"Title: A Novel Memory Gradient Based for Efficient Image Segmentation.\\nYear: 2017\\n\\nAbstract: Image segmentation is a very important phase in automatic image analysis. Of the developed techniques for image segmentation, iterative methods have been proven to be one of the most effective algorithms in the literature. Mean shift algorithms is one of the iterative approaches which have been successfully deployed to many applications. However, despite its promising performance, mean shift has shown its weaknesses in convergence in some of the application areas. In this paper, an improved version of the standard mean-shift algorithm using a memory gradient method is proposed and implemented in order to achieve fast convergence rates by integrating mean shift and memory gradient. Experimental results on real images demonstrate that our proposed algorithm not only improves the efficiency of the classical mean shift algorithm, but also achieves better segmentation results.\",\n \"Title: Identification of Load Categories in Rotor System Based on Vibration Analysis.\\nYear: 2017\\n\\nAbstract: Rotating machinery is often subjected to variable loads during operation. Thus, monitoring and identifying different load types is important. Here, five typical load types have been qualitatively studied for a rotor system. A novel load category identification method for rotor system based on vibration signals is proposed. This method is a combination of ensemble empirical mode decomposition (EEMD), energy feature extraction, and back propagation (BP) neural network. A dedicated load identification test bench for rotor system was developed. According to loads characteristics and test conditions, an experimental plan was formulated, and loading tests for five loads were conducted. Corresponding vibration signals of the rotor system were collected for each load condition via eddy current displacement sensor. Signals were reconstructed using EEMD, and then features were extracted followed by energy calculations. Finally, characteristics were input to the BP neural network, to identify different load types. Comparison and analysis of identifying data and test data revealed a general identification rate of 94.54%, achieving high identification accuracy and good robustness. This shows that the proposed method is feasible. Due to reliable and experimentally validated theoretical results, this method can be applied to load identification and fault diagnosis for rotor equipment used in engineering applications.\",\n \"Title: Data-driven adaptive dynamic programming schemes for non-zero-sum games of unknown discrete-time nonlinear systems.\\nYear: 2018\\n\\nAbstract: This paper integrates game theory, optimal control theory and reinforcement learning to deal with the discrete-time (DT) multi-player non-zero-sum game issue. As is known, the solutions to non-zero-sum game problems are the outcomes of coupled Riccati equations or coupled Hamilton–Jacobi ones, which are generally difficult to solve analytically and require the knowledge of accurate system mathematical models. However, for most practical industrial systems, the system dynamics cannot be obtained accurately or even unavailable, and the conventional model-based methods will be invalid. To overcome this deficiency, we develop data-based adaptive dynamic programming (ADP) algorithms for completely unknown multi-player systems. Firstly, the Nash equilibrium and stationarity conditions are used to formulate the DT multi-player non-zero-sum game, and then policy iteration algorithm is applied to approximate optimal solutions successively. Secondly, a novel online ADP algorithm combined with a neural-network-based identification scheme is designed and only requires the system data instead of the real system models. Subsequently, a data-driven action-dependent heuristic dynamic programming approach is presented and circumvents the estimation errors caused by the identification learning procedure. Finally, two simulation examples are provided to illustrate the feasibility of our schemes.\",\n \"Title: Using an optimization algorithm to establish a network of video surveillance for the protection of Golden Camellia.\\nYear: 2017\\n\\nAbstract: The natural environment is facing increasing human disturbance. Many species of flora are extinct or endangered. To improve the efficiency of ecological management and monitoring, this study proposed to establish a video monitoring network to protect a world-famous rare flora: Golden Camellia, in Fangcheng nature reserve, Guangxi Province, China. Based on the model of LSCP (location set covering problem), we attempted to establish full monitoring coverage of camellias while minimizing the number of video cameras. The model was solved by integer programming. In case of multiple solutions, this study proposed two additional criteria, maximize monitoring area and maximize overlapping count, to eliminate suboptimal solutions. Both of the two optimal solutions included 80 cameras covering a monitoring area of over 55km2. Together, these cameras are able to monitor 97.2% of golden camellia in the reserve. The findings of this study suggest that this location optimization model can be used to improve the conservation effectiveness of rare species.\",\n \"Title: Causality Refined Diagnostic Prediction.\\nYear: 2017\\n\\nAbstract: Applying machine learning in the health care domain has shown promising results in recent years. Interpretable outputs from learning algorithms are desirable for decision making by health care personnel. In this work, we explore the possibility of utilizing causal relationships to refine diagnostic prediction. We focus on the task of diagnostic prediction using discomfort drawings, and explore two ways to employ causal identification to improve the diagnostic results. Firstly, we use causal identification to infer the causal relationships among diagnostic labels which, by itself, provides interpretable results to aid the decision making and training of health care personnel. Secondly, we suggest a post-processing approach where the inferred causal relationships are used to refine the prediction accuracy of a multi-view probabilistic model. Experimental results show firstly that causal identification is capable of detecting the causal relationships among diagnostic labels correctly, and secondly that there is potential for improving pain diagnostics prediction accuracy using the causal relationships.\",\n \"Title: Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation\\nYear: 2018\\n\\nAbstract: This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar preferences with their social friends. Which, however, may not hold true due to various motivations of making online friends and dynamics of online social networks. Inspired by recent causal process based recommendations that first model user exposures towards items and then use these exposures to guide rating prediction, we utilize social information to capture user exposures rather than user preferences. We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality. Under this new assumption, in this paper, we present a novel recommendation approach (named SERec) to integrate social exposure into collaborative filtering. We propose two methods to implement SERec, namely social regularization and social boosting, each with different ways to construct social exposures. Experiments on four real-world datasets demonstrate that our methods outperform the state-of-the-art methods on top-N recommendations. Further study compares the robustness and scalability of the two proposed methods.\",\n \"Title: Portfolio risk measurement via stochastic mesh\\nYear: 2017\\n\\nAbstract: We propose a stochastic mesh approach to portfolio risk measurement under the nested setting in which revaluation of the portfolio value requires simulations. While stochastic mesh was originally proposed as a tool for American option pricing, we are interested in estimating via simulation the risk of the portfolio. We establish asymptotic properties of the stochastic mesh estimator for portfolio risk. In particular, we show that the estimator is asymptotically unbiased and consistent, and its mean squared error (MSE) converges to zero in a rate of Γ-1, where Γ is the effort required to simulate the sample paths. This rate of convergence is the same as that under the non-nested setting. The proposed method allows for path dependence of financial instruments in the portfolio. Preliminary numerical experiments show that the proposed method works reasonably well.\",\n \"Title: A misspecification test for simulation metamodels\\nYear: 2017\\n\\nAbstract: In this paper we propose a novel misspecification test for simulation metamodels. It is a consistent test that helps to assess the adequacy of simulation metamodels. The test statistic we construct is shown to be asymptotically normally distributed under the null hypothesis that the metamodel is correct, while diverging to infinity at a rate of [EQUATION}, where n is the test sample size if the given metamodel is inadequate. Furthermore, as a by-product, we construct confidence intervals for mean squared errors of the metamodels. Preliminary numerical studies show that the test works quite well and has good finite-sample properties.\",\n \"Title: Characteristics Analysis and Image Processing for Full-Polarization Synthetic Aperture Radar Based on Electromagnetic Scattering From Flat Horizontal Perfect Electric Conducting Reflector.\\nYear: 2018\\n\\nAbstract: In this paper, the physical optics method is employed to study the problem of characteristics analysis and image processing for full-polarization synthetic aperture radar (SAR), where electromagnetic scattering from a flat horizontal perfect electric conducting (PEC) reflector is involved. The model of the full-polarization SAR echo from a small region of flat horizontal PEC reflector is deduced b...\",\n \"Title: Varcards: An Integrated Genetic And Clinical Database For Coding Variants In The Human Genome\\nYear: 2018\\n\\nAbstract: A growing number of genomic tools and databases were developed to facilitate the interpretation of genomic variants, particularly in coding regions. However, these tools are separately available in different online websites or databases, making it challenging for general clinicians, geneticists and biologists to obtain the first-hand information regarding some particular variants and genes of interest. Starting with coding regions and splice sties, we artificially generated all possible single nucleotide variants (n = 110 154 363) and cataloged all reported insertion and deletions (n = 1 223 370). We then annotated these variants with respect to functional consequences from more than 60 genomic data sources to develop a database, named VarCards(http://varcards.biols.ac.cn/), by which users can conveniently search, browse and annotate the variant-and gene-level implications of given variants, including the following information: (i) functional effects; (ii) functional consequences through different in silico algorithms; (iii) allele frequencies in different populations; (iv) disease-and phenotyperelated knowledge; (v) generalmeaningful gene-level information; and (vi) drug-gene interactions. As a case study, we successfully employed VarCards in interpretation of de novo mutations in autism spectrum disorders. In conclusion, VarCards provides an intuitive interface of necessary information for researchers to prioritize candidate variations and genes.\",\n \"Title: Value iteration based integral reinforcement learning approach for H∞ controller design of continuous-time nonlinear systems.\\nYear: 2018\\n\\nAbstract: In this paper, a novel integral reinforcement learning approach is developed based on value iteration (VI) for designing the H∞ controller of continuous-time (CT) nonlinear systems. First, the VI learning mechanism is introduced to solve the zero-sum game problems, which is equivalent to the Hamilton–Jacobi–Isaacs (HJI) equation arising in H∞ control problems. Since the proposed method is based on VI learning mechanism, it does not require the admissible control for the implementation, and thus satisfies a more general initial condition than the works based on policy iteration (PI). The iterative property of the value function is analysed with an arbitrary initial positive function, and the H∞ controller can be derived as the iteration converges. For the implementation of the proposed method, three neural networks are introduced to approximate the iterative value function, the iterative control policy and the iterative disturbance policy, respectively. To verify the effectiveness of the VI based method, a linear case and a nonlinear case are presented, respectively.\",\n \"Title: An Efficient Visualization Method for Polygonal Data with Dynamic Simplification.\\nYear: 2018\\n\\nAbstract: Polygonal data often require rendering with symbolization and simplification in geovisualization. A common issue in existing methods is that simplification, symbolization and rendering are addressed separately, causing computational and data redundancies that reduce efficiency, especially when handling large complex polygonal data. Here, we present an efficient polygonal data visualization method by organizing the simplification, tessellation and rendering operations into a single mesh generalization process. First, based on the sweep line method, we propose a topology embedded trapezoidal mesh data structure to organize the tessellated polygons. Second, we introduce horizontal and vertical generalization operations to simplify the trapezoidal meshes. Finally, we define a heuristic testing algorithm to efficiently preserve the topological consistency. The method is tested using three OpenStreetMap datasets and compared with the Douglas Peucker algorithm and the Binary Line Generalization tree-based method. The results show that the proposed method improves the rendering efficiency by a factor of six. Efficiency-sensitive mapping applications such as emergency mapping could benefit from this method, which would significantly improve their visualization performances.\",\n \"Title: Research on the Factors Influencing the Measurement Errors of the Discrete Rogowski Coil.\\nYear: 2018\\n\\nAbstract: An innovative array of magnetic coils (the discrete Rogowski coil-RC) with the advantages of flexible structure, miniaturization and mass producibility is investigated. First, the mutual inductance between the discrete RC and circular and rectangular conductors are calculated using the magnetic vector potential (MVP) method. The results are found to be consistent with those calculated using the finite element method, but the MVP method is simpler and more practical. Then, the influence of conductor section parameters, inclination, and eccentricity on the accuracy of the discrete RC is calculated to provide a reference. Studying the influence of an external current on the discrete RC's interference error reveals optimal values for length, winding density, and position arrangement of the solenoids. It has also found that eccentricity and interference errors decreasing with increasing number of solenoids. Finally, a discrete RC prototype is devised and manufactured. The experimental results show consistent output characteristics, with the calculated sensitivity and mutual inductance of the discrete RC being very close to the experimental results. The influence of an external conductor on the measurement of the discrete RC is analyzed experimentally, and the results show that interference from an external current decreases with increasing distance between the external and measured conductors.\",\n \"Title: Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks.\\nYear: 2018\\n\\nAbstract: In recent years, the classification of breast cancer has been the topic of interest in the field of Healthcare informatics, because it is the second main cause of cancer-related deaths in women. Breast cancer can be identified using a biopsy where tissue is removed and studied under microscope. The diagnosis is based on the qualification of the histopathologist, who will look for abnormal cells. However, if the histopathologist is not well-trained, this may lead to wrong diagnosis. With the recent advances in image processing and machine learning, there is an interest in attempting to develop a reliable pattern recognition based systems to improve the quality of diagnosis. In this paper, we compare two machine learning approaches for the automatic classification of breast cancer histology images into benign and malignant and into benign and malignant sub-classes. The first approach is based on the extraction of a set of handcrafted features encoded by two coding models (bag of words and locality constrained linear coding) and trained by support vector machines, while the second approach is based on the design of convolutional neural networks. We have also experimentally tested dataset augmentation techniques to enhance the accuracy of the convolutional neural network as well as \\\"handcrafted features + convolutional neural network\\\" and \\\"convolutional neural network features + classifier\\\" configurations. The results show convolutional neural networks outperformed the handcrafted feature based classifier, where we achieved accuracy between 96.15% and 98.33% for the binary classification and 83.31% and 88.23% for the multi-class classification.\",\n \"Title: Quantum partial search for uneven distribution of multiple target items.\\nYear: 2018\\n\\nAbstract: Quantum partial search algorithm is an approximate search. It aims to find a target block (which has the target items). It runs a little faster than full Grover search. In this paper, we consider quantum partial search algorithm for multiple target items unevenly distributed in a database (target blocks have different number of target items). The algorithm we describe can locate one of the target blocks. Efficiency of the algorithm is measured by number of queries to the oracle. We optimize the algorithm in order to improve efficiency. By perturbation method, we find that the algorithm runs the fastest when target items are evenly distributed in database.\",\n \"Title: User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning.\\nYear: 2018\\n\\nAbstract: This paper considers recommendation algorithm ensembles in a user-sensitive manner. Recently researchers have proposed various effective recommendation algorithms, which utilized different aspects of the data and different techniques. However, the skewed prediction problem may exist for almost all recommendation algorithms -- algorithms with best average predictive accuracy may cover up that the algorithms may perform poorly for some part of users, which will lead to biased services in real scenarios. In this paper, we propose a user-sensitive ensemble method named UREC to address this issue. We first cluster users based on the recommendation predictions, then we use multi-task learning to learn the user-sensitive ensemble function for the users. In addition, to alleviate the negative effects of new user problem to clustering users, we propose an approximate approach based on a spectral relaxation. Experiments on real-world datasets demonstrate the superiority of our methods.\",\n \"Title: Causal Generative Domain Adaptation Networks.\\nYear: 2018\\n\\nAbstract: An essential problem in domain adaptation is to understand and make use of distribution changes across domains. For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains. By explicitly modeling the changes, one can even generate data in new domains using the generating process with new values for the latent variables in G-DAN. In practice, the process to generate all features together may involve high-dimensional latent variables, requiring dealing with distributions in high dimensions and making it difficult to learn domain changes from few source domains. Interestingly, by further making use of the causal representation of joint distributions, we then decompose the joint distribution into separate modules, each of which involves different low-dimensional latent variables and can be learned separately, leading to a Causal G-DAN (CG-DAN). This improves both statistical and computational efficiency of the learning procedure. Finally, by matching the feature distribution in the target domain, we can recover the target-domain joint distribution and derive the learning machine for the target domain. We demonstrate the efficacy of both G-DAN and CG-DAN in domain generation and cross-domain prediction on both synthetic and real data experiments.\",\n \"Title: Iterative adaptive dynamic programming methods with neural network implementation for multi-player zero-sum games.\\nYear: 2018\\n\\nAbstract: This paper presents novel iterative learning methods along with the neural network implementation for multi-player zero-sum games. Solving zero-sum games depends on the solutions of Hamilton–Jacobi-Isaacs equations, which are nonlinear partial differential equations. These solutions are generally difficult or even impossible to be obtained analytically. To overcome this difficulty, iterative adaptive dynamic programming algorithms are utilized. In the related research works, three-network architecture, i.e., critic-actor-disturbance structure, is used to approximate the value function, control policies and disturbance policies. Different from the previous works, this paper employs single-network architecture, i.e., critic-only structure, to implement the proposed algorithms, which reduces the computation burden and the complexity of design procedure. Finally, two simulation examples are provided to illustrate the effectiveness of our proposed methods.\",\n \"Title: Lung sounds classification using convolutional neural networks.\\nYear: 2018\\n\\nAbstract: •We have proposed an approach for lung sounds classification using CNNs and compared it with two features-based approaches.•In the first handcrafted features-based approach, we used MFCC’s statistics extracted from the signals.•In the second one, we used local binary patterns extracted from spectrograms.•We also opted for ‘Handcrafted Features + CNN’ and ‘CNN Features + Classifier’ configurations.•The final results show that CNNs outperformed the handcrafted features-based approaches.\",\n \"Title: Comparing Digital Phase-Locked Loop and Kalman Filter for Clock Tracking in Ultrawideband Location System.\\nYear: 2018\\n\\nAbstract: For timing and synchronization system, digital phase-locked loop (DPLL) and Kalman filter all have been widely used as the clock tracking and clock correction schemes for the similar structure and properties. This paper compares the two schemes used for ultrawideband (UWB) location system. The improved Kalman filter is more immune to interference.\",\n \"Title: Quantum teleportation and Birman–Murakami–Wenzl algebra\\nYear: 2017\\n\\nAbstract: In this paper, we investigate the relationship of quantum teleportation in quantum information science and the Birman–Murakami–Wenzl (BMW) algebra in low-dimensional topology. For simplicity, we focus on the two spin-1/2 representation of the BMW algebra, which is generated by both the Temperley–Lieb projector and the Yang–Baxter gate. We describe quantum teleportation using the Temperley–Lieb projector and the Yang–Baxter gate, respectively, and study teleportation-based quantum computation using the Yang–Baxter gate. On the other hand, we exploit the extended Temperley–Lieb diagrammatical approach to clearly show that the tangle relations of the BMW algebra have a natural interpretation of quantum teleportation. Inspired by this interpretation, we construct a general representation of the tangle relations of the BMW algebra and obtain interesting representations of the BMW algebra. Therefore, our research sheds a light on a link between quantum information science and low-dimensional topology.\",\n \"Title: XiaoIce Band: A Melody and Arrangement Generation Framework for Pop Music.\\nYear: 2018\\n\\nAbstract: With the development of knowledge of music composition and the recent increase in demand, an increasing number of companies and research institutes have begun to study the automatic generation of music. However, previous models have limitations when applying to song generation, which requires both the melody and arrangement. Besides, many critical factors related to the quality of a song such as chord progression and rhythm patterns are not well addressed. In particular, the problem of how to ensure the harmony of multi-track music is still underexplored. To this end, we present a focused study on pop music generation, in which we take both chord and rhythm influence of melody generation and the harmony of music arrangement into consideration. We propose an end-to-end melody and arrangement generation framework, called XiaoIce Band, which generates a melody track with several accompany tracks played by several types of instruments. Specifically, we devise a Chord based Rhythm and Melody Cross-Generation Model (CRMCG) to generate melody with chord progressions. Then, we propose a Multi-Instrument Co-Arrangement Model (MICA) using multi-task learning for multi-track music arrangement. Finally, we conduct extensive experiments on a real-world dataset, where the results demonstrate the effectiveness of XiaoIce Band.\",\n \"Title: Driver gene mutations based clustering of tumors: methods and applications.\\nYear: 2018\\n\\nAbstract: Motivation: Somatic mutations in proto-oncogenes and tumor suppressor genes constitute a major category of causal genetic abnormalities in tumor cells. The mutation spectra of thousands of tumors have been generated by The Cancer Genome Atlas (TCGA) and other whole genome (exome) sequencing projects. A promising approach to utilizing these resources for precision medicine is to identify genetic similarity-based sub-types within a cancer type and relate the pinpointed sub-types to the clinical outcomes and pathologic characteristics of patients. Results: We propose two novel methods, ccpwModel and xGeneModel, for mutation-based clustering of tumors. In the former, binary variables indicating the status of cancer driver genes in tumors and the genes' involvement in the core cancer pathways are treated as the features in the clustering process. In the latter, the functional similarities of putative cancer driver genes and their confidence scores as the 'true' driver genes are integrated with the mutation spectra to calculate the genetic distances between tumors. We apply both methods to the TCGA data of 16 cancer types. Promising results are obtained when these methods are compared to state-of-the-art approaches as to the associations between the determined tumor clusters and patient race ( or survival time). We further extend the analysis to detect mutation-characterized transcriptomic prognostic signatures, which are directly relevant to the etiology of carcinogenesis.\",\n \"Title: Near-optimal output tracking controller design for nonlinear systems using an event-driven ADP approach.\\nYear: 2018\\n\\nAbstract: In this paper, a novel output tracking control scheme for a class of continuous-time nonlinear system is proposed by using an event-driven adaptive dynamic programming (ADP) approach. Based on the controlled system and the desired reference trajectory, an augmented system is constructed for the optimal tracking control problem (OTCP), where the control law is composed of the steady-state part and the feedback part. According to the standard solution of the OTCP, the feedback part associated to the cost function is to be solved and the steady-state part of the control can be obtained directly. Thus the event-driven near-optimal control method is proposed to solve feedback control of the OTCP, where a single network ADP approach is developed to approximate the optimal cost function. The output tracking error dynamic get converged to zero and the parameters of critic neural network (NN) get converged to the optimal ones by using the designed weight tuning law and event-triggering condition. The stability of the augmented system under the novel control scheme is guaranteed based on Lyapunov theory and two simulation examples are presented to demonstrate the effectiveness of the proposed method.\",\n \"Title: Causal discovery in the presence of missing data.\\nYear: 2018\\n\\nAbstract: Missing data are ubiquitous in many domains such as healthcare. When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be different from those in the complete data generated by the underlying causal process. Consequently, simply applying existing causal discovery methods to the observed data may lead to wrong conclusions. In this paper, we aim at developing a causal discovery method to recover the underlying causal structure from observed data that are missing under different mechanisms, including missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). With missingness mechanisms represented by missingness graphs (m-graphs), we analyze conditions under which additional correction is needed to derive conditional independence/dependence relations in the complete data. Based on our analysis, we propose Missing Value PC (MVPC), which extends the PC algorithm to incorporate additional corrections. Our proposed MVPC is shown in theory to give asymptotically correct results even on data that are MAR or MNAR. Experimental results on both synthetic data and real healthcare applications illustrate that the proposed algorithm is able to find correct causal relations even in the general case of MNAR.\",\n \"Title: Network Scalability for Ultra-Wideband Real-Time Location Systems Based on vMISO.\\nYear: 2018\\n\\nAbstract: For time-based location systems, a common time base is important for obtaining the correct position. However, wireless clock synchronization is considerably easier to deploy in certain environments, such as large areas. A single-region positioning network is often restricted to the anchor communication range. Therefore, multiple master anchors form the basis of an extensible location network and provide an effective means of adapting the system to a complex propagation environment. In this article, we consider simultaneous network scalability with the relative clock synchronization and localization. We first establish an extendable UWB location system model through intelligent response across the routing layer and physical layer. Anchors act as master or slave anchors in different cluster areas and advance the clock check packet (CCP) from an initial region to the surrounding area to achieve clock synchronization of the entire network. The intracluster CCP transmission is achieved by broadcasting, and the intercluster CCP transmissions use vMISO and are automatically driven by the broadcast arrival time for better results. Furthermore, the physical-access and clock synchronization algorithms are discussed. Finally, location tests combined with routing simulations are conducted to demonstrate the performance of the proposed scheme.\",\n \"Title: PCA: Page Correlation Aggregation for Memory Deduplication in Virtualized Environments.\\nYear: 2018\\n\\nAbstract: To intelligently share limited memory across VMs in IaaS cloud, content-based page sharing (CBPS), like KSM, is utilized to greatly reduce the memory footprint of VMs. CBPS merges same-content pages into a single copy. However, it introduces some serious cross-VM covert channel threats. Besides, it has heavy overhead due to vast otiose operations, such as page comparisons and checksum calculations, when detecting page sharing opportunities. In this paper, we propose a novel memory deduplication approach called page correlation aggregation (PCA), which can efficiently reduce otiose operations. Meanwhile defends covert channels. One key idea of PCA is to divide VMs’ pages into several sets, since pages with similar attributes have the greatest possibility with the same content. In PCA, the pages of VMs are firstly divided into different groups according to VMs’ attributes. In each group pages are further separated into different classifications based on their access permissions. Thus page comparisons are restricted to the same classification for sharing. The other is that PCA introduces a dedicated cache to mitigate the latency of COW (Copy- On-Write) used for conducting covert channels. We have conducted a prototype on KSM, one popular CBPS technique. Our experimental results show that PCA reduces otiose operations about 40%, and can effectively resist covert channels.\",\n \"Title: Online adaptive policy iteration based fault-tolerant control algorithm for continuous-time nonlinear tracking systems with actuator failures.\\nYear: 2018\\n\\nAbstract: In this paper, a novel tracking control scheme for continuous-time nonlinear affine systems with actuator faults is proposed by using a policy iteration (PI) based adaptive control algorithm. According to the controlled system and desired reference trajectory, a novel augmented tracking system is constructed and the tracking control problem is converted to the stabilizing issue of the corresponding error dynamic system. PI algorithm, generally used in optimal control and intelligence technique fields, is an important reinforcement learning method to solve the performance function by critic neural network (NN) approximation, which satisfies the Lyapunov equation. For the augmented tracking error system with actuator faults, an online PI based fault-tolerant control law is proposed, where a new tuning law of the adaptive parameter is designed to tolerate four common kinds of actuator faults. The stability of the tracking error dynamic with actuator faults is guaranteed by using Lyapunov theory, and the tracking errors satisfy uniformly bounded as the adaptive parameters get converged. Finally, the designed fault-tolerant feedback control algorithm for nonlinear tracking system with actuator faults is applied in two cases to track the desired reference trajectory, and the simulation results demonstrate the effectiveness and applicability of the proposed method.\",\n \"Title: Real-Valued Sparse DOA Estimation for MIMO Array System Under Unknown Nonuniform Noise.\\nYear: 2018\\n\\nAbstract: In this paper, the problem of the direction of arrival (DOA) estimation for the multiple input multiple output (MIMO) array system is considered as a real-valued sparse signal recover procedure under the condition of unknown nonuniform noise. Then, a real-valued covariance vector-based sparse Bayesian learning framework is proposed, in which the reduced dimensional (RD) transformation is utilized to remove the redundant elements of MIMO array system, and a linear transformation is applied to eliminate the influence of unknown non-uniform noise. Then by supposing that the source powers follow an independent prior Gaussian distribution with zero-mean, a real-valued covariance vector-based sparse Bayesian model is formulated. And considering its unknown variance as hyperparameters, they can be estimated by adopting the expectation-maximization algorithm. Finally, the DOA can be achieved according to the spatial spectrum of hyperparameters. Simulation results have demonstrated that our proposed method not only achieves more superior performance but also provides robustness against nonuniform noise, compared with other recently reported sparse signal representation based methods.\",\n \"Title: Random Occlusion-recovery for Person Re-identification.\\nYear: 2018\\n\\nAbstract: As a basic task of multi-camera surveillance system, person re-identification aims to re-identify a query pedestrian observed from non-overlapping cameras or across different time with a single camera. Recently, Deep learning based person re-identification models have achieved great success in many benchmarks. However, these supervised models require a large amount of labeled image data and the process of manual labeling spends much manpower and time. In this study, we introduce a method to automatically synthesize labeled person images and adopt them to increase the sample number for per identity in datasets. Specifically, we use the block rectangles to occlude the random parts of the persons in the images. Then, a generative adversarial network (GAN) model is proposed to use paired occlusion and original images to synthesize the de-occluded images that similar but not identical to the original images. Afterwards, we annotate the de-occluded images with the same labels of their corresponding raw image and use them to augment the training samples. We use the augmented datasets to train baseline model. The experiment results on Market-1501 and CUHK03 datasets show that the effectiveness of the proposed method.\",\n \"Title: Distributed leader-following consensus of heterogeneous second-order time-varying nonlinear multi-agent systems under directed switching topology.\\nYear: 2019\\n\\nAbstract: This paper focuses on studying the distributed leader-following consensus problem of heterogeneous second-order time-varying nonlinear multi-agent systems (HSTN_MASs) under directed switching topology. Firstly, based on the relative position and relative velocity measurements among neighborhood agents, a class of distributed tracking protocols are proposed. Then the state transformations are provided to solve the time-varying nonlinearity and translate the leader-following problem into the stabilization control problem. By constructing the topology-dependent Lyapunov function and choosing appropriate time-varying regulation factor and coupling strength, it is shown that the exponential tracking of HSTN_MASs can be achieved. Moreover, this paper discusses the distributed leaderless consensus problem of HSTN_MASs. Finally, several simulation examples are given to illustrate the validity of theoretical results.\",\n \"Title: A pilot study on evaluating children with autism spectrum disorder using computer games.\\nYear: 2019\\n\\nAbstract: Evaluation of children with Autism Spectrum Disorders (ASD) is crucial to clinical diagnosis and educational intervention. The traditional evaluation methods based on questionnaires and scales rely on the experience and expertise of the evaluator, are time-consuming and clinically demanding. Computer games can provide an objective, motiving and safe way for evaluating and reflecting children's development. Therefore, the study aimed to investigate a technology-based method using computer games to evaluate children with ASD. The performance of 40 children with ASD and 51 aged-matched typically developing (TD) children was compared. We found: 1) The completion ratio for children with ASD was lower than TD children for the tasks in most of the games. 2) Significant differences between the ASD and TD groups, but no significant differences within group. 3) The performance of the TD group was better than ASD and the efficiency of TD group was proportional to age. While more research is needed to confirm its reliability and validity, the findings indicate that computer games have great potential in the field of special education as an evaluation tool to clarify difficulties associated with autism.\",\n \"Title: Evaluation of Radiation Response in CoCrFeCuNi High-Entropy Alloys.\\nYear: 2018\\n\\nAbstract: CoCrFeCuNi high-entropy alloys (HEAs) prepared by arc melting were irradiated with a 100 keV He+ ion beam. Volume swelling and hardening induced by irradiation were evaluated. When the dose reached 5.0 x 10(17) ions/cm(2), the Cu-rich phases exhibited more severe volume swelling compared with the matrix phases. This result indicated that the Cu-rich phases were favorable sites for the nucleation and gathering of He bubbles. X-ray diffraction indicated that all diffraction peak intensities decreased regularly. This reduction suggested loosening of the irradiated layer, thereby reducing crystallinity, under He+ ion irradiation. The Nix-Gao model was used to fit the measured hardness in order to obtain a hardness value H-0 that excludes the indentation size effect. At ion doses of 2.5 x 10(17) ions/cm(2) and 5.0 x 10(17) ions/cm(2), the HEAs showed obvious hardening, which could be attributed to the formation of large amounts of irradiation defects. At the ion dose of 1.0 x 10(18) ions/cm(2), hardening was reduced, owing to the exfoliation of the original irradiation layer, combined with recovery induced by long-term thermal spike. This study is important to explore the potential uses of HEAs under extreme irradiation conditions.\",\n \"Title: Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition.\\nYear: 2018\\n\\nAbstract: Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods.\",\n \"Title: A two-stage social trust network partition model for large-scale group decision-making problems.\\nYear: 2019\\n\\nAbstract: With the development of big data and social computing, large-scale group decision making (LGDM) problems attract much attention and become merging with social networks or behavioral factors. In this paper, by considering the trust of social behavioral factor, a two-stage trust network partition algorithm is proposed to reduce the complex of LGDM problems. The large-scale decision makers (DMs) are classified into some leader–follower sub-networks through a network partition algorithm. And a solution method based on trust relationship is proposed to keep the independency of the sub-networks when one follower belongs to more than one leader. Next, weights of independent sub-networks and their individual members are computed, and are further used to aggregate the comprehensive decision information. Finally, alternatives of LGDM problems are sorted with the comprehensive decision information. An experiment with MovieLens data is given to illustrate the proposed LGDM algorithm. And a comparison analysis with general clustering method is provided to verify the effectiveness and feasibility of the proposed method.\",\n \"Title: A Practice to Search the Summit of a DEM Using Simulated Annealing Technique\\nYear: 2018\\n\\nAbstract: Simulated annealing (SA) algorithm is based on iterative improvement but with stochastic acceptance of bad moves. This allows the algorithm to escape local optimal solution in early sampling rounds and the progressive refinement into efficient solutions in later sampling rounds. In this paper, we show how this algorithm is used to find the highest peak of a Digital Elevation Model (DEM). In order to search efficiently in the terrain data, we proposed a reformed cost function which used logarithmic transformation. A set of parameters were confirmed after many experiments. The result shows that SA is not guarantee to find the optimal solution. However, there are 16% chances to find the highest peak during 400 experiments. Specifically, the search process is demonstrated by 3D visualization in GIS, which can help understand the mechanism of the algorithm and provide convenience for teaching.\",\n \"Title: Adaptive Data Sampling Mechanism for Process Object.\\nYear: 2018\\n\\nAbstract: Process object is the abstraction of process. In process object, there are different type of entities and associations. The entities vary dependent on other entities. The performance and evolution of process object are affected by the association between entities. These changes could be reflected in the data collected from the process objects. These data from process object could be regard as big data stream. In the context of big data, how to find appropriate data for process object is a challenge. The data sampling should reflect the performance change of process object, and should be adaptive to the current underlying distribution of data in data stream. For finding appropriate data in big data stream to model process object, an adaptive data sampling mechanism is proposed in this paper. Experiments demonstrate the effectiveness of the proposed adaptive data sampling mechanism for process object.\",\n \"Title: Task Allocation Method For Multi-Uav Teams With Limited Communication Bandwidth\\nYear: 2018\\n\\nAbstract: Multiple unmanned aerial vehicle (UAV) team often needs to perform some certain tasks cooperatively. In the process of UAVs performing these tasks, the bandwidth of the communication network will affect the task assignment results, then the problem of task allocation for multi-UAV teams under the condition of limited communication bandwidth is considered. For a target, UAVs need to perform reconnaissance, attack and assessment tasks in coordination. The Consensus-Based Bundle Algorithm(CBBA) requires each task is assigned to no more than one UAV, so this paper extends CBBA by duplicating cooperative tasks to modify the task list and adding a judgment mechanism to ensure the uniqueness of the duplicate tasks' allocation in order to achieve the purpose of allocating multiple UAVs to the same task. At the same time, this paper utilizes a bid warping link to improve the algorithm performance and applies CBBA in asynchronous environment to reduce communication burden. Simulation results show that this improved algorithm is feasible and more efficient to improve the task allocation result for multi-UAVs teams with fewer messages transmission and a larger global reward.\",\n \"Title: Image-Enhanced Multi-level Sentence Representation Net for Natural Language Inference\\nYear: 2018\\n\\nAbstract: Natural Language Inference (NLI) task requires an agent to determine the semantic relation between a premise sentence (p) and a hypothesis sentence (h), which demands sufficient understanding about sentences from lexical knowledge to global semantic. Due to the issues such as polysemy, ambiguity, as well as fuzziness of sentences, fully understanding sentences is still challenging. To this end, we propose an Image-Enhanced Multi-Level Sentence Representation Net (IEMLRN), a novel architecture that is able to utilize the image to enhance the sentence semantic understanding at different scales. To be specific, we introduce the corresponding image of sentences as reference information, which can be helpful for sentence semantic understanding and inference relation evaluation. Since image information might be related to the sentence semantics at different scales, we design a multi-level architecture to understand sentences from different granularity and generate the sentence representation more precisely. Experimental results on the large-scale NLI corpus and real-world NLI alike corpus demonstrate that IEMLRN can simultaneously improve the performance. It is noteworthy that IEMLRN significantly outperforms the state-of-the-art sentence-encoding based models on the challenging hard subset and challenging lexical subset of SNLI corpus.\",\n \"Title: Causal Discovery With Linear Non-Gaussian Models Under Measurement Error: Structural Identifiability Results\\nYear: 2018\\n\\nAbstract: Causal discovery methods aim to recover the causal process that generated purely observational data. Despite its successes on a number of real problems, the presence of measurement error in the observed data can produce serious mistakes in the output of various causal discovery methods. Given the ubiquity of measurement error caused by instruments or proxies used in the measuring process, this problem is one of the main obstacles to reliable causal discovery. It is still unknown to what extent the causal structure of relevant variables can be identified in principle. This study aims to take a step towards filling that void. We assume that the underlining process or the measurement-error free variables follows a linear, non-Guassian causal model, and show that the so-called ordered group decomposition of the causal model, which contains major causal information, is identifiable. The causal structure identifiability is further improved with different types of sparsity constraints on the causal structure. Finally, we give rather mild conditions under which the whole causal structure is fully identifiable.\",\n \"Title: Online Density Estimation over Streaming Data: A Local Adaptive Solution\\nYear: 2018\\n\\nAbstract: Accurate online density estimation is crucial to numerous applications that are prevalent with streaming data. Existing online approaches for density estimation somewhat lack prompt adaptability when facing drifting concepts, resulting in delayed or even deteriorated approximations. To alleviate this issue, in this work, we propose an adaptive local online density estimator, i.e. ALoKDE, for real-time density estimation on data streams. Two strategies, a statistical test for concept drift detection and an adaptive weighted local online density estimation when the drift occurs, are tightly integrated into ALoKDE. Specifically, using a weighted form, ALoKDE seeks to provide an unbiased estimation by factoring in the statistical hallmarks of the latest learned distribution and any potential distributional changes that could be introduced by each incoming instance. To ensure a high-precision estimate, ALoKDE integrates three key components: local sampling, optimal bandwidth selection at a temporal basis, and adaptive weighting factor determination. We further analyze the asymptotic properties of ALoKDE and derive its theoretical error bounds regarding bias, variance, MSE and MISE. Extensive comparative studies on various artificial and real-world streaming data demonstrate the efficacy of ALoKDE in online density estimation and real-time classification.\",\n \"Title: Surface Reconstruction Algorithm Based On Local Data Features\\nYear: 2018\\n\\nAbstract: With the development of reverse engineering device, the point cloud data, as a common and important form, is applied to the surface reconstruction domain. Since the point data is so large, scattered and unordered, the representation of point cloud and surface reconstruction is critical contents in reverse engineering system. In this paper, we redefine the representation of point cloud based on set theory, and derive the rules of neighbour relationship of data. Then, we adopt the KD-tree algorithm and improve its searching approach. In addition, the GeoSurface algorithm is proposed, which uses iterative strategy to mesh surface and uses the depth of KD-tree to control the parameters. Finally, we verify the GeoSurface algorithm. The experimental results show the GeoSurface algorithm is effective, and achieves better results in running time and quality of surface reconstruction compared with greedy and Poisson reconstruction algorithm.\",\n \"Title: Thermal Stability Optimization of the Luojia 1-01 Nighttime Light Remote-Sensing Camera's Principal Distance.\\nYear: 2019\\n\\nAbstract: The instability of the principal distance of the nighttime light remote-sensing camera of the Luojia 1-01 satellite directly affects the geometric accuracy of images, consequently affecting the results of analysis of nighttime light remote-sensing data. Based on the theory of optical passive athermal design, a mathematical model of optical-passive athermal design for principal distance stabilization is established. Positive and negative lenses of different materials and the mechanical structures of different materials are matched to optimize the optical system. According to the index requirements of the Luojia 1-01 camera, an image-telecentric optical system was designed under the guidance of the established mathematical model. In the temperature range of -20 degrees C to +60 degrees C, the principal distance of the system changes from -0.01 m to +0.28 m. After on-orbit testing, the geometric accuracy of the designed nighttime light remote-sensing camera is better than 0.20 pixels and less than index requirement of 0.3 pixels, which indicating that the principal distance maintains good stability on-orbit and meets the application requirements of nighttime light remote sensing.\",\n \"Title: Real-Time Feature-Based Video Stabilization on FPGA.\\nYear: 2017\\n\\nAbstract: Digital video stabilization is an important video enhancement technology that aims to remove unwanted camera vibrations from video sequences. Trading off between stabilization performance and real-time hardware implementation feasibility, this paper presents a feature-based full-frame video stabilization method and a novel complete fully pipelined architectural design to implement it on field-programmable gate array (FPGA). In the proposed method, feature points are first extracted with the oriented features from accelerated segment test and rotated binary robust independent elementary features algorithm and matched between consecutive frames. Next, the matched point pairs are fitted to the affine transformation model using a random-sample consensus-based approach to estimate inter-frame motion robustly. Then, the estimated results are accumulated to compute the cumulative motion parameters between the current and reference frames, and the translational components are smoothed by a Kalman filter representing intentional camera movement. Finally, a mosaicked image is constructed based on cumulative motion parameters using an image mosaicking technique, and then a display window is created with the desired frame size according to the computed intentional camera movement to obtain a full motion-compensated frame. Using pipelining and parallel processing strategies, the whole process has been designed using a novel complete fully pipelined architecture and implemented on Altera’s Cyclone III FPGA to build a real-time stabilization system. The experimental results have shown that the proposed system can deal with standard PAL video input including arbitrate translation and rotation and can produce full-frame stabilized output providing a better viewing experience at 22.37 ms/frame, thus achieving real-time processing performance.\",\n \"Title: A Threat Assessment Method Based On Prioritized Visibility Graph Ordered Weighted Averaging Operator\\nYear: 2016\\n\\nAbstract: Multi-target threat assessment is a considerable issue of air combat tactical decision. As the conventional approaches are deficient and imperfect in the procedure of determining the attribute weights and result display of the threats, a novel threat assessment method based on Prioritized Visibility Graph Ordered Weighted Averaging (PVGOWA) operator is proposed. In this work, the relative threat function and the multi-target assessment model of air combat are established. The VGOWA operator is used to calculate the objective weights, which can fully utilize the information in the combat situation. In order to avoid the randomness and uncertainty of expert scoring in the subjective weight determination process, the POWA operator is used to get the final attribute weights by fusing the objective weights and the priorities of the attributes of each target, which are identified by the decision maker. Besides, the radar-chart model is adopted to display the assessment results. Simulation indicates that the proposed method is more reasonable and intuitional for multi-target threat assessment compared to the conventional method.\",\n \"Title: Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model.\\nYear: 2019\\n\\nAbstract: Zebrafish embryo fluorescent vessel analysis, which aims to automatically investigate the pathogenesis of diseases, has attracted much attention in medical imaging. Zebrafish vessel segmentation is a fairly challenging task, which requires distinguishing foreground and background vessels from the 3D projection images. Recently, there has been a trend to introduce domain knowledge to deep learning algorithms for handling complex environment segmentation problems with accurate achievements. In this paper, a novel dual deep learning framework called Dual ResUNet is developed to conduct zebrafish embryo fluorescent vessel segmentation. To avoid the loss of spatial and identity information, the U-Net model is extended to a dual model with a new residual unit. To achieve stable and robust segmentation performance, our proposed approach merges domain knowledge with a novel contour term and shape constraint. We compare our method qualitatively and quantitatively with several standard segmentation models. Our experimental results show that the proposed method achieves better results than the state-of-art segmentation methods. By investigating the quality of the vessel segmentation, we come to the conclusion that our Dual ResUNet model can learn the characteristic features in those cases where fluorescent protein is deficient or blood vessels are overlapped and achieves robust performance in complicated environments.\",\n \"Title: Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination.\\nYear: 2017\\n\\nAbstract: It is commonplace to encounter nonstationary or heterogeneous data, of which the underlying generating process changes over time or across data sets (the data sets may have different experimental conditions or data collection conditions). Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper we develop a principled framework for causal discovery from such data, called Constraint-based causal Discovery from Nonstationary/heterogeneous Data (CD-NOD), which addresses two important questions. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a way to determine causal orientations by making use of independence changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.\",\n \"Title: Research on adaptive robust guidance law for passive homing missile against maneuvering target\\nYear: 2017\\n\\nAbstract: An improved three dimensional adaptive robust guidance law is proposed for the passive homing guidance in which only line-of-sight angular rate can be measured when target maneuvered. The nonlinear control system of missile is designed according to relative motion model of missile and target, which regards coupling and target acceleration as interference. Then a sliding surface and control law is designed based on approximate parallel approaching principle and variable structure control theory. The self-learning ability of RBF neural network is used to approximate the uncertain nonlinear time-varying functions in the control law online. Finally, the network weights adaptive law is proposed according to Lyapunov theory. This method can assure the excellent adaptive ability and robustness of the system. Simulation results indicate that the guidance law is better than traditional proportional navigation and variable structure guidance law in performance.\",\n \"Title: Learning Vector Autoregressive Models With Latent Processes.\\nYear: 2018\\n\\nAbstract: We study the problem of learning the support of transition matrix between random processes in a Vector Autoregressive (VAR) model from samples when a subset of the processes are latent. It is well known that ignoring the effect of the latent processes may lead to very different estimates of the influences among observed processes, and we are concerned with identifying the influences among the observed processes, those between the latent ones, and those from the latent to the observed ones. We show that the support of transition matrix among the observed processes and lengths of all latent paths between any two observed processes can be identified successfully under some conditions on the VAR model. From the lengths of latent paths, we reconstruct the latent subgraph (representing the influences among the latent processes) with a minimum number of variables uniquely if its topology is a directed tree. Furthermore, we propose an algorithm that finds all possible minimal latent graphs under some conditions on the lengths of latent paths. Our results apply to both non-Gaussian and Gaussian cases, and experimental results on various synthetic and real-world datasets validate our theoretical results.\",\n \"Title: A combined GPS UWB and MARG locationing algorithm for indoor and outdoor mixed scenario\\nYear: 2019\\n\\nAbstract: An indoor or outdoor positioning system is hard to position in cross-region and complex environment. In order to make up for the loss of blind zone in scenario change, we proposed a cooperative positioning system based on GPS/UWB/MARG, which can achieve the seamless positioning between buildings in the hybrid scene. In the hybrid scene, using a weighted fusion algorithm to achieve the cooperative positioning of GPS and UWB, and MARG is used to improve the positioning accuracy of GPS. With data optimization and performance analysis of a subsystem, we process GPS/MARG data and UWB data by weighted fusion method. The optimal positioning information in different positioning environments will be output after independent judgment. The results show that, compared with GPS/MARG positioning system, the average positioning accuracy of the cooperative positioning system is improved by 64% in the hybrid scene. In addition, it expands the application scenario of a single positioning system.\",\n \"Title: Cluster computing data mining based on massive intrusion interference constraints in hybrid networks\\nYear: 2019\\n\\nAbstract: To solve the problem that massive intrusion data in hybrid networks greatly interfere network intrusion detection and cause relatively great difficulty to detection due to their frequency discontinuity, a mining algorithm of massive intrusion cluster computing data in hybrid networks based on spectral feature extraction under fixed constraints of time–frequency window is proposed in this paper. The multi-component cross-detection method is used to collect massive intrusion information in hybrid networks and construct a model of massive intrusion signal in hybrid networks. Cascading notch method is used to suppress intrusion interference under constraints of fixed time–frequency window, and extract fundamental quantity and primary function with locality in massive interference information, and obtain a complete energy distribution spectrum on the time–frequency plane. The energy distribution spectrum is used as guidance function to realize cluster calculating data mining with massive intrusion interference constraints. Simulation results show that, in the intrusion detection process of signal-to-noise ratio from − 15 to − 5 dB, the detection accuracy of the method proposed in this paper is always better than that of others. When the signal-to-noise ratio is − 9 dB, the detection accuracy of this method is over 90%. When the signal-to-noise ratio is −9 dB, the detection accuracy of this method can reach 100%. About the detection time, especially after the Number of intrusion date is 2000, the detection time difference between the three methods is increased, and the detection time of other methods is 2–3 times of paper method. The method proposed can accurately locate mass intrusion distribution sources in hybrid networks under the large frequency oscillation of intrusion data in the hybrid networks, realize network intrusion detection and interference suppression and can filter interference information well, so this method improves intrusion detection probability in hybrid networks.\",\n \"Title: Dynamic Subspace Models for High-Sulfur Gas Sweetening Process Monitoring\\nYear: 2017\\n\\nAbstract: The acid component in high-sulfur gas (HSG) is as high as 5%-15%, which will causes great security risk and has strong corrosive effect to the sweetening equipment. Therefore, productive process monitoring has important significance to ensure the system normal work and safety. However, due to the time lag shift of the operating parameters, static methods seem powerless. Thus, in this paper, we proposed a dynamic subspace model for HSG sweetening process monitoring. Specifically, we first introduce the operating parameters and time series as input candidate features, and get the dynamic expansion matrix by time-lag order analysis. Then we combined the matrix with static PCA model to achieve the process monitoring. The experiments on the actual data of a HSG sweetening plant show that, the false alarm rate (FAR) and missing alarm rate (MAR) of Hotelling T2 and Q statistic (SPE) in DPCA are much lower than those of PCA. In particular, compared to PCA, the SPE MAR in DPCA reduced by 13.42%, and the T2 MAR in DPCA is 4 times lower than that of PCA. Therefore, the proposed monitoring method for HSG sweetening process is efficient and feasible.\",\n \"Title: Preference-driven yield-and-quality optimization for high-sulfur gas sweetening process by extreme learning machine model\\nYear: 2019\\n\\nAbstract: Yield and quality, as the two most important output variables of high-sulfur gas (HSG) sweetening process, are affected by the operating parameters. The HSG sweetening process involves more than ten operating parameters, so the relationship between the parameters and output variables is complex, non-linear and strong coupling. This paper tries to use data mining methods to explore this relationship and apply it to optimize the yield and quality. First, a ten (inputs)-to-three (outputs) model is established by extreme learning machine (ELM). Then, a preference-driven multi-objective optimization algorithm is used to maximize the yield while ensuring that the concentration of carbon dioxide (CO2) and the concentration of hydrogen sulfide (H2S) in the treated gas are close to but not exceeding 3% and 4 ppm respectively. The proposed method is validated in a HSG purification plant in southwest China. A set of 3044 production data is collected and randomly divided into 80 and 20% for training and testing. The results show that the established ELM model is in good agreement with the actual operation data. The maximum deviation of mean square error (MSE), mean absolute error (MAE) and average absolute deviation percent (AAD %) of the predictions in three scenarios are 0.2047, 0.3177 and 7.91% respectively. Moreover, the optimization based on the obtained ELM model is also validated. In particular, the H2S concentration and CO2 concentration in the treated gas are significantly higher than those before optimization, but have not exceeding the limits. Thus, the consumptions of energy and amine solvents decreased, while the yield increased.\",\n \"Title: Interrupt protection control of anti-interference nodes in network based on band sampling decision filter modulation\\nYear: 2019\\n\\nAbstract: In network real-time communication, interrupt protection control needs to be conducted for interference nodes due to the multipath interference in the link layer. However, there have always been some incorrect operations in selecting optimal nodes, such as large errors and non-optimal masking results. Therefore, an interrupt protection method for anti-interference nodes in network real-time communication based on band sampling decision filter modulation is proposed in this paper. In this method, a multipath transmission link model of network real-time communication is constructed; an impulse response function of network communication is established; copy autocorrelation processing for signals of communication node sampling is conducted to realize the transformation from time domain to frequency domain; band sampling decision filter is used to conduct noise suppression for interference nodes; an interrupt decision is conducted for filtering output signals in network real-time communication according to the least mean square error criterion, and whether to conduct interrupt mask for nodes is decided according to the threshold, so as to realize the interrupt protection control for anti-interference nodes in network real-time communication. The simulation results show that by using this method to conduct interrupt protection for communication nodes, the output bit error rate is in general lower than those got by the traditional methods, and the highest bit error rate does not exceed 0.5. It tends to be flat from − 4SNR/dB, and completely unaffected by the interference nodes after 4SNR/dB. The method proposed in this paper restrains the interference nodes, and effectively improves the anti-interference capability and the communication quality.\",\n \"Title: Real-Time Visual Measurement With Opponent Hitting Behavior for Table Tennis Robot.\\nYear: 2018\\n\\nAbstract: In this paper, a visual-based method is proposed for the table tennis robot to estimate the hitting point from the opponent, which can provide a better understanding of the opponent, and thus improve the responsiveness of the table tennis robot. As an essential manifestation of opponent hitting behavior, the information of hitting point includes not only the motion status variation of the ball bef...\",\n \"Title: Fault Estimation and Fault-Tolerant Control for Switched Fuzzy Stochastic Systems.\\nYear: 2018\\n\\nAbstract: This paper addresses the problems of fault estimation and fault-tolerant control for switched fuzzy stochastic systems with actuator fault and sensor fault. A novel observer is proposed to estimate the system states, actuator, and sensor faults, simultaneously. The proposed observer can be treated as an extension of the traditional proportional-integral observer. The estimation information is util...\",\n \"Title: Generalized Score Functions for Causal Discovery.\\nYear: 2018\\n\\nAbstract: Discovery of causal relationships from observational data is a fundamental problem. Roughly speaking, there are two types of methods for causal discovery, constraint-based ones and score-based ones. Score-based methods avoid the multiple testing problem and enjoy certain advantages compared to constraint-based ones. However, most of them need strong assumptions on the functional forms of causal mechanisms, as well as on data distributions, which limit their applicability. In practice the precise information of the underlying model class is usually unknown. If the above assumptions are violated, both spurious and missing edges may result. In this paper, we introduce generalized score functions for causal discovery based on the characterization of general (conditional) independence relationships between random variables, without assuming particular model classes. In particular, we exploit regression in RKHS to capture the dependence in a nonparametric way. The resulting causal discovery approach produces asymptotically correct results in rather general cases, which may have nonlinear causal mechanisms, a wide class of data distributions, mixed continuous and discrete data, and multidimensional variables. Experimental results on both synthetic and real-world data demonstrate the efficacy of our proposed approach.\",\n \"Title: Counting and Sampling from Markov Equivalent DAGs Using Clique Trees\\nYear: 2019\\n\\nAbstract: A directed acyclic graph (DAG) is the most common graphical model for representing causal relationships among a set of variables. When restricted to using only observational data, the structure of the ground truth DAG is identifiable only up to Markov equivalence, based on conditional independence relations among the variables. Therefore, the number of DAGs equivalent to the ground truth DAG is an indicator of the causal complexity of the underlying structure-roughly speaking, it shows how many interventions or how much additional information is further needed to recover the underlying DAG. In this paper, we propose a new technique for counting the number of DAGs in a Markov equivalence class. Our approach is based on the clique tree representation of chordal graphs. We show that in the case of bounded degree graphs, the proposed algorithm is polynomial time. We further demonstrate that this technique can be utilized for uniform sampling from a Markov equivalence class, which provides a stochastic way to enumerate DAGs in the equivalence class and may be needed for finding the best DAG or for causal inference given the equivalence class as input. We also extend our counting and sampling method to the case where prior knowledge about the underlying DAG is available, and present applications of this extension in causal experiment design and estimating the causal effect of joint interventions.\",\n \"Title: A fast optimal node locating algorithm for MIMO system communication based on link balance control.\\nYear: 2019\\n\\nAbstract: In order to solve the frequent communication obstacle problem in multiple-input and multiple-output (MIMO) system communication caused by routing conflicts, it is necessary to quickly locate the optimal nodes for communication, so a fast optimal node locating algorithm for MIMO system communication based on link balance control is proposed in this paper. In this algorithm, a MIMO system communication channel model is constructed through the link balance control method, and interference features of signals are separated by analyzing the spectrum of communication signals based on the model. Simulation results show that this algorithm can provide 100% accuracy in optimal node locating under signal-to-noise ratio of 80 dB, an average improvement of 13.8% over that in traditional algorithms. The results show that our algorithm can provide high accuracy in locating the optimal nodes for communication, and can ensure high locating efficiency, which effectively improves the communication transmission quality of MIMO system.\",\n \"Title: The novel part-based cascaded regression algorithm research combining with pose estimation\\nYear: 2019\\n\\nAbstract: Facial feature location is the key procedure in the process of face image analysis. To increase the localization accuracy and success rate of complex facial images due to lack of shape constraint in robust cascaded pose regression algorithm, the paper had proposed the novel part-based cascaded regression algorithm combining pose estimation. The facial landmarks are divided into several areas and pose estimation has used as shape constraint. To enhance the algorithm’s performance, a priori knowledge has been exerted during sampling shape-indexed features to constrain the distance between pixel’s pair, and the number of regression has reduced. The experimental results of proposed algorithm and others have demonstrated that the proposed algorithm has outperformed in localization accuracy and achieved better robustness for occlusion and complex facial images, the better localization success rate has been obtained, and the proposed method can realize real-time processing standard.\",\n \"Title: Research on Urban Expansion Methods Based on Lacunarity Index\\nYear: 2019\\n\\nAbstract: The problem of urban expansion is an important part of urban development, the domestic research methods were summarized and analyzed. The study found that existing methods can be divided into exponential changes, land use changes and monitoring simulations. However, the research results of existing methods are homogenized. In this study, the gap index model was established and Kunming and Yuxi were taken as examples to systematically introduce the gap index to calculate urban expansion. The study found that the global gap index can better describe the urban expansion characteristics and can explain the heterogeneity between urban land use by scale shifting; The local clearance index can not only describe the urban expansion characteristics, but also analyze the urban expansion direction and expansion trend in detail to describe the spatial pattern change of urban expansion. The application of the gap index in urban expansion has a very good reference for future urban expansion research.\",\n \"Title: Multiobjective Weapon-Target Assignment Problem By Two-Stage Evolutionary Multiobjective Particle Swarm Optimization\\nYear: 2016\\n\\nAbstract: This paper presents a two-stage evolutionary multiobjective particle swarm optimization (TSMOPSO) algorithm to solve the multiobjective weapon-target assignment (WTA) problem. In order to improve the convergence and the rate of convergence of the proposed algorithm, a two-stage evolutionary strategy is employed. In the first stage of evolution, the population is evolved by the particle status updating rules defined in particle swarm algorithm (PSO). In the second stage of evolution, a novel evolution operator is designed to update the Pareto front solutions which is obtained in the first stage of evolution. Experiments indicate that TSMOPSO algorithm is an effective method to solve the multiobjective weapon-target assignment problem, and the convergence and the rate of convergence of this algorithm have an obvious improvement.\",\n \"Title: Tensor-Based Angle and Array Gain-Phase Error Estimation Scheme in Bistatic MIMO Radar.\\nYear: 2019\\n\\nAbstract: This paper investigates the issue of angle and array gain-phase error estimation in multiple-input-multiple-output (MIMO) radar, and a tensor-based angle and gain-phase error estimation scheme is proposed. In our approach, the parallel factor (PARAFAC) decomposition is performed to estimate the transmit and receive direction matrices. Then the estimation of gain error can be obtained according to the relationship between the columns of direction matrices. After that, the linear feature of the phase in the additional well-calibrated array element is utilized to estimate the angles. Finally, by fully using the phase characteristics of all arrays, the phase error can be obtained. Our approach can remove the influence of error accumulation, and thus it has a superior angle and gain-phase error estimation performance, particularly under the condition of low signal-to-noise ratio (SNR). The numerical examples validate the superiority and effectiveness of the proposed scheme.\",\n \"Title: An Adaptive Spectrum Segmentation Method to Optimize Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis.\\nYear: 2019\\n\\nAbstract: Bearings play an important role in the industrial system. Among all kinds of diagnosis methods, empirical wavelet transform has been widely used for its characteristic of separating empirical modes from the spectrum. Although the empirical wavelet transform restrains the mode aliasing of extracting modal functions from the time domain, it runs slowly and separates a large number of invalid components. In this paper, an adaptive and fast empirical wavelet transform method is proposed. The method extracts the first feature cluster in the Fourier transform of the spectrum to reconstruct the trend spectrum. The minimum points are regarded as the initial boundaries. The key spectral negentropy is proposed to extract the frequency band which may contain main information. This method reduces the number of invalid components and computation time by filtering components before reconstruction. The simulation signal proves that the proposed method is effective and the proposed key spectral negentropy has stronger anti-noise ability than kurtosis. The experimental signals show that the method can successfully extract the fault features of inner or outer rings of bearings, and is suitable for the diagnosis of composite faults.\",\n \"Title: Causal Discovery from Heterogeneous/Nonstationary Data.\\nYear: 2019\\n\\nAbstract: It is commonplace to encounter heterogeneous or nonstationary data, of which the underlying generating process changes across domains or over time. Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper, we develop a framework for causal discovery from such data, called Constraint-based causal Discovery from heterogeneous/NOnstationary Data (CD-NOD), to find causal skeleton and directions and estimate the properties of mechanism changes. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a method to determine causal orientations by making use of independent changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. After learning the causal structure, next, we investigate how to efficiently estimate the `driving forceu0027 of the nonstationarity of a causal mechanism. That is, we aim to extract from data a low-dimensional representation of changes. The proposed methods are nonparametric, with no hard restrictions on data distributions and causal mechanisms, and do not rely on window segmentation. Furthermore, we find that data heterogeneity benefits causal structure identification even with particular types of confounders. Finally, we show the connection between heterogeneity/nonstationarity and soft intervention in causal discovery. Experimental results on various synthetic and real-world data sets (task-fMRI and stock market data) are presented to demonstrate the efficacy of the proposed methods.\",\n \"Title: On Learning Invariant Representation for Domain Adaptation.\\nYear: 2019\\n\\nAbstract: Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achieve a small error on the source domain. The hope is that the learnt representation, together with the hypothesis learnt from the source domain, can generalize to the target domain. In this paper, we first construct a simple counterexample showing that, contrary to common belief, the above conditions are not sufficient to guarantee successful domain adaptation. In particular, the counterexample (Fig. 1) exhibits emph{conditional shift}: the class-conditional distributions of input features change between source and target domains. To give a sufficient condition for domain adaptation, we propose a natural and interpretable generalization upper bound that explicitly takes into account the aforementioned shift. Moreover, we shed new light on the problem by proving an information-theoretic lower bound on the joint error of emph{any} domain adaptation method that attempts to learn invariant representations. Our result characterizes a fundamental tradeoff between learning invariant representations and achieving small joint error on both domains when the marginal label distributions differ from source to target. Finally, we conduct experiments on real-world datasets that corroborate our theoretical findings. We believe these insights are helpful in guiding the future design of domain adaptation and representation learning algorithms.\",\n \"Title: Generative-Discriminative Complementary Learning\\nYear: 2019\\n\\nAbstract: The majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases. In this paper, we study the complementary learning problem. Unlike ordinary labels, complementary labels are easy to obtain because an annotator only needs to provide a yes/no answer to a randomly chosen candidate class for each instance. We propose a generative-discriminative complementary learning method that estimates the ordinary labels by modeling both the conditional (discriminative) and instance (generative) distributions. Our method, we call Complementary Conditional GAN (CCGAN), improves the accuracy of predicting ordinary labels and is able to generate high-quality instances in spite of weak supervision. In addition to the extensive empirical studies, we also theoretically show that our model can retrieve the true conditional distribution from the complementarily-labeled data.\",\n \"Title: Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA.\\nYear: 2019\\n\\nAbstract: We consider the problem of inferring causal relationships between two or more passively observed variables. While the problem of such causal discovery has been extensively studied especially in the bivariate setting, the majority of current methods assume a linear causal relationship, and the few methods which consider non-linear dependencies usually make the assumption of additive noise. Here, we propose a framework through which we can perform causal discovery in the presence of general non-linear relationships. The proposed method is based on recent progress in non-linear independent component analysis and exploits the non-stationarity of observations in order to recover the underlying sources or latent disturbances. We show rigorously that in the case of bivariate causal discovery, such non-linear ICA can be used to infer the causal direction via a series of independence tests. We further propose an alternative measure of causal direction based on asymptotic approximations to the likelihood ratio, as well as an extension to multivariate causal discovery. We demonstrate the capabilities of the proposed method via a series of simulation studies and conclude with an application to neuroimaging data.\",\n \"Title: Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data.\\nYear: 2019\\n\\nAbstract: Autism spectrum disorder (ASD) is one of the major developmental disorders affecting children. Recently, it has been hypothesized that ASD is associated with atypical brain connectivities. A substantial body of researches use Pearsonu0027s correlation coefficients, mutual information, or partial correlation to investigate the differences in brain connectivities between ASD and typical controls from functional Magnetic Resonance Imaging (fMRI). However, correlation or partial correlation does not directly reveal causal influences - the information flow - between brain regions. Comparing to correlation, causality pinpoints the key connectivity characteristics and removes redundant features for diagnosis. In this paper, we propose a two-step method for large-scale and cyclic causal discovery from fMRI. It can identify brain causal structures without doing interventional experiments. The learned causal structure, as well as the causal influence strength, provides us the path and effectiveness of information flow. With the recovered causal influence strength as candidate features, we then perform ASD diagnosis by further doing feature selection and classification. We apply our methods to three datasets from Autism Brain Imaging Data Exchange (ABIDE). From experimental results, it shows that with causal connectivities, the diagnostic accuracy largely improves. A closer examination shows that information flows starting from the superior front gyrus to default mode network and posterior areas are largely reduced. Moreover, all enhanced information flows are from posterior to anterior or in local areas. Overall, it shows that long-range influences have a larger proportion of reductions than local ones, while local influences have a larger proportion of increases than long-range ones. By examining the graph properties of brain causal structure, the group of ASD shows reduced small-worldness.\",\n \"Title: DRr-Net: Dynamic Re-read Network for Sentence Semantic Matching\\nYear: 2019\\n\\nAbstract: Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all matching methods, attention mechanism plays an important role in capturing the semantic relations and properly aligning the elements of two sentences. Previous methods utilized attention mechanism to select important parts of sentences at one time. However, the important parts of the sentence during semantic matching are dynamically changing with the degree of sentence understanding. Selecting the important parts at one time may be insufficient for semantic understanding. To this end, we propose a Dynamic Re-read Network (DRr-Net) approach for sentence semantic matching, which is able to pay close attention to a small region of sentences at each step and re-read the important words for better sentence semantic understanding. To be specific, we first employ Attention Stack-GRU (ASG) unit to model the original sentence repeatedly and preserve all the information from bottom-most word embedding input to up-most recurrent output. Second, we utilize Dynamic Re-read (DRr) unit to pay close attention to one important word at one time with the consideration of learned information and re-read the important words for better sentence semantic understanding. Extensive experiments on three sentence matching benchmark datasets demonstrate that DRr-Net has the ability to model sentence semantic more precisely and significantly improve the performance of sentence semantic matching. In addition, it is very interesting that some of finding in our experiments are consistent with the findings of psychological research.\",\n \"Title: Adaptive Fuzzy Fault-Tolerant Tracking Control for Partially Unknown Systems with Actuator Faults via Integral Reinforcement Learning Method\\nYear: 2019\\n\\nAbstract: In this paper, the fuzzy reinforcement learning based tracking control algorithm is first proposed for partially unknown systems with actuator faults. Based on Takagi-Sugeno fuzzy model, a novel fuzzy-augmented tracking dynamic is developed and the overall fuzzy control policy with corresponding performance index is designed, where four kinds of actuator faults, including actuator loss of effectiveness and bias fault, are considered. Combining the reinforcement learning technique and fuzzy-augmented model, the new fuzzy integral reinforcement learning based fault-tolerant control algorithm is designed and it runs in real time for the system with actuator faults. The dynamic matrices can be partially unknown and the online algorithm requires less information transmissions or computational load along with the learning process. Under the overall fuzzy fault-tolerant policy, the tracking objective is achieved and the stability is proved by Lyapunov theory. Finally, the applications in the single-link robot arm system and the complex pitch-rate control problem of F-16 fighter aircraft demonstrate the effectiveness of the proposed method.\",\n \"Title: Robust Optimal Control Scheme for Unknown Constrained-Input Nonlinear Systems via a Plug-n-Play Event-Sampled Critic-Only Algorithm\\nYear: 2020\\n\\nAbstract: In this paper, a novel event-sampled robust optimal controller is proposed for a class of continuous-time constrained-input nonlinear systems with unknown dynamics. In order to solve the robust optimal control problem, an online data-driven identifier is established to construct the system dynamics, and an event-sampled critic-only adaptive dynamic programming method is developed to replace the conventional time-driven actor-critic structure. The designed online identification method runs during the solving process and is not applied as a priori part for the solutions, which simplifies the architecture and reduces computational load. The proposed robust optimal control algorithm tunes the parameters of critic-only neural network (NN) by event-triggering condition and runs in a plug-n-play framework without system functions, where fewer transmissions and less computation are required as all the measurements received simultaneously. Based on the novel design, the stability of system and the convergence of critic NN are demonstrated by Lyapunov theory, where the state is asymptotically stable and weight error is guaranteed to be uniformly ultimately bounded. Finally, the applications in a basic nonlinear system and the complex rotational-translational actuator problem demonstrate the effectiveness of the proposed method.\",\n \"Title: Context-Aware Dual-Attention Network for Natural Language Inference\\nYear: 2019\\n\\nAbstract: Natural Language Inference (NLI) is a fundamental task in natural language understanding. In spite of the importance of existing research on NLI, the problem of how to exploit the contexts of sentences for more precisely capturing the inference relations (i.e. by addressing the issues such as polysemy and ambiguity) is still much open. In this paper, we introduce the corresponding image into inference process. Along this line, we design a novel Context-Aware Dual-Attention Network (CADAN) for tackling NLI task. To be specific, we first utilize the corresponding images as the Image Attention to construct an enriched representation for sentences. Then, we use the enriched representation as the Sentence Attention to analyze the inference relations from detailed perspectives. Finally, a sentence matching method is designed to determine the inference relation in sentence pairs. Experimental results on large-scale NLI corpora and real-world NLI alike corpus demonstrate the superior effectiveness of our CADAN model.\",\n \"Title: Photonic Generation and Transmission of Linearly Chirped Microwave Waveform With Increased Time-Bandwidth Product\\nYear: 2019\\n\\nAbstract: Photonic generation and transmission of linearly chirped microwave waveform with the increased time-bandwidth product (TBWP) is proposed and demonstrated by phase-encoding and splitting parabolic waveform. In the approach, dual-polarization Mach-Zehnder modulator (DPol-MZM) along with polarization controller (PC) is employed to generate two orthogonally polarized wavelengths, and polarization modulator is driven by a parabolic signal. After splitting parabolic waveform and convolving with a binary pseudo random sequence, chirped microwave waveform with high bandwidth and large temporal duration is generated, corresponding to an improved TBWP. Dispersion-induced power fading can be effectively avoided in the approach, and chirped signal with constant initial phase can be achieved by adjusting the PC. The proposed approach is verified by simulations. Linearly chirped microwave waveform with the central frequency of 40 GHz, bandwidth of 12.5 GHz, and TBWPof 20 160 is generated. RF spurious suppression ratios of the generated signal are investigated, and pulse compression performances are also analyzed. The system features filter-free, high tunability, compact structure, and large TBWP, which has potential application in the modern radar system.\",\n \"Title: Causal Discovery with Cascade Nonlinear Additive Noise Models.\\nYear: 2019\\n\\nAbstract: Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class is not transitive--even if each direct causal relation follows this model, indirect causal influences, which result from omitted intermediate causal variables and are frequently encountered in practice, do not necessarily follow the model constraints; as a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured intermediate variables, from data, under the variational auto-encoder framework. Our theoretical results show that with our model, causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.\",\n \"Title: Negentropy Spectrum Decomposition and Its Application in Compound Fault Diagnosis of Rolling Bearing.\\nYear: 2019\\n\\nAbstract: The rolling bearings often suffer from compound fault in practice. Compared with single fault, compound fault contains multiple fault features that are coupled together and make it difficult to detect and extract all fault features by traditional methods such as Hilbert envelope demodulation, wavelet transform and empirical node decomposition (EMD). In order to realize the compound fault diagnosis of rolling bearings and improve the diagnostic accuracy, we developed negentropy spectrum decomposition (NSD), which is based on fast empirical wavelet transform (FEWT) and spectral negentropy, with cyclic extraction as the extraction method. The infogram is constructed by FEWT combined with spectral negentropy to select the best band center and bandwidth for band-pass filtering. The filtered signal is used as a new measured signal, and the fast empirical wavelet transform combined with spectral negentropy is used to filter the new measured signal again. This operation is repeated to achieve cyclic extraction, until the signal no longer contains obvious fault features. After obtaining the envelope of all extracted components, compound fault diagnosis of rolling bearings can be realized. The analysis of the simulation signal and the experimental signal shows that the method can realize the compound fault diagnosis of rolling bearings, which verifies the feasibility and effectiveness of the method. The method proposed in this paper can detect and extract all the fault features of compound fault completely, and it is more reliable for the diagnosis of compound fault. Therefore, the method has practical significance in rolling bearing compound fault diagnosis.\",\n \"Title: Tracking control optimization scheme for a class of partially unknown fuzzy systems by using integral reinforcement learning architecture.\\nYear: 2019\\n\\nAbstract: In this paper, a novel fuzzy integral reinforcement learning (RL) based tracking control algorithm is first proposed for partially unknown fuzzy systems. Firstly, by using the precompensation and augmentation techniques, a new augmented fuzzy tracking system is constructed by combining the fuzzy logic model and desired reference trajectory, where the solution of actual working feedback control policy is converted into a virtual optimal control problem. Secondly, to overcome the requirements of exact original system information, the integral RL technique is utilized to learn the fuzzy control solution, which relaxes the repeatedly transmissions of system matrices during the solving process. Thirdly, compared with the existing standard solution, some crucial and strict aforementioned assumptions are removed and the system can be partially unknown by using the designed algorithm. Besides, under the novel fuzzy control policy, the tracking objective is achieved and the stability is guaranteed by Lyapunov theory. Finally, the developed integral RL tracking control algorithm for partially unknown systems is applied in a mechanical system and the simulation results demonstrate the effectiveness of the proposed new method.\",\n \"Title: Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation.\\nYear: 2019\\n\\nAbstract: Discovery of causal relations from observational data is essential for many disciplines of science and real-world applications. However, unlike other machine learning algorithms, whose development has been greatly fostered by a large amount of available benchmark datasets, causal discovery algorithms are notoriously difficult to be systematically evaluated because few datasets with known ground-truth causal relations are available. In this work, we handle the problem of evaluating causal discovery algorithms by building a flexible simulator in the medical setting. We develop a neuropathic pain diagnosis simulator, inspired by the fact that the biological processes of neuropathic pathophysiology are well studied with well-understood causal influences. Our simulator exploits the causal graph of the neuropathic pain pathology and its parameters in the generator are estimated from real-life patient cases. We show that the data generated from our simulator have similar statistics as real-world data. As a clear advantage, the simulator can produce infinite samples without jeopardizing the privacy of real-world patients. Our simulator provides a natural tool for evaluating various types of causal discovery algorithms, including those to deal with practical issues in causal discovery, such as unknown confounders, selection bias, and missing data. Using our simulator, we have evaluated extensively causal discovery algorithms under various settings.\",\n \"Title: Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models.\\nYear: 2019\\n\\nAbstract: In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments. In this paper, we study causal discovery and forecasting for nonstationary time series. By exploiting a particular type of state-space model to represent the processes, we show that nonstationarity helps to identify causal structure and that forecasting naturally benefits from learned causal knowledge. Specifically, we allow changes in both causal strengths and noise variances in the nonlinear state-space models, which, interestingly, renders both the causal structure and model parameters identifiable. Given the causal model, we treat forecasting as a problem in Bayesian inference in the causal model, which exploits the timevarying property of the data and adapts to new observations in a principled manner. Experimental results on synthetic and real-world data sets demonstrate the efficacy of the proposed methods.\",\n \"Title: Study on novel signal processing and simultaneous-fault diagnostic method for wind turbine:\\nYear: 2019\\n\\nAbstract: High-precision fault diagnosis is important for the widely installed complex industrial product, the wind turbine. However, intelligent monitoring is difficult due to the fuzzy boundaries and individual different variations of the unseen single or simultaneous-fault of such intricate equipment. To solve this problem, this study proposes an ensemble fault diagnostic framework for simultaneous and coupling failure. First, this paper develops novel signal processing methods for effective feature learning and mapping from the non-stationary and nonlinear raw vibrational signals. The adapted variational mode decomposition is introduced based on the particle swarm optimization that applies the minimum mean envelope entropy to optimize the parameters settings. Second, the novel ensemble extreme learning machine-based network is proposed to isolate the faults that applies one extreme learning machine network to count the number of fault scenarios, and the other one to identify the specific single or simultaneous-fault labels. With this scheme, the self-adaptive ensemble extreme learning machine-based fault diagnostic framework is more accurate and faster than the prevailing probabilistic classifier-based methods, as the proposed method does not rely on empirically specified decision-making threshold and generates all the candidate fault labels at the same time. Finally, this study builds the test platform and compares the overall results with the existing feature analysis methods and classifiers. The experimental results verify that the proposed framework detects both single and simultaneous-fault accurately and quickly.\",\n \"Title: Ship Radial Size Estimation In High-Resolution Maritime Surveillance Radars Via Sparse Recovery Using Linear Programming\\nYear: 2019\\n\\nAbstract: For high-resolution maritime surveillance radars operating at scan mode and transmitting long duration pulses, radial size estimation of ships is severely interfered by range sidelobe effect of pulse compression. Due to range sidelobe effect, strong ship returns often cover a range interval wider than its radial size and the range interval covered by detected ship returns is sometimes a severe overestimate of its radial size. An approach to mitigate severe overestimation of ship radial sizes is to find the borders of high-resolution range profile (HRRP) of a ship. Therefore, ship radial size estimation boils down to the recovery of sparse HRRPs of ships from radar returns with heavy-tailed sea clutter in a coherent processing interval (CPI) only containing several pulses. In this paper, a sparse recovery method using linear programming (LP) is proposed to recover sparse HRRPs of ships from radar returns with heavy-tailed sea clutter. As a minor contribution, a full scheme is presented for ship radial size estimation in high-resolution surveillance radars operating at scan mode, which consists of coherent detection along with range cells, annotation of the range interval covered by ship returns, the decision of severe range sidelobe effect, recovery of sparse HRRPs of ships, and radial size computation. The performance of the proposed LP-based method to estimate ship radial size is evaluated by using simulated and real radar data. The experimental results show that it attains more accurate radial size estimates of ships than existing methods.\",\n \"Title: Fuzzy adaptive dynamic programming-based optimal leader-following consensus for heterogeneous nonlinear multi-agent systems\\nYear: 2020\\n\\nAbstract: In this paper, a novel online iterative scheme, based on fuzzy adaptive dynamic programming, is proposed for distributed optimal leader-following consensus of heterogeneous nonlinear multi-agent systems under directed communication graph. This scheme combines game theory, adaptive dynamic programming together with generalized fuzzy hyperbolic model (GFHM). Firstly, based on precompensation technique, an appropriate model transformation is proposed to convert the error system into augmented error system, and an exquisite performance index function is defined for this system. Secondly, on the basis of Hamilton–Jacobi–Bellman (HJB) equation, the optimal consensus control is designed and a novel policy iteration (PI) algorithm is put forward to learn the solutions of the HJB equation online. Here, the proposed PI algorithm is implemented on account of GFHMs. Compared with dual-network model including critic network and action network, the proposed scheme only requires critic network. Thirdly, the augmented consensus error of each agent and the weight estimation error of each GFHM are proved to be uniformly ultimately bounded, and the stability of our method has been verified. Finally, some numerical examples and application examples are conducted to demonstrate the effectiveness of the theoretical results.\",\n \"Title: A Fast Non-Parametric Detection Method For Infrared Small Dim Target\\nYear: 2019\\n\\nAbstract: This paper presents a fast non-parametric detection method for multiple infrared small dim targets against the complex sky background. Traditional methods are based on the entire image processing with a high requirement of accuracy and speediness. However, the proposed method divides the detection into regions of interest (ROIs) searching and precise positioning according to the idea that from coarse searching to precise positioning. The former pays more attention to accuracy while the latter is more concentrated on speediness. First, ROIs sequence can be built by stationary wavelet transform (SWT) followed by Sobel direction directors. Through Hough line transformation, target region proposals which are settled as prior information of precise positioning can be extracted from ROIs sequence. Then, exact target positions are located by auto-regression analysis of temporal profile. Only the max size of targets is assumed which dramatically reduces the dependence of the method on target parameters. Results demonstrate that the proposed method can rapidly locate multiple targets simultaneously in real-time.\",\n \"Title: Domain Generalization via Multidomain Discriminant Analysis.\\nYear: 2019\\n\\nAbstract: Domain generalization (DG) aims to incorporate knowledge from multiple source domains into a single model that could generalize well on unseen target domains. This problem is ubiquitous in practice since the distributions of the target data may rarely be identical to those of the source data. In this paper, we propose Multidomain Discriminant Analysis (MDA) to address DG of classification tasks in general situations. MDA learns a domain-invariant feature transformation that aims to achieve appealing properties, including a minimal divergence among domains within each class, a maximal separability among classes, and overall maximal compactness of all classes. Furthermore, we provide the bounds on excess risk and generalization error by learning theory analysis. Comprehensive experiments on synthetic and real benchmark datasets demonstrate the effectiveness of MDA.\",\n \"Title: Power Demand Response Incentive Pricing Model\\nYear: 2019\\n\\nAbstract: Power demand response aims to clip the peak and fill the valley of power load by interruptible load management. Its closely related to the safety and economic benefit of the power system. At present, various provinces in China have carried out the pilot work of interruptible load management. The power companies signed contracts with power users to stipulate power users to adjust the power load consumption during peak hours or in emergency situations. The singed contracts should be fair for all power users. At the same time, the specific information about the signed contracts should be protected for privacy preservation, which is a typical federated learning scenario. A fair and privacy-preserving incentive pricing model should be proposed to stimulate power uses to participate in thee interruptible load management. To this end, a power demand response incentive pricing model is proposed. Based on the multi-attribute sealed auction game, the bidding model between the power company and the power users is established. In the single bidding model, the risk of the power user's demand response is evaluated firstly. The power users could be classified into different group according to their power load characteristics. Then a user classification selection algorithm is proposed, which enables both the power company's revenue and power user risk to be considered, and enables the selected users to achieve balanced peak clipping. A fair mechanism based on integrals is proposed to assure the fairness between all power users. The case simulations demonstrate that effectiveness of the proposed incentive pricing model.\",\n \"Title: Unpaired Data Empowers Association Tests\\nYear: 2021\\n\\nAbstract: Motivation: There is growing interest in the biomedical research community to incorporate retrospective data, available in healthcare systems, to shed light on associations between different biomarkers. Understanding the association between various types of biomedical data, such as genetic, blood biomarkers, imaging, etc. can provide a holistic understanding of human diseases. To formally test a hypothesized association between two types of data in Electronic Health Records (EHRs), one requires a substantial sample size with both data modalities to achieve a reasonable power. Current association test methods only allow using data from individuals who have both data modalities. Hence, researchers cannot take advantage of much larger EHR samples that includes individuals with at least one of the data types, which limits the power of the association test.Results: We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and to improve the power of the association test. We study the properties of the new test theoretically and empirically, through a series of simulations and by applying our method on real studies in the context of Chronic Obstructive Pulmonary Disease. We are able to identify an association between the high-dimensional characterization of Computed Tomography chest images and several blood biomarkers as well as the expression of dozens of genes involved in the immune system.\",\n \"Title: Ideography Leads Us To The Field Of Cognition: A Radical-Guided Associative Model For Chinese Text Classification\\nYear: 2021\\n\\nAbstract: Cognitive psychology research shows that humans have the instinct for abstract thinking, where association plays an essential role in language comprehension. Especially for Chinese, its ideographic writing system allows radicals to trigger semantic association without the need of phonetics. In fact, subconsciously using the associative information guided by radicals is a key for readers to ensure the robustness of semantic understanding. Fortunately, many basic and extended concepts related to radicals are systematically included in Chinese language dictionaries, which leaves a handy but unexplored way for improving Chinese text representation and classification. To this end, we draw inspirations from cognitive principles between ideography and human associative behavior to propose a novel Radical-guided Associative Model (RAM) for Chinese text classification. RAM comprises two coupled spaces, namely Literal Space and Associative Space, which imitates the real process in people's mind when understanding a Chinese text. To be specific, we first devise a serialized modeling structure in Literal Space to thoroughly capture the sequential information of Chinese text. Then, based on the authoritative information provided by Chinese language dictionaries, we design an association module and put forward a strategy called Radical-Word Association to use ideographic radicals as the medium to associate prior concept words in Associative Space. Afterwards, we design an attention module to imitate people's matching and decision between Literal Space and Associative Space, which can balance the importance of each associative words under specific contexts. Finally, extensive experiments on two real-world datasets prove the effectiveness and rationality of RAM, with good cognitive insights for future language modeling.\",\n \"Title: Testing Independence Between Linear Combinations For Causal Discovery\\nYear: 2021\\n\\nAbstract: Recently, regression based conditional independence (CI) tests have been employed to solve the problem of causal discovery. These methods provide an alternative way to test for CI by transforming CI to independence between residuals. Generally, it is nontrivial to check for independence when these residuals are linearly uncorrelated. With the ability to represent high-order moments, kernel-based methods are usually used to achieve this goal, but at a cost of considerable time. In this paper, we investigate the independence between two linear combinations under linear non-Gaussian structural equation model (SEM). We show that generally the 1-st to 4-th moments of the two linear combinations contain enough information to infer whether or not they are independent. The proposed method provides a simpler but more effective way to measure CIs, with only calculating the 1-st to 4-th moments of the input variables. When applied to causal discovery, the proposed method outperforms kernel-based methods in terms of both speed and accuracy. which is validated by extensive experiments.\",\n \"Title: Making The Relation Matters: Relation Of Relation Learning Network For Sentence Semantic Matching\\nYear: 2021\\n\\nAbstract: Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially BERT. Despite their effectiveness, most of these models treat output labels as meaningless one-hot vectors, underestimating the semantic information and guidance of relations that these labels reveal, especially for tasks with a small number of labels. To address this problem, we propose a Relation of Relation Learning Network (R-2-Net) for sentence semantic matching. Specifically, we first employ BERT to encode the input sentences from a global perspective. Then a CNN-based encoder is designed to capture keywords and phrase information from a local perspective. To fully leverage labels for better relation information extraction, we introduce a self-supervised relation of relation classification task for guiding R-2-Net to consider more about relations. Meanwhile, a triplet loss is employed to distinguish the intra-class and inter-class relations in a finer granularity. Empirical experiments on two sentence semantic matching tasks demonstrate the superiority of our proposed model.\",\n \"Title: Deeptrader: A Deep Reinforcement Learning Approach For Risk-Return Balanced Portfolio Management With Market Conditions Embedding\\nYear: 2021\\n\\nAbstract: Most existing reinforcement learning (RL)-based portfolio management models do not take into account the market conditions, which limits their performance in risk-return balancing. In this paper, we propose DeepTrader, a deep RL method to optimize the investment policy. In particular, to tackle the risk-return balancing problem, our model embeds macro market conditions as an indicator to dynamically adjust the proportion between long and short funds, to lower the risk of market fluctuations, with the negative maximum drawdown as the reward function. Additionally, the model involves a unit to evaluate individual assets, which learns dynamic patterns from historical data with the price rising rate as the reward function. Both temporal and spatial dependencies between assets are captured hierarchically by a specific type of graph structure. Particularly, we find that the estimated causal structure best captures the interrelationships between assets, compared to industry classification and correlation. The two units are complementary and integrated to generate a suitable portfolio which fits the market trend well and strikes a balance between return and risk effectively. Experiments on three well-known stock indexes demonstrate the superiority of DeepTrader in terms of risk-gain criteria.\",\n \"Title: Improving Causal Discovery By Optimal Bayesian Network Learning\\nYear: 2021\\n\\nAbstract: Many widely-used causal discovery methods such as Greedy Equivalent Search (GES), although with asymptotic correctness guarantees, have been reported to produce sub-optimal solutions on finite data, or when the causal faithfulness condition is violated. The constraint-based procedure with Boolean satisfiability (SAT) solver, and the recently proposed Sparsest Permutation (SP) algorithm have shown superb performance, but currently they do not scale well. In this work, we demonstrate that optimal score-based exhaustive search is remarkably useful for causal discovery: it requires weaker conditions to guarantee asymptotic correctness, and outperforms well-known methods including PC, GES, GSP, and NOTEARS. In order to achieve scalability, we also develop an approximation algorithm for larger systems based on the A* method, which scales up to 60+ variables and obtains better results than existing greedy algorithms such as GES, MMHC, and GSP. Our results illustrate the risk of assuming the faithfulness assumption, the advantages of exhaustive search methods, and the limitations of greedy search methods, and shed light on the computational challenges and techniques in scaling up to larger networks and handling unfaithful data.\",\n \"Title: Implementation Of Efficient Quantum Search Algorithms On Nisq Computers\\nYear: 2021\\n\\nAbstract: Despite the advent of Grover's algorithm for the unstructured search, its successful implementation on near-term quantum devices is still limited. We apply three strategies to reduce the errors associated with implementing quantum search algorithms. Our improved search algorithms have been implemented on the IBM quantum processors. Using them, we demonstrate three- and four-qubit search algorithm with higher average success probabilities compared to previous works. We present the successful execution of the five-qubit search on the IBM quantum processor for the first time. The results have been benchmarked using degraded ratio, which is the ratio between the experimental and the theoretical success probabilities. The fast decay of the degraded ratio supports our divide-and-conquer strategy. Our proposed strategies are also useful for implementation of quantum search algorithms in the post-NISQ era.\",\n \"Title: Poorly Differentiated Colorectal Gland Segmentation Approach Based on Internal and External Stress in Histology Images\\nYear: 2020\\n\\nAbstract: Histological assessment of glands is one of the key factors in colon cancer grading. We propose an approach for segmentation of glands in colon cancer histopathology images, which bases on the characteristics of lumens and rough gland boundary. First, we present a U-net for stain separation of H& E image to obtain the H-stain, E-stain, and background stain intensity maps. Subsequently, the epithelial nuclei was identified on the H stain intensity map, and the lumen segmentation was performed on the background intensity map. Then, we employ the axis of least inertia and chain to establish a connection between the lumen and the epithelial nuclei. In the light of connection of the lumen and the epithelial nuclei, more accurately gland segmentation is performed based on the rough gland boundary. The proposed stain separation approach is unsupervised, and the stain separation makes the information contained in the H& E image more independent and easy to identify, so as to deal with the uneven stain intensity and the inconspicuous stain difference. We compare our results on a publicly available data set. The results show that the segmentation approach combining the characteristics of lumens and rough gland boundary has improved the segmentation accuracy.\",\n \"Title: Batch Target Recognition Count under Complex Conditions Based on Att-Unet\\nYear: 2020\\n\\nAbstract: As we all know, the large number of counts is a challenging and time consuming task subject because of oversized number and complex conditions. However, the development of deep learning makes deep learning models very competitive in image segmentation. In this paper, we take cigarette filter rods as the research object. we first evaluate the standard Unet for the filter rod target recognition to separate target and background. Secondly, we use the focal loss function instead of the traditional cross-entropy function to solve the problem of imbalance between target and background area. Thirdly, we add a self-attention module in the traditional Unet convolutional layer to enhance the convolution effect. Fourth, we propose structural element detection criteria and round tangency matching strategy based on HMM (Hidden Markov Model) for the geometric relationship of filter rod position, which further improves the accuracy of the algorithm. We used Qu's [1], Mask-R-CNN [2], FCN [3], Deep-lab-V1 [4] and this paper's algorithm to test the performance of 30000 images from the industrial site. The performance of this paper's algorithm is completely better than the performance of the above algorithm.\",\n \"Title: External Thread Measurement Based on ResUnet and HMM\\nYear: 2020\\n\\nAbstract: There are many methods of thread measurement. In addition, these thread measurement methods require manual segmentation of regions of interest (threaded area) and These methods are easily disturbed by the environment (e.g. dust, iron filings, oil stains, etc.), resulting in inaccurate measurement results. This paper proposes an external thread measurement method based on ResUnet and hidden Markov model (HMM). First, we propose a ResUnet-based thread edge recognition method that omits the process of calibrating the threaded area and identify the thread edge in a complex environment. Secondly, we use HMM to classify the thread edge points so that the threaded parts can be placed at any angle during the measurement, simplifying the measurement steps and calculating the thread parameters based on the classification results. Finally, we evaluated our method using our own dataset, and the results showed that the difference between the measured value and the standard value is within 0.01 mm.\",\n \"Title: DGA-Net - Dynamic Gaussian Attention Network for Sentence Semantic Matching.\\nYear: 2021\\n\\nAbstract: Sentence semantic matching requires an agent to determine the semantic relation between two sentences, where much recent progress has been made by the advancement of representation learning techniques and inspiration of human behaviors. Among all these methods, attention mechanism plays an essential role by selecting important parts effectively. However, current attention methods either focus on all the important parts in a static way or only select one important part at one attention step dynamically, which leaves a large space for further improvement. To this end, in this paper, we design a novel Dynamic Gaussian Attention Network (DGA-Net) to combine the advantages of current static and dynamic attention methods. More specifically, we first leverage pre-trained language model to encode the input sentences and construct semantic representations from a global perspective. Then, we develop a Dynamic Gaussian Attention (DGA) to dynamically capture the important parts and corresponding local contexts from a detailed perspective. Finally, we combine the global information and detailed local information together to decide the semantic relation of sentences comprehensively and precisely. Extensive experiments on two popular sentence semantic matching tasks demonstrate that our proposed DGA-Net is effective in improving the ability of attention mechanism.\",\n \"Title: Observer-Based Output Feedback Event-Triggered Adaptive Control for Linear Multiagent Systems Under Switching Topologies\\nYear: 2022\\n\\nAbstract: The consensus problem of general linear multiagent systems (MASs) is studied under switching topologies by using observer-based event-triggered control method in this article. On the basis of the output information of agents, two kinds of novel event-triggered adaptive control schemes are designed to achieve the leaderless and leader-follower consensus problems, which do not need to utilize the global information of the communication networks. Finally, two simulation examples are introduced to show that the consensus error converges to zero and Zeno behavior is eliminated in MASs. Compared with the existing output feedback control research, one of the significant advantages of our methods is that the controller protocols and triggering mechanisms do not rely on any global information, are independent of the network scale, and are fully distributed ways.\",\n \"Title: Limited Sliding Network: Fine-Grained Target Detection On Electrical Infrastructure For Power Transmission Line Surveillance\\nYear: 2021\\n\\nAbstract: Because of its small size, low local contrast, and much interference, the field image of fine-grained equipment taken from power transmission line surveillance is hard to be sustained by the traditional small target detection technique, which requires the manual extraction of features, making it difficult to accurately detect micro-fine-grained equipment. The deep learning-based algorithms have prospective application but require abundant data to guarantee performance and tackle the problem of foreground-background imbalance. This paper develops an effective pipeline, i.e., limited sliding network (LSNet), to detect the small and fine-grained defects on equipment in power transmission line infrastructure. The model firstly performs the regional analysis on the entire image to determine the potential target locations. The feature extraction and classification on the potential location image blocks are further performed by the VGG-style model for the dense target locations, and the nonmaximum suppression method is finally applied to locate the target. On the other hand, a specific training method is also developed to better deal with a wide range imbalances of positive and negative samples. The proposed method achieves the detection mean average precision (mAP) rate of 98.66% on the real datasets, while limiting the computational overhead of hardware.\",\n \"Title: Adversarial Robustness Through the Lens of Causality.\\nYear: 2022\\n\\nAbstract: The adversarial vulnerability of deep neural networks has attracted significant attention in machine learning. From a causal viewpoint, adversarial attacks can be considered as a specific type of distribution change on natural data. As causal reasoning has an instinct for modeling distribution change, we propose to incorporate causality into mitigating adversarial vulnerability. However, causal formulations of the intuition of adversarial attack and the development of robust DNNs are still lacking in the literature. To bridge this gap, we construct a causal graph to model the generation process of adversarial examples and define the adversarial distribution to formalize the intuition of adversarial attacks. From a causal perspective, we find that the label is spuriously correlated with the style (content-independent) information when an instance is given. The spurious correlation implies that the adversarial distribution is constructed via making the statistical conditional association between style information and labels drastically different from that in natural distribution. Thus, DNNs that fit the spurious correlation are vulnerable to the adversarial distribution. Inspired by the observation, we propose the adversarial distribution alignment method to eliminate the difference between the natural distribution and the adversarial distribution. Extensive experiments demonstrate the efficacy of the proposed method. Our method can be seen as the first attempt to leverage causality for mitigating adversarial vulnerability.\",\n \"Title: AKG: automatic kernel generation for neural processing units using polyhedral transformations\\nYear: 2021\\n\\nAbstract: ABSTRACTExisting tensor compilers have proven their effectiveness in deploying deep neural networks on general-purpose hardware like CPU and GPU, but optimizing for neural processing units (NPUs) is still challenging due to the heterogeneous compute units and complicated memory hierarchy. In this paper, we present AKG, a tensor compiler for NPUs. AKG first lowers the tensor expression language to a polyhedral representation, which is used to automate the memory management of NPUs. Unlike existing approaches that resort to manually written schedules, AKG leverages polyhedral schedulers to perform a much wider class of transformations, and extends the semantics of the polyhedral representation to combine complex tiling techniques and hierarchical fusion strategies. We also implement the domain-specific optimization of convolution in AKG. Moreover, to achieve the optimal performance, we introduce complementary optimizations in code generation, which is followed by an auto-tuner. We conduct extensive experiments on benchmarks ranging from single operators to end-to-end networks. The experimental results show that AKG can obtain superior performance to both manual scheduling approaches and vendor provided libraries. We believe AKG will cast a light on the follow-up compiler works on NPUs.\",\n \"Title: Region-aware network: Model human’s Top-Down visual perception mechanism for crowd counting\\nYear: 2022\\n\\nAbstract: Background noise and scale variation are common problems that have been long recognized in crowd counting. Humans glance at a crowd image and instantly know the approximate number of human and where they are through attention the crowd regions and the congestion degree of crowd regions with a global receptive field. Hence, in this paper, we propose a novel feedback network with Region-Aware block called RANet by modeling human’s Top-Down visual perception mechanism. Firstly, we introduce a feedback architecture to generate priority maps that provide prior about candidate crowd regions in input images. The prior enables the RANet pay more attention to crowd regions. Then we design Region-Aware block that could adaptively encode the contextual information into input images through global receptive field. More specifically, we scan the whole input images and its priority maps in the form of column vector to obtain a relevance matrix estimating their similarity. The relevance matrix obtained would be utilized to build global relationships between pixels. Our method outperforms state-of-the-art crowd counting methods on several public datasets.\",\n \"Title: Progressive Open-Domain Response Generation with Multiple Controllable Attributes.\\nYear: 2021\\n\\nAbstract: It is desirable to include more controllable attributes to enhance the diversity of generated responses in open-domain dialogue systems. However, existing methods can generate responses with only one controllable attribute or lack a flexible way to generate them with multiple controllable attributes. In this paper, we propose a Progressively trained Hierarchical Encoder-Decoder (PHED) to tackle this task. More specifically, PHED deploys Conditional Variational AutoEncoder (CVAE) on Transformer to include one aspect of attributes at one stage. A vital characteristic of the CVAE is to separate the latent variables at each stage into two types: a global variable capturing the common semantic features and a specific variable absorbing the attribute information at that stage. PHED then couples the CVAE latent variables with the Transformer encoder and is trained by minimizing a newly derived ELBO and controlled losses to produce the next stage's input and produce responses as required. Finally, we conduct extensive evaluations to show that PHED significantly outperforms the state-of-the-art neural generation models and produces more diverse responses as expected.\",\n \"Title: SKIP ATTENTION GAN FOR REMOTE SENSING IMAGE SYNTHESIS\\nYear: 2021\\n\\nAbstract: High-quality remote sensing images are difficult to obtain due to limited conditions and high cost for data acquisition. With the development of machine vision and deep learning, some image generation methods (e.g., GANs) are introduced into this field, but it's still hard to generate images with good texture details and structural dependencies. We establish Skip Attention Mechanism to deal with this problem, which learns dependencies between local points on low-resolution feature maps, and then upsample the attention map and combine it with high-resolution feature maps. With this method, long-range dependencies learned from low-resolution are used for generating remote sensing images with more structural details. We name this method as Skip Attention GAN, which is the first method applying cross-scale attention mechanism for unsupervised remote sensing image generation. Experiments show that our method outperforms previous methods under several metrics. Visual and ablation results of attention layers show that Skip Attention has learned long-distance structural dependencies between similar targets.\",\n \"Title: AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning\\nYear: 2022\\n\\nAbstract: Most approaches in reinforcement learning (RL) are data-hungry and specific to fixed environments. In this paper, we propose a principled framework for adaptive RL, called AdaRL, that adapts reliably to changes across domains. Specifically, we construct a generative environment model for the structural relationships among variables in the system and embed the changes in a compact way, which provides a clear and interpretable picture for locating what and where the changes are and how to adapt. Based on the environment model, we characterize a minimal set of representations, including both domain-specific factors and domain-shared state representations, that suffice for reliable and low-cost transfer. Moreover, we show that by explicitly leveraging a compact representation to encode changes, we can adapt the policy with only a few samples without further policy optimization in the target domain. We illustrate the efficacy of AdaRL through a series of experiments that allow for changes in different components of Cartpole and Atari games.\",\n \"Title: A Deep Imputation And Inference Framework For Estimating Personalized And Race-Specific Causal Effects Of Genomic Alterations On Psa\\nYear: 2021\\n\\nAbstract: Prostate Specific Antigen (PSA) level in the serum is one of the most widely used markers in monitoring prostate cancer (PCa) progression, treatment response, and disease relapse. Although significant efforts have been taken to analyze various socioeconomic and cultural factors that contribute to the racial disparities in PCa, limited research has been performed to quantitatively understand how and to what extent molecular alterations may impact differential PSA levels present at varied tumor status between African-American and European-American men. Moreover, missing values among patients add another layer of difficulty in precisely inferring their outcomes. In light of these issues, we propose a data-driven, deep learning-based imputation and inference framework (DIIF). DIIF seamlessly encapsulates two modules: an imputation module driven by a regularized deep autoencoder for imputing critical missing information and an inference module in which two deep variational autoencoders are coupled with a graphical inference model to quantify the personalized and race-specific causal effects. Large-scale empirical studies on the independent sub-cohorts of The Cancer Genome Atlas (TCGA) PCa patients demonstrate the effectiveness of DIIF. We further found that somatic mutations in TP53, ATM, PTEN, FOXA1, and PIK3CA are statistically significant genomic factors that may explain the racial disparities in different PCa features characterized by PSA.\",\n \"Title: Echo state network-based online optimal control for discrete-time nonlinear systems\\nYear: 2021\\n\\nAbstract: This paper investigates the online optimal control problem of discrete-time nonlinear systems using echo state network (ESN)-based adaptive dynamic programming (ADP) method. An online iterative learning algorithm is proposed to solve the partial differential Hamilton-Jacobi-Bellman (HJB) equation in real time. A novel neural networks (NN) critic-actor architecture is presented using two ESNs to implement the ADP method. Then, two online learning laws of the output weights are designed for searching the optimal cost function and control policy. The stability of system and output weights is analysed using Lyapunov approach. Three simulations are given to show the feasibility and effectiveness of the designed algorithm. (C) 2021 Elsevier Inc. All rights reserved.\",\n \"Title: DAE-GAN - Dynamic Aspect-aware GAN for Text-to-Image Synthesis.\\nYear: 2021\\n\\nAbstract: Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency. Previous methods usually generate an initial image with sentence embedding and then refine it with fine-grained word embedding. Despite the significant progress, the 'aspect' information (e.g., red eyes) contained in the text, referring to several words rather than a word that depicts 'a particular part or feature of something', is often ignored, which is highly helpful for synthesizing image details. How to make better utilization of aspect information in text-to-image synthesis still remains an unresolved challenge. To address this problem, in this paper, we propose a Dynamic Aspect-awarE GAN (DAE-GAN) that represents text information comprehensively from multiple granularities, including sentence-level, word-level, and aspect-level. Moreover, inspired by human learning behaviors, we develop a novel Aspect-aware Dynamic Re-drawer (ADR) for image refinement, in which an Attended Global Refinement (AGR) module and an Aspect-aware Local Refinement (ALR) module are alternately employed. AGR utilizes word-level embedding to globally enhance the previously generated image, while ALR dynamically employs aspect-level embedding to refine image details from a local perspective. Finally, a corresponding matching loss function is designed to ensure the text-image semantic consistency at different levels. Extensive experiments on two well-studied and publicly available datasets (i.e., CUB-200 and COCO) demonstrate the superiority and rationality of our method.\",\n \"Title: A New 32-Day Average-Difference Method For Calculating Inter-Sensor Calibration Radiometric Biases Between Snpp And Noaa-20 Instruments Within Icvs Framework\\nYear: 2021\\n\\nAbstract: Two existing double-difference (DD) methods, using either a 3rdSensor or Radiative Transfer Modeling (RTM) as a transfer, are applicable primarily for limited regions and channels, and, thus critical in capturing inter-sensor calibration radiometric bias features. A supplementary method is also desirable for estimating inter-sensor calibration biases at the window and lower sounding channels where the DD methods have non-negligible errors. In this study, using the Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System (JPSS)-1 (alias NOAA-20) as an example, we present a new inter-sensor bias statistical method by calculating 32-day averaged differences (32D-AD) of radiometric measurements between the same instrument onboard two satellites. In the new method, a quality control (QC) scheme using one-sigma (for radiance difference), or two-sigma (for radiance) thresholds are established to remove outliers that are significantly affected by diurnal biases within the 32-day temporal coverage. The performance of the method is assessed by applying it to estimate inter-sensor calibration radiometric biases for four instruments onboard SNPP and NOAA-20, i.e., Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS), Nadir Profiler (NP) within the Ozone Mapping and Profiler Suite (OMPS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Our analyses indicate that the globally-averaged inter-sensor differences using the 32D-AD method agree with those using the existing DD methods for available channels, with margins partially due to remaining diurnal errors. In addition, the new method shows its capability in assessing zonal mean features of inter-sensor calibration biases at upper sounding channels. It also detects the solar intrusion anomaly occurring on NOAA-20 OMPS NP at wavelengths below 300 nm over the Northern Hemisphere. Currently, the new method is being operationally adopted to monitor the long-term trends of (globally-averaged) inter-sensor calibration radiometric biases at all channels for the above sensors in the Integrated Calibration/Validation System (ICVS). It is valuable in demonstrating the quality consistencies of the SDR data at the four instruments between SNPP and NOAA-20 in long-term statistics. The methodology is also applicable for other POES cross-sensor calibration bias assessments with minor changes.\",\n \"Title: Instance-dependent Label-noise Learning under a Structural Causal Model.\\nYear: 2021\\n\\nAbstract: Use the \\\"Report an Issue\\\" link to request a name change.\",\n \"Title: Multi-Attribute Group Decision-Making Method Based On Time-Series Q-Rung Orthopair Fuzzy Sets\\nYear: 2021\\n\\nAbstract: The q-rung orthopair fuzzy set is a powerful and useful tool to deal with uncertainty, but in actual decision-making process, decision-makers are usually required to analyze the actual problem dynamically. Therefore in this paper, we consider the time-series q-rung orthopair fuzzy decision making First, we introduce the new cosine similarity measure of q-ROFS which combines the cosine similarity measure and the Euclidean distance measure. Then, we combine the advantages of projection method and grey correlation degree, establishing the nonlinear programming model to calculate the weights of attributes. Furthermore, we use the exponential decay model to get the weights formulas of q-ROFS at different times. Then we replace the distance function with grey relational projection and extend TOPSIS method. Based on these, we propose a new MAGDM approach to deal with time-series q-rung orthopair fuzzy problem not only from the point of view of geometry but also from the point of view of algebra. Finally, we give a practical example to illustrate effectiveness and feasibility of the new method.\",\n \"Title: MSIPA: Multi-Scale Interval Pattern-Aware Network for ICU Transfer Prediction\\nYear: 2022\\n\\nAbstract: AbstractAccurate prediction of patients’ ICU transfer events is of great significance for improving ICU treatment efficiency. ICU transition prediction task based on Electronic Health Records (EHR) is a temporal mining task like many other health informatics mining tasks. In the EHR-based temporal mining task, existing approaches are usually unable to mine and exploit patterns used to improve model performance. This article proposes a network based on Interval Pattern-Aware, Multi-Scale Interval Pattern-Aware (MSIPA) network. MSIPA mines different interval patterns in temporal EHR data according to the short, medium, and long intervals. MSIPA utilizes the Scaled Dot-Product Attention mechanism to query the contexts corresponding to the three scale patterns. Furthermore, Transformer will use all three types of contextual information simultaneously for ICU transfer prediction. Extensive experiments on real-world data demonstrate that an MSIPA network outperforms state-of-the-art methods.\",\n \"Title: Unaligned Image-to-Image Translation by Learning to Reweight.\\nYear: 2021\\n\\nAbstract: Unsupervised image-to-image translation aims at learning the mapping from the source to target domain without using paired images for training. An essential yet restrictive assumption for unsupervised image translation is that the two domains are aligned, e.g., for the selfie2anime task, the anime (selfie) domain must contain only anime (selfie) face images that can be translated to some images in the other domain. Collecting aligned domains can be laborious and needs lots of attention. In this paper, we consider the task of image translation between two unaligned domains, which may arise for various possible reasons. To solve this problem, we propose to select images based on importance reweighting and develop a method to learn the weights and perform translation simultaneously and automatically. We compare the proposed method with state-of-the-art image translation approaches and present qualitative and quantitative results on different tasks with unaligned domains. Extensive empirical evidence demonstrates the usefulness of the proposed problem formulation and the superiority of our method.\",\n \"Title: Eigenvector-based centralities for multilayer temporal networks under the framework of tensor computation\\nYear: 2021\\n\\nAbstract: •Using sixth-order tensor to represent multilayer temporal networks.•Cosine similarity is proposed to measure interactions between different layers.•Multilayer temporal eigenvector and PageRank centralities are proposed.•The convergence of the proposed iterative algorithms are established.•The proposed two centrality measures are applied to three networks.\",\n \"Title: Variable Spectral Segmentation Empirical Wavelet Transform For Noisy Signal Processing\\nYear: 2021\\n\\nAbstract: Empirical wavelet transform (EWT) can successfully decompose a smooth or noise-free simulated signal into several components. This method has encountered difficulties in processing simulated signals containing noise and in applications. In order to improve the shortcomings of EWT and expand its application, this paper proposes a variable spectral segmentation EWT (VEWT) associated with the trend of spectral fluctuation. Different from scale-space representation and other optimized direct segmentation methods, this paper proposes a selective segmentation method. The extreme points of Multi-taper power spectral density (MPSD) are used to estimate the modes. An extended algorithm is proposed on the basis of Levenberg-Marquardt-Fletcher, which uses the extreme points and positions of MPSD to calculate the bandwidth corresponding to each mode. The information in each frequency band will be determined as the final mode. In the process of loop extraction, a set of boundaries associated with the fluctuations of the spectral will be obtained. The proposed method is more advantageous for the decomposition of signals containing noise. Simulated signals are used to verify the effectiveness of the proposed method. In addition, the MIT-BIH Arrhythmia Database is used to verify the applicability of the proposed method. (C) 2021 Elsevier Inc. All rights reserved.\",\n \"Title: Design and application of facial expression analysis system in empathy ability of children with autism spectrum disorder\\nYear: 2021\\n\\nAbstract: Empathy is an important social ability in the early childhood development. One of the significant characteristics of children with autism spectrum disorder (ASD) is their lack of empathy, which makes it difficult for them to feel and understand other people’s emotions and to judge other people’s behavioral intentions, leading to social disorders. This research designs and implements a facial expre...\",\n \"Title: Learning Temporally Causal Latent Processes from General Temporal Data.\\nYear: 2022\\n\\nAbstract: Our goal is to recover time-delayed latent causal variables and identify their relations from measured temporal data. Estimating causally-related latent variables from observations is particularly challenging as the latent variables are not uniquely recoverable in the most general case. In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be identified from their nonlinear mixtures. We propose LEAP, a theoretically-grounded architecture that extends Variational Autoencoders (VAEs) by enforcing our conditions through proper constraints in causal process prior. Experimental results on various data sets demonstrate that temporally causal latent processes are reliably identified from observed variables under different dependency structures and that our approach considerably outperforms baselines that do not leverage history or nonstationarity information. This is one of the first works that successfully recover time-delayed latent processes from nonlinear mixtures without using sparsity or minimality assumptions.\",\n \"Title: Action-Sufficient State Representation Learning for Control with Structural Constraints.\\nYear: 2022\\n\\nAbstract: Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks. In this paper, we focus on partially observable environments and propose to learn a minimal set of state representations that capture sufficient information for decision-making, termed Action-Sufficient state Representations (ASRs). We build a generative environment model for the structural relationships among variables in the system and present a principled way to characterize ASRs based on structural constraints and the goal of maximizing cumulative reward in policy learning. We then develop a structured sequential Variational Auto-Encoder to estimate the environment model and extract ASRs. Our empirical results on CarRacing and VizDoom demonstrate a clear advantage of learning and using ASRs for policy learning. Moreover, the estimated environment model and ASRs allow learning behaviors from imagined outcomes in the compact latent space to improve sample efficiency.\",\n \"Title: A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction\\nYear: 2021\\n\\nAbstract: A fingerprint-based localization system is an economic way to solve an indoor positioning problem. However, the traditional off-line fingerprint collection stage is a time-consuming and laborious process which limits the use of fingerprint-based localization systems. In this paper, based on ubiquitous Wireless Fidelity (Wi-Fi) equipment and a low-cost Ultra-Wideband (UWB) ranging system (with only one UWB anchor), a ready-to-use indoor localization system is proposed to realize long-term and high-accuracy indoor positioning. More specifically, in this system, it is divided into two stages: (1) an initial stage, and (2) a positioning stage. In the initial stage, an Inertial Measure Unit (IMU) is used to calculate the position using Pedestrian Dead Reckon (PDR) algorithm within a preset number of steps, and the location-related fingerprints are collected to train a Convolutional Neural Network (CNN) regression model; simultaneously, in order to make the UWB ranging system adapt to the Non-Line-of-Sight (NLoS) environment, the increments of acceleration and angular velocity in IMU and the increments of single UWB ranging measures are correlated to pre-train a Supported Vector Regression (SVR). After reaching the threshold of time or step number, the system is changed into a positioning stage, and the CNN predicts the position calibrated by corrected UWB ranging. At last, a series of practical experiments are conducted in the real environment; the experiment results show that, due to the corrected UWB ranging measures calibrating the CNN parameters in every positioning period, this system has stable localization results in a comparative long-term range. Additionally, it has the advantages of stability, low cost, anti-noise, etc.\",\n \"Title: Towards Federated Bayesian Network Structure Learning with Continuous Optimization\\nYear: 2022\\n\\nAbstract: Traditionally, Bayesian network structure learning is often carried out at a central site, in which all data is gathered. However, in practice, data may be distributed across different parties (e.g., companies, devices) who intend to collectively learn a Bayesian network, but are not willing to disclose information related to their data owing to privacy or security concerns. In this work, we present a federated learning approach to estimate the structure of Bayesian network from data that is horizontally partitioned across different parties. We develop a distributed structure learning method based on continuous optimization, using the alternating direction method of multipliers (ADMM), such that only the model parameters have to be exchanged during the optimization process. We demonstrate the flexibility of our approach by adopting it for both linear and nonlinear cases. Experimental results on synthetic and real datasets show that it achieves an improved performance over the other methods, especially when there is a relatively large number of clients and each has a limited sample size.\",\n \"Title: State Estimation for Hybrid Wheeled-Legged Robots Performing Mobile Manipulation Tasks\\nYear: 2021\\n\\nAbstract: This paper introduces a general state estimation framework fusing multiple sensor information for hybrid wheeled-legged robots performing mobile manipulation tasks. At the core of the state estimator is a novel unified odometry for hybrid locomotion which can seamlessly maintain tracking and has no need to switch between stepping and rolling modes. To the best of our knowledge, the proposed odometry is the first work in this area. It is calculated based on the robot kinematics and instantaneous contact points of wheels with sensor inputs from IMU, joint encoders, joint torque sensors estimating wheel contact status, as well as RGB-D camera detecting geometric features of the terrain (e.g. elevation and surface normal vector). Subsequently, the odometry output is utilized as the motion model of a 3D Lidar map-based Monte Carlo Localization module for drift-free state estimation. As part of the framework, visual localization is integrated to provide high precision guidance for the robot movement relative to an object of interest. The proposed approach was verified thoroughly by two experiments conducted on the Pholus robot with OptiTrack measurements as ground truth.\",\n \"Title: Multi-objective Privacy-preserving Text Representation Learning\\nYear: 2021\\n\\nAbstract: BSTRACTPrivate information can either take the form of key phrases that are explicitly contained in the text or be implicit. For example, demographic information about the author of a text can be predicted with above-chance accuracy from linguistic cues in the text itself. Letting alone its explicitness, some of the private information correlates with the output labels and therefore can be learned by a neural network. In such a case, there is a tradeoff between the utility of the representation (measured by the accuracy of the classification network) and its privacy. This problem is inherently a multi-objective problem because these two objectives may conflict, necessitating a trade-off. Thus, we explicitly cast this problem as multi-objective optimization (MOO) with the overall objective of finding a Pareto stationary solution. We, therefore, propose a multiple-gradient descent algorithm (MGDA) that enables the efficient application of the Frank-Wolfe algorithm [10] using the line search. Experimental results on sentiment analysis and part-of-speech (POS) tagging show that MGDA produces higher-performing models than most recent proxy objective approaches, and performs as well as single objective baselines.\",\n \"Title: Non-Nested Estimators For The Central Moments Of A Conditional Expectation And Their Convergence Properties\\nYear: 2021\\n\\nAbstract: The central moments of a conditional expectation exhibit important statistical properties, and the variance is used in many important applications. This paper proposes three non-nested estimators for the central moments that guarantee the asymptotic mean squared error (MSE) of order Gamma(-1), where Gamma represents the total computational budget. Moreover, the central limit theorems are provided, along with the confidence intervals with a convergence rate of Gamma(-1/2). Examples are used to verify the theoretical results and compare the finite sample performance. (C) 2021 Elsevier B.V. All rights reserved.\",\n \"Title: Compressed Self-Attention For Deep Metric Learning\\nYear: 2020\\n\\nAbstract: In this paper, we aim to enhance self-attention (SA) mechanism for deep metric learning in visual perception, by capturing richer contextual dependencies in visual data. To this end, we propose a novel module, named compressed self-attention (CSA), which significantly reduces the computation and memory cost with a neglectable decrease in accuracy with respect to the original SA mechanism, thanks to the following two characteristics: i) it only needs to compute a small number of base attention maps for a small number of base feature vectors; and ii) the output at each spatial location can be simply obtained by an adaptive weighted average of the outputs calculated from the base attention maps. The high computational efficiency of CSA enables the application to high-resolution shallow layers in convolutional neural networks with little additional cost. In addition, CSA makes it practical to further partition the feature maps into groups along the channel dimension and compute attention maps for features in each group separately, thus increasing the diversity of long-range dependencies and accordingly boosting the accuracy. We evaluate the performance of CSA via extensive experiments on two metric learning tasks: person re-identification and local descriptor learning. Qualitative and quantitative comparisons with latest methods demonstrate the significance of CSA in this topic.\",\n \"Title: Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases.\\nYear: 2021\\n\\nAbstract: In causal discovery, linear non-Gaussian acyclic models (LiNGAMs) have been studied extensively. While the causally sufficient case is well understood, in many real problems the observed variables are not causally related. Rather, they are generated by latent variables, such as confounders and mediators, which may themselves be causally related. Existing results on the identification of the causal structure among the latent variables often require very strong graphical assumptions. In this paper, we consider partially observed linear models with either non-Gaussian or heterogeneous errors. In that case we give two graphical conditions which are necessary for identification of the causal structure. These conditions are closely related to sparsity of the causal edges. Together with one additional condition on the coefficients, which holds generically for any graph, the two graphical conditions are also sufficient for identifiability. These new conditions can be satisfied even when there is a large number of latent variables. We demonstrate the validity of our results on synthetic data.\",\n \"Title: Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions.\\nYear: 2021\\n\\nAbstract: Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness. However, the assumption can be approximately violated in many ways, leading to sub-optimal solutions. Although there is a line of research in Bayesian network structure learning that focuses on weakening the assumption, such as exact search methods with well-defined score functions, they do not scale well to large graphs. In this work, we introduce several strategies to improve the scalability of exact score-based methods in the linear Gaussian setting. In particular, we develop a super-structure estimation method based on the support of inverse covariance matrix which requires assumptions that are strictly weaker than faithfulness, and apply it to restrict the search space of exact search. We also propose a local search strategy that performs exact search on the local clusters formed by each variable and its neighbors within two hops in the super-structure. Numerical experiments validate the efficacy of the proposed procedure, and demonstrate that it scales up to hundreds of nodes with a high accuracy.\",\n \"Title: Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?\\nYear: 2021\\n\\nAbstract: Use the \\\"Report an Issue\\\" link to request a name change.\",\n \"Title: Brain Functional Connectivity Analysis via Graphical Deep Learning\\nYear: 2022\\n\\nAbstract: Objective: Graphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and complex relationships between different brain regions. Method: In this work, a graph convolutional network (GCN) based framework is propo...\",\n \"Title: GLaM: Efficient Scaling of Language Models with Mixture-of-Experts.\\nYear: 2022\\n\\nAbstract: Scaling language models with more data, compute and parameters has driven significant progress in natural language processing. For example, thanks to scaling, GPT-3 was able to achieve strong results on in-context learning tasks. However, training these large dense models requires significant amounts of computing resources. In this paper, we propose and develop a family of language models named \\\\glam (\\\\textbf{G}eneralist \\\\textbf{La}nguage \\\\textbf{M}odel), which uses a sparsely activated mixture-of-experts architecture to scale the model capacity while also incurring substantially less training cost compared to dense variants. The largest \\\\glam has 1.2 trillion parameters, which is approximately 7x larger than GPT-3. It consumes only 1/3 of the energy used to train GPT-3 and requires half of the computation flops for inference, while still achieving better overall fewshot performance across 29 NLP tasks.\",\n \"Title: AddrArmor: An Address-based Runtime Code-reuse Attack Mitigation for Shared Objects at the Binary-level\\nYear: 2021\\n\\nAbstract: The widespread adoption of DEP has made most modern attacks follow the same general steps: Attackers try to construct code-reuse attacks by using vulnerable indirect branch instructions in shared objects after successful exploits on memory vulnerabilities. In response to code-reuse attacks, researchers have proposed a large number of defenses. However, most of them require access to source code an...\",\n \"Title: Indoor Location of UWB-TDOA Based on Genetic Algorithm\\nYear: 2021\\n\\nAbstract: With the rapid change of life style, people have higher and higher requirements for high-tech, from outdoor positioning to indoor environment with higher accuracy. In this paper, based on the time difference of arrival (TDOA) location technology, a robust genetic algorithm with good optimization effect is proposed, and the ranging experiment is completed in the environment with UWB signal, which c...\",\n \"Title: Recognition and Location Based on Fusion of FAST Algorithm and SIFT Algorithm\\nYear: 2021\\n\\nAbstract: SLAM technology is the core technology of robot positioning and navigation, and its application in indoor positioning is a new popular trend. Starting from the application of SLAM in indoor positioning, this paper focuses on the performance improvement of SLAM front-end. This paper mainly analyzes the front-end feature extraction algorithm of vision SLAM technology, and proposes a hybrid algorithm...\",\n \"Title: Hybrid Location Algorithm of TDOA / AOA Based on Extended Kalman Filter\\nYear: 2021\\n\\nAbstract: with the development of wireless communication technology, in order to achieve high-precision positioning in indoor environment, this paper proposes a TDOA / AOA hybrid positioning algorithm based on extended Kalman filter. Because NLOS (non line of sight) error is the main error of the system, this paper uses Kalman filter to eliminate NLOS error, so as to reduce the TOA error of NLOS in indoor U...\",\n \"Title: Indoor Sound Source Localization Algorithm Based on BP Neural Network\\nYear: 2021\\n\\nAbstract: Sound source localization is an important part of the perception of things around. Sound source localization can overcome the shortcomings of visual localization, and can also locate the invisible place. The application of sound source localization in indoor is the latest trend. Starting from the application of deep learning to indoor sound source localization, this paper focuses on the analysis a...\",\n \"Title: Gated Graph Neural Networks (GG-NNs) for Abstractive Multi-Comment Summarization\\nYear: 2021\\n\\nAbstract: Summarization of long sequences into a concise statement is a core problem in natural language processing, which requires a non-trivial understanding of the weakly structured text. Therefore, integrating crowdsourced multiple users' comments into a concise summary is even harder because (1) it requires transferring the weakly structured comments to structured knowledge. Besides, (2) the users comm...\",\n \"Title: CSRDA: Cost-sensitive Regularized Dual Averaging for Handling Imbalanced and High-dimensional Streaming Data\\nYear: 2021\\n\\nAbstract: Class-imbalance is one of the most challenging problems in online learning due to its impact on the prediction capability of data stream mining models. Most existing approaches for online learning lack an effective mechanism to handle high-dimensional streaming data with skewed class distributions, resulting in insufficient model interpretation and deterioration of online performance. In this pape...\",\n \"Title: Optimizing smart grid operations from the demand side\\nYear: 2021\\n\\nAbstract: As demand for electricity grows in China, the existing power grid is coming under increasing pressure. Expansion of power generation and delivery capacities across the country requires years of planning and construction. In the meantime, to ensure safe operation of the power grid, it is important to coordinate and optimize the demand side usage. In this paper, we report on our experience deploying an artificial intelligence (AI)-empowered demand-side management platform - the Power Intelligent Decision Support (PIDS) platform - in Shandong Province, China. It consists of three main components: 1) short-term power consumption gap prediction, 2) fine-grained Demand Response (DR) with optimal power adjustment planning, and 3) Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. PIDS has been deployed since August 2018. It is helping over 400 companies optimize their power usage through DR, while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS due to forced shutdown has been reduced from 16% to 0.56%.\",\n \"Title: Optimal Transport for Causal Discovery\\nYear: 2022\\n\\nAbstract: Approaches based on Functional Causal Models (FCMs) have been proposed to determine causal direction between two variables, by properly restricting model classes; however, their performance is sensitive to the model assumptions, which makes it difficult for practitioners to use. In this paper, we provide a novel dynamical-system view of FCMs and propose a new framework for identifying causal direction in the bivariate case. We first show the connection between FCMs and optimal transport, and then study optimal transport under the constraints of FCMs. Furthermore, by exploiting the dynamical interpretation of optimal transport under the FCM constraints, we determine the corresponding underlying dynamical process of the static cause-effect pair data under the least action principle. It provides a new dimension for describing static causal discovery tasks, while enjoying more freedom for modeling the quantitative causal influences. In particular, we show that Additive Noise Models (ANMs) correspond to volume-preserving pressureless flows. Consequently, based on their velocity field divergence, we introduce a criterion to determine causal direction. With this criterion, we propose a novel optimal transport-based algorithm for ANMs which is robust to the choice of models and extend it to post-noninear models. Our method demonstrated state-of-the-art results on both synthetic and causal discovery benchmark datasets.\",\n \"Title: Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models\\nYear: 2021\\n\\nAbstract: AbstractIdentification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class does not allow any confounding or intermediate variables between a cause pair–even if each direct causal relation follows this model. However, omitting the latent causal variables is frequently encountered in practice. After the omission, the model does not necessarily follow the model constraints. As a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a confounding cascade nonlinear additive noise model to represent such causal influences–each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured confounding and intermediate variables, from data under the variational auto-encoder framework. Our theoretical results show that with our model, the causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.\",\n \"Title: HGATs: hierarchical graph attention networks for multiple comments integration\\nYear: 2021\\n\\nAbstract: ABSTRACTFor decades, research in natural language processing (NLP) has focused on summarization. Sequence-to-sequence models for abstractive summarization have been studied extensively, yet generated summaries commonly suffer from fabricated content, and are often found to be near-extractive. We argue that, to address these issues, summarizers need to acquire the co-references that form multiple types of relations over input sentences, e.g., 1-to-N, N-to-1, and N-to-N relations, since the structured knowledge for text usually appears on these relations. By allowing the decoder to pay different attention to the input sentences for the same entity at different generation states, the structured graph representations generate more informative summaries. In this paper, we propose a hierarchical graph attention networks (HGATs) for abstractive summarization with a topic-sensitive PageRank augmented graph. Specifically, we utilize dual decoders, a sequential sentence decoder, and a graph-structured decoder (which are built hierarchically) to maintain the global context and local characteristics of entities, complementing each other. We further design a greedy heuristic to extract salient users' comments while avoiding redundancy to drive a model to better capture entity interactions. Our experimental results show that our models produce significantly higher ROUGE scores than variants without graph-based attention on both SSECIF and CNN/Daily Mail (CNN/DM) datasets.\",\n \"Title: Conditional Contrastive Learning with Kernel\\nYear: 2022\\n\\nAbstract: Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables. Fair contrastive learning constructs negative pairs, for example, from the same gender (conditioning on sensitive information), which in turn reduces undesirable information from the learned representations; weakly supervised contrastive learning constructs positive pairs with similar annotative attributes (conditioning on auxiliary information), which in turn are incorporated into the representations. Although conditional contrastive learning enables many applications, the conditional sampling procedure can be challenging if we cannot obtain sufficient data pairs for some values of the conditioning variable. This paper presents Conditional Contrastive Learning with Kernel (CCL-K) that converts existing conditional contrastive objectives into alternative forms that mitigate the insufficient data problem. Instead of sampling data according to the value of the conditioning variable, CCL-K uses the Kernel Conditional Embedding Operator that samples data from all available data and assigns weights to each sampled data given the kernel similarity between the values of the conditioning variable. We conduct experiments using weakly supervised, fair, and hard negatives contrastive learning, showing CCL-K outperforms state-of-the-art baselines.\",\n \"Title: HyperKRP: A Kernel Runtime Security Architecture with A Tiny Hypervisor on Commodity Hardware\\nYear: 2021\\n\\nAbstract: The large body of kernel code provides broad attack surfaces to exploitable bugs or misconfigurations. Current mitigations are difficult to be integrated together or have a non-trivial performance or code size impact. Thus, systematical protection for the kernel is of critical importance and is required. In this paper, we propose a kernel runtime security architecture, called HyperKRP, to provide systematical protection for kernel code, critical kernel data, and efficient kernel page tables. We have implemented a fully working prototype for a recent Linux kernel running on the Intel x86 processor. Our prototype is compromised of three protection engines based on a small size hypervisor. The evaluation shows that HyperKRP effectively ensures kernel runtime security with acceptable overhead.\",\n \"Title: Invariant Action Effect Model for Reinforcement Learning.\\nYear: 2022\\n\\nAbstract: Good representations can help RL agents perform concise modeling of their surroundings, and thus support effective decision-making in complex environments. Previous methods learn good representations by imposing extra constraints on dynamics. However, in the causal perspective, the causation between the action and its effect is not fully considered in those methods, which leads to the ignorance of the underlying relations among the action effects on the transitions. Based on the intuition that the same action always causes similar effects among different states, we induce such causation by taking the invariance of action effects among states as the relation. By explicitly utilizing such invariance, in this paper, we show that a better representation can be learned and potentially improves the sample efficiency and the generalization ability of the learned policy. We propose Invariant Action Effect Model (IAEM) to capture the invariance in action effects, where the effect of an action is represented as the residual of representations from neighboring states. IAEM is composed of two parts: (1) a new contrastive-based loss to capture the underlying invariance of action effects; (2) an individual action effect and provides a self-adapted weighting strategy to tackle the corner cases where the invariance does not hold. The extensive experiments on two benchmarks, i.e. Grid-World and Atari, show that the representations learned by IAEM preserve the invariance of action effects. Moreover, with the invariant action effect, IAEM can accelerate the learning process by 1.6x, rapidly generalize to new environments by fine-tuning on a few components, and outperform other dynamics-based representation methods by 1.4x in limited steps.\",\n \"Title: Show Your Faith: Cross-Modal Confidence-Aware Network for Image-Text Matching.\\nYear: 2022\\n\\nAbstract: Image-text matching bridges vision and language, which is a crucial task in the field of multi-modal intelligence. The key challenge lies in how to measure image-text relevance accurately as matching evidence. Most existing works aggregate the local semantic similarities of matched region-word pairs as the overall relevance, and they typically assume that the matched pairs are equally reliable. However, although a region-word pair is locally matched across modalities, it may be inconsistent/unreliable from the global perspective of image-text, resulting in inaccurate relevance measurement. In this paper, we propose a novel Cross-Modal Confidence-Aware Network to infer the matching confidence that indicates the reliability of matched region-word pairs, which is combined with the local semantic similarities to refine the relevance measurement. Specifically, we first calculate the matching confidence via the relevance between the semantic of image regions and the complete described semantic in the image, with the text as a bridge. Further, to richly express the region semantics, we extend the region to its visual context in the image. Then, local semantic similarities are weighted with the inferred confidence to filter out unreliable matched pairs in aggregating. Comprehensive experiments show that our method achieves state-of-the-art performance on benchmarks Flickr30K and MSCOCO.\",\n \"Title: Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery.\\nYear: 2022\\n\\nAbstract: Recently, many regression based conditional independence (CI) test methods have been proposed to solve the problem of causal discovery. These methods provide alternatives to test CI by first removing the information of the controlling set from the two target variables, and then testing the independence between the corresponding residuals Res1 and Res2. When the residuals are linearly uncorrelated, the independence test between them is nontrivial. With the ability to calculate inner product in high-dimensional space, kernel-based methods are usually used to achieve this goal, but still consume considerable time. In this paper, we investigate the independence between two linear combinations under linear non-Gaussian structural equation model. We show that the dependence between the two residuals can be captured by the difference between the similarity of (Res1, Res2) and that of (Res1, Res3) (Res3 is generated by random permutation) in high-dimensional space. With this result, we design a new method called SCIT for CI test, where permutation test is performed to control Type I error rate. The proposed method is simpler yet more efficient and effective than the existing ones. When applied to causal discovery, the proposed method outperforms the counterparts in terms of both speed and Type II error rate, especially in the case of small sample size, which is validated by our extensive experiments on various datasets.\",\n \"Title: Identification of Linear Latent Variable Model with Arbitrary Distribution.\\nYear: 2022\\n\\nAbstract: An important problem across multiple disciplines is to infer and understand meaningful latent variables. One strategy commonly used is to model the measured variables in terms of the latent variables under suitable assumptions on the connectivity from the latents to the measured (known as measurement model). Furthermore, it might be even more interesting to discover the causal relations among the latent variables (known as structural model). Recently, some methods have been proposed to estimate the structural model by assuming that the noise terms in the measured and latent variables are non-Gaussian. However, they are not suitable when some of the noise terms become Gaussian. To bridge this gap, we investigate the problem of identification of the structural model with arbitrary noise distributions. We provide necessary and sufficient condition under which the structural model is identifiable: it is identifiable iff for each pair of adjacent latent variables Lx, Ly, (1) at least one of Lx and Ly has non-Gaussian noise, or (2) at least one of them has a non-Gaussian ancestor and is not d-separated from the non-Gaussian component of this ancestor by the common causes of Lx and Ly. This identifiability result relaxes the non-Gaussianity requirements to only a (hopefully small) subset of variables, and accordingly elegantly extends the application scope of the structural model. Based on the above identifiability result, we further propose a practical algorithm to learn the structural model. We verify the correctness of the identifiability result and the effectiveness of the proposed method through empirical studies.\",\n \"Title: Research on the Algorithmic Layout optimization of Prefabricated Building’s Exterior Wall Panels\\nYear: 2021\\n\\nAbstract: This research was carried out to solve the problem of inefficiency and material waste caused by the lack of optimization in the layout design and cutting of the exterior wall panels of prefabricated steel high-rise residential buildings in China. Combined with building performance simulation, it first studies the relationship between relevant factors in the process of exterior wall panel layout and establishes corresponding mathematical model. Then, genetic and heuristic algorithm program are used to find the optimal solution of the panel cutting methods, thereby improving work efficiency and reducing environment pollution.\",\n \"Title: Attainability and Optimality: The Equalized Odds Fairness Revisited.\\nYear: 2022\\n\\nAbstract: Fairness of machine learning algorithms has been of increasing interest. In order to suppress or eliminate discrimination in prediction, various notions as well as approaches have been proposed to impose fairness. Given a notion of fairness, an essential problem is then whether or not it can always be attained, even if with an unlimited amount of data. This issue is, however, not well addressed yet. In this paper, focusing on the Equalized Odds notion of fairness, we consider the attainability of this criterion and, furthermore, if it is attainable, the optimality of the prediction performance under various settings. In particular, for prediction performed by a deterministic function of input features, we give conditions under which Equalized Odds can hold true; if the stochastic prediction is acceptable, we show that under mild assumptions, fair predictors can always be derived. For classification, we further prove that compared to enforcing fairness by post-processing, one can always benefit from exploiting all available features during training and get potentially better prediction performance while remaining fair. Moreover, while stochastic prediction can attain Equalized Odds with theoretical guarantees, we also discuss its limitation and potential negative social impacts.\",\n \"Title: A Drug Recommendation Model Based on Message Propagation and DDI Gating Mechanism\\nYear: 2022\\n\\nAbstract: Drug recommendation task based on the deep learning model has been widely studied and applied in the health care field in recent years. However, the accuracy of drug recommendation models still needs to be improved. In addition, the existing recommendation models either give only one recommendation (however, there may be a variety of drug combination options in practice) or can not provide the confidence level of the recommended result. To fill these gaps, a Drug Recommendation model based on Message Propagation neural network (denoted as DRMP) is proposed in this paper. Then, the Drug-Drug Interaction (DDI) knowledge is introduced into the proposed model to reduce the DDI rate in recommended drugs. Finally, the proposed model is extended to Bayesian Neural Network (BNN) to realize multiple recommendations and give the confidence of each recommendation result, so as to provide richer information to help doctors make decisions. Experimental results on public data sets show that the proposed model is superior to the best existing models.\",\n \"Title: A study on spinal level, length, and branch type of the inferior mesenteric artery and the position relationship between the inferior mesenteric artery, left colic artery, and inferior mesenteric vein\\nYear: 2022\\n\\nAbstract: This study was aimed to explore the clinical application of dual-energy computed tomography (DECT) monoenergetic plus (mono+) imaging to evaluate anatomical variations in the inferior mesenteric artery (IMA). The clinical and imaging data of 212 patients who had undergone total abdominal DECT were retrospectively analyzed. The post-processing mono+ technique was used to obtain 40-keV single-level images in the arterial phase. Three-dimensional reconstruction was performed to evaluate the relationship between the IMA root position and the spinal level, IMA length, and IMA branch type, as well as the position of the left colic artery (LCA) and inferior mesenteric vein (IMV) at the IMA root level. The IMA root was located at the L3 level in 78.3% of cases and at the L2/L3 level in 3.3%. The highest vertebral level of IMA origin was L2 (4.2%), and the lowest was L4 (7.1%). The distance from the IMA root to the level of the sacral promontory was 99.58 ± 13.07 mm, which increased with the elevation of the IMA root at the spinal level. Of the patients, 53.8% demonstrated Type I IMA, 23.1% Type II, 20.7% Type III, and 2.4% Type IV. The length of the IMA varied from 13.6 to 66.0 mm. 77.3% of the IMAs belonged to Type A, the adjacent type, and 22.7% to Type B, the distant type. DECT mono+ can preoperatively evaluate the anatomical characteristics of the IMA and the positional relationship between the LCA and IMV at the IMA root level, which would help clinicians plan individualized surgery for patients.\",\n \"Title: Validation of CrIS Radiometric Performance through Its Comparison to ABI\\nYear: 2022\\n\\nAbstract: Radiometric intercomparison between satellite remote sensing instruments has become an increasingly common practice to monitor the stability and even the accuracy of their radiance products. The assessment also enables the evaluation of calibration improvements made to these products, as well as the identification and resolution of remaining calibration inadequacies. In this paper, the radiance products of the Cross-track Infrared Sounder (CrIS), an interferometer-based hyperspectral IR sounder in low Earth orbit (LEO), is compared with the level-1b (L1b) radiance products of the infrared (IR) bands of the Advanced Baseline Imager (ABI), an imaging radiometer in geostationary (GEO) orbit. Two CrIS instruments are currently in operation on S-NPP and NOAA-20 satellites, respectively, and two ABI instruments are in operation on GOES-16 and GOES-17 satellites, respectively. Radiometric intercomparisons are performed between each CrIS-ABI pair. An established procedure by GSICS for such GEO-LEO instrument comparison is principally followed to emulate the radiance of ABI IR bands from CrIS spectra of the collocated pixels to be compared with the actual ABI radiance. Results show that the long-term time series of CrIS-ABI radiance bias have been stable within 0.2 K for nearly all ABI IR bands within a spectral range from 3.7 mu m to 13.3 mu m, except those with known calibration issues. Miscellaneous calibration events that had occurred to either instrument and altered the biases are identified and explained. While the main goal of the work is to support the on-orbit Cal/Val of CrIS, including the future JPSS-2/3/4 CrIS, such observations can also be referenced to further improve the calibration of ABI.\",\n \"Title: A Method for Extracting Fault Features Using Variable Multilevel Spectral Segmentation Framework and Harmonic Correlation Index\\nYear: 2022\\n\\nAbstract: Adaptive intelligent fault diagnosis technology can improve the automatic status monitoring capability and the success rate of fault diagnosis. This article proposed an adaptive mode decomposition and noise reduction algorithm (VAMD) that use a variable spectral segmentation framework to optimize analytical mode decomposition (AMD) to automatically decompose the mode information in rotating machinery signals. The framework relies on the variability of the window width and envelope estimation characteristics of the order statistics filter (OSF) to increase the diversity of the center frequencies and bandwidth. A novel harmonic correlation index (HCI) is designed to identify the characteristics of rotating machinery faults from various levels of results and improve the usability in mechanical equipment fault diagnosis. The proposed method has successfully achieved fault diagnosis of rolling bearing.\",\n \"Title: Relevance attack on detectors\\nYear: 2022\\n\\nAbstract: •We propose a novel Relevance Attack on Detectors (RAD). We extend DNN interpreters to detectors, find out the most suitable nodes to attack by relevance maps, and explore the best update techniques to increase the transferability.•We evaluate RAD on 8 black-box models and find its state-of-the-art transferability, which exceeds existing results by above 20%. Detection and segmentation performance is greatly impaired in various metrics, invalidating the state-of-the-art DNN to a very rudimentary counterpart.•By RAD, we create the first adversarial dataset for object detection and instance segmentation, i.e., AOCO. As a potential benchmark, AOCO is generated from COCO and contains 10K high-transferable samples. AOCO helps to quickly evaluate and improve the robustness of detectors.\",\n \"Title: Exploiting User Friendship Networks for User Identification across Social Networks\\nYear: 2022\\n\\nAbstract: Identifying offline entities corresponding to multiple virtual accounts of users across social networks is crucial for the development of related fields, such as user recommendation system, network security, and user behavior pattern analysis. The data generated by users on multiple social networks has similarities. Thus, the concept of symmetry can be used to analyze user-generated information for user identification. In this paper, we propose a friendship networks-based user identification across social networks algorithm (FNUI), which performs the similarity of multi-hop neighbor nodes of a user to characterize the information redundancy in the friend networks fully. Subsequently, a gradient descent algorithm is used to optimize the contribution of the user's multi-hop nodes in the user identification process. Ultimately, user identification is achieved in conjunction with the Gale-Shapley matching algorithm. Experimental results show that compared with baselines, such as friend relationship-based user identification (FRUI) and friendship learning-based user identification (FBI): (1) The contribution of single-hop neighbor nodes in the user identification process is higher than other multi-hop neighbor nodes; (2) The redundancy of information contained in multi-hop neighbor nodes has a more significant impact on user identification; (3) The precision rate, recall rate, comprehensive evaluation index (F1), and area under curve (AUC) of user identification have been improved.\",\n \"Title: Joint extraction of entities and overlapping relations by improved graph convolutional networks\\nYear: 2022\\n\\nAbstract: Joint extraction of entities and relations is to recognize entities and semantic relations simultaneously, which is significant for knowledge graph construction. Recently, many effective joint models use dependency trees to capture the structural information of sentences. However, most dependency-based methods cannot make full use of the dependency information. This is because these methods just consider the connection information of dependency trees and ignore the influence of different nodes on the connected edges. In this paper, we establish a novel model to extract entities and relations simultaneously by using improved Multi-task Graph Convolutional networks, called MGCN. Specifically, considering the importance of node information, we merge both node and edge information into Graph Convolutional networks (GCN). In addition, in order to recognize the overlapping relations, we propose an efficient strategy to map multiple relational labels of a sentence into a unique code. Finally, we evaluate our joint model on two public datasets, and the experimental results show that our model outperforms the state-of-art models.\",\n \"Title: Mixture quantized error entropy for recursive least squares adaptive filtering\\nYear: 2022\\n\\nAbstract: Error entropy is a well-known learning criterion in information theoretic learning (ITL), and it has been successfully applied in robust signal processing and machine learning. To date, many robust learning algorithms have been devised based on the minimum error entropy (MEE) criterion, and the Gaussian kernel function is always utilized as the default kernel function in these algorithms, which is not always the best option. To further improve learning performance, two concepts using a mixture of two Gaussian functions as kernel functions, called mixture error entropy and mixture quantized error entropy, are proposed in this paper. We further propose two new recursive least-squares algorithms based on mixture minimum error entropy (MMEE) and mixture quantized minimum error entropy (MQMEE) optimization criteria. The convergence analysis, steady-state mean-square performance, and computational complexity of the two proposed algorithms are investigated. In addition, the reason why the mixture mechanism (mixture correntropy and mixture error entropy) can improve the performance of adaptive filtering algorithms is explained. Simulation results show that the proposed new recursive least-squares algorithms outperform other RLS-type algorithms, and the practicality of the proposed algorithms is verified by the electro-encephalography application.\",\n \"Title: Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation\\nYear: 2022\\n\\nAbstract: Unpaired image-to-image translation (I2I) is an ill-posed problem, as an infinite number of translation functions can map the source domain distribution to the target distribution. Therefore, much effort has been put into designing suitable constraints, e.g., cycle consistency (CycleGAN), geometry consistency (GCGAN), and contrastive learning-based constraints (CUTGAN), that help better pose the problem. However, these well-known constraints have limitations: (1) they are either too restrictive or too weak for specific I2I tasks; (2) these methods result in content distortion when there is a significant spatial variation between the source and target domains. This paper proposes a universal regularization technique called maximum spatial perturbation consistency (MSPC), which enforces a spatial perturbation function $(T)$ and the translation operator $(G)$ to be commutative (i.e., $T\\\\circ G=G\\\\circ T)$ . In addition, we introduce two adversarial training components for learning the spatial perturbation function. The first one lets $T$ compete with $G$ to achieve maximum perturbation. The second one lets $G$ and $T$ compete with discriminators to align the spatial variations caused by the change of object size, object distortion, background interruptions, etc. Our method outperforms the state-of-the-art methods on most I2I benchmarks. We also introduce a new benchmark, namely the front face to profile face dataset, to emphasize the underlying challenges of I2I for real-world applications. We finally perform ablation experiments to study the sensitivity of our method to the severity of spatial perturbation and its effectiveness for distribution alignment.\",\n \"Title: Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis\\nYear: 2022\\n\\nAbstract: Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in the given sentence. While pre-trained language models such as BERT have achieved great success, incorporating dynamic semantic changes into ABSA remains challenging. To this end, in this paper, we propose to address this problem by Dynamic Re-weighting BERT (DR-BERT), a novel method designed to learn dynamic aspect-oriented semantics for ABSA. Specifically, we first take the Stack-BERT layers as a primary encoder to grasp the overall semantic of the sentence and then fine-tune it by incorporating a lightweight Dynamic Reweighting Adapter (DRA). Note that the DRA can pay close attention to a small region of the sentences at each step and re-weigh the vitally important words for better aspect-aware sentiment understanding. Finally, experimental results on three benchmark datasets demonstrate the effectiveness and the rationality of our proposed model and provide good interpretable insights for future semantic modeling.\",\n \"Title: Driver gene detection through Bayesian network integration of mutation and expression profiles\\nYear: 2022\\n\\nAbstract: Motivation: The identification of mutated driver genes and the corresponding pathways is one of the primary goals in understanding tumorigenesis at the patient level. Integration of multi-dimensional genomic data from existing repositories, e.g., The Cancer Genome Atlas (TCGA), offers an effective way to tackle this issue. In this study, we aimed to leverage the complementary genomic information of individuals and create an integrative framework to identify cancer-related driver genes. Specifically, based on pinpointed differentially expressed genes, variants in somatic mutations and a gene interaction network, we proposed an unsupervised Bayesian network integration (BNI) method to detect driver genes and estimate the disease propagation at the patient and/or cohort levels. This new method first captures inherent structural information to construct a functional gene mutation network and then extracts the driver genes and their controlled downstream modules using the minimum cover subset method. Results: Using other credible sources (e.g. Cancer Gene Census and Network of Cancer Genes), we validated the driver genes predicted by the BNI method in three TCGA pan-cancer cohorts. The proposed method provides an effective approach to address tumor heterogeneity faced by personalized medicine. The pinpointed drivers warrant further wet laboratory validation.\",\n \"Title: Understanding Dynamics of Pandemic Models to Support Predictions of COVID-19 Transmission: Parameter Sensitivity Analysis of SIR-Type Models\\nYear: 2022\\n\\nAbstract: Despite efforts made to model and predict COVID-19 transmission, large predictive uncertainty remains. Failure to understand the dynamics of the nonlinear pandemic prediction model is an important reason. To this end, local and multiple global sensitivity analysis approaches are synthetically applied to analyze the sensitivities of parameters and initial state variables and community size (N) in susceptible-infected-recovered (SIR) and its variant susceptible-exposed-infected-recovered (SEIR) models and basic reproduction number (\\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">R0</i>\\n), aiming to provide prior information for parameter estimation and suggestions for COVID-19 prevention and control measures. We found that N influences both the maximum number of actively infected cases and the date on which the maximum number of actively infected cases is reached. The high effect of N on maximum actively infected cases and peak date suggests the necessity of isolating the infected cases in a small community. The protection rate and average quarantined time are most sensitive to the infected populations, with a summation of their first-order sensitivity indices greater than 0.585, and their interactions are also substantial, being 0.389 and 0.334, respectively. The high sensitivities and interaction between the protection rate and average quarantined time suggest that protection and isolation measures should always be implemented in conjunction and started as early as possible. These findings provide insights into the predictability of the pandemic models by estimating influential parameters and suggest how to effectively prevent and control epidemic transmission.\",\n \"Title: A Multiple Salient Features-Based User Identification across Social Media\\nYear: 2022\\n\\nAbstract: Identifying users across social media has practical applications in many research areas, such as user behavior prediction, commercial recommendation systems, and information retrieval. In this paper, we propose a multiple salient features-based user identification across social media (MSF-UI), which extracts and fuses the rich redundant features contained in user display name, network topology, and published content. According to the differences between users' different features, a multi-module calculation method is used to obtain the similarity between various redundant features. Finally, the bidirectional stable marriage matching algorithm is used for user identification across social media. Experimental results show that: (1) Compared with single-attribute features, the multi-dimensional information generated by users is integrated to optimize the universality of user identification; (2) Compared with baseline methods such as ranking-based cross-matching (RCM) and random forest confirmation algorithm based on stable marriage matching (RFCA-SMM), this method can effectively improve precision rate, recall rate, and comprehensive evaluation index (F1).\",\n \"Title: Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models\\nYear: 2022\\n\\nAbstract: This paper investigates the problem of selecting instrumental variables relative to a target causal influence X -> Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders. We propose a necessary condition for detecting variables that cannot serve as instrumental variables. Unlike many existing conditions for continuous variables, i.e., that at least two or more valid instrumental variables are present in the system, our condition is designed with a single instrumental variable. We then characterize the graphical implications of our condition in linear non-Gaussian acyclic causal models. Given that the existing graphical criteria for the instrument validity are not directly testable given observational data, we further show whether and how such graphical criteria can be checked by exploiting our condition. Finally, we develop a method to select the set of candidate instrumental variables given observational data. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method.\",\n \"Title: A Review-aware Graph Contrastive Learning Framework for Recommendation\\nYear: 2022\\n\\nAbstract: Most modern recommender systems predict users' preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the existing review-based recommendation models enriched user/item embedding learning ability with historical reviews or better modeled user-item interactions with the help of available user-item target reviews. Though significant progress has been made, we argue that current solutions for review-based recommendation suffer from two drawbacks. First, as review-based recommendation can be naturally formed as a user-item bipartite graph with edge features from corresponding user-item reviews, how to better exploit this unique graph structure for recommendation? Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better? To this end, in this paper, we propose a novel Review-aware Graph Contrastive Learning (RGCL) framework for review-based recommendation. Specifically, we first construct a review-aware user-item graph with feature-enhanced edges from reviews, where each edge feature is composed of both the user-item rating and the corresponding review semantics. This graph with feature-enhanced edges can help attentively learn each neighbor node weight for user and item representation learning. After that, we design two additional contrastive learning tasks (i.e., Node Discrimination and Edge Discrimination) to provide self-supervised signals for the two components in recommendation process. Finally, extensive experiments over five benchmark datasets demonstrate the superiority of our proposed RGCL compared to the state-of-the-art baselines.\",\n \"Title: Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification\\nYear: 2022\\n\\nAbstract: Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models.\",\n \"Title: Real-time adaptive skin detection using skin color model updating unit in videos\\nYear: 2022\\n\\nAbstract: Skin color plays an important role in color image processing and human–computer interaction. However, factors such as rapidly changing illumination, various color styles, and camera characteristics also make skin detection a challenging task. In particular, the real-time requirement of practical applications is a challenging task in skin detection. In this paper, face detection and alignment are applied to select facial reference points for modeling the skin color distribution. Moreover, we propose the conception and detection approach of skin color model updating unit (SCMUU) according to the fact of skin color distribution remains consistent in a range of frames. The redundant operation of frame by frame updating is avoided using one model in frames of SCMUU. When no reliable faces are detected, two strategies are introduced to remedy and reduce the computational cost. It uses the corresponding model parameters if a similar previous SCMUU is found. Otherwise, we use fixed thresholds instead and increase the interval between two consecutive face detection. Besides, the time-consuming steps are accelerated using a graphic processing unit (GPU) with CUDA in this paper. Experimental results show that, compared with other existing methods, the proposed method has good real time and accuracy for skin detection of various resolution videos under different illumination conditions.\",\n \"Title: Understanding the Users and Videos by Mining a Novel Danmu Dataset\\nYear: 2022\\n\\nAbstract: Recent years have witnessed a successful rise of the time synchronized gossiping comment, or so-called danmu combined with online videos. This new business mode has enriched communication among users by sending users’ feelings through danmus and sharing these danmus on time synchronized videos. Can danmu communication be helpful for better user behavior modeling or video analyzing? To this ...\",\n \"Title: Detection of Sea-Surface Small Targets Masked by Range Sidelobes of Large Objects\\nYear: 2022\\n\\nAbstract: Many high-resolution maritime radars for sea-surface small target detection transmit pulses with large time-bandwidth-products to increase transmitter power and to use the pulse compression technique for high range resolution. On the sea surface, besides small targets of interest, there exist many uninteresting large objects such as big ships, reefs, and offshore platforms for oil production. Large objects have too strong radar returns and their range sidelobes often mask wide range intervals. In these range intervals, masked small targets are difficult to be detected directly. In this article, a complete scheme is proposed to find small targets masked by range sidelobes of large objects in high-resolution maritime radars. In this scheme, high-precision reconstruction of radar returns of large objects is crucial because tiny reconstruction errors are enough to mask weak returns of small targets of interest. An algorithm using the linear programming and oversampled high-resolution range profile model of a large object is given to obtain high-precision reconstruction of the strong radar returns of a large object. From residual radar returns removed the reconstructed strong returns, a near-optimal coherent detector under sea clutter modeled by the spherically invariant random vector with inverse Gamma texture plus colored Gaussian noise is constructed to find masked small targets. Simulated experiments and real measured radar data are used to verify the effectiveness of the proposed scheme and method and reveal their limitations. The results show that the proposed scheme and method are all effective unless small targets fall into the range mainlobe regions of large objects.\",\n \"Title: Recalibration and Assessment of the SNPP CrIS Instrument: A Successful History of Restoration After Midwave Infrared Band Anomaly\\nYear: 2022\\n\\nAbstract: The Suomi National Polar-orbiting Partnership (SNPP) cross-track infrared sounder (CrIS) has provided critical observations for environmental applications for nearly ten years. However, on 26 March 2019, the Joint Polar Satellite System (JPSS) interface data processing segment (IDPS) stopped producing the operational SNPP CrIS sensor data record (SDR) product due to a failure of the midwave infrared (MWIR) band. Following a comprehensive risk assessment, the switch from primary Side-1 to redundant Side-2 electronics was made on 24 June 2019, successfully recovering the full capabilities of the sensor. Comprehensive assessment results demonstrate the high quality of the CrIS SDR product resulting from the sensor recalibration, thus meeting the JPSS Level-1 requirements with margin. The spectral calibration prioritized consistency with the CrIS SDR product prior to the side switch to minimize the impact on users. The results show that the radiometric impact on the CrIS SDR product resulting from the side switch is not significant and is within the calibration radiometric uncertainty. It is demonstrated that after instrument restoration, the SNPP CrIS SDR product recovers the quality needed to be used as radiometric reference for calibration and validation of infrared remote sensing instruments. The recovery of the SNPP CrIS MWIR band is expected to support improvements in numerical weather forecasting by restoring the MWIR band channels sensitive to tropospheric water vapor. This should also help maintain continuity and redundancy of one of the backbone observations of the global observing system.\",\n \"Title: Simulation of three-dimensional phase field model with LBM method using OpenCL\\nYear: 2022\\n\\nAbstract: Multi-field coupled three-dimensional phase field model is a research hotspot for simulating the growth mechanism of solidification microstructure. Single-CPU has become the main bottleneck in the development of phase field model due to its computing power and storage limitations. Based on this, the PF-LBM model of three-dimensional dendritic growth of supercooled pure substance solution under flow is established in this study. We take the succinonitrile (SCN) as the research object and use OpenCL heterogeneous parallel technology to realize the parallel solution. Firstly, the performance of the phase field model solution process is analyzed to find out the time-consuming modules; then, a parallel solution is proposed, and the preliminary parallelization of the phase field model is realized; finally, the problems existing in the parallel algorithm are optimized. The experimental results show that compared with the serial algorithm on the CPU platform under the same conditions, the speedup ratio reaches the highest when the computing scale is 128*128*128, which is 62.10. And the average speedup is 57.46. The scale after parallel is also expanded from 189*189*189 to 357*357*357, which effectively solves the problems existing in single-CPU simulation.\",\n \"Title: McHa: a multistage clustering-based hierarchical attention model for knowledge graph-aware recommendation\\nYear: 2022\\n\\nAbstract: Knowledge graph-aware recommendation has become an important research topic in recent years. The user preference representation, which preserves the user’s taste towards items (e.g., movies, books.), is obtained through aggregating the information of entities or attributes in knowledge graphs directly. However, the fine-grained heterogeneity information, which can be derived from the groups of items or entities, remains barely exploited in the process of encoding the user interaction intention for the items. To fill up this gap, we propose a Multistage Clustering-based Hierarchical Attention (McHa) model to capture the user preference representation. In our work, we first group the items and their neighboring entities in the knowledge graph into item clusters and entity clusters (jointly referred to as multistage clusters), respectively. Then, the user preference representation is obtained by hierarchically aggregating the heterogeneity information derived from the multistage clusters with the weights generated by the hierarchical attention layers. We conduct extensive experimental comparisons with baselines and the variants. The experimental results indicate that McHa has achieved state-of-the-art performance on three benchmark datasets in two scenarios.\",\n \"Title: Analysis, design and preliminary evaluation of an anthropometric self-stabilization passive exoskeleton for enhancing the ability of walking with loads\\nYear: 2022\\n\\nAbstract: This paper introduces the analysis, design and preliminary evaluation of an anthropometric self-stabilization passive exoskeleton (ASPE) with elastic band to increase the load transmission efficiency and protect and strengthen the human body during loaded walking. Firstly, we analyze the working principle of passive exoskeleton, and propose an efficient method of reducing the degrees of freedom in exoskeleton relatively, which contributes to a self-stabilization mechanism for balancing the back torque causing by the loaded backpack and assisting human hip flexion. The design of the ASPE is then introduced in detail. The novelty of the ASPE is that the human–machine interaction forces are significantly reduced by integrating the elastic band into the exoskeleton hip joint and converting load gravity into the elastic potential energy of elastic band to assist hip joint flexion. Furthermore, we analyze the dynamic modeling of the ASPE to preliminarily calculate the transmission efficiency regarding the ratio of plantar pressure of the ASPE to load gravity. Besides, we conduct the experiment of human wearing the ASPE to evaluate the performance of the ASPE regarding the ratio of the difference of human plantar pressure with and without the ASPE to load gravity. The results show that the ASPE can effectively transfer load gravity to the ground and maintain the human natural movement. The ratio of the plantar pressure of the ASPE to load gravity is more than 70% in the simulation, and when the walking speed is 4 km/h, the ASPE reduce the human plantar pressure of 68.9% compared to without wearing the ASPE during human walking with load. The results provide evidence for the efficient transmission of the newly designed ASPE during walking with loads. The application of the ASPE have benefits for subjects walking with loads, such as soldiers, to decrease their injuries and strengthen their ability.\",\n \"Title: Log-based Anomaly Detection from Multi-view by Associating Anomaly Scores with User Trust\\nYear: 2021\\n\\nAbstract: Logs, prevalent among nearly all computer systems, contain rich information that helps with troubleshooting or root cause analysis. Therefore, logs are excellent information sources for anomaly detection. Since logs are diverse and heterogeneous, to deal with all of them requires maintenance personnel to check detection results one by one, which is troublesome. This paper applies an ensemble metho...\",\n \"Title: Remote Attestation of Large-scale Virtual Machines in the Cloud Data Center\\nYear: 2021\\n\\nAbstract: With the development of cloud computing, remote attestation of virtual machines has received extensive attention. However, the current schemes mainly concentrate on the single prover, and the attestation of a large-scale virtualization environment will cause TPM bottleneck and network congestion, resulting in low efficiency of attestation. This paper proposes CloudTA, an extensible remote attestat...\",\n \"Title: Dual-Channel and Hierarchical Graph Convolutional Networks for document-level relation extraction\\nYear: 2022\\n\\nAbstract: Document-level relation extraction aims to infer complex semantic relations among entities in an entire document. Compared with the sentence-level relation extraction, document-level relational facts are expressed by multiple mentions across the sentences in a long-distance, requiring excellent reasoning. In this paper, we propose Dual-Channel and Hierarchical Graph Convolutional Networks (DHGCN), which constructs three graphs in token-level, mention-level, and entity-level to model complex interactions among different semantic representations across the document. Based on the multi-level graphs, we apply the Graph Convolutional Network (GCN) for each level to aggregate the relevant information scattered throughout the document for better inferring the implicit relations. Moreover, we propose a dual-channel encoder to capture structural and contextual information simultaneously, which also supplies the contextual representation for the higher layer to avoid losing low-dimension information. Our DHGCN yields significant improvements over the state-of-the-art methods by 2.75, 5.5, and 3.5 F1 on DocRED, CDR, and GDA, respectively, which are popular document-level relation extraction datasets. Furthermore, to demonstrate the effectiveness of our method, we evaluate DHGCN on a fine gained clinical document-level dataset Symptom-Acupoint Relation (SAR) proposed by ourselves and available at https://github.com/QiSun123/SAR. The experimental results illustrate that DHGCN is able to infer more valuable relations among entities in the document.\",\n \"Title: Partial disentanglement for domain adaptation.\\nYear: 2022\\n\\nAbstract: Unsupervised domain adaptation is critical to many real-world applications where label information is unavailable in the target domain. In general, without further assumptions, the joint distribution of the features and the label is not identifiable in the target domain. To address this issue, we rely on a property of minimal changes of causal mechanisms across domains to minimize unnecessary influences of domain shift. To encode this property, we first formulate the data generating process using a latent variable model with two partitioned latent subspaces: invariant components whose distributions stay the same across domains, and sparse changing components that vary across domains. We further constrain the domain shift to have a restrictive influence on the changing components. Under mild conditions, we show that the latent variables are partially identifiable, from which it follows that the joint distribution of data and labels in the target domain is also identifiable. Given the theoretical insights, we propose a practical domain adaptation framework, called iMSDA. Extensive experimental results reveal that iMSDA outperforms state-of-the-art domain adaptation algorithms on benchmark datasets, demonstrating the effectiveness of our framework.\",\n \"Title: Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint\\nYear: 2022\\n\\nAbstract: Unsupervised image-to-image (I21) translation aims to learn a domain mapping function that can preserve the semantics of the input images without paired data. However, because the underlying semantics distributions in the source and target domains are often mismatched, current distribution matching-based methods may distort the semantics when matching distributions, resulting in the inconsistency between the input and translated images, which is known as the semantics distortion problem. In this paper, we focus on the low-level I21 translation, where the structure of images is highly related to their semantics. To alleviate semantic distortions in such translation tasks without paired supervision, we propose a novel I21 translation constraint, called Structure Consistency Constraint (SCC), to promote the consistency of image structures by reducing the randomness of color transformation in the translation process. To facilitate estimation and maximization of SCC, we propose an approximate representation of mutual information called relative Squared-loss Mutual Information (rSMI) that enjoys efficient analytic solutions. Our SCC can be easily incorporated into most existing translation models. Quantitative and qualitative comparisons on a range of low-level I21 translation tasks show that translation models with SCC outperform the original models by a significant margin with little additional computational and memory costs.\",\n \"Title: Negative-Aware Attention Framework for Image-Text Matching\\nYear: 2022\\n\\nAbstract: Image-text matching, as a fundamental task, bridges the gap between vision and language. The key of this task is to accurately measure similarity between these two modalities. Prior work measuring this similarity mainly based on matched fragments (i.e., word/region with high relevance), while underestimating or even ignoring the effect of mismatched fragments (i.e., word/region with low relevance), e.g., via a typical LeaklyReLU or ReLU operation that forces negative scores close or exact to zero in attention. This work argues that mismatched textual fragments, which contain rich mismatching clues, are also crucial for image-text matching. We thereby propose a novel Negative-Aware Attention Framework (NAAF), which explicitly exploits both the positive effect of matched fragments and the negative effect of mismatched fragments to jointly infer image-text similarity. NAAF (1) delicately designs an iterative optimization method to maximally mine the mismatched fragments, facilitating more discriminative and robust negative effects, and (2) devises the two-branch matching mechanism to precisely calculate similarity/dissimilarity degrees for matched/mismatched fragments with different masks. Extensive experiments on two benchmark datasets, i.e., Flickr30K and MSCOCO, demonstrate the superior effectiveness of our NAAF, achieving state-of-the-art performance. Code will be released at: https://github.com/CrossmodalGroup/NAAF.\",\n \"Title: Soft Subspace Fuzzy Clustering with Dimension Affinity Constraint\\nYear: 2022\\n\\nAbstract: For high-dimensional data, the cluster structure often exists in a feature subset instead of the whole feature space. Soft subspace clustering can efficiently extract the important subspace by allocating a weight to each dimension on the basis of the contribution of this dimension to the cluster identification. However, this kind of method does not consider the correlations between data dimensions in the clustering process. In high-dimensional data, when two dimensions are closely correlated, they should have similar weight assignments, and vice versa. Inspired by the way of clustering with graph embedding technique, we present a novel soft subspace clustering algorithm with considering the correlations between data dimensions. In this method, a novel dimension affinity regularization term is included into the objective function to further highlight those correlated dimensions that are important to the formation of clusters and compress the feature subspaces. Moreover, the alternating direction method of multipliers is adopted to solve the linear optimization problem regarding the dimension weight lasso regularization. In addition, as an extension, the kernelized version is explored to address the non-linear data clustering. Experiments on the real-world datasets demonstrate the efficiency of the presented algorithms in comparison with the conventional clustering methods.\",\n \"Title: Video emotion analysis enhanced by recognizing emotion in video comments\\nYear: 2022\\n\\nAbstract: Video emotion analysis is one of the hottest topics in the video understanding community to cognize the emotion in videos for affective computing, video recommendation, and so on. Currently, many studies tend to employ different deep structures to model video contents for this task. In fact, audiences responses (e.g., physiological signals and comments) are also important since they are directly related to video emotional content and can reflect the emotions in videos. But current work did not fully explore the potential of viewers’ responses due to the following possible reasons: collecting users’ reactions are time-consuming, and importing collection devices will affect viewers’ normal reactions. Fortunately, as a kind of available users’ reactions on many video websites, user-generated comments have strong connections with users’ reactions to video content, and can indirectly reflect video emotions. To this end, we focus on user-generated comments emotion recognition for video emotion analysis. To overcome the shortcoming that user-generate comments are usually informal and related to plots, we design a novel Visual Enhanced Comments Emotion Recognition Model (VECERM) that leverages the related visual information to enhance comments context and identify comments emotions. Based on this, we can leverage the recognized emotion of comments to enhance the emotion analysis of videos via the temporal association between users’ comments and video emotional contents. Many experiments demonstrate our work has promising results for comments emotion recognition and can analyze video emotion with high efficiency and low cost.\",\n \"Title: On the shortest path problem of uncertain random digraphs\\nYear: 2022\\n\\nAbstract: In the field of graph theory, the shortest path problem is one of the most significant problems. However, since varieties of indeterminated factors appear in complex networks, determining of the shortest path from one vertex to another in complex networks may be a lot more complicated than the cases in deterministic networks. To illustrate this problem, the model of uncertain random digraph will be proposed via chance theory, in which some arcs exist with degrees in probability measure and others exist with degrees in uncertain measure. The main focus of this paper is to investigate the main properties of the shortest path in uncertain random digraph. Methods and algorithms are designed to calculate the distribution of shortest path more efficiently. Besides, some numerical examples are presented to show the efficiency of these methods and algorithms.\",\n \"Title: A Novel PDF Shape Control Approach for Nonlinear Stochastic Systems\\nYear: 2022\\n\\nAbstract: In this work, a novel shape control approach of the probability density function (PDF) for nonlinear stochastic systems is presented. First, we provide the formula for the PDF shape controller without devising the control law of the controller. Then, based on the exact analytical solution of the Fokker-Planck-Kolmogorov (FPK) equation, the product function of the polynomial and the exponential polynomial is regarded as the stationary PDF of the state response. To validate the performance of the proposed control approach, we compared it with the exponential polynomial method and the multi-Gaussian closure method by implementing comparative simulation experiments. The results show that the novel PDF shape control approach is effective and feasible. Using an equal number of parameters, our method can achieve a similar or better control effect as the exponential polynomial method. By comparison with the multi-Gaussian closure method, our method has clear advantages in PDF shape control performance. For all cases, the integral of squared error and the errors of first four moments of our proposed method were very small, indicating superior performance and promising good overall control effects of our method. The approach presented in this study provides an alternative for PDF shape control in nonlinear stochastic systems.\",\n \"Title: Enhanced graph convolutional network based on node importance for document-level relation extraction\\nYear: 2022\\n\\nAbstract: Document-level relation extraction aims to reason complex semantic relations among entities expressed by multiple associated mentions in a document. Existing methods construct document-level graphs to model interactions between entities. However, these methods only pay attention to the connection relationship of nodes, yet ignore the importance of nodes decided by topological structure. In this paper, we propose a novel method, named Enhanced Graph Convolutional Network (EGCN), to extract document-level relations. Unlike previous methods that only model the connection relationship between two nodes, we further exploit the global topological structural information by measuring node importance. We merge these non-local relationship into a Graph Convolutional Network to aggregate relevant information. In addition, to model semantic and syntactic interactions in documents, we design a novel strategy to construct document-level heterogeneous graphs with different types of edges. Experimental results demonstrate that our EGCN outperforms the previous models by 5.54%, 1.7%, and 2.9% \\n \\n \\n \\n $$F_1$$\\n \\n on three public document-level relation extraction datasets.\",\n \"Title: Optical Turbulence Characteristics in the Upper Troposphere-Lower Stratosphere over the Lhasa within the Asian Summer Monsoon Anticyclone\\nYear: 2022\\n\\nAbstract: The high elevation, complex topography, and unique atmospheric circulations of the Tibetan Plateau (TP) make its optical turbulence characteristics different from those in low-elevation regions. In this study, the characteristics of the atmospheric refractive index structure constant (C-n(2)) profiles in the Lhasa area at different strength states of the Asian summer monsoon anticyclone (ASMA) are analyzed based on precious in situ sounding data measured over the Lhasa in August 2018. C-n(2) in the upper troposphere-lower stratosphere fluctuates significantly within a few days during the ASMA, particularly in the upper troposphere. The effect of the ASMA on C-n(2) varies among the upper troposphere, tropopause, and lower stratosphere. The stronger and closer the ASMA is to Lhasa, the more pronounced is the \\\"upper highs and lower lows\\\" pressure field structure, which is beneficial for decreasing the potential temperature lapse rate. The decrease in static stability is an important condition for developing optical turbulence, elevating the tropopause height, and reducing the tropopause temperature. However, if strong high-pressure activity occurs at the lower pressure layer, such as at 500 hPa, an \\\"upper highs and lower highs\\\" pressure field structure forms over the Lhasa, increasing the potential temperature lapse rate and suppressing the convective intensity. Being almost unaffected by low-level atmospheric high-pressure activities, the ASMA, as the main influencing factor, mainly inhibits C-n(2) in the tropopause and lower stratosphere. The variations of turbulence intensity in UTLS caused by ASMA activities also have a great influence on astronomical parameters, which will have certain guiding significance for astronomical site testing and observations.\",\n \"Title: An immune algorithm for solving the optimization problem of locating the battery swapping stations\\nYear: 2022\\n\\nAbstract: Compared with fuel-powered vehicles, new energy electric vehicles have less carbon emissions. The application of new energy electric vehicles in logistics transportation is very significant to reduce vehicle-emitted greenhouse gases and improve environmental protection. In the logistics network using electric transport vehicles, the reasonable layout of battery swapping stations not only improves the distribution efficiency but also saves the distribution cost. In this paper, an integer programming problem model was formulated for reasonably locating battery swapping stations, where the total costs (including construction costs, operation and maintenance costs, and travel costs) were minimized. To solve this problem, we proposed a solution strategy based on an immune optimization algorithm, two crossover operators, and variable crossover and mutation rates. The effectiveness of the presented model and the performance of the proposed solution approach were evaluated using a case study. Compared with the commercial software CPLEX, the numerical results showed that the proposed algorithm can provide the optimal solution with less computing time.\",\n \"Title: Multi-objective two-level medical facility location problem and tabu search algorithm\\nYear: 2022\\n\\nAbstract: The medical facility location problem has attracted increasing attention in recent years because the rational layout of medical facilities plays an important role in implementing the hierarchical diagnosis and treatment system in many countries. To promote the implementation of a hierarchical diagnosis and treatment system, a two-level medical facility location problem with multiple patient flows between demand points and medical facilities is studied in this paper. We use a direct interpretation of the two-level medical facility location problem as a multi-objective problem to effectively balance different conflicting objectives. Based on the multi-objective tabu search algorithm and the fast construction method of the Pareto non-dominated solution, we also propose a solution approach to yield a set of solutions that can represent the trade-offs among conflicting objectives. The applicability and validation of the presented model and performance of the proposed optimization approach are evaluated using a real case. The experimental results not only indicate that the proposed solution approach can effectively solve the two-level medical facility location problem, but also suggest that the presented model can provide significant results in determining locations for medical facilities.\",\n \"Title: Evaluation and Optimization on Virtualization Performance Cost under Semantic Gap\\nYear: 2022\\n\\nAbstract: Virtualization is a key enabling technology in modern data centers. While it provides numerous benefits, it also creates new problems. Virtualization requires the hypervisor to treat the virtual machine as a black box, limiting the ability of information exchange between the hypervisor and the virtual machine, bringing a problem known as the semantic gap. Currently, much research on the semantic gap mainly focuses on bridging the semantic gap. The evaluation of the semantic gap, on the other hand, is a neglected but crucial problem, and relevant research is currently lacking. Therefore, this paper proposes a corresponding virtualization performance cost model to better evaluate the semantic gap. Based on this cost model, we summarize solutions that can be used to alleviate the semantic gap. Furthermore, we propose a novel evaluation method for the CPU double scheduling semantic gap. Finally, we propose an effective virtio-balloon based dynamic memory tuning strategy to alleviate the memory semantic gap. The experiments show that for 400.perlbench, our strategy saves 551MB of memory on average during running and reclaims 990MB of memory after running, with a performance cost of only 1.1%. For 429.mcf, our strategy saves 815MB of memory on average during running and reclaims 2130MB of memory after running, although with the performance cost of 27.6%, it prevents performance cliff-like drop caused by memory shortage.\",\n \"Title: Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases.\\nYear: 2022\\n\\nAbstract: Complex question generation over knowledge bases (KB) aims to generate natural language questions involving multiple KB relations or functional constraints. Existing methods train one encoder-decoder-based model to fit all questions. However, such a one-size-fits-all strategy may not perform well since complex questions exhibit an uneven distribution in many dimensions, such as question types, involved KB relations, and query structures, resulting in insufficient learning for long-tailed samples under different dimensions. To address this problem, we propose a meta-learning framework for complex question generation. The meta-trained generator can acquire universal and transferable meta-knowledge and quickly adapt to long-tailed samples through a few most related training samples. To retrieve similar samples for each input query, we design a self-supervised graph retriever to learn distributed representations for samples, and contrastive learning is leveraged to improve the learned representations. We conduct experiments on both WebQuestionsSP and ComplexWebQuestion, and results on long-tailed samples of different dimensions have been significantly improved, which demonstrates the effectiveness of the proposed framework.\",\n \"Title: Research on Intelligent Adaptive Control Method for Waste Heat Power Generation Stability Problem\\nYear: 2022\\n\\nAbstract: The effective recovery and utilization of waste heat is an important part of the energy industry chain, but the gas source of waste heat has large fluctuations and strong random frequency changes, which easily affects the quality of power generation. In order to obtain more continuous and stable electricity in the process of waste heat power generation, this paper proposes an intelligent adaptive control method. This control method decouples the variables for the characteristics of strong coupling of the waste heat power generation system, which improves the anti-disturbance performance of the system on the one hand and reduces the steady-state tracking error of the system on the other hand. Experiments show that when the gas source fluctuates, the system fluctuation rate is stable within 7%; when the set value changes, the adjustment time is within 7 s, and the overshoot is small, so the performance is better than the traditional PID control method. The intelligent adaptive control method proposed in this paper is of great significance for the future development of intelligent grid-connected power generation.\",\n \"Title: Adaptive Backstepping Fuzzy Control for Elevation Equilibrator Electro-Hydraulic Servo System\\nYear: 2022\\n\\nAbstract: An ABFC (adaptive backstepping fuzzy control) strategy is presented for one elevation equilibrator electro-hydraulic servo system of vehicle-mounted artillery with nonmatching uncertainty and parameters time-varying. Method of backstepping is used to overcome the problem of nonmatching uncertainty, adaption laws are adopted to eliminate the unknown parameters online. Fuzzy control is adopted to reduce the switching chattering phenomenon. Co-simulation results show that the ABFC strategy tracks the desired tracking trajectory well. The designed controller has nicer robustness and lower steady-state errors.\",\n \"Title: Reconfigurable Bit-Serial Operation Using Toggle SOT-MRAM for High-Performance Computing in Memory Architecture\\nYear: 2022\\n\\nAbstract: Computing in memory (CIM) is a promising candidate for high throughput and energy-efficient data-driven applications, which mitigates the well-known memory bottleneck in Von Neumann architecture. In this paper, we present a reconfigurable bit-serial operation using toggle spin-orbit torque magnetic random access memory (TSOT-MRAM) to perform the computation completely in the bit-cell array instead of in a peripheral circuit. This bit-serial CIM (BSCIM) scheme achieves higher throughput and energy efficiency in CIM. First, basic Boolean logic operations are realized by utilizing the feature of TSOT device. A bit-cell array that implements the bit-serial operation is then built to provide the communication between column and row necessary for arithmetic operations, such as the carry propagation of addition and multiplication. Finally, we analyze the reliability of BSCIM scheme and demonstrate the performance advantage by performing convolution operations for \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$28\\\\times 28$ </tex-math></inline-formula>\\n handwritten digit images in a BSCIM architecture. The results show that the delay and energy of BSCIM architecture are respectively reduced by 1.16-5.49 times and 1.12-1.43 times compared with the existing digital CIM architectures. Besides, its throughput and energy efficiency are also enhanced to 51.2 GOPS and 9.9 TOPS/W respectively.\",\n \"Title: Causal Narrative Comprehension: A New Perspective for Emotion Cause Extraction\\nYear: 2022\\n\\nAbstract: Emotion Cause Extraction (ECE) aims to reveal the cause clauses behind a given emotion expressed in a text, which has become an emerging topic in broad research communities, such as affective computing and natural language processing. Despite the fact that current methods about the ECE task have made great progress in text semantic understanding from lexicon- and sentence-level, they always ignore the certain causal narratives of emotion text. Significantly, these causal narratives are presented in the form of semantic structure and highly helpful for structure-level emotion cause understanding. Nevertheless, causal narrative is just an abstract narratological concept and its involving semantics is quite different from the common sequential information. Thus, how to properly model and utilize such particular narrative information to boost the ECE performance still remains an unresolved challenge. To this end, in this paper, we propose a novel Causal Narrative Comprehension Model (CNCM) for emotion cause extraction, which learns and leverages causal narrative information smartly to address the above problem. Specifically, we develop a Narrative-aware Causal Association (NCA) unit, which mines the narrative cue about emotional results and uses the semantic correlation between causes and results to model causal narratives of documents. Besides, we design a Result-aware Emotion Attention (REA) unit to make full use of the known result of causal narrative for multiple understanding about emotional causal associations. Through the ingenious combination and collaborative utilization of these two units, we could better identify the emotion cause in the text with causal narrative comprehension. Extensive experiments on the public English and Chinese benchmark datasets of ECE task have validated the effectiveness of CNCM with significant margin by comparing with the state-of-the-art baselines, which demonstrates the potential of narrative information in long text understanding.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":200,"string":"200"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13270,"string":"13,270"}}},{"rowIdx":13271,"cells":{"question_id":{"kind":"string","value":"Sara Comai"},"question":{"kind":"string","value":"Generate an abstract for the title \"A CASE tool for modelling and automatically generating web service-enabled applications\" using the following items: 1. CASE tool\n2. WebML\n3. Web services modelling\n4. Application deployment\n5. Industrial applications"},"answer":{"kind":"string","value":"This paper presents a CASE tool for the high-level specification of web applications integrated with web services. The CASE tool is based on WebML, a conceptual modelling language used for the specification of the front end of web applications. WebML, extended with new primitives for modelling web services, allows one to specify complex interactions between a web application, driven by the user, and remote processes, represented by services. The CASE tool also allows the specification and deployment of application functionalities as web services and automatically generates the running code, including web pages, web services calls and the exposed web services. In this paper, we describe the primitives for web services modelling, the architecture of the CASE tool for designing the applications, and the runtime architecture of the generated applications. Finally, we give an overview of some industrial applications developed with the described approach."},"correct_answer":{"kind":"string","value":"This paper presents a CASE tool for the high-level specification of web applications integrated with web services. The CASE tool is based on WebML, a conceptual modelling language used for the specification of the front end of web applications. WebML, extended with new primitives for modelling web services, allows one to specify complex interactions between a web application, driven by the user, and remote processes, represented by services. The CASE tool also allows the specification and deployment of application functionalities as web services and automatically generates the running code, including web pages, web services calls and the exposed web services. In this paper, we describe the primitives for web services modelling, the architecture of the CASE tool for designing the applications, and the runtime architecture of the generated applications. Finally, we give an overview of some industrial applications developed with the described approach."},"documents":{"kind":"list like","value":["Title: Second International Workshop on DATA Visualization and Integration on the Web (DATAVIEW'11)\nYear: 2011\n\nAbstract: Welcome to the 2nd International Workshop on DATA Visualization and Integration on the Web (DATAVIEW'11), held in conjunction with the European Conference on Web Services (ECOWS'11), September 14-16, 2011 in Lugano, Switzerland. The goal of the DATAVIEW workshop series is to gather researchers and practitioners in the diverse fields related to the integration, interaction and visualization of data to explore the feasibility of using and combining different approaches to create effective data-centric Web-service based applications.","Title: Context-Driven Hypertext Specification\nYear: 2009\n\nAbstract: The aim of the paper is the introduction of a compositional methodology for specifying context-driven hypertexts, that is\n the possibility to filter the available -usually too rich- knowledge and personalize the hypertext specification according\n to the notion of context. We use a methodology and a context model, recently proposed in the database literature, to tailor\n data on the basis of some predefined aspects on the user, the environment, and the possible scenarios, and we adapt them to\n specify also the hypertext schemata of the target application. The applicability of the work is shown on a running example\n and the advantages w.r.t. well known approaches for designing Web applications are highlighted.","Title: XML Document Summarization: Using XQuery for Synopsis Creation\nYear: 2004\n\nAbstract: This paper presents a methodology to support approximate queries over massive and heterogeneous XML datasets, based on concise data statistics such as histograms or other statistical techniques. The basic idea for approximate answers is to store pre-computed summaries of the XML data, also called synopses, and to query them instead of the original database, thus saving time and computational costs. In particular, in this paper we concentrate on a set of XQuery transformation rules for the construction of the synopses collection and for querying the synopsis.","Title: Flexible Queries to Semistructured Datasources: The WG-log Approach\nYear: 1999\n\nAbstract: A line of research is presented aimed at specifying both logical and navigational aspects of semi-structured data sources such as Web sites through the unifying notion of schema. Gracefully supporting schemata that are huge or subject to change, the WG-Log language allows for a uniform representation of queries and views, the latter expressing customized access structures to site information. A survey of related work and some directions for future research involving fuzzy query techniques are...","Title: A characterization of the layout definition problem for web search results\nYear: 2010\n\nAbstract: In the last years the user information seeking process on the Web has shifted from document search to object search. Hence, the answers provided by Web search engines cannot consist any more in a mere list of pages. In this paper we consider queries returning mono-, ambiguous, or multi-domain results, where the domain represents a specific field of interests such as City, People, Movies, etc. and we characterize the problem for the definition of the layout of such results. In particular, we describe a conceptual definition of the Web search result layout problem, by identifying: the parameters involved in the layout design, the tuning dimensions available for optimizing the result layout, and the possible strategies that can be adopted for producing such layouts. Finally, we provide an outlook on the possible future research directions on this topic.","Title: A system for teaching web engineering concepts in a supervised way using rich internet applications\nYear: 2009\n\nAbstract: In the past, different methodologies have been proposed to systematise web development processes. Some of them are supported by Computer Aided Software Engineering (CASE) tools that help the user in all or part of the phases of application development. A number of these tools are used with the didactic aim of teaching the development process of web applications. Nevertheless, to our knowledge, the currently available CASE tools neither support the online cooperative design nor allow the modelling of a process with the supervision of a teacher. In this paper, we present a system for teaching web engineering concepts based on Rich Internet Applications (RIAs), i.e., web applications with multimedia, high levels of interactivity and collaborative work support. The proposed tool allows different modellers to design web applications in a cooperative and supervised way, as well as automatically generate its code.","Title: Model-driven web engineering performance prediction with layered queue networks\nYear: 2010\n\nAbstract: This position paper describes an approach to predict the performances of a Web application already in the early stages of application development. It leverages the wealth of information of MDWE solutions to automatically obtain accurate representations of the running application in terms of layered queue networks (LQNs), i.e., analytical models simulating the behavior of the system and computing the performances mathematically. In particular, the paper discusses how a MDWE methodology can be exploited to generate such performance models and presents a proof of concept example.","Title: Using the Properties of Datalog to Prove Termination and Confluence in Active Databases\nYear: 1997\n\nAbstract: An active database system is a DBMS endowed with active rules, i.e. stored procedures activated by the system when specific events occur. The processing of active rules is characterized by two important properties: termination and confluence. We say that the processing of a set of active rules terminates if, given any initial active database state, the execution of the rules does not continue indefinitely; it is confluent if, for any active database state, the final state does not depend on the order of execution of the rules. Finding sufficient conditions is a nontrivial problem and the lack of a structured theory for the design of a system of active rules makes the analysis of the two properties more difficult. In this work, we see ECA rules as an evolution of deductive rules: we translate the active rules into logical clauses, taking into account the system's execution semantics, then we try to transfer to the active process the known results about termination and determinism available in the literature for deductive rules: sufficient conditions for the two properties of the ECA rules to hold can be found.","Title: Specification and Design of Workflow-driven Hypertexts\nYear: 2003\n\nAbstract: While the Web consolidates as the ubiquitous application delivery platform, the features of Web applications evolve to cover new requirements, like the capability of managing complex workflows spanning multiple users and organizations. This scenario challenges the Web engineering methods to address a broader class of applications. This paper introduces Workflow Driven Hypertexts, defined as Web-enabled hypertextual applications serving the workflow of multiple users, and proposes a design method integrating process, data, and hypertext modeling, expressly conceived to support the development of this class of applications.","Title: Modeling distributed events in data-intensive rich internet applications\nYear: 2007\n\nAbstract: Rich Internet applications (RIAs) enable novel usage scenarios by overcoming the traditional paradigms of Web interaction. Conventional Web applications can be seen as reactive systems in which events are 1) produced by the user acting upon the browser HTML interface, and 2) processed by the server hosting the application state and logic. In RIAs, distribution of data and computation across client and server broadens the classes and features of the produced events as they can originate, be detected, notified, and processed in a variety of ways. In this work, we investigate how events can be explicitly described and coupled to the other concepts of a Web modeling language in order to specify collaborative Rich Internet applications.","Title: Current Research on the Design of Web 2.0 Applications Based on Model-Driven Approaches\nYear: 2008\n\nAbstract: This paper presents our research activities on the design of Web 2.0 applications currently ongoing at Politecnico di Milano. Our approaches are based on model-driven devel- opment techniques: in particular, they extend Web 1.0 mod- els to cope with the technological characteristics of the new applications and exploit design patterns to capture the de- sired behaviors. Two main research activities are presented, considering two orthogonal issues of Web 2.0 applications: the social and the technological aspects. Moreover, other ongoing research directions are outlined.","Title: Automatic generation of workflow-extended domain models\nYear: 2007\n\nAbstract: The specification of business processes is becoming a more and more critical aspect for organizations. Such processes are specified as workflow models expressing the logical precedence among the different business activities (i.e. the units of work). Up to now, workflow models have been commonly managed through specific subsystems, called workflow management systems. In this paper we advocate for the integration of the workflow specification in the system domain model. This workflow-extended domain model is automatically derived from the initial workflow specification. Then, model-driven development methods may depart from the extended domain model to automatically generate an implementation of the system enforcing the business processes in any final technology platform, thus avoiding the need of basing the implementation on a dedicated workflow engine.","Title: A constraint programming approach to automatic layout definition for search results\nYear: 2011\n\nAbstract: In this paper we describe a general framework based on constraint programming techniques to address the automatic layout definition problem for Web search result pages, considering heterogeneous result items types (e.g., web links, images, videos, maps, etc.). Starting from the entity type(s) specified in the search query and the result types deemed more relevant for the given entity type, we define an optimization problem and a set of constraints that grant the optimal positioning of results in the page, modeled as a grid with assigned weights depending on the visibility.","Title: A Model and an XSL Framework for Analyzing the Quality of WebML Conceptual Schemas\nYear: 2002\n\nAbstract: Web conceptual models certainly represent a successful approach to the design of complex Web applications. Wrong uses of conceptual models may however introduce problems, which void the numerous advantages deriving from their exploitation in Web applications design. In this paper, we present a quality model for WebML, an XML-based Web conceptual model having a graphical notation. We also introduce an XSL-based framework able to automatically verify a set of quality attributes identified for WebML conceptual schemas. Our quality framework was born as a WebML-specific framework, but it is flexible enough to be applied to other Web conceptual models based on an XML specification.","Title: A Visual Data Mapping Tool for Software Components Interactions in Service-Oriented Architectures\nYear: 2006\n\nAbstract: With the spread of Service-Oriented Architectures, software components are exposed as Web services whose orchestration is gaining more and more importance. Several CASE tools and development frameworks have been put in place for representing conversations and composition without requiring designers to bother about implementation issues. However, as data sources for the exposed software components are arbitrary, data management problems within Web services interactions are often underestimated. This work addresses the topic of data mapping between XML contents of Web service messages and relational databases, which typically stores such information at the various peers. We propose a high- level approach to this problem, by offering (a) a very simple and straightforward visual transformation model as a software layer that enables the communication between peers based on various data formats, and (b) a software tool that allows the designer to draw the transformation diagrams between the message contents and the database structure. We provide the description of an algorithm for the automatic translation of visual mappings to XSLT rules, and the approach application within the WebML modeling framework for Web services design.","Title: An e-Learning collaborative RIA environment for Web Application Modeling and Code Generation\nYear: 2007\n\nAbstract: In the last years different methodologies have been proposed to systematize the Web development processes. Some of them are supported by CASE tools that help the user in all or in part of the phases of the application development. A number of these tools are used with didactic aims, for teaching the development process of Web applications. 4evertheless, to our knowledge, none of the currently available CASE tools neither supports the online cooperative design, nor allows the modelling of a process with the supervision of a teacher. Our technological solution is based on Rich Internet Applications (RIAs), i.e., Web applications with multimedia, high levels of interactivity, and collaborative work support. This document presents a Rich Internet application for the automatic code generation of Web applications that allows different modellers to design Web applications in a cooperative and supervised way, as well as to automatically generate its code. This work is inspired by the Web modelling language WebML.","Title: Haptic and visual rendering for multi-modal exploration of molecular information\nYear: 2010\n\nAbstract: The paper presents a system for the multi-modal rendering of molecules. Chemists typically deal with phenomena that are not directly experienceable and usually described by huge amount of data awkward to understand directly. Software tools have been introduced to support data interpretation, without a high degree of interactivity and explorability of the data. This work goes beyond the classical visual representations of molecules and introduces some new ways of exploration and navigation of information on which inter-molecular interactions are based. Target users are researchers and teachers, as integration to their activities. Two different modalities are used: haptical and visual. The former modality consists in the addition of a haptic interface that enables users to feel the interaction forces exerted by the electric field around a molecule. The latter modality shows the features of the same electric field by allowing the user to navigate the information about the values of the field using a visual color-based rendering and adding other visual cues. Implementation details, case studies, and results on the two modalities are described. Eye-tracking, awareness tools, machine learning, coordination, expertise","Title: Computing graphical queries over XML data\nYear: 2001\n\nAbstract: The rapid evolution of XML from a mere data exchange format to a universal syntax for encoding domain-specific information raises the need for new query languages specifically conceived to address the characteristics of XML. Such languages should be able not only to extract information from XML documents, but also to apply powerful transformation and restructuring operators, based on a well-defined semantics. Moreover, XML queries should be natural to write and understand, as nontechnical persons also are expected to access the large XML information bases supporting their businesses. This article describes XML-GL, a graphical query language for XML data. XML-GL's uniqueness is in the definition of a graph-based syntax to express a wide variety of XML queries, ranging from simple selections to expressive data transformations involving grouping, aggregation, and arithmetic calculations. XML-GL has an operational semantics based on the notion of graph matching, which serves as a guideline both for the implementation of native processors, and for the adoption of XML-GL as a front-end to any of the XML query languages that are presently under discussion as the standard paradigm for querying XML data.","Title: Declarative specification of Web applications exploiting Web services and workflows\nYear: 2004\n\nAbstract: This demo presents an extension of a declarative language for specifying data-intensive Web applications. We demonstrate a scenario extracted from a real-life application, the Web portal of a computer manufacturer, including interactions with third-party service providers and enabling distributors to participate in well-defined business processes. The crucial advantage of our framework is the high-level modeling of a complex Web application, extended with Web service and workflow capabilities. The application is automatically verified for correctness and the code is automatically generated and deployed.","Title: Editorial message: special track on web technologies and applications\nYear: 2005\n\nAbstract: The special track on Web Technologies and Applications is already at its fourth edition and has seen a constant evolution in the research issues. Currently, Web applications are required to rely on open, flexible, adaptable, and distributed infrastructure and to be ubiquitous, highly scalable, reliable, accessible from different devices, and personalized with respect to various user requirements. Web applications are becoming increasingly complex thereby creating a need for appropriate theoretical foundations, development methodologies, and supporting technology, drawing on different areas of computer science such as databases, artificial intelligence, programming languages, distributed computing, information retrieval, semantic modeling, human-computer interaction, etc.","Title: A Service-Based Framework for Flexible Business Processes\nYear: 2011\n\nAbstract: A framework for the design and enactment of flexible and adaptive business processes combines design-time and runtime mechanisms to offer a single integrated solution. The design-time environment supports the specification of process-driven Web applications with quality-of-service (QoS) constraints and monitoring annotations. The runtime identifies the actual services, from the QoS perspective, oversees the execution through monitoring, and reacts to failures and infringement of QoS constraints. This article discusses these issues on a proof-of-concept application developed for an industrial supply-chain scenario.","Title: Model-driven design and deployment of service-enabled web applications\nYear: 2005\n\nAbstract: Significant effort is currently invested in application integration, enabling business processes of different companies to interact and form complex multiparty processes. Web service standards, based on WSDL (Web Service Definition Language), have been adopted as process-to-process communication paradigms. However, the conceptual modeling of applications using Web services has not yet been addressed. Interaction with Web services is often specified at the level of the source code; thus, Web service interfaces are buried within a programmatic specification.In this article, we argue that Web services should be considered first-class citizens in the specification of Web applications. Thus, service-enabled Web applications should benefit from the high-level modeling and automatic code generation techniques that have long been advocated for Web application design and implementation. To this end, we extend a declarative model for specifying data-intensive Web applications in two directions: (i) high-level modeling of Web services and their interactions with the Web applications which use them, and (ii) modeling and specification of Web applications implementing new, complex Web services.Our approach is fully implemented within a CASE tool allowing the high-level modeling and automatic deployment of service-enabled Web applications.","Title: Hypertext Semantics for Web Applications\nYear: 2002\n\nAbstract: Web applications integrate dynamic pages for publishing data, functions for content management, and generic business services. To support the model-driven design and automatic generation of Web applications, an extended notion of hypertext is required, whose semantics is scarcely investigated. In this paper, we analyse and classify the semantic problems encountered in the computation of Web pages, characterised by multiple interrelated pieces of content, inter-page and intra-page navigation, and content update operations. The identified semantic framework may help in better understanding the existing Web conceptual models, and in reasoning at a higher level about the implementation problems daily faced by Web developers.","Title: DATAVIEW'10: PC co-chairs message\nYear: 2010\n\nAbstract: Welcome to the 1st International Workshop on DATA Visualization and Integration in Enterprise and on the Web (DATAVIEW’10),\n held in conjunction with the On The Move Federated Conference and Workshops (OTM’10), October 25-29, 2010 in Hersonissou,\n Crete, Greece.\n \n \n The increasing availability of machine-consumable data on the web offers developers and end-users new challenging opportunities\n for data-intensive application development. In such a context, there is an increasing need for theories, methods, and tools\n that address data integration and visualization, and user interaction. This workshop aims at bringing together researchers\n and practitioners in the fields of software engineering, web engineering, semantic web, databases, (multimedia) information\n retrieval and data visualization to discuss the feasibility of using and combining different approaches for creating effective\n data-intensive web and enterprise applications where the integration, visualization and interaction with data is a main concern.","Title: Exception handling in workflow-driven Web applications\nYear: 2005\n\nAbstract: As the Web becomes a platform for implementing B2B applications, the need arises of Web conceptual models for describing Web oriented workflow applications implementing business processes. In this context, new problems about process correctness arise, due to the loose control of Web applications upon the behavior of their Web clients. Indeed, incoherent user's behavior can lead to inconsistent processes.This paper presents a high level approach to the management of exceptions that occur during the execution of processes on the Web. We present a classification of exceptions that can be raised inside workflow-driven Web applications, and recovery policies to retrieve coherent status and data after an exception. We devise these concepts at high level and then we exploit them using a Web modeling language (WebML) that in turn provides development facilities like automatic code generation, validation of hypertext models, and so on. An industrial implementation experience is briefly presented too.","Title: Exception management within web applications implementing business processes\nYear: 2006\n\nAbstract: Web applications are more and more used nowadays to implement business processes that have to be executed on the Web. These applications support the modeling, execution, and monitoring of B2B interactions, as well as the management and publishing of content data, value-added services, and so on. The integration of these different technologies raises new problems that may occur during the process execution on the Web. Exceptional situations may be caused by the improper user navigation through Web pages, by system failures that interrupt the B2B interaction, or by events semantically related to the business process. We present a classification of exceptions, the capturing and notification mechanisms, and the recovery policies that may be applied on the business process in order to solve the exception. We also show how the proposed approach can be applied to WebML, a high-level modeling language for Web applications, and compare it with existing standards.","Title: Engineering rich internet applications with a model-driven approach\nYear: 2010\n\nAbstract: Rich Internet Applications (RIAs) have introduced powerful novel functionalities into the Web architecture, borrowed from client-server and desktop applications. The resulting platforms allow designers to improve the user's experience, by exploiting client-side data and computation, bidirectional client-server communication, synchronous and asynchronous events, and rich interface widgets. However, the rapid evolution of RIA technologies challenges the Model-Driven Development methodologies that have been successfully applied in the past decade to traditional Web solutions. This paper illustrates an evolutionary approach for incorporating a wealth of RIA features into an existing Web engineering methodology and notation. The experience demonstrates that it is possible to model RIA application requirements at a high-level using a platform-independent notation, and generate the client-side and server-side code automatically. The resulting approach is evaluated in terms of expressive power, ease of use, and implementability.","Title: A behavioral model for rich internet applications\nYear: 2007\n\nAbstract: Rich Internet Applications (RIAs) are reshaping the way in which the Web works. They change not only the appearance of the Web interfaces, but also the behavior of applications, permitting novel operations, like data distribution, partial page computation, and disconnected work. In this paper we try to understand the differences between the behavior of traditional dynamic Web applications and RIAs, considering the WebML modeling language and its actual implementation.","Title: Termination and Confluence by Rule Prioritization\nYear: 2003\n\nAbstract: An active database system is a DBMS endowed with active rules, i.e., stored procedures activated by the system when specific events occur. The processing of active rules is characterized by two important properties: termination and confluence. We say that the processing of a set of active rules terminates if, given any initial active database state, the execution of the rules does not continue indefinitely; it is confluent if, for any initial database state, the final state is not influenced by the order of execution of the rules. Finding sufficient conditions for these properties to hold is a nontrivial problem, and the lack of a structured theory for the design of a system of active rules makes the analysis of the two properties more difficult. In this work, we translate a set of rules from any of the existing systems into an internal format; then, we translate the active rules into logical clauses, taking into account the system's execution semantics, and transfer to the active process known simple results about termination and determinism available in the literature for deductive rules.","Title: Data and web management research at Politecnico di Milano\nYear: 2007\n\nAbstract: Research in data management at Politecnico di Milano has a long and solid tradition; forefront books on distributed databases, conceptual database design, logical databases, and active databases contributed to shape the foundations of this discipline in the last two decades. Historically, our work has addressed both all aspects of innovation in the technology of modern data management systems and the consequent support of design methods and tools.","Title: A framework for the development of haptic-enhanced web applications\nYear: 2012\n\nAbstract: In the last years we have witnessed an increasing adoption of haptic devices (allowing the user to feel forces or vibrations) in several fields of applications, from gaming, to mobile, automotive, etc. Some efforts have been done to enhance also Web applications interfaces with haptics, either to improve accessibility or, more in general, to improve usability. Despite the spreading of haptic applications, their development is still a time consuming task that requires significant programming skills. In particular, in the Web context no plug-ins or style extensions are currently available and applications must be developed from scratch. In this paper we describe a framework to easily include haptic interaction in Web applications, focusing both on haptic interaction modeling and on its implementation.","Title: State-of-the Art and trends in the Systematic Development of Rich Internet Applications\nYear: 2011\n\nAbstract: Rich Internet Applications (RIAs) are widely adopted Web applications that add the richer interaction,presentation, and client-side computation capabilities of desktop applications to the Web. However, the evolutionfrom Web applications towards RIAs comes at the cost of increased complexity in their development. For thisreason, a wide variety of tools and technologies have been proposed in order to streamline their developmenteffort. This paper investigates the current state of the art of the RIA development approaches. The review showsthat the current industrial development practice lacks a comprehensive approach to RIA development, supportingall the development steps from the design to implementation, test and maintenance, and helping identifying correctdesign choices. This is in part due to the severe fragmentation of current RIA technologies that prevents theadoption of a commonly recognized set of best practices resulting in ad-hoc development processes. These aspectsare in part treated by research methodologies and some innovative industrial solutions, but also these approachespresent some limitations. The paper identifies future research directions for RIAs to fully support theirdevelopment process and to support their design in a more comprehensive and systematic way, from bothindustrial and research perspectives.","Title: Level of detail concepts in data-intensive web applications\nYear: 2005\n\nAbstract: Current data-intensive Web applications, such as on-line trading, e-commerce, corporate portals and so on, are becoming more and more complex, both in terms of density of information and in terms of navigational paths. At this aim different techniques have been proposed in literature for optimizing the information to be shown to the user. In this paper we present a technique for tuning the amount of data presented to the user, directly inspired to the concept of Levels of Details (LoD), commonly used in computer graphics. Like in computer graphics the idea is to simplify the original model, without loosing the main characteristics of the objects to be shown. The approach is based on the application of LoD operators to the compositional and navigational structure of a Web application, expressed through an hypertextual model.","Title: A model-driven methodology to the content layout problem in web applications\nYear: 2012\n\nAbstract: This article presents a model-driven approach for the design of the layout in a complex Web application, where large amounts of data are accessed. The aim of this work is to reduce, as much as possible, repetitive tasks and to factor out common aspects into different kinds of rules that can be reused across different applications. In particular, exploiting the conceptual elements of the typical models used for the design of a Web application, it defines presentation and layout rules at different levels of abstraction and granularity. A procedure for the automatic layout of the content of a page is proposed and evaluated, and the layout of advanced Web applications is discussed.","Title: XML-GL: a graphical language for querying and restructuring XML documents\nYear: 1999\n\nAbstract: The growing acceptance of XML as a standard for semi-structured documents on the Web opens up challenging opportunities for Web query languages. In this paper we introduce XML-GL, a graphical query language for XML documents. The use of a visual formalism for representing both the content of XML documents (and of their DTDs) and the syntax and semantics of queries enables an intuitive expression of queries, even when they are rather complex. XML-GL is inspired by G-log, a general purpose, logic-based language for querying structured and semi-structured data. The paper presents the basic capabilities of XML-GL through a sequence of examples of increasing complexity.","Title: A semantic model for specifying data-intensive Web applications using WebML\nYear: 2001\n\nAbstract: WebML (Web Modelling Language) is a language for the design of data- intensive Web sites. It is supported by visual tools allowing the definition of the con- ceptual data organization and of the pages and links of the actual hypertext(s) which constitute a Web application. In this paper we describe a semantic model for WebML hypertexts by means of Statecharts. Statecharts provide a formal description of the clicking behavior and page data fill of WebML applications. The proposed semantic model has guided the implementation of the WebML runtime and the construction of advanced specification checking functions embedded in the WebML design tools. In particular, developers are supported in the identification of design and runtime prob- lems caused by non-determinism, racing conditions and deadlocks.","Title: Model-driven Specification of Web Services Composition and Integration with Data-intensive Web Applications\nYear: 2002\n\nAbstract: The integration of Web services and Web applications offers challenging opportunities to academic and industrial research; the relevant issues include new methods, paradigms, and standards for support- ing Web services and their composition. This paper focuses on the specification of Web applications that compose Web services in order to support arbitrarily complex processes. The proposed approach extends a high-level modeling language, called WebML, used for the specification of the front-end of Web applications. We develop two orthogonal extensions for the inclusion of Web services inside Web applications, accompanied by suitable protocols for message exchange, and the empowerment of Web modeling primitives with workflow capabilities. The combined use of these two features gives to WebML enough expressive power for specifying complex Web service interactions.","Title: Designing Rich Internet Applications with Web Engineering Methodologies\nYear: 2007\n\nAbstract: Web sites serve to publish information, both locally in intranets as well as on a global scale. Like all software systems, they have to cope with changing requirements and evolving technologies. The reference process model for software migration, ReMiP, ...","Title: Graphical Query Languages for Semi-structured Information\nYear: 2000\n\nAbstract: The amount of different kinds of data availableelectronically has increased rapidly in the lastyears. Beside strongly structured data obeying arigid schema (e.g. data stored in databases), lotsof data which do not present an a priori-knowninternal structure are spreading. Much of suchdata, though unstructured at a first sight, presenta logical organization and can be considered semistructured [1]. The most known example of semistructureddata is represented by the Web. Forsuch data...","Title: Conceptual modeling and code generation for rich internet applications\nYear: 2006\n\nAbstract: This paper addresses conceptual modeling and automaticcode generation for Rich Internet Applications, a variant ofWeb-based systems bridging desktop and thin-client Webinterfaces. We show how classical Web modeling conceptsare not enough to capture the specificity of RIAs, extend anexisting Web modeling language, and provide an implementationof a CASE tool for visual modeling and code generationfrom RIA-aware specifications. Experimentation of theproposed approach in real-world scenarios is also reported.","Title: Static hand poses for gestural interaction: a study\nYear: 2014\n\nAbstract: Gestural interaction leveraging the expressiveness of the human hand, either in touch or in air gestures has been a subject of much research. In this work, a user study focusing mainly on static hand poses has been conducted on a heterogeneous group of participants covering different aspects of this interaction method: role of the participants in the creation of the hand poses, context-free pose-action mapping, learnability and memory issues, and physical comfort. Results of the study are discussed.","Title: Extending conceptual schemas with business process information\nYear: 2010\n\nAbstract: The specification of business processes is becoming a more and more critical aspect for organizations. Such processes are specified as workflow models expressing the logical precedence among the different business activities (i.e., the units of work). Typically, workflow models are managed through specific subsystems, called workflow management systems, to ensure a consistent behavior of the applications with respect to the organization business process. However, for small organizations and/or simple business processes, the complexity and capabilities of these dedicated workflow engines may be overwhelming. In this paper, we therefore, advocate for a different and lightweight approach, consisting in the integration of the business process specification within the system conceptual schema. We show how a workflow-extended conceptual schema can be automatically obtained, which serves both to enforce the organization business process and to manage all its relevant domain data in a unified way. This extended model can be directly processed with current CASE tools, for instance, to generate an implementation of the system (including its business process) in any technological platform.","Title: A Schema-Based Approach to Modeling and Querying WWW Data\nYear: 1998\n\nAbstract: Abstract The steady growth of the amount of data published on the World Wide Web has led to a number of attempts to provide e ective Web querying, as a complement to conventional navigation techniques In this paper, we propose to support WWW querying by specifying both logical and navigational aspects of a Web site through the unifying notion of schema Schemata are a powerful tool for classifying and maintaining WWW data as well as for semantics - aware search on the Web Moreover, schema availability is the basis for an e ective exible querying mechan - ism Following the style of such languages as Good and G - Log, the WG - Log language described in the paper uniformly represents site schemata, instances and queries as graphs Gracefully supporting schemata that are huge or subject to change, WG - Log adequately represents data whose structure is less rigid than that of traditional databases; moreover, it allows for a uniform representation of exible queries and views, the latter expressing customized access structures to the site information","Title: Capturing RIA concepts in a web modeling language\nYear: 2006\n\nAbstract: This work addresses conceptual modeling and automatic code generation for Rich Internet Applications, a variant of Web-based systems bridging the gap between desktop and Web interfaces. The approach we propose is a first step towards a full integration of RIA paradigms into the Web development process, enabling the specification of complex Web solutions mixing HTTP+HTML and Rich Internet Applications, using a single modeling language and tool.","Title: A Haptic-Enhanced System for Molecular Sensing\nYear: 2009\n\nAbstract: The science of haptics has received an enormous attention in the last decade. One of the major application trends of haptics technology is data visualization and training. In this paper, we present a haptically-enhanced system for manipulation and tactile exploration of molecules.The geometrical models of molecules is extracted either from theoretical or empirical data using file formats widely adopted in chemical and biological fields. The addition of information computed with computational chemistry tools, allows users to feel the interaction forces between an explored molecule and a charge associated to the haptic device, and to visualize a huge amount of numerical data in a more comprehensible way. The developed tool can be used either for teaching or research purposes due to its high reliance on both theoretical and experimental data.","Title: MDWEnet: A Practical Approach to Achieving Interoperability of Model-Driven Web Engineering Methods\nYear: 2007\n\nAbstract: Current model-driven Web Engineering approaches (such as OO-H, UWE or WebML) provide a set of methods and supporting tools for a system- atic design and development of Web applications. Each method addresses dif- ferent concerns using separate models (content, nav igation, presentation, busi- ness logic, etc.), and provide model compilers that produce most of the logic and Web pages of the application from these models. However, these proposals also have some limitations, especially for exchangi ng models or representing further modeling concerns, such as architectural st yles, technology independ- ence, or distribution. A possible solution to these issues is provided by making model-driven Web Engineering proposals interoperate, being able to comple- ment each other, and to exchange models between the different tools. MDWEnet is a recent initiative started by a small group of researchers working on model-driven Web Engineering (MDWE). Its goal is to improve current practices and tools for the model-driven developmen t of Web applications for better interoperability. The proposal is based on t he strengths of current model- driven Web Engineering methods, and the existing experience and knowledge in the field. This paper presents the background, m otivation, scope, and objec- tives of MDWEnet. Furthermore, it reports on the MDWEnet results and achievements so far, and its future plan of actions .","Title: A haptic-based framework for chemistry education\nYear: 2010\n\nAbstract: In this demo we present a haptic-based framework for the exploration of the electrical surface of molecules. The geometrical models of molecules are extracted from theoretical data using file formats widely adopted in chemical and biological fields. The addition of information computed with computational chemistry tools allows users to feel the interaction forces between an explored molecule and a charge associated to the haptic device. The developed tool can be used to explore electrostatic fields of molecules, either for didactic or research purposes due to its reliance on computational data.","Title: HaptiChem: Haptic and Visual Support in Interactions with the Microscopic World.\nYear: 2014\n\nAbstract: Haptic technologies provide physical sensations in the interaction with a computing system, by exploiting the human sense of touch and by applying forces, vibrations, or motions to the user hands or body. Considering their features, they can be a useful tool in life-science teaching, especially when molecules are involved. For this purpose, a framework composed of an haptic device and a visual interface for molecular exploration has been developed to simulate molecular and intermolecular interactions. Furthermore, this work evaluates the visual and haptic tool for molecular exploration in a didactic context, performing tests and interviews with students. The final aim is to properly develop the features of the tool, in order to make it suitable for the introduction in chemistry education. Preliminary results show positive and effective responses and learning gains from the tasks. It has also been noticed that the use of such an innovative instrument raises the interest of students in the learning process, which is one of the main benefits of the haptic device.","Title: Editorial message: special track on web and E-business applications\nYear: 2002\n\nAbstract: The World WideWeb has become the standard computing platform for the development of new-generation information systems. A new tide of Web-based e-business applications (such as corporate portals, network-based supply chains and market places, etc.) is driving the need for a more open, flexible and distributed infrastructure, together with appropriate development methodologies and theoretical settings. Today's web applications involve skills from many different areas of computer science, including databases, AI and agent based applications, programming languages and algorithms, distributed computing, information retrieval, semantic modeling, etc. For this reason we proposed a track on Web and E-business applications based on the following main topics: data models for the World Wide Web, Web data management, languages for the World Wide Web and XML, E- business and Web services, transactions on the World Wide Web, security and integrity issues for the WWW, query systems for the World Wide Web, management and storage of Web information, information retrieval and search engines for the Web, Web semantics, data integration over the World Wide Web, data-intensive applications on the World Wide Web, Web architectures.We received 30 submissions, which were extensively reviewed for originality, significance, technical soundness and clarity of presentation. The submitted papers covered most of the proposed topics. The number of submissions distributed on each continent has been the following: 16 from Europe (53%), 9 from North America (30%), 3 from Asia (10%), 1 from Africa (3%) and 1 from Australia (3%). 12 papers corresponding to the 40% of the submitted papers have been selected for presentation at the conference, with the following distribution: 7 from Europe, 3 from America, 1 from Asia and 1 from Australia.","Title: Usability assessment of a multimodal visual-haptic framework for chemistry education\nYear: 2011\n\nAbstract: In this work, we assess the usability of a virtual environment where the force of interaction between the electrostatic field around the molecule and a charge associated to the proxy of a haptic device can be felt. Feedbacks to user are provided in a multimodal visual and haptic way, and auxiliary information are also rendered.","Title: Semantics-aware querying in the WWW: the WG-Log Web query system\nYear: 1999\n\nAbstract: In this paper we introduce a general model of a Web Query System, i.e. a set ofintegrated tools for the efficient and effective retrieval of hypermedia information onthe World Wide Web. Then we describe a prototype environment based on WG-Log, agraph-oriented query and data description language for Web sites, focusing on metadataprocessing and storage.1 Introduction and MotivationsIn this paper we sketch a general model of a Web Query System (WQS); by WQS we meana set of integrated ...","Title: Integrating Haptics in Web Interfaces: State of the Art and Open Issues\nYear: 2012\n\nAbstract: Several application fields have adopted haptic technologies to provide tactile and force effects to users. Some efforts have attempted to enhance Web application interfaces with haptics. This article examines the current state of the art in this area, highlighting the technology's benefits and pointing out issues with haptics integration.","Title: Accessible Urban Routes Reconstruction by Fusing Mobile Sensors Data.\nYear: 2015\n\nAbstract: Despite the recent attitude toward making modern urban cities accessible, many barriers and obstacles still challenge impaired people mobility every day. Having a complete and accurate map of both accessible paths and barriers could support people mobility and could drive urban planning renewing efforts. However, mapping accessible paths and barriers timely, and in an accurate way, requires a significant effort and it is often left to volunteers and no-profit associations. The Maps for Easy Paths (MEP) project represents an attempt to build accessibility maps (i.e., maps with accessible routes) fusing, in an automatic fashion, data implicitly collected through mobile devices carried by interested people when they move around the city. In particular, the paper focuses on the design of the MEP-Traces app used for the collection of relevant data and the MEP-Fusion engine for the reconstruction of accessible routes out of inertial readings and GPS measurements. Experimental results in reconstructing accessible routes in urban scenarios show the effectiveness of the approach in dealing with typical problems related to poor inertial sensors quality.","Title: Online Model Editing, Simulation and Code Generation for Web and Mobile Applications.\nYear: 2017\n\nAbstract: The wide range of different platforms for web and mobile applications development requires fast prototyping and the evaluation of multiple releases. Model Driven Development (MDD) represents the application at high level with textual or visual languages and can be used to automatically generate the final product by means of model-to-model and model-to-code transformations. We describe IFMLEdit.org, an open-source, online MDD tool for the specification and automatic generation of fast prototypes of Web and mobile application from IFML (Interaction Flow Modeling Language) specifications. The tool also supports the semantic mapping from IFML to PCN, a variant of Petri Nets, for model simulation and checking.","Title: IFMLEdit.org: Model Driven Rapid Prototyping of Mobile Apps.\nYear: 2017\n\nAbstract: We demonstrate IFMLEdit.org, an online tool for the rapid prototyping of web and mobile applications. IFMLEdit.org is based on the Interaction Flow Modeling Language (IFML), an OMG standard for the description of the interaction between users and applications by means of flows of information in reaction to user events. In the demo, attendees will be able to edit IFML specifications with IFMLEdit.org, investigate their properties by transforming them into Place Chart Nets, a variant of Petri Nets, and generate the code of web and mobile applications from the validated IFML model. Video: https://youtu.be/y_hDVeUbi7g","Title: Quantitative Indicators for Behaviour Drift Detection from Home Automation Data.\nYear: 2017\n\nAbstract: Smart Homes diffusion provides an opportunity to implement elderly monitoring, extending seniors' independence and avoiding unnecessary assistance costs. Information concerning the inhabitant behaviour is contained in home automation data, and can be extracted by means of quantitative indicators. The application of such approach proves it can evidence behaviour changes.","Title: Designing a HL7 compatible personal health record for mobile devices\nYear: 2017\n\nAbstract: Personal health records (PHRs) support health self-management for any individual. Currently, smart phones and tablets are widespread among the population and can collect and visualize health data, and get home care plan instructions. In this paper we describe an Android based PHR system that guarantees technological and structural interoperability using technological and communicational standards, respectively. Grounded on the standard-based architecture by the Health Level Seven (HL7) organization, we describe a prototype implementing a HL7 compatible personal health record system. We considered some functionalities that may support the discovery of potential risk of cardiovascular diseases, the leading cause of death in industrialized countries according to the WHO (World Health Organization). This work aims at helping people raising their lives quality by participating in vital parameters' examinations. Individuals install a mobile Health app (called mHealth) on their devices and can directly exchange clinical and medical data with health care services through HL7 Fast Healthcare Interoperability Resources (FHIR). FHIR compliant JSON (Java Script Object Notation) encrypted files implement the integration architecture. Direct and standard information exchange based on a regular home care plan between a PHR system used by patients and a healthcare information system used by physicians can boost the active contribution of patients and caregivers to a safe homecare management. The proposed solution may provide faster and better care, reduce economic costs for National Health Services (NHS), and increase quality of individual's life. Moreover, by delivering healthcare services through a mobile application we can also overcome temporal and geographical barriers.","Title: PIR Probability Model for a Cost/Reliability Tradeoff Unobtrusive Indoor Monitoring System.\nYear: 2016\n\nAbstract: PIR (Pyroelectric InfraRed) sensors can be used to detect the presence of humans without the need for them to wear any device. By construction, the fields of view of the sensors are not uniform both in terms of vision space and of sensitivity. The aim of this work is twofold: to provide a probabilistic model of the sensors' detection sensitivity with respect to the movement of the person and of his/her emission surface, and to identify the probability of detection within an area covered by multiple PIR sensors. This allows the computation of the coverage of the PIRs and their optimal arrangement that maximizes the probability of detection of the person.","Title: Maps for Easy Paths (MEP): Enriching Maps with Accessible Paths Using MEP Traces.\nYear: 2016\n\nAbstract: MEP (Maps for Easy Paths) is a project for the enrichment of geographical maps with information about accessibility of urban pedestrian pathways, targeted at people with mobility problems and, in particular, with motor impairments. In this paper we describe the application to collect data along the paths travelled by target people and show experimental results.","Title: IOM-Internet of Mobility: A Wearable Device for Outdoor Data Collection.\nYear: 2016\n\nAbstract: Current technology allows the collection of data about cities by communities collaborating for the wellbeing of the city. The solution described in this paper is a wearable device ecosystem, called IOM (Internet of Mobility), consisting of a wearable device collecting environmental data through sensors to be visualized on a mobile or web platform. The paper focuses on the requirements analysis involving different types of target users and on the design of the wearable device.","Title: Formal semantics of OMG's Interaction Flow Modeling Language (IFML) for mobile and rich-client application model driven development.\nYear: 2018\n\nAbstract: •Formalization semantics of the Interaction Flow Modeling Language (IFML).•Online tool support for generation and validation of model semantics.•Online tool support for generation of semantics compliant code.","Title: Indoor Activity Monitoring for Mutual Reassurance.\nYear: 2016\n\nAbstract: Population ageing is rising issues concerning the sustainability of older seniors assistance. A possible solution can be avoiding early retirement in nurse houses by providing the family and the senior(s) with an unobtrusive monitoring system, mainly based on motion sensors, capable of extending their independent living. The presented system processes the collected data to infer when the person exits and enters, his/her position inside the house, and the occupancy of the house areas. Such information is made available to the family through a set of purposefully designed graphical interfaces and prompt notifications. Preliminary results are satisfying, showing it is possible to restore Mutual Reassurance.","Title: Clone: A Promising System For The Remote Monitoring Of Alzheimer'S Patients An Experimentation With A Wearable Device In A Village For Alzheimer'S Care\nYear: 2018\n\nAbstract: Alzheimer's disease is a form of dementia that affects a large segment of the older population and, due to aging, its prevalence will substantially increase in the next decades. This paper describes an experiment of application of remote monitoring technologies for the collection of data to detect the onset of crises in patients affected by Alzheimer's in an Italian village dedicated to the treatment of the Alzheimer's disease. Furthermore, the paper details the design concept of Eclipse, a smart wearable that compensates for the current lack of Alzheimer's specific wearables on the market.","Title: Non-Invasive Monitoring System To Detect Sitting People\nYear: 2018\n\nAbstract: Determining the position of a body is crucial in the context of the Ambient Assisted Living: knowing if a person is standing, lying down or sitting makes the difference in the process of detecting anomalies due to inactivity. We present a non-invasive monitoring system to recognize if a person is seated using Bluetooth Low Energy technology. The system is composed by devices that can be easily attached to the furniture: a transmitter module that continuously broadcasts Bluetooth signal, and a receiver which collects the RSSi value of the received messages. Finally, a central server evaluates the attenuation of the Bluetooth signal within the 2.4GHz band, correlating it to the presence of a human body in the proximity of the device.","Title: Evolving Experiences of Participation: Towards an e-ILAUD Tool\nYear: 2021\n\nAbstract: The role of participatory communities all over the world calls for new models and tools to support citizens in taking part in decisions about the design and planning of urban design. Information and Communication Technologies (ICT) can be the basis for effective ways of participation for citizens and for helping them achieve a more active role in society. This paper describes e-ILAUD, a tool inspi...","Title: High-Efficiency Multi-Sensor System for Chair Usage Detection\nYear: 2021\n\nAbstract: Recognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such activities in an unobtrusive way, ambient-based solutions are available (e.g., based on PIRs, contact sensors, etc.). In this paper, we focus on the problem of sitting detection exploiting only unobtrusive sensors. In fact, sitting detection can be useful to understand the position of the user in many activities of the daily routines. While identifying sitting/lying on a sofa or bed is reasonably simple with pressure sensors, detecting whether a person is sitting on a chair is an open problem due to the natural chair position volatility. This paper proposes a reliable, not invasive and energetically sustainable system that can be used on chairs already present in the home. In particular, the proposed solution fuses the data of an accelerometer and a capacitive coupling sensor to understand if a person is sitting or not, discriminating the case of objects left on the chair. The results obtained in a real environment setting show an accuracy of 98.6% and a precision of 95%.","Title: Hyperspectral Image Analysis for Automatic Detection and Discrimination of Residual Manufacturing Contaminants\nYear: 2021\n\nAbstract: In modern manufacturing, divergent market dynamics impel companies to move toward a zero-defect production by reducing the risk of errors and defects down to zero. Paint-coating of metal surfaces is one of such process steps and most prominent as consumers will be animated to buy based on their first impression. Despite significant advances in automation and precision engineering of paint-coating,...","Title: WECRAFT: a Platform for Networking Rural Craftsmen in Co-Production of Artisanal Crafts\nYear: 2021\n\nAbstract: India is a growing country with over 1.2 billion people, out of which more than two-thirds still live in rural areas. Indian rural areas are vibrant heritage-wise. They have both a fascinating hard and soft heritage legacy. Each region has many kinds of villages, and each is characterized by specific arts and crafts skills developed over the centuries. Small settlements retain fascinating heritage...","Title: A Multi-Resident Number Estimation Method for Smart Homes\nYear: 2022\n\nAbstract: Population aging requires innovative solutions to increase the quality of life and preserve autonomous and independent living at home. A need of particular significance is the identification of behavioral drifts. A relevant behavioral drift concerns sociality: older people tend to isolate themselves. There is therefore the need to find methodologies to identify if, when, and how long the person is in the company of other people (possibly, also considering the number). The challenge is to address this task in poorly sensorized apartments, with non-intrusive sensors that are typically wireless and can only provide local and simple information. The proposed method addresses technological issues, such as PIR (Passive InfraRed) blind times, topological issues, such as sensor interference due to the inability to separate detection areas, and algorithmic issues. The house is modeled as a graph to constrain transitions between adjacent rooms. Each room is associated with a set of values, for each identified person. These values decay over time and represent the probability that each person is still in the room. Because the used sensors cannot determine the number of people, the approach is based on a multi-branch inference that, over time, differentiates the movements in the apartment and estimates the number of people. The proposed algorithm has been validated with real data obtaining an accuracy of 86.8%.","Title: A Hybrid Agent-Based Model to Simulate and Re-Think Post-COVID-19 Use Processes in Educational Facilities\nYear: 2021\n\nAbstract: The paper presents the development and application of a hybrid multi-agent system to simulate people’s behavior in educational facilities, to support decisions and strategies related to the post-COVID-19 scenarios. Complex use phenomena as the ones occurring in schools and educational facilities, required mixed, hybrid simulation models where the agent-based component, usually controlling single users/bots, is combined with a process-driven engine that ensures correspondence of the users’ behaviors to the general scenario. In our case, hybridization includes also the direct interaction of the intended users through a virtual reality 3D game, to further increase accuracy and adherence to the reality of the simulated phenomena. This paper also presents the application of the simulation model to a real case study, a school in Italy, where use processes have been simulated and currently under assessment during the school opening."],"string":"[\n \"Title: Second International Workshop on DATA Visualization and Integration on the Web (DATAVIEW'11)\\nYear: 2011\\n\\nAbstract: Welcome to the 2nd International Workshop on DATA Visualization and Integration on the Web (DATAVIEW'11), held in conjunction with the European Conference on Web Services (ECOWS'11), September 14-16, 2011 in Lugano, Switzerland. The goal of the DATAVIEW workshop series is to gather researchers and practitioners in the diverse fields related to the integration, interaction and visualization of data to explore the feasibility of using and combining different approaches to create effective data-centric Web-service based applications.\",\n \"Title: Context-Driven Hypertext Specification\\nYear: 2009\\n\\nAbstract: The aim of the paper is the introduction of a compositional methodology for specifying context-driven hypertexts, that is\\n the possibility to filter the available -usually too rich- knowledge and personalize the hypertext specification according\\n to the notion of context. We use a methodology and a context model, recently proposed in the database literature, to tailor\\n data on the basis of some predefined aspects on the user, the environment, and the possible scenarios, and we adapt them to\\n specify also the hypertext schemata of the target application. The applicability of the work is shown on a running example\\n and the advantages w.r.t. well known approaches for designing Web applications are highlighted.\",\n \"Title: XML Document Summarization: Using XQuery for Synopsis Creation\\nYear: 2004\\n\\nAbstract: This paper presents a methodology to support approximate queries over massive and heterogeneous XML datasets, based on concise data statistics such as histograms or other statistical techniques. The basic idea for approximate answers is to store pre-computed summaries of the XML data, also called synopses, and to query them instead of the original database, thus saving time and computational costs. In particular, in this paper we concentrate on a set of XQuery transformation rules for the construction of the synopses collection and for querying the synopsis.\",\n \"Title: Flexible Queries to Semistructured Datasources: The WG-log Approach\\nYear: 1999\\n\\nAbstract: A line of research is presented aimed at specifying both logical and navigational aspects of semi-structured data sources such as Web sites through the unifying notion of schema. Gracefully supporting schemata that are huge or subject to change, the WG-Log language allows for a uniform representation of queries and views, the latter expressing customized access structures to site information. A survey of related work and some directions for future research involving fuzzy query techniques are...\",\n \"Title: A characterization of the layout definition problem for web search results\\nYear: 2010\\n\\nAbstract: In the last years the user information seeking process on the Web has shifted from document search to object search. Hence, the answers provided by Web search engines cannot consist any more in a mere list of pages. In this paper we consider queries returning mono-, ambiguous, or multi-domain results, where the domain represents a specific field of interests such as City, People, Movies, etc. and we characterize the problem for the definition of the layout of such results. In particular, we describe a conceptual definition of the Web search result layout problem, by identifying: the parameters involved in the layout design, the tuning dimensions available for optimizing the result layout, and the possible strategies that can be adopted for producing such layouts. Finally, we provide an outlook on the possible future research directions on this topic.\",\n \"Title: A system for teaching web engineering concepts in a supervised way using rich internet applications\\nYear: 2009\\n\\nAbstract: In the past, different methodologies have been proposed to systematise web development processes. Some of them are supported by Computer Aided Software Engineering (CASE) tools that help the user in all or part of the phases of application development. A number of these tools are used with the didactic aim of teaching the development process of web applications. Nevertheless, to our knowledge, the currently available CASE tools neither support the online cooperative design nor allow the modelling of a process with the supervision of a teacher. In this paper, we present a system for teaching web engineering concepts based on Rich Internet Applications (RIAs), i.e., web applications with multimedia, high levels of interactivity and collaborative work support. The proposed tool allows different modellers to design web applications in a cooperative and supervised way, as well as automatically generate its code.\",\n \"Title: Model-driven web engineering performance prediction with layered queue networks\\nYear: 2010\\n\\nAbstract: This position paper describes an approach to predict the performances of a Web application already in the early stages of application development. It leverages the wealth of information of MDWE solutions to automatically obtain accurate representations of the running application in terms of layered queue networks (LQNs), i.e., analytical models simulating the behavior of the system and computing the performances mathematically. In particular, the paper discusses how a MDWE methodology can be exploited to generate such performance models and presents a proof of concept example.\",\n \"Title: Using the Properties of Datalog to Prove Termination and Confluence in Active Databases\\nYear: 1997\\n\\nAbstract: An active database system is a DBMS endowed with active rules, i.e. stored procedures activated by the system when specific events occur. The processing of active rules is characterized by two important properties: termination and confluence. We say that the processing of a set of active rules terminates if, given any initial active database state, the execution of the rules does not continue indefinitely; it is confluent if, for any active database state, the final state does not depend on the order of execution of the rules. Finding sufficient conditions is a nontrivial problem and the lack of a structured theory for the design of a system of active rules makes the analysis of the two properties more difficult. In this work, we see ECA rules as an evolution of deductive rules: we translate the active rules into logical clauses, taking into account the system's execution semantics, then we try to transfer to the active process the known results about termination and determinism available in the literature for deductive rules: sufficient conditions for the two properties of the ECA rules to hold can be found.\",\n \"Title: Specification and Design of Workflow-driven Hypertexts\\nYear: 2003\\n\\nAbstract: While the Web consolidates as the ubiquitous application delivery platform, the features of Web applications evolve to cover new requirements, like the capability of managing complex workflows spanning multiple users and organizations. This scenario challenges the Web engineering methods to address a broader class of applications. This paper introduces Workflow Driven Hypertexts, defined as Web-enabled hypertextual applications serving the workflow of multiple users, and proposes a design method integrating process, data, and hypertext modeling, expressly conceived to support the development of this class of applications.\",\n \"Title: Modeling distributed events in data-intensive rich internet applications\\nYear: 2007\\n\\nAbstract: Rich Internet applications (RIAs) enable novel usage scenarios by overcoming the traditional paradigms of Web interaction. Conventional Web applications can be seen as reactive systems in which events are 1) produced by the user acting upon the browser HTML interface, and 2) processed by the server hosting the application state and logic. In RIAs, distribution of data and computation across client and server broadens the classes and features of the produced events as they can originate, be detected, notified, and processed in a variety of ways. In this work, we investigate how events can be explicitly described and coupled to the other concepts of a Web modeling language in order to specify collaborative Rich Internet applications.\",\n \"Title: Current Research on the Design of Web 2.0 Applications Based on Model-Driven Approaches\\nYear: 2008\\n\\nAbstract: This paper presents our research activities on the design of Web 2.0 applications currently ongoing at Politecnico di Milano. Our approaches are based on model-driven devel- opment techniques: in particular, they extend Web 1.0 mod- els to cope with the technological characteristics of the new applications and exploit design patterns to capture the de- sired behaviors. Two main research activities are presented, considering two orthogonal issues of Web 2.0 applications: the social and the technological aspects. Moreover, other ongoing research directions are outlined.\",\n \"Title: Automatic generation of workflow-extended domain models\\nYear: 2007\\n\\nAbstract: The specification of business processes is becoming a more and more critical aspect for organizations. Such processes are specified as workflow models expressing the logical precedence among the different business activities (i.e. the units of work). Up to now, workflow models have been commonly managed through specific subsystems, called workflow management systems. In this paper we advocate for the integration of the workflow specification in the system domain model. This workflow-extended domain model is automatically derived from the initial workflow specification. Then, model-driven development methods may depart from the extended domain model to automatically generate an implementation of the system enforcing the business processes in any final technology platform, thus avoiding the need of basing the implementation on a dedicated workflow engine.\",\n \"Title: A constraint programming approach to automatic layout definition for search results\\nYear: 2011\\n\\nAbstract: In this paper we describe a general framework based on constraint programming techniques to address the automatic layout definition problem for Web search result pages, considering heterogeneous result items types (e.g., web links, images, videos, maps, etc.). Starting from the entity type(s) specified in the search query and the result types deemed more relevant for the given entity type, we define an optimization problem and a set of constraints that grant the optimal positioning of results in the page, modeled as a grid with assigned weights depending on the visibility.\",\n \"Title: A Model and an XSL Framework for Analyzing the Quality of WebML Conceptual Schemas\\nYear: 2002\\n\\nAbstract: Web conceptual models certainly represent a successful approach to the design of complex Web applications. Wrong uses of conceptual models may however introduce problems, which void the numerous advantages deriving from their exploitation in Web applications design. In this paper, we present a quality model for WebML, an XML-based Web conceptual model having a graphical notation. We also introduce an XSL-based framework able to automatically verify a set of quality attributes identified for WebML conceptual schemas. Our quality framework was born as a WebML-specific framework, but it is flexible enough to be applied to other Web conceptual models based on an XML specification.\",\n \"Title: A Visual Data Mapping Tool for Software Components Interactions in Service-Oriented Architectures\\nYear: 2006\\n\\nAbstract: With the spread of Service-Oriented Architectures, software components are exposed as Web services whose orchestration is gaining more and more importance. Several CASE tools and development frameworks have been put in place for representing conversations and composition without requiring designers to bother about implementation issues. However, as data sources for the exposed software components are arbitrary, data management problems within Web services interactions are often underestimated. This work addresses the topic of data mapping between XML contents of Web service messages and relational databases, which typically stores such information at the various peers. We propose a high- level approach to this problem, by offering (a) a very simple and straightforward visual transformation model as a software layer that enables the communication between peers based on various data formats, and (b) a software tool that allows the designer to draw the transformation diagrams between the message contents and the database structure. We provide the description of an algorithm for the automatic translation of visual mappings to XSLT rules, and the approach application within the WebML modeling framework for Web services design.\",\n \"Title: An e-Learning collaborative RIA environment for Web Application Modeling and Code Generation\\nYear: 2007\\n\\nAbstract: In the last years different methodologies have been proposed to systematize the Web development processes. Some of them are supported by CASE tools that help the user in all or in part of the phases of the application development. A number of these tools are used with didactic aims, for teaching the development process of Web applications. 4evertheless, to our knowledge, none of the currently available CASE tools neither supports the online cooperative design, nor allows the modelling of a process with the supervision of a teacher. Our technological solution is based on Rich Internet Applications (RIAs), i.e., Web applications with multimedia, high levels of interactivity, and collaborative work support. This document presents a Rich Internet application for the automatic code generation of Web applications that allows different modellers to design Web applications in a cooperative and supervised way, as well as to automatically generate its code. This work is inspired by the Web modelling language WebML.\",\n \"Title: Haptic and visual rendering for multi-modal exploration of molecular information\\nYear: 2010\\n\\nAbstract: The paper presents a system for the multi-modal rendering of molecules. Chemists typically deal with phenomena that are not directly experienceable and usually described by huge amount of data awkward to understand directly. Software tools have been introduced to support data interpretation, without a high degree of interactivity and explorability of the data. This work goes beyond the classical visual representations of molecules and introduces some new ways of exploration and navigation of information on which inter-molecular interactions are based. Target users are researchers and teachers, as integration to their activities. Two different modalities are used: haptical and visual. The former modality consists in the addition of a haptic interface that enables users to feel the interaction forces exerted by the electric field around a molecule. The latter modality shows the features of the same electric field by allowing the user to navigate the information about the values of the field using a visual color-based rendering and adding other visual cues. Implementation details, case studies, and results on the two modalities are described. Eye-tracking, awareness tools, machine learning, coordination, expertise\",\n \"Title: Computing graphical queries over XML data\\nYear: 2001\\n\\nAbstract: The rapid evolution of XML from a mere data exchange format to a universal syntax for encoding domain-specific information raises the need for new query languages specifically conceived to address the characteristics of XML. Such languages should be able not only to extract information from XML documents, but also to apply powerful transformation and restructuring operators, based on a well-defined semantics. Moreover, XML queries should be natural to write and understand, as nontechnical persons also are expected to access the large XML information bases supporting their businesses. This article describes XML-GL, a graphical query language for XML data. XML-GL's uniqueness is in the definition of a graph-based syntax to express a wide variety of XML queries, ranging from simple selections to expressive data transformations involving grouping, aggregation, and arithmetic calculations. XML-GL has an operational semantics based on the notion of graph matching, which serves as a guideline both for the implementation of native processors, and for the adoption of XML-GL as a front-end to any of the XML query languages that are presently under discussion as the standard paradigm for querying XML data.\",\n \"Title: Declarative specification of Web applications exploiting Web services and workflows\\nYear: 2004\\n\\nAbstract: This demo presents an extension of a declarative language for specifying data-intensive Web applications. We demonstrate a scenario extracted from a real-life application, the Web portal of a computer manufacturer, including interactions with third-party service providers and enabling distributors to participate in well-defined business processes. The crucial advantage of our framework is the high-level modeling of a complex Web application, extended with Web service and workflow capabilities. The application is automatically verified for correctness and the code is automatically generated and deployed.\",\n \"Title: Editorial message: special track on web technologies and applications\\nYear: 2005\\n\\nAbstract: The special track on Web Technologies and Applications is already at its fourth edition and has seen a constant evolution in the research issues. Currently, Web applications are required to rely on open, flexible, adaptable, and distributed infrastructure and to be ubiquitous, highly scalable, reliable, accessible from different devices, and personalized with respect to various user requirements. Web applications are becoming increasingly complex thereby creating a need for appropriate theoretical foundations, development methodologies, and supporting technology, drawing on different areas of computer science such as databases, artificial intelligence, programming languages, distributed computing, information retrieval, semantic modeling, human-computer interaction, etc.\",\n \"Title: A Service-Based Framework for Flexible Business Processes\\nYear: 2011\\n\\nAbstract: A framework for the design and enactment of flexible and adaptive business processes combines design-time and runtime mechanisms to offer a single integrated solution. The design-time environment supports the specification of process-driven Web applications with quality-of-service (QoS) constraints and monitoring annotations. The runtime identifies the actual services, from the QoS perspective, oversees the execution through monitoring, and reacts to failures and infringement of QoS constraints. This article discusses these issues on a proof-of-concept application developed for an industrial supply-chain scenario.\",\n \"Title: Model-driven design and deployment of service-enabled web applications\\nYear: 2005\\n\\nAbstract: Significant effort is currently invested in application integration, enabling business processes of different companies to interact and form complex multiparty processes. Web service standards, based on WSDL (Web Service Definition Language), have been adopted as process-to-process communication paradigms. However, the conceptual modeling of applications using Web services has not yet been addressed. Interaction with Web services is often specified at the level of the source code; thus, Web service interfaces are buried within a programmatic specification.In this article, we argue that Web services should be considered first-class citizens in the specification of Web applications. Thus, service-enabled Web applications should benefit from the high-level modeling and automatic code generation techniques that have long been advocated for Web application design and implementation. To this end, we extend a declarative model for specifying data-intensive Web applications in two directions: (i) high-level modeling of Web services and their interactions with the Web applications which use them, and (ii) modeling and specification of Web applications implementing new, complex Web services.Our approach is fully implemented within a CASE tool allowing the high-level modeling and automatic deployment of service-enabled Web applications.\",\n \"Title: Hypertext Semantics for Web Applications\\nYear: 2002\\n\\nAbstract: Web applications integrate dynamic pages for publishing data, functions for content management, and generic business services. To support the model-driven design and automatic generation of Web applications, an extended notion of hypertext is required, whose semantics is scarcely investigated. In this paper, we analyse and classify the semantic problems encountered in the computation of Web pages, characterised by multiple interrelated pieces of content, inter-page and intra-page navigation, and content update operations. The identified semantic framework may help in better understanding the existing Web conceptual models, and in reasoning at a higher level about the implementation problems daily faced by Web developers.\",\n \"Title: DATAVIEW'10: PC co-chairs message\\nYear: 2010\\n\\nAbstract: Welcome to the 1st International Workshop on DATA Visualization and Integration in Enterprise and on the Web (DATAVIEW’10),\\n held in conjunction with the On The Move Federated Conference and Workshops (OTM’10), October 25-29, 2010 in Hersonissou,\\n Crete, Greece.\\n \\n \\n The increasing availability of machine-consumable data on the web offers developers and end-users new challenging opportunities\\n for data-intensive application development. In such a context, there is an increasing need for theories, methods, and tools\\n that address data integration and visualization, and user interaction. This workshop aims at bringing together researchers\\n and practitioners in the fields of software engineering, web engineering, semantic web, databases, (multimedia) information\\n retrieval and data visualization to discuss the feasibility of using and combining different approaches for creating effective\\n data-intensive web and enterprise applications where the integration, visualization and interaction with data is a main concern.\",\n \"Title: Exception handling in workflow-driven Web applications\\nYear: 2005\\n\\nAbstract: As the Web becomes a platform for implementing B2B applications, the need arises of Web conceptual models for describing Web oriented workflow applications implementing business processes. In this context, new problems about process correctness arise, due to the loose control of Web applications upon the behavior of their Web clients. Indeed, incoherent user's behavior can lead to inconsistent processes.This paper presents a high level approach to the management of exceptions that occur during the execution of processes on the Web. We present a classification of exceptions that can be raised inside workflow-driven Web applications, and recovery policies to retrieve coherent status and data after an exception. We devise these concepts at high level and then we exploit them using a Web modeling language (WebML) that in turn provides development facilities like automatic code generation, validation of hypertext models, and so on. An industrial implementation experience is briefly presented too.\",\n \"Title: Exception management within web applications implementing business processes\\nYear: 2006\\n\\nAbstract: Web applications are more and more used nowadays to implement business processes that have to be executed on the Web. These applications support the modeling, execution, and monitoring of B2B interactions, as well as the management and publishing of content data, value-added services, and so on. The integration of these different technologies raises new problems that may occur during the process execution on the Web. Exceptional situations may be caused by the improper user navigation through Web pages, by system failures that interrupt the B2B interaction, or by events semantically related to the business process. We present a classification of exceptions, the capturing and notification mechanisms, and the recovery policies that may be applied on the business process in order to solve the exception. We also show how the proposed approach can be applied to WebML, a high-level modeling language for Web applications, and compare it with existing standards.\",\n \"Title: Engineering rich internet applications with a model-driven approach\\nYear: 2010\\n\\nAbstract: Rich Internet Applications (RIAs) have introduced powerful novel functionalities into the Web architecture, borrowed from client-server and desktop applications. The resulting platforms allow designers to improve the user's experience, by exploiting client-side data and computation, bidirectional client-server communication, synchronous and asynchronous events, and rich interface widgets. However, the rapid evolution of RIA technologies challenges the Model-Driven Development methodologies that have been successfully applied in the past decade to traditional Web solutions. This paper illustrates an evolutionary approach for incorporating a wealth of RIA features into an existing Web engineering methodology and notation. The experience demonstrates that it is possible to model RIA application requirements at a high-level using a platform-independent notation, and generate the client-side and server-side code automatically. The resulting approach is evaluated in terms of expressive power, ease of use, and implementability.\",\n \"Title: A behavioral model for rich internet applications\\nYear: 2007\\n\\nAbstract: Rich Internet Applications (RIAs) are reshaping the way in which the Web works. They change not only the appearance of the Web interfaces, but also the behavior of applications, permitting novel operations, like data distribution, partial page computation, and disconnected work. In this paper we try to understand the differences between the behavior of traditional dynamic Web applications and RIAs, considering the WebML modeling language and its actual implementation.\",\n \"Title: Termination and Confluence by Rule Prioritization\\nYear: 2003\\n\\nAbstract: An active database system is a DBMS endowed with active rules, i.e., stored procedures activated by the system when specific events occur. The processing of active rules is characterized by two important properties: termination and confluence. We say that the processing of a set of active rules terminates if, given any initial active database state, the execution of the rules does not continue indefinitely; it is confluent if, for any initial database state, the final state is not influenced by the order of execution of the rules. Finding sufficient conditions for these properties to hold is a nontrivial problem, and the lack of a structured theory for the design of a system of active rules makes the analysis of the two properties more difficult. In this work, we translate a set of rules from any of the existing systems into an internal format; then, we translate the active rules into logical clauses, taking into account the system's execution semantics, and transfer to the active process known simple results about termination and determinism available in the literature for deductive rules.\",\n \"Title: Data and web management research at Politecnico di Milano\\nYear: 2007\\n\\nAbstract: Research in data management at Politecnico di Milano has a long and solid tradition; forefront books on distributed databases, conceptual database design, logical databases, and active databases contributed to shape the foundations of this discipline in the last two decades. Historically, our work has addressed both all aspects of innovation in the technology of modern data management systems and the consequent support of design methods and tools.\",\n \"Title: A framework for the development of haptic-enhanced web applications\\nYear: 2012\\n\\nAbstract: In the last years we have witnessed an increasing adoption of haptic devices (allowing the user to feel forces or vibrations) in several fields of applications, from gaming, to mobile, automotive, etc. Some efforts have been done to enhance also Web applications interfaces with haptics, either to improve accessibility or, more in general, to improve usability. Despite the spreading of haptic applications, their development is still a time consuming task that requires significant programming skills. In particular, in the Web context no plug-ins or style extensions are currently available and applications must be developed from scratch. In this paper we describe a framework to easily include haptic interaction in Web applications, focusing both on haptic interaction modeling and on its implementation.\",\n \"Title: State-of-the Art and trends in the Systematic Development of Rich Internet Applications\\nYear: 2011\\n\\nAbstract: Rich Internet Applications (RIAs) are widely adopted Web applications that add the richer interaction,presentation, and client-side computation capabilities of desktop applications to the Web. However, the evolutionfrom Web applications towards RIAs comes at the cost of increased complexity in their development. For thisreason, a wide variety of tools and technologies have been proposed in order to streamline their developmenteffort. This paper investigates the current state of the art of the RIA development approaches. The review showsthat the current industrial development practice lacks a comprehensive approach to RIA development, supportingall the development steps from the design to implementation, test and maintenance, and helping identifying correctdesign choices. This is in part due to the severe fragmentation of current RIA technologies that prevents theadoption of a commonly recognized set of best practices resulting in ad-hoc development processes. These aspectsare in part treated by research methodologies and some innovative industrial solutions, but also these approachespresent some limitations. The paper identifies future research directions for RIAs to fully support theirdevelopment process and to support their design in a more comprehensive and systematic way, from bothindustrial and research perspectives.\",\n \"Title: Level of detail concepts in data-intensive web applications\\nYear: 2005\\n\\nAbstract: Current data-intensive Web applications, such as on-line trading, e-commerce, corporate portals and so on, are becoming more and more complex, both in terms of density of information and in terms of navigational paths. At this aim different techniques have been proposed in literature for optimizing the information to be shown to the user. In this paper we present a technique for tuning the amount of data presented to the user, directly inspired to the concept of Levels of Details (LoD), commonly used in computer graphics. Like in computer graphics the idea is to simplify the original model, without loosing the main characteristics of the objects to be shown. The approach is based on the application of LoD operators to the compositional and navigational structure of a Web application, expressed through an hypertextual model.\",\n \"Title: A model-driven methodology to the content layout problem in web applications\\nYear: 2012\\n\\nAbstract: This article presents a model-driven approach for the design of the layout in a complex Web application, where large amounts of data are accessed. The aim of this work is to reduce, as much as possible, repetitive tasks and to factor out common aspects into different kinds of rules that can be reused across different applications. In particular, exploiting the conceptual elements of the typical models used for the design of a Web application, it defines presentation and layout rules at different levels of abstraction and granularity. A procedure for the automatic layout of the content of a page is proposed and evaluated, and the layout of advanced Web applications is discussed.\",\n \"Title: XML-GL: a graphical language for querying and restructuring XML documents\\nYear: 1999\\n\\nAbstract: The growing acceptance of XML as a standard for semi-structured documents on the Web opens up challenging opportunities for Web query languages. In this paper we introduce XML-GL, a graphical query language for XML documents. The use of a visual formalism for representing both the content of XML documents (and of their DTDs) and the syntax and semantics of queries enables an intuitive expression of queries, even when they are rather complex. XML-GL is inspired by G-log, a general purpose, logic-based language for querying structured and semi-structured data. The paper presents the basic capabilities of XML-GL through a sequence of examples of increasing complexity.\",\n \"Title: A semantic model for specifying data-intensive Web applications using WebML\\nYear: 2001\\n\\nAbstract: WebML (Web Modelling Language) is a language for the design of data- intensive Web sites. It is supported by visual tools allowing the definition of the con- ceptual data organization and of the pages and links of the actual hypertext(s) which constitute a Web application. In this paper we describe a semantic model for WebML hypertexts by means of Statecharts. Statecharts provide a formal description of the clicking behavior and page data fill of WebML applications. The proposed semantic model has guided the implementation of the WebML runtime and the construction of advanced specification checking functions embedded in the WebML design tools. In particular, developers are supported in the identification of design and runtime prob- lems caused by non-determinism, racing conditions and deadlocks.\",\n \"Title: Model-driven Specification of Web Services Composition and Integration with Data-intensive Web Applications\\nYear: 2002\\n\\nAbstract: The integration of Web services and Web applications offers challenging opportunities to academic and industrial research; the relevant issues include new methods, paradigms, and standards for support- ing Web services and their composition. This paper focuses on the specification of Web applications that compose Web services in order to support arbitrarily complex processes. The proposed approach extends a high-level modeling language, called WebML, used for the specification of the front-end of Web applications. We develop two orthogonal extensions for the inclusion of Web services inside Web applications, accompanied by suitable protocols for message exchange, and the empowerment of Web modeling primitives with workflow capabilities. The combined use of these two features gives to WebML enough expressive power for specifying complex Web service interactions.\",\n \"Title: Designing Rich Internet Applications with Web Engineering Methodologies\\nYear: 2007\\n\\nAbstract: Web sites serve to publish information, both locally in intranets as well as on a global scale. Like all software systems, they have to cope with changing requirements and evolving technologies. The reference process model for software migration, ReMiP, ...\",\n \"Title: Graphical Query Languages for Semi-structured Information\\nYear: 2000\\n\\nAbstract: The amount of different kinds of data availableelectronically has increased rapidly in the lastyears. Beside strongly structured data obeying arigid schema (e.g. data stored in databases), lotsof data which do not present an a priori-knowninternal structure are spreading. Much of suchdata, though unstructured at a first sight, presenta logical organization and can be considered semistructured [1]. The most known example of semistructureddata is represented by the Web. Forsuch data...\",\n \"Title: Conceptual modeling and code generation for rich internet applications\\nYear: 2006\\n\\nAbstract: This paper addresses conceptual modeling and automaticcode generation for Rich Internet Applications, a variant ofWeb-based systems bridging desktop and thin-client Webinterfaces. We show how classical Web modeling conceptsare not enough to capture the specificity of RIAs, extend anexisting Web modeling language, and provide an implementationof a CASE tool for visual modeling and code generationfrom RIA-aware specifications. Experimentation of theproposed approach in real-world scenarios is also reported.\",\n \"Title: Static hand poses for gestural interaction: a study\\nYear: 2014\\n\\nAbstract: Gestural interaction leveraging the expressiveness of the human hand, either in touch or in air gestures has been a subject of much research. In this work, a user study focusing mainly on static hand poses has been conducted on a heterogeneous group of participants covering different aspects of this interaction method: role of the participants in the creation of the hand poses, context-free pose-action mapping, learnability and memory issues, and physical comfort. Results of the study are discussed.\",\n \"Title: Extending conceptual schemas with business process information\\nYear: 2010\\n\\nAbstract: The specification of business processes is becoming a more and more critical aspect for organizations. Such processes are specified as workflow models expressing the logical precedence among the different business activities (i.e., the units of work). Typically, workflow models are managed through specific subsystems, called workflow management systems, to ensure a consistent behavior of the applications with respect to the organization business process. However, for small organizations and/or simple business processes, the complexity and capabilities of these dedicated workflow engines may be overwhelming. In this paper, we therefore, advocate for a different and lightweight approach, consisting in the integration of the business process specification within the system conceptual schema. We show how a workflow-extended conceptual schema can be automatically obtained, which serves both to enforce the organization business process and to manage all its relevant domain data in a unified way. This extended model can be directly processed with current CASE tools, for instance, to generate an implementation of the system (including its business process) in any technological platform.\",\n \"Title: A Schema-Based Approach to Modeling and Querying WWW Data\\nYear: 1998\\n\\nAbstract: Abstract The steady growth of the amount of data published on the World Wide Web has led to a number of attempts to provide e ective Web querying, as a complement to conventional navigation techniques In this paper, we propose to support WWW querying by specifying both logical and navigational aspects of a Web site through the unifying notion of schema Schemata are a powerful tool for classifying and maintaining WWW data as well as for semantics - aware search on the Web Moreover, schema availability is the basis for an e ective exible querying mechan - ism Following the style of such languages as Good and G - Log, the WG - Log language described in the paper uniformly represents site schemata, instances and queries as graphs Gracefully supporting schemata that are huge or subject to change, WG - Log adequately represents data whose structure is less rigid than that of traditional databases; moreover, it allows for a uniform representation of exible queries and views, the latter expressing customized access structures to the site information\",\n \"Title: Capturing RIA concepts in a web modeling language\\nYear: 2006\\n\\nAbstract: This work addresses conceptual modeling and automatic code generation for Rich Internet Applications, a variant of Web-based systems bridging the gap between desktop and Web interfaces. The approach we propose is a first step towards a full integration of RIA paradigms into the Web development process, enabling the specification of complex Web solutions mixing HTTP+HTML and Rich Internet Applications, using a single modeling language and tool.\",\n \"Title: A Haptic-Enhanced System for Molecular Sensing\\nYear: 2009\\n\\nAbstract: The science of haptics has received an enormous attention in the last decade. One of the major application trends of haptics technology is data visualization and training. In this paper, we present a haptically-enhanced system for manipulation and tactile exploration of molecules.The geometrical models of molecules is extracted either from theoretical or empirical data using file formats widely adopted in chemical and biological fields. The addition of information computed with computational chemistry tools, allows users to feel the interaction forces between an explored molecule and a charge associated to the haptic device, and to visualize a huge amount of numerical data in a more comprehensible way. The developed tool can be used either for teaching or research purposes due to its high reliance on both theoretical and experimental data.\",\n \"Title: MDWEnet: A Practical Approach to Achieving Interoperability of Model-Driven Web Engineering Methods\\nYear: 2007\\n\\nAbstract: Current model-driven Web Engineering approaches (such as OO-H, UWE or WebML) provide a set of methods and supporting tools for a system- atic design and development of Web applications. Each method addresses dif- ferent concerns using separate models (content, nav igation, presentation, busi- ness logic, etc.), and provide model compilers that produce most of the logic and Web pages of the application from these models. However, these proposals also have some limitations, especially for exchangi ng models or representing further modeling concerns, such as architectural st yles, technology independ- ence, or distribution. A possible solution to these issues is provided by making model-driven Web Engineering proposals interoperate, being able to comple- ment each other, and to exchange models between the different tools. MDWEnet is a recent initiative started by a small group of researchers working on model-driven Web Engineering (MDWE). Its goal is to improve current practices and tools for the model-driven developmen t of Web applications for better interoperability. The proposal is based on t he strengths of current model- driven Web Engineering methods, and the existing experience and knowledge in the field. This paper presents the background, m otivation, scope, and objec- tives of MDWEnet. Furthermore, it reports on the MDWEnet results and achievements so far, and its future plan of actions .\",\n \"Title: A haptic-based framework for chemistry education\\nYear: 2010\\n\\nAbstract: In this demo we present a haptic-based framework for the exploration of the electrical surface of molecules. The geometrical models of molecules are extracted from theoretical data using file formats widely adopted in chemical and biological fields. The addition of information computed with computational chemistry tools allows users to feel the interaction forces between an explored molecule and a charge associated to the haptic device. The developed tool can be used to explore electrostatic fields of molecules, either for didactic or research purposes due to its reliance on computational data.\",\n \"Title: HaptiChem: Haptic and Visual Support in Interactions with the Microscopic World.\\nYear: 2014\\n\\nAbstract: Haptic technologies provide physical sensations in the interaction with a computing system, by exploiting the human sense of touch and by applying forces, vibrations, or motions to the user hands or body. Considering their features, they can be a useful tool in life-science teaching, especially when molecules are involved. For this purpose, a framework composed of an haptic device and a visual interface for molecular exploration has been developed to simulate molecular and intermolecular interactions. Furthermore, this work evaluates the visual and haptic tool for molecular exploration in a didactic context, performing tests and interviews with students. The final aim is to properly develop the features of the tool, in order to make it suitable for the introduction in chemistry education. Preliminary results show positive and effective responses and learning gains from the tasks. It has also been noticed that the use of such an innovative instrument raises the interest of students in the learning process, which is one of the main benefits of the haptic device.\",\n \"Title: Editorial message: special track on web and E-business applications\\nYear: 2002\\n\\nAbstract: The World WideWeb has become the standard computing platform for the development of new-generation information systems. A new tide of Web-based e-business applications (such as corporate portals, network-based supply chains and market places, etc.) is driving the need for a more open, flexible and distributed infrastructure, together with appropriate development methodologies and theoretical settings. Today's web applications involve skills from many different areas of computer science, including databases, AI and agent based applications, programming languages and algorithms, distributed computing, information retrieval, semantic modeling, etc. For this reason we proposed a track on Web and E-business applications based on the following main topics: data models for the World Wide Web, Web data management, languages for the World Wide Web and XML, E- business and Web services, transactions on the World Wide Web, security and integrity issues for the WWW, query systems for the World Wide Web, management and storage of Web information, information retrieval and search engines for the Web, Web semantics, data integration over the World Wide Web, data-intensive applications on the World Wide Web, Web architectures.We received 30 submissions, which were extensively reviewed for originality, significance, technical soundness and clarity of presentation. The submitted papers covered most of the proposed topics. The number of submissions distributed on each continent has been the following: 16 from Europe (53%), 9 from North America (30%), 3 from Asia (10%), 1 from Africa (3%) and 1 from Australia (3%). 12 papers corresponding to the 40% of the submitted papers have been selected for presentation at the conference, with the following distribution: 7 from Europe, 3 from America, 1 from Asia and 1 from Australia.\",\n \"Title: Usability assessment of a multimodal visual-haptic framework for chemistry education\\nYear: 2011\\n\\nAbstract: In this work, we assess the usability of a virtual environment where the force of interaction between the electrostatic field around the molecule and a charge associated to the proxy of a haptic device can be felt. Feedbacks to user are provided in a multimodal visual and haptic way, and auxiliary information are also rendered.\",\n \"Title: Semantics-aware querying in the WWW: the WG-Log Web query system\\nYear: 1999\\n\\nAbstract: In this paper we introduce a general model of a Web Query System, i.e. a set ofintegrated tools for the efficient and effective retrieval of hypermedia information onthe World Wide Web. Then we describe a prototype environment based on WG-Log, agraph-oriented query and data description language for Web sites, focusing on metadataprocessing and storage.1 Introduction and MotivationsIn this paper we sketch a general model of a Web Query System (WQS); by WQS we meana set of integrated ...\",\n \"Title: Integrating Haptics in Web Interfaces: State of the Art and Open Issues\\nYear: 2012\\n\\nAbstract: Several application fields have adopted haptic technologies to provide tactile and force effects to users. Some efforts have attempted to enhance Web application interfaces with haptics. This article examines the current state of the art in this area, highlighting the technology's benefits and pointing out issues with haptics integration.\",\n \"Title: Accessible Urban Routes Reconstruction by Fusing Mobile Sensors Data.\\nYear: 2015\\n\\nAbstract: Despite the recent attitude toward making modern urban cities accessible, many barriers and obstacles still challenge impaired people mobility every day. Having a complete and accurate map of both accessible paths and barriers could support people mobility and could drive urban planning renewing efforts. However, mapping accessible paths and barriers timely, and in an accurate way, requires a significant effort and it is often left to volunteers and no-profit associations. The Maps for Easy Paths (MEP) project represents an attempt to build accessibility maps (i.e., maps with accessible routes) fusing, in an automatic fashion, data implicitly collected through mobile devices carried by interested people when they move around the city. In particular, the paper focuses on the design of the MEP-Traces app used for the collection of relevant data and the MEP-Fusion engine for the reconstruction of accessible routes out of inertial readings and GPS measurements. Experimental results in reconstructing accessible routes in urban scenarios show the effectiveness of the approach in dealing with typical problems related to poor inertial sensors quality.\",\n \"Title: Online Model Editing, Simulation and Code Generation for Web and Mobile Applications.\\nYear: 2017\\n\\nAbstract: The wide range of different platforms for web and mobile applications development requires fast prototyping and the evaluation of multiple releases. Model Driven Development (MDD) represents the application at high level with textual or visual languages and can be used to automatically generate the final product by means of model-to-model and model-to-code transformations. We describe IFMLEdit.org, an open-source, online MDD tool for the specification and automatic generation of fast prototypes of Web and mobile application from IFML (Interaction Flow Modeling Language) specifications. The tool also supports the semantic mapping from IFML to PCN, a variant of Petri Nets, for model simulation and checking.\",\n \"Title: IFMLEdit.org: Model Driven Rapid Prototyping of Mobile Apps.\\nYear: 2017\\n\\nAbstract: We demonstrate IFMLEdit.org, an online tool for the rapid prototyping of web and mobile applications. IFMLEdit.org is based on the Interaction Flow Modeling Language (IFML), an OMG standard for the description of the interaction between users and applications by means of flows of information in reaction to user events. In the demo, attendees will be able to edit IFML specifications with IFMLEdit.org, investigate their properties by transforming them into Place Chart Nets, a variant of Petri Nets, and generate the code of web and mobile applications from the validated IFML model. Video: https://youtu.be/y_hDVeUbi7g\",\n \"Title: Quantitative Indicators for Behaviour Drift Detection from Home Automation Data.\\nYear: 2017\\n\\nAbstract: Smart Homes diffusion provides an opportunity to implement elderly monitoring, extending seniors' independence and avoiding unnecessary assistance costs. Information concerning the inhabitant behaviour is contained in home automation data, and can be extracted by means of quantitative indicators. The application of such approach proves it can evidence behaviour changes.\",\n \"Title: Designing a HL7 compatible personal health record for mobile devices\\nYear: 2017\\n\\nAbstract: Personal health records (PHRs) support health self-management for any individual. Currently, smart phones and tablets are widespread among the population and can collect and visualize health data, and get home care plan instructions. In this paper we describe an Android based PHR system that guarantees technological and structural interoperability using technological and communicational standards, respectively. Grounded on the standard-based architecture by the Health Level Seven (HL7) organization, we describe a prototype implementing a HL7 compatible personal health record system. We considered some functionalities that may support the discovery of potential risk of cardiovascular diseases, the leading cause of death in industrialized countries according to the WHO (World Health Organization). This work aims at helping people raising their lives quality by participating in vital parameters' examinations. Individuals install a mobile Health app (called mHealth) on their devices and can directly exchange clinical and medical data with health care services through HL7 Fast Healthcare Interoperability Resources (FHIR). FHIR compliant JSON (Java Script Object Notation) encrypted files implement the integration architecture. Direct and standard information exchange based on a regular home care plan between a PHR system used by patients and a healthcare information system used by physicians can boost the active contribution of patients and caregivers to a safe homecare management. The proposed solution may provide faster and better care, reduce economic costs for National Health Services (NHS), and increase quality of individual's life. Moreover, by delivering healthcare services through a mobile application we can also overcome temporal and geographical barriers.\",\n \"Title: PIR Probability Model for a Cost/Reliability Tradeoff Unobtrusive Indoor Monitoring System.\\nYear: 2016\\n\\nAbstract: PIR (Pyroelectric InfraRed) sensors can be used to detect the presence of humans without the need for them to wear any device. By construction, the fields of view of the sensors are not uniform both in terms of vision space and of sensitivity. The aim of this work is twofold: to provide a probabilistic model of the sensors' detection sensitivity with respect to the movement of the person and of his/her emission surface, and to identify the probability of detection within an area covered by multiple PIR sensors. This allows the computation of the coverage of the PIRs and their optimal arrangement that maximizes the probability of detection of the person.\",\n \"Title: Maps for Easy Paths (MEP): Enriching Maps with Accessible Paths Using MEP Traces.\\nYear: 2016\\n\\nAbstract: MEP (Maps for Easy Paths) is a project for the enrichment of geographical maps with information about accessibility of urban pedestrian pathways, targeted at people with mobility problems and, in particular, with motor impairments. In this paper we describe the application to collect data along the paths travelled by target people and show experimental results.\",\n \"Title: IOM-Internet of Mobility: A Wearable Device for Outdoor Data Collection.\\nYear: 2016\\n\\nAbstract: Current technology allows the collection of data about cities by communities collaborating for the wellbeing of the city. The solution described in this paper is a wearable device ecosystem, called IOM (Internet of Mobility), consisting of a wearable device collecting environmental data through sensors to be visualized on a mobile or web platform. The paper focuses on the requirements analysis involving different types of target users and on the design of the wearable device.\",\n \"Title: Formal semantics of OMG's Interaction Flow Modeling Language (IFML) for mobile and rich-client application model driven development.\\nYear: 2018\\n\\nAbstract: •Formalization semantics of the Interaction Flow Modeling Language (IFML).•Online tool support for generation and validation of model semantics.•Online tool support for generation of semantics compliant code.\",\n \"Title: Indoor Activity Monitoring for Mutual Reassurance.\\nYear: 2016\\n\\nAbstract: Population ageing is rising issues concerning the sustainability of older seniors assistance. A possible solution can be avoiding early retirement in nurse houses by providing the family and the senior(s) with an unobtrusive monitoring system, mainly based on motion sensors, capable of extending their independent living. The presented system processes the collected data to infer when the person exits and enters, his/her position inside the house, and the occupancy of the house areas. Such information is made available to the family through a set of purposefully designed graphical interfaces and prompt notifications. Preliminary results are satisfying, showing it is possible to restore Mutual Reassurance.\",\n \"Title: Clone: A Promising System For The Remote Monitoring Of Alzheimer'S Patients An Experimentation With A Wearable Device In A Village For Alzheimer'S Care\\nYear: 2018\\n\\nAbstract: Alzheimer's disease is a form of dementia that affects a large segment of the older population and, due to aging, its prevalence will substantially increase in the next decades. This paper describes an experiment of application of remote monitoring technologies for the collection of data to detect the onset of crises in patients affected by Alzheimer's in an Italian village dedicated to the treatment of the Alzheimer's disease. Furthermore, the paper details the design concept of Eclipse, a smart wearable that compensates for the current lack of Alzheimer's specific wearables on the market.\",\n \"Title: Non-Invasive Monitoring System To Detect Sitting People\\nYear: 2018\\n\\nAbstract: Determining the position of a body is crucial in the context of the Ambient Assisted Living: knowing if a person is standing, lying down or sitting makes the difference in the process of detecting anomalies due to inactivity. We present a non-invasive monitoring system to recognize if a person is seated using Bluetooth Low Energy technology. The system is composed by devices that can be easily attached to the furniture: a transmitter module that continuously broadcasts Bluetooth signal, and a receiver which collects the RSSi value of the received messages. Finally, a central server evaluates the attenuation of the Bluetooth signal within the 2.4GHz band, correlating it to the presence of a human body in the proximity of the device.\",\n \"Title: Evolving Experiences of Participation: Towards an e-ILAUD Tool\\nYear: 2021\\n\\nAbstract: The role of participatory communities all over the world calls for new models and tools to support citizens in taking part in decisions about the design and planning of urban design. Information and Communication Technologies (ICT) can be the basis for effective ways of participation for citizens and for helping them achieve a more active role in society. This paper describes e-ILAUD, a tool inspi...\",\n \"Title: High-Efficiency Multi-Sensor System for Chair Usage Detection\\nYear: 2021\\n\\nAbstract: Recognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such activities in an unobtrusive way, ambient-based solutions are available (e.g., based on PIRs, contact sensors, etc.). In this paper, we focus on the problem of sitting detection exploiting only unobtrusive sensors. In fact, sitting detection can be useful to understand the position of the user in many activities of the daily routines. While identifying sitting/lying on a sofa or bed is reasonably simple with pressure sensors, detecting whether a person is sitting on a chair is an open problem due to the natural chair position volatility. This paper proposes a reliable, not invasive and energetically sustainable system that can be used on chairs already present in the home. In particular, the proposed solution fuses the data of an accelerometer and a capacitive coupling sensor to understand if a person is sitting or not, discriminating the case of objects left on the chair. The results obtained in a real environment setting show an accuracy of 98.6% and a precision of 95%.\",\n \"Title: Hyperspectral Image Analysis for Automatic Detection and Discrimination of Residual Manufacturing Contaminants\\nYear: 2021\\n\\nAbstract: In modern manufacturing, divergent market dynamics impel companies to move toward a zero-defect production by reducing the risk of errors and defects down to zero. Paint-coating of metal surfaces is one of such process steps and most prominent as consumers will be animated to buy based on their first impression. Despite significant advances in automation and precision engineering of paint-coating,...\",\n \"Title: WECRAFT: a Platform for Networking Rural Craftsmen in Co-Production of Artisanal Crafts\\nYear: 2021\\n\\nAbstract: India is a growing country with over 1.2 billion people, out of which more than two-thirds still live in rural areas. Indian rural areas are vibrant heritage-wise. They have both a fascinating hard and soft heritage legacy. Each region has many kinds of villages, and each is characterized by specific arts and crafts skills developed over the centuries. Small settlements retain fascinating heritage...\",\n \"Title: A Multi-Resident Number Estimation Method for Smart Homes\\nYear: 2022\\n\\nAbstract: Population aging requires innovative solutions to increase the quality of life and preserve autonomous and independent living at home. A need of particular significance is the identification of behavioral drifts. A relevant behavioral drift concerns sociality: older people tend to isolate themselves. There is therefore the need to find methodologies to identify if, when, and how long the person is in the company of other people (possibly, also considering the number). The challenge is to address this task in poorly sensorized apartments, with non-intrusive sensors that are typically wireless and can only provide local and simple information. The proposed method addresses technological issues, such as PIR (Passive InfraRed) blind times, topological issues, such as sensor interference due to the inability to separate detection areas, and algorithmic issues. The house is modeled as a graph to constrain transitions between adjacent rooms. Each room is associated with a set of values, for each identified person. These values decay over time and represent the probability that each person is still in the room. Because the used sensors cannot determine the number of people, the approach is based on a multi-branch inference that, over time, differentiates the movements in the apartment and estimates the number of people. The proposed algorithm has been validated with real data obtaining an accuracy of 86.8%.\",\n \"Title: A Hybrid Agent-Based Model to Simulate and Re-Think Post-COVID-19 Use Processes in Educational Facilities\\nYear: 2021\\n\\nAbstract: The paper presents the development and application of a hybrid multi-agent system to simulate people’s behavior in educational facilities, to support decisions and strategies related to the post-COVID-19 scenarios. Complex use phenomena as the ones occurring in schools and educational facilities, required mixed, hybrid simulation models where the agent-based component, usually controlling single users/bots, is combined with a process-driven engine that ensures correspondence of the users’ behaviors to the general scenario. In our case, hybridization includes also the direct interaction of the intended users through a virtual reality 3D game, to further increase accuracy and adherence to the reality of the simulated phenomena. This paper also presents the application of the simulation model to a real case study, a school in Italy, where use processes have been simulated and currently under assessment during the school opening.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":70,"string":"70"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13271,"string":"13,271"}}},{"rowIdx":13272,"cells":{"question_id":{"kind":"string","value":"Alexandre X Falc o"},"question":{"kind":"string","value":"Generate an abstract for the title \"User-steered image segmentation paradigms: live wire and live lane\" using the following items: 1. Image segmentation\n2. User assistance\n3. Live wire\n4. Live lane\n5. Optimal feature selection"},"answer":{"kind":"string","value":"In multidimensional image analysis, there are, and will continue to be, situations wherein automatic image segmentation methods fail, calling for considerable user assistance in the process. The main goals of segmentation research for such situations ought to be (i) to provide effective control to the user on the segmentation process while it is being executed, and (ii) to minimize the total user's time required in the process. With these goals in mind, we present in this paper two paradigms, referred to as live wire and live lane , for practical image segmentation in large applications. For both approaches, we think of the pixel vertices and oriented edges as forming a graph, assign a set of features to each oriented edge to characterize its ``boundariness,'' and transform feature values to costs. We provide training facilities and automatic optimal feature and transform selection methods so that these assignments can be made with consistent effectiveness in any application. In live wire, the user first selects an initial point on the boundary. For any subsequent point indicated by the cursor, an optimal path from the initial point to the current point is found and displayed in real time. The user thus has a live wire on hand which is moved by moving the cursor. If the cursor goes close to the boundary, the live wire snaps onto the boundary. At this point, if the live wire describes the boundary appropriately, the user deposits the cursor which now becomes the new starting point and the process continues. A few points (live-wire segments) are usually adequate to segment the whole 2D boundary. In live lane, the user selects only the initial point. Subsequent points are selected automatically as the cursor is moved within a lane surrounding the boundary whose width changes as a function of the speed and acceleration of cursor motion. Live-wire segments are generated and displayed in real time between successive points. The users get the feeling that the curve snaps onto the boundary as and while they roughly mark in the vicinity of the boundary. We describe formal evaluation studies to compare the utility of the new methods with that of manual tracing based on speed and repeatability of tracing and on data taken from a large ongoing application. The studies indicate that the new methods are statistically significantly more repeatable and 1.5–2.5 times faster than manual tracing."},"correct_answer":{"kind":"string","value":"In multidimensional image analysis, there are, and will continue to be, situations wherein automatic image segmentation methods fail, calling for considerable user assistance in the process. The main goals of segmentation research for such situations ought to be (i) to provide effective control to the user on the segmentation process while it is being executed, and (ii) to minimize the total user's time required in the process. With these goals in mind, we present in this paper two paradigms, referred to as live wire and live lane , for practical image segmentation in large applications. For both approaches, we think of the pixel vertices and oriented edges as forming a graph, assign a set of features to each oriented edge to characterize its ``boundariness,'' and transform feature values to costs. We provide training facilities and automatic optimal feature and transform selection methods so that these assignments can be made with consistent effectiveness in any application. In live wire, the user first selects an initial point on the boundary. For any subsequent point indicated by the cursor, an optimal path from the initial point to the current point is found and displayed in real time. The user thus has a live wire on hand which is moved by moving the cursor. If the cursor goes close to the boundary, the live wire snaps onto the boundary. At this point, if the live wire describes the boundary appropriately, the user deposits the cursor which now becomes the new starting point and the process continues. A few points (live-wire segments) are usually adequate to segment the whole 2D boundary. In live lane, the user selects only the initial point. Subsequent points are selected automatically as the cursor is moved within a lane surrounding the boundary whose width changes as a function of the speed and acceleration of cursor motion. Live-wire segments are generated and displayed in real time between successive points. The users get the feeling that the curve snaps onto the boundary as and while they roughly mark in the vicinity of the boundary. We describe formal evaluation studies to compare the utility of the new methods with that of manual tracing based on speed and repeatability of tracing and on data taken from a large ongoing application. The studies indicate that the new methods are statistically significantly more repeatable and 1.5–2.5 times faster than manual tracing."},"documents":{"kind":"list like","value":["Title: E-Shell Renderin\nYear: 2002\n\nAbstract: In Medical Imaging, shell rendering is considered one of the most efficient and effective methods for volume visualization. It requires a compact data structure of voxels and uses the voxel splatting technique for surface and volume rendering. In order to avoid holes in the rendition, the size of the voxels are usually made bigger than the size of the pixels for voxel splatting. In such a case, we have identified a conceptual problem in shell rendering which affects the correctness and quality of the renditions. In this paper, we discuss this problem and propose a solution, which improves image quality without affecting the speed of the method. The new approach is called E-Shell Rendering (extended-shell rendering). It requires an extension of the shell data structure and a variant oftheoriginal algorithm. We illustrate and discuss the results with examples created by surface and volume rendering.","Title: A graph-based approach for multiscale shape analysis\nYear: 2004\n\nAbstract: This paper presents two shape descriptors, multiscale fractal dimension and contour saliences, using a graph-based approach— the image foresting transform. It introduces a robust approach to locate contour saliences from the relation between contour and skeleton. The contour salience descriptor consists of a vector, with salience location and value along the contour, and a matching algorithm. We compare both descriptors with fractal dimension, Fourier descriptors, moment invariants, Curvature Scale Space and Beam Angle Statistics regarding to their invariance to object characteristics that belong to a same class (compact-ability) and to their ability to separate objects of distinct classes (separability).","Title: Effective Image Retrieval by Shape Saliences\nYear: 2003\n\nAbstract: Content-Based Image Retrieval (CBIR) systems have been developed aiming at enabling users to search and re- trieve images based on their properties such as shape, color and texture. In this paper, we are concerned with shape- based image retrieval. Here, we discuss a recently proposed shape descriptor, called contour saliences, defined as the influence areas of its higher curvature points. This paper introduces a robust approach to estimate contour saliences by exploiting the relation between a contour and its skele- ton, modifies the original definition to include the location and the value of saliences along the contour, and proposes a new metric to compare contour saliences. The paper also evaluates the effectiveness of the proposed descriptor with respect to Fourier Descriptors, Curvature Scale Space and Moment Invariants.","Title: Content-Based Image Retrieval: Theory and Applications\nYear: 2006\n\nAbstract: Advances in data storage and image acquisition technologies have enabled the cre- ation of large image datasets. In this scenario, it is necess ary to develop appropri- ate information systems to efficiently manage these collect ions. The commonest ap- proaches use the so-called Content-Based Image Retrieval (CBIR) systems . Basically, these systems try to retrieve images similar to a user-define d specification or pattern (e.g., shape sketch, image example). Their goal is to support image retrieval based on content properties (e.g., shape, color, texture), usually encoded into feature vectors. One of the main advantages of the CBIR approach is the possibility of an automatic retrieval process, instead of the traditional keyword-bas ed approach, which usually requires very laborious and time-consuming previous annotation of database images. The CBIR technology has been used in several applications such as fingerprint identifi- cation, biodiversity information systems, digital librar ies, crime prevention, medicine, historical research, among others. This paper aims to introduce the problems and challenges concerned with the creation of CBIR systems, to describe the existing solutions and applications, and to present the state of the art of the existing research in this area.","Title: On the Training Patterns Pruning for Optimum-Path Forest\nYear: 2009\n\nAbstract: The Optimum-Path Forest (OPF) classifier is a novel graph-based supervised pattern recognition technique that has been demonstrated to be superior to Artificial Neural Networks and similar to Support Vector Machines, but much faster. The OPF classifier reduces the problem of pattern recognition to a computation of an optimum-path forest in the feature space induced by a graph, creating discrete optimal partitions, which are optimum-path trees rooted by prototypes, i.e., key samples that will compete among themselves trying to conquer the remaining samples. Some applications, such that medical specialist systems for image-based diseases identification, need to be constantly re-trained with new instances (diagnostics) to achieve a better generalization of the problem, which requires large storage devices, due to the high number of generated data (millions of voxels). In that way, we present here a pruning algorithm for the OPF classifier that learns the most irrelevant samples and eliminate them from the training set, without compromising the classifier's accuracy.","Title: Rotation-invariant texture recognition\nYear: 2007\n\nAbstract: This paper proposes a new texture classification system, which is distinguished by: (1) a new rotation-invariant image descriptor based on Steerable Pyramid Decomposition, and (2) by a novel multi-class recognition method based on Optimum Path Forest. By combining the discriminating power of our image descriptor and classifier, our system uses small size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz dataset. High classification rates demonstrate the superiority of the proposed method.","Title: FCD segmentation using texture asymmetry of MR-T1 images of the brain\nYear: 2008\n\nAbstract: Focal cortical dysplasia is the most common malformation in patients with intractable epilepsy. The segmentation of FCD lesions in MR-T1 images of the brain is a crucial step for treatment planning. In this work we present a new FCD segmentation technique based on analysis of texture asymme try. This technique does not rely on template-based segmentation and is applicable to patients of any age, regardless of anatomic variations. We evaluated the method on 5 patients (aged 11y-51y), and obtained 100% detection rate and coverage of 76.9% of the lesional voxels.","Title: Person-Specific Subspace Analysis For Unconstrained Familiar Face Identification\nYear: 2012\n\nAbstract: While significant strides have been made in the recognition of faces under controlled viewing conditions, face recognition \"in the wild\" remains a challenging unsolved problem [12, 13, 21]. Interestingly, while humans are generally excellent at identifying familiar individuals under such conditions, their performance is significantly worse with unfamiliar individuals [5] and groups [19], leading to the idea that brain may have enhanced or specialized representations of familiar individuals [6]. Inspired by these observations, we explored the use of a number of subspace analysis techniques, applied to various visual representations, to generate person-specific subspaces of \"familiar\" individuals for face identification. In particular, we introduce a person-specific application of partial least squares (PS-PLS) to generate per-individual subspaces, and show that operating in these subspaces yields state-of-the-art performance on the challenging PubFig83 familiar face identification benchmark. The results underscore the potential importance of incorporating a notion of familiarity into face recognition systems.","Title: Automatic Image Segmentation by Tree Pruning\nYear: 2007\n\nAbstract: The Image Foresting Transform (IFT) is a tool for the design of image processing operators based on connectivity, which reduces image processing problems into an optimum-path forest problem in a graph derived from the image. A new image operator is presented, which solves segmentation by pruning trees of the forest. An IFT is applied to create an optimum-path forest whose roots are seed pixels, selected inside a desired object. In this forest, object and background are connected by optimum paths (leaking paths), which cross the object's boundary through its \"most weakly connected\" parts (leaking pixels). These leaking pixels are automatically identified and their subtrees are eliminated, such that the remaining forest defines the object. Tree pruning runs in linear time, is extensible to multidimensional images, is free of ad hoc parameters, and requires only internal seeds, with little interference from the heterogeneity of the background. These aspects favor solutions for automatic segmentation. We present a formal definition of the obtained objects, algorithms, sufficient conditions for tree pruning, and two applications involving automatic segmentation: 3D MR-image segmentation of the human brain and image segmentation of license plates. Given that its most competitive approach is the watershed transform by markers, we also include a comparative analysis between them.","Title: Image segmentation by combining the strengths of Relative Fuzzy Connectedness and Graph Cut.\nYear: 2012\n\nAbstract: We introduce an image segmentation algorithm GC(sum)(max), which combines, in a novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that GC(sum)(max) preserves robustness of RFC with respect to the seed choice (thus, avoiding \"shrinking problem\" of GC), while keeping GC's bigger control over \"leaking though the weak boundary.\" The theoretical analysis of GC(sum)(max) is greatly facilitated by our recent theoretical results that RFC belongs to the Generalized GC (GGC) segmentation algorithms framework. In our implementation of GC(sum)(max) we use, as a subroutine, a version of RFC algorithm (based on Image Foresting Transform) that runs (provably) in linear time with respect to the image size. This results in GC(sum)(max) running in a time close to linear.","Title: Interactive Segmentation by Image Foresting Transform on Superpixel Graphs.\nYear: 2013\n\nAbstract: There are many scenarios in which user interaction is essential for effective image segmentation. In this paper, we present a new interactive segmentation method based on the Image Foresting Transform (IFT). The method over segments the input image, creates a graph based on these segments (super pixels), receives markers (labels) drawn by the user on some super pixels and organizes a competition to label every pixel in the image. Our method has several interesting properties: it is effective, efficient, capable of segmenting multiple objects in almost linear time on the number of super pixels, readily extendable through previously published techniques, and benefits from domain-specific feature extraction. We also present a comparison with another technique based on the IFT, which can be seen as its pixel-based counterpart. Another contribution of this paper is the description of automatic (robot) users. Given a ground truth image, these robots simulate interactive segmentation by trained and untrained users, reducing the costs and biases involved in comparing segmentation techniques.","Title: Fast and accurate holistic face recognition using optimum-path forest\nYear: 2009\n\nAbstract: This paper presents a novel, fast and accurate holistic method for face-recognition using the Optimum-Path Forest (OPF) classifier. Our objective is to improve the face recognition accuracy against traditional methods and to reduce the computational effort in face recognition tasks. During the feature extraction stage we apply Principal Component Analysis to reduce feature vectors in several dimensionalities. Experiments using face images from three public datasets (ORL, CBCL and YALE) present good results. Comparison among two other widely used supervised classifiers, Artificial Neural Networks based on Multilayer Perceptron and Support Vector Machines, show that the proposed method drastically reduces the computational cost, achieving correct classification rates at least identical to SVM.","Title: Prontovideo: An Image Sequence Segmentation Tool Applied to Video Edition\nYear: 2001\n\nAbstract: This work proposes an image sequence segmentation tool for video masking. It consist of a semi-automatic delineation tool of the object of interest, its segmentation, through the video sequence. The tool is based on watershed by markers technique, where the markers are propagated from frame to frame using normalized correlation pattern matching algorithm.","Title: Design of pattern classifiers using optimum-path forest with applications in image analysis\nYear: 2010\n\nAbstract: Current image acquisition and storage technologies have provided large data sets (with millions of samples) for analysis. Samples may be images from an image database, objects extracted from several images, or image pixels. This scenario is very challenging for traditional machine learning and pattern recognition techniques, which need to be more efficient and effective in large data sets. This lecture presents a recent and successful methodology, which links training samples in a given feature space and exploits optimum connectivity between them to the design of pattern classifiers. The methodology essentially extends the Image Foresting Transform, successfully used for filtering, segmentation and shape description, from the image domain to the feature space. Several supervised and unsupervised learning techniques may be created from the specification of two parameters: an adjacency relation and a connectivity function. The adjacency relation defines which samples form arcs of a graph in the feature space. The connectivity function assigns a value to any path in the graph. The path value indicates the strength of connectedness of its terminal node with respect to its source node. A connectivity map is maximized by partitioning the graph into an optimum-path forest rooted at its maxima (i.e., representative samples of each class/group, called prototypes). The optimum-path forest is then a pattern classifier, which assigns to any new sample the class (or group label) of its most strongly connected root. The methods have been successfully applied to several applications and this lecture demonstrates two recent ones: content-based image retrieval (CBIR) and 3D segmentation of brain tissues in MR images. In CBIR, user interaction is considerably reduced to a few clicks on relevant/irrelevant images along 3 iterations of relevance feedback followed by supervised learning in order to achieve satisfactory query results. The 3D segmentation of brain tissues is automatically obtained in less than 2 minutes. It exploits voxel clustering, some prior knowledge and does not require a brain atlas for that purpose, while many other brain tissue segmentation methods do. The lecture concludes by discussing some open problems and perspectives for the optimum-path forest classifiers.","Title: A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection\nYear: 2011\n\nAbstract: Finding an optimal subset of features that maximizes classification accuracy is still an open problem. In this paper, we exploit the speed of the Harmony Search algorithm and the Optimum-Path Forest classifier in order to propose a new fast and accurate approach for feature selection. Comparisons to some other pattern recognition and feature selection techniques showed that the proposed hybrid algorithm for feature selection outperformed them. The experiments were carried out in the context of identifying non-technical losses in power distribution systems.","Title: A Fast and Automatic Method for 3D Rigid Registration of MR Images of the Human Brain\nYear: 2008\n\nAbstract: We propose a system to solve a multi-class produce categorization problem. For that, we use statistical color, texture, and structural appearance descriptors (bag-of-features). As the best combination setup is not known for our problem, we combine several ...","Title: Adapted Dynamic Meshes for Deformable Surfaces\nYear: 2006\n\nAbstract: Deformable objects play an important role in many applications, such as animation and simulation. Effective computation with deformable surfaces can be achieved through the use of dynamic meshes. In this paper we introduce a framework for constructing and maintaining a time-varying adapted mesh structure that conforms to the underlying deformable surface. The adaptation function employs error metrics based on stochastic sampling. Our scheme combines normal and tangential geometric correction with refinement and simplification resolution control. Furthermore, it applies to both parametric and implicit surface descriptions. As the result, we obtain a simple and efficient general scheme that can be used for a wide range of computations.","Title: Joint graph cut and relative fuzzy connectedness image segmentation algorithm.\nYear: 2013\n\nAbstract: •Novel image segmentation algorithm robust w.r.t. seed choice.•Synergistic combinantion of fuzzy connectedness (FC) and Graph Cut (GC) algorithms.•Better delineation of poorly defined boundary segments than either FC or GC alone.","Title: Clustering by optimum path forest and its application to automatic GM/WM classification in MR-T1 images of the brain\nYear: 2008\n\nAbstract: A new approach to identify clusters as trees of an optimum- path forest has been presented. We are extending the method for large datasets with application to automatic GM/WM classification in MR-T1 images of the brain. The method is computed for a few randomly selected voxels, such that GM and WM define two optimum-path trees. The remaining voxels are classified incrementally, by identifying which tree would contain each voxel if it were part of the forest. Our method produces accurate results on phantom and real images, similarly to those obtained by the state-of-the-art, does not rely on templates, and takes less than 1.5 minute on modern PCs.","Title: A New Symmetry-Based Method For Mid-Sagittal Plane Extraction In Neuroimages\nYear: 2011\n\nAbstract: The estimation of the mid-sagittal plane (MSP) is a known problem with several applications in neuroimage analysis. As advance to the state-of-the-art, we present a considerably better approach for MSP extraction based on bilateral symmetry maximization and a more suitable error metric to compare MSP estimation methods. The proposed method was quantitatively evaluated using three other state-of-the-art approaches as baselines and a heterogeneous dataset with 164 clinical images. It outperformed the others in accuracy and precision, being well succeeded on all images. Besides, it does not present limitations with respect to the imaging protocol and initial position of the head, and it is one of the fastest methods in the literature, taking around 30 seconds on a regular workstation.","Title: Multiscale Classification of Remote Sensing Images.\nYear: 2012\n\nAbstract: A huge effort has been applied in image classification to create high-quality thematic maps and to establish precise inventories about land cover use. The peculiarities of remote sensing images (RSIs) combined with the traditional image classification challenges made RSI classification a hard task. Our aim is to propose a kind of boost-classifier adapted to multiscale segmentation. We use the para...","Title: Interactive volume segmentation with differential image foresting transforms.\nYear: 2004\n\nAbstract: The absence of object information very often asks for considerable human assistance in medical image segmentation. Many interactive two-dimensional and three-dimensional (3-D) segmentation methods have been proposed, but their response time to user's actions should be considerably reduced to make them viable from the practical point of view. We circumvent this problem in the framework of the image...","Title: Automatic Subcortical Tissue Segmentation Of Mr Images Using Optimum-Path Forest Clustering\nYear: 2011\n\nAbstract: Automatic MR-image segmentation of brain tissues is an important issue in neuroimaging. For instance, it is a key methodological component of a popular technique denominated voxel-based morphometry (VBM), which quantifies gray-matter (GM) volumes from MR images. However, segmentation accuracy in some subcortical regions on the basis of extant methods is not satisfactory, compromising VBM results. We combine a probabilistic atlas and a fast clustering approach based on optimum connectivity between voxels in their feature space. The algorithm exploits local image properties and global information from the atlas as features to group GM and white-matter (WM) voxels in distinct clusters, and uses the total probability values inside the clusters to label them as GM or WM. This new method is validated in the region of the thalamus and outperformed two widely used methods packaged in SPM and FSL.","Title: Intelligent Understanding Of User Interaction In Image Segmentation\nYear: 2012\n\nAbstract: We have developed interactive tools for graph-based segmentation of natural images, in which the user guides object delineation by drawing strokes (markers) inside and outside the object. A suitable arc-weight estimation is paramount to minimize user time and maximize segmentation accuracy in these tools. However, it depends on discriminative image properties for object and background. These properties can be obtained from some marker pixels, but their identification is a hard problemduring delineation. Careless arc-weight re-estimation reduces user control and drops performance, while interactive arc-weight estimation in a step before interactive object extraction is the best option so far, albeit it is not intuitive for nonexpert users. We present an effective solution using the unified framework of the image foresting transform (IFT) with three operators: clustering for interpreting user interaction and determining when and where arc weights need to be re-estimated; fuzzy classification for arc-weight estimation; and marker competition based on optimum connectivity for object extraction. For validation, we compared the proposed approach with another interactive IFT-based method, which computes arc weights before extraction. Evaluation involved multiple users (experts and nonexperts), a dataset with several natural images, and measurements to quantify accuracy, precision, efficiency (user time and computation time), and user control, being some of them novel measurements, proposed in this work.","Title: MiCRoM: A Metric Distance to Compare Segmented Images\nYear: 2002\n\nAbstract: Recently, several content-based image retrieval (CBIR) systems that make use of segmented images have been proposed. In these systems, images are segmented and represented as a set of regions, and the distance between images is computed according to the visual features of their regions. A major problem of existing distance functions used to compare segmented images is that they are not metrics. Hence, it is not possible to exploit filtering techniques and/or access methods to speedup query processing, as both techniques make extensive use of the triangular inequality property - one of the metric axioms. In this work, we propose MiCROM (Minimum-Cost Region Matching), an effective metric distance which models the comparison of segmented images as a minimum-cost network flow problem. To our knowledge, this is the first time a true metric distance function is proposed to evaluate the distance between segmented images. Our experiments show that MiCROM is at least as effective as existing non-metric distances. Moreover, we have been able to use the recently proposed Omni-sequential filtering technique, and have achieved nearly 2/3 savings in retrieval/query processing time.","Title: Fast Digital Perspective Shell Rendering\nYear: 1999\n\nAbstract: Volume visualization methods usually require extensive storage and time-intensive computations. To make them viable in many applications, researchers have proposed various types of hardware-based and software-based solutions. In most of these solutions, the rendering is based on orthographic parallel projection. However, in applications such as virtual endoscopy, perspective projection is necessary to allow the observer to travel inside the structures. In this paper, we extend one of these software-based solutions, called shell rendering, to provide fast digital perspective projection. The main idea in digital perspective is that each voxel should be rendered using an integer number of pixels which is inversely proportional to the distance between the voxel and the observer and that that distance can be discretized. We show that the new method, called digital perspective shell rendering, provides the same image quality as orthographic parallel projection","Title: A discrete approach for supervised pattern recognition\nYear: 2008\n\nAbstract: We present an approach for supervised pattern recognition based on combinatorial analysis of optimum paths from key samples (prototypes), which creates a discrete optimal partition of the feature space such that any unknown sample can be classified according to this partition. A training set is interpreted as a complete graph with at least one prototype in each class. They compete among themselves and each prototype defines an optimum-path tree, whose nodes are the samples more strongly connected to it than to any other. The result is an optimumpath forest in the training set. A test sample is assigned to the class of the prototype which offers it the optimum path in the forest. The classifier is designed to achieve zero classification errors in the training set, without over-fitting, and to learn from its errors. A comparison with several datasets shows the advantages of the method in accuracy and efficiency with respect to support vector machines.","Title: Remote sensing image representation based on hierarchical histogram propagation.\nYear: 2013\n\nAbstract: Many methods have been recently proposed to deal with the large amount of data provided by high-resolution remote sensing technologies. Several of these methods rely on the use of image segmentation algorithms for delineating target objects. However, a common issue in geographic object-based applications is the definition of the appropriate data representation scale, a problem that can be addressed by exploiting multiscale segmentation. The use of multiple scales, however, raises new challenges related to the definition of effective and efficient mechanisms for extracting features. In this paper, we address the problem of extracting histogram-based features from a hierarchy of regions for multiscale classification. The strategy, called H-Propagation, exploits the existing relationships among regions in a hierarchy to iteratively propagate features along multiple scales. The proposed method speeds up the feature extraction process and yields good results when compared with global low-level extraction approaches.","Title: Spoken emotion recognition through optimum-path forest classification using glottal features\nYear: 2010\n\nAbstract: A new method for the recognition of spoken emotions is presented based on features of the glottal airflow signal. Its effectiveness is tested on the new optimum path classifier (OPF) as well as on six other previously established classification methods that included the Gaussian mixture model (GMM), support vector machine (SVM), artificial neural networks - multi layer perceptron (ANN-MLP), k-nearest neighbor rule (k-NN), Bayesian classifier (BC) and the C4.5 decision tree. The speech database used in this work was collected in an anechoic environment with ten speakers (5M and 5F) each speaking ten sentences in four different emotions: Happy, Angry, Sad, and Neutral. The glottal waveform was extracted from fluent speech via inverse filtering. The investigated features included the glottal symmetry and MFCC vectors of various lengths both for the glottal and the corresponding speech signal. Experimental results indicate that best performance is obtained for the glottal-only features with SVM and OPF generally providing the highest recognition rates, while for GMM or the combination of glottal and speech features performance was relatively inferior. For this text dependent, multi speaker task the top performing classifiers achieved perfect recognition rates for the case of 6th order glottal MFCCs.","Title: Fast and automatic curvilinear reformatting of MR images of the brain for diagnosis of dysplastic lesions\nYear: 2006\n\nAbstract: Curvilinear reformatting is known as the best non-invasive technique for diagnosis of dysplastic lesions of the brain. It consists of computing surfaces that follow the brain's curvature at various depths, making the diagnosis possible by visual inspection of the voxel intensities on these surfaces. Traditional approaches require user intervention and present curvature artifacts. We present a new method for curvilinear reformatting that solves both problems. It uses a graph-based approach to segment the brain, extract its envelope, and compute the isosurfaces at all possible depths by Euclidean distance transform. It requires no user input, no ad-hoc parameters, and takes less than 1 minute to run on a common PC","Title: Automatic Segmentation and Classification of Human Intestinal Parasites From Microscopy Images.\nYear: 2013\n\nAbstract: Human intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis.","Title: Supervised pattern classification based on optimum-path forest\nYear: 2009\n\nAbstract: We present a supervised classification method which represents each class by one or more optimum-path trees rooted at some key samples, called prototypes. The training samples are nodes of a complete graph, whose arcs are weighted by the distances between the feature vectors of their nodes. Prototypes are identified in all classes and the minimization of a connectivity function by dynamic programming assigns to each training sample a minimum-cost path from its most strongly connected prototype. This competition among prototypes partitions the graph into an optimum-path forest rooted at them. The class of the samples in an optimum-path tree is assumed to be the same of its root. A test sample is classified similarly, by identifying which tree would contain it, if the sample were part of the training set. By choice of the graph model and connectivity function, one can devise other optimum-path forest classifiers. We present one of them, which is fast, simple, multiclass, parameter independent, does not make any assumption about the shapes of the classes, and can handle some degree of overlapping between classes. We also propose a general algorithm to learn from errors on an evaluation set without increasing the training set, and show the advantages of our method with respect to SVM, ANN-MLP, and k-NN classifiers in several experiments with datasets of various types. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 120–131, 2009. A preliminary version of the paper was presented at the 12th International Workshop on Combinatorial Image Analysis (Papa et al.,2008a).","Title: Automatic fusion of region-based classifiers for coffee crop recognition\nYear: 2012\n\nAbstract: Coffee crop recognition in remote sensing images is a complex task. It poses several challenges due to different spectral responses and texture patterns that can be extracted from coffee regions. This paper presents a novel framework for combining different classifiers using support vector machine technique (SVM), which try to learn with each one of classifiers previews experiences (meta-learning). We investigate the combination of seven learning methods and seven image descriptors aiming at creating low-cost classifiers for coffee crops recognition. The objective is to provide an effective mechanism for coffee crop recognition by fusion of region-based classifiers in remote sensing images. The experiments showed that the proposed framework for fusion of classifiers produces better results than the traditional majority voting fusion approach and all base classifiers tested.","Title: Fuzzy Connectedness Image Segmentation in Graph Cut Formulation: A Linear-Time Algorithm and a Comparative Analysis.\nYear: 2012\n\nAbstract: A deep theoretical analysis of the graph cut image segmentation framework presented in this paper simultaneously translates into important contributions in several directions.The most important practical contribution of this work is a full theoretical description, and implementation, of a novel powerful segmentation algorithm, GC. The output of GC coincides with a version of a segmentation algorithm known as Iterative Relative Fuzzy Connectedness, IRFC. However, GC is considerably faster than the classic IRFC algorithm, which we prove theoretically and show experimentally. Specifically, we prove that, in the worst case scenario, the GC algorithm runs in linear time with respect to the variable =||+||, where || is the image scene size and || is the size of the allowable range, , of the associated weight/affinity function. For most implementations, is identical to the set of allowable image intensity values, and its size can be treated as small with respect to ||, meaning that ()=(||). In such a situation, GC runs in linear time with respect to the image size ||.We show that the output of GC constitutes a solution of a graph cut energy minimization problem, in which the energy is defined as the norm ∥ ∥ of the map that associates, with every element from the boundary of an object , its weight (). This formulation brings IRFC algorithms to the realm of the graph cut energy minimizers, with energy functions ∥ ∥ for ∈[1,∞]. Of these, the best known minimization problem is for the energy ∥ ∥, which is solved by the classic min-cut/max-flow algorithm, referred to often as the Graph Cut algorithm.We notice that a minimization problem for ∥ ∥, ∈[1,∞), is identical to that for ∥ ∥, when the original weight function is replaced by . Thus, any algorithm GC solving the ∥ ∥ minimization problem, solves also one for ∥ ∥ with ∈[1,∞), so just two algorithms, GC and GC, are enough to solve all ∥ ∥-minimization problems. We also show that, for any fixed weight assignment, the solutions of the ∥ ∥-minimization problems converge to a solution of the ∥ ∥-minimization problem (∥ ∥=lim∥ ∥ is not enough to deduce that).An experimental comparison of the performance of GC and GC algorithms is included. This concentrates on comparing the actual (as opposed to provable worst scenario) algorithms’ running time, as well as the influence of the choice of the seeds on the output.","Title: On “shapes” of colors for content-based image retrieval\nYear: 2000\n\nAbstract: Color is a commonly used feature for realizing content-based image retrieval (CBIR). Towards this goal, this paper presents a new approach for CBIR which is based on well known and widely used color histograms. Contrasting to previous approaches, such as using a single color histogram for the whole image, or local color histograms for a fixed number of image cells, the one we propose (named Color Shape) uses a variable number of histograms, depending only on the actual number of colors present in the image. Our experiments using a large set of heterogeneous images and pre-defined query/answer sets show that the Color Shape approach offers good retrieval quality with relatively low space overhead, outperforming previous approaches.","Title: Descriptor correlation analysis for remote sensing image multi-scale classification.\nYear: 2012\n\nAbstract: This paper addresses the problem of remote sensing image multi-scale classification by: (i) showing that using multiple scales does improve classification results, but not all scales have the same importance; (ii) showing that image descriptors do not offer the same contribution at all scales, as commonly thought, and some of them are very correlated; (iii) introducing a simple approach to automatically select segmentation scales, descriptors, and classifiers based on correlation and accuracy analysis.","Title: Optimizing Contextual-Based Optimum-Forest Classification through Swarm Intelligence\nYear: 2013\n\nAbstract: Several works have been conducted in order to improve classification problems. However, a considerable amount of them do not consider the contextual information in the learning process, which may help the classification step by providing additional information about the relation between a sample and its neighbourhood. Recently, a previous work have proposed a hybrid approach between Optimum-Path Forest classifier and Markov Random Fields (OPF-MRF) aiming to provide contextual information for this classifier. However, the contextual information was restricted to a spatial/temporal-dependent parameter, which has been empirically chosen in that work. We propose here an improvement of OPF-MRF by modelling the problem of finding such parameter as a swarm-based optimization task, which is carried out Particle Swarm Optimization and Harmony Search. The results have been conducted over the classification of Magnetic Ressonance Images of the brain, and the proposed approach seemed to find close results to the ones obtained by an exhaustive search for this parameter, but much faster for that.","Title: Robust And Fast Vowel Recognition Using Optimum-Path Forest\nYear: 2010\n\nAbstract: The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy.","Title: A genetic programming framework for content-based image retrieval\nYear: 2009\n\nAbstract: The effectiveness of content-based image retrieval (CBIR) systems can be improved by combining image features or by weighting image similarities, as computed from multiple feature vectors. However, feature combination do not make sense always and the combined similarity function can be more complex than weight-based functions to better satisfy the users' expectations. We address this problem by presenting a Genetic Programming framework to the design of combined similarity functions. Our method allows nonlinear combination of image similarities and is validated through several experiments, where the images are retrieved based on the shape of their objects. Experimental results demonstrate that the GP framework is suitable for the design of effective combinations functions.","Title: An Adaptive and Efficient Clustering-Based Approach for Content-Based Image Retrieval in Image Databases\nYear: 2001\n\nAbstract: Abstract: In this paper, we present a new Content-Based Image Retrieval (CBIR) approach, for image databases, based on cluster analysis. CBIR relies on the representation (meta-data) of images' visual content. In order to produce such metadata, we propose an efficient and adaptive clustering algorithm to segment the images into regions of high-similarity. This approach contrasts with those that use a single color histogram for the whole image (global methods), or local color histograms for a fixed number of image cells (partition-based methods). Our experimental results show that our clustering approach offers high retrieval effectiveness with low space overhead. For example, using a database of 20,000 images we obtained higher retrieval effectiveness than partition-based methods with about the same space overhead of global methods, which are typically regarded to as storage-wise compact.","Title: Object delineation by κ-connected components\nYear: 2008\n\nAbstract: The notion of \"strength of connectedness\" between pixels has been successfully used in image segmentation. We present extensions to these works, which can considerably improve the efficiency of object delineation tasks. A set of pixels is said to be a κ-connected component with respect to a seed pixel, when the strength of connectedness of any pixel in that set with respect to the seed is higher than or equal to a threshold. We discuss two approaches that define objects based on κ-connected components with respect to a given seed set: with and without competition among seeds. While the previous approaches either assume no competition with a single threshold for all seeds or eliminate the threshold for seed competition, we show that seeds with different thresholds can improve segmentation in both paradigms. We also propose automatic and user-friendly interactive methods to determining the thresholds. The proposed methods are presented in the framework of the image foresting transform, which naturally leads to efficient and correct graph algorithms. The improvements are demonstrated through several segmentation experiments involving medical images.","Title: Cloud bank: a multiple clouds model and its use in MR brain image segmentation\nYear: 2009\n\nAbstract: We present improvements to a recently proposed model-based segmentation strategy, called Clouds. We demonstrate the usefulness of the new model Cloud Bank in the automatic MR-image segmentation of the brain (without medulla and spinal cord), the cerebellum, and each brain hemisphere. These structures are connected in several parts, imposing serious challenges for segmentation. The results indicate that the new model is a fast and accurate tool to eliminate user's intervention or, at least, reduce it to simple corrections, in the context of brain image segmentation.","Title: A data reduction and organization approach for efficient image annotation\nYear: 2013\n\nAbstract: The labor-intensive and time-consuming process of annotating data is a serious bottleneck in many pattern recognition applications when handling massive datasets. Active learning strategies have been sought to reduce the cost on human annotation, by means of automatically selecting the most informative unlabeled samples for annotation. The critical issue lies on the selection of such samples. As an effective solution, we propose an active learning approach that preprocesses the dataset, efficiently reduces and organizes a learning set of samples and selects the most representative ones for human annotation. Experiments performed on real datasets show that the proposed approach requires only a few iterations to achieve high accuracy, keeping user involvement to a minimum.","Title: A Comparative Study among Pattern Classifiers in Interactive Image Segmentation\nYear: 2009\n\nAbstract: Edition of natural images usually asks for considerable userinvolvement, being segmentation one of the main challenges. This paper describes an unified graph-based framework for fast, precise and accurate interactive image segmentation. The method divides segmentation into object recognition, enhancement and extraction. Recognition is done by the user when markers are selected inside and outside the object. Enhancement increases the dissimilarities between object and background and Extraction separates them. Enhancement is done by a fuzzy pixel classifier and it has a great impact in the number of markers required for extraction. In view of minimizing user involvement, we focus this paper on a comparative study among popular classifiers for enhancement, conducting experiments with several natural images and seven users.","Title: Feature selection through gravitational search algorithm\nYear: 2011\n\nAbstract: In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.","Title: Speeding up optimum-path forest training by path-cost propagation\nYear: 2012\n\nAbstract: In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost function. Experiments on public datasets have shown that the proposed approach can obtain similar accuracy to the traditional one but with faster data training.","Title: A linear-time approach for image segmentation using graph-cut measures\nYear: 2006\n\nAbstract: Image segmentation using graph cuts have become very popular in the last years. These methods are computationally expensive, even with hard constraints (seed pixels). We present a solution that runs in time proportional to the number of pixels. Our method computes an ordered region growing from a set of seeds inside the object, where the propagation order of each pixel is proportional to the cost of an optimum path in the image graph from the seed set to that pixel. Each pixel defines a region which includes it and all pixels with lower propagation order. The boundary of each region is a possible cut boundary, whose cut measure is also computed and assigned to the corresponding pixel on-the-fly. The object is obtained by selecting the pixel with minimum-cut measure and all pixels within its respective cut boundary. Approaches for graph-cut segmentation usually assume that the desired cut is a global minimum. We show that this can be only verified within a reduced search space under certain hard constraints. We present and evaluate our method with three cut measures: normalized cut, mean cut and an energy function.","Title: Precipitates segmentation from scanning electron microscope images through machine learning techniques\nYear: 2011\n\nAbstract: The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis.","Title: Fast, Accurate and Precise Mid-Sagittal Plane Location in 3D MR Images of the Brain\nYear: 2008\n\nAbstract: Extraction of the mid-sagittal plane (MSP) is a key step for brain image registration and asymmetry analysis. We present a fast MSP extraction method for 3D MR images, based on automatic segmentation of the brain and on heuristic maximization of the cerebro-spinal fluid within the MSP. The method is robust to severe anatomical asymmetries between the hemispheres, caused by surgical procedures and lesions. The method is also accurate with respect to MSP delineations done by a specialist. The method was evaluated on 64 MR images (36 pathological, 20 healthy, 8 synthetic), and it found a precise and accurate approximation of the MSP in all of them with a mean time of 60.0 seconds per image, mean angular variation within a same image (precision) of 1.26 degrees and mean angular difference from specialist delineations (accuracy) of 1.64 degrees.","Title: Automated diagnosis of human intestinal parasites using optical microscopy images\nYear: 2013\n\nAbstract: Intestinal parasitosis constitutes a serious health problem in most tropical countries. The diagnosis of enteroparasites in laboratory routine relies on the examination of stool samples using optical microscopy and the error rates usually range from moderate to high. Approaches based on automatic image analysis have been proposed, but the methods are usually specific for some species, some of them are computationally expensive, and image acquisition and focus are manual. We present a solution to automate the diagnosis of the 15 most common species of enteroparasites in Brazil, using a sensitive parasitological technique, a motorized microscope with digital camera for automatic image acquisition and focus, and fast image analysis methods. The results indicate that our solution is effective and suitable for laboratory routine, in which the exam must be concluded in a few minutes.","Title: Fuzzy-connected 3D image segmentation at interactive speeds\nYear: 2002\n\nAbstract: Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem with these algorithms has been their excessive computational requirements. In an attempt to substantially speed them up, in the present paper, we study systematically a host of 18 'optimal' graph search algorithms. Extensive testing of these algorithms on a variety of 3D medical images taken from large ongoing applications demonstrates that a 20-1000-fold improvement over current speeds is achievable with a combination of algorithms and fast modern PCs. Utilizing efficient algorithms and careful selection of implementations can speed up the computation of fuzzy connectedness values by a factor of 16-29 (on the same hard-ware), as compared to the implementation previously used in our applications utilizing fuzzy object segmentation. The optimality of an algorithm depends on the input data as well as on the choice of the fuzzy affinity relation. The running time is reduced considerably (by a factor up to 34 for brain MR and even more for bone CT), when the algorithms make use of pre-determined thresholds for the fuzzy objects. The reliable recognition (assisted by human operators) and the accurate, efficient, and sophisticated delineation (automatically performed by the computer) can be effectively incorporated into a single interactive process. If images having intensities with tissue-specific meaning (such as CT or standardized MR images) are utilized, most of the parameters for the segmentation method can be fixed once for all, all intermediate data (feature and fuzzy affinity values for the whole scene) can be computed before the user interaction is needed and the user can be provided with more information at the time of interaction.","Title: The Image Forest Transform Architecture\nYear: 2007\n\nAbstract: The Image Foresting Transform (IFT) is e a generic technique that uses simple variations of the same core algorithm to construct many image processing operators like watershed transforms, edge tracking, geodesic paths, among others. In this paper we propose the Silicon IFT (SIFT), an FPGA-based architecture that leverages on the IFT flexibility to build a fast image processing architecture capable of implementing IFT operators in hardware. Our experiments have shown that SIFT can reach speedups of 5600 upon the correspondent software implementation. Moreover, they exhibit excellent execution times as compared to recent dedicated image processing architectures.","Title: Clouds: A Model For Synergistic Image Segmentation\nYear: 2008\n\nAbstract: Image segmentation consists of recognizing the object in the image and precisely delineating its spatial extent. We present a model, called clouds, that exploits the synergism which commonly exists between recognition and delineation for more effective segmentation. The model can reduce user's intervention to simple corrections or even eliminate it altogether, achieving high accuracy. We evaluate the method in the task of 3D MR image segmentation of the brain in isolating automatically: brain without medulla and spinal cord; just the cerebellum; and the brain hemispheres without medulla, spinal cord, and cerebellum. These structures are connected in several parts, which poses a serious challenge for simplistic segmentation strategies. The entire process takes a few seconds on modem PCs and provides accurate results. The applications for clouds go beyond medical imaging, opening new vistas in a variety of areas served by segmentation.","Title: A compact and efficient image retrieval approach based on border/interior pixel classification\nYear: 2002\n\nAbstract: This paper presents \\bic (Border/Interior pixel Classification), a compact and efficient CBIR approach suitable for broad image domains. It has three main components: (1) a simple and powerful image analysis algorithm that classifies image pixels as either border or interior, (2) a new logarithmic distance (dLog) for comparing histograms, and (3) a compact representation for the visual features extracted from images. Experimental results show that the BIC approach is consistently more compact, more efficient and more effective than state-of-the-art CBIR approaches based on sophisticated image analysis algorithms and complex distance functions. It was also observed that the dLog distance function has two main advantages over vectorial distances (e.g., L1): (1) it is able to increase substantially the effectiveness of (several) histogram-based CBIR approaches and, at the same time, (2) it reduces by 50% the space requirement to represent a histogram.","Title: IFT-Watershed from Gray-Scale Marker\nYear: 2002\n\nAbstract: Abstract. The watershed transform and the morphological reconstruction are two of the most important operators for image segmentation in the framework of mathematical morphology. In many situations, the segmentation requires the classical watershed transform of a reconstructed image. In this paper, we introduce the IFT-watershed from gray scale marker - a method to compute at same time, the reconstruction and the classical watershed transformof the reconstructed image, without explicit computation of any regional minima. The method is based on the Image Foresting Transform (IFT) - a unified and efficient approach to reduce image processing problems to a minimum-cost path forest problem in a graph. As additional contributions, we demonstrate that (i) the cost map of the IFT-watershed from markers is identical to the output of the superior gray scale reconstruction; (ii) other reconstruction algorithms are not watersheds; and (iii) the proposed method achieves competitive advantages as compared to the current classical watershed approach.","Title: Novel Approaches for Exclusive and Continuous Fingerprint Classification\nYear: 2009\n\nAbstract: This paper proposes novel exclusive and continuous approaches to guide the search and the retrieval in fingerprint image databases. Both approaches are useful to perform a coarse level classification of fingerprint images before fingerprint authentication tasks. Our approaches are characterized by: (1) texture image descriptors based on pairs of multi-resolution decomposition methods that encode effectively global and local fingerprint information, with similarity measures used for fingerprint matching purposes, and (2) a novel multi-class object recognition method based on the Optimum Path Forest classifier. Experiments were carried out on the standard NIST-4 dataset aiming to study the discriminative and scalability capabilities of our approaches. The high classification rates allow us demonstrate the feasibility and validity of our approaches for characterizing fingerprint images accurately.","Title: Improving the accuracy of the optimum-path forest supervised classifier for large datasets\nYear: 2010\n\nAbstract: In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OPF while still gaining in classification time, at the expense of a slight increase in training time.","Title: A new framework to combine descriptors for content-based image retrieval\nYear: 2005\n\nAbstract: In this paper, we propose a novel framework using Genetic Programming to combine image database descriptors for content-based image retrieval (CBIR). Our framework is validated through several experiments involving two image databases and specific domains, where the images are retrieved based on the shape of their objects.","Title: A 3D generalization of user-steered live-wire segmentation\nYear: 2000\n\nAbstract: We have been developing user-steered image segmentation methods for situations which require considerable human assistance in object definition. In the past, we have presented two paradigms, referred to as live-wire and live-lane, for segmenting 2D/3D/4D object boundaries in a slice-by-slice fashion, and demonstrated that live-wire and live-lane are more repeatable, with a statistical significance level of P<0.03, and are 1.5–2.5 times faster, with a statistical significance level of P<0.02, than manual tracing. In this paper, we introduce a 3D generalization of the live-wire approach for segmenting 3D/4D object boundaries which further reduces the time spent by the user in segmentation. In a 2D live-wire, given a slice, for two specified points (pixel vertices) on the boundary of the object, the best boundary segment is the minimum-cost path between the two points, described as a set of oriented pixel edges. This segment is found via Dijkstra’s algorithm as the user anchors the first point and moves the cursor to indicate the second point. A complete 2D boundary is identified as a set of consecutive boundary segments forming a “closed”, “connected”, “oriented” contour. The strategy of the 3D extension is that, first, users specify contours via live-wiring on a few slices that are orthogonal to the natural slices of the original scene. If these slices are selected strategically, then we have a sufficient number of points on the 3D boundary of the object to subsequently trace optimum boundary segments automatically in all natural slices of the 3D scene. A 3D object boundary may define multiple 2D boundaries per slice. The points on each 2D boundary form an ordered set such that when the best boundary segment is computed between each pair of consecutive points, a closed, connected, oriented boundary results. The ordered set of points on each 2D boundary is found from the way the users select the orthogonal slices. Based on several validation studies involving segmentation of the bones of the foot in MR images, we found that the 3D extension of live-wire is more repeatable, with a statistical significance level of P<0.0001, and 2–6 times faster, with a statistical significance level of P<0.01, than the 2D live-wire method, and 3–15 times faster than manual tracing.","Title: What is the importance of selecting features for non-technical losses identification?\nYear: 2011\n\nAbstract: Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles.","Title: Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images\nYear: 2008\n\nAbstract: Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system.","Title: The riverbed approach for user-steered image segmentation\nYear: 2011\n\nAbstract: This work presents an optimum user-steered boundary tracking approach for image segmentation, which simulates the behavior of water flowing through a riverbed. The riverbed approach was devised using the Image Foresting Transform with a never exploited connectivity function. We analyze its properties in the derived image graphs and discuss its theoretical relation with other popular methods, such as live wire and graph cuts. Riverbed can significantly reduce the number of user interactions (anchor points) as compared to live wire for objects with complex shapes.","Title: Improving texture description in remote sensing image multi-scale classification tasks by using visual words.\nYear: 2012\n\nAbstract: Although texture features are important for region-based classification of remote sensing images, the literature shows that texture descriptors usually have poor performance when compared and combined with color descriptors. In this paper, we propose a bag-of-visual-words (BOW) “propagation” approach to extract texture features from a hierarchy of regions. This strategy improves efficacy of feature as it encodes texture information independently of the region shape. Experiments show that the proposed approach improves the classification results when compared with global descriptors using the bounding box padding strategy.","Title: An Ultra-Fast User-Steered Image Segmentation Paradigm: Live Wire On The Fly\nYear: 2000\n\nAbstract: We have been developing general user steered image segmentation strategies for routine use in applications involving a large number of data sets. In the past, we have presented three segmentation paradigms: live wire, live lane, and a three-dimensional (3-D) extension of the live-wire method. In this paper, we introduce an ultra-fast live-wire method, referred to as live wire on the fly, for further reducing user's time compared to the basic live-wire method. In live wire, 3-D/four-dimensional (4-D) object boundaries are segmented in a slice-by-slice fashion. To segment a two-dimensional (2-D) boundary, the user initially picks a point on the boundary and all possible minimum-cost paths from this point to all other points in the image are computed via Dijkstra's algorithm. Subsequently a live wire is displayed in real time From the initial point to any subsequent position taken by the cursor. If the cursor is close to the desired boundary, the live wire snaps on to the boundary. The cursor is then deposited and a new live-wire segment is Found next, The entire 2-D boundary is specified via a set of live-wire segments in this fashion. A drawback of this method is that the speed of optimal path computation depends on image size. On modestly powered computers, for images of even modest size, some sluggishness appears in user interaction, which reduces the overall segmentation efficiency. In this work, we solve this problem by exploiting some known properties of graphs to avoid unnecessary minimum-cost path computation during segmentation. In live wire on the fly, when the user selects a point on the boundary the live-wire segment is computed and displayed in real time from the selected point to any subsequent position of the cursor in the image, even for large images and even on low-powered computers. Based on 492 tracing experiments from an actual medical application, we demonstrate that live wire on the fly is 1.3-31 times faster than live wire for actual segmentation for varying image sizes, although the pure computational part alone is found to be about 120 times faster.","Title: Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images.\nYear: 2014\n\nAbstract: •Fuzzy hierarchical modeling of all major organs in a body region.•Detailed object relationship information encoded into models.•Hierarchical object recognition and delineation.•Optimal threshold-based recognition strategy.•Demonstration of the same method on several body regions and different modalities.","Title: Cell histograms versus color histograms for image representation and retrieval\nYear: 2003\n\nAbstract: This paper presents a new approach for content-based image retrieval, which is based on the well-known and widely used color histograms. Previous approaches have used a single global color histogram (GCH) for the whole image, or local color histograms (LCHs) for cells within a grid of fixed size. Our approach is also based on a grid of cells, but unlike the latter it uses a cell histogram for each of the colors actually present in the images, representing how that color is distributed among the image cells - hence the name Cell/Color Histograms. Our experiments have shown that the actual number of colors present in images is often low. Thus we are able to achieve performance comparable to using LCHs within a grid, but with a much smaller space overhead. Furthermore, the proposed approach is very flexible in the sense that the user has alternative ways to calibrate the trade-off between space overhead and retrieval effectiveness. In fact, we have been able to outperform GCHs (typically a compact representation) in terms of effectiveness requiring less storage space.","Title: Learning Person-Specific Representations From Faces in the Wild\nYear: 2014\n\nAbstract: Humans are natural face recognition experts, far out-performing current automated face recognition algorithms, especially in naturalistic, “in the wild” settings. However, a striking feature of human face recognition is that we are dramatically better at recognizing highly familiar faces, presumably because we can leverage large amounts of past experience with the appearance of an individual to aid future recognition. Meanwhile, the analogous situation in automated face recognition, where a large number of training examples of an individual are available, has been largely underexplored, in spite of the increasing relevance of this setting in the age of social media. Inspired by these observations, we propose to explicitly learn enhanced face representations on a per-individual basis, and we present two methods enabling this approach. By learning and operating within person-specific representations, we are able to significantly outperform the previous state-of-the-art on PubFig83, a challenging benchmark for familiar face recognition in the wild, using a novel method for learning representations in deep visual hierarchies. We suggest that such person-specific representations aid recognition by introducing an intermediate form of regularization to the problem.","Title: An active learning paradigm based on a priori data reduction and organization.\nYear: 2014\n\nAbstract: •A novel active learning paradigm, called DROP, based on a priori data reduction and organization.•DROP does not require classification and reorganization of all non-annotated samples in the dataset at each iteration.•The proposed paradigm allows to achieve high accuracy quickly with minimum user interaction.•Results are shown with different clustering and classification strategies, and on a variety of real-world datasets.","Title: Improving Atlas-Based Medical Image Segmentation With A Relaxed Object Search\nYear: 2014\n\nAbstract: Medical image segmentation using 3D probabilistic atlases has been actively pursued to avoid the time-consuming involvement of experts in manual object (organ) delineation for quantitative analysis. By mapping a new 3D image onto the reference coordinate system of the atlas, built for some organ of interest, these techniques take a binary decision based on the probability of each voxel to be part of that organ. However, image-based techniques have also been proposed to refine object delineation at the initial position given by the atlas-based segmentation. In this paper, we relax this condition for delineation refinement by moving an atlas based on the prior probability map to search for the organ around that initial position. Our method uses the multi-scale parameter search algorithm with a suitable criterion function to evaluate automatic 3D organ delineations, as obtained by the image foresting transform algorithm in an uncertainty region of the atlas. Experiments with eight organs in CT and MR images have indicated that our method can improve atlas-based segmentation with statistical significance. Moreover, the relaxed object search consistently found the organ with higher accuracy outside the position obtained by the atlas, which reinforces our claim.","Title: Superpixel-Based Interactive Classification of Very High Resolution Images\nYear: 2014\n\nAbstract: Very high resolution (VHR) images are large datasets for pixel annotation--a process that has depended on the supervised training of an effective pixel classifier. Active learning techniques have mitigated this problem, but pixel descriptors are limited to local image information and the large number of pixels makes the response time to the user's actions impractical, during active learning. To circumvent the problem, we present an active learning strategy that relies on superpixel descriptors and a priori dataset reduction. Firstly, we compare VHR image annotation using superpixel- and pixel-based classifiers, as designed by the same state-of-the-art active learning technique--Multi-Class Level Uncertainty (MCLU). Even with the dataset reduction provided by the superpixel representation, MCLU remains unfeasible for user interaction. Therefore, we propose a technique to considerably reduce the superpixel dataset for active learning. Moreover, we subdivide the reduced dataset into a list of subsets with random sample rearrangement to gain both speed and sample diversity during the active learning process.","Title: Vehicle License Plate Recognition With Random Convolutional Networks\nYear: 2014\n\nAbstract: Despite decades of research on automatic license plate recognition (ALPR), optical character recognition (OCR) still leaves room for improvement in this context, given that a single OCR miss is enough to miss the entire plate. We propose an OCR approach based on convolutional neural networks (CNNs) for feature extraction. The architecture of our CNN is chosen from thousands of random possibilities and its filter weights are set at random and normalized to zero mean and unit norm. By training linear support vector machines (SVMs) on the resulting CNN features, we can achieve recognition rates of over 98% for digits and 96% for letters, something that neither SVMs operating on image pixels nor CNNs trained via back-propagation can achieve. The results are obtained in a dataset that has 182 samples per digit and 28 per letter, and suggest the use of random CNNs as a promising alternative approach to ALPR systems.","Title: On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest Clustering\nYear: 2014\n\nAbstract: In this paper, we show how to improve the Radial Basis Function Neural Networks effectiveness by using the Optimum-Path Forest clustering algorithm, since it computes the number of clusters on-the-fly, which can be very interesting for finding the Gaussians that cover the feature space. Some commonly used approaches for this task, such as the well-known fc-means, require the number of classes/clusters previous its performance. Although the number of classes is known in supervised applications, the real number of clusters is extremely hard to figure out, since one class may be represented by more than one cluster. Experiments over 9 datasets together with statistical analysis have shown the suitability of OPF clustering for the RBF training step.","Title: Active Semi-supervised Learning Using Optimum-Path Forest\nYear: 2014\n\nAbstract: The development of effective and efficient ways of handling real-world applications is becoming increasingly widespread, yet it still faces a number of practical challenges. First and foremost, we have the limited availability of labeled data in contrast to an unbounded number of unlabeled ones. Despite some efforts in active semi-supervised learning, their success depends on an approach suitable to be applied to real massive data. In this paper, we introduce a novel integration of semi-supervised learning and a priori-reduction and organization criteria for active learning based on Optimum-Path Forest classifiers. Encouraging results on both public and real data show the synergy of these strategies jointly. Our approach iteratively generates semi-supervised classifiers that attain high accuracy by selecting the most representative labeled set, while decreasing the propagated errors on the unlabeled set. In addition, it is able to identify samples from all classes quickly while keeping user interaction to a minimum throughout the learning iterations.","Title: Robot users for the evaluation of boundary-tracking approaches in interactive image segmentation\nYear: 2014\n\nAbstract: Recent advances in interactive image segmentation focused on eliminating the user bias during evaluation by simulating their behavior using robot users. However, these robots only work for region-based methods, excluding the important class of approaches that rely on the boundary-tracking paradigm. We propose completely novel robot users that are able to simulate the human user behavior when segmenting an image through the addition of anchor points close to the object's boundary. Our robots constantly evaluate the optimum-boundary segment being computed from a previously selected anchor point to the current virtual mouse position, seeking for the longest possible segment with minimum acceptable error. A new anchor is added when the error is too high and the robots iterate until closing the contour, just like real users. We validate our robots by conducting a user study and extensive experiments, considering two boundary-tracking methods: live-wire-on-the-fly and Riverbed. We further show how our robot users can be used to assess hybrid approaches that combine boundary-tracking with region-based delineation, such as LiveMarkers, while conjecturing that robots might lead to new methods for automatic foreground segmentation.","Title: Differential and relaxed image foresting transform for graph-cut segmentation of multiple 3D objects.\nYear: 2014\n\nAbstract: Graph-cut algorithms have been extensively investigated for interactive binary segmentation, when the simultaneous delineation of multiple objects can save considerable user's time. We present an algorithm (named DRIFT) for 3D multiple object segmentation based on seed voxels and Differential Image Foresting Transforms (DIFTs) with relaxation. DRIFT stands behind efficient implementations of some stateof- the-art methods. The user can add/remove markers (seed voxels) along a sequence of executions of the DRIFT algorithm to improve segmentation. Its first execution takes linear time with the image's size, while the subsequent executions for corrections take sublinear time in practice. At each execution, DRIFT first runs the DIFT algorithm, then it applies diffusion filtering to smooth boundaries between objects (and background) and, finally, it corrects possible objects' disconnection occurrences with respect to their seeds. We evaluate DRIFT in 3D CT-images of the thorax for segmenting the arterial system, esophagus, left pleural cavity, right pleural cavity, trachea and bronchi, and the venous system.","Title: The Ordered Queue and the Optimality of the Watershed Approaches.\nYear: 2000\n\nAbstract: This work reviews the watershed in the graph framework of a shortest-path forest problem using a texicographic path cost formulation. This formulation reflects the behavior of the ordered queue-based watershed algorithm. This algorithm is compared with our proposed shortest-path forest (IFT-Image Foresting Transform), concluding that the watershed is a special case of that. Recently many different watershed approaches are being used. We point out that ill some cases the watershed algorithm does riot keep the optimality of the shortest-path forest solution unless the IFT algorithm is used. The main difference between the algorithms is: related to permanently labeling a pixel when inserting or removing it from the queue. The watershed based on the pixel dissimilarity using IFT carl segment one-pixel width regions while keeping the optimality of the shortest-path forest solution.","Title: 3D blob based brain tumor detection and segmentation in MR images\nYear: 2014\n\nAbstract: Automatic detection and segmentation of brain tumors in 3D MR neuroimages can significantly aid early diagnosis, surgical planning, and follow-up assessment. However, due to diverse location and varying size, primary and metastatic tumors present substantial challenges for detection. We present a fully automatic, unsupervised algorithm that can detect single and multiple tumors from 3 to 28,079 mm3 in volume. Using 20 clinical 3D MR scans containing from 1 to 15 tumors per scan, the proposed approach achieves between 87.84% and 95.30% detection rate and an average end-to-end running time of under 3 minutes. In addition, 5 normal clinical 3D MR scans are evaluated quantitatively to demonstrate that the approach has the potential to discriminate between abnormal and normal brains.","Title: Characterization of the human cortex in MR images through the image foresting transform\nYear: 2003\n\nAbstract: In this work we discuss an approach to characterize the hu- man cortex in MRI data through the segmentation of the cortical layer with deep penetration into the sulci, using the Image Forest- ing Transform. This technique, based on graph theory, provides a sound framework for the implementation of many image process- ing operators, and has been successfully applied to the solution of many problems, including some in medical imaging. We show the results of applying the technique to slices of MRI data from four subjects and compare these to the computation of the morphologi- cal skeleton.","Title: Iso-shaping rigid bodies for estimating their motion from image sequences.\nYear: 2004\n\nAbstract: In many medical imaging applications, due to the limited field of view of imaging devices, acquired images often include only a part of a structure. In such situations, it is impossible to guarantee that the images will contain exactly the same physical extent of the structure at different scans, which leads to difficulties in registration and in many other tasks, such as the analysis of the morphology, architecture, and kinematics of the structures. To facilitate such analysis, we developed a general method, referred to as iso-shaping, that generates structures of the same shape from segmented image sequences. The basis for this method is to automatically find a set of key points, called shape centers, in the segmented partial anatomic structure such that these points are present in all images and that they represent the same physical location in the object, and then trim the structure using these points as reference. The application area considered here is the analysis of the morphology, architecture, and kinematics of the joints of the foot from magnetic resonance images acquired at different joint positions and load conditions. The accuracy of the method is analyzed by utilizing ten data sets for iso-shaping the tibia and the fibula via four evaluative experiments. The analysis indicates that iso-shaping produces results as predicted by the theoretical framework.","Title: The image foresting transform: theory, algorithms, and applications.\nYear: 2004\n\nAbstract: Abstract--The image foresting transform (IFT) is a graph-based approach to the design of image processing operators based on connectivity. It naturally leads to correct and efficient implementations and to a better understanding of how different operators relate to each other. We give here a precise definition of the IFT, and a procedure to compute it--a generalization of Dijkstra's algorithm--with a proof of correctness. We also discuss implementation issues and illustrate the use of the IFT in a few applications.","Title: Tree-Pruning: A New Algorithm and Its Comparative Analysis with the Watershed Transform for Automatic Image Segmentation\nYear: 2006\n\nAbstract: Image segmentation using tree pruning (TP) and water- shed (WS) has been presented in the framework of the im- age forest transform (IFT)— a method to reduce image pro- cessing problems related to connectivity into an optimum- path forest problem in a graph. Given that both algorithms use the IFT with similar parameters, they usually produce similar segmentation results. However, they rely on dif- ferent properties of the IFT which make TP more robust than WS for automatic segmentation tasks. We propose and demonstrate an important improvement in the TP al- gorithm, clarify the differences between TP and WS, and provide their comparative analysis from the theoretical and practical points of view. The experiments involve automatic segmentation of license plates in a database with 990 im- ages.","Title: How to fix any 3D segmentation interactively via Image Foresting Transform and its use in MRI brain segmentation\nYear: 2011\n\nAbstract: In medical imaging, there are many approaches for automatic segmentation. However, none of these methods provide any effective solution to correct segmentation interactively, which becomes a necessity in the case of poorly defined structures. Manual segmentation can not be an alternative given that it might be unfeasible in many cases. On the other hand, how to complete a poor automatic segmentation in an interactive tool is an issue, since the automatic approach and the tool may have been designed with different optimization criteria. We propose solutions to this problem using the framework of the “Image Foresting Transform” (IFT), with evaluation in the context of the segmentation of MR-T1 brain structures. The results indicate that effective semi-automatic correction is possible using just a few markers.","Title: Improving land cover classification through contextual-based optimum-path forest\nYear: 2015\n\nAbstract: A new contextual classifier based on optimum-path forest has been presented (OPF-MRF).A meta-heuristic-based framework has been proposed to estimate the contextual-dependent parameter for OPF-MRF.The proposed approach has been validated in the context of satellite image classification. Traditional machine learning algorithms very often assume statistically independent data samples. However, this is clearly not the case in remote sensing image applications, in which pixels present spatial and/or temporal dependencies. In this work, it has been presented an approach to improve land cover image classification using a contextual approach based on optimum-path forest (OPF) and the well-known Markov random fields (MRFs), hereinafter called OPF-MRF. In addition, it is also introduced a framework to the optimization of the amount of contextual information used by OPF-MRF. Experiments over high- and medium-resolution satellite (CBERS-2B, Landsat 5 TM, Ikonos-2 MS and Geoeye) and radar (ALOS-PALSAR) images covering the area of two Brazilian cities have shown the proposed approach can overcome several shortcomings related to standard OPF classification. In some cases, the proposed approach outperformed traditional OPF in about 9% of recognition rate, which is crucial for land cover classification.","Title: Robust active learning for the diagnosis of parasites\nYear: 2015\n\nAbstract: We have developed an automated system for the diagnosis of intestinal parasites from optical microscopy images. The objects (species of parasites and impurities) segmented from these images form a large dataset. We are interested in the active learning problem of selecting a reasonably small number of objects to be labeled under an expert's supervision for use in training a pattern classifier. However, impurities are very numerous, constitute several clusters in the feature space, and can be quite similar to some species of parasites, leading to a significant challenge for active learning methods. We propose a technique that pre-organizes the data and then properly balances the selection of samples from all classes and uncertain samples for training. Early data organization avoids reprocessing of the large dataset at each learning iteration, enabling the halting of sample selection after a desired number of samples per iteration, yielding interactive response time. We validate our method by comparing it with state-of-the-art approaches, using a previously labeled dataset of almost 6000 objects. Moreover, we report results from experiments on a very realistic scenario, consisting of a dataset with over 140,000 unlabeled objects, under unbalanced classes, the absence of some classes, and the presence of a very large set of impurities. HighlightsA robust active learning method, called RDS, based on a priori data organization.RDS properly balances sample diversity and uncertainty for useful sample selection.It provides high classification accuracy for the automated diagnosis of parasites.Comparisons with different clustering, classification and other literature methods.RDS was evaluated by an experienced expert in parasitology using a realistic scenario.","Title: Visualizing the Hidden Activity of Artificial Neural Networks.\nYear: 2017\n\nAbstract: In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons. Through experiments conducted in three traditional image classification benchmark datasets, we show how visualization can provide highly valuable feedback for network designers. For instance, our discoveries in one of these datasets (SVHN) include the presence of interpretable clusters of learned representations, and the partitioning of artificial neurons into groups with apparently related discriminative roles.","Title: Improving semi-supervised learning through optimum connectivity.\nYear: 2016\n\nAbstract: The annotation of large data sets by a classifier is a problem whose challenge increases as the number of labeled samples used to train the classifier reduces in comparison to the number of unlabeled samples. In this context, semi-supervised learning methods aim at discovering and labeling informative samples among the unlabeled ones, such that their addition to the correct class in the training set can improve classification performance. We present a semi-supervised learning approach that connects unlabeled and labeled samples as nodes of a minimum-spanning tree and partitions the tree into an optimum-path forest rooted at the labeled nodes. It is suitable when most samples from a same class are more closely connected through sequences of nearby samples than samples from distinct classes, which is usually the case in data sets with a reasonable relation between number of samples and feature space dimension. The proposed solution is validated by using several data sets and state-of-the-art methods as baselines. HighlightsA new algorithm for semi-supervised learning based on optimum-path forest.The algorithm provides significant improvements in accuracy and efficiency.Labels are propagated from labeled to unlabeled training samples with less errors.The novel classifier can be more accurate than other state-of-the-art methods.A fast and effective algorithm suitable for developing active learning methods.","Title: Visualizing time-dependent data using dynamic t-SNE\nYear: 2016\n\nAbstract: Many interesting processes can be represented as time-dependent datasets. We define a time-dependent dataset as a sequence of datasets captured at particular time steps. In such a sequence, each dataset is composed of observations (high-dimensional real vectors), and each observation has a corresponding observation across time steps. Dimensionality reduction provides a scalable alternative to create visualizations (projections) that enable insight into the structure of such datasets. However, applying dimensionality reduction independently for each dataset in a sequence may introduce unnecessary variability in the resulting sequence of projections, which makes tracking the evolution of the data significantly more challenging. We show that this issue affects t-SNE, a widely used dimensionality reduction technique. In this context, we propose dynamic t-SNE, an adaptation of t-SNE that introduces a controllable trade-off between temporal coherence and projection reliability. Our evaluation in two time-dependent datasets shows that dynamic t-SNE eliminates unnecessary temporal variability and encourages smooth changes between projections.","Title: Vascular Segmentation In Tof Mra Images Of The Brain Using A Deep Convolutional Neural Network\nYear: 2017\n\nAbstract: Cerebrovascular diseases are one of the main causes of death and disability in the world. Within this context, fast and accurate automatic cerebrovascular segmentation is important for clinicians and researchers to analyze the vessels of the brain, determine criteria of normality, and identify and study cerebrovascular diseases. Nevertheless, automatic segmentation is challenging due to the complex shape, inhomogeneous intensity, and inter-person variability of normal and malformed vessels. In this paper, a deep convolutional neural network (CNN) architecture is used to automatically segment the vessels of the brain in time-of-flight magnetic resonance angiography (TOF MRA) images of healthy subjects. Bi-dimensional manually annotated image patches are extracted in the axial, coronal, and sagittal directions and used as input for training the CNN. For segmentation, each voxel is individually analyzed using the trained CNN by considering the intensity values of neighboring voxels that belong to its patch. Experiments were performed with TOF MRA images of five healthy subjects, using varying numbers of images to train the CNN. Cross validations revealed that the proposed framework is able to segment the vessels with an average Dice coefficient ranging from 0.764 to 0.786 depending on the number of images used for training. In conclusion, the results of this work suggest that CNNs can be used to segment cerebrovascular structures with an accuracy similar to other high-level segmentation methods.","Title: Cell segmentation in 3D confocal images using supervoxel merge-forests with CNN-based hypothesis selection\nYear: 2018\n\nAbstract: Automated segmentation approaches are crucial to quantitatively analyze large-scale 3D microscopy images. Particularly in deep tissue regions, automatic methods still fail to provide error-free segmentations. To improve the segmentation quality throughout imaged samples, we present a new supervoxel-based 3D segmentation approach that outperforms current methods and reduces the manual correction effort. The algorithm consists of gentle preprocessing and a conservative super-voxel generation method followed by supervoxel agglomeration based on local signal properties and a postprocessing step to fix under-segmentation errors using a Convolutional Neural Network. We validate the functionality of the algorithm on manually labeled 3D confocal images of the plant Arabidopsis thaliana and compare the results to a state-of-the-art meristem segmentation algorithm.","Title: SEGMENT3D: A web-based application for collaborative segmentation of 3D images used in the shoot apical meristem\nYear: 2018\n\nAbstract: The quantitative analysis of 3D confocal microscopy images of the shoot apical meristem helps understanding the growth process of some plants. Cell segmentation in these images is crucial for computational plant analysis and many automated methods have been proposed. However, variations in signal intensity across the image mitigate the effectiveness of those approaches with no easy way for user correction. We propose a web-based collaborative 3D image segmentation application, SEGMENT3D, to leverage automatic segmentation results. The image is divided into 3D tiles that can be either segmented interactively from scratch or corrected from a pre-existing segmentation. Individual segmentation results per tile are then automatically merged via consensus analysis and then stitched to complete the segmentation for the entire image stack. SEGMENT3D is a comprehensive application that can be applied to other 3D imaging modalities and general objects. It also provides an easy way to create supervised data to advance segmentation using machine learning models.","Title: A multi-object statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation.\nYear: 2017\n\nAbstract: Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach.","Title: Robust Cerebrovascular Segmentation In 4d Asl Mra Images\nYear: 2018\n\nAbstract: Cerebrovascular diseases are one of the main causes of disability and death in the world. Methods for segmentation of the cerebral blood vessels can help clinicians to visualize the cerebrovascular system, determine age-related normal values, and diagnose and study cerebrovascular diseases. A common problem for these methods is the precise delineation of small vessels, due to different artifacts depending on the medical imaging modality. In this work, we present an automatic segmentation method for four dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) images of the brain, which allows an improved segmentation of small vessels, reaching an average Dice similarity coefficient (DSC) of 0.946 based on an evaluation of five datasets from healthy subjects. The results show that the proposed method is able to account for the magnetic labeling decay and flow related artifacts, which considerably reduce the contrast of small vessels present in the image, leading to improved segmentation results.","Title: Links Between Image Segmentation Based on Optimum-Path Forest and Minimum Cut in Graph\nYear: 2009\n\nAbstract: Image segmentation can be elegantly solved by optimum-path forest and minimum cut in graph. Given that both approaches exploit similar image graphs, some comparative analysis is expected between them. We clarify their differences and provide their comparative analysis from the theoretical point of view, for the case of binary segmentation (object/background) in which hard constraints (seeds) are provided interactively. Particularly, we formally prove that some optimum-path forest methods from two distinct region-based segmentation paradigms, with internal and external seeds and with only internal seeds, indeed minimize some graph-cut measures. This leads to a proof of the necessary conditions under which the optimum-path forest algorithm and the min-cut/max-flow algorithm produce exactly the same segmentation result, allowing a comparative analysis between them.","Title: Multi-label semi-supervised classification through optimum-path forest.\nYear: 2018\n\nAbstract: •An extension of the OPF-based semi-supervised learning for multi-label classification.•A more robust approach to propagate labels to unsupervised samples.•An extensive experimental validation for multi-label classification.","Title: A New Variant of the Optimum-Path Forest Classifier\nYear: 2008\n\nAbstract: We have shown a supervised approach for pattern classification, which interprets the training samples as nodes of a complete arc-weighted graph and computes an optimum-path forest rooted at some of the closest samples between distinct classes. A new sample is classified by the label of the root which offers to it the optimum path. We propose a variant, in which the training samples are the nodes of a graph, whose the arcs are the k -nearest neighbors in the feature space. The graph is weighted on the nodes by their probability density values (pdf) and the optimum-path forest is rooted at the maxima of the pdf. The best value of k is computed by the maximum accuracy of classification in the training set. A test sample is assigned to the class of the maximum, which offers to it the optimum path. Preliminary results have shown that the proposed approach can outperform the previous one and the SVM classifier in some datasets.","Title: Vessel segmentation in 4D arterial spin labeling magnetic resonance angiography images of the brain.\nYear: 2017\n\nAbstract: 4D arterial spin labeling magnetic resonance angiography (4D ASL MRA) is a non-invasive and safe modality for cerebrovascular imaging procedures. It uses the patient's magnetically labeled blood as intrinsic contrast agent, so that no external contrast media is required. It provides important 3D structure and blood flow information but a sufficient cerebrovascular segmentation is important since it can help clinicians to analyze and diagnose vascular diseases faster, and with higher confidence as compared to simple visual rating of raw ASL MRA images. This work presents a new method for automatic cerebrovascular segmentation in 4D ASL MRA images of the brain. In this process images are denoised, corresponding image label/control image pairs of the 4D ASL MRA sequences are subtracted, and temporal intensity averaging is used to generate a static representation of the vascular system. After that, sets of vessel and background seeds are extracted and provided as input for the image foresting transform algorithm to segment the vascular system. Four 4D ASL MRA datasets of the brain arteries of healthy subjects and corresponding time-of-flight (TOF) MRA images were available for this preliminary study. For evaluation of the segmentation results of the proposed method, the cerebrovascular system was automatically segmented in the high-resolution TOF MRA images using a validated algorithm and the segmentation results were registered to the 4D ASL datasets. Corresponding segmentation pairs were compared using the Dice similarity coefficient (DSC). On average, a DSC of 0.9025 was achieved, indicating that vessels can be extracted successfully from 4D ASL MRA datasets by the proposed segmentation method.","Title: Projections As Visual Aids For Classification System Design\nYear: 2018\n\nAbstract: Dimensionality reduction is a compelling alternative for high-dimensional data visualization. This method provides insight into high-dimensional feature spaces by mapping relationships between observations (high-dimensional vectors) to low (two or three) dimensional spaces. These low-dimensional representations support tasks such as outlier and group detection based on direct visualization. Supervised learning, a subfield of machine learning, is also concerned with observations. A key task in supervised learning consists of assigning class labels to observations based on generalization from previous experience. Effective development of such classification systems depends on many choices, including features descriptors, learning algorithms, and hyperparameters. These choices are not trivial, and there is no simple recipe to improve classification systems that perform poorly. In this context, we first propose the use of visual representations based on dimensionality reduction (projections) for predictive feedback on classification efficacy. Second, we propose a projection-based visual analytics methodology, and supportive tooling, that can be used to improve classification systems through feature selection. We evaluate our proposal through experiments involving four datasets and three representative learning algorithms.","Title: Automatic Temporal Segmentation of Vessels of the Brain Using 4D ASL MRA Images.\nYear: 2018\n\nAbstract: Objective: Automatic vessel segmentation can be used to process the considerable amount of data generated by four-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) images. Previous segmentation approaches for dynamic series of images propose either reducing the series to a temporal average (tAIP) or maximum intensity projection (tMIP) prior to vessel segmentation, or a...","Title: A methodology for generating four-dimensional arterial spin labeling MR angiography virtual phantoms.\nYear: 2019\n\nAbstract: •Methodology for the generation of annotated 4D ASL MRA virtual phantoms, with geometric and blood flow ground-truth data.•The phantoms can be used as ground-truth for developing and evaluating new image processing methods for 4D ASL MRA.•In this work, the phantoms are used for image acquisition optimization, by evaluating different scenarios with different acquisition parameter settings.","Title: Iterative Pseudo-Labeling with Deep Feature Annotation and Confidence-Based Sampling\nYear: 2021\n\nAbstract: Training deep neural networks is challenging when large and annotated datasets are unavailable. Extensive manual annotation of data samples is time-consuming, expensive, and error-prone, notably when it needs to be done by experts. To address this issue, increased attention has been devoted to techniques that propagate uncertain labels (also called pseudo labels) to large amounts of unsupervised s...","Title: CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images\nYear: 2021\n\nAbstract: Early detection of COVID-19 is vital to control its spread. Deep learning methods have been presented to detect suggestive signs of COVID-19 from chest CT images. However, due to the novelty of the disease, annotated volumetric data are scarce. Here we propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN). For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones, and the decision layer of our CNN is a support vector machine. As we have no control over the CT image acquisition, we also propose an intensity standardization approach. Our method can achieve mean accuracy and kappa values of 0.97 and 0.93, respectively, on a dataset with 117 CT images extracted from different sites, surpassing its counterpart in all scenarios.","Title: Towards a Simple and Efficient Object-based Superpixel Delineation Framework\nYear: 2021\n\nAbstract: Superpixel segmentation methods are widely used in computer vision applications due to their properties in border delineation. These methods do not usually take into account any prior object information. Although there are a few exceptions, such methods significantly rely on the quality of the object information provided and present high computational cost in most practical cases. Inspired by such...","Title: Improving Deep Learning Projections by Neighborhood Analysis\nYear: 2020\n\nAbstract: Visualization of multidimensional data is a difficult task, for which there are many tools. Among these tools, dimensionality reduction methods were shown to be particularly helpful to explore data visually. Techniques with good visual separation are very popular, such as those from the SNE-class, but those often are computationally expensive and non-parametric. An approach based on neural networks was recently proposed to address those shortcomings, but it introduces some fuzziness in the generated projection, which is not desired. In this paper we thoroughly explain the parameter space of this neural network approach and propose a new neighborhood-based learning paradigm, which further improves the quality of the projections learned by the neural networks, and we illustrate our approach on large real-world datasets."],"string":"[\n \"Title: E-Shell Renderin\\nYear: 2002\\n\\nAbstract: In Medical Imaging, shell rendering is considered one of the most efficient and effective methods for volume visualization. It requires a compact data structure of voxels and uses the voxel splatting technique for surface and volume rendering. In order to avoid holes in the rendition, the size of the voxels are usually made bigger than the size of the pixels for voxel splatting. In such a case, we have identified a conceptual problem in shell rendering which affects the correctness and quality of the renditions. In this paper, we discuss this problem and propose a solution, which improves image quality without affecting the speed of the method. The new approach is called E-Shell Rendering (extended-shell rendering). It requires an extension of the shell data structure and a variant oftheoriginal algorithm. We illustrate and discuss the results with examples created by surface and volume rendering.\",\n \"Title: A graph-based approach for multiscale shape analysis\\nYear: 2004\\n\\nAbstract: This paper presents two shape descriptors, multiscale fractal dimension and contour saliences, using a graph-based approach— the image foresting transform. It introduces a robust approach to locate contour saliences from the relation between contour and skeleton. The contour salience descriptor consists of a vector, with salience location and value along the contour, and a matching algorithm. We compare both descriptors with fractal dimension, Fourier descriptors, moment invariants, Curvature Scale Space and Beam Angle Statistics regarding to their invariance to object characteristics that belong to a same class (compact-ability) and to their ability to separate objects of distinct classes (separability).\",\n \"Title: Effective Image Retrieval by Shape Saliences\\nYear: 2003\\n\\nAbstract: Content-Based Image Retrieval (CBIR) systems have been developed aiming at enabling users to search and re- trieve images based on their properties such as shape, color and texture. In this paper, we are concerned with shape- based image retrieval. Here, we discuss a recently proposed shape descriptor, called contour saliences, defined as the influence areas of its higher curvature points. This paper introduces a robust approach to estimate contour saliences by exploiting the relation between a contour and its skele- ton, modifies the original definition to include the location and the value of saliences along the contour, and proposes a new metric to compare contour saliences. The paper also evaluates the effectiveness of the proposed descriptor with respect to Fourier Descriptors, Curvature Scale Space and Moment Invariants.\",\n \"Title: Content-Based Image Retrieval: Theory and Applications\\nYear: 2006\\n\\nAbstract: Advances in data storage and image acquisition technologies have enabled the cre- ation of large image datasets. In this scenario, it is necess ary to develop appropri- ate information systems to efficiently manage these collect ions. The commonest ap- proaches use the so-called Content-Based Image Retrieval (CBIR) systems . Basically, these systems try to retrieve images similar to a user-define d specification or pattern (e.g., shape sketch, image example). Their goal is to support image retrieval based on content properties (e.g., shape, color, texture), usually encoded into feature vectors. One of the main advantages of the CBIR approach is the possibility of an automatic retrieval process, instead of the traditional keyword-bas ed approach, which usually requires very laborious and time-consuming previous annotation of database images. The CBIR technology has been used in several applications such as fingerprint identifi- cation, biodiversity information systems, digital librar ies, crime prevention, medicine, historical research, among others. This paper aims to introduce the problems and challenges concerned with the creation of CBIR systems, to describe the existing solutions and applications, and to present the state of the art of the existing research in this area.\",\n \"Title: On the Training Patterns Pruning for Optimum-Path Forest\\nYear: 2009\\n\\nAbstract: The Optimum-Path Forest (OPF) classifier is a novel graph-based supervised pattern recognition technique that has been demonstrated to be superior to Artificial Neural Networks and similar to Support Vector Machines, but much faster. The OPF classifier reduces the problem of pattern recognition to a computation of an optimum-path forest in the feature space induced by a graph, creating discrete optimal partitions, which are optimum-path trees rooted by prototypes, i.e., key samples that will compete among themselves trying to conquer the remaining samples. Some applications, such that medical specialist systems for image-based diseases identification, need to be constantly re-trained with new instances (diagnostics) to achieve a better generalization of the problem, which requires large storage devices, due to the high number of generated data (millions of voxels). In that way, we present here a pruning algorithm for the OPF classifier that learns the most irrelevant samples and eliminate them from the training set, without compromising the classifier's accuracy.\",\n \"Title: Rotation-invariant texture recognition\\nYear: 2007\\n\\nAbstract: This paper proposes a new texture classification system, which is distinguished by: (1) a new rotation-invariant image descriptor based on Steerable Pyramid Decomposition, and (2) by a novel multi-class recognition method based on Optimum Path Forest. By combining the discriminating power of our image descriptor and classifier, our system uses small size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz dataset. High classification rates demonstrate the superiority of the proposed method.\",\n \"Title: FCD segmentation using texture asymmetry of MR-T1 images of the brain\\nYear: 2008\\n\\nAbstract: Focal cortical dysplasia is the most common malformation in patients with intractable epilepsy. The segmentation of FCD lesions in MR-T1 images of the brain is a crucial step for treatment planning. In this work we present a new FCD segmentation technique based on analysis of texture asymme try. This technique does not rely on template-based segmentation and is applicable to patients of any age, regardless of anatomic variations. We evaluated the method on 5 patients (aged 11y-51y), and obtained 100% detection rate and coverage of 76.9% of the lesional voxels.\",\n \"Title: Person-Specific Subspace Analysis For Unconstrained Familiar Face Identification\\nYear: 2012\\n\\nAbstract: While significant strides have been made in the recognition of faces under controlled viewing conditions, face recognition \\\"in the wild\\\" remains a challenging unsolved problem [12, 13, 21]. Interestingly, while humans are generally excellent at identifying familiar individuals under such conditions, their performance is significantly worse with unfamiliar individuals [5] and groups [19], leading to the idea that brain may have enhanced or specialized representations of familiar individuals [6]. Inspired by these observations, we explored the use of a number of subspace analysis techniques, applied to various visual representations, to generate person-specific subspaces of \\\"familiar\\\" individuals for face identification. In particular, we introduce a person-specific application of partial least squares (PS-PLS) to generate per-individual subspaces, and show that operating in these subspaces yields state-of-the-art performance on the challenging PubFig83 familiar face identification benchmark. The results underscore the potential importance of incorporating a notion of familiarity into face recognition systems.\",\n \"Title: Automatic Image Segmentation by Tree Pruning\\nYear: 2007\\n\\nAbstract: The Image Foresting Transform (IFT) is a tool for the design of image processing operators based on connectivity, which reduces image processing problems into an optimum-path forest problem in a graph derived from the image. A new image operator is presented, which solves segmentation by pruning trees of the forest. An IFT is applied to create an optimum-path forest whose roots are seed pixels, selected inside a desired object. In this forest, object and background are connected by optimum paths (leaking paths), which cross the object's boundary through its \\\"most weakly connected\\\" parts (leaking pixels). These leaking pixels are automatically identified and their subtrees are eliminated, such that the remaining forest defines the object. Tree pruning runs in linear time, is extensible to multidimensional images, is free of ad hoc parameters, and requires only internal seeds, with little interference from the heterogeneity of the background. These aspects favor solutions for automatic segmentation. We present a formal definition of the obtained objects, algorithms, sufficient conditions for tree pruning, and two applications involving automatic segmentation: 3D MR-image segmentation of the human brain and image segmentation of license plates. Given that its most competitive approach is the watershed transform by markers, we also include a comparative analysis between them.\",\n \"Title: Image segmentation by combining the strengths of Relative Fuzzy Connectedness and Graph Cut.\\nYear: 2012\\n\\nAbstract: We introduce an image segmentation algorithm GC(sum)(max), which combines, in a novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that GC(sum)(max) preserves robustness of RFC with respect to the seed choice (thus, avoiding \\\"shrinking problem\\\" of GC), while keeping GC's bigger control over \\\"leaking though the weak boundary.\\\" The theoretical analysis of GC(sum)(max) is greatly facilitated by our recent theoretical results that RFC belongs to the Generalized GC (GGC) segmentation algorithms framework. In our implementation of GC(sum)(max) we use, as a subroutine, a version of RFC algorithm (based on Image Foresting Transform) that runs (provably) in linear time with respect to the image size. This results in GC(sum)(max) running in a time close to linear.\",\n \"Title: Interactive Segmentation by Image Foresting Transform on Superpixel Graphs.\\nYear: 2013\\n\\nAbstract: There are many scenarios in which user interaction is essential for effective image segmentation. In this paper, we present a new interactive segmentation method based on the Image Foresting Transform (IFT). The method over segments the input image, creates a graph based on these segments (super pixels), receives markers (labels) drawn by the user on some super pixels and organizes a competition to label every pixel in the image. Our method has several interesting properties: it is effective, efficient, capable of segmenting multiple objects in almost linear time on the number of super pixels, readily extendable through previously published techniques, and benefits from domain-specific feature extraction. We also present a comparison with another technique based on the IFT, which can be seen as its pixel-based counterpart. Another contribution of this paper is the description of automatic (robot) users. Given a ground truth image, these robots simulate interactive segmentation by trained and untrained users, reducing the costs and biases involved in comparing segmentation techniques.\",\n \"Title: Fast and accurate holistic face recognition using optimum-path forest\\nYear: 2009\\n\\nAbstract: This paper presents a novel, fast and accurate holistic method for face-recognition using the Optimum-Path Forest (OPF) classifier. Our objective is to improve the face recognition accuracy against traditional methods and to reduce the computational effort in face recognition tasks. During the feature extraction stage we apply Principal Component Analysis to reduce feature vectors in several dimensionalities. Experiments using face images from three public datasets (ORL, CBCL and YALE) present good results. Comparison among two other widely used supervised classifiers, Artificial Neural Networks based on Multilayer Perceptron and Support Vector Machines, show that the proposed method drastically reduces the computational cost, achieving correct classification rates at least identical to SVM.\",\n \"Title: Prontovideo: An Image Sequence Segmentation Tool Applied to Video Edition\\nYear: 2001\\n\\nAbstract: This work proposes an image sequence segmentation tool for video masking. It consist of a semi-automatic delineation tool of the object of interest, its segmentation, through the video sequence. The tool is based on watershed by markers technique, where the markers are propagated from frame to frame using normalized correlation pattern matching algorithm.\",\n \"Title: Design of pattern classifiers using optimum-path forest with applications in image analysis\\nYear: 2010\\n\\nAbstract: Current image acquisition and storage technologies have provided large data sets (with millions of samples) for analysis. Samples may be images from an image database, objects extracted from several images, or image pixels. This scenario is very challenging for traditional machine learning and pattern recognition techniques, which need to be more efficient and effective in large data sets. This lecture presents a recent and successful methodology, which links training samples in a given feature space and exploits optimum connectivity between them to the design of pattern classifiers. The methodology essentially extends the Image Foresting Transform, successfully used for filtering, segmentation and shape description, from the image domain to the feature space. Several supervised and unsupervised learning techniques may be created from the specification of two parameters: an adjacency relation and a connectivity function. The adjacency relation defines which samples form arcs of a graph in the feature space. The connectivity function assigns a value to any path in the graph. The path value indicates the strength of connectedness of its terminal node with respect to its source node. A connectivity map is maximized by partitioning the graph into an optimum-path forest rooted at its maxima (i.e., representative samples of each class/group, called prototypes). The optimum-path forest is then a pattern classifier, which assigns to any new sample the class (or group label) of its most strongly connected root. The methods have been successfully applied to several applications and this lecture demonstrates two recent ones: content-based image retrieval (CBIR) and 3D segmentation of brain tissues in MR images. In CBIR, user interaction is considerably reduced to a few clicks on relevant/irrelevant images along 3 iterations of relevance feedback followed by supervised learning in order to achieve satisfactory query results. The 3D segmentation of brain tissues is automatically obtained in less than 2 minutes. It exploits voxel clustering, some prior knowledge and does not require a brain atlas for that purpose, while many other brain tissue segmentation methods do. The lecture concludes by discussing some open problems and perspectives for the optimum-path forest classifiers.\",\n \"Title: A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection\\nYear: 2011\\n\\nAbstract: Finding an optimal subset of features that maximizes classification accuracy is still an open problem. In this paper, we exploit the speed of the Harmony Search algorithm and the Optimum-Path Forest classifier in order to propose a new fast and accurate approach for feature selection. Comparisons to some other pattern recognition and feature selection techniques showed that the proposed hybrid algorithm for feature selection outperformed them. The experiments were carried out in the context of identifying non-technical losses in power distribution systems.\",\n \"Title: A Fast and Automatic Method for 3D Rigid Registration of MR Images of the Human Brain\\nYear: 2008\\n\\nAbstract: We propose a system to solve a multi-class produce categorization problem. For that, we use statistical color, texture, and structural appearance descriptors (bag-of-features). As the best combination setup is not known for our problem, we combine several ...\",\n \"Title: Adapted Dynamic Meshes for Deformable Surfaces\\nYear: 2006\\n\\nAbstract: Deformable objects play an important role in many applications, such as animation and simulation. Effective computation with deformable surfaces can be achieved through the use of dynamic meshes. In this paper we introduce a framework for constructing and maintaining a time-varying adapted mesh structure that conforms to the underlying deformable surface. The adaptation function employs error metrics based on stochastic sampling. Our scheme combines normal and tangential geometric correction with refinement and simplification resolution control. Furthermore, it applies to both parametric and implicit surface descriptions. As the result, we obtain a simple and efficient general scheme that can be used for a wide range of computations.\",\n \"Title: Joint graph cut and relative fuzzy connectedness image segmentation algorithm.\\nYear: 2013\\n\\nAbstract: •Novel image segmentation algorithm robust w.r.t. seed choice.•Synergistic combinantion of fuzzy connectedness (FC) and Graph Cut (GC) algorithms.•Better delineation of poorly defined boundary segments than either FC or GC alone.\",\n \"Title: Clustering by optimum path forest and its application to automatic GM/WM classification in MR-T1 images of the brain\\nYear: 2008\\n\\nAbstract: A new approach to identify clusters as trees of an optimum- path forest has been presented. We are extending the method for large datasets with application to automatic GM/WM classification in MR-T1 images of the brain. The method is computed for a few randomly selected voxels, such that GM and WM define two optimum-path trees. The remaining voxels are classified incrementally, by identifying which tree would contain each voxel if it were part of the forest. Our method produces accurate results on phantom and real images, similarly to those obtained by the state-of-the-art, does not rely on templates, and takes less than 1.5 minute on modern PCs.\",\n \"Title: A New Symmetry-Based Method For Mid-Sagittal Plane Extraction In Neuroimages\\nYear: 2011\\n\\nAbstract: The estimation of the mid-sagittal plane (MSP) is a known problem with several applications in neuroimage analysis. As advance to the state-of-the-art, we present a considerably better approach for MSP extraction based on bilateral symmetry maximization and a more suitable error metric to compare MSP estimation methods. The proposed method was quantitatively evaluated using three other state-of-the-art approaches as baselines and a heterogeneous dataset with 164 clinical images. It outperformed the others in accuracy and precision, being well succeeded on all images. Besides, it does not present limitations with respect to the imaging protocol and initial position of the head, and it is one of the fastest methods in the literature, taking around 30 seconds on a regular workstation.\",\n \"Title: Multiscale Classification of Remote Sensing Images.\\nYear: 2012\\n\\nAbstract: A huge effort has been applied in image classification to create high-quality thematic maps and to establish precise inventories about land cover use. The peculiarities of remote sensing images (RSIs) combined with the traditional image classification challenges made RSI classification a hard task. Our aim is to propose a kind of boost-classifier adapted to multiscale segmentation. We use the para...\",\n \"Title: Interactive volume segmentation with differential image foresting transforms.\\nYear: 2004\\n\\nAbstract: The absence of object information very often asks for considerable human assistance in medical image segmentation. Many interactive two-dimensional and three-dimensional (3-D) segmentation methods have been proposed, but their response time to user's actions should be considerably reduced to make them viable from the practical point of view. We circumvent this problem in the framework of the image...\",\n \"Title: Automatic Subcortical Tissue Segmentation Of Mr Images Using Optimum-Path Forest Clustering\\nYear: 2011\\n\\nAbstract: Automatic MR-image segmentation of brain tissues is an important issue in neuroimaging. For instance, it is a key methodological component of a popular technique denominated voxel-based morphometry (VBM), which quantifies gray-matter (GM) volumes from MR images. However, segmentation accuracy in some subcortical regions on the basis of extant methods is not satisfactory, compromising VBM results. We combine a probabilistic atlas and a fast clustering approach based on optimum connectivity between voxels in their feature space. The algorithm exploits local image properties and global information from the atlas as features to group GM and white-matter (WM) voxels in distinct clusters, and uses the total probability values inside the clusters to label them as GM or WM. This new method is validated in the region of the thalamus and outperformed two widely used methods packaged in SPM and FSL.\",\n \"Title: Intelligent Understanding Of User Interaction In Image Segmentation\\nYear: 2012\\n\\nAbstract: We have developed interactive tools for graph-based segmentation of natural images, in which the user guides object delineation by drawing strokes (markers) inside and outside the object. A suitable arc-weight estimation is paramount to minimize user time and maximize segmentation accuracy in these tools. However, it depends on discriminative image properties for object and background. These properties can be obtained from some marker pixels, but their identification is a hard problemduring delineation. Careless arc-weight re-estimation reduces user control and drops performance, while interactive arc-weight estimation in a step before interactive object extraction is the best option so far, albeit it is not intuitive for nonexpert users. We present an effective solution using the unified framework of the image foresting transform (IFT) with three operators: clustering for interpreting user interaction and determining when and where arc weights need to be re-estimated; fuzzy classification for arc-weight estimation; and marker competition based on optimum connectivity for object extraction. For validation, we compared the proposed approach with another interactive IFT-based method, which computes arc weights before extraction. Evaluation involved multiple users (experts and nonexperts), a dataset with several natural images, and measurements to quantify accuracy, precision, efficiency (user time and computation time), and user control, being some of them novel measurements, proposed in this work.\",\n \"Title: MiCRoM: A Metric Distance to Compare Segmented Images\\nYear: 2002\\n\\nAbstract: Recently, several content-based image retrieval (CBIR) systems that make use of segmented images have been proposed. In these systems, images are segmented and represented as a set of regions, and the distance between images is computed according to the visual features of their regions. A major problem of existing distance functions used to compare segmented images is that they are not metrics. Hence, it is not possible to exploit filtering techniques and/or access methods to speedup query processing, as both techniques make extensive use of the triangular inequality property - one of the metric axioms. In this work, we propose MiCROM (Minimum-Cost Region Matching), an effective metric distance which models the comparison of segmented images as a minimum-cost network flow problem. To our knowledge, this is the first time a true metric distance function is proposed to evaluate the distance between segmented images. Our experiments show that MiCROM is at least as effective as existing non-metric distances. Moreover, we have been able to use the recently proposed Omni-sequential filtering technique, and have achieved nearly 2/3 savings in retrieval/query processing time.\",\n \"Title: Fast Digital Perspective Shell Rendering\\nYear: 1999\\n\\nAbstract: Volume visualization methods usually require extensive storage and time-intensive computations. To make them viable in many applications, researchers have proposed various types of hardware-based and software-based solutions. In most of these solutions, the rendering is based on orthographic parallel projection. However, in applications such as virtual endoscopy, perspective projection is necessary to allow the observer to travel inside the structures. In this paper, we extend one of these software-based solutions, called shell rendering, to provide fast digital perspective projection. The main idea in digital perspective is that each voxel should be rendered using an integer number of pixels which is inversely proportional to the distance between the voxel and the observer and that that distance can be discretized. We show that the new method, called digital perspective shell rendering, provides the same image quality as orthographic parallel projection\",\n \"Title: A discrete approach for supervised pattern recognition\\nYear: 2008\\n\\nAbstract: We present an approach for supervised pattern recognition based on combinatorial analysis of optimum paths from key samples (prototypes), which creates a discrete optimal partition of the feature space such that any unknown sample can be classified according to this partition. A training set is interpreted as a complete graph with at least one prototype in each class. They compete among themselves and each prototype defines an optimum-path tree, whose nodes are the samples more strongly connected to it than to any other. The result is an optimumpath forest in the training set. A test sample is assigned to the class of the prototype which offers it the optimum path in the forest. The classifier is designed to achieve zero classification errors in the training set, without over-fitting, and to learn from its errors. A comparison with several datasets shows the advantages of the method in accuracy and efficiency with respect to support vector machines.\",\n \"Title: Remote sensing image representation based on hierarchical histogram propagation.\\nYear: 2013\\n\\nAbstract: Many methods have been recently proposed to deal with the large amount of data provided by high-resolution remote sensing technologies. Several of these methods rely on the use of image segmentation algorithms for delineating target objects. However, a common issue in geographic object-based applications is the definition of the appropriate data representation scale, a problem that can be addressed by exploiting multiscale segmentation. The use of multiple scales, however, raises new challenges related to the definition of effective and efficient mechanisms for extracting features. In this paper, we address the problem of extracting histogram-based features from a hierarchy of regions for multiscale classification. The strategy, called H-Propagation, exploits the existing relationships among regions in a hierarchy to iteratively propagate features along multiple scales. The proposed method speeds up the feature extraction process and yields good results when compared with global low-level extraction approaches.\",\n \"Title: Spoken emotion recognition through optimum-path forest classification using glottal features\\nYear: 2010\\n\\nAbstract: A new method for the recognition of spoken emotions is presented based on features of the glottal airflow signal. Its effectiveness is tested on the new optimum path classifier (OPF) as well as on six other previously established classification methods that included the Gaussian mixture model (GMM), support vector machine (SVM), artificial neural networks - multi layer perceptron (ANN-MLP), k-nearest neighbor rule (k-NN), Bayesian classifier (BC) and the C4.5 decision tree. The speech database used in this work was collected in an anechoic environment with ten speakers (5M and 5F) each speaking ten sentences in four different emotions: Happy, Angry, Sad, and Neutral. The glottal waveform was extracted from fluent speech via inverse filtering. The investigated features included the glottal symmetry and MFCC vectors of various lengths both for the glottal and the corresponding speech signal. Experimental results indicate that best performance is obtained for the glottal-only features with SVM and OPF generally providing the highest recognition rates, while for GMM or the combination of glottal and speech features performance was relatively inferior. For this text dependent, multi speaker task the top performing classifiers achieved perfect recognition rates for the case of 6th order glottal MFCCs.\",\n \"Title: Fast and automatic curvilinear reformatting of MR images of the brain for diagnosis of dysplastic lesions\\nYear: 2006\\n\\nAbstract: Curvilinear reformatting is known as the best non-invasive technique for diagnosis of dysplastic lesions of the brain. It consists of computing surfaces that follow the brain's curvature at various depths, making the diagnosis possible by visual inspection of the voxel intensities on these surfaces. Traditional approaches require user intervention and present curvature artifacts. We present a new method for curvilinear reformatting that solves both problems. It uses a graph-based approach to segment the brain, extract its envelope, and compute the isosurfaces at all possible depths by Euclidean distance transform. It requires no user input, no ad-hoc parameters, and takes less than 1 minute to run on a common PC\",\n \"Title: Automatic Segmentation and Classification of Human Intestinal Parasites From Microscopy Images.\\nYear: 2013\\n\\nAbstract: Human intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis.\",\n \"Title: Supervised pattern classification based on optimum-path forest\\nYear: 2009\\n\\nAbstract: We present a supervised classification method which represents each class by one or more optimum-path trees rooted at some key samples, called prototypes. The training samples are nodes of a complete graph, whose arcs are weighted by the distances between the feature vectors of their nodes. Prototypes are identified in all classes and the minimization of a connectivity function by dynamic programming assigns to each training sample a minimum-cost path from its most strongly connected prototype. This competition among prototypes partitions the graph into an optimum-path forest rooted at them. The class of the samples in an optimum-path tree is assumed to be the same of its root. A test sample is classified similarly, by identifying which tree would contain it, if the sample were part of the training set. By choice of the graph model and connectivity function, one can devise other optimum-path forest classifiers. We present one of them, which is fast, simple, multiclass, parameter independent, does not make any assumption about the shapes of the classes, and can handle some degree of overlapping between classes. We also propose a general algorithm to learn from errors on an evaluation set without increasing the training set, and show the advantages of our method with respect to SVM, ANN-MLP, and k-NN classifiers in several experiments with datasets of various types. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 120–131, 2009. A preliminary version of the paper was presented at the 12th International Workshop on Combinatorial Image Analysis (Papa et al.,2008a).\",\n \"Title: Automatic fusion of region-based classifiers for coffee crop recognition\\nYear: 2012\\n\\nAbstract: Coffee crop recognition in remote sensing images is a complex task. It poses several challenges due to different spectral responses and texture patterns that can be extracted from coffee regions. This paper presents a novel framework for combining different classifiers using support vector machine technique (SVM), which try to learn with each one of classifiers previews experiences (meta-learning). We investigate the combination of seven learning methods and seven image descriptors aiming at creating low-cost classifiers for coffee crops recognition. The objective is to provide an effective mechanism for coffee crop recognition by fusion of region-based classifiers in remote sensing images. The experiments showed that the proposed framework for fusion of classifiers produces better results than the traditional majority voting fusion approach and all base classifiers tested.\",\n \"Title: Fuzzy Connectedness Image Segmentation in Graph Cut Formulation: A Linear-Time Algorithm and a Comparative Analysis.\\nYear: 2012\\n\\nAbstract: A deep theoretical analysis of the graph cut image segmentation framework presented in this paper simultaneously translates into important contributions in several directions.The most important practical contribution of this work is a full theoretical description, and implementation, of a novel powerful segmentation algorithm, GC. The output of GC coincides with a version of a segmentation algorithm known as Iterative Relative Fuzzy Connectedness, IRFC. However, GC is considerably faster than the classic IRFC algorithm, which we prove theoretically and show experimentally. Specifically, we prove that, in the worst case scenario, the GC algorithm runs in linear time with respect to the variable =||+||, where || is the image scene size and || is the size of the allowable range, , of the associated weight/affinity function. For most implementations, is identical to the set of allowable image intensity values, and its size can be treated as small with respect to ||, meaning that ()=(||). In such a situation, GC runs in linear time with respect to the image size ||.We show that the output of GC constitutes a solution of a graph cut energy minimization problem, in which the energy is defined as the norm ∥ ∥ of the map that associates, with every element from the boundary of an object , its weight (). This formulation brings IRFC algorithms to the realm of the graph cut energy minimizers, with energy functions ∥ ∥ for ∈[1,∞]. Of these, the best known minimization problem is for the energy ∥ ∥, which is solved by the classic min-cut/max-flow algorithm, referred to often as the Graph Cut algorithm.We notice that a minimization problem for ∥ ∥, ∈[1,∞), is identical to that for ∥ ∥, when the original weight function is replaced by . Thus, any algorithm GC solving the ∥ ∥ minimization problem, solves also one for ∥ ∥ with ∈[1,∞), so just two algorithms, GC and GC, are enough to solve all ∥ ∥-minimization problems. We also show that, for any fixed weight assignment, the solutions of the ∥ ∥-minimization problems converge to a solution of the ∥ ∥-minimization problem (∥ ∥=lim∥ ∥ is not enough to deduce that).An experimental comparison of the performance of GC and GC algorithms is included. This concentrates on comparing the actual (as opposed to provable worst scenario) algorithms’ running time, as well as the influence of the choice of the seeds on the output.\",\n \"Title: On “shapes” of colors for content-based image retrieval\\nYear: 2000\\n\\nAbstract: Color is a commonly used feature for realizing content-based image retrieval (CBIR). Towards this goal, this paper presents a new approach for CBIR which is based on well known and widely used color histograms. Contrasting to previous approaches, such as using a single color histogram for the whole image, or local color histograms for a fixed number of image cells, the one we propose (named Color Shape) uses a variable number of histograms, depending only on the actual number of colors present in the image. Our experiments using a large set of heterogeneous images and pre-defined query/answer sets show that the Color Shape approach offers good retrieval quality with relatively low space overhead, outperforming previous approaches.\",\n \"Title: Descriptor correlation analysis for remote sensing image multi-scale classification.\\nYear: 2012\\n\\nAbstract: This paper addresses the problem of remote sensing image multi-scale classification by: (i) showing that using multiple scales does improve classification results, but not all scales have the same importance; (ii) showing that image descriptors do not offer the same contribution at all scales, as commonly thought, and some of them are very correlated; (iii) introducing a simple approach to automatically select segmentation scales, descriptors, and classifiers based on correlation and accuracy analysis.\",\n \"Title: Optimizing Contextual-Based Optimum-Forest Classification through Swarm Intelligence\\nYear: 2013\\n\\nAbstract: Several works have been conducted in order to improve classification problems. However, a considerable amount of them do not consider the contextual information in the learning process, which may help the classification step by providing additional information about the relation between a sample and its neighbourhood. Recently, a previous work have proposed a hybrid approach between Optimum-Path Forest classifier and Markov Random Fields (OPF-MRF) aiming to provide contextual information for this classifier. However, the contextual information was restricted to a spatial/temporal-dependent parameter, which has been empirically chosen in that work. We propose here an improvement of OPF-MRF by modelling the problem of finding such parameter as a swarm-based optimization task, which is carried out Particle Swarm Optimization and Harmony Search. The results have been conducted over the classification of Magnetic Ressonance Images of the brain, and the proposed approach seemed to find close results to the ones obtained by an exhaustive search for this parameter, but much faster for that.\",\n \"Title: Robust And Fast Vowel Recognition Using Optimum-Path Forest\\nYear: 2010\\n\\nAbstract: The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy.\",\n \"Title: A genetic programming framework for content-based image retrieval\\nYear: 2009\\n\\nAbstract: The effectiveness of content-based image retrieval (CBIR) systems can be improved by combining image features or by weighting image similarities, as computed from multiple feature vectors. However, feature combination do not make sense always and the combined similarity function can be more complex than weight-based functions to better satisfy the users' expectations. We address this problem by presenting a Genetic Programming framework to the design of combined similarity functions. Our method allows nonlinear combination of image similarities and is validated through several experiments, where the images are retrieved based on the shape of their objects. Experimental results demonstrate that the GP framework is suitable for the design of effective combinations functions.\",\n \"Title: An Adaptive and Efficient Clustering-Based Approach for Content-Based Image Retrieval in Image Databases\\nYear: 2001\\n\\nAbstract: Abstract: In this paper, we present a new Content-Based Image Retrieval (CBIR) approach, for image databases, based on cluster analysis. CBIR relies on the representation (meta-data) of images' visual content. In order to produce such metadata, we propose an efficient and adaptive clustering algorithm to segment the images into regions of high-similarity. This approach contrasts with those that use a single color histogram for the whole image (global methods), or local color histograms for a fixed number of image cells (partition-based methods). Our experimental results show that our clustering approach offers high retrieval effectiveness with low space overhead. For example, using a database of 20,000 images we obtained higher retrieval effectiveness than partition-based methods with about the same space overhead of global methods, which are typically regarded to as storage-wise compact.\",\n \"Title: Object delineation by κ-connected components\\nYear: 2008\\n\\nAbstract: The notion of \\\"strength of connectedness\\\" between pixels has been successfully used in image segmentation. We present extensions to these works, which can considerably improve the efficiency of object delineation tasks. A set of pixels is said to be a κ-connected component with respect to a seed pixel, when the strength of connectedness of any pixel in that set with respect to the seed is higher than or equal to a threshold. We discuss two approaches that define objects based on κ-connected components with respect to a given seed set: with and without competition among seeds. While the previous approaches either assume no competition with a single threshold for all seeds or eliminate the threshold for seed competition, we show that seeds with different thresholds can improve segmentation in both paradigms. We also propose automatic and user-friendly interactive methods to determining the thresholds. The proposed methods are presented in the framework of the image foresting transform, which naturally leads to efficient and correct graph algorithms. The improvements are demonstrated through several segmentation experiments involving medical images.\",\n \"Title: Cloud bank: a multiple clouds model and its use in MR brain image segmentation\\nYear: 2009\\n\\nAbstract: We present improvements to a recently proposed model-based segmentation strategy, called Clouds. We demonstrate the usefulness of the new model Cloud Bank in the automatic MR-image segmentation of the brain (without medulla and spinal cord), the cerebellum, and each brain hemisphere. These structures are connected in several parts, imposing serious challenges for segmentation. The results indicate that the new model is a fast and accurate tool to eliminate user's intervention or, at least, reduce it to simple corrections, in the context of brain image segmentation.\",\n \"Title: A data reduction and organization approach for efficient image annotation\\nYear: 2013\\n\\nAbstract: The labor-intensive and time-consuming process of annotating data is a serious bottleneck in many pattern recognition applications when handling massive datasets. Active learning strategies have been sought to reduce the cost on human annotation, by means of automatically selecting the most informative unlabeled samples for annotation. The critical issue lies on the selection of such samples. As an effective solution, we propose an active learning approach that preprocesses the dataset, efficiently reduces and organizes a learning set of samples and selects the most representative ones for human annotation. Experiments performed on real datasets show that the proposed approach requires only a few iterations to achieve high accuracy, keeping user involvement to a minimum.\",\n \"Title: A Comparative Study among Pattern Classifiers in Interactive Image Segmentation\\nYear: 2009\\n\\nAbstract: Edition of natural images usually asks for considerable userinvolvement, being segmentation one of the main challenges. This paper describes an unified graph-based framework for fast, precise and accurate interactive image segmentation. The method divides segmentation into object recognition, enhancement and extraction. Recognition is done by the user when markers are selected inside and outside the object. Enhancement increases the dissimilarities between object and background and Extraction separates them. Enhancement is done by a fuzzy pixel classifier and it has a great impact in the number of markers required for extraction. In view of minimizing user involvement, we focus this paper on a comparative study among popular classifiers for enhancement, conducting experiments with several natural images and seven users.\",\n \"Title: Feature selection through gravitational search algorithm\\nYear: 2011\\n\\nAbstract: In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.\",\n \"Title: Speeding up optimum-path forest training by path-cost propagation\\nYear: 2012\\n\\nAbstract: In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost function. Experiments on public datasets have shown that the proposed approach can obtain similar accuracy to the traditional one but with faster data training.\",\n \"Title: A linear-time approach for image segmentation using graph-cut measures\\nYear: 2006\\n\\nAbstract: Image segmentation using graph cuts have become very popular in the last years. These methods are computationally expensive, even with hard constraints (seed pixels). We present a solution that runs in time proportional to the number of pixels. Our method computes an ordered region growing from a set of seeds inside the object, where the propagation order of each pixel is proportional to the cost of an optimum path in the image graph from the seed set to that pixel. Each pixel defines a region which includes it and all pixels with lower propagation order. The boundary of each region is a possible cut boundary, whose cut measure is also computed and assigned to the corresponding pixel on-the-fly. The object is obtained by selecting the pixel with minimum-cut measure and all pixels within its respective cut boundary. Approaches for graph-cut segmentation usually assume that the desired cut is a global minimum. We show that this can be only verified within a reduced search space under certain hard constraints. We present and evaluate our method with three cut measures: normalized cut, mean cut and an energy function.\",\n \"Title: Precipitates segmentation from scanning electron microscope images through machine learning techniques\\nYear: 2011\\n\\nAbstract: The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis.\",\n \"Title: Fast, Accurate and Precise Mid-Sagittal Plane Location in 3D MR Images of the Brain\\nYear: 2008\\n\\nAbstract: Extraction of the mid-sagittal plane (MSP) is a key step for brain image registration and asymmetry analysis. We present a fast MSP extraction method for 3D MR images, based on automatic segmentation of the brain and on heuristic maximization of the cerebro-spinal fluid within the MSP. The method is robust to severe anatomical asymmetries between the hemispheres, caused by surgical procedures and lesions. The method is also accurate with respect to MSP delineations done by a specialist. The method was evaluated on 64 MR images (36 pathological, 20 healthy, 8 synthetic), and it found a precise and accurate approximation of the MSP in all of them with a mean time of 60.0 seconds per image, mean angular variation within a same image (precision) of 1.26 degrees and mean angular difference from specialist delineations (accuracy) of 1.64 degrees.\",\n \"Title: Automated diagnosis of human intestinal parasites using optical microscopy images\\nYear: 2013\\n\\nAbstract: Intestinal parasitosis constitutes a serious health problem in most tropical countries. The diagnosis of enteroparasites in laboratory routine relies on the examination of stool samples using optical microscopy and the error rates usually range from moderate to high. Approaches based on automatic image analysis have been proposed, but the methods are usually specific for some species, some of them are computationally expensive, and image acquisition and focus are manual. We present a solution to automate the diagnosis of the 15 most common species of enteroparasites in Brazil, using a sensitive parasitological technique, a motorized microscope with digital camera for automatic image acquisition and focus, and fast image analysis methods. The results indicate that our solution is effective and suitable for laboratory routine, in which the exam must be concluded in a few minutes.\",\n \"Title: Fuzzy-connected 3D image segmentation at interactive speeds\\nYear: 2002\\n\\nAbstract: Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem with these algorithms has been their excessive computational requirements. In an attempt to substantially speed them up, in the present paper, we study systematically a host of 18 'optimal' graph search algorithms. Extensive testing of these algorithms on a variety of 3D medical images taken from large ongoing applications demonstrates that a 20-1000-fold improvement over current speeds is achievable with a combination of algorithms and fast modern PCs. Utilizing efficient algorithms and careful selection of implementations can speed up the computation of fuzzy connectedness values by a factor of 16-29 (on the same hard-ware), as compared to the implementation previously used in our applications utilizing fuzzy object segmentation. The optimality of an algorithm depends on the input data as well as on the choice of the fuzzy affinity relation. The running time is reduced considerably (by a factor up to 34 for brain MR and even more for bone CT), when the algorithms make use of pre-determined thresholds for the fuzzy objects. The reliable recognition (assisted by human operators) and the accurate, efficient, and sophisticated delineation (automatically performed by the computer) can be effectively incorporated into a single interactive process. If images having intensities with tissue-specific meaning (such as CT or standardized MR images) are utilized, most of the parameters for the segmentation method can be fixed once for all, all intermediate data (feature and fuzzy affinity values for the whole scene) can be computed before the user interaction is needed and the user can be provided with more information at the time of interaction.\",\n \"Title: The Image Forest Transform Architecture\\nYear: 2007\\n\\nAbstract: The Image Foresting Transform (IFT) is e a generic technique that uses simple variations of the same core algorithm to construct many image processing operators like watershed transforms, edge tracking, geodesic paths, among others. In this paper we propose the Silicon IFT (SIFT), an FPGA-based architecture that leverages on the IFT flexibility to build a fast image processing architecture capable of implementing IFT operators in hardware. Our experiments have shown that SIFT can reach speedups of 5600 upon the correspondent software implementation. Moreover, they exhibit excellent execution times as compared to recent dedicated image processing architectures.\",\n \"Title: Clouds: A Model For Synergistic Image Segmentation\\nYear: 2008\\n\\nAbstract: Image segmentation consists of recognizing the object in the image and precisely delineating its spatial extent. We present a model, called clouds, that exploits the synergism which commonly exists between recognition and delineation for more effective segmentation. The model can reduce user's intervention to simple corrections or even eliminate it altogether, achieving high accuracy. We evaluate the method in the task of 3D MR image segmentation of the brain in isolating automatically: brain without medulla and spinal cord; just the cerebellum; and the brain hemispheres without medulla, spinal cord, and cerebellum. These structures are connected in several parts, which poses a serious challenge for simplistic segmentation strategies. The entire process takes a few seconds on modem PCs and provides accurate results. The applications for clouds go beyond medical imaging, opening new vistas in a variety of areas served by segmentation.\",\n \"Title: A compact and efficient image retrieval approach based on border/interior pixel classification\\nYear: 2002\\n\\nAbstract: This paper presents \\\\bic (Border/Interior pixel Classification), a compact and efficient CBIR approach suitable for broad image domains. It has three main components: (1) a simple and powerful image analysis algorithm that classifies image pixels as either border or interior, (2) a new logarithmic distance (dLog) for comparing histograms, and (3) a compact representation for the visual features extracted from images. Experimental results show that the BIC approach is consistently more compact, more efficient and more effective than state-of-the-art CBIR approaches based on sophisticated image analysis algorithms and complex distance functions. It was also observed that the dLog distance function has two main advantages over vectorial distances (e.g., L1): (1) it is able to increase substantially the effectiveness of (several) histogram-based CBIR approaches and, at the same time, (2) it reduces by 50% the space requirement to represent a histogram.\",\n \"Title: IFT-Watershed from Gray-Scale Marker\\nYear: 2002\\n\\nAbstract: Abstract. The watershed transform and the morphological reconstruction are two of the most important operators for image segmentation in the framework of mathematical morphology. In many situations, the segmentation requires the classical watershed transform of a reconstructed image. In this paper, we introduce the IFT-watershed from gray scale marker - a method to compute at same time, the reconstruction and the classical watershed transformof the reconstructed image, without explicit computation of any regional minima. The method is based on the Image Foresting Transform (IFT) - a unified and efficient approach to reduce image processing problems to a minimum-cost path forest problem in a graph. As additional contributions, we demonstrate that (i) the cost map of the IFT-watershed from markers is identical to the output of the superior gray scale reconstruction; (ii) other reconstruction algorithms are not watersheds; and (iii) the proposed method achieves competitive advantages as compared to the current classical watershed approach.\",\n \"Title: Novel Approaches for Exclusive and Continuous Fingerprint Classification\\nYear: 2009\\n\\nAbstract: This paper proposes novel exclusive and continuous approaches to guide the search and the retrieval in fingerprint image databases. Both approaches are useful to perform a coarse level classification of fingerprint images before fingerprint authentication tasks. Our approaches are characterized by: (1) texture image descriptors based on pairs of multi-resolution decomposition methods that encode effectively global and local fingerprint information, with similarity measures used for fingerprint matching purposes, and (2) a novel multi-class object recognition method based on the Optimum Path Forest classifier. Experiments were carried out on the standard NIST-4 dataset aiming to study the discriminative and scalability capabilities of our approaches. The high classification rates allow us demonstrate the feasibility and validity of our approaches for characterizing fingerprint images accurately.\",\n \"Title: Improving the accuracy of the optimum-path forest supervised classifier for large datasets\\nYear: 2010\\n\\nAbstract: In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OPF while still gaining in classification time, at the expense of a slight increase in training time.\",\n \"Title: A new framework to combine descriptors for content-based image retrieval\\nYear: 2005\\n\\nAbstract: In this paper, we propose a novel framework using Genetic Programming to combine image database descriptors for content-based image retrieval (CBIR). Our framework is validated through several experiments involving two image databases and specific domains, where the images are retrieved based on the shape of their objects.\",\n \"Title: A 3D generalization of user-steered live-wire segmentation\\nYear: 2000\\n\\nAbstract: We have been developing user-steered image segmentation methods for situations which require considerable human assistance in object definition. In the past, we have presented two paradigms, referred to as live-wire and live-lane, for segmenting 2D/3D/4D object boundaries in a slice-by-slice fashion, and demonstrated that live-wire and live-lane are more repeatable, with a statistical significance level of P<0.03, and are 1.5–2.5 times faster, with a statistical significance level of P<0.02, than manual tracing. In this paper, we introduce a 3D generalization of the live-wire approach for segmenting 3D/4D object boundaries which further reduces the time spent by the user in segmentation. In a 2D live-wire, given a slice, for two specified points (pixel vertices) on the boundary of the object, the best boundary segment is the minimum-cost path between the two points, described as a set of oriented pixel edges. This segment is found via Dijkstra’s algorithm as the user anchors the first point and moves the cursor to indicate the second point. A complete 2D boundary is identified as a set of consecutive boundary segments forming a “closed”, “connected”, “oriented” contour. The strategy of the 3D extension is that, first, users specify contours via live-wiring on a few slices that are orthogonal to the natural slices of the original scene. If these slices are selected strategically, then we have a sufficient number of points on the 3D boundary of the object to subsequently trace optimum boundary segments automatically in all natural slices of the 3D scene. A 3D object boundary may define multiple 2D boundaries per slice. The points on each 2D boundary form an ordered set such that when the best boundary segment is computed between each pair of consecutive points, a closed, connected, oriented boundary results. The ordered set of points on each 2D boundary is found from the way the users select the orthogonal slices. Based on several validation studies involving segmentation of the bones of the foot in MR images, we found that the 3D extension of live-wire is more repeatable, with a statistical significance level of P<0.0001, and 2–6 times faster, with a statistical significance level of P<0.01, than the 2D live-wire method, and 3–15 times faster than manual tracing.\",\n \"Title: What is the importance of selecting features for non-technical losses identification?\\nYear: 2011\\n\\nAbstract: Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles.\",\n \"Title: Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images\\nYear: 2008\\n\\nAbstract: Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system.\",\n \"Title: The riverbed approach for user-steered image segmentation\\nYear: 2011\\n\\nAbstract: This work presents an optimum user-steered boundary tracking approach for image segmentation, which simulates the behavior of water flowing through a riverbed. The riverbed approach was devised using the Image Foresting Transform with a never exploited connectivity function. We analyze its properties in the derived image graphs and discuss its theoretical relation with other popular methods, such as live wire and graph cuts. Riverbed can significantly reduce the number of user interactions (anchor points) as compared to live wire for objects with complex shapes.\",\n \"Title: Improving texture description in remote sensing image multi-scale classification tasks by using visual words.\\nYear: 2012\\n\\nAbstract: Although texture features are important for region-based classification of remote sensing images, the literature shows that texture descriptors usually have poor performance when compared and combined with color descriptors. In this paper, we propose a bag-of-visual-words (BOW) “propagation” approach to extract texture features from a hierarchy of regions. This strategy improves efficacy of feature as it encodes texture information independently of the region shape. Experiments show that the proposed approach improves the classification results when compared with global descriptors using the bounding box padding strategy.\",\n \"Title: An Ultra-Fast User-Steered Image Segmentation Paradigm: Live Wire On The Fly\\nYear: 2000\\n\\nAbstract: We have been developing general user steered image segmentation strategies for routine use in applications involving a large number of data sets. In the past, we have presented three segmentation paradigms: live wire, live lane, and a three-dimensional (3-D) extension of the live-wire method. In this paper, we introduce an ultra-fast live-wire method, referred to as live wire on the fly, for further reducing user's time compared to the basic live-wire method. In live wire, 3-D/four-dimensional (4-D) object boundaries are segmented in a slice-by-slice fashion. To segment a two-dimensional (2-D) boundary, the user initially picks a point on the boundary and all possible minimum-cost paths from this point to all other points in the image are computed via Dijkstra's algorithm. Subsequently a live wire is displayed in real time From the initial point to any subsequent position taken by the cursor. If the cursor is close to the desired boundary, the live wire snaps on to the boundary. The cursor is then deposited and a new live-wire segment is Found next, The entire 2-D boundary is specified via a set of live-wire segments in this fashion. A drawback of this method is that the speed of optimal path computation depends on image size. On modestly powered computers, for images of even modest size, some sluggishness appears in user interaction, which reduces the overall segmentation efficiency. In this work, we solve this problem by exploiting some known properties of graphs to avoid unnecessary minimum-cost path computation during segmentation. In live wire on the fly, when the user selects a point on the boundary the live-wire segment is computed and displayed in real time from the selected point to any subsequent position of the cursor in the image, even for large images and even on low-powered computers. Based on 492 tracing experiments from an actual medical application, we demonstrate that live wire on the fly is 1.3-31 times faster than live wire for actual segmentation for varying image sizes, although the pure computational part alone is found to be about 120 times faster.\",\n \"Title: Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images.\\nYear: 2014\\n\\nAbstract: •Fuzzy hierarchical modeling of all major organs in a body region.•Detailed object relationship information encoded into models.•Hierarchical object recognition and delineation.•Optimal threshold-based recognition strategy.•Demonstration of the same method on several body regions and different modalities.\",\n \"Title: Cell histograms versus color histograms for image representation and retrieval\\nYear: 2003\\n\\nAbstract: This paper presents a new approach for content-based image retrieval, which is based on the well-known and widely used color histograms. Previous approaches have used a single global color histogram (GCH) for the whole image, or local color histograms (LCHs) for cells within a grid of fixed size. Our approach is also based on a grid of cells, but unlike the latter it uses a cell histogram for each of the colors actually present in the images, representing how that color is distributed among the image cells - hence the name Cell/Color Histograms. Our experiments have shown that the actual number of colors present in images is often low. Thus we are able to achieve performance comparable to using LCHs within a grid, but with a much smaller space overhead. Furthermore, the proposed approach is very flexible in the sense that the user has alternative ways to calibrate the trade-off between space overhead and retrieval effectiveness. In fact, we have been able to outperform GCHs (typically a compact representation) in terms of effectiveness requiring less storage space.\",\n \"Title: Learning Person-Specific Representations From Faces in the Wild\\nYear: 2014\\n\\nAbstract: Humans are natural face recognition experts, far out-performing current automated face recognition algorithms, especially in naturalistic, “in the wild” settings. However, a striking feature of human face recognition is that we are dramatically better at recognizing highly familiar faces, presumably because we can leverage large amounts of past experience with the appearance of an individual to aid future recognition. Meanwhile, the analogous situation in automated face recognition, where a large number of training examples of an individual are available, has been largely underexplored, in spite of the increasing relevance of this setting in the age of social media. Inspired by these observations, we propose to explicitly learn enhanced face representations on a per-individual basis, and we present two methods enabling this approach. By learning and operating within person-specific representations, we are able to significantly outperform the previous state-of-the-art on PubFig83, a challenging benchmark for familiar face recognition in the wild, using a novel method for learning representations in deep visual hierarchies. We suggest that such person-specific representations aid recognition by introducing an intermediate form of regularization to the problem.\",\n \"Title: An active learning paradigm based on a priori data reduction and organization.\\nYear: 2014\\n\\nAbstract: •A novel active learning paradigm, called DROP, based on a priori data reduction and organization.•DROP does not require classification and reorganization of all non-annotated samples in the dataset at each iteration.•The proposed paradigm allows to achieve high accuracy quickly with minimum user interaction.•Results are shown with different clustering and classification strategies, and on a variety of real-world datasets.\",\n \"Title: Improving Atlas-Based Medical Image Segmentation With A Relaxed Object Search\\nYear: 2014\\n\\nAbstract: Medical image segmentation using 3D probabilistic atlases has been actively pursued to avoid the time-consuming involvement of experts in manual object (organ) delineation for quantitative analysis. By mapping a new 3D image onto the reference coordinate system of the atlas, built for some organ of interest, these techniques take a binary decision based on the probability of each voxel to be part of that organ. However, image-based techniques have also been proposed to refine object delineation at the initial position given by the atlas-based segmentation. In this paper, we relax this condition for delineation refinement by moving an atlas based on the prior probability map to search for the organ around that initial position. Our method uses the multi-scale parameter search algorithm with a suitable criterion function to evaluate automatic 3D organ delineations, as obtained by the image foresting transform algorithm in an uncertainty region of the atlas. Experiments with eight organs in CT and MR images have indicated that our method can improve atlas-based segmentation with statistical significance. Moreover, the relaxed object search consistently found the organ with higher accuracy outside the position obtained by the atlas, which reinforces our claim.\",\n \"Title: Superpixel-Based Interactive Classification of Very High Resolution Images\\nYear: 2014\\n\\nAbstract: Very high resolution (VHR) images are large datasets for pixel annotation--a process that has depended on the supervised training of an effective pixel classifier. Active learning techniques have mitigated this problem, but pixel descriptors are limited to local image information and the large number of pixels makes the response time to the user's actions impractical, during active learning. To circumvent the problem, we present an active learning strategy that relies on superpixel descriptors and a priori dataset reduction. Firstly, we compare VHR image annotation using superpixel- and pixel-based classifiers, as designed by the same state-of-the-art active learning technique--Multi-Class Level Uncertainty (MCLU). Even with the dataset reduction provided by the superpixel representation, MCLU remains unfeasible for user interaction. Therefore, we propose a technique to considerably reduce the superpixel dataset for active learning. Moreover, we subdivide the reduced dataset into a list of subsets with random sample rearrangement to gain both speed and sample diversity during the active learning process.\",\n \"Title: Vehicle License Plate Recognition With Random Convolutional Networks\\nYear: 2014\\n\\nAbstract: Despite decades of research on automatic license plate recognition (ALPR), optical character recognition (OCR) still leaves room for improvement in this context, given that a single OCR miss is enough to miss the entire plate. We propose an OCR approach based on convolutional neural networks (CNNs) for feature extraction. The architecture of our CNN is chosen from thousands of random possibilities and its filter weights are set at random and normalized to zero mean and unit norm. By training linear support vector machines (SVMs) on the resulting CNN features, we can achieve recognition rates of over 98% for digits and 96% for letters, something that neither SVMs operating on image pixels nor CNNs trained via back-propagation can achieve. The results are obtained in a dataset that has 182 samples per digit and 28 per letter, and suggest the use of random CNNs as a promising alternative approach to ALPR systems.\",\n \"Title: On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest Clustering\\nYear: 2014\\n\\nAbstract: In this paper, we show how to improve the Radial Basis Function Neural Networks effectiveness by using the Optimum-Path Forest clustering algorithm, since it computes the number of clusters on-the-fly, which can be very interesting for finding the Gaussians that cover the feature space. Some commonly used approaches for this task, such as the well-known fc-means, require the number of classes/clusters previous its performance. Although the number of classes is known in supervised applications, the real number of clusters is extremely hard to figure out, since one class may be represented by more than one cluster. Experiments over 9 datasets together with statistical analysis have shown the suitability of OPF clustering for the RBF training step.\",\n \"Title: Active Semi-supervised Learning Using Optimum-Path Forest\\nYear: 2014\\n\\nAbstract: The development of effective and efficient ways of handling real-world applications is becoming increasingly widespread, yet it still faces a number of practical challenges. First and foremost, we have the limited availability of labeled data in contrast to an unbounded number of unlabeled ones. Despite some efforts in active semi-supervised learning, their success depends on an approach suitable to be applied to real massive data. In this paper, we introduce a novel integration of semi-supervised learning and a priori-reduction and organization criteria for active learning based on Optimum-Path Forest classifiers. Encouraging results on both public and real data show the synergy of these strategies jointly. Our approach iteratively generates semi-supervised classifiers that attain high accuracy by selecting the most representative labeled set, while decreasing the propagated errors on the unlabeled set. In addition, it is able to identify samples from all classes quickly while keeping user interaction to a minimum throughout the learning iterations.\",\n \"Title: Robot users for the evaluation of boundary-tracking approaches in interactive image segmentation\\nYear: 2014\\n\\nAbstract: Recent advances in interactive image segmentation focused on eliminating the user bias during evaluation by simulating their behavior using robot users. However, these robots only work for region-based methods, excluding the important class of approaches that rely on the boundary-tracking paradigm. We propose completely novel robot users that are able to simulate the human user behavior when segmenting an image through the addition of anchor points close to the object's boundary. Our robots constantly evaluate the optimum-boundary segment being computed from a previously selected anchor point to the current virtual mouse position, seeking for the longest possible segment with minimum acceptable error. A new anchor is added when the error is too high and the robots iterate until closing the contour, just like real users. We validate our robots by conducting a user study and extensive experiments, considering two boundary-tracking methods: live-wire-on-the-fly and Riverbed. We further show how our robot users can be used to assess hybrid approaches that combine boundary-tracking with region-based delineation, such as LiveMarkers, while conjecturing that robots might lead to new methods for automatic foreground segmentation.\",\n \"Title: Differential and relaxed image foresting transform for graph-cut segmentation of multiple 3D objects.\\nYear: 2014\\n\\nAbstract: Graph-cut algorithms have been extensively investigated for interactive binary segmentation, when the simultaneous delineation of multiple objects can save considerable user's time. We present an algorithm (named DRIFT) for 3D multiple object segmentation based on seed voxels and Differential Image Foresting Transforms (DIFTs) with relaxation. DRIFT stands behind efficient implementations of some stateof- the-art methods. The user can add/remove markers (seed voxels) along a sequence of executions of the DRIFT algorithm to improve segmentation. Its first execution takes linear time with the image's size, while the subsequent executions for corrections take sublinear time in practice. At each execution, DRIFT first runs the DIFT algorithm, then it applies diffusion filtering to smooth boundaries between objects (and background) and, finally, it corrects possible objects' disconnection occurrences with respect to their seeds. We evaluate DRIFT in 3D CT-images of the thorax for segmenting the arterial system, esophagus, left pleural cavity, right pleural cavity, trachea and bronchi, and the venous system.\",\n \"Title: The Ordered Queue and the Optimality of the Watershed Approaches.\\nYear: 2000\\n\\nAbstract: This work reviews the watershed in the graph framework of a shortest-path forest problem using a texicographic path cost formulation. This formulation reflects the behavior of the ordered queue-based watershed algorithm. This algorithm is compared with our proposed shortest-path forest (IFT-Image Foresting Transform), concluding that the watershed is a special case of that. Recently many different watershed approaches are being used. We point out that ill some cases the watershed algorithm does riot keep the optimality of the shortest-path forest solution unless the IFT algorithm is used. The main difference between the algorithms is: related to permanently labeling a pixel when inserting or removing it from the queue. The watershed based on the pixel dissimilarity using IFT carl segment one-pixel width regions while keeping the optimality of the shortest-path forest solution.\",\n \"Title: 3D blob based brain tumor detection and segmentation in MR images\\nYear: 2014\\n\\nAbstract: Automatic detection and segmentation of brain tumors in 3D MR neuroimages can significantly aid early diagnosis, surgical planning, and follow-up assessment. However, due to diverse location and varying size, primary and metastatic tumors present substantial challenges for detection. We present a fully automatic, unsupervised algorithm that can detect single and multiple tumors from 3 to 28,079 mm3 in volume. Using 20 clinical 3D MR scans containing from 1 to 15 tumors per scan, the proposed approach achieves between 87.84% and 95.30% detection rate and an average end-to-end running time of under 3 minutes. In addition, 5 normal clinical 3D MR scans are evaluated quantitatively to demonstrate that the approach has the potential to discriminate between abnormal and normal brains.\",\n \"Title: Characterization of the human cortex in MR images through the image foresting transform\\nYear: 2003\\n\\nAbstract: In this work we discuss an approach to characterize the hu- man cortex in MRI data through the segmentation of the cortical layer with deep penetration into the sulci, using the Image Forest- ing Transform. This technique, based on graph theory, provides a sound framework for the implementation of many image process- ing operators, and has been successfully applied to the solution of many problems, including some in medical imaging. We show the results of applying the technique to slices of MRI data from four subjects and compare these to the computation of the morphologi- cal skeleton.\",\n \"Title: Iso-shaping rigid bodies for estimating their motion from image sequences.\\nYear: 2004\\n\\nAbstract: In many medical imaging applications, due to the limited field of view of imaging devices, acquired images often include only a part of a structure. In such situations, it is impossible to guarantee that the images will contain exactly the same physical extent of the structure at different scans, which leads to difficulties in registration and in many other tasks, such as the analysis of the morphology, architecture, and kinematics of the structures. To facilitate such analysis, we developed a general method, referred to as iso-shaping, that generates structures of the same shape from segmented image sequences. The basis for this method is to automatically find a set of key points, called shape centers, in the segmented partial anatomic structure such that these points are present in all images and that they represent the same physical location in the object, and then trim the structure using these points as reference. The application area considered here is the analysis of the morphology, architecture, and kinematics of the joints of the foot from magnetic resonance images acquired at different joint positions and load conditions. The accuracy of the method is analyzed by utilizing ten data sets for iso-shaping the tibia and the fibula via four evaluative experiments. The analysis indicates that iso-shaping produces results as predicted by the theoretical framework.\",\n \"Title: The image foresting transform: theory, algorithms, and applications.\\nYear: 2004\\n\\nAbstract: Abstract--The image foresting transform (IFT) is a graph-based approach to the design of image processing operators based on connectivity. It naturally leads to correct and efficient implementations and to a better understanding of how different operators relate to each other. We give here a precise definition of the IFT, and a procedure to compute it--a generalization of Dijkstra's algorithm--with a proof of correctness. We also discuss implementation issues and illustrate the use of the IFT in a few applications.\",\n \"Title: Tree-Pruning: A New Algorithm and Its Comparative Analysis with the Watershed Transform for Automatic Image Segmentation\\nYear: 2006\\n\\nAbstract: Image segmentation using tree pruning (TP) and water- shed (WS) has been presented in the framework of the im- age forest transform (IFT)— a method to reduce image pro- cessing problems related to connectivity into an optimum- path forest problem in a graph. Given that both algorithms use the IFT with similar parameters, they usually produce similar segmentation results. However, they rely on dif- ferent properties of the IFT which make TP more robust than WS for automatic segmentation tasks. We propose and demonstrate an important improvement in the TP al- gorithm, clarify the differences between TP and WS, and provide their comparative analysis from the theoretical and practical points of view. The experiments involve automatic segmentation of license plates in a database with 990 im- ages.\",\n \"Title: How to fix any 3D segmentation interactively via Image Foresting Transform and its use in MRI brain segmentation\\nYear: 2011\\n\\nAbstract: In medical imaging, there are many approaches for automatic segmentation. However, none of these methods provide any effective solution to correct segmentation interactively, which becomes a necessity in the case of poorly defined structures. Manual segmentation can not be an alternative given that it might be unfeasible in many cases. On the other hand, how to complete a poor automatic segmentation in an interactive tool is an issue, since the automatic approach and the tool may have been designed with different optimization criteria. We propose solutions to this problem using the framework of the “Image Foresting Transform” (IFT), with evaluation in the context of the segmentation of MR-T1 brain structures. The results indicate that effective semi-automatic correction is possible using just a few markers.\",\n \"Title: Improving land cover classification through contextual-based optimum-path forest\\nYear: 2015\\n\\nAbstract: A new contextual classifier based on optimum-path forest has been presented (OPF-MRF).A meta-heuristic-based framework has been proposed to estimate the contextual-dependent parameter for OPF-MRF.The proposed approach has been validated in the context of satellite image classification. Traditional machine learning algorithms very often assume statistically independent data samples. However, this is clearly not the case in remote sensing image applications, in which pixels present spatial and/or temporal dependencies. In this work, it has been presented an approach to improve land cover image classification using a contextual approach based on optimum-path forest (OPF) and the well-known Markov random fields (MRFs), hereinafter called OPF-MRF. In addition, it is also introduced a framework to the optimization of the amount of contextual information used by OPF-MRF. Experiments over high- and medium-resolution satellite (CBERS-2B, Landsat 5 TM, Ikonos-2 MS and Geoeye) and radar (ALOS-PALSAR) images covering the area of two Brazilian cities have shown the proposed approach can overcome several shortcomings related to standard OPF classification. In some cases, the proposed approach outperformed traditional OPF in about 9% of recognition rate, which is crucial for land cover classification.\",\n \"Title: Robust active learning for the diagnosis of parasites\\nYear: 2015\\n\\nAbstract: We have developed an automated system for the diagnosis of intestinal parasites from optical microscopy images. The objects (species of parasites and impurities) segmented from these images form a large dataset. We are interested in the active learning problem of selecting a reasonably small number of objects to be labeled under an expert's supervision for use in training a pattern classifier. However, impurities are very numerous, constitute several clusters in the feature space, and can be quite similar to some species of parasites, leading to a significant challenge for active learning methods. We propose a technique that pre-organizes the data and then properly balances the selection of samples from all classes and uncertain samples for training. Early data organization avoids reprocessing of the large dataset at each learning iteration, enabling the halting of sample selection after a desired number of samples per iteration, yielding interactive response time. We validate our method by comparing it with state-of-the-art approaches, using a previously labeled dataset of almost 6000 objects. Moreover, we report results from experiments on a very realistic scenario, consisting of a dataset with over 140,000 unlabeled objects, under unbalanced classes, the absence of some classes, and the presence of a very large set of impurities. HighlightsA robust active learning method, called RDS, based on a priori data organization.RDS properly balances sample diversity and uncertainty for useful sample selection.It provides high classification accuracy for the automated diagnosis of parasites.Comparisons with different clustering, classification and other literature methods.RDS was evaluated by an experienced expert in parasitology using a realistic scenario.\",\n \"Title: Visualizing the Hidden Activity of Artificial Neural Networks.\\nYear: 2017\\n\\nAbstract: In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons. Through experiments conducted in three traditional image classification benchmark datasets, we show how visualization can provide highly valuable feedback for network designers. For instance, our discoveries in one of these datasets (SVHN) include the presence of interpretable clusters of learned representations, and the partitioning of artificial neurons into groups with apparently related discriminative roles.\",\n \"Title: Improving semi-supervised learning through optimum connectivity.\\nYear: 2016\\n\\nAbstract: The annotation of large data sets by a classifier is a problem whose challenge increases as the number of labeled samples used to train the classifier reduces in comparison to the number of unlabeled samples. In this context, semi-supervised learning methods aim at discovering and labeling informative samples among the unlabeled ones, such that their addition to the correct class in the training set can improve classification performance. We present a semi-supervised learning approach that connects unlabeled and labeled samples as nodes of a minimum-spanning tree and partitions the tree into an optimum-path forest rooted at the labeled nodes. It is suitable when most samples from a same class are more closely connected through sequences of nearby samples than samples from distinct classes, which is usually the case in data sets with a reasonable relation between number of samples and feature space dimension. The proposed solution is validated by using several data sets and state-of-the-art methods as baselines. HighlightsA new algorithm for semi-supervised learning based on optimum-path forest.The algorithm provides significant improvements in accuracy and efficiency.Labels are propagated from labeled to unlabeled training samples with less errors.The novel classifier can be more accurate than other state-of-the-art methods.A fast and effective algorithm suitable for developing active learning methods.\",\n \"Title: Visualizing time-dependent data using dynamic t-SNE\\nYear: 2016\\n\\nAbstract: Many interesting processes can be represented as time-dependent datasets. We define a time-dependent dataset as a sequence of datasets captured at particular time steps. In such a sequence, each dataset is composed of observations (high-dimensional real vectors), and each observation has a corresponding observation across time steps. Dimensionality reduction provides a scalable alternative to create visualizations (projections) that enable insight into the structure of such datasets. However, applying dimensionality reduction independently for each dataset in a sequence may introduce unnecessary variability in the resulting sequence of projections, which makes tracking the evolution of the data significantly more challenging. We show that this issue affects t-SNE, a widely used dimensionality reduction technique. In this context, we propose dynamic t-SNE, an adaptation of t-SNE that introduces a controllable trade-off between temporal coherence and projection reliability. Our evaluation in two time-dependent datasets shows that dynamic t-SNE eliminates unnecessary temporal variability and encourages smooth changes between projections.\",\n \"Title: Vascular Segmentation In Tof Mra Images Of The Brain Using A Deep Convolutional Neural Network\\nYear: 2017\\n\\nAbstract: Cerebrovascular diseases are one of the main causes of death and disability in the world. Within this context, fast and accurate automatic cerebrovascular segmentation is important for clinicians and researchers to analyze the vessels of the brain, determine criteria of normality, and identify and study cerebrovascular diseases. Nevertheless, automatic segmentation is challenging due to the complex shape, inhomogeneous intensity, and inter-person variability of normal and malformed vessels. In this paper, a deep convolutional neural network (CNN) architecture is used to automatically segment the vessels of the brain in time-of-flight magnetic resonance angiography (TOF MRA) images of healthy subjects. Bi-dimensional manually annotated image patches are extracted in the axial, coronal, and sagittal directions and used as input for training the CNN. For segmentation, each voxel is individually analyzed using the trained CNN by considering the intensity values of neighboring voxels that belong to its patch. Experiments were performed with TOF MRA images of five healthy subjects, using varying numbers of images to train the CNN. Cross validations revealed that the proposed framework is able to segment the vessels with an average Dice coefficient ranging from 0.764 to 0.786 depending on the number of images used for training. In conclusion, the results of this work suggest that CNNs can be used to segment cerebrovascular structures with an accuracy similar to other high-level segmentation methods.\",\n \"Title: Cell segmentation in 3D confocal images using supervoxel merge-forests with CNN-based hypothesis selection\\nYear: 2018\\n\\nAbstract: Automated segmentation approaches are crucial to quantitatively analyze large-scale 3D microscopy images. Particularly in deep tissue regions, automatic methods still fail to provide error-free segmentations. To improve the segmentation quality throughout imaged samples, we present a new supervoxel-based 3D segmentation approach that outperforms current methods and reduces the manual correction effort. The algorithm consists of gentle preprocessing and a conservative super-voxel generation method followed by supervoxel agglomeration based on local signal properties and a postprocessing step to fix under-segmentation errors using a Convolutional Neural Network. We validate the functionality of the algorithm on manually labeled 3D confocal images of the plant Arabidopsis thaliana and compare the results to a state-of-the-art meristem segmentation algorithm.\",\n \"Title: SEGMENT3D: A web-based application for collaborative segmentation of 3D images used in the shoot apical meristem\\nYear: 2018\\n\\nAbstract: The quantitative analysis of 3D confocal microscopy images of the shoot apical meristem helps understanding the growth process of some plants. Cell segmentation in these images is crucial for computational plant analysis and many automated methods have been proposed. However, variations in signal intensity across the image mitigate the effectiveness of those approaches with no easy way for user correction. We propose a web-based collaborative 3D image segmentation application, SEGMENT3D, to leverage automatic segmentation results. The image is divided into 3D tiles that can be either segmented interactively from scratch or corrected from a pre-existing segmentation. Individual segmentation results per tile are then automatically merged via consensus analysis and then stitched to complete the segmentation for the entire image stack. SEGMENT3D is a comprehensive application that can be applied to other 3D imaging modalities and general objects. It also provides an easy way to create supervised data to advance segmentation using machine learning models.\",\n \"Title: A multi-object statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation.\\nYear: 2017\\n\\nAbstract: Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach.\",\n \"Title: Robust Cerebrovascular Segmentation In 4d Asl Mra Images\\nYear: 2018\\n\\nAbstract: Cerebrovascular diseases are one of the main causes of disability and death in the world. Methods for segmentation of the cerebral blood vessels can help clinicians to visualize the cerebrovascular system, determine age-related normal values, and diagnose and study cerebrovascular diseases. A common problem for these methods is the precise delineation of small vessels, due to different artifacts depending on the medical imaging modality. In this work, we present an automatic segmentation method for four dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) images of the brain, which allows an improved segmentation of small vessels, reaching an average Dice similarity coefficient (DSC) of 0.946 based on an evaluation of five datasets from healthy subjects. The results show that the proposed method is able to account for the magnetic labeling decay and flow related artifacts, which considerably reduce the contrast of small vessels present in the image, leading to improved segmentation results.\",\n \"Title: Links Between Image Segmentation Based on Optimum-Path Forest and Minimum Cut in Graph\\nYear: 2009\\n\\nAbstract: Image segmentation can be elegantly solved by optimum-path forest and minimum cut in graph. Given that both approaches exploit similar image graphs, some comparative analysis is expected between them. We clarify their differences and provide their comparative analysis from the theoretical point of view, for the case of binary segmentation (object/background) in which hard constraints (seeds) are provided interactively. Particularly, we formally prove that some optimum-path forest methods from two distinct region-based segmentation paradigms, with internal and external seeds and with only internal seeds, indeed minimize some graph-cut measures. This leads to a proof of the necessary conditions under which the optimum-path forest algorithm and the min-cut/max-flow algorithm produce exactly the same segmentation result, allowing a comparative analysis between them.\",\n \"Title: Multi-label semi-supervised classification through optimum-path forest.\\nYear: 2018\\n\\nAbstract: •An extension of the OPF-based semi-supervised learning for multi-label classification.•A more robust approach to propagate labels to unsupervised samples.•An extensive experimental validation for multi-label classification.\",\n \"Title: A New Variant of the Optimum-Path Forest Classifier\\nYear: 2008\\n\\nAbstract: We have shown a supervised approach for pattern classification, which interprets the training samples as nodes of a complete arc-weighted graph and computes an optimum-path forest rooted at some of the closest samples between distinct classes. A new sample is classified by the label of the root which offers to it the optimum path. We propose a variant, in which the training samples are the nodes of a graph, whose the arcs are the k -nearest neighbors in the feature space. The graph is weighted on the nodes by their probability density values (pdf) and the optimum-path forest is rooted at the maxima of the pdf. The best value of k is computed by the maximum accuracy of classification in the training set. A test sample is assigned to the class of the maximum, which offers to it the optimum path. Preliminary results have shown that the proposed approach can outperform the previous one and the SVM classifier in some datasets.\",\n \"Title: Vessel segmentation in 4D arterial spin labeling magnetic resonance angiography images of the brain.\\nYear: 2017\\n\\nAbstract: 4D arterial spin labeling magnetic resonance angiography (4D ASL MRA) is a non-invasive and safe modality for cerebrovascular imaging procedures. It uses the patient's magnetically labeled blood as intrinsic contrast agent, so that no external contrast media is required. It provides important 3D structure and blood flow information but a sufficient cerebrovascular segmentation is important since it can help clinicians to analyze and diagnose vascular diseases faster, and with higher confidence as compared to simple visual rating of raw ASL MRA images. This work presents a new method for automatic cerebrovascular segmentation in 4D ASL MRA images of the brain. In this process images are denoised, corresponding image label/control image pairs of the 4D ASL MRA sequences are subtracted, and temporal intensity averaging is used to generate a static representation of the vascular system. After that, sets of vessel and background seeds are extracted and provided as input for the image foresting transform algorithm to segment the vascular system. Four 4D ASL MRA datasets of the brain arteries of healthy subjects and corresponding time-of-flight (TOF) MRA images were available for this preliminary study. For evaluation of the segmentation results of the proposed method, the cerebrovascular system was automatically segmented in the high-resolution TOF MRA images using a validated algorithm and the segmentation results were registered to the 4D ASL datasets. Corresponding segmentation pairs were compared using the Dice similarity coefficient (DSC). On average, a DSC of 0.9025 was achieved, indicating that vessels can be extracted successfully from 4D ASL MRA datasets by the proposed segmentation method.\",\n \"Title: Projections As Visual Aids For Classification System Design\\nYear: 2018\\n\\nAbstract: Dimensionality reduction is a compelling alternative for high-dimensional data visualization. This method provides insight into high-dimensional feature spaces by mapping relationships between observations (high-dimensional vectors) to low (two or three) dimensional spaces. These low-dimensional representations support tasks such as outlier and group detection based on direct visualization. Supervised learning, a subfield of machine learning, is also concerned with observations. A key task in supervised learning consists of assigning class labels to observations based on generalization from previous experience. Effective development of such classification systems depends on many choices, including features descriptors, learning algorithms, and hyperparameters. These choices are not trivial, and there is no simple recipe to improve classification systems that perform poorly. In this context, we first propose the use of visual representations based on dimensionality reduction (projections) for predictive feedback on classification efficacy. Second, we propose a projection-based visual analytics methodology, and supportive tooling, that can be used to improve classification systems through feature selection. We evaluate our proposal through experiments involving four datasets and three representative learning algorithms.\",\n \"Title: Automatic Temporal Segmentation of Vessels of the Brain Using 4D ASL MRA Images.\\nYear: 2018\\n\\nAbstract: Objective: Automatic vessel segmentation can be used to process the considerable amount of data generated by four-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) images. Previous segmentation approaches for dynamic series of images propose either reducing the series to a temporal average (tAIP) or maximum intensity projection (tMIP) prior to vessel segmentation, or a...\",\n \"Title: A methodology for generating four-dimensional arterial spin labeling MR angiography virtual phantoms.\\nYear: 2019\\n\\nAbstract: •Methodology for the generation of annotated 4D ASL MRA virtual phantoms, with geometric and blood flow ground-truth data.•The phantoms can be used as ground-truth for developing and evaluating new image processing methods for 4D ASL MRA.•In this work, the phantoms are used for image acquisition optimization, by evaluating different scenarios with different acquisition parameter settings.\",\n \"Title: Iterative Pseudo-Labeling with Deep Feature Annotation and Confidence-Based Sampling\\nYear: 2021\\n\\nAbstract: Training deep neural networks is challenging when large and annotated datasets are unavailable. Extensive manual annotation of data samples is time-consuming, expensive, and error-prone, notably when it needs to be done by experts. To address this issue, increased attention has been devoted to techniques that propagate uncertain labels (also called pseudo labels) to large amounts of unsupervised s...\",\n \"Title: CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images\\nYear: 2021\\n\\nAbstract: Early detection of COVID-19 is vital to control its spread. Deep learning methods have been presented to detect suggestive signs of COVID-19 from chest CT images. However, due to the novelty of the disease, annotated volumetric data are scarce. Here we propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN). For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones, and the decision layer of our CNN is a support vector machine. As we have no control over the CT image acquisition, we also propose an intensity standardization approach. Our method can achieve mean accuracy and kappa values of 0.97 and 0.93, respectively, on a dataset with 117 CT images extracted from different sites, surpassing its counterpart in all scenarios.\",\n \"Title: Towards a Simple and Efficient Object-based Superpixel Delineation Framework\\nYear: 2021\\n\\nAbstract: Superpixel segmentation methods are widely used in computer vision applications due to their properties in border delineation. These methods do not usually take into account any prior object information. Although there are a few exceptions, such methods significantly rely on the quality of the object information provided and present high computational cost in most practical cases. Inspired by such...\",\n \"Title: Improving Deep Learning Projections by Neighborhood Analysis\\nYear: 2020\\n\\nAbstract: Visualization of multidimensional data is a difficult task, for which there are many tools. Among these tools, dimensionality reduction methods were shown to be particularly helpful to explore data visually. Techniques with good visual separation are very popular, such as those from the SNE-class, but those often are computationally expensive and non-parametric. An approach based on neural networks was recently proposed to address those shortcomings, but it introduces some fuzziness in the generated projection, which is not desired. In this paper we thoroughly explain the parameter space of this neural network approach and propose a new neighborhood-based learning paradigm, which further improves the quality of the projections learned by the neural networks, and we illustrate our approach on large real-world datasets.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":103,"string":"103"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13272,"string":"13,272"}}},{"rowIdx":13273,"cells":{"question_id":{"kind":"string","value":"Toshihiko Yamakami"},"question":{"kind":"string","value":"Generate an abstract for the title \"From User Experience to Social Experience: A New Perspective for Mobile Social Game Design\" using the following items: 1. Mobile social games\n2. Revenue-generating engines \n3. Social experience \n4. Design \n5. Service engineering"},"answer":{"kind":"string","value":"The strong revenue generating engines of mobile social games have caught attention of both researchers and the industry. The analysis of revenue-generating engines has identified the characteristics of service engineering in the era of social media and services. One such finding is that mobile social game design creates different types of satisfaction with a single service, which is unique in social service engineering. The author coins the term 聛gsocial experience聛h following the term 聛guser experience聛h. Then the author discusses how the viewpoint of social experience impacts the design of social games. Finally, the author summarizes the general implications for social service engineering using the metaphor of social experience."},"correct_answer":{"kind":"string","value":"The strong revenue generating engines of mobile social games have caught attention of both researchers and the industry. The analysis of revenue-generating engines has identified the characteristics of service engineering in the era of social media and services. One such finding is that mobile social game design creates different types of satisfaction with a single service, which is unique in social service engineering. The author coins the term 聛gsocial experience聛h following the term 聛guser experience聛h. Then the author discusses how the viewpoint of social experience impacts the design of social games. Finally, the author summarizes the general implications for social service engineering using the metaphor of social experience."},"documents":{"kind":"list like","value":["Title: A 2-Dimensional Evolution Model of Value-Added Mobile Internet Services\nYear: 2005\n\nAbstract: The mobile Internet quickly penetrates every niche of our daily life. With its saturation, the computer communication industries encounter the new challenges searching for the next mobile Internet evolution. The author discusses a wide range of the value-added mobile Internet services in order to identify the evolution driving force. The author presents a 2-dimentional-factor model to identify the market positions of the value-added mobile Internet services using the two dimensions: network coupling and richness in media modality. Finally, the author discusses the uniqueness of this approach compared to the related studies in the mobile broadband Internet considering mobile multimedia evolution assumptions.","Title: Self-Innovation Skill-Based Change Management: An Approach Toward Flexible Organizational Management.\nYear: 2013\n\nAbstract: As changes have prevailed throughout a wide range of aspects of modern industrialization, it is crucial to manage changes in organizations. The author proposes a bottom-up approach for change management in organizational contexts. The author analyzes the differences between top-down approaches and bottom-up approaches. The proposed approach focuses on teaching self-innovation skills through a habit-building capability that utilizes persuasive technology.","Title: An Exploratory Long-Term Open Source Activity Analysis: Implications from Empirical Findings on Activity Statistics\nYear: 2010\n\nAbstract: Open source software (OSS) activities are diverse and difficult to capture. The author attempts a web service-based observation of OSS activities. Small community factor is discussed from a social viewpoint.","Title: Servicenics approach: A social service engineering framework.\nYear: 2013\n\nAbstract: Servicenics theory [1] is an extension of Gamenics theory for facilitating service engineering. It is an attempt to extend game design to the general principles of service engineering. It is based on the assumption that mobile social game design is based on the generally-applicable design paradigms of service engineering. The author proposes a six-stage view of Servicenics principles that are augmented from the four-stage golden rules of mobile social game design.","Title: Leveraging Information Appliances: A Browser Architecture Perspective in the Mobile Multimedia Age\nYear: 2002\n\nAbstract: As the mobile handsets emerges as Internet ready devices, network enabled are among the most useful and visible aspects of the Internet. A wide variety of information appliances are ready for Internet access and advances in network technologies accelerate this trend. Such devices cope with constraints that do not exist in PC. The mobile multimedia services require improvements in embedded network software engineering. The issues in embedded network software engineering are presented. A framework of new network software architecture is presented. Implications towards mobile multimedia are discussed.","Title: A two-layer ecosystem evolution model: lessons from stages of mobile data services\nYear: 2010\n\nAbstract: The development of mobile services in Japan shows that there are three chasms: one for walled garden, one for SNS-based community services, and one for mobile open platforms. These chasms were discussed from the context of a series of discrete events. The author tries to provide a birds-eye view of this development. The author proposes a two-layer structure for mobile service ecosystems, where each chasm represents the lower-layer component and the upper layer represents the cross-stage ecosystem. The author maps the development of mobile services into this proposed model, and discusses the ecosystem factor for each layer, in order provide a universal basis for generating eco-system-aware mobile data services.","Title: A 3-Stage Transition Model of the Architecture of Mobile Social Games: Lessons from Mobile Social Games in Japan\nYear: 2011\n\nAbstract: Since the wide adoption of Open Social by SNS providers between late 2009 and middle 2010, the mobile business in Japan has quickly turned into the direction of item purchasing. This revenue-generating engine is so powerful that it has changed the landscape of mobile business and the game business in Japan. In less than 2 years, the market involving the item purchasing of mobile social games quickly reached the level of a billion US dollars. This gold-rush has facilitated the rapid evolution of mobile social games as massive numbers of users have gained and incorporated their collective mobile social experience. This has also leveraged the evolution of the architecture of mobile social games. The author proposes a three-stage evolutionary view of the architecture of mobile social games and presents its implications for mobile business engineering.","Title: A Weak-Tie Based Regularity Analysis of Mobile Clickstreams\nYear: 2008\n\nAbstract: Advances in wireless technologies and their penetration into every-day life can create a new context with multiple simultaneous sub-contexts. The mobile Internet leverages multiple contexts with its any-time/any-place capability. This leads to a new third state for Internet use contexts, a weakly-tied state, in addition to the two existing states: active (strongly-tied) and inactive. The author attempts to identify the user behavior patterns derived from this weak-tie based approach. The author proposes an interval analysis of the mobile clickstream in order to identify regular users. From the observation, the author shows a positive relation between the hourly-scale interval of mobile web visits and the revisit ratio in the next month, indicating the degree of the user loyalty to a specific mobile web site.","Title: MobileWeb 2.0: Lessons from Web 2.0 and Past Mobile Internet Development\nYear: 2007\n\nAbstract: The key driving factors for ubiquitous services include deployment, management and service development. The mobile Internet services are milestones how the future ubiquitous services will evolve. The Web 2.0 design patterns can deal with and go beyond the various restrictions inherited in the mobile Internet. The author considers the Web 2.0 service design transitions in the context of leveraging the mobile Internet. The trend called Web 2.0 provides a new perspective for frameworks, architectures, content supply chains, and business models in the Internet business. As the mobile Internet is expected to emerge, it is important to consider the mobile-enabled Web 2.0. The author discusses the mobile Internet evolution in the perspective of the two-path evolution from Web 1.0 to Mobile Web 2.0, either though Mobile Web 1.0 or though Web 2.0. The mobile specific factors and sustained challenges of Web 2.0 are discussed in this evolution path perspective.","Title: A Persistence Analysis of Smartphone Application Stores: An Approach to Understanding Gateways to the Internet\nYear: 2013\n\nAbstract: As smart phones have prevailed across this planet, application store search engine optimization has become a hot issue in mobile business. The author analyzes this phenomenon including past experiences such as standalone applications and Widget applications. The author analyzes the factors that drive the evolution of mobile business in terms of the positions of application stores.","Title: A Fast-Boot Method for Embedded Mobile Linux: Toward a Single-Digit User Sensed Boot Time for Full-Featured Commercial Phones\nYear: 2011\n\nAbstract: Software on mobile devices has become increasingly complicated as end users demand a PC-converged user experience on the mobile Internet. This has complicated the software configuration and boot procedures, resulting in a longer boot time. This is damaging to the mobile user experience. Many legacy techniques to enable the fast boot time differentiation in mobile handsets are not applicable to the today's advanced handsets, which are capable of handling rich multimedia applications. The authors propose a fast boot method for embedded mobile Linux, that is suitable for mobile handsets. They evaluate the proposed method in a prototype for a commercial handset and show positive results in a single-digit user sensed boot time.","Title: A Hybrid Software Architecture for Rich Media Applications on Android Phones\nYear: 2011\n\nAbstract: The mobile Internet has reached a certain stage of preparation for the newly emerging rich media applications. Emerging smartphones are beginning to provide a new infrastructure for rich media applications on mobile handsets. The constraints of the smartphones still require careful software design in order to achieve a rich media user experience. We present a hybrid software architecture to cope with this challenge. We outline the requirements and overall design of the proposed software architecture. Then, we present results from a case study of magazine viewers. The proposed architecture provides improved time to market and improved performance for large sized image data in magazine applications.","Title: Cross-Culture Analysis of Mobile Social Games: Toward Design Guidelines of Lessons Learned from Globalized Mobile Social Games.\nYear: 2013\n\nAbstract: As the mobile social game business heats up, it is crucial to establish a systematic methodology that can deal with the globalization of mobile social games. There are many challenges that come from this globalization. One of them, is the cross-cultural analysis of user behavior in mobile social game contexts. Many Japanese vendors had difficulties with hidden cross-cultural issues in 2012. The author analyzes the cross-cultural lessons learned from the endeavors to globalize mobile social games. Then, the author discusses a basis for the systematical analysis of cross-cultural challenges.","Title: A Four-Stage Gate-Keeper Model of Social Service Engineering: Lessons from Golden Rules of Mobile Social Game Design\nYear: 2012\n\nAbstract: The rapid growth in revenue-generating capabilities of mobile social games has caught the attention of both researchers and industry. The author analyzes the key factors of revenue-generating engines in mobile social games in Japan, and presents the four key factors involved in revenue-generation mechanisms of those games. The author discusses the four golden rules of mobile social games and presents them as parts of a fourstage gatekeeper model for success in mobile social games.","Title: A Social View on Unsolicited Bulk Email Impacts on Mobile Mail Addresses\nYear: 2006\n\nAbstract: The mobile Internet has been threatened by unsolicited bulk mail attacks. This threat has caused user adoption of the longer email addresses and exposes new user behaviors in always-on ubiquitous world. The author discusses the three factors of mail address evolution from the three perspectives: defensive, cognitive and social views. The author analyzes the address length transition over 4 years in Japan. The author describes an excessive user behavior on the use of special characters from the viewpoint of trade-offs among defensive, cognitive and social aspects.","Title: Impact from mobile SPAM mail on mobile internet services\nYear: 2003\n\nAbstract: Rapid penetration of internet-enabled mobile handsets is being witnessed in worldwide. It also creates some new social frictions like SPAM mail to mobile handsets. The emergence of mobile Internet services in Japan soon triggered wide spread mobile SPAM mail. It reached a certain point to attract significant social attention in April 2001. The SPAM mail impact is analyzed from the long-term transition of user mail address pattern changes. This study examines the SPAM mail impact from the viewpoint of charged mobile Internet services.","Title: An Approach Toward Fits Evaluation Between Organizational Culture and Groupware\nYear: 1997\n\nAbstract: The previous research trying to formulate methodologies to evaluate groupware has encountered various difficulties due to complicated characteristics of social process. From our experience of several groupware prototype systems, we became aware of the importance to understand the fits between organizational culture and system characteristics. To identify the fits, a two-dimensional classification is proposed. From the observation that the only one adoption path has been observed, the fits between organizational culture and system characteristics are discussed. In addition, the long-term adoption model is proposed to identify the several boundaries of several types of system acceptance. The method can be used to identify the system acceptance stages in a wide range of applications.","Title: Towards Know-When Technology in the Mobile Information Space: Long-Term User Trace Log Analysis in the Mobile Internet\nYear: 2003\n\nAbstract: The mobile Internet is one of the most promising application domains in the computer communications. The mobile handsets close the gap between end users and the computercommunications using the 24-hour 365-day availability. The emerging success in Japan has demonstrated the potential capabilities for the next generation Internet platform. The close relationship to the end user reveals a new research area of the user behavior analysis. Using the user transaction logs for over a year, the author tries to build up a new knowledge domain: know-when knowledge. A methodology to capture the user behavior change over a span of time is important to manage the subscription-based mobile Internet services. Using the know-when knowledge acquisition, the author explores the possible cues for the long-term analysis of user behavior patterns using the long-term transaction logs. Considering the behavior dynamism of the mobile Internet users, it is important to explore the know-when technologies for the future mobile Internet marketing.","Title: Mobile Social Game Design from the Perspective of Persuasive Technology\nYear: 2012\n\nAbstract: There is an increasing visibility of the mobile social game business in the landscape of the Internet content business. The analysis of mobile social game design shows many similarities between design guidelines in game design and in persuasive technology. The author examines the design guidelines in persuasive technology in order to find similarities in the design guidelines in game design. He concludes that the best practices in mobile social game design follow the design guidelines in persuasive technology.","Title: Business Model Analysis on Vertical Integration of Mobile Data Services\nYear: 2008\n\nAbstract: Mobile Internet is becoming a reality and rapidly penetrating everyday life. Mobile Internet business models shift over a span of time. An emerging business model trend in the mobile Internet is vertical integration by carriers and leading service providers. In order to identify a model to analyze the feasibility of vertical business models, the author analyzes the lessons in the past and proposes an evolution model for mobile Internet business. From this perspective, the author discusses mobile-specific technical and behavioral factors impacting vertical integration of the mobile data services.","Title: Unique Identifier Tracking Analysis: A Methodology to Capture Wireless Internet User Behaviors\nYear: 2001\n\nAbstract: Rapid penetration of Internet-enabled cellular phones is witnessed in worldwide. Cellular phone-based Internet has significantly different behaviors. Wireless Internet users have rather short and transient behaviors. This paper discusses the distinguished characteristics of cellular phone-based Internet users. Issues to understand the user behaviors are presented. A new methodology, unique user analysis, is proposed to cope with the issues of personal user behavior. A case study is presented using unique user analysis. The methodology is effective to capture the characteristics of growing wireless Internet users.","Title: A Three-Dimension Analysis of Driving Factors for Mobile Application Stores: Implications of Open Mobile Business Engineering\nYear: 2011\n\nAbstract: The mobile industry is witnessing a turning point in terms of the increased penetration of PC Internet players such as Apple and Google. The business models of Apple and Google in the mobile industry are very different, however, they are shaking up the legacy mobile software industry by representing a departure from the legacy closed business models. The AppStore represents a great success in the mobile industry. It is a single device with a closed market, however, it has provided the successful vertical integration business model for enclosing end users with iPhone-style services. The App Store has created a market bandwagon that everyone is eager to jump onto. The author provides business model analysis of the AppStore, as compared to the i-mode and Android. Then, the author presents implications for the today's mobile software industry.","Title: A one-seg service development model: ecosystem considerations in the context of mobile communication/broadcast convergence\nYear: 2010\n\nAbstract: Business model engineering is crucial in the context of convergence of mobile communications and broadcasting The author presents a stage model of one-seg service evolution from lessons learned with one-seg in Japan The author focuses awareness on the stages in the complicated relationship between stake-holders With the examination of the transitions between stages, the author also proposes a three-dimensional fit model for mobile services The author discusses the chronological order of fit identification during the stage transitions This service transition with fit exploration and staged view is not specific to one-seg Therefore, it can be applied to a wide range of mobile service development that incorporates new technological elements It should be noted that the mobile technology brings a new perspective to service development using mobility Mobile services require long-term incremental fit exploration which is crucial in mobile service development.","Title: Network software platform design for wireless real-world integration applications\nYear: 2005\n\nAbstract: The mobile Internet quickly penetrates the every-day life. After reaching the mobile multimedia stage, the real world integration is one of the next challenges in the mobile software frameworks. The author proposes an Internet-converged embedded web solution for the real world integration named DirectConnect in the analogy of the Live Connect in the Netscape Navigator. The author describes evaluation on five approaches for the enhanced real world integration and presents advantages of the minimum modification approach. The proposed approach is a browser-centric Internet-converged solution that enables the micro-browser on the mobile handsets to be an integration point of the real world integration. It uses the common Internet framework, and is adaptable to a wide range of web and real world integration. The scheme encapsulates the security issue in each object implementation.","Title: A Long Interval Method to Identify Regular Monthly Mobile Internet Users\nYear: 2008\n\nAbstract: The Internet access from mobile handsets is extending its coverage. It increases the importance to analyze mobile Internet use in research communities and industries. To cope with the increased demands on mobile Internet user analysis, the author describes a regularity-oriented service model. In order to capture user regularity, the author performs an exploratory clickstream analysis to identify users with high loyalty. Based on the author's previous experience on the relationship between middle-range visit intervals and revisit ratio in the next month, the author tries to identify hour-scale intervals linked to the high-loyalty-users. The author obtains the result that the users with intervals with 480 minutes and 720 minutes show the high revisit ratio. The author analyzes key time zones for high- loyalty users. This method is applicable to a wide range of applications with clickstream logs. Using the long-interval characteristics, the author discusses a double peak model of sub-day-scale activity behaviors in the case study.","Title: Regularity Analysis Using Time Slot Counting in the Mobile Clickstream\nYear: 2006\n\nAbstract: The ever-changing nature of the mobile Internet contributes to the difficulties encountered in user behavior research. Regularity is an important aspect of the mobile Internet in research and marketing, because it end users easily lose their interest and leave the mobile web sites due to the limited visibility of the web. Maintaining user loyalty is a vital challenge for mobile webs. Therefore, the methods to identify loyalty transitions are important. The author proposes a regularity measure using click counting in the time slots. With the assumption that the users with regular access have more chances to continue to use the mobile webs, the author examines the monthly prediction of user behavior based on the user access regularity in the previous month. The author obtains approximate 80 % accuracy of prediction in the case study. The author discusses the limitation and implications of the comparison.","Title: Lessons in business model development from early mobile Internet services in Japan\nYear: 2006\n\nAbstract: The author summarizes the lessons learned in the 6-year evolution in the mobile Internet business development in Japan. The lessons about telephony/Internet service development characteristics, a spiral service evolution pattern, and mobile content business model development are studied from a retrospective view on the mobile business evolution. Based on the basic mobile interaction model, the author describes implications for the future mobile business model development.","Title: Mood-Based E-Commerce: A New Approach For Sub-Personality-Based Marketing\nYear: 2013\n\nAbstract: E-commerce has been growing and has reached a point of new motivational approaches that explore a frontier of marketing. Such new approaches include sub-personality-based marketing, which addresses the multi-faceted personalities of human beings. The author explores sub-personality-based e-commerce in order to develop deeper insight into human behavior. Emotion is a fundamental factor in Internet services. In E-commerce, positive emotion is a driving factor for generating sales. In order to facilitate effective emotion-based e-commerce, the author analyzes two dimensions of motivational direction. From the quadrants of these two dimensions, the author extracts three factors: energization, healing, and change. The author proposes an emotion-centric commerce concept that focuses on these three factors, and discusses the framework and business models upon which it is based.","Title: Foundation-based mobile platform software engineering: implications to convergence to open source software\nYear: 2009\n\nAbstract: Open source is impacting software industry in a significant manner. It is critical for any software vendor to take an appropriate position in the open-source-involved software engineering. It is also true in the platform software for mobile handsets. The author analyzes the platform software landscapes in the mobile industry with a perspective of utilizing open source software. The author performs competitive analysis for many stake holders in this landscape.","Title: Mobile Data Services 2.0: Analysis of 3G and Flat Rate Impacts on Mobile Data Services\nYear: 2009\n\nAbstract: Emerging 3G services offering flat rates have brought notable impacts on the mobile content providers in Japan. Mobile business 2.0 based on the high speed Internet is discussed. A business model shift caused by 3G and flat rate penetration is analyzed and its implications are presented.","Title: A Zipf-Like Distribution of Popularity and Hits in the Mobile Web Pages with Short Life Time\nYear: 2006\n\nAbstract: The mobile Internet quickly penetrates the society. The mobile clickstream is a valuable resource to identify the personal web access behaviors. The author makes an empirical study on the mobile clickstream to identify the difference between the mobile information with limited life time and the traditional PC web information with long life time. The observation shows that the mobile web access indicates a zipf-like distribution even though the each web content has a limited life time. The author discusses the implications of this similarity from the view point of the mobile user behavior characteristics.","Title: From Gamenics to Servicenics: Lessons Learned in Mobile Social Games in Japan toward Service Engineering\nYear: 2012\n\nAbstract: Gamenics Theory is a massive collection of know-how for improving usability and playability based on lessons learned from Nintendo video games. The author comes to recognize that many design social game design principles can be interpreted as applications of Gamenics Theory. Gamenics Theory covers the highlights of emerging social game design, however, some of the social and revenue-generating factors are not covered because Gamenics Theory was originally coined for console games. In order to facilitate a better base for service engineering, the author proposes to extend Gamenics Theory to the wider scope of service engineering. The author coins the term ``Servicenics'' and discusses its implications for service engineering based on lessons learned in mobile social games in Japan.","Title: A 3-stage Evolutionary Model of Mobile Social Game Design in Japan\nYear: 2011\n\nAbstract: The mobile Internet business is entering a new era in Japan with the emerging revenue being generated from item purchasing in mobile social games. Users using the mobile high speed Internet with flat-rate data services have acceptance of micro-payments and increased payment acceptance in mobile social games. This has been impacting mobile business and the game business in Japan since the emergence of Open Social based platforms in October 2009. With all of the hype being generated by mobile social games, the author attempts to identify the key success factors in this mobile business landscape. The author examines the current business landscape in mobile social games in Japan. The author recognizes the rapid changes in the ecosystem in mobile social games. The author proposes a 3-stage transition model for describing the underlying ecosystem paradigm in Japan.","Title: An empirical analysis of revisit behaviors of monthly subscription-based mobile video services\nYear: 2010\n\nAbstract: The Mobile Internet is becoming a first-class citizen of the Internet in many advanced countries It is also increasing its broadband capabilities with 3G penetration As increased penetration and bandwidth leverage mobile application business opportunities, it is important to identify methodologies for serving mobile-specific demands Regularity is one of the important means of retaining and enclosing easy-come, easy-go mobile users It is known that users with multiple visits in one day and a long interval have a higher possibility of revisiting in the following month than others The author applies this empirical law to his investigation of the mobile broadband services The result shows that the method proposed for the text-based mobile services is also applicable to mobile video services in the mobile broadband context The author shows that the method based with 2 bits per day can provide results that can be used for classifying monthly-scale regular users in the case study.","Title: Challenges for Mobile Middleware Platform: Issues for Embedded Open Source Software Integration\nYear: 2010\n\nAbstract: Linux is penetrating into mobile software as the basis for the mobile middleware platform. It accelerates the increasing visibility of open source software (OSS) components in the mobile middleware platform. Despite multiple challenges in mobile embedded software engineering, it is crucial to promote open source-aware development of the mobile software platform. The author presents the challenges to open source software integration in embedded software development. The author discusses the open source-aware software development and identifies a path to transition for moving toward it.","Title: Extensible Platform Software Architecture Design and its impact in the embedded network software engineering\nYear: 2005\n\nAbstract: With 3G system introduction, the data communication applications are a key for the mobile Internet evolution. It is a challenge to design an extensible and resource-efficient software platform for mobile multimedia services. The methodology to identify the trade-off in the software architecture design was unexplored in the past literature. The author compares the two software platforms for micro browsers in order to identify the trade-off factors between extensibility and compactness. The author summarizes the three extension schemes, abstract rendering, XML framework, and extensible common components. The author proposes a discrete component analysis to evaluate the impact from the extensible architecture. With this analysis, the author presents the footprint overhead estimate for the embedded software environment designed for mobile handsets. It is a useful measure when the trade-off between the hard-wired concise implementation and the extension-tolerant implementations.","Title: A User-Perceived Freshness Clustering Method to Identify Three Subgroups in Mobile Internet Users\nYear: 2008\n\nAbstract: The user behavior analysis of the mobile Internet is still to be explored. This increases importance in research and business as the mobile Internet penetration becomes visible. The author performs a preparatory survey to identify the user behavior scenarios during a day. It indicates that there are three distinguished mobile Internet behavior patterns: (a) always active, (b) irregular, (c) prime time use. This gives a novel perspective to understand user interactions in the mobile Internet. Intelligent mobile services require addressing these differences in order to improve user experience. The author proposes a user-perceived freshness method to cluster users with the content access logs in order to utilize this three-subgroup-based view. The revisit ratio in the following month is used to characterize the user clusters and the middle users show a high revisit ratio. The author discusses the characteristics of this middle user segment.","Title: FISH: Flexible information sharing and handling system\nYear: 1994\n\nAbstract: With the progress of network technology and workstation technology as the background, there has been progress in the exchange/transmission of information as well as document sharing by electronic means in the office environment. At present, however, techniques have not been developed to the stage where such fragmented information as knowhow is stored/retrieved/managed in the distributed environment. From such a viewpoint, the authors have constructed the knowhow storage system FISH (flexible information sharing and handling system), which can store the knowhow in the text format. In FISH, the minimum unit decomposition of the information and the flexible structurization are realized by decomposing the knowhow into multiple virtual cards to be stored in the computer. Knowhow, which is difficult to be given a systematic structure, is managed by automatically providing links among the card based on the keywords attached to the individual cards. This paper describes first the management method for the knowhow which is the basic design of FISH. Then the features, the functions that can be used, and the implementation of the system are discussed. The result of the one-year operation experiment of the system in the office of the authors is presented, together with discussions. Using FISH, it is made possible to store the knowhow and share the use in the distributed environment.","Title: An Emotion-Object-Interaction Framework for Exploration of Design Patterns of Massive Interpersonal Persuasion\nYear: 2013\n\nAbstract: Social media has been growing since the emergence of Web 2.0. Interpersonal diffusion has big potential for a new kind of media marketing. As the stay time in social media increases, it is crucial to make use of social media for marketing utilities. In order to cope with this need, it is important to create a framework for diffusion using a user experience diffusion mechanism. The large social graph capability of social networks has significant potential. However, the difficulty of controlling the behavior of social interaction presents challenges to coining a systematic methodology to deal with it. For facilitating the automation of user experience diffusion, the author proposes a framework called the \"Emotion-Object-Interaction framework.\" The author discusses the best practices of the Emotion-Object-Interaction framework using the examples of social network services and mobile social games. The proposed framework focuses on the emotion of first user, then on symbolization of the emotion and interaction with it. This simple scheme provides a general framework for mass interpersonal persuasion.","Title: Transition Analysis of Mobile Social Games from Feature-Phone to Smart-Phone\nYear: 2012\n\nAbstract: The Japanese feature-phone has traced a relatively unique evolutionary path considering its unique service development context in the last decade. The global trend of smart phones has provided a new direction in evolution for the mobile service business in Japan. This transition in a unique ecosystem and user experience provides an opportunity to identify the driving factors for mobile service business. The author discusses the possible scenarios in Japan for this ongoing transition from feature-phones to smart-phones. The author analyzes the impacts from the transition using a three-dimensional analysis of the user adoption dimension, content production dimension, and business model dimension.","Title: Social Pattern Development Analysis: A Case Study in a Regional Community Network\nYear: 1998\n\nAbstract: Community development is a dynamic process in which a group of users show their own communication style and culture. Communication development patterns are different from ones from the common diffusion theory since it has a wide variety of use development patterns. This variety comes from the richness of the community communication contents and variety of time spans of transitions. We learned some lessons from two-year experience of a regional community network trial based on a fiber network. To observe the social pattern development, we analyze a social network development patterns in our system. We propose the adaptive time span analysis. In this method, multiple time spans are compared until the stable communication patterns are obtained. This analysis lead to the estimation of context duration time and social pattern development time to understand the dynamics of the community evolution. The case studies in done in NTT-Hayashi trial e-mail communication log data to obtain the time span of persistent bilateral email communication relationship.","Title: Persuasive Strength Analysis of the Mobile Gang of Four: Toward Understanding Mobile Business Engineering in the Era of Mobile Persuasion\nYear: 2013\n\nAbstract: The increasing number of smart phones is leveraging changes of business landscapes in the Internet. This demonstrates the importance of business model engineering in the post-PC era. The author proposes the hypothesis of mobile persuasive strength. Using this hypothesis, the author provides a strength analysis of the Mobile Gang of Four: Apple, Google, Amazon, and Facebook. The analysis provides a systematic trend analysis of the key mobile business players. The author then presents the implications of the hypothesis of mobile persuasive strength for today's business model engineering.","Title: A Trap-Door Method for Subscription-Based Mobile Content\nYear: 2007\n\nAbstract: Scalability design in the mobile Internet web servers is increasing its importance. The performance improve- ment for scalable mobile Internet service requirement is in- creased as the mobile Internet penetrates into every day life. The mobile Internet enabled opportunities for subscription- based charged content for content providers. In order to cope with the scalability design issues, it is important to identify the bottlenecks and propose improvement in the practical use scenes. The author analyzed a content adap- tation system for subscription-based content. The author identified the inefficiencies in the subscription check part and the content adaptation part. The author proposes a novel approach, a trap-door method, to minimize the sub- scription check overhead and the adaptation overhead. The trap-door method utilizes the authentication only at the first access to the charged content. The processing flow is de- scribed. The performance improvement based on the real server performance observation is discussed.","Title: It is not a Game, Not Even Social: New Service Engineering Paradigms from Lessons Learned in Mobile Social Games in Japan\nYear: 2012\n\nAbstract: There is an increasing visibility of mobile social games in Japan. The radical growth of item-purchasing in mobile social games is generating new hype in mobile business. The author revisits the game and social factors of the revenue-generating engines of mobile social games. The author examines the underlying principles of revenue-generating engines in mobile social games, which leads to exploration of a new framework of service engineering for a wider scope beyond game design.","Title: A 4-layer robust mobile web front-end design model\nYear: 2004\n\nAbstract: The author describes the lessons learned from the 4-year non-stop operation of the mobile Internet services to identify the robust mobile web design issues from an operational viewpoint. In order to maintain the user confidence during the unexpected situations, the author describes a 4-layer fallback design model to manage the real world mobile web operations and to give a sense of continuity to the mobile users. The author discusses issues in the open mobile Internet services in relation to the proposed front-end design model for the robust tolerant mobile web operations.","Title: Knowledge Awareness in Asynchronous Information Sharing\nYear: 1993\n\nAbstract: Based on two years of experience using a know-how management system called FISH, we have come to recognize the importance of awareness support in asynchronous information sharing. Here, we propose a new concept of knowledge awareness. The concept represents an attempt to support cognitive process among group members. Three elements of awareness, (1) knowledge existence, (2) interaction, and (3) meta-knowledge are discussed. In addition to the concept, we present the enhancements of FISH based on the concept. The results of knowledge sharing are discussed from the viewpoint of meta knowledge.","Title: An interactivity model of mobile interactive TV: a oneseg case for mobile glue\nYear: 2009\n\nAbstract: One-seg is a new mobile use context combining user experience for mobile and broadcast. This new technology enables to combine real-time broadcast contexts and personal mobile interactive contexts. The author coins an interactivity model for service development of one-seg applications on a mobile handset. The author discusses work-flow based service scenarios for four interactivity aspects of the one-seg applications based on the proposed model. The author presents a mobile glue model to enable a service composition platform perspective for flexible service composition for mobile communication/broadcast converged services.","Title: A Third Service Evolution Dimension inWeb 2.0 and Real-world Integration Inspired Services in the 3G Mobile Data Communications\nYear: 2007\n\nAbstract: It is reported that 67 % of global mobile content is SMSbased, compared to 0 The Japanese market shows the two new directions: emergence of mobile Web 2.0 and distinguished transition from the current business models to the vertical integration ones by mobile operators. Some of Japanese carriers press their own content like music download, auction, micro-payment and so on. It is interesting to investigate how this mobile-specific vertical integration will work in the future as the handset and network converges to the PCs. In addition, the emerging increase of search use and social network use in the mobile contexts initiates the mobile-specific Web 2.0 penetration, another trend of convergence. The high penetration of 3G networks inevitably leads to the flat rate charging, which drives the wireless carriers to seek for other revenue sources. The past lessons told that the mobile service business model evolution was not monotonic due to the complexity of interactions of users, content providers and wireless carriers. It is critical to identify the key factors of business model development, either in Web 2.0-related aspects (e.g. user-generated content) or in vertical integration (e.g. carrier-owned content business or real-world integration services). The two paths toward Mobile Web 2.0 are depicted in Fig. 1, with a specific strategy for each path.","Title: The Third Generation of OSS: A Three-Stage Evolution from Gift to Commerce-Economy.\nYear: 2011\n\nAbstract: Linux is penetrating into mobile software as the basis for a mobile middleware platform. It is accelerating the increasing visibility of open source software (OSS) components in mobile middleware platforms. Considering the 10-million lines of code of OSS-based industrial platforms such as a mobile middleware platform, engagement in foundations is inevitable for large-scale packages of OSS for industrial solutions. The author discusses the driving factors toward a foundation-based OSS and the transition of the underlying economy types to analyze the transitions to the third-generation OSS.","Title: Analysis of Mobile Social Games from the Viewpoint of Gamenics Theory\nYear: 2011\n\nAbstract: The mobile Internet business is entering a new era in Japan with the emergence of revenue being generated from mobile social games. Users using high-speed mobile Internet with flat-rate data services have acceptance of micro-payment and increased payment acceptance in mobile social games. This has been impacting mobile business and the game business in Japan for the last 12 months. Given the hype surrounding mobile social games, the author attempts to identify mobile social extensions of Gamenics theory. Observation of mobile social game design has revealed that most of the design guidelines follow Gamenics theory. The mobile-specific uniqueness resides in the extension of the 4th rule of Gamenics theory. The author presents new design rules from this viewpoint of augmenting Gamenics theory for practices of mobile social game.","Title: Exploratory Day-Scale Behavior Assumption-Based User Clustering with the Mobile Clickstream\nYear: 2007\n\nAbstract: The mobile Internet enables 24-hour always-on comput- ing with increased demands on time-based analysis. From an assumption that the mobile users are split into three dif- ferent clusters, the author proposes a method to identify the three different user clusters. The author evaluates the user segments in a subscription-based commercial service using the revisiting ratio in the next month.","Title: Mobile Application Platform Strategies: Business Model Engineering for the Data Intensive Mobile Age\nYear: 2005\n\nAbstract: As the mobile telephony users exceeds 1 billions, it is apparent that the next challenges for the wireless telephony is data communication services. It involves a wide spectrum of challenges from software engineering, service evolutions, user acceptance management and business model engineering. There are conflicting demands for the integrated software platform for mobile handsets. The author summarizes unique challenges in the mobile data service evolution with a time-consuming spiral evolution pattern. the author discusses the business model engineering for the mobile software platform in the data intensive mobile age using the technology-business combined perspective from lessons learned in the recent mobile Internet evolution. The author provides business model patterns in the mobile software platform engineering and discusses their competence in the two different evolution scenarios.","Title: Command transition probability analysis on mobile internet command sequences\nYear: 2007\n\nAbstract: We have witnessed rapid growth of mobile Internet capabilities in the mobile handsets. The penetration of the user life increases the need to establish a methodology to capture the user behavior patterns in order to improve satisfaction of the mobile web users. In order to cope with the design evaluation needs, the author proposes a long-term command transition probability analysis on mobile clickstreams. The observation showed that the first command ratio did not depend on the menu order, but that the ratio to terminate the command sequence depended on the order. This gives a positive outcome from the command transition analysis in the mobile clickstream.","Title: Intraday-scale Long Interval Method of Classifying Intramonth-Scale Revisiting Mobile Users\nYear: 2008\n\nAbstract: Penetration of the mobile Internet has increased its visibility worldwide. This enables analysis of detailed time-dimensional user behavior data. It also increases the industry need to identify and retain mobile users with strong loyalty to a particular mobile Web site. The author proposes an intramonth-scale revisit classification method for identifying intramonth-scale, revisiting mobile users. The author performs a case study and the result shows that the proposed method shows 87% classifier accuracy. The author discusses a trade-off between classifier accuracy and a true positive ratio.","Title: A Time Slot Count in Window Method Suitable for Long-Term Regularity-Based User Classification for Mobile Internet\nYear: 2008\n\nAbstract: The mobile Internet proliferates and penetrates all aspects of daily life. It is crucial to codify methodologies to fit mobile-specific demands, e.g. regularity. Subscription-based mobile services require the analysis of regularity to capture and retain easy-come, easy-go users. The author proposes a method called the \"time slot count in window\" method (TCW-method) to efficiently capture regular users, while relaxing the explicit splits among time slots with visits. The author shows the case study result from 2001-2002 commercial service logs. A case study conducted in 2001-2002 with commercial news service subscribers shows that the relaxation in the proposed method loses 2-3 % in the recall rate of regularity, but still obtained 88-91 % recall rate. Considering the trade-off with the on-the-fly nature of coping with limited storage capacity, the author considers this method to be very promising for the mobile Internet.","Title: A Mobile Clickstream Time Zone Analysis: Implications for Real-Time Mobile Collaboration\nYear: 2004\n\nAbstract: In order to support the collaboration in the mobile Internet platform, it is important to increase awareness about the mobile user behavior in the time-dimension. The author proposes a qualitative time zone analysis and examines the 3-year mobile click stream logs and discusses the user models on sub-day access patterns. The four mobile user access models are examined from the real world mobile web logs. It indicates that the mature mobile Internet user click stream is driven by the user-specific selection. It shows the exploratory result for know-when technologies in intelligent mobile collaboration support.","Title: A Four-Layer Success Factor View Model of Mobile Social Games: Analysis of Mobage Success in Japan\nYear: 2011\n\nAbstract: The mobile Internet business is entering a new era in Japan with the emerging revenue being generated from item purchasing in mobile social games. Users using high-speed mobile Internet with flat-rate data services have acceptance of micro-payments and increased payment acceptance in mobile social games. This has been impacting mobile business and the game business in Japan since the emergence of Open Social-based platforms in October 2009.With all of hype being generated by mobile social games, the author attempts to identify the key success factors in this mobile business landscape. The author proposes a four-layer approach to identify the success factors for the mobile social games business in Japan. The author presents the implications for mobile business ecosystems from the emerging real-time Web. Then, the author provides a strength analysis of Mob age, one of the leading service providers of mobile social games.","Title: A Regularity-Oriented Service Model with Sub-Day-Scale User Classifications for Mobile Internet\nYear: 2008\n\nAbstract: The Internet has been rapidly mobilized by increasing penetration to wireless users. Important issues in mobile user behaviors include identification of long-term regular users in dynamic 24-hour behaviors. The author proposes a regularity-oriented service model to outline the mobile- specific aspects of usage regularity to obtain clues to user preferences on content and services. Then, the author performs some user surveys to identify the diversity of mobile Internet use. It brings our attention on user segment awareness to identification of regularity. The author proposes a clustering method based on an assumption of three different segments of mobile users. The author examines the clustering result with the revisit ratio in the next month to demonstrate the usefulness of the method in the context of long- term use regularity.","Title: Ubiquitous Computing Literacy: A New Challenge for Social Computing\nYear: 2004\n\nAbstract: As the emerging technologies enable the embedded ubiquitous intelligence with sensors and controllers, the empowered environment encounters multiple social contexts. The collision to the legacy social contexts leads to the technosocial situations that axe the social situations created by the technologies. The author discusses the lessons in the literature and discusses the issues in the social and cognitive model of the ubiquitous computing empowered society. Then, the author proposes the new type of human skill called ubiquitous computing literacy to cope with the intelligence embedded environment. Finally, the author discusses the staged model to implement such large-scale new social knowledge in order to use the ubiquitous computing in a socially friendly manner.","Title: Mobile User Drop Out Ratio Observation with Time zone and Day-of-week Regularity Analysis\nYear: 2007\n\nAbstract: The mobile Internet fills the gap between 24-hour user behavior and Internet clickstreams. The mobile-Internet specific methodology to capture the user behavior characteristics is on the hot research agenda. The long-term stability of the mobile user behavior characteristics is still unexplored. The author proposes a method to identify the user behavior patterns using transition pattern clustering over a long span of time. The author gives an exploratory analysis to find best-fit threshold value to identify regular user behaviors with a 3-month transition patterns. From this observation, the day-of-week behavior gives better indication compared to that of time zone-based one.","Title: An Extensible Monolithic Software Design Approach for Internet-Convergent Embedded Applications\nYear: 2004\n\nAbstract: As the mobile multi-media applications emerge with the mobile handsets, the embedded network software needs a new paradigm to cope with the conflicting demands from the constraint embedded software and the Internet multimedia applications. The author describes the issues in comparisons with the traditional software engineering. The application demands for the extensible and modular software engineering are outlined. Then, the author proposes a modular and monolithic software design approach with the embedded network software architecture that is aligned to the proposed framework. The author presents two aspects of the monolithic and extensible software implementations in the embedded Internet applications. Finally, the author describes the three challenges in the mobile multimedia applications from the view point of the proposed design paradigm.","Title: A 4+1 Bit Month-Scale Regularity Mining Algorithm with One-Path and Distributed Server Constraints for Mobile Internet\nYear: 2008\n\nAbstract: Mobile Internet becomes increasing visible in everyday life. As increased penetration leverages mobile application business opportunities, it is crucial to identify methodologies to fit mobile-specific demands. Regularity is one of the important measures to enclose easy-come, easy-gomobile users. It is known that a user with multiple visits in one day with a long interval has a larger revisiting possibility in the following month than the others. The author proposes a 4+1 bit method to incorporate this empirical law in order to cope with the two major mobile restrictions: distributed server environments and large data stream. The proposed method can be performed in a one-path manner with 32-bit word boundary-aware memory compaction. The experimental result shows the method is promising to identify revisiting users under mobile-specific constraints.","Title: An Embedded Intelligence Model: Issues in the Real-World Empowered Organizational Computing\nYear: 2006\n\nAbstract: Ubiquitous computing will penetrate into the cooperate workplaces. Additions of machine-machine interfaces in the organizational computing pose new framework challenges as well as implementation and deployment issues. The simple monotonic-increase knowledge model does not work in a complicated workplace with many context boundaries. The author enhances issues in hybrid organizational computing to cope with the pervasive and ubiquitous intelligence in the workplaces. The author discusses various dualities exposed in the ubiquitous workplace like a) space vs. place and b) logical organization and real world. The ubiquitous workplace will pose more challenges for the organizational computing from the dualities. For the resolution of these issues, the author proposes and discusses an embedded intelligence model for organizational computing. This model facilitates the framework for the authoring tools for organizational learning systems to complement the dynamic learning contexts in workplaces.","Title: Inter-service revisit analysis of three user groups using intra-day behavior in the mobile clickstream\nYear: 2009\n\nAbstract: The mobile Internet proliferates and penetrates all aspects of daily life. The 4 billion mobile users will increase their impacts on data services. The author came to a sense that the intra-day mobile behaviors are classified into three user groups: always-on, night, and infrequent users. The author proposes a two-phase method to identify these three user groups. The author shows the case study result from 2001 commercial service logs with two different mobile services. The derived user groups are evaluated by the revisit ratio in the following month. The results are discussed to highlight the three-segment model in intra-day mobile user behaviors. They show the clear revisit ratio difference among the three user groups in the four services in observation. The two services in the two case studies are significantly different, however, they still exhibit consistent patterns in the three group classification.","Title: An exploratory analysis on user behavior regularity in the mobile internet\nYear: 2006\n\nAbstract: The ever-changing nature of the mobile Internet contributes to the difficulties encountered when experts try to identify the user behavior characteristics. Using thin channels with so-called 24-hour 365-day always on nature, it is crucial to understand regularity of user access in the mobile Internet. It is leveraged by the mobile Internet-specific features like user identifies provided by wireless carriers. The author attempts to identify the easy-gone mobile Internet users from regularity dimension using a long-term user log with user identifiers. The author proposes an interval probability comparison method to predict the user behavior in the next month. The experiment from the mobile clickstream data shows the positive effect of the proposed method.","Title: Classification of Mobile Internet User Behaviors using Qualitative Transition Patterns\nYear: 2007\n\nAbstract: The mobile-Internet specific methodology to capture the user behavior characteristics is still on the hot research agenda. Based on an ad hoc assumption that the user loyalty relates to web visit regularity, the author explores regularity measures to track user behaviors. The author performed a 3-month transition pattern using regular users that visits a web every month for three months. The author discusses the stability of the measures in the long-term behaviors.","Title: OSS as a digital ecosystem: a reference model for digital ecosystem of OSS\nYear: 2010\n\nAbstract: The author presents an analytical view of OSS from the perspective of a digital ecosystem. The author proposes a three-dimensional reference model of the ecosystem of OSS.","Title: Mobile Content Operation Design: Lessons learned in the long-term experience\nYear: 2003\n\nAbstract: The mobile Internet is one of the most fast growing applicationdomains in the computer communications. The newtechnology components in the mobile Internet have beenhighlighted, however, the every day mobile content operationdesign lacked the attention in the past literature. Themobile content operation encounters a wide range of networkintegration challenges. It is difficult to identify theall operation requirements in advance in the rapidly emergingmobile Internet services. The author presents the challengesand issues from a 3-year experience of non-stop mobilecontent operation. From the lessons learned, the designissues for mobile content operation design criteria are presented.","Title: Markov model based mobile clickstream analysis with sub-day, day and week-scale transitions\nYear: 2007\n\nAbstract: Advanced cooperative work needs user context knowledge in spatial and temporal dimensions. The always-on property of the mobile Internet enables further extension of the cooperative work. It needs to extend the temporal knowledge of the user behaviors for this purpose. This paper explores the temporal dimension: different end-user behavior parameters in different time-scales using the mobile clickstream. The Markov model-based estimation in sub-day scale, day-scale and week-scale transition patterns is analyzed from monthly mobile clickstreams and hierarchical clustering is performed with the three different time-scale behaviors.","Title: Star economy in the user generated content: a new perspective for digital ecosystems\nYear: 2009\n\nAbstract: User generated content plays an important role in Web 2.0-based digital economy. The author focuses an emerging aspect of user participation-based digital economy. Cultural aspects of digital economy are largely unexplored in literature. They identified a new Internet-based social behavior so-called star-economy in a Japanese kid-based network service with the user content sharing. The author analyzed the star-economy in the emerging social game context. The author revisits the network effect (network externality) in the digital eco system in order to identify the landing pad for the star-economy. The author presents the lessons from the new dimension of network effect and exploits the staged based consideration of digital social systems.","Title: Dilemma of Service Engineering in the Era of Social Services: Understanding the Ongoing Challenges of Service Engineering\nYear: 2014\n\nAbstract: The hype surrounding the mobile social game business started in 2009 and peaked in 2012. This rise and decline provides two sets of lessons learned in the game business in Japan. The author provides a retrospective view of the mobile social game business during this period of hype until its decline. From the lessons learned, the author presents the challenges of service engineering in the era of social services.","Title: A Non-Zero-Sum Social Experience Approach: Building Blocks of Social Experience Design\nYear: 2014\n\nAbstract: Service engineering is starting to cope with the increasing needs of facilitating socially empowered services. In order to implement socially-empowered services, it requires design methodologies that deal with social experience beyond just a collection of user experience. Social experience design is promising methodology, however, the concrete building blocks of social experience design are still far from being clear. The author analyzes possible approaches and proposes a non-zero-sum social experience approach in order to facilitate social experience design in design phases and operational phases.","Title: A social process model for cyber mind in the mobile Internet\nYear: 2003\n\nAbstract: As the number of the Internet-enabled mobile handsets increases, it is apparent that the mobile Internet deeply penetrates into the every day life and impacts social interactions. The new ways of communications expose new social interaction patterns. Some of them are uncommon in the real world interactions. The author describes some of the case studies on new social interaction patterns and presents the implications for mobile Internet-augmented societies. Especially, these interaction patterns are distinguished in the viewpoints of quality of interaction modalities and loose identification. The author assumes a new social relation building process in the three aspects; communication modality, identity and distance. A mobile cyber mind is defined and several aspects of it are contrasted with those in the real world interactions. A staged AIMS model is proposed to provide a basic measure to analyze the cyber mind in the emerged technologies to augment the social interactions. The model focuses on the two aspects of the interactions: the successful communication experience and development of social bindings during interactions.","Title: An interoperability design model: lessons learned in computer communication standardization\nYear: 2005\n\nAbstract: Interoperability is one of the top priority issues in the computer communication standardizations. The computer communication protocol design becomes complicated in order to cover both of the communication parts and content parts. The content parts need more flexibility for the future evolution. The author uses the underlying assumptions, architectural aspects and processing model aspects in order to identify the protocol design strategy with lessons learned in the past. The author describes existing extensibility frameworks. Then the author proposes a logical model for architectural design and processing model selection. This model gives a measure to verify the overall consistency for the evolution-tolerant protocol design. The author compares the fits among the logical model components and the possible interoperability strategies.","Title: An application layer protocol interoperability design model for content evolution in the mobile Internet\nYear: 2005\n\nAbstract: Interoperability is one of the top priority issues in the computer communication standardizations. In the content side, the evolution is not continuous, therefore, the interoperability needs to be embedded in the communication protocol design. In the past, the communication protocol design is done in an ad hoc manner in each sequence of protocol evolution. It leads to the lack of the design strategy reuse among the different communication protocol domains. In order to provide a general guideline for the interoperable communication protocol design, a general methodology to pursue the interoperability considering the key aspects of the content evolution. The author analyzes the interoperability-aware protocol design and proposes an interoperability design model. First, the author proposes a communication protocol design framework to clarify the components to be considered in the interoperable communication protocol design. Then, the author describes the different patterns from the past lessons learned in the communication protocol design in each aspect: evolution patterns, underlying assumptions, communication architecture, and protocol processing models.","Title: Time Management in Mobile Social Game Design: Lessons from Mobile Social Games in Japan\nYear: 2011\n\nAbstract: The mobile Internet business is entering a new era in Japan with the emerging revenue being generated from mobile social games. Users using high-speed mobile Internet with flat-rate data services have acceptance of micro-payments and increased payment acceptance in mobile social games. This has been impacting mobile business and the game business in Japan for the last 12 months. Given the hype surrounding mobile social games, the author attempts to identify the guidelines in game design that were not explored in traditional game design. Time-factor is one of the to-be-explored factors in the human factors. It provides a rich source of industrial applications including social games in the anyplace and anytime context. The author highlights the time-dimensional factors in this emerging design methodology for mobile social games.","Title: A Mobile Digital Ecosystem Framework: Lessons from the Evolution of Mobile Data Services\nYear: 2010\n\nAbstract: The mobile Internet has come to permeate into everyday life. It is expected to accommodate a larger part of the digital economy in the near future. Mobile digital ecosystems have more complicated dynamisms due to the mobile-specific dependencies among their stake-holders. The author presents a framework for mobile digital ecosystems with an entity model, a dynamics model, and an acceptance model. This framework provides a basis for analyzing mobile digital ecosystems. The author identifies the factors that drive mobile digital ecosystems, and presents the challenges and factors that shape these ecosystems from the viewpoint of time dimensional evolution.","Title: Horizontal Requirement Engineering in Integration of Multiple IoT Use Cases of City Platform as a Service\nYear: 2017\n\nAbstract: As deployment of IoT grows, potential and real conflicts and interference of IoT systems also grow. Many problems are potentially born in the requirement phase. However, the vertical nature of IoT deployment prevents identification of the challenges in the difficult in the early design phase. The author is engaged in a city-area IoT and open data project, and becomes aware that the necessity of systematic methodologies at the requirement phase. The author presents a 3-stage framework of requirement engineering of joint use cases of vertical IoT services.","Title: Toward positioning IoT-empowered service engineering in city platform as a service\nYear: 2017\n\nAbstract: Internet of Things, IoT, connects physical objects and the cyberspace, which provides potentials to leverage organization and business redesign. An increasing need of digital business integration presents a broad challenge for a wide range of industries. It requires changes in design and operation processes, which ultimately leads to an organizational change. The author proposes dimensions to provide positioning of an IoT service in a comparative framework. Then, the author presents a framework using the proposed dimensions to classify IoT-empowered service engineering. This provides a general framework to assess coverage of city-scale IoT platforms.","Title: A Social Dimension View Model of Divergence of IoT Standardization.\nYear: 2018\n\nAbstract: As IoT (Internet of Things) continues to penetrate everyday life, we witness the increase in the number of IoT standardization bodies. As the coverage of standardization bodies overlap, interoperability is threatened despite of good wills of each standardization body. The author analyzes the causes of fragmentation of IoT standardization from the current landscape of IoT standardization. Then, the author presents a broken assumption model that explains the disorganized status of IoT standardization.","Title: A Dynamism View Model of Convergence and Divergence of IoT Standardization.\nYear: 2018\n\nAbstract: As IoT (Internet of Things) continues to penetrate everyday life, we witness the increase in the number of IoT standardization activities. This is a kind of business and political conflicts. It can be also viewed as an emergence of new types of standardization. In a world where complicated cyber-physical systems come to exist, legacy view models such as layered view models are not adequate to the new landscape of IoT standardization. As a departure of static structural view of standardization, the author proposes a dynamism model for divergence and convergence.","Title: Lessons Learned in Tokyo Public Transportation Open Data APIs.\nYear: 2018\n\nAbstract: Open data is a vital part of new digital economy. It facilitates value creation from combining data from multiple sources. It also important to utilize a massive data flow from emerging IoT (Internet of Things) devices. Future smart cities will consist of a large aggregation of open data APIs. The size and variety of open data APIs provide challenges for usability, consistency, and integrity. The author analyzes issues in open data APIs in the Tokyo public transportation. The author discusses multiple aspects of issues. Then, the author presents a framework with three view models to deal with open data APIs: outcome, cause, and fixes. Finally, the author discusses lessons learned in the Tokyo public transportation open data APIs.","Title: A migration-oriented partial adaptation architecture for IoT-empowered city platform as a service\nYear: 2017\n\nAbstract: Evolution of IoT-empowered city-scale platform leads to migration requirements. All successful IoT systems need to address migration of existing or external systems. The author discusses the challenges of phased migration of heterogeneous IoT systems. Then, the author presents approaches of migratable architecture. Finally, the author proposes a partial adaptation migration architecture to cope with the challenges. The mapping architecture can provide a framework of accommodating evolution of heterogeneous IoT systems.","Title: Device Stand-by Management of IoT: A Framework for Dealing with Real-World Device Fault in City Platform as a Service.\nYear: 2018\n\nAbstract: Expansion of IoT and increasing computing resources provides opportunities in edge computing. There are two types of edge computing: heavy edge computing and lightweight edge computing. The author discusses lightweight edge computing with resource-constraint from real-world use cases from a smart city project. The author proposes device management framework with stand by mechanism and device characteristics from data mining of past device behavior."],"string":"[\n \"Title: A 2-Dimensional Evolution Model of Value-Added Mobile Internet Services\\nYear: 2005\\n\\nAbstract: The mobile Internet quickly penetrates every niche of our daily life. With its saturation, the computer communication industries encounter the new challenges searching for the next mobile Internet evolution. The author discusses a wide range of the value-added mobile Internet services in order to identify the evolution driving force. The author presents a 2-dimentional-factor model to identify the market positions of the value-added mobile Internet services using the two dimensions: network coupling and richness in media modality. Finally, the author discusses the uniqueness of this approach compared to the related studies in the mobile broadband Internet considering mobile multimedia evolution assumptions.\",\n \"Title: Self-Innovation Skill-Based Change Management: An Approach Toward Flexible Organizational Management.\\nYear: 2013\\n\\nAbstract: As changes have prevailed throughout a wide range of aspects of modern industrialization, it is crucial to manage changes in organizations. The author proposes a bottom-up approach for change management in organizational contexts. The author analyzes the differences between top-down approaches and bottom-up approaches. The proposed approach focuses on teaching self-innovation skills through a habit-building capability that utilizes persuasive technology.\",\n \"Title: An Exploratory Long-Term Open Source Activity Analysis: Implications from Empirical Findings on Activity Statistics\\nYear: 2010\\n\\nAbstract: Open source software (OSS) activities are diverse and difficult to capture. The author attempts a web service-based observation of OSS activities. Small community factor is discussed from a social viewpoint.\",\n \"Title: Servicenics approach: A social service engineering framework.\\nYear: 2013\\n\\nAbstract: Servicenics theory [1] is an extension of Gamenics theory for facilitating service engineering. It is an attempt to extend game design to the general principles of service engineering. It is based on the assumption that mobile social game design is based on the generally-applicable design paradigms of service engineering. The author proposes a six-stage view of Servicenics principles that are augmented from the four-stage golden rules of mobile social game design.\",\n \"Title: Leveraging Information Appliances: A Browser Architecture Perspective in the Mobile Multimedia Age\\nYear: 2002\\n\\nAbstract: As the mobile handsets emerges as Internet ready devices, network enabled are among the most useful and visible aspects of the Internet. A wide variety of information appliances are ready for Internet access and advances in network technologies accelerate this trend. Such devices cope with constraints that do not exist in PC. The mobile multimedia services require improvements in embedded network software engineering. The issues in embedded network software engineering are presented. A framework of new network software architecture is presented. Implications towards mobile multimedia are discussed.\",\n \"Title: A two-layer ecosystem evolution model: lessons from stages of mobile data services\\nYear: 2010\\n\\nAbstract: The development of mobile services in Japan shows that there are three chasms: one for walled garden, one for SNS-based community services, and one for mobile open platforms. These chasms were discussed from the context of a series of discrete events. The author tries to provide a birds-eye view of this development. The author proposes a two-layer structure for mobile service ecosystems, where each chasm represents the lower-layer component and the upper layer represents the cross-stage ecosystem. The author maps the development of mobile services into this proposed model, and discusses the ecosystem factor for each layer, in order provide a universal basis for generating eco-system-aware mobile data services.\",\n \"Title: A 3-Stage Transition Model of the Architecture of Mobile Social Games: Lessons from Mobile Social Games in Japan\\nYear: 2011\\n\\nAbstract: Since the wide adoption of Open Social by SNS providers between late 2009 and middle 2010, the mobile business in Japan has quickly turned into the direction of item purchasing. This revenue-generating engine is so powerful that it has changed the landscape of mobile business and the game business in Japan. In less than 2 years, the market involving the item purchasing of mobile social games quickly reached the level of a billion US dollars. This gold-rush has facilitated the rapid evolution of mobile social games as massive numbers of users have gained and incorporated their collective mobile social experience. This has also leveraged the evolution of the architecture of mobile social games. The author proposes a three-stage evolutionary view of the architecture of mobile social games and presents its implications for mobile business engineering.\",\n \"Title: A Weak-Tie Based Regularity Analysis of Mobile Clickstreams\\nYear: 2008\\n\\nAbstract: Advances in wireless technologies and their penetration into every-day life can create a new context with multiple simultaneous sub-contexts. The mobile Internet leverages multiple contexts with its any-time/any-place capability. This leads to a new third state for Internet use contexts, a weakly-tied state, in addition to the two existing states: active (strongly-tied) and inactive. The author attempts to identify the user behavior patterns derived from this weak-tie based approach. The author proposes an interval analysis of the mobile clickstream in order to identify regular users. From the observation, the author shows a positive relation between the hourly-scale interval of mobile web visits and the revisit ratio in the next month, indicating the degree of the user loyalty to a specific mobile web site.\",\n \"Title: MobileWeb 2.0: Lessons from Web 2.0 and Past Mobile Internet Development\\nYear: 2007\\n\\nAbstract: The key driving factors for ubiquitous services include deployment, management and service development. The mobile Internet services are milestones how the future ubiquitous services will evolve. The Web 2.0 design patterns can deal with and go beyond the various restrictions inherited in the mobile Internet. The author considers the Web 2.0 service design transitions in the context of leveraging the mobile Internet. The trend called Web 2.0 provides a new perspective for frameworks, architectures, content supply chains, and business models in the Internet business. As the mobile Internet is expected to emerge, it is important to consider the mobile-enabled Web 2.0. The author discusses the mobile Internet evolution in the perspective of the two-path evolution from Web 1.0 to Mobile Web 2.0, either though Mobile Web 1.0 or though Web 2.0. The mobile specific factors and sustained challenges of Web 2.0 are discussed in this evolution path perspective.\",\n \"Title: A Persistence Analysis of Smartphone Application Stores: An Approach to Understanding Gateways to the Internet\\nYear: 2013\\n\\nAbstract: As smart phones have prevailed across this planet, application store search engine optimization has become a hot issue in mobile business. The author analyzes this phenomenon including past experiences such as standalone applications and Widget applications. The author analyzes the factors that drive the evolution of mobile business in terms of the positions of application stores.\",\n \"Title: A Fast-Boot Method for Embedded Mobile Linux: Toward a Single-Digit User Sensed Boot Time for Full-Featured Commercial Phones\\nYear: 2011\\n\\nAbstract: Software on mobile devices has become increasingly complicated as end users demand a PC-converged user experience on the mobile Internet. This has complicated the software configuration and boot procedures, resulting in a longer boot time. This is damaging to the mobile user experience. Many legacy techniques to enable the fast boot time differentiation in mobile handsets are not applicable to the today's advanced handsets, which are capable of handling rich multimedia applications. The authors propose a fast boot method for embedded mobile Linux, that is suitable for mobile handsets. They evaluate the proposed method in a prototype for a commercial handset and show positive results in a single-digit user sensed boot time.\",\n \"Title: A Hybrid Software Architecture for Rich Media Applications on Android Phones\\nYear: 2011\\n\\nAbstract: The mobile Internet has reached a certain stage of preparation for the newly emerging rich media applications. Emerging smartphones are beginning to provide a new infrastructure for rich media applications on mobile handsets. The constraints of the smartphones still require careful software design in order to achieve a rich media user experience. We present a hybrid software architecture to cope with this challenge. We outline the requirements and overall design of the proposed software architecture. Then, we present results from a case study of magazine viewers. The proposed architecture provides improved time to market and improved performance for large sized image data in magazine applications.\",\n \"Title: Cross-Culture Analysis of Mobile Social Games: Toward Design Guidelines of Lessons Learned from Globalized Mobile Social Games.\\nYear: 2013\\n\\nAbstract: As the mobile social game business heats up, it is crucial to establish a systematic methodology that can deal with the globalization of mobile social games. There are many challenges that come from this globalization. One of them, is the cross-cultural analysis of user behavior in mobile social game contexts. Many Japanese vendors had difficulties with hidden cross-cultural issues in 2012. The author analyzes the cross-cultural lessons learned from the endeavors to globalize mobile social games. Then, the author discusses a basis for the systematical analysis of cross-cultural challenges.\",\n \"Title: A Four-Stage Gate-Keeper Model of Social Service Engineering: Lessons from Golden Rules of Mobile Social Game Design\\nYear: 2012\\n\\nAbstract: The rapid growth in revenue-generating capabilities of mobile social games has caught the attention of both researchers and industry. The author analyzes the key factors of revenue-generating engines in mobile social games in Japan, and presents the four key factors involved in revenue-generation mechanisms of those games. The author discusses the four golden rules of mobile social games and presents them as parts of a fourstage gatekeeper model for success in mobile social games.\",\n \"Title: A Social View on Unsolicited Bulk Email Impacts on Mobile Mail Addresses\\nYear: 2006\\n\\nAbstract: The mobile Internet has been threatened by unsolicited bulk mail attacks. This threat has caused user adoption of the longer email addresses and exposes new user behaviors in always-on ubiquitous world. The author discusses the three factors of mail address evolution from the three perspectives: defensive, cognitive and social views. The author analyzes the address length transition over 4 years in Japan. The author describes an excessive user behavior on the use of special characters from the viewpoint of trade-offs among defensive, cognitive and social aspects.\",\n \"Title: Impact from mobile SPAM mail on mobile internet services\\nYear: 2003\\n\\nAbstract: Rapid penetration of internet-enabled mobile handsets is being witnessed in worldwide. It also creates some new social frictions like SPAM mail to mobile handsets. The emergence of mobile Internet services in Japan soon triggered wide spread mobile SPAM mail. It reached a certain point to attract significant social attention in April 2001. The SPAM mail impact is analyzed from the long-term transition of user mail address pattern changes. This study examines the SPAM mail impact from the viewpoint of charged mobile Internet services.\",\n \"Title: An Approach Toward Fits Evaluation Between Organizational Culture and Groupware\\nYear: 1997\\n\\nAbstract: The previous research trying to formulate methodologies to evaluate groupware has encountered various difficulties due to complicated characteristics of social process. From our experience of several groupware prototype systems, we became aware of the importance to understand the fits between organizational culture and system characteristics. To identify the fits, a two-dimensional classification is proposed. From the observation that the only one adoption path has been observed, the fits between organizational culture and system characteristics are discussed. In addition, the long-term adoption model is proposed to identify the several boundaries of several types of system acceptance. The method can be used to identify the system acceptance stages in a wide range of applications.\",\n \"Title: Towards Know-When Technology in the Mobile Information Space: Long-Term User Trace Log Analysis in the Mobile Internet\\nYear: 2003\\n\\nAbstract: The mobile Internet is one of the most promising application domains in the computer communications. The mobile handsets close the gap between end users and the computercommunications using the 24-hour 365-day availability. The emerging success in Japan has demonstrated the potential capabilities for the next generation Internet platform. The close relationship to the end user reveals a new research area of the user behavior analysis. Using the user transaction logs for over a year, the author tries to build up a new knowledge domain: know-when knowledge. A methodology to capture the user behavior change over a span of time is important to manage the subscription-based mobile Internet services. Using the know-when knowledge acquisition, the author explores the possible cues for the long-term analysis of user behavior patterns using the long-term transaction logs. Considering the behavior dynamism of the mobile Internet users, it is important to explore the know-when technologies for the future mobile Internet marketing.\",\n \"Title: Mobile Social Game Design from the Perspective of Persuasive Technology\\nYear: 2012\\n\\nAbstract: There is an increasing visibility of the mobile social game business in the landscape of the Internet content business. The analysis of mobile social game design shows many similarities between design guidelines in game design and in persuasive technology. The author examines the design guidelines in persuasive technology in order to find similarities in the design guidelines in game design. He concludes that the best practices in mobile social game design follow the design guidelines in persuasive technology.\",\n \"Title: Business Model Analysis on Vertical Integration of Mobile Data Services\\nYear: 2008\\n\\nAbstract: Mobile Internet is becoming a reality and rapidly penetrating everyday life. Mobile Internet business models shift over a span of time. An emerging business model trend in the mobile Internet is vertical integration by carriers and leading service providers. In order to identify a model to analyze the feasibility of vertical business models, the author analyzes the lessons in the past and proposes an evolution model for mobile Internet business. From this perspective, the author discusses mobile-specific technical and behavioral factors impacting vertical integration of the mobile data services.\",\n \"Title: Unique Identifier Tracking Analysis: A Methodology to Capture Wireless Internet User Behaviors\\nYear: 2001\\n\\nAbstract: Rapid penetration of Internet-enabled cellular phones is witnessed in worldwide. Cellular phone-based Internet has significantly different behaviors. Wireless Internet users have rather short and transient behaviors. This paper discusses the distinguished characteristics of cellular phone-based Internet users. Issues to understand the user behaviors are presented. A new methodology, unique user analysis, is proposed to cope with the issues of personal user behavior. A case study is presented using unique user analysis. The methodology is effective to capture the characteristics of growing wireless Internet users.\",\n \"Title: A Three-Dimension Analysis of Driving Factors for Mobile Application Stores: Implications of Open Mobile Business Engineering\\nYear: 2011\\n\\nAbstract: The mobile industry is witnessing a turning point in terms of the increased penetration of PC Internet players such as Apple and Google. The business models of Apple and Google in the mobile industry are very different, however, they are shaking up the legacy mobile software industry by representing a departure from the legacy closed business models. The AppStore represents a great success in the mobile industry. It is a single device with a closed market, however, it has provided the successful vertical integration business model for enclosing end users with iPhone-style services. The App Store has created a market bandwagon that everyone is eager to jump onto. The author provides business model analysis of the AppStore, as compared to the i-mode and Android. Then, the author presents implications for the today's mobile software industry.\",\n \"Title: A one-seg service development model: ecosystem considerations in the context of mobile communication/broadcast convergence\\nYear: 2010\\n\\nAbstract: Business model engineering is crucial in the context of convergence of mobile communications and broadcasting The author presents a stage model of one-seg service evolution from lessons learned with one-seg in Japan The author focuses awareness on the stages in the complicated relationship between stake-holders With the examination of the transitions between stages, the author also proposes a three-dimensional fit model for mobile services The author discusses the chronological order of fit identification during the stage transitions This service transition with fit exploration and staged view is not specific to one-seg Therefore, it can be applied to a wide range of mobile service development that incorporates new technological elements It should be noted that the mobile technology brings a new perspective to service development using mobility Mobile services require long-term incremental fit exploration which is crucial in mobile service development.\",\n \"Title: Network software platform design for wireless real-world integration applications\\nYear: 2005\\n\\nAbstract: The mobile Internet quickly penetrates the every-day life. After reaching the mobile multimedia stage, the real world integration is one of the next challenges in the mobile software frameworks. The author proposes an Internet-converged embedded web solution for the real world integration named DirectConnect in the analogy of the Live Connect in the Netscape Navigator. The author describes evaluation on five approaches for the enhanced real world integration and presents advantages of the minimum modification approach. The proposed approach is a browser-centric Internet-converged solution that enables the micro-browser on the mobile handsets to be an integration point of the real world integration. It uses the common Internet framework, and is adaptable to a wide range of web and real world integration. The scheme encapsulates the security issue in each object implementation.\",\n \"Title: A Long Interval Method to Identify Regular Monthly Mobile Internet Users\\nYear: 2008\\n\\nAbstract: The Internet access from mobile handsets is extending its coverage. It increases the importance to analyze mobile Internet use in research communities and industries. To cope with the increased demands on mobile Internet user analysis, the author describes a regularity-oriented service model. In order to capture user regularity, the author performs an exploratory clickstream analysis to identify users with high loyalty. Based on the author's previous experience on the relationship between middle-range visit intervals and revisit ratio in the next month, the author tries to identify hour-scale intervals linked to the high-loyalty-users. The author obtains the result that the users with intervals with 480 minutes and 720 minutes show the high revisit ratio. The author analyzes key time zones for high- loyalty users. This method is applicable to a wide range of applications with clickstream logs. Using the long-interval characteristics, the author discusses a double peak model of sub-day-scale activity behaviors in the case study.\",\n \"Title: Regularity Analysis Using Time Slot Counting in the Mobile Clickstream\\nYear: 2006\\n\\nAbstract: The ever-changing nature of the mobile Internet contributes to the difficulties encountered in user behavior research. Regularity is an important aspect of the mobile Internet in research and marketing, because it end users easily lose their interest and leave the mobile web sites due to the limited visibility of the web. Maintaining user loyalty is a vital challenge for mobile webs. Therefore, the methods to identify loyalty transitions are important. The author proposes a regularity measure using click counting in the time slots. With the assumption that the users with regular access have more chances to continue to use the mobile webs, the author examines the monthly prediction of user behavior based on the user access regularity in the previous month. The author obtains approximate 80 % accuracy of prediction in the case study. The author discusses the limitation and implications of the comparison.\",\n \"Title: Lessons in business model development from early mobile Internet services in Japan\\nYear: 2006\\n\\nAbstract: The author summarizes the lessons learned in the 6-year evolution in the mobile Internet business development in Japan. The lessons about telephony/Internet service development characteristics, a spiral service evolution pattern, and mobile content business model development are studied from a retrospective view on the mobile business evolution. Based on the basic mobile interaction model, the author describes implications for the future mobile business model development.\",\n \"Title: Mood-Based E-Commerce: A New Approach For Sub-Personality-Based Marketing\\nYear: 2013\\n\\nAbstract: E-commerce has been growing and has reached a point of new motivational approaches that explore a frontier of marketing. Such new approaches include sub-personality-based marketing, which addresses the multi-faceted personalities of human beings. The author explores sub-personality-based e-commerce in order to develop deeper insight into human behavior. Emotion is a fundamental factor in Internet services. In E-commerce, positive emotion is a driving factor for generating sales. In order to facilitate effective emotion-based e-commerce, the author analyzes two dimensions of motivational direction. From the quadrants of these two dimensions, the author extracts three factors: energization, healing, and change. The author proposes an emotion-centric commerce concept that focuses on these three factors, and discusses the framework and business models upon which it is based.\",\n \"Title: Foundation-based mobile platform software engineering: implications to convergence to open source software\\nYear: 2009\\n\\nAbstract: Open source is impacting software industry in a significant manner. It is critical for any software vendor to take an appropriate position in the open-source-involved software engineering. It is also true in the platform software for mobile handsets. The author analyzes the platform software landscapes in the mobile industry with a perspective of utilizing open source software. The author performs competitive analysis for many stake holders in this landscape.\",\n \"Title: Mobile Data Services 2.0: Analysis of 3G and Flat Rate Impacts on Mobile Data Services\\nYear: 2009\\n\\nAbstract: Emerging 3G services offering flat rates have brought notable impacts on the mobile content providers in Japan. Mobile business 2.0 based on the high speed Internet is discussed. A business model shift caused by 3G and flat rate penetration is analyzed and its implications are presented.\",\n \"Title: A Zipf-Like Distribution of Popularity and Hits in the Mobile Web Pages with Short Life Time\\nYear: 2006\\n\\nAbstract: The mobile Internet quickly penetrates the society. The mobile clickstream is a valuable resource to identify the personal web access behaviors. The author makes an empirical study on the mobile clickstream to identify the difference between the mobile information with limited life time and the traditional PC web information with long life time. The observation shows that the mobile web access indicates a zipf-like distribution even though the each web content has a limited life time. The author discusses the implications of this similarity from the view point of the mobile user behavior characteristics.\",\n \"Title: From Gamenics to Servicenics: Lessons Learned in Mobile Social Games in Japan toward Service Engineering\\nYear: 2012\\n\\nAbstract: Gamenics Theory is a massive collection of know-how for improving usability and playability based on lessons learned from Nintendo video games. The author comes to recognize that many design social game design principles can be interpreted as applications of Gamenics Theory. Gamenics Theory covers the highlights of emerging social game design, however, some of the social and revenue-generating factors are not covered because Gamenics Theory was originally coined for console games. In order to facilitate a better base for service engineering, the author proposes to extend Gamenics Theory to the wider scope of service engineering. The author coins the term ``Servicenics'' and discusses its implications for service engineering based on lessons learned in mobile social games in Japan.\",\n \"Title: A 3-stage Evolutionary Model of Mobile Social Game Design in Japan\\nYear: 2011\\n\\nAbstract: The mobile Internet business is entering a new era in Japan with the emerging revenue being generated from item purchasing in mobile social games. Users using the mobile high speed Internet with flat-rate data services have acceptance of micro-payments and increased payment acceptance in mobile social games. This has been impacting mobile business and the game business in Japan since the emergence of Open Social based platforms in October 2009. With all of the hype being generated by mobile social games, the author attempts to identify the key success factors in this mobile business landscape. The author examines the current business landscape in mobile social games in Japan. The author recognizes the rapid changes in the ecosystem in mobile social games. The author proposes a 3-stage transition model for describing the underlying ecosystem paradigm in Japan.\",\n \"Title: An empirical analysis of revisit behaviors of monthly subscription-based mobile video services\\nYear: 2010\\n\\nAbstract: The Mobile Internet is becoming a first-class citizen of the Internet in many advanced countries It is also increasing its broadband capabilities with 3G penetration As increased penetration and bandwidth leverage mobile application business opportunities, it is important to identify methodologies for serving mobile-specific demands Regularity is one of the important means of retaining and enclosing easy-come, easy-go mobile users It is known that users with multiple visits in one day and a long interval have a higher possibility of revisiting in the following month than others The author applies this empirical law to his investigation of the mobile broadband services The result shows that the method proposed for the text-based mobile services is also applicable to mobile video services in the mobile broadband context The author shows that the method based with 2 bits per day can provide results that can be used for classifying monthly-scale regular users in the case study.\",\n \"Title: Challenges for Mobile Middleware Platform: Issues for Embedded Open Source Software Integration\\nYear: 2010\\n\\nAbstract: Linux is penetrating into mobile software as the basis for the mobile middleware platform. It accelerates the increasing visibility of open source software (OSS) components in the mobile middleware platform. Despite multiple challenges in mobile embedded software engineering, it is crucial to promote open source-aware development of the mobile software platform. The author presents the challenges to open source software integration in embedded software development. The author discusses the open source-aware software development and identifies a path to transition for moving toward it.\",\n \"Title: Extensible Platform Software Architecture Design and its impact in the embedded network software engineering\\nYear: 2005\\n\\nAbstract: With 3G system introduction, the data communication applications are a key for the mobile Internet evolution. It is a challenge to design an extensible and resource-efficient software platform for mobile multimedia services. The methodology to identify the trade-off in the software architecture design was unexplored in the past literature. The author compares the two software platforms for micro browsers in order to identify the trade-off factors between extensibility and compactness. The author summarizes the three extension schemes, abstract rendering, XML framework, and extensible common components. The author proposes a discrete component analysis to evaluate the impact from the extensible architecture. With this analysis, the author presents the footprint overhead estimate for the embedded software environment designed for mobile handsets. It is a useful measure when the trade-off between the hard-wired concise implementation and the extension-tolerant implementations.\",\n \"Title: A User-Perceived Freshness Clustering Method to Identify Three Subgroups in Mobile Internet Users\\nYear: 2008\\n\\nAbstract: The user behavior analysis of the mobile Internet is still to be explored. This increases importance in research and business as the mobile Internet penetration becomes visible. The author performs a preparatory survey to identify the user behavior scenarios during a day. It indicates that there are three distinguished mobile Internet behavior patterns: (a) always active, (b) irregular, (c) prime time use. This gives a novel perspective to understand user interactions in the mobile Internet. Intelligent mobile services require addressing these differences in order to improve user experience. The author proposes a user-perceived freshness method to cluster users with the content access logs in order to utilize this three-subgroup-based view. The revisit ratio in the following month is used to characterize the user clusters and the middle users show a high revisit ratio. The author discusses the characteristics of this middle user segment.\",\n \"Title: FISH: Flexible information sharing and handling system\\nYear: 1994\\n\\nAbstract: With the progress of network technology and workstation technology as the background, there has been progress in the exchange/transmission of information as well as document sharing by electronic means in the office environment. At present, however, techniques have not been developed to the stage where such fragmented information as knowhow is stored/retrieved/managed in the distributed environment. From such a viewpoint, the authors have constructed the knowhow storage system FISH (flexible information sharing and handling system), which can store the knowhow in the text format. In FISH, the minimum unit decomposition of the information and the flexible structurization are realized by decomposing the knowhow into multiple virtual cards to be stored in the computer. Knowhow, which is difficult to be given a systematic structure, is managed by automatically providing links among the card based on the keywords attached to the individual cards. This paper describes first the management method for the knowhow which is the basic design of FISH. Then the features, the functions that can be used, and the implementation of the system are discussed. The result of the one-year operation experiment of the system in the office of the authors is presented, together with discussions. Using FISH, it is made possible to store the knowhow and share the use in the distributed environment.\",\n \"Title: An Emotion-Object-Interaction Framework for Exploration of Design Patterns of Massive Interpersonal Persuasion\\nYear: 2013\\n\\nAbstract: Social media has been growing since the emergence of Web 2.0. Interpersonal diffusion has big potential for a new kind of media marketing. As the stay time in social media increases, it is crucial to make use of social media for marketing utilities. In order to cope with this need, it is important to create a framework for diffusion using a user experience diffusion mechanism. The large social graph capability of social networks has significant potential. However, the difficulty of controlling the behavior of social interaction presents challenges to coining a systematic methodology to deal with it. For facilitating the automation of user experience diffusion, the author proposes a framework called the \\\"Emotion-Object-Interaction framework.\\\" The author discusses the best practices of the Emotion-Object-Interaction framework using the examples of social network services and mobile social games. The proposed framework focuses on the emotion of first user, then on symbolization of the emotion and interaction with it. This simple scheme provides a general framework for mass interpersonal persuasion.\",\n \"Title: Transition Analysis of Mobile Social Games from Feature-Phone to Smart-Phone\\nYear: 2012\\n\\nAbstract: The Japanese feature-phone has traced a relatively unique evolutionary path considering its unique service development context in the last decade. The global trend of smart phones has provided a new direction in evolution for the mobile service business in Japan. This transition in a unique ecosystem and user experience provides an opportunity to identify the driving factors for mobile service business. The author discusses the possible scenarios in Japan for this ongoing transition from feature-phones to smart-phones. The author analyzes the impacts from the transition using a three-dimensional analysis of the user adoption dimension, content production dimension, and business model dimension.\",\n \"Title: Social Pattern Development Analysis: A Case Study in a Regional Community Network\\nYear: 1998\\n\\nAbstract: Community development is a dynamic process in which a group of users show their own communication style and culture. Communication development patterns are different from ones from the common diffusion theory since it has a wide variety of use development patterns. This variety comes from the richness of the community communication contents and variety of time spans of transitions. We learned some lessons from two-year experience of a regional community network trial based on a fiber network. To observe the social pattern development, we analyze a social network development patterns in our system. We propose the adaptive time span analysis. In this method, multiple time spans are compared until the stable communication patterns are obtained. This analysis lead to the estimation of context duration time and social pattern development time to understand the dynamics of the community evolution. The case studies in done in NTT-Hayashi trial e-mail communication log data to obtain the time span of persistent bilateral email communication relationship.\",\n \"Title: Persuasive Strength Analysis of the Mobile Gang of Four: Toward Understanding Mobile Business Engineering in the Era of Mobile Persuasion\\nYear: 2013\\n\\nAbstract: The increasing number of smart phones is leveraging changes of business landscapes in the Internet. This demonstrates the importance of business model engineering in the post-PC era. The author proposes the hypothesis of mobile persuasive strength. Using this hypothesis, the author provides a strength analysis of the Mobile Gang of Four: Apple, Google, Amazon, and Facebook. The analysis provides a systematic trend analysis of the key mobile business players. The author then presents the implications of the hypothesis of mobile persuasive strength for today's business model engineering.\",\n \"Title: A Trap-Door Method for Subscription-Based Mobile Content\\nYear: 2007\\n\\nAbstract: Scalability design in the mobile Internet web servers is increasing its importance. The performance improve- ment for scalable mobile Internet service requirement is in- creased as the mobile Internet penetrates into every day life. The mobile Internet enabled opportunities for subscription- based charged content for content providers. In order to cope with the scalability design issues, it is important to identify the bottlenecks and propose improvement in the practical use scenes. The author analyzed a content adap- tation system for subscription-based content. The author identified the inefficiencies in the subscription check part and the content adaptation part. The author proposes a novel approach, a trap-door method, to minimize the sub- scription check overhead and the adaptation overhead. The trap-door method utilizes the authentication only at the first access to the charged content. The processing flow is de- scribed. The performance improvement based on the real server performance observation is discussed.\",\n \"Title: It is not a Game, Not Even Social: New Service Engineering Paradigms from Lessons Learned in Mobile Social Games in Japan\\nYear: 2012\\n\\nAbstract: There is an increasing visibility of mobile social games in Japan. The radical growth of item-purchasing in mobile social games is generating new hype in mobile business. The author revisits the game and social factors of the revenue-generating engines of mobile social games. The author examines the underlying principles of revenue-generating engines in mobile social games, which leads to exploration of a new framework of service engineering for a wider scope beyond game design.\",\n \"Title: A 4-layer robust mobile web front-end design model\\nYear: 2004\\n\\nAbstract: The author describes the lessons learned from the 4-year non-stop operation of the mobile Internet services to identify the robust mobile web design issues from an operational viewpoint. In order to maintain the user confidence during the unexpected situations, the author describes a 4-layer fallback design model to manage the real world mobile web operations and to give a sense of continuity to the mobile users. The author discusses issues in the open mobile Internet services in relation to the proposed front-end design model for the robust tolerant mobile web operations.\",\n \"Title: Knowledge Awareness in Asynchronous Information Sharing\\nYear: 1993\\n\\nAbstract: Based on two years of experience using a know-how management system called FISH, we have come to recognize the importance of awareness support in asynchronous information sharing. Here, we propose a new concept of knowledge awareness. The concept represents an attempt to support cognitive process among group members. Three elements of awareness, (1) knowledge existence, (2) interaction, and (3) meta-knowledge are discussed. In addition to the concept, we present the enhancements of FISH based on the concept. The results of knowledge sharing are discussed from the viewpoint of meta knowledge.\",\n \"Title: An interactivity model of mobile interactive TV: a oneseg case for mobile glue\\nYear: 2009\\n\\nAbstract: One-seg is a new mobile use context combining user experience for mobile and broadcast. This new technology enables to combine real-time broadcast contexts and personal mobile interactive contexts. The author coins an interactivity model for service development of one-seg applications on a mobile handset. The author discusses work-flow based service scenarios for four interactivity aspects of the one-seg applications based on the proposed model. The author presents a mobile glue model to enable a service composition platform perspective for flexible service composition for mobile communication/broadcast converged services.\",\n \"Title: A Third Service Evolution Dimension inWeb 2.0 and Real-world Integration Inspired Services in the 3G Mobile Data Communications\\nYear: 2007\\n\\nAbstract: It is reported that 67 % of global mobile content is SMSbased, compared to 0 The Japanese market shows the two new directions: emergence of mobile Web 2.0 and distinguished transition from the current business models to the vertical integration ones by mobile operators. Some of Japanese carriers press their own content like music download, auction, micro-payment and so on. It is interesting to investigate how this mobile-specific vertical integration will work in the future as the handset and network converges to the PCs. In addition, the emerging increase of search use and social network use in the mobile contexts initiates the mobile-specific Web 2.0 penetration, another trend of convergence. The high penetration of 3G networks inevitably leads to the flat rate charging, which drives the wireless carriers to seek for other revenue sources. The past lessons told that the mobile service business model evolution was not monotonic due to the complexity of interactions of users, content providers and wireless carriers. It is critical to identify the key factors of business model development, either in Web 2.0-related aspects (e.g. user-generated content) or in vertical integration (e.g. carrier-owned content business or real-world integration services). The two paths toward Mobile Web 2.0 are depicted in Fig. 1, with a specific strategy for each path.\",\n \"Title: The Third Generation of OSS: A Three-Stage Evolution from Gift to Commerce-Economy.\\nYear: 2011\\n\\nAbstract: Linux is penetrating into mobile software as the basis for a mobile middleware platform. It is accelerating the increasing visibility of open source software (OSS) components in mobile middleware platforms. Considering the 10-million lines of code of OSS-based industrial platforms such as a mobile middleware platform, engagement in foundations is inevitable for large-scale packages of OSS for industrial solutions. The author discusses the driving factors toward a foundation-based OSS and the transition of the underlying economy types to analyze the transitions to the third-generation OSS.\",\n \"Title: Analysis of Mobile Social Games from the Viewpoint of Gamenics Theory\\nYear: 2011\\n\\nAbstract: The mobile Internet business is entering a new era in Japan with the emergence of revenue being generated from mobile social games. Users using high-speed mobile Internet with flat-rate data services have acceptance of micro-payment and increased payment acceptance in mobile social games. This has been impacting mobile business and the game business in Japan for the last 12 months. Given the hype surrounding mobile social games, the author attempts to identify mobile social extensions of Gamenics theory. Observation of mobile social game design has revealed that most of the design guidelines follow Gamenics theory. The mobile-specific uniqueness resides in the extension of the 4th rule of Gamenics theory. The author presents new design rules from this viewpoint of augmenting Gamenics theory for practices of mobile social game.\",\n \"Title: Exploratory Day-Scale Behavior Assumption-Based User Clustering with the Mobile Clickstream\\nYear: 2007\\n\\nAbstract: The mobile Internet enables 24-hour always-on comput- ing with increased demands on time-based analysis. From an assumption that the mobile users are split into three dif- ferent clusters, the author proposes a method to identify the three different user clusters. The author evaluates the user segments in a subscription-based commercial service using the revisiting ratio in the next month.\",\n \"Title: Mobile Application Platform Strategies: Business Model Engineering for the Data Intensive Mobile Age\\nYear: 2005\\n\\nAbstract: As the mobile telephony users exceeds 1 billions, it is apparent that the next challenges for the wireless telephony is data communication services. It involves a wide spectrum of challenges from software engineering, service evolutions, user acceptance management and business model engineering. There are conflicting demands for the integrated software platform for mobile handsets. The author summarizes unique challenges in the mobile data service evolution with a time-consuming spiral evolution pattern. the author discusses the business model engineering for the mobile software platform in the data intensive mobile age using the technology-business combined perspective from lessons learned in the recent mobile Internet evolution. The author provides business model patterns in the mobile software platform engineering and discusses their competence in the two different evolution scenarios.\",\n \"Title: Command transition probability analysis on mobile internet command sequences\\nYear: 2007\\n\\nAbstract: We have witnessed rapid growth of mobile Internet capabilities in the mobile handsets. The penetration of the user life increases the need to establish a methodology to capture the user behavior patterns in order to improve satisfaction of the mobile web users. In order to cope with the design evaluation needs, the author proposes a long-term command transition probability analysis on mobile clickstreams. The observation showed that the first command ratio did not depend on the menu order, but that the ratio to terminate the command sequence depended on the order. This gives a positive outcome from the command transition analysis in the mobile clickstream.\",\n \"Title: Intraday-scale Long Interval Method of Classifying Intramonth-Scale Revisiting Mobile Users\\nYear: 2008\\n\\nAbstract: Penetration of the mobile Internet has increased its visibility worldwide. This enables analysis of detailed time-dimensional user behavior data. It also increases the industry need to identify and retain mobile users with strong loyalty to a particular mobile Web site. The author proposes an intramonth-scale revisit classification method for identifying intramonth-scale, revisiting mobile users. The author performs a case study and the result shows that the proposed method shows 87% classifier accuracy. The author discusses a trade-off between classifier accuracy and a true positive ratio.\",\n \"Title: A Time Slot Count in Window Method Suitable for Long-Term Regularity-Based User Classification for Mobile Internet\\nYear: 2008\\n\\nAbstract: The mobile Internet proliferates and penetrates all aspects of daily life. It is crucial to codify methodologies to fit mobile-specific demands, e.g. regularity. Subscription-based mobile services require the analysis of regularity to capture and retain easy-come, easy-go users. The author proposes a method called the \\\"time slot count in window\\\" method (TCW-method) to efficiently capture regular users, while relaxing the explicit splits among time slots with visits. The author shows the case study result from 2001-2002 commercial service logs. A case study conducted in 2001-2002 with commercial news service subscribers shows that the relaxation in the proposed method loses 2-3 % in the recall rate of regularity, but still obtained 88-91 % recall rate. Considering the trade-off with the on-the-fly nature of coping with limited storage capacity, the author considers this method to be very promising for the mobile Internet.\",\n \"Title: A Mobile Clickstream Time Zone Analysis: Implications for Real-Time Mobile Collaboration\\nYear: 2004\\n\\nAbstract: In order to support the collaboration in the mobile Internet platform, it is important to increase awareness about the mobile user behavior in the time-dimension. The author proposes a qualitative time zone analysis and examines the 3-year mobile click stream logs and discusses the user models on sub-day access patterns. The four mobile user access models are examined from the real world mobile web logs. It indicates that the mature mobile Internet user click stream is driven by the user-specific selection. It shows the exploratory result for know-when technologies in intelligent mobile collaboration support.\",\n \"Title: A Four-Layer Success Factor View Model of Mobile Social Games: Analysis of Mobage Success in Japan\\nYear: 2011\\n\\nAbstract: The mobile Internet business is entering a new era in Japan with the emerging revenue being generated from item purchasing in mobile social games. Users using high-speed mobile Internet with flat-rate data services have acceptance of micro-payments and increased payment acceptance in mobile social games. This has been impacting mobile business and the game business in Japan since the emergence of Open Social-based platforms in October 2009.With all of hype being generated by mobile social games, the author attempts to identify the key success factors in this mobile business landscape. The author proposes a four-layer approach to identify the success factors for the mobile social games business in Japan. The author presents the implications for mobile business ecosystems from the emerging real-time Web. Then, the author provides a strength analysis of Mob age, one of the leading service providers of mobile social games.\",\n \"Title: A Regularity-Oriented Service Model with Sub-Day-Scale User Classifications for Mobile Internet\\nYear: 2008\\n\\nAbstract: The Internet has been rapidly mobilized by increasing penetration to wireless users. Important issues in mobile user behaviors include identification of long-term regular users in dynamic 24-hour behaviors. The author proposes a regularity-oriented service model to outline the mobile- specific aspects of usage regularity to obtain clues to user preferences on content and services. Then, the author performs some user surveys to identify the diversity of mobile Internet use. It brings our attention on user segment awareness to identification of regularity. The author proposes a clustering method based on an assumption of three different segments of mobile users. The author examines the clustering result with the revisit ratio in the next month to demonstrate the usefulness of the method in the context of long- term use regularity.\",\n \"Title: Ubiquitous Computing Literacy: A New Challenge for Social Computing\\nYear: 2004\\n\\nAbstract: As the emerging technologies enable the embedded ubiquitous intelligence with sensors and controllers, the empowered environment encounters multiple social contexts. The collision to the legacy social contexts leads to the technosocial situations that axe the social situations created by the technologies. The author discusses the lessons in the literature and discusses the issues in the social and cognitive model of the ubiquitous computing empowered society. Then, the author proposes the new type of human skill called ubiquitous computing literacy to cope with the intelligence embedded environment. Finally, the author discusses the staged model to implement such large-scale new social knowledge in order to use the ubiquitous computing in a socially friendly manner.\",\n \"Title: Mobile User Drop Out Ratio Observation with Time zone and Day-of-week Regularity Analysis\\nYear: 2007\\n\\nAbstract: The mobile Internet fills the gap between 24-hour user behavior and Internet clickstreams. The mobile-Internet specific methodology to capture the user behavior characteristics is on the hot research agenda. The long-term stability of the mobile user behavior characteristics is still unexplored. The author proposes a method to identify the user behavior patterns using transition pattern clustering over a long span of time. The author gives an exploratory analysis to find best-fit threshold value to identify regular user behaviors with a 3-month transition patterns. From this observation, the day-of-week behavior gives better indication compared to that of time zone-based one.\",\n \"Title: An Extensible Monolithic Software Design Approach for Internet-Convergent Embedded Applications\\nYear: 2004\\n\\nAbstract: As the mobile multi-media applications emerge with the mobile handsets, the embedded network software needs a new paradigm to cope with the conflicting demands from the constraint embedded software and the Internet multimedia applications. The author describes the issues in comparisons with the traditional software engineering. The application demands for the extensible and modular software engineering are outlined. Then, the author proposes a modular and monolithic software design approach with the embedded network software architecture that is aligned to the proposed framework. The author presents two aspects of the monolithic and extensible software implementations in the embedded Internet applications. Finally, the author describes the three challenges in the mobile multimedia applications from the view point of the proposed design paradigm.\",\n \"Title: A 4+1 Bit Month-Scale Regularity Mining Algorithm with One-Path and Distributed Server Constraints for Mobile Internet\\nYear: 2008\\n\\nAbstract: Mobile Internet becomes increasing visible in everyday life. As increased penetration leverages mobile application business opportunities, it is crucial to identify methodologies to fit mobile-specific demands. Regularity is one of the important measures to enclose easy-come, easy-gomobile users. It is known that a user with multiple visits in one day with a long interval has a larger revisiting possibility in the following month than the others. The author proposes a 4+1 bit method to incorporate this empirical law in order to cope with the two major mobile restrictions: distributed server environments and large data stream. The proposed method can be performed in a one-path manner with 32-bit word boundary-aware memory compaction. The experimental result shows the method is promising to identify revisiting users under mobile-specific constraints.\",\n \"Title: An Embedded Intelligence Model: Issues in the Real-World Empowered Organizational Computing\\nYear: 2006\\n\\nAbstract: Ubiquitous computing will penetrate into the cooperate workplaces. Additions of machine-machine interfaces in the organizational computing pose new framework challenges as well as implementation and deployment issues. The simple monotonic-increase knowledge model does not work in a complicated workplace with many context boundaries. The author enhances issues in hybrid organizational computing to cope with the pervasive and ubiquitous intelligence in the workplaces. The author discusses various dualities exposed in the ubiquitous workplace like a) space vs. place and b) logical organization and real world. The ubiquitous workplace will pose more challenges for the organizational computing from the dualities. For the resolution of these issues, the author proposes and discusses an embedded intelligence model for organizational computing. This model facilitates the framework for the authoring tools for organizational learning systems to complement the dynamic learning contexts in workplaces.\",\n \"Title: Inter-service revisit analysis of three user groups using intra-day behavior in the mobile clickstream\\nYear: 2009\\n\\nAbstract: The mobile Internet proliferates and penetrates all aspects of daily life. The 4 billion mobile users will increase their impacts on data services. The author came to a sense that the intra-day mobile behaviors are classified into three user groups: always-on, night, and infrequent users. The author proposes a two-phase method to identify these three user groups. The author shows the case study result from 2001 commercial service logs with two different mobile services. The derived user groups are evaluated by the revisit ratio in the following month. The results are discussed to highlight the three-segment model in intra-day mobile user behaviors. They show the clear revisit ratio difference among the three user groups in the four services in observation. The two services in the two case studies are significantly different, however, they still exhibit consistent patterns in the three group classification.\",\n \"Title: An exploratory analysis on user behavior regularity in the mobile internet\\nYear: 2006\\n\\nAbstract: The ever-changing nature of the mobile Internet contributes to the difficulties encountered when experts try to identify the user behavior characteristics. Using thin channels with so-called 24-hour 365-day always on nature, it is crucial to understand regularity of user access in the mobile Internet. It is leveraged by the mobile Internet-specific features like user identifies provided by wireless carriers. The author attempts to identify the easy-gone mobile Internet users from regularity dimension using a long-term user log with user identifiers. The author proposes an interval probability comparison method to predict the user behavior in the next month. The experiment from the mobile clickstream data shows the positive effect of the proposed method.\",\n \"Title: Classification of Mobile Internet User Behaviors using Qualitative Transition Patterns\\nYear: 2007\\n\\nAbstract: The mobile-Internet specific methodology to capture the user behavior characteristics is still on the hot research agenda. Based on an ad hoc assumption that the user loyalty relates to web visit regularity, the author explores regularity measures to track user behaviors. The author performed a 3-month transition pattern using regular users that visits a web every month for three months. The author discusses the stability of the measures in the long-term behaviors.\",\n \"Title: OSS as a digital ecosystem: a reference model for digital ecosystem of OSS\\nYear: 2010\\n\\nAbstract: The author presents an analytical view of OSS from the perspective of a digital ecosystem. The author proposes a three-dimensional reference model of the ecosystem of OSS.\",\n \"Title: Mobile Content Operation Design: Lessons learned in the long-term experience\\nYear: 2003\\n\\nAbstract: The mobile Internet is one of the most fast growing applicationdomains in the computer communications. The newtechnology components in the mobile Internet have beenhighlighted, however, the every day mobile content operationdesign lacked the attention in the past literature. Themobile content operation encounters a wide range of networkintegration challenges. It is difficult to identify theall operation requirements in advance in the rapidly emergingmobile Internet services. The author presents the challengesand issues from a 3-year experience of non-stop mobilecontent operation. From the lessons learned, the designissues for mobile content operation design criteria are presented.\",\n \"Title: Markov model based mobile clickstream analysis with sub-day, day and week-scale transitions\\nYear: 2007\\n\\nAbstract: Advanced cooperative work needs user context knowledge in spatial and temporal dimensions. The always-on property of the mobile Internet enables further extension of the cooperative work. It needs to extend the temporal knowledge of the user behaviors for this purpose. This paper explores the temporal dimension: different end-user behavior parameters in different time-scales using the mobile clickstream. The Markov model-based estimation in sub-day scale, day-scale and week-scale transition patterns is analyzed from monthly mobile clickstreams and hierarchical clustering is performed with the three different time-scale behaviors.\",\n \"Title: Star economy in the user generated content: a new perspective for digital ecosystems\\nYear: 2009\\n\\nAbstract: User generated content plays an important role in Web 2.0-based digital economy. The author focuses an emerging aspect of user participation-based digital economy. Cultural aspects of digital economy are largely unexplored in literature. They identified a new Internet-based social behavior so-called star-economy in a Japanese kid-based network service with the user content sharing. The author analyzed the star-economy in the emerging social game context. The author revisits the network effect (network externality) in the digital eco system in order to identify the landing pad for the star-economy. The author presents the lessons from the new dimension of network effect and exploits the staged based consideration of digital social systems.\",\n \"Title: Dilemma of Service Engineering in the Era of Social Services: Understanding the Ongoing Challenges of Service Engineering\\nYear: 2014\\n\\nAbstract: The hype surrounding the mobile social game business started in 2009 and peaked in 2012. This rise and decline provides two sets of lessons learned in the game business in Japan. The author provides a retrospective view of the mobile social game business during this period of hype until its decline. From the lessons learned, the author presents the challenges of service engineering in the era of social services.\",\n \"Title: A Non-Zero-Sum Social Experience Approach: Building Blocks of Social Experience Design\\nYear: 2014\\n\\nAbstract: Service engineering is starting to cope with the increasing needs of facilitating socially empowered services. In order to implement socially-empowered services, it requires design methodologies that deal with social experience beyond just a collection of user experience. Social experience design is promising methodology, however, the concrete building blocks of social experience design are still far from being clear. The author analyzes possible approaches and proposes a non-zero-sum social experience approach in order to facilitate social experience design in design phases and operational phases.\",\n \"Title: A social process model for cyber mind in the mobile Internet\\nYear: 2003\\n\\nAbstract: As the number of the Internet-enabled mobile handsets increases, it is apparent that the mobile Internet deeply penetrates into the every day life and impacts social interactions. The new ways of communications expose new social interaction patterns. Some of them are uncommon in the real world interactions. The author describes some of the case studies on new social interaction patterns and presents the implications for mobile Internet-augmented societies. Especially, these interaction patterns are distinguished in the viewpoints of quality of interaction modalities and loose identification. The author assumes a new social relation building process in the three aspects; communication modality, identity and distance. A mobile cyber mind is defined and several aspects of it are contrasted with those in the real world interactions. A staged AIMS model is proposed to provide a basic measure to analyze the cyber mind in the emerged technologies to augment the social interactions. The model focuses on the two aspects of the interactions: the successful communication experience and development of social bindings during interactions.\",\n \"Title: An interoperability design model: lessons learned in computer communication standardization\\nYear: 2005\\n\\nAbstract: Interoperability is one of the top priority issues in the computer communication standardizations. The computer communication protocol design becomes complicated in order to cover both of the communication parts and content parts. The content parts need more flexibility for the future evolution. The author uses the underlying assumptions, architectural aspects and processing model aspects in order to identify the protocol design strategy with lessons learned in the past. The author describes existing extensibility frameworks. Then the author proposes a logical model for architectural design and processing model selection. This model gives a measure to verify the overall consistency for the evolution-tolerant protocol design. The author compares the fits among the logical model components and the possible interoperability strategies.\",\n \"Title: An application layer protocol interoperability design model for content evolution in the mobile Internet\\nYear: 2005\\n\\nAbstract: Interoperability is one of the top priority issues in the computer communication standardizations. In the content side, the evolution is not continuous, therefore, the interoperability needs to be embedded in the communication protocol design. In the past, the communication protocol design is done in an ad hoc manner in each sequence of protocol evolution. It leads to the lack of the design strategy reuse among the different communication protocol domains. In order to provide a general guideline for the interoperable communication protocol design, a general methodology to pursue the interoperability considering the key aspects of the content evolution. The author analyzes the interoperability-aware protocol design and proposes an interoperability design model. First, the author proposes a communication protocol design framework to clarify the components to be considered in the interoperable communication protocol design. Then, the author describes the different patterns from the past lessons learned in the communication protocol design in each aspect: evolution patterns, underlying assumptions, communication architecture, and protocol processing models.\",\n \"Title: Time Management in Mobile Social Game Design: Lessons from Mobile Social Games in Japan\\nYear: 2011\\n\\nAbstract: The mobile Internet business is entering a new era in Japan with the emerging revenue being generated from mobile social games. Users using high-speed mobile Internet with flat-rate data services have acceptance of micro-payments and increased payment acceptance in mobile social games. This has been impacting mobile business and the game business in Japan for the last 12 months. Given the hype surrounding mobile social games, the author attempts to identify the guidelines in game design that were not explored in traditional game design. Time-factor is one of the to-be-explored factors in the human factors. It provides a rich source of industrial applications including social games in the anyplace and anytime context. The author highlights the time-dimensional factors in this emerging design methodology for mobile social games.\",\n \"Title: A Mobile Digital Ecosystem Framework: Lessons from the Evolution of Mobile Data Services\\nYear: 2010\\n\\nAbstract: The mobile Internet has come to permeate into everyday life. It is expected to accommodate a larger part of the digital economy in the near future. Mobile digital ecosystems have more complicated dynamisms due to the mobile-specific dependencies among their stake-holders. The author presents a framework for mobile digital ecosystems with an entity model, a dynamics model, and an acceptance model. This framework provides a basis for analyzing mobile digital ecosystems. The author identifies the factors that drive mobile digital ecosystems, and presents the challenges and factors that shape these ecosystems from the viewpoint of time dimensional evolution.\",\n \"Title: Horizontal Requirement Engineering in Integration of Multiple IoT Use Cases of City Platform as a Service\\nYear: 2017\\n\\nAbstract: As deployment of IoT grows, potential and real conflicts and interference of IoT systems also grow. Many problems are potentially born in the requirement phase. However, the vertical nature of IoT deployment prevents identification of the challenges in the difficult in the early design phase. The author is engaged in a city-area IoT and open data project, and becomes aware that the necessity of systematic methodologies at the requirement phase. The author presents a 3-stage framework of requirement engineering of joint use cases of vertical IoT services.\",\n \"Title: Toward positioning IoT-empowered service engineering in city platform as a service\\nYear: 2017\\n\\nAbstract: Internet of Things, IoT, connects physical objects and the cyberspace, which provides potentials to leverage organization and business redesign. An increasing need of digital business integration presents a broad challenge for a wide range of industries. It requires changes in design and operation processes, which ultimately leads to an organizational change. The author proposes dimensions to provide positioning of an IoT service in a comparative framework. Then, the author presents a framework using the proposed dimensions to classify IoT-empowered service engineering. This provides a general framework to assess coverage of city-scale IoT platforms.\",\n \"Title: A Social Dimension View Model of Divergence of IoT Standardization.\\nYear: 2018\\n\\nAbstract: As IoT (Internet of Things) continues to penetrate everyday life, we witness the increase in the number of IoT standardization bodies. As the coverage of standardization bodies overlap, interoperability is threatened despite of good wills of each standardization body. The author analyzes the causes of fragmentation of IoT standardization from the current landscape of IoT standardization. Then, the author presents a broken assumption model that explains the disorganized status of IoT standardization.\",\n \"Title: A Dynamism View Model of Convergence and Divergence of IoT Standardization.\\nYear: 2018\\n\\nAbstract: As IoT (Internet of Things) continues to penetrate everyday life, we witness the increase in the number of IoT standardization activities. This is a kind of business and political conflicts. It can be also viewed as an emergence of new types of standardization. In a world where complicated cyber-physical systems come to exist, legacy view models such as layered view models are not adequate to the new landscape of IoT standardization. As a departure of static structural view of standardization, the author proposes a dynamism model for divergence and convergence.\",\n \"Title: Lessons Learned in Tokyo Public Transportation Open Data APIs.\\nYear: 2018\\n\\nAbstract: Open data is a vital part of new digital economy. It facilitates value creation from combining data from multiple sources. It also important to utilize a massive data flow from emerging IoT (Internet of Things) devices. Future smart cities will consist of a large aggregation of open data APIs. The size and variety of open data APIs provide challenges for usability, consistency, and integrity. The author analyzes issues in open data APIs in the Tokyo public transportation. The author discusses multiple aspects of issues. Then, the author presents a framework with three view models to deal with open data APIs: outcome, cause, and fixes. Finally, the author discusses lessons learned in the Tokyo public transportation open data APIs.\",\n \"Title: A migration-oriented partial adaptation architecture for IoT-empowered city platform as a service\\nYear: 2017\\n\\nAbstract: Evolution of IoT-empowered city-scale platform leads to migration requirements. All successful IoT systems need to address migration of existing or external systems. The author discusses the challenges of phased migration of heterogeneous IoT systems. Then, the author presents approaches of migratable architecture. Finally, the author proposes a partial adaptation migration architecture to cope with the challenges. The mapping architecture can provide a framework of accommodating evolution of heterogeneous IoT systems.\",\n \"Title: Device Stand-by Management of IoT: A Framework for Dealing with Real-World Device Fault in City Platform as a Service.\\nYear: 2018\\n\\nAbstract: Expansion of IoT and increasing computing resources provides opportunities in edge computing. There are two types of edge computing: heavy edge computing and lightweight edge computing. The author discusses lightweight edge computing with resource-constraint from real-world use cases from a smart city project. The author proposes device management framework with stand by mechanism and device characteristics from data mining of past device behavior.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":84,"string":"84"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13273,"string":"13,273"}}},{"rowIdx":13274,"cells":{"question_id":{"kind":"string","value":"Zhiguo Wang"},"question":{"kind":"string","value":"Generate an abstract for the title \"Bilateral Multi-Perspective Matching for Natural Language Sentences.\" using the following items: 1. Natural language sentence matching \n2. Bilateral multi-perspective matching \n3. BiLSTM encoder \n4. Fix-length matching vector \n5. State-of-the-art performance"},"answer":{"kind":"string","value":"Natural language sentence matching is a fundamental technology for a variety of tasks. Previous approaches either match sentences from a single direction or only apply single granular (word-by-word or sentence-by-sentence) matching. In this work, we propose a bilateral multi-perspective matching (BiMPM) model under the matching-aggregation framework. Given two sentences $P$ and $Q$, our model first encodes them with a BiLSTM encoder. Next, we match the two encoded sentences in two directions $P rightarrow Q$ and $P leftarrow Q$. In each matching direction, each time step of one sentence is matched against all time-steps of the other sentence from multiple perspectives. Then, another BiLSTM layer is utilized to aggregate the matching results into a fix-length matching vector. Finally, based on the matching vector, the decision is made through a fully connected layer. We evaluate our model on three tasks: paraphrase identification, natural language inference and answer sentence selection. Experimental results on standard benchmark datasets show that our model achieves the state-of-the-art performance on all tasks."},"correct_answer":{"kind":"string","value":"Natural language sentence matching is a fundamental technology for a variety of tasks. Previous approaches either match sentences from a single direction or only apply single granular (word-by-word or sentence-by-sentence) matching. In this work, we propose a bilateral multi-perspective matching (BiMPM) model under the matching-aggregation framework. Given two sentences $P$ and $Q$, our model first encodes them with a BiLSTM encoder. Next, we match the two encoded sentences in two directions $P rightarrow Q$ and $P leftarrow Q$. In each matching direction, each time step of one sentence is matched against all time-steps of the other sentence from multiple perspectives. Then, another BiLSTM layer is utilized to aggregate the matching results into a fix-length matching vector. Finally, based on the matching vector, the decision is made through a fully connected layer. We evaluate our model on three tasks: paraphrase identification, natural language inference and answer sentence selection. Experimental results on standard benchmark datasets show that our model achieves the state-of-the-art performance on all tasks."},"documents":{"kind":"list like","value":["Title: Statistical modeling of syllable-level F0 features for HMM-based unit selection speech synthesis\nYear: 2010\n\nAbstract: In current hidden Markov model(HMM) based unit selection speech synthesis method, the optimal phone-sized candidate units are selected following the maximum likelihood(ML) criterion of the HMMs trained for various acoustic features. This paper introduces the statistical models for syllable-level F0 features into this method. Different from the frame-level F0 parameters used in the current framework, the pitch contour of the vowel in each syllable and its combination for adjacent syllables are extracted to represent the suprasegmental property of F0 features. A context-dependent statistical model is trained using these syllable-level F0 features and the likelihood function of this model is integrated into the unit selection criterion to evaluate the suprasegmental prosody of a given unit sequence. The conventional dynamic programming search algorithm for the phone-sized unit selection is modified to take into account the dependency between the candidate units for the vowels of adjacent syllables which is caused by the syllable-level F0 modeling. Our experiment results prove that this method can improve the naturalness of synthesized speech significantly.","Title: A modified conjugate gradient algorithm with cyclic Barzilai-Borwein steplength for unconstrained optimization\nYear: 2012\n\nAbstract: For solving large-scale unconstrained minimization problems, the nonlinear conjugate gradient method is welcome due to its simplicity, low storage, efficiency and nice convergence properties. Among all the methods in the framework, the conjugate gradient descent algorithm - CG_DESCENT is very popular, in which the generated directions descend automatically, and this nice property is independent of any line search used. In this paper, we generalize CG_DESCENT with two Barzilai-Borwein steplength reused cyclically. We show that the resulting algorithm owns attractive sufficient descent property and converges globally under some mild conditions. We test the proposed algorithm by using a large set of unconstrained problems with high dimensions in CUTEr library. The numerical comparisons with the state-of-the-art algorithm CG_DESCENT illustrate that the proposed method is effective, competitive, and promising.","Title: Phrase structure parsing with dependency structure\nYear: 2010\n\nAbstract: In this paper we present a novel phrase structure parsing approach with the help of dependency structure. Different with existing phrase parsers, in our approach the inference procedure is guided by dependency structure, which makes the parsing procedure flexibly. The experimental results show our approach is much more accurate. With the help of golden dependency trees, F1 score of our parser achieves 96.08% on Penn English Treebank and 90.61% on Penn Chinese Treebank. With the help of N-best dependency trees generated by modified MSTParser, F1 score achieves 90.54% for English and 83.93% for Chinese.","Title: Investigation of deep neural networks (DNN) for large vocabulary continuous speech recognition: Why DNN surpasses GMMS in acoustic modeling\nYear: 2012\n\nAbstract: Recently, it has been reported that context-dependent deep neural network (DNN) has achieved some unprecedented gains in many challenging ASR tasks, including the well-known Switchboard task. In this paper, we first investigate DNN for several large vocabulary speech recognition tasks. Our results have confirmed that DNN can consistently achieve about 25-30% relative error reduction over the best discriminatively trained GMMs even in some ASR tasks with up to 700 hours of training data. Next, we have conducted a series of experiments to study where the unprecedented gain of DNN comes from. Our experiments show the gain of DNN is almost entirely attributed to DNN's feature vectors that are concatenated from several consecutive speech frames within a relatively long context window. At last, we have proposed a few ideas to reconfigure the DNN input features, such as using logarithm spectrum features or VTLN normalized features in DNN. Our results have shown that each of these methods yields over 3% relative error reduction over the traditional MFCC or PLP features in DNN.","Title: Dsp-Based Parallel Implementation Of Speeded-Up Robust Features\nYear: 2011\n\nAbstract: Robust local image features have become crucial components of many state-of-the-art computer vision algorithms. Due to limited hardware resources, computing local features on embedded system is not an easy task. In this paper, we propose an efficient parallel computing framework for speeded-up robust features with an orientation towards multi-DSP based embedded system. We optimize modules in SURF to better utilize the capability of DSP chips. We also design a compact data layout to adapt to the limited memory resource and to increase data access bandwidth. A data-driven barrier and workload balance schemes are presented to synchronize parallel working chips and reduce overall cost. The experiment shows our implementation achieves competitive time efficiency compared with related works.","Title: Constructing up to G2 continuous curve on freeform surface\nYear: 2010\n\nAbstract: This paper presents new methods for G (1) and G (2) continuous interpolation of an arbitrary sequence of points on an implicit or parametric surface with prescribed tangent direction and both tangent direction and curvature vector, respectively, at every point. We design a G (1) or G (2) continuous curve in three-dimensional space, construct a so-called directrix vector field using the space curve and then project a special straight line segment onto the given surface along the directrix vector field. With the techniques in classical differential geometry, we derive a system of differential equations for the projection curve. The desired interpolation curve is just the projection curve, which can be obtained by numerically solving the initial-value problems for a system of first-order ordinary differential equations in the parametric domain associated to the surface representation for the parametric case or in three-dimensional space for the implicit case. Several shape parameters are introduced into the resulting curve, which can be used in subsequent interactive modification such that the shape of the resulting curve meets our demand. The presented method is independent of the geometry and parameterization of the base surface, and numerical experiments demonstrate that it is effective and potentially useful in patterns design on surface.","Title: An implicit control-volume finite element method and its time step strategies for injection molding simulation\nYear: 2007\n\nAbstract: An implicit control-volume finite element method was proposed for simulation of injection molding, the time steps were controlled for both flow and thermal simulation by local flow information, and then the computing complexity analysis was conducted. The implicit scheme was based on updating the melt–air interface. The time steps of implicit algorithm were controlled by injection ratio and local mesh information of flow front to achieve “one time step, one element-layer” for filling simulation; the sub-time steps of computing temperatures were calculated for each node according to local courant number, and the thermal simulation was conducted by the filled order of nodes. Both analysis and case studies showed that the computing complexity of flow simulation was second-order of mesh size (nodal number) and one of thermal simulation was 1.5-order. As a result, the computing times and complexity of solving temperature were less than those of solving pressure.","Title: A Trellis Based Fast Lattice Generating Algorithm\nYear: 2008\n\nAbstract: Lattice is widely used as a kind of the search results in Large Vocabulary Continuous Speech Recognition (LVCSR). A new lattice-generation algorithm is presented in this paper. The algorithm is based on a classical forward-backward decoding method, which is proved to be highly efficient. Moreover, some improvements have been done to satisfy the requirements in the lattice decoding. Two Chinese mandarin large-scale speech recognition tasks are used to evaluate the proposed algorithm and the experimental results show that our algorithm can both improve decoding speed and save decoding space significantly without sacrificing the recognition accuracy, compared with the widely used Lattice decoding method as [7].","Title: Automating nurse self-rostering: A multiagent systems model\nYear: 2009\n\nAbstract: This paper presents a multiagent systems model for constructing nurse rosters. Over the past decade self-rostering has become more favorable in nursing personnel scheduling, due to its empowerment and motivational benefits. However, the labor intensive negotiation procedure among participants has limited its application to medium sized and large wards. To overcome this limitation, we propose an automated negotiation tool utilizing economic-based negotiation mechanisms. We model the environment as a multiagent system. Nurses can indicate their preferences by configuring the preference profiles of the nurse agents, and the nurse agents collectively construct rosters through the negotiation with the management agents and among themselves. To support the design and implementation of automated self-rostering systems, we present a multiagent systems architecture, a structure of preference information, and a negotiation mechanism.","Title: Due-Date Management Through Iterative Bidding\nYear: 2011\n\nAbstract: This paper proposes an iterative bidding framework for integrated due-date management (DDM) decision making. We focus on a type of make-to-order environment in which a firm needs to quote due dates and prices and to schedule the production of a variety of job orders required by a large group of customers. In most cases, customers prefer shorter due dates. However, given limited production capacity and various cost constraints, the firm has to balance the attractiveness of its due-date quotations and the reliability of delivering accepted job orders. The key issue is how to integrate DDM decisions such that high-quality solutions, which benefit both the firm and the customers, can be obtained. We study the integrated DDM in an economic setting where customers are modeled as self-interested agents and the objective of the firm is to maximize social welfare. We present an iterative bidding framework as a decentralized decision support tool which enables the integration of key DDM decisions. Effective solutions are achieved through the automated negotiation between the firm and its customers. We provide analytical results on the application of the proposed framework to two special cases of the integrated DDM. We also evaluate the performance of the framework on general DDM problems through a computational study.","Title: A limited memory BFGS-type method for large-scale unconstrained optimization\nYear: 2008\n\nAbstract: In this paper, a new numerical method for solving large-scale unconstrained optimization problems is presented. It is derived from a modified BFGS-type update formula by Wei, Li, and Qi. It is observed that the update formula can be extended to the framework of limited memory scheme with hardly more storage or arithmetic operations. Under some suitable conditions, the global convergence property is established. The implementations of the method on a set of CUTE problems indicate that this extension is beneficial to the performance of the algorithm.","Title: Bidirectional Sequence Labeling Via Dual Decomposition\nYear: 2013\n\nAbstract: In this paper, we propose a bidirectional algorithm for sequence labeling to capture the influence of both the left-to-right and the right-to-left directions. We combine the optimization of two unidirectional models from opposite directions via the dual decomposition method to jointly label the input sequence. Experiments on three sequence labeling tasks (Chinese word segmentation, English POS tagging and text chunking) show that our approach can improve the accuracy of sequence labeling tasks when the two unidirectional models individually make highly different predictions.","Title: An integer programming model and heuristic algorithm for automatic scheduling in synchrotron facilities\nYear: 2009\n\nAbstract: This paper studies the automatic scheduling problem at the Canadian national synchrotron facility, Canadian Light Source (CLS). An automatic scheduling tool needs to be developed to replace the current manual approach for scheduling experiments on a set of beamlines - resources that generate high-intensity X-rays for use in many kinds of scientific experiments. We present an, Integer programming model for this scheduling activity by formulating it as a problem of unrelated and paralleled machines with partially overlapping capabilities. Furthermore a heuristic based approach is used that can save computation time by pruning the search space. Using realistic data sets generated using parameters made available by CLS, we compare the performance of the base line approach that uses ILOG CPLEX implementation of the Integer programming algorithm with one that uses heuristics. The results show that the heuristic approach runs faster than the base-line, but at the cost of producing a less optimal scheduling solution. An obvious advantage of the study presented in this paper is that the automatic scheduling can handle more scheduling conditions and constraints than humans are able to handle manually and can reach optimal solutions. As far as we know, this is the first attempt to propose an automatic scheduling approach for synchrotron facilities like CLS around the world.","Title: Phonetic clustering based confidence measure for embedded speech recognition\nYear: 2010\n\nAbstract: Word posterior probability (WPP) based confidence measure (CM) has been applied successfully in LVCSR tasks. However, for embedded speech recognition in which system resource is limited, not only performance of CM but also efficiency of the algorithm need to be considered. One of the most important issue in calculating WPP is how to obtain reliable estimation of the normalization term. So in this paper we investigate several methods to estimate the normalization term and focus on methods using different phone-based grammar. Furthermore, to make good trade-off between performance and efficiency for embedded system, we present a general approach of estimating WPP based confidence score based on data-driven phonetic clustering, where Kullback-Leibler divergence (KLD) is employed for grouping all phones into different clusters. Corresponding acoustic and language models for calculating CM score can be re-trained based on the clustering phones. Experimental results on different Mandarin command word and digit recognition tasks show that the proposed method can significantly improve the efficiency with little degradation in CM performance, where more than 90% processing time of CM module is saved.","Title: Optimal system, symmetry reductions and new closed form solutions for the geometric average Asian options\nYear: 2014\n\nAbstract: In this paper, the Lie group analysis method is applied to the geometric average Asian option pricing equation in financial problems. Firstly, the complete Lie symmetry group and infinitesimal generators of this equation are derived. Then the optimal system with one parameter for the Lie symmetry algebra are obtained, which gives the possibility to describe a complete set of invariant solutions to the pricing equation. Finally, based on the optimal system the symmetry reductions and corresponding closed form solutions for the pricing equation are proposed.","Title: Existence of quasiperiodic solutions and Littlewood’s boundedness problem of super-linear impact oscillators\nYear: 2011\n\nAbstract: So far most application of Kolmogorov–Arnold–Moser (KAM) theory has been restricted to smooth dynamical systems. In this paper, it is shown by a series of transformations that how KAM theory can be used to analyze the dynamical behavior of Duffing-type equations with impact. The analysis is carried out for the example(0.1)x¨+x2n+1=p(t),forx(t)>0,x(t)⩾0,x˙(t0+)=-x˙(t0-),ifx(t0)=0with p∈C5 being periodic. We prove that all solutions are bounded, and that there are infinitely many periodic and quasiperiodic solutions in this case.","Title: The effect of traffic on situation awareness and mental workload: simulator-based study\nYear: 2007\n\nAbstract: In the present study, we investigated the effects of the different traffic on the driver's situation awareness and the mental workload (MWL). The task used in this study was a medium fidelity, 3-dimensional simulation of a driving environment. The simulation required participants to drive the user's car and perform a real-world driving task. After the simulated driving, participants were asked to complete two tests which assessed their situation awareness (SA). The mental workload measures in this study consisted of the physiological measures and the subjective assessment. Every participant performed two different traffic simulated driving conditions, one was low traffic, the other was high traffic. The results showed that with the increasing of traffic, the driving performance did not worsen, however participant's mental workload increased, at the same time, the participant's situation awareness performance deteriorated. Meanwhile, our results also demonstrated that recall-based SA test and recognition-based test was heterogeneous.","Title: Time-Frequency Analysis of Seismic Data Using Synchrosqueezing Transform\nYear: 2014\n\nAbstract: Time-frequency analysis can provide useful information in seismic data processing and interpretation. An accurate time-frequency representation is important in highlighting subtle geologic structures and in detecting anomalies associated with hydrocarbon reservoirs. The popular methods, like short-time Fourier transform and wavelet analysis, have limitations in dealing with fast varying instantaneous frequencies, which is often the characteristic of seismic data. The synchrosqueezing transform (SST) is a promising tool to provide a detailed time-frequency representation. We apply the SST to seismic data and show its potential to seismic signal processing applications.","Title: Joint Pos Tagging And Transition-Based Constituent Parsing In Chinese With Non-Local Features\nYear: 2014\n\nAbstract: We propose three improvements to address the drawbacks of state-of-the-art transition-based constituent parsers. First, to resolve the error propagation problem of the traditional pipeline approach, we incorporate POS tagging into the syntactic parsing process. Second, to alleviate the negative influence of size differences among competing action sequences, we align parser states during beam-search decoding. Third, to enhance the power of parsing models, we enlarge the feature set with non-local features and semi-supervised word cluster features. Experimental results show that these modifications improve parsing performance significantly. Evaluated on the Chinese TreeBank (CTB), our final performance reaches 86.3% (F1) when trained on CTB 5.1, and 87.1% when trained on CTB 6.0, and these results outperform all state-of-the-art parsers.","Title: Nonlinear elasto-mammography for characterization of breast tissue properties\nYear: 2011\n\nAbstract: Quantification of the mechanical behavior of normal and cancerous tissues has important implication in the diagnosis of breast tumor. The present work extends the authors' nonlinear elastography framework to incorporate the conventional X-ray mammography, where the projection of displacement information is acquired instead of full three-dimensional (3D) vector. The elastic parameters of normal and cancerous breast tissues are identified by minimizing the difference between the measurement and the corresponding computational prediction. An adjoint method is derived to calculate the gradient of the objective function. Simulations are conducted on a 3D breast phantom consisting of the fatty tissue, glandular tissue, and cancerous tumor, whose mechanical responses are hyperelastic in nature. The material parameters are identified with consideration of measurement error. The results demonstrate that the projective displacements acquired in X-ray mammography provide sufficient constitutive information of the tumor and prove the usability and robustness of the proposed method and algorithm.","Title: Adaptive Variable Time Fractional Anisotropic Diffusion Filtering for Seismic Data Noise Attenuation\nYear: 2016\n\nAbstract: Seismic records are often contaminated with various kinds of noise, which makes it very difficult to distinguish the expected geological features. In this paper, we introduce a novel adaptive variable time fractional-order anisotropic diffusion equation for seismic data noise removal and strongly oriented structure enhancement. Since the time fractional-order differential equation interpolates a parabolic equation and a hyperbolic equation, the solution benefits both of these approaches. The presented differential equation can be written as a Volterra integral equation, and its well-posedness can be guaranteed for all time. We employ a structure tensor to analyze the flow-like texture characteristic which is typical in seismic data. Then, the diffusion process is guided reasonably by a diffusion tensor based on the structure tensor analysis which allows real anisotropic behavior comparing to the classical scalar diffusion approach. In reference to the numerical implementation, we utilize the predictor–corrector algorithm to solve the Volterra integral equation which provides high-order numerical precision jointly with good stability property. Finally, numerical experiments involved with synthetic and prestacked real seismic data are presented. The obtained results demonstrate that the noise is effectively removed, and the coherent seismic events that express some important geological structures are not only preserved but significantly enhanced.","Title: Semi-supervised Clustering for Short Text via Deep Representation Learning.\nYear: 2016\n\nAbstract: In this work, we propose a semisupervised method for short text clustering, where we represent texts as distributed vectors with neural networks, and use a small amount of labeled data to specify our intention for clustering. We design a novel objective to combine the representation learning process and the kmeans clustering process together, and optimize the objective with both labeled data and unlabeled data iteratively until convergence through three steps: (1) assign each short text to its nearest centroid based on its representation from the current neural networks; (2) re-estimate the cluster centroids based on cluster assignments from step (1); (3) update neural networks according to the objective by keeping centroids and cluster assignments fixed. Experimental results on four datasets show that our method works significantly better than several other text clustering methods.","Title: Sentence Similarity Learning by Lexical Decomposition and Composition.\nYear: 2016\n\nAbstract: Most conventional sentence similarity methods only focus on similar parts of two input sentences, and simply ignore the dissimilar parts, which usually give us some clues and semantic meanings about the sentences. In this work, we propose a model to take into account both the similarities and dissimilarities by decomposing and composing lexical semantics over sentences. The model represents each word as a vector, and calculates a semantic matching vector for each word based on all words in the other sentence. Then, each word vector is decomposed into a similar component and a dissimilar component based on the semantic matching vector. After this, a two-channel CNN model is employed to capture features by composing the similar and dissimilar components. Finally, a similarity score is estimated over the composed feature vectors. Experimental results show that our model gets the state-of-the-art performance on the answer sentence selection task, and achieves a comparable result on the paraphrase identification task.","Title: Feature Optimization For Constituent Parsing Via Neural Networks\nYear: 2015\n\nAbstract: The performance of discriminative constituent parsing relies crucially on feature engineering, and effective features usually have to be carefully selected through a painful manual process. In this paper, we propose to automatically learn a set of effective features via neural networks. Specifically, we build a feedforward neural network model, which takes as input a few primitive units (words, POS tags and certain contextual tokens) from the local context, induces the feature representation in the hidden layer and makes parsing predictions in the output layer. The network simultaneously learns the feature representation and the prediction model parameters using a back propagation algorithm. By pre-training the model on a large amount of automatically parsed data, and then fine-tuning on the manually annotated Treebank data, our parser achieves the highest F-1 score at 86.6% on Chinese Treebank 5.1, and a competitive F-1 score at 90.7% on English Treebank. More importantly, our parser generalizes well on cross-domain test sets, where we significantly outperform Berkeley parser by 3.4 points on average for Chinese and 2.5 points for English.","Title: Vocabulary Manipulation For Neural Machine Translation\nYear: 2016\n\nAbstract: In order to capture rich language phenomena, neural machine translation models have to use a large vocabulary size, which requires high computing time and large memory usage. In this paper, we alleviate this issue by introducing a sentence-level or batch-level vocabulary, which is only a very small sub-set of the full output vocabulary. For each sentence or batch, we only predict the target words in its sentence-level or batch-level vocabulary. Thus, we reduce both the computing time and the memory usage. Our method simply takes into account the translation options of each word or phrase in the source sentence, and picks a very small target vocabulary for each sentence based on a word-to-word translation model or a bilingual phrase library learned from a traditional machine translation model. Experimental results on the large-scale English-to-French task show that our method achieves better translation performance by 1 BLEU point over the large vocabulary neural machine translation system of Jean et al. (2015).","Title: Sense Embedding Learning for Word Sense Induction.\nYear: 2016\n\nAbstract: Conventional word sense induction (WSI) methods usually represent each instance with discrete linguistic features or cooccurrence features, and train a model for each polysemous word individually. In this work, we propose to learn sense embeddings for the WSI task. In the training stage, our method induces several sense centroids (embedding) for each polysemous word. In the testing stage, our method represents each instance as a contextual vector, and induces its sense by finding the nearest sense centroid in the embedding space. The advantages of our method are (1) distributed sense vectors are taken as the knowledge representations which are trained discriminatively, and usually have better performance than traditional count-based distributional models, and (2) a general model for the whole vocabulary is jointly trained to induce sense centroids under the mutlitask learning framework. Evaluated on SemEval-2010 WSI dataset, our method outperforms all participants and most of the recent state-of-the-art methods. We further verify the two advantages by comparing with carefully designed baselines.","Title: Supervised Attentions for Neural Machine Translation.\nYear: 2016\n\nAbstract: In this paper, we improve the attention or alignment accuracy of neural machine translation by utilizing the alignments of training sentence pairs. We simply compute the distance between the machine attentions and the true alignments, and minimize this cost in the training procedure. Our experiments on large-scale Chinese-to-English task show that our model improves both translation and alignment qualities significantly over the large-vocabulary neural machine translation system, and even beats a state-of-the-art traditional syntax-based system.","Title: AMR-to-text generation as a Traveling Salesman Problem.\nYear: 2016\n\nAbstract: The task of AMR-to-text generation is to generate grammatical text that sustains the semantic meaning for a given AMR graph. We at- tack the task by first partitioning the AMR graph into smaller fragments, and then generating the translation for each fragment, before finally deciding the order by solving an asymmetric generalized traveling salesman problem (AGTSP). A Maximum Entropy classifier is trained to estimate the traveling costs, and a TSP solver is used to find the optimized solution. The final model reports a BLEU score of 22.44 on the SemEval-2016 Task8 dataset.","Title: Coverage Embedding Models for Neural Machine Translation.\nYear: 2016\n\nAbstract: In this paper, we enhance the attention-based neural machine translation (NMT) by adding explicit coverage embedding models to alleviate issues of repeating and dropping translations in NMT. For each source word, our model starts with a full coverage embedding vector to track the coverage status, and then keeps updating it with neural networks as the translation goes. Experiments on the large-scale Chinese-to-English task show that our enhanced model improves the translation quality significantly on various test sets over the strong large vocabulary NMT system.","Title: Multi-Perspective Context Matching for Machine Comprehension.\nYear: 2016\n\nAbstract: Previous machine comprehension (MC) datasets are either too small to train end-to-end deep learning models, or not difficult enough to evaluate the ability of current MC techniques. The newly released SQuAD dataset alleviates these limitations, and gives us a chance to develop more realistic MC models. Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM) model, which is an end-to-end system that directly predicts the answer beginning and ending points in a passage. Our model first adjusts each word-embedding vector in the passage by multiplying a relevancy weight computed against the question. Then, we encode the question and weighted passage by using bi-directional LSTMs. For each point in the passage, our model matches the context of this point against the encoded question from multiple perspectives and produces a matching vector. Given those matched vectors, we employ another bi-directional LSTM to aggregate all the information and predict the beginning and ending points. Experimental result on the test set of SQuAD shows that our model achieves a competitive result on the leaderboard.","Title: Amr-To-Text Generation With Synchronous Node Replacement Grammar\nYear: 2017\n\nAbstract: This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar. During training, graph-to-string rules are learned using a heuristic extraction algorithm. At test time, a graph transducer is applied to collapse input AMRs and generate output sentences. Evaluated on a standard benchmark, our method gives the state-of-the-art result.","Title: A Unified Query-based Generative Model for Question Generation and Question Answering.\nYear: 2017\n\nAbstract: We propose a query-based generative model for solving both tasks of question generation (QG) and question an- swering (QA). The model follows the classic encoder- decoder framework. The encoder takes a passage and a query as input then performs query understanding by matching the query with the passage from multiple per- spectives. The decoder is an attention-based Long Short Term Memory (LSTM) model with copy and coverage mechanisms. In the QG task, a question is generated from the system given the passage and the target answer, whereas in the QA task, the answer is generated given the question and the passage. During the training stage, we leverage a policy-gradient reinforcement learning algorithm to overcome exposure bias, a major prob- lem resulted from sequence learning with cross-entropy loss. For the QG task, our experiments show higher per- formances than the state-of-the-art results. When used as additional training data, the automatically generated questions even improve the performance of a strong ex- tractive QA system. In addition, our model shows bet- ter performance than the state-of-the-art baselines of the generative QA task.","Title: R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering.\nYear: 2017\n\nAbstract: In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et al., 2016) dataset, which provides a pre-selected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e.g., wikipedia) instead of a pre-selected passage (Chen et al., 2017a). This setting is more complex as it requires large-scale search for relevant passages by an information retrieval component, combined with a reading comprehension model that reads the passages to generate an answer to the question. Performance in this setting lags considerably behind closed-domain performance. In this paper, we present a novel open-domain QA system called Reinforced Ranker-Reader $(R^3)$, based on two algorithmic innovations. First, we propose a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of generating the ground-truth answer to a given question. Second, we propose a novel method that jointly trains the Ranker along with an answer-generation Reader model, based on reinforcement learning. We report extensive experimental results showing that our method significantly improves on the state of the art for multiple open-domain QA datasets.","Title: k-Nearest Neighbor Augmented Neural Networks for Text Classification.\nYear: 2017\n\nAbstract: In recent years, many deep-learning based models are proposed for text classification. This kind of models well fits the training set from the statistical point of view. However, it lacks the capacity of utilizing instance-level information from individual instances in the training set. In this work, we propose to enhance neural network models by allowing them to leverage information from $k$-nearest neighbor (kNN) of the input text. Our model employs a neural network that encodes texts into text embeddings. Moreover, we also utilize $k$-nearest neighbor of the input text as an external memory, and utilize it to capture instance-level information from the training set. The final prediction is made based on features from both the neural network encoder and the kNN memory. Experimental results on several standard benchmark datasets show that our model outperforms the baseline model on all the datasets, and it even beats a very deep neural network model (with 29 layers) in several datasets. Our model also shows superior performance when training instances are scarce, and when the training set is severely unbalanced. Our model also leverages techniques such as semi-supervised training and transfer learning quite well.","Title: Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering.\nYear: 2017\n\nAbstract: Very recently, it comes to be a popular approach for answering open-domain questions by first searching question-related passages, then applying reading comprehension models to extract answers. Existing works usually extract answers from single passages independently, thus not fully make use of the multiple searched passages, especially for the some questions requiring several evidences, which can appear in different passages, to be answered. The above observations raise the problem of evidence aggregation from multiple passages. In this paper, we deal with this problem as answer re-ranking. Specifically, based on the answer candidates generated from the existing state-of-the-art QA model, we propose two different re-ranking methods, strength-based and coverage-based re-rankers, which make use of the aggregated evidences from different passages to help entail the ground-truth answer for the question. Our model achieved state-of-the-arts on three public open-domain QA datasets, Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8% improvement on the former two datasets.","Title: Knowledge Base Relation Detection via Multi-View Matching.\nYear: 2018\n\nAbstract: Relation detection is a core component for Knowledge Base Question Answering (KBQA). In this paper, we propose a knowledge base (KB) relation detection model based on multi-view matching, which utilizes useful information extracted from questions and KB. The matching inside each view is through multiple perspectives to compare two input texts thoroughly. All these components are trained in an end-to-end neural network model. Experiments on SimpleQuestions and WebQSP yield state-of-the-art results on relation detection.","Title: Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks.\nYear: 2018\n\nAbstract: Celebrated emph{Sequence to Sequence learning (Seq2Seq)} and its fruitful variants are powerful models to achieve excellent performance on the tasks that map sequences to sequences. However, these are many machine learning tasks with inputs naturally represented in a form of graphs, which imposes significant challenges to existing Seq2Seq models for lossless conversion from its graph form to the sequence. In this work, we present a general end-to-end approach to map the input graph to a sequence of vectors, and then another attention-based LSTM to decode the target sequence from these vectors. Specifically, to address inevitable information loss for data conversion, we introduce a novel graph-to-sequence neural network model that follows the encoder-decoder architecture. Our method first uses an improved graph-based neural network to generate the node and graph embeddings by a novel aggregation strategy to incorporate the edge direction information into the node embeddings. We also propose an attention based mechanism that aligns node embeddings and decoding sequence to better cope with large graphs. Experimental results on bAbI task, Shortest Path Task, and Natural Language Generation Task demonstrate that our model achieves the state-of-the-art performance and significantly outperforms other baselines. We also show that with the proposed aggregation strategy, our proposed model is able to quickly converge to good performance.","Title: R3: Reinforced Ranker-Reader for Open-Domain Question Answering.\nYear: 2018\n\nAbstract: In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings(1) such as the SQuAD (Rajpurkar et al. 2016) dataset, which provides a pre-selected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e.g., wikipedia) instead of a pre-selected passage (Chen et al. 2017a). This setting is more complex as it requires large-scale search for relevant passages by an information retrieval component, combined with a reading comprehension model that \"reads\" the passages to generate an answer to the question. Performance in this setting lags well behind closed-domain performance. In this paper, we present a novel open-domain QA system called Reinforced Ranker-Reader (R-3), based on two algorithmic innovations. First, we propose a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of extracting the ground-truth answer to a given question. Second, we propose a novel method that jointly trains the Ranker along with an answer-extraction Reader model, based on reinforcement learning. We report extensive experimental results showing that our method significantly improves on the state of the art for multiple open-domain QA datasets.(2)","Title: A Tighter Set-Membership Filter for Some Nonlinear Dynamic Systems.\nYear: 2018\n\nAbstract: In this paper, we propose a tighter set-membership filter for some nonlinear dynamic systems by using an analytic method and a boundary sampling technique. The nonlinear dynamic systems can be linearized about the current estimate, then the remainder term is bounded in real time by an optimization ellipsoid, other than a priori remainder bound. For a 2-D radar system and a quadratic system, some regular properties can be derived for the remainder term, which helps us obtain a tighter bounding ellipsoid to cover the remainder. Moreover, the prediction step and the measurement update step are derived based on the recent optimization method and the on-line bounding ellipsoid of the remainder, so that a tighter set-membership filter can be achieved. The numerical examples demonstrate the effectiveness of the proposed filter.","Title: The Cramér-Rao Bounds and Sensor Selection for Nonlinear Systems with Uncertain Observations.\nYear: 2018\n\nAbstract: This paper considers the problems of the posterior Cramer-Rao bound and sensor selection for multi-sensor nonlinear systems with uncertain observations. In order to effectively overcome the difficulties caused by uncertainty, we investigate two methods to derive the posterior Cramer-Rao bound. The first method is based on the recursive formula of the Cramer-Rao bound and the Gaussian mixture model. Nevertheless, it needs to compute a complex integral based on the joint probability density function of the sensor measurements and the target state. The computation burden of this method is relatively high, especially in large sensor networks. Inspired by the idea of the expectation maximization algorithm, the second method is to introduce some 0-1 latent variables to deal with the Gaussian mixture model. Since the regular condition of the posterior Cramer-Rao bound is unsatisfied for the discrete uncertain system, we use some continuous variables to approximate the discrete latent variables. Then, a new Cramer-Rao bound can be achieved by a limiting process of the Cramer-Rao bound of the continuous system. It avoids the complex integral, which can reduce the computation burden. Based on the new posterior Cramer-Rao bound, the optimal solution of the sensor selection problem can be derived analytically. Thus, it can be used to deal with the sensor selection of a large-scale sensor networks. Two typical numerical examples verify the effectiveness of the proposed methods.","Title: A Graph-To-Sequence Model For Amr-To-Text Generation\nYear: 2018\n\nAbstract: The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. Although it is able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus faces challenges with large graphs, which result in long sequences. We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics. On a standard benchmark, our model shows superior results to existing methods in the literature.","Title: Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model.\nYear: 2018\n\nAbstract: Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees. In this paper, we first propose to use the textit{syntactic graph} to represent three types of syntactic information, i.e., word order, dependency and constituency features. We further employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.","Title: N-ary Relation Extraction using Graph State LSTM.\nYear: 2018\n\nAbstract: Cross-sentence $n$-ary relation extraction detects relations among $n$ entities across multiple sentences. Typical methods formulate an input as a textit{document graph}, integrating various intra-sentential and inter-sentential dependencies. The current state-of-the-art method splits the input graph into two DAGs, adopting a DAG-structured LSTM for each. Though being able to model rich linguistic knowledge by leveraging graph edges, important information can be lost in the splitting procedure. We propose a graph-state LSTM model, which uses a parallel state to model each word, recurrently enriching state values via message passing. Compared with DAG LSTMs, our graph LSTM keeps the original graph structure, and speeds up computation by allowing more parallelization. On a standard benchmark, our model shows the best result in the literature.","Title: SQL-to-Text Generation with Graph-to-Sequence Model.\nYear: 2018\n\nAbstract: Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. In this paper, we first introduce a strategy to represent the SQL query as a directed graph and then employ a graph-to-sequence model to encode the global structure information into node embeddings. This model can effectively learn the correlation between the SQL query pattern and its interpretation. Experimental results on the WikiSQL dataset and Stackoverflow dataset show that our model significantly outperforms the Seq2Seq and Tree2Seq baselines, achieving the state-of-the-art performance.","Title: An effective computer aided diagnosis model for pancreas cancer on PET/CT images.\nYear: 2018\n\nAbstract: •This paper proposes a new CAD model for pancreas cancer on PET/CT images, including (1) pancreas segmentation, (2) feature extraction and selection, (3) classifier design, respectively.•Pancreas segmentation is performed using SLIC on CT pseudo-color images generated by the gray interval mapping (GIP) method.•Dual threshold principal component analysis (DT-PCA) is developed to select the most beneficial feature combination, which not only considers principal features but also integrates some non-principal features into a new polar angle representation.•Hybrid feedback support vector machine-random forest (HFB-SVM-RF) model is designed to identify normal pancreas or pancreas cancer and the key is to use 8 kinds of SVMs to establish the decision trees of RF.•Contrast experiments in 10-fold cross validation demonstrate the efficiency and accuracy of the proposed CAD model as well as its performance advantages as compared with related methods.","Title: Data-driven scheduling optimization under uncertainty using Renyi entropy and skewness criterion.\nYear: 2018\n\nAbstract: •A scheduling optimization problem under resource cost uncertainties is studied.•An integrated Renyi mean-entropy-skewness information criterion is proposed.•Data-driven approximations of the information criterion are developed.•Real industrial applications with diverse system scales and data types are studied.•Reduced average dispersion rates are achieved in all the computational experiments.","Title: Using a Dynamic Neural Field Model to Explore a Direct Collicular Inhibition Account of Inhibition of Return.\nYear: 2013\n\nAbstract: When the interval between a transient flash of light (a \"cue\"\r\n) and a second visual response signal (a \"target\") exceeds at least 200\r\nms, responding is slowest in the direction indicated by the first signal.\r\nThis phenomenon is commonly referred to as inhibition of return (IOR).\r\nThe dynamic neural field model (DNF) has proven to have broad explanatory\r\npower for IOR, effectively capturing many empirical results.\r\nPrevious work has used a short-term depression (STD) implementation\r\nof IOR, but this approach fails to explain many behavioral phenomena\r\nobserved in the literature. Here, we explore a variant model of IOR involving\r\na combination of STD and delayed direct collicular inhibition.\r\nWe demonstrate that this hybrid model can better reproduce established\r\nbehavioural results. We use the results of this model to propose several\r\nexperiments that would yield particularly valuable insight into the nature\r\nof the neurophysiological mechanisms underlying IOR.","Title: Novel Disturbance-Observer-Based Control For Systems With High-Order Mismatched Disturbances\nYear: 2018\n\nAbstract: A novel disturbance-observer-based control method is investigated to attenuate the high-order mismatched disturbances. First, a finite-time disturbance observer (FTDO) is proposed to estimate the disturbances as well as the derivatives. By incorporating the outputs of FTDO, the original system is then reconstructed, where the mismatched disturbances are transformed to the matched ones that are compensated by feed-forward algorithm. Moreover, a feedback control law is developed to achieve the stability and tracking performance requirements for the systems. Finally, the proposed composite control method is applied to an unmanned helicopter system. The simulation results demonstrate that the proposed control method exhibits excellent control performance in the presence of high-order matched and mismatched disturbances.","Title: Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks.\nYear: 2018\n\nAbstract: Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Previous work approximates global evidence with local coreference information, encoding coreference chains with DAG-styled GRU layers within a gated-attention reader. However, coreference is limited in providing information for rich inference. We introduce a new method for better connecting global evidence, which forms more complex graphs compared to DAGs. To perform evidence integration on our graphs, we investigate two recent graph neural networks, namely graph convolutional network (GCN) and graph recurrent network (GRN). Experiments on two standard datasets show that richer global information leads to better answers. Our method performs better than all published results on these datasets.","Title: Learning 3D non-rigid deformation based on an unsupervised deep learning for PET/CT image registration.\nYear: 2019\n\nAbstract: This study proposes a novel method to learn a three-dimensional (3D) non-rigid deformation for automatic image registration between positron emission tomography (PET) and computed tomography (CT) scans obtained from the same patient. The proposed scheme includes two modules: (1) A low-resolution displacement vector field (LR-DVF) estimator, which employs a 3D deep convolutional network (ConvNet) to directly estimate the voxel-wise displacement (a 3D vector field) between PET/CT images; and (2) a 3D spatial transformer and re-sampler, which warps the PET images to match the anatomical structures in the CT images using the estimated 3D vector field. The parameters of the ConvNet are learned from a number of PET/CT image pairs, through an unsupervised learning method. The normalized cross correlation (NCC) between the PET/CT images is employed as the similarity metric to guide an end-to-end learning process, with a constraint (regularization term) to preserve the smoothness of the 3D deformations. A dataset containing 170 PET/CT scans is utilized in experiments, based on 10-fold cross-validation, where 22,338 3D patches are sampled from the dataset. In each fold, 3D patches from 153 patients (90%) are utilized to train the parameters, while the remaining whole-body voxels from 17 patients (10%) are used to test the image registration performance. The experimental results demonstrate that the image registration accuracy (the mean value of NCCs) is increased from 0.402 (the initial situation) to 0.567 on PET/CT scans using the proposed scheme. We also compare the performance of our scheme with previous work (DIRNet), and the advantages of our scheme are confirmed by promising results.","Title: Semantic Neural Machine Translation using AMR.\nYear: 2019\n\nAbstract: It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models. On the other hand, little work has been done on leveraging semantics for neural machine translation (NMT). In this work, we study the usefulness of AMR (short for abstract meaning representation) on NMT. Experiments on a standard English-to-German dataset show that incorporating AMR as additional knowledge can significantly improve a strong attention-based sequence-to-sequence neural translation model.","Title: On equivalence of major relaxation methods for minimum ellipsoid covering intersection of ellipsoids\nYear: 2019\n\nAbstract: This paper investigates the problem on the minimum trace or volume ellipsoid covering intersection of ellipsoids. This problem can be solved by three major relaxation methods involving semi-definite programming relaxation, S-procedure relaxation and parameterized bounding ellipsoid relaxation. They are proposed from the different ideas or viewpoints, so that it is difficult to judge which relaxation method is the most suitable for the different practical implementations. To the best of our knowledge, the problem of proving the convexity of the optimization problem derived by the third relaxation method remains open. By rigorous analysis, we reveal the equivalence among the three relaxation methods, and address the open problem, i.e., the optimization problem derived by the third relaxation method can be equivalently transformed to a convex optimization problem. The computation complexity of these relaxation methods are also discussed, and the equivalent result is helpful to set membership filter and multisensor fusion obtaining a tighter ellipsoid to contain the state with lower computation complexity. Finally, the performance of each method is evaluated by several typical numerical examples in information fusion and filtering.","Title: Ellipsoidal Fusion Estimation for Multisensor Dynamic Systems with Bounded Noises\nYear: 2019\n\nAbstract: This paper considers the set membership fusion estimation problem for the general multisensor dynamic systems with unknown but bounded noises. In order to achieve the ellipsoidal fusion formulae, we divide the set membership filter problem into two steps: the prediction step and the fusion update step. The proposed method has the following nice properties. First, each step can be converted into a semidefinite programming problem, which can be efficiently computed. Second, part-analytical formulae of the shape matrix and the center of the bounding ellipsoid can be derived by using a decoupled technique, which can significantly reduce the computational complexity. Moreover, the relationship between the fusion center and local sensors can be clearly revealed based on the ellipsoidal fusion formula. Finally, it is interesting that the ellipsoidal fusion formula is similar in form to the classic mean-squared error filter fusion, although they are of the different optimization frameworks. However, the fusion weights of the information matrix provided by the different sensors are different and the optimal fusion weights can be calculated by solving a convex optimization problem. A typical numerical example in target tracking demonstrates the effectiveness of the proposed centralized and distributed fusion formula.","Title: Global Dynamics Of An Ecosystem In Open Advective Environments\nYear: 2021\n\nAbstract: This paper deals with a kind of reaction-diffusion-advection model which depicts a predator-prey ecosystem in rivers or streams. We obtain a complete classification on the dynamical behavior of the system in the parameter space of the predator's mortality rate mu and the prey's intrinsic growth rate r. More precisely, both species fail to survive when r is small. With the increase of r, there exist two critical values r* and sigma* such that both species can coexist in the long run when r > r* and 0 < mu < sigma*, otherwise the prey survives alone. Finally, with the aid of numerical simulations, we investigate the effects of diffusion and advection on the global dynamics of the system by computing these two critical values. Numerical simulations illustrate that the diffusion of both predators and prey would benefit the invasion of the predators, and the advection would be unfavorable to the survival of the predators. Moreover, numerical simulations also suggest that the unique positive steady state is globally asymptotically stable among all non-negative and nontrivial initial data.","Title: DAFV: A Unified and Real-Time Framework of Joint Detection and Attributes Recognition for Fast Vehicles\nYear: 2021\n\nAbstract: In the past decade, with the development of computing equipment and CNN, target detection has made great progress, which has promoted the development of specific target detection. The purpose of vehicle detection is not only to extract the vehicle from a large number of traffic surveillance cameras, but also for some follow-up research, such as the structured storage of vehicle information, which needs to quickly identify the attributes of the vehicle. Based on those demands, we propose a method of joint Detection and Attributes recognition for Fast Vehicles (DAFV). Firstly, we present Feature Rapidly Extract Module (FREM), which is to quickly shrink the feature map size and enhance the runtime efficiency. Secondly, we present Feature Refinement Module (FRM) to increase feature utilization rate and improve the performance. Lastly, we present the Cross-Stage and Multi-Scale (CS-MS) Module to optimize scale-invariant design. Related experiments based on UA-DETRAC dataset proves that DAFV is a feasible and effective method. The DAFV is fast and the speed does not change with the number of vehicles. For 416 x 416 pictures, DAFV can reach 53 FPS with only 775 Mib GPU memory, which can meet the needs of real-time applications.","Title: Ts-Unet: A Temporal Smoothed Unet for Video Anomaly Detection\nYear: 2021\n\nAbstract: U A Temporal-Smoothed Unet (TS-Unet) to detect video anomalies is proposed in this paper. The model is trained on regular videos to predict future frames, and uses prediction errors of test videos to detect anomalies. In addition to constraining model's prediction result, particularly, a temporal-smooth loss is presented to force time-continuous feature maps changing smoothly in the latent space. By utilizing the temporal-smooth loss, the model learns more videos' temporal regularity information; and achieve better anomaly detection performances. Experiments on multiple datasets show the superiority and validate the effectiveness of the proposed model.","Title: Pedestrian Re-Identification Based On Attribute Mining And Reasoning\nYear: 2021\n\nAbstract: The high-level semantic information extracted from the pedestrian attribute feature is an important element for pedestrian recognition. Pedestrian attribute recognition plays an important role in both intelligent video surveillance and pedestrian re-identification promoting the convenience of searching and performance of model. This paper tries finding a practical method to improve the performance of the pedestrian re-identification by combining pedestrian attributes and identities. The multi-task learning method combines pedestrian recognition and attribute information in a direct way that considers the correlation between pedestrian attributes and identities but ignores the principle and degree of such correlation. To solve this problem, a new pedestrian recognition framework based on attribute mining and reasoning is proposed in this paper. To enhance the expression ability of attribute features, it designs spatial channel attention module (SCAM) based on attention mechanism to extract features from every attribute. SCAM can not only locate the attributes on the feature map, but also effectively mine channel features with a higher degree of association with attributes. In addition, both spatial attention model and channel attention model are integrated by multiple groups of parallel branches, which further improve the network performance. Finally, using the semantic reasoning and information transmission function of graph convolutional network, the relationship between attribute features and pedestrian features can be mined. Besides, pedestrian features with stronger expression ability can also be obtained. Experiment work is conducted in two databases, DukeMTMC-reID and Market-1501, which are commonly used in pedestrian recognition tasks. On the Market-1501 dataset, the final effect of the algorithm model CMC-1 can reach 94.74%, and mAP can reach 87.02%; on the DukeMTMC-reID dataset, CMC-1 can reach 87.03%, and mAP can reach 77.11%. The results show that our method is at the top of the existing pedestrian recognition methods.","Title: Evidence Integration for Multi-Hop Reading Comprehension With Graph Neural Networks\nYear: 2022\n\nAbstract: Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Previous work approximates global evidence with local coreference information, encoding coreference chains with DAG-styled GRU layers within a gated-attention reader. However, coreference is limited in providing information ...","Title: Knowledge Graph Representation via Hierarchical Hyperbolic Neural Graph Embedding\nYear: 2021\n\nAbstract: Knowledge graph enhanced information retrieval systems have attracted considerable attention due to their ability to improve performance and provide additional explainability. As the knowledge graphs usually include fruitful facts, they are also good sources of side information. However, recent studies have shown that the usefulness of knowledge graphs depends highly on their representation, e.g.,...","Title: Generation-Focused Table-Based Intermediate Pre-training for Free-Form Question Answering.\nYear: 2022\n\nAbstract: Question answering over semi-structured tables has attracted significant attention in the NLP community. However, most of the existing work focus on questions that can be answered with short-form answer, i.e. the answer is often a table cell or aggregation of multiple cells. This can mismatch with the intents of users who want to ask more complex questions that require free-form answers such as explanations. To bridge the gap, most recently, pre-trained sequence-to-sequence language models such as T5 are used for generating free-form answers based on the question and table inputs. However, these pre-trained language models have weaker encoding abilities over table cells and schema. To mitigate this issue, in this work, we present an intermediate pre-training framework, Generation-focused Table-based Intermediate Pre-training (GENTAP), that jointly learns representations of natural language questions and tables. GENTAP learns to generate via two training objectives to enhance the question understanding and table representation abilities for complex questions. Based on experimental results, models that leverage GENTAP framework outperform the existing baselines on FETAQA benchmark. The pre-trained models are not only useful for free-form question answering, but also for few-shot data-to-text generation task, thus showing good transfer ability by obtaining new state-of-the-art results.","Title: An Unbiased Symmetric Matrix Estimator for Topology Inference Under Partial Observability\nYear: 2022\n\nAbstract: Network topology inference is a fundamental problem in many applications of network science, such as locating the source of fake news and brain connectivity network detection. Many real-world situations suffer from a critical problem in which only a limited number of observed nodes are available. In this work, the problem of network topology inference under the framework of partial observability is considered. Based on the vector autoregression model, we propose a novel unbiased estimator for symmetric network topology with Gaussian noise and the Laplacian combination rule. Theoretically, we prove that this estimator converges in probability to the network combination matrix. Furthermore, by utilizing the Gaussian mixture model algorithm, an effective algorithm called the network inference Gauss algorithm is developed to infer the network structure. Finally, compared with state-of-the-art methods, numerical experiments demonstrate that better performance is obtained in the case of small sample sizes when using the proposed algorithm.","Title: Multi-Synchrosqueezing Wavelet Transform for Time-Frequency Localization of Reservoir Characterization in Seismic Data\nYear: 2022\n\nAbstract: Time-frequency analysis (TFA) technology plays a significant role in seismic signal processing. The time-frequency representation (TFR) calculated using the TFA method is helpful for the localization of time-varying frequencies within the signal. Nevertheless, limited by the Heisenberg uncertainty principle, traditional linear TFA methods always provide blurred TFRs, which makes them difficult to distinguish details of time-frequency structures. Recently, the synchrosqueezing transform (SST) was designed to improve the concentration of the TFR. The SST can provide a much concentrated TFR for the weakly frequency-modulated (FM) signal, but it is not effective for the interpretation of strongly FM signals, such as the thin interbed in seismic exploration. In this work, we propose a new tool by introducing the multi-synchrosqueezing operator to the frame of wavelet transform (WT). Employing an iterative operator to correct the frequency estimation of the original SST step-by-step, it thus can calculate a TFR with better concentration and robustness. Synthetic signals and a field seismic data are employed to verify the performance of the proposed method for characterizing the time-varying frequency features.","Title: Data-Driven Time-Frequency Method and Its Application in Detection of Free Gas Beneath a Gas Hydrate Deposit\nYear: 2022\n\nAbstract: The time-frequency (TF) analysis method plays a significant role in the detection of natural gas hydrates. As a data-driven method, compressed sensing (CS) has been widely used in the TF methods due to the sparsity of the TF representation. This study proposes a data-driven TF method based on the CS theory and a non-convex regularization. In the implementation, a continuous wavelet transform (CWT) with a generalized beta wavelet (GBW) is formulated as an inverse problem based on the CS theory. The selection of appropriate parameters enables the GBW to match the seismic wavelets better than the widely used Morlet wavelet. The GBW can constitute a tight frame to reduce calculation time, particularly for large-scale field data processing. Additionally, the proposed TF method introduces the generalized minimax concave (GMC) penalty function as a non-convex regularization term. Compared with the classical sparse approximation method with l(1) regularization, the GMC regularization term can enhance the sparsity in sparse inverse problems and ensure the convexity of sparse inversions. This article also presents an exponentially decreasing threshold scheme to adaptively select the regularization parameters. Three synthetic examples are investigated to demonstrate the performance of the proposed sparse TF representation with GMC regularization. Finally, the proposed TF method's performance in detecting free gas of gas hydrates is validated using field seismic data obtained from the Blake Ridge.","Title: Seismic Impedance Inversion Using Conditional Generative Adversarial Network\nYear: 2022\n\nAbstract: Deep-learning methods, such as convolutional neural networks (CNNs), have been successfully applied to seismic impedance inversion in recent years. Compared with traditional geophysical inversion, deep-learning inversion can give inversion results with higher resolution. In this letter, we further improve the performance of deep-learning inversion and propose a seismic impedance inversion method based on conditional generative adversarial network (cGAN). In the proposed method, a generator learns to predict seismic impedance from seismic data, and a discriminator learns to distinguish between fake and real impedance. We mix the cGAN objective with mean square error (MSE) loss to bring in more information for model training. Besides, a CNN-based seismic forward model is trained to introduce the constraint of unlabeled data in the training of cGAN. Tests on Marmousi2 model and overthrust model show that the proposed method can obtain more accurate impedance and have better robustness against random noise than CNN method.","Title: DLDP-FL: Dynamic local differential privacy federated learning method based on mesh network edge devices\nYear: 2022\n\nAbstract: With increasing concerns about data security, privacy protection has become a necessary consideration in machine learning. Federated Learning (FL) solves the problem of data islands in machine learning and protects the privacy of participants by enabling further encryption through Differential Privacy (DP). In Artificial Intelligence of Things (AIoT), edge devices also suffer from data islands, which are addressed by applying FL to AIoT. However, the privacy and efficiency of existing FL needs to be further improved, and the integration with edge computing is not yet high enough. In this paper, first, we propose a novel edge FL architecture based on edge devices in mesh network architecture; next, we exploit the mesh networking features to address the problem of possible internal attacks from edge devices and design a Dynamic Local Differential Privacy (DLDP) algorithm; then, according to the communication characteristics of mesh network, we design Edge-FedAvg algorithm to reduce the communication cost; finally, to enhance the response to untrusted center servers, embed watermark in the model to further enhance the privacy protection capability. The Dynamic Local Differential Privacy Federated Learning (DLDP-FL) framework designed in this paper is used for FL of edge devices under mesh network, which can improve communication efficiency and enhance privacy protection capabilities at the same time.","Title: Numerical Investigation on the Symmetric Breakup of Bubble within a Heated Microfluidic Y-Junction\nYear: 2022\n\nAbstract: This study numerically investigated the symmetric breakup of bubble within a heated microfluidic Y-junction. The established three-dimensional model was verified with the results in the literature. Two crucial variables, Reynolds number (Re) and heat flux (q), were considered. Numerical results demonstrated that the bubble breakup was significantly affected by phase change under the heated environment. The \"breakup with tunnel\" and \"breakup with obstruction\" modes respectively occurred at the low and high q. The breakup rate in pinch-off stage was much larger than that in squeezing stage. As Re increased, the bubble broke more rapidly, and the critical neck thickness tended to decrease. The bubble annihilated the vortices existing in the divergence region and made the fluid flow more uniform. The heat transfer was enhanced more drastically as Re was decreased or q was increased, where the maximum Nusselt number under two-phase case was 6.53 times larger than single-phase case. The present study not only helps understanding of the physical mechanisms of bubble behaviors and heat transfer within microfluidic Y-junction, but also informs design of microfluidic devices.","Title: Sleep Staging Method for Imbalanced EEG Data Based on Differential Privacy Federated Learning\nYear: 2022\n\nAbstract: EEG is a personal privacy information, that is difficult to share data and not conducive to the study of sleep staging. In this paper, the Federated Learning (FL) method is introduced to solve the problem of data island in sleep staging EEG signals. To simulate the distribution of different user data, we introduce the K-means algorithm into EEG dataset partitioning and transform it into a non-independent and identically distributed unbalanced dataset. Then, based on the classic Multilayer Perceptron (MLP) network, we analyse the privacy leakage problem in FL, and Differential Privacy (DP) is used to protect the privacy of user EEG data. Next, we analyse the reason for the accuracy loss between FL and Centralized Learning (CL), and the effect of the imbalance of the data distribution of each client. We propose a self-adaptive batch method to adaptively balance the batch size of each client, and experiments show that the training results of imbalanced datasets can be improved. Finally, when the accuracy of the model does not reach the historical best value in the set epoch interval, the Mandatory Optimization Strategy (MOS) is used to further improve the accuracy and reduce the randomness of the model. The DP-FL based on the self-adaptive batch and MOS strategy is tested on our dataset and the Sleep-EDF Database Expanded dataset, which not only protects user privacy but also achieves the same accuracy as CL. The accuracy is 82.88% on our dataset and 91.81% on the subset of the Sleep-EDF Database Expanded dataset.","Title: Gaussian Process Regression with Grid Spectral Mixture Kernel: Distributed Learning for Multidimensional Data\nYear: 2022\n\nAbstract: Kernel design for Gaussian processes (GPs) along with the associated hyper-parameter optimization is a challenging problem. In this paper, we propose a novel grid spectral mixture (GSM) kernel design for GPs that can automatically fit multidimensional data with affordable model complexity and superior modeling capability. To alleviate the computational complexity due to the curse of dimensionality, we leverage a multicore computing environment to optimize the kernel hyper-parameters in a distributed manner. We further propose a doubly distributed learning algorithm based on the alternating direction method of multipliers (ADMM) which enables multiple agents to learn the kernel hyper-parameters collaboratively. The doubly distributed learning algorithm is shown to be effective in reducing the overall computational complexity while preserving data privacy during the learning process. Experiments on various one-dimensional and multidimensional data sets demonstrate that the proposed kernel design yields superior training and prediction performance compared to its competitors."],"string":"[\n \"Title: Statistical modeling of syllable-level F0 features for HMM-based unit selection speech synthesis\\nYear: 2010\\n\\nAbstract: In current hidden Markov model(HMM) based unit selection speech synthesis method, the optimal phone-sized candidate units are selected following the maximum likelihood(ML) criterion of the HMMs trained for various acoustic features. This paper introduces the statistical models for syllable-level F0 features into this method. Different from the frame-level F0 parameters used in the current framework, the pitch contour of the vowel in each syllable and its combination for adjacent syllables are extracted to represent the suprasegmental property of F0 features. A context-dependent statistical model is trained using these syllable-level F0 features and the likelihood function of this model is integrated into the unit selection criterion to evaluate the suprasegmental prosody of a given unit sequence. The conventional dynamic programming search algorithm for the phone-sized unit selection is modified to take into account the dependency between the candidate units for the vowels of adjacent syllables which is caused by the syllable-level F0 modeling. Our experiment results prove that this method can improve the naturalness of synthesized speech significantly.\",\n \"Title: A modified conjugate gradient algorithm with cyclic Barzilai-Borwein steplength for unconstrained optimization\\nYear: 2012\\n\\nAbstract: For solving large-scale unconstrained minimization problems, the nonlinear conjugate gradient method is welcome due to its simplicity, low storage, efficiency and nice convergence properties. Among all the methods in the framework, the conjugate gradient descent algorithm - CG_DESCENT is very popular, in which the generated directions descend automatically, and this nice property is independent of any line search used. In this paper, we generalize CG_DESCENT with two Barzilai-Borwein steplength reused cyclically. We show that the resulting algorithm owns attractive sufficient descent property and converges globally under some mild conditions. We test the proposed algorithm by using a large set of unconstrained problems with high dimensions in CUTEr library. The numerical comparisons with the state-of-the-art algorithm CG_DESCENT illustrate that the proposed method is effective, competitive, and promising.\",\n \"Title: Phrase structure parsing with dependency structure\\nYear: 2010\\n\\nAbstract: In this paper we present a novel phrase structure parsing approach with the help of dependency structure. Different with existing phrase parsers, in our approach the inference procedure is guided by dependency structure, which makes the parsing procedure flexibly. The experimental results show our approach is much more accurate. With the help of golden dependency trees, F1 score of our parser achieves 96.08% on Penn English Treebank and 90.61% on Penn Chinese Treebank. With the help of N-best dependency trees generated by modified MSTParser, F1 score achieves 90.54% for English and 83.93% for Chinese.\",\n \"Title: Investigation of deep neural networks (DNN) for large vocabulary continuous speech recognition: Why DNN surpasses GMMS in acoustic modeling\\nYear: 2012\\n\\nAbstract: Recently, it has been reported that context-dependent deep neural network (DNN) has achieved some unprecedented gains in many challenging ASR tasks, including the well-known Switchboard task. In this paper, we first investigate DNN for several large vocabulary speech recognition tasks. Our results have confirmed that DNN can consistently achieve about 25-30% relative error reduction over the best discriminatively trained GMMs even in some ASR tasks with up to 700 hours of training data. Next, we have conducted a series of experiments to study where the unprecedented gain of DNN comes from. Our experiments show the gain of DNN is almost entirely attributed to DNN's feature vectors that are concatenated from several consecutive speech frames within a relatively long context window. At last, we have proposed a few ideas to reconfigure the DNN input features, such as using logarithm spectrum features or VTLN normalized features in DNN. Our results have shown that each of these methods yields over 3% relative error reduction over the traditional MFCC or PLP features in DNN.\",\n \"Title: Dsp-Based Parallel Implementation Of Speeded-Up Robust Features\\nYear: 2011\\n\\nAbstract: Robust local image features have become crucial components of many state-of-the-art computer vision algorithms. Due to limited hardware resources, computing local features on embedded system is not an easy task. In this paper, we propose an efficient parallel computing framework for speeded-up robust features with an orientation towards multi-DSP based embedded system. We optimize modules in SURF to better utilize the capability of DSP chips. We also design a compact data layout to adapt to the limited memory resource and to increase data access bandwidth. A data-driven barrier and workload balance schemes are presented to synchronize parallel working chips and reduce overall cost. The experiment shows our implementation achieves competitive time efficiency compared with related works.\",\n \"Title: Constructing up to G2 continuous curve on freeform surface\\nYear: 2010\\n\\nAbstract: This paper presents new methods for G (1) and G (2) continuous interpolation of an arbitrary sequence of points on an implicit or parametric surface with prescribed tangent direction and both tangent direction and curvature vector, respectively, at every point. We design a G (1) or G (2) continuous curve in three-dimensional space, construct a so-called directrix vector field using the space curve and then project a special straight line segment onto the given surface along the directrix vector field. With the techniques in classical differential geometry, we derive a system of differential equations for the projection curve. The desired interpolation curve is just the projection curve, which can be obtained by numerically solving the initial-value problems for a system of first-order ordinary differential equations in the parametric domain associated to the surface representation for the parametric case or in three-dimensional space for the implicit case. Several shape parameters are introduced into the resulting curve, which can be used in subsequent interactive modification such that the shape of the resulting curve meets our demand. The presented method is independent of the geometry and parameterization of the base surface, and numerical experiments demonstrate that it is effective and potentially useful in patterns design on surface.\",\n \"Title: An implicit control-volume finite element method and its time step strategies for injection molding simulation\\nYear: 2007\\n\\nAbstract: An implicit control-volume finite element method was proposed for simulation of injection molding, the time steps were controlled for both flow and thermal simulation by local flow information, and then the computing complexity analysis was conducted. The implicit scheme was based on updating the melt–air interface. The time steps of implicit algorithm were controlled by injection ratio and local mesh information of flow front to achieve “one time step, one element-layer” for filling simulation; the sub-time steps of computing temperatures were calculated for each node according to local courant number, and the thermal simulation was conducted by the filled order of nodes. Both analysis and case studies showed that the computing complexity of flow simulation was second-order of mesh size (nodal number) and one of thermal simulation was 1.5-order. As a result, the computing times and complexity of solving temperature were less than those of solving pressure.\",\n \"Title: A Trellis Based Fast Lattice Generating Algorithm\\nYear: 2008\\n\\nAbstract: Lattice is widely used as a kind of the search results in Large Vocabulary Continuous Speech Recognition (LVCSR). A new lattice-generation algorithm is presented in this paper. The algorithm is based on a classical forward-backward decoding method, which is proved to be highly efficient. Moreover, some improvements have been done to satisfy the requirements in the lattice decoding. Two Chinese mandarin large-scale speech recognition tasks are used to evaluate the proposed algorithm and the experimental results show that our algorithm can both improve decoding speed and save decoding space significantly without sacrificing the recognition accuracy, compared with the widely used Lattice decoding method as [7].\",\n \"Title: Automating nurse self-rostering: A multiagent systems model\\nYear: 2009\\n\\nAbstract: This paper presents a multiagent systems model for constructing nurse rosters. Over the past decade self-rostering has become more favorable in nursing personnel scheduling, due to its empowerment and motivational benefits. However, the labor intensive negotiation procedure among participants has limited its application to medium sized and large wards. To overcome this limitation, we propose an automated negotiation tool utilizing economic-based negotiation mechanisms. We model the environment as a multiagent system. Nurses can indicate their preferences by configuring the preference profiles of the nurse agents, and the nurse agents collectively construct rosters through the negotiation with the management agents and among themselves. To support the design and implementation of automated self-rostering systems, we present a multiagent systems architecture, a structure of preference information, and a negotiation mechanism.\",\n \"Title: Due-Date Management Through Iterative Bidding\\nYear: 2011\\n\\nAbstract: This paper proposes an iterative bidding framework for integrated due-date management (DDM) decision making. We focus on a type of make-to-order environment in which a firm needs to quote due dates and prices and to schedule the production of a variety of job orders required by a large group of customers. In most cases, customers prefer shorter due dates. However, given limited production capacity and various cost constraints, the firm has to balance the attractiveness of its due-date quotations and the reliability of delivering accepted job orders. The key issue is how to integrate DDM decisions such that high-quality solutions, which benefit both the firm and the customers, can be obtained. We study the integrated DDM in an economic setting where customers are modeled as self-interested agents and the objective of the firm is to maximize social welfare. We present an iterative bidding framework as a decentralized decision support tool which enables the integration of key DDM decisions. Effective solutions are achieved through the automated negotiation between the firm and its customers. We provide analytical results on the application of the proposed framework to two special cases of the integrated DDM. We also evaluate the performance of the framework on general DDM problems through a computational study.\",\n \"Title: A limited memory BFGS-type method for large-scale unconstrained optimization\\nYear: 2008\\n\\nAbstract: In this paper, a new numerical method for solving large-scale unconstrained optimization problems is presented. It is derived from a modified BFGS-type update formula by Wei, Li, and Qi. It is observed that the update formula can be extended to the framework of limited memory scheme with hardly more storage or arithmetic operations. Under some suitable conditions, the global convergence property is established. The implementations of the method on a set of CUTE problems indicate that this extension is beneficial to the performance of the algorithm.\",\n \"Title: Bidirectional Sequence Labeling Via Dual Decomposition\\nYear: 2013\\n\\nAbstract: In this paper, we propose a bidirectional algorithm for sequence labeling to capture the influence of both the left-to-right and the right-to-left directions. We combine the optimization of two unidirectional models from opposite directions via the dual decomposition method to jointly label the input sequence. Experiments on three sequence labeling tasks (Chinese word segmentation, English POS tagging and text chunking) show that our approach can improve the accuracy of sequence labeling tasks when the two unidirectional models individually make highly different predictions.\",\n \"Title: An integer programming model and heuristic algorithm for automatic scheduling in synchrotron facilities\\nYear: 2009\\n\\nAbstract: This paper studies the automatic scheduling problem at the Canadian national synchrotron facility, Canadian Light Source (CLS). An automatic scheduling tool needs to be developed to replace the current manual approach for scheduling experiments on a set of beamlines - resources that generate high-intensity X-rays for use in many kinds of scientific experiments. We present an, Integer programming model for this scheduling activity by formulating it as a problem of unrelated and paralleled machines with partially overlapping capabilities. Furthermore a heuristic based approach is used that can save computation time by pruning the search space. Using realistic data sets generated using parameters made available by CLS, we compare the performance of the base line approach that uses ILOG CPLEX implementation of the Integer programming algorithm with one that uses heuristics. The results show that the heuristic approach runs faster than the base-line, but at the cost of producing a less optimal scheduling solution. An obvious advantage of the study presented in this paper is that the automatic scheduling can handle more scheduling conditions and constraints than humans are able to handle manually and can reach optimal solutions. As far as we know, this is the first attempt to propose an automatic scheduling approach for synchrotron facilities like CLS around the world.\",\n \"Title: Phonetic clustering based confidence measure for embedded speech recognition\\nYear: 2010\\n\\nAbstract: Word posterior probability (WPP) based confidence measure (CM) has been applied successfully in LVCSR tasks. However, for embedded speech recognition in which system resource is limited, not only performance of CM but also efficiency of the algorithm need to be considered. One of the most important issue in calculating WPP is how to obtain reliable estimation of the normalization term. So in this paper we investigate several methods to estimate the normalization term and focus on methods using different phone-based grammar. Furthermore, to make good trade-off between performance and efficiency for embedded system, we present a general approach of estimating WPP based confidence score based on data-driven phonetic clustering, where Kullback-Leibler divergence (KLD) is employed for grouping all phones into different clusters. Corresponding acoustic and language models for calculating CM score can be re-trained based on the clustering phones. Experimental results on different Mandarin command word and digit recognition tasks show that the proposed method can significantly improve the efficiency with little degradation in CM performance, where more than 90% processing time of CM module is saved.\",\n \"Title: Optimal system, symmetry reductions and new closed form solutions for the geometric average Asian options\\nYear: 2014\\n\\nAbstract: In this paper, the Lie group analysis method is applied to the geometric average Asian option pricing equation in financial problems. Firstly, the complete Lie symmetry group and infinitesimal generators of this equation are derived. Then the optimal system with one parameter for the Lie symmetry algebra are obtained, which gives the possibility to describe a complete set of invariant solutions to the pricing equation. Finally, based on the optimal system the symmetry reductions and corresponding closed form solutions for the pricing equation are proposed.\",\n \"Title: Existence of quasiperiodic solutions and Littlewood’s boundedness problem of super-linear impact oscillators\\nYear: 2011\\n\\nAbstract: So far most application of Kolmogorov–Arnold–Moser (KAM) theory has been restricted to smooth dynamical systems. In this paper, it is shown by a series of transformations that how KAM theory can be used to analyze the dynamical behavior of Duffing-type equations with impact. The analysis is carried out for the example(0.1)x¨+x2n+1=p(t),forx(t)>0,x(t)⩾0,x˙(t0+)=-x˙(t0-),ifx(t0)=0with p∈C5 being periodic. We prove that all solutions are bounded, and that there are infinitely many periodic and quasiperiodic solutions in this case.\",\n \"Title: The effect of traffic on situation awareness and mental workload: simulator-based study\\nYear: 2007\\n\\nAbstract: In the present study, we investigated the effects of the different traffic on the driver's situation awareness and the mental workload (MWL). The task used in this study was a medium fidelity, 3-dimensional simulation of a driving environment. The simulation required participants to drive the user's car and perform a real-world driving task. After the simulated driving, participants were asked to complete two tests which assessed their situation awareness (SA). The mental workload measures in this study consisted of the physiological measures and the subjective assessment. Every participant performed two different traffic simulated driving conditions, one was low traffic, the other was high traffic. The results showed that with the increasing of traffic, the driving performance did not worsen, however participant's mental workload increased, at the same time, the participant's situation awareness performance deteriorated. Meanwhile, our results also demonstrated that recall-based SA test and recognition-based test was heterogeneous.\",\n \"Title: Time-Frequency Analysis of Seismic Data Using Synchrosqueezing Transform\\nYear: 2014\\n\\nAbstract: Time-frequency analysis can provide useful information in seismic data processing and interpretation. An accurate time-frequency representation is important in highlighting subtle geologic structures and in detecting anomalies associated with hydrocarbon reservoirs. The popular methods, like short-time Fourier transform and wavelet analysis, have limitations in dealing with fast varying instantaneous frequencies, which is often the characteristic of seismic data. The synchrosqueezing transform (SST) is a promising tool to provide a detailed time-frequency representation. We apply the SST to seismic data and show its potential to seismic signal processing applications.\",\n \"Title: Joint Pos Tagging And Transition-Based Constituent Parsing In Chinese With Non-Local Features\\nYear: 2014\\n\\nAbstract: We propose three improvements to address the drawbacks of state-of-the-art transition-based constituent parsers. First, to resolve the error propagation problem of the traditional pipeline approach, we incorporate POS tagging into the syntactic parsing process. Second, to alleviate the negative influence of size differences among competing action sequences, we align parser states during beam-search decoding. Third, to enhance the power of parsing models, we enlarge the feature set with non-local features and semi-supervised word cluster features. Experimental results show that these modifications improve parsing performance significantly. Evaluated on the Chinese TreeBank (CTB), our final performance reaches 86.3% (F1) when trained on CTB 5.1, and 87.1% when trained on CTB 6.0, and these results outperform all state-of-the-art parsers.\",\n \"Title: Nonlinear elasto-mammography for characterization of breast tissue properties\\nYear: 2011\\n\\nAbstract: Quantification of the mechanical behavior of normal and cancerous tissues has important implication in the diagnosis of breast tumor. The present work extends the authors' nonlinear elastography framework to incorporate the conventional X-ray mammography, where the projection of displacement information is acquired instead of full three-dimensional (3D) vector. The elastic parameters of normal and cancerous breast tissues are identified by minimizing the difference between the measurement and the corresponding computational prediction. An adjoint method is derived to calculate the gradient of the objective function. Simulations are conducted on a 3D breast phantom consisting of the fatty tissue, glandular tissue, and cancerous tumor, whose mechanical responses are hyperelastic in nature. The material parameters are identified with consideration of measurement error. The results demonstrate that the projective displacements acquired in X-ray mammography provide sufficient constitutive information of the tumor and prove the usability and robustness of the proposed method and algorithm.\",\n \"Title: Adaptive Variable Time Fractional Anisotropic Diffusion Filtering for Seismic Data Noise Attenuation\\nYear: 2016\\n\\nAbstract: Seismic records are often contaminated with various kinds of noise, which makes it very difficult to distinguish the expected geological features. In this paper, we introduce a novel adaptive variable time fractional-order anisotropic diffusion equation for seismic data noise removal and strongly oriented structure enhancement. Since the time fractional-order differential equation interpolates a parabolic equation and a hyperbolic equation, the solution benefits both of these approaches. The presented differential equation can be written as a Volterra integral equation, and its well-posedness can be guaranteed for all time. We employ a structure tensor to analyze the flow-like texture characteristic which is typical in seismic data. Then, the diffusion process is guided reasonably by a diffusion tensor based on the structure tensor analysis which allows real anisotropic behavior comparing to the classical scalar diffusion approach. In reference to the numerical implementation, we utilize the predictor–corrector algorithm to solve the Volterra integral equation which provides high-order numerical precision jointly with good stability property. Finally, numerical experiments involved with synthetic and prestacked real seismic data are presented. The obtained results demonstrate that the noise is effectively removed, and the coherent seismic events that express some important geological structures are not only preserved but significantly enhanced.\",\n \"Title: Semi-supervised Clustering for Short Text via Deep Representation Learning.\\nYear: 2016\\n\\nAbstract: In this work, we propose a semisupervised method for short text clustering, where we represent texts as distributed vectors with neural networks, and use a small amount of labeled data to specify our intention for clustering. We design a novel objective to combine the representation learning process and the kmeans clustering process together, and optimize the objective with both labeled data and unlabeled data iteratively until convergence through three steps: (1) assign each short text to its nearest centroid based on its representation from the current neural networks; (2) re-estimate the cluster centroids based on cluster assignments from step (1); (3) update neural networks according to the objective by keeping centroids and cluster assignments fixed. Experimental results on four datasets show that our method works significantly better than several other text clustering methods.\",\n \"Title: Sentence Similarity Learning by Lexical Decomposition and Composition.\\nYear: 2016\\n\\nAbstract: Most conventional sentence similarity methods only focus on similar parts of two input sentences, and simply ignore the dissimilar parts, which usually give us some clues and semantic meanings about the sentences. In this work, we propose a model to take into account both the similarities and dissimilarities by decomposing and composing lexical semantics over sentences. The model represents each word as a vector, and calculates a semantic matching vector for each word based on all words in the other sentence. Then, each word vector is decomposed into a similar component and a dissimilar component based on the semantic matching vector. After this, a two-channel CNN model is employed to capture features by composing the similar and dissimilar components. Finally, a similarity score is estimated over the composed feature vectors. Experimental results show that our model gets the state-of-the-art performance on the answer sentence selection task, and achieves a comparable result on the paraphrase identification task.\",\n \"Title: Feature Optimization For Constituent Parsing Via Neural Networks\\nYear: 2015\\n\\nAbstract: The performance of discriminative constituent parsing relies crucially on feature engineering, and effective features usually have to be carefully selected through a painful manual process. In this paper, we propose to automatically learn a set of effective features via neural networks. Specifically, we build a feedforward neural network model, which takes as input a few primitive units (words, POS tags and certain contextual tokens) from the local context, induces the feature representation in the hidden layer and makes parsing predictions in the output layer. The network simultaneously learns the feature representation and the prediction model parameters using a back propagation algorithm. By pre-training the model on a large amount of automatically parsed data, and then fine-tuning on the manually annotated Treebank data, our parser achieves the highest F-1 score at 86.6% on Chinese Treebank 5.1, and a competitive F-1 score at 90.7% on English Treebank. More importantly, our parser generalizes well on cross-domain test sets, where we significantly outperform Berkeley parser by 3.4 points on average for Chinese and 2.5 points for English.\",\n \"Title: Vocabulary Manipulation For Neural Machine Translation\\nYear: 2016\\n\\nAbstract: In order to capture rich language phenomena, neural machine translation models have to use a large vocabulary size, which requires high computing time and large memory usage. In this paper, we alleviate this issue by introducing a sentence-level or batch-level vocabulary, which is only a very small sub-set of the full output vocabulary. For each sentence or batch, we only predict the target words in its sentence-level or batch-level vocabulary. Thus, we reduce both the computing time and the memory usage. Our method simply takes into account the translation options of each word or phrase in the source sentence, and picks a very small target vocabulary for each sentence based on a word-to-word translation model or a bilingual phrase library learned from a traditional machine translation model. Experimental results on the large-scale English-to-French task show that our method achieves better translation performance by 1 BLEU point over the large vocabulary neural machine translation system of Jean et al. (2015).\",\n \"Title: Sense Embedding Learning for Word Sense Induction.\\nYear: 2016\\n\\nAbstract: Conventional word sense induction (WSI) methods usually represent each instance with discrete linguistic features or cooccurrence features, and train a model for each polysemous word individually. In this work, we propose to learn sense embeddings for the WSI task. In the training stage, our method induces several sense centroids (embedding) for each polysemous word. In the testing stage, our method represents each instance as a contextual vector, and induces its sense by finding the nearest sense centroid in the embedding space. The advantages of our method are (1) distributed sense vectors are taken as the knowledge representations which are trained discriminatively, and usually have better performance than traditional count-based distributional models, and (2) a general model for the whole vocabulary is jointly trained to induce sense centroids under the mutlitask learning framework. Evaluated on SemEval-2010 WSI dataset, our method outperforms all participants and most of the recent state-of-the-art methods. We further verify the two advantages by comparing with carefully designed baselines.\",\n \"Title: Supervised Attentions for Neural Machine Translation.\\nYear: 2016\\n\\nAbstract: In this paper, we improve the attention or alignment accuracy of neural machine translation by utilizing the alignments of training sentence pairs. We simply compute the distance between the machine attentions and the true alignments, and minimize this cost in the training procedure. Our experiments on large-scale Chinese-to-English task show that our model improves both translation and alignment qualities significantly over the large-vocabulary neural machine translation system, and even beats a state-of-the-art traditional syntax-based system.\",\n \"Title: AMR-to-text generation as a Traveling Salesman Problem.\\nYear: 2016\\n\\nAbstract: The task of AMR-to-text generation is to generate grammatical text that sustains the semantic meaning for a given AMR graph. We at- tack the task by first partitioning the AMR graph into smaller fragments, and then generating the translation for each fragment, before finally deciding the order by solving an asymmetric generalized traveling salesman problem (AGTSP). A Maximum Entropy classifier is trained to estimate the traveling costs, and a TSP solver is used to find the optimized solution. The final model reports a BLEU score of 22.44 on the SemEval-2016 Task8 dataset.\",\n \"Title: Coverage Embedding Models for Neural Machine Translation.\\nYear: 2016\\n\\nAbstract: In this paper, we enhance the attention-based neural machine translation (NMT) by adding explicit coverage embedding models to alleviate issues of repeating and dropping translations in NMT. For each source word, our model starts with a full coverage embedding vector to track the coverage status, and then keeps updating it with neural networks as the translation goes. Experiments on the large-scale Chinese-to-English task show that our enhanced model improves the translation quality significantly on various test sets over the strong large vocabulary NMT system.\",\n \"Title: Multi-Perspective Context Matching for Machine Comprehension.\\nYear: 2016\\n\\nAbstract: Previous machine comprehension (MC) datasets are either too small to train end-to-end deep learning models, or not difficult enough to evaluate the ability of current MC techniques. The newly released SQuAD dataset alleviates these limitations, and gives us a chance to develop more realistic MC models. Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM) model, which is an end-to-end system that directly predicts the answer beginning and ending points in a passage. Our model first adjusts each word-embedding vector in the passage by multiplying a relevancy weight computed against the question. Then, we encode the question and weighted passage by using bi-directional LSTMs. For each point in the passage, our model matches the context of this point against the encoded question from multiple perspectives and produces a matching vector. Given those matched vectors, we employ another bi-directional LSTM to aggregate all the information and predict the beginning and ending points. Experimental result on the test set of SQuAD shows that our model achieves a competitive result on the leaderboard.\",\n \"Title: Amr-To-Text Generation With Synchronous Node Replacement Grammar\\nYear: 2017\\n\\nAbstract: This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar. During training, graph-to-string rules are learned using a heuristic extraction algorithm. At test time, a graph transducer is applied to collapse input AMRs and generate output sentences. Evaluated on a standard benchmark, our method gives the state-of-the-art result.\",\n \"Title: A Unified Query-based Generative Model for Question Generation and Question Answering.\\nYear: 2017\\n\\nAbstract: We propose a query-based generative model for solving both tasks of question generation (QG) and question an- swering (QA). The model follows the classic encoder- decoder framework. The encoder takes a passage and a query as input then performs query understanding by matching the query with the passage from multiple per- spectives. The decoder is an attention-based Long Short Term Memory (LSTM) model with copy and coverage mechanisms. In the QG task, a question is generated from the system given the passage and the target answer, whereas in the QA task, the answer is generated given the question and the passage. During the training stage, we leverage a policy-gradient reinforcement learning algorithm to overcome exposure bias, a major prob- lem resulted from sequence learning with cross-entropy loss. For the QG task, our experiments show higher per- formances than the state-of-the-art results. When used as additional training data, the automatically generated questions even improve the performance of a strong ex- tractive QA system. In addition, our model shows bet- ter performance than the state-of-the-art baselines of the generative QA task.\",\n \"Title: R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering.\\nYear: 2017\\n\\nAbstract: In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et al., 2016) dataset, which provides a pre-selected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e.g., wikipedia) instead of a pre-selected passage (Chen et al., 2017a). This setting is more complex as it requires large-scale search for relevant passages by an information retrieval component, combined with a reading comprehension model that reads the passages to generate an answer to the question. Performance in this setting lags considerably behind closed-domain performance. In this paper, we present a novel open-domain QA system called Reinforced Ranker-Reader $(R^3)$, based on two algorithmic innovations. First, we propose a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of generating the ground-truth answer to a given question. Second, we propose a novel method that jointly trains the Ranker along with an answer-generation Reader model, based on reinforcement learning. We report extensive experimental results showing that our method significantly improves on the state of the art for multiple open-domain QA datasets.\",\n \"Title: k-Nearest Neighbor Augmented Neural Networks for Text Classification.\\nYear: 2017\\n\\nAbstract: In recent years, many deep-learning based models are proposed for text classification. This kind of models well fits the training set from the statistical point of view. However, it lacks the capacity of utilizing instance-level information from individual instances in the training set. In this work, we propose to enhance neural network models by allowing them to leverage information from $k$-nearest neighbor (kNN) of the input text. Our model employs a neural network that encodes texts into text embeddings. Moreover, we also utilize $k$-nearest neighbor of the input text as an external memory, and utilize it to capture instance-level information from the training set. The final prediction is made based on features from both the neural network encoder and the kNN memory. Experimental results on several standard benchmark datasets show that our model outperforms the baseline model on all the datasets, and it even beats a very deep neural network model (with 29 layers) in several datasets. Our model also shows superior performance when training instances are scarce, and when the training set is severely unbalanced. Our model also leverages techniques such as semi-supervised training and transfer learning quite well.\",\n \"Title: Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering.\\nYear: 2017\\n\\nAbstract: Very recently, it comes to be a popular approach for answering open-domain questions by first searching question-related passages, then applying reading comprehension models to extract answers. Existing works usually extract answers from single passages independently, thus not fully make use of the multiple searched passages, especially for the some questions requiring several evidences, which can appear in different passages, to be answered. The above observations raise the problem of evidence aggregation from multiple passages. In this paper, we deal with this problem as answer re-ranking. Specifically, based on the answer candidates generated from the existing state-of-the-art QA model, we propose two different re-ranking methods, strength-based and coverage-based re-rankers, which make use of the aggregated evidences from different passages to help entail the ground-truth answer for the question. Our model achieved state-of-the-arts on three public open-domain QA datasets, Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8% improvement on the former two datasets.\",\n \"Title: Knowledge Base Relation Detection via Multi-View Matching.\\nYear: 2018\\n\\nAbstract: Relation detection is a core component for Knowledge Base Question Answering (KBQA). In this paper, we propose a knowledge base (KB) relation detection model based on multi-view matching, which utilizes useful information extracted from questions and KB. The matching inside each view is through multiple perspectives to compare two input texts thoroughly. All these components are trained in an end-to-end neural network model. Experiments on SimpleQuestions and WebQSP yield state-of-the-art results on relation detection.\",\n \"Title: Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks.\\nYear: 2018\\n\\nAbstract: Celebrated emph{Sequence to Sequence learning (Seq2Seq)} and its fruitful variants are powerful models to achieve excellent performance on the tasks that map sequences to sequences. However, these are many machine learning tasks with inputs naturally represented in a form of graphs, which imposes significant challenges to existing Seq2Seq models for lossless conversion from its graph form to the sequence. In this work, we present a general end-to-end approach to map the input graph to a sequence of vectors, and then another attention-based LSTM to decode the target sequence from these vectors. Specifically, to address inevitable information loss for data conversion, we introduce a novel graph-to-sequence neural network model that follows the encoder-decoder architecture. Our method first uses an improved graph-based neural network to generate the node and graph embeddings by a novel aggregation strategy to incorporate the edge direction information into the node embeddings. We also propose an attention based mechanism that aligns node embeddings and decoding sequence to better cope with large graphs. Experimental results on bAbI task, Shortest Path Task, and Natural Language Generation Task demonstrate that our model achieves the state-of-the-art performance and significantly outperforms other baselines. We also show that with the proposed aggregation strategy, our proposed model is able to quickly converge to good performance.\",\n \"Title: R3: Reinforced Ranker-Reader for Open-Domain Question Answering.\\nYear: 2018\\n\\nAbstract: In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings(1) such as the SQuAD (Rajpurkar et al. 2016) dataset, which provides a pre-selected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e.g., wikipedia) instead of a pre-selected passage (Chen et al. 2017a). This setting is more complex as it requires large-scale search for relevant passages by an information retrieval component, combined with a reading comprehension model that \\\"reads\\\" the passages to generate an answer to the question. Performance in this setting lags well behind closed-domain performance. In this paper, we present a novel open-domain QA system called Reinforced Ranker-Reader (R-3), based on two algorithmic innovations. First, we propose a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of extracting the ground-truth answer to a given question. Second, we propose a novel method that jointly trains the Ranker along with an answer-extraction Reader model, based on reinforcement learning. We report extensive experimental results showing that our method significantly improves on the state of the art for multiple open-domain QA datasets.(2)\",\n \"Title: A Tighter Set-Membership Filter for Some Nonlinear Dynamic Systems.\\nYear: 2018\\n\\nAbstract: In this paper, we propose a tighter set-membership filter for some nonlinear dynamic systems by using an analytic method and a boundary sampling technique. The nonlinear dynamic systems can be linearized about the current estimate, then the remainder term is bounded in real time by an optimization ellipsoid, other than a priori remainder bound. For a 2-D radar system and a quadratic system, some regular properties can be derived for the remainder term, which helps us obtain a tighter bounding ellipsoid to cover the remainder. Moreover, the prediction step and the measurement update step are derived based on the recent optimization method and the on-line bounding ellipsoid of the remainder, so that a tighter set-membership filter can be achieved. The numerical examples demonstrate the effectiveness of the proposed filter.\",\n \"Title: The Cramér-Rao Bounds and Sensor Selection for Nonlinear Systems with Uncertain Observations.\\nYear: 2018\\n\\nAbstract: This paper considers the problems of the posterior Cramer-Rao bound and sensor selection for multi-sensor nonlinear systems with uncertain observations. In order to effectively overcome the difficulties caused by uncertainty, we investigate two methods to derive the posterior Cramer-Rao bound. The first method is based on the recursive formula of the Cramer-Rao bound and the Gaussian mixture model. Nevertheless, it needs to compute a complex integral based on the joint probability density function of the sensor measurements and the target state. The computation burden of this method is relatively high, especially in large sensor networks. Inspired by the idea of the expectation maximization algorithm, the second method is to introduce some 0-1 latent variables to deal with the Gaussian mixture model. Since the regular condition of the posterior Cramer-Rao bound is unsatisfied for the discrete uncertain system, we use some continuous variables to approximate the discrete latent variables. Then, a new Cramer-Rao bound can be achieved by a limiting process of the Cramer-Rao bound of the continuous system. It avoids the complex integral, which can reduce the computation burden. Based on the new posterior Cramer-Rao bound, the optimal solution of the sensor selection problem can be derived analytically. Thus, it can be used to deal with the sensor selection of a large-scale sensor networks. Two typical numerical examples verify the effectiveness of the proposed methods.\",\n \"Title: A Graph-To-Sequence Model For Amr-To-Text Generation\\nYear: 2018\\n\\nAbstract: The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. Although it is able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus faces challenges with large graphs, which result in long sequences. We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics. On a standard benchmark, our model shows superior results to existing methods in the literature.\",\n \"Title: Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model.\\nYear: 2018\\n\\nAbstract: Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees. In this paper, we first propose to use the textit{syntactic graph} to represent three types of syntactic information, i.e., word order, dependency and constituency features. We further employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.\",\n \"Title: N-ary Relation Extraction using Graph State LSTM.\\nYear: 2018\\n\\nAbstract: Cross-sentence $n$-ary relation extraction detects relations among $n$ entities across multiple sentences. Typical methods formulate an input as a textit{document graph}, integrating various intra-sentential and inter-sentential dependencies. The current state-of-the-art method splits the input graph into two DAGs, adopting a DAG-structured LSTM for each. Though being able to model rich linguistic knowledge by leveraging graph edges, important information can be lost in the splitting procedure. We propose a graph-state LSTM model, which uses a parallel state to model each word, recurrently enriching state values via message passing. Compared with DAG LSTMs, our graph LSTM keeps the original graph structure, and speeds up computation by allowing more parallelization. On a standard benchmark, our model shows the best result in the literature.\",\n \"Title: SQL-to-Text Generation with Graph-to-Sequence Model.\\nYear: 2018\\n\\nAbstract: Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. In this paper, we first introduce a strategy to represent the SQL query as a directed graph and then employ a graph-to-sequence model to encode the global structure information into node embeddings. This model can effectively learn the correlation between the SQL query pattern and its interpretation. Experimental results on the WikiSQL dataset and Stackoverflow dataset show that our model significantly outperforms the Seq2Seq and Tree2Seq baselines, achieving the state-of-the-art performance.\",\n \"Title: An effective computer aided diagnosis model for pancreas cancer on PET/CT images.\\nYear: 2018\\n\\nAbstract: •This paper proposes a new CAD model for pancreas cancer on PET/CT images, including (1) pancreas segmentation, (2) feature extraction and selection, (3) classifier design, respectively.•Pancreas segmentation is performed using SLIC on CT pseudo-color images generated by the gray interval mapping (GIP) method.•Dual threshold principal component analysis (DT-PCA) is developed to select the most beneficial feature combination, which not only considers principal features but also integrates some non-principal features into a new polar angle representation.•Hybrid feedback support vector machine-random forest (HFB-SVM-RF) model is designed to identify normal pancreas or pancreas cancer and the key is to use 8 kinds of SVMs to establish the decision trees of RF.•Contrast experiments in 10-fold cross validation demonstrate the efficiency and accuracy of the proposed CAD model as well as its performance advantages as compared with related methods.\",\n \"Title: Data-driven scheduling optimization under uncertainty using Renyi entropy and skewness criterion.\\nYear: 2018\\n\\nAbstract: •A scheduling optimization problem under resource cost uncertainties is studied.•An integrated Renyi mean-entropy-skewness information criterion is proposed.•Data-driven approximations of the information criterion are developed.•Real industrial applications with diverse system scales and data types are studied.•Reduced average dispersion rates are achieved in all the computational experiments.\",\n \"Title: Using a Dynamic Neural Field Model to Explore a Direct Collicular Inhibition Account of Inhibition of Return.\\nYear: 2013\\n\\nAbstract: When the interval between a transient flash of light (a \\\"cue\\\"\\r\\n) and a second visual response signal (a \\\"target\\\") exceeds at least 200\\r\\nms, responding is slowest in the direction indicated by the first signal.\\r\\nThis phenomenon is commonly referred to as inhibition of return (IOR).\\r\\nThe dynamic neural field model (DNF) has proven to have broad explanatory\\r\\npower for IOR, effectively capturing many empirical results.\\r\\nPrevious work has used a short-term depression (STD) implementation\\r\\nof IOR, but this approach fails to explain many behavioral phenomena\\r\\nobserved in the literature. Here, we explore a variant model of IOR involving\\r\\na combination of STD and delayed direct collicular inhibition.\\r\\nWe demonstrate that this hybrid model can better reproduce established\\r\\nbehavioural results. We use the results of this model to propose several\\r\\nexperiments that would yield particularly valuable insight into the nature\\r\\nof the neurophysiological mechanisms underlying IOR.\",\n \"Title: Novel Disturbance-Observer-Based Control For Systems With High-Order Mismatched Disturbances\\nYear: 2018\\n\\nAbstract: A novel disturbance-observer-based control method is investigated to attenuate the high-order mismatched disturbances. First, a finite-time disturbance observer (FTDO) is proposed to estimate the disturbances as well as the derivatives. By incorporating the outputs of FTDO, the original system is then reconstructed, where the mismatched disturbances are transformed to the matched ones that are compensated by feed-forward algorithm. Moreover, a feedback control law is developed to achieve the stability and tracking performance requirements for the systems. Finally, the proposed composite control method is applied to an unmanned helicopter system. The simulation results demonstrate that the proposed control method exhibits excellent control performance in the presence of high-order matched and mismatched disturbances.\",\n \"Title: Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks.\\nYear: 2018\\n\\nAbstract: Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Previous work approximates global evidence with local coreference information, encoding coreference chains with DAG-styled GRU layers within a gated-attention reader. However, coreference is limited in providing information for rich inference. We introduce a new method for better connecting global evidence, which forms more complex graphs compared to DAGs. To perform evidence integration on our graphs, we investigate two recent graph neural networks, namely graph convolutional network (GCN) and graph recurrent network (GRN). Experiments on two standard datasets show that richer global information leads to better answers. Our method performs better than all published results on these datasets.\",\n \"Title: Learning 3D non-rigid deformation based on an unsupervised deep learning for PET/CT image registration.\\nYear: 2019\\n\\nAbstract: This study proposes a novel method to learn a three-dimensional (3D) non-rigid deformation for automatic image registration between positron emission tomography (PET) and computed tomography (CT) scans obtained from the same patient. The proposed scheme includes two modules: (1) A low-resolution displacement vector field (LR-DVF) estimator, which employs a 3D deep convolutional network (ConvNet) to directly estimate the voxel-wise displacement (a 3D vector field) between PET/CT images; and (2) a 3D spatial transformer and re-sampler, which warps the PET images to match the anatomical structures in the CT images using the estimated 3D vector field. The parameters of the ConvNet are learned from a number of PET/CT image pairs, through an unsupervised learning method. The normalized cross correlation (NCC) between the PET/CT images is employed as the similarity metric to guide an end-to-end learning process, with a constraint (regularization term) to preserve the smoothness of the 3D deformations. A dataset containing 170 PET/CT scans is utilized in experiments, based on 10-fold cross-validation, where 22,338 3D patches are sampled from the dataset. In each fold, 3D patches from 153 patients (90%) are utilized to train the parameters, while the remaining whole-body voxels from 17 patients (10%) are used to test the image registration performance. The experimental results demonstrate that the image registration accuracy (the mean value of NCCs) is increased from 0.402 (the initial situation) to 0.567 on PET/CT scans using the proposed scheme. We also compare the performance of our scheme with previous work (DIRNet), and the advantages of our scheme are confirmed by promising results.\",\n \"Title: Semantic Neural Machine Translation using AMR.\\nYear: 2019\\n\\nAbstract: It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models. On the other hand, little work has been done on leveraging semantics for neural machine translation (NMT). In this work, we study the usefulness of AMR (short for abstract meaning representation) on NMT. Experiments on a standard English-to-German dataset show that incorporating AMR as additional knowledge can significantly improve a strong attention-based sequence-to-sequence neural translation model.\",\n \"Title: On equivalence of major relaxation methods for minimum ellipsoid covering intersection of ellipsoids\\nYear: 2019\\n\\nAbstract: This paper investigates the problem on the minimum trace or volume ellipsoid covering intersection of ellipsoids. This problem can be solved by three major relaxation methods involving semi-definite programming relaxation, S-procedure relaxation and parameterized bounding ellipsoid relaxation. They are proposed from the different ideas or viewpoints, so that it is difficult to judge which relaxation method is the most suitable for the different practical implementations. To the best of our knowledge, the problem of proving the convexity of the optimization problem derived by the third relaxation method remains open. By rigorous analysis, we reveal the equivalence among the three relaxation methods, and address the open problem, i.e., the optimization problem derived by the third relaxation method can be equivalently transformed to a convex optimization problem. The computation complexity of these relaxation methods are also discussed, and the equivalent result is helpful to set membership filter and multisensor fusion obtaining a tighter ellipsoid to contain the state with lower computation complexity. Finally, the performance of each method is evaluated by several typical numerical examples in information fusion and filtering.\",\n \"Title: Ellipsoidal Fusion Estimation for Multisensor Dynamic Systems with Bounded Noises\\nYear: 2019\\n\\nAbstract: This paper considers the set membership fusion estimation problem for the general multisensor dynamic systems with unknown but bounded noises. In order to achieve the ellipsoidal fusion formulae, we divide the set membership filter problem into two steps: the prediction step and the fusion update step. The proposed method has the following nice properties. First, each step can be converted into a semidefinite programming problem, which can be efficiently computed. Second, part-analytical formulae of the shape matrix and the center of the bounding ellipsoid can be derived by using a decoupled technique, which can significantly reduce the computational complexity. Moreover, the relationship between the fusion center and local sensors can be clearly revealed based on the ellipsoidal fusion formula. Finally, it is interesting that the ellipsoidal fusion formula is similar in form to the classic mean-squared error filter fusion, although they are of the different optimization frameworks. However, the fusion weights of the information matrix provided by the different sensors are different and the optimal fusion weights can be calculated by solving a convex optimization problem. A typical numerical example in target tracking demonstrates the effectiveness of the proposed centralized and distributed fusion formula.\",\n \"Title: Global Dynamics Of An Ecosystem In Open Advective Environments\\nYear: 2021\\n\\nAbstract: This paper deals with a kind of reaction-diffusion-advection model which depicts a predator-prey ecosystem in rivers or streams. We obtain a complete classification on the dynamical behavior of the system in the parameter space of the predator's mortality rate mu and the prey's intrinsic growth rate r. More precisely, both species fail to survive when r is small. With the increase of r, there exist two critical values r* and sigma* such that both species can coexist in the long run when r > r* and 0 < mu < sigma*, otherwise the prey survives alone. Finally, with the aid of numerical simulations, we investigate the effects of diffusion and advection on the global dynamics of the system by computing these two critical values. Numerical simulations illustrate that the diffusion of both predators and prey would benefit the invasion of the predators, and the advection would be unfavorable to the survival of the predators. Moreover, numerical simulations also suggest that the unique positive steady state is globally asymptotically stable among all non-negative and nontrivial initial data.\",\n \"Title: DAFV: A Unified and Real-Time Framework of Joint Detection and Attributes Recognition for Fast Vehicles\\nYear: 2021\\n\\nAbstract: In the past decade, with the development of computing equipment and CNN, target detection has made great progress, which has promoted the development of specific target detection. The purpose of vehicle detection is not only to extract the vehicle from a large number of traffic surveillance cameras, but also for some follow-up research, such as the structured storage of vehicle information, which needs to quickly identify the attributes of the vehicle. Based on those demands, we propose a method of joint Detection and Attributes recognition for Fast Vehicles (DAFV). Firstly, we present Feature Rapidly Extract Module (FREM), which is to quickly shrink the feature map size and enhance the runtime efficiency. Secondly, we present Feature Refinement Module (FRM) to increase feature utilization rate and improve the performance. Lastly, we present the Cross-Stage and Multi-Scale (CS-MS) Module to optimize scale-invariant design. Related experiments based on UA-DETRAC dataset proves that DAFV is a feasible and effective method. The DAFV is fast and the speed does not change with the number of vehicles. For 416 x 416 pictures, DAFV can reach 53 FPS with only 775 Mib GPU memory, which can meet the needs of real-time applications.\",\n \"Title: Ts-Unet: A Temporal Smoothed Unet for Video Anomaly Detection\\nYear: 2021\\n\\nAbstract: U A Temporal-Smoothed Unet (TS-Unet) to detect video anomalies is proposed in this paper. The model is trained on regular videos to predict future frames, and uses prediction errors of test videos to detect anomalies. In addition to constraining model's prediction result, particularly, a temporal-smooth loss is presented to force time-continuous feature maps changing smoothly in the latent space. By utilizing the temporal-smooth loss, the model learns more videos' temporal regularity information; and achieve better anomaly detection performances. Experiments on multiple datasets show the superiority and validate the effectiveness of the proposed model.\",\n \"Title: Pedestrian Re-Identification Based On Attribute Mining And Reasoning\\nYear: 2021\\n\\nAbstract: The high-level semantic information extracted from the pedestrian attribute feature is an important element for pedestrian recognition. Pedestrian attribute recognition plays an important role in both intelligent video surveillance and pedestrian re-identification promoting the convenience of searching and performance of model. This paper tries finding a practical method to improve the performance of the pedestrian re-identification by combining pedestrian attributes and identities. The multi-task learning method combines pedestrian recognition and attribute information in a direct way that considers the correlation between pedestrian attributes and identities but ignores the principle and degree of such correlation. To solve this problem, a new pedestrian recognition framework based on attribute mining and reasoning is proposed in this paper. To enhance the expression ability of attribute features, it designs spatial channel attention module (SCAM) based on attention mechanism to extract features from every attribute. SCAM can not only locate the attributes on the feature map, but also effectively mine channel features with a higher degree of association with attributes. In addition, both spatial attention model and channel attention model are integrated by multiple groups of parallel branches, which further improve the network performance. Finally, using the semantic reasoning and information transmission function of graph convolutional network, the relationship between attribute features and pedestrian features can be mined. Besides, pedestrian features with stronger expression ability can also be obtained. Experiment work is conducted in two databases, DukeMTMC-reID and Market-1501, which are commonly used in pedestrian recognition tasks. On the Market-1501 dataset, the final effect of the algorithm model CMC-1 can reach 94.74%, and mAP can reach 87.02%; on the DukeMTMC-reID dataset, CMC-1 can reach 87.03%, and mAP can reach 77.11%. The results show that our method is at the top of the existing pedestrian recognition methods.\",\n \"Title: Evidence Integration for Multi-Hop Reading Comprehension With Graph Neural Networks\\nYear: 2022\\n\\nAbstract: Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Previous work approximates global evidence with local coreference information, encoding coreference chains with DAG-styled GRU layers within a gated-attention reader. However, coreference is limited in providing information ...\",\n \"Title: Knowledge Graph Representation via Hierarchical Hyperbolic Neural Graph Embedding\\nYear: 2021\\n\\nAbstract: Knowledge graph enhanced information retrieval systems have attracted considerable attention due to their ability to improve performance and provide additional explainability. As the knowledge graphs usually include fruitful facts, they are also good sources of side information. However, recent studies have shown that the usefulness of knowledge graphs depends highly on their representation, e.g.,...\",\n \"Title: Generation-Focused Table-Based Intermediate Pre-training for Free-Form Question Answering.\\nYear: 2022\\n\\nAbstract: Question answering over semi-structured tables has attracted significant attention in the NLP community. However, most of the existing work focus on questions that can be answered with short-form answer, i.e. the answer is often a table cell or aggregation of multiple cells. This can mismatch with the intents of users who want to ask more complex questions that require free-form answers such as explanations. To bridge the gap, most recently, pre-trained sequence-to-sequence language models such as T5 are used for generating free-form answers based on the question and table inputs. However, these pre-trained language models have weaker encoding abilities over table cells and schema. To mitigate this issue, in this work, we present an intermediate pre-training framework, Generation-focused Table-based Intermediate Pre-training (GENTAP), that jointly learns representations of natural language questions and tables. GENTAP learns to generate via two training objectives to enhance the question understanding and table representation abilities for complex questions. Based on experimental results, models that leverage GENTAP framework outperform the existing baselines on FETAQA benchmark. The pre-trained models are not only useful for free-form question answering, but also for few-shot data-to-text generation task, thus showing good transfer ability by obtaining new state-of-the-art results.\",\n \"Title: An Unbiased Symmetric Matrix Estimator for Topology Inference Under Partial Observability\\nYear: 2022\\n\\nAbstract: Network topology inference is a fundamental problem in many applications of network science, such as locating the source of fake news and brain connectivity network detection. Many real-world situations suffer from a critical problem in which only a limited number of observed nodes are available. In this work, the problem of network topology inference under the framework of partial observability is considered. Based on the vector autoregression model, we propose a novel unbiased estimator for symmetric network topology with Gaussian noise and the Laplacian combination rule. Theoretically, we prove that this estimator converges in probability to the network combination matrix. Furthermore, by utilizing the Gaussian mixture model algorithm, an effective algorithm called the network inference Gauss algorithm is developed to infer the network structure. Finally, compared with state-of-the-art methods, numerical experiments demonstrate that better performance is obtained in the case of small sample sizes when using the proposed algorithm.\",\n \"Title: Multi-Synchrosqueezing Wavelet Transform for Time-Frequency Localization of Reservoir Characterization in Seismic Data\\nYear: 2022\\n\\nAbstract: Time-frequency analysis (TFA) technology plays a significant role in seismic signal processing. The time-frequency representation (TFR) calculated using the TFA method is helpful for the localization of time-varying frequencies within the signal. Nevertheless, limited by the Heisenberg uncertainty principle, traditional linear TFA methods always provide blurred TFRs, which makes them difficult to distinguish details of time-frequency structures. Recently, the synchrosqueezing transform (SST) was designed to improve the concentration of the TFR. The SST can provide a much concentrated TFR for the weakly frequency-modulated (FM) signal, but it is not effective for the interpretation of strongly FM signals, such as the thin interbed in seismic exploration. In this work, we propose a new tool by introducing the multi-synchrosqueezing operator to the frame of wavelet transform (WT). Employing an iterative operator to correct the frequency estimation of the original SST step-by-step, it thus can calculate a TFR with better concentration and robustness. Synthetic signals and a field seismic data are employed to verify the performance of the proposed method for characterizing the time-varying frequency features.\",\n \"Title: Data-Driven Time-Frequency Method and Its Application in Detection of Free Gas Beneath a Gas Hydrate Deposit\\nYear: 2022\\n\\nAbstract: The time-frequency (TF) analysis method plays a significant role in the detection of natural gas hydrates. As a data-driven method, compressed sensing (CS) has been widely used in the TF methods due to the sparsity of the TF representation. This study proposes a data-driven TF method based on the CS theory and a non-convex regularization. In the implementation, a continuous wavelet transform (CWT) with a generalized beta wavelet (GBW) is formulated as an inverse problem based on the CS theory. The selection of appropriate parameters enables the GBW to match the seismic wavelets better than the widely used Morlet wavelet. The GBW can constitute a tight frame to reduce calculation time, particularly for large-scale field data processing. Additionally, the proposed TF method introduces the generalized minimax concave (GMC) penalty function as a non-convex regularization term. Compared with the classical sparse approximation method with l(1) regularization, the GMC regularization term can enhance the sparsity in sparse inverse problems and ensure the convexity of sparse inversions. This article also presents an exponentially decreasing threshold scheme to adaptively select the regularization parameters. Three synthetic examples are investigated to demonstrate the performance of the proposed sparse TF representation with GMC regularization. Finally, the proposed TF method's performance in detecting free gas of gas hydrates is validated using field seismic data obtained from the Blake Ridge.\",\n \"Title: Seismic Impedance Inversion Using Conditional Generative Adversarial Network\\nYear: 2022\\n\\nAbstract: Deep-learning methods, such as convolutional neural networks (CNNs), have been successfully applied to seismic impedance inversion in recent years. Compared with traditional geophysical inversion, deep-learning inversion can give inversion results with higher resolution. In this letter, we further improve the performance of deep-learning inversion and propose a seismic impedance inversion method based on conditional generative adversarial network (cGAN). In the proposed method, a generator learns to predict seismic impedance from seismic data, and a discriminator learns to distinguish between fake and real impedance. We mix the cGAN objective with mean square error (MSE) loss to bring in more information for model training. Besides, a CNN-based seismic forward model is trained to introduce the constraint of unlabeled data in the training of cGAN. Tests on Marmousi2 model and overthrust model show that the proposed method can obtain more accurate impedance and have better robustness against random noise than CNN method.\",\n \"Title: DLDP-FL: Dynamic local differential privacy federated learning method based on mesh network edge devices\\nYear: 2022\\n\\nAbstract: With increasing concerns about data security, privacy protection has become a necessary consideration in machine learning. Federated Learning (FL) solves the problem of data islands in machine learning and protects the privacy of participants by enabling further encryption through Differential Privacy (DP). In Artificial Intelligence of Things (AIoT), edge devices also suffer from data islands, which are addressed by applying FL to AIoT. However, the privacy and efficiency of existing FL needs to be further improved, and the integration with edge computing is not yet high enough. In this paper, first, we propose a novel edge FL architecture based on edge devices in mesh network architecture; next, we exploit the mesh networking features to address the problem of possible internal attacks from edge devices and design a Dynamic Local Differential Privacy (DLDP) algorithm; then, according to the communication characteristics of mesh network, we design Edge-FedAvg algorithm to reduce the communication cost; finally, to enhance the response to untrusted center servers, embed watermark in the model to further enhance the privacy protection capability. The Dynamic Local Differential Privacy Federated Learning (DLDP-FL) framework designed in this paper is used for FL of edge devices under mesh network, which can improve communication efficiency and enhance privacy protection capabilities at the same time.\",\n \"Title: Numerical Investigation on the Symmetric Breakup of Bubble within a Heated Microfluidic Y-Junction\\nYear: 2022\\n\\nAbstract: This study numerically investigated the symmetric breakup of bubble within a heated microfluidic Y-junction. The established three-dimensional model was verified with the results in the literature. Two crucial variables, Reynolds number (Re) and heat flux (q), were considered. Numerical results demonstrated that the bubble breakup was significantly affected by phase change under the heated environment. The \\\"breakup with tunnel\\\" and \\\"breakup with obstruction\\\" modes respectively occurred at the low and high q. The breakup rate in pinch-off stage was much larger than that in squeezing stage. As Re increased, the bubble broke more rapidly, and the critical neck thickness tended to decrease. The bubble annihilated the vortices existing in the divergence region and made the fluid flow more uniform. The heat transfer was enhanced more drastically as Re was decreased or q was increased, where the maximum Nusselt number under two-phase case was 6.53 times larger than single-phase case. The present study not only helps understanding of the physical mechanisms of bubble behaviors and heat transfer within microfluidic Y-junction, but also informs design of microfluidic devices.\",\n \"Title: Sleep Staging Method for Imbalanced EEG Data Based on Differential Privacy Federated Learning\\nYear: 2022\\n\\nAbstract: EEG is a personal privacy information, that is difficult to share data and not conducive to the study of sleep staging. In this paper, the Federated Learning (FL) method is introduced to solve the problem of data island in sleep staging EEG signals. To simulate the distribution of different user data, we introduce the K-means algorithm into EEG dataset partitioning and transform it into a non-independent and identically distributed unbalanced dataset. Then, based on the classic Multilayer Perceptron (MLP) network, we analyse the privacy leakage problem in FL, and Differential Privacy (DP) is used to protect the privacy of user EEG data. Next, we analyse the reason for the accuracy loss between FL and Centralized Learning (CL), and the effect of the imbalance of the data distribution of each client. We propose a self-adaptive batch method to adaptively balance the batch size of each client, and experiments show that the training results of imbalanced datasets can be improved. Finally, when the accuracy of the model does not reach the historical best value in the set epoch interval, the Mandatory Optimization Strategy (MOS) is used to further improve the accuracy and reduce the randomness of the model. The DP-FL based on the self-adaptive batch and MOS strategy is tested on our dataset and the Sleep-EDF Database Expanded dataset, which not only protects user privacy but also achieves the same accuracy as CL. The accuracy is 82.88% on our dataset and 91.81% on the subset of the Sleep-EDF Database Expanded dataset.\",\n \"Title: Gaussian Process Regression with Grid Spectral Mixture Kernel: Distributed Learning for Multidimensional Data\\nYear: 2022\\n\\nAbstract: Kernel design for Gaussian processes (GPs) along with the associated hyper-parameter optimization is a challenging problem. In this paper, we propose a novel grid spectral mixture (GSM) kernel design for GPs that can automatically fit multidimensional data with affordable model complexity and superior modeling capability. To alleviate the computational complexity due to the curse of dimensionality, we leverage a multicore computing environment to optimize the kernel hyper-parameters in a distributed manner. We further propose a doubly distributed learning algorithm based on the alternating direction method of multipliers (ADMM) which enables multiple agents to learn the kernel hyper-parameters collaboratively. The doubly distributed learning algorithm is shown to be effective in reducing the overall computational complexity while preserving data privacy during the learning process. Experiments on various one-dimensional and multidimensional data sets demonstrate that the proposed kernel design yields superior training and prediction performance compared to its competitors.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":68,"string":"68"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13274,"string":"13,274"}}},{"rowIdx":13275,"cells":{"question_id":{"kind":"string","value":"Tsutomu Terada"},"question":{"kind":"string","value":"Generate an abstract for the title \"Input interface using wrinkles on clothes.\" using the following items: Input interface, wrinkles, tactile feedback, eye-free, accuracy."},"answer":{"kind":"string","value":"We propose an input interface that uses wrinkles on clothes. The interface generates several wrinkles on clothes when an input action is required from applications. The user senses the current situation and the number of choices by touching the wrinkles. He/she can input commands by selecting one of the wrinkles, and these wrinkles disappear after input operation. This sequence can be performed inconspicuously since the wrinkles provide tactile feedback in an eye-free input interface. The interface is naturally buried in natural wrinkles on clothes. We implemented a prototype of the proposed input interface and evaluated users' learning on it. The evaluation results confirmed that users can operate the proposed interface with more than 95% accuracy and in approximately two seconds per input."},"correct_answer":{"kind":"string","value":"We propose an input interface that uses wrinkles on clothes. The interface generates several wrinkles on clothes when an input action is required from applications. The user senses the current situation and the number of choices by touching the wrinkles. He/she can input commands by selecting one of the wrinkles, and these wrinkles disappear after input operation. This sequence can be performed inconspicuously since the wrinkles provide tactile feedback in an eye-free input interface. The interface is naturally buried in natural wrinkles on clothes. We implemented a prototype of the proposed input interface and evaluated users' learning on it. The evaluation results confirmed that users can operate the proposed interface with more than 95% accuracy and in approximately two seconds per input."},"documents":{"kind":"list like","value":["Title: A Context-Aware System that Changes Sensor Combinations Considering Energy Consumption\nYear: 2008\n\nAbstract: In wearable computing environments, a wearable computer runs various applications using various sensors (wearable sensors). In the area of context awareness, though various systems using accelerometers have been proposed to recognize very minute motions and states, energy consumption was not taken into consideration. We propose a context-aware system that reduces energy consumption. In life, the granularity of required contexts differs according to the situation. Therefore, the proposed system changes the granularity of cognitive contexts of a user's situation and supplies power on the basis of the optimal sensor combination. Higher accuracy is achieved with fewer sensors. In addition, in proportion to the remainder of power resources, the proposed system reduces the number of sensors within the tolerance of accuracy. Moreover, the accuracy is improved by considering context transition. Even if the number of sensors changes, no extra classifiers or training data are required because the data for shutting off sensors is complemented by our proposed algorithm. By using our system, power consumption can be reduced without large losses in accuracy.","Title: A fault diagnostic system by line status monitoring for ubiquitous computers connected with multiple communication lines\nYear: 2014\n\nAbstract: In ubiquitous computing environments, many computers should be controlled in cooperation to support human daily-life. We pick up a dance performance that performers wear a large amount of LEDs to combine body expression and lighting effects. In such situation, there is a problem that failure frequently occurs caused by dance movements. In the environment involving hundreds of computers, it costs much time and effort to check where failures have occurred. To solve this problem, we propose a system for identifying the position of the failure easily by checking the status of communication among computers. In addition, we implemented a fault diagnostic system that can provide a series of flows for detecting the failure, guessing why the failure occurred and taking a measure. Furthermore, we actually used our system into LED dance performances, conducted an evaluative experiment, and confirmed its usefulness.","Title: A wearable system for supporting pit-crew and audiences on motorbike races\nYear: 2008\n\nAbstract: Motorbike racing is one of the most popular motorsports and many fans visit circuits to watch races. However, since audiences and pit crews can only obtain limited information, it is difficult for them to get detailed information about teams except for high ranking teams and few popular teams. To solve this problem, we have already presented an information browsing system for pit crews who used wearable computers. This paper describes an extension to the system. We also applied our system to racing audiences. Since our system can provide various information such as detailed ranking tables and favorite teams, it can make supporting the teams more advantageous and entertain audiences much more.","Title: Query processing methods considering the deadline of queries for database broadcasting systems\nYear: 2007\n\nAbstract: In recent years, there has been an increasing interest in the database broadcasting system where the server periodically broadcasts contents of a database to mobile clients such as portable computers and PDAs. There are three query processing methods in the database broadcasting system: (i) the server processes a query and then broadcasts the query result to the client, (ii) the client stores all data that are necessary to process the query and then processes it locally, and (iii) the server and the client collaborate in processing the query. Since the performance of each method changes according to the situation such as the interval of query generation and the size of query results, it is difficult to choose the optimal method among them statically. In this paper, we propose new query processing methods which dynamically change the order of queries submitted in the queue at the server and also change processing methods for the queries according to the deadline of queries and the system situation. These methods improve the success rate as well as reduce the response time in most cases. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(2): 21– 31, 2007; Published online in Wiley InterScience (). DOI 10.1002/scj.20689","Title: A context-aware audio presentation method in wearable computing\nYear: 2011\n\nAbstract: Audio usage is one of the more widely-applicable methods of information presentation in wearable computing environments since it can be used hands-free, requires only small devices like earphones, and does not interfere with most tasks compared with other methods including visual displays. However, since presented sound is often drowned out by ambient noise or conversational voice, a user is forced to turn the volume up to catch the audio information. Therefore, we propose an audio information presentation method that takes into account the user contexts. In our proposed method, a system estimates the user contexts based on the data from wearable sensors and a microphone, and then controls and presents the audio information so that it can be surely audible by changing the volume and the timing for presentation. The evaluation results confirmed the effectiveness of our proposed method.","Title: Designing gestures for hands and feet in daily life\nYear: 2011\n\nAbstract: In wearable computing environments, people handles various information anytime and anywhere with a wearing computer. In such situation, a gesture is one of powerful methods as input method because it needs no physical devices to touch and a user can input quickly. However, there are various restrictions for gesture input in daily life; gestures must be socially acceptable because a user has to gesture with unusual movements in a crowd, gestures must be flexible because a user cannot gesture when he/she has a bag with his hand that is used for a gesture. In this paper, we clarify the restrictions on gesture interfaces in daily life, then propose practical gestures for selecting simple menu items with hands and feet.","Title: An interactive system for recognizing user actions on a surface using accelerometers\nYear: 2014\n\nAbstract: There are various approaches to recognizing user actions for interactive arts. By making a system interactive, the audience has more fun because they are participating, and the artists can translate what is in their imagination more richly. Since user actions have great variety and the restrictions on installations are diverse, conventional systems use mechanisms for recognizing user motions that are specialized to their own work, i.e., that are not general. In this paper, we propose a method that adds interactivity to any surface and recognizes the position and intensity of a preformed action by using multiple accelerometers.","Title: User Preference Learning System For Tangible User Interfaces\nYear: 2009\n\nAbstract: There is increasing demand for tangible user interfaces (TUIs), for easy and intuitive control of applications. In order to allow use of TUI devices for controlling applications, a system needs to associate functions with the TUI devices. Although several TUI applications and toolkits have been proposed, it is difficult to use them for various applications since they require the user to perform several inconvenient operations. Therefore, we propose a preference learning algorithm for an automatic matching between TUI devices and application functions. A user can freely place and easily operate the TUI devices, and our system creates and learns users preference data. Our system does this by determining three concrete types of user preferences specialized for each application and six abstract types of those common to all applications. Our proposed system has achieved over 80% accuracy in automatic matching.","Title: A Ubiquitous Data Logger that Selects an Efficient Data Compression Method\nYear: 2012\n\nAbstract: Recently, ubiquitous computing has been garnering a great deal of attention. In ubiquitous computing environments, sensor nodes connected to a network are used for collecting sensor data. However, considering the cost needed to set up the network, it may be more convenient to use data loggers that directly send data to users than to use a sensor network. In this paper, we propose a ubiquitous data logger that takes sensor characteristics into consideration in managing data. The logger reduces data volume by selecting an appropriate compression algorithm that takes sensor characteristics into consideration, because the sensor characteristics vary depending on the sensor data.","Title: HASC Challenge: gathering large scale human activity corpus for the real-world activity understandings\nYear: 2011\n\nAbstract: Understandings of human activity through wearable sensors will enable the next-generation human-oriented computing. However, most of researches on the activity recognition so far are based on small number of test subjects, and not well adapted for real world applications. To overcome the situation, we have started a project named \"HASC Challenge\" to collect a large scale human activity corpus. By the end of 2010, by the collaboration of 20 teams, more than 6700 accelerometer data with 540 subjects have been collected through our project. We also developed a tool named \"HASC Tool\" for management, evaluation and collection of the large number of activity sensor data.","Title: An Information Layout Method for an Optical See-through HMD Considering the Background\nYear: 2007\n\nAbstract: We present a framework for 3D spatial gesture design and modeling. A wearable input device that facilitates the use of visual sensors and body sensors is proposed for gesture acquisition. We adapted two different pattern matching techniques, Dynamic ...","Title: Composition of Filtering Functions\nYear: 2003\n\nAbstract: In recent years, due to the increasing popularization of various broadcast services, there has been anincreasing demand for information fltering techniques.Although filtering methods that have been proposed filterdata in their own ways, mathematical representation ofthese methods does not exist. Consequently, it is notpossible to qualitatively evaluate various filtering methods, to optimize processing methods in filtering, or todesign a declarative language for filtering processes. Inour previous work we have defined a filtering functionthat represents filtering as a function, and clarified theproperties of different filtering methods. In practice,current filtering methods actually consist of multiplemethods. In this paper, we make it possible to qualitatively represent those filtering methods by introducingthe concept of composition into the framework of filtering function. Moreover, we reveal the characteristicsof those combined filtering methods by clarifying theproperties of composite filtering functions.","Title: A system for practicing formations in dance performance using a two-axis movable electric curtain track\nYear: 2014\n\nAbstract: Improving physical expressions and the sense of rhythm in dance performances has become important in recent years due to the increase in child dancers and dance studios. Even beginners in dance gain more opportunities to perform dances in groups. When dancing in a group, collapsed formation will greatly reduce the quality of dance performance even if the choreography is synchronized with the music. Therefore, learning the dance formation in a group is as important as learning its choreography. It is also important to be aware of keeping the proper formation and moving smoothly into the next formation to perform professional level group dances. However, it is difficult to obtain the sense of a proper formation if some members of the dance cannot participate in the practice. We have proposed a practice-support system for performing the formation smoothly using a self-propelled screen even if there is no dance partner. However, the movement of people was limited more than necessary by the excessive presence of a self-propelled screen moving irregular and the fear of the collision with the screen. Therefore, the reproducibility of the trajectory in the case where the user danced with another dancer was low. In this work, we propose a practice-support system for performing the formation using the two-axis movable curtain rail, whose movement direction does not drift and the material used for projection is soft. These characteristics reduce the fear of the collision and improve the accuracy in movement of the screen.","Title: Evaluating Effect of Concreteness in Instructions for Gesture Recognition\nYear: 2011\n\nAbstract: Gesture input has an advantage of rich expressive power over conventional input devices, but it is difficult to share the gesture trajectory with other people through writing or verbally. We obtained acceleration data for 10 kinds of gestures instructed through text, figures, and movies, totaling 44 patterns from 13 test subjects, for a total of 2630 data samples. We then examined the differences in the gestures.","Title: A Query Processing Method Considering the Characteristic of Energy Consumption for Broadcast Database Systems\nYear: 2009\n\nAbstract: In recent years, there has been an increasing interest in a broadcast database system where the server periodically broadcasts contents of a database to mobile clients such as PDAs and smartphones. There are three query processing methods in the broadcast database system. Generally, mobile clients have limits in energy consumption, i.e., battery, and each of the three methods consumes different amount of energy for query processing. In this paper we propose a new method which dynamically chooses one of the three query processing methods considering energy consumption.","Title: A system for visualizing human behavior based on car metaphors\nYear: 2013\n\nAbstract: There are many accidents such as bumping between walkers in crowded places. One of reasons for them is that it is difficult for each person to predict the behaviors of other people. On the other hand, cars implicitly communicate with other cars by presenting their contexts with equipments such as brake lights and turn signals. In this paper, we propose a system for visualizing the user context by using information presentation methods based on those found in cars, such as wearing LEDs as brake lights, which can be seen by surrounding people. The evaluation results when using our prototype system confirmed that our method visually and intuitively presented the user context. In addition, we evaluated the visibility effects of changing the mounting position of the wearable devices.","Title: Method of determining training data for gesture recognition considering decay in gesture movements.\nYear: 2013\n\nAbstract: Mobile phones and video game controllers using gesture recognition technologies enable easy and intuitive operations, such as those in drawing objects. Gesture recognition systems generally require several samples of training data before recognition takes place. However, recognition accuracy deteriorates as time passes since the trajectory of the gestures changes due to fatigue or forgetfulness. We investigated the change in gestures and fast found that several samples of gestures were not suitable for training data. Therefore, we propose two methods of finding appropriate data for training. We confirmed that the proposed methods found better training data than the conventional method from the viewpoints of the number of data collected and the accuracy of recognition.","Title: A text input method for half-sized keyboard using keying interval\nYear: 2012\n\nAbstract: In various environments, such as mobile and wearable computing, compact I/O devices are desirable from the viewpoint of portability. Now, many users are accustomed to input with a keyboard, however, there is a limitation of miniaturization because it degrades the performance of key touch. Therefore, in this paper, we propose a method to miniaturize a keyboard by excluding the half of it. In using the proposed method, one hand hits keys as usual, and the other hand hits the place outside the keyboard as if the user types with both hands. The user can input words with only one hand because the proposed system estimates the input word using keying interval, which appears also when the user inputs with both hands. From the results of user study, we confirmed that the user can input with only one hand and that it does not decrease input speed drastically.","Title: A framework for constructing entertainment contents using flash and wearable sensors\nYear: 2010\n\nAbstract: Multimedia interactive contents that can be controlled by user's motion attract a great deal of attention especially in entertainment such as gesture-based games. A system that provides such interactive contents detects the human motions using several body-worn sensors. To develop such a system, the contents creator must have enough knowledge about various sensors. In addition, since sensors and contents are deeply associated in contents, it is difficult to change/add sensors for such contents. In this paper, we propose a framework that helps contents creators who do not have enough knowledge on sensors. In our framework, an interactive content is divided into two layers; sensor management layer and content layer. We confirmed that creators can create interactive contents easier with our framework.","Title: Implementation and evaluation on a concealed interface using abdominal circumference\nYear: 2014\n\nAbstract: The downsizing of computers enables users to operate computers anywhere. Generally, since a user operates his/her computer by his/her hands, computer operation is unconcealed to surrounding people. On the other hand, there are demands for hidden operation of the computer in situations such as face to face communication and during a meeting. The operation of computer in those scenes often gives a bad impression to surroundings and discommunication. Therefore, in this study, we propose an interface using a user's abdominal circumference as input. The motion of the abdomen is hard to be recognized by surroundings, and a user can move one's abdomen independently of the other body parts. We implemented a prototype of input method using the moving velocity and the absolute size of abdomen as input. Then, we evaluated the granularity, reproducibility, parallelism, resistance to environment, confidentiality and resistance to mis-recognition on the proposed interface.","Title: A User Recognition Method Using Accelerometer for Electric Appliances\nYear: 2013\n\nAbstract: We propose a method to identify persons using home electric appliances with accelerometer. Proposing method improves the accuracy of the power management system in the home, such as HEMS (Home Energy Management System) and EoD (Energy on Demand). We aim to detect the timing of using appliances before using them, and to identify who use them with acceleration sensor. Detecting the timing of using appliances makes it possible to prevent accidents caused by high electric current (for example breaker falls) before the accidents occur. Furthermore, identifying who use the appliances make it easy to plan the power consumption, because there are large difference among the operating time of appliances.","Title: Design and implementation of an extensible rule processing system for wearable computing\nYear: 2004\n\nAbstract: In wearable computing environments, a user brings and uses his/her own computer to acquire various services wherever he/she goes. This type of computer, which acts as a service platform for wearable computing, needs autonomy and simplicity of services, flexibility of services/devices configuration, and power saving functions. Since most conventional wearable computing systems do not fulfil all of these requirements, we propose A-WEAR, which is a rule-based wearable computing system. We employ an event-driven rule as a behavior description language of A-WEAR to achieve autonomy and simplicity. Furthermore, we employ a plug-in mechanism to achieve flexibility and power saving. Using our system, we can easily provide and use various services for wearable computing environments.","Title: A ZigBee-based sensor node for tracking people's locations\nYear: 2008\n\nAbstract: A sensor network system has been developed for tracking people's locations in workplaces as part of a ubiquitous network system for providing context-aware services in daily activities. Since the installation of such a sensor is desired any place within its target domain with few limitations, it must operate by battery for a relatively long time, e.g., one month. To satisfy this requirement, we designed a battery-operated sensor node based on ZigBee technology and extended its operation period by developing a flexible sleep control protocol and a high-accuracy time synchronization mechanism between sensor nodes to reduce power consumption. From simulations based on actual data collected, we confirmed that a sensor node located in a hospital's medical ward can work over 21 days using four AA Ni-H batteries.","Title: Evaluation study on sensor placement and gesture selection for mobile devices\nYear: 2012\n\nAbstract: Mobile phones and video game controllers using gesture recognition technologies enable easy and intuitive operations, such as scrolling a browser and drawing objects. However, usually only one of each kind of sensor is installed in a device, and the effect of multiple homogeneous sensors on recognition accuracy has not been investigated. Moreover, the effect of the differences in the motion of a gesture has not been examined. We have investigated the use of a test mobile device with nine accelerometers and nine gyroscopes. We have captured the data for 27 kinds of gestures for a mobile tablet. We experimentally investigated the effects on recognition accuracy of changing the number and positions of the sensors and of the number and kinds of gestures. The results showed that the use of multiple homogeneous sensors has zero or negligible effect on recognition accuracy, but that using an accelerometer along with a gyroscope improves recognition accuracy. They also showed that some gestures were not consistent among test subjects and interdependent, so selecting specific gestures to use can improve recognition accuracy.","Title: Labeling method for acceleration data using an execution sequence of activities\nYear: 2013\n\nAbstract: In the area of activity recognition, many systems using accelerometers have been proposed. Common method for activity recognition requires raw data labeled with ground truth to learn the model. To obtain ground truth, a wearer records his/her activities during data logging through video camera or handwritten memo. However, referring a video takes long time and taking a memo interrupts natural activity. We propose a labeling method for activity recognition using an execution sequence of activities. The execution sequence includes activities in performed order, does not include time stamps, and is made based on his/her memory. Our proposed method partitions and classifies unlabeled data into segments and clusters, and assigns a cluster to each segment, then assign labels according to the best-matching assignment of clusters with the user-recorded activities. The proposed method gave a precision of 0.812 for data including seven kinds of activities. We also confirmed that recognition accuracy with training data labeled with our proposal gave a recall of 0.871, which is equivalent to that with ground truth.","Title: An information layout method for an optical see-through head mounted display focusing on the viewability\nYear: 2008\n\nAbstract: Accessing information when we are on the move is a key feature if mobile computing environments, and using an optical see-through head mounted display (HMD) is one of the most suitable ways to do this. Although the HMD can display information without interfering with the user’s view, when the sight behind the display is too complex or too bright, the information displayed can bee very difficult to see. To solve this problem, we have created a way of laying out information for the optical see-through HMD. The ideal area for displaying information is determined by evaluating the sight image behind the HMD captured by a pantoscopic camera mounted on it. Moreover, if there is no suitable area for displaying information, our method select involves using the sight image around users use to the ideal direction and instructing them to face the direction. Our method displays information to ideal areas.","Title: A destination prediction method using driving contexts and trajectory for car navigation systems\nYear: 2009\n\nAbstract: Car navigation systems provide the best route to a destination quickly and effectively. However, during daily driving, this information is not necessary since drivers already know the route to the destination very well. In addition, it is time-consuming for drivers to input the destination. Thus, our research group has proposed a new car navigation system that provides information related to the destination by predicting the user's destination automatically. We propose the use of a new method that predicts the destination on the basis of the driving trajectory and the contexts in which the user drives. A system that uses our method knows the destination without user interaction and provides information related to the correct destination.","Title: A system for supporting performers in stuffed suits\nYear: 2012\n\nAbstract: Stuffed suits have been widely used at various events. However, performances with stuffed suits present two main difficulties in that performers are not aware of their postures because of the difference in the shape and size of the stuffed suits and the physical human body, and it is difficult for them to communicate smoothly with others because of limited visibility. These problems lead performers to train excessively to acquire a high degree of skill in performances. The main goal of our study was to construct a system to support performers in stuffed suits, which would enable them to act like the characters they represented. From the results we obtained from evaluating our prototype system, we confirmed that our method could effectively support performers in stuffed suits.","Title: A Prototype System of Remote Music Therapy Using the Latest Communication Technology in Japan\nYear: 2013\n\nAbstract: This paper describes a prototype system of remote music therapy for elderly people with dementia using the latest communication technology in Japan. The system uses the latest high-speed optical network, called NGN (Next Generation Network) and the latest terminal especially for audio data communication called Hikari DUETTO NY1, which was released at the end of the last year. We did an experiment of the system using the real communication environment in this January between Tokyo and Osaka. The communication time of the system between Tokyo and Osaka was about 25.5 msec. Thus, we confirmed that the system was able to provide almost real-time audio data communication. Based on the result, the feasibility of music therapy activities, such as singing and exercises with music, using the system was examined. We confirmed that the singing activity which is the most important one in the music therapy was feasible.","Title: Airstic drum: a drumstick for integration of real and virtual drums\nYear: 2012\n\nAbstract: Drum kits consist of various kinds of percussion instruments. As all percussion instruments are large and heavy, they are inconvenient for drummers to carry and set up. Virtual drums, which include motion sensors and enable drummers to imitate playing drums by stroking a virtual drum, are highly portable. However, drummers, who are used to playing real drums, have difficulty in demonstrating their drum skills with virtual drums because of the lack of feedback from stroking, low sound quality, and so on. Our proposed Airstic Drum achieves high portability and performance quality by integrating real and virtual drums. Airstic Drum can distinguish the stroking of virtual drums from the stroking of real drums, and it outputs digital sound only when the drummer strokes virtual drums. We have developed a prototype system and evaluated its effectiveness by actual use.","Title: A multi-modal interface for performers in stuffed suits\nYear: 2014\n\nAbstract: In wearable computing environments, a user can obtain information safely and efficiently without disturbing their daily life. However, since the surrounding conditions change frequently according to the situation, the information presentation method of the wearable system needs to adapt to the change. The main purpose of our research is to construct a system that changes information presentation methods according to the user situation. We focus on supporting performers in stuffed suits with multi-modal information presentation. We investigated the interfaces for these performers who cannot acquire sufficient information of the surrounding environments.","Title: On achieving dependability for wearable computing by device bypassing\nYear: 2014\n\nAbstract: When using wearable computers used for medical operation and aviation safety, disrupted information presentation due to hardware/software problems can have serious consequences including medical accidents. We propose a mechanism that maintains information presentation in such situatoins by I/O device bypassing. In out method, I/O devices directly communicate with other devices if a system failure happens. The proposed method selects appropriate data converters by considering recognizability in order to present information that is easily understandable to users. We confirmed that the proposed method works effectively by implementing several applications with our prototype system.","Title: Musical B-boying: A Wearable Musical Instrument by Dancing\nYear: 2008\n\nAbstract: Advances in computer technologies have enabled new means of musical expression with motion enabled musical instruments, which have attracted a great deal of attention. We created a new performance system that enables a dancer to make music while b-boying, which is a dance style. We implemented sensor-equipped shoes and a system that recognizes motions and that controls music. We used an implemented prototype on stage, and we found that dancers were able to control the music flexibly while b-boying. Our system will bring about a new style of musical performance for both musicians and dancers.","Title: On query processing considering energy consumption for broadcast database systems\nYear: 2007\n\nAbstract: In recent years, there has been an increasing interest in the broadcast database system where the server periodically broadcasts contents in a database to mobile clients such as portable computers and PDAs. There are three query processing methods in the broadcast database system, while each method consumes different amount of power for query processing. In this paper, we propose a new query processing method which dynamically chooses an appropriate method among the three query processing methods by considering energy consumption.","Title: An Active Database System for Receiving Broadcast Data\nYear: 2002\n\nAbstract: In recent years, many broadcast and communication satellites have been launched to start various new services, which attracts a great deal of attention on data broadcasting system using satellites. In such systems, the volume and variety of data being broadcast will increase significantly. This results in a growing demand on a system that stores a large volume of data and presents the necessary information for users in an efficient way. Highly motivated by such requirements, we propose the super active database(SADB) system which is a data receiving system based on an active database in this paper. Since the use of active database can increase the flexibility in describing the system behavior, the proposed system is well suited to receive and process many types of data. On SADB, the receiving operation management and the received data processing are separated from the rule processing unit. As a result, the SADB can process a great deal of data faster than the conventional active databases.","Title: Primer Streamer: a System to Attract Users to Interests via Images on HMD\nYear: 2013\n\nAbstract: Recently, environments in which users can always see information is becoming realistic due to the development of wearable computing. Researchers have proposed various information presentation methods for different situations. However, in wearable computing environments, since the user does not always need a particular piece of information, there should a specified duration for which the system can show information that does not have any specific importance for the user. In this research, we propose a system that prompts the user to become unconsciously attracted to certain information by presenting visual information related to their interest. Our system utilizes the concept of the priming effect, whereby individuals are affected by content that has been presented before. Evaluation results confirmed that presenting information prompts the user to obtain the intended information. We also implemented a prototype of the system, Primer Streamer, that presents information related to user interest.","Title: Construction of a prototyping support system for painted musical instruments\nYear: 2012\n\nAbstract: Recently there have been many works of research and products which make use of electronic and information technology to equip a piece of paper with interactive functions. Examples include picture books which output animal noises, and the use of electric circuits drawn on paper, in conductive ink, to facilitate the output of light and sound. However, these technologies do not have a function that enables customization of the output that is linked to an input interface. The instrument's sound is composed of various kinds of tone and pitch and the effect of a single note is different from that of chords and melody. When users are designing a painted musical instrument they find out problems with the instrument and then discuss and test the design using the customize function, which selects and outputs the sound of the instrument. Therefore, the goal of our study is to construct a prototyping support system for painted musical instruments. By drawing shapes on the paper with conductive ink users create input interfaces to which they can then assign different sounds flexibly and intuitively.","Title: Mobile Clavier: New Music Keyboard for Flexible Key Transpose.\nYear: 2007\n\nAbstract: Musical performers need to show off their virtuosity for selfexpression and communicate with other people. Therefore, they are prepared to perform at any time and anywhere. However, a musical keyboard of 88 keys is too large and too heavy to carry around. When a portable keyboard that is suitable for carrying around is played over a wide range, the notes being played frequently cause the diapason of the keyboard to protrude. It is common to use Key Transpose in conventional portable keyboards, which shifts the diapason of the keyboard. However, this function creates several problems such as the feeling of discomfort from the misalignment between the keying positions and their output sounds. Therefore, the goal of our study is to construct Mobile Clavier, which enables the diapason to be changed smoothly. Mobile Clavier resolves the problems with Key Transpose by having black keys inserted between any two side-by-side white keys. This paper also discusses how effective Mobile Clavier was in an experiment conducted using professional pianists. We can play music at any time and anywhere with Mobile Clavier.","Title: A rule-based acceleration data processing engine for small sensor node\nYear: 2008\n\nAbstract: In recent years, various small sensor nodes have been developed for recognizing real world situations and events for the development of context-aware systems. We consider that the acceleration sensor is one crucial element for recognizing various types of situations because it has rich and simple information. An application system using acceleration data requires the following features: 1) rapid processing of data without large memory since the amount of acceleration data is much greater than the amount of other sensor data, 2) a node that reduces the amount of data sent to a server, and 3) systems that can be easily configured by users at low cost. Current sensor nodes, however, do not have enough functions to satisfy these requirements. We propose a rule-based data processing engine for processing acceleration data. Our proposed engine rewrites the rules on each node with a few bytes of data. We evaluated our rule-based engine on our small sensor node called the Motion sensing and Communication Minimized Chip (MoCoMi-Chip).","Title: Ubiquitous Gadgets for Constructing Flexible Ubiquitous Services\nYear: 2006\n\nAbstract: In the near future, small ubiquitous devices will be embedded into most of artifacts to support human dailylife. In such environments, we need multiple types of human-computer interfaces for enjoying ubiquitous services. This paper describes several ubiquitous gadgets; ubiquitous chip, Pin&Play, and VCC, to provide flexible ubiquitous services and user interfaces. The integrative use of these devices achieves the physical and intuitive interaction between users and ubiquitous services.","Title: On a Cooperation of Broadcast Scheduling and Base Station Caching in the Hybrid Wireless Broadcast Environment\nYear: 2006\n\nAbstract: In this paper, based on the Hybrid Wireless Broadcast (HWB) model, we investigate a cooperative data management strategy to provide more efficient data delivery to the mobile clients. This strategy integrates broadcast scheduling with cache management of the base station by making effective use of the HWB data dissemination, namely push- and pull- based broadcast and pull-based point-topoint wireless communication. Simulation study demonstrates that the proposed cooperation strategy can improve the system performance.","Title: A method for distance estimation using intra-frame optical flow with an interlace camera\nYear: 2011\n\nAbstract: Recently, there are many researches on location estimation using optical flow, which is a well-known distance estimation method without any infrastructure. However, since the calculation of optical flow needs high computational power, it cannot adapt to high-speed movement. Therefore, in this paper, we propose intra-frame optical flow, which is a new distance estimation method using an interlace camera. It can estimate the high speed moving objects accurately because it uses two successive images with a very short scanning interval extracted from one image captured by an interlace camera. The evaluation result confirmed the effectiveness of our method.","Title: Dynamic Construction Mechanism of a Trigger Graph on Active Databases in Mobile Computing Environments\nYear: 2003\n\nAbstract: As a result of the rapid development of wirelesscommunications and computer hardware technologies,we can now access various information anywhere using handy terminals. To support the integrative useof data held by mobile hosts in this environment, weproposed the AMDS (Active Mobile Database System)as the kernel system for data management. The behavior definition language of this system, ECA(Event-Condition-Action) rules, has a high description capability that enables users to define complicated behavior. However, the execution of the ECA rules may fallinto a chain of unexpected behaviors. In general, adirected graph, called a trigger graph, is used for detecting chains. Since the trigger graph highly dependson network topology, it is difficult to employ the trigger graph in a mobile computing environment. In thispaper, we propose a method that reconstructs the trigger graph dynamically to adapt to changes in networktopology. Using this mechanism,mobile applicationswith ECA rules can be used more safely.","Title: Early Gesture Recognition Method with an accelerometer.\nYear: 2014\n\nAbstract: An accelerometer is installed in most current mobile phones, such as the iPhones, Android-powered devices, and video game controllers for Wii or PS3, which enable easy and intuitive operations such as scrolling browsers and drawing 3D objects by detecting the inclination and motion of devices. Therefore, many gesture-based user interfaces with accelerometers are expected to appear in the future. Gesture recognition systems with accelerometers generally have to construct gesture models with user's gesture data before use, and recognize unknown gestures by comparing them with training data. As recognition process generally starts after the gesture has finished, output of the recognition result and feedback, e.g. scrolling, have a delay, which may cause users to retry gestures and degrade interface usability. We propose a method of early gesture recognition that calculates the distance between input data and training data sequentially, and outputs recognition results only when one output candidate has a stronger likelihood than the others. Additionally, we implemented a gesture-based photo viewer as an example of useful applications of our proposed method.","Title: A Method for Wearable Projector Selection that Considers the Viewability of Projected Images\nYear: 2010\n\nAbstract: Many projects and practices can now support user tasks by presenting images on the floor or wall using a mobile projector. However, unstable images make communication and task support difficult. The viewability of a projected image depends on several factors; the position of the wearable projector, user contexts such as whether he/she is walking or standing, and the type of presentation content. We describe a method for selecting a projector from multiple wearable projectors, wherein the method considers these factors. We investigated the characteristics of viewability by changing various factors in various contexts and with different content. We also investigated the appropriate position for a wearable mobile projector in various situations and with different content in subjective evaluations. The results indicated that our system dynamically changes the characteristics of a projector for presenting content. We implemented a prototype system that selects the appropriate projector dynamically in response to the situation.","Title: A system for visualizing sound source using augmented reality\nYear: 2012\n\nAbstract: In recent years, Augmented Reality (AR) has been widely used in various research. AR augments human sensation and offers information to support users in daily life. In this research, we propose an AR system that recognizes sound sources to augment human's vision. In our system, a sound source and its position are detected by acoustic processing. The system notifies a user through different visual markers, which are allocated on different kinds of sound sources. Our system detects different kinds of sound sources in daily life and notifies user of the information and the position of sound source. Using our system, the user can find out which object is making sound in the environment without hearing it.","Title: A pointing method using accelerometers for graphical user interfaces\nYear: 2012\n\nAbstract: Graphical User Interfaces (GUIs) are widely used and pointing devices are required to operate most of them. We have proposed Xangle, a pointing method using two accelerometers for wearable computing environments. The cursor is positioned at the intersection of two straight lines, which are synchronized with the angles of the accelerometers at fingers. However, Xangle is difficult to be used in daily-life, when the user frequently changes which part of the body they point with. Therefore, we propose a method of changing the body parts used for pointing according to the situation. Additionally, we proposed a method to accelerate the pointer and a method to layout menu items for Xangle since these methods are suitable for using GUI in wearable computing environments. We confirmed that the proposed method was effective from the results of evaluations.","Title: Intra-frame scan: a method for estimating object movements using an interlace camera and visual markers\nYear: 2008\n\nAbstract: There has recently been a great deal of research on location-dependent services, such as navigation systems where these estimate the location of participants in event spaces and provide navigation for them. There are methods of estimating absolute positions such as those using GPS and those using RFID tags. However, they have problems with the high installation cost and environmental restrictions. Therefore, relative-position estimation is a strong candidate that is inexpensive and can be broadly adapted. Although there are existing methods of estimating relative positions using wearable sensors, they have problems with the cost of managing multiple sensors. There is a method using an optical flow of camera images. However, since it needs a high degree of computational power for calculating optical flows, it cannot respond to high-speed motion. In this paper, we propose a new method of estimating relative positions using simple markers and an interlace camera. Since our system estimates the relative distance by using two successive images with a very short scanning interval captured by an interlace camera, it can adapt to high-speed movement.","Title: Functional Properties of Information Filtering\nYear: 2001\n\nAbstract: In recent years, due to the increasing popularizationof data broadcasting, the volume andvariety of data being broadcast are rapidlyincreasing. In this environment, as it isdifficult for users to search for informationfrom a large amount of broadcast data, thereis an increasing demand for filtering techniquesthat automatically extract only thenecessary data. Consequently, a number offiltering methods have been proposed. However,mathematical representation of thesemethods...","Title: Development of a navigation system with a route planning algorithm using body-worn sensors\nYear: 2008\n\nAbstract: There are many kinds of events where participants converge and move around freely looking at points of interest. Since in these event spaces, the participants go where they want, some problems may arise for the event manager such as people staying in one area too long or congestion that occurs at specific attractions. Therefore, we propose here a new navigation system that has a route planning algorithm to satisfy the objectives of the event manager. In an actual test of our system, we found that the participants' actions were in line with the objectives of the event manager. Moreover, we also found that our system performs better by using wearable computing technologies because detailed information on participants is acquired.","Title: Wearable DJ system: a new motion-controlled DJ system\nYear: 2009\n\nAbstract: The recent reappreciation of music has helped to reestablish the need for the Disk Jockey(DJ), which is someone who selects and plays music. The DJ selects the music, smoothly changes from one song to another, applies sound effects to the music, and this in turn excites the audience. However, conventional DJs cannot get away from the booth because of all the equipment, and thus they are unable to freely perform. Therefore, in this study, we propose a Wearable DJ system that solves this problem that uses wearable computing and gesture recognition technologies. The proposed system enables DJs to perform DJ techniques by performing intuitive gesture operations using wearable acceleration sensors. We have actually used the proposed system on several event stages such as Kobe Luminarie 2007 in Japan. We have evaluated the accuracy of its gesture recognition by changing the parameters and have confirmed its effectiveness.","Title: A method to control LED blinking for position detection of devices on conductive clothes\nYear: 2011\n\nAbstract: Various wearable computing devices face problems with their power supplies, communication channels, and placement. Conductive clothes can resolve these problems, but it is still difficult to know the positions of devices on the conductive fabric. Therefore, we have devised a method to detect the positions of such devices by using a camera. To detect the positions our method blinks the LEDs on the devices according to their ID. Additionally, we propose several methods to shorten the time for detection. An experimental evaluation confirmed that compared with conventional method our methods reduce the time to detect the positions of the devices.","Title: Ultrasound-based movement sensing, gesture-, and context-recognition\nYear: 2013\n\nAbstract: We propose an activity and context recognition method where the user carries a neck-worn receiver comprising a microphone, and small speakers on his wrists that generate ultrasounds. The system recognizes gestures on the basis of the volume of the received sound and the Doppler effect. The former indicates the distance between the neck and wrists, and the later indicates the speed of motions. Thus, our approach substitutes the wired or wireless communication typically required in body area motion sensing networks by ultrasounds. Our system also recognizes the place where the user is in and the people who are near the user by ID signals generated from speakers placed in rooms and on people. The strength of the approach is that, for offline recognition, a simple audio recorder can be used for the receiver. We evaluate the approach in one scenario on nine gestures/activities with 10 users. Evaluation results confirmed that when there was no environmental sound generated from other people, the recognition rate was 87% on average. When there was environmental sound generated from other people, we compare approach ultrasound-based recognition which uses only the feature value of ultrasound against standard approach, which uses feature value of ultrasound and environmental sound. Results for the proposed approach are 65%, for the standard approach are 57%.","Title: A method for identification of moving objects by integrative use of a camera and accelerometers\nYear: 2012\n\nAbstract: In sports events shown on TV or on the Internet, having the names and uniform numbers of the players displayed beside the players on the screen helps viewers to identify the players. Conventional methods of annotating and tracking objects utilize image recognition techniques with high-resolution images, so players captured on camera from a long distance are too small for accurate recognition. In response to this problem, we propose a method for identifying and tracking moving objects with a combination of wearable acceleration sensors and image recognition techniques. In our method, the method recognizes moving objects by matching the context picked up by the wearable sensors with the context inferred during the image processing. Evaluation results demonstrated the effectiveness of the proposed method.","Title: A sound-based lifelog system using ultrasound\nYear: 2014\n\nAbstract: We propose an activity and context recognition method where the user carries a neck-worn receiver comprising a microphone, and small speakers on his wrists that generate ultrasounds. The system recognizes gestures on the basis of the volume of the received sound and the Doppler effect. The former indicates the distance between the neck and wrists, and the latter indicates the speed of motions. Thus, our approach substitutes the wired or wireless communication typically required in body area motion sensing networks by ultrasounds. Our system also recognizes the place where the user is in and the people who are near the user by ID signals generated from speakers placed in rooms and on people. The strength of the approach is that, for offline recognition, a simple audio recorder can be used for the receiver. In this paper, we introduce our new device.","Title: A Text Input Interface using a Portable Clavier for Pianists\nYear: 2007\n\nAbstract: Due to the miniaturization of computers, wearable computing has attracted great attention. Several text input interfaces for wearable computing devices have already been proposed. However; those devices are not customized for specific users, so the devices do not facilitate sufficiently easy and quick text input. Therefore, the goal of our study is to construct a text input method specialized to pianists by exploiting characteristics of pianists and claviers. The proposed method achieves simple and quick text input by restricting users to pianists. The results of the evaluation suggest that the input speed achieved using proposed interface is twice as fast as that of the Twiddler.","Title: A distance estimation method using intra-frame optical flow with interlace camera\nYear: 2010\n\nAbstract: There has recently been much research on estimates of location using optical flow, which is a well-known for estimating distances. However, since high computational power is needed to calculate optical flow, it cannot be adapted to high-speed movement. This paper proposes an intra-frame optical flow, which is a new method of estimating distances using an interlace camera. It estimates high-speed moving objects accurately because it uses two successive images with a very short scanning interval extracted from one image captured by an interlace camera. The result obtained from evaluation confirmed the effectiveness of our method.","Title: An information presentation method for head mounted display considering surrounding environments\nYear: 2014\n\nAbstract: When wearing a head mounted display (HMD), the degree of concentration on the HMD varies depending on the surrounding environment. In this work, we developed an information presentation method considering cognitive cost and safety in wearable computing environments. The proposed method changes its information presentation method based on possible gazing time that varies in according with the surrounding environment and user context. We used an eye tracker to measure the relationship between eyestrain and watching an HMD and also clarified the relationship between gaze time and surrounding environment. We then used the results to develop an algorithm to change the information presentation method. Evaluation results revealed cases in which it was difficult and dangerous to gaze at an HMD and therefore necessary to change the information presentation.","Title: Design of a Car Navigation System that Predicts User Destination\nYear: 2006\n\nAbstract: Because of advances in information technologies, car navigation systems have come into widespread use as useful tools to guide drivers where they want to go. Conventional car navigation systems present the most suitable route according to a destination input into the system. However, since the required operation to input the destination costs so much, users do not usually use car navigation systems for daily driving. In this paper, to exploit the effective functions of car navigation systems, we propose a new system that automatically predicts user purpose and destination. The proposed car navigation system presents various information based on predicted purpose without interaction from users.","Title: Aware-Mail: An Event-Driven Mail System for Wearable Computing Environments\nYear: 2004\n\nAbstract: In recent years, e-mail has become widespread explosively and it became one of very important communication tools which anyone can use easily. Here, the wearable computing has also attracted a lot of attention. In wearable computing environments, a user brings and uses his/her own computer wherever he/she goes. Therefore, we can construct a new mail system by utilizing the features of wearable computing such as \"hands-free\", \"always on\", and \"supporting daily life\". In this paper, we propose a mail system for wearable computing environments, called aware-mail (active wearable system applying rule-based engine for mail). Our system provides new type of communications in wearable computing environments, such as automatic mail processing according to the user's situation and exchanging information with other users by mail-based P2P communications.","Title: A pointing method using two accelerometers for wearable computing\nYear: 2009\n\nAbstract: A variety of real-world situations are beneficial for wearable computing since it provides information services while users are doing other jobs. Therefore, a simple and hands-free input interfaces have suitable for these computer operations. However, such interface has not been achieved with conventional input devices such as mice or track balls. Although gesture or eye-gaze input techniques have also been developed for wearable computing, they also suffer from problems i.e., a slow pointing speed, difficulty in carrying devices, and complexity in parallel use when doing tasks. We propose a new method of pointing using input of simple gestures with two accelerometers. By dividing the specifications of two coordinates into a combination of two independent motions, we accomplish accurate and intuitive pointing. A user attaches two small accelerometers to both his/her hands or both elbows. The pointing is done by using the intersection of two straight lines, and the movement of the lines is synchronized with that of the accelerometers. In addition, we also propose a method of changing the position of objects being pointed at that is new approach. The results we obtained from our evaluation experiments confirmed that our method was effective.","Title: Implicit context awareness by face recognition\nYear: 2011\n\nAbstract: In recent years, technical improvements to sensors have attracted a great deal of attention, in particular due to the sensors' capability recognizing user contexts. In this paper, we propose an implicit context awareness system that identifies user context by sensing the context of surrounding environments. We implemented a prototype two cameras in a variely of situations. Evaluation results showed that the system was effective and improved context recognition. Our method can be used to identify rich contexts that cannot be recognized by conventional methods.","Title: An application development environment for rule-based i/o control devices\nYear: 2005\n\nAbstract: In this paper, we propose an application development environment for the ubiquitous chip, which is a rule-based event-driven input/output (I/O) control device for constructing ubiquitous computing environments. The proposed development environment simulates the behaviors of multiple ubiquitous chips and helps users to create rules. Moreover, it has a function for developing applications by cooperation between virtual ubiquitous chips and real ubiquitous chips. The application environment enables both programmers and general users to develop and customize applications for ubiquitous computing environments.","Title: HASC2011corpus: towards the common ground of human activity recognition\nYear: 2011\n\nAbstract: Human activity recognition through the wearable sensor will enable a next-generation human-oriented ubiquitous computing. However, most of research on human activity recognition so far is based on small number of subjects, and non-public data. To overcome the situation, we have gathered 4897 accelerometer data with 116 subjects and compose them as HASC2011corpus. In the field of pattern recognition, it is very important to evaluate and to improve the recognition methods by using the same dataset as a common ground. We make the HASC2011corpus into public for the research community to use it as a common ground of the Human Activity Recognition. We also show several facts and results of obtained from the corpus.","Title: A Map Matching Algorithm for Car Navigation Systems that Predict User Destination\nYear: 2008\n\nAbstract: In this paper, we propose a map matching algorithm for car navigation systems that predict user destination. This car navigation system is a novel system that automatically predicts user purpose and destination to present various information based on predicted purpose without user interaction. It requires the correct road where the car drives in real time, and it also need to know the route from the start point to the current point correctly. The proposal map matching method divides the trajectory into equal intervals and calculates the shortest path score for each one. Testing using GPS data for actual car trips showed that its use results in better destination prediction than with conventional methods in most cases. The results were the best for intervals of 5 minutes.","Title: A Motion Recognition Method for a Wearable Dancing Musical Instrument\nYear: 2009\n\nAbstract: In this paper, we constructed a system for realizing a new style of dance performance that dancers play music by dancing. From pilot study, we have found that the motion recognition for dance performance needed the synchronism to back ground music (BGM). Therefore, we propose a new motion recognition method specialized to dance performances. The key techniques of the proposed method are (1) adaptive decision of the size of recognition window to recognize a motion in sync with BGM, and (2) motion recognition in two-phase (rough and detailed) to fulfill the accuracy in high speed recognition. Data was recorded using a 3-axis wireless accelerometers mounted on both shoes. We evaluated the method on a dataset of 5 different dance steps (each repeated 100 times).The results show that this method is capable of improving recognition for all steps (in one case improving recognition from 62% to 99%) while retaining a feeling of seamless connection between movement and sound.","Title: An Information Retrieval System for Supporting Casual Conversation in Wearable Computing Environments\nYear: 2005\n\nAbstract: In the future, wearable computers are going to be exploited for different applications to support human life. In this paper, we present a new wearable computing system that supports daily conversation based on speech recognition and information retrieval technologies. In daily conversation, sometimes we do not know the meaning of a new word or we want to know more information about a topic. In such cases, we can get more information to make our conversation more interesting and fluent by receiving auxiliary information from a wearable computer. The proposed system analyzes conversation content and retrieves information related to ongoing conversations from the Internet to make them more interesting and beneficial. We have implemented a prototype system, and the results of actual use have shown the need for such a system.","Title: TEMPEST: a text input system for musical performers\nYear: 2006\n\nAbstract: Recently, due to the widespread use of computers, text-based communication methods, such as e-mail and chat, have attracted a great deal of attention. If pianists can apply their musical expressions to the text input, they can input texts with their own emotion richly. Therefore, the goal of our study is to construct TEMPEST (TExt input and Musical PErforming SysTem) that is a text input system to input texts with various musical expressions using a clavier. Since this system provides text input methods considering musical artistry, a performer can input texts like playing music.","Title: Application Development Environment For Event-Driven Ubiquitous Devices\nYear: 2009\n\nAbstract: Purpose - In ubiquitous computing environments, not only programmers but also general users come to develop/customize applications. Therefore, the purpose of this paper is to propose a new application development environment for ubiquitous computing environments.Design/methodology/approach - The paper clarifies the requirements for application development environments of ubiquitous computing environments. It then designs and implements a prototype of a development environment that fulfills these requirements.Findings - It is found that the requirements for application development in ubiquitous computing environments are: easy programming, detection of current status, programming awareness of network connections between multiple ubiquitous devices, and debugging with cooperation among real/virtual environments.Research limitations/implications - This prototype of application development environment is designed for event-driven ubiquitous devices.Practical implications - A prototype development environment has been implemented to show the effectiveness of this approach, and is presented an example of an application development to show the effectiveness of the approach.Originality/value - This paper proposes a new style of application development for ubiquitous computing environments","Title: Evaluating effect of types of instructions for gesture recognition with an accelerometer\nYear: 2014\n\nAbstract: Mobile phones or remotes for video games using gesture recognition technologies enable easy and intuitive operations such as scrolling browser and drawing objects. Gesture input has an advantage of rich expressive power over the conventional interfaces, but it is difficult to share the gesture trajectory with other people through writing or verbally. In this paper, we evaluate how user gestures change according to the types of the instruction. We obtained acceleration data for 10 kinds of gestures instructed through three types of texts, figures, and videos, totalling 44 patterns from 13 test subjects, for a total of 2,630 data samples.","Title: Design and implementation on a pie menu interface for analog joysticks\nYear: 2009\n\nAbstract: There have been recently several input methods that combine pen-based input with a pie menu as an input interface in environments where working space is limited. However, they suffer from various problems in that input operation is difficult to learn where there are many items on the menu. In this research, we propose a new space-saving input interface that combines an analog joystick and a hierarchical pie menu. The proposed method employs a hierarchical pie menu to enhance the number of items, and a frequency-based area division algorithm that works to expand the area for important items. Using our method, an intuitive input interface is achieved that is high speed and highly accurate.","Title: Toward Achieving On-Site Programming\nYear: 2009\n\nAbstract: Wearable computing, where users can wear computers anywhere and at any time, is slowly becoming a reality because of recent technological advancements. When users access various services in wearable computing environments, they want to define new services by themselves. Therefore, we propose a new framework for constructing context-aware applications in wearable computing environments. Our framework, called Wearable Toolkit, consists of an event-driven rule processing engine and tools to develop applications. By using our framework, we can define and customise services anywhere and at any time. We found our system helps to create context-aware wearable services from the results we obtained by evaluating it.","Title: A Ubiquitous Computing Environment Composed by Cooperation between Visual Markers and Event-Driven Compact Devices\nYear: 2005\n\nAbstract: In this paper, we propose a new ubiquitous computing environment composed by visual markers and event-driven compact devices. We employ the VCC (Visual Computer Communication) method, which we have already proposed as an information displaying method for visual markers. In the VCC method, a marker is composed of multiple tiles whose color changes at a certain interval of time, by which the marker can send dozens of bytes of information. In our research, to enhance the facility of VCC markers, we use rule-based compact devices to process various types of data and to change information shown in a VCC marker dynamically. The combination of VCC markers and compact devices enables various services such as the presentation of real-world information collected by compact devices and control of appliances via compact devices.","Title: IT-enabled donation boxes to promote donation\nYear: 2009\n\nAbstract: Electronics and information technology have great potential to attract people's attention. In this paper, we describe our use of IT (Information Technology)-enabled donation boxes to promote donations. We implemented four donation boxes, including a colorfully illuminated box and a sensor-enabled interactive box. We put them into actual use in a fundraising activity at Kobe Luminarie and evaluated their effect. Through this activity, we found that IT-enabled donation boxes have the potential to increase donations.","Title: Lighting choreographer: an LED control system for dance performances\nYear: 2011\n\nAbstract: There have been various approaches to enhance physical performances by adding visuals and/or audio according to the motions of performers. However, these methods were too simple to explain the performers' will of their body expression. Furthermore, they did not consider exceeding the physical limitation of body expression, such as providing the sense of faster movement of arms by changing colors in costumes dynamically. We propose a new LED performance system to expand the expression capability of human body. We can create new generation performances by combining body expression and lighting effects.","Title: An Event-driven Navigation Platform forWearable Computing Environments\nYear: 2005\n\nAbstract: Wearable computing environments are slowly becoming a reality, worn and used by a user wherever he or she goes. As a result, a lot of attention is given to navigation systems that provide various information according to the users' situation. In this paper, we identify the requirements of wearable navigation systems and propose an event-driven navigation platform to fulfill them. Since the behaviors of our platform are described in a set of event-driven rules, users can adopt a variety of attached devices and customize system configurations. Moreover, we have developed a prototype and made an experimental study for its evaluation. Using our system, service providers can easily construct flexible wearable navigations.","Title: Evaluation on performer support methods for interactive performances using projector\nYear: 2009\n\nAbstract: Recently, performances that combine performersu0027 actions and images projected from a projector have attracted a great deal of attention. In such performances, since the performer usually faces the audience, it is difficult for him/her to watch the projected images on a background screen. This means that he/she cannot make the performances dynamic in response to changes in the situation. Therefore, we evaluate multiple information presentation methods for interactive performances. We have developed a prototype system for supporting performers and evaluated its effectiveness. We confirmed that differences in display devices and in the types of presenting images affected the quality of performances. The implemented prototype was actually used in several stage performances, and we confirmed that the system was effective and improved the visibility of projected images.","Title: An event-driven wearable systems for supporting pit-crew and audiences on motorbike races\nYear: 2009\n\nAbstract: Motorbike racing is one of the most popular motorsports and many fans visit circuits to watch races. However, since audiences and pit crews can only obtain limited information, it is difficult for them to get detailed information about teams except for high ranking teams and few popular teams. To solve this problem, we propose an information browsing system for pit crews and audiences who use wearable computers. This system allows pit crew to browse for various race information easily and effortlessly, and it can entertain audiences much more.","Title: CLAD: a Sensor Management Device forWearable Computing\nYear: 2007\n\nAbstract: There has been increasing interest in wearable computing. In wearable computing environments, a wearable computer runs various applications with various sensors (wearable sensors). Since conventional wearable systems do not manage the power supply flexibly, they consume excess power resources for unused sensors. Additionally, sensors frequently become unstable for several reasons such as a breakdown. This instability is hard to detect simply from the sensed data. To solve these problems, we propose a new sensor management device CLAD (cross-linkage for assembled devices) that has various functions for power management and sensed-data management. CLAD improves power saving, data accuracy, and operational reliability.","Title: Evaluation function of sensor position for activity recognition considering wearability\nYear: 2013\n\nAbstract: In the wearable computing environment, a computer provides many kinds of services by using the values from wearable sensors to recognize the user's movements or situations. In the research on activity recognition, accelerometers are attached on the user's body such as wrists, waist and, feet. Though researches on best sensor placement for context aware systems has been released thus far, they do not use enough number of sensors to really decide the best sensor placement. When using these context aware systems in our daily life, we also need to consider the discomfort that the user gets from attaching the sensors. The sensor might get in the user's way or feel uncomfortable for the user, however, as far as we know, the sensor's wearability is not taken into consideration in these researches. This paper proposes an evaluation function that scores sensor placement considering both recognition accuracy and sensor wearability, with twenty sensors on the user's body and thirty kinds of exercises including aerobic exercise, weight training, and yoga. Then we experimentally evaluated sensor placement, resulted in high degree of accuracy achieved without feeling stressful.","Title: Design and implementation of a ubiquitous optical device controlled with a projector\nYear: 2008\n\nAbstract: We recently have seen illumination using Light Emitting Diodes (LEDs) on the street. The current illumination is difficult to control of flexibly and dynamically. In response to this problem, we propose a method controlling numerous LEDs collectively, dynamically, and flexibly by using a projector and small devices with optical sensors. We designed and implemented a small device to achieve our mechanism, which has an LED, an optical sensor, and a microcomputer to react to the light illuminated from a projector. Moreover, it has functions to self adjust the threshold and recognize optical commands, which are required for collective control. We discuss how effective our proposed device was in a study to evaluate it and in field tests done at actual events.","Title: A Context Aware System Based on Scent\nYear: 2011\n\nAbstract: Conventional context-aware systems normally use accelerometers and gyroscopes, and it is difficult to recognize contexts such as having a meal, or going to the toilet. We propose a new context recognition method based on scent using a wearable scent sensor. Since our algorithm considers the characteristics of scent, it recognizes contexts that find difficult to recognize by conventional sensors. Evaluation results demonstrated that scent sensors identify the context of having a meal with 94\\% accuracy and going to the toilet with 97\\% accuracy. We also implemented a context-aware life-log system for healthcare.","Title: A Position Detection Method Of Devices On Conductive Clothes By Controlling Led Blinking\nYear: 2013\n\nAbstract: Various wearable computing devices face problems with their power supplies, communication channels, and placement. Conductive clothes can resolve these problems, but it is still difficult to know the positions of devices on the conductive fabric. Therefore, we propose a method to detect the positions of such devices by using a camera. To detect the positions, our method blinks the LEDs on the devices according to their ID. Additionally, we propose several methods to shorten the time for detection. An experimental evaluation confirmed that compared with conventional method our methods reduce the time to detect the positions of the devices.","Title: A Query Processing Method Considering Query Frequency for Broadcast Database Systems\nYear: 2006\n\nAbstract: In recent years, there has been an increasing interest in broadcast database systems, where the server periodically broadcasts contents of a database to mobile clients such as PDAs. There are three query processing methods in the broadcast database system: (i) the server processes a query and then broadcasts the query result to the client; (ii) the client stores all data that are necessary in processing the query and then processes it locally; and (iii) the server and the client collaborate in processing the query. Since the performance of each method changes according to the system situation, such as query frequency, it is difficult to decide the optimal method among them statically. In this paper, we propose a new query processing method which dynamically changes the query processing method based on query frequency. This method improves not only the response time but also the success rate of query processing compared with the traditional methods.","Title: A query processing method for hybrid wireless-broadcast networks\nYear: 2005\n\nAbstract: In this paper, we propose a new effective query processing method through our hybrid wireless broadcast (HWB) model, which combines push and pull based broadcast and pull-based point-to-point wireless communication. Our method provides a flexible and complementary information service in different bandwidths and service ranges. The results of simulation studies show the HWB approach shortens the average waiting time and enhances the performance of the system.","Title: A smile/laughter recognition mechanism for smile-based life logging\nYear: 2013\n\nAbstract: Most situations that cause people to smile are important and treasured events that happen in front of the other people. In life-logging systems that record everything with wearable cameras and microphones, it is difficult to extract the important events from a large amount of recordings. In this research, we design and implement a smile-based life-logging system that focuses on smile/laughter for indexing the interesting/enjoyable events on a recorded video. Our system, features an original smile/laughter recognition device using photo interrupters that is comfortable enough for daily use and proposed an algorithm that detects smile/laughter separately by threshold-based clustering. The main challenge is that, since the reasons people smile and laugh are quite diverse, the system has to detect a smile/laughter as different events. Evaluation results showed that our mechanism achieved a 73%/94% accuracy in detecting smile/laughter, while actual use of the system showed that it can accurately detect interesting scenes from a recorded life log.","Title: Ubiquitous Chip: A Rule-Based I/O Control Device for Ubiquitous Computing\nYear: 2004\n\nAbstract: In this paper, we propose a new framework for ubiquitous computing by rule-based, event-driven I/O (input/output) control devices. Our approach is flexible and autonomous because it employs a behavior-description language based on ECA (Event, Condition, Action) rules with simple I/O control functions. We have implemented a prototype ubiquitous device with connectors and several sensors to show the effectiveness of our approach.","Title: Wearable MC system a system for supporting MC performances using wearable computing technologies\nYear: 2011\n\nAbstract: A master of ceremonies (MC) plays an important role to ensure all events progress smoothly because unexpected interruption make them unsuccessful. MCs must have various abilities such as being able to memorize the content of given scenarios and manage problems that occur unexpectedly. Moreover, since unskilled MCs cannot intuit the atmosphere in the audiences during an event, they cannot control this smoothly. Therefore, we propose a wearable system that solves these problems for MCs achieved through wearable computing technologies. Our system has functions to support MCs in carrying out their duties smoothly, such as a robust voice-tracking function for them to read scripts, a user interface that does not interrupt other tasks, and a function that enables MCs intuit grasp the atmosphere of the audience. We implemented a prototype of the wearable MC system and actually used it at several events. The results we obtained from actually using it confirmed that it worked well and helped MCs to carry out their official duties smoothly.","Title: Evaluating Gesture Recognition by Multiple-Sensor-Containing Mobile Devices\nYear: 2011\n\nAbstract: Mobile phones or video game controllers using gesture recognition technologies enable easy and intuitive operations. However, usually only one of each type of sensor is installed in each device, and the effect of multiple sensors on recognition accuracy has not been investigated. Moreover, the effect of the differences in the motion of a gesture has not been examined. We captured data for 27 kinds of gestures by using a mobile device with 9 accelerometers and 9 gyroscopes, we then experimentally investigated the effects on recognition accuracy of changing the number and positions of sensors, and the number and kinds of gestures. The results showed that the use of multiple sensors and of sensors positioned at specific positions affects accuracy. It was also shown that gestures are interdependent and selecting specific gestures improves recognition accuracy.","Title: An event-driven wearable system for supporting motorbike racing teams\nYear: 2004\n\nAbstract: Exploiting the latest information is very important for winning a motorbike race. However, it is difficult for a pit crew to acquire the latest information while working. The miniaturization of computers has enabled the creation of wearable computers, which permit users instant, and hands-free access to information. Therefore, we propose a wearable system that supports motorbike racing teams. This system allows pit crew to browse for various race information easily and effortlessly.","Title: A system for practicing formations in dance performance supported by self-propelled screen\nYear: 2013\n\nAbstract: Collapsed formation in a group dance will greatly reduce the quality of the performance even if the dance in the group is synchronized with music. Therefore, learning the formation of a dance in a group is as important as learning its choreography. However, if someone cannot participate in practice, it is difficult for the rest of the members to gain a sense of the proper formation in practice. We propose a practice-support system for performing the formation smoothly using a self-propelled screen even if there is no dance partner. We developed a prototype of the system and investigated whether a sense of presence provided by both methods of practicing formations was close to the sense we really obtain when we dance with humans. The result verified that the sense of dancing with a projected video was closest to the sense of dancing with a dancer, and the trajectory information from dancing with a self-propelled robot was close to the trajectory information from dancing with a dancer. Practicing in situations similar to real ones is able to be done by combining these two methods. Furthermore, we investigated whether the self-propelled screen obtained the advantages of dancing with both methods and found that it only obtained advantages of dancing with projected video.","Title: An on-site programming environment for wearable computing\nYear: 2012\n\nAbstract: In wearable computing environments, it is difficult for users to prepare applications that are used beforehand since there are various situations and places. Therefore, they want to define new services by themselves. In this study, we present a development framework and several tools for developing services in wearable computing environments. The framework consists of an event-driven rule processing engine and service implementation tools, which enable users to program services easily and quickly. The proposed system shows elements of event-driven rules as chips, and we can program services by selecting chips on graphical user interfaces. In addition, the proposed system has two functions considering programming features on wearable computing: genetic-algorithm-based programming and social-network-based programming.","Title: A life log system that recognizes the objects in a pocket\nYear: 2015\n\nAbstract: A novel approach has been developed for recognizing objects in pockets and for recording the events related to the objects. Information on putting an object into or taking it out of a pocket is closely related to user contexts. For example, when a house key is taken out from a pocket, the owner of the key is likely just getting home. We implemented a objects-in-pocket recognition device, which has a pair of infrared sensors arranged in a matrix, and life log software to obtain the time stamp of events happening. We evaluated whether or not the system could deal with one of five objects (a smartphone, ticket, hand, key, and lip balm) using template matching. When one registered object (the smartphone, ticket, or key) was put in the pocket, our system recognized the object correctly 91% of the time on average. We also evaluated our system in one action scenario. With our system's time stamps, user could easily remember what he took on that day and when he used the items.","Title: A recognition method for combined activities with accelerometers\nYear: 2014\n\nAbstract: Many activity recognition systems using accelerometers have been proposed. Activities that have been recognized are \\\"single\\\" activities which can be expressed with one verb, such as sitting, walking, holding a mobile phone, and throwing a ball. In actual, however, \\\"combined\\\" activities including more than two kinds of state and movement are often taken place. Focusing on hand gestures, they are performed not only while standing, but also while walking and sitting. Though the simplest way to recognize such combined activities is to construct the recognition models for all the possible combinations of the activities, the number of combinations becomes immense. In this paper, we propose a recognition method for combined activities by learning single activities only. Evaluation results confirmed that our proposed method achieved 0.84 of recall and 0.85 of precision, which is comparable to the method that had learned all the combined activities.","Title: Mobile Phone User Authentication With Grip Gestures Using Pressure Sensors\nYear: 2014\n\nAbstract: Purpose - User authentication is generally used to protect personal information such as phone numbers, photos and account information stored in a mobile device by limiting the user to a specific person, e.g. the owner of the device. Authentication methods with password, PIN, face recognition and fingerprint identification have been widely used; however, these methods have problems of difficulty in one-handed operation, vulnerability to shoulder hacking and illegal access using fingerprint with either super glue or facial portrait. From viewpoints of usability and safety, strong and uncomplicated method is required.Design/methodology/approach - In this paper, a user authentication method is proposed based on grip gestures using pressure sensors mounted on the lateral and back sides of a mobile phone. Grip gesture is an operation of grasping a mobile phone, which is assumed to be done instead of conventional unlock procedure. Grip gesture can be performed with one hand. Moreover, it is hard to imitate grip gestures, as finger movements and grip force during a grip gesture are hardly seen by the others.Findings - The feature values of grip force are experimentally investigated and the proposed method from viewpoint of error rate is evaluated. From the result, this method achieved 0.02 of equal error rate, which is equivalent to face recognition.Originality/value - Many researches using pressure sensors to recognize grip pattern have been proposed thus far; however, the conventional works just recognize grip patterns and do not identify users, or need long pressure data to finish confident authentication. This proposed method authenticates users with a short grip gesture.","Title: Method for visualizing undone operations based on changes on desktop screen\nYear: 2014\n\nAbstract: The undo operation of graphical user interfaces is widely used. However, conventional implementations of undo have two problems: 1) users may not notice undone parts since the operation is instantaneous, and 2) it is difficult to know the undone contents if the user temporarily interrupts a work the user works on multiple operations in parallel. Although there have been several methods developed that improve undo operations, they are for specific applications. Here, we devised a visualization for undone operations that, is independent of the running applications and based on the idea that an undo operation causes display to change. Our method specifies the area affected by the undo operation by screens. We implementedseveral visualization methods including emphasizing the undone area, presenting the undone content, and notifying the user that the undone is not visible.","Title: Filtering Order Adaptation Based on Attractor Selection for Data Broadcasting System\nYear: 2009\n\nAbstract: Recent spread of various data broadcasting services leads to provide enormous and various data. Since, data that a client needs are a part of them, there has been an increasing interest in information filtering techniques where a client automatically chooses and stores the necessary data. Generally, when a client performs filtering, it applies some filters sequentially and the time required for filtering changes according to the order of filters. On the other hand,in recent years, there have been many studies about attractor selection which is an autonomous parameter control technique based on the knowledge from living organisms.In this paper, in order to reduce the load for filtering, we propose a novel method which adaptively changes the order of filters according to the change in broadcast contents.This method adaptively decides the control parameters for filtering by using attractor selection.","Title: A method for energy saving on context-aware system by sampling control and data complement\nYear: 2010\n\nAbstract: Although various systems use multiple accelerometers to recognize minute motions and states in the research area of context-awareness, a conventional architecture can be optimized from the viewpoint of its energy consumption and accuracy. In this paper, we propose a context-aware system that reduces the energy consumption by controlling the sampling frequency of wearable sensors. Even if the sampling frequency changes, no extra configuration is required because the missing data for the controlled sensors are complemented by our proposed algorithm. The evaluation results confirmed a power-reduction of 34.28% with keeping the accuracy. The energy consumption can be reduced without a large loss in accuracy using our system.","Title: A motion recognition method by constancy-decision\nYear: 2010\n\nAbstract: Many context-aware systems using accelerometers have been proposed. Contexts that have been recognized are categorized into postures (e.g. sitting), behaviors (e.g. walking), and gestures (e.g. a punch). Postures and behaviors are states lasting for a certain length of time. Gestures, however, are sporadic or once-off actions. It has been a challenging task to find gestures buried in other contexts. In this paper, we propose a method that classifies contexts into postures, behaviors, and gestures by using the autocorrelation of the acceleration values and recognizes contexts with an appropriate method. We evaluated the recall and precision of recognition for seven kinds of gestures while five kinds of behaviors; The conventional method gave values of 0.75 and 0.59 whereas our method gave 0.93 and 0.93. Our system enables a user to input by gesturing even while he or she is performing a behavior.","Title: A Prototyping Support System For The Creation Of Painted Musical Instruments\nYear: 2015\n\nAbstract: Currently there are numerous works of research and products which utilise electronic and information technology to equip a piece of paper with interactive functions, including illustrated books which output sounds and the process of drawing electric circuits onto paper, in conductive ink, to facilitate the output of light and sound. However, these technologies do not facilitate customisation of the output that is linked to an input interface. In the case of producing a musical instrument, the sound comprises a wide range of tones and pitches and naturally a single note will produce a different effect than chords and melody. When users are creating a painted musical instrument they identify and discuss any design flaws and then test the design through use of the customise function, which controls the sound output. Therefore, our study aims to construct a prototyping support system for the creation of painted musical instruments. By using conductive ink to draw various designs onto paper, users create input interfaces to which they can then assign different sounds freely and easily. To evaluate the usability of the proposed system, we conducted a basic experiment, as well as an exhibition and a workshop, using our proposed system.","Title: An evaluation on behaviors in taking photos by changing icon images on head mounted display\nYear: 2015\n\nAbstract: Recently, users can always see the information by wearable computing technologies. Researchers have proposed various information presentation methods according to user context. We have proposed the system called Primer Streamer [2] that unconsciously leads the user to the specific information by presenting the visual information that is related to user's interest. This system utilizes the concept of the priming effect, whereby individuals are affected by content that has been presented before. In this research, we aim to expand the concept to more realistic situation. We showed that the image of desktop icons affects human behaviors. If the small image like the icon image also affects human behaviors, the proposed system can simultaneously work with other applications. In this paper, we performed an experiment that participants took photos in changing the icon image of camera application. Evaluation results confirmed that they changed the objects of shooting according to the icon image.","Title: A Navigation System for Crowd Flow Control by Controlling Information Presentation.\nYear: 2015\n\nAbstract: In recent years, navigation systems have been widely used in various amusement parks and sightseeing spots. Navigation systems, such as car navigation systems and navigation applications in smartphones, are also used in our daily life. By using a navigation system, users can not only find their location but also browse information on nearby attractions such as restaurants or department stores; furthermore, it can tell users the route to lead them to their destination. In these situations users will set their destinations by using the navigation system, but in tourism or for events, users have no idea about the current conditions. On the other hand, organizers will have a hard time trying to control the crowd flow as more and more tourists use their own mobile devices to search for the information they want. In this research, we propose a navigation system to control the crowd flow and lead users to specific destinations or areas naturally by presenting suitable information to them. In this paper, we will explain the experiments performed using the proposed system during an event held in Osaka called \\\"Osaka Mizube Bar\\\". The results of the experiment confirmed that the behavior of users can be affected by controlling the rank list that they check on their devices; furthermore, users can be led to a specific destination or area by taking advantage of this function.","Title: YOUPLAY: Designing Participatory Theatrical Performance using Wearable Sensors.\nYear: 2016\n\nAbstract: This paper describes a participatory theatrical performance named YOUPLAY. In YOUPLAY, a general person who is usually treated as an audience member becomes an actor and plays a character role in the performance. He/she wears several types of equipments such as sensors and a microphone, and experiences an interactive story. We found during the development and actual use of the system that such interactive systems need robust activity recognition techniques. We explain the details of the system for YOUPLAY and present the knowledge gathered from 80 actual stages.","Title: Multi-tiles: a System for Information Presentation using Divided Rotatable Screens.\nYear: 2015\n\nAbstract: With the recent fall in the prices of small and high resolution projectors, it has become increasingly popular to project images onto a screen. However, because viewers can see all of the images projected on a screen at a given time, the presenter is not able to present only a portion of the projected information. In other words, the viewers cannot look at a part of the projected information selectively: they must see it all, including that which they do not want to see. To overcome this limitation, we propose a presentation system using a multi-tiled screen. The screen is divided into 3x3 multiple rotatable panels and by rotating the surface of the projection, the presenter can select which information the viewers can see. We implemented a prototype system and confirmed that the presenter can conceal a part of the projected information from viewers who cannot see the projection surface of certain panels.","Title: Success Imprinter: A Method for Controlling Mental Preparedness Using Psychological Conditioned Information.\nYear: 2016\n\nAbstract: It is difficult to control one's mental preparedness in important situations such as sports performances. We propose Success Imprinter, a new mental control system that enables users to strengthen their mental preparedness simply by presenting information. With Success Imprinter, users can strengthen their mental preparedness more easily than by using the previous methods. We utilize the concept of conditioning, which is a learning principle. Our system presents users with a stimulus, which is presented upon the success of their performance repeatedly, to strengthen their mental preparedness. An evaluation confirmed that Success Imprinter has a consistent effect on user's mental preparedness and results of darts competition although its effect was different for each user. Moreover, we discuss a method to identify the effect on each user on the basis of their individual characteristics. From these results, we implemented a prototype that presents conditioned information automatically.","Title: An Activity Recognition Method by Measuring Circumference of Body Parts.\nYear: 2016\n\nAbstract: This paper investigates an activity recognition method where the user wears stretch sensors made of conductive fabric on each part of the body. The system recognizes actions by measuring the circumference of body parts. The electrical resistivity of our stretch sensors changes in accordance with their expansion/shrinkage. The strengths of this approach are that the appearance of the user is not interfered with and that the sensor is less expensive than other sensors such as the myoelectric sensor. The results of evaluation confirmed that our method can recognize eight contexts by measuring the circumference of four body parts (abdominal region, waist, wrist, ankle) with 99.92% accuracy.","Title: A Lifelog System for Detecting Psychological Stress with Glass-equipped Temperature Sensors.\nYear: 2016\n\nAbstract: Stress is extremely harmful to one's health. It is important to know which situations or events cause us to feel stressed: if we know the factors behind the stress, we can take corrective action. However, it is hard to perceive stress in everyday life by ourselves. Automatically detecting stress from biological information is one method for dealing with this. Stress is generally detected by using a physiological index pulse, brain activity, and breathing in order to ensure universality and accuracy. This biological information reacts to sudden stressors, not chronic stressors. However, it is difficult to use measuring devices for such data in everyday life because the devices require expertise for operation and are expensive. Our goal in this study is to develop a lifelog system featuring glass-equipped sensors that can be used on a daily basis. We detect stress by examining nasal skin temperature, which is decreased by sudden stressors. In order to investigate the recognition accuracy of the proposed system, we performed experiments at the scenes of feeling stress. Results showed that the system can distinguish factors other than stress from the change in nasal skin temperature with sufficient precision. Moreover, we investigated the optimum locations to attach temperature sensors to ensure that they have both reactivity and comfort. We also implemented an application for analyzing the measured data. The application calculates the time at which a user feels stress by analyzing the measured data and extracts a stressful scene from a video recorded from the point of view of the user.","Title: A Dance Performance Environment in which Performers Dance with Multiple Robotic Balls.\nYear: 2016\n\nAbstract: In recent years, as robotics technology progresses, various mobile robots have been developed to dance with humans. However, up until now there have been no system for interactively creating a performance using multiple mobile robots. Therefore, performance using multiple mobile robots is still difficult. In this study, we construct a mechanism by which a performer can interactively create a performance while he/she considers the correspondence between his/her motion and the mobile robots' movement and light. Specifically, we developed a system that enables performers to freely create performances with multiple robotics balls that can move omunidirectionnally and have full color LEDs. Performers can design both the movements of the robotic balls and the colors of the LEDs. To evaluate the effectiveness of the system, we had four performers use the system to create and demonstrate performances. Moreover, we confirmed that the system performed reliably in a real environment.","Title: Recognizing activities and identifying users based on tabletop activities with load cells.\nYear: 2015\n\nAbstract: There have been several studies on object detection and activity recognition on a table conducted thus far. Most of these studies use image processing with cameras or a specially configured table with electrodes and an RFID reader. In private homes, methods using cameras are not preferable since cameras might invade the privacy of inhabitants and give them the impression of being monitored. In addition, it is difficult to apply the specially configured system to off-the-shelf tables. In this work, we propose a system that recognizes activities conducted on a table and identifies which user conducted the activities with load cells only. The proposed system uses four load cells installed on the four corners of the table or under the four legs of the table. User privacy is protected because only the data on actions through the load cells is obtained. Load cells are easily installed on off-the-shelf tables with four legs and installing our system does not change the appearance of the table. The results of experiments using a table we manufactured revealed that the weight error was 38 g, the position error was 6.8 cm, the average recall of recognition for four activities was 0.96, and the average recalls of user identification were 0.65 for ten users and 0.89 for four users.","Title: A system for memorizing chinese characters using a song based on strokes and structures of the character.\nYear: 2015\n\nAbstract: Hundreds of thousands of Chinese characters are used in the Japanese and Chinese writing systems. Memorizing all the Chinese characters requires great effort because learners have to memorize not only the shape of the character but also its meaning and usage. Research to find a method to present the shape of the figures using sound has been conducted. Consequently, it is possible to generate a song for learning Chinese characters comprehensively using sounds that represent the shapes of the characters and lyrics that represent the meaning and usage. We propose a method to generate a song based on the structures, strokes, and usage of Chinese characters and a method to memorize Chinese characters using the generated song.","Title: A Method For Controlling Crowd Flow By Changing Recommender Information On Navigation Application\nYear: 2016\n\nAbstract: Purpose - This paper aims to control the crowd flow naturally by presenting appropriate information.Design/methodology/approach - The authors developed a navigation application for an event held in Osaka called \"Osaka Mizube Bar\", and divided users into three groups to present different information in restaurants' view list.Findings - The results of the experiment confirmed that users will focus on the position of the rank list, regardless of the information for each item.Originality/value - This paper used persuasive technology in information presentation for event application.","Title: A percussion learning system by rhythm internalization using haptic indication.\nYear: 2015\n\nAbstract: Mastering the correct stroking order and accent technique when playing percussion is important. Percussion learning systems using haptic and visual indications have been developed. Thus, it is efficient for the learner to internalize performance information when learning how to play percussion. In this paper, we describe this process of rhythm internalization. We consider that rhythm internalization enables learners to perceive what is correct playing and to improve their performance. Additionally, because learners using previous learning method are stimulated by the indications of percussion performance information while stroking, perceiving these indications is not easy. We propose a learning method that presents the two phases separately. One of them is a phase where the learners receive the indications of performance information, and the other is a phase where they actually practice stroking. Learners first internalize rhythm through the indications of performance information. This separated learning method enables learners to learn the percussion more efficiently. This paper describes a percussion learning system using rhythm internalization with haptic, visual, and auditory indications and the design of the prototype system. We also present our evaluation of the prototype system's effectiveness.","Title: A system for dancing with multiple robotic balls.\nYear: 2015\n\nAbstract: In recent years, as robotics technology progresses, various mobile robots have been developed to dance with humans. There have been different approaches to enhance physical expression by adding these robots. However, in the performance of using these mobile robots, it is not clear the correspondence between the body expression and the mobile robot's expression. For the sake of the investigation what changes occur by the combination of the performer's motion and the mobile robot's movement and light, we built a mechanism to consider the correspondence between the body expression and the mobile robot's expression. Specifically, we developed a system that a performer dances with multiple robotic balls and performed a demonstration by using it. Additionally, we investigated what possibility performers can imagine by using our proposed system.","Title: UltraSoundLog: location/person-aware sound log system for museums.\nYear: 2015\n\nAbstract: In wearable computing environments, various sensors enable the recording of user context as a life log. For enhancing the experience in a museum or exhibition, a voice recorder is useful for recording users' comments and providing explanations for the exhibitions. However, to utilize the sound log after the event, the voice recorder should attach appropriate tags to the sound data for extracting intended data from the long-term sound log. In a previous study, we have proposed an activity and context recognition method where the user carries a neck-worn receiver comprising a microphone and small speakers on his/her wrists that generate ultrasound. Our system also recognizes the location of the user and the people whom are near the user by ultrasonic ID signals generated from speakers placed in rooms and on people. In this study, we focus on embedding location/person IDs to sound logs by using ultrasonic IDs. We actually used the proposed method during a real event, and we confirmed that the wearing position of the microphone affects the accuracy of ultrasonic ID recognition because the acquired volume of ultrasound is different at each position. We propose a new method for recognizing the position of a microphone to improve the accuracy of ID recognition, and we actually use the improved method at another real event. Evaluation results confirmed that the accuracy of position recognition was 84.7 %, and the accuracy of ID recognition was 68.0 % for the proposed method, improved from 55.2 % for the conventional method.","Title: Training system of bicycle pedaling using auditory feedback.\nYear: 2015\n\nAbstract: Recently, bicycling as a sport has attracted a great deal of attention. Previous research on bicycles suggests that pedaling at high frequency at a constant speed is most effective. However, it is hard for beginners to acquire such pedaling skills since expert cyclists develop the skills through long-term training. We propose a bicycle pedaling training system using auditory feedback. The system generates feedback sound every time a pedal crank turns a quarter rotation. Users can keep the pedaling speed constant by synchronizing pedaling with the feedback sound with background music whose tempo is constant. We conducted an experiment with eleven subjects for four weeks and confirmed that the variances of pedaling speed for the subjects trained with the proposed system decreased significantly compared with those of the conventional method.","Title: A system for determining which player touches a card first using a wrist-worn sensor in competitive karuta.\nYear: 2016\n\nAbstract: Competitive karuta is a Japanese card game. It is played without a referee. Therefore, the players sometimes get into an argument over their judgement. We propose a system that judges the player who took a card first in a competitive karuta match by using a wrist-worn accelerometer and gyroscope. From the experiments, 69.2% of rounds were estimated without error and 99.0% of rounds were estimated within 20-ms error. When our system was introduced on the close game, the accuracy of deciding the player taking a card was 75%.","Title: STICK TRACK: a Musical Score Generation System for Drums Considering Hitting Hand.\nYear: 2016\n\nAbstract: Drummers are required to learn the correct stroking order to play drums efficiently. However, general musical scores for drums do not indicate the annotation which drummers use to stroke each drum with a left hand or a right hand. Therefore, drum teachers have to handwrite such annotation to musical score or use the soft application. There is not musical score generation system for drums that indicates hitting hands. In this research, we proposed the system that generates musical score that indicates the hitting hand for drum performance. Our proposed STICK TRACK recognizes the hitting hand on the basis of the data of a gyro sensor that are embedded in the drumsticks and MIDI message from an electronic drum. We constructed the prototype system and evaluated its effectiveness.","Title: A System for Identifying Toilet User by Characteristics of Paper Roll Rotation.\nYear: 2016\n\nAbstract: Along with the progress of miniaturization and energy saving technologies of sensors, biological information in our daily life can be monitored by installing the sensors to a lavatory bowl. Lavatory is usually shared among several people, therefore biological information need to be identified. Using camera, microphone, or scales is not appropriate considering privacy in a lavatory. In this paper, we focus on the difference in the way of pulling a toilet paper roll and propose a system that identifies individuals based on features of rotation of a toilet paper roll with a gyroscope. The evaluation results confirmed that 85.8% accuracy was achieved for a five-people group in a laboratory environment.","Title: Gesture Recognition Method Based on Ultrasound Propagation in Body.\nYear: 2016\n\nAbstract: We propose a method for gesture recognition that utilizes active acoustic sensing, which transmits acoustic signals to a target, and recognizes the target's state by analyzing the response. In this study, the user wore a contact speaker that transmitted ultrasonic sweep signals to the user's body and a contact microphone that detected the ultrasound propagated through the body. The propagation characteristics of the ultrasound changed depending on the user movements. We utilized these changes to recognize the user's gestures. The novel point of our method is that the user's gestures can be acquired not only from the apparent movement but also from the user's internal state, such as muscle activity, since ultrasound is transmitted to the user's internal body and body surface. We implemented a prototype device and investigated the performance of the proposed method in 21 contexts with 10 subjects. The evaluation results confirmed that the recognition accuracy was 91.6 % on average when we trained/tested using the same data set.","Title: Mimebot: Sphere-shaped Mobile Robot Imitating Rotational Movement.\nYear: 2016\n\nAbstract: When designing a performance involving people and mobile robots, we must consider the required functions and shape of the robot. However, it can be difficult to account for all of the requirements. In this paper, we discuss a mobile robot in the shape of a ball that is used in theatrical performances. Such a spherical robot should be agile and be able to roll like a ball. However, it is difficult to create a robot with all of these characteristics. Instead, we propose a mobile robot that can give the audience the optical illusion of the unique movements of a sphere by mounting a spherical LED display on a high-agility wheeled robot. The results of an experiment using a prototype indicate that this sort of robot can broaden the range of possible performances by giving the optical illusion of being a rolling sphere.","Title: A Navigation System for Controlling Sightseeing Route by Changing Presenting Information\nYear: 2016\n\nAbstract: Many applications have recently been developed for guided sightseeing on smartphones. In these applications, developers provide recommended routes for users beforehand so that they can better enjoy their trips. However, these guided sightseeing applications cannot satisfy all situations for all users, moreover, it is difficult for these applications to control crowd flow during peak seasons. Therefore, we propose a navigation system for sightseeing that generates a route plan on the basis of users' current situations and persuades users to pick specific spots from a route customization view. Furthermore, we evaluate the methods for controlling crowd flow by changing the presented information.","Title: Estimating Trajectory of Inhabitants with Sparsely Aligned Infrared Sensors\nYear: 2016\n\nAbstract: We propose in this paper a method for estimating trajectories of the inhabitants in a home environment, which exploits the synergy between location and movement to provide the information necessary for intelligent home appliance control. Our goal is to carry out accurate movement estimation for multiple people in a home environment. We propose an approach that uses information gathered using only passive infrared sensors commonly found in lighting control systems. No special devices or video cameras are needed. Moreover, it is not necessary to carry out data collection for training. We evaluated our approach by conducting experiments in a real home fitted with sensors and we confirmed that trajectories were almost completely detected for four inhabitants who moved upon scenarios.","Title: Mimebot: Spherical Robot Visually Imitating A Rolling Sphere\nYear: 2017\n\nAbstract: Purpose - When designing a performance involving people and mobile robots, the required functions andshape of the robot must be considered. However, it can be difficult to account for all of the requirements. The purpose of this paper is to discuss a mobile robot in the shape of a ball that is used in theatrical performances.Design/methodology/approach - The paper proposes a mobile robot that can give the audience the optical illusion of the unique movements of a sphere by mounting a spherical light-emitting diode (LED) display on a high-agility wheeled robot.Findings - It was found that movements that are difficult to implement with existing mechanisms can nonetheless be visualized through the use of light.Originality/value - The paper proposes the concept of using pseudo-physical movements in performances with robots. The authors built a robot that visually reproduces the movements of a rolling sphere and is capable of faster movements and easier position estimations in comparison with previous spherical robots.","Title: A User Identification Method Based On Features Of Opening/Closing A Refrigerator Door\nYear: 2017\n\nAbstract: Refrigerators are commonly used by multiple users in the home and office. However, expired food is sometimes left in the refrigerator, and users may eat food belonging to others since food is often arranged in the refrigerator in a disorderly manner. This happens because food is not organized by owner. If food can be linked with its owner, users will not eat food belonging to others, and food in the refrigerator will be consumed prior to expiration by informing the owner of the expiration date. The simplest way to link food with its owner is to input the name of the owner manually every time he or she puts food in the refrigerator, which, however, is tedious and impractical. We propose a method that identifies who put food in the refrigerator by using pressure sensors, an accelerometer, and a gyroscope attached to the refrigerator door. The method analyzes the motions of opening/closing a refrigerator door and the pressure distribution of gripping the door-handle. In the future, we aim to link food with its owner. From the experiment, we confirmed that the method achieves 90.3% accuracy in user identification for a group of four.","Title: Personal Identification System Based On Rotation Of Toilet Paper Rolls\nYear: 2017\n\nAbstract: Biological information can easily be monitored by installing sensors in a lavatory bowl. Lavatories are usually shared by several people, so users need to be identified. Because of the need for privacy, using cameras, microphones, or scales is not appropriate. Though personal identification can be done using a touch panel, the user may forget to use it because the action is not necessary. In this paper, we focus on the differences in the way of pulling a toilet paper roll and propose a system that identifies individuals based on features of rotating of toilet paper rolls with a gyroscope. The evaluation results revealed that 83.9% accuracy was achieved for a five-person group in a laboratory environment, and 69.2% accuracy was achieved for a five-person group in a practical environment.","Title: Toward Interest Estimation from Head Motion Using Wearable Sensors: A Case Study in Story Time for Children.\nYear: 2017\n\nAbstract: Learning activities were evaluated using questionnaire survey, video, or audio. However, these methods have the following problems. First, writing on the questionnaire paper is difficult, especially for little children. Second, because the answering questionnaires was performed after the experiments were finished. They were different temporally and spatially from the scene to be evaluated. Moreover, sometimes participants has forgot the part of the contents. Finally, by recording video or audio, we can look back at each scene and evaluate them, however, video or audio analysis takes a very long time. This research aims to solve these three problems and evaluate natural reactions; first, for children of a low age group, second, including changes in the state of participants during an activity, and third, as much as possible without wasting time and effort. In this paper, during storytelling events for children, we attempted to obtain the values of acceleration and angular velocity sensors with sensors placed on the participant’s heads, and tried to estimate their motions and degree of interests. Motions were calculated using the F-value, with accuracies of 0.66 in “Sitting state”, 0.26 in “Sitting again”, 0.47 in “Wriggling”, and 0.93 in “Playing with hands”. From these results, “Playing with hands” had the highest degree of interest, with a motion recognition rate of 0.93 in F-value. Comparing the proposed method with the video evaluation later, the proposed method can obtain the evaluation result during the learning activity. Therefore, by feeding back the estimation result in real time, we can make improvements while doing activities.","Title: WAHM - 4th workshop on ubiquitous technologies to augment the human mind: sharing experiences.\nYear: 2017\n\nAbstract: A recurring science fiction theme is the downloading of abilities from another human to one's own mind. Emerging technologies beyond simple audio/video recordings such as: 360° videos, tactile recorders and odor recorders are promising tools to enable skill transfer and empathy. However, the produced large datasets require new means for selecting, displaying and sharing experiences. This workshop will bring together researchers from a wide range of computing disciplines, such as virtual reality, mobile computing, privacy and security, social computing and ethnography, usability, and systems research. Furthermore, we will invite researchers from related disciplines such as psychology and economics. The objective is to discuss how these trends are changing our existing research on sharing experiences and knowledge to augment the human mind.","Title: Evaluating effects of listening to content with lip-sync animation on head mounted displays.\nYear: 2017\n\nAbstract: Users should always be able to receive information when using a head-mounted display (HMD) anytime, anywhere. Users can watch content shown on an HMD hands-free even when moving or working. It seems that presenting specific information affects humans. In this paper, we investigate the effects on listening to speech information that are caused by presenting animation on an HMD. It is difficult to listen to information that is presented in noisy surroundings. If the solution were only to turn up the volume, we would feel uncomfortable because this is very inconvenient. Therefore, by additionally presenting animation, we aim to make it easy for users to listen to speech information. With our method, we use lip-sync animation that matches specific speech information. We performed two experiments to determine whether it is easier to get speech information with animation.","Title: Smart eye mask: eye-mask shaped sleep monitoring device.\nYear: 2017\n\nAbstract: We present Smart Eye Mask, which is a portable and comfortable sleep sensing system that classifies sleep stages (REM or NREM) by using infrared sensors on an eye mask. Smart Eye Mask has 19 photo-reflectors and measures the change in eye movement from the distance between the eyelid and the device. Since Smart Eye Mask is integrated into an eye mask, users do not feel uncomfortable during sleep. Moreover, users can utilize it anywhere because eye masks are easy to carry. We conducted an evaluation of Smart Eye Mask, and the recognition rate of classifying sleep stages as REM or NREM was 80.0 %.","Title: Smart eye mask: sleep sensing system using infrared sensors.\nYear: 2017\n\nAbstract: We present Smart Eye Mask, which is a portable and comfortable sleep sensing system that classifies sleep stages (REM or NREM) by using infrared sensors on an eye mask. Smart Eye Mask has 19 photo-reflectors and measures the change in eye movement from the distance between the eyelid and the device. Since Smart Eye Mask is integrated into an eye mask, users do not feel uncomfortable during sleep. Moreover, users can utilize it anywhere because eye masks are easy to carry. We conducted an evaluation of Smart Eye Mask, and the recognition rate of classifying sleep stages as REM or NREM was 80.0 %.","Title: Improving Ultrasound-Based Gesture Recognition by Partially Shielded Microphone.\nYear: 2018\n\nAbstract: We propose a method to improve ultrasound-based in-air gesture recognition. Doppler effect is often used to recognize ultrasound-based gesture. However, increasing the number of gesture is difficult because of limited information obtained from that. In this study, we partially shield the microphone by a 3D printed cover. Acoustic characteristics of the microphone is changed by the cover, and it increases the obtained information. Since the proposed method utilizes a 3D printed cover and a pair of embedded speaker and microphone of a device, it does not require additional electrical device to improve gesture recognition. We implemented five microphone covers and investigated the performance of the proposed method in six gestures with four participants. Evaluation results confirmed that the recognition accuracy increased 12% in the most effective device by using the proposed method.","Title: An Eyeglass to Present Information to a User and Others Separately by LED Blinking.\nYear: 2018\n\nAbstract: We propose an eyeglass to present information to a user and others. The proposed eyeglass is equipped with LED tapeson the outside and the inside of its edges. We also propose some applications that control LED blinking of the proposed eyeglass. In addition, we apply the proposed system in a variety of situation. We discuss about the effectiveness of the system in those situations.","Title: A context recognition method using temperature sensors in the nostrils.\nYear: 2018\n\nAbstract: We can benefit from various services with context recognition using wearable sensors. In this study, we focus on the contexts acquired from sensor data in the nostrils. Nostrils can provide various contexts on breathing, nasal congestion, and higher level contexts including psychological and health states. In this paper, we propose a context recognition method using the information in the nostril. We develop a system to acquire the temperature in the nostrils using small temperature sensors connected to glasses. As a result of the evaluations, the proposed system can detect workload at an accuracy of 96.4%.","Title: Improving ultrasound-based gesture recognition using a partially shielded single microphone.\nYear: 2018\n\nAbstract: We propose a method to improve ultrasound-based in-air gesture recognition by altering the acoustic characteristics of a microphone. The Doppler effect is often utilized to recognize ultrasound-based gestures. However, increasing the number of gestures is difficult because of the limited information obtained from the Doppler effect. In this study, we partially shield a microphone with a 3D-printed cover. The cover alters the sensitivity of the microphone and the characteristics of the obtained Doppler effect. Since the proposed method utilizes a 3D-printed cover with a single microphone and speaker embedded in a device, it does not require additional electronic devices to improve gesture recognition. We design four different microphone covers and evaluate the performance of the proposed method on six gestures with eight participants. The evaluation results confirm that recognition accuracy is increased by 15.3% by utilizing the proposed method.","Title: A Meeting Log Structuring System Using Wearable Sensors.\nYear: 2018\n\nAbstract: We propose a system that structures a meeting log by detecting and tagging the participants' actions in the meeting using acceleration sensors. The proposed system detects head movement such as nodding of each participant or motion during utterances by using acceleration sensors attached to the heads of all participants in a meeting. In addition, we developed a Meeting Review Tree, which is an application that recognizes a meeting participants' utterances and three kinds of actions using acceleration and angular velocity sensors and tags them to recorded movies. In the proposed system, the structure of the meeting is hierarchized into three layers and tagged contexts as follows: The first layer represents the transition of the reporter during the meeting, the second layer represents changes in information of speakers in the report, and the third layer represents motions such as nodding. As a result of the evaluation experiment, the recognition accuracy of the stratified first layer was 57.0% and that of the second layer was 61.0%.","Title: Passerby Crowdsourcing: Workers' Behavior and Data Quality Management.\nYear: 2018\n\nAbstract: Worker recruitment is one of the important problems in crowdsourcing, and many proposals have been presented for placing equipment in physical spaces for recruiting workers. One of the essential challenges of the approach is how to keep people attracted because those who perform tasks at first gradually lose interest and do not access the equipment. This study uses a different approach to the worker recruitment problem. In our approach, we dive into people's personal spaces by projecting task images on the floor, thereby allowing the passersby to effortlessly access tasks while walking. The problem then changes from how to keep people engaged to how to manage data quality because many passersby unconsciously or intentionally walk through the task screen on the floor without doing the task, which produces unintended results. We explore a machine-learning approach to select only the intended results and manage the data quality. The system assesses the workers' intention from their behavior. We identify the features for classifiers based on our observations of the passersby. We then conduct extensive evaluations with real data. The results show that the features are effective in practice, and the classifiers improve the data quality.","Title: Screen Unlocking Method using Behavioral Characteristics when Taking Mobile Phone from Pocket.\nYear: 2016\n\nAbstract: In this paper, we propose a screen unlocking system using an accelerometer and pressure sensor arrays mounted on a mobile phone as casual identification. The proposed system focuses on the user's behavioral characteristics when taking their mobile phone from their pocket and the pressure distribution when gripping the mobile phone during this motion. In the evaluation experiment, 12 out of 30 taking-out actions succeed to unlock the mobile phone screen, and our system achieved 0.43 FAR after 18 templates had been trained.","Title: A Motion Recognition Method Using Foot Pressure Sensors.\nYear: 2018\n\nAbstract: This paper proposes a method for recognizing postures and gestures using foot pressure sensors, and we investigate optimal positions for pressure sensors on soles are the best for motion recognition. In experiments, the recognition accuracies of 22 kinds of daily postures and gestures were evaluated from foot-pressure sensor values. Furthermore, the optimum measurement points for high recognition accuracy were examined by evaluating combinations of two foot pressure measurement areas on a round-robin basis. As a result, when selecting the optimum two points for a user, the recognition accuracy was about 93.6% on average. Although individual differences were seen, the best combinations of areas for each subject were largely divided into two major patterns. When two points were chosen, combinations of the near thenar, which is located near the thumb ball, and near the heel or point of the outside of the middle of the foot were highly recognized. Of the best two points, one was commonly the near thenar for subjects. By taking three points of data and covering these two combinations, it will be possible to cope with individual differences. The recognition accuracy of the averaged combinations of the best two combinations for all subjects was classified with an accuracy of about 91.0% on average. On the basis of these results, two types of pressure sensing shoes were developed.","Title: Evaluation on Context Recognition Using Temperature Sensors in the Nostrils.\nYear: 2019\n\nAbstract: We can benefit from various services with context recognition using wearable sensors. In this study, we focus on the contexts acquired from sensor data in the nostrils. Nostrils can provide various contexts on breathing, nasal congestion, and higher level contexts including psychological and health states. In this paper, we propose a context recognition method using the information in the nostril. We develop a system to acquire the temperature in the nostrils using small temperature sensors connected to glasses. As a result of the evaluations, the proposed system can detect breathing correctly, workload at an accuracy of 96.4%, six behaviors at an accuracy of 54%, and eight behaviors in daily life at an accuracy of 86%. Moreover, the proposed system can detect nasal congestion, therefore, it can log nasal cycles that are considered to have a relationship with the autonomic nerves and/or biological states.","Title: A method for structuring meeting logs using wearable sensors\nYear: 2019\n\nAbstract: We propose a system that structures a meeting log by detecting and tagging the participants’ actions in the meeting using acceleration sensors. The proposed system detects head movement such as nodding of each participant or motion during utterances by using acceleration sensors attached to the heads of all participants in a meeting. As an evaluation result of detection of utterance and three types of head movement, which are nodding, looking around, and tilting one’s head, the recognition accuracy of utterance was 44.4%, nodding was 38.9%, and looking around was 39.4%. In addition, we developed a Meeting Review Tree, which is an application that recognizes a meeting participants’ utterances and three kinds of actions using acceleration and angular velocity sensors and tags them to recorded movies. In the proposed system, the structure of the meeting is hierarchized into three layers and tagged contexts as follows: The first layer represents the transition of the reporter during the meeting, the second layer represents changes in information of speakers in the report, and the third layer represents motions such as nodding. As a result of the evaluation experiment, the recognition accuracy of the stratified first layer was 57.0% and that of the second layer was 61.0%. The recognition accuracies of the utterance and motions were improved by the machine learning approach. The utterance recognition rate was 97.5%. The motion recognition rate was 52.4% for nodding and 53.6% for looking around.","Title: Gaze-Adaptive Subtitles Considering the Balance among Vertical/Horizontal and Depth of Eye Movement\nYear: 2021\n\nAbstract: Subtitles (captions displayed on the screen) are important in 3D content, such as virtual reality (VR) and 3D movies, to help users understand the content. However, an optimal displaying method and framework for subtitles have not been established for 3D content because 3D has a depth factor. To determine how to place text in 3D content, we propose four methods of moving subtitles dynamically cons...","Title: A Method for Behavior Change Support by Controlling Psychological Effects on Walking Motivation Caused by Step Count Log Competition System\nYear: 2021\n\nAbstract: Systems presenting information that encourages competition by using rankings and scores (hereafter referred to as competition information) have become widespread to support behavioral change. However, users without high levels of motivation, such as behavior change support targets, do not necessarily benefit from competition information. In this study, we propose a method to control the psychological effects caused by competition information to support behavior change. We implemented a competition information presentation system using step counts logs to support increasing one's daily steps. We designed two patterns of competition information considering psychological effects. One is likely to have good effects, using three mechanisms to easily obtain results corresponding to the effort, make closely matched rivals with similar abilities, and pay attention to a small number of rivals. The other is unlikely to have positive effects and may potentially even have negative ones, using a mechanism that brings about the opposite results of the former pattern. We evaluated 42 participants with low levels of motivation over six weeks. The results showed that the former information pattern increased participants' step counts by about 1000 steps per day, and the latter information pattern did not lead to an increase. We confirmed the feasibility of the proposed method and discussed the possibility of the appropriate use and potential abuse of such techniques for manipulating motivation. Our study can be helpful in designing a competition information presentation system considering psychological effects.","Title: Extensible Neck: A Gesture Input Method to Extend/Contract Neck Virtually in Video See-through AR Environment\nYear: 2022\n\nAbstract: With the popularization of head-mounted displays (HMDs), many systems for human augmentation have been developed. This will increase the opportunities to use such systems in daily life. Therefore, the user interfaces for these systems must be designed to be intuitive and highly responsive. This paper proposes an intuitive input method that uses natural gestures as input cues for systems for human augmentation. We investigated the appropriate gestures for a system that expands the movements of the user's viewpoint by extending and contracting the neck in a video see-through AR environment. We conducted an experiment to investigate natural gestures by observing the motions when a person wants to extend his/her neck. Furthermore, we determined the operation method for extending/contracting the neck and holding the position through additional experiments. Based on this investigation, we implemented a prototype of the proposed system in a VR environment. Note that we employed a VR environment since we could test our method in various situations, although our target environment is AR. We compared the operability of the proposed method and the handheld controller using our prototype. The results confirmed that the participants felt more immersed using our method, although the positioning speed using controller input was faster than that of our method.","Title: Color-Path: Hair Arrangement Reproduction Support System by Displaying Target Motion in AR\nYear: 2022\n\nAbstract: Curling irons can create curled hair by heating it. However, because the operation of the curling iron involves six degrees of freedom (6DoF) movements, such as translation and rotation, it is difficult to understand and reproduce by simply watching instructional videos. In this study, we proposed Color-Path, a smart mirror-shaped system that allows users to easily understand how to move a curling iron. The proposed system acquires the moving paths of the curling iron using a camera on a smart mirror and an accelerometer on the curling iron. The system displays the curling iron paths of the target hairstyle on a smart mirror in Augmented Reality (AR). From the evaluation experiments, we confirmed that the system contributed to the reproduction of the moving paths of the curling iron through quantitative evaluation. However, a subjective evaluation showed that our system could not reproduce the appearance of the target hairstyle. The results indicate that the time to heat the hair should be considered."],"string":"[\n \"Title: A Context-Aware System that Changes Sensor Combinations Considering Energy Consumption\\nYear: 2008\\n\\nAbstract: In wearable computing environments, a wearable computer runs various applications using various sensors (wearable sensors). In the area of context awareness, though various systems using accelerometers have been proposed to recognize very minute motions and states, energy consumption was not taken into consideration. We propose a context-aware system that reduces energy consumption. In life, the granularity of required contexts differs according to the situation. Therefore, the proposed system changes the granularity of cognitive contexts of a user's situation and supplies power on the basis of the optimal sensor combination. Higher accuracy is achieved with fewer sensors. In addition, in proportion to the remainder of power resources, the proposed system reduces the number of sensors within the tolerance of accuracy. Moreover, the accuracy is improved by considering context transition. Even if the number of sensors changes, no extra classifiers or training data are required because the data for shutting off sensors is complemented by our proposed algorithm. By using our system, power consumption can be reduced without large losses in accuracy.\",\n \"Title: A fault diagnostic system by line status monitoring for ubiquitous computers connected with multiple communication lines\\nYear: 2014\\n\\nAbstract: In ubiquitous computing environments, many computers should be controlled in cooperation to support human daily-life. We pick up a dance performance that performers wear a large amount of LEDs to combine body expression and lighting effects. In such situation, there is a problem that failure frequently occurs caused by dance movements. In the environment involving hundreds of computers, it costs much time and effort to check where failures have occurred. To solve this problem, we propose a system for identifying the position of the failure easily by checking the status of communication among computers. In addition, we implemented a fault diagnostic system that can provide a series of flows for detecting the failure, guessing why the failure occurred and taking a measure. Furthermore, we actually used our system into LED dance performances, conducted an evaluative experiment, and confirmed its usefulness.\",\n \"Title: A wearable system for supporting pit-crew and audiences on motorbike races\\nYear: 2008\\n\\nAbstract: Motorbike racing is one of the most popular motorsports and many fans visit circuits to watch races. However, since audiences and pit crews can only obtain limited information, it is difficult for them to get detailed information about teams except for high ranking teams and few popular teams. To solve this problem, we have already presented an information browsing system for pit crews who used wearable computers. This paper describes an extension to the system. We also applied our system to racing audiences. Since our system can provide various information such as detailed ranking tables and favorite teams, it can make supporting the teams more advantageous and entertain audiences much more.\",\n \"Title: Query processing methods considering the deadline of queries for database broadcasting systems\\nYear: 2007\\n\\nAbstract: In recent years, there has been an increasing interest in the database broadcasting system where the server periodically broadcasts contents of a database to mobile clients such as portable computers and PDAs. There are three query processing methods in the database broadcasting system: (i) the server processes a query and then broadcasts the query result to the client, (ii) the client stores all data that are necessary to process the query and then processes it locally, and (iii) the server and the client collaborate in processing the query. Since the performance of each method changes according to the situation such as the interval of query generation and the size of query results, it is difficult to choose the optimal method among them statically. In this paper, we propose new query processing methods which dynamically change the order of queries submitted in the queue at the server and also change processing methods for the queries according to the deadline of queries and the system situation. These methods improve the success rate as well as reduce the response time in most cases. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(2): 21– 31, 2007; Published online in Wiley InterScience (). DOI 10.1002/scj.20689\",\n \"Title: A context-aware audio presentation method in wearable computing\\nYear: 2011\\n\\nAbstract: Audio usage is one of the more widely-applicable methods of information presentation in wearable computing environments since it can be used hands-free, requires only small devices like earphones, and does not interfere with most tasks compared with other methods including visual displays. However, since presented sound is often drowned out by ambient noise or conversational voice, a user is forced to turn the volume up to catch the audio information. Therefore, we propose an audio information presentation method that takes into account the user contexts. In our proposed method, a system estimates the user contexts based on the data from wearable sensors and a microphone, and then controls and presents the audio information so that it can be surely audible by changing the volume and the timing for presentation. The evaluation results confirmed the effectiveness of our proposed method.\",\n \"Title: Designing gestures for hands and feet in daily life\\nYear: 2011\\n\\nAbstract: In wearable computing environments, people handles various information anytime and anywhere with a wearing computer. In such situation, a gesture is one of powerful methods as input method because it needs no physical devices to touch and a user can input quickly. However, there are various restrictions for gesture input in daily life; gestures must be socially acceptable because a user has to gesture with unusual movements in a crowd, gestures must be flexible because a user cannot gesture when he/she has a bag with his hand that is used for a gesture. In this paper, we clarify the restrictions on gesture interfaces in daily life, then propose practical gestures for selecting simple menu items with hands and feet.\",\n \"Title: An interactive system for recognizing user actions on a surface using accelerometers\\nYear: 2014\\n\\nAbstract: There are various approaches to recognizing user actions for interactive arts. By making a system interactive, the audience has more fun because they are participating, and the artists can translate what is in their imagination more richly. Since user actions have great variety and the restrictions on installations are diverse, conventional systems use mechanisms for recognizing user motions that are specialized to their own work, i.e., that are not general. In this paper, we propose a method that adds interactivity to any surface and recognizes the position and intensity of a preformed action by using multiple accelerometers.\",\n \"Title: User Preference Learning System For Tangible User Interfaces\\nYear: 2009\\n\\nAbstract: There is increasing demand for tangible user interfaces (TUIs), for easy and intuitive control of applications. In order to allow use of TUI devices for controlling applications, a system needs to associate functions with the TUI devices. Although several TUI applications and toolkits have been proposed, it is difficult to use them for various applications since they require the user to perform several inconvenient operations. Therefore, we propose a preference learning algorithm for an automatic matching between TUI devices and application functions. A user can freely place and easily operate the TUI devices, and our system creates and learns users preference data. Our system does this by determining three concrete types of user preferences specialized for each application and six abstract types of those common to all applications. Our proposed system has achieved over 80% accuracy in automatic matching.\",\n \"Title: A Ubiquitous Data Logger that Selects an Efficient Data Compression Method\\nYear: 2012\\n\\nAbstract: Recently, ubiquitous computing has been garnering a great deal of attention. In ubiquitous computing environments, sensor nodes connected to a network are used for collecting sensor data. However, considering the cost needed to set up the network, it may be more convenient to use data loggers that directly send data to users than to use a sensor network. In this paper, we propose a ubiquitous data logger that takes sensor characteristics into consideration in managing data. The logger reduces data volume by selecting an appropriate compression algorithm that takes sensor characteristics into consideration, because the sensor characteristics vary depending on the sensor data.\",\n \"Title: HASC Challenge: gathering large scale human activity corpus for the real-world activity understandings\\nYear: 2011\\n\\nAbstract: Understandings of human activity through wearable sensors will enable the next-generation human-oriented computing. However, most of researches on the activity recognition so far are based on small number of test subjects, and not well adapted for real world applications. To overcome the situation, we have started a project named \\\"HASC Challenge\\\" to collect a large scale human activity corpus. By the end of 2010, by the collaboration of 20 teams, more than 6700 accelerometer data with 540 subjects have been collected through our project. We also developed a tool named \\\"HASC Tool\\\" for management, evaluation and collection of the large number of activity sensor data.\",\n \"Title: An Information Layout Method for an Optical See-through HMD Considering the Background\\nYear: 2007\\n\\nAbstract: We present a framework for 3D spatial gesture design and modeling. A wearable input device that facilitates the use of visual sensors and body sensors is proposed for gesture acquisition. We adapted two different pattern matching techniques, Dynamic ...\",\n \"Title: Composition of Filtering Functions\\nYear: 2003\\n\\nAbstract: In recent years, due to the increasing popularization of various broadcast services, there has been anincreasing demand for information fltering techniques.Although filtering methods that have been proposed filterdata in their own ways, mathematical representation ofthese methods does not exist. Consequently, it is notpossible to qualitatively evaluate various filtering methods, to optimize processing methods in filtering, or todesign a declarative language for filtering processes. Inour previous work we have defined a filtering functionthat represents filtering as a function, and clarified theproperties of different filtering methods. In practice,current filtering methods actually consist of multiplemethods. In this paper, we make it possible to qualitatively represent those filtering methods by introducingthe concept of composition into the framework of filtering function. Moreover, we reveal the characteristicsof those combined filtering methods by clarifying theproperties of composite filtering functions.\",\n \"Title: A system for practicing formations in dance performance using a two-axis movable electric curtain track\\nYear: 2014\\n\\nAbstract: Improving physical expressions and the sense of rhythm in dance performances has become important in recent years due to the increase in child dancers and dance studios. Even beginners in dance gain more opportunities to perform dances in groups. When dancing in a group, collapsed formation will greatly reduce the quality of dance performance even if the choreography is synchronized with the music. Therefore, learning the dance formation in a group is as important as learning its choreography. It is also important to be aware of keeping the proper formation and moving smoothly into the next formation to perform professional level group dances. However, it is difficult to obtain the sense of a proper formation if some members of the dance cannot participate in the practice. We have proposed a practice-support system for performing the formation smoothly using a self-propelled screen even if there is no dance partner. However, the movement of people was limited more than necessary by the excessive presence of a self-propelled screen moving irregular and the fear of the collision with the screen. Therefore, the reproducibility of the trajectory in the case where the user danced with another dancer was low. In this work, we propose a practice-support system for performing the formation using the two-axis movable curtain rail, whose movement direction does not drift and the material used for projection is soft. These characteristics reduce the fear of the collision and improve the accuracy in movement of the screen.\",\n \"Title: Evaluating Effect of Concreteness in Instructions for Gesture Recognition\\nYear: 2011\\n\\nAbstract: Gesture input has an advantage of rich expressive power over conventional input devices, but it is difficult to share the gesture trajectory with other people through writing or verbally. We obtained acceleration data for 10 kinds of gestures instructed through text, figures, and movies, totaling 44 patterns from 13 test subjects, for a total of 2630 data samples. We then examined the differences in the gestures.\",\n \"Title: A Query Processing Method Considering the Characteristic of Energy Consumption for Broadcast Database Systems\\nYear: 2009\\n\\nAbstract: In recent years, there has been an increasing interest in a broadcast database system where the server periodically broadcasts contents of a database to mobile clients such as PDAs and smartphones. There are three query processing methods in the broadcast database system. Generally, mobile clients have limits in energy consumption, i.e., battery, and each of the three methods consumes different amount of energy for query processing. In this paper we propose a new method which dynamically chooses one of the three query processing methods considering energy consumption.\",\n \"Title: A system for visualizing human behavior based on car metaphors\\nYear: 2013\\n\\nAbstract: There are many accidents such as bumping between walkers in crowded places. One of reasons for them is that it is difficult for each person to predict the behaviors of other people. On the other hand, cars implicitly communicate with other cars by presenting their contexts with equipments such as brake lights and turn signals. In this paper, we propose a system for visualizing the user context by using information presentation methods based on those found in cars, such as wearing LEDs as brake lights, which can be seen by surrounding people. The evaluation results when using our prototype system confirmed that our method visually and intuitively presented the user context. In addition, we evaluated the visibility effects of changing the mounting position of the wearable devices.\",\n \"Title: Method of determining training data for gesture recognition considering decay in gesture movements.\\nYear: 2013\\n\\nAbstract: Mobile phones and video game controllers using gesture recognition technologies enable easy and intuitive operations, such as those in drawing objects. Gesture recognition systems generally require several samples of training data before recognition takes place. However, recognition accuracy deteriorates as time passes since the trajectory of the gestures changes due to fatigue or forgetfulness. We investigated the change in gestures and fast found that several samples of gestures were not suitable for training data. Therefore, we propose two methods of finding appropriate data for training. We confirmed that the proposed methods found better training data than the conventional method from the viewpoints of the number of data collected and the accuracy of recognition.\",\n \"Title: A text input method for half-sized keyboard using keying interval\\nYear: 2012\\n\\nAbstract: In various environments, such as mobile and wearable computing, compact I/O devices are desirable from the viewpoint of portability. Now, many users are accustomed to input with a keyboard, however, there is a limitation of miniaturization because it degrades the performance of key touch. Therefore, in this paper, we propose a method to miniaturize a keyboard by excluding the half of it. In using the proposed method, one hand hits keys as usual, and the other hand hits the place outside the keyboard as if the user types with both hands. The user can input words with only one hand because the proposed system estimates the input word using keying interval, which appears also when the user inputs with both hands. From the results of user study, we confirmed that the user can input with only one hand and that it does not decrease input speed drastically.\",\n \"Title: A framework for constructing entertainment contents using flash and wearable sensors\\nYear: 2010\\n\\nAbstract: Multimedia interactive contents that can be controlled by user's motion attract a great deal of attention especially in entertainment such as gesture-based games. A system that provides such interactive contents detects the human motions using several body-worn sensors. To develop such a system, the contents creator must have enough knowledge about various sensors. In addition, since sensors and contents are deeply associated in contents, it is difficult to change/add sensors for such contents. In this paper, we propose a framework that helps contents creators who do not have enough knowledge on sensors. In our framework, an interactive content is divided into two layers; sensor management layer and content layer. We confirmed that creators can create interactive contents easier with our framework.\",\n \"Title: Implementation and evaluation on a concealed interface using abdominal circumference\\nYear: 2014\\n\\nAbstract: The downsizing of computers enables users to operate computers anywhere. Generally, since a user operates his/her computer by his/her hands, computer operation is unconcealed to surrounding people. On the other hand, there are demands for hidden operation of the computer in situations such as face to face communication and during a meeting. The operation of computer in those scenes often gives a bad impression to surroundings and discommunication. Therefore, in this study, we propose an interface using a user's abdominal circumference as input. The motion of the abdomen is hard to be recognized by surroundings, and a user can move one's abdomen independently of the other body parts. We implemented a prototype of input method using the moving velocity and the absolute size of abdomen as input. Then, we evaluated the granularity, reproducibility, parallelism, resistance to environment, confidentiality and resistance to mis-recognition on the proposed interface.\",\n \"Title: A User Recognition Method Using Accelerometer for Electric Appliances\\nYear: 2013\\n\\nAbstract: We propose a method to identify persons using home electric appliances with accelerometer. Proposing method improves the accuracy of the power management system in the home, such as HEMS (Home Energy Management System) and EoD (Energy on Demand). We aim to detect the timing of using appliances before using them, and to identify who use them with acceleration sensor. Detecting the timing of using appliances makes it possible to prevent accidents caused by high electric current (for example breaker falls) before the accidents occur. Furthermore, identifying who use the appliances make it easy to plan the power consumption, because there are large difference among the operating time of appliances.\",\n \"Title: Design and implementation of an extensible rule processing system for wearable computing\\nYear: 2004\\n\\nAbstract: In wearable computing environments, a user brings and uses his/her own computer to acquire various services wherever he/she goes. This type of computer, which acts as a service platform for wearable computing, needs autonomy and simplicity of services, flexibility of services/devices configuration, and power saving functions. Since most conventional wearable computing systems do not fulfil all of these requirements, we propose A-WEAR, which is a rule-based wearable computing system. We employ an event-driven rule as a behavior description language of A-WEAR to achieve autonomy and simplicity. Furthermore, we employ a plug-in mechanism to achieve flexibility and power saving. Using our system, we can easily provide and use various services for wearable computing environments.\",\n \"Title: A ZigBee-based sensor node for tracking people's locations\\nYear: 2008\\n\\nAbstract: A sensor network system has been developed for tracking people's locations in workplaces as part of a ubiquitous network system for providing context-aware services in daily activities. Since the installation of such a sensor is desired any place within its target domain with few limitations, it must operate by battery for a relatively long time, e.g., one month. To satisfy this requirement, we designed a battery-operated sensor node based on ZigBee technology and extended its operation period by developing a flexible sleep control protocol and a high-accuracy time synchronization mechanism between sensor nodes to reduce power consumption. From simulations based on actual data collected, we confirmed that a sensor node located in a hospital's medical ward can work over 21 days using four AA Ni-H batteries.\",\n \"Title: Evaluation study on sensor placement and gesture selection for mobile devices\\nYear: 2012\\n\\nAbstract: Mobile phones and video game controllers using gesture recognition technologies enable easy and intuitive operations, such as scrolling a browser and drawing objects. However, usually only one of each kind of sensor is installed in a device, and the effect of multiple homogeneous sensors on recognition accuracy has not been investigated. Moreover, the effect of the differences in the motion of a gesture has not been examined. We have investigated the use of a test mobile device with nine accelerometers and nine gyroscopes. We have captured the data for 27 kinds of gestures for a mobile tablet. We experimentally investigated the effects on recognition accuracy of changing the number and positions of the sensors and of the number and kinds of gestures. The results showed that the use of multiple homogeneous sensors has zero or negligible effect on recognition accuracy, but that using an accelerometer along with a gyroscope improves recognition accuracy. They also showed that some gestures were not consistent among test subjects and interdependent, so selecting specific gestures to use can improve recognition accuracy.\",\n \"Title: Labeling method for acceleration data using an execution sequence of activities\\nYear: 2013\\n\\nAbstract: In the area of activity recognition, many systems using accelerometers have been proposed. Common method for activity recognition requires raw data labeled with ground truth to learn the model. To obtain ground truth, a wearer records his/her activities during data logging through video camera or handwritten memo. However, referring a video takes long time and taking a memo interrupts natural activity. We propose a labeling method for activity recognition using an execution sequence of activities. The execution sequence includes activities in performed order, does not include time stamps, and is made based on his/her memory. Our proposed method partitions and classifies unlabeled data into segments and clusters, and assigns a cluster to each segment, then assign labels according to the best-matching assignment of clusters with the user-recorded activities. The proposed method gave a precision of 0.812 for data including seven kinds of activities. We also confirmed that recognition accuracy with training data labeled with our proposal gave a recall of 0.871, which is equivalent to that with ground truth.\",\n \"Title: An information layout method for an optical see-through head mounted display focusing on the viewability\\nYear: 2008\\n\\nAbstract: Accessing information when we are on the move is a key feature if mobile computing environments, and using an optical see-through head mounted display (HMD) is one of the most suitable ways to do this. Although the HMD can display information without interfering with the user’s view, when the sight behind the display is too complex or too bright, the information displayed can bee very difficult to see. To solve this problem, we have created a way of laying out information for the optical see-through HMD. The ideal area for displaying information is determined by evaluating the sight image behind the HMD captured by a pantoscopic camera mounted on it. Moreover, if there is no suitable area for displaying information, our method select involves using the sight image around users use to the ideal direction and instructing them to face the direction. Our method displays information to ideal areas.\",\n \"Title: A destination prediction method using driving contexts and trajectory for car navigation systems\\nYear: 2009\\n\\nAbstract: Car navigation systems provide the best route to a destination quickly and effectively. However, during daily driving, this information is not necessary since drivers already know the route to the destination very well. In addition, it is time-consuming for drivers to input the destination. Thus, our research group has proposed a new car navigation system that provides information related to the destination by predicting the user's destination automatically. We propose the use of a new method that predicts the destination on the basis of the driving trajectory and the contexts in which the user drives. A system that uses our method knows the destination without user interaction and provides information related to the correct destination.\",\n \"Title: A system for supporting performers in stuffed suits\\nYear: 2012\\n\\nAbstract: Stuffed suits have been widely used at various events. However, performances with stuffed suits present two main difficulties in that performers are not aware of their postures because of the difference in the shape and size of the stuffed suits and the physical human body, and it is difficult for them to communicate smoothly with others because of limited visibility. These problems lead performers to train excessively to acquire a high degree of skill in performances. The main goal of our study was to construct a system to support performers in stuffed suits, which would enable them to act like the characters they represented. From the results we obtained from evaluating our prototype system, we confirmed that our method could effectively support performers in stuffed suits.\",\n \"Title: A Prototype System of Remote Music Therapy Using the Latest Communication Technology in Japan\\nYear: 2013\\n\\nAbstract: This paper describes a prototype system of remote music therapy for elderly people with dementia using the latest communication technology in Japan. The system uses the latest high-speed optical network, called NGN (Next Generation Network) and the latest terminal especially for audio data communication called Hikari DUETTO NY1, which was released at the end of the last year. We did an experiment of the system using the real communication environment in this January between Tokyo and Osaka. The communication time of the system between Tokyo and Osaka was about 25.5 msec. Thus, we confirmed that the system was able to provide almost real-time audio data communication. Based on the result, the feasibility of music therapy activities, such as singing and exercises with music, using the system was examined. We confirmed that the singing activity which is the most important one in the music therapy was feasible.\",\n \"Title: Airstic drum: a drumstick for integration of real and virtual drums\\nYear: 2012\\n\\nAbstract: Drum kits consist of various kinds of percussion instruments. As all percussion instruments are large and heavy, they are inconvenient for drummers to carry and set up. Virtual drums, which include motion sensors and enable drummers to imitate playing drums by stroking a virtual drum, are highly portable. However, drummers, who are used to playing real drums, have difficulty in demonstrating their drum skills with virtual drums because of the lack of feedback from stroking, low sound quality, and so on. Our proposed Airstic Drum achieves high portability and performance quality by integrating real and virtual drums. Airstic Drum can distinguish the stroking of virtual drums from the stroking of real drums, and it outputs digital sound only when the drummer strokes virtual drums. We have developed a prototype system and evaluated its effectiveness by actual use.\",\n \"Title: A multi-modal interface for performers in stuffed suits\\nYear: 2014\\n\\nAbstract: In wearable computing environments, a user can obtain information safely and efficiently without disturbing their daily life. However, since the surrounding conditions change frequently according to the situation, the information presentation method of the wearable system needs to adapt to the change. The main purpose of our research is to construct a system that changes information presentation methods according to the user situation. We focus on supporting performers in stuffed suits with multi-modal information presentation. We investigated the interfaces for these performers who cannot acquire sufficient information of the surrounding environments.\",\n \"Title: On achieving dependability for wearable computing by device bypassing\\nYear: 2014\\n\\nAbstract: When using wearable computers used for medical operation and aviation safety, disrupted information presentation due to hardware/software problems can have serious consequences including medical accidents. We propose a mechanism that maintains information presentation in such situatoins by I/O device bypassing. In out method, I/O devices directly communicate with other devices if a system failure happens. The proposed method selects appropriate data converters by considering recognizability in order to present information that is easily understandable to users. We confirmed that the proposed method works effectively by implementing several applications with our prototype system.\",\n \"Title: Musical B-boying: A Wearable Musical Instrument by Dancing\\nYear: 2008\\n\\nAbstract: Advances in computer technologies have enabled new means of musical expression with motion enabled musical instruments, which have attracted a great deal of attention. We created a new performance system that enables a dancer to make music while b-boying, which is a dance style. We implemented sensor-equipped shoes and a system that recognizes motions and that controls music. We used an implemented prototype on stage, and we found that dancers were able to control the music flexibly while b-boying. Our system will bring about a new style of musical performance for both musicians and dancers.\",\n \"Title: On query processing considering energy consumption for broadcast database systems\\nYear: 2007\\n\\nAbstract: In recent years, there has been an increasing interest in the broadcast database system where the server periodically broadcasts contents in a database to mobile clients such as portable computers and PDAs. There are three query processing methods in the broadcast database system, while each method consumes different amount of power for query processing. In this paper, we propose a new query processing method which dynamically chooses an appropriate method among the three query processing methods by considering energy consumption.\",\n \"Title: An Active Database System for Receiving Broadcast Data\\nYear: 2002\\n\\nAbstract: In recent years, many broadcast and communication satellites have been launched to start various new services, which attracts a great deal of attention on data broadcasting system using satellites. In such systems, the volume and variety of data being broadcast will increase significantly. This results in a growing demand on a system that stores a large volume of data and presents the necessary information for users in an efficient way. Highly motivated by such requirements, we propose the super active database(SADB) system which is a data receiving system based on an active database in this paper. Since the use of active database can increase the flexibility in describing the system behavior, the proposed system is well suited to receive and process many types of data. On SADB, the receiving operation management and the received data processing are separated from the rule processing unit. As a result, the SADB can process a great deal of data faster than the conventional active databases.\",\n \"Title: Primer Streamer: a System to Attract Users to Interests via Images on HMD\\nYear: 2013\\n\\nAbstract: Recently, environments in which users can always see information is becoming realistic due to the development of wearable computing. Researchers have proposed various information presentation methods for different situations. However, in wearable computing environments, since the user does not always need a particular piece of information, there should a specified duration for which the system can show information that does not have any specific importance for the user. In this research, we propose a system that prompts the user to become unconsciously attracted to certain information by presenting visual information related to their interest. Our system utilizes the concept of the priming effect, whereby individuals are affected by content that has been presented before. Evaluation results confirmed that presenting information prompts the user to obtain the intended information. We also implemented a prototype of the system, Primer Streamer, that presents information related to user interest.\",\n \"Title: Construction of a prototyping support system for painted musical instruments\\nYear: 2012\\n\\nAbstract: Recently there have been many works of research and products which make use of electronic and information technology to equip a piece of paper with interactive functions. Examples include picture books which output animal noises, and the use of electric circuits drawn on paper, in conductive ink, to facilitate the output of light and sound. However, these technologies do not have a function that enables customization of the output that is linked to an input interface. The instrument's sound is composed of various kinds of tone and pitch and the effect of a single note is different from that of chords and melody. When users are designing a painted musical instrument they find out problems with the instrument and then discuss and test the design using the customize function, which selects and outputs the sound of the instrument. Therefore, the goal of our study is to construct a prototyping support system for painted musical instruments. By drawing shapes on the paper with conductive ink users create input interfaces to which they can then assign different sounds flexibly and intuitively.\",\n \"Title: Mobile Clavier: New Music Keyboard for Flexible Key Transpose.\\nYear: 2007\\n\\nAbstract: Musical performers need to show off their virtuosity for selfexpression and communicate with other people. Therefore, they are prepared to perform at any time and anywhere. However, a musical keyboard of 88 keys is too large and too heavy to carry around. When a portable keyboard that is suitable for carrying around is played over a wide range, the notes being played frequently cause the diapason of the keyboard to protrude. It is common to use Key Transpose in conventional portable keyboards, which shifts the diapason of the keyboard. However, this function creates several problems such as the feeling of discomfort from the misalignment between the keying positions and their output sounds. Therefore, the goal of our study is to construct Mobile Clavier, which enables the diapason to be changed smoothly. Mobile Clavier resolves the problems with Key Transpose by having black keys inserted between any two side-by-side white keys. This paper also discusses how effective Mobile Clavier was in an experiment conducted using professional pianists. We can play music at any time and anywhere with Mobile Clavier.\",\n \"Title: A rule-based acceleration data processing engine for small sensor node\\nYear: 2008\\n\\nAbstract: In recent years, various small sensor nodes have been developed for recognizing real world situations and events for the development of context-aware systems. We consider that the acceleration sensor is one crucial element for recognizing various types of situations because it has rich and simple information. An application system using acceleration data requires the following features: 1) rapid processing of data without large memory since the amount of acceleration data is much greater than the amount of other sensor data, 2) a node that reduces the amount of data sent to a server, and 3) systems that can be easily configured by users at low cost. Current sensor nodes, however, do not have enough functions to satisfy these requirements. We propose a rule-based data processing engine for processing acceleration data. Our proposed engine rewrites the rules on each node with a few bytes of data. We evaluated our rule-based engine on our small sensor node called the Motion sensing and Communication Minimized Chip (MoCoMi-Chip).\",\n \"Title: Ubiquitous Gadgets for Constructing Flexible Ubiquitous Services\\nYear: 2006\\n\\nAbstract: In the near future, small ubiquitous devices will be embedded into most of artifacts to support human dailylife. In such environments, we need multiple types of human-computer interfaces for enjoying ubiquitous services. This paper describes several ubiquitous gadgets; ubiquitous chip, Pin&Play, and VCC, to provide flexible ubiquitous services and user interfaces. The integrative use of these devices achieves the physical and intuitive interaction between users and ubiquitous services.\",\n \"Title: On a Cooperation of Broadcast Scheduling and Base Station Caching in the Hybrid Wireless Broadcast Environment\\nYear: 2006\\n\\nAbstract: In this paper, based on the Hybrid Wireless Broadcast (HWB) model, we investigate a cooperative data management strategy to provide more efficient data delivery to the mobile clients. This strategy integrates broadcast scheduling with cache management of the base station by making effective use of the HWB data dissemination, namely push- and pull- based broadcast and pull-based point-topoint wireless communication. Simulation study demonstrates that the proposed cooperation strategy can improve the system performance.\",\n \"Title: A method for distance estimation using intra-frame optical flow with an interlace camera\\nYear: 2011\\n\\nAbstract: Recently, there are many researches on location estimation using optical flow, which is a well-known distance estimation method without any infrastructure. However, since the calculation of optical flow needs high computational power, it cannot adapt to high-speed movement. Therefore, in this paper, we propose intra-frame optical flow, which is a new distance estimation method using an interlace camera. It can estimate the high speed moving objects accurately because it uses two successive images with a very short scanning interval extracted from one image captured by an interlace camera. The evaluation result confirmed the effectiveness of our method.\",\n \"Title: Dynamic Construction Mechanism of a Trigger Graph on Active Databases in Mobile Computing Environments\\nYear: 2003\\n\\nAbstract: As a result of the rapid development of wirelesscommunications and computer hardware technologies,we can now access various information anywhere using handy terminals. To support the integrative useof data held by mobile hosts in this environment, weproposed the AMDS (Active Mobile Database System)as the kernel system for data management. The behavior definition language of this system, ECA(Event-Condition-Action) rules, has a high description capability that enables users to define complicated behavior. However, the execution of the ECA rules may fallinto a chain of unexpected behaviors. In general, adirected graph, called a trigger graph, is used for detecting chains. Since the trigger graph highly dependson network topology, it is difficult to employ the trigger graph in a mobile computing environment. In thispaper, we propose a method that reconstructs the trigger graph dynamically to adapt to changes in networktopology. Using this mechanism,mobile applicationswith ECA rules can be used more safely.\",\n \"Title: Early Gesture Recognition Method with an accelerometer.\\nYear: 2014\\n\\nAbstract: An accelerometer is installed in most current mobile phones, such as the iPhones, Android-powered devices, and video game controllers for Wii or PS3, which enable easy and intuitive operations such as scrolling browsers and drawing 3D objects by detecting the inclination and motion of devices. Therefore, many gesture-based user interfaces with accelerometers are expected to appear in the future. Gesture recognition systems with accelerometers generally have to construct gesture models with user's gesture data before use, and recognize unknown gestures by comparing them with training data. As recognition process generally starts after the gesture has finished, output of the recognition result and feedback, e.g. scrolling, have a delay, which may cause users to retry gestures and degrade interface usability. We propose a method of early gesture recognition that calculates the distance between input data and training data sequentially, and outputs recognition results only when one output candidate has a stronger likelihood than the others. Additionally, we implemented a gesture-based photo viewer as an example of useful applications of our proposed method.\",\n \"Title: A Method for Wearable Projector Selection that Considers the Viewability of Projected Images\\nYear: 2010\\n\\nAbstract: Many projects and practices can now support user tasks by presenting images on the floor or wall using a mobile projector. However, unstable images make communication and task support difficult. The viewability of a projected image depends on several factors; the position of the wearable projector, user contexts such as whether he/she is walking or standing, and the type of presentation content. We describe a method for selecting a projector from multiple wearable projectors, wherein the method considers these factors. We investigated the characteristics of viewability by changing various factors in various contexts and with different content. We also investigated the appropriate position for a wearable mobile projector in various situations and with different content in subjective evaluations. The results indicated that our system dynamically changes the characteristics of a projector for presenting content. We implemented a prototype system that selects the appropriate projector dynamically in response to the situation.\",\n \"Title: A system for visualizing sound source using augmented reality\\nYear: 2012\\n\\nAbstract: In recent years, Augmented Reality (AR) has been widely used in various research. AR augments human sensation and offers information to support users in daily life. In this research, we propose an AR system that recognizes sound sources to augment human's vision. In our system, a sound source and its position are detected by acoustic processing. The system notifies a user through different visual markers, which are allocated on different kinds of sound sources. Our system detects different kinds of sound sources in daily life and notifies user of the information and the position of sound source. Using our system, the user can find out which object is making sound in the environment without hearing it.\",\n \"Title: A pointing method using accelerometers for graphical user interfaces\\nYear: 2012\\n\\nAbstract: Graphical User Interfaces (GUIs) are widely used and pointing devices are required to operate most of them. We have proposed Xangle, a pointing method using two accelerometers for wearable computing environments. The cursor is positioned at the intersection of two straight lines, which are synchronized with the angles of the accelerometers at fingers. However, Xangle is difficult to be used in daily-life, when the user frequently changes which part of the body they point with. Therefore, we propose a method of changing the body parts used for pointing according to the situation. Additionally, we proposed a method to accelerate the pointer and a method to layout menu items for Xangle since these methods are suitable for using GUI in wearable computing environments. We confirmed that the proposed method was effective from the results of evaluations.\",\n \"Title: Intra-frame scan: a method for estimating object movements using an interlace camera and visual markers\\nYear: 2008\\n\\nAbstract: There has recently been a great deal of research on location-dependent services, such as navigation systems where these estimate the location of participants in event spaces and provide navigation for them. There are methods of estimating absolute positions such as those using GPS and those using RFID tags. However, they have problems with the high installation cost and environmental restrictions. Therefore, relative-position estimation is a strong candidate that is inexpensive and can be broadly adapted. Although there are existing methods of estimating relative positions using wearable sensors, they have problems with the cost of managing multiple sensors. There is a method using an optical flow of camera images. However, since it needs a high degree of computational power for calculating optical flows, it cannot respond to high-speed motion. In this paper, we propose a new method of estimating relative positions using simple markers and an interlace camera. Since our system estimates the relative distance by using two successive images with a very short scanning interval captured by an interlace camera, it can adapt to high-speed movement.\",\n \"Title: Functional Properties of Information Filtering\\nYear: 2001\\n\\nAbstract: In recent years, due to the increasing popularizationof data broadcasting, the volume andvariety of data being broadcast are rapidlyincreasing. In this environment, as it isdifficult for users to search for informationfrom a large amount of broadcast data, thereis an increasing demand for filtering techniquesthat automatically extract only thenecessary data. Consequently, a number offiltering methods have been proposed. However,mathematical representation of thesemethods...\",\n \"Title: Development of a navigation system with a route planning algorithm using body-worn sensors\\nYear: 2008\\n\\nAbstract: There are many kinds of events where participants converge and move around freely looking at points of interest. Since in these event spaces, the participants go where they want, some problems may arise for the event manager such as people staying in one area too long or congestion that occurs at specific attractions. Therefore, we propose here a new navigation system that has a route planning algorithm to satisfy the objectives of the event manager. In an actual test of our system, we found that the participants' actions were in line with the objectives of the event manager. Moreover, we also found that our system performs better by using wearable computing technologies because detailed information on participants is acquired.\",\n \"Title: Wearable DJ system: a new motion-controlled DJ system\\nYear: 2009\\n\\nAbstract: The recent reappreciation of music has helped to reestablish the need for the Disk Jockey(DJ), which is someone who selects and plays music. The DJ selects the music, smoothly changes from one song to another, applies sound effects to the music, and this in turn excites the audience. However, conventional DJs cannot get away from the booth because of all the equipment, and thus they are unable to freely perform. Therefore, in this study, we propose a Wearable DJ system that solves this problem that uses wearable computing and gesture recognition technologies. The proposed system enables DJs to perform DJ techniques by performing intuitive gesture operations using wearable acceleration sensors. We have actually used the proposed system on several event stages such as Kobe Luminarie 2007 in Japan. We have evaluated the accuracy of its gesture recognition by changing the parameters and have confirmed its effectiveness.\",\n \"Title: A method to control LED blinking for position detection of devices on conductive clothes\\nYear: 2011\\n\\nAbstract: Various wearable computing devices face problems with their power supplies, communication channels, and placement. Conductive clothes can resolve these problems, but it is still difficult to know the positions of devices on the conductive fabric. Therefore, we have devised a method to detect the positions of such devices by using a camera. To detect the positions our method blinks the LEDs on the devices according to their ID. Additionally, we propose several methods to shorten the time for detection. An experimental evaluation confirmed that compared with conventional method our methods reduce the time to detect the positions of the devices.\",\n \"Title: Ultrasound-based movement sensing, gesture-, and context-recognition\\nYear: 2013\\n\\nAbstract: We propose an activity and context recognition method where the user carries a neck-worn receiver comprising a microphone, and small speakers on his wrists that generate ultrasounds. The system recognizes gestures on the basis of the volume of the received sound and the Doppler effect. The former indicates the distance between the neck and wrists, and the later indicates the speed of motions. Thus, our approach substitutes the wired or wireless communication typically required in body area motion sensing networks by ultrasounds. Our system also recognizes the place where the user is in and the people who are near the user by ID signals generated from speakers placed in rooms and on people. The strength of the approach is that, for offline recognition, a simple audio recorder can be used for the receiver. We evaluate the approach in one scenario on nine gestures/activities with 10 users. Evaluation results confirmed that when there was no environmental sound generated from other people, the recognition rate was 87% on average. When there was environmental sound generated from other people, we compare approach ultrasound-based recognition which uses only the feature value of ultrasound against standard approach, which uses feature value of ultrasound and environmental sound. Results for the proposed approach are 65%, for the standard approach are 57%.\",\n \"Title: A method for identification of moving objects by integrative use of a camera and accelerometers\\nYear: 2012\\n\\nAbstract: In sports events shown on TV or on the Internet, having the names and uniform numbers of the players displayed beside the players on the screen helps viewers to identify the players. Conventional methods of annotating and tracking objects utilize image recognition techniques with high-resolution images, so players captured on camera from a long distance are too small for accurate recognition. In response to this problem, we propose a method for identifying and tracking moving objects with a combination of wearable acceleration sensors and image recognition techniques. In our method, the method recognizes moving objects by matching the context picked up by the wearable sensors with the context inferred during the image processing. Evaluation results demonstrated the effectiveness of the proposed method.\",\n \"Title: A sound-based lifelog system using ultrasound\\nYear: 2014\\n\\nAbstract: We propose an activity and context recognition method where the user carries a neck-worn receiver comprising a microphone, and small speakers on his wrists that generate ultrasounds. The system recognizes gestures on the basis of the volume of the received sound and the Doppler effect. The former indicates the distance between the neck and wrists, and the latter indicates the speed of motions. Thus, our approach substitutes the wired or wireless communication typically required in body area motion sensing networks by ultrasounds. Our system also recognizes the place where the user is in and the people who are near the user by ID signals generated from speakers placed in rooms and on people. The strength of the approach is that, for offline recognition, a simple audio recorder can be used for the receiver. In this paper, we introduce our new device.\",\n \"Title: A Text Input Interface using a Portable Clavier for Pianists\\nYear: 2007\\n\\nAbstract: Due to the miniaturization of computers, wearable computing has attracted great attention. Several text input interfaces for wearable computing devices have already been proposed. However; those devices are not customized for specific users, so the devices do not facilitate sufficiently easy and quick text input. Therefore, the goal of our study is to construct a text input method specialized to pianists by exploiting characteristics of pianists and claviers. The proposed method achieves simple and quick text input by restricting users to pianists. The results of the evaluation suggest that the input speed achieved using proposed interface is twice as fast as that of the Twiddler.\",\n \"Title: A distance estimation method using intra-frame optical flow with interlace camera\\nYear: 2010\\n\\nAbstract: There has recently been much research on estimates of location using optical flow, which is a well-known for estimating distances. However, since high computational power is needed to calculate optical flow, it cannot be adapted to high-speed movement. This paper proposes an intra-frame optical flow, which is a new method of estimating distances using an interlace camera. It estimates high-speed moving objects accurately because it uses two successive images with a very short scanning interval extracted from one image captured by an interlace camera. The result obtained from evaluation confirmed the effectiveness of our method.\",\n \"Title: An information presentation method for head mounted display considering surrounding environments\\nYear: 2014\\n\\nAbstract: When wearing a head mounted display (HMD), the degree of concentration on the HMD varies depending on the surrounding environment. In this work, we developed an information presentation method considering cognitive cost and safety in wearable computing environments. The proposed method changes its information presentation method based on possible gazing time that varies in according with the surrounding environment and user context. We used an eye tracker to measure the relationship between eyestrain and watching an HMD and also clarified the relationship between gaze time and surrounding environment. We then used the results to develop an algorithm to change the information presentation method. Evaluation results revealed cases in which it was difficult and dangerous to gaze at an HMD and therefore necessary to change the information presentation.\",\n \"Title: Design of a Car Navigation System that Predicts User Destination\\nYear: 2006\\n\\nAbstract: Because of advances in information technologies, car navigation systems have come into widespread use as useful tools to guide drivers where they want to go. Conventional car navigation systems present the most suitable route according to a destination input into the system. However, since the required operation to input the destination costs so much, users do not usually use car navigation systems for daily driving. In this paper, to exploit the effective functions of car navigation systems, we propose a new system that automatically predicts user purpose and destination. The proposed car navigation system presents various information based on predicted purpose without interaction from users.\",\n \"Title: Aware-Mail: An Event-Driven Mail System for Wearable Computing Environments\\nYear: 2004\\n\\nAbstract: In recent years, e-mail has become widespread explosively and it became one of very important communication tools which anyone can use easily. Here, the wearable computing has also attracted a lot of attention. In wearable computing environments, a user brings and uses his/her own computer wherever he/she goes. Therefore, we can construct a new mail system by utilizing the features of wearable computing such as \\\"hands-free\\\", \\\"always on\\\", and \\\"supporting daily life\\\". In this paper, we propose a mail system for wearable computing environments, called aware-mail (active wearable system applying rule-based engine for mail). Our system provides new type of communications in wearable computing environments, such as automatic mail processing according to the user's situation and exchanging information with other users by mail-based P2P communications.\",\n \"Title: A pointing method using two accelerometers for wearable computing\\nYear: 2009\\n\\nAbstract: A variety of real-world situations are beneficial for wearable computing since it provides information services while users are doing other jobs. Therefore, a simple and hands-free input interfaces have suitable for these computer operations. However, such interface has not been achieved with conventional input devices such as mice or track balls. Although gesture or eye-gaze input techniques have also been developed for wearable computing, they also suffer from problems i.e., a slow pointing speed, difficulty in carrying devices, and complexity in parallel use when doing tasks. We propose a new method of pointing using input of simple gestures with two accelerometers. By dividing the specifications of two coordinates into a combination of two independent motions, we accomplish accurate and intuitive pointing. A user attaches two small accelerometers to both his/her hands or both elbows. The pointing is done by using the intersection of two straight lines, and the movement of the lines is synchronized with that of the accelerometers. In addition, we also propose a method of changing the position of objects being pointed at that is new approach. The results we obtained from our evaluation experiments confirmed that our method was effective.\",\n \"Title: Implicit context awareness by face recognition\\nYear: 2011\\n\\nAbstract: In recent years, technical improvements to sensors have attracted a great deal of attention, in particular due to the sensors' capability recognizing user contexts. In this paper, we propose an implicit context awareness system that identifies user context by sensing the context of surrounding environments. We implemented a prototype two cameras in a variely of situations. Evaluation results showed that the system was effective and improved context recognition. Our method can be used to identify rich contexts that cannot be recognized by conventional methods.\",\n \"Title: An application development environment for rule-based i/o control devices\\nYear: 2005\\n\\nAbstract: In this paper, we propose an application development environment for the ubiquitous chip, which is a rule-based event-driven input/output (I/O) control device for constructing ubiquitous computing environments. The proposed development environment simulates the behaviors of multiple ubiquitous chips and helps users to create rules. Moreover, it has a function for developing applications by cooperation between virtual ubiquitous chips and real ubiquitous chips. The application environment enables both programmers and general users to develop and customize applications for ubiquitous computing environments.\",\n \"Title: HASC2011corpus: towards the common ground of human activity recognition\\nYear: 2011\\n\\nAbstract: Human activity recognition through the wearable sensor will enable a next-generation human-oriented ubiquitous computing. However, most of research on human activity recognition so far is based on small number of subjects, and non-public data. To overcome the situation, we have gathered 4897 accelerometer data with 116 subjects and compose them as HASC2011corpus. In the field of pattern recognition, it is very important to evaluate and to improve the recognition methods by using the same dataset as a common ground. We make the HASC2011corpus into public for the research community to use it as a common ground of the Human Activity Recognition. We also show several facts and results of obtained from the corpus.\",\n \"Title: A Map Matching Algorithm for Car Navigation Systems that Predict User Destination\\nYear: 2008\\n\\nAbstract: In this paper, we propose a map matching algorithm for car navigation systems that predict user destination. This car navigation system is a novel system that automatically predicts user purpose and destination to present various information based on predicted purpose without user interaction. It requires the correct road where the car drives in real time, and it also need to know the route from the start point to the current point correctly. The proposal map matching method divides the trajectory into equal intervals and calculates the shortest path score for each one. Testing using GPS data for actual car trips showed that its use results in better destination prediction than with conventional methods in most cases. The results were the best for intervals of 5 minutes.\",\n \"Title: A Motion Recognition Method for a Wearable Dancing Musical Instrument\\nYear: 2009\\n\\nAbstract: In this paper, we constructed a system for realizing a new style of dance performance that dancers play music by dancing. From pilot study, we have found that the motion recognition for dance performance needed the synchronism to back ground music (BGM). Therefore, we propose a new motion recognition method specialized to dance performances. The key techniques of the proposed method are (1) adaptive decision of the size of recognition window to recognize a motion in sync with BGM, and (2) motion recognition in two-phase (rough and detailed) to fulfill the accuracy in high speed recognition. Data was recorded using a 3-axis wireless accelerometers mounted on both shoes. We evaluated the method on a dataset of 5 different dance steps (each repeated 100 times).The results show that this method is capable of improving recognition for all steps (in one case improving recognition from 62% to 99%) while retaining a feeling of seamless connection between movement and sound.\",\n \"Title: An Information Retrieval System for Supporting Casual Conversation in Wearable Computing Environments\\nYear: 2005\\n\\nAbstract: In the future, wearable computers are going to be exploited for different applications to support human life. In this paper, we present a new wearable computing system that supports daily conversation based on speech recognition and information retrieval technologies. In daily conversation, sometimes we do not know the meaning of a new word or we want to know more information about a topic. In such cases, we can get more information to make our conversation more interesting and fluent by receiving auxiliary information from a wearable computer. The proposed system analyzes conversation content and retrieves information related to ongoing conversations from the Internet to make them more interesting and beneficial. We have implemented a prototype system, and the results of actual use have shown the need for such a system.\",\n \"Title: TEMPEST: a text input system for musical performers\\nYear: 2006\\n\\nAbstract: Recently, due to the widespread use of computers, text-based communication methods, such as e-mail and chat, have attracted a great deal of attention. If pianists can apply their musical expressions to the text input, they can input texts with their own emotion richly. Therefore, the goal of our study is to construct TEMPEST (TExt input and Musical PErforming SysTem) that is a text input system to input texts with various musical expressions using a clavier. Since this system provides text input methods considering musical artistry, a performer can input texts like playing music.\",\n \"Title: Application Development Environment For Event-Driven Ubiquitous Devices\\nYear: 2009\\n\\nAbstract: Purpose - In ubiquitous computing environments, not only programmers but also general users come to develop/customize applications. Therefore, the purpose of this paper is to propose a new application development environment for ubiquitous computing environments.Design/methodology/approach - The paper clarifies the requirements for application development environments of ubiquitous computing environments. It then designs and implements a prototype of a development environment that fulfills these requirements.Findings - It is found that the requirements for application development in ubiquitous computing environments are: easy programming, detection of current status, programming awareness of network connections between multiple ubiquitous devices, and debugging with cooperation among real/virtual environments.Research limitations/implications - This prototype of application development environment is designed for event-driven ubiquitous devices.Practical implications - A prototype development environment has been implemented to show the effectiveness of this approach, and is presented an example of an application development to show the effectiveness of the approach.Originality/value - This paper proposes a new style of application development for ubiquitous computing environments\",\n \"Title: Evaluating effect of types of instructions for gesture recognition with an accelerometer\\nYear: 2014\\n\\nAbstract: Mobile phones or remotes for video games using gesture recognition technologies enable easy and intuitive operations such as scrolling browser and drawing objects. Gesture input has an advantage of rich expressive power over the conventional interfaces, but it is difficult to share the gesture trajectory with other people through writing or verbally. In this paper, we evaluate how user gestures change according to the types of the instruction. We obtained acceleration data for 10 kinds of gestures instructed through three types of texts, figures, and videos, totalling 44 patterns from 13 test subjects, for a total of 2,630 data samples.\",\n \"Title: Design and implementation on a pie menu interface for analog joysticks\\nYear: 2009\\n\\nAbstract: There have been recently several input methods that combine pen-based input with a pie menu as an input interface in environments where working space is limited. However, they suffer from various problems in that input operation is difficult to learn where there are many items on the menu. In this research, we propose a new space-saving input interface that combines an analog joystick and a hierarchical pie menu. The proposed method employs a hierarchical pie menu to enhance the number of items, and a frequency-based area division algorithm that works to expand the area for important items. Using our method, an intuitive input interface is achieved that is high speed and highly accurate.\",\n \"Title: Toward Achieving On-Site Programming\\nYear: 2009\\n\\nAbstract: Wearable computing, where users can wear computers anywhere and at any time, is slowly becoming a reality because of recent technological advancements. When users access various services in wearable computing environments, they want to define new services by themselves. Therefore, we propose a new framework for constructing context-aware applications in wearable computing environments. Our framework, called Wearable Toolkit, consists of an event-driven rule processing engine and tools to develop applications. By using our framework, we can define and customise services anywhere and at any time. We found our system helps to create context-aware wearable services from the results we obtained by evaluating it.\",\n \"Title: A Ubiquitous Computing Environment Composed by Cooperation between Visual Markers and Event-Driven Compact Devices\\nYear: 2005\\n\\nAbstract: In this paper, we propose a new ubiquitous computing environment composed by visual markers and event-driven compact devices. We employ the VCC (Visual Computer Communication) method, which we have already proposed as an information displaying method for visual markers. In the VCC method, a marker is composed of multiple tiles whose color changes at a certain interval of time, by which the marker can send dozens of bytes of information. In our research, to enhance the facility of VCC markers, we use rule-based compact devices to process various types of data and to change information shown in a VCC marker dynamically. The combination of VCC markers and compact devices enables various services such as the presentation of real-world information collected by compact devices and control of appliances via compact devices.\",\n \"Title: IT-enabled donation boxes to promote donation\\nYear: 2009\\n\\nAbstract: Electronics and information technology have great potential to attract people's attention. In this paper, we describe our use of IT (Information Technology)-enabled donation boxes to promote donations. We implemented four donation boxes, including a colorfully illuminated box and a sensor-enabled interactive box. We put them into actual use in a fundraising activity at Kobe Luminarie and evaluated their effect. Through this activity, we found that IT-enabled donation boxes have the potential to increase donations.\",\n \"Title: Lighting choreographer: an LED control system for dance performances\\nYear: 2011\\n\\nAbstract: There have been various approaches to enhance physical performances by adding visuals and/or audio according to the motions of performers. However, these methods were too simple to explain the performers' will of their body expression. Furthermore, they did not consider exceeding the physical limitation of body expression, such as providing the sense of faster movement of arms by changing colors in costumes dynamically. We propose a new LED performance system to expand the expression capability of human body. We can create new generation performances by combining body expression and lighting effects.\",\n \"Title: An Event-driven Navigation Platform forWearable Computing Environments\\nYear: 2005\\n\\nAbstract: Wearable computing environments are slowly becoming a reality, worn and used by a user wherever he or she goes. As a result, a lot of attention is given to navigation systems that provide various information according to the users' situation. In this paper, we identify the requirements of wearable navigation systems and propose an event-driven navigation platform to fulfill them. Since the behaviors of our platform are described in a set of event-driven rules, users can adopt a variety of attached devices and customize system configurations. Moreover, we have developed a prototype and made an experimental study for its evaluation. Using our system, service providers can easily construct flexible wearable navigations.\",\n \"Title: Evaluation on performer support methods for interactive performances using projector\\nYear: 2009\\n\\nAbstract: Recently, performances that combine performersu0027 actions and images projected from a projector have attracted a great deal of attention. In such performances, since the performer usually faces the audience, it is difficult for him/her to watch the projected images on a background screen. This means that he/she cannot make the performances dynamic in response to changes in the situation. Therefore, we evaluate multiple information presentation methods for interactive performances. We have developed a prototype system for supporting performers and evaluated its effectiveness. We confirmed that differences in display devices and in the types of presenting images affected the quality of performances. The implemented prototype was actually used in several stage performances, and we confirmed that the system was effective and improved the visibility of projected images.\",\n \"Title: An event-driven wearable systems for supporting pit-crew and audiences on motorbike races\\nYear: 2009\\n\\nAbstract: Motorbike racing is one of the most popular motorsports and many fans visit circuits to watch races. However, since audiences and pit crews can only obtain limited information, it is difficult for them to get detailed information about teams except for high ranking teams and few popular teams. To solve this problem, we propose an information browsing system for pit crews and audiences who use wearable computers. This system allows pit crew to browse for various race information easily and effortlessly, and it can entertain audiences much more.\",\n \"Title: CLAD: a Sensor Management Device forWearable Computing\\nYear: 2007\\n\\nAbstract: There has been increasing interest in wearable computing. In wearable computing environments, a wearable computer runs various applications with various sensors (wearable sensors). Since conventional wearable systems do not manage the power supply flexibly, they consume excess power resources for unused sensors. Additionally, sensors frequently become unstable for several reasons such as a breakdown. This instability is hard to detect simply from the sensed data. To solve these problems, we propose a new sensor management device CLAD (cross-linkage for assembled devices) that has various functions for power management and sensed-data management. CLAD improves power saving, data accuracy, and operational reliability.\",\n \"Title: Evaluation function of sensor position for activity recognition considering wearability\\nYear: 2013\\n\\nAbstract: In the wearable computing environment, a computer provides many kinds of services by using the values from wearable sensors to recognize the user's movements or situations. In the research on activity recognition, accelerometers are attached on the user's body such as wrists, waist and, feet. Though researches on best sensor placement for context aware systems has been released thus far, they do not use enough number of sensors to really decide the best sensor placement. When using these context aware systems in our daily life, we also need to consider the discomfort that the user gets from attaching the sensors. The sensor might get in the user's way or feel uncomfortable for the user, however, as far as we know, the sensor's wearability is not taken into consideration in these researches. This paper proposes an evaluation function that scores sensor placement considering both recognition accuracy and sensor wearability, with twenty sensors on the user's body and thirty kinds of exercises including aerobic exercise, weight training, and yoga. Then we experimentally evaluated sensor placement, resulted in high degree of accuracy achieved without feeling stressful.\",\n \"Title: Design and implementation of a ubiquitous optical device controlled with a projector\\nYear: 2008\\n\\nAbstract: We recently have seen illumination using Light Emitting Diodes (LEDs) on the street. The current illumination is difficult to control of flexibly and dynamically. In response to this problem, we propose a method controlling numerous LEDs collectively, dynamically, and flexibly by using a projector and small devices with optical sensors. We designed and implemented a small device to achieve our mechanism, which has an LED, an optical sensor, and a microcomputer to react to the light illuminated from a projector. Moreover, it has functions to self adjust the threshold and recognize optical commands, which are required for collective control. We discuss how effective our proposed device was in a study to evaluate it and in field tests done at actual events.\",\n \"Title: A Context Aware System Based on Scent\\nYear: 2011\\n\\nAbstract: Conventional context-aware systems normally use accelerometers and gyroscopes, and it is difficult to recognize contexts such as having a meal, or going to the toilet. We propose a new context recognition method based on scent using a wearable scent sensor. Since our algorithm considers the characteristics of scent, it recognizes contexts that find difficult to recognize by conventional sensors. Evaluation results demonstrated that scent sensors identify the context of having a meal with 94\\\\% accuracy and going to the toilet with 97\\\\% accuracy. We also implemented a context-aware life-log system for healthcare.\",\n \"Title: A Position Detection Method Of Devices On Conductive Clothes By Controlling Led Blinking\\nYear: 2013\\n\\nAbstract: Various wearable computing devices face problems with their power supplies, communication channels, and placement. Conductive clothes can resolve these problems, but it is still difficult to know the positions of devices on the conductive fabric. Therefore, we propose a method to detect the positions of such devices by using a camera. To detect the positions, our method blinks the LEDs on the devices according to their ID. Additionally, we propose several methods to shorten the time for detection. An experimental evaluation confirmed that compared with conventional method our methods reduce the time to detect the positions of the devices.\",\n \"Title: A Query Processing Method Considering Query Frequency for Broadcast Database Systems\\nYear: 2006\\n\\nAbstract: In recent years, there has been an increasing interest in broadcast database systems, where the server periodically broadcasts contents of a database to mobile clients such as PDAs. There are three query processing methods in the broadcast database system: (i) the server processes a query and then broadcasts the query result to the client; (ii) the client stores all data that are necessary in processing the query and then processes it locally; and (iii) the server and the client collaborate in processing the query. Since the performance of each method changes according to the system situation, such as query frequency, it is difficult to decide the optimal method among them statically. In this paper, we propose a new query processing method which dynamically changes the query processing method based on query frequency. This method improves not only the response time but also the success rate of query processing compared with the traditional methods.\",\n \"Title: A query processing method for hybrid wireless-broadcast networks\\nYear: 2005\\n\\nAbstract: In this paper, we propose a new effective query processing method through our hybrid wireless broadcast (HWB) model, which combines push and pull based broadcast and pull-based point-to-point wireless communication. Our method provides a flexible and complementary information service in different bandwidths and service ranges. The results of simulation studies show the HWB approach shortens the average waiting time and enhances the performance of the system.\",\n \"Title: A smile/laughter recognition mechanism for smile-based life logging\\nYear: 2013\\n\\nAbstract: Most situations that cause people to smile are important and treasured events that happen in front of the other people. In life-logging systems that record everything with wearable cameras and microphones, it is difficult to extract the important events from a large amount of recordings. In this research, we design and implement a smile-based life-logging system that focuses on smile/laughter for indexing the interesting/enjoyable events on a recorded video. Our system, features an original smile/laughter recognition device using photo interrupters that is comfortable enough for daily use and proposed an algorithm that detects smile/laughter separately by threshold-based clustering. The main challenge is that, since the reasons people smile and laugh are quite diverse, the system has to detect a smile/laughter as different events. Evaluation results showed that our mechanism achieved a 73%/94% accuracy in detecting smile/laughter, while actual use of the system showed that it can accurately detect interesting scenes from a recorded life log.\",\n \"Title: Ubiquitous Chip: A Rule-Based I/O Control Device for Ubiquitous Computing\\nYear: 2004\\n\\nAbstract: In this paper, we propose a new framework for ubiquitous computing by rule-based, event-driven I/O (input/output) control devices. Our approach is flexible and autonomous because it employs a behavior-description language based on ECA (Event, Condition, Action) rules with simple I/O control functions. We have implemented a prototype ubiquitous device with connectors and several sensors to show the effectiveness of our approach.\",\n \"Title: Wearable MC system a system for supporting MC performances using wearable computing technologies\\nYear: 2011\\n\\nAbstract: A master of ceremonies (MC) plays an important role to ensure all events progress smoothly because unexpected interruption make them unsuccessful. MCs must have various abilities such as being able to memorize the content of given scenarios and manage problems that occur unexpectedly. Moreover, since unskilled MCs cannot intuit the atmosphere in the audiences during an event, they cannot control this smoothly. Therefore, we propose a wearable system that solves these problems for MCs achieved through wearable computing technologies. Our system has functions to support MCs in carrying out their duties smoothly, such as a robust voice-tracking function for them to read scripts, a user interface that does not interrupt other tasks, and a function that enables MCs intuit grasp the atmosphere of the audience. We implemented a prototype of the wearable MC system and actually used it at several events. The results we obtained from actually using it confirmed that it worked well and helped MCs to carry out their official duties smoothly.\",\n \"Title: Evaluating Gesture Recognition by Multiple-Sensor-Containing Mobile Devices\\nYear: 2011\\n\\nAbstract: Mobile phones or video game controllers using gesture recognition technologies enable easy and intuitive operations. However, usually only one of each type of sensor is installed in each device, and the effect of multiple sensors on recognition accuracy has not been investigated. Moreover, the effect of the differences in the motion of a gesture has not been examined. We captured data for 27 kinds of gestures by using a mobile device with 9 accelerometers and 9 gyroscopes, we then experimentally investigated the effects on recognition accuracy of changing the number and positions of sensors, and the number and kinds of gestures. The results showed that the use of multiple sensors and of sensors positioned at specific positions affects accuracy. It was also shown that gestures are interdependent and selecting specific gestures improves recognition accuracy.\",\n \"Title: An event-driven wearable system for supporting motorbike racing teams\\nYear: 2004\\n\\nAbstract: Exploiting the latest information is very important for winning a motorbike race. However, it is difficult for a pit crew to acquire the latest information while working. The miniaturization of computers has enabled the creation of wearable computers, which permit users instant, and hands-free access to information. Therefore, we propose a wearable system that supports motorbike racing teams. This system allows pit crew to browse for various race information easily and effortlessly.\",\n \"Title: A system for practicing formations in dance performance supported by self-propelled screen\\nYear: 2013\\n\\nAbstract: Collapsed formation in a group dance will greatly reduce the quality of the performance even if the dance in the group is synchronized with music. Therefore, learning the formation of a dance in a group is as important as learning its choreography. However, if someone cannot participate in practice, it is difficult for the rest of the members to gain a sense of the proper formation in practice. We propose a practice-support system for performing the formation smoothly using a self-propelled screen even if there is no dance partner. We developed a prototype of the system and investigated whether a sense of presence provided by both methods of practicing formations was close to the sense we really obtain when we dance with humans. The result verified that the sense of dancing with a projected video was closest to the sense of dancing with a dancer, and the trajectory information from dancing with a self-propelled robot was close to the trajectory information from dancing with a dancer. Practicing in situations similar to real ones is able to be done by combining these two methods. Furthermore, we investigated whether the self-propelled screen obtained the advantages of dancing with both methods and found that it only obtained advantages of dancing with projected video.\",\n \"Title: An on-site programming environment for wearable computing\\nYear: 2012\\n\\nAbstract: In wearable computing environments, it is difficult for users to prepare applications that are used beforehand since there are various situations and places. Therefore, they want to define new services by themselves. In this study, we present a development framework and several tools for developing services in wearable computing environments. The framework consists of an event-driven rule processing engine and service implementation tools, which enable users to program services easily and quickly. The proposed system shows elements of event-driven rules as chips, and we can program services by selecting chips on graphical user interfaces. In addition, the proposed system has two functions considering programming features on wearable computing: genetic-algorithm-based programming and social-network-based programming.\",\n \"Title: A life log system that recognizes the objects in a pocket\\nYear: 2015\\n\\nAbstract: A novel approach has been developed for recognizing objects in pockets and for recording the events related to the objects. Information on putting an object into or taking it out of a pocket is closely related to user contexts. For example, when a house key is taken out from a pocket, the owner of the key is likely just getting home. We implemented a objects-in-pocket recognition device, which has a pair of infrared sensors arranged in a matrix, and life log software to obtain the time stamp of events happening. We evaluated whether or not the system could deal with one of five objects (a smartphone, ticket, hand, key, and lip balm) using template matching. When one registered object (the smartphone, ticket, or key) was put in the pocket, our system recognized the object correctly 91% of the time on average. We also evaluated our system in one action scenario. With our system's time stamps, user could easily remember what he took on that day and when he used the items.\",\n \"Title: A recognition method for combined activities with accelerometers\\nYear: 2014\\n\\nAbstract: Many activity recognition systems using accelerometers have been proposed. Activities that have been recognized are \\\\\\\"single\\\\\\\" activities which can be expressed with one verb, such as sitting, walking, holding a mobile phone, and throwing a ball. In actual, however, \\\\\\\"combined\\\\\\\" activities including more than two kinds of state and movement are often taken place. Focusing on hand gestures, they are performed not only while standing, but also while walking and sitting. Though the simplest way to recognize such combined activities is to construct the recognition models for all the possible combinations of the activities, the number of combinations becomes immense. In this paper, we propose a recognition method for combined activities by learning single activities only. Evaluation results confirmed that our proposed method achieved 0.84 of recall and 0.85 of precision, which is comparable to the method that had learned all the combined activities.\",\n \"Title: Mobile Phone User Authentication With Grip Gestures Using Pressure Sensors\\nYear: 2014\\n\\nAbstract: Purpose - User authentication is generally used to protect personal information such as phone numbers, photos and account information stored in a mobile device by limiting the user to a specific person, e.g. the owner of the device. Authentication methods with password, PIN, face recognition and fingerprint identification have been widely used; however, these methods have problems of difficulty in one-handed operation, vulnerability to shoulder hacking and illegal access using fingerprint with either super glue or facial portrait. From viewpoints of usability and safety, strong and uncomplicated method is required.Design/methodology/approach - In this paper, a user authentication method is proposed based on grip gestures using pressure sensors mounted on the lateral and back sides of a mobile phone. Grip gesture is an operation of grasping a mobile phone, which is assumed to be done instead of conventional unlock procedure. Grip gesture can be performed with one hand. Moreover, it is hard to imitate grip gestures, as finger movements and grip force during a grip gesture are hardly seen by the others.Findings - The feature values of grip force are experimentally investigated and the proposed method from viewpoint of error rate is evaluated. From the result, this method achieved 0.02 of equal error rate, which is equivalent to face recognition.Originality/value - Many researches using pressure sensors to recognize grip pattern have been proposed thus far; however, the conventional works just recognize grip patterns and do not identify users, or need long pressure data to finish confident authentication. This proposed method authenticates users with a short grip gesture.\",\n \"Title: Method for visualizing undone operations based on changes on desktop screen\\nYear: 2014\\n\\nAbstract: The undo operation of graphical user interfaces is widely used. However, conventional implementations of undo have two problems: 1) users may not notice undone parts since the operation is instantaneous, and 2) it is difficult to know the undone contents if the user temporarily interrupts a work the user works on multiple operations in parallel. Although there have been several methods developed that improve undo operations, they are for specific applications. Here, we devised a visualization for undone operations that, is independent of the running applications and based on the idea that an undo operation causes display to change. Our method specifies the area affected by the undo operation by screens. We implementedseveral visualization methods including emphasizing the undone area, presenting the undone content, and notifying the user that the undone is not visible.\",\n \"Title: Filtering Order Adaptation Based on Attractor Selection for Data Broadcasting System\\nYear: 2009\\n\\nAbstract: Recent spread of various data broadcasting services leads to provide enormous and various data. Since, data that a client needs are a part of them, there has been an increasing interest in information filtering techniques where a client automatically chooses and stores the necessary data. Generally, when a client performs filtering, it applies some filters sequentially and the time required for filtering changes according to the order of filters. On the other hand,in recent years, there have been many studies about attractor selection which is an autonomous parameter control technique based on the knowledge from living organisms.In this paper, in order to reduce the load for filtering, we propose a novel method which adaptively changes the order of filters according to the change in broadcast contents.This method adaptively decides the control parameters for filtering by using attractor selection.\",\n \"Title: A method for energy saving on context-aware system by sampling control and data complement\\nYear: 2010\\n\\nAbstract: Although various systems use multiple accelerometers to recognize minute motions and states in the research area of context-awareness, a conventional architecture can be optimized from the viewpoint of its energy consumption and accuracy. In this paper, we propose a context-aware system that reduces the energy consumption by controlling the sampling frequency of wearable sensors. Even if the sampling frequency changes, no extra configuration is required because the missing data for the controlled sensors are complemented by our proposed algorithm. The evaluation results confirmed a power-reduction of 34.28% with keeping the accuracy. The energy consumption can be reduced without a large loss in accuracy using our system.\",\n \"Title: A motion recognition method by constancy-decision\\nYear: 2010\\n\\nAbstract: Many context-aware systems using accelerometers have been proposed. Contexts that have been recognized are categorized into postures (e.g. sitting), behaviors (e.g. walking), and gestures (e.g. a punch). Postures and behaviors are states lasting for a certain length of time. Gestures, however, are sporadic or once-off actions. It has been a challenging task to find gestures buried in other contexts. In this paper, we propose a method that classifies contexts into postures, behaviors, and gestures by using the autocorrelation of the acceleration values and recognizes contexts with an appropriate method. We evaluated the recall and precision of recognition for seven kinds of gestures while five kinds of behaviors; The conventional method gave values of 0.75 and 0.59 whereas our method gave 0.93 and 0.93. Our system enables a user to input by gesturing even while he or she is performing a behavior.\",\n \"Title: A Prototyping Support System For The Creation Of Painted Musical Instruments\\nYear: 2015\\n\\nAbstract: Currently there are numerous works of research and products which utilise electronic and information technology to equip a piece of paper with interactive functions, including illustrated books which output sounds and the process of drawing electric circuits onto paper, in conductive ink, to facilitate the output of light and sound. However, these technologies do not facilitate customisation of the output that is linked to an input interface. In the case of producing a musical instrument, the sound comprises a wide range of tones and pitches and naturally a single note will produce a different effect than chords and melody. When users are creating a painted musical instrument they identify and discuss any design flaws and then test the design through use of the customise function, which controls the sound output. Therefore, our study aims to construct a prototyping support system for the creation of painted musical instruments. By using conductive ink to draw various designs onto paper, users create input interfaces to which they can then assign different sounds freely and easily. To evaluate the usability of the proposed system, we conducted a basic experiment, as well as an exhibition and a workshop, using our proposed system.\",\n \"Title: An evaluation on behaviors in taking photos by changing icon images on head mounted display\\nYear: 2015\\n\\nAbstract: Recently, users can always see the information by wearable computing technologies. Researchers have proposed various information presentation methods according to user context. We have proposed the system called Primer Streamer [2] that unconsciously leads the user to the specific information by presenting the visual information that is related to user's interest. This system utilizes the concept of the priming effect, whereby individuals are affected by content that has been presented before. In this research, we aim to expand the concept to more realistic situation. We showed that the image of desktop icons affects human behaviors. If the small image like the icon image also affects human behaviors, the proposed system can simultaneously work with other applications. In this paper, we performed an experiment that participants took photos in changing the icon image of camera application. Evaluation results confirmed that they changed the objects of shooting according to the icon image.\",\n \"Title: A Navigation System for Crowd Flow Control by Controlling Information Presentation.\\nYear: 2015\\n\\nAbstract: In recent years, navigation systems have been widely used in various amusement parks and sightseeing spots. Navigation systems, such as car navigation systems and navigation applications in smartphones, are also used in our daily life. By using a navigation system, users can not only find their location but also browse information on nearby attractions such as restaurants or department stores; furthermore, it can tell users the route to lead them to their destination. In these situations users will set their destinations by using the navigation system, but in tourism or for events, users have no idea about the current conditions. On the other hand, organizers will have a hard time trying to control the crowd flow as more and more tourists use their own mobile devices to search for the information they want. In this research, we propose a navigation system to control the crowd flow and lead users to specific destinations or areas naturally by presenting suitable information to them. In this paper, we will explain the experiments performed using the proposed system during an event held in Osaka called \\\\\\\"Osaka Mizube Bar\\\\\\\". The results of the experiment confirmed that the behavior of users can be affected by controlling the rank list that they check on their devices; furthermore, users can be led to a specific destination or area by taking advantage of this function.\",\n \"Title: YOUPLAY: Designing Participatory Theatrical Performance using Wearable Sensors.\\nYear: 2016\\n\\nAbstract: This paper describes a participatory theatrical performance named YOUPLAY. In YOUPLAY, a general person who is usually treated as an audience member becomes an actor and plays a character role in the performance. He/she wears several types of equipments such as sensors and a microphone, and experiences an interactive story. We found during the development and actual use of the system that such interactive systems need robust activity recognition techniques. We explain the details of the system for YOUPLAY and present the knowledge gathered from 80 actual stages.\",\n \"Title: Multi-tiles: a System for Information Presentation using Divided Rotatable Screens.\\nYear: 2015\\n\\nAbstract: With the recent fall in the prices of small and high resolution projectors, it has become increasingly popular to project images onto a screen. However, because viewers can see all of the images projected on a screen at a given time, the presenter is not able to present only a portion of the projected information. In other words, the viewers cannot look at a part of the projected information selectively: they must see it all, including that which they do not want to see. To overcome this limitation, we propose a presentation system using a multi-tiled screen. The screen is divided into 3x3 multiple rotatable panels and by rotating the surface of the projection, the presenter can select which information the viewers can see. We implemented a prototype system and confirmed that the presenter can conceal a part of the projected information from viewers who cannot see the projection surface of certain panels.\",\n \"Title: Success Imprinter: A Method for Controlling Mental Preparedness Using Psychological Conditioned Information.\\nYear: 2016\\n\\nAbstract: It is difficult to control one's mental preparedness in important situations such as sports performances. We propose Success Imprinter, a new mental control system that enables users to strengthen their mental preparedness simply by presenting information. With Success Imprinter, users can strengthen their mental preparedness more easily than by using the previous methods. We utilize the concept of conditioning, which is a learning principle. Our system presents users with a stimulus, which is presented upon the success of their performance repeatedly, to strengthen their mental preparedness. An evaluation confirmed that Success Imprinter has a consistent effect on user's mental preparedness and results of darts competition although its effect was different for each user. Moreover, we discuss a method to identify the effect on each user on the basis of their individual characteristics. From these results, we implemented a prototype that presents conditioned information automatically.\",\n \"Title: An Activity Recognition Method by Measuring Circumference of Body Parts.\\nYear: 2016\\n\\nAbstract: This paper investigates an activity recognition method where the user wears stretch sensors made of conductive fabric on each part of the body. The system recognizes actions by measuring the circumference of body parts. The electrical resistivity of our stretch sensors changes in accordance with their expansion/shrinkage. The strengths of this approach are that the appearance of the user is not interfered with and that the sensor is less expensive than other sensors such as the myoelectric sensor. The results of evaluation confirmed that our method can recognize eight contexts by measuring the circumference of four body parts (abdominal region, waist, wrist, ankle) with 99.92% accuracy.\",\n \"Title: A Lifelog System for Detecting Psychological Stress with Glass-equipped Temperature Sensors.\\nYear: 2016\\n\\nAbstract: Stress is extremely harmful to one's health. It is important to know which situations or events cause us to feel stressed: if we know the factors behind the stress, we can take corrective action. However, it is hard to perceive stress in everyday life by ourselves. Automatically detecting stress from biological information is one method for dealing with this. Stress is generally detected by using a physiological index pulse, brain activity, and breathing in order to ensure universality and accuracy. This biological information reacts to sudden stressors, not chronic stressors. However, it is difficult to use measuring devices for such data in everyday life because the devices require expertise for operation and are expensive. Our goal in this study is to develop a lifelog system featuring glass-equipped sensors that can be used on a daily basis. We detect stress by examining nasal skin temperature, which is decreased by sudden stressors. In order to investigate the recognition accuracy of the proposed system, we performed experiments at the scenes of feeling stress. Results showed that the system can distinguish factors other than stress from the change in nasal skin temperature with sufficient precision. Moreover, we investigated the optimum locations to attach temperature sensors to ensure that they have both reactivity and comfort. We also implemented an application for analyzing the measured data. The application calculates the time at which a user feels stress by analyzing the measured data and extracts a stressful scene from a video recorded from the point of view of the user.\",\n \"Title: A Dance Performance Environment in which Performers Dance with Multiple Robotic Balls.\\nYear: 2016\\n\\nAbstract: In recent years, as robotics technology progresses, various mobile robots have been developed to dance with humans. However, up until now there have been no system for interactively creating a performance using multiple mobile robots. Therefore, performance using multiple mobile robots is still difficult. In this study, we construct a mechanism by which a performer can interactively create a performance while he/she considers the correspondence between his/her motion and the mobile robots' movement and light. Specifically, we developed a system that enables performers to freely create performances with multiple robotics balls that can move omunidirectionnally and have full color LEDs. Performers can design both the movements of the robotic balls and the colors of the LEDs. To evaluate the effectiveness of the system, we had four performers use the system to create and demonstrate performances. Moreover, we confirmed that the system performed reliably in a real environment.\",\n \"Title: Recognizing activities and identifying users based on tabletop activities with load cells.\\nYear: 2015\\n\\nAbstract: There have been several studies on object detection and activity recognition on a table conducted thus far. Most of these studies use image processing with cameras or a specially configured table with electrodes and an RFID reader. In private homes, methods using cameras are not preferable since cameras might invade the privacy of inhabitants and give them the impression of being monitored. In addition, it is difficult to apply the specially configured system to off-the-shelf tables. In this work, we propose a system that recognizes activities conducted on a table and identifies which user conducted the activities with load cells only. The proposed system uses four load cells installed on the four corners of the table or under the four legs of the table. User privacy is protected because only the data on actions through the load cells is obtained. Load cells are easily installed on off-the-shelf tables with four legs and installing our system does not change the appearance of the table. The results of experiments using a table we manufactured revealed that the weight error was 38 g, the position error was 6.8 cm, the average recall of recognition for four activities was 0.96, and the average recalls of user identification were 0.65 for ten users and 0.89 for four users.\",\n \"Title: A system for memorizing chinese characters using a song based on strokes and structures of the character.\\nYear: 2015\\n\\nAbstract: Hundreds of thousands of Chinese characters are used in the Japanese and Chinese writing systems. Memorizing all the Chinese characters requires great effort because learners have to memorize not only the shape of the character but also its meaning and usage. Research to find a method to present the shape of the figures using sound has been conducted. Consequently, it is possible to generate a song for learning Chinese characters comprehensively using sounds that represent the shapes of the characters and lyrics that represent the meaning and usage. We propose a method to generate a song based on the structures, strokes, and usage of Chinese characters and a method to memorize Chinese characters using the generated song.\",\n \"Title: A Method For Controlling Crowd Flow By Changing Recommender Information On Navigation Application\\nYear: 2016\\n\\nAbstract: Purpose - This paper aims to control the crowd flow naturally by presenting appropriate information.Design/methodology/approach - The authors developed a navigation application for an event held in Osaka called \\\"Osaka Mizube Bar\\\", and divided users into three groups to present different information in restaurants' view list.Findings - The results of the experiment confirmed that users will focus on the position of the rank list, regardless of the information for each item.Originality/value - This paper used persuasive technology in information presentation for event application.\",\n \"Title: A percussion learning system by rhythm internalization using haptic indication.\\nYear: 2015\\n\\nAbstract: Mastering the correct stroking order and accent technique when playing percussion is important. Percussion learning systems using haptic and visual indications have been developed. Thus, it is efficient for the learner to internalize performance information when learning how to play percussion. In this paper, we describe this process of rhythm internalization. We consider that rhythm internalization enables learners to perceive what is correct playing and to improve their performance. Additionally, because learners using previous learning method are stimulated by the indications of percussion performance information while stroking, perceiving these indications is not easy. We propose a learning method that presents the two phases separately. One of them is a phase where the learners receive the indications of performance information, and the other is a phase where they actually practice stroking. Learners first internalize rhythm through the indications of performance information. This separated learning method enables learners to learn the percussion more efficiently. This paper describes a percussion learning system using rhythm internalization with haptic, visual, and auditory indications and the design of the prototype system. We also present our evaluation of the prototype system's effectiveness.\",\n \"Title: A system for dancing with multiple robotic balls.\\nYear: 2015\\n\\nAbstract: In recent years, as robotics technology progresses, various mobile robots have been developed to dance with humans. There have been different approaches to enhance physical expression by adding these robots. However, in the performance of using these mobile robots, it is not clear the correspondence between the body expression and the mobile robot's expression. For the sake of the investigation what changes occur by the combination of the performer's motion and the mobile robot's movement and light, we built a mechanism to consider the correspondence between the body expression and the mobile robot's expression. Specifically, we developed a system that a performer dances with multiple robotic balls and performed a demonstration by using it. Additionally, we investigated what possibility performers can imagine by using our proposed system.\",\n \"Title: UltraSoundLog: location/person-aware sound log system for museums.\\nYear: 2015\\n\\nAbstract: In wearable computing environments, various sensors enable the recording of user context as a life log. For enhancing the experience in a museum or exhibition, a voice recorder is useful for recording users' comments and providing explanations for the exhibitions. However, to utilize the sound log after the event, the voice recorder should attach appropriate tags to the sound data for extracting intended data from the long-term sound log. In a previous study, we have proposed an activity and context recognition method where the user carries a neck-worn receiver comprising a microphone and small speakers on his/her wrists that generate ultrasound. Our system also recognizes the location of the user and the people whom are near the user by ultrasonic ID signals generated from speakers placed in rooms and on people. In this study, we focus on embedding location/person IDs to sound logs by using ultrasonic IDs. We actually used the proposed method during a real event, and we confirmed that the wearing position of the microphone affects the accuracy of ultrasonic ID recognition because the acquired volume of ultrasound is different at each position. We propose a new method for recognizing the position of a microphone to improve the accuracy of ID recognition, and we actually use the improved method at another real event. Evaluation results confirmed that the accuracy of position recognition was 84.7 %, and the accuracy of ID recognition was 68.0 % for the proposed method, improved from 55.2 % for the conventional method.\",\n \"Title: Training system of bicycle pedaling using auditory feedback.\\nYear: 2015\\n\\nAbstract: Recently, bicycling as a sport has attracted a great deal of attention. Previous research on bicycles suggests that pedaling at high frequency at a constant speed is most effective. However, it is hard for beginners to acquire such pedaling skills since expert cyclists develop the skills through long-term training. We propose a bicycle pedaling training system using auditory feedback. The system generates feedback sound every time a pedal crank turns a quarter rotation. Users can keep the pedaling speed constant by synchronizing pedaling with the feedback sound with background music whose tempo is constant. We conducted an experiment with eleven subjects for four weeks and confirmed that the variances of pedaling speed for the subjects trained with the proposed system decreased significantly compared with those of the conventional method.\",\n \"Title: A system for determining which player touches a card first using a wrist-worn sensor in competitive karuta.\\nYear: 2016\\n\\nAbstract: Competitive karuta is a Japanese card game. It is played without a referee. Therefore, the players sometimes get into an argument over their judgement. We propose a system that judges the player who took a card first in a competitive karuta match by using a wrist-worn accelerometer and gyroscope. From the experiments, 69.2% of rounds were estimated without error and 99.0% of rounds were estimated within 20-ms error. When our system was introduced on the close game, the accuracy of deciding the player taking a card was 75%.\",\n \"Title: STICK TRACK: a Musical Score Generation System for Drums Considering Hitting Hand.\\nYear: 2016\\n\\nAbstract: Drummers are required to learn the correct stroking order to play drums efficiently. However, general musical scores for drums do not indicate the annotation which drummers use to stroke each drum with a left hand or a right hand. Therefore, drum teachers have to handwrite such annotation to musical score or use the soft application. There is not musical score generation system for drums that indicates hitting hands. In this research, we proposed the system that generates musical score that indicates the hitting hand for drum performance. Our proposed STICK TRACK recognizes the hitting hand on the basis of the data of a gyro sensor that are embedded in the drumsticks and MIDI message from an electronic drum. We constructed the prototype system and evaluated its effectiveness.\",\n \"Title: A System for Identifying Toilet User by Characteristics of Paper Roll Rotation.\\nYear: 2016\\n\\nAbstract: Along with the progress of miniaturization and energy saving technologies of sensors, biological information in our daily life can be monitored by installing the sensors to a lavatory bowl. Lavatory is usually shared among several people, therefore biological information need to be identified. Using camera, microphone, or scales is not appropriate considering privacy in a lavatory. In this paper, we focus on the difference in the way of pulling a toilet paper roll and propose a system that identifies individuals based on features of rotation of a toilet paper roll with a gyroscope. The evaluation results confirmed that 85.8% accuracy was achieved for a five-people group in a laboratory environment.\",\n \"Title: Gesture Recognition Method Based on Ultrasound Propagation in Body.\\nYear: 2016\\n\\nAbstract: We propose a method for gesture recognition that utilizes active acoustic sensing, which transmits acoustic signals to a target, and recognizes the target's state by analyzing the response. In this study, the user wore a contact speaker that transmitted ultrasonic sweep signals to the user's body and a contact microphone that detected the ultrasound propagated through the body. The propagation characteristics of the ultrasound changed depending on the user movements. We utilized these changes to recognize the user's gestures. The novel point of our method is that the user's gestures can be acquired not only from the apparent movement but also from the user's internal state, such as muscle activity, since ultrasound is transmitted to the user's internal body and body surface. We implemented a prototype device and investigated the performance of the proposed method in 21 contexts with 10 subjects. The evaluation results confirmed that the recognition accuracy was 91.6 % on average when we trained/tested using the same data set.\",\n \"Title: Mimebot: Sphere-shaped Mobile Robot Imitating Rotational Movement.\\nYear: 2016\\n\\nAbstract: When designing a performance involving people and mobile robots, we must consider the required functions and shape of the robot. However, it can be difficult to account for all of the requirements. In this paper, we discuss a mobile robot in the shape of a ball that is used in theatrical performances. Such a spherical robot should be agile and be able to roll like a ball. However, it is difficult to create a robot with all of these characteristics. Instead, we propose a mobile robot that can give the audience the optical illusion of the unique movements of a sphere by mounting a spherical LED display on a high-agility wheeled robot. The results of an experiment using a prototype indicate that this sort of robot can broaden the range of possible performances by giving the optical illusion of being a rolling sphere.\",\n \"Title: A Navigation System for Controlling Sightseeing Route by Changing Presenting Information\\nYear: 2016\\n\\nAbstract: Many applications have recently been developed for guided sightseeing on smartphones. In these applications, developers provide recommended routes for users beforehand so that they can better enjoy their trips. However, these guided sightseeing applications cannot satisfy all situations for all users, moreover, it is difficult for these applications to control crowd flow during peak seasons. Therefore, we propose a navigation system for sightseeing that generates a route plan on the basis of users' current situations and persuades users to pick specific spots from a route customization view. Furthermore, we evaluate the methods for controlling crowd flow by changing the presented information.\",\n \"Title: Estimating Trajectory of Inhabitants with Sparsely Aligned Infrared Sensors\\nYear: 2016\\n\\nAbstract: We propose in this paper a method for estimating trajectories of the inhabitants in a home environment, which exploits the synergy between location and movement to provide the information necessary for intelligent home appliance control. Our goal is to carry out accurate movement estimation for multiple people in a home environment. We propose an approach that uses information gathered using only passive infrared sensors commonly found in lighting control systems. No special devices or video cameras are needed. Moreover, it is not necessary to carry out data collection for training. We evaluated our approach by conducting experiments in a real home fitted with sensors and we confirmed that trajectories were almost completely detected for four inhabitants who moved upon scenarios.\",\n \"Title: Mimebot: Spherical Robot Visually Imitating A Rolling Sphere\\nYear: 2017\\n\\nAbstract: Purpose - When designing a performance involving people and mobile robots, the required functions andshape of the robot must be considered. However, it can be difficult to account for all of the requirements. The purpose of this paper is to discuss a mobile robot in the shape of a ball that is used in theatrical performances.Design/methodology/approach - The paper proposes a mobile robot that can give the audience the optical illusion of the unique movements of a sphere by mounting a spherical light-emitting diode (LED) display on a high-agility wheeled robot.Findings - It was found that movements that are difficult to implement with existing mechanisms can nonetheless be visualized through the use of light.Originality/value - The paper proposes the concept of using pseudo-physical movements in performances with robots. The authors built a robot that visually reproduces the movements of a rolling sphere and is capable of faster movements and easier position estimations in comparison with previous spherical robots.\",\n \"Title: A User Identification Method Based On Features Of Opening/Closing A Refrigerator Door\\nYear: 2017\\n\\nAbstract: Refrigerators are commonly used by multiple users in the home and office. However, expired food is sometimes left in the refrigerator, and users may eat food belonging to others since food is often arranged in the refrigerator in a disorderly manner. This happens because food is not organized by owner. If food can be linked with its owner, users will not eat food belonging to others, and food in the refrigerator will be consumed prior to expiration by informing the owner of the expiration date. The simplest way to link food with its owner is to input the name of the owner manually every time he or she puts food in the refrigerator, which, however, is tedious and impractical. We propose a method that identifies who put food in the refrigerator by using pressure sensors, an accelerometer, and a gyroscope attached to the refrigerator door. The method analyzes the motions of opening/closing a refrigerator door and the pressure distribution of gripping the door-handle. In the future, we aim to link food with its owner. From the experiment, we confirmed that the method achieves 90.3% accuracy in user identification for a group of four.\",\n \"Title: Personal Identification System Based On Rotation Of Toilet Paper Rolls\\nYear: 2017\\n\\nAbstract: Biological information can easily be monitored by installing sensors in a lavatory bowl. Lavatories are usually shared by several people, so users need to be identified. Because of the need for privacy, using cameras, microphones, or scales is not appropriate. Though personal identification can be done using a touch panel, the user may forget to use it because the action is not necessary. In this paper, we focus on the differences in the way of pulling a toilet paper roll and propose a system that identifies individuals based on features of rotating of toilet paper rolls with a gyroscope. The evaluation results revealed that 83.9% accuracy was achieved for a five-person group in a laboratory environment, and 69.2% accuracy was achieved for a five-person group in a practical environment.\",\n \"Title: Toward Interest Estimation from Head Motion Using Wearable Sensors: A Case Study in Story Time for Children.\\nYear: 2017\\n\\nAbstract: Learning activities were evaluated using questionnaire survey, video, or audio. However, these methods have the following problems. First, writing on the questionnaire paper is difficult, especially for little children. Second, because the answering questionnaires was performed after the experiments were finished. They were different temporally and spatially from the scene to be evaluated. Moreover, sometimes participants has forgot the part of the contents. Finally, by recording video or audio, we can look back at each scene and evaluate them, however, video or audio analysis takes a very long time. This research aims to solve these three problems and evaluate natural reactions; first, for children of a low age group, second, including changes in the state of participants during an activity, and third, as much as possible without wasting time and effort. In this paper, during storytelling events for children, we attempted to obtain the values of acceleration and angular velocity sensors with sensors placed on the participant’s heads, and tried to estimate their motions and degree of interests. Motions were calculated using the F-value, with accuracies of 0.66 in “Sitting state”, 0.26 in “Sitting again”, 0.47 in “Wriggling”, and 0.93 in “Playing with hands”. From these results, “Playing with hands” had the highest degree of interest, with a motion recognition rate of 0.93 in F-value. Comparing the proposed method with the video evaluation later, the proposed method can obtain the evaluation result during the learning activity. Therefore, by feeding back the estimation result in real time, we can make improvements while doing activities.\",\n \"Title: WAHM - 4th workshop on ubiquitous technologies to augment the human mind: sharing experiences.\\nYear: 2017\\n\\nAbstract: A recurring science fiction theme is the downloading of abilities from another human to one's own mind. Emerging technologies beyond simple audio/video recordings such as: 360° videos, tactile recorders and odor recorders are promising tools to enable skill transfer and empathy. However, the produced large datasets require new means for selecting, displaying and sharing experiences. This workshop will bring together researchers from a wide range of computing disciplines, such as virtual reality, mobile computing, privacy and security, social computing and ethnography, usability, and systems research. Furthermore, we will invite researchers from related disciplines such as psychology and economics. The objective is to discuss how these trends are changing our existing research on sharing experiences and knowledge to augment the human mind.\",\n \"Title: Evaluating effects of listening to content with lip-sync animation on head mounted displays.\\nYear: 2017\\n\\nAbstract: Users should always be able to receive information when using a head-mounted display (HMD) anytime, anywhere. Users can watch content shown on an HMD hands-free even when moving or working. It seems that presenting specific information affects humans. In this paper, we investigate the effects on listening to speech information that are caused by presenting animation on an HMD. It is difficult to listen to information that is presented in noisy surroundings. If the solution were only to turn up the volume, we would feel uncomfortable because this is very inconvenient. Therefore, by additionally presenting animation, we aim to make it easy for users to listen to speech information. With our method, we use lip-sync animation that matches specific speech information. We performed two experiments to determine whether it is easier to get speech information with animation.\",\n \"Title: Smart eye mask: eye-mask shaped sleep monitoring device.\\nYear: 2017\\n\\nAbstract: We present Smart Eye Mask, which is a portable and comfortable sleep sensing system that classifies sleep stages (REM or NREM) by using infrared sensors on an eye mask. Smart Eye Mask has 19 photo-reflectors and measures the change in eye movement from the distance between the eyelid and the device. Since Smart Eye Mask is integrated into an eye mask, users do not feel uncomfortable during sleep. Moreover, users can utilize it anywhere because eye masks are easy to carry. We conducted an evaluation of Smart Eye Mask, and the recognition rate of classifying sleep stages as REM or NREM was 80.0 %.\",\n \"Title: Smart eye mask: sleep sensing system using infrared sensors.\\nYear: 2017\\n\\nAbstract: We present Smart Eye Mask, which is a portable and comfortable sleep sensing system that classifies sleep stages (REM or NREM) by using infrared sensors on an eye mask. Smart Eye Mask has 19 photo-reflectors and measures the change in eye movement from the distance between the eyelid and the device. Since Smart Eye Mask is integrated into an eye mask, users do not feel uncomfortable during sleep. Moreover, users can utilize it anywhere because eye masks are easy to carry. We conducted an evaluation of Smart Eye Mask, and the recognition rate of classifying sleep stages as REM or NREM was 80.0 %.\",\n \"Title: Improving Ultrasound-Based Gesture Recognition by Partially Shielded Microphone.\\nYear: 2018\\n\\nAbstract: We propose a method to improve ultrasound-based in-air gesture recognition. Doppler effect is often used to recognize ultrasound-based gesture. However, increasing the number of gesture is difficult because of limited information obtained from that. In this study, we partially shield the microphone by a 3D printed cover. Acoustic characteristics of the microphone is changed by the cover, and it increases the obtained information. Since the proposed method utilizes a 3D printed cover and a pair of embedded speaker and microphone of a device, it does not require additional electrical device to improve gesture recognition. We implemented five microphone covers and investigated the performance of the proposed method in six gestures with four participants. Evaluation results confirmed that the recognition accuracy increased 12% in the most effective device by using the proposed method.\",\n \"Title: An Eyeglass to Present Information to a User and Others Separately by LED Blinking.\\nYear: 2018\\n\\nAbstract: We propose an eyeglass to present information to a user and others. The proposed eyeglass is equipped with LED tapeson the outside and the inside of its edges. We also propose some applications that control LED blinking of the proposed eyeglass. In addition, we apply the proposed system in a variety of situation. We discuss about the effectiveness of the system in those situations.\",\n \"Title: A context recognition method using temperature sensors in the nostrils.\\nYear: 2018\\n\\nAbstract: We can benefit from various services with context recognition using wearable sensors. In this study, we focus on the contexts acquired from sensor data in the nostrils. Nostrils can provide various contexts on breathing, nasal congestion, and higher level contexts including psychological and health states. In this paper, we propose a context recognition method using the information in the nostril. We develop a system to acquire the temperature in the nostrils using small temperature sensors connected to glasses. As a result of the evaluations, the proposed system can detect workload at an accuracy of 96.4%.\",\n \"Title: Improving ultrasound-based gesture recognition using a partially shielded single microphone.\\nYear: 2018\\n\\nAbstract: We propose a method to improve ultrasound-based in-air gesture recognition by altering the acoustic characteristics of a microphone. The Doppler effect is often utilized to recognize ultrasound-based gestures. However, increasing the number of gestures is difficult because of the limited information obtained from the Doppler effect. In this study, we partially shield a microphone with a 3D-printed cover. The cover alters the sensitivity of the microphone and the characteristics of the obtained Doppler effect. Since the proposed method utilizes a 3D-printed cover with a single microphone and speaker embedded in a device, it does not require additional electronic devices to improve gesture recognition. We design four different microphone covers and evaluate the performance of the proposed method on six gestures with eight participants. The evaluation results confirm that recognition accuracy is increased by 15.3% by utilizing the proposed method.\",\n \"Title: A Meeting Log Structuring System Using Wearable Sensors.\\nYear: 2018\\n\\nAbstract: We propose a system that structures a meeting log by detecting and tagging the participants' actions in the meeting using acceleration sensors. The proposed system detects head movement such as nodding of each participant or motion during utterances by using acceleration sensors attached to the heads of all participants in a meeting. In addition, we developed a Meeting Review Tree, which is an application that recognizes a meeting participants' utterances and three kinds of actions using acceleration and angular velocity sensors and tags them to recorded movies. In the proposed system, the structure of the meeting is hierarchized into three layers and tagged contexts as follows: The first layer represents the transition of the reporter during the meeting, the second layer represents changes in information of speakers in the report, and the third layer represents motions such as nodding. As a result of the evaluation experiment, the recognition accuracy of the stratified first layer was 57.0% and that of the second layer was 61.0%.\",\n \"Title: Passerby Crowdsourcing: Workers' Behavior and Data Quality Management.\\nYear: 2018\\n\\nAbstract: Worker recruitment is one of the important problems in crowdsourcing, and many proposals have been presented for placing equipment in physical spaces for recruiting workers. One of the essential challenges of the approach is how to keep people attracted because those who perform tasks at first gradually lose interest and do not access the equipment. This study uses a different approach to the worker recruitment problem. In our approach, we dive into people's personal spaces by projecting task images on the floor, thereby allowing the passersby to effortlessly access tasks while walking. The problem then changes from how to keep people engaged to how to manage data quality because many passersby unconsciously or intentionally walk through the task screen on the floor without doing the task, which produces unintended results. We explore a machine-learning approach to select only the intended results and manage the data quality. The system assesses the workers' intention from their behavior. We identify the features for classifiers based on our observations of the passersby. We then conduct extensive evaluations with real data. The results show that the features are effective in practice, and the classifiers improve the data quality.\",\n \"Title: Screen Unlocking Method using Behavioral Characteristics when Taking Mobile Phone from Pocket.\\nYear: 2016\\n\\nAbstract: In this paper, we propose a screen unlocking system using an accelerometer and pressure sensor arrays mounted on a mobile phone as casual identification. The proposed system focuses on the user's behavioral characteristics when taking their mobile phone from their pocket and the pressure distribution when gripping the mobile phone during this motion. In the evaluation experiment, 12 out of 30 taking-out actions succeed to unlock the mobile phone screen, and our system achieved 0.43 FAR after 18 templates had been trained.\",\n \"Title: A Motion Recognition Method Using Foot Pressure Sensors.\\nYear: 2018\\n\\nAbstract: This paper proposes a method for recognizing postures and gestures using foot pressure sensors, and we investigate optimal positions for pressure sensors on soles are the best for motion recognition. In experiments, the recognition accuracies of 22 kinds of daily postures and gestures were evaluated from foot-pressure sensor values. Furthermore, the optimum measurement points for high recognition accuracy were examined by evaluating combinations of two foot pressure measurement areas on a round-robin basis. As a result, when selecting the optimum two points for a user, the recognition accuracy was about 93.6% on average. Although individual differences were seen, the best combinations of areas for each subject were largely divided into two major patterns. When two points were chosen, combinations of the near thenar, which is located near the thumb ball, and near the heel or point of the outside of the middle of the foot were highly recognized. Of the best two points, one was commonly the near thenar for subjects. By taking three points of data and covering these two combinations, it will be possible to cope with individual differences. The recognition accuracy of the averaged combinations of the best two combinations for all subjects was classified with an accuracy of about 91.0% on average. On the basis of these results, two types of pressure sensing shoes were developed.\",\n \"Title: Evaluation on Context Recognition Using Temperature Sensors in the Nostrils.\\nYear: 2019\\n\\nAbstract: We can benefit from various services with context recognition using wearable sensors. In this study, we focus on the contexts acquired from sensor data in the nostrils. Nostrils can provide various contexts on breathing, nasal congestion, and higher level contexts including psychological and health states. In this paper, we propose a context recognition method using the information in the nostril. We develop a system to acquire the temperature in the nostrils using small temperature sensors connected to glasses. As a result of the evaluations, the proposed system can detect breathing correctly, workload at an accuracy of 96.4%, six behaviors at an accuracy of 54%, and eight behaviors in daily life at an accuracy of 86%. Moreover, the proposed system can detect nasal congestion, therefore, it can log nasal cycles that are considered to have a relationship with the autonomic nerves and/or biological states.\",\n \"Title: A method for structuring meeting logs using wearable sensors\\nYear: 2019\\n\\nAbstract: We propose a system that structures a meeting log by detecting and tagging the participants’ actions in the meeting using acceleration sensors. The proposed system detects head movement such as nodding of each participant or motion during utterances by using acceleration sensors attached to the heads of all participants in a meeting. As an evaluation result of detection of utterance and three types of head movement, which are nodding, looking around, and tilting one’s head, the recognition accuracy of utterance was 44.4%, nodding was 38.9%, and looking around was 39.4%. In addition, we developed a Meeting Review Tree, which is an application that recognizes a meeting participants’ utterances and three kinds of actions using acceleration and angular velocity sensors and tags them to recorded movies. In the proposed system, the structure of the meeting is hierarchized into three layers and tagged contexts as follows: The first layer represents the transition of the reporter during the meeting, the second layer represents changes in information of speakers in the report, and the third layer represents motions such as nodding. As a result of the evaluation experiment, the recognition accuracy of the stratified first layer was 57.0% and that of the second layer was 61.0%. The recognition accuracies of the utterance and motions were improved by the machine learning approach. The utterance recognition rate was 97.5%. The motion recognition rate was 52.4% for nodding and 53.6% for looking around.\",\n \"Title: Gaze-Adaptive Subtitles Considering the Balance among Vertical/Horizontal and Depth of Eye Movement\\nYear: 2021\\n\\nAbstract: Subtitles (captions displayed on the screen) are important in 3D content, such as virtual reality (VR) and 3D movies, to help users understand the content. However, an optimal displaying method and framework for subtitles have not been established for 3D content because 3D has a depth factor. To determine how to place text in 3D content, we propose four methods of moving subtitles dynamically cons...\",\n \"Title: A Method for Behavior Change Support by Controlling Psychological Effects on Walking Motivation Caused by Step Count Log Competition System\\nYear: 2021\\n\\nAbstract: Systems presenting information that encourages competition by using rankings and scores (hereafter referred to as competition information) have become widespread to support behavioral change. However, users without high levels of motivation, such as behavior change support targets, do not necessarily benefit from competition information. In this study, we propose a method to control the psychological effects caused by competition information to support behavior change. We implemented a competition information presentation system using step counts logs to support increasing one's daily steps. We designed two patterns of competition information considering psychological effects. One is likely to have good effects, using three mechanisms to easily obtain results corresponding to the effort, make closely matched rivals with similar abilities, and pay attention to a small number of rivals. The other is unlikely to have positive effects and may potentially even have negative ones, using a mechanism that brings about the opposite results of the former pattern. We evaluated 42 participants with low levels of motivation over six weeks. The results showed that the former information pattern increased participants' step counts by about 1000 steps per day, and the latter information pattern did not lead to an increase. We confirmed the feasibility of the proposed method and discussed the possibility of the appropriate use and potential abuse of such techniques for manipulating motivation. Our study can be helpful in designing a competition information presentation system considering psychological effects.\",\n \"Title: Extensible Neck: A Gesture Input Method to Extend/Contract Neck Virtually in Video See-through AR Environment\\nYear: 2022\\n\\nAbstract: With the popularization of head-mounted displays (HMDs), many systems for human augmentation have been developed. This will increase the opportunities to use such systems in daily life. Therefore, the user interfaces for these systems must be designed to be intuitive and highly responsive. This paper proposes an intuitive input method that uses natural gestures as input cues for systems for human augmentation. We investigated the appropriate gestures for a system that expands the movements of the user's viewpoint by extending and contracting the neck in a video see-through AR environment. We conducted an experiment to investigate natural gestures by observing the motions when a person wants to extend his/her neck. Furthermore, we determined the operation method for extending/contracting the neck and holding the position through additional experiments. Based on this investigation, we implemented a prototype of the proposed system in a VR environment. Note that we employed a VR environment since we could test our method in various situations, although our target environment is AR. We compared the operability of the proposed method and the handheld controller using our prototype. The results confirmed that the participants felt more immersed using our method, although the positioning speed using controller input was faster than that of our method.\",\n \"Title: Color-Path: Hair Arrangement Reproduction Support System by Displaying Target Motion in AR\\nYear: 2022\\n\\nAbstract: Curling irons can create curled hair by heating it. However, because the operation of the curling iron involves six degrees of freedom (6DoF) movements, such as translation and rotation, it is difficult to understand and reproduce by simply watching instructional videos. In this study, we proposed Color-Path, a smart mirror-shaped system that allows users to easily understand how to move a curling iron. The proposed system acquires the moving paths of the curling iron using a camera on a smart mirror and an accelerometer on the curling iron. The system displays the curling iron paths of the target hairstyle on a smart mirror in Augmented Reality (AR). From the evaluation experiments, we confirmed that the system contributed to the reproduction of the moving paths of the curling iron through quantitative evaluation. However, a subjective evaluation showed that our system could not reproduce the appearance of the target hairstyle. The results indicate that the time to heat the hair should be considered.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":144,"string":"144"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13275,"string":"13,275"}}},{"rowIdx":13276,"cells":{"question_id":{"kind":"string","value":"Huijuan Wang"},"question":{"kind":"string","value":"Generate an abstract for the title \"Topology of molecular interaction networks.\" using the following items: 1. Molecular interactions \n2. Network models \n3. Graph topology \n4. Local vs global analysis \n5. Predictive approach"},"answer":{"kind":"string","value":"Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further."},"correct_answer":{"kind":"string","value":"Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further."},"documents":{"kind":"list like","value":["Title: 2-Adic Complexity Of Self-Shrinking Sequence\nYear: 2011\n\nAbstract: This paper studies the 2-adic complexity of the self-shrinking sequence under the relationship between 2-adic integers and binary sequences. Based on the linear complexity and the number of the sequences which have the same connection integer, we conclude that the 2-adic complexity of the self-shrinking sequence constructed by a binary m-sequence of order n has a lower bound 2(n-2) - 1. Furthermore, it is shown that its 2-adic complexity has a bigger lower bound under some circumstances.","Title: Degree and principal eigenvectors in complex networks\nYear: 2012\n\nAbstract: The largest eigenvalue λ 1 of the adjacency matrix powerfully characterizes dynamic processes on networks, such as virus spread and synchronization. The minimization of the spectral radius by removing a set of links (or nodes) has been shown to be an NP-complete problem. So far, the best heuristic strategy is to remove links/nodes based on the principal eigenvector corresponding to the largest eigenvalue λ 1 . This motivates us to investigate properties of the principal eigenvector x 1 and its relation with the degree vector. (a) We illustrate and explain why the average E [x 1 ] decreases with the linear degree correlation coefficient ρ D in a network with a given degree vector; (b) The difference between the principal eigenvector and the scaled degree vector is proved to be the smallest, when $\\lambda _{1}=\\frac{N_{2}}{N_{1}}$ , where N k is the total number walks in the network with k hops; (c) The correlation between the principal eigenvector and the degree vector decreases when the degree correlation ρ D is decreased.","Title: Non-consensus opinion models on complex networks\nYear: 2012\n\nAbstract: Social dynamic opinion models have been widely studied to understand how interactions among individuals cause opinions to evolve. Most opinion models that utilize spin interaction models usually produce a consensus steady state in which only one opinion exists. Because in reality different opinions usually coexist, we focus on non-consensus opinion models in which above a certain threshold two opinions coexist in a stable relationship. We revisit and extend the non-consensus opinion (NCO) model introduced by Shao et al. (Phys. Rev. Lett. 103:01870, ). The NCO model in random networks displays a second order phase transition that belongs to regular mean field percolation and is characterized by the appearance (above a certain threshold) of a large spanning cluster of the minority opinion. We generalize the NCO model by adding a weight factor to each individual’s original opinion when determining their future opinion (NCO model). We find that as increases the minority opinion holders tend to form stable clusters with a smaller initial minority fraction than in the NCO model. We also revisit another non-consensus opinion model based on the NCO model, the inflexible contrarian opinion (ICO) model (Li et al. in Phys. Rev. E 84:066101, ), which introduces inflexible contrarians to model the competition between two opinions in a steady state. Inflexible contrarians are individuals that never change their original opinion but may influence the opinions of others. To place the inflexible contrarians in the ICO model we use two different strategies, random placement and one in which high-degree nodes are targeted. The inflexible contrarians effectively decrease the size of the largest rival-opinion cluster in both strategies, but the effect is more pronounced under the targeted method. All of the above models have previously been explored in terms of a single network, but human communities are usually interconnected, not isolated. Because opinions propagate not only within single networks but also between networks, and because the rules of opinion formation within a network may differ from those between networks, we study here the opinion dynamics in coupled networks. Each network represents a social group or community and the interdependent links joining individuals from different networks may be social ties that are unusually strong, e.g., married couples. We apply the non-consensus opinion (NCO) rule on each individual network and the global majority rule on interdependent pairs such that two interdependent agents with different opinions will, due to the influence of mass media, follow the majority opinion of the entire population. The opinion interactions within each network and the interdependent links across networks interlace periodically until a steady state is reached. We find that the interdependent links effectively force the system from a second order phase transition, which is characteristic of the NCO model on a single network, to a hybrid phase transition, i.e., a mix of second-order and abrupt jump-like transitions that ultimately becomes, as we increase the percentage of interdependent agents, a pure abrupt transition. We conclude that for the NCO model on coupled networks, interactions through interdependent links could push the non-consensus opinion model to a consensus opinion model, which mimics the reality that increased mass communication causes people to hold opinions that are increasingly similar. We also find that the effect of interdependent links is more pronounced in interdependent scale free networks than in interdependent Erdős Rényi networks.","Title: Robust IP watermarking methodologies for physical design\nYear: 1998\n\nAbstract: Increasingly popular reuse-based design paradigms create a pressing need for authorship enforcement techniques that protect the intellectual property rights of designers. We develop the first intellectual property protection protocols for embedding design watermarks at the physical design level. We demonstrate that these protocols are tarnsparent with respect to existing industrial tools and design flows, and that they can embed watermarks into real-world industrial designs with very low implementation overhead (as measured by such standard metrics as wirelength, layout area, number of vias, routing congestion and CPU time). On several industrial test cases, we obtain extremely strong, tamper-resistant proofs of authorship for placement and routing solutions.","Title: The Properties Of The Fcsr-Based Self-Shrinking Sequence\nYear: 2013\n\nAbstract: In the construction of a no-linear key-stream generator, self-shrinking is an established way of getting the binary pseudo-random periodic sequences in cryptography design. In this paper, using the theoretical analysis, we mainly study the self-shrinking sequence based on the 1-sequence, and the theoretical results reflect its good cryptography properties accurately, such that it has the last period T = p(e)(p - 1)/2 when T is an odd number, and the expected value of its autocorrelation belongs to {0, 1/T} and the variance is O(T / ln(4) T). Furthermore, we find that the 2-adic complexity of the self-shrinking sequence based on the I-sequence is large enough to resist the Rational Approximation attack.","Title: A 130.7mm2 2-layer 32Gb ReRAM memory device in 24nm technology\nYear: 2013\n\nAbstract: ReRAM has been considered as one of the potential technologies for the next-generation nonvolatile memory, given its fast access speed, high reliability, and multi-level capability. Multiple-layered architectures have been used for several megabit test-chips and memory macros [1-3]. This paper presents a MeOx-based 32Gb ReRAM test chip developed in 24nm technology.","Title: Algebraic connectivity optimization via link addition.\nYear: 2008\n\nAbstract: Significant research effort has been devoted to the topological features of complex networks to enhance the performance of dynamic processes implemented on these networks. In this work, we investigate how to optimize a network for a given dynamic process via a minor topological modification. The algebraic connectivity a(G) of a network G characterizes the performance of e.g. synchronization of dynamic processes at the nodes of the network G and random walks on networks which models e.g. the dispersion phenomena. We confine ourselves to the problem: \"Where to add a link in a network G such that the algebraic connectivity is increased the most?\" Exhaustive searching for the optimal link addition is computationally infeasible. Hence, we propose two strategies: 1. adding a link between the minimal degree node and a random other node; 2. adding a link between a node pair with the maximal |ui − uj|, the absolute difference between the i − th and j − th elements of the Fiedler vector of G. Strategy 1 and 2 are compared with random link addition in three classes of networks: the Erdös-Rényi random graph, the BA model and the k-ary tree. The Fiedler vector based strategy 2 performs better than strategy 1. However, strategy 1 requires only local information, i.e. node degree.","Title: Soft Sensing Based on LS-SVM and Its Application to a Distillation Column\nYear: 2006\n\nAbstract: Dry point of aviation kerosene in the atmospheric distillation column is a very important process value for quality controlling. But unfortunately few on-line hardware sensors are available to this value or such sensors are difficult to maintain. This paper adopts a novel method based on least squares support vector machine (LS-SVM) regression to implement on-line estimation of aviation kerosene dry point. Compared to traditional radial basis function (RBF) neural network and squares support vector machine (SVM) regression methods, using the same sample data, the simulation results show that the soft sensing based on LS-SVM regression has better abilities of model generalization and real-time character.","Title: The observable part of a network\nYear: 2009\n\nAbstract: The union of all shortest path trees GUspt is the maximally observable part of a network when traffic follows shortest paths. Overlay networks such as peer to peer networks or virtual private networks can be regarded as a subgraph of GUspt. We investigate properties of GUspt in different underlying topologies with regular i.i.d. link weights. In particular, we show that the overlay GUspt in an Erdös-Rényi random graph Gp (N) is a connected GPc (N) where Pc ∼ log N/N is the critical link density, an observation with potential for ad-hoc networks. Shortest paths and, thus also the overlay GUspt, can be controlled by link weights. By tuning the power exponent α of polynomial link weights in different underlying graphs, the phase transitions in the structure of GUspt are shown by simulations to follow a same universal curve FT (α) = Pr[GUspt is a tree]. The existence of a controllable phase transition in networks may allow network operators to steer and balance flows in their network. The structure of GUspt in terms of the extreme value index α is further examined together with its spectrum, the eigenvalues of the corresponding adjacency matrix of GUspt.","Title: The stability of paths in a dynamic network\nYear: 2005\n\nAbstract: Dynamic networks appear in several contexts: QoS routing faces the difficult problem of accurately and efficiently maintaining, distributing and updating network state information, and in wireless ad hoc networking, signal strength fluctuations complicate the choice of stable paths. In this paper we will focus on the stability of paths in a network with dynamically changing link weights. The level of path stability has a direct relation to the number of updates that are necessary to maintain an accurate view of the network state. If a small change in the network state does not affect the shortest path, then such a change need not be distributed throughout the network. We evaluate path stability by adding noise and observing the change in paths.","Title: On the linear arboricity of graphs embeddable in surfaces.\nYear: 2014\n\nAbstract: The linear arboricity of a graph G, denoted by la(G), is the minimum number of linear forest required to partition the edge set E(G). Akiyama, Exoo and Harary conjectured that ⌈Δ2⌉⩽la(G)⩽⌈Δ+12⌉ for any simple graph G, where Δ is the maximum degree of G. In this paper, it is proved that this conjecture is true for any graph G which can be embedded in a surface of nonnegative Euler characteristic, and furthermore, la(G)=⌈Δ2⌉ if Δ⩾9.","Title: Minimum number of disjoint linear forests covering a planar graph\nYear: 2014\n\nAbstract: Graph coloring has interesting real-life applications in optimization, computer science and network design, such as file transferring in a computer network, computation of Hessians matrix and so on. In this paper, we consider one important coloring, linear arboricity, which is an improper edge coloring. Moreover, we study linear arboricity on planar graphs with maximum degree $$\\varDelta \\ge 7$$ Δ 7 . We have proved that the linear arboricity of $$G$$ G is $$\\lceil \\frac{\\varDelta }{2}\\rceil $$ Δ 2 , if for each vertex $$v\\in V(G)$$ v V ( G ) , there are two integers $$i_v,j_v\\in \\{3,4,5,6,7,8\\}$$ i v , j v { 3 , 4 , 5 , 6 , 7 , 8 } such that any two cycles of length $$i_v$$ i v and $$j_v$$ j v , which contain $$v$$ v , are not adjacent. Clearly, if $$i_v=i, j_v=j$$ i v = i , j v = j for each vertex $$v\\in V(G)$$ v V ( G ) , then we can easily get one corollary: for two fixed integers $$i,j\\in \\{3,4,5,6,7,8\\}$$ i , j { 3 , 4 , 5 , 6 , 7 , 8 } , if there is no adjacent cycles with length $$i$$ i and $$j$$ j in $$G$$ G , then the linear arboricity of $$G$$ G is $$\\lceil \\frac{\\varDelta }{2}\\rceil $$ ¿ Δ 2 ¿ .","Title: Filling and slotting: analysis and algorithms\nYear: 1998\n\nAbstract: In very deep-submicron VLSI, certain manufacturing steps &mdash notably optical exposure, resist development and etch, chemical vapor deposition and chemical-mechanical polishing (CMP)&mdash have varying effects on device and interconnect features depending on local characteristics of the layout. To make these effects uniform and predictable, the layout itself must be made uniform with respect to certain density parameters. Traditionally, only foundries have performed the post-processing needed to achieve this uniformity, via insertion (“filling”) or partial deletion (“slotting”) of features in the layout. Today, however, physical design and verification tools cannot remain oblivious to such foundry post-processing. Without an accurate estimate of the filling and slotting, RC extraction, delay calculation, and timing and noise analysis flows will all suffer from wild inaccuracies. Therefore, future place-and-route tools must efficiently perform filling and slotting prior to performance analysis within the layout optimization loop. We give the first formulations of the filling and slotting problems that arise in layout post-processing or layout optimization for manufacturability. Such formulations seek to add or remove features to a given process layer, so that the local area or perimeter density of features satisfies prescribed upper and lower bounds in all windows of a given size. We also present efficient algorithms for density analysis as well as for filling/slotting synthesis. Our work provides a new unification between manufacturing and physical design, and captures a number of general requirements imposed on layout by the manufacturing process.","Title: GLS: New class of generalized Legendre sequences with optimal arithmetic cross-correlation.\nYear: 2013\n\nAbstract: The Legendre symbol has been used to construct sequences with ideal cross-correlation, but it was never used in the arithmetic cross-correlation. In this paper, a new class of generalized Legendre sequences are described and analyzed with respect to their period, distributional, arithmetic cross-correlation and distinctness properties. This analysis gives a new approach to study the connection between the Legendre symbol and the arithmetic cross-correlation. In the end of this paper, possible application of these sequences with optimal arithmetic cross-correlation is indicated.","Title: On Synchronization of Interdependent Networks\nYear: 2013\n\nAbstract: It is well-known that the synchronization of diffusively-coupled systems on networks strongly depends on the network topology. In particular, the so-called algebraic connectivity $\\mu_{N-1}$, or the smallest non-zero eigenvalue of the discrete Laplacian operator plays a crucial role on synchronization, graph partitioning, and network robustness. In our study, synchronization is placed in the general context of networks-of-networks, where single network models are replaced by a more realistic hierarchy of interdependent networks. The present work shows, analytically and numerically, how the algebraic connectivity experiences sharp transitions after the addition of sufficient links among interdependent networks.","Title: Multi-Weighted Monetary Transaction Network.\nYear: 2011\n\nAbstract: This paper aims to both develop and apply advances from the field of complex networks to large economic systems and explore the (dis)similarities between economic systems and other real-world complex networks. For the first time, the nature and evolution of the Dutch economy are captured by means of a data set analysis that describes the monetary transactions among 105 economical activity clusters over the period 1987-2007. We propose to represent this data set as a multi-weighted network, called the monetary transaction network. Each node represents a unique activity cluster. Nodes are interconnected via monetary transactions. The millions of euros that traverse the links and that circulate inside each activity cluster are denoted by a link weight and a node weight respectively. By applying innovative methodologies from network theory, we observe important features of the monetary transaction network as well as its evolution: (a) Activity clusters with a large internal flow tend to cooperate with many other clusters via high volume monetary transactions. (b) Activity clusters with a lower internal transaction volume prefer to transact with fewer neighboring nodes that have a higher internal flow. (c) The node weights seem to follow a power law distribution. Surprisingly, (b) and (c) have been observed in community structures of many real-world networks as well. (d) Activity clusters tend to balance the monetary volume of their transactions with their neighbors, reflected by a positive link weight correlation around each node. This correlation becomes stronger over time while the number of links increases over time as well.","Title: Epidemic threshold in directed networks\nYear: 2013\n\nAbstract: Epidemics have so far been mostly studied in undirected networks. However, many real-world networks, such as the online social network Twitter and the world wide web, on which information, emotion, or malware spreads, are directed networks, composed of both unidirectional links and bidirectional links. We define the directionality xi as the percentage of unidirectional links. The epidemic threshold tc for the susceptible-infected-susceptible ( SIS) epidemic is lower bounded by 1/lambda(1) in directed networks, where lambda(1), also called the spectral radius, is the largest eigenvalue of the adjacency matrix. In this work, we propose two algorithms to generate directed networks with a given directionality xi. The effect of xi on the spectral radius lambda 1, principal eigenvector x(1), spectral gap (lambda(1) -|lambda(2)|), and algebraic connectivity mu(N-1) is studied. Important findings are that the spectral radius.1 decreases with the directionality xi, whereas the spectral gap and the algebraic connectivity increase with the directionality xi. The extent of the decrease of the spectral radius depends on both the degree distribution and the degree-degree correlation rho(D). Hence, in directed networks, the epidemic threshold is larger and a random walk converges to its steady state faster than that in undirected networks with the same degree distribution.","Title: Sampling networks by the union of m shortest path trees\nYear: 2010\n\nAbstract: Many network topology measurements capture or sample only a partial view of the actual network structure, which we call the underlying network. Sampling bias is a critical problem in the field of complex networks ranging from biological networks, social networks and artificial networks like the Internet. This bias phenomenon depends on both the sampling method of the measurements and the features of the underlying networks. In RIPE NCC and the PlanetLab measurement architectures, the Internet is mapped as G\"@?\"\"\"m\"s\"p\"t, the union of shortest paths between each pair of a set M of m testboxes, or equivalently, m shortest path trees. In this paper, we investigate this sampling method on a wide class of real-world complex networks as well as on the weighted Erdos-Renyi random graphs. This general framework examines the effect of the set of testboxes on G\"@?\"\"\"m\"s\"p\"t. We establish the correlation between the subgraph G\"M of the underlying network, i.e. the set M and the direct links between nodes of set M, and the sampled network G\"@?\"\"\"m\"s\"p\"t. Furthermore, we illustrate that in order to obtain an increasingly accurate view of a given network, a higher than linear detection/measuring effort (the relative size m/N of set M) is needed, where N is the size of the underlying network. Finally, when the relative size m/N of set M is small, we characterize the kind of networks possessing small sampling bias, which provides insights on how to place the testboxes for good network topology measurement.","Title: Total coloring of embedded graphs with maximum degree at least seven\nYear: 2014\n\nAbstract: A k-total-coloring of a graph G is a coloring of V(G)@?E(G) using k colors such that no two adjacent or incident elements receive the same color. A graph G is k-total-colorable if it admits a k-total-coloring. In this paper, it is proved that any graph G which can be embedded in a surface @S of Euler characteristic @g(@S)=0 is (@D(G)+2)-total-colorable if @D(G)=7, where @D(G) denotes the maximum degree of G.","Title: Optimal phase conflict removal for layout of dark field alternatingphase shifting masks\nYear: 2000\n\nAbstract: We describe new, efficient algorithms for layout modification and phase assignment for dark field alternating-type phase shifting masks in the single exposure regime. We make the following contributions. First, we suggest new two-coloring and compaction approach that simultaneously optimizes layout and phase assignment which is based on planar embedding of an associated conflict graph. We also des...","Title: Total coloring of planar graphs with maximum degree 8\nYear: 2014\n\nAbstract: Let G be a planar graph with @D=8 and without adjacent cycles of size i and j, for some 3=","Title: Local topological signatures for network-based prediction of biological function\nYear: 2013\n\nAbstract: In biology, similarity in structure or sequence between molecules is often used as evidence of functional similarity. In protein interaction networks, structural similarity of nodes (i.e., proteins) is often captured by comparing node signatures (vectors of topological properties of neighborhoods surrounding the nodes). In this paper, we ask how well such topological signatures predict protein function, using protein interaction networks of the organism Saccharomyces cerevisiae. To this end, we compare two node signatures from the literature --- the graphlet degree vector and a signature based on the graph spectrum --- and our own simple node signature based on basic topological properties. We find the connection between topology and protein function to be weak but statistically significant. Surprisingly, our node signature, despite its simplicity, performs on par with the other more sophisticated node signatures. In fact, we show that just two metrics, the link count and transitivity, are enough to classify protein function at a level on par with the other signatures suggesting that detailed topological characteristics are unlikely to aid in protein function prediction based on protein interaction networks.","Title: Electric field-enhanced metal-induced lateral crystallization and P-channel poly-Si TFTs fabricated by it\nYear: 2008\n\nAbstract: The poly-Si thin film was obtained by electric field-enhanced metal-induced lateral crystallization technique at low temperature. Raman spectra, X-ray diffraction (XRD) and scan electron microscope (SEM) were used to analyze the crystallization state, crystal structure and surface morphology of the poly-Si thin film. Results show that the poly-Si has good crystallinity, and the electric field has the effect of enhancing the crystallization when DC electric voltage is added to the film during annealing. Secondary ion mass spectroscopy (SIMS) shows that the metal Ni improves the crystallization by diffusing into the a-Si thin film, so the crystallization of the lateral diffused region of Ni is the best. The p-channel poly-Si thin film transistors (TFTs) were fabricated by this large-size grain technique. The I\"D\"S-V\"D\"S and the transfer characteristics of the TFTs were measured, from which, the hole mobility of TFTs was 65cm^2/Vs, the on and off current ratio was 5x10^6. It is a promising method to fabricate high-performance poly-Si TFTs at low temperature for applications in AMLCD and AMOLED.","Title: Metabolic network destruction: Relating topology to robustness\nYear: 2011\n\nAbstract: Biological networks exhibit intriguing topological properties such as small-worldness. In this paper, we investigate whether the topology of a particular type of biological network, a metabolic network, is related to its robustness. We do so by perturbing a metabolic system in silico, one reaction at a time and studying the correlations between growth, as predicted by flux balance analysis, and a number of topological metrics, as computed from three network representations of the metabolic system.","Title: Did combination exercises of LBNP and ergometer prevent the muscle atrophy induced by 15-day bed rest in women?\nYear: 2011\n\nAbstract: Lower body negative pressure (LBNP) and cycle ergometer have been used in space flight as effective countermeasures in several years. But the physiological effects of their combination on preventing the muscle atrophy induced by microgravity or simulated microgravity have been less reported, especially in women. This study was to investigate the effects of combined LBNP and ergometer on preventing the muscle atrophy induced by 15-day bed rest in women. Method: 22 healthy female subjects were assigned to 1) group A (no exercise counter-measure, control group, n = 8), 2) group B (LBNP exercise group, n = 7) and 3) group C (LBNP + ergometer exercise group, n = 7). They all experienced the bed rest with 6° head-down tilt position for 15 days. At the end of bed rest, the subjects were examined the peak torque of knee joint for muscle strength, DEXA examination for lean mass of lower body, and MRI for muscle volume (quadriceps femoris, gastrocnemius and soleus). Results: The muscle strength, lean mass of lower body and muscle volume were significant decreased after 15-day bed rest (P<;0.05). The decrement of muscle strength, mass and volume was less in LBNP exercise group and the combined exercise group, the maximum decrement happened in control group. Conclusion: There were a few benefits from combined exercise to prevent the muscle atrophy induced by 15-day bed rest, but its effect of sustaining the muscle function was almost same as LBNP exercise alone. Considering its advantage in enhancing aerobic capacity (paper in press), the combined LBNP and ergometer exercise has the potential to be an effective countermeasure which could be used in space flight for female astronaut.","Title: Optimal phase conflict removal for layout of dark field alternating phase shifting masks\nYear: 1999\n\nAbstract: We describe new, efficient algorithms for layout modification and phase assignment for dark field alternating-type phase shifting masks in the single exposure regime. We make the following contributions. First, we suggest new two-coloring and compaction approach that simultaneously optimizes layout and phase assignment which is based on planar embedding of an associated conflict graph. We also describe additional approaches to cooptimization of layout and phase assignment for alternating PSM. Second, we give optimal and fast algorithms to minimize the number of phase conflicts that must be removed to ensure two colorability of the conflict graph. We reduce this problem to the T-join problem which asks for a minimum weight edge set A such that a node u is incident to an odd number of edges of A if u belongs to a given node subset T of a weighted graph. Third, we suggest several practical algorithms for the T-join problem. In sparse graphs, our algorithms are faster than previously known methods. Computational experience with industrial VLSI layout benchmarks shows the advantages of the new algorithms","Title: Suturing and tying knots assisted by a surgical robot system in laryngeal mis\nYear: 2010\n\nAbstract: Suturing and tying knots assisted by surgical robot systems are complicated and time-consuming tasks in minimally invasive surgery (MIS). It is almost impossible to perform these operations in laryngeal MIS because motions of the end-effectors are greatly confined by a narrow and long laryngoscope tube. This paper presents the robot-assisted operations of suturing and knot-tying in a laryngeal surgery under a self-retaining laryngoscope, which has a greatly confined workspace. In order to use robot assistance to perform the suturing and knot-tying tasks in such a workspace, an appropriate suturing path is planned. The suturing path planning is completed based on a knot-tying algorithm called the bending-twisting knot-tying (BTKT). A robot system for laryngeal MIS called MicroHand III is designed. The kinematical model of the system is developed in the paper. The simulation and experimental results have shown that suturing and knot-tying assisted by MicroHand III system are successful.","Title: Emergence of Modular Structure in a Large-Scale Brain Network with Interactions between Dynamics and Connectivity.\nYear: 2010\n\nAbstract: A network of 32 or 64 connected neural masses, each representing a large population of interacting excitatory and inhibitory neurons and generating an electroencephalography/ magnetoencephalography like output signal, was used to demonstrate how an interaction between dynamics and connectivity might explain the emergence of complex network features, in particular modularity. Network evolution was modeled by two processes: (i) synchronization dependent plasticity (SDP) and (ii) growth dependent plasticity (GDP). In the case of SDP, connections between neural masses were strengthened when they were strongly synchronized, and were weakened when they were not. GDP was modeled as a homeostatic process with random, distance dependent outgrowth of new connections between neural masses. GDP alone resulted in stable networks with distance dependent connection strengths, typical small-world features, but no degree correlations and only weak modularity. SDP applied to random networks induced clustering, but no clear modules. Stronger modularity evolved only through an interaction of SDP and GDP, with the number and size of the modules depending on the relative strength of both processes, as well as on the size of the network. Lesioning part of the network, after a stable state was achieved, resulted in a temporary disruption of the network structure. The model gives a possible scenario to explain how modularity can arise in developing brain networks, and makes predictions about the time course of network changes during development and following acute lesions.","Title: Heterogeneous recovery rates against SIS epidemics in directed networks\nYear: 2014\n\nAbstract: The nodes in communication networks are possibly and most likely equipped with different recovery resources, which allow them to recover from a virus with different rates. In this paper, we aim to understand know how to allocate the limited recovery resources to efficiently prevent the spreading of epidemics. We study the susceptible-infected-susceptible (SIS) epidemic model on directed scale-free networks. In the classic SIS model, a susceptible node can be infected by an infected neighbor with the infection rate β and an infected node can be recovered to be susceptible again with the recovery rate δ. In the steady state a fraction y\n<sub xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">∞</sub>\n of nodes are infected, which shows how severely the network is infected. Instead of considering the same recovery rate for all the nodes in the SIS model, we propose to allocate the recovery rate δ\n<sub xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">ί</sub>\n for node i according to its indegree and outdegree-δ\n<sub xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">i</sub>\n~ k\n<sup xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">α in</sup>\n<sub xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">i, in</sub>\n k\n<sup xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">α out</sup>\n<sub xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">i, out</sub>\n given the finite average recovery rate (δ) representing the limited recovery resources over the whole network. We consider directed networks with the same power law indegree and outdegree distribution, but different values of the directionality ξ (the proportion of unidirectional links) and linear correlation coefficients ρ between the indegree and outdegree. We find that, by tuning the two scaling exponents α\n<sub xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">in</sub>\n and α\n<sub xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">out</sub>\n, we can always reduce the infection fraction y\n<sub xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">∞</sub>\n thus reducing the extent of infections, comparing to the homogeneous recovery rates allocation. Moreover, we can find our optimal strategy via the optimal choice of the exponent α\n<sub xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">in</sub>\n and α\n<sub xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">out</sub>\n. Our optimal strategy indicates that when the recovery resources are sufficient, more resources should be allocated to the nodes with a larger indegree or outdegree, but when the recovery resource is very limited, only the nodes with a larger outdegree should be equipped with more resources. We also find that our optimal strategy works better when the recovery resources are sufficient but not yet able to make the epidemic die out, and when the indegree outdegree correlation is small.","Title: Correlation between centrality metrics and their application to the opinion model.\nYear: 2014\n\nAbstract: In recent decades, a number of centrality metrics describing network properties of nodes have been proposed to rank the importance of nodes. In order to understand the correlations between centrality metrics and to approximate a high-complexity centrality metric by a strongly correlated low-complexity metric, we first study the correlation between centrality metrics in terms of their Pearson correlation coefficient and their similarity in ranking of nodes. In addition to considering the widely used centrality metrics, we introduce a new centrality measure, the degree mass. The mth-order degree mass of a node is the sum of the weighted degree of the node and its neighbors no further than m hops away. We find that the betweenness, the closeness, and the components of the principal eigenvector of the adjacency matrix are strongly correlated with the degree, the 1st-order degree mass and the 2nd-order degree mass, respectively, in both network models and real-world networks. We then theoretically prove that the Pearson correlation coefficient between the principal eigenvector and the 2nd-order degree mass is larger than that between the principal eigenvector and a lower order degree mass. Finally, we investigate the effect of the inflexible contrarians selected based on different centrality metrics in helping one opinion to compete with another in the inflexible contrarian opinion (ICO) model. Interestingly, we find that selecting the inflexible contrarians based on the leverage, the betweenness, or the degree is more effective in opinion-competition than using other centrality metrics in all types of networks. This observation is supported by our previous observations, i.e., that there is a strong linear correlation between the degree and the betweenness, as well as a high centrality similarity between the leverage and the degree.","Title: Total colorings of planar graphs without chordal 6-cycles.\nYear: 2014\n\nAbstract: A total k-coloring of a graph G is a coloring of V(G)∪E(G) using k colors such that no two adjacent or incident elements receive the same color. The total chromatic number of G is the smallest integer k such that G has a total k-coloring. In this paper, it is proved that if G is a planar graph with maximum degree Δ≥7 and without chordal 6-cycles, then the total chromatic number of G is Δ+1.","Title: Minimum total coloring of planar graph\nYear: 2014\n\nAbstract: Graph coloring is an important tool in the study of optimization, computer science, network design, e.g., file transferring in a computer network, pattern matching, computation of Hessians matrix and so on. In this paper, we consider one important coloring, vertex coloring of a total graph, which is familiar to us by the name of \"total coloring\". Total coloring is a coloring of $$V\\cup {E}$$ V E such that no two adjacent or incident elements receive the same color. In other words, total chromatic number of $$G$$ G is the minimum number of disjoint vertex independent sets covering a total graph of $$G$$ G . Here, let $$G$$ G be a planar graph with $$\\varDelta \\ge 8$$ Δ 8 . We proved that if for every vertex $$v\\in V$$ v V , there exists two integers $$i_{v},j_{v} \\in \\{3,4,5,6,7,8\\}$$ i v , j v { 3 , 4 , 5 , 6 , 7 , 8 } such that $$v$$ v is not incident with intersecting $$i_v$$ i v -cycles and $$j_v$$ j v -cycles, then the vertex chromatic number of total graph of $$G$$ G is $$\\varDelta +1$$ Δ + 1 , i.e., the total chromatic number of $$G$$ G is $$\\varDelta +1$$ Δ + 1 .","Title: An Optimal Preemptive Algorithm for the Single-Server Parallel-Machine Scheduling with Loading and Unloading Times.\nYear: 2014\n\nAbstract: We study a preemptive scheduling problem on two identical parallel machines that share a common server. Each job has to be loaded by the server before being processed on one of the machines and unloaded by the server after its processing. The loading and unloading times are both equal to one time unit. The goal is to minimize the makespan. We propose a O(n log n) solution algorithm for the preemptive variant of the problem.","Title: New Lower Bounds for the Fundamental Weight of the Principal Eigenvector in Complex Networks\nYear: 2014\n\nAbstract: The principal eigenvector x1 belonging to the largest adjacency Eigen value (i.e. The spectral radius) of a graph is one of the most popular centrality metrics. The spectral radius of the adjacency matrix powerfully characterizes the dynamic processes on networks, such as virus spread and synchronization. The sum of components of the principal eigenvector, which is also called the fundamental weight w1, is argued to be as important as the Eigen values of the graph matrix. Here we theoretically prove two new types of lower bound wL and wD for the fundamental weight w1 in any network. The lower bound wL is related to the clique number (the size of the largest clique) of the network. The lower bound wL is sharper than the wD whereas the computational complexity of wD is lower. We compare the sharper lower bound wL with w1 in different networks. The effect of the network structure on the relative deviation of wL is studied. Based on wL, another new lower bound for w1 is proposed for a special type of networks.","Title: Complete game-theoretic characterization of SIS epidemics protection strategies\nYear: 2014\n\nAbstract: Defining an optimal protection strategy against viruses, spam propagation or any other kind of contamination process is an important feature for designing new networks and architectures. In this work, we consider decentralized optimal protection strategies when a virus is propagating over a network through a Susceptible Infected Susceptible (SIS) epidemic process. We assume that each node in the network can fully protect itself from infection at a constant cost, or the node can use recovery software, once it is infected. We model our system using a game theoretic framework. Based on this model, we find pure and mixed equilibria, and evaluate the performance of the equilibria by finding the Price of Anarchy (PoA) in several network topologies. Finally, we give numerical illustrations of our results.","Title: Nonconsensus opinion model on directed networks.\nYear: 2014\n\nAbstract: Dynamic social opinion models have been widely studied on undirected networks, and most of them are based on spin interaction models that produce a consensus. In reality, however, many networks such as Twitter and the World Wide Web are directed and are composed of both unidirectional and bidirectional links. Moreover, from choosing a coffee brand to deciding who to vote for in an election, two or more competing opinions often coexist. In response to this ubiquity of directed networks and the coexistence of two or more opinions in decision-making situations, we study a nonconsensus opinion model introduced by Shao et al. [Phys. Rev. Lett. 103, 018701 (2009)] on directed networks. We define directionality xi as the percentage of unidirectional links in a network, and we use the linear correlation coefficient rho between the in-degree and out-degree of a node to quantify the relation between the in-degree and out-degree. We introduce two degree-preserving rewiring approaches which allow us to construct directed networks that can have a broad range of possible combinations of directionality xi and linear correlation coefficient rho and to study how xi and rho impact opinion competitions. We find that, as the directionality xi or the in-degree and out-degree correlation rho increases, the majority opinion becomes more dominant and the minority opinion's ability to survive is lowered.","Title: Design of a compact silicon-based integrated passive band-pass filter with two tunable finite transmission zeros.\nYear: 2016\n\nAbstract: The thin film integrated passive device (IPD) has caused intensively attention due to its high integrated level and application in System in Package (SiP). Meanwhile, the IPD filter has shown great application value in modern wireless communication system. In this paper, the design method of a compact silicon-based lumped IPD band-pass filter with two tunable finite transmission zeros is shown. The passband of the designed filter is 2.4–2.5GHz. Specific schematic is used to introduce two independently tunable transmission zeros. The specific schematic contains mutual inductor structure, which is realized by making two planar spiral inductors to partially overlap with each other and is helpful to decrease the size of the filter. The mutual inductance is adjustable by changing the overlap degree. To design and optimize the layout of the filter more efficiently, the schematic-layout mutual feedback method is presented. Finally, the measurement results verify the validity of the whole design process and method. The size of the designed second-order band-pass filter is 0.85mm×0.85mm×0.35mm. To the best of our knowledge, the band-pass-filter developed in this paper has the smallest size while achieving equivalent electrical performance.","Title: Total Coloring of Planar Graphs Without Chordal Short Cycles.\nYear: 2015\n\nAbstract: Let $$G$$G be a planar graph with $$\\\\varDelta \\\\ge 8$$Δ¿8 and $$v$$v be a vertex of $$G$$G. It is proved that $$\\\\chi ''(G)=\\\\varDelta +1$$¿¿¿(G)=Δ+1 if $$v$$v is not incident with chordal $$5$$5- or $$6$$6-cycles by Shen et al. (Appl Math Lett 22:1369---1373, 2009), or $$v$$v is not incident with $$2$$2-chordal $$5$$5-cycle by Chang et al. (Theor Comput Sci 476:16---23, 2013). In this paper we generalize these results and prove that if $$v$$v is not incident with chordal $$6$$6-cycle, or chordal $$7$$7-cycle, or $$2$$2-chordal $$5$$5-cycle, then $$\\\\chi ''(G)=\\\\varDelta +1$$¿¿¿(G)=Δ+1.","Title: MaPU: A novel mathematical computing architecture\nYear: 2016\n\nAbstract: As the feature size of the semiconductor process is scaling down to 10nm and below, it is possible to assemble systems with high performance processors that can theoretically provide computational power of up to tens of PLOPS. However, the power consumption of these systems is also rocketing up to tens of millions watts, and the actual performance is only around 60% of the theoretical performance. Today, power efficiency and sustained performance have become the main foci of processor designers. Traditional computing architecture such as superscalar and GPGPU are proven to be power inefficient, and there is a big gap between the actual and peak performance. In this paper, we present the MaPU architecture, a novel architecture which is suitable for data-intensive computing with great power efficiency and sustained computation throughput. To achieve this goal, MaPU attempts to optimize the application from a system perspective, including the hardware, algorithm and corresponding program model. It uses an innovative multi-granularity parallel memory system with intrinsic shuffle ability, cascading pipelines with wide SIMD data paths and a state-machine-based program model. When executing typical signal processing algorithms, a single MaPU core implemented with a 40nm process exhibits a sustained performance of 134 GLOPS while consuming only 2.8 W in power, which increases the actual power efficiency by an order of magnitude comparable with the traditional CPU and GPGPU.","Title: Activation Games In Online Dating Platforms\nYear: 2015\n\nAbstract: In this paper we describe a model for the activation level of users in online dating platforms (ODPs). Such popular systems are conceived in order to match individuals from two groups of potential mates. The business of such platforms pivots around the customers' expectancy to get in contact with their future dates: upon the payment of a fee to the platform owner, ODPs provide specific tools to improve reach and visibilityHowever, ODPs require a critical number of active users in order to sustain their operations (and their business). Customers of the platform trade off on the price for being more visible and attract mates' contacts. A user becomes inactive if he or she is not contacted by others for some time: being contacted by potential mates acts as an activation signal.The aim of our analysis is to propose a game theoretical framework to capture such a complex activation problem in strategic form. We unveil the structure of Nash equilibria and we further derive a Stackelberg formulation. The latter is a hierarchical game where the platform owner aims at maximizing profits while preserving the ODP activity level above a critical epidemic threshold.","Title: Non-rigid 3D Model Retrieval Based on Weighted Bags-of-Phrases and LDA.\nYear: 2016\n\nAbstract: This paper presents an improved BOP model, called weighted bags-of-phrases (W-BOP), and its application in non-rigid 3D shape retrieval. The W-BOP model uses the Gaussian weighted function and the 3D points' ring based neighborhood to construct the spatial arrangement model of visual words. Compared with BOP model, it can describe the 3D model more detailedly. Compared with the SS-BOW model and the BOFG model, it needn't perform feature detection step and has higher computation efficiency. To further improve the retrieval performance, the LDA algorithm is used to reduce the dimension of the W-BOP descriptor. Extensive experiments have validated the effectiveness of the designed W-BOP model and LDA based non-rigid 3D model retrieval method.","Title: Applying the locality principle to improve the shortest path algorithm.\nYear: 2017\n\nAbstract: When shortest path algorithm be used in real time road network monitoring system, the time complexity of the algorithm is becoming the bottleneck of the system. the paper proposes a solution to improve the shortest path algorithm. By making use of the k-ary-reverse tree and locality principle, this improved algorithm keeps the data operation, in priority queue, be localized in a small range. So the time complexity of this improved shortest path algorithm is optimized from O(nlog2n) to $$O(k\\\\times n)$$O(k×n); the parameter k denotes the average count of adjacency edges to each vertex, and n denotes the total count of vertexes in the network. Note that, the parameter k mainly depends on the edges density in the network. Generally speaking, the mean value of parameter k is not more than 4 in the road network, i.e. the time complexity of the improved algorithm is optimized from O(nlog2n) to O(n) in the road network. So the paper makes a conclusion that there is a linear correlation between the time complexity and the vertexes count n in the improved algorithm.","Title: Total coloring of planar graphs without adjacent chordal 6-cycles.\nYear: 2017\n\nAbstract: A total coloring of a graph G is a coloring such that no two adjacent or incident elements receive the same color. In this field there is a famous conjecture, named Total Coloring Conjecture, saying that the the total chromatic number of each graph G is at most $$\\\\Delta +2$$Δ+2. Let G be a planar graph with maximum degree $$\\\\Delta \\\\ge 7$$Δź7 and without adjacent chordal 6-cycles, that is, two cycles of length 6 with chord do not share common edges. In this paper, it is proved that the total chromatic number of G is $$\\\\Delta +1$$Δ+1, which partly confirmed Total Coloring Conjecture.","Title: Modeling of Information Diffusion on Social Networks with Applications to WeChat.\nYear: 2017\n\nAbstract: Traces of user activities recorded in online social networks open new possibilities to systematically understand the information diffusion process on social networks. From the online social network WeChat, we collected a large number of information cascade trees, each of which tells the spreading trajectory of a message/information such as which user creates the information and which users view or forward the information shared by which neighbours. In this work, we propose two heterogeneous non-linear models, one for the topologies of the information cascade trees and the other for the stochastic process of information diffusion on a social network. Both models are validated by the WeChat data in reproducing and explaining key features of cascade trees.","Title: The Accuracy of Mean-Field Approximation for Susceptible-Infected-Susceptible Epidemic Spreading with Heterogeneous Infection Rates.\nYear: 2016\n\nAbstract: The epidemic spreading over a network has been studied for years by applying the mean-field approach in both homogeneous case, where each node may get infected by an infected neighbor with the same rate, and heterogeneous case, where the infection rates between different pairs of nodes are also different. Researchers have discussed whether the mean-field approaches could accurately describe the epidemic spreading for the homogeneous cases but not for the heterogeneous cases. In this paper, we explore if and under what conditions the mean-field approach could perform well when the infection rates are heterogeneous. In particular, we employ the Susceptible-Infected-Susceptible (SIS) model and compare the average fraction of infected nodes in the metastable state, where the fraction of infected nodes remains stable for a long time, obtained by the continuous-time simulation and the mean-field approximation. We concentrate on an individual-based mean-field approximation called the N-intertwined Mean Field Approximation (NIMFA), which is an advanced approach considered the underlying network topology. Moreover, for the heterogeneity of the infection rates, we consider not only the independent and identically distributed (i.i.d.) infection rate but also the infection rate correlated with the degree of the two end nodes. We conclude that NIMFA is generally more accurate when the prevalence of the epidemic is higher. Given the same effective infection rate, NIMFA is less accurate when the variance of the i.i.d. infection rate or the correlation between the infection rate and the nodal degree leads to a lower prevalence. Moreover, given the same actual prevalence, NIMFA performs better in the cases: 1) when the variance of the i.i.d. infection rates is smaller (while the average is unchanged); 2) when the correlation between the infection rate and the nodal degree is positive. Our work suggests the conditions when the mean-field approach, in particular NIMFA, is more accurate in the approximation of the SIS epidemic with heterogeneous infection rates.","Title: Robust 3D keypoint detection method based on double Gaussian weighted dissimilarity measure.\nYear: 2017\n\nAbstract: This paper proposes a novel multiscale 3D keypoint detection method via the double Gaussian weighted dissimilarity measure. At each scale, the shape index value and the double Gaussian weighted dissimilarity measure value of each 3D point are firstly computed. Then the candidate keypoints with local maximum dissimilarity measure values are determined. Finally the final 3D keypoints are detected under our proposed multiscale detection scheme. As the dissimilarity measure used in this paper has better robust descriptive ability and is rotation and translation transformation invariant, the proposed detection method is robust to noise, rotation and translation transformation. Extensive experimental results have shown that using our proposed multiscale detection method, we can detect the keypoints with higher repeatability under different noise levels.","Title: Hesitant Picture 2-Tuple Linguistic Aggregation Operators Based on Archimedean T-Norm and T-Conorm and Their Use in Decision-Making.\nYear: 2018\n\nAbstract: An aggregation operator performs the task of fusing multiple sources of information, which plays a pivotal part in realizing a collective opinion in most decision-making activities. Considering the increasing complexity of decision-making situations, it is imperative to extend aggregation operators for fusing uncertain information with the different forms of attribute values. This study focuses on the development of picture fuzzy sets and aims to design a managerial decision-making solving method. Some operational principles of hesitant picture 2-tuple linguistic variables on account of the Archimedean t-norm and t-conorm are initiated, on which two hesitant picture 2-tuple linguistic weighted operators are established by taking various weight forms. Moreover, we explore the aggregation operators' idempotency, boundedness, and monotonicity, as well as analyze some particular forms of these operators. Furthermore, these aggregation operators are employed to design a method of deriving an overall performance from evaluation of experts with hesitant picture 2-tuple linguistic terms. An example of selecting service outsourcing supplier is carried out to show the procedures of decision-making with a detailed comparative analysis.","Title: The indirect carbon emission from household consumption in China between 1995-2009 and 2010-2030: A decomposition and prediction analysis.\nYear: 2019\n\nAbstract: •A framework of determining and predicting to assess indirect emissions is developed.•Indirect carbon emissions from household consumption is redefined.•Multivariable regressions is inserted into IO and SDA model to predict indirect emissions.•The population variable has a positive role.","Title: A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data.\nYear: 2019\n\nAbstract: In order to achieve more efficient energy consumption, it is crucial that accurate detailed information is given on how power is consumed. Electricity details benefit both market utilities and also power consumers. Non-intrusive load monitoring (NILM), a novel and economic technology, obtains single-appliance power consumption through a single total power meter. This paper, focusing on load disaggregation with low hardware costs, proposed a load disaggregation method for low sampling data from smart meters based on a clustering algorithm and support vector regression optimization. This approach combines the k-median algorithm and dynamic time warping to identify the operating appliance and retrieves single energy consumption from an aggregate smart meter signal via optimized support vector regression (OSVR). Experiments showed that the technique can recognize multiple devices switching on at the same time using low-frequency data and achieve a high load disaggregation performance. The proposed method employs low sampling data acquired by smart meters without installing extra measurement equipment, which lowers hardware cost and is suitable for applications in smart grid environments.","Title: Information Diffusion Backbones In Temporal Networks\nYear: 2018\n\nAbstract: Progress has been made in understanding how temporal network features affect the percentage of nodes reached by an information diffusion process. In this work, we explore further: which node pairs are likely to contribute to the actual diffusion of information, i.e., appear in a diffusion trajectory? How is this likelihood related to the local temporal connection features of the node pair? Such deep understanding of the role of node pairs is crucial to tackle challenging optimization problems such as which kind of node pairs or temporal contacts should be stimulated in order to maximize the prevalence of information spreading. We start by using Susceptible-Infected (SI) model, in which an infected (information possessing) node could spread the information to a susceptible node with a given infection probability beta whenever a contact happens between the two nodes, as the information diffusion process. We consider a large number of real-world temporal networks. First, we propose the construction of an information diffusion backbone G(B)(beta) for a S/spreading process with an infection probability beta on a temporal network. The backbone is a weighted network where the weight of each node pair indicates how likely the node pair appears in a diffusion trajectory starting from an arbitrary node. Second, we investigate the relation between the backbones with different infection probabilities on a temporal network. We find that the backbone topology obtained for low and high infection probabilities approach the backbone G(B)(beta -> 0) and G(B)(beta=1), respectively. The backbone G(B)(beta -> 0) equals the integrated weighted network, where the weight of a node pair counts the total number of contacts in between. Finally, we explore node pairs with what local connection features tend to appear in G(B)(beta=1), thus actually contribute to the global information diffusion. We discover that a local connection feature among many other features we proposed, could well identify the (high-weight) links in G(B)(beta=1). This local feature encodes the time that each contact occurs, pointing out the importance of temporal features in determining the role of node pairs in a dynamic process.","Title: Optimal channel assignment with list-edge coloring\nYear: 2019\n\nAbstract: Motivated from optimal channel assignment in optical networks, the list edge (and total) coloring are studied in this paper. In a special case of planar graphs, we determined the list edge (and total) coloring number.","Title: Maximizing profit of multiple adoptions in social networks with a martingale approach\nYear: 2019\n\nAbstract: Information propagation plays an important role in social network, which helps shaping consumer’s purchasing decisions. Most of existing works focus on maximizing the influence of one product. But in our reality life, the majority of the companies produce various products for meeting customer needs. So it is important to learn about how to distribute the limited budget to maximize the companies profits. In this paper, we use the martingale technique to handle the Profit Maximization with Multiple Adoptions (\\(PM^{2}A\\)) problem, which aims to identify a seed set for each product with overall activation cost at most B such that the expected total profit is maximized. We design a \\(PM^{2}AM\\) algorithm which returns a \\((\\frac{1}{2}(1-\\frac{1}{e^{2}})-\\varepsilon )\\)-approximate solution and runs in \\(O\\left( (k^{*}+\\ell )(m+n)nqp_{max}\\ln nq/ (\\varepsilon ^{2}\\cdot p_{min})\\right) \\) expected time.","Title: Social Structure Decomposition With Security Issue.\nYear: 2019\n\nAbstract: Social structure decomposition has been a valuable research topic in the study of social networks, and it still has many unresolved problems. At the same time, an increasing number of people pays attention to the security issue that is worth considering and researching. With security consideration, we study a new social structure decomposition problem that can be formulated as a minimization problem in graph theory as follows: given a social network with formulation as a graph G, partition the vertex set of G into the minimum number of subsets, such that each subset induces an acyclic graph called a forest. This minimum number is called the vertex arboricity, denoted by va(G). It is well known that the vertex arboricity va (G) <= 3 for each planar graph G in the graph theory. In this paper, we prove that for the planar graph G, if no 3-cycle intersects a 4-cycle or no 3-cycle intersects a 5-cycle, then va(G) <= 2.","Title: An Adversarial Transfer Network for Knowledge Representation Learning\nYear: 2021\n\nAbstract: ABSTRACT Knowledge representation learning has received a lot of attention in the past few years. The success of existing methods heavily relies on the quality of knowledge graphs. The entities with few triplets tend to be learned with less expressive power. Fortunately, there are many knowledge graphs constructed from various sources, the representations of which could contain much information. We propose an adversarial embedding transfer network ATransN, which transfers knowledge from one or more teacher knowledge graphs to a target one through an aligned entity set without explicit data leakage. Specifically, we add soft constraints on aligned entity pairs and neighbours to the existing knowledge representation learning methods. To handle the problem of possible distribution differences between teacher and target knowledge graphs, we introduce an adversarial adaption module. The discriminator of this module evaluates the degree of consistency between the embeddings of an aligned entity pair. The consistency score is then used as the weights of soft constraints. It is not necessary to acquire the relations and triplets in teacher knowledge graphs because we only utilize the entity representations. Knowledge graph completion results show that ATransN achieves better performance against baselines without transfer on three datasets, CN3l, WK3l, and DWY100k. The ablation study demonstrates that ATransN can bring steady and consistent improvement in different settings. The extension of combining other knowledge graph embedding algorithms and the extension with three teacher graphs display the promising generalization of the adversarial transfer network.","Title: Quantifying the hierarchy of public transport networks\nYear: 2021\n\nAbstract: Public transport networks constitute critical infrastructure in urban systems. Public transport networks are characterised by their hierarchical structure, yet methods to quantify their underlying hierarchy are lacking. We propose a metric for quantifying the hierarchy in public transport networks which incorporates topological as well as passenger flow information. Our proposed metric consists of...","Title: Topological-temporal properties of evolving networks\nYear: 2022\n\nAbstract: Many real-world complex systems including human interactions can be represented by temporal (or evolving) networks, where links activate or deactivate over time. Characterizing temporal networks is crucial to compare different real-world networks and to detect their common patterns or differences. A systematic method that can characterize simultaneously the temporal and topological relations of the time-specific interactions (also called contacts or events) of a temporal network, is still missing. In this article, we propose a method to characterize to what extent contacts that happen close in time occur also close in topology. Specifically, we study the interrelation between temporal and topological properties of the contacts from three perspectives: (1) the correlation (among the elements) of the activity time series which records the total number of contacts in a network that happen at each time step; (2) the interplay between the topological distance and time difference of two arbitrary contacts; (3) the temporal correlation of contacts within the local neighbourhood centred at each link (so-called ego-network) to explore whether such contacts that happen close in topology are also close in time. By applying our method to 13 real-world temporal networks, we found that temporal-topological correlation of contacts is more evident in virtual contact networks than in physical contact networks. This could be due to the lower cost and easier access of online communications than physical interactions, allowing and possibly facilitating social contagion, that is, interactions of one individual may influence the activity of its neighbours. We also identify different patterns between virtual and physical networks and among physical contact networks at, for example, school and workplace, in the formation of correlation in local neighbourhoods. Patterns and differences detected via our method may further inspire the development of more realistic temporal network models, that could reproduce jointly temporal and topological properties of contacts.","Title: A Traceability System of Livestock Products Based on Blockchain and the Internet of Things\nYear: 2021\n\nAbstract: The safety of animal products has always been the focus of people's attention, especially the frequent occurrence of food safety problems in recent years, which has caused a series of bad social impact, and the traceability system of the whole industry chain is a solution to the food safety problem. Based on the analysis of the problems existing in the application of traditional traceability syste...","Title: Mitigate SIR epidemic spreading via contact blocking in temporal networks\nYear: 2022\n\nAbstract: Progress has been made in how to suppress epidemic spreading on temporal networks via blocking all contacts of targeted nodes or node pairs. In this work, we develop contact blocking strategies that remove a fraction of contacts from a temporal (time evolving) human contact network to mitigate the spread of a Susceptible-Infected-Recovered epidemic. We define the probability that a contact c(i, j, t) is removed as a function of a given centrality metric of the corresponding link l(i, j) in the aggregated network and the time t of the contact. The aggregated network captures the number of contacts between each node pair. A set of 12 link centrality metrics have been proposed and each centrality metric leads to a unique contact removal strategy. These strategies together with a baseline strategy (random removal) are evaluated in empirical contact networks via the average prevalence, the peak prevalence and the time to reach the peak prevalence. We find that the epidemic spreading can be mitigated the best when contacts between node pairs that have fewer contacts and early contacts are more likely to be removed. A strategy tends to perform better when the average number contacts removed from each node pair varies less. The aggregated pruned network resulted from the best contact removal strategy tends to have a large largest eigenvalue, a large modularity and probably a small largest connected component size.","Title: A 1-Tb 4b/Cell 4-Plane 162-Layer 3D Flash Memory With a 2.4-Gb/s I/O Speed Interface\nYear: 2022\n\nAbstract: The presented 1Tb 4b/cell 162 WL layer 3D Flash memory achieves an areal density of 15 Gb/mm2 which is 8.7% higher than prior work [1]. High-performance is the key enabler for 4b/cell NAND Flash to enter mainstream systems. This work delivers a 60MB/s programming throughput and a 65μs tR with an 8kB central WL stair architecture and contact-through-WL (CTW) region. 2.4Gb/s I/...","Title: Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning.\nYear: 2022\n\nAbstract: Relational graph neural networks have garnered particular attention to encode graph context in knowledge graphs (KGs). Although they achieved competitive performance on small KGs, how to efficiently and effectively utilize graph context for large KGs remains an open problem. To this end, we propose the Relation-based Embedding Propagation (REP) method. It is a post-processing technique to adapt pre-trained KG embeddings with graph context. As relations in KGs are directional, we model the incoming head context and the outgoing tail context separately. Accordingly, we design relational context functions with no external parameters. Besides, we use averaging to aggregate context information, making REP more computation-efficient. We theoretically prove that such designs can avoid information distortion during propagation. Extensive experiments also demonstrate that REP has significant scalability while improving or maintaining prediction quality. Notably, it averagely brings about 10% relative improvement to triplet-based embedding methods on OGBL-WikiKG2 and takes 5%-83% time to achieve comparable results as the state-of-the-art GC-OTE.","Title: Influence of clustering coefficient on network embedding in link prediction\nYear: 2022\n\nAbstract: Multiple network embedding algorithms have been proposed to perform the prediction of missing or future links in complex networks. However, we lack the understanding of how network topology affects their performance, or which algorithms are more likely to perform better given the topological properties of the network. In this paper, we investigate how the clustering coefficient of a network, i.e., the probability that the neighbours of a node are also connected, affects network embedding algorithms’ performance in link prediction, in terms of the AUC (area under the ROC curve). We evaluate classic embedding algorithms, i.e., Matrix Factorisation, Laplacian Eigenmaps and node2vec, in both synthetic networks and (rewired) real-world networks with variable clustering coefficient. Specifically, a rewiring algorithm is applied to each real-world network to change the clustering coefficient while keeping key network properties. We find that a higher clustering coefficient tends to lead to a higher AUC in link prediction, except for Matrix Factorisation, which is not sensitive to the change of clustering coefficient. To understand such influence of the clustering coefficient, we (1) explore the relation between the link rating (probability that a node pair is the missing link) derived from the aforementioned algorithms and the number of common neighbours of the node pair, and (2) evaluate these embedding algorithms’ ability to reconstruct the original training (sub)network. All the network embedding algorithms that we tested tend to assign higher likelihood of connection to node pairs that share an intermediate or high number of common neighbours, independently of the clustering coefficient of the training network. Then, the predicted networks will have more triangles, thus a higher clustering coefficient. As the clustering coefficient increases, all the algorithms but Matrix Factorisation could also better reconstruct the training network. These two observations may partially explain why increasing the clustering coefficient improves the prediction performance.","Title: GROWN+UP: A ''Graph Representation Of a Webpage\" Network Utilizing Pre-training\nYear: 2022\n\nAbstract: ABSTRACTLarge pre-trained neural networks are ubiquitous and critical to the success of many downstream tasks in natural language processing and computer vision. However, within the field of web information retrieval, there is a stark contrast in the lack of similarly flexible and powerful pre-trained models that can properly parse webpages. Consequently, we believe that common machine learning tasks like content extraction and information mining from webpages have low-hanging gains that yet remain untapped. We aim to close the gap by introducing an agnostic deep graph neural network feature extractor that can ingest webpage structures, pre-train self-supervised on massive unlabeled data, and fine-tune to arbitrary tasks on webpages effectually. Finally, we show that our pre-trained model achieves state-of-the-art results using multiple datasets on two very different benchmarks: webpage boilerplate removal and genre classification, thus lending support to its potential application in diverse downstream tasks.","Title: A Lip Reading Method Based on 3D Convolutional Vision Transformer\nYear: 2022\n\nAbstract: Lip reading has received increasing attention in recent years. It judges the content of speech based on the movement of the speaker's lips. The rapid development of deep learning has promoted progress in lip reading. However, due to lip reading needs to process the information of continuous video frames, it is necessary to consider the correlation information between adjacent images and the correlation between long-distance images. Moreover, lip reading recognition mainly focuses on the subtle changes of lips and their surrounding environment, and it is necessary to extract the subtle features of small-size images. Therefore, the performance of machine lip reading is generally not high, and the research progress is slow. In order to improve the performance of machine lip reading, we propose a lip reading method based on 3D convolutional vision transformer (3DCvT), which combines vision transformer and 3D convolution to extract the spatio-temporal feature of continuous images, and take full advantage of the properties of convolutions and transformers to extract local and global features from continuous images effectively. The extracted features are then sent to a Bidirectional Gated Recurrent Unit (BiGRU) for sequence modeling. We proved the effectiveness of our method on large-scale lip reading datasets LRW and LRW-1000 and achieved state-of-the-art performance.","Title: Intelligent Grazing UAV Based on Airborne Depth Reasoning\nYear: 2022\n\nAbstract: The existing precision grazing technology helps to improve the utilization rate of livestock to pasture, but it is still at the level of \"collectivization\" and cannot provide more accurate grazing management and control. (1) Background: In recent years, with the rapid development of agent-related technologies such as deep learning, visual navigation and tracking, more and more lightweight edge computing cell target detection algorithms have been proposed. (2) Methods: In this study, the improved YOLOv5 detector combined with the extended dataset realized the accurate identification and location of domestic cattle; with the help of the kernel correlation filter (KCF) automatic tracking framework, the long-term cyclic convolution network (LRCN) was used to analyze the texture characteristics of animal fur and effectively distinguish the individual cattle. (3) Results: The intelligent UAV equipped with an AGX Xavier high-performance computing unit ran the above algorithm through edge computing and effectively realized the individual identification and positioning of cattle during the actual flight. (4) Conclusion: The UAV platform based on airborne depth reasoning is expected to help the development of smart ecological animal husbandry and provide better precision services for herdsmen."],"string":"[\n \"Title: 2-Adic Complexity Of Self-Shrinking Sequence\\nYear: 2011\\n\\nAbstract: This paper studies the 2-adic complexity of the self-shrinking sequence under the relationship between 2-adic integers and binary sequences. Based on the linear complexity and the number of the sequences which have the same connection integer, we conclude that the 2-adic complexity of the self-shrinking sequence constructed by a binary m-sequence of order n has a lower bound 2(n-2) - 1. Furthermore, it is shown that its 2-adic complexity has a bigger lower bound under some circumstances.\",\n \"Title: Degree and principal eigenvectors in complex networks\\nYear: 2012\\n\\nAbstract: The largest eigenvalue λ 1 of the adjacency matrix powerfully characterizes dynamic processes on networks, such as virus spread and synchronization. The minimization of the spectral radius by removing a set of links (or nodes) has been shown to be an NP-complete problem. So far, the best heuristic strategy is to remove links/nodes based on the principal eigenvector corresponding to the largest eigenvalue λ 1 . This motivates us to investigate properties of the principal eigenvector x 1 and its relation with the degree vector. (a) We illustrate and explain why the average E [x 1 ] decreases with the linear degree correlation coefficient ρ D in a network with a given degree vector; (b) The difference between the principal eigenvector and the scaled degree vector is proved to be the smallest, when $\\\\lambda _{1}=\\\\frac{N_{2}}{N_{1}}$ , where N k is the total number walks in the network with k hops; (c) The correlation between the principal eigenvector and the degree vector decreases when the degree correlation ρ D is decreased.\",\n \"Title: Non-consensus opinion models on complex networks\\nYear: 2012\\n\\nAbstract: Social dynamic opinion models have been widely studied to understand how interactions among individuals cause opinions to evolve. Most opinion models that utilize spin interaction models usually produce a consensus steady state in which only one opinion exists. Because in reality different opinions usually coexist, we focus on non-consensus opinion models in which above a certain threshold two opinions coexist in a stable relationship. We revisit and extend the non-consensus opinion (NCO) model introduced by Shao et al. (Phys. Rev. Lett. 103:01870, ). The NCO model in random networks displays a second order phase transition that belongs to regular mean field percolation and is characterized by the appearance (above a certain threshold) of a large spanning cluster of the minority opinion. We generalize the NCO model by adding a weight factor to each individual’s original opinion when determining their future opinion (NCO model). We find that as increases the minority opinion holders tend to form stable clusters with a smaller initial minority fraction than in the NCO model. We also revisit another non-consensus opinion model based on the NCO model, the inflexible contrarian opinion (ICO) model (Li et al. in Phys. Rev. E 84:066101, ), which introduces inflexible contrarians to model the competition between two opinions in a steady state. Inflexible contrarians are individuals that never change their original opinion but may influence the opinions of others. To place the inflexible contrarians in the ICO model we use two different strategies, random placement and one in which high-degree nodes are targeted. The inflexible contrarians effectively decrease the size of the largest rival-opinion cluster in both strategies, but the effect is more pronounced under the targeted method. All of the above models have previously been explored in terms of a single network, but human communities are usually interconnected, not isolated. Because opinions propagate not only within single networks but also between networks, and because the rules of opinion formation within a network may differ from those between networks, we study here the opinion dynamics in coupled networks. Each network represents a social group or community and the interdependent links joining individuals from different networks may be social ties that are unusually strong, e.g., married couples. We apply the non-consensus opinion (NCO) rule on each individual network and the global majority rule on interdependent pairs such that two interdependent agents with different opinions will, due to the influence of mass media, follow the majority opinion of the entire population. The opinion interactions within each network and the interdependent links across networks interlace periodically until a steady state is reached. We find that the interdependent links effectively force the system from a second order phase transition, which is characteristic of the NCO model on a single network, to a hybrid phase transition, i.e., a mix of second-order and abrupt jump-like transitions that ultimately becomes, as we increase the percentage of interdependent agents, a pure abrupt transition. We conclude that for the NCO model on coupled networks, interactions through interdependent links could push the non-consensus opinion model to a consensus opinion model, which mimics the reality that increased mass communication causes people to hold opinions that are increasingly similar. We also find that the effect of interdependent links is more pronounced in interdependent scale free networks than in interdependent Erdős Rényi networks.\",\n \"Title: Robust IP watermarking methodologies for physical design\\nYear: 1998\\n\\nAbstract: Increasingly popular reuse-based design paradigms create a pressing need for authorship enforcement techniques that protect the intellectual property rights of designers. We develop the first intellectual property protection protocols for embedding design watermarks at the physical design level. We demonstrate that these protocols are tarnsparent with respect to existing industrial tools and design flows, and that they can embed watermarks into real-world industrial designs with very low implementation overhead (as measured by such standard metrics as wirelength, layout area, number of vias, routing congestion and CPU time). On several industrial test cases, we obtain extremely strong, tamper-resistant proofs of authorship for placement and routing solutions.\",\n \"Title: The Properties Of The Fcsr-Based Self-Shrinking Sequence\\nYear: 2013\\n\\nAbstract: In the construction of a no-linear key-stream generator, self-shrinking is an established way of getting the binary pseudo-random periodic sequences in cryptography design. In this paper, using the theoretical analysis, we mainly study the self-shrinking sequence based on the 1-sequence, and the theoretical results reflect its good cryptography properties accurately, such that it has the last period T = p(e)(p - 1)/2 when T is an odd number, and the expected value of its autocorrelation belongs to {0, 1/T} and the variance is O(T / ln(4) T). Furthermore, we find that the 2-adic complexity of the self-shrinking sequence based on the I-sequence is large enough to resist the Rational Approximation attack.\",\n \"Title: A 130.7mm2 2-layer 32Gb ReRAM memory device in 24nm technology\\nYear: 2013\\n\\nAbstract: ReRAM has been considered as one of the potential technologies for the next-generation nonvolatile memory, given its fast access speed, high reliability, and multi-level capability. Multiple-layered architectures have been used for several megabit test-chips and memory macros [1-3]. This paper presents a MeOx-based 32Gb ReRAM test chip developed in 24nm technology.\",\n \"Title: Algebraic connectivity optimization via link addition.\\nYear: 2008\\n\\nAbstract: Significant research effort has been devoted to the topological features of complex networks to enhance the performance of dynamic processes implemented on these networks. In this work, we investigate how to optimize a network for a given dynamic process via a minor topological modification. The algebraic connectivity a(G) of a network G characterizes the performance of e.g. synchronization of dynamic processes at the nodes of the network G and random walks on networks which models e.g. the dispersion phenomena. We confine ourselves to the problem: \\\"Where to add a link in a network G such that the algebraic connectivity is increased the most?\\\" Exhaustive searching for the optimal link addition is computationally infeasible. Hence, we propose two strategies: 1. adding a link between the minimal degree node and a random other node; 2. adding a link between a node pair with the maximal |ui − uj|, the absolute difference between the i − th and j − th elements of the Fiedler vector of G. Strategy 1 and 2 are compared with random link addition in three classes of networks: the Erdös-Rényi random graph, the BA model and the k-ary tree. The Fiedler vector based strategy 2 performs better than strategy 1. However, strategy 1 requires only local information, i.e. node degree.\",\n \"Title: Soft Sensing Based on LS-SVM and Its Application to a Distillation Column\\nYear: 2006\\n\\nAbstract: Dry point of aviation kerosene in the atmospheric distillation column is a very important process value for quality controlling. But unfortunately few on-line hardware sensors are available to this value or such sensors are difficult to maintain. This paper adopts a novel method based on least squares support vector machine (LS-SVM) regression to implement on-line estimation of aviation kerosene dry point. Compared to traditional radial basis function (RBF) neural network and squares support vector machine (SVM) regression methods, using the same sample data, the simulation results show that the soft sensing based on LS-SVM regression has better abilities of model generalization and real-time character.\",\n \"Title: The observable part of a network\\nYear: 2009\\n\\nAbstract: The union of all shortest path trees GUspt is the maximally observable part of a network when traffic follows shortest paths. Overlay networks such as peer to peer networks or virtual private networks can be regarded as a subgraph of GUspt. We investigate properties of GUspt in different underlying topologies with regular i.i.d. link weights. In particular, we show that the overlay GUspt in an Erdös-Rényi random graph Gp (N) is a connected GPc (N) where Pc ∼ log N/N is the critical link density, an observation with potential for ad-hoc networks. Shortest paths and, thus also the overlay GUspt, can be controlled by link weights. By tuning the power exponent α of polynomial link weights in different underlying graphs, the phase transitions in the structure of GUspt are shown by simulations to follow a same universal curve FT (α) = Pr[GUspt is a tree]. The existence of a controllable phase transition in networks may allow network operators to steer and balance flows in their network. The structure of GUspt in terms of the extreme value index α is further examined together with its spectrum, the eigenvalues of the corresponding adjacency matrix of GUspt.\",\n \"Title: The stability of paths in a dynamic network\\nYear: 2005\\n\\nAbstract: Dynamic networks appear in several contexts: QoS routing faces the difficult problem of accurately and efficiently maintaining, distributing and updating network state information, and in wireless ad hoc networking, signal strength fluctuations complicate the choice of stable paths. In this paper we will focus on the stability of paths in a network with dynamically changing link weights. The level of path stability has a direct relation to the number of updates that are necessary to maintain an accurate view of the network state. If a small change in the network state does not affect the shortest path, then such a change need not be distributed throughout the network. We evaluate path stability by adding noise and observing the change in paths.\",\n \"Title: On the linear arboricity of graphs embeddable in surfaces.\\nYear: 2014\\n\\nAbstract: The linear arboricity of a graph G, denoted by la(G), is the minimum number of linear forest required to partition the edge set E(G). Akiyama, Exoo and Harary conjectured that ⌈Δ2⌉⩽la(G)⩽⌈Δ+12⌉ for any simple graph G, where Δ is the maximum degree of G. In this paper, it is proved that this conjecture is true for any graph G which can be embedded in a surface of nonnegative Euler characteristic, and furthermore, la(G)=⌈Δ2⌉ if Δ⩾9.\",\n \"Title: Minimum number of disjoint linear forests covering a planar graph\\nYear: 2014\\n\\nAbstract: Graph coloring has interesting real-life applications in optimization, computer science and network design, such as file transferring in a computer network, computation of Hessians matrix and so on. In this paper, we consider one important coloring, linear arboricity, which is an improper edge coloring. Moreover, we study linear arboricity on planar graphs with maximum degree $$\\\\varDelta \\\\ge 7$$ Δ 7 . We have proved that the linear arboricity of $$G$$ G is $$\\\\lceil \\\\frac{\\\\varDelta }{2}\\\\rceil $$ Δ 2 , if for each vertex $$v\\\\in V(G)$$ v V ( G ) , there are two integers $$i_v,j_v\\\\in \\\\{3,4,5,6,7,8\\\\}$$ i v , j v { 3 , 4 , 5 , 6 , 7 , 8 } such that any two cycles of length $$i_v$$ i v and $$j_v$$ j v , which contain $$v$$ v , are not adjacent. Clearly, if $$i_v=i, j_v=j$$ i v = i , j v = j for each vertex $$v\\\\in V(G)$$ v V ( G ) , then we can easily get one corollary: for two fixed integers $$i,j\\\\in \\\\{3,4,5,6,7,8\\\\}$$ i , j { 3 , 4 , 5 , 6 , 7 , 8 } , if there is no adjacent cycles with length $$i$$ i and $$j$$ j in $$G$$ G , then the linear arboricity of $$G$$ G is $$\\\\lceil \\\\frac{\\\\varDelta }{2}\\\\rceil $$ ¿ Δ 2 ¿ .\",\n \"Title: Filling and slotting: analysis and algorithms\\nYear: 1998\\n\\nAbstract: In very deep-submicron VLSI, certain manufacturing steps &mdash notably optical exposure, resist development and etch, chemical vapor deposition and chemical-mechanical polishing (CMP)&mdash have varying effects on device and interconnect features depending on local characteristics of the layout. To make these effects uniform and predictable, the layout itself must be made uniform with respect to certain density parameters. Traditionally, only foundries have performed the post-processing needed to achieve this uniformity, via insertion (“filling”) or partial deletion (“slotting”) of features in the layout. Today, however, physical design and verification tools cannot remain oblivious to such foundry post-processing. Without an accurate estimate of the filling and slotting, RC extraction, delay calculation, and timing and noise analysis flows will all suffer from wild inaccuracies. Therefore, future place-and-route tools must efficiently perform filling and slotting prior to performance analysis within the layout optimization loop. We give the first formulations of the filling and slotting problems that arise in layout post-processing or layout optimization for manufacturability. Such formulations seek to add or remove features to a given process layer, so that the local area or perimeter density of features satisfies prescribed upper and lower bounds in all windows of a given size. We also present efficient algorithms for density analysis as well as for filling/slotting synthesis. Our work provides a new unification between manufacturing and physical design, and captures a number of general requirements imposed on layout by the manufacturing process.\",\n \"Title: GLS: New class of generalized Legendre sequences with optimal arithmetic cross-correlation.\\nYear: 2013\\n\\nAbstract: The Legendre symbol has been used to construct sequences with ideal cross-correlation, but it was never used in the arithmetic cross-correlation. In this paper, a new class of generalized Legendre sequences are described and analyzed with respect to their period, distributional, arithmetic cross-correlation and distinctness properties. This analysis gives a new approach to study the connection between the Legendre symbol and the arithmetic cross-correlation. In the end of this paper, possible application of these sequences with optimal arithmetic cross-correlation is indicated.\",\n \"Title: On Synchronization of Interdependent Networks\\nYear: 2013\\n\\nAbstract: It is well-known that the synchronization of diffusively-coupled systems on networks strongly depends on the network topology. In particular, the so-called algebraic connectivity $\\\\mu_{N-1}$, or the smallest non-zero eigenvalue of the discrete Laplacian operator plays a crucial role on synchronization, graph partitioning, and network robustness. In our study, synchronization is placed in the general context of networks-of-networks, where single network models are replaced by a more realistic hierarchy of interdependent networks. The present work shows, analytically and numerically, how the algebraic connectivity experiences sharp transitions after the addition of sufficient links among interdependent networks.\",\n \"Title: Multi-Weighted Monetary Transaction Network.\\nYear: 2011\\n\\nAbstract: This paper aims to both develop and apply advances from the field of complex networks to large economic systems and explore the (dis)similarities between economic systems and other real-world complex networks. For the first time, the nature and evolution of the Dutch economy are captured by means of a data set analysis that describes the monetary transactions among 105 economical activity clusters over the period 1987-2007. We propose to represent this data set as a multi-weighted network, called the monetary transaction network. Each node represents a unique activity cluster. Nodes are interconnected via monetary transactions. The millions of euros that traverse the links and that circulate inside each activity cluster are denoted by a link weight and a node weight respectively. By applying innovative methodologies from network theory, we observe important features of the monetary transaction network as well as its evolution: (a) Activity clusters with a large internal flow tend to cooperate with many other clusters via high volume monetary transactions. (b) Activity clusters with a lower internal transaction volume prefer to transact with fewer neighboring nodes that have a higher internal flow. (c) The node weights seem to follow a power law distribution. Surprisingly, (b) and (c) have been observed in community structures of many real-world networks as well. (d) Activity clusters tend to balance the monetary volume of their transactions with their neighbors, reflected by a positive link weight correlation around each node. This correlation becomes stronger over time while the number of links increases over time as well.\",\n \"Title: Epidemic threshold in directed networks\\nYear: 2013\\n\\nAbstract: Epidemics have so far been mostly studied in undirected networks. However, many real-world networks, such as the online social network Twitter and the world wide web, on which information, emotion, or malware spreads, are directed networks, composed of both unidirectional links and bidirectional links. We define the directionality xi as the percentage of unidirectional links. The epidemic threshold tc for the susceptible-infected-susceptible ( SIS) epidemic is lower bounded by 1/lambda(1) in directed networks, where lambda(1), also called the spectral radius, is the largest eigenvalue of the adjacency matrix. In this work, we propose two algorithms to generate directed networks with a given directionality xi. The effect of xi on the spectral radius lambda 1, principal eigenvector x(1), spectral gap (lambda(1) -|lambda(2)|), and algebraic connectivity mu(N-1) is studied. Important findings are that the spectral radius.1 decreases with the directionality xi, whereas the spectral gap and the algebraic connectivity increase with the directionality xi. The extent of the decrease of the spectral radius depends on both the degree distribution and the degree-degree correlation rho(D). Hence, in directed networks, the epidemic threshold is larger and a random walk converges to its steady state faster than that in undirected networks with the same degree distribution.\",\n \"Title: Sampling networks by the union of m shortest path trees\\nYear: 2010\\n\\nAbstract: Many network topology measurements capture or sample only a partial view of the actual network structure, which we call the underlying network. Sampling bias is a critical problem in the field of complex networks ranging from biological networks, social networks and artificial networks like the Internet. This bias phenomenon depends on both the sampling method of the measurements and the features of the underlying networks. In RIPE NCC and the PlanetLab measurement architectures, the Internet is mapped as G\\\"@?\\\"\\\"\\\"m\\\"s\\\"p\\\"t, the union of shortest paths between each pair of a set M of m testboxes, or equivalently, m shortest path trees. In this paper, we investigate this sampling method on a wide class of real-world complex networks as well as on the weighted Erdos-Renyi random graphs. This general framework examines the effect of the set of testboxes on G\\\"@?\\\"\\\"\\\"m\\\"s\\\"p\\\"t. We establish the correlation between the subgraph G\\\"M of the underlying network, i.e. the set M and the direct links between nodes of set M, and the sampled network G\\\"@?\\\"\\\"\\\"m\\\"s\\\"p\\\"t. Furthermore, we illustrate that in order to obtain an increasingly accurate view of a given network, a higher than linear detection/measuring effort (the relative size m/N of set M) is needed, where N is the size of the underlying network. Finally, when the relative size m/N of set M is small, we characterize the kind of networks possessing small sampling bias, which provides insights on how to place the testboxes for good network topology measurement.\",\n \"Title: Total coloring of embedded graphs with maximum degree at least seven\\nYear: 2014\\n\\nAbstract: A k-total-coloring of a graph G is a coloring of V(G)@?E(G) using k colors such that no two adjacent or incident elements receive the same color. A graph G is k-total-colorable if it admits a k-total-coloring. In this paper, it is proved that any graph G which can be embedded in a surface @S of Euler characteristic @g(@S)=0 is (@D(G)+2)-total-colorable if @D(G)=7, where @D(G) denotes the maximum degree of G.\",\n \"Title: Optimal phase conflict removal for layout of dark field alternatingphase shifting masks\\nYear: 2000\\n\\nAbstract: We describe new, efficient algorithms for layout modification and phase assignment for dark field alternating-type phase shifting masks in the single exposure regime. We make the following contributions. First, we suggest new two-coloring and compaction approach that simultaneously optimizes layout and phase assignment which is based on planar embedding of an associated conflict graph. We also des...\",\n \"Title: Total coloring of planar graphs with maximum degree 8\\nYear: 2014\\n\\nAbstract: Let G be a planar graph with @D=8 and without adjacent cycles of size i and j, for some 3=\",\n \"Title: Local topological signatures for network-based prediction of biological function\\nYear: 2013\\n\\nAbstract: In biology, similarity in structure or sequence between molecules is often used as evidence of functional similarity. In protein interaction networks, structural similarity of nodes (i.e., proteins) is often captured by comparing node signatures (vectors of topological properties of neighborhoods surrounding the nodes). In this paper, we ask how well such topological signatures predict protein function, using protein interaction networks of the organism Saccharomyces cerevisiae. To this end, we compare two node signatures from the literature --- the graphlet degree vector and a signature based on the graph spectrum --- and our own simple node signature based on basic topological properties. We find the connection between topology and protein function to be weak but statistically significant. Surprisingly, our node signature, despite its simplicity, performs on par with the other more sophisticated node signatures. In fact, we show that just two metrics, the link count and transitivity, are enough to classify protein function at a level on par with the other signatures suggesting that detailed topological characteristics are unlikely to aid in protein function prediction based on protein interaction networks.\",\n \"Title: Electric field-enhanced metal-induced lateral crystallization and P-channel poly-Si TFTs fabricated by it\\nYear: 2008\\n\\nAbstract: The poly-Si thin film was obtained by electric field-enhanced metal-induced lateral crystallization technique at low temperature. Raman spectra, X-ray diffraction (XRD) and scan electron microscope (SEM) were used to analyze the crystallization state, crystal structure and surface morphology of the poly-Si thin film. Results show that the poly-Si has good crystallinity, and the electric field has the effect of enhancing the crystallization when DC electric voltage is added to the film during annealing. Secondary ion mass spectroscopy (SIMS) shows that the metal Ni improves the crystallization by diffusing into the a-Si thin film, so the crystallization of the lateral diffused region of Ni is the best. The p-channel poly-Si thin film transistors (TFTs) were fabricated by this large-size grain technique. The I\\\"D\\\"S-V\\\"D\\\"S and the transfer characteristics of the TFTs were measured, from which, the hole mobility of TFTs was 65cm^2/Vs, the on and off current ratio was 5x10^6. It is a promising method to fabricate high-performance poly-Si TFTs at low temperature for applications in AMLCD and AMOLED.\",\n \"Title: Metabolic network destruction: Relating topology to robustness\\nYear: 2011\\n\\nAbstract: Biological networks exhibit intriguing topological properties such as small-worldness. In this paper, we investigate whether the topology of a particular type of biological network, a metabolic network, is related to its robustness. We do so by perturbing a metabolic system in silico, one reaction at a time and studying the correlations between growth, as predicted by flux balance analysis, and a number of topological metrics, as computed from three network representations of the metabolic system.\",\n \"Title: Did combination exercises of LBNP and ergometer prevent the muscle atrophy induced by 15-day bed rest in women?\\nYear: 2011\\n\\nAbstract: Lower body negative pressure (LBNP) and cycle ergometer have been used in space flight as effective countermeasures in several years. But the physiological effects of their combination on preventing the muscle atrophy induced by microgravity or simulated microgravity have been less reported, especially in women. This study was to investigate the effects of combined LBNP and ergometer on preventing the muscle atrophy induced by 15-day bed rest in women. Method: 22 healthy female subjects were assigned to 1) group A (no exercise counter-measure, control group, n = 8), 2) group B (LBNP exercise group, n = 7) and 3) group C (LBNP + ergometer exercise group, n = 7). They all experienced the bed rest with 6° head-down tilt position for 15 days. At the end of bed rest, the subjects were examined the peak torque of knee joint for muscle strength, DEXA examination for lean mass of lower body, and MRI for muscle volume (quadriceps femoris, gastrocnemius and soleus). Results: The muscle strength, lean mass of lower body and muscle volume were significant decreased after 15-day bed rest (P<;0.05). The decrement of muscle strength, mass and volume was less in LBNP exercise group and the combined exercise group, the maximum decrement happened in control group. Conclusion: There were a few benefits from combined exercise to prevent the muscle atrophy induced by 15-day bed rest, but its effect of sustaining the muscle function was almost same as LBNP exercise alone. Considering its advantage in enhancing aerobic capacity (paper in press), the combined LBNP and ergometer exercise has the potential to be an effective countermeasure which could be used in space flight for female astronaut.\",\n \"Title: Optimal phase conflict removal for layout of dark field alternating phase shifting masks\\nYear: 1999\\n\\nAbstract: We describe new, efficient algorithms for layout modification and phase assignment for dark field alternating-type phase shifting masks in the single exposure regime. We make the following contributions. First, we suggest new two-coloring and compaction approach that simultaneously optimizes layout and phase assignment which is based on planar embedding of an associated conflict graph. We also describe additional approaches to cooptimization of layout and phase assignment for alternating PSM. Second, we give optimal and fast algorithms to minimize the number of phase conflicts that must be removed to ensure two colorability of the conflict graph. We reduce this problem to the T-join problem which asks for a minimum weight edge set A such that a node u is incident to an odd number of edges of A if u belongs to a given node subset T of a weighted graph. Third, we suggest several practical algorithms for the T-join problem. In sparse graphs, our algorithms are faster than previously known methods. Computational experience with industrial VLSI layout benchmarks shows the advantages of the new algorithms\",\n \"Title: Suturing and tying knots assisted by a surgical robot system in laryngeal mis\\nYear: 2010\\n\\nAbstract: Suturing and tying knots assisted by surgical robot systems are complicated and time-consuming tasks in minimally invasive surgery (MIS). It is almost impossible to perform these operations in laryngeal MIS because motions of the end-effectors are greatly confined by a narrow and long laryngoscope tube. This paper presents the robot-assisted operations of suturing and knot-tying in a laryngeal surgery under a self-retaining laryngoscope, which has a greatly confined workspace. In order to use robot assistance to perform the suturing and knot-tying tasks in such a workspace, an appropriate suturing path is planned. The suturing path planning is completed based on a knot-tying algorithm called the bending-twisting knot-tying (BTKT). A robot system for laryngeal MIS called MicroHand III is designed. The kinematical model of the system is developed in the paper. The simulation and experimental results have shown that suturing and knot-tying assisted by MicroHand III system are successful.\",\n \"Title: Emergence of Modular Structure in a Large-Scale Brain Network with Interactions between Dynamics and Connectivity.\\nYear: 2010\\n\\nAbstract: A network of 32 or 64 connected neural masses, each representing a large population of interacting excitatory and inhibitory neurons and generating an electroencephalography/ magnetoencephalography like output signal, was used to demonstrate how an interaction between dynamics and connectivity might explain the emergence of complex network features, in particular modularity. Network evolution was modeled by two processes: (i) synchronization dependent plasticity (SDP) and (ii) growth dependent plasticity (GDP). In the case of SDP, connections between neural masses were strengthened when they were strongly synchronized, and were weakened when they were not. GDP was modeled as a homeostatic process with random, distance dependent outgrowth of new connections between neural masses. GDP alone resulted in stable networks with distance dependent connection strengths, typical small-world features, but no degree correlations and only weak modularity. SDP applied to random networks induced clustering, but no clear modules. Stronger modularity evolved only through an interaction of SDP and GDP, with the number and size of the modules depending on the relative strength of both processes, as well as on the size of the network. Lesioning part of the network, after a stable state was achieved, resulted in a temporary disruption of the network structure. The model gives a possible scenario to explain how modularity can arise in developing brain networks, and makes predictions about the time course of network changes during development and following acute lesions.\",\n \"Title: Heterogeneous recovery rates against SIS epidemics in directed networks\\nYear: 2014\\n\\nAbstract: The nodes in communication networks are possibly and most likely equipped with different recovery resources, which allow them to recover from a virus with different rates. In this paper, we aim to understand know how to allocate the limited recovery resources to efficiently prevent the spreading of epidemics. We study the susceptible-infected-susceptible (SIS) epidemic model on directed scale-free networks. In the classic SIS model, a susceptible node can be infected by an infected neighbor with the infection rate β and an infected node can be recovered to be susceptible again with the recovery rate δ. In the steady state a fraction y\\n<sub xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">∞</sub>\\n of nodes are infected, which shows how severely the network is infected. Instead of considering the same recovery rate for all the nodes in the SIS model, we propose to allocate the recovery rate δ\\n<sub xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">ί</sub>\\n for node i according to its indegree and outdegree-δ\\n<sub xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">i</sub>\\n~ k\\n<sup xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">α in</sup>\\n<sub xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">i, in</sub>\\n k\\n<sup xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">α out</sup>\\n<sub xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">i, out</sub>\\n given the finite average recovery rate (δ) representing the limited recovery resources over the whole network. We consider directed networks with the same power law indegree and outdegree distribution, but different values of the directionality ξ (the proportion of unidirectional links) and linear correlation coefficients ρ between the indegree and outdegree. We find that, by tuning the two scaling exponents α\\n<sub xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">in</sub>\\n and α\\n<sub xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">out</sub>\\n, we can always reduce the infection fraction y\\n<sub xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">∞</sub>\\n thus reducing the extent of infections, comparing to the homogeneous recovery rates allocation. Moreover, we can find our optimal strategy via the optimal choice of the exponent α\\n<sub xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">in</sub>\\n and α\\n<sub xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">out</sub>\\n. Our optimal strategy indicates that when the recovery resources are sufficient, more resources should be allocated to the nodes with a larger indegree or outdegree, but when the recovery resource is very limited, only the nodes with a larger outdegree should be equipped with more resources. We also find that our optimal strategy works better when the recovery resources are sufficient but not yet able to make the epidemic die out, and when the indegree outdegree correlation is small.\",\n \"Title: Correlation between centrality metrics and their application to the opinion model.\\nYear: 2014\\n\\nAbstract: In recent decades, a number of centrality metrics describing network properties of nodes have been proposed to rank the importance of nodes. In order to understand the correlations between centrality metrics and to approximate a high-complexity centrality metric by a strongly correlated low-complexity metric, we first study the correlation between centrality metrics in terms of their Pearson correlation coefficient and their similarity in ranking of nodes. In addition to considering the widely used centrality metrics, we introduce a new centrality measure, the degree mass. The mth-order degree mass of a node is the sum of the weighted degree of the node and its neighbors no further than m hops away. We find that the betweenness, the closeness, and the components of the principal eigenvector of the adjacency matrix are strongly correlated with the degree, the 1st-order degree mass and the 2nd-order degree mass, respectively, in both network models and real-world networks. We then theoretically prove that the Pearson correlation coefficient between the principal eigenvector and the 2nd-order degree mass is larger than that between the principal eigenvector and a lower order degree mass. Finally, we investigate the effect of the inflexible contrarians selected based on different centrality metrics in helping one opinion to compete with another in the inflexible contrarian opinion (ICO) model. Interestingly, we find that selecting the inflexible contrarians based on the leverage, the betweenness, or the degree is more effective in opinion-competition than using other centrality metrics in all types of networks. This observation is supported by our previous observations, i.e., that there is a strong linear correlation between the degree and the betweenness, as well as a high centrality similarity between the leverage and the degree.\",\n \"Title: Total colorings of planar graphs without chordal 6-cycles.\\nYear: 2014\\n\\nAbstract: A total k-coloring of a graph G is a coloring of V(G)∪E(G) using k colors such that no two adjacent or incident elements receive the same color. The total chromatic number of G is the smallest integer k such that G has a total k-coloring. In this paper, it is proved that if G is a planar graph with maximum degree Δ≥7 and without chordal 6-cycles, then the total chromatic number of G is Δ+1.\",\n \"Title: Minimum total coloring of planar graph\\nYear: 2014\\n\\nAbstract: Graph coloring is an important tool in the study of optimization, computer science, network design, e.g., file transferring in a computer network, pattern matching, computation of Hessians matrix and so on. In this paper, we consider one important coloring, vertex coloring of a total graph, which is familiar to us by the name of \\\"total coloring\\\". Total coloring is a coloring of $$V\\\\cup {E}$$ V E such that no two adjacent or incident elements receive the same color. In other words, total chromatic number of $$G$$ G is the minimum number of disjoint vertex independent sets covering a total graph of $$G$$ G . Here, let $$G$$ G be a planar graph with $$\\\\varDelta \\\\ge 8$$ Δ 8 . We proved that if for every vertex $$v\\\\in V$$ v V , there exists two integers $$i_{v},j_{v} \\\\in \\\\{3,4,5,6,7,8\\\\}$$ i v , j v { 3 , 4 , 5 , 6 , 7 , 8 } such that $$v$$ v is not incident with intersecting $$i_v$$ i v -cycles and $$j_v$$ j v -cycles, then the vertex chromatic number of total graph of $$G$$ G is $$\\\\varDelta +1$$ Δ + 1 , i.e., the total chromatic number of $$G$$ G is $$\\\\varDelta +1$$ Δ + 1 .\",\n \"Title: An Optimal Preemptive Algorithm for the Single-Server Parallel-Machine Scheduling with Loading and Unloading Times.\\nYear: 2014\\n\\nAbstract: We study a preemptive scheduling problem on two identical parallel machines that share a common server. Each job has to be loaded by the server before being processed on one of the machines and unloaded by the server after its processing. The loading and unloading times are both equal to one time unit. The goal is to minimize the makespan. We propose a O(n log n) solution algorithm for the preemptive variant of the problem.\",\n \"Title: New Lower Bounds for the Fundamental Weight of the Principal Eigenvector in Complex Networks\\nYear: 2014\\n\\nAbstract: The principal eigenvector x1 belonging to the largest adjacency Eigen value (i.e. The spectral radius) of a graph is one of the most popular centrality metrics. The spectral radius of the adjacency matrix powerfully characterizes the dynamic processes on networks, such as virus spread and synchronization. The sum of components of the principal eigenvector, which is also called the fundamental weight w1, is argued to be as important as the Eigen values of the graph matrix. Here we theoretically prove two new types of lower bound wL and wD for the fundamental weight w1 in any network. The lower bound wL is related to the clique number (the size of the largest clique) of the network. The lower bound wL is sharper than the wD whereas the computational complexity of wD is lower. We compare the sharper lower bound wL with w1 in different networks. The effect of the network structure on the relative deviation of wL is studied. Based on wL, another new lower bound for w1 is proposed for a special type of networks.\",\n \"Title: Complete game-theoretic characterization of SIS epidemics protection strategies\\nYear: 2014\\n\\nAbstract: Defining an optimal protection strategy against viruses, spam propagation or any other kind of contamination process is an important feature for designing new networks and architectures. In this work, we consider decentralized optimal protection strategies when a virus is propagating over a network through a Susceptible Infected Susceptible (SIS) epidemic process. We assume that each node in the network can fully protect itself from infection at a constant cost, or the node can use recovery software, once it is infected. We model our system using a game theoretic framework. Based on this model, we find pure and mixed equilibria, and evaluate the performance of the equilibria by finding the Price of Anarchy (PoA) in several network topologies. Finally, we give numerical illustrations of our results.\",\n \"Title: Nonconsensus opinion model on directed networks.\\nYear: 2014\\n\\nAbstract: Dynamic social opinion models have been widely studied on undirected networks, and most of them are based on spin interaction models that produce a consensus. In reality, however, many networks such as Twitter and the World Wide Web are directed and are composed of both unidirectional and bidirectional links. Moreover, from choosing a coffee brand to deciding who to vote for in an election, two or more competing opinions often coexist. In response to this ubiquity of directed networks and the coexistence of two or more opinions in decision-making situations, we study a nonconsensus opinion model introduced by Shao et al. [Phys. Rev. Lett. 103, 018701 (2009)] on directed networks. We define directionality xi as the percentage of unidirectional links in a network, and we use the linear correlation coefficient rho between the in-degree and out-degree of a node to quantify the relation between the in-degree and out-degree. We introduce two degree-preserving rewiring approaches which allow us to construct directed networks that can have a broad range of possible combinations of directionality xi and linear correlation coefficient rho and to study how xi and rho impact opinion competitions. We find that, as the directionality xi or the in-degree and out-degree correlation rho increases, the majority opinion becomes more dominant and the minority opinion's ability to survive is lowered.\",\n \"Title: Design of a compact silicon-based integrated passive band-pass filter with two tunable finite transmission zeros.\\nYear: 2016\\n\\nAbstract: The thin film integrated passive device (IPD) has caused intensively attention due to its high integrated level and application in System in Package (SiP). Meanwhile, the IPD filter has shown great application value in modern wireless communication system. In this paper, the design method of a compact silicon-based lumped IPD band-pass filter with two tunable finite transmission zeros is shown. The passband of the designed filter is 2.4–2.5GHz. Specific schematic is used to introduce two independently tunable transmission zeros. The specific schematic contains mutual inductor structure, which is realized by making two planar spiral inductors to partially overlap with each other and is helpful to decrease the size of the filter. The mutual inductance is adjustable by changing the overlap degree. To design and optimize the layout of the filter more efficiently, the schematic-layout mutual feedback method is presented. Finally, the measurement results verify the validity of the whole design process and method. The size of the designed second-order band-pass filter is 0.85mm×0.85mm×0.35mm. To the best of our knowledge, the band-pass-filter developed in this paper has the smallest size while achieving equivalent electrical performance.\",\n \"Title: Total Coloring of Planar Graphs Without Chordal Short Cycles.\\nYear: 2015\\n\\nAbstract: Let $$G$$G be a planar graph with $$\\\\\\\\varDelta \\\\\\\\ge 8$$Δ¿8 and $$v$$v be a vertex of $$G$$G. It is proved that $$\\\\\\\\chi ''(G)=\\\\\\\\varDelta +1$$¿¿¿(G)=Δ+1 if $$v$$v is not incident with chordal $$5$$5- or $$6$$6-cycles by Shen et al. (Appl Math Lett 22:1369---1373, 2009), or $$v$$v is not incident with $$2$$2-chordal $$5$$5-cycle by Chang et al. (Theor Comput Sci 476:16---23, 2013). In this paper we generalize these results and prove that if $$v$$v is not incident with chordal $$6$$6-cycle, or chordal $$7$$7-cycle, or $$2$$2-chordal $$5$$5-cycle, then $$\\\\\\\\chi ''(G)=\\\\\\\\varDelta +1$$¿¿¿(G)=Δ+1.\",\n \"Title: MaPU: A novel mathematical computing architecture\\nYear: 2016\\n\\nAbstract: As the feature size of the semiconductor process is scaling down to 10nm and below, it is possible to assemble systems with high performance processors that can theoretically provide computational power of up to tens of PLOPS. However, the power consumption of these systems is also rocketing up to tens of millions watts, and the actual performance is only around 60% of the theoretical performance. Today, power efficiency and sustained performance have become the main foci of processor designers. Traditional computing architecture such as superscalar and GPGPU are proven to be power inefficient, and there is a big gap between the actual and peak performance. In this paper, we present the MaPU architecture, a novel architecture which is suitable for data-intensive computing with great power efficiency and sustained computation throughput. To achieve this goal, MaPU attempts to optimize the application from a system perspective, including the hardware, algorithm and corresponding program model. It uses an innovative multi-granularity parallel memory system with intrinsic shuffle ability, cascading pipelines with wide SIMD data paths and a state-machine-based program model. When executing typical signal processing algorithms, a single MaPU core implemented with a 40nm process exhibits a sustained performance of 134 GLOPS while consuming only 2.8 W in power, which increases the actual power efficiency by an order of magnitude comparable with the traditional CPU and GPGPU.\",\n \"Title: Activation Games In Online Dating Platforms\\nYear: 2015\\n\\nAbstract: In this paper we describe a model for the activation level of users in online dating platforms (ODPs). Such popular systems are conceived in order to match individuals from two groups of potential mates. The business of such platforms pivots around the customers' expectancy to get in contact with their future dates: upon the payment of a fee to the platform owner, ODPs provide specific tools to improve reach and visibilityHowever, ODPs require a critical number of active users in order to sustain their operations (and their business). Customers of the platform trade off on the price for being more visible and attract mates' contacts. A user becomes inactive if he or she is not contacted by others for some time: being contacted by potential mates acts as an activation signal.The aim of our analysis is to propose a game theoretical framework to capture such a complex activation problem in strategic form. We unveil the structure of Nash equilibria and we further derive a Stackelberg formulation. The latter is a hierarchical game where the platform owner aims at maximizing profits while preserving the ODP activity level above a critical epidemic threshold.\",\n \"Title: Non-rigid 3D Model Retrieval Based on Weighted Bags-of-Phrases and LDA.\\nYear: 2016\\n\\nAbstract: This paper presents an improved BOP model, called weighted bags-of-phrases (W-BOP), and its application in non-rigid 3D shape retrieval. The W-BOP model uses the Gaussian weighted function and the 3D points' ring based neighborhood to construct the spatial arrangement model of visual words. Compared with BOP model, it can describe the 3D model more detailedly. Compared with the SS-BOW model and the BOFG model, it needn't perform feature detection step and has higher computation efficiency. To further improve the retrieval performance, the LDA algorithm is used to reduce the dimension of the W-BOP descriptor. Extensive experiments have validated the effectiveness of the designed W-BOP model and LDA based non-rigid 3D model retrieval method.\",\n \"Title: Applying the locality principle to improve the shortest path algorithm.\\nYear: 2017\\n\\nAbstract: When shortest path algorithm be used in real time road network monitoring system, the time complexity of the algorithm is becoming the bottleneck of the system. the paper proposes a solution to improve the shortest path algorithm. By making use of the k-ary-reverse tree and locality principle, this improved algorithm keeps the data operation, in priority queue, be localized in a small range. So the time complexity of this improved shortest path algorithm is optimized from O(nlog2n) to $$O(k\\\\\\\\times n)$$O(k×n); the parameter k denotes the average count of adjacency edges to each vertex, and n denotes the total count of vertexes in the network. Note that, the parameter k mainly depends on the edges density in the network. Generally speaking, the mean value of parameter k is not more than 4 in the road network, i.e. the time complexity of the improved algorithm is optimized from O(nlog2n) to O(n) in the road network. So the paper makes a conclusion that there is a linear correlation between the time complexity and the vertexes count n in the improved algorithm.\",\n \"Title: Total coloring of planar graphs without adjacent chordal 6-cycles.\\nYear: 2017\\n\\nAbstract: A total coloring of a graph G is a coloring such that no two adjacent or incident elements receive the same color. In this field there is a famous conjecture, named Total Coloring Conjecture, saying that the the total chromatic number of each graph G is at most $$\\\\\\\\Delta +2$$Δ+2. Let G be a planar graph with maximum degree $$\\\\\\\\Delta \\\\\\\\ge 7$$Δź7 and without adjacent chordal 6-cycles, that is, two cycles of length 6 with chord do not share common edges. In this paper, it is proved that the total chromatic number of G is $$\\\\\\\\Delta +1$$Δ+1, which partly confirmed Total Coloring Conjecture.\",\n \"Title: Modeling of Information Diffusion on Social Networks with Applications to WeChat.\\nYear: 2017\\n\\nAbstract: Traces of user activities recorded in online social networks open new possibilities to systematically understand the information diffusion process on social networks. From the online social network WeChat, we collected a large number of information cascade trees, each of which tells the spreading trajectory of a message/information such as which user creates the information and which users view or forward the information shared by which neighbours. In this work, we propose two heterogeneous non-linear models, one for the topologies of the information cascade trees and the other for the stochastic process of information diffusion on a social network. Both models are validated by the WeChat data in reproducing and explaining key features of cascade trees.\",\n \"Title: The Accuracy of Mean-Field Approximation for Susceptible-Infected-Susceptible Epidemic Spreading with Heterogeneous Infection Rates.\\nYear: 2016\\n\\nAbstract: The epidemic spreading over a network has been studied for years by applying the mean-field approach in both homogeneous case, where each node may get infected by an infected neighbor with the same rate, and heterogeneous case, where the infection rates between different pairs of nodes are also different. Researchers have discussed whether the mean-field approaches could accurately describe the epidemic spreading for the homogeneous cases but not for the heterogeneous cases. In this paper, we explore if and under what conditions the mean-field approach could perform well when the infection rates are heterogeneous. In particular, we employ the Susceptible-Infected-Susceptible (SIS) model and compare the average fraction of infected nodes in the metastable state, where the fraction of infected nodes remains stable for a long time, obtained by the continuous-time simulation and the mean-field approximation. We concentrate on an individual-based mean-field approximation called the N-intertwined Mean Field Approximation (NIMFA), which is an advanced approach considered the underlying network topology. Moreover, for the heterogeneity of the infection rates, we consider not only the independent and identically distributed (i.i.d.) infection rate but also the infection rate correlated with the degree of the two end nodes. We conclude that NIMFA is generally more accurate when the prevalence of the epidemic is higher. Given the same effective infection rate, NIMFA is less accurate when the variance of the i.i.d. infection rate or the correlation between the infection rate and the nodal degree leads to a lower prevalence. Moreover, given the same actual prevalence, NIMFA performs better in the cases: 1) when the variance of the i.i.d. infection rates is smaller (while the average is unchanged); 2) when the correlation between the infection rate and the nodal degree is positive. Our work suggests the conditions when the mean-field approach, in particular NIMFA, is more accurate in the approximation of the SIS epidemic with heterogeneous infection rates.\",\n \"Title: Robust 3D keypoint detection method based on double Gaussian weighted dissimilarity measure.\\nYear: 2017\\n\\nAbstract: This paper proposes a novel multiscale 3D keypoint detection method via the double Gaussian weighted dissimilarity measure. At each scale, the shape index value and the double Gaussian weighted dissimilarity measure value of each 3D point are firstly computed. Then the candidate keypoints with local maximum dissimilarity measure values are determined. Finally the final 3D keypoints are detected under our proposed multiscale detection scheme. As the dissimilarity measure used in this paper has better robust descriptive ability and is rotation and translation transformation invariant, the proposed detection method is robust to noise, rotation and translation transformation. Extensive experimental results have shown that using our proposed multiscale detection method, we can detect the keypoints with higher repeatability under different noise levels.\",\n \"Title: Hesitant Picture 2-Tuple Linguistic Aggregation Operators Based on Archimedean T-Norm and T-Conorm and Their Use in Decision-Making.\\nYear: 2018\\n\\nAbstract: An aggregation operator performs the task of fusing multiple sources of information, which plays a pivotal part in realizing a collective opinion in most decision-making activities. Considering the increasing complexity of decision-making situations, it is imperative to extend aggregation operators for fusing uncertain information with the different forms of attribute values. This study focuses on the development of picture fuzzy sets and aims to design a managerial decision-making solving method. Some operational principles of hesitant picture 2-tuple linguistic variables on account of the Archimedean t-norm and t-conorm are initiated, on which two hesitant picture 2-tuple linguistic weighted operators are established by taking various weight forms. Moreover, we explore the aggregation operators' idempotency, boundedness, and monotonicity, as well as analyze some particular forms of these operators. Furthermore, these aggregation operators are employed to design a method of deriving an overall performance from evaluation of experts with hesitant picture 2-tuple linguistic terms. An example of selecting service outsourcing supplier is carried out to show the procedures of decision-making with a detailed comparative analysis.\",\n \"Title: The indirect carbon emission from household consumption in China between 1995-2009 and 2010-2030: A decomposition and prediction analysis.\\nYear: 2019\\n\\nAbstract: •A framework of determining and predicting to assess indirect emissions is developed.•Indirect carbon emissions from household consumption is redefined.•Multivariable regressions is inserted into IO and SDA model to predict indirect emissions.•The population variable has a positive role.\",\n \"Title: A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data.\\nYear: 2019\\n\\nAbstract: In order to achieve more efficient energy consumption, it is crucial that accurate detailed information is given on how power is consumed. Electricity details benefit both market utilities and also power consumers. Non-intrusive load monitoring (NILM), a novel and economic technology, obtains single-appliance power consumption through a single total power meter. This paper, focusing on load disaggregation with low hardware costs, proposed a load disaggregation method for low sampling data from smart meters based on a clustering algorithm and support vector regression optimization. This approach combines the k-median algorithm and dynamic time warping to identify the operating appliance and retrieves single energy consumption from an aggregate smart meter signal via optimized support vector regression (OSVR). Experiments showed that the technique can recognize multiple devices switching on at the same time using low-frequency data and achieve a high load disaggregation performance. The proposed method employs low sampling data acquired by smart meters without installing extra measurement equipment, which lowers hardware cost and is suitable for applications in smart grid environments.\",\n \"Title: Information Diffusion Backbones In Temporal Networks\\nYear: 2018\\n\\nAbstract: Progress has been made in understanding how temporal network features affect the percentage of nodes reached by an information diffusion process. In this work, we explore further: which node pairs are likely to contribute to the actual diffusion of information, i.e., appear in a diffusion trajectory? How is this likelihood related to the local temporal connection features of the node pair? Such deep understanding of the role of node pairs is crucial to tackle challenging optimization problems such as which kind of node pairs or temporal contacts should be stimulated in order to maximize the prevalence of information spreading. We start by using Susceptible-Infected (SI) model, in which an infected (information possessing) node could spread the information to a susceptible node with a given infection probability beta whenever a contact happens between the two nodes, as the information diffusion process. We consider a large number of real-world temporal networks. First, we propose the construction of an information diffusion backbone G(B)(beta) for a S/spreading process with an infection probability beta on a temporal network. The backbone is a weighted network where the weight of each node pair indicates how likely the node pair appears in a diffusion trajectory starting from an arbitrary node. Second, we investigate the relation between the backbones with different infection probabilities on a temporal network. We find that the backbone topology obtained for low and high infection probabilities approach the backbone G(B)(beta -> 0) and G(B)(beta=1), respectively. The backbone G(B)(beta -> 0) equals the integrated weighted network, where the weight of a node pair counts the total number of contacts in between. Finally, we explore node pairs with what local connection features tend to appear in G(B)(beta=1), thus actually contribute to the global information diffusion. We discover that a local connection feature among many other features we proposed, could well identify the (high-weight) links in G(B)(beta=1). This local feature encodes the time that each contact occurs, pointing out the importance of temporal features in determining the role of node pairs in a dynamic process.\",\n \"Title: Optimal channel assignment with list-edge coloring\\nYear: 2019\\n\\nAbstract: Motivated from optimal channel assignment in optical networks, the list edge (and total) coloring are studied in this paper. In a special case of planar graphs, we determined the list edge (and total) coloring number.\",\n \"Title: Maximizing profit of multiple adoptions in social networks with a martingale approach\\nYear: 2019\\n\\nAbstract: Information propagation plays an important role in social network, which helps shaping consumer’s purchasing decisions. Most of existing works focus on maximizing the influence of one product. But in our reality life, the majority of the companies produce various products for meeting customer needs. So it is important to learn about how to distribute the limited budget to maximize the companies profits. In this paper, we use the martingale technique to handle the Profit Maximization with Multiple Adoptions (\\\\(PM^{2}A\\\\)) problem, which aims to identify a seed set for each product with overall activation cost at most B such that the expected total profit is maximized. We design a \\\\(PM^{2}AM\\\\) algorithm which returns a \\\\((\\\\frac{1}{2}(1-\\\\frac{1}{e^{2}})-\\\\varepsilon )\\\\)-approximate solution and runs in \\\\(O\\\\left( (k^{*}+\\\\ell )(m+n)nqp_{max}\\\\ln nq/ (\\\\varepsilon ^{2}\\\\cdot p_{min})\\\\right) \\\\) expected time.\",\n \"Title: Social Structure Decomposition With Security Issue.\\nYear: 2019\\n\\nAbstract: Social structure decomposition has been a valuable research topic in the study of social networks, and it still has many unresolved problems. At the same time, an increasing number of people pays attention to the security issue that is worth considering and researching. With security consideration, we study a new social structure decomposition problem that can be formulated as a minimization problem in graph theory as follows: given a social network with formulation as a graph G, partition the vertex set of G into the minimum number of subsets, such that each subset induces an acyclic graph called a forest. This minimum number is called the vertex arboricity, denoted by va(G). It is well known that the vertex arboricity va (G) <= 3 for each planar graph G in the graph theory. In this paper, we prove that for the planar graph G, if no 3-cycle intersects a 4-cycle or no 3-cycle intersects a 5-cycle, then va(G) <= 2.\",\n \"Title: An Adversarial Transfer Network for Knowledge Representation Learning\\nYear: 2021\\n\\nAbstract: ABSTRACT Knowledge representation learning has received a lot of attention in the past few years. The success of existing methods heavily relies on the quality of knowledge graphs. The entities with few triplets tend to be learned with less expressive power. Fortunately, there are many knowledge graphs constructed from various sources, the representations of which could contain much information. We propose an adversarial embedding transfer network ATransN, which transfers knowledge from one or more teacher knowledge graphs to a target one through an aligned entity set without explicit data leakage. Specifically, we add soft constraints on aligned entity pairs and neighbours to the existing knowledge representation learning methods. To handle the problem of possible distribution differences between teacher and target knowledge graphs, we introduce an adversarial adaption module. The discriminator of this module evaluates the degree of consistency between the embeddings of an aligned entity pair. The consistency score is then used as the weights of soft constraints. It is not necessary to acquire the relations and triplets in teacher knowledge graphs because we only utilize the entity representations. Knowledge graph completion results show that ATransN achieves better performance against baselines without transfer on three datasets, CN3l, WK3l, and DWY100k. The ablation study demonstrates that ATransN can bring steady and consistent improvement in different settings. The extension of combining other knowledge graph embedding algorithms and the extension with three teacher graphs display the promising generalization of the adversarial transfer network.\",\n \"Title: Quantifying the hierarchy of public transport networks\\nYear: 2021\\n\\nAbstract: Public transport networks constitute critical infrastructure in urban systems. Public transport networks are characterised by their hierarchical structure, yet methods to quantify their underlying hierarchy are lacking. We propose a metric for quantifying the hierarchy in public transport networks which incorporates topological as well as passenger flow information. Our proposed metric consists of...\",\n \"Title: Topological-temporal properties of evolving networks\\nYear: 2022\\n\\nAbstract: Many real-world complex systems including human interactions can be represented by temporal (or evolving) networks, where links activate or deactivate over time. Characterizing temporal networks is crucial to compare different real-world networks and to detect their common patterns or differences. A systematic method that can characterize simultaneously the temporal and topological relations of the time-specific interactions (also called contacts or events) of a temporal network, is still missing. In this article, we propose a method to characterize to what extent contacts that happen close in time occur also close in topology. Specifically, we study the interrelation between temporal and topological properties of the contacts from three perspectives: (1) the correlation (among the elements) of the activity time series which records the total number of contacts in a network that happen at each time step; (2) the interplay between the topological distance and time difference of two arbitrary contacts; (3) the temporal correlation of contacts within the local neighbourhood centred at each link (so-called ego-network) to explore whether such contacts that happen close in topology are also close in time. By applying our method to 13 real-world temporal networks, we found that temporal-topological correlation of contacts is more evident in virtual contact networks than in physical contact networks. This could be due to the lower cost and easier access of online communications than physical interactions, allowing and possibly facilitating social contagion, that is, interactions of one individual may influence the activity of its neighbours. We also identify different patterns between virtual and physical networks and among physical contact networks at, for example, school and workplace, in the formation of correlation in local neighbourhoods. Patterns and differences detected via our method may further inspire the development of more realistic temporal network models, that could reproduce jointly temporal and topological properties of contacts.\",\n \"Title: A Traceability System of Livestock Products Based on Blockchain and the Internet of Things\\nYear: 2021\\n\\nAbstract: The safety of animal products has always been the focus of people's attention, especially the frequent occurrence of food safety problems in recent years, which has caused a series of bad social impact, and the traceability system of the whole industry chain is a solution to the food safety problem. Based on the analysis of the problems existing in the application of traditional traceability syste...\",\n \"Title: Mitigate SIR epidemic spreading via contact blocking in temporal networks\\nYear: 2022\\n\\nAbstract: Progress has been made in how to suppress epidemic spreading on temporal networks via blocking all contacts of targeted nodes or node pairs. In this work, we develop contact blocking strategies that remove a fraction of contacts from a temporal (time evolving) human contact network to mitigate the spread of a Susceptible-Infected-Recovered epidemic. We define the probability that a contact c(i, j, t) is removed as a function of a given centrality metric of the corresponding link l(i, j) in the aggregated network and the time t of the contact. The aggregated network captures the number of contacts between each node pair. A set of 12 link centrality metrics have been proposed and each centrality metric leads to a unique contact removal strategy. These strategies together with a baseline strategy (random removal) are evaluated in empirical contact networks via the average prevalence, the peak prevalence and the time to reach the peak prevalence. We find that the epidemic spreading can be mitigated the best when contacts between node pairs that have fewer contacts and early contacts are more likely to be removed. A strategy tends to perform better when the average number contacts removed from each node pair varies less. The aggregated pruned network resulted from the best contact removal strategy tends to have a large largest eigenvalue, a large modularity and probably a small largest connected component size.\",\n \"Title: A 1-Tb 4b/Cell 4-Plane 162-Layer 3D Flash Memory With a 2.4-Gb/s I/O Speed Interface\\nYear: 2022\\n\\nAbstract: The presented 1Tb 4b/cell 162 WL layer 3D Flash memory achieves an areal density of 15 Gb/mm2 which is 8.7% higher than prior work [1]. High-performance is the key enabler for 4b/cell NAND Flash to enter mainstream systems. This work delivers a 60MB/s programming throughput and a 65μs tR with an 8kB central WL stair architecture and contact-through-WL (CTW) region. 2.4Gb/s I/...\",\n \"Title: Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning.\\nYear: 2022\\n\\nAbstract: Relational graph neural networks have garnered particular attention to encode graph context in knowledge graphs (KGs). Although they achieved competitive performance on small KGs, how to efficiently and effectively utilize graph context for large KGs remains an open problem. To this end, we propose the Relation-based Embedding Propagation (REP) method. It is a post-processing technique to adapt pre-trained KG embeddings with graph context. As relations in KGs are directional, we model the incoming head context and the outgoing tail context separately. Accordingly, we design relational context functions with no external parameters. Besides, we use averaging to aggregate context information, making REP more computation-efficient. We theoretically prove that such designs can avoid information distortion during propagation. Extensive experiments also demonstrate that REP has significant scalability while improving or maintaining prediction quality. Notably, it averagely brings about 10% relative improvement to triplet-based embedding methods on OGBL-WikiKG2 and takes 5%-83% time to achieve comparable results as the state-of-the-art GC-OTE.\",\n \"Title: Influence of clustering coefficient on network embedding in link prediction\\nYear: 2022\\n\\nAbstract: Multiple network embedding algorithms have been proposed to perform the prediction of missing or future links in complex networks. However, we lack the understanding of how network topology affects their performance, or which algorithms are more likely to perform better given the topological properties of the network. In this paper, we investigate how the clustering coefficient of a network, i.e., the probability that the neighbours of a node are also connected, affects network embedding algorithms’ performance in link prediction, in terms of the AUC (area under the ROC curve). We evaluate classic embedding algorithms, i.e., Matrix Factorisation, Laplacian Eigenmaps and node2vec, in both synthetic networks and (rewired) real-world networks with variable clustering coefficient. Specifically, a rewiring algorithm is applied to each real-world network to change the clustering coefficient while keeping key network properties. We find that a higher clustering coefficient tends to lead to a higher AUC in link prediction, except for Matrix Factorisation, which is not sensitive to the change of clustering coefficient. To understand such influence of the clustering coefficient, we (1) explore the relation between the link rating (probability that a node pair is the missing link) derived from the aforementioned algorithms and the number of common neighbours of the node pair, and (2) evaluate these embedding algorithms’ ability to reconstruct the original training (sub)network. All the network embedding algorithms that we tested tend to assign higher likelihood of connection to node pairs that share an intermediate or high number of common neighbours, independently of the clustering coefficient of the training network. Then, the predicted networks will have more triangles, thus a higher clustering coefficient. As the clustering coefficient increases, all the algorithms but Matrix Factorisation could also better reconstruct the training network. These two observations may partially explain why increasing the clustering coefficient improves the prediction performance.\",\n \"Title: GROWN+UP: A ''Graph Representation Of a Webpage\\\" Network Utilizing Pre-training\\nYear: 2022\\n\\nAbstract: ABSTRACTLarge pre-trained neural networks are ubiquitous and critical to the success of many downstream tasks in natural language processing and computer vision. However, within the field of web information retrieval, there is a stark contrast in the lack of similarly flexible and powerful pre-trained models that can properly parse webpages. Consequently, we believe that common machine learning tasks like content extraction and information mining from webpages have low-hanging gains that yet remain untapped. We aim to close the gap by introducing an agnostic deep graph neural network feature extractor that can ingest webpage structures, pre-train self-supervised on massive unlabeled data, and fine-tune to arbitrary tasks on webpages effectually. Finally, we show that our pre-trained model achieves state-of-the-art results using multiple datasets on two very different benchmarks: webpage boilerplate removal and genre classification, thus lending support to its potential application in diverse downstream tasks.\",\n \"Title: A Lip Reading Method Based on 3D Convolutional Vision Transformer\\nYear: 2022\\n\\nAbstract: Lip reading has received increasing attention in recent years. It judges the content of speech based on the movement of the speaker's lips. The rapid development of deep learning has promoted progress in lip reading. However, due to lip reading needs to process the information of continuous video frames, it is necessary to consider the correlation information between adjacent images and the correlation between long-distance images. Moreover, lip reading recognition mainly focuses on the subtle changes of lips and their surrounding environment, and it is necessary to extract the subtle features of small-size images. Therefore, the performance of machine lip reading is generally not high, and the research progress is slow. In order to improve the performance of machine lip reading, we propose a lip reading method based on 3D convolutional vision transformer (3DCvT), which combines vision transformer and 3D convolution to extract the spatio-temporal feature of continuous images, and take full advantage of the properties of convolutions and transformers to extract local and global features from continuous images effectively. The extracted features are then sent to a Bidirectional Gated Recurrent Unit (BiGRU) for sequence modeling. We proved the effectiveness of our method on large-scale lip reading datasets LRW and LRW-1000 and achieved state-of-the-art performance.\",\n \"Title: Intelligent Grazing UAV Based on Airborne Depth Reasoning\\nYear: 2022\\n\\nAbstract: The existing precision grazing technology helps to improve the utilization rate of livestock to pasture, but it is still at the level of \\\"collectivization\\\" and cannot provide more accurate grazing management and control. (1) Background: In recent years, with the rapid development of agent-related technologies such as deep learning, visual navigation and tracking, more and more lightweight edge computing cell target detection algorithms have been proposed. (2) Methods: In this study, the improved YOLOv5 detector combined with the extended dataset realized the accurate identification and location of domestic cattle; with the help of the kernel correlation filter (KCF) automatic tracking framework, the long-term cyclic convolution network (LRCN) was used to analyze the texture characteristics of animal fur and effectively distinguish the individual cattle. (3) Results: The intelligent UAV equipped with an AGX Xavier high-performance computing unit ran the above algorithm through edge computing and effectively realized the individual identification and positioning of cattle during the actual flight. (4) Conclusion: The UAV platform based on airborne depth reasoning is expected to help the development of smart ecological animal husbandry and provide better precision services for herdsmen.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":64,"string":"64"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13276,"string":"13,276"}}},{"rowIdx":13277,"cells":{"question_id":{"kind":"string","value":"L szl Lov sz"},"question":{"kind":"string","value":"Generate an abstract for the title \"Global Information from Local Observation\" using the following items: random process, graph, global properties, genus, polynomial time."},"answer":{"kind":"string","value":"We observe a certain random process on a graph \"locally\", i.e., in the neighborhood of a node, and would like to derive information about \"global\" properties of the graph. For example, what can we know about a graph based on observing the returns of a random walk to a given node?Our main result concerns a graph embedded in an orientable surface with genus g, and a process, consisting of random excitations of edges and random balancing around nodes and faces. It is shown how to obtain the genus of the surface in polynomial time from local observations of the process restricted to a connected subgraph whose size is (essentially)O(g2)."},"correct_answer":{"kind":"string","value":"We observe a certain random process on a graph \"locally\", i.e., in the neighborhood of a node, and would like to derive information about \"global\" properties of the graph. For example, what can we know about a graph based on observing the returns of a random walk to a given node?Our main result concerns a graph embedded in an orientable surface with genus g, and a process, consisting of random excitations of edges and random balancing around nodes and faces. It is shown how to obtain the genus of the surface in polynomial time from local observations of the process restricted to a connected subgraph whose size is (essentially)O(g2)."},"documents":{"kind":"list like","value":["Title: Discrete Quantitative Nodal Theorem\nYear: 2021\n\nAbstract: We prove a theorem that can be thought of as a common generalization of the Discrete Nodal Theorem and (one direction of) Cheeger's Inequality for graphs. A special case of this result will assert that if the second and third eigenvalues of the Laplacian are at least epsilon apart, then the subgraphs induced by the positive and negative supports of the eigenvector belonging to lambda(2) are not only connected, but edge-expanders (in a weighted sense, with expansion depending on epsilon).","Title: Polymatroid greedoids\nYear: 1985\n\nAbstract: This paper discusses polymatroid greedoids, a superclass of them, called local poset greedoids, and their relations to other subclasses of greedoids. Polymatroid greedoids combine in a certain sense the different relaxation concepts of matroids as polymatroids and as greedoids. Some characterization results are given especially for local poset greedoids via excluded minors. General construction principles for intersection of matroids and polymatroid greedoids with shelling structures are given. Furthermore, relations among many subclasses of greedoids which are known so far, are demonstrated.","Title: Tibor Gallai\nYear: 1982\n\nAbstract: Without Abstract","Title: Generalized quasirandom graphs\nYear: 2008\n\nAbstract: We prove that if a sequence of graphs has (asymptotically) the same distribution of small subgraphs as a generalized random graph modeled on a fixed weighted graph H, then these graphs have a structure that is asymptotically the same as the structure of H. Furthermore, it suffices to require this for a finite number of subgraphs, whose number and size is bounded by a function of |V(H)|.","Title: Finitely forcible graphons\nYear: 2011\n\nAbstract: We investigate families of graphs and graphons (graph limits) that are determined by a finite number of prescribed subgraph densities. Our main focus is the case when the family contains only one element, i.e., a unique structure is forced by finitely many subgraph densities. Generalizing results of Turan, Erdos-Simonovits and Chung-Graham-Wilson, we construct numerous finitely forcible graphons. Most of these fall into two categories: one type has an algebraic structure and the other type has an iterated (fractal-like) structure. We also give some necessary conditions for forcibility, which imply that finitely forcible graphons are ''rare'', and exhibit simple and explicit non-forcible graphons.","Title: Vertex Packing Algorithms\nYear: 1985\n\nAbstract: Without Abstract","Title: Limits of randomly grown graph sequences\nYear: 2011\n\nAbstract: Motivated in part by various sequences of graphs growing under random rules (such as Internet models), Borgs, Chayes, Lovasz, Sos, Szegedy and Vesztergombi introduced convergent sequences of dense graphs and their limits. In this paper we use this framework to study one of the motivating classes of examples, namely randomly growing graphs. We prove the (almost sure) convergence of several such randomly growing graph sequences, and determine their limit. The analysis is not always straightforward: in some cases the cut-distance from a limit object can be directly estimated, while in other cases densities of subgraphs can be shown to converge.","Title: A note on the last new vertex visited by a random walk\nYear: 1993\n\nAbstract: A \"cover tour\" of a connected graph G from a vertex x is a random walk that begins at x, moves at each step with equal probability to any neighbor of its current vertex, and ends when it has hit every vertex of G. The cycle C(n) is well known to have the curious property that a cover tour from any vertex is equally likely to end at any other vertex; the complete graph K(n) shares this property, trivially, by symmetry. Ronald L. Graham has asked whether there are any other graphs with this property; we show that there are not. (C) 1993 John Wiley & Sons, Inc.","Title: Semi-matchings for bipartite graphs and load balancing\nYear: 2006\n\nAbstract: We consider the problem of fairly matching the left-hand vertices of a bipartite graph to the right-hand vertices. We refer to this problem as the optimal semi-matching problem; it is a relaxation of the known bipartite matching problem. We present a way to evaluate the quality of a given semi-matching and show that, under this measure, an optimal semi-matching balances the load on the right-hand vertices with respect to any Lp-norm. In particular, when modeling a job assignment system, an optimal semi-matching achieves the minimal makespan and the minimal flow time for the system.The problem of finding optimal semi-matchings is a special case of certain scheduling problems for which known solutions exist. However, these known solutions are based on general network optimization algorithms, and are not the most efficient way to solve the optimal semi-matching problem. To compute optimal semi-matchings efficiently, we present and analyze two new algorithms. The first algorithm generalizes the Hungarian method for computing maximum bipartite matchings, while the second, more efficient algorithm is based on a new notion of cost-reducing paths. Our experimental results demonstrate that the second algorithm is vastly superior to using known network optimization algorithms to solve the optimal semi-matching problem. Furthermore, this same algorithm can also be used to find maximum bipartite matchings and is shown to be roughly as efficient as the best known algorithms for this goal.","Title: Dual graph homomorphism functions\nYear: 2010\n\nAbstract: For any two graphs F and G, let hom(F,G) denote the number of homomorphisms F→G, that is, adjacency preserving maps V(F)→V(G) (graphs may have loops but no multiple edges). We characterize graph parameters f for which there exists a graph F such that f(G)=hom(F,G) for each graph G.","Title: Structural properties of greedoids\nYear: 1983\n\nAbstract: Greedoids were introduced by the authors as generalizations of matroids providing a framework for the greedy algorithm. In\n this paper they are studied from a structural aspect. Definitions of basic matroid-theoretical concepts such as rank and closure\n can be generalized to greedoids, even though they loose some of their fundamental properties. The rank function of a greedoid\n is only “locally” submodular. The closure operator is not monotone but possesses a (relaxed) Steinitz—McLane exchange property.\n We define two classes of subsets, called rank-feasible and closure-feasible, so that the rank and closure behave nicely for\n them. In particular, restricted to rank-feasible sets the rank function is submodular. Finally we show that Rado’s theorem\n on independent transversals of subsets of matroids remains valid for feasible transversals of certain sets of greedoids.","Title: Semidefinite Functions on Categories.\nYear: 2009\n\nAbstract: Freedman, Lovasz and Schrijver characterized graph parameters that can be represented as the (weighted) number of homomorphisms into a fixed graph. Several extensions of this result have been proved. We use the framework of categories to prove a general theorem of this kind. Similarly as previous resuts, the characterization uses certain infinite matrices, called connection matrices, which are required to be positive semidefinite.","Title: Ear-decompositions of matching covered graphs\nYear: 1983\n\nAbstract: We call a graphmatching-covered if every line belongs to a perfect matching. We study the technique of “ear-decompositions” of such graphs. We prove that\n a non-bipartite matching-covered graph containsK\n 4 orK\n 2⊕K\n 3 (the triangular prism). Using this result, we give new characterizations of those graphs whose matching and covering numbers\n are equal. We apply these results to the theory of τ-critical graphs.","Title: Random walks and the regeneration time\nYear: 1998\n\nAbstract: Consider a graph G and a random walk on it. We want tostop the random walk at certain times (using an optimal stoppingrule) to obtain independent samples from a given distribution ρon the nodes. For an undirected graph, the expected time betweenconsecutive samples is maximized by a distribution equally dividedbetween two nodes, and so the worst expected time is 1-4 of themaximum commute time between two nodes. In the directed case, theexpected time for this distribution is within a factor of 2 of themaximum. © 1998 John Wiley & Sons, Inc. J. Graph Theory29: 5762, 1998","Title: A localization inequality for set functions\nYear: 2006\n\nAbstract: We prove the following theorem, which is an analog for discrete set functions of a geometric result of Lovász and Simonovits. Given two real-valued set functions f1, f2 defined on the subsets of a finite set S satisfying, ΣX⊆S fi(X) ≥0 for i ∈ {1, 2}, there exists a positive multiplicative set function µ over S and two subsets A, B ⊆ S such that for i ∈ {1,2} µ(A)fi(A)+µ(B)fi(B)+µ(A∪B)fi(A∪ B) + µ(A ∩ B)fi (A∩B) ≥0. The Ahlswede-Daykin four function theorem can be deduced easily from this.","Title: Isoperimetric Problems for Convex Bodies and a Localization Lemama\nYear: 1995\n\nAbstract: . We study the smallest number /(K) such that a given convex body K in IRncan be cut into two parts K 1 and K 2 by a surface with an (n \\Gamma 1)-dimensional measure/(K)vol(K 1 ) \\Delta vol(K 2 )=vol(K). Let M 1 (K) be the average distance of a point of K fromits center of gravity. We prove for the "isoperimetric coefficient" that/(K) ln 2M 1 (K);and give other upper and lower bounds. We conjecture that our upper bound is bestpossible up to a constant.Our main tool is a...","Title: Rubber bands, convex embeddings and graph connectivity\nYear: 1988\n\nAbstract: We give various characterizations ofk-vertex connected graphs by geometric, algebraic, and “physical” properties. As an example, a graphG isk-connected if and only if, specifying anyk vertices ofG, the vertices ofG can be represented by points of ℝk−1 so that nok are on a hyper-plane and each vertex is in the convex hull of its neighbors, except for thek specified vertices. The proof of this theorem appeals to physics. The embedding is found by letting the edges of the graph behave like ideal springs and letting its vertices settle in equilibrium.","Title: Covering minima and lattice point free convex bodies\nYear: 1986\n\nAbstract: Suppose K is a convex set of nonzero volume in Euclidean n-space R\nn and it is symmetric about the origin (i.e., if x belongs to K, so does - x). For any real number t, let tK={tx : xK}. The infimum over all positive real numbers t such that if a copy of tK is placed centered at every integer point, all of R\nn is covered, is called the covering radius of K (with respect to the lattice Z\nn). The covering radius and related quantities have been studied extensively in Geometry of Numbers. In this paper, we define and study the covering minima of a convex body which is not necessarily symmetric about the origin; the covering radius will be a special case of one of of these minima. This extension to general convex bodies has among other things, applications to algorithms for Integer Programming which was our initial motivation. This motivation is explained in some detail later. We use the results of the paper to derive bounds on the width of lattice point free convex bodies and analyze their structure.","Title: Simulated annealing in convex bodies and an O*(n4) volume algorithm\nYear: 2006\n\nAbstract: We present a new algorithm for computing the volume of a convex body in R^n. The main ingredients of the algorithm are (i) a ''morphing'' technique that can be viewed as a variant of simulated annealing and (ii) a new rounding algorithm to put a convex body in near-isotropic position. The complexity is O^*(n^4), improving on the previous best algorithm by a factor of n.","Title: Left and right convergence of graphs with bounded degree\nYear: 2013\n\nAbstract: The theory of convergent graph sequences has been worked out in two extreme cases, dense graphs and bounded degree graphs. One can define convergence in terms of counting homomorphisms from fixed graphs into members of the sequence (left-convergence), or counting homomorphisms into fixed graphs (right-convergence). Under appropriate conditions, these two ways of defining convergence was proved to be equivalent in the dense case by Borgs, Chayes, Lovász, Sós and Vesztergombi. In this paper a similar equivalence is established in the bounded degree case, if the set of graphs in the definition of right-convergence is appropriately restricted. In terms of statistical physics, the implication that left convergence implies right convergence means that for a left-convergent sequence, partition functions of a large class of statistical physics models converge. The proof relies on techniques from statistical physics, like cluster expansion and Dobrushin Uniqueness. © 2012 Wiley Periodicals, Inc. Random Struct. 2012 (Supported by OTKA (67867) and ERC (227701).)","Title: Random-Walks In A Convex Body And An Improved Volume Algorithm\nYear: 1993\n\nAbstract: We give a randomized algorithm using O(n7 log2 n) separation calls to approximate the volume of a convex body with a fixed relative error. The bound is O(n6 log4 n) for centrally symmetric bodies and for polytopes with a polynomial number of facets, and O(n5 log4 n) for centrally symmetric polytopes with a polynomial number of facets. We also give an O(n6 log n) algorithm to sample a point from the uniform distribution over a convex body. Several tools are developed that may be interesting on their own. We extend results of Sinclair-Jerrum [43] and the authors [34] on the mixing rate of Markov chains from finite to arbitrary Markov chains. We also analyze the mixing rate of various random walks on convex bodies, in particular the random walk with steps from the uniform distribution over a unit ball. (C) 1993 John Wiley & Sons, Inc.","Title: Subgraph Densities in Signed Graphons and the Local Simonovits-Sidorenko Conjecture.\nYear: 2011\n\nAbstract: We prove inequalities between the densities of various bipartite subgraphs in signed graphs. One of the main inequalities is that the density of any bipartite graph with girth 2r cannot exceed the density of the 2r-cycle. This study is motivated by the Simonovits-Sidorenko conjecture, which states that the density of a bipartite graph F with m edges in any graph G is at least the m-th power of the edge density of G. Another way of stating this is that the graph G with given edge density minimizing the number of copies of F is, asymptotically, a random graph. We prove that this is true locally, i.e., for graphs G that are \"close\" to a random graph. Both kinds of results are treated in the framework of graphons (2-variable functions serving as limit objects for graph sequences), which in this context was already used by Sidorenko.","Title: Matching structure and the matching lattice\nYear: 1987\n\nAbstract: The matching polyhedron, i.e., the convex hull of (incidence vectors of) perfect matchings of a graph was characterized by Edmonds; this result is the key to a large part of polyhedral combinatorics and is used in many combinatorial algorithms. The linear hull of perfect matchings was characterized by Naddef, and by Edmonds, Lovász, and Pulleyblank. In this paper we describe the lattice generated by these vectors, i.e., the set of all integer linear combinations of perfect matchings. It turns out that the Petersen graph is, in a sense, the only difficult example. Our results also imply a characterization of the linear hull of perfect matchings over fields of characteristic different from 0. The main method is a decomposition theory developed by Kotzig, Lovász, and Plummer, which breaks down every graph into a number of graphs called bricks with very good matching properties. The number of Petersen graphs among these bricks will turn out to be an essential parameter of the matching lattice. Some refinements of the decomposition theory are also given. Among others, we show that the list of bricks obtained during the decomposition procedure is independent of the special choices made during the procedure.","Title: Waiting for a Bat to Fly By (in Polynomial Time)\nYear: 2006\n\nAbstract: We observe returns of a simple random walk on a finite graph to a fixed node, and would like to infer properties of the graph, in particular properties of the spectrum of the transition matrix. This is not possible in general, but at least the set of eigenvalues can be recovered under fairly general conditions, e.g., when the graph has a node-transitive automorphism group. The main result is that by observing polynomially many returns, it is possible to estimate the spectral gap of such a graph up to a constant factor.","Title: Approximating Graphs by Graphs and Functions (Abstract)\nYear: 2007\n\nAbstract: In many areas of science huge networks (graphs) are central objects of study: the internet, the brain, various social networks, VLSI, statistical physics. To study these graphs, new paradigms are needed: What are meaningful questions to ask? When are two huge graphs \"similar\"? How to \"scale down\" these graphs without changing their fundamental structure and algorithmic properties? How to generate random examples with the desired properties? A reasonably complete answer can be given in the case when the huge graphs are dense (in the more difficult case of sparse graphs there are only partial results).","Title: Hit-and-Run from a Corner\nYear: 2006\n\nAbstract: We show that the hit-and-run random walk mixes rapidly starting from any interior point of a convex body. This is the first random walk known to have this property. In contrast, the ball walk can take exponentially many steps from some starting points. The proof extends to sampling an exponential density over a convex body.","Title: Entropy Splitting For Antiblocking Corners And Perfect Graphs\nYear: 1990\n\nAbstract: We characterize pairs of convex sets A, B in the k-dimensional space with the property that every probability distribution (p1, ..., pk) has a repsesentationpt = a(i).b(i), a is-a-member-of, b is-a-member-of B.Minimal pairs with this property are antiblocking pairs of convex corners. This result is closely related to a new entropy concept. The main application is an information theoretic characterization of perfect graphs.","Title: Search Problems in the Decision Tree Model\nYear: 1995\n\nAbstract: The relative power of determinism, randomness, and nondeterminism for search problems in the Boolean decision tree model is studied. It is shown that the gaps between the nondeterministic, the randomized, and the deterministic complexities can be arbitrarily large for search problems. An interesting connection of this model to the complexity of resolution proofs is also mentioned.","Title: Cycles through specified vertices of a graph\nYear: 1981\n\nAbstract: We prove that ifS is a set ofk−1 vertices in ak-connected graphG, then the cycles throughS generate the cycle space ofG. Moreover, whenk≧3, each cycle ofG can be expressed as the sum of an odd number of cycles throughS. On the other hand, ifS is a set ofk vertices, these conclusions do not necessarily hold, and we characterize the exceptional cases. As corollaries, we establish\n the existence of odd and even cycles through specified vertices and deduce the existence of long odd and even cycles in graphs\n of high connectivity.","Title: The ellipsoid method and its consequences in combinatorial optimization.\nYear: 1981\n\nAbstract: L. G. Khachiyan recently published a polynomial algorithm to check feasibility of a system of linear inequalities. The method\n is an adaptation of an algorithm proposed by Shor for non-linear optimization problems. In this paper we show that the method\n also yields interesting results in combinatorial optimization. Thus it yields polynomial algorithms for vertex packing in\n perfect graphs; for the matching and matroid intersection problems; for optimum covering of directed cuts of a digraph; for\n the minimum value of a submodular set function; and for other important combinatorial problems. On the negative side, it yields\n a proof that weighted fractional chromatic number is NP-hard.","Title: Communication complexity and combinatorial lattice theory\nYear: 1993\n\nAbstract: In a recent paper, Hajnal, Maass, and Turán analyzed the communication complexity of graph connectivity. Building on this work, we develop a general framework for the study of a broad class of communication problems which has several interesting special cases including the graph connectivity problem. The approach is based on the combinatorial theory of alignments and lattices.","Title: The rank and size of graphs\nYear: 1996\n\nAbstract: We show that the number of points with pairwise different sets of neighbors in a graph is O(2(r)/2), where r is the rank of the adjacency matrix. We also give an example that achieves this bound. (C) 1996 John Wiley & Sons, Inc.","Title: Logconcave functions: geometry and efficient sampling algorithms\nYear: 2003\n\nAbstract: The class of logconcave functions in Rn is a common generalization of Gaussians and of indicator functions of convex sets. Motivated by the problem of sampling from a logconcave density function, we study their geometry and introduce an analysis technique for \"smoothing\" them out. This leads to efficient sampling algorithms with no assumptions on the local smoothness of the density function. After appropriate preprocessing, both the ball walk (with a Metropolis filter) and a generalization of hit-and-run produce a point from approximately the right distribution in time O*(n4), and in amortized time O*(n3) if many sample points are needed (where the asterisk indicates that dependence on the error parameter and factors of log n are not shown). The bounds are optimal in terms of a \"roundness\" parameter and match the best-known bounds for the special case of the uniform density over a convex set.","Title: The membership problem in jump systems\nYear: 1997\n\nAbstract: A jump system is a set of lattice points satisfying a certain exchange axiom. This notion was introduced by Bouchet and Cunningham [2], as a common generalization of (among others) the sets of bases of a matroid and degree sequences of subgraphs of a graph. We prove, under additional assumptions, a min-max formula for the distance of a lattice point from a jump system. The conditions are met in the examples above, and so our formula contains, as special cases, Tutte's f -factor-theorem and Edmonds' matroid intersection theorem.","Title: (Almost) tight bounds and existence theorems for confluent flows\nYear: 2004\n\nAbstract: A flow is said to be confluent if at any node all the flow leaves along a single edge. Given a directed graph G with k sinks and non-negative demands on all the nodes of G, we consider the problem of determining a confluent flow that routes every node demand to some sink such that the maximum congestion at a sink is minimized. Confluent flows arise in a variety of application areas, most notably in networking; in fact, most flows in the Internet are confluent since Internet routing is destination based.We present near-tight approximation algorithms, hardness results, and existence theorems for confluent flows. The main result of this paper is a polynomial-time algorithm for determining a confluent flow with congestion at most 1 + ln(k) in G, if G admits a splittable flow with congestion at most 1. We complement this result in two directions. First, we present a graph G that admits a splittable flow with congestion at most 1, yet no confluent flow with congestion smaller than Hk, thus establishing tight upper and lower bounds to within an additive constant less than 1. Second, we show that it is NP-hard to approximate the congestion of an optimal confluent flow to within a factor of (lg k)/2, thus resolving the polynomial-time approximability to within a multiplicative constant. We also consider a demand maximization version of the problem. We show that if G admits a splittable flow of congestion at most 1, then a variant of the congestion minimization algorithm yields a confluent flow in G with congestion at most 1 that satisfies 1/3 fraction of total demand.We show that the gap between confluent flows and splittable flows is much smaller, if the underlying graph were k connected. In particular, we prove that k-connected graphs with k sinks admit confluent flows of congestion less than C + dmax, where C is the congestion of the best splittable flow, and dmax is the maximum demand of any node in G. The proof of this existence theorem is non-constructive and relies on topological techniques introduced in [16].","Title: Graph limits and parameter testing\nYear: 2006\n\nAbstract: We define a distance of two graphs that reflects the closeness of both local and global properties. We also define convergence of a sequence of graphs, and show that a graph sequence is convergent if and only if it is Cauchy in this distance. Every convergent graph sequence has a limit in the form of a symmetric measurable function in two variables. We use these notions of distance and graph limits to give a general theory for parameter testing. As examples, we provide short proofs of the testability of MaxCut and the recent result of Alon and Shapira about the testability of hereditary graph properties.","Title: Graph parameters and semigroup functions\nYear: 2008\n\nAbstract: We prove a general theorem on semigroup functions that implies characterizations of graph partition functions in terms of the positive semidefiniteness ('reflection positivity') and rank of certain derived matrices. The theorem can be applied to undirected and directed graphs as well as hypergraphs.","Title: Corrigendum to our paper \"The ellipsoid method and its consequences in combinatorial optimization\nYear: 1984\n\nAbstract: An error in the proof, and in the statement of a generalization, of the result that submodular setfunctions can be minimized\n over the subsets with odd cardinality is corrected.","Title: Hit-and-run mixes fast\nYear: 1999\n\nAbstract: . It is shown that the “hit-and-run” algorithm for sampling from a convex body K (introduced by R.L. Smith) mixes in time O\n *(n\n 2\n R\n 2/r\n 2), where R and r are the radii of the inscribed and circumscribed balls of K. Thus after appropriate preprocessing, hit-and-run produces an approximately uniformly distributed sample point in time O\n *(n\n 3), which matches the best known bound for other sampling algorithms. We show that the bound is best possible in terms of R,r and n.","Title: Contractors and connectors of graph algebras\nYear: 2009\n\nAbstract: We study generalizations of the “contraction-deletion” relation of the Tutte polynomial, and other similar simple operations, to other graph parameters. The question can be set in the framework of graph algebras introduced by Freedman at al [Reflection positivity, rank connectivity, and homomorphisms of graphs, J. Amer. Math. Soc. 20 (2007), 37–51.] Graph algebras are defined by a graph parameter, and they were introduced in order to study and characterize homomorphism functions. We prove that for homomorphism functions, these graph algebras have special elements called “contractors” and “connectors”. This gives a new characterization of homomorphism functions. © 2008 Wiley Periodicals, Inc. J Graph Theory 60: 11–30, 2009","Title: Random walks and an O*(n5) volume algorithm for convex bodies\nYear: 1997\n\nAbstract: Given a high dimensional convex body K µ IRn by a separation oracle, we can approx- imate its volume with relative error \", using O⁄(n5) oracle calls. Our algorithm also brings the body into isotropic position. As all previous randomized volume algorithms, we use \"rounding\" followed by a multi- phase Monte-Carlo (product estimator) technique. Both parts rely on sampling (generating random points in K), which is done by random walk. Our algorithm introduces three new ideas: † the use of the isotropic position (or at least an approximation of it) for rounding, † the separation of global obstructions (diameter) and local obstructions (boundary problems) for fast mixing, and † a stepwise interlacing of rounding and sampling.","Title: Approximating Min-sum Set Cover\nYear: 2002\n\nAbstract: Abstract The input to the min sum set cover problem is a collection of n sets that jointly cover m elements. The output is a linear order on the sets, namely, in every time step from 1 to n exactly one set is chosen. For every element, this induces a rst time step by which it is covered. The objective is to,nd a linear arrangement of the sets that minimizes the sum of these rst time steps over all elements. We show that a greedy algorithm approximates min sum set cover within a ratio of 4. This result was implicit in work of Bar-Noy, Bellare, Halldorsson, Shachnai and Tamir (1998) on chromatic sums, but we present a simpler proof. We also show that for every >0, achieving an approximation ratio of 4 is NP-hard. For the min sum vertex cover version of the problem (which comes up as a heuristic for speeding up solvers of semidenite programs) we show that it can be approximated within a ratio of 2, and is NP-hard to approximate within some constant >1. a preliminary version of this paper appeared in the conference proceedings of APPROX 2002. School of Mathematics and College of Computing, Georgia Institute of Technology, Atlanta, GA 30332- 0160; research in part supported by the NSF grant DMS-0100298. tetali@math.gatech.edu","Title: The Colin de Verdière Number and Sphere Representations of a Graph.\nYear: 1997\n\nAbstract: Colin de Verdiere introduced an interesting linear algebraic invariant mu(G) Of graphs. He proved that mu(G) less than or equal to 2 if anti only if G is outerplanar, and mu(G) less than or equal to 3 if and only if G is planar. We prove that if the complement of a graph G on a nodes is outerplanar, then mu(G) greater than or equal to n - 4, and if it is planar, then mu(G) greater than or equal to n - 5. We give a full characterization of maximal planar graphs whose complements G have mu(G) = n - 5. In the opposite direction we show that if G does not have \"twin\" nodes, then mu(G) greater than or equal to n - 3 implies that the complement of G is outerplanar, and mu(G) greater than or equal to n - 4 implies that the complement of G is planar.Our main tools are a geometric formulation of the invariant, and constructing representations of graphs by spheres, related to the classical result of Koebe about representing planar graphs by touching disks. In particular we show that such sphere representations characterize outerplanar and planar graphs.","Title: Exact Mixing in an Unknown Markov Chain\nYear: 1995\n\nAbstract: We give a simple stopping rule which will stop an unknown, irreducible n-state Markov chain at a state whose probability distribution is exactly the stationary distribution of the chain. The expected stopping time of the rule is bounded by a polynomial in the maximum mean hitting time of the chain. Our stopping rule can be made deterministic unless the chain itself has no random transitions.","Title: Local Versus Global Properties of Metric Spaces\nYear: 2012\n\nAbstract: Motivated by applications in combinatorial optimization, we study the extent to which the global properties of a metric space, and especially its embeddability into $\\ell_1$ with low distortion, are determined by the properties of its small subspaces. We establish both upper and lower bounds on the distortion of embedding locally constrained metrics into various target spaces. Other aspects of locally constrained metrics are studied as well, in particular, how far are those metrics from general metrics.","Title: On Conway's thrackle conjecture\nYear: 1997\n\nAbstract: Abstract. A thrackle is a graph drawn in the plane so that its edges are represented by Jordan arcs and any two distinct arcs either meet at exactly one common vertex or cross at exactly one point interior to both arcs. About 40 years ago, J. H. Conway conjectured that the number of edges of a thrackle cannot exceed the number of its vertices. We show that a thrackle has at most twice as many edges as vertices. Some related problems and generalizations are also considered.","Title: Steinitz representations of polyhedra and the Colin de Verdiére number\nYear: 2001\n\nAbstract: We show that the Steinitz representations of 3-connected planargraphs are correspond, in a well described way, to Colin de Verdierematrices of such graphs.1 IntroductionColin de Verdiere [1] introduced a spectral invariant (G) of a graph G.Roughly speaking, (G) is the multiplicity of the second largest eigenvalueof the adjacency matrix of G, maximized by weighting the edges and nodes(the exact definition will be given in section 3). The matrix that attainsthis maximum will be called a ...","Title: Lecture Notes on Evasiveness of Graph Properties\nYear: 2002\n\nAbstract: This report presents notes from the first eight lectures of the class Many\nModels of Complexity taught by Laszlo Lovasz at Princeton University in the\nfall of 1990. The topic is evasiveness of graph properties: given a graph\nproperty, how many edges of the graph an algorithm must check in the worst case\nbefore it knows whether the property holds.","Title: On the graph of large distances\nYear: 1989\n\nAbstract: For a set of points in the plane, let>>... denote the different distances determined by Consider the graph() whose vertices are the elements of, and two are joined by an edge iff their distance is at least. It is proved that the chromatic number of() is at most 7 if ||≥const. If consists of the vertices of a convex polygon and ||≥const, then the chromatic number of() is at most 3. Both bounds are best possible. If consists of the vertices of a convex polygon then() has a vertex of degree at most 3 − 1. This implies that in this case the chromatic number of() is at most 3. The best bound here is probably 2+1, which is tight for the regular (2+1)-gon.","Title: Limits of dense graph sequences\nYear: 2006\n\nAbstract: We show that if a sequence of dense graphs G\"n has the property that for every fixed graph F, the density of copies of F in G\"n tends to a limit, then there is a natural ''limit object,'' namely a symmetric measurable function W:[0,1]^2-[0,1]. This limit object determines all the limits of subgraph densities. Conversely, every such function arises as a limit object. We also characterize graph parameters that are obtained as limits of subgraph densities by the ''reflection positivity'' property. Along the way we introduce a rather general model of random graphs, which seems to be interesting on its own right.","Title: Non-Deterministic Graph Property Testing.\nYear: 2013\n\nAbstract: A property of finite graphs is called non-deterministically testable if it has a 'certificate' such that once the certificate is specified, its correctness can be verified by random local testing. In this paper we study certificates that consist of one or more unary and/or binary relations on the nodes, in the case of dense graphs. Using the theory of graph limits, we prove that non-deterministically testable properties are also deterministically testable.","Title: Note: Dual graph homomorphism functions\nYear: 2010\n\nAbstract: For any two graphs F and G, let hom(F,G) denote the number of homomorphisms F-G, that is, adjacency preserving maps V(F)-V(G) (graphs may have loops but no multiple edges). We characterize graph parameters f for which there exists a graph F such that f(G)=hom(F,G) for each graph G. The result may be considered as a certain dual of a characterization of graph parameters of the form hom(.,H), given by Freedman, Lovasz and Schrijver [M. Freedman, L. Lovasz, A. Schrijver, Reflection positivity, rank connectivity, and homomorphisms of graphs, J. Amer. Math. Soc. 20 (2007) 37-51]. The conditions amount to the multiplicativity of f and to the positive semidefiniteness of certain matrices N(f,k).","Title: Mathematical Structures Underlying Greedy Algorithms\nYear: 1981\n\nAbstract: Without Abstract","Title: Building scalable and robust peer-to-peer overlay networks for broadcasting using network coding\nYear: 2007\n\nAbstract: We propose a scheme for building peer-to-peer overlay networks for broadcasting using network coding. The scheme addresses\n many practical issues such as scalability, robustness, constraints on bandwidth, and locality of decisions. We analyze the\n system theoretically and prove near optimal bounds on the parameters defining robustness and scalability. As a result we show\n that the effects of failures are contained locally, allowing the network to grow exponentially with server load. We also argue\n that adversarial failures are no more harmful than random failures.","Title: (Almost) Tight bounds and existence theorems for single-commodity confluent flows\nYear: 2007\n\nAbstract: A flow of a commodity is said to be confluent if at any node all the flow of the commodity leaves along a single edge. In this article, we study single-commodity confluent flow problems, where we need to route given node demands to a single destination using a confluent flow. Single- and multi-commodity confluent flows arise in a variety of application areas, most notably in networking; in fact, most flows in the Internet are (multi-commodity) confluent flows since Internet routing is destination based. We present near-tight approximation algorithms, hardness results, and existence theorems for minimizing congestion in single-commodity confluent flows. The maximum edge congestion of a single-commodity confluent flow occurs at one of the incoming edges of the destination. Therefore, finding a minimum-congestion confluent flow is equivalent to the following problem: given a directed graph G with k sinks and non-negative demands on all the nodes of G, determine a confluent flow that routes every node demand to some sink such that the maximum congestion at a sink is minimized. The main result of this article is a polynomial-time algorithm for determining a confluent flow with congestion at most 1 + ln(k) in G, if G admits a splittable flow with congestion at most 1. We complement this result in two directions. First, we present a graph G that admits a splittable flow with congestion at most 1, yet no confluent flow with congestion smaller than Hk, the kth harmonic number, thus establishing tight upper and lower bounds to within an additive constant less than 1. Second, we show that it is NP-hard to approximate the congestion of an optimal confluent flow to within a factor of (log2k)/2, thus resolving the polynomial-time approximability to within a multiplicative constant. We also consider a demand maximization version of the problem. We show that if G admits a splittable flow of congestion at most 1, then a variant of the congestion minimization algorithm yields a confluent flow in G with congestion at most 1 that satisfies 1/3 fraction of total demand. We show that the gap between confluent flows and splittable flows is much smaller, if the underlying graph is k-connected. In particular, we prove that k-connected graphs with k sinks admit confluent flows of congestion less than C + dmax, where C is the congestion of the best splittable flow, and dmax is the maximum demand of any node in G. The proof of this existence theorem is non-constructive and relies on topological techniques introduced by Lovász.","Title: Computational results of an O∗(n4) volume algorithm.\nYear: 2012\n\nAbstract: Recently an O*(n(4)) volume algorithm has been presented for convex bodies by Lovasz and Vempala, where n is the number of dimensions of the convex body. Essentially the algorithm is a series of Monte Carlo integrations. In this paper we describe a computer implementation of the volume algorithm, where we improved the computational aspects of the original algorithm by adding variance decreasing modifications: a stratified sampling strategy, double point integration and orthonormalised estimators. Formulas and methodology were developed so that the errors in each phase of the algorithm can be controlled. Some computational results for convex bodies in dimensions ranging from 2 to 10 are presented as well. (C) 2011 Elsevier B.V. All rights reserved.","Title: The cocycle lattice of binary matroids\nYear: 1993\n\nAbstract: We continue the study initiated in (LS) of the lattice (grid) generated by the incidence vectors of cocycles of a binary matroid and its dual lattice. In (LS), we proved that every denominator in the dual lattice is a power of 2, and characterized those binary matroids M for which the largest exponent k(M) is 1. In this paper, we characterize the matroids with k(M) = 2 and, for each constant k, give a polynomial time algorithm to decide whether k(M) ‚ k.","Title: Random graphons and a weak Positivstellensatz for graphs.\nYear: 2012\n\nAbstract: In an earlier article, the authors proved that limits of convergent graph sequences can be described by various structures, including certain 2-variable real functions called graphons, random graph models satisfying certain consistency conditions, and normalized, multiplicative and reflection positive graph parameters. In this article we show that each of these structures has a related, relaxed version, which are also equivalent. Using this, we describe a further structure equivalent to graph limits, namely probability measures on countable graphs that are ergodic with respect to the group of permutations of the nodes. As an application, we prove an analogue of the Positivstellensatz for graphs: we show that every linear inequality between subgraph densities that holds asymptotically for all graphs has a formal proof in the following sense: it can be approximated arbitrarily well by another valid inequality that is a sum of squares in the algebra of partially labeled graphs. (C) 2011 Wiley Periodicals, Inc. J Graph Theory","Title: Search problems in the decision tree model (preliminary version)\nYear: 1991\n\nAbstract: The relative power of determinism, randomness, and nondeterminism for search problems in the Boolean decision tree model is studied. It is shown that the CNF search problem is complete for all the variants of decision trees. It is then shown that the gaps between the nondeterministic, the randomized, and the deterministic complexities can be arbitrarily large for search problems. The special case of nondeterministic complexity is discussed.","Title: The generalized basis reduction algorithm\nYear: 1992\n\nAbstract: Let F(x) be a convex function defined in R(n), which is symmetric about the origin and homogeneous of degree 1, and let L be the lattice of integers Z(n). A definition of a reduced basis, b1,...,b(n), of the lattice with respect to the distance function F is presented, and we describe an algorithm which yields a reduced basis in polynomial time, for fixed n. In the special case in which the bodies {x: F(x) less-than-or-equal-to t) are ellipsoids, the definition of a reduced basis is identical with that given by Lenstra, Lenstra and Lovasz (1982) and the algorithm is the well-known basis reduction algorithm. We show that the basis vector b1, in a reduced basis, is an approximation to a shortest nonzero lattice point with respect to F and relate the basis vectors b(i) to Minkowski's successive minima. The results lead to an algorithm for integer programming which executes in polynomial time for fixed n, but which avoids the ellipsoidal approximations required by Lenstra's algorithm. We also discuss the properties of a Korkine-Zolotarev basis for the lattice.","Title: Two-prover one-round proof systems: their power and their problems (extended abstract)\nYear: 1992\n\nAbstract: We characterize the power of two-prover one-round (MIP(2,1)) proof systems, showing that MIP(2,1)=NEXPTIME. However, the following intriguing question remains open: Does parallel repetition decrease the error probability of MIP(2,1) proof systems?.We use techniques based on quadratic programming to study this problem, and prove the parallel repetition conjecture in some special cases. Interestingly, our work leads to a general polynomial time heuristic for any NP-problem. We prove the effectiveness of this heuristic for several problems, such as computing the chromatic number of perfect graphs.","Title: On the Shannon capacity of a graph\nYear: 1979\n\nAbstract: It is proved that the Shannon zero-error capacity of the pentagon issqrt{5}. The method is then generalized to obtain upper bounds on the capacity of an arbitrary graph. A well-characterized, and in a sense easily computable, function is introduced which bounds the capacity from above and equals the capacity in a large number of cases. Several results are obtained on the capacity of special graphs; for example, the Petersen graph has capacity four and a self-complementary graph with n points and with a vertex-transitive automorphism group has capacitysqrt{5}.","Title: On integer points in polyhedra: a lower bound\nYear: 1992\n\nAbstract: Given a polyhedronP?R we writePI for the convex hull of the integral points inP. It is known thatPI can have at most135-2 vertices ifP is a rational polyhedron with size f. Here we give an example showing thatPI can have as many as O(?n-1) vertices. The construction uses the Dirichlet unit theorem.","Title: Exit frequency matrices for finite markov chains\nYear: 2010\n\nAbstract: Consider a finite irreducible Markov chain on state space S with transition matrix M and stationary distribution π. Let R be the diagonal matrix of return times, Rii = 1/πi. Given distributions σ, τ and k ∈ S, the exit frequency xk(σ, τ) denotes the expected number of times a random walk exits state k before an optimal stopping rule from σ to τ halts the walk. For a target distribution τ, we define Xτ as the n × n matrix given by (Xτ)ij = xj(i, τ), where i also denotes the singleton distribution on state i. The dual Markov chain with transition matrix = RM⊤R−1 is called the reverse chain. We prove that Markov chain duality extends to matrices of exit frequencies. Specifically, for each target distribution τ, we associate a unique dual distribution τ*. Let $\\rX_{\\fc{\\t}}$ denote the matrix of exit frequencies from singletons to τ* on the reverse chain. We show that $\\rX_{\\fc{\\t}} = R (X_{\\t}^{\\top}-\\vb^{\\top} \\one)R^{-1}$, where b is a non-negative constant vector (depending on τ). We explore this exit frequency duality and further illuminate the relationship between stopping rules on the original chain and reverse chain.","Title: Reversal of Markov Chains and the Forget Time\nYear: 1998\n\nAbstract: We study three quantities that can each be viewed as the time needed for a finite irreducible Markov chain to ‘forget’ where it started. One of these is the mixing time, the minimum mean length of a stopping rule that yields the stationary distribution from the worst starting state. A second is the forget time, the minimum mean length of any stopping rule that yields the same distribution from any starting state. The third is the reset time, the minimum expected time between independent samples from the stationary distribution.Our main results state that the mixing time of a chain is equal to the mixing time of the time-reversed chain, while the forget time of a chain is equal to the reset time of the reverse chain. In particular, the forget time and the reset time of a time-reversible chain are equal. Moreover, the mixing time lies between absolute constant multiples of the sum of the forget time and the reset time.We also derive an explicit formula for the forget time, in terms of the 'access times' introduced in [11]. This enables us to relate the forget and reset times to other mixing measures of the chain.","Title: Some Mathematics behind Graph Property Testing\nYear: 2008\n\nAbstract: Graph property testing is the third reincarnation of the same general question, after statistics and learning theory. In its\n simplest form, we have a huge graph (we don’t even know its size), and we draw a sample of the node set of bounded size. What\n properties of the graph can be deduced from this sample?\n \n The graph property testing model was first introduced by Goldreich, Goldwasser and Ron (but related questions were considered\n before). In the context of dense graphs, a very general result is due to Alon and Shapira, who proved that every hereditary\n graph property is testable.\n \n \n \n Using the theory of graph limits, Lovász and Szegedy defined an analytic version of the (dense) graph property testing problem,\n which can be formulated as studying an unknown 2-variable symmetric function through sampling from its domain and studying\n the random graph obtained when using the function values as edge probabilities. This analytic version allows for simpler formulation\n of the problems, and leads to various characterizations of testable properties. These results can be applied to the original\n graph-theoretic property testing. In particular, they lead to a new combinatorial characterization of testable graph properties.\n \n \n \n We survey these results, along with analogous results for graphs with bounded degree.","Title: The rank of connection matrices and the dimension of graph algebras\nYear: 2006\n\nAbstract: Connection matrices were introduced in [M. Freedman, L. Lovász, A. Schrijver, Reflection positivity, rank connectivity, and homomorphism of graphs (MSR Tech Report # MSR-TR-2004-41) ftp://ftp.research.microsoft.com/pub/tr/TR-2004-41.pdf], where they were used to characterize graph homomorphism functions. The goal of this note is to determine the exact rank of these matrices. The result can be rephrased in terms of the dimension of graph algebras, also introduced in the same paper. Yet another version proves that if two k-tuples of nodes behave in the same way from the point of view of graph homomorphisms, then they are equivalent under the automorphism group.","Title: Proving Integrality Gaps without Knowing the Linear Program\nYear: 2006\n\nAbstract: Proving integrality gaps for linear relaxations of NP optimization problems is a difficult task and usually undertaken on a case-by-case basis. We initiate a more systematic approach. We prove an integrality gap of 2 o(1) for three families of linear relaxations for VERTEX COVER, and our methods seem relevant to other problems as well.","Title: Brick Decompositions And The Matching Rank Of Graphs\nYear: 1982\n\nAbstract: The number of linearly independent perfect matchings of a graph — or, equivalently, the dimension of the perfect matching\n polytope — is determined in various senses. First it is shown that the fact that every linear objective function can be optimized\n over the perfect matchings in polynomial time implies the existence of a polynomial-time algorithm to determine the dimension\n of this set. This observation also yields polynomial algorithms to determine, among others, the number of linearly independent\n common bases of two matroids and the number of linearly independent maximum stable sets in claw-free or perfect graphs. For\n the case of perfect matchings, Naddef’s minimax theorem for the dimension of the perfect matching polytope is strengthened\n and it is shown how the decomposition theory of matchings in graphs can be applied to derive a particularly simple formula\n for this dimension. This formula is based upon the number of constituents of a certain decomposition of the graph which we\n call a brick decomposition. Finally, these results are applied to obtain a description of the facets of the perfect matching\n polytope.","Title: Energy of convex sets, shortest paths, and resistance\nYear: 2001\n\nAbstract: Let us assign independent, exponentially distributed random edge lengths to the edges of an undirected graph. Lyons, Pemantle, and Peres (1999, J. Combin. Theory Ser. A 86 (1999), 158–168) proved that the expected length of the shortest path between two given nodes is bounded from below by the resistance between these nodes, where the resistance of an edge is the expectation of its length. They remarked that instead of exponentially distributed variables, one could consider random variables with a log-concave tail. We generalize this result in two directions. First, we note that the variables do not have to be independent: it suffices to assume that their joint distribution is log-concave. Second, the inequality can be formulated as follows: the expected length of a shortest path between two given nodes is the expected optimum of a stochastic linear program over a flow polytope, while the resistance is the minimum of a convex quadratic function over this polytope. We show that the inequality between these quantities holds true for an arbitrary polytope provided its blocker has integral vertices.","Title: Critical Facets of the Stable Set Polytope\nYear: 2001\n\nAbstract: Dedicated to the memory of Paul Erdős \n A facet of the stable set polytope of a graph G can be viewed as a generalization of the notion of an -critical graph. We extend several results from the theory of -critical graphs to facets. The defect of a nontrivial, full-dimensional facet of the stable set polytope of a graph G is defined by . We prove the upper bound for the degree of any node u in a critical facet-graph, and show that can occur only when . We also give a simple proof of the characterization of critical facet-graphs with defect 2 proved by Sewell [11]. As an\n application of these techniques we sharpen a result of Surányi [13] by showing that if an -critical graph has defect and contains nodes of degree , then the graph is an odd subdivision of .","Title: The geometry of logconcave functions and sampling algorithms\nYear: 2007\n\nAbstract: We derive several limit results for the profile of random plane-oriented recursive trees. These include the limit distribution of the normalized profile, asymptotic bimodality of the variance, asymptotic approximation to the expected width, and the correlation ...","Title: Positive graphs.\nYear: 2016\n\nAbstract: We study \"positive\" graphs that have a nonnegative homomorphism number into every edge-weighted graph (where the edgeweights may be negative). We conjecture that all positive graphs can be obtained by taking two copies of an arbitrary simple graph and gluing them together along an independent set of nodes. We prove the conjecture for various classes of graphs including all trees. We prove a number of properties of positive graphs, including the fact that they have a homomorphic image which has at least half the original number of nodes but in which every edge has an even number of pre-images. The results, combined with a computer program, imply that the conjecture is true for all graphs up to 9 nodes.","Title: Measures on the square as sparse graph limits\nYear: 2019\n\nAbstract: We study a metric on the set of finite graphs in which two graphs are considered to be similar if they have similar bounded dimensional “factors”. We show that limits of convergent graph sequences in this metric can be represented by symmetric Borel measures on [0,1]2. This leads to a generalization of dense graph limit theory to sparse graph sequences."],"string":"[\n \"Title: Discrete Quantitative Nodal Theorem\\nYear: 2021\\n\\nAbstract: We prove a theorem that can be thought of as a common generalization of the Discrete Nodal Theorem and (one direction of) Cheeger's Inequality for graphs. A special case of this result will assert that if the second and third eigenvalues of the Laplacian are at least epsilon apart, then the subgraphs induced by the positive and negative supports of the eigenvector belonging to lambda(2) are not only connected, but edge-expanders (in a weighted sense, with expansion depending on epsilon).\",\n \"Title: Polymatroid greedoids\\nYear: 1985\\n\\nAbstract: This paper discusses polymatroid greedoids, a superclass of them, called local poset greedoids, and their relations to other subclasses of greedoids. Polymatroid greedoids combine in a certain sense the different relaxation concepts of matroids as polymatroids and as greedoids. Some characterization results are given especially for local poset greedoids via excluded minors. General construction principles for intersection of matroids and polymatroid greedoids with shelling structures are given. Furthermore, relations among many subclasses of greedoids which are known so far, are demonstrated.\",\n \"Title: Tibor Gallai\\nYear: 1982\\n\\nAbstract: Without Abstract\",\n \"Title: Generalized quasirandom graphs\\nYear: 2008\\n\\nAbstract: We prove that if a sequence of graphs has (asymptotically) the same distribution of small subgraphs as a generalized random graph modeled on a fixed weighted graph H, then these graphs have a structure that is asymptotically the same as the structure of H. Furthermore, it suffices to require this for a finite number of subgraphs, whose number and size is bounded by a function of |V(H)|.\",\n \"Title: Finitely forcible graphons\\nYear: 2011\\n\\nAbstract: We investigate families of graphs and graphons (graph limits) that are determined by a finite number of prescribed subgraph densities. Our main focus is the case when the family contains only one element, i.e., a unique structure is forced by finitely many subgraph densities. Generalizing results of Turan, Erdos-Simonovits and Chung-Graham-Wilson, we construct numerous finitely forcible graphons. Most of these fall into two categories: one type has an algebraic structure and the other type has an iterated (fractal-like) structure. We also give some necessary conditions for forcibility, which imply that finitely forcible graphons are ''rare'', and exhibit simple and explicit non-forcible graphons.\",\n \"Title: Vertex Packing Algorithms\\nYear: 1985\\n\\nAbstract: Without Abstract\",\n \"Title: Limits of randomly grown graph sequences\\nYear: 2011\\n\\nAbstract: Motivated in part by various sequences of graphs growing under random rules (such as Internet models), Borgs, Chayes, Lovasz, Sos, Szegedy and Vesztergombi introduced convergent sequences of dense graphs and their limits. In this paper we use this framework to study one of the motivating classes of examples, namely randomly growing graphs. We prove the (almost sure) convergence of several such randomly growing graph sequences, and determine their limit. The analysis is not always straightforward: in some cases the cut-distance from a limit object can be directly estimated, while in other cases densities of subgraphs can be shown to converge.\",\n \"Title: A note on the last new vertex visited by a random walk\\nYear: 1993\\n\\nAbstract: A \\\"cover tour\\\" of a connected graph G from a vertex x is a random walk that begins at x, moves at each step with equal probability to any neighbor of its current vertex, and ends when it has hit every vertex of G. The cycle C(n) is well known to have the curious property that a cover tour from any vertex is equally likely to end at any other vertex; the complete graph K(n) shares this property, trivially, by symmetry. Ronald L. Graham has asked whether there are any other graphs with this property; we show that there are not. (C) 1993 John Wiley & Sons, Inc.\",\n \"Title: Semi-matchings for bipartite graphs and load balancing\\nYear: 2006\\n\\nAbstract: We consider the problem of fairly matching the left-hand vertices of a bipartite graph to the right-hand vertices. We refer to this problem as the optimal semi-matching problem; it is a relaxation of the known bipartite matching problem. We present a way to evaluate the quality of a given semi-matching and show that, under this measure, an optimal semi-matching balances the load on the right-hand vertices with respect to any Lp-norm. In particular, when modeling a job assignment system, an optimal semi-matching achieves the minimal makespan and the minimal flow time for the system.The problem of finding optimal semi-matchings is a special case of certain scheduling problems for which known solutions exist. However, these known solutions are based on general network optimization algorithms, and are not the most efficient way to solve the optimal semi-matching problem. To compute optimal semi-matchings efficiently, we present and analyze two new algorithms. The first algorithm generalizes the Hungarian method for computing maximum bipartite matchings, while the second, more efficient algorithm is based on a new notion of cost-reducing paths. Our experimental results demonstrate that the second algorithm is vastly superior to using known network optimization algorithms to solve the optimal semi-matching problem. Furthermore, this same algorithm can also be used to find maximum bipartite matchings and is shown to be roughly as efficient as the best known algorithms for this goal.\",\n \"Title: Dual graph homomorphism functions\\nYear: 2010\\n\\nAbstract: For any two graphs F and G, let hom(F,G) denote the number of homomorphisms F→G, that is, adjacency preserving maps V(F)→V(G) (graphs may have loops but no multiple edges). We characterize graph parameters f for which there exists a graph F such that f(G)=hom(F,G) for each graph G.\",\n \"Title: Structural properties of greedoids\\nYear: 1983\\n\\nAbstract: Greedoids were introduced by the authors as generalizations of matroids providing a framework for the greedy algorithm. In\\n this paper they are studied from a structural aspect. Definitions of basic matroid-theoretical concepts such as rank and closure\\n can be generalized to greedoids, even though they loose some of their fundamental properties. The rank function of a greedoid\\n is only “locally” submodular. The closure operator is not monotone but possesses a (relaxed) Steinitz—McLane exchange property.\\n We define two classes of subsets, called rank-feasible and closure-feasible, so that the rank and closure behave nicely for\\n them. In particular, restricted to rank-feasible sets the rank function is submodular. Finally we show that Rado’s theorem\\n on independent transversals of subsets of matroids remains valid for feasible transversals of certain sets of greedoids.\",\n \"Title: Semidefinite Functions on Categories.\\nYear: 2009\\n\\nAbstract: Freedman, Lovasz and Schrijver characterized graph parameters that can be represented as the (weighted) number of homomorphisms into a fixed graph. Several extensions of this result have been proved. We use the framework of categories to prove a general theorem of this kind. Similarly as previous resuts, the characterization uses certain infinite matrices, called connection matrices, which are required to be positive semidefinite.\",\n \"Title: Ear-decompositions of matching covered graphs\\nYear: 1983\\n\\nAbstract: We call a graphmatching-covered if every line belongs to a perfect matching. We study the technique of “ear-decompositions” of such graphs. We prove that\\n a non-bipartite matching-covered graph containsK\\n 4 orK\\n 2⊕K\\n 3 (the triangular prism). Using this result, we give new characterizations of those graphs whose matching and covering numbers\\n are equal. We apply these results to the theory of τ-critical graphs.\",\n \"Title: Random walks and the regeneration time\\nYear: 1998\\n\\nAbstract: Consider a graph G and a random walk on it. We want tostop the random walk at certain times (using an optimal stoppingrule) to obtain independent samples from a given distribution ρon the nodes. For an undirected graph, the expected time betweenconsecutive samples is maximized by a distribution equally dividedbetween two nodes, and so the worst expected time is 1-4 of themaximum commute time between two nodes. In the directed case, theexpected time for this distribution is within a factor of 2 of themaximum. © 1998 John Wiley & Sons, Inc. J. Graph Theory29: 5762, 1998\",\n \"Title: A localization inequality for set functions\\nYear: 2006\\n\\nAbstract: We prove the following theorem, which is an analog for discrete set functions of a geometric result of Lovász and Simonovits. Given two real-valued set functions f1, f2 defined on the subsets of a finite set S satisfying, ΣX⊆S fi(X) ≥0 for i ∈ {1, 2}, there exists a positive multiplicative set function µ over S and two subsets A, B ⊆ S such that for i ∈ {1,2} µ(A)fi(A)+µ(B)fi(B)+µ(A∪B)fi(A∪ B) + µ(A ∩ B)fi (A∩B) ≥0. The Ahlswede-Daykin four function theorem can be deduced easily from this.\",\n \"Title: Isoperimetric Problems for Convex Bodies and a Localization Lemama\\nYear: 1995\\n\\nAbstract: . We study the smallest number /(K) such that a given convex body K in IRncan be cut into two parts K 1 and K 2 by a surface with an (n \\\\Gamma 1)-dimensional measure/(K)vol(K 1 ) \\\\Delta vol(K 2 )=vol(K). Let M 1 (K) be the average distance of a point of K fromits center of gravity. We prove for the "isoperimetric coefficient" that/(K) ln 2M 1 (K);and give other upper and lower bounds. We conjecture that our upper bound is bestpossible up to a constant.Our main tool is a...\",\n \"Title: Rubber bands, convex embeddings and graph connectivity\\nYear: 1988\\n\\nAbstract: We give various characterizations ofk-vertex connected graphs by geometric, algebraic, and “physical” properties. As an example, a graphG isk-connected if and only if, specifying anyk vertices ofG, the vertices ofG can be represented by points of ℝk−1 so that nok are on a hyper-plane and each vertex is in the convex hull of its neighbors, except for thek specified vertices. The proof of this theorem appeals to physics. The embedding is found by letting the edges of the graph behave like ideal springs and letting its vertices settle in equilibrium.\",\n \"Title: Covering minima and lattice point free convex bodies\\nYear: 1986\\n\\nAbstract: Suppose K is a convex set of nonzero volume in Euclidean n-space R\\nn and it is symmetric about the origin (i.e., if x belongs to K, so does - x). For any real number t, let tK={tx : xK}. The infimum over all positive real numbers t such that if a copy of tK is placed centered at every integer point, all of R\\nn is covered, is called the covering radius of K (with respect to the lattice Z\\nn). The covering radius and related quantities have been studied extensively in Geometry of Numbers. In this paper, we define and study the covering minima of a convex body which is not necessarily symmetric about the origin; the covering radius will be a special case of one of of these minima. This extension to general convex bodies has among other things, applications to algorithms for Integer Programming which was our initial motivation. This motivation is explained in some detail later. We use the results of the paper to derive bounds on the width of lattice point free convex bodies and analyze their structure.\",\n \"Title: Simulated annealing in convex bodies and an O*(n4) volume algorithm\\nYear: 2006\\n\\nAbstract: We present a new algorithm for computing the volume of a convex body in R^n. The main ingredients of the algorithm are (i) a ''morphing'' technique that can be viewed as a variant of simulated annealing and (ii) a new rounding algorithm to put a convex body in near-isotropic position. The complexity is O^*(n^4), improving on the previous best algorithm by a factor of n.\",\n \"Title: Left and right convergence of graphs with bounded degree\\nYear: 2013\\n\\nAbstract: The theory of convergent graph sequences has been worked out in two extreme cases, dense graphs and bounded degree graphs. One can define convergence in terms of counting homomorphisms from fixed graphs into members of the sequence (left-convergence), or counting homomorphisms into fixed graphs (right-convergence). Under appropriate conditions, these two ways of defining convergence was proved to be equivalent in the dense case by Borgs, Chayes, Lovász, Sós and Vesztergombi. In this paper a similar equivalence is established in the bounded degree case, if the set of graphs in the definition of right-convergence is appropriately restricted. In terms of statistical physics, the implication that left convergence implies right convergence means that for a left-convergent sequence, partition functions of a large class of statistical physics models converge. The proof relies on techniques from statistical physics, like cluster expansion and Dobrushin Uniqueness. © 2012 Wiley Periodicals, Inc. Random Struct. 2012 (Supported by OTKA (67867) and ERC (227701).)\",\n \"Title: Random-Walks In A Convex Body And An Improved Volume Algorithm\\nYear: 1993\\n\\nAbstract: We give a randomized algorithm using O(n7 log2 n) separation calls to approximate the volume of a convex body with a fixed relative error. The bound is O(n6 log4 n) for centrally symmetric bodies and for polytopes with a polynomial number of facets, and O(n5 log4 n) for centrally symmetric polytopes with a polynomial number of facets. We also give an O(n6 log n) algorithm to sample a point from the uniform distribution over a convex body. Several tools are developed that may be interesting on their own. We extend results of Sinclair-Jerrum [43] and the authors [34] on the mixing rate of Markov chains from finite to arbitrary Markov chains. We also analyze the mixing rate of various random walks on convex bodies, in particular the random walk with steps from the uniform distribution over a unit ball. (C) 1993 John Wiley & Sons, Inc.\",\n \"Title: Subgraph Densities in Signed Graphons and the Local Simonovits-Sidorenko Conjecture.\\nYear: 2011\\n\\nAbstract: We prove inequalities between the densities of various bipartite subgraphs in signed graphs. One of the main inequalities is that the density of any bipartite graph with girth 2r cannot exceed the density of the 2r-cycle. This study is motivated by the Simonovits-Sidorenko conjecture, which states that the density of a bipartite graph F with m edges in any graph G is at least the m-th power of the edge density of G. Another way of stating this is that the graph G with given edge density minimizing the number of copies of F is, asymptotically, a random graph. We prove that this is true locally, i.e., for graphs G that are \\\"close\\\" to a random graph. Both kinds of results are treated in the framework of graphons (2-variable functions serving as limit objects for graph sequences), which in this context was already used by Sidorenko.\",\n \"Title: Matching structure and the matching lattice\\nYear: 1987\\n\\nAbstract: The matching polyhedron, i.e., the convex hull of (incidence vectors of) perfect matchings of a graph was characterized by Edmonds; this result is the key to a large part of polyhedral combinatorics and is used in many combinatorial algorithms. The linear hull of perfect matchings was characterized by Naddef, and by Edmonds, Lovász, and Pulleyblank. In this paper we describe the lattice generated by these vectors, i.e., the set of all integer linear combinations of perfect matchings. It turns out that the Petersen graph is, in a sense, the only difficult example. Our results also imply a characterization of the linear hull of perfect matchings over fields of characteristic different from 0. The main method is a decomposition theory developed by Kotzig, Lovász, and Plummer, which breaks down every graph into a number of graphs called bricks with very good matching properties. The number of Petersen graphs among these bricks will turn out to be an essential parameter of the matching lattice. Some refinements of the decomposition theory are also given. Among others, we show that the list of bricks obtained during the decomposition procedure is independent of the special choices made during the procedure.\",\n \"Title: Waiting for a Bat to Fly By (in Polynomial Time)\\nYear: 2006\\n\\nAbstract: We observe returns of a simple random walk on a finite graph to a fixed node, and would like to infer properties of the graph, in particular properties of the spectrum of the transition matrix. This is not possible in general, but at least the set of eigenvalues can be recovered under fairly general conditions, e.g., when the graph has a node-transitive automorphism group. The main result is that by observing polynomially many returns, it is possible to estimate the spectral gap of such a graph up to a constant factor.\",\n \"Title: Approximating Graphs by Graphs and Functions (Abstract)\\nYear: 2007\\n\\nAbstract: In many areas of science huge networks (graphs) are central objects of study: the internet, the brain, various social networks, VLSI, statistical physics. To study these graphs, new paradigms are needed: What are meaningful questions to ask? When are two huge graphs \\\"similar\\\"? How to \\\"scale down\\\" these graphs without changing their fundamental structure and algorithmic properties? How to generate random examples with the desired properties? A reasonably complete answer can be given in the case when the huge graphs are dense (in the more difficult case of sparse graphs there are only partial results).\",\n \"Title: Hit-and-Run from a Corner\\nYear: 2006\\n\\nAbstract: We show that the hit-and-run random walk mixes rapidly starting from any interior point of a convex body. This is the first random walk known to have this property. In contrast, the ball walk can take exponentially many steps from some starting points. The proof extends to sampling an exponential density over a convex body.\",\n \"Title: Entropy Splitting For Antiblocking Corners And Perfect Graphs\\nYear: 1990\\n\\nAbstract: We characterize pairs of convex sets A, B in the k-dimensional space with the property that every probability distribution (p1, ..., pk) has a repsesentationpt = a(i).b(i), a is-a-member-of, b is-a-member-of B.Minimal pairs with this property are antiblocking pairs of convex corners. This result is closely related to a new entropy concept. The main application is an information theoretic characterization of perfect graphs.\",\n \"Title: Search Problems in the Decision Tree Model\\nYear: 1995\\n\\nAbstract: The relative power of determinism, randomness, and nondeterminism for search problems in the Boolean decision tree model is studied. It is shown that the gaps between the nondeterministic, the randomized, and the deterministic complexities can be arbitrarily large for search problems. An interesting connection of this model to the complexity of resolution proofs is also mentioned.\",\n \"Title: Cycles through specified vertices of a graph\\nYear: 1981\\n\\nAbstract: We prove that ifS is a set ofk−1 vertices in ak-connected graphG, then the cycles throughS generate the cycle space ofG. Moreover, whenk≧3, each cycle ofG can be expressed as the sum of an odd number of cycles throughS. On the other hand, ifS is a set ofk vertices, these conclusions do not necessarily hold, and we characterize the exceptional cases. As corollaries, we establish\\n the existence of odd and even cycles through specified vertices and deduce the existence of long odd and even cycles in graphs\\n of high connectivity.\",\n \"Title: The ellipsoid method and its consequences in combinatorial optimization.\\nYear: 1981\\n\\nAbstract: L. G. Khachiyan recently published a polynomial algorithm to check feasibility of a system of linear inequalities. The method\\n is an adaptation of an algorithm proposed by Shor for non-linear optimization problems. In this paper we show that the method\\n also yields interesting results in combinatorial optimization. Thus it yields polynomial algorithms for vertex packing in\\n perfect graphs; for the matching and matroid intersection problems; for optimum covering of directed cuts of a digraph; for\\n the minimum value of a submodular set function; and for other important combinatorial problems. On the negative side, it yields\\n a proof that weighted fractional chromatic number is NP-hard.\",\n \"Title: Communication complexity and combinatorial lattice theory\\nYear: 1993\\n\\nAbstract: In a recent paper, Hajnal, Maass, and Turán analyzed the communication complexity of graph connectivity. Building on this work, we develop a general framework for the study of a broad class of communication problems which has several interesting special cases including the graph connectivity problem. The approach is based on the combinatorial theory of alignments and lattices.\",\n \"Title: The rank and size of graphs\\nYear: 1996\\n\\nAbstract: We show that the number of points with pairwise different sets of neighbors in a graph is O(2(r)/2), where r is the rank of the adjacency matrix. We also give an example that achieves this bound. (C) 1996 John Wiley & Sons, Inc.\",\n \"Title: Logconcave functions: geometry and efficient sampling algorithms\\nYear: 2003\\n\\nAbstract: The class of logconcave functions in Rn is a common generalization of Gaussians and of indicator functions of convex sets. Motivated by the problem of sampling from a logconcave density function, we study their geometry and introduce an analysis technique for \\\"smoothing\\\" them out. This leads to efficient sampling algorithms with no assumptions on the local smoothness of the density function. After appropriate preprocessing, both the ball walk (with a Metropolis filter) and a generalization of hit-and-run produce a point from approximately the right distribution in time O*(n4), and in amortized time O*(n3) if many sample points are needed (where the asterisk indicates that dependence on the error parameter and factors of log n are not shown). The bounds are optimal in terms of a \\\"roundness\\\" parameter and match the best-known bounds for the special case of the uniform density over a convex set.\",\n \"Title: The membership problem in jump systems\\nYear: 1997\\n\\nAbstract: A jump system is a set of lattice points satisfying a certain exchange axiom. This notion was introduced by Bouchet and Cunningham [2], as a common generalization of (among others) the sets of bases of a matroid and degree sequences of subgraphs of a graph. We prove, under additional assumptions, a min-max formula for the distance of a lattice point from a jump system. The conditions are met in the examples above, and so our formula contains, as special cases, Tutte's f -factor-theorem and Edmonds' matroid intersection theorem.\",\n \"Title: (Almost) tight bounds and existence theorems for confluent flows\\nYear: 2004\\n\\nAbstract: A flow is said to be confluent if at any node all the flow leaves along a single edge. Given a directed graph G with k sinks and non-negative demands on all the nodes of G, we consider the problem of determining a confluent flow that routes every node demand to some sink such that the maximum congestion at a sink is minimized. Confluent flows arise in a variety of application areas, most notably in networking; in fact, most flows in the Internet are confluent since Internet routing is destination based.We present near-tight approximation algorithms, hardness results, and existence theorems for confluent flows. The main result of this paper is a polynomial-time algorithm for determining a confluent flow with congestion at most 1 + ln(k) in G, if G admits a splittable flow with congestion at most 1. We complement this result in two directions. First, we present a graph G that admits a splittable flow with congestion at most 1, yet no confluent flow with congestion smaller than Hk, thus establishing tight upper and lower bounds to within an additive constant less than 1. Second, we show that it is NP-hard to approximate the congestion of an optimal confluent flow to within a factor of (lg k)/2, thus resolving the polynomial-time approximability to within a multiplicative constant. We also consider a demand maximization version of the problem. We show that if G admits a splittable flow of congestion at most 1, then a variant of the congestion minimization algorithm yields a confluent flow in G with congestion at most 1 that satisfies 1/3 fraction of total demand.We show that the gap between confluent flows and splittable flows is much smaller, if the underlying graph were k connected. In particular, we prove that k-connected graphs with k sinks admit confluent flows of congestion less than C + dmax, where C is the congestion of the best splittable flow, and dmax is the maximum demand of any node in G. The proof of this existence theorem is non-constructive and relies on topological techniques introduced in [16].\",\n \"Title: Graph limits and parameter testing\\nYear: 2006\\n\\nAbstract: We define a distance of two graphs that reflects the closeness of both local and global properties. We also define convergence of a sequence of graphs, and show that a graph sequence is convergent if and only if it is Cauchy in this distance. Every convergent graph sequence has a limit in the form of a symmetric measurable function in two variables. We use these notions of distance and graph limits to give a general theory for parameter testing. As examples, we provide short proofs of the testability of MaxCut and the recent result of Alon and Shapira about the testability of hereditary graph properties.\",\n \"Title: Graph parameters and semigroup functions\\nYear: 2008\\n\\nAbstract: We prove a general theorem on semigroup functions that implies characterizations of graph partition functions in terms of the positive semidefiniteness ('reflection positivity') and rank of certain derived matrices. The theorem can be applied to undirected and directed graphs as well as hypergraphs.\",\n \"Title: Corrigendum to our paper \\\"The ellipsoid method and its consequences in combinatorial optimization\\nYear: 1984\\n\\nAbstract: An error in the proof, and in the statement of a generalization, of the result that submodular setfunctions can be minimized\\n over the subsets with odd cardinality is corrected.\",\n \"Title: Hit-and-run mixes fast\\nYear: 1999\\n\\nAbstract: . It is shown that the “hit-and-run” algorithm for sampling from a convex body K (introduced by R.L. Smith) mixes in time O\\n *(n\\n 2\\n R\\n 2/r\\n 2), where R and r are the radii of the inscribed and circumscribed balls of K. Thus after appropriate preprocessing, hit-and-run produces an approximately uniformly distributed sample point in time O\\n *(n\\n 3), which matches the best known bound for other sampling algorithms. We show that the bound is best possible in terms of R,r and n.\",\n \"Title: Contractors and connectors of graph algebras\\nYear: 2009\\n\\nAbstract: We study generalizations of the “contraction-deletion” relation of the Tutte polynomial, and other similar simple operations, to other graph parameters. The question can be set in the framework of graph algebras introduced by Freedman at al [Reflection positivity, rank connectivity, and homomorphisms of graphs, J. Amer. Math. Soc. 20 (2007), 37–51.] Graph algebras are defined by a graph parameter, and they were introduced in order to study and characterize homomorphism functions. We prove that for homomorphism functions, these graph algebras have special elements called “contractors” and “connectors”. This gives a new characterization of homomorphism functions. © 2008 Wiley Periodicals, Inc. J Graph Theory 60: 11–30, 2009\",\n \"Title: Random walks and an O*(n5) volume algorithm for convex bodies\\nYear: 1997\\n\\nAbstract: Given a high dimensional convex body K µ IRn by a separation oracle, we can approx- imate its volume with relative error \\\", using O⁄(n5) oracle calls. Our algorithm also brings the body into isotropic position. As all previous randomized volume algorithms, we use \\\"rounding\\\" followed by a multi- phase Monte-Carlo (product estimator) technique. Both parts rely on sampling (generating random points in K), which is done by random walk. Our algorithm introduces three new ideas: † the use of the isotropic position (or at least an approximation of it) for rounding, † the separation of global obstructions (diameter) and local obstructions (boundary problems) for fast mixing, and † a stepwise interlacing of rounding and sampling.\",\n \"Title: Approximating Min-sum Set Cover\\nYear: 2002\\n\\nAbstract: Abstract The input to the min sum set cover problem is a collection of n sets that jointly cover m elements. The output is a linear order on the sets, namely, in every time step from 1 to n exactly one set is chosen. For every element, this induces a rst time step by which it is covered. The objective is to,nd a linear arrangement of the sets that minimizes the sum of these rst time steps over all elements. We show that a greedy algorithm approximates min sum set cover within a ratio of 4. This result was implicit in work of Bar-Noy, Bellare, Halldorsson, Shachnai and Tamir (1998) on chromatic sums, but we present a simpler proof. We also show that for every >0, achieving an approximation ratio of 4 is NP-hard. For the min sum vertex cover version of the problem (which comes up as a heuristic for speeding up solvers of semidenite programs) we show that it can be approximated within a ratio of 2, and is NP-hard to approximate within some constant >1. a preliminary version of this paper appeared in the conference proceedings of APPROX 2002. School of Mathematics and College of Computing, Georgia Institute of Technology, Atlanta, GA 30332- 0160; research in part supported by the NSF grant DMS-0100298. tetali@math.gatech.edu\",\n \"Title: The Colin de Verdière Number and Sphere Representations of a Graph.\\nYear: 1997\\n\\nAbstract: Colin de Verdiere introduced an interesting linear algebraic invariant mu(G) Of graphs. He proved that mu(G) less than or equal to 2 if anti only if G is outerplanar, and mu(G) less than or equal to 3 if and only if G is planar. We prove that if the complement of a graph G on a nodes is outerplanar, then mu(G) greater than or equal to n - 4, and if it is planar, then mu(G) greater than or equal to n - 5. We give a full characterization of maximal planar graphs whose complements G have mu(G) = n - 5. In the opposite direction we show that if G does not have \\\"twin\\\" nodes, then mu(G) greater than or equal to n - 3 implies that the complement of G is outerplanar, and mu(G) greater than or equal to n - 4 implies that the complement of G is planar.Our main tools are a geometric formulation of the invariant, and constructing representations of graphs by spheres, related to the classical result of Koebe about representing planar graphs by touching disks. In particular we show that such sphere representations characterize outerplanar and planar graphs.\",\n \"Title: Exact Mixing in an Unknown Markov Chain\\nYear: 1995\\n\\nAbstract: We give a simple stopping rule which will stop an unknown, irreducible n-state Markov chain at a state whose probability distribution is exactly the stationary distribution of the chain. The expected stopping time of the rule is bounded by a polynomial in the maximum mean hitting time of the chain. Our stopping rule can be made deterministic unless the chain itself has no random transitions.\",\n \"Title: Local Versus Global Properties of Metric Spaces\\nYear: 2012\\n\\nAbstract: Motivated by applications in combinatorial optimization, we study the extent to which the global properties of a metric space, and especially its embeddability into $\\\\ell_1$ with low distortion, are determined by the properties of its small subspaces. We establish both upper and lower bounds on the distortion of embedding locally constrained metrics into various target spaces. Other aspects of locally constrained metrics are studied as well, in particular, how far are those metrics from general metrics.\",\n \"Title: On Conway's thrackle conjecture\\nYear: 1997\\n\\nAbstract: Abstract. A thrackle is a graph drawn in the plane so that its edges are represented by Jordan arcs and any two distinct arcs either meet at exactly one common vertex or cross at exactly one point interior to both arcs. About 40 years ago, J. H. Conway conjectured that the number of edges of a thrackle cannot exceed the number of its vertices. We show that a thrackle has at most twice as many edges as vertices. Some related problems and generalizations are also considered.\",\n \"Title: Steinitz representations of polyhedra and the Colin de Verdiére number\\nYear: 2001\\n\\nAbstract: We show that the Steinitz representations of 3-connected planargraphs are correspond, in a well described way, to Colin de Verdierematrices of such graphs.1 IntroductionColin de Verdiere [1] introduced a spectral invariant (G) of a graph G.Roughly speaking, (G) is the multiplicity of the second largest eigenvalueof the adjacency matrix of G, maximized by weighting the edges and nodes(the exact definition will be given in section 3). The matrix that attainsthis maximum will be called a ...\",\n \"Title: Lecture Notes on Evasiveness of Graph Properties\\nYear: 2002\\n\\nAbstract: This report presents notes from the first eight lectures of the class Many\\nModels of Complexity taught by Laszlo Lovasz at Princeton University in the\\nfall of 1990. The topic is evasiveness of graph properties: given a graph\\nproperty, how many edges of the graph an algorithm must check in the worst case\\nbefore it knows whether the property holds.\",\n \"Title: On the graph of large distances\\nYear: 1989\\n\\nAbstract: For a set of points in the plane, let>>... denote the different distances determined by Consider the graph() whose vertices are the elements of, and two are joined by an edge iff their distance is at least. It is proved that the chromatic number of() is at most 7 if ||≥const. If consists of the vertices of a convex polygon and ||≥const, then the chromatic number of() is at most 3. Both bounds are best possible. If consists of the vertices of a convex polygon then() has a vertex of degree at most 3 − 1. This implies that in this case the chromatic number of() is at most 3. The best bound here is probably 2+1, which is tight for the regular (2+1)-gon.\",\n \"Title: Limits of dense graph sequences\\nYear: 2006\\n\\nAbstract: We show that if a sequence of dense graphs G\\\"n has the property that for every fixed graph F, the density of copies of F in G\\\"n tends to a limit, then there is a natural ''limit object,'' namely a symmetric measurable function W:[0,1]^2-[0,1]. This limit object determines all the limits of subgraph densities. Conversely, every such function arises as a limit object. We also characterize graph parameters that are obtained as limits of subgraph densities by the ''reflection positivity'' property. Along the way we introduce a rather general model of random graphs, which seems to be interesting on its own right.\",\n \"Title: Non-Deterministic Graph Property Testing.\\nYear: 2013\\n\\nAbstract: A property of finite graphs is called non-deterministically testable if it has a 'certificate' such that once the certificate is specified, its correctness can be verified by random local testing. In this paper we study certificates that consist of one or more unary and/or binary relations on the nodes, in the case of dense graphs. Using the theory of graph limits, we prove that non-deterministically testable properties are also deterministically testable.\",\n \"Title: Note: Dual graph homomorphism functions\\nYear: 2010\\n\\nAbstract: For any two graphs F and G, let hom(F,G) denote the number of homomorphisms F-G, that is, adjacency preserving maps V(F)-V(G) (graphs may have loops but no multiple edges). We characterize graph parameters f for which there exists a graph F such that f(G)=hom(F,G) for each graph G. The result may be considered as a certain dual of a characterization of graph parameters of the form hom(.,H), given by Freedman, Lovasz and Schrijver [M. Freedman, L. Lovasz, A. Schrijver, Reflection positivity, rank connectivity, and homomorphisms of graphs, J. Amer. Math. Soc. 20 (2007) 37-51]. The conditions amount to the multiplicativity of f and to the positive semidefiniteness of certain matrices N(f,k).\",\n \"Title: Mathematical Structures Underlying Greedy Algorithms\\nYear: 1981\\n\\nAbstract: Without Abstract\",\n \"Title: Building scalable and robust peer-to-peer overlay networks for broadcasting using network coding\\nYear: 2007\\n\\nAbstract: We propose a scheme for building peer-to-peer overlay networks for broadcasting using network coding. The scheme addresses\\n many practical issues such as scalability, robustness, constraints on bandwidth, and locality of decisions. We analyze the\\n system theoretically and prove near optimal bounds on the parameters defining robustness and scalability. As a result we show\\n that the effects of failures are contained locally, allowing the network to grow exponentially with server load. We also argue\\n that adversarial failures are no more harmful than random failures.\",\n \"Title: (Almost) Tight bounds and existence theorems for single-commodity confluent flows\\nYear: 2007\\n\\nAbstract: A flow of a commodity is said to be confluent if at any node all the flow of the commodity leaves along a single edge. In this article, we study single-commodity confluent flow problems, where we need to route given node demands to a single destination using a confluent flow. Single- and multi-commodity confluent flows arise in a variety of application areas, most notably in networking; in fact, most flows in the Internet are (multi-commodity) confluent flows since Internet routing is destination based. We present near-tight approximation algorithms, hardness results, and existence theorems for minimizing congestion in single-commodity confluent flows. The maximum edge congestion of a single-commodity confluent flow occurs at one of the incoming edges of the destination. Therefore, finding a minimum-congestion confluent flow is equivalent to the following problem: given a directed graph G with k sinks and non-negative demands on all the nodes of G, determine a confluent flow that routes every node demand to some sink such that the maximum congestion at a sink is minimized. The main result of this article is a polynomial-time algorithm for determining a confluent flow with congestion at most 1 + ln(k) in G, if G admits a splittable flow with congestion at most 1. We complement this result in two directions. First, we present a graph G that admits a splittable flow with congestion at most 1, yet no confluent flow with congestion smaller than Hk, the kth harmonic number, thus establishing tight upper and lower bounds to within an additive constant less than 1. Second, we show that it is NP-hard to approximate the congestion of an optimal confluent flow to within a factor of (log2k)/2, thus resolving the polynomial-time approximability to within a multiplicative constant. We also consider a demand maximization version of the problem. We show that if G admits a splittable flow of congestion at most 1, then a variant of the congestion minimization algorithm yields a confluent flow in G with congestion at most 1 that satisfies 1/3 fraction of total demand. We show that the gap between confluent flows and splittable flows is much smaller, if the underlying graph is k-connected. In particular, we prove that k-connected graphs with k sinks admit confluent flows of congestion less than C + dmax, where C is the congestion of the best splittable flow, and dmax is the maximum demand of any node in G. The proof of this existence theorem is non-constructive and relies on topological techniques introduced by Lovász.\",\n \"Title: Computational results of an O∗(n4) volume algorithm.\\nYear: 2012\\n\\nAbstract: Recently an O*(n(4)) volume algorithm has been presented for convex bodies by Lovasz and Vempala, where n is the number of dimensions of the convex body. Essentially the algorithm is a series of Monte Carlo integrations. In this paper we describe a computer implementation of the volume algorithm, where we improved the computational aspects of the original algorithm by adding variance decreasing modifications: a stratified sampling strategy, double point integration and orthonormalised estimators. Formulas and methodology were developed so that the errors in each phase of the algorithm can be controlled. Some computational results for convex bodies in dimensions ranging from 2 to 10 are presented as well. (C) 2011 Elsevier B.V. All rights reserved.\",\n \"Title: The cocycle lattice of binary matroids\\nYear: 1993\\n\\nAbstract: We continue the study initiated in (LS) of the lattice (grid) generated by the incidence vectors of cocycles of a binary matroid and its dual lattice. In (LS), we proved that every denominator in the dual lattice is a power of 2, and characterized those binary matroids M for which the largest exponent k(M) is 1. In this paper, we characterize the matroids with k(M) = 2 and, for each constant k, give a polynomial time algorithm to decide whether k(M) ‚ k.\",\n \"Title: Random graphons and a weak Positivstellensatz for graphs.\\nYear: 2012\\n\\nAbstract: In an earlier article, the authors proved that limits of convergent graph sequences can be described by various structures, including certain 2-variable real functions called graphons, random graph models satisfying certain consistency conditions, and normalized, multiplicative and reflection positive graph parameters. In this article we show that each of these structures has a related, relaxed version, which are also equivalent. Using this, we describe a further structure equivalent to graph limits, namely probability measures on countable graphs that are ergodic with respect to the group of permutations of the nodes. As an application, we prove an analogue of the Positivstellensatz for graphs: we show that every linear inequality between subgraph densities that holds asymptotically for all graphs has a formal proof in the following sense: it can be approximated arbitrarily well by another valid inequality that is a sum of squares in the algebra of partially labeled graphs. (C) 2011 Wiley Periodicals, Inc. J Graph Theory\",\n \"Title: Search problems in the decision tree model (preliminary version)\\nYear: 1991\\n\\nAbstract: The relative power of determinism, randomness, and nondeterminism for search problems in the Boolean decision tree model is studied. It is shown that the CNF search problem is complete for all the variants of decision trees. It is then shown that the gaps between the nondeterministic, the randomized, and the deterministic complexities can be arbitrarily large for search problems. The special case of nondeterministic complexity is discussed.\",\n \"Title: The generalized basis reduction algorithm\\nYear: 1992\\n\\nAbstract: Let F(x) be a convex function defined in R(n), which is symmetric about the origin and homogeneous of degree 1, and let L be the lattice of integers Z(n). A definition of a reduced basis, b1,...,b(n), of the lattice with respect to the distance function F is presented, and we describe an algorithm which yields a reduced basis in polynomial time, for fixed n. In the special case in which the bodies {x: F(x) less-than-or-equal-to t) are ellipsoids, the definition of a reduced basis is identical with that given by Lenstra, Lenstra and Lovasz (1982) and the algorithm is the well-known basis reduction algorithm. We show that the basis vector b1, in a reduced basis, is an approximation to a shortest nonzero lattice point with respect to F and relate the basis vectors b(i) to Minkowski's successive minima. The results lead to an algorithm for integer programming which executes in polynomial time for fixed n, but which avoids the ellipsoidal approximations required by Lenstra's algorithm. We also discuss the properties of a Korkine-Zolotarev basis for the lattice.\",\n \"Title: Two-prover one-round proof systems: their power and their problems (extended abstract)\\nYear: 1992\\n\\nAbstract: We characterize the power of two-prover one-round (MIP(2,1)) proof systems, showing that MIP(2,1)=NEXPTIME. However, the following intriguing question remains open: Does parallel repetition decrease the error probability of MIP(2,1) proof systems?.We use techniques based on quadratic programming to study this problem, and prove the parallel repetition conjecture in some special cases. Interestingly, our work leads to a general polynomial time heuristic for any NP-problem. We prove the effectiveness of this heuristic for several problems, such as computing the chromatic number of perfect graphs.\",\n \"Title: On the Shannon capacity of a graph\\nYear: 1979\\n\\nAbstract: It is proved that the Shannon zero-error capacity of the pentagon issqrt{5}. The method is then generalized to obtain upper bounds on the capacity of an arbitrary graph. A well-characterized, and in a sense easily computable, function is introduced which bounds the capacity from above and equals the capacity in a large number of cases. Several results are obtained on the capacity of special graphs; for example, the Petersen graph has capacity four and a self-complementary graph with n points and with a vertex-transitive automorphism group has capacitysqrt{5}.\",\n \"Title: On integer points in polyhedra: a lower bound\\nYear: 1992\\n\\nAbstract: Given a polyhedronP?R we writePI for the convex hull of the integral points inP. It is known thatPI can have at most135-2 vertices ifP is a rational polyhedron with size f. Here we give an example showing thatPI can have as many as O(?n-1) vertices. The construction uses the Dirichlet unit theorem.\",\n \"Title: Exit frequency matrices for finite markov chains\\nYear: 2010\\n\\nAbstract: Consider a finite irreducible Markov chain on state space S with transition matrix M and stationary distribution π. Let R be the diagonal matrix of return times, Rii = 1/πi. Given distributions σ, τ and k ∈ S, the exit frequency xk(σ, τ) denotes the expected number of times a random walk exits state k before an optimal stopping rule from σ to τ halts the walk. For a target distribution τ, we define Xτ as the n × n matrix given by (Xτ)ij = xj(i, τ), where i also denotes the singleton distribution on state i. The dual Markov chain with transition matrix = RM⊤R−1 is called the reverse chain. We prove that Markov chain duality extends to matrices of exit frequencies. Specifically, for each target distribution τ, we associate a unique dual distribution τ*. Let $\\\\rX_{\\\\fc{\\\\t}}$ denote the matrix of exit frequencies from singletons to τ* on the reverse chain. We show that $\\\\rX_{\\\\fc{\\\\t}} = R (X_{\\\\t}^{\\\\top}-\\\\vb^{\\\\top} \\\\one)R^{-1}$, where b is a non-negative constant vector (depending on τ). We explore this exit frequency duality and further illuminate the relationship between stopping rules on the original chain and reverse chain.\",\n \"Title: Reversal of Markov Chains and the Forget Time\\nYear: 1998\\n\\nAbstract: We study three quantities that can each be viewed as the time needed for a finite irreducible Markov chain to ‘forget’ where it started. One of these is the mixing time, the minimum mean length of a stopping rule that yields the stationary distribution from the worst starting state. A second is the forget time, the minimum mean length of any stopping rule that yields the same distribution from any starting state. The third is the reset time, the minimum expected time between independent samples from the stationary distribution.Our main results state that the mixing time of a chain is equal to the mixing time of the time-reversed chain, while the forget time of a chain is equal to the reset time of the reverse chain. In particular, the forget time and the reset time of a time-reversible chain are equal. Moreover, the mixing time lies between absolute constant multiples of the sum of the forget time and the reset time.We also derive an explicit formula for the forget time, in terms of the 'access times' introduced in [11]. This enables us to relate the forget and reset times to other mixing measures of the chain.\",\n \"Title: Some Mathematics behind Graph Property Testing\\nYear: 2008\\n\\nAbstract: Graph property testing is the third reincarnation of the same general question, after statistics and learning theory. In its\\n simplest form, we have a huge graph (we don’t even know its size), and we draw a sample of the node set of bounded size. What\\n properties of the graph can be deduced from this sample?\\n \\n The graph property testing model was first introduced by Goldreich, Goldwasser and Ron (but related questions were considered\\n before). In the context of dense graphs, a very general result is due to Alon and Shapira, who proved that every hereditary\\n graph property is testable.\\n \\n \\n \\n Using the theory of graph limits, Lovász and Szegedy defined an analytic version of the (dense) graph property testing problem,\\n which can be formulated as studying an unknown 2-variable symmetric function through sampling from its domain and studying\\n the random graph obtained when using the function values as edge probabilities. This analytic version allows for simpler formulation\\n of the problems, and leads to various characterizations of testable properties. These results can be applied to the original\\n graph-theoretic property testing. In particular, they lead to a new combinatorial characterization of testable graph properties.\\n \\n \\n \\n We survey these results, along with analogous results for graphs with bounded degree.\",\n \"Title: The rank of connection matrices and the dimension of graph algebras\\nYear: 2006\\n\\nAbstract: Connection matrices were introduced in [M. Freedman, L. Lovász, A. Schrijver, Reflection positivity, rank connectivity, and homomorphism of graphs (MSR Tech Report # MSR-TR-2004-41) ftp://ftp.research.microsoft.com/pub/tr/TR-2004-41.pdf], where they were used to characterize graph homomorphism functions. The goal of this note is to determine the exact rank of these matrices. The result can be rephrased in terms of the dimension of graph algebras, also introduced in the same paper. Yet another version proves that if two k-tuples of nodes behave in the same way from the point of view of graph homomorphisms, then they are equivalent under the automorphism group.\",\n \"Title: Proving Integrality Gaps without Knowing the Linear Program\\nYear: 2006\\n\\nAbstract: Proving integrality gaps for linear relaxations of NP optimization problems is a difficult task and usually undertaken on a case-by-case basis. We initiate a more systematic approach. We prove an integrality gap of 2 o(1) for three families of linear relaxations for VERTEX COVER, and our methods seem relevant to other problems as well.\",\n \"Title: Brick Decompositions And The Matching Rank Of Graphs\\nYear: 1982\\n\\nAbstract: The number of linearly independent perfect matchings of a graph — or, equivalently, the dimension of the perfect matching\\n polytope — is determined in various senses. First it is shown that the fact that every linear objective function can be optimized\\n over the perfect matchings in polynomial time implies the existence of a polynomial-time algorithm to determine the dimension\\n of this set. This observation also yields polynomial algorithms to determine, among others, the number of linearly independent\\n common bases of two matroids and the number of linearly independent maximum stable sets in claw-free or perfect graphs. For\\n the case of perfect matchings, Naddef’s minimax theorem for the dimension of the perfect matching polytope is strengthened\\n and it is shown how the decomposition theory of matchings in graphs can be applied to derive a particularly simple formula\\n for this dimension. This formula is based upon the number of constituents of a certain decomposition of the graph which we\\n call a brick decomposition. Finally, these results are applied to obtain a description of the facets of the perfect matching\\n polytope.\",\n \"Title: Energy of convex sets, shortest paths, and resistance\\nYear: 2001\\n\\nAbstract: Let us assign independent, exponentially distributed random edge lengths to the edges of an undirected graph. Lyons, Pemantle, and Peres (1999, J. Combin. Theory Ser. A 86 (1999), 158–168) proved that the expected length of the shortest path between two given nodes is bounded from below by the resistance between these nodes, where the resistance of an edge is the expectation of its length. They remarked that instead of exponentially distributed variables, one could consider random variables with a log-concave tail. We generalize this result in two directions. First, we note that the variables do not have to be independent: it suffices to assume that their joint distribution is log-concave. Second, the inequality can be formulated as follows: the expected length of a shortest path between two given nodes is the expected optimum of a stochastic linear program over a flow polytope, while the resistance is the minimum of a convex quadratic function over this polytope. We show that the inequality between these quantities holds true for an arbitrary polytope provided its blocker has integral vertices.\",\n \"Title: Critical Facets of the Stable Set Polytope\\nYear: 2001\\n\\nAbstract: Dedicated to the memory of Paul Erdős \\n A facet of the stable set polytope of a graph G can be viewed as a generalization of the notion of an -critical graph. We extend several results from the theory of -critical graphs to facets. The defect of a nontrivial, full-dimensional facet of the stable set polytope of a graph G is defined by . We prove the upper bound for the degree of any node u in a critical facet-graph, and show that can occur only when . We also give a simple proof of the characterization of critical facet-graphs with defect 2 proved by Sewell [11]. As an\\n application of these techniques we sharpen a result of Surányi [13] by showing that if an -critical graph has defect and contains nodes of degree , then the graph is an odd subdivision of .\",\n \"Title: The geometry of logconcave functions and sampling algorithms\\nYear: 2007\\n\\nAbstract: We derive several limit results for the profile of random plane-oriented recursive trees. These include the limit distribution of the normalized profile, asymptotic bimodality of the variance, asymptotic approximation to the expected width, and the correlation ...\",\n \"Title: Positive graphs.\\nYear: 2016\\n\\nAbstract: We study \\\"positive\\\" graphs that have a nonnegative homomorphism number into every edge-weighted graph (where the edgeweights may be negative). We conjecture that all positive graphs can be obtained by taking two copies of an arbitrary simple graph and gluing them together along an independent set of nodes. We prove the conjecture for various classes of graphs including all trees. We prove a number of properties of positive graphs, including the fact that they have a homomorphic image which has at least half the original number of nodes but in which every edge has an even number of pre-images. The results, combined with a computer program, imply that the conjecture is true for all graphs up to 9 nodes.\",\n \"Title: Measures on the square as sparse graph limits\\nYear: 2019\\n\\nAbstract: We study a metric on the set of finite graphs in which two graphs are considered to be similar if they have similar bounded dimensional “factors”. We show that limits of convergent graph sequences in this metric can be represented by symmetric Borel measures on [0,1]2. This leads to a generalization of dense graph limit theory to sparse graph sequences.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":74,"string":"74"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13277,"string":"13,277"}}},{"rowIdx":13278,"cells":{"question_id":{"kind":"string","value":"Hideki Koike"},"question":{"kind":"string","value":"Generate an abstract for the title \"IPMatrix: an effective visualization framework for cyber threat monitoring\" using the following items: 1. Internet cyber threat monitoring\n2. Statistical analysis\n3. 2-dimensional matrix representation\n4. Visualization framework\n5. Propagation of Welchia and Sasser.D worms"},"answer":{"kind":"string","value":"An effective Internet cyber threat monitoring system detects cyber threats using network sensors deployed at particular points on the Internet, statistically analyses the time of attack, source of attack, and type of attack, and then visualizes the result of this analysis. Existing systems, however, simply visualize country-by-country statistics of attacks or hourly changes of attacks. Using these systems, it is difficult to understand the source of attack, the diffusion of the attack, or the relation between the target and the source of the attack. This paper described a method for visualizing cyber threats by using 2-dimensional matrix representation of IP addresses. The advantages of this method are that: (I) the logical distance of IP addresses is represented intuitively, (2) Internet address space is visualized economically, (3) macroscopic information (site level) and microscopic information (local level) are visualized simultaneously. By using this visualization framework, propagation of the Welchia worm and the Sasser.D worm are visualized."},"correct_answer":{"kind":"string","value":"An effective Internet cyber threat monitoring system detects cyber threats using network sensors deployed at particular points on the Internet, statistically analyses the time of attack, source of attack, and type of attack, and then visualizes the result of this analysis. Existing systems, however, simply visualize country-by-country statistics of attacks or hourly changes of attacks. Using these systems, it is difficult to understand the source of attack, the diffusion of the attack, or the relation between the target and the source of the attack. This paper described a method for visualizing cyber threats by using 2-dimensional matrix representation of IP addresses. The advantages of this method are that: (I) the logical distance of IP addresses is represented intuitively, (2) Internet address space is visualized economically, (3) macroscopic information (site level) and microscopic information (local level) are visualized simultaneously. By using this visualization framework, propagation of the Welchia worm and the Sasser.D worm are visualized."},"documents":{"kind":"list like","value":["Title: SplashDisplay\nYear: 2012\n\nAbstract: 'SplashDisplay' is a system developed to attempt real time volumetric display. This system implements air pressure generated by an x-y coordinate based projectile launching speaker through a bed of projectile beads to simulate a real-time 3D \"explosion\" like effect. The projectile beads act as a projection medium for a top-mounted visible light projector; and through synchronized timing of these components, it is possible to create 3D, tangible effects at will. Also, by using IR LED and IR sensitive cameras, user interaction can be added to this system to allow for an interactive surface. The result of these components is a dynamic, interactive, real-time \"explosion\" simulation game that can be used to confirm the innovative construct of this suggested system.","Title: AquaTop display\nYear: 2013\n\nAbstract: AquaTop display is a projection system that uses white water as a screen surface. This system allows the user ' s limbs to freely move through, under and over the projection surface. Using the unique characteristics of fluid, we propose new interactions methods specific to the projection medium, water. Our system uses a depth camera to detect input on and over the water surface to allow for interactions such as protruding fingers out from under the water surface and scooping up the water with both hands. This type of interaction is not capable with current impenetrable, rigid body, flat surfaces. For example, by floating one's limbs on the water surface, it is also possible to fuse one's body with the displayed objects for further augmented interaction by 'becoming one' with the screen.","Title: AquaTop display: a true \\\"immersive\\\" water display system\nYear: 2013\n\nAbstract: The AquaTop Display is system that uses the projection of images onto cloudy water. This system allows the users limbs to freely move through, under and over the projection surface. As the projection medium is fluid, we propose new interaction methods specific to this medium by using a Kinect Depth Sensor. Scooping up water, protruding fingers out from underneath the water surface are capable with this system. These type of interactions are not normally possible with current impenetrable rigid surfaces. Using mapped projection, AquaTop Display also augments one's limbs on the water surface, providing a environment for an 'immersible' experience, allowing the users to become one with the screen.","Title: Ubiquitous Display for Dynamically Changing Environments\nYear: 2003\n\nAbstract: This paper proposes a novel method for ubiquitous displays using projectors in indoor environments. In particular, our method consists of two distinct features: automatic scene modeling of a dynamically changing indoor environment, and automatic selection of surfaces onto which various contents are displayed by taking into account both geometric and photometric properties. As a result, our method can be applied to dynamically changing scenes such as a meeting room where furniture and other objects are moved frequently.","Title: 3-D interaction with a large wall display using transparent markers\nYear: 2010\n\nAbstract: In this paper we proposed a new interface for interacting with large displays via small video devices such as a cell phone. We estimate the location of the camera relative to the display using a matrix of transparent markers embedded on display. As a result, our interface allows the user to interact with digital contents without being distracted by opaque visual markers. Our interface enables intuitive interactions such as pointing, rotating, dragging and dropping. Moreover, our use of a small hand-held camera device allows for interaction with large scale displays without the need for direct contact with the display surface. Thus our system is well suited for interactions when there is some distance between the user and the display. Our proposed system has applications to large scale advertisement displays and can enable interactions between individuals and large scale digital content.","Title: Bouncing Star project: design and development of augmented sports application using a ball including electronic and wireless modules\nYear: 2010\n\nAbstract: In our project, we created a new ball, \"Bouncing Star\" (Hane-Boshi in Japanese), comprised of electronic devices. We also created augmented sports system using Bouncing Star and a computer program to support an interface between the digital and the physical world. This program is able to recognize the ball's state of motion (static, rolled, thrown, bound, etc.) by analyzing data received through a wireless module. The program also tracks the ball's position through image recognition techniques. On this system, we developed augmented sports applications which integrate real time dynamic computer graphics and responsive sounds which are synchronized with the ball's characteristics of motion. Our project's goal is to establish a new dynamic form of entertainment which can be realized through the combination of the ball and digital technologies.","Title: Online Anomal Movement Detection Based On Unsupervised Incremental Learning\nYear: 2008\n\nAbstract: We propose an online anomal movement detection method using incremental unsupervised learning. As the feature for discrimination, vile extract the principal component of the spatio-temporal feature by incremental PCA. We then detect anomal movements by an incremental 1-class SVM. In order to use principal component as the feature for discrimination while supporting incrementation of the subspace, we modify the SVM kernel function to take account of the difference in distance scale between the principal component feature vectors and that of the feature vectors after the subspace is recremented. This allows us to efficiently conduct the re-learning process even though the dimension of the original input spatio-temporal feature is high. Experiments show that anomal scenes can be detected without the cost of preparing a lot of labeled data for preliminary learning.","Title: EdgeSonic: image feature sonification for the visually impaired\nYear: 2011\n\nAbstract: We propose a framework to aid a visually impaired user to recognize objects in an image by sonifying image edge features and distance-to-edge maps. Visually impaired people usually touch objects to recognize their shape. However, it is difficult to recognize objects printed on flat surfaces or objects that can only be viewed from a distance, solely with our haptic senses. Our ultimate goal is to aid a visually impaired user to recognize basic object shapes, by transposing them to aural information. Our proposed method provides two types of image sonification: (1) local edge gradient sonification and (2) sonification of the distance to the closest image edge. Our method was implemented on a touch-panel mobile device, which allows the user to aurally explore image context by sliding his finger across the image on the touch screen. Preliminary experiments show that the combination of local edge gradient sonification and distance-to-edge sonification are effective for understanding basic line drawings. Furthermore, our tests show a significant improvement in image understanding with the introduction of proper user training.","Title: Interactive object registration and recognition for augmented desk interface\nYear: 2001\n\nAbstract: Identification of objects in a real world plays a key role for human-computer interaction in a computer-augmented environment using augmented reality techniques. For providing natural interaction in such environments, it is necessary for an interface system to know which objects a user is using. In the previously developed interface systems, real objects are identified by using specially designed tags attached to objects. In this work, we propose a new method for interactive object recognition and registration for more natural and intuitive interaction without using any tags. In particular, we introduce interactive object registration and recognition by combining direct manipulation with user's hands and a color-based object recognition algorithm.","Title: Image correction techniques for 3D interactive surface using a transparent elastic gels\nYear: 2012\n\nAbstract: There are many kinds of three dimensional displays that have been developed to date. Most of them provide 3D visual sensation to the users, but they do not provide 3D haptic feedback. On the other hand, an interactive surface system using transparent gels enables users to touch a 3D surface. The main issue of the system, however, is that the image is distorted due to \"lens effect\" of the gels. This paper describes a method to solve the image distortion through the use of a light field display (LFD) which is discussed in detail in section 2. By combining the LFD and transparent gel interface, it becomes possible to show correct 3D images on gels from any viewing position.","Title: Monocular 3D tracking of multiple interacting targets\nYear: 2008\n\nAbstract: In this paper we present a new approach based on Markov Chain Monte Carlo (MCMC) for the stable monocular tracking of variable interacting targets in 3D space. The crucial problem with monocular tracking multiple targets is that mutual occlusions on the 2D image cause target conflict(change ID, merge targets...). We focus on the fact that multiple targets cannot occupy the same position in 3D space and propose to track multiple interacting targets using relative position of targets in 3D space. Experiments show that our system can stably track multiple humans that are interacting with each other","Title: An augmented tabletop video game with pinching gesture recognition\nYear: 2008\n\nAbstract: A novel interaction technique for multi-player tabletop entertainment systems. The proposed technique achieves high-response bi-manual interaction by multiple users, and it simultaneously recognises the positions and orientations of the users' hands and pinching gestures.","Title: PhotoelasticBall: a touch detectable ball using photoelasticity\nYear: 2014\n\nAbstract: Balls are a key equipment for sports and entertainments such as juggling, etc. Then, much research has been conducted for developing balls of the next generation to enhance ball related entertainments. Such balls have plenty of special effects such as sound and light, but have limited input method. Those effects are often controlled through ball's native motion by using accelerometers, etc. However, as increasing the variety of special functions of such balls, the appropriate input method should be required. In this research, we developed a force vector sensor sheet unit that can be implemented on the surface of the ball. In this paper, we report the detail of the sensory system and its experimental results.","Title: An Incremental Learning Method for Unconstrained Gaze Estimation\nYear: 2008\n\nAbstract: This paper presents an online learning algorithm for appea- rance-based gaze estimation that allows free head movement in a casual desktop environment. Our method avoids the lengthy calibration stage using an incremental learning approach. Our system keeps running as a background process on the desktop PC and continuously updates the estimation parameters by taking user's operations on the PC monitor as input. To handle free head movement of a user, we propose a pose-based clustering approach that efficiently extends an appearance manifold model to handle the large variations of the head pose. The effectiveness of the proposed method is validated by quantitative performance evaluation with three users.","Title: NIGELOG: Protecting Logging Information by Hiding Multiple Backups in Directories\nYear: 1999\n\nAbstract: This paper proposed a novel method to protect logging information more securely. The method, named NIGELOG, produces multiple backups of the logging information, hides them in arbitrary directories, and periodically moves these backups to other directories. Also, NIGELOG can automatically restore the original logging information by using the backups even if the original file is altered or deleted by intruders. NIGELOG is implemented as a C++ class library with some standard software libraries and does not require additional hardwares such as write-once media. It is easier to apply NIGELOG to UNIX applications which produces log-files.","Title: Real-Time Tracking of Multiple Fingertips and Gesture Recognition for Augmented Desk Interface Systems\nYear: 2002\n\nAbstract: In this paper, we propose a fast and robust method for tracking a user's hand and multiple fingertips; we then demonstrate gesture recognition based on measured fingertip trajectories for augmented desk interface systems.Our tracking method is capable of tracking multiple fingertips in a reliable manner even in a complex background under a dynamically changing lighting condition without any markers. First, based on its geometrical features, the location of each fingertip is located in each input infrared image frame. Then, correspondences of detected fingertips between successive image frames are determined based on a prediction technique.Our gesture recognition system is particularly advantageous for Human-Computer Interaction (HCI) in that users can achieve interactions based on symbolic gestures at the same time that they perform direct manipulation with their own hands and fingers. The effectiveness of our proposed method has been successfully demonstrated via a number of experiments.","Title: Information retrieval system using real world objects\nYear: 2000\n\nAbstract: In this position paper we bring a new approach — Value-Sensitive Design — to understanding the value implications of augmented reality. We examine seven values: psychological well-being, physical well-being, privacy, deception, informed consent, ownership and property, and trust. In addition, we briefly describe our work where we apply a Value-Sensitive Design approach to augmented reality of the natural world.","Title: Transparent 2-D markers on an LCD tabletop system\nYear: 2009\n\nAbstract: Tabletop systems are currently being focused on and many applications using these systems are being developed. In such tabletop systems, how to recognize real objects on the table is an essential and important issue. In existing tabletop systems, 2-D markers have been often used. However, their black-and-white pattern, which means nothing to humans, spoils the appearance of the object. We developed transparent markers on a liquid crystal display (LCD) tabletop system by using the polarization features of the LCD and optical lms. In particular, through experiments with various kinds of optical films, we found that two halfwave plates make the markers rotation invariant. By using the transparent markers, tangible transparent Magic Lenses(TM) applications were developed.","Title: Two-handed drawing on augmented desk system\nYear: 2002\n\nAbstract: This paper describes a two-handed drawing tool developed on our augmented desk system. Using our real-time finger tracking method, a user can draw and manipulate objects interactively by his/her own finger/hand. Based on the former work on two-handed interaction, different roles are assigned to each hand. The right hand is used to draw and to manipulate objects. Using gesture recognition, primitive objects can be drawn by users' handwriting. On the other hand, the left hand is used to manipulate menus and to assist the right hand. By closing all left hand fingers, users can initiate the appearance of structural radial menus around their left hands, and can select appropriate items by using a left hand finger. The left hand is also used to assist in the performance of drawing tasks, e.g., specifying the center of a circle or top-left corner of a rectangle, or specifying the object to be copied.","Title: Analysis and evaluation of recommendation systems\nYear: 2007\n\nAbstract: Popular online services, such as Amazon.com, provide recommendations for users by using other users' rating scores for items. In this study, we describe three types of rating systems: score-rated, count-rated, and digital-rated. We hypothesize that digital-rated systems provide the most useful recommendations. Then we analyze the differences in the results of the rating when the granularity of the score changes. Finally, we visualize users by developing a 2-D visualization system that uses a multi-dimensional scaling method.","Title: EnhancedTable: an augmented table system for supporting face-to-face meeting in ubiquitous environment\nYear: 2004\n\nAbstract: This paper describes our design and implementation of an augmented table system for face-to-face meetings. The system was designed to be used by multiple users in the ubiquitous environment, where people do not need to bring their laptop PCs. With effective use of the advantage of computer vision, we implemented the concept of ubiquitous desktop as personal workspace and virtual Chinese table as shared workspace. User can share/personalize files by drag-and-dropping icons to/from the shared workspace. The system also provides capabilities for interactive image capturing and finger position sharing, both of which would be useful for the meetings.","Title: Model based human motion tracking using probability evolutionary algorithm\nYear: 2008\n\nAbstract: A novel evolutionary algorithm called probability evolutionary algorithm (PEA), and a method based on PEA for visual tracking of human motion are presented. PEA is inspired by estimation of distribution algorithms and quantum-inspired evolutionary algorithm, and it has a good balance between exploration and exploitation with very fast computation speed. The individual in PEA is encoded by the probability vector, defined as the smallest unit of information, for the probabilistic representation. The observation step is used in PEA to obtain the observed states of the individual, and the update operator is used to evolve the individual. In the PEA based human tracking framework, tracking is considered to be a function optimization problem, so the aim is to optimize the matching function between the model and the image observation. Since the matching function is a very complex function in high-dimensional space, PEA is used to optimize it. Experiments on 2D and 3D human motion tracking demonstrate the effectiveness, significance and computation efficiency of the proposed human tracking method.","Title: PAC-PAC: pinching gesture recognition for augmented tabletop video game\nYear: 2009\n\nAbstract: A novel tabletop entertainment system that allows simultaneous interactions by multiple participants was developed. The newly developed interaction technique of this system recognizes a pinching gesture performed with the thumb and forefinger. This gesture recognition technique enables rapid response and high degree-of-freedom input for the players.","Title: Highly deformable interactive 3D surface display\nYear: 2012\n\nAbstract: In this research, we focused on the flexibility limitation of a display material as one of the main causes for height con-straints in deformable surfaces. We propose a method that does not only utilize the material flexibility but also allows for increased variations of shapes and their corresponding interaction possibilities. Using this method, our proposed display design can then support additional expansion via protrusion of an air-pressure-controlled moldable display surface using a residual cloth-excess method and a fixed airbag mount.","Title: Task-Oriented Evaluation of Super-Resolution Techniques\nYear: 2010\n\nAbstract: The goal of super-resolution (SR) techniques is to enhance the resolution of low-resolution (LR) images. How to evaluate the performance of an SR algorithm seems to be forgotten when researchers keep producing algorithms. This paper presents a task-oriented method for evaluating SR techniques. Our method includes both objective and subjective measures and is designed from the viewpoint of how SR impacts many essential image processing and vision tasks. We evaluate some state-of-the-art SR algorithms and the results suggest that different SR algorithms should be utilized for different applications. In general, they reflect the consistency and conflict between objective and subjective measures as well as computer vision systems and human vision systems do.","Title: Information layout and interaction techniques on an augmented round table\nYear: 2005\n\nAbstract: Round tabletop display systems are currently being promoted, but the optimal ways to use these systems to display a large amount of information or how to interact with them have not been considered. This paper describes information presentation and interaction technique for a large number of files on a round tabletop display system. Three layouts are explored on our augmented table system: sequential layout, classification layout, and spiral layout. Users can search and find files by virtually rotating the circular display using a ”hands-on” technique.","Title: STARMINE: a visualization system for cyber attacks\nYear: 2006\n\nAbstract: In cyber attack monitoring systems, various types of visualizations, such as geographical visualization, temporal visualization, logical visualization, are being used. Each visualization has its own advantages and disadvantages. Since it is important to analyze the information from different viewpoints and to make a right decision in practical cyber attack monitoring, these visualization should be highly integrated.This paper describes a visualization system for cyber threat monitoring named STARMINE, which integrates three different views, that is geographical, temporal, and logical views, of the cyber threat in 3-D space. Since three views are seen simultaneously and are synchronized, it is helpful for administrators to analyze the threats much more easily. As an example, the propagation of Sasser.D worm were shown.","Title: Pac-pac: pinching gesture recognition for tabletop entertainment system\nYear: 2010\n\nAbstract: We present our new interaction technique for tabletop system and video game application using it. This technique is for recognizing a pinching gesture performed with the thumb and forefinger and tapping gestures with them by a ceiling camera above the table. The positions and orientations of multiple gestures are recognized at 200Hz, and the users around the table are tracked by their arm positions. This technique is useful for large-sized tabletop system for multiple users because it does not require any additional equipments to them nor registration process before using the system. Some pilot studies demonstrated the robustness and accuracy of the proposed technique with displaying appropriate guide on the display. We also developed a video game application called \"Pac-pac\". A player can shoot a bullet along the orientation of his hand by tapping, from any side of the table. We exhibited Pac-pac several time and well-received.","Title: AquaTop display: interactive water surface for viewing and manipulating information in a bathroom\nYear: 2013\n\nAbstract: Due to the wide spread use of smart phones and PCs, people can access information everywhere in everyday life. However, there are very few methods to access content within an bathing environment. Some people carry smart phones into a bathroom but it is unnatural to be holding a device during bathing. This paper proposes an interactive water surface display system, in which information is projected on the surface of a white water solution and users can interact with this information using gestures. In this paper, we discuss interaction design in a bathroom, describing an implementation of our system and its proposed applications.","Title: Enhanced interaction with physical toys\nYear: 2011\n\nAbstract: We developed an entertainment system that enhances the experience of playing with tops by employing augmented reality technologies. A tabletop system tracks the positions and rotation speeds of multiple tops with a high-speed camera and displays audio and visual effects. A hand-held device, called an accelerator, enables virtual and physical contact between the user and top by allowing the user to move and accelerate the top and obtain force feedback from the top. We proposed a top battle game in which the player will interact with these tops.","Title: ZASH: A Browsing System for Multi-Dimensional Data\nYear: 1999\n\nAbstract: This paper described a browsing system for movie database. The system, named ZASH, was designed and developed to explore the following features: (1) the use of multiple 2D planes to separate different types of information; (2) the use of 3D space to improve the visibility of links between nodes; (3) the use of multidimensional scaling to lay out movies and commentators so that the similar data are placed physically near each other; (4) the use of graphical fisheye views to improve the visibility of users' focus and its neighbors. The system also provides traditional searching methods (e.g. keyword, director, production year, etc.). By integrating browsing techniques and searching techniques, the system gave users more chances to find movies.","Title: How does 3-D visualization work in software engineering?: empirical study of a 3-D version/module visualization system\nYear: 1998\n\nAbstract: Version control and module management are very important in practical software development. In UNIX, RCS or SCCS is used in general as version control tools. They, however, have a couple of drawbacks. This paper proposed a solution for these issues by applying 3-D visualization. The prototype system, VRCS, was developed. In our system, each version information stored in a RCS history file is displayed as a 2-D tree by taking the z-axis as time. Other 2-D trees are laid out in 3-D space in the same way. In our visualization, files which compose a certain release of the software are connected by line called relation-link. By using GUIs, users can check in/out each version easily and interactively. More importantly, just by choosing the relation-link, a certain release is rebuilt automatically. Three comparative experiments between VRCS and RCS were conducted to know the effectiveness of VRCS. The result shows that VRCS is faster in checking in/out than RCS.","Title: Human-centric panoramic imaging stitching\nYear: 2012\n\nAbstract: We introduce a novel image mosaicing algorithm to generate 360° landscape images while also taking into account the presence of people at the boundaries between stitched images. Current image mosaicing techniques tend to fail when there is extreme parallax caused by nearby objects or moving objects at the boundary between images. This parallax causes ghosting or unnatural discontinuities in the image. To address this problem, we present an image mosaicing algorithm that is robust to parallax and misalignment, and is also able to preserve the important human-centric content, specifically faces. In particular, we find an optimal path between the boundary of two images that preserves color continuity and peoples' faces in the scene. Preliminary results show promising results of preserving close-up faces with parallax while also being able to generate a perceptually plausible 360° panoramic image.","Title: The role of another spatial dimension in software visualization\nYear: 1993\n\nAbstract: The pr]mary objective of this article is to demonstrate the use of 3D-computer graphics m visualizing shapeless software information by focusing on performance monitormg of parallel/concurrent computer systems. Issues are addressed from two different perspectives. expressiveness of output media and user cognition The former describes the limitations of 2D output media. The latter refers to a user’s cognitive load when us]ng 2D representations in a multlple-window environment. We show how these problems can be minim]zed by using a 3D framework A prototype vlsuabzation system called VOGUE has been developed A 3D framework is used to visualize the execution pattern of two parallel,’concurrent computer systems an electrlc power control system and a parallel mampulator system Thrmgb these visuahzations, we show the effectiveness of our framework The apphcations of 3D frameworks to other kinds of software reformation are also described.","Title: SHIRI: buttocks humanoid that represents emotions with visual and tactual transformation of the muscles\nYear: 2012\n\nAbstract: In this paper, we propose a novel interface design for \"human-robot\" communication by focusing on visual and tactual transformation of the muscles. Since recent humanoids may appear as humanoid figures using human-like body gestures and behavior, it is hard to say that they have enough elements to cover the complex composition that is a human. The muscles that constitute the human body work by not only turning joints and generating limb and body movements, but also control skin surface shape and firmness, allowing the various levels of touch response. Therefore, we attempt to approach the creation of sensitive and subtle expression by a humanoid robot using organic constructs. In this project, we produce \"SHIRI\", which represents emotions with organic movements of the Gluteus Maximus Actuator (GMA). In addition, we also implement user interaction for SHIRI and consider how perceptions the user can obtain by communicating with SHIRI.","Title: Interactive surface that have dynamic softness control\nYear: 2012\n\nAbstract: In the field of interface surface research, the idea of the 'softness' of a surface medium is one significant factor in determining a suitable means of interaction with the user. With direct touch input, for example, the degree of surface softness allows for the generation various touch sensations and tactile feedback. Additionally, the softness also affects the shape of the surface: a soft surface will allow the user to deform the surface at will while a hard surface will maintain its shape easier. In many traditional flexible surfaces to date, this element has been considered static and thus unchangeable. This project, in contrast, considers the softness of a surface to be dynamic and thus further explores the interaction possibilities with this type of surface. We demonstrate the possibilities of dynamically changing surfaces and their derived user interaction.","Title: Recognizing objects with multiple configurations\nYear: 2014\n\nAbstract: Computer vision has been extensively adopted in many domains during the last three decades. One of the main goals of computer vision applications is to recognize objects. Generally, computers can successfully achieve object recognition by relying on a large quantity of data. In real world, some objects may own diverse configurations or/and be observed at various angles and positions, and the process of object recognition is denoted as recognizing objects in dynamic state. It is difficult to collect enough data to achieve the sorts of objects recognition. In order to resolve the problem, we propose a technique to achieve object recognition which is not only in static state where the objects do not own multiple configurations, but also in dynamic state. First, we apply an effective robust algorithm to obtain landmarks from objects in two dimensional images. With the algorithm, the number of landmarks from different objects can be appointed in advance. A set of landmarks as a point is projected into a pre-shape space and a shape space. Next, a method is proposed to create a surface among three basic data models in a pre-shape space. If basic data are too few to create a surface or a curve, a new basic data can be built from the basic data. Then, a series of new data models can be obtained from these basic data in a pre-shape space. Finally, object recognition can be achieved by using the new data models in shape space. We give some examples to show the algorithms are efficient not only for the objects with noises, but also for the ones with various configurations.","Title: Head Pose Estimation System Based on Particle Filtering with Adaptive Diffusion Control\nYear: 2005\n\nAbstract: In this paper, we propose a new tracking system based on a stochastic filtering framework for reliably estimating the 3D pose of a user's head in real-time. Our system es- timates the pose of a user's head in each image frame whose 3D model is automatically obtained at an initiali- zation step. In particular, our estimation method is designed to control the diffusion factor of a motion model adaptively. This technique contributes significantly to improving the following performance simultaneously: the robust tracking against abrupt head motion and the accu- rate pose estimation when the user is staring at a point in a scene. The performance of our proposed method has been successfully demonstrated via experiments.","Title: Facial parts-based face hallucination method\nYear: 2010\n\nAbstract: Face hallucination produces high-resolution facial images from low-resolution inputs. In this paper, we propose a facial-parts-based face hallucination method. Since our goal is face recognition rather than face reconstruction, the contour information of facial-parts(such as eyes) is important. This method reconstructs facial parts as entities instead of dividing them into small blocks. We obtain the contours of facial parts by using the Active Appearance Model(AAM), and transform training images based on contours. We confirm that the proposed method significantly enhances face recognition performance.","Title: Vision-Based Face Tracking System for Large Displays\nYear: 2002\n\nAbstract: In this paper, we present a stereo-based face tracking system which can track the 3D position and orientation of a user in real-time, and the system's application for interaction with a large display. Our tracking system incorporates dynamic update of template images for tracking facial features so that the system can successfully track a user's face for a large angle of rotation. Another advantage of our tracking system is that it does not require a user to manually initialize the tracking process, which is critical for natural and intuitive interaction. Based on our face tracking system, we have implemented several prototype applications which change information shown on a large display adaptively according to the location looked at by a user.","Title: HOJI*HOJI: the hole-type interactive device for entertainment\nYear: 2012\n\nAbstract: Holes often excite our curiosity and eventually people will want to look inside them. In this research, focusing the attention on this \"hole exploration\" and corresponding actions as an element of interactive entertainment, we developed a hole-type device named \"HOJI*HOJI\". HOJI*HOJI is equipped with a hole that can recognize finger position through the use of pressure sensors. Force feedback is also implemented within the device for realizing interaction between the 'hole' and the user's finger. For proof of concept, an interactive game application was made for HOJI*HOJI and was exhibited at 2 events to confirm whether this 'hole' can attract people's curiosity.","Title: Fast Tracking of Hands and Fingertips in Infrared Images for Augmented Desk Interface\nYear: 2000\n\nAbstract: In this paper, we introduce a fast and robust method for tracking positions of the centers and the fingertips of both right and left hands. Our method makes use of infrared camera images for reliable detection of user's hands, and uses template matching strategy for finding fingertips. This method is an essential part of our augmented desk interface in which a user can, with natural hand gestures, simultaneously manipulate both physical objects and electronically projected objects on a desk, e.g., a textbook and related WWW pages. Previous tracking methods which are typically based on color segmentation or background subtraction simply do not perform well in this type of application because an observed color of human skin and image backgrounds may change significantly due to projection of various objects onto a desk. In contrast, our proposed method was shown to be effective even in such a challenging situation through demonstration in our augmented desk interface. This paper describes the details of our tracking method as well as typical applications in our augmented desk interface.","Title: Experiencing the ball's POV for ballistic sports\nYear: 2013\n\nAbstract: We place a small wireless camera inside an American football to capture the ball's point-of-view during flight to augment a spectator's experience of the game of football. To this end, we propose a robust video synthesis algorithm that leverages the unique constraints of fast spinning cameras to obtain a stabilized bird's eye point-of-view video clip. Our algorithm uses a coarse-to-fine image homography computation technique to progressively register images. We then optimize an energy function defined over pixel-wise color similarity and distance to image borders, to find optimal image seams to create panoramic composite images. Our results show that we can generate realistic videos from a camera spinning at speeds of up to 600 RPM.","Title: Snaplink: Interactive Object Registration And Recognition For Augmented Desk Interface\nYear: 2001\n\nAbstract: Identification of objects in a real world plays a key role for human-computer interaction in a computer-augmented environment using augmented reality techniques. To provide natural and intuitive interaction in such environments, it is necessary for an interface system to know which objects a user is using. In previously developed interface systems, real objects are identified by using specially designed tags attached to objects. In this work, we propose a new method for interactive object recognition and registration; the method permits more natural and intuitive interaction without using any tags. In particular, we introduce interactive object registration and recognition by combining direct manipulation by the user's hands with a color-based object recognition algorithm.","Title: Tudumi: information visualization system for monitoring and auditing computer logs\nYear: 2002\n\nAbstract: Computer security breaches are already a major problem in using computers. The most basic defense against it is to monitor and audit the computer logs. Computer logs, however have a huge amount of textual data. It is, therefore, almost impossible to inspect them manually using current systems. We propose a log visualization system called \"Tudumi\". Tudumi consists of several functions which assist system administrators to perform such tasks manually. These functions are information visualization, log summarization and reflecting known rules into the visualization method. Tudumi makes it easier to detect anomalous user activities, such as intrusion, from a huge amount of computer logs.","Title: LivingClay: particle actuation to control display volume and stiffness\nYear: 2013\n\nAbstract: We present a new type of display actuation that is able to control both display geometry and stiffness properties using a filler material and air flow control technique. The display consists of a flat, flexible layer of cells on the surface and chamber filled with particles under it. Display geometries can be changed by transporting an amount of particles between display cells and the particle chamber using pressured air and vacuum to control the air flows. This system also allow for variable stiffness using vacuum technique to harden the particles inside chamber. In this paper, we present the design and control technique of this new type actuator and also possible interaction on a single actuator display. We also propose a low-cost, effective way to control an array of actuators where the air flow line and particle line are arranged in a multiplexed grid configuration.","Title: InteractiveTop: an entertainment system that enhances the experience of playing with tops\nYear: 2011\n\nAbstract: A top is a simple toy, yet its behavior is unique when compared with the behaviors of other physical toys. Its attractive behavior is based on the gyroscopic effect acting within the top. We sense the force of the gyroscopic effect when touching or handling them. We consider that this experience helps children become aware of a basic phenomenon of physics. In traditional research[Ishii et al. 1999], a visual and audio effect are often used to augment playging experience of sports or physical toys. In this project, we especially focused on augmentation of physical contact between the tops and the players by using force feedback.","Title: Two-handed drawing on augmented desk\nYear: 2002\n\nAbstract: This paper describes a two-handed drawing tool on Enhanced Desk. Through the experiments, our tool showed better performance when drawing simple figures than traditional drawing tools. The subjects also reported that it was easier to learn the usage of the tool.","Title: Video-based tracking of user's motion for augmented desk interface\nYear: 2004\n\nAbstract: This paper presents an overview of our project on an augmented desk interface system called the EnhancedDesk. The EnhancedDesk is equipped with an infrared camera for tracking a users' hand motions, a color video camera for recognizing objects, and projectors for displaying various kinds of digital information onto the desk. The key technical innovations of the EnhancedDesk include: fast and accurate tracking of multiple fingers for direct manipulation of both real and projected objects with bare hands, interactive object registration and recognition with hand gestures, vision-based tracking of a user's gaze, and addition of interactive functionality overlaid on the desk. This paper presents a brief description of the components, as well as some prototypes of applications of our augmented desk interface system.","Title: Estimating Anomality of the Video Sequences for Surveillance Using 1-Class SVM\nYear: 2008\n\nAbstract: We have proposed a method to detect and quantitatively extract anomalies from surveillance videos. Using our method, anomalies are detected as patterns based on spatio-temporal features that are outliers in new feature space. Conventional anomaly detection methods use features such as tracks or local spatio-temporal features, both of which provide insufficient timing information. Using our method, the principal components of spatio-temporal features of change are extracted from the frames of video sequences of several seconds duration. This enables anomalies based on movement irregularity, both position and speed, to be determined and thus permits the automatic detection of anomal events in sequences of constant length without regard to their start and end. We used a 1-class SVM, which is a non-supervised outlier detection method. The output from the SVM indicates the distance between the outlier and the concentrated base pattern. We demonstrated that the anomalies extracted using our method subjectively matched perceived irregularities in the pattern of movements. Our method is useful in surveillance services because the captured images can be shown in the order of anomality, which significantly reduces the time needed.","Title: Estimating Number Of People Using Calibrated Monocular Camera Based On Geometrical Analysis Of Surface Area\nYear: 2009\n\nAbstract: We propose a novel technique for estimating the number of people in a video sequence; it has the advantages of being stable even in crowded situations and needing no ground-truth data. By analyzing the geometrical relationships between image pixels and their intersection volumes in the real world quantitatively, a foreground image directly indicates the number of people. Because foreground detection is possible even in crowded situations, the proposed method can be applied in such situations. Moreover, it can estimate the number of people in an a priori manner, so it needs no ground-truth data unlike existing feature-based estimation techniques. Experiments show the validity of the proposed method.","Title: ClaytricSurface: an interactive surface with dynamic softness control capability\nYear: 2012\n\nAbstract: For a long time, solid planar surfaces have been commonly used for computer displays. Recently, there have been many attempts to provide a flexible element with the display surface to extend possibilities of interaction by utilizing many flexible materials, such as elastomer, sand or clay [Piper et al. 2002]. However, in many traditional flexible display to date, this softness element has been considered as a static element and thus unchangeable. Thus, traditional flexible surface limits the possible interaction that is supported on each surface due to the interactive element being strongly dependent on the physical/chemical properties of display material.","Title: Real-Time Fingertip Tracking and Gesture Recognition\nYear: 2002\n\nAbstract: Augmented desk interfaces and other virtual reality systems depend on accurate, real-time hand and fingertip tracking for seamless integration between real objects and associated digital information. We introduce a method for discerning fingertip locations in image frames and measuring fingertip trajectories across image frames. We also propose a mechanism for combining direct manipulation and symbolic gestures based on multiple fingertip motions. Our method uses a filtering technique, in addition to detecting fingertips in each image frame, to predict fingertip locations in successive image frames and to examine the correspondences between the predicted locations and detected fingertips. This lets us obtain multiple complex fingertip trajectories in real time and improves fingertip tracking. This method can track multiple fingertips reliably even on a complex background under changing lighting conditions without invasive devices or color markers.","Title: A vision-based non-contact interactive advertisement with a display wall\nYear: 2006\n\nAbstract: We developed an advertising system that enables users to interact with advertisements on a large display wall without any physical contact. The system recognizes positions of bodies or hands of users in front of the display by stereo cameras. The system recognizes a distance between a user and the display as the user's interests in the advertisement, and changes two display modes according to the distance. One of the modes shows advertisements as many as possible to attract interests of passers-by. The other allows a user to interact with the advertisement on the display by recognizing a gesture of the user.","Title: EnhancedTable: supporting a small meeting in ubiquitous and augmented environment\nYear: 2004\n\nAbstract: This paper describes our design and implementation of an augmented table interface for small group meetings. The system was designed to be used by multiple users in the ubiquitous environment, where people do not need to bring their laptop PCs. With effective use of the advantage of computer vision, we implemented the concept of ubiquitous desktop as personal workspace and virtual Chinese table as shared workspace. User can share/personalize files by drag-and-dropping icons to/from the shared workspace. The system also provides capabilities for interactive image capturing and finger position sharing, both of which would be useful for the group meetings.","Title: Visualizing Cyber Attacks using IP Matrix\nYear: 2005\n\nAbstract: An Internet cyber threat monitoring system detects cyber threats using network sensors deployed at particular points on the Internet, statistically analyzes the time of attack, source of attack, and type of attack, and then visualizes the result of this analysis. Existing systems, however, simply visualize country-by-country statistics of attacks or hourly changes of attacks. Using these systems, it is difficult to understand the source of attack, the diffusion of the attack, or the relation between the target and the source of the attack. This paper described a method for visualizing cyber threats by using 2-dimensional matrix representation of IP addresses. The advantages of this method are that: (1) the logical distance of IP addresses is represented intuitively; (2) Internet address space is visualized economically; (3) macroscopic information (Internet level) and microscopic information (local level) are visualized simultaneously. By using this visualization framework, propagation of the Welchia worm and the Sasser.D worm are visualized.","Title: VRCS: Integrating Version Control and Module Management Using Interactive Three-Dimensional Graphics\nYear: 1997\n\nAbstract: Version control and module management are very important in practical software development. In UNIX, RCS or SCCS is used in general as version control tools. However, they (1) lack: an ability of explicitly displaying the version changes; (2) require complicated commands to be typed; and (3) lack an ability of managing multiple files and modules. This paper proposed a solution for these issues by applying 3-D visualization. The prototype system, VRCS, was developed. In our system, each version information stored in a RCS history Jibe is displayed as a 2-D tree by taking the z-axis as time. Other 2-D trees are laid out in 3-D space in the same way. In our visualization files which compose a certain release of the software are connected by line called relation-link. By using GUIs, users can check; in/out each version easily and interactively. More importantly, just by choosing the relation-link, a certain release is rebuilt automatically.","Title: PhotoelasticTouch: transparent rubbery tangible interface using an LCD and photoelasticity\nYear: 2009\n\nAbstract: PhotoelasticTouch is a novel tabletop system designed to intuitively facilitate touch-based interaction via real objects made from transparent elastic material. The system utilizes vision-based recognition techniques and the photoelastic properties of the transparent rubber to recognize deformed regions of the elastic material. Our system works with elastic materials over a wide variety of shapes and does not require any explicit visual markers. Compared to traditional interactive surfaces, our 2.5 dimensional interface system enables direct touch interaction and soft tactile feedback. In this paper we present our force sensing technique using photoelasticity and describe the implementation of our prototype system. We also present three practical applications of PhotoelasticTouch, a force-sensitive touch panel, a tangible face application, and a paint application.","Title: PhotoelasticTouch: transparent rubbery interface using a LCD and photoelasticity\nYear: 2009\n\nAbstract: PhotoelasticTouch is a tabletop system designed to intuitively facilitate touch-based interaction via real objects made from transparent elastic material. The transparent elastic material (transparent rubber) provides a realistic haptic interface, which when combined with the visual content displayed on the LCD tabletop, enables a balanced coupling of the physical world and digital content. The system utilizes the photo-elastic properties of the transparent rubber to recognize when a user pushes, pulls or pinches the object, while the LCD provides the appropriate visual feedback in accordance to the stress applied to the elastic material.","Title: SnortView: visualization system of snort logs\nYear: 2004\n\nAbstract: False detection is a major issue in deploying and maintaining Network-based Intrusion Detection Systems (NIDS). Traditionally, it is recommended to customize its signature database (DB) to reduce false detections. However, it requires quite deep knowledge and skills to appropriately customize the signature DB. Inappropriate customization causes the increase of false negatives as well as false positives. In this paper, we propose a visualization system of a NIDS log, named SnortView, which supports administrators in analyzing NIDS alerts much faster and much more easily. Instead of customizing the signature DB, we propose to utilize visualization to recognize not only each alert but also false detections. The system is based on a 2-D time diagram and alerts are shown as icons with different styles and colors. In addition, the system introduces some visualization techniques such as overlayed statistical information, source-destination matrix, and so on. The system was used to detect real attacks while recognizing some false detections.","Title: SplashDisplay: volumetric projection using projectile beads\nYear: 2012\n\nAbstract: The prime feature of the SplashDisplay is that it uses projectile beads as a display medium that are launched from the table; this means that the tangible medium can be removed from the surface and allowed free movement in air. In current research [1] [2], displays have tended toward fixed mediums and therefore defining and visualizing explosive activity on such displays has been difficult. However, the SplashDisplay is made for this particular purpose, and given the nature of the display medium it pulls away from conventional standard surfaces. Given this tradeoff, it is possible for the SplashDisplay to launch projectile beads from millimeters to meters into the air freely making it possible to attain an image 'depth' much like the Z-axis in 3D. In the simulation of 'explosions', this system launches beads into the air much like the physical phenomenon, making the projected object 'feel' like it actually exploded (Figure 1). As the beads are white in color, it is possible for these beads to act as a display backing. When the system is still, then the beads play the role of a stationary screen; once the beads are in air, they can still be recognized as 'screen' material. If light is projected onto these airborne beads, they will illuminate as they fall, giving a 'fireworks' like effect in real time.","Title: Fluid surface: interactive water surface display for viewing information in a bathroom\nYear: 2012\n\nAbstract: Information is becoming accessible everywhere in everyday life due of the spread of smart phones and portable personal computers; however are very few methods in accessing contents in a bathing environment. Sometimes smart phones can be carried into a bathroom but it is unnatural to be holding a device at during bathing, so a suitable technique for information browsing in a bathing environment is required. We propose an interactive water surface display system, which uses image-recognition techniques. By using water, the system can perform an intuitive interaction peculiar to water such as poking a finger up from under the water surface, stroking the water surface and scooping up water. In this paper, we discuss interaction design in a bathroom, describing an implementation of our system and its applications.","Title: Interactive textbook and interactive Venn diagram: natural and intuitive interfaces on augmented desk system\nYear: 2000\n\nAbstract: This paper describes two interface prototypes which we have developed on our augmented desk interface system, EnhancedDesk. The first application is Interactive Textbook, which is aimed at providing an effective learning environment. When a student opens a page which describes experiments or simulations, Interactive Textbook automatically retrieves digital contents from its database and projects them onto the desk. Interactive Textbook also allows the student hands-on ability to interact with the digital contents. The second application is the Interactive Venn Diagram, which is aimed at supporting effective information retrieval. Instead of keywords, the system uses real objects such as books or CDs as keys for retrieval. The system projects a circle around each book; data corresponding the book are then retrieved and projected inside the circle. By moving two or more circles so that the circles intersect each other, the user can compose a Venn diagram interactively on the desk. We also describe the new technologies introduced in EnhancedDesk which enable us to implement these applications.","Title: LumoSpheres: real-time tracking of flying objects and image projection for a volumetric display.\nYear: 2015\n\nAbstract: This paper proposes a method for real-time tracking of flying objects and image projection onto them for developing a particle-based volumetric 3D display. The first section describes the concept using high-speed cameras and projectors for a particle-based volumetric 3D display. Our solution suggests a prediction model with kinematic laws and uses Kalman Filters to address latency issues within the projector-camera system. We conducted experiments to show the accuracy of the image projection. We also present an application of our method in entertainment, Digital Juggling.","Title: Augmented dodgeball: an approach to designing augmented sports\nYear: 2015\n\nAbstract: Ubiquitous computing offers enhanced interactive, human-centric experiences including sporting and fitness-based applications. To enhance this experience further, we consider augmenting dodgeball by adding digital elements to a traditional ball game. To achieve this, an understanding of the game mechanics with participating movable bodies, is required. This paper discusses the design process of a ball--player-centric interface that uses live data acquisition during gameplay for augmented dodgeball, which is presented as an application of augmented sports. Initial prototype testing shows that player detection can be achieved using a low-energy wireless sensor based network such as that used with fitness sensors, and a ball with an embedded sensor together with proximity tagging.","Title: Appearance-Based Gaze Estimation With Online Calibration From Mouse Operations\nYear: 2015\n\nAbstract: This paper presents an unconstrained gaze estimation method using an online learning algorithm. We focus on a desktop scenario, where a user operates a personal computer, and use the mouse-clicked positions to infer, where on the screen the user is looking at. Our method continuously captures the user’s head pose and eye images with a monocular camera, and each mouse click triggers learning sample acquisition. In order to handle head pose variations, the samples are adaptively clustered according to the estimated head pose. Then, local reconstruction-based gaze estimation models are incrementally updated in each cluster. We conducted a prototype evaluation in real-world environments, and our method achieved an estimation accuracy of 2.9 .","Title: Recognition of objects in various situations from two dimensional images\nYear: 2010\n\nAbstract: Generally, computers can successfully achieve object recognition by relying on sufficient information of observed objects. However, in real world, many objects own diverse configurations or objects are observed at various angles and positions, which make it difficult to match the observed objects with data models in a limited database. In this paper, to resolve the above problem, we propose an algorithm to achieve this kind of object recognition in shape space. Firstly, we describe the algorithm of extracting landmarks from outer contour of a shape by using recursive landmarks determination, in which the number of the landmarks can be appointed. Then, for the objects with many configurations, a series of new data are generated from one or two data models in pre-shape space. Finally, we achieve object recognition with shape space theory. The proposed method is efficient not only for the objects with noises, but also for the ones with various situations.","Title: ClaytricSurface: An Interactive Deformable Display with Dynamic Stiffness Control\nYear: 2014\n\nAbstract: ClaytricSurface is a shape-changeable display with dynamic stiffness control. The prototype uses vacuum pressure control on an enclosed volume of particles. Users can mold 3D shapes and apply textures to them while experiencing tactile feedback through dynamically changing stiffness. The system's developers have added both touch input using a depth camera and peripheral devices to expand the system's practical application. They also developed applications in the form of design tools that have great entertainment potential.","Title: Fractal approaches for visualizing huge hierarchies\nYear: 1993\n\nAbstract: This paper describes fractal approaches to the problemswhich associate with visualizing huge hierarchies.The geometrical characteristic of a fractal, selfsimilarity,allows users to visually interact with a hugetree in the same manner at every level of the tree. Thefractal dimension, a measure of complexity, makes itpossible to control the total amount of displayed nodes.A prototype visualization system for UNIX directoriesis also shown.1 IntroductionVisualization systems for...","Title: VisuaLinda: a framework for visualizing parallel Linda programs\nYear: 1997\n\nAbstract: This paper describes the VisuaLinda system, which is an integration of a Linda server and a visualizer of parallel Linda programs. Since the visualization module is built in the Linda server, programmers do not need to put additional visualization primitives in their client programs in order to visualize the behavior of their programs. This framework significantly reduces the programmers' burden in debugging parallel programs, owing to the following two features. First, it minimizes the ``probe effect,'' which is one of the main concerns in monitoring parallel programs. Second, VisuaLinda uses three-dimensional space to display both the relation between the Linda server and the client programs, and the execution of client programs. This framework can be used to display a much larger number of processes than using 2D visualization techniques, see two relations simultaneously, improve the visibility of communication lines, and see each process's state as well as the overview of the execution.","Title: Information Layout and Interaction on Virtual and Real Rotary Tables\nYear: 2007\n\nAbstract: Abstract Many tabletop systems have been developed, but few of them deal with the problems of visualizing and manipulat- ing a large amount,of information such as files on a table- top that is physically limited in size. In order to address this issue, we developed a rotary table system. The system rec- ognizes users’ hand gestures, and the users can rotate the table virtually. The table acts as a scroll wheel, and users can see a great deal of information by scrolling the table. We investigated three layout methods: sequential, classifi- cation, and spiral. We investigated these on the system and conducted user studies. Moreover, we also developed a real rotary table by using a roller bearing and a round table- top. Then, we conducted comparative experiments on the usability and intuitiveness of the two rotary tables.","Title: Implementation and evaluations of vision-based finger flicking gesture recognition for tabletops\nYear: 2008\n\nAbstract: Finger flicking is a familiar finger movement, and we often flick a small object on the table with a finger in our daily life. We developed a flicking gesture recognition system called the dasiaOHAJIKI interfacepsila, which can track very rapid flicking movements using a high-speed camera, and estimate the flicking power and direction in real time. In this paper, we describe the recognition techniques of the flicking gesture on a large LCD to produce a novel tabletop system that enables us to use the gesture to flick a virtual marble on the table. Moreover, we also present an evaluation of a vision-based flicking tabletop system as an entertainment application.","Title: Estimating the number of people in a video sequence via geometrical model\nYear: 2008\n\nAbstract: We propose a novel technique for estimating the number of people in a video sequence; it has the advantages of being stable even in crowded situations and needing no ground-truth data. By analyzing the geometrical relationships between image pixels and their intersection volumes in the real world quantitatively, a foreground image can be directly indicate the number of people. Because foreground detection can be done even in crowded situations, the proposed method can be applied to such situations. Also it can estimate the number of people in an a-priori manner, so it needs no ground-truth data which is necessary for existing feature-based estimating techniques. Experiments show the validity of the proposed method.","Title: Contrasting shadow for occluder light suppression from one-shot image\nYear: 2010\n\nAbstract: Two main problems for front projection systems when a user appears between a screen and a projector are 1) shadows being cast on a screen and 2) the user being illuminated due to undesirable strong projection light. To solve these problems, it is necessary to know which projectors are occluded by a user and cast a shadow. We propose a method that suppresses occluder light from a single image based on the fact that the darkness of shadows on a screen is differentiated when projectors have different luminance. Contrary to previous approaches, our method utilizes different alpha masks for each projector to contrast shadows on a screen. This enables us to clarify the relationship between the shadow on a screen and the occluded projector. Additionally, by differentiating all projector luminances, our method makes it possible to discriminate shadows even when some shadow regions overlap on a screen. We implemented the proposed method into a three-projector system to demonstrate its effectiveness.","Title: Simple Camera Calibration From a Single Image Using Five Points on Two Orthogonal 1-D Objects\nYear: 2010\n\nAbstract: We propose a simple and practical calibration technique that effectively estimates camera parameters from just five points on two orthogonal 1-D objects, each which has three collinear points, one of which is shared. We derive the basic equations needed to realize camera calibration from just five points observed on a single image that captures the objects. We describe a new camera calibration algorithm that estimates the camera parameters based on the basic equations and optimizes them by the bundle adjustment technique. Our method is validated by both computer simulated data and real images. The results show that the camera parameters yielded by our method are close to those yielded by existing methods. The tests demonstrate that our method is both effective and practical.","Title: Smart curtain as interactive display in living space.\nYear: 2015\n\nAbstract: In the era of IoT, there is the necessity of information display for IoT devices in living space. One ideal realization should be 'ubiquitous' for such a purpose. We propose smart curtain as an implementation of ubiquitous computing in living space, which has the following four advantages; (a) a role of boundary between two spaces such as indoor and outdoor, (b) natural layer structure which consists of translucent curtain and drape curtain, (c) relatively large area which can offer enough space for displaying a lot of information and image which can make good atmosphere, and (d) context awareness by sensing surrounding environment and the state (opening/closing) of curtain. Though its visual quality is not expected to be so high, there should be many kinds of contents which don't need high visual quality.","Title: Ballumiere: real-time tracking and projection system for high-speed flying balls.\nYear: 2015\n\nAbstract: We present Ballumiere, a novel projection system for the balls moving in the air. Recently projection mapping has attracted much attention. \\\"Shader Lamps\\\" [Raskar et al. 2001] enable to render computer graphics to real objects in three dimensional space using projectors. However, almost all the projection mapping techniques only allow projecting to static objects or projection targets that move slowly. When we try to track and project to moving objects, the conventional method of projection mapping is unsatisfactory due to the delay between tracking and projection. \\\"Lumipen\\\" [Okumura et al. 2013] tracks and projects to a small object in real-time using a high-speed vision sensor and a projector combination using a high-speed optical gaze controller. This system successfully projects to an object moving quickly, but only allows for one object to be a projection target due to restrictions of the optical axis of the vision sensors and the projector being the same. To solve these problems, we present Ballumiere as a new real-time tracking and projection system that can project to the multiple fast moving airborne balls using a motion capture camera and a standard off the shelf projector. We overcome the delay problem using prediction algorithms using Kalman filters and kinetic models. In this demo, we project images and movies onto juggling balls and soccer balls.","Title: MultiMosaic: simultaneous visualization of multiple images for near and far viewers using image mosaic and saliency map\nYear: 2013\n\nAbstract: This paper presents a method to display two types of images simultaneously in one image. One is for viewers at a far distance, and the other is for viewers closer to the screen. The basic idea is to overlay small images in high resolution (i.e. near-images) on a larger image in low resolution (i.e. far-image) by adjusting transparency of the near images. However, this simple overlay approach fails to provide acceptable visibility for both images. We propose to use a saliency map to emphasize important features of each image. By using this saliency map, it is possible to provide better visibility for both images, which this paper will illustrate with various examples.","Title: Vision-based face tracking system for window interface: prototype application and empirical studies\nYear: 2001\n\nAbstract: In this paper, we study the effective use of gaze information for human-computer interaction based on a stereo-based vision system which can track the 3D position and orientation of a user in real-time. We have integrated our face-tracking system into the X Window interface system, and conducted experiments to evaluate the effectiveness of our proposed framework for using gaze information for window interfaces.","Title: Synthesizing Pseudo Straight View from A Spinning Camera Ball.\nYear: 2016\n\nAbstract: This paper proposed a spherical camera ball and its image processing algorithm, designed to provide a ball's point of view (POV) for spectating of ball sports. The proposed spherical camera ball has six cameras embedded at fixed intervals around the surface of the ball. One of the main issues for such ball-type cameras is that the device is spinning and therefore it is hard to obtain stable video stream from such spinning cameras. This paper proposed automatic selection of cameras using matching scores between the anchor frame and current frame. The resultant movie will then always shows the point of interest within the frame. We then applied image translation to obtain a pseudo straight view in which the point of interest is always shown in the center of the frame.","Title: A Remote Pointing Method with Dynamic C-D Ratio during a Pinching Gesture for Large Tabletop Systems.\nYear: 2016\n\nAbstract: This paper proposes a simple but efficient remote pointing method for large tabletop systems using a pinching gesture with a thumb and a forefinger. The proposed method uses a control-display (C-D) ratio that dynamically changes depending on the height of the user's hand. The user can intuitively control the speed of the pointer and its travel distance by changing his hand position in 3-D. The method can also be used in a multi-user environment. We applied the method to a photo viewer application.","Title: Visual Guidance with Unnoticed Blur Effect.\nYear: 2016\n\nAbstract: In information media such as TV programs, digital signage, or web pages, information content providers often want to guide viewers' attention to a particular location of the display. However, \\\"active\\\" methods, such as flashing displays, using animation, or changing colors, often interrupt viewers' concentration and makes viewers feel annoyed. This paper proposes a method for guiding viewers' attention without viewers noticing. By focusing on a characteristic of the human visual system, we propose a dynamic blur control method. Our method gradually blurs the image on the display to the threshold at which viewers are aware of the modulation of the display, while the region where viewers' attention should be guided remains unblurred. Two subjective experiments were conducted to show the effectiveness of our method. In the first, viewers' attention was guided to the unblurred region using blur control. In the second, a threshold was found at which viewers were aware of the modulation, and viewers' gaze is guided below this threshold. This means that the viewers' attention can be guided without them noticing.","Title: OmniEyeball: Spherical Display Embedded With Omnidirectional Camera Using Dynamic Spherical Mapping.\nYear: 2016\n\nAbstract: Recently, 360-degree panorama and spherical displays have received more and more attention due to their unique panoramic properties. Compared with existing works, we plan to utilize omnidirectional cameras in our spherical display system to enable omnidirectional panoramic image as input and output. In our work, we present a novel movable spherical display embedded with omnidirectional cameras. Our system can use embedded cameras to shoot 360-degree panoramic video and project the live stream from its cameras onto its spherical display in real time. In addition, we implemented an approach to achieve the dynamic spherical projection mapping in order to project to moving spherical devices. We have also been creating applications utilizing system's features by using 360-degree panoramic image as input and output.","Title: Ballumiere: Real-Time Tracking and Projection for High-Speed Moving Balls.\nYear: 2016\n\nAbstract: Projection onto moving objects has a serious slipping problem due to delay between tracking and projection. We propose a new method to overcome the delay problem, and we succeed in increasing the accuracy of projection. We present Ballumiere as a demo for projection to volleyballs and juggling balls.","Title: MlioLight: Multi-Layered Image Overlay using Multiple Flashlight Devices.\nYear: 2016\n\nAbstract: We propose a technique that overlays natural images on the real world using the information from multiple flashlight devices. We focus on finding areas of overlapping lights in a multiple light-source scenario and overlaying multi-layered information on a real world object in these areas.In order to mix multiple images, we developed a light identification and overlapping area detection technique using rapid synchronization between high-speed cameras and multiple light devices.In this paper, we describe the concept of our system and a prototype implementation.We also describe two different applications.","Title: Activity-Aware Video Stabilization for BallCam.\nYear: 2016\n\nAbstract: We present a video stabilization algorithm for ball camera systems that undergo extreme egomotion during sports play. In particular, we focus on the BallCam system which is an American football embedded with an action camera at the tip of the ball. We propose an activity-aware video stabilization algorithm which is able to understand the current activity of the BallCam, which uses estimated activity labels to inform a robust video stabilization algorithm. Activity recognition is performed with a deep convolutional neural network, which uses optical flow.","Title: Ballumiere: Real-Time Tracking and Spherical Projection for High-Speed Moving Balls.\nYear: 2016\n\nAbstract: Projections on moving objects have a problem in that the projection may slip because of the delay between tracking and projection. Here, we propose a new prediction method combining a Kalman filter and a three-frame feedback model that switches between these models according to the ball's state of motion. We developed a real-time tracking and projection system named \\\"Ballumiere\\\", which uses motion capture cameras for tracking and multiple projectors for spherical projection. We conducted a comparative experiment with an existing prediction model and showed that our method minimizes slipping and increases the accuracy of the projection.","Title: Video segmentation and stabilization for BallCam.\nYear: 2017\n\nAbstract: We present a video stabilization algorithm for ball camera systems that undergo extreme egomotion during sports play. In particular, we focus on the BallCam system which is an American football embedded with an action camera at the tip of the ball. We propose an activity-aware video stabilization algorithm which is able to understand the current activity of the BallCam, which uses estimated activity labels to inform a robust video stabilization algorithm. Activity recognition is performed with a deep convolutional neural network, which uses optical flow.","Title: Synthesizing fixed point of views from a spinning omnidirectional ball camera.\nYear: 2017\n\nAbstract: Recent years have seen the emergence of a new way to watch sports, that is, watching them from the ball's point of view (POV) taken with cameras embedded in the ball. The problem, however, is that the ball's POV is unstable since the ball spins. In this paper, we describe how we developed a prototype of the ball camera with an omnidirectional camera and propose an algorithm to fix the ball's POV to an arbitrary direction. Feature points are extracted from an entire omnidirectional image and the pose change between frames is estimated by feature matching and the five-point algorithm. The POV of the current frame is modified to the one of the initial frame by applying the product of estimated rotation matrices between all adjacent frames up to the current one. Our algorithm is not limited to our hardware and can be applied to any omnidirectional videos taken with different camera systems.","Title: Qoom: An Interactive Omnidirectional Ball Display.\nYear: 2017\n\nAbstract: We present a sphere-shaped interactive display system, named Qoom, as a new input and output device. Unlike existing sphere-shaped displays, Qoom is a perfectly spherical ball that can be rotated, thrown, or even kicked. First, we discuss how spherical displays can be used in daily life and describe how users interact with spheres. Then, we show how we developed the Qoom prototype that uses touch and rotation detection, real-time object tracking, and spherical projection mapping. We implemented actions including touching, rotating, bouncing and throwing as controls. We also developed applications for Qoom that utilize the unique advantages of ball displays.","Title: Rapid Prototyping of Accessible Interfaces With Gaze-Contingent Tunnel Vision Simulation.\nYear: 2017\n\nAbstract: Active involvement of users with disabilities is difficult to employ during the iterative stages of the design process due to high costs and effort associated with user studies. This research proposes a user centered design (UCD) strategy to incorporate the use of gaze-contingent tunnel vision simulation with sighted individuals to facilitate rapid prototyping of accessible interfaces. Through three types of validation studies, we examined how our simulation techniques can provide the opportunity for continued evaluation and refinement of the design. Our simulation approach was effective in emulating scanning behaviors caused by tunnel vision along with grasping user feedback to recognize user interface and usability criteria early in the design cycle.","Title: Shin-Tai: A Method For Controlling Characteristics Of A Humanoid Robot'S Body Using Artificial Muscles And Fats\nYear: 2017\n\nAbstract: \"SHIN-TAI\" is an upper humanoid body composed of artificial muscles and artificial fats covered with artificial skin. The artificial muscles reproduce natural changes in shape and stiffness of real human muscles by a pressure-regulating system, and the amount of artificial fats can be increased or decreased by controlling liquid flow rate into liquid bags under the skin. By controlling these two programmable functions, SHIN-TAI can assume various body types, namely, \"slim,\" \"muscular,\" \"voluptuous,\" or \"overweight.\" Even its gender can be changed by region-selective control of the fats. It is demonstrated that a controllable character of a humanoid can be expressed by using our method instead of conversations or body gestures, which depend on a static human-like housing as in recent humanoid implementations.","Title: ParaPara: Synthesizing Pseudo-2.5D Content from Monocular Videos for Mixed Reality.\nYear: 2018\n\nAbstract: In this work, we propose a simple yet effective method for synthesizing a pseudo-2.5D scene from a monocular video for mixed reality (MR) content. We also propose the ParaPara system, which applies this method. Most previously proposed systems convert real-world objects into 3D graphic models using expensive equipment; this is a barrier for individuals or small groups to create MR content. ParaPara uses four points in an image and their manually estimated distances to synthesize MR content by applying deep neural networks and simple image processing techniques to monocular videos. The synthesized content can be observed through an MR head-mounted display, and spatial mapping and spatial sound are applied to support the interaction between the real-world and MR content. The proposed system is expected to reduce the entry barriers to create MR content because it can create such content from a large number of previously captured videos.","Title: A Study On Gaze Guidance Using Artificial Color Shifts\nYear: 2018\n\nAbstract: In Web or digital signage, content providers want to guide users' attention to the intended regions. Using active visual stimuli, such as animated or flashing objects, is effective for gaze guidance; however, it has been reported that such an approach often results in unpleasant feelings for users. This paper proposed a new method for gaze guidance using artificial color shifts that does not induce unpleasant feelings in the user. We created an image filter that separates the image in three layers, i.e., cyan, magenta, and yellow, and slightly shifted each layer the left, right, and down, respectively. The filter was applied to the entire image except the region where the user's gaze was to be guided. We conducted experiments using a gaze tracker. The experimental results showed that the proposed method can guide the user's gaze to a particular region with less unpleasant feelings.","Title: A Real-Time Image Processing Framework With An Aerial Overhead Camera For Sports\nYear: 2018\n\nAbstract: Recently, large horizontal interactive surfaces have begun to be developed. In these systems, an overhead camera is often used to detect the position of objects on the surface even if they are not in contact with the surface. However the issues that these systems face are that they are expensive and a camera cannot be easily attached on top of the surface in some situations. This paper proposes a framework that uses a camera on a drone (UAV) as an overhead camera unit to convert arbitrary horizontal rectangular regions into interactive surfaces. Although commercially available drones that are equipped with cameras have high latencies and are difficult to use in real-time interactive systems, we solved this latency issue using a small PC that performs primitive image processing tasks onboard. First, we describe a drone unit that has an infrared camera and a small PC for real-time image processing, such as surface detection and object detection. Second, we describe novel infrared markers for the robust detection of the four corners of a rectangular region and the objects within that region. Finally, we describe an interactive sports coaching application in which a drone unit is used as an overhead camera both for a large playing field and small tabletop.","Title: How Display Shapes Affect 360-Degree Panoramic Video Communication\nYear: 2018\n\nAbstract: Field-of-view limitation has been a long-standing issue in video communication systems. With the advancement of omni-directional panoramic technology, the omnidirectional camera, which can provide a 360 degrees field of view, has become increasingly popular in the last few years. Previous research indicated that one-way video communication systems with a wider field of view improve task efficiency. Therefore, we propose to utilize omnidirectional cameras in a symmetrical video communication system and study how this configuration affects remote collaboration. In this study, we conducted experiments based on two conditions, which are an omnidirectional camera with a spherical display and an omnidirectional camera with a horizontally placed 2D flat display. Under these conditions, we analyzed how the display types affected remote collaboration. Our results show that participants marginally preferred the spherical display to the 2D flat display. We also show the advantages and disadvantages of each display. The findings contribute to our understanding of how to design an environment for remote collaboration that captures and shows a 360 degrees panoramic view of a remote site.","Title: OmniEyeball: Spherical Display Equipped With Omnidirectional Camera And Its Application For 360-Degree Video Communication.\nYear: 2018\n\nAbstract: We propose OmniEyeball (OEB), which is a novel interactive 360° image I/O system. It integrates the spherical display system with an omnidirectional camera to enable both capturing the 360° panoramic live streaming video as well as displaying it. We also present its unique application for symmetric 360° video communication by utilizing two OEB terminals, which may solve the narrow field-of-view problem in video communication. In addition, we designed a vision-based touch detection technique as well as some features to support 360° video communication.","Title: DisplayBowl: A Bowl-Shaped Display for Omnidirectional Videos.\nYear: 2018\n\nAbstract: We introduce DisplayBowl which is a concept of a bowl shaped hemispherical display for showing omnidirectional images. This display provides three-way observation for omnidirectional images. DisplayBowl allows users to observe an omnidirectional image by looking the image from above. In addition, users can see it with a first-person-viewpoint, by looking into the inside of the hemispherical surface from diagonally above. Furthermore, by observing both the inside and the outside of the hemispherical surface at the same time from obliquely above, it is possible to observe it by a pseudo third-person-viewpoint, like watching the drone obliquely from behind. These ways of viewing solve the problem of inability of pilots controlling a remote vehicle such as a drone to notice what happens behind them, which happen with conventional displays such as flat displays and head mounted displays.","Title: A real-time golf-swing training system using sonification and sound image localization\nYear: 2018\n\nAbstract: There are real-time training systems to learn the correct golf swing form by providing visual feedback to the users. However, real-time visual feedback requires the users to see the display during their motion that leads to the wrong posture. This paper proposed a real-time golf-swing training system using sonification and sound image localization. The system provides real-time audio feedback based on the difference between the pre-recorded model data and real-time user data, which consists of the roll, pitch, and yaw angles of a golf club shaft. The system also used sound image localization so that the user can hear the audio feedback from the direction of the club head. The user can recognize the current posture of the club without moving their gaze.","Title: Image compensation and stabilization for immersive 360-degree videos from capsule endoscopy\nYear: 2018\n\nAbstract: This paper describes image processing that can be used to develop immersive 360-degree videos using capsule endoscopy procedures. When viewed through a head-mounted display (HMD), doctors are able to inspect the human gastrointestinal tract as if they were inside the patient's body. Although the endoscopy capsule has two tiny fisheye cameras, the images captured by these cameras cannot be converted to equirectangular images which is the basic format used to produce 360-degree videos. This study proposes a method to generate a pseudo-omnidirectional video from the original images and stabilizes the video to prevent virtual reality (VR) sickness.","Title: Real-Time Human Motion Forecasting Using A Rgb Camera\nYear: 2018\n\nAbstract: This paper propose a real-time human motion forecasting system which visualize the future pose in virtual reality using a RGB camera. Our system consists of three parts: 2D pose estimation from RGB frames using a residual neural network, 2D pose forecasting using a recurrent neural network, and 3D recovery from the predicted 2D pose using a residual linear network. To improve the prediction learning quantity of temporal feature, we propose a special method using lattice optical flow for the joints movement estimation. After fitting the skeleton, a predicted 3d model of target human will be built 0.5s in advance in a 30-fps video.","Title: Balloonygen: extended tabletop display embedded with balloon-like deformable spherical screen\nYear: 2018\n\nAbstract: Balloonygen, an extended tabletop display embedded with a balloon-like deformable spherical screen, is a display that can seamlessly expose a spherical screen for three-dimensional contents, such as omnidirectional images, in a conventional flat display. By continuously morphing between a two-dimensional shape called tabletop and a three-dimensional shape called sphere, we render the benefits of a flat display and a spherical display to coexist and propose a smoother approach for information sharing. Balloonygen dynamically provides an optimal way to display the contents by inflating the rubber membrane installed at the center of a tabletop display and morphing between the two- and three-dimensional shapes. In this study, by prototyping and designing the application scenario, we discuss the advantages and disadvantages of this display and possible interactions involved.","Title: Gaze navigation in the real world by changing visual appearance of objects using projector-camera system\nYear: 2018\n\nAbstract: This paper proposes a method for gaze navigation in the real world by projecting an image onto a real object and changing its appearance. In the proposed method, a camera captures an image of objects in the real world. Next all the pixels in the image but those in a specified region are slightly shifted to left and right. Then the obtained image is projected onto the original objects. As a result, the objects not in the specified region looks blurred. We conducted user experiments and showed that the users' gaze were navigated to the specified region.","Title: BitoBody: Real-time human contact detection and dynamic projection system\nYear: 2019\n\nAbstract: In this research, we propose a novel human body contact detection and projection system with dynamic mesh collider. We use motion capture camera and generated human 3D models to detect the contact between user's bodies. Since it is difficult to update human mesh collider every frame, a special algorithm that divides body meshes into small pieces of polygons to do collision detection is developed and detected hit information will be dynamically projected according to its magnitude of damage. The maximum deviation of damage projection is about 7.9cm under a 240-fps optitrack motion capture system and 12.0cm under a 30-fps Kinect camera. The proposed system can be used in various sports where bodies come in contact and it allows the audience and players to understand the context easier.","Title: OHAJIKI interface: flicking gesture recognition with a high-speed camera\nYear: 2006\n\nAbstract: This paper describes a novel interaction technique that recognizes a finger flicking gesture for power adjustment input for a sports game, such as golf swing or hitting. Our system measures speed of the finger motion and direction of the gesture by using a high-speed camera and a high frame rate image processing technique. By using this system, users can adjust power and angle intuitively. We developed a 3D golf game using this interaction technique to provide an intuitive golf swing input.","Title: Futurepose - Mixed Reality Martial Arts Training Using Real-Time 3d Human Pose Forecasting With A Rgb Camera\nYear: 2019\n\nAbstract: In this paper, we propose a novel mixed reality martial arts training system using deep learning based real-time human pose forecasting.Our training system is based on 3D pose estimation using a residual neural network with input from a RGB camera, which captures the motion of a trainer. The student wearing a head mounted display can see the virtual model of the trainer and his forecasted future pose. The pose forecasting is based on recurrent networks, to improve the learning quantity of the motion's temporal feature, we use a special lattice optical flow method for the joints movement estimation. We visualize the real-time human motion by a generated human model while the forecasted pose is shown by a red skeleton model. In our experiments, we evaluated the performance of our system when predicting 15 frames ahead in a 30-fps video (0.5s forecasting), the accuracies were acceptable since they are equal to or even outperforms some methods using depth IR cameras or fabric technologies, user studies showed that our system is helpful for beginners to understand martial arts and the usability is comfortable since the motions were captured by RGB camera.","Title: OmniGlobe: An Interactive I/O System For Symmetric 360-Degree Video Communication\nYear: 2019\n\nAbstract: Video communication systems have been suffered from the narrow field of view. To solve this limitation, one study proposed symmetric 360° video communication system by combining an omnidirectional camera and a hemispherical display. However, the system still had several issues, e.g., the invisibility of hemisphere which was at the opposite side from a user caused the inconvenience of observing the remote environment. To solve these issues, we introduce OmniGlobe, a novel symmetric full 360° video communication system which incorporates an omnidirectional camera, a full spherical display, and several visual or interactive techniques. Based on an experiment, we could indicate that our system is effective in reducing the inconvenience of observing the remote environment and increased the remote space awareness and user's gaze awareness to support remote collaboration. We also discuss the takeaways, limitations and application areas in our system which help improve the system.","Title: Golf training system using sonification and virtual shadow\nYear: 2019\n\nAbstract: This paper proposed a golf training system using real-time audio-visual feedback. The system captures user's motion with an optical motion tracking system and projects his/her posture as a virtual shadow on the ground. Unlike other golf training systems, our system enables the user to keep his/her gaze on the ball. The model swing motion of the expert golfer is also overlaid so that the user can understand the difference between his/her motion and expert's. Moreover, the system provides audio feedback using sound image localization. It enables the user to understand the position and orientation of the club face, which is often out of sight of the user during his/her swing motion.","Title: Remote control experiment with displaybowl and 360-degree video\nYear: 2019\n\nAbstract: DisplayBowl is a bowl-shaped hemispherical display for showing omnidirectional images with direction data. It provides users with a novel way of observing 360-degree video streams, which improves the awareness of the surroundings when operating a remote-controlled vehicle compared to conventional flat displays and HMDs. In this paper, we present a user study, in which we asked participants to control a remote drone using an omnidirectional video streaming, to compare the uniqueness and advantages of three displays: a flat panel display, a head-mounted display and DisplayBowl.","Title: Visualizing expert motion for guidance in a VR ski simulator\nYear: 2019\n\nAbstract: While humans are quite good at copying motions from others, it is difficult to do so in a dynamic sport such as skiing. Hence, we propose a virtual reality ski training system, which visualizes prerecorded expert motion in different ways and enables users to learn by copying. The system is based on a commercial indoor ski simulator, a VR headset, and two VR trackers to capture the ski's motion. Users can control their skis on the virtual ski slope and improve their skills by following a digital avatar of the expert skier replayed in front of them. We investigate 3 types of visualizations for training: Graphs to visualize the angle of feet compared to the expert, periodic copies of the expert's pose to show the spatial and temporal motion of the key movements, and a more minimal ribbon-trace of the leading skier to point out the optimized trajectory.","Title: PushToSki - An Indoor Ski Training System Using Haptic Feedback\nYear: 2021\n\nAbstract: ABSTRACTHaptic feedback is an intuitive way of improving required postures in sports without having the trainee change their head-pose towards visual cues and therefore possibly worsening their overall body-pose. However, this feedback is not possible in a dynamic sport like alpine skiing which is why we propose a virtual reality ski training system that uses vibration as a haptic feedback method. Our system uses a commercially available indoor ski simulator and several trackers to capture the user’s motion together with a set of vibration motors which will provide direct, haptic feedback to the user. Our system therefore allows giving haptic feedback even while the trainee is moving on the simulator.","Title: Real-time Projection of Lip Animation onto Face Masks using OmniProcam\nYear: 2021\n\nAbstract: ABSTRACT This paper describes an OmniProcam system, which enables 360 degree horizontal projection by a fisheye lens with a coaxial procam in which the optical axes of the camera and projector are exactly matched. Combined with 2D marker recognition, the OmniProcam can display images onto screens at arbitrary positions in 3D space. As an example application, we developed a system which projects lip animation onto the user’s face masks for better communication at the physical meeting in current COVID-19 situation. The system recognizes the user’s speech, generates the lip animation using Lipsync, and projects the animation onto the user’s face masks.","Title: Watch-Your-Skiing: Visualizations for VR Skiing using Real-time Body Tracking\nYear: 2021\n\nAbstract: Correcting one’s body posture is necessary when acquiring specific skills, especially for some sports such as skiing or gymnastics. However, it is difficult to observe our posture objectively, which is the reason why a trainer is required. In this paper, we introduce a VR ski training system using full body motion capture to provide real-time feedback for the user. Two types of different visual cu...","Title: Augmenting VR Ski Training using Time Distortion\nYear: 2022\n\nAbstract: Virtual reality-based sports simulators are widely developed, which makes training in a virtual environment possible. On the other hand, methods using temporal features are also introduced to realize an adaptive training. In this paper, we study the effect of time distortion on alpine ski training to find out how modifying the temporal space can affect sports training. Experiments are conducted to investigate how a fast/slow and a static/dynamic time distortion-based training, respectively, can impact the performance of users.","Title: ASTRE: Prototyping Technique for Modular Soft Robots With Variable Stiffness\nYear: 2022\n\nAbstract: Soft robots are advantageous for human interaction owing to their adaptability and safe interactivity. However, research on soft robots is challenging because of the complex fabrication process of elastomeric materials with multiple channels. In this study, we introduce a prototyping technique for the fabrication-friendly soft robots using pneumatic artificial muscle(PAMs) and modular 3D printed reinforcement. We presented three primitive deformation structures: bending, twisting, and contraction. Moreover, we propose a novel variable stiffness technique that alters PAMs contraction and radial expansion behavior into locking, malleable, and rotational brake features. We explore both the parallel and series arrangement of the reinforcement module and propose new types of mixing configurations and scaling techniques. We quantitatively verified the force scaling technique on different types of features. We demonstrate the feasibility of this prototyping technique through six application examples and conclude with a discussion of the limitations and possible future improvements.","Title: AI Golf: Golf Swing Analysis Tool for Self-Training\nYear: 2022\n\nAbstract: In the field of the acquisition of sports skills, a common way to improve sports skills, such as golf swings, is to imitate professional players' motions. However, it is difficult for beginners to specify the keyframes on which they should focus and which part of the body they should correct because of inconsistent timing and lack of knowledge. In this study, a golf swing analysis tool using neural networks is proposed to address this gap. The proposed system compares two motion sequences and specifies keyframes in which significant differences can be observed between the two motions. In addition, the system helps users intuitively understand the differences between themselves and professional players by using interpretable clues. The main challenge of this study is to target the fine-grained differences between users and professionals that can be used for self-training. Moreover, the significance of the proposed approach is the use of an unsupervised learning method without prior knowledge and labeled data, which will benefit future applications and research in other sports and skill training processes. In our approach, neural networks are first used to create a motion synchronizer to align motions with different phases and timing. Next, a motion discrepancy detector is implemented to find fine-grained differences between motions in latent spaces that are learned by the networks. Furthermore, we consider that learning intermediate motions may be feasible for beginners because, in this way, they can gradually change their pose to match the ideal form. Therefore, based on the synchronization and discrepancy detection results, we utilize a decoder to restore the intermediate human poses between two motions from the latent space. Finally, we suggest possible applications for analyzing and visualizing the discrepancy between the two input motions and interacting with the users. With the proposed application, users can easily understand the differences between their motions and those of various experts during self-training and learn how to improve their motions."],"string":"[\n \"Title: SplashDisplay\\nYear: 2012\\n\\nAbstract: 'SplashDisplay' is a system developed to attempt real time volumetric display. This system implements air pressure generated by an x-y coordinate based projectile launching speaker through a bed of projectile beads to simulate a real-time 3D \\\"explosion\\\" like effect. The projectile beads act as a projection medium for a top-mounted visible light projector; and through synchronized timing of these components, it is possible to create 3D, tangible effects at will. Also, by using IR LED and IR sensitive cameras, user interaction can be added to this system to allow for an interactive surface. The result of these components is a dynamic, interactive, real-time \\\"explosion\\\" simulation game that can be used to confirm the innovative construct of this suggested system.\",\n \"Title: AquaTop display\\nYear: 2013\\n\\nAbstract: AquaTop display is a projection system that uses white water as a screen surface. This system allows the user ' s limbs to freely move through, under and over the projection surface. Using the unique characteristics of fluid, we propose new interactions methods specific to the projection medium, water. Our system uses a depth camera to detect input on and over the water surface to allow for interactions such as protruding fingers out from under the water surface and scooping up the water with both hands. This type of interaction is not capable with current impenetrable, rigid body, flat surfaces. For example, by floating one's limbs on the water surface, it is also possible to fuse one's body with the displayed objects for further augmented interaction by 'becoming one' with the screen.\",\n \"Title: AquaTop display: a true \\\\\\\"immersive\\\\\\\" water display system\\nYear: 2013\\n\\nAbstract: The AquaTop Display is system that uses the projection of images onto cloudy water. This system allows the users limbs to freely move through, under and over the projection surface. As the projection medium is fluid, we propose new interaction methods specific to this medium by using a Kinect Depth Sensor. Scooping up water, protruding fingers out from underneath the water surface are capable with this system. These type of interactions are not normally possible with current impenetrable rigid surfaces. Using mapped projection, AquaTop Display also augments one's limbs on the water surface, providing a environment for an 'immersible' experience, allowing the users to become one with the screen.\",\n \"Title: Ubiquitous Display for Dynamically Changing Environments\\nYear: 2003\\n\\nAbstract: This paper proposes a novel method for ubiquitous displays using projectors in indoor environments. In particular, our method consists of two distinct features: automatic scene modeling of a dynamically changing indoor environment, and automatic selection of surfaces onto which various contents are displayed by taking into account both geometric and photometric properties. As a result, our method can be applied to dynamically changing scenes such as a meeting room where furniture and other objects are moved frequently.\",\n \"Title: 3-D interaction with a large wall display using transparent markers\\nYear: 2010\\n\\nAbstract: In this paper we proposed a new interface for interacting with large displays via small video devices such as a cell phone. We estimate the location of the camera relative to the display using a matrix of transparent markers embedded on display. As a result, our interface allows the user to interact with digital contents without being distracted by opaque visual markers. Our interface enables intuitive interactions such as pointing, rotating, dragging and dropping. Moreover, our use of a small hand-held camera device allows for interaction with large scale displays without the need for direct contact with the display surface. Thus our system is well suited for interactions when there is some distance between the user and the display. Our proposed system has applications to large scale advertisement displays and can enable interactions between individuals and large scale digital content.\",\n \"Title: Bouncing Star project: design and development of augmented sports application using a ball including electronic and wireless modules\\nYear: 2010\\n\\nAbstract: In our project, we created a new ball, \\\"Bouncing Star\\\" (Hane-Boshi in Japanese), comprised of electronic devices. We also created augmented sports system using Bouncing Star and a computer program to support an interface between the digital and the physical world. This program is able to recognize the ball's state of motion (static, rolled, thrown, bound, etc.) by analyzing data received through a wireless module. The program also tracks the ball's position through image recognition techniques. On this system, we developed augmented sports applications which integrate real time dynamic computer graphics and responsive sounds which are synchronized with the ball's characteristics of motion. Our project's goal is to establish a new dynamic form of entertainment which can be realized through the combination of the ball and digital technologies.\",\n \"Title: Online Anomal Movement Detection Based On Unsupervised Incremental Learning\\nYear: 2008\\n\\nAbstract: We propose an online anomal movement detection method using incremental unsupervised learning. As the feature for discrimination, vile extract the principal component of the spatio-temporal feature by incremental PCA. We then detect anomal movements by an incremental 1-class SVM. In order to use principal component as the feature for discrimination while supporting incrementation of the subspace, we modify the SVM kernel function to take account of the difference in distance scale between the principal component feature vectors and that of the feature vectors after the subspace is recremented. This allows us to efficiently conduct the re-learning process even though the dimension of the original input spatio-temporal feature is high. Experiments show that anomal scenes can be detected without the cost of preparing a lot of labeled data for preliminary learning.\",\n \"Title: EdgeSonic: image feature sonification for the visually impaired\\nYear: 2011\\n\\nAbstract: We propose a framework to aid a visually impaired user to recognize objects in an image by sonifying image edge features and distance-to-edge maps. Visually impaired people usually touch objects to recognize their shape. However, it is difficult to recognize objects printed on flat surfaces or objects that can only be viewed from a distance, solely with our haptic senses. Our ultimate goal is to aid a visually impaired user to recognize basic object shapes, by transposing them to aural information. Our proposed method provides two types of image sonification: (1) local edge gradient sonification and (2) sonification of the distance to the closest image edge. Our method was implemented on a touch-panel mobile device, which allows the user to aurally explore image context by sliding his finger across the image on the touch screen. Preliminary experiments show that the combination of local edge gradient sonification and distance-to-edge sonification are effective for understanding basic line drawings. Furthermore, our tests show a significant improvement in image understanding with the introduction of proper user training.\",\n \"Title: Interactive object registration and recognition for augmented desk interface\\nYear: 2001\\n\\nAbstract: Identification of objects in a real world plays a key role for human-computer interaction in a computer-augmented environment using augmented reality techniques. For providing natural interaction in such environments, it is necessary for an interface system to know which objects a user is using. In the previously developed interface systems, real objects are identified by using specially designed tags attached to objects. In this work, we propose a new method for interactive object recognition and registration for more natural and intuitive interaction without using any tags. In particular, we introduce interactive object registration and recognition by combining direct manipulation with user's hands and a color-based object recognition algorithm.\",\n \"Title: Image correction techniques for 3D interactive surface using a transparent elastic gels\\nYear: 2012\\n\\nAbstract: There are many kinds of three dimensional displays that have been developed to date. Most of them provide 3D visual sensation to the users, but they do not provide 3D haptic feedback. On the other hand, an interactive surface system using transparent gels enables users to touch a 3D surface. The main issue of the system, however, is that the image is distorted due to \\\"lens effect\\\" of the gels. This paper describes a method to solve the image distortion through the use of a light field display (LFD) which is discussed in detail in section 2. By combining the LFD and transparent gel interface, it becomes possible to show correct 3D images on gels from any viewing position.\",\n \"Title: Monocular 3D tracking of multiple interacting targets\\nYear: 2008\\n\\nAbstract: In this paper we present a new approach based on Markov Chain Monte Carlo (MCMC) for the stable monocular tracking of variable interacting targets in 3D space. The crucial problem with monocular tracking multiple targets is that mutual occlusions on the 2D image cause target conflict(change ID, merge targets...). We focus on the fact that multiple targets cannot occupy the same position in 3D space and propose to track multiple interacting targets using relative position of targets in 3D space. Experiments show that our system can stably track multiple humans that are interacting with each other\",\n \"Title: An augmented tabletop video game with pinching gesture recognition\\nYear: 2008\\n\\nAbstract: A novel interaction technique for multi-player tabletop entertainment systems. The proposed technique achieves high-response bi-manual interaction by multiple users, and it simultaneously recognises the positions and orientations of the users' hands and pinching gestures.\",\n \"Title: PhotoelasticBall: a touch detectable ball using photoelasticity\\nYear: 2014\\n\\nAbstract: Balls are a key equipment for sports and entertainments such as juggling, etc. Then, much research has been conducted for developing balls of the next generation to enhance ball related entertainments. Such balls have plenty of special effects such as sound and light, but have limited input method. Those effects are often controlled through ball's native motion by using accelerometers, etc. However, as increasing the variety of special functions of such balls, the appropriate input method should be required. In this research, we developed a force vector sensor sheet unit that can be implemented on the surface of the ball. In this paper, we report the detail of the sensory system and its experimental results.\",\n \"Title: An Incremental Learning Method for Unconstrained Gaze Estimation\\nYear: 2008\\n\\nAbstract: This paper presents an online learning algorithm for appea- rance-based gaze estimation that allows free head movement in a casual desktop environment. Our method avoids the lengthy calibration stage using an incremental learning approach. Our system keeps running as a background process on the desktop PC and continuously updates the estimation parameters by taking user's operations on the PC monitor as input. To handle free head movement of a user, we propose a pose-based clustering approach that efficiently extends an appearance manifold model to handle the large variations of the head pose. The effectiveness of the proposed method is validated by quantitative performance evaluation with three users.\",\n \"Title: NIGELOG: Protecting Logging Information by Hiding Multiple Backups in Directories\\nYear: 1999\\n\\nAbstract: This paper proposed a novel method to protect logging information more securely. The method, named NIGELOG, produces multiple backups of the logging information, hides them in arbitrary directories, and periodically moves these backups to other directories. Also, NIGELOG can automatically restore the original logging information by using the backups even if the original file is altered or deleted by intruders. NIGELOG is implemented as a C++ class library with some standard software libraries and does not require additional hardwares such as write-once media. It is easier to apply NIGELOG to UNIX applications which produces log-files.\",\n \"Title: Real-Time Tracking of Multiple Fingertips and Gesture Recognition for Augmented Desk Interface Systems\\nYear: 2002\\n\\nAbstract: In this paper, we propose a fast and robust method for tracking a user's hand and multiple fingertips; we then demonstrate gesture recognition based on measured fingertip trajectories for augmented desk interface systems.Our tracking method is capable of tracking multiple fingertips in a reliable manner even in a complex background under a dynamically changing lighting condition without any markers. First, based on its geometrical features, the location of each fingertip is located in each input infrared image frame. Then, correspondences of detected fingertips between successive image frames are determined based on a prediction technique.Our gesture recognition system is particularly advantageous for Human-Computer Interaction (HCI) in that users can achieve interactions based on symbolic gestures at the same time that they perform direct manipulation with their own hands and fingers. The effectiveness of our proposed method has been successfully demonstrated via a number of experiments.\",\n \"Title: Information retrieval system using real world objects\\nYear: 2000\\n\\nAbstract: In this position paper we bring a new approach — Value-Sensitive Design — to understanding the value implications of augmented reality. We examine seven values: psychological well-being, physical well-being, privacy, deception, informed consent, ownership and property, and trust. In addition, we briefly describe our work where we apply a Value-Sensitive Design approach to augmented reality of the natural world.\",\n \"Title: Transparent 2-D markers on an LCD tabletop system\\nYear: 2009\\n\\nAbstract: Tabletop systems are currently being focused on and many applications using these systems are being developed. In such tabletop systems, how to recognize real objects on the table is an essential and important issue. In existing tabletop systems, 2-D markers have been often used. However, their black-and-white pattern, which means nothing to humans, spoils the appearance of the object. We developed transparent markers on a liquid crystal display (LCD) tabletop system by using the polarization features of the LCD and optical lms. In particular, through experiments with various kinds of optical films, we found that two halfwave plates make the markers rotation invariant. By using the transparent markers, tangible transparent Magic Lenses(TM) applications were developed.\",\n \"Title: Two-handed drawing on augmented desk system\\nYear: 2002\\n\\nAbstract: This paper describes a two-handed drawing tool developed on our augmented desk system. Using our real-time finger tracking method, a user can draw and manipulate objects interactively by his/her own finger/hand. Based on the former work on two-handed interaction, different roles are assigned to each hand. The right hand is used to draw and to manipulate objects. Using gesture recognition, primitive objects can be drawn by users' handwriting. On the other hand, the left hand is used to manipulate menus and to assist the right hand. By closing all left hand fingers, users can initiate the appearance of structural radial menus around their left hands, and can select appropriate items by using a left hand finger. The left hand is also used to assist in the performance of drawing tasks, e.g., specifying the center of a circle or top-left corner of a rectangle, or specifying the object to be copied.\",\n \"Title: Analysis and evaluation of recommendation systems\\nYear: 2007\\n\\nAbstract: Popular online services, such as Amazon.com, provide recommendations for users by using other users' rating scores for items. In this study, we describe three types of rating systems: score-rated, count-rated, and digital-rated. We hypothesize that digital-rated systems provide the most useful recommendations. Then we analyze the differences in the results of the rating when the granularity of the score changes. Finally, we visualize users by developing a 2-D visualization system that uses a multi-dimensional scaling method.\",\n \"Title: EnhancedTable: an augmented table system for supporting face-to-face meeting in ubiquitous environment\\nYear: 2004\\n\\nAbstract: This paper describes our design and implementation of an augmented table system for face-to-face meetings. The system was designed to be used by multiple users in the ubiquitous environment, where people do not need to bring their laptop PCs. With effective use of the advantage of computer vision, we implemented the concept of ubiquitous desktop as personal workspace and virtual Chinese table as shared workspace. User can share/personalize files by drag-and-dropping icons to/from the shared workspace. The system also provides capabilities for interactive image capturing and finger position sharing, both of which would be useful for the meetings.\",\n \"Title: Model based human motion tracking using probability evolutionary algorithm\\nYear: 2008\\n\\nAbstract: A novel evolutionary algorithm called probability evolutionary algorithm (PEA), and a method based on PEA for visual tracking of human motion are presented. PEA is inspired by estimation of distribution algorithms and quantum-inspired evolutionary algorithm, and it has a good balance between exploration and exploitation with very fast computation speed. The individual in PEA is encoded by the probability vector, defined as the smallest unit of information, for the probabilistic representation. The observation step is used in PEA to obtain the observed states of the individual, and the update operator is used to evolve the individual. In the PEA based human tracking framework, tracking is considered to be a function optimization problem, so the aim is to optimize the matching function between the model and the image observation. Since the matching function is a very complex function in high-dimensional space, PEA is used to optimize it. Experiments on 2D and 3D human motion tracking demonstrate the effectiveness, significance and computation efficiency of the proposed human tracking method.\",\n \"Title: PAC-PAC: pinching gesture recognition for augmented tabletop video game\\nYear: 2009\\n\\nAbstract: A novel tabletop entertainment system that allows simultaneous interactions by multiple participants was developed. The newly developed interaction technique of this system recognizes a pinching gesture performed with the thumb and forefinger. This gesture recognition technique enables rapid response and high degree-of-freedom input for the players.\",\n \"Title: Highly deformable interactive 3D surface display\\nYear: 2012\\n\\nAbstract: In this research, we focused on the flexibility limitation of a display material as one of the main causes for height con-straints in deformable surfaces. We propose a method that does not only utilize the material flexibility but also allows for increased variations of shapes and their corresponding interaction possibilities. Using this method, our proposed display design can then support additional expansion via protrusion of an air-pressure-controlled moldable display surface using a residual cloth-excess method and a fixed airbag mount.\",\n \"Title: Task-Oriented Evaluation of Super-Resolution Techniques\\nYear: 2010\\n\\nAbstract: The goal of super-resolution (SR) techniques is to enhance the resolution of low-resolution (LR) images. How to evaluate the performance of an SR algorithm seems to be forgotten when researchers keep producing algorithms. This paper presents a task-oriented method for evaluating SR techniques. Our method includes both objective and subjective measures and is designed from the viewpoint of how SR impacts many essential image processing and vision tasks. We evaluate some state-of-the-art SR algorithms and the results suggest that different SR algorithms should be utilized for different applications. In general, they reflect the consistency and conflict between objective and subjective measures as well as computer vision systems and human vision systems do.\",\n \"Title: Information layout and interaction techniques on an augmented round table\\nYear: 2005\\n\\nAbstract: Round tabletop display systems are currently being promoted, but the optimal ways to use these systems to display a large amount of information or how to interact with them have not been considered. This paper describes information presentation and interaction technique for a large number of files on a round tabletop display system. Three layouts are explored on our augmented table system: sequential layout, classification layout, and spiral layout. Users can search and find files by virtually rotating the circular display using a ”hands-on” technique.\",\n \"Title: STARMINE: a visualization system for cyber attacks\\nYear: 2006\\n\\nAbstract: In cyber attack monitoring systems, various types of visualizations, such as geographical visualization, temporal visualization, logical visualization, are being used. Each visualization has its own advantages and disadvantages. Since it is important to analyze the information from different viewpoints and to make a right decision in practical cyber attack monitoring, these visualization should be highly integrated.This paper describes a visualization system for cyber threat monitoring named STARMINE, which integrates three different views, that is geographical, temporal, and logical views, of the cyber threat in 3-D space. Since three views are seen simultaneously and are synchronized, it is helpful for administrators to analyze the threats much more easily. As an example, the propagation of Sasser.D worm were shown.\",\n \"Title: Pac-pac: pinching gesture recognition for tabletop entertainment system\\nYear: 2010\\n\\nAbstract: We present our new interaction technique for tabletop system and video game application using it. This technique is for recognizing a pinching gesture performed with the thumb and forefinger and tapping gestures with them by a ceiling camera above the table. The positions and orientations of multiple gestures are recognized at 200Hz, and the users around the table are tracked by their arm positions. This technique is useful for large-sized tabletop system for multiple users because it does not require any additional equipments to them nor registration process before using the system. Some pilot studies demonstrated the robustness and accuracy of the proposed technique with displaying appropriate guide on the display. We also developed a video game application called \\\"Pac-pac\\\". A player can shoot a bullet along the orientation of his hand by tapping, from any side of the table. We exhibited Pac-pac several time and well-received.\",\n \"Title: AquaTop display: interactive water surface for viewing and manipulating information in a bathroom\\nYear: 2013\\n\\nAbstract: Due to the wide spread use of smart phones and PCs, people can access information everywhere in everyday life. However, there are very few methods to access content within an bathing environment. Some people carry smart phones into a bathroom but it is unnatural to be holding a device during bathing. This paper proposes an interactive water surface display system, in which information is projected on the surface of a white water solution and users can interact with this information using gestures. In this paper, we discuss interaction design in a bathroom, describing an implementation of our system and its proposed applications.\",\n \"Title: Enhanced interaction with physical toys\\nYear: 2011\\n\\nAbstract: We developed an entertainment system that enhances the experience of playing with tops by employing augmented reality technologies. A tabletop system tracks the positions and rotation speeds of multiple tops with a high-speed camera and displays audio and visual effects. A hand-held device, called an accelerator, enables virtual and physical contact between the user and top by allowing the user to move and accelerate the top and obtain force feedback from the top. We proposed a top battle game in which the player will interact with these tops.\",\n \"Title: ZASH: A Browsing System for Multi-Dimensional Data\\nYear: 1999\\n\\nAbstract: This paper described a browsing system for movie database. The system, named ZASH, was designed and developed to explore the following features: (1) the use of multiple 2D planes to separate different types of information; (2) the use of 3D space to improve the visibility of links between nodes; (3) the use of multidimensional scaling to lay out movies and commentators so that the similar data are placed physically near each other; (4) the use of graphical fisheye views to improve the visibility of users' focus and its neighbors. The system also provides traditional searching methods (e.g. keyword, director, production year, etc.). By integrating browsing techniques and searching techniques, the system gave users more chances to find movies.\",\n \"Title: How does 3-D visualization work in software engineering?: empirical study of a 3-D version/module visualization system\\nYear: 1998\\n\\nAbstract: Version control and module management are very important in practical software development. In UNIX, RCS or SCCS is used in general as version control tools. They, however, have a couple of drawbacks. This paper proposed a solution for these issues by applying 3-D visualization. The prototype system, VRCS, was developed. In our system, each version information stored in a RCS history file is displayed as a 2-D tree by taking the z-axis as time. Other 2-D trees are laid out in 3-D space in the same way. In our visualization, files which compose a certain release of the software are connected by line called relation-link. By using GUIs, users can check in/out each version easily and interactively. More importantly, just by choosing the relation-link, a certain release is rebuilt automatically. Three comparative experiments between VRCS and RCS were conducted to know the effectiveness of VRCS. The result shows that VRCS is faster in checking in/out than RCS.\",\n \"Title: Human-centric panoramic imaging stitching\\nYear: 2012\\n\\nAbstract: We introduce a novel image mosaicing algorithm to generate 360° landscape images while also taking into account the presence of people at the boundaries between stitched images. Current image mosaicing techniques tend to fail when there is extreme parallax caused by nearby objects or moving objects at the boundary between images. This parallax causes ghosting or unnatural discontinuities in the image. To address this problem, we present an image mosaicing algorithm that is robust to parallax and misalignment, and is also able to preserve the important human-centric content, specifically faces. In particular, we find an optimal path between the boundary of two images that preserves color continuity and peoples' faces in the scene. Preliminary results show promising results of preserving close-up faces with parallax while also being able to generate a perceptually plausible 360° panoramic image.\",\n \"Title: The role of another spatial dimension in software visualization\\nYear: 1993\\n\\nAbstract: The pr]mary objective of this article is to demonstrate the use of 3D-computer graphics m visualizing shapeless software information by focusing on performance monitormg of parallel/concurrent computer systems. Issues are addressed from two different perspectives. expressiveness of output media and user cognition The former describes the limitations of 2D output media. The latter refers to a user’s cognitive load when us]ng 2D representations in a multlple-window environment. We show how these problems can be minim]zed by using a 3D framework A prototype vlsuabzation system called VOGUE has been developed A 3D framework is used to visualize the execution pattern of two parallel,’concurrent computer systems an electrlc power control system and a parallel mampulator system Thrmgb these visuahzations, we show the effectiveness of our framework The apphcations of 3D frameworks to other kinds of software reformation are also described.\",\n \"Title: SHIRI: buttocks humanoid that represents emotions with visual and tactual transformation of the muscles\\nYear: 2012\\n\\nAbstract: In this paper, we propose a novel interface design for \\\"human-robot\\\" communication by focusing on visual and tactual transformation of the muscles. Since recent humanoids may appear as humanoid figures using human-like body gestures and behavior, it is hard to say that they have enough elements to cover the complex composition that is a human. The muscles that constitute the human body work by not only turning joints and generating limb and body movements, but also control skin surface shape and firmness, allowing the various levels of touch response. Therefore, we attempt to approach the creation of sensitive and subtle expression by a humanoid robot using organic constructs. In this project, we produce \\\"SHIRI\\\", which represents emotions with organic movements of the Gluteus Maximus Actuator (GMA). In addition, we also implement user interaction for SHIRI and consider how perceptions the user can obtain by communicating with SHIRI.\",\n \"Title: Interactive surface that have dynamic softness control\\nYear: 2012\\n\\nAbstract: In the field of interface surface research, the idea of the 'softness' of a surface medium is one significant factor in determining a suitable means of interaction with the user. With direct touch input, for example, the degree of surface softness allows for the generation various touch sensations and tactile feedback. Additionally, the softness also affects the shape of the surface: a soft surface will allow the user to deform the surface at will while a hard surface will maintain its shape easier. In many traditional flexible surfaces to date, this element has been considered static and thus unchangeable. This project, in contrast, considers the softness of a surface to be dynamic and thus further explores the interaction possibilities with this type of surface. We demonstrate the possibilities of dynamically changing surfaces and their derived user interaction.\",\n \"Title: Recognizing objects with multiple configurations\\nYear: 2014\\n\\nAbstract: Computer vision has been extensively adopted in many domains during the last three decades. One of the main goals of computer vision applications is to recognize objects. Generally, computers can successfully achieve object recognition by relying on a large quantity of data. In real world, some objects may own diverse configurations or/and be observed at various angles and positions, and the process of object recognition is denoted as recognizing objects in dynamic state. It is difficult to collect enough data to achieve the sorts of objects recognition. In order to resolve the problem, we propose a technique to achieve object recognition which is not only in static state where the objects do not own multiple configurations, but also in dynamic state. First, we apply an effective robust algorithm to obtain landmarks from objects in two dimensional images. With the algorithm, the number of landmarks from different objects can be appointed in advance. A set of landmarks as a point is projected into a pre-shape space and a shape space. Next, a method is proposed to create a surface among three basic data models in a pre-shape space. If basic data are too few to create a surface or a curve, a new basic data can be built from the basic data. Then, a series of new data models can be obtained from these basic data in a pre-shape space. Finally, object recognition can be achieved by using the new data models in shape space. We give some examples to show the algorithms are efficient not only for the objects with noises, but also for the ones with various configurations.\",\n \"Title: Head Pose Estimation System Based on Particle Filtering with Adaptive Diffusion Control\\nYear: 2005\\n\\nAbstract: In this paper, we propose a new tracking system based on a stochastic filtering framework for reliably estimating the 3D pose of a user's head in real-time. Our system es- timates the pose of a user's head in each image frame whose 3D model is automatically obtained at an initiali- zation step. In particular, our estimation method is designed to control the diffusion factor of a motion model adaptively. This technique contributes significantly to improving the following performance simultaneously: the robust tracking against abrupt head motion and the accu- rate pose estimation when the user is staring at a point in a scene. The performance of our proposed method has been successfully demonstrated via experiments.\",\n \"Title: Facial parts-based face hallucination method\\nYear: 2010\\n\\nAbstract: Face hallucination produces high-resolution facial images from low-resolution inputs. In this paper, we propose a facial-parts-based face hallucination method. Since our goal is face recognition rather than face reconstruction, the contour information of facial-parts(such as eyes) is important. This method reconstructs facial parts as entities instead of dividing them into small blocks. We obtain the contours of facial parts by using the Active Appearance Model(AAM), and transform training images based on contours. We confirm that the proposed method significantly enhances face recognition performance.\",\n \"Title: Vision-Based Face Tracking System for Large Displays\\nYear: 2002\\n\\nAbstract: In this paper, we present a stereo-based face tracking system which can track the 3D position and orientation of a user in real-time, and the system's application for interaction with a large display. Our tracking system incorporates dynamic update of template images for tracking facial features so that the system can successfully track a user's face for a large angle of rotation. Another advantage of our tracking system is that it does not require a user to manually initialize the tracking process, which is critical for natural and intuitive interaction. Based on our face tracking system, we have implemented several prototype applications which change information shown on a large display adaptively according to the location looked at by a user.\",\n \"Title: HOJI*HOJI: the hole-type interactive device for entertainment\\nYear: 2012\\n\\nAbstract: Holes often excite our curiosity and eventually people will want to look inside them. In this research, focusing the attention on this \\\"hole exploration\\\" and corresponding actions as an element of interactive entertainment, we developed a hole-type device named \\\"HOJI*HOJI\\\". HOJI*HOJI is equipped with a hole that can recognize finger position through the use of pressure sensors. Force feedback is also implemented within the device for realizing interaction between the 'hole' and the user's finger. For proof of concept, an interactive game application was made for HOJI*HOJI and was exhibited at 2 events to confirm whether this 'hole' can attract people's curiosity.\",\n \"Title: Fast Tracking of Hands and Fingertips in Infrared Images for Augmented Desk Interface\\nYear: 2000\\n\\nAbstract: In this paper, we introduce a fast and robust method for tracking positions of the centers and the fingertips of both right and left hands. Our method makes use of infrared camera images for reliable detection of user's hands, and uses template matching strategy for finding fingertips. This method is an essential part of our augmented desk interface in which a user can, with natural hand gestures, simultaneously manipulate both physical objects and electronically projected objects on a desk, e.g., a textbook and related WWW pages. Previous tracking methods which are typically based on color segmentation or background subtraction simply do not perform well in this type of application because an observed color of human skin and image backgrounds may change significantly due to projection of various objects onto a desk. In contrast, our proposed method was shown to be effective even in such a challenging situation through demonstration in our augmented desk interface. This paper describes the details of our tracking method as well as typical applications in our augmented desk interface.\",\n \"Title: Experiencing the ball's POV for ballistic sports\\nYear: 2013\\n\\nAbstract: We place a small wireless camera inside an American football to capture the ball's point-of-view during flight to augment a spectator's experience of the game of football. To this end, we propose a robust video synthesis algorithm that leverages the unique constraints of fast spinning cameras to obtain a stabilized bird's eye point-of-view video clip. Our algorithm uses a coarse-to-fine image homography computation technique to progressively register images. We then optimize an energy function defined over pixel-wise color similarity and distance to image borders, to find optimal image seams to create panoramic composite images. Our results show that we can generate realistic videos from a camera spinning at speeds of up to 600 RPM.\",\n \"Title: Snaplink: Interactive Object Registration And Recognition For Augmented Desk Interface\\nYear: 2001\\n\\nAbstract: Identification of objects in a real world plays a key role for human-computer interaction in a computer-augmented environment using augmented reality techniques. To provide natural and intuitive interaction in such environments, it is necessary for an interface system to know which objects a user is using. In previously developed interface systems, real objects are identified by using specially designed tags attached to objects. In this work, we propose a new method for interactive object recognition and registration; the method permits more natural and intuitive interaction without using any tags. In particular, we introduce interactive object registration and recognition by combining direct manipulation by the user's hands with a color-based object recognition algorithm.\",\n \"Title: Tudumi: information visualization system for monitoring and auditing computer logs\\nYear: 2002\\n\\nAbstract: Computer security breaches are already a major problem in using computers. The most basic defense against it is to monitor and audit the computer logs. Computer logs, however have a huge amount of textual data. It is, therefore, almost impossible to inspect them manually using current systems. We propose a log visualization system called \\\"Tudumi\\\". Tudumi consists of several functions which assist system administrators to perform such tasks manually. These functions are information visualization, log summarization and reflecting known rules into the visualization method. Tudumi makes it easier to detect anomalous user activities, such as intrusion, from a huge amount of computer logs.\",\n \"Title: LivingClay: particle actuation to control display volume and stiffness\\nYear: 2013\\n\\nAbstract: We present a new type of display actuation that is able to control both display geometry and stiffness properties using a filler material and air flow control technique. The display consists of a flat, flexible layer of cells on the surface and chamber filled with particles under it. Display geometries can be changed by transporting an amount of particles between display cells and the particle chamber using pressured air and vacuum to control the air flows. This system also allow for variable stiffness using vacuum technique to harden the particles inside chamber. In this paper, we present the design and control technique of this new type actuator and also possible interaction on a single actuator display. We also propose a low-cost, effective way to control an array of actuators where the air flow line and particle line are arranged in a multiplexed grid configuration.\",\n \"Title: InteractiveTop: an entertainment system that enhances the experience of playing with tops\\nYear: 2011\\n\\nAbstract: A top is a simple toy, yet its behavior is unique when compared with the behaviors of other physical toys. Its attractive behavior is based on the gyroscopic effect acting within the top. We sense the force of the gyroscopic effect when touching or handling them. We consider that this experience helps children become aware of a basic phenomenon of physics. In traditional research[Ishii et al. 1999], a visual and audio effect are often used to augment playging experience of sports or physical toys. In this project, we especially focused on augmentation of physical contact between the tops and the players by using force feedback.\",\n \"Title: Two-handed drawing on augmented desk\\nYear: 2002\\n\\nAbstract: This paper describes a two-handed drawing tool on Enhanced Desk. Through the experiments, our tool showed better performance when drawing simple figures than traditional drawing tools. The subjects also reported that it was easier to learn the usage of the tool.\",\n \"Title: Video-based tracking of user's motion for augmented desk interface\\nYear: 2004\\n\\nAbstract: This paper presents an overview of our project on an augmented desk interface system called the EnhancedDesk. The EnhancedDesk is equipped with an infrared camera for tracking a users' hand motions, a color video camera for recognizing objects, and projectors for displaying various kinds of digital information onto the desk. The key technical innovations of the EnhancedDesk include: fast and accurate tracking of multiple fingers for direct manipulation of both real and projected objects with bare hands, interactive object registration and recognition with hand gestures, vision-based tracking of a user's gaze, and addition of interactive functionality overlaid on the desk. This paper presents a brief description of the components, as well as some prototypes of applications of our augmented desk interface system.\",\n \"Title: Estimating Anomality of the Video Sequences for Surveillance Using 1-Class SVM\\nYear: 2008\\n\\nAbstract: We have proposed a method to detect and quantitatively extract anomalies from surveillance videos. Using our method, anomalies are detected as patterns based on spatio-temporal features that are outliers in new feature space. Conventional anomaly detection methods use features such as tracks or local spatio-temporal features, both of which provide insufficient timing information. Using our method, the principal components of spatio-temporal features of change are extracted from the frames of video sequences of several seconds duration. This enables anomalies based on movement irregularity, both position and speed, to be determined and thus permits the automatic detection of anomal events in sequences of constant length without regard to their start and end. We used a 1-class SVM, which is a non-supervised outlier detection method. The output from the SVM indicates the distance between the outlier and the concentrated base pattern. We demonstrated that the anomalies extracted using our method subjectively matched perceived irregularities in the pattern of movements. Our method is useful in surveillance services because the captured images can be shown in the order of anomality, which significantly reduces the time needed.\",\n \"Title: Estimating Number Of People Using Calibrated Monocular Camera Based On Geometrical Analysis Of Surface Area\\nYear: 2009\\n\\nAbstract: We propose a novel technique for estimating the number of people in a video sequence; it has the advantages of being stable even in crowded situations and needing no ground-truth data. By analyzing the geometrical relationships between image pixels and their intersection volumes in the real world quantitatively, a foreground image directly indicates the number of people. Because foreground detection is possible even in crowded situations, the proposed method can be applied in such situations. Moreover, it can estimate the number of people in an a priori manner, so it needs no ground-truth data unlike existing feature-based estimation techniques. Experiments show the validity of the proposed method.\",\n \"Title: ClaytricSurface: an interactive surface with dynamic softness control capability\\nYear: 2012\\n\\nAbstract: For a long time, solid planar surfaces have been commonly used for computer displays. Recently, there have been many attempts to provide a flexible element with the display surface to extend possibilities of interaction by utilizing many flexible materials, such as elastomer, sand or clay [Piper et al. 2002]. However, in many traditional flexible display to date, this softness element has been considered as a static element and thus unchangeable. Thus, traditional flexible surface limits the possible interaction that is supported on each surface due to the interactive element being strongly dependent on the physical/chemical properties of display material.\",\n \"Title: Real-Time Fingertip Tracking and Gesture Recognition\\nYear: 2002\\n\\nAbstract: Augmented desk interfaces and other virtual reality systems depend on accurate, real-time hand and fingertip tracking for seamless integration between real objects and associated digital information. We introduce a method for discerning fingertip locations in image frames and measuring fingertip trajectories across image frames. We also propose a mechanism for combining direct manipulation and symbolic gestures based on multiple fingertip motions. Our method uses a filtering technique, in addition to detecting fingertips in each image frame, to predict fingertip locations in successive image frames and to examine the correspondences between the predicted locations and detected fingertips. This lets us obtain multiple complex fingertip trajectories in real time and improves fingertip tracking. This method can track multiple fingertips reliably even on a complex background under changing lighting conditions without invasive devices or color markers.\",\n \"Title: A vision-based non-contact interactive advertisement with a display wall\\nYear: 2006\\n\\nAbstract: We developed an advertising system that enables users to interact with advertisements on a large display wall without any physical contact. The system recognizes positions of bodies or hands of users in front of the display by stereo cameras. The system recognizes a distance between a user and the display as the user's interests in the advertisement, and changes two display modes according to the distance. One of the modes shows advertisements as many as possible to attract interests of passers-by. The other allows a user to interact with the advertisement on the display by recognizing a gesture of the user.\",\n \"Title: EnhancedTable: supporting a small meeting in ubiquitous and augmented environment\\nYear: 2004\\n\\nAbstract: This paper describes our design and implementation of an augmented table interface for small group meetings. The system was designed to be used by multiple users in the ubiquitous environment, where people do not need to bring their laptop PCs. With effective use of the advantage of computer vision, we implemented the concept of ubiquitous desktop as personal workspace and virtual Chinese table as shared workspace. User can share/personalize files by drag-and-dropping icons to/from the shared workspace. The system also provides capabilities for interactive image capturing and finger position sharing, both of which would be useful for the group meetings.\",\n \"Title: Visualizing Cyber Attacks using IP Matrix\\nYear: 2005\\n\\nAbstract: An Internet cyber threat monitoring system detects cyber threats using network sensors deployed at particular points on the Internet, statistically analyzes the time of attack, source of attack, and type of attack, and then visualizes the result of this analysis. Existing systems, however, simply visualize country-by-country statistics of attacks or hourly changes of attacks. Using these systems, it is difficult to understand the source of attack, the diffusion of the attack, or the relation between the target and the source of the attack. This paper described a method for visualizing cyber threats by using 2-dimensional matrix representation of IP addresses. The advantages of this method are that: (1) the logical distance of IP addresses is represented intuitively; (2) Internet address space is visualized economically; (3) macroscopic information (Internet level) and microscopic information (local level) are visualized simultaneously. By using this visualization framework, propagation of the Welchia worm and the Sasser.D worm are visualized.\",\n \"Title: VRCS: Integrating Version Control and Module Management Using Interactive Three-Dimensional Graphics\\nYear: 1997\\n\\nAbstract: Version control and module management are very important in practical software development. In UNIX, RCS or SCCS is used in general as version control tools. However, they (1) lack: an ability of explicitly displaying the version changes; (2) require complicated commands to be typed; and (3) lack an ability of managing multiple files and modules. This paper proposed a solution for these issues by applying 3-D visualization. The prototype system, VRCS, was developed. In our system, each version information stored in a RCS history Jibe is displayed as a 2-D tree by taking the z-axis as time. Other 2-D trees are laid out in 3-D space in the same way. In our visualization files which compose a certain release of the software are connected by line called relation-link. By using GUIs, users can check; in/out each version easily and interactively. More importantly, just by choosing the relation-link, a certain release is rebuilt automatically.\",\n \"Title: PhotoelasticTouch: transparent rubbery tangible interface using an LCD and photoelasticity\\nYear: 2009\\n\\nAbstract: PhotoelasticTouch is a novel tabletop system designed to intuitively facilitate touch-based interaction via real objects made from transparent elastic material. The system utilizes vision-based recognition techniques and the photoelastic properties of the transparent rubber to recognize deformed regions of the elastic material. Our system works with elastic materials over a wide variety of shapes and does not require any explicit visual markers. Compared to traditional interactive surfaces, our 2.5 dimensional interface system enables direct touch interaction and soft tactile feedback. In this paper we present our force sensing technique using photoelasticity and describe the implementation of our prototype system. We also present three practical applications of PhotoelasticTouch, a force-sensitive touch panel, a tangible face application, and a paint application.\",\n \"Title: PhotoelasticTouch: transparent rubbery interface using a LCD and photoelasticity\\nYear: 2009\\n\\nAbstract: PhotoelasticTouch is a tabletop system designed to intuitively facilitate touch-based interaction via real objects made from transparent elastic material. The transparent elastic material (transparent rubber) provides a realistic haptic interface, which when combined with the visual content displayed on the LCD tabletop, enables a balanced coupling of the physical world and digital content. The system utilizes the photo-elastic properties of the transparent rubber to recognize when a user pushes, pulls or pinches the object, while the LCD provides the appropriate visual feedback in accordance to the stress applied to the elastic material.\",\n \"Title: SnortView: visualization system of snort logs\\nYear: 2004\\n\\nAbstract: False detection is a major issue in deploying and maintaining Network-based Intrusion Detection Systems (NIDS). Traditionally, it is recommended to customize its signature database (DB) to reduce false detections. However, it requires quite deep knowledge and skills to appropriately customize the signature DB. Inappropriate customization causes the increase of false negatives as well as false positives. In this paper, we propose a visualization system of a NIDS log, named SnortView, which supports administrators in analyzing NIDS alerts much faster and much more easily. Instead of customizing the signature DB, we propose to utilize visualization to recognize not only each alert but also false detections. The system is based on a 2-D time diagram and alerts are shown as icons with different styles and colors. In addition, the system introduces some visualization techniques such as overlayed statistical information, source-destination matrix, and so on. The system was used to detect real attacks while recognizing some false detections.\",\n \"Title: SplashDisplay: volumetric projection using projectile beads\\nYear: 2012\\n\\nAbstract: The prime feature of the SplashDisplay is that it uses projectile beads as a display medium that are launched from the table; this means that the tangible medium can be removed from the surface and allowed free movement in air. In current research [1] [2], displays have tended toward fixed mediums and therefore defining and visualizing explosive activity on such displays has been difficult. However, the SplashDisplay is made for this particular purpose, and given the nature of the display medium it pulls away from conventional standard surfaces. Given this tradeoff, it is possible for the SplashDisplay to launch projectile beads from millimeters to meters into the air freely making it possible to attain an image 'depth' much like the Z-axis in 3D. In the simulation of 'explosions', this system launches beads into the air much like the physical phenomenon, making the projected object 'feel' like it actually exploded (Figure 1). As the beads are white in color, it is possible for these beads to act as a display backing. When the system is still, then the beads play the role of a stationary screen; once the beads are in air, they can still be recognized as 'screen' material. If light is projected onto these airborne beads, they will illuminate as they fall, giving a 'fireworks' like effect in real time.\",\n \"Title: Fluid surface: interactive water surface display for viewing information in a bathroom\\nYear: 2012\\n\\nAbstract: Information is becoming accessible everywhere in everyday life due of the spread of smart phones and portable personal computers; however are very few methods in accessing contents in a bathing environment. Sometimes smart phones can be carried into a bathroom but it is unnatural to be holding a device at during bathing, so a suitable technique for information browsing in a bathing environment is required. We propose an interactive water surface display system, which uses image-recognition techniques. By using water, the system can perform an intuitive interaction peculiar to water such as poking a finger up from under the water surface, stroking the water surface and scooping up water. In this paper, we discuss interaction design in a bathroom, describing an implementation of our system and its applications.\",\n \"Title: Interactive textbook and interactive Venn diagram: natural and intuitive interfaces on augmented desk system\\nYear: 2000\\n\\nAbstract: This paper describes two interface prototypes which we have developed on our augmented desk interface system, EnhancedDesk. The first application is Interactive Textbook, which is aimed at providing an effective learning environment. When a student opens a page which describes experiments or simulations, Interactive Textbook automatically retrieves digital contents from its database and projects them onto the desk. Interactive Textbook also allows the student hands-on ability to interact with the digital contents. The second application is the Interactive Venn Diagram, which is aimed at supporting effective information retrieval. Instead of keywords, the system uses real objects such as books or CDs as keys for retrieval. The system projects a circle around each book; data corresponding the book are then retrieved and projected inside the circle. By moving two or more circles so that the circles intersect each other, the user can compose a Venn diagram interactively on the desk. We also describe the new technologies introduced in EnhancedDesk which enable us to implement these applications.\",\n \"Title: LumoSpheres: real-time tracking of flying objects and image projection for a volumetric display.\\nYear: 2015\\n\\nAbstract: This paper proposes a method for real-time tracking of flying objects and image projection onto them for developing a particle-based volumetric 3D display. The first section describes the concept using high-speed cameras and projectors for a particle-based volumetric 3D display. Our solution suggests a prediction model with kinematic laws and uses Kalman Filters to address latency issues within the projector-camera system. We conducted experiments to show the accuracy of the image projection. We also present an application of our method in entertainment, Digital Juggling.\",\n \"Title: Augmented dodgeball: an approach to designing augmented sports\\nYear: 2015\\n\\nAbstract: Ubiquitous computing offers enhanced interactive, human-centric experiences including sporting and fitness-based applications. To enhance this experience further, we consider augmenting dodgeball by adding digital elements to a traditional ball game. To achieve this, an understanding of the game mechanics with participating movable bodies, is required. This paper discusses the design process of a ball--player-centric interface that uses live data acquisition during gameplay for augmented dodgeball, which is presented as an application of augmented sports. Initial prototype testing shows that player detection can be achieved using a low-energy wireless sensor based network such as that used with fitness sensors, and a ball with an embedded sensor together with proximity tagging.\",\n \"Title: Appearance-Based Gaze Estimation With Online Calibration From Mouse Operations\\nYear: 2015\\n\\nAbstract: This paper presents an unconstrained gaze estimation method using an online learning algorithm. We focus on a desktop scenario, where a user operates a personal computer, and use the mouse-clicked positions to infer, where on the screen the user is looking at. Our method continuously captures the user’s head pose and eye images with a monocular camera, and each mouse click triggers learning sample acquisition. In order to handle head pose variations, the samples are adaptively clustered according to the estimated head pose. Then, local reconstruction-based gaze estimation models are incrementally updated in each cluster. We conducted a prototype evaluation in real-world environments, and our method achieved an estimation accuracy of 2.9 .\",\n \"Title: Recognition of objects in various situations from two dimensional images\\nYear: 2010\\n\\nAbstract: Generally, computers can successfully achieve object recognition by relying on sufficient information of observed objects. However, in real world, many objects own diverse configurations or objects are observed at various angles and positions, which make it difficult to match the observed objects with data models in a limited database. In this paper, to resolve the above problem, we propose an algorithm to achieve this kind of object recognition in shape space. Firstly, we describe the algorithm of extracting landmarks from outer contour of a shape by using recursive landmarks determination, in which the number of the landmarks can be appointed. Then, for the objects with many configurations, a series of new data are generated from one or two data models in pre-shape space. Finally, we achieve object recognition with shape space theory. The proposed method is efficient not only for the objects with noises, but also for the ones with various situations.\",\n \"Title: ClaytricSurface: An Interactive Deformable Display with Dynamic Stiffness Control\\nYear: 2014\\n\\nAbstract: ClaytricSurface is a shape-changeable display with dynamic stiffness control. The prototype uses vacuum pressure control on an enclosed volume of particles. Users can mold 3D shapes and apply textures to them while experiencing tactile feedback through dynamically changing stiffness. The system's developers have added both touch input using a depth camera and peripheral devices to expand the system's practical application. They also developed applications in the form of design tools that have great entertainment potential.\",\n \"Title: Fractal approaches for visualizing huge hierarchies\\nYear: 1993\\n\\nAbstract: This paper describes fractal approaches to the problemswhich associate with visualizing huge hierarchies.The geometrical characteristic of a fractal, selfsimilarity,allows users to visually interact with a hugetree in the same manner at every level of the tree. Thefractal dimension, a measure of complexity, makes itpossible to control the total amount of displayed nodes.A prototype visualization system for UNIX directoriesis also shown.1 IntroductionVisualization systems for...\",\n \"Title: VisuaLinda: a framework for visualizing parallel Linda programs\\nYear: 1997\\n\\nAbstract: This paper describes the VisuaLinda system, which is an integration of a Linda server and a visualizer of parallel Linda programs. Since the visualization module is built in the Linda server, programmers do not need to put additional visualization primitives in their client programs in order to visualize the behavior of their programs. This framework significantly reduces the programmers' burden in debugging parallel programs, owing to the following two features. First, it minimizes the ``probe effect,'' which is one of the main concerns in monitoring parallel programs. Second, VisuaLinda uses three-dimensional space to display both the relation between the Linda server and the client programs, and the execution of client programs. This framework can be used to display a much larger number of processes than using 2D visualization techniques, see two relations simultaneously, improve the visibility of communication lines, and see each process's state as well as the overview of the execution.\",\n \"Title: Information Layout and Interaction on Virtual and Real Rotary Tables\\nYear: 2007\\n\\nAbstract: Abstract Many tabletop systems have been developed, but few of them deal with the problems of visualizing and manipulat- ing a large amount,of information such as files on a table- top that is physically limited in size. In order to address this issue, we developed a rotary table system. The system rec- ognizes users’ hand gestures, and the users can rotate the table virtually. The table acts as a scroll wheel, and users can see a great deal of information by scrolling the table. We investigated three layout methods: sequential, classifi- cation, and spiral. We investigated these on the system and conducted user studies. Moreover, we also developed a real rotary table by using a roller bearing and a round table- top. Then, we conducted comparative experiments on the usability and intuitiveness of the two rotary tables.\",\n \"Title: Implementation and evaluations of vision-based finger flicking gesture recognition for tabletops\\nYear: 2008\\n\\nAbstract: Finger flicking is a familiar finger movement, and we often flick a small object on the table with a finger in our daily life. We developed a flicking gesture recognition system called the dasiaOHAJIKI interfacepsila, which can track very rapid flicking movements using a high-speed camera, and estimate the flicking power and direction in real time. In this paper, we describe the recognition techniques of the flicking gesture on a large LCD to produce a novel tabletop system that enables us to use the gesture to flick a virtual marble on the table. Moreover, we also present an evaluation of a vision-based flicking tabletop system as an entertainment application.\",\n \"Title: Estimating the number of people in a video sequence via geometrical model\\nYear: 2008\\n\\nAbstract: We propose a novel technique for estimating the number of people in a video sequence; it has the advantages of being stable even in crowded situations and needing no ground-truth data. By analyzing the geometrical relationships between image pixels and their intersection volumes in the real world quantitatively, a foreground image can be directly indicate the number of people. Because foreground detection can be done even in crowded situations, the proposed method can be applied to such situations. Also it can estimate the number of people in an a-priori manner, so it needs no ground-truth data which is necessary for existing feature-based estimating techniques. Experiments show the validity of the proposed method.\",\n \"Title: Contrasting shadow for occluder light suppression from one-shot image\\nYear: 2010\\n\\nAbstract: Two main problems for front projection systems when a user appears between a screen and a projector are 1) shadows being cast on a screen and 2) the user being illuminated due to undesirable strong projection light. To solve these problems, it is necessary to know which projectors are occluded by a user and cast a shadow. We propose a method that suppresses occluder light from a single image based on the fact that the darkness of shadows on a screen is differentiated when projectors have different luminance. Contrary to previous approaches, our method utilizes different alpha masks for each projector to contrast shadows on a screen. This enables us to clarify the relationship between the shadow on a screen and the occluded projector. Additionally, by differentiating all projector luminances, our method makes it possible to discriminate shadows even when some shadow regions overlap on a screen. We implemented the proposed method into a three-projector system to demonstrate its effectiveness.\",\n \"Title: Simple Camera Calibration From a Single Image Using Five Points on Two Orthogonal 1-D Objects\\nYear: 2010\\n\\nAbstract: We propose a simple and practical calibration technique that effectively estimates camera parameters from just five points on two orthogonal 1-D objects, each which has three collinear points, one of which is shared. We derive the basic equations needed to realize camera calibration from just five points observed on a single image that captures the objects. We describe a new camera calibration algorithm that estimates the camera parameters based on the basic equations and optimizes them by the bundle adjustment technique. Our method is validated by both computer simulated data and real images. The results show that the camera parameters yielded by our method are close to those yielded by existing methods. The tests demonstrate that our method is both effective and practical.\",\n \"Title: Smart curtain as interactive display in living space.\\nYear: 2015\\n\\nAbstract: In the era of IoT, there is the necessity of information display for IoT devices in living space. One ideal realization should be 'ubiquitous' for such a purpose. We propose smart curtain as an implementation of ubiquitous computing in living space, which has the following four advantages; (a) a role of boundary between two spaces such as indoor and outdoor, (b) natural layer structure which consists of translucent curtain and drape curtain, (c) relatively large area which can offer enough space for displaying a lot of information and image which can make good atmosphere, and (d) context awareness by sensing surrounding environment and the state (opening/closing) of curtain. Though its visual quality is not expected to be so high, there should be many kinds of contents which don't need high visual quality.\",\n \"Title: Ballumiere: real-time tracking and projection system for high-speed flying balls.\\nYear: 2015\\n\\nAbstract: We present Ballumiere, a novel projection system for the balls moving in the air. Recently projection mapping has attracted much attention. \\\\\\\"Shader Lamps\\\\\\\" [Raskar et al. 2001] enable to render computer graphics to real objects in three dimensional space using projectors. However, almost all the projection mapping techniques only allow projecting to static objects or projection targets that move slowly. When we try to track and project to moving objects, the conventional method of projection mapping is unsatisfactory due to the delay between tracking and projection. \\\\\\\"Lumipen\\\\\\\" [Okumura et al. 2013] tracks and projects to a small object in real-time using a high-speed vision sensor and a projector combination using a high-speed optical gaze controller. This system successfully projects to an object moving quickly, but only allows for one object to be a projection target due to restrictions of the optical axis of the vision sensors and the projector being the same. To solve these problems, we present Ballumiere as a new real-time tracking and projection system that can project to the multiple fast moving airborne balls using a motion capture camera and a standard off the shelf projector. We overcome the delay problem using prediction algorithms using Kalman filters and kinetic models. In this demo, we project images and movies onto juggling balls and soccer balls.\",\n \"Title: MultiMosaic: simultaneous visualization of multiple images for near and far viewers using image mosaic and saliency map\\nYear: 2013\\n\\nAbstract: This paper presents a method to display two types of images simultaneously in one image. One is for viewers at a far distance, and the other is for viewers closer to the screen. The basic idea is to overlay small images in high resolution (i.e. near-images) on a larger image in low resolution (i.e. far-image) by adjusting transparency of the near images. However, this simple overlay approach fails to provide acceptable visibility for both images. We propose to use a saliency map to emphasize important features of each image. By using this saliency map, it is possible to provide better visibility for both images, which this paper will illustrate with various examples.\",\n \"Title: Vision-based face tracking system for window interface: prototype application and empirical studies\\nYear: 2001\\n\\nAbstract: In this paper, we study the effective use of gaze information for human-computer interaction based on a stereo-based vision system which can track the 3D position and orientation of a user in real-time. We have integrated our face-tracking system into the X Window interface system, and conducted experiments to evaluate the effectiveness of our proposed framework for using gaze information for window interfaces.\",\n \"Title: Synthesizing Pseudo Straight View from A Spinning Camera Ball.\\nYear: 2016\\n\\nAbstract: This paper proposed a spherical camera ball and its image processing algorithm, designed to provide a ball's point of view (POV) for spectating of ball sports. The proposed spherical camera ball has six cameras embedded at fixed intervals around the surface of the ball. One of the main issues for such ball-type cameras is that the device is spinning and therefore it is hard to obtain stable video stream from such spinning cameras. This paper proposed automatic selection of cameras using matching scores between the anchor frame and current frame. The resultant movie will then always shows the point of interest within the frame. We then applied image translation to obtain a pseudo straight view in which the point of interest is always shown in the center of the frame.\",\n \"Title: A Remote Pointing Method with Dynamic C-D Ratio during a Pinching Gesture for Large Tabletop Systems.\\nYear: 2016\\n\\nAbstract: This paper proposes a simple but efficient remote pointing method for large tabletop systems using a pinching gesture with a thumb and a forefinger. The proposed method uses a control-display (C-D) ratio that dynamically changes depending on the height of the user's hand. The user can intuitively control the speed of the pointer and its travel distance by changing his hand position in 3-D. The method can also be used in a multi-user environment. We applied the method to a photo viewer application.\",\n \"Title: Visual Guidance with Unnoticed Blur Effect.\\nYear: 2016\\n\\nAbstract: In information media such as TV programs, digital signage, or web pages, information content providers often want to guide viewers' attention to a particular location of the display. However, \\\\\\\"active\\\\\\\" methods, such as flashing displays, using animation, or changing colors, often interrupt viewers' concentration and makes viewers feel annoyed. This paper proposes a method for guiding viewers' attention without viewers noticing. By focusing on a characteristic of the human visual system, we propose a dynamic blur control method. Our method gradually blurs the image on the display to the threshold at which viewers are aware of the modulation of the display, while the region where viewers' attention should be guided remains unblurred. Two subjective experiments were conducted to show the effectiveness of our method. In the first, viewers' attention was guided to the unblurred region using blur control. In the second, a threshold was found at which viewers were aware of the modulation, and viewers' gaze is guided below this threshold. This means that the viewers' attention can be guided without them noticing.\",\n \"Title: OmniEyeball: Spherical Display Embedded With Omnidirectional Camera Using Dynamic Spherical Mapping.\\nYear: 2016\\n\\nAbstract: Recently, 360-degree panorama and spherical displays have received more and more attention due to their unique panoramic properties. Compared with existing works, we plan to utilize omnidirectional cameras in our spherical display system to enable omnidirectional panoramic image as input and output. In our work, we present a novel movable spherical display embedded with omnidirectional cameras. Our system can use embedded cameras to shoot 360-degree panoramic video and project the live stream from its cameras onto its spherical display in real time. In addition, we implemented an approach to achieve the dynamic spherical projection mapping in order to project to moving spherical devices. We have also been creating applications utilizing system's features by using 360-degree panoramic image as input and output.\",\n \"Title: Ballumiere: Real-Time Tracking and Projection for High-Speed Moving Balls.\\nYear: 2016\\n\\nAbstract: Projection onto moving objects has a serious slipping problem due to delay between tracking and projection. We propose a new method to overcome the delay problem, and we succeed in increasing the accuracy of projection. We present Ballumiere as a demo for projection to volleyballs and juggling balls.\",\n \"Title: MlioLight: Multi-Layered Image Overlay using Multiple Flashlight Devices.\\nYear: 2016\\n\\nAbstract: We propose a technique that overlays natural images on the real world using the information from multiple flashlight devices. We focus on finding areas of overlapping lights in a multiple light-source scenario and overlaying multi-layered information on a real world object in these areas.In order to mix multiple images, we developed a light identification and overlapping area detection technique using rapid synchronization between high-speed cameras and multiple light devices.In this paper, we describe the concept of our system and a prototype implementation.We also describe two different applications.\",\n \"Title: Activity-Aware Video Stabilization for BallCam.\\nYear: 2016\\n\\nAbstract: We present a video stabilization algorithm for ball camera systems that undergo extreme egomotion during sports play. In particular, we focus on the BallCam system which is an American football embedded with an action camera at the tip of the ball. We propose an activity-aware video stabilization algorithm which is able to understand the current activity of the BallCam, which uses estimated activity labels to inform a robust video stabilization algorithm. Activity recognition is performed with a deep convolutional neural network, which uses optical flow.\",\n \"Title: Ballumiere: Real-Time Tracking and Spherical Projection for High-Speed Moving Balls.\\nYear: 2016\\n\\nAbstract: Projections on moving objects have a problem in that the projection may slip because of the delay between tracking and projection. Here, we propose a new prediction method combining a Kalman filter and a three-frame feedback model that switches between these models according to the ball's state of motion. We developed a real-time tracking and projection system named \\\\\\\"Ballumiere\\\\\\\", which uses motion capture cameras for tracking and multiple projectors for spherical projection. We conducted a comparative experiment with an existing prediction model and showed that our method minimizes slipping and increases the accuracy of the projection.\",\n \"Title: Video segmentation and stabilization for BallCam.\\nYear: 2017\\n\\nAbstract: We present a video stabilization algorithm for ball camera systems that undergo extreme egomotion during sports play. In particular, we focus on the BallCam system which is an American football embedded with an action camera at the tip of the ball. We propose an activity-aware video stabilization algorithm which is able to understand the current activity of the BallCam, which uses estimated activity labels to inform a robust video stabilization algorithm. Activity recognition is performed with a deep convolutional neural network, which uses optical flow.\",\n \"Title: Synthesizing fixed point of views from a spinning omnidirectional ball camera.\\nYear: 2017\\n\\nAbstract: Recent years have seen the emergence of a new way to watch sports, that is, watching them from the ball's point of view (POV) taken with cameras embedded in the ball. The problem, however, is that the ball's POV is unstable since the ball spins. In this paper, we describe how we developed a prototype of the ball camera with an omnidirectional camera and propose an algorithm to fix the ball's POV to an arbitrary direction. Feature points are extracted from an entire omnidirectional image and the pose change between frames is estimated by feature matching and the five-point algorithm. The POV of the current frame is modified to the one of the initial frame by applying the product of estimated rotation matrices between all adjacent frames up to the current one. Our algorithm is not limited to our hardware and can be applied to any omnidirectional videos taken with different camera systems.\",\n \"Title: Qoom: An Interactive Omnidirectional Ball Display.\\nYear: 2017\\n\\nAbstract: We present a sphere-shaped interactive display system, named Qoom, as a new input and output device. Unlike existing sphere-shaped displays, Qoom is a perfectly spherical ball that can be rotated, thrown, or even kicked. First, we discuss how spherical displays can be used in daily life and describe how users interact with spheres. Then, we show how we developed the Qoom prototype that uses touch and rotation detection, real-time object tracking, and spherical projection mapping. We implemented actions including touching, rotating, bouncing and throwing as controls. We also developed applications for Qoom that utilize the unique advantages of ball displays.\",\n \"Title: Rapid Prototyping of Accessible Interfaces With Gaze-Contingent Tunnel Vision Simulation.\\nYear: 2017\\n\\nAbstract: Active involvement of users with disabilities is difficult to employ during the iterative stages of the design process due to high costs and effort associated with user studies. This research proposes a user centered design (UCD) strategy to incorporate the use of gaze-contingent tunnel vision simulation with sighted individuals to facilitate rapid prototyping of accessible interfaces. Through three types of validation studies, we examined how our simulation techniques can provide the opportunity for continued evaluation and refinement of the design. Our simulation approach was effective in emulating scanning behaviors caused by tunnel vision along with grasping user feedback to recognize user interface and usability criteria early in the design cycle.\",\n \"Title: Shin-Tai: A Method For Controlling Characteristics Of A Humanoid Robot'S Body Using Artificial Muscles And Fats\\nYear: 2017\\n\\nAbstract: \\\"SHIN-TAI\\\" is an upper humanoid body composed of artificial muscles and artificial fats covered with artificial skin. The artificial muscles reproduce natural changes in shape and stiffness of real human muscles by a pressure-regulating system, and the amount of artificial fats can be increased or decreased by controlling liquid flow rate into liquid bags under the skin. By controlling these two programmable functions, SHIN-TAI can assume various body types, namely, \\\"slim,\\\" \\\"muscular,\\\" \\\"voluptuous,\\\" or \\\"overweight.\\\" Even its gender can be changed by region-selective control of the fats. It is demonstrated that a controllable character of a humanoid can be expressed by using our method instead of conversations or body gestures, which depend on a static human-like housing as in recent humanoid implementations.\",\n \"Title: ParaPara: Synthesizing Pseudo-2.5D Content from Monocular Videos for Mixed Reality.\\nYear: 2018\\n\\nAbstract: In this work, we propose a simple yet effective method for synthesizing a pseudo-2.5D scene from a monocular video for mixed reality (MR) content. We also propose the ParaPara system, which applies this method. Most previously proposed systems convert real-world objects into 3D graphic models using expensive equipment; this is a barrier for individuals or small groups to create MR content. ParaPara uses four points in an image and their manually estimated distances to synthesize MR content by applying deep neural networks and simple image processing techniques to monocular videos. The synthesized content can be observed through an MR head-mounted display, and spatial mapping and spatial sound are applied to support the interaction between the real-world and MR content. The proposed system is expected to reduce the entry barriers to create MR content because it can create such content from a large number of previously captured videos.\",\n \"Title: A Study On Gaze Guidance Using Artificial Color Shifts\\nYear: 2018\\n\\nAbstract: In Web or digital signage, content providers want to guide users' attention to the intended regions. Using active visual stimuli, such as animated or flashing objects, is effective for gaze guidance; however, it has been reported that such an approach often results in unpleasant feelings for users. This paper proposed a new method for gaze guidance using artificial color shifts that does not induce unpleasant feelings in the user. We created an image filter that separates the image in three layers, i.e., cyan, magenta, and yellow, and slightly shifted each layer the left, right, and down, respectively. The filter was applied to the entire image except the region where the user's gaze was to be guided. We conducted experiments using a gaze tracker. The experimental results showed that the proposed method can guide the user's gaze to a particular region with less unpleasant feelings.\",\n \"Title: A Real-Time Image Processing Framework With An Aerial Overhead Camera For Sports\\nYear: 2018\\n\\nAbstract: Recently, large horizontal interactive surfaces have begun to be developed. In these systems, an overhead camera is often used to detect the position of objects on the surface even if they are not in contact with the surface. However the issues that these systems face are that they are expensive and a camera cannot be easily attached on top of the surface in some situations. This paper proposes a framework that uses a camera on a drone (UAV) as an overhead camera unit to convert arbitrary horizontal rectangular regions into interactive surfaces. Although commercially available drones that are equipped with cameras have high latencies and are difficult to use in real-time interactive systems, we solved this latency issue using a small PC that performs primitive image processing tasks onboard. First, we describe a drone unit that has an infrared camera and a small PC for real-time image processing, such as surface detection and object detection. Second, we describe novel infrared markers for the robust detection of the four corners of a rectangular region and the objects within that region. Finally, we describe an interactive sports coaching application in which a drone unit is used as an overhead camera both for a large playing field and small tabletop.\",\n \"Title: How Display Shapes Affect 360-Degree Panoramic Video Communication\\nYear: 2018\\n\\nAbstract: Field-of-view limitation has been a long-standing issue in video communication systems. With the advancement of omni-directional panoramic technology, the omnidirectional camera, which can provide a 360 degrees field of view, has become increasingly popular in the last few years. Previous research indicated that one-way video communication systems with a wider field of view improve task efficiency. Therefore, we propose to utilize omnidirectional cameras in a symmetrical video communication system and study how this configuration affects remote collaboration. In this study, we conducted experiments based on two conditions, which are an omnidirectional camera with a spherical display and an omnidirectional camera with a horizontally placed 2D flat display. Under these conditions, we analyzed how the display types affected remote collaboration. Our results show that participants marginally preferred the spherical display to the 2D flat display. We also show the advantages and disadvantages of each display. The findings contribute to our understanding of how to design an environment for remote collaboration that captures and shows a 360 degrees panoramic view of a remote site.\",\n \"Title: OmniEyeball: Spherical Display Equipped With Omnidirectional Camera And Its Application For 360-Degree Video Communication.\\nYear: 2018\\n\\nAbstract: We propose OmniEyeball (OEB), which is a novel interactive 360° image I/O system. It integrates the spherical display system with an omnidirectional camera to enable both capturing the 360° panoramic live streaming video as well as displaying it. We also present its unique application for symmetric 360° video communication by utilizing two OEB terminals, which may solve the narrow field-of-view problem in video communication. In addition, we designed a vision-based touch detection technique as well as some features to support 360° video communication.\",\n \"Title: DisplayBowl: A Bowl-Shaped Display for Omnidirectional Videos.\\nYear: 2018\\n\\nAbstract: We introduce DisplayBowl which is a concept of a bowl shaped hemispherical display for showing omnidirectional images. This display provides three-way observation for omnidirectional images. DisplayBowl allows users to observe an omnidirectional image by looking the image from above. In addition, users can see it with a first-person-viewpoint, by looking into the inside of the hemispherical surface from diagonally above. Furthermore, by observing both the inside and the outside of the hemispherical surface at the same time from obliquely above, it is possible to observe it by a pseudo third-person-viewpoint, like watching the drone obliquely from behind. These ways of viewing solve the problem of inability of pilots controlling a remote vehicle such as a drone to notice what happens behind them, which happen with conventional displays such as flat displays and head mounted displays.\",\n \"Title: A real-time golf-swing training system using sonification and sound image localization\\nYear: 2018\\n\\nAbstract: There are real-time training systems to learn the correct golf swing form by providing visual feedback to the users. However, real-time visual feedback requires the users to see the display during their motion that leads to the wrong posture. This paper proposed a real-time golf-swing training system using sonification and sound image localization. The system provides real-time audio feedback based on the difference between the pre-recorded model data and real-time user data, which consists of the roll, pitch, and yaw angles of a golf club shaft. The system also used sound image localization so that the user can hear the audio feedback from the direction of the club head. The user can recognize the current posture of the club without moving their gaze.\",\n \"Title: Image compensation and stabilization for immersive 360-degree videos from capsule endoscopy\\nYear: 2018\\n\\nAbstract: This paper describes image processing that can be used to develop immersive 360-degree videos using capsule endoscopy procedures. When viewed through a head-mounted display (HMD), doctors are able to inspect the human gastrointestinal tract as if they were inside the patient's body. Although the endoscopy capsule has two tiny fisheye cameras, the images captured by these cameras cannot be converted to equirectangular images which is the basic format used to produce 360-degree videos. This study proposes a method to generate a pseudo-omnidirectional video from the original images and stabilizes the video to prevent virtual reality (VR) sickness.\",\n \"Title: Real-Time Human Motion Forecasting Using A Rgb Camera\\nYear: 2018\\n\\nAbstract: This paper propose a real-time human motion forecasting system which visualize the future pose in virtual reality using a RGB camera. Our system consists of three parts: 2D pose estimation from RGB frames using a residual neural network, 2D pose forecasting using a recurrent neural network, and 3D recovery from the predicted 2D pose using a residual linear network. To improve the prediction learning quantity of temporal feature, we propose a special method using lattice optical flow for the joints movement estimation. After fitting the skeleton, a predicted 3d model of target human will be built 0.5s in advance in a 30-fps video.\",\n \"Title: Balloonygen: extended tabletop display embedded with balloon-like deformable spherical screen\\nYear: 2018\\n\\nAbstract: Balloonygen, an extended tabletop display embedded with a balloon-like deformable spherical screen, is a display that can seamlessly expose a spherical screen for three-dimensional contents, such as omnidirectional images, in a conventional flat display. By continuously morphing between a two-dimensional shape called tabletop and a three-dimensional shape called sphere, we render the benefits of a flat display and a spherical display to coexist and propose a smoother approach for information sharing. Balloonygen dynamically provides an optimal way to display the contents by inflating the rubber membrane installed at the center of a tabletop display and morphing between the two- and three-dimensional shapes. In this study, by prototyping and designing the application scenario, we discuss the advantages and disadvantages of this display and possible interactions involved.\",\n \"Title: Gaze navigation in the real world by changing visual appearance of objects using projector-camera system\\nYear: 2018\\n\\nAbstract: This paper proposes a method for gaze navigation in the real world by projecting an image onto a real object and changing its appearance. In the proposed method, a camera captures an image of objects in the real world. Next all the pixels in the image but those in a specified region are slightly shifted to left and right. Then the obtained image is projected onto the original objects. As a result, the objects not in the specified region looks blurred. We conducted user experiments and showed that the users' gaze were navigated to the specified region.\",\n \"Title: BitoBody: Real-time human contact detection and dynamic projection system\\nYear: 2019\\n\\nAbstract: In this research, we propose a novel human body contact detection and projection system with dynamic mesh collider. We use motion capture camera and generated human 3D models to detect the contact between user's bodies. Since it is difficult to update human mesh collider every frame, a special algorithm that divides body meshes into small pieces of polygons to do collision detection is developed and detected hit information will be dynamically projected according to its magnitude of damage. The maximum deviation of damage projection is about 7.9cm under a 240-fps optitrack motion capture system and 12.0cm under a 30-fps Kinect camera. The proposed system can be used in various sports where bodies come in contact and it allows the audience and players to understand the context easier.\",\n \"Title: OHAJIKI interface: flicking gesture recognition with a high-speed camera\\nYear: 2006\\n\\nAbstract: This paper describes a novel interaction technique that recognizes a finger flicking gesture for power adjustment input for a sports game, such as golf swing or hitting. Our system measures speed of the finger motion and direction of the gesture by using a high-speed camera and a high frame rate image processing technique. By using this system, users can adjust power and angle intuitively. We developed a 3D golf game using this interaction technique to provide an intuitive golf swing input.\",\n \"Title: Futurepose - Mixed Reality Martial Arts Training Using Real-Time 3d Human Pose Forecasting With A Rgb Camera\\nYear: 2019\\n\\nAbstract: In this paper, we propose a novel mixed reality martial arts training system using deep learning based real-time human pose forecasting.Our training system is based on 3D pose estimation using a residual neural network with input from a RGB camera, which captures the motion of a trainer. The student wearing a head mounted display can see the virtual model of the trainer and his forecasted future pose. The pose forecasting is based on recurrent networks, to improve the learning quantity of the motion's temporal feature, we use a special lattice optical flow method for the joints movement estimation. We visualize the real-time human motion by a generated human model while the forecasted pose is shown by a red skeleton model. In our experiments, we evaluated the performance of our system when predicting 15 frames ahead in a 30-fps video (0.5s forecasting), the accuracies were acceptable since they are equal to or even outperforms some methods using depth IR cameras or fabric technologies, user studies showed that our system is helpful for beginners to understand martial arts and the usability is comfortable since the motions were captured by RGB camera.\",\n \"Title: OmniGlobe: An Interactive I/O System For Symmetric 360-Degree Video Communication\\nYear: 2019\\n\\nAbstract: Video communication systems have been suffered from the narrow field of view. To solve this limitation, one study proposed symmetric 360° video communication system by combining an omnidirectional camera and a hemispherical display. However, the system still had several issues, e.g., the invisibility of hemisphere which was at the opposite side from a user caused the inconvenience of observing the remote environment. To solve these issues, we introduce OmniGlobe, a novel symmetric full 360° video communication system which incorporates an omnidirectional camera, a full spherical display, and several visual or interactive techniques. Based on an experiment, we could indicate that our system is effective in reducing the inconvenience of observing the remote environment and increased the remote space awareness and user's gaze awareness to support remote collaboration. We also discuss the takeaways, limitations and application areas in our system which help improve the system.\",\n \"Title: Golf training system using sonification and virtual shadow\\nYear: 2019\\n\\nAbstract: This paper proposed a golf training system using real-time audio-visual feedback. The system captures user's motion with an optical motion tracking system and projects his/her posture as a virtual shadow on the ground. Unlike other golf training systems, our system enables the user to keep his/her gaze on the ball. The model swing motion of the expert golfer is also overlaid so that the user can understand the difference between his/her motion and expert's. Moreover, the system provides audio feedback using sound image localization. It enables the user to understand the position and orientation of the club face, which is often out of sight of the user during his/her swing motion.\",\n \"Title: Remote control experiment with displaybowl and 360-degree video\\nYear: 2019\\n\\nAbstract: DisplayBowl is a bowl-shaped hemispherical display for showing omnidirectional images with direction data. It provides users with a novel way of observing 360-degree video streams, which improves the awareness of the surroundings when operating a remote-controlled vehicle compared to conventional flat displays and HMDs. In this paper, we present a user study, in which we asked participants to control a remote drone using an omnidirectional video streaming, to compare the uniqueness and advantages of three displays: a flat panel display, a head-mounted display and DisplayBowl.\",\n \"Title: Visualizing expert motion for guidance in a VR ski simulator\\nYear: 2019\\n\\nAbstract: While humans are quite good at copying motions from others, it is difficult to do so in a dynamic sport such as skiing. Hence, we propose a virtual reality ski training system, which visualizes prerecorded expert motion in different ways and enables users to learn by copying. The system is based on a commercial indoor ski simulator, a VR headset, and two VR trackers to capture the ski's motion. Users can control their skis on the virtual ski slope and improve their skills by following a digital avatar of the expert skier replayed in front of them. We investigate 3 types of visualizations for training: Graphs to visualize the angle of feet compared to the expert, periodic copies of the expert's pose to show the spatial and temporal motion of the key movements, and a more minimal ribbon-trace of the leading skier to point out the optimized trajectory.\",\n \"Title: PushToSki - An Indoor Ski Training System Using Haptic Feedback\\nYear: 2021\\n\\nAbstract: ABSTRACTHaptic feedback is an intuitive way of improving required postures in sports without having the trainee change their head-pose towards visual cues and therefore possibly worsening their overall body-pose. However, this feedback is not possible in a dynamic sport like alpine skiing which is why we propose a virtual reality ski training system that uses vibration as a haptic feedback method. Our system uses a commercially available indoor ski simulator and several trackers to capture the user’s motion together with a set of vibration motors which will provide direct, haptic feedback to the user. Our system therefore allows giving haptic feedback even while the trainee is moving on the simulator.\",\n \"Title: Real-time Projection of Lip Animation onto Face Masks using OmniProcam\\nYear: 2021\\n\\nAbstract: ABSTRACT This paper describes an OmniProcam system, which enables 360 degree horizontal projection by a fisheye lens with a coaxial procam in which the optical axes of the camera and projector are exactly matched. Combined with 2D marker recognition, the OmniProcam can display images onto screens at arbitrary positions in 3D space. As an example application, we developed a system which projects lip animation onto the user’s face masks for better communication at the physical meeting in current COVID-19 situation. The system recognizes the user’s speech, generates the lip animation using Lipsync, and projects the animation onto the user’s face masks.\",\n \"Title: Watch-Your-Skiing: Visualizations for VR Skiing using Real-time Body Tracking\\nYear: 2021\\n\\nAbstract: Correcting one’s body posture is necessary when acquiring specific skills, especially for some sports such as skiing or gymnastics. However, it is difficult to observe our posture objectively, which is the reason why a trainer is required. In this paper, we introduce a VR ski training system using full body motion capture to provide real-time feedback for the user. Two types of different visual cu...\",\n \"Title: Augmenting VR Ski Training using Time Distortion\\nYear: 2022\\n\\nAbstract: Virtual reality-based sports simulators are widely developed, which makes training in a virtual environment possible. On the other hand, methods using temporal features are also introduced to realize an adaptive training. In this paper, we study the effect of time distortion on alpine ski training to find out how modifying the temporal space can affect sports training. Experiments are conducted to investigate how a fast/slow and a static/dynamic time distortion-based training, respectively, can impact the performance of users.\",\n \"Title: ASTRE: Prototyping Technique for Modular Soft Robots With Variable Stiffness\\nYear: 2022\\n\\nAbstract: Soft robots are advantageous for human interaction owing to their adaptability and safe interactivity. However, research on soft robots is challenging because of the complex fabrication process of elastomeric materials with multiple channels. In this study, we introduce a prototyping technique for the fabrication-friendly soft robots using pneumatic artificial muscle(PAMs) and modular 3D printed reinforcement. We presented three primitive deformation structures: bending, twisting, and contraction. Moreover, we propose a novel variable stiffness technique that alters PAMs contraction and radial expansion behavior into locking, malleable, and rotational brake features. We explore both the parallel and series arrangement of the reinforcement module and propose new types of mixing configurations and scaling techniques. We quantitatively verified the force scaling technique on different types of features. We demonstrate the feasibility of this prototyping technique through six application examples and conclude with a discussion of the limitations and possible future improvements.\",\n \"Title: AI Golf: Golf Swing Analysis Tool for Self-Training\\nYear: 2022\\n\\nAbstract: In the field of the acquisition of sports skills, a common way to improve sports skills, such as golf swings, is to imitate professional players' motions. However, it is difficult for beginners to specify the keyframes on which they should focus and which part of the body they should correct because of inconsistent timing and lack of knowledge. In this study, a golf swing analysis tool using neural networks is proposed to address this gap. The proposed system compares two motion sequences and specifies keyframes in which significant differences can be observed between the two motions. In addition, the system helps users intuitively understand the differences between themselves and professional players by using interpretable clues. The main challenge of this study is to target the fine-grained differences between users and professionals that can be used for self-training. Moreover, the significance of the proposed approach is the use of an unsupervised learning method without prior knowledge and labeled data, which will benefit future applications and research in other sports and skill training processes. In our approach, neural networks are first used to create a motion synchronizer to align motions with different phases and timing. Next, a motion discrepancy detector is implemented to find fine-grained differences between motions in latent spaces that are learned by the networks. Furthermore, we consider that learning intermediate motions may be feasible for beginners because, in this way, they can gradually change their pose to match the ideal form. Therefore, based on the synchronization and discrepancy detection results, we utilize a decoder to restore the intermediate human poses between two motions from the latent space. Finally, we suggest possible applications for analyzing and visualizing the discrepancy between the two input motions and interacting with the users. With the proposed application, users can easily understand the differences between their motions and those of various experts during self-training and learn how to improve their motions.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":116,"string":"116"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13278,"string":"13,278"}}},{"rowIdx":13279,"cells":{"question_id":{"kind":"string","value":"Tao Gu"},"question":{"kind":"string","value":"Generate an abstract for the title \"Device-free indoor localization and tracking through Human-Object Interactions\" using the following items: 1. Device-free indoor localization\n2. Received Signal Strength Indicator (RSSI)\n3. Human-object interaction (HOI)\n4. Bayesian probabilistic framework\n5. COTS RFID-based localization"},"answer":{"kind":"string","value":"Device-free indoor localization aims to localize people without requiring them to carry any devices or being actively involved in the localizing process. It underpins a wide range of applications including older people surveillance, intruder detection and indoor navigation. However, in a cluttered environment such as a residential home, the Received Signal Strength Indicator (RSSI) is heavily obstructed by furniture or metallic appliances, thus reducing the localization accuracy. This environment is important to observe as human-object interaction (HOI) events, detected by pervasive sensors, can potentially reveal people's interleaved locations during daily living activities, such as watching TV, opening the fridge door. This paper aims to enhance the performance of commercial off-the-shelf (COTS) RFID-based localization system by leveraging HOI contexts in a furnished home. Specifically, we propose a general Bayesian probabilistic framework to integrate both RSSI signals and HOI events to infer the most likely location and trajectory. Experiments conducted in a residential house demonstrate the effectiveness of our proposed method, in which we can localize a resident with average 95% accuracy and track a moving subject with 0.58m mean error distance."},"correct_answer":{"kind":"string","value":"Device-free indoor localization aims to localize people without requiring them to carry any devices or being actively involved in the localizing process. It underpins a wide range of applications including older people surveillance, intruder detection and indoor navigation. However, in a cluttered environment such as a residential home, the Received Signal Strength Indicator (RSSI) is heavily obstructed by furniture or metallic appliances, thus reducing the localization accuracy. This environment is important to observe as human-object interaction (HOI) events, detected by pervasive sensors, can potentially reveal people's interleaved locations during daily living activities, such as watching TV, opening the fridge door. This paper aims to enhance the performance of commercial off-the-shelf (COTS) RFID-based localization system by leveraging HOI contexts in a furnished home. Specifically, we propose a general Bayesian probabilistic framework to integrate both RSSI signals and HOI events to infer the most likely location and trajectory. Experiments conducted in a residential house demonstrate the effectiveness of our proposed method, in which we can localize a resident with average 95% accuracy and track a moving subject with 0.58m mean error distance."},"documents":{"kind":"list like","value":["Title: BloomCast: Efficient and Effective Full-Text Retrieval in Unstructured P2P Networks\nYear: 2012\n\nAbstract: Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P systems to effectively locate items with guaranteed recall. Second, existing schemes to improve search success rate often rely on replicating a large number of item replicas across the wide area network, incurring a large amount of communication and storage costs. In this paper, we propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. By leveraging a hybrid P2P protocol, BloomCast replicates the items uniformly at random across the P2P networks, achieving a guaranteed recall at a communication cost of O(\\sqrt{N}), where N is the size of the network. Furthermore, by casting Bloom Filters instead of the raw documents across the network, BloomCast significantly reduces the communication and storage costs for replication. We demonstrate the power of BloomCast design through both mathematical proof and comprehensive simulations based on the query logs from a major commercial search engine and NIST TREC WT10G data collection. Results show that BloomCast achieves an average query recall of 91 percent, which outperforms the existing WP algorithm by 18 percent, while BloomCast greatly reduces the search latency for query processing by 57 percent.","Title: Development of urine glucose level monitor for home healthcare using near infrared spectroscopy\nYear: 2008\n\nAbstract: We have been developing a urine glucose level monitoring technique using near infrared spectroscopy in conjunction with the chemometric method aiming for the use of home health care. In this study, validity of the calibration models were assessed using urine samples from nine healthy male subjects. For the individual measurement, reasonable accuracy was obtained in predicting urine glucose level for each subject (mean value of standard error of prediction; 25.8 mg/dl, S.D.; 10.8 mg/dl), however, the accuracy decreased (around 60 mg/dl) when the calibration models were generated using data from multiple subjects. One of the causative factors seemed to be urine urea concentration level which may vary within very wide range (0-2000 mg/dl) under physiological condition, and therefore, it was suggested that any idea, e.g., classification of data by urea level, would be required for improving the accuracy.","Title: A hierarchical approach to real-time activity recognition in body sensor networks\nYear: 2012\n\nAbstract: Real-time activity recognition in body sensor networks is an important and challenging task. In this paper, we propose a real-time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we first use a fast and lightweight algorithm to detect gestures at the sensor node level, and then propose a pattern based real-time algorithm to recognize complex, high-level activities at the portable device level. We evaluate our algorithms over a real-world dataset. The results show that the proposed system not only achieves good performance (an average utility of 0.81, an average accuracy of 82.87%, and an average real-time delay of 5.7 seconds), but also significantly reduces the network's communication cost by 60.2%.","Title: A distributed hierarchical structure for object networks supporting human activity recognition\nYear: 2006\n\nAbstract: Pervasive environments will witness heterogeneous smart embedded devices (e.g. sensors, actuators) integrated into user's living environment (e.g. smart homes and hospitals) and provide a multitude of information that can transparently support user's lifestyle. One promising application resulting from the management and exploitation of this information is the human activity recognition. In this paper we briefly describe our activity recognition architecture and focus on an important management component of this architecture using the concept of object networks. We explore how object networks can integrate various sensor networks and heterogeneous devices into a coherent network through embedded context and role profile and at the same time support distributed context reasoning. The paper also describes the mechanisms used to eliminate and refine context information that is deemed irrelevant due to user behaviour changes over time, by employing the idea of role fitness.","Title: Information retrieval in schema-based P2P systems using one-dimensional semantic space\nYear: 2007\n\nAbstract: The widespread use of RDF-based information necessitates efficient information retrieval techniques in wide-area networks. In this paper, we present Dynamic Semantic Space, a schema-based peer-to-peer overlay network that facilitates efficient lookup for RDF-based information in dynamic environments. Peers in this overlay are grouped based on the semantics of their data which are extracted according to a set of schemas, and self-organized as a semantic overlay network. To reduce overheads incurred by peer joining, leaving and content changes in a high-dimensional overlay network, peers are constructed as a one-dimensional semantic space that facilitates efficient routing for both pull and push requests. A search or a subscription request is only routed to the appropriate cluster that holds related data, thus reducing unnecessary search cost and increasing the efficiency of locating information. Through a comprehensive simulation study, we demonstrate the effectiveness of our proposed techniques.","Title: Context-aware content filtering & presentation for pervasive & mobile information systems.\nYear: 2008\n\nAbstract: What constitutes relevant information to an individual may vary widely under different contexts. However, previous work on pervasive information systems has mostly focused on context-aware delivery of application-specific information. Such systems are only able to operate within narrow application domains and cannot be generalized to handle other heterogeneous types of information. To fill this gap, we propose a context-aware system for information integration that can handle arbitrary information types and determine their relevance to the user's current context. In contrast to existing model-based approaches to context reasoning, we log user interaction and perform usage mining using OLAP to discover context-dependent preferences for different information types. This allows us to build a more generic and adaptive system that automatically selects the most relevant content and presents it to the user in a succinct manner that supports ease of consumption and comprehension.","Title: FTrack: Infrastructure-free floor localization via mobile phone sensing\nYear: 2012\n\nAbstract: Mobile phone localization plays a key role in the fast-growing Location Based Applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, WiFi or GPS. In this paper, we present FTrack, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. FTrack uses the mobile phone's accelerometer only without any infrastructure support. It does not require any prior knowledge of the building such as floor height. By capturing user encounters and analyzing user trails, FTrack finds the mapping from the traveling time (when taking the elevator) or the step counts (when walking on the stairs) between any two floors to the number of floor levels. The mapping can then be used for mobile users to pinpoint their current floor levels. We conduct both simulation and field studies to demonstrate the effectiveness of FTrack. Our field trial in a 10-floor building shows that FTrack achieves an accuracy of over 90% after two hours in our experiment.","Title: A Peer-to-Peer Architecture for Context Lookup\nYear: 2005\n\nAbstract: As computing technology moves towards pervasive computing, many applications are beginning to make use of context information to adapt to and respond appropriately to their environments. Such a trend necessitates efficient search for context information in wide-area networks. In this paper, we propose a semantic P2P context lookup system. Peers are grouped based on the semantics of their local data which are extracted according to a set of schemas and are self-organized as a semantic overlay network. Context search requests are only routed to the appropriate nodes that have relevant data, reducing unnecessary query traffic and increasing the chances that the context data will be found quickly. To reduce maintenance overheads incurred by high-dimensional semantic overlay networks, we propose a onedimensional ring space to construct peers and facilitate efficient query routing. Our simulation studies demonstrate the effectiveness of our proposed routing techniques.","Title: PAS: Prediction-based Adaptive Sleeping for Diffusion Stimulus Monitoring Sensor Networks\nYear: 2008\n\nAbstract: Energy efficiency has proven to be a dominating factor for the lifetime of WSN surveillance systems. Intensive studies have been done to provide energy efficient power management mechanisms, but few directly address the issues specifically for the sensor networks that monitoring Diffusion Stimulus (DS), a scenario that is the most common for environment monitoring applications. In this paper, we present our Prediction-based Adaptive Sleeping (PAS) solution that is designed directly for DS monitoring WSNs. The proposed PAS takes into account the characteristics of the DS spreading process and optimizes the sensors' sleeping schedules accordingly. The sensors near the DS boundary stay awake to timely capture the possible arrival of stimulus while the sensors far from the DS boundary keep in the energy-saving mode. The novelty design of PAS strikes a good balance between response time and energy conservation. Our simulation results also prove that the proposed PAS can greatly reduce the energy utilizations without noticeably sacrificing the system's performance.","Title: PAS: Prediction-based Adaptive Sleeping for Environment Monitoring in Sensor Networks\nYear: 2007\n\nAbstract: Energy efficiency has proven to be an important factor dominating the working period of WSN surveillance systems. Intensive studies have been done to provide energy efficient power management mechanisms. In this paper, we present PAS, a Prediction-based Adaptive Sleeping mechanism for environment monitoring sensor networks to conserve energy. PAS focuses on the diffusion stimulus (DS) scenario, which is very common and important in the application of environment monitoring. Different with most of previous works, PAS explores the features of DS spreading process to obtain higher energy efficiency. In PAS, sensors determine their sleeping schedules based on the observed emergency of DS spreading. While sensors near the DS boundary stay awake to accurately capture the possible stimulus arrival, the far away sensors turn into sleeping mode to conserve energy. Simulation experiment shows that PAS largely reduces the energy cost without decreasing system performance.","Title: A Pattern Mining Approach to Sensor-Based Human Activity Recognition\nYear: 2011\n\nAbstract: Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing due to its potential in many applications, such as assistive living and healthcare. This task is particularly challenging because human activities are often performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved or concurrent) manner in real life. Little work has been done in addressing complex issues in such a situation. The existing models of interleaved and concurrent activities are typically learning-based. Such models lack of flexibility in real life because activities can be interleaved and performed concurrently in many different ways. In this paper, we propose a novel pattern mining approach to recognize sequential, interleaved, and concurrent activities in a unified framework. We exploit Emerging Pattern-a discriminative pattern that describes significant changes between classes of data-to identify sensor features for classifying activities. Different from existing learning-based approaches which require different training data sets for building activity models, our activity models are built upon the sequential activity trace only and can be applied to recognize both simple and complex activities. We conduct our empirical studies by collecting real-world traces, evaluating the performance of our algorithm, and comparing our algorithm with static and temporal models. Our results demonstrate that, with a time slice of 15 seconds, we achieve an accuracy of 90.96 percent for sequential activity, 88.1 percent for interleaved activity, and 82.53 percent for concurrent activity.","Title: Managing Quality of Context in Pervasive Computing\nYear: 2006\n\nAbstract: Context-awareness plays a key role in a paradigm shift from traditional desktop styled computing to emerging pervasive computing. Many context-aware systems have been built to achieve the vision of pervasive computing and alleviate the human attention bottleneck; however, these systems are far from real world applications. Quality of context is critical in reducing the gap between existing systems and real-life applications. Aiming to provide the support of quality of context, in this paper, we propose a novel quality model for context information and a context management mechanism for inconsistency resolution. We also build a prototype system to validate our proposed model and mechanism, and to assist the development of contextaware applications. Through our evaluations and case study, context-aware applications can be built with the support of quality of context.","Title: A semantic P2P framework for building context-aware applications in multiple smart spaces\nYear: 2007\n\nAbstract: Context information has emerged as an important resource to enable autonomy and flexibility of ubiquitous applications. The widespread use of context information necessitates an efficient lookup service in a wide-area network over multiple smart spaces. In this paper, we propose a context lookup framework based on a semantic peer-to-peer network to support the building of context-aware applications in multiple smart spaces. Collaborative contextaware applications that utilize different context information in multiple smart spaces can be easily built by invoking a pull or push service provided by our framework. We describe the design of our system, demonstrate the development process of context-aware applications, and report the measurements obtained from our prototype.","Title: Minimizing inter-server communications by exploiting self-similarity in online social networks\nYear: 2012\n\nAbstract: Efficiently operating on relevant data for users in large-scale online social network (OSN) systems is a challenging problem. Storage systems used by popular OSN systems often rely on key-value stores, where randomly partitioning the data of users among servers across the data centers is the defacto standard. Although by using DHTs, the random partition scheme is highly scalable for hosting a large number of users, it leads to costly inter-server communications across data centers due to the complexity of interconnection and interaction between OSN users. In this paper, we explore how to reduce the inter-server communications by retaining the simple and robust nature of OSNs. We propose a data placement solution atop OSN systems to divide users among servers according to the interaction-locality-based structure. Our approach exploits a simple, yet powerful principle of OSN interactions, self-similarity, which reveals that the inter-server communication cost is minimized under such intrinsic structure. Our algorithm avoids a significant amount of inter-server traffic as well as achieves load balance among servers across the data centers. We demonstrate the existence of self-similarity in large-scale Facebook traces including 10 million Facebook users and 24 million interaction events. We conduct comprehensive trace-driven simulations to evaluate this design exploiting the unique feature of self-similarity. Results show that our scheme significantly reduces the traffic and latency of the existing schemes.","Title: Recognizing multi-user activities using wearable sensors in a smart home\nYear: 2011\n\nAbstract: The advances of wearable sensors and wireless networks offer many opportunities to recognize human activities from sensor readings in pervasive computing. Existing work so far focuses mainly on recognizing activities of a single user in a home environment. However, there are typically multiple inhabitants in a real home and they often perform activities together. In this paper, we investigate the problem of recognizing multi-user activities using wearable sensors in a home setting. We develop a multi-modal, wearable sensor platform to collect sensor data for multiple users, and study two temporal probabilistic models-Coupled Hidden Markov Model (CHMM) and Factorial Conditional Random Field (FCRF)-to model interacting processes in a sensor-based, multi-user scenario. We conduct a real-world trace collection done by two subjects over two weeks, and evaluate these two models through our experimental studies. Our experimental results show that we achieve an accuracy of 96.41% with CHMM and an accuracy of 87.93% with FCRF, respectively, for recognizing multi-user activities.","Title: Complete Bipartite Anonymity: Confusing Anonymous Mobility Traces for Location Privacy\nYear: 2012\n\nAbstract: Using mobile devices, people can easily obtain their location information, and access a wide range of location based services (LBSs). Many existing LBSs rely in accurate, continuous, and real-time streams of location information to provide quality of service guarantees. In this case, even if an user accesses LBSs anonymously, the identity of the user can still be revealed by analyzing the mobility trace. To protect user privacy, existing work sacrifice the quality of LBSs by degrading spatial and temporal accuracy. To achieve a better tradeoff between user privacy and the quality of service, we present a novel approach, Complete Bipartite Anonymity (CBA), to confuse the paths of nearby users by connecting different users' real traces with fake ones. CBA protects user privacy as users become indistinguishable after their paths are confused, the quality of service of LBSs is also guaranteed since users are able to report their accurate locations. We evaluate CBA by comparing the system and privacy performance with existing techniques such as Path Confusion or Query Obfuscation using a real-world data set, the results show that our scheme increases the chance for a user joining an anonymity group by 10 times in low user density areas, and reduces the resources consumed by about 90% for achieving the same anonymity degree.","Title: An unsupervised approach to activity recognition and segmentation based on object-use fingerprints\nYear: 2010\n\nAbstract: Human activity recognition is an important task which has many potential applications. In recent years, researchers from pervasive computing are interested in deploying on-body sensors to collect observations and applying machine learning techniques to model and recognize activities. Supervised machine learning techniques typically require an appropriate training process in which training data need to be labeled manually. In this paper, we propose an unsupervised approach based on object-use fingerprints to recognize activities without human labeling. We show how to build our activity models based on object-use fingerprints, which are sets of contrast patterns describing significant differences of object use between any two activity classes. We then propose a fingerprint-based algorithm to recognize activities. We also propose two heuristic algorithms based on object relevance to segment a trace and detect the boundary of any two adjacent activities. We develop a wearable RFID system and conduct a real-world trace collection done by seven volunteers in a smart home over a period of 2 weeks. We conduct comprehensive experimental evaluations and comparison study. The results show that our recognition algorithm achieves a precision of 91.4% and a recall 92.8%, and the segmentation algorithm achieves an accuracy of 93.1% on the dataset we collected.","Title: Peer-to-Peer Context Reasoning in Pervasive Computing Environments\nYear: 2008\n\nAbstract: In this paper, we propose a peer-to-peer approach to derive and obtain additional context data from low-level context data that may be spread over multiple domains in pervasive computing environments. In this system, peers are self-organized into a semantic peer-to-peer network as the underlying communication substrate. Context reasoning is done in a distributed fashion through logical reasoning according to a set of user-defined rules. Both pull and push services are supported in the system to enable message exchange during the reasoning process. We present our design concepts, and prove the effectiveness of our system through the prototype evaluation.","Title: Ontology Modeling of a Dynamic Protocol Stack\nYear: 2006\n\nAbstract: This paper proposes a formal approach for protocol information modeling and validation leveraging on ontological techniques. We demonstrate the advantage of our approach through prototyping a protocol management system for representation of communication protocols and composition of protocol stacks. The prototype has an ontology-based model to describe meta-data of protocols and protocol stacks in a systematic way. Consequently, the retrieval of protocols and the validation of protocol stacks are realized by corresponding operations on the ontology model. Owing to the better expressiveness of RDFS, the ontology model can describe protocols with higher fidelity. Our experimental results show that the ontology-based protocol management system is operable and provides expressive knowledge modeling without compromising the performance","Title: Application Based Distance Measurement for Context Retrieval in Ubiquitous Computing\nYear: 2007\n\nAbstract: Building large-scale smart environments is one of the long-term goals of ubiquitous computing. The widespread of context information in such environments necessitates an effective context retrieval mechanism. This paper proposes a novel context retrieval method based on applications' query patterns. We propose high dimensional vector to model contexts from applications' perspective, and apply the normalized inner product of high dimensional vectors to measure context distance. Contexts with similar query patterns are clustered into the same group. To improve the performance of context retrieval, we build distributed indices on each node to speed up a local search, and create shortcuts based on clustering results to facilitate query routing. We show how our proposed methods can be applied to existing context retrieval mechanisms. Our experimental results show that our method can significantly reduce retrieval cost.","Title: Supporting pervasive computing applications with active context fusion and semantic context delivery\nYear: 2010\n\nAbstract: Future pervasive computing applications are envisioned to adapt the applications' behaviors by utilizing various contexts of an environment and its users. Such context information may often be ambiguous and also heterogeneous, which make the delivery of unambiguous context information to real applications extremely challenging. Thus, a significant challenge facing the development of realistic and deployable context-aware services for pervasive computing applications is the ability to deal with these ambiguous contexts. In this paper, we propose a resource optimized quality assured context mediation framework based on efficient context-aware data fusion and semantic-based context delivery. In this framework, contexts are first fused by an active fusion technique based on Dynamic Bayesian Networks and ontology, and further mediated using a composable ontological rule-based model with the involvement of users or application developers. The fused context data are then organized into an ontology-based semantic network together with the associated ontologies in order to facilitate efficient context delivery. Experimental results using SunSPOT and other sensors demonstrate the promise of this approach.","Title: Season: Shelving Interference And Joint Identification In Large-Scale Rfid Systems\nYear: 2011\n\nAbstract: Prior work on anti-collision for Radio Frequency IDentification (RFID) systems usually schedule adjacent readers to exclusively interrogate tags for avoiding reader collisions. Although such a pattern can effectively deal with collisions, the lack of readers' collaboration wastes numerous time on the scheduling process and dramatically degrades the throughput of identification. Even worse, the tags within the overlapped interrogation regions of adjacent readers (termed as contentious tags), even if the number of such tags is very small, introduce a significant delay to the identification process. In this paper, we propose a new strategy for collision resolution. First, we shelve the collisions and identify the tags that do not involve reader collisions. Second, we perform a joint identification, in which adjacent readers collaboratively identify the contentious tags. In particular, we find that neighboring readers can cause a new type of collisions, cross-tag-collision, which may impede the joint identification. We propose a protocol stack, named Season, to undertake the tasks in two phases and solve the cross-tag-collision. We conduct extensive simulations and preliminary implementation to demonstrate the efficiency of our scheme. T he results show that our scheme can achieve above 6 times improvement on the identification throughput in a large-scale dense reader environment.","Title: Jointcache: Collaborative Path Confusion Through Lightweight P2p Communication\nYear: 2013\n\nAbstract: The emerging location based services have brought forth a privacy risk where users expose their location information. Most of the existing solutions preserve location privacy relying on a trusted anonymizer, which may not exist in real situation. Several peer-to-peer approaches are proposed, but these techniques require large-scale data storage and communication, and they cannot provide up-to-date context information to users. To address this issue, we propose a novel method JointCache to preserve location privacy by confusing nearby users' paths. Using this technique, users do not need to buffer context information of a large area and they only need to transfer little information to each other for cooperation. We use simulation to compare JointCache with the traditional path confusion, the results show that our technique solves the same place same time problem, and achieves higher anonymity even in a low user density situation.","Title: Data Management for Context-Aware Computing\nYear: 2008\n\nAbstract: We envisage future context-aware applications will dynamically adapt their behaviors to various context data from sources in wide-area networks, such as the Internet. Facing the changing context and the sheer number of context sources, a data management system that supports effective source organization and efficient data lookup becomes crucial to the easy development of context-aware applications. In this paper, we propose the design of a new context data management system that is equipped with query processing capabilities. We encapsulate the context sources into physical spaces belonging to different context spaces and organize them as peers in semantic overlay networks. Initial evaluation results of an experimental system prototype demonstrate the effectiveness of our design.","Title: A Loose-Pattern Process Approach to Clustering Fuzzy Data Sets\nYear: 1985\n\nAbstract: A loose-pattern process approach to clustering sets consists of three main computations: loose-pattern reject option, tight-pattern classifcation, and loose-pattern assigning classes. The loose-pattern rejection is implemented using a rule based on q nearest neighbors of each point. Two clustering methods, GLC and OUPIC, are introduced as tight-pattern clustering techniques. The decisions of loose-pattern assigning classes are related to a heuristic membership function. The function and experiments with one set is discussed.","Title: A service-oriented middleware for building context-aware services\nYear: 2005\n\nAbstract: The advancement of wireless networks and mobile computing necessitates more advanced applications and services to be built with context-awareness enabled and adaptability to their changing contexts. Today, building context-aware services is a complex task due to the lack of an adequate infrastructure support in pervasive computing environments. In this article, we propose a Service-Oriented Context-Aware Middleware (SOCAM) architecture for the building and rapid prototyping of context-aware services. It provides efficient support for acquiring, discovering, interpreting and accessing various contexts to build context-aware services. We also propose a formal context model based on ontology using Web Ontology Language to address issues including semantic representation, context reasoning, context classification and dependency. We describe our context model and the middleware architecture, and present a performance study for our prototype in a smart home environment.","Title: Object relevance weight pattern mining for activity recognition and segmentation\nYear: 2010\n\nAbstract: Monitoring daily activities of a person has many potential benefits in pervasive computing. These include providing proactive support for the elderly and monitoring anomalous behaviors. A typical approach in existing research on activity detection is to construct sequence-based models of low-level activity features based on the order of object usage. However, these models have poor accuracy, require many parameters to estimate, and demand excessive computational effort. Many other supervised learning approaches have been proposed but they all suffer from poor scalability due to the manual labeling involved in the training process. In this paper, we simplify the activity modeling process by relying on the relevance weights of objects as the basis of activity discrimination rather than on sequence information. For each activity, we mine the web to extract the most relevant objects according to their normalized usage frequency. We develop a KeyExtract algorithm for activity recognition and two algorithms, MaxGap and MaxGain, for activity segmentation with linear time complexities. Simulation results indicate that our proposed algorithms achieve high accuracy in the presence of different noise levels indicating their good potential in real-world deployment.","Title: An architecture for flexible service discovery in OCTOPUS\nYear: 2003\n\nAbstract: Service discovery has been drawing much attention from researchers and practitioners. The existing service discovery systems, like SLP, Jini, UPnP and Salutation, provide basic infrastructures where services can announce their presence and users can locate these services across the network. However there are several key issues which are partially solved or have not been well addressed - such as scalability, availability, dynamics and support for multiple matching mechanisms. In this paper, we propose a design for a service locating manager (SLM) system which addresses some of these issues. The SLM system adopts a dynamic hierarchical tree structure and service aggregation for scalability, availability and dynamics, and introduces multiple matching mechanisms which contain an attribute-based and a semantic matching engine. It provides a scalable, distributed, dynamic and robust solution to establish a flexible service discovery architecture. We describe our concepts, architecture and implementation, and present a performance study for our prototype.","Title: Towards a flexible service discovery\nYear: 2005\n\nAbstract: To support m-commerce applications, a service discovery mechanism where services can announce their presence and mobile users can locate these services is needed. Service discovery in dynamic mobile environments poses many challenges such as service providers may create and delete services or servers anytime; mobile services may be deployed in various forms, etc. In this paper, we propose a design for a Service Locating Service (SLS), which addresses some of these issues to provide a flexible service discovery mechanism for m-commerce applications. In our architecture, we adopt a dynamic tree structure for organizing SLS servers to meet the dynamic requirements of services and servers; we introduce service aggregation for fast locating; and we also propose multiple service matching mechanisms, which contain an attribute matching engine and a semantic matching engine for different service interfaces. We describe our concepts, architecture and implementation, and present a performance study for our prototype.","Title: Context-aware middleware for pervasive elderly homecare\nYear: 2009\n\nAbstract: The growing aging population faces a number of challenges, including rising medical cost, inadequate number of medical doctors and healthcare professionals, as well as higher incidence of misdiagnosis. There is an increasing demand for a better healthcare support for the elderly and one promising solution is the development of a context-aware middleware infrastructure for pervasive health/wellness-care. This allows the accurate and timely delivery of health/medical information among the patients, doctors and healthcare workers through a widespread deployment of wireless sensor networks and mobile devices. In this paper, we present our design and implementation of such a Context-Aware Middleware for Pervasive Homecare (CAMPH). The middleware offers several key-enabling system services that consist of P2P-based context query processing, context reasoning for activity recognition and context-aware service management. It can be used to support the development and deployment of various homecare services for the elderly such as patient monitoring, location-based emergency response, anomalous daily activity detection, pervasive access to medical data and social networking. We have developed a prototype of the middleware and demonstrated the concept of providing a continuing-care to an elderly with the collaborative interactions spanning multiple physical spaces: person, home, office and clinic. The results of the prototype show that our middleware approach achieves good efficiency of context query processing and good accuracy of activity recognition.","Title: A two-tier semantic overlay network for P2P search\nYear: 2007\n\nAbstract: This paper proposes a two-tier semantic peer-to-peer network that facilitates efficient search for context information in wide-area networks. Context data with the same semantics are grouped together into a one-dimensional semantic ring space in the upper-tier network. This is achieved by applying an ontology- based semantic clustering technique and dedicating part of node identifiers to correspond to their data semantics. In the lower-tier network, peers in each semantic cluster are organized as Chord identifier space. Thus, all the nodes in the same semantic cluster know which node is responsible for storing context data triples they are looking for, and context queries can be efficiently routed to those nodes. Through the simulation studies, we demonstrate the effectiveness of our proposed scheme.","Title: Privacy Protection in Participatory Sensing Applications Requiring Fine-Grained Locations\nYear: 2010\n\nAbstract: The emerging participatory sensing applications have brought a privacy risk where users expose their location information. Most of the existing solutions preserve location privacy by generalizing a precise user location to a coarse-grained location, and hence they cannot be applied in those applications requiring fine-grained location information. To address this issue, in this paper we propose a novel method to preserve location privacy by anonymizing coarse-grained locations and retaining fine-grained locations using Attribute Based Encryption (ABE). In addition, we do not assume the service provider is an trustworthy entity, making our solution more feasible to practical applications. We present and analyze our security model, and evaluate the performance and scalability of our system.","Title: Sensor-Based Human Activity Recognition in a Multi-user Scenario\nYear: 2009\n\nAbstract: Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activities from sensor readings in a smart home environment. We develop a multimodal sensing platform and present a theoretical framework to recognize both single-user and multi-user activities. We conduct our trace collection done in a smart home, and evaluate our framework through experimental studies. Our experimental result shows that we achieve an average accuracy of 85.46% with CHMMs.","Title: Secure RFID Identification and Authentication with Triggered Hash Chain Variants\nYear: 2008\n\nAbstract: In this paper, we propose two RFID identification and authentication schemes based on the previously proposed Triggered Hash Chain scheme by Henrici and Muller [1]. The schemes are designed to mitigate the shortcomings observed in the Triggered Hash Chain scheme and to ensure privacy preserving identification, tag-reader mutual authentication, as well as forward-privacy in the case of RFID tags that have been compromised. The first scheme uses a challenge-response mechanism to defend against an obvious weakness of the Triggered Hash Chain scheme. The second scheme uses an authenticated monotonic counter to defend against a session linking attack that the first scheme is vulnerable to. We compare the level of security offered by our proposed schemes against other previous schemes and find that the schemes perform well, while keeping within reasonable overheads in terms of computational, storage and communication requirements.","Title: Audio-on-demand over wireless sensor networks\nYear: 2012\n\nAbstract: Audio represents one of the most appealing yet least exploited modalities in wireless sensor networks, due to the potentially extremely large data volumes and limited wireless capacity. Therefore, how to effectively collect audio sensing information remains a challenging problem. In this paper, we propose a new paradigm of audio information collection based on the concept of audio-on-demand. We consider a sink-free environment targeting for disaster management, where audio chunks are stored inside the network for retrieval. The difficulty is to guarantee a high search success rate without infrastructure support. To solve the problem, we design a novel replication algorithm that deploys an optimal number of O([EQUATION]) replicas across the sensor network. We prove the optimality of the energy consumption of the algorithm, and use real test-bed experiments and extensive simulations to evaluate the performance and efficiency of our design. The experimental results show that our design can provide satisfactory quality of audio-on-demand service with short startup latency and slight playback jitter. Extensive simulation results show that this design achieves a search success rate of 98% while reducing the search energy consumption by an order of magnitude compared with existing schemes.","Title: Scheduling Divisible Loads from Multiple Input Sources in MapReduce\nYear: 2013\n\nAbstract: MapReduce is a programming model for data-intensive applications. The performance of MapReduce is greatly affected by the scheduling efficiency, which needs to consider both the parallelism of processing and overhead of data transmission. Lots of researches only focus on optimizing scheduling mechanisms or data storage strategies to reduce the overhead of data transmission in big data applications. The analysis of MapReduce carried out doesn't consider the overhead of data transmission sufficiently. This paper proposes a divisible load scheduling model to describe the data transmission and task execution in MapReduce. The task allocation and corresponding data transmission are abstracted as scheduling divisible loads from multiple input sources. With the established model, the optimal distribution of input data and scheduling of loads in map phase are presented with linear programming. The performance of map phase is evaluated and analyzed under different environments.","Title: Energy balanced data collection in Wireless Sensor Networks\nYear: 2012\n\nAbstract: In this paper, we investigate the energy balanced data collection problem in WSNs, aiming to balance the energy consumption among all the sensor nodes in the data propagation process. Energy balanced data collection can potentially save energy consumption and prolong the network lifetime, and hence it has many practical implications for WSN design and deployment. The traditional hop-by-hop transmission model allows a sensor node to propagate its packets in a hop-by-hop manner towards the sink, resulting in poor energy balance for the entire network. To address the problem, we apply a slice based energy model, and divide the energy balanced data collection problem into inter- and intra-slice energy balance problems. We then propose a novel Inter-slice Mixed Transmission strategy and an Intra-slice Forwarding technique to address each of the problems. Finally, we design an Energy-balanced Transmission Protocol (ETP) to combine both techniques to achieve total energy balance in data collection. Through extensive simulation studies, we demonstrate that, while ETP achieves energy balanced data collection, the network lifespan is increased by 10 times and the network delay is decreased by more than 70% compared to the hop-by-hop transmission in a general square area WSN.","Title: Schema matching for context-aware computing\nYear: 2008\n\nAbstract: Context-aware computing is a key paradigm of ubiquitous computing in which applications automatically adapt their operations to dynamic context data from multiple sources. Managing a number of distributed sources, a middleware that facilitates the development of context-aware applications must provide a uniform view of all these sources to the applications. Local schemas of context data from individual sources need to be matched into a set of global schemas in the middleware, upon which applications can issue context queries to acquire data. In this paper, we study this problem of schema matching for context-aware computing. We propose a multi-criteria algorithm to determine candidate attribute matches between two schemas. The algorithm adaptively adjusts the priorities of different criteria based on previous matching results to improve the efficiency and accuracy of succeeding operations. We further develop an algorithm to categorize a new local schema into one of the global schemas whenever possible via a shared attribute dictionary. Our results based on schemas from real-world websites demonstrate the good matching accuracy achieved by our algorithms.","Title: A peer-to-peer overlay for context information search.\nYear: 2005\n\nAbstract: The widespread use of context information necessitates an efficient wide-area lookup service in pervasive computing. In this paper, we present Semantic Context Space (SCS), a semantic overlay network that facilitates efficient search for context information in distributed environments. Peers in SCS are grouped based on the semantics of their local data and self-organized into a one-dimensional ring space. Context search requests are only routed to the appropriate semantic clusters, reducing unnecessary search cost on peers that have irrelevant context data, and increasing the chances that the context data will be found quickly. By exploring parallelism in a semantic cluster, search request can be found quickly. Our simulation studies demonstrate the effectiveness of SCS.","Title: Ontology Based Context Modeling and Reasoning using OWL\nYear: 2004\n\nAbstract: Here we propose an OWL encoded context ontology (CONON) for modeling context in pervasive computing environments, and for supporting logic-based context reasoning. CONON provides an upper context ontology that captures general concepts about basic context, and also provides extensibility for adding domain-specific ontology in a hierarchical manner. Based on this context ontology, we have studied the use of logic reasoning to check the consistency of context information, and to reason over low-level, explicit context to derive high-level, implicit context. By giving a performance study for our prototype, we quantitatively evaluate the feasibility of logic based context reasoning for nontime-critical applications in pervasive computing environments, where we always have to deal carefully with the limitation of computational resources.","Title: Research On The Evaluation Index System And Method For Intelligent Performance Of Smart Space Based On Internet Of Things Technology\nYear: 2011\n\nAbstract: This article firstly defines the smart living space and it's six application aspect which is widely used under the Internet of Things, subdivide and analysis the researches home and abroad when study the relative issues about smart living space assess theory system of the \"Internet of Things\" technology, concluding the grey multi-levels methods on the basis of \"Internet of Things\" technology assess theory system at last. Then specify the evaluation of the intelligence storage.","Title: Recognizing Multiuser Activities Using Wireless Body Sensor Networks\nYear: 2011\n\nAbstract: The advances of wireless networking and sensor technology open up an interesting opportunity to infer human activities in a smart home environment. Existing work in this paradigm focuses mainly on recognizing activities of single user. In this work, we focus on the fundamental problem of recognizing activities of multiple users using a wireless body sensor network, and propose a scalable pattern mining approach to recognize both single- and multiuser activities in a unified framework. We exploit Emerging Pattern-a discriminative knowledge pattern which describes significant changes among activity classes of data-for building activity models and design a scalable, noise-resistant, Emerging Pattern-based Multiuser Activity Recognizer (epMAR) to recognize both single- and multiuser activities. We develop a multimodal, wireless body sensor network for collecting real-world traces in a smart home environment, and conduct comprehensive empirical studies to evaluate our system. Results show that epMAR outperforms existing schemes in terms of accuracy, scalability, and robustness.","Title: An Ontology- Based P2p Network For Semantic Search\nYear: 2009\n\nAbstract: This article presents an ontology-based peer-to-peer network that facilitates efficient search for data in wide-area networks. Data with the same semantics are grouped together into one-dimensional semantic ring space in the upper-tier network. This is achieved by applying an ontology-based semantic clustering technique and dedicating part of node identifiers to correspond to their data semantics. In the lower-tier network, peers in each semantic cluster are organized as Chord identifier space. Thus, all the nodes in the same semantic cluster know which node is responsible for storing context data triples they are looking for, and context queries can be efficiently routed to those nodes. Through the simulation studies, the authors demonstrate the effectiveness of our proposed scheme.","Title: KQStream: Kindred-Based QoS-Aware Live Media Streaming in Heterogeneous Peer-to-Peer Environments\nYear: 2007\n\nAbstract: This paper presents the design of a Kindred-based QoS-aware Live Media Streaming (KQStream) system in heterogeneous peer-to-peer environments. Motivated by the Multiple Description Coding technique, we propose a novel kindred-based approach to construct a peer-to-peer overlay topology. To achieve QoS-awareness during the streaming process, we propose a dynamic QoS-aware regulating technique which takes into account of user's preference, network bandwidth and buffer size. With the integration of the two proposed techniques, KQStream provides more flexibility in heterogeneous peer-to-peer environments and configurable playback with dynamic QoS-aware regulating.","Title: Real-time activity recognition inwireless body sensor networks: From simple gestures to complex activities\nYear: 2010\n\nAbstract: Real-time activity recognition using body sensor networks is an important and challenging task and it has many potential applications. In this paper, we propose a realtime, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we first use a fast, lightweight template matching algorithm to detect gestures at the sensor node level, and then use a discriminative pattern based real-time algorithm to recognize high-level activities at the portable device level. We evaluate our algorithms over a real-world dataset. The results show that the proposed system not only achieves good performance (an average precision of 94.9%, an average recall of 82.5%, and an average real-time delay of 5.7 seconds), but also significantly reduces the network communication cost by 60.2%. © 2010 IEEE.","Title: Complete Bipartite Anonymity for Location Privacy.\nYear: 2014\n\nAbstract: Users are vulnerable to privacy risks when providing their location information to location-based services (LBS). Existing work sacrifices the quality of LBS by degrading spatial and temporal accuracy for ensuring user privacy. In this paper, we propose a novel approach, Complete Bipartite Anonymity (CBA), aiming to achieve both user privacy and quality of service. The theoretical basis of CBA is that: if the bipartite graph of k nearby users’ paths can be transformed into a complete bipartite graph, then these users achieve k-anonymity since the set of “end points connecting to a specific start point in a graph” is an equivalence class. To achieve CBA, we design a Collaborative Path Confusion (CPC) protocol which enables nearby users to discover and authenticate each other without knowing their real identities or accurate locations, predict the encounter location using users’ moving pattern information, and generate fake traces obfuscating the real ones. We evaluate CBA using a real-world dataset, and compare its privacy performance with existing path confusion approach. The results show that CBA enhances location privacy by increasing the chance for a user confusing his/her path with others by 4 to 16 times in low user density areas. We also demonstrate that CBA is secure under the trace identification attack.","Title: MaLoc: a practical magnetic fingerprinting approach to indoor localization using smartphones\nYear: 2014\n\nAbstract: Using magnetic field data as fingerprints for localization in indoor environment has become popular in recent years. Particle filter is often used to improve accuracy. However, most of existing particle filter based approaches either are heavily affected by motion estimation errors, which makes the system unreliable, or impose strong restrictions on smartphone such as fixed phone orientation, which is not practical for real-life use. In this paper, we present an indoor localization system named MaLoc, built on our proposed augmented particle filter. We create several innovations on the motion model, the measurement model and the resampling model to enhance the traditional particle filter. To minimize errors in motion estimation and improve the robustness of particle filter, we augment the particle filter with a dynamic step length estimation algorithm and a heuristic particle resampling algorithm. We use a hybrid measurement model which combines a new magnetic fingerprinting model and the existing magnitude fingerprinting model to improve the system performance and avoid calibrating different smartphone magnetometers. In addition, we present a novel localization quality estimation method and a localization failure detection method to address the \\\"Kidnapped Robot Problem\\\" and improve the overall usability. Our experimental studies show that MaLoc achieves a localization accuracy of 1~2.8m on average in a large building.","Title: B-Loc: Scalable Floor Localization Using Barometer on Smartphone\nYear: 2014\n\nAbstract: Traditional fingerprint based localization techniques mainly rely on infrastructure support such as GSM and Wi-Fi. They require war-driving which is both time-consuming and labor-intensive. With recent advances of smartphone sensors, sensor-assisted localization techniques are emerging. However, they often need user-specific training and more power intensive sensing, resulting in infeasible solutions for real deployment. In this paper, we present B-Loc, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. It makes use of the barometer on smartphone only. B-Loc does not rely on any Wi-Fi infrastructure and requires neither war-driving nor prior knowledge of the buildings. Leveraging on crowd sourcing, B-Loc builds the barometer fingerprint map which contains the barometric pressure value for each floor level to locate users' floor levels. We conduct both simulation and field studies to demonstrate the accuracy, scalability, and robustness of B-Loc. Our field study in a 10-floor building shows that B-Loc achieves an accuracy of over 98%.","Title: A Wearable RFID System for Real-Time Activity Recognition Using Radio Patterns.\nYear: 2013\n\nAbstract: Much work have been done in activity recognition using wearable sensors organized in a body sensor network. The quality and communication reliability of the sensor data much affects the system performance. Recent studies show the potential of using RFID radio information instead of sensor data for activity recognition. This approach has the advantages of low cost and high reliability. Radio-based recognition method is also amiable to packet loss and has the advantages including MAC layer simplicity and low transmission power level. In this paper, we present a novel wearable Radio Frequency Identification (RFID) system using passive tags which are smaller and more cost-effective to recognize human activities in real-time. We exploit RFID radio patterns and extract both spatial and temporal features to characterize various activities. We also address two issues - the false negative issue of tag readings and tag/antenna calibration, and design a fast online recognition system. We develop a prototype system which consists of a wearable RFID system and a smartphone to demonstrate the working principles, and conduct experimental studies with four subjects over two weeks. The results show that our system achieves a high recognition accuracy of 93.6% with a latency of 5 s.","Title: F-Loc: Floor localization via crowdsourcing\nYear: 2014\n\nAbstract: Traditional fingerprint based localization techniques mainly rely on infrastructure support such as GSM, Wi-Fi or GPS. They work by war-driving the entire indoor spaces which is both time-consuming and labor-intensive. With recent advances of smartphone and sensing technologies, sensor-assisted localization techniques leveraging on mobile phone sensing are emerging. However, sensors are inherently noisy, making this technique challenging for real deployment. In this paper, we present F-Loc, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. It does not need to war-drive the entire building. Leveraging on crowdsourcing and mobile phone sensing, we collect users' Wi-Fi traces and accelerometer readings. Through advanced clustering and cluster manipulating techniques, we are able to build the Wi-Fi map of the entire building, which can then be used for floor localization. We conduct both simulation and field studies to demonstrate the accuracy, scalability, and robustness of F-Loc. Our field study in a 10-floor building shows that F-Loc achieves an accuracy of over 98%.","Title: A level set method for structural shape and topology optimization using Radial Basis Function\nYear: 2014\n\nAbstract: In order to address the efficient issue of the topological shape optimization problem, this paper presents a parametric level set method for the problem of continuum structures by using the Radial Basis Functions (RBFs). The level set-based method is introduced to implicitly represent the free boundary of a structure. To evolve the level set front, the Wu's compactly supported radial basis function (CSRBF) with C4 smoothness is applied. Then, the Hamilton-Jacobi partial differential equation (PDE) is transformed into a relatively easier ordinary differential equation (ODE). Furthermore, a self-adaptive moving limit scheme is incorporated into the optimality criteria to achieve a better optimal result. Finally, the numerical ex-ample is provided to show the effectiveness of the proposed method.","Title: Crowdsourced smartphone sensing for localization in metro trains\nYear: 2014\n\nAbstract: Traditional fingerprint based localization techniques mainly rely on infrastructure support such as RFID, Wi-Fi or GPS. They operate by war-driving the entire space which is both time-consuming and labor-intensive. In this paper, we present M-Loc, a novel infrastructure-free localization system to locate mobile users in a metro line. It does not rely on any Wi-Fi infrastructure, and does not need to war-drive the metro line. Leveraging crowdsourcing, we collect accelerometer, magnetometer and barometer readings on smartphones, and analyze these sensor data to extract patterns. Through advanced data manipulating techniques, we build the pattern map for the entire metro line, which can then be used for localization. We conduct field studies to demonstrate the accuracy, scalability, and robustness of M-Loc. The results of our field studies in 3 metro lines with 55 stations show that M-Loc achieves an accuracy of 93% when travelling 3 stations, 98% when travelling 5 stations.","Title: Exploiting link correlation for core-based dissemination in wireless sensor networks\nYear: 2014\n\nAbstract: Bulk data dissemination is a basic building block for enabling software update and reprogramming in wireless sensor networks. The recent structure based approach looks promising for efficient dissemination since it facilitates transmission and sleep scheduling. However, a number of limitations exist in existing structured protocols. In this paper, we propose a correlated core based solution for efficient bulk data dissemination in wireless sensor networks. We propose an efficient backbone node selection algorithm to construct the core structure by exploiting link correlation. We also design a novel negotiation mechanism which greatly reduces the control message overhead as compared to the existing structured protocols. We conduct both simulation and testbed experiments, and the results show that our proposed solution outperforms the state-of-the-art in terms of both the number of transmissions and the completion time.","Title: Chemotaxis and Quorum Sensing Inspired Device Interaction Supporting Social Networking\nYear: 2007\n\nAbstract: Conference and social events provides an opportunity for people to interact and develop formal contacts with various groups of individuals. In this paper, we propose an efficient interaction mechanism in a pervasive computing environment that provide recommendation to users of suitable locations within a conference or expo hall to meet and interact with individuals of similar interests. The proposed solution is based on evaluation of context information to deduce each user's interests as well as bioinspired self-organisation mechanism to direct users towards appropriate locations. Simulation results have also been provided to validate our proposed solution.","Title: Minimizing inter-server communications by exploiting self-similarity in online social networks\nYear: 2016\n\nAbstract: Efficiently operating on relevant data for users in large-scale online social network (OSN) systems is a challenging problem. Storage systems used by popular OSN systems often rely on key-value stores, where randomly partitioning the data of users among servers across the data centers is the defacto standard. Although by using DHTs, the random partition scheme is highly scalable for hosting a large number of users, it leads to costly inter-server communications across data centers due to the complexity of interconnection and interaction between OSN users. In this paper, we explore how to reduce the inter-server communications by retaining the simple and robust nature of OSNs. We propose a data placement solution atop OSN systems to divide users among servers according to the interaction-locality-based structure. Our approach exploits a simple, yet powerful principle of OSN interactions, self-similarity, which reveals that the inter-server communication cost is minimized under such intrinsic structure. Our algorithm avoids a significant amount of inter-server traffic as well as achieves load balance among servers across the data centers. We demonstrate the existence of self-similarity in large-scale Facebook traces including 10 million Facebook users and 24 million interaction events. We conduct comprehensive trace-driven simulations to evaluate this design exploiting the unique feature of self-similarity. Results show that our scheme significantly reduces the traffic and latency of the existing schemes.","Title: Infrastructure-Free Floor Localization Through Crowdsourcing.\nYear: 2015\n\nAbstract: Mobile phone localization plays a key role in the fast-growing location-based applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, Wi-Fi or GPS. In this paper, we present FTrack, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. FTrack uses the mobile phone’s sensors only without any infrastructure support. It does not require any prior knowledge of the building such as floor height or floor levels. Through crowdsourcing, FTrack builds a mapping table which contains the magnetic field signature of users taking the elevator/escalator or walking on the stairs between any two floors. The table can then be used for mobile users to pinpoint their current floor levels. We conduct both simulation and field studies to demonstrate the efficiency, scalability and robustness of FTrack. Our field trial shows that FTrack achieves an accuracy of over 96% in three different buildings.","Title: RF-Care: Device-Free Posture Recognition for Elderly People Using A Passive RFID Tag Array.\nYear: 2015\n\nAbstract: Activity recognition is a fundamental research topic for a wide range of important applications such as fall detection for elderly people. Existing techniques mainly rely on wearable sensors, which may not be reliable and practical in real-world situations since people often forget to wear these sensors. For this reason, device-free activity recognition has gained the popularity in recent years. In this paper, we propose an RFID (radio frequency identification) based, device-free posture recognition system. More specifically, we analyze Received Signal Strength Indicator (RSSI) signal patterns from an RFID tag array, and systematically examine the impact of tag configuration on system performance. On top of selected optimal subset of tags, we study the challenges on posture recognition. Apart from exploring posture classification, we specially propose to infer posture transitions via Dirichlet Process Gaussian Mixture Model (DPGMM) based Hidden Markov Model (HMM), which effectively captures the nature of uncertainty caused by signal strength varieties during posture transitions. We run a pilot study to evaluate our system with 12 orientation-sensitive postures and a series of posture change sequences. We conduct extensive experiments in both lab and real-life home environments. The results demonstrate that our system achieves high accuracy in both environments, which holds the potential to support assisted living of elderly people.","Title: Supporting Serendipitous Social Interaction Using Human Mobility Prediction\nYear: 2015\n\nAbstract: Leveraging the regularities of people's trajectories, mobility prediction can help forecast social interaction opportunities. In this paper, in order to facilitate real-world social interaction, we aim to predict “serendipitous” social interactions, which are defined as unplanned encounters and interaction opportunities and regarded as emerging social interactions. We collected GPS trajectory data...","Title: CoCo+: Exploiting correlated core for energy efficient dissemination in wireless sensor networks.\nYear: 2016\n\nAbstract: Bulk data dissemination is a basic building block for enabling a variety of applications in wireless sensor networks such as software update, network bug fixing, surveillance video distribution, etc. The recent structure based approach looks promising for efficient dissemination since it facilitates transmission and sleep scheduling. However, a number of limitations exist in existing structured protocols. In this paper, we propose a correlated core based solution for efficient bulk data dissemination in wireless sensor networks (called CoCo+). CoCo+ has three salient features. First, CoCo+ is based on an efficient node selection algorithm for constructing the core structure by exploiting link correlation (called Correlated Core). Second, CoCo+ employs a novel consecutive transmission mechanism, which allows out-of-order transmissions and reduces propagation delay. Third, CoCo+ uses a novel lightweight negotiation mechanism that greatly reduces negotiation overhead as compared to the existing structured protocols. We implement CoCo+ with TinyOS/TelosB and conduct both simulation and testbed experiments. Results show that our proposed solution outperforms the state-of-the-art by 52.3 and 49.6% in terms of the number of transmissions and the completion time, respectively.","Title: Who should I invite for my party?: combining user preference and influence maximization for social events\nYear: 2015\n\nAbstract: The newly emerging event-based social networks (EBSNs) extend social interaction from online to offline, providing an appealing platform for people to organize and participate realworld social events. In this paper, we investigate how to select potential participants in EBSNs from an event host's point of view. We formulate the problem as mining influential and preferable invitee set, considering from two complementary aspects. The first aspect concerns users' preference with respect to the event. The second aspect is influence maximization, which aims to influence the largest number of users to participate the event. In particular, we propose a novel Credit Distribution-User Influence Preference (CD-UIP) algorithm to find the most influential and preferable followers as the invitees. We collect a real-world dataset from a popular EBSNs called \\\"Douban Events\\\", and the experimental results on the dataset demonstrate the proposed algorithm outperforms the state-of-the-art prediction methods.","Title: Freedom: Online Activity Recognition via Dictionary-Based Sparse Representation of RFID Sensing Data\nYear: 2015\n\nAbstract: Understanding and recognizing the activities performed by people is a fundamental research topic for a wide range of important applications such as fall detection of elderly people. In this paper, we present the technical details behind Freedom, a low-cost, unobtrusive system that supports independent livingof the older people. The Freedom system interprets what aperson is doing by leveraging machine learning algorithmsand radio-frequency identification (RFID) technology. To dealwith noisy, streaming, unstable RFID signals, we particularlydevelop a dictionary-based approach that can learn dictionariesfor activities using an unsupervised sparse coding algorithm. Our approach achieves efficient and robust activity recognitionvia a more compact representation of the activities. Extensiveexperiments conducted in a real-life residential environmentdemonstrate that our proposed system offers a good overallperformance (e.g., achieving over 96% accuracy in recognizing23 activities) and has the potential to be further developed tosupport the independent living of elderly people.","Title: Modeling Link Correlation In Low-Power Wireless Networks\nYear: 2015\n\nAbstract: Wireless link correlation can greatly affect the performance of wireless protocols such as flooding, and opportunistic routing. Researchers have proposed a variety of approaches to optimize existing protocols exploiting link correlation. Most existing works directly measure link correlation using packet-level transmissions and receptions. Measurement alone is insufficient because it lacks predictive power and scalability. In this paper, we present CorModel, a model for predicting link correlation in low-power wireless networks. Based on the underlying causes of link correlation, we explore four easily measurable parameters for our modeling. Besides PHY-layer parameters that previous studies have explored, we find that network-layer parameters can also have significant impact on link correlation. We validate our model and illustrate its usefulness by integrating it into existing protocols for more accurate correlation estimation. Experimental results show that our model can significantly increase the accuracy of wireless link estimation, resulting in better protocol performance.","Title: Node Capability Modeling for Reduce Phase's Scheduling in MapReduce Environment.\nYear: 2015\n\nAbstract: MapReduce is a programming model widely used in big data processing. Reduce tasks scheduling in MapReduce is a key issue which affect the performance significantly. Unfortunately, because of the complication of reduce tasks scheduling, there are no acknowledged solution in this issue. Main ideas in optimizing reduce tasks scheduling emphasizes features of computation or data locality. Although few researches tried to explore solutions with theoretical modeling, their models are oversimplified. Aiming to optimizing reduce tasks scheduling, we propose a method of modeling node's computation and communication capability uniformly based on analyzing the procedure of reduce phase theoretically. In the analysis, cost of reduce tasks in intermediate data fetching and processing are integrated. With the proposed model, the optimal load balance of reduce phase is concluded and proved. Evaluations under different environments show that load balance of reduce phase is improved significantly with the scheduling method instructed by the optimal principle.","Title: A Novel Metric for Opportunistic Routing in Heterogenous Duty-Cycled Wireless Sensor Networks\nYear: 2015\n\nAbstract: This paper investigates the suboptimal problem of existing state-of-the-art routing protocols when they are applied to heterogeneous duty-cycled wireless sensor networks (WSNs). In particular, we discover that the selected optimal routes with the least cost based their routing metric may not always lead to the least transmission cost. The key reason is that the existing routing metrics used do not sufficiently capture packet transmission cost in heterogeneous duty-cycled WSNs. To address this issue, we propose a novel routing metric, namely expected transmission cost (ETC), which efficiently captures packet transmission cost in heterogeneous duty-cycled WSNs by estimating both expected rendezvous cost and communication cost. Based on ETC, we design an opportunistic routing protocol (EoR) which is proved to select optimal routes with the least packet transmission cost. Our experimental results show that EoR outperforms the state-of-the-art protocols in terms of energy efficiency, latency, and packet delivery ratio.","Title: L-MAC: A wake-up time self-learning MAC protocol for wireless sensor networks.\nYear: 2016\n\nAbstract: This paper analyzes the trade-off issue between energy efficiency and packet delivery latency among existing duty-cycling MAC protocols in wireless sensor networks for low data-rate periodic-reporting applications. We then propose a novel and practical wake-up time self-learning MAC (L-MAC) protocol in which the key idea is to reuse beacon messages of receiver-initiated MAC protocols to enable nodes to coordinate their wakeup time with their parent nodes without incurring extra communication overhead. Based on the self-learning mechanism we propose, L-MAC builds an on-demand staggered scheduler to allow any node to forward packets continuously to the sink node. We present an analytical model, and conduct extensive simulations and experiments on Telosb sensors to show that L-MAC achieves significant higher energy efficiency compared to state-of-the-art asynchronous MAC protocols and a similar result of latency compared to synchronous MAC protocols. In particular, under QoS requirements with an upper bound value for one-hop packet delivery latency within 1 s and a lower bound value for packet delivery ratio within 95%, results show that the duty cycle of L-MAC is improved by more than 3.8 times and the end-to-end packet delivery latency of L-MAC is reduced by more than 7 times compared to those of AS-MAC and other state-of-the-art MAC protocols, respectively, in case of the packet generation interval of 1 min. L-MAC hence achieves high performance in both energy efficiency and packet delivery latency.","Title: TagFall: Towards Unobstructive Fine-Grained Fall Detection based on UHF Passive RFID Tags.\nYear: 2015\n\nAbstract: Falls are among the leading causes of hospitalization for the elderly and illness individuals. Considering that the elderly often live alone and receive only irregular visits, it is essential to develop such a system that can effectively detect a fall or abnormal activities. However, previous fall detection systems either require to wear sensors or are able to detect a fall but fail to provide fine-grained contextual information (e.g., what is the person doing before falling, falling directions). In this paper, we propose a device-free, fine-grained fall detection system based on pure passive UHF RFID tags, which not only is capable of sensing regular actions and fall events simultaneously, but also provide caregivers the contexts of fall orientations. We first augment the Angle-based Outlier Detection Method (ABOD) to classify normal actions (e.g., standing, sitting, lying and walking) and detect a fall event. Once a fall event is detected, we first segment a fix-length RSSI data stream generated by the fall and then utilize Dynamic Time Warping (DTW) based kNN to distinguish the falling direction. The experimental results demonstrate that our proposed approach can distinguish the living status before fall happening, as well as the fall orientations with a high accuracy. The experiments also show that our device-free, fine-grained fall detection system offers a good overall performance and has the potential to better support the assisted living of older people.","Title: Towards Energy-Balanced Data Transmission For Lifetime Optimization In Wireless Sensor Networks\nYear: 2016\n\nAbstract: Energy balance is a critical issue in wireless sensor networks. Several mixed data transmission (MDT) schemes have been proposed to achieve energy balance. However, most existing works are lack of theoretical study, especially understanding the relationship between network-wide energy balancing and lifetime optimization. In this paper, we conduct comprehensive theoretical analysis to the two-level based MDT scheme when applying to network-wide energy balancing, and eventually to maximize the network lifetime. We propose a novel network model, named energy balance area (EBA), and formally analyze its characteristics under the two-level based MDT scheme. To maximize the network lifetime, we convert the transmission probability allocation problem in the MDT scheme into an EBA partitioning (EBA-PT) problem, which is shown to be NPhard. We then propose a heuristic approximation algorithm to determine the optimal configuration of EBAs, which is proven in this paper to be the key for maximizing the network lifetime. In this way, we obtain a near-optimal result. Our experimental studies show that network lifetime can be further improved as compared the hop-by-hop and the two-level based MDT schemes.","Title: City-Scale Localization with Telco Big Data\nYear: 2016\n\nAbstract: It is still challenging in telecommunication (telco) industry to accurately locate mobile devices (MDs) at city-scale using the measurement report (MR) data, which measure parameters of radio signal strengths when MDs connect with base stations (BSs) in telco networks for making/receiving calls or mobile broadband (MBB) services. In this paper, we find that the widely-used location based services (LBSs) have accumulated lots of over-the-top (OTT) global positioning system (GPS) data in telco networks, which can be automatically used as training labels for learning accurate MRbased positioning systems. Benefiting from these telco big data, we deploy a context-aware coarse-to-fine regression (CCR) model in Spark/Hadoop-based telco big data platform for city-scale localization of MDs with two novel contributions. First, we design map-matching and interpolation algorithms to encode contextual information of road networks. Second, we build a two-layer regression model to capture coarse-to-fine contextual features in a short time window for improved localization performance. In our experiments, we collect 108 GPS-associated MR records in the centroid of Shanghai city with 12 × 11 square kilometers for 30 days, and measure four important properties of real-world MR data related to localization errors: stability, sensitivity, uncertainty and missing values. The proposed CCR works well under different properties of MR data and achieves a mean error of 110m and a median error of 80m, outperforming the state-of-art range-based and fingerprinting localization methods.","Title: Recognizing Parkinsonian Gait Pattern by Exploiting Fine-Grained Movement Function Features.\nYear: 2016\n\nAbstract: Parkinson's disease (PD) is one of the typical movement disorder diseases among elderly people, which has a serious impact on their daily lives. In this article, we propose a novel computation framework to recognize gait patterns in patients with PD. The key idea of our approach is to distinguish gait patterns in PD patients from healthy individuals by accurately extracting gait features that capture all three aspects of movement functions, that is, stability, symmetry, and harmony. The proposed framework contains three steps: gait phase discrimination, feature extraction and selection, and pattern classification. In the first step, we put forward a sliding window--based method to discriminate four gait phases from plantar pressure data. Based on the gait phases, we extract and select gait features that characterize stability, symmetry, and harmony of movement functions. Finally, we recognize PD gait patterns by applying a hybrid classification model. We evaluate the framework using an open dataset that contains real plantar pressure data of 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework significantly outperforms the four baseline approaches.","Title: Scalable floor localization using barometer on smartphone.\nYear: 2016\n\nAbstract: Traditional fingerprint-based localization techniques mainly rely on infrastructure support such as GSM and Wi-Fi. They require war-driving, which is both time-consuming and labor-intensive. With recent advances of smartphone sensors, sensor-assisted localization techniques are emerging. However, they often need user-specific training and more power intensive sensing, resulting in infeasible solutions for real deployment. In this paper, we present Barometer-based floor Localization system B-Loc, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. It makes use of the barometer on smartphone. B-Loc does not rely on any Wi-Fi infrastructure and requires neither war-driving nor prior knowledge of the buildings. Leveraging on crowdsourcing, B-Loc builds the barometer fingerprint map, which contains the barometric pressure value for each floor level to locate users' floor levels. We conduct both simulation and field studies to demonstrate the accuracy, scalability, and robustness of B-Loc. Our simulation shows that B-Loc can locate the user fast and the field study in a 10-floor building shows that B-Loc achieves an accuracy of over 98%. Copyright © 2016 John Wiley & Sons, Ltd.","Title: AudioGest: enabling fine-grained hand gesture detection by decoding echo signal.\nYear: 2016\n\nAbstract: Hand gesture is becoming an increasingly popular means of interacting with consumer electronic devices, such as mobile phones, tablets and laptops. In this paper, we present AudioGest, a device-free gesture recognition system that can accurately sense the hand in-air movement around user's devices. Compared to the state-of-the-art, AudioGest is superior in using only one pair of built-in speaker and microphone, without any extra hardware or infrastructure support and with no training, to achieve fine-grained hand detection. Our system is able to accurately recognize various hand gestures, estimate the hand in-air time, as well as average moving speed and waving range. We achieve this by transforming the device into an active sonar system that transmits inaudible audio signal and decodes the echoes of hand at its microphone. We address various challenges including cleaning the noisy reflected sound signal, interpreting the echo spectrogram into hand gestures, decoding the Doppler frequency shifts into the hand waving speed and range, as well as being robust to the environmental motion and signal drifting. We implement the proof-of-concept prototype in three different electronic devices and extensively evaluate the system in four real-world scenarios using 3,900 hand gestures that collected by five users for more than two weeks. Our results show that AudioGest can detect six hand gestures with an accuracy up to 96%, and by distinguishing the gesture attributions, it can provide up to 162 control commands for various applications.","Title: Learning from less for better: semi-supervised activity recognition via shared structure discovery.\nYear: 2016\n\nAbstract: Despite the active research into, and the development of, human activity recognition over the decades, existing techniques still have several limitations, in particular, poor performance due to insufficient ground-truth data and little support of intra-class variability of activities (i.e., the same activity may be performed in different ways by different individuals, or even by the same individuals with different time frames). Aiming to tackle these two issues, in this paper, we present a robust activity recognition approach by extracting the intrinsic shared structures from activities to handle intra-class variability, and the approach is embedded into a semi-supervised learning framework by utilizing the learned correlations from both labeled and easily-obtained unlabeled data simultaneously. We use l2,1 minimization on both loss function and regularizations to effectively resist outliers in noisy sensor data and improve recognition accuracy by discerning underlying commonalities from activities. Extensive experimental evaluations on four community-contributed public datasets indicate that with little training samples, our proposed approach outperforms a set of classical supervised learning methods as well as those recently proposed semi-supervised approaches.","Title: Human respiration detection with commodity wifi devices: do user location and body orientation matter?\nYear: 2016\n\nAbstract: Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's breathing depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.","Title: An Audio-based Hierarchical Smoking Behavior Detection System Based on A Smart Neckband Platform.\nYear: 2016\n\nAbstract: Smoking behavior detection has attracted much research interest for its significant impact on smokers' physical and mental health. Existing research has shown the potential of using wearable devices for fine-grained smoking puff and session detection by detecting a smoker's content of breathing, lighter usage, breathing, and gesture patterns. However, the existing systems are complex, and they are usually vulnerable to confounding activities and diversity of smoking behavior. To address these limitations, this paper proposes the design and implementation of a simple and compact smart neckband device for smoking detection. The device is equipped with both passive and active acoustic sensors to detect smoking sessions and puffs. We propose a hierarchical processing framework in which the lower-layer detects the sub-movements, i.e., lighter usage, hand-to-mouth gesture and deep breathing, from perceived audio data; and the higher-layer, based on the lower-layerąŕs detection results, detects smoking puffs and sessions using temporal sequence analysis techniques. Real-world experiments suggest our system can accurately detect smoking puffs and sessions with F1 score of respectively 93.59% and 92.96% in complex environments with the presence of confounding activities and diverse ways of smoking.","Title: Integrating Wi-Fi And Magnetic Field For Fingerprinting Based Indoor Positioning System\nYear: 2016\n\nAbstract: Smartphone based personal tracking is very important for people to find their destination in large complex buildings (e.g. shopping malls, airports and museums). Such applications are highly demanded in both industries and research organizations. One critical issue for these applications is lack of mature technologies for highly accurate indoor location tracking. In this paper, a new Wi-Fi and magnetic field based smartphone tracking system, named WMLoc, was introduced. The system is a part of a collaborative project between the RMIT University and a famous Australian software company. A number of tracking algorithms such as K nearest neighbor (KNN), artificial neural network (ANN) and back tracing (BT) have been developed or adopted for the system in order to obtain a real-time precise location of the smartphone user. The integration of Wi-Fi and magnetic field includes physical floor analysis, label pattern creation using ANN and BT for enhancing the tracking reliability and improving positioning accuracy. The WMLoc system was tested in two buildings at the RMIT University, Australia. The preliminary results showed that the average root-mean-square (RMS) error of the WMLoc system was less than 2.6 m.","Title: An Analytical Model for Coding-Based Reprogramming Protocols in Lossy Wireless Sensor Networks.\nYear: 2017\n\nAbstract: Multi-hop over-the-air reprogramming is essential for remote installation of software patches and upgrades in wireless sensor networks (WSNs). Several recent coding-based reprogramming protocols have been proposed to enable efficient code dissemination in high packet loss environments. An accurate and formal analysis of the performance of these protocols, however, has not been studied sufficiently in the literature. In this paper, we present a novel high-fidelity analytical model based on the shortest path algorithm to measure the completion time by incorporating overhearing and packet coding. This model can be applied to any coding-based reprogramming protocol by substituting the coding part with protocol specific operations. We conduct extensive testbed experiments to evaluate the performance of our proposed model. Based on the analytical and numerical experiments, we find that 1) overhearing causes significant reduction of the completion time in dense wireless sensor networks, particularly, it reduces 50-70 percent of the total completion time when the packet reception rate is 0.896; 2) coding delay plays a key role in the total completion time compared to the communication delay when the packet coding parameters are selected appropriately, for example, the communication delay is about 65 percent of the coding delay when the number of packets per page is 16 for the finite field size $2^8$ ; 3) the total completion time can be minimized when the number of packets per page is close to 24 and the finite field size is close to $2^4$ .","Title: Toward a Wearable RFID System for Real-Time Activity Recognition Using Radio Patterns.\nYear: 2017\n\nAbstract: Elderly care is one of the many applications supported by real-time activity recognition systems. Traditional approaches use cameras, body sensor networks, or radio patterns from various sources for activity recognition. However, these approaches are limited due to ease-of-use, coverage, or privacy preserving issues. In this paper, we present a novel wearable Radio Frequency Identification (RFID) system aims at providing an easy-to-use solution with high detection coverage. Our system uses passive tags which are maintenance-free and can be embedded into the clothes to reduce the wearing and maintenance efforts. A small RFID reader is also worn on the user's body to extend the detection coverage as the user moves. We exploit RFID radio patterns and extract both spatial and temporal features to characterize various activities. We also address the issues of false negative of tag readings and tag/antenna calibration, and design a fast online recognition system. Antenna and tag selection is done automatically to explore the minimum number of devices required to achieve target accuracy. We develop a prototype system which consists of a wearable RFID system and a smartphone to demonstrate the working principles, and conduct experimental studies with four subjects over two weeks. The results show that our system achieves a high recognition accuracy of 93.6 percent with a latency of 5 seconds. Additionally, we show that the system only requires two antennas and four tagged body parts to achieve a high recognition accuracy of 85 percent.","Title: A Mixed Transmission Strategy to Achieve Energy Balancing in Wireless Sensor Networks\nYear: 2017\n\nAbstract: In this paper, we investigate the problem of energy balanced data collection in wireless sensor networks, aiming to balance energy consumption among all sensor nodes during the data propagation process. Energy balanced data collection can potentially save energy consumption and prolong network lifetime, and hence, it has many practical implications for sensor network design and deployment. The traditional hop-by-hop transmission model allows a sensor node to propagate its packets in a hop-by-hop manner toward the sink, resulting in poor energy balancing for the entire network. To address the problem, we apply a slice-based energy model, and divide the problem into inter-slice and intra-slice energy balancing problems. We then propose a probability-based strategy named inter-slice mixed transmission protocol and an intra-slice forwarding technique to address each of the problems. We propose an energy-balanced transmission protocol by combining both techniques to achieve total energy balancing. In addition, we study the condition of switching between inter-slice transmission and intra-slice transmission, and the limitation of hops in an intra-slice transmission. Through our extensive simulation studies, we demonstrate that the proposed protocols achieve energy balancing, prolong network lifespan, and decrease network delay, compared with the hop-by-hop transmission and a cluster-based routing protocol under various parameter settings.","Title: Accurate and Generic Sender Selection for Bulk Data Dissemination in Low-Power Wireless Networks\nYear: 2017\n\nAbstract: Data dissemination is a fundamental service offered by low-power wireless networks. Sender selection is the key to the dissemination performance and has been extensively studied. Sender impact metric plays a significant role in sender selection, since it determines which senders are selected for transmission. Recent studies have shown that spatial link diversity has a significant impact on the efficiency of broadcast. However, the existing metrics overlook such impact. Besides, they consider only gains but ignore the costs of sender candidates. As a result, existing works cannot achieve accurate estimation of the sender impact. Moreover, they cannot well support data dissemination with network coding, which is commonly used for lossy environments. In this paper, we first propose a novel sender impact metric, namely, $\\gamma $ , which jointly exploits link quality and spatial link diversity to calculate the gain/cost ratio of the sender candidates. Then, we develop a generic sender selection scheme based on the $\\gamma $ metric (called $\\gamma $ -component) that can generally support both types of dissemination using native packets and network coding. Extensive evaluations are conducted through real testbed experiments and large-scale simulations. The performance results and analysis show that $\\gamma $ achieves far more accurate impact estimation than the existing works. In addition, the dissemination protocols based on $\\gamma $ -component outperform the existing protocols in terms of completion time and transmissions (by 20.5% and 23.1%, respectively).","Title: \"Knowing value\" logic as a normal modal logic.\nYear: 2016\n\nAbstract: Recent years witness a growing interest in nonstandard epistemic logics of knowing whether, knowing what, knowing how, and so on. These logics are usually not normal, i.e., the standard axioms and reasoning rules for modal logic may be invalid. In this paper, we show that the conditional knowing logic proposed by Wang and Fan cite{WF13} can be viewed as a disguised normal modal logic by treating the negation of the Kv operator as a special diamond. Under this perspective, it turns out that the original first-order Kripke semantics can be greatly simplified by introducing a ternary relation $R_i^c$ in standard Kripke models, which associates one world with two $i$-accessible worlds that do not agree on the value of constant $c$. Under intuitive constraints, the modal logic based on such Kripke models is exactly the one studied by Wang and Fan (2013,2014}. Moreover, there is a very natural binary generalization of the knowing diamond, which, surprisingly, does not increase the expressive power of the logic. The resulting logic with the binary diamond has a transparent normal modal system, which sharpens our understanding of the knowing logic and simplifies some previously hard problems.","Title: DeepKey: An EEG and Gait Based Dual-Authentication System.\nYear: 2017\n\nAbstract: Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are increasingly facing the risk of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoders, or fingerprint films. In this regard, we design a multimodal biometric authentication system named DeepKey which uses both gait and Electroencephalography (EEG) signals to provide better protection against such risks. DeepKey consists of three key components: an Invalid ID Filter Model to block invalid subjects, a Gait Identification Model to recognize Gait IDs and an EEG Identification Model to recognize EEG IDs. In particular, the first two models employ a one-class SVM algorithm and a Recurrent Neural Network based deep learning model, respectively. The third model combines autoregressive coefficients, an RNN structure, and an SVM classifier. DeepKey is trained with a gait dataset of 160,000 samples and an EEG dataset of 108,000 samples. Experimental results show DeepKey outperforms a series of comparison methods and achieves an overall accuracy of 0.983 along with an overall false acceptance rate (FAR) of 0.0 and a false rejection rate (FRR) of 0.019.","Title: Towards Accurate Corruption Estimation in ZigBee Under Cross-Technology Interference\nYear: 2017\n\nAbstract: Cross-Technology Interference affects the operation of low-power ZigBee networks, especially under severe WiFi interference. Accurate corruption estimation is very important to improve the resilience of ZigBee transmissions. However, there are many limitations in existing approaches such as low accuracy, high overhead, and requiring hardware modification. In this paper, we propose an accurate corruption estimation approach, AccuEst, which utilizes per-byte SINR (Signal-to-Interference-and-Noise Ratio) to detect corruption. We combine the use of pilot symbols with per-byte SINR to improve corruption detection accuracy, especially in highly noisy environments (i.e., noise and interference are at the same level). In addition, we design an adaptive pilot instrumentation scheme to strike a good balance between accuracy and overhead. We implement AccuEst on the TinyOS 2.1.1/TelosB platform and evaluate its performance through extensive experiments. Results show that AccuEst improves corruption detection accuracy by 78.6% on average compared with state-of-the-art approach (i.e., CARE) in highly noisy environments. In addition, AccuEst reduces pilot overhead by 53.7% on average compared to the traditional pilot-based approach. We implement AccuEst in a coding-based transmission protocol, and results show that with AccuEst, the packet delivery ratio is improved by 20.3% on average.","Title: Embracing Corruption Burstiness: Fast Error Recovery for ZigBee under Wi-Fi Interference.\nYear: 2017\n\nAbstract: The ZigBee communication can be easily and severely interfered by Wi-Fi traffic. Error recovery, as an important means for ZigBee to survive Wi-Fi interference, has been extensively studied in recent years. The existing works add upfront redundancy to in-packet blocks for recovering a certain number of random corruptions. Therefore, the bursty nature of ZigBee in-packet corruptions under Wi-Fi interference is often considered harmful, since some blocks are full of errors which cannot be recovered and some blocks have no errors but are still requiring redundancy. As a result, they often use interleaving to reshape the bursty errors, before applying complex FEC codes to recover the re-shaped random distributed errors. In this paper, we take a different view that burstiness may be helpful. With burstiness, the in-packet corruptions are often consecutive and the requirement for error recovery is reduced as recovering any k consecutive errors instead of recovering any random k errors. This lowered requirement allows us to design far more efficient code than the existing FEC codes. Motivated by this implication, we exploit the corruption burstiness to design a simple yet effective error recovery code using XOR operations (called ZiXOR). ZiXOR uses XOR code and the delay is significantly reduced. More, ZiXOR uses RSSI-hinted approach to detect in packet corruptions without CRC, incurring almost no extra transmission overhead. The testbed evaluation results show that ZiXOR outperforms the state-of-the-art works in terms of the throughput (by 47 percent) and latency (by 22 percent).","Title: Exploring traffic congestion correlation from multiple data sources.\nYear: 2017\n\nAbstract: Traffic congestion is a major concern in many cities around the world. Previous work mainly focuses on the prediction of congestion and analysis of traffic flows, while the congestion correlation between road segments has not been studied yet. In this paper, we propose a three-phase framework to explore the congestion correlation between road segments from multiple real world data. In the first phase, we extract congestion information on each road segment from GPS trajectories of over 10,000 taxis, define congestion correlation and propose a corresponding mining algorithm to find out all the existing correlations. In the second phase, we extract various features on each pair of road segments from road network and POI data. In the last phase, the results of the first two phases are input into several classifiers to predict congestion correlation. We further analyze the important features and evaluate the results of the trained classifiers through experiments. We found some important patterns that lead to a high/low congestion correlation, and they can facilitate building various transportation applications. In addition, we found that traffic congestion correlation has obvious directionality and transmissibility. The proposed techniques in our framework are general, and can be applied to other pairwise correlation analysis.","Title: Rapid: A Multimodal and Device-free Approach Using Noise Estimation for Robust Person Identification\nYear: 2017\n\nAbstract: Device-free human sensing is a key technology to support many applications such as indoor navigation and activity recognition. By exploiting WiFi signals reflected by human body, there have been many WiFi-based device-free human sensing applications. Among these applications, person identification is a fundamental technology to enable user-specific services. In this paper, we present Rapid, a system that can perform robust person identification in a device-free and low-cost manner, using fine-grained channel information (i.e., CSI) of WiFi and acoustic information from footstep sound. In order to achieve high accuracy in real-life scenarios with both system and environment noise, we perform noise estimation and include two different confidence values to quantify the impact of noise to both CSI and acoustic measurements. Based on an accurate gait analysis, we then adaptively fuse CSI and acoustic measurements to achieve robust person identification. We implement low-cost Rapid nodes and evaluate our system using experiments at multiple locations with a total of 1800 gait instances from 20 volunteers, and the results show that Rapid identifies a subject with an average accuracy of 92% to 82% from a group of 2 to 6 subjects, respectively.","Title: Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals\nYear: 2018\n\nAbstract: An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subject's active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large-scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the-art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.","Title: Fullie and Wiselie: A Dual-Stream Recurrent Convolutional Attention Model for Activity Recognition.\nYear: 2017\n\nAbstract: Multimodal features play a key role in wearable sensor based Human Activity Recognition (HAR). Selecting the most salient features adaptively is a promising way to maximize the effectiveness of multimodal sensor data. In this regard, we propose a fully and select wisely (Fullie and Wiselie) principle as well as a dual-stream recurrent convolutional attention model, Recurrent Attention and Activity Frame (RAAF), to improve the recognition performance. We first collect modality features and the relations between each pair of features to generate activity frames, and then introduce an attention mechanism to select the most prominent regions from activity frames precisely. The selected frames not only maximize the utilization of valid features but also reduce the number of features to be computed effectively. We further analyze the hyper-parameters, accuracy, interpretability, and annotation dependency of the proposed model based on extensive experiments. The results show that RAAF achieves competitive performance on two benchmarked datasets and works well in real life scenarios.","Title: FastDesk: A remote desktop virtualization system for multi-tenant.\nYear: 2018\n\nAbstract: Classical remote desktop systems are considered for a single host, thus hindering their applicability on virtualization environment. Remote desktop virtualization is rising in the recent years as a new advanced extension to virtualization technology. However, existing remote desktop virtualization solutions introduce weak performance in relation to response time, video quality, and cost saving. This paper proposes a novel remote desktop virtualization system for multi-tenant, namely FastDesk, which is based on a server-push mode streaming mechanism and a heuristic virtual desktop placement algorithm. Through extensive experiments, the results show that FastDesk outperforms other popular platforms in terms of bandwidth with less than 2 Mbps and 94% video quality. Meanwhile, it creates the minimal resource wastage compared with other virtual machine placement algorithms. Furthermore, the proposed FastDesk achieves low CPU utilization, low bandwidth and good scalability. At last, to widen its applicability, FastDesk has been implemented on VirtualBox for running 3D programs.","Title: C-FMCW Based Contactless Respiration Detection Using Acoustic Signal.\nYear: 2017\n\nAbstract: Recent advances in ubiquitous sensing technologies have exploited various approaches to monitoring vital signs. One of the vital signs is human respiration which typically requires reliable monitoring with low error rate in practice. Previous works in respiration monitoring however either incur high cost or suffer from poor error rate. In this paper, we propose a Correlation based Frequency Modulated Continuous Wave method (C-FMCW) which is able to achieve high ranging resolution. Based on C-FMCW, we present the design and implementation of an audio-based highly-accurate system for human respiration monitoring, leveraging on commodity speaker and microphone widely available in home environments. The basic idea behind the audio-based method is that when a user is close to a pair of speaker and microphone, body movement during respiration causes periodic audio signal changes, which can be extracted to obtain the respiration rate. However, several technical challenges exist when applying C-FMCW to detect respiration with commodity acoustic devices. First, the sampling frequency offset between speakers and microphones if not being corrected properly would cause high ranging errors. Second, the uncertain starting time difference between the speaker and microphone varies over time. Moreover, due to multipath effect, weak periodic components due to respiration can easily be overwhelmed by strong static components in practice. To address those challenges, we 1) propose an algorithm to compensate dynamically acoustic signal and counteract the offset between speaker and microphone; 2) co-locate speaker and microphone and use the received signal without reflection (self-interference) as a reference to eliminate the starting time difference; and 3) leverage the periodicity of respiration to extract weak periodic components with autocorrelation. Extensive experimental results show that our system detects respiration in real environments with the median error lower than 0.35 breaths/min, outperforming the state-of-the-arts.","Title: Spatial Multiplexing For Non-Line-Of-Sight Light-To-Camera Communications\nYear: 2018\n\nAbstract: Light-to-Camera Communications (LCC) have emerged as a new wireless communication technology with great potential to benefit a broad range of applications. However, the existing LCC systems either require cameras directly facing to the lights or can only communicate over a single link, resulting in low throughputs and being fragile to ambient illuminant interference. We present HYCACO, a novel LCC system, which enables multiple light emitting diodes (LEDs) with an unaltered camera to communicate via the non-line-of-sight (NLoS) links. Different from other NLoS LCC systems, the proposed scheme is resilient to the complex indoor luminous environment. HYCACO can decode the messages by exploring the mixed reflected optical signals transmitted from multiple LEDs. By further exploiting the rolling shutter mechanism, we present the optimal optical frequencies and camera exposure duration selection strategy to achieve the best performance. We built a hardware prototype to demonstrate the efficiency of the proposed scheme under different application scenarios. The experimental results show that the system throughput reaches 4.5 kbps on iPhone 6s with three transmitters. With the robustness, improved system throughput and ease of use, HYCACO has great potentials to be used in a wide range of applications such as advertising, tagging objects, and device certifications.","Title: Traveling Officer Problem: Managing Car Parking Violations Efficiently Using Sensor Data.\nYear: 2018\n\nAbstract: The on-street parking system is an indispensable part of civic projects, as it provides travelers and shoppers with parking spaces. With the recent in-ground sensors deployed throughout the Melbourne central business district (CBD), there is a significant problem on how to use the sensor data to manage parking violations and issue infringement notices efficiently in a short time-window. In this pa...","Title: FinDroidHR: Smartwatch Gesture Input with Optical Heartrate Monitor.\nYear: 2018\n\nAbstract: We present FinDroidHR, a novel gesture input technique for off-the-shelf smartwatches. Our technique is designed to detect 10 hand gestures on the hand wearing a smartwatch. The technique is enabled by analysing features of the Photoplethysmography (PPG) signal that optical heart-rate sensors capture. In a study with 20 participants, we show that FinDroidHR achieves 90.55% accuracy and 90.73% recall. Our work is the first study to explore the feasibility of using optical sensors on the off-the-shelf wearable devices to recognise gestures. Without requiring bespoke hardware, FinDroidHR can be readily used on existing smartwatches.","Title: SpiderWalk: Circumstance-aware Transportation Activity Detection Using a Novel Contact Vibration Sensor.\nYear: 2018\n\nAbstract: This paper presents the design and implementation of the SpiderWalk system for circumstance-aware transportation activity detection using a novel contact vibration sensor. Different from existing systems that only report the type of activity, our system detects not only the activity but also its circumstances (e.g., road surface, vehicle, and shoe types) to provide better support for applications such as activity logging, location tracking, and smart persuasive applications. Inspired by but different from existing audio-based context detection approaches using microphones, the SpiderWalk system is designed and implemented using an ultra-sensitive, flexible contact vibration sensor which mimics the spiders' sensory slit organs. By sensing vibration patterns from the soles of shoes, the system can accurately detect transportation activities with rich circumstance information while resisting undesirable external signals from other sources or speech that may cause the data assignment and privacy preserving issues. Moreover, our system is implemented by reusing existing audio devices and can be used by an unmodified smartphone, making it ready for large-scale deployments. Finally, a novel temporal and spatial correlated classification approach is proposed to accurately detect the complex combinations of transportation activities and circumstances based on the output of each individual classifiers. Experiments conducted on a real-world data set suggest our system can accurately detect different transportation activities and their circumstances with an average detection accuracy of 93.8% with resource overheads comparable to existing audio- and GPS-based systems.","Title: Exploiting Delay-Aware Load Balance for Scalable 802.11 PSM in Crowd Event Environments.\nYear: 2017\n\nAbstract: This paper presents ScaPSM (i.e., Scalable Power-Saving Mode Scheduler), a design that enables scalable competing background traffic scheduling in crowd event 802.11 deployments with Power-Saving Mode (PSM) radio operation. ScaPSM prevents the packet delay proliferation of previous study, if applied in the crowd events scenario, by introducing a new strategy of adequate competition among multiple PSM clients to optimize overall energy saving without degrading packet delay performance. The key novelty behind ScaPSM is that it exploits delay-aware load balance to control judiciously the qualification and the number of competing PSM clients before every beacon frame's transmission, which helps to mitigate congestion at the peak period with increasing the number of PSM clients. With ScaPSM, the average packet delay is bounded and fairness among PSM clients is simultaneously achieved. ScaPSM is incrementally deployable due to only AP-side changes and does not require any modification to the 802.11 protocol or the clients. We theoretically analyze the performance of ScaPSM. Our experimental results show that the proposed design is practical, effective, and featuring with significantly improved scalability for crowd events.","Title: Making Sense of Doppler Effect for Multi-Modal Hand Motion Detection.\nYear: 2018\n\nAbstract: Hand gesture is becoming an increasingly popular means of interacting with consumer electronic devices, such as mobile phones, tablets and laptops. In this paper, we present AudioGest, a device-free gesture recognition system that can accurately sense the hand in-air movement around user's devices. Compared to the state-of-the-art techniques, AudioGest is superior in using only one pair of built-i...","Title: HiMeter: Telling You the Height Rather than the Altitude.\nYear: 2018\n\nAbstract: The altitude of a moving user is important context information for mobile technologies and applications. However, with the increasing pervasiveness of smartphones and abundant mobile applications, developers and users have gradually discovered that the height is more useful than altitude in many situations. The height is often a relative value, which is the vertical distance to the ground rather than the vertical distance to sea level, and we believe that it is useful in many applications, such as localization/navigation, sport/health and tourism/travel. In this paper, we first carried out a nation-wide online survey to confirm the desirability for the height information in mobile applications, and the result is positive. Then, we proposed HiMeter, an effective and accurate approach to calculating the height of the smartphone. HiMeter makes use of a low-power barometer on the smartphone and does not require GPS or back-server support. We concentrate on the vertical moving pattern of the user and designed several novel techniques, resulting in HiMeter not needing any reference points, and the complex process of calculating the absolute altitude can be avoided. The field studies show that HiMeter can achieve an accuracy of within 5 m in 90% of cases indoors and an accuracy of 10 m in 83% of cases outdoors. Compared to the existing works, HiMeter is more accurate and practical and is more suitable for usage in many mobile applications.","Title: An Adaptive Low-Power Listening Protocol for Wireless Sensor Networks in Noisy Environments.\nYear: 2018\n\nAbstract: This paper investigates the energy consumption minimization problem for wireless sensor networks running low-power listening (LPL) protocols in noisy environments. We observe that the energy consumption by false wakeups (i.e., wakeup without receiving any packet) of a node in noisy environments can be a dominant factor in many cases while the false wakeup rate is spatially and temporarily dynamic....","Title: Towards Repeatable Wireless Network Simulation Using Performance Aware Markov Model.\nYear: 2018\n\nAbstract: Wireless network simulation is a fundamental service aiming at providing controlled and repeatable environment for protocol design, performance testing, etc. The existing simulators focus on reproducing the packet behaviors on individual links. However, as observed in some recent works, individual link behaviors alone are not enough to characterize the protocol performance. As a result, while the existing works can mimic the link behaviors very closely, they often fail to simulate protocol level performance. In this paper, we propose a novel performance-aware simulation approach which can preserve not only the link-level behaviors but also the performance-level behaviors. We first devise an accurate performance model by combining link quality and the spatial temporal link correlation. Based on the performance modeling, we then propose a Performance Aware Hidden Markov Model (PA-HMM), where the protocol performance is directly fed into the Markov state transitions. PA-HMM is able to simulate both link-level behaviors and high-level protocol performance. We conduct extensive testbed and simulation experiments with broadcast and anycast protocols. The results show that compared to the state-of-the-art work, 1) the performance model is able to accurately characterize wireless communication performance and 2) the protocol performance is closely simulated as compared to the empirical results.","Title: FullBreathe: Full Human Respiration Detection Exploiting Complementarity of CSI Phase and Amplitude of WiFi Signals.\nYear: 2018\n\nAbstract: Human respiration detection based on Wi-Fi signals does not require users to carry any device, hence it has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, recent studies show that respiration sensing performance varies in different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call \"blind spots\". In this paper, we aim to address the blind spot problem to ensure full coverage of respiration detection. Basically, the amplitude and phase of Wi-Fi channel state information (CSI) are orthogonal and complementary to each other, so they can be combined to eliminate the blind spots. However, accurate CSI phase cannot be obtained from commodity Wi-Fi due to the clock-unsynchronized transceivers. Thus, we apply conjugate multiplication (CM) of CSI between two antennas to remove the phase offset and construct two orthogonal signals--new \"amplitude and phase\" which are still complementary to each other. In this way, we can ensure full human respiration detection. Based on these ideas, We design and implement a real-time respiration detection system with commodity Wi-Fi devices. We conduct extensive experiments to validate our model and design. The results show that, with only one transceiver pair and without leveraging multiple sub-carriers, our system enables full location coverage with no blind spot, showing great potential for real deployment.","Title: Gazerevealer: Inferring Password Using Smartphone Front Camera\nYear: 2018\n\nAbstract: The widespread use of smartphones has brought great convenience to our daily lives, while at the same time we have been increasingly exposed to security threats. Keystroke security is an essential element in user privacy protection. In this paper, we present GazeRevealer, a novel side-channel based keystroke inference framework to infer sensitive inputs on smartphone from video recordings of victim's eye patterns captured from smartphone front camera. We observe that eye movements typically follow the keystrokes typing on the number-only soft keyboard during password input. By exploiting eye patterns, we are able to infer the passwords being entered. We propose a novel algorithm to extract sensitive eye pattern images from video streams, and classify different eye patterns with Support Vector Classification. We also propose a novel enhanced method to boost the inference accuracy. Compared with prior keystroke detection approaches, GazeRevealer does not require any external auxiliary devices, and it relies only on smartphone front camera. We evaluate the performance of GazeRevealer with three different types of smartphones, and the result shows that GazeRevealer achieves 77.43% detection accuracy for a single key number and 83.33% inference rate for the 6-digit password in the ideal case.","Title: BTrack: Using Barometer for Energy Efficient Location Tracking on Mountain Roads.\nYear: 2018\n\nAbstract: This paper presents the design and implementation of BTrack, a new approach for location tracking on mountain roads. It is as accurate as the GPS on common smartphones, but costs much less energy. To design such a location system, we carried out a nation-wide online survey to confirm the desirability for low power location tracking system, and the result is positive. Then, we proposed BTrack, and it makes two key technical contributions. The first is to propose a dynamic transition matrix hidden Markov model to combine the barometer and accelerometer reading hints for estimating the location of the user. The second is to design some novel techniques for parameter estimation, and proposed an adaptive algorithm to reduce the computational complexity. The field studies show that the accuracy of BTrack is no worse than GPS in 56% cases, meanwhile, the energy consumption is only about 30%. Compared with the existing works, BTrack is more suitable for location tracking on mountain roads.","Title: A VR-based, hybrid modeling approach to fire evacuation simulation.\nYear: 2018\n\nAbstract: VR-based simulation could significantly improve the user experience by offering users vivid and near-life visual scenes, hence helping users better handle dangerous situations safely such as fire accidents. In this paper, we design a fire evacuation simulation system and propose a hybrid crowd evacuation modeling and simulation approach, which is a layer-based model adopting both local and global techniques partially into different layers. In essence, this model integrates an agent-based model with an improved dynamical network flow model, which is capable of taking into account issues both from individual diversity and from crowd movement tendency to simulate crowd evacuation. An emergency response mechanism driven by videos is then designed according to the model. Once fire accidents are detected in videos, the system will first simulate accidents according to the fire level provided by the monitoring module and then start an evacuation routine or adjust evacuation routes. The simulation system can be experienced by users in a virtual environment. Finally, evaluations have been conducted to test the rationality of our model and results show that the proposed model can simulate the crowd movement and agent behavior in dynamic environments efficiently.","Title: Minimum Cost Deployment of Bistatic Radar Sensor for Perimeter Barrier Coverage.\nYear: 2019\n\nAbstract: Perimeter barriers can provide intrusion detection for a closed area. It is efficient for practical applications, such as coastal shoreline monitoring and international boundary surveillance. Perimeter barrier coverage construction in some regions of interest with irregular boundaries can be represented by its minimum circumcircle and every point on the perimeter can be covered. This paper studies circle barrier coverage in Bistatic Radar Sensor Network (BRSN) which encircles a region of interest. To improve the coverage quality, it is required to construct a circle barrier with a predefined width. Firstly, we consider a BR deployment problem to constructing a single BR circular barrier with minimum threshold of detectability. We study the optimized BR placement patterns on the single circular ring. Then the unit costs of the BR sensor are taken into account to derive the minimum cost placement sequence. Secondly, we further consider a circular BR barrier with a predefined width, which is wider than the breadth of Cassini oval sensing area with minimum threshold of detectability. We propose two segment strategies to efficiently divide a circular barrier to several adjacent sub-ring with some appropriate width: Circular equipartition strategy and an adaptive segmentation strategy. Finally, we propose approximate optimization placement algorithms for minimum cost placement of BR sensor for circular barrier coverage with required width and detection threshold. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.","Title: ReLog: A systematic approach for supporting efficient reprogramming in wireless sensor networks.\nYear: 2017\n\nAbstract: Abstract Wireless sensor networks are shifting to application platforms that poses several challenges on reprogramming efficiency. To better support the efficient reprogramming, this paper proposes a systematic approach named ReLog which consists of a programming language, a compiler, and a virtual machine. To make application programs concise and easy to modify, the ReLog language extends from a traditional logical programming language and makes the extension part have the similar coding style. To reduce the size of data for reprogramming, the compiler first produces extremely compact executable code by compiling application programs into high-level representations. It also implements efficient incremental reprogramming to diminish differences between the current and new executable code. To mitigate the energy consumption incurred by interpretive execution, the virtual machine optimizes the executable code as well as the execution process to improve the runtime efficiency. We have implemented ReLog and evaluated it with respect to real reprogramming cases. Our experimental results show that it is easy to modify ReLog programs to satisfy new application requirements. Meanwhile, the compiler reduces the size of executable code by 61.4%–83.2% compared to the existing work. In addition, the lifetime of sensors running the ReLog virtual machine is close (97.04%–98.31%) to that running the native code.","Title: Compressive Representation for Device-Free Activity Recognition with Passive RFID Signal Strength.\nYear: 2018\n\nAbstract: Understanding and recognizing human activities is a fundamental research topic for a wide range of important applications such as fall detection and remote health monitoring and intervention. Despite active research in human activity recognition over the past years, existing approaches based on computer vision or wearable sensor technologies present several significant issues such as privacy (e.g., using video camera to monitor the elderly at home) and practicality (e.g., not possible for an older person with dementia to remember wearing devices). In this paper, we present a low-cost, unobtrusive, and robust system that supports independent living of older people. The system interprets what a person is doing by deciphering signal fluctuations using radio-frequency identification (RFID) technology and machine learning algorithms. To deal with noisy, streaming, and unstable RFID signals, we develop a compressive sensing, dictionary-based approach that can learn a set of compact and informative dictionaries of activities using an unsupervised subspace decomposition. In particular, we devise a number of approaches to explore the properties of sparse coefficients of the learned dictionaries for fully utilizing the embodied discriminative information on the activity recognition task. Our approach achieves efficient and robust activity recognition via a more compact and robust representation of activities. Extensive experiments conducted in a real-life residential environment demonstrate that our proposed system offers a good overall performance and shows the promising practical potential to underpin the applications for the independent living of the elderly.","Title: Accurate Corruption Estimation in ZigBee Under Cross-Technology Interference\nYear: 2019\n\nAbstract: Cross-Technology Interference affects the operation of low-power ZigBee networks, especially under severe WiFi interference. Accurate corruption estimation is very important to improve the resilience of ZigBee transmissions. However, there are many limitations in existing approaches such as low accuracy, high overhead, and requirement of hardware modification. In this paper, we propose an accurate corruption estimation approach, AccuEst, which utilizes per-byte SINR (Signal-to-Interference-and-Noise Ratio) to detect corruption. We combine the use of pilot symbols with per-byte SINR to improve corruption detection accuracy, especially in highly noisy environments (i.e., noise and interference are at the same level). We extract pilot symbols by leveraging protocol signatures. In addition, we design an adaptive pilot instrumentation scheme to strike a good balance between accuracy and overhead. We implement AccuEst on the TinyOS 2.1.1/TelosB platform and evaluate its performance through extensive experiments. Results show that AccuEst improves corruption detection accuracy by 79.4 percent on average compared with state-of-the-art approach (i.e., CARE) in highly noisy environments. In addition, AccuEst reduces pilot overhead by 83.7 percent on average compared to the traditional pilot-based approach. We implement AccuEst in a coding-based transmission protocol, and results show that with AccuEst, the packet delivery ratio is improved by 22.1 percent on average.","Title: Contactless Respiration Monitoring Using Ultrasound Signal With Off-The-Shelf Audio Devices\nYear: 2019\n\nAbstract: Recent years have witnessed advances of Internet of Things technologies and their applications to enable contact-less sensing and elderly care in smart homes. Continuous and real-time respiration monitoring is one of the important applications to promote assistive living for elders during sleep and attracted wide attention in both academia and industry. Most of the existing respiration monitoring systems require expensive and specialized devices to sense chest displacement. However, chest displacement is not a direct indicator of breathing and thus false detection may often occur. In this paper, we design and implement a real-time and contactless respiration monitoring system by directly sensing the exhaled airflow from breathing using ultrasound signals with off-the-shelf speaker and microphone. Exhaled airflow from breathing can be regarded as air turbulence, which scatters the sound wave and results in Doppler effect. Our system works as an acoustic radar which transmits sound wave and detects the Doppler effect caused by breathing airflow. We mathematically model the relationship between the Doppler frequency change and the direction of breathing airflow. Based on this model, we design a minimum description length-based algorithm to effectively capture the Doppler effect caused by exhaled airflow. We conduct extensive experiments with 25 participants (7 elders, 2 young kids, and 16 adults, including 11 females and 14 males) in four different rooms. The participants take four different sleep postures (lying on one's back, on right/left side, and on one's stomach) in different positions of the bed. Experiment results show that our system achieves a median error lower than 0.3 breaths/min (2%) for respiration monitoring and can accurately identify Apnea. The results also demonstrate that the system is robust to different respiration styles (shallow, normal, and deep), respiration rate variation, ambient noise, sensing distance variation (within 0.7 m), and transmitted signal frequency variation.","Title: Towards a Diffraction-based Sensing Approach on Human Activity Recognition\nYear: 2019\n\nAbstract: In recent years, wireless sensing has been exploited as a promising research direction for contactless human activity recognition. However, one major issue hindering the real deployment of these systems is that the signal variation patterns induced by the human activities with different devices and environmental settings are neither stable nor consistent, resulting in unstable system performance. The existing machine learning based methods usually take the \"black box\" approach and fails to achieve consistent performance. In this paper, we argue that a deep understanding of radio signal propagation in wireless sensing is needed, and it may be possible to develop a deterministic sensing model to make the signal variation patterns predictable.\n\nWith this intuition, in this paper we investigate: 1) how wireless signals are affected by human activities taking transceiver location and environment settings into consideration; 2) a new deterministic sensing approach to model the received signal variation patterns for different human activities; 3) a proof-of-concept prototype to demonstrate our approach and a case study to detect diverse activities. In particular, we propose a diffraction-based sensing model to quantitatively determine the signal change with respect to a target's motions, which eventually links signal variation patterns with motions, and hence can be used to recognize human activities. Through our case study, we demonstrate that the diffraction-based sensing model is effective and robust in recognizing exercises and daily activities. In addition, we demonstrate that the proposed model improves the recognition accuracy of existing machine learning systems by above 10%.","Title: A Novel Privacy Preserving Search Technique For Stego Data In Untrusted Cloud\nYear: 2019\n\nAbstract: We propose the first privacy preserving search technique for stego health data in untrusted cloud in this paper. The Cloud computing is a popular technology to the healthcare providers for outsourcing health data due to flexibility and cost effectiveness. However, outsourcing health data to the cloud introduces serious privacy issues to the patient. For example, dishonest personnel of the cloud provider may disclose patient sensitive information to business organizations for some financial benefits. Using steganography, patient sensitive information is hidden within health data for privacy preservation. As a result, stego health data is generated. To the best of our knowledge, no method exists for searching a particular stego data without disclosing any information to the cloud. We propose a framework for privacy preserving search over stego health data. We systematically describe each component of the proposed framework. We conduct several experiments to evaluate the performance of the framework.","Title: Enabling Out-of-Band Coordination of Wi-Fi Communications on Smartphones\nYear: 2019\n\nAbstract: This paper identifies two energy saving opportunities of Wi-Fi interface emerged during smartphone’s screen-off periods. Exploiting the opportunities, we propose a new power saving strategy, BackPSM, for screen-off Wi-Fi communications. BackPSM regulates client to send and receive packets in batches and coordinates multiple clients to communicate at different slots (i.e., beacon interval). The core problem in BackPSM is how to coordinate client without incurring extra traffic overheads. To handle the problem, we propose a novel paradigm, Out-of-Band Communication (OBC), for client-to-client direct communications. OBC exploits the Traffic Indication Map (TIM) field of Wi-Fi Beacon to create a free side-channel between clients. It is based upon the observation that a client may control \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$1 \\rightarrow 0$ </tex-math></inline-formula>\n appearing on TIM bit by locally regulating packet receiving operations. We adopt this \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$1 \\rightarrow 0$ </tex-math></inline-formula>\n as the basic signal, and leverage the time length in between two signals to encode information. We demonstrate that OBC can be used to convey coordination information with close to 100% accuracy. We have implemented and evaluated BackPSM on a testbed. The results show that BackPSM can decode the traffic pattern of peers reliably using OBC, and establish collision-free schedules fast to achieve out-of-band coordination of client communications. BackPSM reduces screen-off energy by up to 60% and outperforms the state-of-the-art strategies by 16%–42%.","Title: SMinder: Detect a Left-behind Phone using Sensor-based Context Awareness.\nYear: 2019\n\nAbstract: Forget your smartphone in the car again? This happens often in our daily lives, sometimes even makes troubles. In this paper, we present SMinder, an effective, low power approach to remind user take the phone when getting off the car. Based on the context awareness techniques in mobile sensing, we detect the situation of forgetting to take the phone when getting off the car. SMinder requires neither any infrastructure nor any human intervention. It only uses low power smartphone sensors. Namely, the smartphone detects by itself whether it is left behind and remind the user before he leaves the car. SMinder reminds the user with high accuracy and minimum energy consumption, making it realistic for real-world use. Compared to the existing approaches, SMinder is cheaper and easier to use. Our experiments with the prototype system demonstrate the performance, scalability, and robustness of SMinder.","Title: Assessing User Mental Workload for Smartphone Applications With Built-In Sensors.\nYear: 2019\n\nAbstract: This work proposes a novel three-dimensional model to represent users' mental workload when using smartphone applications. We validate this model by studying the factors' perceptual independence and interactions using data collected from 22 participants. By analyzing the correlations between the factors of mental workload and tap strength captured by smartphones' built-in sensors, we discover tap ...","Title: Key analysis of smart tourism project setting and tourists' satisfaction degree based on data mining.\nYear: 2019\n\nAbstract: By improving the mining efficiency and enhancing the scalability of the algorithm, it solves the problem that the traditional recommendation algorithm has a long response time and low recommendation efficiency in the recommendation of tourist spots, and cannot adapt to the needs of big data mining. An in-depth analysis of the existing collaborative filtering recommendation algorithm was conducted, and the Slope One algorithm and the Item-based algorithm recommended for the tourist attractions were selected. Combine these two algorithms efficiently, parallelize the algorithm based on MapReduce programming on the Hadoop cloud platform, and verify the validity of the algorithm by collecting the tourism evaluation network real tourist attraction scoring data. By testing the real tourist attraction score data, it shows that the algorithm not only improves the accuracy of the recommendation but also has higher running speed than the traditional collaborative filtering algorithm. The experimental results show that the algorithm has higher mining performance and scalability and can be better adapted to the characteristics of large tourist sites, sparse data matrix, and meet the high hit rate and personalized requirements of tourist attractions.","Title: Approximate Optimal Deployment of Barrier Coverage on Heterogeneous Bistatic Radar Sensors.\nYear: 2019\n\nAbstract: Heterogeneous Bistatic Radars (BR) have different sensing ranges and couplings of sensing regions, which provide more flexible coverage for the boundary at complex terrain such as across rivers and valleys. Due to the Cassini oval sensing region of a BR and the coupling of sensing regions among different BRs, the coverage problem of BR sensor networks is very challenging. Existing works in BR barrier coverage focus mainly on homogeneous BR sensor networks. This paper studies the heterogeneous BR placement problem on a line barrier to achieve optimal coverage. 1) We investigate coverage differences of the basic placement sequences of heterogeneous BRs on the line barrier, and prove the optimal basic placement spacing patterns of heterogeneous BRs. 2) We study the coverage coupling effect among adjacent BRs on the line barrier, and determine that different placement sequences of heterogeneous BR transmitters will affect the barrier's coverage performance and length. The optimal placement sequence of heterogeneous BR barrier cannot be solved through the greedy algorithm. 3) We propose an optimal BRs placement algorithm on a line barrier when the heterogeneous BR transmitters' placement sequence is predetermined on the barrier, and prove it to be optimal. Through simulation experiments, we determine that the different placement sequences of heterogeneous BR transmitters have little influence on the barrier's maximum length. Then, we propose an approximate algorithm to optimize the BR placement spacing sequence on the heterogeneous line barrier. 4) As a heterogeneous barrier case study, a minimum cost coverage algorithm of heterogeneous BR barrier is presented. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.","Title: Spatial Multiplexing for Non-Line-of-Sight Light-to-Camera Communications\nYear: 2019\n\nAbstract: Light-to-Camera Communications (LCC) have emerged as a new wireless communication technology with great potential to benefit a broad range of applications. However, the existing LCC systems either require cameras directly facing to the lights or can only communicate over a single link, resulting in low throughputs and being fragile to ambient illuminant interference. We present HYCACO, a novel LCC system, which enables multiple light emitting diodes (LEDs) with an unaltered camera to communicate via the non-line-of-sight (NLoS) links. Different from other NLoS LCC systems, the proposed scheme is resilient to the complex indoor luminous environment. HYCACO can decode the messages by exploring the mixed reflected optical signals transmitted from multiple LEDs. By further exploiting the rolling shutter mechanism, we present the optimal optical frequencies and camera exposure duration selection strategy to achieve the best performance. We built a hardware prototype to demonstrate the efficiency of the proposed scheme under different application scenarios. The experimental results show that the system throughput reaches 4.5 kbps on iPhone 6s with three transmitters. With the robustness, improved system throughput and ease of use, HYCACO has great potentials to be used in a wide range of applications such as advertising, tagging objects, and device certifications.","Title: Queec: QoE-aware edge computing for complex IoT event processing under dynamic workloads\nYear: 2019\n\nAbstract: Many IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) which are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this paper, we present Queec, a QoE-aware edge computing system for complex IoT event processing under dynamic workloads. With Queec, the complex IoT event processing tasks that are relative computation-intensive for low-end IoT devices can be transparently offloaded to nearby edge nodes at runtime. We formulate the problem of scheduling multi-user tasks to multiple edge nodes as an optimization problem which minimizes the overall offloading latency of all tasks while avoiding the overloading problem. We implement Queec on low-end IoT devices, edge nodes and the cloud. We conduct extensive evaluations and the results show that Queec reduces 56.98% of the offloading latency on average compared with the state of art under dynamic workloads, while incurring acceptable overhead.","Title: DeepKey: A Multimodal Biometric Authentication System via Deep Decoding Gaits and Brainwaves\nYear: 2020\n\nAbstract: AbstractBiometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are at increasing risks of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoder, or fingerprint films. In this article, we design a multimodal biometric authentication system named DeepKey, which uses both Electroencephalography (EEG) and gait signals to better protect against such risk. DeepKey consists of two key components: an Invalid ID Filter Model to block unauthorized subjects, and an identification model based on attention-based Recurrent Neural Network (RNN) to identify a subject’s EEG IDs and gait IDs in parallel. The subject can only be granted access while all the components produce consistent affirmations to match the user’s proclaimed identity. We implement DeepKey with a live deployment in our university and conduct extensive empirical experiments to study its technical feasibility in practice. DeepKey achieves the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) of 0 and 1.0%, respectively. The preliminary results demonstrate that DeepKey is feasible, shows consistent superior performance compared to a set of methods, and has the potential to be applied to the authentication deployment in real-world settings.","Title: SateLoc: A Virtual Fingerprinting Approach to Outdoor LoRa Localization Using Satellite Images\nYear: 2021\n\nAbstract: AbstractWith the increasing relevance of the Internet of Things and large-scale location-based services, LoRa localization has been attractive due to its low-cost, low-power, and long-range properties. However, existing localization approaches based on received signal strength indicators are either easily affected by signal fading of different land-cover types or labor intensive. In this work, we propose SateLoc, a LoRa localization system that utilizes satellite images to generate virtual fingerprints. Specifically, SateLoc first uses high-resolution satellite images to identify land-cover types. With the path loss parameters of each land-cover type, SateLoc can automatically generate a virtual fingerprinting map for each gateway. We then propose a novel multi-gateway combination strategy, which is weighted by the environmental interference of each gateway, to produce a joint likelihood distribution for localization and tracking. We implement SateLoc with commercial LoRa devices without any hardware modification, and evaluate its performance in a 227,500-m\\(\\) urban area. Experimental results show that SateLoc achieves a median localization error of 43.5 m, improving more than 50% compared to state-of-the-art model-based approaches. Moreover, SateLoc can achieve a median tracking error of 37.9 m with the distance constraint of adjacent estimated locations. More importantly, compared to fingerprinting-based approaches, SateLoc does not require the labor-intensive fingerprint acquisition process.","Title: PRComm: Anti-Interference Cross-Technology Communication Based on Pseudo-random Sequence\nYear: 2021\n\nAbstract: ABSTRACTWith the rapid development of the Internet of Things (IoT), we have seen a larger number of devices deployed with different wireless communication protocols (i.e., WiFi, ZigBee, Bluetooth). Working in the same place opens a new opportunity for these devices to communicate directly with each other, leveraging on Cross-technology Communication (CTC). However, since these devices operate in the same frequency band which results in the competition against each other for network resources, severe interfere may arise. In this paper, we explore pseudo-random sequence (PR sequence) to design a novel CTC protocol that enables low-cost direct communication between WiFi and ZigBee in noisy indoor environments. Pseudorandom sequence offers a unique statistical feature to accomplish both information transmission and synchronization between heterogeneous devices. We design a dynamic synchronous decoding strategy to handle interference coexisted among different wireless protocols. Our system does not require any modification of communication protocol and underlying hardware and firmware. We implement our system on commercial devices (Intel 5300 WiFi NIC and MicaZ CC2420), and conduct extensive experiments to evaluate the system performance in three typical scenarios. The experimental results show that the synchronization time of our approach is lower than 0.5 ms, and the accuracy is greater than 84% while the channel occupancy is as high as 50%.","Title: Queec: QoE-aware Edge Computing for IoT Devices under Dynamic Workloads\nYear: 2021\n\nAbstract: AbstractMany IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) that are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this article, we present Queec, a QoE-aware edge computing system for complex IoT event processing under dynamic workloads. With Queec, the complex IoT event processing tasks that are relatively computation-intensive for low-end IoT devices can be transparently offloaded to nearby edge nodes at runtime. We formulate the problem of scheduling multi-user tasks to multiple edge nodes as an optimization problem, which minimizes the overall offloading latency of all tasks while avoiding the overloading problem. We implement Queec on low-end IoT devices, edge nodes, and the cloud. We conduct extensive evaluations, and the results show that Queec reduces 56.98% of the offloading latency on average compared with the state-of-the-art under dynamic workloads, while incurring acceptable overhead.","Title: PCube: scaling LoRa concurrent transmissions with reception diversities\nYear: 2021\n\nAbstract: ABSTRACTThis paper presents the design and implementation of PCube, a phase-based parallel packet decoder for concurrent transmissions of LoRa nodes. The key enabling technology behind PCube is a novel air-channel phase measurement technique which is able to extract phase differences of air-channels between LoRa nodes and multiple antennas of a gateway. PCube leverages the reception diversities of multiple receiving antennas of a gateway and scales the concurrent transmissions of a large number of LoRa nodes, even exceeding the number of receiving antennas at a gateway. As a phase-based parallel decoder, PCube provides a new dimension to resolve collisions and supports more concurrent transmissions by complementing time and frequency based parallel decoders. PCube is implemented and evaluated with synchronized software defined radios and off-the-shelf LoRa nodes in both indoors and outdoors. Results demonstrate that PCube can substantially outperform state-of-the-art works in terms of aggregated throughput by 4.9× and the number of concurrent nodes by up to 5×. More importantly, PCube scales well with the number of receiving antennas of a gateway, which is promising to break the barrier of concurrent transmissions.","Title: DynaKey: Dynamic Keystroke Tracking Using a Head-Mounted Camera Device\nYear: 2022\n\nAbstract: Mobile and wearable devices have become more and more popular. However, the tiny touch screen leads to inefficient interaction with these devices, especially for text input. In this article, we propose DynaKey, which allows people to type on a virtual keyboard printed on a piece of article or drawn on a desk, for inputting text into a head-mounted camera device (e.g., smart glasses). By usi...","Title: LiteNap: Downclocking LoRa Reception\nYear: 2021\n\nAbstract: This paper presents LiteNap which improves the energy efficiency of LoRa by enabling LoRa nodes to operate in a downclocked ‘light sleep’ mode for packet reception. A fundamental limit that prevents radio downclocking is the Nyquist sampling theorem which demands the clock-rate being at least twice the bandwidth of LoRa chirps. Our study reveals under-sampled LoRa chirps suffer frequency aliasing ...","Title: MDLdroid: A ChainSGD-Reduce Approach to Mobile Deep Learning for Personal Mobile Sensing\nYear: 2022\n\nAbstract: Personal mobile sensing is fast permeating our daily lives to enable activity monitoring, healthcare and rehabilitation. Combined with deep learning, these applications have achieved significant success in recent years. Different from conventional cloud-based paradigms, running deep learning on devices offers several advantages including data privacy preservation and low-latency response for both ...","Title: Message from the iThings 2021 General Chairs: iThings-GreenCom-CPSCom-SmartData-Cybermatics 2021\nYear: 2021\n\nAbstract: Welcome to the 14th IEEE International Conference on Internet of Things (iThings 2021)! The iThings 2021 is a top publication venue sponsored by the IEEE Computer Society. The IEEE iThings 2021 Conference was planned to be held in Melbourne, Australia, December 06-08, 2021. Given the COVID- 19 pandemic and associated travel restrictions, as the safety of people is of the highest priority, the conference will be held virtually on December 06-08, 2021. On behalf of the Organizing Committee of iThings 2021, we would like to extend a warm welcome to all the delegates attending iThings-2021.","Title: Smart Diagnosis: Deep Learning Boosted Driver Inattention Detection and Abnormal Driving Prediction\nYear: 2022\n\nAbstract: Inattentive driving is one of the high-risk factors that causes a large number of traffic accidents every year. In this article, we aim to detect driver inattention leveraging on large-scale vehicle trajectory data while at the same time explore how do these inattentive events affect driver behaviors and what following reactions they may cause, especially, for commercial vehicles. Specifically, th...","Title: GaitTracker: 3D Skeletal Tracking for Gait Analysis Based on Inertial Measurement Units\nYear: 2022\n\nAbstract: AbstractGait rehabilitation is a common method of postoperative recovery after the user sustains an injury or disability. However, traditional gait rehabilitations are usually performed under the supervision of rehabilitation specialists, which implies that the patients cannot receive adequate gait assessment anytime and anywhere. In this article, we propose GaitTracker, a novel system to remotely and continuously perform gait monitoring and analysis by three-dimensional (3D) skeletal tracking in a wearable approach. Specifically, this system consists of four Inertial Measurement Units (IMU), which are attached on the shanks and thighs of the human body. According to the measurements from these IMUs, we can obtain the motion signals of lower limbs during gait rehabilitation. By adaptively synchronizing coordinate systems of different IMUs and building the geometric model of lower limbs, the exact gait movements can be reconstructed, and gait parameters can be extracted without any prior knowledge. GaitTracker offers three key features: (1) a unified 3D skeletal model to depict the precise gait movement and parameters in 3D space, (2) a coordinate system synchronization scheme to perform space synchronization over all the IMU sensors, and (3) an automatic estimation method for the user-specific geometric parameters. In this way, GaitTracker is able to accurately perform 3D skeletal tracking of lower limbs for gait analysis, such as evaluating the gait symmetry and the gait parameters including the swing/stance time. We implemented GaitTracker and evaluated its performance in real applications. The experimental results show that, the average error for skeleton angle estimation, joint displacement estimation, and gait parameter estimation are 3∘, 2.3%, and 3%, respectively, outperforming the state of the art.","Title: Separating Voices from Multiple Sound Sources using 2D Microphone Array\nYear: 2022\n\nAbstract: Voice assistant has been widely used for human-computer interaction and automatic meeting minutes. However, for multiple sound sources, the performance of speech recognition in voice assistant decreases dramatically. Therefore, it is crucial to separate multiple voices efficiently for an effective voice assistant application in multi-user scenarios. In this paper, we present a novel voice separation system using a 2D microphone array in multiple sound source scenarios. Specifically, we propose a spatial filtering-based method to iteratively estimate the Angle of Arrival (AoA) of each sound source and separate the voice signals with adaptive beamforming. We use BeamForming-based cross-Correlation (BF-Correlation) to accurately assess the performance of beamforming and automatically optimize the voice separation in the iterative framework. Different from cross-correlation, BF-Correlation further performs cross-correlation among the after-beamforming voice signals processed with each linear microphone array. In this way, the mutual interference from voice signals out of the specified direction can be effectively suppressed or mitigated via the spatial filtering technique. We implement a prototype system and evaluate its performance in real environments. Experimental results show that the average AoA error is 1.4 degree and the average ratio of automatic speech recognition accuracy is 90.2% in the presence of three sound sources.","Title: Edge-Eye: Rectifying Millimeter-level Edge Deviation in Manufacturing using Camera-enabled IoT Edge Device\nYear: 2022\n\nAbstract: Irradiated Cross-linked Polyethylene Foam (IXPE) has been one of the most commonly used materials in industry. During the production process of IXPE sheets, their edges need keep aligned strictly, otherwise, they could quickly get out of the border of the rolling plate and cause the huge economic loss. In this paper, we propose a camera-enabled approach, called Edge-Eye, to rectify the edge deviation automatically for IXPE production with millimeter-level accuracy. We deploy a commercial camera with mobile edge node in front of the IXPE sheet to continuously detect and rectify the edge deviation. Particularly, to handle the complex production en-vironment when extracting the edge of IXPE sheet, we deploy a pair of reference bars with high-contrast colors to efficiently differ-entiate the sheet edge from the background. Then, we propose a Bi-direction Edge Tracking method to perform the edge detection from both vertical and horizontal aspects. To realize the rectification using mobile edge nodes with limited computing resources, we reduce the cost of computation by extracting the Minimized Region of Interest, i.e., the edge area overlapped with the higher contrast reference bar on both sides. We further design a negative feedback control system with multi-stage feedback regulation mechanism, keeping the edge deviation within millimeter-level. We implemented Edge-Eye on the ARM64 platform and performed evaluation in the practical IXPE production process. The experimental results show that Edge-Eye achieves the average accuracy of 5mm for the edge deviation rectification, with the average latency of 200ms for edge deviation detection. During the process of 20-month real deployment for 36 production lines, 66 manpower per day (90% of the overall manpower) has been saved, and the utilization rate of IXPE material increases from 87% to 94%.","Title: Your Breath Doesn't Lie: Multi-user Authentication by Sensing Respiration Using mmWave Radar\nYear: 2022\n\nAbstract: User authentication is critical to privacy preservation. Most of the existing works focus on single-user authentication, which may not work efficiently and practically in multi-user scenarios. To this end, we present a Multi-user Authentication system (M-Auth) that employs a single COTS mmWave radar to capture the user's unique breathing pattern. It exploits the phenomenon that radio frequency (RF) signals are affected by chest displacements due to breathing. We specifically design an auxiliary rotating gadget to dynamically adjust radar orientation, making it more effective in capturing respiration signals from multiple users. To profile individual components from the entangled RF signals, we leverage mmWave's high directivity to locate each user and separately focus on reflections from different positions. We propose a signal energy comparison method to eliminate the irrelevant body movements for preserving fine-grained respiration traits. Afterward, we develop a feature selection pipeline to elicit the most informative features and train a machine learning-based classifier to identify each user. M-Auth is practical due to its non-contact and passive nature, and it is secure as respiration is unique and difficult-to-forge. Extensive experiments involving 37 participants demonstrate that M-Auth is effective in verifying legitimate users and thwarting spoofing attacks, with an authentication accuracy of over 96 % and an attack detection rate of over 95%."],"string":"[\n \"Title: BloomCast: Efficient and Effective Full-Text Retrieval in Unstructured P2P Networks\\nYear: 2012\\n\\nAbstract: Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P systems to effectively locate items with guaranteed recall. Second, existing schemes to improve search success rate often rely on replicating a large number of item replicas across the wide area network, incurring a large amount of communication and storage costs. In this paper, we propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. By leveraging a hybrid P2P protocol, BloomCast replicates the items uniformly at random across the P2P networks, achieving a guaranteed recall at a communication cost of O(\\\\sqrt{N}), where N is the size of the network. Furthermore, by casting Bloom Filters instead of the raw documents across the network, BloomCast significantly reduces the communication and storage costs for replication. We demonstrate the power of BloomCast design through both mathematical proof and comprehensive simulations based on the query logs from a major commercial search engine and NIST TREC WT10G data collection. Results show that BloomCast achieves an average query recall of 91 percent, which outperforms the existing WP algorithm by 18 percent, while BloomCast greatly reduces the search latency for query processing by 57 percent.\",\n \"Title: Development of urine glucose level monitor for home healthcare using near infrared spectroscopy\\nYear: 2008\\n\\nAbstract: We have been developing a urine glucose level monitoring technique using near infrared spectroscopy in conjunction with the chemometric method aiming for the use of home health care. In this study, validity of the calibration models were assessed using urine samples from nine healthy male subjects. For the individual measurement, reasonable accuracy was obtained in predicting urine glucose level for each subject (mean value of standard error of prediction; 25.8 mg/dl, S.D.; 10.8 mg/dl), however, the accuracy decreased (around 60 mg/dl) when the calibration models were generated using data from multiple subjects. One of the causative factors seemed to be urine urea concentration level which may vary within very wide range (0-2000 mg/dl) under physiological condition, and therefore, it was suggested that any idea, e.g., classification of data by urea level, would be required for improving the accuracy.\",\n \"Title: A hierarchical approach to real-time activity recognition in body sensor networks\\nYear: 2012\\n\\nAbstract: Real-time activity recognition in body sensor networks is an important and challenging task. In this paper, we propose a real-time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we first use a fast and lightweight algorithm to detect gestures at the sensor node level, and then propose a pattern based real-time algorithm to recognize complex, high-level activities at the portable device level. We evaluate our algorithms over a real-world dataset. The results show that the proposed system not only achieves good performance (an average utility of 0.81, an average accuracy of 82.87%, and an average real-time delay of 5.7 seconds), but also significantly reduces the network's communication cost by 60.2%.\",\n \"Title: A distributed hierarchical structure for object networks supporting human activity recognition\\nYear: 2006\\n\\nAbstract: Pervasive environments will witness heterogeneous smart embedded devices (e.g. sensors, actuators) integrated into user's living environment (e.g. smart homes and hospitals) and provide a multitude of information that can transparently support user's lifestyle. One promising application resulting from the management and exploitation of this information is the human activity recognition. In this paper we briefly describe our activity recognition architecture and focus on an important management component of this architecture using the concept of object networks. We explore how object networks can integrate various sensor networks and heterogeneous devices into a coherent network through embedded context and role profile and at the same time support distributed context reasoning. The paper also describes the mechanisms used to eliminate and refine context information that is deemed irrelevant due to user behaviour changes over time, by employing the idea of role fitness.\",\n \"Title: Information retrieval in schema-based P2P systems using one-dimensional semantic space\\nYear: 2007\\n\\nAbstract: The widespread use of RDF-based information necessitates efficient information retrieval techniques in wide-area networks. In this paper, we present Dynamic Semantic Space, a schema-based peer-to-peer overlay network that facilitates efficient lookup for RDF-based information in dynamic environments. Peers in this overlay are grouped based on the semantics of their data which are extracted according to a set of schemas, and self-organized as a semantic overlay network. To reduce overheads incurred by peer joining, leaving and content changes in a high-dimensional overlay network, peers are constructed as a one-dimensional semantic space that facilitates efficient routing for both pull and push requests. A search or a subscription request is only routed to the appropriate cluster that holds related data, thus reducing unnecessary search cost and increasing the efficiency of locating information. Through a comprehensive simulation study, we demonstrate the effectiveness of our proposed techniques.\",\n \"Title: Context-aware content filtering & presentation for pervasive & mobile information systems.\\nYear: 2008\\n\\nAbstract: What constitutes relevant information to an individual may vary widely under different contexts. However, previous work on pervasive information systems has mostly focused on context-aware delivery of application-specific information. Such systems are only able to operate within narrow application domains and cannot be generalized to handle other heterogeneous types of information. To fill this gap, we propose a context-aware system for information integration that can handle arbitrary information types and determine their relevance to the user's current context. In contrast to existing model-based approaches to context reasoning, we log user interaction and perform usage mining using OLAP to discover context-dependent preferences for different information types. This allows us to build a more generic and adaptive system that automatically selects the most relevant content and presents it to the user in a succinct manner that supports ease of consumption and comprehension.\",\n \"Title: FTrack: Infrastructure-free floor localization via mobile phone sensing\\nYear: 2012\\n\\nAbstract: Mobile phone localization plays a key role in the fast-growing Location Based Applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, WiFi or GPS. In this paper, we present FTrack, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. FTrack uses the mobile phone's accelerometer only without any infrastructure support. It does not require any prior knowledge of the building such as floor height. By capturing user encounters and analyzing user trails, FTrack finds the mapping from the traveling time (when taking the elevator) or the step counts (when walking on the stairs) between any two floors to the number of floor levels. The mapping can then be used for mobile users to pinpoint their current floor levels. We conduct both simulation and field studies to demonstrate the effectiveness of FTrack. Our field trial in a 10-floor building shows that FTrack achieves an accuracy of over 90% after two hours in our experiment.\",\n \"Title: A Peer-to-Peer Architecture for Context Lookup\\nYear: 2005\\n\\nAbstract: As computing technology moves towards pervasive computing, many applications are beginning to make use of context information to adapt to and respond appropriately to their environments. Such a trend necessitates efficient search for context information in wide-area networks. In this paper, we propose a semantic P2P context lookup system. Peers are grouped based on the semantics of their local data which are extracted according to a set of schemas and are self-organized as a semantic overlay network. Context search requests are only routed to the appropriate nodes that have relevant data, reducing unnecessary query traffic and increasing the chances that the context data will be found quickly. To reduce maintenance overheads incurred by high-dimensional semantic overlay networks, we propose a onedimensional ring space to construct peers and facilitate efficient query routing. Our simulation studies demonstrate the effectiveness of our proposed routing techniques.\",\n \"Title: PAS: Prediction-based Adaptive Sleeping for Diffusion Stimulus Monitoring Sensor Networks\\nYear: 2008\\n\\nAbstract: Energy efficiency has proven to be a dominating factor for the lifetime of WSN surveillance systems. Intensive studies have been done to provide energy efficient power management mechanisms, but few directly address the issues specifically for the sensor networks that monitoring Diffusion Stimulus (DS), a scenario that is the most common for environment monitoring applications. In this paper, we present our Prediction-based Adaptive Sleeping (PAS) solution that is designed directly for DS monitoring WSNs. The proposed PAS takes into account the characteristics of the DS spreading process and optimizes the sensors' sleeping schedules accordingly. The sensors near the DS boundary stay awake to timely capture the possible arrival of stimulus while the sensors far from the DS boundary keep in the energy-saving mode. The novelty design of PAS strikes a good balance between response time and energy conservation. Our simulation results also prove that the proposed PAS can greatly reduce the energy utilizations without noticeably sacrificing the system's performance.\",\n \"Title: PAS: Prediction-based Adaptive Sleeping for Environment Monitoring in Sensor Networks\\nYear: 2007\\n\\nAbstract: Energy efficiency has proven to be an important factor dominating the working period of WSN surveillance systems. Intensive studies have been done to provide energy efficient power management mechanisms. In this paper, we present PAS, a Prediction-based Adaptive Sleeping mechanism for environment monitoring sensor networks to conserve energy. PAS focuses on the diffusion stimulus (DS) scenario, which is very common and important in the application of environment monitoring. Different with most of previous works, PAS explores the features of DS spreading process to obtain higher energy efficiency. In PAS, sensors determine their sleeping schedules based on the observed emergency of DS spreading. While sensors near the DS boundary stay awake to accurately capture the possible stimulus arrival, the far away sensors turn into sleeping mode to conserve energy. Simulation experiment shows that PAS largely reduces the energy cost without decreasing system performance.\",\n \"Title: A Pattern Mining Approach to Sensor-Based Human Activity Recognition\\nYear: 2011\\n\\nAbstract: Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing due to its potential in many applications, such as assistive living and healthcare. This task is particularly challenging because human activities are often performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved or concurrent) manner in real life. Little work has been done in addressing complex issues in such a situation. The existing models of interleaved and concurrent activities are typically learning-based. Such models lack of flexibility in real life because activities can be interleaved and performed concurrently in many different ways. In this paper, we propose a novel pattern mining approach to recognize sequential, interleaved, and concurrent activities in a unified framework. We exploit Emerging Pattern-a discriminative pattern that describes significant changes between classes of data-to identify sensor features for classifying activities. Different from existing learning-based approaches which require different training data sets for building activity models, our activity models are built upon the sequential activity trace only and can be applied to recognize both simple and complex activities. We conduct our empirical studies by collecting real-world traces, evaluating the performance of our algorithm, and comparing our algorithm with static and temporal models. Our results demonstrate that, with a time slice of 15 seconds, we achieve an accuracy of 90.96 percent for sequential activity, 88.1 percent for interleaved activity, and 82.53 percent for concurrent activity.\",\n \"Title: Managing Quality of Context in Pervasive Computing\\nYear: 2006\\n\\nAbstract: Context-awareness plays a key role in a paradigm shift from traditional desktop styled computing to emerging pervasive computing. Many context-aware systems have been built to achieve the vision of pervasive computing and alleviate the human attention bottleneck; however, these systems are far from real world applications. Quality of context is critical in reducing the gap between existing systems and real-life applications. Aiming to provide the support of quality of context, in this paper, we propose a novel quality model for context information and a context management mechanism for inconsistency resolution. We also build a prototype system to validate our proposed model and mechanism, and to assist the development of contextaware applications. Through our evaluations and case study, context-aware applications can be built with the support of quality of context.\",\n \"Title: A semantic P2P framework for building context-aware applications in multiple smart spaces\\nYear: 2007\\n\\nAbstract: Context information has emerged as an important resource to enable autonomy and flexibility of ubiquitous applications. The widespread use of context information necessitates an efficient lookup service in a wide-area network over multiple smart spaces. In this paper, we propose a context lookup framework based on a semantic peer-to-peer network to support the building of context-aware applications in multiple smart spaces. Collaborative contextaware applications that utilize different context information in multiple smart spaces can be easily built by invoking a pull or push service provided by our framework. We describe the design of our system, demonstrate the development process of context-aware applications, and report the measurements obtained from our prototype.\",\n \"Title: Minimizing inter-server communications by exploiting self-similarity in online social networks\\nYear: 2012\\n\\nAbstract: Efficiently operating on relevant data for users in large-scale online social network (OSN) systems is a challenging problem. Storage systems used by popular OSN systems often rely on key-value stores, where randomly partitioning the data of users among servers across the data centers is the defacto standard. Although by using DHTs, the random partition scheme is highly scalable for hosting a large number of users, it leads to costly inter-server communications across data centers due to the complexity of interconnection and interaction between OSN users. In this paper, we explore how to reduce the inter-server communications by retaining the simple and robust nature of OSNs. We propose a data placement solution atop OSN systems to divide users among servers according to the interaction-locality-based structure. Our approach exploits a simple, yet powerful principle of OSN interactions, self-similarity, which reveals that the inter-server communication cost is minimized under such intrinsic structure. Our algorithm avoids a significant amount of inter-server traffic as well as achieves load balance among servers across the data centers. We demonstrate the existence of self-similarity in large-scale Facebook traces including 10 million Facebook users and 24 million interaction events. We conduct comprehensive trace-driven simulations to evaluate this design exploiting the unique feature of self-similarity. Results show that our scheme significantly reduces the traffic and latency of the existing schemes.\",\n \"Title: Recognizing multi-user activities using wearable sensors in a smart home\\nYear: 2011\\n\\nAbstract: The advances of wearable sensors and wireless networks offer many opportunities to recognize human activities from sensor readings in pervasive computing. Existing work so far focuses mainly on recognizing activities of a single user in a home environment. However, there are typically multiple inhabitants in a real home and they often perform activities together. In this paper, we investigate the problem of recognizing multi-user activities using wearable sensors in a home setting. We develop a multi-modal, wearable sensor platform to collect sensor data for multiple users, and study two temporal probabilistic models-Coupled Hidden Markov Model (CHMM) and Factorial Conditional Random Field (FCRF)-to model interacting processes in a sensor-based, multi-user scenario. We conduct a real-world trace collection done by two subjects over two weeks, and evaluate these two models through our experimental studies. Our experimental results show that we achieve an accuracy of 96.41% with CHMM and an accuracy of 87.93% with FCRF, respectively, for recognizing multi-user activities.\",\n \"Title: Complete Bipartite Anonymity: Confusing Anonymous Mobility Traces for Location Privacy\\nYear: 2012\\n\\nAbstract: Using mobile devices, people can easily obtain their location information, and access a wide range of location based services (LBSs). Many existing LBSs rely in accurate, continuous, and real-time streams of location information to provide quality of service guarantees. In this case, even if an user accesses LBSs anonymously, the identity of the user can still be revealed by analyzing the mobility trace. To protect user privacy, existing work sacrifice the quality of LBSs by degrading spatial and temporal accuracy. To achieve a better tradeoff between user privacy and the quality of service, we present a novel approach, Complete Bipartite Anonymity (CBA), to confuse the paths of nearby users by connecting different users' real traces with fake ones. CBA protects user privacy as users become indistinguishable after their paths are confused, the quality of service of LBSs is also guaranteed since users are able to report their accurate locations. We evaluate CBA by comparing the system and privacy performance with existing techniques such as Path Confusion or Query Obfuscation using a real-world data set, the results show that our scheme increases the chance for a user joining an anonymity group by 10 times in low user density areas, and reduces the resources consumed by about 90% for achieving the same anonymity degree.\",\n \"Title: An unsupervised approach to activity recognition and segmentation based on object-use fingerprints\\nYear: 2010\\n\\nAbstract: Human activity recognition is an important task which has many potential applications. In recent years, researchers from pervasive computing are interested in deploying on-body sensors to collect observations and applying machine learning techniques to model and recognize activities. Supervised machine learning techniques typically require an appropriate training process in which training data need to be labeled manually. In this paper, we propose an unsupervised approach based on object-use fingerprints to recognize activities without human labeling. We show how to build our activity models based on object-use fingerprints, which are sets of contrast patterns describing significant differences of object use between any two activity classes. We then propose a fingerprint-based algorithm to recognize activities. We also propose two heuristic algorithms based on object relevance to segment a trace and detect the boundary of any two adjacent activities. We develop a wearable RFID system and conduct a real-world trace collection done by seven volunteers in a smart home over a period of 2 weeks. We conduct comprehensive experimental evaluations and comparison study. The results show that our recognition algorithm achieves a precision of 91.4% and a recall 92.8%, and the segmentation algorithm achieves an accuracy of 93.1% on the dataset we collected.\",\n \"Title: Peer-to-Peer Context Reasoning in Pervasive Computing Environments\\nYear: 2008\\n\\nAbstract: In this paper, we propose a peer-to-peer approach to derive and obtain additional context data from low-level context data that may be spread over multiple domains in pervasive computing environments. In this system, peers are self-organized into a semantic peer-to-peer network as the underlying communication substrate. Context reasoning is done in a distributed fashion through logical reasoning according to a set of user-defined rules. Both pull and push services are supported in the system to enable message exchange during the reasoning process. We present our design concepts, and prove the effectiveness of our system through the prototype evaluation.\",\n \"Title: Ontology Modeling of a Dynamic Protocol Stack\\nYear: 2006\\n\\nAbstract: This paper proposes a formal approach for protocol information modeling and validation leveraging on ontological techniques. We demonstrate the advantage of our approach through prototyping a protocol management system for representation of communication protocols and composition of protocol stacks. The prototype has an ontology-based model to describe meta-data of protocols and protocol stacks in a systematic way. Consequently, the retrieval of protocols and the validation of protocol stacks are realized by corresponding operations on the ontology model. Owing to the better expressiveness of RDFS, the ontology model can describe protocols with higher fidelity. Our experimental results show that the ontology-based protocol management system is operable and provides expressive knowledge modeling without compromising the performance\",\n \"Title: Application Based Distance Measurement for Context Retrieval in Ubiquitous Computing\\nYear: 2007\\n\\nAbstract: Building large-scale smart environments is one of the long-term goals of ubiquitous computing. The widespread of context information in such environments necessitates an effective context retrieval mechanism. This paper proposes a novel context retrieval method based on applications' query patterns. We propose high dimensional vector to model contexts from applications' perspective, and apply the normalized inner product of high dimensional vectors to measure context distance. Contexts with similar query patterns are clustered into the same group. To improve the performance of context retrieval, we build distributed indices on each node to speed up a local search, and create shortcuts based on clustering results to facilitate query routing. We show how our proposed methods can be applied to existing context retrieval mechanisms. Our experimental results show that our method can significantly reduce retrieval cost.\",\n \"Title: Supporting pervasive computing applications with active context fusion and semantic context delivery\\nYear: 2010\\n\\nAbstract: Future pervasive computing applications are envisioned to adapt the applications' behaviors by utilizing various contexts of an environment and its users. Such context information may often be ambiguous and also heterogeneous, which make the delivery of unambiguous context information to real applications extremely challenging. Thus, a significant challenge facing the development of realistic and deployable context-aware services for pervasive computing applications is the ability to deal with these ambiguous contexts. In this paper, we propose a resource optimized quality assured context mediation framework based on efficient context-aware data fusion and semantic-based context delivery. In this framework, contexts are first fused by an active fusion technique based on Dynamic Bayesian Networks and ontology, and further mediated using a composable ontological rule-based model with the involvement of users or application developers. The fused context data are then organized into an ontology-based semantic network together with the associated ontologies in order to facilitate efficient context delivery. Experimental results using SunSPOT and other sensors demonstrate the promise of this approach.\",\n \"Title: Season: Shelving Interference And Joint Identification In Large-Scale Rfid Systems\\nYear: 2011\\n\\nAbstract: Prior work on anti-collision for Radio Frequency IDentification (RFID) systems usually schedule adjacent readers to exclusively interrogate tags for avoiding reader collisions. Although such a pattern can effectively deal with collisions, the lack of readers' collaboration wastes numerous time on the scheduling process and dramatically degrades the throughput of identification. Even worse, the tags within the overlapped interrogation regions of adjacent readers (termed as contentious tags), even if the number of such tags is very small, introduce a significant delay to the identification process. In this paper, we propose a new strategy for collision resolution. First, we shelve the collisions and identify the tags that do not involve reader collisions. Second, we perform a joint identification, in which adjacent readers collaboratively identify the contentious tags. In particular, we find that neighboring readers can cause a new type of collisions, cross-tag-collision, which may impede the joint identification. We propose a protocol stack, named Season, to undertake the tasks in two phases and solve the cross-tag-collision. We conduct extensive simulations and preliminary implementation to demonstrate the efficiency of our scheme. T he results show that our scheme can achieve above 6 times improvement on the identification throughput in a large-scale dense reader environment.\",\n \"Title: Jointcache: Collaborative Path Confusion Through Lightweight P2p Communication\\nYear: 2013\\n\\nAbstract: The emerging location based services have brought forth a privacy risk where users expose their location information. Most of the existing solutions preserve location privacy relying on a trusted anonymizer, which may not exist in real situation. Several peer-to-peer approaches are proposed, but these techniques require large-scale data storage and communication, and they cannot provide up-to-date context information to users. To address this issue, we propose a novel method JointCache to preserve location privacy by confusing nearby users' paths. Using this technique, users do not need to buffer context information of a large area and they only need to transfer little information to each other for cooperation. We use simulation to compare JointCache with the traditional path confusion, the results show that our technique solves the same place same time problem, and achieves higher anonymity even in a low user density situation.\",\n \"Title: Data Management for Context-Aware Computing\\nYear: 2008\\n\\nAbstract: We envisage future context-aware applications will dynamically adapt their behaviors to various context data from sources in wide-area networks, such as the Internet. Facing the changing context and the sheer number of context sources, a data management system that supports effective source organization and efficient data lookup becomes crucial to the easy development of context-aware applications. In this paper, we propose the design of a new context data management system that is equipped with query processing capabilities. We encapsulate the context sources into physical spaces belonging to different context spaces and organize them as peers in semantic overlay networks. Initial evaluation results of an experimental system prototype demonstrate the effectiveness of our design.\",\n \"Title: A Loose-Pattern Process Approach to Clustering Fuzzy Data Sets\\nYear: 1985\\n\\nAbstract: A loose-pattern process approach to clustering sets consists of three main computations: loose-pattern reject option, tight-pattern classifcation, and loose-pattern assigning classes. The loose-pattern rejection is implemented using a rule based on q nearest neighbors of each point. Two clustering methods, GLC and OUPIC, are introduced as tight-pattern clustering techniques. The decisions of loose-pattern assigning classes are related to a heuristic membership function. The function and experiments with one set is discussed.\",\n \"Title: A service-oriented middleware for building context-aware services\\nYear: 2005\\n\\nAbstract: The advancement of wireless networks and mobile computing necessitates more advanced applications and services to be built with context-awareness enabled and adaptability to their changing contexts. Today, building context-aware services is a complex task due to the lack of an adequate infrastructure support in pervasive computing environments. In this article, we propose a Service-Oriented Context-Aware Middleware (SOCAM) architecture for the building and rapid prototyping of context-aware services. It provides efficient support for acquiring, discovering, interpreting and accessing various contexts to build context-aware services. We also propose a formal context model based on ontology using Web Ontology Language to address issues including semantic representation, context reasoning, context classification and dependency. We describe our context model and the middleware architecture, and present a performance study for our prototype in a smart home environment.\",\n \"Title: Object relevance weight pattern mining for activity recognition and segmentation\\nYear: 2010\\n\\nAbstract: Monitoring daily activities of a person has many potential benefits in pervasive computing. These include providing proactive support for the elderly and monitoring anomalous behaviors. A typical approach in existing research on activity detection is to construct sequence-based models of low-level activity features based on the order of object usage. However, these models have poor accuracy, require many parameters to estimate, and demand excessive computational effort. Many other supervised learning approaches have been proposed but they all suffer from poor scalability due to the manual labeling involved in the training process. In this paper, we simplify the activity modeling process by relying on the relevance weights of objects as the basis of activity discrimination rather than on sequence information. For each activity, we mine the web to extract the most relevant objects according to their normalized usage frequency. We develop a KeyExtract algorithm for activity recognition and two algorithms, MaxGap and MaxGain, for activity segmentation with linear time complexities. Simulation results indicate that our proposed algorithms achieve high accuracy in the presence of different noise levels indicating their good potential in real-world deployment.\",\n \"Title: An architecture for flexible service discovery in OCTOPUS\\nYear: 2003\\n\\nAbstract: Service discovery has been drawing much attention from researchers and practitioners. The existing service discovery systems, like SLP, Jini, UPnP and Salutation, provide basic infrastructures where services can announce their presence and users can locate these services across the network. However there are several key issues which are partially solved or have not been well addressed - such as scalability, availability, dynamics and support for multiple matching mechanisms. In this paper, we propose a design for a service locating manager (SLM) system which addresses some of these issues. The SLM system adopts a dynamic hierarchical tree structure and service aggregation for scalability, availability and dynamics, and introduces multiple matching mechanisms which contain an attribute-based and a semantic matching engine. It provides a scalable, distributed, dynamic and robust solution to establish a flexible service discovery architecture. We describe our concepts, architecture and implementation, and present a performance study for our prototype.\",\n \"Title: Towards a flexible service discovery\\nYear: 2005\\n\\nAbstract: To support m-commerce applications, a service discovery mechanism where services can announce their presence and mobile users can locate these services is needed. Service discovery in dynamic mobile environments poses many challenges such as service providers may create and delete services or servers anytime; mobile services may be deployed in various forms, etc. In this paper, we propose a design for a Service Locating Service (SLS), which addresses some of these issues to provide a flexible service discovery mechanism for m-commerce applications. In our architecture, we adopt a dynamic tree structure for organizing SLS servers to meet the dynamic requirements of services and servers; we introduce service aggregation for fast locating; and we also propose multiple service matching mechanisms, which contain an attribute matching engine and a semantic matching engine for different service interfaces. We describe our concepts, architecture and implementation, and present a performance study for our prototype.\",\n \"Title: Context-aware middleware for pervasive elderly homecare\\nYear: 2009\\n\\nAbstract: The growing aging population faces a number of challenges, including rising medical cost, inadequate number of medical doctors and healthcare professionals, as well as higher incidence of misdiagnosis. There is an increasing demand for a better healthcare support for the elderly and one promising solution is the development of a context-aware middleware infrastructure for pervasive health/wellness-care. This allows the accurate and timely delivery of health/medical information among the patients, doctors and healthcare workers through a widespread deployment of wireless sensor networks and mobile devices. In this paper, we present our design and implementation of such a Context-Aware Middleware for Pervasive Homecare (CAMPH). The middleware offers several key-enabling system services that consist of P2P-based context query processing, context reasoning for activity recognition and context-aware service management. It can be used to support the development and deployment of various homecare services for the elderly such as patient monitoring, location-based emergency response, anomalous daily activity detection, pervasive access to medical data and social networking. We have developed a prototype of the middleware and demonstrated the concept of providing a continuing-care to an elderly with the collaborative interactions spanning multiple physical spaces: person, home, office and clinic. The results of the prototype show that our middleware approach achieves good efficiency of context query processing and good accuracy of activity recognition.\",\n \"Title: A two-tier semantic overlay network for P2P search\\nYear: 2007\\n\\nAbstract: This paper proposes a two-tier semantic peer-to-peer network that facilitates efficient search for context information in wide-area networks. Context data with the same semantics are grouped together into a one-dimensional semantic ring space in the upper-tier network. This is achieved by applying an ontology- based semantic clustering technique and dedicating part of node identifiers to correspond to their data semantics. In the lower-tier network, peers in each semantic cluster are organized as Chord identifier space. Thus, all the nodes in the same semantic cluster know which node is responsible for storing context data triples they are looking for, and context queries can be efficiently routed to those nodes. Through the simulation studies, we demonstrate the effectiveness of our proposed scheme.\",\n \"Title: Privacy Protection in Participatory Sensing Applications Requiring Fine-Grained Locations\\nYear: 2010\\n\\nAbstract: The emerging participatory sensing applications have brought a privacy risk where users expose their location information. Most of the existing solutions preserve location privacy by generalizing a precise user location to a coarse-grained location, and hence they cannot be applied in those applications requiring fine-grained location information. To address this issue, in this paper we propose a novel method to preserve location privacy by anonymizing coarse-grained locations and retaining fine-grained locations using Attribute Based Encryption (ABE). In addition, we do not assume the service provider is an trustworthy entity, making our solution more feasible to practical applications. We present and analyze our security model, and evaluate the performance and scalability of our system.\",\n \"Title: Sensor-Based Human Activity Recognition in a Multi-user Scenario\\nYear: 2009\\n\\nAbstract: Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activities from sensor readings in a smart home environment. We develop a multimodal sensing platform and present a theoretical framework to recognize both single-user and multi-user activities. We conduct our trace collection done in a smart home, and evaluate our framework through experimental studies. Our experimental result shows that we achieve an average accuracy of 85.46% with CHMMs.\",\n \"Title: Secure RFID Identification and Authentication with Triggered Hash Chain Variants\\nYear: 2008\\n\\nAbstract: In this paper, we propose two RFID identification and authentication schemes based on the previously proposed Triggered Hash Chain scheme by Henrici and Muller [1]. The schemes are designed to mitigate the shortcomings observed in the Triggered Hash Chain scheme and to ensure privacy preserving identification, tag-reader mutual authentication, as well as forward-privacy in the case of RFID tags that have been compromised. The first scheme uses a challenge-response mechanism to defend against an obvious weakness of the Triggered Hash Chain scheme. The second scheme uses an authenticated monotonic counter to defend against a session linking attack that the first scheme is vulnerable to. We compare the level of security offered by our proposed schemes against other previous schemes and find that the schemes perform well, while keeping within reasonable overheads in terms of computational, storage and communication requirements.\",\n \"Title: Audio-on-demand over wireless sensor networks\\nYear: 2012\\n\\nAbstract: Audio represents one of the most appealing yet least exploited modalities in wireless sensor networks, due to the potentially extremely large data volumes and limited wireless capacity. Therefore, how to effectively collect audio sensing information remains a challenging problem. In this paper, we propose a new paradigm of audio information collection based on the concept of audio-on-demand. We consider a sink-free environment targeting for disaster management, where audio chunks are stored inside the network for retrieval. The difficulty is to guarantee a high search success rate without infrastructure support. To solve the problem, we design a novel replication algorithm that deploys an optimal number of O([EQUATION]) replicas across the sensor network. We prove the optimality of the energy consumption of the algorithm, and use real test-bed experiments and extensive simulations to evaluate the performance and efficiency of our design. The experimental results show that our design can provide satisfactory quality of audio-on-demand service with short startup latency and slight playback jitter. Extensive simulation results show that this design achieves a search success rate of 98% while reducing the search energy consumption by an order of magnitude compared with existing schemes.\",\n \"Title: Scheduling Divisible Loads from Multiple Input Sources in MapReduce\\nYear: 2013\\n\\nAbstract: MapReduce is a programming model for data-intensive applications. The performance of MapReduce is greatly affected by the scheduling efficiency, which needs to consider both the parallelism of processing and overhead of data transmission. Lots of researches only focus on optimizing scheduling mechanisms or data storage strategies to reduce the overhead of data transmission in big data applications. The analysis of MapReduce carried out doesn't consider the overhead of data transmission sufficiently. This paper proposes a divisible load scheduling model to describe the data transmission and task execution in MapReduce. The task allocation and corresponding data transmission are abstracted as scheduling divisible loads from multiple input sources. With the established model, the optimal distribution of input data and scheduling of loads in map phase are presented with linear programming. The performance of map phase is evaluated and analyzed under different environments.\",\n \"Title: Energy balanced data collection in Wireless Sensor Networks\\nYear: 2012\\n\\nAbstract: In this paper, we investigate the energy balanced data collection problem in WSNs, aiming to balance the energy consumption among all the sensor nodes in the data propagation process. Energy balanced data collection can potentially save energy consumption and prolong the network lifetime, and hence it has many practical implications for WSN design and deployment. The traditional hop-by-hop transmission model allows a sensor node to propagate its packets in a hop-by-hop manner towards the sink, resulting in poor energy balance for the entire network. To address the problem, we apply a slice based energy model, and divide the energy balanced data collection problem into inter- and intra-slice energy balance problems. We then propose a novel Inter-slice Mixed Transmission strategy and an Intra-slice Forwarding technique to address each of the problems. Finally, we design an Energy-balanced Transmission Protocol (ETP) to combine both techniques to achieve total energy balance in data collection. Through extensive simulation studies, we demonstrate that, while ETP achieves energy balanced data collection, the network lifespan is increased by 10 times and the network delay is decreased by more than 70% compared to the hop-by-hop transmission in a general square area WSN.\",\n \"Title: Schema matching for context-aware computing\\nYear: 2008\\n\\nAbstract: Context-aware computing is a key paradigm of ubiquitous computing in which applications automatically adapt their operations to dynamic context data from multiple sources. Managing a number of distributed sources, a middleware that facilitates the development of context-aware applications must provide a uniform view of all these sources to the applications. Local schemas of context data from individual sources need to be matched into a set of global schemas in the middleware, upon which applications can issue context queries to acquire data. In this paper, we study this problem of schema matching for context-aware computing. We propose a multi-criteria algorithm to determine candidate attribute matches between two schemas. The algorithm adaptively adjusts the priorities of different criteria based on previous matching results to improve the efficiency and accuracy of succeeding operations. We further develop an algorithm to categorize a new local schema into one of the global schemas whenever possible via a shared attribute dictionary. Our results based on schemas from real-world websites demonstrate the good matching accuracy achieved by our algorithms.\",\n \"Title: A peer-to-peer overlay for context information search.\\nYear: 2005\\n\\nAbstract: The widespread use of context information necessitates an efficient wide-area lookup service in pervasive computing. In this paper, we present Semantic Context Space (SCS), a semantic overlay network that facilitates efficient search for context information in distributed environments. Peers in SCS are grouped based on the semantics of their local data and self-organized into a one-dimensional ring space. Context search requests are only routed to the appropriate semantic clusters, reducing unnecessary search cost on peers that have irrelevant context data, and increasing the chances that the context data will be found quickly. By exploring parallelism in a semantic cluster, search request can be found quickly. Our simulation studies demonstrate the effectiveness of SCS.\",\n \"Title: Ontology Based Context Modeling and Reasoning using OWL\\nYear: 2004\\n\\nAbstract: Here we propose an OWL encoded context ontology (CONON) for modeling context in pervasive computing environments, and for supporting logic-based context reasoning. CONON provides an upper context ontology that captures general concepts about basic context, and also provides extensibility for adding domain-specific ontology in a hierarchical manner. Based on this context ontology, we have studied the use of logic reasoning to check the consistency of context information, and to reason over low-level, explicit context to derive high-level, implicit context. By giving a performance study for our prototype, we quantitatively evaluate the feasibility of logic based context reasoning for nontime-critical applications in pervasive computing environments, where we always have to deal carefully with the limitation of computational resources.\",\n \"Title: Research On The Evaluation Index System And Method For Intelligent Performance Of Smart Space Based On Internet Of Things Technology\\nYear: 2011\\n\\nAbstract: This article firstly defines the smart living space and it's six application aspect which is widely used under the Internet of Things, subdivide and analysis the researches home and abroad when study the relative issues about smart living space assess theory system of the \\\"Internet of Things\\\" technology, concluding the grey multi-levels methods on the basis of \\\"Internet of Things\\\" technology assess theory system at last. Then specify the evaluation of the intelligence storage.\",\n \"Title: Recognizing Multiuser Activities Using Wireless Body Sensor Networks\\nYear: 2011\\n\\nAbstract: The advances of wireless networking and sensor technology open up an interesting opportunity to infer human activities in a smart home environment. Existing work in this paradigm focuses mainly on recognizing activities of single user. In this work, we focus on the fundamental problem of recognizing activities of multiple users using a wireless body sensor network, and propose a scalable pattern mining approach to recognize both single- and multiuser activities in a unified framework. We exploit Emerging Pattern-a discriminative knowledge pattern which describes significant changes among activity classes of data-for building activity models and design a scalable, noise-resistant, Emerging Pattern-based Multiuser Activity Recognizer (epMAR) to recognize both single- and multiuser activities. We develop a multimodal, wireless body sensor network for collecting real-world traces in a smart home environment, and conduct comprehensive empirical studies to evaluate our system. Results show that epMAR outperforms existing schemes in terms of accuracy, scalability, and robustness.\",\n \"Title: An Ontology- Based P2p Network For Semantic Search\\nYear: 2009\\n\\nAbstract: This article presents an ontology-based peer-to-peer network that facilitates efficient search for data in wide-area networks. Data with the same semantics are grouped together into one-dimensional semantic ring space in the upper-tier network. This is achieved by applying an ontology-based semantic clustering technique and dedicating part of node identifiers to correspond to their data semantics. In the lower-tier network, peers in each semantic cluster are organized as Chord identifier space. Thus, all the nodes in the same semantic cluster know which node is responsible for storing context data triples they are looking for, and context queries can be efficiently routed to those nodes. Through the simulation studies, the authors demonstrate the effectiveness of our proposed scheme.\",\n \"Title: KQStream: Kindred-Based QoS-Aware Live Media Streaming in Heterogeneous Peer-to-Peer Environments\\nYear: 2007\\n\\nAbstract: This paper presents the design of a Kindred-based QoS-aware Live Media Streaming (KQStream) system in heterogeneous peer-to-peer environments. Motivated by the Multiple Description Coding technique, we propose a novel kindred-based approach to construct a peer-to-peer overlay topology. To achieve QoS-awareness during the streaming process, we propose a dynamic QoS-aware regulating technique which takes into account of user's preference, network bandwidth and buffer size. With the integration of the two proposed techniques, KQStream provides more flexibility in heterogeneous peer-to-peer environments and configurable playback with dynamic QoS-aware regulating.\",\n \"Title: Real-time activity recognition inwireless body sensor networks: From simple gestures to complex activities\\nYear: 2010\\n\\nAbstract: Real-time activity recognition using body sensor networks is an important and challenging task and it has many potential applications. In this paper, we propose a realtime, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we first use a fast, lightweight template matching algorithm to detect gestures at the sensor node level, and then use a discriminative pattern based real-time algorithm to recognize high-level activities at the portable device level. We evaluate our algorithms over a real-world dataset. The results show that the proposed system not only achieves good performance (an average precision of 94.9%, an average recall of 82.5%, and an average real-time delay of 5.7 seconds), but also significantly reduces the network communication cost by 60.2%. © 2010 IEEE.\",\n \"Title: Complete Bipartite Anonymity for Location Privacy.\\nYear: 2014\\n\\nAbstract: Users are vulnerable to privacy risks when providing their location information to location-based services (LBS). Existing work sacrifices the quality of LBS by degrading spatial and temporal accuracy for ensuring user privacy. In this paper, we propose a novel approach, Complete Bipartite Anonymity (CBA), aiming to achieve both user privacy and quality of service. The theoretical basis of CBA is that: if the bipartite graph of k nearby users’ paths can be transformed into a complete bipartite graph, then these users achieve k-anonymity since the set of “end points connecting to a specific start point in a graph” is an equivalence class. To achieve CBA, we design a Collaborative Path Confusion (CPC) protocol which enables nearby users to discover and authenticate each other without knowing their real identities or accurate locations, predict the encounter location using users’ moving pattern information, and generate fake traces obfuscating the real ones. We evaluate CBA using a real-world dataset, and compare its privacy performance with existing path confusion approach. The results show that CBA enhances location privacy by increasing the chance for a user confusing his/her path with others by 4 to 16 times in low user density areas. We also demonstrate that CBA is secure under the trace identification attack.\",\n \"Title: MaLoc: a practical magnetic fingerprinting approach to indoor localization using smartphones\\nYear: 2014\\n\\nAbstract: Using magnetic field data as fingerprints for localization in indoor environment has become popular in recent years. Particle filter is often used to improve accuracy. However, most of existing particle filter based approaches either are heavily affected by motion estimation errors, which makes the system unreliable, or impose strong restrictions on smartphone such as fixed phone orientation, which is not practical for real-life use. In this paper, we present an indoor localization system named MaLoc, built on our proposed augmented particle filter. We create several innovations on the motion model, the measurement model and the resampling model to enhance the traditional particle filter. To minimize errors in motion estimation and improve the robustness of particle filter, we augment the particle filter with a dynamic step length estimation algorithm and a heuristic particle resampling algorithm. We use a hybrid measurement model which combines a new magnetic fingerprinting model and the existing magnitude fingerprinting model to improve the system performance and avoid calibrating different smartphone magnetometers. In addition, we present a novel localization quality estimation method and a localization failure detection method to address the \\\\\\\"Kidnapped Robot Problem\\\\\\\" and improve the overall usability. Our experimental studies show that MaLoc achieves a localization accuracy of 1~2.8m on average in a large building.\",\n \"Title: B-Loc: Scalable Floor Localization Using Barometer on Smartphone\\nYear: 2014\\n\\nAbstract: Traditional fingerprint based localization techniques mainly rely on infrastructure support such as GSM and Wi-Fi. They require war-driving which is both time-consuming and labor-intensive. With recent advances of smartphone sensors, sensor-assisted localization techniques are emerging. However, they often need user-specific training and more power intensive sensing, resulting in infeasible solutions for real deployment. In this paper, we present B-Loc, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. It makes use of the barometer on smartphone only. B-Loc does not rely on any Wi-Fi infrastructure and requires neither war-driving nor prior knowledge of the buildings. Leveraging on crowd sourcing, B-Loc builds the barometer fingerprint map which contains the barometric pressure value for each floor level to locate users' floor levels. We conduct both simulation and field studies to demonstrate the accuracy, scalability, and robustness of B-Loc. Our field study in a 10-floor building shows that B-Loc achieves an accuracy of over 98%.\",\n \"Title: A Wearable RFID System for Real-Time Activity Recognition Using Radio Patterns.\\nYear: 2013\\n\\nAbstract: Much work have been done in activity recognition using wearable sensors organized in a body sensor network. The quality and communication reliability of the sensor data much affects the system performance. Recent studies show the potential of using RFID radio information instead of sensor data for activity recognition. This approach has the advantages of low cost and high reliability. Radio-based recognition method is also amiable to packet loss and has the advantages including MAC layer simplicity and low transmission power level. In this paper, we present a novel wearable Radio Frequency Identification (RFID) system using passive tags which are smaller and more cost-effective to recognize human activities in real-time. We exploit RFID radio patterns and extract both spatial and temporal features to characterize various activities. We also address two issues - the false negative issue of tag readings and tag/antenna calibration, and design a fast online recognition system. We develop a prototype system which consists of a wearable RFID system and a smartphone to demonstrate the working principles, and conduct experimental studies with four subjects over two weeks. The results show that our system achieves a high recognition accuracy of 93.6% with a latency of 5 s.\",\n \"Title: F-Loc: Floor localization via crowdsourcing\\nYear: 2014\\n\\nAbstract: Traditional fingerprint based localization techniques mainly rely on infrastructure support such as GSM, Wi-Fi or GPS. They work by war-driving the entire indoor spaces which is both time-consuming and labor-intensive. With recent advances of smartphone and sensing technologies, sensor-assisted localization techniques leveraging on mobile phone sensing are emerging. However, sensors are inherently noisy, making this technique challenging for real deployment. In this paper, we present F-Loc, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. It does not need to war-drive the entire building. Leveraging on crowdsourcing and mobile phone sensing, we collect users' Wi-Fi traces and accelerometer readings. Through advanced clustering and cluster manipulating techniques, we are able to build the Wi-Fi map of the entire building, which can then be used for floor localization. We conduct both simulation and field studies to demonstrate the accuracy, scalability, and robustness of F-Loc. Our field study in a 10-floor building shows that F-Loc achieves an accuracy of over 98%.\",\n \"Title: A level set method for structural shape and topology optimization using Radial Basis Function\\nYear: 2014\\n\\nAbstract: In order to address the efficient issue of the topological shape optimization problem, this paper presents a parametric level set method for the problem of continuum structures by using the Radial Basis Functions (RBFs). The level set-based method is introduced to implicitly represent the free boundary of a structure. To evolve the level set front, the Wu's compactly supported radial basis function (CSRBF) with C4 smoothness is applied. Then, the Hamilton-Jacobi partial differential equation (PDE) is transformed into a relatively easier ordinary differential equation (ODE). Furthermore, a self-adaptive moving limit scheme is incorporated into the optimality criteria to achieve a better optimal result. Finally, the numerical ex-ample is provided to show the effectiveness of the proposed method.\",\n \"Title: Crowdsourced smartphone sensing for localization in metro trains\\nYear: 2014\\n\\nAbstract: Traditional fingerprint based localization techniques mainly rely on infrastructure support such as RFID, Wi-Fi or GPS. They operate by war-driving the entire space which is both time-consuming and labor-intensive. In this paper, we present M-Loc, a novel infrastructure-free localization system to locate mobile users in a metro line. It does not rely on any Wi-Fi infrastructure, and does not need to war-drive the metro line. Leveraging crowdsourcing, we collect accelerometer, magnetometer and barometer readings on smartphones, and analyze these sensor data to extract patterns. Through advanced data manipulating techniques, we build the pattern map for the entire metro line, which can then be used for localization. We conduct field studies to demonstrate the accuracy, scalability, and robustness of M-Loc. The results of our field studies in 3 metro lines with 55 stations show that M-Loc achieves an accuracy of 93% when travelling 3 stations, 98% when travelling 5 stations.\",\n \"Title: Exploiting link correlation for core-based dissemination in wireless sensor networks\\nYear: 2014\\n\\nAbstract: Bulk data dissemination is a basic building block for enabling software update and reprogramming in wireless sensor networks. The recent structure based approach looks promising for efficient dissemination since it facilitates transmission and sleep scheduling. However, a number of limitations exist in existing structured protocols. In this paper, we propose a correlated core based solution for efficient bulk data dissemination in wireless sensor networks. We propose an efficient backbone node selection algorithm to construct the core structure by exploiting link correlation. We also design a novel negotiation mechanism which greatly reduces the control message overhead as compared to the existing structured protocols. We conduct both simulation and testbed experiments, and the results show that our proposed solution outperforms the state-of-the-art in terms of both the number of transmissions and the completion time.\",\n \"Title: Chemotaxis and Quorum Sensing Inspired Device Interaction Supporting Social Networking\\nYear: 2007\\n\\nAbstract: Conference and social events provides an opportunity for people to interact and develop formal contacts with various groups of individuals. In this paper, we propose an efficient interaction mechanism in a pervasive computing environment that provide recommendation to users of suitable locations within a conference or expo hall to meet and interact with individuals of similar interests. The proposed solution is based on evaluation of context information to deduce each user's interests as well as bioinspired self-organisation mechanism to direct users towards appropriate locations. Simulation results have also been provided to validate our proposed solution.\",\n \"Title: Minimizing inter-server communications by exploiting self-similarity in online social networks\\nYear: 2016\\n\\nAbstract: Efficiently operating on relevant data for users in large-scale online social network (OSN) systems is a challenging problem. Storage systems used by popular OSN systems often rely on key-value stores, where randomly partitioning the data of users among servers across the data centers is the defacto standard. Although by using DHTs, the random partition scheme is highly scalable for hosting a large number of users, it leads to costly inter-server communications across data centers due to the complexity of interconnection and interaction between OSN users. In this paper, we explore how to reduce the inter-server communications by retaining the simple and robust nature of OSNs. We propose a data placement solution atop OSN systems to divide users among servers according to the interaction-locality-based structure. Our approach exploits a simple, yet powerful principle of OSN interactions, self-similarity, which reveals that the inter-server communication cost is minimized under such intrinsic structure. Our algorithm avoids a significant amount of inter-server traffic as well as achieves load balance among servers across the data centers. We demonstrate the existence of self-similarity in large-scale Facebook traces including 10 million Facebook users and 24 million interaction events. We conduct comprehensive trace-driven simulations to evaluate this design exploiting the unique feature of self-similarity. Results show that our scheme significantly reduces the traffic and latency of the existing schemes.\",\n \"Title: Infrastructure-Free Floor Localization Through Crowdsourcing.\\nYear: 2015\\n\\nAbstract: Mobile phone localization plays a key role in the fast-growing location-based applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, Wi-Fi or GPS. In this paper, we present FTrack, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. FTrack uses the mobile phone’s sensors only without any infrastructure support. It does not require any prior knowledge of the building such as floor height or floor levels. Through crowdsourcing, FTrack builds a mapping table which contains the magnetic field signature of users taking the elevator/escalator or walking on the stairs between any two floors. The table can then be used for mobile users to pinpoint their current floor levels. We conduct both simulation and field studies to demonstrate the efficiency, scalability and robustness of FTrack. Our field trial shows that FTrack achieves an accuracy of over 96% in three different buildings.\",\n \"Title: RF-Care: Device-Free Posture Recognition for Elderly People Using A Passive RFID Tag Array.\\nYear: 2015\\n\\nAbstract: Activity recognition is a fundamental research topic for a wide range of important applications such as fall detection for elderly people. Existing techniques mainly rely on wearable sensors, which may not be reliable and practical in real-world situations since people often forget to wear these sensors. For this reason, device-free activity recognition has gained the popularity in recent years. In this paper, we propose an RFID (radio frequency identification) based, device-free posture recognition system. More specifically, we analyze Received Signal Strength Indicator (RSSI) signal patterns from an RFID tag array, and systematically examine the impact of tag configuration on system performance. On top of selected optimal subset of tags, we study the challenges on posture recognition. Apart from exploring posture classification, we specially propose to infer posture transitions via Dirichlet Process Gaussian Mixture Model (DPGMM) based Hidden Markov Model (HMM), which effectively captures the nature of uncertainty caused by signal strength varieties during posture transitions. We run a pilot study to evaluate our system with 12 orientation-sensitive postures and a series of posture change sequences. We conduct extensive experiments in both lab and real-life home environments. The results demonstrate that our system achieves high accuracy in both environments, which holds the potential to support assisted living of elderly people.\",\n \"Title: Supporting Serendipitous Social Interaction Using Human Mobility Prediction\\nYear: 2015\\n\\nAbstract: Leveraging the regularities of people's trajectories, mobility prediction can help forecast social interaction opportunities. In this paper, in order to facilitate real-world social interaction, we aim to predict “serendipitous” social interactions, which are defined as unplanned encounters and interaction opportunities and regarded as emerging social interactions. We collected GPS trajectory data...\",\n \"Title: CoCo+: Exploiting correlated core for energy efficient dissemination in wireless sensor networks.\\nYear: 2016\\n\\nAbstract: Bulk data dissemination is a basic building block for enabling a variety of applications in wireless sensor networks such as software update, network bug fixing, surveillance video distribution, etc. The recent structure based approach looks promising for efficient dissemination since it facilitates transmission and sleep scheduling. However, a number of limitations exist in existing structured protocols. In this paper, we propose a correlated core based solution for efficient bulk data dissemination in wireless sensor networks (called CoCo+). CoCo+ has three salient features. First, CoCo+ is based on an efficient node selection algorithm for constructing the core structure by exploiting link correlation (called Correlated Core). Second, CoCo+ employs a novel consecutive transmission mechanism, which allows out-of-order transmissions and reduces propagation delay. Third, CoCo+ uses a novel lightweight negotiation mechanism that greatly reduces negotiation overhead as compared to the existing structured protocols. We implement CoCo+ with TinyOS/TelosB and conduct both simulation and testbed experiments. Results show that our proposed solution outperforms the state-of-the-art by 52.3 and 49.6% in terms of the number of transmissions and the completion time, respectively.\",\n \"Title: Who should I invite for my party?: combining user preference and influence maximization for social events\\nYear: 2015\\n\\nAbstract: The newly emerging event-based social networks (EBSNs) extend social interaction from online to offline, providing an appealing platform for people to organize and participate realworld social events. In this paper, we investigate how to select potential participants in EBSNs from an event host's point of view. We formulate the problem as mining influential and preferable invitee set, considering from two complementary aspects. The first aspect concerns users' preference with respect to the event. The second aspect is influence maximization, which aims to influence the largest number of users to participate the event. In particular, we propose a novel Credit Distribution-User Influence Preference (CD-UIP) algorithm to find the most influential and preferable followers as the invitees. We collect a real-world dataset from a popular EBSNs called \\\\\\\"Douban Events\\\\\\\", and the experimental results on the dataset demonstrate the proposed algorithm outperforms the state-of-the-art prediction methods.\",\n \"Title: Freedom: Online Activity Recognition via Dictionary-Based Sparse Representation of RFID Sensing Data\\nYear: 2015\\n\\nAbstract: Understanding and recognizing the activities performed by people is a fundamental research topic for a wide range of important applications such as fall detection of elderly people. In this paper, we present the technical details behind Freedom, a low-cost, unobtrusive system that supports independent livingof the older people. The Freedom system interprets what aperson is doing by leveraging machine learning algorithmsand radio-frequency identification (RFID) technology. To dealwith noisy, streaming, unstable RFID signals, we particularlydevelop a dictionary-based approach that can learn dictionariesfor activities using an unsupervised sparse coding algorithm. Our approach achieves efficient and robust activity recognitionvia a more compact representation of the activities. Extensiveexperiments conducted in a real-life residential environmentdemonstrate that our proposed system offers a good overallperformance (e.g., achieving over 96% accuracy in recognizing23 activities) and has the potential to be further developed tosupport the independent living of elderly people.\",\n \"Title: Modeling Link Correlation In Low-Power Wireless Networks\\nYear: 2015\\n\\nAbstract: Wireless link correlation can greatly affect the performance of wireless protocols such as flooding, and opportunistic routing. Researchers have proposed a variety of approaches to optimize existing protocols exploiting link correlation. Most existing works directly measure link correlation using packet-level transmissions and receptions. Measurement alone is insufficient because it lacks predictive power and scalability. In this paper, we present CorModel, a model for predicting link correlation in low-power wireless networks. Based on the underlying causes of link correlation, we explore four easily measurable parameters for our modeling. Besides PHY-layer parameters that previous studies have explored, we find that network-layer parameters can also have significant impact on link correlation. We validate our model and illustrate its usefulness by integrating it into existing protocols for more accurate correlation estimation. Experimental results show that our model can significantly increase the accuracy of wireless link estimation, resulting in better protocol performance.\",\n \"Title: Node Capability Modeling for Reduce Phase's Scheduling in MapReduce Environment.\\nYear: 2015\\n\\nAbstract: MapReduce is a programming model widely used in big data processing. Reduce tasks scheduling in MapReduce is a key issue which affect the performance significantly. Unfortunately, because of the complication of reduce tasks scheduling, there are no acknowledged solution in this issue. Main ideas in optimizing reduce tasks scheduling emphasizes features of computation or data locality. Although few researches tried to explore solutions with theoretical modeling, their models are oversimplified. Aiming to optimizing reduce tasks scheduling, we propose a method of modeling node's computation and communication capability uniformly based on analyzing the procedure of reduce phase theoretically. In the analysis, cost of reduce tasks in intermediate data fetching and processing are integrated. With the proposed model, the optimal load balance of reduce phase is concluded and proved. Evaluations under different environments show that load balance of reduce phase is improved significantly with the scheduling method instructed by the optimal principle.\",\n \"Title: A Novel Metric for Opportunistic Routing in Heterogenous Duty-Cycled Wireless Sensor Networks\\nYear: 2015\\n\\nAbstract: This paper investigates the suboptimal problem of existing state-of-the-art routing protocols when they are applied to heterogeneous duty-cycled wireless sensor networks (WSNs). In particular, we discover that the selected optimal routes with the least cost based their routing metric may not always lead to the least transmission cost. The key reason is that the existing routing metrics used do not sufficiently capture packet transmission cost in heterogeneous duty-cycled WSNs. To address this issue, we propose a novel routing metric, namely expected transmission cost (ETC), which efficiently captures packet transmission cost in heterogeneous duty-cycled WSNs by estimating both expected rendezvous cost and communication cost. Based on ETC, we design an opportunistic routing protocol (EoR) which is proved to select optimal routes with the least packet transmission cost. Our experimental results show that EoR outperforms the state-of-the-art protocols in terms of energy efficiency, latency, and packet delivery ratio.\",\n \"Title: L-MAC: A wake-up time self-learning MAC protocol for wireless sensor networks.\\nYear: 2016\\n\\nAbstract: This paper analyzes the trade-off issue between energy efficiency and packet delivery latency among existing duty-cycling MAC protocols in wireless sensor networks for low data-rate periodic-reporting applications. We then propose a novel and practical wake-up time self-learning MAC (L-MAC) protocol in which the key idea is to reuse beacon messages of receiver-initiated MAC protocols to enable nodes to coordinate their wakeup time with their parent nodes without incurring extra communication overhead. Based on the self-learning mechanism we propose, L-MAC builds an on-demand staggered scheduler to allow any node to forward packets continuously to the sink node. We present an analytical model, and conduct extensive simulations and experiments on Telosb sensors to show that L-MAC achieves significant higher energy efficiency compared to state-of-the-art asynchronous MAC protocols and a similar result of latency compared to synchronous MAC protocols. In particular, under QoS requirements with an upper bound value for one-hop packet delivery latency within 1 s and a lower bound value for packet delivery ratio within 95%, results show that the duty cycle of L-MAC is improved by more than 3.8 times and the end-to-end packet delivery latency of L-MAC is reduced by more than 7 times compared to those of AS-MAC and other state-of-the-art MAC protocols, respectively, in case of the packet generation interval of 1 min. L-MAC hence achieves high performance in both energy efficiency and packet delivery latency.\",\n \"Title: TagFall: Towards Unobstructive Fine-Grained Fall Detection based on UHF Passive RFID Tags.\\nYear: 2015\\n\\nAbstract: Falls are among the leading causes of hospitalization for the elderly and illness individuals. Considering that the elderly often live alone and receive only irregular visits, it is essential to develop such a system that can effectively detect a fall or abnormal activities. However, previous fall detection systems either require to wear sensors or are able to detect a fall but fail to provide fine-grained contextual information (e.g., what is the person doing before falling, falling directions). In this paper, we propose a device-free, fine-grained fall detection system based on pure passive UHF RFID tags, which not only is capable of sensing regular actions and fall events simultaneously, but also provide caregivers the contexts of fall orientations. We first augment the Angle-based Outlier Detection Method (ABOD) to classify normal actions (e.g., standing, sitting, lying and walking) and detect a fall event. Once a fall event is detected, we first segment a fix-length RSSI data stream generated by the fall and then utilize Dynamic Time Warping (DTW) based kNN to distinguish the falling direction. The experimental results demonstrate that our proposed approach can distinguish the living status before fall happening, as well as the fall orientations with a high accuracy. The experiments also show that our device-free, fine-grained fall detection system offers a good overall performance and has the potential to better support the assisted living of older people.\",\n \"Title: Towards Energy-Balanced Data Transmission For Lifetime Optimization In Wireless Sensor Networks\\nYear: 2016\\n\\nAbstract: Energy balance is a critical issue in wireless sensor networks. Several mixed data transmission (MDT) schemes have been proposed to achieve energy balance. However, most existing works are lack of theoretical study, especially understanding the relationship between network-wide energy balancing and lifetime optimization. In this paper, we conduct comprehensive theoretical analysis to the two-level based MDT scheme when applying to network-wide energy balancing, and eventually to maximize the network lifetime. We propose a novel network model, named energy balance area (EBA), and formally analyze its characteristics under the two-level based MDT scheme. To maximize the network lifetime, we convert the transmission probability allocation problem in the MDT scheme into an EBA partitioning (EBA-PT) problem, which is shown to be NPhard. We then propose a heuristic approximation algorithm to determine the optimal configuration of EBAs, which is proven in this paper to be the key for maximizing the network lifetime. In this way, we obtain a near-optimal result. Our experimental studies show that network lifetime can be further improved as compared the hop-by-hop and the two-level based MDT schemes.\",\n \"Title: City-Scale Localization with Telco Big Data\\nYear: 2016\\n\\nAbstract: It is still challenging in telecommunication (telco) industry to accurately locate mobile devices (MDs) at city-scale using the measurement report (MR) data, which measure parameters of radio signal strengths when MDs connect with base stations (BSs) in telco networks for making/receiving calls or mobile broadband (MBB) services. In this paper, we find that the widely-used location based services (LBSs) have accumulated lots of over-the-top (OTT) global positioning system (GPS) data in telco networks, which can be automatically used as training labels for learning accurate MRbased positioning systems. Benefiting from these telco big data, we deploy a context-aware coarse-to-fine regression (CCR) model in Spark/Hadoop-based telco big data platform for city-scale localization of MDs with two novel contributions. First, we design map-matching and interpolation algorithms to encode contextual information of road networks. Second, we build a two-layer regression model to capture coarse-to-fine contextual features in a short time window for improved localization performance. In our experiments, we collect 108 GPS-associated MR records in the centroid of Shanghai city with 12 × 11 square kilometers for 30 days, and measure four important properties of real-world MR data related to localization errors: stability, sensitivity, uncertainty and missing values. The proposed CCR works well under different properties of MR data and achieves a mean error of 110m and a median error of 80m, outperforming the state-of-art range-based and fingerprinting localization methods.\",\n \"Title: Recognizing Parkinsonian Gait Pattern by Exploiting Fine-Grained Movement Function Features.\\nYear: 2016\\n\\nAbstract: Parkinson's disease (PD) is one of the typical movement disorder diseases among elderly people, which has a serious impact on their daily lives. In this article, we propose a novel computation framework to recognize gait patterns in patients with PD. The key idea of our approach is to distinguish gait patterns in PD patients from healthy individuals by accurately extracting gait features that capture all three aspects of movement functions, that is, stability, symmetry, and harmony. The proposed framework contains three steps: gait phase discrimination, feature extraction and selection, and pattern classification. In the first step, we put forward a sliding window--based method to discriminate four gait phases from plantar pressure data. Based on the gait phases, we extract and select gait features that characterize stability, symmetry, and harmony of movement functions. Finally, we recognize PD gait patterns by applying a hybrid classification model. We evaluate the framework using an open dataset that contains real plantar pressure data of 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework significantly outperforms the four baseline approaches.\",\n \"Title: Scalable floor localization using barometer on smartphone.\\nYear: 2016\\n\\nAbstract: Traditional fingerprint-based localization techniques mainly rely on infrastructure support such as GSM and Wi-Fi. They require war-driving, which is both time-consuming and labor-intensive. With recent advances of smartphone sensors, sensor-assisted localization techniques are emerging. However, they often need user-specific training and more power intensive sensing, resulting in infeasible solutions for real deployment. In this paper, we present Barometer-based floor Localization system B-Loc, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. It makes use of the barometer on smartphone. B-Loc does not rely on any Wi-Fi infrastructure and requires neither war-driving nor prior knowledge of the buildings. Leveraging on crowdsourcing, B-Loc builds the barometer fingerprint map, which contains the barometric pressure value for each floor level to locate users' floor levels. We conduct both simulation and field studies to demonstrate the accuracy, scalability, and robustness of B-Loc. Our simulation shows that B-Loc can locate the user fast and the field study in a 10-floor building shows that B-Loc achieves an accuracy of over 98%. Copyright © 2016 John Wiley & Sons, Ltd.\",\n \"Title: AudioGest: enabling fine-grained hand gesture detection by decoding echo signal.\\nYear: 2016\\n\\nAbstract: Hand gesture is becoming an increasingly popular means of interacting with consumer electronic devices, such as mobile phones, tablets and laptops. In this paper, we present AudioGest, a device-free gesture recognition system that can accurately sense the hand in-air movement around user's devices. Compared to the state-of-the-art, AudioGest is superior in using only one pair of built-in speaker and microphone, without any extra hardware or infrastructure support and with no training, to achieve fine-grained hand detection. Our system is able to accurately recognize various hand gestures, estimate the hand in-air time, as well as average moving speed and waving range. We achieve this by transforming the device into an active sonar system that transmits inaudible audio signal and decodes the echoes of hand at its microphone. We address various challenges including cleaning the noisy reflected sound signal, interpreting the echo spectrogram into hand gestures, decoding the Doppler frequency shifts into the hand waving speed and range, as well as being robust to the environmental motion and signal drifting. We implement the proof-of-concept prototype in three different electronic devices and extensively evaluate the system in four real-world scenarios using 3,900 hand gestures that collected by five users for more than two weeks. Our results show that AudioGest can detect six hand gestures with an accuracy up to 96%, and by distinguishing the gesture attributions, it can provide up to 162 control commands for various applications.\",\n \"Title: Learning from less for better: semi-supervised activity recognition via shared structure discovery.\\nYear: 2016\\n\\nAbstract: Despite the active research into, and the development of, human activity recognition over the decades, existing techniques still have several limitations, in particular, poor performance due to insufficient ground-truth data and little support of intra-class variability of activities (i.e., the same activity may be performed in different ways by different individuals, or even by the same individuals with different time frames). Aiming to tackle these two issues, in this paper, we present a robust activity recognition approach by extracting the intrinsic shared structures from activities to handle intra-class variability, and the approach is embedded into a semi-supervised learning framework by utilizing the learned correlations from both labeled and easily-obtained unlabeled data simultaneously. We use l2,1 minimization on both loss function and regularizations to effectively resist outliers in noisy sensor data and improve recognition accuracy by discerning underlying commonalities from activities. Extensive experimental evaluations on four community-contributed public datasets indicate that with little training samples, our proposed approach outperforms a set of classical supervised learning methods as well as those recently proposed semi-supervised approaches.\",\n \"Title: Human respiration detection with commodity wifi devices: do user location and body orientation matter?\\nYear: 2016\\n\\nAbstract: Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's breathing depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.\",\n \"Title: An Audio-based Hierarchical Smoking Behavior Detection System Based on A Smart Neckband Platform.\\nYear: 2016\\n\\nAbstract: Smoking behavior detection has attracted much research interest for its significant impact on smokers' physical and mental health. Existing research has shown the potential of using wearable devices for fine-grained smoking puff and session detection by detecting a smoker's content of breathing, lighter usage, breathing, and gesture patterns. However, the existing systems are complex, and they are usually vulnerable to confounding activities and diversity of smoking behavior. To address these limitations, this paper proposes the design and implementation of a simple and compact smart neckband device for smoking detection. The device is equipped with both passive and active acoustic sensors to detect smoking sessions and puffs. We propose a hierarchical processing framework in which the lower-layer detects the sub-movements, i.e., lighter usage, hand-to-mouth gesture and deep breathing, from perceived audio data; and the higher-layer, based on the lower-layerąŕs detection results, detects smoking puffs and sessions using temporal sequence analysis techniques. Real-world experiments suggest our system can accurately detect smoking puffs and sessions with F1 score of respectively 93.59% and 92.96% in complex environments with the presence of confounding activities and diverse ways of smoking.\",\n \"Title: Integrating Wi-Fi And Magnetic Field For Fingerprinting Based Indoor Positioning System\\nYear: 2016\\n\\nAbstract: Smartphone based personal tracking is very important for people to find their destination in large complex buildings (e.g. shopping malls, airports and museums). Such applications are highly demanded in both industries and research organizations. One critical issue for these applications is lack of mature technologies for highly accurate indoor location tracking. In this paper, a new Wi-Fi and magnetic field based smartphone tracking system, named WMLoc, was introduced. The system is a part of a collaborative project between the RMIT University and a famous Australian software company. A number of tracking algorithms such as K nearest neighbor (KNN), artificial neural network (ANN) and back tracing (BT) have been developed or adopted for the system in order to obtain a real-time precise location of the smartphone user. The integration of Wi-Fi and magnetic field includes physical floor analysis, label pattern creation using ANN and BT for enhancing the tracking reliability and improving positioning accuracy. The WMLoc system was tested in two buildings at the RMIT University, Australia. The preliminary results showed that the average root-mean-square (RMS) error of the WMLoc system was less than 2.6 m.\",\n \"Title: An Analytical Model for Coding-Based Reprogramming Protocols in Lossy Wireless Sensor Networks.\\nYear: 2017\\n\\nAbstract: Multi-hop over-the-air reprogramming is essential for remote installation of software patches and upgrades in wireless sensor networks (WSNs). Several recent coding-based reprogramming protocols have been proposed to enable efficient code dissemination in high packet loss environments. An accurate and formal analysis of the performance of these protocols, however, has not been studied sufficiently in the literature. In this paper, we present a novel high-fidelity analytical model based on the shortest path algorithm to measure the completion time by incorporating overhearing and packet coding. This model can be applied to any coding-based reprogramming protocol by substituting the coding part with protocol specific operations. We conduct extensive testbed experiments to evaluate the performance of our proposed model. Based on the analytical and numerical experiments, we find that 1) overhearing causes significant reduction of the completion time in dense wireless sensor networks, particularly, it reduces 50-70 percent of the total completion time when the packet reception rate is 0.896; 2) coding delay plays a key role in the total completion time compared to the communication delay when the packet coding parameters are selected appropriately, for example, the communication delay is about 65 percent of the coding delay when the number of packets per page is 16 for the finite field size $2^8$ ; 3) the total completion time can be minimized when the number of packets per page is close to 24 and the finite field size is close to $2^4$ .\",\n \"Title: Toward a Wearable RFID System for Real-Time Activity Recognition Using Radio Patterns.\\nYear: 2017\\n\\nAbstract: Elderly care is one of the many applications supported by real-time activity recognition systems. Traditional approaches use cameras, body sensor networks, or radio patterns from various sources for activity recognition. However, these approaches are limited due to ease-of-use, coverage, or privacy preserving issues. In this paper, we present a novel wearable Radio Frequency Identification (RFID) system aims at providing an easy-to-use solution with high detection coverage. Our system uses passive tags which are maintenance-free and can be embedded into the clothes to reduce the wearing and maintenance efforts. A small RFID reader is also worn on the user's body to extend the detection coverage as the user moves. We exploit RFID radio patterns and extract both spatial and temporal features to characterize various activities. We also address the issues of false negative of tag readings and tag/antenna calibration, and design a fast online recognition system. Antenna and tag selection is done automatically to explore the minimum number of devices required to achieve target accuracy. We develop a prototype system which consists of a wearable RFID system and a smartphone to demonstrate the working principles, and conduct experimental studies with four subjects over two weeks. The results show that our system achieves a high recognition accuracy of 93.6 percent with a latency of 5 seconds. Additionally, we show that the system only requires two antennas and four tagged body parts to achieve a high recognition accuracy of 85 percent.\",\n \"Title: A Mixed Transmission Strategy to Achieve Energy Balancing in Wireless Sensor Networks\\nYear: 2017\\n\\nAbstract: In this paper, we investigate the problem of energy balanced data collection in wireless sensor networks, aiming to balance energy consumption among all sensor nodes during the data propagation process. Energy balanced data collection can potentially save energy consumption and prolong network lifetime, and hence, it has many practical implications for sensor network design and deployment. The traditional hop-by-hop transmission model allows a sensor node to propagate its packets in a hop-by-hop manner toward the sink, resulting in poor energy balancing for the entire network. To address the problem, we apply a slice-based energy model, and divide the problem into inter-slice and intra-slice energy balancing problems. We then propose a probability-based strategy named inter-slice mixed transmission protocol and an intra-slice forwarding technique to address each of the problems. We propose an energy-balanced transmission protocol by combining both techniques to achieve total energy balancing. In addition, we study the condition of switching between inter-slice transmission and intra-slice transmission, and the limitation of hops in an intra-slice transmission. Through our extensive simulation studies, we demonstrate that the proposed protocols achieve energy balancing, prolong network lifespan, and decrease network delay, compared with the hop-by-hop transmission and a cluster-based routing protocol under various parameter settings.\",\n \"Title: Accurate and Generic Sender Selection for Bulk Data Dissemination in Low-Power Wireless Networks\\nYear: 2017\\n\\nAbstract: Data dissemination is a fundamental service offered by low-power wireless networks. Sender selection is the key to the dissemination performance and has been extensively studied. Sender impact metric plays a significant role in sender selection, since it determines which senders are selected for transmission. Recent studies have shown that spatial link diversity has a significant impact on the efficiency of broadcast. However, the existing metrics overlook such impact. Besides, they consider only gains but ignore the costs of sender candidates. As a result, existing works cannot achieve accurate estimation of the sender impact. Moreover, they cannot well support data dissemination with network coding, which is commonly used for lossy environments. In this paper, we first propose a novel sender impact metric, namely, $\\\\gamma $ , which jointly exploits link quality and spatial link diversity to calculate the gain/cost ratio of the sender candidates. Then, we develop a generic sender selection scheme based on the $\\\\gamma $ metric (called $\\\\gamma $ -component) that can generally support both types of dissemination using native packets and network coding. Extensive evaluations are conducted through real testbed experiments and large-scale simulations. The performance results and analysis show that $\\\\gamma $ achieves far more accurate impact estimation than the existing works. In addition, the dissemination protocols based on $\\\\gamma $ -component outperform the existing protocols in terms of completion time and transmissions (by 20.5% and 23.1%, respectively).\",\n \"Title: \\\"Knowing value\\\" logic as a normal modal logic.\\nYear: 2016\\n\\nAbstract: Recent years witness a growing interest in nonstandard epistemic logics of knowing whether, knowing what, knowing how, and so on. These logics are usually not normal, i.e., the standard axioms and reasoning rules for modal logic may be invalid. In this paper, we show that the conditional knowing logic proposed by Wang and Fan cite{WF13} can be viewed as a disguised normal modal logic by treating the negation of the Kv operator as a special diamond. Under this perspective, it turns out that the original first-order Kripke semantics can be greatly simplified by introducing a ternary relation $R_i^c$ in standard Kripke models, which associates one world with two $i$-accessible worlds that do not agree on the value of constant $c$. Under intuitive constraints, the modal logic based on such Kripke models is exactly the one studied by Wang and Fan (2013,2014}. Moreover, there is a very natural binary generalization of the knowing diamond, which, surprisingly, does not increase the expressive power of the logic. The resulting logic with the binary diamond has a transparent normal modal system, which sharpens our understanding of the knowing logic and simplifies some previously hard problems.\",\n \"Title: DeepKey: An EEG and Gait Based Dual-Authentication System.\\nYear: 2017\\n\\nAbstract: Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are increasingly facing the risk of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoders, or fingerprint films. In this regard, we design a multimodal biometric authentication system named DeepKey which uses both gait and Electroencephalography (EEG) signals to provide better protection against such risks. DeepKey consists of three key components: an Invalid ID Filter Model to block invalid subjects, a Gait Identification Model to recognize Gait IDs and an EEG Identification Model to recognize EEG IDs. In particular, the first two models employ a one-class SVM algorithm and a Recurrent Neural Network based deep learning model, respectively. The third model combines autoregressive coefficients, an RNN structure, and an SVM classifier. DeepKey is trained with a gait dataset of 160,000 samples and an EEG dataset of 108,000 samples. Experimental results show DeepKey outperforms a series of comparison methods and achieves an overall accuracy of 0.983 along with an overall false acceptance rate (FAR) of 0.0 and a false rejection rate (FRR) of 0.019.\",\n \"Title: Towards Accurate Corruption Estimation in ZigBee Under Cross-Technology Interference\\nYear: 2017\\n\\nAbstract: Cross-Technology Interference affects the operation of low-power ZigBee networks, especially under severe WiFi interference. Accurate corruption estimation is very important to improve the resilience of ZigBee transmissions. However, there are many limitations in existing approaches such as low accuracy, high overhead, and requiring hardware modification. In this paper, we propose an accurate corruption estimation approach, AccuEst, which utilizes per-byte SINR (Signal-to-Interference-and-Noise Ratio) to detect corruption. We combine the use of pilot symbols with per-byte SINR to improve corruption detection accuracy, especially in highly noisy environments (i.e., noise and interference are at the same level). In addition, we design an adaptive pilot instrumentation scheme to strike a good balance between accuracy and overhead. We implement AccuEst on the TinyOS 2.1.1/TelosB platform and evaluate its performance through extensive experiments. Results show that AccuEst improves corruption detection accuracy by 78.6% on average compared with state-of-the-art approach (i.e., CARE) in highly noisy environments. In addition, AccuEst reduces pilot overhead by 53.7% on average compared to the traditional pilot-based approach. We implement AccuEst in a coding-based transmission protocol, and results show that with AccuEst, the packet delivery ratio is improved by 20.3% on average.\",\n \"Title: Embracing Corruption Burstiness: Fast Error Recovery for ZigBee under Wi-Fi Interference.\\nYear: 2017\\n\\nAbstract: The ZigBee communication can be easily and severely interfered by Wi-Fi traffic. Error recovery, as an important means for ZigBee to survive Wi-Fi interference, has been extensively studied in recent years. The existing works add upfront redundancy to in-packet blocks for recovering a certain number of random corruptions. Therefore, the bursty nature of ZigBee in-packet corruptions under Wi-Fi interference is often considered harmful, since some blocks are full of errors which cannot be recovered and some blocks have no errors but are still requiring redundancy. As a result, they often use interleaving to reshape the bursty errors, before applying complex FEC codes to recover the re-shaped random distributed errors. In this paper, we take a different view that burstiness may be helpful. With burstiness, the in-packet corruptions are often consecutive and the requirement for error recovery is reduced as recovering any k consecutive errors instead of recovering any random k errors. This lowered requirement allows us to design far more efficient code than the existing FEC codes. Motivated by this implication, we exploit the corruption burstiness to design a simple yet effective error recovery code using XOR operations (called ZiXOR). ZiXOR uses XOR code and the delay is significantly reduced. More, ZiXOR uses RSSI-hinted approach to detect in packet corruptions without CRC, incurring almost no extra transmission overhead. The testbed evaluation results show that ZiXOR outperforms the state-of-the-art works in terms of the throughput (by 47 percent) and latency (by 22 percent).\",\n \"Title: Exploring traffic congestion correlation from multiple data sources.\\nYear: 2017\\n\\nAbstract: Traffic congestion is a major concern in many cities around the world. Previous work mainly focuses on the prediction of congestion and analysis of traffic flows, while the congestion correlation between road segments has not been studied yet. In this paper, we propose a three-phase framework to explore the congestion correlation between road segments from multiple real world data. In the first phase, we extract congestion information on each road segment from GPS trajectories of over 10,000 taxis, define congestion correlation and propose a corresponding mining algorithm to find out all the existing correlations. In the second phase, we extract various features on each pair of road segments from road network and POI data. In the last phase, the results of the first two phases are input into several classifiers to predict congestion correlation. We further analyze the important features and evaluate the results of the trained classifiers through experiments. We found some important patterns that lead to a high/low congestion correlation, and they can facilitate building various transportation applications. In addition, we found that traffic congestion correlation has obvious directionality and transmissibility. The proposed techniques in our framework are general, and can be applied to other pairwise correlation analysis.\",\n \"Title: Rapid: A Multimodal and Device-free Approach Using Noise Estimation for Robust Person Identification\\nYear: 2017\\n\\nAbstract: Device-free human sensing is a key technology to support many applications such as indoor navigation and activity recognition. By exploiting WiFi signals reflected by human body, there have been many WiFi-based device-free human sensing applications. Among these applications, person identification is a fundamental technology to enable user-specific services. In this paper, we present Rapid, a system that can perform robust person identification in a device-free and low-cost manner, using fine-grained channel information (i.e., CSI) of WiFi and acoustic information from footstep sound. In order to achieve high accuracy in real-life scenarios with both system and environment noise, we perform noise estimation and include two different confidence values to quantify the impact of noise to both CSI and acoustic measurements. Based on an accurate gait analysis, we then adaptively fuse CSI and acoustic measurements to achieve robust person identification. We implement low-cost Rapid nodes and evaluate our system using experiments at multiple locations with a total of 1800 gait instances from 20 volunteers, and the results show that Rapid identifies a subject with an average accuracy of 92% to 82% from a group of 2 to 6 subjects, respectively.\",\n \"Title: Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals\\nYear: 2018\\n\\nAbstract: An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subject's active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large-scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the-art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.\",\n \"Title: Fullie and Wiselie: A Dual-Stream Recurrent Convolutional Attention Model for Activity Recognition.\\nYear: 2017\\n\\nAbstract: Multimodal features play a key role in wearable sensor based Human Activity Recognition (HAR). Selecting the most salient features adaptively is a promising way to maximize the effectiveness of multimodal sensor data. In this regard, we propose a fully and select wisely (Fullie and Wiselie) principle as well as a dual-stream recurrent convolutional attention model, Recurrent Attention and Activity Frame (RAAF), to improve the recognition performance. We first collect modality features and the relations between each pair of features to generate activity frames, and then introduce an attention mechanism to select the most prominent regions from activity frames precisely. The selected frames not only maximize the utilization of valid features but also reduce the number of features to be computed effectively. We further analyze the hyper-parameters, accuracy, interpretability, and annotation dependency of the proposed model based on extensive experiments. The results show that RAAF achieves competitive performance on two benchmarked datasets and works well in real life scenarios.\",\n \"Title: FastDesk: A remote desktop virtualization system for multi-tenant.\\nYear: 2018\\n\\nAbstract: Classical remote desktop systems are considered for a single host, thus hindering their applicability on virtualization environment. Remote desktop virtualization is rising in the recent years as a new advanced extension to virtualization technology. However, existing remote desktop virtualization solutions introduce weak performance in relation to response time, video quality, and cost saving. This paper proposes a novel remote desktop virtualization system for multi-tenant, namely FastDesk, which is based on a server-push mode streaming mechanism and a heuristic virtual desktop placement algorithm. Through extensive experiments, the results show that FastDesk outperforms other popular platforms in terms of bandwidth with less than 2 Mbps and 94% video quality. Meanwhile, it creates the minimal resource wastage compared with other virtual machine placement algorithms. Furthermore, the proposed FastDesk achieves low CPU utilization, low bandwidth and good scalability. At last, to widen its applicability, FastDesk has been implemented on VirtualBox for running 3D programs.\",\n \"Title: C-FMCW Based Contactless Respiration Detection Using Acoustic Signal.\\nYear: 2017\\n\\nAbstract: Recent advances in ubiquitous sensing technologies have exploited various approaches to monitoring vital signs. One of the vital signs is human respiration which typically requires reliable monitoring with low error rate in practice. Previous works in respiration monitoring however either incur high cost or suffer from poor error rate. In this paper, we propose a Correlation based Frequency Modulated Continuous Wave method (C-FMCW) which is able to achieve high ranging resolution. Based on C-FMCW, we present the design and implementation of an audio-based highly-accurate system for human respiration monitoring, leveraging on commodity speaker and microphone widely available in home environments. The basic idea behind the audio-based method is that when a user is close to a pair of speaker and microphone, body movement during respiration causes periodic audio signal changes, which can be extracted to obtain the respiration rate. However, several technical challenges exist when applying C-FMCW to detect respiration with commodity acoustic devices. First, the sampling frequency offset between speakers and microphones if not being corrected properly would cause high ranging errors. Second, the uncertain starting time difference between the speaker and microphone varies over time. Moreover, due to multipath effect, weak periodic components due to respiration can easily be overwhelmed by strong static components in practice. To address those challenges, we 1) propose an algorithm to compensate dynamically acoustic signal and counteract the offset between speaker and microphone; 2) co-locate speaker and microphone and use the received signal without reflection (self-interference) as a reference to eliminate the starting time difference; and 3) leverage the periodicity of respiration to extract weak periodic components with autocorrelation. Extensive experimental results show that our system detects respiration in real environments with the median error lower than 0.35 breaths/min, outperforming the state-of-the-arts.\",\n \"Title: Spatial Multiplexing For Non-Line-Of-Sight Light-To-Camera Communications\\nYear: 2018\\n\\nAbstract: Light-to-Camera Communications (LCC) have emerged as a new wireless communication technology with great potential to benefit a broad range of applications. However, the existing LCC systems either require cameras directly facing to the lights or can only communicate over a single link, resulting in low throughputs and being fragile to ambient illuminant interference. We present HYCACO, a novel LCC system, which enables multiple light emitting diodes (LEDs) with an unaltered camera to communicate via the non-line-of-sight (NLoS) links. Different from other NLoS LCC systems, the proposed scheme is resilient to the complex indoor luminous environment. HYCACO can decode the messages by exploring the mixed reflected optical signals transmitted from multiple LEDs. By further exploiting the rolling shutter mechanism, we present the optimal optical frequencies and camera exposure duration selection strategy to achieve the best performance. We built a hardware prototype to demonstrate the efficiency of the proposed scheme under different application scenarios. The experimental results show that the system throughput reaches 4.5 kbps on iPhone 6s with three transmitters. With the robustness, improved system throughput and ease of use, HYCACO has great potentials to be used in a wide range of applications such as advertising, tagging objects, and device certifications.\",\n \"Title: Traveling Officer Problem: Managing Car Parking Violations Efficiently Using Sensor Data.\\nYear: 2018\\n\\nAbstract: The on-street parking system is an indispensable part of civic projects, as it provides travelers and shoppers with parking spaces. With the recent in-ground sensors deployed throughout the Melbourne central business district (CBD), there is a significant problem on how to use the sensor data to manage parking violations and issue infringement notices efficiently in a short time-window. In this pa...\",\n \"Title: FinDroidHR: Smartwatch Gesture Input with Optical Heartrate Monitor.\\nYear: 2018\\n\\nAbstract: We present FinDroidHR, a novel gesture input technique for off-the-shelf smartwatches. Our technique is designed to detect 10 hand gestures on the hand wearing a smartwatch. The technique is enabled by analysing features of the Photoplethysmography (PPG) signal that optical heart-rate sensors capture. In a study with 20 participants, we show that FinDroidHR achieves 90.55% accuracy and 90.73% recall. Our work is the first study to explore the feasibility of using optical sensors on the off-the-shelf wearable devices to recognise gestures. Without requiring bespoke hardware, FinDroidHR can be readily used on existing smartwatches.\",\n \"Title: SpiderWalk: Circumstance-aware Transportation Activity Detection Using a Novel Contact Vibration Sensor.\\nYear: 2018\\n\\nAbstract: This paper presents the design and implementation of the SpiderWalk system for circumstance-aware transportation activity detection using a novel contact vibration sensor. Different from existing systems that only report the type of activity, our system detects not only the activity but also its circumstances (e.g., road surface, vehicle, and shoe types) to provide better support for applications such as activity logging, location tracking, and smart persuasive applications. Inspired by but different from existing audio-based context detection approaches using microphones, the SpiderWalk system is designed and implemented using an ultra-sensitive, flexible contact vibration sensor which mimics the spiders' sensory slit organs. By sensing vibration patterns from the soles of shoes, the system can accurately detect transportation activities with rich circumstance information while resisting undesirable external signals from other sources or speech that may cause the data assignment and privacy preserving issues. Moreover, our system is implemented by reusing existing audio devices and can be used by an unmodified smartphone, making it ready for large-scale deployments. Finally, a novel temporal and spatial correlated classification approach is proposed to accurately detect the complex combinations of transportation activities and circumstances based on the output of each individual classifiers. Experiments conducted on a real-world data set suggest our system can accurately detect different transportation activities and their circumstances with an average detection accuracy of 93.8% with resource overheads comparable to existing audio- and GPS-based systems.\",\n \"Title: Exploiting Delay-Aware Load Balance for Scalable 802.11 PSM in Crowd Event Environments.\\nYear: 2017\\n\\nAbstract: This paper presents ScaPSM (i.e., Scalable Power-Saving Mode Scheduler), a design that enables scalable competing background traffic scheduling in crowd event 802.11 deployments with Power-Saving Mode (PSM) radio operation. ScaPSM prevents the packet delay proliferation of previous study, if applied in the crowd events scenario, by introducing a new strategy of adequate competition among multiple PSM clients to optimize overall energy saving without degrading packet delay performance. The key novelty behind ScaPSM is that it exploits delay-aware load balance to control judiciously the qualification and the number of competing PSM clients before every beacon frame's transmission, which helps to mitigate congestion at the peak period with increasing the number of PSM clients. With ScaPSM, the average packet delay is bounded and fairness among PSM clients is simultaneously achieved. ScaPSM is incrementally deployable due to only AP-side changes and does not require any modification to the 802.11 protocol or the clients. We theoretically analyze the performance of ScaPSM. Our experimental results show that the proposed design is practical, effective, and featuring with significantly improved scalability for crowd events.\",\n \"Title: Making Sense of Doppler Effect for Multi-Modal Hand Motion Detection.\\nYear: 2018\\n\\nAbstract: Hand gesture is becoming an increasingly popular means of interacting with consumer electronic devices, such as mobile phones, tablets and laptops. In this paper, we present AudioGest, a device-free gesture recognition system that can accurately sense the hand in-air movement around user's devices. Compared to the state-of-the-art techniques, AudioGest is superior in using only one pair of built-i...\",\n \"Title: HiMeter: Telling You the Height Rather than the Altitude.\\nYear: 2018\\n\\nAbstract: The altitude of a moving user is important context information for mobile technologies and applications. However, with the increasing pervasiveness of smartphones and abundant mobile applications, developers and users have gradually discovered that the height is more useful than altitude in many situations. The height is often a relative value, which is the vertical distance to the ground rather than the vertical distance to sea level, and we believe that it is useful in many applications, such as localization/navigation, sport/health and tourism/travel. In this paper, we first carried out a nation-wide online survey to confirm the desirability for the height information in mobile applications, and the result is positive. Then, we proposed HiMeter, an effective and accurate approach to calculating the height of the smartphone. HiMeter makes use of a low-power barometer on the smartphone and does not require GPS or back-server support. We concentrate on the vertical moving pattern of the user and designed several novel techniques, resulting in HiMeter not needing any reference points, and the complex process of calculating the absolute altitude can be avoided. The field studies show that HiMeter can achieve an accuracy of within 5 m in 90% of cases indoors and an accuracy of 10 m in 83% of cases outdoors. Compared to the existing works, HiMeter is more accurate and practical and is more suitable for usage in many mobile applications.\",\n \"Title: An Adaptive Low-Power Listening Protocol for Wireless Sensor Networks in Noisy Environments.\\nYear: 2018\\n\\nAbstract: This paper investigates the energy consumption minimization problem for wireless sensor networks running low-power listening (LPL) protocols in noisy environments. We observe that the energy consumption by false wakeups (i.e., wakeup without receiving any packet) of a node in noisy environments can be a dominant factor in many cases while the false wakeup rate is spatially and temporarily dynamic....\",\n \"Title: Towards Repeatable Wireless Network Simulation Using Performance Aware Markov Model.\\nYear: 2018\\n\\nAbstract: Wireless network simulation is a fundamental service aiming at providing controlled and repeatable environment for protocol design, performance testing, etc. The existing simulators focus on reproducing the packet behaviors on individual links. However, as observed in some recent works, individual link behaviors alone are not enough to characterize the protocol performance. As a result, while the existing works can mimic the link behaviors very closely, they often fail to simulate protocol level performance. In this paper, we propose a novel performance-aware simulation approach which can preserve not only the link-level behaviors but also the performance-level behaviors. We first devise an accurate performance model by combining link quality and the spatial temporal link correlation. Based on the performance modeling, we then propose a Performance Aware Hidden Markov Model (PA-HMM), where the protocol performance is directly fed into the Markov state transitions. PA-HMM is able to simulate both link-level behaviors and high-level protocol performance. We conduct extensive testbed and simulation experiments with broadcast and anycast protocols. The results show that compared to the state-of-the-art work, 1) the performance model is able to accurately characterize wireless communication performance and 2) the protocol performance is closely simulated as compared to the empirical results.\",\n \"Title: FullBreathe: Full Human Respiration Detection Exploiting Complementarity of CSI Phase and Amplitude of WiFi Signals.\\nYear: 2018\\n\\nAbstract: Human respiration detection based on Wi-Fi signals does not require users to carry any device, hence it has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, recent studies show that respiration sensing performance varies in different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call \\\"blind spots\\\". In this paper, we aim to address the blind spot problem to ensure full coverage of respiration detection. Basically, the amplitude and phase of Wi-Fi channel state information (CSI) are orthogonal and complementary to each other, so they can be combined to eliminate the blind spots. However, accurate CSI phase cannot be obtained from commodity Wi-Fi due to the clock-unsynchronized transceivers. Thus, we apply conjugate multiplication (CM) of CSI between two antennas to remove the phase offset and construct two orthogonal signals--new \\\"amplitude and phase\\\" which are still complementary to each other. In this way, we can ensure full human respiration detection. Based on these ideas, We design and implement a real-time respiration detection system with commodity Wi-Fi devices. We conduct extensive experiments to validate our model and design. The results show that, with only one transceiver pair and without leveraging multiple sub-carriers, our system enables full location coverage with no blind spot, showing great potential for real deployment.\",\n \"Title: Gazerevealer: Inferring Password Using Smartphone Front Camera\\nYear: 2018\\n\\nAbstract: The widespread use of smartphones has brought great convenience to our daily lives, while at the same time we have been increasingly exposed to security threats. Keystroke security is an essential element in user privacy protection. In this paper, we present GazeRevealer, a novel side-channel based keystroke inference framework to infer sensitive inputs on smartphone from video recordings of victim's eye patterns captured from smartphone front camera. We observe that eye movements typically follow the keystrokes typing on the number-only soft keyboard during password input. By exploiting eye patterns, we are able to infer the passwords being entered. We propose a novel algorithm to extract sensitive eye pattern images from video streams, and classify different eye patterns with Support Vector Classification. We also propose a novel enhanced method to boost the inference accuracy. Compared with prior keystroke detection approaches, GazeRevealer does not require any external auxiliary devices, and it relies only on smartphone front camera. We evaluate the performance of GazeRevealer with three different types of smartphones, and the result shows that GazeRevealer achieves 77.43% detection accuracy for a single key number and 83.33% inference rate for the 6-digit password in the ideal case.\",\n \"Title: BTrack: Using Barometer for Energy Efficient Location Tracking on Mountain Roads.\\nYear: 2018\\n\\nAbstract: This paper presents the design and implementation of BTrack, a new approach for location tracking on mountain roads. It is as accurate as the GPS on common smartphones, but costs much less energy. To design such a location system, we carried out a nation-wide online survey to confirm the desirability for low power location tracking system, and the result is positive. Then, we proposed BTrack, and it makes two key technical contributions. The first is to propose a dynamic transition matrix hidden Markov model to combine the barometer and accelerometer reading hints for estimating the location of the user. The second is to design some novel techniques for parameter estimation, and proposed an adaptive algorithm to reduce the computational complexity. The field studies show that the accuracy of BTrack is no worse than GPS in 56% cases, meanwhile, the energy consumption is only about 30%. Compared with the existing works, BTrack is more suitable for location tracking on mountain roads.\",\n \"Title: A VR-based, hybrid modeling approach to fire evacuation simulation.\\nYear: 2018\\n\\nAbstract: VR-based simulation could significantly improve the user experience by offering users vivid and near-life visual scenes, hence helping users better handle dangerous situations safely such as fire accidents. In this paper, we design a fire evacuation simulation system and propose a hybrid crowd evacuation modeling and simulation approach, which is a layer-based model adopting both local and global techniques partially into different layers. In essence, this model integrates an agent-based model with an improved dynamical network flow model, which is capable of taking into account issues both from individual diversity and from crowd movement tendency to simulate crowd evacuation. An emergency response mechanism driven by videos is then designed according to the model. Once fire accidents are detected in videos, the system will first simulate accidents according to the fire level provided by the monitoring module and then start an evacuation routine or adjust evacuation routes. The simulation system can be experienced by users in a virtual environment. Finally, evaluations have been conducted to test the rationality of our model and results show that the proposed model can simulate the crowd movement and agent behavior in dynamic environments efficiently.\",\n \"Title: Minimum Cost Deployment of Bistatic Radar Sensor for Perimeter Barrier Coverage.\\nYear: 2019\\n\\nAbstract: Perimeter barriers can provide intrusion detection for a closed area. It is efficient for practical applications, such as coastal shoreline monitoring and international boundary surveillance. Perimeter barrier coverage construction in some regions of interest with irregular boundaries can be represented by its minimum circumcircle and every point on the perimeter can be covered. This paper studies circle barrier coverage in Bistatic Radar Sensor Network (BRSN) which encircles a region of interest. To improve the coverage quality, it is required to construct a circle barrier with a predefined width. Firstly, we consider a BR deployment problem to constructing a single BR circular barrier with minimum threshold of detectability. We study the optimized BR placement patterns on the single circular ring. Then the unit costs of the BR sensor are taken into account to derive the minimum cost placement sequence. Secondly, we further consider a circular BR barrier with a predefined width, which is wider than the breadth of Cassini oval sensing area with minimum threshold of detectability. We propose two segment strategies to efficiently divide a circular barrier to several adjacent sub-ring with some appropriate width: Circular equipartition strategy and an adaptive segmentation strategy. Finally, we propose approximate optimization placement algorithms for minimum cost placement of BR sensor for circular barrier coverage with required width and detection threshold. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.\",\n \"Title: ReLog: A systematic approach for supporting efficient reprogramming in wireless sensor networks.\\nYear: 2017\\n\\nAbstract: Abstract Wireless sensor networks are shifting to application platforms that poses several challenges on reprogramming efficiency. To better support the efficient reprogramming, this paper proposes a systematic approach named ReLog which consists of a programming language, a compiler, and a virtual machine. To make application programs concise and easy to modify, the ReLog language extends from a traditional logical programming language and makes the extension part have the similar coding style. To reduce the size of data for reprogramming, the compiler first produces extremely compact executable code by compiling application programs into high-level representations. It also implements efficient incremental reprogramming to diminish differences between the current and new executable code. To mitigate the energy consumption incurred by interpretive execution, the virtual machine optimizes the executable code as well as the execution process to improve the runtime efficiency. We have implemented ReLog and evaluated it with respect to real reprogramming cases. Our experimental results show that it is easy to modify ReLog programs to satisfy new application requirements. Meanwhile, the compiler reduces the size of executable code by 61.4%–83.2% compared to the existing work. In addition, the lifetime of sensors running the ReLog virtual machine is close (97.04%–98.31%) to that running the native code.\",\n \"Title: Compressive Representation for Device-Free Activity Recognition with Passive RFID Signal Strength.\\nYear: 2018\\n\\nAbstract: Understanding and recognizing human activities is a fundamental research topic for a wide range of important applications such as fall detection and remote health monitoring and intervention. Despite active research in human activity recognition over the past years, existing approaches based on computer vision or wearable sensor technologies present several significant issues such as privacy (e.g., using video camera to monitor the elderly at home) and practicality (e.g., not possible for an older person with dementia to remember wearing devices). In this paper, we present a low-cost, unobtrusive, and robust system that supports independent living of older people. The system interprets what a person is doing by deciphering signal fluctuations using radio-frequency identification (RFID) technology and machine learning algorithms. To deal with noisy, streaming, and unstable RFID signals, we develop a compressive sensing, dictionary-based approach that can learn a set of compact and informative dictionaries of activities using an unsupervised subspace decomposition. In particular, we devise a number of approaches to explore the properties of sparse coefficients of the learned dictionaries for fully utilizing the embodied discriminative information on the activity recognition task. Our approach achieves efficient and robust activity recognition via a more compact and robust representation of activities. Extensive experiments conducted in a real-life residential environment demonstrate that our proposed system offers a good overall performance and shows the promising practical potential to underpin the applications for the independent living of the elderly.\",\n \"Title: Accurate Corruption Estimation in ZigBee Under Cross-Technology Interference\\nYear: 2019\\n\\nAbstract: Cross-Technology Interference affects the operation of low-power ZigBee networks, especially under severe WiFi interference. Accurate corruption estimation is very important to improve the resilience of ZigBee transmissions. However, there are many limitations in existing approaches such as low accuracy, high overhead, and requirement of hardware modification. In this paper, we propose an accurate corruption estimation approach, AccuEst, which utilizes per-byte SINR (Signal-to-Interference-and-Noise Ratio) to detect corruption. We combine the use of pilot symbols with per-byte SINR to improve corruption detection accuracy, especially in highly noisy environments (i.e., noise and interference are at the same level). We extract pilot symbols by leveraging protocol signatures. In addition, we design an adaptive pilot instrumentation scheme to strike a good balance between accuracy and overhead. We implement AccuEst on the TinyOS 2.1.1/TelosB platform and evaluate its performance through extensive experiments. Results show that AccuEst improves corruption detection accuracy by 79.4 percent on average compared with state-of-the-art approach (i.e., CARE) in highly noisy environments. In addition, AccuEst reduces pilot overhead by 83.7 percent on average compared to the traditional pilot-based approach. We implement AccuEst in a coding-based transmission protocol, and results show that with AccuEst, the packet delivery ratio is improved by 22.1 percent on average.\",\n \"Title: Contactless Respiration Monitoring Using Ultrasound Signal With Off-The-Shelf Audio Devices\\nYear: 2019\\n\\nAbstract: Recent years have witnessed advances of Internet of Things technologies and their applications to enable contact-less sensing and elderly care in smart homes. Continuous and real-time respiration monitoring is one of the important applications to promote assistive living for elders during sleep and attracted wide attention in both academia and industry. Most of the existing respiration monitoring systems require expensive and specialized devices to sense chest displacement. However, chest displacement is not a direct indicator of breathing and thus false detection may often occur. In this paper, we design and implement a real-time and contactless respiration monitoring system by directly sensing the exhaled airflow from breathing using ultrasound signals with off-the-shelf speaker and microphone. Exhaled airflow from breathing can be regarded as air turbulence, which scatters the sound wave and results in Doppler effect. Our system works as an acoustic radar which transmits sound wave and detects the Doppler effect caused by breathing airflow. We mathematically model the relationship between the Doppler frequency change and the direction of breathing airflow. Based on this model, we design a minimum description length-based algorithm to effectively capture the Doppler effect caused by exhaled airflow. We conduct extensive experiments with 25 participants (7 elders, 2 young kids, and 16 adults, including 11 females and 14 males) in four different rooms. The participants take four different sleep postures (lying on one's back, on right/left side, and on one's stomach) in different positions of the bed. Experiment results show that our system achieves a median error lower than 0.3 breaths/min (2%) for respiration monitoring and can accurately identify Apnea. The results also demonstrate that the system is robust to different respiration styles (shallow, normal, and deep), respiration rate variation, ambient noise, sensing distance variation (within 0.7 m), and transmitted signal frequency variation.\",\n \"Title: Towards a Diffraction-based Sensing Approach on Human Activity Recognition\\nYear: 2019\\n\\nAbstract: In recent years, wireless sensing has been exploited as a promising research direction for contactless human activity recognition. However, one major issue hindering the real deployment of these systems is that the signal variation patterns induced by the human activities with different devices and environmental settings are neither stable nor consistent, resulting in unstable system performance. The existing machine learning based methods usually take the \\\"black box\\\" approach and fails to achieve consistent performance. In this paper, we argue that a deep understanding of radio signal propagation in wireless sensing is needed, and it may be possible to develop a deterministic sensing model to make the signal variation patterns predictable.\\n\\nWith this intuition, in this paper we investigate: 1) how wireless signals are affected by human activities taking transceiver location and environment settings into consideration; 2) a new deterministic sensing approach to model the received signal variation patterns for different human activities; 3) a proof-of-concept prototype to demonstrate our approach and a case study to detect diverse activities. In particular, we propose a diffraction-based sensing model to quantitatively determine the signal change with respect to a target's motions, which eventually links signal variation patterns with motions, and hence can be used to recognize human activities. Through our case study, we demonstrate that the diffraction-based sensing model is effective and robust in recognizing exercises and daily activities. In addition, we demonstrate that the proposed model improves the recognition accuracy of existing machine learning systems by above 10%.\",\n \"Title: A Novel Privacy Preserving Search Technique For Stego Data In Untrusted Cloud\\nYear: 2019\\n\\nAbstract: We propose the first privacy preserving search technique for stego health data in untrusted cloud in this paper. The Cloud computing is a popular technology to the healthcare providers for outsourcing health data due to flexibility and cost effectiveness. However, outsourcing health data to the cloud introduces serious privacy issues to the patient. For example, dishonest personnel of the cloud provider may disclose patient sensitive information to business organizations for some financial benefits. Using steganography, patient sensitive information is hidden within health data for privacy preservation. As a result, stego health data is generated. To the best of our knowledge, no method exists for searching a particular stego data without disclosing any information to the cloud. We propose a framework for privacy preserving search over stego health data. We systematically describe each component of the proposed framework. We conduct several experiments to evaluate the performance of the framework.\",\n \"Title: Enabling Out-of-Band Coordination of Wi-Fi Communications on Smartphones\\nYear: 2019\\n\\nAbstract: This paper identifies two energy saving opportunities of Wi-Fi interface emerged during smartphone’s screen-off periods. Exploiting the opportunities, we propose a new power saving strategy, BackPSM, for screen-off Wi-Fi communications. BackPSM regulates client to send and receive packets in batches and coordinates multiple clients to communicate at different slots (i.e., beacon interval). The core problem in BackPSM is how to coordinate client without incurring extra traffic overheads. To handle the problem, we propose a novel paradigm, Out-of-Band Communication (OBC), for client-to-client direct communications. OBC exploits the Traffic Indication Map (TIM) field of Wi-Fi Beacon to create a free side-channel between clients. It is based upon the observation that a client may control \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$1 \\\\rightarrow 0$ </tex-math></inline-formula>\\n appearing on TIM bit by locally regulating packet receiving operations. We adopt this \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$1 \\\\rightarrow 0$ </tex-math></inline-formula>\\n as the basic signal, and leverage the time length in between two signals to encode information. We demonstrate that OBC can be used to convey coordination information with close to 100% accuracy. We have implemented and evaluated BackPSM on a testbed. The results show that BackPSM can decode the traffic pattern of peers reliably using OBC, and establish collision-free schedules fast to achieve out-of-band coordination of client communications. BackPSM reduces screen-off energy by up to 60% and outperforms the state-of-the-art strategies by 16%–42%.\",\n \"Title: SMinder: Detect a Left-behind Phone using Sensor-based Context Awareness.\\nYear: 2019\\n\\nAbstract: Forget your smartphone in the car again? This happens often in our daily lives, sometimes even makes troubles. In this paper, we present SMinder, an effective, low power approach to remind user take the phone when getting off the car. Based on the context awareness techniques in mobile sensing, we detect the situation of forgetting to take the phone when getting off the car. SMinder requires neither any infrastructure nor any human intervention. It only uses low power smartphone sensors. Namely, the smartphone detects by itself whether it is left behind and remind the user before he leaves the car. SMinder reminds the user with high accuracy and minimum energy consumption, making it realistic for real-world use. Compared to the existing approaches, SMinder is cheaper and easier to use. Our experiments with the prototype system demonstrate the performance, scalability, and robustness of SMinder.\",\n \"Title: Assessing User Mental Workload for Smartphone Applications With Built-In Sensors.\\nYear: 2019\\n\\nAbstract: This work proposes a novel three-dimensional model to represent users' mental workload when using smartphone applications. We validate this model by studying the factors' perceptual independence and interactions using data collected from 22 participants. By analyzing the correlations between the factors of mental workload and tap strength captured by smartphones' built-in sensors, we discover tap ...\",\n \"Title: Key analysis of smart tourism project setting and tourists' satisfaction degree based on data mining.\\nYear: 2019\\n\\nAbstract: By improving the mining efficiency and enhancing the scalability of the algorithm, it solves the problem that the traditional recommendation algorithm has a long response time and low recommendation efficiency in the recommendation of tourist spots, and cannot adapt to the needs of big data mining. An in-depth analysis of the existing collaborative filtering recommendation algorithm was conducted, and the Slope One algorithm and the Item-based algorithm recommended for the tourist attractions were selected. Combine these two algorithms efficiently, parallelize the algorithm based on MapReduce programming on the Hadoop cloud platform, and verify the validity of the algorithm by collecting the tourism evaluation network real tourist attraction scoring data. By testing the real tourist attraction score data, it shows that the algorithm not only improves the accuracy of the recommendation but also has higher running speed than the traditional collaborative filtering algorithm. The experimental results show that the algorithm has higher mining performance and scalability and can be better adapted to the characteristics of large tourist sites, sparse data matrix, and meet the high hit rate and personalized requirements of tourist attractions.\",\n \"Title: Approximate Optimal Deployment of Barrier Coverage on Heterogeneous Bistatic Radar Sensors.\\nYear: 2019\\n\\nAbstract: Heterogeneous Bistatic Radars (BR) have different sensing ranges and couplings of sensing regions, which provide more flexible coverage for the boundary at complex terrain such as across rivers and valleys. Due to the Cassini oval sensing region of a BR and the coupling of sensing regions among different BRs, the coverage problem of BR sensor networks is very challenging. Existing works in BR barrier coverage focus mainly on homogeneous BR sensor networks. This paper studies the heterogeneous BR placement problem on a line barrier to achieve optimal coverage. 1) We investigate coverage differences of the basic placement sequences of heterogeneous BRs on the line barrier, and prove the optimal basic placement spacing patterns of heterogeneous BRs. 2) We study the coverage coupling effect among adjacent BRs on the line barrier, and determine that different placement sequences of heterogeneous BR transmitters will affect the barrier's coverage performance and length. The optimal placement sequence of heterogeneous BR barrier cannot be solved through the greedy algorithm. 3) We propose an optimal BRs placement algorithm on a line barrier when the heterogeneous BR transmitters' placement sequence is predetermined on the barrier, and prove it to be optimal. Through simulation experiments, we determine that the different placement sequences of heterogeneous BR transmitters have little influence on the barrier's maximum length. Then, we propose an approximate algorithm to optimize the BR placement spacing sequence on the heterogeneous line barrier. 4) As a heterogeneous barrier case study, a minimum cost coverage algorithm of heterogeneous BR barrier is presented. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.\",\n \"Title: Spatial Multiplexing for Non-Line-of-Sight Light-to-Camera Communications\\nYear: 2019\\n\\nAbstract: Light-to-Camera Communications (LCC) have emerged as a new wireless communication technology with great potential to benefit a broad range of applications. However, the existing LCC systems either require cameras directly facing to the lights or can only communicate over a single link, resulting in low throughputs and being fragile to ambient illuminant interference. We present HYCACO, a novel LCC system, which enables multiple light emitting diodes (LEDs) with an unaltered camera to communicate via the non-line-of-sight (NLoS) links. Different from other NLoS LCC systems, the proposed scheme is resilient to the complex indoor luminous environment. HYCACO can decode the messages by exploring the mixed reflected optical signals transmitted from multiple LEDs. By further exploiting the rolling shutter mechanism, we present the optimal optical frequencies and camera exposure duration selection strategy to achieve the best performance. We built a hardware prototype to demonstrate the efficiency of the proposed scheme under different application scenarios. The experimental results show that the system throughput reaches 4.5 kbps on iPhone 6s with three transmitters. With the robustness, improved system throughput and ease of use, HYCACO has great potentials to be used in a wide range of applications such as advertising, tagging objects, and device certifications.\",\n \"Title: Queec: QoE-aware edge computing for complex IoT event processing under dynamic workloads\\nYear: 2019\\n\\nAbstract: Many IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) which are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this paper, we present Queec, a QoE-aware edge computing system for complex IoT event processing under dynamic workloads. With Queec, the complex IoT event processing tasks that are relative computation-intensive for low-end IoT devices can be transparently offloaded to nearby edge nodes at runtime. We formulate the problem of scheduling multi-user tasks to multiple edge nodes as an optimization problem which minimizes the overall offloading latency of all tasks while avoiding the overloading problem. We implement Queec on low-end IoT devices, edge nodes and the cloud. We conduct extensive evaluations and the results show that Queec reduces 56.98% of the offloading latency on average compared with the state of art under dynamic workloads, while incurring acceptable overhead.\",\n \"Title: DeepKey: A Multimodal Biometric Authentication System via Deep Decoding Gaits and Brainwaves\\nYear: 2020\\n\\nAbstract: AbstractBiometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are at increasing risks of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoder, or fingerprint films. In this article, we design a multimodal biometric authentication system named DeepKey, which uses both Electroencephalography (EEG) and gait signals to better protect against such risk. DeepKey consists of two key components: an Invalid ID Filter Model to block unauthorized subjects, and an identification model based on attention-based Recurrent Neural Network (RNN) to identify a subject’s EEG IDs and gait IDs in parallel. The subject can only be granted access while all the components produce consistent affirmations to match the user’s proclaimed identity. We implement DeepKey with a live deployment in our university and conduct extensive empirical experiments to study its technical feasibility in practice. DeepKey achieves the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) of 0 and 1.0%, respectively. The preliminary results demonstrate that DeepKey is feasible, shows consistent superior performance compared to a set of methods, and has the potential to be applied to the authentication deployment in real-world settings.\",\n \"Title: SateLoc: A Virtual Fingerprinting Approach to Outdoor LoRa Localization Using Satellite Images\\nYear: 2021\\n\\nAbstract: AbstractWith the increasing relevance of the Internet of Things and large-scale location-based services, LoRa localization has been attractive due to its low-cost, low-power, and long-range properties. However, existing localization approaches based on received signal strength indicators are either easily affected by signal fading of different land-cover types or labor intensive. In this work, we propose SateLoc, a LoRa localization system that utilizes satellite images to generate virtual fingerprints. Specifically, SateLoc first uses high-resolution satellite images to identify land-cover types. With the path loss parameters of each land-cover type, SateLoc can automatically generate a virtual fingerprinting map for each gateway. We then propose a novel multi-gateway combination strategy, which is weighted by the environmental interference of each gateway, to produce a joint likelihood distribution for localization and tracking. We implement SateLoc with commercial LoRa devices without any hardware modification, and evaluate its performance in a 227,500-m\\\\(\\\\) urban area. Experimental results show that SateLoc achieves a median localization error of 43.5 m, improving more than 50% compared to state-of-the-art model-based approaches. Moreover, SateLoc can achieve a median tracking error of 37.9 m with the distance constraint of adjacent estimated locations. More importantly, compared to fingerprinting-based approaches, SateLoc does not require the labor-intensive fingerprint acquisition process.\",\n \"Title: PRComm: Anti-Interference Cross-Technology Communication Based on Pseudo-random Sequence\\nYear: 2021\\n\\nAbstract: ABSTRACTWith the rapid development of the Internet of Things (IoT), we have seen a larger number of devices deployed with different wireless communication protocols (i.e., WiFi, ZigBee, Bluetooth). Working in the same place opens a new opportunity for these devices to communicate directly with each other, leveraging on Cross-technology Communication (CTC). However, since these devices operate in the same frequency band which results in the competition against each other for network resources, severe interfere may arise. In this paper, we explore pseudo-random sequence (PR sequence) to design a novel CTC protocol that enables low-cost direct communication between WiFi and ZigBee in noisy indoor environments. Pseudorandom sequence offers a unique statistical feature to accomplish both information transmission and synchronization between heterogeneous devices. We design a dynamic synchronous decoding strategy to handle interference coexisted among different wireless protocols. Our system does not require any modification of communication protocol and underlying hardware and firmware. We implement our system on commercial devices (Intel 5300 WiFi NIC and MicaZ CC2420), and conduct extensive experiments to evaluate the system performance in three typical scenarios. The experimental results show that the synchronization time of our approach is lower than 0.5 ms, and the accuracy is greater than 84% while the channel occupancy is as high as 50%.\",\n \"Title: Queec: QoE-aware Edge Computing for IoT Devices under Dynamic Workloads\\nYear: 2021\\n\\nAbstract: AbstractMany IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) that are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this article, we present Queec, a QoE-aware edge computing system for complex IoT event processing under dynamic workloads. With Queec, the complex IoT event processing tasks that are relatively computation-intensive for low-end IoT devices can be transparently offloaded to nearby edge nodes at runtime. We formulate the problem of scheduling multi-user tasks to multiple edge nodes as an optimization problem, which minimizes the overall offloading latency of all tasks while avoiding the overloading problem. We implement Queec on low-end IoT devices, edge nodes, and the cloud. We conduct extensive evaluations, and the results show that Queec reduces 56.98% of the offloading latency on average compared with the state-of-the-art under dynamic workloads, while incurring acceptable overhead.\",\n \"Title: PCube: scaling LoRa concurrent transmissions with reception diversities\\nYear: 2021\\n\\nAbstract: ABSTRACTThis paper presents the design and implementation of PCube, a phase-based parallel packet decoder for concurrent transmissions of LoRa nodes. The key enabling technology behind PCube is a novel air-channel phase measurement technique which is able to extract phase differences of air-channels between LoRa nodes and multiple antennas of a gateway. PCube leverages the reception diversities of multiple receiving antennas of a gateway and scales the concurrent transmissions of a large number of LoRa nodes, even exceeding the number of receiving antennas at a gateway. As a phase-based parallel decoder, PCube provides a new dimension to resolve collisions and supports more concurrent transmissions by complementing time and frequency based parallel decoders. PCube is implemented and evaluated with synchronized software defined radios and off-the-shelf LoRa nodes in both indoors and outdoors. Results demonstrate that PCube can substantially outperform state-of-the-art works in terms of aggregated throughput by 4.9× and the number of concurrent nodes by up to 5×. More importantly, PCube scales well with the number of receiving antennas of a gateway, which is promising to break the barrier of concurrent transmissions.\",\n \"Title: DynaKey: Dynamic Keystroke Tracking Using a Head-Mounted Camera Device\\nYear: 2022\\n\\nAbstract: Mobile and wearable devices have become more and more popular. However, the tiny touch screen leads to inefficient interaction with these devices, especially for text input. In this article, we propose DynaKey, which allows people to type on a virtual keyboard printed on a piece of article or drawn on a desk, for inputting text into a head-mounted camera device (e.g., smart glasses). By usi...\",\n \"Title: LiteNap: Downclocking LoRa Reception\\nYear: 2021\\n\\nAbstract: This paper presents LiteNap which improves the energy efficiency of LoRa by enabling LoRa nodes to operate in a downclocked ‘light sleep’ mode for packet reception. A fundamental limit that prevents radio downclocking is the Nyquist sampling theorem which demands the clock-rate being at least twice the bandwidth of LoRa chirps. Our study reveals under-sampled LoRa chirps suffer frequency aliasing ...\",\n \"Title: MDLdroid: A ChainSGD-Reduce Approach to Mobile Deep Learning for Personal Mobile Sensing\\nYear: 2022\\n\\nAbstract: Personal mobile sensing is fast permeating our daily lives to enable activity monitoring, healthcare and rehabilitation. Combined with deep learning, these applications have achieved significant success in recent years. Different from conventional cloud-based paradigms, running deep learning on devices offers several advantages including data privacy preservation and low-latency response for both ...\",\n \"Title: Message from the iThings 2021 General Chairs: iThings-GreenCom-CPSCom-SmartData-Cybermatics 2021\\nYear: 2021\\n\\nAbstract: Welcome to the 14th IEEE International Conference on Internet of Things (iThings 2021)! The iThings 2021 is a top publication venue sponsored by the IEEE Computer Society. The IEEE iThings 2021 Conference was planned to be held in Melbourne, Australia, December 06-08, 2021. Given the COVID- 19 pandemic and associated travel restrictions, as the safety of people is of the highest priority, the conference will be held virtually on December 06-08, 2021. On behalf of the Organizing Committee of iThings 2021, we would like to extend a warm welcome to all the delegates attending iThings-2021.\",\n \"Title: Smart Diagnosis: Deep Learning Boosted Driver Inattention Detection and Abnormal Driving Prediction\\nYear: 2022\\n\\nAbstract: Inattentive driving is one of the high-risk factors that causes a large number of traffic accidents every year. In this article, we aim to detect driver inattention leveraging on large-scale vehicle trajectory data while at the same time explore how do these inattentive events affect driver behaviors and what following reactions they may cause, especially, for commercial vehicles. Specifically, th...\",\n \"Title: GaitTracker: 3D Skeletal Tracking for Gait Analysis Based on Inertial Measurement Units\\nYear: 2022\\n\\nAbstract: AbstractGait rehabilitation is a common method of postoperative recovery after the user sustains an injury or disability. However, traditional gait rehabilitations are usually performed under the supervision of rehabilitation specialists, which implies that the patients cannot receive adequate gait assessment anytime and anywhere. In this article, we propose GaitTracker, a novel system to remotely and continuously perform gait monitoring and analysis by three-dimensional (3D) skeletal tracking in a wearable approach. Specifically, this system consists of four Inertial Measurement Units (IMU), which are attached on the shanks and thighs of the human body. According to the measurements from these IMUs, we can obtain the motion signals of lower limbs during gait rehabilitation. By adaptively synchronizing coordinate systems of different IMUs and building the geometric model of lower limbs, the exact gait movements can be reconstructed, and gait parameters can be extracted without any prior knowledge. GaitTracker offers three key features: (1) a unified 3D skeletal model to depict the precise gait movement and parameters in 3D space, (2) a coordinate system synchronization scheme to perform space synchronization over all the IMU sensors, and (3) an automatic estimation method for the user-specific geometric parameters. In this way, GaitTracker is able to accurately perform 3D skeletal tracking of lower limbs for gait analysis, such as evaluating the gait symmetry and the gait parameters including the swing/stance time. We implemented GaitTracker and evaluated its performance in real applications. The experimental results show that, the average error for skeleton angle estimation, joint displacement estimation, and gait parameter estimation are 3∘, 2.3%, and 3%, respectively, outperforming the state of the art.\",\n \"Title: Separating Voices from Multiple Sound Sources using 2D Microphone Array\\nYear: 2022\\n\\nAbstract: Voice assistant has been widely used for human-computer interaction and automatic meeting minutes. However, for multiple sound sources, the performance of speech recognition in voice assistant decreases dramatically. Therefore, it is crucial to separate multiple voices efficiently for an effective voice assistant application in multi-user scenarios. In this paper, we present a novel voice separation system using a 2D microphone array in multiple sound source scenarios. Specifically, we propose a spatial filtering-based method to iteratively estimate the Angle of Arrival (AoA) of each sound source and separate the voice signals with adaptive beamforming. We use BeamForming-based cross-Correlation (BF-Correlation) to accurately assess the performance of beamforming and automatically optimize the voice separation in the iterative framework. Different from cross-correlation, BF-Correlation further performs cross-correlation among the after-beamforming voice signals processed with each linear microphone array. In this way, the mutual interference from voice signals out of the specified direction can be effectively suppressed or mitigated via the spatial filtering technique. We implement a prototype system and evaluate its performance in real environments. Experimental results show that the average AoA error is 1.4 degree and the average ratio of automatic speech recognition accuracy is 90.2% in the presence of three sound sources.\",\n \"Title: Edge-Eye: Rectifying Millimeter-level Edge Deviation in Manufacturing using Camera-enabled IoT Edge Device\\nYear: 2022\\n\\nAbstract: Irradiated Cross-linked Polyethylene Foam (IXPE) has been one of the most commonly used materials in industry. During the production process of IXPE sheets, their edges need keep aligned strictly, otherwise, they could quickly get out of the border of the rolling plate and cause the huge economic loss. In this paper, we propose a camera-enabled approach, called Edge-Eye, to rectify the edge deviation automatically for IXPE production with millimeter-level accuracy. We deploy a commercial camera with mobile edge node in front of the IXPE sheet to continuously detect and rectify the edge deviation. Particularly, to handle the complex production en-vironment when extracting the edge of IXPE sheet, we deploy a pair of reference bars with high-contrast colors to efficiently differ-entiate the sheet edge from the background. Then, we propose a Bi-direction Edge Tracking method to perform the edge detection from both vertical and horizontal aspects. To realize the rectification using mobile edge nodes with limited computing resources, we reduce the cost of computation by extracting the Minimized Region of Interest, i.e., the edge area overlapped with the higher contrast reference bar on both sides. We further design a negative feedback control system with multi-stage feedback regulation mechanism, keeping the edge deviation within millimeter-level. We implemented Edge-Eye on the ARM64 platform and performed evaluation in the practical IXPE production process. The experimental results show that Edge-Eye achieves the average accuracy of 5mm for the edge deviation rectification, with the average latency of 200ms for edge deviation detection. During the process of 20-month real deployment for 36 production lines, 66 manpower per day (90% of the overall manpower) has been saved, and the utilization rate of IXPE material increases from 87% to 94%.\",\n \"Title: Your Breath Doesn't Lie: Multi-user Authentication by Sensing Respiration Using mmWave Radar\\nYear: 2022\\n\\nAbstract: User authentication is critical to privacy preservation. Most of the existing works focus on single-user authentication, which may not work efficiently and practically in multi-user scenarios. To this end, we present a Multi-user Authentication system (M-Auth) that employs a single COTS mmWave radar to capture the user's unique breathing pattern. It exploits the phenomenon that radio frequency (RF) signals are affected by chest displacements due to breathing. We specifically design an auxiliary rotating gadget to dynamically adjust radar orientation, making it more effective in capturing respiration signals from multiple users. To profile individual components from the entangled RF signals, we leverage mmWave's high directivity to locate each user and separately focus on reflections from different positions. We propose a signal energy comparison method to eliminate the irrelevant body movements for preserving fine-grained respiration traits. Afterward, we develop a feature selection pipeline to elicit the most informative features and train a machine learning-based classifier to identify each user. M-Auth is practical due to its non-contact and passive nature, and it is secure as respiration is unique and difficult-to-forge. Extensive experiments involving 37 participants demonstrate that M-Auth is effective in verifying legitimate users and thwarting spoofing attacks, with an authentication accuracy of over 96 % and an attack detection rate of over 95%.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":130,"string":"130"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13279,"string":"13,279"}}},{"rowIdx":13280,"cells":{"question_id":{"kind":"string","value":"S Das"},"question":{"kind":"string","value":"Generate an abstract for the title \"Mobility management for VoIP service: Mobile IP vs. SIP\" using the following items: Wireless, VoIP, Mobility management, Mobile IP, SIP-based solutions."},"answer":{"kind":"string","value":"Wireless Internet access has gained significant attention as wireless/mobile communications and networking become widespread. The voice over IP service is likely to play a key role in the convergence of IP-based Internet and mobile cellular networks. We explore different mobility management schemes from the perspective of VoIP services, with a focus on Mobile IP and session initiation protocol. After illustrating the signaling message flows in these two protocols for diverse cases of mobility management, we propose a shadow registration concept to reduce the interdomain handoff (macro-mobility) delay in the VoIP service in mobile environments. We also analytically compute and compare the delay and disruption time for exchanging signaling messages associated with the Mobile IP and SIP-based solutions."},"correct_answer":{"kind":"string","value":"Wireless Internet access has gained significant attention as wireless/mobile communications and networking become widespread. The voice over IP service is likely to play a key role in the convergence of IP-based Internet and mobile cellular networks. We explore different mobility management schemes from the perspective of VoIP services, with a focus on Mobile IP and session initiation protocol. After illustrating the signaling message flows in these two protocols for diverse cases of mobility management, we propose a shadow registration concept to reduce the interdomain handoff (macro-mobility) delay in the VoIP service in mobile environments. We also analytically compute and compare the delay and disruption time for exchanging signaling messages associated with the Mobile IP and SIP-based solutions."},"documents":{"kind":"list like","value":["Title: A Multiobjective Evolutionary-Simplex Hybrid Approach for the Optimization of Differential Equation Models of Gene Networks\nYear: 2008\n\nAbstract: This paper describes genetic and hybrid approaches for multiobjective optimization using a numerical measure called fuzzy dominance. Fuzzy dominance is used when implementing tournament selection within the genetic algorithm (GA). In the hybrid version, it is also used to carry out a Nelder-Mead simplex-based local search. The proposed GA is shown to perform better than NSGA-II and SPEA-2 on standard benchmarks, as well as for the optimization of a genetic model for flowering time control in rice. Adding the local search achieves faster convergence, an important feature in computationally intensive optimization of gene networks. The hybrid version also compares well with ParEGO on a few other benchmarks. The proposed hybrid algorithm is then applied to estimate the parameters of an elaborate gene network model of flowering time control in Arabidopsis. Overall solution quality is quite good by biological standards. Tradeoffs are discussed between accuracy in gene activity levels versus in the plant traits that they influence. These tradeoffs suggest that data mining the Pareto front may be useful in bioinformatics.","Title: Peristaltic flow of viscoelastic fluid with fractional Maxwell model through a channel\nYear: 2010\n\nAbstract: The paper presents the transportation of viscoelastic fluid with fractional Maxwell model by peristalsis through a channel under long wavelength and low Reynolds number approximations. The propagation of wall of channel is taken as sinusoidal wave propagation (contraction and relaxation). Homotopy perturbation method (HPM) and Adomian decomposition method (ADM) are used to obtain the analytical approximate solutions of the problem. The expressions of axial velocity, volume flow rate and pressure gradient are obtained. The effects of fractional parameters (@a), relaxation time (@l\\\"1) and amplitude (@f) on the pressure difference and friction force across one wavelength are calculated numerically for different particular cases and depicted through graphs.","Title: IDMP-based fast handoffs and paging in IP-based 4G mobile networks\nYear: 2002\n\nAbstract: We consider the use of our previously proposed Intra-Domain Mobility Management Protocol (IDMP) in fourth-generation mobile networks. On evaluating the heterogeneous access technologies, cellular layouts, and application characteristics of 4G environments, we realize a need to reduce both handoff latency and the frequency of mobility-related signaling. We first present IDMP's fast intradomain handoff mechanism that uses a duration-limited proactive packet multicasting solution. We quantify the expected buffering requirements of our proposed multicasting scheme for typical 4G network characteristics and compare it with alternative IP-based fast handoff solutions. We also present a paging scheme under IDMP that replicates the current cellular paging structure. Our paging mechanism supports generic paging strategies and can significantly reduce the mobility-related IP signaling load","Title: Ge based nanostructures for electronic and photonic devices\nYear: 2010\n\nAbstract: Self-assembled GexSi1−x islands were grown on Si(001) substrates by solid source molecular beam epitaxy. Two different morphological shapes with different sizes were evolved by tuning the growth time at a constant deposition temperature. Micro-Raman analysis was carried out to investigate the composition, intermixing and strain of resultant islands. The observed broad infra-red photoluminescence signal from grown samples was associated with radiative recombination of holes confined in the Ge islands and electrons localized in the Si buffer layer. The PL peak position and intensity were found to be influenced by the islands size and intermixing of Si and Ge. The electrical properties of the islands were studied through photoexcited I–V characteristics and current imaging using conducting mode atomic force microscopy.","Title: A QoS Network Management System for Robust and Reliable Multimedia Services\nYear: 2002\n\nAbstract: In this paper, we introduce a practical network management system with QoS for multimedia traffic. Today, multimedia applications have evolved significantly and have become an essential part of the Internet. In order to effectively support the newly emerging network traffic, the underlying network protocols need to be aware of the characteristics and demands of the traffic. The proposed network system is ready to address the varied characteristics of multimedia traffic and can assure a high degree of adherence to the quality of service demanded from it. Considering the need for reliable QoS services, the system is also equipped with fault tolerance capability by provisioning multiple QoS paths. Moreover, the system has been practically designed and implemented to provide \"cost-effective\" QoS support with respect to control overhead. It deploys measurement-based QoS path computation and call admission scheme which may be deemed ineffective for bursty multimedia traffic. However, results conclusively prove that provisioning multiple paths and utilizing them in parallel in our system not only provides high fault tolerance capability but also effectively accommodates multimedia traffic by relieving its burstiness with multiple paths. We present the architecture of the system and discuss the benefits gained for multimedia traffic.","Title: Media-independent pre-authentication supporting secure interdomain handover optimization\nYear: 2008\n\nAbstract: Handovers may cause delays and packet losses that affect real-time communication performance. Mobility protocols at several layers are designed to support handover, but they need to be optimized to ensure high-quality application performance. Existing optimization techniques are not sufficient to take care of interdomain and intertechnology handovers involving different access technologies, such as Wi-Fi, GSM, CDMA, and WiMAX. We categorize several types of handover, describe handover delay components, and propose a handover optimization framework called media independent pre-authentication that can provide optimizations for interdomain and intertechnology handover in a manner that is transparent to mobility management protocols. In addition, we also present experimental results demonstrating that this framework can achieve a significant reduction in handover delays for both network-layer and application-layer mobility management protocols.","Title: Distributed paging and registration in wireless networks\nYear: 2005\n\nAbstract: The success of current and future wireless networks depends on their ability to provide connections to mobile terminals anywhere and at any time. It is therefore of crucial importance that wireless networks are able to quickly and efficiently locate mobile users at the time of an incoming call, which is achieved in current networks through a combination of paging and registration. In this article we present novel distributed paging and registration procedures that are naturally suited to future distributed wireless network architectures. In addition, the distributed nature of these strategies allows us to effectively balance the paging and registration traffic and the required signal processing throughout the network and alleviate any potential overloads of individual base stations.","Title: Practical QoS Network System with Fault Tolerance\nYear: 2003\n\nAbstract: In this paper, we present an "emulation environment" forthe design and planning of intranets. Intranets must support Quality-ofService(QoS) for real-time traffic and must be fault-tolerant for missioncriticalapplications such as tele-medicine. Our goal is to design the intranetas an overlay network within the IP network and to manage traffic in the intranetso that QoS requirements are satisfied. Routing is a key componentof this architecture, in that it enables the efficient use of the...","Title: Channel routing in Manhattan-diagonal model\nYear: 1996\n\nAbstract: This paper presents a new technique of channel routing based on the Manhattan-Diagonal (MD) model. The layout grid is assumed to consist of two layers with tracks in horizontal, vertical and /spl plusmn/45/spl deg/ directions. First, we consider the non-overlap model and present a simple O(l,d) time algorithm that routes an arbitrary channel with no cyclic vertical constraints in w tracks, where l is the length of the channel, d is the channel density, and d/spl les/w/spl les/(d+1). Next, we describe an output-sensitive algorithm that can route general channels with cyclic vertical constraints using w tracks, in O(l,w) time allowing overlapping of wiring segments in two layers. The router outputs an 18-track solution for the Deutsch's difficult example, a 2-track solution for Burstein's difficult channel, and a 15-track solution for cycle.tough without inserting any extra row or column. Apart from quick termination, the proposed algorithms provide solutions with significantly low via count and reduced wire length. The study thus reveals the superiority of MD-routing strategy to classical techniques.","Title: User-transparent reconfiguration method for self-organizing IP multimedia subsystem\nYear: 2011\n\nAbstract: The NGN (Next Generation Network), which can provide advanced multimedia services over an all-IP based network, has been the subject of considerable attention for several years. While there have been tremendous efforts to develop its architecture and protocols, especially for IMS, a key technology of the NGN, its wide deployment is still a long way off. However, efforts to create an advanced signaling infrastructure able to meet many requirements have resulted in a large number of functional components and interactions between these components. Thus, the carriers are trying to explore effective ways to deploy IMS while offering value-added services. As one such approach, we have proposed a self-organizing IMS. A self-organizing IMS enables IMS functional components and corresponding physical nodes to adapt dynamically and automatically based on actual conditions such as network load and available system resources while continuing IMS operation. To realize this, service continuity for users is an important requirement when a reconfiguration occurs during operation. In this paper, we propose a mechanism that will provide service continuity to users without any impact on clients by extending the SBC (Session Border Controller). Furthermore, we implement it, show its behavior and evaluate its processing time.","Title: A comparison of reverse link access schemes for next-generation cellular systems\nYear: 2006\n\nAbstract: We consider different transmission options on the reverse link of cellular systems for packet data. The different transmission options are classified based on the nature of in-cell and out-of-cell interference power statistics. The categories are: (a) no in-cell interference, averaged out-of cell interference; (b) no in-cell interference, bursty out-of-cell interference; and (c) averaged in-cell interference, averaged out-of-cell interference. Depending on whether the reverse link transmission is time multiplexed one user at a time transmission, or simultaneous transmission by multiple users with or without in-cell orthogonality, the interference structure falls into one of the above three categories. We analyze the throughput performance of the system in each of these cases when incremental redundancy is employed to combat uncertainty in the interference power. We compare the different options under an in-cell rise-over-thermal (IROT) constraint and provide some insights for reverse link design for next-generation cellular systems. Our results show that transmission option (a) with an optimal choice of the number of simultaneous transmissions within the cell has the best performance over several different scenarios. Time-multiplexed transmissions, despite the bursty out-of-cell interference power structure, has throughput comparable to that of a multiple-user orthogonal transmission system for small cells where mobiles have sufficient transmit power to meet the target IROT.","Title: Parallel algorithms for single row routing in narrow streets\nYear: 1995\n\nAbstract: In this paper, the problem, of obtaining an optimal layout for the single row routing problem in narrow streets (maximum number of tracks in either street is at most 2) on parallel computers is studied. An O(log n) time parallel algorithm with O(n/logn) processors on a CREW PRAM and an O(log/sup 2/n) time cost-optimal algorithm on a tree machine is obtained.","Title: Unsteady MHD Couette flow in a rotating system\nYear: 2009\n\nAbstract: The unsteady hydromagnetic Couette flow of a viscous incompressible electrically conducting fluid in a rotating system has been considered. An exact solution of the governing equations has been obtained by using a Laplace transform. Solutions for the velocity distributions as well as shear stresses have been obtained for small times as well as for large times. It is found that for large times the primary velocity decreases with increase in the rotation parameter K^2 while it increases with increase in the magnetic parameter M^2. It is also found that with increase in K^2, the secondary velocity v\"1 decreases near the stationary plate while it increases near the moving plate. On the other hand, the secondary velocity decreases with increase in the magnetic parameter.","Title: Design-Intent Coverage—A New Paradigm for Formal Property Verification\nYear: 2006\n\nAbstract: It is essential to formally ascertain whether the register-transfer level (RTL) validation effort effectively guarantees the correctness with respect to the design's architectural intent. The design's architectural intent can be expressed in formal properties. However, due to the capacity limitations of formal verification, these architectural properties cannot be directly verified on the RTL. As a result, a set of lower level RTL properties are developed and verified against the RTL modules. In a top-down design approach, the architect would ideally like to formally guarantee the coverage of the architectural intent at the time of creating the specifications for the component RTL modules (that is, before they are passed to the designers for implementation). In this paper, the authors present: 1) a method for checking whether the RTL properties are covering the architectural properties, that is, whether verifying the RTL properties guarantees the correctness of the design's architectural intent; 2) a method to identify which architectural properties are still uncovered, that is, not guaranteed by the RTL properties; and 3) a methodology for representing the gap between the specifications in a legible form","Title: Homotopy analysis method for solving fractional hyperbolic partial differential equations\nYear: 2011\n\nAbstract: In the present paper, the solutions of the hyperbolic partial differential equation with fractional time derivative of order α (1","Title: CMOS compatible bulk micromachined silicon piezoresistive accelerometer with low off-axis sensitivity\nYear: 2006\n\nAbstract: The CMOS compatible bulk micromachined piezoresistive accelerometer presented in this paper consists of four flexures supporting a proof mass. Four pairs of boron-diffused piezoresistors are located at maximum stress points on the flexures near the proof mass and frame ends. Because of the opposite nature of stress at the two ends, these piezoresistors can be connected to form a Wheatstone bridge such that the off-axis responses are practically cancelled while the on-axis (along perpendicular to proof mass) response is maximized. The device is simulated using CoventorWare. In the fabrication process, dual-doped TMAH solution is used for wet anisotropic etching. The novelty of this etching process is that the bulk micromachining can be performed after aluminum metallization. The etched surface is also smooth. The fabrication is thus CMOS compatible. The accelerometer exhibits good linearity over 0–10g.","Title: Emotion clustering from stimulated electroencephalographic signals using a Duffing oscillator\nYear: 2010\n\nAbstract: The paper proposes an alternative approach to emotion recognition from stimulated EEG signals using Duffing oscillator. Reported works on emotion clustering generally employ the principles of supervised learning. Unfortunately, because of noisy and limited feature set, the classification problem often suffers from high inaccuracy. This has been overcome in this paper by submitting the EEG signals directly to a Duffing oscillator and the phase portraits constructed from its time-response demonstrate structural similarity to similar emotion excitatory stimuli. The accuracy in clustering was experimentally validated even with injection of Gaussian noise over the EEG signal up to a signal-to-noise ratio of 25 dB. The results of clustering in presence of low signal-to-noise ratio confirm the robustness of the proposed scheme.","Title: An interval digraph in relation to its associated bipartite graph\nYear: 1993\n\nAbstract: The intersection digraph of a family of ordered pairs of sets {( S v , T v ): v ∈ V } is the digraph D ( V , E ) such that uv ∈ E if and only if S u ∩ T v ≠0. Internal digraphs are those intersection digraphs for which the subsets are intervals on the real line. In a previous paper, they were characterized in terms of Ferrers digraphs and a close relationship was obtained between an interval digraph and a digraph of Ferrers dimension 2. In order to characterize a digraph D of Ferrers dimension 2, Cogis associated an undirected graph H ( D ) with D in a suitable way, the vertices of H ( D ) corresponding to the zeros of the adjacency matrix of D . He proved that D has Ferrers dimension at most 2 if and only if H ( D ) is bipartite. Depending on the above characterization, this paper first obtains some properties of a digraph of Ferrers dimension 2; then it is shown how the notion of interior edges is related to an interval digraph.","Title: Approximate analytical solutions of fractional gas dynamic equations\nYear: 2011\n\nAbstract: In this article, differential transform method (DTM) has been successfully applied to obtain the approximate analytical solutions of the nonlinear homogeneous and non-homogeneous gas dynamic equations, shock wave equation and shallow water equations with fractional order time derivatives. The true beauty of the article is manifested in its emphatic application of Caputo fractional order time derivative on the classical equations with the achievement of the highly accurate solutions by the known series solutions and even for more complicated nonlinear fractional partial differential equations (PDEs). The method is really capable of reducing the size of the computational work besides being effective and convenient for solving fractional nonlinear equations. Numerical results for different particular cases of the equations are depicted through graphs.","Title: Fade-resistant CDMA transmission and reception over time-varying wireless channels\nYear: 2006\n\nAbstract: Physical limitations due to fading and interference in wireless channels pose technical challenges in achieving reliable communication. To adapt to this adverse radio environment efficiently, we propose a fade-resistant transmission system based on code division multiple access (CDMA) technology. In this system, all the symbols of a user are transmitted simultaneously using parallel sequences over a time-varying wireless channel. We derive the relationship between the self-interference and the multiple-access interference in such a system. We prove that instantaneous signal-to-interference-plus-noise ratio (SINR) at the matched filter output of a system with parallel sequences is statistically higher than that of the conventional CDMA system. Numerical results demonstrate that the parallel sequence transmission scheme can provide capacity improvement over the conventional CDMA system depending on variations in the channel.","Title: An Economical RC Active Equalizer with Applications in Communications\nYear: 1977\n\nAbstract: A simple active equalizer network employing two operational amplifiers (OA's), five resistors, and a pair of capacitors is presented. The same network can be used for both amplitude and delay equalizations. Experimental results show that the circuit can be conveniently used for amplitude equalization in voice communications, and for delay equalization in private line data circuits.","Title: Noisy recurrent neural networks: the discrete-time case.\nYear: 1998\n\nAbstract: The results of the companion paper on the behavior of continuous-time recurrent neural networks (RNN) are specialized to the discrete-time (so-called time-lagged recurrent) case. Uniform boundedness of the first and second moment sequences of the trajectory is established. For practical design purposes, estimates and bounds on the bias and variance sequences of the stochastic discrete-time RNN are derived. A script file for estimating the variance bounds for specified designs is provided in an Appendix.","Title: Gap Analysis and Deployment Architectures for 3GPP2 MMD Networks\nYear: 2007\n\nAbstract: Mobile and fixed operators are offering broadband access to the Internet by either adding wireless access to their existing core networks (e.g., CDMA IxEVDO. GPRS, and WiFi) or continuing to evolve existing broadband infrastructures (e.g., DSL, Cable and FTTx). Consequently, IMS and MMD architectures as defined by 3GPP and 3GPP2 are becoming increasingly important to network operators, in particul...","Title: A novel strategy for plant breeding based on simulations of gene network models.\nYear: 2013\n\nAbstract: This paper describes a new computational method to make predictions on the outcome of pair-wise crosses of plant lines limiting expensive laboratory breeding experiments to carry out crosses of the most promising pairs of lines. Compared to the well-known marker assisted breeding, the proposed approach approximates plant gene regulatory networks to estimate outcomes of all possible crossovers, thereby taking into account epistatic relationships between alleles. The proposed method is tested and compare with various breeding approaches on artificial NK landscape models and an extensive synthetic model of Arabidopsis plant's flowering time system. The results show our method outperforms other breeding strategies.","Title: Detection of hard cuts and gradual transitions from video using fuzzy logic\nYear: 2008\n\nAbstract: In this paper, we introduce a novel approach for the detection and classification of shot boundaries based on interval type-2 fuzzy logic. We consider fuzzy low level features (Fuzzy Histogram (FH) and Fuzzy Co-occurrence Matrix (FCM)) and evaluate a fuzzy rule base modelled by interval type-2 sets to detect cut boundaries. After detection of cuts, a recovery mechanism is used to reduce false positives caused by illumination variation (e.g., camera flash light). The fuzzy rules used for gradual transition form a unified approach to detect various types gradual transitions like dissolves, fades and wipes, by a single method. Test results indicate that very high precision and recall could be achieved for the test videos, particularly the recall value of gradual transitions.","Title: Analytical solution of a fractional diffusion equation by variational iteration method\nYear: 2009\n\nAbstract: In the present paper the Analytical approximate solution of a fractional diffusion equation is deduced with the help of powerful Variational Iteration method. By using an initial value, the explicit solutions of the equation for different cases have been derived, which accelerate the rapid convergence of the series solution. The present method performs extremely well in terms of efficiency and simplicity. Numerical results for different particular cases of the problem are presented graphically.","Title: Realizing mobile wireless Internet telephony and streaming multimedia testbed\nYear: 2004\n\nAbstract: Streaming real-time multimedia content over the Internet is gaining momentum in the communications, entertainment, music and interactive game industries as well as in the military. In general, streaming applications include IP telephony, multimedia broadcasts and various interactive applications such as multi-party conferences, collaborations and multiplayer games. Successfully realizing such applications in a highly mobile environment, however, presents many research challenges. In order to investigate such challenges and demonstrate viable solutions, we have developed an experimental indoor and outdoor testbed laboratory. By implementing standard IETF protocols into this testbed, we have demonstrated the basic functionalities required of the mobile wireless Internet to successfully support mobile multimedia access. These requirements include signaling, registration, dynamic configuration, mobility binding, location management, Authentication Authorization and Accounting (AAA), and quality of service over a variety of radio access network (RAN) technologies (e.g. 802.11b, CDMA/GPRS). In this paper, we describe this testbed and discuss important design issues and tradeoffs. We detail the incorporation and inter-relation of a wide catalog of IETF protocols-such as SIP, SAP, SDP, RTP/RTCP/RTSP, MGCP, variants of Mobile-IP, DRCP, HMMP, PANA, and DSNP-to achieve our goals. We believe that the results and experiences obtained from this experimental testbed will advance the understanding of the pertinent deployment issues for a Mobile Wireless Internet.","Title: Extrapolated Biogeography-Based Optimization (eBBO) for Global Numerical Optimization and Microstrip Patch Antenna Design\nYear: 2010\n\nAbstract: Biogeography-Based Optimization (BBO) uses the idea of probabilistically sharing features between solutions based on the solutions' fitness values. Therefore, its exploitation ability is good but it lacks in exploration ability. In this paper, the authors extend the original BBO and propose a hybrid version combined with ePSO (particle swarm optimization with extrapolation technique), namely eBBO, for unconstrained global numerical optimization problems in the continuous domain. eBBO combines the exploitation ability of BBO with the exploration ability of ePSO effectively, which can generate global optimum solutions. To validate the performance of eBBO, experiments have been conducted on 23 standard benchmark problems with a range of dimensions and diverse complexities and compared with original BBO and other versions of BBO in terms of the quality of the final solution and the convergence rate. Influence of population size and scalability study is also considered and results are compared with statistical paired t-test. Experimental analysis indicates that the proposed approach is effective and efficient and improves the exploration ability of BBO.","Title: A 10-Year Mechatronics Curriculum Development Initiative: Relevance, Content, and Results—Part I\nYear: 2010\n\nAbstract: This paper describes the first phase of a Mechatronics Curriculum Development effort-the design of an 驴Introduction to Mechatronics驴 course, the infusion of mechatronics activities throughout the curriculum and in outreach activities, and assessment results. In addition, the relevance and impact of such a curriculum on the education of engineers in relation to the evolving global economy is discussed. The second and third phases of this effort included development of two advanced mechatronics courses and the formulation of a mechatronics curriculum, which will be addressed in a companion paper, 驴A 10-Year Mechatronics Curriculum Development Initiative: Relevance, Content, and Results-Part II驴 (IEEE Transactions on Education, vol. 53, no. 2, May 2010).","Title: Interaction between Griffith cracks in a sandwiched orthotropic layer\nYear: 2003\n\nAbstract: A study is made of the interaction between three coplanar Griffith cracks which are located symmetrically in the midplane of an orthotropic layer of finite thickness 2h sandwiched between two identical orthotropic half planes. The Fourier transform technique is used to reduce the elastostatic problem to a set of integral equations which have been solved by using the finite Hilbert transform and Cooke's results. Analytical expressions for the stress intensity factors at the tips of cracks are obtained for large values of h. Numerical results concerning the interaction effects are presented with physical significance. It is shown that interaction effects are either shielding or amplification depending on the location of cracks, spacing of crack-tips, and the thickness of the layer. The stress magnification factors at the crack-tips are also calculated.","Title: An approximate analytical solution of one-dimensional phase change problems in a finite domain\nYear: 2011\n\nAbstract: In this paper, we present an approximate analytical solution for solving one dimensional two phase Stefan problem. The finite sine transform technique is used to convert the non dimensional form from a space domain to a wave number domain. Inverse finite sine transform is used to obtain the desired solution. The location of moving interface during freezing process in a finite domain is studied and the result thus obtained are discussed graphically. The whole analysis is presented in a non dimensional form.","Title: Analysis and prevention of convex corner undercutting in bulk micromachined silicon microstructures\nYear: 2006\n\nAbstract: Convex corner undercutting in <100> silicon is an undesired phenomena during bulk micromachining of crystalline silicon substrate using anisotropic wet chemical etching process. The present investigation concentrates on the studies of convex corner undercutting at the free end of silicon cantilever beams released by anisotropic etching process. It also reports a simple, space efficient compensation design for complete prevention of corner deformation. Various compensation patterns such as square, rectangle and superposition of square and rectangular blocks of various dimensions have been employed at the free end corners of cantilever beam to protect corner deformation due to undercutting. The experiment was carried out in 44wt.% KOH at 70°C using <100> oriented silicon wafer. Both n-type and p-type silicon wafers were used to study the variations in the nature of corner deformation. A simple empirical relation has been obtained from the experimental data to calculate the lateral dimensions of the compensation layout from the total etch depth required to release the structure.","Title: An approximate analytical solution of time-fractional telegraph equation\nYear: 2011\n\nAbstract: In this article, the powerful, easy-to-use and effective approximate analytical mathematical tool like homotopy analysis method (HAM) is used to solve the telegraph equation with fractional time derivative α (1<α⩽2). By using initial values, the explicit solutions of telegraph equation for different particular cases have been derived. The numerical solutions show that only a few iterations are needed to obtain accurate approximate solutions. The method performs extremely well in terms of efficiency and simplicity to solve this historical model.","Title: Binary knapsack problems with random budgets\nYear: 2003\n\nAbstract: The binary knapsack problem is a combinatorial optimization problem in which a subset of a given set of elements needs to be chosen in order to maximize profit, given a budget constraint. In this paper, we study a stochastic version of the problem in which the budget is random. We propose two different formulations of this problem, based on different ways of handling infeasibility, and propose an exact algorithm and a local search-based heuristic to solve the problems represented by these formulations. We also present the results from some computational experiments.","Title: Robust Chaos - The Theoretical Formulation And Experimental Evidence\nYear: 1999\n\nAbstract: Practical applications of chaos are likely to face problems of reliability because of the existence of multiple attractors and periodic windows in most chaotic dynamical systems. In this paper we show that robust chaos, defined by the absence of periodic windows and coexisting attractors in some neighborhood of the parameter space, can occur in piecewise smooth systems and obtain the conditions of its occurrence. We demonstrate experimental evidence of this phenomenon in power electronic circuits.","Title: Architecture of a VLSI Chip for Modeling Amino Acid Sequence in Proteins\nYear: 1996\n\nAbstract: A Cellular Automata (CA) based model of amino acids which constitute different types protein is re-ported in this paper. A simulation engine is being developed based on this model to study protein behaviour.","Title: On the solutions of fractional Swift Hohenberg equation with dispersion\nYear: 2013\n\nAbstract: In this article, the approximate solutions of the non-linear Swift Hohenberg equation with fractional time derivative in the presence of dispersive term have been obtained. The fractional derivative is described in Caputo sense. Time fractional nonlinear partial differential equations in the presence of dispersion and bifurcation parameters have been computed numerically to predict hydrodynamic fluctuations at convective instability for different particular cases and results are depicted through graphs.","Title: Low-complexity iterative multiuser detection and decoding for real-time applications\nYear: 2005\n\nAbstract: This paper presents a low-complexity multiuser decoding technique that can be implemented in real time for a convolutionally coded direct sequence code division multiple access (DS-CDMA) system. The main contribution, denoted here as the iterative prior update (IPU), consists of iterative interference cancellation and prior updates on sequences of coded bits combined with M-algorithm and list decoding. We illustrate performance gains over other low-complexity sequence detection and decoding strategies and argue that the algorithm converges within a few iterations and requires only a small size buffer for keeping track of the priors along iterations. The fact that the we can use existing available architectures for Viterbi decoding with slight modifications and can meet the real-time processing constraints makes the IPU algorithm an attractive alternative for cellular systems.","Title: Open-loop simulations of the primate saccadic system using burst cell discharge from the superior colliculus.\nYear: 1995\n\nAbstract: Saccade-related burst neurons (SRBNs) in the monkey superior colliculus (SC) have been hypothesized to provide the brainstem saccadic burst generator with the dynamic error signal and the movement initiating trigger signal. To test this claim, we performed two sets of open-loop simulations on a burst generator model with the local feedback disconnected using experimentally obtained SRBN activity as both the driving and trigger signal inputs to the model. First, using neural data obtained from cells located near the middle of the rostral to caudal extent of the SC, the internal parameters of the model were optimized by means of a stochastic hillclimbing algorithm to produce an intermediate-sized saccade. The parameter values obtained from the optimization were then fixed and additional simulations were done using the experimental data from rostral collicular neurons (small saccades) and from more caudal neurons (large saccades); the model generated realistic saccades, matching both position and velocity profiles of real saccades to the centers of the movement fields of all these cells. Second, the model was driven by SRBN activity affiliated with interrupted saccades, the resumed eye movements observed following electrical stimulation of the omnipause region. Once again, the model produced eye movements that closely resembled the interrupted saccades produced by such simulations, but minor readjustment of parameters reflecting the weight of the projection of the trigger signal was required. Our study demonstrates that a model of the burst generator produces reasonably realistic saccades when driven with actual samples of SRBN discharges.","Title: Application of homotopy perturbation method and homotopy analysis method to fractional vibration equation\nYear: 2011\n\nAbstract: In this paper, the approximate analytical solutions of the mathematical model of vibration equation with fractional-order time derivative β (1","Title: Bulk micromachining of silicon in TMAH-based etchants for aluminum passivation and smooth surface\nYear: 2006\n\nAbstract: The fabrication of silicon based micromechanical sensors often requires bulk silicon etching after aluminum metallization. All wet silicon etchants including ordinary undoped tetramethyl ammonium hydroxide (TMAH)–water solution attack the overlaying aluminum metal interconnect during the anisotropic etching of (100) silicon. This paper presents a TMAH–water based etching recipe to achieve high silicon etch rate, a smooth etched surface and almost total protection of the exposed aluminum metallization. The etch rate measurements of (100) silicon, silicon dioxide and aluminum along with the morphology studies of etched surfaces are performed on both n-type and p-type silicon wafers at different concentrations (2, 5, 10 and 15%) for undoped TMAH treated at various temperatures as well as for TMAH solution doped separately and simultaneously with silicic acid and ammonium peroxodisulphate (AP). It is established through a detailed study that 5% TMAH–water solution dual doped with 38gm/l silicic acid and 7gm/l AP yields a reasonably high (100) silicon etch rate of 70μm/h at 80°C, very small etch rates of SiO2 and pure aluminum (around 80Å/h and 50Å/h, respectively), and a smooth surface (±7nm) at a bath temperature of 80°C. The etchant has been successfully used for fabricating several MEMS structures like piezoresistive accelerometer, vaporizing liquid micro-thruster and flow sensor. In all cases, the bulk micromachining is carried out after the formation of aluminum interconnects which is found to remain unaffected during the prolonged etching process at 80°C. The TMAH based etchant may be attractive in industry due to its compatibility with standard CMOS process.","Title: A distributed model of the saccade system: simulations of temporally perturbed saccades using position and velocity feedback.\nYear: 1999\n\nAbstract: Interrupted saccades, movements that are perturbed in mid-flight by pulsatile electrical stimulation in the omnipause neuron region, are known to achieve final eye displacements with accuracies that are similar to normal saccades even in the absence of visual input following the perturbation. In an attempt to explain the neurophysiological basis for this phenomenon, the present paper describes a model of the saccadic system that represents the superior colliculus as a dynamic two-dimensional, topographically arranged array of laterally interconnected units. A distributed feedback pathway to the colliculus from downstream elements, providing both eye position and velocity signals is incorporated in the model. With the help of a training procedure based on a genetic algorithm and gradient descent, the model is optimized to produce both the normal as well as slow saccades with similar accuracy. The slow movements are included in the training set to mimic the accurate saccades that occur despite alterations in alertness, as well as following various degenerative oculomotor diseases. Although interrupted saccades were not included in the training set, the model is able to produce accurate movement of this type as an emergent property for a wide range of perturbed eye velocity trajectories. Our model demonstrates for the first time, that by means of an appropriate feedback mechanism, a single-layered dynamic network can be made to retain a distributed memory of the remaining ocular displacement error even for interrupted and slow saccades. These results support the hypothesis that saccades are controlled by error feedback of signals that code efference copies of eye motion, and further, suggest a possible answer to a long standing question about the kind of the feedback signal, if any, that is received by the superior colliculus during saccadic eye movements.","Title: Max-SINR ISI/ICI-Shaping Multicarrier Communication Over the Doubly Dispersive Channel\nYear: 2007\n\nAbstract: For communication over doubly dispersive channels, we consider the design of multicarrier modulation (MCM) schemes based on time-frequency shifts of prototype pulses. We consider the case where the receiver knows the channel state and the transmitter knows the channel statistics (e.g., delay spread and Doppler spread) but not the channel state. Previous work has examined MCM pulses designed for suppression of inter-symbol/inter-carrier interference (ISI/ICI) subject to orthogonal or biorthogonal constraints. In doubly dispersive channels, however, complete suppression of ISI/ICI is impossible, and the ISI/ICI pattern generated by these (bi)orthogonal schemes can be difficult to equalize, especially when operating at high bandwidth efficiency. We propose a different approach to MCM pulse design, whereby a limited expanse of ISI/ICI is tolerated in modulation/demodulation and treated near-optimally by a downstream equalizer. Specifically, we propose MCM pulse designs that maximize a signal-to-interference-plus-noise ratio (SINR) which suppresses ISI/ICI outside a target pattern. In addition, we propose two low-complexity turbo equalizers, based on minimum mean-squared error and maximum likelihood criteria, respectively, that leverage the structure of the target ISI/ICI pattern. The resulting system exhibits an excellent combination of low complexity, low bit-error rate, and high spectral efficiency.","Title: Moving Griffith crack in an orthotropic strip with punches at boundary faces\nYear: 2005\n\nAbstract: Integral transform technique is employed to solve the elastodynamic problem of steady- state propagation of a Griffith crack centrally situated along the midplane of orthotropic strip of finite thickness 2h and subjected to point loading with centrally situated moving punches under constant pressure along the boundaries of the layer. The problem is re- duced to the solution of a pair of simultaneous singular integral equations with Cauchy- type singularities which have finally been solved through the finite Hilbert transform technique. For large h, analytical expression for the stress intensity factor at the crack tip is obtained. Graphical plots of the numerical results are also presented.","Title: Mobility Testbed for 3GPP2-Based Multimedia Domain Networks\nYear: 2007\n\nAbstract: Wireless service providers strive to preserve the quality of service and user experience for mobile users. Several standards bodies are defining architectures that can be used as a platform to provide secure and seamless services to these mobile users. These architectures aim to provide several required functions such as signaling, configuration, security association, encryption, and billing. However, the placement of several functional components and their interaction at several layers contributes to the operational complexity and thus affects the optimal results. Testbed realization of any standardized architecture can help investigate the underlying networking issues. In this article, we describe a mobility test bed implementation based on one of the architecture alternatives of 3GPP2, where the outbound signaling servers are distributed around the edges of the network. We experiment with three different hand-off techniques and analyze the associated experimental results. Analysis of these experimental results and experiences obtained from the testbed implementation can be helpful to any service provider that plans to deploy a version of the MMD (multimedia domain) architecture with distributed signaling servers.","Title: Rerouting for handoff based on the distance in wireless ATM networks\nYear: 2002\n\nAbstract: Whenever handoff for a mobile user occurs in wireless ATM network, its connection must be reestablished in the new area. This paper proposes a new path rerouting scheme based on the incremental reestablishment approach. It decides the crossover node by comparing the distances between each node in the existing path and the new location, while traversing the path backward. This scheme allows us to efficiently decide a new path whose length is close to the shortest path. In order to realistically evaluate and compare the proposed scheme, we model the distance a mobile user travels based on move speed and call duration statistics and propose a new approach for generating realistic random network. Comprehensive computer simulation for various random networks shows that the proposed scheme outperforms the previous schemes in terms of path efficiency and handoff delay. We also obtain path reuse efficiency of higher than 80%. The proposed scheme can be implemented using the information in the routing table of the underlying network.","Title: A 90-nm Low-Power FPGA for Battery-Powered Applications\nYear: 2007\n\nAbstract: Programmable logic devices such as field-programmable gate arrays (FPGAs) are useful for a wide range of applications. However, FPGAs are not commonly used in battery-powered applications because they consume more power than application-specified integrated circuits and lack power management features. In this paper, we describe the design and implementation of Pika, a low-power FPGA core targeting battery-powered applications. Our design is based on a commercial low-cost FPGA and achieves substantial power savings through a series of power optimizations. The resulting architecture is compatible with existing commercial design tools. The implementation is done in a 90-nm triple-oxide CMOS process. Compared to the baseline design, Pika consumes 46% less active power and 99% less standby power. Furthermore, it retains circuit and configuration state during standby mode and wakes up from standby mode in approximately 100 ns","Title: A Linear State-Space Analysis of the Migration Model in an Island Biogeography System\nYear: 2011\n\nAbstract: Biogeography deals with the study of the distribution of biodiversity over space and time and has been well studied by naturists and biologists for over the last five decades. Recently, the theory of biogeography has been applied to solve difficult engineering optimization problems in the form of a nature-inspired metaheuristic, known as biogeography-based optimization (BBO) algorithm. In this correspondence paper, we present an in-depth analysis of the linear time-invariant (LTI) system model of immigration and emigration of organisms in an island biogeography system that forms the basis of BBO. We find the bound of the eigenvalues of the general LTI system matrix using the Perron-Frobenius theorem from linear algebra. Based on the bounds of the eigenvalues, we further investigate four important properties of the LTI biogeography system, including the system reasonability with probability distribution vectors, stability, convergence, and nature of the equilibrium state. Our analysis gives a better insight into the dynamics of migration in actual biogeography systems and also helps in the understanding of the search mechanism of BBO on multimodal fitness landscapes.","Title: Embedded Network Test-Bed for Validating Real-Time Control Algorithms to Ensure Optimal Time Domain Performance\nYear: 2012\n\nAbstract: The paper presents a Stateflow based network test-bed to validate real-time optimal control algorithms. Genetic Algorithm (GA) based time domain performance index minimization is attempted for tuning of PI controller to handle a balanced lag and delay type First Order Plus Time Delay (FOPTD) process over network. The tuning performance is validated on a real-time communication network with artificially simulated stochastic delay, packet loss and out-of-order packets characterizing the network.","Title: Adaptive Gain and Order Scheduling of Optimal Fractional Order PIlamdaDµ Controllers with Radial Basis Function Neural-Network\nYear: 2012\n\nAbstract: Gain and order scheduling of fractional order (FO) PIλDμ controllers are studied in this paper considering four different classes of higher order processes. The mapping between the optimum PID/FOPID controller parameters and the reduced order process models are done using Radial Basis Function (RBF) type Artificial Neural Network (ANN). Simulation studies have been done to show the effectiveness of the RBFNN for online scheduling of such controllers with random change in set-point and process parameters.","Title: Optimizing Continued Fraction Expansion Based IIR Realization of Fractional Order Differ-Integrators with Genetic Algorithm\nYear: 2012\n\nAbstract: Rational approximation of fractional order (FO) differ-integrators via Continued Fraction Expansion (CFE) is a well known technique. In this paper, the nominal structures of various generating functions are optimized using Genetic Algorithm (GA) to minimize the deviation in magnitude and phase response between the original FO element and the rationalized discrete time filter in Infinite Impulse Response (IIR) structure. The optimized filter based realizations show better approximation of the FO elements in comparison with the existing methods and is demonstrated by the frequency response of the IIR filters.","Title: Optimized quality factor of fractional order analog filters with band-pass and band-stop characteristics\nYear: 2012\n\nAbstract: Fractional order (FO) filters have been investigated in this paper, with band-pass (BP) and band-stop (BS) characteristics, which can not be achieved with conventional integer order filters with orders lesser then two. The quality factors for symmetric and asymmetric magnitude response have been optimized using real coded Genetic Algorithm (GA) for a user specified center frequency. Parametric influence of the FO filters on the magnitude response is also illustrated with credible numerical simulations.","Title: Optimum weight selection based LQR formulation for the design of fractional order PIλDμ controllers to handle a class of fractional order systems\nYear: 2013\n\nAbstract: A weighted summation of Integral of Time Multiplied Absolute Error (ITAE) and Integral of Squared Controller Output (ISCO) minimization based time domain optimal tuning of fractional-order (FO) PID or PIλDμ controller is proposed in this paper with a Linear Quadratic Regulator (LQR) based technique that minimizes the change in trajectories of the state variables and the control signal. A class of fractional order systems having single non-integer order element which show highly sluggish and oscillatory open loop responses have been tuned with an LQR based FOPID controller. The proposed controller design methodology is compared with the existing time domain optimal tuning techniques with respect to change in the trajectory of state variables, tracking performance for change in set-point, magnitude of control signal and also the capability of load disturbance suppression. A real coded genetic algorithm (GA) has been used for the optimal choice of weighting matrices while designing the quadratic regulator by minimizing the time domain integral performance index. Credible simulation studies have been presented to justify the proposition.","Title: Optimum weight selection based LQR formulation for the design of fractional order PIλDμ controllers to handle a class of fractional order systems\nYear: 2013\n\nAbstract: A weighted summation of Integral of Time Multiplied Absolute Error (ITAE) and Integral of Squared Controller Output (ISCO) minimization based time domain optimal tuning of fractional-order (FO) PID or PIλDμ controller is proposed in this paper with a Linear Quadratic Regulator (LQR) based technique that minimizes the change in trajectories of the state variables and the control signal. A class of fractional order systems having single non-integer order element which show highly sluggish and oscillatory open loop responses have been tuned with an LQR based FOPID controller. The proposed controller design methodology is compared with the existing time domain optimal tuning techniques with respect to change in the trajectory of state variables, tracking performance for change in set-point, magnitude of control signal and also the capability of load disturbance suppression. A real coded genetic algorithm (GA) has been used for the optimal choice of weighting matrices while designing the quadratic regulator by minimizing the time domain integral performance index. Credible simulation studies have been presented to justify the proposition.","Title: An Optimized HPWL Model for VLSI Analytical Placement\nYear: 2015\n\nAbstract: In VLSI physical design, placement is an important stage. Analytical placer minimizes the half-perimeter wire lengh (HPWL) of the circuit as an objective function to place blocks optimally in the chip. This paper introduces a new smooth recursive model for HPWL function which can be used inside the analytical placers. The proposed model is more accurate than widely used log-sum-exponent (LSE)[9], weighted average (WA)[3] and ABSWL[7] models. The error upper bound of new approximation is also tightest among the existing wire length models. When deployed inside placement engine NUPlacer[1], it shows reduction in wire lengh by 12.3%, 10% and 3% on ISPD 2005 placement benchmarks compared to LSE, WA and ABSWL models respectively.","Title: Analytical Study Of Primary User Emulation Attack Detection Techniques In Cognitive Radio Adhoc Network\nYear: 2017\n\nAbstract: To provide security in wireless medium is very difficult as compared to wired medium. Specifically in case of cognitive radio network the malicious user can behave like a legitimate user either to capture the communication process or to launch many attacks. In this research endeavor, our objectives are to deal with the various security issues in cognitive radio networks and then to obtain a detection technique for Primary User Emulation Attack (PUK)","Title: Numerical solution of non-linear partial differential equation for porous media using operational matrices.\nYear: 2019\n\nAbstract: Nonlinear partial differential equations (NPDEs) play a significant role in modeling of many complex physical problems and engineering processes. Development of reliable and efficient techniques to solve such types of NPDEs is the need of the hour. In this article, numerical solutions of a class of NPDEs subject to initial and boundary conditions are derived. In the proposed approach, shifted Chebyshev polynomials are considered to approximate the solutions together with shifted Chebyshev derivative operational matrix and spectral collocation method. The benefit of this method is that it converts such problems in the systems of algebraic equations which can be solved easily. To show the efficiency, high accuracy and reliability of the proposed approach, a comparison between the numerical results of some illustrative examples and their existing analytical results from the literature is reported. There is high consistency between the approximate solutions and their exact solutions to a higher order of accuracy. The error analysis for each case exhibited through graphs and tables confirms the super-linear convergence rate of the proposed method.","Title: Rf-Based Analytics Generated By Tag-To-Tag Networks\nYear: 2019\n\nAbstract: We have developed a type of RFID tags that can communicate with each other directly if there is an RF signal in their environment to support backscattering. These tags are passive and they can form a tag-to-tag network. Our tags communicate by what we refer to as multiphase probing. With this technique, we basically explore the backscatter channel by reflecting the incident RF signal with different changes in the phase. We define a measure of the backscatter channel, which we call backscatter channel state information (BCSI). The BCSI is composed of backscatter channel phase, backscatter amplitude, and change in baseline excitation level. When acquired over time, this measure provides rich RF analytics that can be used to extract various types of information from the environment of the tags by signal processing/machine learning methods. We show in the paper that this analytics is invariant w.r.t. to some variables including the deployment environment. We provide results from experiments with our tags that demonstrate the invariance of the BCSI.","Title: Parallel and Communication Avoiding Least Angle Regression.\nYear: 2019\n\nAbstract: We are interested in parallelizing the Least Angle Regression (LARS) algorithm for fitting linear regression models to high-dimensional data. We consider two parallel and communication avoiding versions of the basic LARS algorithm. The two algorithms apply to data that have different layout patterns (one is appropriate for row-partitioned data, and the other is appropriate for column-partitioned data), and they have different asymptotic costs and practical performance. The first is bLARS, a block version of LARS algorithm where we update b columns at each iteration. Assuming that the data are row-partitioned, bLARS reduces the number of arithmetic operations, latency, and bandwidth by a factor of b. The second is Tournament-bLARS (T-bLARS), a tournament version of LARS, in which case processors compete, by running several LARS computations in parallel, to choose b new columns to be added into the solution. Assuming that the data are column-partitioned, T-bLARS reduces latency by a factor of b. Similarly to LARS, our proposed methods generate a sequence of linear models. We present extensive numerical experiments that illustrate speed-ups up to 25x compared to LARS.","Title: Design And Simulation Of Rfid Array Antenna 2x1 For Detection System Of Objects Or Living Things In Motion\nYear: 2019\n\nAbstract: This paper presents the improvement of radiation characteristics performances of the RFID array antenna for detection system of objects or living things in motion. The proposed array antenna is composed of two identical elements, connected in parallel via simple microstrip line divider, having a power port adapted to 5012. This array is designed using RT/duroid-5880 substrate with the following parameters: loss tangent tan delta = 9.10(-4) , epsilon(r) of 2.2 and substrate's height of 1.56 mm. The total size of this array antenna is (110.5 x 83)mm(2).The simulation results obtained in this work show that the proposed array antenna has a high gain about of 9.22 dB, a good reflection coefficient S-11 of -25.36 dB, and a good adaptation at the 2.4 GHz microwave band. (C) 2019 The Authors. Published by Elsevier B.V.","Title: Smart Webcam Cover: Exploring the Design of an Intelligent Webcam Cover to Improve Usability and Trust\nYear: 2021\n\nAbstract: AbstractLaptop webcams can be covertly activated by malware and law enforcement agencies. Consequently, 59% percent of Americans manually cover their webcams to avoid being surveilled. However, manual covers are prone to human error---through a survey with 200 users, we found that 61.5% occasionally forget to re-attach their cover after using their webcam. To address this problem, we developed Smart Webcam Cover (SWC): a thin film that covers the webcam (PDLC-overlay) by default until a user manually uncovers the webcam, and automatically covers the webcam when not in use. Through a two-phased design iteration process, we evaluated SWC with 20 webcam cover users through a remote study with a video prototype of SWC, compared to manual operation, and discussed factors that influence users' trust in the effectiveness of SWC and their perceptions of its utility.","Title: An Enhanced Ultra-High Gain Active-Switched Quasi Z-Source Inverter\nYear: 2022\n\nAbstract: This brief proposes an enhanced ultra-high gain quasi Z-source inverter (EUHG-qZSI) having single unit of active switched capacitive network and switched inductor respectively. This inverter provides very high voltage gain profile with limiting value of shoot-through duty ratio of 0.186 and also, numerator factor of its gain is two times of (1+shoot-through duty ratio). Thus, the suggested inverter achieves higher voltage gain than the existing ultra-high/high gain/two-switched topologies. The voltage stresses on switches and capacitors of the proposed inverter are reduced. It also retains desirable qualities such as common ground with input DC supply and continuous source-side current for providing relief from current stress on input supply. The steady-state voltage gain, parameter values, current and voltage stresses of its components, loss and input current ripple are given to analyze the operation and characteristics of proposed inverter. The comparative voltage gains, voltage stresses and average switching device power are given to indicate its decent performances. Observations of both simulation and experimental verifications indicate real-time working of the suggested inverter.","Title: \"How Do You Not Lose Friends?\": Synthesizing a Design Space of Social Controls for Securing Shared Digital Resources Via Participatory Design Jams\nYear: 2022\n\nAbstract: Digital resources (streaming services, banking accounts, collaborative documents, etc.) are commonly shared among small, social groups. Yet, the security and privacy (S&P) controls for these resources map poorly onto the reality of shared access and ownership (e.g., one shared Netflix password for roommates). One challenge is that the design space for social S&P controls remains unclear. We bridged this gap by engaging end-users in participatory design workshops to envision social solutions to S&P challenges common to their groups. In analyzing the generated ideas and group discussions, we identified four design considerations salient to social S&P controls: social transparency; structures of governance; stakes and responsibility; and, promoting pro-group S&P behaviors. Additionally, we discovered trade-offs and challenges that arise when designing social S&P controls: balancing group security versus individual privacy; combating social friction; mitigating social herding behaviors; and, minimizing coordination costs.","Title: Electrothermal blood streaming conveying hybridized nanoparticles in a non-uniform endoscopic conduit\nYear: 2022\n\nAbstract: The novelty of nanoparticles in transferrals of medications and biological fluids via electrokinetic mechanism has been competently recognized. Due to the impressive role of nanoparticles suspended in blood or physiological fluids in medical fields, the current research article is planned to formulate an effective mathematical model to analyze the dynamism of bloodstream infused with hybridized nanoparticles in a non-uniform endoscopic conduit (space between two coaxial tubes) under the interactivities of electroosmosis, peristalsis, and buoyancy forces. The dual impact of heat source, Joule heating, and convectively cooling wall condition is examined. The geometrical shapes (sphere, brick, cylinder, and platelet) of nanoparticles injected into blood are accounted for in the formulation of modelled equations. The blood doped with hybridized nanoparticles is regarded as an electrolyte solution. The lubrication and Debye-Hückel linearization estimations are invoked in order to linearize the flow equations. Analytical solutions for the resulting leading equations are computed by implementing an analytical approach. The amendments in the physiognomies under variations in sundry parameters are explained through the line, bar graphs, and numerical tables. Outcomes admit that the flow of ionized blood is significantly amended across the endoscopic conduit due to the electrostatic body force. Blood is warmed or cooled with positive or negative values of Joule heating parameter. Blood is cooled with augmenting volumetric concentration of hybridized nanoparticles. The trapping phenomenon is also described by designing streamline plots. The size of confined blood boluses expands due to the thin electric double layer (EDL). The novel findings of this hemodynamic simulation furnish significant applicabilities in modelling of transportation of medications and drugs, physiological fluid mixers, testing and assessment of human diseases, detection of bacteria and viruses, etc."],"string":"[\n \"Title: A Multiobjective Evolutionary-Simplex Hybrid Approach for the Optimization of Differential Equation Models of Gene Networks\\nYear: 2008\\n\\nAbstract: This paper describes genetic and hybrid approaches for multiobjective optimization using a numerical measure called fuzzy dominance. Fuzzy dominance is used when implementing tournament selection within the genetic algorithm (GA). In the hybrid version, it is also used to carry out a Nelder-Mead simplex-based local search. The proposed GA is shown to perform better than NSGA-II and SPEA-2 on standard benchmarks, as well as for the optimization of a genetic model for flowering time control in rice. Adding the local search achieves faster convergence, an important feature in computationally intensive optimization of gene networks. The hybrid version also compares well with ParEGO on a few other benchmarks. The proposed hybrid algorithm is then applied to estimate the parameters of an elaborate gene network model of flowering time control in Arabidopsis. Overall solution quality is quite good by biological standards. Tradeoffs are discussed between accuracy in gene activity levels versus in the plant traits that they influence. These tradeoffs suggest that data mining the Pareto front may be useful in bioinformatics.\",\n \"Title: Peristaltic flow of viscoelastic fluid with fractional Maxwell model through a channel\\nYear: 2010\\n\\nAbstract: The paper presents the transportation of viscoelastic fluid with fractional Maxwell model by peristalsis through a channel under long wavelength and low Reynolds number approximations. The propagation of wall of channel is taken as sinusoidal wave propagation (contraction and relaxation). Homotopy perturbation method (HPM) and Adomian decomposition method (ADM) are used to obtain the analytical approximate solutions of the problem. The expressions of axial velocity, volume flow rate and pressure gradient are obtained. The effects of fractional parameters (@a), relaxation time (@l\\\\\\\"1) and amplitude (@f) on the pressure difference and friction force across one wavelength are calculated numerically for different particular cases and depicted through graphs.\",\n \"Title: IDMP-based fast handoffs and paging in IP-based 4G mobile networks\\nYear: 2002\\n\\nAbstract: We consider the use of our previously proposed Intra-Domain Mobility Management Protocol (IDMP) in fourth-generation mobile networks. On evaluating the heterogeneous access technologies, cellular layouts, and application characteristics of 4G environments, we realize a need to reduce both handoff latency and the frequency of mobility-related signaling. We first present IDMP's fast intradomain handoff mechanism that uses a duration-limited proactive packet multicasting solution. We quantify the expected buffering requirements of our proposed multicasting scheme for typical 4G network characteristics and compare it with alternative IP-based fast handoff solutions. We also present a paging scheme under IDMP that replicates the current cellular paging structure. Our paging mechanism supports generic paging strategies and can significantly reduce the mobility-related IP signaling load\",\n \"Title: Ge based nanostructures for electronic and photonic devices\\nYear: 2010\\n\\nAbstract: Self-assembled GexSi1−x islands were grown on Si(001) substrates by solid source molecular beam epitaxy. Two different morphological shapes with different sizes were evolved by tuning the growth time at a constant deposition temperature. Micro-Raman analysis was carried out to investigate the composition, intermixing and strain of resultant islands. The observed broad infra-red photoluminescence signal from grown samples was associated with radiative recombination of holes confined in the Ge islands and electrons localized in the Si buffer layer. The PL peak position and intensity were found to be influenced by the islands size and intermixing of Si and Ge. The electrical properties of the islands were studied through photoexcited I–V characteristics and current imaging using conducting mode atomic force microscopy.\",\n \"Title: A QoS Network Management System for Robust and Reliable Multimedia Services\\nYear: 2002\\n\\nAbstract: In this paper, we introduce a practical network management system with QoS for multimedia traffic. Today, multimedia applications have evolved significantly and have become an essential part of the Internet. In order to effectively support the newly emerging network traffic, the underlying network protocols need to be aware of the characteristics and demands of the traffic. The proposed network system is ready to address the varied characteristics of multimedia traffic and can assure a high degree of adherence to the quality of service demanded from it. Considering the need for reliable QoS services, the system is also equipped with fault tolerance capability by provisioning multiple QoS paths. Moreover, the system has been practically designed and implemented to provide \\\"cost-effective\\\" QoS support with respect to control overhead. It deploys measurement-based QoS path computation and call admission scheme which may be deemed ineffective for bursty multimedia traffic. However, results conclusively prove that provisioning multiple paths and utilizing them in parallel in our system not only provides high fault tolerance capability but also effectively accommodates multimedia traffic by relieving its burstiness with multiple paths. We present the architecture of the system and discuss the benefits gained for multimedia traffic.\",\n \"Title: Media-independent pre-authentication supporting secure interdomain handover optimization\\nYear: 2008\\n\\nAbstract: Handovers may cause delays and packet losses that affect real-time communication performance. Mobility protocols at several layers are designed to support handover, but they need to be optimized to ensure high-quality application performance. Existing optimization techniques are not sufficient to take care of interdomain and intertechnology handovers involving different access technologies, such as Wi-Fi, GSM, CDMA, and WiMAX. We categorize several types of handover, describe handover delay components, and propose a handover optimization framework called media independent pre-authentication that can provide optimizations for interdomain and intertechnology handover in a manner that is transparent to mobility management protocols. In addition, we also present experimental results demonstrating that this framework can achieve a significant reduction in handover delays for both network-layer and application-layer mobility management protocols.\",\n \"Title: Distributed paging and registration in wireless networks\\nYear: 2005\\n\\nAbstract: The success of current and future wireless networks depends on their ability to provide connections to mobile terminals anywhere and at any time. It is therefore of crucial importance that wireless networks are able to quickly and efficiently locate mobile users at the time of an incoming call, which is achieved in current networks through a combination of paging and registration. In this article we present novel distributed paging and registration procedures that are naturally suited to future distributed wireless network architectures. In addition, the distributed nature of these strategies allows us to effectively balance the paging and registration traffic and the required signal processing throughout the network and alleviate any potential overloads of individual base stations.\",\n \"Title: Practical QoS Network System with Fault Tolerance\\nYear: 2003\\n\\nAbstract: In this paper, we present an "emulation environment" forthe design and planning of intranets. Intranets must support Quality-ofService(QoS) for real-time traffic and must be fault-tolerant for missioncriticalapplications such as tele-medicine. Our goal is to design the intranetas an overlay network within the IP network and to manage traffic in the intranetso that QoS requirements are satisfied. Routing is a key componentof this architecture, in that it enables the efficient use of the...\",\n \"Title: Channel routing in Manhattan-diagonal model\\nYear: 1996\\n\\nAbstract: This paper presents a new technique of channel routing based on the Manhattan-Diagonal (MD) model. The layout grid is assumed to consist of two layers with tracks in horizontal, vertical and /spl plusmn/45/spl deg/ directions. First, we consider the non-overlap model and present a simple O(l,d) time algorithm that routes an arbitrary channel with no cyclic vertical constraints in w tracks, where l is the length of the channel, d is the channel density, and d/spl les/w/spl les/(d+1). Next, we describe an output-sensitive algorithm that can route general channels with cyclic vertical constraints using w tracks, in O(l,w) time allowing overlapping of wiring segments in two layers. The router outputs an 18-track solution for the Deutsch's difficult example, a 2-track solution for Burstein's difficult channel, and a 15-track solution for cycle.tough without inserting any extra row or column. Apart from quick termination, the proposed algorithms provide solutions with significantly low via count and reduced wire length. The study thus reveals the superiority of MD-routing strategy to classical techniques.\",\n \"Title: User-transparent reconfiguration method for self-organizing IP multimedia subsystem\\nYear: 2011\\n\\nAbstract: The NGN (Next Generation Network), which can provide advanced multimedia services over an all-IP based network, has been the subject of considerable attention for several years. While there have been tremendous efforts to develop its architecture and protocols, especially for IMS, a key technology of the NGN, its wide deployment is still a long way off. However, efforts to create an advanced signaling infrastructure able to meet many requirements have resulted in a large number of functional components and interactions between these components. Thus, the carriers are trying to explore effective ways to deploy IMS while offering value-added services. As one such approach, we have proposed a self-organizing IMS. A self-organizing IMS enables IMS functional components and corresponding physical nodes to adapt dynamically and automatically based on actual conditions such as network load and available system resources while continuing IMS operation. To realize this, service continuity for users is an important requirement when a reconfiguration occurs during operation. In this paper, we propose a mechanism that will provide service continuity to users without any impact on clients by extending the SBC (Session Border Controller). Furthermore, we implement it, show its behavior and evaluate its processing time.\",\n \"Title: A comparison of reverse link access schemes for next-generation cellular systems\\nYear: 2006\\n\\nAbstract: We consider different transmission options on the reverse link of cellular systems for packet data. The different transmission options are classified based on the nature of in-cell and out-of-cell interference power statistics. The categories are: (a) no in-cell interference, averaged out-of cell interference; (b) no in-cell interference, bursty out-of-cell interference; and (c) averaged in-cell interference, averaged out-of-cell interference. Depending on whether the reverse link transmission is time multiplexed one user at a time transmission, or simultaneous transmission by multiple users with or without in-cell orthogonality, the interference structure falls into one of the above three categories. We analyze the throughput performance of the system in each of these cases when incremental redundancy is employed to combat uncertainty in the interference power. We compare the different options under an in-cell rise-over-thermal (IROT) constraint and provide some insights for reverse link design for next-generation cellular systems. Our results show that transmission option (a) with an optimal choice of the number of simultaneous transmissions within the cell has the best performance over several different scenarios. Time-multiplexed transmissions, despite the bursty out-of-cell interference power structure, has throughput comparable to that of a multiple-user orthogonal transmission system for small cells where mobiles have sufficient transmit power to meet the target IROT.\",\n \"Title: Parallel algorithms for single row routing in narrow streets\\nYear: 1995\\n\\nAbstract: In this paper, the problem, of obtaining an optimal layout for the single row routing problem in narrow streets (maximum number of tracks in either street is at most 2) on parallel computers is studied. An O(log n) time parallel algorithm with O(n/logn) processors on a CREW PRAM and an O(log/sup 2/n) time cost-optimal algorithm on a tree machine is obtained.\",\n \"Title: Unsteady MHD Couette flow in a rotating system\\nYear: 2009\\n\\nAbstract: The unsteady hydromagnetic Couette flow of a viscous incompressible electrically conducting fluid in a rotating system has been considered. An exact solution of the governing equations has been obtained by using a Laplace transform. Solutions for the velocity distributions as well as shear stresses have been obtained for small times as well as for large times. It is found that for large times the primary velocity decreases with increase in the rotation parameter K^2 while it increases with increase in the magnetic parameter M^2. It is also found that with increase in K^2, the secondary velocity v\\\"1 decreases near the stationary plate while it increases near the moving plate. On the other hand, the secondary velocity decreases with increase in the magnetic parameter.\",\n \"Title: Design-Intent Coverage—A New Paradigm for Formal Property Verification\\nYear: 2006\\n\\nAbstract: It is essential to formally ascertain whether the register-transfer level (RTL) validation effort effectively guarantees the correctness with respect to the design's architectural intent. The design's architectural intent can be expressed in formal properties. However, due to the capacity limitations of formal verification, these architectural properties cannot be directly verified on the RTL. As a result, a set of lower level RTL properties are developed and verified against the RTL modules. In a top-down design approach, the architect would ideally like to formally guarantee the coverage of the architectural intent at the time of creating the specifications for the component RTL modules (that is, before they are passed to the designers for implementation). In this paper, the authors present: 1) a method for checking whether the RTL properties are covering the architectural properties, that is, whether verifying the RTL properties guarantees the correctness of the design's architectural intent; 2) a method to identify which architectural properties are still uncovered, that is, not guaranteed by the RTL properties; and 3) a methodology for representing the gap between the specifications in a legible form\",\n \"Title: Homotopy analysis method for solving fractional hyperbolic partial differential equations\\nYear: 2011\\n\\nAbstract: In the present paper, the solutions of the hyperbolic partial differential equation with fractional time derivative of order α (1\",\n \"Title: CMOS compatible bulk micromachined silicon piezoresistive accelerometer with low off-axis sensitivity\\nYear: 2006\\n\\nAbstract: The CMOS compatible bulk micromachined piezoresistive accelerometer presented in this paper consists of four flexures supporting a proof mass. Four pairs of boron-diffused piezoresistors are located at maximum stress points on the flexures near the proof mass and frame ends. Because of the opposite nature of stress at the two ends, these piezoresistors can be connected to form a Wheatstone bridge such that the off-axis responses are practically cancelled while the on-axis (along perpendicular to proof mass) response is maximized. The device is simulated using CoventorWare. In the fabrication process, dual-doped TMAH solution is used for wet anisotropic etching. The novelty of this etching process is that the bulk micromachining can be performed after aluminum metallization. The etched surface is also smooth. The fabrication is thus CMOS compatible. The accelerometer exhibits good linearity over 0–10g.\",\n \"Title: Emotion clustering from stimulated electroencephalographic signals using a Duffing oscillator\\nYear: 2010\\n\\nAbstract: The paper proposes an alternative approach to emotion recognition from stimulated EEG signals using Duffing oscillator. Reported works on emotion clustering generally employ the principles of supervised learning. Unfortunately, because of noisy and limited feature set, the classification problem often suffers from high inaccuracy. This has been overcome in this paper by submitting the EEG signals directly to a Duffing oscillator and the phase portraits constructed from its time-response demonstrate structural similarity to similar emotion excitatory stimuli. The accuracy in clustering was experimentally validated even with injection of Gaussian noise over the EEG signal up to a signal-to-noise ratio of 25 dB. The results of clustering in presence of low signal-to-noise ratio confirm the robustness of the proposed scheme.\",\n \"Title: An interval digraph in relation to its associated bipartite graph\\nYear: 1993\\n\\nAbstract: The intersection digraph of a family of ordered pairs of sets {( S v , T v ): v ∈ V } is the digraph D ( V , E ) such that uv ∈ E if and only if S u ∩ T v ≠0. Internal digraphs are those intersection digraphs for which the subsets are intervals on the real line. In a previous paper, they were characterized in terms of Ferrers digraphs and a close relationship was obtained between an interval digraph and a digraph of Ferrers dimension 2. In order to characterize a digraph D of Ferrers dimension 2, Cogis associated an undirected graph H ( D ) with D in a suitable way, the vertices of H ( D ) corresponding to the zeros of the adjacency matrix of D . He proved that D has Ferrers dimension at most 2 if and only if H ( D ) is bipartite. Depending on the above characterization, this paper first obtains some properties of a digraph of Ferrers dimension 2; then it is shown how the notion of interior edges is related to an interval digraph.\",\n \"Title: Approximate analytical solutions of fractional gas dynamic equations\\nYear: 2011\\n\\nAbstract: In this article, differential transform method (DTM) has been successfully applied to obtain the approximate analytical solutions of the nonlinear homogeneous and non-homogeneous gas dynamic equations, shock wave equation and shallow water equations with fractional order time derivatives. The true beauty of the article is manifested in its emphatic application of Caputo fractional order time derivative on the classical equations with the achievement of the highly accurate solutions by the known series solutions and even for more complicated nonlinear fractional partial differential equations (PDEs). The method is really capable of reducing the size of the computational work besides being effective and convenient for solving fractional nonlinear equations. Numerical results for different particular cases of the equations are depicted through graphs.\",\n \"Title: Fade-resistant CDMA transmission and reception over time-varying wireless channels\\nYear: 2006\\n\\nAbstract: Physical limitations due to fading and interference in wireless channels pose technical challenges in achieving reliable communication. To adapt to this adverse radio environment efficiently, we propose a fade-resistant transmission system based on code division multiple access (CDMA) technology. In this system, all the symbols of a user are transmitted simultaneously using parallel sequences over a time-varying wireless channel. We derive the relationship between the self-interference and the multiple-access interference in such a system. We prove that instantaneous signal-to-interference-plus-noise ratio (SINR) at the matched filter output of a system with parallel sequences is statistically higher than that of the conventional CDMA system. Numerical results demonstrate that the parallel sequence transmission scheme can provide capacity improvement over the conventional CDMA system depending on variations in the channel.\",\n \"Title: An Economical RC Active Equalizer with Applications in Communications\\nYear: 1977\\n\\nAbstract: A simple active equalizer network employing two operational amplifiers (OA's), five resistors, and a pair of capacitors is presented. The same network can be used for both amplitude and delay equalizations. Experimental results show that the circuit can be conveniently used for amplitude equalization in voice communications, and for delay equalization in private line data circuits.\",\n \"Title: Noisy recurrent neural networks: the discrete-time case.\\nYear: 1998\\n\\nAbstract: The results of the companion paper on the behavior of continuous-time recurrent neural networks (RNN) are specialized to the discrete-time (so-called time-lagged recurrent) case. Uniform boundedness of the first and second moment sequences of the trajectory is established. For practical design purposes, estimates and bounds on the bias and variance sequences of the stochastic discrete-time RNN are derived. A script file for estimating the variance bounds for specified designs is provided in an Appendix.\",\n \"Title: Gap Analysis and Deployment Architectures for 3GPP2 MMD Networks\\nYear: 2007\\n\\nAbstract: Mobile and fixed operators are offering broadband access to the Internet by either adding wireless access to their existing core networks (e.g., CDMA IxEVDO. GPRS, and WiFi) or continuing to evolve existing broadband infrastructures (e.g., DSL, Cable and FTTx). Consequently, IMS and MMD architectures as defined by 3GPP and 3GPP2 are becoming increasingly important to network operators, in particul...\",\n \"Title: A novel strategy for plant breeding based on simulations of gene network models.\\nYear: 2013\\n\\nAbstract: This paper describes a new computational method to make predictions on the outcome of pair-wise crosses of plant lines limiting expensive laboratory breeding experiments to carry out crosses of the most promising pairs of lines. Compared to the well-known marker assisted breeding, the proposed approach approximates plant gene regulatory networks to estimate outcomes of all possible crossovers, thereby taking into account epistatic relationships between alleles. The proposed method is tested and compare with various breeding approaches on artificial NK landscape models and an extensive synthetic model of Arabidopsis plant's flowering time system. The results show our method outperforms other breeding strategies.\",\n \"Title: Detection of hard cuts and gradual transitions from video using fuzzy logic\\nYear: 2008\\n\\nAbstract: In this paper, we introduce a novel approach for the detection and classification of shot boundaries based on interval type-2 fuzzy logic. We consider fuzzy low level features (Fuzzy Histogram (FH) and Fuzzy Co-occurrence Matrix (FCM)) and evaluate a fuzzy rule base modelled by interval type-2 sets to detect cut boundaries. After detection of cuts, a recovery mechanism is used to reduce false positives caused by illumination variation (e.g., camera flash light). The fuzzy rules used for gradual transition form a unified approach to detect various types gradual transitions like dissolves, fades and wipes, by a single method. Test results indicate that very high precision and recall could be achieved for the test videos, particularly the recall value of gradual transitions.\",\n \"Title: Analytical solution of a fractional diffusion equation by variational iteration method\\nYear: 2009\\n\\nAbstract: In the present paper the Analytical approximate solution of a fractional diffusion equation is deduced with the help of powerful Variational Iteration method. By using an initial value, the explicit solutions of the equation for different cases have been derived, which accelerate the rapid convergence of the series solution. The present method performs extremely well in terms of efficiency and simplicity. Numerical results for different particular cases of the problem are presented graphically.\",\n \"Title: Realizing mobile wireless Internet telephony and streaming multimedia testbed\\nYear: 2004\\n\\nAbstract: Streaming real-time multimedia content over the Internet is gaining momentum in the communications, entertainment, music and interactive game industries as well as in the military. In general, streaming applications include IP telephony, multimedia broadcasts and various interactive applications such as multi-party conferences, collaborations and multiplayer games. Successfully realizing such applications in a highly mobile environment, however, presents many research challenges. In order to investigate such challenges and demonstrate viable solutions, we have developed an experimental indoor and outdoor testbed laboratory. By implementing standard IETF protocols into this testbed, we have demonstrated the basic functionalities required of the mobile wireless Internet to successfully support mobile multimedia access. These requirements include signaling, registration, dynamic configuration, mobility binding, location management, Authentication Authorization and Accounting (AAA), and quality of service over a variety of radio access network (RAN) technologies (e.g. 802.11b, CDMA/GPRS). In this paper, we describe this testbed and discuss important design issues and tradeoffs. We detail the incorporation and inter-relation of a wide catalog of IETF protocols-such as SIP, SAP, SDP, RTP/RTCP/RTSP, MGCP, variants of Mobile-IP, DRCP, HMMP, PANA, and DSNP-to achieve our goals. We believe that the results and experiences obtained from this experimental testbed will advance the understanding of the pertinent deployment issues for a Mobile Wireless Internet.\",\n \"Title: Extrapolated Biogeography-Based Optimization (eBBO) for Global Numerical Optimization and Microstrip Patch Antenna Design\\nYear: 2010\\n\\nAbstract: Biogeography-Based Optimization (BBO) uses the idea of probabilistically sharing features between solutions based on the solutions' fitness values. Therefore, its exploitation ability is good but it lacks in exploration ability. In this paper, the authors extend the original BBO and propose a hybrid version combined with ePSO (particle swarm optimization with extrapolation technique), namely eBBO, for unconstrained global numerical optimization problems in the continuous domain. eBBO combines the exploitation ability of BBO with the exploration ability of ePSO effectively, which can generate global optimum solutions. To validate the performance of eBBO, experiments have been conducted on 23 standard benchmark problems with a range of dimensions and diverse complexities and compared with original BBO and other versions of BBO in terms of the quality of the final solution and the convergence rate. Influence of population size and scalability study is also considered and results are compared with statistical paired t-test. Experimental analysis indicates that the proposed approach is effective and efficient and improves the exploration ability of BBO.\",\n \"Title: A 10-Year Mechatronics Curriculum Development Initiative: Relevance, Content, and Results—Part I\\nYear: 2010\\n\\nAbstract: This paper describes the first phase of a Mechatronics Curriculum Development effort-the design of an 驴Introduction to Mechatronics驴 course, the infusion of mechatronics activities throughout the curriculum and in outreach activities, and assessment results. In addition, the relevance and impact of such a curriculum on the education of engineers in relation to the evolving global economy is discussed. The second and third phases of this effort included development of two advanced mechatronics courses and the formulation of a mechatronics curriculum, which will be addressed in a companion paper, 驴A 10-Year Mechatronics Curriculum Development Initiative: Relevance, Content, and Results-Part II驴 (IEEE Transactions on Education, vol. 53, no. 2, May 2010).\",\n \"Title: Interaction between Griffith cracks in a sandwiched orthotropic layer\\nYear: 2003\\n\\nAbstract: A study is made of the interaction between three coplanar Griffith cracks which are located symmetrically in the midplane of an orthotropic layer of finite thickness 2h sandwiched between two identical orthotropic half planes. The Fourier transform technique is used to reduce the elastostatic problem to a set of integral equations which have been solved by using the finite Hilbert transform and Cooke's results. Analytical expressions for the stress intensity factors at the tips of cracks are obtained for large values of h. Numerical results concerning the interaction effects are presented with physical significance. It is shown that interaction effects are either shielding or amplification depending on the location of cracks, spacing of crack-tips, and the thickness of the layer. The stress magnification factors at the crack-tips are also calculated.\",\n \"Title: An approximate analytical solution of one-dimensional phase change problems in a finite domain\\nYear: 2011\\n\\nAbstract: In this paper, we present an approximate analytical solution for solving one dimensional two phase Stefan problem. The finite sine transform technique is used to convert the non dimensional form from a space domain to a wave number domain. Inverse finite sine transform is used to obtain the desired solution. The location of moving interface during freezing process in a finite domain is studied and the result thus obtained are discussed graphically. The whole analysis is presented in a non dimensional form.\",\n \"Title: Analysis and prevention of convex corner undercutting in bulk micromachined silicon microstructures\\nYear: 2006\\n\\nAbstract: Convex corner undercutting in <100> silicon is an undesired phenomena during bulk micromachining of crystalline silicon substrate using anisotropic wet chemical etching process. The present investigation concentrates on the studies of convex corner undercutting at the free end of silicon cantilever beams released by anisotropic etching process. It also reports a simple, space efficient compensation design for complete prevention of corner deformation. Various compensation patterns such as square, rectangle and superposition of square and rectangular blocks of various dimensions have been employed at the free end corners of cantilever beam to protect corner deformation due to undercutting. The experiment was carried out in 44wt.% KOH at 70°C using <100> oriented silicon wafer. Both n-type and p-type silicon wafers were used to study the variations in the nature of corner deformation. A simple empirical relation has been obtained from the experimental data to calculate the lateral dimensions of the compensation layout from the total etch depth required to release the structure.\",\n \"Title: An approximate analytical solution of time-fractional telegraph equation\\nYear: 2011\\n\\nAbstract: In this article, the powerful, easy-to-use and effective approximate analytical mathematical tool like homotopy analysis method (HAM) is used to solve the telegraph equation with fractional time derivative α (1<α⩽2). By using initial values, the explicit solutions of telegraph equation for different particular cases have been derived. The numerical solutions show that only a few iterations are needed to obtain accurate approximate solutions. The method performs extremely well in terms of efficiency and simplicity to solve this historical model.\",\n \"Title: Binary knapsack problems with random budgets\\nYear: 2003\\n\\nAbstract: The binary knapsack problem is a combinatorial optimization problem in which a subset of a given set of elements needs to be chosen in order to maximize profit, given a budget constraint. In this paper, we study a stochastic version of the problem in which the budget is random. We propose two different formulations of this problem, based on different ways of handling infeasibility, and propose an exact algorithm and a local search-based heuristic to solve the problems represented by these formulations. We also present the results from some computational experiments.\",\n \"Title: Robust Chaos - The Theoretical Formulation And Experimental Evidence\\nYear: 1999\\n\\nAbstract: Practical applications of chaos are likely to face problems of reliability because of the existence of multiple attractors and periodic windows in most chaotic dynamical systems. In this paper we show that robust chaos, defined by the absence of periodic windows and coexisting attractors in some neighborhood of the parameter space, can occur in piecewise smooth systems and obtain the conditions of its occurrence. We demonstrate experimental evidence of this phenomenon in power electronic circuits.\",\n \"Title: Architecture of a VLSI Chip for Modeling Amino Acid Sequence in Proteins\\nYear: 1996\\n\\nAbstract: A Cellular Automata (CA) based model of amino acids which constitute different types protein is re-ported in this paper. A simulation engine is being developed based on this model to study protein behaviour.\",\n \"Title: On the solutions of fractional Swift Hohenberg equation with dispersion\\nYear: 2013\\n\\nAbstract: In this article, the approximate solutions of the non-linear Swift Hohenberg equation with fractional time derivative in the presence of dispersive term have been obtained. The fractional derivative is described in Caputo sense. Time fractional nonlinear partial differential equations in the presence of dispersion and bifurcation parameters have been computed numerically to predict hydrodynamic fluctuations at convective instability for different particular cases and results are depicted through graphs.\",\n \"Title: Low-complexity iterative multiuser detection and decoding for real-time applications\\nYear: 2005\\n\\nAbstract: This paper presents a low-complexity multiuser decoding technique that can be implemented in real time for a convolutionally coded direct sequence code division multiple access (DS-CDMA) system. The main contribution, denoted here as the iterative prior update (IPU), consists of iterative interference cancellation and prior updates on sequences of coded bits combined with M-algorithm and list decoding. We illustrate performance gains over other low-complexity sequence detection and decoding strategies and argue that the algorithm converges within a few iterations and requires only a small size buffer for keeping track of the priors along iterations. The fact that the we can use existing available architectures for Viterbi decoding with slight modifications and can meet the real-time processing constraints makes the IPU algorithm an attractive alternative for cellular systems.\",\n \"Title: Open-loop simulations of the primate saccadic system using burst cell discharge from the superior colliculus.\\nYear: 1995\\n\\nAbstract: Saccade-related burst neurons (SRBNs) in the monkey superior colliculus (SC) have been hypothesized to provide the brainstem saccadic burst generator with the dynamic error signal and the movement initiating trigger signal. To test this claim, we performed two sets of open-loop simulations on a burst generator model with the local feedback disconnected using experimentally obtained SRBN activity as both the driving and trigger signal inputs to the model. First, using neural data obtained from cells located near the middle of the rostral to caudal extent of the SC, the internal parameters of the model were optimized by means of a stochastic hillclimbing algorithm to produce an intermediate-sized saccade. The parameter values obtained from the optimization were then fixed and additional simulations were done using the experimental data from rostral collicular neurons (small saccades) and from more caudal neurons (large saccades); the model generated realistic saccades, matching both position and velocity profiles of real saccades to the centers of the movement fields of all these cells. Second, the model was driven by SRBN activity affiliated with interrupted saccades, the resumed eye movements observed following electrical stimulation of the omnipause region. Once again, the model produced eye movements that closely resembled the interrupted saccades produced by such simulations, but minor readjustment of parameters reflecting the weight of the projection of the trigger signal was required. Our study demonstrates that a model of the burst generator produces reasonably realistic saccades when driven with actual samples of SRBN discharges.\",\n \"Title: Application of homotopy perturbation method and homotopy analysis method to fractional vibration equation\\nYear: 2011\\n\\nAbstract: In this paper, the approximate analytical solutions of the mathematical model of vibration equation with fractional-order time derivative β (1\",\n \"Title: Bulk micromachining of silicon in TMAH-based etchants for aluminum passivation and smooth surface\\nYear: 2006\\n\\nAbstract: The fabrication of silicon based micromechanical sensors often requires bulk silicon etching after aluminum metallization. All wet silicon etchants including ordinary undoped tetramethyl ammonium hydroxide (TMAH)–water solution attack the overlaying aluminum metal interconnect during the anisotropic etching of (100) silicon. This paper presents a TMAH–water based etching recipe to achieve high silicon etch rate, a smooth etched surface and almost total protection of the exposed aluminum metallization. The etch rate measurements of (100) silicon, silicon dioxide and aluminum along with the morphology studies of etched surfaces are performed on both n-type and p-type silicon wafers at different concentrations (2, 5, 10 and 15%) for undoped TMAH treated at various temperatures as well as for TMAH solution doped separately and simultaneously with silicic acid and ammonium peroxodisulphate (AP). It is established through a detailed study that 5% TMAH–water solution dual doped with 38gm/l silicic acid and 7gm/l AP yields a reasonably high (100) silicon etch rate of 70μm/h at 80°C, very small etch rates of SiO2 and pure aluminum (around 80Å/h and 50Å/h, respectively), and a smooth surface (±7nm) at a bath temperature of 80°C. The etchant has been successfully used for fabricating several MEMS structures like piezoresistive accelerometer, vaporizing liquid micro-thruster and flow sensor. In all cases, the bulk micromachining is carried out after the formation of aluminum interconnects which is found to remain unaffected during the prolonged etching process at 80°C. The TMAH based etchant may be attractive in industry due to its compatibility with standard CMOS process.\",\n \"Title: A distributed model of the saccade system: simulations of temporally perturbed saccades using position and velocity feedback.\\nYear: 1999\\n\\nAbstract: Interrupted saccades, movements that are perturbed in mid-flight by pulsatile electrical stimulation in the omnipause neuron region, are known to achieve final eye displacements with accuracies that are similar to normal saccades even in the absence of visual input following the perturbation. In an attempt to explain the neurophysiological basis for this phenomenon, the present paper describes a model of the saccadic system that represents the superior colliculus as a dynamic two-dimensional, topographically arranged array of laterally interconnected units. A distributed feedback pathway to the colliculus from downstream elements, providing both eye position and velocity signals is incorporated in the model. With the help of a training procedure based on a genetic algorithm and gradient descent, the model is optimized to produce both the normal as well as slow saccades with similar accuracy. The slow movements are included in the training set to mimic the accurate saccades that occur despite alterations in alertness, as well as following various degenerative oculomotor diseases. Although interrupted saccades were not included in the training set, the model is able to produce accurate movement of this type as an emergent property for a wide range of perturbed eye velocity trajectories. Our model demonstrates for the first time, that by means of an appropriate feedback mechanism, a single-layered dynamic network can be made to retain a distributed memory of the remaining ocular displacement error even for interrupted and slow saccades. These results support the hypothesis that saccades are controlled by error feedback of signals that code efference copies of eye motion, and further, suggest a possible answer to a long standing question about the kind of the feedback signal, if any, that is received by the superior colliculus during saccadic eye movements.\",\n \"Title: Max-SINR ISI/ICI-Shaping Multicarrier Communication Over the Doubly Dispersive Channel\\nYear: 2007\\n\\nAbstract: For communication over doubly dispersive channels, we consider the design of multicarrier modulation (MCM) schemes based on time-frequency shifts of prototype pulses. We consider the case where the receiver knows the channel state and the transmitter knows the channel statistics (e.g., delay spread and Doppler spread) but not the channel state. Previous work has examined MCM pulses designed for suppression of inter-symbol/inter-carrier interference (ISI/ICI) subject to orthogonal or biorthogonal constraints. In doubly dispersive channels, however, complete suppression of ISI/ICI is impossible, and the ISI/ICI pattern generated by these (bi)orthogonal schemes can be difficult to equalize, especially when operating at high bandwidth efficiency. We propose a different approach to MCM pulse design, whereby a limited expanse of ISI/ICI is tolerated in modulation/demodulation and treated near-optimally by a downstream equalizer. Specifically, we propose MCM pulse designs that maximize a signal-to-interference-plus-noise ratio (SINR) which suppresses ISI/ICI outside a target pattern. In addition, we propose two low-complexity turbo equalizers, based on minimum mean-squared error and maximum likelihood criteria, respectively, that leverage the structure of the target ISI/ICI pattern. The resulting system exhibits an excellent combination of low complexity, low bit-error rate, and high spectral efficiency.\",\n \"Title: Moving Griffith crack in an orthotropic strip with punches at boundary faces\\nYear: 2005\\n\\nAbstract: Integral transform technique is employed to solve the elastodynamic problem of steady- state propagation of a Griffith crack centrally situated along the midplane of orthotropic strip of finite thickness 2h and subjected to point loading with centrally situated moving punches under constant pressure along the boundaries of the layer. The problem is re- duced to the solution of a pair of simultaneous singular integral equations with Cauchy- type singularities which have finally been solved through the finite Hilbert transform technique. For large h, analytical expression for the stress intensity factor at the crack tip is obtained. Graphical plots of the numerical results are also presented.\",\n \"Title: Mobility Testbed for 3GPP2-Based Multimedia Domain Networks\\nYear: 2007\\n\\nAbstract: Wireless service providers strive to preserve the quality of service and user experience for mobile users. Several standards bodies are defining architectures that can be used as a platform to provide secure and seamless services to these mobile users. These architectures aim to provide several required functions such as signaling, configuration, security association, encryption, and billing. However, the placement of several functional components and their interaction at several layers contributes to the operational complexity and thus affects the optimal results. Testbed realization of any standardized architecture can help investigate the underlying networking issues. In this article, we describe a mobility test bed implementation based on one of the architecture alternatives of 3GPP2, where the outbound signaling servers are distributed around the edges of the network. We experiment with three different hand-off techniques and analyze the associated experimental results. Analysis of these experimental results and experiences obtained from the testbed implementation can be helpful to any service provider that plans to deploy a version of the MMD (multimedia domain) architecture with distributed signaling servers.\",\n \"Title: Rerouting for handoff based on the distance in wireless ATM networks\\nYear: 2002\\n\\nAbstract: Whenever handoff for a mobile user occurs in wireless ATM network, its connection must be reestablished in the new area. This paper proposes a new path rerouting scheme based on the incremental reestablishment approach. It decides the crossover node by comparing the distances between each node in the existing path and the new location, while traversing the path backward. This scheme allows us to efficiently decide a new path whose length is close to the shortest path. In order to realistically evaluate and compare the proposed scheme, we model the distance a mobile user travels based on move speed and call duration statistics and propose a new approach for generating realistic random network. Comprehensive computer simulation for various random networks shows that the proposed scheme outperforms the previous schemes in terms of path efficiency and handoff delay. We also obtain path reuse efficiency of higher than 80%. The proposed scheme can be implemented using the information in the routing table of the underlying network.\",\n \"Title: A 90-nm Low-Power FPGA for Battery-Powered Applications\\nYear: 2007\\n\\nAbstract: Programmable logic devices such as field-programmable gate arrays (FPGAs) are useful for a wide range of applications. However, FPGAs are not commonly used in battery-powered applications because they consume more power than application-specified integrated circuits and lack power management features. In this paper, we describe the design and implementation of Pika, a low-power FPGA core targeting battery-powered applications. Our design is based on a commercial low-cost FPGA and achieves substantial power savings through a series of power optimizations. The resulting architecture is compatible with existing commercial design tools. The implementation is done in a 90-nm triple-oxide CMOS process. Compared to the baseline design, Pika consumes 46% less active power and 99% less standby power. Furthermore, it retains circuit and configuration state during standby mode and wakes up from standby mode in approximately 100 ns\",\n \"Title: A Linear State-Space Analysis of the Migration Model in an Island Biogeography System\\nYear: 2011\\n\\nAbstract: Biogeography deals with the study of the distribution of biodiversity over space and time and has been well studied by naturists and biologists for over the last five decades. Recently, the theory of biogeography has been applied to solve difficult engineering optimization problems in the form of a nature-inspired metaheuristic, known as biogeography-based optimization (BBO) algorithm. In this correspondence paper, we present an in-depth analysis of the linear time-invariant (LTI) system model of immigration and emigration of organisms in an island biogeography system that forms the basis of BBO. We find the bound of the eigenvalues of the general LTI system matrix using the Perron-Frobenius theorem from linear algebra. Based on the bounds of the eigenvalues, we further investigate four important properties of the LTI biogeography system, including the system reasonability with probability distribution vectors, stability, convergence, and nature of the equilibrium state. Our analysis gives a better insight into the dynamics of migration in actual biogeography systems and also helps in the understanding of the search mechanism of BBO on multimodal fitness landscapes.\",\n \"Title: Embedded Network Test-Bed for Validating Real-Time Control Algorithms to Ensure Optimal Time Domain Performance\\nYear: 2012\\n\\nAbstract: The paper presents a Stateflow based network test-bed to validate real-time optimal control algorithms. Genetic Algorithm (GA) based time domain performance index minimization is attempted for tuning of PI controller to handle a balanced lag and delay type First Order Plus Time Delay (FOPTD) process over network. The tuning performance is validated on a real-time communication network with artificially simulated stochastic delay, packet loss and out-of-order packets characterizing the network.\",\n \"Title: Adaptive Gain and Order Scheduling of Optimal Fractional Order PIlamdaDµ Controllers with Radial Basis Function Neural-Network\\nYear: 2012\\n\\nAbstract: Gain and order scheduling of fractional order (FO) PIλDμ controllers are studied in this paper considering four different classes of higher order processes. The mapping between the optimum PID/FOPID controller parameters and the reduced order process models are done using Radial Basis Function (RBF) type Artificial Neural Network (ANN). Simulation studies have been done to show the effectiveness of the RBFNN for online scheduling of such controllers with random change in set-point and process parameters.\",\n \"Title: Optimizing Continued Fraction Expansion Based IIR Realization of Fractional Order Differ-Integrators with Genetic Algorithm\\nYear: 2012\\n\\nAbstract: Rational approximation of fractional order (FO) differ-integrators via Continued Fraction Expansion (CFE) is a well known technique. In this paper, the nominal structures of various generating functions are optimized using Genetic Algorithm (GA) to minimize the deviation in magnitude and phase response between the original FO element and the rationalized discrete time filter in Infinite Impulse Response (IIR) structure. The optimized filter based realizations show better approximation of the FO elements in comparison with the existing methods and is demonstrated by the frequency response of the IIR filters.\",\n \"Title: Optimized quality factor of fractional order analog filters with band-pass and band-stop characteristics\\nYear: 2012\\n\\nAbstract: Fractional order (FO) filters have been investigated in this paper, with band-pass (BP) and band-stop (BS) characteristics, which can not be achieved with conventional integer order filters with orders lesser then two. The quality factors for symmetric and asymmetric magnitude response have been optimized using real coded Genetic Algorithm (GA) for a user specified center frequency. Parametric influence of the FO filters on the magnitude response is also illustrated with credible numerical simulations.\",\n \"Title: Optimum weight selection based LQR formulation for the design of fractional order PIλDμ controllers to handle a class of fractional order systems\\nYear: 2013\\n\\nAbstract: A weighted summation of Integral of Time Multiplied Absolute Error (ITAE) and Integral of Squared Controller Output (ISCO) minimization based time domain optimal tuning of fractional-order (FO) PID or PIλDμ controller is proposed in this paper with a Linear Quadratic Regulator (LQR) based technique that minimizes the change in trajectories of the state variables and the control signal. A class of fractional order systems having single non-integer order element which show highly sluggish and oscillatory open loop responses have been tuned with an LQR based FOPID controller. The proposed controller design methodology is compared with the existing time domain optimal tuning techniques with respect to change in the trajectory of state variables, tracking performance for change in set-point, magnitude of control signal and also the capability of load disturbance suppression. A real coded genetic algorithm (GA) has been used for the optimal choice of weighting matrices while designing the quadratic regulator by minimizing the time domain integral performance index. Credible simulation studies have been presented to justify the proposition.\",\n \"Title: Optimum weight selection based LQR formulation for the design of fractional order PIλDμ controllers to handle a class of fractional order systems\\nYear: 2013\\n\\nAbstract: A weighted summation of Integral of Time Multiplied Absolute Error (ITAE) and Integral of Squared Controller Output (ISCO) minimization based time domain optimal tuning of fractional-order (FO) PID or PIλDμ controller is proposed in this paper with a Linear Quadratic Regulator (LQR) based technique that minimizes the change in trajectories of the state variables and the control signal. A class of fractional order systems having single non-integer order element which show highly sluggish and oscillatory open loop responses have been tuned with an LQR based FOPID controller. The proposed controller design methodology is compared with the existing time domain optimal tuning techniques with respect to change in the trajectory of state variables, tracking performance for change in set-point, magnitude of control signal and also the capability of load disturbance suppression. A real coded genetic algorithm (GA) has been used for the optimal choice of weighting matrices while designing the quadratic regulator by minimizing the time domain integral performance index. Credible simulation studies have been presented to justify the proposition.\",\n \"Title: An Optimized HPWL Model for VLSI Analytical Placement\\nYear: 2015\\n\\nAbstract: In VLSI physical design, placement is an important stage. Analytical placer minimizes the half-perimeter wire lengh (HPWL) of the circuit as an objective function to place blocks optimally in the chip. This paper introduces a new smooth recursive model for HPWL function which can be used inside the analytical placers. The proposed model is more accurate than widely used log-sum-exponent (LSE)[9], weighted average (WA)[3] and ABSWL[7] models. The error upper bound of new approximation is also tightest among the existing wire length models. When deployed inside placement engine NUPlacer[1], it shows reduction in wire lengh by 12.3%, 10% and 3% on ISPD 2005 placement benchmarks compared to LSE, WA and ABSWL models respectively.\",\n \"Title: Analytical Study Of Primary User Emulation Attack Detection Techniques In Cognitive Radio Adhoc Network\\nYear: 2017\\n\\nAbstract: To provide security in wireless medium is very difficult as compared to wired medium. Specifically in case of cognitive radio network the malicious user can behave like a legitimate user either to capture the communication process or to launch many attacks. In this research endeavor, our objectives are to deal with the various security issues in cognitive radio networks and then to obtain a detection technique for Primary User Emulation Attack (PUK)\",\n \"Title: Numerical solution of non-linear partial differential equation for porous media using operational matrices.\\nYear: 2019\\n\\nAbstract: Nonlinear partial differential equations (NPDEs) play a significant role in modeling of many complex physical problems and engineering processes. Development of reliable and efficient techniques to solve such types of NPDEs is the need of the hour. In this article, numerical solutions of a class of NPDEs subject to initial and boundary conditions are derived. In the proposed approach, shifted Chebyshev polynomials are considered to approximate the solutions together with shifted Chebyshev derivative operational matrix and spectral collocation method. The benefit of this method is that it converts such problems in the systems of algebraic equations which can be solved easily. To show the efficiency, high accuracy and reliability of the proposed approach, a comparison between the numerical results of some illustrative examples and their existing analytical results from the literature is reported. There is high consistency between the approximate solutions and their exact solutions to a higher order of accuracy. The error analysis for each case exhibited through graphs and tables confirms the super-linear convergence rate of the proposed method.\",\n \"Title: Rf-Based Analytics Generated By Tag-To-Tag Networks\\nYear: 2019\\n\\nAbstract: We have developed a type of RFID tags that can communicate with each other directly if there is an RF signal in their environment to support backscattering. These tags are passive and they can form a tag-to-tag network. Our tags communicate by what we refer to as multiphase probing. With this technique, we basically explore the backscatter channel by reflecting the incident RF signal with different changes in the phase. We define a measure of the backscatter channel, which we call backscatter channel state information (BCSI). The BCSI is composed of backscatter channel phase, backscatter amplitude, and change in baseline excitation level. When acquired over time, this measure provides rich RF analytics that can be used to extract various types of information from the environment of the tags by signal processing/machine learning methods. We show in the paper that this analytics is invariant w.r.t. to some variables including the deployment environment. We provide results from experiments with our tags that demonstrate the invariance of the BCSI.\",\n \"Title: Parallel and Communication Avoiding Least Angle Regression.\\nYear: 2019\\n\\nAbstract: We are interested in parallelizing the Least Angle Regression (LARS) algorithm for fitting linear regression models to high-dimensional data. We consider two parallel and communication avoiding versions of the basic LARS algorithm. The two algorithms apply to data that have different layout patterns (one is appropriate for row-partitioned data, and the other is appropriate for column-partitioned data), and they have different asymptotic costs and practical performance. The first is bLARS, a block version of LARS algorithm where we update b columns at each iteration. Assuming that the data are row-partitioned, bLARS reduces the number of arithmetic operations, latency, and bandwidth by a factor of b. The second is Tournament-bLARS (T-bLARS), a tournament version of LARS, in which case processors compete, by running several LARS computations in parallel, to choose b new columns to be added into the solution. Assuming that the data are column-partitioned, T-bLARS reduces latency by a factor of b. Similarly to LARS, our proposed methods generate a sequence of linear models. We present extensive numerical experiments that illustrate speed-ups up to 25x compared to LARS.\",\n \"Title: Design And Simulation Of Rfid Array Antenna 2x1 For Detection System Of Objects Or Living Things In Motion\\nYear: 2019\\n\\nAbstract: This paper presents the improvement of radiation characteristics performances of the RFID array antenna for detection system of objects or living things in motion. The proposed array antenna is composed of two identical elements, connected in parallel via simple microstrip line divider, having a power port adapted to 5012. This array is designed using RT/duroid-5880 substrate with the following parameters: loss tangent tan delta = 9.10(-4) , epsilon(r) of 2.2 and substrate's height of 1.56 mm. The total size of this array antenna is (110.5 x 83)mm(2).The simulation results obtained in this work show that the proposed array antenna has a high gain about of 9.22 dB, a good reflection coefficient S-11 of -25.36 dB, and a good adaptation at the 2.4 GHz microwave band. (C) 2019 The Authors. Published by Elsevier B.V.\",\n \"Title: Smart Webcam Cover: Exploring the Design of an Intelligent Webcam Cover to Improve Usability and Trust\\nYear: 2021\\n\\nAbstract: AbstractLaptop webcams can be covertly activated by malware and law enforcement agencies. Consequently, 59% percent of Americans manually cover their webcams to avoid being surveilled. However, manual covers are prone to human error---through a survey with 200 users, we found that 61.5% occasionally forget to re-attach their cover after using their webcam. To address this problem, we developed Smart Webcam Cover (SWC): a thin film that covers the webcam (PDLC-overlay) by default until a user manually uncovers the webcam, and automatically covers the webcam when not in use. Through a two-phased design iteration process, we evaluated SWC with 20 webcam cover users through a remote study with a video prototype of SWC, compared to manual operation, and discussed factors that influence users' trust in the effectiveness of SWC and their perceptions of its utility.\",\n \"Title: An Enhanced Ultra-High Gain Active-Switched Quasi Z-Source Inverter\\nYear: 2022\\n\\nAbstract: This brief proposes an enhanced ultra-high gain quasi Z-source inverter (EUHG-qZSI) having single unit of active switched capacitive network and switched inductor respectively. This inverter provides very high voltage gain profile with limiting value of shoot-through duty ratio of 0.186 and also, numerator factor of its gain is two times of (1+shoot-through duty ratio). Thus, the suggested inverter achieves higher voltage gain than the existing ultra-high/high gain/two-switched topologies. The voltage stresses on switches and capacitors of the proposed inverter are reduced. It also retains desirable qualities such as common ground with input DC supply and continuous source-side current for providing relief from current stress on input supply. The steady-state voltage gain, parameter values, current and voltage stresses of its components, loss and input current ripple are given to analyze the operation and characteristics of proposed inverter. The comparative voltage gains, voltage stresses and average switching device power are given to indicate its decent performances. Observations of both simulation and experimental verifications indicate real-time working of the suggested inverter.\",\n \"Title: \\\"How Do You Not Lose Friends?\\\": Synthesizing a Design Space of Social Controls for Securing Shared Digital Resources Via Participatory Design Jams\\nYear: 2022\\n\\nAbstract: Digital resources (streaming services, banking accounts, collaborative documents, etc.) are commonly shared among small, social groups. Yet, the security and privacy (S&P) controls for these resources map poorly onto the reality of shared access and ownership (e.g., one shared Netflix password for roommates). One challenge is that the design space for social S&P controls remains unclear. We bridged this gap by engaging end-users in participatory design workshops to envision social solutions to S&P challenges common to their groups. In analyzing the generated ideas and group discussions, we identified four design considerations salient to social S&P controls: social transparency; structures of governance; stakes and responsibility; and, promoting pro-group S&P behaviors. Additionally, we discovered trade-offs and challenges that arise when designing social S&P controls: balancing group security versus individual privacy; combating social friction; mitigating social herding behaviors; and, minimizing coordination costs.\",\n \"Title: Electrothermal blood streaming conveying hybridized nanoparticles in a non-uniform endoscopic conduit\\nYear: 2022\\n\\nAbstract: The novelty of nanoparticles in transferrals of medications and biological fluids via electrokinetic mechanism has been competently recognized. Due to the impressive role of nanoparticles suspended in blood or physiological fluids in medical fields, the current research article is planned to formulate an effective mathematical model to analyze the dynamism of bloodstream infused with hybridized nanoparticles in a non-uniform endoscopic conduit (space between two coaxial tubes) under the interactivities of electroosmosis, peristalsis, and buoyancy forces. The dual impact of heat source, Joule heating, and convectively cooling wall condition is examined. The geometrical shapes (sphere, brick, cylinder, and platelet) of nanoparticles injected into blood are accounted for in the formulation of modelled equations. The blood doped with hybridized nanoparticles is regarded as an electrolyte solution. The lubrication and Debye-Hückel linearization estimations are invoked in order to linearize the flow equations. Analytical solutions for the resulting leading equations are computed by implementing an analytical approach. The amendments in the physiognomies under variations in sundry parameters are explained through the line, bar graphs, and numerical tables. Outcomes admit that the flow of ionized blood is significantly amended across the endoscopic conduit due to the electrostatic body force. Blood is warmed or cooled with positive or negative values of Joule heating parameter. Blood is cooled with augmenting volumetric concentration of hybridized nanoparticles. The trapping phenomenon is also described by designing streamline plots. The size of confined blood boluses expands due to the thin electric double layer (EDL). The novel findings of this hemodynamic simulation furnish significant applicabilities in modelling of transportation of medications and drugs, physiological fluid mixers, testing and assessment of human diseases, detection of bacteria and viruses, etc.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":64,"string":"64"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13280,"string":"13,280"}}},{"rowIdx":13281,"cells":{"question_id":{"kind":"string","value":"Zuqing Zhu"},"question":{"kind":"string","value":"Generate an abstract for the title \"Scalable network planning for elastic optical orthogonal frequency division multiplexing (OFDM) networks\" using the following items: scalable O-OFDM network planning, genetic algorithm, adaptive optimization, routing-spectrum-modulation assignments, convergence performance."},"answer":{"kind":"string","value":"We propose to achieve scalable O-OFDM network planning with a genetic algorithm (GA) that operates in an adaptive way for high optimization efficiency. When the network topology and lightpath requests are given, the GA encodes the routing, spectrum and modulation assignments (RSMA) as genes and optimizes them iteratively. Both the crossover and mutation schemes in our proposed GA operate adaptively based on the fitness of individuals. Specifically, when the individuals are not fit yet, their genes can be modified significantly with crossover and mutation. On the other hand, for individuals that are already fit, we limit the crossover and mutation rate to avoid chromosomal disruption. The simulation results with the NSFNET topology and up to 1000 lightpath requests show that the proposed adaptive GA converges faster with better optimization performance, when comparing to a non-adaptive one. For the 1000-request case, the proposed algorithm can converge with a relatively small population size (e.g. 50) within 80 generations."},"correct_answer":{"kind":"string","value":"We propose to achieve scalable O-OFDM network planning with a genetic algorithm (GA) that operates in an adaptive way for high optimization efficiency. When the network topology and lightpath requests are given, the GA encodes the routing, spectrum and modulation assignments (RSMA) as genes and optimizes them iteratively. Both the crossover and mutation schemes in our proposed GA operate adaptively based on the fitness of individuals. Specifically, when the individuals are not fit yet, their genes can be modified significantly with crossover and mutation. On the other hand, for individuals that are already fit, we limit the crossover and mutation rate to avoid chromosomal disruption. The simulation results with the NSFNET topology and up to 1000 lightpath requests show that the proposed adaptive GA converges faster with better optimization performance, when comparing to a non-adaptive one. For the 1000-request case, the proposed algorithm can converge with a relatively small population size (e.g. 50) within 80 generations."},"documents":{"kind":"list like","value":["Title: Energy-efficient scheduling and energy-delay tradeoff in green hybrid fiber-coaxial networks.\nYear: 2012\n\nAbstract: Hybrid Fiber Coaxial (HFC) networks support broadband Internet access with the existing cable TV systems. Recent advances on HFC networks have demonstrated effective improvements on the customers' access speeds with the channel-bonding technology. In this paper, we develop a novel energy-efficient traffic scheduling algorithm for the HFC networks that support channel bonding. The proposed algorithm is compliant with the newly-released DOCSIS 3.0 standard. We come up with a system model of the channel-bonding transmitters (TXs) on a Cable Modem (CM), and then define several operation modes for them. At the beginning of each scheduling cycle, the proposed algorithm adjusts the TXs' operation modes based on the traffic status. Both analytical analysis and numerical simulations are then developed to investigate the energy-saving and delay introduced by the algorithm. The results on energy-saving indicate that the energy-consumption of the TXs scales almost linearly with the input traffic load and effective energy-saving can be achieved. We also investigate the tradeoff between the energy-saving and the average delay to optimize the parameters for the energy-efficient scheduling, and to make sure that the traffic will not experience significant delay increase due to the energy-saving operations.","Title: A Two-Population Based Evolutionary Approach for Optimizing Routing, Modulation and Spectrum Assignments (RMSA) in O-OFDM Networks\nYear: 2012\n\nAbstract: We propose a novel two-population genetic algorithm (MPGA) to optimize the routing, modulation and spectrum assignments (RMSA) in optical orthogonal frequency-division multiplexing (O-OFDM) networks. The proposed MPGA makes two populations evolve in parallel with different selection and mutation strategies, and incorporates a migration operation to exchange individuals between them. Performance evaluations show that the MPGA outperforms several existing algorithms.","Title: Dynamic RMSA in spectrum-sliced elastic optical networks for high-throughput service provisioning\nYear: 2013\n\nAbstract: We propose several novel dynamic routing, modulation and spectrum assignment (RMSA) algorithms for high-throughput service provisioning in spectrum-sliced elastic optical networks. The proposed algorithms can be applied to network provisioning, where a network operator needs to figure out an efficient way to serve each dynamic connection request based on current network status. The proposed algorithms can be categorized into two categories, dynamic RMSA using online path computation and dynamic RMSA using path-set updates. The former one updates link metrics based on their spectrum utilization status dynamically, and performs RMSA with online routing path computation for each request. The latter one pre-computes K shortest routing paths as the path-set for each source-destination pair in the network topology, performs RMSA based on different path-selection policies during dynamic provisioning, and updates a path-set by adding a new path in when a path in the set is not available for a request. We evaluate the proposed algorithms with simulations using the Poisson traffic model in a 14-node NSFNET topology. Simulation results indicate that the proposed algorithms outperform existing dynamic RMSA algorithm by providing lower bandwidth blocking probability, and reduce the blocking probability by one magnitude or more when the traffic load is light (≤ 700 Erlangs). The comparisons on network resource utilization also indicate that the proposed algorithms achieve larger resource utilization and hence provide higher throughput for network operations.","Title: Dynamic Multi-Path Service Provisioning under Differential Delay Constraint in Elastic Optical Networks\nYear: 2013\n\nAbstract: Optical orthogonal frequency-division multiplexing (O-OFDM) technology has the elastic feature of allocating spectrum resources based on subcarrier slots with bandwidths at a few GHz or even narrower. This feature enables us to utilize link capacity more efficiently by splitting a connection's traffic over multiple routing paths. In this paper, we propose a novel dynamic multi-path provisioning algorithm for O-OFDM based elastic optical networks. The algorithm tries to set up dynamic connections with single-path routing in a best-effort manner. When a connection cannot be served with a single routing path, the algorithm uses an auxiliary-graph based approach to calculate a multi-path provisioning scheme based on two parameters, i.e., the differential delay upper-bound and the bandwidth allocation granularity. Simulation results indicate that compared with several existing single-path provisioning algorithms, the proposed algorithm provides lower bandwidth blocking probability and achieves 10-18% improvement on average network throughput.","Title: Design green Hybrid Fiber-Coaxial networks: A traffic-aware and cooperative approach\nYear: 2012\n\nAbstract: We propose a traffic-aware and cooperative approach to achieve energy-saving in Hybrid Fiber-Coaxial (HFC) networks that support DOCSIS 3.0 standard. The approach incorporates a two-step operation that consists of two algorithms, one for the Cable Modem (CM) side and the other for the Cable Modem Termination System (CMTS) side. To avoid transition overheads, we design both algorithms with the consideration of energy-to-NoC (Number of Changes) tradeoff and try to minimize the number of Dynamic Bonding Change (DBC) operations while saving the energy. Simulations verify the effectiveness of the algorithms by using a realistic HFC network traffic model, and the results demonstrate significant saving on the ON-time (energy consumption) of the CMs' channels and the CMTS' ports.","Title: Bandwidth defragmentation in dynamic elastic optical networks with minimum traffic disruptions\nYear: 2013\n\nAbstract: Bandwidth defragmentation, i.e., the operation to reconfigure existing connections for making the spectrum usage less fragmented and less misaligned, has recently been recognized as one of the most important features for elastic optical networks (EONs). In this paper, we propose a novel comprehensive bandwidth defragmentation algorithm that considers the problems of 1) When to defragment? 2) What for defragment? and 3) How to defragment? jointly. The proposed algorithm accomplishes defragmentation through proactive network reconfiguration that only reroutes a portion of existing connections. In each defragmentation operation, we first choose the connections to reroute using a selection strategy, then determine how to reroute them with the defragmentation based routing and spectrum assignment (DF-RSA), and finally perform rerouting with best-effort traffic migration to minimize traffic disruptions. Simulation results indicate that in order to make the bandwidth blocking probability (BBP) comparable with that from the Greenfield scenario (100% rerouting all the time), the proposed algorithm only needs to reroute ~30% existing connections. The simulations also demonstrate that the traffic disruption percentages are less than 1% for defragmentations with 30% rerouting and can be further reduced to within 0.25% by adding a move-to-vacancy (MTV) approach in the traffic migration.","Title: On the Effect of Bandwidth Fragmentation on Blocking Probability in Elastic Optical Networks\nYear: 2013\n\nAbstract: In elastic optical networks (EONs), bandwidth fragmentation refers to the existence of non-aligned, isolated and small-sized blocks of contiguous subcarrier slots in the optical spectrum. As they are neither contiguous in the spectrum domain nor aligned along the routing paths, the network operator will have difficulty to use these slots for future connections. In this work, we analyze the effect of bandwidth fragmentation on the blocking probability in EONs. Our theoretical analysis indicates that two factors related to bandwidth fragmentation have effects on the blocking probability: 1) the extent that the available slot-blocks (i.e., blocks of contiguous slots) on different links are aligned on spectrum locations, and 2) the sizes of the available slot-blocks in links' spectra for future requests. When an EON's spectrum becomes more fragmented, the first factor actually reduces the blocking probability, while the second one increases the blocking probability. Their mixed effect determines the overall trend of how the blocking probability will change with bandwidth fragmentation. Our theoretical model can forecast this trend and reveal the relation among the blocking probability, bandwidth fragmentation, request bandwidth distribution, and spectrum utilization. We have also conducted numerical simulations to verify the theoretical analysis, and the simulation results exhibit similar trends as predicted by the theoretical model.","Title: Design QoS-Aware Multi-Path Provisioning Strategies for Efficient Cloud-Assisted SVC Video Streaming to Heterogeneous Clients\nYear: 2013\n\nAbstract: We layout a network infrastructure that leverages the storage and computing power of a cloud residing in the core for collecting network status and computing multi-path scalable video coding (SVC) streaming provisioning strategies. Therefore, in addition to its conventional tasks in the application layer, the cloud also gets involved in the network layer for the optimization of routing and forwarding. We call this scheme as cloud-assisted SVC streaming, and use it to further improve the performance of SVC streaming by using close cooperation between cloud and network. Compared to source-routing based provisioning, the cloud-assisted scheme can provide more cost-effective provisioning strategies by utilizing better knowledge of network environment together with more powerful computation power. We then propose several multi-path provisioning algorithms for cloud-assisted SVC streaming in heterogeneous networks. To the best of our knowledge, these are the first proposals to work on the problem of adaptive multi-path SVC streaming under the bandwidth, delay and differential delay constraints. Our design of the provisioning algorithms starts from an approach that is based on Max Flow and an Auxiliary Graph. Several extensions are then made based on this approach to address the situations such as provisioning from multiple sources and provisioning in dynamic network environments with rapid background traffic fluctuations. Simulations in both static and dynamic network environments show that the proposed algorithms can achieve effective performance improvements in terms of request blocking probability, bandwidth utilization, packet delay, packet loss rate, and video playback quality.","Title: On the Spectrum-Efficient Overlay Multicast in Elastic Optical Networks Built with Multicast-Incapable Switches.\nYear: 2013\n\nAbstract: We study overlay multicast (OL-M) in multicast-incapable elastic optical networks (EONs), and propose a spectrum-efficient OL-M scheme that relies on the spectrum-flexible member-only relay, i.e., OL-M-SFMOR. Our simulation results indicate that OL-M-SFMOR achieves significant improvements on the spectrum-efficiency of multicast in EONs, when compared with other multicast schemes, even including the all-optical multicast schemes in multicast-capable EONs. © 2013 IEEE.","Title: On the Performance Analysis of Energy-Efficient Upstream Scheduling for Hybrid Fiber-Coaxial Networks with Channel Bonding\nYear: 2013\n\nAbstract: We develop a novel QoS-aware energy-efficient upstream traffic scheduling algorithm for channel-bonding cable modems (CMs) in hybrid fiber-coaxial (HFC) networks. Based on their QoS requirements, the algorithm sends incoming packets to different priority queues and controls channel-bonding transmitters (TXs) to forward them. Based on a vacation M/M/n queuing model, we analyze the algorithm's performance with a three-dimensional Markov chain and derive the analytical expressions of packet delay and power consumption.","Title: Dynamic transparent virtual network embedding over elastic optical infrastructures\nYear: 2013\n\nAbstract: We propose a novel dynamic transparent virtual network embedding (VNE) algorithm, which considers node mapping and link mapping jointly, for network virtualization over optical orthogonal frequency-division multiplexing (O-OFDM) based elastic optical infrastructures. For each virtual optical network (VON) request, the algorithm first transfers the substrate optical network into a layered-auxiliary-graph according to the spectrum usage of each fiber link, then applies a node mapping approach that considers the local information of all substrate nodes, and accomplishes the link mapping, in a single layer of the auxiliary graph. The simulation results verify that the proposed algorithm considers the uniqueness of O-OFDM networks and outperforms two reference algorithms that directly apply the VNE schemes developed for Layer 2/3 or WDM network virtualization, by providing lower VON blocking probability. The simulations with a realistic topology also demonstrate that the average lengths of embedded substrate paths are well-controlled within the typical transmission reaches of O-OFDM signals. To the best of our knowledge, this is the first proposal that includes both link mapping and node mapping to address dynamic transparent VNE over elastic optical infrastructures.","Title: Mixed Placement of 1R/2R/3R Regenerators in Translucent Optical Networks to Achieve Green and Cost-Effective Design.\nYear: 2011\n\nAbstract: We develop a theoretical model to estimate the BER evolutions in optical translucent networks in a more accurate way. The model calculates the BER increment at each regeneration site with considerations of reshaping nonlinearity and jitter effect for 40 Gb/s signals. Based on the model, we propose a novel translucent optical network infrastructure that includes all-optical 2R regenerators and uses mixed operation of 1R/2R/3R regenerators for cost and power consumption savings. Simulation results show that our proposed infrastructure reduces the number of O/E/O 3R regenerators to less than 50% and achieves effective power saving. © 2011 IEEE.","Title: Energy-efficient wideband cable access networks in future smart cities.\nYear: 2013\n\nAbstract: The Internet explosion makes energy consumption of the ICT sector a global concern. Nowadays, the power consumption of access networks increases rapidly due to the rollout of broadband services. Cable access networks provide Internet access over the existing cable television systems. Among all wired access networks, they have the second biggest user base, which is still growing fast due to the rel...","Title: Crosstalk due to optical demultiplexing in subcarrier multiplexed systems\nYear: 2011\n\nAbstract: We report an in-depth investigation of the inter-modulation crosstalk in subcarrier multiplexing (SCM) systems with optical demultiplexing (ODeMux). Both theoretical derivations and numerical simulations show that the crosstalk in ODeMux systems mainly comes from the nonlinear mixing of the baseband and subcarrier modulations inside the signal channels. Several key parameters are then studied to estimate their effects on the magnitude of the crosstalk. As a result, performance optimization strategies are proposed for ODeMux SCM systems. In order to further enhance the transmission performance of ODeMux SCM systems, we discuss two techniques to suppress the inter-modulation crosstalk and analyze their effectiveness with numerical simulations.","Title: Optical networks in cloud computing [Guest Editorial]\nYear: 2013\n\nAbstract: The seven articles in this special section focus on optical networking deployed in cloud computing.","Title: An Empirical Investigation of the Impact of Server Virtualization on Energy Efficiency for Green Data Center.\nYear: 2013\n\nAbstract: The swift adoption of cloud services is accelerating the deployment of data centers. These data centers are consuming a large amount of energy, which is expected to grow dramatically under the existing technological trends. Therefore, research efforts are in great need to architect green data centers with better energy efficiency. The most prominent approach is the consolidation enabled by virtual...","Title: A Novel Energy-Aware Design to Build Green Broadband Cable Access Networks.\nYear: 2011\n\nAbstract: We propose and investigate energy-saving algorithms for DOCSIS-3.0 networks. By proactively monitoring traffic load, the algorithms achieve network-wide energy saving by re-adjusting CM's bonding groups and shutting down under-utilized upstream/downstream ports on the CMTS. Numerical simulations indicate that by applying the CM side algorithm only, we can achieve a possible 17.6% energy saving for 8192 CMs. We also study the fundamental tradeoff between energy-saving and packet queuing delay and estimate the upper bounds of the delays. © 2011 IEEE.","Title: Genetic algorithms for designing energy-efficient optical transport networks with mixed regenerator placement\nYear: 2012\n\nAbstract: We design genetic algorithms (GA) to solve the mixed regenerator placement (MRP) problem of lightpaths with different lengths in optical transport networks, and investigate their performance with numerical simulations. By incorporating a theoretical model that can estimate BER changes hop-by-hop along lightpaths, the GA encodes the placements of 1R/2R/3R at intermediate regeneration sites as genes, and takes quality-of-transmission (QoT) and energy-efficiency as the fitness functions. With a relatively small population size (e.g. 30-50), the algorithms obtain multiple qualified MRP results that can satisfy both the QoT and energy requirements within 32 generations, for lightpaths with lengths up to 28 hops. Two crossover and two mutation operators are investigated within the GA. By adjusting the possibilities of the crossover and mutation intelligently, the adaptive scheme outperforms the uniform one by getting a larger percentage of fit individuals. We also propose two selection operators, and demonstrate an adjustable tradeoff between QoT and energy-efficiency.","Title: Optical regenerator placement strategy to achieve green design of translucent optical networks\nYear: 2011\n\nAbstract: We report a novel optical regenerator placement strategy to achieve green and cost-effective design of optical translucent networks. Instead of treating the BER independently before and after a regenerator, we calculate the BER increment at each regeneration site with the considerations of reshaping nonlinearity and jitter effect. Simulation results demonstrate that the cost and power consumption of a translucent network can be effectively reduced with our proposed scheme.","Title: Experimental Demonstration Of Svc Video Streaming Using Qos-Aware Multi-Path Routing Over Integrated Services Routers\nYear: 2013\n\nAbstract: We construct an experiment testbed using commercial routers and demonstrate QoS-aware multi-path SVC video streaming with it. The testbed consists of six integrated services routers (Cisco 2900 Series) that are configured using a mesh topology. To realize QoS-aware multi-path routing efficiently, we develop a centralized automatic NC&M system that monitors link status proactively, calculates the multi-path routing scheme for each streaming session, and communicates with the routers' control plane to adjust their routing policies. For each streaming session, when the NC&M finds a better multi-path routing scheme, it reconfigures the routers to invoke a path-switching. The experimental results indicate that the multi-path SVC video streaming scheme reduces the packet loss rate (PLR) from 3.33% to 0.62% for the base layer (BL) packets, and to 1.71% for the enhancement layer (EL) packets. Additional experiments on video playback quality, video playback peak signal-to-noise ratio (PSNR), and delay jitter also verify that the multi-path scheme outperforms the single-path one significantly and utilizes the network resources more efficiently.","Title: Dynamic RMSA in elastic optical networks with an adaptive genetic algorithm\nYear: 2012\n\nAbstract: We develop an adaptive and efficient genetic algorithm (GA) to solve the dynamic routing, modulation and spectrum assignments (RMSA) for elastic O-OFDM networks. The algorithm offers an efficient way of serving the dynamic lightpath requests based on the current network status at each service provision time. The GA is designed for multi-objective optimization. For low traffic cases when there is no blocking, the GA minimizes the maximum number of slots required on any fiber in the network; otherwise, it minimizes the blocking probability. The performance of the proposed GA is evaluated in dynamic RMSA simulations with the 14-node NSFNET and the 28-node US Backbone topologies, and the results show that it converges within 25 generations. The simulation results also verify that the GA-RMSA outperforms several existing algorithms by providing more load-balanced network provisioning solutions with lower blocking probabilities. Specifically, when the traffic load is same, the GA can achieve more than one order-of-magnitude reduction on blocking probability. To the best of our knowledge, this is the first attempt to solve dynamic RMSA in elastic O-OFDM networks with a GA.","Title: Improving Energy-Efficiency Of Hfc Networks With A Master-Slave Linecard Configuration\nYear: 2013\n\nAbstract: We develop a novel traffic scheduling algorithm based on a master-slave linecard (LC) configuration to improve the energy-efficiency of hybrid fiber-coaxial (HFC) networks. The algorithm forwards packets to the master or slave LC adaptively according to the traffic load, and toggles the LCs between working and sleeping modes for energy-saving. To optimize the algorithm's key parameters, we model the system using a two-dimensional Markov process and derive the analytical expressions of several performance metrics, including average packet delay, LC switching frequency, and energy efficiency improvement. We then verify the theoretical analysis with numerical simulations using the Monte Carlo method. Both the theoretical and simulation results indicate that the proposed algorithm can achieve significant energy efficiency improvement, while keeping the average packet delay and LC switching frequency low.","Title: Using Genetic Algorithm to Optimize Mixed Placement of 1R/2R/3R Regenerators in Translucent Lightpaths for Energy-Efficient Design.\nYear: 2012\n\nAbstract: We propose an evolutionary approach to efficiently optimize the mixed placements of 1R/2R/3R regenerators in translucent lightpaths. The genetic algorithm encodes the placements of mixed types of regenerators as genes, and incorporates adaptive genetic operators for effective search strategy. We consider both end-to-end signal transmission quality and regeneration energy cost, and design the fitness function for multi-objective optimization. The simulation results show that the proposed approach converges within 50 generations even when the lightpath under investigation is as long as 31 hops. To improve the flexibility of network designs, multiple energyefficient mixed regenerator placement (MRP) results can be obtained simultaneously. © 2012 IEEE.","Title: Fragmentation-aware routing, modulation and spectrum assignment algorithms in elastic optical networks\nYear: 2013\n\nAbstract: We investigate the principle of how dynamic service provisioning fragments the spectral resources on links along a path, and propose corresponding RMSA algorithms to alleviate spectrum fragmentation in dynamic network environments. © 2013 OSA.","Title: Energy-Efficient Protection Designs For Translucent Optical Networks Using Mixed Regenerator Placement\nYear: 2013\n\nAbstract: We investigate energy-efficient protection designs for translucent networks using mixed regenerator placement (MRP). For link-based p-cycle designs, we develop an ILP model and a heuristic.We also propose a path-based shared backup path protection design algorithm","Title: Spectrum defragmentation algorithms for elastic optical networks using hitless spectrum retuning techniques\nYear: 2013\n\nAbstract: We propose several algorithms to achieve hitless bandwidth defragmentation using spectrum retuning in elastic optical networks. Two retuning techniques, spectrum sweeping and hop tuning, are studied. Simulation results show that the hop tuning technique achieves better defragmentation performance. © 2013 OSA.","Title: Ambient assisted living communications [Guest Editorial]\nYear: 2015\n\nAbstract: Nowadays taking care of elderly people and the disabled has become a very important but challenging task. Although the elderly have wisdom and wealth gathered from their life experiences, they require special assistance, higher health insurance costs, and even constant monitoring. By utilizing information process and communications technology, ambient assisted living (AAL) communications open up a new way to address such needs for the aged and the sick. Specifically, AAL uses ambient technologies, including data sensing, processing, transmission, and artificial intelligence, to enable new products, services, and processes that help to provide safe, healthy lives for the aged and disabled. It also supports improved social connections and accessibility to the external world. With the growth of AAL environment, the accessibility gets more challenging for the complex data structure.","Title: Implementation and Demonstration of Revenue-Driven Provisioning for Advance Reservation Requests in OpenFlow-Controlled SD-EONs\nYear: 2014\n\nAbstract: This paper aims to realize OpenFlow-controlled revenue-driven advance reservation (AR) provisioning in software-defined elastic optical networks (SD-EONs). We design control plane framework, propose two algorithms for the OpenFlow controller, implement the whole system in a semi-practical SD-EON testbed, and conduct experiments with two payment models. Experimental results indicate the proposed system performs well and can increase total revenue-gain effectively.","Title: Flexible Traffic Engineering: When OpenFlow Meets Multi-Protocol IP-Forwarding\nYear: 2014\n\nAbstract: In this letter, we present flexible traffic engineering (F-TE), a solution to achieve highly efficient traffic engineering in a hybrid network where IPv4 and IPv6 protocols co-exist. By leveraging the programmability of OpenFlow (OF), we design the OF system to facilitate IP forwarding interchanging (i.e., switching the packets between IPv4 and IPv6 at each hop according to the network status). The OF system is implemented in a real network testbed. We conduct experiments with video streaming to verify that the proposed F-TE can improve network throughput effectively with adaptive IP interchanging.","Title: Spectrum-efficient anycast in elastic optical inter-datacenter networks.\nYear: 2014\n\nAbstract: With the rapid development of cloud computing, optical inter-datacenter (inter-DC) networks have attracted intensive research attentions. Meanwhile, recent advances on the flexible-grid elastic optical networks (EONs) have demonstrated agile spectrum management in the optical layer. Therefore, we expect EONs to become a promising underlying infrastructure for optical inter-DC networks. In this paper, we investigate offline and online routing and spectrum assignment (RSA) problems for anycast requests in elastic optical inter-DC networks. For the offline problem, we formulate an integer linear programming (ILP) model and propose several heuristics based on single-DC destination selection. The optimal solutions for small-scale problems are obtained by solving the ILP, and we compare them with those from the heuristics for measuring the optimization gaps. For the online problem, we design several heuristics to consider the computing and bandwidth resources jointly for efficient service provisioning, including an algorithm that uses multi-DC destination selection. Our simulation results demonstrate that the anycast algorithm with multi-DC destination selection can fully utilize the bandwidth resources in the elastic optical inter-DC network, make computing resources become the bottleneck, and reduce the bandwidth blocking probability of anycast requests effectively.","Title: Efficient and scalable cloud-assisted SVC video streaming through mesh networks\nYear: 2012\n\nAbstract: To improve the efficiency and scalability of video streaming in large scale mesh networks, we propose a cloud-assisted video delivering strategy that can distribute video contents to subscribers over multiple routing paths dynamically. By leveraging the computation power and numerous storage of a cloud and the flexibility of scalable video coding (SVC) scenarios, we design algorithms for multiple routing path selection and assignment, and propose to run them in a cloud for fast processing. The multi-path routing is then achieved by label-switching in the network layer. The simulation results show that the proposed algorithms achieved efficient usage of the network resources and improved user experience in terms of bandwidth, delay and jitter.","Title: Dynamic anycast in inter-datacenter networks over elastic optical infrastructure\nYear: 2014\n\nAbstract: Since the optical orthogonal frequency-division multiplexing (O-OFDM) technology can facilitate agile spectrum management, the elastic optical infrastructure based on it has become a promising candidate for the physical-layer of inter-datacenter networks. In this paper, we investigate dynamic anycast in such networks. Firstly, we design three metrics to consider computing and bandwidth resources jointly, and propose anycast algorithms with single-path routing based on them. Then, we propose an anycast algorithm with multipath routing to further improve the network performance. Our simulation results indicate that the anycast algorithm with multipath routing can fully utilize the bandwidth resources in the optical infrastructure, make computing resources become the bottleneck, and reduce the bandwidth blocking probability of anycast requests effectively.","Title: Toward profit-seeking virtual network embedding algorithm via global resource capacity\nYear: 2014\n\nAbstract: In this paper, after proposing a novel metric, i.e., global resource capacity (GRC), to quantify the embedding potential of each substrate node, we propose an efficient heuristic virtual network embedding (VNE) algorithm, called as GRC-VNE. The proposed algorithm aims to maximize the revenue and to minimize the cost of the infrastructure provider (InP). Based on GRC, the proposed algorithm applies a greedy load-balance manner to embed each virtual node sequentially, and then adopts the shortest path routing to embed each virtual link. Simulation results demonstrate that our proposed GRC-VNE algorithm achieves lower request blocking probability and higher revenue due to the more appropriate consideration of the resource distribution of the entire network, when compared to the two lastest VNE algorithms that also consider the resources of entire substrate network. Then, we introduce a classical reserved cloud revenue model, which consists of fixed revenue and variable one. Based on this revenue model, we design a novel admission control policy selectively accepting the VNR with high revenue-to-cost ratio to maximize the InP's profit based on an empirical threshold. Through extensive simulations, we observe that the optimal empirical threshold is proportional to the ratio of variable revenue to the fixed one.","Title: Revenue-driven virtual network embedding based on global resource information.\nYear: 2013\n\nAbstract: Virtual network embedding (VNE), working as a key step for network virtualization, has recently gained intensive attentions from the research community. In this paper, we propose a novel VNE algorithm that aims to maximize the infrastructure provider's revenue from serving virtual network (VN) requests, with the help of the global resource information. The proposed algorithm, named as revenue-driven VNE (RD-VNE), adopts a node-ranking approach that takes the global resource information into account in a recursive manner to assist the greedy node mapping, and leverages the shortest-path routing for link mapping. Our simulation results suggest that the proposed VNE algorithm outperforms two existing VNE algorithms that also take global resource information into consideration, in terms of request blocking probability, and brings higher time-average revenue to the infrastructure provider (InP).","Title: Design integrated RSA for multicast in elastic optical networks with a layered approach\nYear: 2013\n\nAbstract: In this paper, we incorporate a layered approach to design integrated multicast-capable routing and spectrum assignment (MC-RSA) algorithms for achieving efficient all-optical multicasting in spectrum-sliced elastic optical networks (EONs), which are based on the optical orthogonal frequency-division multiplexing (O-OFDM) technology. For each multicast request, the proposed algorithms decompose the physical topology into several layered auxiliary graphs according to the network spectrum utilization. Then, based on the request's bandwidth requirement, we select a proper layer and calculate a multicast light-tree within it. With these procedures, the routing and spectrum assignment (RSA) for each multicast request is done in an integrated way. We evaluate the proposed algorithms in simulations of static network planning and dynamic network provisioning. The simulation results demonstrate that compared to the existing MC-RSA algorithms, our approaches achieve more efficient network planning in terms of spectrum utilization, and provide lower blocking probabilities in network provisioning.","Title: Dynamic p-cycle configuration in spectrum-sliced elastic optical networks\nYear: 2013\n\nAbstract: We propose three algorithms for dynamic pre-configured-cycle (p-cycle) configuration in the spectrum-sliced elastic optical networks (EONs) based on the optical orthogonal frequency-division multiplexing (O-OFDM) technology, aiming to provide 100% restoration against single-link failures. The first one (PE-p-cycle) configures the working path and p-cycles of a request together according to the protection efficiencies of the p-cycles. In order to reduce request blocking probability, we then propose to use the spectrum planning technique to regulate the spectra of working and protection resources and design two algorithms based on the protected working capacity envelope (PWCE) cycles (PWCE-p-cycle-SP) and Hamiltonian cycles (Ham-p-cycle-SP). The three algorithms are then evaluated in two mesh network topologies, using dynamic Poisson traffic. The simulation results indicate that with the spectrum planning, PWCE-p-cycle-SP and Ham-p-cycle-SP reduce the blocking probability effectively, when comparing with the one without it, i.e., PE-p-cycle. To the best of our knowledge, this is the first attempt to address dynamic p-cycle configuration in EONs.","Title: Performance evaluation of an enhanced cryptography solution for m-Health applications in cooperative environments.\nYear: 2013\n\nAbstract: Mobile health (m-Health) applications delivers healthcare services through mobile applications regardless of time and place. An m-Health application makes use of wireless communications to sustain its health services and often providing a patient-doctor interaction. Therefore, m-Health applications present several challenging issues and constraints, such as, mobile devices battery and storage capacity, broadcast constraints, interferences, disconnections, noises, limited bandwidths, network delays, and of most importance, privacy and confidentiality concerns. This paper proposes a novel and enhanced cryptography solution in a cooperative environment considering a novel and early-proposed cooperation strategy for m-Health Applications. This proposal aims to face the challenges related to privacy and security issues of all forwarded and retrieved data concerning user sensitive information. Furthermore, it presents a performance evaluation of this proposal considering a comparison with an earlier proposed encryption strategy for the same cooperative environment.","Title: Minimizing disaster backup window for geo-distributed multi-datacenter cloud systems\nYear: 2014\n\nAbstract: We optimize the disaster backup in multi-datacenter (multi-DC) cloud systems and design disaster-aware algorithms to realize rapid backup with the objective of minimizing the backup window for all the DCs in the network. A mixed integer linear programming (MILP) model is first formulated to optimize the backup processes of all production DCs jointly. We then develop three heuristics that use the one-step or two-step approaches for the selection of backup DCs and the calculation of backup routing paths. Simulation results show that the Two-Step algorithm can achieve the shortest backup window with the lowest operation complexity.","Title: Joint defragmentation of spectrum and computing resources in inter-datacenter networks over elastic optical infrastructure\nYear: 2014\n\nAbstract: In this paper, we consider to apply a joint defrag-mentation (DF) of spectrum and computing resources in the inter-datacenter (inter-DC) networks built over elastic optical infrastructure based on the optical orthogonal frequency-division multiplexing (O-OFDM) technology. We propose joint DF algorithms that consist of a request selection strategy to select active requests for reconfiguring and an anycast-based DF routing and spectrum assignment (DF-RSA) to reconfigure the selected requests. For the joint DF, two request selection strategies and three anycast-based DF-RSA schemes are designed, and their combinations are evaluated with simulations. The simulation results show that by considering the usages of spectrum and computing resources jointly, the proposed algorithms can improve the blocking performance of the inter-DC network significantly with controlled numbers of DF operations.","Title: Reduce spectrum defragmentation latency in EONs with effective parallelization of connection reconfigurations\nYear: 2014\n\nAbstract: We investigate parallel defragmentation and propose a novel algorithm to achieve effective parallelization of the connection reconfigurations with a conflict graph. Simulation results show that the algorithm can effectively reduce the latency of traffic migrations.","Title: Distributed Online Hybrid Cloud Management for Profit-Driven Multimedia Cloud Computing\nYear: 2015\n\nAbstract: It is known that with hybrid cloud, a multimedia cloud service provider (MCSP) can quickly extend its services to multiple geographical locations with quality-of-service (QoS) guarantees. Meanwhile, to maximize its profit, the MCSP needs an online management mechanism to operate the hybrid cloud efficiently. In this paper, we study how to maximize an MCSP’s profit from provisioning multimedia services to geographically distributed users with hybrid cloud. We first design a service provisioning model to manage the resources in hybrid cloud. Here, in order to make the model practical and address the different situations in private and public clouds, we consider different time granularities for resource reservations. Then, we leverage the Lyapunov optimization technique to maximize the profit of MCSP and propose an online algorithm that can manage hybrid cloud in the distributed manner. Specifically, the algorithm determines the access control and routing of each multimedia service request, and allocates the resources in hybrid cloud accordingly. We also apply the ??-persistent technique to ensure that the worst-case latency of the provisioned requests is bounded. Finally, the proposed algorithm is evaluated with extensive simulations using both synthetical and real traces. Simulation results indicate that the algorithm can manage the hybrid cloud efficiently and maximize the profit of MCSP.","Title: Data-oriented malleable reservation to revitalize spectrum fragments in elastic optical networks\nYear: 2015\n\nAbstract: This paper investigates malleable reservation (MR) for data-oriented requests to revitalize spectrum fragments in elastic optical networks. We formulate a mixed integer linear programming model and propose a time-efficient heuristic to solve the MR problem.","Title: Highly efficient data migration and backup for big data applications in elastic optical inter-data-center networks\nYear: 2015\n\nAbstract: This article discusses the technologies for realizing highly efficient data migration and backup for big data applications in elastic optical inter-data-center (inter-DC) networks. We first describe the impacts of big data applications on underlying network infrastructure and introduce the concept of flexible-grid elastic optical inter-DC networks. Then we model the data migration in such networks as dynamic anycast and propose several efficient algorithms. Joint resource defragmentation is also discussed to further improve network performance. For efficient data backup, we leverage a mutual backup model and investigate how to avoid the prolonged negative impacts on DCs' normal operation by minimizing the DC backup window.","Title: Distributed Online Scheduling and Routing of Multicast-Oriented Tasks for Profit-Driven Cloud Computing.\nYear: 2016\n\nAbstract: It is known that to support a few common applications well, e.g., datacenter (DC) backup, multicast-oriented tasks need to be handled in inter-DC networks. In this letter, we propose an approach to schedule and route multicast-oriented tasks in inter-DC networks with arbitrary topologies. Specifically, we leverage Lyapunov optimization to develop a distributed online approach that can maximize the time-average profit with only local information. Besides, we also design a destination grouping scheme to address the scalability issue of our proposed approach and demonstrate that the number of queues in the system can be reduced significantly. Extensive simulations verify the performance of the proposed approaches.","Title: Design and Demonstration of SDN-Based Flexible Flow Converging with Protocol-Oblivious Forwarding (POF)\nYear: 2015\n\nAbstract: With the development of software-defined networking (SDN), people start to realize that the protocol-dependent nature of OpenFlow, i.e., the matching fields are defined according to existing network protocols (e.g., Ethernet and IP), will limit the programmability of forwarding plane and cause scalability issues. In this work, we focus on Protocol-Oblivious Forwarding (POF) [1], which can make the forwarding plane reconfigurable, programmable and future-proof with a protocol-independent instruction set. We design and implement a POF-based flexible flow converging (F-FC) scheme to reduce the number of flow-entries for enhanced scalability. To evaluate the POF system experimentally, we build a network testbed that consists of both commercial and software-based POF switches. Network experiments with real-time video streaming in the proposed POF system demonstrate that our POF-based F-FC approach can outperform conventional schemes.","Title: On efficient protection design for dynamic multipath provisioning in elastic optical networks\nYear: 2015\n\nAbstract: Multipath provisioning (MPP) in elastic optical networks (EONs) can improve the network performance effectively. In this paper, we study the protection schemes for MPP to ensure 100% restoration against single-link failures. We design three algorithms, namely, Instant-ILP, PWCE-MPP and mPWCEMPP. The first one leverages a simple integer linear programming (ILP) model to design the optimal working-backup structure for each request instantly, while the rest two utilize protected working capacity envelop (PWCE) and spectrum planning to further reduce bandwidth blocking probability (BBP). Moreover, mPWCE-MPP considers the differences among working and backup paths, and ensures that the maximum path-difference (MPD) of a request will not increase dramatically in restoration. Simulation results show that mPWCE-MPP can obtain the best trade-off between BBP and average MPD.","Title: Toward online profit-driven scheduling of inter-DC data-transfers for cloud applications\nYear: 2015\n\nAbstract: For an inter-datacenter (inter-DC) network that carries multiple cloud applications, tasks may arise in the DCs and need to transfer data to others with different latency requirements. Therefore, it is desired that a highly efficient online scheduling algorithm could be developed to consider the request admission, routing selection and bandwidth allocation for the data-transfers jointly. In this work, we investigate this problem, and propose an online scheduling algorithm, namely, GlobeAny, to maximize the time-average profit from provisioning the requests. The proposed algorithm leverages Lyapunov optimization techniques and can achieve arbitrarily approaching to the optimal value within O(1/V) gap. We also show that by adjusting the application weights, it can provide differentiated services to requests with different latency requirements.","Title: Maximizing Utility of Time-Constrained Emergency Backup in Inter-Datacenter Networks.\nYear: 2016\n\nAbstract: This work addresses the emergency backup problem in inter-datacenter (inter-DC) network, which is triggered in response to predictable destructive events. We consider the value of data and study how to maximize the utility of time-constrained emergency backup. Specifically, we first formulate the utility-maximization problem for a time-varying network environment and then leverage the time-expanded network (TEN) approach to transform it into a static network problem. To finally solve the problem, a time-efficient and distributed algorithm is proposed based on the dual decomposition technique. Simulation results show that our algorithm outperforms existing algorithms.","Title: Joint Spectrum and IT Resource Allocation for Efficient VNF Service Chaining in Inter-Datacenter Elastic Optical Networks.\nYear: 2016\n\nAbstract: We study how to allocate spectrum and IT resources jointly for realizing efficient virtual network function (VNF) service chaining in inter-datacenter elastic optical networks. We first formulate an integer linear programming model to solve the problem exactly, and then a longest common subsequence-based heuristic is proposed. The simulation results indicate that the proposed algorithms can reuse the deployed VNFs efficiently and arrange the spectrum utilization in a much more load-balanced manner.","Title: p-Cycle design without candidate cycle enumeration in mixed-line-rate optical networks\nYear: 2015\n\nAbstract: This paper develops and evaluates a new protection solution for pre-configured-cycle (p-cycle) design in Mixed-Line-Rate (MLR) optical networks. Conventional p-cycle approaches require enumerating candidate cycles in advance and screening p-cycles using heuristic algorithms. Our method generates p-cycles directly in one-step using an Integer Linear Programming (ILP) model. Cost-effective transponders and distance-adaptive line rates are provisioned for every p-cycle to minimize joint cost of transponders and spare capacity. The design problem is solved together with spectral clustering based graph partitioning, which permits to compute the optimal solution in independent sub-graphs concurrently. The results show that our protection method is cost-efficient for p-cycle design with mixed line rates and scalable for large optical networks.","Title: Source routing with protocol-oblivious forwarding (POF) to enable efficient e-Health data transfers.\nYear: 2016\n\nAbstract: It has already been confirmed that software-defined networking (SDN) can make the networks more programmable, adaptive and application aware. However, due to the large-scale and geographically-distributed nature of wide-area networks (WAN), the scalability could become a critical issue if we incorporate SDN for WANs (i.e., realizing SD-WANs). In this paper, we design and implement a novel network system that can leverage source routing with the protocol-oblivious forwarding (POF) to facilitate efficient e-Health data transfers with low setup latency. We develop the POF-based source routing protocol to realize a pipeline based packet processing procedure, which can replace the table-lookup based approach in traditional SDN networks and make the forwarding plane more efficient. The proposed scheme is demonstrated experimentally, and the results verify that with it, the flow-tables installed in each core switches in a POF-controlled SD-WAN can be minimized and the path setup latency of traffic flows can be reduced significantly as well.","Title: Orchestrating Multicast-Oriented Nfv Trees In Inter-Dc Elastic Optical Networks\nYear: 2016\n\nAbstract: It is known that by incorporating network function virtualization (NFV) in inter-datacenter (inter-DC) networks, we can use the network resources more intelligently to deploy new services faster. This paper considers an inter-DC elastic optical network (IDC-EON) and studies how to orchestrate the multicast-oriented NFV trees (M-NFV-Ts) in it effciently. We first consider an of fine scenario in which all the M-NFV-Ts are known and need to be served in the network. A mixed integer linear programming (MILP) model is formulated to solve the problem exactly, and we also propose a heuristic based on path-intersection (PI) to reduce the time complexity. With extensive simulations, we show that the proposed heuristic can approximate the MILP's performance on low-cost M-NFV-T provisioning but only requires much shorter running time. Next, the online scenario where the M-NFV-Ts can come and leave on-the-fly is addressed, and we leverage PI to design two online algorithms for orchestrating dynamic M-NFV-Ts in IDC-EONs, i.e., with either the batch (B-PI) or sequential (S-PI) scheme. Simulation results indicate that compared with S-PI, B-PI can reduce blocking probability effectively.","Title: Flexible Flow Converging: A Systematic Case Study on Forwarding Plane Programmability of Protocol-Oblivious Forwarding (POF).\nYear: 2016\n\nAbstract: It is known that software-defined networking (SDN) can support effective traffic engineering (TE) with the global view of networks. Hence, OpenFlow was used to realize flow-based TE. However, there is a tradeoff between the volume of installed flow entries and the granularity of TE. Moreover, the protocol-dependent nature of OpenFlow might limit the flexibility and adaptivity of TE. In this paper, we leverage the protocol-oblivious forwarding technology that can make each switch work as a protocol independent white box and utilize its forwarding plane programmability to design a novel flexible flow converging (F-FC) scheme for realizing SDN-based fine-grained TE. Specifically, we design both the network system and the F-FC algorithms running on it and conduct experiments to demonstrate that our proposed scheme can not only reduce the volume of installed flow entries in switches but also realize fine-grained TE to achieve better utilization on network bandwidth.","Title: On the Parallelization of Spectrum Defragmentation Reconfigurations in Elastic Optical Networks.\nYear: 2016\n\nAbstract: Flexible-grid elastic optical networks (EONs) have attracted intensive research interests for the agile spectrum management in the optical layer. Meanwhile, due to the relatively small spectrum allocation granularity, spectrum fragmentation has been commonly recognized as one of the key factors that can deteriorate the performance of EONs. To alleviate spectrum fragmentation, various defragmentati...","Title: Advances in Optical Communications Technologies.\nYear: 2017\n\nAbstract: The articles in this special section focus on recent advancements in optical communication. In 2017, as fiber optic communications technologies continue to demonstrate their advantages in telecom and data center networks, we are also witnessing attractive and promising progress on optical wireless communications, which can potentially work with RF communications to meet the ever-increasing demands for higher date transmission rates and smart home networking. Meanwhile, the innovations in optical communications and networking systems can never be realized without fundamental support from the physical infrastructure layer. The recent advances on few-mode fibers open up appealing opportunities for equipment vendors and network operators to solve the capacity crunch problem using mode-division multiplexing. To integrate the momentum gained from these technical advances, an efficient and intelligent network control and management (NCu0026M) mechanism is essential, especially for building heterogeneous networks to support a wide range of services with various traffic patterns. Therefore, the implementation of software-defined networking (SDN) in optical communications networks appears inevitable, and we expect to see more advances in this area in 2017.","Title: Control plane innovations to realize dynamic formulation of multicast sessions in inter-DC software-defined elastic optical networks.\nYear: 2017\n\nAbstract: It is known that to support the applications such as datacenter backup and migration, multicast should be supported efficiently in inter-datacenter (inter-DC) networks to carry the corresponding point-to multiple -point communications. Moreover, due to the traffic dynamics in inter-DC networks, we might have to consider the case that the multicast members can join or leave a multicast session dynamically. Therefore, in this work, we try to leverage control plane innovations to realize dynamic formulation of multicast sessions in inter-DC software-defined elastic optical networks (SD-EONS), which are equipped with multicast-incapable bandwidth-variable wavelength selective switches (MI-BV-WSS'). Here, one key issue to address is that the continuous changing of multicast group members can degrade the optimality of a multicast-tree. Hence, we propose to rearrange the multicast-trees adaptively to reduce their spectrum usage. Meanwhile, we try to minimize the frequency of rearrangements to avoid unnecessary operation complexity. Based on these considerations, we propose several multicast-tree rearrangement algorithms for updating multicast sessions dynamically with lightpath reroutings in inter DC SD-EONs. Both partial and full multicast-tree rearrangements are studied. Simulation results indicate that the proposed algorithms can rearrange the multicast-trees intelligently such that the blocking probability can be reduced effectively with the least lightpath reroutings. Next, based on these theoretical investigations, we consider how to implement the proposed algorithms in the control plane of an inter-DC SD-EON, We extend the OpenFlow (OF) protocol to support the dynamic formulation of multicast sessions and also design the functional models in the control plane elements to realize multicasttree rearrangements. Experiment results verify the effectiveness of our proposed algorithms and system design. (C) 2016 Elsevier B.V. All rights reserved.","Title: Novel Location-Constrained Virtual Network Embedding (LC-VNE) Algorithms Towards Integrated Node and Link Mapping.\nYear: 2016\n\nAbstract: This paper tries to solve the location-constrained virtual network embedding LC-VNE problem efficiently. We first investigate the complexity of LC-VNE, and by leveraging the graph bisection problem, we provide the first formal proof of the $\\\\mathcal {NP}$ -completeness and inapproximability result of LC-VNE. Then, we propose two novel LC-VNE algorithms based on a compatibility graph CG to achieve integrated node and link mapping. In particular, in the CG, each node represents a candidate substrate path for a virtual link, and each link indicates the compatible relation between its two endnodes. Our theoretical analysis proves that the maximal clique in the CG is also the maximum one when the substrate network has sufficient resources. With CG, we reduce LC-VNE to the minimum-cost maximum clique problem, which inspires us to propose two efficient LC-VNE heuristics. Extensive numerical simulations demonstrate that compared with the existing ones, our proposed LC-VNE algorithms have significantly reduced time complexity and can provide smaller gaps to the optimal solutions, lower blocking probabilities, and higher time-average revenue as well.","Title: On-Demand and Reliable vSD-EON Provisioning with Correlated Data and Control Plane Embedding.\nYear: 2016\n\nAbstract: Software-defined elastic optical networks (SDEONs) provide operators more flexibility to customize their optical infrastructure dynamically and adaptively, and network virtualization, i.e., infrastructure-as-a-service (IaaS), enables multiple tenants to share the substrate infrastructure efficiently. In this paper, we study how to provision virtual SD-EONs (vSD-EONs) with the correlated data and control plane embedding (chi-VNE) that considers the quality-of-service (QoS) of virtual control plane (vCP), i.e., availability and control channel latency. We propose a chi-VNE algorithm to solve the problem, design the network system to realize it, and accomplish proof-of-concept experimental demonstrations in an OpenFlow-based network testbed. Numerical and experimental results indicate that the proposed algorithm and system function well and can realize on-demand and reliable vSD-EON provisioning.","Title: Throughput–Power Tradeoff Association for User Equipment in WLAN/Cellular Integrated Networks\nYear: 2017\n\nAbstract: To meet the dramatic growth in mobile traffic, wireless local area networks (WLANs) have been integrated with cellular networks. We investigate the user equipment (UE) association problem in WLAN/cellular integrated networks from game-theoretic perspective by taking into account throughput and power consumption. In the case of WLAN offloading, random uplink traffic from UEs inevitably brings conte...","Title: Impairment- and Splitting-Aware Cloud-Ready Multicast Provisioning in Elastic Optical Networks\nYear: 2017\n\nAbstract: It is known that multicast provisioning is important for supporting cloud-based applications, and as the traffics from these applications are increasing quickly, we may rely on optical networks to realize high-throughput multicast. Meanwhile, the flexible-grid elastic optical networks (EONs) achieve agile access to the massive bandwidth in optical fibers, and hence can provision variable bandwidths to adapt to the dynamic demands from the cloud-based applications. In this paper, we consider all-optical multicast in EONs in a practical manner and focus on designing impairment- and splitting-aware multicast provisioning schemes. We first study the procedure of adaptive modulation selection for a light-tree, and point out that the multicast scheme in EONs is fundamentally different from that in the fixed-grid wavelength-division multiplexing networks. Then, we formulate the problem of impairment- and splitting-aware routing, modulation and spectrum assignment (ISa-RMSA) for all-optical multicast in EONs and analyze its hardness. Next, we analyze the advantages brought by the flexibility of routing structures and discuss the ISa-RMSA schemes based on light-trees and light-forests. This paper suggests that for ISa-RMSA, the light-forest-based approach can use less bandwidth than the light-tree-based one, while still satisfying the quality of transmission requirement. Therefore, we establish the minimum light-forest problem for optimizing a light-forest in ISa-RMSA. Finally, we design several time-efficient ISa-RMSA algorithms, and prove that one of them can solve the minimum light-forest problem with a fixed approximation ratio.","Title: SR-PVX: A Source Routing Based Network Virtualization Hypervisor to Enable POF-FIS Programmability in vSDNs.\nYear: 2017\n\nAbstract: We design and implement a novel network virtualization hypervisor (NVH), which leverages source routing (SR) and provides network programmability based on the protocol-oblivious forwarding flow instruction set (POF-FIS), to realize protocol-independent virtual software-defined networks (vSDNs). The NVH, namely SR-PVX, uses a novel SR-based mechanism to ensure that the substrate switches can process packets from vSDNs with very small flow-table consumption. We design the network architecture, packet format, and packet processing procedure, and implement them to experimentally demonstrate that SR-PVX can control substrate POF switches to accomplish the SR-based vSDN slicing. We also perform an experiment to show that the POF-enabled vSDN created by SR-PVX can easily realize a stateful firewall with its POF-FIS programmability.","Title: Protocol Oblivious Forwarding (POF): Software-Defined Networking with Enhanced Programmability.\nYear: 2017\n\nAbstract: Software-defined networking separates the control and forwarding planes of a network to make it more programmable and application- aware. As one of the initial implementations of SDN, OpenFlow abstracts a forwarding device as a flow table and realizes flow processing by applying the \\\"match-and-act\\\" principle. However, the protocol-dependent nature of OpenFlow still limits the programmability of the forwarding plane. Hence, in this article, we discuss how to leverage protocol-oblivious forwarding (POF) to further enhance the network programmability such that the forwarding plane becomes protocol-independent and can be dynamically reprogrammed to support new protocol stacks seamlessly. We first review the development of OpenFlow and explain the motivations for introducing POF. Then we explain the working principle of POF, discuss our efforts on realizing the POF development ecosystem, and show our implementation of POF-based source routing as a novel use case. Finally, we elaborate on the first WAN-based POF network testbed that includes POF switches located in two cities in China.","Title: Orchestrating data-intensive vNF service chains in inter-DC elastic optical networks\nYear: 2017\n\nAbstract: We investigate the problem of data-intensive vNF service chain (vNF-SC) orchestration in inter-datacenter EONs. After analyzing the NP-hardness of this problem, we solve it in a sequential manner by optimizing both the request serving sequence and the data-intensive vNF-SC orchestration. Specifically, we propose a request sorting algorithm and a data-intensive vNF-SC orchestration algorithm based on dynamic programming to minimize the service completion time. We conduct simulations to evaluate the proposed algorithms, and simulation results verify their effectiveness.","Title: On the Distance Spectrum Assignment in Elastic Optical Networks.\nYear: 2017\n\nAbstract: In elastic optical networks, two lightpaths sharing common fiber links might have to be isolated in the spectrum domain with a proper guard-band to prevent crosstalk and/or reduce physical-layer security threats. Meanwhile, the actual requirements on guard-band sizes can vary for different lightpath pairs, because of various reasons. Therefore, in this paper, we consider the situation in which the...","Title: Admm-Based Distributed Algorithm For Emergency Backup In Time-Variant Inter-Dc Networks\nYear: 2017\n\nAbstract: This paper considers the emergency backup in an inter-datacenter (inter-DC) network whose topology is time-variant due to the progress of a disaster. We first transform the dynamic backup into a static flow problem through building a variable time-expanded network (V-TEN). Then, by considering both data utility and resource cost, we formulate an optimization to maximize the backup profit and leverage the alternating direction method of multipliers (ADMM) to design a time-efficient and distributed algorithm. Simulation results show that our ADMM-based algorithm outperforms several existing ones.","Title: Embedding Virtual Software-Defined Networks Over Distributed Hypervisors For Vdc Formulation\nYear: 2017\n\nAbstract: We study how to embed virtual software-defined networks (vSDNs) cost-effectively over a substrate network with distributed network virtualization hypervisors (NVHs) for virtual datacenter (vDC) formulation to support Big Data applications. Specifically, we try to jointly optimize the embedding schemes of the control and data planes of each vSDN, i.e., to minimize the data plane's resource consumption and limit the number of NVHs used in the control plane simultaneously. We first formulate an integer linear programming (ILP) model to solve the problem exactly, and then design a heuristic to reduce the time complexity. Simulation results suggest that our proposed algorithms can embed vSDNs cost-effectively and significantly outperform the existing scheme based on global resource capacity (GRC).","Title: Design and Demonstration of High-Throughput Protocol Oblivious Packet Forwarding to Support Software-Defined Vehicular Networks.\nYear: 2017\n\nAbstract: Software-defined networking (SDN) is a promising technology that can resolve the challenges faced by vehicular networks. However, the OpenFlow-based SDN implementations can only provide a protocol-dependent data plane. This can restrict the effectiveness of software-defined vehicular networks, since special-purpose protocols that have not been standardized in OpenFlow specifications are used frequently in vehicular networks. To address this issue, this paper studies how to realize a protocol independent data plane by leveraging the protocol oblivious forwarding (POF). Specifically, we present the design of a software-based POF switch (PVS) that supports runtime protocol customization in principle. We implement PVS in a switch box, and propose a flow table management scheme to ensure high-throughput packet forwarding. The experimental results verify that PVS can achieve line-rate packet forwarding at 10 Gbps when the packets' size is 512 B, and the proposed flow table management scheme is effective.","Title: On Dynamic Service Function Chain Deployment and Readjustment.\nYear: 2017\n\nAbstract: Network function virtualization (NFV) is a promising technology to decouple the network functions from dedicated hardware elements, leading to the significant cost reduction in network service provisioning. As more and more users are trying to access their services wherever and whenever, we expect the NFV-related service function chains (SFCs) to be dynamic and adaptive, i.e., they can be readjust...","Title: Leveraging Protocol-Oblivious Forwarding (POF) to Realize NFV-Assisted Mobility Management.\nYear: 2017\n\nAbstract: In the Internet, to enable emerging network services such as e-Health to be delivered to numerous people anytime and anywhere, mobility management plays an important role. In this work, we leverage the protocol-oblivious forwarding (POF) to design a novel network system that can realize highly-efficient mobility management and overcome the drawbacks of existing approaches. Moreover, we explore the forwarding plane programmability provided by POF to realize dynamic virtual network function (vNF) deployment for traffic adaption. We implement our system and conduct experiments to verify that it functions well for NFV-assisted mobility management and achieves reduced handover latency and enhanced QoE.","Title: Improving SDN Scalability With Protocol-Oblivious Source Routing: A System-Level Study.\nYear: 2018\n\nAbstract: Software-defined networking (SDN) has been considered as a break-through technology for the next-generation Internet. It enables fine-grained flow control that can make networks more flexible and programmable. However, this might lead to scalability issues due to the possible flow state explosion in SDN switches. SDN-based source routing can reduce the volume of flow-tables significantly by encodi...","Title: On vNF-SC deployment and task scheduling for bulk-data transfers in inter-DC EONs\nYear: 2017\n\nAbstract: We study how to deploy vNF service chains (vNF-SCs) and perform task scheduling for bulk-data transfers in inter-datacenter (inter-DC) elastic optical networks (EONs). We call this type of request a data-intensive vNF-SC request. According to the number of required vNF-SCs, the data-intensive vNF-SC requests are classified into linear and multi-branch data-intensive vNF-SC requests. For linear data-intensive vNF-SC requests, we propose a dynamic-programming-based vNF-SC deployment and task scheduling algorithm to minimize the average service completion time (SCT). For multi-branch data-intensive vNF-SC requests, we propose a correlation-aware vNF-SC deployment and task scheduling algorithm to minimize the average SCT, since there are multiple vNF-SC branches that are correlated and the SCT of a multi-branch data-intensive vNF-SC request is determined by the SCT of the latest vNF-SC branch. Simulation results verify that our algorithms can reduce the average SCTs of data-intensive vNF-SC requests of the two types effectively.","Title: Evacuate Before Too Late: Distributed Backup in Inter-DC Networks with Progressive Disasters.\nYear: 2018\n\nAbstract: Inter-datacenter (inter-DC) networks are essential for large enterprises to deliver high-quality services to end-users. Since DCs are vulnerable to natural disasters, an inter-DC network operator needs an effective emergency backup plan to evacuate the endangered data out in case of a progressive disaster whose status can be predicted by an early warning system. In this paper, we try to solve the ...","Title: Profit-Aware Distributed Online Scheduling for Data-Oriented Tasks in Cloud Datacenters.\nYear: 2018\n\nAbstract: As there is an increasing trend to deploy geographically distributed (geo-distributed) cloud datacenters (DCs), the scheduling of data-oriented tasks in such cloud DC systems becomes an appealing research topic. Specifically, it is challenging to achieve the distributed online scheduling that can handle the tasks' acceptance, data-transfers, and processing jointly and efficiently. In this paper, by considering the store-and-forward and anycast schemes, we formulate an optimization problem to maximize the time average profit from serving data-oriented tasks in a cloud DC system and then leverage the Lyapunov optimization techniques to propose an efficient scheduling algorithm, i.e., GlobalAny. We also extend the proposed algorithm by designing a data-transfer acceleration scheme to reduce the data-transfer latency. Extensive simulations verify that our algorithms can maximize the time-average profit in a distributed online manner. The results also indicate that GlobalAny and GlobalAny_Ext (i.e., GlobalAny with data-transfer acceleration) outperform several existing algorithms in terms of both time-average profit and computation time.","Title: PVFlow: Flow-Table Virtualization in POF-based vSDN Hypervisor (PVX)\nYear: 2018\n\nAbstract: It is known that by combining network virtualization with software-defined network (SDN), people can realize virtual SDNs (vSDNs) for enhanced programmability, adaptivity and cost-effectiveness. Meanwhile, protocol-oblivious forwarding (POF) can overcome the protocol-dependent restriction of Open-Flow and provide a more generic abstraction model of the forwarding elements in SDNs to realize a protocol-independent data plane. In this work, we design a flow-table virtualization module, i.e., PVFlow, to realize resource-efficient flow-table virtualization for POF-based vSDNs. To fully explore the flexibility of POF and support arbitrary matching fields in vSDNs' flow-tables, we propose the idea of “BigMatch”, which enables different flow-tables in vSDNs to share a matching stage in a substrate POF switch efficiently. We implement PVFlow in a POF-based network virtualization hypervisor (NVH) system, i.e., PVX, and verify its effectiveness on improving the efficiency of flow-table virtualization with experiments.","Title: Application-Driven End-to-End Slicing: When Wireless Network Virtualization Orchestrates With NFV-Based Mobile Edge Computing.\nYear: 2018\n\nAbstract: Recently, to adapt to the various quality-of-service (QoS) requirements of emerging applications, application-driven network slicing has attracted intensive interests. In this paper, we apply the idea of software-defined wireless network virtualization (WiNV) to WiFi networks and design and demonstrate a novel network system, namely, ADE(2)WiNFV. The proposed system can orchestrate software-defined WiNV with network-function-virtualization-based mobile edge computing (MEC) to realize application-driven end-to-end (E2E) slicing over heterogeneous wireline/wireless networks. Our experimental demonstrations verify that ADE(2)WiNFV can realize application-aware E2E slices on demand, each of which contains not only guaranteed E2E bandwidth resources (i.e., in the forms of virtual links, virtual switches, and virtual access points) but also isolated IT resources (i.e., in the form of virtual network functions) to carry specific applications with QoS guarantees.","Title: Availability-aware service provisioning in SD-EON-based inter-datacenter networks\nYear: 2016\n\nAbstract: The tremendous transmission capacity and flexible spectrum allocation scheme make elastic optical networks (EONs) one of the most promising infrastructures for constructing the interconnection in datacenter (DC) networks. Meanwhile, as DC traffics exhibit highly dynamic and heterogeneous features, differentiated service provisioning schemes are desired. In this paper, we take the advantage of centralized network control and management provided by the software-defined elastic optical networks (SD-EONs) and investigate availability-aware service provisioning in SD-EON-based inter-datacenter (inter-DC) networks. We first describe the problem of availability-aware service provisioning in SD-EON-based inter-DC networks and present the theoretical analysis for service availability. Then, we propose an availability-aware service provisioning algorithm (ASP) that leverages different path protection schemes to satisfy different service availability requirements. A service downgrading (SD) strategy is also designed as a supplement of ASP to further improve its performance. Simulation results show that the proposed ASP-SD algorithm can effectively improve the spectrum efficiency without sacrificing availability.","Title: Advances in Optical Communications Technologies.\nYear: 2018\n\nAbstract: The articles in this special section address advancements in optical communications. The field of optical communications and networking technologies is gaining new momentum from artificial intelligence (AI) and quantum communications, while there has been continuous attention on emerging topics such as TWDM/ WDM passive optical networks (PONs), software-defined networking (SDN), and smart cities. ...","Title: How the Network Topology, Traffic Distribution, and Routing Scheme Impact on the Spectrum Usage in Elastic Optical Networks.\nYear: 2018\n\nAbstract: Elastic Optical Network (EON) has been considered as a promising optical networking technology to architect the next-generation backbone networks. Routing and Spectrum Assignment (RSA) is the fundamental problem in EONs to realize service provisioning. Generally, the RSA is solved by routing the requests with lightpaths first and then assigning spectrum resources to the lightpaths to optimize the spectrum usage. Thus, the spectrum assignment explicitly decide the final spectrum usage of EONs. However, besides the spectrum assignment, there are three other factors, the network topology, traffic distribution and routing scheme, implicitly impact on the spectrum usage. Few related work involves in the implicit impact mechanism. In this paper, we aim to provide a thoroughly theoretical analysis on the impact of the three key factors on the spectrum usage. To this end, two theoretical chains are proposed: (1) The optimal spectrum usage can be measured by the chromatic number of the conflict graph, which is positively correlated to the intersecting probability, emph{i.e.}, the smaller the intersecting probability, the smaller the optimal spectrum usage; (2) The intersecting probability is decided by the network topology, traffic distribution and routing scheme via a quadratic programming parameterized with a matrix of conflict coefficients. The effectiveness of our theoretical analysis has been validated by extensive numerical results. Meanwhile, our theoretical deductions also permit to give several constant approximation ratios for RSA algorithms.","Title: SPARSE - Privacy-Aware and Collusion Resistant Location Proof Generation and Verification.\nYear: 2018\n\nAbstract: Recently, there has been an increase in the number of location-based services and applications. It is common for these applications to provide facilities or rewards for users who visit specific venues frequently. This creates the incentive for dishonest users to lie about their location and submit fake check- ins by changing their GPS data. To solve this issue, different distributed location proof schemes have been proposed to generate location proofs for mobile users. However, these schemes have some drawbacks: (1) they are vulnerable to either Prover-Prover or Prover-Witness collusions, (2) the location proof generation process is slow when users adopt a long private key, and (3) their implementation requires some hardware changes on mobile devices. To address these issues, we propose the Secure, Privacy-Aware and collusion Resistant poSition vErification (SPARSE) scheme to generate private location proofs for mobile users. SPARSE has a distributed architecture designed for ad-hoc scenarios in which mobile users generate location proofs for each other. Since we do not integrate any distance bounding protocol into SPARSE, it becomes an easy-to-implement scheme in which the location proof generation process is independent of the length of the users' private key. We provide a comprehensive security analysis and simulation which show that SPARSE provides privacy protection as well as security properties for users including integrity, unforgeability and non-transferability of the location proofs. Moreover, it achieves a highly reliable performance against collusions.","Title: Virtualization of Table Resources in Programmable Data Plane with Global Consideration.\nYear: 2018\n\nAbstract: In this work, we try to address the problem of memory fragmentation in ternary content addressable memory (TCAM) in programmable data plane (PDP), by designing and implementing a novel network hypervisor for PDP, namely, TPVX. TPVX realizes the virtualization of table resources in PDP with global consideration, i.e., when mapping tenant flow tables to physical switches, TPVX considers their table sizes and the pre-formatted sub-tables in the physical network to improve TCAM utilization and avoid memory fragmentation. Our experimental results verify that with TPVX, the utilization of the table resources in PDP can be improved dramatically and the extra processing latency due to the newly-introduced overheads can be maintained well simultaneously.","Title: Economics of Multi-Domain Software-Defined EONs - Games Among Brokers.\nYear: 2018\n\nAbstract: This paper discusses the network architecture and mechanisms used for advancing the economics of multi-domain software-defined elastic optical networks (SD-EONs) with the considerations of both noncooperative and cooperative games among the incentive-driven brokers. Specifically, in order to architect multi-domain SD-EONs such that the end-to-end light-path provisioning across multiple domains can be facilitated cost-effectively, we propose to introduce a management plane that contains incentive-driven brokers. As the brokers may compete or cooperate with each other to offer cross-domain lightpath provisioning services due to profits, we design and elaborate on the operation mechanisms for both incentive-driven broker competition (i.e., noncooperative gaming) and market-share based broker bargain (i.e., cooperative gaming). The experimental results that are obtained with an OpenFlow-based control plane SD-EON testbed are also presented to verify the cost-effectiveness of the proposed mechanisms.","Title: Service Provisioning with Energy-Aware Regenerator Allocation in Multi-Domain EONs\nYear: 2015\n\nAbstract: As multi-domain elastic optical networks (EONs) can enhance network scalability, extend service reach, and accommodate the inter-operability issues, it is very relevant to consider them in practical network operations. In this work, we study the problem of how to achieve energy-aware service provisioning in a multi-domain EON, where the optoelectronic regenerators only exist in border nodes. We consider dynamic lightpath requests and propose two algorithms, i.e., the greedy regenerator allocation (GRA) and the set-cover based regenerator allocation (STC), to realize the joint optimization of routing, modulation and spectrum assignment (RMSA) and regenerator allocation. The algorithms are evaluated with extensive simulations that use multi-domain EONs built with different regenerator placement strategies. The results verify that GRA and STC outperform the existing algorithm for energy-aware multi-domain service provisioning, and STC achieves the best performance in terms of both blocking probability and power efficiency.","Title: On Incentive-Driven VNF Service Chaining in Inter-Datacenter Elastic Optical Networks: A Hierarchical Game-Theoretic Mechanism\nYear: 2019\n\nAbstract: In this paper, we propose an incentive-driven virtual network function service chaining (VNF-SC) framework for optimizing the cross-stratum resource provisioning in multi-broker orchestrated inter-datacenter elastic optical networks (IDC-EONs). The proposed framework employs a non-cooperative hierarchical game-theoretic mechanism, where the resource brokers and the VNF-SC users play the leader and the follower games, respectively. In the leader game, the brokers calculate VNF-SC service schemes for users and compete for the provisioning tasks. While in the follower game, the users compete for VNF-SC services for jointly optimizing the resource cost and the received quality-of-service. We first elaborate on the modeling of the follower game, discuss the existence of Nash equilibrium and propose a mixed-strategy gaming approach enabled by an auxiliary graph-based algorithm to facilitate users selecting the most appropriate service schemes. Then, under the assumption that the brokers are aware of the principle of the follower game, we present the model for the leader game and develop a time-efficient heuristic algorithm for brokers to compete for the provisioning tasks. Simulations show that the proposed incentive-driven VNF-SC framework significantly improves the network throughput (i.e., \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$>4.8 \\times$ </tex-math></inline-formula>\n blocking reduction) while assisting users and brokers in achieving higher utilities compared with existing solutions.","Title: Forecast-Assisted NFV Service Chain Deployment Based on Affiliation-Aware vNF Placement.\nYear: 2016\n\nAbstract: This paper studies the problem of service chain (SC) deployment. Specifically, we try to place virtual network functions (vNFs) on network nodes and connect the vNFs in sequence through link mapping. We start with the offline problem. An integer linear programming (ILP) model is formulated to minimize the total SC deployment cost. With the ILP, we prove that the offline problem is NP-hard and propose a time-efficient heuristic based on affiliation-aware vNF placement. Then, we move to the online problem, and design a forecast-assisted online SC deployment algorithm that includes the prediction of future vNF requirements. Simulation results show that the online algorithms can reduce the blocking probability of SC requests and increase the service provider's profit from SC deployment effectively.","Title: On Efficient Incentive-Driven Vnf Service Chain Provisioning With Mixed-Strategy Gaming In Broker-Based Eo-Idcns\nYear: 2017\n\nAbstract: We propose to realize incentive-driven virtual network function service chain provisioning in broker-based elastic optical inter-datacenter networks with mixed-strategy gaining and design a heuristic to find the near-equilibrium solutions. Simulation results verify both the effectiveness and stability of the proposed approach.","Title: Demonstration Of Survivable Vsd-Eon Slicing With Automatic Data Plane Restoration To Support Reliable Video Streaming\nYear: 2017\n\nAbstract: We design and experimentally demonstrate a network slicing system which can not only construct vSD-EONs dynamically for upper-layer applications but also recover their data plane services automatically and timely during substrate link failures.","Title: Cost-Efficient Multi-Layer Restoration To Address Ip Router Outages In Ip-Over-Eons\nYear: 2017\n\nAbstract: We study how to address the IP router outages in an IP-over-EON with multi-layer restoration (MLR), and propose an auxiliary-graph (AG) based scheme that can minimize the additional OPEX of MLR with the help of the spectrum expansion capability of sliceable bandwidth variable transponders (SBV-Ts)","Title: Experimental Demonstration Of Cognitive Provisioning And Alien Wavelength Monitoring In Multi-Domain Eon\nYear: 2018\n\nAbstract: This paper proposes a cognitive multi-domain EON architecture with machine-learning aided RMSA and alien wavelength monitoring. Testbed experiments show modulation format recognition, QoT monitoring and cognitive routing for a 160 GBd alien multi-wavelength lightpath.","Title: Leveraging Predictive Analytics To Achieve Knowledge-Defined Orchestration In A Hybrid Optical/Electrical Dc Network: Collaborative Forecasting And Decision Making\nYear: 2018\n\nAbstract: We design and experimentally demonstrate a hybrid optical/electrical DC network that achieves knowledge-defi ned orchestration with two collaborative machine learning modules.","Title: On Multi-Layer Restoration In Optical Networks With Encryption Solution Deployment\nYear: 2018\n\nAbstract: We consider the scenario in which an optical network with encryption solution deployment can he affected by electrical layer failures, and propose an algorithm to improve the cost-effectiveness of multi-layer restoration in it.","Title: The First Testbed Demonstration Of Cognitive End-To-End Optical Service Provisioning With Hierarchical Learning Across Multiple Autonomous Systems\nYear: 2018\n\nAbstract: We demonstrate the first experimental testbed with a hierarchical machine-learning network management framework for impairment-aware end-to-end elastic optical RMSA service provisioning across multi autonomous domains with QoT estimation deviations below 10%. (C) 2018 The Authors(s)","Title: Realizing Highly-Available, Scalable, and Protocol-Independent vSDN Slicing With a Distributed Network Hypervisor System.\nYear: 2018\n\nAbstract: In this paper, we design and implement a distributed network virtualization hypervisor (NVH) system, namely, DPVisor, which can provide superior network programmability based on protocol-oblivious forwarding to realize highly-available, scalable, and protocol-independent virtual software-defined network slicing. The experimental comparisons indicate that DPVisor achieves comparable performance as ONVisor (i.e., an OpenFlow-based ONOS benchmark), in terms of the message processing latency, message processing throughput, and failure recovery time. Moreover, to optimize the performance of the distributed NVH systems, we carefully adjust the consistency model used in the state synchronization of NVH instances and propose a local cache-based scheme to balance the tradeoff between the consistency and availability of network status. Our experimental results confirm that the message processing throughput of the distributed NVH systems can be greatly improved (i.e., for both DPVisor and ONVisor), while the data consistency among NVH instances is still maintained well.","Title: Deep-Rmsa: A Deep-Reinforcement-Learning Routing, Modulation And Spectrum Assignment Agent For Elastic Optical Networks\nYear: 2018\n\nAbstract: This paper demonstrates Deep-RMSA, a deep reinforcement learning based self-learning RMSA agent that can learn successful policies from dynamic network operations while realizing cognitive and autonomous RMSA in EONs.","Title: Realizing AI-AssistedMulti-Layer Restoration in a Software-Defined IP-over-EON with Deep Learning: An Experimental Study\nYear: 2018\n\nAbstract: By using deep learning, we experimentally demonstrate AI-assisted multi-layer restoration in an IP-over-EON, which recovers affected traffic timely with congestion-avoidance rerouting.","Title: Multi-Agent Deep Reinforcement Learning in Cognitive Inter-Domain Networking with Multi-Broker Orchestration\nYear: 2019\n\nAbstract: This paper proposes, for the first time, a cognitive inter-domain networking framework with multi-broker orchestration and multi-agent deep reinforcement learning for multi-domain optical networks. Simulation results show > 17% blocking reduction compared to the baselines.","Title: Building Network Nervous System with Multilayer Telemetry to Realize AI-Assisted Reflexes in Software-Defined IP-Over-EONs for Application-Aware Service Provisioning\nYear: 2019\n\nAbstract: We design and experimentally demonstrate a network nervous system that can leverage multilayer telemetry to realize artificial intelligence (AI) assisted network reconfigurations (i.e., reflexes) in a software-defined IP over elastic optical network, for application-aware provisioning.","Title: Advances in Optical Communications and Networking Technologies\nYear: 2019\n\nAbstract: The articles in this special section focus on advancements in optical communications and networking technologies (OCNS). OCNS has been playing an increasingly important role in interconnecting a large variety of IT resources over globally distributed autonomous network systems, to facilitate cost-effective information exchange and data processing. In 2018, with the emergence of fifth generation (5G)-oriented optical transmission and access networks, we witnessed optical communication networks becoming more adaptive and reconfigurable to support dynamic optical service provisioning, protection, and restoration, and to deliver short time to market and cost-effective services and solutions. Following this trend, we expect to observe continued expansion of optical transmission and access networks and faster convergence of optical and wireless infrastructures in 2019.","Title: On Throughput Optimization and Bound Analysis in Cache-Enabled Fiber-Wireless Networks\nYear: 2019\n\nAbstract: With the dense deployment of millimeter wave (mmWave) front ends and popularization of bandwidth-intensive applications, shared backhaul in fiber-wireless (FiWi) networks is still facing a bandwidth crunch. To alleviate the backhaul pressure, in this paper, caching capability is enabled at the edge of FiWi networks, i.e., optical network unit access points (ONU-APs). On the other hand, as both power budget and backhaul bandwidth in FiWi networks are constrained, it is challenging to properly leverage power for caching and that for wireless transmission to achieve superior system performance. As caching has a significant impact on resource allocation, we reconsider performance optimization and analysis in cache-enabled FiWi networks. Firstly, in the cache-enabled FiWi network with mmWave, we formulate the joint power allocation and caching problem, with the goal to maximize the downlink throughput. A two-stage algorithm is then proposed to solve the problem. Secondly, to investigate the theoretical capacity of the cache-enabled FiWi network with mmWave, we derive an upper bound of the downlink throughput by analyzing properties of the average rate of wireless links. Particularly, we show that appropriate power allocation for wireless transmission and caching at ONU-APs is essential to achieve higher throughput. The numerical and simulation results validate our theoretical analysis and demonstrate the proposed algorithm can approach the analytical upper bound.","Title: Proactive and Hitless vSDN Reconfiguration to Balance Substrate TCAM Utilization: From Algorithm Design to System Prototype\nYear: 2019\n\nAbstract: The combination of network virtualization and software-defined networking enables an infrastructure provider to create software-defined virtual networks (vSDNs) over a shared substrate network (SNT), for supporting new network services more timely and cost-effectively. Meanwhile, as both the services and traffic in the Internet are becoming more and more dynamic, how to properly maintain vSDNs in ...","Title: DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks.\nYear: 2019\n\nAbstract: This paper proposes DeepRMSA, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs). DeepRMSA learns the correct online RMSA policies by parameterizing the policies with deep neural networks (DNNs) that can sense complex EON states. The DNNs are trained with experiences of dynamic lightpath provisioning. We first modify the asynchronous advantage actor-critic algorithm and present an episode-based training mechanism for DeepRMSA, namely, DeepRMSA-EP. DeepRMSA-EP divides the dynamic provisioning process into multiple episodes (each containing the servicing of a fixed number of lightpath requests) and performs training by the end of each episode. The optimization target of DeepRMSA-EP at each step of servicing a request is to maximize the cumulative reward within the rest of the episode. Thus, we obviate the need for estimating the rewards related to unknown future states. To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i.e., DeepRMSA-FLX. DeepRMSA-FLX attempts to smooth out the oscillations by defining the optimization scope at each step as a sliding window, and ensuring that the cumulative rewards always include rewards from a fixed number of requests. Evaluations with the two sample topologies show that DeepRMSA-FLX can effectively stabilize the training while achieving blocking probability reductions of more than 20.3% and 14.3%, when compared with the baselines.","Title: Energy-Efficient WLANs With Resource and Re-Association Scheduling Optimization.\nYear: 2019\n\nAbstract: Recently, a number of WiFi access points (APs) have been densely deployed to provide widely available, high-performance Internet services. As such, an energy efficiency issue becomes crucial toward the design of green wireless local area networks (WLANs). In this paper, we propose a resource and re-association scheduling algorithm (referred to RAS) based on Benders' decomposition to reduce the ene...","Title: Visualize Your IP-Over-Optical Network in Realtime: A P4-Based Flexible Multilayer In-Band Network Telemetry (ML-INT) System.\nYear: 2019\n\nAbstract: Nowadays, with the fast development of backbone networks, performance monitoring and troubleshooting on IP-over-optical networks have become increasingly important. However, a real-time monitoring scheme, which is programmable to reveal the end-to-end multilayer information of an arbitrary flow in an IP-over-optical network, is still absent. Also, for such a monitoring scheme, how to balance the tradeoff between the accuracy and the overhead has not been studied yet. To address these challenges, we design a P4-based flexible multilayer in-band network telemetry (ML-INT) system to visualize an IP-over-optical network in real time. Specifically, the flexible ML-INT scheme only selects a small portion of packets in an IP flow to encode INT headers, while each INT header only contains a part of the statistics of all the electrical/optical network elements (NEs) on the flow's routing path. We design the packet processing pipelines for the scheme, program P4-based hardware programmable data-plane (PDP) switches to implement the pipelines, develop the optical performance monitors that can cooperate with the PDP switches to facilitate ML-INT, and implement a high-performance data analyzer that can extract, parse, and analyze the INT data carried by the high-speed IP flows [i.e., with an arrival rate up to 2 million packets per second (Mp/s)]. The whole flexible ML-INT system is experimentally demonstrated in a small-scale but real IP-over-optical network testbed. The experimental results verify that our proposal only introduces very small overhead and can make the IP-over-optical network more visible in real time for performance monitoring and troubleshooting.","Title: Leveraging Heterogeneous NFV Platforms to Upgrade Service Function Chains in DCNs\nYear: 2021\n\nAbstract: The advances on SmartNICs and programmable data plane (PDP) switches motivate people to deploy virtual network functions (vNFs) on these devices directly for better packet processing performance. Hence, this work considers how to leverage heterogeneous network function virtualization (NFV) platforms to upgrade the provisioning of vNF service chains (vNF-SCs) in a datacenter network (DCN). We design a novel time-efficient heuristic, to plan the service upgrade under a limited budget such that the QoS improvement on latency reduction can be maximized. Specifically, our algorithm first chooses nodes in the DCN to upgrade (i.e., by either equipping SmartNICs on certain servers or replacing switches with PDPSWs), and then adjusts vNF-SC provisioning accordingly.","Title: Offloading NFV Orchestration to ToR Switches: How to Leverage PDP to Realize Agile Service Function Chaining in HOE-DCNs\nYear: 2021\n\nAbstract: The advances on hybrid optical/electrical DCNs (HOE-DCNs) make it easier to support network function virtualization (NFV) with various quality-of-service (QoS) demands. However, the existing approaches for NFV orchestration rely heavily on the control plane, which might lead to scalability and performance issues. In this work, we propose to offload certain network orchestration tasks from the control plane to top-of-rack (ToR) switches, by leveraging the flexibility of programmable data plane (PDP). Specifically, we architect an HOE-DCN whose ToR switches are PDP switches, design the network orchestration system for it, and program the ToR switches, such that they can make quick decisions locally to assist the control plane to provision the service chains of virtual network functions (vNFSCs) agilely. We prototype our proposal in a small-scale HOE-DCN testbed, and the experimental results show that without much involvement of the control plane, the ToR switches can make local decisions and readjust the provisioning schemes of vNF-SCs adaptively to satisfy their QoS demands.","Title: Security-Aware Planning of Packet-Over-Optical Networks in Consideration of OTN Encryption\nYear: 2021\n\nAbstract: The fast development of cloud computing and Big Data applications has promoted virtualization technologies such as network function virtualization (NFV), which in turn dramatically increased the amount of sensitive data being transmitted over the optical networks for datacenter interconnections (DCIs). To ensure the physical-layer security in DCIs, people have developed optical transport network (...","Title: Series Editorial: Optical Communications and Networks\nYear: 2021\n\nAbstract: In this second issue of the Optical Communications and Networks Series in 2021, we have selected three articles that present recent advances in optical communications and networking. These advances and innovations are aimed at making the optical infrastructure of future Internet more secure, energy-efficient, and intelligent.","Title: Guest Editorial Latest Advances in Optical Networks for 5G Communications and Beyond\nYear: 2021\n\nAbstract: This Special Issue contains a collection of outstanding papers covering several recent advances in optical networks for 5G communications and beyond. Papers are organized into four categories: network resource planning; optical access networks; optical fronthaul solutions; and autonomous and data-driven network management. In this introduction, a brief overview of the field is given, followed by a summary of the seventeen papers of this Special Issue, and a discussion of future directions in the field.","Title: Disaster Protection in Inter-DataCenter Networks Leveraging Cooperative Storage\nYear: 2021\n\nAbstract: Natural disasters have challenged the survivability of Elastic Optical Inter-DataCenter Networks (EO-DCNs), and it is urgent to establish efficient disaster protection schemes. In this paper, we investigate the disaster-resilient service provisioning problem leveraging cooperative storage system (CSS). Instead of mirrored content backup on a single DC, our proposed CSS partitions a required conten...","Title: Closed-Loop Network Automation With Generic Programmable Data Plane (G-Pdp)\nYear: 2021\n\nAbstract: In this work, we try to combine software-defined networking (SDN), in-band network telemetry (INT), and data analytics to realize a novel closed-loop network automation system. To architect the data plane, we jointly consider P4-based and protocol-oblivious forwarding (POF) based programmable data plane switches (PDP-SWs) to build a generic programmable data plane (G-PDP), such that the two types of PDP-SWs can benefit each other mutually to overcome their own drawbacks. In the control plane, we expand ONOS to ensure that it can effectively manage the PDP-SWs in the G-PDP. We also design and implement data analyzers (DAs) in its data analytics subsystem, and deploy them distributedly in the G-PDP to alleviate the burden of data processing in the control plane. Our proposal is demonstrated experimentally with a network system prototype that consists of six PDP-SWs, and the results confirm that it can realize closed-loop network automation effectively and balance the tradeoff between flexibility and performance properly.","Title: Series Editorial: Optical Communications and Networks\nYear: 2021\n\nAbstract: In 2021, as optical technologies continue to demonstrate their advantages in telecom and data-center networks, we are also witnessing the attractive and promising progress on free-space optical communications and new technologies for fiber optic communications. Therefore, in this last issue of the Optical Communications and Networks Series in 2021, we have selected three contributions that address...","Title: Cognitive Control-Loop for Elastic Optical Networks with Space-Division Multiplexing\nYear: 2021\n\nAbstract: This paper introduces a new solution to improve network performance by decreasing spectrum fragmentation, crosstalk interference, blocking of virtual networks, cost, and link load imbalance. These problems degrade the performance of Elastic Optical Networks with Space-Division Multiplexing. The proposed solution, called Cognitive control loop (CO-OP), is capable of identifying a set of problems and creating plans to mitigate these problems. The CO-OP comprises four functions that employ learning algorithms to identify problems and plan a series of actions to reduce or eliminate them. The results show that the CO-OP can effectively decrease up to 30% the blocking of requests and up to 50% the crosstalk occurrence compared to existing algorithms.","Title: On the Upgrade of Service Function Chains With Heterogeneous NFV Platforms\nYear: 2021\n\nAbstract: The fast development of high-performance and flexible SmartNICs and programmable data plane switches (PDP-SWs) has motivated people to consider the deployment of virtual network functions (vNFs) on them. Hence, together with traditional virtual machines (VMs), SmartNICs and PDP-SWs form heterogeneous network function virtualization (NFV) platforms for realizing vNF service chains (vNF-SCs). In this work, we consider the transition from software-based homogeneous NFV platforms to the heterogeneous ones, and study how to optimize the service upgrade of vNF-SCs. Specifically, the service upgrade is divided into two steps, which are 1) selecting servers/switches in the substrate network (SNT) to upgrade, which is done by adding SmartNICs to servers and replacing traditional switches with PDP-SWs, under a fixed budget, and 2) redeploying the existing vNF-SCs in the updated SNT to maximize the quality-of-service (QoS) improvement on latency reductions. We first formulate an integer linear programming (ILP) model to optimize the overall service upgrade, then design two correlated optimizations for its two steps, and finally propose polynomial-time approximation algorithms to solve the optimizations. The results of extensive simulations confirm that our proposed algorithm outperforms the existing benchmarks in various network scenarios, and achieves better tradeoff between performance and time-efficiency.","Title: Multi-Agent and Cooperative Deep Reinforcement Learning for Scalable Network Automation in Multi-Domain SD-EONs\nYear: 2021\n\nAbstract: The service provisioning in multi-domain software-defined elastic optical networks (SD-EONs) is an interesting but difficult problem to tackle, because the basic problem of lightpath provisioning, i.e., the routing and spectrum assignment (RSA), is \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$\\mathcal {NP}$ </tex-math></inline-formula>\n-hard, and each domain is owned and operated by a different carrier. Therefore, even though numerous RSA heuristics have been proposed, there does not exist a universal winner that can always achieve the lowest blocking probability in all the scenarios of a multi-domain SD-EON. This motivates us to revisit the inter-domain provisioning problem in this paper by leveraging deep reinforcement learning (DRL). Specifically, we propose DeepCoop, which is an inter-domain service framework that uses multiple cooperative DRL agents to achieve scalable network automation in a multi-domain SD-EON. DeepCoop employs a DRL agent in each domain to optimize intra-domain service provisioning, while a domain-level path computation element (PCE) is introduced to obtain the sequence of the domains to go through for each lightpath request. By sharing a restricted amount of information among each other, the DRL agents can make their decisions distributedly. To ensure scalability and universality, we design the action space of each DRL agent based on well-known RSA heuristics, and architect the agents based on the soft actor-critic (SAC) scenario. We run extensive simulations to evaluate DeepCoop, and the results show that DeepCoop can adapt to the dynamic environment in a multi-domain SD-EON to always select the best RSA heuristic for minimizing blocking probability, and it outperforms the existing algorithms on inter-domain provisioning in various scenarios. Moreover, we verify that the distributed training implemented in DeepCoop ensures its universality and scalability (i.e., its training and operation do not depend on the topology of the SD-EON).","Title: Edge-cloud Collaborative Heterogeneous Task Scheduling in Multilayer Elastic Optical Networks\nYear: 2021\n\nAbstract: With the explosive growth of edge applications in the 5G/B5G era, edge-cloud collaboration (ECC) is playing a prominent role in edge service provisioning. For highly diversified edge-cloud collaborative services (ECSs), the joint allocation of heterogeneous computing resources in heterogeneous servers and multi-dimensional underlying optical network resources should be conducted. In this paper, we investigate the heterogeneous task scheduling for ECSs over multilayer elastic optical network (ML-EON), which involves the joint allocation of heterogeneous computing resources in edge and cloud servers and high-dimensional network resources. We propose a Task-Node Matching Score (TNMS) based method, which evaluates the fitness for each mapping tuple between each task in ECS and each substrate node in ML-EON, and adaptively generates a specific matching score for each task-node pair. Furthermore, TNMS is extended with a pre-allocation mechanism (TNMS-Pre) to estimate the costs of multi-dimensional resources in ML-EON for virtual link (VL) mapping. The estimated VL mapping costs are integrated into the matching scores to guide the task placement to be cost-efficient. To guarantee the feasibility, a maximal weight matching (MWM) based method is presented to determine the task placement schemes. Simulation results demonstrate the effectiveness of the adaptive scoring for heterogeneous task placement and the pre-allocation mechanism for reducing the ML-EON costs.","Title: On the Bilevel Optimization for Remapping Virtual Networks in an HOE-DCN\nYear: 2022\n\nAbstract: Hybrid optical/electrical datacenter network (HOE-DCN) uses the inter-rack networks that consist of both electrical Ethernet switches and optical cross-connects (OXCs), for better cost-efficiency and scalability. Meanwhile, to provision dynamic network services well, the operator of an HOE-DCN needs to deploy virtual networks (VNTs) and remap them adaptively. Therefore, this work studies the problem of VNT remapping in an HOE-DCN from a novel perspective, i.e., the remapping schemes should be optimized for not only the network status after the remapping but also the transition to realize it. Specifically, we model this problem as a bilevel optimization, where the upper-level optimization aims at selecting proper virtual machines (VMs) to migrate such that the estimated latency of VM migration can be minimized, and the lower-level optimization determines the actual scheme of VNT remapping for minimizing the number of resource hot-spots. We first formulate a bilevel mixed integer linear programming (BMILP) model for the bilevel optimization, and then propose a polynomial time algorithm based on enumeration to solve it approximately. Extensive simulations verify the effectiveness of our proposal.","Title: To Cooperate or Not to Cooperate: Service Function Chaining in Multi-Domain Edge-Cloud Elastic Optical Networks\nYear: 2022\n\nAbstract: We study the the non-cooperative provisioning of service function chains in a multi-domain edge-cloud elastic optical network (EC-EON), leverage game theory to design an algorithm for it, and analyze its performance difference from the cooperative scheme with simulations.","Title: Which can Accelerate Distributed Machine Learning Faster: Hybrid Optical/Electrical or Optical Reconfigurable DCN?\nYear: 2022\n\nAbstract: We run various distributed machine learning (DML) architectures in a hybrid optical/electrical DCN and an optical DCN based on Hyper-FleX-LION. Experimental results show that Hyper-FleX-LION gains faster DML acceleration and improves acceleration ratio by up to 22.3%.","Title: Dynamic Cross-Layer Restoration to Resolve Packet Layer Outages in FlexE-Over-EONs\nYear: 2022\n\nAbstract: As a promising technology, Flex Ethernet (FlexE) helps to realize deterministic and ultra-low latency in metro and transport networks. Meanwhile, previous studies have confirmed the advantages of the symbiosis of FlexE and elastic optical network (EON) (i.e., a FlexE-over-EON) on resource utilization and cost-effectiveness. In this paper, we consider the cross-layer restoration (CLR) in FlexE-over-EONs based on the FlexE-aware architecture. Specifically, we address the situation where an outage happened on one FlexE switch in the packet layer to bring it offline temporarily and then the affected client flows need to be recovered quickly and proactively. Three CLR strategies are first proposed to fully explore the flexibility of FlexE-over-EON for restoring the affected flows. Then, with the strategies, we formulate an integer linear programming (ILP) model and design an auxiliary graph (AG) based algorithm to reroute the affected flows as well as minimize the additional operational expense (OPEX) incurred during the CLR. Extensive simulations verify the effectiveness of our proposed CLR algorithms."],"string":"[\n \"Title: Energy-efficient scheduling and energy-delay tradeoff in green hybrid fiber-coaxial networks.\\nYear: 2012\\n\\nAbstract: Hybrid Fiber Coaxial (HFC) networks support broadband Internet access with the existing cable TV systems. Recent advances on HFC networks have demonstrated effective improvements on the customers' access speeds with the channel-bonding technology. In this paper, we develop a novel energy-efficient traffic scheduling algorithm for the HFC networks that support channel bonding. The proposed algorithm is compliant with the newly-released DOCSIS 3.0 standard. We come up with a system model of the channel-bonding transmitters (TXs) on a Cable Modem (CM), and then define several operation modes for them. At the beginning of each scheduling cycle, the proposed algorithm adjusts the TXs' operation modes based on the traffic status. Both analytical analysis and numerical simulations are then developed to investigate the energy-saving and delay introduced by the algorithm. The results on energy-saving indicate that the energy-consumption of the TXs scales almost linearly with the input traffic load and effective energy-saving can be achieved. We also investigate the tradeoff between the energy-saving and the average delay to optimize the parameters for the energy-efficient scheduling, and to make sure that the traffic will not experience significant delay increase due to the energy-saving operations.\",\n \"Title: A Two-Population Based Evolutionary Approach for Optimizing Routing, Modulation and Spectrum Assignments (RMSA) in O-OFDM Networks\\nYear: 2012\\n\\nAbstract: We propose a novel two-population genetic algorithm (MPGA) to optimize the routing, modulation and spectrum assignments (RMSA) in optical orthogonal frequency-division multiplexing (O-OFDM) networks. The proposed MPGA makes two populations evolve in parallel with different selection and mutation strategies, and incorporates a migration operation to exchange individuals between them. Performance evaluations show that the MPGA outperforms several existing algorithms.\",\n \"Title: Dynamic RMSA in spectrum-sliced elastic optical networks for high-throughput service provisioning\\nYear: 2013\\n\\nAbstract: We propose several novel dynamic routing, modulation and spectrum assignment (RMSA) algorithms for high-throughput service provisioning in spectrum-sliced elastic optical networks. The proposed algorithms can be applied to network provisioning, where a network operator needs to figure out an efficient way to serve each dynamic connection request based on current network status. The proposed algorithms can be categorized into two categories, dynamic RMSA using online path computation and dynamic RMSA using path-set updates. The former one updates link metrics based on their spectrum utilization status dynamically, and performs RMSA with online routing path computation for each request. The latter one pre-computes K shortest routing paths as the path-set for each source-destination pair in the network topology, performs RMSA based on different path-selection policies during dynamic provisioning, and updates a path-set by adding a new path in when a path in the set is not available for a request. We evaluate the proposed algorithms with simulations using the Poisson traffic model in a 14-node NSFNET topology. Simulation results indicate that the proposed algorithms outperform existing dynamic RMSA algorithm by providing lower bandwidth blocking probability, and reduce the blocking probability by one magnitude or more when the traffic load is light (≤ 700 Erlangs). The comparisons on network resource utilization also indicate that the proposed algorithms achieve larger resource utilization and hence provide higher throughput for network operations.\",\n \"Title: Dynamic Multi-Path Service Provisioning under Differential Delay Constraint in Elastic Optical Networks\\nYear: 2013\\n\\nAbstract: Optical orthogonal frequency-division multiplexing (O-OFDM) technology has the elastic feature of allocating spectrum resources based on subcarrier slots with bandwidths at a few GHz or even narrower. This feature enables us to utilize link capacity more efficiently by splitting a connection's traffic over multiple routing paths. In this paper, we propose a novel dynamic multi-path provisioning algorithm for O-OFDM based elastic optical networks. The algorithm tries to set up dynamic connections with single-path routing in a best-effort manner. When a connection cannot be served with a single routing path, the algorithm uses an auxiliary-graph based approach to calculate a multi-path provisioning scheme based on two parameters, i.e., the differential delay upper-bound and the bandwidth allocation granularity. Simulation results indicate that compared with several existing single-path provisioning algorithms, the proposed algorithm provides lower bandwidth blocking probability and achieves 10-18% improvement on average network throughput.\",\n \"Title: Design green Hybrid Fiber-Coaxial networks: A traffic-aware and cooperative approach\\nYear: 2012\\n\\nAbstract: We propose a traffic-aware and cooperative approach to achieve energy-saving in Hybrid Fiber-Coaxial (HFC) networks that support DOCSIS 3.0 standard. The approach incorporates a two-step operation that consists of two algorithms, one for the Cable Modem (CM) side and the other for the Cable Modem Termination System (CMTS) side. To avoid transition overheads, we design both algorithms with the consideration of energy-to-NoC (Number of Changes) tradeoff and try to minimize the number of Dynamic Bonding Change (DBC) operations while saving the energy. Simulations verify the effectiveness of the algorithms by using a realistic HFC network traffic model, and the results demonstrate significant saving on the ON-time (energy consumption) of the CMs' channels and the CMTS' ports.\",\n \"Title: Bandwidth defragmentation in dynamic elastic optical networks with minimum traffic disruptions\\nYear: 2013\\n\\nAbstract: Bandwidth defragmentation, i.e., the operation to reconfigure existing connections for making the spectrum usage less fragmented and less misaligned, has recently been recognized as one of the most important features for elastic optical networks (EONs). In this paper, we propose a novel comprehensive bandwidth defragmentation algorithm that considers the problems of 1) When to defragment? 2) What for defragment? and 3) How to defragment? jointly. The proposed algorithm accomplishes defragmentation through proactive network reconfiguration that only reroutes a portion of existing connections. In each defragmentation operation, we first choose the connections to reroute using a selection strategy, then determine how to reroute them with the defragmentation based routing and spectrum assignment (DF-RSA), and finally perform rerouting with best-effort traffic migration to minimize traffic disruptions. Simulation results indicate that in order to make the bandwidth blocking probability (BBP) comparable with that from the Greenfield scenario (100% rerouting all the time), the proposed algorithm only needs to reroute ~30% existing connections. The simulations also demonstrate that the traffic disruption percentages are less than 1% for defragmentations with 30% rerouting and can be further reduced to within 0.25% by adding a move-to-vacancy (MTV) approach in the traffic migration.\",\n \"Title: On the Effect of Bandwidth Fragmentation on Blocking Probability in Elastic Optical Networks\\nYear: 2013\\n\\nAbstract: In elastic optical networks (EONs), bandwidth fragmentation refers to the existence of non-aligned, isolated and small-sized blocks of contiguous subcarrier slots in the optical spectrum. As they are neither contiguous in the spectrum domain nor aligned along the routing paths, the network operator will have difficulty to use these slots for future connections. In this work, we analyze the effect of bandwidth fragmentation on the blocking probability in EONs. Our theoretical analysis indicates that two factors related to bandwidth fragmentation have effects on the blocking probability: 1) the extent that the available slot-blocks (i.e., blocks of contiguous slots) on different links are aligned on spectrum locations, and 2) the sizes of the available slot-blocks in links' spectra for future requests. When an EON's spectrum becomes more fragmented, the first factor actually reduces the blocking probability, while the second one increases the blocking probability. Their mixed effect determines the overall trend of how the blocking probability will change with bandwidth fragmentation. Our theoretical model can forecast this trend and reveal the relation among the blocking probability, bandwidth fragmentation, request bandwidth distribution, and spectrum utilization. We have also conducted numerical simulations to verify the theoretical analysis, and the simulation results exhibit similar trends as predicted by the theoretical model.\",\n \"Title: Design QoS-Aware Multi-Path Provisioning Strategies for Efficient Cloud-Assisted SVC Video Streaming to Heterogeneous Clients\\nYear: 2013\\n\\nAbstract: We layout a network infrastructure that leverages the storage and computing power of a cloud residing in the core for collecting network status and computing multi-path scalable video coding (SVC) streaming provisioning strategies. Therefore, in addition to its conventional tasks in the application layer, the cloud also gets involved in the network layer for the optimization of routing and forwarding. We call this scheme as cloud-assisted SVC streaming, and use it to further improve the performance of SVC streaming by using close cooperation between cloud and network. Compared to source-routing based provisioning, the cloud-assisted scheme can provide more cost-effective provisioning strategies by utilizing better knowledge of network environment together with more powerful computation power. We then propose several multi-path provisioning algorithms for cloud-assisted SVC streaming in heterogeneous networks. To the best of our knowledge, these are the first proposals to work on the problem of adaptive multi-path SVC streaming under the bandwidth, delay and differential delay constraints. Our design of the provisioning algorithms starts from an approach that is based on Max Flow and an Auxiliary Graph. Several extensions are then made based on this approach to address the situations such as provisioning from multiple sources and provisioning in dynamic network environments with rapid background traffic fluctuations. Simulations in both static and dynamic network environments show that the proposed algorithms can achieve effective performance improvements in terms of request blocking probability, bandwidth utilization, packet delay, packet loss rate, and video playback quality.\",\n \"Title: On the Spectrum-Efficient Overlay Multicast in Elastic Optical Networks Built with Multicast-Incapable Switches.\\nYear: 2013\\n\\nAbstract: We study overlay multicast (OL-M) in multicast-incapable elastic optical networks (EONs), and propose a spectrum-efficient OL-M scheme that relies on the spectrum-flexible member-only relay, i.e., OL-M-SFMOR. Our simulation results indicate that OL-M-SFMOR achieves significant improvements on the spectrum-efficiency of multicast in EONs, when compared with other multicast schemes, even including the all-optical multicast schemes in multicast-capable EONs. © 2013 IEEE.\",\n \"Title: On the Performance Analysis of Energy-Efficient Upstream Scheduling for Hybrid Fiber-Coaxial Networks with Channel Bonding\\nYear: 2013\\n\\nAbstract: We develop a novel QoS-aware energy-efficient upstream traffic scheduling algorithm for channel-bonding cable modems (CMs) in hybrid fiber-coaxial (HFC) networks. Based on their QoS requirements, the algorithm sends incoming packets to different priority queues and controls channel-bonding transmitters (TXs) to forward them. Based on a vacation M/M/n queuing model, we analyze the algorithm's performance with a three-dimensional Markov chain and derive the analytical expressions of packet delay and power consumption.\",\n \"Title: Dynamic transparent virtual network embedding over elastic optical infrastructures\\nYear: 2013\\n\\nAbstract: We propose a novel dynamic transparent virtual network embedding (VNE) algorithm, which considers node mapping and link mapping jointly, for network virtualization over optical orthogonal frequency-division multiplexing (O-OFDM) based elastic optical infrastructures. For each virtual optical network (VON) request, the algorithm first transfers the substrate optical network into a layered-auxiliary-graph according to the spectrum usage of each fiber link, then applies a node mapping approach that considers the local information of all substrate nodes, and accomplishes the link mapping, in a single layer of the auxiliary graph. The simulation results verify that the proposed algorithm considers the uniqueness of O-OFDM networks and outperforms two reference algorithms that directly apply the VNE schemes developed for Layer 2/3 or WDM network virtualization, by providing lower VON blocking probability. The simulations with a realistic topology also demonstrate that the average lengths of embedded substrate paths are well-controlled within the typical transmission reaches of O-OFDM signals. To the best of our knowledge, this is the first proposal that includes both link mapping and node mapping to address dynamic transparent VNE over elastic optical infrastructures.\",\n \"Title: Mixed Placement of 1R/2R/3R Regenerators in Translucent Optical Networks to Achieve Green and Cost-Effective Design.\\nYear: 2011\\n\\nAbstract: We develop a theoretical model to estimate the BER evolutions in optical translucent networks in a more accurate way. The model calculates the BER increment at each regeneration site with considerations of reshaping nonlinearity and jitter effect for 40 Gb/s signals. Based on the model, we propose a novel translucent optical network infrastructure that includes all-optical 2R regenerators and uses mixed operation of 1R/2R/3R regenerators for cost and power consumption savings. Simulation results show that our proposed infrastructure reduces the number of O/E/O 3R regenerators to less than 50% and achieves effective power saving. © 2011 IEEE.\",\n \"Title: Energy-efficient wideband cable access networks in future smart cities.\\nYear: 2013\\n\\nAbstract: The Internet explosion makes energy consumption of the ICT sector a global concern. Nowadays, the power consumption of access networks increases rapidly due to the rollout of broadband services. Cable access networks provide Internet access over the existing cable television systems. Among all wired access networks, they have the second biggest user base, which is still growing fast due to the rel...\",\n \"Title: Crosstalk due to optical demultiplexing in subcarrier multiplexed systems\\nYear: 2011\\n\\nAbstract: We report an in-depth investigation of the inter-modulation crosstalk in subcarrier multiplexing (SCM) systems with optical demultiplexing (ODeMux). Both theoretical derivations and numerical simulations show that the crosstalk in ODeMux systems mainly comes from the nonlinear mixing of the baseband and subcarrier modulations inside the signal channels. Several key parameters are then studied to estimate their effects on the magnitude of the crosstalk. As a result, performance optimization strategies are proposed for ODeMux SCM systems. In order to further enhance the transmission performance of ODeMux SCM systems, we discuss two techniques to suppress the inter-modulation crosstalk and analyze their effectiveness with numerical simulations.\",\n \"Title: Optical networks in cloud computing [Guest Editorial]\\nYear: 2013\\n\\nAbstract: The seven articles in this special section focus on optical networking deployed in cloud computing.\",\n \"Title: An Empirical Investigation of the Impact of Server Virtualization on Energy Efficiency for Green Data Center.\\nYear: 2013\\n\\nAbstract: The swift adoption of cloud services is accelerating the deployment of data centers. These data centers are consuming a large amount of energy, which is expected to grow dramatically under the existing technological trends. Therefore, research efforts are in great need to architect green data centers with better energy efficiency. The most prominent approach is the consolidation enabled by virtual...\",\n \"Title: A Novel Energy-Aware Design to Build Green Broadband Cable Access Networks.\\nYear: 2011\\n\\nAbstract: We propose and investigate energy-saving algorithms for DOCSIS-3.0 networks. By proactively monitoring traffic load, the algorithms achieve network-wide energy saving by re-adjusting CM's bonding groups and shutting down under-utilized upstream/downstream ports on the CMTS. Numerical simulations indicate that by applying the CM side algorithm only, we can achieve a possible 17.6% energy saving for 8192 CMs. We also study the fundamental tradeoff between energy-saving and packet queuing delay and estimate the upper bounds of the delays. © 2011 IEEE.\",\n \"Title: Genetic algorithms for designing energy-efficient optical transport networks with mixed regenerator placement\\nYear: 2012\\n\\nAbstract: We design genetic algorithms (GA) to solve the mixed regenerator placement (MRP) problem of lightpaths with different lengths in optical transport networks, and investigate their performance with numerical simulations. By incorporating a theoretical model that can estimate BER changes hop-by-hop along lightpaths, the GA encodes the placements of 1R/2R/3R at intermediate regeneration sites as genes, and takes quality-of-transmission (QoT) and energy-efficiency as the fitness functions. With a relatively small population size (e.g. 30-50), the algorithms obtain multiple qualified MRP results that can satisfy both the QoT and energy requirements within 32 generations, for lightpaths with lengths up to 28 hops. Two crossover and two mutation operators are investigated within the GA. By adjusting the possibilities of the crossover and mutation intelligently, the adaptive scheme outperforms the uniform one by getting a larger percentage of fit individuals. We also propose two selection operators, and demonstrate an adjustable tradeoff between QoT and energy-efficiency.\",\n \"Title: Optical regenerator placement strategy to achieve green design of translucent optical networks\\nYear: 2011\\n\\nAbstract: We report a novel optical regenerator placement strategy to achieve green and cost-effective design of optical translucent networks. Instead of treating the BER independently before and after a regenerator, we calculate the BER increment at each regeneration site with the considerations of reshaping nonlinearity and jitter effect. Simulation results demonstrate that the cost and power consumption of a translucent network can be effectively reduced with our proposed scheme.\",\n \"Title: Experimental Demonstration Of Svc Video Streaming Using Qos-Aware Multi-Path Routing Over Integrated Services Routers\\nYear: 2013\\n\\nAbstract: We construct an experiment testbed using commercial routers and demonstrate QoS-aware multi-path SVC video streaming with it. The testbed consists of six integrated services routers (Cisco 2900 Series) that are configured using a mesh topology. To realize QoS-aware multi-path routing efficiently, we develop a centralized automatic NC&M system that monitors link status proactively, calculates the multi-path routing scheme for each streaming session, and communicates with the routers' control plane to adjust their routing policies. For each streaming session, when the NC&M finds a better multi-path routing scheme, it reconfigures the routers to invoke a path-switching. The experimental results indicate that the multi-path SVC video streaming scheme reduces the packet loss rate (PLR) from 3.33% to 0.62% for the base layer (BL) packets, and to 1.71% for the enhancement layer (EL) packets. Additional experiments on video playback quality, video playback peak signal-to-noise ratio (PSNR), and delay jitter also verify that the multi-path scheme outperforms the single-path one significantly and utilizes the network resources more efficiently.\",\n \"Title: Dynamic RMSA in elastic optical networks with an adaptive genetic algorithm\\nYear: 2012\\n\\nAbstract: We develop an adaptive and efficient genetic algorithm (GA) to solve the dynamic routing, modulation and spectrum assignments (RMSA) for elastic O-OFDM networks. The algorithm offers an efficient way of serving the dynamic lightpath requests based on the current network status at each service provision time. The GA is designed for multi-objective optimization. For low traffic cases when there is no blocking, the GA minimizes the maximum number of slots required on any fiber in the network; otherwise, it minimizes the blocking probability. The performance of the proposed GA is evaluated in dynamic RMSA simulations with the 14-node NSFNET and the 28-node US Backbone topologies, and the results show that it converges within 25 generations. The simulation results also verify that the GA-RMSA outperforms several existing algorithms by providing more load-balanced network provisioning solutions with lower blocking probabilities. Specifically, when the traffic load is same, the GA can achieve more than one order-of-magnitude reduction on blocking probability. To the best of our knowledge, this is the first attempt to solve dynamic RMSA in elastic O-OFDM networks with a GA.\",\n \"Title: Improving Energy-Efficiency Of Hfc Networks With A Master-Slave Linecard Configuration\\nYear: 2013\\n\\nAbstract: We develop a novel traffic scheduling algorithm based on a master-slave linecard (LC) configuration to improve the energy-efficiency of hybrid fiber-coaxial (HFC) networks. The algorithm forwards packets to the master or slave LC adaptively according to the traffic load, and toggles the LCs between working and sleeping modes for energy-saving. To optimize the algorithm's key parameters, we model the system using a two-dimensional Markov process and derive the analytical expressions of several performance metrics, including average packet delay, LC switching frequency, and energy efficiency improvement. We then verify the theoretical analysis with numerical simulations using the Monte Carlo method. Both the theoretical and simulation results indicate that the proposed algorithm can achieve significant energy efficiency improvement, while keeping the average packet delay and LC switching frequency low.\",\n \"Title: Using Genetic Algorithm to Optimize Mixed Placement of 1R/2R/3R Regenerators in Translucent Lightpaths for Energy-Efficient Design.\\nYear: 2012\\n\\nAbstract: We propose an evolutionary approach to efficiently optimize the mixed placements of 1R/2R/3R regenerators in translucent lightpaths. The genetic algorithm encodes the placements of mixed types of regenerators as genes, and incorporates adaptive genetic operators for effective search strategy. We consider both end-to-end signal transmission quality and regeneration energy cost, and design the fitness function for multi-objective optimization. The simulation results show that the proposed approach converges within 50 generations even when the lightpath under investigation is as long as 31 hops. To improve the flexibility of network designs, multiple energyefficient mixed regenerator placement (MRP) results can be obtained simultaneously. © 2012 IEEE.\",\n \"Title: Fragmentation-aware routing, modulation and spectrum assignment algorithms in elastic optical networks\\nYear: 2013\\n\\nAbstract: We investigate the principle of how dynamic service provisioning fragments the spectral resources on links along a path, and propose corresponding RMSA algorithms to alleviate spectrum fragmentation in dynamic network environments. © 2013 OSA.\",\n \"Title: Energy-Efficient Protection Designs For Translucent Optical Networks Using Mixed Regenerator Placement\\nYear: 2013\\n\\nAbstract: We investigate energy-efficient protection designs for translucent networks using mixed regenerator placement (MRP). For link-based p-cycle designs, we develop an ILP model and a heuristic.We also propose a path-based shared backup path protection design algorithm\",\n \"Title: Spectrum defragmentation algorithms for elastic optical networks using hitless spectrum retuning techniques\\nYear: 2013\\n\\nAbstract: We propose several algorithms to achieve hitless bandwidth defragmentation using spectrum retuning in elastic optical networks. Two retuning techniques, spectrum sweeping and hop tuning, are studied. Simulation results show that the hop tuning technique achieves better defragmentation performance. © 2013 OSA.\",\n \"Title: Ambient assisted living communications [Guest Editorial]\\nYear: 2015\\n\\nAbstract: Nowadays taking care of elderly people and the disabled has become a very important but challenging task. Although the elderly have wisdom and wealth gathered from their life experiences, they require special assistance, higher health insurance costs, and even constant monitoring. By utilizing information process and communications technology, ambient assisted living (AAL) communications open up a new way to address such needs for the aged and the sick. Specifically, AAL uses ambient technologies, including data sensing, processing, transmission, and artificial intelligence, to enable new products, services, and processes that help to provide safe, healthy lives for the aged and disabled. It also supports improved social connections and accessibility to the external world. With the growth of AAL environment, the accessibility gets more challenging for the complex data structure.\",\n \"Title: Implementation and Demonstration of Revenue-Driven Provisioning for Advance Reservation Requests in OpenFlow-Controlled SD-EONs\\nYear: 2014\\n\\nAbstract: This paper aims to realize OpenFlow-controlled revenue-driven advance reservation (AR) provisioning in software-defined elastic optical networks (SD-EONs). We design control plane framework, propose two algorithms for the OpenFlow controller, implement the whole system in a semi-practical SD-EON testbed, and conduct experiments with two payment models. Experimental results indicate the proposed system performs well and can increase total revenue-gain effectively.\",\n \"Title: Flexible Traffic Engineering: When OpenFlow Meets Multi-Protocol IP-Forwarding\\nYear: 2014\\n\\nAbstract: In this letter, we present flexible traffic engineering (F-TE), a solution to achieve highly efficient traffic engineering in a hybrid network where IPv4 and IPv6 protocols co-exist. By leveraging the programmability of OpenFlow (OF), we design the OF system to facilitate IP forwarding interchanging (i.e., switching the packets between IPv4 and IPv6 at each hop according to the network status). The OF system is implemented in a real network testbed. We conduct experiments with video streaming to verify that the proposed F-TE can improve network throughput effectively with adaptive IP interchanging.\",\n \"Title: Spectrum-efficient anycast in elastic optical inter-datacenter networks.\\nYear: 2014\\n\\nAbstract: With the rapid development of cloud computing, optical inter-datacenter (inter-DC) networks have attracted intensive research attentions. Meanwhile, recent advances on the flexible-grid elastic optical networks (EONs) have demonstrated agile spectrum management in the optical layer. Therefore, we expect EONs to become a promising underlying infrastructure for optical inter-DC networks. In this paper, we investigate offline and online routing and spectrum assignment (RSA) problems for anycast requests in elastic optical inter-DC networks. For the offline problem, we formulate an integer linear programming (ILP) model and propose several heuristics based on single-DC destination selection. The optimal solutions for small-scale problems are obtained by solving the ILP, and we compare them with those from the heuristics for measuring the optimization gaps. For the online problem, we design several heuristics to consider the computing and bandwidth resources jointly for efficient service provisioning, including an algorithm that uses multi-DC destination selection. Our simulation results demonstrate that the anycast algorithm with multi-DC destination selection can fully utilize the bandwidth resources in the elastic optical inter-DC network, make computing resources become the bottleneck, and reduce the bandwidth blocking probability of anycast requests effectively.\",\n \"Title: Efficient and scalable cloud-assisted SVC video streaming through mesh networks\\nYear: 2012\\n\\nAbstract: To improve the efficiency and scalability of video streaming in large scale mesh networks, we propose a cloud-assisted video delivering strategy that can distribute video contents to subscribers over multiple routing paths dynamically. By leveraging the computation power and numerous storage of a cloud and the flexibility of scalable video coding (SVC) scenarios, we design algorithms for multiple routing path selection and assignment, and propose to run them in a cloud for fast processing. The multi-path routing is then achieved by label-switching in the network layer. The simulation results show that the proposed algorithms achieved efficient usage of the network resources and improved user experience in terms of bandwidth, delay and jitter.\",\n \"Title: Dynamic anycast in inter-datacenter networks over elastic optical infrastructure\\nYear: 2014\\n\\nAbstract: Since the optical orthogonal frequency-division multiplexing (O-OFDM) technology can facilitate agile spectrum management, the elastic optical infrastructure based on it has become a promising candidate for the physical-layer of inter-datacenter networks. In this paper, we investigate dynamic anycast in such networks. Firstly, we design three metrics to consider computing and bandwidth resources jointly, and propose anycast algorithms with single-path routing based on them. Then, we propose an anycast algorithm with multipath routing to further improve the network performance. Our simulation results indicate that the anycast algorithm with multipath routing can fully utilize the bandwidth resources in the optical infrastructure, make computing resources become the bottleneck, and reduce the bandwidth blocking probability of anycast requests effectively.\",\n \"Title: Toward profit-seeking virtual network embedding algorithm via global resource capacity\\nYear: 2014\\n\\nAbstract: In this paper, after proposing a novel metric, i.e., global resource capacity (GRC), to quantify the embedding potential of each substrate node, we propose an efficient heuristic virtual network embedding (VNE) algorithm, called as GRC-VNE. The proposed algorithm aims to maximize the revenue and to minimize the cost of the infrastructure provider (InP). Based on GRC, the proposed algorithm applies a greedy load-balance manner to embed each virtual node sequentially, and then adopts the shortest path routing to embed each virtual link. Simulation results demonstrate that our proposed GRC-VNE algorithm achieves lower request blocking probability and higher revenue due to the more appropriate consideration of the resource distribution of the entire network, when compared to the two lastest VNE algorithms that also consider the resources of entire substrate network. Then, we introduce a classical reserved cloud revenue model, which consists of fixed revenue and variable one. Based on this revenue model, we design a novel admission control policy selectively accepting the VNR with high revenue-to-cost ratio to maximize the InP's profit based on an empirical threshold. Through extensive simulations, we observe that the optimal empirical threshold is proportional to the ratio of variable revenue to the fixed one.\",\n \"Title: Revenue-driven virtual network embedding based on global resource information.\\nYear: 2013\\n\\nAbstract: Virtual network embedding (VNE), working as a key step for network virtualization, has recently gained intensive attentions from the research community. In this paper, we propose a novel VNE algorithm that aims to maximize the infrastructure provider's revenue from serving virtual network (VN) requests, with the help of the global resource information. The proposed algorithm, named as revenue-driven VNE (RD-VNE), adopts a node-ranking approach that takes the global resource information into account in a recursive manner to assist the greedy node mapping, and leverages the shortest-path routing for link mapping. Our simulation results suggest that the proposed VNE algorithm outperforms two existing VNE algorithms that also take global resource information into consideration, in terms of request blocking probability, and brings higher time-average revenue to the infrastructure provider (InP).\",\n \"Title: Design integrated RSA for multicast in elastic optical networks with a layered approach\\nYear: 2013\\n\\nAbstract: In this paper, we incorporate a layered approach to design integrated multicast-capable routing and spectrum assignment (MC-RSA) algorithms for achieving efficient all-optical multicasting in spectrum-sliced elastic optical networks (EONs), which are based on the optical orthogonal frequency-division multiplexing (O-OFDM) technology. For each multicast request, the proposed algorithms decompose the physical topology into several layered auxiliary graphs according to the network spectrum utilization. Then, based on the request's bandwidth requirement, we select a proper layer and calculate a multicast light-tree within it. With these procedures, the routing and spectrum assignment (RSA) for each multicast request is done in an integrated way. We evaluate the proposed algorithms in simulations of static network planning and dynamic network provisioning. The simulation results demonstrate that compared to the existing MC-RSA algorithms, our approaches achieve more efficient network planning in terms of spectrum utilization, and provide lower blocking probabilities in network provisioning.\",\n \"Title: Dynamic p-cycle configuration in spectrum-sliced elastic optical networks\\nYear: 2013\\n\\nAbstract: We propose three algorithms for dynamic pre-configured-cycle (p-cycle) configuration in the spectrum-sliced elastic optical networks (EONs) based on the optical orthogonal frequency-division multiplexing (O-OFDM) technology, aiming to provide 100% restoration against single-link failures. The first one (PE-p-cycle) configures the working path and p-cycles of a request together according to the protection efficiencies of the p-cycles. In order to reduce request blocking probability, we then propose to use the spectrum planning technique to regulate the spectra of working and protection resources and design two algorithms based on the protected working capacity envelope (PWCE) cycles (PWCE-p-cycle-SP) and Hamiltonian cycles (Ham-p-cycle-SP). The three algorithms are then evaluated in two mesh network topologies, using dynamic Poisson traffic. The simulation results indicate that with the spectrum planning, PWCE-p-cycle-SP and Ham-p-cycle-SP reduce the blocking probability effectively, when comparing with the one without it, i.e., PE-p-cycle. To the best of our knowledge, this is the first attempt to address dynamic p-cycle configuration in EONs.\",\n \"Title: Performance evaluation of an enhanced cryptography solution for m-Health applications in cooperative environments.\\nYear: 2013\\n\\nAbstract: Mobile health (m-Health) applications delivers healthcare services through mobile applications regardless of time and place. An m-Health application makes use of wireless communications to sustain its health services and often providing a patient-doctor interaction. Therefore, m-Health applications present several challenging issues and constraints, such as, mobile devices battery and storage capacity, broadcast constraints, interferences, disconnections, noises, limited bandwidths, network delays, and of most importance, privacy and confidentiality concerns. This paper proposes a novel and enhanced cryptography solution in a cooperative environment considering a novel and early-proposed cooperation strategy for m-Health Applications. This proposal aims to face the challenges related to privacy and security issues of all forwarded and retrieved data concerning user sensitive information. Furthermore, it presents a performance evaluation of this proposal considering a comparison with an earlier proposed encryption strategy for the same cooperative environment.\",\n \"Title: Minimizing disaster backup window for geo-distributed multi-datacenter cloud systems\\nYear: 2014\\n\\nAbstract: We optimize the disaster backup in multi-datacenter (multi-DC) cloud systems and design disaster-aware algorithms to realize rapid backup with the objective of minimizing the backup window for all the DCs in the network. A mixed integer linear programming (MILP) model is first formulated to optimize the backup processes of all production DCs jointly. We then develop three heuristics that use the one-step or two-step approaches for the selection of backup DCs and the calculation of backup routing paths. Simulation results show that the Two-Step algorithm can achieve the shortest backup window with the lowest operation complexity.\",\n \"Title: Joint defragmentation of spectrum and computing resources in inter-datacenter networks over elastic optical infrastructure\\nYear: 2014\\n\\nAbstract: In this paper, we consider to apply a joint defrag-mentation (DF) of spectrum and computing resources in the inter-datacenter (inter-DC) networks built over elastic optical infrastructure based on the optical orthogonal frequency-division multiplexing (O-OFDM) technology. We propose joint DF algorithms that consist of a request selection strategy to select active requests for reconfiguring and an anycast-based DF routing and spectrum assignment (DF-RSA) to reconfigure the selected requests. For the joint DF, two request selection strategies and three anycast-based DF-RSA schemes are designed, and their combinations are evaluated with simulations. The simulation results show that by considering the usages of spectrum and computing resources jointly, the proposed algorithms can improve the blocking performance of the inter-DC network significantly with controlled numbers of DF operations.\",\n \"Title: Reduce spectrum defragmentation latency in EONs with effective parallelization of connection reconfigurations\\nYear: 2014\\n\\nAbstract: We investigate parallel defragmentation and propose a novel algorithm to achieve effective parallelization of the connection reconfigurations with a conflict graph. Simulation results show that the algorithm can effectively reduce the latency of traffic migrations.\",\n \"Title: Distributed Online Hybrid Cloud Management for Profit-Driven Multimedia Cloud Computing\\nYear: 2015\\n\\nAbstract: It is known that with hybrid cloud, a multimedia cloud service provider (MCSP) can quickly extend its services to multiple geographical locations with quality-of-service (QoS) guarantees. Meanwhile, to maximize its profit, the MCSP needs an online management mechanism to operate the hybrid cloud efficiently. In this paper, we study how to maximize an MCSP’s profit from provisioning multimedia services to geographically distributed users with hybrid cloud. We first design a service provisioning model to manage the resources in hybrid cloud. Here, in order to make the model practical and address the different situations in private and public clouds, we consider different time granularities for resource reservations. Then, we leverage the Lyapunov optimization technique to maximize the profit of MCSP and propose an online algorithm that can manage hybrid cloud in the distributed manner. Specifically, the algorithm determines the access control and routing of each multimedia service request, and allocates the resources in hybrid cloud accordingly. We also apply the ??-persistent technique to ensure that the worst-case latency of the provisioned requests is bounded. Finally, the proposed algorithm is evaluated with extensive simulations using both synthetical and real traces. Simulation results indicate that the algorithm can manage the hybrid cloud efficiently and maximize the profit of MCSP.\",\n \"Title: Data-oriented malleable reservation to revitalize spectrum fragments in elastic optical networks\\nYear: 2015\\n\\nAbstract: This paper investigates malleable reservation (MR) for data-oriented requests to revitalize spectrum fragments in elastic optical networks. We formulate a mixed integer linear programming model and propose a time-efficient heuristic to solve the MR problem.\",\n \"Title: Highly efficient data migration and backup for big data applications in elastic optical inter-data-center networks\\nYear: 2015\\n\\nAbstract: This article discusses the technologies for realizing highly efficient data migration and backup for big data applications in elastic optical inter-data-center (inter-DC) networks. We first describe the impacts of big data applications on underlying network infrastructure and introduce the concept of flexible-grid elastic optical inter-DC networks. Then we model the data migration in such networks as dynamic anycast and propose several efficient algorithms. Joint resource defragmentation is also discussed to further improve network performance. For efficient data backup, we leverage a mutual backup model and investigate how to avoid the prolonged negative impacts on DCs' normal operation by minimizing the DC backup window.\",\n \"Title: Distributed Online Scheduling and Routing of Multicast-Oriented Tasks for Profit-Driven Cloud Computing.\\nYear: 2016\\n\\nAbstract: It is known that to support a few common applications well, e.g., datacenter (DC) backup, multicast-oriented tasks need to be handled in inter-DC networks. In this letter, we propose an approach to schedule and route multicast-oriented tasks in inter-DC networks with arbitrary topologies. Specifically, we leverage Lyapunov optimization to develop a distributed online approach that can maximize the time-average profit with only local information. Besides, we also design a destination grouping scheme to address the scalability issue of our proposed approach and demonstrate that the number of queues in the system can be reduced significantly. Extensive simulations verify the performance of the proposed approaches.\",\n \"Title: Design and Demonstration of SDN-Based Flexible Flow Converging with Protocol-Oblivious Forwarding (POF)\\nYear: 2015\\n\\nAbstract: With the development of software-defined networking (SDN), people start to realize that the protocol-dependent nature of OpenFlow, i.e., the matching fields are defined according to existing network protocols (e.g., Ethernet and IP), will limit the programmability of forwarding plane and cause scalability issues. In this work, we focus on Protocol-Oblivious Forwarding (POF) [1], which can make the forwarding plane reconfigurable, programmable and future-proof with a protocol-independent instruction set. We design and implement a POF-based flexible flow converging (F-FC) scheme to reduce the number of flow-entries for enhanced scalability. To evaluate the POF system experimentally, we build a network testbed that consists of both commercial and software-based POF switches. Network experiments with real-time video streaming in the proposed POF system demonstrate that our POF-based F-FC approach can outperform conventional schemes.\",\n \"Title: On efficient protection design for dynamic multipath provisioning in elastic optical networks\\nYear: 2015\\n\\nAbstract: Multipath provisioning (MPP) in elastic optical networks (EONs) can improve the network performance effectively. In this paper, we study the protection schemes for MPP to ensure 100% restoration against single-link failures. We design three algorithms, namely, Instant-ILP, PWCE-MPP and mPWCEMPP. The first one leverages a simple integer linear programming (ILP) model to design the optimal working-backup structure for each request instantly, while the rest two utilize protected working capacity envelop (PWCE) and spectrum planning to further reduce bandwidth blocking probability (BBP). Moreover, mPWCE-MPP considers the differences among working and backup paths, and ensures that the maximum path-difference (MPD) of a request will not increase dramatically in restoration. Simulation results show that mPWCE-MPP can obtain the best trade-off between BBP and average MPD.\",\n \"Title: Toward online profit-driven scheduling of inter-DC data-transfers for cloud applications\\nYear: 2015\\n\\nAbstract: For an inter-datacenter (inter-DC) network that carries multiple cloud applications, tasks may arise in the DCs and need to transfer data to others with different latency requirements. Therefore, it is desired that a highly efficient online scheduling algorithm could be developed to consider the request admission, routing selection and bandwidth allocation for the data-transfers jointly. In this work, we investigate this problem, and propose an online scheduling algorithm, namely, GlobeAny, to maximize the time-average profit from provisioning the requests. The proposed algorithm leverages Lyapunov optimization techniques and can achieve arbitrarily approaching to the optimal value within O(1/V) gap. We also show that by adjusting the application weights, it can provide differentiated services to requests with different latency requirements.\",\n \"Title: Maximizing Utility of Time-Constrained Emergency Backup in Inter-Datacenter Networks.\\nYear: 2016\\n\\nAbstract: This work addresses the emergency backup problem in inter-datacenter (inter-DC) network, which is triggered in response to predictable destructive events. We consider the value of data and study how to maximize the utility of time-constrained emergency backup. Specifically, we first formulate the utility-maximization problem for a time-varying network environment and then leverage the time-expanded network (TEN) approach to transform it into a static network problem. To finally solve the problem, a time-efficient and distributed algorithm is proposed based on the dual decomposition technique. Simulation results show that our algorithm outperforms existing algorithms.\",\n \"Title: Joint Spectrum and IT Resource Allocation for Efficient VNF Service Chaining in Inter-Datacenter Elastic Optical Networks.\\nYear: 2016\\n\\nAbstract: We study how to allocate spectrum and IT resources jointly for realizing efficient virtual network function (VNF) service chaining in inter-datacenter elastic optical networks. We first formulate an integer linear programming model to solve the problem exactly, and then a longest common subsequence-based heuristic is proposed. The simulation results indicate that the proposed algorithms can reuse the deployed VNFs efficiently and arrange the spectrum utilization in a much more load-balanced manner.\",\n \"Title: p-Cycle design without candidate cycle enumeration in mixed-line-rate optical networks\\nYear: 2015\\n\\nAbstract: This paper develops and evaluates a new protection solution for pre-configured-cycle (p-cycle) design in Mixed-Line-Rate (MLR) optical networks. Conventional p-cycle approaches require enumerating candidate cycles in advance and screening p-cycles using heuristic algorithms. Our method generates p-cycles directly in one-step using an Integer Linear Programming (ILP) model. Cost-effective transponders and distance-adaptive line rates are provisioned for every p-cycle to minimize joint cost of transponders and spare capacity. The design problem is solved together with spectral clustering based graph partitioning, which permits to compute the optimal solution in independent sub-graphs concurrently. The results show that our protection method is cost-efficient for p-cycle design with mixed line rates and scalable for large optical networks.\",\n \"Title: Source routing with protocol-oblivious forwarding (POF) to enable efficient e-Health data transfers.\\nYear: 2016\\n\\nAbstract: It has already been confirmed that software-defined networking (SDN) can make the networks more programmable, adaptive and application aware. However, due to the large-scale and geographically-distributed nature of wide-area networks (WAN), the scalability could become a critical issue if we incorporate SDN for WANs (i.e., realizing SD-WANs). In this paper, we design and implement a novel network system that can leverage source routing with the protocol-oblivious forwarding (POF) to facilitate efficient e-Health data transfers with low setup latency. We develop the POF-based source routing protocol to realize a pipeline based packet processing procedure, which can replace the table-lookup based approach in traditional SDN networks and make the forwarding plane more efficient. The proposed scheme is demonstrated experimentally, and the results verify that with it, the flow-tables installed in each core switches in a POF-controlled SD-WAN can be minimized and the path setup latency of traffic flows can be reduced significantly as well.\",\n \"Title: Orchestrating Multicast-Oriented Nfv Trees In Inter-Dc Elastic Optical Networks\\nYear: 2016\\n\\nAbstract: It is known that by incorporating network function virtualization (NFV) in inter-datacenter (inter-DC) networks, we can use the network resources more intelligently to deploy new services faster. This paper considers an inter-DC elastic optical network (IDC-EON) and studies how to orchestrate the multicast-oriented NFV trees (M-NFV-Ts) in it effciently. We first consider an of fine scenario in which all the M-NFV-Ts are known and need to be served in the network. A mixed integer linear programming (MILP) model is formulated to solve the problem exactly, and we also propose a heuristic based on path-intersection (PI) to reduce the time complexity. With extensive simulations, we show that the proposed heuristic can approximate the MILP's performance on low-cost M-NFV-T provisioning but only requires much shorter running time. Next, the online scenario where the M-NFV-Ts can come and leave on-the-fly is addressed, and we leverage PI to design two online algorithms for orchestrating dynamic M-NFV-Ts in IDC-EONs, i.e., with either the batch (B-PI) or sequential (S-PI) scheme. Simulation results indicate that compared with S-PI, B-PI can reduce blocking probability effectively.\",\n \"Title: Flexible Flow Converging: A Systematic Case Study on Forwarding Plane Programmability of Protocol-Oblivious Forwarding (POF).\\nYear: 2016\\n\\nAbstract: It is known that software-defined networking (SDN) can support effective traffic engineering (TE) with the global view of networks. Hence, OpenFlow was used to realize flow-based TE. However, there is a tradeoff between the volume of installed flow entries and the granularity of TE. Moreover, the protocol-dependent nature of OpenFlow might limit the flexibility and adaptivity of TE. In this paper, we leverage the protocol-oblivious forwarding technology that can make each switch work as a protocol independent white box and utilize its forwarding plane programmability to design a novel flexible flow converging (F-FC) scheme for realizing SDN-based fine-grained TE. Specifically, we design both the network system and the F-FC algorithms running on it and conduct experiments to demonstrate that our proposed scheme can not only reduce the volume of installed flow entries in switches but also realize fine-grained TE to achieve better utilization on network bandwidth.\",\n \"Title: On the Parallelization of Spectrum Defragmentation Reconfigurations in Elastic Optical Networks.\\nYear: 2016\\n\\nAbstract: Flexible-grid elastic optical networks (EONs) have attracted intensive research interests for the agile spectrum management in the optical layer. Meanwhile, due to the relatively small spectrum allocation granularity, spectrum fragmentation has been commonly recognized as one of the key factors that can deteriorate the performance of EONs. To alleviate spectrum fragmentation, various defragmentati...\",\n \"Title: Advances in Optical Communications Technologies.\\nYear: 2017\\n\\nAbstract: The articles in this special section focus on recent advancements in optical communication. In 2017, as fiber optic communications technologies continue to demonstrate their advantages in telecom and data center networks, we are also witnessing attractive and promising progress on optical wireless communications, which can potentially work with RF communications to meet the ever-increasing demands for higher date transmission rates and smart home networking. Meanwhile, the innovations in optical communications and networking systems can never be realized without fundamental support from the physical infrastructure layer. The recent advances on few-mode fibers open up appealing opportunities for equipment vendors and network operators to solve the capacity crunch problem using mode-division multiplexing. To integrate the momentum gained from these technical advances, an efficient and intelligent network control and management (NCu0026M) mechanism is essential, especially for building heterogeneous networks to support a wide range of services with various traffic patterns. Therefore, the implementation of software-defined networking (SDN) in optical communications networks appears inevitable, and we expect to see more advances in this area in 2017.\",\n \"Title: Control plane innovations to realize dynamic formulation of multicast sessions in inter-DC software-defined elastic optical networks.\\nYear: 2017\\n\\nAbstract: It is known that to support the applications such as datacenter backup and migration, multicast should be supported efficiently in inter-datacenter (inter-DC) networks to carry the corresponding point-to multiple -point communications. Moreover, due to the traffic dynamics in inter-DC networks, we might have to consider the case that the multicast members can join or leave a multicast session dynamically. Therefore, in this work, we try to leverage control plane innovations to realize dynamic formulation of multicast sessions in inter-DC software-defined elastic optical networks (SD-EONS), which are equipped with multicast-incapable bandwidth-variable wavelength selective switches (MI-BV-WSS'). Here, one key issue to address is that the continuous changing of multicast group members can degrade the optimality of a multicast-tree. Hence, we propose to rearrange the multicast-trees adaptively to reduce their spectrum usage. Meanwhile, we try to minimize the frequency of rearrangements to avoid unnecessary operation complexity. Based on these considerations, we propose several multicast-tree rearrangement algorithms for updating multicast sessions dynamically with lightpath reroutings in inter DC SD-EONs. Both partial and full multicast-tree rearrangements are studied. Simulation results indicate that the proposed algorithms can rearrange the multicast-trees intelligently such that the blocking probability can be reduced effectively with the least lightpath reroutings. Next, based on these theoretical investigations, we consider how to implement the proposed algorithms in the control plane of an inter-DC SD-EON, We extend the OpenFlow (OF) protocol to support the dynamic formulation of multicast sessions and also design the functional models in the control plane elements to realize multicasttree rearrangements. Experiment results verify the effectiveness of our proposed algorithms and system design. (C) 2016 Elsevier B.V. All rights reserved.\",\n \"Title: Novel Location-Constrained Virtual Network Embedding (LC-VNE) Algorithms Towards Integrated Node and Link Mapping.\\nYear: 2016\\n\\nAbstract: This paper tries to solve the location-constrained virtual network embedding LC-VNE problem efficiently. We first investigate the complexity of LC-VNE, and by leveraging the graph bisection problem, we provide the first formal proof of the $\\\\\\\\mathcal {NP}$ -completeness and inapproximability result of LC-VNE. Then, we propose two novel LC-VNE algorithms based on a compatibility graph CG to achieve integrated node and link mapping. In particular, in the CG, each node represents a candidate substrate path for a virtual link, and each link indicates the compatible relation between its two endnodes. Our theoretical analysis proves that the maximal clique in the CG is also the maximum one when the substrate network has sufficient resources. With CG, we reduce LC-VNE to the minimum-cost maximum clique problem, which inspires us to propose two efficient LC-VNE heuristics. Extensive numerical simulations demonstrate that compared with the existing ones, our proposed LC-VNE algorithms have significantly reduced time complexity and can provide smaller gaps to the optimal solutions, lower blocking probabilities, and higher time-average revenue as well.\",\n \"Title: On-Demand and Reliable vSD-EON Provisioning with Correlated Data and Control Plane Embedding.\\nYear: 2016\\n\\nAbstract: Software-defined elastic optical networks (SDEONs) provide operators more flexibility to customize their optical infrastructure dynamically and adaptively, and network virtualization, i.e., infrastructure-as-a-service (IaaS), enables multiple tenants to share the substrate infrastructure efficiently. In this paper, we study how to provision virtual SD-EONs (vSD-EONs) with the correlated data and control plane embedding (chi-VNE) that considers the quality-of-service (QoS) of virtual control plane (vCP), i.e., availability and control channel latency. We propose a chi-VNE algorithm to solve the problem, design the network system to realize it, and accomplish proof-of-concept experimental demonstrations in an OpenFlow-based network testbed. Numerical and experimental results indicate that the proposed algorithm and system function well and can realize on-demand and reliable vSD-EON provisioning.\",\n \"Title: Throughput–Power Tradeoff Association for User Equipment in WLAN/Cellular Integrated Networks\\nYear: 2017\\n\\nAbstract: To meet the dramatic growth in mobile traffic, wireless local area networks (WLANs) have been integrated with cellular networks. We investigate the user equipment (UE) association problem in WLAN/cellular integrated networks from game-theoretic perspective by taking into account throughput and power consumption. In the case of WLAN offloading, random uplink traffic from UEs inevitably brings conte...\",\n \"Title: Impairment- and Splitting-Aware Cloud-Ready Multicast Provisioning in Elastic Optical Networks\\nYear: 2017\\n\\nAbstract: It is known that multicast provisioning is important for supporting cloud-based applications, and as the traffics from these applications are increasing quickly, we may rely on optical networks to realize high-throughput multicast. Meanwhile, the flexible-grid elastic optical networks (EONs) achieve agile access to the massive bandwidth in optical fibers, and hence can provision variable bandwidths to adapt to the dynamic demands from the cloud-based applications. In this paper, we consider all-optical multicast in EONs in a practical manner and focus on designing impairment- and splitting-aware multicast provisioning schemes. We first study the procedure of adaptive modulation selection for a light-tree, and point out that the multicast scheme in EONs is fundamentally different from that in the fixed-grid wavelength-division multiplexing networks. Then, we formulate the problem of impairment- and splitting-aware routing, modulation and spectrum assignment (ISa-RMSA) for all-optical multicast in EONs and analyze its hardness. Next, we analyze the advantages brought by the flexibility of routing structures and discuss the ISa-RMSA schemes based on light-trees and light-forests. This paper suggests that for ISa-RMSA, the light-forest-based approach can use less bandwidth than the light-tree-based one, while still satisfying the quality of transmission requirement. Therefore, we establish the minimum light-forest problem for optimizing a light-forest in ISa-RMSA. Finally, we design several time-efficient ISa-RMSA algorithms, and prove that one of them can solve the minimum light-forest problem with a fixed approximation ratio.\",\n \"Title: SR-PVX: A Source Routing Based Network Virtualization Hypervisor to Enable POF-FIS Programmability in vSDNs.\\nYear: 2017\\n\\nAbstract: We design and implement a novel network virtualization hypervisor (NVH), which leverages source routing (SR) and provides network programmability based on the protocol-oblivious forwarding flow instruction set (POF-FIS), to realize protocol-independent virtual software-defined networks (vSDNs). The NVH, namely SR-PVX, uses a novel SR-based mechanism to ensure that the substrate switches can process packets from vSDNs with very small flow-table consumption. We design the network architecture, packet format, and packet processing procedure, and implement them to experimentally demonstrate that SR-PVX can control substrate POF switches to accomplish the SR-based vSDN slicing. We also perform an experiment to show that the POF-enabled vSDN created by SR-PVX can easily realize a stateful firewall with its POF-FIS programmability.\",\n \"Title: Protocol Oblivious Forwarding (POF): Software-Defined Networking with Enhanced Programmability.\\nYear: 2017\\n\\nAbstract: Software-defined networking separates the control and forwarding planes of a network to make it more programmable and application- aware. As one of the initial implementations of SDN, OpenFlow abstracts a forwarding device as a flow table and realizes flow processing by applying the \\\\\\\"match-and-act\\\\\\\" principle. However, the protocol-dependent nature of OpenFlow still limits the programmability of the forwarding plane. Hence, in this article, we discuss how to leverage protocol-oblivious forwarding (POF) to further enhance the network programmability such that the forwarding plane becomes protocol-independent and can be dynamically reprogrammed to support new protocol stacks seamlessly. We first review the development of OpenFlow and explain the motivations for introducing POF. Then we explain the working principle of POF, discuss our efforts on realizing the POF development ecosystem, and show our implementation of POF-based source routing as a novel use case. Finally, we elaborate on the first WAN-based POF network testbed that includes POF switches located in two cities in China.\",\n \"Title: Orchestrating data-intensive vNF service chains in inter-DC elastic optical networks\\nYear: 2017\\n\\nAbstract: We investigate the problem of data-intensive vNF service chain (vNF-SC) orchestration in inter-datacenter EONs. After analyzing the NP-hardness of this problem, we solve it in a sequential manner by optimizing both the request serving sequence and the data-intensive vNF-SC orchestration. Specifically, we propose a request sorting algorithm and a data-intensive vNF-SC orchestration algorithm based on dynamic programming to minimize the service completion time. We conduct simulations to evaluate the proposed algorithms, and simulation results verify their effectiveness.\",\n \"Title: On the Distance Spectrum Assignment in Elastic Optical Networks.\\nYear: 2017\\n\\nAbstract: In elastic optical networks, two lightpaths sharing common fiber links might have to be isolated in the spectrum domain with a proper guard-band to prevent crosstalk and/or reduce physical-layer security threats. Meanwhile, the actual requirements on guard-band sizes can vary for different lightpath pairs, because of various reasons. Therefore, in this paper, we consider the situation in which the...\",\n \"Title: Admm-Based Distributed Algorithm For Emergency Backup In Time-Variant Inter-Dc Networks\\nYear: 2017\\n\\nAbstract: This paper considers the emergency backup in an inter-datacenter (inter-DC) network whose topology is time-variant due to the progress of a disaster. We first transform the dynamic backup into a static flow problem through building a variable time-expanded network (V-TEN). Then, by considering both data utility and resource cost, we formulate an optimization to maximize the backup profit and leverage the alternating direction method of multipliers (ADMM) to design a time-efficient and distributed algorithm. Simulation results show that our ADMM-based algorithm outperforms several existing ones.\",\n \"Title: Embedding Virtual Software-Defined Networks Over Distributed Hypervisors For Vdc Formulation\\nYear: 2017\\n\\nAbstract: We study how to embed virtual software-defined networks (vSDNs) cost-effectively over a substrate network with distributed network virtualization hypervisors (NVHs) for virtual datacenter (vDC) formulation to support Big Data applications. Specifically, we try to jointly optimize the embedding schemes of the control and data planes of each vSDN, i.e., to minimize the data plane's resource consumption and limit the number of NVHs used in the control plane simultaneously. We first formulate an integer linear programming (ILP) model to solve the problem exactly, and then design a heuristic to reduce the time complexity. Simulation results suggest that our proposed algorithms can embed vSDNs cost-effectively and significantly outperform the existing scheme based on global resource capacity (GRC).\",\n \"Title: Design and Demonstration of High-Throughput Protocol Oblivious Packet Forwarding to Support Software-Defined Vehicular Networks.\\nYear: 2017\\n\\nAbstract: Software-defined networking (SDN) is a promising technology that can resolve the challenges faced by vehicular networks. However, the OpenFlow-based SDN implementations can only provide a protocol-dependent data plane. This can restrict the effectiveness of software-defined vehicular networks, since special-purpose protocols that have not been standardized in OpenFlow specifications are used frequently in vehicular networks. To address this issue, this paper studies how to realize a protocol independent data plane by leveraging the protocol oblivious forwarding (POF). Specifically, we present the design of a software-based POF switch (PVS) that supports runtime protocol customization in principle. We implement PVS in a switch box, and propose a flow table management scheme to ensure high-throughput packet forwarding. The experimental results verify that PVS can achieve line-rate packet forwarding at 10 Gbps when the packets' size is 512 B, and the proposed flow table management scheme is effective.\",\n \"Title: On Dynamic Service Function Chain Deployment and Readjustment.\\nYear: 2017\\n\\nAbstract: Network function virtualization (NFV) is a promising technology to decouple the network functions from dedicated hardware elements, leading to the significant cost reduction in network service provisioning. As more and more users are trying to access their services wherever and whenever, we expect the NFV-related service function chains (SFCs) to be dynamic and adaptive, i.e., they can be readjust...\",\n \"Title: Leveraging Protocol-Oblivious Forwarding (POF) to Realize NFV-Assisted Mobility Management.\\nYear: 2017\\n\\nAbstract: In the Internet, to enable emerging network services such as e-Health to be delivered to numerous people anytime and anywhere, mobility management plays an important role. In this work, we leverage the protocol-oblivious forwarding (POF) to design a novel network system that can realize highly-efficient mobility management and overcome the drawbacks of existing approaches. Moreover, we explore the forwarding plane programmability provided by POF to realize dynamic virtual network function (vNF) deployment for traffic adaption. We implement our system and conduct experiments to verify that it functions well for NFV-assisted mobility management and achieves reduced handover latency and enhanced QoE.\",\n \"Title: Improving SDN Scalability With Protocol-Oblivious Source Routing: A System-Level Study.\\nYear: 2018\\n\\nAbstract: Software-defined networking (SDN) has been considered as a break-through technology for the next-generation Internet. It enables fine-grained flow control that can make networks more flexible and programmable. However, this might lead to scalability issues due to the possible flow state explosion in SDN switches. SDN-based source routing can reduce the volume of flow-tables significantly by encodi...\",\n \"Title: On vNF-SC deployment and task scheduling for bulk-data transfers in inter-DC EONs\\nYear: 2017\\n\\nAbstract: We study how to deploy vNF service chains (vNF-SCs) and perform task scheduling for bulk-data transfers in inter-datacenter (inter-DC) elastic optical networks (EONs). We call this type of request a data-intensive vNF-SC request. According to the number of required vNF-SCs, the data-intensive vNF-SC requests are classified into linear and multi-branch data-intensive vNF-SC requests. For linear data-intensive vNF-SC requests, we propose a dynamic-programming-based vNF-SC deployment and task scheduling algorithm to minimize the average service completion time (SCT). For multi-branch data-intensive vNF-SC requests, we propose a correlation-aware vNF-SC deployment and task scheduling algorithm to minimize the average SCT, since there are multiple vNF-SC branches that are correlated and the SCT of a multi-branch data-intensive vNF-SC request is determined by the SCT of the latest vNF-SC branch. Simulation results verify that our algorithms can reduce the average SCTs of data-intensive vNF-SC requests of the two types effectively.\",\n \"Title: Evacuate Before Too Late: Distributed Backup in Inter-DC Networks with Progressive Disasters.\\nYear: 2018\\n\\nAbstract: Inter-datacenter (inter-DC) networks are essential for large enterprises to deliver high-quality services to end-users. Since DCs are vulnerable to natural disasters, an inter-DC network operator needs an effective emergency backup plan to evacuate the endangered data out in case of a progressive disaster whose status can be predicted by an early warning system. In this paper, we try to solve the ...\",\n \"Title: Profit-Aware Distributed Online Scheduling for Data-Oriented Tasks in Cloud Datacenters.\\nYear: 2018\\n\\nAbstract: As there is an increasing trend to deploy geographically distributed (geo-distributed) cloud datacenters (DCs), the scheduling of data-oriented tasks in such cloud DC systems becomes an appealing research topic. Specifically, it is challenging to achieve the distributed online scheduling that can handle the tasks' acceptance, data-transfers, and processing jointly and efficiently. In this paper, by considering the store-and-forward and anycast schemes, we formulate an optimization problem to maximize the time average profit from serving data-oriented tasks in a cloud DC system and then leverage the Lyapunov optimization techniques to propose an efficient scheduling algorithm, i.e., GlobalAny. We also extend the proposed algorithm by designing a data-transfer acceleration scheme to reduce the data-transfer latency. Extensive simulations verify that our algorithms can maximize the time-average profit in a distributed online manner. The results also indicate that GlobalAny and GlobalAny_Ext (i.e., GlobalAny with data-transfer acceleration) outperform several existing algorithms in terms of both time-average profit and computation time.\",\n \"Title: PVFlow: Flow-Table Virtualization in POF-based vSDN Hypervisor (PVX)\\nYear: 2018\\n\\nAbstract: It is known that by combining network virtualization with software-defined network (SDN), people can realize virtual SDNs (vSDNs) for enhanced programmability, adaptivity and cost-effectiveness. Meanwhile, protocol-oblivious forwarding (POF) can overcome the protocol-dependent restriction of Open-Flow and provide a more generic abstraction model of the forwarding elements in SDNs to realize a protocol-independent data plane. In this work, we design a flow-table virtualization module, i.e., PVFlow, to realize resource-efficient flow-table virtualization for POF-based vSDNs. To fully explore the flexibility of POF and support arbitrary matching fields in vSDNs' flow-tables, we propose the idea of “BigMatch”, which enables different flow-tables in vSDNs to share a matching stage in a substrate POF switch efficiently. We implement PVFlow in a POF-based network virtualization hypervisor (NVH) system, i.e., PVX, and verify its effectiveness on improving the efficiency of flow-table virtualization with experiments.\",\n \"Title: Application-Driven End-to-End Slicing: When Wireless Network Virtualization Orchestrates With NFV-Based Mobile Edge Computing.\\nYear: 2018\\n\\nAbstract: Recently, to adapt to the various quality-of-service (QoS) requirements of emerging applications, application-driven network slicing has attracted intensive interests. In this paper, we apply the idea of software-defined wireless network virtualization (WiNV) to WiFi networks and design and demonstrate a novel network system, namely, ADE(2)WiNFV. The proposed system can orchestrate software-defined WiNV with network-function-virtualization-based mobile edge computing (MEC) to realize application-driven end-to-end (E2E) slicing over heterogeneous wireline/wireless networks. Our experimental demonstrations verify that ADE(2)WiNFV can realize application-aware E2E slices on demand, each of which contains not only guaranteed E2E bandwidth resources (i.e., in the forms of virtual links, virtual switches, and virtual access points) but also isolated IT resources (i.e., in the form of virtual network functions) to carry specific applications with QoS guarantees.\",\n \"Title: Availability-aware service provisioning in SD-EON-based inter-datacenter networks\\nYear: 2016\\n\\nAbstract: The tremendous transmission capacity and flexible spectrum allocation scheme make elastic optical networks (EONs) one of the most promising infrastructures for constructing the interconnection in datacenter (DC) networks. Meanwhile, as DC traffics exhibit highly dynamic and heterogeneous features, differentiated service provisioning schemes are desired. In this paper, we take the advantage of centralized network control and management provided by the software-defined elastic optical networks (SD-EONs) and investigate availability-aware service provisioning in SD-EON-based inter-datacenter (inter-DC) networks. We first describe the problem of availability-aware service provisioning in SD-EON-based inter-DC networks and present the theoretical analysis for service availability. Then, we propose an availability-aware service provisioning algorithm (ASP) that leverages different path protection schemes to satisfy different service availability requirements. A service downgrading (SD) strategy is also designed as a supplement of ASP to further improve its performance. Simulation results show that the proposed ASP-SD algorithm can effectively improve the spectrum efficiency without sacrificing availability.\",\n \"Title: Advances in Optical Communications Technologies.\\nYear: 2018\\n\\nAbstract: The articles in this special section address advancements in optical communications. The field of optical communications and networking technologies is gaining new momentum from artificial intelligence (AI) and quantum communications, while there has been continuous attention on emerging topics such as TWDM/ WDM passive optical networks (PONs), software-defined networking (SDN), and smart cities. ...\",\n \"Title: How the Network Topology, Traffic Distribution, and Routing Scheme Impact on the Spectrum Usage in Elastic Optical Networks.\\nYear: 2018\\n\\nAbstract: Elastic Optical Network (EON) has been considered as a promising optical networking technology to architect the next-generation backbone networks. Routing and Spectrum Assignment (RSA) is the fundamental problem in EONs to realize service provisioning. Generally, the RSA is solved by routing the requests with lightpaths first and then assigning spectrum resources to the lightpaths to optimize the spectrum usage. Thus, the spectrum assignment explicitly decide the final spectrum usage of EONs. However, besides the spectrum assignment, there are three other factors, the network topology, traffic distribution and routing scheme, implicitly impact on the spectrum usage. Few related work involves in the implicit impact mechanism. In this paper, we aim to provide a thoroughly theoretical analysis on the impact of the three key factors on the spectrum usage. To this end, two theoretical chains are proposed: (1) The optimal spectrum usage can be measured by the chromatic number of the conflict graph, which is positively correlated to the intersecting probability, emph{i.e.}, the smaller the intersecting probability, the smaller the optimal spectrum usage; (2) The intersecting probability is decided by the network topology, traffic distribution and routing scheme via a quadratic programming parameterized with a matrix of conflict coefficients. The effectiveness of our theoretical analysis has been validated by extensive numerical results. Meanwhile, our theoretical deductions also permit to give several constant approximation ratios for RSA algorithms.\",\n \"Title: SPARSE - Privacy-Aware and Collusion Resistant Location Proof Generation and Verification.\\nYear: 2018\\n\\nAbstract: Recently, there has been an increase in the number of location-based services and applications. It is common for these applications to provide facilities or rewards for users who visit specific venues frequently. This creates the incentive for dishonest users to lie about their location and submit fake check- ins by changing their GPS data. To solve this issue, different distributed location proof schemes have been proposed to generate location proofs for mobile users. However, these schemes have some drawbacks: (1) they are vulnerable to either Prover-Prover or Prover-Witness collusions, (2) the location proof generation process is slow when users adopt a long private key, and (3) their implementation requires some hardware changes on mobile devices. To address these issues, we propose the Secure, Privacy-Aware and collusion Resistant poSition vErification (SPARSE) scheme to generate private location proofs for mobile users. SPARSE has a distributed architecture designed for ad-hoc scenarios in which mobile users generate location proofs for each other. Since we do not integrate any distance bounding protocol into SPARSE, it becomes an easy-to-implement scheme in which the location proof generation process is independent of the length of the users' private key. We provide a comprehensive security analysis and simulation which show that SPARSE provides privacy protection as well as security properties for users including integrity, unforgeability and non-transferability of the location proofs. Moreover, it achieves a highly reliable performance against collusions.\",\n \"Title: Virtualization of Table Resources in Programmable Data Plane with Global Consideration.\\nYear: 2018\\n\\nAbstract: In this work, we try to address the problem of memory fragmentation in ternary content addressable memory (TCAM) in programmable data plane (PDP), by designing and implementing a novel network hypervisor for PDP, namely, TPVX. TPVX realizes the virtualization of table resources in PDP with global consideration, i.e., when mapping tenant flow tables to physical switches, TPVX considers their table sizes and the pre-formatted sub-tables in the physical network to improve TCAM utilization and avoid memory fragmentation. Our experimental results verify that with TPVX, the utilization of the table resources in PDP can be improved dramatically and the extra processing latency due to the newly-introduced overheads can be maintained well simultaneously.\",\n \"Title: Economics of Multi-Domain Software-Defined EONs - Games Among Brokers.\\nYear: 2018\\n\\nAbstract: This paper discusses the network architecture and mechanisms used for advancing the economics of multi-domain software-defined elastic optical networks (SD-EONs) with the considerations of both noncooperative and cooperative games among the incentive-driven brokers. Specifically, in order to architect multi-domain SD-EONs such that the end-to-end light-path provisioning across multiple domains can be facilitated cost-effectively, we propose to introduce a management plane that contains incentive-driven brokers. As the brokers may compete or cooperate with each other to offer cross-domain lightpath provisioning services due to profits, we design and elaborate on the operation mechanisms for both incentive-driven broker competition (i.e., noncooperative gaming) and market-share based broker bargain (i.e., cooperative gaming). The experimental results that are obtained with an OpenFlow-based control plane SD-EON testbed are also presented to verify the cost-effectiveness of the proposed mechanisms.\",\n \"Title: Service Provisioning with Energy-Aware Regenerator Allocation in Multi-Domain EONs\\nYear: 2015\\n\\nAbstract: As multi-domain elastic optical networks (EONs) can enhance network scalability, extend service reach, and accommodate the inter-operability issues, it is very relevant to consider them in practical network operations. In this work, we study the problem of how to achieve energy-aware service provisioning in a multi-domain EON, where the optoelectronic regenerators only exist in border nodes. We consider dynamic lightpath requests and propose two algorithms, i.e., the greedy regenerator allocation (GRA) and the set-cover based regenerator allocation (STC), to realize the joint optimization of routing, modulation and spectrum assignment (RMSA) and regenerator allocation. The algorithms are evaluated with extensive simulations that use multi-domain EONs built with different regenerator placement strategies. The results verify that GRA and STC outperform the existing algorithm for energy-aware multi-domain service provisioning, and STC achieves the best performance in terms of both blocking probability and power efficiency.\",\n \"Title: On Incentive-Driven VNF Service Chaining in Inter-Datacenter Elastic Optical Networks: A Hierarchical Game-Theoretic Mechanism\\nYear: 2019\\n\\nAbstract: In this paper, we propose an incentive-driven virtual network function service chaining (VNF-SC) framework for optimizing the cross-stratum resource provisioning in multi-broker orchestrated inter-datacenter elastic optical networks (IDC-EONs). The proposed framework employs a non-cooperative hierarchical game-theoretic mechanism, where the resource brokers and the VNF-SC users play the leader and the follower games, respectively. In the leader game, the brokers calculate VNF-SC service schemes for users and compete for the provisioning tasks. While in the follower game, the users compete for VNF-SC services for jointly optimizing the resource cost and the received quality-of-service. We first elaborate on the modeling of the follower game, discuss the existence of Nash equilibrium and propose a mixed-strategy gaming approach enabled by an auxiliary graph-based algorithm to facilitate users selecting the most appropriate service schemes. Then, under the assumption that the brokers are aware of the principle of the follower game, we present the model for the leader game and develop a time-efficient heuristic algorithm for brokers to compete for the provisioning tasks. Simulations show that the proposed incentive-driven VNF-SC framework significantly improves the network throughput (i.e., \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$>4.8 \\\\times$ </tex-math></inline-formula>\\n blocking reduction) while assisting users and brokers in achieving higher utilities compared with existing solutions.\",\n \"Title: Forecast-Assisted NFV Service Chain Deployment Based on Affiliation-Aware vNF Placement.\\nYear: 2016\\n\\nAbstract: This paper studies the problem of service chain (SC) deployment. Specifically, we try to place virtual network functions (vNFs) on network nodes and connect the vNFs in sequence through link mapping. We start with the offline problem. An integer linear programming (ILP) model is formulated to minimize the total SC deployment cost. With the ILP, we prove that the offline problem is NP-hard and propose a time-efficient heuristic based on affiliation-aware vNF placement. Then, we move to the online problem, and design a forecast-assisted online SC deployment algorithm that includes the prediction of future vNF requirements. Simulation results show that the online algorithms can reduce the blocking probability of SC requests and increase the service provider's profit from SC deployment effectively.\",\n \"Title: On Efficient Incentive-Driven Vnf Service Chain Provisioning With Mixed-Strategy Gaming In Broker-Based Eo-Idcns\\nYear: 2017\\n\\nAbstract: We propose to realize incentive-driven virtual network function service chain provisioning in broker-based elastic optical inter-datacenter networks with mixed-strategy gaining and design a heuristic to find the near-equilibrium solutions. Simulation results verify both the effectiveness and stability of the proposed approach.\",\n \"Title: Demonstration Of Survivable Vsd-Eon Slicing With Automatic Data Plane Restoration To Support Reliable Video Streaming\\nYear: 2017\\n\\nAbstract: We design and experimentally demonstrate a network slicing system which can not only construct vSD-EONs dynamically for upper-layer applications but also recover their data plane services automatically and timely during substrate link failures.\",\n \"Title: Cost-Efficient Multi-Layer Restoration To Address Ip Router Outages In Ip-Over-Eons\\nYear: 2017\\n\\nAbstract: We study how to address the IP router outages in an IP-over-EON with multi-layer restoration (MLR), and propose an auxiliary-graph (AG) based scheme that can minimize the additional OPEX of MLR with the help of the spectrum expansion capability of sliceable bandwidth variable transponders (SBV-Ts)\",\n \"Title: Experimental Demonstration Of Cognitive Provisioning And Alien Wavelength Monitoring In Multi-Domain Eon\\nYear: 2018\\n\\nAbstract: This paper proposes a cognitive multi-domain EON architecture with machine-learning aided RMSA and alien wavelength monitoring. Testbed experiments show modulation format recognition, QoT monitoring and cognitive routing for a 160 GBd alien multi-wavelength lightpath.\",\n \"Title: Leveraging Predictive Analytics To Achieve Knowledge-Defined Orchestration In A Hybrid Optical/Electrical Dc Network: Collaborative Forecasting And Decision Making\\nYear: 2018\\n\\nAbstract: We design and experimentally demonstrate a hybrid optical/electrical DC network that achieves knowledge-defi ned orchestration with two collaborative machine learning modules.\",\n \"Title: On Multi-Layer Restoration In Optical Networks With Encryption Solution Deployment\\nYear: 2018\\n\\nAbstract: We consider the scenario in which an optical network with encryption solution deployment can he affected by electrical layer failures, and propose an algorithm to improve the cost-effectiveness of multi-layer restoration in it.\",\n \"Title: The First Testbed Demonstration Of Cognitive End-To-End Optical Service Provisioning With Hierarchical Learning Across Multiple Autonomous Systems\\nYear: 2018\\n\\nAbstract: We demonstrate the first experimental testbed with a hierarchical machine-learning network management framework for impairment-aware end-to-end elastic optical RMSA service provisioning across multi autonomous domains with QoT estimation deviations below 10%. (C) 2018 The Authors(s)\",\n \"Title: Realizing Highly-Available, Scalable, and Protocol-Independent vSDN Slicing With a Distributed Network Hypervisor System.\\nYear: 2018\\n\\nAbstract: In this paper, we design and implement a distributed network virtualization hypervisor (NVH) system, namely, DPVisor, which can provide superior network programmability based on protocol-oblivious forwarding to realize highly-available, scalable, and protocol-independent virtual software-defined network slicing. The experimental comparisons indicate that DPVisor achieves comparable performance as ONVisor (i.e., an OpenFlow-based ONOS benchmark), in terms of the message processing latency, message processing throughput, and failure recovery time. Moreover, to optimize the performance of the distributed NVH systems, we carefully adjust the consistency model used in the state synchronization of NVH instances and propose a local cache-based scheme to balance the tradeoff between the consistency and availability of network status. Our experimental results confirm that the message processing throughput of the distributed NVH systems can be greatly improved (i.e., for both DPVisor and ONVisor), while the data consistency among NVH instances is still maintained well.\",\n \"Title: Deep-Rmsa: A Deep-Reinforcement-Learning Routing, Modulation And Spectrum Assignment Agent For Elastic Optical Networks\\nYear: 2018\\n\\nAbstract: This paper demonstrates Deep-RMSA, a deep reinforcement learning based self-learning RMSA agent that can learn successful policies from dynamic network operations while realizing cognitive and autonomous RMSA in EONs.\",\n \"Title: Realizing AI-AssistedMulti-Layer Restoration in a Software-Defined IP-over-EON with Deep Learning: An Experimental Study\\nYear: 2018\\n\\nAbstract: By using deep learning, we experimentally demonstrate AI-assisted multi-layer restoration in an IP-over-EON, which recovers affected traffic timely with congestion-avoidance rerouting.\",\n \"Title: Multi-Agent Deep Reinforcement Learning in Cognitive Inter-Domain Networking with Multi-Broker Orchestration\\nYear: 2019\\n\\nAbstract: This paper proposes, for the first time, a cognitive inter-domain networking framework with multi-broker orchestration and multi-agent deep reinforcement learning for multi-domain optical networks. Simulation results show > 17% blocking reduction compared to the baselines.\",\n \"Title: Building Network Nervous System with Multilayer Telemetry to Realize AI-Assisted Reflexes in Software-Defined IP-Over-EONs for Application-Aware Service Provisioning\\nYear: 2019\\n\\nAbstract: We design and experimentally demonstrate a network nervous system that can leverage multilayer telemetry to realize artificial intelligence (AI) assisted network reconfigurations (i.e., reflexes) in a software-defined IP over elastic optical network, for application-aware provisioning.\",\n \"Title: Advances in Optical Communications and Networking Technologies\\nYear: 2019\\n\\nAbstract: The articles in this special section focus on advancements in optical communications and networking technologies (OCNS). OCNS has been playing an increasingly important role in interconnecting a large variety of IT resources over globally distributed autonomous network systems, to facilitate cost-effective information exchange and data processing. In 2018, with the emergence of fifth generation (5G)-oriented optical transmission and access networks, we witnessed optical communication networks becoming more adaptive and reconfigurable to support dynamic optical service provisioning, protection, and restoration, and to deliver short time to market and cost-effective services and solutions. Following this trend, we expect to observe continued expansion of optical transmission and access networks and faster convergence of optical and wireless infrastructures in 2019.\",\n \"Title: On Throughput Optimization and Bound Analysis in Cache-Enabled Fiber-Wireless Networks\\nYear: 2019\\n\\nAbstract: With the dense deployment of millimeter wave (mmWave) front ends and popularization of bandwidth-intensive applications, shared backhaul in fiber-wireless (FiWi) networks is still facing a bandwidth crunch. To alleviate the backhaul pressure, in this paper, caching capability is enabled at the edge of FiWi networks, i.e., optical network unit access points (ONU-APs). On the other hand, as both power budget and backhaul bandwidth in FiWi networks are constrained, it is challenging to properly leverage power for caching and that for wireless transmission to achieve superior system performance. As caching has a significant impact on resource allocation, we reconsider performance optimization and analysis in cache-enabled FiWi networks. Firstly, in the cache-enabled FiWi network with mmWave, we formulate the joint power allocation and caching problem, with the goal to maximize the downlink throughput. A two-stage algorithm is then proposed to solve the problem. Secondly, to investigate the theoretical capacity of the cache-enabled FiWi network with mmWave, we derive an upper bound of the downlink throughput by analyzing properties of the average rate of wireless links. Particularly, we show that appropriate power allocation for wireless transmission and caching at ONU-APs is essential to achieve higher throughput. The numerical and simulation results validate our theoretical analysis and demonstrate the proposed algorithm can approach the analytical upper bound.\",\n \"Title: Proactive and Hitless vSDN Reconfiguration to Balance Substrate TCAM Utilization: From Algorithm Design to System Prototype\\nYear: 2019\\n\\nAbstract: The combination of network virtualization and software-defined networking enables an infrastructure provider to create software-defined virtual networks (vSDNs) over a shared substrate network (SNT), for supporting new network services more timely and cost-effectively. Meanwhile, as both the services and traffic in the Internet are becoming more and more dynamic, how to properly maintain vSDNs in ...\",\n \"Title: DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks.\\nYear: 2019\\n\\nAbstract: This paper proposes DeepRMSA, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs). DeepRMSA learns the correct online RMSA policies by parameterizing the policies with deep neural networks (DNNs) that can sense complex EON states. The DNNs are trained with experiences of dynamic lightpath provisioning. We first modify the asynchronous advantage actor-critic algorithm and present an episode-based training mechanism for DeepRMSA, namely, DeepRMSA-EP. DeepRMSA-EP divides the dynamic provisioning process into multiple episodes (each containing the servicing of a fixed number of lightpath requests) and performs training by the end of each episode. The optimization target of DeepRMSA-EP at each step of servicing a request is to maximize the cumulative reward within the rest of the episode. Thus, we obviate the need for estimating the rewards related to unknown future states. To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i.e., DeepRMSA-FLX. DeepRMSA-FLX attempts to smooth out the oscillations by defining the optimization scope at each step as a sliding window, and ensuring that the cumulative rewards always include rewards from a fixed number of requests. Evaluations with the two sample topologies show that DeepRMSA-FLX can effectively stabilize the training while achieving blocking probability reductions of more than 20.3% and 14.3%, when compared with the baselines.\",\n \"Title: Energy-Efficient WLANs With Resource and Re-Association Scheduling Optimization.\\nYear: 2019\\n\\nAbstract: Recently, a number of WiFi access points (APs) have been densely deployed to provide widely available, high-performance Internet services. As such, an energy efficiency issue becomes crucial toward the design of green wireless local area networks (WLANs). In this paper, we propose a resource and re-association scheduling algorithm (referred to RAS) based on Benders' decomposition to reduce the ene...\",\n \"Title: Visualize Your IP-Over-Optical Network in Realtime: A P4-Based Flexible Multilayer In-Band Network Telemetry (ML-INT) System.\\nYear: 2019\\n\\nAbstract: Nowadays, with the fast development of backbone networks, performance monitoring and troubleshooting on IP-over-optical networks have become increasingly important. However, a real-time monitoring scheme, which is programmable to reveal the end-to-end multilayer information of an arbitrary flow in an IP-over-optical network, is still absent. Also, for such a monitoring scheme, how to balance the tradeoff between the accuracy and the overhead has not been studied yet. To address these challenges, we design a P4-based flexible multilayer in-band network telemetry (ML-INT) system to visualize an IP-over-optical network in real time. Specifically, the flexible ML-INT scheme only selects a small portion of packets in an IP flow to encode INT headers, while each INT header only contains a part of the statistics of all the electrical/optical network elements (NEs) on the flow's routing path. We design the packet processing pipelines for the scheme, program P4-based hardware programmable data-plane (PDP) switches to implement the pipelines, develop the optical performance monitors that can cooperate with the PDP switches to facilitate ML-INT, and implement a high-performance data analyzer that can extract, parse, and analyze the INT data carried by the high-speed IP flows [i.e., with an arrival rate up to 2 million packets per second (Mp/s)]. The whole flexible ML-INT system is experimentally demonstrated in a small-scale but real IP-over-optical network testbed. The experimental results verify that our proposal only introduces very small overhead and can make the IP-over-optical network more visible in real time for performance monitoring and troubleshooting.\",\n \"Title: Leveraging Heterogeneous NFV Platforms to Upgrade Service Function Chains in DCNs\\nYear: 2021\\n\\nAbstract: The advances on SmartNICs and programmable data plane (PDP) switches motivate people to deploy virtual network functions (vNFs) on these devices directly for better packet processing performance. Hence, this work considers how to leverage heterogeneous network function virtualization (NFV) platforms to upgrade the provisioning of vNF service chains (vNF-SCs) in a datacenter network (DCN). We design a novel time-efficient heuristic, to plan the service upgrade under a limited budget such that the QoS improvement on latency reduction can be maximized. Specifically, our algorithm first chooses nodes in the DCN to upgrade (i.e., by either equipping SmartNICs on certain servers or replacing switches with PDPSWs), and then adjusts vNF-SC provisioning accordingly.\",\n \"Title: Offloading NFV Orchestration to ToR Switches: How to Leverage PDP to Realize Agile Service Function Chaining in HOE-DCNs\\nYear: 2021\\n\\nAbstract: The advances on hybrid optical/electrical DCNs (HOE-DCNs) make it easier to support network function virtualization (NFV) with various quality-of-service (QoS) demands. However, the existing approaches for NFV orchestration rely heavily on the control plane, which might lead to scalability and performance issues. In this work, we propose to offload certain network orchestration tasks from the control plane to top-of-rack (ToR) switches, by leveraging the flexibility of programmable data plane (PDP). Specifically, we architect an HOE-DCN whose ToR switches are PDP switches, design the network orchestration system for it, and program the ToR switches, such that they can make quick decisions locally to assist the control plane to provision the service chains of virtual network functions (vNFSCs) agilely. We prototype our proposal in a small-scale HOE-DCN testbed, and the experimental results show that without much involvement of the control plane, the ToR switches can make local decisions and readjust the provisioning schemes of vNF-SCs adaptively to satisfy their QoS demands.\",\n \"Title: Security-Aware Planning of Packet-Over-Optical Networks in Consideration of OTN Encryption\\nYear: 2021\\n\\nAbstract: The fast development of cloud computing and Big Data applications has promoted virtualization technologies such as network function virtualization (NFV), which in turn dramatically increased the amount of sensitive data being transmitted over the optical networks for datacenter interconnections (DCIs). To ensure the physical-layer security in DCIs, people have developed optical transport network (...\",\n \"Title: Series Editorial: Optical Communications and Networks\\nYear: 2021\\n\\nAbstract: In this second issue of the Optical Communications and Networks Series in 2021, we have selected three articles that present recent advances in optical communications and networking. These advances and innovations are aimed at making the optical infrastructure of future Internet more secure, energy-efficient, and intelligent.\",\n \"Title: Guest Editorial Latest Advances in Optical Networks for 5G Communications and Beyond\\nYear: 2021\\n\\nAbstract: This Special Issue contains a collection of outstanding papers covering several recent advances in optical networks for 5G communications and beyond. Papers are organized into four categories: network resource planning; optical access networks; optical fronthaul solutions; and autonomous and data-driven network management. In this introduction, a brief overview of the field is given, followed by a summary of the seventeen papers of this Special Issue, and a discussion of future directions in the field.\",\n \"Title: Disaster Protection in Inter-DataCenter Networks Leveraging Cooperative Storage\\nYear: 2021\\n\\nAbstract: Natural disasters have challenged the survivability of Elastic Optical Inter-DataCenter Networks (EO-DCNs), and it is urgent to establish efficient disaster protection schemes. In this paper, we investigate the disaster-resilient service provisioning problem leveraging cooperative storage system (CSS). Instead of mirrored content backup on a single DC, our proposed CSS partitions a required conten...\",\n \"Title: Closed-Loop Network Automation With Generic Programmable Data Plane (G-Pdp)\\nYear: 2021\\n\\nAbstract: In this work, we try to combine software-defined networking (SDN), in-band network telemetry (INT), and data analytics to realize a novel closed-loop network automation system. To architect the data plane, we jointly consider P4-based and protocol-oblivious forwarding (POF) based programmable data plane switches (PDP-SWs) to build a generic programmable data plane (G-PDP), such that the two types of PDP-SWs can benefit each other mutually to overcome their own drawbacks. In the control plane, we expand ONOS to ensure that it can effectively manage the PDP-SWs in the G-PDP. We also design and implement data analyzers (DAs) in its data analytics subsystem, and deploy them distributedly in the G-PDP to alleviate the burden of data processing in the control plane. Our proposal is demonstrated experimentally with a network system prototype that consists of six PDP-SWs, and the results confirm that it can realize closed-loop network automation effectively and balance the tradeoff between flexibility and performance properly.\",\n \"Title: Series Editorial: Optical Communications and Networks\\nYear: 2021\\n\\nAbstract: In 2021, as optical technologies continue to demonstrate their advantages in telecom and data-center networks, we are also witnessing the attractive and promising progress on free-space optical communications and new technologies for fiber optic communications. Therefore, in this last issue of the Optical Communications and Networks Series in 2021, we have selected three contributions that address...\",\n \"Title: Cognitive Control-Loop for Elastic Optical Networks with Space-Division Multiplexing\\nYear: 2021\\n\\nAbstract: This paper introduces a new solution to improve network performance by decreasing spectrum fragmentation, crosstalk interference, blocking of virtual networks, cost, and link load imbalance. These problems degrade the performance of Elastic Optical Networks with Space-Division Multiplexing. The proposed solution, called Cognitive control loop (CO-OP), is capable of identifying a set of problems and creating plans to mitigate these problems. The CO-OP comprises four functions that employ learning algorithms to identify problems and plan a series of actions to reduce or eliminate them. The results show that the CO-OP can effectively decrease up to 30% the blocking of requests and up to 50% the crosstalk occurrence compared to existing algorithms.\",\n \"Title: On the Upgrade of Service Function Chains With Heterogeneous NFV Platforms\\nYear: 2021\\n\\nAbstract: The fast development of high-performance and flexible SmartNICs and programmable data plane switches (PDP-SWs) has motivated people to consider the deployment of virtual network functions (vNFs) on them. Hence, together with traditional virtual machines (VMs), SmartNICs and PDP-SWs form heterogeneous network function virtualization (NFV) platforms for realizing vNF service chains (vNF-SCs). In this work, we consider the transition from software-based homogeneous NFV platforms to the heterogeneous ones, and study how to optimize the service upgrade of vNF-SCs. Specifically, the service upgrade is divided into two steps, which are 1) selecting servers/switches in the substrate network (SNT) to upgrade, which is done by adding SmartNICs to servers and replacing traditional switches with PDP-SWs, under a fixed budget, and 2) redeploying the existing vNF-SCs in the updated SNT to maximize the quality-of-service (QoS) improvement on latency reductions. We first formulate an integer linear programming (ILP) model to optimize the overall service upgrade, then design two correlated optimizations for its two steps, and finally propose polynomial-time approximation algorithms to solve the optimizations. The results of extensive simulations confirm that our proposed algorithm outperforms the existing benchmarks in various network scenarios, and achieves better tradeoff between performance and time-efficiency.\",\n \"Title: Multi-Agent and Cooperative Deep Reinforcement Learning for Scalable Network Automation in Multi-Domain SD-EONs\\nYear: 2021\\n\\nAbstract: The service provisioning in multi-domain software-defined elastic optical networks (SD-EONs) is an interesting but difficult problem to tackle, because the basic problem of lightpath provisioning, i.e., the routing and spectrum assignment (RSA), is \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$\\\\mathcal {NP}$ </tex-math></inline-formula>\\n-hard, and each domain is owned and operated by a different carrier. Therefore, even though numerous RSA heuristics have been proposed, there does not exist a universal winner that can always achieve the lowest blocking probability in all the scenarios of a multi-domain SD-EON. This motivates us to revisit the inter-domain provisioning problem in this paper by leveraging deep reinforcement learning (DRL). Specifically, we propose DeepCoop, which is an inter-domain service framework that uses multiple cooperative DRL agents to achieve scalable network automation in a multi-domain SD-EON. DeepCoop employs a DRL agent in each domain to optimize intra-domain service provisioning, while a domain-level path computation element (PCE) is introduced to obtain the sequence of the domains to go through for each lightpath request. By sharing a restricted amount of information among each other, the DRL agents can make their decisions distributedly. To ensure scalability and universality, we design the action space of each DRL agent based on well-known RSA heuristics, and architect the agents based on the soft actor-critic (SAC) scenario. We run extensive simulations to evaluate DeepCoop, and the results show that DeepCoop can adapt to the dynamic environment in a multi-domain SD-EON to always select the best RSA heuristic for minimizing blocking probability, and it outperforms the existing algorithms on inter-domain provisioning in various scenarios. Moreover, we verify that the distributed training implemented in DeepCoop ensures its universality and scalability (i.e., its training and operation do not depend on the topology of the SD-EON).\",\n \"Title: Edge-cloud Collaborative Heterogeneous Task Scheduling in Multilayer Elastic Optical Networks\\nYear: 2021\\n\\nAbstract: With the explosive growth of edge applications in the 5G/B5G era, edge-cloud collaboration (ECC) is playing a prominent role in edge service provisioning. For highly diversified edge-cloud collaborative services (ECSs), the joint allocation of heterogeneous computing resources in heterogeneous servers and multi-dimensional underlying optical network resources should be conducted. In this paper, we investigate the heterogeneous task scheduling for ECSs over multilayer elastic optical network (ML-EON), which involves the joint allocation of heterogeneous computing resources in edge and cloud servers and high-dimensional network resources. We propose a Task-Node Matching Score (TNMS) based method, which evaluates the fitness for each mapping tuple between each task in ECS and each substrate node in ML-EON, and adaptively generates a specific matching score for each task-node pair. Furthermore, TNMS is extended with a pre-allocation mechanism (TNMS-Pre) to estimate the costs of multi-dimensional resources in ML-EON for virtual link (VL) mapping. The estimated VL mapping costs are integrated into the matching scores to guide the task placement to be cost-efficient. To guarantee the feasibility, a maximal weight matching (MWM) based method is presented to determine the task placement schemes. Simulation results demonstrate the effectiveness of the adaptive scoring for heterogeneous task placement and the pre-allocation mechanism for reducing the ML-EON costs.\",\n \"Title: On the Bilevel Optimization for Remapping Virtual Networks in an HOE-DCN\\nYear: 2022\\n\\nAbstract: Hybrid optical/electrical datacenter network (HOE-DCN) uses the inter-rack networks that consist of both electrical Ethernet switches and optical cross-connects (OXCs), for better cost-efficiency and scalability. Meanwhile, to provision dynamic network services well, the operator of an HOE-DCN needs to deploy virtual networks (VNTs) and remap them adaptively. Therefore, this work studies the problem of VNT remapping in an HOE-DCN from a novel perspective, i.e., the remapping schemes should be optimized for not only the network status after the remapping but also the transition to realize it. Specifically, we model this problem as a bilevel optimization, where the upper-level optimization aims at selecting proper virtual machines (VMs) to migrate such that the estimated latency of VM migration can be minimized, and the lower-level optimization determines the actual scheme of VNT remapping for minimizing the number of resource hot-spots. We first formulate a bilevel mixed integer linear programming (BMILP) model for the bilevel optimization, and then propose a polynomial time algorithm based on enumeration to solve it approximately. Extensive simulations verify the effectiveness of our proposal.\",\n \"Title: To Cooperate or Not to Cooperate: Service Function Chaining in Multi-Domain Edge-Cloud Elastic Optical Networks\\nYear: 2022\\n\\nAbstract: We study the the non-cooperative provisioning of service function chains in a multi-domain edge-cloud elastic optical network (EC-EON), leverage game theory to design an algorithm for it, and analyze its performance difference from the cooperative scheme with simulations.\",\n \"Title: Which can Accelerate Distributed Machine Learning Faster: Hybrid Optical/Electrical or Optical Reconfigurable DCN?\\nYear: 2022\\n\\nAbstract: We run various distributed machine learning (DML) architectures in a hybrid optical/electrical DCN and an optical DCN based on Hyper-FleX-LION. Experimental results show that Hyper-FleX-LION gains faster DML acceleration and improves acceleration ratio by up to 22.3%.\",\n \"Title: Dynamic Cross-Layer Restoration to Resolve Packet Layer Outages in FlexE-Over-EONs\\nYear: 2022\\n\\nAbstract: As a promising technology, Flex Ethernet (FlexE) helps to realize deterministic and ultra-low latency in metro and transport networks. Meanwhile, previous studies have confirmed the advantages of the symbiosis of FlexE and elastic optical network (EON) (i.e., a FlexE-over-EON) on resource utilization and cost-effectiveness. In this paper, we consider the cross-layer restoration (CLR) in FlexE-over-EONs based on the FlexE-aware architecture. Specifically, we address the situation where an outage happened on one FlexE switch in the packet layer to bring it offline temporarily and then the affected client flows need to be recovered quickly and proactively. Three CLR strategies are first proposed to fully explore the flexibility of FlexE-over-EON for restoring the affected flows. Then, with the strategies, we formulate an integer linear programming (ILP) model and design an auxiliary graph (AG) based algorithm to reroute the affected flows as well as minimize the additional operational expense (OPEX) incurred during the CLR. Extensive simulations verify the effectiveness of our proposed CLR algorithms.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":118,"string":"118"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13281,"string":"13,281"}}},{"rowIdx":13282,"cells":{"question_id":{"kind":"string","value":"Michael Georgiopoulos"},"question":{"kind":"string","value":"Generate an abstract for the title \"Learning Hash Function through Codewords.\" using the following items: 1. Hash learning \n2. Codewords \n3. Regularization term \n4. Block Coordinate Descent algorithm \n5. Content-based image retrieval"},"answer":{"kind":"string","value":"In this paper, we propose a novel hash learning approach that has the following main distinguishing features, when compared to past frameworks. First, the codewords are utilized in the Hamming space as ancillary techniques to accomplish its hash learning task. These codewords, which are inferred from the data, attempt to capture grouping aspects of the datau0027s hash codes. Furthermore, the proposed framework is capable of addressing supervised, unsupervised and, even, semi-supervised hash learning scenarios. Additionally, the framework adopts a regularization term over the codewords, which automatically chooses the codewords for the problem. To efficiently solve the problem, one Block Coordinate Descent algorithm is showcased in the paper. We also show that one step of the algorithms can be casted into several Support Vector Machine problems which enables our algorithms to utilize efficient software package. For the regularization term, a closed form solution of the proximal operator is provided in the paper. A series of comparative experiments focused on content-based image retrieval highlights its performance advantages."},"correct_answer":{"kind":"string","value":"In this paper, we propose a novel hash learning approach that has the following main distinguishing features, when compared to past frameworks. First, the codewords are utilized in the Hamming space as ancillary techniques to accomplish its hash learning task. These codewords, which are inferred from the data, attempt to capture grouping aspects of the datau0027s hash codes. Furthermore, the proposed framework is capable of addressing supervised, unsupervised and, even, semi-supervised hash learning scenarios. Additionally, the framework adopts a regularization term over the codewords, which automatically chooses the codewords for the problem. To efficiently solve the problem, one Block Coordinate Descent algorithm is showcased in the paper. We also show that one step of the algorithms can be casted into several Support Vector Machine problems which enables our algorithms to utilize efficient software package. For the regularization term, a closed form solution of the proximal operator is provided in the paper. A series of comparative experiments focused on content-based image retrieval highlights its performance advantages."},"documents":{"kind":"list like","value":["Title: Fuzzy ART properties\nYear: 1995\n\nAbstract: This paper presents some important properties of the Fuzzy ART neural network algorithm introduced by Carpenter, Grossberg, and Rosen. The properties described in the paper are distinguished into a number of categories. These include template, access, and reset properties, as well as properties related to the number of list presentations needed for weight stabilization. These properties provide numerous insights as to how Fuzzy ART operates. Furthermore, the effects of the Fuzzy ART parameters a and p on the functionality of the algorithm are clearly illustrated.","Title: Learning affine transformations\nYear: 1999\n\nAbstract: Under the assumption of weak perspective, two views of the same planar object are related through an affine transformation. In this paper, we consider the problem of training a simple neural network to learn to predict the parameters of the affine transformation. Although the proposed scheme has similarities with other neural network schemes, its practical advantages are more profound. First of all, the views used to train the neural network are not obtained by taking pictures of the object from different viewpoints. Instead, the training views are obtained by sampling the space of affine transformed views of the object. This space is constructed using a single view of the object. Fundamental to this procedure is a methodology, based on singular-value decomposition (SVD) and interval arithmetic (IA), for estimating the ranges of values that the parameters of affine transformation can assume. Second, the accuracy of the proposed scheme is very close to that of a traditional least squares approach with slightly better space and time requirements. A front-end stage to the neural network, based on principal components analysis (PCA), shows to increase its noise tolerance dramatically and also to guides us in deciding how many training views are necessary in order for the network to learn a good, noise tolerant, mapping. The proposed approach has been tested using both artificial and real data.","Title: Order of search in fuzzy ART and fuzzy ARTMAP: effect of the choice parameter\nYear: 1996\n\nAbstract: This paper focuses on two ART architectures, the Fuzzy ART and the Fuzzy ARTMAP. Fuzzy ART is a pattern clustering machine, while Fuzzy ARTMAP is a pattern classification machine. Our study concentrates on the order according to which categories in Fuzzy ART, or the ART a model of Fuzzy ARTMAP are chosen. Our work provides a geometrical, and clearer understanding of why, and in what order, these categories are chosen for various ranges of the choice parameter of the Fuzzy ART module. This understanding serves as a powerful tool in developing properties of learning pertaining to these neural network architectures; to strengthen this argument, it is worth mentioning that the order according to which categories are chosen in ART 1 and ARTMAP provided a valuable tool in proving important properties about these architectures. Copyright © 1996 Elsevier Science Ltd.","Title: APHID: An architecture for private, high-performance integrated data mining\nYear: 2010\n\nAbstract: While the emerging field of privacy preserving data mining (PPDM) will enable many new data mining applications, it suffers from several practical difficulties. PPDM algorithms are challenging to develop and computationally intensive to execute. Developers need convenient abstractions to simplify the engineering of PPDM applications. The individual parties involved in the data mining process need a way to bring high-performance, parallel computers to bear on the computationally intensive parts of the PPDM tasks. This paper discusses APHID (Architecture for Private and High-performance Integrated Data mining), a practical architecture and software framework for developing and executing large scale PPDM applications. At one tier, the system supports simplified use of cluster and grid resources, and at another tier, the system abstracts communication for easy PPDM algorithm development. This paper offers a detailed analysis of the challenges in developing PPDM algorithms with existing frameworks, and motivates the design of a new infrastructure based on these challenges.","Title: Fast parallel outlier detection for categorical datasets using MapReduce\nYear: 2008\n\nAbstract: Outlier detection has received considerable attention in many applications, such as detecting network attacks or credit card fraud. The massive datasets currently available for mining in some of these outlier detection applications require large parallel systems, and consequently parallelizable outlier detection methods. Most existing outlier detection methods assume that all of the attributes of a dataset are numerical, usually have a quadratic time complexity with respect to the number of points in the dataset, and quite often they require multiple dataset scans. In this paper, we propose a fast parallel outlier detection strategy based on the Attribute Value Frequency (AVF) approach, a high-speed, scalable outlier detection method for categorical data that is inherently easy to parallelize. Our proposed solution, MR-AVF, is based on the MapReduce paradigm for parallel programming, which offers load balancing and fault tolerance. MR-AVF is particularly simple to develop and it is shown to be highly scalable with respect to the number of cluster nodes.","Title: Pipelining of Fuzzy ARTMAP without matchtracking: correctness, performance bound, and Beowulf evaluation.\nYear: 2007\n\nAbstract: Fuzzy ARTMAP neural networks have been proven to be good classifiers on a variety of classification problems. However, the time that Fuzzy ARTMAP takes to converge to a solution increases rapidly as the number of patterns used for training is increased. In this paper we examine the time Fuzzy ARTMAP takes to converge to a solution and we propose a coarse grain parallelization technique, based on a pipeline approach, to speed-up the training process. In particular, we have parallelized Fuzzy ARTMAP without the match-tracking mechanism. We provide a series of theorems and associated proofs that show the characteristics of Fuzzy ARTMAP's, without matchtracking, parallel implementation. Results run on a BEOWULF cluster with three large databases show linear speedup as a function of the number of processors used in the pipeline. The databases used for our experiments are the Forrest CoverType database from the UCI Machine Learning repository and two artificial databases, where the data generated were 16-dimensional Gaussian distributed data belonging to two distinct classes, with different amounts of overlap (5% and 15%).","Title: Gap-based estimation: choosing the smoothing parameters for probabilistic and general regression neural networks.\nYear: 2007\n\nAbstract: Probabilistic neural networks (PNN) and general regression neural networks (GRNN) represent knowledge by simple but interpretable models that approximate the optimal classifier or predictor in the sense of expected value of the accuracy. These models require the specification of an important smoothing parameter, which is usually chosen by cross-validation or clustering. In this article, we demonstrate the problems with the cross-validation and clustering approaches to specify the smoothing parameter, discuss the relationship between this parameter and some of the data statistics, and attempt to develop a fast approach to determine the optimal value of this parameter. Finally, through experimentation, we show that our approach, referred to as a gap-based estimation approach, is superior in speed to the compared approaches, including support vector machine, and yields good and stable accuracy.","Title: A Neural Net Associative Memory for Real-Time Applications.\nYear: 1990\n\nAbstract: A parallel hardware implementation of the associative memory neural network introduced by Hopfield is described. The design utilizes the Geometric Arithmetic Parallel Processor (GAPP), a commercially available single-chip VLSI general-purpose array processor consisting of 72 processing elements. The ability to cascade these chips allows large arrays of processors to be easily constructed and used to implement the Hopfield network. The memory requirements and processing times of such arrays are analyzed based on the number of nodes in the network and the number of exemplar patterns. Compared with other digital implementations, this design yields significant improvements in runtime performance and offers the capability of using large neural network associative memories in real-time applications.","Title: Accelerated learning of Generalized Sammon Mappings\nYear: 2011\n\nAbstract: The Sammon Mapping (SM) has established itself as a valuable tool in dimensionality reduction, manifold learning, exploratory data analysis and, particularly, in data visualization. The SM is capable of projecting high-dimensional data into a low-dimensional space, so that they can be visualized and interpreted. This is accomplished by representing inter-sample dissimilarities in the original space by Euclidean inter-sample distances in the projection space. Recently, Kernel Sammon Mapping (KSM) has been shown to subsume the SM and a few other related extensions to SM. Both of the aforementioned models feature a set of linear weights that are estimated via Iterative Majorization (IM). While IM is significantly faster than other standard gradient-based methods, tackling data sets of larger than moderate sizes becomes a challenging learning task, as IM's convergence significantly slows down with increasing data set cardinality. In this paper we derive two improved training algorithms based on Successive Over-Relaxation (SOR) and Parallel Tangents (PARTAN) acceleration, that, while still being first-order methods, exhibit faster convergence than IM. Both algorithms are relatively easy to understand, straightforward to implement and, performance-wise, are as robust as IM. We also present comparative results that illustrate their computational advantages on a set of benchmark problems.","Title: Efficient evolution of ART neural networks\nYear: 2008\n\nAbstract: Genetic algorithms have been used to evolve sev- eral neural network architectures. In a previous effort, we introduced the evolution of three well known ART architects; Fuzzy ARTMAP (FAM), Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM). The resulting architectures were shown to achieve competitive generalization and exceptionally small size. A major concern regarding these architectures, and any evolved neural network architecture in general, is the added overhead in terms of computational time needed to produce the finally evolved network. In this paper we investigate ways of reducing this computational overhead by reducing the computations needed for the calculation of the fitness value of the evolved ART architectures. The results obtained in this paper can be directly extended to many other evolutionary neural network architectures, beyond the studied evolution of ART neural network architectures.","Title: APHID: A Practical Architecture for High-Performance, Privacy-Preserving Data Mining\nYear: 2009\n\nAbstract: While the emerging field of privacy preserving data mining (PPDM) will enable many new data mining applications, it suffers from several practical difficulties. PPDM algorithms are difficult to develop and computationally intensive to execute. Developers need convenient abstractions to reduce the costs of engineering PPDM applications. The individual parties involved in the data mining process need a way to bring high- performance, parallel computers to bear on the computationally intensive parts of the PPDM tasks. This paper discusses APHID (Architecture for Private and High-performance Integrated Data mining), a practical software architecture for developing and executing large-scale PPDM applications. At one level, the system supports simplified use of cluster and grid resources, and at another level, the system abstracts communication for easy PPDM algorithm development. This paper offers an analysis of the challenges in developing PPDM algorithms with existing frameworks, and motivates the design of a new infrastructure based on these challenges. Index Terms—Privacy-Preserving Data Mining, Distributed Data Mining, Cluster Computing, MapReduce","Title: Boosted ARTMAP: Modifications to fuzzy ARTMAP motivated by boosting theory\nYear: 2006\n\nAbstract: In this paper, several modifications to the Fuzzy ARTMAP neural network architecture are proposed for conducting classification in complex, possibly noisy, environments. The goal of these modifications is to improve upon the generalization performance of Fuzzy ART-based neural networks, such as Fuzzy ARTMAP, in these situations. One of the major difficulties of employing Fuzzy ARTMAP on such learning problems involves over-fitting of the training data. Structural risk minimization is a machine-learning framework that addresses the issue of over-fitting by providing a backbone for analysis as well as an impetus for the design of better learning algorithms. The theory of structural risk minimization reveals a trade-off between training error and classifier complexity in reducing generalization error, which will be exploited in the learning algorithms proposed in this paper. Boosted ART extends Fuzzy ART by allowing the spatial extent of each cluster formed to be adjusted independently. Boosted ARTMAP generalizes upon Fuzzy ARTMAP by allowing non-zero training error in an effort to reduce the hypothesis complexity and hence improve overall generalization performance. Although Boosted ARTMAP is strictly speaking not a boosting algorithm, the changes it encompasses were motivated by the goals that one strives to achieve when employing boosting. Boosted ARTMAP is an on-line learner, it does not require excessive parameter tuning to operate, and it reduces precisely to Fuzzy ARTMAP for particular parameter values. Another architecture described in this paper is Structural Boosted ARTMAP, which uses both Boosted ART and Boosted ARTMAP to perform structural risk minimization learning. Structural Boosted ARTMAP will allow comparison of the capabilities of off-line versus on-line learning as well as empirical risk minimization versus structural risk minimization using Fuzzy ARTMAP-based neural network architectures. Both empirical and theoretical results are presented to enhance the understanding of these architectures.","Title: The N-N-N conjecture in ART1\nYear: 1992\n\nAbstract: In this paper we consider the ART1 neural network architecture introduced by Carpenter and Grossberg. In their original paper, Carpenter and Grossberg made the following conjecture: In the fast learning case, if the F 2 layer in ART1 has at least N nodes, then each member of a list of N input patterns presented cyclically at the F 1 layer of ART1 will have direct access to an F 2 layer node after at most N list presentations. In this paper, we demonstrate that the conjecture is not valid for certain large L values, where L is a network parameter associated with the adaptation of the bottom-up traces in ART1 . It is worth noting that previous work has shown the conjecture to be true for small L values.","Title: Using Algebraic Functions of Views for Indexing-Based Object Recognition\nYear: 1998\n\nAbstract: Current indexing-based approaches build the hash table using either a large number of reference views or 3D models. In this paper, we propose building the hash table using algebraic functions of views. During preprocessing, we consider groups of model points and we represent all the views (i.e., images) that they can produce in a hash table. These views are computed using algebraic functions of a small number of reference views which contain the group. Fundamental to this procedure is a methodology based on Singular Value Decomposition and Interval Arithmetic for estimating the ranges of values that the parameters of algebraic functions can assume. During recognition, scene groups are used to retrieve from the hash table the model groups that might have produced them. Using algebraic functions of views for indexing-based recognition offers a number of advantages. First of all, the hash table can be built easier, without requiring 3D models or a large number of reference views. Second, recognition does not rely on the similarities between new and reference views. Third, verification becomes simpler. Finally, the approach is more general and extendible.","Title: Kernel-based distance metric learning in the output space\nYear: 2013\n\nAbstract: In this paper we present two related, kernel-based Distance Metric Learning (DML) methods. Their respective models non-linearly map data from their original space to an output space, and subsequent distance measurements are performed in the output space via a Mahalanobis metric. The dimensionality of the output space can be directly controlled to facilitate the learning of a low-rank metric. Both methods allow for simultaneous inference of the associated metric and the mapping to the output space, which can be used to visualize the data, when the output space is 2-or 3-dimensional. Experimental results for a collection of classification tasks illustrate the advantages of the proposed methods over other traditional and kernel-based DML approaches.","Title: A winner-take-all methodology: finding the best evolutionary algorithm for the global optimization of functions\nYear: 2012\n\nAbstract: The problem of effectively and efficiently finding the global optimum of a function by using evolutionary algorithms is current and pertinent, and two of the evolutionary techniques that have received significant attention in the literature are Particle Swarm Optimization (PSO), and Differential Evolution (DE), as well as their numerous variants. One way of taking advantage of the many good PSO and DE variant algorithms that have appeared in the literature is to run them all for a particular optimization problem and choose the best answer provided. This approach, referred to as the Naive Approach (NA) is time consuming. In this paper, we are using the naive approach with a suite of algorithms for each function minimization problem and we run the algorithms, in the suite, for a specific number of function evaluations (typically much smaller than the number of function evaluations needed for each specific algorithm to converge to a solution) and decide, using appropriate performance measures of merit, which one of these algorithms will continue running until convergence; the rest of the algorithms, deemed as algorithms that will eventually produce inferior solutions, are aborted. We refer to our methodology, for obvious reasons, as the Winner-Take-All (WTA) methodology. Using this methodology we introduce WTA algorithmic variants that are efficient and effective solvers of global optimization problems. In this paper, we report results on one of these variants, called WTA1, and show that it is very competitive compared to the constituent algorithms in the suite used for its design, and efficient compared to NA.","Title: Experiments with an innovative tree pruning algorithm\nYear: 2007\n\nAbstract: The pruning phase is one of the necessary steps in decision tree induction. Existing pruning algorithms tend to have some or all of the following difficulties: 1) lack of theoretical support; 2) high computational complexity; 3) dependence on validation; 4) complicated implementation. The 2-norm pruning algorithm proposed here addresses all of the above difficulties. This paper demonstrates the experimental results of the comparison among the 2-norm pruning algorithm and two classical pruning algorithms, the Minimal Cost-Complexity algorithm (used in CART) and the Error-based pruning algorithm (used in C4.5), and confirms that the 2-norm pruning algorithm is superior in accuracy and speed.","Title: Efficient revised simplex method for SVM training.\nYear: 2011\n\nAbstract: Existing active set methods reported in the literature for support vector machine (SVM) training must contend with singularities when solving for the search direction. When a singularity is encountered, an infinite descent direction can be carefully chosen that avoids cycling and allows the algorithm to converge. However, the algorithm implementation is likely to be more complex and less computationally efficient than would otherwise be required for an algorithm that does not have to contend with the singularities. We show that the revised simplex method introduced by Rusin provides a guarantee of nonsingularity when solving for the search direction. This method provides for a simpler and more computationally efficient implementation, as it avoids the need to test for rank degeneracies and also the need to modify factorizations or solution methods based upon those rank degeneracies. In our approach, we take advantage of the guarantee of nonsingularity by implementing an efficient method for solving the search direction and show that our algorithm is competitive with SVM-QP and also that it is a particularly effective when the fraction of nonbound support vectors is large. In addition, we show competitive performance of the proposed algorithm against two popular SVM training algorithms, SVMLight and LIBSVM.","Title: A dictionary-based approach to fast and accurate name matching in large law enforcement databases\nYear: 2006\n\nAbstract: In the presence of dirty data, a search for specific information by a standard query (e.g., search for a name that is misspelled or mistyped) does not return all needed information. This is an issue of grave importance in homeland security, criminology, medical applications, GIS (geographic information systems) and so on. Different techniques, such as soundex, phonix, n-grams, edit-distance, have been used to improve the matching rate in these name-matching applications. There is a pressing need for name matching approaches that provide high levels of accuracy, while at the same time maintaining the computational complexity of achieving this goal reasonably low. In this paper, we present ANSWER, a name matching approach that utilizes a prefix-tree of available names in the database. Creating and searching the name dictionary tree is fast and accurate and, thus, ANSWER is superior to other techniques of retrieving fuzzy name matches in large databases.","Title: A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes\nYear: 2010\n\nAbstract: Outlier detection has attracted substantial attention in many applications and research areas; some of the most prominent applications are network intrusion detection or credit card fraud detection. Many of the existing approaches are based on calculating distances among the points in the dataset. These approaches cannot easily adapt to current datasets that usually contain a mix of categorical and continuous attributes, and may be distributed among different geographical locations. In addition, current datasets usually have a large number of dimensions. These datasets tend to be sparse, and traditional concepts such as Euclidean distance or nearest neighbor become unsuitable. We propose a fast distributed outlier detection strategy intended for datasets containing mixed attributes. The proposed method takes into consideration the sparseness of the dataset, and is experimentally shown to be highly scalable with the number of points and the number of attributes in the dataset. Experimental results show that the proposed outlier detection method compares very favorably with other state-of-the art outlier detection strategies proposed in the literature and that the speedup achieved by its distributed version is very close to linear.","Title: Generalized Entropy for Splitting on Numerical Attributes in Decision Trees\nYear: 2006\n\nAbstract: Decision Trees are well known for their training efficiency and their interpretable knowledge representation. They apply a greedy search and a divide-and-conquer approach to learn patterns. The greedy search is based on the evaluation criterion on the candidate splits at each node. Although research has been performed on various such criteria, there is no significant improvement from the classical split approaches introduced in the early decision tree literature. This paper presents a new evaluation rule to determine candidate splits in decision tree classifiers. The experiments show that this new evaluation rule reduces the size of the resulting tree, while maintaining the tree's accuracy.","Title: Direct-sequence Spread-Spectrum with Transform Domain Interference Suppression.\nYear: 1995\n\nAbstract: The performance of a direct-sequence spread-spectrum (DS-SS) receiver employing new techniques for multiple non-stationary interference suppression is presented. These techniques are based on transform domain order statistics and they selectively suppress spectral components that are sufficiently large and narrow. Other important advantages are also discussed. The bit error rate performance is determined by Monte-Carlo simulations and it is compared to the performance of fixed notch filtering and of the well-known LMS algorithm. The effect of using data windowing is examined, and results with single-tone, narrow-band, stationary, non-stationary and multiple interference are presented. A comparison of the amount of computation required by the proposed transform domain approach to that of the time-domain LMS algorithm is also presented.","Title: A Partitioned Fuzzy ARTMAP Implementation for Fast Processing of Large Databases on Sequential Machines\nYear: 2004\n\nAbstract: Fuzzy ARTMAP (FAM) is a neural network architec- ture that can establish the correct mapping between real valued input patterns and their correct labels. FAM can learn quickly compared to other neural network paradigms and has the advantage of incremental/online learning capabilities. Nevertheless FAM tends to slow down as the size of the data set grows. This problem is analyzed and a solution is proposed that can speed up the algorithm in sequential as well as parallel settings. Experimental results are presented that show a consid- erable improvement in speed of the algorithm at the cost of creating larger size FAM architectures. Directions for future work are also discussed.","Title: Data-partitioning using the Hilbert space filling curves: effect on the speed of convergence of Fuzzy ARTMAP for large database problems.\nYear: 2005\n\nAbstract: The Fuzzy ARTMAP algorithm has been proven to be one of the premier neural network architectures for classification problems. One of the properties of Fuzzy ARTMAP, which can be both an asset and a liability, is its capacity to produce new nodes (templates) on demand to represent classification categories. This property allows Fuzzy ARTMAP to automatically adapt to the database without having to a priori specify its network size. On the other hand, it has the undesirable side effect that large databases might produce a large network size (node proliferation) that can dramatically slow down the training speed of the algorithm. To address the slow convergence speed of Fuzzy ARTMAP for large database problems, we propose the use of space-filling curves, specifically the Hilbert space-filling curves (HSFC). Hilbert space-filling curves allow us to divide the problem into smaller sub-problems, each focusing on a smaller than the original dataset. For learning each partition of data, a different Fuzzy ARTMAP network is used. Through this divide-and-conquer approach we are avoiding the node proliferation problem, and consequently we speedup Fuzzy ARTMAP's training. Results have been produced for a two-class, 16-dimensional Gaussian data, and on the Forest database, available at the UCI repository. Our results indicate that the Hilbert space-filling curve approach reduces the time that it takes to train Fuzzy ARTMAP without affecting the generalization performance attained by Fuzzy ARTMAP trained on the original large dataset. Given that the resulting smaller datasets that the HSFC approach produces can independently be learned by different Fuzzy ARTMAP networks, we have also implemented and tested a parallel implementation of this approach on a Beowulf cluster of workstations that further speeds up Fuzzy ARTMAP's convergence to a solution for large database problems.","Title: Pareto-Path Multitask Multiple Kernel Learning\nYear: 2014\n\nAbstract: A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.","Title: A Connectionist-Symbolic Approach to Modeling Agent Behavior: Neural Networks Grouped by Contexts\nYear: 2001\n\nAbstract: A recent report by the National Research Council (NRC) declares neural networks \"hold the most promise for providing powerful learning models\". While some researchers have experimented with using neural networks to model battlefield behavior for Computer Generated Forces (CGF) systems used in distributed simulations, the NRC report indicates that further research is needed to develop a hybrid system that will integrate the newer neural network technology into the current rule-based paradigms. This paper supports this solicitation by examining the use of a context structure to modularly organize the application of neural networks to a low-level Semi-Automated Forces (SAF) reactive task. Specifically, it reports on the development of a neural network movement model and illustrates how its performance is improved through the use of the modular context paradigm. Further, this paper introduces the theory behind the neural networks' architecture and training algorithms as well as the specifics of how the networks were developed for this investigation. Lastly, it illustrates how the networks were integrated with SAF software, defines the networks' performance measures, presents the results of the scenarios considered in this investigation, and offers directions for future work.","Title: Putting the Utility of Match Tracking in Fuzzy ARTMAP Training to the Test\nYear: 2003\n\nAbstract: An integral component of Fuzzy ARTMAP's training phase is the use of Match Tracking (MT), whose functionality is to search for an appropriate category that will correctly classify a presented training pattern in case this particular pattern was originally misclassified. In this paper we explain the MT's role in detail, why it actually works and finally we put its usefulness to the test by comparing it to the simpler, faster alternative of not using MT at all during training. Finally, we present a series of experimental results that eventually raise questions about the MT's utility. More specifically, we show that in the absence of MT the resulting, trained FAM networks are of reasonable size and exhibit better generalization performance.","Title: Similarity kernels for nearest neighbor-based outlier detection\nYear: 2010\n\nAbstract: Outlier detection is an important research topic that focuses on detecting abnormal information in data sets and processes. This paper addresses the problem of determining which class of kernels should be used in a geometric framework for nearest neighbor-based outlier detection. It introduces the class of similarity kernels and employs it within that framework. We also propose the use of isotropic stationary kernels for the case of normed input spaces. Two definitions of similarity scores using kernels are given: the k-NN kernel similarity score (kNNSS) and the summation kernel similarity score (SKSS). The paper concludes with preliminary experimental results comparing the performance of kNNSS and SKSS for outlier detection on four data sets. SKSS compared favorably to kNNSS.","Title: A Scalable and Efficient Outlier Detection Strategy for Categorical Data\nYear: 2007\n\nAbstract: An important factor for discriminating between webpages is their genre (e.g., blogs, personal homepages, e-shops, online newspapers, etc). Webpage genre identification has a great potential in information retrieval since users of search engines can combine ...","Title: Classification Of Noisy Patterns Using Artmap-Based Neural Networks\nYear: 2000\n\nAbstract: In this paper we present a modification of the test phase of ARTMAP-based neural networks that improves the classification performance of the networks when the patterns that are used for classification are extracted from noisy signals. The signals that are considered in this work are textured images, which are a case of 2D signals. Two neural networks from the ARTMAP family are examined, namely the Fuzzy ARTMAP (FAM) neural network and the Hypersphere ARTMAP (HAM) neural network. We compare the original FAM and KAM architectures with the modified ones, which we name FAM-m and HAM-m respectively. We also compare the classification performance of the modified networks, and of the original networks when they are trained with patterns extracted from noisy textures. Finally, we illustrate how combination of features can improve the classification performance for both the noiseless and noisy textures.","Title: Learning in the feed-forward random neural network: A critical review\nYear: 2010\n\nAbstract: The Random Neural Network (RNN) has received, since its inception in 1989, considerable attention and has been successfully used in a number of applications. In this critical review paper we focus on the feed-forward RNN model and its ability to solve classification problems. In particular, we paid special attention to the RNN literature related with learning algorithms that discover the RNN interconnection weights, suggested other potential algorithms that can be used to find the RNN interconnection weights, and compared the RNN model with other neural-network based and non-neural network based classifier models. In review, the extensive literature review and experimentation with the RNN feed-forward model provided us with the necessary guidance to introduce six critical review comments that identify some gaps in the RNN's related literature and suggest directions for future research.","Title: Properties of learning in ARTMAP\nYear: 1994\n\nAbstract: In this paper we consider the ARTMAP architecture for situations requiring learning of many-to-one maps. It is shown that if ARTMAP is repeatedly presented with a list of input/output pairs, it establishes the required mapping in at most M a −1 list presentations, where M a corresponds to the total number of ones in each one of the input patterns. Other useful properties, associated with the learning of the mapping represented by an arbitrary list of input/output pairs, are also examined. These properties reveal some of the characteristics of learning in ARTMAP when it is used as a tool in establishing an arbitrary mapping from a binary input space to a binary output space. The results presented in this paper are valid for the fast learning case, and for small β a values, where β a is a parameter associated with the adaptation of bottom-up weights in one of the ART1 modules of ARTMAP.","Title: Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization\nYear: 1997\n\nAbstract: Recent theoretical results support that decreasing the number of free parameters in a neural network (i.e., weights) can improve generalization. These results have triggered the development of many approaches which try to determine an “appropriate” network size for a given problem. The main goal has been to find a network size just large enough to capture the general class properties of the data. In some cases, however, network size is not reduced significantly or the reduction is satisfactory but generalization is affected. In this paper, we propose the coupling of genetic algorithms with weight elimination. Our objective is not only to significantly reduce network size, by pruning larger size networks, but also to preserve generalization, that is, to come up with pruned networks which generalize as good or even better than their unpruned counterparts. The innovation of our work relies on a fitness function which uses an adaptive parameter to encourage reproduction of networks having small size and good generalization. The proposed approach has been tested using both artificial and real databases demonstrating good performance.","Title: A k-norm pruning algorithm for decision tree classifiers based on error rate estimation\nYear: 2008\n\nAbstract: Decision trees are well-known and established models for classification and regression. In this paper, we focus on the estimation and the minimization of the misclassification rate of decision tree classifiers. We apply Lidstone's Law of Succession for the estimation of the class probabilities and error rates. In our work, we take into account not only the expected values of the error rate, which has been the norm in existing research, but also the corresponding reliability (measured by standard deviations) of the error rate. Based on this estimation, we propose an efficient pruning algorithm, called k-norm pruning, that has a clear theoretical interpretation, is easily implemented, and does not require a validation set. Our experiments show that our proposed pruning algorithm produces accurate trees quickly, and compares very favorably with two other well-known pruning algorithms, CCP of CART and EBP of C4.5.","Title: Index based on algebraic functions of views\nYear: 1998\n\nAbstract: In this paper, we propose the use of algebraic functions of views for indexing-based object recognition. During indexing, we consider groups of model points and we represent all the views (i.e., images) that they can produce in a hash table. The images that a group of model points can produce are computed by combining a small number of reference views which contain the group using algebraic functions of views. Fundamental to this procedure is a methodology, based on Singular Value Decomposition and Interval Arithmetic, for estimating the allowable ranges of values that the parameters of algebraic functions can assume. During recognition, scene groups are used to retrieve from the hash table the most feasible model groups that might have produced the scene groups. The use of al- gebraic functions of views for indexing-based recognition offers a number of advantages. First of all, the hash table can be built using a small number of reference views per object. This is in contrast to cur- rent approaches which build the hash table using either a large num- ber of reference views or 3D models. Most importantly, recognition does not rely on the similarity between reference views and novel views; all that is required for the novel views is to contain common groups of points with a small number of reference views. Second, verification becomes simpler. This is because candidate models can now be back-projected onto the scene by applying a linear transfor- mation on a small number of reference views of the candidate model. Finally, the proposed approach is more general and extendible. This is because algebraic functions of views have been shown to exist over a wide range of transformations and projections. The recognition performance of the proposed approach is demonstrated using both artificial and real data. c","Title: Intelligent Trading Agents for Massively Multi-player Game Economies\nYear: 2008\n\nAbstract: As massively multi-player gaming environments become more detailed, developing agents to populate these virtual worlds as capable non-player characters poses an increas- ingly complex problem. Human players in many games must achieve their objectives through financial skills such as tr ad- ing and supply chain management as well as through com- bat and diplomacy. In this paper, we examine the problem of creating intelligent trading agents for virtual markets . Us- ing historical data from EVE Online, a science-fiction based MMORPG, we evaluate several strategies for buying, sell- ing, and supply chain management. We demonstrate that us- ing reinforcement learning to determine policies based on the market microstructure gives trading agents a competitive ad- vantage in amassing wealth. Imbuing agents with the ability to adapt their trading policies can make them more resistant to exploitation by other traders and capable of participati ng in virtual economies on an equal footing with humans.","Title: S-Race: a multi-objective racing algorithm\nYear: 2013\n\nAbstract: This paper presents a multi-objective racing algorithm, S-Race, which efficiently addresses multi-objective model selection problems in the sense of Pareto optimality. As a racing algorithm, S-Race attempts to eliminate candidate models as soon as there is sufficient statistical evidence of their inferiority relative to other models with respect to all objectives. This approach is followed in the interest of controlling the computational effort. S-Race adopts a non-parametric sign test to identify pair-wise domination relationship between models. Meanwhile, Holm's Step-Down method is employed to control the overall family-wise error rate of simultaneous hypotheses testing during the race. Experimental results involving the selection of superior Support Vector Machine classifiers according to 2 and 3 performance criteria indicate that S-Race is an efficient and effective algorithm for automatic model selection, when compared to a brute-force, multi-objective selection approach.","Title: Experiments with Safe ARTMAP and Comparisons to Other ART Networks\nYear: 2006\n\nAbstract: Fuzzy ARTMAP (FAM) is currently considered as one of the premier neural network architectures in solving classification problems. Safe muARTMAP, a modified version of FAM, was introduced to remedy the category proliferation problem that has been extensively reported in the literature. However, Safe muARTMAP's performance depends on a number of parameters. In this paper, we analyzed each parameter to set up the candidate values for evaluation. We performed an exhaustive experimentation to identify good default values for these parameters for a variety of problems, and compared the best performing Safe muARTMAP network with other best performing ART networks, including those that claim to solve the category proliferation problem.","Title: Properties of learning related to pattern diversity in ART1\nYear: 1991\n\nAbstract: In this paper we consider a special class of the ART1 neural network. It is shown that if this network is repeatedly presented with an arbitrary list of binary input patterns, learning self-stabilizes in at most m list presentations, where m corresponds to the number of patterns of distinct size in the input list. Other useful properties of the ART1 network, associated with the learning of an arbitrary list of binary input patterns, are also examined. These properties reveal some of the “good” characteristics of the ART1 network when it is used as a tool for the learning of recognition categories.","Title: A Fast Revised Simplex Method For Svm Training\nYear: 2008\n\nAbstract: Active set methods for training the Support Vector Machines (SVM) are advantageous since they enable incremental training and, as we show in this research, do not exhibit exponentially increasing training times commonly associated with the decomposition methods as the SVM training parameter, C, is increased or the classification difficulty increases. Previous implementations of the active set method must contend with singularities, especially associated with the linear kernel, and must compute infinite descent directions, which may be inefficient, especially as C is increased. In this research, we propose a revised simplex method for quadratic programming, which has a guarantee of non-singularity for the sub-problem, and show how this can be adapted to SVM training.","Title: A General Framework for Concurrent Simulation on Neural Network Models\nYear: 1992\n\nAbstract: The analysis of complex neural network models via analytical techniques is often quite difficult due to the large numbers of components involved and the nonlinearities associated with these components. The authors present a framework for simulating neural networks as discrete event nonlinear dynamical systems. This includes neural network models whose components are described by continuous-time differential equations or by discrete-time difference equations. Specifically, the authors consider the design and construction of a concurrent object-oriented discrete event simulation environment for neural networks. The use of an object-oriented language provides the data abstraction facilities necessary to support modification and extension of the simulation system at a high level of abstraction. Furthermore, the ability to specify concurrent processing supports execution on parallel architectures. The use of this system is demonstrated by simulating a specific neural network model on a general-purpose parallel computer.","Title: A Privacy Preserving Probabilistic Neural Network For Horizontally Partitioned Databases\nYear: 2007\n\nAbstract: In this paper, we present a version of the Probabilistic Neural Network (PNN) that is capable of operating on a distributed database that is horizontally partitioned. It does so in a way that is privacy-preserving: that is, a test point can be evaluated by the algorithm without any party knowing the data owned by the other parties. We present an analysis of this algorithm from the standpoints of security and computational performance. Finally, we provide performance results of an implementation of this privacy preserving, distributed PNN algorithm.","Title: Category regions as new geometrical concepts in Fuzzy-ART and Fuzzy-ARTMAP\nYear: 2002\n\nAbstract: In this paper we introduce novel geometric concepts, namely category regions, in the original framework of Fuzzy-ART (FA) and Fuzzy-ARTMAP (FAM). The definitions of these regions are based on geometric interpretations of the vigilance test and the F2 layer competition of committed nodes with uncommitted ones, that we call commitment test. It turns out that not only these regions have the same geometrical shape (polytope structure), but they also share a lot of common and interesting properties that are demonstrated in this paper. One of these properties is the shrinking of the volume that each one of these polytope structures occupies, as training progresses, which alludes to the stability of learning in FA and FAM, a well-known result. Furthermore, properties of learning of FA and FAM are also proven utilizing the geometrical structure and properties that these regions possess; some of these properties were proven before using counterintuitive, algebraic manipulations and are now demonstrated again via intuitive geometrical arguments. One of the results that is worth mentioning as having practical ramifications is the one which states that for certain areas of the vigilance-choice parameter space (p,a), the training and performance (testing) phases of FA and FAM do not depend on the particular choices of the vigilance parameter. Finally, it is worth noting that, although the idea of the category regions has been developed under the premises of FA and FAM, category regions are also meaningful for later developed ART neural network structures, such as ARTEMAP, ARTMAP-IC, Boosted ARTMAP, Micro-ARTMAP, Ellipsoid-ART/ARTMAP, among others.","Title: <Bold>Properties Of The K-Norm Pruning Algorithm For Decision Tree Classifiers</Bold>\nYear: 2008\n\nAbstract: Pruning is one of the key procedures in training decision tree classifiers. It removes trivial rules from the raw knowledge base built from training examples, in order to avoid over-using noisy, conflicting, or fuzzy inputs, so that the refined model can generalize better with unseen cases. In this paper we present a number of properties of k-norm pruning, a recently proposed pruning algorithm, which has clear theoretical interpretation. In an earlier paper it was shown that k-norm pruning compares very favorably in terms of accuracy and size with Minimal Cost-Complexity Pruning and Error Based Pruning, two of the most cited decision tree pruning methods; it was also shown that k-norm pruning is more efficient, at times orders of magnitude more efficient than Minimal Cost-Complexity Pruning and Error Based Pruning. In this paper, we demonstrate the validity of the k-norm properties through a series of theorems, and explain their practical significance.","Title: Non-derivable itemsets for fast outlier detection in large high-dimensional categorical data\nYear: 2011\n\nAbstract: Detecting outliers in a dataset is an important data mining task with many applications, such as detection of credit card fraud or network intrusions. Traditional methods assume numerical data and compute pair-wise distances among points. Recently, outlier detection methods were proposed for categorical and mixed-attribute data using the concept of Frequent Itemsets (FIs). These methods face challenges when dealing with large high-dimensional data, where the number of generated FIs can be extremely large. To address this issue, we propose several outlier detection schemes inspired by the well-known condensed representation of FIs, Non-Derivable Itemsets (NDIs). Specifically, we contrast a method based on frequent NDIs, FNDI-OD, and a method based on the negative border of NDIs, NBNDI-OD, with their previously proposed FI-based counterparts. We also explore outlier detection based on Non-Almost Derivable Itemsets (NADIs), which approximate the NDIs in the data given a δ parameter. Our proposed methods use a far smaller collection of sets than the FI collection in order to compute an anomaly score for each data point. Experiments on real-life data show that, as expected, methods based on NDIs and NADIs offer substantial advantages in terms of speed and scalability over FI-based Outlier Detection method. What is significant is that NDI-based methods exhibit similar or better detection accuracy compared to the FI-based methods, which supports our claim that the NDI representation is especially well-suited for the task of detecting outliers. At the same time, the NDI approximation scheme, NADIs is shown to exhibit similar accuracy to the NDI-based method for various δ values and further runtime performance gains. Finally, we offer an in-depth discussion and experimentation regarding the trade-offs of the proposed algorithms and the choice of parameter values.","Title: Answer: Approximate Name Search With Errors In Large Databases By A Novel Approach Based On Prefix-Dictionary\nYear: 2006\n\nAbstract: The obvious need for using modem computer networking capabilities to enable the effective sharing of information has resulted in data-sharing systems, which store, and manage large amounts of data. These data need to be effectively searched and analyzed. More specifically, in the presence of dirty data, a search for specific information by a standard query (e.g., search for a name that is misspelled or mistyped) does not return all needed information, as required in homeland security, criminology, and medical applications, amongst others. Different techniques, such as soundex, phonix, n-grams, edit-distance, have been used to improve the matching rate in these name-matching applications. These techniques have demonstrated varying levels of success, but there is a pressing need for name matching approaches that provide high levels of accuracy in matching names, while at the same time maintaining low computational complexity. In this paper, such a technique, called ANSWER, is proposed and its characteristics are discussed. Our results demonstrate that ANSWER possesses high accuracy, as well as high speed and is superior to other techniques of retrieving fuzzy name matches in large databases.","Title: Learning tactical human behavior through observation of human performance\nYear: 2006\n\nAbstract: It is widely accepted that the difficulty and expense involved in acquiring the knowledge behind tactical behaviors has been one limiting factor in the development of simulated agents representing adversaries and teammates in military and game simulations. Several researchers have addressed this problem with varying degrees of success. The problem mostly lies in the fact that tactical knowledge is difficult to elicit and represent through interactive sessions between the model developer and the subject matter expert. This paper describes a novel approach that employs genetic programming in conjunction with context-based reasoning to evolve tactical agents based upon automatic observation of a human performing a mission on a simulator. In this paper, we describe the process used to carry out the learning. A prototype was built to demonstrate feasibility and it is described herein. The prototype was rigorously and extensively tested. The evolved agents exhibited good fidelity to the observed human performance, as well as the capacity to generalize from it.","Title: Generative Neural Networks for Multi-task Life-Long Learning.\nYear: 2014\n\nAbstract: Multi-task learning (MTL) is an established method of inducing bias in neural network learning. It has been used as a method of knowledge transfer in sequential learning tasks, and as a method of overcoming the problem of impoverished training data. A standard problem in neural networks is the establishment of the proper network structure for a given problem. This problem is compounded in the MTL ...","Title: Experiments with Safe muARTMAP : effect of the network parameters on the network performance.\nYear: 2007\n\nAbstract: Fuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is, Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data are of the noisy and/or overlapping nature. To remedy this problem a number of researchers have designed modifications to the training phase of Fuzzy ARTMAP that had the beneficial effect of reducing this category proliferation. One of these modified Fuzzy ARTMAP architectures was the one proposed by Gomez-Sanchez, and his colleagues, referred to as Safe μARTMAP. In this paper we present reasonable analytical arguments that demonstrate of how we should choose the range of some of the Safe μARTMAP network parameters. Through a combination of these analytical arguments and experimentation we were able to identify good default parameter values for some of the Safe μARTMAP network parameters. This feat would allow one to save computations when a good performing Safe μARTMAP network is needed to be identified for a new classification problem. Furthermore, we performed an exhaustive experimentation to find the best Safe μARTMAP network for a variety of problems (simulated and real problems), and we compared it with other best performing ART networks, including other ART networks that claim to resolve the category proliferation problem in Fuzzy ARTMAP. These experimental results allow one to make appropriate statements regarding the pair-wise comparison of a number of ART networks (including Safe μARTMAP).","Title: Detecting Outliers in High-Dimensional Datasets with Mixed Attributes\nYear: 2008\n\nAbstract: Outlier Detection has attracted substantial attention in many applications and research areas. Examples include detection of network intrusions or credit card fraud. Many of the existing approaches are based on pair-wise distances among all points in the dataset. These approaches cannot easily extend to current datasets that usually contain a mix of categorical and continuous attributes, and may be scattered over large geographical areas. In addition, current datasets usually have a large number of dimensions. These datasets tend to be sparse, and traditional concepts such as Euclidean distance or nearest neighbor become unsuitable. We propose ODMAD, a fast outlier detection strategy intended for datasets containing mixed attributes. ODMAD takes into consideration the sparseness of the dataset, and is experimentally shown to be highly scalable with the number of points and number of attributes in the dataset.","Title: Educating middle and high school students in space operations: the simulation approach\nYear: 2004\n\nAbstract: This paper describes a proposed pilot educational program to teach middle and high school students the space shuttle processing operations. This pilot program involves a partnership of two Universities (University of Central Florida and University of Florida), a small business (Productivity Apex), and three middle/high schools in the Florida area.Our proposed pilot program is an innovative approach designed to teach students about the complexities of space vehicle processing through a simulation environment. Through the many planned activities that we propose to achieve our goal, we will be able to introduce middle and high school students to aerospace operations, excite them about space education, and enthusiastically engage them in science and technology related projects. The ultimate objective of this effort is to motivate middle and high school students to pursue careers in Science, Technology, Engineering, and Math (STEM) fields.","Title: GFAM: evolving Fuzzy ARTMAP neural networks.\nYear: 2006\n\nAbstract: This paper focuses on the evolution of Fuzzy ARTMAP neural network classifiers, using genetic algorithms, with the objective of improving generalization performance (classification accuracy of the ART network on unseen test data) and alleviating the ART category proliferation problem (the problem of creating more than necessary ART network categories to solve a classification problem). We refer to the resulting architecture as GFAM. We demonstrate through extensive experimentation that GFAM exhibits good generalization and is of small size (creates few ART categories), while consuming reasonable computational effort. In a number of classification problems, GFAM produces the optimal classifier. Furthermore, we compare the performance of GFAM with other competitive ARTMAP classifiers that have appeared in the literature and addressed the category proliferation problem in ART. We illustrate that GFAM produces improved results over these architectures, as well as other competitive classifiers.","Title: A Unifying Framework for Typical Multitask Multiple Kernel Learning Problems\nYear: 2014\n\nAbstract: Over the past few years, multiple kernel learning (MKL) has received significant attention among data-driven feature selection techniques in the context of kernel-based learning. MKL formulations have been devised and solved for a broad spectrum of machine learning problems, including multitask learning (MTL). Solving different MKL formulations usually involves designing algorithms that are tailored to the problem at hand, which is, typically, a nontrivial accomplishment. In this paper we present a general multitask multiple kernel learning (MT-MKL) framework that subsumes well-known MT-MKL formulations, as well as several important MKL approaches on single-task problems. We then derive a simple algorithm that can solve the unifying framework. To demonstrate the flexibility of the proposed framework, we formulate a new learning problem, namely partially-shared common space MT-MKL, and demonstrate its merits through experimentation.","Title: Conic Multi-task Classification.\nYear: 2014\n\nAbstract: Traditionally, Multi-task Learning (MTL) models optimize the average of task-related objective functions, which is an intuitive approach and which we will be referring to as Average MTL. However, a more general framework, referred to as Conic MTL, can be formulated by considering conic combinations of the objective functions instead; in this framework, Average MTL arises as a special case, when all combination coefficients equal 1. Although the advantage of Conic MTL over Average MTL has been shown experimentally in previous works, no theoretical justification has been provided to date. In this paper, we derive a generalization bound for the Conic MTL method, and demonstrate that the tightest bound is not necessarily achieved, when all combination coefficients equal 1; hence, Average MTL may not always be the optimal choice, and it is important to consider Conic MTL. As a byproduct of the generalization bound, it also theoretically explains the good experimental results of previous relevant works. Finally, we propose a new Conic MTL model, whose conic combination coefficients minimize the generalization bound, instead of choosing them heuristically as has been done in previous methods. The rationale and advantage of our model is demonstrated and verified via a series of experiments by comparing with several other methods.","Title: Online model racing based on extreme performance\nYear: 2014\n\nAbstract: Racing algorithms are often used for offline model selection, where models are compared in terms of their average performance over a collection of problems. In this paper, we present a new racing algorithm variant, Max-Race, which makes decisions based on the maximum performance of models. It is an online algorithm, whose goal is to optimally allocate computational resources in a portfolio of evolutionary algorithms, while solving a particular problem instance. It employs a hypothesis test based on extreme value theory in order to decide, which component algorithms to retire, while avoiding unnecessary computations. Experimental results confirm that Max-Race is able to identify the best individual with high precision and low computational overhead. When used as a scheme to select the best from a portfolio of algorithms, the results compare favorably to the ones of other popular algorithm portfolio approaches.","Title: Multitask Classification Hypothesis Space With Improved Generalization Bounds.\nYear: 2013\n\nAbstract: This paper presents a pair of hypothesis spaces (HSs) of vector-valued functions intended to be used in the context of multitask classification. While both are parameterized on the elements of reproducing kernel Hilbert spaces and impose a feature mapping that is common to all tasks, one of them assumes this mapping as fixed, while the more general one learns the mapping via multiple kernel learning. For these new HSs, empirical Rademacher complexity-based generalization bounds are derived, and are shown to be tighter than the bound of a particular HS, which has appeared recently in the literature, leading to improved performance. As a matter of fact, the latter HS is shown to be a special case of ours. Based on an equivalence to Group-Lasso type HSs, the proposed HSs are utilized toward corresponding support vector machine-based formulations. Finally, experimental results on multitask learning problems underline the quality of the derived bounds and validate this paper's analysis.","Title: Correction to 'Packet error probabilities in frequency hopped spread-spectrum packet radio networks-memoryless frequency hopping patterns considered'\nYear: 1991\n\nAbstract: The author corrects the approach followed in the above-titled paper (see ibid., vol.36, no.6, p.720-3, 1988) to compute packet error probabilities in frequency-hopped spread-spectrum packet radio networks with memoryless frequency-hopping patterns. The nature of the error is that the sequence of symbol errors is not Markov, so that a formula which is instrumental for the computation of packet error probabilities, is incorrect. He obtains exact and easy to compute expressions for the evaluation of the packet error probabilities.<>","Title: Analyzing the Fuzzy ARTMAP Matchtracking mechanism with Co-Objective Optimization Theory\nYear: 2007\n\nAbstract: In the process of learning a pattern I, the fuzzy ARTMAP algorithm templates (i.e., the weight vectors corresponding to nodes of its category representation layer) compete for the representation of the given pattern. This competition can induce matchtracking: a process that iterates a number of times over the template set searching for a template w* of the correct class that best represents the pattern I. In this paper, we analyze the search for a winning template from the perspective of bi-criterion optimization and prove that it is actually a walk along the Pareto front of an appropriately defined co-objective optimization problem. This observation allows us to propose the basis for an implementation variant of fuzzy ARTMAP that (a) produces exactly the same network as fuzzy ARTMAP, (b) avoids matchtracking by explicitly keeping track of a subset of the Pareto front, (c) finds the correct template to represent an input pattern through a single pass over the template set and (d) eliminates the need for the fuzzy ARTMAP parameter epsiv.","Title: On Extending the SMO Algorithm Sub-Problem\nYear: 2007\n\nAbstract: The Support Vector Machine is a widely employed machine learning model due to its repeatedly demonstrated superior generalization performance. The Sequential Minimal Optimization (SMO) algorithm is one of the most popular SVM training approaches. SMO is fast, as well as easy to implement; however, it has a limited working set size (2 points only). Faster training times can result if the working set size can be increased without significantly increasing the computational complexity. In this paper, we extend the 2-point SMO formulation to a 4-point formulation and address the theoretical issues associated with such an extension. We show that modifying the SMO algorithm to increase the working set size is beneficial in terms of the number of iterations required for convergence, and shows promise for reducing the overall training time. I. INTRODUCTION he Support Vector Machine is a classification model, maintaining excellent generalization capabilities along with a built-in resistance to overtraining. This generalization performance is based upon strong theoretical foundations first introduced by Vapnik (1). Instead of performing empirical risk minimization, as many other models do, the SVM algorithm performs structural risk minimization. This simply means that the algorithm seeks a balance between fitting function complexity and training error. The SVM algorithm works by finding a separating hyperplane between two classes of data that maximizes the margin between the closest data point and the separating hyperplane. Vapnik shows that this is equivalent to structural risk minimization. The primal, soft-margin problem formulation is shown in (1).","Title: MO-GART: Multiobjective genetic ART architectures\nYear: 2008\n\nAbstract: In this work we present, for the first time, the evolution of ART Neural Network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to evolve simultaneously the weights, as well as the topology of three well-known ART architectures; Fuzzy ARTMAP (FAM), Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM). We refer to the resulting architectures as MO-GFAM, MOGEAM, or MO-GGAM, and collectively as MO-GART. The major advantage of MO-GART is that it produces a number of solutions for the classification problem at hand that have different levels of merit (accuracy on unseen data (generalization) and size (number of categories created)). MO-GART is shown to be more elegant (does not require user intervention to define the network parameters), more effective (of better accuracy and smaller size), and more efficient (faster to produce the solution networks) than other ART neural network architectures that have appeared in the literature.","Title: An efficient active set method for SVM training without singular inner problems\nYear: 2009\n\nAbstract: Efficiently implemented active set methods have been successfully applied to support vector machine (SVM) training. These active set methods offer higher precision and incremental training at the cost of additional memory requirements when compared to decomposition methods such as sequential minimal optimization (SMO). However, all existing active set methods must deal with singularities occurring within the inner problem solved at each iteration, a problem that leads to more complex implementation and potential inefficiencies. Here, we introduce a revised simplex method, originally introduced by Rusin, adapted for SVM training and show this is an active set method similar to most existing methods with the advantage of maintaining nonsingularity of the inner problem. We compare performance to an existing active set method introduced by Scheinberg and demonstrate an improvement in training times, in some cases. We show our method maintains a slightly simpler implementation and offers advantages in terms of applying iterative methods to alleviate memory concerns. We also show performance of the active set methods when compared to state-of-the-art decomposition implementations such as SVMLight and SMO.","Title: Convergence Properties of Learning in ART1\nYear: 1990\n\nAbstract: We consider the ART1 neural network architecture. It is shown that in the fast learning case, an ART1 network that is repeatedly presented with an arbitrary list of binary input patterns, self-stabilizes the recognition code of every size-l pattern in at most l list presentations.","Title: SPRINT Multi-Objective Model Racing\nYear: 2015\n\nAbstract: Multi-objective model selection, which is an important aspect of Machine Learning, refers to the problem of identifying a set of Pareto optimal models from a given ensemble of models. This paper proposes SPRINT-Race, a multi-objective racing algorithm based on the Sequential Probability Ratio Test with an Indifference Zone. In SPRINT-Race, a non-parametric ternary-decision sequential analogue of the sign test is adopted to identify pair-wise dominance and non-dominance relationship. In addition, a Bonferroni approach is employed to control the overall probability of any erroneous decisions. In the fixed confidence setting, SPRINT-Race tries to minimize the computational effort needed to achieve a predefined confidence about the quality of the returned models. The efficiency of SPRINT-Race is analyzed on artificially-constructed multi-objective model selection problems with known ground-truth. Moreover, SPRINT-Race is applied to identifying the Pareto optimal parameter settings of Ant Colony Optimization algorithms in the context of solving Traveling Salesman Problems. The experimental results confirm the advantages of SPRINT-Race for multi-objective model selection.","Title: Hash Function Learning via Codewords\nYear: 2015\n\nAbstract: In this paper we introduce a novel hash learning framework that has two main distinguishing features, when compared to past approaches. First, it utilizes codewords in the Hamming space as ancillary means to accomplish its hash learning task. These codewords, which are inferred from the data, attempt to capture similarity aspects of the data's hash codes. Secondly and more importantly, the same framework is capable of addressing supervised, unsupervised and, even, semi-supervised hash learning tasks in a natural manner. A series of comparative experiments focused on content-based image retrieval highlights its performance advantages.","Title: Multi-Task Learning with Group-Specific Feature Space Sharing\nYear: 2015\n\nAbstract: When faced with learning a set of inter-related tasks from a limited amount of usable data, learning each task independently may lead to poor generalization performance. (MTL) exploits the latent relations between tasks and overcomes data scarcity limitations by co-learning all these tasks simultaneously to offer improved performance. We propose a novel Multi-Task Multiple Kernel Learning framework based on Support Vector Machines for binary classification tasks. By considering pair-wise task affinity in terms of similarity between a pair's respective feature spaces, the new framework, compared to other similar MTL approaches, offers a high degree of flexibility in determining how similar feature spaces should be, as well as which pairs of tasks should share a common feature space in order to benefit overall performance. The associated optimization problem is solved via a block coordinate descent, which employs a consensus-form Alternating Direction Method of Multipliers algorithm to optimize the Multiple Kernel Learning weights and, hence, to determine task affinities. Empirical evaluation on seven data sets exhibits a statistically significant improvement of our framework's results compared to the ones of several other Clustered Multi-Task Learning methods.","Title: Multi-Task Learning Using Neighborhood Kernels.\nYear: 2017\n\nAbstract: This paper introduces a new and effective algorithm for learning kernels in a Multi-Task Learning (MTL) setting. Although, we consider a MTL scenario here, our approach can be easily applied to standard single task learning, as well. As shown by our empirical results, our algorithm consistently outperforms the traditional kernel learning algorithms such as uniform combination solution, convex combinations of base kernels as well as some kernel alignment-based models, which have been proven to give promising results in the past. We present a Rademacher complexity bound based on which a new Multi-Task Multiple Kernel Learning (MT-MKL) model is derived. In particular, we propose a Support Vector Machine-regularized model in which, for each task, an optimal kernel is learned based on a neighborhood-defining kernel that is not restricted to be positive semi-definite. Comparative experimental results are showcased that underline the merits of our neighborhood-defining framework in both classification and regression problems.","Title: Pareto-Optimal Model Selection via SPRINT-Race.\nYear: 2018\n\nAbstract: In machine learning, the notion of multi-objective model selection (MOMS) refers to the problem of identifying the set of Pareto-optimal models that optimize by compromising more than one predefined objectives simultaneously. This paper introduces SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting, which is based on the sequential probability ratio with indiffere...","Title: Sparse Inductive Embedding: An Explorative Data Visualization Technique\nYear: 2017\n\nAbstract: Metric Multi-Dimensional Scaling is one of the most popular visualization techniques aiming to determine a point configuration of the data in a low-dimensional space, while preserving the original pairwise dissimilarities amongst the data points. In this paper we propose Sparse Inductive Multi- Dimensional Scaling approach, an exploratory data analysis and visualization tool. Its kernel-based nature provides an inductive method, and hence allows for embedding of previously unseen data even with non-numerical features. Furthermore, via the use of a sparsity-promoting regularizer, the technique is capable of identifying key training samples, whose contribution in defining the embedded manifold's specific structure is most influential. As we will show, the influence of these samples leads us to interesting interpretations in the context of the data at hand. We adequately train the consequent non-convex framework via an Iterative Majorization approach, for which a stable closed-form solution is obtained. Experimental results showcase the potential benefits of the proposed embedding method on both artificial and real-world data.","Title: Multi-Label Learning Via Codewords\nYear: 2018\n\nAbstract: In this paper, we introduce a novel hash learning framework for multi-label learning which employs structured prediction. A hash function is learned to embed samples in Hamming spaces, and for each label, a pair of codewords are simultaneously inferred from the available data. These codewords are then used to determine label predictions based on Hamming proximity. The key advantage of this framework is it's computational efficiency in tackling multi-label problems without making restrictive, simplifying assumptions about the structure of the output space, or developing problem-dependent heuristics. Our method not only enjoys considerably better scalability while capturing label inter-dependence, but also yields an exact training algorithm. Experimental results on a collection of benchmark multi-label datasets demonstrate that our model attains higher performance over alternative state-of-the-art multi-label approaches. It is also worth noting that our method can be extended to semi-supervised and missing labels scenarios.","Title: Robust Manifold Learning via Conformity Pursuit.\nYear: 2019\n\nAbstract: This letter presents an effective and simple method, termed Global Conformity Pursuit (GCP), for robust manifold learning. Data points lying on a union of low-dimensional nonlinear manifolds are expected to be highly conforming. On the other hand, outliers do not typically adhere to low-dimensional structures or otherwise do not exist in large numbers. Hence, they can be identified by their low ov...","Title: A Structural Equation Model Analysis of Computing Identity Sub-Constructs and Student Academic Persistence.\nYear: 2018\n\nAbstract: This Research Full Paper presents the effects of computing identity sub-constructs on the persistence of computer science students. Computer science (CS) is one of the fastest growing disciplines in the world and an emerging critical field for all students to obtain vital skills to be successful in the 21st century. Despite the growing importance of computer science, many university and college programs suffer from low student persistence rates. Disciplinary identity is a theoretical framework that refers to how students see themselves with respect to a discipline and is related to long-term membership in a disciplinary community. The theory has been effectively applied in Science, Technology, Engineering, and Mathematics (STEM) to understand students' success and persistence. This study examines the effects of performance/competence, recognition, interest and sense of belonging on the academic persistence of computer science students. A survey of approximately 1,640 computing students as part of a National Science Foundation (NSF) funded project was developed and administered at three metropolitan public institutions. Confirmatory Factor Analysis (CFA) was performed to validate the sub-constructs of identity for use in a computing identity model. Then, a structural equation model (SEM) was constructed as a snapshot of the structural relationships for describing and quantifying the impact of the identity sub-constructs on persistence. The results indicated that our model for CS aligns with prior research on disciplinary identity but also adds the importance of sense of belonging. In addition, the findings indicate that students' academic persistence is directly influenced by their interest. A better understanding of these factors may leverage insight into students' academic persistence in computer science/engineering programs as well as a meaningful lens of analysis for further curriculum and extracurricular activities.","Title: Reduced-Rank Local Distance Metric Learning for k-NN Classification.\nYear: 2019\n\nAbstract: We propose a new method for local distance metric learning based on sample similarity as side information. These local metrics, which utilize conical combinations of metric weight matrices, are learned from the pooled spatial characteristics of the data, as well as the similarity profiles between the pairs of samples, whose distances are measured. The main objective of our framework is to yield metrics, such that the resulting distances between similar samples are small and distances between dissimilar samples are above a certain threshold. For learning and inference purposes, we describe a transductive, as well as an inductive algorithm; the former approach naturally befits our framework, while the latter one is provided in the interest of faster learning. Experimental results on a collection of classification problems imply that the new methods may exhibit notable performance advantages over alternative metric learning approaches that have recently appeared in the literature.","Title: The Impact of Technical Interviews, and other Professional and Cultural Experiences on Students' Computing Identity\nYear: 2021\n\nAbstract: ABSTRACTIncreasingly companies assess a computing candidate's capabilities using technical interviews (TIs). Yet students struggle to code on demand, and there is already an insufficient amount of computing graduates to meet industry needs. Therefore, it is important to understand students' perceptions of TIs, and other professional experiences (e.g., computing jobs). We surveyed 740 undergraduate computing students at three universities to examine their experiences with the hiring process, as well as the impact of professional and cultural experiences (e.g., familial support) on computing identity. We considered the interactions between these experiences and social identity for groups underrepresented in computing - women, Black/African American, and Hispanic/Latinx students. Among other findings, we observed that students that did not have positive experiences with TIs had a reduced computing identity, but that facing discrimination during technical interviews had the opposite effect. Social support may play a role. Having friends in computing bolsters computing identity for Hispanic/Latinx students, as does a supportive home environment for women. Also, freelance computing jobs increase computing identity for Black/African American students. Our findings are intended to raise awareness of the best way for educators to help diverse groups of students to succeed, and to inform them of the experiences that may influence students' engagement, resilience, and computing identity development.","Title: Sketches by MoSSaRT: Representative selection from manifolds with gross sparse corruptions\nYear: 2022\n\nAbstract: •A reproduction profile encodes the data relations of grossly corrupted manifold structures.•Approximate feature maps emulate a desired feature mapping associated with a RKHS.•Scalable and parallelizable ADMM-based algorithm has nearly linear complexity in the data size.•The proxy objective function induced by the approximate features converges exponentially fast.•The representatives are vertices of the symmetrized convex hull of the data in a transformed space."],"string":"[\n \"Title: Fuzzy ART properties\\nYear: 1995\\n\\nAbstract: This paper presents some important properties of the Fuzzy ART neural network algorithm introduced by Carpenter, Grossberg, and Rosen. The properties described in the paper are distinguished into a number of categories. These include template, access, and reset properties, as well as properties related to the number of list presentations needed for weight stabilization. These properties provide numerous insights as to how Fuzzy ART operates. Furthermore, the effects of the Fuzzy ART parameters a and p on the functionality of the algorithm are clearly illustrated.\",\n \"Title: Learning affine transformations\\nYear: 1999\\n\\nAbstract: Under the assumption of weak perspective, two views of the same planar object are related through an affine transformation. In this paper, we consider the problem of training a simple neural network to learn to predict the parameters of the affine transformation. Although the proposed scheme has similarities with other neural network schemes, its practical advantages are more profound. First of all, the views used to train the neural network are not obtained by taking pictures of the object from different viewpoints. Instead, the training views are obtained by sampling the space of affine transformed views of the object. This space is constructed using a single view of the object. Fundamental to this procedure is a methodology, based on singular-value decomposition (SVD) and interval arithmetic (IA), for estimating the ranges of values that the parameters of affine transformation can assume. Second, the accuracy of the proposed scheme is very close to that of a traditional least squares approach with slightly better space and time requirements. A front-end stage to the neural network, based on principal components analysis (PCA), shows to increase its noise tolerance dramatically and also to guides us in deciding how many training views are necessary in order for the network to learn a good, noise tolerant, mapping. The proposed approach has been tested using both artificial and real data.\",\n \"Title: Order of search in fuzzy ART and fuzzy ARTMAP: effect of the choice parameter\\nYear: 1996\\n\\nAbstract: This paper focuses on two ART architectures, the Fuzzy ART and the Fuzzy ARTMAP. Fuzzy ART is a pattern clustering machine, while Fuzzy ARTMAP is a pattern classification machine. Our study concentrates on the order according to which categories in Fuzzy ART, or the ART a model of Fuzzy ARTMAP are chosen. Our work provides a geometrical, and clearer understanding of why, and in what order, these categories are chosen for various ranges of the choice parameter of the Fuzzy ART module. This understanding serves as a powerful tool in developing properties of learning pertaining to these neural network architectures; to strengthen this argument, it is worth mentioning that the order according to which categories are chosen in ART 1 and ARTMAP provided a valuable tool in proving important properties about these architectures. Copyright © 1996 Elsevier Science Ltd.\",\n \"Title: APHID: An architecture for private, high-performance integrated data mining\\nYear: 2010\\n\\nAbstract: While the emerging field of privacy preserving data mining (PPDM) will enable many new data mining applications, it suffers from several practical difficulties. PPDM algorithms are challenging to develop and computationally intensive to execute. Developers need convenient abstractions to simplify the engineering of PPDM applications. The individual parties involved in the data mining process need a way to bring high-performance, parallel computers to bear on the computationally intensive parts of the PPDM tasks. This paper discusses APHID (Architecture for Private and High-performance Integrated Data mining), a practical architecture and software framework for developing and executing large scale PPDM applications. At one tier, the system supports simplified use of cluster and grid resources, and at another tier, the system abstracts communication for easy PPDM algorithm development. This paper offers a detailed analysis of the challenges in developing PPDM algorithms with existing frameworks, and motivates the design of a new infrastructure based on these challenges.\",\n \"Title: Fast parallel outlier detection for categorical datasets using MapReduce\\nYear: 2008\\n\\nAbstract: Outlier detection has received considerable attention in many applications, such as detecting network attacks or credit card fraud. The massive datasets currently available for mining in some of these outlier detection applications require large parallel systems, and consequently parallelizable outlier detection methods. Most existing outlier detection methods assume that all of the attributes of a dataset are numerical, usually have a quadratic time complexity with respect to the number of points in the dataset, and quite often they require multiple dataset scans. In this paper, we propose a fast parallel outlier detection strategy based on the Attribute Value Frequency (AVF) approach, a high-speed, scalable outlier detection method for categorical data that is inherently easy to parallelize. Our proposed solution, MR-AVF, is based on the MapReduce paradigm for parallel programming, which offers load balancing and fault tolerance. MR-AVF is particularly simple to develop and it is shown to be highly scalable with respect to the number of cluster nodes.\",\n \"Title: Pipelining of Fuzzy ARTMAP without matchtracking: correctness, performance bound, and Beowulf evaluation.\\nYear: 2007\\n\\nAbstract: Fuzzy ARTMAP neural networks have been proven to be good classifiers on a variety of classification problems. However, the time that Fuzzy ARTMAP takes to converge to a solution increases rapidly as the number of patterns used for training is increased. In this paper we examine the time Fuzzy ARTMAP takes to converge to a solution and we propose a coarse grain parallelization technique, based on a pipeline approach, to speed-up the training process. In particular, we have parallelized Fuzzy ARTMAP without the match-tracking mechanism. We provide a series of theorems and associated proofs that show the characteristics of Fuzzy ARTMAP's, without matchtracking, parallel implementation. Results run on a BEOWULF cluster with three large databases show linear speedup as a function of the number of processors used in the pipeline. The databases used for our experiments are the Forrest CoverType database from the UCI Machine Learning repository and two artificial databases, where the data generated were 16-dimensional Gaussian distributed data belonging to two distinct classes, with different amounts of overlap (5% and 15%).\",\n \"Title: Gap-based estimation: choosing the smoothing parameters for probabilistic and general regression neural networks.\\nYear: 2007\\n\\nAbstract: Probabilistic neural networks (PNN) and general regression neural networks (GRNN) represent knowledge by simple but interpretable models that approximate the optimal classifier or predictor in the sense of expected value of the accuracy. These models require the specification of an important smoothing parameter, which is usually chosen by cross-validation or clustering. In this article, we demonstrate the problems with the cross-validation and clustering approaches to specify the smoothing parameter, discuss the relationship between this parameter and some of the data statistics, and attempt to develop a fast approach to determine the optimal value of this parameter. Finally, through experimentation, we show that our approach, referred to as a gap-based estimation approach, is superior in speed to the compared approaches, including support vector machine, and yields good and stable accuracy.\",\n \"Title: A Neural Net Associative Memory for Real-Time Applications.\\nYear: 1990\\n\\nAbstract: A parallel hardware implementation of the associative memory neural network introduced by Hopfield is described. The design utilizes the Geometric Arithmetic Parallel Processor (GAPP), a commercially available single-chip VLSI general-purpose array processor consisting of 72 processing elements. The ability to cascade these chips allows large arrays of processors to be easily constructed and used to implement the Hopfield network. The memory requirements and processing times of such arrays are analyzed based on the number of nodes in the network and the number of exemplar patterns. Compared with other digital implementations, this design yields significant improvements in runtime performance and offers the capability of using large neural network associative memories in real-time applications.\",\n \"Title: Accelerated learning of Generalized Sammon Mappings\\nYear: 2011\\n\\nAbstract: The Sammon Mapping (SM) has established itself as a valuable tool in dimensionality reduction, manifold learning, exploratory data analysis and, particularly, in data visualization. The SM is capable of projecting high-dimensional data into a low-dimensional space, so that they can be visualized and interpreted. This is accomplished by representing inter-sample dissimilarities in the original space by Euclidean inter-sample distances in the projection space. Recently, Kernel Sammon Mapping (KSM) has been shown to subsume the SM and a few other related extensions to SM. Both of the aforementioned models feature a set of linear weights that are estimated via Iterative Majorization (IM). While IM is significantly faster than other standard gradient-based methods, tackling data sets of larger than moderate sizes becomes a challenging learning task, as IM's convergence significantly slows down with increasing data set cardinality. In this paper we derive two improved training algorithms based on Successive Over-Relaxation (SOR) and Parallel Tangents (PARTAN) acceleration, that, while still being first-order methods, exhibit faster convergence than IM. Both algorithms are relatively easy to understand, straightforward to implement and, performance-wise, are as robust as IM. We also present comparative results that illustrate their computational advantages on a set of benchmark problems.\",\n \"Title: Efficient evolution of ART neural networks\\nYear: 2008\\n\\nAbstract: Genetic algorithms have been used to evolve sev- eral neural network architectures. In a previous effort, we introduced the evolution of three well known ART architects; Fuzzy ARTMAP (FAM), Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM). The resulting architectures were shown to achieve competitive generalization and exceptionally small size. A major concern regarding these architectures, and any evolved neural network architecture in general, is the added overhead in terms of computational time needed to produce the finally evolved network. In this paper we investigate ways of reducing this computational overhead by reducing the computations needed for the calculation of the fitness value of the evolved ART architectures. The results obtained in this paper can be directly extended to many other evolutionary neural network architectures, beyond the studied evolution of ART neural network architectures.\",\n \"Title: APHID: A Practical Architecture for High-Performance, Privacy-Preserving Data Mining\\nYear: 2009\\n\\nAbstract: While the emerging field of privacy preserving data mining (PPDM) will enable many new data mining applications, it suffers from several practical difficulties. PPDM algorithms are difficult to develop and computationally intensive to execute. Developers need convenient abstractions to reduce the costs of engineering PPDM applications. The individual parties involved in the data mining process need a way to bring high- performance, parallel computers to bear on the computationally intensive parts of the PPDM tasks. This paper discusses APHID (Architecture for Private and High-performance Integrated Data mining), a practical software architecture for developing and executing large-scale PPDM applications. At one level, the system supports simplified use of cluster and grid resources, and at another level, the system abstracts communication for easy PPDM algorithm development. This paper offers an analysis of the challenges in developing PPDM algorithms with existing frameworks, and motivates the design of a new infrastructure based on these challenges. Index Terms—Privacy-Preserving Data Mining, Distributed Data Mining, Cluster Computing, MapReduce\",\n \"Title: Boosted ARTMAP: Modifications to fuzzy ARTMAP motivated by boosting theory\\nYear: 2006\\n\\nAbstract: In this paper, several modifications to the Fuzzy ARTMAP neural network architecture are proposed for conducting classification in complex, possibly noisy, environments. The goal of these modifications is to improve upon the generalization performance of Fuzzy ART-based neural networks, such as Fuzzy ARTMAP, in these situations. One of the major difficulties of employing Fuzzy ARTMAP on such learning problems involves over-fitting of the training data. Structural risk minimization is a machine-learning framework that addresses the issue of over-fitting by providing a backbone for analysis as well as an impetus for the design of better learning algorithms. The theory of structural risk minimization reveals a trade-off between training error and classifier complexity in reducing generalization error, which will be exploited in the learning algorithms proposed in this paper. Boosted ART extends Fuzzy ART by allowing the spatial extent of each cluster formed to be adjusted independently. Boosted ARTMAP generalizes upon Fuzzy ARTMAP by allowing non-zero training error in an effort to reduce the hypothesis complexity and hence improve overall generalization performance. Although Boosted ARTMAP is strictly speaking not a boosting algorithm, the changes it encompasses were motivated by the goals that one strives to achieve when employing boosting. Boosted ARTMAP is an on-line learner, it does not require excessive parameter tuning to operate, and it reduces precisely to Fuzzy ARTMAP for particular parameter values. Another architecture described in this paper is Structural Boosted ARTMAP, which uses both Boosted ART and Boosted ARTMAP to perform structural risk minimization learning. Structural Boosted ARTMAP will allow comparison of the capabilities of off-line versus on-line learning as well as empirical risk minimization versus structural risk minimization using Fuzzy ARTMAP-based neural network architectures. Both empirical and theoretical results are presented to enhance the understanding of these architectures.\",\n \"Title: The N-N-N conjecture in ART1\\nYear: 1992\\n\\nAbstract: In this paper we consider the ART1 neural network architecture introduced by Carpenter and Grossberg. In their original paper, Carpenter and Grossberg made the following conjecture: In the fast learning case, if the F 2 layer in ART1 has at least N nodes, then each member of a list of N input patterns presented cyclically at the F 1 layer of ART1 will have direct access to an F 2 layer node after at most N list presentations. In this paper, we demonstrate that the conjecture is not valid for certain large L values, where L is a network parameter associated with the adaptation of the bottom-up traces in ART1 . It is worth noting that previous work has shown the conjecture to be true for small L values.\",\n \"Title: Using Algebraic Functions of Views for Indexing-Based Object Recognition\\nYear: 1998\\n\\nAbstract: Current indexing-based approaches build the hash table using either a large number of reference views or 3D models. In this paper, we propose building the hash table using algebraic functions of views. During preprocessing, we consider groups of model points and we represent all the views (i.e., images) that they can produce in a hash table. These views are computed using algebraic functions of a small number of reference views which contain the group. Fundamental to this procedure is a methodology based on Singular Value Decomposition and Interval Arithmetic for estimating the ranges of values that the parameters of algebraic functions can assume. During recognition, scene groups are used to retrieve from the hash table the model groups that might have produced them. Using algebraic functions of views for indexing-based recognition offers a number of advantages. First of all, the hash table can be built easier, without requiring 3D models or a large number of reference views. Second, recognition does not rely on the similarities between new and reference views. Third, verification becomes simpler. Finally, the approach is more general and extendible.\",\n \"Title: Kernel-based distance metric learning in the output space\\nYear: 2013\\n\\nAbstract: In this paper we present two related, kernel-based Distance Metric Learning (DML) methods. Their respective models non-linearly map data from their original space to an output space, and subsequent distance measurements are performed in the output space via a Mahalanobis metric. The dimensionality of the output space can be directly controlled to facilitate the learning of a low-rank metric. Both methods allow for simultaneous inference of the associated metric and the mapping to the output space, which can be used to visualize the data, when the output space is 2-or 3-dimensional. Experimental results for a collection of classification tasks illustrate the advantages of the proposed methods over other traditional and kernel-based DML approaches.\",\n \"Title: A winner-take-all methodology: finding the best evolutionary algorithm for the global optimization of functions\\nYear: 2012\\n\\nAbstract: The problem of effectively and efficiently finding the global optimum of a function by using evolutionary algorithms is current and pertinent, and two of the evolutionary techniques that have received significant attention in the literature are Particle Swarm Optimization (PSO), and Differential Evolution (DE), as well as their numerous variants. One way of taking advantage of the many good PSO and DE variant algorithms that have appeared in the literature is to run them all for a particular optimization problem and choose the best answer provided. This approach, referred to as the Naive Approach (NA) is time consuming. In this paper, we are using the naive approach with a suite of algorithms for each function minimization problem and we run the algorithms, in the suite, for a specific number of function evaluations (typically much smaller than the number of function evaluations needed for each specific algorithm to converge to a solution) and decide, using appropriate performance measures of merit, which one of these algorithms will continue running until convergence; the rest of the algorithms, deemed as algorithms that will eventually produce inferior solutions, are aborted. We refer to our methodology, for obvious reasons, as the Winner-Take-All (WTA) methodology. Using this methodology we introduce WTA algorithmic variants that are efficient and effective solvers of global optimization problems. In this paper, we report results on one of these variants, called WTA1, and show that it is very competitive compared to the constituent algorithms in the suite used for its design, and efficient compared to NA.\",\n \"Title: Experiments with an innovative tree pruning algorithm\\nYear: 2007\\n\\nAbstract: The pruning phase is one of the necessary steps in decision tree induction. Existing pruning algorithms tend to have some or all of the following difficulties: 1) lack of theoretical support; 2) high computational complexity; 3) dependence on validation; 4) complicated implementation. The 2-norm pruning algorithm proposed here addresses all of the above difficulties. This paper demonstrates the experimental results of the comparison among the 2-norm pruning algorithm and two classical pruning algorithms, the Minimal Cost-Complexity algorithm (used in CART) and the Error-based pruning algorithm (used in C4.5), and confirms that the 2-norm pruning algorithm is superior in accuracy and speed.\",\n \"Title: Efficient revised simplex method for SVM training.\\nYear: 2011\\n\\nAbstract: Existing active set methods reported in the literature for support vector machine (SVM) training must contend with singularities when solving for the search direction. When a singularity is encountered, an infinite descent direction can be carefully chosen that avoids cycling and allows the algorithm to converge. However, the algorithm implementation is likely to be more complex and less computationally efficient than would otherwise be required for an algorithm that does not have to contend with the singularities. We show that the revised simplex method introduced by Rusin provides a guarantee of nonsingularity when solving for the search direction. This method provides for a simpler and more computationally efficient implementation, as it avoids the need to test for rank degeneracies and also the need to modify factorizations or solution methods based upon those rank degeneracies. In our approach, we take advantage of the guarantee of nonsingularity by implementing an efficient method for solving the search direction and show that our algorithm is competitive with SVM-QP and also that it is a particularly effective when the fraction of nonbound support vectors is large. In addition, we show competitive performance of the proposed algorithm against two popular SVM training algorithms, SVMLight and LIBSVM.\",\n \"Title: A dictionary-based approach to fast and accurate name matching in large law enforcement databases\\nYear: 2006\\n\\nAbstract: In the presence of dirty data, a search for specific information by a standard query (e.g., search for a name that is misspelled or mistyped) does not return all needed information. This is an issue of grave importance in homeland security, criminology, medical applications, GIS (geographic information systems) and so on. Different techniques, such as soundex, phonix, n-grams, edit-distance, have been used to improve the matching rate in these name-matching applications. There is a pressing need for name matching approaches that provide high levels of accuracy, while at the same time maintaining the computational complexity of achieving this goal reasonably low. In this paper, we present ANSWER, a name matching approach that utilizes a prefix-tree of available names in the database. Creating and searching the name dictionary tree is fast and accurate and, thus, ANSWER is superior to other techniques of retrieving fuzzy name matches in large databases.\",\n \"Title: A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes\\nYear: 2010\\n\\nAbstract: Outlier detection has attracted substantial attention in many applications and research areas; some of the most prominent applications are network intrusion detection or credit card fraud detection. Many of the existing approaches are based on calculating distances among the points in the dataset. These approaches cannot easily adapt to current datasets that usually contain a mix of categorical and continuous attributes, and may be distributed among different geographical locations. In addition, current datasets usually have a large number of dimensions. These datasets tend to be sparse, and traditional concepts such as Euclidean distance or nearest neighbor become unsuitable. We propose a fast distributed outlier detection strategy intended for datasets containing mixed attributes. The proposed method takes into consideration the sparseness of the dataset, and is experimentally shown to be highly scalable with the number of points and the number of attributes in the dataset. Experimental results show that the proposed outlier detection method compares very favorably with other state-of-the art outlier detection strategies proposed in the literature and that the speedup achieved by its distributed version is very close to linear.\",\n \"Title: Generalized Entropy for Splitting on Numerical Attributes in Decision Trees\\nYear: 2006\\n\\nAbstract: Decision Trees are well known for their training efficiency and their interpretable knowledge representation. They apply a greedy search and a divide-and-conquer approach to learn patterns. The greedy search is based on the evaluation criterion on the candidate splits at each node. Although research has been performed on various such criteria, there is no significant improvement from the classical split approaches introduced in the early decision tree literature. This paper presents a new evaluation rule to determine candidate splits in decision tree classifiers. The experiments show that this new evaluation rule reduces the size of the resulting tree, while maintaining the tree's accuracy.\",\n \"Title: Direct-sequence Spread-Spectrum with Transform Domain Interference Suppression.\\nYear: 1995\\n\\nAbstract: The performance of a direct-sequence spread-spectrum (DS-SS) receiver employing new techniques for multiple non-stationary interference suppression is presented. These techniques are based on transform domain order statistics and they selectively suppress spectral components that are sufficiently large and narrow. Other important advantages are also discussed. The bit error rate performance is determined by Monte-Carlo simulations and it is compared to the performance of fixed notch filtering and of the well-known LMS algorithm. The effect of using data windowing is examined, and results with single-tone, narrow-band, stationary, non-stationary and multiple interference are presented. A comparison of the amount of computation required by the proposed transform domain approach to that of the time-domain LMS algorithm is also presented.\",\n \"Title: A Partitioned Fuzzy ARTMAP Implementation for Fast Processing of Large Databases on Sequential Machines\\nYear: 2004\\n\\nAbstract: Fuzzy ARTMAP (FAM) is a neural network architec- ture that can establish the correct mapping between real valued input patterns and their correct labels. FAM can learn quickly compared to other neural network paradigms and has the advantage of incremental/online learning capabilities. Nevertheless FAM tends to slow down as the size of the data set grows. This problem is analyzed and a solution is proposed that can speed up the algorithm in sequential as well as parallel settings. Experimental results are presented that show a consid- erable improvement in speed of the algorithm at the cost of creating larger size FAM architectures. Directions for future work are also discussed.\",\n \"Title: Data-partitioning using the Hilbert space filling curves: effect on the speed of convergence of Fuzzy ARTMAP for large database problems.\\nYear: 2005\\n\\nAbstract: The Fuzzy ARTMAP algorithm has been proven to be one of the premier neural network architectures for classification problems. One of the properties of Fuzzy ARTMAP, which can be both an asset and a liability, is its capacity to produce new nodes (templates) on demand to represent classification categories. This property allows Fuzzy ARTMAP to automatically adapt to the database without having to a priori specify its network size. On the other hand, it has the undesirable side effect that large databases might produce a large network size (node proliferation) that can dramatically slow down the training speed of the algorithm. To address the slow convergence speed of Fuzzy ARTMAP for large database problems, we propose the use of space-filling curves, specifically the Hilbert space-filling curves (HSFC). Hilbert space-filling curves allow us to divide the problem into smaller sub-problems, each focusing on a smaller than the original dataset. For learning each partition of data, a different Fuzzy ARTMAP network is used. Through this divide-and-conquer approach we are avoiding the node proliferation problem, and consequently we speedup Fuzzy ARTMAP's training. Results have been produced for a two-class, 16-dimensional Gaussian data, and on the Forest database, available at the UCI repository. Our results indicate that the Hilbert space-filling curve approach reduces the time that it takes to train Fuzzy ARTMAP without affecting the generalization performance attained by Fuzzy ARTMAP trained on the original large dataset. Given that the resulting smaller datasets that the HSFC approach produces can independently be learned by different Fuzzy ARTMAP networks, we have also implemented and tested a parallel implementation of this approach on a Beowulf cluster of workstations that further speeds up Fuzzy ARTMAP's convergence to a solution for large database problems.\",\n \"Title: Pareto-Path Multitask Multiple Kernel Learning\\nYear: 2014\\n\\nAbstract: A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.\",\n \"Title: A Connectionist-Symbolic Approach to Modeling Agent Behavior: Neural Networks Grouped by Contexts\\nYear: 2001\\n\\nAbstract: A recent report by the National Research Council (NRC) declares neural networks \\\"hold the most promise for providing powerful learning models\\\". While some researchers have experimented with using neural networks to model battlefield behavior for Computer Generated Forces (CGF) systems used in distributed simulations, the NRC report indicates that further research is needed to develop a hybrid system that will integrate the newer neural network technology into the current rule-based paradigms. This paper supports this solicitation by examining the use of a context structure to modularly organize the application of neural networks to a low-level Semi-Automated Forces (SAF) reactive task. Specifically, it reports on the development of a neural network movement model and illustrates how its performance is improved through the use of the modular context paradigm. Further, this paper introduces the theory behind the neural networks' architecture and training algorithms as well as the specifics of how the networks were developed for this investigation. Lastly, it illustrates how the networks were integrated with SAF software, defines the networks' performance measures, presents the results of the scenarios considered in this investigation, and offers directions for future work.\",\n \"Title: Putting the Utility of Match Tracking in Fuzzy ARTMAP Training to the Test\\nYear: 2003\\n\\nAbstract: An integral component of Fuzzy ARTMAP's training phase is the use of Match Tracking (MT), whose functionality is to search for an appropriate category that will correctly classify a presented training pattern in case this particular pattern was originally misclassified. In this paper we explain the MT's role in detail, why it actually works and finally we put its usefulness to the test by comparing it to the simpler, faster alternative of not using MT at all during training. Finally, we present a series of experimental results that eventually raise questions about the MT's utility. More specifically, we show that in the absence of MT the resulting, trained FAM networks are of reasonable size and exhibit better generalization performance.\",\n \"Title: Similarity kernels for nearest neighbor-based outlier detection\\nYear: 2010\\n\\nAbstract: Outlier detection is an important research topic that focuses on detecting abnormal information in data sets and processes. This paper addresses the problem of determining which class of kernels should be used in a geometric framework for nearest neighbor-based outlier detection. It introduces the class of similarity kernels and employs it within that framework. We also propose the use of isotropic stationary kernels for the case of normed input spaces. Two definitions of similarity scores using kernels are given: the k-NN kernel similarity score (kNNSS) and the summation kernel similarity score (SKSS). The paper concludes with preliminary experimental results comparing the performance of kNNSS and SKSS for outlier detection on four data sets. SKSS compared favorably to kNNSS.\",\n \"Title: A Scalable and Efficient Outlier Detection Strategy for Categorical Data\\nYear: 2007\\n\\nAbstract: An important factor for discriminating between webpages is their genre (e.g., blogs, personal homepages, e-shops, online newspapers, etc). Webpage genre identification has a great potential in information retrieval since users of search engines can combine ...\",\n \"Title: Classification Of Noisy Patterns Using Artmap-Based Neural Networks\\nYear: 2000\\n\\nAbstract: In this paper we present a modification of the test phase of ARTMAP-based neural networks that improves the classification performance of the networks when the patterns that are used for classification are extracted from noisy signals. The signals that are considered in this work are textured images, which are a case of 2D signals. Two neural networks from the ARTMAP family are examined, namely the Fuzzy ARTMAP (FAM) neural network and the Hypersphere ARTMAP (HAM) neural network. We compare the original FAM and KAM architectures with the modified ones, which we name FAM-m and HAM-m respectively. We also compare the classification performance of the modified networks, and of the original networks when they are trained with patterns extracted from noisy textures. Finally, we illustrate how combination of features can improve the classification performance for both the noiseless and noisy textures.\",\n \"Title: Learning in the feed-forward random neural network: A critical review\\nYear: 2010\\n\\nAbstract: The Random Neural Network (RNN) has received, since its inception in 1989, considerable attention and has been successfully used in a number of applications. In this critical review paper we focus on the feed-forward RNN model and its ability to solve classification problems. In particular, we paid special attention to the RNN literature related with learning algorithms that discover the RNN interconnection weights, suggested other potential algorithms that can be used to find the RNN interconnection weights, and compared the RNN model with other neural-network based and non-neural network based classifier models. In review, the extensive literature review and experimentation with the RNN feed-forward model provided us with the necessary guidance to introduce six critical review comments that identify some gaps in the RNN's related literature and suggest directions for future research.\",\n \"Title: Properties of learning in ARTMAP\\nYear: 1994\\n\\nAbstract: In this paper we consider the ARTMAP architecture for situations requiring learning of many-to-one maps. It is shown that if ARTMAP is repeatedly presented with a list of input/output pairs, it establishes the required mapping in at most M a −1 list presentations, where M a corresponds to the total number of ones in each one of the input patterns. Other useful properties, associated with the learning of the mapping represented by an arbitrary list of input/output pairs, are also examined. These properties reveal some of the characteristics of learning in ARTMAP when it is used as a tool in establishing an arbitrary mapping from a binary input space to a binary output space. The results presented in this paper are valid for the fast learning case, and for small β a values, where β a is a parameter associated with the adaptation of bottom-up weights in one of the ART1 modules of ARTMAP.\",\n \"Title: Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization\\nYear: 1997\\n\\nAbstract: Recent theoretical results support that decreasing the number of free parameters in a neural network (i.e., weights) can improve generalization. These results have triggered the development of many approaches which try to determine an “appropriate” network size for a given problem. The main goal has been to find a network size just large enough to capture the general class properties of the data. In some cases, however, network size is not reduced significantly or the reduction is satisfactory but generalization is affected. In this paper, we propose the coupling of genetic algorithms with weight elimination. Our objective is not only to significantly reduce network size, by pruning larger size networks, but also to preserve generalization, that is, to come up with pruned networks which generalize as good or even better than their unpruned counterparts. The innovation of our work relies on a fitness function which uses an adaptive parameter to encourage reproduction of networks having small size and good generalization. The proposed approach has been tested using both artificial and real databases demonstrating good performance.\",\n \"Title: A k-norm pruning algorithm for decision tree classifiers based on error rate estimation\\nYear: 2008\\n\\nAbstract: Decision trees are well-known and established models for classification and regression. In this paper, we focus on the estimation and the minimization of the misclassification rate of decision tree classifiers. We apply Lidstone's Law of Succession for the estimation of the class probabilities and error rates. In our work, we take into account not only the expected values of the error rate, which has been the norm in existing research, but also the corresponding reliability (measured by standard deviations) of the error rate. Based on this estimation, we propose an efficient pruning algorithm, called k-norm pruning, that has a clear theoretical interpretation, is easily implemented, and does not require a validation set. Our experiments show that our proposed pruning algorithm produces accurate trees quickly, and compares very favorably with two other well-known pruning algorithms, CCP of CART and EBP of C4.5.\",\n \"Title: Index based on algebraic functions of views\\nYear: 1998\\n\\nAbstract: In this paper, we propose the use of algebraic functions of views for indexing-based object recognition. During indexing, we consider groups of model points and we represent all the views (i.e., images) that they can produce in a hash table. The images that a group of model points can produce are computed by combining a small number of reference views which contain the group using algebraic functions of views. Fundamental to this procedure is a methodology, based on Singular Value Decomposition and Interval Arithmetic, for estimating the allowable ranges of values that the parameters of algebraic functions can assume. During recognition, scene groups are used to retrieve from the hash table the most feasible model groups that might have produced the scene groups. The use of al- gebraic functions of views for indexing-based recognition offers a number of advantages. First of all, the hash table can be built using a small number of reference views per object. This is in contrast to cur- rent approaches which build the hash table using either a large num- ber of reference views or 3D models. Most importantly, recognition does not rely on the similarity between reference views and novel views; all that is required for the novel views is to contain common groups of points with a small number of reference views. Second, verification becomes simpler. This is because candidate models can now be back-projected onto the scene by applying a linear transfor- mation on a small number of reference views of the candidate model. Finally, the proposed approach is more general and extendible. This is because algebraic functions of views have been shown to exist over a wide range of transformations and projections. The recognition performance of the proposed approach is demonstrated using both artificial and real data. c\",\n \"Title: Intelligent Trading Agents for Massively Multi-player Game Economies\\nYear: 2008\\n\\nAbstract: As massively multi-player gaming environments become more detailed, developing agents to populate these virtual worlds as capable non-player characters poses an increas- ingly complex problem. Human players in many games must achieve their objectives through financial skills such as tr ad- ing and supply chain management as well as through com- bat and diplomacy. In this paper, we examine the problem of creating intelligent trading agents for virtual markets . Us- ing historical data from EVE Online, a science-fiction based MMORPG, we evaluate several strategies for buying, sell- ing, and supply chain management. We demonstrate that us- ing reinforcement learning to determine policies based on the market microstructure gives trading agents a competitive ad- vantage in amassing wealth. Imbuing agents with the ability to adapt their trading policies can make them more resistant to exploitation by other traders and capable of participati ng in virtual economies on an equal footing with humans.\",\n \"Title: S-Race: a multi-objective racing algorithm\\nYear: 2013\\n\\nAbstract: This paper presents a multi-objective racing algorithm, S-Race, which efficiently addresses multi-objective model selection problems in the sense of Pareto optimality. As a racing algorithm, S-Race attempts to eliminate candidate models as soon as there is sufficient statistical evidence of their inferiority relative to other models with respect to all objectives. This approach is followed in the interest of controlling the computational effort. S-Race adopts a non-parametric sign test to identify pair-wise domination relationship between models. Meanwhile, Holm's Step-Down method is employed to control the overall family-wise error rate of simultaneous hypotheses testing during the race. Experimental results involving the selection of superior Support Vector Machine classifiers according to 2 and 3 performance criteria indicate that S-Race is an efficient and effective algorithm for automatic model selection, when compared to a brute-force, multi-objective selection approach.\",\n \"Title: Experiments with Safe ARTMAP and Comparisons to Other ART Networks\\nYear: 2006\\n\\nAbstract: Fuzzy ARTMAP (FAM) is currently considered as one of the premier neural network architectures in solving classification problems. Safe muARTMAP, a modified version of FAM, was introduced to remedy the category proliferation problem that has been extensively reported in the literature. However, Safe muARTMAP's performance depends on a number of parameters. In this paper, we analyzed each parameter to set up the candidate values for evaluation. We performed an exhaustive experimentation to identify good default values for these parameters for a variety of problems, and compared the best performing Safe muARTMAP network with other best performing ART networks, including those that claim to solve the category proliferation problem.\",\n \"Title: Properties of learning related to pattern diversity in ART1\\nYear: 1991\\n\\nAbstract: In this paper we consider a special class of the ART1 neural network. It is shown that if this network is repeatedly presented with an arbitrary list of binary input patterns, learning self-stabilizes in at most m list presentations, where m corresponds to the number of patterns of distinct size in the input list. Other useful properties of the ART1 network, associated with the learning of an arbitrary list of binary input patterns, are also examined. These properties reveal some of the “good” characteristics of the ART1 network when it is used as a tool for the learning of recognition categories.\",\n \"Title: A Fast Revised Simplex Method For Svm Training\\nYear: 2008\\n\\nAbstract: Active set methods for training the Support Vector Machines (SVM) are advantageous since they enable incremental training and, as we show in this research, do not exhibit exponentially increasing training times commonly associated with the decomposition methods as the SVM training parameter, C, is increased or the classification difficulty increases. Previous implementations of the active set method must contend with singularities, especially associated with the linear kernel, and must compute infinite descent directions, which may be inefficient, especially as C is increased. In this research, we propose a revised simplex method for quadratic programming, which has a guarantee of non-singularity for the sub-problem, and show how this can be adapted to SVM training.\",\n \"Title: A General Framework for Concurrent Simulation on Neural Network Models\\nYear: 1992\\n\\nAbstract: The analysis of complex neural network models via analytical techniques is often quite difficult due to the large numbers of components involved and the nonlinearities associated with these components. The authors present a framework for simulating neural networks as discrete event nonlinear dynamical systems. This includes neural network models whose components are described by continuous-time differential equations or by discrete-time difference equations. Specifically, the authors consider the design and construction of a concurrent object-oriented discrete event simulation environment for neural networks. The use of an object-oriented language provides the data abstraction facilities necessary to support modification and extension of the simulation system at a high level of abstraction. Furthermore, the ability to specify concurrent processing supports execution on parallel architectures. The use of this system is demonstrated by simulating a specific neural network model on a general-purpose parallel computer.\",\n \"Title: A Privacy Preserving Probabilistic Neural Network For Horizontally Partitioned Databases\\nYear: 2007\\n\\nAbstract: In this paper, we present a version of the Probabilistic Neural Network (PNN) that is capable of operating on a distributed database that is horizontally partitioned. It does so in a way that is privacy-preserving: that is, a test point can be evaluated by the algorithm without any party knowing the data owned by the other parties. We present an analysis of this algorithm from the standpoints of security and computational performance. Finally, we provide performance results of an implementation of this privacy preserving, distributed PNN algorithm.\",\n \"Title: Category regions as new geometrical concepts in Fuzzy-ART and Fuzzy-ARTMAP\\nYear: 2002\\n\\nAbstract: In this paper we introduce novel geometric concepts, namely category regions, in the original framework of Fuzzy-ART (FA) and Fuzzy-ARTMAP (FAM). The definitions of these regions are based on geometric interpretations of the vigilance test and the F2 layer competition of committed nodes with uncommitted ones, that we call commitment test. It turns out that not only these regions have the same geometrical shape (polytope structure), but they also share a lot of common and interesting properties that are demonstrated in this paper. One of these properties is the shrinking of the volume that each one of these polytope structures occupies, as training progresses, which alludes to the stability of learning in FA and FAM, a well-known result. Furthermore, properties of learning of FA and FAM are also proven utilizing the geometrical structure and properties that these regions possess; some of these properties were proven before using counterintuitive, algebraic manipulations and are now demonstrated again via intuitive geometrical arguments. One of the results that is worth mentioning as having practical ramifications is the one which states that for certain areas of the vigilance-choice parameter space (p,a), the training and performance (testing) phases of FA and FAM do not depend on the particular choices of the vigilance parameter. Finally, it is worth noting that, although the idea of the category regions has been developed under the premises of FA and FAM, category regions are also meaningful for later developed ART neural network structures, such as ARTEMAP, ARTMAP-IC, Boosted ARTMAP, Micro-ARTMAP, Ellipsoid-ART/ARTMAP, among others.\",\n \"Title: <Bold>Properties Of The K-Norm Pruning Algorithm For Decision Tree Classifiers</Bold>\\nYear: 2008\\n\\nAbstract: Pruning is one of the key procedures in training decision tree classifiers. It removes trivial rules from the raw knowledge base built from training examples, in order to avoid over-using noisy, conflicting, or fuzzy inputs, so that the refined model can generalize better with unseen cases. In this paper we present a number of properties of k-norm pruning, a recently proposed pruning algorithm, which has clear theoretical interpretation. In an earlier paper it was shown that k-norm pruning compares very favorably in terms of accuracy and size with Minimal Cost-Complexity Pruning and Error Based Pruning, two of the most cited decision tree pruning methods; it was also shown that k-norm pruning is more efficient, at times orders of magnitude more efficient than Minimal Cost-Complexity Pruning and Error Based Pruning. In this paper, we demonstrate the validity of the k-norm properties through a series of theorems, and explain their practical significance.\",\n \"Title: Non-derivable itemsets for fast outlier detection in large high-dimensional categorical data\\nYear: 2011\\n\\nAbstract: Detecting outliers in a dataset is an important data mining task with many applications, such as detection of credit card fraud or network intrusions. Traditional methods assume numerical data and compute pair-wise distances among points. Recently, outlier detection methods were proposed for categorical and mixed-attribute data using the concept of Frequent Itemsets (FIs). These methods face challenges when dealing with large high-dimensional data, where the number of generated FIs can be extremely large. To address this issue, we propose several outlier detection schemes inspired by the well-known condensed representation of FIs, Non-Derivable Itemsets (NDIs). Specifically, we contrast a method based on frequent NDIs, FNDI-OD, and a method based on the negative border of NDIs, NBNDI-OD, with their previously proposed FI-based counterparts. We also explore outlier detection based on Non-Almost Derivable Itemsets (NADIs), which approximate the NDIs in the data given a δ parameter. Our proposed methods use a far smaller collection of sets than the FI collection in order to compute an anomaly score for each data point. Experiments on real-life data show that, as expected, methods based on NDIs and NADIs offer substantial advantages in terms of speed and scalability over FI-based Outlier Detection method. What is significant is that NDI-based methods exhibit similar or better detection accuracy compared to the FI-based methods, which supports our claim that the NDI representation is especially well-suited for the task of detecting outliers. At the same time, the NDI approximation scheme, NADIs is shown to exhibit similar accuracy to the NDI-based method for various δ values and further runtime performance gains. Finally, we offer an in-depth discussion and experimentation regarding the trade-offs of the proposed algorithms and the choice of parameter values.\",\n \"Title: Answer: Approximate Name Search With Errors In Large Databases By A Novel Approach Based On Prefix-Dictionary\\nYear: 2006\\n\\nAbstract: The obvious need for using modem computer networking capabilities to enable the effective sharing of information has resulted in data-sharing systems, which store, and manage large amounts of data. These data need to be effectively searched and analyzed. More specifically, in the presence of dirty data, a search for specific information by a standard query (e.g., search for a name that is misspelled or mistyped) does not return all needed information, as required in homeland security, criminology, and medical applications, amongst others. Different techniques, such as soundex, phonix, n-grams, edit-distance, have been used to improve the matching rate in these name-matching applications. These techniques have demonstrated varying levels of success, but there is a pressing need for name matching approaches that provide high levels of accuracy in matching names, while at the same time maintaining low computational complexity. In this paper, such a technique, called ANSWER, is proposed and its characteristics are discussed. Our results demonstrate that ANSWER possesses high accuracy, as well as high speed and is superior to other techniques of retrieving fuzzy name matches in large databases.\",\n \"Title: Learning tactical human behavior through observation of human performance\\nYear: 2006\\n\\nAbstract: It is widely accepted that the difficulty and expense involved in acquiring the knowledge behind tactical behaviors has been one limiting factor in the development of simulated agents representing adversaries and teammates in military and game simulations. Several researchers have addressed this problem with varying degrees of success. The problem mostly lies in the fact that tactical knowledge is difficult to elicit and represent through interactive sessions between the model developer and the subject matter expert. This paper describes a novel approach that employs genetic programming in conjunction with context-based reasoning to evolve tactical agents based upon automatic observation of a human performing a mission on a simulator. In this paper, we describe the process used to carry out the learning. A prototype was built to demonstrate feasibility and it is described herein. The prototype was rigorously and extensively tested. The evolved agents exhibited good fidelity to the observed human performance, as well as the capacity to generalize from it.\",\n \"Title: Generative Neural Networks for Multi-task Life-Long Learning.\\nYear: 2014\\n\\nAbstract: Multi-task learning (MTL) is an established method of inducing bias in neural network learning. It has been used as a method of knowledge transfer in sequential learning tasks, and as a method of overcoming the problem of impoverished training data. A standard problem in neural networks is the establishment of the proper network structure for a given problem. This problem is compounded in the MTL ...\",\n \"Title: Experiments with Safe muARTMAP : effect of the network parameters on the network performance.\\nYear: 2007\\n\\nAbstract: Fuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is, Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data are of the noisy and/or overlapping nature. To remedy this problem a number of researchers have designed modifications to the training phase of Fuzzy ARTMAP that had the beneficial effect of reducing this category proliferation. One of these modified Fuzzy ARTMAP architectures was the one proposed by Gomez-Sanchez, and his colleagues, referred to as Safe μARTMAP. In this paper we present reasonable analytical arguments that demonstrate of how we should choose the range of some of the Safe μARTMAP network parameters. Through a combination of these analytical arguments and experimentation we were able to identify good default parameter values for some of the Safe μARTMAP network parameters. This feat would allow one to save computations when a good performing Safe μARTMAP network is needed to be identified for a new classification problem. Furthermore, we performed an exhaustive experimentation to find the best Safe μARTMAP network for a variety of problems (simulated and real problems), and we compared it with other best performing ART networks, including other ART networks that claim to resolve the category proliferation problem in Fuzzy ARTMAP. These experimental results allow one to make appropriate statements regarding the pair-wise comparison of a number of ART networks (including Safe μARTMAP).\",\n \"Title: Detecting Outliers in High-Dimensional Datasets with Mixed Attributes\\nYear: 2008\\n\\nAbstract: Outlier Detection has attracted substantial attention in many applications and research areas. Examples include detection of network intrusions or credit card fraud. Many of the existing approaches are based on pair-wise distances among all points in the dataset. These approaches cannot easily extend to current datasets that usually contain a mix of categorical and continuous attributes, and may be scattered over large geographical areas. In addition, current datasets usually have a large number of dimensions. These datasets tend to be sparse, and traditional concepts such as Euclidean distance or nearest neighbor become unsuitable. We propose ODMAD, a fast outlier detection strategy intended for datasets containing mixed attributes. ODMAD takes into consideration the sparseness of the dataset, and is experimentally shown to be highly scalable with the number of points and number of attributes in the dataset.\",\n \"Title: Educating middle and high school students in space operations: the simulation approach\\nYear: 2004\\n\\nAbstract: This paper describes a proposed pilot educational program to teach middle and high school students the space shuttle processing operations. This pilot program involves a partnership of two Universities (University of Central Florida and University of Florida), a small business (Productivity Apex), and three middle/high schools in the Florida area.Our proposed pilot program is an innovative approach designed to teach students about the complexities of space vehicle processing through a simulation environment. Through the many planned activities that we propose to achieve our goal, we will be able to introduce middle and high school students to aerospace operations, excite them about space education, and enthusiastically engage them in science and technology related projects. The ultimate objective of this effort is to motivate middle and high school students to pursue careers in Science, Technology, Engineering, and Math (STEM) fields.\",\n \"Title: GFAM: evolving Fuzzy ARTMAP neural networks.\\nYear: 2006\\n\\nAbstract: This paper focuses on the evolution of Fuzzy ARTMAP neural network classifiers, using genetic algorithms, with the objective of improving generalization performance (classification accuracy of the ART network on unseen test data) and alleviating the ART category proliferation problem (the problem of creating more than necessary ART network categories to solve a classification problem). We refer to the resulting architecture as GFAM. We demonstrate through extensive experimentation that GFAM exhibits good generalization and is of small size (creates few ART categories), while consuming reasonable computational effort. In a number of classification problems, GFAM produces the optimal classifier. Furthermore, we compare the performance of GFAM with other competitive ARTMAP classifiers that have appeared in the literature and addressed the category proliferation problem in ART. We illustrate that GFAM produces improved results over these architectures, as well as other competitive classifiers.\",\n \"Title: A Unifying Framework for Typical Multitask Multiple Kernel Learning Problems\\nYear: 2014\\n\\nAbstract: Over the past few years, multiple kernel learning (MKL) has received significant attention among data-driven feature selection techniques in the context of kernel-based learning. MKL formulations have been devised and solved for a broad spectrum of machine learning problems, including multitask learning (MTL). Solving different MKL formulations usually involves designing algorithms that are tailored to the problem at hand, which is, typically, a nontrivial accomplishment. In this paper we present a general multitask multiple kernel learning (MT-MKL) framework that subsumes well-known MT-MKL formulations, as well as several important MKL approaches on single-task problems. We then derive a simple algorithm that can solve the unifying framework. To demonstrate the flexibility of the proposed framework, we formulate a new learning problem, namely partially-shared common space MT-MKL, and demonstrate its merits through experimentation.\",\n \"Title: Conic Multi-task Classification.\\nYear: 2014\\n\\nAbstract: Traditionally, Multi-task Learning (MTL) models optimize the average of task-related objective functions, which is an intuitive approach and which we will be referring to as Average MTL. However, a more general framework, referred to as Conic MTL, can be formulated by considering conic combinations of the objective functions instead; in this framework, Average MTL arises as a special case, when all combination coefficients equal 1. Although the advantage of Conic MTL over Average MTL has been shown experimentally in previous works, no theoretical justification has been provided to date. In this paper, we derive a generalization bound for the Conic MTL method, and demonstrate that the tightest bound is not necessarily achieved, when all combination coefficients equal 1; hence, Average MTL may not always be the optimal choice, and it is important to consider Conic MTL. As a byproduct of the generalization bound, it also theoretically explains the good experimental results of previous relevant works. Finally, we propose a new Conic MTL model, whose conic combination coefficients minimize the generalization bound, instead of choosing them heuristically as has been done in previous methods. The rationale and advantage of our model is demonstrated and verified via a series of experiments by comparing with several other methods.\",\n \"Title: Online model racing based on extreme performance\\nYear: 2014\\n\\nAbstract: Racing algorithms are often used for offline model selection, where models are compared in terms of their average performance over a collection of problems. In this paper, we present a new racing algorithm variant, Max-Race, which makes decisions based on the maximum performance of models. It is an online algorithm, whose goal is to optimally allocate computational resources in a portfolio of evolutionary algorithms, while solving a particular problem instance. It employs a hypothesis test based on extreme value theory in order to decide, which component algorithms to retire, while avoiding unnecessary computations. Experimental results confirm that Max-Race is able to identify the best individual with high precision and low computational overhead. When used as a scheme to select the best from a portfolio of algorithms, the results compare favorably to the ones of other popular algorithm portfolio approaches.\",\n \"Title: Multitask Classification Hypothesis Space With Improved Generalization Bounds.\\nYear: 2013\\n\\nAbstract: This paper presents a pair of hypothesis spaces (HSs) of vector-valued functions intended to be used in the context of multitask classification. While both are parameterized on the elements of reproducing kernel Hilbert spaces and impose a feature mapping that is common to all tasks, one of them assumes this mapping as fixed, while the more general one learns the mapping via multiple kernel learning. For these new HSs, empirical Rademacher complexity-based generalization bounds are derived, and are shown to be tighter than the bound of a particular HS, which has appeared recently in the literature, leading to improved performance. As a matter of fact, the latter HS is shown to be a special case of ours. Based on an equivalence to Group-Lasso type HSs, the proposed HSs are utilized toward corresponding support vector machine-based formulations. Finally, experimental results on multitask learning problems underline the quality of the derived bounds and validate this paper's analysis.\",\n \"Title: Correction to 'Packet error probabilities in frequency hopped spread-spectrum packet radio networks-memoryless frequency hopping patterns considered'\\nYear: 1991\\n\\nAbstract: The author corrects the approach followed in the above-titled paper (see ibid., vol.36, no.6, p.720-3, 1988) to compute packet error probabilities in frequency-hopped spread-spectrum packet radio networks with memoryless frequency-hopping patterns. The nature of the error is that the sequence of symbol errors is not Markov, so that a formula which is instrumental for the computation of packet error probabilities, is incorrect. He obtains exact and easy to compute expressions for the evaluation of the packet error probabilities.<>\",\n \"Title: Analyzing the Fuzzy ARTMAP Matchtracking mechanism with Co-Objective Optimization Theory\\nYear: 2007\\n\\nAbstract: In the process of learning a pattern I, the fuzzy ARTMAP algorithm templates (i.e., the weight vectors corresponding to nodes of its category representation layer) compete for the representation of the given pattern. This competition can induce matchtracking: a process that iterates a number of times over the template set searching for a template w* of the correct class that best represents the pattern I. In this paper, we analyze the search for a winning template from the perspective of bi-criterion optimization and prove that it is actually a walk along the Pareto front of an appropriately defined co-objective optimization problem. This observation allows us to propose the basis for an implementation variant of fuzzy ARTMAP that (a) produces exactly the same network as fuzzy ARTMAP, (b) avoids matchtracking by explicitly keeping track of a subset of the Pareto front, (c) finds the correct template to represent an input pattern through a single pass over the template set and (d) eliminates the need for the fuzzy ARTMAP parameter epsiv.\",\n \"Title: On Extending the SMO Algorithm Sub-Problem\\nYear: 2007\\n\\nAbstract: The Support Vector Machine is a widely employed machine learning model due to its repeatedly demonstrated superior generalization performance. The Sequential Minimal Optimization (SMO) algorithm is one of the most popular SVM training approaches. SMO is fast, as well as easy to implement; however, it has a limited working set size (2 points only). Faster training times can result if the working set size can be increased without significantly increasing the computational complexity. In this paper, we extend the 2-point SMO formulation to a 4-point formulation and address the theoretical issues associated with such an extension. We show that modifying the SMO algorithm to increase the working set size is beneficial in terms of the number of iterations required for convergence, and shows promise for reducing the overall training time. I. INTRODUCTION he Support Vector Machine is a classification model, maintaining excellent generalization capabilities along with a built-in resistance to overtraining. This generalization performance is based upon strong theoretical foundations first introduced by Vapnik (1). Instead of performing empirical risk minimization, as many other models do, the SVM algorithm performs structural risk minimization. This simply means that the algorithm seeks a balance between fitting function complexity and training error. The SVM algorithm works by finding a separating hyperplane between two classes of data that maximizes the margin between the closest data point and the separating hyperplane. Vapnik shows that this is equivalent to structural risk minimization. The primal, soft-margin problem formulation is shown in (1).\",\n \"Title: MO-GART: Multiobjective genetic ART architectures\\nYear: 2008\\n\\nAbstract: In this work we present, for the first time, the evolution of ART Neural Network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to evolve simultaneously the weights, as well as the topology of three well-known ART architectures; Fuzzy ARTMAP (FAM), Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM). We refer to the resulting architectures as MO-GFAM, MOGEAM, or MO-GGAM, and collectively as MO-GART. The major advantage of MO-GART is that it produces a number of solutions for the classification problem at hand that have different levels of merit (accuracy on unseen data (generalization) and size (number of categories created)). MO-GART is shown to be more elegant (does not require user intervention to define the network parameters), more effective (of better accuracy and smaller size), and more efficient (faster to produce the solution networks) than other ART neural network architectures that have appeared in the literature.\",\n \"Title: An efficient active set method for SVM training without singular inner problems\\nYear: 2009\\n\\nAbstract: Efficiently implemented active set methods have been successfully applied to support vector machine (SVM) training. These active set methods offer higher precision and incremental training at the cost of additional memory requirements when compared to decomposition methods such as sequential minimal optimization (SMO). However, all existing active set methods must deal with singularities occurring within the inner problem solved at each iteration, a problem that leads to more complex implementation and potential inefficiencies. Here, we introduce a revised simplex method, originally introduced by Rusin, adapted for SVM training and show this is an active set method similar to most existing methods with the advantage of maintaining nonsingularity of the inner problem. We compare performance to an existing active set method introduced by Scheinberg and demonstrate an improvement in training times, in some cases. We show our method maintains a slightly simpler implementation and offers advantages in terms of applying iterative methods to alleviate memory concerns. We also show performance of the active set methods when compared to state-of-the-art decomposition implementations such as SVMLight and SMO.\",\n \"Title: Convergence Properties of Learning in ART1\\nYear: 1990\\n\\nAbstract: We consider the ART1 neural network architecture. It is shown that in the fast learning case, an ART1 network that is repeatedly presented with an arbitrary list of binary input patterns, self-stabilizes the recognition code of every size-l pattern in at most l list presentations.\",\n \"Title: SPRINT Multi-Objective Model Racing\\nYear: 2015\\n\\nAbstract: Multi-objective model selection, which is an important aspect of Machine Learning, refers to the problem of identifying a set of Pareto optimal models from a given ensemble of models. This paper proposes SPRINT-Race, a multi-objective racing algorithm based on the Sequential Probability Ratio Test with an Indifference Zone. In SPRINT-Race, a non-parametric ternary-decision sequential analogue of the sign test is adopted to identify pair-wise dominance and non-dominance relationship. In addition, a Bonferroni approach is employed to control the overall probability of any erroneous decisions. In the fixed confidence setting, SPRINT-Race tries to minimize the computational effort needed to achieve a predefined confidence about the quality of the returned models. The efficiency of SPRINT-Race is analyzed on artificially-constructed multi-objective model selection problems with known ground-truth. Moreover, SPRINT-Race is applied to identifying the Pareto optimal parameter settings of Ant Colony Optimization algorithms in the context of solving Traveling Salesman Problems. The experimental results confirm the advantages of SPRINT-Race for multi-objective model selection.\",\n \"Title: Hash Function Learning via Codewords\\nYear: 2015\\n\\nAbstract: In this paper we introduce a novel hash learning framework that has two main distinguishing features, when compared to past approaches. First, it utilizes codewords in the Hamming space as ancillary means to accomplish its hash learning task. These codewords, which are inferred from the data, attempt to capture similarity aspects of the data's hash codes. Secondly and more importantly, the same framework is capable of addressing supervised, unsupervised and, even, semi-supervised hash learning tasks in a natural manner. A series of comparative experiments focused on content-based image retrieval highlights its performance advantages.\",\n \"Title: Multi-Task Learning with Group-Specific Feature Space Sharing\\nYear: 2015\\n\\nAbstract: When faced with learning a set of inter-related tasks from a limited amount of usable data, learning each task independently may lead to poor generalization performance. (MTL) exploits the latent relations between tasks and overcomes data scarcity limitations by co-learning all these tasks simultaneously to offer improved performance. We propose a novel Multi-Task Multiple Kernel Learning framework based on Support Vector Machines for binary classification tasks. By considering pair-wise task affinity in terms of similarity between a pair's respective feature spaces, the new framework, compared to other similar MTL approaches, offers a high degree of flexibility in determining how similar feature spaces should be, as well as which pairs of tasks should share a common feature space in order to benefit overall performance. The associated optimization problem is solved via a block coordinate descent, which employs a consensus-form Alternating Direction Method of Multipliers algorithm to optimize the Multiple Kernel Learning weights and, hence, to determine task affinities. Empirical evaluation on seven data sets exhibits a statistically significant improvement of our framework's results compared to the ones of several other Clustered Multi-Task Learning methods.\",\n \"Title: Multi-Task Learning Using Neighborhood Kernels.\\nYear: 2017\\n\\nAbstract: This paper introduces a new and effective algorithm for learning kernels in a Multi-Task Learning (MTL) setting. Although, we consider a MTL scenario here, our approach can be easily applied to standard single task learning, as well. As shown by our empirical results, our algorithm consistently outperforms the traditional kernel learning algorithms such as uniform combination solution, convex combinations of base kernels as well as some kernel alignment-based models, which have been proven to give promising results in the past. We present a Rademacher complexity bound based on which a new Multi-Task Multiple Kernel Learning (MT-MKL) model is derived. In particular, we propose a Support Vector Machine-regularized model in which, for each task, an optimal kernel is learned based on a neighborhood-defining kernel that is not restricted to be positive semi-definite. Comparative experimental results are showcased that underline the merits of our neighborhood-defining framework in both classification and regression problems.\",\n \"Title: Pareto-Optimal Model Selection via SPRINT-Race.\\nYear: 2018\\n\\nAbstract: In machine learning, the notion of multi-objective model selection (MOMS) refers to the problem of identifying the set of Pareto-optimal models that optimize by compromising more than one predefined objectives simultaneously. This paper introduces SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting, which is based on the sequential probability ratio with indiffere...\",\n \"Title: Sparse Inductive Embedding: An Explorative Data Visualization Technique\\nYear: 2017\\n\\nAbstract: Metric Multi-Dimensional Scaling is one of the most popular visualization techniques aiming to determine a point configuration of the data in a low-dimensional space, while preserving the original pairwise dissimilarities amongst the data points. In this paper we propose Sparse Inductive Multi- Dimensional Scaling approach, an exploratory data analysis and visualization tool. Its kernel-based nature provides an inductive method, and hence allows for embedding of previously unseen data even with non-numerical features. Furthermore, via the use of a sparsity-promoting regularizer, the technique is capable of identifying key training samples, whose contribution in defining the embedded manifold's specific structure is most influential. As we will show, the influence of these samples leads us to interesting interpretations in the context of the data at hand. We adequately train the consequent non-convex framework via an Iterative Majorization approach, for which a stable closed-form solution is obtained. Experimental results showcase the potential benefits of the proposed embedding method on both artificial and real-world data.\",\n \"Title: Multi-Label Learning Via Codewords\\nYear: 2018\\n\\nAbstract: In this paper, we introduce a novel hash learning framework for multi-label learning which employs structured prediction. A hash function is learned to embed samples in Hamming spaces, and for each label, a pair of codewords are simultaneously inferred from the available data. These codewords are then used to determine label predictions based on Hamming proximity. The key advantage of this framework is it's computational efficiency in tackling multi-label problems without making restrictive, simplifying assumptions about the structure of the output space, or developing problem-dependent heuristics. Our method not only enjoys considerably better scalability while capturing label inter-dependence, but also yields an exact training algorithm. Experimental results on a collection of benchmark multi-label datasets demonstrate that our model attains higher performance over alternative state-of-the-art multi-label approaches. It is also worth noting that our method can be extended to semi-supervised and missing labels scenarios.\",\n \"Title: Robust Manifold Learning via Conformity Pursuit.\\nYear: 2019\\n\\nAbstract: This letter presents an effective and simple method, termed Global Conformity Pursuit (GCP), for robust manifold learning. Data points lying on a union of low-dimensional nonlinear manifolds are expected to be highly conforming. On the other hand, outliers do not typically adhere to low-dimensional structures or otherwise do not exist in large numbers. Hence, they can be identified by their low ov...\",\n \"Title: A Structural Equation Model Analysis of Computing Identity Sub-Constructs and Student Academic Persistence.\\nYear: 2018\\n\\nAbstract: This Research Full Paper presents the effects of computing identity sub-constructs on the persistence of computer science students. Computer science (CS) is one of the fastest growing disciplines in the world and an emerging critical field for all students to obtain vital skills to be successful in the 21st century. Despite the growing importance of computer science, many university and college programs suffer from low student persistence rates. Disciplinary identity is a theoretical framework that refers to how students see themselves with respect to a discipline and is related to long-term membership in a disciplinary community. The theory has been effectively applied in Science, Technology, Engineering, and Mathematics (STEM) to understand students' success and persistence. This study examines the effects of performance/competence, recognition, interest and sense of belonging on the academic persistence of computer science students. A survey of approximately 1,640 computing students as part of a National Science Foundation (NSF) funded project was developed and administered at three metropolitan public institutions. Confirmatory Factor Analysis (CFA) was performed to validate the sub-constructs of identity for use in a computing identity model. Then, a structural equation model (SEM) was constructed as a snapshot of the structural relationships for describing and quantifying the impact of the identity sub-constructs on persistence. The results indicated that our model for CS aligns with prior research on disciplinary identity but also adds the importance of sense of belonging. In addition, the findings indicate that students' academic persistence is directly influenced by their interest. A better understanding of these factors may leverage insight into students' academic persistence in computer science/engineering programs as well as a meaningful lens of analysis for further curriculum and extracurricular activities.\",\n \"Title: Reduced-Rank Local Distance Metric Learning for k-NN Classification.\\nYear: 2019\\n\\nAbstract: We propose a new method for local distance metric learning based on sample similarity as side information. These local metrics, which utilize conical combinations of metric weight matrices, are learned from the pooled spatial characteristics of the data, as well as the similarity profiles between the pairs of samples, whose distances are measured. The main objective of our framework is to yield metrics, such that the resulting distances between similar samples are small and distances between dissimilar samples are above a certain threshold. For learning and inference purposes, we describe a transductive, as well as an inductive algorithm; the former approach naturally befits our framework, while the latter one is provided in the interest of faster learning. Experimental results on a collection of classification problems imply that the new methods may exhibit notable performance advantages over alternative metric learning approaches that have recently appeared in the literature.\",\n \"Title: The Impact of Technical Interviews, and other Professional and Cultural Experiences on Students' Computing Identity\\nYear: 2021\\n\\nAbstract: ABSTRACTIncreasingly companies assess a computing candidate's capabilities using technical interviews (TIs). Yet students struggle to code on demand, and there is already an insufficient amount of computing graduates to meet industry needs. Therefore, it is important to understand students' perceptions of TIs, and other professional experiences (e.g., computing jobs). We surveyed 740 undergraduate computing students at three universities to examine their experiences with the hiring process, as well as the impact of professional and cultural experiences (e.g., familial support) on computing identity. We considered the interactions between these experiences and social identity for groups underrepresented in computing - women, Black/African American, and Hispanic/Latinx students. Among other findings, we observed that students that did not have positive experiences with TIs had a reduced computing identity, but that facing discrimination during technical interviews had the opposite effect. Social support may play a role. Having friends in computing bolsters computing identity for Hispanic/Latinx students, as does a supportive home environment for women. Also, freelance computing jobs increase computing identity for Black/African American students. Our findings are intended to raise awareness of the best way for educators to help diverse groups of students to succeed, and to inform them of the experiences that may influence students' engagement, resilience, and computing identity development.\",\n \"Title: Sketches by MoSSaRT: Representative selection from manifolds with gross sparse corruptions\\nYear: 2022\\n\\nAbstract: •A reproduction profile encodes the data relations of grossly corrupted manifold structures.•Approximate feature maps emulate a desired feature mapping associated with a RKHS.•Scalable and parallelizable ADMM-based algorithm has nearly linear complexity in the data size.•The proxy objective function induced by the approximate features converges exponentially fast.•The representatives are vertices of the symmetrized convex hull of the data in a transformed space.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":74,"string":"74"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13282,"string":"13,282"}}},{"rowIdx":13283,"cells":{"question_id":{"kind":"string","value":"Chuang Lin"},"question":{"kind":"string","value":"Generate an abstract for the title \"Reliability-Aware Energy Efficiency in Web Service Provision and Placement\" using the following items: 1. Reliability\n2. Web services\n3. Energy consumption\n4. Optimization framework\n5. Heuristic algorithm"},"answer":{"kind":"string","value":"Reliability is a critical concern in the provision and placement of web services. A breakdown of service would seriously reduce customers' satisfaction, and thus harm the revenue of service providers. To maintain a high reliability, the common approach is deploying multiple service instances across different physical servers. This would inevitably raise another concern of energy consumption. Thus, greening web services also becomes an important issue. In this paper, we study the fundamental tradeoff between reliability and energy consumption, and propose an optimization framework that considers both factors. In specific, we build a continuous-time Markov model to analyze the steady-state reliability and mean time to failure (MTTF) from a service-oriented perspective, and obtain the minimum number of service instances to meet the given reliability requirement. Then, we show that deploying these instances in the server cluster to minimize energy consumption is NP-hard. To this end, we propose a heuristic algorithm to approximate the result. The analytical and experimental results show the effectiveness, and the approximation ratio is less than 1.25 for 90% of the data sets we use."},"correct_answer":{"kind":"string","value":"Reliability is a critical concern in the provision and placement of web services. A breakdown of service would seriously reduce customers' satisfaction, and thus harm the revenue of service providers. To maintain a high reliability, the common approach is deploying multiple service instances across different physical servers. This would inevitably raise another concern of energy consumption. Thus, greening web services also becomes an important issue. In this paper, we study the fundamental tradeoff between reliability and energy consumption, and propose an optimization framework that considers both factors. In specific, we build a continuous-time Markov model to analyze the steady-state reliability and mean time to failure (MTTF) from a service-oriented perspective, and obtain the minimum number of service instances to meet the given reliability requirement. Then, we show that deploying these instances in the server cluster to minimize energy consumption is NP-hard. To this end, we propose a heuristic algorithm to approximate the result. The analytical and experimental results show the effectiveness, and the approximation ratio is less than 1.25 for 90% of the data sets we use."},"documents":{"kind":"list like","value":["Title: Queue management for QoS provision build on network processor\nYear: 2003\n\nAbstract: Network processor is a kind of programmable processor performing network computing with special design and optimization. It can be viewed as a tight-coupled multi-processor system due to its architecture of multiple in-chip processors, high-speed buses and other function components. The design and development of networking systems using network processors is an emerging field that offers numerous challenges and opportunities. This paper provides the design and implementation of queue management module combining buffer management and packet scheduling for QoS provision, which uses Intel's network processor and follows the relative differentiated service model. As the key issues, system synchronization, task assignment to threads, and design difficulties are discussed in the paper.","Title: Modeling Hierarchical Caches in Content-Centric Networks\nYear: 2013\n\nAbstract: Content-Centric Network (CCN) provides a cleanslate design for the Internet, where content becomes the primitive of communications. In CCN, routers are equipped with content stores, which act as caches for frequently requested content. This design enables the Internet to provide content distribution services without any application-layer support. On the other hand, as caches are integrated into routers, the overall performance of CCN will be influenced by the caching efficiency. This paper studies the performance issues of caches in CCN, with the aim to gain some understanding on how caches should be designed to maintain a high performance in a cost-efficient way. Specifically, we use a two-dimensional discrete-time Markov chain to model the two-layer cache hierarchy formed by CCN routers, and develop an efficient algorithm to calculate the hit ratios of these caches. Simulations validate the accuracy of our modeling method, and convey some understanding on cache design in CCN.","Title: Effective Data Aggregation Supported by Dynamic Routing in Wireless Sensor Networks\nYear: 2010\n\nAbstract: Data aggregation is an main method to conserve energy in wireless sensor network (WSN). Prior work on data aggregation protocols are generally based on static routing schemes, such as tree-based, cluster-based or chain-based routing schemes. Although they can save energy to some extent, in dynamic scenarios where the source nodes are changing frequently, they will not only incur high overhead to continuously reconstruct the routing but also can not reduce the communication overhead effectively. Our work aims to design an effective data aggregation mechanism supported by dynamic routing (DASDR) which can adapt to different scenarios without incurring much overhead. Enlightened by the concept of potential field in the discipline of physics, the dynamic routing in DASDR is designed based on two potential fields: depth potential field which guarantees packets reaching the sink at last and queue potential field which makes packets more spatially convergent and thus data aggregation will be more efficient. Simulation results show that DASDR is more effective in energy savings as well as scales well with regard to the network size.","Title: Performability Evaluation of Resource Scheduling Algorithms for Computational Grids\nYear: 2006\n\nAbstract: Resource scheduling is a fundamental issue in achieving high performance on computational grids. In this paper, we concentrate on parallel resource scheduling problems for hierarchical resource scheduling architecture of computational Grids, and propose a Stochastic Reward Nets (SRN) based performability evaluation framework for evaluating the performability of resource scheduling algorithms for computational Grid with resource unavailability. Within the proposed framework, performability model of hierarchical resource scheduling is constructed, 36 combinations of 6 global job scheduling algorithms and 6 local processor allocation algorithms are specified in the uniform performability model, and performability metrics are derived.","Title: A sufficient condition for instability of buffer priority policies in re-entrant lines\nYear: 2003\n\nAbstract: We use a buffer-boundedness approach to study the stability of re-entrant lines with a buffer priority scheduling policy. Using Petri net models we establish a sufficient condition for instability of such systems having a positive feedback loop. An example of unstable systems is also given.","Title: Performance Analysis of Retransmission and Redundancy Schemes in Sensor Networks\nYear: 2008\n\nAbstract: In this paper, by establishing the probability models, we systematically and comprehensively analyze the roles of the packet retransmission, the block retransmission, and the erasure coding in the reliable transport of wireless sensor networks. And as well as the three kinds of packet level schemes, we also consider the effect of two kinds of bit level strategies, CRC and FEC. At last, based on the numeric results, the appropriate schemes for different BER (high, medium, and low) are determined, and we also present some principles that reveal profound insights in designing reliable protocols and mechanisms in wireless sensor networks.","Title: MediaCloud: A New Paradigm of Multimedia Computing.\nYear: 2012\n\nAbstract: Multimedia computing has attracted considerable attention with the rapid growth in the development and application of multimedia technology. Current studies have attempted to support the increasing resource consumption and computational overhead caused by multimedia computing. In this paper, we propose MediaCloud, a new multimedia computing paradigm that integrates the concept of cloud computing in handling multimedia applications and services effectively and efficiently. MediaCloud faces the following key challenges: heterogeneity, scalability, and multimedia Quality of Service (QoS) provisioning. To address the challenges above, first, a layered architecture of MediaCloud, which can provide scalable multimedia services, is presented. Then, MediaCloud technologies by which users can access multimedia services from different terminals anytime and anywhere with QoS provisioning are introduced. Finally, MediaCloud implementation and applications are presented, and media retrieval and delivery are adopted as case studies to demonstrate the feasibility of the proposed MediaCloud design.","Title: An Improved Markov Model for IEEE 802.15.4 Slotted CSMA/CA Mechanism.\nYear: 2009\n\nAbstract: IEEE 802.15.4 protocol is proposed to meet the low latency and energy consumption needs in low-rate wireless applications,\n however, few analytical models are tractable enough for comprehensive evaluation of the protocol. To evaluate the IEEE 802.15.4\n slotted CSMA/CA channel access mechanism in this paper, we propose a practical and accurate discrete Markov chain model, which\n can dynamically represent different network loads. By computing the steady-state distribution probability of the Markov chain,\n we obtain an evaluation formula for throughput, energy consumption, and access latency. Then we further analyze the parameters\n that influence performance including packet arrival rate, initial backoff exponent and maximum backoff number. Finally, NS2\n simulator has been used to evaluate the performance of the 802.15.4 CSMA/CA mechanism under different scenarios and to validate\n the accuracy of the proposed model.","Title: Analysis of a hybrid cutoff priority scheme for multiple classes of traffic in multimedia wireless networks\nYear: 1998\n\nAbstract: In this paper, we propose and analyze the performance of a new handoff scheme called hybrid cutoff priority scheme for wireless networks carrying multimedia traffic. The unique characteristics of this scheme include support for N classes of traffic, each may have different QoS requirements in terms of number of channels needed, holding time of the connection and cutoff priority. The proposed scheme can handle finite buffering for both new calls and handoffs. Futhermore, we take into consideration the departure of new calls due to caller impatience and the dropping of queued handoff calls due to unavailability of channels during the handoff period. The performance indices adopted in the evaluation using the Stochastic Petri Net (SPN) model include new call and handoff blocking probabilities, call forced termination probability, and channel utilization for each type of traffic. Impact on the performance measures by various system parameters such as queue length, traffic input and QoS of different traffic has also been studied.","Title: Towards a Universal Friendly P2P Media Streaming Application: An Evaluation Framework\nYear: 2008\n\nAbstract: Network-oblivious P2P applications have produced high cross-ISP traffics and led to serious disruption to ISP economics. While end users are concerned with quality, Internet Content Provider (ICP) cares more about service scale and ISP focuses on operating cost. Thus this paper provides a framework of evaluating the P2P streaming application performance from perspectives of all entities involved, i.e ISP, ICP and end users. Based on the analytical study, we propose potential solutions to define unprecedented friendly and cost-effective P2P streaming application to achieve the ideal philosophy of \"More Users =Better performance + Lower cost\".","Title: Throughput Analysis of Wireless Multi-hop Chain Networks\nYear: 2009\n\nAbstract: It is important but challenging to understand the throughput performance in wireless multi-hop networks. Most previous works show that the M/M/1 model is accurate enough for throughput analysis most of the time. However, we notice that without considering the interrelationship between the traffic on nodes, the M/M/1 model is quite inaccurate sometimes, especially on the typical chain topology. Therefore in this paper, we propose a model called Traffic-Analysis-Based (TAB) for throughput analysis focusing on chains. TAB obtains the throughput by analyzing the state transition process of the chain as the traffic rate is increasing, based on the traffic interrelationships. Besides the throughput of each node, we also get two significant state transition points. One is the optimal sending rate of traffic, and the other is the lower bound when M/M/1 can be used. Simulation results show that TAB is much more accurate than M/M/1 when the traffic rate is under the second transition point.","Title: VCNF: A Secure Video Conferencing System Based on P2P Technology\nYear: 2008\n\nAbstract: The goal of a video conferencing system is to deliver multimedia data to the conference participants efficiently and securely. To meet the requirement of a video conferencing system over the internet, many issues have to be addressed, such as security, scalability and heterogeneity. In this paper, we propose a secure and scalable video conferencing system named VCNF (Video Conference Network Foundation), to support large number of conferencing groups simultaneously with each group has limited members. We use a P2PSIP overlay to store and lookup the user and group contact information. SIP protocol is used to setup media sessions and a new kind of session-- VPN session. We adopt a layered data transfer mechanism which traits different kinds of media-streams as of different importance level. We have implemented the system and analyzed its performance and security. An Internet video-based game based on our platform has also been deployed over the Internet.","Title: A scheduling model for maximizing availability with makespan constraint based on residual lifetime in heterogeneous clusters\nYear: 2007\n\nAbstract: A notable requirement of clusters is to maximize its processing performance. Lots of work in this area has been done to optimize the system performance by improving certain metric such as reliability, availability, security and so on. However, most of them assumes that the system is running without interruption and seldom considers the system's intrinsic characteristics, such as failure rate, repair rate and lifetime. In this paper, we study how to achieve high availability based on residual lifetime analysis for the repairable heterogeneous clusters with makespan constraints. First, we provide an availability model based on addressing the cluster's residual lifetime model. Second, we give an objective function about the model and develop a heuristic scheduling algorithm to maximize the availability the makespan constraint. At last, we demonstrate these advantages through the extensive simulated experiments.","Title: Stochastic game net and applications in security analysis for enterprise network\nYear: 2012\n\nAbstract: Stochastic game theoretic framework has been used in many fields of networks with interactive behaviors. However, further use of this framework is limited due to the following reasons. Firstly, it is difficult to build comprehensive and rigorous models for complex network structures by the state-based game model. Secondly, solving and extending the dynamic behaviors of participators of the network are nearly impossible, because of the complexity of state transitions. Last but not least, general game model is not able to describe and analyze specific events and behaviors in some kinds of networks, like enterprise networks. In this paper, we propose a new modeling paradigm (stochastic game net, or SGN) for stochastic games representation with Petri nets. Based on our graphical tool, stochastic game problems can be described clearly, and the model can be solved and extended easily. Moreover, this paper puts forth a series of methods for modeling and analyzing the competitive game by SGN, which is the main contribution of this work. Our achievements are applied to the security analysis for enterprise networks. The analysis results prove the powerful ability of our achievements in solving the complicated and dynamic game problems. Furthermore, our approaches can be used to calculate the existence and the value of an equilibrium point.","Title: QoS-aware Scheduling Algorithm Based on Complete Matching of User Jobs and Grid Services\nYear: 2006\n\nAbstract: Grid Services Scheduling is a challenging problem under Open Grid Service Architecture(OGSA). Grid Services running on Clusters must have mechanism and policies for establishing and supporting QoS. A graph theory formal description is introduced into the Service Grid Model in this paper. The necessary and sufficient condition of complete matching of user job and service resources has been given and proved. Optimal Solution to matchmaking of grid jobs and grid services is developed based on the running time weight matrix, and the arithmetic has been verified by simulation analysis which proved to be more efficient than the alike arithmetic. The arithmetic has been implemented and running well.","Title: Security Analysis of Enterprise Network Based on Stochastic Game Nets Model\nYear: 2009\n\nAbstract: In this paper, we propose a novel modeling method, Stochastic Game Nets (SGN), and use it to model and analyze the security issues in enterprise networks. Firstly, the definition and modeling algorithm of Stochastic Game Nets are given. And then we apply the Stochastic Game Nets method to describe the attack and defense course in the enterprise networks successfully, and find a Nash equilibrium. Finally we analyze the confidentiality and integrity of the enterprise network quantificationally based on the model. The method can also be applied to other areas with respect to a game.","Title: Enhanced low-latency detection of motor intention from EEG for closed-loop brain-computer interface applications.\nYear: 2014\n\nAbstract: In recent years, the detection of voluntary motor intentions from electroencephalogram (EEG) has been used for triggering external devices in closed-loop brain-computer interface (BCI) research. Movement-related cortical potentials (MRCP), a type of slow cortical potentials, have been recently used for detection. In order to enhance the efficacy of closed-loop BCI systems based on MRCPs, a manifold method called Locality Preserving Projection, followed by a linear discriminant analysis (LDA) classifier (LPP-LDA) is proposed in this paper to detect MRCPs from scalp EEG in real time. In an online experiment on nine healthy subjects, LPP-LDA statistically outperformed the classic matched filter approach with greater true positive rate (79 ± 11% versus 68 ± 10%; p = 0.007) and less false positives (1.4 ± 0.8/min versus 2.3 ± 1.1/min; p = 0.016 ). Moreover, the proposed system performed detections with significantly shorter latency (315 ± 165 ms versus 460 ± 123 ms; p = 0.013), which is a fundamental characteristics to induce neuroplastic changes in closed-loop BCIs, following the Hebbian principle. In conclusion, the proposed system works as a generic brain switch, with high accuracy, low latency, and easy online implementation. It can thus be used as a fundamental element of BCI systems for neuromodulation and motor function rehabilitation.","Title: An Efficient Recovery and Survival Scheme against Malware Attacks\nYear: 2010\n\nAbstract: Intricate malware can result in the failure of on-line Comprehensive Protection (CP) in distributed systems, and place the system in an unsafe state which is difficult to recover from. There lacks an effective scheme to defend against this extreme attack. In this paper, based on the Two-layer Protection and Co-operative Recovery (TPCRS) mechanism, we propose an efficient survivable scheme against malware attacks in distributed systems. The basic strategy is to deploy an Emergency Response/Recovery (ER) agent at each node to recognize the state of the system whenever the CP fails, and to carry out cooperative security among multiple nodes so that the infected nodes can be rapidly recovered. Furthermore, a Preventive Maintenance (PM) model is adopted to enhance the reliability of the distributed system. Si-mulation results demonstrate the practicality and efficiency of the proposed schemes.","Title: Adding Security to Network Via Network Processors\nYear: 2004\n\nAbstract: With the increasing need of security, cryptographic processing becomes a crucial issue for network devices. Traditionally security functions are implemented with Application Specific Integrated Circuit (ASIC) or General-Purposed Processors (GPPs). Network processors (NPs) are emerging as a programmable alternative to the conventional solutions to deliver high performance and flexibility at moderate cost. This work compares and analyzes architectural characteristics of many widespread cryptographic algorithms on Intel IXP2800 network processor. In addition, we investigate several implementation and optimization principles that can improve the cryptographic performance on NPs. Most of the results reported here should be applicable to other network processors since they have similar components and architectures.","Title: Design and analysis of an ONOFF variable structure controller for AQM routers supporting TCP flows.\nYear: 2008\n\nAbstract: Active queue management (AQM) can maintain smaller queuing delay and higher throughput by purposefully dropping packets at\n intermediate nodes. Most of the existing AQM schemes follow the probability dropping mechanism originating from random early\n detection (RED). This paper develops a novel packet dropping mechanism for AQM through designing an ONOFF controller applying\n the variable structure control theory. Because the binary ONOFF controller can considerably simplify the manipulation on the\n AQM router, it is helpful for implementing the high performance router. The design principles of ONOFF controller are discussed\n in detail. The guidelines towards parameter settings are presented. The performance is extensively evaluated and compared\n with other well-known controllers through simulations and theoretical analysis. The results demonstrate that the ONOFF controller\n is responsive and robust against external disturbances, and is insensitive to variances of the system parameters. Therefore,\n it is very suitable for the time-varying network system, and at the same time, it can also keep the instantaneous queue length\n at a desired level with rather small oscillations, which is conducive to achieving the technical objectives of AQM.","Title: Dependability Analysis of Control Center Networks in Smart Grid Using Stochastic Petri Nets\nYear: 2012\n\nAbstract: As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs, and increase reliability. In smart grid, control center networks have attracted a great deal of attention, because their security and dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of smart grid security, but few work focuses on the dependability analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the dependability of control center networks in smart grid. We present the general model of control center networks by considering different backup strategies of critical components. With the general SPNs model, we can measure the dependability from two metrics, i.e., the reliability and availability, through analyzing the transient and steady-state probabilities simultaneously. To avoid the state-space explosion problem in computing, the state-space explosion avoidance method is proposed as well. Finally, we study a specific case to demonstrate the feasibility and efficiency of the proposed model in the dependability analysis of control center networks in smart grid.","Title: Modeling and survivability analysis of service composition using Stochastic Petri Nets\nYear: 2011\n\nAbstract: In this paper, we propose a service composition model method that supports quantitative computation based on Stochastic Petri Nets (SPN). It can capture the semantics of complex service combinations and their respective specifications. In this method, services are divided into interior services and exterior services. The exterior services will be published to the users, while the interior ones do not need to be published. Six equivalent simplified theorems which can be used to simplify the complex models of interior services to simple models of exterior services are presented. They enable the minimization of the state space of the model and make quantitative computation feasible. In addition, since Grid services are always affected by all kinds of churns in actual applications, we also research survivability and its main attributes for Grid service composition. The definition and computational methods based on the model are put forward. In the end, we use the method presented above to describe and analyze an example of travel Grid services successfully.","Title: Reputation-Based QoS Provisioning in Cloud Computing via Dirichlet Multinomial Model\nYear: 2010\n\nAbstract: In Cloud computing, users with different service requirements often need to negotiate with service provider via Service Level Agreement (SLA). The unique pay-as-you-go billing way in Cloud computing challenges resource provisioning for service providers. In this paper, based on the Dirichlet multinomial model, we present an efficient reputation-based QoS provisioning scheme, which can minimize the cost of computing resources, while satisfying the desired QoS metrics. Unlike the previous counterparts, we consider the statistical probability of the response time as a practical metric rather than the typical mean response time. Numerical results show the efficiency and effectiveness of the proposed scheme.","Title: Optimization and Benchmark of Cryptographic Algorithms on Network Processors\nYear: 2004\n\nAbstract: With the increasing needs of security, cryptographic functions have been exploited in network devices. Besides time consuming, security protocols are flexible in algorithm selections. Fortunately, network processors, which serve as the backbone of intelligent network devices, hold performance and flexibility at the same time. In this article, we investigate several principles that can be used with implementing and optimizing cpptographic algorithms on network processors. Also, these principles are applied in real life algontltms, including stream ciphers, block ciphers and digital signatures. Related experiments and benchmark results on Intel LYPl200 network processor are provided.","Title: Mutual Authentication and Key Exchange Protocols for Roaming Services in Wireless Mobile Networks\nYear: 2006\n\nAbstract: Two novel mutual authentication and key exchange protocols with anonymity are proposed for different roaming scenarios in the global mobility network. The new features in the proposed protocols include identity anonymity and one-time session key renewal. Identity anonymity protects mobile users privacy in the roaming network environment. One-time session key progression frequently renews the session key for mobile users and reduces the risk of using a compromised session key to communicate with visited networks. It has demonstrated that the computation complexity of the proposed protocols is similar to the existing ones, while the security has been significantly improved","Title: Saturation aware TCP throughput prediction\nYear: 2009\n\nAbstract: With the ever growing network traffic, capacity of paths in the networks nowadays has become increasingly easy to saturate. This brings new challenge to TCP throughput prediction. The main problem is loss rate and RTT during the TCP flow are often expected as inputs for the throughput models used in the prediction; however, only loss rate and RTT before the flow are available and are used instead. If the flow itself causes significant changes in loss rate and RTT, e.g., when the TCP flow attempts to saturate the underlying available bandwidth, the prediction error can be unacceptably large. Though new prediction approaches are being proposed, they basic require record of previous TCP transfers and are applicable only when TCP transfers are performed repeatedly, which limits their application. In this work, by properly using a measurement of the underlying available bandwidth, we develop an analytical TCP throughput model which can explicitly capture changes in loss rate and RTT caused by the target TCP flow and hence, can largely improve the prediction accuracy by making the prediction aware of capacity saturation of paths while at the same does not require any history record of previous TCP transfers as current newly proposed works do. Results show that when the changes in loss rate and RTT are large, the errors by traditional models can be as large as over 200%, whereas the error by the proposed model is usually very small, e.g., with the average error below 10% and the maximum error below 20% for general settings, and is bounded by the measurement error of available bandwidth in the worse case; when the changes in loss rate and RTT are small, even a very rough estimation of available bandwidth, e.g., with an error of around 50%, can lead to very accurate prediction by the proposed model.","Title: SRD Service: A Stochastic Network Calculus Approach for Service Differentiation Analysis\nYear: 2010\n\nAbstract: The popularity of delay-sensitive applications has been rapidly evolving in recent years. However, the current Internet is not well designed to support the delivery of such traffic. The SRD (Stochastic Rate-Delay) services proposed in this paper empower a user to choose either a higher transmission rate or a low queuing delay at a congested network link. While average low delay remains guaranteed, the delay constraint is allowed to violate occasionally. The implementation enforces the intended service differentiation via link scheduling and sizing of the buffer. We build a model using stochastic network calculus to analyze the SRD services proposal and shed light on its fundamental characteristics quantitatively.","Title: Scalable parameter tuning for AVQ\nYear: 2005\n\nAbstract: This letter proposes a simple, scalable, practical and systematic rule for tuning the control parameter of the adaptive virtual queue (AVQ) active queue management scheme. An explicit stability condition of AVQ is proposed using classical control theory. Theoretical analyses as well as simulation results are used to validate the result.","Title: An Analytical Method to Predict Packet Losses over Bursty Wireless Channels.\nYear: 2011\n\nAbstract: In this letter, we perform an analytical investigation of packet losses for bursty wireless channels and present a closed-form solution for the packet loss rate. The solution can adapt to the channel dynamics and predict the packet loss rate more fast and accurately. With the results, we can design cross-layer protocols over wireless networks more conveniently. © 2006 IEEE.","Title: HMM-Based Predictive Power Saving Mechanism in WiMAX\nYear: 2010\n\nAbstract: Several studies have showed that network features (e.g., packet interval and packet size) may be well modeled by a hidden Markov model (HMM) with appropriate hidden variables that capture the current state of the network. In this paper, we propose a prediction mechanism on the basis of the HMM model to assist the Power Saving (PS) in WiMAX. In comparison with prior models whose analyses are often with the assumption of Poisson arrival, the prediction-based PS relies no more on that, which means a rich variety of traffic patterns such as heavy-tailed or burst that are often encountered in usual networks can be applied. In addition, our mechanism balances in a more effective way the tradeoff between packet delay and energy consumption, and we find that in fact they can be both improved sometimes. Validation results confirm that the prediction-based PS is of great reduction in energy (about 5% to Instant Message and 5%-25% to HTTP) under the trace driven simulations.","Title: A self-adaptive probabilistic packet filtering scheme against entropy attacks in network coding\nYear: 2009\n\nAbstract: In this paper, based on a novel self-adaptive probabilistic subset linear-dependency detection (S-PSLD) algorithm, we propose an efficient packet filtering scheme against entropy attacks in network coding. The scheme verifies the received packets probabilistically instead of exactly, and thus it can rapidly filter out the resultant packets from entropy attacks. Moreover, to minimize the packet detection cost at forwarder while keeping the false positive rate at an expected low level, a self-adaptive algorithm is introduced such that each forwarder can dynamically tune the system security parameters according to the available bandwidth or the number of the received packets in buffer. Theoretical analysis and performance evaluation are given to demonstrate the validity and efficiency of the proposed scheme.","Title: Improving the Convergence and Stability of Congestion Control Algorithm\nYear: 2007\n\nAbstract: The traditional TCP congestion control is inefficient for high speed networks and it is a challenge to design a high speed replacement for TCP. By simulating some existing high speed protocols, we find that these high speed protocols have limitations in convergence and stability. To address these problems, we apply a population ecology model to design a novel congestion control algorithm-Coupling Logistic TCP(CLTCP). It is based on bandwidth pre-assignment that is similar to XCP and MaxNet. The pre-assignment rate factor is computed in the routers based on the information of the router capacity, the aggregate incoming traffic and the queue length. Then the senders adjust the sending rate according to the pre-assignment rate factor which carries by the packet to strengthen the convergence and stability of transport protocol. The theoretical analysis and simulation results show that CLTCP provides not only fast convergence and strong stability, but also high utilization and fair bandwidth allocation regardless of round trip time.","Title: Survey on transport control in data center networks.\nYear: 2013\n\nAbstract: Traditional fair bandwidth sharing by leveraging AIMD-based congestion control mechanisms faces great challenges in data center networks. Much work has been done to solve one of the various challenges. However, no single transport layer protocol can solve all of them. In this article, we focus on the transport layer in data centers, and present a comprehensive survey of existing problems and their current solutions. We hope that this article can help readers quickly understand the causes of each problem and learn about current research progress, so as to help them make new contributions in this field. © 1986-2012 IEEE.","Title: On the Optimal Request Routing Strategy in CDN Live Streaming Application\nYear: 2011\n\nAbstract: In this paper, we consider the Request Routing (RR) strategy in the CDN live streaming application. We show that to find an optimal RR strategy is correspond to a static optimization problem if the total number of clients is known in advance. However, this static approach is ineffective due to the difficulty in precisely estimating the number of clients off-line. We then develop the MPS scheduling algorithm to compute the multiphase RR strategy. Experimental study shows that our algorithm can generate a close-to-optimal strategy with respect to a wide range of the number of clients.","Title: Security Analysis for Online Banking System Using Hierarchical Stochastic Game Nets Model\nYear: 2009\n\nAbstract: In this paper, we propose hierarchical Stochastic Game Nets, and use it to model and analyze the security issues in online banking system. Firstly, the definition and modeling algorithm of hierarchical Stochastic Game Nets are given. In which, some theorems are proved. Then we apply hierarchical Stochastic Game Nets to describe the attack and defense course in the online banking. Finally we analyze the attack time and attack probability in the online banking quantitatively based on the model successfully.","Title: Sliding Mode Congestion Control for data center Ethernet networks\nYear: 2012\n\nAbstract: Recently, Ethernet is being enhanced as the unified switch fabric of data centers, called Data Center Ethernet. The end-to-end congestion management is one of the indispensable enhancements, and Quantized Congestion Notification (QCN) has been ratified to be the standard. Our experiments show that QCN suffers from the oscillation of the queue at the bottleneck link. With the changes of system parameters and network configurations, the oscillation may become so serious that the queue is emptied frequently. As a result, the utilization of the bottleneck link degrades. Theoretical analysis shows that QCN approaches to the equilibrium point mainly through the sliding mode motion. But whether QCN enters into the sliding mode motion also depends on both system parameters and network configurations. Hence, we present the Sliding Mode Congestion Control (SMCC) scheme, which can drive the system into the sliding mode motion under any conditions. SMCC benefits from the advantage that the sliding mode motion is insensitive to system parameters and external disturbances. Moreover, SMCC is simple, stable and has short response time. QCN can be replaced by SMCC easily since both of them follow the framework developed by the IEEE 802.1 Qau work group. Experiments on the NetFPGA platform show that SMCC is superior to QCN, especially in the condition that the traffic pattern and the network state are variable.","Title: Modeling and Analysis of Dependability Attributes of Service Computing Systems\nYear: 2011\n\nAbstract: Dependability is an important consideration during the design and development of IT systems and services. But in service computing systems, the traditional definition and evaluation methods from the systems' and components' point of view meet challenges. In this paper, we change the angle of view, and study the dependability and their attributes from the service-oriented perspective. A stochastic model using semi-Markov process is put forward, and the quantitative analysis of the dependability attributes is carried out. By extending and transforming this model, the mean time to dependability attributes failure is computed. Based on the analysis and calculations, some theorems are proposed and proved, to show the interrelationships and comparisons of the different dependability attributes.","Title: Inside the bird's nest: measurements of large-scale live VoD from the 2008 olympics\nYear: 2009\n\nAbstract: The 2008 Beijing Olympics was an interesting event from a VoD perspective because it involved near real-time video delivery at massive scales over multiple days of a high-profile event. We present some measurement-driven insights into this event through a unique dataset obtained from ChinaCache, the largest CDN in China. The dataset is unique in three respects. First, it gives a \"white-box\" view into user access patterns which would otherwise be impossible. Second, since the CDN serves different content providers, it allows to compare and contrast the effects of different presentation models on end users. Third, the nature of the content itself is vastly different from traditional VoD systems in terms of the real-time and event-driven nature, which gives rise to unique effects. The dataset allows us to investigate a wide range of interesting issues: (1) how the live nature of the events causes differences in access patterns compared to traditional VoD and User-Generated Content (UGC) systems, (2) how the presentation models affect user behavior, and (3) flash-crowd phenomena. Based on these observations, we discuss implications for future live VoD systems.","Title: Experimental Analysis Of Super-Seeding In Bittorrent\nYear: 2008\n\nAbstract: With the popularity of BitTorrent, improving its performance has been an active research area. Super-seeding, a special upload policy for initial seeds, improves the efficiency in producing multiple seeds and reduces the uploading cost of the initial seeders. However, the overall benefit of super seeding remains a question. In this paper, we conduct an experimental study over the performance of super-seeding scheme of BitTornado. We attempt to answer the following questions: whether and how much super-seeding saves uploading cost, whether the download time of all peers is decreased by super-seeding, and in which scenario super-seeding performs worse. With varying seed bandwidth and peer behavior, we analyze the overall download time and upload cost of super seeding scheme during random period tests over 250 widely distributed PlanetLab nodes. The results show that benefits of super-seeding depend highly on the upload bandwidth of the initial seeds and the behavior of individual peers. Our work not only provides reference for the potential adoption of super-seeding in BitTorrent, but also much insights for the balance of enhancing Quality of Experience (QoE) and saving cost for a large-scale BitTorrent-like P2P commercial application.","Title: Idle Detection Based Optimal Throughput Rate Adaptation in Multi-Rate WLANs\nYear: 2009\n\nAbstract: Multiple transmission rates are supported in the current 802.11 WLANs, which allows stations to exploit different rates in an adaptive manner to cope with the variability of wireless channels and achieve the higher system throughput. Many efforts have been made at rate adaptation in the literature. The design of rate adaptation algorithms needs to take four basic issues into account. In this paper, by properly addressing the four issues, we present a novel rate adaptation algorithm, idle detection based optimal throughput rate adaptation (ITRA). We apply a comprehensive approach to designing the rate adaptation mechanism and attempt to maximize the overall system throughput. ITRA is furnished with two instructive features. First, ITRA modifies the conventional exponential backoff rule to a constant one. With the help of constant contention window (CW), ITRA can discriminate collisions from channel errors, and does not need RTS/CTS handshakes which are adopted by many existing works. We derive an explicit relation between the throughput and the CW size, and choose the CW size to optimize the throughput as well as improve the fairness. Secondly, ITRA directly estimates the current network throughput and changes transmission rate according to the estimation. Since the fundamental purpose of rate adaptation is to improve the network throughput, using the throughput as the metric is more straightforward than applying indirect criteria like frame transmission statistics or PHY metrics. We evaluate our proposed scheme by simulation, and the result shows that ITRA achieves satisfactory performance.","Title: A novel multiple time scale congestion control scheme for multimedia traffic\nYear: 2004\n\nAbstract: A novel two time scale congestion control scheme is proposed in this paper, which is called Adaptive Wavelet and Probability-based scheme (AWP). On normal time scales of 20-200 ms, it is a smooth and TCP-friendly congestion control; on large time scales of 1-5 second, it detects persistent shifts in overall network contention and modulates the rate control exhibited on normal scale. AWP exploits self-similar property of network traffic and predicts future network available bandwidth to enhance performance, and it is adaptive to network environment changes through an auto-correction algorithm. With comparable experiments among TFRC, TCP and AWP over NS platform, AWP has been proved to be able to greatly improve the quality of service(QoS) of multimedia data transmission while avoiding congestion collapse on the network.","Title: Improving TCP Throughput over HSDPA Networks\nYear: 2008\n\nAbstract: The various link adaptation techniques employed by High Speed Downlink Packet Access (HSDPA) in the third generation (3G) networks augment the bandwidth oscillation, which is identified as one of the most important factors resulting in the throughput deterioration of Transmission Control Protocol (TCP). In this paper, we firstly explain why the bandwidth oscillation degrades the TCP performance through a special simulation experiment. Subsequently, a split connection Window Adaptation TCP Proxy is proposed to improve the TCP throughput over HSDPA networks. In this solution, the built-in attributes of HSDAP system are sufficiently utilized. In order to effectively use the precious cellular link resources, the length of the queue connected with it is intentionally kept around the reference value through adjusting the sending window size of TCP proxy based on the dynamic values of varying bandwidth. A discrete-time stochastic state space model is formulated to analyze the system stability. The validity of enhanced scheme is verified through simulation experiments. The performance of TCP proxy is compared with the standard TCP protocol. The numerical results show that our TCP proxy is able to keep the cellular link utilization over 90%, and to improve TCP throughput by 100% under most conditions.","Title: Risk-Aware QoP/QoS Optimization for Multimedia Applications in Wireless Networks\nYear: 2009\n\nAbstract: The unique characteristics of wireless networks pose a number of nontrivial challenges to multimedia applications with security and rigorous QoS requirements. Lack of adequate security protection is incapable of meeting security requirements of applications, whereas enabling excessive security services inevitably leads to further degradation in QoS due to additional computation and payload encapsulation. Early work, e.g. LAP, achieves balance by adjusting security policy according to QoS metrics; but none of them are security guaranteed. In this paper, we present an efficient risk-aware QoP (Quality of Protection) and QoS optimization algorithm for multimedia applications in wireless networks. It can achieve an optimization for QoP/QoS performance metrics through offering hierarchical security services and QoS support. Experiment demonstrates that even in high risk environments our scheme can efficiently balance QoP and QoS requirements.","Title: CASM: a content-aware protocol for secure video multicast\nYear: 2006\n\nAbstract: Information security has been a critical issue in the design and development of reliable distributed communication systems and has attracted significant research efforts. A challenging task is how to maintain information security at a high level for multiple-destination video applications with the huge volume of data and dynamic property of clients. This paper proposes a novel Content-Aware Secure Multicast (CASM) protocol for video distribution that seamlessly integrates three important modules: 1)a scalable light-weight algorithm for group key management; 2) a content-aware key embedding algorithm that can make video quality distortion imperceptible and is reliable for clients to detect embedded keys; and 3) a smart two-level video encryption algorithm that can selectively encrypt a small set of video data only, and yet ensure the video as well as the embedded keys unrecognizable without a genuine key. The implementation of the CASM protocol is independent of the underlying multicast mechanism and is fully compatible with existing coding standards. Performance evaluation studies built upon a CASM prototype have demonstrated that CASM is highly robust and scalable in dynamic multicast environments. Moreover, it ensures secure distribution of key and video data with minimized communication and computation overheads. The proposed content-aware key embedding and encryption algorithms are fast enough to support real-time video multicasting.","Title: Traffic-Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks\nYear: 2011\n\nAbstract: The congestion problem in Wireless Sensor Networks (WSNs) is quite different from that in traditional networks. Most current congestion control algorithms try to alleviate the congestion by reducing the rate at which the source nodes inject packets into the network. However, this traffic control scheme always decreases the throughput so as to violate fidelity level required by the applications. In this paper, we present a solution that sufficiently exerts the idle or underloaded nodes to alleviate congestion and improve the overall throughput in WSNs. To achieve this goal, a traffic-aware dynamic routing (TADR) algorithm is proposed to route packets around the congestion areas and scatter the excessive packets along multiple paths consisting of idle and underloaded nodes. Utilizing the concept of potential in classical physics, our TADR algorithm is designed through constructing a hybrid virtual potential field using depth and normalized queue length to force the packets to steer clear of obstacles created by congestion and eventually move toward the sink. The simulation results show that the proposed solution improves the overall throughput by around 370 percent as compared to MintRoute, which is one of benchmark routing protocols. Furthermore, TADR scheme has low overhead suitable for large-scale, dense sensor networks.","Title: An Authorization Model without Central Authority for Service Collaboration\nYear: 2011\n\nAbstract: In the service-oriented computing, a single transaction initiated by a client might invoke many different services in other administrative domains. Existing models for authorizing the access assume that all services involved in collaboration are managed by the central authority, which is not always a realistic premise. In this paper, we propose a novel authorization model for dynamic service collaboration. With the authorization discovery process, the client can discover the needed authorization for service access available in other autonomous domains. With extensions to SoD relationship, the conflicts of client interests can be formalized and expressed as constraints. The authorization problems are formalized to choose the optimal access path for each task. At last, the example and experiments show the practicality and the effectiveness of our scheme.","Title: An Efficient Privacy-Preserving Publish-Subscribe Service Scheme for Cloud Computing\nYear: 2010\n\nAbstract: Cloud computing provides a novel computing paradigm for enterprises to store programs and data in the Cloud in a transparent manner, which poses the challenge of security and privacy. In this paper, based on homomorphic cryptography and Zero-Knowledge Proof, we present a novel privacy-preserving scheme for Cloud publish/subscribe service, which achieve efficient privacy-preserving authentication, data integrity, and publish-subscribe confidentiality. The performance evaluation and security analysis demonstrate the practice and validity of the proposed scheme.","Title: Antiworm Npu-Based Parallel Bloom Filters In Giga-Ethernet Lan\nYear: 2006\n\nAbstract: In this paper, an AntiWorm system based on the Intel IXP Network Processor was implemented using the Parallel Bloom filters technique. The AntiWorm system consists of two components: Bloom filters and Exact Matching engines. The Parallel Bloom filters can identify the suspicious traffic quickly and effectively, and then dispatch them to Exact Matching engines for further investigation. Both the principles and the implementation of the AntiWorm system are introduced in detail. With the consideration of the system performance parameters, two feasible implementation solutions are investigated and the advantages and disadvantages are also compared. The selections of configuration parameters of the AntiWorm system are also discussed. A hash scheme based on MD5's function is proposed for implementing fast hash functions. To test the performance of the AntiWorm system, such as throughput and delay, some experiments are carried out with different simulated traffic condition. The internal statistics of IXP network processor are also collected and analyzed for optimizing the system performance. To demonstrate the operation of the AntiWorm system, assaults by Worm Blaster are used in the test bed, and the experimental results prove the effectiveness of the AntiWorm system. The Software Package WormDetector1.0 is also provided as a software release from the research.","Title: Secure, Disjoint, Multipath Source Routing Protocol(SDMSR) for Mobile Ad-Hoc Networks\nYear: 2006\n\nAbstract: Mobile Ad-hoc Network (MANET) is an appealing technology that has attracted lots of research efforts over past years. Although the principle of wireless, structure-less, dynamic networks is attractive, there are still some major flaws that prevent commercial expansion. Security is one of these main barriers; MANETs are known to be particularly vulnerable to security attack. One solution proposed to increase security resilience is to use multipath routing algorithms [1]. However multipath routing also introduces new challenges in terms of security and security overhead. In this paper we consider the problem of secure routing in fully distributed MANETs using multipath routing. After studying the effect of multipath in terms of security, we propose an efficient multipath heuristic and a new approach to protect the route discovery called SDMSR to secure the routing protocol while mitigating security overhead.","Title: Self-healing group key distribution with time-limited node revocation for wireless sensor networks\nYear: 2007\n\nAbstract: A novel key distribution scheme with time-limited node revocation is proposed for secure group communications in wireless sensor networks. The proposed scheme offers two important security properties: the seal-healing re-keying message distribution which features periodic one-way re-keying with implicitly authentication, efficient tolerance for the lost re-keying messages, and seamless Traffic Encryption Key (TEK) switch without disrupting ongoing data transmissions; and the time-limited dynamic node attachment and detachment, so that both forward and backward secrecy is assured by dual directional hash chains. It is shown that the communication and computation overhead of the proposed protocol is light, and the protocol is robust under poor communication channel quality and frequent group node topology change.","Title: A Fast Multi-pattern Matching Algorithm for Deep Packet Inspection on a Network Processor\nYear: 2007\n\nAbstract: Deep Packet Inspection (DPI) is a critical function in network security applications such as Firewalls and Intrusion Detection Systems (IDS). Signature based scanners used in DPI apply multi-pattern matching algorithms to check whether the packet payload or flow content contains a specified signature in a signature set. Existing multi-pattern matching algorithms sacrifice memory space to achieve better performance. In this paper a novel fast multi-pattern matching algorithm, the Hash Boyer-Moore (HBM) Algorithm, is presented, which reduces the memory footprint of the heuristic table using a hash function and adds another heuristic table to reduce the false-positive ratio. Analyses and simulations show HBM offers higher speed and lower memory cost than some existing algorithms. The HBM algorithm was implemented on the Intel IXP 2400 Network Processor (NP) platform and experiments show suitable performance results in a Gigabit Ethernet LAN environment.","Title: Stochastic Performance Analysis of a Wireless Finite-State Markov Channel\nYear: 2013\n\nAbstract: Wireless networks are expected to support a diverse range of quality of service requirements and traffic characteristics. This paper undertakes stochastic performance analysis of a wireless finite-state Markov channel (FSMC) by using stochastic network calculus. Particularly, delay and backlog upper bounds are derived directly based on the analytical principle behind stochastic network calculus. Both the single user and multi-user cases are considered. For the multi-user case, two channel sharing methods among eligible users are studied, i.e., the even sharing and exclusive use methods. In the former, the channel service rate is evenly divided among eligible users, whereas in the latter, it is exclusively used by a user randomly selected from the eligible users. When studying the exclusive use method, the problem that the state space increases exponentially with the user number is addressed using a novel approach. The essential idea of this approach is to construct a new Markov modulation process from the channel state process. In the new process, the multi-user effect is equivalently manifested by its transition and steady-state probabilities, and the state space size remains unchanged even with the increase of the user number. This significantly reduces the complexity in computing the derived backlog and delay bounds. The presented analysis is validated through comparison between analytical and simulation results.","Title: Performance Analysis of Channel Contention in Wireless Ad Hoc Networks: A Stochastic Game Nets Approach\nYear: 2010\n\nAbstract: This paper concentrates on the performance analysis of channel contention in wireless ad hoc networks by using Stochastic Game Nets (SGN). We refine the definition of SGN so that it can be used to precisely capture the underlying details of a given system. A SGN model is developed to evaluate the channel utilization in a two-node wireless system. We quantify the system performance under equilibrium strategies. The findings of this paper are instructive for the design and deployment of wireless ad hoc networks.","Title: A secure and scalable video conference system based on peer-assisted content delivery networks.\nYear: 2009\n\nAbstract: With the ever-increasing demand for multimedia conferencing over the Internet, video conferencing systems that enable efficient and secure data delivery have been a hot yet challenging research topic in both academy and industry. To deploy large-scale commercial video conferencing services, we need to address critical issues, such as Quality-of-Service (QoS), security and scalability. In this paper, we propose a secure and scalable video conferencing system over the Internet, namely Video Conference Network Platform (VCNP). VCNP organizes users into a Chord-based Peer-to-Peer (P2P) overlay, and utilizes Peer-to-Peer Session Initiation Protocol (P2PSIP) for the management of conference members, which includes the setup of media sessions and the storage of user contact information and conferencing group information over distributed peers. A VPN session over P2PSIP is implemented to act as a secure infrastructure for VCNP. For video data delivery, we design a framework of Peer-assisted CDN (Content Delivery Network), which uses both edge servers and fiber network in CDN, and the principle of structured P2P management to enhance the QoS, scalability and flexibility of the system. Based on the proposed architecture and protocols, VCNP has already been implemented and deployed over the Internet. Experimental results demonstrate the effectiveness of VCNP in delivering the unprecedented security, scalability, and certain QoS simultaneously in a unified architecture.","Title: Quality of Protection with Performance Analysis in IP Multimedia Subsystem\nYear: 2009\n\nAbstract: IP multimedia subsystem (IMS) is considered as the main solution to the next generation multimedia communication.By examining potential security threats to IMS, we initiate a set of security policies for choice and consider their quality of protection (QoP) based on the strength of the security mechanism. In order to provide secure service to users, it is insufficient to just take the security benefits into consideration. An adequate analysis of the impact of security policies in IMS on the performance quantitatively is necessary and significant. We present a novel study of IMS performance using queuing Petri nets (QPN) to predict the system performance metrics, i.e., the signaling delay, server utilization, which then be used to evaluate the impacts of different security mechanism on system performance quantitatively. With the multi-view security partition introduced, multi-level security service can be provided to diverse users and applications for a best tradeoff between security requirements and system performance.","Title: IVForensic: a digital forensics service platform for internet videos\nYear: 2009\n\nAbstract: IVForensic is a digital forensics service platform for Internet videos, with the aim of revealing illegal videos and preventing them from spreading over the Internet. It a) implements a flexible, secure and scalable architecture for large-scale online monitoring, b) provides an effective and efficient forensic countermeasure for widely-sourced Internet videos, c) guarantees good end-user experience such as flexible settings, simple operations and low startup latency. The results of performance evaluation using data obtained from real-world deployments demonstrate the effectiveness of the platform.","Title: Distributed KDC-based random pairwise key establishment in wireless sensor networks\nYear: 2010\n\nAbstract: Key management is a core mechanism to provide secure and reliable communications in wireless sensor networks (WSNs). In large-scale WSNs, due to the resource constraint on sensor nodes, it is still an extremely challenging task to achieve good performance in terms of high network connectivity and strong resilience against sensor nodes capture with low overheads. To address this issue, in this paper we propose a novel random pairwise key establishment scheme, called RPKE. In RPKE, sensor nodes differentiate their roles as either auxiliary nodes or ordinary nodes prior to network deployment. The auxiliary nodes act as distributed key distribution center (KDC), and neighboring ordinary nodes can establish pairwise key with the help of the distributed KDC. Theoretical analysis and simulation evaluation demonstrate that RPKE performs well in terms of network connectivity and resilience at the cost of low computation/communication/storage overheads, compared to the existing counterparts.","Title: Frequency Domain Packet Scheduling with Stability Analysis for 3GPP LTE Uplink\nYear: 2013\n\nAbstract: In this paper, we investigate the Frequency Domain Packet Scheduling (FDPS) problem for 3GPP Long Term Evolution (LTE) Uplink (UL). Instead of studying a specific scheduling policy, we provide a unified approach to tackle this issue. First, we formalize a general LTE UL FDPS problem, which is suitable for various scheduling policies. Then, we prove that the problem is MAX SNP-hard, which implies that approximation algorithms with constant approximation ratios are the best that we can hope for. Therefore, we design two approximation algorithms, both of which have polynomial runtime. The first algorithm is based on a simple greedy method. The second one is based on the Local Ratio (L-R) technique and it can approximately solve the LTE UL FDPS problem with an approximation ratio of 2. To further analyze the stability of the 2-approximation L-R algorithm, we derive a specific FDPS problem, which incorporates the queue length and channel quality information. We utilize the Lyapunov Drift to prove the L-R algorithm is stable for any $((\\omega_0, \\epsilon_0))$-admissible LTE UL systems. The simulation results indicate good performance of the L-R scheduler.","Title: A Novel Cookie-Based DDoS Protection Scheme and its Performance Analysis\nYear: 2009\n\nAbstract: Seamless handover is one of the most attractive research fields in B3G systems. Many mechanisms are proposed to provide certain QoS guarantees in handovers of mobile systems, which would also introduce new threats, such as DoS and DDOS attacks. In this paper, we extend a cookie-based scheme to protect systems from DoS and DDoS attacks. We also adopt a novel queuing network model to analyze our scheme by estimating two essential metrics, i.e. the mean total response time and the mean queue length of MAP. Numerical results indicate that our mechanism works better than the traditional cookie-based scheme and it could effectively help networks defend against abnormal attacks in the handover process.","Title: Self-certified mutual authentication and key exchange protocol for roaming services\nYear: 2006\n\nAbstract: In this paper, based on self-certified mechanism, we propose a novel mutual authentication and key exchange protocol for roaming services in the global mobility network. The main new features included in the proposed protocol are (1) identity anonymity, which protects location privacy of mobile users in the roaming network environment; (2) one-time session key renewal, which frequently updates the session key for mobile users and hence reduces the risk of using a compromised session key to communicate with the visited network. The performance analysis shows that the computational complexity of our protocol is low and suitable to be used on mobile equipments.","Title: Enhancing tit-for-tat for incentive in BitTorrent networks\nYear: 2010\n\nAbstract: BitTorrent achives great success in its high performance for file distribution. It is commonly believed that this owes to\n Tit-for-Tat strategy adopted in choking algorithm for incentive in BitTorrent protocol. But there are still many issues in\n incentive mechanism in BitTorrent. In this paper, we study Tit-for-Tat strategy by constructing a game model in P2P environments\n and deducing the constraint under which the strict Tit-for-Tat is an equilibrium strategy. We then enhance and adapt the Tit-for-Tat\n strategy to the dynamic property in P2P networks, and deduce the constraint under which it is still an equilibrium strategy.\n We also study through simulations the performance improvement of the enhanced Tit-for-Tat, and give suggestion on how to choose\n proper system parameters under different network conditions. Finally, based on the enhanced Tit-for-Tat strategy, we propose\n a method to improve incentive in BitTorrent networks to cope with free-riding and fairness issues.","Title: Linear Temporal Inference of Workflow Management Systems Based on Timed Petri Nets Models\nYear: 2002\n\nAbstract: In recent years a significant body of research has been dedicated to the analysis and verification of workflows without taking\n into account the temporal dimension of activities involved. Once the temporal aspects of the activities in a process description\n are taken into account, various types of synchronization anomalies and other undesirable behavior may be detected. In this\n paper, we use a timed Petri nets to represent workflows and introduce a set of linear inference algorithms for the quantitative\n analysis of temporal constraints of workflows. We apply our models to the analysis of several workflow patterns.","Title: Energy and Delay Minimization in Cluster-Based Wireless Sensor Networks\nYear: 2012\n\nAbstract: Wireless sensor networks (WSNs) usually consist of a large number of power-constrained sensors deployed to collect information in a specific sensed field. How to gather data in an energy-efficient manner is always a very important issue in WSNs. Recently, cluster-based data aggregation is widely used to reduce energy consumption and prolong network lifetime. However, most existing schemes proposed to reduce energy consumption fail to consider the factor of quality of service (QoS), especially the source-to-sink delay and data-loss probability. To address this problem, we propose a novel model which can capture both the factors of energy efficiency and QoS guarantee in WSNs. Through analytical and simulative results, we show that our model has the potential to help deploy the wireless sensor nodes in an energy-efficient manner while still providing a certain level of QoS.","Title: A robust active queue management algorithm in large delay networks\nYear: 2005\n\nAbstract: Active Queue Management (AQM) can maintain smaller queuing delay and higher throughput by purposefully dropping the packets at intermediate nodes. Almost all the existing AQM schemes neglect the impact of large delay on their performance. In this study, we first show an unexpected fact through simulation experiments, which is the dramatic oscillations appear in the queues controlled by several popular AQM schemes, including RED, PI controller and REM, in large delay networks. With appropriate model approximation, we design a robust AQM controller to compensate for the delay using the principle of internal mode compensation in control theory. Our novel algorithm restrains the negative effect on queue stability caused by large delay. Simulation results show that the integrated performance of the proposed algorithm outperforms that of several well-known schemes when the connections have large delay, while keeping buffer queue short.","Title: General gap-utilizing strategy for non-persistent traffic in wireless networks\nYear: 2009\n\nAbstract: We focus on a general strategy which exploits nonpersistent traffic gaps to reduce data delay/loss caused by some communication-breaking actions, e.g. handover and neighbor scanning. Those actions usually interrupt transmission or take up transferring chances for a period time, hence incur QoS downgrades. Our study discusses the details on how to choose the optimal gaps for them in order to minimize the bad impact, which provides a potential guidance for protocol and system design.","Title: Self-correcting time synchronization using reference broadcast in wireless sensor network\nYear: 2008\n\nAbstract: Time synchronization is one of the most fundamental services for numerous wireless sensor network applications. In this article the definition and basic concepts of time synchronization are introduced, and the related work is summarized in brief. Through analyzing the characteristics of the existing typical synchronization protocols and making a comprehensive comparison of the performance of various algorithms, we present a common guideline for designing the time synchronization protocol in WSN. Following this guideline, we develop a new time synchronization protocol called Self-Correcting Time Synchronization (SCTS), which converts the time synchronization problem into an online dynamic self-adjusting optimizing process to make the offset compensation and drift compensation simultaneously. The time and space complexities of the algorithm implementation are very low. In addition, the SCTS protocol fully exploits the inherent broadcast property of wireless channel, so the communication overhead is rather low. Because the algorithm implementation is based on the phase locked loop principle, an equivalent digital PLL without an actual voltage controlled oscillator is also proposed to avoid introducing the extra hardware required by a traditional PLL circuit. Finally, we validate SCTS on the Berkeley Mica2 experimental platform, and the performance is evaluated and compared to the existing typical time synchronization protocol.","Title: Trust Based Access Control in Infrastructure-Centric Environment\nYear: 2011\n\nAbstract: The rapid development of applications running on global information infrastructure poses the problem of securing information sharing among domain collaborations. Existing access control models are defective in dynamic authorization based on user's trustworthiness and do not take full advantages of the infrastructure in implementing access control system. In this work, we propose a trust and role based access control model and the corresponding framework in infrastructure-centric environment. With the extension to RBAC model, trust level requirements, which dictate that the roles in the privilege context must be activated by the trustworthy user, can be specified. The comprehensive trust model, which calculates the user's trust level in multiple trust contexts based on behavior histories, is proposed. Moreover, by taking advantages of the infrastructure services, our scheme is flexible and scalable in that system administrators are free to choose custom scoring functions while the infrastructure trust evaluation services are relieved of the heavy burdens of history record maintenance and trust level update.","Title: Performance Analysis of Data Management in Sensor Data Storage via Stochastic Petri Nets.\nYear: 2010\n\nAbstract: Recently, sensor data storage has gained increasing popularity for reliable access to data through redundancy spread over unreliable nodes in wireless sensor networks. In storage-centric sensor networks, several schemes have been proposed to optimize the performance of data management in terms of data availability, repair bandwidth, etc. However, few works have been undertaken to study the performance of these data management schemes from a comprehensive point of view. In this paper, we adopt a concise graphic model, i.e., Stochastic Petri Nets (SPNs), to analyze the performance of three representative data management schemes. From the steady state probability matrix of the SPNs models, we can easily get the average energy consumption, repair bandwidth, reliability and data availability. Based on numerical results, we provide guidelines for designing sensor data storage systems. The results also demonstrate that our proposed models are suitable for analyzing data management schemes in sensor data storage.","Title: Logical Inference Of Clauses Based On Petri Net Models\nYear: 1998\n\nAbstract: This paper extends the logical inference of Horn clauses in Petri net models to cover a large class of non-Horn clauses. Based on four-valued logic and the conflict transition concept, we show how the Petri net model for this class of non-Horn clauses can be constructed. The clause inference is solved by the T-invariant method or the fixpoint of markings. Both forward and backward inference can be used in our:model. It is further shown that these techniques are efficient for the common classes of monotonic reasoning. (C) 1998 John Wiley & Sons, Inc.","Title: Efficient Re-Keying Scheme for Group Key Distribution\nYear: 2008\n\nAbstract: In this paper, we propose a communication-efficient re-keying scheme by using a polynomial-based efficient code method. Compared with the previous schemes, the re-keying cost is significantly reduced, since no extra re-keying message header is needed. In addition, the computation overhead is also lightweight since only 2logn hash operations are required for each re-keying. Therefore, the proposed scheme is more suitable for deploying in the group application scenarios with both high dynamic memberships and limited communication channel capacity.","Title: Session-Affinity Aware Request Allocation for Web Clusters\nYear: 2004\n\nAbstract: Persistent connections are increasingly being used in web retrieval due to wide adoption of HTTP/1.1 standards. With persistent connections, the request allocation algorithm used by web clusters is often session-grained. This paper studies the caching performance of web clusters under session-grained request allocation. It is shown that although content-based algorithms considerably improve caching performance over content-blind algorithms at the request-grained level, most performance gain is offset by the allocation dependency that arises when the requests are allocated at the session-grained level. The performance loss increases with cluster size and connection holding time. An optimization problem is then formulated for improving the caching effectiveness of session-grained allocation. The problem is proven to be NP-complete. Based on a heuristic approach, a session-affinity aware algorithm is presented that makes use of the correlation between the requests in a session. The new algorithm is shown to significantly outperform the content-based algorithm undersession-grained allocation. It is also shown that optimizing session-grained allocation cannot fully compensate for the performance loss caused by allocation dependency.","Title: Modeling and analysis of a p2p-vod system based on stochastic network calculus\nYear: 2012\n\nAbstract: Peer-to-peer video-on-demand (P2P-VoD) is one of the most commercially important and technically challenging applications. In such systems, each video file is typically divided into sub-pieces that are stored possibly at different sending peers, and the receiving peer often needs to coordinate multiple senders by specifying a transmission schedule for them. In this paper, we investigate this transmission scheduling problem for a specific P2P-VoD system which is PPLive. We formalize the problem of scheduling sub-piece transmission based on stochastic network calculus, and analyze its delay performance. Based on the delay analysis, an optimization problem is formulated to design the schedule for sending peers to transmit their sub-pieces. The objective of this optimization is to maximize a defined reward and at the same time meet the delay constraint required to ensure video quality at the receiver.","Title: A Trust and Context Based Access Control Model for Distributed Systems\nYear: 2008\n\nAbstract: In order to overcome the limitations in traditional access control models such as identity-based access control and meet the access requirements in distributed systems, we propose a Trust and Context based Access Control model called TCAC, it extends the traditional RBAC (role based access control) model with the notion of trust and context. Role assignment in TCAC is based on the trustworthiness and context information of users. The TCAC model is flexible, scalable, and well suitable for the dynamic and distributed systems. Then we provide a trust evaluation mechanism based on the local and global reputation to compute the trust value of a user in distributed system, which can avoid malicious nodes behave correctly in order to get the highest possible trust value. Finally an implementation framework of the access control system based on TCAC is described.","Title: Modeling and analyzing power management policies in server farms using Stochastic Petri Nets\nYear: 2012\n\nAbstract: Server farms are playing an important role in the Internet infrastructure today. However, the increasing power consumption of server farms makes them expensive to operate. Thus, how to reduce the power consumed by server farms has become a important research topic. Power can be thought as a resource of system, just like traditional resources, and we can manage power via improved resource management strategies. In recent studies on power management, the system is attached with multiple states of different power consumption, and by switching among these states, power consumption can be made proportional to the work load. As different job scheduling policies will result in different performance and power consumption, an optimized policy with power as a factor can achieve a better tradeoff between performance and power consumption. In this paper, we summarize some familiar power management policies and propose a novel model using Stochastic Reward Nets(SRN). Based on this model, we analyze the performance and power consumption of different power management policies, and propose a novel cost-aware job scheduling algorithm.","Title: VISA: a visual sentiment analysis system\nYear: 2012\n\nAbstract: Sentiment plays a critical role in many information-centric business scenarios. The opinion mining methods proposed in the recent decade have formed a solid foundation to investigate the sentiment analysis tasks, but are often too complicated and scattered to serve the needs of real customers. We introduce the VISA system in this paper, which applies the visualization technology to synthesize the sentiment analysis results and present to the end user in an interactive manner. VISA builds on the generic sentiment tuple based data model and consumes the different facets of sentiment data with coordinated multiple views, hence is scalable to work with most of existing sentiment analysis engines on various application domains. We showcase the usage of VISA in a real world example and demonstrate the system's effectiveness through the user trail in finding an appropriate hotel for his family trip.","Title: A novel key management and distribution solution for secure video multicast\nYear: 2005\n\nAbstract: In secure video multicast systems, access control is a very important and challenging issue. A common practice is to encrypt the video content using a group key, shared only by legitimate clients. Since the clients can join and leave the multicast session frequently, the group key needs to be updated and delivered to the legitimate clients accordingly in order to provide forward/backward secrecy. Therefore, key management and distribution plays a crucial role in building a secure and scalable video multicast system. Conventional approaches usually use a dedicated secure channel independent of the video data channel, which is expensive and inefficient. In this paper, we propose a novel and scalable Media-dependent Key Management and Distribution (MKMD) solution that embeds key management messages in video data so as to save network bandwidth and enhance the security level, and most importantly, without sacrificing the quality of video content too much. We have built a prototype system of MKMD, and our extensive experimental results show that MKMD is a secure, scalable, reliable and efficient key management and distribution solution for video multicast system.","Title: Grid Service Workflow Models and Their Equivalent Simplification Methods\nYear: 2006\n\nAbstract: In this paper, we propose a Grid service workflow model framework based on Stochastic Petri net (SPN) and equivalent methods to simplify the complex service workflow model to a simple model. In our models, the Grid service can be of inner services and exterior services. The exterior service will be published to the users and the inner one needs not to be published. The models are expressive enough to capture the inner information of complex service combinations and their respective specificities. The obtained framework enables declarative composition of Grid services. It shows dynamic and transient relationships among services. On the other hand, by the equivalent simplification to the models, the inner information of complex services can be protected, and the computation and analysis course can be simplified for the Grid service workflow.","Title: Effective load balancing for cloud-based multimedia system.\nYear: 2011\n\nAbstract: Cloud computing, as a new concept, provides a good solution for handling multimedia applications effectively and efficiently. It can facilitate the execution of complicated multimedia tasks, as well as supports specific and stringent multimedia QoS provisioning. However, for this new idea, a key challenge is how to make each multimedia service task to obtain the required resources in the shortest time. In this paper, we propose an effective load balancing approach for cloud-based multimedia system, called CMLB. Its main advantage is fully considering the load of all servers and the network conditions, and thus achieving reasonable resource allocation and scheduling. The evaluation results demonstrate the effectiveness of our approach. © 2011 IEEE.","Title: A Hybrid Modeling Approach to Microarchitecture Design Space Exploring\nYear: 2010\n\nAbstract: The micro architectural design space of a new processor is too huge for architects to handle with cycle-accurate simulators. Previous researches attack this problem by statistical learning methods such as Artificial Neural Networks (ANN) and statistical sampling solutions such as SimPoint. These approaches greatly reduce the simulation time while keeping the results of CPI precisely. However, all these machine learning and sampling methods are “black boxes”: although we can get CPI accurately, we can’t get detailed information of on-chip components which makes it difficult to find relationships between these components and bottlenecks of a design. Thus these approaches are not sufficient to provide enough intuitions for architects to find potential improvements. This paper proposes a novel “white box” Decision-free Generalized Stochastic Petri Nets (Decision-free GSPN) model. We adopt ANN to estimate certain parameters for GSPN when we consider new design points. Our hybrid approach could predict CPI accurately and produce the usage state of instruction queue (IQ), micro operation queue (uopQ), reservation station (RS), reorder buffer (ROB) and so on precise enough to give architects intuitions into the new design. Our solution takes only several minutes to finish comparing to days when we adopt cycle-accurate software simulator, with less than 8.6% error rate for CPI. We believe the information our method produces is precise enough to give architects more intuitions and insights about how to change a design comparing to previous machine learning methods.","Title: Supervised Gabor-Based Kernel Locality Preserving Projections for Face Recognition\nYear: 2011\n\nAbstract: Locality Preserving Projections (LPP) is an unsupervised method which seeks to optimally preserve the neighborhood structure of the dataset. LPP has been used wildly, but it has limits to solve the classification problem, such as the ignorance of the label information. Supervised Kernel Locality Preserving Projections (SKLPP) can preserve within-class geometric structures and represent the complex nonlinear variations of the face manifold by nonlinear kernel projection. Kernel method projects data from low-dimensional space to high-dimensional space. It can overcome the difficult when it is hard to use linear method in low-dimensional. In this paper, a novel Supervised Gabor-based Kernel Locality Preserving Projections (SGKLPP) method was proposed. This method integrates the Gabor wavelet representation of face images and the Supervised Kernel Locality Preserving Projections methods and it is robust to variations of illumination and facial expression. Experiments are performed to test the proposed algorithm on ORL dataset and Yale dataset. Results show that our new algorithm outperforms Supervised LPP (SLPP) method, SKLPP method and Supervised Gabor-based LPP (SGLPP) method.","Title: Minimum average-case queries of q+1-ary search game with small sets\nYear: 2012\n\nAbstract: Given a search space S={1,2,...,n}, an unknown element x^*@?S and fixed integers @?=1 and q=1, a q+1-ary @?-restricted query is of the following form: which one of the set {A\"0,A\"1,...,A\"q} is the x^* in?, where (A\"0,A\"1,...,A\"q) is a partition of S and |A\"i|@?@? for i=1,2,...,q. The problem of finding x^* from S with q+1-ary size-restricted queries is called as a q+1-ary search game with small sets. In this paper, we consider sequential algorithms for the above problem, and establish the minimum number of average-case sequential queries when x^* satisfies the uniform distribution on S.","Title: Efficient resource allocation for China's 3G/4G wireless networks\nYear: 2005\n\nAbstract: The all-IP DiffServ model is expected to be the most promising architecture for QoS provisioning in China's next-generation wireless networks, due to its scalability, convenience for mobility support, and capability of interworking heterogeneous radio access networks. This article focuses on efficient resource allocation in a wireless DiffServ architecture. Resource utilization efficiency is particularly important for China's wireless networks as the mobile user density in China is and will continue to be much higher than that in other countries. More specifically, we propose a novel buffer sharing scheme to provide assured service for real-time layer-coded multimedia traffic, which can guarantee the specific packet loss requirement of each layer with UDP as the transport layer protocol. An adaptive optimal buffer configuration can be applied to achieve maximum resource utilization over the time-varying channel. Assured service is also provided to TCP data traffic for guaranteed throughput, where the cross-layer coupling between the TCP layer and link layer is exploited to efficiently utilize the wireless resources.","Title: Exploiting Gaps of Real-Time Traffic to Improve Handover Performance in Wireless Networks\nYear: 2010\n\nAbstract: The handover mechanism in wireless networks has been intensively investigated in the past years. The traditional solutions are to customize various protocols in different layers to provide uninterrupted communication, which is particularly important for real time traffic. In this paper, we intend to exploit silence gap, inherently existing in realtime traffic, to improve handover performance. The proposed strategy can be appended to most of handover mechanisms without extra overhead. We theoretically analyze the relationship between gap distribution and handover performance under the assumption of different statistical distribution of gaps on a case-by-case basis. Subsequently, we insert an additional module into the WiMAX network and conduct the simulations using real voice traces. The results confirm the analytical conclusions, showing that the gap-exploiting approach can obtain significant gain in terms of packet loss particularly in low-speed mobile environment.","Title: Investigating the interacting two-way tcp connections over 3GPP LTE networks\nYear: 2012\n\nAbstract: This paper investigates the interactions between two-way TCP connections over 3GPP LTE networks. In the LTE network, the two-way TCP flows share buffers on a common bottleneck, i.e., the radio access links. The behaviors of TCPs significantly influence the others in the opposite direction. Specifically, the radio links of LTE are asymmetric, which may induce drastic interactions of TCPs and rapid draining of downlink buffer. The periodic idleness of downlink is a huge waste of the precious radio bandwidth and results in considerable performance degradation. In the viewpoint of Coupled Queues, we thoroughly understand the interacting TCPs and explain the reason for performance degradation. Based on a straightforward modeling procedure, we formalize the evolution of two-way TCPs and model the bottleneck queue size in every slot. The model indicates the queues are close coupled, which is verified with simulations on NS2. If the uplink (queue) is fully utilized, the downlink (queue) will always be underutilized even idle, and vice versa. Furthermore, an effective solution called Preemptive ACK Queueing (PAQ) is designed to decouple the queues, which improves the performance of two-way TCPs over LTE networks.","Title: Scale Congestion Control to Ultra-High Speed Ethernet.\nYear: 2014\n\nAbstract: Currently, Ethernet is broadly used in LAN, datacenter and enterprise networks, storage networks, high performance computing networks and so on. Along with the popularity of Ethernet comes the requirement of enhancing Ethernet with congestion control. On the other hand, Ethernet speed extends to 40Gbps and 100Gbps recently, and even 400Gbps in the near future. The ultra-high speed requires congestion control algorithms to adapt to the broad changes of bandwidth, and highlights the impacts of small delay by enlarging the bandwidth delay product. The state-of-art standard QCN is heuristically designed for the 1Gbps and 10Gbps Ethernet, and unaware of the challenges accompanying the ultra-high speed. To scale congestion control to ultra-high speed Ethernet, we propose the Adaptive Sliding Mode (ASM) congestion control algorithm, which is simple, stable, has fast and smooth convergence process, can tolerate the impacts of delay and adapt to the wide changes of bandwidth. Real experiments and simulations confirm these good properties and show that ASM outperforms QCN. Designing ASM, we find that the derivative of queue length is helpful to rate adjustment because it reflects the difference between bandwidth and aggregated sending rate. We also argue for enforcing congestion control system staying at the congestion boundary line, along which it automatically slides to stable point. These insights are also valuable to develop other congestion control algorithms in ultra-high speed networks.","Title: Performance analysis of email systems under three types of attacks\nYear: 2010\n\nAbstract: Email is a crucial part of our daily life, but email systems are facing increasing security threats such as attacks and spam. Because of this, new mechanisms are being provided to defend against the attacks and to reduce the amount of spam in email systems. Up to now, few available works have been reported on the performance evaluation of email systems under attack, which has become necessary for enhancing email security. In this paper, we propose a novel method to study the impact of three types of attacks on email systems. We construct a multiple queueing model to characterize three types of attacks integrally, and study the performance metrics of system security such as system availability, average queue length and information leakage probability. Numerical examples indicate that the approach of this paper is effective and efficient for dealing with the security analysis of email systems under attack. We believe that this work will open a new avenue for the performance evaluation of computer networks under email attack and other forms of attacks.","Title: Adaptive resource management for mobile multimedia communications in asymmetric wireless network\nYear: 2005\n\nAbstract: In the multi-service wireless networks, the bandwidth asymmetry is the distinctive characteristic. To avoid wasting uplink and downlink bandwidth in asymmetric network, admissible region for the nonreal-time (NRT) calls has been proposed. But in that scheme the static guard capacity for RT and NRT calls cannot adapt to traffic variation. In addition, the differentiated QoS services to users are not considered. In this paper, we propose an adaptive guard channel reservation (AGCR) scheme, which has the following novelties: i) the guard capacity for each class is dynamically adjusted so as to adapt to dynamic traffic, ii) the blocking probabilities of classes with higher priority are kept below certain targets and iii) differentiated QoS services to users are provided by maintaining the blocking probability ratios over different classes at the predefined targets. Simulation results have demonstrated the proposed AGCR scheme can keep the blocking probabilities of classes with higher priority below certain targets, track the ratios close to the targets well while maintaining a high bandwidth utilization","Title: Scheduling gain analysis of opportunistic OFDMA and OFDM-TDMA systems\nYear: 2008\n\nAbstract: In this paper, the performance of opportunistic scheduling in orthogonal frequency division multiplexing (OFDM) networks is studied. An analytical model is developed to extend the multi-class processor-sharing model in single-carrier networks to multi-carrier OFDM networks, where the total service rate depends on the total number of users. Based on the analytical model, the scheduling gains in both OFDM-TDMA (time division multiple access) and OFDMA (orthogonal frequency division multiple access) networks are evaluated. Different from previous works in this area, we focus on the scheduling performance at the flow level and consider a dynamic network setting with random finite-size service demands. Simulations are performed to verify the analytical results.","Title: A Distributed Fault/Intrusion-Tolerant Sensor Data Storage Scheme Based on Network Coding and Homomorphic Fingerprinting\nYear: 2012\n\nAbstract: Recently, distributed data storage has gained increasing popularity for reliable access to data through redundancy spread over unreliable nodes in wireless sensor networks (WSNs). However, without any protection to guarantee the data integrity and availability, the reliable data storage cannot be achieved since sensor nodes are prone to various failures, and attackers may compromise sensor nodes to pollute or destroy the stored data. Therefore, how to design a robust sensor data storage scheme to efficiently guarantee the data integrity and availability becomes a critical issue for distributed sensor storage networks. In this paper, we propose a distributed fault/intrusion-tolerant data storage scheme based on network coding and homomorphic fingerprinting in volatile WSNs environments. For high data availability, the proposed scheme uses network coding to encode the source data and distribute encoded fragments with original data pieces. With secure, compact, and efficient homomorphic fingerprinting, our scheme can fast locate incorrect fragments and then initialize data maintenance. Extensive theoretical analysis and simulative results demonstrate the efficacy and efficiency of the proposed scheme.","Title: Survivability analysis of grid resource management system topology\nYear: 2005\n\nAbstract: The resource management system is one of the essential components of Grids. A resource management system may be made up of one or many resource management nodes. The organization manner of the resource management nodes, i.e. the topology of a resource management system, is a key factor that should be considered carefully during the architecture design of resource management systems with respect to some specified requirements (e.g., performance, survivability). In this paper, a new survivability definition is proposed and the survivability of three typical Grid resource management system topologies (centralized, hierarchical, and peer-to-peer) is analyzed.","Title: Accelerating large-scale data distribution in booming internet: effectiveness, bottlenecks and practices\nYear: 2009\n\nAbstract: With the booming of Internet and consumer electronics industry, there are rising amount of content (audio, video, software) that needs to be distributed to increasing number of end consumers in higher speed. Whereas the explosive growth of data-concentrated applications over Internet contributes to the entertainment electronics, the large-scale distribution of huge data to end consumers with guarantee of Quality of Service (QoS), remains an elusive goal. The potential integration of Peer-to-Peer (P2P) paradigm into the Internet content distribution infrastructure provides a disruptive market opportunity to scale the Internet for higher quality data delivery. While the theoretical benefits of P2P in regular Internet has been widely reported, it remains unknown on its performance for large-scale mass data delivery over high-speed networks, highly promising yet controversial. This paper thus provides an experimental and analytical performance study over BitTorrent-like P2P networks for accelerating large-scale content distribution over booming Internet. Our study explores the unique strength of P2P in high speed networks, identifies performance bottlenecks, and quantifies the special requirements in the new scenario, i.e. file piece length and seed capacity. Further, to enable services before full downloading, this paper proposes a Piece-On-Demand (POD) scheme to modify BitTorrent in integration with Filesystem in Userspace (FUSE) to both decrease file distribution time and increase service availability. To demonstrate the feasibility of adopting P2P as a reliable consumer network for content distribution, we further illustrate our industrial exploration on utilizing P2P in deployment for application/services with huge image, providing unprecedented deployment speed and scalability for enabling on-demand services to large-scale users.","Title: Padding for orthogonality: Efficient subspace authentication for network coding\nYear: 2011\n\nAbstract: Network coding provides a promising alternative to traditional store-and-forward transmission paradigm. However, due to its information-mixing nature, network coding is notoriously susceptible to pollution attacks: a single polluted packet can end up corrupting bunches of good ones. Existing authentication mechanisms either incur high computation/bandwidth overheads, or cannot resist the tag pollution proposed recently. This paper presents a novel idea termed “padding for orthogonality” for network coding authentication. Inspired by it, we design a public-key based signature scheme and a symmetric-key based MAC scheme, which can both effectively contain pollution attacks at forwarders. In particular, we combine them to propose a unified scheme termed MacSig, the first hybrid-key cryptographic approach to network coding authentication. It can thwart both normal pollution and tag pollution attacks in an efficient way. Simulative results show that our MacSig scheme has a low bandwidth overhead, and a verification process 2-4 times faster than typical signature-based solutions in some circumstances.","Title: Frequency Domain Packet Scheduling with MIMO for 3GPP LTE Downlink\nYear: 2013\n\nAbstract: In this paper, we formalize a general Frequency Domain Packet Scheduling (FDPS) problem for 3GPP LTE Downlink (DL). The DL FDPS problem incorporates the SingleUser Multiple Input Multiple Output (SU-MIMO) technique, and can express various scheduling policies, including the Proportional-Fair metric, the MaxWeight scheduling, etc. For LTE DL SU-MIMO, the constraint of selecting only one MIMO mode (transmit diversity or spatial multiplexing) per user in each transmission time interval (TTI) increases the hardness of the FDPS problem. We prove the problem is MAX SNP-hard, which implies approximation algorithms with constant approximation ratios are the best we can expect. Subsequently, we propose an approximation algorithm of polynomial runtime. The solution is based on a greedy method for maximizing a non-decreasing submodular function over a matroid. The algorithm can solve the general DL FDPS problem with an approximation ratio of 4. We implement the proposed algorithm and compare its performance with other well-known schedulers.","Title: Frequency-Domain Packet Scheduling for 3GPP LTE Uplink\nYear: 2010\n\nAbstract: In this paper, we investigate the frequency-domain packet scheduling (FDPS) problem for 3GPP LTE Uplink (UL). Instead of studying a specific scheduling policy, we provide a unified approach to tackle this issue. First we formalize a general LTE UL FDPS problem which is suitable for various scheduling policies. Then we prove that the problem is MAX SNP-hard, which implies that approximation algorithms with constant approximation ratios are the best that we can hope for. Therefore we design two approximation algorithms, both of which have polynomial runtime. Subsequently, we analyze the two algorithms and find their approximation ratios. The first algorithm is easy to follow, since it is based on a simple greedy method. The second one is based on the local ratio technique and it can approximately solve the LTE UL FDPS problem with a approximation ratio of 2.","Title: H-PFSP: Efficient Hybrid Parallel PFSP Protected Scheduling for MapReduce System\nYear: 2013\n\nAbstract: MapReduce provides a data-parallel computing framework, and has emerged as a popular processing model due to the simplicity of operations for big data application developers. Data processing applications from many different domains such as search and data mining are usually developed using open-source Hadoop implementation of MapReduce or self-developed MapReduce-like implementations like Dryad [1] and Ciel [2]. In cloud environments, products like Amazon's Elastic Compute Cloud (EC2) [3] provide MapReduce services as third-party multi-tenant service. Even within a company, a number of products may share the MapReduce cluster. Therefore, a fair and efficient scheduler is crucial to improve performance of submitted jobs and guarantee multi-user fairness. However, in practice, it is hard to guarantee both fairness and per-job performance, especially when jobs are scheduled without accurate estimation. We show that processor sharing (PS) type of schedulers like Fair Scheduling degrade the per-job performance in a multi-user environment. We present a new scheduling policy, Hybrid Parallel pessimistic Fair Schedule Protocol (H-PFSP), that can finish every job no later than Fair scheduler does. Unlike Fair scheduler, however, it can improve the per-job performance of MapReduce systems with relatively accurate job progress estimation.","Title: Modeling, Analysis and Optimization of Dependability-Aware Energy Efficiency in Services Computing Systems\nYear: 2013\n\nAbstract: Besides performance, dependability and energy efficiency are two critical concerns during the design, development and management of large-scale services computing systems. In this paper, we jointly consider the performance, dependability and energy efficiency, and optimize the dependability-aware energy efficiency of services computing systems by maximizing the quality of service and dependability revenue and minimizing energy costs. Markov reward models are put forward, and quantitative analysis of them is carried out. In addition, the methodologies for hierarchical model composition and state aggregation are proposed. Furthermore, the optimization problem is formulated as an average reward criterion Markov decision problem, and the algorithm to solve it is introduced. Finally, the LANL service systems are analyzed and optimized as a case study to illuminate how this approach can apply to large-scale systems in reality.","Title: Application level network access control system based on TNC architecture for enterprise network.\nYear: 2010\n\nAbstract: Traditional NAC system in enterprise network is in coarse granularity (e.g. IP or MAC address) and lack of flexibility. Recently the demand in tight control of the enterprise network to defense the misuse and security issues become more and more urgent. Based on the TCG TNC standard, an application level network access control mechanism is proposed and implemented. With TNC client/server model in hand, a client is designed to enhance TNC client with the function of host flow controller (HFC), and intercepts each application network access request(ANAR) and transfer it to PDP server to authorize the access request. When a sensor (i.e. intrusion detection system) detects any malicious traffic, host flow controller and network flow controller can identify the application that origins this traffic by querying Metadata Access Point (MAP) server and block this application's network access. A prototype system is implemented to demonstrate the design and can be used to defense the anomaly network behaviors. The prototype system demonstrates that the hosts, switches, firewalls and IDS can work together to detect, diagnose and protect enterprise network from the malicious applications attack initiated inside or outside of an enterprise network, quarantine unhealthy hosts and make the enterprise network more reliable and trustworthy.","Title: A novel data transmission scheme for mobile sensor network.\nYear: 2009\n\nAbstract: Discard the traditional methods based routing, we develop an Activity-Based data delivery scheme for mobile sensor network. Using Brown Motion Model and Multi-Dimensional Normal Distribution method, it is proved that a sensor node has higher probability to meet the sink node if and only if it moves more actively. Then, we propose an activity-based approach, in which, node compares the activity with others to make decision on when and how to exchange data message. The proposed scheme is generally applicable to any mobile sensor network, because sensors in our approach can have different speed and different activities. Extensive simulations are conducted for performance evaluation. The results show that activity-based data delivery scheme achieves the comparatively high successful delivery ratio with small delay and acceptable transmission overhead.","Title: How stochastic network calculus concepts help green the power grid\nYear: 2011\n\nAbstract: The renewable energy generation such as solar and wind will constitute an important part of the next generation grid. As the variations of renewable sources may not match the time distribution of load, energy storage is essential for grid stability. Supplemented with energy storage, we investigate the feasibility of integrating solar photovoltaic (PV) panels and wind turbines into the grid. To deal with the fluctuation in both the power generation and demand, we borrow the ideas from stochastic network calculus and build a stochastic model for the power supply reliability with different renewable energy configurations. To illustrate the validity of the model, we conduct a case study for the integration of renewable energy sources into the power system of an island off the coast of Southern California. Performance of the hybrid system under study is assessed by employing the stochastic model, e.g., with a set of system configurations, the long-term expected Fraction of Time that energy Not-Served (FTNS) of a given period can be obtained.","Title: A secure multipath routing protocol in mobile ad hoc networks\nYear: 2010\n\nAbstract: Multipath routing can adapt to traffic changes, increase reliability and enhance the Quality of Service support in communication networks. This routing algorithm has been recognized as one of the salient features in Mobile Ad hoc Networks (MANETs). However, existing multipath routing protocols are often practically infeasible due to its security venerability if they suffer from attacks in MANETs, such as repudiation attacks, Denial of Service attacks. In this paper we propose a novel secure multipath routing protocol, referred to as SMRP, and investigate its feasibility and performance. SMRP applies a new heuristic algorithm increasing the number of disjoint paths and a smart authentication mechanism to enhance the security against the attacks in MANETs. The performance results from extensive analysis and experiments show that SMRP can efficiently enhance the security in MANETs while preserving the low overhead of computing and communication. Copyright © 2009 John Wiley & Sons, Ltd.","Title: Dependability Modeling and Analysis for the Virtual Data Center of Cloud Computing\nYear: 2011\n\nAbstract: Virtual data center is becoming increasingly popular as the infrastructure of Cloud computing. In order to provide efficient and uninterrupted service, the dependability of the Cloud infrastructure has received extensive attention. But modeling and analysis of dependability of the virtual data center is challenging for the particular characteristics and mechanisms of virtualization. In this paper, we use hierarchical method and develop the hybrid models combining reliability block diagrams and general stochastic Petri nets. Focusing on the two attributes reliability and availability, we give the computation expressions by solving the models. The impact of the characteristics and mechanisms of virtualization such as consolidation backup and live migration on dependability is studied. We analyze the relation between reliability and consolidation ratio, as well as the relation between availability and the workload. In addition, some useful rules are summarized and discussed, which are believed to be helpful to the design and construction of more dependable virtual data center.","Title: Graph regularized non-negative matrix factorization with sparse coding\nYear: 2013\n\nAbstract: Matrix factorization techniques have been frequently utilized in pattern recognition and machine learning. Among them, Non-negative Matrix Factorization (NMF) has received considerable attention because it represents the naturally occurring data by parts of it. On the other hand, from the geometric perspective, the data is usually sampled from a low dimensional manifold embedded in a high dimensional ambient space. One hopes then to find a compact representation which uncovers the intrinsic geometric structure. In this paper, we propose a novel method, called Graph Regularized Non-negative Matrix Factorization with Sparse Coding (GRNMF_SC), the new model can learn much sparser representation and more discriminating power via imposing sparse constraint and Laplacian regularization explicitly. Experimental results on the ORL and Yale databases demonstrate encouraging performance of the proposed algorithm when compared with the state-of-the-art algorithms.","Title: Iterative learning control for nonlinear distributed parameter system with state time delay\nYear: 2013\n\nAbstract: The iterative learning control method is applied to distributed parameter systems governed by parabolic partial differential equations (PDE). In this paper, an iterative learning control topic for a kind of nonlinear distributed parameter system which initial value can vary in a little space has been discussed. And an high level algorithm is extended and proven, using Green formula and Gronwall inequality, that it is suitable for the given system. Numerical simulation is presented for a parabolic PDE solved using ILC based on Eular difference format. The numerical example is given to illustrate the effectiveness of the proposed method.","Title: Integrative scheme of Differentiated Services: Modeling and performance analysis\nYear: 2000\n\nAbstract: Proposes an integrative scheme of differentiated services (DiffServ) for the Internet. In our scheme, the quality of service (QoS) is ranked according to the levels of both importance and urgency, which makes the service negotiation and the implementation mechanism more flexible and universal. Samples of the implementation mechanism at the edge nodes and interior nodes are described, and stochastic Petri net models of them are given. The performance of the per-hop behavior (PHB) implementation mechanism is analyzed, under both Poisson and self-similar arrivals. An approximate analysis technique of decomposition and iteration is used to reduce the complexity. For self-similar traffic, an MMPP (Markov-modulated Poisson process) based self-similar model of A.T. Andersen et al. (1998) is used","Title: A Petri Net Approach To Stability Analysis Of Buffer Priority Scheduling Policies In Manufacturing Systems\nYear: 2001\n\nAbstract: The concept of buffer boundedness as an alternative criterion for characterizing the stability of scheduling policies was emphasized. The stability of re-entrant line systems was analyzed using the concept of buffer boundedness. The dynamic behavior of re-entrant line systems was represented by a Petri net based framework. The Petri net method was observed to be intuitive, simple and efficient.","Title: An accurate scene-based traffic model for MPEG video stream\nYear: 2003\n\nAbstract: In this paper, we propose an accurate scene-based traffic model for MPEG video stream. The autocorrelation structure of video stream is modeled by two Auto-Regress (AR) processes. We also take the correlation in GOP (Group of Picture) into account. According to the size of I frame, the size of B frame and P frame in the same GOP are determined by a lognormal distribution. As for the marginal distribution of the MPEG video stream, we use normal mixture distribution to fit it, and the parameters of normal mixture distribution are estimated through an Expecting Maximum (EM) algorithm. At last, the accuracy of the proposed model is validated with simulation experiments and the application of this model in network QoS (Quality of Service) evaluation.","Title: A stochastic Petri nets approach to dependability analysis of control center networks in smart grid.\nYear: 2011\n\nAbstract: As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs and increase reliability. In smart grid, control center networks have attracted a great number of attentions, because their security and dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of security, but few work focuses on the dependability analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the dependability of control center networks in smart grid. With the proposed SPNs model, we can measure the dependability from two metrics, i.e., the reliability and availability, through analyzing the transient probability. We also study a specific case to demonstrate the feasibility and efficiency of the proposed model in the dependability analysis of control center networks in smart grid. © 2011 IEEE.","Title: EcoPlan: energy-efficient downlink and uplink data transmission in mobile cloud computing.\nYear: 2015\n\nAbstract: Mobile cloud computing (MC2) is emerging as a new computing paradigm that seeks to augment resource-constrained mobile devices for executing computing- and/or data-intensive mobile applications. Nonetheless, the energy-poverty nature of mobile devices has become a stumbling block that greatly impedes the practical application of MC2. Fortunately, for delay-tolerant mobile applications, energy conservation is achievable via two means: (1) dynamic selection of energy-efficient links (e.g., WiFi interface); and (2) deferring data transmission in bad connectivity. In this paper, we study the problem of energy-efficient downlink and uplink data transmission between mobile devices and clouds. In the presence of unpredictable data arrival, network availability and link quality, our objective is to minimize the time average energy consumption of a mobile device while ensuring the stability of both device-end and cloud-end queues. To achieve this goal, we propose an online control framework named EcoPlan under which mobile users can make flexible link selection and data transmission scheduling decisions to achieve arbitrary energy-delay tradeoffs. Real-world trace-driven simulations demonstrate the effectiveness of EcoPlan, along with its superior energy-efficiency over alternative WiFi-prioritized, minimum-delay and SALSA schemes.","Title: Phase Plane Analysis of Quantized Congestion Notification for Data Center Ethernet\nYear: 2015\n\nAbstract: Currently, Ethernet is being enhanced to become the unified switch fabric in data centers. With the unified switch fabric, the cost on redundant devices is reduced, while the design and management of data center networks are simplified. Congestion management is one of the indispensable enhancements on Ethernet, and Quantized Congestion Notification (QCN) has just been ratified as the formal standard. Though QCN has been investigated for several years, there exist few in-depth theoretical analyses on QCN. The most possible reason is that QCN is heuristically designed and involves the property of variable structure. The classic linear analysis method is incapable of handling the segmented nonlinearity of the variable structure system. In this paper, we use the phase plane method, which is suitable for systems of segmented nonlinearity, to analyze the QCN system. The overall dynamic behaviors of the QCN system are presented, and the sufficient conditions for the stable QCN system are deduced. These sufficient conditions serve as guidelines toward proper parameters setting. Moreover, we find that the stability of QCN is mainly promised by the sliding mode motion, which is the underlying reason for QCN's stable queue shown in numerous simulations and experiments. Experiments on the NetFPGA platform verify that the analytical results can explain the complex behaviors of QCN.","Title: Dynamic Routing for Data Integrity and Delay Differentiated Services in Wireless Sensor Networks\nYear: 2015\n\nAbstract: Applications running on the same Wireless Sensor Network (WSN) platform usually have different Quality of Service (QoS) requirements. Two basic requirements are low delay and high data integrity. However, in most situations, these two requirements cannot be satisfied simultaneously. In this paper, based on the concept of potential in physics, we propose IDDR, a multi-path dynamic routing algorithm, to resolve this conflict. By constructing a virtual hybrid potential field, IDDR separates packets of applications with different QoS requirements according to the weight assigned to each packet, and routes them towards the sink through different paths to improve the data fidelity for integrity-sensitive applications as well as reduce the end-to-end delay for delay-sensitive ones. Using the Lyapunov drift technique, we prove that IDDR is stable. Simulation results demonstrate that IDDR provides data integrity and delay differentiated services.","Title: Modeling and Solving TCP Incast Problem in Data Center Networks\nYear: 2015\n\nAbstract: TCP Incast problem attracts much attention due to the catastrophic goodput drop. In this paper, a goodput model of the problem is built to understand why goodput collapse occurs and a solution to the problem based on the theoretical analysis is proposed. We found that the TCP Incast goodput deterioration is mainly caused by two types of timeouts, one happens at the tail of data blocks and dominates the goodput when the number of senders is small, while the other one at the head of data blocks and governs the goodput when the number of senders is large. The proposed model describes the relationship between these two types of timeouts and the Incast communication pattern, block size, bottleneck buffer size, and so on. The simulation results indicate that the model well characterizes the features of the TCP Incast problem. Enlightened by the analysis, a PRiority-based solution to the TCP INcast problem (PRIN) is proposed, which avoids timeouts at the head of blocks by reducing TCP send window and prevents timeouts at the tail of blocks by leveraging priority technology. The experimental results show that PRIN solves the TCP Incast problem.","Title: Managing performance and power consumption tradeoff for multiple heterogeneous servers in cloud computing\nYear: 2014\n\nAbstract: There are typically multiple heterogeneous servers providing various services in cloud computing. High power consumption of these servers increases the cost of running a data center. Thus, there is a problem of reducing the power cost with tolerable performance degradation. In this paper, we optimize the performance and power consumption tradeoff for multiple heterogeneous servers. We consider the following problems: (1) optimal job scheduling with fixed service rates; (2) joint optimal service speed scaling and job scheduling. For problem (1), we present the Karush-Kuhn-Tucker (KKT) conditions and provide a closed-form solution. For problem (2), both continuous speed scaling and discrete speed scaling are considered. In discrete speed scaling, the feasible service rates are discrete and bounded. We formulate the problem as an MINLP problem and propose a distributed algorithm by online value iteration, which has lower complexity than a centralized algorithm. Our approach provides an analytical way to manage the tradeoff between performance and power consumption. The simulation results show the gain of using speed scaling, and also prove the effectiveness and efficiency of the proposed algorithms.","Title: Queuing Models With Applications to Mode Selection in Device-to-Device Communications Underlaying Cellular Networks\nYear: 2014\n\nAbstract: In this paper, we study the performance of mode selections in device-to-device (D2D) communications in terms of end-to-end average throughput, average delay, and dropping probability, considering dynamic data arrival with non-saturated buffers. We first introduce a general framework that includes three canonical routing modes, namely D2D mode, cellular mode, and hybrid mode, which can be combined with different resource allocation restrictions to represent the semi-static and dynamic selections of the three resource sharing modes. A queuing model is developed when the routing mode for every D2D connection is chosen, and an exact numerical analysis and an approximate decomposition and iteration approach are proposed. The performance measures are obtained from the decomposition approach and validated by means of simulation. We further introduce a mode selection scheme that adaptively chooses to semi-statically or dynamically select the resource sharing modes according to the estimated performance measures.","Title: Modeling and Analysis of Dependability Attributes for Services Computing Systems\nYear: 2014\n\nAbstract: Dependability is an important consideration during the design and development of IT systems and services. But in services computing systems, the traditional definition and evaluation methods from the systems' and components' point of view meet challenges. In this paper, we veer from the angle of view and study the dependability and their attributes from the service-oriented perspective. A stochastic model using the semi-Markov process is put forward, and the quantitative analysis of the dependability attributes is carried out. By extending and transforming this model, the mean time to dependability attributes failure is calculated. Based on the analysis and calculations, some theorems are proposed and proved, to show the inter-relationships and comparisons of the different dependability attributes. Furthermore, we model the service composition and conduct workflow analysis to show how this model could deal with complex services. In addition, LANL service systems are analyzed as a case study to show how the proposed model and calculation methods could apply to real systems, and sensitivity analysis is also performed to identify the bottlenecks and find effective ways for dependability optimization.","Title: Resource control in network assisted device-to-device communications: solutions and challenges\nYear: 2014\n\nAbstract: Network assisted Device-to-Device (D2D) communications is envisioned as a promising technology to facilitate the discovery of geographically close cellular devices and reduce the communication cost between these devices. In order to fully take advantages of the freedom in resource management introduced by D2D communications, it is essential to design optimal resource control policies. In this article, we first review and classify the state-of-the-art research based on different considered network scenarios. We then introduce our preliminary studies on delay-aware resource control with bursty traffic, in which we propose an optimization framework and formulate a general queuing model for performance evaluation and optimization. Finally, the challenges and open issues are highlighted to serve as guidelines for future research.","Title: 1.5D Egocentric Dynamic Network Visualization\nYear: 2015\n\nAbstract: Dynamic network visualization has been a challenging research topic due to the visual and computational complexity introduced by the extra time dimension. Existing solutions are usually good for overview and presentation tasks, but not for the interactive analysis of a large dynamic network. We introduce in this paper a new approach which considers only the dynamic network central to a focus node, also known as the egocentric dynamic network. Our major contribution is a novel 1.5D visualization design which greatly reduces the visual complexity of the dynamic network without sacrificing the topological and temporal context central to the focus node. In our design, the egocentric dynamic network is presented in a single static view, supporting rich analysis through user interactions on both time and network. We propose a general framework for the 1.5D visualization approach, including the data processing pipeline, the visualization algorithm design, and customized interaction methods. Finally, we demonstrate the effectiveness of our approach on egocentric dynamic network analysis tasks, through case studies and a controlled user experiment comparing with three baseline dynamic network visualization methods.","Title: Neighbourhood sensitive preserving embedding for pattern classification\nYear: 2014\n\nAbstract: Recently, a large family of supervised or unsupervised manifold learning algorithms that stem from statistical or geometrical theory has been designed to solve the problem of pattern classification. In this study, consider the fact that the data are usually sampled from a low-dimensional manifold space which resides in a high-dimensional Euclidean space, the authors propose a novel two-graph-based supervised linear classification algorithm called neighbourhood sensitive preserving embedding (NSPE). Different from local linear embedding (LLE) (or neighbourhood preserving embedding (NPE)) which preserves the local neighbourhood structure with one graph, NSPE can discover both the intrinsic and discriminant structure of the data manifold by constructing two graphs, that is, the within-class graph and the between-class graph. Thus, the data are mapped into a subspace where the nearby points with the same label are close to each other, whereas the nearby points with different labels are far apart. As a classification method, besides being defined on training samples, NSPE is also defined on testing samples. Experiments carried on the real-world face databases demonstrate that the results of all two-graph-based spectral methods are comparable and better than that of one-graph-based methods.","Title: Flow-Based Influence Graph Visual Summarization\nYear: 2014\n\nAbstract: Visually mining a large influence graph is appealing yet challenging. Existing summarization methods enhance the visualization with blocked views, but have adverse effect on the latent influence structure. How can we visually summarize a large graph to maximize influence flows? In particular, how can we illustrate the impact of an individual node through the summarization? Can we maintain the appealing graph metaphor while preserving both the overall influence pattern and fine readability? To answer these questions, we first formally define the influence graph summarization problem. Second, we propose an end-to-end framework to solve the new problem. Last, we report our experiment results. Evidences demonstrate that our framework can effectively approximate the proposed influence graph summarization objective while outperforming previous methods in a typical scenario of visually mining academic citation networks.","Title: Toward a practical reconfigurable router: a software component development approach\nYear: 2014\n\nAbstract: Modern routers are no longer simple packet routing equipment. They are extremely complex systems that incorporate various network management functionalities. Due to system complexity, most commercial routers are developed by a few equipment vendors in a closed development pattern, which not only hinders the broad participation of most routing function research and development (R&D) teams, but also deter the wide deployment of novel network architectures such as Software Defined Networks (SDNs). In this article a practical approach is introduced to build an open, flexible, and modularized reconfigurable router. A reconfigurable routing software platform supporting functional modules is dynamically assembled, replaced, and updated in the form of components. So far the platform has been adopted by vendors such as Ruijie Networks in their commercial routers, and it is running well all the time. Moreover, a component development environment is provided, which consists of a code editor, a multi-platform compiler, and an automatic testing tool. A component sharing website has also been built to encourage the participation of various R&D teams and promote the spread of our open reconfigurable development pattern.","Title: Big data: transforming the design philosophy of future internet.\nYear: 2014\n\nAbstract: Big data opens the era of the fourth paradigm for science discovery through datadriven computing. This new paradigm applies to the design of the future Internet, which currently faces issues in supporting new applications, efficient resource utilization, and continuous evolvement. We observe several technological transformations in network architecture, services, and applications, and point out the grand opportunities for designing future Internet architecture, communication models, and resource management mechanisms enabled by the availability of massive network data. In particular, we envision in the future Internet: 1) computational complexity replaces state complexity in the control plane; 2) data intelligence enables user choices and rewards innovations; and 3) correlations from data analytics help solve inherently hard optimization problems. Finally, we identify the key challenges in data-driven Internet design and outline future research directions.","Title: A closed-loop brain-computer interface triggering an active ankle-foot orthosis for inducing cortical neural plasticity.\nYear: 2014\n\nAbstract: In this paper, we present a brain-computer interface (BCI) driven motorized ankle-foot orthosis (BCI-MAFO), intended for stroke rehabilitation, and we demonstrate its efficacy in inducing cortical neuroplasticity in healthy subjects with a short intervention procedure (∼15 min). This system detects imaginary dorsiflexion movements within a short latency from scalp EEG through the analysis of movement-related cortical potentials (MRCPs). A manifold-based method, called locality preserving projection, detected the motor imagery online with a true positive rate of 73.0 ± 10.3%. Each detection triggered the MAFO to elicit a passive dorsiflexion. In nine healthy subjects, the size of the motor-evoked potential (MEP) elicited by transcranial magnetic stimulation increased significantly immediately following and 30 min after the cessation of this BCI-MAFO intervention for ∼15 min ( p = 0.009 and , respectively), indicating neural plasticity. In four subjects, the size of the short latency stretch reflex component did not change following the intervention, suggesting that the site of the induced plasticity was cortical. All but one subject also performed two control conditions where they either imagined only or where the MAFO was randomly triggered. Both of these control conditions resulted in no significant changes in MEP size (p = 0.38 and p = 0.15). The proposed system provides a fast and effective approach for inducing cortical plasticity through BCI and has potential in motor function rehabilitation following stroke.","Title: A Lightweight Encryption Scheme for Network-Coded Mobile Ad Hoc Networks\nYear: 2014\n\nAbstract: Energy saving is an important issue in Mobile Ad Hoc Networks (MANETs). Recent studies show that network coding can help reduce the energy consumption in MANETs by using less transmissions. However, apart from transmission cost, there are other sources of energy consumption, e.g, data encryption/decryption. In this paper, we study how to leverage network coding to reduce the energy consumed by data encryption in MANETs. It is interesting that network coding has a nice property of intrinsic security, based on which encryption can be done quite efficiently. To this end, we propose P-Coding, a lightweight encryption scheme to provide confidentiality for network-coded MANETs in an energy-efficient way. The basic idea of P-Coding is to let the source randomly permute the symbols of each packet (which is prefixed with its coding vector), before performing network coding operations. Without knowing the permutation, eavesdroppers cannot locate coding vectors for correct decoding, and thus cannot obtain any meaningful information. We demonstrate that due to its lightweight nature, P-Coding incurs minimal energy consumption compared to other encryption schemes.","Title: Dimensioning the packet loss burstiness over wireless channels: a novel metric, its analysis and application\nYear: 2014\n\nAbstract: AbstractThe packet loss burstiness over wireless channels is commonly acknowledged as a key impacting factor on the performance of networking protocols. An accurate evaluation of the packet loss burstiness, which reveals the characteristics and performance of the wireless channels, is crucial to the design of wireless systems and the quality-of-service provisioning to end users. In this paper, a simple yet accurate analytical framework is developed to dimension the packet loss burstiness over generic wireless channels. In specific, we first propose a novel and effective metric to characterize the packet loss burstiness, which is shown to be more compact, effective, and accurate than the metrics proposed in existing literature for the same purpose. With this metric, we then develop an analytical framework and derive the closed-form solutions of the packet loss performance, including the packet loss rate and the loss-burst/loss-gap length distributions. Lastly, as an example to show how the derived results can be applied to the design of wireless systems, we apply the analytical results to devise an adaptive packetization scheme. The proposed packetization scheme adaptively adjusts the packet length of transmissions based on the prediction of the packet loss rate and loss-burst/loss-gap lengths of the wireless channel. Via extensive simulations, we show that with the proposed packetization scheme, the channel throughput can be enhanced by more than 10% than the traditional scheme.Copyright © 2012 John Wiley & Sons, Ltd.","Title: Optimal power scheduling in 802.11n wireless networks for real-time services\nYear: 2014\n\nAbstract: AbstractThe growing popularity of mobile devices in our daily life demands higher throughput of wireless networks. The new communication standard 802.11n has significantly improved throughput because of the use of advanced technologies such as the multiple-input multiple-output communication technique. Because mobile devices are usually battery-operated, power efficiency is critical; on the other hand, delay performance can be improved by transmitting at high power. To address the conflicting requirement of power saving and small delay, power scheduling is needed. In the past, many approaches to power scheduling have been proposed for real-time applications, but few of them have considered complicated modes of channel state informationCSI in multiple-input multiple-output. In this paper, we study this and classify the CSI into four types, namely, constant, slow fading, fast fading, and unknown. For known CSI, we propose an optimal algorithm for power scheduling. For unknown CSI, we propose an approximate algorithm based on some heuristics. To improve resource utilization, a stochastic delay-bound method is proposed for fast-fading condition. Simulation results demonstrate that the performance achieved by the optimal and heuristic algorithms agrees well with the analysis. Copyright © 2012 John Wiley & Sons, Ltd.","Title: Energy-Efficient Link Selection and Transmission Scheduling in Mobile Cloud Computing.\nYear: 2014\n\nAbstract: Mobile cloud computing (MCC) converges mobile computing and Cloud computing for augmenting resource-poor mobile devices to run ¿¿heavier¿¿ applications. With the increasing ubiquity of mobile devices, MCC exhibits vast application potential in various areas. Energy-efficient data transmission is a key issue in MCC due to energy-poverty of mobile devices. In this letter, we address the issue of energy-efficient link selection and data transmission scheduling for delay-tolerant and data-intensive applications in MCC. We first formulate the problem as a discrete-time stochastic dynamic program (SDP) that aims to optimize both system throughput and energy consumption. To solve the formulated SDP, we then propose a scalable approximate dynamic programming (ADP) algorithm that does not require the statistics of exogenous stochastic information (e.g., data arrival). Simulation studies show that the proposed ADP algorithm can reduce the average energy consumed for delivering a packet by a maximum of over 40 percent compared to alternative minimum-delay and SALSA policies.","Title: Wireless channel model using stochastic high-level Petri nets for cross-layer performance analysis in orthogonal frequency-division multiplexing system\nYear: 2014\n\nAbstract: In this study, the authors form a wireless channel model for orthogonal frequency-division multiplexing (OFDM) systems with stochastic high-level Petri net (SHLPN) formalism in order to simplify the cross-layer performance analysis of modern wireless systems. Compared with existing finite state Markov channel model whose state space grows exponentially with the number of OFDM subchannels, the author's proposed SHLPN model uses state aggregation technique to deal with this problem. Closed-form expressions to calculate the transition probabilities among the compound markings of the SHLPN model are provided. When applied to derive the performance measures for OFDM system in terms of the average throughput, average delay and packet dropping probability, the SHLPN model can accurately capture the correlated time-varying nature of wireless channels. Simulation is performed to show that the numerical results offered by the proposed model are more accurate compared with other simplified channel models for avoiding state space complexity.","Title: Hierarchical Bayes based Adaptive Sparsity in Gaussian Mixture Model.\nYear: 2014\n\nAbstract: •A hierarchical Bayes Gaussian Mixture Model (HBGMM) with adaptive sparsity is proposed.•We impose our hierarchical Bayes prior on the entries of precision matrices in GMM to encourage sparsity.•Our adaptive sparsity can reduce the dimensionality of the parameters in GMM to be estimated.•Our HBGMM method does not involve any hyperparameters to be estimated, i.e., it is hyperparameter-free.","Title: Analysis and Improvement of Makespan and Utilization for MapReduce\nYear: 2013\n\nAbstract: A MapReduce cluster is usually shared by multiple users or products, aiming at accelerating their own job. In contrast, the utilization of the cluster is the main concern for the system itself. MapReduce jobs are split into independent tasks during execution. However, in practice, the number of tasks per stage for each job and the system settings are often sub-optimal to support certain workload. As far as we know, few research focused on the impacts of influential factors such as task granularity, slot count and workload, and the performance analysis from the perspectives of each job and system. We construct models to describe the effects of these factors on the performance from both the per-job and system perspectives. Based on the understanding provided by analytical model, we discuss the optimal settings of job and system parameters, then propose a batch scheduling policy instead of FIFO, so as to improve the system utilization and reduce the average job processing time. The simulation results show that the average gap time is reduced by 10% and the mean job make span is improved by 15% when each job is preempted 6 times on average using the batch scheduling policy.","Title: Energy Efficient Dynamic Service Selection for Large-Scale Web Service Systems\nYear: 2014\n\nAbstract: With the increasing popularity of web services on the Internet, service selection has become an important issue in large-scale web service systems. In recent years, with the growing energy consumption associated with IT systems and services, energy efficiency has drawn extensive attention in service allocation and selection. However, existing approaches for energy efficient web service selection face great challenges, because of the high dynamics and unpredictability of task arrivals, and large scale of current web service systems. In this paper, we present a distributed online approach for web service selection that jointly considers the conflicting performance metrics including response time, queue congestion and energy consumption. Targeting at optimizing the long-term average system reward, our approach does not require any priori knowledge of the statistics or prediction on task arrivals. Mathematical analysis as well as simulation experiments demonstrate its effectiveness in optimizing energy efficiency while stabilizing the system.","Title: Ranking Web Services with Limited and Noisy Information\nYear: 2014\n\nAbstract: With the increasing popularity of web services on the Internet, besides functionalities, Quality of Service (QoS) is becoming an important concern for describing characteristics of web services. QoS rankings provide valuable information for making optimal service selection and recommendation from a set of functionally similar or equivalent service candidates. However, in order to obtain such rankings, a huge number of invocations on the services are usually required, which is extremely expensive and even impractical in reality. To tackle this challenge, this paper proposes a scheme to derive the global ranking from observations of QoS rankings on subsets of the services, while the observations may also be contaminated by noise and errors. We introduce a pairwise comparison model to describe the relationships between services, and thus the ranking can be formulated as random walks over the services. A Markov chain based approach is proposed, and algorithms for deriving global rankings are designed. The efficacy of our approach is validated by both mathematical analysis and simulation experiments.","Title: Partial Selection: An Efficient Approach for QoS-Aware Web Service Composition\nYear: 2014\n\nAbstract: With the increasing presence of web services on the Internet, Quality of Service (QoS) is becoming important for describing non-functional characteristics of web services, and is often employed in web service composition. As QoS is an aggregated concept consisting of several attributes, service composition on enormous candidate sets is a challenging multi-objective optimization problem. In this paper, we study the problem from a general Pareto-optimal angle, seeking to reduce the search space in service composition. QoS attributes are systematically studied according to their different types of aggregation pattern in service composition, and QoS-based dominance relationships between candidates and between workflows are defined. Taking advantage of pruning candidates by dominance relationships and constraint validations at candidate level, a service composition algorithm using partial selection technique is proposed, which is able to significantly reduce the search space and achieve great performance gains. A careful analysis of the optimality of our approach is provided, and its efficacy is further validated by empirical evaluation.","Title: Error estimating codes for insertion and deletion channels\nYear: 2014\n\nAbstract: Error estimating codes (EEC) have recently been proposed for measuring the bit error rate (BER) in packets transmitted over wireless links. They however can provide such measurements only when there are no insertion and deletion errors, which could occur in various wireless network environments. In this work, we propose ``idEEC'', the first technique that can do so even in the presence of insertion and deletion errors. We show that idEEC is provable robust under most bit insertion and deletion scenarios, provided insertion/deletion errors occur with much lower probability than bit flipping errors. Our idEEC design can build upon any existing EEC scheme. The basic idea of the idEEC encoding is to divide the packet into a number of segments, each of which is encoded using the underlying EEC scheme. The basic idea of the idEEC decoding is to divide the packet into a few slices in a randomized manner -- each of which may contain several segments -- and then try to identify a slice that has no insertion and deletion errors in it (called a ``clean slice''). Once such a clean slice is found, it is removed from the packet for later processing, and this ``randomized divide and search'' procedure will be iteratively performed on the rest of the packet until no more clean slices can be found. The BER will then be estimated from all the clean slices discovered through all the iterations. A careful analysis of the accuracy guarantees of the idEEC decoding is provided, and the efficacy of idEEC is further validated by simulation experiments.","Title: Delay guaranteed live migration of Virtual Machines\nYear: 2014\n\nAbstract: The proliferation of cloud services makes virtualization technology more important. One important feature of virtualization is live Virtual Machine (VM) migration, which can be employed to facilitate load balancing, fault management and server maintenance etc. Two main metrics of evaluating a live VM migration mechanism are total migration time and downtime. The existing literature on live VM migration mainly focus on designing migration mechanisms to shorten these two metrics or making a tradeoff between them. Few of them can be applied to the applications with delay requirements, such as, delay-sensitive web services or a VM backup process that needs to be done in a specific time. This will not only negatively impact the user experiences, but also reduce the profit of cloud service providers. Besides, the frequently varied bandwidth required by the widely used pre-copy mechanism is difficult to be provided by current network technologies. In this work, we theoretically analyze how much bandwidth is required to guarantee the total migration time and downtime of a live VM migration. We first propose a deterministic-based model as a simple example, then assume that the dirtying frequency of each page obeys the bernoulli distribution. At last, we analyze the statistic features of the typical workload running in a VM and build a reciprocal-based workload model, and theoretically give the required bandwidth value to satisfy the performance metrics of a live VM migration. The experimental results demonstrate that the bandwidth obtained from the reciprocal-based model can guarantee the expected total migration time and downtime.","Title: Grid Service Interaction Pattern and Composition Verification\nYear: 2008\n\nAbstract: It is an important direction of the development of Parallel Computer Architecture to implement Single System Image (SSI) on clusters, since SSI supports easier programming and administration on clusters. Currently, most SSI studies focused on the middleware ...","Title: HEIR: Heterogeneous interference recognition for wireless sensor networks\nYear: 2014\n\nAbstract: With the rapid development of wireless communication technology, a large number of wireless networks and devices that have different PHY and MAC layers coexist with each other. 2.4GHz Industrial, Scientific and Medical (ISM) band is becoming increasingly crowded. Wireless Sensor Networks (WSNs) which use low-power communication standard IEEE802.15.4 share the unlicensed spectrum with a plethora of other devices and technologies, such as WiFi systems underlying IEEE802.11, Bluetooth systems underlying IEEE802.15.1, and even non-communication appliance like microwave ovens. The ability to detect what radios are operating in the neighborhood is a fundamental need of WSNs, ranging from network management to network security. Since there are no explicit mechanisms to recognize such heterogeneous interference sources, WSNs often have no reasonable way to guard against them. In this paper, we describe the main working principle of heterogeneous interference sources, and present HEIR, a detector that is able to accurately identify heterogeneous interference sources. HEIR builds on the insight that there are hidden repeating patterns of signals which can be used to construct unique signatures and identify different types in most wireless protocols. The method can be implemented through signal sampling, interference estimation, feature extraction, and device classification. We show the experimental evaluation in an indoor testbed that HEIR is accurate in several different scenarios, and can live on sensor nodes' hardware. Since no any channel changes, the network topology is not interrupted, and the stable communication in real-time is ensured then.","Title: Design and analysis of fault tolerance mechanism for sparrow\nYear: 2014\n\nAbstract: Big data processing frameworks are developing towards larger degrees of parallelism and shorter task durations in order to achieve lower response time. Scheduling highly parallel tasks that complete in nearly 100 milliseconds poses a major challenge for task schedulers. Taking the challenge, researchers turn to decentralized frameworks to relieve the pressure of task schedulers, among which Sparrow is a good choice. However, little efforts are devoted to fault tolerance of Sparrow, which does not handle worker failures, giving rise to incomplete tasks. We present a fault tolerance mechanism named Heartbeat on Sparrow to handle failures of worker machines. Through simulation, we compare it with a simple mechanism. The result shows that Heartbeat on Sparrow can detect worker failures faster and reschedule all failed tasks more efficiently, achieving recovery of tasks and states in sub-second time. We hope this mechanism will make some contributions to Sparrow and other decentralized designs on fault tolerance side.","Title: Analysing convergence of Quantized Congestion Notification in Data Center Ethernet\nYear: 2014\n\nAbstract: Enhancing Ethernet as the unified data center fabric to concurrently handle the traffic of Local Area Network (LAN), Storage Area Network (SAN), and High Performance Computing (HPC) has attracted much attention. Congestion management is one critical enhancement to fill the performance gap between traditional Ethernet and the unified data center fabric. Currently, Quantized Congestion Notification (QCN) has been approved as the standard congestion management mechanism. However, lots of work pointed out that QCN suffers from the problem of unfairness among different flows. In this paper, we found that QCN could achieve fairness, merely the convergence time to fairness is quite long. Thus, we build a convergence time model to investigate the reasons of the slow convergence process of QCN. The model indicates that the convergence time of QCN can be decreased if RPs have the same rate increase probability or the rate increase step becomes larger at steady state. We validate the precise of our model by comparing with experimental data on the NetFPGA platform. The results show that it well characterizes the convergence time to fairness of QCN. Based on the proposed model, the impact of QCN parameters, network parameters, and QCN variants on the convergence time is analysed. Finally, enlightened by the analysis, we proposed a mechanism, called QCN-T, which replaces the Byte Counter and Timer at sources with a single modified Timer, to reduce the convergence time of QCN.","Title: Scalability of control planes for Software defined networks: Modeling and evaluation\nYear: 2014\n\nAbstract: With the increasing popularity of Software defined network (SDN), designing a scalable SDN control plane becomes a critical problem. An effective approach to improving the scalability is to design distributed architecture of SDN control plane. However, how to evaluate the scalability of SDN control planes remains unexplored. In this paper, we propose a metric of scalability for SDN control planes, and study three typical SDN control plane structures, including centralized, decentralized and hierarchical architectures. We build performance models for response time, based on which we evaluate the scalability of these three structures. Furthermore, the comparison between different architectures are analyzed by mathematical methods. Numerical evaluations are also conducted to validate the conclusions drawn in this paper.","Title: An online mechanism for dynamic instance allocation in reserved instance marketplace\nYear: 2014\n\nAbstract: As one of the pricing model offered by Amazon, reserved instance enables users to reserve capacities for their EC2 instances and lowers their average instance cost. To attract more users to adopt the reserved instance, Amazon has provided a platform named Reserved Instance Marketplace to give users the flexibility to sell the remainder of their reserved instances as their needs change. However, the present trading mechanism inside the Reserved Instance Marketplace requires the reserved instance sellers to sell their reserved capacities at month level and set the upfront fee by themselves, which is not flexible enough and hard for an inexperienced seller to specify a suitable upfront fee. To address this problem, this paper proposes an online mechanism for the Reserved Instance Marketplace. Our online mechanism tries to maximize the sellers' revenue by dynamically allocating the reserved capacities among various buyers without the need of specifying upfront fee in advance. The competitive ratio of the online allocation algorithm inside our mechanism is proved to be within a small constant factor of optimal competitive ratio in theory. To evaluate the performance of our online mechanism, we conduct simulations on synthetic data and real data trace from one of Google clusters. The simulation results show that our online mechanism can achieve at least 55% of the offline optimal algorithm in most cases.","Title: Modeling and Analysis of Availability for SaaS Multi-tenant Architecture\nYear: 2014\n\nAbstract: Software as a Service(SaaS) is increasingly becoming an important application development and delivery model. Among different implementation architectures, multi-tenant architecture(MTA) has received most attentions. Not only because it is easier to be maintained, but also increases the resource utilization. However, sharing the same instance will have impact on the availability of each tenant. Availability is a very critical attribute of tenants' SLA, but modeling and analysis of availability for MTA SaaS is challenging. In this paper, we firstly discuss the differences of availability between one-tenant and multi-tenant from tenant's view. Then we analyze the repair and failure rate based on the MTA feature and give the function expression with parameters of the number of tenant's users. And a Markov model is built for availability analysis. We solve the model and get the computation expression of the availability in a case study. Then a numerical result is obtained under the specific conditions and finally we have a simply discussion on it.","Title: Toward Optimal Admission Control and Resource Allocation for LTE-A Femtocell Uplink\nYear: 2015\n\nAbstract: The Third Generation Partnership Project (3GPP) has incorporated femtocell (FC) technology in the Long Term Evolution Advanced (LTE-A) standard to enhance the quality of service of indoor mobile users and extend the coverage area of existing macrocells (MCs). In such two-tier LTE-A MC/FC systems, co-tier and cross-tier interference exists in co-channel deployment, exerting adverse effects on system performance. In this paper, we study Single-Carrier Frequency Division Multiple Access (SC-FDMA) based LTE-A FC uplink. We propose the use of transport-layer data Admission Control (AC) in each Femto User Equipment (FUE) as well as interference-aware Resource Allocation (RA) in each base station to manage the inter-cell interference. We first formulate the problem as a constrained Markov decision problem which aims at maximizing the time average throughput of the entire FC tier subject to queue stability constraint for each FUE. Then we propose a Joint Admission Control and Resource Allocation (JACRA) algorithm to obtain the optimal AC and RA policies. In light of the NP-hardness of the resource allocation subproblem, we further propose an iterative heuristic with polynomial time complexity. Simulation studies show that the proposed JACRA algorithm is throughput optimal, outperforming alternative proportional fair and round robin scheduling schemes. Moreover, the proposed heuristic achieves near-optimal throughput with substantial improvement in computational complexity.","Title: Dynamic queue length thresholds for scheduling real-time traffic in ATM networks\nYear: 1999\n\nAbstract: New schemes of scheduling real-time traffic in high speed ATM networks are proposed for multiple priority queues based on dynamic queue length thresholds in real-time scheduling. The proposed schemes will provide guaranteed real-time service for each connection and cell sequence integrity for virtual channel/path characteristics. We provide the stochastic Petri net model of these schemes and an approximate analysis technique, which significantly reduce the complexity of the model solution and can be applied to real ATM switch models. Using numerical results we show that our schemes outperform well known schemes such as the head-of-line priority control and the queue length threshold policy","Title: A modeling reliability analysis technique for cloud storage system\nYear: 2013\n\nAbstract: Cloud storage system can reshape the way how kinds of contents are generated, stored and distributed on today's Internet. It is intriguing to study the service provision of cloud storage system for the sake of providing global users with satisfactory service. This paper conducts modeling analysis of a cloud storage system, and proposes a Markov decision process modeling framework to analyze the reliability of the storage system. Based on the modeling analysis, the optimal strategy can be got after executing two solution algorithms. By incorporating these techniques into cloud storage system design, the authors can significantly improve the replication performance and reliability of the system in this paper. Our numerical results further demonstrate the effectiveness and superiority of our technique against traditional static approaches.","Title: BEC: A reliable and efficient mechanism for cloud storage service\nYear: 2013\n\nAbstract: In a cloud storage system, the hardware equipments storing the contents are located in many places and the capabilities of them are various. These realities draw a big challenge to providing constant and efficient service to the storage system's users. In this paper, intrigued from the features of erasure coding, we design a content storage and delivery mechanism which can tolerate cloud servers' failures, so that ensure the reliability of the cloud storage system while distributing contents efficiently. In this mechanism, the users can still retrieve an adequate number of coded packets to recover the original packets after a fixed number of servers collapse. Afterwards, through experiments we compare our mechanism to the erasure coding method and the replication redundancy strategy, and validate that our technology takes advantages in fault tolerance and repair efficiency.","Title: A PetriNet-Based Approach for Supporting Traceability in Cyber-Physical Manufacturing Systems.\nYear: 2016\n\nAbstract: With the growing popularity of complex dynamic activities in manufacturing processes, traceability of the entire life of every product has drawn significant attention especially for food, clinical materials, and similar items. This paper studies the traceability issue in cyber-physical manufacturing systems from a theoretical viewpoint. Petri net models are generalized for formulating dynamic manufacturing processes, based on which a detailed approach for enabling traceability analysis is presented. Models as well as algorithms are carefully designed, which can trace back the lifecycle of a possibly contaminated item. A practical prototype system for supporting traceability is designed, and a real-life case study of a quality control system for bee products is presented to validate the effectiveness of the approach.","Title: Joint study on VMs deployment, assignment and migration in geographically distributed data centers\nYear: 2016\n\nAbstract: Abstract Enterprises build private clouds to provide IT resources for geographically distributed subsidiaries or product divisions. Public cloud providers like Amazon lease their platforms to enterprise users, thus, enterprises can also rent a number of virtual machines (VMs) from their data centers in the service provider networks. Unfortunately, the network cannot always guarantee stable connectivity for their clients to access the VMs or low-latency transfer among data centers. Usually, both latency and bandwidth are in unstable network environment. Being affected by background traffics, the network status can be volatile. To reduce the latency uncertainty of client accesses, enterprises should consider the network status when they deploy data centers or rent virtual data centers from cloud providers. In this paper, we first develop a data center deployment and assignment scheme for an enterprise to meet its users’ requirements under uncertain network status. To accommodate to the changes of the network status and users’ demands, a VMs migration-based redeployment scheme is adopted. These two schemes work in a joint way, and lay out a framework to help enterprises make better use of private or public clouds.","Title: Hierarchical caches in content-centric networks: modeling and analysis\nYear: 2015\n\nAbstract: Content-centric network (CCN) is a new Internet architecture in which content is treated as the primitive of communication. In CCN, routers are equipped with content stores at the content level, which act as caches for frequently requested content. Based on this design, the Internet is available to provide content distribution services without any application-layer support. In addition, as caches are integrated into routers, the overall performance of CCN will be deeply affected by the caching efficiency.In this paper, our aim is to gain some insights on how caches should be designed to maintain a high performance in a cost-efficient way. We try to model the two-layer cache hierarchy composed of CCN routers using a two-dimensional discrete-time Markov chain, and develop an efficient algorithm to calculate the hit ratios of these caches. Simulations validate the accuracy of our modeling method, and convey some meaningful information which can help us better understand the caching mechanism of CCN.","Title: Privacy Petri Net and Privacy Leak Software.\nYear: 2015\n\nAbstract: Private information leak behavior has been widely discovered in malware and suspicious applications. We refer to such software as privacy leak software (PLS). Nowadays, PLS has become a serious and challenging problem to cyber security. Previous methodologies are of two categories: one focuses on the outbound network traffic of the applications; the other dives into the inside information flow of the applications. We present an abstract model called Privacy Petri Net (PPN) which is more applicable to various applications and more intuitive and vivid to users. We apply our approach to both malware and suspicious applications in real world. The experimental result shows that our approach can effectively find categories, content, procedure, destination and severity of the private information leaks for the target software.","Title: VEGAS: Visual influEnce GrAph Summarization on Citation Networks\nYear: 2015\n\nAbstract: Visually analyzing citation networks poses challenges to many fields of the data mining research. How can we summarize a large citation graph according to the user’s interest? In particular, how can we illustrate the impact of a highly influential paper through the summarization? Can we maintain the sensory node-link graph structure while revealing the flow-based influence patterns and preserving a fine readability? The state-of-the-art influence maximization algorithms can detect the most influential node in a citation network, but fail to summarize a graph structure to account for its influence. On the other hand, existing graph summarization methods fold large graphs into clustered views, but can not reveal the hidden influence patterns underneath the citation network. In this paper, we first formally define the Influence Graph Summarization problem on citation networks. Second, we propose a matrix decomposition based algorithm pipeline to solve the IGS problem. Our method can not only highlight the flow-based influence patterns, but also easily extend to support the rich attribute information. A prototype system called VEGAS implementing this pipeline is also developed. Third, we present a theoretical analysis on our main algorithm, which is equivalent to the kernel k-mean clustering. It can be proved that the matrix decomposition based algorithm can approximate the objective of the proposed IGS problem. Last, we conduct comprehensive experiments with real-world citation networks to compare the proposed algorithm with classical graph summarization methods. Evaluation results demonstrate that our method significantly outperforms the previous ones in optimizing both the quantitative IGS objective and the quality of the visual summarizations.","Title: Relocation routing for energy balancing in mobile sensor networks\nYear: 2015\n\nAbstract: Wireless sensor networksWSNs have been widely investigated in the past decades because of its applicability in various extreme environments. As sensors use battery, most works on WSNs focus on energy efficiency issuese.g., local energy balancing problems in statically deployed WSNs. Few works have paid attention to the global energy balancing problem for the scenario that mobile sensor nodes can move freely. In this paper, we propose a new routing protocol called global energy balancing routing protocol GEBRP based on an active network framework and node relocation in mobile sensor networks. This protocol achieves global energy efficiency by repairing coverage holes and replacing invalid nodes dynamically. Simulation and experiment results demonstrate that the proposed GEBRP achieves superior performance over the existing scheme. In addition, we analyze the delay performance of GEBRP and study how the delay performance is affected by various system parameters.Copyright © 2013 John Wiley & Sons, Ltd.","Title: Performance modelling for transparent computing using stochastic Petri nets.\nYear: 2016\n\nAbstract: Massive applications, large-scale and disparity of demands tend to be the main characteristics of information technology. Therefore, to define an efficient computing paradigm with high quality of service has become an urgent issue both in academia and industry. An effective approach named transparent computing paradigm is proposed to meet the demands, which leaves computation and storage in different places. This paper studies the performance evaluation of such computing paradigm. We propose the stochastic Petri net models of transparent computing, based on which mathematical analysis is provided. Furthermore, we compare transparent computing paradigm with some traditional computing paradigms, both quantitatively and qualitatively.","Title: SLA-Aware Tenant Placement and Dynamic Resource Provision in SaaS\nYear: 2015\n\nAbstract: Software as a Service (SaaS) is an increasingly important service delivery model in cloud computing, and multitenancy makes it possible to support large scale customized tenants with only one code base. However, the complexity of multi-tenant architecture may lead to poor performance and low resource utilization. The customized demands may also lead to high operating cost. It is very important to develop an accurate model to predict the performance of the multi-tenant SaaS. To this end, a multi-tenant queueing network model is developed. Based on the model, a balanced SLA-aware tenant placement algorithm is proposed considering that customized tenants may need more resources to be placed together. The algorithm is effective in nearly 90% of the simulations comparing with other heuristic algorithms. Furthermore, the optimization problem on dynamic resource provision to minimize the operating cost is studied. As the original optimization problem is NPhard, a continuous upper bound is used to convert the original optimization problem into a convex optimization which can be solved efficiently in polynomial time. Finally, it is demonstrated that the approximate ratio of the proposed approach is no greater than 1.2 in more than 90% of the simulations.","Title: Greening Geo-distributed Data Centers by Joint Optimization of Request Routing and Virtual Machine Scheduling\nYear: 2014\n\nAbstract: In this paper, we present a unifying optimization framework for greening geographically distributed Data Centers (DCs) by means of three control operations: (1) inter-DC request routing at front-end proxies, (2) intra-DC request routing at tiered core switches and edge switches, and (3) Virtual Machine (VM) scheduling on heterogeneous physical servers. We first formulate the request routing and VM scheduling problem as a stochastic program which aims to minimize the time average electricity cost, carbon taxes and bandwidth cost for Cloud Service Providers (CSPs) while guaranteeing the long-term request queue stability for cloud users. Then we propose an online and decentralized algorithm named GREEN to address the randomness of external workload, power price, and carbon emission rate. The proposed algorithm provably achieves arbitrary cost-delay tradeoffs, thereby enabling CSPs to make flexible scheduling choices toward the desired objective. Trace-driven simulations confirm the efficacy and adaptivity of GREEN in non-stationary environments.","Title: Matrix Factorization with Column L0-Norm Constraint for Robust Multi-subspace Analysis.\nYear: 2015\n\nAbstract: We aim to study the subspace structure of dataapproximately generated from multiple categories and removeerrors (e.g., noise, corruptions, and outliers) in the data aswell. Most previous methods for subspace analysis learn onlyone subspace, failing to discover the intrinsic complex structure, while state-of-the-art methods use data itself as the basis (self-expressiveness property), showing degraded performance whendata contain errors. To tackle the problem, we propose anovel method, called Matrix Factorization with Column L0-normconstraint (MFC0), from the matrix factorization perspective. MFC0 simultaneously discovers the multi-subspace structure ofeither clean or contaminated data, and learns the basis for eachsubspace. Speciflcally, the learnt basis with the orthonormal constraint shows high robustness to errors by adding a regularizationterm. Owing to the column l0-norm constraint, the generatedrepresentation matrix can be (approximate) block-diagonal afterreordering its columns, with each block characterizing onesubspace. We develop an efflcient flrst-order optimization schemeto stably solve the nonconvex and nonsmooth objective function ofMFC0. Experimental results on synthetic data and real-world facedatasets demonstrate the superiority over traditional and state-of-the-art methods on both representation learning, subspacerecovery and clustering.","Title: Comprehensive Understanding Of Tcp Incast Problem\nYear: 2015\n\nAbstract: Since TCP Incast has been identified as a catastrophic problem in many typical data center applications, a lot of efforts have been made to analyze or solve it. The analysis work intends to model Incast problem from certain perspective, and the solutions try to solve the problem through designing enhanced mechanisms or algorithms. However, the proposed models are either closely coupled with particular protocol version or dependent on empirical observations, and the solutions cannot eliminate Incast problem entirely because the underlying issues are not identified completely. There is little work which attempts to close the gap between \"analyzing\" and \"solving\", and present a comprehensive understanding.In this paper, we provide an in-depth understanding of how TCP Incast problem happens. We build up an interpretive model which emphasizes particularly on describing qualitatively how various factors, including system parameters and mechanism variables, affect network performances in Incast traffic pattern, but not on calculating the accurate throughput. With this model, we give plausible explanations why the various solutions for TCP Incast problem can help, but do not solve it entirely.","Title: Discriminant Manifold Learning via Sparse Coding for Image Analysis.\nYear: 2016\n\nAbstract: Traditional subspace learning methods directly calculate the statistical properties of the original input images, while ignoring different contributions of different image components. In fact, the noise (e.g., illumination, shadow) in the image often has a negative influence on learning the desired subspace and should have little contribution to image recognition. To tackle this problem, we propose a novel subspace learning method named Discriminant Manifold Learning via Sparse Coding (DML_SC). In our method, we first decompose the input image into several components via dictionary learning, and then regroup the components into a More Important Part (MIP) and a Less Important Part (LIP). The MIP can be regarded as the clean part of the original image residing on a nonlinear submanifold, while LIP as noise in the image. Finally, the MIP and LIP are incorporated into manifold learning to learn a desired discriminative subspace. The proposed method is able to deal with data with and without labels, yielding supervised and unsupervised DML SCs. Experimental results show that DML_SC achieves best performance on image recognition and clustering tasks compared with well-known subspace learning and sparse representation methods.","Title: Modeling and Analysis of Availability in Multi-Tenant SaaS\nYear: 2015\n\nAbstract: Software as a Service (SaaS) has become an important application development and service delivery model. Among different architectures, multi-tenant architecture (MTA) not only has advantage on maintenance, but also increases resource utilization by sharing instances. However, sharing instances brings challenges to the security of the service. As one of the three principal properties of the security, availability receives more and more attentions. Recently, there are extensive efforts on technical methods to implement a secure multi-tenant SaaS, but few works on the modeling and analysis of its availability. In this paper, we firstly present the availability issues of the multi-tenant SaaS. Two important mechanisms to implement the MTA SaaS are then introduced: network isolation and database sharing. After that, a stochastic Petri net (SPN) model is developed to analyze the availability. Specific metrics are proposed to measure the availability both from the aspects of the system and the tenant. To extend the SPN model for large scale analysis, we solve the state space explosion problem of SPN model based on the theory of Markov chain aggregation. Finally, numerical results are provided to demonstrate the effectiveness of the SPN model and the analysis is efficient.","Title: An Effective Policy Relocation Scheme for VM Migration in Software-Defined Networks\nYear: 2015\n\nAbstract: To achieve the flexibility in resource optimization, fault tolerance and load balancing, virtual machines(VMs) should be effectively migrated in data center networks(DCN). VMs are regulated by security policies and also required to relocate the policy during VM migration, which is a challenge problem to maintain not only the isolation among different domains and tenants, but also the consistency of policy configuration. In this paper, we consider the problem of policy relocation in Software-defined Network(SDN) context since it provides flexible control. We introduce a formal model describing the problem and present a novel policy relocation scheme called VPRS(a Virtual Policy Relocation Scheme), which is based on policy-aware principle and space-optimized method. It can prevent fragmentation of policy and reduce the memory usage while accurately relocating influenced policies. On the other hand, it also simplifies the management of policy. Specifically, VPRS contains novel algorithms for policy relocation, an app-layer policy management, policy translator and component for controllers notification. We define three actions of policy relocation based on the policy abstraction, and then describe the advantage of our scheme about selecting an optimized network address as target address. Then we introduce the core space-optimized algorithms, and evaluate the effectiveness of our scheme by providing consumption on their running overhead and utilization of rule space. Our scheme performs well on real policy sets, and has dramatic reduction by even up to 90\\% on rule space costs with high utilization.","Title: A Method for Detection of Anomaly Node in IOT.\nYear: 2015\n\nAbstract: Perceived data would be affected by the node status in the Internet of things. If the status of node was abnormal, the perception data would not reliable. In order to guarantee the reliability of the perception data in the Internet of things, a method for detecting the anomaly of nodes is proposed. Detection unit was constructed firstly. Each detection unit included working node, associated node and decision node. The detection unit can realize self-detection, which can detect the abnormal nodes in time. The task of the working node was to perceive the data and submit it to the data center. It would always be in working status until it was abnormal. When the work node has an exception, the alarm information would be issued. Then, the work node will be replaced by the associated node. Data perception of monitoring area would be responsible for the associated nodes. In each detection unit, three levels of detection can be completed. The first level is the work node self-check. The second stage is the auxiliary detection of associated nodes. The third level is the final decision of the node. Using the simulation tool Matlab7.0, the results show that our method is effective.","Title: Prototype Implementation and Performance Evaluation of a QoS-based Web Server\nYear: 2005\n\nAbstract: With the evolvement of the E-commerce, the Internet is undergoing substantial changes from a communication and browsing infrastructure to a medium for conducting business and selling services. These changes place the Web server at the center of the E-commerce infrastructure with increasing requirements for providing service differentiation and performance assurance. To that end, Web servers must have mechanisms and policies for establishing and supporting Quality of Service (QoS).In this paper, a Web server prototype with priority-based QoS control mechanisms, called QoS Apache, is implemented. A performance testing platform is established, and the performance measurement software httperf is used to test the popular Apache and the QoS Apache. The performance evaluation results show that the QoS Apache server can provide different types of requests with differentiated Web QoS, in comparison with the popular QoS-unaware Apache.","Title: Game-theoretic Analysis of Computation Offloading for Cloudlet-based Mobile Cloud Computing\nYear: 2015\n\nAbstract: Mobile cloud computing (MC2) is emerging as a promising computing paradigm which helps alleviate the conflict between resource-constrained mobile devices and resource-consuming mobile applications through computation offloading. In this paper, we analyze the computation offloading problem in cloudlet-based mobile cloud computing. Different from most of the previous works which are either from the perspective of a single user or under the setting of a single wireless access point (AP), we research the computation offloading strategy of multiple users via multiple wireless APs. With the widespread deployment of WLAN, offloading via multiple wireless APs will obtain extensive application. Taking energy consumption and delay (including computing and transmission delay) into account, we present a game-theoretic analysis of the computation offloading problem while mimicking the selfish nature of the individuals. In the case of homogeneous mobile users, conditions of Nash equilibrium are analyzed, and an algorithm that admits a Nash equilibrium is proposed. For heterogeneous users, we prove the existence of Nash equilibrium by introducing the definition of exact potential game and design a distributed computation offloading algorithm to help mobile users choose proper offloading strategies. Numerical extensive simulations have been conducted and results demonstrate that the proposed algorithm can achieve desired system performance.","Title: Guest Editorial Special Issue on Cloud Computing for IoT.\nYear: 2016\n\nAbstract: In recent years, there has been a growing interest in the ability of embedded devices, sensors, and actuators to communicate, and create a ubiquitous cyber-physical world. The growth of the notion of the Internet of Things (IoT) and the rapid development of technologies such as short range mobile communication and improved energy-efficiency is expected to create a pervasive connection of “things.” This will inevitably result in the generation of enormous amount of data, which have to be stored, processed, and accessed. Cloud computing has long been recognized as a paradigm for big data storage and analytics. The combination of cloud computing and IoT can enable ubiquitous sensing services and powerful processing of sensing data streams beyond the capability of individual things, thus stimulating innovations in both fields. For example, cloud platforms allow the sensing data to be stored and used intelligently for smart monitoring and actuation with the smart devices. Novel data fusion algorithms, machine learning methods, and artificial intelligence techniques can be implemented and run centralized or distributed on the cloud to achieve automated decision making. These will boost the development of new applications, such as smart cities, grids, and transportation systems. New challenges, however, arise when IoT meets cloud—there is an urgent need for novel network architectures that seamlessly integrate them, and protocols that facilitate big data streaming from IoT to the cloud. QoS and QoE, as well as data security, privacy, and reliability, are critical concerns during the integration.","Title: A Study on Online Social Networks Theme Semantic Computing Model\nYear: 2016\n\nAbstract: Online information sources including Weibo and Wechat bring huge impact to the society. Only a few words of network information can expand rapidly and catalyze the generation of a huge amount of information. Semantic computing on online social networks and research on topics about emergencies have great significance. In this article, a numerical model of text semantic analysis based on artificial neural network is proposed, and a semantic computational algorithm for social network texts as well as a discovery algorithm for emergencies is provided with reference to the information provided by the social nodes itself and the semantic of the text. Semantic vector of micro-information for nodes and closure extension of semantic extensions are defined in order to build up an equivalence of short sentences, and in turn realize the discovery of emergencies.","Title: Cost-Effective Request Scheduling for Greening Cloud Data Centers\nYear: 2016\n\nAbstract: With the popularity of cloud computing, many cloud service providers deploy regional data centers to offer services and pplications. These large-scale data centers have drawn extensive attention in terms of the huge energy demand and carbon emission. Thus, how to make use of their spatial diversities to green data centers and reduce cloud provider's costs is an important concern. In this paper, we integrate service reward, electricity cost, carbon taxes and service performance to study cost-effective request scheduling for cloud data centers. We propose an online and distributed scheduling algorithm CESA to chieve the flexible tradeoff between these conflicting objectives. The time complexity of CESA is polynomial, and it can be implemented in a parallel way. CESA requires no prior knowledge of the statistics of request arrivals or future electricity prices, yet it provably approximates the optimal system profit while bounding the queue length. Real-trace based simulations are conducted which verify the effectiveness of our CESA algorithm.","Title: Monitoring-Based Task Scheduling in Large-Scale SaaS Cloud.\nYear: 2016\n\nAbstract: With the increasing scale of SaaS and the continuous growth in server failures, task scheduling problems become more intricate, and both scheduling quality and scheduling speed raise further concerns. In this paper, we first propose a virtualized and monitoring SaaS model with predictive maintenance to minimize the costs of fault tolerance. Then with the monitored and predicted available states of servers, we focus on dynamic real-time task scheduling in large-scale heterogeneous SaaS, targeting at jointly optimizing the long-term performance benefits and energy costs in order to improve scheduling quality. We formulate a dynamic programming problem, where both the state and action spaces are too large to be solved by simple iterations. To address these issues, we take advantage of Machine Learning theory, and put forward an approximate dynamic programming algorithm. We utilize value function approximation and candidate-heuristic method to separately solve state and action explosions. Thus, computation complexity is significantly reduced and scheduling speed is greatly enhanced. Finally, we conduct experiments with both random simulation data and Google cloud trace-logs. Qos evaluations and comparisons demonstrate that our approach is effective and efficient under bursty requests and high throughputs.","Title: On Denial of Service Attacks in Software Defined Networks.\nYear: 2016\n\nAbstract: Software defined networking greatly simplifies network management by decoupling control functions from the network data plane. However, such a decoupling also opens SDN to various denial of service attacks: an adversary can easily exhaust network resources by flooding short-lived spoofed flows. Toward this issue, we present a comprehensive study of DoS attacks in SDN, and propose multi-layer fair queueing (MLFQ), a simple but effective DoS mitigation method. MLFQ enforces fair sharing of an SDN controller¿s resources with multiple layers of queues, which can dynamically expand and aggregate according to controller load. Both testbed-based and emulation-based experiments demonstrate the effectiveness of MLFQ in mitigating DoS attacks targeted at SDN controllers.","Title: Modeling and understanding burst transmission for energy efficient ethernet.\nYear: 2017\n\nAbstract: Recently, the energy consumption of Ethernet has become one of the hottest topics focused by both academic committee and industry, especially with the increase of the link speed from 1Gbps to 10/40Gbps nowadays or even 100/400Gbps in the near future. To save the energy consumed by the Ethernet, the Energy Efficient Ethernet (EEE) is developed and standardized by the IEEE 802.3az work group. When there is no incoming traffic, the EEE can saves 90% of its energy consumption by entering into the Low Power Idle (LPI) mode. To maximize the energy saving of Ethernet, the Burst TRansmission (BTR) mechanism, which defines a new way to utilize the LPI mode, is developed as a mechanism for EEE. Prior work theoretically shows that the BTR mechanism makes a tradeoff between the energy saving and the queuing delay. However, the traffic pattern, on which the performance of EEE greatly depends, is assumed to be deterministic in their analyses. Besides, their models made estimation for many situations. In this paper, we model EEE with the BTR mechanism and provide analytical understanding on the BTR mechanism. We propose two actual models: one focuses on the buffer size limit, the other concentrates on tolerable packet delay additionally. We draw some guidelines of parameter selection and policies design for EEE from combination of theory conclusions and simulation results. The results show that the saved energy can be constrained by link occupancy even though the buffer size is variational. The buffer full triggered wake-up policy can achieve ideal ratio of energy consumption and arrival rate within the scope of the buffer as well. However, the tolerable delay cannot be guaranteed by any policies. The buffer size is even fixed, which affects the flexibility of demanded delay for different business. The policy considering tolerable delay is supposed to be a little better than the other policy, with a little more complicated design. Thus we design an adaptive mechanism both in quantitative and qualitative: detect the load utilization, apply the buffer full triggered wake-up policy for higher load utilization link, while applying the buffer full and timeout triggered wake-up policy for the delay sensitive business and tiny arrival rate.","Title: Trust Model of Wireless Sensor Networks and Its Application in Data Fusion.\nYear: 2017\n\nAbstract: In order to ensure the reliability and credibility of the data in wireless sensor networks (WSNs), this paper proposes a trust evaluation model and data fusion mechanism based on trust. First of all, it gives the model structure. Then, the calculation rules of trust are given. In the trust evaluation model, comprehensive trust consists of three parts: behavior trust, data trust, and historical trust. Data trust can be calculated by processing the sensor data. Based on the behavior of nodes in sensing and forwarding, the behavior trust is obtained. The initial value of historical trust is set to the maximum and updated with comprehensive trust. Comprehensive trust can be obtained by weighted calculation, and then the model is used to construct the trust list and guide the process of data fusion. Using the trust model, simulation results indicate that energy consumption can be reduced by an average of 15%. The detection rate of abnormal nodes is at least 10% higher than that of the lightweight and dependable trust system (LDTS) model. Therefore, this model has good performance in ensuring the reliability and credibility of the data. Moreover, the energy consumption of transmitting was greatly reduced.","Title: Discriminant Manifold Learning via Sparse Coding for Robust Feature Extraction.\nYear: 2017\n\nAbstract: Most off-the-shelf subspace learning methods directly calculate the statistical characteristics of the original input images, while ignoring different contributions of different image components. In fact, to extract efficient features for image analysis, the noise or trivial structure in images should have little contribution and the intrinsic structure should be uncovered. Motivated by this observation, we propose a new subspace learning method, namely, discriminant manifold learning via sparse coding (DML_SC) for robust feature extraction. Specifically, we first decompose each input image into several components via dictionary learning, and then regroup the components into a more important part (MIP) and a less important part (LIP). The MIP can be considered as the clean portion of the image residing on a low-dimensional submanifold, while the LIP as noise or trivial structure within the image. Finally, the MIP and LIP are incorporated into manifold learning to learn a desired discriminative subspace. The proposed method is general for both cases with and without class labels, hence generating supervised DML_SC (SDML_SC) and unsupervised DML_SC (UDML_SC). Experimental results on four benchmark data sets demonstrate the efficacy of the proposed DML_SCs on both image recognition and clustering tasks.","Title: Assessment Of Elbow Spasticity With Surface Electromyography And Mechanomyography Based On Support Vector Machine\nYear: 2017\n\nAbstract: The Modified Ashworth Scale (MAS) is the gold standard in clinical for grading spasticity. However, its results greatly depend on the physician evaluations and are subjective. In this study, we investigated the feasibility of using support vector machine (SVM) to objectively assess elbow spasticity based on both surface electromyography (sEMG) and mechanomyography (MMG). sEMG signals and tri-axial accelerometer mechanomyography (ACC-MMG) signals were recorded simultaneously on patients' biceps and triceps when they extended or bended elbow passively. 39 post-stroke patients participated in the study, and were divided into four groups regarding MAS level (MAS=0, 1, 1+ or 2). The three types of features, root mean square (RMS), mean power frequency (MPF), and median frequency (MF), were calculated from sEMG and MMG signal recordings. Spearman correlation analysis was used to investigate the relationship between the features and spasticity grades. The results showed that the correlation between MAS and each of the five features (MMG-RMS of the biceps, MMG-RMS of the triceps, the EMG-RMS of the biceps, EMG-RMS of the triceps, EMG-MPF of the triceps) was significant (p<0.05). The four spasticity grades were identified with SVM, and the classification accuracy of SVM with sEMG, MMG, sEMG-MMG were 70.9%, 83.3%, 91.7%, respectively. Our results suggest that using the SVM-based method with sEMG and MMG to assess elbow spasticity would be suitable for clinical management of spasticity.","Title: Analysing The Evolution Of Contrary Opinions On A Controversial Network Event\nYear: 2017\n\nAbstract: With the growing popularity of social networking services, network public opinion gradually plays an important role in social life. When a controversial network event happens, what people concern about is which opinion in the contrary opinions will be widely accepted by people and how long this event lasts. To solve this problem, we propose a social evolutionary game model based on Hawk-Dove game to simulate how contrary opinions evolve in social network. The effectiveness of our model is validated by actual data. This model can be used to estimate the potential dominant opinion group and the time length of the controversy. Besides, our simulation reveals some special features of the evolution process and results. This study may be useful for network public opinion supervision and market research.","Title: Long-Term Multi-objective Task Scheduling with Diff-Serv in Hybrid Clouds.\nYear: 2017\n\nAbstract: With the speedy development of E-commerce, requests over the internet from intensive users are soaring, especially in global online shopping festivals. In order to meet the increasing demands of temporary capacity and reduce daily expenses, hybrid clouds are often used, and the task scheduling problem with multi-objectives is further investigated. In this paper, we firstly build a differentiated-service (Diff-Serv) task scheduling model, and formulate a dynamic programming problem, where the state space is too large to be solved by exhaustive iterations. Therefore, we carefully design the value approximation function, and with reference to the reinforcement learning theory, we put forward an approximate dynamic programming (ADP) algorithm so as to conduct the long-term optimization for performance benefit, energy and rental costs. Furthermore, both scheduling quality and scheduling speed are taken into consideration in this algorithm. Experiments with both random synthetic workloads and Google cloud trace-logs are conducted to evaluate the proposed algorithm, and results demonstrate that our algorithm is effective and efficient, especially under bursty requests.","Title: Thinking And Modeling For Big Data From The Perspective Of The I Ching\nYear: 2017\n\nAbstract: Data is growing faster than ever before and is changing our daily life. However it is rather challenging to manage the big data [F. H. Cate, The big debate, Science 346 (2014) 810, J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh and A. H. Byers, Big Data: The Next Frontier for Innovation, Competition, and Productivity (Mckinsey global Institute, 2011), S. Lohr, The Age of Big Data (New York Times, 2012), p. 11, L. Einav and J. Levin, Economics in the age of big data, Science 345 (2014) 715, M. J. Khoury and J. P. A. Ioannidis, Big data meets public health, Science 346 (2014) 1054-1055, V. Marx, Biology: The big challenges of big data, Nature 498(7453) (2013) 255-260.]. In this paper, we propose the big data thinking and modeling techniques from the perspective of the I Ching, which is a very famous imaginal thinking theory in China with 3,000 years history. The I Ching has been proven to be very useful and practical in many domains, e.g., 36 stratagems.Firstly, inspired from the three components of the I Ching, image, number and principle, we propose a new three-cycle big data thinking way, from data to phenomenon, from phenomenon to correlation, and from correlation to knowledge, which is a generalization of the fourth paradigm (from causality to correlation) proposed by Jim Gray.Secondly, inspired from the three entities of the I Ching, heaven, earth and human, we propose a new big data modeling method. We use the tree entities to represent the big data. We map the 4V of big data (volume, variety, velocity, veracity) to four opposition and uniform relations in the I Ching, and generate the eight diagrams. By capturing the relationships between eight diagrams, we generate the 64 hexagrams, and use 64 hexagrams to model big data. We also provide the principle rules to understand the knowledge generated by the model.Thirdly, we discuss how to utilize our model to describe big-data management tools, including, MapReduce, Spark, Storm. We also provide a new model for handling distributed data streams.We do think that we provide a new practical way of thinking and modeling for big data. We also believe that this will open up many new research directions on big data.","Title: Cost-Efficient Resource Provisioning in Cloud Assisted Mobile Edge Computing.\nYear: 2017\n\nAbstract: Mobile edge computing (MEC) is emerging as an effective computing paradigm which alleviates the conflict between computation-intensive mobile applications and resource-constrained mobile devices. In this paper, a Cloud Assisted Mobile Edge computing (CAME) framework is adopted to enhance the adaptability of MEC to time-varying mobile requests. The resource provisioning problem is investigated to provide guaranteed quality of service (QoS) with minimum system cost. By exploiting the piecewise convexity of the problem, the Optimal Resource Provisioning (ORP) algorithm is developed, which determines the computation capacity at mobile edge and dynamically tunes the usage of cloud resources. Extensive simulations demonstrate that the ORP algorithm yields the minimum system cost, compared with the local-first and the cloud-first algorithms. In addition, the ORP algorithm can flexibly adapt to the time-varying mobile requests.","Title: Analysing and improving convergence of quantized congestion notification in Data Center Ethernet.\nYear: 2018\n\nAbstract: Quantized Congestion Notification (QCN) has been approved as the standard congestion management mechanism for the Data Center Ethernet (DCE). However, lots of work pointed out that QCN suffers from the problem of unfairness among different flows. In this paper, we found that QCN could achieve fairness, merely the convergence time to fairness is quite long. Thus, we build a convergence time model to investigate the reasons of the slow convergence process of QCN. We validate the precision of our model by comparing with experimental data on the NetFPGA platform. The results show that the proposed model accurately well characterizes the convergence time to fairness of QCN. Based on the model, the impact of QCN parameters, network parameters, and QCN variants on the convergence time is analysed in detail. Results indicate that the convergence time of QCN can be decreased if sources have the same rate increase probability or the rate increase step becomes larger at steady state. Enlightened by the analysis, we proposed a mechanism called QCN-T, which replaces the original Byte Counter and Timer at sources with a single modified Timer to reduce the convergence time. Finally, evaluations show great improvements of QCN-T in both convergence and stability.","Title: Robust Multi-subspace Analysis Using Novel Column L0-norm Constrained Matrix Factorization.\nYear: 2018\n\nAbstract: We study the underlying structure of data (approximately) generated from a union of independent subspaces. Traditional methods learn only one subspace, failing to discover the multi-subspace structure, while state-of-the-art methods analyze the multi-subspace structure using data themselves as the dictionary, which cannot offer the explicit basis to span each subspace and are sensitive to errors via an indirect representation. Additionally, they also suffer from a high computational complexity, being quadratic or cubic to the sample size. To tackle all these problems, we propose a method, called Matrix Factorization with Column L0-norm constraint (MFC0), that can simultaneously learn the basis for each subspace, generate a direct sparse representation for each data sample, as well as removing errors in the data in an efficient way. Furthermore, we develop a first-order alternating direction algorithm, whose computational complexity is linear to the sample size, to stably and effectively solve the nonconvex objective function and non- smooth l0-norm constraint of MFC0. Experimental results on both synthetic and real-world datasets demonstrate that besides the superiority over traditional and state-of-the-art methods for subspace clustering, data reconstruction, error correction, MFC0 also shows its uniqueness for multi-subspace basis learning and direct sparse representation.","Title: FTLLS: A fault tolerant, low latency, distributed scheduling approach based on sparrow.\nYear: 2018\n\nAbstract: Big data processing systems are developing towards larger degrees of parallelism and shorter task durations in order to achieve lower response time. Scheduling highly parallel tasks that complete in sub-seconds poses a great challenge to traditional centralized schedulers. Taking the challenge, researchers turn to distributed scheduling approaches to avoid the throughput limitation of centralized schedulers, among which Sparrow is a leading design. However, little effort is devoted to the fault tolerance of Sparrow and there are problems with Sparrow’s sample-based techniques, which gives rise to incomplete jobs and large scheduling latency. We then present Fault Tolerant, Low Latency Sparrow (FTLLS). It extends Sparrow with an assistant machine to handle worker failures and to make better scheduling decisions. Through simulations, it is proved that FTLLS can detect worker failures more quickly than a naive timeout approach and make better scheduling decisions than native Sparrow. Through implementation, the results show that FTLLS guarantees no incomplete jobs at the presence of worker failures and reduces scheduling latencies by over 1.5 × when compared to native Sparrow. In addition, the simplicity of the idea adopted by FTLLS makes it applicable to a wide variety of distributed scheduling approaches.","Title: Trust Evaluation Model Of Cloud User Based On Behavior Data\nYear: 2018\n\nAbstract: In the process of using the cloud platform, how to ensure the safety of users is a matter we must concern. The user authentication can provide a certain degree of security, but when the user information was leaked, this method will not be effective. Therefore, this article proposes a trust evaluation model based on user behavior data. In this model, the user's historical behavior will be used to construct a set of trusted behavior of the cloud users. On this basis, the direct trust of the user's behavior can be obtained. Then, the recommendation trust can be calculated by the interaction between the users and other cloud users. Given the current historical trust, the comprehensive trust can be obtained using the weighted average method. Among them, the initial value of historical trust is set to a constant and then updated by the comprehensive trust. In order to control the user's abnormal behavior more effectively, the suspicious threshold value and the abnormal threshold value were defined, which are used to punish the historical trust. Through the simulation of the virtual digital library cloud platform, the method can effectively evaluate the cloud users.","Title: Optimizing availability and QoS of heterogeneous distributed system based on residual lifetime in uncertain environment\nYear: 2009\n\nAbstract: A notable requirement of heterogeneous parallel and distributed computing systems is to maximize their processing performance and agreed upon QoS. Lots of work in this field has been done to optimize the system performance by improving certain metrics such as reliability, robustness, security, and so on. However, most of them assume that systems are running without interruption all the time and seldom consider the system’s intrinsic characteristics, such as failure rate, repair rate, and lifetime. In this paper, we study how to achieve high availability based on residual lifetime analysis for heterogeneous distributed computational systems with considering their essential features. First, we provide an availability model taking into account system’s expected residual lifetime. Second, we propose an objective function about the model and develop a heuristic scheduling algorithm to maximize the availability with the makespan constraint. At last, we demonstrate these advantages through the extensive simulated experiments.","Title: A Robust Sparse Representation Based Pattern Recognition Approach for Myoelectric Control.\nYear: 2018\n\nAbstract: Interferences in the form of white Gaussian noise (WGN) are inevitable during long-term electromyogram (EMG) recordings. Even with the aid of advanced signal denoising techniques, such an intermittent interference is hardly detected and attenuated in the practical use of EMG-driven control systems. Hence, a robust pattern recognition scheme that is invariant to noise contamination would potentially aid the realization of an efficient EMG-based pattern recognition (EMG-PR) control system. To this end, an EMG-PR scheme driven by sparse representation-based classification (SRC) algorithm and root mean square (rms) descriptor (RMS-SRC) is proposed in this paper. The accuracy and the robustness of the proposed scheme were investigated using the high-density surface EMG recordings from 12 traumatic brain-injured patients and 5 post-stroke survivors. For benchmark comparison, another three different feature sets and four pattern recognition algorithms were considered. The optimal pattern recognition schemes with respect to each feature-classifier combination were first selected in the absence of WGN contamination. Then, six levels of WGN with a signal-to-noise ratio (SNR) ranging from 5 to 30 dB were added into the EMG recordings, respectively, to mimic the different WGN interferences. Our result showed that the proposed RMS-SRC scheme could achieve a similar accuracy with the benchmark schemes in the presence of limited noise contamination (0-15 dB), and significantly outperformed the other schemes when the SNR of WGN increased (20-30 dB). More notably, the RMS-SRC scheme significantly outperformed the other pattern recognition schemes when the WGN existed in either training set or testing set only. The findings proved the comparative advantage of the proposed RMS-SRC pattern recognition scheme over the other currently used schemes in the myoelectric control. Thus, the proposed scheme would potentially facilitate the development of EMG-driven rehabilitation robots for accurate and dexterous assistive training for patients with neurological disorders.","Title: Energy-Aware Computation Offloading of IoT Sensors in Cloudlet-Based Mobile Edge Computing.\nYear: 2018\n\nAbstract: Mobile edge computing is proposed as a promising computing paradigm to relieve the excessive burden of data centers and mobile networks, which is induced by the rapid growth of Internet of Things (IoT). This work introduces the cloud-assisted multi-cloudlet framework to provision scalable services in cloudlet-based mobile edge computing. Due to the constrained computation resources of cloudlets and limited communication resources of wireless access points (APs), IoT sensors with identical computation offloading decisions interact with each other. To optimize the processing delay and energy consumption of computation tasks, theoretic analysis of the computation offloading decision problem of IoT sensors is presented in this paper. In more detail, the computation offloading decision problem of IoT sensors is formulated as a computation offloading game and the condition of Nash equilibrium is derived by introducing the tool of a potential game. By exploiting the finite improvement property of the game, the Computation Offloading Decision (COD) algorithm is designed to provide decentralized computation offloading strategies for IoT sensors. Simulation results demonstrate that the COD algorithm can significantly reduce the system cost compared with the random-selection algorithm and the cloud-first algorithm. Furthermore, the COD algorithm can scale well with increasing IoT sensors.","Title: Cycle-Consistency Based Hierarchical Dense Semantic Correspondence\nYear: 2018\n\nAbstract: This work aims to estimate dense correspondences between the images from same visual class but with different geometries and visual similarities. This task is particularly challenging because (i) most image pairs have large intra-class variations, and (ii) their visual content is similar only on the high-level structure.To address these problems, this paper proposed a multi-level method to estimate per-pixel correspondences from high-level semantic to low-level structural details by the guidance of cycle-consistency. We utilize CNN feature pyramid to represent images level by level. Meanwhile, we introduce cycle-consistency to measure the reliability of flow vector, which further affects the guidance from higher level to lower level. The proposed method has been extensively evaluated on various challenging benchmarks. The results show that our method significantly outperforms the state-of-the-arts in terms of semantic flow accuracy.","Title: A Novel Time-Domain Descriptor for Improved Prediction of Upper Limb Movement Intent in EMG-PR System.\nYear: 2018\n\nAbstract: Electromyogram pattern recognition (EMG-PR) based control is a potential method capable of providing intuitively dexterous control functions in upper limb prostheses. Meanwhile, the feature extraction method adopted in EMG-PR based control is considered as an important factor that influences the performance of the prostheses. By exploiting the limitations of the existing feature extraction methods, this study proposed a new feature extraction method to effectively characterize EMG signal patterns associated with different limb movement intent. The performance of the proposed 2-dimensional novel time-domain feature set (NTDFS) was investigated using classification accuracy and feature space separability metrics across five subjects' EMG recordings, and compared with four different existing methods. In comparison to four other previously proposed feature extraction methods, the NTDFS achieved significantly better performance with increment in accuracy in the range of 5.20% ∼ 8.40% at p<0.05. Additionally, by applying principal component analysis (PCA) technique, the PCA feature space for NTDFS show obvious class separability in comparison to the other existing feature extraction methods. Thus, the proposed NTDFS may facilitate the development of accurate and robust clinically viable EMG-PR based prostheses.","Title: Flexible Non-contact Electrodes for Bioelectrical Signal Monitoring.\nYear: 2018\n\nAbstract: Physiological signals such as ECG and EMG are closely related with human health and a long term monitoring of the physiological signals would be beneficial to detect possible disorders at the early stage. The wet electrodes currently used in the clinics require adhesives gels to record high-quality signals, which might cause discomfort of the patients and introduce some risk of skin allergy. Non-contact capacitive electrodes that can be operated without skin contact have been developed in previous studies, but these electrodes were rigid with electronic components on the back, which might not be an optimal solution for long term healthcare monitoring. In this study, a flexible non-contact electrode without any electronic components on both sides was designed for the long term acquisition of bioelectrical signals to maximize subject comfort. The flexible electrode was made up of multi-layer flexible printed circuits (FPC) materials and could be bent according to the local shape to achieve better non-contact capacitive coupling with the skin. The performance of the proposed flexible electrode was compared with that of the conventional wet electrodes in different signal recording conditions. The results showed that the proposed non-contact flexible electrode can capture high quality ECG and EMG signals, and its performance was comparable to the commonly used wet electrodes. This study might provide a reliable solution with great patient comfort for the long-term monitoring of biological signals.","Title: Thinking and methodology of multi-objective optimization.\nYear: 2018\n\nAbstract: Multi-objective optimization is one of the most important aspects of operational research, and has been applied widely. Most of the existing research works on multi-objective optimization focused on the concrete solutions to detailed optimization problem in reality, lacking of the deep investigation on the fundamental thinking and methodology of the multi-objective optimization problem. To fill this gap, this paper studies the multi-objective optimization problem from a Chinese traditional philosophic angle. Based on the theories and ideology of I Ching, we investigate the basic thinking and methodology of multi-objective optimization, and summarize three methodological patterns for modeling and solving the problems, which are local optimization for global moderation, divide and conquer after comprehensive consideration, and combination of division and integration. Basic ideas, approaches and examples for such three patterns are introduced, and the relationships among the three patterns are discussed from both philosophic and mathematical aspects. Furthermore, for the computational solutions of multi-objective optimization problems, we summarize four thinking patterns based on traditional Chinese philosophy, which are changing thinking, fuzzy thinking, uncertainty thinking, and imaginable thinking. Algorithms that are inspired by the four thinking patterns are presented. This work seeks to study the fundamental thinking and methodology of multi-objective optimization problem from a combination angle of science and philosophy, which is expected to inspire new ideas and methods for solving the problem.","Title: Activity Recognition Based on Deep Learning and Android Software\nYear: 2018\n\nAbstract: This project transplants the deep learning framework to the android platform. It combines Deep learning based on Tensor Flow (TF) with Android software to recognize people's activities. Provided by android sensors, dataset is in the form of float arrays. And then we used them to train TF model with the data that was tagged by the different actions. Then we transplant the model into android to achieve activity recognition in mobile phone. In this project, six activities are recognized, including downstairs, upstairs, jogging, walking, sitting and standing. Finally, the probability of each activity will be seen in an android mobile phone. According to the results of experiments, almost all of six types of activities can be recognized successfully.","Title: An LSTM-Based Method with Attention Mechanism for Travel Time Prediction.\nYear: 2019\n\nAbstract: Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic dynamics in road network. In recent years, Long Short-Term Memory has been applied to traffic prediction, achieving better performance. The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction. Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks. In this paper, we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction. We present the proposed model in a tree structure. The proposed model substitutes a tree structure with attention mechanism for the unfold way of standard Long Short-Term Memory to construct the depth of Long Short-Term Memory and modeling long-term dependence. The attention mechanism is over the output layer of each Long Short-Term Memory unit. The departure time is used as the aspect of the attention mechanism and the attention mechanism integrates departure time into the proposed model. We use AdaGrad method for training the proposed model. Based on the datasets provided by Highways England, the experimental results show that the proposed model can achieve better accuracy than the Long Short-Term Memory and other baseline methods. The case study suggests that the departure time is effectively employed by using attention mechanism.","Title: Multi-Objective Service Composition with QoS Dependencies\nYear: 2019\n\nAbstract: Service composition is popular for composing a set of existing services to provide complex services. With the increasing number of services deployed in cloud computing environments, many service providers have started to offer candidate services with equivalent functionality but different Quality of Service (QoS) levels. Therefore, QoS-aware service composition has drawn extensive attention. Most existing approaches for QoS-aware service composition assume a service's QoS values are not correlated to those of other services. However, QoS dependency exists in real life, and impacts the overall QoS values of the composite services. In this article, we study QoS dependency-aware service composition considering multiple QoS attributes. Based on the Pareto set model, we focus on searching for a set of Pareto optimal solutions. A candidate pruning algorithm for removing the unpromising candidates is proposed, and a service composition algorithm using Vector Ordinal Optimization techniques is designed. Simulation experiments are conducted to validate the efficiency and effectiveness of our algorithms. We are the first to take advantage of Vector Ordinal Optimization techniques to search for Pareto optimal composition solutions with QoS dependency involved. The capturing of QoS dependency enables us to find truly desirable solutions.","Title: Multi-level Semantic Representation for Flower Classification.\nYear: 2017\n\nAbstract: Fine-grained classification is challenging since sub-categories have little intra-class variances and large intra-class variations. The task of flower classification can be achieved through highlighting the discriminative parts. Most traditional methods trained Convolutional Neural Networks (CNN) to handle the variations of pose, color and rotation, which only utilize single-level semantic information. In this paper, we propose a fine-grained classification approach with multi-level semantic representation. With the complementary strengths of multi-level semantic representation, we attempt to capture the subtle differences between sub-categories. One object-level model and multiple part-level model are trained as a multi-scale classifier. We test our method on the Oxford Flower dataset with 102 categories, and our result achieves the best performance over other state-of-the-art approaches.","Title: Travel Time Prediction by Providing Constraints on a Convolutional Neural Network.\nYear: 2018\n\nAbstract: Traffic prediction is a complex, nonlinear spatiotemporal modeling problem. Data-based methods have been widely applied to traffic information prediction because of their ability to model the complex, nonlinear spatiotemporal traffic dynamics. These abilities are based on statistical methods and machine learning methods to extract features from historical traffic information. Despite the success of these methods, the local-level representation in the data-based methods is clearly limited because it does not directly reflect the relationship between each historical data point in the input sequence and the predicted information. Convolutional neural networks (CNNs) have also been widely applied in many other fields because of their feature extraction ability, which greatly enhances the learning ability of the standard, fully connected layers of neural networks by providing constraints in the task domain. Thus, we create a global-level representation to reflect the relationship between each historical traffic data point and the predicted information by using the proposed window that provides constraints in the task domain and exploits a max-pooled CNN to automatically extract global-level features for traffic information. To the best of our knowledge, our proposed method is the first example of using the new window to provide constraints in the task domain for CNNs. The proposed method is trained in an end-to-end way by the back-propagation approach in which the AdaGrad method is used to update the parameters of the proposed method. Based on the data set provided by Highways England, the experimental results show that the proposed method outperforms all baseline methods in terms of accuracy.","Title: Two-level task scheduling with multi-objectives in geo-distributed and large-scale SaaS cloud\nYear: 2019\n\nAbstract: With the exploding of data-intensive web applications and requests (tasks), geo-distributed and large-scale data centers (DCs) are widely deployed in Software as a Service (SaaS) cloud, but server failures continue to grow at the same time. In this context, task scheduling problems become more intricate and both scheduling quality and scheduling speed raise further concerns. In this paper, we first propose a virtualized & monitoring SaaS model with predictive maintenance to minimize the costs of fault tolerance. Then with the monitored and predicted available states of servers, we focus on dynamic real-time task scheduling in geo-distributed and large-scale DCs with heterogeneous servers. Multiple objectives, including the long-term performance benefits, energy and communication costs, are taken into consideration in order to improve scheduling quality. For inter-DC and intra-DC task scheduling, two dynamic programming problems are formulated respectively, but there exists the problem that both state and action spaces are too large to be solved by simple iterations. To address this issue, we introduce the idea of reinforcement learning theory into solving traditional stochastic dynamic programming problems in the large-scale SaaS cloud, and put forward a cascaded two-level (inter-DC and intra-DC level) approximate dynamic programming (ADP) task-scheduling algorithm. The computation complexity can be significantly reduced and scheduling speed can be greatly improved. Finally, we conduct experiments with both random simulation data and Google cloud trace-logs. QoS evaluations and comparisons demonstrate that two ADP algorithms can work cooperatively, and our two-level ADP algorithm is more effective under large quantity of bursty requests.","Title: A Convolution Component-Based Method with Attention Mechanism for Travel-Time Prediction.\nYear: 2019\n\nAbstract: Deep learning approaches have been recently applied to traffic prediction because of their ability to extract features of traffic data. While convolutional neural networks may improve the predictive accuracy by transiting traffic data to images and extracting features in the images, the convolutional results can be improved by using the global-level representation that is a direct way to extract features. The time intervals are not considered as aspects of convolutional neural networks for traffic prediction. The attention mechanism may adaptively select a sequence of regions and only process the selected regions to better extract features when aspects are considered. In this paper, we propose the attention mechanism over the convolutional result for traffic prediction. The proposed method is based on multiple links. The time interval is considered as the aspect of attention mechanism. Based on the dataset provided by Highways England, the experimental results show that the proposed method can achieve better accuracy than the baseline methods.","Title: Toward Efficient Image Representation: Sparse Concept Discriminant Matrix Factorization\nYear: 2019\n\nAbstract: The key ingredients of matrix factorization lie in basic learning and coefficient representation. To enhance the discriminant ability of the learned basis, discriminant graph embedding is usually introduced in the matrix factorization model. However, the existing matrix factorization methods based on graph embedding generally conduct discriminant analysis via a single type of adjacency graph, either similarity-based graphs (e.g., Laplacian eigenmaps graph) or reconstruction-based graphs (e.g., \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">${L}_{1}$ </tex-math></inline-formula>\n-graph), while ignoring the cooperation of the different types of adjacency graphs that can better depict the discriminant structure of original data. To address the above issue, we propose a novel Fisher-like criterion, based on graph embedding, to extract sufficient discriminant information via two different types of adjacency graphs. One graph preserves the reconstruction relationships of neighboring samples in the same category, and the other suppresses the similarity relationships of neighboring samples from different categories. Moreover, we also leverage the sparse coding to promote the sparsity of the coefficients. By virtue of the proposed Fisher-like criterion and sparse coding, a new matrix factorization framework called Sparse concept Discriminant Matrix Factorization (SDMF) is proposed for efficient image representation. Furthermore, we extend the Fisher-like criterion to an unsupervised context, thus yielding an unsupervised version of SDMF. Experimental results on seven benchmark datasets demonstrate the effectiveness and efficiency of the proposed SDMFs on both image classification and clustering tasks.","Title: CycleEmotionGAN: Emotional Semantic Consistency Preserved CycleGAN for Adapting Image Emotions\nYear: 2019\n\nAbstract: Deep neural networks excel at learning from large-scale labeled training data, but cannot well generalize the learned knowledge to new domains or datasets. Domain adaptation studies how to transfer models trained on one labeled source domain to another sparsely labeled or unlabeled target domain. In this paper, we investigate the unsupervised domain adaptation (UDA) problem in image emotion classification. Specifically, we develop a novel cycle-consistent adversarial model, termed CycleEmotionGAN, by enforcing emotional semantic consistency while adapting images cycle consistently. By alternately optimizing the CycleGAN loss, the emotional semantic consistency loss, and the target classification loss, CycleEmotionGAN can adapt source domain images to have similar distributions to the target domain without using aligned image pairs. Simultaneously, the annotation information of the source images is preserved. Extensive experiments are conducted on the ArtPhoto and FI datasets, and the results demonstrate that CycleEmotionGAN significantly outperforms the state-of-the-art UDA approaches.","Title: Short-Term Travel Time Prediction: A Spatiotemporal Deep Learning Approach\nYear: 2019\n\nAbstract: Traffic prediction is a complex, nonlinear spatiotemporal relationship modeling task with the randomness of Traffic demand, the spatial and temporal dependency between Traffic flows, and other recurrent and nonrecurrent factors. Based on the ability to learn generic features from history information, deep learning approaches have been recently applied to Traffic prediction. Convolutional neural network (CNN) methods that learn Traffic as images can improve the predictive accuracy by leveraging the implicit correlations among nearby links. Traffic prediction based on CNN is still in its initial stage without making full use of spatiotemporal Traffic information. In this paper, we improve the predictive accuracy by directly capturing the relationship between the input sequence and the predicted value. We propose the new local receptive fields for spatiotemporal Traffic information to provide the constraints in the task domain for CNN which is different from traditionally learning Traffic as images. We explore a max-pooled CNN followed by a fully connected layer with a nonlinear activation function to convolute the new local receptive fields. The higher global-level features are fed into a predictor to generate the predicted output. Based on the dataset provided by Highways England, we validate the assumption that there exists direct relationship between the input sequence and the predicted value. We train the proposed method by using the backpropagation approach, and we employ the AdaGrad method to update the parameters of the proposed method. The experimental results show that the proposed method can improve the predictive accuracy.","Title: Cost-Efficient Resource Provisioning for Dynamic Requests in Cloud Assisted Mobile Edge Computing\nYear: 2021\n\nAbstract: Mobile edge computing is emerging as a new computing paradigm that provides enhanced experience to mobile users via low latency connections and augmented computation capacity. As the amount of user requests is time-varying, while the computation capacity of edge hosts is limited, Cloud Assisted Mobile Edge (CAME) computing framework is introduced to improve the scalability of the edge platform. By...","Title: Multimodal Transformer with Variable-Length Memory for Vision-and-Language Navigation.\nYear: 2022\n\nAbstract: Vision-and-Language Navigation (VLN) is a task that an agent is required to follow a language instruction to navigate to the goal position, which relies on the ongoing interactions with the environment during moving. Recent Transformer-based VLN methods have made great progress benefiting from the direct connections between visual observations and language instructions via the multimodal cross-attention mechanism. However, these methods usually represent temporal context as a fixed-length vector by using an LSTM decoder or using manually designed hidden states to build a recurrent Transformer. Considering a single fixed-length vector is often insufficient to capture long-term temporal context, in this paper, we introduce Multimodal Transformer with Variable-length Memory (MTVM) for visually-grounded natural language navigation by modeling the temporal context explicitly. Specifically, MTVM enables the agent to keep track of the navigation trajectory by directly storing activations in the previous time step in a memory bank. To further boost the performance, we propose a memory-aware consistency loss to help learn a better joint representation of temporal context with random masked instructions. We evaluate MTVM on popular R2R and CVDN datasets. Our model improves Success Rate on R2R test set by 2% and reduces Goal Process by 1.5 m on CVDN test set. Code is available at: https://github.com/clin1223/MTVM.","Title: Preliminary evaluation of a novel vision-guided hybrid robot system for capsulotomy in cataract surgery☆☆\nYear: 2022\n\nAbstract: •A novel 6-DoF hybrid robot is introduced to improve the stability and compactness of surgery robot.•A robot control algorithm with an RCM constraint is proposed to reduce the position error of the robot.•An edge detection method based on the U-Net is proposed to provide visual feedback to the robot and increase the precision of tracking the edge of the anterior capsule.•Experimental results are presented to indicate that the proposed robot system can increase the performance of cataract surgery.","Title: Continuous Finger Kinematics Estimation Based on sEMG and Attention-ConvGRU Network\nYear: 2022\n\nAbstract: sEMG is an efficient media for human-computer interactions, especially in the controlling of artificial limbs and other mechanical arms. In the paper, we propose a new Attention-ConvGRU model which can continuously estimate the finger joint angles based on sEMG when executing classical grasping motions. The experimental results show that the average correlation coefficient (CC) and the root mean square error (RMSE) of the proposed Attention-ConvGRU method are 0.8320 +/- 0.04 and 8.8717 +/- 0.98, respectively, which are significantly better than that of the GRU method (0.8093 +/- 0.05, p < 0.01, 9.3716 +/- 0.95, p < 0.01). Moreover, the training speed of Attention-ConvGRU is 4.5 times faster than that of GRU method.","Title: Preliminary Estimation of the Friction between Force-sensing Forceps and Cornea\nYear: 2022\n\nAbstract: Continuous curvilinear capsulorhexis (CCC) is a delicate ophthalmic procedure that may benefit from robot technology. In CCC, surgeons make an incision in the cornea, insert forceps into the anterior segment through the incision, and peel the anterior lens capsule (ALC) from the lens. The absence of perception of axial peeling force applied to ALC is challenging in developing robot-assisted CCC. A potential way to deal with this challenge is measuring axial force with a mercantile force sensor, which has the advantage of sub-millinewton precision, high axial stiffness, high stability, and ease of use. However, the measured results of the mercantile force sensor contain the axial peeling force and forceps-cornea friction. Therefore, we seek to estimate the friction at the incision with the distributed LuGre friction model. The normal force and friction at incision are measured by fiber bragg grating (FBG) sensors and a mercantile force sensor, respectively. Then, the parameters of the LuGre model are identified by applying a low-frequency sinusoidal movement on the forceps. An identification experiment is performed on ex-vivo pig cornea samples. Finally, friction during inserting forceps into the anterior segment of ex-vivo pig eyes is estimated. The root mean square error of friction identification and estimation are 0.59 mN and 1.59 mN, respectively."],"string":"[\n \"Title: Queue management for QoS provision build on network processor\\nYear: 2003\\n\\nAbstract: Network processor is a kind of programmable processor performing network computing with special design and optimization. It can be viewed as a tight-coupled multi-processor system due to its architecture of multiple in-chip processors, high-speed buses and other function components. The design and development of networking systems using network processors is an emerging field that offers numerous challenges and opportunities. This paper provides the design and implementation of queue management module combining buffer management and packet scheduling for QoS provision, which uses Intel's network processor and follows the relative differentiated service model. As the key issues, system synchronization, task assignment to threads, and design difficulties are discussed in the paper.\",\n \"Title: Modeling Hierarchical Caches in Content-Centric Networks\\nYear: 2013\\n\\nAbstract: Content-Centric Network (CCN) provides a cleanslate design for the Internet, where content becomes the primitive of communications. In CCN, routers are equipped with content stores, which act as caches for frequently requested content. This design enables the Internet to provide content distribution services without any application-layer support. On the other hand, as caches are integrated into routers, the overall performance of CCN will be influenced by the caching efficiency. This paper studies the performance issues of caches in CCN, with the aim to gain some understanding on how caches should be designed to maintain a high performance in a cost-efficient way. Specifically, we use a two-dimensional discrete-time Markov chain to model the two-layer cache hierarchy formed by CCN routers, and develop an efficient algorithm to calculate the hit ratios of these caches. Simulations validate the accuracy of our modeling method, and convey some understanding on cache design in CCN.\",\n \"Title: Effective Data Aggregation Supported by Dynamic Routing in Wireless Sensor Networks\\nYear: 2010\\n\\nAbstract: Data aggregation is an main method to conserve energy in wireless sensor network (WSN). Prior work on data aggregation protocols are generally based on static routing schemes, such as tree-based, cluster-based or chain-based routing schemes. Although they can save energy to some extent, in dynamic scenarios where the source nodes are changing frequently, they will not only incur high overhead to continuously reconstruct the routing but also can not reduce the communication overhead effectively. Our work aims to design an effective data aggregation mechanism supported by dynamic routing (DASDR) which can adapt to different scenarios without incurring much overhead. Enlightened by the concept of potential field in the discipline of physics, the dynamic routing in DASDR is designed based on two potential fields: depth potential field which guarantees packets reaching the sink at last and queue potential field which makes packets more spatially convergent and thus data aggregation will be more efficient. Simulation results show that DASDR is more effective in energy savings as well as scales well with regard to the network size.\",\n \"Title: Performability Evaluation of Resource Scheduling Algorithms for Computational Grids\\nYear: 2006\\n\\nAbstract: Resource scheduling is a fundamental issue in achieving high performance on computational grids. In this paper, we concentrate on parallel resource scheduling problems for hierarchical resource scheduling architecture of computational Grids, and propose a Stochastic Reward Nets (SRN) based performability evaluation framework for evaluating the performability of resource scheduling algorithms for computational Grid with resource unavailability. Within the proposed framework, performability model of hierarchical resource scheduling is constructed, 36 combinations of 6 global job scheduling algorithms and 6 local processor allocation algorithms are specified in the uniform performability model, and performability metrics are derived.\",\n \"Title: A sufficient condition for instability of buffer priority policies in re-entrant lines\\nYear: 2003\\n\\nAbstract: We use a buffer-boundedness approach to study the stability of re-entrant lines with a buffer priority scheduling policy. Using Petri net models we establish a sufficient condition for instability of such systems having a positive feedback loop. An example of unstable systems is also given.\",\n \"Title: Performance Analysis of Retransmission and Redundancy Schemes in Sensor Networks\\nYear: 2008\\n\\nAbstract: In this paper, by establishing the probability models, we systematically and comprehensively analyze the roles of the packet retransmission, the block retransmission, and the erasure coding in the reliable transport of wireless sensor networks. And as well as the three kinds of packet level schemes, we also consider the effect of two kinds of bit level strategies, CRC and FEC. At last, based on the numeric results, the appropriate schemes for different BER (high, medium, and low) are determined, and we also present some principles that reveal profound insights in designing reliable protocols and mechanisms in wireless sensor networks.\",\n \"Title: MediaCloud: A New Paradigm of Multimedia Computing.\\nYear: 2012\\n\\nAbstract: Multimedia computing has attracted considerable attention with the rapid growth in the development and application of multimedia technology. Current studies have attempted to support the increasing resource consumption and computational overhead caused by multimedia computing. In this paper, we propose MediaCloud, a new multimedia computing paradigm that integrates the concept of cloud computing in handling multimedia applications and services effectively and efficiently. MediaCloud faces the following key challenges: heterogeneity, scalability, and multimedia Quality of Service (QoS) provisioning. To address the challenges above, first, a layered architecture of MediaCloud, which can provide scalable multimedia services, is presented. Then, MediaCloud technologies by which users can access multimedia services from different terminals anytime and anywhere with QoS provisioning are introduced. Finally, MediaCloud implementation and applications are presented, and media retrieval and delivery are adopted as case studies to demonstrate the feasibility of the proposed MediaCloud design.\",\n \"Title: An Improved Markov Model for IEEE 802.15.4 Slotted CSMA/CA Mechanism.\\nYear: 2009\\n\\nAbstract: IEEE 802.15.4 protocol is proposed to meet the low latency and energy consumption needs in low-rate wireless applications,\\n however, few analytical models are tractable enough for comprehensive evaluation of the protocol. To evaluate the IEEE 802.15.4\\n slotted CSMA/CA channel access mechanism in this paper, we propose a practical and accurate discrete Markov chain model, which\\n can dynamically represent different network loads. By computing the steady-state distribution probability of the Markov chain,\\n we obtain an evaluation formula for throughput, energy consumption, and access latency. Then we further analyze the parameters\\n that influence performance including packet arrival rate, initial backoff exponent and maximum backoff number. Finally, NS2\\n simulator has been used to evaluate the performance of the 802.15.4 CSMA/CA mechanism under different scenarios and to validate\\n the accuracy of the proposed model.\",\n \"Title: Analysis of a hybrid cutoff priority scheme for multiple classes of traffic in multimedia wireless networks\\nYear: 1998\\n\\nAbstract: In this paper, we propose and analyze the performance of a new handoff scheme called hybrid cutoff priority scheme for wireless networks carrying multimedia traffic. The unique characteristics of this scheme include support for N classes of traffic, each may have different QoS requirements in terms of number of channels needed, holding time of the connection and cutoff priority. The proposed scheme can handle finite buffering for both new calls and handoffs. Futhermore, we take into consideration the departure of new calls due to caller impatience and the dropping of queued handoff calls due to unavailability of channels during the handoff period. The performance indices adopted in the evaluation using the Stochastic Petri Net (SPN) model include new call and handoff blocking probabilities, call forced termination probability, and channel utilization for each type of traffic. Impact on the performance measures by various system parameters such as queue length, traffic input and QoS of different traffic has also been studied.\",\n \"Title: Towards a Universal Friendly P2P Media Streaming Application: An Evaluation Framework\\nYear: 2008\\n\\nAbstract: Network-oblivious P2P applications have produced high cross-ISP traffics and led to serious disruption to ISP economics. While end users are concerned with quality, Internet Content Provider (ICP) cares more about service scale and ISP focuses on operating cost. Thus this paper provides a framework of evaluating the P2P streaming application performance from perspectives of all entities involved, i.e ISP, ICP and end users. Based on the analytical study, we propose potential solutions to define unprecedented friendly and cost-effective P2P streaming application to achieve the ideal philosophy of \\\"More Users =Better performance + Lower cost\\\".\",\n \"Title: Throughput Analysis of Wireless Multi-hop Chain Networks\\nYear: 2009\\n\\nAbstract: It is important but challenging to understand the throughput performance in wireless multi-hop networks. Most previous works show that the M/M/1 model is accurate enough for throughput analysis most of the time. However, we notice that without considering the interrelationship between the traffic on nodes, the M/M/1 model is quite inaccurate sometimes, especially on the typical chain topology. Therefore in this paper, we propose a model called Traffic-Analysis-Based (TAB) for throughput analysis focusing on chains. TAB obtains the throughput by analyzing the state transition process of the chain as the traffic rate is increasing, based on the traffic interrelationships. Besides the throughput of each node, we also get two significant state transition points. One is the optimal sending rate of traffic, and the other is the lower bound when M/M/1 can be used. Simulation results show that TAB is much more accurate than M/M/1 when the traffic rate is under the second transition point.\",\n \"Title: VCNF: A Secure Video Conferencing System Based on P2P Technology\\nYear: 2008\\n\\nAbstract: The goal of a video conferencing system is to deliver multimedia data to the conference participants efficiently and securely. To meet the requirement of a video conferencing system over the internet, many issues have to be addressed, such as security, scalability and heterogeneity. In this paper, we propose a secure and scalable video conferencing system named VCNF (Video Conference Network Foundation), to support large number of conferencing groups simultaneously with each group has limited members. We use a P2PSIP overlay to store and lookup the user and group contact information. SIP protocol is used to setup media sessions and a new kind of session-- VPN session. We adopt a layered data transfer mechanism which traits different kinds of media-streams as of different importance level. We have implemented the system and analyzed its performance and security. An Internet video-based game based on our platform has also been deployed over the Internet.\",\n \"Title: A scheduling model for maximizing availability with makespan constraint based on residual lifetime in heterogeneous clusters\\nYear: 2007\\n\\nAbstract: A notable requirement of clusters is to maximize its processing performance. Lots of work in this area has been done to optimize the system performance by improving certain metric such as reliability, availability, security and so on. However, most of them assumes that the system is running without interruption and seldom considers the system's intrinsic characteristics, such as failure rate, repair rate and lifetime. In this paper, we study how to achieve high availability based on residual lifetime analysis for the repairable heterogeneous clusters with makespan constraints. First, we provide an availability model based on addressing the cluster's residual lifetime model. Second, we give an objective function about the model and develop a heuristic scheduling algorithm to maximize the availability the makespan constraint. At last, we demonstrate these advantages through the extensive simulated experiments.\",\n \"Title: Stochastic game net and applications in security analysis for enterprise network\\nYear: 2012\\n\\nAbstract: Stochastic game theoretic framework has been used in many fields of networks with interactive behaviors. However, further use of this framework is limited due to the following reasons. Firstly, it is difficult to build comprehensive and rigorous models for complex network structures by the state-based game model. Secondly, solving and extending the dynamic behaviors of participators of the network are nearly impossible, because of the complexity of state transitions. Last but not least, general game model is not able to describe and analyze specific events and behaviors in some kinds of networks, like enterprise networks. In this paper, we propose a new modeling paradigm (stochastic game net, or SGN) for stochastic games representation with Petri nets. Based on our graphical tool, stochastic game problems can be described clearly, and the model can be solved and extended easily. Moreover, this paper puts forth a series of methods for modeling and analyzing the competitive game by SGN, which is the main contribution of this work. Our achievements are applied to the security analysis for enterprise networks. The analysis results prove the powerful ability of our achievements in solving the complicated and dynamic game problems. Furthermore, our approaches can be used to calculate the existence and the value of an equilibrium point.\",\n \"Title: QoS-aware Scheduling Algorithm Based on Complete Matching of User Jobs and Grid Services\\nYear: 2006\\n\\nAbstract: Grid Services Scheduling is a challenging problem under Open Grid Service Architecture(OGSA). Grid Services running on Clusters must have mechanism and policies for establishing and supporting QoS. A graph theory formal description is introduced into the Service Grid Model in this paper. The necessary and sufficient condition of complete matching of user job and service resources has been given and proved. Optimal Solution to matchmaking of grid jobs and grid services is developed based on the running time weight matrix, and the arithmetic has been verified by simulation analysis which proved to be more efficient than the alike arithmetic. The arithmetic has been implemented and running well.\",\n \"Title: Security Analysis of Enterprise Network Based on Stochastic Game Nets Model\\nYear: 2009\\n\\nAbstract: In this paper, we propose a novel modeling method, Stochastic Game Nets (SGN), and use it to model and analyze the security issues in enterprise networks. Firstly, the definition and modeling algorithm of Stochastic Game Nets are given. And then we apply the Stochastic Game Nets method to describe the attack and defense course in the enterprise networks successfully, and find a Nash equilibrium. Finally we analyze the confidentiality and integrity of the enterprise network quantificationally based on the model. The method can also be applied to other areas with respect to a game.\",\n \"Title: Enhanced low-latency detection of motor intention from EEG for closed-loop brain-computer interface applications.\\nYear: 2014\\n\\nAbstract: In recent years, the detection of voluntary motor intentions from electroencephalogram (EEG) has been used for triggering external devices in closed-loop brain-computer interface (BCI) research. Movement-related cortical potentials (MRCP), a type of slow cortical potentials, have been recently used for detection. In order to enhance the efficacy of closed-loop BCI systems based on MRCPs, a manifold method called Locality Preserving Projection, followed by a linear discriminant analysis (LDA) classifier (LPP-LDA) is proposed in this paper to detect MRCPs from scalp EEG in real time. In an online experiment on nine healthy subjects, LPP-LDA statistically outperformed the classic matched filter approach with greater true positive rate (79 ± 11% versus 68 ± 10%; p = 0.007) and less false positives (1.4 ± 0.8/min versus 2.3 ± 1.1/min; p = 0.016 ). Moreover, the proposed system performed detections with significantly shorter latency (315 ± 165 ms versus 460 ± 123 ms; p = 0.013), which is a fundamental characteristics to induce neuroplastic changes in closed-loop BCIs, following the Hebbian principle. In conclusion, the proposed system works as a generic brain switch, with high accuracy, low latency, and easy online implementation. It can thus be used as a fundamental element of BCI systems for neuromodulation and motor function rehabilitation.\",\n \"Title: An Efficient Recovery and Survival Scheme against Malware Attacks\\nYear: 2010\\n\\nAbstract: Intricate malware can result in the failure of on-line Comprehensive Protection (CP) in distributed systems, and place the system in an unsafe state which is difficult to recover from. There lacks an effective scheme to defend against this extreme attack. In this paper, based on the Two-layer Protection and Co-operative Recovery (TPCRS) mechanism, we propose an efficient survivable scheme against malware attacks in distributed systems. The basic strategy is to deploy an Emergency Response/Recovery (ER) agent at each node to recognize the state of the system whenever the CP fails, and to carry out cooperative security among multiple nodes so that the infected nodes can be rapidly recovered. Furthermore, a Preventive Maintenance (PM) model is adopted to enhance the reliability of the distributed system. Si-mulation results demonstrate the practicality and efficiency of the proposed schemes.\",\n \"Title: Adding Security to Network Via Network Processors\\nYear: 2004\\n\\nAbstract: With the increasing need of security, cryptographic processing becomes a crucial issue for network devices. Traditionally security functions are implemented with Application Specific Integrated Circuit (ASIC) or General-Purposed Processors (GPPs). Network processors (NPs) are emerging as a programmable alternative to the conventional solutions to deliver high performance and flexibility at moderate cost. This work compares and analyzes architectural characteristics of many widespread cryptographic algorithms on Intel IXP2800 network processor. In addition, we investigate several implementation and optimization principles that can improve the cryptographic performance on NPs. Most of the results reported here should be applicable to other network processors since they have similar components and architectures.\",\n \"Title: Design and analysis of an ONOFF variable structure controller for AQM routers supporting TCP flows.\\nYear: 2008\\n\\nAbstract: Active queue management (AQM) can maintain smaller queuing delay and higher throughput by purposefully dropping packets at\\n intermediate nodes. Most of the existing AQM schemes follow the probability dropping mechanism originating from random early\\n detection (RED). This paper develops a novel packet dropping mechanism for AQM through designing an ONOFF controller applying\\n the variable structure control theory. Because the binary ONOFF controller can considerably simplify the manipulation on the\\n AQM router, it is helpful for implementing the high performance router. The design principles of ONOFF controller are discussed\\n in detail. The guidelines towards parameter settings are presented. The performance is extensively evaluated and compared\\n with other well-known controllers through simulations and theoretical analysis. The results demonstrate that the ONOFF controller\\n is responsive and robust against external disturbances, and is insensitive to variances of the system parameters. Therefore,\\n it is very suitable for the time-varying network system, and at the same time, it can also keep the instantaneous queue length\\n at a desired level with rather small oscillations, which is conducive to achieving the technical objectives of AQM.\",\n \"Title: Dependability Analysis of Control Center Networks in Smart Grid Using Stochastic Petri Nets\\nYear: 2012\\n\\nAbstract: As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs, and increase reliability. In smart grid, control center networks have attracted a great deal of attention, because their security and dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of smart grid security, but few work focuses on the dependability analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the dependability of control center networks in smart grid. We present the general model of control center networks by considering different backup strategies of critical components. With the general SPNs model, we can measure the dependability from two metrics, i.e., the reliability and availability, through analyzing the transient and steady-state probabilities simultaneously. To avoid the state-space explosion problem in computing, the state-space explosion avoidance method is proposed as well. Finally, we study a specific case to demonstrate the feasibility and efficiency of the proposed model in the dependability analysis of control center networks in smart grid.\",\n \"Title: Modeling and survivability analysis of service composition using Stochastic Petri Nets\\nYear: 2011\\n\\nAbstract: In this paper, we propose a service composition model method that supports quantitative computation based on Stochastic Petri Nets (SPN). It can capture the semantics of complex service combinations and their respective specifications. In this method, services are divided into interior services and exterior services. The exterior services will be published to the users, while the interior ones do not need to be published. Six equivalent simplified theorems which can be used to simplify the complex models of interior services to simple models of exterior services are presented. They enable the minimization of the state space of the model and make quantitative computation feasible. In addition, since Grid services are always affected by all kinds of churns in actual applications, we also research survivability and its main attributes for Grid service composition. The definition and computational methods based on the model are put forward. In the end, we use the method presented above to describe and analyze an example of travel Grid services successfully.\",\n \"Title: Reputation-Based QoS Provisioning in Cloud Computing via Dirichlet Multinomial Model\\nYear: 2010\\n\\nAbstract: In Cloud computing, users with different service requirements often need to negotiate with service provider via Service Level Agreement (SLA). The unique pay-as-you-go billing way in Cloud computing challenges resource provisioning for service providers. In this paper, based on the Dirichlet multinomial model, we present an efficient reputation-based QoS provisioning scheme, which can minimize the cost of computing resources, while satisfying the desired QoS metrics. Unlike the previous counterparts, we consider the statistical probability of the response time as a practical metric rather than the typical mean response time. Numerical results show the efficiency and effectiveness of the proposed scheme.\",\n \"Title: Optimization and Benchmark of Cryptographic Algorithms on Network Processors\\nYear: 2004\\n\\nAbstract: With the increasing needs of security, cryptographic functions have been exploited in network devices. Besides time consuming, security protocols are flexible in algorithm selections. Fortunately, network processors, which serve as the backbone of intelligent network devices, hold performance and flexibility at the same time. In this article, we investigate several principles that can be used with implementing and optimizing cpptographic algorithms on network processors. Also, these principles are applied in real life algontltms, including stream ciphers, block ciphers and digital signatures. Related experiments and benchmark results on Intel LYPl200 network processor are provided.\",\n \"Title: Mutual Authentication and Key Exchange Protocols for Roaming Services in Wireless Mobile Networks\\nYear: 2006\\n\\nAbstract: Two novel mutual authentication and key exchange protocols with anonymity are proposed for different roaming scenarios in the global mobility network. The new features in the proposed protocols include identity anonymity and one-time session key renewal. Identity anonymity protects mobile users privacy in the roaming network environment. One-time session key progression frequently renews the session key for mobile users and reduces the risk of using a compromised session key to communicate with visited networks. It has demonstrated that the computation complexity of the proposed protocols is similar to the existing ones, while the security has been significantly improved\",\n \"Title: Saturation aware TCP throughput prediction\\nYear: 2009\\n\\nAbstract: With the ever growing network traffic, capacity of paths in the networks nowadays has become increasingly easy to saturate. This brings new challenge to TCP throughput prediction. The main problem is loss rate and RTT during the TCP flow are often expected as inputs for the throughput models used in the prediction; however, only loss rate and RTT before the flow are available and are used instead. If the flow itself causes significant changes in loss rate and RTT, e.g., when the TCP flow attempts to saturate the underlying available bandwidth, the prediction error can be unacceptably large. Though new prediction approaches are being proposed, they basic require record of previous TCP transfers and are applicable only when TCP transfers are performed repeatedly, which limits their application. In this work, by properly using a measurement of the underlying available bandwidth, we develop an analytical TCP throughput model which can explicitly capture changes in loss rate and RTT caused by the target TCP flow and hence, can largely improve the prediction accuracy by making the prediction aware of capacity saturation of paths while at the same does not require any history record of previous TCP transfers as current newly proposed works do. Results show that when the changes in loss rate and RTT are large, the errors by traditional models can be as large as over 200%, whereas the error by the proposed model is usually very small, e.g., with the average error below 10% and the maximum error below 20% for general settings, and is bounded by the measurement error of available bandwidth in the worse case; when the changes in loss rate and RTT are small, even a very rough estimation of available bandwidth, e.g., with an error of around 50%, can lead to very accurate prediction by the proposed model.\",\n \"Title: SRD Service: A Stochastic Network Calculus Approach for Service Differentiation Analysis\\nYear: 2010\\n\\nAbstract: The popularity of delay-sensitive applications has been rapidly evolving in recent years. However, the current Internet is not well designed to support the delivery of such traffic. The SRD (Stochastic Rate-Delay) services proposed in this paper empower a user to choose either a higher transmission rate or a low queuing delay at a congested network link. While average low delay remains guaranteed, the delay constraint is allowed to violate occasionally. The implementation enforces the intended service differentiation via link scheduling and sizing of the buffer. We build a model using stochastic network calculus to analyze the SRD services proposal and shed light on its fundamental characteristics quantitatively.\",\n \"Title: Scalable parameter tuning for AVQ\\nYear: 2005\\n\\nAbstract: This letter proposes a simple, scalable, practical and systematic rule for tuning the control parameter of the adaptive virtual queue (AVQ) active queue management scheme. An explicit stability condition of AVQ is proposed using classical control theory. Theoretical analyses as well as simulation results are used to validate the result.\",\n \"Title: An Analytical Method to Predict Packet Losses over Bursty Wireless Channels.\\nYear: 2011\\n\\nAbstract: In this letter, we perform an analytical investigation of packet losses for bursty wireless channels and present a closed-form solution for the packet loss rate. The solution can adapt to the channel dynamics and predict the packet loss rate more fast and accurately. With the results, we can design cross-layer protocols over wireless networks more conveniently. © 2006 IEEE.\",\n \"Title: HMM-Based Predictive Power Saving Mechanism in WiMAX\\nYear: 2010\\n\\nAbstract: Several studies have showed that network features (e.g., packet interval and packet size) may be well modeled by a hidden Markov model (HMM) with appropriate hidden variables that capture the current state of the network. In this paper, we propose a prediction mechanism on the basis of the HMM model to assist the Power Saving (PS) in WiMAX. In comparison with prior models whose analyses are often with the assumption of Poisson arrival, the prediction-based PS relies no more on that, which means a rich variety of traffic patterns such as heavy-tailed or burst that are often encountered in usual networks can be applied. In addition, our mechanism balances in a more effective way the tradeoff between packet delay and energy consumption, and we find that in fact they can be both improved sometimes. Validation results confirm that the prediction-based PS is of great reduction in energy (about 5% to Instant Message and 5%-25% to HTTP) under the trace driven simulations.\",\n \"Title: A self-adaptive probabilistic packet filtering scheme against entropy attacks in network coding\\nYear: 2009\\n\\nAbstract: In this paper, based on a novel self-adaptive probabilistic subset linear-dependency detection (S-PSLD) algorithm, we propose an efficient packet filtering scheme against entropy attacks in network coding. The scheme verifies the received packets probabilistically instead of exactly, and thus it can rapidly filter out the resultant packets from entropy attacks. Moreover, to minimize the packet detection cost at forwarder while keeping the false positive rate at an expected low level, a self-adaptive algorithm is introduced such that each forwarder can dynamically tune the system security parameters according to the available bandwidth or the number of the received packets in buffer. Theoretical analysis and performance evaluation are given to demonstrate the validity and efficiency of the proposed scheme.\",\n \"Title: Improving the Convergence and Stability of Congestion Control Algorithm\\nYear: 2007\\n\\nAbstract: The traditional TCP congestion control is inefficient for high speed networks and it is a challenge to design a high speed replacement for TCP. By simulating some existing high speed protocols, we find that these high speed protocols have limitations in convergence and stability. To address these problems, we apply a population ecology model to design a novel congestion control algorithm-Coupling Logistic TCP(CLTCP). It is based on bandwidth pre-assignment that is similar to XCP and MaxNet. The pre-assignment rate factor is computed in the routers based on the information of the router capacity, the aggregate incoming traffic and the queue length. Then the senders adjust the sending rate according to the pre-assignment rate factor which carries by the packet to strengthen the convergence and stability of transport protocol. The theoretical analysis and simulation results show that CLTCP provides not only fast convergence and strong stability, but also high utilization and fair bandwidth allocation regardless of round trip time.\",\n \"Title: Survey on transport control in data center networks.\\nYear: 2013\\n\\nAbstract: Traditional fair bandwidth sharing by leveraging AIMD-based congestion control mechanisms faces great challenges in data center networks. Much work has been done to solve one of the various challenges. However, no single transport layer protocol can solve all of them. In this article, we focus on the transport layer in data centers, and present a comprehensive survey of existing problems and their current solutions. We hope that this article can help readers quickly understand the causes of each problem and learn about current research progress, so as to help them make new contributions in this field. © 1986-2012 IEEE.\",\n \"Title: On the Optimal Request Routing Strategy in CDN Live Streaming Application\\nYear: 2011\\n\\nAbstract: In this paper, we consider the Request Routing (RR) strategy in the CDN live streaming application. We show that to find an optimal RR strategy is correspond to a static optimization problem if the total number of clients is known in advance. However, this static approach is ineffective due to the difficulty in precisely estimating the number of clients off-line. We then develop the MPS scheduling algorithm to compute the multiphase RR strategy. Experimental study shows that our algorithm can generate a close-to-optimal strategy with respect to a wide range of the number of clients.\",\n \"Title: Security Analysis for Online Banking System Using Hierarchical Stochastic Game Nets Model\\nYear: 2009\\n\\nAbstract: In this paper, we propose hierarchical Stochastic Game Nets, and use it to model and analyze the security issues in online banking system. Firstly, the definition and modeling algorithm of hierarchical Stochastic Game Nets are given. In which, some theorems are proved. Then we apply hierarchical Stochastic Game Nets to describe the attack and defense course in the online banking. Finally we analyze the attack time and attack probability in the online banking quantitatively based on the model successfully.\",\n \"Title: Sliding Mode Congestion Control for data center Ethernet networks\\nYear: 2012\\n\\nAbstract: Recently, Ethernet is being enhanced as the unified switch fabric of data centers, called Data Center Ethernet. The end-to-end congestion management is one of the indispensable enhancements, and Quantized Congestion Notification (QCN) has been ratified to be the standard. Our experiments show that QCN suffers from the oscillation of the queue at the bottleneck link. With the changes of system parameters and network configurations, the oscillation may become so serious that the queue is emptied frequently. As a result, the utilization of the bottleneck link degrades. Theoretical analysis shows that QCN approaches to the equilibrium point mainly through the sliding mode motion. But whether QCN enters into the sliding mode motion also depends on both system parameters and network configurations. Hence, we present the Sliding Mode Congestion Control (SMCC) scheme, which can drive the system into the sliding mode motion under any conditions. SMCC benefits from the advantage that the sliding mode motion is insensitive to system parameters and external disturbances. Moreover, SMCC is simple, stable and has short response time. QCN can be replaced by SMCC easily since both of them follow the framework developed by the IEEE 802.1 Qau work group. Experiments on the NetFPGA platform show that SMCC is superior to QCN, especially in the condition that the traffic pattern and the network state are variable.\",\n \"Title: Modeling and Analysis of Dependability Attributes of Service Computing Systems\\nYear: 2011\\n\\nAbstract: Dependability is an important consideration during the design and development of IT systems and services. But in service computing systems, the traditional definition and evaluation methods from the systems' and components' point of view meet challenges. In this paper, we change the angle of view, and study the dependability and their attributes from the service-oriented perspective. A stochastic model using semi-Markov process is put forward, and the quantitative analysis of the dependability attributes is carried out. By extending and transforming this model, the mean time to dependability attributes failure is computed. Based on the analysis and calculations, some theorems are proposed and proved, to show the interrelationships and comparisons of the different dependability attributes.\",\n \"Title: Inside the bird's nest: measurements of large-scale live VoD from the 2008 olympics\\nYear: 2009\\n\\nAbstract: The 2008 Beijing Olympics was an interesting event from a VoD perspective because it involved near real-time video delivery at massive scales over multiple days of a high-profile event. We present some measurement-driven insights into this event through a unique dataset obtained from ChinaCache, the largest CDN in China. The dataset is unique in three respects. First, it gives a \\\"white-box\\\" view into user access patterns which would otherwise be impossible. Second, since the CDN serves different content providers, it allows to compare and contrast the effects of different presentation models on end users. Third, the nature of the content itself is vastly different from traditional VoD systems in terms of the real-time and event-driven nature, which gives rise to unique effects. The dataset allows us to investigate a wide range of interesting issues: (1) how the live nature of the events causes differences in access patterns compared to traditional VoD and User-Generated Content (UGC) systems, (2) how the presentation models affect user behavior, and (3) flash-crowd phenomena. Based on these observations, we discuss implications for future live VoD systems.\",\n \"Title: Experimental Analysis Of Super-Seeding In Bittorrent\\nYear: 2008\\n\\nAbstract: With the popularity of BitTorrent, improving its performance has been an active research area. Super-seeding, a special upload policy for initial seeds, improves the efficiency in producing multiple seeds and reduces the uploading cost of the initial seeders. However, the overall benefit of super seeding remains a question. In this paper, we conduct an experimental study over the performance of super-seeding scheme of BitTornado. We attempt to answer the following questions: whether and how much super-seeding saves uploading cost, whether the download time of all peers is decreased by super-seeding, and in which scenario super-seeding performs worse. With varying seed bandwidth and peer behavior, we analyze the overall download time and upload cost of super seeding scheme during random period tests over 250 widely distributed PlanetLab nodes. The results show that benefits of super-seeding depend highly on the upload bandwidth of the initial seeds and the behavior of individual peers. Our work not only provides reference for the potential adoption of super-seeding in BitTorrent, but also much insights for the balance of enhancing Quality of Experience (QoE) and saving cost for a large-scale BitTorrent-like P2P commercial application.\",\n \"Title: Idle Detection Based Optimal Throughput Rate Adaptation in Multi-Rate WLANs\\nYear: 2009\\n\\nAbstract: Multiple transmission rates are supported in the current 802.11 WLANs, which allows stations to exploit different rates in an adaptive manner to cope with the variability of wireless channels and achieve the higher system throughput. Many efforts have been made at rate adaptation in the literature. The design of rate adaptation algorithms needs to take four basic issues into account. In this paper, by properly addressing the four issues, we present a novel rate adaptation algorithm, idle detection based optimal throughput rate adaptation (ITRA). We apply a comprehensive approach to designing the rate adaptation mechanism and attempt to maximize the overall system throughput. ITRA is furnished with two instructive features. First, ITRA modifies the conventional exponential backoff rule to a constant one. With the help of constant contention window (CW), ITRA can discriminate collisions from channel errors, and does not need RTS/CTS handshakes which are adopted by many existing works. We derive an explicit relation between the throughput and the CW size, and choose the CW size to optimize the throughput as well as improve the fairness. Secondly, ITRA directly estimates the current network throughput and changes transmission rate according to the estimation. Since the fundamental purpose of rate adaptation is to improve the network throughput, using the throughput as the metric is more straightforward than applying indirect criteria like frame transmission statistics or PHY metrics. We evaluate our proposed scheme by simulation, and the result shows that ITRA achieves satisfactory performance.\",\n \"Title: A novel multiple time scale congestion control scheme for multimedia traffic\\nYear: 2004\\n\\nAbstract: A novel two time scale congestion control scheme is proposed in this paper, which is called Adaptive Wavelet and Probability-based scheme (AWP). On normal time scales of 20-200 ms, it is a smooth and TCP-friendly congestion control; on large time scales of 1-5 second, it detects persistent shifts in overall network contention and modulates the rate control exhibited on normal scale. AWP exploits self-similar property of network traffic and predicts future network available bandwidth to enhance performance, and it is adaptive to network environment changes through an auto-correction algorithm. With comparable experiments among TFRC, TCP and AWP over NS platform, AWP has been proved to be able to greatly improve the quality of service(QoS) of multimedia data transmission while avoiding congestion collapse on the network.\",\n \"Title: Improving TCP Throughput over HSDPA Networks\\nYear: 2008\\n\\nAbstract: The various link adaptation techniques employed by High Speed Downlink Packet Access (HSDPA) in the third generation (3G) networks augment the bandwidth oscillation, which is identified as one of the most important factors resulting in the throughput deterioration of Transmission Control Protocol (TCP). In this paper, we firstly explain why the bandwidth oscillation degrades the TCP performance through a special simulation experiment. Subsequently, a split connection Window Adaptation TCP Proxy is proposed to improve the TCP throughput over HSDPA networks. In this solution, the built-in attributes of HSDAP system are sufficiently utilized. In order to effectively use the precious cellular link resources, the length of the queue connected with it is intentionally kept around the reference value through adjusting the sending window size of TCP proxy based on the dynamic values of varying bandwidth. A discrete-time stochastic state space model is formulated to analyze the system stability. The validity of enhanced scheme is verified through simulation experiments. The performance of TCP proxy is compared with the standard TCP protocol. The numerical results show that our TCP proxy is able to keep the cellular link utilization over 90%, and to improve TCP throughput by 100% under most conditions.\",\n \"Title: Risk-Aware QoP/QoS Optimization for Multimedia Applications in Wireless Networks\\nYear: 2009\\n\\nAbstract: The unique characteristics of wireless networks pose a number of nontrivial challenges to multimedia applications with security and rigorous QoS requirements. Lack of adequate security protection is incapable of meeting security requirements of applications, whereas enabling excessive security services inevitably leads to further degradation in QoS due to additional computation and payload encapsulation. Early work, e.g. LAP, achieves balance by adjusting security policy according to QoS metrics; but none of them are security guaranteed. In this paper, we present an efficient risk-aware QoP (Quality of Protection) and QoS optimization algorithm for multimedia applications in wireless networks. It can achieve an optimization for QoP/QoS performance metrics through offering hierarchical security services and QoS support. Experiment demonstrates that even in high risk environments our scheme can efficiently balance QoP and QoS requirements.\",\n \"Title: CASM: a content-aware protocol for secure video multicast\\nYear: 2006\\n\\nAbstract: Information security has been a critical issue in the design and development of reliable distributed communication systems and has attracted significant research efforts. A challenging task is how to maintain information security at a high level for multiple-destination video applications with the huge volume of data and dynamic property of clients. This paper proposes a novel Content-Aware Secure Multicast (CASM) protocol for video distribution that seamlessly integrates three important modules: 1)a scalable light-weight algorithm for group key management; 2) a content-aware key embedding algorithm that can make video quality distortion imperceptible and is reliable for clients to detect embedded keys; and 3) a smart two-level video encryption algorithm that can selectively encrypt a small set of video data only, and yet ensure the video as well as the embedded keys unrecognizable without a genuine key. The implementation of the CASM protocol is independent of the underlying multicast mechanism and is fully compatible with existing coding standards. Performance evaluation studies built upon a CASM prototype have demonstrated that CASM is highly robust and scalable in dynamic multicast environments. Moreover, it ensures secure distribution of key and video data with minimized communication and computation overheads. The proposed content-aware key embedding and encryption algorithms are fast enough to support real-time video multicasting.\",\n \"Title: Traffic-Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks\\nYear: 2011\\n\\nAbstract: The congestion problem in Wireless Sensor Networks (WSNs) is quite different from that in traditional networks. Most current congestion control algorithms try to alleviate the congestion by reducing the rate at which the source nodes inject packets into the network. However, this traffic control scheme always decreases the throughput so as to violate fidelity level required by the applications. In this paper, we present a solution that sufficiently exerts the idle or underloaded nodes to alleviate congestion and improve the overall throughput in WSNs. To achieve this goal, a traffic-aware dynamic routing (TADR) algorithm is proposed to route packets around the congestion areas and scatter the excessive packets along multiple paths consisting of idle and underloaded nodes. Utilizing the concept of potential in classical physics, our TADR algorithm is designed through constructing a hybrid virtual potential field using depth and normalized queue length to force the packets to steer clear of obstacles created by congestion and eventually move toward the sink. The simulation results show that the proposed solution improves the overall throughput by around 370 percent as compared to MintRoute, which is one of benchmark routing protocols. Furthermore, TADR scheme has low overhead suitable for large-scale, dense sensor networks.\",\n \"Title: An Authorization Model without Central Authority for Service Collaboration\\nYear: 2011\\n\\nAbstract: In the service-oriented computing, a single transaction initiated by a client might invoke many different services in other administrative domains. Existing models for authorizing the access assume that all services involved in collaboration are managed by the central authority, which is not always a realistic premise. In this paper, we propose a novel authorization model for dynamic service collaboration. With the authorization discovery process, the client can discover the needed authorization for service access available in other autonomous domains. With extensions to SoD relationship, the conflicts of client interests can be formalized and expressed as constraints. The authorization problems are formalized to choose the optimal access path for each task. At last, the example and experiments show the practicality and the effectiveness of our scheme.\",\n \"Title: An Efficient Privacy-Preserving Publish-Subscribe Service Scheme for Cloud Computing\\nYear: 2010\\n\\nAbstract: Cloud computing provides a novel computing paradigm for enterprises to store programs and data in the Cloud in a transparent manner, which poses the challenge of security and privacy. In this paper, based on homomorphic cryptography and Zero-Knowledge Proof, we present a novel privacy-preserving scheme for Cloud publish/subscribe service, which achieve efficient privacy-preserving authentication, data integrity, and publish-subscribe confidentiality. The performance evaluation and security analysis demonstrate the practice and validity of the proposed scheme.\",\n \"Title: Antiworm Npu-Based Parallel Bloom Filters In Giga-Ethernet Lan\\nYear: 2006\\n\\nAbstract: In this paper, an AntiWorm system based on the Intel IXP Network Processor was implemented using the Parallel Bloom filters technique. The AntiWorm system consists of two components: Bloom filters and Exact Matching engines. The Parallel Bloom filters can identify the suspicious traffic quickly and effectively, and then dispatch them to Exact Matching engines for further investigation. Both the principles and the implementation of the AntiWorm system are introduced in detail. With the consideration of the system performance parameters, two feasible implementation solutions are investigated and the advantages and disadvantages are also compared. The selections of configuration parameters of the AntiWorm system are also discussed. A hash scheme based on MD5's function is proposed for implementing fast hash functions. To test the performance of the AntiWorm system, such as throughput and delay, some experiments are carried out with different simulated traffic condition. The internal statistics of IXP network processor are also collected and analyzed for optimizing the system performance. To demonstrate the operation of the AntiWorm system, assaults by Worm Blaster are used in the test bed, and the experimental results prove the effectiveness of the AntiWorm system. The Software Package WormDetector1.0 is also provided as a software release from the research.\",\n \"Title: Secure, Disjoint, Multipath Source Routing Protocol(SDMSR) for Mobile Ad-Hoc Networks\\nYear: 2006\\n\\nAbstract: Mobile Ad-hoc Network (MANET) is an appealing technology that has attracted lots of research efforts over past years. Although the principle of wireless, structure-less, dynamic networks is attractive, there are still some major flaws that prevent commercial expansion. Security is one of these main barriers; MANETs are known to be particularly vulnerable to security attack. One solution proposed to increase security resilience is to use multipath routing algorithms [1]. However multipath routing also introduces new challenges in terms of security and security overhead. In this paper we consider the problem of secure routing in fully distributed MANETs using multipath routing. After studying the effect of multipath in terms of security, we propose an efficient multipath heuristic and a new approach to protect the route discovery called SDMSR to secure the routing protocol while mitigating security overhead.\",\n \"Title: Self-healing group key distribution with time-limited node revocation for wireless sensor networks\\nYear: 2007\\n\\nAbstract: A novel key distribution scheme with time-limited node revocation is proposed for secure group communications in wireless sensor networks. The proposed scheme offers two important security properties: the seal-healing re-keying message distribution which features periodic one-way re-keying with implicitly authentication, efficient tolerance for the lost re-keying messages, and seamless Traffic Encryption Key (TEK) switch without disrupting ongoing data transmissions; and the time-limited dynamic node attachment and detachment, so that both forward and backward secrecy is assured by dual directional hash chains. It is shown that the communication and computation overhead of the proposed protocol is light, and the protocol is robust under poor communication channel quality and frequent group node topology change.\",\n \"Title: A Fast Multi-pattern Matching Algorithm for Deep Packet Inspection on a Network Processor\\nYear: 2007\\n\\nAbstract: Deep Packet Inspection (DPI) is a critical function in network security applications such as Firewalls and Intrusion Detection Systems (IDS). Signature based scanners used in DPI apply multi-pattern matching algorithms to check whether the packet payload or flow content contains a specified signature in a signature set. Existing multi-pattern matching algorithms sacrifice memory space to achieve better performance. In this paper a novel fast multi-pattern matching algorithm, the Hash Boyer-Moore (HBM) Algorithm, is presented, which reduces the memory footprint of the heuristic table using a hash function and adds another heuristic table to reduce the false-positive ratio. Analyses and simulations show HBM offers higher speed and lower memory cost than some existing algorithms. The HBM algorithm was implemented on the Intel IXP 2400 Network Processor (NP) platform and experiments show suitable performance results in a Gigabit Ethernet LAN environment.\",\n \"Title: Stochastic Performance Analysis of a Wireless Finite-State Markov Channel\\nYear: 2013\\n\\nAbstract: Wireless networks are expected to support a diverse range of quality of service requirements and traffic characteristics. This paper undertakes stochastic performance analysis of a wireless finite-state Markov channel (FSMC) by using stochastic network calculus. Particularly, delay and backlog upper bounds are derived directly based on the analytical principle behind stochastic network calculus. Both the single user and multi-user cases are considered. For the multi-user case, two channel sharing methods among eligible users are studied, i.e., the even sharing and exclusive use methods. In the former, the channel service rate is evenly divided among eligible users, whereas in the latter, it is exclusively used by a user randomly selected from the eligible users. When studying the exclusive use method, the problem that the state space increases exponentially with the user number is addressed using a novel approach. The essential idea of this approach is to construct a new Markov modulation process from the channel state process. In the new process, the multi-user effect is equivalently manifested by its transition and steady-state probabilities, and the state space size remains unchanged even with the increase of the user number. This significantly reduces the complexity in computing the derived backlog and delay bounds. The presented analysis is validated through comparison between analytical and simulation results.\",\n \"Title: Performance Analysis of Channel Contention in Wireless Ad Hoc Networks: A Stochastic Game Nets Approach\\nYear: 2010\\n\\nAbstract: This paper concentrates on the performance analysis of channel contention in wireless ad hoc networks by using Stochastic Game Nets (SGN). We refine the definition of SGN so that it can be used to precisely capture the underlying details of a given system. A SGN model is developed to evaluate the channel utilization in a two-node wireless system. We quantify the system performance under equilibrium strategies. The findings of this paper are instructive for the design and deployment of wireless ad hoc networks.\",\n \"Title: A secure and scalable video conference system based on peer-assisted content delivery networks.\\nYear: 2009\\n\\nAbstract: With the ever-increasing demand for multimedia conferencing over the Internet, video conferencing systems that enable efficient and secure data delivery have been a hot yet challenging research topic in both academy and industry. To deploy large-scale commercial video conferencing services, we need to address critical issues, such as Quality-of-Service (QoS), security and scalability. In this paper, we propose a secure and scalable video conferencing system over the Internet, namely Video Conference Network Platform (VCNP). VCNP organizes users into a Chord-based Peer-to-Peer (P2P) overlay, and utilizes Peer-to-Peer Session Initiation Protocol (P2PSIP) for the management of conference members, which includes the setup of media sessions and the storage of user contact information and conferencing group information over distributed peers. A VPN session over P2PSIP is implemented to act as a secure infrastructure for VCNP. For video data delivery, we design a framework of Peer-assisted CDN (Content Delivery Network), which uses both edge servers and fiber network in CDN, and the principle of structured P2P management to enhance the QoS, scalability and flexibility of the system. Based on the proposed architecture and protocols, VCNP has already been implemented and deployed over the Internet. Experimental results demonstrate the effectiveness of VCNP in delivering the unprecedented security, scalability, and certain QoS simultaneously in a unified architecture.\",\n \"Title: Quality of Protection with Performance Analysis in IP Multimedia Subsystem\\nYear: 2009\\n\\nAbstract: IP multimedia subsystem (IMS) is considered as the main solution to the next generation multimedia communication.By examining potential security threats to IMS, we initiate a set of security policies for choice and consider their quality of protection (QoP) based on the strength of the security mechanism. In order to provide secure service to users, it is insufficient to just take the security benefits into consideration. An adequate analysis of the impact of security policies in IMS on the performance quantitatively is necessary and significant. We present a novel study of IMS performance using queuing Petri nets (QPN) to predict the system performance metrics, i.e., the signaling delay, server utilization, which then be used to evaluate the impacts of different security mechanism on system performance quantitatively. With the multi-view security partition introduced, multi-level security service can be provided to diverse users and applications for a best tradeoff between security requirements and system performance.\",\n \"Title: IVForensic: a digital forensics service platform for internet videos\\nYear: 2009\\n\\nAbstract: IVForensic is a digital forensics service platform for Internet videos, with the aim of revealing illegal videos and preventing them from spreading over the Internet. It a) implements a flexible, secure and scalable architecture for large-scale online monitoring, b) provides an effective and efficient forensic countermeasure for widely-sourced Internet videos, c) guarantees good end-user experience such as flexible settings, simple operations and low startup latency. The results of performance evaluation using data obtained from real-world deployments demonstrate the effectiveness of the platform.\",\n \"Title: Distributed KDC-based random pairwise key establishment in wireless sensor networks\\nYear: 2010\\n\\nAbstract: Key management is a core mechanism to provide secure and reliable communications in wireless sensor networks (WSNs). In large-scale WSNs, due to the resource constraint on sensor nodes, it is still an extremely challenging task to achieve good performance in terms of high network connectivity and strong resilience against sensor nodes capture with low overheads. To address this issue, in this paper we propose a novel random pairwise key establishment scheme, called RPKE. In RPKE, sensor nodes differentiate their roles as either auxiliary nodes or ordinary nodes prior to network deployment. The auxiliary nodes act as distributed key distribution center (KDC), and neighboring ordinary nodes can establish pairwise key with the help of the distributed KDC. Theoretical analysis and simulation evaluation demonstrate that RPKE performs well in terms of network connectivity and resilience at the cost of low computation/communication/storage overheads, compared to the existing counterparts.\",\n \"Title: Frequency Domain Packet Scheduling with Stability Analysis for 3GPP LTE Uplink\\nYear: 2013\\n\\nAbstract: In this paper, we investigate the Frequency Domain Packet Scheduling (FDPS) problem for 3GPP Long Term Evolution (LTE) Uplink (UL). Instead of studying a specific scheduling policy, we provide a unified approach to tackle this issue. First, we formalize a general LTE UL FDPS problem, which is suitable for various scheduling policies. Then, we prove that the problem is MAX SNP-hard, which implies that approximation algorithms with constant approximation ratios are the best that we can hope for. Therefore, we design two approximation algorithms, both of which have polynomial runtime. The first algorithm is based on a simple greedy method. The second one is based on the Local Ratio (L-R) technique and it can approximately solve the LTE UL FDPS problem with an approximation ratio of 2. To further analyze the stability of the 2-approximation L-R algorithm, we derive a specific FDPS problem, which incorporates the queue length and channel quality information. We utilize the Lyapunov Drift to prove the L-R algorithm is stable for any $((\\\\omega_0, \\\\epsilon_0))$-admissible LTE UL systems. The simulation results indicate good performance of the L-R scheduler.\",\n \"Title: A Novel Cookie-Based DDoS Protection Scheme and its Performance Analysis\\nYear: 2009\\n\\nAbstract: Seamless handover is one of the most attractive research fields in B3G systems. Many mechanisms are proposed to provide certain QoS guarantees in handovers of mobile systems, which would also introduce new threats, such as DoS and DDOS attacks. In this paper, we extend a cookie-based scheme to protect systems from DoS and DDoS attacks. We also adopt a novel queuing network model to analyze our scheme by estimating two essential metrics, i.e. the mean total response time and the mean queue length of MAP. Numerical results indicate that our mechanism works better than the traditional cookie-based scheme and it could effectively help networks defend against abnormal attacks in the handover process.\",\n \"Title: Self-certified mutual authentication and key exchange protocol for roaming services\\nYear: 2006\\n\\nAbstract: In this paper, based on self-certified mechanism, we propose a novel mutual authentication and key exchange protocol for roaming services in the global mobility network. The main new features included in the proposed protocol are (1) identity anonymity, which protects location privacy of mobile users in the roaming network environment; (2) one-time session key renewal, which frequently updates the session key for mobile users and hence reduces the risk of using a compromised session key to communicate with the visited network. The performance analysis shows that the computational complexity of our protocol is low and suitable to be used on mobile equipments.\",\n \"Title: Enhancing tit-for-tat for incentive in BitTorrent networks\\nYear: 2010\\n\\nAbstract: BitTorrent achives great success in its high performance for file distribution. It is commonly believed that this owes to\\n Tit-for-Tat strategy adopted in choking algorithm for incentive in BitTorrent protocol. But there are still many issues in\\n incentive mechanism in BitTorrent. In this paper, we study Tit-for-Tat strategy by constructing a game model in P2P environments\\n and deducing the constraint under which the strict Tit-for-Tat is an equilibrium strategy. We then enhance and adapt the Tit-for-Tat\\n strategy to the dynamic property in P2P networks, and deduce the constraint under which it is still an equilibrium strategy.\\n We also study through simulations the performance improvement of the enhanced Tit-for-Tat, and give suggestion on how to choose\\n proper system parameters under different network conditions. Finally, based on the enhanced Tit-for-Tat strategy, we propose\\n a method to improve incentive in BitTorrent networks to cope with free-riding and fairness issues.\",\n \"Title: Linear Temporal Inference of Workflow Management Systems Based on Timed Petri Nets Models\\nYear: 2002\\n\\nAbstract: In recent years a significant body of research has been dedicated to the analysis and verification of workflows without taking\\n into account the temporal dimension of activities involved. Once the temporal aspects of the activities in a process description\\n are taken into account, various types of synchronization anomalies and other undesirable behavior may be detected. In this\\n paper, we use a timed Petri nets to represent workflows and introduce a set of linear inference algorithms for the quantitative\\n analysis of temporal constraints of workflows. We apply our models to the analysis of several workflow patterns.\",\n \"Title: Energy and Delay Minimization in Cluster-Based Wireless Sensor Networks\\nYear: 2012\\n\\nAbstract: Wireless sensor networks (WSNs) usually consist of a large number of power-constrained sensors deployed to collect information in a specific sensed field. How to gather data in an energy-efficient manner is always a very important issue in WSNs. Recently, cluster-based data aggregation is widely used to reduce energy consumption and prolong network lifetime. However, most existing schemes proposed to reduce energy consumption fail to consider the factor of quality of service (QoS), especially the source-to-sink delay and data-loss probability. To address this problem, we propose a novel model which can capture both the factors of energy efficiency and QoS guarantee in WSNs. Through analytical and simulative results, we show that our model has the potential to help deploy the wireless sensor nodes in an energy-efficient manner while still providing a certain level of QoS.\",\n \"Title: A robust active queue management algorithm in large delay networks\\nYear: 2005\\n\\nAbstract: Active Queue Management (AQM) can maintain smaller queuing delay and higher throughput by purposefully dropping the packets at intermediate nodes. Almost all the existing AQM schemes neglect the impact of large delay on their performance. In this study, we first show an unexpected fact through simulation experiments, which is the dramatic oscillations appear in the queues controlled by several popular AQM schemes, including RED, PI controller and REM, in large delay networks. With appropriate model approximation, we design a robust AQM controller to compensate for the delay using the principle of internal mode compensation in control theory. Our novel algorithm restrains the negative effect on queue stability caused by large delay. Simulation results show that the integrated performance of the proposed algorithm outperforms that of several well-known schemes when the connections have large delay, while keeping buffer queue short.\",\n \"Title: General gap-utilizing strategy for non-persistent traffic in wireless networks\\nYear: 2009\\n\\nAbstract: We focus on a general strategy which exploits nonpersistent traffic gaps to reduce data delay/loss caused by some communication-breaking actions, e.g. handover and neighbor scanning. Those actions usually interrupt transmission or take up transferring chances for a period time, hence incur QoS downgrades. Our study discusses the details on how to choose the optimal gaps for them in order to minimize the bad impact, which provides a potential guidance for protocol and system design.\",\n \"Title: Self-correcting time synchronization using reference broadcast in wireless sensor network\\nYear: 2008\\n\\nAbstract: Time synchronization is one of the most fundamental services for numerous wireless sensor network applications. In this article the definition and basic concepts of time synchronization are introduced, and the related work is summarized in brief. Through analyzing the characteristics of the existing typical synchronization protocols and making a comprehensive comparison of the performance of various algorithms, we present a common guideline for designing the time synchronization protocol in WSN. Following this guideline, we develop a new time synchronization protocol called Self-Correcting Time Synchronization (SCTS), which converts the time synchronization problem into an online dynamic self-adjusting optimizing process to make the offset compensation and drift compensation simultaneously. The time and space complexities of the algorithm implementation are very low. In addition, the SCTS protocol fully exploits the inherent broadcast property of wireless channel, so the communication overhead is rather low. Because the algorithm implementation is based on the phase locked loop principle, an equivalent digital PLL without an actual voltage controlled oscillator is also proposed to avoid introducing the extra hardware required by a traditional PLL circuit. Finally, we validate SCTS on the Berkeley Mica2 experimental platform, and the performance is evaluated and compared to the existing typical time synchronization protocol.\",\n \"Title: Trust Based Access Control in Infrastructure-Centric Environment\\nYear: 2011\\n\\nAbstract: The rapid development of applications running on global information infrastructure poses the problem of securing information sharing among domain collaborations. Existing access control models are defective in dynamic authorization based on user's trustworthiness and do not take full advantages of the infrastructure in implementing access control system. In this work, we propose a trust and role based access control model and the corresponding framework in infrastructure-centric environment. With the extension to RBAC model, trust level requirements, which dictate that the roles in the privilege context must be activated by the trustworthy user, can be specified. The comprehensive trust model, which calculates the user's trust level in multiple trust contexts based on behavior histories, is proposed. Moreover, by taking advantages of the infrastructure services, our scheme is flexible and scalable in that system administrators are free to choose custom scoring functions while the infrastructure trust evaluation services are relieved of the heavy burdens of history record maintenance and trust level update.\",\n \"Title: Performance Analysis of Data Management in Sensor Data Storage via Stochastic Petri Nets.\\nYear: 2010\\n\\nAbstract: Recently, sensor data storage has gained increasing popularity for reliable access to data through redundancy spread over unreliable nodes in wireless sensor networks. In storage-centric sensor networks, several schemes have been proposed to optimize the performance of data management in terms of data availability, repair bandwidth, etc. However, few works have been undertaken to study the performance of these data management schemes from a comprehensive point of view. In this paper, we adopt a concise graphic model, i.e., Stochastic Petri Nets (SPNs), to analyze the performance of three representative data management schemes. From the steady state probability matrix of the SPNs models, we can easily get the average energy consumption, repair bandwidth, reliability and data availability. Based on numerical results, we provide guidelines for designing sensor data storage systems. The results also demonstrate that our proposed models are suitable for analyzing data management schemes in sensor data storage.\",\n \"Title: Logical Inference Of Clauses Based On Petri Net Models\\nYear: 1998\\n\\nAbstract: This paper extends the logical inference of Horn clauses in Petri net models to cover a large class of non-Horn clauses. Based on four-valued logic and the conflict transition concept, we show how the Petri net model for this class of non-Horn clauses can be constructed. The clause inference is solved by the T-invariant method or the fixpoint of markings. Both forward and backward inference can be used in our:model. It is further shown that these techniques are efficient for the common classes of monotonic reasoning. (C) 1998 John Wiley & Sons, Inc.\",\n \"Title: Efficient Re-Keying Scheme for Group Key Distribution\\nYear: 2008\\n\\nAbstract: In this paper, we propose a communication-efficient re-keying scheme by using a polynomial-based efficient code method. Compared with the previous schemes, the re-keying cost is significantly reduced, since no extra re-keying message header is needed. In addition, the computation overhead is also lightweight since only 2logn hash operations are required for each re-keying. Therefore, the proposed scheme is more suitable for deploying in the group application scenarios with both high dynamic memberships and limited communication channel capacity.\",\n \"Title: Session-Affinity Aware Request Allocation for Web Clusters\\nYear: 2004\\n\\nAbstract: Persistent connections are increasingly being used in web retrieval due to wide adoption of HTTP/1.1 standards. With persistent connections, the request allocation algorithm used by web clusters is often session-grained. This paper studies the caching performance of web clusters under session-grained request allocation. It is shown that although content-based algorithms considerably improve caching performance over content-blind algorithms at the request-grained level, most performance gain is offset by the allocation dependency that arises when the requests are allocated at the session-grained level. The performance loss increases with cluster size and connection holding time. An optimization problem is then formulated for improving the caching effectiveness of session-grained allocation. The problem is proven to be NP-complete. Based on a heuristic approach, a session-affinity aware algorithm is presented that makes use of the correlation between the requests in a session. The new algorithm is shown to significantly outperform the content-based algorithm undersession-grained allocation. It is also shown that optimizing session-grained allocation cannot fully compensate for the performance loss caused by allocation dependency.\",\n \"Title: Modeling and analysis of a p2p-vod system based on stochastic network calculus\\nYear: 2012\\n\\nAbstract: Peer-to-peer video-on-demand (P2P-VoD) is one of the most commercially important and technically challenging applications. In such systems, each video file is typically divided into sub-pieces that are stored possibly at different sending peers, and the receiving peer often needs to coordinate multiple senders by specifying a transmission schedule for them. In this paper, we investigate this transmission scheduling problem for a specific P2P-VoD system which is PPLive. We formalize the problem of scheduling sub-piece transmission based on stochastic network calculus, and analyze its delay performance. Based on the delay analysis, an optimization problem is formulated to design the schedule for sending peers to transmit their sub-pieces. The objective of this optimization is to maximize a defined reward and at the same time meet the delay constraint required to ensure video quality at the receiver.\",\n \"Title: A Trust and Context Based Access Control Model for Distributed Systems\\nYear: 2008\\n\\nAbstract: In order to overcome the limitations in traditional access control models such as identity-based access control and meet the access requirements in distributed systems, we propose a Trust and Context based Access Control model called TCAC, it extends the traditional RBAC (role based access control) model with the notion of trust and context. Role assignment in TCAC is based on the trustworthiness and context information of users. The TCAC model is flexible, scalable, and well suitable for the dynamic and distributed systems. Then we provide a trust evaluation mechanism based on the local and global reputation to compute the trust value of a user in distributed system, which can avoid malicious nodes behave correctly in order to get the highest possible trust value. Finally an implementation framework of the access control system based on TCAC is described.\",\n \"Title: Modeling and analyzing power management policies in server farms using Stochastic Petri Nets\\nYear: 2012\\n\\nAbstract: Server farms are playing an important role in the Internet infrastructure today. However, the increasing power consumption of server farms makes them expensive to operate. Thus, how to reduce the power consumed by server farms has become a important research topic. Power can be thought as a resource of system, just like traditional resources, and we can manage power via improved resource management strategies. In recent studies on power management, the system is attached with multiple states of different power consumption, and by switching among these states, power consumption can be made proportional to the work load. As different job scheduling policies will result in different performance and power consumption, an optimized policy with power as a factor can achieve a better tradeoff between performance and power consumption. In this paper, we summarize some familiar power management policies and propose a novel model using Stochastic Reward Nets(SRN). Based on this model, we analyze the performance and power consumption of different power management policies, and propose a novel cost-aware job scheduling algorithm.\",\n \"Title: VISA: a visual sentiment analysis system\\nYear: 2012\\n\\nAbstract: Sentiment plays a critical role in many information-centric business scenarios. The opinion mining methods proposed in the recent decade have formed a solid foundation to investigate the sentiment analysis tasks, but are often too complicated and scattered to serve the needs of real customers. We introduce the VISA system in this paper, which applies the visualization technology to synthesize the sentiment analysis results and present to the end user in an interactive manner. VISA builds on the generic sentiment tuple based data model and consumes the different facets of sentiment data with coordinated multiple views, hence is scalable to work with most of existing sentiment analysis engines on various application domains. We showcase the usage of VISA in a real world example and demonstrate the system's effectiveness through the user trail in finding an appropriate hotel for his family trip.\",\n \"Title: A novel key management and distribution solution for secure video multicast\\nYear: 2005\\n\\nAbstract: In secure video multicast systems, access control is a very important and challenging issue. A common practice is to encrypt the video content using a group key, shared only by legitimate clients. Since the clients can join and leave the multicast session frequently, the group key needs to be updated and delivered to the legitimate clients accordingly in order to provide forward/backward secrecy. Therefore, key management and distribution plays a crucial role in building a secure and scalable video multicast system. Conventional approaches usually use a dedicated secure channel independent of the video data channel, which is expensive and inefficient. In this paper, we propose a novel and scalable Media-dependent Key Management and Distribution (MKMD) solution that embeds key management messages in video data so as to save network bandwidth and enhance the security level, and most importantly, without sacrificing the quality of video content too much. We have built a prototype system of MKMD, and our extensive experimental results show that MKMD is a secure, scalable, reliable and efficient key management and distribution solution for video multicast system.\",\n \"Title: Grid Service Workflow Models and Their Equivalent Simplification Methods\\nYear: 2006\\n\\nAbstract: In this paper, we propose a Grid service workflow model framework based on Stochastic Petri net (SPN) and equivalent methods to simplify the complex service workflow model to a simple model. In our models, the Grid service can be of inner services and exterior services. The exterior service will be published to the users and the inner one needs not to be published. The models are expressive enough to capture the inner information of complex service combinations and their respective specificities. The obtained framework enables declarative composition of Grid services. It shows dynamic and transient relationships among services. On the other hand, by the equivalent simplification to the models, the inner information of complex services can be protected, and the computation and analysis course can be simplified for the Grid service workflow.\",\n \"Title: Effective load balancing for cloud-based multimedia system.\\nYear: 2011\\n\\nAbstract: Cloud computing, as a new concept, provides a good solution for handling multimedia applications effectively and efficiently. It can facilitate the execution of complicated multimedia tasks, as well as supports specific and stringent multimedia QoS provisioning. However, for this new idea, a key challenge is how to make each multimedia service task to obtain the required resources in the shortest time. In this paper, we propose an effective load balancing approach for cloud-based multimedia system, called CMLB. Its main advantage is fully considering the load of all servers and the network conditions, and thus achieving reasonable resource allocation and scheduling. The evaluation results demonstrate the effectiveness of our approach. © 2011 IEEE.\",\n \"Title: A Hybrid Modeling Approach to Microarchitecture Design Space Exploring\\nYear: 2010\\n\\nAbstract: The micro architectural design space of a new processor is too huge for architects to handle with cycle-accurate simulators. Previous researches attack this problem by statistical learning methods such as Artificial Neural Networks (ANN) and statistical sampling solutions such as SimPoint. These approaches greatly reduce the simulation time while keeping the results of CPI precisely. However, all these machine learning and sampling methods are “black boxes”: although we can get CPI accurately, we can’t get detailed information of on-chip components which makes it difficult to find relationships between these components and bottlenecks of a design. Thus these approaches are not sufficient to provide enough intuitions for architects to find potential improvements. This paper proposes a novel “white box” Decision-free Generalized Stochastic Petri Nets (Decision-free GSPN) model. We adopt ANN to estimate certain parameters for GSPN when we consider new design points. Our hybrid approach could predict CPI accurately and produce the usage state of instruction queue (IQ), micro operation queue (uopQ), reservation station (RS), reorder buffer (ROB) and so on precise enough to give architects intuitions into the new design. Our solution takes only several minutes to finish comparing to days when we adopt cycle-accurate software simulator, with less than 8.6% error rate for CPI. We believe the information our method produces is precise enough to give architects more intuitions and insights about how to change a design comparing to previous machine learning methods.\",\n \"Title: Supervised Gabor-Based Kernel Locality Preserving Projections for Face Recognition\\nYear: 2011\\n\\nAbstract: Locality Preserving Projections (LPP) is an unsupervised method which seeks to optimally preserve the neighborhood structure of the dataset. LPP has been used wildly, but it has limits to solve the classification problem, such as the ignorance of the label information. Supervised Kernel Locality Preserving Projections (SKLPP) can preserve within-class geometric structures and represent the complex nonlinear variations of the face manifold by nonlinear kernel projection. Kernel method projects data from low-dimensional space to high-dimensional space. It can overcome the difficult when it is hard to use linear method in low-dimensional. In this paper, a novel Supervised Gabor-based Kernel Locality Preserving Projections (SGKLPP) method was proposed. This method integrates the Gabor wavelet representation of face images and the Supervised Kernel Locality Preserving Projections methods and it is robust to variations of illumination and facial expression. Experiments are performed to test the proposed algorithm on ORL dataset and Yale dataset. Results show that our new algorithm outperforms Supervised LPP (SLPP) method, SKLPP method and Supervised Gabor-based LPP (SGLPP) method.\",\n \"Title: Minimum average-case queries of q+1-ary search game with small sets\\nYear: 2012\\n\\nAbstract: Given a search space S={1,2,...,n}, an unknown element x^*@?S and fixed integers @?=1 and q=1, a q+1-ary @?-restricted query is of the following form: which one of the set {A\\\"0,A\\\"1,...,A\\\"q} is the x^* in?, where (A\\\"0,A\\\"1,...,A\\\"q) is a partition of S and |A\\\"i|@?@? for i=1,2,...,q. The problem of finding x^* from S with q+1-ary size-restricted queries is called as a q+1-ary search game with small sets. In this paper, we consider sequential algorithms for the above problem, and establish the minimum number of average-case sequential queries when x^* satisfies the uniform distribution on S.\",\n \"Title: Efficient resource allocation for China's 3G/4G wireless networks\\nYear: 2005\\n\\nAbstract: The all-IP DiffServ model is expected to be the most promising architecture for QoS provisioning in China's next-generation wireless networks, due to its scalability, convenience for mobility support, and capability of interworking heterogeneous radio access networks. This article focuses on efficient resource allocation in a wireless DiffServ architecture. Resource utilization efficiency is particularly important for China's wireless networks as the mobile user density in China is and will continue to be much higher than that in other countries. More specifically, we propose a novel buffer sharing scheme to provide assured service for real-time layer-coded multimedia traffic, which can guarantee the specific packet loss requirement of each layer with UDP as the transport layer protocol. An adaptive optimal buffer configuration can be applied to achieve maximum resource utilization over the time-varying channel. Assured service is also provided to TCP data traffic for guaranteed throughput, where the cross-layer coupling between the TCP layer and link layer is exploited to efficiently utilize the wireless resources.\",\n \"Title: Exploiting Gaps of Real-Time Traffic to Improve Handover Performance in Wireless Networks\\nYear: 2010\\n\\nAbstract: The handover mechanism in wireless networks has been intensively investigated in the past years. The traditional solutions are to customize various protocols in different layers to provide uninterrupted communication, which is particularly important for real time traffic. In this paper, we intend to exploit silence gap, inherently existing in realtime traffic, to improve handover performance. The proposed strategy can be appended to most of handover mechanisms without extra overhead. We theoretically analyze the relationship between gap distribution and handover performance under the assumption of different statistical distribution of gaps on a case-by-case basis. Subsequently, we insert an additional module into the WiMAX network and conduct the simulations using real voice traces. The results confirm the analytical conclusions, showing that the gap-exploiting approach can obtain significant gain in terms of packet loss particularly in low-speed mobile environment.\",\n \"Title: Investigating the interacting two-way tcp connections over 3GPP LTE networks\\nYear: 2012\\n\\nAbstract: This paper investigates the interactions between two-way TCP connections over 3GPP LTE networks. In the LTE network, the two-way TCP flows share buffers on a common bottleneck, i.e., the radio access links. The behaviors of TCPs significantly influence the others in the opposite direction. Specifically, the radio links of LTE are asymmetric, which may induce drastic interactions of TCPs and rapid draining of downlink buffer. The periodic idleness of downlink is a huge waste of the precious radio bandwidth and results in considerable performance degradation. In the viewpoint of Coupled Queues, we thoroughly understand the interacting TCPs and explain the reason for performance degradation. Based on a straightforward modeling procedure, we formalize the evolution of two-way TCPs and model the bottleneck queue size in every slot. The model indicates the queues are close coupled, which is verified with simulations on NS2. If the uplink (queue) is fully utilized, the downlink (queue) will always be underutilized even idle, and vice versa. Furthermore, an effective solution called Preemptive ACK Queueing (PAQ) is designed to decouple the queues, which improves the performance of two-way TCPs over LTE networks.\",\n \"Title: Scale Congestion Control to Ultra-High Speed Ethernet.\\nYear: 2014\\n\\nAbstract: Currently, Ethernet is broadly used in LAN, datacenter and enterprise networks, storage networks, high performance computing networks and so on. Along with the popularity of Ethernet comes the requirement of enhancing Ethernet with congestion control. On the other hand, Ethernet speed extends to 40Gbps and 100Gbps recently, and even 400Gbps in the near future. The ultra-high speed requires congestion control algorithms to adapt to the broad changes of bandwidth, and highlights the impacts of small delay by enlarging the bandwidth delay product. The state-of-art standard QCN is heuristically designed for the 1Gbps and 10Gbps Ethernet, and unaware of the challenges accompanying the ultra-high speed. To scale congestion control to ultra-high speed Ethernet, we propose the Adaptive Sliding Mode (ASM) congestion control algorithm, which is simple, stable, has fast and smooth convergence process, can tolerate the impacts of delay and adapt to the wide changes of bandwidth. Real experiments and simulations confirm these good properties and show that ASM outperforms QCN. Designing ASM, we find that the derivative of queue length is helpful to rate adjustment because it reflects the difference between bandwidth and aggregated sending rate. We also argue for enforcing congestion control system staying at the congestion boundary line, along which it automatically slides to stable point. These insights are also valuable to develop other congestion control algorithms in ultra-high speed networks.\",\n \"Title: Performance analysis of email systems under three types of attacks\\nYear: 2010\\n\\nAbstract: Email is a crucial part of our daily life, but email systems are facing increasing security threats such as attacks and spam. Because of this, new mechanisms are being provided to defend against the attacks and to reduce the amount of spam in email systems. Up to now, few available works have been reported on the performance evaluation of email systems under attack, which has become necessary for enhancing email security. In this paper, we propose a novel method to study the impact of three types of attacks on email systems. We construct a multiple queueing model to characterize three types of attacks integrally, and study the performance metrics of system security such as system availability, average queue length and information leakage probability. Numerical examples indicate that the approach of this paper is effective and efficient for dealing with the security analysis of email systems under attack. We believe that this work will open a new avenue for the performance evaluation of computer networks under email attack and other forms of attacks.\",\n \"Title: Adaptive resource management for mobile multimedia communications in asymmetric wireless network\\nYear: 2005\\n\\nAbstract: In the multi-service wireless networks, the bandwidth asymmetry is the distinctive characteristic. To avoid wasting uplink and downlink bandwidth in asymmetric network, admissible region for the nonreal-time (NRT) calls has been proposed. But in that scheme the static guard capacity for RT and NRT calls cannot adapt to traffic variation. In addition, the differentiated QoS services to users are not considered. In this paper, we propose an adaptive guard channel reservation (AGCR) scheme, which has the following novelties: i) the guard capacity for each class is dynamically adjusted so as to adapt to dynamic traffic, ii) the blocking probabilities of classes with higher priority are kept below certain targets and iii) differentiated QoS services to users are provided by maintaining the blocking probability ratios over different classes at the predefined targets. Simulation results have demonstrated the proposed AGCR scheme can keep the blocking probabilities of classes with higher priority below certain targets, track the ratios close to the targets well while maintaining a high bandwidth utilization\",\n \"Title: Scheduling gain analysis of opportunistic OFDMA and OFDM-TDMA systems\\nYear: 2008\\n\\nAbstract: In this paper, the performance of opportunistic scheduling in orthogonal frequency division multiplexing (OFDM) networks is studied. An analytical model is developed to extend the multi-class processor-sharing model in single-carrier networks to multi-carrier OFDM networks, where the total service rate depends on the total number of users. Based on the analytical model, the scheduling gains in both OFDM-TDMA (time division multiple access) and OFDMA (orthogonal frequency division multiple access) networks are evaluated. Different from previous works in this area, we focus on the scheduling performance at the flow level and consider a dynamic network setting with random finite-size service demands. Simulations are performed to verify the analytical results.\",\n \"Title: A Distributed Fault/Intrusion-Tolerant Sensor Data Storage Scheme Based on Network Coding and Homomorphic Fingerprinting\\nYear: 2012\\n\\nAbstract: Recently, distributed data storage has gained increasing popularity for reliable access to data through redundancy spread over unreliable nodes in wireless sensor networks (WSNs). However, without any protection to guarantee the data integrity and availability, the reliable data storage cannot be achieved since sensor nodes are prone to various failures, and attackers may compromise sensor nodes to pollute or destroy the stored data. Therefore, how to design a robust sensor data storage scheme to efficiently guarantee the data integrity and availability becomes a critical issue for distributed sensor storage networks. In this paper, we propose a distributed fault/intrusion-tolerant data storage scheme based on network coding and homomorphic fingerprinting in volatile WSNs environments. For high data availability, the proposed scheme uses network coding to encode the source data and distribute encoded fragments with original data pieces. With secure, compact, and efficient homomorphic fingerprinting, our scheme can fast locate incorrect fragments and then initialize data maintenance. Extensive theoretical analysis and simulative results demonstrate the efficacy and efficiency of the proposed scheme.\",\n \"Title: Survivability analysis of grid resource management system topology\\nYear: 2005\\n\\nAbstract: The resource management system is one of the essential components of Grids. A resource management system may be made up of one or many resource management nodes. The organization manner of the resource management nodes, i.e. the topology of a resource management system, is a key factor that should be considered carefully during the architecture design of resource management systems with respect to some specified requirements (e.g., performance, survivability). In this paper, a new survivability definition is proposed and the survivability of three typical Grid resource management system topologies (centralized, hierarchical, and peer-to-peer) is analyzed.\",\n \"Title: Accelerating large-scale data distribution in booming internet: effectiveness, bottlenecks and practices\\nYear: 2009\\n\\nAbstract: With the booming of Internet and consumer electronics industry, there are rising amount of content (audio, video, software) that needs to be distributed to increasing number of end consumers in higher speed. Whereas the explosive growth of data-concentrated applications over Internet contributes to the entertainment electronics, the large-scale distribution of huge data to end consumers with guarantee of Quality of Service (QoS), remains an elusive goal. The potential integration of Peer-to-Peer (P2P) paradigm into the Internet content distribution infrastructure provides a disruptive market opportunity to scale the Internet for higher quality data delivery. While the theoretical benefits of P2P in regular Internet has been widely reported, it remains unknown on its performance for large-scale mass data delivery over high-speed networks, highly promising yet controversial. This paper thus provides an experimental and analytical performance study over BitTorrent-like P2P networks for accelerating large-scale content distribution over booming Internet. Our study explores the unique strength of P2P in high speed networks, identifies performance bottlenecks, and quantifies the special requirements in the new scenario, i.e. file piece length and seed capacity. Further, to enable services before full downloading, this paper proposes a Piece-On-Demand (POD) scheme to modify BitTorrent in integration with Filesystem in Userspace (FUSE) to both decrease file distribution time and increase service availability. To demonstrate the feasibility of adopting P2P as a reliable consumer network for content distribution, we further illustrate our industrial exploration on utilizing P2P in deployment for application/services with huge image, providing unprecedented deployment speed and scalability for enabling on-demand services to large-scale users.\",\n \"Title: Padding for orthogonality: Efficient subspace authentication for network coding\\nYear: 2011\\n\\nAbstract: Network coding provides a promising alternative to traditional store-and-forward transmission paradigm. However, due to its information-mixing nature, network coding is notoriously susceptible to pollution attacks: a single polluted packet can end up corrupting bunches of good ones. Existing authentication mechanisms either incur high computation/bandwidth overheads, or cannot resist the tag pollution proposed recently. This paper presents a novel idea termed “padding for orthogonality” for network coding authentication. Inspired by it, we design a public-key based signature scheme and a symmetric-key based MAC scheme, which can both effectively contain pollution attacks at forwarders. In particular, we combine them to propose a unified scheme termed MacSig, the first hybrid-key cryptographic approach to network coding authentication. It can thwart both normal pollution and tag pollution attacks in an efficient way. Simulative results show that our MacSig scheme has a low bandwidth overhead, and a verification process 2-4 times faster than typical signature-based solutions in some circumstances.\",\n \"Title: Frequency Domain Packet Scheduling with MIMO for 3GPP LTE Downlink\\nYear: 2013\\n\\nAbstract: In this paper, we formalize a general Frequency Domain Packet Scheduling (FDPS) problem for 3GPP LTE Downlink (DL). The DL FDPS problem incorporates the SingleUser Multiple Input Multiple Output (SU-MIMO) technique, and can express various scheduling policies, including the Proportional-Fair metric, the MaxWeight scheduling, etc. For LTE DL SU-MIMO, the constraint of selecting only one MIMO mode (transmit diversity or spatial multiplexing) per user in each transmission time interval (TTI) increases the hardness of the FDPS problem. We prove the problem is MAX SNP-hard, which implies approximation algorithms with constant approximation ratios are the best we can expect. Subsequently, we propose an approximation algorithm of polynomial runtime. The solution is based on a greedy method for maximizing a non-decreasing submodular function over a matroid. The algorithm can solve the general DL FDPS problem with an approximation ratio of 4. We implement the proposed algorithm and compare its performance with other well-known schedulers.\",\n \"Title: Frequency-Domain Packet Scheduling for 3GPP LTE Uplink\\nYear: 2010\\n\\nAbstract: In this paper, we investigate the frequency-domain packet scheduling (FDPS) problem for 3GPP LTE Uplink (UL). Instead of studying a specific scheduling policy, we provide a unified approach to tackle this issue. First we formalize a general LTE UL FDPS problem which is suitable for various scheduling policies. Then we prove that the problem is MAX SNP-hard, which implies that approximation algorithms with constant approximation ratios are the best that we can hope for. Therefore we design two approximation algorithms, both of which have polynomial runtime. Subsequently, we analyze the two algorithms and find their approximation ratios. The first algorithm is easy to follow, since it is based on a simple greedy method. The second one is based on the local ratio technique and it can approximately solve the LTE UL FDPS problem with a approximation ratio of 2.\",\n \"Title: H-PFSP: Efficient Hybrid Parallel PFSP Protected Scheduling for MapReduce System\\nYear: 2013\\n\\nAbstract: MapReduce provides a data-parallel computing framework, and has emerged as a popular processing model due to the simplicity of operations for big data application developers. Data processing applications from many different domains such as search and data mining are usually developed using open-source Hadoop implementation of MapReduce or self-developed MapReduce-like implementations like Dryad [1] and Ciel [2]. In cloud environments, products like Amazon's Elastic Compute Cloud (EC2) [3] provide MapReduce services as third-party multi-tenant service. Even within a company, a number of products may share the MapReduce cluster. Therefore, a fair and efficient scheduler is crucial to improve performance of submitted jobs and guarantee multi-user fairness. However, in practice, it is hard to guarantee both fairness and per-job performance, especially when jobs are scheduled without accurate estimation. We show that processor sharing (PS) type of schedulers like Fair Scheduling degrade the per-job performance in a multi-user environment. We present a new scheduling policy, Hybrid Parallel pessimistic Fair Schedule Protocol (H-PFSP), that can finish every job no later than Fair scheduler does. Unlike Fair scheduler, however, it can improve the per-job performance of MapReduce systems with relatively accurate job progress estimation.\",\n \"Title: Modeling, Analysis and Optimization of Dependability-Aware Energy Efficiency in Services Computing Systems\\nYear: 2013\\n\\nAbstract: Besides performance, dependability and energy efficiency are two critical concerns during the design, development and management of large-scale services computing systems. In this paper, we jointly consider the performance, dependability and energy efficiency, and optimize the dependability-aware energy efficiency of services computing systems by maximizing the quality of service and dependability revenue and minimizing energy costs. Markov reward models are put forward, and quantitative analysis of them is carried out. In addition, the methodologies for hierarchical model composition and state aggregation are proposed. Furthermore, the optimization problem is formulated as an average reward criterion Markov decision problem, and the algorithm to solve it is introduced. Finally, the LANL service systems are analyzed and optimized as a case study to illuminate how this approach can apply to large-scale systems in reality.\",\n \"Title: Application level network access control system based on TNC architecture for enterprise network.\\nYear: 2010\\n\\nAbstract: Traditional NAC system in enterprise network is in coarse granularity (e.g. IP or MAC address) and lack of flexibility. Recently the demand in tight control of the enterprise network to defense the misuse and security issues become more and more urgent. Based on the TCG TNC standard, an application level network access control mechanism is proposed and implemented. With TNC client/server model in hand, a client is designed to enhance TNC client with the function of host flow controller (HFC), and intercepts each application network access request(ANAR) and transfer it to PDP server to authorize the access request. When a sensor (i.e. intrusion detection system) detects any malicious traffic, host flow controller and network flow controller can identify the application that origins this traffic by querying Metadata Access Point (MAP) server and block this application's network access. A prototype system is implemented to demonstrate the design and can be used to defense the anomaly network behaviors. The prototype system demonstrates that the hosts, switches, firewalls and IDS can work together to detect, diagnose and protect enterprise network from the malicious applications attack initiated inside or outside of an enterprise network, quarantine unhealthy hosts and make the enterprise network more reliable and trustworthy.\",\n \"Title: A novel data transmission scheme for mobile sensor network.\\nYear: 2009\\n\\nAbstract: Discard the traditional methods based routing, we develop an Activity-Based data delivery scheme for mobile sensor network. Using Brown Motion Model and Multi-Dimensional Normal Distribution method, it is proved that a sensor node has higher probability to meet the sink node if and only if it moves more actively. Then, we propose an activity-based approach, in which, node compares the activity with others to make decision on when and how to exchange data message. The proposed scheme is generally applicable to any mobile sensor network, because sensors in our approach can have different speed and different activities. Extensive simulations are conducted for performance evaluation. The results show that activity-based data delivery scheme achieves the comparatively high successful delivery ratio with small delay and acceptable transmission overhead.\",\n \"Title: How stochastic network calculus concepts help green the power grid\\nYear: 2011\\n\\nAbstract: The renewable energy generation such as solar and wind will constitute an important part of the next generation grid. As the variations of renewable sources may not match the time distribution of load, energy storage is essential for grid stability. Supplemented with energy storage, we investigate the feasibility of integrating solar photovoltaic (PV) panels and wind turbines into the grid. To deal with the fluctuation in both the power generation and demand, we borrow the ideas from stochastic network calculus and build a stochastic model for the power supply reliability with different renewable energy configurations. To illustrate the validity of the model, we conduct a case study for the integration of renewable energy sources into the power system of an island off the coast of Southern California. Performance of the hybrid system under study is assessed by employing the stochastic model, e.g., with a set of system configurations, the long-term expected Fraction of Time that energy Not-Served (FTNS) of a given period can be obtained.\",\n \"Title: A secure multipath routing protocol in mobile ad hoc networks\\nYear: 2010\\n\\nAbstract: Multipath routing can adapt to traffic changes, increase reliability and enhance the Quality of Service support in communication networks. This routing algorithm has been recognized as one of the salient features in Mobile Ad hoc Networks (MANETs). However, existing multipath routing protocols are often practically infeasible due to its security venerability if they suffer from attacks in MANETs, such as repudiation attacks, Denial of Service attacks. In this paper we propose a novel secure multipath routing protocol, referred to as SMRP, and investigate its feasibility and performance. SMRP applies a new heuristic algorithm increasing the number of disjoint paths and a smart authentication mechanism to enhance the security against the attacks in MANETs. The performance results from extensive analysis and experiments show that SMRP can efficiently enhance the security in MANETs while preserving the low overhead of computing and communication. Copyright © 2009 John Wiley & Sons, Ltd.\",\n \"Title: Dependability Modeling and Analysis for the Virtual Data Center of Cloud Computing\\nYear: 2011\\n\\nAbstract: Virtual data center is becoming increasingly popular as the infrastructure of Cloud computing. In order to provide efficient and uninterrupted service, the dependability of the Cloud infrastructure has received extensive attention. But modeling and analysis of dependability of the virtual data center is challenging for the particular characteristics and mechanisms of virtualization. In this paper, we use hierarchical method and develop the hybrid models combining reliability block diagrams and general stochastic Petri nets. Focusing on the two attributes reliability and availability, we give the computation expressions by solving the models. The impact of the characteristics and mechanisms of virtualization such as consolidation backup and live migration on dependability is studied. We analyze the relation between reliability and consolidation ratio, as well as the relation between availability and the workload. In addition, some useful rules are summarized and discussed, which are believed to be helpful to the design and construction of more dependable virtual data center.\",\n \"Title: Graph regularized non-negative matrix factorization with sparse coding\\nYear: 2013\\n\\nAbstract: Matrix factorization techniques have been frequently utilized in pattern recognition and machine learning. Among them, Non-negative Matrix Factorization (NMF) has received considerable attention because it represents the naturally occurring data by parts of it. On the other hand, from the geometric perspective, the data is usually sampled from a low dimensional manifold embedded in a high dimensional ambient space. One hopes then to find a compact representation which uncovers the intrinsic geometric structure. In this paper, we propose a novel method, called Graph Regularized Non-negative Matrix Factorization with Sparse Coding (GRNMF_SC), the new model can learn much sparser representation and more discriminating power via imposing sparse constraint and Laplacian regularization explicitly. Experimental results on the ORL and Yale databases demonstrate encouraging performance of the proposed algorithm when compared with the state-of-the-art algorithms.\",\n \"Title: Iterative learning control for nonlinear distributed parameter system with state time delay\\nYear: 2013\\n\\nAbstract: The iterative learning control method is applied to distributed parameter systems governed by parabolic partial differential equations (PDE). In this paper, an iterative learning control topic for a kind of nonlinear distributed parameter system which initial value can vary in a little space has been discussed. And an high level algorithm is extended and proven, using Green formula and Gronwall inequality, that it is suitable for the given system. Numerical simulation is presented for a parabolic PDE solved using ILC based on Eular difference format. The numerical example is given to illustrate the effectiveness of the proposed method.\",\n \"Title: Integrative scheme of Differentiated Services: Modeling and performance analysis\\nYear: 2000\\n\\nAbstract: Proposes an integrative scheme of differentiated services (DiffServ) for the Internet. In our scheme, the quality of service (QoS) is ranked according to the levels of both importance and urgency, which makes the service negotiation and the implementation mechanism more flexible and universal. Samples of the implementation mechanism at the edge nodes and interior nodes are described, and stochastic Petri net models of them are given. The performance of the per-hop behavior (PHB) implementation mechanism is analyzed, under both Poisson and self-similar arrivals. An approximate analysis technique of decomposition and iteration is used to reduce the complexity. For self-similar traffic, an MMPP (Markov-modulated Poisson process) based self-similar model of A.T. Andersen et al. (1998) is used\",\n \"Title: A Petri Net Approach To Stability Analysis Of Buffer Priority Scheduling Policies In Manufacturing Systems\\nYear: 2001\\n\\nAbstract: The concept of buffer boundedness as an alternative criterion for characterizing the stability of scheduling policies was emphasized. The stability of re-entrant line systems was analyzed using the concept of buffer boundedness. The dynamic behavior of re-entrant line systems was represented by a Petri net based framework. The Petri net method was observed to be intuitive, simple and efficient.\",\n \"Title: An accurate scene-based traffic model for MPEG video stream\\nYear: 2003\\n\\nAbstract: In this paper, we propose an accurate scene-based traffic model for MPEG video stream. The autocorrelation structure of video stream is modeled by two Auto-Regress (AR) processes. We also take the correlation in GOP (Group of Picture) into account. According to the size of I frame, the size of B frame and P frame in the same GOP are determined by a lognormal distribution. As for the marginal distribution of the MPEG video stream, we use normal mixture distribution to fit it, and the parameters of normal mixture distribution are estimated through an Expecting Maximum (EM) algorithm. At last, the accuracy of the proposed model is validated with simulation experiments and the application of this model in network QoS (Quality of Service) evaluation.\",\n \"Title: A stochastic Petri nets approach to dependability analysis of control center networks in smart grid.\\nYear: 2011\\n\\nAbstract: As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs and increase reliability. In smart grid, control center networks have attracted a great number of attentions, because their security and dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of security, but few work focuses on the dependability analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the dependability of control center networks in smart grid. With the proposed SPNs model, we can measure the dependability from two metrics, i.e., the reliability and availability, through analyzing the transient probability. We also study a specific case to demonstrate the feasibility and efficiency of the proposed model in the dependability analysis of control center networks in smart grid. © 2011 IEEE.\",\n \"Title: EcoPlan: energy-efficient downlink and uplink data transmission in mobile cloud computing.\\nYear: 2015\\n\\nAbstract: Mobile cloud computing (MC2) is emerging as a new computing paradigm that seeks to augment resource-constrained mobile devices for executing computing- and/or data-intensive mobile applications. Nonetheless, the energy-poverty nature of mobile devices has become a stumbling block that greatly impedes the practical application of MC2. Fortunately, for delay-tolerant mobile applications, energy conservation is achievable via two means: (1) dynamic selection of energy-efficient links (e.g., WiFi interface); and (2) deferring data transmission in bad connectivity. In this paper, we study the problem of energy-efficient downlink and uplink data transmission between mobile devices and clouds. In the presence of unpredictable data arrival, network availability and link quality, our objective is to minimize the time average energy consumption of a mobile device while ensuring the stability of both device-end and cloud-end queues. To achieve this goal, we propose an online control framework named EcoPlan under which mobile users can make flexible link selection and data transmission scheduling decisions to achieve arbitrary energy-delay tradeoffs. Real-world trace-driven simulations demonstrate the effectiveness of EcoPlan, along with its superior energy-efficiency over alternative WiFi-prioritized, minimum-delay and SALSA schemes.\",\n \"Title: Phase Plane Analysis of Quantized Congestion Notification for Data Center Ethernet\\nYear: 2015\\n\\nAbstract: Currently, Ethernet is being enhanced to become the unified switch fabric in data centers. With the unified switch fabric, the cost on redundant devices is reduced, while the design and management of data center networks are simplified. Congestion management is one of the indispensable enhancements on Ethernet, and Quantized Congestion Notification (QCN) has just been ratified as the formal standard. Though QCN has been investigated for several years, there exist few in-depth theoretical analyses on QCN. The most possible reason is that QCN is heuristically designed and involves the property of variable structure. The classic linear analysis method is incapable of handling the segmented nonlinearity of the variable structure system. In this paper, we use the phase plane method, which is suitable for systems of segmented nonlinearity, to analyze the QCN system. The overall dynamic behaviors of the QCN system are presented, and the sufficient conditions for the stable QCN system are deduced. These sufficient conditions serve as guidelines toward proper parameters setting. Moreover, we find that the stability of QCN is mainly promised by the sliding mode motion, which is the underlying reason for QCN's stable queue shown in numerous simulations and experiments. Experiments on the NetFPGA platform verify that the analytical results can explain the complex behaviors of QCN.\",\n \"Title: Dynamic Routing for Data Integrity and Delay Differentiated Services in Wireless Sensor Networks\\nYear: 2015\\n\\nAbstract: Applications running on the same Wireless Sensor Network (WSN) platform usually have different Quality of Service (QoS) requirements. Two basic requirements are low delay and high data integrity. However, in most situations, these two requirements cannot be satisfied simultaneously. In this paper, based on the concept of potential in physics, we propose IDDR, a multi-path dynamic routing algorithm, to resolve this conflict. By constructing a virtual hybrid potential field, IDDR separates packets of applications with different QoS requirements according to the weight assigned to each packet, and routes them towards the sink through different paths to improve the data fidelity for integrity-sensitive applications as well as reduce the end-to-end delay for delay-sensitive ones. Using the Lyapunov drift technique, we prove that IDDR is stable. Simulation results demonstrate that IDDR provides data integrity and delay differentiated services.\",\n \"Title: Modeling and Solving TCP Incast Problem in Data Center Networks\\nYear: 2015\\n\\nAbstract: TCP Incast problem attracts much attention due to the catastrophic goodput drop. In this paper, a goodput model of the problem is built to understand why goodput collapse occurs and a solution to the problem based on the theoretical analysis is proposed. We found that the TCP Incast goodput deterioration is mainly caused by two types of timeouts, one happens at the tail of data blocks and dominates the goodput when the number of senders is small, while the other one at the head of data blocks and governs the goodput when the number of senders is large. The proposed model describes the relationship between these two types of timeouts and the Incast communication pattern, block size, bottleneck buffer size, and so on. The simulation results indicate that the model well characterizes the features of the TCP Incast problem. Enlightened by the analysis, a PRiority-based solution to the TCP INcast problem (PRIN) is proposed, which avoids timeouts at the head of blocks by reducing TCP send window and prevents timeouts at the tail of blocks by leveraging priority technology. The experimental results show that PRIN solves the TCP Incast problem.\",\n \"Title: Managing performance and power consumption tradeoff for multiple heterogeneous servers in cloud computing\\nYear: 2014\\n\\nAbstract: There are typically multiple heterogeneous servers providing various services in cloud computing. High power consumption of these servers increases the cost of running a data center. Thus, there is a problem of reducing the power cost with tolerable performance degradation. In this paper, we optimize the performance and power consumption tradeoff for multiple heterogeneous servers. We consider the following problems: (1) optimal job scheduling with fixed service rates; (2) joint optimal service speed scaling and job scheduling. For problem (1), we present the Karush-Kuhn-Tucker (KKT) conditions and provide a closed-form solution. For problem (2), both continuous speed scaling and discrete speed scaling are considered. In discrete speed scaling, the feasible service rates are discrete and bounded. We formulate the problem as an MINLP problem and propose a distributed algorithm by online value iteration, which has lower complexity than a centralized algorithm. Our approach provides an analytical way to manage the tradeoff between performance and power consumption. The simulation results show the gain of using speed scaling, and also prove the effectiveness and efficiency of the proposed algorithms.\",\n \"Title: Queuing Models With Applications to Mode Selection in Device-to-Device Communications Underlaying Cellular Networks\\nYear: 2014\\n\\nAbstract: In this paper, we study the performance of mode selections in device-to-device (D2D) communications in terms of end-to-end average throughput, average delay, and dropping probability, considering dynamic data arrival with non-saturated buffers. We first introduce a general framework that includes three canonical routing modes, namely D2D mode, cellular mode, and hybrid mode, which can be combined with different resource allocation restrictions to represent the semi-static and dynamic selections of the three resource sharing modes. A queuing model is developed when the routing mode for every D2D connection is chosen, and an exact numerical analysis and an approximate decomposition and iteration approach are proposed. The performance measures are obtained from the decomposition approach and validated by means of simulation. We further introduce a mode selection scheme that adaptively chooses to semi-statically or dynamically select the resource sharing modes according to the estimated performance measures.\",\n \"Title: Modeling and Analysis of Dependability Attributes for Services Computing Systems\\nYear: 2014\\n\\nAbstract: Dependability is an important consideration during the design and development of IT systems and services. But in services computing systems, the traditional definition and evaluation methods from the systems' and components' point of view meet challenges. In this paper, we veer from the angle of view and study the dependability and their attributes from the service-oriented perspective. A stochastic model using the semi-Markov process is put forward, and the quantitative analysis of the dependability attributes is carried out. By extending and transforming this model, the mean time to dependability attributes failure is calculated. Based on the analysis and calculations, some theorems are proposed and proved, to show the inter-relationships and comparisons of the different dependability attributes. Furthermore, we model the service composition and conduct workflow analysis to show how this model could deal with complex services. In addition, LANL service systems are analyzed as a case study to show how the proposed model and calculation methods could apply to real systems, and sensitivity analysis is also performed to identify the bottlenecks and find effective ways for dependability optimization.\",\n \"Title: Resource control in network assisted device-to-device communications: solutions and challenges\\nYear: 2014\\n\\nAbstract: Network assisted Device-to-Device (D2D) communications is envisioned as a promising technology to facilitate the discovery of geographically close cellular devices and reduce the communication cost between these devices. In order to fully take advantages of the freedom in resource management introduced by D2D communications, it is essential to design optimal resource control policies. In this article, we first review and classify the state-of-the-art research based on different considered network scenarios. We then introduce our preliminary studies on delay-aware resource control with bursty traffic, in which we propose an optimization framework and formulate a general queuing model for performance evaluation and optimization. Finally, the challenges and open issues are highlighted to serve as guidelines for future research.\",\n \"Title: 1.5D Egocentric Dynamic Network Visualization\\nYear: 2015\\n\\nAbstract: Dynamic network visualization has been a challenging research topic due to the visual and computational complexity introduced by the extra time dimension. Existing solutions are usually good for overview and presentation tasks, but not for the interactive analysis of a large dynamic network. We introduce in this paper a new approach which considers only the dynamic network central to a focus node, also known as the egocentric dynamic network. Our major contribution is a novel 1.5D visualization design which greatly reduces the visual complexity of the dynamic network without sacrificing the topological and temporal context central to the focus node. In our design, the egocentric dynamic network is presented in a single static view, supporting rich analysis through user interactions on both time and network. We propose a general framework for the 1.5D visualization approach, including the data processing pipeline, the visualization algorithm design, and customized interaction methods. Finally, we demonstrate the effectiveness of our approach on egocentric dynamic network analysis tasks, through case studies and a controlled user experiment comparing with three baseline dynamic network visualization methods.\",\n \"Title: Neighbourhood sensitive preserving embedding for pattern classification\\nYear: 2014\\n\\nAbstract: Recently, a large family of supervised or unsupervised manifold learning algorithms that stem from statistical or geometrical theory has been designed to solve the problem of pattern classification. In this study, consider the fact that the data are usually sampled from a low-dimensional manifold space which resides in a high-dimensional Euclidean space, the authors propose a novel two-graph-based supervised linear classification algorithm called neighbourhood sensitive preserving embedding (NSPE). Different from local linear embedding (LLE) (or neighbourhood preserving embedding (NPE)) which preserves the local neighbourhood structure with one graph, NSPE can discover both the intrinsic and discriminant structure of the data manifold by constructing two graphs, that is, the within-class graph and the between-class graph. Thus, the data are mapped into a subspace where the nearby points with the same label are close to each other, whereas the nearby points with different labels are far apart. As a classification method, besides being defined on training samples, NSPE is also defined on testing samples. Experiments carried on the real-world face databases demonstrate that the results of all two-graph-based spectral methods are comparable and better than that of one-graph-based methods.\",\n \"Title: Flow-Based Influence Graph Visual Summarization\\nYear: 2014\\n\\nAbstract: Visually mining a large influence graph is appealing yet challenging. Existing summarization methods enhance the visualization with blocked views, but have adverse effect on the latent influence structure. How can we visually summarize a large graph to maximize influence flows? In particular, how can we illustrate the impact of an individual node through the summarization? Can we maintain the appealing graph metaphor while preserving both the overall influence pattern and fine readability? To answer these questions, we first formally define the influence graph summarization problem. Second, we propose an end-to-end framework to solve the new problem. Last, we report our experiment results. Evidences demonstrate that our framework can effectively approximate the proposed influence graph summarization objective while outperforming previous methods in a typical scenario of visually mining academic citation networks.\",\n \"Title: Toward a practical reconfigurable router: a software component development approach\\nYear: 2014\\n\\nAbstract: Modern routers are no longer simple packet routing equipment. They are extremely complex systems that incorporate various network management functionalities. Due to system complexity, most commercial routers are developed by a few equipment vendors in a closed development pattern, which not only hinders the broad participation of most routing function research and development (R&D) teams, but also deter the wide deployment of novel network architectures such as Software Defined Networks (SDNs). In this article a practical approach is introduced to build an open, flexible, and modularized reconfigurable router. A reconfigurable routing software platform supporting functional modules is dynamically assembled, replaced, and updated in the form of components. So far the platform has been adopted by vendors such as Ruijie Networks in their commercial routers, and it is running well all the time. Moreover, a component development environment is provided, which consists of a code editor, a multi-platform compiler, and an automatic testing tool. A component sharing website has also been built to encourage the participation of various R&D teams and promote the spread of our open reconfigurable development pattern.\",\n \"Title: Big data: transforming the design philosophy of future internet.\\nYear: 2014\\n\\nAbstract: Big data opens the era of the fourth paradigm for science discovery through datadriven computing. This new paradigm applies to the design of the future Internet, which currently faces issues in supporting new applications, efficient resource utilization, and continuous evolvement. We observe several technological transformations in network architecture, services, and applications, and point out the grand opportunities for designing future Internet architecture, communication models, and resource management mechanisms enabled by the availability of massive network data. In particular, we envision in the future Internet: 1) computational complexity replaces state complexity in the control plane; 2) data intelligence enables user choices and rewards innovations; and 3) correlations from data analytics help solve inherently hard optimization problems. Finally, we identify the key challenges in data-driven Internet design and outline future research directions.\",\n \"Title: A closed-loop brain-computer interface triggering an active ankle-foot orthosis for inducing cortical neural plasticity.\\nYear: 2014\\n\\nAbstract: In this paper, we present a brain-computer interface (BCI) driven motorized ankle-foot orthosis (BCI-MAFO), intended for stroke rehabilitation, and we demonstrate its efficacy in inducing cortical neuroplasticity in healthy subjects with a short intervention procedure (∼15 min). This system detects imaginary dorsiflexion movements within a short latency from scalp EEG through the analysis of movement-related cortical potentials (MRCPs). A manifold-based method, called locality preserving projection, detected the motor imagery online with a true positive rate of 73.0 ± 10.3%. Each detection triggered the MAFO to elicit a passive dorsiflexion. In nine healthy subjects, the size of the motor-evoked potential (MEP) elicited by transcranial magnetic stimulation increased significantly immediately following and 30 min after the cessation of this BCI-MAFO intervention for ∼15 min ( p = 0.009 and , respectively), indicating neural plasticity. In four subjects, the size of the short latency stretch reflex component did not change following the intervention, suggesting that the site of the induced plasticity was cortical. All but one subject also performed two control conditions where they either imagined only or where the MAFO was randomly triggered. Both of these control conditions resulted in no significant changes in MEP size (p = 0.38 and p = 0.15). The proposed system provides a fast and effective approach for inducing cortical plasticity through BCI and has potential in motor function rehabilitation following stroke.\",\n \"Title: A Lightweight Encryption Scheme for Network-Coded Mobile Ad Hoc Networks\\nYear: 2014\\n\\nAbstract: Energy saving is an important issue in Mobile Ad Hoc Networks (MANETs). Recent studies show that network coding can help reduce the energy consumption in MANETs by using less transmissions. However, apart from transmission cost, there are other sources of energy consumption, e.g, data encryption/decryption. In this paper, we study how to leverage network coding to reduce the energy consumed by data encryption in MANETs. It is interesting that network coding has a nice property of intrinsic security, based on which encryption can be done quite efficiently. To this end, we propose P-Coding, a lightweight encryption scheme to provide confidentiality for network-coded MANETs in an energy-efficient way. The basic idea of P-Coding is to let the source randomly permute the symbols of each packet (which is prefixed with its coding vector), before performing network coding operations. Without knowing the permutation, eavesdroppers cannot locate coding vectors for correct decoding, and thus cannot obtain any meaningful information. We demonstrate that due to its lightweight nature, P-Coding incurs minimal energy consumption compared to other encryption schemes.\",\n \"Title: Dimensioning the packet loss burstiness over wireless channels: a novel metric, its analysis and application\\nYear: 2014\\n\\nAbstract: AbstractThe packet loss burstiness over wireless channels is commonly acknowledged as a key impacting factor on the performance of networking protocols. An accurate evaluation of the packet loss burstiness, which reveals the characteristics and performance of the wireless channels, is crucial to the design of wireless systems and the quality-of-service provisioning to end users. In this paper, a simple yet accurate analytical framework is developed to dimension the packet loss burstiness over generic wireless channels. In specific, we first propose a novel and effective metric to characterize the packet loss burstiness, which is shown to be more compact, effective, and accurate than the metrics proposed in existing literature for the same purpose. With this metric, we then develop an analytical framework and derive the closed-form solutions of the packet loss performance, including the packet loss rate and the loss-burst/loss-gap length distributions. Lastly, as an example to show how the derived results can be applied to the design of wireless systems, we apply the analytical results to devise an adaptive packetization scheme. The proposed packetization scheme adaptively adjusts the packet length of transmissions based on the prediction of the packet loss rate and loss-burst/loss-gap lengths of the wireless channel. Via extensive simulations, we show that with the proposed packetization scheme, the channel throughput can be enhanced by more than 10% than the traditional scheme.Copyright © 2012 John Wiley & Sons, Ltd.\",\n \"Title: Optimal power scheduling in 802.11n wireless networks for real-time services\\nYear: 2014\\n\\nAbstract: AbstractThe growing popularity of mobile devices in our daily life demands higher throughput of wireless networks. The new communication standard 802.11n has significantly improved throughput because of the use of advanced technologies such as the multiple-input multiple-output communication technique. Because mobile devices are usually battery-operated, power efficiency is critical; on the other hand, delay performance can be improved by transmitting at high power. To address the conflicting requirement of power saving and small delay, power scheduling is needed. In the past, many approaches to power scheduling have been proposed for real-time applications, but few of them have considered complicated modes of channel state informationCSI in multiple-input multiple-output. In this paper, we study this and classify the CSI into four types, namely, constant, slow fading, fast fading, and unknown. For known CSI, we propose an optimal algorithm for power scheduling. For unknown CSI, we propose an approximate algorithm based on some heuristics. To improve resource utilization, a stochastic delay-bound method is proposed for fast-fading condition. Simulation results demonstrate that the performance achieved by the optimal and heuristic algorithms agrees well with the analysis. Copyright © 2012 John Wiley & Sons, Ltd.\",\n \"Title: Energy-Efficient Link Selection and Transmission Scheduling in Mobile Cloud Computing.\\nYear: 2014\\n\\nAbstract: Mobile cloud computing (MCC) converges mobile computing and Cloud computing for augmenting resource-poor mobile devices to run ¿¿heavier¿¿ applications. With the increasing ubiquity of mobile devices, MCC exhibits vast application potential in various areas. Energy-efficient data transmission is a key issue in MCC due to energy-poverty of mobile devices. In this letter, we address the issue of energy-efficient link selection and data transmission scheduling for delay-tolerant and data-intensive applications in MCC. We first formulate the problem as a discrete-time stochastic dynamic program (SDP) that aims to optimize both system throughput and energy consumption. To solve the formulated SDP, we then propose a scalable approximate dynamic programming (ADP) algorithm that does not require the statistics of exogenous stochastic information (e.g., data arrival). Simulation studies show that the proposed ADP algorithm can reduce the average energy consumed for delivering a packet by a maximum of over 40 percent compared to alternative minimum-delay and SALSA policies.\",\n \"Title: Wireless channel model using stochastic high-level Petri nets for cross-layer performance analysis in orthogonal frequency-division multiplexing system\\nYear: 2014\\n\\nAbstract: In this study, the authors form a wireless channel model for orthogonal frequency-division multiplexing (OFDM) systems with stochastic high-level Petri net (SHLPN) formalism in order to simplify the cross-layer performance analysis of modern wireless systems. Compared with existing finite state Markov channel model whose state space grows exponentially with the number of OFDM subchannels, the author's proposed SHLPN model uses state aggregation technique to deal with this problem. Closed-form expressions to calculate the transition probabilities among the compound markings of the SHLPN model are provided. When applied to derive the performance measures for OFDM system in terms of the average throughput, average delay and packet dropping probability, the SHLPN model can accurately capture the correlated time-varying nature of wireless channels. Simulation is performed to show that the numerical results offered by the proposed model are more accurate compared with other simplified channel models for avoiding state space complexity.\",\n \"Title: Hierarchical Bayes based Adaptive Sparsity in Gaussian Mixture Model.\\nYear: 2014\\n\\nAbstract: •A hierarchical Bayes Gaussian Mixture Model (HBGMM) with adaptive sparsity is proposed.•We impose our hierarchical Bayes prior on the entries of precision matrices in GMM to encourage sparsity.•Our adaptive sparsity can reduce the dimensionality of the parameters in GMM to be estimated.•Our HBGMM method does not involve any hyperparameters to be estimated, i.e., it is hyperparameter-free.\",\n \"Title: Analysis and Improvement of Makespan and Utilization for MapReduce\\nYear: 2013\\n\\nAbstract: A MapReduce cluster is usually shared by multiple users or products, aiming at accelerating their own job. In contrast, the utilization of the cluster is the main concern for the system itself. MapReduce jobs are split into independent tasks during execution. However, in practice, the number of tasks per stage for each job and the system settings are often sub-optimal to support certain workload. As far as we know, few research focused on the impacts of influential factors such as task granularity, slot count and workload, and the performance analysis from the perspectives of each job and system. We construct models to describe the effects of these factors on the performance from both the per-job and system perspectives. Based on the understanding provided by analytical model, we discuss the optimal settings of job and system parameters, then propose a batch scheduling policy instead of FIFO, so as to improve the system utilization and reduce the average job processing time. The simulation results show that the average gap time is reduced by 10% and the mean job make span is improved by 15% when each job is preempted 6 times on average using the batch scheduling policy.\",\n \"Title: Energy Efficient Dynamic Service Selection for Large-Scale Web Service Systems\\nYear: 2014\\n\\nAbstract: With the increasing popularity of web services on the Internet, service selection has become an important issue in large-scale web service systems. In recent years, with the growing energy consumption associated with IT systems and services, energy efficiency has drawn extensive attention in service allocation and selection. However, existing approaches for energy efficient web service selection face great challenges, because of the high dynamics and unpredictability of task arrivals, and large scale of current web service systems. In this paper, we present a distributed online approach for web service selection that jointly considers the conflicting performance metrics including response time, queue congestion and energy consumption. Targeting at optimizing the long-term average system reward, our approach does not require any priori knowledge of the statistics or prediction on task arrivals. Mathematical analysis as well as simulation experiments demonstrate its effectiveness in optimizing energy efficiency while stabilizing the system.\",\n \"Title: Ranking Web Services with Limited and Noisy Information\\nYear: 2014\\n\\nAbstract: With the increasing popularity of web services on the Internet, besides functionalities, Quality of Service (QoS) is becoming an important concern for describing characteristics of web services. QoS rankings provide valuable information for making optimal service selection and recommendation from a set of functionally similar or equivalent service candidates. However, in order to obtain such rankings, a huge number of invocations on the services are usually required, which is extremely expensive and even impractical in reality. To tackle this challenge, this paper proposes a scheme to derive the global ranking from observations of QoS rankings on subsets of the services, while the observations may also be contaminated by noise and errors. We introduce a pairwise comparison model to describe the relationships between services, and thus the ranking can be formulated as random walks over the services. A Markov chain based approach is proposed, and algorithms for deriving global rankings are designed. The efficacy of our approach is validated by both mathematical analysis and simulation experiments.\",\n \"Title: Partial Selection: An Efficient Approach for QoS-Aware Web Service Composition\\nYear: 2014\\n\\nAbstract: With the increasing presence of web services on the Internet, Quality of Service (QoS) is becoming important for describing non-functional characteristics of web services, and is often employed in web service composition. As QoS is an aggregated concept consisting of several attributes, service composition on enormous candidate sets is a challenging multi-objective optimization problem. In this paper, we study the problem from a general Pareto-optimal angle, seeking to reduce the search space in service composition. QoS attributes are systematically studied according to their different types of aggregation pattern in service composition, and QoS-based dominance relationships between candidates and between workflows are defined. Taking advantage of pruning candidates by dominance relationships and constraint validations at candidate level, a service composition algorithm using partial selection technique is proposed, which is able to significantly reduce the search space and achieve great performance gains. A careful analysis of the optimality of our approach is provided, and its efficacy is further validated by empirical evaluation.\",\n \"Title: Error estimating codes for insertion and deletion channels\\nYear: 2014\\n\\nAbstract: Error estimating codes (EEC) have recently been proposed for measuring the bit error rate (BER) in packets transmitted over wireless links. They however can provide such measurements only when there are no insertion and deletion errors, which could occur in various wireless network environments. In this work, we propose ``idEEC'', the first technique that can do so even in the presence of insertion and deletion errors. We show that idEEC is provable robust under most bit insertion and deletion scenarios, provided insertion/deletion errors occur with much lower probability than bit flipping errors. Our idEEC design can build upon any existing EEC scheme. The basic idea of the idEEC encoding is to divide the packet into a number of segments, each of which is encoded using the underlying EEC scheme. The basic idea of the idEEC decoding is to divide the packet into a few slices in a randomized manner -- each of which may contain several segments -- and then try to identify a slice that has no insertion and deletion errors in it (called a ``clean slice''). Once such a clean slice is found, it is removed from the packet for later processing, and this ``randomized divide and search'' procedure will be iteratively performed on the rest of the packet until no more clean slices can be found. The BER will then be estimated from all the clean slices discovered through all the iterations. A careful analysis of the accuracy guarantees of the idEEC decoding is provided, and the efficacy of idEEC is further validated by simulation experiments.\",\n \"Title: Delay guaranteed live migration of Virtual Machines\\nYear: 2014\\n\\nAbstract: The proliferation of cloud services makes virtualization technology more important. One important feature of virtualization is live Virtual Machine (VM) migration, which can be employed to facilitate load balancing, fault management and server maintenance etc. Two main metrics of evaluating a live VM migration mechanism are total migration time and downtime. The existing literature on live VM migration mainly focus on designing migration mechanisms to shorten these two metrics or making a tradeoff between them. Few of them can be applied to the applications with delay requirements, such as, delay-sensitive web services or a VM backup process that needs to be done in a specific time. This will not only negatively impact the user experiences, but also reduce the profit of cloud service providers. Besides, the frequently varied bandwidth required by the widely used pre-copy mechanism is difficult to be provided by current network technologies. In this work, we theoretically analyze how much bandwidth is required to guarantee the total migration time and downtime of a live VM migration. We first propose a deterministic-based model as a simple example, then assume that the dirtying frequency of each page obeys the bernoulli distribution. At last, we analyze the statistic features of the typical workload running in a VM and build a reciprocal-based workload model, and theoretically give the required bandwidth value to satisfy the performance metrics of a live VM migration. The experimental results demonstrate that the bandwidth obtained from the reciprocal-based model can guarantee the expected total migration time and downtime.\",\n \"Title: Grid Service Interaction Pattern and Composition Verification\\nYear: 2008\\n\\nAbstract: It is an important direction of the development of Parallel Computer Architecture to implement Single System Image (SSI) on clusters, since SSI supports easier programming and administration on clusters. Currently, most SSI studies focused on the middleware ...\",\n \"Title: HEIR: Heterogeneous interference recognition for wireless sensor networks\\nYear: 2014\\n\\nAbstract: With the rapid development of wireless communication technology, a large number of wireless networks and devices that have different PHY and MAC layers coexist with each other. 2.4GHz Industrial, Scientific and Medical (ISM) band is becoming increasingly crowded. Wireless Sensor Networks (WSNs) which use low-power communication standard IEEE802.15.4 share the unlicensed spectrum with a plethora of other devices and technologies, such as WiFi systems underlying IEEE802.11, Bluetooth systems underlying IEEE802.15.1, and even non-communication appliance like microwave ovens. The ability to detect what radios are operating in the neighborhood is a fundamental need of WSNs, ranging from network management to network security. Since there are no explicit mechanisms to recognize such heterogeneous interference sources, WSNs often have no reasonable way to guard against them. In this paper, we describe the main working principle of heterogeneous interference sources, and present HEIR, a detector that is able to accurately identify heterogeneous interference sources. HEIR builds on the insight that there are hidden repeating patterns of signals which can be used to construct unique signatures and identify different types in most wireless protocols. The method can be implemented through signal sampling, interference estimation, feature extraction, and device classification. We show the experimental evaluation in an indoor testbed that HEIR is accurate in several different scenarios, and can live on sensor nodes' hardware. Since no any channel changes, the network topology is not interrupted, and the stable communication in real-time is ensured then.\",\n \"Title: Design and analysis of fault tolerance mechanism for sparrow\\nYear: 2014\\n\\nAbstract: Big data processing frameworks are developing towards larger degrees of parallelism and shorter task durations in order to achieve lower response time. Scheduling highly parallel tasks that complete in nearly 100 milliseconds poses a major challenge for task schedulers. Taking the challenge, researchers turn to decentralized frameworks to relieve the pressure of task schedulers, among which Sparrow is a good choice. However, little efforts are devoted to fault tolerance of Sparrow, which does not handle worker failures, giving rise to incomplete tasks. We present a fault tolerance mechanism named Heartbeat on Sparrow to handle failures of worker machines. Through simulation, we compare it with a simple mechanism. The result shows that Heartbeat on Sparrow can detect worker failures faster and reschedule all failed tasks more efficiently, achieving recovery of tasks and states in sub-second time. We hope this mechanism will make some contributions to Sparrow and other decentralized designs on fault tolerance side.\",\n \"Title: Analysing convergence of Quantized Congestion Notification in Data Center Ethernet\\nYear: 2014\\n\\nAbstract: Enhancing Ethernet as the unified data center fabric to concurrently handle the traffic of Local Area Network (LAN), Storage Area Network (SAN), and High Performance Computing (HPC) has attracted much attention. Congestion management is one critical enhancement to fill the performance gap between traditional Ethernet and the unified data center fabric. Currently, Quantized Congestion Notification (QCN) has been approved as the standard congestion management mechanism. However, lots of work pointed out that QCN suffers from the problem of unfairness among different flows. In this paper, we found that QCN could achieve fairness, merely the convergence time to fairness is quite long. Thus, we build a convergence time model to investigate the reasons of the slow convergence process of QCN. The model indicates that the convergence time of QCN can be decreased if RPs have the same rate increase probability or the rate increase step becomes larger at steady state. We validate the precise of our model by comparing with experimental data on the NetFPGA platform. The results show that it well characterizes the convergence time to fairness of QCN. Based on the proposed model, the impact of QCN parameters, network parameters, and QCN variants on the convergence time is analysed. Finally, enlightened by the analysis, we proposed a mechanism, called QCN-T, which replaces the Byte Counter and Timer at sources with a single modified Timer, to reduce the convergence time of QCN.\",\n \"Title: Scalability of control planes for Software defined networks: Modeling and evaluation\\nYear: 2014\\n\\nAbstract: With the increasing popularity of Software defined network (SDN), designing a scalable SDN control plane becomes a critical problem. An effective approach to improving the scalability is to design distributed architecture of SDN control plane. However, how to evaluate the scalability of SDN control planes remains unexplored. In this paper, we propose a metric of scalability for SDN control planes, and study three typical SDN control plane structures, including centralized, decentralized and hierarchical architectures. We build performance models for response time, based on which we evaluate the scalability of these three structures. Furthermore, the comparison between different architectures are analyzed by mathematical methods. Numerical evaluations are also conducted to validate the conclusions drawn in this paper.\",\n \"Title: An online mechanism for dynamic instance allocation in reserved instance marketplace\\nYear: 2014\\n\\nAbstract: As one of the pricing model offered by Amazon, reserved instance enables users to reserve capacities for their EC2 instances and lowers their average instance cost. To attract more users to adopt the reserved instance, Amazon has provided a platform named Reserved Instance Marketplace to give users the flexibility to sell the remainder of their reserved instances as their needs change. However, the present trading mechanism inside the Reserved Instance Marketplace requires the reserved instance sellers to sell their reserved capacities at month level and set the upfront fee by themselves, which is not flexible enough and hard for an inexperienced seller to specify a suitable upfront fee. To address this problem, this paper proposes an online mechanism for the Reserved Instance Marketplace. Our online mechanism tries to maximize the sellers' revenue by dynamically allocating the reserved capacities among various buyers without the need of specifying upfront fee in advance. The competitive ratio of the online allocation algorithm inside our mechanism is proved to be within a small constant factor of optimal competitive ratio in theory. To evaluate the performance of our online mechanism, we conduct simulations on synthetic data and real data trace from one of Google clusters. The simulation results show that our online mechanism can achieve at least 55% of the offline optimal algorithm in most cases.\",\n \"Title: Modeling and Analysis of Availability for SaaS Multi-tenant Architecture\\nYear: 2014\\n\\nAbstract: Software as a Service(SaaS) is increasingly becoming an important application development and delivery model. Among different implementation architectures, multi-tenant architecture(MTA) has received most attentions. Not only because it is easier to be maintained, but also increases the resource utilization. However, sharing the same instance will have impact on the availability of each tenant. Availability is a very critical attribute of tenants' SLA, but modeling and analysis of availability for MTA SaaS is challenging. In this paper, we firstly discuss the differences of availability between one-tenant and multi-tenant from tenant's view. Then we analyze the repair and failure rate based on the MTA feature and give the function expression with parameters of the number of tenant's users. And a Markov model is built for availability analysis. We solve the model and get the computation expression of the availability in a case study. Then a numerical result is obtained under the specific conditions and finally we have a simply discussion on it.\",\n \"Title: Toward Optimal Admission Control and Resource Allocation for LTE-A Femtocell Uplink\\nYear: 2015\\n\\nAbstract: The Third Generation Partnership Project (3GPP) has incorporated femtocell (FC) technology in the Long Term Evolution Advanced (LTE-A) standard to enhance the quality of service of indoor mobile users and extend the coverage area of existing macrocells (MCs). In such two-tier LTE-A MC/FC systems, co-tier and cross-tier interference exists in co-channel deployment, exerting adverse effects on system performance. In this paper, we study Single-Carrier Frequency Division Multiple Access (SC-FDMA) based LTE-A FC uplink. We propose the use of transport-layer data Admission Control (AC) in each Femto User Equipment (FUE) as well as interference-aware Resource Allocation (RA) in each base station to manage the inter-cell interference. We first formulate the problem as a constrained Markov decision problem which aims at maximizing the time average throughput of the entire FC tier subject to queue stability constraint for each FUE. Then we propose a Joint Admission Control and Resource Allocation (JACRA) algorithm to obtain the optimal AC and RA policies. In light of the NP-hardness of the resource allocation subproblem, we further propose an iterative heuristic with polynomial time complexity. Simulation studies show that the proposed JACRA algorithm is throughput optimal, outperforming alternative proportional fair and round robin scheduling schemes. Moreover, the proposed heuristic achieves near-optimal throughput with substantial improvement in computational complexity.\",\n \"Title: Dynamic queue length thresholds for scheduling real-time traffic in ATM networks\\nYear: 1999\\n\\nAbstract: New schemes of scheduling real-time traffic in high speed ATM networks are proposed for multiple priority queues based on dynamic queue length thresholds in real-time scheduling. The proposed schemes will provide guaranteed real-time service for each connection and cell sequence integrity for virtual channel/path characteristics. We provide the stochastic Petri net model of these schemes and an approximate analysis technique, which significantly reduce the complexity of the model solution and can be applied to real ATM switch models. Using numerical results we show that our schemes outperform well known schemes such as the head-of-line priority control and the queue length threshold policy\",\n \"Title: A modeling reliability analysis technique for cloud storage system\\nYear: 2013\\n\\nAbstract: Cloud storage system can reshape the way how kinds of contents are generated, stored and distributed on today's Internet. It is intriguing to study the service provision of cloud storage system for the sake of providing global users with satisfactory service. This paper conducts modeling analysis of a cloud storage system, and proposes a Markov decision process modeling framework to analyze the reliability of the storage system. Based on the modeling analysis, the optimal strategy can be got after executing two solution algorithms. By incorporating these techniques into cloud storage system design, the authors can significantly improve the replication performance and reliability of the system in this paper. Our numerical results further demonstrate the effectiveness and superiority of our technique against traditional static approaches.\",\n \"Title: BEC: A reliable and efficient mechanism for cloud storage service\\nYear: 2013\\n\\nAbstract: In a cloud storage system, the hardware equipments storing the contents are located in many places and the capabilities of them are various. These realities draw a big challenge to providing constant and efficient service to the storage system's users. In this paper, intrigued from the features of erasure coding, we design a content storage and delivery mechanism which can tolerate cloud servers' failures, so that ensure the reliability of the cloud storage system while distributing contents efficiently. In this mechanism, the users can still retrieve an adequate number of coded packets to recover the original packets after a fixed number of servers collapse. Afterwards, through experiments we compare our mechanism to the erasure coding method and the replication redundancy strategy, and validate that our technology takes advantages in fault tolerance and repair efficiency.\",\n \"Title: A PetriNet-Based Approach for Supporting Traceability in Cyber-Physical Manufacturing Systems.\\nYear: 2016\\n\\nAbstract: With the growing popularity of complex dynamic activities in manufacturing processes, traceability of the entire life of every product has drawn significant attention especially for food, clinical materials, and similar items. This paper studies the traceability issue in cyber-physical manufacturing systems from a theoretical viewpoint. Petri net models are generalized for formulating dynamic manufacturing processes, based on which a detailed approach for enabling traceability analysis is presented. Models as well as algorithms are carefully designed, which can trace back the lifecycle of a possibly contaminated item. A practical prototype system for supporting traceability is designed, and a real-life case study of a quality control system for bee products is presented to validate the effectiveness of the approach.\",\n \"Title: Joint study on VMs deployment, assignment and migration in geographically distributed data centers\\nYear: 2016\\n\\nAbstract: Abstract Enterprises build private clouds to provide IT resources for geographically distributed subsidiaries or product divisions. Public cloud providers like Amazon lease their platforms to enterprise users, thus, enterprises can also rent a number of virtual machines (VMs) from their data centers in the service provider networks. Unfortunately, the network cannot always guarantee stable connectivity for their clients to access the VMs or low-latency transfer among data centers. Usually, both latency and bandwidth are in unstable network environment. Being affected by background traffics, the network status can be volatile. To reduce the latency uncertainty of client accesses, enterprises should consider the network status when they deploy data centers or rent virtual data centers from cloud providers. In this paper, we first develop a data center deployment and assignment scheme for an enterprise to meet its users’ requirements under uncertain network status. To accommodate to the changes of the network status and users’ demands, a VMs migration-based redeployment scheme is adopted. These two schemes work in a joint way, and lay out a framework to help enterprises make better use of private or public clouds.\",\n \"Title: Hierarchical caches in content-centric networks: modeling and analysis\\nYear: 2015\\n\\nAbstract: Content-centric network (CCN) is a new Internet architecture in which content is treated as the primitive of communication. In CCN, routers are equipped with content stores at the content level, which act as caches for frequently requested content. Based on this design, the Internet is available to provide content distribution services without any application-layer support. In addition, as caches are integrated into routers, the overall performance of CCN will be deeply affected by the caching efficiency.In this paper, our aim is to gain some insights on how caches should be designed to maintain a high performance in a cost-efficient way. We try to model the two-layer cache hierarchy composed of CCN routers using a two-dimensional discrete-time Markov chain, and develop an efficient algorithm to calculate the hit ratios of these caches. Simulations validate the accuracy of our modeling method, and convey some meaningful information which can help us better understand the caching mechanism of CCN.\",\n \"Title: Privacy Petri Net and Privacy Leak Software.\\nYear: 2015\\n\\nAbstract: Private information leak behavior has been widely discovered in malware and suspicious applications. We refer to such software as privacy leak software (PLS). Nowadays, PLS has become a serious and challenging problem to cyber security. Previous methodologies are of two categories: one focuses on the outbound network traffic of the applications; the other dives into the inside information flow of the applications. We present an abstract model called Privacy Petri Net (PPN) which is more applicable to various applications and more intuitive and vivid to users. We apply our approach to both malware and suspicious applications in real world. The experimental result shows that our approach can effectively find categories, content, procedure, destination and severity of the private information leaks for the target software.\",\n \"Title: VEGAS: Visual influEnce GrAph Summarization on Citation Networks\\nYear: 2015\\n\\nAbstract: Visually analyzing citation networks poses challenges to many fields of the data mining research. How can we summarize a large citation graph according to the user’s interest? In particular, how can we illustrate the impact of a highly influential paper through the summarization? Can we maintain the sensory node-link graph structure while revealing the flow-based influence patterns and preserving a fine readability? The state-of-the-art influence maximization algorithms can detect the most influential node in a citation network, but fail to summarize a graph structure to account for its influence. On the other hand, existing graph summarization methods fold large graphs into clustered views, but can not reveal the hidden influence patterns underneath the citation network. In this paper, we first formally define the Influence Graph Summarization problem on citation networks. Second, we propose a matrix decomposition based algorithm pipeline to solve the IGS problem. Our method can not only highlight the flow-based influence patterns, but also easily extend to support the rich attribute information. A prototype system called VEGAS implementing this pipeline is also developed. Third, we present a theoretical analysis on our main algorithm, which is equivalent to the kernel k-mean clustering. It can be proved that the matrix decomposition based algorithm can approximate the objective of the proposed IGS problem. Last, we conduct comprehensive experiments with real-world citation networks to compare the proposed algorithm with classical graph summarization methods. Evaluation results demonstrate that our method significantly outperforms the previous ones in optimizing both the quantitative IGS objective and the quality of the visual summarizations.\",\n \"Title: Relocation routing for energy balancing in mobile sensor networks\\nYear: 2015\\n\\nAbstract: Wireless sensor networksWSNs have been widely investigated in the past decades because of its applicability in various extreme environments. As sensors use battery, most works on WSNs focus on energy efficiency issuese.g., local energy balancing problems in statically deployed WSNs. Few works have paid attention to the global energy balancing problem for the scenario that mobile sensor nodes can move freely. In this paper, we propose a new routing protocol called global energy balancing routing protocol GEBRP based on an active network framework and node relocation in mobile sensor networks. This protocol achieves global energy efficiency by repairing coverage holes and replacing invalid nodes dynamically. Simulation and experiment results demonstrate that the proposed GEBRP achieves superior performance over the existing scheme. In addition, we analyze the delay performance of GEBRP and study how the delay performance is affected by various system parameters.Copyright © 2013 John Wiley & Sons, Ltd.\",\n \"Title: Performance modelling for transparent computing using stochastic Petri nets.\\nYear: 2016\\n\\nAbstract: Massive applications, large-scale and disparity of demands tend to be the main characteristics of information technology. Therefore, to define an efficient computing paradigm with high quality of service has become an urgent issue both in academia and industry. An effective approach named transparent computing paradigm is proposed to meet the demands, which leaves computation and storage in different places. This paper studies the performance evaluation of such computing paradigm. We propose the stochastic Petri net models of transparent computing, based on which mathematical analysis is provided. Furthermore, we compare transparent computing paradigm with some traditional computing paradigms, both quantitatively and qualitatively.\",\n \"Title: SLA-Aware Tenant Placement and Dynamic Resource Provision in SaaS\\nYear: 2015\\n\\nAbstract: Software as a Service (SaaS) is an increasingly important service delivery model in cloud computing, and multitenancy makes it possible to support large scale customized tenants with only one code base. However, the complexity of multi-tenant architecture may lead to poor performance and low resource utilization. The customized demands may also lead to high operating cost. It is very important to develop an accurate model to predict the performance of the multi-tenant SaaS. To this end, a multi-tenant queueing network model is developed. Based on the model, a balanced SLA-aware tenant placement algorithm is proposed considering that customized tenants may need more resources to be placed together. The algorithm is effective in nearly 90% of the simulations comparing with other heuristic algorithms. Furthermore, the optimization problem on dynamic resource provision to minimize the operating cost is studied. As the original optimization problem is NPhard, a continuous upper bound is used to convert the original optimization problem into a convex optimization which can be solved efficiently in polynomial time. Finally, it is demonstrated that the approximate ratio of the proposed approach is no greater than 1.2 in more than 90% of the simulations.\",\n \"Title: Greening Geo-distributed Data Centers by Joint Optimization of Request Routing and Virtual Machine Scheduling\\nYear: 2014\\n\\nAbstract: In this paper, we present a unifying optimization framework for greening geographically distributed Data Centers (DCs) by means of three control operations: (1) inter-DC request routing at front-end proxies, (2) intra-DC request routing at tiered core switches and edge switches, and (3) Virtual Machine (VM) scheduling on heterogeneous physical servers. We first formulate the request routing and VM scheduling problem as a stochastic program which aims to minimize the time average electricity cost, carbon taxes and bandwidth cost for Cloud Service Providers (CSPs) while guaranteeing the long-term request queue stability for cloud users. Then we propose an online and decentralized algorithm named GREEN to address the randomness of external workload, power price, and carbon emission rate. The proposed algorithm provably achieves arbitrary cost-delay tradeoffs, thereby enabling CSPs to make flexible scheduling choices toward the desired objective. Trace-driven simulations confirm the efficacy and adaptivity of GREEN in non-stationary environments.\",\n \"Title: Matrix Factorization with Column L0-Norm Constraint for Robust Multi-subspace Analysis.\\nYear: 2015\\n\\nAbstract: We aim to study the subspace structure of dataapproximately generated from multiple categories and removeerrors (e.g., noise, corruptions, and outliers) in the data aswell. Most previous methods for subspace analysis learn onlyone subspace, failing to discover the intrinsic complex structure, while state-of-the-art methods use data itself as the basis (self-expressiveness property), showing degraded performance whendata contain errors. To tackle the problem, we propose anovel method, called Matrix Factorization with Column L0-normconstraint (MFC0), from the matrix factorization perspective. MFC0 simultaneously discovers the multi-subspace structure ofeither clean or contaminated data, and learns the basis for eachsubspace. Speciflcally, the learnt basis with the orthonormal constraint shows high robustness to errors by adding a regularizationterm. Owing to the column l0-norm constraint, the generatedrepresentation matrix can be (approximate) block-diagonal afterreordering its columns, with each block characterizing onesubspace. We develop an efflcient flrst-order optimization schemeto stably solve the nonconvex and nonsmooth objective function ofMFC0. Experimental results on synthetic data and real-world facedatasets demonstrate the superiority over traditional and state-of-the-art methods on both representation learning, subspacerecovery and clustering.\",\n \"Title: Comprehensive Understanding Of Tcp Incast Problem\\nYear: 2015\\n\\nAbstract: Since TCP Incast has been identified as a catastrophic problem in many typical data center applications, a lot of efforts have been made to analyze or solve it. The analysis work intends to model Incast problem from certain perspective, and the solutions try to solve the problem through designing enhanced mechanisms or algorithms. However, the proposed models are either closely coupled with particular protocol version or dependent on empirical observations, and the solutions cannot eliminate Incast problem entirely because the underlying issues are not identified completely. There is little work which attempts to close the gap between \\\"analyzing\\\" and \\\"solving\\\", and present a comprehensive understanding.In this paper, we provide an in-depth understanding of how TCP Incast problem happens. We build up an interpretive model which emphasizes particularly on describing qualitatively how various factors, including system parameters and mechanism variables, affect network performances in Incast traffic pattern, but not on calculating the accurate throughput. With this model, we give plausible explanations why the various solutions for TCP Incast problem can help, but do not solve it entirely.\",\n \"Title: Discriminant Manifold Learning via Sparse Coding for Image Analysis.\\nYear: 2016\\n\\nAbstract: Traditional subspace learning methods directly calculate the statistical properties of the original input images, while ignoring different contributions of different image components. In fact, the noise (e.g., illumination, shadow) in the image often has a negative influence on learning the desired subspace and should have little contribution to image recognition. To tackle this problem, we propose a novel subspace learning method named Discriminant Manifold Learning via Sparse Coding (DML_SC). In our method, we first decompose the input image into several components via dictionary learning, and then regroup the components into a More Important Part (MIP) and a Less Important Part (LIP). The MIP can be regarded as the clean part of the original image residing on a nonlinear submanifold, while LIP as noise in the image. Finally, the MIP and LIP are incorporated into manifold learning to learn a desired discriminative subspace. The proposed method is able to deal with data with and without labels, yielding supervised and unsupervised DML SCs. Experimental results show that DML_SC achieves best performance on image recognition and clustering tasks compared with well-known subspace learning and sparse representation methods.\",\n \"Title: Modeling and Analysis of Availability in Multi-Tenant SaaS\\nYear: 2015\\n\\nAbstract: Software as a Service (SaaS) has become an important application development and service delivery model. Among different architectures, multi-tenant architecture (MTA) not only has advantage on maintenance, but also increases resource utilization by sharing instances. However, sharing instances brings challenges to the security of the service. As one of the three principal properties of the security, availability receives more and more attentions. Recently, there are extensive efforts on technical methods to implement a secure multi-tenant SaaS, but few works on the modeling and analysis of its availability. In this paper, we firstly present the availability issues of the multi-tenant SaaS. Two important mechanisms to implement the MTA SaaS are then introduced: network isolation and database sharing. After that, a stochastic Petri net (SPN) model is developed to analyze the availability. Specific metrics are proposed to measure the availability both from the aspects of the system and the tenant. To extend the SPN model for large scale analysis, we solve the state space explosion problem of SPN model based on the theory of Markov chain aggregation. Finally, numerical results are provided to demonstrate the effectiveness of the SPN model and the analysis is efficient.\",\n \"Title: An Effective Policy Relocation Scheme for VM Migration in Software-Defined Networks\\nYear: 2015\\n\\nAbstract: To achieve the flexibility in resource optimization, fault tolerance and load balancing, virtual machines(VMs) should be effectively migrated in data center networks(DCN). VMs are regulated by security policies and also required to relocate the policy during VM migration, which is a challenge problem to maintain not only the isolation among different domains and tenants, but also the consistency of policy configuration. In this paper, we consider the problem of policy relocation in Software-defined Network(SDN) context since it provides flexible control. We introduce a formal model describing the problem and present a novel policy relocation scheme called VPRS(a Virtual Policy Relocation Scheme), which is based on policy-aware principle and space-optimized method. It can prevent fragmentation of policy and reduce the memory usage while accurately relocating influenced policies. On the other hand, it also simplifies the management of policy. Specifically, VPRS contains novel algorithms for policy relocation, an app-layer policy management, policy translator and component for controllers notification. We define three actions of policy relocation based on the policy abstraction, and then describe the advantage of our scheme about selecting an optimized network address as target address. Then we introduce the core space-optimized algorithms, and evaluate the effectiveness of our scheme by providing consumption on their running overhead and utilization of rule space. Our scheme performs well on real policy sets, and has dramatic reduction by even up to 90\\\\% on rule space costs with high utilization.\",\n \"Title: A Method for Detection of Anomaly Node in IOT.\\nYear: 2015\\n\\nAbstract: Perceived data would be affected by the node status in the Internet of things. If the status of node was abnormal, the perception data would not reliable. In order to guarantee the reliability of the perception data in the Internet of things, a method for detecting the anomaly of nodes is proposed. Detection unit was constructed firstly. Each detection unit included working node, associated node and decision node. The detection unit can realize self-detection, which can detect the abnormal nodes in time. The task of the working node was to perceive the data and submit it to the data center. It would always be in working status until it was abnormal. When the work node has an exception, the alarm information would be issued. Then, the work node will be replaced by the associated node. Data perception of monitoring area would be responsible for the associated nodes. In each detection unit, three levels of detection can be completed. The first level is the work node self-check. The second stage is the auxiliary detection of associated nodes. The third level is the final decision of the node. Using the simulation tool Matlab7.0, the results show that our method is effective.\",\n \"Title: Prototype Implementation and Performance Evaluation of a QoS-based Web Server\\nYear: 2005\\n\\nAbstract: With the evolvement of the E-commerce, the Internet is undergoing substantial changes from a communication and browsing infrastructure to a medium for conducting business and selling services. These changes place the Web server at the center of the E-commerce infrastructure with increasing requirements for providing service differentiation and performance assurance. To that end, Web servers must have mechanisms and policies for establishing and supporting Quality of Service (QoS).In this paper, a Web server prototype with priority-based QoS control mechanisms, called QoS Apache, is implemented. A performance testing platform is established, and the performance measurement software httperf is used to test the popular Apache and the QoS Apache. The performance evaluation results show that the QoS Apache server can provide different types of requests with differentiated Web QoS, in comparison with the popular QoS-unaware Apache.\",\n \"Title: Game-theoretic Analysis of Computation Offloading for Cloudlet-based Mobile Cloud Computing\\nYear: 2015\\n\\nAbstract: Mobile cloud computing (MC2) is emerging as a promising computing paradigm which helps alleviate the conflict between resource-constrained mobile devices and resource-consuming mobile applications through computation offloading. In this paper, we analyze the computation offloading problem in cloudlet-based mobile cloud computing. Different from most of the previous works which are either from the perspective of a single user or under the setting of a single wireless access point (AP), we research the computation offloading strategy of multiple users via multiple wireless APs. With the widespread deployment of WLAN, offloading via multiple wireless APs will obtain extensive application. Taking energy consumption and delay (including computing and transmission delay) into account, we present a game-theoretic analysis of the computation offloading problem while mimicking the selfish nature of the individuals. In the case of homogeneous mobile users, conditions of Nash equilibrium are analyzed, and an algorithm that admits a Nash equilibrium is proposed. For heterogeneous users, we prove the existence of Nash equilibrium by introducing the definition of exact potential game and design a distributed computation offloading algorithm to help mobile users choose proper offloading strategies. Numerical extensive simulations have been conducted and results demonstrate that the proposed algorithm can achieve desired system performance.\",\n \"Title: Guest Editorial Special Issue on Cloud Computing for IoT.\\nYear: 2016\\n\\nAbstract: In recent years, there has been a growing interest in the ability of embedded devices, sensors, and actuators to communicate, and create a ubiquitous cyber-physical world. The growth of the notion of the Internet of Things (IoT) and the rapid development of technologies such as short range mobile communication and improved energy-efficiency is expected to create a pervasive connection of “things.” This will inevitably result in the generation of enormous amount of data, which have to be stored, processed, and accessed. Cloud computing has long been recognized as a paradigm for big data storage and analytics. The combination of cloud computing and IoT can enable ubiquitous sensing services and powerful processing of sensing data streams beyond the capability of individual things, thus stimulating innovations in both fields. For example, cloud platforms allow the sensing data to be stored and used intelligently for smart monitoring and actuation with the smart devices. Novel data fusion algorithms, machine learning methods, and artificial intelligence techniques can be implemented and run centralized or distributed on the cloud to achieve automated decision making. These will boost the development of new applications, such as smart cities, grids, and transportation systems. New challenges, however, arise when IoT meets cloud—there is an urgent need for novel network architectures that seamlessly integrate them, and protocols that facilitate big data streaming from IoT to the cloud. QoS and QoE, as well as data security, privacy, and reliability, are critical concerns during the integration.\",\n \"Title: A Study on Online Social Networks Theme Semantic Computing Model\\nYear: 2016\\n\\nAbstract: Online information sources including Weibo and Wechat bring huge impact to the society. Only a few words of network information can expand rapidly and catalyze the generation of a huge amount of information. Semantic computing on online social networks and research on topics about emergencies have great significance. In this article, a numerical model of text semantic analysis based on artificial neural network is proposed, and a semantic computational algorithm for social network texts as well as a discovery algorithm for emergencies is provided with reference to the information provided by the social nodes itself and the semantic of the text. Semantic vector of micro-information for nodes and closure extension of semantic extensions are defined in order to build up an equivalence of short sentences, and in turn realize the discovery of emergencies.\",\n \"Title: Cost-Effective Request Scheduling for Greening Cloud Data Centers\\nYear: 2016\\n\\nAbstract: With the popularity of cloud computing, many cloud service providers deploy regional data centers to offer services and pplications. These large-scale data centers have drawn extensive attention in terms of the huge energy demand and carbon emission. Thus, how to make use of their spatial diversities to green data centers and reduce cloud provider's costs is an important concern. In this paper, we integrate service reward, electricity cost, carbon taxes and service performance to study cost-effective request scheduling for cloud data centers. We propose an online and distributed scheduling algorithm CESA to chieve the flexible tradeoff between these conflicting objectives. The time complexity of CESA is polynomial, and it can be implemented in a parallel way. CESA requires no prior knowledge of the statistics of request arrivals or future electricity prices, yet it provably approximates the optimal system profit while bounding the queue length. Real-trace based simulations are conducted which verify the effectiveness of our CESA algorithm.\",\n \"Title: Monitoring-Based Task Scheduling in Large-Scale SaaS Cloud.\\nYear: 2016\\n\\nAbstract: With the increasing scale of SaaS and the continuous growth in server failures, task scheduling problems become more intricate, and both scheduling quality and scheduling speed raise further concerns. In this paper, we first propose a virtualized and monitoring SaaS model with predictive maintenance to minimize the costs of fault tolerance. Then with the monitored and predicted available states of servers, we focus on dynamic real-time task scheduling in large-scale heterogeneous SaaS, targeting at jointly optimizing the long-term performance benefits and energy costs in order to improve scheduling quality. We formulate a dynamic programming problem, where both the state and action spaces are too large to be solved by simple iterations. To address these issues, we take advantage of Machine Learning theory, and put forward an approximate dynamic programming algorithm. We utilize value function approximation and candidate-heuristic method to separately solve state and action explosions. Thus, computation complexity is significantly reduced and scheduling speed is greatly enhanced. Finally, we conduct experiments with both random simulation data and Google cloud trace-logs. Qos evaluations and comparisons demonstrate that our approach is effective and efficient under bursty requests and high throughputs.\",\n \"Title: On Denial of Service Attacks in Software Defined Networks.\\nYear: 2016\\n\\nAbstract: Software defined networking greatly simplifies network management by decoupling control functions from the network data plane. However, such a decoupling also opens SDN to various denial of service attacks: an adversary can easily exhaust network resources by flooding short-lived spoofed flows. Toward this issue, we present a comprehensive study of DoS attacks in SDN, and propose multi-layer fair queueing (MLFQ), a simple but effective DoS mitigation method. MLFQ enforces fair sharing of an SDN controller¿s resources with multiple layers of queues, which can dynamically expand and aggregate according to controller load. Both testbed-based and emulation-based experiments demonstrate the effectiveness of MLFQ in mitigating DoS attacks targeted at SDN controllers.\",\n \"Title: Modeling and understanding burst transmission for energy efficient ethernet.\\nYear: 2017\\n\\nAbstract: Recently, the energy consumption of Ethernet has become one of the hottest topics focused by both academic committee and industry, especially with the increase of the link speed from 1Gbps to 10/40Gbps nowadays or even 100/400Gbps in the near future. To save the energy consumed by the Ethernet, the Energy Efficient Ethernet (EEE) is developed and standardized by the IEEE 802.3az work group. When there is no incoming traffic, the EEE can saves 90% of its energy consumption by entering into the Low Power Idle (LPI) mode. To maximize the energy saving of Ethernet, the Burst TRansmission (BTR) mechanism, which defines a new way to utilize the LPI mode, is developed as a mechanism for EEE. Prior work theoretically shows that the BTR mechanism makes a tradeoff between the energy saving and the queuing delay. However, the traffic pattern, on which the performance of EEE greatly depends, is assumed to be deterministic in their analyses. Besides, their models made estimation for many situations. In this paper, we model EEE with the BTR mechanism and provide analytical understanding on the BTR mechanism. We propose two actual models: one focuses on the buffer size limit, the other concentrates on tolerable packet delay additionally. We draw some guidelines of parameter selection and policies design for EEE from combination of theory conclusions and simulation results. The results show that the saved energy can be constrained by link occupancy even though the buffer size is variational. The buffer full triggered wake-up policy can achieve ideal ratio of energy consumption and arrival rate within the scope of the buffer as well. However, the tolerable delay cannot be guaranteed by any policies. The buffer size is even fixed, which affects the flexibility of demanded delay for different business. The policy considering tolerable delay is supposed to be a little better than the other policy, with a little more complicated design. Thus we design an adaptive mechanism both in quantitative and qualitative: detect the load utilization, apply the buffer full triggered wake-up policy for higher load utilization link, while applying the buffer full and timeout triggered wake-up policy for the delay sensitive business and tiny arrival rate.\",\n \"Title: Trust Model of Wireless Sensor Networks and Its Application in Data Fusion.\\nYear: 2017\\n\\nAbstract: In order to ensure the reliability and credibility of the data in wireless sensor networks (WSNs), this paper proposes a trust evaluation model and data fusion mechanism based on trust. First of all, it gives the model structure. Then, the calculation rules of trust are given. In the trust evaluation model, comprehensive trust consists of three parts: behavior trust, data trust, and historical trust. Data trust can be calculated by processing the sensor data. Based on the behavior of nodes in sensing and forwarding, the behavior trust is obtained. The initial value of historical trust is set to the maximum and updated with comprehensive trust. Comprehensive trust can be obtained by weighted calculation, and then the model is used to construct the trust list and guide the process of data fusion. Using the trust model, simulation results indicate that energy consumption can be reduced by an average of 15%. The detection rate of abnormal nodes is at least 10% higher than that of the lightweight and dependable trust system (LDTS) model. Therefore, this model has good performance in ensuring the reliability and credibility of the data. Moreover, the energy consumption of transmitting was greatly reduced.\",\n \"Title: Discriminant Manifold Learning via Sparse Coding for Robust Feature Extraction.\\nYear: 2017\\n\\nAbstract: Most off-the-shelf subspace learning methods directly calculate the statistical characteristics of the original input images, while ignoring different contributions of different image components. In fact, to extract efficient features for image analysis, the noise or trivial structure in images should have little contribution and the intrinsic structure should be uncovered. Motivated by this observation, we propose a new subspace learning method, namely, discriminant manifold learning via sparse coding (DML_SC) for robust feature extraction. Specifically, we first decompose each input image into several components via dictionary learning, and then regroup the components into a more important part (MIP) and a less important part (LIP). The MIP can be considered as the clean portion of the image residing on a low-dimensional submanifold, while the LIP as noise or trivial structure within the image. Finally, the MIP and LIP are incorporated into manifold learning to learn a desired discriminative subspace. The proposed method is general for both cases with and without class labels, hence generating supervised DML_SC (SDML_SC) and unsupervised DML_SC (UDML_SC). Experimental results on four benchmark data sets demonstrate the efficacy of the proposed DML_SCs on both image recognition and clustering tasks.\",\n \"Title: Assessment Of Elbow Spasticity With Surface Electromyography And Mechanomyography Based On Support Vector Machine\\nYear: 2017\\n\\nAbstract: The Modified Ashworth Scale (MAS) is the gold standard in clinical for grading spasticity. However, its results greatly depend on the physician evaluations and are subjective. In this study, we investigated the feasibility of using support vector machine (SVM) to objectively assess elbow spasticity based on both surface electromyography (sEMG) and mechanomyography (MMG). sEMG signals and tri-axial accelerometer mechanomyography (ACC-MMG) signals were recorded simultaneously on patients' biceps and triceps when they extended or bended elbow passively. 39 post-stroke patients participated in the study, and were divided into four groups regarding MAS level (MAS=0, 1, 1+ or 2). The three types of features, root mean square (RMS), mean power frequency (MPF), and median frequency (MF), were calculated from sEMG and MMG signal recordings. Spearman correlation analysis was used to investigate the relationship between the features and spasticity grades. The results showed that the correlation between MAS and each of the five features (MMG-RMS of the biceps, MMG-RMS of the triceps, the EMG-RMS of the biceps, EMG-RMS of the triceps, EMG-MPF of the triceps) was significant (p<0.05). The four spasticity grades were identified with SVM, and the classification accuracy of SVM with sEMG, MMG, sEMG-MMG were 70.9%, 83.3%, 91.7%, respectively. Our results suggest that using the SVM-based method with sEMG and MMG to assess elbow spasticity would be suitable for clinical management of spasticity.\",\n \"Title: Analysing The Evolution Of Contrary Opinions On A Controversial Network Event\\nYear: 2017\\n\\nAbstract: With the growing popularity of social networking services, network public opinion gradually plays an important role in social life. When a controversial network event happens, what people concern about is which opinion in the contrary opinions will be widely accepted by people and how long this event lasts. To solve this problem, we propose a social evolutionary game model based on Hawk-Dove game to simulate how contrary opinions evolve in social network. The effectiveness of our model is validated by actual data. This model can be used to estimate the potential dominant opinion group and the time length of the controversy. Besides, our simulation reveals some special features of the evolution process and results. This study may be useful for network public opinion supervision and market research.\",\n \"Title: Long-Term Multi-objective Task Scheduling with Diff-Serv in Hybrid Clouds.\\nYear: 2017\\n\\nAbstract: With the speedy development of E-commerce, requests over the internet from intensive users are soaring, especially in global online shopping festivals. In order to meet the increasing demands of temporary capacity and reduce daily expenses, hybrid clouds are often used, and the task scheduling problem with multi-objectives is further investigated. In this paper, we firstly build a differentiated-service (Diff-Serv) task scheduling model, and formulate a dynamic programming problem, where the state space is too large to be solved by exhaustive iterations. Therefore, we carefully design the value approximation function, and with reference to the reinforcement learning theory, we put forward an approximate dynamic programming (ADP) algorithm so as to conduct the long-term optimization for performance benefit, energy and rental costs. Furthermore, both scheduling quality and scheduling speed are taken into consideration in this algorithm. Experiments with both random synthetic workloads and Google cloud trace-logs are conducted to evaluate the proposed algorithm, and results demonstrate that our algorithm is effective and efficient, especially under bursty requests.\",\n \"Title: Thinking And Modeling For Big Data From The Perspective Of The I Ching\\nYear: 2017\\n\\nAbstract: Data is growing faster than ever before and is changing our daily life. However it is rather challenging to manage the big data [F. H. Cate, The big debate, Science 346 (2014) 810, J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh and A. H. Byers, Big Data: The Next Frontier for Innovation, Competition, and Productivity (Mckinsey global Institute, 2011), S. Lohr, The Age of Big Data (New York Times, 2012), p. 11, L. Einav and J. Levin, Economics in the age of big data, Science 345 (2014) 715, M. J. Khoury and J. P. A. Ioannidis, Big data meets public health, Science 346 (2014) 1054-1055, V. Marx, Biology: The big challenges of big data, Nature 498(7453) (2013) 255-260.]. In this paper, we propose the big data thinking and modeling techniques from the perspective of the I Ching, which is a very famous imaginal thinking theory in China with 3,000 years history. The I Ching has been proven to be very useful and practical in many domains, e.g., 36 stratagems.Firstly, inspired from the three components of the I Ching, image, number and principle, we propose a new three-cycle big data thinking way, from data to phenomenon, from phenomenon to correlation, and from correlation to knowledge, which is a generalization of the fourth paradigm (from causality to correlation) proposed by Jim Gray.Secondly, inspired from the three entities of the I Ching, heaven, earth and human, we propose a new big data modeling method. We use the tree entities to represent the big data. We map the 4V of big data (volume, variety, velocity, veracity) to four opposition and uniform relations in the I Ching, and generate the eight diagrams. By capturing the relationships between eight diagrams, we generate the 64 hexagrams, and use 64 hexagrams to model big data. We also provide the principle rules to understand the knowledge generated by the model.Thirdly, we discuss how to utilize our model to describe big-data management tools, including, MapReduce, Spark, Storm. We also provide a new model for handling distributed data streams.We do think that we provide a new practical way of thinking and modeling for big data. We also believe that this will open up many new research directions on big data.\",\n \"Title: Cost-Efficient Resource Provisioning in Cloud Assisted Mobile Edge Computing.\\nYear: 2017\\n\\nAbstract: Mobile edge computing (MEC) is emerging as an effective computing paradigm which alleviates the conflict between computation-intensive mobile applications and resource-constrained mobile devices. In this paper, a Cloud Assisted Mobile Edge computing (CAME) framework is adopted to enhance the adaptability of MEC to time-varying mobile requests. The resource provisioning problem is investigated to provide guaranteed quality of service (QoS) with minimum system cost. By exploiting the piecewise convexity of the problem, the Optimal Resource Provisioning (ORP) algorithm is developed, which determines the computation capacity at mobile edge and dynamically tunes the usage of cloud resources. Extensive simulations demonstrate that the ORP algorithm yields the minimum system cost, compared with the local-first and the cloud-first algorithms. In addition, the ORP algorithm can flexibly adapt to the time-varying mobile requests.\",\n \"Title: Analysing and improving convergence of quantized congestion notification in Data Center Ethernet.\\nYear: 2018\\n\\nAbstract: Quantized Congestion Notification (QCN) has been approved as the standard congestion management mechanism for the Data Center Ethernet (DCE). However, lots of work pointed out that QCN suffers from the problem of unfairness among different flows. In this paper, we found that QCN could achieve fairness, merely the convergence time to fairness is quite long. Thus, we build a convergence time model to investigate the reasons of the slow convergence process of QCN. We validate the precision of our model by comparing with experimental data on the NetFPGA platform. The results show that the proposed model accurately well characterizes the convergence time to fairness of QCN. Based on the model, the impact of QCN parameters, network parameters, and QCN variants on the convergence time is analysed in detail. Results indicate that the convergence time of QCN can be decreased if sources have the same rate increase probability or the rate increase step becomes larger at steady state. Enlightened by the analysis, we proposed a mechanism called QCN-T, which replaces the original Byte Counter and Timer at sources with a single modified Timer to reduce the convergence time. Finally, evaluations show great improvements of QCN-T in both convergence and stability.\",\n \"Title: Robust Multi-subspace Analysis Using Novel Column L0-norm Constrained Matrix Factorization.\\nYear: 2018\\n\\nAbstract: We study the underlying structure of data (approximately) generated from a union of independent subspaces. Traditional methods learn only one subspace, failing to discover the multi-subspace structure, while state-of-the-art methods analyze the multi-subspace structure using data themselves as the dictionary, which cannot offer the explicit basis to span each subspace and are sensitive to errors via an indirect representation. Additionally, they also suffer from a high computational complexity, being quadratic or cubic to the sample size. To tackle all these problems, we propose a method, called Matrix Factorization with Column L0-norm constraint (MFC0), that can simultaneously learn the basis for each subspace, generate a direct sparse representation for each data sample, as well as removing errors in the data in an efficient way. Furthermore, we develop a first-order alternating direction algorithm, whose computational complexity is linear to the sample size, to stably and effectively solve the nonconvex objective function and non- smooth l0-norm constraint of MFC0. Experimental results on both synthetic and real-world datasets demonstrate that besides the superiority over traditional and state-of-the-art methods for subspace clustering, data reconstruction, error correction, MFC0 also shows its uniqueness for multi-subspace basis learning and direct sparse representation.\",\n \"Title: FTLLS: A fault tolerant, low latency, distributed scheduling approach based on sparrow.\\nYear: 2018\\n\\nAbstract: Big data processing systems are developing towards larger degrees of parallelism and shorter task durations in order to achieve lower response time. Scheduling highly parallel tasks that complete in sub-seconds poses a great challenge to traditional centralized schedulers. Taking the challenge, researchers turn to distributed scheduling approaches to avoid the throughput limitation of centralized schedulers, among which Sparrow is a leading design. However, little effort is devoted to the fault tolerance of Sparrow and there are problems with Sparrow’s sample-based techniques, which gives rise to incomplete jobs and large scheduling latency. We then present Fault Tolerant, Low Latency Sparrow (FTLLS). It extends Sparrow with an assistant machine to handle worker failures and to make better scheduling decisions. Through simulations, it is proved that FTLLS can detect worker failures more quickly than a naive timeout approach and make better scheduling decisions than native Sparrow. Through implementation, the results show that FTLLS guarantees no incomplete jobs at the presence of worker failures and reduces scheduling latencies by over 1.5 × when compared to native Sparrow. In addition, the simplicity of the idea adopted by FTLLS makes it applicable to a wide variety of distributed scheduling approaches.\",\n \"Title: Trust Evaluation Model Of Cloud User Based On Behavior Data\\nYear: 2018\\n\\nAbstract: In the process of using the cloud platform, how to ensure the safety of users is a matter we must concern. The user authentication can provide a certain degree of security, but when the user information was leaked, this method will not be effective. Therefore, this article proposes a trust evaluation model based on user behavior data. In this model, the user's historical behavior will be used to construct a set of trusted behavior of the cloud users. On this basis, the direct trust of the user's behavior can be obtained. Then, the recommendation trust can be calculated by the interaction between the users and other cloud users. Given the current historical trust, the comprehensive trust can be obtained using the weighted average method. Among them, the initial value of historical trust is set to a constant and then updated by the comprehensive trust. In order to control the user's abnormal behavior more effectively, the suspicious threshold value and the abnormal threshold value were defined, which are used to punish the historical trust. Through the simulation of the virtual digital library cloud platform, the method can effectively evaluate the cloud users.\",\n \"Title: Optimizing availability and QoS of heterogeneous distributed system based on residual lifetime in uncertain environment\\nYear: 2009\\n\\nAbstract: A notable requirement of heterogeneous parallel and distributed computing systems is to maximize their processing performance and agreed upon QoS. Lots of work in this field has been done to optimize the system performance by improving certain metrics such as reliability, robustness, security, and so on. However, most of them assume that systems are running without interruption all the time and seldom consider the system’s intrinsic characteristics, such as failure rate, repair rate, and lifetime. In this paper, we study how to achieve high availability based on residual lifetime analysis for heterogeneous distributed computational systems with considering their essential features. First, we provide an availability model taking into account system’s expected residual lifetime. Second, we propose an objective function about the model and develop a heuristic scheduling algorithm to maximize the availability with the makespan constraint. At last, we demonstrate these advantages through the extensive simulated experiments.\",\n \"Title: A Robust Sparse Representation Based Pattern Recognition Approach for Myoelectric Control.\\nYear: 2018\\n\\nAbstract: Interferences in the form of white Gaussian noise (WGN) are inevitable during long-term electromyogram (EMG) recordings. Even with the aid of advanced signal denoising techniques, such an intermittent interference is hardly detected and attenuated in the practical use of EMG-driven control systems. Hence, a robust pattern recognition scheme that is invariant to noise contamination would potentially aid the realization of an efficient EMG-based pattern recognition (EMG-PR) control system. To this end, an EMG-PR scheme driven by sparse representation-based classification (SRC) algorithm and root mean square (rms) descriptor (RMS-SRC) is proposed in this paper. The accuracy and the robustness of the proposed scheme were investigated using the high-density surface EMG recordings from 12 traumatic brain-injured patients and 5 post-stroke survivors. For benchmark comparison, another three different feature sets and four pattern recognition algorithms were considered. The optimal pattern recognition schemes with respect to each feature-classifier combination were first selected in the absence of WGN contamination. Then, six levels of WGN with a signal-to-noise ratio (SNR) ranging from 5 to 30 dB were added into the EMG recordings, respectively, to mimic the different WGN interferences. Our result showed that the proposed RMS-SRC scheme could achieve a similar accuracy with the benchmark schemes in the presence of limited noise contamination (0-15 dB), and significantly outperformed the other schemes when the SNR of WGN increased (20-30 dB). More notably, the RMS-SRC scheme significantly outperformed the other pattern recognition schemes when the WGN existed in either training set or testing set only. The findings proved the comparative advantage of the proposed RMS-SRC pattern recognition scheme over the other currently used schemes in the myoelectric control. Thus, the proposed scheme would potentially facilitate the development of EMG-driven rehabilitation robots for accurate and dexterous assistive training for patients with neurological disorders.\",\n \"Title: Energy-Aware Computation Offloading of IoT Sensors in Cloudlet-Based Mobile Edge Computing.\\nYear: 2018\\n\\nAbstract: Mobile edge computing is proposed as a promising computing paradigm to relieve the excessive burden of data centers and mobile networks, which is induced by the rapid growth of Internet of Things (IoT). This work introduces the cloud-assisted multi-cloudlet framework to provision scalable services in cloudlet-based mobile edge computing. Due to the constrained computation resources of cloudlets and limited communication resources of wireless access points (APs), IoT sensors with identical computation offloading decisions interact with each other. To optimize the processing delay and energy consumption of computation tasks, theoretic analysis of the computation offloading decision problem of IoT sensors is presented in this paper. In more detail, the computation offloading decision problem of IoT sensors is formulated as a computation offloading game and the condition of Nash equilibrium is derived by introducing the tool of a potential game. By exploiting the finite improvement property of the game, the Computation Offloading Decision (COD) algorithm is designed to provide decentralized computation offloading strategies for IoT sensors. Simulation results demonstrate that the COD algorithm can significantly reduce the system cost compared with the random-selection algorithm and the cloud-first algorithm. Furthermore, the COD algorithm can scale well with increasing IoT sensors.\",\n \"Title: Cycle-Consistency Based Hierarchical Dense Semantic Correspondence\\nYear: 2018\\n\\nAbstract: This work aims to estimate dense correspondences between the images from same visual class but with different geometries and visual similarities. This task is particularly challenging because (i) most image pairs have large intra-class variations, and (ii) their visual content is similar only on the high-level structure.To address these problems, this paper proposed a multi-level method to estimate per-pixel correspondences from high-level semantic to low-level structural details by the guidance of cycle-consistency. We utilize CNN feature pyramid to represent images level by level. Meanwhile, we introduce cycle-consistency to measure the reliability of flow vector, which further affects the guidance from higher level to lower level. The proposed method has been extensively evaluated on various challenging benchmarks. The results show that our method significantly outperforms the state-of-the-arts in terms of semantic flow accuracy.\",\n \"Title: A Novel Time-Domain Descriptor for Improved Prediction of Upper Limb Movement Intent in EMG-PR System.\\nYear: 2018\\n\\nAbstract: Electromyogram pattern recognition (EMG-PR) based control is a potential method capable of providing intuitively dexterous control functions in upper limb prostheses. Meanwhile, the feature extraction method adopted in EMG-PR based control is considered as an important factor that influences the performance of the prostheses. By exploiting the limitations of the existing feature extraction methods, this study proposed a new feature extraction method to effectively characterize EMG signal patterns associated with different limb movement intent. The performance of the proposed 2-dimensional novel time-domain feature set (NTDFS) was investigated using classification accuracy and feature space separability metrics across five subjects' EMG recordings, and compared with four different existing methods. In comparison to four other previously proposed feature extraction methods, the NTDFS achieved significantly better performance with increment in accuracy in the range of 5.20% ∼ 8.40% at p<0.05. Additionally, by applying principal component analysis (PCA) technique, the PCA feature space for NTDFS show obvious class separability in comparison to the other existing feature extraction methods. Thus, the proposed NTDFS may facilitate the development of accurate and robust clinically viable EMG-PR based prostheses.\",\n \"Title: Flexible Non-contact Electrodes for Bioelectrical Signal Monitoring.\\nYear: 2018\\n\\nAbstract: Physiological signals such as ECG and EMG are closely related with human health and a long term monitoring of the physiological signals would be beneficial to detect possible disorders at the early stage. The wet electrodes currently used in the clinics require adhesives gels to record high-quality signals, which might cause discomfort of the patients and introduce some risk of skin allergy. Non-contact capacitive electrodes that can be operated without skin contact have been developed in previous studies, but these electrodes were rigid with electronic components on the back, which might not be an optimal solution for long term healthcare monitoring. In this study, a flexible non-contact electrode without any electronic components on both sides was designed for the long term acquisition of bioelectrical signals to maximize subject comfort. The flexible electrode was made up of multi-layer flexible printed circuits (FPC) materials and could be bent according to the local shape to achieve better non-contact capacitive coupling with the skin. The performance of the proposed flexible electrode was compared with that of the conventional wet electrodes in different signal recording conditions. The results showed that the proposed non-contact flexible electrode can capture high quality ECG and EMG signals, and its performance was comparable to the commonly used wet electrodes. This study might provide a reliable solution with great patient comfort for the long-term monitoring of biological signals.\",\n \"Title: Thinking and methodology of multi-objective optimization.\\nYear: 2018\\n\\nAbstract: Multi-objective optimization is one of the most important aspects of operational research, and has been applied widely. Most of the existing research works on multi-objective optimization focused on the concrete solutions to detailed optimization problem in reality, lacking of the deep investigation on the fundamental thinking and methodology of the multi-objective optimization problem. To fill this gap, this paper studies the multi-objective optimization problem from a Chinese traditional philosophic angle. Based on the theories and ideology of I Ching, we investigate the basic thinking and methodology of multi-objective optimization, and summarize three methodological patterns for modeling and solving the problems, which are local optimization for global moderation, divide and conquer after comprehensive consideration, and combination of division and integration. Basic ideas, approaches and examples for such three patterns are introduced, and the relationships among the three patterns are discussed from both philosophic and mathematical aspects. Furthermore, for the computational solutions of multi-objective optimization problems, we summarize four thinking patterns based on traditional Chinese philosophy, which are changing thinking, fuzzy thinking, uncertainty thinking, and imaginable thinking. Algorithms that are inspired by the four thinking patterns are presented. This work seeks to study the fundamental thinking and methodology of multi-objective optimization problem from a combination angle of science and philosophy, which is expected to inspire new ideas and methods for solving the problem.\",\n \"Title: Activity Recognition Based on Deep Learning and Android Software\\nYear: 2018\\n\\nAbstract: This project transplants the deep learning framework to the android platform. It combines Deep learning based on Tensor Flow (TF) with Android software to recognize people's activities. Provided by android sensors, dataset is in the form of float arrays. And then we used them to train TF model with the data that was tagged by the different actions. Then we transplant the model into android to achieve activity recognition in mobile phone. In this project, six activities are recognized, including downstairs, upstairs, jogging, walking, sitting and standing. Finally, the probability of each activity will be seen in an android mobile phone. According to the results of experiments, almost all of six types of activities can be recognized successfully.\",\n \"Title: An LSTM-Based Method with Attention Mechanism for Travel Time Prediction.\\nYear: 2019\\n\\nAbstract: Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic dynamics in road network. In recent years, Long Short-Term Memory has been applied to traffic prediction, achieving better performance. The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction. Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks. In this paper, we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction. We present the proposed model in a tree structure. The proposed model substitutes a tree structure with attention mechanism for the unfold way of standard Long Short-Term Memory to construct the depth of Long Short-Term Memory and modeling long-term dependence. The attention mechanism is over the output layer of each Long Short-Term Memory unit. The departure time is used as the aspect of the attention mechanism and the attention mechanism integrates departure time into the proposed model. We use AdaGrad method for training the proposed model. Based on the datasets provided by Highways England, the experimental results show that the proposed model can achieve better accuracy than the Long Short-Term Memory and other baseline methods. The case study suggests that the departure time is effectively employed by using attention mechanism.\",\n \"Title: Multi-Objective Service Composition with QoS Dependencies\\nYear: 2019\\n\\nAbstract: Service composition is popular for composing a set of existing services to provide complex services. With the increasing number of services deployed in cloud computing environments, many service providers have started to offer candidate services with equivalent functionality but different Quality of Service (QoS) levels. Therefore, QoS-aware service composition has drawn extensive attention. Most existing approaches for QoS-aware service composition assume a service's QoS values are not correlated to those of other services. However, QoS dependency exists in real life, and impacts the overall QoS values of the composite services. In this article, we study QoS dependency-aware service composition considering multiple QoS attributes. Based on the Pareto set model, we focus on searching for a set of Pareto optimal solutions. A candidate pruning algorithm for removing the unpromising candidates is proposed, and a service composition algorithm using Vector Ordinal Optimization techniques is designed. Simulation experiments are conducted to validate the efficiency and effectiveness of our algorithms. We are the first to take advantage of Vector Ordinal Optimization techniques to search for Pareto optimal composition solutions with QoS dependency involved. The capturing of QoS dependency enables us to find truly desirable solutions.\",\n \"Title: Multi-level Semantic Representation for Flower Classification.\\nYear: 2017\\n\\nAbstract: Fine-grained classification is challenging since sub-categories have little intra-class variances and large intra-class variations. The task of flower classification can be achieved through highlighting the discriminative parts. Most traditional methods trained Convolutional Neural Networks (CNN) to handle the variations of pose, color and rotation, which only utilize single-level semantic information. In this paper, we propose a fine-grained classification approach with multi-level semantic representation. With the complementary strengths of multi-level semantic representation, we attempt to capture the subtle differences between sub-categories. One object-level model and multiple part-level model are trained as a multi-scale classifier. We test our method on the Oxford Flower dataset with 102 categories, and our result achieves the best performance over other state-of-the-art approaches.\",\n \"Title: Travel Time Prediction by Providing Constraints on a Convolutional Neural Network.\\nYear: 2018\\n\\nAbstract: Traffic prediction is a complex, nonlinear spatiotemporal modeling problem. Data-based methods have been widely applied to traffic information prediction because of their ability to model the complex, nonlinear spatiotemporal traffic dynamics. These abilities are based on statistical methods and machine learning methods to extract features from historical traffic information. Despite the success of these methods, the local-level representation in the data-based methods is clearly limited because it does not directly reflect the relationship between each historical data point in the input sequence and the predicted information. Convolutional neural networks (CNNs) have also been widely applied in many other fields because of their feature extraction ability, which greatly enhances the learning ability of the standard, fully connected layers of neural networks by providing constraints in the task domain. Thus, we create a global-level representation to reflect the relationship between each historical traffic data point and the predicted information by using the proposed window that provides constraints in the task domain and exploits a max-pooled CNN to automatically extract global-level features for traffic information. To the best of our knowledge, our proposed method is the first example of using the new window to provide constraints in the task domain for CNNs. The proposed method is trained in an end-to-end way by the back-propagation approach in which the AdaGrad method is used to update the parameters of the proposed method. Based on the data set provided by Highways England, the experimental results show that the proposed method outperforms all baseline methods in terms of accuracy.\",\n \"Title: Two-level task scheduling with multi-objectives in geo-distributed and large-scale SaaS cloud\\nYear: 2019\\n\\nAbstract: With the exploding of data-intensive web applications and requests (tasks), geo-distributed and large-scale data centers (DCs) are widely deployed in Software as a Service (SaaS) cloud, but server failures continue to grow at the same time. In this context, task scheduling problems become more intricate and both scheduling quality and scheduling speed raise further concerns. In this paper, we first propose a virtualized & monitoring SaaS model with predictive maintenance to minimize the costs of fault tolerance. Then with the monitored and predicted available states of servers, we focus on dynamic real-time task scheduling in geo-distributed and large-scale DCs with heterogeneous servers. Multiple objectives, including the long-term performance benefits, energy and communication costs, are taken into consideration in order to improve scheduling quality. For inter-DC and intra-DC task scheduling, two dynamic programming problems are formulated respectively, but there exists the problem that both state and action spaces are too large to be solved by simple iterations. To address this issue, we introduce the idea of reinforcement learning theory into solving traditional stochastic dynamic programming problems in the large-scale SaaS cloud, and put forward a cascaded two-level (inter-DC and intra-DC level) approximate dynamic programming (ADP) task-scheduling algorithm. The computation complexity can be significantly reduced and scheduling speed can be greatly improved. Finally, we conduct experiments with both random simulation data and Google cloud trace-logs. QoS evaluations and comparisons demonstrate that two ADP algorithms can work cooperatively, and our two-level ADP algorithm is more effective under large quantity of bursty requests.\",\n \"Title: A Convolution Component-Based Method with Attention Mechanism for Travel-Time Prediction.\\nYear: 2019\\n\\nAbstract: Deep learning approaches have been recently applied to traffic prediction because of their ability to extract features of traffic data. While convolutional neural networks may improve the predictive accuracy by transiting traffic data to images and extracting features in the images, the convolutional results can be improved by using the global-level representation that is a direct way to extract features. The time intervals are not considered as aspects of convolutional neural networks for traffic prediction. The attention mechanism may adaptively select a sequence of regions and only process the selected regions to better extract features when aspects are considered. In this paper, we propose the attention mechanism over the convolutional result for traffic prediction. The proposed method is based on multiple links. The time interval is considered as the aspect of attention mechanism. Based on the dataset provided by Highways England, the experimental results show that the proposed method can achieve better accuracy than the baseline methods.\",\n \"Title: Toward Efficient Image Representation: Sparse Concept Discriminant Matrix Factorization\\nYear: 2019\\n\\nAbstract: The key ingredients of matrix factorization lie in basic learning and coefficient representation. To enhance the discriminant ability of the learned basis, discriminant graph embedding is usually introduced in the matrix factorization model. However, the existing matrix factorization methods based on graph embedding generally conduct discriminant analysis via a single type of adjacency graph, either similarity-based graphs (e.g., Laplacian eigenmaps graph) or reconstruction-based graphs (e.g., \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">${L}_{1}$ </tex-math></inline-formula>\\n-graph), while ignoring the cooperation of the different types of adjacency graphs that can better depict the discriminant structure of original data. To address the above issue, we propose a novel Fisher-like criterion, based on graph embedding, to extract sufficient discriminant information via two different types of adjacency graphs. One graph preserves the reconstruction relationships of neighboring samples in the same category, and the other suppresses the similarity relationships of neighboring samples from different categories. Moreover, we also leverage the sparse coding to promote the sparsity of the coefficients. By virtue of the proposed Fisher-like criterion and sparse coding, a new matrix factorization framework called Sparse concept Discriminant Matrix Factorization (SDMF) is proposed for efficient image representation. Furthermore, we extend the Fisher-like criterion to an unsupervised context, thus yielding an unsupervised version of SDMF. Experimental results on seven benchmark datasets demonstrate the effectiveness and efficiency of the proposed SDMFs on both image classification and clustering tasks.\",\n \"Title: CycleEmotionGAN: Emotional Semantic Consistency Preserved CycleGAN for Adapting Image Emotions\\nYear: 2019\\n\\nAbstract: Deep neural networks excel at learning from large-scale labeled training data, but cannot well generalize the learned knowledge to new domains or datasets. Domain adaptation studies how to transfer models trained on one labeled source domain to another sparsely labeled or unlabeled target domain. In this paper, we investigate the unsupervised domain adaptation (UDA) problem in image emotion classification. Specifically, we develop a novel cycle-consistent adversarial model, termed CycleEmotionGAN, by enforcing emotional semantic consistency while adapting images cycle consistently. By alternately optimizing the CycleGAN loss, the emotional semantic consistency loss, and the target classification loss, CycleEmotionGAN can adapt source domain images to have similar distributions to the target domain without using aligned image pairs. Simultaneously, the annotation information of the source images is preserved. Extensive experiments are conducted on the ArtPhoto and FI datasets, and the results demonstrate that CycleEmotionGAN significantly outperforms the state-of-the-art UDA approaches.\",\n \"Title: Short-Term Travel Time Prediction: A Spatiotemporal Deep Learning Approach\\nYear: 2019\\n\\nAbstract: Traffic prediction is a complex, nonlinear spatiotemporal relationship modeling task with the randomness of Traffic demand, the spatial and temporal dependency between Traffic flows, and other recurrent and nonrecurrent factors. Based on the ability to learn generic features from history information, deep learning approaches have been recently applied to Traffic prediction. Convolutional neural network (CNN) methods that learn Traffic as images can improve the predictive accuracy by leveraging the implicit correlations among nearby links. Traffic prediction based on CNN is still in its initial stage without making full use of spatiotemporal Traffic information. In this paper, we improve the predictive accuracy by directly capturing the relationship between the input sequence and the predicted value. We propose the new local receptive fields for spatiotemporal Traffic information to provide the constraints in the task domain for CNN which is different from traditionally learning Traffic as images. We explore a max-pooled CNN followed by a fully connected layer with a nonlinear activation function to convolute the new local receptive fields. The higher global-level features are fed into a predictor to generate the predicted output. Based on the dataset provided by Highways England, we validate the assumption that there exists direct relationship between the input sequence and the predicted value. We train the proposed method by using the backpropagation approach, and we employ the AdaGrad method to update the parameters of the proposed method. The experimental results show that the proposed method can improve the predictive accuracy.\",\n \"Title: Cost-Efficient Resource Provisioning for Dynamic Requests in Cloud Assisted Mobile Edge Computing\\nYear: 2021\\n\\nAbstract: Mobile edge computing is emerging as a new computing paradigm that provides enhanced experience to mobile users via low latency connections and augmented computation capacity. As the amount of user requests is time-varying, while the computation capacity of edge hosts is limited, Cloud Assisted Mobile Edge (CAME) computing framework is introduced to improve the scalability of the edge platform. By...\",\n \"Title: Multimodal Transformer with Variable-Length Memory for Vision-and-Language Navigation.\\nYear: 2022\\n\\nAbstract: Vision-and-Language Navigation (VLN) is a task that an agent is required to follow a language instruction to navigate to the goal position, which relies on the ongoing interactions with the environment during moving. Recent Transformer-based VLN methods have made great progress benefiting from the direct connections between visual observations and language instructions via the multimodal cross-attention mechanism. However, these methods usually represent temporal context as a fixed-length vector by using an LSTM decoder or using manually designed hidden states to build a recurrent Transformer. Considering a single fixed-length vector is often insufficient to capture long-term temporal context, in this paper, we introduce Multimodal Transformer with Variable-length Memory (MTVM) for visually-grounded natural language navigation by modeling the temporal context explicitly. Specifically, MTVM enables the agent to keep track of the navigation trajectory by directly storing activations in the previous time step in a memory bank. To further boost the performance, we propose a memory-aware consistency loss to help learn a better joint representation of temporal context with random masked instructions. We evaluate MTVM on popular R2R and CVDN datasets. Our model improves Success Rate on R2R test set by 2% and reduces Goal Process by 1.5 m on CVDN test set. Code is available at: https://github.com/clin1223/MTVM.\",\n \"Title: Preliminary evaluation of a novel vision-guided hybrid robot system for capsulotomy in cataract surgery☆☆\\nYear: 2022\\n\\nAbstract: •A novel 6-DoF hybrid robot is introduced to improve the stability and compactness of surgery robot.•A robot control algorithm with an RCM constraint is proposed to reduce the position error of the robot.•An edge detection method based on the U-Net is proposed to provide visual feedback to the robot and increase the precision of tracking the edge of the anterior capsule.•Experimental results are presented to indicate that the proposed robot system can increase the performance of cataract surgery.\",\n \"Title: Continuous Finger Kinematics Estimation Based on sEMG and Attention-ConvGRU Network\\nYear: 2022\\n\\nAbstract: sEMG is an efficient media for human-computer interactions, especially in the controlling of artificial limbs and other mechanical arms. In the paper, we propose a new Attention-ConvGRU model which can continuously estimate the finger joint angles based on sEMG when executing classical grasping motions. The experimental results show that the average correlation coefficient (CC) and the root mean square error (RMSE) of the proposed Attention-ConvGRU method are 0.8320 +/- 0.04 and 8.8717 +/- 0.98, respectively, which are significantly better than that of the GRU method (0.8093 +/- 0.05, p < 0.01, 9.3716 +/- 0.95, p < 0.01). Moreover, the training speed of Attention-ConvGRU is 4.5 times faster than that of GRU method.\",\n \"Title: Preliminary Estimation of the Friction between Force-sensing Forceps and Cornea\\nYear: 2022\\n\\nAbstract: Continuous curvilinear capsulorhexis (CCC) is a delicate ophthalmic procedure that may benefit from robot technology. In CCC, surgeons make an incision in the cornea, insert forceps into the anterior segment through the incision, and peel the anterior lens capsule (ALC) from the lens. The absence of perception of axial peeling force applied to ALC is challenging in developing robot-assisted CCC. A potential way to deal with this challenge is measuring axial force with a mercantile force sensor, which has the advantage of sub-millinewton precision, high axial stiffness, high stability, and ease of use. However, the measured results of the mercantile force sensor contain the axial peeling force and forceps-cornea friction. Therefore, we seek to estimate the friction at the incision with the distributed LuGre friction model. The normal force and friction at incision are measured by fiber bragg grating (FBG) sensors and a mercantile force sensor, respectively. Then, the parameters of the LuGre model are identified by applying a low-frequency sinusoidal movement on the forceps. An identification experiment is performed on ex-vivo pig cornea samples. Finally, friction during inserting forceps into the anterior segment of ex-vivo pig eyes is estimated. The root mean square error of friction identification and estimation are 0.59 mN and 1.59 mN, respectively.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":200,"string":"200"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13283,"string":"13,283"}}},{"rowIdx":13284,"cells":{"question_id":{"kind":"string","value":"Jayaram K Udupa"},"question":{"kind":"string","value":"Generate an abstract for the title \"Architectural analysis on dynamic MRI to study thoracic insufficiency syndrome.\" using the following items: 1. Thoracic insufficiency syndrome (TIS)\n2. Quantitative dynamic magnetic resonance imaging (QdMRI)\n3. 3D landmark points\n4. Pathophysiological mechanisms"},"answer":{"kind":"string","value":"The major hurdles currently preventing advance and innovation in thoracic insufficiency syndrome (TIS) assessment and treatment are the lack of standardizable objective diagnostic measurement techniques that describe the 3D thoracoabdominal structures and the dynamics of respiration. Our goal is to develop, test, and evaluate a quantitative dynamic magnetic resonance imaging (QdMRI) methodology and a biomechanical understanding for deriving key quantitative parameters from free-tidal-breathing dMRI image data for describing the 3D structure and dynamics of the thoracoabdominal organs of TIS patients. In this paper, we propose an idea of a shape sketch to codify and then quantify the overall thoracic architecture, which involves the selection of 3D landmark points and computation of 3D dynamic distances over a respiratory cycle. We perform two statistical analyses of distance sketches on 25 different TIS patients to try to understand the pathophysiological mechanisms in relation to spine deformity and to quantitatively evaluate improvements from pre-operative to post-operative states. This QdMRI methodology involves developing: (1) a 4D image construction method; (2) an algorithm for the 4D segmentation of thoraco-abdominal structures; and (3) a set of key quantitative parameters. We illustrate that the TIS dynamic distance analysis method produces results previously unknown and precisely describes the morphologic and dynamic alterations of the thorax in TIS. A set of 3D thoracoabdominal distances and/or distance differences enables the precise estimation of key measures such as left & right differences, differences over tidal breathing, and differences from pre- to post-operative condition."},"correct_answer":{"kind":"string","value":"The major hurdles currently preventing advance and innovation in thoracic insufficiency syndrome (TIS) assessment and treatment are the lack of standardizable objective diagnostic measurement techniques that describe the 3D thoracoabdominal structures and the dynamics of respiration. Our goal is to develop, test, and evaluate a quantitative dynamic magnetic resonance imaging (QdMRI) methodology and a biomechanical understanding for deriving key quantitative parameters from free-tidal-breathing dMRI image data for describing the 3D structure and dynamics of the thoracoabdominal organs of TIS patients. In this paper, we propose an idea of a shape sketch to codify and then quantify the overall thoracic architecture, which involves the selection of 3D landmark points and computation of 3D dynamic distances over a respiratory cycle. We perform two statistical analyses of distance sketches on 25 different TIS patients to try to understand the pathophysiological mechanisms in relation to spine deformity and to quantitatively evaluate improvements from pre-operative to post-operative states. This QdMRI methodology involves developing: (1) a 4D image construction method; (2) an algorithm for the 4D segmentation of thoraco-abdominal structures; and (3) a set of key quantitative parameters. We illustrate that the TIS dynamic distance analysis method produces results previously unknown and precisely describes the morphologic and dynamic alterations of the thorax in TIS. A set of 3D thoracoabdominal distances and/or distance differences enables the precise estimation of key measures such as left & right differences, differences over tidal breathing, and differences from pre- to post-operative condition."},"documents":{"kind":"list like","value":["Title: Multidimensional digital boundaries\nYear: 1994\n\nAbstract: In many imaging applications, boundaries of objects need to be identified in multidimensional digital image data for the visualization and analysis of object information captured in the images. This article addresses the question of how to define boundaries in multidimensional digital spaces so that they are \"closed\" and connected, and so that they partition the digital space into an interior set that is connected and an exterior set that is connected. Using adjacency relations defined on the elements of the digital space and on boundary elements, we prove some basic results relating to these properties of boundaries. We examine in detail some specific boundary element adjacency relations and present efficient algorithms that track boundaries defined in binary images of any (finite) diinensionality. We conclude with two conjectures relating to the connectedness of boundaries.","Title: Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation\nYear: 1996\n\nAbstract: Images are by nature fuzzy. Approaches to object information extraction from images should attempt to use this fact and retain fuzziness as realistically as possible. In past image segmentation research, the notion of “hanging togetherness” of image elements specified by their fuzzy connectedness has been lacking. We present a theory of fuzzy objects for n -dimensional digital spaces based on a notion of fuzzy connectedness of image elements. Although our definitions lead to problems of enormous combinatorial complexity, the theoretical results allow us to reduce this dramatically, leading us to practical algorithms for fuzzy object extraction. We present algorithms for extracting a specified fuzzy object and for identifying all fuzzy objects present in the image data. We demonstrate the utility of the theory and algorithms in image segmentation based on several practical examples all drawn from medical imaging.","Title: Fast Visualization, Manipulation, and Analysis of Binary Volumetric Objects\nYear: 1991\n\nAbstract: A set of algorithms for interactive visualization, manipulation, and measurement of large 3-D objects on general-purpose workstations is described. They are based on a method of representing digital structures called a semiboundary. This data structure stores boundary and interior information. The use of these algorithms for visualizing medical data is addressed. Examples of their use are given.","Title: Co-segmentation of functional and anatomical images.\nYear: 2012\n\nAbstract: This paper presents a novel method for segmenting functional and anatomical structures simultaneously. The proposed method unifies domains of anatomical and functional images (PET-CT), represents them in a product lattice, and performs simultaneous delineation of regions based on a random walk image segmentation. In addition, we propose a simple yet efficient object/background seed localization method, where background and foreground object cues are automatically obtained from PET images and propagated onto the corresponding anatomical images (CT). In our experiments, abnormal anatomies on PET-CT images from human subjects are segmented synergistically by the proposed fully automatic co-segmentation method with high precision (mean DSC of 91.44%) in seconds (avg. 40 seconds).","Title: Boundary Detection in Multidimensions\nYear: 1982\n\nAbstract: The development of image processing algorithms for time-varying imagery and computerized tomography data calls for generalization of the concepts of adjacency, connectivity, boundary, etc., to three and four-dimensional discrete spaces. This paper defines these basic concepts in unified terminology and presents algorithms for a boundary detection task in multidimensional space. The performance of these algorithms is discussed with respect to theoretical maximum complexity, and is illustrated with simulated computerized tomography data.","Title: Oriented active shape models.\nYear: 2009\n\nAbstract: Active shape models (ASM) are widely employed for recognizing anatomic structures and for delineating them in medical images. In this paper, a novel strategy called oriented active shape models (OASM) is presented in an attempt to overcome the following five limitations of ASM: 1) lower delineation accuracy, 2) the requirement of a large number of landmarks, 3) sensitivity to search range, 4) sensitivity to initialization, and 5) inability to fully exploit the specific information present in the given image to be segmented. OASM effectively combines the rich statistical shape information embodied in ASM with the boundary orientedness property and the globally optimal delineation capability of the live wire methodology of boundary segmentation. The latter characteristics allow live wire to effectively separate an object boundary from other nonobject boundaries with similar properties especially when they come very close in the image domain. The approach leads to a two-level dynamic programming method, wherein the first level corresponds to boundary recognition and the second level corresponds to boundary delineation, and to an effective automatic initialization method. The method outputs a globally optimal boundary that agrees with the shape model if the recognition step is successful in bringing the model close to the boundary in the image. Extensive evaluation experiments have been conducted by utilizing 40 image (magnetic resonance and computed tomography) data sets in each of five different application areas for segmenting breast, liver, bones of the foot, and cervical vertebrae of the spine. Comparisons are made between OASM and ASM based on precision, accuracy, and efficiency of segmentation. Accuracy is assessed using both region-based false positive and false negative measures and boundary-based distance measures. The results indicate the following: 1) The accuracy of segmentation via OASM is considerably better than that of ASM; 2) The number of landmarks can be reduced by a factor of 3 in OASM over that in ASM; 3) OASM becomes largely independent of search range and initialization becomes automatic. All three benefits of OASM ensue mainly from the severe constraints brought in by the boundary-orientedness property of live wire and the globally optimal solution found by the 2-level dynamic programming algorithm.","Title: Go digital, go fuzzy\nYear: 2002\n\nAbstract: In many application areas of imaging sciences, object information captured in multi-dimensional images needs to be extracted,\n visualized, manipulated, and analyzed. These four groups of operations have been (and are being) intensively investigated,\n developed, and applied in a variety of applications. In this paper, after giving a brief overview of the four groups of operations,\n we put forth two main arguments: (1) Computers are digital, and most image acquisition and communication efforts at present\n are toward digital approaches. In the same vein, there are considerable advantages to taking an inherently digital approach\n to the above four groups of operations rather than using concepts based on continuous approximations. (2) Considering the\n fact that images are inherently fuzzy, to handle uncertainties and heterogeneity of object properties realistically, approaches\n based on fuzzy sets should be taken to the above four groups of operations. We give two examples in support of these arguments.","Title: The Influence of Intensity Standardization on Medical Image Registration\nYear: 2010\n\nAbstract: Acquisition-to-acquisition signal intensity variations (non-standardness) are inherent in MR images. Standardization is a post processing method for correcting inter-subject intensity variations through transforming all images from the given image gray scale into a standard gray scale wherein similar intensities achieve similar tissue meanings. The lack of a standard image intensity scale in MRI leads to many difficulties in tissue characterizability, image display, and analysis, including image segmentation. This phenomenon has been documented well; however, effects of standardization on medical image registration have not been studied yet. In this paper, we investigate the influence of intensity standardization in registration tasks with systematic and analytic evaluations involving clinical MR images. We conducted nearly 20,000 clinical MR image registration experiments and evaluated the quality of registrations both quantitatively and qualitatively. The evaluations show that intensity variations between images degrades the accuracy of registration performance. The results imply that the accuracy of image registration not only depends on spatial and geometric similarity but also on the similarity of the intensity values for the same tissues in different images.","Title: Iterative relative fuzzy connectedness for multiple objects with multiple seeds\nYear: 2007\n\nAbstract: Estimating intrinsic geometric properties of a surface from a polygonal mesh obtained from range data is an important stage of numerous algorithms in computer and robot vision, computer graphics, geometric modeling, and industrial and biomedical engineering. ...","Title: Fuzzy object model based fuzzy connectedness image segmentation of newborn brain MR images.\nYear: 2012\n\nAbstract: Cerebral parenchyma segmentation in newborn magnetic resonance (MR) images is crucial for developing computer-aided diagnosis systems in newborn cerebral diseases. However, there is limited number of studies on newborn brain MR image analysis. This study presents a novel method for fully automatically segmenting the cerebral parenchyma region using scale-based fuzzy connected image segmentation and fuzzy object models. The proposed method evaluates object affinity and homogeneity using the MR signal, and employs a fuzzy object model, which is built from training datasets. We have evaluated the proposed method based on 10 newborn MR images with subject revised age between -1 month and 2 months. These studies indicate that the use of a fuzzy object model is effective in improving the segmentation accuracy.","Title: A framework for comparing different image segmentation methods and its use in studying equivalences between level set and fuzzy connectedness frameworks\nYear: 2011\n\nAbstract: In the current vast image segmentation literature, there seems to be considerable redundancy among algorithms, while there is a serious lack of methods that would allow their theoretical comparison to establish their similarity, equivalence, or distinctness. In this paper, we make an attempt to fill this gap. To accomplish this goal, we argue that: (1) every digital segmentation algorithm A should have a well defined continuous counterpart M\"A, referred to as its model, which constitutes an asymptotic of A when image resolution goes to infinity; (2) the equality of two such models M\"A and M\"A\"^\"' establishes a theoretical (asymptotic) equivalence of their digital counterparts A and A^'. Such a comparison is of full theoretical value only when, for each involved algorithm A, its model M\"A is proved to be an asymptotic of A. So far, such proofs do not appear anywhere in the literature, even in the case of algorithms introduced as digitizations of continuous models, like level set segmentation algorithms. The main goal of this article is to explore a line of investigation for formally pairing the digital segmentation algorithms with their asymptotic models, justifying such relations with mathematical proofs, and using the results to compare the segmentation algorithms in this general theoretical framework. As a first step towards this general goal, we prove here that the gradient based thresholding model M\"@? is the asymptotic for the fuzzy connectedness Udupa and Samarasekera segmentation algorithm used with gradient based affinity A\"@?. We also argue that, in a sense, M\"@? is the asymptotic for the original front propagation level set algorithm of Malladi, Sethian, and Vemuri, thus establishing a theoretical equivalence between these two specific algorithms. Experimental evidence of this last equivalence is also provided.","Title: Image segmentation by combining the strengths of Relative Fuzzy Connectedness and Graph Cut.\nYear: 2012\n\nAbstract: We introduce an image segmentation algorithm GC(sum)(max), which combines, in a novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that GC(sum)(max) preserves robustness of RFC with respect to the seed choice (thus, avoiding \"shrinking problem\" of GC), while keeping GC's bigger control over \"leaking though the weak boundary.\" The theoretical analysis of GC(sum)(max) is greatly facilitated by our recent theoretical results that RFC belongs to the Generalized GC (GGC) segmentation algorithms framework. In our implementation of GC(sum)(max) we use, as a subroutine, a version of RFC algorithm (based on Image Foresting Transform) that runs (provably) in linear time with respect to the image size. This results in GC(sum)(max) running in a time close to linear.","Title: Medical image segmentation by combining graph cuts and oriented active appearance models.\nYear: 2012\n\nAbstract: In this paper, we propose a novel method based on a strategic combination of the active appearance model (AAM), live wire (LW), and graph cuts (GCs) for abdominal 3-D organ segmentation. The proposed method consists of three main parts: model building, object recognition, and delineation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the recognition part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW methods, resulting in the oriented AAM (OAAM). A multiobject strategy is utilized to help in object initialization. We employ a pseudo-3-D initialization strategy and segment the organs slice by slice via a multiobject OAAM method. For the object delineation part, a 3-D shape-constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC-OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT data set and also on the MICCAI 2007 Grand Challenge liver data set. The results show the following: 1) The overall segmentation accuracy of true positive volume fraction TPVF > 94.3% and false positive volume fraction can be achieved; 2) the initialization performance can be improved by combining the AAM and LW; 3) the multiobject strategy greatly facilitates initialization; 4) compared with the traditional 3-D AAM method, the pseudo-3-D OAAM method achieves comparable performance while running 12 times faster; and 5) the performance of the proposed method is comparable to state-of-the-art liver segmentation algorithm. The executable version of the 3-D shape-constrained GC method with a user interface can be downloaded from http://xinjianchen.wordpress.com/research/.","Title: Scale-based fuzzy connected image segmentation: theory, algorithms, and validation\nYear: 2000\n\nAbstract: This paper extends a previously reported theory and algorithms for object definition based on fuzzy connectedness. In this approach, a strength of connectedness is determined between every pair of image elements. This is done by considering all possible connecting paths between the two elements in each pair. The strength assigned to a particular path is defined as the weakest affinity between successive pairs of elements along the path. Affinity specifies the degree to which elements hang together locally in the image. Although the theory allowed any neighborhood size for affinity definition, it did not indicate how this was to be selected. By bringing object scale into the framework in this paper, not only the size of the neighborhood is specified but also it is allowed to change in different parts of the image. This paper argues that scale-based affinity, and hence connectedness, is natural in object definition and demonstrates that this leads to more effective object segmentation. The approach presented here considers affinity to consist of two components. The homogeneity-based component indicates the degree of affinity between image elements based on the homogeneity of their intensity properties. The object-feature-based component captures the degree of closeness of their intensity properties to some expected values of those properties for the object. A family of non-scale-based and scale-based affinity relations are constructed dictated by how we envisage the two components to characterize objects. A simple and effective method for giving a rough estimate of scale at different locations in the image is presented. The original theoretical and algorithmic framework remains more-or-less the same but considerably improved segmentations result. The method has been tested in several applications qualitatively. A quantitative statistical comparison between the non-scale-based and the scale-based methods was made based on 250 phantom images. These were generated from 10 patient MR brain studies by first segmenting the objects, then setting up appropriate intensity levels for the object and the background, and then by adding five different levels for each of noise and blurring and a fixed slow varying background component. Both the statistical and the subjective tests clearly indicate that the scale-based method is superior to the non-scale-based method in capturing details and in robustness to noise. It is also shown, based on these phantom images, that any (global) optimum threshold selection method will perform inferior to the fuzzy connectedness methods described in this paper.","Title: Landmark Selection For Shape Model Construction Via Equalization Of Variance\nYear: 2008\n\nAbstract: Model-based segmentation approaches, such as those employing Active Shape Models (ASMs), have proved to be useful for medical image segmentation and understanding. To build the model, we need an annotated training set representing correspondences among shapes. Manual positioning of landmarks is a tedious, time consuming, and error prone task, and almost impossible in the 3D space. To overcome some of these drawbacks, we devised an automatic method. Our method is guided by the strategy of equalization of the variance contained in a training set for selecting landmarks. The main premise here is that this strategy itself takes care of the correspondence issue and at the same time deploys landmarks very frugally and optimally considering shape variations. The desired landmarks are positioned around each contour in such a manner as to equally distribute the total variance existing in the training set. The method is evaluated on 40 MRI foot data sets. The results show that, for the same number of landmarks, the proposed method is significantly more compact than manual and equally spaced annotations.","Title: GC-ASM: Synergistic Integration of Graph-Cut and Active Shape Model Strategies for Medical Image Segmentation.\nYear: 2013\n\nAbstract: Image segmentation methods may be classified into two categories: purely image based and model based. Each of these two classes has its own advantages and disadvantages. In this paper, we propose a novel synergistic combination of the image based graph-cut (GC) method with the model based ASM method to arrive at the GC-ASM method for medical image segmentation. A multi-object GC cost function is proposed which effectively integrates the ASM shape information into the GC framework. The proposed method consists of two phases: model building and segmentation. In the model building phase, the ASM model is built and the parameters of the GC are estimated. The segmentation phase consists of two main steps: initialization (recognition) and delineation. For initialization, an automatic method is proposed which estimates the pose (translation, orientation, and scale) of the model, and obtains a rough segmentation result which also provides the shape information for the GC method. For delineation, an iterative GC-ASM algorithm is proposed which performs finer delineation based on the initialization results. The proposed methods are implemented to operate on 2D images and evaluated on clinical chest CT, abdominal CT, and foot MRI data sets. The results show the following: (a) An overall delineation accuracy of TPVF > 96%, FPVF < 0.6% can be achieved via GC-ASM for different objects, modalities, and body regions. (b) GC-ASM improves over ASM in its accuracy and precision to search region. (c) GC-ASM requires far fewer landmarks (about 1/3 of ASM) than ASM. (d) GC-ASM achieves full automation in the segmentation step compared to GC which requires seed specification and improves on the accuracy of GC. (e) One disadvantage of GC-ASM is its increased computational expense owing to the iterative nature of the algorithm.","Title: Local Scale for Boundary Shape Description: Application in Locating Landmarks Automatically\nYear: 2007\n\nAbstract: A good shape descriptor is necessary for automatically identifying landmarks on boundaries. Our method of boundary shape description is based on the notion of cscale, which is a new local scale concept, defined at each boundary element. From this representation we can extract special points of interest such as convex and concave corners, straight lines, circular segments, and inflection points. The results show that this method gives a complete description of shape and allows the automatic positioning of mathematical landmarks, which agree with our intuitive ideas of where landmarks may be defined. This method is applicable to spaces of any dimensionality, although we have focused in this paper on 2D shapes.","Title: Joint graph cut and relative fuzzy connectedness image segmentation algorithm.\nYear: 2013\n\nAbstract: •Novel image segmentation algorithm robust w.r.t. seed choice.•Synergistic combinantion of fuzzy connectedness (FC) and Graph Cut (GC) algorithms.•Better delineation of poorly defined boundary segments than either FC or GC alone.","Title: Linear time algorithms for exact distance transform: elaboration on Maurer et al. algorithm\nYear: 2009\n\nAbstract: In 2003, Maurer at al. (7) published a paper describing an algorithm that computes the exact distance transform in a linear time (with respect to image size) for the rectangular binary images in the k-dimensional space Rk and distance measured with respect to Lp-metric for 1 ≤ p ≤∞ , which includes Euclidean distance L2. In this paper we discuss this algorithm from theoretical and practical points of view. On the practical side, we concentrate on its Euclidean distance version, discuss the possible ways of implementing it as signed distance transform, and experimentally compare implemented algorithms. We also describe the parallelization of these algorithms and the computation time savings associated with such an implementation. The discussed implementations will be made available as a part of the CAVASS software system developed and maintained in our group (5). On the theoretical side, we prove that our version of the signed distance transform algorithm, GBDT , returns, in a linear time, the exact value of the distance from the geometrically defined object boundary. We notice that, actually, the precise form of the algorithm from (7) is not well defined for L1 and L∞ metrics and point to our complete proof (not given in (7)) that all these algorithms work correctly for the Lp-metric with 1 <p< ∞.","Title: Vectorial scale-based fuzzy-connected image segmentation\nYear: 2006\n\nAbstract: This paper presents an extension of previously published theory and algorithms for fuzzy-connected image segmentation. In this approach, a strength of connectedness is assigned to every pair of image elements. This is done by finding the strongest among all possible connecting paths between the two elements in each pair. The strength assigned to a particular path is defined as the weakest affinity between successive pairs of elements along the path. Affinity specifies the degree to which elements hang together locally in the image. A scale is determined at every element in the image that indicates the size of the largest homogeneous hyperball region centered at the element. In determining affinity between any two elements, all elements within their scale regions are considered. This method has been effectively utilized in several medical applications. In this paper, we generalize this method from scalar images to vectorial images. In a vectorial image, scale is defined as the radius of the largest hyperball contained in the same homogeneous region under a predefined condition of homogeneity of the image vector field. Two different components of affinity, namely homogeneity-based affinity and object-feature-based affinity, are devised in a fully vectorial manner. The original relative fuzzy connectedness algorithm is utilized to delineate a specified object via a competing strategy among multiple objects. We have presented several studies to evaluate the performance of this method based on simulated MR images, 20 clinical MR images, and 250 mathematical phantom images. These studies indicate that the fully vectorial fuzzy connectedness formulation has generally overall better accuracy than the method using some intermediate ad hoc steps to fit the vectorial image to a scalar fuzzy connectedness formulation, and precision and efficiency are similar for these two methods.","Title: A GENERAL THEORY OF IMAGE SEGMENTATION: LEVEL SET SEGMENTATION IN THE FUZZY CONNECTEDNESS FRAMEWORK\nYear: 2007\n\nAbstract: In the current vast image segmentation literature, there is a serious lack of methods that would allow theoretical comparison of the algorithms introduced by using different mathematical methodologies. The main goal of this article is to introduce a general theoretical framework for image segmentation that would allow such comparison. The framework is based on the formal definitions designed to answer the following fundamental questions: What is the relation between an idealized image and its digital representation? What properties a segmentation algorithm must satisfy to be acknowledged as acceptable? What does it mean that a digital image segmentation algorithm truly approximates an idealized segmentation model? We use the formulated framework to analyze the front propagation (FP) level set algorithm of Malladi, Sethian, and Vemuri and compare it with the fuzzy connectedness family of algorithms. In particular, we prove that the FP algorithm is weakly model-equivalent with the absolute fuzzy connectedness algorithm of Udupa and Samarasekera used with gradient based affinity. Experimental evidence of this equivalence is also provided. The presented theoretical framework can be used to analyze any arbitrary segmentation algorithm. This line of investigation is a subject of our forthcoming work.","Title: Brain tissue MR-image segmentation via optimum-path forest clustering\nYear: 2012\n\nAbstract: We present an accurate and fast approach for MR-image segmentation of brain tissues, that is robust to anatomical variations and takes an average of less than 1min for completion on modern PCs. The method first corrects voxel values in the brain based on local estimations of the white-matter intensities. This strategy is inspired by other works, but it is simple, fast, and very effective. Tissue classification exploits a recent clustering approach based on the motion of optimum-path forest (OPF), which can find natural groups such that the absolute majority of voxels in each group belongs to the same class. First, a small random set of brain voxels is used for OPF clustering. Cluster labels are propagated to the remaining voxels, and then class labels are assigned to each group. The experiments used several datasets from three protocols (involving normal subjects, phantoms, and patients), two state-of-the-art approaches, and a novel methodology which finds the best choice of parameters for each method within the operational range of these parameters using a training dataset. The proposed method outperformed the compared approaches in speed, accuracy, and robustness.","Title: Joint segmentation of anatomical and functional images: applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images.\nYear: 2013\n\nAbstract: •We present a novel method for co-segmentation of anatomical and functional images.•We extend the random walk method for jointly delineating multiple objects from multiple image modalities.•Our method segments PET, PET-CT, MRI-PET, and fused MRI-PET-CT images with high accuracy in seconds.•Users can easily adapt our algorithm to cases where one-to-one correspondence between different modalities is not satisfied.•Our method offers automated object detection via interesting uptake region algorithm to avoid users having to provide seeds.","Title: Relative fuzzy connectedness among multiple objects: theory, algorithms, and applications in image segmentation\nYear: 2001\n\nAbstract: The notion of fuzzy connectedness captures the idea of “hanging-togetherness” of image elements in an object by assigning a strength of connectedness to every possible path between every possible pair of image elements. This concept leads to powerful image segmentation algorithms based on dynamic programming whose effectiveness has been demonstrated on thousands of images in a variety of applications. In a previous framework, we introduced the notion of relative fuzzy connectedness for separating a foreground object from a background object. In this framework, an image element c is considered to belong to that among these two objects with respect to whose reference image element c has the higher strength of connectedness. In fuzzy connectedness, a local fuzzy relation called affinity is used on the image domain. This relation was required for theoretical reasons to be of fixed form in the previous framework. In the present paper, we generalize relative connectedness to multiple objects, allowing all objects (of importance) to compete among themselves to grab membership of image elements based on their relative strength of connectedness to reference elements. We also allow affinity to be tailored to the individual objects. We present a theoretical and algorithmic framework and demonstrate that the objects defined are independent of the reference elements chosen as long as they are not in the fuzzy boundary between objects. Examples from medical imaging are presented to illustrate visually the effectiveness of multiple object relative fuzzy connectedness. A quantitative evaluation based on 160 mathematical phantom images demonstrates objectively the effectiveness of relative fuzzy connectedness with object-tailored affinity relation.","Title: Linear Time Algorithms for Exact Distance Transform\nYear: 2011\n\nAbstract: In 2003, Maurer et al. (IEEE Trans. Pattern Anal. Mach. Intell. 25:265---270, 2003) published a paper describing an algorithm that computes the exact distance transform in linear time (with respect to image size) for the rectangular binary images in the k-dimensional space 驴 k and distance measured with respect to L p -metric for 1驴p驴驴, which includes Euclidean distance L 2. In this paper we discuss this algorithm from theoretical and practical points of view. On the practical side, we concentrate on its Euclidean distance version, discuss the possible ways of implementing it as signed distance transform, and experimentally compare implemented algorithms. We also describe the parallelization of these algorithms and discuss the computational time savings associated with them. All these implementations will be made available as a part of the CAVASS software system developed and maintained in our group (Grevera et al. in J. Digit. Imaging 20:101---118, 2007). On the theoretical side, we prove that our version of the signed distance transform algorithm, GBDT, returns the exact value of the distance from the geometrically defined object boundary. We provide a complete proof (which was not given of Maurer et al. (IEEE Trans. Pattern Anal. Mach. Intell. 25:265---270, 2003) that all these algorithms work correctly for L p -metric with 1pL 1 and L 驴 metrics. In addition, we show that the algorithm can be used to find, in linear time, the exact value of the diameter of an object, that is, the largest possible distance between any two of its elements.","Title: A new method of automatic landmark tagging for shape model construction via local curvature scale\nYear: 2008\n\nAbstract: Segmentation of organs in medical images is a difficult task requiring very often the use of model-based approaches. To build the model, we need an annotated training set of shape examples with correspondences indicated among shapes. Manual positioning of landmarks is a tedious, time-consuming, and error prone task, and almost impossible in the 3D space. To overcome some of these drawbacks, we devised an automatic method based on the notion of c-scale, a new local scale concept. For each boundary element b, the arc length of the largest homogeneous curvature region connected to b is estimated as well as the orientation of the tangent at b. With this shape description method, we can automatically locate mathematical landmarks selected at different levels of detail. The method avoids the use of landmarks for the generation of the mean shape. The selection of landmarks on the mean. shape is done automatically using the c-scale method. Then, these landmarks are propagated to each shape in the training set, defining this way the correspondences among the shapes. Altogether 12 strategies are described along these lines. The methods are evaluated on 40 MRI foot data sets, the object of interest being the talus bone. The results show that, for the same number of landmarks, the proposed methods are more compact than manual and equally spaced annotations. The approach is applicable to spaces of any dimensionality, although we have focused in this paper on 2D shapes.","Title: Some new concepts for encoding line patterns\nYear: 1975\n\nAbstract: A scheme for encoding patterns composed of straight strokes is presented. The code for such a configuration is a sequence of turning and end points. These points which completely characterize the patterns considered are extracted from the straight line segments in the pattern oriented in eight principal directions. The method has the advantage of working directly with the given thick pattern rather than with its thinned version. It is shown how these two new concepts viz. turning and end points can be extended to the recognition problem. Alphanumerals of a particular font are analysed using simple decision methods. The scheme seems to have wider applicability in the area of network analysis, graph theory and computer perception of planar drawings.","Title: New Variants of a Method of MRI Scale Normalization\nYear: 1999\n\nAbstract: One of the major drawbacks of Magnetic Resonance Imaging (MRI) has been the lack of a standard and quantifiable interpretation of image intensities. This causes many diffculties in image display and analysis. We have devised a two-step method wherein all images can be transformed in such a way that for the same protocol and body region, in the transformed images similar intensities will have similar tissue meaning. Normalized images can be displayed with fixed windows without the need of per case adjustment. More importantly, extraction of quantitative information about healthy organs or about abnormities, such as tumors, can considerably be simplified. This paper introduces and compares new variants of this normalization method that can help to overcome some of the problems with the original method.","Title: A framework for evaluating image segmentation algorithms\nYear: 2006\n\nAbstract: The purpose of this paper is to describe a framework for evaluating image segmentation algorithms. Image segmentation consists of object recognition and delineation. For evaluating segmentation methods, three factors—precision (reliability), accuracy (validity), and efficiency (viability)—need to be considered for both recognition and delineation. To assess precision, we need to choose a figure of merit, repeat segmentation considering all sources of variation, and determine variations in figure of merit via statistical analysis. It is impossible usually to establish true segmentation. Hence, to assess accuracy, we need to choose a surrogate of true segmentation and proceed as for precision. In determining accuracy, it may be important to consider different ‘landmark’ areas of the structure to be segmented depending on the application. To assess efficiency, both the computational and the user time required for algorithm training and for algorithm execution should be measured and analyzed. Precision, accuracy, and efficiency factors have an influence on one another. It is difficult to improve one factor without affecting others. Segmentation methods must be compared based on all three factors, as illustrated in an example wherein two methods are compared in a particular application domain. The weight given to each factor depends on application.","Title: Fast Digital Perspective Shell Rendering\nYear: 1999\n\nAbstract: Volume visualization methods usually require extensive storage and time-intensive computations. To make them viable in many applications, researchers have proposed various types of hardware-based and software-based solutions. In most of these solutions, the rendering is based on orthographic parallel projection. However, in applications such as virtual endoscopy, perspective projection is necessary to allow the observer to travel inside the structures. In this paper, we extend one of these software-based solutions, called shell rendering, to provide fast digital perspective projection. The main idea in digital perspective is that each voxel should be rendered using an integer number of pixels which is inversely proportional to the distance between the voxel and the observer and that that distance can be discretized. We show that the new method, called digital perspective shell rendering, provides the same image quality as orthographic parallel projection","Title: Image filtering via generalized scale.\nYear: 2008\n\nAbstract: In medical imaging, low signal-to-noise ratio (SNR) and/or contrast-to-noise ratio (CNR) often cause many image processing algorithms to perform poorly. Postacquisition image filtering is an important off-line image processing approach widely employed to enhance the SNR and CNR. A major drawback of many filtering techniques is image degradation by diffusing/blurring edges and/or fine structures. In this paper, we introduce a scale-based filtering method that employs scale-dependent diffusion conductance to perform filtering. This approach utilizes novel object scale information via a concept called generalized scale, which imposes no shape, size, or anisotropic constraints unlike previously published ball scale-based filtering strategies. The object scale allows us to better control the filtering process by constraining smoothing in regions with fine details and in the vicinity of boundaries while permitting effective smoothing in the interior of homogeneous regions. A new quantitative evaluation strategy that captures the SNR to CNR trade-off behavior of filtering methods is presented. The evaluations based on the Brainweb data sets show superior performance of generalized scale-based diffusive filtering over two existing methods, namely, ball scale-based and nonlinear complex diffusion processes. Qualitative experiments based on both phantom and patient magnetic resonance images demonstrate that the generalized scale-based approach leads to better preservation of fine details and edges.","Title: Generalized Scale-Based Image Filtering\nYear: 2005\n\nAbstract: In medical imaging, low signal-to-noise ratio (SNR) and/or contrast-to-noise ratio (CNR) often cause many image processing algorithms to perform poorly. Postacquisition image filtering is an important off-line image processing approach widely employed to enhance the SNR and CNR. A major drawback of many filtering techniques is image degradation by diffusing/blurring edges and/or fine structures. In this paper, we introduce a scale-based filtering method that employs scale-dependent diffusion conductance to perform filtering. This approach utilizes novel object scale information via a concept called generalized ball scale, which imposes no shape, size, or anisotropic constraints unlike previously published ball scale-based filtering strategies. The object scale allows us to better control the filtering process by constraining smoothing in regions with fine details and in the vicinity of boundaries while permitting effective smoothing in the interior of homogeneous regions. Quantitative evaluations based on the Brainweb data sets show superior performance of generalized ball scale-based diffusive filtering over two existing methods. namely, ball scale-based and nonlinear complex diffusion process. Qualitative experiments based on both phantom and patient magnetic resonance images demonstrate that the generalized ball scale-based approach leads to better preserved fine details and edges.","Title: Hybrid Segmentation of Anatomical Data\nYear: 2001\n\nAbstract: We propose new hybrid methods for automated segmentation of radiological patient data and the Visible Human data. In this paper, we integrate boundary-based and region-based segmentation methods which amplifies the strength but reduces the weakness of both approaches. The novelty comes from combining a boundary-based method, the deformable model-based segmentation with region-based segmentation methods, the fuzzy connectedness and Voronoi Diagram-based segmentation, to develop hybrid methods that yield high precision, accuracy and efficiency. This work is a part of a NLM funded effort to provide a fully implemented and tested Visible Human Project Segmentation and Registration Toolkit (Insight).","Title: Intensity Standardization Simplifies Brain MR Image Segmentation.\nYear: 2009\n\nAbstract: Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity-inhomogeneity-correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.","Title: Performance evaluation of finite normal mixture model-based image segmentation techniques.\nYear: 2003\n\nAbstract: Finite Normal Mixture (FNM) model-based image segmentation techniques adopt the following detection-estimation-classification paradigm: 1) detect the number of image regions by using theoretical information criteria; 2) estimate model parameters by using expectation-maximization (EM)/classification-maximization (CM) algorithms; and 3) classify pixels into regions by using various classifiers. This paper presents a theoretical framework to evaluate the performance of this class of image segmentation techniques. For the detection performance, probabilities of over-detection and under-detection of the number of image regions are defined, and the associated formulae in terms of model parameters and image quality are derived. For the estimation performance, both EM and CM algorithms are showed to produce asymptotically unbiased ML estimates of model parameters in the case of no-overlap. Cramer-Rao bounds of variances of these estimates are derived. For the classification performance, misclassification probability for the Bayesian classifier is defined, and a simple formula based on parameter estimates and classified data is derived to evaluate segmentation errors. This evaluation method provides both theoretically approachable accuracy limits of the techniques and practically achievable performance of the given images. Theoretical and experimental results are in good agreement and indicate that, for images of moderate quality, the detection operation is robust, the parameter estimates are accurate, and the segmentation errors are small.","Title: Ball-Scale Based Hierarchical Multi-Object Recognition in 3D Medical Images\nYear: 2010\n\nAbstract: This paper investigates, using prior shape models and the concept of ball scale (b-scale), ways of automatically recognizing objects in 3D images without performing elaborate searches or optimization. That is, the goal is to place the model in a single shot close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. This is achieved via the following set of key ideas: (a) A semi-automatic way of constructing a multi-object shape model assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship between objects in the training images and their intensity patterns captured in b-scale images. (c) A hierarchical mechanism of positioning the model, in a one-shot way, in a given image from a knowledge of the learnt pose relationship and the b-scale image of the given image to be segmented. The evaluation results on a set of 20 routine clinical abdominal female and male CT data sets indicate the following: (1) Incorporating a large number of objects improves the recognition accuracy dramatically. (2) The recognition algorithm can be thought as a hierarchical framework such that quick replacement of the model assembly is defined as coarse recognition and delineation itself is known as finest recognition. (3) Scale yields useful information about the relationship between the model assembly and any given image such that the recognition results in a placement of the model close to the actual pose without doing any elaborate searches or optimization. (4) Effective object recognition can make delineation most accurate.","Title: Clutter-Free Volume Rendering For Magnetic Resonance Angiography Using Fuzzy Connectedness\nYear: 2000\n\nAbstract: The purpose of this paper is to describe a practical method for the clutter-free, three-dimensional (3D) volume rendering of magnetic resonance angiographic (MRA) data. In MRA, clutter due to artifacts or nearby high-intensity structures prevents clear visualization of the vessel under investigation. We offer an alternative to the manual editing that is commonly used to remove clutter. The method is near automatic and requires the user to point at structures on a 3D maximum intensity projection (MIP) display. It utilizes recently developed fuzzy connected object delineation algorithms to extract the vessels of interest. Because the resulting definition is nonbinary, it can be displayed via MIP or more sophisticated volume-rendering techniques. The improved renditions are illustrated with several MRA studies. Implementation of the fuzzy connectedness method proved to be effective in removing the associated clutter in the images and, in some cases, dramatically improving visibility. Additionally, vessels could be extracted with a nominal number of points selected within the object by the user that retained most of the information present in the conventional MIP display. This could all be performed in a practical time frame: the first vessel delineation in 30 s, subsequent delineations in 2-10 s per view, all on a 100-MHz Pentium PC. Automatic delineation of vessels for 3D MRA visualization is feasible via fuzzy connectedness principles. The method retains the original intensity constitution, an advantage of the MIP method, and mostly eliminates the clutter commonly observed in MIP. Its speed and effectiveness make it feasible for routine clinical use. (C) 2000 John Wiley & Sons, Inc.","Title: Fuzzy Connectedness Image Segmentation in Graph Cut Formulation: A Linear-Time Algorithm and a Comparative Analysis.\nYear: 2012\n\nAbstract: A deep theoretical analysis of the graph cut image segmentation framework presented in this paper simultaneously translates into important contributions in several directions.The most important practical contribution of this work is a full theoretical description, and implementation, of a novel powerful segmentation algorithm, GC. The output of GC coincides with a version of a segmentation algorithm known as Iterative Relative Fuzzy Connectedness, IRFC. However, GC is considerably faster than the classic IRFC algorithm, which we prove theoretically and show experimentally. Specifically, we prove that, in the worst case scenario, the GC algorithm runs in linear time with respect to the variable =||+||, where || is the image scene size and || is the size of the allowable range, , of the associated weight/affinity function. For most implementations, is identical to the set of allowable image intensity values, and its size can be treated as small with respect to ||, meaning that ()=(||). In such a situation, GC runs in linear time with respect to the image size ||.We show that the output of GC constitutes a solution of a graph cut energy minimization problem, in which the energy is defined as the norm ∥ ∥ of the map that associates, with every element from the boundary of an object , its weight (). This formulation brings IRFC algorithms to the realm of the graph cut energy minimizers, with energy functions ∥ ∥ for ∈[1,∞]. Of these, the best known minimization problem is for the energy ∥ ∥, which is solved by the classic min-cut/max-flow algorithm, referred to often as the Graph Cut algorithm.We notice that a minimization problem for ∥ ∥, ∈[1,∞), is identical to that for ∥ ∥, when the original weight function is replaced by . Thus, any algorithm GC solving the ∥ ∥ minimization problem, solves also one for ∥ ∥ with ∈[1,∞), so just two algorithms, GC and GC, are enough to solve all ∥ ∥-minimization problems. We also show that, for any fixed weight assignment, the solutions of the ∥ ∥-minimization problems converge to a solution of the ∥ ∥-minimization problem (∥ ∥=lim∥ ∥ is not enough to deduce that).An experimental comparison of the performance of GC and GC algorithms is included. This concentrates on comparing the actual (as opposed to provable worst scenario) algorithms’ running time, as well as the influence of the choice of the seeds on the output.","Title: Fuzzy Connected Object Delineation: Axiomatic Path Strength Definition and the Case of Multiple Seeds\nYear: 2001\n\nAbstract: This paper presents an extension of previously published theory and algorithms for object definition based on fuzzy connectedness. In this approach, a strength of connectedness is assigned between every pair of image elements. This is done by finding the strongest among all possible connecting paths between the two elements in each pair. The strength assigned to a particular path is defined as the weakest affinity between successive pairs of elements along the path. Affinity specifies the degree to which elements hang together locally in the image and is determined considering how close the elements are in space and in intensity features. A fuzzy connected object containing a particular seed element specified within the object is computed via dynamic programming. In all reported works so far, the minimum of affinities has been considered to define path strength and the maximum of path strengths has been used to define fuzzy connectedness. The question thus remained all along as to whether there are other valid formulations for fuzzy connectedness. One of the main contributions of this paper is a theoretical investigation under reasonable axioms to establish that maximum of path strengths of minimum of affinities along each path is indeed the one and only valid choice for defining fuzzy connectedness. In the past, a single fuzzy connected object was specified through a single seed element indicated interactively within the object region. When many objects are to be identified in a single image, interactive seed specification becomes cumbersome. Further, selecting exactly one element in each region automatically is more difficult than identifying a set of elements. Hence the theory and algorithms for the single-element case need to be generalized to multiple elements for more effective practical use. This is the second main contribution of this paper. The importance of multiple seeded fuzzy connected object definition from the considerations of both practicality and automation in image segmentation are described and illustrated with examples taken from several real medical applications.","Title: Cloud bank: a multiple clouds model and its use in MR brain image segmentation\nYear: 2009\n\nAbstract: We present improvements to a recently proposed model-based segmentation strategy, called Clouds. We demonstrate the usefulness of the new model Cloud Bank in the automatic MR-image segmentation of the brain (without medulla and spinal cord), the cerebellum, and each brain hemisphere. These structures are connected in several parts, imposing serious challenges for segmentation. The results indicate that the new model is a fast and accurate tool to eliminate user's intervention or, at least, reduce it to simple corrections, in the context of brain image segmentation.","Title: User-steered image segmentation paradigms: live wire and live lane\nYear: 1998\n\nAbstract: In multidimensional image analysis, there are, and will continue to be, situations wherein automatic image segmentation methods fail, calling for considerable user assistance in the process. The main goals of segmentation research for such situations ought to be (i) to provide effective control to the user on the segmentation process while it is being executed, and (ii) to minimize the total user's time required in the process. With these goals in mind, we present in this paper two paradigms, referred to as live wire and live lane , for practical image segmentation in large applications. For both approaches, we think of the pixel vertices and oriented edges as forming a graph, assign a set of features to each oriented edge to characterize its ``boundariness,'' and transform feature values to costs. We provide training facilities and automatic optimal feature and transform selection methods so that these assignments can be made with consistent effectiveness in any application. In live wire, the user first selects an initial point on the boundary. For any subsequent point indicated by the cursor, an optimal path from the initial point to the current point is found and displayed in real time. The user thus has a live wire on hand which is moved by moving the cursor. If the cursor goes close to the boundary, the live wire snaps onto the boundary. At this point, if the live wire describes the boundary appropriately, the user deposits the cursor which now becomes the new starting point and the process continues. A few points (live-wire segments) are usually adequate to segment the whole 2D boundary. In live lane, the user selects only the initial point. Subsequent points are selected automatically as the cursor is moved within a lane surrounding the boundary whose width changes as a function of the speed and acceleration of cursor motion. Live-wire segments are generated and displayed in real time between successive points. The users get the feeling that the curve snaps onto the boundary as and while they roughly mark in the vicinity of the boundary. We describe formal evaluation studies to compare the utility of the new methods with that of manual tracing based on speed and repeatability of tracing and on data taken from a large ongoing application. The studies indicate that the new methods are statistically significantly more repeatable and 1.5–2.5 times faster than manual tracing.","Title: Fuzzy-connected 3D image segmentation at interactive speeds\nYear: 2002\n\nAbstract: Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem with these algorithms has been their excessive computational requirements. In an attempt to substantially speed them up, in the present paper, we study systematically a host of 18 'optimal' graph search algorithms. Extensive testing of these algorithms on a variety of 3D medical images taken from large ongoing applications demonstrates that a 20-1000-fold improvement over current speeds is achievable with a combination of algorithms and fast modern PCs. Utilizing efficient algorithms and careful selection of implementations can speed up the computation of fuzzy connectedness values by a factor of 16-29 (on the same hard-ware), as compared to the implementation previously used in our applications utilizing fuzzy object segmentation. The optimality of an algorithm depends on the input data as well as on the choice of the fuzzy affinity relation. The running time is reduced considerably (by a factor up to 34 for brain MR and even more for bone CT), when the algorithms make use of pre-determined thresholds for the fuzzy objects. The reliable recognition (assisted by human operators) and the accurate, efficient, and sophisticated delineation (automatically performed by the computer) can be effectively incorporated into a single interactive process. If images having intensities with tissue-specific meaning (such as CT or standardized MR images) are utilized, most of the parameters for the segmentation method can be fixed once for all, all intermediate data (feature and fuzzy affinity values for the whole scene) can be computed before the user interaction is needed and the user can be provided with more information at the time of interaction.","Title: Local curvature scale: a new concept of shape description\nYear: 2008\n\nAbstract: Shape description plays a fundamental role in computer vision and pattern recognition, especially in the fields of shape analysis, image segmentation, and registration. Shape representations must be unique, complete and should be able to reflect the differences between similar objects while abstracting from detail and keeping the basic features. Although many methods for shape description exist, they are usually application dependent. The proposed method of boundary shape description is based on the notion of curvature-scale, which is a new local scale concept, defined at each boundary element. From this representation, we can extract special points of interest such as convex and concave corners, straight lines, circular segments, and inflection points. This method is different from existing methods of curvature estimation and can be directly applied to digital boundaries without requiring prior approximation of the boundary. The results show that it produces a complete boundary shape description capable of handling different levels of shape detail. It also has numerous potential applications such as automatic landmark tagging which becomes necessary to build model-based approaches toward the goal of organ modeling and segmentation. The method is applicable to spaces of any dimensionality, although we have focused in this paper on 2D shapes.","Title: A justification of a fast surface tracking algorithm\nYear: 1992\n\nAbstract: In many three-dimensional (3D) imaging applications in science, medicine, and engineering, object information captured in a 3D digital image is visualized and analyzed based on object surfaces extracted from the image. Recently Gordon and Udupa (Comput. Vision Graphics Image Process.45, 1989, 196–214) reported a fast algorithm for tracking such surfaces, whose correct behavior has only been a conjecture but observed nonetheless in all uses of this algorithm in a variety of biomedical applications. In the present paper, we prove the correctness of their algorithm by showing that the surfaces generated by the algorithm correspond to connected components of boundary elements and that they possess Jordan boundary properties.","Title: Clouds: A Model For Synergistic Image Segmentation\nYear: 2008\n\nAbstract: Image segmentation consists of recognizing the object in the image and precisely delineating its spatial extent. We present a model, called clouds, that exploits the synergism which commonly exists between recognition and delineation for more effective segmentation. The model can reduce user's intervention to simple corrections or even eliminate it altogether, achieving high accuracy. We evaluate the method in the task of 3D MR image segmentation of the brain in isolating automatically: brain without medulla and spinal cord; just the cerebellum; and the brain hemispheres without medulla, spinal cord, and cerebellum. These structures are connected in several parts, which poses a serious challenge for simplistic segmentation strategies. The entire process takes a few seconds on modem PCs and provides accurate results. The applications for clouds go beyond medical imaging, opening new vistas in a variety of areas served by segmentation.","Title: Boundary and object labelling in three-dimensional images\nYear: 1990\n\nAbstract: There are many imaging modalities (e.g., medical imaging scanners) that capture information about internal structures and generate three-dimensional (3D) digital images of the distribution of some physical property of the material of the structure. Such images have been found to be very useful in analyzing the form and function of the structure and in detecting and correcting deformities in the structure. Visualization of 3D structures is an essential component of such analyses. One commonly used approach to visualization consists of identifying the structure of interest, forming its surfaces, and then rendering the surfaces on a two-dimensional screen. This paper addresses the surface formation problem assuming that object identification has already been done and a 3D binary image is available that represents the structure. For the existing 3D boundary tracking algorithms, the user has to somehow specify each surface that is to be tracked. Often, the 3D image consists of many surfaces of interest. Their manual specification is very tedious and may be impossible if the structure is of complex shape. This paper describes a methodology for automatically tracking all boundary surfaces—i.e., labelling boundary surfaces—in the given 3D image. The algorithms also generate additional information from which the 3D connected components in the image are trivially obtained. Examples from medical imaging are included to illustrate the usefulness of the new methodology.","Title: Image background inhomogeneity correction in MRI via intensity standardization.\nYear: 2009\n\nAbstract: An automatic, simple, and image intensity standardization-based strategy for correcting background inhomogeneity in MR images is presented in this paper. Image intensities are first transformed to a standard intensity gray scale by a standardization process. Different tissue sample regions are then obtained from the standardized image by simply thresholding based on fixed intensity intervals. For each tissue region, a polynomial is fitted to the estimated discrete background intensity variation. Finally, a combined polynomial is determined and used for correcting the intensity inhomogeneity in the whole image. The above procedure is repeated on the corrected image iteratively until the size of the extracted tissue regions does not change significantly in two successive iterations. Intensity scale standardization is effected to make sure that the corrected image is not biased by the fitting strategy. The method has been tested on a number of simulated and clinical MR images. These tests and a comparison with the method of non-parametric non-uniform intensity normalization (N3) indicate that the method is effective in background intensity inhomogeneity correction and may have a slight edge over the N3method.","Title: Automatic Landmark Selection for Active Shape Models\nYear: 2005\n\nAbstract: The first step in Active Shape Model (ASM) based image segmentation and processing is to create a point distribution model (PDM) during the training phase. Correct point (landmark) correspondences across each of the training shapes must be determined for a successful and effective statistical model building process. Effective and automatic solutions for this problem are needed for the practical use of ASM methods. In this paper, we provide a solution for this problem which consists of: (i) a process of generating a mean shape without requiring landmarks, (ii) a process of automatic landmark selection for the mean shape, and (iii) a process of propagating landmarks on to each training shape for defining, landmarks in them. This paper describes the method of generating the mean shape, and the landmark selection and correspondence process. Although the method is generally applicable to spaces of any dimensionality, our first implementation and evaluation has been carried out for 2D shapes. The method is evaluated on 20 MRI foot data sets, the object of interest being the talus bone. The results indicate that, for the same given number of points, better compactness (number of parameters) of the ASM by using our method can be achieved than by using the commonly used equi-spaced point selection method.","Title: Generalized scale: theory, algorithms, and application to image inhomogeneity correction\nYear: 2006\n\nAbstract: Scale is a fundamental concept useful in almost all image processing and analysis tasks including segmentation, filtering, interpolation, registration, visualization, and quantitative analysis. Broadly speaking, scale related work can be divided into three categories: (1) multi-scale or scale-space representation, (2) local scale, and (3) locally adaptive scale. The original formulation of scale in the form of scale-space theory came from the consideration of the presence of multiple scales in nature and the desire to represent measured signals at multiple scales. However, since this representation did not suggest how to select appropriate scales, the notion of local scale was proposed to pick the right scale for a particular application from the multi-scale representation of the image. Recently, there has been considerable interest in developing locally adaptive scales, the idea being to consider the local size of object in carrying out whatever local operations that are to be done on the image. However, existing locally adaptive models are limited by shape, size, and anisotropic constraints. In this work, we propose a generalized scale model which is spatially adaptive like other local morphometric models, and yet possesses the global spirit of multi-scale representations. We postulate that this semi-locally adaptive nature of generalized scale confers it certain distinct advantages over other global and local scale formulations. We also present a variant of the generalized scale notion that we refer to as the generalized ball scale, which, in addition to having the advantages of the generalized scale model, also has superior noise resistance properties. Both scale models are dependent only on two parameters for their estimation, and we demonstrate means by which optimal values for these parameters can be estimated. Both the generalized scales can be readily applied to solving a range of image processing problems. One such problem that we address in this paper is correcting for slowly varying background spatial intensity in Magnetic Resonance images. We demonstrate the superiority of the generalized scale based inhomogeneity correction methods over an existing scale-based correction technique.","Title: Shape modeling via local curvature scale\nYear: 2010\n\nAbstract: Segmentation and modeling of organs using model-based approaches require a priori information which is often given by manually tagging landmarks on a training set of shapes. This is a tedious, time-consuming, and error prone task. To overcome some of these drawbacks, focusing on 2D shapes, we devised an automatic method based on the notion of curvature scale - a new local scale concept. This shape descriptor is used to automatically locate mathematical landmarks on the mean of the shapes in the training set, which are then propagated to the training shapes. Altogether 12 different strategies are described and are evaluated in different combinations in terms of compactness on two data sets - 40 CT images of the liver and 40 MR images of the talus bone of the foot. The results show that, for the same number of landmarks, the proposed methods are more compact than manual and equally spaced annotations.","Title: The role of intensity standardization in medical image registration\nYear: 2010\n\nAbstract: Acquisition-to-acquisition signal intensity variations (non-standardness) are inherent in MR images. Standardization is a post processing method for correcting inter-subject intensity variations through transforming all images from the given image gray scale into a standard gray scale wherein similar intensities achieve similar tissue meanings. The lack of a standard image intensity scale in MRI leads to many difficulties in tissue characterizability, image display, and analysis, including image segmentation. The influence of standardization on these tasks has been documented well; however, effects of standardization on medical image registration have not been studied yet. In this paper, we investigate the role of intensity standardization in registration tasks with systematic and analytic evaluations involving clinical MR images. We conducted nearly 20,000 clinical MR image registration experiments and evaluated the quality of registrations both quantitatively and qualitatively. The evaluations show that intensity variations between images degrades the accuracy of registration performance. The results imply that the accuracy of image registration not only depends on spatial and geometric similarity but also on the similarity of the intensity values for the same tissues in different images.","Title: Breast Tissue Density Quantification via Digitized Mammograms.\nYear: 2001\n\nAbstract: Studies reported in the literature indicate that breast cancer risk is associated with mammographic densities. An objective, repeatable, and a quantitative measure of risk derived from mammographic densities will be of considerable use in recommending alternative screening paradigms and/or preventive measures. However, image processing efforts toward this goal seem to be sparse in the literature, and automatic and efficient methods do not seem to exist. In this paper, we describe and validate an automatic and reproducible method to segment dense tissue regions from fat within breasts from digitized mammograms using scale-based fuzzy connectivity methods. Different measures for characterizing mammographic density are computed from the segmented regions and their robustness in terms of their linear correlation across two different projections-cranio-caudal and medio-lateral-oblique-are studied. The accuracy of the method is studied by computing the area of mismatch of segmented dense regions using the proposed method and using manual outlining. A comparison between the mammographic density parameter taking into account the original intensities and that just considering the segmented area indicates that the former may have some advantages over the latter.","Title: A Task-Specific Evaluation of Three-Dimensional Image Interpolation Techniques.\nYear: 1999\n\nAbstract: Image interpolation is an important operation that is widely used in medical imaging, image processing and computer graphics. A variety of interpolation methods are available in the literature. However, their systematic evaluation is lacking. In a previous paper (ibid., vol. 15, pp. 881-92, Dec. 1996), we presented a framework for the task-independent comparison of interpolation methods based on c...","Title: Affinity functions in fuzzy connectedness based image segmentation I: Equivalence of affinities\nYear: 2010\n\nAbstract: Fuzzy connectedness (FC) constitutes an important class of image segmentation schemas. Although affinity functions represent the core aspect (main variability parameter) of FC algorithms, they have not been studied systematically in the literature. In this paper, we began filling this gap by introducing and studying the notion of equivalent affinities: if any two equivalent affinities are used in the same FC schema to produce two versions of the algorithm, then these algorithms are equivalent in the sense that they lead to identical segmentations. We give a complete and elegant characterization of the affinity equivalence. We also demonstrate that any segmentation obtained via a relative fuzzy connectedness (RFC) algorithm can be viewed as segmentation obtained via absolute fuzzy connectedness (AFC) algorithm with an automatic and adaptive threshold detection. Since the main goal of the paper is to identify, by formal mathematical arguments, the affinity functions that are equivalent, extensive experimental confirmations are not needed - they show completely identical segmentations - and as such, only relevant examples of the theoretical results are provided.","Title: A 3D generalization of user-steered live-wire segmentation\nYear: 2000\n\nAbstract: We have been developing user-steered image segmentation methods for situations which require considerable human assistance in object definition. In the past, we have presented two paradigms, referred to as live-wire and live-lane, for segmenting 2D/3D/4D object boundaries in a slice-by-slice fashion, and demonstrated that live-wire and live-lane are more repeatable, with a statistical significance level of P<0.03, and are 1.5–2.5 times faster, with a statistical significance level of P<0.02, than manual tracing. In this paper, we introduce a 3D generalization of the live-wire approach for segmenting 3D/4D object boundaries which further reduces the time spent by the user in segmentation. In a 2D live-wire, given a slice, for two specified points (pixel vertices) on the boundary of the object, the best boundary segment is the minimum-cost path between the two points, described as a set of oriented pixel edges. This segment is found via Dijkstra’s algorithm as the user anchors the first point and moves the cursor to indicate the second point. A complete 2D boundary is identified as a set of consecutive boundary segments forming a “closed”, “connected”, “oriented” contour. The strategy of the 3D extension is that, first, users specify contours via live-wiring on a few slices that are orthogonal to the natural slices of the original scene. If these slices are selected strategically, then we have a sufficient number of points on the 3D boundary of the object to subsequently trace optimum boundary segments automatically in all natural slices of the 3D scene. A 3D object boundary may define multiple 2D boundaries per slice. The points on each 2D boundary form an ordered set such that when the best boundary segment is computed between each pair of consecutive points, a closed, connected, oriented boundary results. The ordered set of points on each 2D boundary is found from the way the users select the orthogonal slices. Based on several validation studies involving segmentation of the bones of the foot in MR images, we found that the 3D extension of live-wire is more repeatable, with a statistical significance level of P<0.0001, and 2–6 times faster, with a statistical significance level of P<0.01, than the 2D live-wire method, and 3–15 times faster than manual tracing.","Title: Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation\nYear: 2002\n\nAbstract: The notion of fuzzy connectedness captures the idea of \"hanging-togetherness\" of image elements in an object by assigning a strength of connectedness to every possible path between every possible pair of image elements. This concept leads to powerful image segmentation algorithms based on dynamic programming whose effectiveness has been demonstrated on 1,000s of images in a variety of applications. In the previous framework, a fuzzy connected object is defined with a threshold on the strength of connectedness. In this paper, we introduce the notion of relative connectedness that overcomes the need for a threshold and that leads to more effective segmentations. The central idea is that an object gets defined in an image because of the presence of other co-objects. Each object is initialized by a seed element. An image element c is considered to belong to that object with respect to whose reference image element c has the highest strength of connectedness. In this fashion, objects compete among each other utilizing fuzzy connectedness to grab membership of image elements. We present a theoretical and algorithmic framework for defining objects via relative connectedness and demonstrate utilizing the theory that the objects defined are independent of reference elements chosen as long as they are not in the fuzzy boundary between objects. An iterative strategy is also introduced wherein the strongest relative connected core parts are first defined and iteratively relaxed to conservatively capture the more fuzzy parts subsequently. Examples from medical imaging are presented to illustrate visually the effectiveness of relative fuzzy connectedness. A quantitative mathematical phantom study involving 160 images is conducted to demonstrate objectively the effectiveness of relative fuzzy connectedness.","Title: Efficient computation of enclosed volume and surface area from the same triangulated surface representation.\nYear: 2011\n\nAbstract: We demonstrate that the volume enclosed by triangulated surfaces can be computed efficiently in the same elegant way the volume enclosed by digital surfaces can be computed by digital surface integration. Although digital surfaces are effective and efficient for visualization and volume measurement, their drawback is that surface area measurements derived from them are inaccurate. On the other hand, triangulated surfaces give more accurate surface area measurements, but volume measurements and visualization are less efficient. Our data structure (called t-shell) for representing triangulated digital surfaces retains advantages and overcomes difficulties of both the digital and the triangulated surfaces. We create a lookup table with area and volume contributions for each of the 256 Marching Cubes configurations. When scanning the shell (e.g., while creating it), the surface area and volume are incrementally computed by using the lookup table and the current x co-ordinate, where the sign of the x component of the triangle normal indicates the sign of the volume contribution. We have computed surface area and volume for digitized mathematical phantoms, physical phantoms, and real objects. The experiments show that triangulated surface area is more accurate, triangulated volume follows digital volume closely, and that the values get closer to the true value with decreasing voxel size.","Title: Multiple Sclerosis Lesion Quantification Using Fuzzy-Connectedness Principles.\nYear: 1997\n\nAbstract: Multiple sclerosis (MS) is a disease of the white matter. Magnetic resonance imaging (MRI) is proven to be a sensitive method of monitoring the progression of this disease and of its changes due to treatment protocols. Quantification of the severity of the disease through estimation of MS lesion volume via MR imaging is vital for understanding and monitoring the disease and its treatment. This pap...","Title: Segmentation and Evaluation of Adipose Tissue from Whole Body MRI Scans\nYear: 2003\n\nAbstract: Accurate quantification of total body and the distribution of regional adipose tissue using manual segmentation is a challenging problem due to the high variation between manual delineations. Manual segmentation also requires highly trained experts with knowledge of anatomy. We present a hybrid segmentation method that provides robust delineation results for adipose tissue from whole body MRI scans. A formal evaluation of accuracy of the segmentation method is performed. This semi-automatic segmentation algorithm reduces significantly the time required for quantification of adipose tissue, and the accuracy measurements show that the results are close to the ground truth obtained from manual segmentations.","Title: Optimum Image Thresholding via Class Uncertainty and Region Homogeneity\nYear: 2001\n\nAbstract: Thresholding is a popular image segmentation method that converts a gray-level image into a binary image. The selection of optimum thresholds has remained a challenge over decades. Besides being a segmentation tool on its own, often it is also a step in many advanced image segmentation techniques in spaces other than the image space. Most of the thresholding methods reported to date are based on histogram analysis using information-theoretic approaches. These methods have not harnessed the information captured in image morphology. Here, we introduce a novel thresholding method that accounts for both intensity-based class uncertainty驴a histogram-based property驴and region homogeneity驴an image morphology-based property. A scale-based formulation is used for region homogeneity computation. At any threshold, intensity-based class uncertainty is computed by fitting a Gaussian to the intensity distribution of each of the two regions segmented at that threshold. The theory of the optimum thresholding method is based on the postulate that objects manifest themselves with fuzzy boundaries in any digital image acquired by an imaging device. The main idea here is to select that threshold at which pixels with high class uncertainty accumulate mostly around object boundaries. To achieve this, a new threshold energy criterion is formulated using class-uncertainty and region homogeneity such that, at any image location, a high energy is created when both class uncertainty and region homogeneity are high or both are low. Finally, the method selects that threshold which corresponds to the minimum overall energy. The method has been compared to a recently-published maximum segmented image information ($MSII$) method. Superiority of the proposed method was observed both qualitatively on clinical medical images as well as quantitatively on 250 realistic phantom images generated by adding different degrees of blurring, noise, and background variation to real objects segmented from clinical images.","Title: An Ultra-Fast User-Steered Image Segmentation Paradigm: Live Wire On The Fly\nYear: 2000\n\nAbstract: We have been developing general user steered image segmentation strategies for routine use in applications involving a large number of data sets. In the past, we have presented three segmentation paradigms: live wire, live lane, and a three-dimensional (3-D) extension of the live-wire method. In this paper, we introduce an ultra-fast live-wire method, referred to as live wire on the fly, for further reducing user's time compared to the basic live-wire method. In live wire, 3-D/four-dimensional (4-D) object boundaries are segmented in a slice-by-slice fashion. To segment a two-dimensional (2-D) boundary, the user initially picks a point on the boundary and all possible minimum-cost paths from this point to all other points in the image are computed via Dijkstra's algorithm. Subsequently a live wire is displayed in real time From the initial point to any subsequent position taken by the cursor. If the cursor is close to the desired boundary, the live wire snaps on to the boundary. The cursor is then deposited and a new live-wire segment is Found next, The entire 2-D boundary is specified via a set of live-wire segments in this fashion. A drawback of this method is that the speed of optimal path computation depends on image size. On modestly powered computers, for images of even modest size, some sluggishness appears in user interaction, which reduces the overall segmentation efficiency. In this work, we solve this problem by exploiting some known properties of graphs to avoid unnecessary minimum-cost path computation during segmentation. In live wire on the fly, when the user selects a point on the boundary the live-wire segment is computed and displayed in real time from the selected point to any subsequent position of the cursor in the image, even for large images and even on low-powered computers. Based on 492 tracing experiments from an actual medical application, we demonstrate that live wire on the fly is 1.3-31 times faster than live wire for actual segmentation for varying image sizes, although the pure computational part alone is found to be about 120 times faster.","Title: Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images.\nYear: 2014\n\nAbstract: •Fuzzy hierarchical modeling of all major organs in a body region.•Detailed object relationship information encoded into models.•Hierarchical object recognition and delineation.•Optimal threshold-based recognition strategy.•Demonstration of the same method on several body regions and different modalities.","Title: Affinity functions in fuzzy connectedness based image segmentation II: Defining and recognizing truly novel affinities\nYear: 2010\n\nAbstract: Affinity functions - the measure of how strongly pairs of adjacent spels in the image hang together - represent the core aspect (main variability parameter) of the fuzzy connectedness (FC) algorithms, an important class of image segmentation schemas. In this paper, we present the first ever theoretical analysis of the two standard affinities, homogeneity and object-feature, the way they can be combined, and which combined versions are truly distinct from each other. The analysis is based on the notion of equivalent affinities, the theory of which comes from a companion Part I of this paper (Ciesielski and Udupa, in this issue) [11]. We demonstrate that the homogeneity based and object feature based affinities are equivalent, respectively, to the difference quotient of the intensity function and Rosenfeld's degree of connectivity. We also show that many parameters used in the definitions of these two affinities are redundant in the sense that changing their values lead to equivalent affinities. We finish with an analysis of possible ways of combining different component affinities that result in non-equivalent affinities. In particular, we investigate which of these methods, when applied to homogeneity based and object-feature based components lead to truly novel (non-equivalent) affinities, and how this is affected by different choices of parameters. Since the main goal of the paper is to identify, by formal mathematical arguments, the affinity functions that are equivalent, extensive experimental confirmations are not needed - they show completely identical FC segmentations - and as such, only relevant examples of the theoretical results are provided. Instead, we focus mainly on theoretical results within a perspective of the fuzzy connectedness segmentation literature.","Title: Cluster of Workstation based Nonrigid Image Registration Using Free-Form Deformation\nYear: 2009\n\nAbstract: Nonrigid image registration plays an important role in medical application fields. Owing to its complex com- putations, it incurs high computational cost. In this paper, a parallel algorithm schema for nonrigid image registration methods that use B-splines for deformation and mutual information as a similarity measure is pro- posed. It involves a complex interplay of various steps which are analyzed in considerable detail from the view point of parallelizing registration. The algorithms are implemented on a cluster of workstations. We present some results on a 10 processor cluster of PCs and compare them with a sequential implementation. The results show that a speed up of n/2 for n processors in registering large images. The method is fully portable and seamlessly expandable.","Title: New Concepts for Three-Dimensional Shape Analysis\nYear: 1977\n\nAbstract: A new approach to machine representation and analysis of three-dimensional objects is presented. The representation, based on the notion of \"skeleton\" of an object leads to a scheme for comparing two given object views for shape relations. The objects are composed of long, thin, rectangular prisms joined at their ends. The input picture to the program is the digitized line drawing portraying the three-dimensional object. To compare two object views, two characteristic vertices called \"cardinal point\" and \"end-cardinal point,\" occurring consistently at the bends and open ends of the object are detected. The skeletons are then obtained as a connected path passing through these points. The shape relationships between the objects are then obtained from the matching characteristics of their skeletons. The method explores the possibility of a more detailed and finer analysis leading to detection of features like symmetry, asymmetry and other shape properties of an object.","Title: CIDI-lung-seg: A single-click annotation tool for automatic delineation of lungs from CT scans\nYear: 2014\n\nAbstract: Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies. Manual annotation, current gold standard, is time consuming and often subject to human bias. On the other hand, current state-of-the-art fully automated lung segmentation methods fail to make their way into the clinical practice due to their inability to efficiently incorporate human input for handling misclassifications and praxis. This paper presents a lung annotation tool for CT images that is interactive, efficient, and robust. The proposed annotation tool produces an “as accurate as possible” initial annotation based on the fuzzy-connectedness image segmentation, followed by efficient manual fixation of the initial extraction if deemed necessary by the practitioner. To provide maximum flexibility to the users, our annotation tool is supported in three major operating systems (Windows, Linux, and the Mac OS X). The quantitative results comparing our free software with commercially available lung segmentation tools show higher degree of consistency and precision of our software with a considerable potential to enhance the performance of routine clinical tasks.","Title: A generic approach to pathological lung segmentation.\nYear: 2014\n\nAbstract: In this study, we propose a novel pathological lung segmentation method that takes into account neighbor prior constraints and a novel pathology recognition system. Our proposed framework has two stages; during stage one, we adapted the fuzzy connectedness (FC) image segmentation algorithm to perform initial lung parenchyma extraction. In parallel, we estimate the lung volume using rib-cage information without explicitly delineating lungs. This rudimentary, but intelligent lung volume estimation system allows comparison of volume differences between rib cage and FC based lung volume measurements. Significant volume difference indicates the presence of pathology, which invokes the second stage of the proposed framework for the refinement of segmented lung. In stage two, texture-based features are utilized to detect abnormal imaging patterns (consolidations, ground glass, interstitial thickening, tree-inbud, honeycombing, nodules, and micro-nodules) that might have been missed during the first stage of the algorithm. This refinement stage is further completed by a novel neighboring anatomy-guided segmentation approach to include abnormalities with weak textures, and pleura regions. We evaluated the accuracy and efficiency of the proposed method on more than 400 CT scans with the presence of a wide spectrum of abnormalities. To our best of knowledge, this is the first study to evaluate all abnormal imaging patterns in a single segmentation framework. The quantitative results show that our pathological lung segmentation method improves on current standards because of its high sensitivity and specificity and may have considerable potential to enhance the performance of routine clinical tasks.","Title: Improving Atlas-Based Medical Image Segmentation With A Relaxed Object Search\nYear: 2014\n\nAbstract: Medical image segmentation using 3D probabilistic atlases has been actively pursued to avoid the time-consuming involvement of experts in manual object (organ) delineation for quantitative analysis. By mapping a new 3D image onto the reference coordinate system of the atlas, built for some organ of interest, these techniques take a binary decision based on the probability of each voxel to be part of that organ. However, image-based techniques have also been proposed to refine object delineation at the initial position given by the atlas-based segmentation. In this paper, we relax this condition for delineation refinement by moving an atlas based on the prior probability map to search for the organ around that initial position. Our method uses the multi-scale parameter search algorithm with a suitable criterion function to evaluate automatic 3D organ delineations, as obtained by the image foresting transform algorithm in an uncertainty region of the atlas. Experiments with eight organs in CT and MR images have indicated that our method can improve atlas-based segmentation with statistical significance. Moreover, the relaxed object search consistently found the organ with higher accuracy outside the position obtained by the atlas, which reinforces our claim.","Title: Differential and relaxed image foresting transform for graph-cut segmentation of multiple 3D objects.\nYear: 2014\n\nAbstract: Graph-cut algorithms have been extensively investigated for interactive binary segmentation, when the simultaneous delineation of multiple objects can save considerable user's time. We present an algorithm (named DRIFT) for 3D multiple object segmentation based on seed voxels and Differential Image Foresting Transforms (DIFTs) with relaxation. DRIFT stands behind efficient implementations of some stateof- the-art methods. The user can add/remove markers (seed voxels) along a sequence of executions of the DRIFT algorithm to improve segmentation. Its first execution takes linear time with the image's size, while the subsequent executions for corrections take sublinear time in practice. At each execution, DRIFT first runs the DIFT algorithm, then it applies diffusion filtering to smooth boundaries between objects (and background) and, finally, it corrects possible objects' disconnection occurrences with respect to their seeds. We evaluate DRIFT in 3D CT-images of the thorax for segmenting the arterial system, esophagus, left pleural cavity, right pleural cavity, trachea and bronchi, and the venous system.","Title: Registration of 3D objects and surfaces\nYear: 1990\n\nAbstract: An approach to the problem of comparative analysis of objects and their surfaces that arises in medical imaging is presented and illustrated with an application to postsurgical bone-graft separation. A common requirement in medical imaging applications is the registration of multiple representations of the object prior to mensuration. The modeling of the registration process and the registration system are described. The measurement of change in volume of bone grafts implanted inside the human body in a corrective surgical procedure is discussed, and the results are evaluated.<>","Title: Medical image reconstruction, processing, visualization, and analysis: the MIPG perspective\nYear: 2002\n\nAbstract: In this paper, we identify and describe the key ideas, concepts and software related to Medical Imaging that emerged from our research group, which is commonly known by the acronym MIPG, denoting Medical Image Processing Group. For the purpose of our presentation, we consider the entire discipline of Medical Imaging to consist of the areas of acquisition, postprocessing, evaluation, and application. We are indeed fortunate to be able to discuss seminal research from our group in each of these areas. In Section II, we identify relevant moments of inception and describe our past contributions. Our achievements, current activities, and future directions are summarized in Section III, Section IV, and Section V, respectively; the keys to our success are listed in Section V.","Title: Shell rendering\nYear: 1993\n\nAbstract: A structure model for volume rendering, called a shell, is introduced. Roughly, a shell consists of a set of voxels in the vicinity of the structure boundary together with a number of attributes associated with the voxels in this set. By carefully choosing the attributes and storing the shell in a special data structure that allows random access to the voxels and their attributes, storage and computational requirements can be reduced drastically. Only the voxels that potentially contribute to the rendition actually enter into major computation. Instead of the commonly used ray-casting paradigm, voxel projection is used. This eliminates the need for render-time interpolation and further enhances the speed. By having one of the attributes as a boundary likelihood function that determines the most likely location of voxels in the shell to be on the structure boundary, surface-based measurements can be made. The shell concept, the data structure, the rendering and measurement algorithms, and examples drawn from medical imaging that illustrate these concepts are described.<>","Title: Incorporating a measure of local scale in voxel-based 3-D image registration.\nYear: 2003\n\nAbstract: We present a new class of approaches for rigid-body registration and their evaluation in studying multiple sclerosis (MS) via multiprotocol magnetic resonance imaging (MRI). Three pairs of rigid-body registration algorithms were implemented, using cross-correlation and mutual information (MI), operating on original gray-level images, and utilizing the intermediate images resulting from our new scale-based method. In the scale image, every voxel has the local \"scale\" value assigned to it, defined as the radius of the largest ball centered at the voxel with homogeneous intensities. Three-dimensional image data of the head were acquired from ten MS patients for each of six MRI protocols. Images in some of the protocols were acquired in registration. The registered pairs were used as ground truth. Accuracy and consistency of the six registration methods were measured within and between protocols for known amounts of misregistrations. Our analysis indicates that there is no \"best\" method. For medium misregistration, the method using MI, for small add large misregistration the method using normalized cross-correlation performs best. For high-resolution data the correlation method and for low-resolution data the MI method, both using the original gray-level images, are the most consistent. We have previously demonstrated the use of local scale information in fuzzy connectedness segmentation and image filtering. Scale may also have potential for image registration as suggested by this work.","Title: Fuzzy connectedness and image segmentation\nYear: 2003\n\nAbstract: Image segmentation-the process of defining objects in images-remains the most challenging problem in image processing despite decades of research. Many general methodologies have been proposed to date to tackle this problem. An emerging framework that has shown considerable promise recently is that of fuzzy connectedness. Images are by nature fuzzy. Object regions manifest themselves in images with a heterogeneity of image intensities owing to the inherent object material heterogeneity, and artifacts such as blurring, noise and background variation introduced by the imaging device. In spite of this gradation of intensities, knowledgeable observers can perceive object regions as a gestalt. The fuzzy connectedness framework aims at capturing this notion via a fuzzy topological notion called fuzzy connectedness which defines how the image elements hang together spatially in spite of their gradation of intensities. In defining objects in a given image, the strength of connectedness between every pair of image elements is considered, which in turn is determined by considering all possible connecting paths between the pair. In spite of a high combinatorial complexity, theoretical advances in fuzzy connectedness have made it possible to delineate objects via dynamic programming at close to interactive speeds on modern PCs. This paper gives a tutorial review of the fuzzy connectedness framework delineating the various advances that have been made. These are illustrated with several medical applications in the areas of Multiple Sclerosis of the brain, magnetic resonance (MR) and computer tomographic (CT) angiography, brain tumor, mammography, upper airway disorders in children, and colonography.","Title: Iso-shaping rigid bodies for estimating their motion from image sequences.\nYear: 2004\n\nAbstract: In many medical imaging applications, due to the limited field of view of imaging devices, acquired images often include only a part of a structure. In such situations, it is impossible to guarantee that the images will contain exactly the same physical extent of the structure at different scans, which leads to difficulties in registration and in many other tasks, such as the analysis of the morphology, architecture, and kinematics of the structures. To facilitate such analysis, we developed a general method, referred to as iso-shaping, that generates structures of the same shape from segmented image sequences. The basis for this method is to automatically find a set of key points, called shape centers, in the segmented partial anatomic structure such that these points are present in all images and that they represent the same physical location in the object, and then trim the structure using these points as reference. The application area considered here is the analysis of the morphology, architecture, and kinematics of the joints of the foot from magnetic resonance images acquired at different joint positions and load conditions. The accuracy of the method is analyzed by utilizing ten data sets for iso-shaping the tibia and the fibula via four evaluative experiments. The analysis indicates that iso-shaping produces results as predicted by the theoretical framework.","Title: Volume rendering in the presence of partial volume effects\nYear: 2005\n\nAbstract: In tomographic imagery, partial volume effects (PVEs) cause several artifacts in volume renditions. In X-ray computed tomography (CT), for example, soft-tissue-like pseudo structures appear in bone-to-air and bone-to-fat interfaces. Further, skin, which is identical to soft tissue in terms of CT number, obscures the rendition of the latter. The purpose of This work is to demonstrate these phenomena and to provide effective solutions that yield significantly improved renditions. We introduce two methods that detect and classify voxels with PVE in X-ray CT. Further, a method is described to automatically peel skin so that PVE-resolved renditions of bone and soft tissue reveal considerably more detail. In the first method to address PVE, called the fraction measure (FM) method, the fraction of each tissue material in each voxel v is estimated by taking into account the intensities of the voxels neighboring v. The second method, called uncertainty principle (UP) method, is based on the following postulate (Saha and Udupa, 2001): In any acquired image, voxels with the highest uncertainty occur in the vicinity of object boundaries. The removal of skin is achieved by means of mathematical morphology. Volume renditions have been created before and after applying the methods for several patient CT datasets. A mathematical phantom experiment involving different levels of PVE has been conducted by adding different degrees of noise and blurring. A quantitative evaluation is done utilizing the mathematical phantom and clinical CT data wherein an operator carefully masked out voxels with PVE in the segmented images. All results have demonstrated the enhanced quality of display of bone and soft tissue after applying the proposed methods. The quantitative evaluations indicate that more than 98% of the voxels with PVE are removed by the two methods and the second method performs slightly better than the first. Further, skin peeling vividly reveals fine details in the soft tissue str- - uctures.","Title: Interplay between intensity standardization and inhomogeneity correction in MR image processing\nYear: 2005\n\nAbstract: Image intensity standardization is a postprocessing method designed for correcting acquisition-to-acquisition signal intensity variations (nonstandardness) inherent in magnetic resonance (MR) images. Inhomogeneity correction is a process used to suppress the low frequency background nonuniformities (inhomogeneities) of the image domain that exist in MR images. Both these procedures have important implications in MR image analysis. The effects of these postprocessing operations on improvement of image quality in isolation has been well documented. However, the combined effects of these two processes on MR images and how the processes influence each other have not been studied thus far. In this paper, we evaluate the effect of inhomogeneity correction followed by standardization and vice-versa on MR images in order to determine the best sequence to follow for enhancing image quality. We conducted experiments on several clinical and phantom data sets (nearly 4000 three-dimensional MR images were analyzed) corresponding to four different MRI protocols. Different levels of artificial nonstandardness, and different models and levels of artificial background inhomogeneity were used in these experiments. Our results indicate that improved standardization can be achieved by preceding it with inhomogeneity correction. There is no statistically significant difference in image quality obtained between the results of standardization followed by correction and that of correction followed by standardization from the perspective of inhomogeneity correction. The correction operation is found to bias the effect of standardization. We demonstrate this bias both qualitatively and quantitatively by using two different methods of inhomogeneity correction. We also show that this bias in standardization is independent of the specific inhomogeneity correction method used. The effect of this bias due to correction was also seen in magnetization transfer ratio (MTR) images, which are naturall- - y endowed with the standardness property. Standardization, on the other hand, does not seem to influence the correction operation. It is also found that longer sequences of repeated correction and standardization operations do not considerably improve image quality. These results were found to hold for the clinical and the phantom data sets, for different MRI protocols, for different levels of artificial nonstandardness, for different models and levels of artificial inhomogeneity, for different correction methods, and for images that were endowed with inherent standardness as well as for those that were standardized by using the intensity standardization method. Overall, we conclude that inhomogeneity correction followed by intensity standardization is the best sequence to follow from the perspective of both image quality and computational efficiency.","Title: A characterization of the geometric architecture of the peritalar joint complex via MRI, an aid to classification of foot type\nYear: 1999\n\nAbstract: The purpose of this work is to study the architecture of the rearfoot using in vivo MR image data. Each data set used in this study is made of sixty sagittal slices of the foot acquired in a 1.5-T commercial GE MR system. The authors use the live-wire method to delineate boundaries and form the surfaces of the bones. In the first part of this work, they describe a new method to characterize the three-dimensional (3-D) relationships of four bones of the peritalar complex and apply this description technique to data sets from ten normal subjects and from seven pathological cases. In the second part, the authors propose a procedure to classify feet, based on the values of these new architectural parameters. They conclude that this noninvasive method offers a unique tool to characterize the 3-D architecture of the feet in live patients, based on a set of new architectural parameters. This can be integrated into a set of tools to improve diagnosis and treatment of foot malformations.","Title: New variants of a method of MRI scale standardization.\nYear: 2000\n\nAbstract: One of the major drawbacks of magnetic resonance imaging (MRI) has been the lack of a standard and quantifiable interpretation of image intensities. Unlike in other modalities, such as X-ray computerized tomography, MR images taken for the same patient on the same scanner at different times may appear different from each other due to a variety of scanner-dependent variations and, therefore, the absolute intensity values do not have a fixed meaning. We have devised a two-step method wherein all images (independent of patients and the specific brand of the MR scanner used) can be transformed in such a way that for the same protocol and body region, in the transformed images similar intensities will have similar tissue meaning. Standardized images can be displayed with fixed windows without the need of per-case adjustment. More importantly, extraction of quantitative information about healthy organs or about abnormalities can be considerably simplified. This paper introduces and compares new vari- ants of this standardizing method that can help to overcome some of the problems with the original method.","Title: An order of magnitude faster isosurface rendering in software on a PC than using dedicated, general purpose rendering hardware\nYear: 2000\n\nAbstract: The purpose of this work is to compare the speed of isosurface rendering in software with that using dedicated hardware. Input data consist of 10 different objects from various parts of the body and various modalities (CT, MR, and MRA) with a variety of surface sizes (up to 1 million voxels/2 million triangles) and shapes. The software rendering technique consists of a particular method of voxel-based surface rendering, called shell rendering. The hardware method is OpenGL-based and uses the surfaces constructed from our implementation of the 驴Marching Cubes驴 algorithm. The hardware environment consists of a variety of platforms, including a Sun Ultra I with a Creator3D graphics card and a Silicon Graphics Reality Engine II, both with polygon rendering hardware, and a 300Mhz Pentium PC. The results indicate that the software method (shell rendering) was 18 to 31 times faster than any hardware rendering methods. This work demonstrates that a software implementation of a particular rendering algorithm (shell rendering) can outperform dedicated hardware. We conclude that, for medical surface visualization, expensive dedicated hardware engines are not required. More importantly, available software algorithms (shell rendering) on a 300Mhz Pentium PC outperform the speed of rendering via hardware engines by a factor of 18 to 31.","Title: Scale-based diffusive image filtering preserving boundary sharpness and fine structures.\nYear: 2001\n\nAbstract: Image acquisition techniques often suffer from low signal-to-noise ratio (SNR) and/or contrast-to-noise ratio (CNR). Although many acquisition techniques are available to minimize these, post acquisition filtering is a major off-line image processing technique commonly used to improve the SNR and CNR. A major drawback of filtering is that it often diffuses/blurs important structures along with noise. In this paper, we introduce two scale-based filtering methods that use local structure size or \"object scale\" information to arrest smoothing around fine structures and across even low-gradient boundaries. The first of these methods uses a weighted average over a scale-dependent neighborhood while the other employs scale-dependent diffusion conductance to perform filtering. Both methods adaptively modify the degree of filtering at any image location depending on local object scale. Object scale allows us to accurately use a restricted homogeneity parameter for filtering in regions with fine details and in the vicinity of boundaries while a generous parameter in the interiors of homogeneous regions. Qualitative experiments based on both phantoms and patient magnetic resonance images show significant improvements using the scale-based methods over the extant anisotropic diffusive filtering method in preserving fine details and sharpness of object boundaries. Quantitative analyses utilizing 25 phantom images generated under a range of conditions of blurring, noise, and background variation confirm the superiority of the new scale-based approaches.","Title: Hierarchical Scale-Based Multiobject Recognition of 3-D Anatomical Structures\nYear: 2012\n\nAbstract: Segmentation of anatomical structures from medical images is a challenging problem, which depends on the accurate recognition (localization) of anatomical structures prior to delineation. This study generalizes anatomy segmentation problem via attacking two major challenges: 1) automatically locating anatomical structures without doing search or optimization, and 2) automatically delineating the anatomical structures based on the located model assembly. For 1), we propose intensity weighted ball-scale object extraction concept to build a hierarchical transfer function from image space to object (shape) space such that anatomical structures in 3-D medical images can be recognized without the need to perform search or optimization. For 2), we integrate the graph-cut (GC) segmentation algorithm with prior shape model. This integrated segmentation framework is evaluated on clinical 3-D images consisting of a set of 20 abdominal CT scans. In addition, we use a set of 11 foot MR images to test the generalizability of our method to the different imaging modalities as well as robustness and accuracy of the proposed methodology. Since MR image intensities do not possess a tissue specific numeric meaning, we also explore the effects of intensity nonstandardness on anatomical object recognition. Experimental results indicate that: 1) effective recognition can make the delineation more accurate; 2) incorporating a large number of anatomical structures via a model assembly in the shape model improves the recognition and delineation accuracy dramatically; 3) ball-scale yields useful information about the relationship between the objects and the image; 4) intensity variation among scenes in an ensemble degrades object recognition performance.","Title: Fuzzy connectedness image segmentation for newborn brain extraction in MR images.\nYear: 2013\n\nAbstract: Newborn's brain has a various shape, and easily changes with not only brain developing and cerebral diseases. Although the brain segmentation in MR images is an effective way to quantify the brain shape and size, there are few studies in neonatal brain MR image analysis. This paper introduces a novel method based on fuzzy connectedness (FC) with fuzzy object model (FOM). FOM is built from a training dataset, and gives fuzzy degree belonging to parenchyma with respect to location and intensity. FC is calculated from object affinity and homogeneous affinity, and the object affinity is given by the FOM. The method first segments the white matter, and then segments the surrounding cortex. The propose method has been applied to 10 newborn subjects whose revised age was between -1 month and +2 month. Leave-on-out cross-validation (LOOCV) was conducted, and the mean false-positive volume fraction was 1.33%, the mean false-negative volume fraction was 2.90%, and geometric-mean was 1.42%.","Title: Imaging transforms for visualizing surfaces and volumes\nYear: 1993\n\nAbstract: Three-dimensional (3D) visualization in biomedical and other imaging areas is a rapidly emerging discipline. The major developments\n in this field are described in a unified and concise way. To this end, we introduce an operator notation to describe the basic\n imaging transforms commonly used in 3D visualization and to identify a comprehensive set of basic transforms. We also introduce\n several new basic transforms for filtering and interpolating scenes and structures and for rendering surfaces and volumes.\n We demonstrate not only how the existing visualization methodologies can be described concisely, but we also show how a great\n variety of new methodologies can be generated using both the existing imaging transforms and the new transforms introduced\n in this paper. A comprehensive evaluation method to compare objectively rendering methods used in visualization based on task-specific\n mathematical phantoms is described. We examine in detail separate transform sequences that are best suited for rendering robust\n and frail structures (ie, structures with well- and poorly defined boundaries).","Title: Multidimensional data format specification: A generalization of the American College of Radiology-National Electric Manufacturers Association standards\nYear: 1992\n\nAbstract: Multidimensional image data are becoming increasingly common in biomedical imaging. Three-dimensional visualization and analysis\n techniques based on three-dimensional image data have become an established discipline in biomedicine. Some imaging problems\n generate image data of even higher dimensions. It often becomes necessary, rather than just convenient, to consider the higher-dimensional\n data as a whole to adequately answer the underlying imaging questions. Despite this established need for convenient exchange\n of image and image-derived information, no exchange protocols are available that adequately meet the needs of multidimensional\n imaging systems. This paper describes an exchange protocol that has been designed after careful consideration of the common\n requirements of methodologies for visualization and analysis of multidimensional data. It is based on and is a generalization\n of the widely accepted American College of Radiology-National Electrical Manufacturers Association (ACR-NEMA) standards specified\n for two-dimensional images. It is implemented and actively being used in a data-, application-, and machine-independent software\n environment, being developed in the authors’ department, for the visualization and analysis of multidimensional images.","Title: Interactive segmentation and boundary surface formation for 3-D digital images\nYear: 1982\n\nAbstract: In visualizing objects in 3-D digital images based on shaded-surface displays, segmentation of the 3-D regions is a basic processing operation. When object and nonobject regions touch or overlap and have identical features, automatic segmentation methods fail. We present an interactive technique which permits isolation of arbitrary subregions of the 3-D image by a region filling procedure. We illustrate the usefulness of this capability, through computerized tomography data, in preoperative surgical planning and for producing arbitrary fragmentations of objects in 3-D images. The techniques are applicable in other fields as well, where independent display of overlapping structures is important. When the object regions are specified through a set of contours in a sequence of slices, we show that the boundary surfaces of the object are produceable in an efficient way using the cuberille-based discrete representation of the object surfaces. The algorithms we present for forming boundary surfaces do not impose restrictions on the shape and pattern of distribution of contours in the slices as do the surface tiling schemes reported in the literature. We assume that a contour is made up of at least three points.","Title: A search algorithm for skeletonization of thick patterns\nYear: 1974\n\nAbstract: An algorithm is proposed in this paper for obtaining the \"medial line\" or the \"skeleton\" of a thick pattern. The method introduces new concepts of a \"turning point\" and an \"end point.\" The present method for thinning binary patterns is a two stage process. During the first stage the method employs four different pairs of operators to test and indicate the existence of vertical, horizontal, right and left inclined limbs in the pattern and to extract the respective end and turning points. During the second stage the medial line of the pattern is obtained by constructing the limbs from a knowledge of the already extracted end and turning points by a simple search procedure. The results obtained by the present method on hand printed alphanumerics are better than those obtained by the existing methods, The extraction of turning and end points also facilitates coding and easy recognition of the given patterns. These results will be reported elsewhere.","Title: Surface Shading in the Cuberille Environment\nYear: 1985\n\nAbstract: Computed tomography and the cuberiile model驴an effort to better serve the medical profession and its patients.","Title: Interactive Surgical Planning\nYear: 1984\n\nAbstract: With this tool for visualizing and manipulating organ images in three dimensions, surgeons can plan and test their strategies before they get to the operating room.","Title: A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT\nYear: 2015\n\nAbstract: •An accurate and efficient computational framework for airway quantification is proposed..•A novel hybrid approach for precise segmentation of the lumen presented..•Two novel methods both in 2-D and 3-D to estimate the airway walls are explored..•Better identification of airway surfaces than the widely applied methods is reported..","Title: Quantitative normal thoracic anatomy at CT.\nYear: 2016\n\nAbstract: •It points out detailed quantitative anatomic (QA) information currently not existing.•We demonstrate the non-linear geometric and geographic relationships among objects.•We show the highly non-linear variations of object-specific properties of 11 objects.•QA information is useful in creating effective models for the object segmentation.","Title: Soft Computing in Medical Image Processing.\nYear: 2016\n\nAbstract: Recent advances in medical imaging modalities such as magnetic resonance (MR) imaging and multidetector computed tomography (MDCT) enable us to acquire high-dimensional, sectional, thin-sliced, and a large number of images within a short acquisition time. The sheer volume of data poses great challenges for human interpretation of images by radiologists. Image processing becomes essential to analyze such complex data.Soft computing has been introduced into medical image processing because it is an effective approach to handle uncertainties inherent in acquired image data. Some examples in the past 20 years are fuzzy connectedness approaches to image segmentation, fuzzy clustering methods particularly for human brain MR image segmentation, and statistical atlases and fuzzy models for object recognition and delineation. Soft computing approaches include fuzzy logic, neural networks, support vector machines, evolutional computation, probabilistic approaches, and chaos theory.This special issue aims to showcase recent advances in soft computing approaches in medical image processing. In response to the call for papers, we received 26 submissions from all over the world. All manuscripts underwent a very rigorous peer review process. We finally selected 8 full papers for this special issue.Image segmentation plays an important role in medical image processing. L. Ma et al. propose a new method which combines artificial fish swarm algorithm and fuzzy c-means and demonstrates the performance on brain MR images. K. B. Kim et al. propose a contour extraction method for segmenting deep cervical flexor (DCF) muscles in ultrasonic images. The method is based on fuzzy sigma binarization process. L. Zhao and K. Jia introduce multiscale convolutional neural network (CNN) to brain tumor segmentation. Y. Li et al. show a method for segmenting white blood cells using a dual-thresholding approach.Image fusion is also an effective computational methodology that is hard for a human to deal with. It combines images from two or more different modalities and generates a new image. G. Yang et al. propose a new method that fills nonsubsampled contourlet transform (NCST) coefficients using generalized Gaussian density. It has been applied to CT/MRI, MRI/positron emission tomography (PET) and MRI/PET image fusion, and the results are superior to conventional NCST-based methods.In addition, there are unique medical image processing schemes that have been proposed. Jing and Sheng introduce a method to retrieve a 3D model from freehand sketch. It may be very useful for medical education, informed consent for patients, and so on. B. J. Park et al. propose a gesture based interface, and it can be utilized in the operating room where surgeons cannot touch computer interface devices such as mouse and keyboard during operation. L. Huang et al. show a dense trajectory and classification method on echocardiogram video images.Each paper contributes an improvement of medical image processing in each application area, and also the approaches are applicable to other areas. We hope that the papers will enrich the readersu0027 knowledge of biomedical image analysis and will in turn lead them to the exploration of new advances and avenues.Syoji KobashiLaszlo G. NyulJayaram K. Udupa","Title: Virtual landmarks.\nYear: 2017\n\nAbstract: Much has been published on finding landmarks object surfaces in the context of shape modeling. While this is still an open problem, many of the challenges of past approaches can be overcome by removing the restriction that landmarks on the object surface. The we propose may reside inside, on the boundary of, or outside the object and are tethered to the object. Our solution is straightforward, simple, and recursive in nature, proceeding from global features initially to local features in later levels to detect landmarks. Principal component analysis (PCA) is used as an engine to recursively subdivide the object region. The object itself may be represented in binary or fuzzy form or with gray values. The method is illustrated in 3D space (although it generalizes readily to spaces of any dimensionality) on four objects (liver, trachea and bronchi, and outer boundaries of left and right lungs along pleura) derived from 5 patient computed tomography (CT) image data sets of the thorax and abdomen. The virtual landmark identification approach seems to work well on different structures in different subjects and seems to detect landmarks that are homologously located in different samples of the same object. The approach guarantees that virtual landmarks are invariant to translation, scaling, and rotation of the object/image. Landmarking techniques are fundamental for many computer vision and image processing applications, and we are currently exploring the use virtual landmarks in automatic anatomy recognition and object analytics.","Title: Fat segmentation on chest CT images via fuzzy models.\nYear: 2016\n\nAbstract: Quantification of fat throughout the body is vital for the study of many diseases. In the thorax, it is important for lung transplant candidates since obesity and being underweight are contraindications to lung transplantation given their associations with increased mortality. Common approaches for thoracic fat segmentation are all interactive in nature, requiring significant manual effort to draw the interfaces between fat and muscle with low efficiency and questionable repeatability. The goal of this paper is to explore a practical way for the segmentation of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) components of chest fat based on a recently developed body-wide automatic anatomy recognition (AAR) methodology. The AAR approach involves 3 main steps: building a fuzzy anatomy model of the body region involving all its major representative objects, recognizing objects in any given test image, and delineating the objects. We made several modifications to these steps to develop an effective solution to delineate SAT/VAT components of fat. Two new objects representing interfaces of SAT and VAT regions with other tissues, SatIn and VatIn are defined, rather than using directly the SAT and VAT components as objects for constructing the models. A hierarchical arrangement of these new and other reference objects is built to facilitate their recognition in the hierarchical order. Subsequently, accurate delineations of the SAT/VAT components are derived from these objects. Unenhanced CT images from 40 lung transplant candidates were utilized in experimentally evaluating this new strategy. Mean object location error achieved was about 2 voxels and delineation error in terms of false positive and false negative volume fractions were, respectively, 0.07 and 0.1 for SAT and 0.04 and 0.2 for VAT.","Title: A general approach to liver lesion segmentation in CT images.\nYear: 2016\n\nAbstract: Lesion segmentation has remained a challenge in different body regions. Generalizability is lacking in published methods as variability in results is common, even for a given organ and modality, such that it becomes difficult to establish standardized methods of disease quantification and reporting. This paper makes an attempt at a generalizable method based on classifying lesions along with their background into groups using clinically used visual attributes. Using an Iterative Relative Fuzzy Connectedness (IRFC) delineation engine, the ideas are implemented for the task of liver lesion segmentation in computed tomography (CT) images. For lesion groups with the same background properties, a few subjects are chosen as the training set to obtain the optimal IRFC parameters for the background tissue components. For lesion groups with similar foreground properties, optimal foreground parameters for IRFC are set as the median intensity value of the training lesion subset. To segment liver lesions belonging to a certain group, the devised method requires manual loading of the corresponding parameters, and correct setting of the foreground and background seeds. The segmentation is then completed in seconds. Segmentation accuracy and repeatability with respect to seed specification are evaluated. Accuracy is assessed by the assignment of a delineation quality score (DQS) to each case. Inter- operator repeatability is assessed by the difference between segmentations carried out independently by two operators. Experiments on 80 liver lesion cases show that the proposed method achieves a mean DQS score of 4.03 and inter- operator repeatability of 92.3%.","Title: Optimal atlas construction through hierarchical image registration.\nYear: 2016\n\nAbstract: Atlases (digital or otherwise) are common in medicine. However, there is no standard framework for creating them from medical images. One traditional approach is to pick a representative subject and then proceed to label structures/regions of interest in this image. Another is to create a \"mean\" or average subject. Atlases may also contain more than a single representative (e.g., the Visible Human contains both a male and a female data set). Other criteria besides gender may be used as well, and the atlas may contain many examples for a given criterion. In this work, we propose that atlases be created in an optimal manner using a well-established graph theoretic approach using a min spanning tree (or more generally, a collection of them). The resulting atlases may contain many examples for a given criterion. In fact, our framework allows for the addition of new subjects to the atlas to allow it to evolve over time. Furthermore, one can apply segmentation methods to the graph (e.g., graph-cut, fuzzy connectedness, or cluster analysis) which allow it to be separated into \"sub-atlases\" as it evolves. We demonstrate our method by applying it to 50 3D CT data sets of the chest region, and by comparing it to a number of traditional methods using measures such as Mean Squared Difference, Mattes Mutual Information, and Correlation, and for rigid registration. Our results demonstrate that optimal atlases can be constructed in this manner and outperform other methods of construction using freely available software.","Title: Lymph node detection in IASLC-defined zones on PET/CT images.\nYear: 2016\n\nAbstract: Lymph node detection is challenging due to the low contrast between lymph nodes as well as surrounding soft tissues and the variation in nodal size and shape. In this paper, we propose several novel ideas which are combined into a system to operate on positron emission tomography/computed tomography (PET/CT) images to detect abnormal thoracic nodes. First, our previous Automatic Anatomy Recognition (AAR) approach is modified where lymph node zones predominantly following International Association for the Study of Lung Cancer (IASLC) specifications are modeled as objects arranged in a hierarchy along with key anatomic anchor objects. This fuzzy anatomy model built from diagnostic CT images is then deployed on PET/CT images for automatically recognizing the zones. A novel globular filter (g-filter) to detect blob-like objects over a specified range of sizes is designed to detect the most likely locations and sizes of diseased nodes. Abnormal nodes within each automatically localized zone are subsequently detected via combined use of different items of information at various scales: lymph node zone model poses found at recognition indicating the geographic layout at the global level of node clusters, g-filter response which hones in on and carefully selects node-like globular objects at the node level, and CT and PET gray value but within only the most plausible nodal regions for node presence at the voxel level. The models are built from 25 diagnostic CT scans and refined for an object hierarchy based on a separate set of 20 diagnostic CT scans. Node detection is tested on an additional set of 20 PET/CT scans. Our preliminary results indicate node detection sensitivity and specificity at around 90% and 85%, respectively.","Title: Automatic anatomy recognition on CT images with pathology.\nYear: 2016\n\nAbstract: Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.","Title: A Critical Analysis Of The Methods Of Evaluating Mri Brain Segmentation Algorithms\nYear: 2017\n\nAbstract: Many papers are published every year containing new methodologies for brain tissue segmentation in magnetic resonance images. The evaluation of these methods is fundamental to understand their behavior and to observe their weak and strong points. Even though improvements have been proposed, the analysis of the segmentation results can still lead to incorrect conclusions. This paper contains an investigation of the state-of-the-art in brain tissue segmentation evaluation, which includes tissue classification or segmentation, handling partial volume effect, and evaluation metrics. It uncovers previously unnoticed pitfalls and proposes standard procedures to avoid them. Experiments show that the proposed evaluation strategy gives a better insight about the method's strong and weak points.","Title: Automatic anatomy recognition using neural network learning of object relationships via virtual landmarks.\nYear: 2018\n\nAbstract: The recently developed body-wide Automatic Anatomy Recognition (AAR) methodology depends on fuzzy modeling of individual objects, hierarchically arranging objects, constructing an anatomy ensemble of these models, and a dichotomous object recognition delineation process. The parent-to-offspring spatial relationship in the object hierarchy is crucial in the AAR method. We have found this relationship to be quite complex, and as such any improvement in capturing this relationship information in the anatomy model will improve the process of recognition itself. Currently, the method encodes this relationship based on the layout of the geometric centers of the objects. Motivated by the concept of virtual landmarks (VLs), this paper presents a new one-shot AAR recognition method that utilizes the VLs to learn object relationships by training a neural network to predict the pose and the VLs of an offspring object given the VLs of the parent object in the hierarchy. We set up two neural networks for each parent-offspring object pair in a body region, one for predicting the VLs and another for predicting the pose parameters. The VL-based learning/prediction method is evaluated on two object hierarchies involving 14 objects. We utilize 54 computed tomography (CT) image data sets of head and neck cancer patients and the associated object contours drawn by dosimetrists for routine radiation therapy treatment planning. The VL neural network method is found to yield more accurate object localization than the currently used simple AAR method.","Title: Thoracic lymph node station recognition on CT images based on automatic anatomy recognition with an optimal parent strategy.\nYear: 2018\n\nAbstract: Currently, there are many papers that have been published on the detection and segmentation of lymph nodes from medical images. However, it is still a challenging problem owing to low contrast with surrounding soft tissues and the variations of lymph node size and shape on computed tomography (CT) images. This is particularly very difficult on low-dose CT of PET/CT acquisitions. In this study, we utilize our previous automatic anatomy recognition (AAR) framework to recognize the thoracic-lymph node stations defined by the International Association for the Study of Lung Cancer (IASLC) lymph node map. The lymph node stations themselves are viewed as anatomic objects and are localized by using a one-shot method in the AAR framework. Two strategies have been taken in this paper for integration into AAR framework. The first is to combine some lymph node stations into composite lymph node stations according to their geometrical nearness. The other is to find the optimal parent (organ or union of organs) as an anchor for each lymph node station based on the recognition error and thereby find an overall optimal hierarchy to arrange anchor organs and lymph node stations. Based on 28 contrast-enhanced thoracic CT image data sets for model building, 12 independent data sets for testing, our results show that thoracic lymph node stations can be localized within 2-3 voxels compared to the ground truth.","Title: Quantitative analysis of adipose tissue on chest CT to predict primary graft dysfunction in lung transplant recipients: a novel optimal biomarker approach.\nYear: 2018\n\nAbstract: In this study, patients who underwent lung transplantation are categorized into two groups of successful (positive) or failed (negative) transplantations according to primary graft dysfunction (PGD), i.e., acute lung injury within 72 hours of lung transplantation. Obesity or being underweight is associated with an increased risk of PGD. Adipose quantification and characterization via computed tomography (CT) imaging is an evolving topic of interest. However, very little research of PGD prediction using adipose quantity or characteristics derived from medical images has been performed. The aim of this study is to explore image based features of thoracic adipose tissue on pre-operative chest CT to distinguish between the above two groups of patients. 140 unenhanced chest CT images from three lung transplant centers (Columbia, Penn, and Duke) are included in this study. 124 patients are in the successful group and 16 in failure group. Chest CT slices at the T7 and T8 vertebral levels are captured to represent the thoracic fat burden by using a standardized anatomic space (SAS) approach. Fat (subcutaneous adipose tissue (SAT)/ visceral adipose tissue (VAT)) intensity and texture properties (1142 in total) for each patient are collected, and then an optimal feature set is selected to maximize feature independence and separation between the two groups. Leave-one-out and leave-ten-out cross validation strategies are adopted to test the prediction ability based on those selected features all of which came from VAT texture properties. Accuracy of prediction (ACC), sensitivity (SEN), specificity (SPE), and area under the curve (AUC) of 0.87/0.97, 0.87/0.97, 0.88/1.00, and 0.88/0.99, respectively are achieved by the method. The optimal feature set includes only 5 features (also all from VAT), which might suggest that thoracic VAT plays a more important role than SAT in predicting PGD in lung transplant recipients.","Title: Urinary bladder cancer T-staging from T2-weighted MR images using an optimal biomarker approach.\nYear: 2018\n\nAbstract: Magnetic resonance imaging (MRI) is often used in clinical practice to stage patients with bladder cancer to help plan treatment. However, qualitative assessment of MR images is prone to inaccuracies, adversely affecting patient outcomes. In this paper, T2-weighted MR image-based quantitative features were extracted from the bladder wall in 65 patients with bladder cancer to classify them into two primary tumor (T) stage groups: group 1 - T stage <= T2, with primary tumor locally confined to the bladder, and group 2 -T stage > T2, with primary tumor locally extending beyond the bladder. The bladder was divided into 8 sectors in the axial plane, where each sector has a corresponding reference standard T stage that is based on expert radiology qualitative MR image review and histopathologic results. The performance of the classification for correct assignment of T stage grouping was then evaluated at both the patient level and the sector level. Each bladder sector was divided into 3 shells (inner, middle, and outer), and 15,834 features including intensity features and texture features from local binary pattern and gray-level co-occurrence matrix were extracted from the 3 shells of each sector. An optimal feature set was selected from all features using an optimal biomarker approach. Nine optimal biomarker features were derived based on texture properties from the middle shell, with an area under the ROC curve of AUC value at the sector and patient level of 0.813 and 0.806, respectively.","Title: Auto-contouring via automatic anatomy recognition of organs at risk in head and neck cancer on CT images.\nYear: 2018\n\nAbstract: Contouring of the organs at risk is a vital part of routine radiation therapy planning. For the head and neck (H&N) region, this is more challenging due to the complexity of anatomy, the presence of streak artifacts, and the variations of object appearance. In this paper, we describe the latest advances in our Automatic Anatomy Recognition (AAR) approach, which aims to automatically contour multiple objects in the head and neck region on planning CT images. Our method has three major steps: model building, object recognition, and object delineation. First, the better-quality images from our cohort of H&N CT studies are used to build fuzzy models and find the optimal hierarchy for arranging objects based on the relationship between objects. Then, the object recognition step exploits the rich prior anatomic information encoded in the hierarchy to derive the location and pose for each object, which leads to generalizable and robust methods and mitigation of object localization challenges. Finally, the delineation algorithms employ local features to contour the boundary based on object recognition results. We make several improvements within the AAR framework, including finding recognition-error-driven optimal hierarchy, modeling boundary relationships, combining texture and intensity, and evaluating object quality. Experiments were conducted on the largest ensemble of clinical data sets reported to date, including 216 planning CT studies and over 2,600 object samples. The preliminary results show that on data sets with minimal (<4 slices) streak artifacts and other deviations, overall recognition accuracy reaches 2 voxels, with overall delineation Dice coefficient close to 0.8 and Hausdorff Distance within 1 voxel.","Title: Image quality and segmentation.\nYear: 2018\n\nAbstract: Algorithms for image segmentation (including object recognition and delineation) are influenced by the quality of object appearance in the image and overall image quality. However, the issue of how to perform segmentation evaluation as a function of these quality factors has not been addressed in the literature. In this paper, we present a solution to this problem. We devised a set of key quality criteria that influence segmentation (global and regional): posture deviations, image noise, beam hardening artifacts (streak artifacts), shape distortion, presence of pathology, object intensity deviation, and object contrast. A trained reader assigned a grade to each object for each criterion in each study. We developed algorithms based on logical predicates for determining a 1 to 10 numeric quality score for each object and each image from reader-assigned quality grades. We analyzed these object and image quality scores (OQS and IQS, respectively) in our data cohort by gender and age. We performed recognition and delineation of all objects using recent adaptations [8, 9] of our Automatic Anatomy Recognition (AAR) framework [6] and analyzed the accuracy of recognition and delineation of each object. We illustrate our method on 216 head & neck and 211 thoracic cancer computed tomography (CT) studies.","Title: Generalized ball-scale: theory, algorithms, and application to image inhomogeneity correction.\nYear: 2005\n\nAbstract: The concept of generalized scale (g-scale) was introduced previously to overcome the shape, topological, and anisotropic constraints imposed by previous local morphometric scale models. Roughly speaking, the g-scale of a voxel in a scene was defined as the largest set of all voxels associated with it, that satisfy some homogeneity criterion. g-scale was shown to have interesting theoretical properties, and its superiority to an existing image background inhomogeneity correction method was demonstrated. In this paper, we present a variant of g-scale that we refer to as g(B)-scale. The difference between g- and gB-scale is that, while for g-scale, individual voxels are included into the g-scale set one at a time, the g(B)-scale set is grown by including hyperballs, the hyperball corresponding to the local ball scale at every voxel (which, briefly, is the radius of the largest hyperball of homogeneous intensity centered at the voxel). The g(B)-scale model was found to be more resistant to severe levels of inhomogeneity and noise compared to g-scale. A methodology to perform image background inhomogeneity correction based on the idea of gB(-)scale was qualitatively and quantitatively compared on nearly 250 clinical and phantom datasets, with a ball-scale- and a g-scale-based correction methodology. For scenes containing inhomogeneity but no noise, the g-, and g(B)-scale methods performed comparably and were superior to the ball-scale method. For scenes containing both noise and inhomogeneity, the g(B)-scale-based method outperformed both the g- and ball-scale correction methods.","Title: Hierarchical model-based object localization for auto-contouring in head and neck radiation therapy planning.\nYear: 2018\n\nAbstract: Segmentation of organs at risk (OARs) is a key step during the radiation therapy (RT) treatment planning process. Automatic anatomy recognition (AAR) is a recently developed body-wide multiple object segmentation approach, where segmentation is designed as two dichotomous steps: object recognition (or localization) and object delineation. Recognition is the high-level process of determining the whereabouts of an object, and delineation is the meticulous low-level process of precisely indicating the space occupied by an object. This study focuses on recognition. The purpose of this paper is to introduce new features of the AAR-recognition approach (abbreviated as AAR-R from now on) of combining texture and intensity information into the recognition procedure, using the optimal spanning tree to achieve the optimal hierarchy for recognition to minimize recognition errors, and to illustrate recognition performance by using large-scale testing computed tomography (CT) data sets. The data sets pertain to 216 non-serial (planning) and 82 serial (re-planning) studies of head and neck (H&N) cancer patients undergoing radiation therapy, involving a total of similar to 2600 object samples. Texture property \"maximum probability of occurrence\" derived from the co-occurrence matrix was determined to be the best property and is utilized in conjunction with intensity properties in AAR-R. An optimal spanning tree is found in the complete graph whose nodes are individual objects, and then the tree is used as the hierarchy in recognition. Texture information combined with intensity can significantly reduce location error for gland-related objects (parotid and submandibular glands). We also report recognition results by considering image quality, which is a novel concept. AAR-R with new features achieves a location error of less than 4 mm (similar to 1.5 voxels in our studies) for good quality images for both serial and non-serial studies.","Title: Interactive iterative relative fuzzy connectedness lung segmentation on thoracic 4D dynamic MR images.\nYear: 2017\n\nAbstract: Lung delineation via dynamic 4D thoracic magnetic resonance imaging (MRI) is necessary for quantitative image analysis for studying pediatric respiratory diseases such as thoracic insufficiency syndrome (TIS). This task is very challenging because of the often-extreme malformations of the thorax in TIS, lack of signal from bone and connective tissues resulting in inadequate image quality, abnormal thoracic dynamics, and the inability of the patients to cooperate with the protocol needed to get good quality images. We propose an interactive fuzzy connectedness approach as a potential practical solution to this difficult problem. Manual segmentation is too labor intensive especially due to the 4D nature of the data and can lead to low repeatability of the segmentation results. Registration-based approaches are somewhat inefficient and may produce inaccurate results due to accumulated registration errors and inadequate boundary information. The proposed approach works in a manner resembling the Iterative Livewire tool but uses iterative relative fuzzy connectedness (IRFC) as the delineation engine. Seeds needed by IRFC are set manually and are propagated from slice-to-slice, decreasing the needed human labor, and then a fuzzy connectedness map is automatically calculated almost instantaneously. If the segmentation is acceptable, the user selects \"next\" slice. Otherwise, the seeds are refined and the process continues. Although human interaction is needed, an advantage of the method is the high level of efficient user-control on the process and non-necessity to refine the results. Dynamic MRI sequences from 5 pediatric TIS patients involving 39 3D spatial volumes are used to evaluate the proposed approach. The method is compared to two other IRFC strategies with a higher level of automation. The proposed method yields an overall true positive and false positive volume fraction of 0.91 and 0.03, respectively, and Hausdorff boundary distance of 2 mm.","Title: Disease quantification on PET/CT images without explicit object delineation.\nYear: 2019\n\nAbstract: •Extension of the previous AAR framework for anatomy recognition to disease quantification.•Disease burden estimation on PET/CT images without explicit operations such as object delineation and partial volume correction but after only object localization and using fuzzy principles.•A general strategy that allows disease quantification at the organ or lesion level via automatic or interactive techniques for object localization.","Title: Fat quantification and analysis of lung transplant patients on unenhanced chest CT images based on standardized anatomic space\nYear: 2016\n\nAbstract: Chest fat estimation is important for identifying high-risk lung transplant candidates. In this paper, an approach to chest fat quantification based on a recently formulated concept of standardized anatomic space (SAS) is presented. The goal of this paper is to seek answers to the following questions related to chest fat quantification on single slice versus whole volume CT, which have not been addressed in the literature. What level of correlation exists between total chest fat volume and fat areas measured on single abdominal and thigh slices? What is the anatomic location in the chest where maximal correlation of fat area with fat volume can be expected? Do the components of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) have the same area-to-volume correlative behavior or do they differ? The SAS approach includes two steps: calibration followed by transformation which will map the patient slice locations non-linearly to SAS. The optimal slice locations found for SAT and VAT based on SAS are different and at the mid-level of the T8 vertebral body for SAT and mid-level of the T7 vertebral body for VAT. Fat volume and area on optimal slices for SAT and VAT are correlated with Pearson correlation coefficients of 0.97 and 0.86, respectively. The correlation of chest fat volume with abdominal and thigh fat areas is weak to modest.","Title: Definition and automatic anatomy recognition of lymph node zones in the pelvis on CT images\nYear: 2016\n\nAbstract: Currently, unlike IALSC-defined thoracic lymph node zones, no explicitly provided definitions for lymph nodes in other body regions are available. Yet, definitions are critical for standardizing the recognition, delineation, quantification, and reporting of lymphadenopathy in other body regions. Continuing from our previous work in the thorax, this paper proposes a standardized definition of the grouping of pelvic lymph nodes into 10 zones. We subsequently employ our earlier Automatic Anatomy Recognition (AAR) framework designed for body-wide organ modeling, recognition, and delineation to actually implement these zonal definitions where the zones are treated as anatomic objects. First, all 10 zones and key anatomic organs used as anchors are manually delineated under expert supervision for constructing fuzzy anatomy models of the assembly of organs together with the zones. Then, optimal hierarchical arrangement of these objects is constructed for the purpose of achieving the best zonal recognition. For actual localization of the objects, two strategies are used - optimal thresholded search for organs and one-shot method for the zones where the known relationship of the zones to key organs is exploited. Based on 50 computed tomography (CT) image data sets for the pelvic body region and an equal division into training and test subsets, automatic zonal localization within 1-3 voxels is achieved.","Title: AAR-RT - A system for auto-contouring organs at risk on CT images for radiation therapy planning: Principles, design, and large-scale evaluation on head-and-neck and thoracic cancer cases.\nYear: 2019\n\nAbstract: •A practical system for auto-contouring organs at risk in radiation therapy planning built around previous AAR framework.•Significantly improved all three stages of AAR – model building, object recognition, and object delineation.•Large-scale evaluation on 503 cancer patient CT scans and involving 4,301 3D object samples from two body regions.•Evaluation as a function of object/ image quality, gender, and age group. Object recognition more robust than delineation.","Title: Retrospective 4D MR image construction from free-breathing slice Acquisitions: A novel graph-based approach.\nYear: 2017\n\nAbstract: •Free-breathing MRI slice acquisition of pediatric thoraces with ailments.•Novel globally optimal graph-based method of 4D construction from 1000s of slices.•Image-based strategy without the need for breath holding or external surrogates.•Consistent and temporally and spatially smooth 4D constructed image.•4D phantom experiment based on 3D printing of a patient thorax for validation.","Title: Automatic thoracic body region localization.\nYear: 2017\n\nAbstract: Radiological imaging and image interpretation for clinical decision making are mostly specific to each body region such as head & neck, thorax, abdomen, pelvis, and extremities. For automating image analysis and consistency of results, standardizing definitions of body regions and the various anatomic objects, tissue regions, and zones in them becomes essential. Assuming that a standardized definition of body regions is available, a fundamental early step needed in automated image and object analytics is to automatically trim the given image stack into image volumes exactly satisfying the body region definition. This paper presents a solution to this problem based on the concept of virtual landmarks and evaluates it on whole-body positron emission tomography/computed tomography (PET/CT) scans. The method first selects a (set of) reference object(s), segments it (them) roughly, and identifies virtual landmarks for the object(s). The geometric relationship between these landmarks and the boundary locations of body regions in the craniocaudal direction is then learned through a neural network regressor, and the locations are predicted. Based on low-dose unenhanced CT images of 180 near whole-body PET/CT scans (which includes 34 whole-body PET/CT scans), the mean localization error for the boundaries of superior of thorax (TS) and inferior of thorax (TI), expressed as number of slices (slice spacing approximate to 4mm)), and using either the skeleton or the pleural spaces as reference objects, is found to be 3,2 (using skeleton) and 3, 5 (using pleural spaces) respectively, or in mm 13, 10 mm (using skeleton) and 10.5, 20 mm (using pleural spaces), respectively. Improvements of this performance via optimal selection of objects and virtual landmarks and other object analytics applications are currently being pursued. and the skeleton and pleural spaces used as a reference objects","Title: Disease quantification on PET/CT images without object delineation\nYear: 2017\n\nAbstract: The derivation of quantitative information from images to make quantitative radiology (QR) clinically practical continues to face a major image analysis hurdle because of image segmentation challenges. This paper presents a novel approach to disease quantification (DQ) via positron emission tomography/computed tomography (PET/CT) images that explores how to decouple DQ methods from explicit dependence on object segmentation through the use of only object recognition results to quantify disease burden. The concept of an object-dependent disease map is introduced to express disease severity without performing explicit delineation and partial volume correction of either objects or lesions. The parameters of the disease map are estimated from a set of training image data sets. The idea is illustrated on 20 lung lesions and 20 liver lesions derived from F-18-2-fluoro-2-deoxy-D-glucose (FDG)-PET/CT scans of patients with various types of cancers and also on 20 NEMA PET/CT phantom data sets. Our preliminary results show that, on phantom data sets, \"disease burden\" can be estimated to within 2% of known absolute true activity. Notwithstanding the difficulty in establishing true quantification on patient PET images, our results achieve 8% deviation from \"true\" estimates, with slightly larger deviations for small and diffuse lesions where establishing ground truth becomes really questionable, and smaller deviations for larger lesions where ground truth set up becomes more reliable. We are currently exploring extensions of the approach to include fully automated body-wide DQ, extensions to just CT or magnetic resonance imaging (MRI) alone, to PET/CT performed with radiotracers other than FDG, and other functional forms of disease maps.","Title: Lung parenchymal analysis on dynamic MRI in thoracic insufficiency syndrome to assess changes following surgical intervention.\nYear: 2018\n\nAbstract: General surgeons, orthopedists, and pulmonologists individually treat patients with thoracic insufficiency syndrome (TIS). The benefits of growth-sparing procedures such as Vertical Expandable Prosthetic Titanium Rib (VEPTR)insertion for treating patients with TIS have been demonstrated. However, at present there is no objective assessment metricto examine different thoracic structural components individually as to their roles in the syndrome, in contributing to dynamics and function, and in influencing treatment outcome. Using thoracic dynamic MRI (dMRI), we have been developing a methodology to overcome this problem. In this paper, we extend this methodology from our previous structural analysis approaches to examining lung tissue properties. We process the T2-weighted dMRI images through a series of steps involving 4D image construction of the acquired dMRI images, intensity non-uniformity correction and standardization of the 4D image, lung segmentation, and estimation of the parameters describing lung tissue intensity distributions in the 4D image. Based on pre- and post-operative dMRI data sets from 25 TIS patients (predominantly neuromuscular and congenital conditions), we demonstrate how lung tissue can be characterized by the estimated distribution parameters. Our results show that standardized T2-weighted image intensity values decrease from the pre- to post-operative condition, likely reflecting improved lung aeration post-operatively. In both pre- and post-operative conditions, the intensity values decrease also from end-expiration to end-inspiration, supporting the basic premise of our results.","Title: Quantitative dynamic MRI (QdMRI) Volumetric Analysis of Pediatric Patients with Thoracic Insufficiency Syndrome.\nYear: 2018\n\nAbstract: The lack of standardizable objective diagnostic measurement techniques is a major hurdle in the assessment and treatment of pediatric patients with thoracic insufficiency syndrome (TIS). The aim of this paper is to explore quantitative dynamic MRI (QdMRI) volumetric parameters derived from thoracic dMRI in pediatric patients with TIS and the relationships between dMRI parameters and clinical measurements. 25 TIS patients treated with vertical expandable prosthetic titanium rib (VEPTR) surgery are included in this retrospective study. Left and right lungs at end-inspiration and end-expiration are segmented from constructed 4D dMRI images. Lung volumes and excursion (or tidal) volumes of the left/right chest wall and hemi-diaphragms are computed. Commonly used clinical parameters include thoracic and lumbar Cobb angles and respiratory measurements from pulmonary function testing (PFT). 200 3D lungs in total (left & right, pre-operative & post-operative, end-inspiration & end-expiration) are segmented for analysis. Our analysis indicates that change of resting breathing rate (RR) following surgery is negatively correlated with that of QdMRI parameters. Chest wall tidal volumes and hemi-diaphragm tidal volumes increase significantly following surgery. Clinical parameter RR reduced after surgical treatment with P values around 0.06 but no significant differences were found on other clinical parameters. The significant increase in post-operative tidal volumes suggests a treatment-related improvement in lung capacity. The reduction of RR following surgery shows that breathing function is improved. The QdMRI parameters may offer an objective marker set for studying TIS, which is currently lacking.","Title: Super-mask-based object localization for auto-contouring in head and neck radiation therapy planning.\nYear: 2019\n\nAbstract: We have presented a variety of methods for object recognition based on the Automatic Anatomy Recognition (AAR) framework at previous SPIE conferences, including AAR recognition via optimal threshold on intensity, AAR recognition via composite information from intensity and texture, and AAR recognition with the optimal hierarchical structure design, and via neural networks to learn object relationships. The purpose of this paper is to introduce new features for the AAR-based recognition procedure and improve the performance of object localization for auto contouring in head and neck (H&N) radiation therapy planning, specifically for some of the most challenging objects. The proposed super-mask technique first registers images used for model building among themselves optimally by using a minimal spanning tree in the complete graph formed with images as nodes to determine the order of registering images. Subsequently, we build a super-mask by combining the similarly registered binary images corresponding to each object by taking (S1) union of all binary images, (S2) intersection among all binary images, or (S3) the voting based fuzzy mask created by adding the binary images. The super-mask is then used to confine search for optimum localization of the object in the given image. A large-scale H&N computed tomography (CT) data set with 216 subjects and over 2000 3D object samples were utilized in this study. The super-mask-based object localization approach within the AAR framework improved the recognition accuracy by 25-45% compared with the previous AAR strategy, especially for the most challenging H&N objects. On low quality images, the new method achieves recognition accuracy within 2 voxels on 50-60% of the cases.","Title: Obtaining the potential number of models/atlases needed for capturing anatomic variations in population images.\nYear: 2019\n\nAbstract: Many medical image processing and analysis operations can benefit a great deal from prior information encoded in the form of models/atlases to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. However, the fundamental question \"How many models/atlases are needed for optimally encoding prior information to address the differing body habitus factor in a given population?\" has remained a difficult and open problem. We propose a method to seek an answer to this question assuming that a set I of images representative of the population for the body region is given. Our approach, after images in I are trimmed to the exact body region, is to create a partition of I into a specified number n of groups by optimizing the collective similarity of images in each group. We then ascertain how the overall goodness of partition P-n(I) varies as we change n from 1 to Subsequently, values of n at which there are significant changes in the goodness value are determined. These breakpoints are taken as the recommended number of groups/models/atlases. Our results on 284 thoracic computed tomography (CT) scans show that at least 8 groups are essential, and 15, 21, or 32 could be optimum numbers if a finer classification is needed for this population. This method may be helpful for constructing high quality models/atlases with a proper grouping of the images from a sufficiently large population and in selecting optimally the training image sets needed for each class in deep learning strategies.","Title: Auto-labeling of respiratory time points in free-breathing thoracic dynamic MR image acquisitions for 4D image construction.\nYear: 2019\n\nAbstract: Determining the EE (End of Expiration) and EI (End of Inspiration) time points in the respiratory cycle is one key step during the 4D image construction from free-breathing dynamic thoracic computed tomography (CT) or magnetic resonance imaging (MRI) acquisitions. However, the cost of manually labeling EE and EI time points is extensive. An automatic image-based EE and EI labeling method makes image annotation independent of the image acquisition process, avoiding use of internal or external markers for the patient during image acquisition. The purpose of this paper is to introduce a novel optical-flow-based technique for finding EE and EI time points from dynamic thoracic MRI acquired during natural tidal-breathing. The diaphragm is tracked as a marker to determine the state of breathing. A region of interest (ROI) containing the diaphragm is selected to calculate the pixel optical flow values between two adjacent time slices. The average optical flow values of all pixels including diaphragm motion speed is used as a reference for labeling EE and EI. When the direction of movement of the diaphragm changes, EE or EI is found depending on the direction of the change. Quantitative evaluation was carried out to evaluate the effectiveness of our method in different locations in the lungs as compared to manual labeling. When tested on 28 patient dynamic thoracic MRI data sets, the average error was found to be less than 1 time point. Automatic labeling greatly shortened the labeling time, requiring less than 8 minutes compared to 4 hours for manual labeling per study.","Title: A Minimally Interactive Method for Labeling Respiratory Phases in Free-Breathing Thoracic Dynamic MRI for Constructing 4D Images\nYear: 2022\n\nAbstract: Objective: Determination of end-expiration (EE) and end-inspiration (EI) time points in the respiratory cycle in free-breathing slice image acquisitions of the thorax is one key step needed for 4D image construction via dynamic magnetic resonance imaging. The purpose of this paper is to realize the automation of the labeling process. Methods: The diaphragm is used as a surrogate for ...","Title: Gradient-Aligned convolution neural network\nYear: 2022\n\nAbstract: •We propose a general Convolution operation, called GAConv, which can replace conventional operations in CNN to help it achieve rotation invariance.•With GAConv, Gradient-Aligned CNN (GACNN) can achieve rotation invariance without any data augmentation, feature-map augmentation, and filter enrichment.•In GACNN, rotation invariance does not learn from the training set, but bases on the network model. Different from the vanilla CNN, GACNN will output invariant results for all rotated versions of an object, no matter whether the network is trained or not.•We conduct classification experiments on designed dataset and realistic datasets. The results show that with the same computation cost, GACNN achieved better results than conventional CNN and some rotational invariant CNN.","Title: Object recognition in medical images via anatomy-guided deep learning\nYear: 2022\n\nAbstract: •A conceptual framework to synergistically marry the unmatched strengths of high-level human knowledge (natural intelligence) and artificial intelligence to arrive at a robust, accurate, and general object recognition method for medical image analysis.•The AAR-DL approach combines an advanced anatomy-modeling strategy (AAR), model-based object recognition (AAR-R), and deep learning object detection networks.•AAR-DL consists of 4 key modules wherein prior knowledge is made use of judiciously at every stage.•AAR-DL has demonstrated high accuracy and robustness to image artifacts and deviations.•AAR-DL performs like an expert human operator in object recognition with localization accuracy within 1–2 voxels and remarkable robustness.","Title: Organ segmentation from computed tomography images using the 3D convolutional neural network: a systematic review\nYear: 2022\n\nAbstract: Computed tomography images are scans that combine a series of X-rays with computer processing techniques to display organs in the body. Recently, 3D CNN models have become effective in tasks relating to recognition, delineation, and classification. Therefore we propose a review to summarize different 3D CNN algorithms for segmenting organs in computed tomography images. This work systematically applies a two-stage procedure for review. A thorough screening of abstracts and titles to ascertain their relevance was done. Research papers published in the academic repositories were selected, analyzed, and reviewed. Insight relating to 3D organ segmentation is provided, with content such as database usage, disadvantages, and advantages. A comparison of two accuracies was carried out with a graph depicting database categories. Important insights, limitations, observations, and future directions were elucidated. After careful investigation, we observe that the encoder-decoder network is predominant for segmentation. The encoder-decoder framework provides a seamless procedure to segment CT images. A prediction of future trends with insightful recommendations for researchers is proposed. Finally, findings suggest that CNN algorithms produce good accuracies despite their limitations."],"string":"[\n \"Title: Multidimensional digital boundaries\\nYear: 1994\\n\\nAbstract: In many imaging applications, boundaries of objects need to be identified in multidimensional digital image data for the visualization and analysis of object information captured in the images. This article addresses the question of how to define boundaries in multidimensional digital spaces so that they are \\\"closed\\\" and connected, and so that they partition the digital space into an interior set that is connected and an exterior set that is connected. Using adjacency relations defined on the elements of the digital space and on boundary elements, we prove some basic results relating to these properties of boundaries. We examine in detail some specific boundary element adjacency relations and present efficient algorithms that track boundaries defined in binary images of any (finite) diinensionality. We conclude with two conjectures relating to the connectedness of boundaries.\",\n \"Title: Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation\\nYear: 1996\\n\\nAbstract: Images are by nature fuzzy. Approaches to object information extraction from images should attempt to use this fact and retain fuzziness as realistically as possible. In past image segmentation research, the notion of “hanging togetherness” of image elements specified by their fuzzy connectedness has been lacking. We present a theory of fuzzy objects for n -dimensional digital spaces based on a notion of fuzzy connectedness of image elements. Although our definitions lead to problems of enormous combinatorial complexity, the theoretical results allow us to reduce this dramatically, leading us to practical algorithms for fuzzy object extraction. We present algorithms for extracting a specified fuzzy object and for identifying all fuzzy objects present in the image data. We demonstrate the utility of the theory and algorithms in image segmentation based on several practical examples all drawn from medical imaging.\",\n \"Title: Fast Visualization, Manipulation, and Analysis of Binary Volumetric Objects\\nYear: 1991\\n\\nAbstract: A set of algorithms for interactive visualization, manipulation, and measurement of large 3-D objects on general-purpose workstations is described. They are based on a method of representing digital structures called a semiboundary. This data structure stores boundary and interior information. The use of these algorithms for visualizing medical data is addressed. Examples of their use are given.\",\n \"Title: Co-segmentation of functional and anatomical images.\\nYear: 2012\\n\\nAbstract: This paper presents a novel method for segmenting functional and anatomical structures simultaneously. The proposed method unifies domains of anatomical and functional images (PET-CT), represents them in a product lattice, and performs simultaneous delineation of regions based on a random walk image segmentation. In addition, we propose a simple yet efficient object/background seed localization method, where background and foreground object cues are automatically obtained from PET images and propagated onto the corresponding anatomical images (CT). In our experiments, abnormal anatomies on PET-CT images from human subjects are segmented synergistically by the proposed fully automatic co-segmentation method with high precision (mean DSC of 91.44%) in seconds (avg. 40 seconds).\",\n \"Title: Boundary Detection in Multidimensions\\nYear: 1982\\n\\nAbstract: The development of image processing algorithms for time-varying imagery and computerized tomography data calls for generalization of the concepts of adjacency, connectivity, boundary, etc., to three and four-dimensional discrete spaces. This paper defines these basic concepts in unified terminology and presents algorithms for a boundary detection task in multidimensional space. The performance of these algorithms is discussed with respect to theoretical maximum complexity, and is illustrated with simulated computerized tomography data.\",\n \"Title: Oriented active shape models.\\nYear: 2009\\n\\nAbstract: Active shape models (ASM) are widely employed for recognizing anatomic structures and for delineating them in medical images. In this paper, a novel strategy called oriented active shape models (OASM) is presented in an attempt to overcome the following five limitations of ASM: 1) lower delineation accuracy, 2) the requirement of a large number of landmarks, 3) sensitivity to search range, 4) sensitivity to initialization, and 5) inability to fully exploit the specific information present in the given image to be segmented. OASM effectively combines the rich statistical shape information embodied in ASM with the boundary orientedness property and the globally optimal delineation capability of the live wire methodology of boundary segmentation. The latter characteristics allow live wire to effectively separate an object boundary from other nonobject boundaries with similar properties especially when they come very close in the image domain. The approach leads to a two-level dynamic programming method, wherein the first level corresponds to boundary recognition and the second level corresponds to boundary delineation, and to an effective automatic initialization method. The method outputs a globally optimal boundary that agrees with the shape model if the recognition step is successful in bringing the model close to the boundary in the image. Extensive evaluation experiments have been conducted by utilizing 40 image (magnetic resonance and computed tomography) data sets in each of five different application areas for segmenting breast, liver, bones of the foot, and cervical vertebrae of the spine. Comparisons are made between OASM and ASM based on precision, accuracy, and efficiency of segmentation. Accuracy is assessed using both region-based false positive and false negative measures and boundary-based distance measures. The results indicate the following: 1) The accuracy of segmentation via OASM is considerably better than that of ASM; 2) The number of landmarks can be reduced by a factor of 3 in OASM over that in ASM; 3) OASM becomes largely independent of search range and initialization becomes automatic. All three benefits of OASM ensue mainly from the severe constraints brought in by the boundary-orientedness property of live wire and the globally optimal solution found by the 2-level dynamic programming algorithm.\",\n \"Title: Go digital, go fuzzy\\nYear: 2002\\n\\nAbstract: In many application areas of imaging sciences, object information captured in multi-dimensional images needs to be extracted,\\n visualized, manipulated, and analyzed. These four groups of operations have been (and are being) intensively investigated,\\n developed, and applied in a variety of applications. In this paper, after giving a brief overview of the four groups of operations,\\n we put forth two main arguments: (1) Computers are digital, and most image acquisition and communication efforts at present\\n are toward digital approaches. In the same vein, there are considerable advantages to taking an inherently digital approach\\n to the above four groups of operations rather than using concepts based on continuous approximations. (2) Considering the\\n fact that images are inherently fuzzy, to handle uncertainties and heterogeneity of object properties realistically, approaches\\n based on fuzzy sets should be taken to the above four groups of operations. We give two examples in support of these arguments.\",\n \"Title: The Influence of Intensity Standardization on Medical Image Registration\\nYear: 2010\\n\\nAbstract: Acquisition-to-acquisition signal intensity variations (non-standardness) are inherent in MR images. Standardization is a post processing method for correcting inter-subject intensity variations through transforming all images from the given image gray scale into a standard gray scale wherein similar intensities achieve similar tissue meanings. The lack of a standard image intensity scale in MRI leads to many difficulties in tissue characterizability, image display, and analysis, including image segmentation. This phenomenon has been documented well; however, effects of standardization on medical image registration have not been studied yet. In this paper, we investigate the influence of intensity standardization in registration tasks with systematic and analytic evaluations involving clinical MR images. We conducted nearly 20,000 clinical MR image registration experiments and evaluated the quality of registrations both quantitatively and qualitatively. The evaluations show that intensity variations between images degrades the accuracy of registration performance. The results imply that the accuracy of image registration not only depends on spatial and geometric similarity but also on the similarity of the intensity values for the same tissues in different images.\",\n \"Title: Iterative relative fuzzy connectedness for multiple objects with multiple seeds\\nYear: 2007\\n\\nAbstract: Estimating intrinsic geometric properties of a surface from a polygonal mesh obtained from range data is an important stage of numerous algorithms in computer and robot vision, computer graphics, geometric modeling, and industrial and biomedical engineering. ...\",\n \"Title: Fuzzy object model based fuzzy connectedness image segmentation of newborn brain MR images.\\nYear: 2012\\n\\nAbstract: Cerebral parenchyma segmentation in newborn magnetic resonance (MR) images is crucial for developing computer-aided diagnosis systems in newborn cerebral diseases. However, there is limited number of studies on newborn brain MR image analysis. This study presents a novel method for fully automatically segmenting the cerebral parenchyma region using scale-based fuzzy connected image segmentation and fuzzy object models. The proposed method evaluates object affinity and homogeneity using the MR signal, and employs a fuzzy object model, which is built from training datasets. We have evaluated the proposed method based on 10 newborn MR images with subject revised age between -1 month and 2 months. These studies indicate that the use of a fuzzy object model is effective in improving the segmentation accuracy.\",\n \"Title: A framework for comparing different image segmentation methods and its use in studying equivalences between level set and fuzzy connectedness frameworks\\nYear: 2011\\n\\nAbstract: In the current vast image segmentation literature, there seems to be considerable redundancy among algorithms, while there is a serious lack of methods that would allow their theoretical comparison to establish their similarity, equivalence, or distinctness. In this paper, we make an attempt to fill this gap. To accomplish this goal, we argue that: (1) every digital segmentation algorithm A should have a well defined continuous counterpart M\\\"A, referred to as its model, which constitutes an asymptotic of A when image resolution goes to infinity; (2) the equality of two such models M\\\"A and M\\\"A\\\"^\\\"' establishes a theoretical (asymptotic) equivalence of their digital counterparts A and A^'. Such a comparison is of full theoretical value only when, for each involved algorithm A, its model M\\\"A is proved to be an asymptotic of A. So far, such proofs do not appear anywhere in the literature, even in the case of algorithms introduced as digitizations of continuous models, like level set segmentation algorithms. The main goal of this article is to explore a line of investigation for formally pairing the digital segmentation algorithms with their asymptotic models, justifying such relations with mathematical proofs, and using the results to compare the segmentation algorithms in this general theoretical framework. As a first step towards this general goal, we prove here that the gradient based thresholding model M\\\"@? is the asymptotic for the fuzzy connectedness Udupa and Samarasekera segmentation algorithm used with gradient based affinity A\\\"@?. We also argue that, in a sense, M\\\"@? is the asymptotic for the original front propagation level set algorithm of Malladi, Sethian, and Vemuri, thus establishing a theoretical equivalence between these two specific algorithms. Experimental evidence of this last equivalence is also provided.\",\n \"Title: Image segmentation by combining the strengths of Relative Fuzzy Connectedness and Graph Cut.\\nYear: 2012\\n\\nAbstract: We introduce an image segmentation algorithm GC(sum)(max), which combines, in a novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that GC(sum)(max) preserves robustness of RFC with respect to the seed choice (thus, avoiding \\\"shrinking problem\\\" of GC), while keeping GC's bigger control over \\\"leaking though the weak boundary.\\\" The theoretical analysis of GC(sum)(max) is greatly facilitated by our recent theoretical results that RFC belongs to the Generalized GC (GGC) segmentation algorithms framework. In our implementation of GC(sum)(max) we use, as a subroutine, a version of RFC algorithm (based on Image Foresting Transform) that runs (provably) in linear time with respect to the image size. This results in GC(sum)(max) running in a time close to linear.\",\n \"Title: Medical image segmentation by combining graph cuts and oriented active appearance models.\\nYear: 2012\\n\\nAbstract: In this paper, we propose a novel method based on a strategic combination of the active appearance model (AAM), live wire (LW), and graph cuts (GCs) for abdominal 3-D organ segmentation. The proposed method consists of three main parts: model building, object recognition, and delineation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the recognition part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW methods, resulting in the oriented AAM (OAAM). A multiobject strategy is utilized to help in object initialization. We employ a pseudo-3-D initialization strategy and segment the organs slice by slice via a multiobject OAAM method. For the object delineation part, a 3-D shape-constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC-OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT data set and also on the MICCAI 2007 Grand Challenge liver data set. The results show the following: 1) The overall segmentation accuracy of true positive volume fraction TPVF > 94.3% and false positive volume fraction can be achieved; 2) the initialization performance can be improved by combining the AAM and LW; 3) the multiobject strategy greatly facilitates initialization; 4) compared with the traditional 3-D AAM method, the pseudo-3-D OAAM method achieves comparable performance while running 12 times faster; and 5) the performance of the proposed method is comparable to state-of-the-art liver segmentation algorithm. The executable version of the 3-D shape-constrained GC method with a user interface can be downloaded from http://xinjianchen.wordpress.com/research/.\",\n \"Title: Scale-based fuzzy connected image segmentation: theory, algorithms, and validation\\nYear: 2000\\n\\nAbstract: This paper extends a previously reported theory and algorithms for object definition based on fuzzy connectedness. In this approach, a strength of connectedness is determined between every pair of image elements. This is done by considering all possible connecting paths between the two elements in each pair. The strength assigned to a particular path is defined as the weakest affinity between successive pairs of elements along the path. Affinity specifies the degree to which elements hang together locally in the image. Although the theory allowed any neighborhood size for affinity definition, it did not indicate how this was to be selected. By bringing object scale into the framework in this paper, not only the size of the neighborhood is specified but also it is allowed to change in different parts of the image. This paper argues that scale-based affinity, and hence connectedness, is natural in object definition and demonstrates that this leads to more effective object segmentation. The approach presented here considers affinity to consist of two components. The homogeneity-based component indicates the degree of affinity between image elements based on the homogeneity of their intensity properties. The object-feature-based component captures the degree of closeness of their intensity properties to some expected values of those properties for the object. A family of non-scale-based and scale-based affinity relations are constructed dictated by how we envisage the two components to characterize objects. A simple and effective method for giving a rough estimate of scale at different locations in the image is presented. The original theoretical and algorithmic framework remains more-or-less the same but considerably improved segmentations result. The method has been tested in several applications qualitatively. A quantitative statistical comparison between the non-scale-based and the scale-based methods was made based on 250 phantom images. These were generated from 10 patient MR brain studies by first segmenting the objects, then setting up appropriate intensity levels for the object and the background, and then by adding five different levels for each of noise and blurring and a fixed slow varying background component. Both the statistical and the subjective tests clearly indicate that the scale-based method is superior to the non-scale-based method in capturing details and in robustness to noise. It is also shown, based on these phantom images, that any (global) optimum threshold selection method will perform inferior to the fuzzy connectedness methods described in this paper.\",\n \"Title: Landmark Selection For Shape Model Construction Via Equalization Of Variance\\nYear: 2008\\n\\nAbstract: Model-based segmentation approaches, such as those employing Active Shape Models (ASMs), have proved to be useful for medical image segmentation and understanding. To build the model, we need an annotated training set representing correspondences among shapes. Manual positioning of landmarks is a tedious, time consuming, and error prone task, and almost impossible in the 3D space. To overcome some of these drawbacks, we devised an automatic method. Our method is guided by the strategy of equalization of the variance contained in a training set for selecting landmarks. The main premise here is that this strategy itself takes care of the correspondence issue and at the same time deploys landmarks very frugally and optimally considering shape variations. The desired landmarks are positioned around each contour in such a manner as to equally distribute the total variance existing in the training set. The method is evaluated on 40 MRI foot data sets. The results show that, for the same number of landmarks, the proposed method is significantly more compact than manual and equally spaced annotations.\",\n \"Title: GC-ASM: Synergistic Integration of Graph-Cut and Active Shape Model Strategies for Medical Image Segmentation.\\nYear: 2013\\n\\nAbstract: Image segmentation methods may be classified into two categories: purely image based and model based. Each of these two classes has its own advantages and disadvantages. In this paper, we propose a novel synergistic combination of the image based graph-cut (GC) method with the model based ASM method to arrive at the GC-ASM method for medical image segmentation. A multi-object GC cost function is proposed which effectively integrates the ASM shape information into the GC framework. The proposed method consists of two phases: model building and segmentation. In the model building phase, the ASM model is built and the parameters of the GC are estimated. The segmentation phase consists of two main steps: initialization (recognition) and delineation. For initialization, an automatic method is proposed which estimates the pose (translation, orientation, and scale) of the model, and obtains a rough segmentation result which also provides the shape information for the GC method. For delineation, an iterative GC-ASM algorithm is proposed which performs finer delineation based on the initialization results. The proposed methods are implemented to operate on 2D images and evaluated on clinical chest CT, abdominal CT, and foot MRI data sets. The results show the following: (a) An overall delineation accuracy of TPVF > 96%, FPVF < 0.6% can be achieved via GC-ASM for different objects, modalities, and body regions. (b) GC-ASM improves over ASM in its accuracy and precision to search region. (c) GC-ASM requires far fewer landmarks (about 1/3 of ASM) than ASM. (d) GC-ASM achieves full automation in the segmentation step compared to GC which requires seed specification and improves on the accuracy of GC. (e) One disadvantage of GC-ASM is its increased computational expense owing to the iterative nature of the algorithm.\",\n \"Title: Local Scale for Boundary Shape Description: Application in Locating Landmarks Automatically\\nYear: 2007\\n\\nAbstract: A good shape descriptor is necessary for automatically identifying landmarks on boundaries. Our method of boundary shape description is based on the notion of cscale, which is a new local scale concept, defined at each boundary element. From this representation we can extract special points of interest such as convex and concave corners, straight lines, circular segments, and inflection points. The results show that this method gives a complete description of shape and allows the automatic positioning of mathematical landmarks, which agree with our intuitive ideas of where landmarks may be defined. This method is applicable to spaces of any dimensionality, although we have focused in this paper on 2D shapes.\",\n \"Title: Joint graph cut and relative fuzzy connectedness image segmentation algorithm.\\nYear: 2013\\n\\nAbstract: •Novel image segmentation algorithm robust w.r.t. seed choice.•Synergistic combinantion of fuzzy connectedness (FC) and Graph Cut (GC) algorithms.•Better delineation of poorly defined boundary segments than either FC or GC alone.\",\n \"Title: Linear time algorithms for exact distance transform: elaboration on Maurer et al. algorithm\\nYear: 2009\\n\\nAbstract: In 2003, Maurer at al. (7) published a paper describing an algorithm that computes the exact distance transform in a linear time (with respect to image size) for the rectangular binary images in the k-dimensional space Rk and distance measured with respect to Lp-metric for 1 ≤ p ≤∞ , which includes Euclidean distance L2. In this paper we discuss this algorithm from theoretical and practical points of view. On the practical side, we concentrate on its Euclidean distance version, discuss the possible ways of implementing it as signed distance transform, and experimentally compare implemented algorithms. We also describe the parallelization of these algorithms and the computation time savings associated with such an implementation. The discussed implementations will be made available as a part of the CAVASS software system developed and maintained in our group (5). On the theoretical side, we prove that our version of the signed distance transform algorithm, GBDT , returns, in a linear time, the exact value of the distance from the geometrically defined object boundary. We notice that, actually, the precise form of the algorithm from (7) is not well defined for L1 and L∞ metrics and point to our complete proof (not given in (7)) that all these algorithms work correctly for the Lp-metric with 1 <p< ∞.\",\n \"Title: Vectorial scale-based fuzzy-connected image segmentation\\nYear: 2006\\n\\nAbstract: This paper presents an extension of previously published theory and algorithms for fuzzy-connected image segmentation. In this approach, a strength of connectedness is assigned to every pair of image elements. This is done by finding the strongest among all possible connecting paths between the two elements in each pair. The strength assigned to a particular path is defined as the weakest affinity between successive pairs of elements along the path. Affinity specifies the degree to which elements hang together locally in the image. A scale is determined at every element in the image that indicates the size of the largest homogeneous hyperball region centered at the element. In determining affinity between any two elements, all elements within their scale regions are considered. This method has been effectively utilized in several medical applications. In this paper, we generalize this method from scalar images to vectorial images. In a vectorial image, scale is defined as the radius of the largest hyperball contained in the same homogeneous region under a predefined condition of homogeneity of the image vector field. Two different components of affinity, namely homogeneity-based affinity and object-feature-based affinity, are devised in a fully vectorial manner. The original relative fuzzy connectedness algorithm is utilized to delineate a specified object via a competing strategy among multiple objects. We have presented several studies to evaluate the performance of this method based on simulated MR images, 20 clinical MR images, and 250 mathematical phantom images. These studies indicate that the fully vectorial fuzzy connectedness formulation has generally overall better accuracy than the method using some intermediate ad hoc steps to fit the vectorial image to a scalar fuzzy connectedness formulation, and precision and efficiency are similar for these two methods.\",\n \"Title: A GENERAL THEORY OF IMAGE SEGMENTATION: LEVEL SET SEGMENTATION IN THE FUZZY CONNECTEDNESS FRAMEWORK\\nYear: 2007\\n\\nAbstract: In the current vast image segmentation literature, there is a serious lack of methods that would allow theoretical comparison of the algorithms introduced by using different mathematical methodologies. The main goal of this article is to introduce a general theoretical framework for image segmentation that would allow such comparison. The framework is based on the formal definitions designed to answer the following fundamental questions: What is the relation between an idealized image and its digital representation? What properties a segmentation algorithm must satisfy to be acknowledged as acceptable? What does it mean that a digital image segmentation algorithm truly approximates an idealized segmentation model? We use the formulated framework to analyze the front propagation (FP) level set algorithm of Malladi, Sethian, and Vemuri and compare it with the fuzzy connectedness family of algorithms. In particular, we prove that the FP algorithm is weakly model-equivalent with the absolute fuzzy connectedness algorithm of Udupa and Samarasekera used with gradient based affinity. Experimental evidence of this equivalence is also provided. The presented theoretical framework can be used to analyze any arbitrary segmentation algorithm. This line of investigation is a subject of our forthcoming work.\",\n \"Title: Brain tissue MR-image segmentation via optimum-path forest clustering\\nYear: 2012\\n\\nAbstract: We present an accurate and fast approach for MR-image segmentation of brain tissues, that is robust to anatomical variations and takes an average of less than 1min for completion on modern PCs. The method first corrects voxel values in the brain based on local estimations of the white-matter intensities. This strategy is inspired by other works, but it is simple, fast, and very effective. Tissue classification exploits a recent clustering approach based on the motion of optimum-path forest (OPF), which can find natural groups such that the absolute majority of voxels in each group belongs to the same class. First, a small random set of brain voxels is used for OPF clustering. Cluster labels are propagated to the remaining voxels, and then class labels are assigned to each group. The experiments used several datasets from three protocols (involving normal subjects, phantoms, and patients), two state-of-the-art approaches, and a novel methodology which finds the best choice of parameters for each method within the operational range of these parameters using a training dataset. The proposed method outperformed the compared approaches in speed, accuracy, and robustness.\",\n \"Title: Joint segmentation of anatomical and functional images: applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images.\\nYear: 2013\\n\\nAbstract: •We present a novel method for co-segmentation of anatomical and functional images.•We extend the random walk method for jointly delineating multiple objects from multiple image modalities.•Our method segments PET, PET-CT, MRI-PET, and fused MRI-PET-CT images with high accuracy in seconds.•Users can easily adapt our algorithm to cases where one-to-one correspondence between different modalities is not satisfied.•Our method offers automated object detection via interesting uptake region algorithm to avoid users having to provide seeds.\",\n \"Title: Relative fuzzy connectedness among multiple objects: theory, algorithms, and applications in image segmentation\\nYear: 2001\\n\\nAbstract: The notion of fuzzy connectedness captures the idea of “hanging-togetherness” of image elements in an object by assigning a strength of connectedness to every possible path between every possible pair of image elements. This concept leads to powerful image segmentation algorithms based on dynamic programming whose effectiveness has been demonstrated on thousands of images in a variety of applications. In a previous framework, we introduced the notion of relative fuzzy connectedness for separating a foreground object from a background object. In this framework, an image element c is considered to belong to that among these two objects with respect to whose reference image element c has the higher strength of connectedness. In fuzzy connectedness, a local fuzzy relation called affinity is used on the image domain. This relation was required for theoretical reasons to be of fixed form in the previous framework. In the present paper, we generalize relative connectedness to multiple objects, allowing all objects (of importance) to compete among themselves to grab membership of image elements based on their relative strength of connectedness to reference elements. We also allow affinity to be tailored to the individual objects. We present a theoretical and algorithmic framework and demonstrate that the objects defined are independent of the reference elements chosen as long as they are not in the fuzzy boundary between objects. Examples from medical imaging are presented to illustrate visually the effectiveness of multiple object relative fuzzy connectedness. A quantitative evaluation based on 160 mathematical phantom images demonstrates objectively the effectiveness of relative fuzzy connectedness with object-tailored affinity relation.\",\n \"Title: Linear Time Algorithms for Exact Distance Transform\\nYear: 2011\\n\\nAbstract: In 2003, Maurer et al. (IEEE Trans. Pattern Anal. Mach. Intell. 25:265---270, 2003) published a paper describing an algorithm that computes the exact distance transform in linear time (with respect to image size) for the rectangular binary images in the k-dimensional space 驴 k and distance measured with respect to L p -metric for 1驴p驴驴, which includes Euclidean distance L 2. In this paper we discuss this algorithm from theoretical and practical points of view. On the practical side, we concentrate on its Euclidean distance version, discuss the possible ways of implementing it as signed distance transform, and experimentally compare implemented algorithms. We also describe the parallelization of these algorithms and discuss the computational time savings associated with them. All these implementations will be made available as a part of the CAVASS software system developed and maintained in our group (Grevera et al. in J. Digit. Imaging 20:101---118, 2007). On the theoretical side, we prove that our version of the signed distance transform algorithm, GBDT, returns the exact value of the distance from the geometrically defined object boundary. We provide a complete proof (which was not given of Maurer et al. (IEEE Trans. Pattern Anal. Mach. Intell. 25:265---270, 2003) that all these algorithms work correctly for L p -metric with 1pL 1 and L 驴 metrics. In addition, we show that the algorithm can be used to find, in linear time, the exact value of the diameter of an object, that is, the largest possible distance between any two of its elements.\",\n \"Title: A new method of automatic landmark tagging for shape model construction via local curvature scale\\nYear: 2008\\n\\nAbstract: Segmentation of organs in medical images is a difficult task requiring very often the use of model-based approaches. To build the model, we need an annotated training set of shape examples with correspondences indicated among shapes. Manual positioning of landmarks is a tedious, time-consuming, and error prone task, and almost impossible in the 3D space. To overcome some of these drawbacks, we devised an automatic method based on the notion of c-scale, a new local scale concept. For each boundary element b, the arc length of the largest homogeneous curvature region connected to b is estimated as well as the orientation of the tangent at b. With this shape description method, we can automatically locate mathematical landmarks selected at different levels of detail. The method avoids the use of landmarks for the generation of the mean shape. The selection of landmarks on the mean. shape is done automatically using the c-scale method. Then, these landmarks are propagated to each shape in the training set, defining this way the correspondences among the shapes. Altogether 12 strategies are described along these lines. The methods are evaluated on 40 MRI foot data sets, the object of interest being the talus bone. The results show that, for the same number of landmarks, the proposed methods are more compact than manual and equally spaced annotations. The approach is applicable to spaces of any dimensionality, although we have focused in this paper on 2D shapes.\",\n \"Title: Some new concepts for encoding line patterns\\nYear: 1975\\n\\nAbstract: A scheme for encoding patterns composed of straight strokes is presented. The code for such a configuration is a sequence of turning and end points. These points which completely characterize the patterns considered are extracted from the straight line segments in the pattern oriented in eight principal directions. The method has the advantage of working directly with the given thick pattern rather than with its thinned version. It is shown how these two new concepts viz. turning and end points can be extended to the recognition problem. Alphanumerals of a particular font are analysed using simple decision methods. The scheme seems to have wider applicability in the area of network analysis, graph theory and computer perception of planar drawings.\",\n \"Title: New Variants of a Method of MRI Scale Normalization\\nYear: 1999\\n\\nAbstract: One of the major drawbacks of Magnetic Resonance Imaging (MRI) has been the lack of a standard and quantifiable interpretation of image intensities. This causes many diffculties in image display and analysis. We have devised a two-step method wherein all images can be transformed in such a way that for the same protocol and body region, in the transformed images similar intensities will have similar tissue meaning. Normalized images can be displayed with fixed windows without the need of per case adjustment. More importantly, extraction of quantitative information about healthy organs or about abnormities, such as tumors, can considerably be simplified. This paper introduces and compares new variants of this normalization method that can help to overcome some of the problems with the original method.\",\n \"Title: A framework for evaluating image segmentation algorithms\\nYear: 2006\\n\\nAbstract: The purpose of this paper is to describe a framework for evaluating image segmentation algorithms. Image segmentation consists of object recognition and delineation. For evaluating segmentation methods, three factors—precision (reliability), accuracy (validity), and efficiency (viability)—need to be considered for both recognition and delineation. To assess precision, we need to choose a figure of merit, repeat segmentation considering all sources of variation, and determine variations in figure of merit via statistical analysis. It is impossible usually to establish true segmentation. Hence, to assess accuracy, we need to choose a surrogate of true segmentation and proceed as for precision. In determining accuracy, it may be important to consider different ‘landmark’ areas of the structure to be segmented depending on the application. To assess efficiency, both the computational and the user time required for algorithm training and for algorithm execution should be measured and analyzed. Precision, accuracy, and efficiency factors have an influence on one another. It is difficult to improve one factor without affecting others. Segmentation methods must be compared based on all three factors, as illustrated in an example wherein two methods are compared in a particular application domain. The weight given to each factor depends on application.\",\n \"Title: Fast Digital Perspective Shell Rendering\\nYear: 1999\\n\\nAbstract: Volume visualization methods usually require extensive storage and time-intensive computations. To make them viable in many applications, researchers have proposed various types of hardware-based and software-based solutions. In most of these solutions, the rendering is based on orthographic parallel projection. However, in applications such as virtual endoscopy, perspective projection is necessary to allow the observer to travel inside the structures. In this paper, we extend one of these software-based solutions, called shell rendering, to provide fast digital perspective projection. The main idea in digital perspective is that each voxel should be rendered using an integer number of pixels which is inversely proportional to the distance between the voxel and the observer and that that distance can be discretized. We show that the new method, called digital perspective shell rendering, provides the same image quality as orthographic parallel projection\",\n \"Title: Image filtering via generalized scale.\\nYear: 2008\\n\\nAbstract: In medical imaging, low signal-to-noise ratio (SNR) and/or contrast-to-noise ratio (CNR) often cause many image processing algorithms to perform poorly. Postacquisition image filtering is an important off-line image processing approach widely employed to enhance the SNR and CNR. A major drawback of many filtering techniques is image degradation by diffusing/blurring edges and/or fine structures. In this paper, we introduce a scale-based filtering method that employs scale-dependent diffusion conductance to perform filtering. This approach utilizes novel object scale information via a concept called generalized scale, which imposes no shape, size, or anisotropic constraints unlike previously published ball scale-based filtering strategies. The object scale allows us to better control the filtering process by constraining smoothing in regions with fine details and in the vicinity of boundaries while permitting effective smoothing in the interior of homogeneous regions. A new quantitative evaluation strategy that captures the SNR to CNR trade-off behavior of filtering methods is presented. The evaluations based on the Brainweb data sets show superior performance of generalized scale-based diffusive filtering over two existing methods, namely, ball scale-based and nonlinear complex diffusion processes. Qualitative experiments based on both phantom and patient magnetic resonance images demonstrate that the generalized scale-based approach leads to better preservation of fine details and edges.\",\n \"Title: Generalized Scale-Based Image Filtering\\nYear: 2005\\n\\nAbstract: In medical imaging, low signal-to-noise ratio (SNR) and/or contrast-to-noise ratio (CNR) often cause many image processing algorithms to perform poorly. Postacquisition image filtering is an important off-line image processing approach widely employed to enhance the SNR and CNR. A major drawback of many filtering techniques is image degradation by diffusing/blurring edges and/or fine structures. In this paper, we introduce a scale-based filtering method that employs scale-dependent diffusion conductance to perform filtering. This approach utilizes novel object scale information via a concept called generalized ball scale, which imposes no shape, size, or anisotropic constraints unlike previously published ball scale-based filtering strategies. The object scale allows us to better control the filtering process by constraining smoothing in regions with fine details and in the vicinity of boundaries while permitting effective smoothing in the interior of homogeneous regions. Quantitative evaluations based on the Brainweb data sets show superior performance of generalized ball scale-based diffusive filtering over two existing methods. namely, ball scale-based and nonlinear complex diffusion process. Qualitative experiments based on both phantom and patient magnetic resonance images demonstrate that the generalized ball scale-based approach leads to better preserved fine details and edges.\",\n \"Title: Hybrid Segmentation of Anatomical Data\\nYear: 2001\\n\\nAbstract: We propose new hybrid methods for automated segmentation of radiological patient data and the Visible Human data. In this paper, we integrate boundary-based and region-based segmentation methods which amplifies the strength but reduces the weakness of both approaches. The novelty comes from combining a boundary-based method, the deformable model-based segmentation with region-based segmentation methods, the fuzzy connectedness and Voronoi Diagram-based segmentation, to develop hybrid methods that yield high precision, accuracy and efficiency. This work is a part of a NLM funded effort to provide a fully implemented and tested Visible Human Project Segmentation and Registration Toolkit (Insight).\",\n \"Title: Intensity Standardization Simplifies Brain MR Image Segmentation.\\nYear: 2009\\n\\nAbstract: Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity-inhomogeneity-correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.\",\n \"Title: Performance evaluation of finite normal mixture model-based image segmentation techniques.\\nYear: 2003\\n\\nAbstract: Finite Normal Mixture (FNM) model-based image segmentation techniques adopt the following detection-estimation-classification paradigm: 1) detect the number of image regions by using theoretical information criteria; 2) estimate model parameters by using expectation-maximization (EM)/classification-maximization (CM) algorithms; and 3) classify pixels into regions by using various classifiers. This paper presents a theoretical framework to evaluate the performance of this class of image segmentation techniques. For the detection performance, probabilities of over-detection and under-detection of the number of image regions are defined, and the associated formulae in terms of model parameters and image quality are derived. For the estimation performance, both EM and CM algorithms are showed to produce asymptotically unbiased ML estimates of model parameters in the case of no-overlap. Cramer-Rao bounds of variances of these estimates are derived. For the classification performance, misclassification probability for the Bayesian classifier is defined, and a simple formula based on parameter estimates and classified data is derived to evaluate segmentation errors. This evaluation method provides both theoretically approachable accuracy limits of the techniques and practically achievable performance of the given images. Theoretical and experimental results are in good agreement and indicate that, for images of moderate quality, the detection operation is robust, the parameter estimates are accurate, and the segmentation errors are small.\",\n \"Title: Ball-Scale Based Hierarchical Multi-Object Recognition in 3D Medical Images\\nYear: 2010\\n\\nAbstract: This paper investigates, using prior shape models and the concept of ball scale (b-scale), ways of automatically recognizing objects in 3D images without performing elaborate searches or optimization. That is, the goal is to place the model in a single shot close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. This is achieved via the following set of key ideas: (a) A semi-automatic way of constructing a multi-object shape model assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship between objects in the training images and their intensity patterns captured in b-scale images. (c) A hierarchical mechanism of positioning the model, in a one-shot way, in a given image from a knowledge of the learnt pose relationship and the b-scale image of the given image to be segmented. The evaluation results on a set of 20 routine clinical abdominal female and male CT data sets indicate the following: (1) Incorporating a large number of objects improves the recognition accuracy dramatically. (2) The recognition algorithm can be thought as a hierarchical framework such that quick replacement of the model assembly is defined as coarse recognition and delineation itself is known as finest recognition. (3) Scale yields useful information about the relationship between the model assembly and any given image such that the recognition results in a placement of the model close to the actual pose without doing any elaborate searches or optimization. (4) Effective object recognition can make delineation most accurate.\",\n \"Title: Clutter-Free Volume Rendering For Magnetic Resonance Angiography Using Fuzzy Connectedness\\nYear: 2000\\n\\nAbstract: The purpose of this paper is to describe a practical method for the clutter-free, three-dimensional (3D) volume rendering of magnetic resonance angiographic (MRA) data. In MRA, clutter due to artifacts or nearby high-intensity structures prevents clear visualization of the vessel under investigation. We offer an alternative to the manual editing that is commonly used to remove clutter. The method is near automatic and requires the user to point at structures on a 3D maximum intensity projection (MIP) display. It utilizes recently developed fuzzy connected object delineation algorithms to extract the vessels of interest. Because the resulting definition is nonbinary, it can be displayed via MIP or more sophisticated volume-rendering techniques. The improved renditions are illustrated with several MRA studies. Implementation of the fuzzy connectedness method proved to be effective in removing the associated clutter in the images and, in some cases, dramatically improving visibility. Additionally, vessels could be extracted with a nominal number of points selected within the object by the user that retained most of the information present in the conventional MIP display. This could all be performed in a practical time frame: the first vessel delineation in 30 s, subsequent delineations in 2-10 s per view, all on a 100-MHz Pentium PC. Automatic delineation of vessels for 3D MRA visualization is feasible via fuzzy connectedness principles. The method retains the original intensity constitution, an advantage of the MIP method, and mostly eliminates the clutter commonly observed in MIP. Its speed and effectiveness make it feasible for routine clinical use. (C) 2000 John Wiley & Sons, Inc.\",\n \"Title: Fuzzy Connectedness Image Segmentation in Graph Cut Formulation: A Linear-Time Algorithm and a Comparative Analysis.\\nYear: 2012\\n\\nAbstract: A deep theoretical analysis of the graph cut image segmentation framework presented in this paper simultaneously translates into important contributions in several directions.The most important practical contribution of this work is a full theoretical description, and implementation, of a novel powerful segmentation algorithm, GC. The output of GC coincides with a version of a segmentation algorithm known as Iterative Relative Fuzzy Connectedness, IRFC. However, GC is considerably faster than the classic IRFC algorithm, which we prove theoretically and show experimentally. Specifically, we prove that, in the worst case scenario, the GC algorithm runs in linear time with respect to the variable =||+||, where || is the image scene size and || is the size of the allowable range, , of the associated weight/affinity function. For most implementations, is identical to the set of allowable image intensity values, and its size can be treated as small with respect to ||, meaning that ()=(||). In such a situation, GC runs in linear time with respect to the image size ||.We show that the output of GC constitutes a solution of a graph cut energy minimization problem, in which the energy is defined as the norm ∥ ∥ of the map that associates, with every element from the boundary of an object , its weight (). This formulation brings IRFC algorithms to the realm of the graph cut energy minimizers, with energy functions ∥ ∥ for ∈[1,∞]. Of these, the best known minimization problem is for the energy ∥ ∥, which is solved by the classic min-cut/max-flow algorithm, referred to often as the Graph Cut algorithm.We notice that a minimization problem for ∥ ∥, ∈[1,∞), is identical to that for ∥ ∥, when the original weight function is replaced by . Thus, any algorithm GC solving the ∥ ∥ minimization problem, solves also one for ∥ ∥ with ∈[1,∞), so just two algorithms, GC and GC, are enough to solve all ∥ ∥-minimization problems. We also show that, for any fixed weight assignment, the solutions of the ∥ ∥-minimization problems converge to a solution of the ∥ ∥-minimization problem (∥ ∥=lim∥ ∥ is not enough to deduce that).An experimental comparison of the performance of GC and GC algorithms is included. This concentrates on comparing the actual (as opposed to provable worst scenario) algorithms’ running time, as well as the influence of the choice of the seeds on the output.\",\n \"Title: Fuzzy Connected Object Delineation: Axiomatic Path Strength Definition and the Case of Multiple Seeds\\nYear: 2001\\n\\nAbstract: This paper presents an extension of previously published theory and algorithms for object definition based on fuzzy connectedness. In this approach, a strength of connectedness is assigned between every pair of image elements. This is done by finding the strongest among all possible connecting paths between the two elements in each pair. The strength assigned to a particular path is defined as the weakest affinity between successive pairs of elements along the path. Affinity specifies the degree to which elements hang together locally in the image and is determined considering how close the elements are in space and in intensity features. A fuzzy connected object containing a particular seed element specified within the object is computed via dynamic programming. In all reported works so far, the minimum of affinities has been considered to define path strength and the maximum of path strengths has been used to define fuzzy connectedness. The question thus remained all along as to whether there are other valid formulations for fuzzy connectedness. One of the main contributions of this paper is a theoretical investigation under reasonable axioms to establish that maximum of path strengths of minimum of affinities along each path is indeed the one and only valid choice for defining fuzzy connectedness. In the past, a single fuzzy connected object was specified through a single seed element indicated interactively within the object region. When many objects are to be identified in a single image, interactive seed specification becomes cumbersome. Further, selecting exactly one element in each region automatically is more difficult than identifying a set of elements. Hence the theory and algorithms for the single-element case need to be generalized to multiple elements for more effective practical use. This is the second main contribution of this paper. The importance of multiple seeded fuzzy connected object definition from the considerations of both practicality and automation in image segmentation are described and illustrated with examples taken from several real medical applications.\",\n \"Title: Cloud bank: a multiple clouds model and its use in MR brain image segmentation\\nYear: 2009\\n\\nAbstract: We present improvements to a recently proposed model-based segmentation strategy, called Clouds. We demonstrate the usefulness of the new model Cloud Bank in the automatic MR-image segmentation of the brain (without medulla and spinal cord), the cerebellum, and each brain hemisphere. These structures are connected in several parts, imposing serious challenges for segmentation. The results indicate that the new model is a fast and accurate tool to eliminate user's intervention or, at least, reduce it to simple corrections, in the context of brain image segmentation.\",\n \"Title: User-steered image segmentation paradigms: live wire and live lane\\nYear: 1998\\n\\nAbstract: In multidimensional image analysis, there are, and will continue to be, situations wherein automatic image segmentation methods fail, calling for considerable user assistance in the process. The main goals of segmentation research for such situations ought to be (i) to provide effective control to the user on the segmentation process while it is being executed, and (ii) to minimize the total user's time required in the process. With these goals in mind, we present in this paper two paradigms, referred to as live wire and live lane , for practical image segmentation in large applications. For both approaches, we think of the pixel vertices and oriented edges as forming a graph, assign a set of features to each oriented edge to characterize its ``boundariness,'' and transform feature values to costs. We provide training facilities and automatic optimal feature and transform selection methods so that these assignments can be made with consistent effectiveness in any application. In live wire, the user first selects an initial point on the boundary. For any subsequent point indicated by the cursor, an optimal path from the initial point to the current point is found and displayed in real time. The user thus has a live wire on hand which is moved by moving the cursor. If the cursor goes close to the boundary, the live wire snaps onto the boundary. At this point, if the live wire describes the boundary appropriately, the user deposits the cursor which now becomes the new starting point and the process continues. A few points (live-wire segments) are usually adequate to segment the whole 2D boundary. In live lane, the user selects only the initial point. Subsequent points are selected automatically as the cursor is moved within a lane surrounding the boundary whose width changes as a function of the speed and acceleration of cursor motion. Live-wire segments are generated and displayed in real time between successive points. The users get the feeling that the curve snaps onto the boundary as and while they roughly mark in the vicinity of the boundary. We describe formal evaluation studies to compare the utility of the new methods with that of manual tracing based on speed and repeatability of tracing and on data taken from a large ongoing application. The studies indicate that the new methods are statistically significantly more repeatable and 1.5–2.5 times faster than manual tracing.\",\n \"Title: Fuzzy-connected 3D image segmentation at interactive speeds\\nYear: 2002\\n\\nAbstract: Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem with these algorithms has been their excessive computational requirements. In an attempt to substantially speed them up, in the present paper, we study systematically a host of 18 'optimal' graph search algorithms. Extensive testing of these algorithms on a variety of 3D medical images taken from large ongoing applications demonstrates that a 20-1000-fold improvement over current speeds is achievable with a combination of algorithms and fast modern PCs. Utilizing efficient algorithms and careful selection of implementations can speed up the computation of fuzzy connectedness values by a factor of 16-29 (on the same hard-ware), as compared to the implementation previously used in our applications utilizing fuzzy object segmentation. The optimality of an algorithm depends on the input data as well as on the choice of the fuzzy affinity relation. The running time is reduced considerably (by a factor up to 34 for brain MR and even more for bone CT), when the algorithms make use of pre-determined thresholds for the fuzzy objects. The reliable recognition (assisted by human operators) and the accurate, efficient, and sophisticated delineation (automatically performed by the computer) can be effectively incorporated into a single interactive process. If images having intensities with tissue-specific meaning (such as CT or standardized MR images) are utilized, most of the parameters for the segmentation method can be fixed once for all, all intermediate data (feature and fuzzy affinity values for the whole scene) can be computed before the user interaction is needed and the user can be provided with more information at the time of interaction.\",\n \"Title: Local curvature scale: a new concept of shape description\\nYear: 2008\\n\\nAbstract: Shape description plays a fundamental role in computer vision and pattern recognition, especially in the fields of shape analysis, image segmentation, and registration. Shape representations must be unique, complete and should be able to reflect the differences between similar objects while abstracting from detail and keeping the basic features. Although many methods for shape description exist, they are usually application dependent. The proposed method of boundary shape description is based on the notion of curvature-scale, which is a new local scale concept, defined at each boundary element. From this representation, we can extract special points of interest such as convex and concave corners, straight lines, circular segments, and inflection points. This method is different from existing methods of curvature estimation and can be directly applied to digital boundaries without requiring prior approximation of the boundary. The results show that it produces a complete boundary shape description capable of handling different levels of shape detail. It also has numerous potential applications such as automatic landmark tagging which becomes necessary to build model-based approaches toward the goal of organ modeling and segmentation. The method is applicable to spaces of any dimensionality, although we have focused in this paper on 2D shapes.\",\n \"Title: A justification of a fast surface tracking algorithm\\nYear: 1992\\n\\nAbstract: In many three-dimensional (3D) imaging applications in science, medicine, and engineering, object information captured in a 3D digital image is visualized and analyzed based on object surfaces extracted from the image. Recently Gordon and Udupa (Comput. Vision Graphics Image Process.45, 1989, 196–214) reported a fast algorithm for tracking such surfaces, whose correct behavior has only been a conjecture but observed nonetheless in all uses of this algorithm in a variety of biomedical applications. In the present paper, we prove the correctness of their algorithm by showing that the surfaces generated by the algorithm correspond to connected components of boundary elements and that they possess Jordan boundary properties.\",\n \"Title: Clouds: A Model For Synergistic Image Segmentation\\nYear: 2008\\n\\nAbstract: Image segmentation consists of recognizing the object in the image and precisely delineating its spatial extent. We present a model, called clouds, that exploits the synergism which commonly exists between recognition and delineation for more effective segmentation. The model can reduce user's intervention to simple corrections or even eliminate it altogether, achieving high accuracy. We evaluate the method in the task of 3D MR image segmentation of the brain in isolating automatically: brain without medulla and spinal cord; just the cerebellum; and the brain hemispheres without medulla, spinal cord, and cerebellum. These structures are connected in several parts, which poses a serious challenge for simplistic segmentation strategies. The entire process takes a few seconds on modem PCs and provides accurate results. The applications for clouds go beyond medical imaging, opening new vistas in a variety of areas served by segmentation.\",\n \"Title: Boundary and object labelling in three-dimensional images\\nYear: 1990\\n\\nAbstract: There are many imaging modalities (e.g., medical imaging scanners) that capture information about internal structures and generate three-dimensional (3D) digital images of the distribution of some physical property of the material of the structure. Such images have been found to be very useful in analyzing the form and function of the structure and in detecting and correcting deformities in the structure. Visualization of 3D structures is an essential component of such analyses. One commonly used approach to visualization consists of identifying the structure of interest, forming its surfaces, and then rendering the surfaces on a two-dimensional screen. This paper addresses the surface formation problem assuming that object identification has already been done and a 3D binary image is available that represents the structure. For the existing 3D boundary tracking algorithms, the user has to somehow specify each surface that is to be tracked. Often, the 3D image consists of many surfaces of interest. Their manual specification is very tedious and may be impossible if the structure is of complex shape. This paper describes a methodology for automatically tracking all boundary surfaces—i.e., labelling boundary surfaces—in the given 3D image. The algorithms also generate additional information from which the 3D connected components in the image are trivially obtained. Examples from medical imaging are included to illustrate the usefulness of the new methodology.\",\n \"Title: Image background inhomogeneity correction in MRI via intensity standardization.\\nYear: 2009\\n\\nAbstract: An automatic, simple, and image intensity standardization-based strategy for correcting background inhomogeneity in MR images is presented in this paper. Image intensities are first transformed to a standard intensity gray scale by a standardization process. Different tissue sample regions are then obtained from the standardized image by simply thresholding based on fixed intensity intervals. For each tissue region, a polynomial is fitted to the estimated discrete background intensity variation. Finally, a combined polynomial is determined and used for correcting the intensity inhomogeneity in the whole image. The above procedure is repeated on the corrected image iteratively until the size of the extracted tissue regions does not change significantly in two successive iterations. Intensity scale standardization is effected to make sure that the corrected image is not biased by the fitting strategy. The method has been tested on a number of simulated and clinical MR images. These tests and a comparison with the method of non-parametric non-uniform intensity normalization (N3) indicate that the method is effective in background intensity inhomogeneity correction and may have a slight edge over the N3method.\",\n \"Title: Automatic Landmark Selection for Active Shape Models\\nYear: 2005\\n\\nAbstract: The first step in Active Shape Model (ASM) based image segmentation and processing is to create a point distribution model (PDM) during the training phase. Correct point (landmark) correspondences across each of the training shapes must be determined for a successful and effective statistical model building process. Effective and automatic solutions for this problem are needed for the practical use of ASM methods. In this paper, we provide a solution for this problem which consists of: (i) a process of generating a mean shape without requiring landmarks, (ii) a process of automatic landmark selection for the mean shape, and (iii) a process of propagating landmarks on to each training shape for defining, landmarks in them. This paper describes the method of generating the mean shape, and the landmark selection and correspondence process. Although the method is generally applicable to spaces of any dimensionality, our first implementation and evaluation has been carried out for 2D shapes. The method is evaluated on 20 MRI foot data sets, the object of interest being the talus bone. The results indicate that, for the same given number of points, better compactness (number of parameters) of the ASM by using our method can be achieved than by using the commonly used equi-spaced point selection method.\",\n \"Title: Generalized scale: theory, algorithms, and application to image inhomogeneity correction\\nYear: 2006\\n\\nAbstract: Scale is a fundamental concept useful in almost all image processing and analysis tasks including segmentation, filtering, interpolation, registration, visualization, and quantitative analysis. Broadly speaking, scale related work can be divided into three categories: (1) multi-scale or scale-space representation, (2) local scale, and (3) locally adaptive scale. The original formulation of scale in the form of scale-space theory came from the consideration of the presence of multiple scales in nature and the desire to represent measured signals at multiple scales. However, since this representation did not suggest how to select appropriate scales, the notion of local scale was proposed to pick the right scale for a particular application from the multi-scale representation of the image. Recently, there has been considerable interest in developing locally adaptive scales, the idea being to consider the local size of object in carrying out whatever local operations that are to be done on the image. However, existing locally adaptive models are limited by shape, size, and anisotropic constraints. In this work, we propose a generalized scale model which is spatially adaptive like other local morphometric models, and yet possesses the global spirit of multi-scale representations. We postulate that this semi-locally adaptive nature of generalized scale confers it certain distinct advantages over other global and local scale formulations. We also present a variant of the generalized scale notion that we refer to as the generalized ball scale, which, in addition to having the advantages of the generalized scale model, also has superior noise resistance properties. Both scale models are dependent only on two parameters for their estimation, and we demonstrate means by which optimal values for these parameters can be estimated. Both the generalized scales can be readily applied to solving a range of image processing problems. One such problem that we address in this paper is correcting for slowly varying background spatial intensity in Magnetic Resonance images. We demonstrate the superiority of the generalized scale based inhomogeneity correction methods over an existing scale-based correction technique.\",\n \"Title: Shape modeling via local curvature scale\\nYear: 2010\\n\\nAbstract: Segmentation and modeling of organs using model-based approaches require a priori information which is often given by manually tagging landmarks on a training set of shapes. This is a tedious, time-consuming, and error prone task. To overcome some of these drawbacks, focusing on 2D shapes, we devised an automatic method based on the notion of curvature scale - a new local scale concept. This shape descriptor is used to automatically locate mathematical landmarks on the mean of the shapes in the training set, which are then propagated to the training shapes. Altogether 12 different strategies are described and are evaluated in different combinations in terms of compactness on two data sets - 40 CT images of the liver and 40 MR images of the talus bone of the foot. The results show that, for the same number of landmarks, the proposed methods are more compact than manual and equally spaced annotations.\",\n \"Title: The role of intensity standardization in medical image registration\\nYear: 2010\\n\\nAbstract: Acquisition-to-acquisition signal intensity variations (non-standardness) are inherent in MR images. Standardization is a post processing method for correcting inter-subject intensity variations through transforming all images from the given image gray scale into a standard gray scale wherein similar intensities achieve similar tissue meanings. The lack of a standard image intensity scale in MRI leads to many difficulties in tissue characterizability, image display, and analysis, including image segmentation. The influence of standardization on these tasks has been documented well; however, effects of standardization on medical image registration have not been studied yet. In this paper, we investigate the role of intensity standardization in registration tasks with systematic and analytic evaluations involving clinical MR images. We conducted nearly 20,000 clinical MR image registration experiments and evaluated the quality of registrations both quantitatively and qualitatively. The evaluations show that intensity variations between images degrades the accuracy of registration performance. The results imply that the accuracy of image registration not only depends on spatial and geometric similarity but also on the similarity of the intensity values for the same tissues in different images.\",\n \"Title: Breast Tissue Density Quantification via Digitized Mammograms.\\nYear: 2001\\n\\nAbstract: Studies reported in the literature indicate that breast cancer risk is associated with mammographic densities. An objective, repeatable, and a quantitative measure of risk derived from mammographic densities will be of considerable use in recommending alternative screening paradigms and/or preventive measures. However, image processing efforts toward this goal seem to be sparse in the literature, and automatic and efficient methods do not seem to exist. In this paper, we describe and validate an automatic and reproducible method to segment dense tissue regions from fat within breasts from digitized mammograms using scale-based fuzzy connectivity methods. Different measures for characterizing mammographic density are computed from the segmented regions and their robustness in terms of their linear correlation across two different projections-cranio-caudal and medio-lateral-oblique-are studied. The accuracy of the method is studied by computing the area of mismatch of segmented dense regions using the proposed method and using manual outlining. A comparison between the mammographic density parameter taking into account the original intensities and that just considering the segmented area indicates that the former may have some advantages over the latter.\",\n \"Title: A Task-Specific Evaluation of Three-Dimensional Image Interpolation Techniques.\\nYear: 1999\\n\\nAbstract: Image interpolation is an important operation that is widely used in medical imaging, image processing and computer graphics. A variety of interpolation methods are available in the literature. However, their systematic evaluation is lacking. In a previous paper (ibid., vol. 15, pp. 881-92, Dec. 1996), we presented a framework for the task-independent comparison of interpolation methods based on c...\",\n \"Title: Affinity functions in fuzzy connectedness based image segmentation I: Equivalence of affinities\\nYear: 2010\\n\\nAbstract: Fuzzy connectedness (FC) constitutes an important class of image segmentation schemas. Although affinity functions represent the core aspect (main variability parameter) of FC algorithms, they have not been studied systematically in the literature. In this paper, we began filling this gap by introducing and studying the notion of equivalent affinities: if any two equivalent affinities are used in the same FC schema to produce two versions of the algorithm, then these algorithms are equivalent in the sense that they lead to identical segmentations. We give a complete and elegant characterization of the affinity equivalence. We also demonstrate that any segmentation obtained via a relative fuzzy connectedness (RFC) algorithm can be viewed as segmentation obtained via absolute fuzzy connectedness (AFC) algorithm with an automatic and adaptive threshold detection. Since the main goal of the paper is to identify, by formal mathematical arguments, the affinity functions that are equivalent, extensive experimental confirmations are not needed - they show completely identical segmentations - and as such, only relevant examples of the theoretical results are provided.\",\n \"Title: A 3D generalization of user-steered live-wire segmentation\\nYear: 2000\\n\\nAbstract: We have been developing user-steered image segmentation methods for situations which require considerable human assistance in object definition. In the past, we have presented two paradigms, referred to as live-wire and live-lane, for segmenting 2D/3D/4D object boundaries in a slice-by-slice fashion, and demonstrated that live-wire and live-lane are more repeatable, with a statistical significance level of P<0.03, and are 1.5–2.5 times faster, with a statistical significance level of P<0.02, than manual tracing. In this paper, we introduce a 3D generalization of the live-wire approach for segmenting 3D/4D object boundaries which further reduces the time spent by the user in segmentation. In a 2D live-wire, given a slice, for two specified points (pixel vertices) on the boundary of the object, the best boundary segment is the minimum-cost path between the two points, described as a set of oriented pixel edges. This segment is found via Dijkstra’s algorithm as the user anchors the first point and moves the cursor to indicate the second point. A complete 2D boundary is identified as a set of consecutive boundary segments forming a “closed”, “connected”, “oriented” contour. The strategy of the 3D extension is that, first, users specify contours via live-wiring on a few slices that are orthogonal to the natural slices of the original scene. If these slices are selected strategically, then we have a sufficient number of points on the 3D boundary of the object to subsequently trace optimum boundary segments automatically in all natural slices of the 3D scene. A 3D object boundary may define multiple 2D boundaries per slice. The points on each 2D boundary form an ordered set such that when the best boundary segment is computed between each pair of consecutive points, a closed, connected, oriented boundary results. The ordered set of points on each 2D boundary is found from the way the users select the orthogonal slices. Based on several validation studies involving segmentation of the bones of the foot in MR images, we found that the 3D extension of live-wire is more repeatable, with a statistical significance level of P<0.0001, and 2–6 times faster, with a statistical significance level of P<0.01, than the 2D live-wire method, and 3–15 times faster than manual tracing.\",\n \"Title: Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation\\nYear: 2002\\n\\nAbstract: The notion of fuzzy connectedness captures the idea of \\\"hanging-togetherness\\\" of image elements in an object by assigning a strength of connectedness to every possible path between every possible pair of image elements. This concept leads to powerful image segmentation algorithms based on dynamic programming whose effectiveness has been demonstrated on 1,000s of images in a variety of applications. In the previous framework, a fuzzy connected object is defined with a threshold on the strength of connectedness. In this paper, we introduce the notion of relative connectedness that overcomes the need for a threshold and that leads to more effective segmentations. The central idea is that an object gets defined in an image because of the presence of other co-objects. Each object is initialized by a seed element. An image element c is considered to belong to that object with respect to whose reference image element c has the highest strength of connectedness. In this fashion, objects compete among each other utilizing fuzzy connectedness to grab membership of image elements. We present a theoretical and algorithmic framework for defining objects via relative connectedness and demonstrate utilizing the theory that the objects defined are independent of reference elements chosen as long as they are not in the fuzzy boundary between objects. An iterative strategy is also introduced wherein the strongest relative connected core parts are first defined and iteratively relaxed to conservatively capture the more fuzzy parts subsequently. Examples from medical imaging are presented to illustrate visually the effectiveness of relative fuzzy connectedness. A quantitative mathematical phantom study involving 160 images is conducted to demonstrate objectively the effectiveness of relative fuzzy connectedness.\",\n \"Title: Efficient computation of enclosed volume and surface area from the same triangulated surface representation.\\nYear: 2011\\n\\nAbstract: We demonstrate that the volume enclosed by triangulated surfaces can be computed efficiently in the same elegant way the volume enclosed by digital surfaces can be computed by digital surface integration. Although digital surfaces are effective and efficient for visualization and volume measurement, their drawback is that surface area measurements derived from them are inaccurate. On the other hand, triangulated surfaces give more accurate surface area measurements, but volume measurements and visualization are less efficient. Our data structure (called t-shell) for representing triangulated digital surfaces retains advantages and overcomes difficulties of both the digital and the triangulated surfaces. We create a lookup table with area and volume contributions for each of the 256 Marching Cubes configurations. When scanning the shell (e.g., while creating it), the surface area and volume are incrementally computed by using the lookup table and the current x co-ordinate, where the sign of the x component of the triangle normal indicates the sign of the volume contribution. We have computed surface area and volume for digitized mathematical phantoms, physical phantoms, and real objects. The experiments show that triangulated surface area is more accurate, triangulated volume follows digital volume closely, and that the values get closer to the true value with decreasing voxel size.\",\n \"Title: Multiple Sclerosis Lesion Quantification Using Fuzzy-Connectedness Principles.\\nYear: 1997\\n\\nAbstract: Multiple sclerosis (MS) is a disease of the white matter. Magnetic resonance imaging (MRI) is proven to be a sensitive method of monitoring the progression of this disease and of its changes due to treatment protocols. Quantification of the severity of the disease through estimation of MS lesion volume via MR imaging is vital for understanding and monitoring the disease and its treatment. This pap...\",\n \"Title: Segmentation and Evaluation of Adipose Tissue from Whole Body MRI Scans\\nYear: 2003\\n\\nAbstract: Accurate quantification of total body and the distribution of regional adipose tissue using manual segmentation is a challenging problem due to the high variation between manual delineations. Manual segmentation also requires highly trained experts with knowledge of anatomy. We present a hybrid segmentation method that provides robust delineation results for adipose tissue from whole body MRI scans. A formal evaluation of accuracy of the segmentation method is performed. This semi-automatic segmentation algorithm reduces significantly the time required for quantification of adipose tissue, and the accuracy measurements show that the results are close to the ground truth obtained from manual segmentations.\",\n \"Title: Optimum Image Thresholding via Class Uncertainty and Region Homogeneity\\nYear: 2001\\n\\nAbstract: Thresholding is a popular image segmentation method that converts a gray-level image into a binary image. The selection of optimum thresholds has remained a challenge over decades. Besides being a segmentation tool on its own, often it is also a step in many advanced image segmentation techniques in spaces other than the image space. Most of the thresholding methods reported to date are based on histogram analysis using information-theoretic approaches. These methods have not harnessed the information captured in image morphology. Here, we introduce a novel thresholding method that accounts for both intensity-based class uncertainty驴a histogram-based property驴and region homogeneity驴an image morphology-based property. A scale-based formulation is used for region homogeneity computation. At any threshold, intensity-based class uncertainty is computed by fitting a Gaussian to the intensity distribution of each of the two regions segmented at that threshold. The theory of the optimum thresholding method is based on the postulate that objects manifest themselves with fuzzy boundaries in any digital image acquired by an imaging device. The main idea here is to select that threshold at which pixels with high class uncertainty accumulate mostly around object boundaries. To achieve this, a new threshold energy criterion is formulated using class-uncertainty and region homogeneity such that, at any image location, a high energy is created when both class uncertainty and region homogeneity are high or both are low. Finally, the method selects that threshold which corresponds to the minimum overall energy. The method has been compared to a recently-published maximum segmented image information ($MSII$) method. Superiority of the proposed method was observed both qualitatively on clinical medical images as well as quantitatively on 250 realistic phantom images generated by adding different degrees of blurring, noise, and background variation to real objects segmented from clinical images.\",\n \"Title: An Ultra-Fast User-Steered Image Segmentation Paradigm: Live Wire On The Fly\\nYear: 2000\\n\\nAbstract: We have been developing general user steered image segmentation strategies for routine use in applications involving a large number of data sets. In the past, we have presented three segmentation paradigms: live wire, live lane, and a three-dimensional (3-D) extension of the live-wire method. In this paper, we introduce an ultra-fast live-wire method, referred to as live wire on the fly, for further reducing user's time compared to the basic live-wire method. In live wire, 3-D/four-dimensional (4-D) object boundaries are segmented in a slice-by-slice fashion. To segment a two-dimensional (2-D) boundary, the user initially picks a point on the boundary and all possible minimum-cost paths from this point to all other points in the image are computed via Dijkstra's algorithm. Subsequently a live wire is displayed in real time From the initial point to any subsequent position taken by the cursor. If the cursor is close to the desired boundary, the live wire snaps on to the boundary. The cursor is then deposited and a new live-wire segment is Found next, The entire 2-D boundary is specified via a set of live-wire segments in this fashion. A drawback of this method is that the speed of optimal path computation depends on image size. On modestly powered computers, for images of even modest size, some sluggishness appears in user interaction, which reduces the overall segmentation efficiency. In this work, we solve this problem by exploiting some known properties of graphs to avoid unnecessary minimum-cost path computation during segmentation. In live wire on the fly, when the user selects a point on the boundary the live-wire segment is computed and displayed in real time from the selected point to any subsequent position of the cursor in the image, even for large images and even on low-powered computers. Based on 492 tracing experiments from an actual medical application, we demonstrate that live wire on the fly is 1.3-31 times faster than live wire for actual segmentation for varying image sizes, although the pure computational part alone is found to be about 120 times faster.\",\n \"Title: Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images.\\nYear: 2014\\n\\nAbstract: •Fuzzy hierarchical modeling of all major organs in a body region.•Detailed object relationship information encoded into models.•Hierarchical object recognition and delineation.•Optimal threshold-based recognition strategy.•Demonstration of the same method on several body regions and different modalities.\",\n \"Title: Affinity functions in fuzzy connectedness based image segmentation II: Defining and recognizing truly novel affinities\\nYear: 2010\\n\\nAbstract: Affinity functions - the measure of how strongly pairs of adjacent spels in the image hang together - represent the core aspect (main variability parameter) of the fuzzy connectedness (FC) algorithms, an important class of image segmentation schemas. In this paper, we present the first ever theoretical analysis of the two standard affinities, homogeneity and object-feature, the way they can be combined, and which combined versions are truly distinct from each other. The analysis is based on the notion of equivalent affinities, the theory of which comes from a companion Part I of this paper (Ciesielski and Udupa, in this issue) [11]. We demonstrate that the homogeneity based and object feature based affinities are equivalent, respectively, to the difference quotient of the intensity function and Rosenfeld's degree of connectivity. We also show that many parameters used in the definitions of these two affinities are redundant in the sense that changing their values lead to equivalent affinities. We finish with an analysis of possible ways of combining different component affinities that result in non-equivalent affinities. In particular, we investigate which of these methods, when applied to homogeneity based and object-feature based components lead to truly novel (non-equivalent) affinities, and how this is affected by different choices of parameters. Since the main goal of the paper is to identify, by formal mathematical arguments, the affinity functions that are equivalent, extensive experimental confirmations are not needed - they show completely identical FC segmentations - and as such, only relevant examples of the theoretical results are provided. Instead, we focus mainly on theoretical results within a perspective of the fuzzy connectedness segmentation literature.\",\n \"Title: Cluster of Workstation based Nonrigid Image Registration Using Free-Form Deformation\\nYear: 2009\\n\\nAbstract: Nonrigid image registration plays an important role in medical application fields. Owing to its complex com- putations, it incurs high computational cost. In this paper, a parallel algorithm schema for nonrigid image registration methods that use B-splines for deformation and mutual information as a similarity measure is pro- posed. It involves a complex interplay of various steps which are analyzed in considerable detail from the view point of parallelizing registration. The algorithms are implemented on a cluster of workstations. We present some results on a 10 processor cluster of PCs and compare them with a sequential implementation. The results show that a speed up of n/2 for n processors in registering large images. The method is fully portable and seamlessly expandable.\",\n \"Title: New Concepts for Three-Dimensional Shape Analysis\\nYear: 1977\\n\\nAbstract: A new approach to machine representation and analysis of three-dimensional objects is presented. The representation, based on the notion of \\\"skeleton\\\" of an object leads to a scheme for comparing two given object views for shape relations. The objects are composed of long, thin, rectangular prisms joined at their ends. The input picture to the program is the digitized line drawing portraying the three-dimensional object. To compare two object views, two characteristic vertices called \\\"cardinal point\\\" and \\\"end-cardinal point,\\\" occurring consistently at the bends and open ends of the object are detected. The skeletons are then obtained as a connected path passing through these points. The shape relationships between the objects are then obtained from the matching characteristics of their skeletons. The method explores the possibility of a more detailed and finer analysis leading to detection of features like symmetry, asymmetry and other shape properties of an object.\",\n \"Title: CIDI-lung-seg: A single-click annotation tool for automatic delineation of lungs from CT scans\\nYear: 2014\\n\\nAbstract: Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies. Manual annotation, current gold standard, is time consuming and often subject to human bias. On the other hand, current state-of-the-art fully automated lung segmentation methods fail to make their way into the clinical practice due to their inability to efficiently incorporate human input for handling misclassifications and praxis. This paper presents a lung annotation tool for CT images that is interactive, efficient, and robust. The proposed annotation tool produces an “as accurate as possible” initial annotation based on the fuzzy-connectedness image segmentation, followed by efficient manual fixation of the initial extraction if deemed necessary by the practitioner. To provide maximum flexibility to the users, our annotation tool is supported in three major operating systems (Windows, Linux, and the Mac OS X). The quantitative results comparing our free software with commercially available lung segmentation tools show higher degree of consistency and precision of our software with a considerable potential to enhance the performance of routine clinical tasks.\",\n \"Title: A generic approach to pathological lung segmentation.\\nYear: 2014\\n\\nAbstract: In this study, we propose a novel pathological lung segmentation method that takes into account neighbor prior constraints and a novel pathology recognition system. Our proposed framework has two stages; during stage one, we adapted the fuzzy connectedness (FC) image segmentation algorithm to perform initial lung parenchyma extraction. In parallel, we estimate the lung volume using rib-cage information without explicitly delineating lungs. This rudimentary, but intelligent lung volume estimation system allows comparison of volume differences between rib cage and FC based lung volume measurements. Significant volume difference indicates the presence of pathology, which invokes the second stage of the proposed framework for the refinement of segmented lung. In stage two, texture-based features are utilized to detect abnormal imaging patterns (consolidations, ground glass, interstitial thickening, tree-inbud, honeycombing, nodules, and micro-nodules) that might have been missed during the first stage of the algorithm. This refinement stage is further completed by a novel neighboring anatomy-guided segmentation approach to include abnormalities with weak textures, and pleura regions. We evaluated the accuracy and efficiency of the proposed method on more than 400 CT scans with the presence of a wide spectrum of abnormalities. To our best of knowledge, this is the first study to evaluate all abnormal imaging patterns in a single segmentation framework. The quantitative results show that our pathological lung segmentation method improves on current standards because of its high sensitivity and specificity and may have considerable potential to enhance the performance of routine clinical tasks.\",\n \"Title: Improving Atlas-Based Medical Image Segmentation With A Relaxed Object Search\\nYear: 2014\\n\\nAbstract: Medical image segmentation using 3D probabilistic atlases has been actively pursued to avoid the time-consuming involvement of experts in manual object (organ) delineation for quantitative analysis. By mapping a new 3D image onto the reference coordinate system of the atlas, built for some organ of interest, these techniques take a binary decision based on the probability of each voxel to be part of that organ. However, image-based techniques have also been proposed to refine object delineation at the initial position given by the atlas-based segmentation. In this paper, we relax this condition for delineation refinement by moving an atlas based on the prior probability map to search for the organ around that initial position. Our method uses the multi-scale parameter search algorithm with a suitable criterion function to evaluate automatic 3D organ delineations, as obtained by the image foresting transform algorithm in an uncertainty region of the atlas. Experiments with eight organs in CT and MR images have indicated that our method can improve atlas-based segmentation with statistical significance. Moreover, the relaxed object search consistently found the organ with higher accuracy outside the position obtained by the atlas, which reinforces our claim.\",\n \"Title: Differential and relaxed image foresting transform for graph-cut segmentation of multiple 3D objects.\\nYear: 2014\\n\\nAbstract: Graph-cut algorithms have been extensively investigated for interactive binary segmentation, when the simultaneous delineation of multiple objects can save considerable user's time. We present an algorithm (named DRIFT) for 3D multiple object segmentation based on seed voxels and Differential Image Foresting Transforms (DIFTs) with relaxation. DRIFT stands behind efficient implementations of some stateof- the-art methods. The user can add/remove markers (seed voxels) along a sequence of executions of the DRIFT algorithm to improve segmentation. Its first execution takes linear time with the image's size, while the subsequent executions for corrections take sublinear time in practice. At each execution, DRIFT first runs the DIFT algorithm, then it applies diffusion filtering to smooth boundaries between objects (and background) and, finally, it corrects possible objects' disconnection occurrences with respect to their seeds. We evaluate DRIFT in 3D CT-images of the thorax for segmenting the arterial system, esophagus, left pleural cavity, right pleural cavity, trachea and bronchi, and the venous system.\",\n \"Title: Registration of 3D objects and surfaces\\nYear: 1990\\n\\nAbstract: An approach to the problem of comparative analysis of objects and their surfaces that arises in medical imaging is presented and illustrated with an application to postsurgical bone-graft separation. A common requirement in medical imaging applications is the registration of multiple representations of the object prior to mensuration. The modeling of the registration process and the registration system are described. The measurement of change in volume of bone grafts implanted inside the human body in a corrective surgical procedure is discussed, and the results are evaluated.<>\",\n \"Title: Medical image reconstruction, processing, visualization, and analysis: the MIPG perspective\\nYear: 2002\\n\\nAbstract: In this paper, we identify and describe the key ideas, concepts and software related to Medical Imaging that emerged from our research group, which is commonly known by the acronym MIPG, denoting Medical Image Processing Group. For the purpose of our presentation, we consider the entire discipline of Medical Imaging to consist of the areas of acquisition, postprocessing, evaluation, and application. We are indeed fortunate to be able to discuss seminal research from our group in each of these areas. In Section II, we identify relevant moments of inception and describe our past contributions. Our achievements, current activities, and future directions are summarized in Section III, Section IV, and Section V, respectively; the keys to our success are listed in Section V.\",\n \"Title: Shell rendering\\nYear: 1993\\n\\nAbstract: A structure model for volume rendering, called a shell, is introduced. Roughly, a shell consists of a set of voxels in the vicinity of the structure boundary together with a number of attributes associated with the voxels in this set. By carefully choosing the attributes and storing the shell in a special data structure that allows random access to the voxels and their attributes, storage and computational requirements can be reduced drastically. Only the voxels that potentially contribute to the rendition actually enter into major computation. Instead of the commonly used ray-casting paradigm, voxel projection is used. This eliminates the need for render-time interpolation and further enhances the speed. By having one of the attributes as a boundary likelihood function that determines the most likely location of voxels in the shell to be on the structure boundary, surface-based measurements can be made. The shell concept, the data structure, the rendering and measurement algorithms, and examples drawn from medical imaging that illustrate these concepts are described.<>\",\n \"Title: Incorporating a measure of local scale in voxel-based 3-D image registration.\\nYear: 2003\\n\\nAbstract: We present a new class of approaches for rigid-body registration and their evaluation in studying multiple sclerosis (MS) via multiprotocol magnetic resonance imaging (MRI). Three pairs of rigid-body registration algorithms were implemented, using cross-correlation and mutual information (MI), operating on original gray-level images, and utilizing the intermediate images resulting from our new scale-based method. In the scale image, every voxel has the local \\\"scale\\\" value assigned to it, defined as the radius of the largest ball centered at the voxel with homogeneous intensities. Three-dimensional image data of the head were acquired from ten MS patients for each of six MRI protocols. Images in some of the protocols were acquired in registration. The registered pairs were used as ground truth. Accuracy and consistency of the six registration methods were measured within and between protocols for known amounts of misregistrations. Our analysis indicates that there is no \\\"best\\\" method. For medium misregistration, the method using MI, for small add large misregistration the method using normalized cross-correlation performs best. For high-resolution data the correlation method and for low-resolution data the MI method, both using the original gray-level images, are the most consistent. We have previously demonstrated the use of local scale information in fuzzy connectedness segmentation and image filtering. Scale may also have potential for image registration as suggested by this work.\",\n \"Title: Fuzzy connectedness and image segmentation\\nYear: 2003\\n\\nAbstract: Image segmentation-the process of defining objects in images-remains the most challenging problem in image processing despite decades of research. Many general methodologies have been proposed to date to tackle this problem. An emerging framework that has shown considerable promise recently is that of fuzzy connectedness. Images are by nature fuzzy. Object regions manifest themselves in images with a heterogeneity of image intensities owing to the inherent object material heterogeneity, and artifacts such as blurring, noise and background variation introduced by the imaging device. In spite of this gradation of intensities, knowledgeable observers can perceive object regions as a gestalt. The fuzzy connectedness framework aims at capturing this notion via a fuzzy topological notion called fuzzy connectedness which defines how the image elements hang together spatially in spite of their gradation of intensities. In defining objects in a given image, the strength of connectedness between every pair of image elements is considered, which in turn is determined by considering all possible connecting paths between the pair. In spite of a high combinatorial complexity, theoretical advances in fuzzy connectedness have made it possible to delineate objects via dynamic programming at close to interactive speeds on modern PCs. This paper gives a tutorial review of the fuzzy connectedness framework delineating the various advances that have been made. These are illustrated with several medical applications in the areas of Multiple Sclerosis of the brain, magnetic resonance (MR) and computer tomographic (CT) angiography, brain tumor, mammography, upper airway disorders in children, and colonography.\",\n \"Title: Iso-shaping rigid bodies for estimating their motion from image sequences.\\nYear: 2004\\n\\nAbstract: In many medical imaging applications, due to the limited field of view of imaging devices, acquired images often include only a part of a structure. In such situations, it is impossible to guarantee that the images will contain exactly the same physical extent of the structure at different scans, which leads to difficulties in registration and in many other tasks, such as the analysis of the morphology, architecture, and kinematics of the structures. To facilitate such analysis, we developed a general method, referred to as iso-shaping, that generates structures of the same shape from segmented image sequences. The basis for this method is to automatically find a set of key points, called shape centers, in the segmented partial anatomic structure such that these points are present in all images and that they represent the same physical location in the object, and then trim the structure using these points as reference. The application area considered here is the analysis of the morphology, architecture, and kinematics of the joints of the foot from magnetic resonance images acquired at different joint positions and load conditions. The accuracy of the method is analyzed by utilizing ten data sets for iso-shaping the tibia and the fibula via four evaluative experiments. The analysis indicates that iso-shaping produces results as predicted by the theoretical framework.\",\n \"Title: Volume rendering in the presence of partial volume effects\\nYear: 2005\\n\\nAbstract: In tomographic imagery, partial volume effects (PVEs) cause several artifacts in volume renditions. In X-ray computed tomography (CT), for example, soft-tissue-like pseudo structures appear in bone-to-air and bone-to-fat interfaces. Further, skin, which is identical to soft tissue in terms of CT number, obscures the rendition of the latter. The purpose of This work is to demonstrate these phenomena and to provide effective solutions that yield significantly improved renditions. We introduce two methods that detect and classify voxels with PVE in X-ray CT. Further, a method is described to automatically peel skin so that PVE-resolved renditions of bone and soft tissue reveal considerably more detail. In the first method to address PVE, called the fraction measure (FM) method, the fraction of each tissue material in each voxel v is estimated by taking into account the intensities of the voxels neighboring v. The second method, called uncertainty principle (UP) method, is based on the following postulate (Saha and Udupa, 2001): In any acquired image, voxels with the highest uncertainty occur in the vicinity of object boundaries. The removal of skin is achieved by means of mathematical morphology. Volume renditions have been created before and after applying the methods for several patient CT datasets. A mathematical phantom experiment involving different levels of PVE has been conducted by adding different degrees of noise and blurring. A quantitative evaluation is done utilizing the mathematical phantom and clinical CT data wherein an operator carefully masked out voxels with PVE in the segmented images. All results have demonstrated the enhanced quality of display of bone and soft tissue after applying the proposed methods. The quantitative evaluations indicate that more than 98% of the voxels with PVE are removed by the two methods and the second method performs slightly better than the first. Further, skin peeling vividly reveals fine details in the soft tissue str- - uctures.\",\n \"Title: Interplay between intensity standardization and inhomogeneity correction in MR image processing\\nYear: 2005\\n\\nAbstract: Image intensity standardization is a postprocessing method designed for correcting acquisition-to-acquisition signal intensity variations (nonstandardness) inherent in magnetic resonance (MR) images. Inhomogeneity correction is a process used to suppress the low frequency background nonuniformities (inhomogeneities) of the image domain that exist in MR images. Both these procedures have important implications in MR image analysis. The effects of these postprocessing operations on improvement of image quality in isolation has been well documented. However, the combined effects of these two processes on MR images and how the processes influence each other have not been studied thus far. In this paper, we evaluate the effect of inhomogeneity correction followed by standardization and vice-versa on MR images in order to determine the best sequence to follow for enhancing image quality. We conducted experiments on several clinical and phantom data sets (nearly 4000 three-dimensional MR images were analyzed) corresponding to four different MRI protocols. Different levels of artificial nonstandardness, and different models and levels of artificial background inhomogeneity were used in these experiments. Our results indicate that improved standardization can be achieved by preceding it with inhomogeneity correction. There is no statistically significant difference in image quality obtained between the results of standardization followed by correction and that of correction followed by standardization from the perspective of inhomogeneity correction. The correction operation is found to bias the effect of standardization. We demonstrate this bias both qualitatively and quantitatively by using two different methods of inhomogeneity correction. We also show that this bias in standardization is independent of the specific inhomogeneity correction method used. The effect of this bias due to correction was also seen in magnetization transfer ratio (MTR) images, which are naturall- - y endowed with the standardness property. Standardization, on the other hand, does not seem to influence the correction operation. It is also found that longer sequences of repeated correction and standardization operations do not considerably improve image quality. These results were found to hold for the clinical and the phantom data sets, for different MRI protocols, for different levels of artificial nonstandardness, for different models and levels of artificial inhomogeneity, for different correction methods, and for images that were endowed with inherent standardness as well as for those that were standardized by using the intensity standardization method. Overall, we conclude that inhomogeneity correction followed by intensity standardization is the best sequence to follow from the perspective of both image quality and computational efficiency.\",\n \"Title: A characterization of the geometric architecture of the peritalar joint complex via MRI, an aid to classification of foot type\\nYear: 1999\\n\\nAbstract: The purpose of this work is to study the architecture of the rearfoot using in vivo MR image data. Each data set used in this study is made of sixty sagittal slices of the foot acquired in a 1.5-T commercial GE MR system. The authors use the live-wire method to delineate boundaries and form the surfaces of the bones. In the first part of this work, they describe a new method to characterize the three-dimensional (3-D) relationships of four bones of the peritalar complex and apply this description technique to data sets from ten normal subjects and from seven pathological cases. In the second part, the authors propose a procedure to classify feet, based on the values of these new architectural parameters. They conclude that this noninvasive method offers a unique tool to characterize the 3-D architecture of the feet in live patients, based on a set of new architectural parameters. This can be integrated into a set of tools to improve diagnosis and treatment of foot malformations.\",\n \"Title: New variants of a method of MRI scale standardization.\\nYear: 2000\\n\\nAbstract: One of the major drawbacks of magnetic resonance imaging (MRI) has been the lack of a standard and quantifiable interpretation of image intensities. Unlike in other modalities, such as X-ray computerized tomography, MR images taken for the same patient on the same scanner at different times may appear different from each other due to a variety of scanner-dependent variations and, therefore, the absolute intensity values do not have a fixed meaning. We have devised a two-step method wherein all images (independent of patients and the specific brand of the MR scanner used) can be transformed in such a way that for the same protocol and body region, in the transformed images similar intensities will have similar tissue meaning. Standardized images can be displayed with fixed windows without the need of per-case adjustment. More importantly, extraction of quantitative information about healthy organs or about abnormalities can be considerably simplified. This paper introduces and compares new vari- ants of this standardizing method that can help to overcome some of the problems with the original method.\",\n \"Title: An order of magnitude faster isosurface rendering in software on a PC than using dedicated, general purpose rendering hardware\\nYear: 2000\\n\\nAbstract: The purpose of this work is to compare the speed of isosurface rendering in software with that using dedicated hardware. Input data consist of 10 different objects from various parts of the body and various modalities (CT, MR, and MRA) with a variety of surface sizes (up to 1 million voxels/2 million triangles) and shapes. The software rendering technique consists of a particular method of voxel-based surface rendering, called shell rendering. The hardware method is OpenGL-based and uses the surfaces constructed from our implementation of the 驴Marching Cubes驴 algorithm. The hardware environment consists of a variety of platforms, including a Sun Ultra I with a Creator3D graphics card and a Silicon Graphics Reality Engine II, both with polygon rendering hardware, and a 300Mhz Pentium PC. The results indicate that the software method (shell rendering) was 18 to 31 times faster than any hardware rendering methods. This work demonstrates that a software implementation of a particular rendering algorithm (shell rendering) can outperform dedicated hardware. We conclude that, for medical surface visualization, expensive dedicated hardware engines are not required. More importantly, available software algorithms (shell rendering) on a 300Mhz Pentium PC outperform the speed of rendering via hardware engines by a factor of 18 to 31.\",\n \"Title: Scale-based diffusive image filtering preserving boundary sharpness and fine structures.\\nYear: 2001\\n\\nAbstract: Image acquisition techniques often suffer from low signal-to-noise ratio (SNR) and/or contrast-to-noise ratio (CNR). Although many acquisition techniques are available to minimize these, post acquisition filtering is a major off-line image processing technique commonly used to improve the SNR and CNR. A major drawback of filtering is that it often diffuses/blurs important structures along with noise. In this paper, we introduce two scale-based filtering methods that use local structure size or \\\"object scale\\\" information to arrest smoothing around fine structures and across even low-gradient boundaries. The first of these methods uses a weighted average over a scale-dependent neighborhood while the other employs scale-dependent diffusion conductance to perform filtering. Both methods adaptively modify the degree of filtering at any image location depending on local object scale. Object scale allows us to accurately use a restricted homogeneity parameter for filtering in regions with fine details and in the vicinity of boundaries while a generous parameter in the interiors of homogeneous regions. Qualitative experiments based on both phantoms and patient magnetic resonance images show significant improvements using the scale-based methods over the extant anisotropic diffusive filtering method in preserving fine details and sharpness of object boundaries. Quantitative analyses utilizing 25 phantom images generated under a range of conditions of blurring, noise, and background variation confirm the superiority of the new scale-based approaches.\",\n \"Title: Hierarchical Scale-Based Multiobject Recognition of 3-D Anatomical Structures\\nYear: 2012\\n\\nAbstract: Segmentation of anatomical structures from medical images is a challenging problem, which depends on the accurate recognition (localization) of anatomical structures prior to delineation. This study generalizes anatomy segmentation problem via attacking two major challenges: 1) automatically locating anatomical structures without doing search or optimization, and 2) automatically delineating the anatomical structures based on the located model assembly. For 1), we propose intensity weighted ball-scale object extraction concept to build a hierarchical transfer function from image space to object (shape) space such that anatomical structures in 3-D medical images can be recognized without the need to perform search or optimization. For 2), we integrate the graph-cut (GC) segmentation algorithm with prior shape model. This integrated segmentation framework is evaluated on clinical 3-D images consisting of a set of 20 abdominal CT scans. In addition, we use a set of 11 foot MR images to test the generalizability of our method to the different imaging modalities as well as robustness and accuracy of the proposed methodology. Since MR image intensities do not possess a tissue specific numeric meaning, we also explore the effects of intensity nonstandardness on anatomical object recognition. Experimental results indicate that: 1) effective recognition can make the delineation more accurate; 2) incorporating a large number of anatomical structures via a model assembly in the shape model improves the recognition and delineation accuracy dramatically; 3) ball-scale yields useful information about the relationship between the objects and the image; 4) intensity variation among scenes in an ensemble degrades object recognition performance.\",\n \"Title: Fuzzy connectedness image segmentation for newborn brain extraction in MR images.\\nYear: 2013\\n\\nAbstract: Newborn's brain has a various shape, and easily changes with not only brain developing and cerebral diseases. Although the brain segmentation in MR images is an effective way to quantify the brain shape and size, there are few studies in neonatal brain MR image analysis. This paper introduces a novel method based on fuzzy connectedness (FC) with fuzzy object model (FOM). FOM is built from a training dataset, and gives fuzzy degree belonging to parenchyma with respect to location and intensity. FC is calculated from object affinity and homogeneous affinity, and the object affinity is given by the FOM. The method first segments the white matter, and then segments the surrounding cortex. The propose method has been applied to 10 newborn subjects whose revised age was between -1 month and +2 month. Leave-on-out cross-validation (LOOCV) was conducted, and the mean false-positive volume fraction was 1.33%, the mean false-negative volume fraction was 2.90%, and geometric-mean was 1.42%.\",\n \"Title: Imaging transforms for visualizing surfaces and volumes\\nYear: 1993\\n\\nAbstract: Three-dimensional (3D) visualization in biomedical and other imaging areas is a rapidly emerging discipline. The major developments\\n in this field are described in a unified and concise way. To this end, we introduce an operator notation to describe the basic\\n imaging transforms commonly used in 3D visualization and to identify a comprehensive set of basic transforms. We also introduce\\n several new basic transforms for filtering and interpolating scenes and structures and for rendering surfaces and volumes.\\n We demonstrate not only how the existing visualization methodologies can be described concisely, but we also show how a great\\n variety of new methodologies can be generated using both the existing imaging transforms and the new transforms introduced\\n in this paper. A comprehensive evaluation method to compare objectively rendering methods used in visualization based on task-specific\\n mathematical phantoms is described. We examine in detail separate transform sequences that are best suited for rendering robust\\n and frail structures (ie, structures with well- and poorly defined boundaries).\",\n \"Title: Multidimensional data format specification: A generalization of the American College of Radiology-National Electric Manufacturers Association standards\\nYear: 1992\\n\\nAbstract: Multidimensional image data are becoming increasingly common in biomedical imaging. Three-dimensional visualization and analysis\\n techniques based on three-dimensional image data have become an established discipline in biomedicine. Some imaging problems\\n generate image data of even higher dimensions. It often becomes necessary, rather than just convenient, to consider the higher-dimensional\\n data as a whole to adequately answer the underlying imaging questions. Despite this established need for convenient exchange\\n of image and image-derived information, no exchange protocols are available that adequately meet the needs of multidimensional\\n imaging systems. This paper describes an exchange protocol that has been designed after careful consideration of the common\\n requirements of methodologies for visualization and analysis of multidimensional data. It is based on and is a generalization\\n of the widely accepted American College of Radiology-National Electrical Manufacturers Association (ACR-NEMA) standards specified\\n for two-dimensional images. It is implemented and actively being used in a data-, application-, and machine-independent software\\n environment, being developed in the authors’ department, for the visualization and analysis of multidimensional images.\",\n \"Title: Interactive segmentation and boundary surface formation for 3-D digital images\\nYear: 1982\\n\\nAbstract: In visualizing objects in 3-D digital images based on shaded-surface displays, segmentation of the 3-D regions is a basic processing operation. When object and nonobject regions touch or overlap and have identical features, automatic segmentation methods fail. We present an interactive technique which permits isolation of arbitrary subregions of the 3-D image by a region filling procedure. We illustrate the usefulness of this capability, through computerized tomography data, in preoperative surgical planning and for producing arbitrary fragmentations of objects in 3-D images. The techniques are applicable in other fields as well, where independent display of overlapping structures is important. When the object regions are specified through a set of contours in a sequence of slices, we show that the boundary surfaces of the object are produceable in an efficient way using the cuberille-based discrete representation of the object surfaces. The algorithms we present for forming boundary surfaces do not impose restrictions on the shape and pattern of distribution of contours in the slices as do the surface tiling schemes reported in the literature. We assume that a contour is made up of at least three points.\",\n \"Title: A search algorithm for skeletonization of thick patterns\\nYear: 1974\\n\\nAbstract: An algorithm is proposed in this paper for obtaining the \\\"medial line\\\" or the \\\"skeleton\\\" of a thick pattern. The method introduces new concepts of a \\\"turning point\\\" and an \\\"end point.\\\" The present method for thinning binary patterns is a two stage process. During the first stage the method employs four different pairs of operators to test and indicate the existence of vertical, horizontal, right and left inclined limbs in the pattern and to extract the respective end and turning points. During the second stage the medial line of the pattern is obtained by constructing the limbs from a knowledge of the already extracted end and turning points by a simple search procedure. The results obtained by the present method on hand printed alphanumerics are better than those obtained by the existing methods, The extraction of turning and end points also facilitates coding and easy recognition of the given patterns. These results will be reported elsewhere.\",\n \"Title: Surface Shading in the Cuberille Environment\\nYear: 1985\\n\\nAbstract: Computed tomography and the cuberiile model驴an effort to better serve the medical profession and its patients.\",\n \"Title: Interactive Surgical Planning\\nYear: 1984\\n\\nAbstract: With this tool for visualizing and manipulating organ images in three dimensions, surgeons can plan and test their strategies before they get to the operating room.\",\n \"Title: A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT\\nYear: 2015\\n\\nAbstract: •An accurate and efficient computational framework for airway quantification is proposed..•A novel hybrid approach for precise segmentation of the lumen presented..•Two novel methods both in 2-D and 3-D to estimate the airway walls are explored..•Better identification of airway surfaces than the widely applied methods is reported..\",\n \"Title: Quantitative normal thoracic anatomy at CT.\\nYear: 2016\\n\\nAbstract: •It points out detailed quantitative anatomic (QA) information currently not existing.•We demonstrate the non-linear geometric and geographic relationships among objects.•We show the highly non-linear variations of object-specific properties of 11 objects.•QA information is useful in creating effective models for the object segmentation.\",\n \"Title: Soft Computing in Medical Image Processing.\\nYear: 2016\\n\\nAbstract: Recent advances in medical imaging modalities such as magnetic resonance (MR) imaging and multidetector computed tomography (MDCT) enable us to acquire high-dimensional, sectional, thin-sliced, and a large number of images within a short acquisition time. The sheer volume of data poses great challenges for human interpretation of images by radiologists. Image processing becomes essential to analyze such complex data.Soft computing has been introduced into medical image processing because it is an effective approach to handle uncertainties inherent in acquired image data. Some examples in the past 20 years are fuzzy connectedness approaches to image segmentation, fuzzy clustering methods particularly for human brain MR image segmentation, and statistical atlases and fuzzy models for object recognition and delineation. Soft computing approaches include fuzzy logic, neural networks, support vector machines, evolutional computation, probabilistic approaches, and chaos theory.This special issue aims to showcase recent advances in soft computing approaches in medical image processing. In response to the call for papers, we received 26 submissions from all over the world. All manuscripts underwent a very rigorous peer review process. We finally selected 8 full papers for this special issue.Image segmentation plays an important role in medical image processing. L. Ma et al. propose a new method which combines artificial fish swarm algorithm and fuzzy c-means and demonstrates the performance on brain MR images. K. B. Kim et al. propose a contour extraction method for segmenting deep cervical flexor (DCF) muscles in ultrasonic images. The method is based on fuzzy sigma binarization process. L. Zhao and K. Jia introduce multiscale convolutional neural network (CNN) to brain tumor segmentation. Y. Li et al. show a method for segmenting white blood cells using a dual-thresholding approach.Image fusion is also an effective computational methodology that is hard for a human to deal with. It combines images from two or more different modalities and generates a new image. G. Yang et al. propose a new method that fills nonsubsampled contourlet transform (NCST) coefficients using generalized Gaussian density. It has been applied to CT/MRI, MRI/positron emission tomography (PET) and MRI/PET image fusion, and the results are superior to conventional NCST-based methods.In addition, there are unique medical image processing schemes that have been proposed. Jing and Sheng introduce a method to retrieve a 3D model from freehand sketch. It may be very useful for medical education, informed consent for patients, and so on. B. J. Park et al. propose a gesture based interface, and it can be utilized in the operating room where surgeons cannot touch computer interface devices such as mouse and keyboard during operation. L. Huang et al. show a dense trajectory and classification method on echocardiogram video images.Each paper contributes an improvement of medical image processing in each application area, and also the approaches are applicable to other areas. We hope that the papers will enrich the readersu0027 knowledge of biomedical image analysis and will in turn lead them to the exploration of new advances and avenues.Syoji KobashiLaszlo G. NyulJayaram K. Udupa\",\n \"Title: Virtual landmarks.\\nYear: 2017\\n\\nAbstract: Much has been published on finding landmarks object surfaces in the context of shape modeling. While this is still an open problem, many of the challenges of past approaches can be overcome by removing the restriction that landmarks on the object surface. The we propose may reside inside, on the boundary of, or outside the object and are tethered to the object. Our solution is straightforward, simple, and recursive in nature, proceeding from global features initially to local features in later levels to detect landmarks. Principal component analysis (PCA) is used as an engine to recursively subdivide the object region. The object itself may be represented in binary or fuzzy form or with gray values. The method is illustrated in 3D space (although it generalizes readily to spaces of any dimensionality) on four objects (liver, trachea and bronchi, and outer boundaries of left and right lungs along pleura) derived from 5 patient computed tomography (CT) image data sets of the thorax and abdomen. The virtual landmark identification approach seems to work well on different structures in different subjects and seems to detect landmarks that are homologously located in different samples of the same object. The approach guarantees that virtual landmarks are invariant to translation, scaling, and rotation of the object/image. Landmarking techniques are fundamental for many computer vision and image processing applications, and we are currently exploring the use virtual landmarks in automatic anatomy recognition and object analytics.\",\n \"Title: Fat segmentation on chest CT images via fuzzy models.\\nYear: 2016\\n\\nAbstract: Quantification of fat throughout the body is vital for the study of many diseases. In the thorax, it is important for lung transplant candidates since obesity and being underweight are contraindications to lung transplantation given their associations with increased mortality. Common approaches for thoracic fat segmentation are all interactive in nature, requiring significant manual effort to draw the interfaces between fat and muscle with low efficiency and questionable repeatability. The goal of this paper is to explore a practical way for the segmentation of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) components of chest fat based on a recently developed body-wide automatic anatomy recognition (AAR) methodology. The AAR approach involves 3 main steps: building a fuzzy anatomy model of the body region involving all its major representative objects, recognizing objects in any given test image, and delineating the objects. We made several modifications to these steps to develop an effective solution to delineate SAT/VAT components of fat. Two new objects representing interfaces of SAT and VAT regions with other tissues, SatIn and VatIn are defined, rather than using directly the SAT and VAT components as objects for constructing the models. A hierarchical arrangement of these new and other reference objects is built to facilitate their recognition in the hierarchical order. Subsequently, accurate delineations of the SAT/VAT components are derived from these objects. Unenhanced CT images from 40 lung transplant candidates were utilized in experimentally evaluating this new strategy. Mean object location error achieved was about 2 voxels and delineation error in terms of false positive and false negative volume fractions were, respectively, 0.07 and 0.1 for SAT and 0.04 and 0.2 for VAT.\",\n \"Title: A general approach to liver lesion segmentation in CT images.\\nYear: 2016\\n\\nAbstract: Lesion segmentation has remained a challenge in different body regions. Generalizability is lacking in published methods as variability in results is common, even for a given organ and modality, such that it becomes difficult to establish standardized methods of disease quantification and reporting. This paper makes an attempt at a generalizable method based on classifying lesions along with their background into groups using clinically used visual attributes. Using an Iterative Relative Fuzzy Connectedness (IRFC) delineation engine, the ideas are implemented for the task of liver lesion segmentation in computed tomography (CT) images. For lesion groups with the same background properties, a few subjects are chosen as the training set to obtain the optimal IRFC parameters for the background tissue components. For lesion groups with similar foreground properties, optimal foreground parameters for IRFC are set as the median intensity value of the training lesion subset. To segment liver lesions belonging to a certain group, the devised method requires manual loading of the corresponding parameters, and correct setting of the foreground and background seeds. The segmentation is then completed in seconds. Segmentation accuracy and repeatability with respect to seed specification are evaluated. Accuracy is assessed by the assignment of a delineation quality score (DQS) to each case. Inter- operator repeatability is assessed by the difference between segmentations carried out independently by two operators. Experiments on 80 liver lesion cases show that the proposed method achieves a mean DQS score of 4.03 and inter- operator repeatability of 92.3%.\",\n \"Title: Optimal atlas construction through hierarchical image registration.\\nYear: 2016\\n\\nAbstract: Atlases (digital or otherwise) are common in medicine. However, there is no standard framework for creating them from medical images. One traditional approach is to pick a representative subject and then proceed to label structures/regions of interest in this image. Another is to create a \\\"mean\\\" or average subject. Atlases may also contain more than a single representative (e.g., the Visible Human contains both a male and a female data set). Other criteria besides gender may be used as well, and the atlas may contain many examples for a given criterion. In this work, we propose that atlases be created in an optimal manner using a well-established graph theoretic approach using a min spanning tree (or more generally, a collection of them). The resulting atlases may contain many examples for a given criterion. In fact, our framework allows for the addition of new subjects to the atlas to allow it to evolve over time. Furthermore, one can apply segmentation methods to the graph (e.g., graph-cut, fuzzy connectedness, or cluster analysis) which allow it to be separated into \\\"sub-atlases\\\" as it evolves. We demonstrate our method by applying it to 50 3D CT data sets of the chest region, and by comparing it to a number of traditional methods using measures such as Mean Squared Difference, Mattes Mutual Information, and Correlation, and for rigid registration. Our results demonstrate that optimal atlases can be constructed in this manner and outperform other methods of construction using freely available software.\",\n \"Title: Lymph node detection in IASLC-defined zones on PET/CT images.\\nYear: 2016\\n\\nAbstract: Lymph node detection is challenging due to the low contrast between lymph nodes as well as surrounding soft tissues and the variation in nodal size and shape. In this paper, we propose several novel ideas which are combined into a system to operate on positron emission tomography/computed tomography (PET/CT) images to detect abnormal thoracic nodes. First, our previous Automatic Anatomy Recognition (AAR) approach is modified where lymph node zones predominantly following International Association for the Study of Lung Cancer (IASLC) specifications are modeled as objects arranged in a hierarchy along with key anatomic anchor objects. This fuzzy anatomy model built from diagnostic CT images is then deployed on PET/CT images for automatically recognizing the zones. A novel globular filter (g-filter) to detect blob-like objects over a specified range of sizes is designed to detect the most likely locations and sizes of diseased nodes. Abnormal nodes within each automatically localized zone are subsequently detected via combined use of different items of information at various scales: lymph node zone model poses found at recognition indicating the geographic layout at the global level of node clusters, g-filter response which hones in on and carefully selects node-like globular objects at the node level, and CT and PET gray value but within only the most plausible nodal regions for node presence at the voxel level. The models are built from 25 diagnostic CT scans and refined for an object hierarchy based on a separate set of 20 diagnostic CT scans. Node detection is tested on an additional set of 20 PET/CT scans. Our preliminary results indicate node detection sensitivity and specificity at around 90% and 85%, respectively.\",\n \"Title: Automatic anatomy recognition on CT images with pathology.\\nYear: 2016\\n\\nAbstract: Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.\",\n \"Title: A Critical Analysis Of The Methods Of Evaluating Mri Brain Segmentation Algorithms\\nYear: 2017\\n\\nAbstract: Many papers are published every year containing new methodologies for brain tissue segmentation in magnetic resonance images. The evaluation of these methods is fundamental to understand their behavior and to observe their weak and strong points. Even though improvements have been proposed, the analysis of the segmentation results can still lead to incorrect conclusions. This paper contains an investigation of the state-of-the-art in brain tissue segmentation evaluation, which includes tissue classification or segmentation, handling partial volume effect, and evaluation metrics. It uncovers previously unnoticed pitfalls and proposes standard procedures to avoid them. Experiments show that the proposed evaluation strategy gives a better insight about the method's strong and weak points.\",\n \"Title: Automatic anatomy recognition using neural network learning of object relationships via virtual landmarks.\\nYear: 2018\\n\\nAbstract: The recently developed body-wide Automatic Anatomy Recognition (AAR) methodology depends on fuzzy modeling of individual objects, hierarchically arranging objects, constructing an anatomy ensemble of these models, and a dichotomous object recognition delineation process. The parent-to-offspring spatial relationship in the object hierarchy is crucial in the AAR method. We have found this relationship to be quite complex, and as such any improvement in capturing this relationship information in the anatomy model will improve the process of recognition itself. Currently, the method encodes this relationship based on the layout of the geometric centers of the objects. Motivated by the concept of virtual landmarks (VLs), this paper presents a new one-shot AAR recognition method that utilizes the VLs to learn object relationships by training a neural network to predict the pose and the VLs of an offspring object given the VLs of the parent object in the hierarchy. We set up two neural networks for each parent-offspring object pair in a body region, one for predicting the VLs and another for predicting the pose parameters. The VL-based learning/prediction method is evaluated on two object hierarchies involving 14 objects. We utilize 54 computed tomography (CT) image data sets of head and neck cancer patients and the associated object contours drawn by dosimetrists for routine radiation therapy treatment planning. The VL neural network method is found to yield more accurate object localization than the currently used simple AAR method.\",\n \"Title: Thoracic lymph node station recognition on CT images based on automatic anatomy recognition with an optimal parent strategy.\\nYear: 2018\\n\\nAbstract: Currently, there are many papers that have been published on the detection and segmentation of lymph nodes from medical images. However, it is still a challenging problem owing to low contrast with surrounding soft tissues and the variations of lymph node size and shape on computed tomography (CT) images. This is particularly very difficult on low-dose CT of PET/CT acquisitions. In this study, we utilize our previous automatic anatomy recognition (AAR) framework to recognize the thoracic-lymph node stations defined by the International Association for the Study of Lung Cancer (IASLC) lymph node map. The lymph node stations themselves are viewed as anatomic objects and are localized by using a one-shot method in the AAR framework. Two strategies have been taken in this paper for integration into AAR framework. The first is to combine some lymph node stations into composite lymph node stations according to their geometrical nearness. The other is to find the optimal parent (organ or union of organs) as an anchor for each lymph node station based on the recognition error and thereby find an overall optimal hierarchy to arrange anchor organs and lymph node stations. Based on 28 contrast-enhanced thoracic CT image data sets for model building, 12 independent data sets for testing, our results show that thoracic lymph node stations can be localized within 2-3 voxels compared to the ground truth.\",\n \"Title: Quantitative analysis of adipose tissue on chest CT to predict primary graft dysfunction in lung transplant recipients: a novel optimal biomarker approach.\\nYear: 2018\\n\\nAbstract: In this study, patients who underwent lung transplantation are categorized into two groups of successful (positive) or failed (negative) transplantations according to primary graft dysfunction (PGD), i.e., acute lung injury within 72 hours of lung transplantation. Obesity or being underweight is associated with an increased risk of PGD. Adipose quantification and characterization via computed tomography (CT) imaging is an evolving topic of interest. However, very little research of PGD prediction using adipose quantity or characteristics derived from medical images has been performed. The aim of this study is to explore image based features of thoracic adipose tissue on pre-operative chest CT to distinguish between the above two groups of patients. 140 unenhanced chest CT images from three lung transplant centers (Columbia, Penn, and Duke) are included in this study. 124 patients are in the successful group and 16 in failure group. Chest CT slices at the T7 and T8 vertebral levels are captured to represent the thoracic fat burden by using a standardized anatomic space (SAS) approach. Fat (subcutaneous adipose tissue (SAT)/ visceral adipose tissue (VAT)) intensity and texture properties (1142 in total) for each patient are collected, and then an optimal feature set is selected to maximize feature independence and separation between the two groups. Leave-one-out and leave-ten-out cross validation strategies are adopted to test the prediction ability based on those selected features all of which came from VAT texture properties. Accuracy of prediction (ACC), sensitivity (SEN), specificity (SPE), and area under the curve (AUC) of 0.87/0.97, 0.87/0.97, 0.88/1.00, and 0.88/0.99, respectively are achieved by the method. The optimal feature set includes only 5 features (also all from VAT), which might suggest that thoracic VAT plays a more important role than SAT in predicting PGD in lung transplant recipients.\",\n \"Title: Urinary bladder cancer T-staging from T2-weighted MR images using an optimal biomarker approach.\\nYear: 2018\\n\\nAbstract: Magnetic resonance imaging (MRI) is often used in clinical practice to stage patients with bladder cancer to help plan treatment. However, qualitative assessment of MR images is prone to inaccuracies, adversely affecting patient outcomes. In this paper, T2-weighted MR image-based quantitative features were extracted from the bladder wall in 65 patients with bladder cancer to classify them into two primary tumor (T) stage groups: group 1 - T stage <= T2, with primary tumor locally confined to the bladder, and group 2 -T stage > T2, with primary tumor locally extending beyond the bladder. The bladder was divided into 8 sectors in the axial plane, where each sector has a corresponding reference standard T stage that is based on expert radiology qualitative MR image review and histopathologic results. The performance of the classification for correct assignment of T stage grouping was then evaluated at both the patient level and the sector level. Each bladder sector was divided into 3 shells (inner, middle, and outer), and 15,834 features including intensity features and texture features from local binary pattern and gray-level co-occurrence matrix were extracted from the 3 shells of each sector. An optimal feature set was selected from all features using an optimal biomarker approach. Nine optimal biomarker features were derived based on texture properties from the middle shell, with an area under the ROC curve of AUC value at the sector and patient level of 0.813 and 0.806, respectively.\",\n \"Title: Auto-contouring via automatic anatomy recognition of organs at risk in head and neck cancer on CT images.\\nYear: 2018\\n\\nAbstract: Contouring of the organs at risk is a vital part of routine radiation therapy planning. For the head and neck (H&N) region, this is more challenging due to the complexity of anatomy, the presence of streak artifacts, and the variations of object appearance. In this paper, we describe the latest advances in our Automatic Anatomy Recognition (AAR) approach, which aims to automatically contour multiple objects in the head and neck region on planning CT images. Our method has three major steps: model building, object recognition, and object delineation. First, the better-quality images from our cohort of H&N CT studies are used to build fuzzy models and find the optimal hierarchy for arranging objects based on the relationship between objects. Then, the object recognition step exploits the rich prior anatomic information encoded in the hierarchy to derive the location and pose for each object, which leads to generalizable and robust methods and mitigation of object localization challenges. Finally, the delineation algorithms employ local features to contour the boundary based on object recognition results. We make several improvements within the AAR framework, including finding recognition-error-driven optimal hierarchy, modeling boundary relationships, combining texture and intensity, and evaluating object quality. Experiments were conducted on the largest ensemble of clinical data sets reported to date, including 216 planning CT studies and over 2,600 object samples. The preliminary results show that on data sets with minimal (<4 slices) streak artifacts and other deviations, overall recognition accuracy reaches 2 voxels, with overall delineation Dice coefficient close to 0.8 and Hausdorff Distance within 1 voxel.\",\n \"Title: Image quality and segmentation.\\nYear: 2018\\n\\nAbstract: Algorithms for image segmentation (including object recognition and delineation) are influenced by the quality of object appearance in the image and overall image quality. However, the issue of how to perform segmentation evaluation as a function of these quality factors has not been addressed in the literature. In this paper, we present a solution to this problem. We devised a set of key quality criteria that influence segmentation (global and regional): posture deviations, image noise, beam hardening artifacts (streak artifacts), shape distortion, presence of pathology, object intensity deviation, and object contrast. A trained reader assigned a grade to each object for each criterion in each study. We developed algorithms based on logical predicates for determining a 1 to 10 numeric quality score for each object and each image from reader-assigned quality grades. We analyzed these object and image quality scores (OQS and IQS, respectively) in our data cohort by gender and age. We performed recognition and delineation of all objects using recent adaptations [8, 9] of our Automatic Anatomy Recognition (AAR) framework [6] and analyzed the accuracy of recognition and delineation of each object. We illustrate our method on 216 head & neck and 211 thoracic cancer computed tomography (CT) studies.\",\n \"Title: Generalized ball-scale: theory, algorithms, and application to image inhomogeneity correction.\\nYear: 2005\\n\\nAbstract: The concept of generalized scale (g-scale) was introduced previously to overcome the shape, topological, and anisotropic constraints imposed by previous local morphometric scale models. Roughly speaking, the g-scale of a voxel in a scene was defined as the largest set of all voxels associated with it, that satisfy some homogeneity criterion. g-scale was shown to have interesting theoretical properties, and its superiority to an existing image background inhomogeneity correction method was demonstrated. In this paper, we present a variant of g-scale that we refer to as g(B)-scale. The difference between g- and gB-scale is that, while for g-scale, individual voxels are included into the g-scale set one at a time, the g(B)-scale set is grown by including hyperballs, the hyperball corresponding to the local ball scale at every voxel (which, briefly, is the radius of the largest hyperball of homogeneous intensity centered at the voxel). The g(B)-scale model was found to be more resistant to severe levels of inhomogeneity and noise compared to g-scale. A methodology to perform image background inhomogeneity correction based on the idea of gB(-)scale was qualitatively and quantitatively compared on nearly 250 clinical and phantom datasets, with a ball-scale- and a g-scale-based correction methodology. For scenes containing inhomogeneity but no noise, the g-, and g(B)-scale methods performed comparably and were superior to the ball-scale method. For scenes containing both noise and inhomogeneity, the g(B)-scale-based method outperformed both the g- and ball-scale correction methods.\",\n \"Title: Hierarchical model-based object localization for auto-contouring in head and neck radiation therapy planning.\\nYear: 2018\\n\\nAbstract: Segmentation of organs at risk (OARs) is a key step during the radiation therapy (RT) treatment planning process. Automatic anatomy recognition (AAR) is a recently developed body-wide multiple object segmentation approach, where segmentation is designed as two dichotomous steps: object recognition (or localization) and object delineation. Recognition is the high-level process of determining the whereabouts of an object, and delineation is the meticulous low-level process of precisely indicating the space occupied by an object. This study focuses on recognition. The purpose of this paper is to introduce new features of the AAR-recognition approach (abbreviated as AAR-R from now on) of combining texture and intensity information into the recognition procedure, using the optimal spanning tree to achieve the optimal hierarchy for recognition to minimize recognition errors, and to illustrate recognition performance by using large-scale testing computed tomography (CT) data sets. The data sets pertain to 216 non-serial (planning) and 82 serial (re-planning) studies of head and neck (H&N) cancer patients undergoing radiation therapy, involving a total of similar to 2600 object samples. Texture property \\\"maximum probability of occurrence\\\" derived from the co-occurrence matrix was determined to be the best property and is utilized in conjunction with intensity properties in AAR-R. An optimal spanning tree is found in the complete graph whose nodes are individual objects, and then the tree is used as the hierarchy in recognition. Texture information combined with intensity can significantly reduce location error for gland-related objects (parotid and submandibular glands). We also report recognition results by considering image quality, which is a novel concept. AAR-R with new features achieves a location error of less than 4 mm (similar to 1.5 voxels in our studies) for good quality images for both serial and non-serial studies.\",\n \"Title: Interactive iterative relative fuzzy connectedness lung segmentation on thoracic 4D dynamic MR images.\\nYear: 2017\\n\\nAbstract: Lung delineation via dynamic 4D thoracic magnetic resonance imaging (MRI) is necessary for quantitative image analysis for studying pediatric respiratory diseases such as thoracic insufficiency syndrome (TIS). This task is very challenging because of the often-extreme malformations of the thorax in TIS, lack of signal from bone and connective tissues resulting in inadequate image quality, abnormal thoracic dynamics, and the inability of the patients to cooperate with the protocol needed to get good quality images. We propose an interactive fuzzy connectedness approach as a potential practical solution to this difficult problem. Manual segmentation is too labor intensive especially due to the 4D nature of the data and can lead to low repeatability of the segmentation results. Registration-based approaches are somewhat inefficient and may produce inaccurate results due to accumulated registration errors and inadequate boundary information. The proposed approach works in a manner resembling the Iterative Livewire tool but uses iterative relative fuzzy connectedness (IRFC) as the delineation engine. Seeds needed by IRFC are set manually and are propagated from slice-to-slice, decreasing the needed human labor, and then a fuzzy connectedness map is automatically calculated almost instantaneously. If the segmentation is acceptable, the user selects \\\"next\\\" slice. Otherwise, the seeds are refined and the process continues. Although human interaction is needed, an advantage of the method is the high level of efficient user-control on the process and non-necessity to refine the results. Dynamic MRI sequences from 5 pediatric TIS patients involving 39 3D spatial volumes are used to evaluate the proposed approach. The method is compared to two other IRFC strategies with a higher level of automation. The proposed method yields an overall true positive and false positive volume fraction of 0.91 and 0.03, respectively, and Hausdorff boundary distance of 2 mm.\",\n \"Title: Disease quantification on PET/CT images without explicit object delineation.\\nYear: 2019\\n\\nAbstract: •Extension of the previous AAR framework for anatomy recognition to disease quantification.•Disease burden estimation on PET/CT images without explicit operations such as object delineation and partial volume correction but after only object localization and using fuzzy principles.•A general strategy that allows disease quantification at the organ or lesion level via automatic or interactive techniques for object localization.\",\n \"Title: Fat quantification and analysis of lung transplant patients on unenhanced chest CT images based on standardized anatomic space\\nYear: 2016\\n\\nAbstract: Chest fat estimation is important for identifying high-risk lung transplant candidates. In this paper, an approach to chest fat quantification based on a recently formulated concept of standardized anatomic space (SAS) is presented. The goal of this paper is to seek answers to the following questions related to chest fat quantification on single slice versus whole volume CT, which have not been addressed in the literature. What level of correlation exists between total chest fat volume and fat areas measured on single abdominal and thigh slices? What is the anatomic location in the chest where maximal correlation of fat area with fat volume can be expected? Do the components of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) have the same area-to-volume correlative behavior or do they differ? The SAS approach includes two steps: calibration followed by transformation which will map the patient slice locations non-linearly to SAS. The optimal slice locations found for SAT and VAT based on SAS are different and at the mid-level of the T8 vertebral body for SAT and mid-level of the T7 vertebral body for VAT. Fat volume and area on optimal slices for SAT and VAT are correlated with Pearson correlation coefficients of 0.97 and 0.86, respectively. The correlation of chest fat volume with abdominal and thigh fat areas is weak to modest.\",\n \"Title: Definition and automatic anatomy recognition of lymph node zones in the pelvis on CT images\\nYear: 2016\\n\\nAbstract: Currently, unlike IALSC-defined thoracic lymph node zones, no explicitly provided definitions for lymph nodes in other body regions are available. Yet, definitions are critical for standardizing the recognition, delineation, quantification, and reporting of lymphadenopathy in other body regions. Continuing from our previous work in the thorax, this paper proposes a standardized definition of the grouping of pelvic lymph nodes into 10 zones. We subsequently employ our earlier Automatic Anatomy Recognition (AAR) framework designed for body-wide organ modeling, recognition, and delineation to actually implement these zonal definitions where the zones are treated as anatomic objects. First, all 10 zones and key anatomic organs used as anchors are manually delineated under expert supervision for constructing fuzzy anatomy models of the assembly of organs together with the zones. Then, optimal hierarchical arrangement of these objects is constructed for the purpose of achieving the best zonal recognition. For actual localization of the objects, two strategies are used - optimal thresholded search for organs and one-shot method for the zones where the known relationship of the zones to key organs is exploited. Based on 50 computed tomography (CT) image data sets for the pelvic body region and an equal division into training and test subsets, automatic zonal localization within 1-3 voxels is achieved.\",\n \"Title: AAR-RT - A system for auto-contouring organs at risk on CT images for radiation therapy planning: Principles, design, and large-scale evaluation on head-and-neck and thoracic cancer cases.\\nYear: 2019\\n\\nAbstract: •A practical system for auto-contouring organs at risk in radiation therapy planning built around previous AAR framework.•Significantly improved all three stages of AAR – model building, object recognition, and object delineation.•Large-scale evaluation on 503 cancer patient CT scans and involving 4,301 3D object samples from two body regions.•Evaluation as a function of object/ image quality, gender, and age group. Object recognition more robust than delineation.\",\n \"Title: Retrospective 4D MR image construction from free-breathing slice Acquisitions: A novel graph-based approach.\\nYear: 2017\\n\\nAbstract: •Free-breathing MRI slice acquisition of pediatric thoraces with ailments.•Novel globally optimal graph-based method of 4D construction from 1000s of slices.•Image-based strategy without the need for breath holding or external surrogates.•Consistent and temporally and spatially smooth 4D constructed image.•4D phantom experiment based on 3D printing of a patient thorax for validation.\",\n \"Title: Automatic thoracic body region localization.\\nYear: 2017\\n\\nAbstract: Radiological imaging and image interpretation for clinical decision making are mostly specific to each body region such as head & neck, thorax, abdomen, pelvis, and extremities. For automating image analysis and consistency of results, standardizing definitions of body regions and the various anatomic objects, tissue regions, and zones in them becomes essential. Assuming that a standardized definition of body regions is available, a fundamental early step needed in automated image and object analytics is to automatically trim the given image stack into image volumes exactly satisfying the body region definition. This paper presents a solution to this problem based on the concept of virtual landmarks and evaluates it on whole-body positron emission tomography/computed tomography (PET/CT) scans. The method first selects a (set of) reference object(s), segments it (them) roughly, and identifies virtual landmarks for the object(s). The geometric relationship between these landmarks and the boundary locations of body regions in the craniocaudal direction is then learned through a neural network regressor, and the locations are predicted. Based on low-dose unenhanced CT images of 180 near whole-body PET/CT scans (which includes 34 whole-body PET/CT scans), the mean localization error for the boundaries of superior of thorax (TS) and inferior of thorax (TI), expressed as number of slices (slice spacing approximate to 4mm)), and using either the skeleton or the pleural spaces as reference objects, is found to be 3,2 (using skeleton) and 3, 5 (using pleural spaces) respectively, or in mm 13, 10 mm (using skeleton) and 10.5, 20 mm (using pleural spaces), respectively. Improvements of this performance via optimal selection of objects and virtual landmarks and other object analytics applications are currently being pursued. and the skeleton and pleural spaces used as a reference objects\",\n \"Title: Disease quantification on PET/CT images without object delineation\\nYear: 2017\\n\\nAbstract: The derivation of quantitative information from images to make quantitative radiology (QR) clinically practical continues to face a major image analysis hurdle because of image segmentation challenges. This paper presents a novel approach to disease quantification (DQ) via positron emission tomography/computed tomography (PET/CT) images that explores how to decouple DQ methods from explicit dependence on object segmentation through the use of only object recognition results to quantify disease burden. The concept of an object-dependent disease map is introduced to express disease severity without performing explicit delineation and partial volume correction of either objects or lesions. The parameters of the disease map are estimated from a set of training image data sets. The idea is illustrated on 20 lung lesions and 20 liver lesions derived from F-18-2-fluoro-2-deoxy-D-glucose (FDG)-PET/CT scans of patients with various types of cancers and also on 20 NEMA PET/CT phantom data sets. Our preliminary results show that, on phantom data sets, \\\"disease burden\\\" can be estimated to within 2% of known absolute true activity. Notwithstanding the difficulty in establishing true quantification on patient PET images, our results achieve 8% deviation from \\\"true\\\" estimates, with slightly larger deviations for small and diffuse lesions where establishing ground truth becomes really questionable, and smaller deviations for larger lesions where ground truth set up becomes more reliable. We are currently exploring extensions of the approach to include fully automated body-wide DQ, extensions to just CT or magnetic resonance imaging (MRI) alone, to PET/CT performed with radiotracers other than FDG, and other functional forms of disease maps.\",\n \"Title: Lung parenchymal analysis on dynamic MRI in thoracic insufficiency syndrome to assess changes following surgical intervention.\\nYear: 2018\\n\\nAbstract: General surgeons, orthopedists, and pulmonologists individually treat patients with thoracic insufficiency syndrome (TIS). The benefits of growth-sparing procedures such as Vertical Expandable Prosthetic Titanium Rib (VEPTR)insertion for treating patients with TIS have been demonstrated. However, at present there is no objective assessment metricto examine different thoracic structural components individually as to their roles in the syndrome, in contributing to dynamics and function, and in influencing treatment outcome. Using thoracic dynamic MRI (dMRI), we have been developing a methodology to overcome this problem. In this paper, we extend this methodology from our previous structural analysis approaches to examining lung tissue properties. We process the T2-weighted dMRI images through a series of steps involving 4D image construction of the acquired dMRI images, intensity non-uniformity correction and standardization of the 4D image, lung segmentation, and estimation of the parameters describing lung tissue intensity distributions in the 4D image. Based on pre- and post-operative dMRI data sets from 25 TIS patients (predominantly neuromuscular and congenital conditions), we demonstrate how lung tissue can be characterized by the estimated distribution parameters. Our results show that standardized T2-weighted image intensity values decrease from the pre- to post-operative condition, likely reflecting improved lung aeration post-operatively. In both pre- and post-operative conditions, the intensity values decrease also from end-expiration to end-inspiration, supporting the basic premise of our results.\",\n \"Title: Quantitative dynamic MRI (QdMRI) Volumetric Analysis of Pediatric Patients with Thoracic Insufficiency Syndrome.\\nYear: 2018\\n\\nAbstract: The lack of standardizable objective diagnostic measurement techniques is a major hurdle in the assessment and treatment of pediatric patients with thoracic insufficiency syndrome (TIS). The aim of this paper is to explore quantitative dynamic MRI (QdMRI) volumetric parameters derived from thoracic dMRI in pediatric patients with TIS and the relationships between dMRI parameters and clinical measurements. 25 TIS patients treated with vertical expandable prosthetic titanium rib (VEPTR) surgery are included in this retrospective study. Left and right lungs at end-inspiration and end-expiration are segmented from constructed 4D dMRI images. Lung volumes and excursion (or tidal) volumes of the left/right chest wall and hemi-diaphragms are computed. Commonly used clinical parameters include thoracic and lumbar Cobb angles and respiratory measurements from pulmonary function testing (PFT). 200 3D lungs in total (left & right, pre-operative & post-operative, end-inspiration & end-expiration) are segmented for analysis. Our analysis indicates that change of resting breathing rate (RR) following surgery is negatively correlated with that of QdMRI parameters. Chest wall tidal volumes and hemi-diaphragm tidal volumes increase significantly following surgery. Clinical parameter RR reduced after surgical treatment with P values around 0.06 but no significant differences were found on other clinical parameters. The significant increase in post-operative tidal volumes suggests a treatment-related improvement in lung capacity. The reduction of RR following surgery shows that breathing function is improved. The QdMRI parameters may offer an objective marker set for studying TIS, which is currently lacking.\",\n \"Title: Super-mask-based object localization for auto-contouring in head and neck radiation therapy planning.\\nYear: 2019\\n\\nAbstract: We have presented a variety of methods for object recognition based on the Automatic Anatomy Recognition (AAR) framework at previous SPIE conferences, including AAR recognition via optimal threshold on intensity, AAR recognition via composite information from intensity and texture, and AAR recognition with the optimal hierarchical structure design, and via neural networks to learn object relationships. The purpose of this paper is to introduce new features for the AAR-based recognition procedure and improve the performance of object localization for auto contouring in head and neck (H&N) radiation therapy planning, specifically for some of the most challenging objects. The proposed super-mask technique first registers images used for model building among themselves optimally by using a minimal spanning tree in the complete graph formed with images as nodes to determine the order of registering images. Subsequently, we build a super-mask by combining the similarly registered binary images corresponding to each object by taking (S1) union of all binary images, (S2) intersection among all binary images, or (S3) the voting based fuzzy mask created by adding the binary images. The super-mask is then used to confine search for optimum localization of the object in the given image. A large-scale H&N computed tomography (CT) data set with 216 subjects and over 2000 3D object samples were utilized in this study. The super-mask-based object localization approach within the AAR framework improved the recognition accuracy by 25-45% compared with the previous AAR strategy, especially for the most challenging H&N objects. On low quality images, the new method achieves recognition accuracy within 2 voxels on 50-60% of the cases.\",\n \"Title: Obtaining the potential number of models/atlases needed for capturing anatomic variations in population images.\\nYear: 2019\\n\\nAbstract: Many medical image processing and analysis operations can benefit a great deal from prior information encoded in the form of models/atlases to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. However, the fundamental question \\\"How many models/atlases are needed for optimally encoding prior information to address the differing body habitus factor in a given population?\\\" has remained a difficult and open problem. We propose a method to seek an answer to this question assuming that a set I of images representative of the population for the body region is given. Our approach, after images in I are trimmed to the exact body region, is to create a partition of I into a specified number n of groups by optimizing the collective similarity of images in each group. We then ascertain how the overall goodness of partition P-n(I) varies as we change n from 1 to Subsequently, values of n at which there are significant changes in the goodness value are determined. These breakpoints are taken as the recommended number of groups/models/atlases. Our results on 284 thoracic computed tomography (CT) scans show that at least 8 groups are essential, and 15, 21, or 32 could be optimum numbers if a finer classification is needed for this population. This method may be helpful for constructing high quality models/atlases with a proper grouping of the images from a sufficiently large population and in selecting optimally the training image sets needed for each class in deep learning strategies.\",\n \"Title: Auto-labeling of respiratory time points in free-breathing thoracic dynamic MR image acquisitions for 4D image construction.\\nYear: 2019\\n\\nAbstract: Determining the EE (End of Expiration) and EI (End of Inspiration) time points in the respiratory cycle is one key step during the 4D image construction from free-breathing dynamic thoracic computed tomography (CT) or magnetic resonance imaging (MRI) acquisitions. However, the cost of manually labeling EE and EI time points is extensive. An automatic image-based EE and EI labeling method makes image annotation independent of the image acquisition process, avoiding use of internal or external markers for the patient during image acquisition. The purpose of this paper is to introduce a novel optical-flow-based technique for finding EE and EI time points from dynamic thoracic MRI acquired during natural tidal-breathing. The diaphragm is tracked as a marker to determine the state of breathing. A region of interest (ROI) containing the diaphragm is selected to calculate the pixel optical flow values between two adjacent time slices. The average optical flow values of all pixels including diaphragm motion speed is used as a reference for labeling EE and EI. When the direction of movement of the diaphragm changes, EE or EI is found depending on the direction of the change. Quantitative evaluation was carried out to evaluate the effectiveness of our method in different locations in the lungs as compared to manual labeling. When tested on 28 patient dynamic thoracic MRI data sets, the average error was found to be less than 1 time point. Automatic labeling greatly shortened the labeling time, requiring less than 8 minutes compared to 4 hours for manual labeling per study.\",\n \"Title: A Minimally Interactive Method for Labeling Respiratory Phases in Free-Breathing Thoracic Dynamic MRI for Constructing 4D Images\\nYear: 2022\\n\\nAbstract: Objective: Determination of end-expiration (EE) and end-inspiration (EI) time points in the respiratory cycle in free-breathing slice image acquisitions of the thorax is one key step needed for 4D image construction via dynamic magnetic resonance imaging. The purpose of this paper is to realize the automation of the labeling process. Methods: The diaphragm is used as a surrogate for ...\",\n \"Title: Gradient-Aligned convolution neural network\\nYear: 2022\\n\\nAbstract: •We propose a general Convolution operation, called GAConv, which can replace conventional operations in CNN to help it achieve rotation invariance.•With GAConv, Gradient-Aligned CNN (GACNN) can achieve rotation invariance without any data augmentation, feature-map augmentation, and filter enrichment.•In GACNN, rotation invariance does not learn from the training set, but bases on the network model. Different from the vanilla CNN, GACNN will output invariant results for all rotated versions of an object, no matter whether the network is trained or not.•We conduct classification experiments on designed dataset and realistic datasets. The results show that with the same computation cost, GACNN achieved better results than conventional CNN and some rotational invariant CNN.\",\n \"Title: Object recognition in medical images via anatomy-guided deep learning\\nYear: 2022\\n\\nAbstract: •A conceptual framework to synergistically marry the unmatched strengths of high-level human knowledge (natural intelligence) and artificial intelligence to arrive at a robust, accurate, and general object recognition method for medical image analysis.•The AAR-DL approach combines an advanced anatomy-modeling strategy (AAR), model-based object recognition (AAR-R), and deep learning object detection networks.•AAR-DL consists of 4 key modules wherein prior knowledge is made use of judiciously at every stage.•AAR-DL has demonstrated high accuracy and robustness to image artifacts and deviations.•AAR-DL performs like an expert human operator in object recognition with localization accuracy within 1–2 voxels and remarkable robustness.\",\n \"Title: Organ segmentation from computed tomography images using the 3D convolutional neural network: a systematic review\\nYear: 2022\\n\\nAbstract: Computed tomography images are scans that combine a series of X-rays with computer processing techniques to display organs in the body. Recently, 3D CNN models have become effective in tasks relating to recognition, delineation, and classification. Therefore we propose a review to summarize different 3D CNN algorithms for segmenting organs in computed tomography images. This work systematically applies a two-stage procedure for review. A thorough screening of abstracts and titles to ascertain their relevance was done. Research papers published in the academic repositories were selected, analyzed, and reviewed. Insight relating to 3D organ segmentation is provided, with content such as database usage, disadvantages, and advantages. A comparison of two accuracies was carried out with a graph depicting database categories. Important insights, limitations, observations, and future directions were elucidated. After careful investigation, we observe that the encoder-decoder network is predominant for segmentation. The encoder-decoder framework provides a seamless procedure to segment CT images. A prediction of future trends with insightful recommendations for researchers is proposed. Finally, findings suggest that CNN algorithms produce good accuracies despite their limitations.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":124,"string":"124"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13284,"string":"13,284"}}},{"rowIdx":13285,"cells":{"question_id":{"kind":"string","value":"Sophia Drossopoulou"},"question":{"kind":"string","value":"Generate an abstract for the title \"Tribe: a simple virtual class calculus\" using the following items: 1. Tribe \n2. Inheritance \n3. Type system \n4. Polymorphism \n5. Language design"},"answer":{"kind":"string","value":"Beginning with BETA, a range of programming language mechanisms such as virtual classes (class-valued attributes of objects) have been developed to allow inheritance in the presence of mutually dependent classes. This paper presents Tribe, a type system which generalises and simplifies other formalisms of such mechanisms, by treating issues which are inessential for soundness, such as the precise details of dispatch and field initialisation, as orthogonal to the core formalism. Tribe can support path types dependent simultaneously on both classes and objects, which is useful for writing library code, and ubiquitous access to an object's family, which offers family polymorphism without the need to drag around family arguments. Languages based on Tribe will be both simpler and more expressive than existing designs, while having a simpler type system, serving as a useful basis for future language designs."},"correct_answer":{"kind":"string","value":"Beginning with BETA, a range of programming language mechanisms such as virtual classes (class-valued attributes of objects) have been developed to allow inheritance in the presence of mutually dependent classes. This paper presents Tribe, a type system which generalises and simplifies other formalisms of such mechanisms, by treating issues which are inessential for soundness, such as the precise details of dispatch and field initialisation, as orthogonal to the core formalism. Tribe can support path types dependent simultaneously on both classes and objects, which is useful for writing library code, and ubiquitous access to an object's family, which offers family polymorphism without the need to drag around family arguments. Languages based on Tribe will be both simpler and more expressive than existing designs, while having a simpler type system, serving as a useful basis for future language designs."},"documents":{"kind":"list like","value":["Title: BabyJFrom Object Based to Class Based Programming via Types\nYear: 2003\n\nAbstract: Object oriented programming can be classified into the object based, and the class based paradigm. Object based languages typically are weakly typed and interpreted, allow member addition and removal, and thus they support flexibility and prototyping. Class based languages are usually strongly typed and compiled, require a rigid class structure and class membership, and thus they support more robust, type safe programs.","Title: Generic Universe Types\nYear: 2007\n\nAbstract: Ownership is a powerful concept to structure the object store and to control aliasing and modifications of objects. This paper presents an ownership type system for a Java-like programming language with generic types. Like our earlier Universe type system, Generic Universe Types enforce the owner-as-modifier discipline. This discipline does not restrict aliasing, but requires modifications of an object to be initiated by its owner. This allows owner objects to control state changes of owned objects, for instance, to maintain invariants. Generic Universe Types re- quire a small annotation overhead and provide strong static guarantees. They are the first type system that combines the owner-as-modifier dis- cipline with type genericity.","Title: Policy as Types.\nYear: 2013\n\nAbstract: Drossopoulou and Noble argue persuasively for the need for a means to express policy in object-capability-based systems. We investigate a practical means to realize their aim via the Curry-Howard isomorphism. Specifically, we investigate representing policy as types in a behavioral type system for the RHO-calculus, a reflective higher-order variant of the pi-calculus.","Title: Formalising Composition Oriented Programming\nYear: 1999\n\nAbstract: We present a snapshot of work in progress to de- fine a formal model of the composition technol- ogy which underpins Subject-oriented and Aspect- oriented programming.","Title: Objects and session types\nYear: 2009\n\nAbstract: A session takes place between two parties; after establishing a connection, each party interleaves local computations and communications (sending or receiving) with the other. Session types characterise such sessions in terms of the types of values communicated and the shape of protocols, and have been developed for the @p-calculus, CORBA interfaces, and functional languages. We study the incorporation of session types into object-oriented languages through MOOSE, a multi-threaded language with session types, thread spawning, iterative, and higher-order sessions. Our design aims to consistently integrate the object-oriented programming style and sessions, and to be able to treat various case studies from the literature. We describe the design of MOOSE, its syntax, operational semantics, and type system, and develop a type inference system. After proving subject reduction, we establish the progress property: once a communication has been established, well-typed programs will never starve at communication points.","Title: Types for hierarchic shapes\nYear: 2006\n\nAbstract: Heap entities tend to contain complex references to each other. To manage this complexity, types which express shapes and hierarchies have been suggested. We survey type systems which describe such hierarchic shapes, how these types are used for reasoning about programs, and applications in concurrent programming.","Title: Overloading in preliminary Ada\nYear: 1980\n\nAbstract: Ada permits the overloading of enumeration literals, aggregates, subprograms and operators, i.e. the declaration of the same designator with different meanings in the same scope. This leads to difficulties during the semantic analysis of expressions and subprogram calls. For selecting the meaning not only the designator but also the types of its operands or parameters and the type of its result must be used. We show that the identification of expressions is possible in two passes, the first bottom-up, the second top-down.","Title: A Model for Java with Wildcards\nYear: 2008\n\nAbstract: Wildcards are a complex and subtle part of the Java type system, present since version 5.0. Although there have been various formalisations and partial type soundness results concerning wildcards, to the best of our knowledge, no system that includes all the key aspects of Java wildcards has been proven type sound. This paper establishes that Java wildcards are type sound. We describe a new formal model based on explicit existential types whose pack and unpack operations are handled implicitly, and prove it type sound. Moreover, we specify a translation from a subset of Java to our formal model, and discuss how several interesting aspects of the Java type system are handled.","Title: An Effective Translation of Fickle into Java\nYear: 2001\n\nAbstract: We present a translation from Fickle (a Java-like language allowing dynamic object re-classification, that is, objects that can change their class at run-time) into plain Java. The translation is proved to preserve static and dynamic semantics; moreover, it is shown to be effective, in the sense that the translation of a Fickle class does not depend on the implementation of used classes, hence can be done in a separate way, that is, without having their sources, exactly as it happens for Java compilation. The aim is to demonstrate that an extension of Java supporting dynamic object re-classification could be fully compatible with the existing Java environment.","Title: Fully concurrent garbage collection of actors on many-core machines\nYear: 2013\n\nAbstract: Disposal of dead actors in actor-model languages is as important as disposal of unreachable objects in object-oriented languages. In current practice, programmers are required to either manually terminate actors, or they have to rely on garbage collection systems that monitor actor mutation through write barriers, thread coordination through locks etc. These techniques, however, prevent the collector from being fully concurrent. We developed a protocol that allows garbage collection to run fully concurrently with all actors. The main challenges in concurrent garbage collection is the detection of cycles of sleeping actors in the actors graph, in the presence of concurrent mutation of this graph. Our protocol is solely built on message passing: it uses deferred direct reference counting, a dedicated actor for the detection of (cyclic) garbage, and a confirmation protocol (to deal with the mutation of the actor graph). We present our ideas informally through an example, and then present a formal model, prove soundness and argue completeness. We have implemented the protocol as part of a runtime library. As a preliminary performance evaluation, we discuss the performance of our approach as currently used at a financial institution, and use four benchmarks from the literature to compare our approach with other actor-model systems. These preliminary results indicate that the overhead of our approach is small.","Title: A distributed object-oriented language with session types\nYear: 2005\n\nAbstract: In the age of the world-wide web and mobile computing, programming communication-centric software is essential. Thus, programmers and program designers are exposed to new levels of complexity, such as ensuring the correct composition of communication behaviours and guaranteeing deadlockfreedom of their protocols. This paper proposes the language Ldoos, a simple distributed object-oriented language augmented with session communication primitives and types. Ldoos provides a flexible object-oriented programming style for structural interaction protocols by prescribing channel usages within signatures of distributed classes. We develop a typing system for Ldoos and prove its soundness with respect to the operational semantics. We also show that in a well-typed Ldoos program, there will never be a connection error, a communication error, nor an incorrect completion between server-client interactions. These results demonstrate that a consistent integration of object-oriented language features and session types can statically check the consistent composition of communication protocols.","Title: Separating ownership topology and encapsulation with generic universe types\nYear: 2011\n\nAbstract: Ownership is a powerful concept to structure the object store and to control aliasing and modifications of objects. This article presents an ownership type system for a Java-like programming language with generic types. Like our earlier Universe type system, Generic Universe Types structure the heap hierarchically. In contrast to earlier work, we separate the enforcement of an ownership topology from an encapsulation system. The topological system uses an existential modifier to express that no ownership information is available statically. On top of the topological system, we build an encapsulation system that enforces the owner-as-modifier discipline. This discipline does not restrict aliasing, but requires modifications of an object to be initiated by its owner. This allows owner objects to control state changes of owned objects—for instance, to maintain invariants. Separating the topological system from the encapsulation system allows for a cleaner formalization, separation of concerns, and simpler reuse of the individual systems in different contexts.","Title: A flexible model for dynamic linking in Java and C#\nYear: 2006\n\nAbstract: Dynamic linking supports flexible code deployment, allowing partially linked code to link further code on the fly, as needed. Thus, end-users enjoy the advantage of automatically receiving any updates, without any need for any explicit actions on their side, such as re-compilation, or re-linking. On the down side, two executions of a program may link in different versions of code, which in some cases causes subtle errors, and may mystify end-users. Dynamic linking in Java and C# are similar: the same linking phases are involved, soundness is based on similar ideas, and executions which do not throw linking errors give the same result. They are, however, not identical: the linking phases are combined differently, and take place in different order. Consequently, linking errors may be detected at different times by Java and C# runtime systems. We develop a non-deterministic model, which describes the behaviour of both Java and C# program executions. The non-determinism allows us to describe the design space, to distill the similarities between the two languages, and to use one proof of soundness for both. We also prove that all execution strategies are equivalent with respect to terminating executions that do not throw link errors: they give the same results.","Title: Refined Effects for Unanticipated Object Re-classification: Fickle3\nYear: 2003\n\nAbstract: Abstract. In previous work on the language Fickle and its extension FickleII Dezani and us introduced language features for object re-classication for imperative, typed, class-based, object-oriented languages. In this paper we present the language Fickle3, which on one side renes FickleII with more expressive eect annotations, and on the other eliminates the need to declare explicitly which are the classes of the objects that may be re-classied. Therefore, Fickle3 allows to correctly type meaningful programs which FickleII rejects. Moreover, re-classication may be decided by the client of a class, allowing unanticipated object reclassic ation. As for FickleII, also the type and eect system for Fickle3 guarantees that, even though objects may be re-classied across classes with dieren t members, they will never attempt to access non existing members. The type and eect,system of Fickle3 has some signican t dierences from the one of FickleII. In particular, besides the fact that intra-class type checking has to track the more rened eects, when a class is combined with other classes some,additional inter-class checking is introduced.","Title: More dynamic object reclassification: Fickle∥\nYear: 2002\n\nAbstract: Reclassification changes the class membership of an object at run-time while retaining its identity. We suggest language features for object reclassification, which extend an imperative, typed, class-based, object-oriented language.We present our proposal through the language Fickle⋄⋄. The imperative features, combined with the requirement for a static and safe type system, provided the main challenges. We develop a type and effect system for Fickle⋄⋄ and prove its soundness with respect to the operational semantics. In particular, even though objects may be reclassified across classes with different members, there will never be an attempt to access nonexisting members.","Title: Formal Techniques for Java Programs\nYear: 2000\n\nAbstract: This report gives an overview of the second ECOOP Workshop on Formal Techniques for Java Programs. It explains the motivation for such a workshop and summarizes the presentations and discussions.","Title: Formal Techniques for Java Programs\nYear: 2001\n\nAbstract: This report gives an overview of the third ECOOP Workshop on Formal Techniques for Java Programs. It explau]ns the motivation\n for such a workshop and summarizes the presentations and discussions.","Title: Flexible models for dynamic linking\nYear: 2003\n\nAbstract: Dynamic linking supports flexible code deployment: partially linked code links further code on the fly, as needed; and thus, end-users receive updates automatically. On the down side, each program run may link different versions of the same code, possibly causing subtle errors which mystify end-users. Dynamic linking in Java and C# are similar: The same linking phases are involved, soundness is based on similar ideas, and executions which do not throw linking errors give the same result. They are, however, not identical: the linking phases are combined differently, and take place in a different order. We develop a non-deterministic model, which includes the behaviour of Java and C#. The non-determinism allows us to describe the design space, to distill the similarities between the two languages, and to use one proof of soundness for both.We also prove that all execution strategies are equivalent in the sense that all terminating executions which do not involve a link error, give the same result.","Title: Existential Quantification for Variant Ownership\nYear: 2009\n\nAbstract: Ownership types characterize the topology of objects in the heap, through a characterization of the context to which an object belongs. They have been used to support reasoning, memory management, concurrency, etc. Subtyping is traditionally invariant w.r.t. contexts, which has often proven inflexible in some situations. Recent work has introduced restricted forms of subtype variance and unknown context, but in a rather ad-hoc and restricted way. We develop Jo***, a calculus which supports parameterisation of types, as well as contexts, and allows variant subtyping of contexts based on existential quantification. Jo*** is more expressive, general, and uniform than previous works which add variance to ownership languages. Our explicit use of existential types makes the connection to type-theoretic foundations from existential types more transparent. We prove type soundness for Jo*** and extend it to Jo*** deep which enforces the owners-as-dominators property.","Title: A formal semantics for isorecursive and equirecursive state abstractions\nYear: 2013\n\nAbstract: Methodologies for static program verification and analysis often support recursive predicates in specifications, in order to reason about recursive data structures. Intuitively, a predicate instance represents the complete unrolling of its definition; this is the equirecursive interpretation. However, this semantics is unsuitable for static verification, when the recursion becomes unbounded. For this reason, most static verifiers differentiate between, e.g., a predicate instance and its corresponding body, while providing a facility to map between the two; this is the isorecursive semantics. While this latter interpretation is usually implemented in practice, only the equirecursive semantics is typically treated in theoretical work. In this paper, we provide both an isorecursive and an equirecursive formal semantics for recursive definitions in the context of Chalice, a verification methodology based on implicit dynamic frames. We show that development of such formalisations requires addressing several subtle issues, such as the possibility of infinitely-recursive definitions and the need for the isorecursive semantics to correctly reflect the restrictions that make it readily implementable. These questions are made more challenging still in the context of implicit dynamic frames, where the use of heap-dependent expressions provides further pitfalls for a correct formal treatment.","Title: Ownership, encapsulation and the disjointness of type and effect\nYear: 2002\n\nAbstract: Ownership types provide a statically enforceable notion of object-level encapsulation. We extend ownership types with computational effects to support reasoning about object-oriented programs. The ensuing system provides both access control and effects reporting. Based on this type system, we codify two formal systems for reasoning about aliasing and the disjointness of computational effects. The first can be used to prove that evaluation of two expressions will never lead to aliases, while the latter can be used to show the non-interference of two expressions.","Title: Fickle: Dynamic Object Re-classification\nYear: 2001\n\nAbstract: Re-classification changes at run-time the class membership of an object while retaining its identity. We suggest language features for object re-classification, which could extend an imperative, typed, classbased, object-oriented language.We present our proposal through the language Fickle. The imperative features combined with the requirement for a static and safe type system provided the main challenges. We develop a type and effect system for Fickle and prove its soundness with respect to the operational semantics. In particular, even though objects may be re-classified across classes with different members, they will never attempt to access nonexisting members.","Title: An Integrated Engineering Study Scheme In Computing\nYear: 1993\n\nAbstract: This paper describes the integrated engineering study scheme, based around a set of 4 year MEng programmes of study, established by Imperial College. The paper outlines the rationale for the scheme and gives an account of its constituent programmes of study and the curriculum. The organisation and pattern of teaching, student workload and assessment methods are discussed. A detailed comparison of the scheme with the proposals and recommendations of the important model curricula are given.","Title: Flexible Bytecode for Linking in .NET\nYear: 2005\n\nAbstract: Dynamic linking in modern execution environments like .NET is considerably more sophisticated than in the days of C shared libraries on UNIX. One aspect of this sophistication is that .NET assemblies embed type information about dynamically linked resources. This type information implicitly represents compile-time assumptions about the resources available at run-time. However, the resources available at run-time may differ from those available at compile-time. For example, the execution environment on a mobile phone might provide fewer, simpler classes than on a desktop PC. As bytecode cannot adapt to its execution environment, component reuse is restricted and development costs are increased. We have designed and implemented a ''flexible'' dynamic linking scheme that binds bytecode as late as possible to the assemblies and classes available in a .NET execution environment. We describe the scheme's integration with the .NET linking infrastructure, review important design decisions and report on experiences with the ''Rotor'' shared source version of .NET.","Title: A type safe state abstraction for coordination in Java-like languages\nYear: 2008\n\nAbstract: The state of a concurrent object, intended as some abstraction over the values of the fields of the object, usually determines its coordination behavior. Therefore, state is always in the programmer’s mind, even though implicitly. We suggest a feature for Java-like languages, which makes the state of a concurrent object explicit and supports the expression of the object’s behavior depending on the state it is currently in. Namely, an object will be in one of the states declared in its class. The state determines the presence of fields and methods. State transition statements explicitly change the state of an object, and thus change the availability of fields and methods. When a thread calls a method which is declared in the object’s class but absent from its current state, it waits, until the state of the object changes to a state which does contain that method. This directly expresses coordination. We claim that this feature makes it easier to understand and develop concurrent programs, and substantiate our claim through the discussion of some popular examples of concurrent programs written using this feature.We develop a type and effect system, which guarantees that, during execution of a method invoked on a concurrent object $${\\tt o}$$: (1) No attempt will be made to access fields not available in the current state of $${\\tt o}$$, and (2) No method invoked on a receiver (syntactically) different from $${\\tt this}$$may cause the invocation of a method on $${\\tt o}$$. The latter guarantee helps to enforce the former and prevents a family of accidental violations of the intended coordination protocol.","Title: Language and Architecture Paradigms as Object Classes\nYear: 1994\n\nAbstract: Computer language paradigms offer linguistic abstractions and proof theories for expressing program implementations. Similarly, system architectures offer the hardware abstractions and quantitative theories applicable to the execution of compiled programs. Although the two entities are usually treated independently, object-oriented technology can be used to obtain a unifying framework. Specifically, inheritance can be used to model both programming languages as extensions to the assembly language executed by the target architecture, and system architectures as the root class of those paradigms. We describe how these principles can be used to model, structure and implement real multiparadigm systems in a portable and extendable way.","Title: Bounded session types for object oriented languages\nYear: 2006\n\nAbstract: Earlier work explored the introduction of session types into object oriented languages. Following the session types literature, two parties would start communicating, provided the types attached to that communication, i.e. the corresponding session types, were dual of each other. Then, the type system was able to ensure soundness, in the sense that two communicating partners were guaranteed to receive/send sequences of values following the order specified by their session types. In the current paper we improve upon our earlier work in two ways: we extend the type system to support bounded polymorphism, and we make the selection more object-oriented, so that control structures determine how to continue evaluation, depending on the class of the object being sent/received. Interestingly, although our notion of selection is more powerful than that in earlier work, the ensuing system turned out not to be more complex, except for the notion of duality, which needed to be extended, to correctly deal with bounded polymorphism, and to capture the new notion of selection. The paper contains an example, informal explanations, a formal description of the operational semantics and of type system, and a proof of subject reduction.","Title: Java is Type Safe - Probably\nYear: 1997\n\nAbstract: Amidst rocketing numbers of enthusiastic Java programmers and internet applet users, there is growing concern about the security of executing Java code produced by external, unknown sources. Rather than waiting to find out empirically what damage Java programs do, we aim to examine first the language and then the environment looking for points of weakness. A proof of the soundness of the Java type system is a first, necessary step towards demonstrating which Java programs won't compromise computer security. We consider a type safe subset of Java describing primitive types, classes, inheritance, instance variables and methods, interfaces, shadowing, dynamic method binding, object creation, null and arrays. We argue that for this subset the type system is sound, by proving that program execution preserves the types, up to subclasses/subinterfaces.","Title: Formal Techniques for Java-Like Programs\nYear: 2008\n\nAbstract: This report gives an overview of the 10th Workshop on Formal Techniques for Java-like Programs at ECOOP 2008. It explains the motivation for the workshop, and summarizes the presentations and discussions.","Title: Formal Techniques for Java-Like Programs\nYear: 2006\n\nAbstract: This report gives an overview of the eighth ECOOP Workshop on Formal Techniques for Java-like Programs. It summarizes the\n workshop preparation, contributions, debates and conclusions.","Title: Formal Techniques for Java-like Programs\nYear: 2002\n\nAbstract: This report gives an overview of the fourth ECOOP Workshop on Formal Techniques for Java-like Programs. It explains the motivation\n for such a workshop and summarizes the presentations and discussions.","Title: Universe-Type-Based Verification Techniques For Mutable Static Fields And Methods\nYear: 2009\n\nAbstract: We present three novel techniques for the verification of invariants in the setting of Java-like languages including static fields and methods. Our techniques structure the heap through universe types, and extend the Visibility Technique of Muller et al.In order to cater for mutable static fields, we extend the classical universe types heap topology with multiple trees, where each tree is rooted in a class. Thus classes may naturally own objects as static fields.We present a basic version of our approach, which allows trees to be visited at the top and then navigated \"downwards\", and which avoids dangerous call-backs through effects which track static method calls. As well as the usual kinds of proof obligations defining that certain invariants must hold at a given state, we employ a second kind of obligation to show that certain other invariants are preserved between two states (i.e., if they hold in the former state then they will still hold in the latter). This allows us to deal with invariants whose expected truth-value cannot always be determined statically in a modular way.We then present two extensions of our basic technique, aimed at improving usability. Firstly, we introduce a new universe annotation to allow safe callbacks between trees, whereby trees may be visited not at the top, but at the point where a previous visit \"had left off\". Secondly, we refine our heap topology with a notion of 'levels', which stratify the heap and provide modularity with regard to library classes and the required effects annotations.","Title: Describing the Semantics of Java and Proving Type Soundness\nYear: 1999\n\nAbstract: Java combines the experience from the development of several object oriented languages, such as C++, Smalltalk and Clos. The philosophy of the language designers was to include only features with already known semantics, and to provide a small and simple language.","Title: Considerate reasoning and the composite design pattern\nYear: 2010\n\nAbstract: We propose Considerate Reasoning, a novel specification and verification technique based on object invariants. This technique supports succinct specifications of implementations which follow the pattern of breaking properties of other objects and then notifying them appropriately. It allows the specification to be concerned only with the properties directly relevant to the current method call, with no need to explicitly mention the concerns of subcalls. In this way, the specification reflects the division of responsibility present in the implementation, and reflects what we regard as the natural argument behind the design. We specify and prove the well-known Composite design pattern using Considerate Reasoning. We show how to encode our approach in Boogie2. The resulting specification verifies automatically within a few seconds; no manual guidance is required beyond the careful representation of the invariants themselves.","Title: Can addresses be types? (A case study: objects with delegation)\nYear: 2003\n\nAbstract: We adapt the aliasing constraints approach for designing a flexible typing of evolving objects. Types are singleton types (addresses of objects, as a matter of fact) whose relevance is mainly due to the sort of safety property they guarantee. In particular we provide a type system for an imperative object based calculus with delegation and which supports method and delegate overriding, addition, and removal.","Title: Towards type inference for javascript\nYear: 2005\n\nAbstract: Object-oriented scripting languages like Javascript and Python are popular partly because of their dynamic features. These include the runtime modification of objects and classes through addition of fields or updating of methods. These features make static typing difficult and so usually dynamic typing is used. Consequently, errors such as access to non-existent members are not detected until runtime. We first develop a formalism for an object based language, JS0 with features from Javascript, including dynamic addition of fields and updating of methods. We give an operational semantics and static type system for JS0using structural types. Our types allow objects to evolve in a controlled manner by classifying members as definite or potential. We define a type inference algorithm for JS0 that is sound with respect to the type system. If the type inference algorithm succeeds, then the program is typeable. Therefore, programmers can benefit from the safety offered by the type system, without the need to write explicitly types in their programs.","Title: Zeno: an automated prover for properties of recursive data structures\nYear: 2012\n\nAbstract: Zeno is a new tool for the automatic generation of proofs of simple properties of functions over recursively defined data structures. It takes a Haskell program and an assertion as its goal and tries to contruct a proof for that goal. If successful, it converts the proof into Isabelle code. Zeno searches for a proof tree by iteratively reducing the goal into a conjunction of sub-goals, terminating when all leaves are proven true. This process requires the exploration of many alternatives. We have adapted known, and developed new, heuristics for the reduction of the search space. Our new heuristics aim to promote the application of function definitions, and avoid the repetition of similar proof steps. We compare with the rippling based tool IsaPlanner and the industrial strength tool ACL2s on the basis of a test suite from the IsaPlanner website. We found that Zeno compared favourably with these tools both in terms of theorem proving power and speed.","Title: Is the Java type system sound?\nYear: 1999\n\nAbstract: Abstract: this paper we address the type soundness of theJava kernel language. We prove for a substantial subsetof the language kernel that its type system is sound, inthe sense that unless an exception is raised, the evaluc fl ??? John Wiley & Sons, Inc","Title: An Abstract Model of Java Dynamic Linking and Loading\nYear: 2000\n\nAbstract: We suggest a model for dynamic loading and linking as in Java. We distinguish five components in a Java implementation: evaluation, resolution, loading, verification, and preparation - with their associated checks. We demonstrate how these five together guarantee type soundness.We take an abstract view, and base our model on a language nearer to Java source than to bytecode. We consider the following features of Java: classes, subclasses, fields and hiding, methods and inheritance, and interfaces.","Title: Universe Types for Topology and Encapsulation\nYear: 2007\n\nAbstract: The Universe Type System is an ownership type system for object-oriented programming languages that hierarchically structures the object store; it is used to reason modularly about programs. We formalise Universe Types for a core subset of Java in two steps: We first define a Topological Type System that structures the object store hierarchically into an ownership tree, and demonstrate soundness of the Topological Type System by proving subject reduction. Motivated by concerns of modular verification, we then present an Encapsulation Type System that enforces the owner-as-modifier discipline; that is, that object updates are initiated by the owner of the object. The contributions of this paper are, firstly, an extensive type-theoretic account of the Universe Type System, with explanations and complete proofs, and secondly, the clean separation of the topological from the encapsulation concerns.","Title: Amalgamating sessions and methods in object-oriented languages with generics\nYear: 2009\n\nAbstract: We suggest an amalgamation of communication-based programming (centered on sessions) and object-oriented programming, whereby sessions between concurrent threads are amalgamated with methods. In our proposal, threads consist of the execution of session bodies on objects and communicate with each other by asynchronously sending/receiving objects on channels. The response to a session request is based on the name of the request and the class of the object receiving the request. The decision of how to continue a session is based on the class of the object sent/received. Sessions can be delegated to other sessions, although sessions themselves are not first class objects. We demonstrate our ideas through a core language with generic types, SAM^g, and an example. We then formalize a small calculus, FSAM^g, and prove subject reduction and progress. The latter property is notoriously difficult to achieve in concurrent calculi.","Title: A Unified Framework for Verification Techniques for Object Invariants\nYear: 2008\n\nAbstract: Verification of object-oriented programs relies on object invariants to express consistency criteria of objects. The semantics of object invariants is subtle, mainly because of call-backs, multi-object in- variants, and subclassing. Several verification techniques for object invariants have been proposed. These techniques are complex and differ in restrictions on programs (for instance, whichfields can be updated), restrictions on invariants (what an invariant may refer to), use of advanced type systems (such as ownership types), meaning of invariants (in which execution states are invariants assumed to hold), and proof obligations (when should an invariant be proven). As a result, it is difficult to compare and understand whether/why these techniques are sound, or to develop better techniques. In this paper, we develop a unified framework to describe ver- ification techniques for object invariants. We separate type system concerns from verification strategy concerns. We distil seven pa- rameters that characterise a verification technique, and identify suf- ficient conditions on these parameters under which a verification technique is sound. To illustrate the generality of our framework, we instantiate it with six verification techniques from the literature. We show how our framework facilitates the assessment and com- parison of the soundness and expressiveness of these techniques.","Title: A type preserving translation of Fickle into Java\nYear: 2001\n\nAbstract: We present a translation from Fickle (a Java-like language allowing objects that can change their class at run-time) into plain Java. The translation, which maps any Fickle class into a Java class, is driven by an invariant that relates the Fickle object to its Java counterpart. The translation, which is proven to preserve both the static and the dynamic semantics of the language, is an enhanced version of a previous proposal by the same authors.","Title: Session types for object-oriented languages\nYear: 2006\n\nAbstract: A session takes place between two parties; after establishing a connection, each party interleaves local computations with communications (sending or receiving) with the other. Session types characterise such sessions in terms of the types of values communicated and the shape of protocols, and have been developed for the π-calculus, CORBA interfaces, and functional languages. We study the incorporation of session types into object-oriented languages through Moose, a multi-threaded language with session types, thread spawning, iterative and higher-order sessions. Our design aims to consistently integrate the object-oriented programming style and sessions, and to be able to treat various case studies from the literature. We describe the design of Moose, its syntax, operational semantics and type system, and develop a type inference system. After proving subject reduction, we establish the progress property: once a communication has been established, well-typed programs will never starve at communication points.","Title: Scaling Future Software: The Manycore Challenge\nYear: 2014\n\nAbstract: Existing software cannot benefit from the revolutionary potential increases in computational power provided by manycore chips unless their design and code are polluted by an unprecedented amount of low-level, fine-grained concurrency detail. As a consequence, the advent of manycore chips threatens to make current main-stream programming approaches obsolete, and thereby, jeopardizes the benefits gained from the last 20 years of development in industrial software engineering. In this article we put forward an argument for a fundamental breakthrough in how parallelism and concurrency are integrated into the software of the future.","Title: Rationally Reconstructing the Escrow Example\nYear: 2014\n\nAbstract: The Escrow Exchange Contract has been used as a case study of building up complex and trustworthy systems from basic object capabilities, in the context of concurrent and distributed programming. In this short paper we present a Rational Reconstruction of the Escrow Exchange Contract case study, expressed in Grace, concentrating on the most essential issues of trustworthiness, and ignoring issues to do with distribution or more complex protocols. We then use our notation for capability policies to specify the key features of the reconstructed case study.","Title: Calculating communication costs with Sessions Types and Sizes.\nYear: 2014\n\nAbstract: We present a small object-oriented language with communication primitives. The language allows the assignment of binary session types to communication channels in order to govern the interaction between different objects and to statically calculate communication costs. Class declarations are annotated with size information in order to determine the cost of sending and receiving objects. This paper describes our first steps in the creation of a session-based, object-oriented\r\nlanguage for communication optimization purposes.","Title: Behavioural Types For Non-Uniform Memory Accesses\nYear: 2015\n\nAbstract: Concurrent programs executing on NUMA architectures consist of concurrent entities (e.g. threads, actors) and data placed on different nodes. Execution of these concurrent entities often reads or updates states from remote nodes. The performance of such systems depends on the extent to which the concurrent entities can be executing in parallel, and on the amount of the remote reads and writes. We consider an actor-based object oriented language, and propose a type system which expresses the topology of the program (the placement of the actors and data on the nodes), and an effect system which characterises remote reads and writes (in terms of which node reads/writes from which other nodes). We use a variant of ownership types for the topology, and a combination of behavioural and ownership types for the effect system.","Title: A sip of the Chalice\nYear: 2011\n\nAbstract: Chalice is a verification tool for object-based concurrent programs. It supports verification of functional properties of the programs as well as providing a deadlock prevention mechanism. It is built on Implicit Dynamic Frames, fractional permissions and permission transfer. Implicit Dynamic Frames have been formulated and proven sound using verification conditions and axiomatisation of the heap and stack. Verification in Chalice is specified in terms of weakest preconditions and havocing the heap. In this paper we give a formalisation of the part of Chalice concerned with functional properties. We describe its operational semantics, Hoare logic and sketch the soundness proof. Our system is parametric with respect to the underlying assertion language.","Title: On subtyping, wildcards, and existential types\nYear: 2009\n\nAbstract: Wildcards are an often confusing part of the Java type system: the behaviour of wildcard types is not fully specified by subtyping, due to wildcard capture, and the rules for type checking are often misunderstood. Their very formulation seems somehow 'different' from the rest of the Java type system, which is based on a simple, nominal hierarchy. We investigate subtyping in models for Java with and without generics and wildcards. We separate subclassing from subtyping, unify subtyping for class and wildcard types using existential types, and show that Java wildcards emerge naturally from the combination of inclusion and parametric polymorphism.","Title: Swapsies on the Internet: First Steps towards Reasoning about Risk and Trust in an Open World\nYear: 2015\n\nAbstract: Contemporary open systems use components developed by many different parties, linked together dynamically in unforeseen constellations. Code needs to live up to strict security specifications: it has to ensure the correct functioning of its objects when they collaborate with external objects which may be malicious. In this paper we propose specifications that model risk and trust in such open systems. We specify Miller, Van Cutsem, and Tulloh's escrow exchange example, and discuss the meaning of such a specification. We argue informally that the code satisfies its specification.","Title: The need for capability policies\nYear: 2013\n\nAbstract: The object-capability model is one of the industry standards adopted for the implementation of security policies for web-based software. Object-capabilities in various forms are supported by programming languages such as E, Joe-E, Newspeak, Grace, and the newer versions of Javascript. Unfortunately, code written using capabilities tends to concentrate on the low-level mechanism rather than the high-level policy. In this position paper, we argue that current specification methodologies cannot adequately capture all aspects of the capability policies required to support object-capability systems. We outline informally the features that such security policies should support, and we demonstrate (also informally) how we can reason that examples satisfy the capability policies.","Title: Towards a semantic model for Java wildcards\nYear: 2010\n\nAbstract: Wildcard types enrich the types expressible in Java, and extend the set of typeable Java programs. Syntactic models and proofs of soundness for type systems related to Java wildcards have been suggested in the past, however, the semantics of wildcards has not yet been studied. In this paper we propose a semantic model for Java wildcards, inspired by work on semantic subtyping, which traditionally interprets types as sets of possible values. To easily reflect the nominal type system of Java, our model is defined in terms of runtime types (closed class types) rather than the structure of the runtime values themselves. To reflect the variance introduced by wildcards, our model interprets types as sets of such closed class types. Having defined our model, we employ a standard semantic notion of subtyping. We show soundness of syntactic subtyping with respect to the semantic version, and demonstrate that completeness fails in the general case. We identify a restricted (but nonetheless rich) type language for which the syntactic notion of subtyping is both sound and complete.","Title: Deny capabilities for safe, fast actors\nYear: 2015\n\nAbstract: Abstract Combining the actor-model with shared memory for performance is efficient but can introduce data-races. Existing approaches to static data-race freedom are based on uniqueness and immutability, but lack flexibility and high performance implementations. Our approach, based on deny properties, allows reading, writing and traversing unique references, introduces a new form of write uniqueness, and guarantees atomic behaviours.","Title: Permission and Authority Revisited towards a formalisation.\nYear: 2016\n\nAbstract: Miller's notions of permissions and authority are foundational to the analysis of object-capability programming. Informal definitions of these concepts were given in Miller's thesis. In this paper we propose precise definitions for permissions and authority, based on a small object-oriented calculus. We quantify their bounds (current, eventual, behavioural, topological), and delineate the relationships between these definitions.","Title: Modular Verification of Procedure Equivalence in the Presence of Memory Allocation.\nYear: 2017\n\nAbstract: For most high level languages, two procedures are equivalent if they transform a pair of isomorphic stores to isomorphic stores. However, tools for modular checking of such equivalence impose a stronger check where isomorphism is strengthened to equality of stores. This results in the inability to prove many interesting program pairs with recursion and dynamic memory allocation. In this work, we present RIE, a methodology to modularly establish equivalence of procedures in the presence of memory allocation, cyclic data structures and recursion. Our technique addresses the need for finding witnesses to isomorphism with angelic allocation, supports reasoning about equivalent procedures calls when the stores are only locally isomorphic, and reasoning about changes in the order of procedure calls. We have implemented RIE by encoding it in the Boogie program verifier. We describe the encoding and prove its soundness.","Title: You can have it all: abstraction and good cache performance.\nYear: 2017\n\nAbstract: On current architectures, the optimisation of an application's performance often involves data being stored according to access affinity --- what is accessed together should be stored together, rather than logical affinity --- what belongs together logically stays together. Such low level techniques lead to faster, but more error prone code, and end up tangling the program's logic with low-level data layout details. \n\n Our vision, which we call SHAPES --- Safe, High-level, Abstractions for oPtimisation of mEmory cacheS --- is that the layout of a data structure should be defined only once, upon instantiation, and the remainder of the code should be layout agnostic. This enables performance improvements while also guaranteeing memory safety, and supports the separation of program logic from low level concerns. In this paper we investigate how this vision can be supported by extending a programming language. \n\n We describe the core language features supporting this vision: classes can be customized to support different layouts, and layout information is carried around in types; the remaining source code is layout-unaware and the compiler emits layout-aware code. We then discuss our SHAPES implementation through a prototype library, which we also used for preliminary evaluations. Finally, we discuss how the core could be expanded so as to deliver SHAPES's full potential: the incorporation of compacting garbage collection, ad hoc polymorphism and late binding, synchronization of representations of different collections, support for dynamic change of representation, etc.","Title: Orca: GC and type system co-design for actor languages\nYear: 2017\n\nAbstract: ORCA is a concurrent and parallel garbage collector for actor programs, which does not require any STW steps, or synchronization mechanisms, and that has been designed to support zero-copy message passing and sharing of mutable data. ORCA is part of a runtime for actor-based languages, which was co-designed with the Pony programming language, and in particular, with its data race free type system. By co-designing an actor language with its runtime, it was possible to exploit certain language properties in order to optimize performance of garbage collection. Namely, ORCA relies on the guarantees of absence of race conditions in order to avoid read/write barriers, and it leverages the actor message passing, for synchronization among actors. \n\n In this paper we briefly describe Pony and its type system. We use pseudo-code in order to introduce how ORCA allocates and deallocates objects, how it shares mutable data without requiring barriers upon data mutation, and how can immutability be used to further optimize garbage collection. Moreover, we discuss the advantages of co-designing an actor language with its runtime, and we demonstrate that ORCA can be implemented in a performant and scalable way through a set of micro-benchmarks, including a comparison with other well-known collectors.","Title: Tree topologies for causal message delivery.\nYear: 2017\n\nAbstract: Causal message delivery, i.e. the requirement that messages are delivered in an order respecting their causal (logical) dependencies, is often mandated in the distributed setting. So far, causal message delivery has been implemented by augmenting messages with meta data information that allows the receiver (or the platform) to re-order, and if necessary hold back, messages upon receipt before processing. \n\n We propose that causal message delivery can be achieved by construction, simply by organizing the nodes of the distributed application into a tree topology, and without the need for any meta data in the messages. \n\n We present our ideas informally through an example application and then develop a formal model and prove that causal message delivery is preserved in tree-based networks.","Title: Correctness of a Concurrent Object Collector for Actor Languages.\nYear: 2018\n\nAbstract: ORCA is a garbage collection protocol for actor-based programs. Multiple actors may mutate the heap while the collector is running without any dedicated synchronisation. ORCA is applicable to any actor language whose type system prevents data races and which supports causal message delivery. We present a model of ORCA which is parametric to the host language and its type system. We describe the interplay between the host language and the collector. We give invariants preserved by ORCA, and prove its soundness and completeness.","Title: Extending SHAPES for SIMD Architectures - An approach to native support for Struct of Arrays in languages.\nYear: 2018\n\nAbstract: SIMD (Single Instruction, Multiple Data) instruction sets are ubiquitous on modern hardware, but rarely used in software projects. A major reason for this is that efficient SIMD code requires data to be laid out in memory in an unconventional manner, forcing developers to explicitly refactor their code and data structures in order to make use of SIMD. In previous work, we proposed SHAPES, an abstract layout specification for enabling memory optimisations for managed, object-oriented languages. In this paper, we explain how, by extending SHAPES with well-known constructs from the literature, which are not specific to SIMD, we can extend SHAPES to compile programs to use SIMD instructions. The resulting language (sketch) seems able to exploit SIMD capabilities without sacrificing ease of development.","Title: Chai: traits for Java-like languages\nYear: 2005\n\nAbstract: Traits support the factoring out of common behaviour, and its integration into classes in a manner that coexists smoothly with inheritance-based structuring mechanisms. We designed the language Chai, which incorporates statically typed traits into a simple Java-inspired base language, and we discuss three versions of the language: Chai1, where traits are only a mechanism for the creation of classes; Chai2 where traits are a mechanism for the creation of classes, and can also introduce types, and Chai3 where traits play a role at runtime, and can can be applied to objects, and change the objects' behaviour. We give formal models for these languages, outline the proof of soundness, and our prototype implementation.","Title: Flint for Safer Smart Contracts.\nYear: 2019\n\nAbstract: The Ethereum blockchain platform supports the execution of decentralised applications or smart contracts. These typically hold and transfer digital currency to other parties on the platform; however, they have been subject to numerous attacks due to the unintentional introduction of bugs. Over a billion dollars worth of currency has been stolen since its release in July 2015. As smart contracts cannot be updated after deployment, it is imperative that the programming language supports the development of robust contracts. We propose Flint, a new statically-typed programming language specifically designed for writing robust smart contracts. Flintu0027s features enforce the writing of safe and predictable code. To encourage good practices, we introduce protection blocks. Protection blocks restrict who can run code and when (using typestate) it can be executed. To prevent vulnerabilities relating to the unintentional loss of currency, Flint Asset traits provide safe atomic operations, ensuring the state of contracts is always consistent. Writes to state are restricted, simplifying reasoning about smart contracts.","Title: Safely Abstracting Memory Layouts.\nYear: 2019\n\nAbstract: Modern architectures require applications to make effective use of caches to achieve high performance and hide memory latency. This in turn requires careful consideration of placement of data in memory to exploit spatial locality, leverage hardware prefetching and conserve memory bandwidth. In unmanaged languages like C++, memory optimisations are common, but at the cost of losing object abstraction and memory safety. In managed languages like Java and C#, the abstract view of memory and proliferation of moving compacting garbage collection does not provide enough control over placement and layout. We have proposed SHAPES, a type-driven abstract placement specification that can be integrated with object-oriented languages to enable memory optimisations. SHAPES preserves both memory and object abstraction. In this paper, we formally specify the SHAPES semantics and describe its memory safety model.","Title: snmalloc: a message passing allocator\nYear: 2019\n\nAbstract: snmalloc is an implementation of malloc aimed at workloads in which objects are typically deallocated by a different thread than the one that had allocated them. We use the term producer/consumer for such workloads. snmalloc uses a novel message passing scheme which returns deallocated objects to the originating allocator in batches without taking any locks. It also uses a novel bump pointer-free list data structure with which just 64-bits of meta-data are sufficient for each 64 KiB slab. On such producer/consumer benchmarks our approach performs better than existing allocators. Snmalloc is available at <a href=\"https://github.com/Microsoft/snmalloc\">https://github.com/Microsoft/snmalloc</a>.","Title: Facebook's Cyber-Cyber and Cyber-Physical Digital Twins\nYear: 2021\n\nAbstract: A cyber-cyber digital twin is a simulation of a software system. By contrast, a cyber-physical digital twin is a simulation of a non-software (physical) system. Although cyber-physical digital twins have received a lot of recent attention, their cyber-cyber counterparts have been comparatively overlooked. In this paper we show how the unique properties of cyber-cyber digital twins open up exciting opportunities for research and development. Like all digital twins, the cyber-cyber digital twin is both informed by and informs the behaviour of the twin it simulates. It is therefore a software system that simulates another software system, making it conceptually truly a twin, blurring the distinction between the simulated and the simulator. Cyber-cyber digital twins can be twins of other cyber-cyber digital twins, leading to a hierarchy of twins. As we shall see, these apparently philosophical observations have practical ramifications for the design, implementation and deployment of digital twins at Facebook."],"string":"[\n \"Title: BabyJFrom Object Based to Class Based Programming via Types\\nYear: 2003\\n\\nAbstract: Object oriented programming can be classified into the object based, and the class based paradigm. Object based languages typically are weakly typed and interpreted, allow member addition and removal, and thus they support flexibility and prototyping. Class based languages are usually strongly typed and compiled, require a rigid class structure and class membership, and thus they support more robust, type safe programs.\",\n \"Title: Generic Universe Types\\nYear: 2007\\n\\nAbstract: Ownership is a powerful concept to structure the object store and to control aliasing and modifications of objects. This paper presents an ownership type system for a Java-like programming language with generic types. Like our earlier Universe type system, Generic Universe Types enforce the owner-as-modifier discipline. This discipline does not restrict aliasing, but requires modifications of an object to be initiated by its owner. This allows owner objects to control state changes of owned objects, for instance, to maintain invariants. Generic Universe Types re- quire a small annotation overhead and provide strong static guarantees. They are the first type system that combines the owner-as-modifier dis- cipline with type genericity.\",\n \"Title: Policy as Types.\\nYear: 2013\\n\\nAbstract: Drossopoulou and Noble argue persuasively for the need for a means to express policy in object-capability-based systems. We investigate a practical means to realize their aim via the Curry-Howard isomorphism. Specifically, we investigate representing policy as types in a behavioral type system for the RHO-calculus, a reflective higher-order variant of the pi-calculus.\",\n \"Title: Formalising Composition Oriented Programming\\nYear: 1999\\n\\nAbstract: We present a snapshot of work in progress to de- fine a formal model of the composition technol- ogy which underpins Subject-oriented and Aspect- oriented programming.\",\n \"Title: Objects and session types\\nYear: 2009\\n\\nAbstract: A session takes place between two parties; after establishing a connection, each party interleaves local computations and communications (sending or receiving) with the other. Session types characterise such sessions in terms of the types of values communicated and the shape of protocols, and have been developed for the @p-calculus, CORBA interfaces, and functional languages. We study the incorporation of session types into object-oriented languages through MOOSE, a multi-threaded language with session types, thread spawning, iterative, and higher-order sessions. Our design aims to consistently integrate the object-oriented programming style and sessions, and to be able to treat various case studies from the literature. We describe the design of MOOSE, its syntax, operational semantics, and type system, and develop a type inference system. After proving subject reduction, we establish the progress property: once a communication has been established, well-typed programs will never starve at communication points.\",\n \"Title: Types for hierarchic shapes\\nYear: 2006\\n\\nAbstract: Heap entities tend to contain complex references to each other. To manage this complexity, types which express shapes and hierarchies have been suggested. We survey type systems which describe such hierarchic shapes, how these types are used for reasoning about programs, and applications in concurrent programming.\",\n \"Title: Overloading in preliminary Ada\\nYear: 1980\\n\\nAbstract: Ada permits the overloading of enumeration literals, aggregates, subprograms and operators, i.e. the declaration of the same designator with different meanings in the same scope. This leads to difficulties during the semantic analysis of expressions and subprogram calls. For selecting the meaning not only the designator but also the types of its operands or parameters and the type of its result must be used. We show that the identification of expressions is possible in two passes, the first bottom-up, the second top-down.\",\n \"Title: A Model for Java with Wildcards\\nYear: 2008\\n\\nAbstract: Wildcards are a complex and subtle part of the Java type system, present since version 5.0. Although there have been various formalisations and partial type soundness results concerning wildcards, to the best of our knowledge, no system that includes all the key aspects of Java wildcards has been proven type sound. This paper establishes that Java wildcards are type sound. We describe a new formal model based on explicit existential types whose pack and unpack operations are handled implicitly, and prove it type sound. Moreover, we specify a translation from a subset of Java to our formal model, and discuss how several interesting aspects of the Java type system are handled.\",\n \"Title: An Effective Translation of Fickle into Java\\nYear: 2001\\n\\nAbstract: We present a translation from Fickle (a Java-like language allowing dynamic object re-classification, that is, objects that can change their class at run-time) into plain Java. The translation is proved to preserve static and dynamic semantics; moreover, it is shown to be effective, in the sense that the translation of a Fickle class does not depend on the implementation of used classes, hence can be done in a separate way, that is, without having their sources, exactly as it happens for Java compilation. The aim is to demonstrate that an extension of Java supporting dynamic object re-classification could be fully compatible with the existing Java environment.\",\n \"Title: Fully concurrent garbage collection of actors on many-core machines\\nYear: 2013\\n\\nAbstract: Disposal of dead actors in actor-model languages is as important as disposal of unreachable objects in object-oriented languages. In current practice, programmers are required to either manually terminate actors, or they have to rely on garbage collection systems that monitor actor mutation through write barriers, thread coordination through locks etc. These techniques, however, prevent the collector from being fully concurrent. We developed a protocol that allows garbage collection to run fully concurrently with all actors. The main challenges in concurrent garbage collection is the detection of cycles of sleeping actors in the actors graph, in the presence of concurrent mutation of this graph. Our protocol is solely built on message passing: it uses deferred direct reference counting, a dedicated actor for the detection of (cyclic) garbage, and a confirmation protocol (to deal with the mutation of the actor graph). We present our ideas informally through an example, and then present a formal model, prove soundness and argue completeness. We have implemented the protocol as part of a runtime library. As a preliminary performance evaluation, we discuss the performance of our approach as currently used at a financial institution, and use four benchmarks from the literature to compare our approach with other actor-model systems. These preliminary results indicate that the overhead of our approach is small.\",\n \"Title: A distributed object-oriented language with session types\\nYear: 2005\\n\\nAbstract: In the age of the world-wide web and mobile computing, programming communication-centric software is essential. Thus, programmers and program designers are exposed to new levels of complexity, such as ensuring the correct composition of communication behaviours and guaranteeing deadlockfreedom of their protocols. This paper proposes the language Ldoos, a simple distributed object-oriented language augmented with session communication primitives and types. Ldoos provides a flexible object-oriented programming style for structural interaction protocols by prescribing channel usages within signatures of distributed classes. We develop a typing system for Ldoos and prove its soundness with respect to the operational semantics. We also show that in a well-typed Ldoos program, there will never be a connection error, a communication error, nor an incorrect completion between server-client interactions. These results demonstrate that a consistent integration of object-oriented language features and session types can statically check the consistent composition of communication protocols.\",\n \"Title: Separating ownership topology and encapsulation with generic universe types\\nYear: 2011\\n\\nAbstract: Ownership is a powerful concept to structure the object store and to control aliasing and modifications of objects. This article presents an ownership type system for a Java-like programming language with generic types. Like our earlier Universe type system, Generic Universe Types structure the heap hierarchically. In contrast to earlier work, we separate the enforcement of an ownership topology from an encapsulation system. The topological system uses an existential modifier to express that no ownership information is available statically. On top of the topological system, we build an encapsulation system that enforces the owner-as-modifier discipline. This discipline does not restrict aliasing, but requires modifications of an object to be initiated by its owner. This allows owner objects to control state changes of owned objects—for instance, to maintain invariants. Separating the topological system from the encapsulation system allows for a cleaner formalization, separation of concerns, and simpler reuse of the individual systems in different contexts.\",\n \"Title: A flexible model for dynamic linking in Java and C#\\nYear: 2006\\n\\nAbstract: Dynamic linking supports flexible code deployment, allowing partially linked code to link further code on the fly, as needed. Thus, end-users enjoy the advantage of automatically receiving any updates, without any need for any explicit actions on their side, such as re-compilation, or re-linking. On the down side, two executions of a program may link in different versions of code, which in some cases causes subtle errors, and may mystify end-users. Dynamic linking in Java and C# are similar: the same linking phases are involved, soundness is based on similar ideas, and executions which do not throw linking errors give the same result. They are, however, not identical: the linking phases are combined differently, and take place in different order. Consequently, linking errors may be detected at different times by Java and C# runtime systems. We develop a non-deterministic model, which describes the behaviour of both Java and C# program executions. The non-determinism allows us to describe the design space, to distill the similarities between the two languages, and to use one proof of soundness for both. We also prove that all execution strategies are equivalent with respect to terminating executions that do not throw link errors: they give the same results.\",\n \"Title: Refined Effects for Unanticipated Object Re-classification: Fickle3\\nYear: 2003\\n\\nAbstract: Abstract. In previous work on the language Fickle and its extension FickleII Dezani and us introduced language features for object re-classication for imperative, typed, class-based, object-oriented languages. In this paper we present the language Fickle3, which on one side renes FickleII with more expressive eect annotations, and on the other eliminates the need to declare explicitly which are the classes of the objects that may be re-classied. Therefore, Fickle3 allows to correctly type meaningful programs which FickleII rejects. Moreover, re-classication may be decided by the client of a class, allowing unanticipated object reclassic ation. As for FickleII, also the type and eect system for Fickle3 guarantees that, even though objects may be re-classied across classes with dieren t members, they will never attempt to access non existing members. The type and eect,system of Fickle3 has some signican t dierences from the one of FickleII. In particular, besides the fact that intra-class type checking has to track the more rened eects, when a class is combined with other classes some,additional inter-class checking is introduced.\",\n \"Title: More dynamic object reclassification: Fickle∥\\nYear: 2002\\n\\nAbstract: Reclassification changes the class membership of an object at run-time while retaining its identity. We suggest language features for object reclassification, which extend an imperative, typed, class-based, object-oriented language.We present our proposal through the language Fickle⋄⋄. The imperative features, combined with the requirement for a static and safe type system, provided the main challenges. We develop a type and effect system for Fickle⋄⋄ and prove its soundness with respect to the operational semantics. In particular, even though objects may be reclassified across classes with different members, there will never be an attempt to access nonexisting members.\",\n \"Title: Formal Techniques for Java Programs\\nYear: 2000\\n\\nAbstract: This report gives an overview of the second ECOOP Workshop on Formal Techniques for Java Programs. It explains the motivation for such a workshop and summarizes the presentations and discussions.\",\n \"Title: Formal Techniques for Java Programs\\nYear: 2001\\n\\nAbstract: This report gives an overview of the third ECOOP Workshop on Formal Techniques for Java Programs. It explau]ns the motivation\\n for such a workshop and summarizes the presentations and discussions.\",\n \"Title: Flexible models for dynamic linking\\nYear: 2003\\n\\nAbstract: Dynamic linking supports flexible code deployment: partially linked code links further code on the fly, as needed; and thus, end-users receive updates automatically. On the down side, each program run may link different versions of the same code, possibly causing subtle errors which mystify end-users. Dynamic linking in Java and C# are similar: The same linking phases are involved, soundness is based on similar ideas, and executions which do not throw linking errors give the same result. They are, however, not identical: the linking phases are combined differently, and take place in a different order. We develop a non-deterministic model, which includes the behaviour of Java and C#. The non-determinism allows us to describe the design space, to distill the similarities between the two languages, and to use one proof of soundness for both.We also prove that all execution strategies are equivalent in the sense that all terminating executions which do not involve a link error, give the same result.\",\n \"Title: Existential Quantification for Variant Ownership\\nYear: 2009\\n\\nAbstract: Ownership types characterize the topology of objects in the heap, through a characterization of the context to which an object belongs. They have been used to support reasoning, memory management, concurrency, etc. Subtyping is traditionally invariant w.r.t. contexts, which has often proven inflexible in some situations. Recent work has introduced restricted forms of subtype variance and unknown context, but in a rather ad-hoc and restricted way. We develop Jo***, a calculus which supports parameterisation of types, as well as contexts, and allows variant subtyping of contexts based on existential quantification. Jo*** is more expressive, general, and uniform than previous works which add variance to ownership languages. Our explicit use of existential types makes the connection to type-theoretic foundations from existential types more transparent. We prove type soundness for Jo*** and extend it to Jo*** deep which enforces the owners-as-dominators property.\",\n \"Title: A formal semantics for isorecursive and equirecursive state abstractions\\nYear: 2013\\n\\nAbstract: Methodologies for static program verification and analysis often support recursive predicates in specifications, in order to reason about recursive data structures. Intuitively, a predicate instance represents the complete unrolling of its definition; this is the equirecursive interpretation. However, this semantics is unsuitable for static verification, when the recursion becomes unbounded. For this reason, most static verifiers differentiate between, e.g., a predicate instance and its corresponding body, while providing a facility to map between the two; this is the isorecursive semantics. While this latter interpretation is usually implemented in practice, only the equirecursive semantics is typically treated in theoretical work. In this paper, we provide both an isorecursive and an equirecursive formal semantics for recursive definitions in the context of Chalice, a verification methodology based on implicit dynamic frames. We show that development of such formalisations requires addressing several subtle issues, such as the possibility of infinitely-recursive definitions and the need for the isorecursive semantics to correctly reflect the restrictions that make it readily implementable. These questions are made more challenging still in the context of implicit dynamic frames, where the use of heap-dependent expressions provides further pitfalls for a correct formal treatment.\",\n \"Title: Ownership, encapsulation and the disjointness of type and effect\\nYear: 2002\\n\\nAbstract: Ownership types provide a statically enforceable notion of object-level encapsulation. We extend ownership types with computational effects to support reasoning about object-oriented programs. The ensuing system provides both access control and effects reporting. Based on this type system, we codify two formal systems for reasoning about aliasing and the disjointness of computational effects. The first can be used to prove that evaluation of two expressions will never lead to aliases, while the latter can be used to show the non-interference of two expressions.\",\n \"Title: Fickle: Dynamic Object Re-classification\\nYear: 2001\\n\\nAbstract: Re-classification changes at run-time the class membership of an object while retaining its identity. We suggest language features for object re-classification, which could extend an imperative, typed, classbased, object-oriented language.We present our proposal through the language Fickle. The imperative features combined with the requirement for a static and safe type system provided the main challenges. We develop a type and effect system for Fickle and prove its soundness with respect to the operational semantics. In particular, even though objects may be re-classified across classes with different members, they will never attempt to access nonexisting members.\",\n \"Title: An Integrated Engineering Study Scheme In Computing\\nYear: 1993\\n\\nAbstract: This paper describes the integrated engineering study scheme, based around a set of 4 year MEng programmes of study, established by Imperial College. The paper outlines the rationale for the scheme and gives an account of its constituent programmes of study and the curriculum. The organisation and pattern of teaching, student workload and assessment methods are discussed. A detailed comparison of the scheme with the proposals and recommendations of the important model curricula are given.\",\n \"